diff --git a/.devcontainer/.env.example b/.devcontainer/.env.example new file mode 100644 index 0000000..0b6d1f9 --- /dev/null +++ b/.devcontainer/.env.example @@ -0,0 +1,18 @@ +# Copy to `.devcontainer/.env` for docker-compose variable substitution. +# Compose reads this file from the `.devcontainer/` directory (not repo-root `.env` for these keys). +# +# Host port mappings (optional): +# SSH_PORT=2220 +# APP_PORT=1234 +# DEV_PORT=8888 +# +# DNS inside the container (optional; defaults are 1.1.1.1 + 8.8.8.8 in docker-compose.yml). +# Use your corporate resolvers if public DNS is blocked: +# DEVCONTAINER_DNS_1=10.0.0.1 +# DEVCONTAINER_DNS_2=10.0.0.2 +# +# Selenium (standalone Firefox service in docker-compose.yml). Override URL if you use an external grid: +# SELENIUM_REMOTE_URL=http://selenium-standalone-firefox:4444 +# Host ports if 4444 / 7900 are already in use: +# SELENIUM_GRID_PORT=4445 +# SELENIUM_VNC_PORT=7901 diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index fca2f0b..6d47dbf 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,10 +1,8 @@ FROM ghcr.io/astral-sh/uv:python3.12-bookworm -# Set locale to avoid warnings ENV LC_ALL=C.UTF-8 ENV LANG=C.UTF-8 -# Install just and other system dependencies RUN apt-get update && apt-get install -y \ sudo \ curl \ @@ -13,16 +11,11 @@ RUN apt-get update && apt-get install -y \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* -# Crear usuario no-root con UID/GID que suele usar VS Code (1000:1000). -# TOOD: Take this user out of sudoers if you want to use this in fully agents mode. RUN useradd -ms /bin/bash -u 1000 vscode \ - && apt-get update && apt-get install -y sudo \ + && apt-get update \ + && apt-get install -y sudo \ && echo "vscode ALL=(ALL) NOPASSWD:ALL" >> /etc/sudoers -# Gemini CLI -# Please login outside of the container and copy your credentials to ~/.gemini/... -RUN curl -fsSL https://deb.nodesource.com/setup_24.x | sudo -E bash - && sudo apt-get install -y nodejs -RUN npm install -g @google/gemini-cli RUN mkdir -p /app/data \ && chown -R 1000:1000 /app/data \ diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 59d883b..983c1de 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -1,16 +1,18 @@ { "name": "git-metadata-extractor-dev", - "build": { - "dockerfile": "Dockerfile" + "dockerComposeFile": "docker-compose.yml", + "service": "devcontainer", + "workspaceFolder": "/workspaces/project", + "containerEnv": { + "UV_CACHE_DIR": "/workspaces/project/.uv-cache" + }, + "overrideCommand": false, + "features": { + "ghcr.io/devcontainers/features/sshd:1": { + "version": "latest" + } }, - "runArgs": [ - "--env-file", - "${localWorkspaceFolder}/.env", - "--network", - "dev" - ], "remoteUser": "vscode", - "workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}", "customizations": { "vscode": { "settings": { @@ -26,8 +28,6 @@ ] } }, - "forwardPorts": [ - 1234 - ], - "postCreateCommand": "rm -rf .venv && uv venv && uv pip install -e .[dev] && echo '. $PWD/.venv/bin/activate' >> /home/vscode/.bashrc" -} \ No newline at end of file + "postCreateCommand": "mkdir -p .uv-cache && rm -rf .venv && uv venv && uv pip install -e .[dev] && echo '. $PWD/.venv/bin/activate' >> /home/vscode/.bashrc", + "postStartCommand": "bash .devcontainer/set-vscode-password.sh" +} diff --git a/.devcontainer/docker-compose.yml b/.devcontainer/docker-compose.yml new file mode 100644 index 0000000..2baac8a --- /dev/null +++ b/.devcontainer/docker-compose.yml @@ -0,0 +1,61 @@ +# Dev container stack. Compose publishes ports on the host (more reliable than +# devcontainer forwardPorts in some setups). Interpolation vars (SSH_PORT, etc.) +# can be set in `.devcontainer/.env` (see `.devcontainer/.env.example`). +# +# Internal SSH: devcontainers `sshd` feature listens on 2222, not 22 — map host:2222. +# +# Explicit DNS: containers on external networks (e.g. `dev`) sometimes get no working resolver +# and `uv pip` fails with "dns error" / "failed to lookup address information". +# Override in `.devcontainer/.env`: DEVCONTAINER_DNS_1 / DEVCONTAINER_DNS_2. +services: + devcontainer: + build: + context: .. + dockerfile: .devcontainer/Dockerfile + dns: + - "${DEVCONTAINER_DNS_1:-1.1.1.1}" + - "${DEVCONTAINER_DNS_2:-8.8.8.8}" + env_file: + - ../.env + environment: + # Avoid ~/.cache/uv (often root-owned after sshd/common-utils); workspace is bind-mounted as vscode. + UV_CACHE_DIR: /workspaces/project/.uv-cache + SELENIUM_REMOTE_URL: ${SELENIUM_REMOTE_URL:-http://gme-selenium-firefox:4444} + ports: + - "${SSH_PORT:-2222}:2222" + - "${APP_PORT:-1234}:1234" + - "${DEV_PORT:-8888}:8888" + volumes: + - ..:/workspaces/project:cached + command: sleep infinity + networks: + - dev + gme-qdrant: + image: qdrant/qdrant:latest + container_name: gme-qdrant + ports: + - "6333:6333" + - "6334:6334" + volumes: + - ../data/qdrant/storage:/qdrant/storage + restart: unless-stopped + networks: + - dev + # README "Option B": multi-session standalone Firefox (ORCID, Selenium-backed tools). + gme-selenium-firefox: + image: selenium/standalone-firefox + container_name: gme-selenium-firefox + ports: + - "${SELENIUM_GRID_PORT:-4444}:4444" + - "${SELENIUM_VNC_PORT:-7900}:7900" + shm_size: "2g" + environment: + SE_NODE_MAX_SESSIONS: "5" + SE_NODE_SESSION_TIMEOUT: "300" + restart: unless-stopped + networks: + - dev + +networks: + dev: + external: true diff --git a/.devcontainer/set-vscode-password.sh b/.devcontainer/set-vscode-password.sh new file mode 100644 index 0000000..4cffe88 --- /dev/null +++ b/.devcontainer/set-vscode-password.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash +# Apply VSCODE_PASSWORD to user vscode at container start (not baked into the image). +# Set VSCODE_PASSWORD in .env (this repo loads it via devcontainer runArgs --env-file). +set -euo pipefail +if [[ -z "${VSCODE_PASSWORD:-}" ]]; then + exit 0 +fi +printf 'vscode:%s\n' "$VSCODE_PASSWORD" | sudo chpasswd diff --git a/.env.dist b/.env.dist deleted file mode 100644 index 5784dac..0000000 --- a/.env.dist +++ /dev/null @@ -1,12 +0,0 @@ -OPENAI_API_KEY= -OPENROUTER_API_KEY= -GITHUB_TOKEN= -GITLAB_TOKEN= -INFOSCIENCE_TOKEN= -MODEL= -PROVIDER= -SELENIUM_REMOTE_URL= -GUNICORN_CMD_ARGS="--timeout=600" -CACHE_DB_PATH=/app/data/cache.db -MAX_SELENIUM_SESSIONS=2 -MAX_CACHE_ENTRIES=10000000 diff --git a/.env.example b/.env.example index 5e20cfd..922c0bb 100644 --- a/.env.example +++ b/.env.example @@ -1,11 +1,305 @@ -# Environment variables for git-metadata-extractor -# Copy this file to .env and fill in your actual values +# Environment variables for git-metadata-extractor. +# Copy this file to `.env` and fill in your actual values. `.env` is gitignored. +# Anything left commented out falls back to the default shown next to it. -# EPFL RCP Token for inference endpoint +# --------------------------------------------------------------------------- +# Dev container +# --------------------------------------------------------------------------- + +# Optional: devcontainer SSH password — applied on each container start +# (see .devcontainer/set-vscode-password.sh). Do not commit a real value. +# VSCODE_PASSWORD= + +# If PyPI fails inside the devcontainer with DNS errors, set custom resolvers +# in `.devcontainer/.env` (see `.devcontainer/.env.example`). No variable here. + +# --------------------------------------------------------------------------- +# Required credentials +# --------------------------------------------------------------------------- + +# GitHub API token. Required for v2 preflight (`/v2/health` flips to +# `degraded` without it), for all real GitHub provider calls, and for the +# `src/index/github/` indexer (REST metadata + README fetches). +GITHUB_TOKEN=your-github-token-here + +# Bound for retrying transient GitHub provider failures (gimie returning +# empty/malformed JSON during 502s, rate-limit blips, etc.). Default 3. +# Set to 0 to disable. Non-transient errors (NOT_FOUND, UnicodeDecodeError) +# never retry — UnicodeDecodeError falls back to a minimal payload instead. +# V2_GITHUB_REPO_MAX_RETRIES=3 + +# --------------------------------------------------------------------------- +# LLM provider credentials (only the one(s) referenced by your model config +# need to be set — `src/v2/agents/llm/runtime.py` validates this at startup +# and surfaces a clear error if the configured profile's env var is missing). +# --------------------------------------------------------------------------- + +# EPFL RCP inference endpoint token. RCP_TOKEN=your-rcp-token-here -# OpenAI API Key (if using OpenAI provider) +# OpenAI API key (only if a model profile is configured for OpenAI). OPENAI_API_KEY=your-openai-key-here -# OpenRouter API Key (if using OpenRouter provider) +# OpenRouter API key (only if a model profile is configured for OpenRouter). OPENROUTER_API_KEY=your-openrouter-key-here + +# --------------------------------------------------------------------------- +# Optional integrations +# --------------------------------------------------------------------------- + +# Infoscience bearer token. Leave unset for public endpoints; required only +# when the deployment hits protected Infoscience routes. +# INFOSCIENCE_TOKEN= + +# Remote Selenium endpoint used by the link-veracity stage and the LLM +# `fetch_link_content_via_selenium` tool. Leave unset to disable Selenium-based +# fetching (the pipeline degrades gracefully with warnings). +# SELENIUM_REMOTE_URL=http://localhost:4444 + +# --------------------------------------------------------------------------- +# V2 pipeline runtime +# --------------------------------------------------------------------------- + +# Default agent runtime when `/v2/extract` requests omit `agent_runtime`. +# One of `rule_based|llm`. Default: `llm`. +# V2_AGENT_RUNTIME_DEFAULT=llm + +# When `true`, `/v2/extract` (GET and POST) wires up mock providers — no +# external GitHub/ORCID/Infoscience/ROR calls. Default: `true`. Set to +# `false` for production deployments that talk to real APIs. +# V2_USE_MOCK_PROVIDERS=true + +# LLM critic pruning. When `false` (default), the critic stage is skipped +# entirely so no persons/orgs/repos/articles are dropped. Set to `true` to +# re-enable critic drop suggestions; flagged entities appear in +# `excluded_entities` with reason "critic pruning". +# V2_APPLY_CRITIC_PRUNING=false + +# Scout-mode upgrade for the `context_summary` LLM stage. When `true`, +# the summary agent gets the broad RAG-search toolkit (orcid, ror, +# infoscience, openalex, zenodo, ethz_research_collection, huggingface, +# renkulab, snsf, epfl_graph + selenium_fetch) on top of the legacy +# `grep_repository_corpus` + DuckDuckGo pair, and switches to a +# scout-style system prompt that produces a structured brief with +# explicit People / Organizations / Articles / Affiliations / Caveats +# sections. Per-entity agents (person, org, article, membership, +# contribution) automatically benefit since they already consume the +# `summary_markdown` field. Trade-off: one heavier upfront LLM call, +# but per-entity calls send less context (raw blobs already stripped) +# and duplicate ORCID/ROR lookups across entities collapse into the +# scout's shared brief. Off by default. +# V2_CONTEXT_SUMMARY_SCOUT_MODE=false + +# --------------------------------------------------------------------------- +# V2 caches +# --------------------------------------------------------------------------- + +# Provider response cache (GitHub users/orgs, ORCID, Infoscience, ROR) AND +# the async-job store backing `POST /v2/extract` + `GET /v2/jobs/{job_id}`. +# Single TTL applied to every cached entry. +# V2_PROVIDER_CACHE_PATH=.cache/v2/providers.db +# V2_PROVIDER_CACHE_TTL_DAYS=30 +# V2_PROVIDER_CACHE_ENABLED=true + +# Pipeline-level response cache (the outer `/extract` → V2ExtractResponse +# cache). Set to `false` to force every `/extract` call to re-run the full +# pipeline while keeping every sub-cache (provider responses, agent verdicts, +# Selenium fetches, link-veracity verdicts) active. Useful when verifying the +# sub-caches are doing their job. Default: `true`. +# V2_PIPELINE_CACHE_ENABLED=true + +# --------------------------------------------------------------------------- +# Logging & observability +# --------------------------------------------------------------------------- + +# FastAPI / Python log level. One of DEBUG|INFO|WARNING|ERROR. Default: INFO. +# LOG_LEVEL=INFO + +# Per-request log of every external-service query the LLM agents try +# (Infoscience, ORCID, ROR, GitHub, DuckDuckGo, Selenium, etc.). Each +# `/extract` call writes one JSON file `.json` here. Default: +# `logs/v2_queries/` (relative to the server's working directory). +# V2_QUERY_LOG_DIR=logs/v2_queries + +# --------------------------------------------------------------------------- +# src/index/* (Infoscience + OpenAlex RAG indexing) +# --------------------------------------------------------------------------- + +# Override default data root for `src/index/*` (where harvested raw JSON, +# extracted text, and the LanceDB / DuckDB stores live). Default: `data/index`. +# OpenAlex artifacts live under `/openalex/`. +# INDEX_DATA_DIR=data/index + +# Pipeline tuning, model pins, and Solr filter terms for Infoscience live in +# `config/index/infoscience.yaml`. RCP and Infoscience auth tokens are +# read from `RCP_TOKEN` / `INFOSCIENCE_TOKEN` above. + +# --------------------------------------------------------------------------- +# src/index/openalex — OpenAlex ingestion + RAG over EPFL/Switzerland +# --------------------------------------------------------------------------- +# Static settings (RCP base URL, model pins, scope ROR/country, Qdrant URL, +# chunking sizes, etc.) live in `config/index/openalex.yaml`. Only secrets +# and the most common runtime overrides go here. + +# Required: email used by OpenAlex polite pool. Without this, requests fall +# back to the slower public pool with stricter rate limits. +# OPENALEX_MAILTO=you@example.com + +# Optional: override the Qdrant API key at runtime (not in YAML). +# INDEX_QDRANT_API_KEY= + +# Optional runtime overrides (rarely needed — defaults from YAML are correct +# for the EPFL deployment). When running inside the devcontainer the Qdrant +# service is reachable as `gme-qdrant`; from a host shell or external runner +# use `localhost`. +# INDEX_QDRANT_URL=http://gme-qdrant:6333 +# INDEX_QDRANT_PREFER_GRPC=false +# INDEX_OPENALEX_SCOPE_ROR=https://ror.org/02s376052 +# INDEX_OPENALEX_SCOPE_COUNTRY=ch + +# --------------------------------------------------------------------------- +# src/index/huggingface — HuggingFace ingestion + RAG over EPFL/Switzerland +# --------------------------------------------------------------------------- +# Static settings (RCP base URL, model pins, EPFL/Swiss org seeds, discovery +# search terms, Qdrant URL, chunking) live in `config/index/huggingface.yaml`. +# Only secrets and the most common runtime overrides go here. + +# Recommended: HuggingFace Hub access token. Anonymous IPs share a 500 req / +# 5 min API bucket; authenticated free tier raises that to 1k API + 5k +# resolver requests per window. Required in practice for the Switzerland +# sweep. +# HF_TOKEN= + +# Optional: override the active scope at runtime (epfl | switzerland). +# INDEX_HUGGINGFACE_SCOPE=epfl + +# --------------------------------------------------------------------------- +# src/index/zenodo — Zenodo ingestion + RAG over EPFL/Switzerland +# --------------------------------------------------------------------------- +# Static settings (RCP base URL, model pins, EPFL/Swiss community lists, +# Qdrant URL, chunking sizes, page size, rate limit) live in +# `config/index/zenodo.yaml`. Only secrets and the most common runtime +# overrides go here. + +# Optional: Zenodo personal access token. Anonymous access works (page size +# capped at 25 server-side); a token raises page size to 100 but is +# recommended for stability. +# ZENODO_TOKEN= + +# Optional: override the active scope at runtime (epfl | switzerland). +# INDEX_ZENODO_SCOPE=epfl + + +# --------------------------------------------------------------------------- +# SWISSUbase index module (`src/index/swissubase/`) +# --------------------------------------------------------------------------- +# SWISSUbase (www.swissubase.ch) is the Swiss national platform for +# sharing/preserving social-science research data, run by FORS / UZH / +# U. Neuchâtel / DASCH. There is no public REST API: every catalogue +# detail endpoint requires the session cookie set by the SPA. Ingest +# therefore drives a Selenium browser session and calls the JSON +# endpoints from inside the authenticated browser context, so +# SELENIUM_REMOTE_URL must be set. +# +# Static settings (catalogue base URL, language, polite delay, scope +# patterns, Qdrant URL, chunking sizes) live in +# `config/index/swissubase.yaml`. Only runtime overrides go here. + +# Override the active ingest scope: +# epfl_sdsc_ethz (default) — embed only studies whose institution +# string matches EPFL / ETHZ / SDSC. +# switzerland — embed every study (full ~13k catalogue). +# INDEX_SWISSUBASE_SCOPE=epfl_sdsc_ethz + +# Set to `false` to disable the V2 SWISSUbase RAG agent tool. Default +# is on, but the agent tool degrades gracefully (returns []) when the +# Qdrant collection isn't built yet. +# V2_SWISSUBASE_RAG_ENABLED=true + + +# --------------------------------------------------------------------------- +# RenkuLab index module (`src/index/renkulab/`) +# --------------------------------------------------------------------------- +# RenkuLab (renkulab.io) is SDSC's data-science platform. The index covers +# four entity types — projects, groups, users, data_connectors — pulled +# from the public `/api/data` REST surface. Three list endpoints are +# anonymous; users are harvested via `/search/query?q=type:User` (the +# dedicated `/users` endpoint requires auth). +# +# Static settings (Renku base URL, page size, rate limit, Qdrant URL, +# chunking sizes, scope keyword filters) live in +# `config/index/renkulab.yaml`. Only secrets and runtime overrides +# go here. + +# Optional: RenkuLab personal access token. Anonymous access works for +# everything the indexer needs today; set this if you want richer user +# records via `/users` in the future. +# RENKULAB_TOKEN= + +# Override the active ingest scope (all | epfl | switzerland). Scope +# filters are heuristic substring matches against namespace, path, +# keywords, and repository URLs — Renku has no first-class community. +# INDEX_RENKULAB_SCOPE=all + +# Set to `false` to disable the V2 RenkuLab RAG agent tool. Default is +# on; the tool degrades gracefully (returns []) when collections are +# missing. +# V2_RENKULAB_RAG_ENABLED=true + + +# --------------------------------------------------------------------------- +# GitHub repository index module (`src/index/github/`) +# --------------------------------------------------------------------------- +# A deterministic GitHub-REST-fed index of EPFL/Swiss/gimie-list repos +# (metadata + README chunks, embedded with Qwen3-Embedding-8B). Reuses +# the existing `GITHUB_TOKEN` for REST + README fetches — no new secret. +# `GITHUB_TOKEN` may be a comma-separated list of PATs to multiply the +# 5K req/h auth budget; the client round-robins and parks rate-limited +# tokens until their reset. +# +# Static settings (RCP base URL, model pins, scope seeds, Qdrant URL, +# chunking sizes, README size cap) live in `config/index/github.yaml`. +# Only runtime overrides go here. + +# Override the active ingest scope (epfl | switzerland | ). +# INDEX_GITHUB_SCOPE=epfl + +# Set to `false` to disable the V2 GitHub RAG agent tool. Default is on; +# the tool degrades gracefully (returns []) when the github_repos +# collection isn't built yet. Wired into the repository agent so the +# LLM can find related repos / canonical implementations while +# reasoning about the repo it is processing. +# V2_GITHUB_RAG_ENABLED=true + + +EPFL_GRAPH_USERNAME= +EPFL_GRAPH_PASSWORD= + +# Disable the EPFL Graph disciplines RAG agent tool / federated adapter. +# When unset (default), the v2 LLM repository agent gets a +# `search_epfl_graph_disciplines` tool backed by the Qdrant collection +# `epfl_graph_disciplines`. Run `just epfl-graph-ingest && just +# epfl-graph-embed` once to populate it. +# V2_EPFL_GRAPH_RAG_ENABLED=true + +# Concept tagging (optional pipeline stage). When enabled, stamps +# `_concepts` and `_keywords` onto the root repository entity from the +# README. Both fields are stripped before strict validation and JSON-LD +# output for now (schema-compliant); they exist as internal pipeline +# metadata until the ontology adds the corresponding terms. +# Backends: `epfl_graph` (requires EPFL_GRAPH_USERNAME/PASSWORD), `wikipedia` +# (credential-free, MediaWiki opensearch), `llm` (uses the project's +# existing model_config — RCP_TOKEN / OPENAI_API_KEY / OPENROUTER_API_KEY). +# V2_CONCEPT_TAGGING_ENABLED=false +# V2_CONCEPT_TAGGING_BACKEND=epfl_graph +# Drop EPFL Graph concepts whose mixed_score is below this threshold (0..1). +# Helps prune low-confidence noise (e.g. "Graph theory" matched on the word +# "graph"). Only applies to the `epfl_graph` backend. +# V2_CONCEPT_TAGGING_EPFL_MIN_SCORE=0.5 +# Enrich each top-N discipline with related publications/people/units via +# OpenAlex (uses pyalex, polite-pool email read from OPENALEX_MAILTO). +# V2_CONCEPT_TAGGING_OPENALEX_RELATED_ENABLED=false + + +ORCID_CLIENT_ID= +ORCID_CLIENT_SECRET= \ No newline at end of file diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..795f232 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,84 @@ +name: CI + +on: + pull_request: + branches: ["main", "develop"] + push: + branches: ["main", "develop"] + workflow_dispatch: + inputs: + run_llm_integration: + description: "Run opt-in LLM integration tests" + required: false + default: "false" + type: choice + options: + - "false" + - "true" + schedule: + - cron: "0 6 * * 1" + +jobs: + v2-ci-gates: + runs-on: ubuntu-latest + timeout-minutes: 20 + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install just + uses: taiki-e/install-action@just + + - name: Install project dependencies + run: | + python -m pip install --upgrade pip + python -m pip install -e ".[dev]" + + - name: Check generated v2 models freshness + run: just v2-models-check + + - name: Run TTL schema alignment gate + run: python -m pytest tests/v2/test_ttl_schema_alignment.py -q + + - name: Run JSON-LD roundtrip gate + run: python -m pytest tests/v2/test_roundtrip.py -q + + - name: Run scoped v2 test suite + run: python -m pytest tests/v2/ -n 4 --dist=loadfile -m "not live_provider and not llm_integration" + + - name: Run v1 parity regression tests + run: | + mkdir -p .tmp + CACHE_DB_PATH=.tmp/cache.db python -m pytest tests/test_cache.py tests/test_orcid_validation_pipeline.py tests/test_v1_parity.py + + llm-integration: + runs-on: ubuntu-latest + timeout-minutes: 20 + if: github.event_name == 'schedule' || (github.event_name == 'workflow_dispatch' && github.event.inputs.run_llm_integration == 'true') + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install project dependencies + run: | + python -m pip install --upgrade pip + python -m pip install -e ".[dev]" + + - name: Run LLM integration tests + env: + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + OPENROUTER_API_KEY: ${{ secrets.OPENROUTER_API_KEY }} + RCP_TOKEN: ${{ secrets.RCP_TOKEN }} + run: python -m pytest tests/v2/test_llm_repository_agent.py -m llm_integration -v diff --git a/.gitignore b/.gitignore index 5df4a1e..e752d20 100644 --- a/.gitignore +++ b/.gitignore @@ -1,8 +1,15 @@ +extraction_results +logs/ .cursor/mcp.json +.cursor/plans +.claude/ +.opentix-worktree/ .ruff_cache +.uv-cache/ data api_cache.db .env2 +.cache/* # Byte-compiled / optimized / DLL files __pycache__/ @@ -55,6 +62,7 @@ coverage.xml *.py,cover .hypothesis/ .pytest_cache/ +.testmondata cover/ # Translations @@ -172,3 +180,4 @@ DeepLabCutDeepLabCut.json MalloryWittwer.json output.jsonld sdsc-ordes.json +.internal/RISKS.md diff --git a/.tmp/.gitignore b/.tmp/.gitignore new file mode 100644 index 0000000..f59ec20 --- /dev/null +++ b/.tmp/.gitignore @@ -0,0 +1 @@ +* \ No newline at end of file diff --git a/AGENTS.md b/AGENTS.md index 11db740..507dd01 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,159 +1,339 @@ # Agent Operating Guide for git-metadata-extractor -## Title + Purpose -This guide defines the operating contract for autonomous and semi-autonomous coding agents working in this repository. -The goal is safe, reproducible contributions with minimal human back-and-forth. - -## Project Snapshot -- Language/runtime: Python project with package code under `src/`. -- Main runtime surfaces: - - API: `src/api.py` - - CLI: `src/main.py` - - Analysis orchestration: `src/analysis/` - - Agent pipelines: `src/agents/` - - Data models/contracts: `src/data_models/` - - Tests: `tests/` -- Core references: - - `README.md` - - `docs/AGENT_STRATEGY.md` - -## Environment & Prerequisites -Required environment variables (from `.env.dist` and `.env.example`): -- `OPENAI_API_KEY` -- `OPENROUTER_API_KEY` -- `GITHUB_TOKEN` -- `GITLAB_TOKEN` -- `INFOSCIENCE_TOKEN` -- `MODEL` -- `PROVIDER` -- `SELENIUM_REMOTE_URL` -- `CACHE_DB_PATH` -- `MAX_SELENIUM_SESSIONS` -- `MAX_CACHE_ENTRIES` -- `GUNICORN_CMD_ARGS` +Operating contract for autonomous and semi-autonomous coding agents working +in this repository. Goal: safe, reproducible contributions with minimal +human back-and-forth. + +## What this tool is + +A FastAPI service that turns a GitHub URL (repository / user / org) into +JSON-LD aligned with **Open Pulse Ontology v2.1.2**. The service runs the +input through a multi-stage pipeline that combines deterministic rules, +provider lookups (GitHub REST, ROR, ORCID, Infoscience), and optional LLM +agents to produce a graph of `schema:SoftwareSourceCode`, +`schema:Person`, `org:Organization`, `org:Membership`, +`pulse:Contribution`, and `schema:ScholarlyArticle` entities. + +V1 (under `src/v1/`) is a **frozen** legacy pipeline kept around for +backwards-compatible endpoints. **All new work targets V2** under +`src/v2/`. + +## Code map + +``` +src/api.py # FastAPI app, mounts /v1 and /v2 routers, /docs UI +src/v1/ # frozen legacy pipeline (no new work) +src/v2/ + api.py # /v2/extract endpoint + pipeline driver + jobs.py # async job store backing POST /v2/extract + config.py # config knobs + dependencies.py # provider wiring, cache resolver + log_context.py # request-id logging context + observation/query_log.py # per-request external-query log + + agents/ + models.py # AgentResult, ProviderSet, TypedEntityBuckets + registry.py # runtime → runner table + llm/ # LLM-backed agents + _payload_helpers.py # force_server_uuid + shared post-LLM stamps + _verdict_cache.py # per-agent result cache + /agent.py # per-entity LLM agents (one Pydantic AI run each) + agent_tools/ # Tool factories (selenium, ROR, ORCID, etc.) + # *_rag.py: per-index Qdrant search tools + # (infoscience, huggingface, openalex, + # zenodo, orcid, ror, renkulab, + # epfl_graph_rag) — see + # docs/v2-rag-tools.md + rule_based/ + _agent.py # deterministic counterparts (no LLM) + refiners/ # hybrid-runtime LLM refiners — propose targeted + /agent.py # patches over rule-based output, whitelisted fields only + # organization/agent.py — pulse:OrganizationType + # repository/agent.py — pulse:discipline, pulse:repositoryType + # person/agent.py — schema:name (handle→canonical) + + ingest/ + cache.py # ProviderCache (SQLite, WAL) + providers/ # github / ror / orcid / infoscience clients + # *_rag.py: async Qdrant-backed RAG providers + # _rag_helpers.py: shared filter/rerank utils + + pipeline/ + orchestrator.py # stage runner, fan-out concurrency, retries + stages/ # the actual stages — see "Pipeline" below + + schema/ # JSON Schemas (agent + strict) + JSON-LD context + validation/ # strict-schema + SHACL validators + canonicalization/ # ID resolution, string normalisation + +src/index/ # 11 sibling RAG indices (DuckDB + Qdrant per index) + + # _federated/ adapter layer. + # epfl_graph/ — disciplines ontology RAG + # (see docs/epfl-graph-disciplines.md) + # See docs/rag-indices.md for the full inventory. + +src/module/ # Standalone analytical modules complementing v2. + # dependents/ — GitHub `/network/dependents` scraper + # epfl_graph/ — graphai-client wrapper + ontology + # endpoints + OpenAlex bridge. + # Used by concept_tagging and the + # src/index/epfl_graph/ ingest pass. + +tests/v2/ # default test target +``` + +## Pipeline (the actual stages, in order) + +`/v2/extract` runs the same pipeline regardless of `agent_runtime`. The +runtime only controls which agent implementations execute (LLM agents vs. +deterministic rule-based agents). All other stages run unconditionally. + +``` +1. classify_url classify the input as repository / user / org +2. gather_context fetch GitHub metadata + GIMIE JSON-LD +3. context_summary [LLM] compile a markdown summary; raw blobs are stripped + from per-agent prompts in LLM mode +4. repo_agent produce the root repository entity +5. person_agents (fan-out) one agent per discovered contributor / user +6. org_agents (fan-out) one agent per discovered organisation +7. article_agents (fan-out) discover scholarly articles tied to the repo +8. membership_agents (fan-out) one agent per (person, org) pair +9. contribution_agents (fan-out) one agent per (person, repo) pair +10. llm_dedup [LLM] cross-bucket entity dedup + ID remap (fail-open) +11. reconcile_entities deterministic ID canonicalisation + linkage +12. llm_critic [LLM, gated] drop-suggestion stage (off by default) +13. guarantee_repo_author stamp github-owner as schema:author when empty +14. strict_validation per-entity strict JSON Schema check +15. assemble_output split graph into root + related + excluded +16. link_veracity [LLM, gated] verify every URL via Selenium fetch + LLM +17. validate_articles drop placeholder/sentinel-DOI articles +18. validate_author_classes drop `schema:author` refs whose target is + not a `schema:Person` (closes a SHACL + class-shape violation that was previously + warning-only) +19. validate_ownership strip mismatched pulse:owns +20. infer_owners stamp pulse:owns / pulse:ownedBy from handles; + also coerces any residual bare-login string + on `pulse:ownedBy` (e.g. "luzpaz") to the + IRI shape `{"@id": "https://github.com/luzpaz"}` + so SHACL never sees a `` URI +21. infer_github_handle_parents fuzzy-search ROR for parent of every github + org; add ROR org entities, stamp unitOf +22. org_relationships [LLM] whole-graph LLM call to refine unitOf edges +23. infer_org_units deterministic name-token fallback for unitOf +24. concept_tagging [gated] pull EPFL Graph concepts/keywords/disciplines + from the README onto the root repo entity as + internal `_concepts`/`_keywords`/`_disciplines` + metadata (off by default; opt-in with + `V2_CONCEPT_TAGGING_ENABLED=true`) +25. build_jsonld_output produce the final JSON-LD graph; also + strips redundant `pulse:ror` from any + `org:Organization` whose `@id` is already + the ROR (closed-shape violation fix) +``` + +**Gates:** + +- Stages tagged `[LLM]` only run in `agent_runtime=llm`. +- `link_veracity` is `[LLM]`-only too: in `agent_runtime=rule_based` it is **always skipped** (rule-based mode is guaranteed LLM-free). +- `agent_runtime=hybrid` runs the rule-based generators (stages 4-9) **and** an LLM refiner stage (`refine_with_llm`, between reconciliation and `guarantee_repo_author`). The refiner proposes whitelisted-field patches per entity type: organizations (`pulse:OrganizationType`), repositories (`pulse:discipline`, `pulse:repositoryType` only when current is `pulse:Other`), and persons (`schema:name` only when current looks like a GitHub handle). LLM-only stages (`llm_dedup`, `llm_critic`, `link_veracity`, `org_relationships`) are **skipped** in hybrid mode. Toggle with `V2_HYBRID_REFINER_ENABLED` (default `true`). +- `llm_critic` is **off by default**. Set `V2_APPLY_CRITIC_PRUNING=true` to enable (LLM mode only). +- `link_veracity` is **on by default in LLM mode**. Set `V2_LINK_VERACITY_ENABLED=false` to skip even in LLM mode (recommended for batch runs). +- `concept_tagging` is **off by default**. Set `V2_CONCEPT_TAGGING_ENABLED=true` to opt in. Backends are pluggable via `V2_CONCEPT_TAGGING_BACKEND` ∈ {`epfl_graph` (default, calls graphai), `wikipedia` (credential-free MediaWiki opensearch), `llm` (pydantic-ai)}. Stamps `_concepts` / `_keywords` / `_disciplines` as internal `_*` metadata (stripped before JSON-LD output and strict validation). Optional OpenAlex enrichment per discipline via `V2_CONCEPT_TAGGING_OPENALEX_RELATED_ENABLED=true` (publications, people, units). Full reference at [`docs/concept-tagging.md`](docs/concept-tagging.md). + +**Hallucination guards baked into agents:** + +- `force_server_uuid` overwrites whatever UUID the LLM emitted with a server-generated one in `identifiers.uuid` only — never as a top-level field (additionalProperties violations). +- Repository agent post-LLM: stars/forks from GitHub REST (deterministic, not LLM-derived); discipline fallback `wd:Q428691` (computer engineering) when LLM emits empty/null; `pulse:repositoryType` keyword heuristic in rule-based mode. +- Contribution agent post-LLM: stamps `schema:author = target_person.id` and `pulse:contributionTo = target_repository.id` from the orchestrator's authoritative pair, regardless of what the LLM emits. +- Article agent post-LLM: drops the entity if `schema:identifier` is a placeholder DOI (`10.0000/...`) or sentinel string (`UNKNOWN`, `N/A`, `TBD`, etc.) and no `pulse:infoscienceArticleIdentifier` is present. +- Article agent (rule-based) defaults to repo-name-only Infoscience queries; opt in to the wider `include_person_queries=True` / `include_organization_queries=True` blend only when over-attribution risk is low. +- Membership agents (both rule-based and LLM): swap `time:hasBeginning` / `time:hasEnd` when ORCID returns the pair inverted, so `hasBeginning <= hasEnd` always holds. + +**SHACL conformance auto-fixes (warning-only `shacl_gate`, but the graph is fixed in place):** + +The SHACL gate emits violations as `result.warnings` rather than aborting, +so the responsibility for producing a SHACL-clean graph lives in the +upstream stages and agents. The four most common violations are +addressed deterministically: + +1. `pulse:ownedBy` IRI shape — `infer_owners` rewrites bare-login strings + to `{"@id": "https://github.com/{handle}"}`. Without this, SHACL + resolves the bare token against the working-directory base URI + (``) and the closed-shape check on + `schema:Person | org:Organization` fails. +2. `pulse:ror` redundancy — `build_jsonld_output` strips the field on + any `org:Organization` whose `@id` is already the ROR. The + Organization shape is `sh:closed` and rejects `pulse:ror`; the + `@id` already carries that information. +3. `Membership` date order — both membership agents swap inverted dates + (above). +4. `schema:author` class — `validate_author_classes` filters refs whose + target is missing from the graph or not typed `schema:Person`. + Catches Membership / Contribution ids leaking into author lists. + +**Orchestrator fanout filtering:** + +- `_filter_person_work_items` skips a queued person fanout when the + GitHub handle resolves to `type=Organization` (cached + `provider.github.get_user(login)` lookup). +- `_filter_org_work_items` mirrors the rule for org fanouts: skips + handles whose GitHub `type` is `User`. This prevents the 4× retry + loop in `org_agent` for personal handles encoded into + `org:hasMembership` composite ids. +- `_person_fanout_contexts` materialises a User-account repo owner as a + Person when missing from `contributors` (abandoned repos, empty + repos). Prevents the owner from leaking as a bare-string ref in + `pulse:ownedBy` / `schema:author` with no backing entity. + +## API surface + +- `GET /v2/health` — health check (open, no auth) +- `POST /v2/extract` — async job. Body: `{source_url, agent_runtime?, output_format?, include_context_summary?}`. Returns `{job_id, status, status_url}`; poll `GET /v2/jobs/{job_id}`. +- `GET /v2/jobs/{job_id}` — job status + result when complete +- `GET /v2/extract/{full_path:path}` — synchronous extract (single repo) +- `GET /docs` — Swagger UI with auto/manual dark-mode toggle (override persisted in `localStorage`) + +**Auth:** every `/v1/*` route plus `/v2/extract` and `/v2/jobs/{id}` requires +`Authorization: Bearer ` (see the `API_TOKEN` row below). `/`, +`/docs`, and `/v2/health` are open. The dependency lives in +`src/v2/auth.py::verify_token`. + +V1 endpoints (`/v1/extract`, `/v1/cache/*`) are still mounted but frozen +(and now also bearer-protected). + +## Configuration (env vars) + +| Var | Default | Purpose | +|---|---|---| +| `GITHUB_TOKEN` | — | required for live GitHub provider | +| `API_TOKEN` | — | bearer token guarding every `/v1/*` route plus `/v2/extract` and `/v2/jobs/{id}`. Fails closed: missing → 503 (no dev bypass). `/`, `/docs`, `/v2/health` stay open. Generate with `python -c "import secrets; print(secrets.token_urlsafe(32))"`. | +| `RCP_TOKEN` / `OPENAI_API_KEY` / `OPENROUTER_API_KEY` | — | one is required for LLM mode | +| `INFOSCIENCE_TOKEN` | unset | only for protected Infoscience routes | +| `SELENIUM_REMOTE_URL` | unset | enables Selenium-backed link veracity + selenium-fetch tool | +| `V2_AGENT_RUNTIME_DEFAULT` | `llm` | default runtime when `/v2/extract` omits `agent_runtime` | +| `V2_USE_MOCK_PROVIDERS` | `true` | swap in mock GitHub/ORCID/Infoscience/ROR providers | +| `V2_LINK_VERACITY_ENABLED` | `true` | turn off to skip the link-veracity stage in LLM mode (rule-based mode skips unconditionally) | +| `V2_CONTEXT_SUMMARY_SCOUT_MODE` | `false` | upgrade `context_summary` LLM stage to scout mode: broad RAG-search toolkit (orcid/ror/infoscience/openalex/zenodo/ethz/huggingface/renkulab/snsf/epfl_graph + selenium_fetch) on top of the legacy `grep_repository_corpus` + DuckDuckGo pair, plus a structured-brief prompt with explicit People / Organizations / Articles / Affiliations / Caveats sections. Per-entity LLM agents (person, org, article, membership, contribution) automatically benefit since they already consume the `summary_markdown`. Trade-off: heavier upfront LLM call, but per-entity calls send less context and duplicate ORCID/ROR lookups across entities collapse into the scout's shared brief. | +| `V2_INFOSCIENCE_RAG_ENABLED` | `true` | enables the Infoscience RAG agent tools (Qdrant-backed semantic search + on-demand chunk/record fetch). Construction degrades gracefully when Qdrant or RCP is unreachable. | +| `V2_ETHZ_RESEARCH_COLLECTION_RAG_ENABLED` | `true` | enables the ETH Research Collection RAG agent tools (DSpace-backed sister index to Infoscience for ETHZ research outputs). Same shape: search + fetch_chunks + fetch_records. | +| `V2_HUGGINGFACE_RAG_ENABLED` | `true` | enables the HuggingFace Hub RAG search tool (collections: `hf_models`, `hf_datasets`, `hf_spaces`, `hf_orgs`). | +| `V2_OPENALEX_RAG_ENABLED` | `true` | enables the OpenAlex RAG search tool (collections: `works`, `authors`, `institutions`, `sources`, `topics`, `concepts`). | +| `V2_ZENODO_RAG_ENABLED` | `true` | enables the Zenodo RAG search tool (collection: `zenodo_records`). | +| `V2_ORCID_RAG_ENABLED` | `true` | enables the ORCID RAG search tool (entities: `persons`, `employments`, `educations`; collections namespaced by scope). | +| `V2_ROR_RAG_ENABLED` | `true` | enables the ROR RAG search tool (scopes: `epfl_ethz`, `switzerland`, `europe`, `worldwide`). | +| `V2_SWISSUBASE_RAG_ENABLED` | `true` | enables the SWISSUbase RAG search tool (collection: `swissubase_entities`; entities: `studies`, `datasets`, `persons`, `institutions`). Ingest is Selenium-driven; default scope embeds only EPFL/ETHZ/SDSC-affiliated studies. | +| `V2_RENKULAB_RAG_ENABLED` | `true` | enables the RenkuLab RAG search tool (renkulab.io). One Qdrant collection per entity type: `renkulab_projects`, `renkulab_groups`, `renkulab_users`, `renkulab_data_connectors`. The single tool searches across all four by default; the `entity_types` argument scopes to a subset. | +| `RENKULAB_TOKEN` | unset | optional; without it the indexer can still ingest public projects/groups/data_connectors and harvest users via `/search/query?q=type:User`. With it, set on `https://renkulab.io/api/data` for richer user records. | +| `V2_EPFL_GRAPH_RAG_ENABLED` | `true` | enables the EPFL Graph disciplines RAG search tool (`search_epfl_graph_disciplines`). Single Qdrant collection `epfl_graph_disciplines` over the curated EPFL Graph academic-discipline ontology (~2226 categories, depth 1..5, embeddings built from `name + canonical Wikipedia lead-section + top anchor concept names`). Wired into the repository, person, organization, and article LLM agents. Refresh with `just epfl-graph-{ingest,enrich-wikipedia,embed}`. See [`docs/epfl-graph-disciplines.md`](docs/epfl-graph-disciplines.md). | +| `EPFL_GRAPH_USERNAME`, `EPFL_GRAPH_PASSWORD` | unset | required by the `epfl-graph-ingest` recipe (the auth handshake against `graphai.epfl.ch`). Not needed at runtime once the index is hydrated — `search_epfl_graph_disciplines` only hits Qdrant + RCP. | +| `INDEX_QDRANT_URL` | `http://qdrant:6333` (yaml default) | Qdrant endpoint for every RAG index. Inside the devcontainer use `http://gme-qdrant:6333`. | +| `V2_APPLY_CRITIC_PRUNING` | `false` | turn on to enable critic drop suggestions | +| `V2_MAX_CONCURRENT_AGENTS` | `6` | per-stage fan-out concurrency | +| `V2_PROVIDER_CACHE_PATH` | `.cache/v2/providers.db` | SQLite path for the provider+verdict+pipeline cache. Use a different path per run profile (e.g. LLM vs rule-based) for isolation. | +| `V2_PROVIDER_CACHE_TTL_DAYS` | `30` | TTL for cached entries | +| `V2_PROVIDER_CACHE_ENABLED` | `true` | when `false`, every external lookup is fresh | +| `V2_PIPELINE_CACHE_ENABLED` | `true` | when `false`, every `/extract` re-runs the full pipeline | +| `V2_QUERY_LOG_DIR` | `logs/v2_queries` | per-request external-query log destination | +| `LOG_LEVEL` | `INFO` | DEBUG/INFO/WARNING/ERROR | + +Required environment **for serving requests**: `GITHUB_TOKEN` and at least +one LLM credential (when LLM mode is the default). Rules: - Never print, log, or commit secrets. -- Never modify secret-bearing files (`.env`, `.env2`, similar secret files) unless explicitly asked. -- If required variables are missing for the requested task, fail fast and report exactly which variables are missing. - -## Canonical Commands -`justfile` is the source of truth for routine operations. Prefer `just` commands over ad-hoc shell commands when equivalent recipes exist. - -- Setup: - - `just install-dev` - - `just setup` -- Run API: - - `just serve-dev` - - `just serve` -- Tests: - - `just test` - - `just test-file tests/.py` -- Quality: - - `just lint` - - `just type-check` - - `just check` -- CI-like local validation: - - `just ci` - -## Architecture Map For Agents -- Repository analysis flow entrypoints: `src/analysis/repositories.py` -- User and organization analysis entrypoints: - - `src/analysis/user.py` - - `src/analysis/organization.py` -- Agent implementations: - - `src/agents/` - - Atomic subpipeline: `src/agents/atomic_agents/` -- Data contracts: - - `src/data_models/` -- Context and external lookups: - - `src/context/` -- Cache layer: - - `src/cache/` - -## Editing Rules (Strict) +- Never modify `.env`, `.env2`, or other secret-bearing files unless explicitly asked. +- Fail fast and report the missing variable name (no value) when a required var is absent. + +## Common commands + +The `justfile` is the source of truth. Prefer `just ` over ad-hoc shell. + +| Recipe | Purpose | +|---|---| +| `just install-dev` | install with dev extras | +| `just setup` | install + scaffold `.env` | +| `just serve-dev` | uvicorn + auto-reload (watches only `src/**/*.py`) | +| `just serve-gunicorn` | gunicorn with 4 workers (production-shape) | +| `just serve-stop` | stop whatever's bound to `:$PORT` | +| `just test` | fast tests via testmon | +| `just test-full` | full deterministic test run | +| `just test-file ` | one file | +| `just lint` / `just type-check` / `just check` | quality gates | +| `just v2-models-generate` | regenerate Pydantic models from strict schemas | +| `just v2-models-check` | assert generated models are in sync | + +For batch extractions over many repos: `scripts/v2/batch_extract.sh` reads +a hardcoded URL list and runs them through `/v2/extract` with configurable +parallelism. Resumable: skips repos whose result file already exists with +a non-`running` status. + +## Pipeline cache topology + +Three caches share a single SQLite DB (path: `V2_PROVIDER_CACHE_PATH`): + +1. **Provider cache** — gimie payloads, GitHub REST responses, ROR / ORCID / Infoscience hits. Deterministic, content-addressed. +2. **Agent verdict cache** — LLM agent results keyed on agent name + identity. Skips a repeat LLM call for a known-good payload. +3. **Pipeline cache** — full `/v2/extract` response for a given source URL. + +Set `V2_PROVIDER_CACHE_PATH` to a different file per run profile (e.g., +`.cache/v2-rule-based/providers.db` for rule-based runs) to keep them +isolated and independently invalidatable. + +## Editing rules + - Keep diffs minimal and scoped to the requested task. - Preserve existing code style, project conventions, and import patterns. - Do not rename or move public modules unless explicitly requested. -- Do not modify `.env`, `.env2`, or other secret-bearing files unless explicitly requested. -- Never run destructive git/file operations unless explicitly requested. -- If unrelated local changes exist, do not revert them; work around them and report context in the completion summary. - -## Task Playbooks -### Bug Fix Playbook -1. Reproduce the issue with a targeted test (or nearest equivalent validation). -2. Patch the minimal root cause. -3. Run focused tests first; run broader checks if shared paths were touched. -4. Report behavior change and residual risk. - -### Feature Playbook -1. Identify API/data-model impact before coding. -2. Implement required model, pipeline, and endpoint wiring. -3. Add or adjust tests in `tests/`. -4. Validate with `just test` plus relevant lint/type checks. - -### Refactor Playbook -1. Preserve behavior unless behavior change is explicitly requested. -2. Keep API contracts stable. -3. Prove parity with tests and checks. - -## Testing & Validation Requirements -Minimum before completion: -- Run the nearest relevant tests. -- Run lint/type checks for touched Python modules when feasible. - -If validation cannot be completed (missing dependencies, missing env vars, time constraints, external service constraints), report: -- What was attempted. -- What failed and why. -- The exact command(s) to run later. - -## API/Schema Change Rules -For changes to FastAPI endpoints in `src/api.py`, include: -- Updated request/response behavior notes. -- Compatibility or migration notes. -- Test coverage for changed endpoint behavior. - -For changes to models in `src/data_models/`, include: -- Impact notes on downstream usage (`src/analysis/`, `src/agents/`, API surface). -- Tests for new/changed fields and validation behavior. - -## Output/Reporting Contract For Agents -Completion reports must include: -- Files changed -- Behavior change -- Commands run and key results -- Risks / follow-ups +- Do not modify secret-bearing files unless explicitly requested. +- Do not run destructive git/file operations unless explicitly requested. +- If unrelated local changes exist, do not revert them — work around them and report context. + +## Schema change rules + +JSON Schemas live in **three byte-identical copies** that must stay in sync: + +1. `src/v2/schema/json/{type}/{entity}.schema.json` (source) +2. `dev/ontology-v2-json-response/a-001/json-schema/{type}/pulse_{Entity}Shape.schema.json` (promoted) +3. `tests/v2/fixtures/schema/{type}/{entity}.schema.json` (test fixture) -No vague "done" messages. Reports must include verifiable evidence. +After any schema edit: copy to all three and run `just v2-models-generate` +to regenerate Pydantic models in `src/v2/schema/models/`. `just v2-models-check` +in CI catches drift. -## Definition of Done -- Requested scope implemented. -- Relevant tests/checks passed, or blockers explicitly documented. -- No secret leakage. -- No unrelated mutations. -- No undocumented behavior changes. +## Identifier conventions -## Test Cases & Scenarios For This Guide -1. Discoverability -- Scenario: A new agent opens the repository root. -- Expectation: `AGENTS.md` is present with quick-start commands and architecture map. +- **Person**: `https://orcid.org/{orcid}` when ORCID known, else `https://github.com/{login}`, else `urn:pulse:{uuid}` +- **Organization**: `https://ror.org/{id}` when ROR known, else `https://github.com/{handle}`, else `urn:pulse:{uuid}` +- **Repository**: `https://github.com/{owner}/{name}` +- **Article**: `https://doi.org/{doi}` when DOI known +- **Membership**: `{person_id}_{org_id}` composite +- **Contribution**: `{person_id}_{repo_id}` composite -2. Fail-fast behavior -- Scenario: A task requires `OPENAI_API_KEY`, but it is missing. -- Expectation: Agent halts and reports the missing variable explicitly; no fabricated results. +`identifiers.uuid` is always a server-generated UUIDv4 (via +`src/v2/agents/models.py::generate_uuid()`); the LLM never controls it. -3. Workflow consistency -- Scenario: Bug fix in `src/agents/organization_enrichment.py`. -- Expectation: Agent follows the bug-fix playbook and runs targeted tests first. +## Internal pipeline metadata + +Fields whose names start with `_` (e.g. `_person_ref` on Memberships) are +internal pipeline metadata. They are **stripped** before strict +validation, JSON-LD output, RDF serialisation, and any external +artefact. Never expose `_`-prefixed fields in API responses. + +## Reporting contract + +Completion reports must include: +- Files changed +- Behaviour change +- Commands run + key results +- Risks / follow-ups -4. Safety guardrails -- Scenario: Dirty worktree with unrelated changes. -- Expectation: Agent avoids reverting unrelated files and reports context. +No vague "done". Reports must include verifiable evidence. -5. Reporting quality -- Scenario: Agent completes a task. -- Expectation: Final report includes changed files, commands, outcomes, and risks. +## Definition of done -## Public API/Type Impact -- No code/API/type changes are introduced by this document. -- This file defines a repository-local agent policy contract only. +- Requested scope implemented +- Relevant tests/checks passed (or blockers explicitly documented) +- No secret leakage +- No unrelated mutations +- No undocumented behaviour changes diff --git a/CHANGELOG.md b/CHANGELOG.md index 3688617..f660245 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,1039 @@ All notable changes to this project will be documented in this file. +## [Unpublished] + +### Changed +- `POST /v2/extract` is now an async job submitter rather than a synchronous proxy to the GET handler. The handler validates the URL synchronously (still 422 for unsupported URLs), persists a `pending` job in the `ProviderCache`-backed job store, schedules the pipeline as a background task, and returns `202 Accepted` with `{job_id, status, status_url, submitted_at}`. Returns `503` if the provider cache is disabled. `GET /v2/extract/{full_path}` remains synchronous and unchanged. +- In `agent_runtime=llm`, orchestrator now performs an initial fail-open context-compilation pass immediately after `context_gather`: downstream LLM agents receive a compiled markdown summary block in prompt appendix context, while raw README/GIMIE/repository-file blobs are stripped from per-agent runtime context payloads. +- `/v2/extract` adds `include_context_summary=true|false` (default `false`). When enabled, responses include `context_summary_markdown` containing the compiled context brief provided to downstream agents (if available for the run/runtime). +- `/v2/extract` link validation is now always-on (`link_veracity` stage) for all runtimes (no request flag). The stage now validates all discovered HTTP(S) links from assembled entities and records per-link diagnostics/intermediates. +- Article link validation now enforces DOI-link checking by normalizing bare `schema:identifier` DOI strings to `https://doi.org/` before verification. Canonical article identity hierarchy remains `DOI > Infoscience > UUID`. +- Link-pruning policy now removes only explicitly unreachable links (`fetched_successfully=false`) and prunes entities when canonical URL/DOI checks fail or no valid URL remains after pruning. +- Added two always-on LLM-only global stages in `/v2/extract`: `llm_dedup` (after permissive validation, before reconciliation) and `llm_critic` (after reconciliation, before strict validation). Both stages are fail-open (warning-only) and append to `stats.stages_completed` in LLM runs. +- `llm_dedup` now applies deterministic constrained merge acceptance on LLM cluster suggestions across organizations/persons/repositories/articles, enforces identifier-conflict rejections, resolves canonical IDs by hierarchy, remaps references, and recomputes membership/contribution composite IDs. +- `llm_critic` now applies deterministic non-root pruning from LLM suggestions with root protection, cascade cleanup for memberships/contributions, relation-array cleanup, and `critic_pruned` entries in `excluded_entities`. +- `llm_critic` now receives explicit `owner_provenance`, can call `search_on_the_internet` (DuckDuckGo) and GitHub organization metadata tooling for relevance checks, and deterministically protects (a) repository owner-organization ancestry (`org:unitOf` chain) and (b) contributor-affiliation organizations tied to kept repository contributors from critic pruning. +- Strengthened v2 organization identity reconciliation to be ROR-first end-to-end. `resolve_organization_id()` now enforces hierarchy precedence even when incoming `id/idSource` is prefilled with a lower-priority source, and reconciliation now merges high-confidence duplicate organization variants (ROR/Infoscience/GitHub signals) before relationship remapping. +- Improved cross-source organization merge equivalence to normalize common name spelling variants (for example `center`/`centre`) so ROR + Infoscience duplicates merge more reliably and membership/org remaps propagate to canonical ROR IDs. +- Changed v2 organization lookup collision behavior during reconciliation to prefer ROR-backed canonical organizations when multiple candidates share tokens, while keeping ambiguity warning-only (no extract hard-fail). +- Changed `PipelineOrchestrator` default prompt-context behavior so downstream LLM agents receive serialized upstream stage JSON by default (`include_upstream_stage_outputs_in_prompt=True`). +- Updated LLM organization and membership system prompts with explicit acronym-disambiguation guidance, context-grounded ROR selection rules, and “leave ROR null when ambiguous” instructions. +- Updated membership fanout context to provide explicit `target_person` and `target_organizations` blocks, and wired `LLMMembershipAgentV2` to expose ORCID lookup tooling (`get_orcid_record`) for role/date grounding when ORCID identifiers are available. +- Tightened organization ownership semantics: reconciliation now rebuilds `pulse:owns` from canonical repository `pulse:ownedBy` links and clears ownership for organizations without a GitHub organization handle, preventing non-owner affiliation organizations from owning source repositories. +- Disabled ownership propagation from GitHub org-account units to canonical parent organizations; `pulse:owns` now remains direct-owner-only while preserving GitHub org-account nodes as `org:Organization` entities for hierarchy linkage. +- Reconciliation now strips organization lookup-only fields (`aliases`, `acronyms`, `labels`) before strict validation/output so `org:Organization` payloads stay strict-schema compliant (`additionalProperties: false`). +- GitHub org-account unit entities synthesized during reconciliation now use canonical GitHub URL IDs (`https://github.com/`) instead of bare-handle IDs, keeping repository ownership links URL-consistent end-to-end. +- Added a dual-provider organization LLM tool (`search_organization_identity`) that queries ROR and Infoscience together and returns linked candidate pairs for coherent identifier assignment. +- Replaced opaque `urn:git-metadata-extractor:entity:` URI prefix with shorter `urn:pulse:` for fallback entity `@id` values. Canonical entities (persons, repositories, organizations, articles) now use dereferenceable URLs (`https://orcid.org/`, `https://github.com/`, `https://ror.org/`, `https://doi.org/`) as `@id` wherever the `resolve_*_id()` functions in `id_resolution.py` produce them. Only UUID-fallback or pre-reconciliation entities receive the `urn:pulse:` prefix. +- Membership and contribution composite IDs now use full canonical URIs for both parts (e.g., `https://github.com/user_https://ror.org/02s376052`). The pipeline no longer relies on splitting composite IDs by `_` to extract person/org references — `_person_ref` and `schema:author`/`org:organization` fields are used instead. +- Crossref validation no longer parses composite membership IDs via string splitting. Ownership checks use an explicit `_person_ref` → person lookup map built from membership entities. +- Strict schema validation now strips `_`-prefixed internal fields before checking `additionalProperties`, preventing false rejections from pipeline-internal metadata. + +### Fixed +- Fixed a regression where link-checker/runtime errors could be misclassified as invalid URLs and incorrectly prune root entities. Link-veracity checker errors are now fail-open warnings and do not trigger automatic pruning. +- Fixed `LLMPersonAgentV2` null optional-field leak: `model_dump(by_alias=True, mode="json")` in pydantic-ai returns all fields including `None`-valued optionals. The strict SHACL schema requires absent fields rather than explicit nulls, so top-level `None` values are now stripped from the payload dict before validation (`{k: v for k, v in payload.items() if v is not None}`). The nested `identifiers` sub-object is preserved intact. +- Fixed `LLMPersonAgentV2` null-list iteration bug: when the LLM returned `"pulse:hasContribution": null` or `"org:hasMembership": null`, `payload.get(key, [])` returned `None` (key exists with null value, default not used), causing `TypeError: 'NoneType' object is not iterable`. Changed to `(payload.get(key) or [])` to handle both absent key and null value. +- Fixed `V2LLMRuntime` token count extraction: `result.usage` in pydantic-ai 1.5.0 is a method, not a property. Added `if callable(usage): usage = usage()` guard before field access so `tokens_prompt`/`tokens_completion` are populated from the real `RunUsage` object instead of always returning `None`. V1 agents had the same bug but silently fell back to tiktoken estimates; v2 now uses the actual API-reported counts. +- Fixed v2 provider rate-limiter cross-loop lock reuse: shared `asyncio.Lock` instances could be reused across different event loops and fail with `"... is bound to a different event loop"`. Locks are now keyed per `(provider, event-loop-id)` to support mixed loop execution safely (script and orchestrator paths). +- Fixed person-stage unbounded waits in LLM fanout by adding a hard timeout around `LLMPersonAgentV2` runtime calls (`llm_call_timeout_seconds`, default `180s`) with explicit timeout errors per contributor. +- Fixed GIMIE-only repository runs when LLM is disabled: if GIMIE returns JSON-LD on `self.gimie`, the run is now marked successful instead of failing with `no data generated`. +- Fixed end-of-run logging noise for GIMIE-only runs (`run_llm=False`): suppress the LLM token-usage summary banner. +- Fixed `/v1/repository/gimie/json-ld` GitHub rate-limit surfacing: map GIMIE `ConnectionError` messages (secondary/primary rate limits) to clearer HTTP `429` / `503` responses instead of generic failures. + +### Added +- Added `make_query_dependencies_tool` factory (`src/v2/agents/llm/agent_tools/query_dependencies.py`): creates a pydantic-ai `Tool` named `query_dependencies` that returns the parsed SPDX dependency list for a repository by calling GitHub's dependency-graph REST endpoint (`GET /repos/{owner}/{repo}/dependency-graph/sbom`). Each entry is shaped as `{name, ecosystem, version, spdxId}` with `ecosystem` parsed from the package's `purl` external reference (pypi, npm, cargo, maven, githubactions, ...). Supports optional `ecosystem` exact-match and `name_contains` (case-insensitive substring) filters, plus a `limit` cap (default 50, max 500). Returns `[]` when no SBOM is available (dependency graph disabled, 404, or 403 for private repos without `repo` scope) so the LLM treats absence as "no data" rather than as an error. Wired into `LLMRepositoryAgentV2` as an optional tool — the system prompt instructs the model to skip the call unless dependency context would inform `pulse:repositoryType`, `pulse:discipline`, or `schema:programmingLanguage`. +- Extended the `GitHubProvider` interface with `get_repository_sbom(full_name) -> list[dict] | None` (`src/v2/ingest/providers/base.py`), with a default `return None` impl so optional fakes need not stub it. Implemented in `RealGitHubProvider` (`github_provider.py`) by calling the SPDX SBOM endpoint with the existing `GITHUB_TOKEN` auth and `ProviderCache` integration; 404/403 collapse to `None`. `MockGitHubProvider` (`mock_github.py`) returns a small fixture for `octocat/Hello-World`. +- Added regression coverage in `tests/v2/test_github_sbom.py` (purl parsing across pypi/npm/maven/cargo/githubactions, version-fallback to `versionInfo`, unwrapped SPDX shapes, HTTP 200/404/403/500/non-JSON/network-failure paths) and `tests/v2/test_llm_query_dependencies_tool.py` (filter combinations, limit clamping, copy-not-reference output, empty-SBOM behavior). +- Added `make_query_orcid_tool` factory (`src/v2/agents/llm/agent_tools/query_orcid.py`): creates a pydantic-ai `Tool` named `query_orcid` that searches ORCID's expanded-search endpoint (`/v3.0/expanded-search/`) by free-text name. The tool wraps the standard edismax query with boosts on `given-and-family-names`, `family-name`, `given-names`, `credit-name`, `other-names`, and `text`, plus boost queries on current/past institution affiliations, and returns up to `rows` candidate hits (`orcid_id`, given/family/credit/other names, `institution_names`, `emails`). Wired into both `LLMPersonAgentV2` and `LLMMembershipAgentV2` so the LLM can discover an ORCID before calling `get_orcid_record` for the full profile. +- Extended the `ORCIDProvider` interface with `search_persons(query, *, rows=50, start=0) -> list[ORCIDSearchHit]` (`src/v2/ingest/providers/base.py`) and implemented it in both `RealORCIDProvider` (`orcid_provider.py`) and `MockORCIDProvider` (`mock_orcid.py`). Real-provider results are cached through `ProviderCache` keyed on `(query, rows, start)`; the mock matches case-insensitive substrings against the existing fixtures so unit tests don't hit the network. +- Updated `LLMPersonAgentV2` and `LLMMembershipAgentV2` system prompts to document `query_orcid`, including when to call it (no ORCID available, no Infoscience match), how to disambiguate hits via `institution_names`, and the rule that `pulse:orcid` and membership dates may only be filled after `get_orcid_record` confirms the candidate. +- Added regression coverage in `tests/v2/test_real_orcid_search.py` for the `RealORCIDProvider.search_persons` path: edismax `q` construction, pagination params, malformed-payload tolerance, query trimming + `rows`/`start` clamping, empty-query short-circuit, missing-`expanded-result` handling, and `ProviderCache` hit/miss behavior. +- Added prototype body-based extract endpoint `POST /v2/extract` (keeps existing `GET /v2/extract/{full_path}` intact). Request body mirrors extract options (`source_url`, `output_format`, `agent_runtime`, `include_intermediates`, `include_context_summary`) and returns the same `V2ExtractResponse` contract. +- Added `GET /v2/jobs/{job_id}` companion retrieval endpoint for jobs submitted via `POST /v2/extract`. Returns the persisted `V2ExtractJob` record (`pending|running|completed|failed`) including `request`, timestamps, and either `result` (full `V2ExtractResponse`) or `error` (`V2ErrorResponse`). Returns `404` for unknown / TTL-expired ids, `503` if the provider cache is disabled. Records persist in `ProviderCache` under the `v2-extract-job` namespace, governed by `V2_PROVIDER_CACHE_PATH` / `V2_PROVIDER_CACHE_TTL_DAYS`. +- Added `JobStore` (`src/v2/jobs.py`), a thin wrapper around `ProviderCache` for `V2ExtractJob` reads/writes, plus models `V2ExtractJob`, `V2ExtractJobAccepted`, and `V2ExtractJobStatus` in `src/v2/api_models/contracts.py`. +- Added `LLMContextSummaryAgentV2` (`src/v2/agents/llm/context_summary/agent.py`) as a beginning-of-pipeline LLM context compiler that ingests raw gathered repository material and emits `summary_markdown` for downstream agent grounding. +- Added `make_repository_corpus_grep_tool` (`src/v2/agents/llm/agent_tools/repository_corpus_grep.py`), a provenance-aware corpus grep tool returning markdown snippets with source metadata and line-numbered context blocks. +- Added `make_duckduckgo_search_tool` (`src/v2/agents/llm/agent_tools/duckduckgo_search.py`), which exposes `search_on_the_internet` for compact DuckDuckGo-backed external context retrieval (title/url/snippet rows) and wired it into `LLMContextSummaryAgentV2`. +- Added `hash_user_email_tool` (`src/v2/agents/llm/agent_tools/email_hash.py`) and wired it into `LLMPersonAgentV2` so person extraction can call a canonical email-anonymization tool instead of reimplementing hashing in-prompt. +- Added `LLMDedupAgentV2` (`src/v2/agents/llm/dedup/agent.py`) and `LLMCriticAgentV2` (`src/v2/agents/llm/critic/agent.py`) with structured JSON outputs for global duplicate-cluster and prune suggestions. +- Added stage helpers `run_llm_dedup_stage` and `run_llm_critic_stage` (`src/v2/pipeline/stages/llm_dedup.py`, `src/v2/pipeline/stages/llm_critic.py`) plus stage result dataclasses in `src/v2/pipeline/stages/models.py`. +- Added regression coverage: + - `tests/v2/test_llm_dedup_stage.py` + - `tests/v2/test_llm_critic_stage.py` + - `tests/v2/test_extract_e2e.py` (LLM stage sequencing, intermediates, and fail-open behavior) +- Added `reconciliation_debug` intermediate emission in `/v2/extract` (when `include_intermediates=true`) with merge/remap diagnostics: merged group/entity counts, org remap sample, and organization lookup token-collision sample. +- Added full 7-stage LLM repository debug flow in `scripts/v2/run_llm_repo_persons_and_orgs.py`: + `context_gather -> repo_agent -> person_agents -> org_agents -> article_agents -> membership_agents -> contribution_agents`, + with deterministic class-stage seed fanout, partial-failure tolerance, stage summaries, and final combined JSON-LD aggregation including class entities from both primary `result.data` and stage list stats. +- Added independent `LLMLinkVeracityAgentV2` (`src/v2/agents/llm/link_veracity/agent.py`) to verify extracted link relationships with boolean verdicts using Selenium-backed content retrieval. +- Added shared LLM agent tools: + - `generate_uuid_v4_tool` / `generate_uuid_v4_batch_tool` (`src/v2/agents/llm/agent_tools/uuid.py`) + - `fetch_link_content_via_selenium_tool` (`src/v2/agents/llm/agent_tools/selenium_fetch.py`) + and wired Selenium tool availability into repository/person/organization/article/membership/contribution LLM agents. +- Added `LLMPersonAgentV2` (`src/v2/agents/llm/person/agent.py`) as the LLM-backed person extraction agent. Uses the same runtime infrastructure as `LLMRepositoryAgentV2` but accepts any combination of identifiers (GitHub username, ORCID, Infoscience ID, or name) and routes tool calls through provider-aware factories built at `run()` time. +- Added `make_orcid_person_tool` factory (`src/v2/agents/llm/agent_tools/orcid_person.py`): creates a pydantic-ai `Tool` named `get_orcid_record` that fetches a full ORCID profile (name, employment, education, affiliations) by ORCID identifier. Tool closure captures the `ORCIDProvider` instance at construction time. +- Added `make_infoscience_search_tool` factory (`src/v2/agents/llm/agent_tools/infoscience_search.py`): creates a pydantic-ai `Tool` named `search_infoscience_person` that queries Infoscience for person records by name or query string. Returns ranked results with `infosciencePersonIdentifier`, `name`, `orcid`, `affiliations`, `profileUrl`, and `score`. +- Added GIMIE JSON-LD context enrichment to both LLM agents. `get_repository_jsonld()` (new non-abstract method on `GitHubProvider` base class) returns the cached raw GIMIE JSON-LD dict populated during `gather_context`. Both `LLMRepositoryAgentV2` (8 000-char limit) and `LLMPersonAgentV2` (4 000-char limit) serialize it as a JSON string and include it in the LLM input, enabling the model to extract DOIs, license IRIs, timestamps, and author credits from the richer RDF graph. +- Added `max_concurrent_agents: int = 3` parameter to `PipelineOrchestrator.__init__`. All fanout stages (`_execute_stage`) now create a fresh `asyncio.Semaphore(max_concurrent_agents)` per call and wrap each agent execution, preventing unbounded parallelism that was saturating the LLM endpoint when many person/org agents were launched simultaneously. +- Added prompt-context propagation controls in `PipelineOrchestrator`: `include_upstream_stage_outputs_in_prompt` (serialized `upstream_stage_outputs_json`) and `user_prompt_appendix` (verbatim text block) to feed downstream child-agent prompts without parsing. +- Added shared prompt helper `src/v2/agents/llm/prompt_context.py` and wired both `LLMRepositoryAgentV2` and `LLMPersonAgentV2` to append optional runtime sections to `user_prompt`. +- Added runtime usage observability fields (`requests`, `tool_calls`) to `LLMRuntimeResult` and runtime completion logs. +- Added local test-runtime tooling for faster loops: `pytest-xdist` and `pytest-testmon` in dev dependencies, plus a new `llm_integration` pytest marker and explicit `just test-llm-integration` command for opt-in real-provider LLM tests. +- Added `src/v2/agents/llm/agent_tools/` package as the shared tool registry for LLM agents. Each tool module exposes a named pydantic-ai `Tool` instance that any LLM agent can import and pass to `V2LLMRuntime.run_json_prompt`. +- Added `list_disciplines_tool` (`src/v2/agents/llm/agent_tools/disciplines.py`): a pydantic-ai tool named `list_disciplines` that returns the complete `DisciplineV2` mapping as a list of `{"wikidata_id", "name"}` objects. The tool description is generated at import time from the enum and contains the full Wikidata-ID-to-name table. Logs an INFO line on each call for observability. +- Extended `V2LLMRuntime.run_json_prompt` with a `tools: list[Any] | None` parameter forwarded directly to the pydantic-ai `Agent` constructor, enabling per-agent tool customization without subclassing the runtime. +- Wired `list_disciplines_tool` into `LLMRepositoryAgentV2`: the agent passes `tools=[list_disciplines_tool]` to the runtime call so the model can look up valid `pulse:discipline` Wikidata IRIs during generation. +- Updated `LLMRepositoryAgentV2` system prompt to add an "Available tools" section that names `list_disciplines`, describes its return shape, and instructs the model to call it before assigning any `pulse:discipline` values. +- Added Plan D runtime-migration implementation artifacts and task breakdown docs in `.internal/plan-d/PD-02` through `.internal/plan-d/PD-10`, including updated Plan D dependency/index metadata in `.internal/plan-d/README.md`. +- Added v2 runtime-selection primitives (`AgentRuntime`, parser, runtime protocol, runtime registry, rule-based/llm namespace scaffolding) to support staged runtime switching. +- Added v2 LLM runtime adapter (`src/v2/llm/runtime.py`) that reuses `src/llm/model_config.py`, enforces provider credential presence by env-var name, and normalizes structured JSON output + token metadata. +- Added `LLMRepositoryAgentV2` as the wave-1 LLM stage replacement for repository extraction, with permissive schema validation and contract-failure rejection behavior. +- Added runtime regression coverage for: + - config/runtime parsing (`tests/v2/test_config.py`), + - API runtime query validation (`tests/v2/test_api_extract_stub.py`), + - orchestrator runtime routing/hard-fail semantics (`tests/v2/test_orchestrator_execution.py`), + - LLM runtime adapter behavior (`tests/v2/test_llm_runtime_adapter.py`), + - LLM repository agent contract behavior (`tests/v2/test_llm_repository_agent.py`), + - runtime-default and no-fallback e2e behavior (`tests/v2/test_extract_e2e.py`). +- Added focused v2 regression coverage for Plan C issues 07/08/09 in `tests/v2/test_jsonld_build.py`, `tests/v2/test_context_versioning.py`, and `tests/v2/test_reconciliation.py`: + - literal `schema:name` / `pulse:githubUsername` values that match entity IDs remain literals, + - `schema:url` compacts/expands as an IRI-valued term, + - organization Infoscience identifiers normalize from URL input to UUID tokens. + +### Changed +- Changed `just v2-run-repo-full-llm` to run the full 7-stage script with `--verify-links`, so the command now appends end-of-run link-veracity checks by default. +- Changed local test workflows in `justfile` to a fast-default model: `just test` now runs `--testmon --no-cov -n auto --dist=loadfile`, while `just test-full` and `just test-coverage` provide deterministic full-suite and coverage-focused runs. +- Changed test invocation guidance and automation to prefer `.venv/bin/python -m pytest` (instead of relying on ad-hoc `PYTHONPATH`/global `pytest`) across `just` recipes, docs, and CI test steps. +- Changed v2 test isolation for safe parallel execution by resetting shared `src.api.app.state` fields between tests, using per-test DB paths (`V2_GRAPH_DB_PATH`/`CACHE_DB_PATH`), and hardening cache-singleton cleanup in v1 cache tests. +- Changed CI test throughput by parallelizing the default v2 suite (`-n 4 --dist=loadfile`) and separating `llm_integration` into a dedicated scheduled/manual job instead of default PR execution. +- Renamed repository identifier field `schema:identifier` to `schema:citation` in both agent and strict repository schemas (`src/v2/schemas/*/repository.schema.json`) to correctly represent the DOI/citation link. Updated `idSource` enum accordingly (`"schema:citation"` replaces `"schema:identifier"`). Regenerated Pydantic models (`IdSource3.schema_citation`). Updated all fixture copies and promoted dev schemas. +- Removed `schema:alternateName` from organization handling end-to-end: agent schema (`src/v2/schemas/agent/organization.schema.json`), generated models, organization agents (rule-based and LLM), prompts, and downstream consumers (article/membership/reconciliation). Alias matching now relies on `aliases`/`acronyms`/`labels` plus normalized name/identifier/handle tokens. +- Added `/v2/extract` runtime selector support with `agent_runtime=rule_based|llm`, defaulting from `V2_AGENT_RUNTIME_DEFAULT` when omitted. +- Updated pipeline orchestrator to resolve stage runners through runtime registry and enforce hard-fail policy for repository-stage LLM failures (`agent_runtime=llm`) without rule-based fallback. +- Updated `AGENTS.md` handoff to Plan D entry task `.internal/plan-d/PD-02-runtime-enum-and-config.md`. +- Updated the v2 standalone script `scripts/v2/run_llm_repo_and_persons.py` to use bounded person-agent concurrency with heartbeat progress output, per-contributor timeout handling, and partial-result completion summaries (`ok`, `timeout`, `error`) plus final successful `Person entities` JSON. +- Updated LLM fanout prompt behavior so downstream child agents can receive accumulated upstream stage JSON context (repository -> persons -> organizations -> ...) and optional raw multi-file text appendix blocks. +- Updated v2 API reference docs with runtime selector semantics and `V2_AGENT_RUNTIME_DEFAULT` environment variable. +- Removed v2 dependency on the v1 TTL-based cache system (`cache_manager`). `RealGitHubProvider` now calls base parsers (`GitHubUsersParser`, `GitHubOrganizationsParser`) directly instead of cached wrappers. The `force_refresh` query parameter and `V2_DISABLE_CACHE` environment variable have been removed from v2 endpoints and provider initialization. +- Removed reconciliation cleanup that explicitly stripped `schema:alternateName` because the field is no longer emitted anywhere in the v2 organization pipeline. +- Updated JSON-LD build normalization to be context-property-aware so string values are promoted to `{"@id": ...}` only for terms declared with `@type: @id`. +- Updated v2 JSON-LD context promotion with explicit `schema:url` IRI typing (`"schema:url": {"@type": "@id"}`) to satisfy SHACL IRI node-kind expectations. +- Updated reconciliation organization identifier normalization so `pulse:infoscienceOrganizationIdentifier` is persisted as UUID form (including URL-input extraction) in both top-level and `identifiers` payload fields. +- Updated `AGENTS.md` handoff to set the next entry task to `.internal/phase-8/P8-01-basic-live-connectivity.md` after completing Plan C issues 07, 08, and 09. +- Updated RDF graph coercion to be predicate-aware for string-constrained fields so URL-looking `schema:identifier` values are emitted as `xsd:string` literals instead of IRIs. +- Updated reconciliation organization normalization to prune unresolved `org:hasUnit` / `org:unitOf` links and emit explicit dropped-reference counters, preventing dangling organization hierarchy edges. +- Updated `AGENTS.md` handoff to set the next entry task to `.internal/plan-c/issue-07-jsonld-literal-to-id-conversion.md` after completing Plan C issues 5 and 6. +- Updated v2 organization alias propagation and reconciliation lookup matching for membership/source-organization resolution to use `aliases`/`acronyms`/`labels` (without `schema:alternateName`) while preserving accent/punctuation-insensitive token variants and GitHub handle matching with/without `@`. +- Updated v2 article-author resolution to use richer person-name alias tokens (including ORCID/Infoscience/GitHub display-name style inputs plus comma-order normalization) before strict unresolved-author skip decisions. +- Updated strict article skip warnings to include per-candidate matched/unmatched author counts for clearer operational diagnostics. +- Updated v2 person-fanout orchestration to skip GitHub contributor accounts whose resolved profile type is `Organization`, preventing organization handles (for example `sdsc-ordes`) from being emitted by `person_agent` as `schema:Person`. +- Updated reconciliation to model GitHub organization accounts as organization units when they act as repository owners under a canonical organization, adding `org:hasUnit` (canonical org) and `org:unitOf` (GitHub org-account node) links. +- Updated `AGENTS.md` handoff to set the next entry task to `.internal/plan-c/issue-05-schema-identifier-literal-vs-iri.md`. +- Coerced Infoscience person `profile_url` values to plain strings in `RealInfoscienceProvider.search_person(...)` so `schema:url` is no longer dropped due to `HttpUrl` object typing in permissive agent validation. +- Updated `PersonAgentV2` payload assembly to omit `schema:email` when anonymization returns `None`, removing noisy optional-field validation warnings without changing SHACL semantics. +- Updated `AGENTS.md` handoff to set the next entry task to `.internal/plan-c/issue-03-membership-organization-resolution.md` after completing Plan C issues 1 and 2. +- Enforced v2 production-safe fallback behavior in `/v2/extract` by introducing an explicit runtime flag (default `false`) and wiring it through article generation and reconciliation to prevent generated fallback entities/values in default production output. +- Standardized v2 agent-emitted `identifiers.uuid` generation on shared UUIDv4 helper `src/v2/agents/models.py::generate_uuid()` across person/repository/organization/article/membership/contribution agents. +- Updated reconciliation controls so unresolved article authors, fallback memberships, and fallback contributions are only generated when explicit fallback mode is enabled. +- Updated article-agent handling for strict mode to drop unresolved author references, reject placeholder-author/date coercion, and skip invalid candidates with explicit warnings. +- Normalized repository `schema:dateCreated` values in `RepositoryAgentV2` to strict UTC timestamp format (`YYYY-MM-DDTHH:MM:SSZ`) so live extracts do not fail root strict validation when providers return date-only strings. +- Updated `AGENTS.md` handoff to the next Phase 8 entry task `.internal/phase-8/P8-02-live-smoke-test-harness.md` after completing live connectivity stabilization in `P8-01`. +- Updated v2b phase-6 regression coverage to lock extract output contracts and stage ordering across repository/user/organization flows (`tests/v2/test_extract_e2e.py`, `tests/v2/test_extract_golden.py`). +- Updated extract golden fixtures to assert broader clean-break JSON envelope expectations (`id` coverage and full six-bucket `entities_by_type` shape for user/org payloads). +- Updated graph regression coverage to assert source-scoped intermediate filtering behavior when secondary-source intermediates exist (`tests/v2/test_api_graph.py`). +- Refreshed v2 API documentation with six-class runtime stage flow, JSON/JSON-LD output contract details, graph-write semantics, and corrected `/v2/graph` intermediate defaults. +- Updated `AGENTS.md` handoff to `.internal/phase-8/P8-01-basic-live-connectivity.md` after completing v2b phase-6 tasks. +- Implemented v2b phase-5 graph integration by wiring `/v2/extract` graph-write execution through `GraphStore.upsert_entity(...)` and persisting agent intermediates through new GraphStore intermediate APIs. +- Refactored intermediates assembly to read from `GraphStore.get_intermediates(...)` instead of direct stage-layer SQLite access, with optional run scoping for extract responses. +- Preserved source-scoped graph filtering correctness by persisting only final included entity IDs in `runs.stats.entity_ids` and excluding strict-invalid entities from graph writes. +- Updated `AGENTS.md` handoff to the next v2b entry task `.internal/v2b-plan/phase-6-regression-docs/P2B-27-e2e-golden-graph-regressions.md` after completing phase-5 tasks `P2B-24` through `P2B-26`. +- Implemented v2b phase-4 output contracts for `/v2/extract`: output assembly now emits a clean JSON envelope (`root_entity`, `related_entities`, `excluded_entities`, `entities_by_type`) and JSON-LD responses are built through a dedicated `jsonld_build` stage before SHACL validation. +- Updated v2 extract contract models to typed output unions (`V2JSONOutputEnvelope` and `V2JSONLDOutput`) with `output_format`/payload consistency checks. +- Promoted additional JSON-LD context term mappings in `src/v2/schemas/context/v2.0.jsonld` (relationship `@id` bindings and xsd datatype annotations) to keep phase-4 JSON-LD payloads compact/typed. +- Updated `AGENTS.md` handoff to the next v2b entry task `.internal/v2b-plan/phase-5-graph-integration/P2B-24-graphstore-intermediates-apis.md` after completing phase-4 tasks `P2B-20` through `P2B-23`. +- Integrated phase-3 v2b validation/reconciliation runtime gates into `/v2/extract`: reconciliation now runs before strict validation, strict root failures return typed 422, non-root strict failures are excluded with warnings, and SHACL validation executes as a non-fatal gate. +- Refactored reconciliation precedence to treat class-agent `articles`/`memberships`/`contributions` as primary outputs, synthesize fallback membership/contribution entities only for uncovered links, and emit explicit synthesis-traceability warnings. +- Aligned canonicalization `idSource` output with strict enums (`pulse:*` / `schema:identifier`) while preserving legacy alias compatibility for pre-existing payloads. +- Restored extract response compatibility for existing v2 contracts by preserving legacy stage-report filtering and legacy `output.entities` keying while keeping strict/SHACL gate execution active. +- Expanded v2 orchestration to a six-class stage graph (`repo/person/org/article/membership/contribution`) for repository/user/organization execution plans. +- Added derivation metadata emission in `AgentResult.stats["derivation"]` for repository, person, and organization agents while keeping entity payloads schema-clean. +- Updated `AGENTS.md` handoff to the next v2b entry task `.internal/v2b-plan/phase-3-validation-reconciliation/P2B-16-reconciliation-primary-class-outputs.md`. +- Added v2 class-agent exports for `ArticleAgentV2`, `MembershipAgentV2`, and `ContributionAgentV2` in `src/v2/agents/__init__.py`. +- Updated `AGENTS.md` v2 handoff to the next v2b orchestration entry task `.internal/v2b-plan/phase-2-orchestration/P2B-11-derivation-metadata-existing-agents.md` after phase-1 class-agent completion. +- Expanded the v2 Infoscience publication provider contract to include normalized article-linking fields (`authors`, `publicationDate`, `doi`, `url`, and optional `sourceOrganization`) with consistent `None`/empty-list fallback semantics. +- Updated `AGENTS.md` v2 handoff to the v2b continuation entry task `.internal/v2b-plan/phase-1-class-agents/P2B-05-article-agent-skeleton.md` after phase-0 foundations completion. +- Restricted v2 repository-mode GitHub expansion to direct entities only: + - Disabled GitHub repo-list expansion for user/org lookups in repository extracts. + - Scoped organization fanout so only direct repository owner org keeps GitHub lookup; membership-derived orgs use non-GitHub enrichment paths. + - Kept ORCID/Infoscience/ROR enrichment active for person/org entities in repository mode. + - Constrained repository-mode `pulse:owns` emission to source repository context where provided. +- Added v2 cache-bypass controls for testing runs: + - `/v2/extract?force_refresh=true` now propagates to real-provider dependency wiring. + - New env switch `V2_DISABLE_CACHE=true` disables v1-backed provider cache for all v2 runs. +- Updated `AGENTS.md` environment/testing guidance with explicit no-cache run instructions for v2 (`V2_DISABLE_CACHE` and `force_refresh`). +- Added CI migration gates for Phase 7 completion: + - JSON-LD roundtrip regression gate (`tests/v2/test_roundtrip.py`) + - v1 parity regression gate (`tests/test_v1_parity.py` plus legacy v1 suites in CI) + - TTL/schema alignment and generated-model freshness gates remain enforced. +- Added v2 privacy parity stage with deterministic v1-compatible email anonymization (`sha256(local_part)[:12]@domain`) and reconciliation-stage application for person entities. +- Added provider-level rate-limit handling with per-provider tracking, proactive throttling near quota exhaustion, and exponential backoff + jitter retry on 429/`ProviderRateLimitError`. +- Changed `BaseProvider` initialization to remain backward-compatible for existing mock/dummy providers by defaulting `provider_name` when omitted. +- Changed v1 parity CI execution to force a writable cache path (`CACHE_DB_PATH=.tmp/cache.db`) for deterministic test runs. +- Added migration documentation for v1-to-v2 endpoint mapping, response-shape differences, environment variables, and deprecation timeline. +- Updated `AGENTS.md` handoff to advance the current entry task to `.internal/phase-8/P8-01-basic-live-connectivity.md`. +- Added Phase 7 CI gating workflow (`.github/workflows/ci.yml`) that runs generated-model freshness checks and the TTL-schema alignment test within scoped `tests/v2` execution. +- Added `just v2-models-generate` / `just v2-models-check` commands and wired deterministic schema-bundle generation so codegen freshness checks fail with schema-specific drift messages. +- Added `datamodel-code-generator` dev dependency and shared `[tool.datamodel-codegen]` defaults in `pyproject.toml`. +- Updated `AGENTS.md` handoff to advance the current v2 entry task to `.internal/v2-plan/phase-7-ci-migration/P7-04-ci-roundtrip.md`. +- Added v2 run-id correlation via `contextvars` so request traces, pipeline/agent spans, structured error events, and response headers share the same `run_id` per request. +- Changed `/v2/extract` run lifecycle handling to persist a run row up-front, propagate that run identifier through stats and response headers, and finalize run status with completion/failure metadata. +- Changed `/v2/extract` stats run-id format from generated `pipeline-*` strings to canonical UUID run identifiers. +- Added v2 structured error event emission (`record_error`) alongside standard Python logging for classified and pipeline execution failures. +- Added v2 observability metrics primitives for token usage, stage latency, validation failure counts, alias lookup hit/miss tracking, and graph upsert counters. +- Updated `AGENTS.md` handoff to advance the current v2 entry task to `.internal/v2-plan/phase-7-ci-migration/P7-01-codegen-setup.md`. +- Added v2 FastAPI request tracing middleware on `/v2/*` routes that emits `X-Run-Id` response headers and records request span attributes (`path`, `method`, `status_code`, `duration_ms`, `response_size`) without affecting non-v2 routes. +- Added v2 pipeline-stage span instrumentation for URL classification, context gather, agent-stage execution, permissive/strict validation, reconciliation, graph-write, and output assembly; strict/reconciliation/graph-write are currently emitted as explicit `status=skipped` spans where execution is not yet wired. +- Added v2 agent-run span instrumentation around retry-wrapped agent execution, including status (`success`/`retry`/`failure`/`error`), model/provider metadata, token usage, and retry count attributes. +- Extended `AgentResult`/pipeline serialization with optional model/provider/token fields to support observability payloads. +- Updated v2 observability probes to be true no-ops unless Logfire is both importable and initialized via bootstrap (`logfire.configure(...)`), avoiding unconfigured-runtime warnings. +- Updated `AGENTS.md` handoff to advance the current v2 entry task to `.internal/v2-plan/phase-6-observability/P6-05-token-latency-metrics.md`. +- Removed hardcoded US Logfire base-url fallback in preflight; base URL now resolves from `LOGFIRE_BASE_URL`, `.logfire` credentials, or token inference, otherwise fails explicitly. +- Changed Logfire preflight credential precedence to accept token from `.logfire/logfire_credentials.json` (from `logfire projects use`) and prefer it over `LOGFIRE_TOKEN` when both are present. +- Changed Logfire preflight behavior to validate connectivity/auth directly against `GET /v1/info` instead of SDK flush heuristics. +- Extended Phase 8 live preflight provider selection to include `logfire` by default. +- Added v2 Logfire bootstrap integration notes to `AGENTS.md` and advanced the current entry task to `.internal/v2-plan/phase-6-observability/P6-02-fastapi-instrumentation.md`. +- Switched v2 intermediates response assembly to a shared stage (`assemble_intermediates`) and reused it in both `/v2/extract` and `/v2/graph`. +- Switched v2 stats generation to a shared stage (`compute_stats`) and reused it in both `/v2/extract` and `/v2/graph`, including run-aware duration/stage metadata and graph-derived triple counts. +- Updated agent handoff in `AGENTS.md` to set the next entry task to `.internal/v2-plan/phase-6-observability/P6-01-logfire-bootstrap.md` after completing Phase 5. +- Replaced the `/v2/graph` stub with a graph-store-backed implementation that exports JSON-LD, applies `source_url`/`entity_type` filters, supports optional intermediate snapshots, and reports computed graph stats. +- Updated v2 graph serialization behavior by expanding namespace-prefix resolution in RDF sync and moving JSON-LD context loading to a versioned context file. +- Updated agent handoff in `AGENTS.md` to set the next entry task to `.internal/v2-plan/phase-5-export/P5-05-intermediates-envelope.md` after completing `P5-01` through `P5-04`. +- Added SQLite concurrent-write safeguards in `GraphStore` by enabling WAL mode, configuring `busy_timeout`/`synchronous`, and applying bounded retry handling for transient busy/locked write failures. +- Updated agent handoff in `AGENTS.md` so the next entry task advances to `.internal/v2-plan/phase-5-export/P5-01-jsonld-export.md` after completing Phase 4. +- Normalized RDF `rdf:type` generation for built-in v2 entity kinds so lowercase stored types (`person`, `repository`, etc.) emit ontology class URIs (`pulse:Person`, `pulse:Repository`, ...). +- Extended `GraphStore` with run tracking (`create_run`, `complete_run`, `fail_run`, `get_run`, `get_runs_by_source`) and entity upsert behavior powered by merge policy + field-level provenance. +- Integrated in-memory RDF synchronization into graph-store lifecycle: + - bootstraps RDF graph from SQLite on startup + - applies entity/edge deltas on insert/update/upsert/delete + - exposes `get_rdf_graph()` for query/serialization surfaces. +- Updated v2 progress handoff in `AGENTS.md` to set the next entry task to `.internal/v2-plan/phase-4-graph-store/P4-09-intermediate-snapshots.md`. +- Clarified the v2 progress handoff in `AGENTS.md` by pointing the entry task to `.internal/v2-plan/phase-4-graph-store/P4-05-runs-table.md` after completing `P4-01` through `P4-04`. +- Added explicit guardrails for destructive graph rollback: `MigrationRunner.rollback_to()` now requires explicit opt-in with `allow_destructive_rollback=True` or `V2_GRAPH_ALLOW_DESTRUCTIVE_ROLLBACK=1`. + +### Testing +- `.venv/bin/python -m pytest tests/v2/test_api_mount_v2_router.py -q` +- `.venv/bin/python -m pytest tests/test_cache.py -q` +- `.venv/bin/python -m pytest tests/v2/test_llm_repository_agent.py -q -m 'not llm_integration'` +- `.venv/bin/python -m pytest tests/v2/test_dependencies.py tests/v2/test_extract_golden.py tests/v2/test_api_extract_stub.py -q -m 'not llm_integration and not live_provider'` +- `.venv/bin/python -m pytest tests/v2 -q -n 4 --dist=loadfile -m 'not live_provider and not llm_integration'` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_config.py tests/v2/test_api_extract_stub.py tests/v2/test_orchestrator_execution.py tests/v2/test_llm_runtime_adapter.py tests/v2/test_llm_repository_agent.py tests/v2/test_agent_runtime_scaffolding.py tests/v2/test_extract_e2e.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/agents/__init__.py src/v2/agents/contracts.py src/v2/agents/runtime.py src/v2/agents/registry.py src/v2/agents/llm/repository_agent.py src/v2/agents/rule_based/__init__.py src/v2/llm/runtime.py src/v2/llm/__init__.py src/v2/config.py src/v2/api.py src/v2/pipeline/orchestrator.py` +- `PYTHONPATH=. .venv/bin/ruff check tests/v2/test_llm_runtime_adapter.py tests/v2/test_llm_repository_agent.py tests/v2/test_agent_runtime_scaffolding.py tests/v2/test_api_extract_stub.py tests/v2/test_config.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/agents/runtime.py src/v2/agents/contracts.py src/v2/agents/registry.py src/v2/agents/llm/repository_agent.py src/v2/llm/runtime.py src/v2/config.py src/v2/api.py src/v2/pipeline/orchestrator.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_jsonld_build.py tests/v2/test_context_versioning.py tests/v2/test_reconciliation.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/pipeline/stages/jsonld_build.py src/v2/pipeline/stages/reconciliation.py tests/v2/test_jsonld_build.py tests/v2/test_context_versioning.py tests/v2/test_reconciliation.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/pipeline/stages/jsonld_build.py src/v2/pipeline/stages/reconciliation.py` +- `python -m json.tool src/v2/schemas/context/v2.0.jsonld` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_rdf_sync.py tests/v2/test_reconciliation.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/graph/rdf_sync.py src/v2/pipeline/stages/reconciliation.py tests/v2/test_rdf_sync.py tests/v2/test_reconciliation.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/graph/rdf_sync.py src/v2/pipeline/stages/reconciliation.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_article_agent.py tests/v2/test_reconciliation.py tests/v2/test_organization_agent.py tests/v2/test_membership_agent.py tests/v2/test_provider_interfaces.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/providers/ror_provider.py src/v2/agents/organization_agent.py src/v2/pipeline/stages/reconciliation.py src/v2/agents/membership_agent.py src/v2/agents/article_agent.py tests/v2/test_reconciliation.py tests/v2/test_organization_agent.py tests/v2/test_article_agent.py tests/v2/test_provider_interfaces.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/providers/ror_provider.py src/v2/agents/organization_agent.py src/v2/pipeline/stages/reconciliation.py src/v2/agents/membership_agent.py src/v2/agents/article_agent.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_promoted_agent_schemas.py tests/v2/test_promoted_strict_schemas.py -q` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_orchestrator_execution.py tests/v2/test_reconciliation.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/pipeline/orchestrator.py src/v2/pipeline/stages/reconciliation.py tests/v2/test_orchestrator_execution.py tests/v2/test_reconciliation.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/pipeline/orchestrator.py src/v2/pipeline/stages/reconciliation.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` (1 known failure remains: `tests/v2/test_extract_golden.py::test_extract_endpoint_matches_golden_contract[...]` expected `entities_count=3`, actual `5`) +- `curl -sS -m 180 "http://localhost:1234/v2/extract/https%3A%2F%2Fwww.github.com%2Fsdsc-ordes%2Fgimie?output_format=json&force_refresh=true"` (timed out with `curl: (28)`; local `serve-dev` endpoint not responding during this run) +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_provider_interfaces.py tests/v2/test_person_agent.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/providers/infoscience_provider.py src/v2/agents/person_agent.py tests/v2/test_provider_interfaces.py tests/v2/test_person_agent.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` (1 known failure remains: `tests/v2/test_extract_golden.py::test_extract_endpoint_matches_golden_contract[...]` expected `entities_count=3`, actual `5`) +- `PYTHONPATH=. .venv/bin/pytest -m v2` (same single known golden failure; no non-v2 collection/import failures) +- `curl -sS -m 180 "http://localhost:1234/v2/extract/https%3A%2F%2Fwww.github.com%2Fsdsc-ordes%2Fgimie?output_format=json&force_refresh=true" > /tmp/issue01_issue02_extract.json && jq '{status: .status, source_url: .source_url, detected_type: .detected_type, warnings_count: (.warnings|length), first_warning: (.warnings[0] // null)}' /tmp/issue01_issue02_extract.json` +- `jq '{schema_url_httpurl_warnings: ([.warnings[] | select(test("schema:url") and test("HttpUrl"))] | length), schema_email_none_warnings: ([.warnings[] | select(test("schema:email") and test("None is not of type '\\''string'\\''"))] | length), sample_schema_url_warning: ([.warnings[] | select(test("schema:url"))][0] // null), sample_schema_email_warning: ([.warnings[] | select(test("schema:email"))][0] // null)}' /tmp/issue01_issue02_extract.json` +- `curl -sS -m 180 "http://localhost:1234/v2/extract/https%3A%2F%2Fwww.github.com%2Fsdsc-ordes%2Fgimie?output_format=json" | jq '{source_url, detected_type, output_format, error_type, entities_count: .stats.entities_count, stages_completed: .stats.stages_completed, output_keys: (.output|keys)}'` +- `curl -sS -m 180 "http://localhost:1234/v2/extract/https%3A%2F%2Fwww.github.com%2Fsdsc-ordes%2Fgimie?output_format=json" | jq '{root_id: .output.root_entity.id, date_created: .output.root_entity[\"schema:dateCreated\"]}'` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_repository_agent.py tests/v2/test_extract_e2e.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/agents/repository_agent.py tests/v2/test_repository_agent.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/agents/repository_agent.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest -m v2` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_extract_e2e.py tests/v2/test_extract_golden.py tests/v2/test_api_graph.py -q` +- `PYTHONPATH=. .venv/bin/ruff check tests/v2/test_extract_e2e.py tests/v2/test_extract_golden.py tests/v2/test_api_graph.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest -m v2` +- `curl -sS -m 30 "http://localhost:1234/v2/extract/https%3A%2F%2Fwww.github.com%2Fsdsc-ordes%2Fgimie?output_format=json"` (timed out with `curl: (28)`; no local server response during run) +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_intermediates_envelope.py tests/v2/test_api_graph.py tests/v2/test_extract_e2e.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/api.py src/v2/graph/store.py src/v2/pipeline/stages/intermediates.py tests/v2/test_intermediates_envelope.py tests/v2/test_api_graph.py tests/v2/test_extract_e2e.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/api.py src/v2/graph/store.py src/v2/pipeline/stages/intermediates.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest -m v2` +- `curl -sS -m 30 "http://localhost:1234/v2/extract/https%3A%2F%2Fwww.github.com%2Fsdsc-ordes%2Fgimie?output_format=json"` (timed out with `curl: (28)`; no local server response during run) +- `PYTHONPATH=. .venv/bin/ruff check src/v2/api.py src/v2/models/contracts.py src/v2/pipeline/stages/output_assembly.py src/v2/pipeline/stages/jsonld_build.py src/v2/pipeline/stages/models.py src/v2/pipeline/stages/__init__.py tests/v2/test_extract_e2e.py tests/v2/test_response_contracts.py tests/v2/test_context_versioning.py tests/v2/test_pipeline_spans.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/api.py src/v2/models/contracts.py src/v2/pipeline/stages/output_assembly.py src/v2/pipeline/stages/jsonld_build.py src/v2/pipeline/stages/models.py src/v2/pipeline/stages/__init__.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest -m v2` +- `curl -sS -m 180 "http://localhost:1234/v2/extract/https%3A%2F%2Fwww.github.com%2Fsdsc-ordes%2Fgimie?output_format=json" | jq '{status: .status, error_type: .error_type, message: .error.message}'` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/api.py src/v2/canonicalization/id_resolution.py src/v2/pipeline/stages/reconciliation.py src/v2/pipeline/stages/output_assembly.py src/v2/validation/shacl_validation.py tests/v2/test_reconciliation.py tests/v2/test_canonical_id_person.py tests/v2/test_canonical_id_organization.py tests/v2/test_canonical_id_repository.py tests/v2/test_canonical_id_article.py tests/v2/test_strict_validation_gate.py tests/v2/test_extract_e2e.py tests/v2/test_shacl_validation.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/api.py src/v2/canonicalization/id_resolution.py src/v2/pipeline/stages/reconciliation.py src/v2/pipeline/stages/output_assembly.py src/v2/validation/shacl_validation.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest -m v2` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/agents/repository_agent.py src/v2/agents/person_agent.py src/v2/agents/organization_agent.py src/v2/pipeline/orchestrator.py tests/v2/test_repository_agent.py tests/v2/test_person_agent.py tests/v2/test_organization_agent.py tests/v2/test_orchestrator_graph.py tests/v2/test_orchestrator_execution.py tests/v2/test_pipeline_spans.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/agents/repository_agent.py src/v2/agents/person_agent.py src/v2/agents/organization_agent.py src/v2/pipeline/orchestrator.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_orchestrator_graph.py tests/v2/test_orchestrator_execution.py tests/v2/test_pipeline_spans.py tests/v2/test_repository_agent.py tests/v2/test_person_agent.py tests/v2/test_organization_agent.py -q` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` (fails only `tests/v2/test_extract_golden.py` fixture drift on `entities_count`/`stages_completed` now that six-class stages are active) +- `PYTHONPATH=. .venv/bin/pytest -m v2` (same three `test_extract_golden.py` failures; no non-v2 collection/import failures) +- `curl -sS -m 180 "http://localhost:1234/v2/extract/https%3A%2F%2Fwww.github.com%2Fsdsc-ordes%2Fgimie?output_format=json&force_refresh=true" | jq '{source_url, detected_type, entities_count: .stats.entities_count, stages_completed: .stats.stages_completed, entity_keys: (.output.entities | keys)}'` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_article_agent.py tests/v2/test_membership_agent.py tests/v2/test_contribution_agent.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/agents/__init__.py src/v2/agents/article_agent.py src/v2/agents/membership_agent.py src/v2/agents/contribution_agent.py tests/v2/test_article_agent.py tests/v2/test_membership_agent.py tests/v2/test_contribution_agent.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/agents/article_agent.py src/v2/agents/membership_agent.py src/v2/agents/contribution_agent.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `curl -sS -m 180 "http://localhost:1234/v2/extract/https%3A%2F%2Fwww.github.com%2Fsdsc-ordes%2Fgimie?output_format=json&force_refresh=true" | jq '{source_url, detected_type, entities_count: .stats.entities_count, stages_completed: .stats.stages_completed, warnings_count: (.warnings | length), sample_entity_keys: ((.output.entities | keys)[:8])}'` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_orchestrator_execution.py tests/v2/test_provider_interfaces.py tests/v2/test_mock_infoscience_provider.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/agents/models.py src/v2/agents/__init__.py src/v2/pipeline/models.py src/v2/providers/base.py src/v2/providers/infoscience_provider.py src/v2/providers/mock_infoscience.py tests/v2/test_orchestrator_execution.py tests/v2/test_provider_interfaces.py tests/v2/test_mock_infoscience_provider.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/agents/models.py src/v2/pipeline/models.py src/v2/providers/base.py src/v2/providers/infoscience_provider.py src/v2/providers/mock_infoscience.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `curl -sS -m 180 "http://localhost:1234/v2/extract/https%3A%2F%2Fwww.github.com%2Fsdsc-ordes%2Fgimie?output_format=json&force_refresh=true" | jq '{source_url, detected_type, output_keys: (.output.entities | keys), entities_count: .stats.entities_count, stages_completed: .stats.stages_completed, warnings_count: (.warnings | length)}'` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/dependencies.py src/v2/providers/github_provider.py src/v2/agents/person_agent.py src/v2/agents/organization_agent.py src/v2/pipeline/orchestrator.py src/cache/cached_parsers.py src/parsers/users_parser.py src/parsers/orgs_parser.py tests/v2/test_dependencies.py tests/v2/test_provider_interfaces.py tests/v2/test_person_agent.py tests/v2/test_organization_agent.py tests/v2/test_orchestrator_execution.py tests/v2/test_extract_e2e.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_dependencies.py tests/v2/test_provider_interfaces.py tests/v2/test_person_agent.py tests/v2/test_organization_agent.py tests/v2/test_orchestrator_execution.py tests/v2/test_extract_e2e.py -v` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/dependencies.py src/v2/providers/github_provider.py tests/v2/test_dependencies.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_dependencies.py -v` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_api_extract_stub.py -v` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/pipeline/stages/privacy.py src/v2/pipeline/stages/reconciliation.py src/v2/providers/base.py src/v2/providers/rate_limiter.py src/v2/providers/github_provider.py src/v2/providers/infoscience_provider.py src/v2/providers/orcid_provider.py src/v2/providers/ror_provider.py src/v2/providers/__init__.py tests/v2/test_roundtrip.py tests/v2/test_email_anonymization.py tests/v2/test_rate_limiter.py tests/test_v1_parity.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/pipeline/stages/privacy.py src/v2/pipeline/stages/reconciliation.py src/v2/providers/base.py src/v2/providers/rate_limiter.py src/v2/providers/github_provider.py src/v2/providers/infoscience_provider.py src/v2/providers/orcid_provider.py src/v2/providers/ror_provider.py tests/v2/test_roundtrip.py tests/v2/test_email_anonymization.py tests/v2/test_rate_limiter.py tests/test_v1_parity.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_roundtrip.py tests/v2/test_email_anonymization.py tests/v2/test_rate_limiter.py -q` +- `CACHE_DB_PATH=.tmp/cache.db PYTHONPATH=. .venv/bin/pytest tests/test_cache.py tests/test_orcid_validation_pipeline.py tests/test_v1_parity.py -q` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest -m v2` +- `PYTHONPATH=. .venv/bin/python scripts/v2/generate_v2_models.py` +- `just v2-models-generate` +- `just v2-models-check` +- `PYTHONPATH=. .venv/bin/ruff check scripts/v2/generate_v2_models.py tests/v2/test_generated_models.py tests/v2/test_ttl_schema_alignment.py` +- `PYTHONPATH=. .venv/bin/mypy scripts/v2/generate_v2_models.py tests/v2/test_generated_models.py tests/v2/test_ttl_schema_alignment.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_generated_models.py tests/v2/test_ttl_schema_alignment.py -q` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest -m v2` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_metrics.py tests/v2/test_error_events.py tests/v2/test_runid_correlation.py tests/v2/test_extract_golden.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/observability/context.py src/v2/observability/log_filter.py src/v2/observability/metrics.py src/v2/observability/error_events.py src/v2/observability/middleware.py src/v2/observability/agent_instrumentation.py src/v2/observability/pipeline_spans.py src/v2/api.py src/v2/observability/__init__.py tests/v2/test_metrics.py tests/v2/test_error_events.py tests/v2/test_runid_correlation.py tests/v2/test_extract_golden.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/api.py src/v2/observability/context.py src/v2/observability/log_filter.py src/v2/observability/metrics.py src/v2/observability/error_events.py src/v2/observability/middleware.py src/v2/observability/agent_instrumentation.py src/v2/observability/pipeline_spans.py src/v2/observability/__init__.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_fastapi_instrumentation.py tests/v2/test_agent_instrumentation.py tests/v2/test_pipeline_spans.py -q` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_api_extract_stub.py tests/v2/test_api_graph.py tests/v2/test_api_health.py tests/v2/test_api_mount_v2_router.py tests/v2/test_extract_e2e.py tests/v2/test_extract_golden.py tests/v2/test_graph_golden.py tests/v2/test_orchestrator_execution.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/agents/models.py src/v2/api.py src/v2/observability/__init__.py src/v2/observability/agent_instrumentation.py src/v2/observability/middleware.py src/v2/observability/pipeline_spans.py src/v2/pipeline/models.py src/v2/pipeline/orchestrator.py tests/v2/test_agent_instrumentation.py tests/v2/test_fastapi_instrumentation.py tests/v2/test_pipeline_spans.py` +- `PYTHONPATH=. .venv/bin/mypy src/v2/api.py src/v2/pipeline/orchestrator.py src/v2/observability/agent_instrumentation.py src/v2/observability/middleware.py src/v2/observability/pipeline_spans.py src/v2/agents/models.py src/v2/pipeline/models.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_provider_connectivity_preflight.py -q` +- `PYTHONPATH=. .venv/bin/ruff check scripts/v2/check_provider_connectivity.py tests/v2/test_provider_connectivity_preflight.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_logfire_bootstrap.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/observability/__init__.py src/v2/observability/bootstrap.py tests/v2/test_logfire_bootstrap.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_intermediates_envelope.py tests/v2/test_stats_computation.py tests/v2/test_api_graph.py tests/v2/test_api_extract_stub.py tests/v2/test_extract_golden.py tests/v2/test_response_contracts.py -q` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/api.py src/v2/models/__init__.py src/v2/models/contracts.py src/v2/pipeline/stages/__init__.py src/v2/pipeline/stages/intermediates.py src/v2/pipeline/stages/stats.py tests/v2/test_intermediates_envelope.py tests/v2/test_stats_computation.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest -m v2` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_jsonld_export.py tests/v2/test_context_versioning.py tests/v2/test_filtered_subgraph.py tests/v2/test_api_graph.py tests/v2/test_graph_golden.py -q` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_org_alias_canonicalization.py tests/v2/test_concurrent_writes.py -v` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` +- `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_entity_crud.py tests/v2/test_edge_crud.py tests/v2/test_alias_crud.py tests/v2/test_runs_crud.py tests/v2/test_upsert_merge.py tests/v2/test_provenance.py tests/v2/test_rdf_sync.py tests/v2/test_canonical_id_organization.py tests/v2/test_org_alias_canonicalization.py tests/v2/test_concurrent_writes.py -v` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/graph/concurrency.py src/v2/graph/store.py src/v2/graph/provenance.py src/v2/graph/__init__.py src/v2/canonicalization/string_utils.py src/v2/canonicalization/organization_alias_map.py src/v2/canonicalization/__init__.py tests/v2/test_org_alias_canonicalization.py tests/v2/test_concurrent_writes.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` +- `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_runs_crud.py tests/v2/test_upsert_merge.py tests/v2/test_provenance.py tests/v2/test_rdf_sync.py -v` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/graph/rdf_sync.py tests/v2/test_rdf_sync.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` +- `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_entity_crud.py tests/v2/test_edge_crud.py tests/v2/test_alias_crud.py tests/v2/test_runs_crud.py tests/v2/test_upsert_merge.py tests/v2/test_provenance.py tests/v2/test_rdf_sync.py -v` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/graph/__init__.py src/v2/graph/models.py src/v2/graph/store.py src/v2/graph/merge.py src/v2/graph/provenance.py src/v2/graph/rdf_sync.py tests/v2/test_runs_crud.py tests/v2/test_upsert_merge.py tests/v2/test_provenance.py tests/v2/test_rdf_sync.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` +- `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_graph_schema.py tests/v2/test_entity_crud.py tests/v2/test_edge_crud.py tests/v2/test_alias_crud.py -v` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_graph_schema.py -v` +- `PYTHONPATH=. .venv/bin/ruff check src/v2/graph/migrations.py tests/v2/test_graph_schema.py` +- `PYTHONPATH=. .venv/bin/pytest tests/v2/test_canonical_id_repository.py tests/v2/test_reconciliation.py tests/v2/test_partial_failure.py tests/v2/test_enum_alignment.py -v` + +### Added +- Added RDF sync regression coverage that locks `schema:identifier` URL values to typed string literals while preserving IRI coercion for predicates such as `schema:url`. +- Added reconciliation regression coverage for organization hierarchy pruning/preservation across unresolved and resolvable `org:hasUnit` / `org:unitOf` references. +- Added orchestrator regression coverage `test_execute_skips_github_organization_accounts_from_person_fanout` to lock the org-account exclusion behavior in person fanout. +- Added reconciliation regression coverage `test_reconcile_models_github_org_account_as_unit_for_repository_owner` to lock `org:hasUnit`/`org:unitOf` modeling for GitHub org accounts. +- Added regression test `test_real_infoscience_person_profile_url_is_coerced_to_string` in `tests/v2/test_provider_interfaces.py` to lock `HttpUrl` to `str` coercion in real Infoscience provider person results. +- Added regression test `test_person_agent_omits_schema_email_when_no_email_is_available` in `tests/v2/test_person_agent.py` to lock `None` email pre-filter behavior. +- Added v2 UUID helper regression coverage (`tests/v2/test_agent_uuid_generation.py`) asserting UUIDv4 generation semantics. +- Added reconciliation and extract e2e regression coverage for fallback-generation policy, including stress coverage for large unresolved Infoscience author lists to prevent fallback-entity explosions in production mode. +- Added repository-agent regression coverage for strict date normalization from date-only source values in `tests/v2/test_repository_agent.py`. +- Added phase-6 extract regression assertions for: + - explicit clean-break JSON envelope keys and six-bucket output grouping. + - detected-type-specific stage sequence locking in integration and golden tests. + - JSON-LD node contract checks (`@id`/`@type`) in golden verification. +- Added phase-6 graph regression assertion that source-scoped `/v2/graph` intermediate responses exclude secondary-source intermediates. +- Added first-class GraphStore intermediate persistence/query APIs in `src/v2/graph/store.py` (`insert_intermediate`, `get_intermediates`) with deterministic ordering and optional source/run filtering. +- Added phase-5 regression coverage for GraphStore-backed intermediates and extract-driven source graph filtering: + - `tests/v2/test_intermediates_envelope.py` + - `tests/v2/test_api_graph.py` + - `tests/v2/test_extract_e2e.py` +- Added a dedicated phase-4 JSON-LD build stage at `src/v2/pipeline/stages/jsonld_build.py` and updated stage exports/wiring. +- Added clean-break extract response contracts and validation tests for JSON/JSON-LD output shapes (`tests/v2/test_response_contracts.py`, `tests/v2/test_extract_e2e.py`). +- Added phase-4 regression coverage for context-term promotion and stage-span sequencing (`tests/v2/test_context_versioning.py`, `tests/v2/test_pipeline_spans.py`) plus refreshed v2 extract/graph golden fixtures. +- Added phase-2 orchestration coverage for class-stage fanout context propagation, deterministic root-type seeding, mixed-success retry behavior, and stage-span assertions (`tests/v2/test_orchestrator_execution.py`, `tests/v2/test_pipeline_spans.py`). +- Added new v2 class-agent implementations and tests: + - `src/v2/agents/article_agent.py` + - `src/v2/agents/membership_agent.py` + - `src/v2/agents/contribution_agent.py` + - `tests/v2/test_article_agent.py` + - `tests/v2/test_membership_agent.py` + - `tests/v2/test_contribution_agent.py` +- Added Infoscience publication fixture ranking signals (`score`) for class-agent coverage in `tests/v2/fixtures/providers/infoscience/publication_result.json`. +- Added six-class runtime typed entity bucket primitives in v2 pipeline/agent models and serialization (`repositories`, `persons`, `organizations`, `articles`, `memberships`, `contributions`) for downstream reconciliation/output stages. +- Added focused v2 dependency coverage for cache-bypass toggles in `tests/v2/test_dependencies.py`. +- Added Phase 7 CI and migration artifacts: + - `tests/v2/test_roundtrip.py` + - `tests/test_v1_parity.py` + - `tests/v2/test_email_anonymization.py` + - `tests/v2/test_rate_limiter.py` + - `src/v2/pipeline/stages/privacy.py` + - `src/v2/providers/rate_limiter.py` + - `docs/migration-v1-to-v2.md` + - `docs/v2-api-reference.md` +- Added committed generated v2 models module at `src/v2/generated/entities.py` (with datamodel-code-generator header and embedded strict-schema SHA metadata) plus package exports in `src/v2/generated/__init__.py`. +- Added `scripts/v2/generate_v2_models.py` to produce/check generated models from strict schemas and detect stale codegen output. +- Added generated-model smoke coverage in `tests/v2/test_generated_models.py` for `PersonModel` and `RepositoryModel` fixture validation. +- Added promoted TTL↔schema CI gate coverage in `tests/v2/test_ttl_schema_alignment.py` for property/enum/pattern/required/datatype consistency and drift detection. +- Added v2 observability primitives for phase-6 completion: + - `src/v2/observability/context.py` (`RunContext`) + - `src/v2/observability/log_filter.py` (`RunIdLogFilter`) + - `src/v2/observability/metrics.py` (`V2Metrics`) + - `src/v2/observability/error_events.py` (`record_error`) +- Added focused coverage for phase-6 completion: + - `tests/v2/test_metrics.py` + - `tests/v2/test_error_events.py` + - `tests/v2/test_runid_correlation.py` +- Added v2 observability primitives and wiring for Phase 6 request/agent/pipeline tracing: + - `src/v2/observability/middleware.py` + - `src/v2/observability/agent_instrumentation.py` + - `src/v2/observability/pipeline_spans.py` +- Added focused observability test coverage: + - `tests/v2/test_fastapi_instrumentation.py` + - `tests/v2/test_agent_instrumentation.py` + - `tests/v2/test_pipeline_spans.py` +- Added a Logfire connectivity branch in `scripts/v2/check_provider_connectivity.py`: + - validates Logfire credentials via `LOGFIRE_TOKEN` or `.logfire/logfire_credentials.json` + - validates token + region with direct `GET /v1/info` checks against the resolved Logfire API base URL +- Added warning output when `LOGFIRE_TOKEN` and `.logfire` credentials both exist but differ. +- Added Logfire preflight coverage in `tests/v2/test_provider_connectivity_preflight.py`. +- Added v2 observability bootstrap primitives: + - `src/v2/observability/bootstrap.py` (`initialize_logfire`) + - `src/v2/observability/__init__.py` (bootstrap export) +- Added focused Logfire bootstrap coverage: + - `tests/v2/test_logfire_bootstrap.py` +- Added intermediates envelope contracts and assembly stage: + - `src/v2/models/contracts.py` (`IntermediateEnvelope`) + - `src/v2/pipeline/stages/intermediates.py` + - `tests/v2/test_intermediates_envelope.py` +- Added shared stats computation stage for v2 API responses: + - `src/v2/pipeline/stages/stats.py` + - `tests/v2/test_stats_computation.py` +- Added v2 graph export primitives and context assets: + - `src/v2/graph/export.py` + - `src/v2/schemas/context/v2.0.jsonld` +- Added run-scoped entity ID lookup for graph filtering in `src/v2/graph/store.py`. +- Added Phase 5 coverage for JSON-LD export, context versioning, filtered subgraphs, and the `/v2/graph` API: + - `tests/v2/test_jsonld_export.py` + - `tests/v2/test_context_versioning.py` + - `tests/v2/test_filtered_subgraph.py` + - `tests/v2/test_api_graph.py` +- Added organization alias canonicalization primitives: + - `src/v2/canonicalization/string_utils.py` + - `src/v2/canonicalization/organization_alias_map.py` +- Added SQLite write-concurrency utility and exports: + - `src/v2/graph/concurrency.py` + - `src/v2/graph/__init__.py` +- Added focused v2 coverage for organization alias resolution and concurrent-write behavior: + - `tests/v2/test_org_alias_canonicalization.py` + - `tests/v2/test_concurrent_writes.py` +- Added RDF sync coverage for ontology class normalization from built-in lowercase entity kinds in `tests/v2/test_rdf_sync.py`. +- **V2 Phase 4 graph-store execution metadata + merge/provenance/RDF sync (`P4-05` to `P4-08`)**: + - Added merge-policy primitive and result contract: + - `src/v2/graph/merge.py` + - Added provenance tracking primitive: + - `src/v2/graph/provenance.py` + - Added RDF bootstrap/delta synchronization primitive: + - `src/v2/graph/rdf_sync.py` + - Extended graph-store data contracts: + - `src/v2/graph/models.py` (`Run`, `ProvenanceEntry`) + - Extended graph-store integration: + - `src/v2/graph/store.py` + - `src/v2/graph/__init__.py` + - Added focused v2 coverage: + - `tests/v2/test_runs_crud.py` + - `tests/v2/test_upsert_merge.py` + - `tests/v2/test_provenance.py` + - `tests/v2/test_rdf_sync.py` +- **V2 Phase 4 graph-store foundation and CRUD (`P4-01` to `P4-04`)**: + - Added SQLite schema constants and migration runner: + - `src/v2/graph/schema.py` + - `src/v2/graph/migrations.py` + - `src/v2/graph/migrations/001_initial.sql` + - Added typed graph-store models and package exports: + - `src/v2/graph/models.py` + - `src/v2/graph/__init__.py` + - Added `GraphStore` CRUD operations for entities, edges, and aliases: + - `src/v2/graph/store.py` + - Added graph-store schema and CRUD coverage: + - `tests/v2/test_graph_schema.py` + - `tests/v2/test_entity_crud.py` + - `tests/v2/test_edge_crud.py` + - `tests/v2/test_alias_crud.py` +- Added a scoped Phase 3 completion checkpoint for repository canonicalization, reconciliation, partial-failure assembly, and enum-alignment validation. +- **V2 Phase 3 reconciliation and enum alignment (`P3-05` to `P3-08`)**: + - Added repository canonical ID resolution with prioritized source selection: + - `src/v2/canonicalization/id_resolution.py` + - `tests/v2/test_canonical_id_repository.py` + - Added cross-entity reconciliation for canonical link normalization plus membership/contribution generation: + - `src/v2/pipeline/stages/reconciliation.py` + - `src/v2/pipeline/stages/models.py` + - `tests/v2/test_reconciliation.py` + - Added partial-failure output assembly that excludes strict-invalid non-root entities and preserves root success semantics: + - `src/v2/pipeline/stages/output_assembly.py` + - `src/v2/pipeline/stages/models.py` + - `tests/v2/test_partial_failure.py` + - Added v2 enum alignment against the TTL ontology and extraction utility: + - `src/v2/models/enums.py` + - `scripts/v2/extract_enums_from_ttl.py` + - `tests/v2/test_enum_alignment.py` +- **V2 validation gates and canonical ID resolution**: + - Added strict JSON Schema gate module and batch result contracts: + - `src/v2/validation/schema_validation.py` + - `tests/v2/test_strict_validation_gate.py` + - Added SHACL validation support with ontology loading cache: + - `src/v2/validation/shacl_validation.py` + - `src/v2/validation/ontology.py` + - `tests/v2/test_shacl_validation.py` + - Added canonical ID resolution for people and organizations: + - `src/v2/canonicalization/id_resolution.py` + - `src/v2/canonicalization/__init__.py` + - `tests/v2/test_canonical_id_person.py` + - `tests/v2/test_canonical_id_organization.py` +- **V2 Phase 0 strict schema promotion (`P0-01`)**: + - Promoted 6 strict JSON Schemas from `dev/ontology-v2-json-response/a-001/json-schema/strict/` to `src/v2/schemas/strict/`: + - `person.schema.json` + - `repository.schema.json` + - `organization.schema.json` + - `membership.schema.json` + - `contribution.schema.json` + - `article.schema.json` + - Added new v2 package markers: + - `src/v2/__init__.py` + - `src/v2/schemas/__init__.py` +- **V2 Phase 0 agent schema promotion (`P0-02`)**: + - Promoted 6 agent JSON Schemas from `dev/ontology-v2-json-response/a-001/json-schema/agent/` to `src/v2/schemas/agent/`: + - `person.schema.json` + - `repository.schema.json` + - `organization.schema.json` + - `membership.schema.json` + - `contribution.schema.json` + - `article.schema.json` +- **V2 Phase 0 test infrastructure (`P0-03`)**: + - Added `tests/v2/conftest.py` with shared session fixtures: + - `v2_test_config` + - `load_schema()` + - `load_fixture()` + - `load_golden()` + - Added v2 fixture/golden scaffold directories: + - `tests/v2/fixtures/schema/{strict,agent}/` + - `tests/v2/fixtures/providers/{github,orcid,infoscience,ror}/` + - `tests/v2/fixtures/scenarios/` + - `tests/v2/golden/{extract,graph}/` + - Added schema fixture copies in: + - `tests/v2/fixtures/schema/strict/*.schema.json` + - `tests/v2/fixtures/schema/agent/*.schema.json` + - Added infrastructure smoke tests: + - `tests/v2/test_test_infrastructure.py` +- **V2 Phase 0 strict schema valid fixtures/tests (`P0-04`)**: + - Added valid strict fixture copies in `tests/v2/fixtures/schema/strict/`: + - `pulse_PersonShape.json` + - `pulse_RepositoryShape.json` + - `pulse_OrganizationShape.json` + - `pulse_MembershipShape.json` + - `pulse_ContributionShape.json` + - `pulse_ArticleShape.json` + - Added strict schema validation tests: + - `tests/v2/test_schema_validation_strict.py` +- **V2 Phase 0 agent schema valid fixtures/tests (`P0-05`)**: + - Added agent schema validation tests: + - `tests/v2/test_schema_validation_agent.py` +- **V2 Phase 0 strict schema negative fixtures/tests (`P0-06`)**: + - Added strict-invalid schema fixtures in `tests/v2/fixtures/schema/invalid/`: + - `person_missing_name.json` + - `person_bad_orcid.json` + - `person_no_identifier.json` + - `repo_bad_github_handle.json` + - `org_unknown_type.json` + - `membership_extra_properties.json` + - `contribution_negative_count.json` + - `article_bad_doi.json` + - Added strict negative schema validation tests: + - `tests/v2/test_schema_validation_negative.py` +- **V2 Phase 0 mock GitHub provider fixtures/interface (`P0-07`)**: + - Added provider package scaffolding: + - `src/v2/providers/__init__.py` + - `src/v2/providers/base.py` + - `src/v2/providers/mock_github.py` + - Added GitHub provider fixtures in `tests/v2/fixtures/providers/github/`: + - `repo_payload.json` + - `user_payload.json` + - `org_payload.json` + - `contributors_payload.json` + - `rate_limited_response.json` + - `not_found_response.json` + - Added mock provider tests: + - `tests/v2/test_mock_github_provider.py` +- **V2 Phase 0 mock Infoscience provider fixtures/interface (`P0-09`)**: + - Added Infoscience provider interface + fixture-backed mock: + - `src/v2/providers/base.py` (new `InfoscienceProvider`) + - `src/v2/providers/mock_infoscience.py` + - Added Infoscience provider fixtures in `tests/v2/fixtures/providers/infoscience/`: + - `person_single_hit.json` + - `person_multi_hit.json` + - `orgunit_result.json` + - `publication_result.json` + - `empty_result.json` + - Added mock provider tests: + - `tests/v2/test_mock_infoscience_provider.py` +- **V2 Phase 0 mock ROR provider fixtures/interface (`P0-10`)**: + - Added ROR provider interface + fixture-backed mock: + - `src/v2/providers/base.py` (new `RORProvider`) + - `src/v2/providers/mock_ror.py` + - Added ROR provider fixtures in `tests/v2/fixtures/providers/ror/`: + - `org_detail.json` + - `search_results.json` + - `org_with_aliases.json` + - `parent_org.json` + - `not_found.json` + - Added mock provider tests: + - `tests/v2/test_mock_ror_provider.py` +- **V2 Phase 0 seed-based mock data generator (`P0-11`)**: + - Promoted deterministic mock data generation tooling: + - `scripts/v2/generate_mock_data.py` + - `src/v2/testing/__init__.py` + - `src/v2/testing/mock_generator.py` + - Added mock generator tests: + - `tests/v2/test_mock_generator.py` +- **V2 Phase 0 mock ORCID provider fixtures/interface (`P0-08`)**: + - Added ORCID provider interface and fixture-backed mock: + - `src/v2/providers/base.py` (new `ORCIDRecord` + `ORCIDProvider`) + - `src/v2/providers/mock_orcid.py` + - `src/v2/providers/__init__.py` (ORCID exports) + - Added ORCID provider fixtures in `tests/v2/fixtures/providers/orcid/`: + - `valid_record.json` + - `no_affiliations.json` + - `multiple_employment.json` + - `invalid_checksum.json` + - Added mock provider tests: + - `tests/v2/test_mock_orcid_provider.py` +- **V2 Phase 0 cross-reference consistency validation (`P0-12`)**: + - Added cross-reference validation module: + - `src/v2/validation/__init__.py` + - `src/v2/validation/crossref.py` + - Added cross-reference consistency tests: + - `tests/v2/test_crossref_consistency.py` +- **V2 Phase 0 golden extract contract tests (red phase) (`P0-13`)**: + - Added extract golden tests: + - `tests/v2/test_extract_golden.py` + - Added extract golden fixtures: + - `tests/v2/golden/extract/repo_github_com_owner_repo.json` + - `tests/v2/golden/extract/user_github_com_username.json` + - `tests/v2/golden/extract/org_github_com_orgname.json` +- **V2 Phase 0 golden graph contract tests (red phase) (`P0-14`)**: + - Added graph golden tests: + - `tests/v2/test_graph_golden.py` + - Added graph golden fixtures: + - `tests/v2/golden/graph/full_graph.json` + - `tests/v2/golden/graph/filtered_by_type.json` + - `tests/v2/golden/graph/filtered_by_source.json` +- **V2 Phase 1 package skeleton (`P1-01`)**: + - Added `src/v2/api.py` placeholder module. + - Added package skeleton `__init__.py` files for: + - `src/v2/agents/` + - `src/v2/canonicalization/` + - `src/v2/detection/` + - `src/v2/generated/` + - `src/v2/graph/` + - `src/v2/models/` + - `src/v2/observability/` + - `src/v2/pipeline/` + - `src/v2/pipeline/stages/` +- **V2 Phase 1 config module (`P1-02`)**: + - Added `src/v2/config.py` with `V2Config` environment loading and defaults: + - `V2_GRAPH_DB_PATH` (default: `data/v2_graph.db`) + - `V2_INTERMEDIATE_HISTORY_LIMIT` (default: `5`) + - `V2_ENABLE_LOGFIRE` (default: `true`) + - `LOGFIRE_TOKEN` (optional) + - `GITHUB_TOKEN` (required via `validate_preflight()`) + - Added `tests/v2/test_config.py`. +- **V2 Phase 1 GitHub URL classifier (`P1-03`)**: + - Added URL detection models in `src/v2/detection/models.py`: + - `GitHubURLType` + - `GitHubURLClassification` + - `UnsupportedGitHubURL` + - Added `classify_github_url()` implementation in `src/v2/detection/github_url_classifier.py`. + - Added exports in `src/v2/detection/__init__.py`. + - Added `tests/v2/test_url_classifier.py`. +- **V2 Phase 1 classifier edge-case handling (`P1-04`)**: + - Extended classifier rejection logic for unsupported GitHub subresource URLs: + - `issues`, `pull`, `blob`, `tree`, `commit`/`commits`, `actions`, `releases`, `wiki`, `settings`, `security` + - Added support for: + - HTTP to HTTPS normalization + - URL-decoded owner/repo path segments + - configurable GitHub Enterprise base URL (`V2_GITHUB_BASE_URL`) + - Added `tests/v2/test_url_classifier_edge_cases.py`. +- **V2 Phase 1 response contracts (`P1-05`)**: + - Added `src/v2/models/contracts.py` with: + - `V2ExtractResponse` + - `V2GraphResponse` + - `V2Stats` + - `V2GraphUpdate` + - Added exports in `src/v2/models/__init__.py`. + - Added `tests/v2/test_response_contracts.py`. +- **V2 Phase 1 error models (`P1-06`)**: + - Added `src/v2/models/errors.py` with: + - `V2ErrorType` + - `V2FieldError` + - `V2ErrorResponse` + - Added exports in `src/v2/models/__init__.py`. + - Added `tests/v2/test_error_models.py`. +- **V2 Phase 1 stub extract endpoint (`P1-07`)**: + - Implemented `src/v2/api.py` router with `GET /v2/extract/{full_path:path}`. + - Added query parameter handling for: + - `output_format` + - `force_refresh` + - `include_intermediates` + - Added typed `422` error payloads for unsupported URLs. + - Added `tests/v2/test_api_extract_stub.py`. +- **V2 Phase 1 stub graph endpoint (`P1-08`)**: + - Added `GET /v2/graph` to `src/v2/api.py`. + - Added query parameter handling for: + - `source_url` + - repeated `entity_type` + - `include_intermediates` + - `intermediate_limit` + - Stub response returns JSON-LD envelope with empty `@graph` and zeroed stats. + - Added `tests/v2/test_api_graph_stub.py`. +- **V2 Phase 1 mount v2 router in main app (`P1-09`)**: + - Mounted `v2_router` in `src/api.py` so `/v2/*` routes are served via the main API app. + - Added main-app routing coverage: + - `tests/v2/test_api_mount_v2_router.py` +- **V2 Phase 1 v2 health check endpoint (`P1-10`)**: + - Added `V2HealthResponse` to `src/v2/models/contracts.py`. + - Exported `V2HealthResponse` from `src/v2/models/__init__.py`. + - Added `GET /v2/health` in `src/v2/api.py` with component-level statuses for: + - `python` + - `config` + - `graph_store` (stubbed healthy) + - `github_token` (healthy/degraded) + - Added health endpoint coverage: + - `tests/v2/test_api_health.py` +- **V2 Phase 2 provider interfaces (`P2-01`)**: + - Added production provider implementations: + - `src/v2/providers/github_provider.py` + - `src/v2/providers/orcid_provider.py` + - `src/v2/providers/infoscience_provider.py` + - `src/v2/providers/ror_provider.py` + - Extended provider exports and factory wiring in: + - `src/v2/providers/__init__.py` + - `src/v2/providers/base.py` + - Added provider interface coverage: + - `tests/v2/test_provider_interfaces.py` +- **V2 Phase 2 agent wrappers (`P2-02`, `P2-03`, `P2-04`)**: + - Added shared v2 agent result/provider models: + - `src/v2/agents/models.py` + - Added permissive-schema wrappers: + - `src/v2/agents/repository_agent.py` + - `src/v2/agents/person_agent.py` + - `src/v2/agents/organization_agent.py` + - `src/v2/agents/__init__.py` + - Added focused v2 agent coverage: + - `tests/v2/test_repository_agent.py` + - `tests/v2/test_person_agent.py` + - `tests/v2/test_organization_agent.py` +- **V2 Phase 2 retry + soft-failure (`P2-05`)**: + - Added reusable retry wrapper: + - `src/v2/agents/retry.py` + - Extended `AgentResult` with partial-failure metadata and retry stats: + - `src/v2/agents/models.py` + - Exported retry helper: + - `src/v2/agents/__init__.py` + - Added retry behavior coverage: + - `tests/v2/test_agent_retry.py` +- **V2 Phase 2 orchestrator graph + execution (`P2-06`, `P2-07`)**: + - Added pipeline contracts and execution runtime: + - `src/v2/pipeline/models.py` + - `src/v2/pipeline/orchestrator.py` + - `src/v2/pipeline/__init__.py` + - Added orchestration graph and execution coverage: + - `tests/v2/test_orchestrator_graph.py` + - `tests/v2/test_orchestrator_execution.py` +- **V2 Phase 2 context-gather stage (`P2-08`)**: + - Added context bundle model and gather stage: + - `src/v2/pipeline/stages/models.py` + - `src/v2/pipeline/stages/context_gather.py` + - `src/v2/pipeline/stages/__init__.py` + - Added stage coverage: + - `tests/v2/test_context_gather.py` +- **V2 Phase 2 `/v2/extract` pipeline wiring (`P2-09`)**: + - Added provider dependency injection: + - `src/v2/dependencies.py` + - Replaced extract stub logic with orchestrator execution: + - `src/v2/api.py` + - Added end-to-end extract coverage and promoted extract golden test to green: + - `tests/v2/test_extract_e2e.py` + - `tests/v2/test_extract_golden.py` + - `tests/v2/golden/extract/repo_github_com_owner_repo.json` + - `tests/v2/golden/extract/user_github_com_username.json` + - `tests/v2/golden/extract/org_github_com_orgname.json` + +### Changed +- **V2 Infoscience canonical URI strategy**: + - Canonical Infoscience IDs for person/orgunit/publication now normalize to the single API endpoint form: + - `https://infoscience.epfl.ch/server/api/core/items/{uuid}` + - Kept support for legacy/alternate inputs during normalization: + - `https://infoscience.epfl.ch/entities/{person|orgunit|publication}/{uuid}` + - `https://infoscience.epfl.ch/server/api/entities/{person|orgunit|publication}/{uuid}/full` + - `https://infoscience.epfl.ch/server/api/core/items/{uuid}` + - Extended reconciliation to canonicalize article IDs and article cross-references (`schema:author`, `schema:sourceOrganization`) against resolved person/organization IDs. +- **V2 export surfaces for reconciliation and enum alignment**: + - Extended `src/v2/canonicalization/__init__.py` with repository ID resolution export. + - Extended `src/v2/pipeline/stages/__init__.py` with reconciliation/output assembly exports. + - Extended `src/v2/models/__init__.py` with v2 enum exports. +- **Dependency management**: + - Pinned `pyshacl` to `==0.22.2` in `pyproject.toml` for deterministic SHACL validation environments. +- **SHACL validation behavior**: + - Updated `src/v2/validation/shacl_validation.py` to validate against a merged data+ontology graph so `sh:class` checks resolve ontology enum instances consistently with installed `pyshacl`. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P4-01-sqlite-schema-migrations.md` after completing `P3-05` through `P3-08`. +- **Validation and canonicalization package exports**: + - Extended `src/v2/validation/__init__.py` exports with strict/SHACL validators and ontology loader helpers. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P3-05-canonical-id-repository.md` after completing `P3-01` through `P3-04`. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P3-01-strict-json-schema-gate.md` after completing `P2-05` through `P2-09`. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P2-01-provider-interfaces.md` after completing `P1-09` and `P1-10`. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P2-05-agent-retry-soft-failure.md` after completing `P2-01`, `P2-02`, `P2-03`, and `P2-04`. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P1-09-mount-v2-router.md` after completing `P1-05`, `P1-06`, `P1-07`, and `P1-08`. +- **Agent workflow documentation**: + - Updated `AGENTS.md` with a dedicated `V2 Phase 0 TDD Track` section. + - Advanced the phase entry task to `P0-03-test-infrastructure.md` after completing `P0-02`. + - Added explicit validation commands for promoted schemas: + - `python -m json.tool src/v2/schemas/strict/*.json` + - `python -m json.tool src/v2/schemas/agent/*.json` + - `just test-file tests/v2/test_promoted_strict_schemas.py` + - `just test-file tests/v2/test_promoted_agent_schemas.py` +- **Pytest configuration**: + - Registered the `v2` marker in `pyproject.toml` under `[tool.pytest.ini_options]`. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P0-04-strict-schema-valid-tests.md` after completing `P0-03`. + - Added explicit v2 infrastructure check commands: + - `pytest tests/v2/ --collect-only` + - `pytest tests/v2 -m v2 --collect-only` + - `just test-file tests/v2/test_test_infrastructure.py` +- **Agent workflow documentation**: + - Advanced the phase entry task to `P0-05-agent-schema-valid-tests.md` after completing `P0-04`. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P0-06-negative-schema-tests.md` after completing `P0-05`. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P0-07-mock-github-provider.md` after completing `P0-06`. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P0-08-mock-orcid-provider.md` after completing `P0-07`. +- **Agent workflow documentation**: + - Kept the phase entry task at `P0-08-mock-orcid-provider.md` as the earliest remaining dependency before `P0-12`. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P1-01-package-skeleton.md` after completing Phase 0 tasks `P0-08`, `P0-12`, `P0-13`, and `P0-14`. +- **Agent workflow documentation**: + - Advanced the phase entry task to `P1-02-config-module.md` after completing `P1-01`. +- **Environment example configuration**: + - Extended `.env.example` with v2-related keys: + - `GITHUB_TOKEN` + - `V2_GRAPH_DB_PATH` + - `V2_INTERMEDIATE_HISTORY_LIMIT` + - `V2_ENABLE_LOGFIRE` + - `LOGFIRE_TOKEN` +- **Agent workflow documentation**: + - Advanced the phase entry task to `P1-05-response-contracts.md` after completing `P1-02`, `P1-03`, and `P1-04`. + +### Testing +- Ran task-focused canonicalization/reconciliation checks with repo venv: + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_canonical_id_person.py tests/v2/test_canonical_id_organization.py tests/v2/test_canonical_id_article.py tests/v2/test_reconciliation.py -v` + - Result: `26 passed` +- Ran scoped reliability checks with repo venv: + - `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` + - Result: `321 collected` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` + - Result: `39 passed` + - `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` + - Result: `321 collected` +- Ran task-focused checks for `P3-05` to `P3-08` with repo venv: + - `PYTHONPATH=. .venv/bin/ruff check src/v2/canonicalization/__init__.py src/v2/canonicalization/id_resolution.py src/v2/pipeline/stages/__init__.py src/v2/pipeline/stages/models.py src/v2/pipeline/stages/reconciliation.py src/v2/pipeline/stages/output_assembly.py src/v2/models/__init__.py src/v2/models/enums.py scripts/v2/extract_enums_from_ttl.py tests/v2/test_canonical_id_repository.py tests/v2/test_reconciliation.py tests/v2/test_partial_failure.py tests/v2/test_enum_alignment.py` + - `PYTHONPATH=. .venv/bin/mypy --follow-imports=skip src/v2/canonicalization src/v2/pipeline/stages src/v2/models/enums.py` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_canonical_id_repository.py tests/v2/test_reconciliation.py tests/v2/test_partial_failure.py tests/v2/test_enum_alignment.py -v` +- Ran scoped reliability checks with repo venv: + - `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` + - `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` + - `PYTHONPATH=. .venv/bin/pytest -m v2 --collect-only` +- Ran SHACL dependency validation checks with repo venv: + - `PYTHONPATH=. .venv/bin/ruff check src/v2/validation/shacl_validation.py` + - `PYTHONPATH=. .venv/bin/mypy --follow-imports=skip src/v2/validation/shacl_validation.py` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_shacl_validation.py -v` +- Ran task-focused checks for strict validation gates and canonical ID resolution with repo venv: + - `PYTHONPATH=. .venv/bin/ruff check src/v2/validation/__init__.py src/v2/validation/schema_validation.py src/v2/validation/ontology.py src/v2/validation/shacl_validation.py src/v2/canonicalization/__init__.py src/v2/canonicalization/id_resolution.py tests/v2/test_strict_validation_gate.py tests/v2/test_shacl_validation.py tests/v2/test_canonical_id_person.py tests/v2/test_canonical_id_organization.py` + - `PYTHONPATH=. .venv/bin/mypy --follow-imports=skip src/v2/validation src/v2/canonicalization` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_strict_validation_gate.py tests/v2/test_canonical_id_person.py tests/v2/test_canonical_id_organization.py tests/v2/test_shacl_validation.py -v` +- Ran scoped reliability checks with repo venv: + - `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` + - `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` + - `PYTHONPATH=. .venv/bin/pytest -m v2 --collect-only` +- Ran task-focused checks for `P2-05` to `P2-09` with repo venv: + - `PYTHONPATH=. .venv/bin/ruff check src/v2/agents/models.py src/v2/agents/retry.py src/v2/agents/__init__.py src/v2/pipeline/__init__.py src/v2/pipeline/models.py src/v2/pipeline/orchestrator.py src/v2/pipeline/stages/__init__.py src/v2/pipeline/stages/context_gather.py src/v2/pipeline/stages/models.py src/v2/dependencies.py src/v2/api.py tests/v2/test_agent_retry.py tests/v2/test_orchestrator_graph.py tests/v2/test_orchestrator_execution.py tests/v2/test_context_gather.py tests/v2/test_extract_e2e.py tests/v2/test_extract_golden.py` + - `PYTHONPATH=. .venv/bin/mypy --follow-imports=skip src/v2/agents/retry.py src/v2/pipeline src/v2/dependencies.py src/v2/api.py` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_agent_retry.py tests/v2/test_orchestrator_graph.py tests/v2/test_orchestrator_execution.py tests/v2/test_context_gather.py tests/v2/test_extract_e2e.py tests/v2/test_extract_golden.py -q` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_api_extract_stub.py::test_extract_repository_url_returns_detected_repository tests/v2/test_extract_e2e.py::test_extract_endpoint_runs_pipeline_with_mock_providers -v` +- Ran scoped reliability checks with repo venv: + - `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` + - `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` + - `PYTHONPATH=. .venv/bin/pytest -m v2 --collect-only` +- Ran task-focused checks for `P1-09` and `P1-10` with repo venv: + - `PYTHONPATH=. .venv/bin/ruff check src/v2/api.py src/v2/models/contracts.py src/v2/models/__init__.py tests/v2/test_api_mount_v2_router.py tests/v2/test_api_health.py` + - `PYTHONPATH=. .venv/bin/ruff check --select I src/api.py` + - `PYTHONPATH=. .venv/bin/mypy --follow-imports=skip src/v2/models/contracts.py src/v2/api.py` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` + - `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` + - `PYTHONPATH=. .venv/bin/pytest -m v2 --collect-only` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_api_mount_v2_router.py tests/v2/test_api_health.py -v` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_api_mount_v2_router.py tests/v2/test_api_health.py tests/v2/test_api_extract_stub.py tests/v2/test_api_graph_stub.py -v` +- Ran task-focused checks for `P1-05` to `P1-08` with repo venv: + - `PYTHONPATH=. .venv/bin/ruff check src/v2/api.py src/v2/models/contracts.py src/v2/models/errors.py src/v2/models/__init__.py tests/v2/test_response_contracts.py tests/v2/test_error_models.py tests/v2/test_api_extract_stub.py tests/v2/test_api_graph_stub.py` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_response_contracts.py tests/v2/test_error_models.py tests/v2/test_api_extract_stub.py tests/v2/test_api_graph_stub.py -v` +- Ran scoped reliability checks with repo venv: + - `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` + - `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` +- Ran task-focused checks for `P2-01` to `P2-04` with repo venv: + - `PYTHONPATH=. .venv/bin/ruff check src/v2/providers/base.py src/v2/providers/__init__.py src/v2/providers/github_provider.py src/v2/providers/infoscience_provider.py src/v2/providers/orcid_provider.py src/v2/providers/ror_provider.py src/v2/agents/__init__.py src/v2/agents/models.py src/v2/agents/repository_agent.py src/v2/agents/person_agent.py src/v2/agents/organization_agent.py tests/v2/test_provider_interfaces.py tests/v2/test_repository_agent.py tests/v2/test_person_agent.py tests/v2/test_organization_agent.py` + - `PYTHONPATH=. .venv/bin/mypy --follow-imports=skip src/v2/agents src/v2/providers` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` + - `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` + - `PYTHONPATH=. .venv/bin/pytest -m v2 --collect-only` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_provider_interfaces.py tests/v2/test_repository_agent.py tests/v2/test_person_agent.py tests/v2/test_organization_agent.py -v` + - `PYTHONPATH=. .venv/bin/pytest -m v2 --collect-only` +- Added `tests/v2/test_promoted_strict_schemas.py` to verify: + - promoted schema files exist and parse as JSON, + - promoted files are byte-identical to source artifacts in `dev/`, + - each promoted schema passes `jsonschema` meta-schema validation. +- Added `tests/v2/test_promoted_agent_schemas.py` to verify: + - promoted agent schema files exist and parse as JSON, + - promoted files are byte-identical to source artifacts in `dev/`, + - each promoted schema passes `jsonschema` meta-schema validation, + - each agent schema preserves all property names present in its strict counterpart. +- Added `tests/v2/test_test_infrastructure.py` to verify: + - `load_schema("strict", "person")` returns a parsed JSON object, + - `load_fixture("schema/strict", "person.schema")` resolves nested fixture groups, + - `v2_test_config` points to expected test fixture/golden roots. +- Added `tests/v2/test_schema_validation_strict.py` to verify: + - strict fixtures meet minimum instance coverage per entity: + - Person (>=5), Repository (>=4), Organization (>=5), Membership (>=6), Contribution (>=9), Article (>=4), + - every valid fixture instance passes `jsonschema.validate()` against promoted strict schemas. +- Added `tests/v2/test_schema_validation_agent.py` to verify: + - each strict valid fixture group has at least one instance for all six entity types, + - every valid strict fixture instance passes `jsonschema.validate()` against promoted agent schemas. +- Added `tests/v2/test_schema_validation_negative.py` to verify: + - each invalid fixture is rejected by its corresponding strict schema with `jsonschema.ValidationError`, + - coverage includes at least 8 distinct invalid fixtures spanning required fields, patterns, enums, `anyOf`, `additionalProperties`, and numeric minimum constraints. +- Added `tests/v2/test_mock_github_provider.py` to verify: + - `MockGitHubProvider` implements the abstract `GitHubProvider` interface methods, + - repository lookup returns a GitHub REST-shaped payload for `octocat/Hello-World`, + - provider error paths raise typed exceptions for not found, rate limit, and private repository access, + - GitHub provider fixture files exist with valid JSON and include both REST and GraphQL mock response shapes. +- Added `tests/v2/test_mock_infoscience_provider.py` to verify: + - `MockInfoscienceProvider` implements the abstract `InfoscienceProvider` interface methods, + - person search returns single-hit and multi-hit fixtures (ambiguous candidate scenario), + - empty-result queries return an empty list without raising provider errors, + - fixture catalog/JSON validity checks for Infoscience payloads. +- Added `tests/v2/test_mock_ror_provider.py` to verify: + - `MockRORProvider` implements the abstract `RORProvider` interface methods, + - organization detail fixtures include names, aliases, types, country data, and parent/child relationships, + - search fixtures return ranked candidate organizations, + - alias fixtures include alternate names, acronyms, and multilingual labels. +- Added `tests/v2/test_mock_generator.py` to verify: + - `generate_dataset(seed=42)` is deterministic and `seed=99` yields different output, + - generated entities validate against promoted strict schemas, + - cross-reference integrity across persons, repositories, organizations, memberships, contributions, and articles, + - `--edge-cases` generation includes UUID-only identities, zero-count contributions, and forked repositories, + - CLI `scripts/v2/generate_mock_data.py` writes expected `pulse_*` JSON files to disk. +- Added `tests/v2/test_mock_orcid_provider.py` to verify: + - `MockORCIDProvider` implements abstract `ORCIDProvider`, + - fixture-backed valid/no-affiliation/multi-employment ORCID responses are returned with normalized structure, + - invalid checksum and malformed ORCID formats are rejected at provider level. +- Added `tests/v2/test_crossref_consistency.py` to verify: + - seed-generated dataset (`seed=42`) passes cross-reference checks with zero invalid references, + - orphaned contribution author references are detected, + - ownership symmetry (`pulse:owns` ↔ `pulse:ownedBy`) mismatches are detected, + - membership composite IDs resolve to valid person+organization pairs. +- Added `tests/v2/test_extract_golden.py` (`xfail`, red phase) to define `/v2/extract` contract expectations for: + - repository URL input, + - user URL input, + - organization URL input. +- Added `tests/v2/test_graph_golden.py` (`xfail`, red phase) to define `/v2/graph` contract expectations for: + - full graph envelope, + - filtering by entity type, + - filtering by source URL. +- Ran scoped reliability checks with repo venv: + - `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` + - `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` +- Ran additional phase-scoped v2 checks with repo venv: + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_promoted_agent_schemas.py -v` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_test_infrastructure.py -v` + - `PYTHONPATH=. .venv/bin/pytest -m v2 --collect-only` +- Added `tests/v2/test_config.py` to verify: + - valid config creation with required environment variables, + - descriptive `ValueError` on missing `GITHUB_TOKEN`, + - default `V2_GRAPH_DB_PATH`, + - `V2_ENABLE_LOGFIRE=false` behavior, + - optional `LOGFIRE_TOKEN` behavior when Logfire is enabled. +- Added `tests/v2/test_url_classifier.py` to verify: + - repository/user/organization detection, + - `.git` stripping, + - default user classification for ambiguous `https://github.com/`, + - normalization of trailing slashes, query params, fragments, + - non-GitHub URL rejection via `ValueError`. +- Added `tests/v2/test_url_classifier_edge_cases.py` to verify: + - at least 10 unsupported subresource rejection cases with specific reasons, + - HTTP to HTTPS upgrade, + - URL-encoded path segment handling, + - configurable GitHub Enterprise base URL behavior. +- Ran task-focused checks with repo venv: + - `PYTHONPATH=. .venv/bin/ruff check src/v2/config.py src/v2/detection/__init__.py src/v2/detection/models.py src/v2/detection/github_url_classifier.py tests/v2/test_config.py tests/v2/test_url_classifier.py tests/v2/test_url_classifier_edge_cases.py` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_config.py tests/v2/test_url_classifier.py tests/v2/test_url_classifier_edge_cases.py -q` +- Ran scoped reliability checks with repo venv: + - `PYTHONPATH=. .venv/bin/pytest tests/v2/ --collect-only` + - `PYTHONPATH=. .venv/bin/pytest tests/v2/test_schema_validation_strict.py -v` + - `PYTHONPATH=. .venv/bin/pytest tests/v2 -m v2 --collect-only` + - `PYTHONPATH=. .venv/bin/pytest -m v2 --collect-only` + ## [2.0.1] - 2026-02-16 diff --git a/CLAUDE.md b/CLAUDE.md new file mode 120000 index 0000000..47dc3e3 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1 @@ +AGENTS.md \ No newline at end of file diff --git a/README.md b/README.md index 5bd714c..28327db 100644 --- a/README.md +++ b/README.md @@ -1,224 +1,285 @@ # Git Metadata Extractor -This project is designed to classify imaging software repositories and extract relevant information using AI models like GPT and Gemini. It integrates with external services to analyze repositories and store the extracted data in JSON-LD format. - -The output of `/v1/extract` aligns with the softwareSourceCodeSchema of Imaging Plaza project. +A FastAPI service that turns a GitHub URL (repository / user / org) into +JSON-LD aligned with **Open Pulse Ontology v2.0.0**. The service runs the +input through a multi-stage pipeline that combines deterministic rules, +provider lookups (GitHub REST, ROR, ORCID, Infoscience, ETH Research +Collection), and optional LLM agents to produce a graph of +`schema:SoftwareSourceCode`, `schema:Person`, `org:Organization`, +`org:Membership`, `pulse:Contribution`, and `schema:ScholarlyArticle` +entities. + +The repository ships **two cooperating subsystems**: + +1. **Extraction service** (`src/v1/`, `src/v2/`) — the FastAPI app that + converts a GitHub URL to JSON-LD. V1 is frozen and kept for backwards + compatibility; **all new work targets V2** under `src/v2/`. +2. **RAG indices** (`src/index/*`) — nine sibling indices over EPFL/Swiss + research catalogues (HuggingFace, OpenAlex, Infoscience, ORCID, ROR, + Zenodo, ETH Research Collection, GitHub, SNSF) plus a federated layer + that fans out across all of them. The v2 LLM agents call these indices + as tools during extraction. + +## Quick links + +- [Documentation site](https://imaging-plaza.github.io/git-metadata-extractor/) (versioned via MkDocs + Mike) +- [`docs/getting-started.md`](docs/getting-started.md) — install + first run +- [`docs/v2-api-reference.md`](docs/v2-api-reference.md) — `/v2/extract`, `/v2/jobs`, `/v2/graph` +- [`docs/rag-indices.md`](docs/rag-indices.md) — the nine RAG indices + federated layer +- [`docs/v2-rag-tools.md`](docs/v2-rag-tools.md) — agent-side RAG tools wired into the v2 pipeline +- [`docs/migration-v1-to-v2.md`](docs/migration-v1-to-v2.md) — endpoint mapping +- [`CLAUDE.md`](CLAUDE.md) / [`AGENTS.md`](AGENTS.md) — agent operating contract ## Features -- Extracts repository metadata using GIMIE and AI models. -- Merges extracted data into JSON-LD format. -- Supports CLI usage for flexible execution. - -## Project Structure +- **JSON-LD extraction** aligned with Open Pulse Ontology v2.0.0 + (repositories, persons, organizations, articles, memberships, + contributions). +- **Multi-stage pipeline** with deterministic and LLM-backed agents, + cross-bucket dedup, reconciliation, strict schema + SHACL validation, + Selenium-backed link veracity, and ROR-driven org-hierarchy inference. +- **RAG-augmented agents** can query nine Qdrant-backed indices on demand + (or the federated layer for whole-corpus queries). +- **Async job API** (`POST /v2/extract` + `GET /v2/jobs/{id}`) backed by + the SQLite provider cache. +- **Three-layer cache** (provider responses, agent verdicts, full pipeline + responses) sharing one SQLite DB. +- **Versioned documentation** site (`mkdocs` + `mike`). + +## Project structure -```bash -. -├── CHANGELOG.md -├── Dockerfile -├── LICENSE -├── pyproject.toml -├── README.md -├── requirements.txt -└── src - ├── __init__.py - ├── __pycache__ - │ └── __init__.cpython-311.pyc - ├── api.py - ├── core - │ ├── __init__.py - │ ├── __pycache__ - │ │ ├── __init__.cpython-311.pyc - │ │ └── models.cpython-311.pyc - │ ├── genai_model.py - │ ├── gimie_methods.py - │ ├── models.py - │ ├── prompts.py - │ └── verification.py - ├── files - │ ├── json-ld-context.json - │ └── output_file.json - ├── main.py - ├── test - │ ├── __pycache__ - │ │ └── test_conversion.cpython-311-pytest-8.4.1.pyc - │ └── test_conversion.py - └── utils - ├── __init__.py - ├── logging_config.py - └── utils.py ``` - +src/ + api.py # FastAPI app, mounts /v1 and /v2 routers, /docs UI + v1/ # frozen legacy pipeline (no new work) + v2/ + api.py # /v2/extract endpoint + pipeline driver + jobs.py # async job store backing POST /v2/extract + pipeline/ # 23-stage extraction pipeline + agents/llm/ # LLM-backed per-entity agents + RAG tools + agents/rule_based/ # deterministic counterparts + ingest/ # provider clients (github, ror, orcid, infoscience, *_rag) + schema/ # JSON Schemas + JSON-LD context + generated Pydantic models + validation/ # strict-schema + SHACL validators + index/ + huggingface/ # HuggingFace Hub RAG index + openalex/ # OpenAlex RAG index + infoscience/ # EPFL Infoscience RAG index + ethz_research_collection/ # ETH Research Collection RAG index + orcid/ # ORCID person RAG index + ror/ # ROR organisation RAG index + zenodo/ # Zenodo records RAG index + github/ # GitHub repo+README RAG index + snsf/ # Swiss National Science Foundation grants + _federated/ # cross-index search + entity lookup + +config/index/ # static YAML config per RAG index +docs/ # mkdocs site source +tests/v2/ # default test target +tests/index/ # per-index test suites +scripts/v2/ # batch extraction + debug runners +justfile # task runner — source of truth for commands +``` ## Installation -Clone the repository and install dependencies: - -``` sh -pip install -r requirements.txt -``` - -Create a `.env` (or modify `.env.dist`) file and fill it as follows: +This project uses [`uv`](https://docs.astral.sh/uv/) for dependency +management. Install with the dev extras and scaffold a `.env` from the +template: -``` bash -OPENAI_API_KEY="your_openai_api_key" -OPENROUTER_API_KEY="your_openrouter_api_key" -GITHUB_TOKEN= -GITLAB_TOKEN= -MODEL="model to be used" -PROVIDER="openai" or "openrouter" +```bash +just install-dev # uv pip install -e ".[dev]" +cp .env.example .env ``` -## Usage - -You can run the script with the default settings or specify parameters via CLI: - -```sh -python src/main.py --url https://github.com/qchapp/lungs-segmentation --output_path output_file.json -``` +Edit `.env` and fill in at minimum: -If no arguments are provided, it will use the default repository and output path. +- `GITHUB_TOKEN` — required for `/v2/health` to report `ok` and for any + real GitHub call (extraction service + `gh-*` indexer). +- One LLM credential — `RCP_TOKEN` (EPFL RCP), `OPENAI_API_KEY`, or + `OPENROUTER_API_KEY`, depending on the model profile in + `src/v2/agents/llm/model_config.py`. Validated at startup; missing keys + surface a clear error. -## Versioned documentation (GitHub Pages) +Optional (only set if you use the corresponding feature): -The repository includes a versioned documentation site under `docs/` powered by MkDocs Material + Mike. +- `INFOSCIENCE_TOKEN` — protected Infoscience routes only. +- `SELENIUM_REMOTE_URL` — enables the link-veracity stage and the + `fetch_link_content_via_selenium` agent tool. +- `HF_TOKEN` — recommended for the HuggingFace indexer (anonymous IPs hit + a 500-req / 5-min bucket and stall around ~50 orgs). +- `ZENODO_TOKEN`, `OPENALEX_MAILTO`, `EPFL_GRAPH_USERNAME` / + `EPFL_GRAPH_PASSWORD` — per-indexer politeness. -Install docs dependencies: +See `.env.example` for the full annotated list. -```bash -uv pip install -e ".[docs]" -``` +## Running the extraction service -Local docs preview: +Development (uvicorn, auto-reload on `src/**/*.py`): ```bash -just docs-serve +just serve-dev ``` -Strict docs build: +Production-shape (gunicorn, 4 workers): ```bash -just docs-build +just serve-gunicorn ``` -Manual publish commands: +Stop whatever is bound to `:$PORT` (default `1234`): ```bash -# Publish dev/latest from current branch -just docs-deploy-dev - -# Publish a release version and update stable alias -just docs-deploy-release 2.0.1 - -# Set default version in selector -just docs-set-default stable +just serve-stop ``` -Automation: - -- `.github/workflows/docs_pages.yml` publishes docs on: - - Pushes to `main` (`dev` + `latest`) - - Pushes of tags matching `v*` (release version + `stable`) -- Configure GitHub Pages to serve from the `gh-pages` branch root. - -## How to run the tool using Docker? - -1. You need to build the image. +Then: - ``` bash - docker build -t git-metadata-extractor -f tools/image/Dockerfile . - ``` +- Swagger UI: (with auto/manual dark-mode toggle) +- v2 health: +- Extract a repo (sync): `curl "http://localhost:1234/v2/extract/github.com/octocat/Hello-World?output_format=jsonld"` +- Extract a repo (async): `POST /v2/extract` returns `202` + `job_id`; poll `GET /v2/jobs/{job_id}`. -2. Run the image. +V1 endpoints (`/v1/extract`, `/v1/cache/*`) are still mounted but frozen. +See [`docs/migration-v1-to-v2.md`](docs/migration-v1-to-v2.md) for the +endpoint mapping. - ``` bash - docker run -it --env-file .env -p 1234:1234 --entrypoint bash git-metadata-extractor - ``` +For batch extractions, `scripts/v2/batch_extract.sh` reads a hardcoded URL +list and drives `/v2/extract` with configurable parallelism (resumable — +skips already-completed result files). - If you are developping the application it's useful to mount the app volume. +## Running the RAG indices - ``` bash - docker run -it --env-file .env -p 1234:1234 -v .:/app --entrypoint bash git-metadata-extractor - ``` +Each index is independent: its own DuckDB, its own Qdrant collections, its +own CLI, its own justfile recipes. The full inventory and per-index +quickstarts live in [`docs/rag-indices.md`](docs/rag-indices.md). Common +shape: -3. Then you can run the tool via - - ``` bash - python src/main.py --url https://github.com/qchapp/lungs-segmentation --output_path output_file.json - ``` +```bash +# HuggingFace example +just hf-status # counts + paths +just hf-ingest --scope switzerland # idempotent +just hf-embed # only embeds new chunks +just hf-search "swiss german LLM" --top-k 5 +just hf-lineage epfl-llm/meditron-7b # ancestors + descendants + +# Cross-index federated search +just gme-indices # list registered adapters +just gme-search "Swiss German LLM" --top-k 10 +just gme-entity 0000-0001-9534-3870 # by any identifier +``` -4. Optional. If you are planning to use the ORCID functionality, you need to start a remote browser and configure the `.env` file. +All indices share a Qdrant instance on the compose service +`gme-qdrant:6333` (NOT `localhost:6333` — running from inside the +devcontainer requires the service name). Embeddings come from +`Qwen/Qwen3-Embedding-8B` on EPFL RCP; reranking from +`Qwen/Qwen3-Reranker-8B`. See [`docs/rag-indices.md`](docs/rag-indices.md) +for storage layout, scopes, and failure modes. - **Option A: Standalone mode (single concurrent session - may cause errors with concurrent requests):** - ``` bash - docker run --rm -d -p 4444:4444 -p 7900:7900 --shm-size="2g" --name selenium-standalone-firefox --network dev selenium/standalone-firefox - ``` +The v2 LLM agents reach the same indices through async providers in +`src/v2/ingest/providers/*_rag.py` and pydantic-ai tool factories in +`src/v2/agents/llm/agent_tools/*_rag.py`. Toggle each with +`V2__RAG_ENABLED` (all default to `true`); see +[`docs/v2-rag-tools.md`](docs/v2-rag-tools.md) for tool schemas, filter +allowlists, and the federated tool flow. - **Option B: Standalone mode with multiple sessions (recommended for concurrent requests):** - ``` bash - docker run --rm -d -p 4444:4444 -p 7900:7900 --shm-size="2g" \ - -e SE_NODE_MAX_SESSIONS=5 \ - -e SE_NODE_SESSION_TIMEOUT=300 \ - --name selenium-standalone-firefox \ - --network dev \ - selenium/standalone-firefox - ``` +## Testing +The `justfile` is the source of truth — it invokes +`.venv/bin/python -m pytest`, so you don't need to set `PYTHONPATH` or +rely on a globally available `pytest`. +```bash +just test # fast loop via testmon +just test-full # full deterministic run +just test-coverage # with coverage report +just test-llm-integration # opt-in real-provider LLM tests +just test-live # opt-in live-provider smoke tests +just lint # ruff +just type-check # mypy +just check # lint + type-check +just ci # lint + type-check + coverage +``` +Per-index test suites live under `tests/index//` and have their own +recipes (`just hf-test`, `just orcid-test`, `just openalex-test`). - **Option C: Grid mode with hub and multiple nodes (best for high concurrency):** - ``` bash - # Start the hub - docker run --rm -d -p 4444:4444 --name selenium-hub --network dev selenium/hub:latest +If testmon selection looks stale, `rm -f .testmondata` then `just +test-full`. - # Start 3 Firefox nodes - docker run --rm -d --shm-size="2g" -e SE_EVENT_BUS_HOST=selenium-hub \ - -e SE_EVENT_BUS_PUBLISH_PORT=4442 -e SE_EVENT_BUS_SUBSCRIBE_PORT=4443 \ - --name selenium-node-firefox-1 --network dev selenium/node-firefox:latest +## Configuration knobs - docker run --rm -d --shm-size="2g" -e SE_EVENT_BUS_HOST=selenium-hub \ - -e SE_EVENT_BUS_PUBLISH_PORT=4442 -e SE_EVENT_BUS_SUBSCRIBE_PORT=4443 \ - --name selenium-node-firefox-2 --network dev selenium/node-firefox:latest +The most-touched env vars (full list in +[`CLAUDE.md`](CLAUDE.md#configuration-env-vars) and `.env.example`): - docker run --rm -d --shm-size="2g" -e SE_EVENT_BUS_HOST=selenium-hub \ - -e SE_EVENT_BUS_PUBLISH_PORT=4442 -e SE_EVENT_BUS_SUBSCRIBE_PORT=4443 \ - --name selenium-node-firefox-3 --network dev selenium/node-firefox:latest +| Var | Default | Purpose | +|---|---|---| +| `V2_AGENT_RUNTIME_DEFAULT` | `llm` | default for `/v2/extract` when query omits `agent_runtime` | +| `V2_USE_MOCK_PROVIDERS` | `true` | mock GitHub/ORCID/Infoscience/ROR; set `false` for real APIs | +| `V2_LINK_VERACITY_ENABLED` | `true` | turn off in batch runs to skip Selenium fetches | +| `V2_APPLY_CRITIC_PRUNING` | `false` | enable critic drop suggestions (LLM mode only) | +| `V2_MAX_CONCURRENT_AGENTS` | `6` | per-stage fan-out concurrency | +| `V2_PROVIDER_CACHE_PATH` | `.cache/v2/providers.db` | shared provider + verdict + pipeline cache | +| `V2_PROVIDER_CACHE_TTL_DAYS` | `30` | TTL for cached entries | +| `V2_PIPELINE_CACHE_ENABLED` | `true` | full `/extract` response cache | +| `V2_QUERY_LOG_DIR` | `logs/v2_queries` | per-request external-query log destination | +| `V2__RAG_ENABLED` | `true` | toggle each RAG index tool (`INFOSCIENCE`, `ETHZ_RESEARCH_COLLECTION`, `HUGGINGFACE`, `OPENALEX`, `ZENODO`, `ORCID`, `ROR`) | +| `INDEX_QDRANT_URL` | `http://qdrant:6333` (yaml default) | Qdrant endpoint; use `http://gme-qdrant:6333` inside the devcontainer | +| `LOG_LEVEL` | `INFO` | DEBUG/INFO/WARNING/ERROR | - # Update .env to use: SELENIUM_REMOTE_URL=http://selenium-hub:4444 - ``` +Use a different `V2_PROVIDER_CACHE_PATH` per run profile (e.g. +`.cache/v2-rule-based/providers.db`) to keep LLM and rule-based runs +independently invalidatable. -## How to develop using Docker? +## Versioned documentation -To facilitate the development we can mount the app folder in the docker. By doing this, all changes made in local will be accesible from the running container. +The repository ships a versioned MkDocs Material + Mike site under +`docs/`. -``` bash -docker run -it --env-file .env -p 1234:1234 -v .:/app git-metadata-extractor +```bash +uv pip install -e ".[docs]" +just docs-serve # local preview +just docs-build # strict build +just docs-deploy-dev # publish dev + latest from current branch +just docs-deploy-release 2.0.1 # publish a release version + update stable alias +just docs-set-default stable # set default version in selector ``` +`.github/workflows/docs_pages.yml` publishes docs on: -## How to start the API? - -Simply run: +- pushes to `main` → `dev` + `latest` +- tags matching `v*` → release version + `stable` -``` bash -docker run -it --rm --env-file .env -p 1234:1234 -v ./data:/app/data --name git-metadata-extractor --network dev git-metadata-extractor -``` +GitHub Pages must be configured to serve from the `gh-pages` branch root. -This mounts the local `data` folder to persist cache files. To configure the cache directory, set `CACHE_DIR=/app/data` in your `.env` file. +## Docker -Then go to `localhost:1234` +```bash +docker build -t git-metadata-extractor -f tools/image/Dockerfile . +docker run -it --rm --env-file .env -p 1234:1234 \ + -v ./data:/app/data \ + --name git-metadata-extractor --network dev \ + git-metadata-extractor +``` -Or if you are running the container with `bash` as the entrypoint, please execute. +To use the link-veracity stage / ORCID Selenium-backed tooling, start a +Selenium container alongside (Grid mode recommended for concurrent +requests): -``` bash -uvicorn src.api:app --host 0.0.0.0 --workers 4 --port 1234 --reload +```bash +docker run --rm -d -p 4444:4444 -p 7900:7900 --shm-size="2g" \ + -e SE_NODE_MAX_SESSIONS=5 -e SE_NODE_SESSION_TIMEOUT=300 \ + --name selenium-standalone-firefox --network dev \ + selenium/standalone-firefox +# then set SELENIUM_REMOTE_URL=http://selenium-standalone-firefox:4444 in .env ``` -`--reload` allows you to modify the files and reload automatically the api endpoint. Excellent for development. +Qdrant is wired up via the devcontainer compose file +(`.devcontainer/docker-compose.yml`); inside the devcontainer reach it at +`http://gme-qdrant:6333`. ## Credits -- Quentin Chappuis - EPFL Center for Imaging -- Robin Franken - SDSC -- Carlos Vivar Rios - SDSC / EPFL Center for Imaging +- Quentin Chappuis — EPFL Center for Imaging +- Robin Franken — SDSC +- Carlos Vivar Rios — SDSC / EPFL Center for Imaging diff --git a/config/index/epfl_graph.yaml b/config/index/epfl_graph.yaml new file mode 100644 index 0000000..e9243b7 --- /dev/null +++ b/config/index/epfl_graph.yaml @@ -0,0 +1,45 @@ +# EPFL Graph disciplines RAG indexer config. +# +# Source: graphai.epfl.ch /ontology/* — a curated tree of academic +# disciplines (~2226 categories, depth 1..5) where each leaf is backed by +# 50-110 anchor Wikipedia concepts. This index turns the tree into a +# semantic-search surface so callers can find the closest EPFL discipline +# for a given README / abstract / arbitrary text. +# +# Resolved by `src/index/epfl_graph/config.py` and merged with env vars +# (`RCP_TOKEN`, `EPFL_GRAPH_USERNAME`, `EPFL_GRAPH_PASSWORD`, +# `INDEX_QDRANT_API_KEY`, `INDEX_DATA_DIR`) at startup. + +rcp: + # Same RCP deployment as openalex / zenodo / infoscience indexers. + base_url: https://inference-rcp.epfl.ch/v1 + embedding_model: Qwen/Qwen3-Embedding-8B + embedding_dim: 4096 + query_instruction: "Given a README or abstract, retrieve the closest EPFL Graph academic disciplines" + reranker_model: Qwen/Qwen3-Reranker-8B + batch_size: 32 + max_concurrency: 4 + timeout_seconds: 120 + +graphai: + base_url: https://graphai.epfl.ch + port: 443 + # Polite pacing for the ~2226-call tree walk during ingest. ~5 req/s is + # well under graphai's per-token rate limit and finishes the full walk + # in ~8 minutes. + rate_per_second: 5 + max_concurrency: 4 + # Number of top anchor concepts to keep per category for embedding + # context. Most leaf categories ship 50-110; embedding text quality + # plateaus well below that. + anchor_concepts_per_category: 12 + +qdrant: + url: http://localhost:6333 + prefer_grpc: false + +filter: + # Skip categories shallower than this depth — depth 1 is "academic + # disciplines" / depth 2 is "applied-sciences" etc., too broad to be + # useful matches. + min_depth: 3 diff --git a/config/index/ethz_research_collection.yaml b/config/index/ethz_research_collection.yaml new file mode 100644 index 0000000..2f30b59 --- /dev/null +++ b/config/index/ethz_research_collection.yaml @@ -0,0 +1,55 @@ +# ETH Research Collection RAG indexer config. +# Resolved by `src/index/ethz_research_collection/config.py` and merged with env vars +# (`RCP_TOKEN`, `ETHZ_RESEARCH_COLLECTION_TOKEN`, `INDEX_DATA_DIR`) at startup. + +rcp: + # EPFL RCP OpenAI-compatible inference endpoint (shared with the other + # RAG indices — same embedding/rerank models keep cross-index hits + # comparable). + base_url: https://inference-rcp.epfl.ch/v1 + embedding_model: Qwen/Qwen3-Embedding-8B + # Native dim for Qwen3-Embedding-8B is 4096. Configurable down to 32. + embedding_dim: 4096 + # Prepended to every query (not passages) per Qwen3-Embedding instruction format. + query_instruction: "Given a web search query, retrieve relevant passages that answer the query" + reranker_model: Qwen/Qwen3-Reranker-8B + # Per-batch input count for /v1/embeddings. Smaller default given 8B params. + batch_size: 16 + # Concurrent in-flight embed/rerank calls. + max_concurrency: 4 + # Generous to accommodate RCP cold-start (model load can take minutes). + timeout_seconds: 120 + +# Pydantic field name is `research_collection` (see EthzResearchCollectionIndexConfig). +research_collection: + # ETH Research Collection runs DSpace 7. The REST API is mounted at /server/api. + # Verify against https://www.research-collection.ethz.ch/server/api when first + # running ingest — no auth needed for read-only endpoints. + base_url: https://www.research-collection.ethz.ch/server/api + page_size: 100 + # DSpace edge layers 405 under burst; keep concurrency modest. + # `dspace.py` retries 405/429/5xx with exponential backoff. + max_concurrency: 2 + +filter: + # Solr `fulltext:` qualifier is applied per-term against + # /discover/search/objects?configuration=researchoutputs. The same + # terms drive the URL-extraction regex in extract_matches.py. + terms: + - github.com + - huggingface.co + - hf.co + +chunking: + size_tokens: 1024 + overlap_tokens: 128 + # tiktoken encoding name. cl100k_base is a reasonable proxy for the + # Qwen3 tokenizer; counts are within ~10% for English/code text. + tokenizer: cl100k_base + +qdrant: + # Devcontainer default. Overridden by INDEX_QDRANT_URL / INDEX_QDRANT_API_KEY + # / INDEX_QDRANT_PREFER_GRPC env vars at load time. + url: http://gme-qdrant:6333 + prefer_grpc: false + api_key: null diff --git a/config/index/github.yaml b/config/index/github.yaml new file mode 100644 index 0000000..e3ef068 --- /dev/null +++ b/config/index/github.yaml @@ -0,0 +1,56 @@ +# GitHub repository RAG indexer config. +# Resolved by `src/index/github/config.py` and merged with env vars +# (`RCP_TOKEN`, `GITHUB_TOKEN`, `INDEX_QDRANT_URL`, `INDEX_QDRANT_API_KEY`, +# `INDEX_DATA_DIR`, `INDEX_GITHUB_SCOPE`) at startup. + +rcp: + # Same RCP deployment as the openalex / zenodo / huggingface / infoscience + # indexers — single Qwen3-Embedding-8B / Qwen3-Reranker-8B pair. + base_url: https://inference-rcp.epfl.ch/v1 + embedding_model: Qwen/Qwen3-Embedding-8B + embedding_dim: 4096 + query_instruction: "Given a search query, retrieve relevant GitHub repositories" + reranker_model: Qwen/Qwen3-Reranker-8B + batch_size: 32 + max_concurrency: 4 + timeout_seconds: 120 + +github: + api_base: https://api.github.com + per_page: 100 + max_concurrency: 4 + # Skip embedding repos whose composite text (full_name + description + + # topics + README) is shorter than this — defends against placeholder + # READMEs (same dynamic as huggingface spaces). + min_card_chars: 64 + # Truncate READMEs larger than this before chunking (1 MiB). + readme_max_bytes: 1048576 + +scope: + active: epfl # epfl | switzerland (or any other key in `seeds`) + seeds: + epfl: + # Curated EPFL starter — extend as we discover. The openalex + # `work_github_urls` table (populated by `openalex find-github`) is + # the principled source of new candidates; review and add here. + - epfl-lts2/gspbox + - epfl-dlab/transformers-CFG + - epfl-lasa/control-libraries + switzerland: + - epfl-lts2/gspbox + - epfl-dlab/transformers-CFG + - epfl-lasa/control-libraries + - eth-cscs/firecrest + - eth-easl/modyn + - ethz-asl/maplab + +qdrant: + # Compose service `gme-qdrant` on the `dev` network. See + # `.internal/ror/qdrant-setup.md` for the hostname rationale. + url: http://gme-qdrant:6333 + prefer_grpc: false + +chunking: + size_tokens: 512 # READMEs are short; bigger chunks than openalex's 256 + overlap_tokens: 64 + tokenizer: cl100k_base diff --git a/config/index/huggingface.yaml b/config/index/huggingface.yaml new file mode 100644 index 0000000..4c83cb9 --- /dev/null +++ b/config/index/huggingface.yaml @@ -0,0 +1,363 @@ +# HuggingFace RAG indexer config. +# Resolved by `src/index/huggingface/config.py` and merged with env vars +# (RCP_TOKEN, HF_TOKEN, INDEX_QDRANT_URL, INDEX_QDRANT_API_KEY, +# INDEX_QDRANT_PREFER_GRPC, INDEX_HUGGINGFACE_SCOPE, INDEX_DATA_DIR) +# at startup. + +rcp: + # OpenAI-compatible inference endpoint. Same RCP deployment as the + # infoscience and openalex indexers. + base_url: https://inference-rcp.epfl.ch/v1 + embedding_model: Qwen/Qwen3-Embedding-8B + # Native dim for Qwen3-Embedding-8B is 4096. Configurable down to 32 if + # RCP deploys with Matryoshka truncation. + embedding_dim: 4096 + query_instruction: "Given a search query, retrieve relevant model and dataset cards" + reranker_model: Qwen/Qwen3-Reranker-8B + batch_size: 32 + max_concurrency: 4 + timeout_seconds: 120 + +huggingface: + api_base: https://huggingface.co + # Default page size for HfApi list endpoints. Hub max is 100 (anonymous) + # and 100 (authenticated); leave at 100. + per_page: 100 + max_concurrency: 4 + # When true, snapshot_download README + *.json + *.yaml + *.md alongside + # the metadata pull. Weight files are always ignored. + full_cards: false + # Skip embedding rows whose card is shorter than this many characters — + # avoids embedding "# My Demo" placeholder cards as standalone chunks. + min_card_chars: 64 + +scope: + # epfl | switzerland — picks which seed list to use. Override via + # INDEX_HUGGINGFACE_SCOPE. + active: epfl + seeds: + # Curated by hand. Add new entries below the relevant section header so + # the file stays scannable. The `discover-orgs` subcommand never edits + # this file — it only proposes candidates in logs/discover_orgs.jsonl. + epfl: + # Original seed (institutional + flagship lab orgs). + - epfl-llm + - epfl-nlp + - EPFL-VILAB + - epfl-ml4ed + - EPFL-ECEO + # Promoted from discover-orgs run on 2026-05-01 (EPFL labs/projects). + - epfl-dlab + - LIONS-EPFL + - EPFL-IVRL + - SAGESSE-EPFL + - EPFL-CVLAB-SPACECRAFT + - ADP-EPFL + - epfl-ml-ytf + - epfl-dhlab + - EPFL-LNMC + - EPFL-CVLab + - EPFL-DrivingVQA + - structure-epflai + - asl-epfl + - epfl-ihl + - epfl-radio-astro + # Promoted personal user accounts (EPFL-affiliated researchers). + - vanek-epfl + - rapieniuta-epfl + # Promoted from wider discover-orgs run on 2026-05-01 (EPFL projects). + - EPFL-DL-CER-project + # Promoted from extended-terms discover-orgs run on 2026-05-01. + - Idiap # Idiap Research Institute, Martigny (EPFL-affiliated) + - epfml # EPFL ML group (Jaggi/MLO alt namespace) + # Promoted 2026-05-01 from EPFL org member rosters (high-confidence: + # every HF org membership is an EPFL org). + - ViktorDo # Viktor Domazetoski (EPFL-ECEO) — 94 models + - inrainbws # Zhuoqian Yang (EPFL-IVRL) — 10 models, 1 dataset + - mdelmas # Maxime Delmas (Idiap) — 6 models, 2 datasets + - faruk-zahiragic # Faruk Zahiragic (epfl-ml-ytf) — 4 models + - matsant01 # Matteo Santelmo (SAGESSE-EPFL) — 2 models, 1 dataset + - Yusif-EPFL # Yusif Askari (epfl-ml-ytf) — 2 models + - miaw1419 # Mahdi Shafiei (EPFL-VILAB) — 2 models + - ZhitongGao # Zhitong Gao (EPFL-VILAB) — 1 model, 1 dataset + - Moglul # Mog (structure-epflai) — 1 model + - cmlavo # Christophe Michaud-Lavoie (EPFL-DL-CER-project) — 1 model + - natasa-jovanovic # Nataša Jovanović (EPFL-VILAB) — 1 model + - sogandstormesalehi # Sogand Salehi (EPFL-VILAB) — 1 model + - SkyeLu # Skye Lu (EPFL-IVRL) — 1 dataset + - chrisfinlay # Chris Finlay (epfl-radio-astro) — 1 dataset + - mxxsc # Oscar Ma (epfl-dlab) — 1 dataset + - sraimund # Raimund Schnürer (epfl-dhlab) — 1 dataset + switzerland: + # EPFL block (mirrors `epfl` seed above). + - epfl-llm + - epfl-nlp + - EPFL-VILAB + - epfl-ml4ed + - EPFL-ECEO + - epfl-dlab + - LIONS-EPFL + - EPFL-IVRL + - SAGESSE-EPFL + - EPFL-CVLAB-SPACECRAFT + - ADP-EPFL + - epfl-ml-ytf + - epfl-dhlab + - EPFL-LNMC + - EPFL-CVLab + - EPFL-DrivingVQA + - structure-epflai + - asl-epfl + - epfl-ihl + - epfl-radio-astro + - vanek-epfl + - rapieniuta-epfl + - EPFL-DL-CER-project + - Idiap + - epfml + - ViktorDo + - inrainbws + - mdelmas + - faruk-zahiragic + - matsant01 + - Yusif-EPFL + - miaw1419 + - ZhitongGao + - Moglul + - cmlavo + - natasa-jovanovic + - sogandstormesalehi + - SkyeLu + - chrisfinlay + - mxxsc + - sraimund + # Original ETH + Swiss block. + - swiss-ai + - swiss-ai-center + - ethz + - ETHZurich + - ETH-CVG + - prs-eth + - ethz-spylab + - lasgroup + - camlab-ethz + - ethzanalytics + - eth-student-project-house + - ZurichNLP + - rcds + # Promoted from discover-orgs run on 2026-05-01 (ETH labs/projects). + - ethz-mtc + - ethz-vlg + - eth-nlped + - eth-easl + - adhd-ethz + # Promoted personal user accounts (ETH-affiliated researchers). + - arahmoun-ethz + - jayzhang-ethz + - ethzhou + - liushu-ethz + # Promoted from wider discover-orgs run on 2026-05-01 + # (other Swiss universities + national centres). + - SDSC # Swiss Data Science Center (EPFL/ETH joint) + - dh-unibe # Universität Bern, Digital Humanities + - cvg-unibe # Universität Bern, Computer Vision Group + - prg-unibe # Universität Bern, Pattern Recognition Group + - ivis-UZH # UZH, Interactive Visualization + - CTPLab-DBE-UniBas # UniBas D-BSSE Cellular Therapy & Pathology Lab + - unige-fti # Université de Genève, Faculté de Traduction + - ALIRE-HESSO # HES-SO ALIRE project + - HSLU-AAI # Hochschule Luzern, Applied AI + - fhnw # FHNW institutional + - fhnwrover # FHNW robotics project + # Promoted 2026-05-01 from extended Swiss probe (ZHAW + SWE-Swiss). + - ZHAW # Zürcher Hochschule für Angewandte Wissenschaften + - SWE-Swiss # Swiss SWE benchmark (SWE-Swiss-32B-SFT, SWESwiss-Repair-RL) + # Promoted 2026-05-01 from ETH/SDSC/UniBe org member rosters. + # High-confidence: every member's HF org affiliations are within our Swiss seed. + # Sorted by upload activity descending; top hauler `mtc` has 225 models. + - mtc # MTC (ETH Media Technology Center) — 225 models + - Stern5497 # Ronja Stern @ rcds — 20 models + - briannnyee # Brian Lee @ ethz — 11 models, 8 datasets + - qchapp # Quentin Chappuis @ SDSC + - AleBurzio # Alessandro Burzio @ ethzanalytics + - Fuqianshi # ChenruiFan @ cvg-unibe + - Paviththiren # Sivasothilingam @ ethz + - haofeixu # Haofei Xu @ ETH-CVG + - slochmann # Sebastian Lochmann @ ethz + - mstupk # Michael Stupka @ dh-unibe + - pstroe # Phillip Benjamin Ströbel @ ZurichNLP/dh-unibe + - rbai86 # Radek @ ETHZurich + - yunkao # Yunke Ao @ ethz + - yyyyifan # Yifan Hou @ eth-nlped + - SinanAkkoyun # Sinan Akkoyun @ ETHZurich + - javirandor # Javier Rando @ ethz-spylab + - RoyYang0714 # Yung-Hsu Yang @ ETH-CVG + - davidhightown # David Hohenstatt @ ethz + - hohs # Ho @ ethz + - igupta # Isha Gupta @ ethz + - moritzmiller # Moritz Miller @ ETHZurich/ethz + - rllover123 # Nicolas @ ethz + - ruili3 # Rui Li @ ETHZurich + - shtosti # Hanna Hubarava @ ZurichNLP + - xarthurx # Arthur MA @ ETHZurich + - SinaAhmadi # Sina Ahmadi @ ZurichNLP + - alexpin03 # Wei Pin @ ethz + - feradauto # Fernando Gonzalez Adauto @ ethzanalytics + - gerchowl # lars gerchow @ ethz + - jmathys # Joel Mathys @ ethz + - leosct # Leo Schmidt-Traub @ lasgroup + - miwytt # Michelle Wastl @ ZurichNLP + - nkristina # Kristina Nikolic @ ethz-spylab + - ollibolli # Oliver @ ethz/ethzanalytics + - pasztorb # Barna Pasztor @ ethz + - AkmalAshirmatov # Akmal Ashirmatov @ lasgroup + - AlexandreBinninger # Alexandre Binninger @ ethz + - LheaB # B @ ethz + - Pavidiran # Pavidiran Ramanathan @ ethz + - abbatea # Alessandro @ eth-nlped + - agrd # Arthur Girard @ ethz + - daemonkiller # Himanshu Bansal @ ETHZurich + - dmacjam # Jakub Macina @ eth-nlped + - isideris # Iason Sideris @ ethz + - kadeck # Karl D @ ethz + - BatofGo # Ray @ ethz + - Danieldcglp # Daniel Gaivao Lozano @ ethz + - MFerrari # Marcel Ferrari @ ethz + - MariusWenk # Marius Wenk @ ETHZurich + - PeterTor # Torben Peters @ prs-eth + - Rsebti # Rayane Sebti @ ethz + - SaraFlick # Sara Flick @ dh-unibe + - Zixiang-Zhao # Zixiang Zhao @ ethz + - cgoeldel # Constantin Goeldel @ ethz + - david-e-g # Davide Guidobene @ ETHZurich/ethz + - debaumann # Dennis Baumann @ ethz + - eishatirraazia19 # Eisha Tir Raazia @ SDSC + - fedecomi # Federico Cominelli @ ethz + - henning101 # Henning Metzmacher @ ethz + - imertan # Ipek Mertan @ ethz + - mchami # Mamoun Chami @ ETHZurich + - mikhaeljohanes # Mikhael Johanes @ ethz + - mlichtsteiner # Lichtsteiner @ ethz + - mortezajkhkhj # Morteza Esmaeilpour @ ethz + - mwertich # Martin Wertich @ ethz + - sarathsuresh # Sarath Suresh @ ETHZurich + - shibuyanights # Jenu @ ethz + - suvodip1212 # suvodip chakraborty @ ethz + - tobinski404 # t @ dh-unibe/prg-unibe + - veichta # Alexander Veicht @ ethz + - zhopto3 # Zachary Hopton @ ZurichNLP + # Promoted 2026-05-02 from targeted Swiss-company probe. + # Verified Swiss-headquartered companies (not big tech) with HF presence. + - lakera # Lakera AI — ETH spin-off, Zurich, AI security (19 repos) + - swisscom # Swisscom — Swiss national telecom, Bern HQ + - LatticeFlow # LatticeFlow AI — ETH spin-off, Zurich, model validation + - squirro # Squirro — Zurich enterprise AI / insight engine + - eraneos # Eraneos — Zurich IT consulting (formerly AWK) + - ubs-ai # UBS AI Community — Swiss bank, Zurich HQ + +discovery: + # Substrings fed to list_models(search=...) / list_datasets(search=...). + # Hits grouped by namespace; new namespaces written to + # logs/discover_orgs.jsonl for human review before promoting into + # `scope.seeds`. Never auto-added. + search_terms: + # EPFL institutional + adjacent institute / lab tokens. Substring + # matches are noisy by design — review the resulting jsonl before + # promoting any namespace to `scope.seeds`. + epfl: + # Institutional + city. + - epfl + - lausanne + # Affiliated / adjacent institutes. + - idiap # Idiap Research Institute (Martigny, EPFL-affiliated) + - martigny + - c4dt # Center for Digital Trust + - ai-center # EPFL AI Center + # Lab / group acronyms commonly used as namespace prefixes. + - mlo # Machine Learning and Optimization (Jaggi) + - lasa # Learning Algorithms and Systems (Billard) + - vita # Visual Intelligence for Transportation + - cvlab # Computer Vision Lab (Fua) + - ivrl # Image and Visual Representation Lab (Süsstrunk) + - vilab # Visual Intelligence and Learning (Zamir) + - chili # Computer-Human Interaction in Learning & Instruction + - lions # Laboratory for Information and Inference Systems (Cevher) + - dlab # Data Science Lab (West) + - dhlab # Digital Humanities Lab (Kaplan) + - nlp # NLP labs (paired with EPFL via namespace) + - lts2 # Signal Processing Lab 2 (Vandergheynst) + - lts4 # Signal Processing Lab 4 (Frossard) + - lcav # Audiovisual Communications Lab (Vetterli) + - mlbio # Machine Learning for Biology + - sage # Scalable Computing Systems Lab + # Cast a wide net — discover-orgs only surfaces candidates for review, + # never auto-adds. Mix of institutional acronyms, university initials, + # and city names. False positives are expected (e.g. "bern" matches + # "Bernarderfds"); the human-review step in logs/discover_orgs.jsonl + # is where they get filtered out. + switzerland: + # Institutional acronyms. + - epfl + - eth + - ethz + - bsse # D-BSSE Basel — ETH Zurich systems biology dept + - cscs # Swiss National Supercomputing Centre + - empa # Federal Laboratories for Materials Science + - psi # Paul Scherrer Institute + - paul-scherrer + - sdsc # Swiss Data Science Center + # Swiss university acronyms. + - usi # Università della Svizzera italiana + - uzh # Universität Zürich + - unibe # Universität Bern + - unibas # Universität Basel + - unil # Université de Lausanne + - unige # Université de Genève + - unifr # Universität Freiburg + - unine # Université de Neuchâtel + - hesso # HES-SO (Western Switzerland) + - hslu # Hochschule Luzern + - fhnw # Fachhochschule Nordwestschweiz + # City + country tokens (noisy but catch the long tail). + - lausanne + - zurich + - geneva + - basel + - bern + - swiss + # EPFL adjacent / lab tokens (mirrors `epfl` scope). + - idiap + - martigny + - c4dt + - ai-center + - mlo + - lasa + - vita + - cvlab + - ivrl + - vilab + - chili + - lions + - dlab + - dhlab + - nlp + - lts2 + - lts4 + - lcav + - mlbio + - sage + +qdrant: + # Compose service `gme-qdrant` on the `dev` network. Use the service name + # (not localhost) so this resolves correctly from inside the devcontainer. + # See `.internal/ror/qdrant-setup.md` for the hostname rationale. + url: http://gme-qdrant:6333 + prefer_grpc: false + +chunking: + # Cards are short — bigger chunks than openalex's 256. + size_tokens: 512 + overlap_tokens: 64 + # tiktoken encoding name. cl100k_base is a reasonable proxy for the Qwen3 + # tokenizer; counts are within ~10% for English text. + tokenizer: cl100k_base diff --git a/config/index/infoscience.yaml b/config/index/infoscience.yaml new file mode 100644 index 0000000..a430bd0 --- /dev/null +++ b/config/index/infoscience.yaml @@ -0,0 +1,51 @@ +# Infoscience RAG indexer config. +# Resolved by `src/index/infoscience/config.py` and merged with env vars +# (`RCP_TOKEN`, `INFOSCIENCE_TOKEN`, `INDEX_DATA_DIR`) at startup. + +rcp: + # EPFL RCP OpenAI-compatible inference endpoint. + base_url: https://inference-rcp.epfl.ch/v1 + embedding_model: Qwen/Qwen3-Embedding-8B + # Native dim for Qwen3-Embedding-8B is 4096. Configurable down to 32. + embedding_dim: 4096 + # Prepended to every query (not passages) per Qwen3-Embedding instruction format. + query_instruction: "Given a web search query, retrieve relevant passages that answer the query" + reranker_model: Qwen/Qwen3-Reranker-8B + # Per-batch input count for /v1/embeddings. Smaller default given 8B params. + batch_size: 16 + # Concurrent in-flight embed/rerank calls. + max_concurrency: 4 + # Generous to accommodate RCP cold-start (model load can take minutes). + timeout_seconds: 120 + +infoscience: + base_url: https://infoscience.epfl.ch/server/api + page_size: 100 + # Infoscience's edge layer 405s under burst; keep concurrency modest. + # `dspace.py` retries 405/429/5xx with exponential backoff. + # Bumped from 2 to 8 for the bulk fetch-text run (the dump-script + # ran fine at 8 with the existing retry/backoff behaviour). + max_concurrency: 8 + +filter: + # Solr `fulltext:` qualifier is applied per-term against + # /discover/search/objects?configuration=researchoutputs. The same + # terms drive the URL-extraction regex in extract_matches.py. + terms: + - github.com + - huggingface.co + - hf.co + +chunking: + size_tokens: 1024 + overlap_tokens: 128 + # tiktoken encoding name. cl100k_base is a reasonable proxy for the + # Qwen3 tokenizer; counts are within ~10% for English/code text. + tokenizer: cl100k_base + +qdrant: + # Devcontainer default. Overridden by INDEX_QDRANT_URL / INDEX_QDRANT_API_KEY + # / INDEX_QDRANT_PREFER_GRPC env vars at load time. + url: http://gme-qdrant:6333 + prefer_grpc: false + api_key: null diff --git a/config/index/openalex.yaml b/config/index/openalex.yaml new file mode 100644 index 0000000..edaaf7e --- /dev/null +++ b/config/index/openalex.yaml @@ -0,0 +1,48 @@ +# OpenAlex RAG indexer config. +# Resolved by `src/index/openalex/config.py` and merged with env vars +# (`RCP_TOKEN`, `OPENALEX_MAILTO`, `INDEX_QDRANT_API_KEY`, `INDEX_DATA_DIR`) +# at startup. + +rcp: + # OpenAI-compatible inference endpoint. Same RCP deployment as the + # infoscience indexer (config/index/infoscience.yaml). + base_url: https://inference-rcp.epfl.ch/v1 + embedding_model: Qwen/Qwen3-Embedding-8B + # Native dim for Qwen3-Embedding-8B is 4096. Configurable down to 32 + # if RCP deploys with Matryoshka truncation. + embedding_dim: 4096 + # Prepended to every query (not passages) per Qwen3-Embedding instruction format. + query_instruction: "Given a search query, retrieve relevant scholarly works" + reranker_model: Qwen/Qwen3-Reranker-8B + # Per-batch input count for /v1/embeddings. Smaller default given 8B params. + batch_size: 32 + # Concurrent in-flight embed/rerank calls. + max_concurrency: 4 + # Generous to accommodate RCP cold-start (model load can take minutes). + timeout_seconds: 120 + +openalex: + base_url: https://api.openalex.org + # OpenAlex max page size. + per_page: 200 + # Thread-pool size when fanning out across entity types. + max_concurrency: 4 + +scope: + # EPFL ROR (Phase 1 default). Override via INDEX_OPENALEX_SCOPE_ROR. + ror: https://ror.org/02s376052 + # Switzerland country code (Phase 2). Override via INDEX_OPENALEX_SCOPE_COUNTRY. + country: ch + +qdrant: + # Compose service exposes 6333 HTTP / 6334 gRPC. + url: http://gme-qdrant:6333 + # Set true to use 6334 (faster bulk upserts). + prefer_grpc: false + +chunking: + size_tokens: 256 + overlap_tokens: 64 + # tiktoken encoding name. cl100k_base is a reasonable proxy for the + # Qwen3 tokenizer; counts are within ~10% for English/code text. + tokenizer: cl100k_base diff --git a/config/index/orcid.yaml b/config/index/orcid.yaml new file mode 100644 index 0000000..ef41bc7 --- /dev/null +++ b/config/index/orcid.yaml @@ -0,0 +1,125 @@ +# ORCID RAG indexer config. +# Resolved by `src/index/orcid/config.py` and merged with env vars +# (`RCP_TOKEN`, `INDEX_QDRANT_API_KEY`, `INDEX_DATA_DIR`, +# `INDEX_ORCID_SCOPE`) at startup. + +rcp: + # Same RCP deployment as openalex/infoscience. + base_url: https://inference-rcp.epfl.ch/v1 + embedding_model: Qwen/Qwen3-Embedding-8B + embedding_dim: 4096 + query_instruction: "Given a search query, retrieve relevant ORCID person profiles" + reranker_model: Qwen/Qwen3-Reranker-8B + batch_size: 32 + max_concurrency: 4 + timeout_seconds: 120 + +orcid: + # Public ORCID API. Member API requires OAuth and is not used here. + base_url: https://pub.orcid.org/v3.0 + timeout_seconds: 20 + # Hard cap from ORCID's expanded-search endpoint. + search_max_rows: 200 + # Rate-limit pacing — tightened after a 429 storm at ~8200/10072 on a fresh + # IP. The public API doesn't return x-ratelimit-remaining, so the proactive + # throttle in v2 RateLimiter never fires; we cap effective rate here instead. + max_retries: 6 # 7 total attempts; worst-case backoff ≈ 60s + base_delay_seconds: 0.5 + max_delay_seconds: 60.0 + request_min_interval_seconds: 1.5 # ≤ 0.67 req/s steady-state + # Identify ourselves on every request — ORCID may fingerprint UA in addition + # to IP. Override via INDEX_ORCID_USER_AGENT. Leave blank/null to fall back + # to the requests default. + user_agent: "git-metadata-extractor-orcid-index/0.1" + +scope: + # EPFL ROR (used by the `epfl` scope). Override via INDEX_ORCID_SCOPE_ROR. + ror: https://ror.org/02s376052 + # Switzerland country code (used by the `switzerland` scope). Override via INDEX_ORCID_SCOPE_COUNTRY. + country: ch + # Per-scope affiliation aliases, used both by ORCID `expanded-search` + # discovery (one search per alias) and by the post-filter that decides + # `in_scope` for ORCID-search-only seeds. Aliases are case-insensitive + # substring-matched against employment.organization / education.organization + # — keep them distinctive (avoid 3-4 letter abbreviations like "PSI", "USI", + # "WSL", "SDSC" that would over-match in non-Swiss contexts). + # `load_config` collapses this dict to the active scope's list at load-time. + affiliation_aliases: + epfl: + - "EPFL" + - "École polytechnique fédérale de Lausanne" + - "Ecole polytechnique federale de Lausanne" + - "Swiss Federal Institute of Technology Lausanne" + switzerland: + # EPFL family + - "EPFL" + - "École polytechnique fédérale de Lausanne" + - "Ecole polytechnique federale de Lausanne" + - "Swiss Federal Institute of Technology Lausanne" + # ETHZ family + - "ETH Zurich" + - "ETH Zürich" + - "Eidgenössische Technische Hochschule Zürich" + # Cantonal universities + - "University of Lausanne" + - "Université de Lausanne" + - "University of Geneva" + - "Université de Genève" + - "University of Zurich" + - "Universität Zürich" + - "University of Bern" + - "Universität Bern" + - "Université de Berne" + - "University of Basel" + - "Universität Basel" + - "University of Fribourg" + - "Université de Fribourg" + - "Universität Freiburg" + - "University of Neuchâtel" + - "Université de Neuchâtel" + - "University of St. Gallen" + - "Universität St. Gallen" + - "Università della Svizzera italiana" + # Universities of applied sciences + - "Haute école spécialisée de Suisse occidentale" + - "HES-SO" + # National research institutes (long-form aliases only) + - "Paul Scherrer Institute" + - "Paul Scherrer Institut" + - "Empa" + - "Swiss Federal Laboratories for Materials Science and Technology" + - "Eawag" + - "Swiss Federal Institute of Aquatic Science and Technology" + - "Swiss Federal Institute for Forest" # WSL — long form to disambiguate + - "Idiap Research Institute" + - "CSEM" + - "CERN" + - "European Organization for Nuclear Research" + - "Swiss Data Science Center" + # University hospitals + - "Centre Hospitalier Universitaire Vaudois" + - "CHUV" + - "Hôpitaux Universitaires de Genève" + - "University Hospital of Lausanne" + - "University Hospital Zurich" + - "Universitätsspital Zürich" + - "University Hospital Basel" + # Industry research + - "IBM Research - Zurich" + - "IBM Research Zurich" + +discovery: + # `openalex` | `orcid_search` | `both`. Override via INDEX_ORCID_DISCOVERY_SOURCE. + source: both + # Path to a previously-built openalex DuckDB (the seed of source A). + # Override via INDEX_ORCID_OPENALEX_DB. + openalex_db: data/index/openalex/duckdb/openalex.duckdb + +qdrant: + url: http://gme-qdrant:6333 + prefer_grpc: false + +chunking: + size_tokens: 256 + overlap_tokens: 64 + tokenizer: cl100k_base diff --git a/config/index/renkulab.yaml b/config/index/renkulab.yaml new file mode 100644 index 0000000..7f05183 --- /dev/null +++ b/config/index/renkulab.yaml @@ -0,0 +1,70 @@ +# RenkuLab RAG indexer config. +# Resolved by `src/index/renkulab/config.py` and merged with env vars +# (`RCP_TOKEN`, `RENKULAB_TOKEN`, `INDEX_QDRANT_API_KEY`, `INDEX_DATA_DIR`, +# `INDEX_QDRANT_URL`) at startup. + +rcp: + # Same RCP deployment as openalex / zenodo / infoscience indexers. + base_url: https://inference-rcp.epfl.ch/v1 + embedding_model: Qwen/Qwen3-Embedding-8B + embedding_dim: 4096 + query_instruction: "Given a search query, retrieve relevant RenkuLab projects, groups, users, and data connectors" + reranker_model: Qwen/Qwen3-Reranker-8B + batch_size: 32 + max_concurrency: 4 + timeout_seconds: 120 + +renkulab: + base_url: https://renkulab.io/api/data + # Documented page-size cap is 100 across the public Renku data API. + page_size: 100 + # Polite default. Renku's nginx fronts a Go service that handles bursts + # well, but we throttle to keep batch jobs from looking abusive. + rate_per_minute: 120 + max_concurrency: 4 + +scope: + # `all` ingests every public project / group / user / data connector. + # `epfl` / `switzerland` filter post-fetch via keyword/namespace match — + # Renku has no first-class community concept, so the filter is heuristic. + default: all + # Substrings matched (case-insensitive) against namespace + keywords + + # repository URLs. Anything matching is kept under the named scope. + epfl_keywords: + - epfl + - lausanne + - .epfl.ch + switzerland_keywords: + - epfl + - eth + - ethz + - unibe + - unil + - unige + - unifr + - unine + - sdsc + - switch + - .ch/ + - swiss + +# Per-entity ingest toggles. Default to all on; flip off for a faster +# partial run. +entities: + projects: true + groups: true + users: true + data_connectors: true + group_members: true + project_members: true + +qdrant: + # Devcontainer compose service name — REST 6333, gRPC 6334. + # Use http://localhost:6333 from a host shell instead. + url: http://gme-qdrant:6333 + prefer_grpc: false + +chunking: + size_tokens: 256 + overlap_tokens: 64 + tokenizer: cl100k_base diff --git a/config/index/ror.yaml b/config/index/ror.yaml new file mode 100644 index 0000000..a13e5ca --- /dev/null +++ b/config/index/ror.yaml @@ -0,0 +1,43 @@ +# ROR (Research Organization Registry) RAG indexer config. +# Resolved by `src/index/ror/config.py` and merged with env vars +# (`RCP_TOKEN`, `INDEX_DATA_DIR`) at startup. + +rcp: + # OpenAI-compatible inference endpoint. Same RCP gateway as infoscience. + base_url: https://inference-rcp.epfl.ch/v1 + embedding_model: Qwen/Qwen3-Embedding-8B + # Native dim for Qwen3-Embedding-8B is 4096. Asserted on first response. + embedding_dim: 4096 + # Prepended to query embeddings (not record embeddings) per Qwen3-Embedding format. + query_instruction: "Given a query, retrieve research organization records that match" + reranker_model: Qwen/Qwen3-Reranker-8B + # Per-batch input count for /v1/embeddings. + batch_size: 32 + # Concurrent in-flight embed/rerank calls. + max_concurrency: 8 + # Generous to accommodate RCP cold-start (model load can take minutes). + timeout_seconds: 120 + +scope: + # `epfl_ethz` (BFS from seeds), `switzerland` (CH country), `europe` (UN geoscheme), or `worldwide` (full dump). + mode: europe + seeds: + - https://ror.org/02s376052 # EPFL + - https://ror.org/05a28rw58 # ETHZ + expand: [parent, child, related] + max_depth: 2 + +ror_dump: + # Zenodo concept DOI resolves to the latest release. + zenodo_concept_doi: 10.5281/zenodo.6347574 + +retrieval: + top_k: 50 + rerank_top_k: 10 + +qdrant: + # Vector DB. Override at runtime with INDEX_QDRANT_URL / INDEX_QDRANT_API_KEY. + url: http://gme-qdrant:6333 + prefer_grpc: false + # Collection name = "_" (e.g. ror_worldwide, ror_switzerland). + collection_prefix: ror diff --git a/config/index/snsf.yaml b/config/index/snsf.yaml new file mode 100644 index 0000000..88c38a8 --- /dev/null +++ b/config/index/snsf.yaml @@ -0,0 +1,51 @@ +# SNSF P3 (Swiss National Science Foundation) RAG indexer config. +# Resolved by `src/index/snsf/config.py`. +# +# Phase 1 only uses the manual bulk-CSV ingest path (data downloaded by hand +# from data.snf.ch and dropped into a local directory). Phase 2 will add +# RCP embeddings + Qdrant — see `.internal/snsf/README.md` for the plan. + +snsf: + base_url: https://data.snf.ch + request_timeout_s: 60 + page_size: 1000 + inter_request_sleep_s: 0.2 + user_agent: >- + git-metadata-extractor/snsf (polite scraper; please email if issues) + +scope: + # Active scope. Drives which subset is materialised into `scope_records` + # at `load-local` time. Override on the CLI with `--scope`. + active: epfl + # Filter clause = (Solr field name → list of UUIDs/IDs). Used by the + # parked API path in `ingest/snsf_client.py` (Phase 2). The CSV ingest + # path in `ingest/local_ingest.py` derives equivalent SQL WHEREs from + # `ingest/scope.py:SCOPE_WHERE` instead — both resolve to the same row + # set, just different filter expressions. + filters: + epfl: + research_institution_id: ["a8095007-f9b3-4170-ab92-59fbecfbba10"] + ethz: + research_institution_id: ["3501eb73-70f2-4abd-b626-2c764d7f73dd"] + eth_domain: + research_institution_type_id: ["6ee0a15d-934b-416f-bfb6-0f0b5638edb3"] + switzerland: {} + +# Phase 2 additions (kept commented so the schema is forward-compatible): +# rcp: +# base_url: https://inference-rcp.epfl.ch/v1 +# embedding_model: Qwen/Qwen3-Embedding-8B +# embedding_dim: 4096 +# query_instruction: "Given a query, retrieve SNSF grant titles and abstracts that match" +# reranker_model: Qwen/Qwen3-Reranker-8B +# batch_size: 32 +# max_concurrency: 4 +# timeout_seconds: 120 +# qdrant: +# url: http://gme-qdrant:6333 +# prefer_grpc: false +# collection_prefix: snsf +# chunking: +# size_tokens: 512 +# overlap_tokens: 64 +# tokenizer: cl100k_base diff --git a/config/index/swissubase.yaml b/config/index/swissubase.yaml new file mode 100644 index 0000000..1f86992 --- /dev/null +++ b/config/index/swissubase.yaml @@ -0,0 +1,76 @@ +# SWISSUbase RAG indexer config. +# Resolved by `src/index/swissubase/config.py` and merged with env vars +# (`RCP_TOKEN`, `SELENIUM_REMOTE_URL`, `INDEX_QDRANT_API_KEY`, +# `INDEX_QDRANT_URL`, `INDEX_DATA_DIR`, `INDEX_SWISSUBASE_SCOPE`) at +# startup. + +rcp: + # Same RCP deployment as openalex / infoscience / zenodo indexers. + base_url: https://inference-rcp.epfl.ch/v1 + embedding_model: Qwen/Qwen3-Embedding-8B + embedding_dim: 4096 + query_instruction: "Given a search query, retrieve relevant SWISSUbase studies, datasets, persons and institutions" + reranker_model: Qwen/Qwen3-Reranker-8B + batch_size: 32 + max_concurrency: 4 + timeout_seconds: 120 + +catalogue: + # SWISSUbase is a Swiss social-science research-data platform run by + # FORS / UZH / U. Neuchâtel / DASCH. It exposes ~13k "studies" (UI + # label: Project) plus their datasets, principal investigators and + # affiliated institutions. + base_url: https://www.swissubase.ch + language: en + # The catalogue UI uses ps=10..50; bumping further hasn't been verified. + page_size: 50 + # Polite spacing between page navigations. + page_delay_seconds: 1.0 + # The Material table mounts after a JSON XHR; timeout on each page. + list_render_timeout_seconds: 30 + # Per-study detail JSON XHR (overview-block + dynamic-blocks). + detail_timeout_seconds: 30 + + # ID-iteration mode: the search-studies endpoint silently caps deep + # pagination, so the ingest enumerates studyVersionIds directly. Per- + # study endpoints have no such cap. Observed live range is ~1..21,500 + # with ~58% density. 25,000 leaves headroom for new studies. + id_start: 1 + id_end: 25000 + # Polite spacing between per-ID requests; lower than page_delay_seconds + # because we now make one Selenium-fetch per ID (vs per page of 50). + per_id_delay_seconds: 0.2 + +scope: + default: epfl_sdsc_ethz + # SWISSUbase has no institution filter server-side, so the scope is + # applied as a post-filter on the rendered institution string. + # Studies that match get `affiliation_match=TRUE` and are the only + # ones embedded by default. Set INDEX_SWISSUBASE_SCOPE=switzerland + # to embed every study (option (a) in the project plan). + epfl_sdsc_ethz_patterns: + - EPFL + - École polytechnique fédérale de Lausanne + - Ecole polytechnique fédérale de Lausanne + - ETH Zurich + - ETHZ + - Eidgenössische Technische Hochschule + - SDSC + - Swiss Data Science Center + +selenium: + # The shared remote Selenium Grid (Firefox). Inside the devcontainer + # this is set by env to gme-selenium-firefox:4444. + page_load_timeout_seconds: 60 + headless: true + +qdrant: + # Devcontainer compose service name — REST 6333, gRPC 6334. + # Use http://localhost:6333 from a host shell instead. + url: http://gme-qdrant:6333 + prefer_grpc: false + +chunking: + size_tokens: 256 + overlap_tokens: 64 + tokenizer: cl100k_base diff --git a/config/index/zenodo.yaml b/config/index/zenodo.yaml new file mode 100644 index 0000000..2a1e940 --- /dev/null +++ b/config/index/zenodo.yaml @@ -0,0 +1,49 @@ +# Zenodo RAG indexer config. +# Resolved by `src/index/zenodo/config.py` and merged with env vars +# (`RCP_TOKEN`, `ZENODO_TOKEN`, `INDEX_QDRANT_API_KEY`, `INDEX_DATA_DIR`) +# at startup. + +rcp: + # Same RCP deployment as openalex + infoscience indexers. + base_url: https://inference-rcp.epfl.ch/v1 + embedding_model: Qwen/Qwen3-Embedding-8B + embedding_dim: 4096 + query_instruction: "Given a search query, retrieve relevant Zenodo records" + reranker_model: Qwen/Qwen3-Reranker-8B + batch_size: 32 + max_concurrency: 4 + timeout_seconds: 120 + +zenodo: + base_url: https://zenodo.org/api + # Authenticated max is 100; anonymous is capped at 25 server-side. + page_size: 100 + # Zenodo's documented hard limit is 30/min — leave a small buffer. + rate_per_minute: 25 + # Conservative default; raise once rate limiter is verified safe. + max_concurrency: 1 + +scope: + default: epfl + # Curated EPFL community list. Add more as discovered via + # https://zenodo.org/api/communities?q=epfl during ingest. + epfl_communities: + - epfl + - eesd_at_epfl + - ivrl-epfl-switzerland + # Phase 2: union with the EPFL list. Swiss-wide community slugs go here. + switzerland_communities: + - supsi + - sweco25 + - plato-ch + +qdrant: + # Devcontainer compose service name — REST 6333, gRPC 6334. + # Use http://localhost:6333 from a host shell instead. + url: http://gme-qdrant:6333 + prefer_grpc: false + +chunking: + size_tokens: 256 + overlap_tokens: 64 + tokenizer: cl100k_base diff --git a/docs/ROADMAP.md b/docs/ROADMAP.md new file mode 100644 index 0000000..4529715 --- /dev/null +++ b/docs/ROADMAP.md @@ -0,0 +1,137 @@ +# RAG Roadmap + +What's left to build across the gme RAG stack, ranked by impact / effort. +Each item links to the relevant module so you can pick one and go. + +> Status as of **2026-05-02**. Re-rank after every major shipment. + +## Tier 1 — quick wins, big impact (do these first) + +> **Items 1, 2, 3 shipped 2026-05-02** — see "Done" section below. Items 4 and 5 are still open. + +### 4. Periodic discover + ingest cron +**Effort**: ~50 LOC + a justfile recipe. +**Impact**: corpus stays fresh without manual nudging. + +Use the [`/schedule`](https://github.com/Imaging-Plaza/git-metadata-extractor) routine harness already in the repo. Schedule: +- weekly `hf-discover-orgs --scope switzerland` + log review prompt +- weekly `-ingest --scope switzerland` for each index (skip-cache makes it cheap) +- weekly `-embed` +- monthly `gme search` smoke-test that all 9 indices respond + +### 5. Skip-already-ingested in remaining indices +**Effort**: ~10 LOC each. +**Impact**: same win as the HF fix on 2026-05-01 — refreshes go from minutes to seconds. + +The pattern is now in `src/index/huggingface/ingest/{models,datasets,spaces}_ingest.py`: ask the listing endpoint to expand `lastModified` + `sha`, compare against the stored `sha`, skip if unchanged. Apply to: +- `openalex/ingest/` (compare `updated_date` per work) +- `orcid/ingest/` (compare `last_modified_date`) +- `zenodo/ingest/` (compare `updated`) +- `gh/ingest/` (compare `updated_at`) +- `infoscience/discover.py` (compare `lastModified`) + +## Tier 2 — quality & coverage + +### 6. Per-adapter timeouts in federated layer +**Effort**: ~15 LOC. +A hung adapter currently can stall the whole `gme search` response. Wrap each `Future` with `wait(timeout=…)` and surface timeouts in the `errors` field. + +### 7. Federated faceting +**Effort**: ~80 LOC. +HF has `--facets license,pipeline_tag,author` natively. Generalise: `gme search --facets license,year,country_code` rolls up keys per adapter that emits them. Useful for "what licenses dominate Swiss-German results" across indices. + +### 8. Time-decay in ranking +**Effort**: ~10 LOC per index. +A 2025 model and a 2023 model rerank symmetrically today. Add a small `last_modified` boost in the rerank step (linear or exponential decay from "now"). Behind a `--fresh` flag so historical research isn't penalised. + +### 9. Sparse-bio HF orgs +**Effort**: ~50 LOC. +Orgs like `mtc` (225 models, fullname just "MTC") rank poorly because their embed text is dominated by repo titles. Two paths: +- Scrape the HF org page HTML (richer than the API exposes). +- Hand-curate `details` for top sparse orgs in a `data/index/huggingface/overrides.yaml`. + +### 10. Citation parsing from READMEs +**Effort**: ~120 LOC. +Most HF model cards have a `## Citation` block with bibtex / arxiv ID. Extract during ingest, store as `models.citations JSON`, and cross-link to OpenAlex/Infoscience records (DOI match). Powers "find papers that introduce this model". + +### 11. Author cross-entity view +**Effort**: ~60 LOC. +A new tool/CLI: `gme-author ` returns one consolidated card — bio, models, datasets, papers (infoscience), citing papers, ORCID employments, openalex works, ROR org. Saves agents from running 5 sequential lookups. + +### 12. Hospitals / clinical AI coverage +**Effort**: 1 day quarterly. +CHUV, HUG, USZ, Inselspital, Universitätsspital Basel — verified zero HF presence today (2026-05-02). Re-check quarterly. The right pattern: `gme-discover --domain medical --scope switzerland` runs the substring search across hospital-typical tokens. + +## Tier 3 — new entity types + +### 13. HF Papers entity +**Effort**: ~150 LOC + a new collection. +`huggingface.co/papers/` is a real HF entity that links models ↔ papers. Index as a 5th HF entity type (`hf_papers`). Most useful as a join column between HF and OpenAlex/arXiv. + +### 14. HF Collections +**Effort**: ~80 LOC. +HF "collections" are author-curated bundles (e.g. `EPFL-VILAB/4M-21B`). Worth indexing because they encode the lab's own narrative about what goes together. + +### 15. arXiv adapter +**Effort**: ~200 LOC + dump pipeline. +Many EPFL papers live on arXiv before infoscience indexes them. arXiv has a clean OAI-PMH bulk API. New module `src/index/arxiv/` mirroring infoscience. + +### 16. Crossref adapter +**Effort**: ~150 LOC. +For DOI → metadata resolution and citing-paper graphs. Useful as a join key between every paper-publishing index. + +### 17. OpenReview adapter +**Effort**: ~150 LOC. +For ML conference papers (ICLR, NeurIPS, ICML). EPFL/ETH groups publish heavily here. Has a public API. + +## Tier 4 — operations & observability + +### 18. Webhook for new EPFL repos +**Effort**: ~100 LOC. +HF doesn't expose webhooks but a poll-and-diff every 6h on the seed authors would catch new releases within hours instead of days. + +### 19. Cross-index dedup in `gme-entity` +**Effort**: ~40 LOC. +If an ORCID resolves to a person AND OpenAlex resolves the same person via their ORCID-link, currently both records come back independently. A canonicalisation pass would group them. + +### 20. Federated faceting + dedup unified +**Effort**: ~100 LOC (depends on #7 + #19). +A `gme-search --merge-by orcid` would dedup hits across indices on a chosen identifier. + +### 21. Persistent corpus stats dashboard +**Effort**: ~100 LOC. +A `gme-stats` command that runs every status check + writes to `data/stats/.json`, then a Markdown report or simple HTML. Useful for "is the corpus growing". + +### 22. Backfill workflow generalised +**Effort**: ~50 LOC. +HF got `hf-backfill-payloads` to retroactively push `base_model` to existing Qdrant points without re-embedding. The same pattern (push new payload fields without re-embed) is going to come up for every index. Extract a `--backfill ` flag in each `-embed`. + +### 23. uv sync auto-load .env +**Effort**: ~5 LOC. +Several modules silently miss `HF_TOKEN` / `RCP_TOKEN` if the shell predates the `.env` edit. Add `python-dotenv.load_dotenv()` at the top of each `cli.py` so the env always reflects the file. + +## Coverage gaps already verified + +These were probed during 2026-05-01/02 and have **no meaningful HF presence today**. Worth re-checking in 6 months but don't expect quick wins: + +- **Hospitals**: CHUV, HUG, Inselspital, USZ, Universitätsspital Basel +- **Federal research centres**: PSI / Paul Scherrer, EAWAG, Empa, WSL, CSEM, RUAG +- **Supercomputing / bioinformatics**: CSCS (only personal user), SIB, Bgee, Expasy, Vital-IT +- **Other Swiss unis (HF presence)**: USI, SUPSI, IDSIA, UniL, UniFR, UniNE +- **Crypto Valley**: DFINITY, Tezos, Cardano Foundation, Casper +- **Big pharma**: Roche, Novartis, Lonza (no HF orgs) +- **Insurance / finance**: Pictet, Julius Bär, Zurich Insurance, Helvetia +- **Industrial giants**: ABB, Sensirion, Logitech, Sonova, Pix4D + +The 6 confirmed Swiss companies on HF (lakera, swisscom, LatticeFlow, squirro, eraneos, ubs-ai) are already in seed. + +## Done ✅ (recent shipments) + +- **2026-05-01**: `chromadb` removed (unused); HuggingFace index built+ingested+embedded; `cited_by_infoscience` cross-link; entity-level dedup in HF search; `--filter` flag. +- **2026-05-01/02**: 16 high-confidence EPFL personal users + 73 ETH/Swiss researchers + ZHAW/SWE-Swiss/lakera/squirro/swisscom/LatticeFlow/eraneos/ubs-ai promoted; corpus grew 18→147 namespaces, 296→1034 models. +- **2026-05-02 morning**: HF `--filter base_model=X`, `--facets`, `hf-backfill-payloads` (retroactive payload updates without re-embed). `models_ingest`/`datasets_ingest`/`spaces_ingest` now skip unchanged repos via sha comparison. +- **2026-05-02 afternoon**: `src/index/_federated/` shipped with 6 adapters; `gme search` / `gme entity` / `gme indices` CLIs; `FederatedRagProvider` + `search_federated_rag` + `lookup_entity_federated` v2 LLM agent tools; full docs ([`federated-search.md`](federated-search.md), [`rag-indices.md`](rag-indices.md), this file). +- **2026-05-02 evening (Tier-1 sweep)**: + - **3 missing federated adapters** — `ethz_research_collection`, `github`, `snsf`. `gme-indices` now lists 9 (was 6). Smoke test: `gme search "Swiss German LLM"` returns hits from at least 5 indices. + - **HF base-model lineage** — `src/index/huggingface/retrieval/lineage.py` walks the `base_models` DAG up + down. New CLI: `hf-lineage [--depth N]`. New v2 LLM tool: `lineage_huggingface` (registered in repository agent). Verified: meditron-7b ↔ Llama-2-7b in both directions. + - **Cross-index reranking** — new `--rerank` flag on `gme search` sends the merged candidate pool through RCP's cross-encoder once for globally-fair ordering. Reranker config borrowed from whichever per-index module loads first (HF/openalex/zenodo/orcid). Response gains `reranked: true|false`. diff --git a/docs/api-and-cli.md b/docs/api-and-cli.md index 1e2a51b..146258a 100644 --- a/docs/api-and-cli.md +++ b/docs/api-and-cli.md @@ -1,66 +1,186 @@ # API and CLI +Quick reference for both the extraction service and the per-index CLIs. +For the full v2 contract see [V2 API Reference](v2-api-reference.md); for +RAG indices see [RAG Indices Overview](rag-indices.md). + ## Main entrypoints -- API app: `src/api.py` -- Repository analysis orchestrator: `src/analysis/repositories.py` -- User analysis orchestrator: `src/analysis/user.py` -- Organization analysis orchestrator: `src/analysis/organization.py` +- API app: `src/api.py` — mounts `/v1/*` (frozen) and `/v2/*` (active). +- V2 router: `src/v2/api.py`. +- V2 pipeline driver: `src/v2/pipeline/orchestrator.py`. +- V1 analysis (frozen): `src/v1/analysis/`. -## Active API endpoints +## Authentication -- `GET /v1/repository/gimie/json-ld/{full_path:path}`: GIMIE-only JSON-LD extraction. -- `GET /v1/repository/llm/json/{full_path:path}`: repository Pydantic output. -- `GET /v1/repository/llm/json-ld/{full_path:path}`: repository JSON-LD output. -- `GET /v1/user/llm/json/{full_path:path}`: user profile analysis. -- `GET /v1/org/llm/json/{full_path:path}`: organization analysis. -- Cache management endpoints under `/v1/cache/*`. +All `/v1/*` routes plus `/v2/extract` and `/v2/jobs/{id}` require a bearer +token; `/`, `/docs`, and `/v2/health` stay open. Send the token from the +server-side `API_TOKEN` env var: -Common query flags: +```http +Authorization: Bearer +``` -- `force_refresh=true`: bypass cache. -- `enrich_orgs=true`: run organization enrichment. -- `enrich_users=true`: run user enrichment (repository/user routes). +Missing or wrong token → `401` (with `WWW-Authenticate: Bearer`). Server +without `API_TOKEN` set → `503`. Generate a value with: -## Response shape +```bash +python -c "import secrets; print(secrets.token_urlsafe(32))" +``` -All analysis endpoints return `APIOutput` (`src/data_models/api.py`): +Full failure-mode table at +[v2-api-reference.md#authentication](v2-api-reference.md#authentication). -- `link` -- `type` (`repository`, `user`, `organization`) -- `parsedTimestamp` -- `output` (model object or JSON-LD dict) -- `stats` (`APIStats` token usage, duration, GitHub rate-limit headers) +## Active endpoints -## Endpoint-to-pipeline map +### V2 (use these for new clients) -```mermaid -flowchart LR - A[/repository/llm/*] --> R[Repository.run_analysis] - B[/user/llm/json/*] --> U[User.run_analysis] - C[/org/llm/json/*] --> O[Organization.run_analysis] +- `GET /v2/health` — health check + provider preflight. +- `GET /v2/extract/{full_path:path}` — synchronous extraction. Path is a + GitHub URL or path (`github.com/owner/name`). +- `POST /v2/extract` — async submission. Returns `202` + `job_id`. Body: + `{source_url, agent_runtime?, output_format?, include_context_summary?}`. +- `GET /v2/jobs/{job_id}` — poll for async job status / result. + +Common query parameters on `GET /v2/extract`: + +- `output_format` — `jsonld` (default) or `json`. +- `agent_runtime` — `rule_based` or `llm` (defaults to + `V2_AGENT_RUNTIME_DEFAULT`, which itself defaults to `llm`). +- `include_context_summary` — `true|false` (default `false`). - R --> RA[Atomic repository pipeline + optional enrichments] - U --> UA[GitHub parse + user/org enrich + linked entities + EPFL] - O --> OA[Atomic organization pipeline] +Example requests (export `API_TOKEN` first so the snippets work as-is): + +```bash +export API_TOKEN=... # value from .env + +# sync, JSON-LD +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/extract/github.com/octocat/Hello-World?output_format=jsonld" | jq + +# sync, JSON, rule-based +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/extract/github.com/octocat/Hello-World?output_format=json&agent_runtime=rule_based" | jq + +# async +curl -s -X POST http://localhost:1234/v2/extract \ + -H "Authorization: Bearer $API_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{"source_url": "github.com/octocat/Hello-World", "output_format": "json", "agent_runtime": "llm"}' | jq +# → {"job_id": "...", "status": "pending", "status_url": "/v2/jobs/..."} + +# poll +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/jobs/" | jq ``` -## Run the API +### V1 (frozen, kept for backwards compatibility) + +- `GET /v1/repository/gimie/json-ld/{full_path:path}` — GIMIE-only JSON-LD. +- `GET /v1/repository/llm/json/{full_path:path}` — repository pydantic output. +- `GET /v1/repository/llm/json-ld/{full_path:path}` — repository JSON-LD. +- `GET /v1/user/llm/json/{full_path:path}` — user profile. +- `GET /v1/org/llm/json/{full_path:path}` — organization analysis. +- `GET /v1/cache/stats|entries`, `POST /v1/cache/{cleanup,clear,enable,disable}`, + `DELETE /v1/cache/invalidate/{api_type}` — v1 cache controls. + +V1 query flags: + +- `force_refresh=true` — bypass cache. +- `enrich_orgs=true` — run organization enrichment. +- `enrich_users=true` — run user enrichment (repository / user routes). + +For mapping V1 calls to V2 see +[Migration: V1 → V2](migration-v1-to-v2.md). + +## V2 response shape + +`GET /v2/extract` and the `result` field of a completed job both return +`V2ExtractResponse`: + +- `source_url` +- `detected_type` — `repository | user | organization` +- `output_format` — `jsonld | json` +- `output` — see below +- `warnings` — non-fatal pipeline warnings +- `stats` — run-scoped counts + duration +- optional `context_summary_markdown` (when requested and available) + +`output_format=json`: + +- `root_entity: object | null` +- `related_entities: list[object]` +- `excluded_entities: list[object]` +- `entities_by_type: {repositories, persons, organizations, articles, memberships, contributions}` + +`output_format=jsonld`: + +- `@context: object` +- `@graph: list[object]` +- optional `excluded_entities` + +## Per-index CLIs + +Every RAG index ships its own CLI (`python -m src.index.`) plus +`just -*` recipes. Common shape: ```bash -just serve-dev +just -status # counts + paths +just -ingest --scope # populate DuckDB +just -embed # push vectors to Qdrant +just -search "" # semantic retrieval +just -query --predefined ... # SQL over DuckDB ``` +Recipes registered in the justfile: + +| Index | Prefix | Notes | +|---|---|---| +| HuggingFace | `hf-*` | adds `hf-discover-orgs`, `hf-lineage` | +| OpenAlex | `openalex-*` | adds `openalex-find-github`, `openalex-rebuild-qdrant`, `openalex-serve` | +| ORCID | `orcid-*` | adds `orcid-discover`, `orcid-serve` | +| Zenodo | `zenodo-*` | adds `zenodo-serve` | +| GitHub | `gh-*` | adds `gh-rebuild-qdrant`, `gh-serve` | +| Infoscience | `index-infoscience-*` | full lifecycle + `ingest-duckdb` + `query` | +| ROR | (per-CLI) | `python -m src.index.ror …` | +| ETH Research Collection | (per-CLI) | `python -m src.index.ethz_research_collection …` | +| SNSF | (per-CLI) | `python -m src.index.snsf …` | +| Federated | `gme-*` | `gme-search`, `gme-entity`, `gme-indices` | + ## Smoke tests ```bash +# v2 — health is open +curl -s "http://localhost:1234/v2/health" | jq + +# v2 extract requires the bearer token +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/extract/github.com/octocat/Hello-World?output_format=json&agent_runtime=rule_based" | jq + +# v1 (legacy) — recipes also need API_TOKEN; they pick it up from .env via just just api-test-gimie just api-test-extract just api-test-extract-refresh ``` -## CLI status and alternatives +## Batch extraction + +`scripts/v2/batch_extract.sh` reads a hardcoded URL list and drives +`/v2/extract` with configurable parallelism. Resumable: skips repos whose +result file already exists with a non-`running` status. + +## Endpoint-to-pipeline map -- `just extract ...` currently calls `src/main.py`, which still imports legacy `core.*` modules. -- Use API endpoints for full extraction flows. -- Use `scripts/convert_json_jsonld.py` for JSON <-> JSON-LD conversion workflows. +```mermaid +flowchart LR + A[GET /v2/extract] --> P[Pipeline orchestrator] + B[POST /v2/extract] --> J[Job store
SQLite-backed] + J --> P + P --> S1[context_summary] + S1 --> S2[per-entity agents] + S2 --> S3[dedup → reconcile → critic] + S3 --> S4[strict + assemble + link veracity] + S4 --> S5[ownership + org-hierarchy inference] + S5 --> S6[build_jsonld_output] + GJ[GET /v2/jobs/id] --> J +``` diff --git a/docs/architecture/design-notes.md b/docs/architecture/design-notes.md index 99101cb..4bb04e8 100644 --- a/docs/architecture/design-notes.md +++ b/docs/architecture/design-notes.md @@ -1,97 +1,203 @@ # Design Notes -This page documents the current runtime architecture in `v2.0.0`. +This page documents the current runtime architecture (`v2.0.0+`). ## Component architecture ```mermaid flowchart TB subgraph Client - C1[HTTP client / frontend] + C1[HTTP client / batch script] end - subgraph API - A1[FastAPI router
src/api.py] - A2[Request logging middleware
AsyncRequestContext] + subgraph API[FastAPI app — src/api.py] + A1[v2_router
src/v2/api.py] + A2[Request logging
AsyncRequestContext] + A3[v1 frozen
src/v1/] end - subgraph Analysis - R[Repository analysis] - U[User analysis] - O[Organization analysis] - AA[Atomic agents] + subgraph Pipeline[V2 pipeline — src/v2/pipeline/] + P0[orchestrator] + P1[stages/* — 23 stages] + P2[per-entity agents
src/v2/agents/llm/* + rule_based/*] end - subgraph Data - M[Data models
src/data_models] - Cache[SQLite cache
src/cache] + subgraph Caches[Shared SQLite — V2_PROVIDER_CACHE_PATH] + K1[Provider cache] + K2[Agent verdict cache] + K3[Pipeline response cache] + K4[Async-job store] + end + + subgraph Providers[src/v2/ingest/providers/] + PG[GitHub REST + GIMIE] + PR[ROR / ORCID / Infoscience] + PRAG[*_rag — Qdrant-backed] + end + + subgraph RAG[RAG indices — src/index/*] + RH[HuggingFace] + ROA[OpenAlex] + RI[Infoscience] + RE[ETHZ Research Collection] + RO[ORCID] + RR[ROR] + RZ[Zenodo] + RG[GitHub] + RS[SNSF] + RFED[Federated layer] end subgraph External - G[GitHub API] - I[Infoscience API] - ORCID[ORCID / Selenium] + GH[GitHub API] + IS[Infoscience API] + ORID[ORCID API] ROR[ROR API] - LLM[Configured LLM provider] - GIMIE[GIMIE] + SEL[Selenium grid] + LLM[LLM provider
RCP / OpenAI / OpenRouter] + QD[(Qdrant
gme-qdrant:6333)] + RCP[EPFL RCP
embed + rerank] end C1 --> A2 --> A1 - A1 --> R - A1 --> U - A1 --> O - - R --> AA - U --> AA - O --> AA - - R --> M - U --> M - O --> M - - R --> Cache - U --> Cache - O --> Cache - - AA --> LLM - R --> GIMIE - R --> G - U --> G - O --> G - AA --> I - AA --> ROR - R --> ORCID + A1 --> P0 + A1 --> A3 + P0 --> P1 + P1 --> P2 + P2 --> Providers + Providers --> Caches + PG --> GH + PR --> IS + PR --> ORID + PR --> ROR + PRAG --> RFED + PRAG --> RH + PRAG --> ROA + PRAG --> RI + PRAG --> RE + PRAG --> RO + PRAG --> RR + PRAG --> RZ + RAG --> QD + RAG --> RCP + P1 --> SEL + P2 --> LLM ``` -## Repository request sequence +## V2 extract sequence ```mermaid sequenceDiagram autonumber participant Client - participant API as FastAPI /v1/repository/llm/json - participant Repo as Repository.run_analysis - participant Cache as CacheManager - participant Pipe as Atomic pipeline + enrichments - - Client->>API: GET /v1/repository/llm/json/{url} - API->>Repo: initialize + run_analysis(...) - Repo->>Cache: check repository cache - alt cache hit and not force_refresh - Cache-->>Repo: cached object - Repo-->>API: output + stats - else cache miss or forced refresh - Repo->>Pipe: run GIMIE + atomic stages - Pipe-->>Repo: structured repository model - Repo->>Pipe: optional enrichments + final EPFL assessment - Repo->>Cache: persist final model - Repo-->>API: output + stats + participant API as /v2/extract + participant Cache as ProviderCache (SQLite) + participant Orch as Pipeline orchestrator + participant Agents as Per-entity agents (LLM or rule-based) + participant RAG as RAG providers + Qdrant + participant Ext as External APIs (GitHub / ROR / ORCID / Infoscience / Selenium) + + Client->>API: GET or POST /v2/extract + API->>Cache: pipeline-cache lookup (full response) + alt pipeline cache hit + Cache-->>API: V2ExtractResponse + API-->>Client: response + else miss + API->>Orch: run pipeline + Orch->>Ext: classify_url + gather_context (GIMIE + GitHub REST) + Orch->>Cache: provider-cache lookup / store + Orch->>Agents: context_summary → per-entity fan-out + Agents->>Cache: agent-verdict cache lookup + alt verdict miss + Agents->>RAG: search_*_rag / lookup_*_rag (optional) + RAG-->>Agents: thin hits + Agents->>Cache: store verdict + end + Orch->>Orch: dedup → reconcile → critic → strict → assemble + Orch->>Ext: link_veracity (Selenium fetch + LLM verdict) + Orch->>Orch: ownership + org-hierarchy inference + Orch->>Orch: build_jsonld_output + Orch->>Cache: store pipeline response + Orch-->>API: V2ExtractResponse + API-->>Client: response end - API-->>Client: APIOutput ``` +For async submission (`POST /v2/extract`), the API persists a job record +into the same SQLite under the `v2-extract-job` namespace and runs the +pipeline as a background task. Clients poll `GET /v2/jobs/{job_id}`. + +## Pipeline cache topology + +Three caches share a single SQLite DB (path: `V2_PROVIDER_CACHE_PATH`): + +1. **Provider cache** — gimie payloads, GitHub REST responses, ROR / + ORCID / Infoscience hits. Deterministic, content-addressed. +2. **Agent verdict cache** — LLM agent results keyed on agent name + + identity. Skips a repeat LLM call for a known-good payload. +3. **Pipeline cache** — full `/v2/extract` response for a given source URL. + +The async-job store also lives in this SQLite (separate namespace, same +TTL). Set `V2_PROVIDER_CACHE_PATH` to a different file per run profile +(e.g. `.cache/v2-rule-based/providers.db` for rule-based runs) to keep +them isolated and independently invalidatable. + +## RAG index architecture + +Each index under `src/index//` is independent: its own DuckDB, its +own Qdrant collections, its own CLI, its own FastAPI app, its own refresh +cadence. The federated layer (`src/index/_federated/`) never shares +state — it just orchestrates fan-out across registered adapters in a +`ThreadPoolExecutor`. + +```mermaid +flowchart LR + Src[upstream API
HF / OA / IS / …] --> ING[ingest CLI
just -ingest] + ING --> DB[(DuckDB
data/index//duckdb/)] + DB --> EMB[embed CLI
just -embed] + EMB --> RCPq[RCP /v1/embeddings] + RCPq --> QD[(Qdrant
gme-qdrant:6333)] + DB --> Q[just -query] + QD --> S[just -search] + S --> RR[RCP /v1/rerank] + RR --> Out[ranked hits] + + subgraph FED[Federated layer] + ADAP[adapters/.py] + end + DB -.-> ADAP + QD -.-> ADAP + ADAP --> GME[just gme-search / gme-entity] + ADAP --> RAGTOOL[v2 LLM RAG tools] +``` + +Shared infrastructure (post-2026-05-01 pattern): + +- **Storage**: DuckDB at `data/index//duckdb/.duckdb` — + canonical records + `chunks` ledger (one row per Qdrant point). +- **Vector store**: Qdrant — per-index collections, cosine distance, + 4096-dim vectors. +- **Embeddings**: `Qwen/Qwen3-Embedding-8B` on EPFL RCP, instruction-aware. +- **Reranker**: `Qwen/Qwen3-Reranker-8B` on EPFL RCP. +- **Chunking**: token-aware sliding window via `tiktoken` (`cl100k_base`). +- **Auth**: `RCP_TOKEN` (required); per-index source tokens (`HF_TOKEN`, + `GITHUB_TOKEN`, `INFOSCIENCE_TOKEN`, …) where the upstream API requires + them. + +The `ror` index is a partial outlier (no DuckDB layer; flat catalog of +orgs in Qdrant + a JSONL dump for lexical lookup). + +See [RAG Indices Overview](../rag-indices.md) for the full inventory and +per-index quickstarts. + ## Notes -- Cache TTL defaults are configured in `src/cache/cache_config.py` (default 365 days unless overridden). -- Repository pipeline includes optional enrichments (`enrich_orgs`, `enrich_users`) and always runs final validation before caching. -- Organization analysis uses an atomic 6-stage flow; user analysis combines GitHub parsing + LLM + enrichment steps. +- The frozen v1 pipeline (`src/v1/`) shares the FastAPI app but has its + own cache (`src/v1/cache/`) and code paths. No new work targets v1. +- Internal pipeline metadata fields whose names start with `_` are + stripped before strict validation, JSON-LD output, and any external + artefact. Never expose `_`-prefixed fields in API responses. +- `identifiers.uuid` on every entity is server-generated + (`src/v2/agents/models.py::generate_uuid()`); the LLM never controls it. +- See [V2 API Reference](../v2-api-reference.md) for the full 23-stage + pipeline and gating rules. diff --git a/docs/concept-tagging.md b/docs/concept-tagging.md new file mode 100644 index 0000000..f78116c --- /dev/null +++ b/docs/concept-tagging.md @@ -0,0 +1,137 @@ +# Concept tagging pipeline stage + +Optional stage that stamps three underscore-prefixed metadata fields onto +the root repository entity, derived from the README: + +- `_keywords` — `list[str]` of plain keyword tokens. +- `_concepts` — `list[dict]` of structured concepts, each shaped + `{label, wikipedia_url, wikidata_id, concept_id, score, source}`. +- `_disciplines` — `list[dict]` of EPFL Graph academic-discipline + matches, each shaped `{category_id, name, score, rank, from_concept, + source, graphsearch_url, wikipedia_url, wikipedia_page_id, chain, + publications?, people?, units?, openalex_topics?}`. + +All three fields are stripped before strict JSON-Schema validation +(handled by `schema_validation.py`: any `_`-prefixed key is dropped) and +before JSON-LD output (handled by `jsonld_build.py`: same rule). They +are purely **internal pipeline metadata**, not part of the public Open +Pulse Ontology surface (yet) — surface them by adding the corresponding +ontology terms (`schema:keywords`, `schema:about`, etc.) to the strict +schema and projecting in the output stage when the schema lands. + +## When the stage runs + +Right after `infer_org_units`, immediately before `build_jsonld_output`. +Operates on `assembled_output.root_entity` and only when the input is a +**repository** (skipped for user/organization extracts). Independent of +`agent_runtime` — runs the same in `llm` and `rule_based` modes. + +## Backends + +Pluggable via `V2_CONCEPT_TAGGING_BACKEND`: + +| Backend | Source | Requirements | Quality | Speed | +|---|---|---|---|---| +| `epfl_graph` (default) | graphai.epfl.ch `/text/wikify` + `/text/keywords` | `EPFL_GRAPH_USERNAME` + `EPFL_GRAPH_PASSWORD` | high — wikified concepts with mixed-score, EPFL ontology disciplines via `/ontology/nearest_neighbor/concept/category` | ~3-8 s per README | +| `wikipedia` | MediaWiki opensearch | none | low — token-frequency heuristic feeds an unranked first-page-hit lookup | ~5 s per README (top-N requests) | +| `llm` | `pydantic-ai` Agent reusing the project's `model_config` | one of `RCP_TOKEN`/`OPENAI_API_KEY`/`OPENROUTER_API_KEY` | medium — depends on the configured model; consumes LLM tokens | ~5-15 s per README | + +The whole stage is **opt-in** because each backend has its own external +dependency. + +## Configuration + +| Env var | Default | Purpose | +|---|---|---| +| `V2_CONCEPT_TAGGING_ENABLED` | `false` | master switch — set to `true` to run the stage | +| `V2_CONCEPT_TAGGING_BACKEND` | `epfl_graph` | one of `epfl_graph` / `wikipedia` / `llm` | +| `V2_CONCEPT_TAGGING_EPFL_MIN_SCORE` | `0.0` | drop EPFL-Graph concepts whose `mixed_score` is below this threshold (0..1). Filters out low-confidence noise like "Graph theory" matched on the word "graph" | +| `V2_CONCEPT_TAGGING_OPENALEX_RELATED_ENABLED` | `false` | per top-discipline, also fetch related publications / people / units via OpenAlex (4 cheap HTTP per discipline). Requires `OPENALEX_MAILTO` for the polite pool | + +Internal knobs (function arguments to `run_concept_tagging_stage` — +defaults are sensible, override only when needed): + +| Argument | Default | Purpose | +|---|---|---| +| `max_keywords` | 25 | cap on `_keywords` length | +| `max_concepts` | 25 | cap on `_concepts` length | +| `max_disciplines` | 10 | cap on `_disciplines` length | +| `max_readme_chars` | 8000 | truncate long READMEs before sending to backends | +| `discipline_top_concepts` | 6 | how many top concepts to look up disciplines for | +| `discipline_top_n_per_concept` | 3 | how many disciplines to fetch per concept | +| `enrich_with_disciplines` | `true` | whether the `epfl_graph` backend also fetches disciplines via `/ontology/nearest_neighbor/concept/category` | +| `related_top_disciplines` | 3 | how many top disciplines to enrich with OpenAlex when `enable_related_openalex=True` | + +## Markdown stripping + +The README is normalised before being shipped to any backend — code +fences, inline code, links, images, ATX/setext headers, bold/italic +markers, list bullets, blockquotes, and HRs are all removed. Markdown +noise like `## Project Structure` and `**three-layer cache**` was the +top source of garbage keywords from the EPFL Graph keyword extractor in +v1; the stripper closes that gap. + +## Discipline shape (with everything turned on) + +```python +{ + "category_id": "topics-in-natural-language-processing", + "label": "Natural language processing", + "score": 15.7, + "rank": 1, + "from_concept": "Information extraction", + "source": "epfl_graph", + "graphsearch_url": "https://graphsearch.epfl.ch/en/category/topics-in-natural-language-processing", + "wikipedia_url": "https://en.wikipedia.org/?curid=21652", + "wikipedia_page_id": "21652", + "chain": [ + {"category_id": "topics-in-natural-language-processing", "label": "Natural language processing", "depth": 0, "wikipedia_url": "..."}, + {"category_id": "natural-language-processing", "label": "Natural language processing", "depth": 1, "wikipedia_url": "..."}, + {"category_id": "information-engineering", "label": "Information engineering", "depth": 2, "wikipedia_url": "..."}, + {"category_id": "applied-sciences", "label": "Outline of applied science", "depth": 3, "wikipedia_url": "..."}, + {"category_id": "academic-disciplines", "label": "Academic disciplines", "depth": 4, "wikipedia_url": "..."}, + ], + # Only present when V2_CONCEPT_TAGGING_OPENALEX_RELATED_ENABLED=true: + "openalex_topics": [ + {"topic_id": "T10395", "topic_name": "...", "score": 0.81}, + ... + ], + "publications": [ + {"openalex_id": "W...", "title": "...", "year": 2024, "doi": "...", "venue": "...", "url": "..."}, + ... + ], + "people": [ + {"openalex_id": "A...", "name": "...", "orcid": "...", "institution": "...", "ror": "..."}, + ... + ], + "units": [ + {"openalex_id": "I...", "name": "...", "ror": "...", "country_code": "CH", "homepage_url": "..."}, + ... + ], +} +``` + +## Why not use the new disciplines RAG index? + +The legacy concept_tagging path calls graphai's +`/ontology/nearest_neighbor/concept/category` once per concept (~3 HTTP +per README). Now that `src/index/epfl_graph/` mirrors the ontology into +Qdrant locally, the same job can be done with a single embed + +vector-search per README. **That swap is a planned follow-up** — the +behaviour change is non-trivial (different scoring, different +discipline counts) so it warrants its own review. For now the two +systems coexist: + +- `concept_tagging` stage → graphai per-concept (this doc). +- `search_epfl_graph_disciplines` agent tool → Qdrant semantic search ([epfl-graph-disciplines.md](epfl-graph-disciplines.md)). + +## File map + +``` +src/v2/pipeline/stages/concept_tagging.py # stage entrypoint + backends +src/v2/pipeline/stages/__init__.py # re-exports run_concept_tagging_stage etc. +src/module/epfl_graph/ontology.py # /ontology/* wrappers (used by epfl_graph backend) +src/module/epfl_graph/openalex_related.py # publications / people / units helpers +src/v2/api.py # wires the stage into the v2 pipeline +.env.example # documents all the V2_CONCEPT_TAGGING_* knobs +``` diff --git a/docs/discover-hydrate.md b/docs/discover-hydrate.md new file mode 100644 index 0000000..bfc8a45 --- /dev/null +++ b/docs/discover-hydrate.md @@ -0,0 +1,229 @@ +# Discover + Hydrate + +Build up the gme RAG indices by streaming candidate identifiers +(**Seeds**) from one source into the right index's hydrator. The two +operations are protocols extending the federated layer; they sit +alongside `gme search` / `gme entity` (read-only) and provide the +write-side of cross-index work. + +> **TL;DR.** ORCID already worked this way (`discover` → `seeds` table +> → `ingest`). The new layer lifts that pattern to the federated CLI +> and makes it cross-index: any discoverer can produce seeds that any +> matching hydrator consumes. Internal design: see +> [`.internal/federated/discover-hydrate-design.md`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/.internal/federated/discover-hydrate-design.md). + +## Mental model + +``` +┌─ Discover ────────────────────────────────────────────────────────────┐ +│ source (web scrape, search, citation graph, dump diff, sibling- │ +│ index extract) │ +│ │ │ +│ ▼ │ +│ Seed{id, seed_type, source, hint?} ← JSONL on stdout / --out │ +└──────┬────────────────────────────────────────────────────────────────┘ + │ pipe or file + ▼ +┌─ Hydrate ─────────────────────────────────────────────────────────────┐ +│ group seeds by `seed_type` → route to matching hydrators │ +│ │ │ +│ ▼ │ +│ per-index fetch + upsert (idempotent, ON CONFLICT DO UPDATE/NOTHING) │ +└───────────────────────────────────────────────────────────────────────┘ +``` + +A `Seed` is a 4-tuple: `id` (canonical identifier), `seed_type` +(open string discriminator: `"doi"`, `"orcid"`, `"openalex_work"`, +`"github_repo"`, `"zenodo_id"`, …), `source` (provenance), and an +optional `hint` dict for opaque metadata that travels with the seed +(e.g. an affiliation ROR to stamp at hydration time). + +## CLI + +Both subcommands live under `python -m src.index._federated` +(aliased as `gme`). + +### `gme discover` + +Run a registered discoverer and emit Seeds as JSONL. + +```bash +# Scrape SDSC's publications page → DOI seeds +gme discover --source datascience-ch --indices openalex --out sdsc-seeds.jsonl + +# Generic OpenAlex /works search +gme discover --source from-search --indices openalex \ + --opt query="machine learning fairness" --out ml-seeds.jsonl + +# Find works in our DB whose referenced_works are not yet populated +gme discover --source from-references --indices openalex --out missing-refs.jsonl + +# ORCID multi-source seed table population (also returns a seed stream) +gme discover --source both --indices orcid --out orcid-seeds.jsonl + +# GitHub: scrape the dependents network of a target repo +gme discover --source dependents --indices github \ + --opt target=Imaging-Plaza/git-metadata-extractor --out gh-seeds.jsonl +``` + +Source-specific options pass through `--opt key=value` (repeatable). + +### `gme hydrate` + +Read Seeds from a JSONL file (or stdin) and dispatch to the matching +hydrators. Idempotent: by default skips seeds whose canonical record +is already in the target store. + +```bash +# Bring missing DOIs from the SDSC scrape into our DB +gme hydrate sdsc-seeds.jsonl + +# Restrict to a subset of indices +gme hydrate seeds.jsonl --indices openalex,zenodo + +# Force re-fetch even if already in DB +gme hydrate seeds.jsonl --all + +# Pipeline form (no temp file) +gme discover --source datascience-ch --indices openalex \ + | gme hydrate - +``` + +The dispatcher groups seeds by `seed_type` and forwards each group to +every hydrator that accepts it. A single seed can therefore be +consumed by multiple indices (e.g. a DOI seed from datascience.ch can +feed both OpenAlex *and* Zenodo when the DOI is `10.5281/zenodo.`). + +### `gme indices` + +Lists every registered adapter / discoverer / hydrator and what each +exposes. + +```bash +gme indices +``` + +```jsonc +{ + "openalex": { + "entity_types": ["works", "authors", "institutions", "sources", "topics", "concepts"], + "search": true, + "lookup": true, + "discover": ["from-search", "from-references", "datascience-ch"], + "hydrate": ["doi", "openalex_work", "openalex_author"] + }, + "orcid": { + "entity_types": ["persons", "employments", "educations"], + "search": true, + "lookup": true, + "discover": ["openalex", "orcid_search", "both"], + "hydrate": ["orcid"] + }, + ... +} +``` + +## Coverage matrix + +State as of 2026-05-04. Stub = surface registered, full implementation +TBD; rest are production-ready paths. + +| Index | Discover sources | Hydrate seed types | Status | +|---|---|---|---| +| **openalex** | `from-search`, `from-references`, `datascience-ch` | `doi`, `openalex_work`, `openalex_author` | production | +| **orcid** | `openalex`, `orcid_search`, `both` | `orcid` | production | +| **zenodo** | `infoscience` | `zenodo_id`, `doi` | production | +| **infoscience** | `from-search` | `infoscience_url` | discover real / hydrate stub | +| **ethz_research_collection** | `from-search` | `ethz_rc_url` | discover real / hydrate stub | +| **github** | `dependents`, `from-references` | `github_repo` | discover real / hydrate stub | +| **huggingface** | `orgs`, `from-search` | `hf_model`, `hf_dataset`, `hf_space`, `hf_org` | discover (orgs) real / hydrate stub | +| **renkulab** | `from-search` | `renkulab_url` | stub | +| **snsf** | — | — | surface only | +| **swissubase** | `from-search` | `swissubase_url` | stub | +| ror | (n/a — dump-driven) | (n/a) | search/lookup only | +| epfl_graph | (n/a — dump-driven) | (n/a) | search/lookup only | + +## Seed shape (JSONL) + +One JSON object per line: + +```json +{"id": "10.1029/2025WR042835", "seed_type": "doi", "source": "datascience.ch", "hint": {"affiliation_ror": "https://ror.org/02hdt9m26"}} +{"id": "https://openalex.org/W4400555555", "seed_type": "openalex_work", "source": "from-references", "hint": {"refs_only": true}} +{"id": "0000-0002-1825-0097", "seed_type": "orcid", "source": "orcid:openalex", "hint": {"scope": "switzerland", "discovered_via": "openalex"}} +``` + +The `hint` dict is opaque — each hydrator interprets keys it +recognises (e.g. OpenAlex respects `affiliation_ror` to stamp +`work_institutions`; ORCID uses `scope`) and ignores the rest. + +## Common recipes + +### Add SDSC publications missing from our OpenAlex DB + +```bash +# 1. Scrape datascience.ch (32 pages, ~300 DOIs). +# 2. Cross-check against works.doi, fetch the missing ones, +# stamp work_institutions with SDSC's OpenAlex institution id. +gme discover --source datascience-ch --indices openalex --out sdsc.jsonl +gme hydrate sdsc.jsonl --indices openalex +``` + +### Backfill OpenAlex `work_references` + +For works ingested before `referenced_works` was in `WORKS_PROJECTION` +(legacy DBs): + +```bash +gme discover --source from-references --indices openalex --out missing-refs.jsonl +gme hydrate missing-refs.jsonl --indices openalex +``` + +The OpenAlex hydrator detects `hint.refs_only=true` and runs the +batched fast path (100 IDs per request, only populates +`work_references`, no work upsert). + +For works whose `raw.referenced_works` *is* populated (newer +ingests), you can skip the API entirely: + +```bash +python -m src.index.openalex.cli references-extract +``` + +### Cross-source dedup via JSONL ops + +Since seeds are JSONL, standard CLI tools work: + +```bash +# Union DOIs from two sources, dedup by id +cat sdsc.jsonl ml.jsonl | jq -c 'select(.seed_type=="doi")' | sort -u > all-dois.jsonl +gme hydrate all-dois.jsonl --indices openalex +``` + +## Adding a new index + +1. Create `src/index//_federated.py`. +2. Implement an `IndexDiscoverer` (declare `name`, `accepted_sources`, + `discover(source, **opts) → Iterator[Seed]`) and/or an + `IndexHydrator` (declare `name`, `accepted_seed_types`, + `hydrate(seeds, only_unfetched) → HydrationSummary`). +3. Call `register_discoverer(...)` / `register_hydrator(...)` at module + top level. +4. Add `` to the `_CANDIDATES` list in + `src/index/_federated/dh_registry.py` so `gme indices` picks it up. +5. Done — `gme discover` and `gme hydrate` route automatically based on + `accepted_sources` / `accepted_seed_types`. + +See `src/index/openalex/_federated.py` for a production reference +implementation. + +## Related documentation + +- [`federated-search.md`](federated-search.md) — `gme search` / + `gme entity` / `gme indices` (read-side companion). +- [`openalex-index.md`](openalex-index.md) — full OpenAlex doc with + the `references-extract` API-free path. +- [`rag-indices.md`](rag-indices.md) — high-level overview of all + twelve indices. +- `.internal/federated/discover-hydrate-design.md` — design rationale, + open questions, migration plan. diff --git a/docs/epfl-graph-disciplines.md b/docs/epfl-graph-disciplines.md new file mode 100644 index 0000000..96b43b4 --- /dev/null +++ b/docs/epfl-graph-disciplines.md @@ -0,0 +1,192 @@ +# EPFL Graph disciplines RAG index + +Mirrors the curated EPFL Graph academic-discipline ontology +(`graphai.epfl.ch/ontology/*`) into a single Qdrant collection so callers +can ask **"what discipline is this README closest to?"** without burning +per-concept HTTP calls. The ontology is a 6-level tree of ~2226 +categories where each leaf is anchored by 50–110 Wikipedia articles that +together define the category. + +## What it indexes + +| Item | Source | Detail | +|---|---|---| +| Categories | `GET /ontology/tree` + `GET /ontology/tree/category/{id}` | name, depth, parent, child categories, anchor concepts, canonical Wikipedia page id + title | +| Anchor concepts | from each `category_info.concepts[*]` | top-N Wikipedia article names per category, used in the embedding text | +| Wikipedia extracts | MediaWiki TextExtracts API (`prop=extracts&exintro=1`) | canonical lead-section plain text per category, mapped via the category's `name` (= article title) with `redirects=1` | + +The full payload returned by graphai for each category is also stored in +the `categories.raw` JSON column so downstream consumers can reach into +it without a re-fetch. + +## Storage layout + +``` +data/index/epfl_graph/ +└── duckdb/ + └── epfl_graph.duckdb # ~8.6 MB after a full ingest +``` + +Two tables: + +- **`categories`** — one row per ontology node. Columns: `category_id`, + `name`, `depth`, `parent_id`, `wikipedia_page_id`, `wikipedia_url`, + `wikipedia_extract`, `graphsearch_url`, `n_concepts`, `n_children`, + `embedding_text`, `raw` (JSON), `fetched_at`. +- **`category_concepts`** — top-N anchor Wikipedia concepts per + category. Columns: `category_id`, `concept_id`, `concept_name`, `rank`. + +A single Qdrant collection — `epfl_graph_disciplines` — holds the +embeddings: one 4096-dim point per category at depth ≥ 3 (the +`filter.min_depth` knob skips the broad ancestor nodes +`academic-disciplines`/`applied-sciences`/etc. by default). + +## Embedding text shape + +``` +{name}. {wikipedia_extract truncated to 1200 chars}. Anchor concepts: c1, c2, … +``` + +Built by `build_embedding_text()` in +`src/index/epfl_graph/ingest/wikipedia_extracts.py`. Categories without +a Wikipedia extract (~36% of the catalogue — mostly EPFL-Graph synthetic +`topics-in-X` / `entities-in-X` taxonomy nodes) fall back to +`name + anchor concepts`. + +## CLI + +```bash +just epfl-graph-status # row counts + Qdrant collection name +just epfl-graph-ingest # walk the 2226-node tree (~25 min, polite ~5 req/s) +just epfl-graph-enrich-wikipedia # fill Wikipedia extracts (~3 min, batched 50/req) +just epfl-graph-embed # push vectors to Qdrant (~5 min, batches of 32) +just epfl-graph-search "extracting research metadata from GitHub" --top-k 10 +just epfl-graph-search "..." --min-depth 4 # leaf-only disciplines +just epfl-graph-search "..." --no-rerank # skip the cross-encoder, vector-only +``` + +Equivalent without `just`: + +```bash +python -m src.index.epfl_graph status +python -m src.index.epfl_graph ingest [--limit N] +python -m src.index.epfl_graph enrich-wikipedia [--limit N] +python -m src.index.epfl_graph embed [--limit N] +python -m src.index.epfl_graph search "" --top-k 10 --candidate-k 50 --min-depth 4 +``` + +## Configuration + +Static settings live in [`config/index/epfl_graph.yaml`](../config/index/epfl_graph.yaml). +The ones worth tuning per deployment: + +| Path | Default | Purpose | +|---|---|---| +| `rcp.embedding_model` | `Qwen/Qwen3-Embedding-8B` | shared with the other indices | +| `rcp.reranker_model` | `Qwen/Qwen3-Reranker-8B` | shared with the other indices | +| `graphai.rate_per_second` | `5` | tree-walk pacing (graphai is gentle but not free) | +| `graphai.anchor_concepts_per_category` | `12` | how many top anchor concepts to keep per category for the embedding text | +| `qdrant.url` | `http://localhost:6333` (override with `INDEX_QDRANT_URL=http://gme-qdrant:6333` inside the devcontainer) | | +| `filter.min_depth` | `3` | shallowest category depth that gets embedded — depth 1–2 are too broad to be useful matches | + +Env vars: + +- `EPFL_GRAPH_USERNAME`, `EPFL_GRAPH_PASSWORD` — required by `ingest` (the auth flows via `src/module/epfl_graph/auth.py`). +- `RCP_TOKEN` — required by `embed` and by `search` (the reranker is opt-in but on by default). +- `INDEX_QDRANT_URL` / `INDEX_QDRANT_API_KEY` — Qdrant overrides. +- `V2_EPFL_GRAPH_RAG_ENABLED` — opt-out flag for the v2 LLM-agent tool. Default `true`. + +## Wired into the v2 pipeline + +When `V2_EPFL_GRAPH_RAG_ENABLED` is set or unset (default `true`), +`src/v2/dependencies.py:_resolve_epfl_graph_rag_provider()` constructs an +`EpflGraphRagProvider` and stamps it onto the `ProviderSet`. + +LLM agents that pick up the tool when the provider is non-None: + +| Agent | Tool registered as | +|---|---| +| Repository | `search_epfl_graph_disciplines` | +| Person | `search_epfl_graph_disciplines` | +| Organization | `search_epfl_graph_disciplines` | +| Article | `search_epfl_graph_disciplines` | + +Contribution and membership agents are intentionally skipped — they are +pure linking agents (person↔repo, person↔org), no discipline benefit. + +The federated layer registers `epfl_graph` as an adapter exposing the +`disciplines` entity type. `gme search` hits it alongside the other +indices when the query has no entity-type hint. + +## Tool signature (LLM-facing) + +```python +search_epfl_graph_disciplines( + query: str, + top_k: int = 10, + filters: dict[str, Any] | None = None, # category_id, depth, parent_id, entity_type + rerank: bool = False, +) -> list[dict[str, Any]] +``` + +Each hit is a thin dict: + +```python +{ + "id": "", # qdrant point id (uuid5(name="epfl_graph|")) + "score": 0.45, # vector cosine score + "category_id": "topics-in-natural-language-processing", + "name": "Natural language processing", + "depth": 4, + "parent_id": "natural-language-processing", + "wikipedia_page_id": "21652", + "wikipedia_url": "https://en.wikipedia.org/?curid=21652", + "graphsearch_url": "https://graphsearch.epfl.ch/en/category/topics-in-natural-language-processing", + "n_concepts": 110, + "n_children": 0, + "collection": "epfl_graph_disciplines", +} +``` + +For `min_depth=4` (leaf-only discipline tagging) pass it via the CLI +flag for direct calls, or via `filters={"depth": {"$gte": 4}}` from +inside an LLM tool call. + +## How the tool actually behaves + +``` +LLM: search_epfl_graph_disciplines( + query="GPU-accelerated finite element solver for elastodynamics in geomechanics", + top_k=5, + filters={"depth": 4}, + rerank=True, + ) +→ [ + {name="Computational mechanics", score=0.94, …}, + {name="Continuum mechanics", score=0.93, …}, + {name="Finite element method", score=0.92, …}, + {name="Geophysics", score=0.90, …}, + {name="High-performance computing", score=0.89, …}, + ] +``` + +## Refreshing + +The ontology changes rarely (months between meaningful tree updates). +Recommended cadence: + +- **Quarterly**: `just epfl-graph-ingest && just epfl-graph-enrich-wikipedia && just epfl-graph-embed`. Total ~35 min. +- **Ad-hoc** when a new EPFL Graph deployment ships ontology changes you care about. + +The ingest is fully idempotent — re-running it upserts on `category_id` +without duplicating concepts. + +## Shape of the underlying graphai endpoints + +For reference (these are NOT exposed by upstream `graphai-client`; they +are wrapped in `src/module/epfl_graph/ontology.py`): + +- `GET /ontology/tree` — returns `{child_to_parent: [{child_id, parent_id}, …]}`. ~2226 edges. +- `GET /ontology/tree/category/{id}` — returns `{info: {category_id, depth, id, name}, parent_category: , child_categories: [, …] | None, clusters: [, …] | None, concepts: [{id, name}, …] | None}`. The `info.id` and `info.name` fields are the Wikipedia page id + canonical title. +- `POST /ontology/nearest_neighbor/concept/category` — concept-id → top-N nearest categories. Used by the legacy concept_tagging stage; superseded for README→category routing by this index's semantic search. +- `GET /ontology/openalex/category/{id}/nearest_topics` — bridges into OpenAlex topic IDs (the basis of the OpenAlex-side related-entity enrichment in `src/v2/pipeline/stages/concept_tagging.py`). diff --git a/docs/federated-search.md b/docs/federated-search.md new file mode 100644 index 0000000..153769e --- /dev/null +++ b/docs/federated-search.md @@ -0,0 +1,241 @@ +# Federated Search & Cross-Index Lookup + +Single-entry-point access to all gme RAG indices. +Lives at `src/index/_federated/`. + +> **Two views, one corpus.** Today the project has twelve independent +> indices (HuggingFace, OpenAlex, Infoscience, ORCID, ROR, Zenodo, ETH +> Research Collection, GitHub, SNSF, RenkuLab, EPFL Graph, SWISSUbase), +> each with its own CLI / FastAPI / Qdrant collections. The federated +> layer **does not replace them** — it sits *above* them, fans out +> queries in parallel, merges results by score, and exposes a single +> `gme search` / `gme entity` interface for analysts and tools (including +> the v2 LLM pipeline). + +## When to reach for it + +| Question | Use | +|---|---| +| "Find anything Swiss-German LLM-related across the whole corpus" | `gme search` | +| "Given this ORCID / ROR / DOI / HF slug, give me everything we have on it" | `gme entity` | +| "I'm scripting against just one index and know which one" | the per-index CLI (`hf-search`, `orcid-search`, …) | +| "What indices are loaded right now?" | `gme indices` | + +## Architecture (1-paragraph version) + +Each per-index module gets a thin **adapter** under +`src/index/_federated/adapters/.py` that implements a 2-method +`IndexAdapter` Protocol — `search()` and `lookup()`. Adapters self-register +on import via `register(adapter)`. The federated layer fans out across all +registered adapters in a `ThreadPoolExecutor`, merges hits by `score`, and +returns a single JSON shape. Adapters never share state — a failure in one +index is logged and skipped, the rest continue. + +``` + ┌─────────────────────────────────────────────┐ + user query ─► │ gme search / gme entity (CLI / API) │ + └────────────────────┬────────────────────────┘ + │ + ThreadPoolExecutor — fans out in parallel + │ + ┌────────┬──────────┬────────┬──────┬──────┬─────────┐ + ▼ ▼ ▼ ▼ ▼ ▼ ▼ + HF adapter OpenAlex Infoscience ORCID ROR Zenodo (more…) + │ │ │ │ │ │ + └────────┴──────────┴────────┴──────┴──────┘ + │ + merge by score → trim to top_k + │ + ▼ + {"hits":[…], "by_index":{…}, "errors":{…}} +``` + +Each adapter is **lazy-imported** inside its methods, so spinning up the +CLI doesn't pay the cost of loading every index's heavy dependencies. + +## Quick start + +### Federated semantic search + +```bash +# Search everything in parallel, top 20 across all indices +just gme-search "Swiss German large language model" + +# Restrict to a subset of indices +just gme-search "EPFL machine learning" --indices huggingface,openalex,orcid + +# Pull more candidates from each index, then trim to top-50 overall +just gme-search "remote sensing earth observation" --top-k 50 --top-k-per-index 10 + +# Forward filters — adapters that recognise the key apply them, others ignore +just gme-search "Swiss researcher" --filter namespace_kind=user +just gme-search "ETH paper" --filter year=2024 --filter publication_year=2024 + +# Restrict each adapter to a single entity type +just gme-search "Swiss German LLM" --entity-type models + +# Cross-index rerank: send the merged candidate pool through RCP's +# cross-encoder once for a globally-fair ordering. Per-adapter scores +# aren't directly comparable (each ranks within its own pool); this +# fixes that. Costs +1 RCP call. +just gme-search "Swiss German LLM" --rerank +``` + +Output shape: + +```json +{ + "hits": [ + { + "index": "huggingface", + "entity_type": "model", + "id": "ZurichNLP/swissbert", + "title": "ZurichNLP/swissbert", + "score": 0.97, + "summary": "ZurichNLP/swissbert — fill-mask — cc-by-nc-4.0", + "url": "https://huggingface.co/ZurichNLP/swissbert", + "payload": { "...": "raw qdrant payload" } + }, + "..." + ], + "by_index": {"huggingface": 5, "openalex": 5, "orcid": 3, "ror": 2, "zenodo": 2, "infoscience": 3, "github": 2, "ethz_research_collection": 2, "snsf": 1}, + "errors": {}, + "registered_indices": ["ethz_research_collection", "github", "huggingface", "infoscience", "openalex", "orcid", "ror", "snsf", "zenodo"], + "reranked": false +} +``` + +`reranked` is `true` when the response went through the cross-index +rerank step (`--rerank` flag); otherwise scores are per-adapter only and +not directly comparable across indices. + +### Cross-index entity lookup + +Pass any identifier — slug, full URL, ORCID, ROR, DOI, UUID — and every +adapter that recognises the shape returns matches. + +```bash +# HF org slug → only HF returns a record (others see no match in their identifier patterns) +just gme-entity epfl-llm + +# ORCID → orcid adapter resolves; the rest return 0 +just gme-entity 0000-0001-9534-3870 +just gme-entity https://orcid.org/0000-0001-9534-3870 + +# ROR +just gme-entity https://ror.org/02s376052 + +# OpenAlex Work / Author / Institution +just gme-entity W2741809807 +just gme-entity https://openalex.org/A5023888391 + +# Zenodo (numeric ID, URL, or DOI) +just gme-entity 5732376 +just gme-entity 10.5281/zenodo.5732376 + +# Infoscience UUID +just gme-entity 5f5978a4-149d-400f-a11b-1786dacea50c + +# HF repo (author/repo) +just gme-entity epfl-llm/meditron-7b +just gme-entity https://huggingface.co/EPFL-VILAB/4M + +# Restrict the lookup to a subset of indices +just gme-entity 0000-0001-9534-3870 --indices orcid,openalex +``` + +Output shape: + +```json +{ + "identifier": "epfl-llm", + "records": [ + { + "index": "huggingface", + "entity_type": "org", + "id": "epfl-llm", + "data": {"slug": "epfl-llm", "fullname": "EPFL LLM Team", "...": "..."}, + "url": "https://huggingface.co/epfl-llm" + } + ], + "by_index": {"huggingface": 1, "openalex": 0, "orcid": 0, "infoscience": 0, "ror": 0, "zenodo": 0}, + "errors": {} +} +``` + +### List registered adapters + +```bash +just gme-indices +``` + +```json +{ + "huggingface": {"entity_types": ["models", "datasets", "spaces", "orgs"]}, + "infoscience": {"entity_types": ["chunks", "articles", "persons", "organizations"]}, + "openalex": {"entity_types": ["works", "authors", "institutions", "sources", "topics", "concepts"]}, + "orcid": {"entity_types": ["persons", "employments", "educations"]}, + "ror": {"entity_types": ["organizations"]}, + "zenodo": {"entity_types": ["zenodo_records"]} +} +``` + +## Identifier patterns recognised by `gme entity` + +| Pattern | Resolved by | +|---|---| +| `/` (e.g. `epfl-llm/meditron-7b`) | `huggingface`, `github` (both try; either may match) | +| `https://huggingface.co/...` URLs | `huggingface` | +| `https://github.com//` URLs | `github` | +| Bare HF slug (e.g. `epfl-llm`) | `huggingface` (org/user) | +| `https://openalex.org/Wxxxxx` or bare `Wxxxxx`, `Axxxxx`, etc. | `openalex` | +| `0000-0001-2345-6789` or `https://orcid.org/...` | `orcid` | +| `https://ror.org/<9-char-id>` or bare 9-char ID | `ror` | +| Numeric Zenodo ID, `https://zenodo.org/record/`, or `10.5281/zenodo.` DOI | `zenodo` | +| UUID (or `https://infoscience.epfl.ch/.../`) | `infoscience` | +| UUID (or `https://www.research-collection.ethz.ch/handle/`) | `ethz_research_collection` | +| 6–7 digit grant id, or `https://data.snf.ch/grants/grant/` | `snsf` | + +When in doubt, paste the URL — every adapter's regex is conservative and +will silently no-op on inputs it doesn't recognise. + +## Adding a new index + +1. Implement `IndexAdapter` (Protocol with `search()` and `lookup()`): + ```python + from src.index._federated.registry import EntityRecord, Hit, register + + class MyAdapter: + name = "my_index" + entity_types = ["thing"] + + def search(self, *, query, entity_type, top_k, filters): + # Lazy import + call your index's semantic_search + return [Hit(index=self.name, entity_type="thing", id=..., score=..., ...)] + + def lookup(self, identifier): + # Detect identifier shape, return [] if not recognised + return [EntityRecord(...)] + + register(MyAdapter()) + ``` +2. Save it as `src/index/_federated/adapters/my_index.py`. +3. Add `"my_index"` to the `candidates` tuple in `registry.py:load_adapters`. +4. The CLI now lists it under `gme indices`. Done. + +Adapters should: +- **Lazy-import** index internals inside the methods (keeps `gme indices` cheap). +- **Catch broad exceptions** — federation must keep working even if one index is down. +- **Return empty** for unknown identifiers in `lookup()` (no exceptions for "not mine"). + +## Limits + open follow-ups + +- **No facet aggregation across indices.** HF has `--facets` natively (`docs/huggingface-index.md`); the federated layer does not yet roll those up. +- **One identifier at a time** in `gme entity`. Batching would be a small extension if needed for scripting. +- **Errors are logged, not surfaced.** The `errors` field in the response is populated when an adapter raises, but per-adapter timeouts aren't enforced — a hung adapter could stall the response. +- **Cross-index entity dedup.** If an ORCID resolves to the same person via both the `orcid` and `openalex` adapters, you currently get two records. A canonicalisation pass would fold them. See `ROADMAP.md`. + +## Related documentation + +- [`huggingface-index.md`](huggingface-index.md) — direct-access guide for the HF index +- [`v2-rag-tools.md`](v2-rag-tools.md) — agent-side tools that the v2 LLM pipeline uses (per-index, the federated tool is plumbed in alongside) diff --git a/docs/getting-started.md b/docs/getting-started.md index 1fb23b8..186eecf 100644 --- a/docs/getting-started.md +++ b/docs/getting-started.md @@ -2,75 +2,185 @@ ## 1. Install dependencies +This project uses [`uv`](https://docs.astral.sh/uv/) for dependency management. + ```bash -just install-dev +just install-dev # uv pip install -e ".[dev]" ``` ## 2. Configure environment -Create `.env` from `.env.dist` (or update your existing `.env`) and set the required variables: - -- `OPENAI_API_KEY` -- `OPENROUTER_API_KEY` -- `GITHUB_TOKEN` -- `GITLAB_TOKEN` -- `INFOSCIENCE_TOKEN` -- `MODEL` -- `PROVIDER` -- `SELENIUM_REMOTE_URL` -- `CACHE_DB_PATH` -- `MAX_SELENIUM_SESSIONS` -- `MAX_CACHE_ENTRIES` -- `GUNICORN_CMD_ARGS` +Create `.env` from the template and edit: -Commonly used additional variable: +```bash +cp .env.example .env +``` -- `RCP_TOKEN` (used by the configured `openai-compatible` endpoint in `src/llm/model_config.py`) +Required to serve `/v2/extract`: + +- `GITHUB_TOKEN` — `/v2/health` flips to `degraded` without it. +- `API_TOKEN` — bearer token guarding every `/v1/*` route plus + `/v2/extract` and `/v2/jobs/{id}`. **Fails closed**: missing → + every protected request returns `503` (no dev bypass). Generate with + `python -c "import secrets; print(secrets.token_urlsafe(32))"`. See + [Authentication](v2-api-reference.md#authentication). +- One LLM credential (validated at startup against the active model + profile in `src/v2/agents/llm/model_config.py`): + - `RCP_TOKEN` (EPFL RCP), or + - `OPENAI_API_KEY`, or + - `OPENROUTER_API_KEY`. + +Optional integrations: + +- `INFOSCIENCE_TOKEN` — protected Infoscience routes only. +- `SELENIUM_REMOTE_URL` — enables the link-veracity pipeline stage and + the `fetch_link_content_via_selenium` LLM tool. + +Per-indexer politeness (only needed when running the corresponding +indexer): + +- `HF_TOKEN` — HuggingFace Hub. Anonymous IPs share a 500-req / 5-min + bucket; authenticated raises that to 1k API + 5k resolver requests. + Required in practice for the Switzerland sweep. +- `ZENODO_TOKEN` — raises page size from 25 to 100. +- `OPENALEX_MAILTO` — moves you to the OpenAlex polite pool. +- `EPFL_GRAPH_USERNAME` / `EPFL_GRAPH_PASSWORD` — EPFL Graph API. + +V2 pipeline knobs (most-touched; full list in [README.md](https://github.com/Imaging-Plaza/git-metadata-extractor/blob/main/README.md) +and `.env.example`): + +- `V2_AGENT_RUNTIME_DEFAULT` (default `llm`) — runtime selector when the + query omits `agent_runtime`. +- `V2_USE_MOCK_PROVIDERS` (default `true`) — set `false` to hit real APIs. +- `V2_LINK_VERACITY_ENABLED` (default `true`) — turn off in batch runs. +- `V2_APPLY_CRITIC_PRUNING` (default `false`) — enable critic drop suggestions. +- `V2_PROVIDER_CACHE_PATH` (default `.cache/v2/providers.db`) — use a + separate path per run profile (LLM vs. rule-based) for isolation. +- `V2__RAG_ENABLED` (default `true`) — toggle RAG tools per index + (`INFOSCIENCE`, `ETHZ_RESEARCH_COLLECTION`, `HUGGINGFACE`, `OPENALEX`, + `ZENODO`, `ORCID`, `ROR`). +- `INDEX_QDRANT_URL` — Qdrant endpoint. **Inside the devcontainer use + `http://gme-qdrant:6333`**, not `localhost:6333`. ## 3. Run the API locally ```bash -just serve-dev +just serve-dev # uvicorn + auto-reload on src/**/*.py ``` -Swagger UI: +Default port is `1234`. Override with `HOST=0.0.0.0 PORT=8080 just serve-dev`. + +- Swagger UI: +- v2 health: +- Stop the server: `just serve-stop` + +Smoke-test extraction (export `API_TOKEN` first so the snippets work as-is): + +```bash +export API_TOKEN=... # value from .env + +# sync, JSON-LD +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/extract/github.com/octocat/Hello-World?output_format=jsonld" | jq -- `http://localhost:1234/docs` +# sync, JSON, rule-based runtime +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/extract/github.com/octocat/Hello-World?output_format=json&agent_runtime=rule_based" | jq + +# async (returns 202 + job_id; poll /v2/jobs/{id}) +curl -s -X POST "http://localhost:1234/v2/extract" \ + -H "Authorization: Bearer $API_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{"source_url": "github.com/octocat/Hello-World", "output_format": "json"}' | jq + +# health check (no auth needed) +curl -s "http://localhost:1234/v2/health" | jq +``` ## 4. Run tests and checks +The `justfile` recipes invoke `.venv/bin/python -m pytest` directly; you +do not need to set `PYTHONPATH` or rely on a global `pytest`. + +```bash +just test # fast loop via testmon +just test-full # full deterministic run (parallelized) +just test-coverage # with coverage +just lint # ruff +just type-check # mypy +just check # lint + type-check +just ci # lint + type-check + coverage +``` + +Per-index test suites: + +```bash +just hf-test +just openalex-test +just orcid-test +``` + +Opt-in real-provider tests (require credentials and live network): + ```bash -just test -just lint -just type-check +just test-llm-integration +just test-live ``` -Or all checks together: +If testmon selection looks stale: `rm -f .testmondata` then `just test-full`. + +## 5. Try the RAG indices + +Each index is independent; common shape: ```bash -just ci +# HuggingFace +just hf-status +just hf-ingest --scope switzerland # idempotent — skips already-ingested +just hf-embed # only embeds new chunks +just hf-search "swiss german LLM" --top-k 5 +just hf-lineage epfl-llm/meditron-7b # walk base_models DAG + +# Federated (cross-index) +just gme-indices # list registered adapters +just gme-search "Swiss German LLM" --top-k 10 +just gme-entity 0000-0001-9534-3870 # by ORCID, ROR, DOI, HF slug, … ``` -## 5. Build and preview docs +See [RAG Indices Overview](rag-indices.md) for the full per-index +inventory, scopes, and storage layout. + +## 6. Build and preview docs ```bash uv pip install -e ".[docs]" -just docs-build -just docs-serve +just docs-serve # local preview +just docs-build # strict build +``` + +Publish (CI handles `main` and tag pushes; manual commands available): + +```bash +just docs-deploy-dev # publish dev + latest +just docs-deploy-release 2.0.1 # publish release + update stable +just docs-set-default stable ``` ## Local workflow ```mermaid flowchart LR - A[Edit code or prompts] --> B[Run tests and checks] - B --> C[Run API locally] - C --> D[Call endpoints / inspect output] + A[Edit code or prompts] --> B[Run tests + lint] + B --> C[just serve-dev] + C --> D[Call /v2/extract / inspect output] D --> A ``` ## CLI status - The primary production interface is the FastAPI service (`src/api.py`). -- `src/main.py` currently references legacy imports (`core.*`) and does not run as-is in the current v2 layout. -- For conversion workflows, use `scripts/convert_json_jsonld.py`. +- For batch extractions: `scripts/v2/batch_extract.sh` reads a hardcoded + URL list and drives `/v2/extract` with configurable parallelism + (resumable — skips already-completed result files). +- Each RAG index ships its own CLI (`python -m src.index.`) wired + up via `just -*` recipes. diff --git a/docs/huggingface-index.md b/docs/huggingface-index.md new file mode 100644 index 0000000..6829be6 --- /dev/null +++ b/docs/huggingface-index.md @@ -0,0 +1,303 @@ +# HuggingFace Index + +Standalone RAG index over EPFL- and Switzerland-affiliated HuggingFace +namespaces (organisations + personal users) and the models / datasets / +spaces they publish. Lives at `src/index/huggingface/`. + +> **Two complementary views.** [`docs/v2-rag-tools.md`](v2-rag-tools.md) +> documents the **agent-facing** tool (`search_huggingface_rag`) used by +> the v2 LLM pipeline. *This* page documents the **direct-access** CLI, +> SQL, and HTTP surfaces — for analysts and scripts that don't want to go +> through an agent. +> +> The design rationale and architectural decisions live in +> [`.internal/huggingface/PLAN.md`](https://github.com/Imaging-Plaza/git-metadata-extractor/blob/main/.internal/huggingface/PLAN.md). + +## Architecture (1-paragraph version) + +DuckDB at `data/index/huggingface/duckdb/huggingface.duckdb` holds the +canonical records (one row per repo / namespace) plus a `chunks` +bookkeeping ledger. Qdrant (compose service `gme-qdrant:6333`) holds the +embedding vectors in four collections — `hf_models`, `hf_datasets`, +`hf_spaces`, `hf_orgs` — each chunk indexed by a deterministic +`uuid5(NAMESPACE_URL, "||")`. Embeddings come +from `Qwen/Qwen3-Embedding-8B` on EPFL RCP; reranking from +`Qwen/Qwen3-Reranker-8B`. README cards land on disk under +`data/index/huggingface/cards/{models,datasets,spaces}///README.md`. + +``` +┌─ Ingest ──────────────────────────────────────────────────────────────┐ +│ HF Hub → DuckDB orgs/models/datasets/spaces + cards/ on disk │ +└──────┬────────────────────────────────────────────────────────────────┘ + ▼ +┌─ Embed ───────────────────────────────────────────────────────────────┐ +│ DuckDB rows → chunker → RCP /v1/embeddings → Qdrant collections│ +│ │ +│ bookkeeping: chunks(chunk_id, entity_type, repo_id, chunk_index, …) │ +└──────┬────────────────────────────────────────────────────────────────┘ + ▼ +┌─ Query ───────────────────────────────────────────────────────────────┐ +│ semantic: query → embed → Qdrant → rerank → entity-dedup → hydrate │ +│ sql: guarded SELECT/WITH over DuckDB │ +└───────────────────────────────────────────────────────────────────────┘ +``` + +## EPFL inventory (2026-05-01 snapshot) + +**25 EPFL namespaces (24 orgs + 1 personal user) → 235 artefacts (177 models · 48 datasets · 10 spaces).** + +| Namespace | Type | Models | Datasets | Spaces | HF followers | +|---|---|---:|---:|---:|---:| +| `Idiap` | org | 56 | 0 | 4 | 65 | +| `EPFL-VILAB` | org | 51 | 3 | 6 | 86 | +| `EPFL-ECEO` | org | 12 | 7 | 1 | 27 | +| `LIONS-EPFL` | org | 9 | 0 | 0 | 4 | +| `vanek-epfl` | user | 9 | 6 | 0 | 0 | +| `epfl-dlab` | org | 6 | 7 | 0 | 15 | +| `EPFL-IVRL` | org | 6 | 0 | 0 | 8 | +| `SAGESSE-EPFL` | org | 4 | 0 | 0 | 5 | +| `epfl-ml4ed` | org | 4 | 0 | 0 | 3 | +| `ADP-EPFL` | org | 3 | 0 | 0 | 3 | +| `epfl-ml-ytf` | org | 3 | 0 | 1 | 3 | +| `rapieniuta-epfl` | org | 3 | 0 | 0 | 1 | +| `epfl-dhlab` | org | 2 | 0 | 0 | 14 | +| `epfml` | org | 2 | 3 | 0 | 52 | +| `epfl-llm` | org | 2 | 1 | 1 | 274 | +| `structure-epflai` | org | 1 | 3 | 0 | 3 | +| `epfl-nlp` | org | 1 | 0 | 0 | 21 | +| `EPFL-DL-CER-project` | org | 1 | 9 | 0 | 3 | +| `EPFL-CVLAB-SPACECRAFT` | org | 1 | 3 | 0 | 14 | +| `epfl-ihl` | org | 1 | 0 | 0 | 3 | +| `EPFL-LNMC` | org | 0 | 2 | 0 | 2 | +| `EPFL-DrivingVQA` | org | 0 | 1 | 0 | 2 | +| `asl-epfl` | org | 0 | 1 | 0 | 2 | +| `epfl-radio-astro` | org | 0 | 1 | 0 | 2 | +| `EPFL-CVLab` | org | 0 | 1 | 0 | 3 | + +The full Switzerland scope (58 namespaces) additionally covers ETH labs, +swiss-ai, ZurichNLP, SDSC, UniBe/UniGe/UniBas/HES-SO/HSLU/FHNW. Live +counts: `just hf-status`. + +The canonical EPFL author list (use as `WHERE author IN (…)` or `--filter author=…`): + +``` +epfl-llm, epfl-nlp, EPFL-VILAB, epfl-ml4ed, EPFL-ECEO, +epfl-dlab, LIONS-EPFL, EPFL-IVRL, SAGESSE-EPFL, EPFL-CVLAB-SPACECRAFT, +ADP-EPFL, epfl-ml-ytf, epfl-dhlab, EPFL-LNMC, EPFL-CVLab, +EPFL-DrivingVQA, structure-epflai, asl-epfl, epfl-ihl, epfl-radio-astro, +vanek-epfl, rapieniuta-epfl, EPFL-DL-CER-project, Idiap, epfml +``` + +## Seven ways to access the data + +### 1. CLI: predefined SQL queries + +```bash +# All EPFL namespaces in seed +just hf-query --predefined orgs_by_scope --param scope=epfl + +# Models by author, sorted by downloads +just hf-query --predefined models_by_author --param author=Idiap --param limit=100 +just hf-query --predefined models_by_author --param author=EPFL-VILAB --param limit=100 + +# Datasets by author +just hf-query --predefined datasets_by_author --param author=epfl-dlab --param limit=100 + +# Top models across the whole index +just hf-query --predefined top_models_by_downloads --param limit=20 + +# Models by HuggingFace pipeline tag +just hf-query --predefined models_by_pipeline_tag --param pipeline_tag=text-generation --param limit=20 +``` + +Other predefined queries: `count_by_entity`, `count_models`, `count_datasets`, `count_spaces`, `top_datasets_by_downloads`. All defined in `src/index/huggingface/retrieval/sql.py`. + +### 2. CLI: ad-hoc SQL across all 25 EPFL namespaces + +```bash +just hf-query "SELECT repo_id, author, downloads, likes + FROM models + WHERE author IN ('epfl-llm','epfl-nlp','EPFL-VILAB','epfl-ml4ed','EPFL-ECEO', + 'epfl-dlab','LIONS-EPFL','EPFL-IVRL','SAGESSE-EPFL', + 'EPFL-CVLAB-SPACECRAFT','ADP-EPFL','epfl-ml-ytf','epfl-dhlab', + 'EPFL-LNMC','EPFL-CVLab','EPFL-DrivingVQA','structure-epflai', + 'asl-epfl','epfl-ihl','epfl-radio-astro','vanek-epfl', + 'rapieniuta-epfl','EPFL-DL-CER-project','Idiap','epfml') + ORDER BY downloads DESC NULLS LAST LIMIT 50" +``` + +Ad-hoc queries are guarded — only `SELECT`/`WITH` is allowed; `;` and `--` injection are blocked. + +### 3. CLI: semantic search + +```bash +# Find an EPFL lab that does X +just hf-search "Swiss German language model" --type orgs --top-k 5 +just hf-search "medical large language model" --type orgs --top-k 5 +just hf-search "remote sensing earth observation" --type orgs --top-k 5 + +# Find a model by what it does +just hf-search "fine-tuned BERT for Swiss German" --type models --top-k 10 + +# Filter to EPFL personal-user accounts only +just hf-search "EPFL researcher computer vision" --type orgs --filter namespace_kind=user --top-k 5 + +# Filter to one author's repos +just hf-search "diffusion model" --type models --filter author=EPFL-VILAB --top-k 5 + +# Combine filters (Apache-2.0 only) +just hf-search "instruction-tuned LLM" --type models --filter license=apache-2.0 --top-k 5 +``` + +`--filter key=value` is repeatable. Multiple values for the same key collapse to Qdrant `MatchAny`; numeric values are coerced to ints. + +Verified working hits (rerank scores): + +| Query | `--type` | Top hit (rerank score) | +|---|---|---| +| `swiss german large language model` | `models` | `ZurichNLP/swissbert` (0.99) | +| `EPFL machine learning and optimization laboratory` | `orgs` | `LIONS-EPFL` — *Laboratory for Information and Inference Systems* (0.98) | +| `Swiss AI Initiative large language models` | `orgs` | `swiss-ai` — *Swiss AI Initiative* (0.96) | +| `computer vision and remote sensing photogrammetry` | `orgs` | `prs-eth` — *Photogrammetry and Remote Sensing Lab of ETH Zurich* (0.96) | + +### 4. Python: direct DuckDB read-only + +```python +import duckdb +con = duckdb.connect('data/index/huggingface/duckdb/huggingface.duckdb', read_only=True) + +EPFL = ['epfl-llm','epfl-nlp','EPFL-VILAB','epfl-ml4ed','EPFL-ECEO', + 'epfl-dlab','LIONS-EPFL','EPFL-IVRL','SAGESSE-EPFL','EPFL-CVLAB-SPACECRAFT', + 'ADP-EPFL','epfl-ml-ytf','epfl-dhlab','EPFL-LNMC','EPFL-CVLab', + 'EPFL-DrivingVQA','structure-epflai','asl-epfl','epfl-ihl','epfl-radio-astro', + 'vanek-epfl','rapieniuta-epfl','EPFL-DL-CER-project','Idiap','epfml'] +ph = ','.join(['?'] * len(EPFL)) + +models = con.execute(f"SELECT repo_id, pipeline_tag, library_name, license, " + f" downloads, likes, last_modified " + f"FROM models WHERE author IN ({ph}) " + f"ORDER BY downloads DESC NULLS LAST", EPFL).fetchall() + +datasets = con.execute(f"SELECT repo_id, license, downloads, likes " + f"FROM datasets WHERE author IN ({ph})", EPFL).fetchall() + +spaces = con.execute(f"SELECT repo_id, sdk, license, likes " + f"FROM spaces WHERE author IN ({ph})", EPFL).fetchall() +``` + +The `raw` JSON column on every table holds the full HF API payload if you +need fields beyond the structured columns. + +### 5. Filesystem: every README is on disk + +``` +data/index/huggingface/cards/{models,datasets,spaces}///README.md +``` + +```bash +# List every EPFL model card directory +find data/index/huggingface/cards/models -mindepth 2 -maxdepth 2 -type d \ + | grep -E "/(epfl|EPFL|Idiap|epfml|LIONS-EPFL|SAGESSE-EPFL|ADP-EPFL|structure-epflai|asl-epfl|vanek-epfl|rapieniuta-epfl)/" +``` + +### 6. Lineage: `hf-lineage ` + +Walks the `base_models` DAG up (ancestors — parent models that this one +was fine-tuned from) and down (descendants — other repos that list this +one as their base). Pure local DuckDB lookup — no RCP / Qdrant calls, +sub-second. + +```bash +# What is meditron-7b fine-tuned from? +just hf-lineage epfl-llm/meditron-7b +# → ancestors: {level_1: [{repo_id: meta-llama/Llama-2-7b}]}, edges: [...] + +# What's been fine-tuned from Llama-2-7b in our index? +just hf-lineage meta-llama/Llama-2-7b +# → descendants: {level_1: [{repo_id: epfl-llm/meditron-7b, downloads: 5251, likes: 321, ...}]} + +# Walk further (default depth=3) +just hf-lineage epfl-llm/meditron-7b --depth 5 +``` + +The same is exposed in the v2 LLM pipeline as the `lineage_huggingface` +tool (registered automatically when the HF RAG provider is enabled). + +### 7. HTTP: FastAPI on `:8002` + +```bash +just hf-serve & # mounts src.index.huggingface.api:app on 0.0.0.0:8002 + +curl -s -X POST http://localhost:8002/search \ + -H 'content-type: application/json' \ + -d '{"query":"medical LLM","entity_type":"models","top_k":5,"filter":{"author":"epfl-llm"}}' +``` + +## Schema reference + +| Table | Primary key | Key columns | JSON columns | +|---|---|---|---| +| `orgs` | `slug` | `namespace_kind` (org\|user), `scope`, `fullname`, `details`, `num_models`, `num_datasets`, `num_spaces`, `num_followers`, `avatar_url` | `raw` | +| `models` | `repo_id` | `author`, `pipeline_tag`, `library_name`, `license`, `downloads`, `downloads_all_time`, `likes`, `gated`, `private`, `created_at`, `last_modified` | `tags`, `card_data`, `base_models`, `raw` | +| `datasets` | `repo_id` | `author`, `license`, `downloads`, `downloads_all_time`, `likes`, `gated`, `private`, `created_at`, `last_modified` | `tags`, `card_data`, `dataset_info`, `raw` | +| `spaces` | `repo_id` | `author`, `sdk`, `runtime_stage`, `hardware`, `license`, `likes`, `created_at`, `last_modified` | `tags`, `card_data`, `raw` | +| `chunks` | `chunk_id` | `entity_type` (model\|dataset\|space\|org), `repo_id`, `chunk_index`, `text`, `token_count`, `vector_id`, `embedded_at` | — | + +`vector_id == chunk_id` and both equal the Qdrant point id, so DuckDB ↔ Qdrant joins are trivial. + +## Running counts + +```bash +just hf-status +``` + +emits a JSON snapshot: + +```json +{ + "duckdb_counts": { + "orgs": 153, "models": 1034, "datasets": 324, "spaces": 70, "chunks": 3163 + }, + "qdrant_counts": { + "hf_orgs": 246, "hf_models": 1804, "hf_datasets": 1021, "hf_spaces": 92 + }, + "active_scope": "epfl", + "rcp_configured": true, + "hf_token_configured": false +} +``` + +(snapshot 2026-05-02 — current with the 73-namespace Swiss expansion + 6 Swiss companies + lineage backfill) + +`duckdb.chunks == sum(qdrant_counts)` should always hold — DuckDB's +`chunks` table is the bookkeeping ledger for what's been pushed to Qdrant. + +## Refresh cycle + +```bash +# 1. (optional) discover new EPFL/Swiss namespaces +just hf-discover-orgs --scope switzerland +# Review data/index/huggingface/logs/discover_orgs.jsonl, edit +# config/index/huggingface.yaml seed by hand (never auto-promotes). + +# 2. Re-ingest (idempotent on repo_id / slug) +just hf-ingest --scope switzerland --types models,datasets,spaces,orgs + +# 3. Re-embed (idempotent — skips chunks already in DuckDB.chunks) +just hf-embed +``` + +If the anonymous HF API rate limit (500 req / 5 min, shared per IP) bites +mid-ingest, split into per-type runs (`--types orgs`, then `--types +spaces`, etc.) and wait between them. Setting `HF_TOKEN` in `.env` +removes the IP-share and bumps the bucket to 1k API + 5k resolver per 5 +min. + +## Related documentation + +- [`v2-rag-tools.md`](v2-rag-tools.md) — the agent-facing + `search_huggingface_rag` tool used by v2 LLM agents +- [`.internal/huggingface/README.md`](https://github.com/Imaging-Plaza/git-metadata-extractor/blob/main/.internal/huggingface/README.md) + — workstream tracker (mirrors this page's access guide) +- [`.internal/huggingface/PLAN.md`](https://github.com/Imaging-Plaza/git-metadata-extractor/blob/main/.internal/huggingface/PLAN.md) + — full design + rationale + risks diff --git a/docs/index.md b/docs/index.md index bfaf66d..6a65a9e 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,39 +1,104 @@ -# Git Metadata Extractor Documentation +# Git Metadata Extractor -Git Metadata Extractor analyzes Git repositories, GitHub users, and GitHub organizations, then enriches the output with LLM-based classification and academic catalog context. +A FastAPI service that turns a GitHub URL (repository / user / org) into +JSON-LD aligned with **Open Pulse Ontology v2.1.2**, plus nine sibling RAG +indices over EPFL/Swiss research catalogues that the v2 LLM agents can +query during extraction. + +The repository ships **two cooperating subsystems**: + +1. **Extraction service** (`src/v1/`, `src/v2/`) — the FastAPI app. V1 is + frozen; all new work targets V2 under `src/v2/`. +2. **RAG indices** (`src/index/*`) — independent Qdrant + DuckDB indices + over HuggingFace, OpenAlex, Infoscience, ETH Research Collection, + ORCID, ROR, Zenodo, GitHub, SNSF, plus a federated layer that fans out + across all of them. ## Documentation map -- [Getting Started](getting-started.md) -- [API and CLI](api-and-cli.md) -- [Design Notes](architecture/design-notes.md) -- [Repository Analysis Strategy](AGENT_STRATEGY.md) +- [Getting Started](getting-started.md) — install, first run, common commands +- [V2 API Reference](v2-api-reference.md) — `/v2/extract`, `/v2/jobs`, `/v2/graph` +- [Migration: V1 → V2](migration-v1-to-v2.md) — endpoint mapping +- [API and CLI](api-and-cli.md) — quick reference +- [RAG Indices Overview](rag-indices.md) — the nine indices + federated layer +- [Federated Search](federated-search.md) — cross-index design +- [HuggingFace Index](huggingface-index.md) — most-used index, deep-dive +- [V2 Agent RAG Tools](v2-rag-tools.md) — agent-side tools wired into the v2 pipeline +- [Roadmap](ROADMAP.md) — what's left to build +- [Design Notes](architecture/design-notes.md) — runtime architecture - [Legacy Releases](releases/legacy-releases.md) -## System overview +## V2 pipeline at a glance + +`/v2/extract` runs the same 23-stage pipeline regardless of `agent_runtime`. +The runtime only controls which agent implementations execute (LLM vs. +deterministic rule-based). Other stages run unconditionally. ```mermaid -flowchart LR - A[Client or Automation] --> B[FastAPI app
src/api.py] +flowchart TB + A[Client / batch script] --> B[FastAPI app
src/api.py] + B --> V2[/v2/extract /v2/jobs /v2/graph
src/v2/api.py/] + B --> V1[/v1/* legacy frozen/] + + V2 --> P[Pipeline orchestrator
src/v2/pipeline/orchestrator.py] + P --> CTX[context_summary] + CTX --> AG[per-entity agents
repo / person / org / article / membership / contribution] + AG --> DEDUP[llm_dedup → reconcile → llm_critic] + DEDUP --> VAL[strict_validation → assemble_output] + VAL --> LV[link_veracity] + LV --> OWN[ownership + org-hierarchy inference] + OWN --> OUT[build_jsonld_output] - B --> C1[Repository analysis
src/analysis/repositories.py] - B --> C2[User analysis
src/analysis/user.py] - B --> C3[Organization analysis
src/analysis/organization.py] + AG -. RAG tools .-> RAG[src/v2/ingest/providers/*_rag.py] + RAG --> Q[(Qdrant
gme-qdrant:6333)] + RAG --> RCP[EPFL RCP
embed + rerank] +``` + +LLM agents call **per-index RAG tools** plus a **federated tool** +(`search_federated_rag`, `lookup_entity_federated`) that fans out across +nine indices in parallel. See +[V2 Agent RAG Tools](v2-rag-tools.md) for the full inventory. + +## RAG indices at a glance - C1 --> D[Atomic agents
src/agents/atomic_agents] - C2 --> D - C3 --> D +```mermaid +flowchart LR + subgraph Sources + HF[huggingface.co] + OA[api.openalex.org] + IS[infoscience.epfl.ch] + ETHZ[research-collection.ethz.ch] + OR[pub.orcid.org] + RO[ror.org] + ZE[zenodo.org] + GH[api.github.com] + SN[data.snf.ch] + end - C1 --> E[Data models
src/data_models] - C2 --> E - C3 --> E + subgraph Indices + HFi[hf] + OAi[openalex] + ISi[infoscience] + ETHi[ethz_research_collection] + ORi[orcid] + ROi[ror] + ZEi[zenodo] + GHi[github] + SNi[snsf] + end - C1 --> F[Cache manager
src/cache] - C2 --> F - C3 --> F + HF --> HFi + OA --> OAi + IS --> ISi + ETHZ --> ETHi + OR --> ORi + RO --> ROi + ZE --> ZEi + GH --> GHi + SN --> SNi - D --> G[LLM provider] - D --> H[Infoscience / ORCID / ROR / GitHub APIs] + Indices --> FED[Federated layer
gme-search / gme-entity] + FED --> AG[v2 LLM agents] ``` ## Versioning diff --git a/docs/issue-brief-gimie-github-cff-edge-cases.md b/docs/issue-brief-gimie-github-cff-edge-cases.md new file mode 100644 index 0000000..b23995a --- /dev/null +++ b/docs/issue-brief-gimie-github-cff-edge-cases.md @@ -0,0 +1,156 @@ +# Issue brief: GIMIE / PyYAML failures on GitHub repos + +This document summarizes failures when calling **`GET /v1/repository/gimie/json-ld/{url}`** (git-metadata-extractor + GIMIE 0.7.x) against certain GitHub repositories. Use it to open issues on **gimie**, **repository templates**, or internal trackers. + +--- + +## Summary + +The endpoint returned **HTTP 400** with bodies like `{"message":"Expecting value: line 1 column 1 (char 0)"}` or `{"message":"month must be in 1..12"}`. The API maps uncaught **`ValueError`** (and related errors) to that JSON shape. + +Root causes fall into four areas: + +1. **GIMIE** mishandles GitHub **`204 No Content`** on the contributors REST endpoint (empty body). +2. **GIMIE** assumes GraphQL **`repository.object`** is always non-null for `HEAD:` (empty / no-tree repos). +3. **PyYAML** parses invalid **`date-released`** (and similar) values in **`CITATION.cff`** as YAML 1.1 timestamps, raising **`ValueError`** during **`safe_load`**. +4. **GitHub** may return **secondary (or primary) rate limit** errors on GraphQL contributor queries; GIMIE surfaces these as **`ConnectionError`**. + +--- + +## Environment + +| Component | Notes | +|-----------|--------| +| Consumer | git-metadata-extractor GIMIE JSON-LD route | +| Library | `gimie==0.7.2` (PyPI) | +| YAML | PyYAML `safe_load` in `gimie.parsers.cff` | +| GitHub | Authenticated API (`GITHUB_TOKEN`) | + +--- + +## Issue A — Empty repository: contributors endpoint returns 204 + +### Reproduction + +- Repository with **no commits** (example: `raj921/dream-home-ai-`). +- GIMIE requests `GET https://api.github.com/repos/{owner}/{repo}/contributors`. +- GitHub responds with **204 No Content** and an **empty body** (contrast: normal repos return **200** and a JSON array). + +### Root cause + +In `gimie.extractors.common.queries.send_rest_query`, any `status_code != 200` path calls `resp.json().get("message", "")`. For **204**, `resp.json()` runs on an empty body and raises **`json.JSONDecodeError`**, which surfaces as **“Expecting value: line 1 column 1 (char 0)”**. + +### Suggested upstream fix (gimie) + +- Treat **204** for list-style reads as an **empty list** `[]`, or avoid calling `resp.json()` when the body is empty. + +--- + +## Issue B — Empty / no-tree repository: GraphQL `object` is null + +### Reproduction + +- Same class of repository (no real `HEAD` tree). +- GraphQL `repository.object(expression: "HEAD:")` returns **`null`**. + +### Root cause + +`GithubExtractor.list_files` uses `self._repo_data["object"]["entries"]`, which raises **`TypeError`** when `"object"` is **`None`**. + +### Suggested upstream fix (gimie) + +- If `object` is **`None`**, return **no files** (e.g. `[]`) and skip file-based parsers. + +--- + +## Issue C — `CITATION.cff` dates: PyYAML timestamp constructor + invalid templates + +### Reproduction + +Repositories ship **`CITATION.cff`** with **`date-released:`** (or related keys) set to **invalid calendar values**. Examples observed in **`sdsc-ordes`** repositories (from raw `CITATION.cff`): + +| Repository | Example `date-released` | +|------------|-------------------------| +| debates-analytics | `2025-28-04` (ISO day/month reversed) | +| nds-lucid-web-app | `2025-13-14` | +| history-rewrite | `2025-19-16` | +| mava-api | `2025-53-05` | +| repository-template-rust | `2025-15-27` | +| open-pulse-hackalysis | `2026-39-13` | + +### Root cause + +PyYAML (YAML 1.1) treats **unquoted** scalars matching `YYYY-M-D` as **timestamps** and constructs `datetime.date(year, month, day)`. Invalid months or impossible dates raise **`ValueError: month must be in 1..12`** (or similar) inside **`yaml.safe_load`**, invoked from **`gimie.parsers.cff`** (e.g. `get_cff_doi`, `get_cff_authors`). + +**Important:** GIMIE’s CFF logic only **uses** identifiers and authors from the parsed dict; it does **not** consume `date-released` for emitted triples. However, **`safe_load` parses the entire document**, so a broken date still **fails extraction** before DOI/author handling. + +### Suggested upstream fixes + +**GIMIE (defensive):** + +- Preprocess CFF text before `safe_load`, use a loader that does not auto-coerce timestamps for these scalars, or wrap `safe_load` and degrade with a warning (skip CFF) on `ValueError`. + +**Templates / authoring (organizational):** + +- Emit valid **`YYYY-MM-DD`** per the [Citation File Format](https://github.com/citation-file-format/citation-file-format) spec. +- If a field must remain free-form, **quote** the value in YAML so it is loaded as a **string**, not a timestamp. + +--- + +## Issue D — GitHub secondary / primary rate limits + +### Reproduction + +- Many **`force_refresh=true`** calls or high crawl concurrency against **`GET /v1/repository/gimie/json-ld/...`** with the same **`GITHUB_TOKEN`**. +- GIMIE runs GraphQL **`query_contributors`**; GitHub responds with an error body such as *“You have exceeded a secondary rate limit…”*. + +### Root cause + +`gimie.extractors.common.queries.send_graphql_query` raises **`ConnectionError`** when the HTTP status is not success or the GraphQL payload reports failure. + +### Mitigation + +- Prefer **cached** responses; avoid **`force_refresh=true`** on every repository in a batch crawl. +- **git-metadata-extractor** maps **`ConnectionError`** on the GIMIE JSON-LD route to **HTTP 429** when the message indicates a rate limit, and **HTTP 503** for other GitHub **`ConnectionError`** cases (clearer than an uncaught stack trace). + +--- + +## Consumer-side mitigation (git-metadata-extractor) + +For production resilience, this repository may apply **local monkeypatches** (not a substitute for fixing upstream gimie or templates): + +- Patch **`send_rest_query`** so **204** → **`[]`**. +- Patch **`GithubExtractor.list_files`** so **`object is None`** → **`[]`**. +- Wrap **`CffParser.parse`**: normalize fixable reversed day/month dates; **quote** remaining invalid `YYYY-M-D` tokens on known keys (`date-released`, `date-published`, `date-last-released`) so PyYAML returns strings. +- **GIMIE route** (`src/api.py`): explicit **`ConnectionError`** handling → **429** / **503** with GitHub’s message in **`detail`**. + +--- + +## How to verify a fix + +After deploying gimie or API changes: + +1. **Empty repo:** + `GET .../v1/repository/gimie/json-ld/https%3A%2F%2Fgithub.com%2Fraj921%2Fdream-home-ai-?force_refresh=true` + Expect **200** and JSON-LD, not 400. + +2. **Bad CFF dates:** + `GET .../v1/repository/gimie/json-ld/https%3A%2F%2Fgithub.com%2Fsdsc-ordes%2Fnds-lucid-web-app?force_refresh=true` + Expect **200** and JSON-LD, not `month must be in 1..12`. + +--- + +## Suggested issue split + +| Audience | Scope | +|----------|--------| +| **gimie** (e.g. sdsc-ordes/gimie) | Issues **A**, **B**, and defensive handling for **C** in one or two GitHub issues. Optional: retries/backoff for **D** (coordinate with GitHub ToS / abuse guidelines). | +| **Template / org owners** | Issue **C** only: correct `CITATION.cff` `date-released` (and generator scripts) across org templates. | +| **API consumers / crawlers** | Issue **D**: backoff, concurrency limits, and avoid unconditional **`force_refresh`**. | + +--- + +## References (in-tree) + +- GIMIE integration and workarounds: `src/gimie_utils/gimie_methods.py` +- Regression tests: `tests/test_gimie_empty_github_repo.py`, `tests/test_gimie_cff_date_normalize.py` diff --git a/docs/migration-v1-to-v2.md b/docs/migration-v1-to-v2.md new file mode 100644 index 0000000..cc7c49b --- /dev/null +++ b/docs/migration-v1-to-v2.md @@ -0,0 +1,114 @@ +# Migration Guide: V1 to V2 + +This guide describes how to run v1 and v2 side by side and migrate clients incrementally. + +## Coexistence Model + +- v1 endpoints remain mounted under `/v1/...`. +- v2 endpoints are mounted under `/v2/...`. +- v2 does not require v1 route changes and can be adopted per client. + +## Authentication (applies to v1 and v2) + +Both v1 and v2 routes are now bearer-protected. Every `/v1/*` route, plus +`/v2/extract` and `/v2/jobs/{id}`, requires: + +```http +Authorization: Bearer +``` + +`/`, `/docs`, and `/v2/health` stay open. See +[v2-api-reference.md#authentication](v2-api-reference.md#authentication) +for failure modes (401 / 503). + +If you're migrating an existing v1 client, you must add the bearer header +**before** switching to v2 — otherwise the v1 client will already be +returning 401 against the new server. + +## Endpoint Mapping + +| V1 usage | V2 equivalent | +| --- | --- | +| `GET /v1/repository/llm/json-ld/{full_path}` | `GET /v2/extract/{full_path}?output_format=jsonld` | +| `GET /v1/repository/llm/json/{full_path}` | `GET /v2/extract/{full_path}?output_format=json` | +| `GET /v1/repository/gimie/json-ld/{full_path}` | `GET /v2/extract/{full_path}?output_format=jsonld` | +| `GET /v1/user/llm/json/{full_path}` | `GET /v2/extract/{full_path}?output_format=json` (user URL) | +| `GET /v1/org/llm/json/{full_path}` | `GET /v2/extract/{full_path}?output_format=json` (org URL) | +| n/a | `POST /v2/extract` (async) + `GET /v2/jobs/{id}` | +| n/a | `GET /v2/health` | + +## Copy-Paste Migration Examples + +### Repository JSON-LD extraction + +V1: + +```bash +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v1/repository/llm/json-ld/https://github.com/octocat/Hello-World" \ + | jq +``` + +V2: + +```bash +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/extract/github.com/octocat/Hello-World?output_format=jsonld" \ + | jq +``` + +### Repository JSON extraction + +V1: + +```bash +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v1/repository/llm/json/https://github.com/octocat/Hello-World" \ + | jq +``` + +V2: + +```bash +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/extract/github.com/octocat/Hello-World?output_format=json" \ + | jq +``` + +## Response Format Differences + +- v1 repository endpoints return `APIOutput` (`link`, `output`, `cached`). +- v2 extract returns `V2ExtractResponse` (`source_url`, `detected_type`, + `output_format`, `output`, `warnings`, `stats`, optional + `context_summary_markdown`). +- v2 also offers an **async** submission flow (`POST /v2/extract` → + `GET /v2/jobs/{id}`); the job record carries the same response payload + in its `result` field once `status == "completed"`. + +## New/Relevant V2 Environment Variables + +See [V2 API Reference — V2 Environment Variables](v2-api-reference.md#v2-environment-variables) +for the full list. Notable additions vs. v1: + +- `API_TOKEN` (required) — bearer token guarding both v1 and v2 routes; + missing → 503. +- `V2_AGENT_RUNTIME_DEFAULT` (default `llm`) — runtime selector. +- `V2_USE_MOCK_PROVIDERS` (default `true`) — set `false` in production. +- `V2_PROVIDER_CACHE_*` — shared provider + verdict + pipeline + job-store cache. +- `V2__RAG_ENABLED` — toggle each RAG tool family. +- `INDEX_QDRANT_URL` — Qdrant endpoint for the RAG indices. +- `GITHUB_TOKEN` — required for healthy provider preflight (same as v1). + +## Breaking/Behavior Changes + +- V2 response envelopes are not shape-identical to v1 responses. +- V2 introduces run-scoped stats and optional intermediates payloads. +- V2 canonicalization and reconciliation pipelines are stricter than v1 heuristics. + +## Deprecation Timeline + +- March 31, 2026: v1 marked as deprecated in docs and release notes. +- June 30, 2026: v1 feature freeze; only critical bug fixes. +- September 30, 2026 (target `v3.0.0`): v1 endpoints removed. + +If timeline dates move, update this document and `CHANGELOG.md` in the same release. diff --git a/docs/openalex-index.md b/docs/openalex-index.md new file mode 100644 index 0000000..8742535 --- /dev/null +++ b/docs/openalex-index.md @@ -0,0 +1,292 @@ +# OpenAlex Index + +Standalone RAG index over the OpenAlex graph (works, authors, +institutions, sources, topics, concepts) scoped to EPFL / Switzerland / +arbitrary ROR. Lives at `src/index/openalex/`. + +> **Two complementary views.** [`docs/v2-rag-tools.md`](v2-rag-tools.md) +> documents the **agent-facing** tool (`search_openalex_rag`) used by +> the v2 LLM pipeline. *This* page documents the **direct-access** CLI, +> ingest pipeline, schema, federated adapter, and the citation-graph +> companion table. + +## Architecture + +DuckDB at `data/index/openalex/duckdb/openalex.duckdb` holds canonical +entity rows (one per `openalex_id`) plus a set of normalised linking +tables (`work_authors`, `work_institutions`, `work_github_urls`, +`work_references`). Qdrant collections — one per entity type +(`works`, `authors`, `institutions`, `sources`, `topics`, `concepts`) +— hold the embedding vectors. Embeddings come from +`Qwen/Qwen3-Embedding-8B` on EPFL RCP; reranking from +`Qwen/Qwen3-Reranker-8B`. + +``` +┌─ Ingest ──────────────────────────────────────────────────────────────┐ +│ pyalex (filter chaining + select projection + retry) → OpenAlex │ +│ REST /works /authors /institutions /sources /topics │ +│ /concepts │ +│ │ │ +│ ▼ │ +│ DuckDB: works / authors / institutions / sources / topics / │ +│ concepts + work_authors / work_institutions / │ +│ work_github_urls / work_references │ +└──────┬────────────────────────────────────────────────────────────────┘ + ▼ +┌─ Discover (GitHub URLs) ─────────────────────────────────────────────┐ +│ works.abstract / fulltext_origin → regex github.com//│ +│ │ │ +│ ▼ │ +│ DuckDB: work_github_urls (citing work → repo, with source provenance)│ +└──────┬────────────────────────────────────────────────────────────────┘ + ▼ +┌─ Embed ───────────────────────────────────────────────────────────────┐ +│ DuckDB rows → chunker (256 tok, 64 overlap) → RCP /v1/embeddings │ +│ │ → Qdrant │ +│ ▼ │ +│ bookkeeping: chunks(chunk_id, entity_type, entity_id, chunk_index, …)│ +└──────┬────────────────────────────────────────────────────────────────┘ + ▼ +┌─ Query ───────────────────────────────────────────────────────────────┐ +│ semantic: query → embed → Qdrant → optional rerank → thin hits │ +│ hydrate: openalex_id → DuckDB → full record │ +│ federated: same query fans out across all gme indices in parallel │ +└───────────────────────────────────────────────────────────────────────┘ +``` + +## Scopes + +The ingest CLI takes `--scope {epfl,switzerland}`. The two scopes apply +different OpenAlex filters: + +| Scope | Works filter | Authors filter | Institutions filter | +|---|---|---|---| +| `epfl` | `authorships.institutions.ror = ` | `affiliations.institution.ror = ` | `ror = ` | +| `switzerland` | `authorships.institutions.country_code = ` | `last_known_institutions.country_code = ` | `country_code = ` | + +Defaults come from `config/index/openalex.yaml`: + +```yaml +scope: + ror: https://ror.org/02s376052 # EPFL ROR + country: ch # Switzerland +``` + +### Reusing `--scope epfl` for any institution + +Set `INDEX_OPENALEX_SCOPE_ROR` to override the ROR at runtime. Same DB, +same paths — just a different filter. Useful for adding a partner +institution's corpus on top of an existing scope: + +```bash +# add SDSC works/authors to the existing DuckDB +INDEX_OPENALEX_SCOPE_ROR=https://ror.org/02hdt9m26 \ + python -m src.index.openalex.cli ingest --scope epfl --entities works,authors + +# add ETH Zurich works +INDEX_OPENALEX_SCOPE_ROR=https://ror.org/05a28rw58 \ + python -m src.index.openalex.cli ingest --scope epfl --entities works,authors +``` + +All upserts are idempotent (`ON CONFLICT DO UPDATE/NOTHING`), so +overlapping works (e.g. EPFL × ETHZ co-authored papers) refresh +cleanly without duplication. + +`INDEX_OPENALEX_SCOPE_COUNTRY` overrides the country filter the same +way. + +## Schema + +```sql +works ( + openalex_id TEXT PRIMARY KEY, + doi TEXT, + title TEXT, + abstract TEXT, -- reconstructed from inverted-index + publication_year INTEGER, + primary_topic_id TEXT, -- FK → topics + primary_source_id TEXT, -- FK → sources + raw JSON, -- full OpenAlex payload (subject to WORKS_PROJECTION) + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +) + +authors ( + openalex_id TEXT PRIMARY KEY, + display_name TEXT, + orcid TEXT, + last_known_institution_id TEXT, -- FK → institutions (current institution) + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +) + +institutions (openalex_id PK, ror, display_name, country_code, raw, ingested_at) +sources (openalex_id PK, issn_l, display_name, type, raw, ingested_at) +topics (openalex_id PK, display_name, domain_id, field_id, raw, ingested_at) +concepts (openalex_id PK, display_name, level, raw, ingested_at) + +-- Linking tables +work_authors (work_id, author_id, position) PK (work_id, author_id) +work_institutions (work_id, institution_id) PK (work_id, institution_id) +work_github_urls (work_id, url, normalized_url, owner, repo, source, found_at) + PK (work_id, normalized_url) + source ∈ {'abstract', 'fulltext'} + +-- Backward citation graph (one row = "citing cites cited") +work_references (citing_work_id, cited_work_id, position) + PK (citing_work_id, cited_work_id) + +chunks (chunk_id, entity_type, entity_id, chunk_index, text, token_count, vector_id, embedded_at) +``` + +`DuckDBStore.bootstrap()` runs `schema.sql` on every `open()`; all +statements are `IF NOT EXISTS`, so re-runs are safe. + +### `WORKS_PROJECTION` and `referenced_works` + +The ingest path narrows the OpenAlex API response via a `select=` +projection (`src/index/openalex/models.py::WORKS_PROJECTION`). The +projection includes `referenced_works` so `works.raw.referenced_works` +is populated for newly-ingested works. **Older DuckDBs ingested before +this field was added** can be backfilled with a one-shot script that +batches missing IDs through `/works?filter=ids.openalex:W1|W2|…&select=id,referenced_works` +and populates `work_references` directly — no full re-ingest needed. + +## CLI subcommands + +| Subcommand | Purpose | +|---|---| +| `ingest` | Pull entities into DuckDB. `--scope {epfl,switzerland}` selects the filter. `--entities` defaults to all six (`works,authors,institutions,sources,topics,concepts`). | +| `find-github` | Discover GitHub URLs in `works.abstract` / `fulltext_origin`; persists matches to `work_github_urls` with provenance | +| `embed` | Chunk + embed entity rows with no chunks yet, push vectors to Qdrant. Idempotent — already-chunked rows are skipped via `stream_rows_for_embedding`. | +| `rebuild-qdrant` | Re-push existing `chunks` rows to Qdrant (re-embedding `chunks.text`). Use after a Qdrant wipe; does not touch DuckDB. | +| `search` | Semantic retrieval (vector + RCP rerank). | +| `query` | Read-only DuckDB SQL — predefined queries or guarded ad-hoc. | +| `serve` | Run the FastAPI app (port 8001). | + +## Federated layer + +OpenAlex registers an `OpenAlexAdapter` at +`src/index/_federated/adapters/openalex.py`, exposing two operations to +the federated layer: + +- **`search`** — wraps `semantic_search()` from + `src/index/openalex/retrieval/semantic.py`. Returns `Hit` objects + with `index="openalex"`, `entity_type` ∈ {work, author, institution, + source, topic, concept}, score, summary, URL. +- **`lookup`** — recognises identifier strings (full OpenAlex URL, + short ID `W…`/`A…`/`I…`/`S…`/`T…`/`C…`, DOI, ROR, ORCID). + +```bash +gme search "machine learning fairness" --indices openalex --top-k 5 +gme entity W2741809807 # OpenAlex Work +gme entity 10.1038/s41586-021-03819-2 # by DOI +``` + +## Agent tools (v2 LLM pipeline) + +Wired into the **repository, person, organization, and article agents**: + +| Tool | Purpose | +|---|---| +| `search_openalex_rag(query, collection, top_k, filters?, rerank?)` | Vector search any of the 6 Qdrant collections (`works`, `authors`, `institutions`, `sources`, `topics`, `concepts`). Returns thin hits hydrated from DuckDB. | + +Tool is gated by `V2_OPENALEX_RAG_ENABLED` (default `true`) and +constructs lazily — if Qdrant or RCP is unreachable, the provider +builder returns `None` and the tool simply isn't attached. + +## Configuration + +Reads `config/index/openalex.yaml`. Notable env vars (merged at +`load_config()`): + +| Var | Default | Purpose | +|---|---|---| +| `OPENALEX_MAILTO` | — | required for the polite-pool API rate. Pyalex sends it as `?mailto=`. | +| `RCP_TOKEN` | — | required for embed / search / rerank | +| `INDEX_OPENALEX_SCOPE_ROR` | unset | override `scope.ror` at runtime (per-institution ingests) | +| `INDEX_OPENALEX_SCOPE_COUNTRY` | unset | override `scope.country` at runtime | +| `INDEX_QDRANT_URL` | `http://gme-qdrant:6333` | Qdrant endpoint | +| `INDEX_QDRANT_API_KEY` | unset | passed through if your Qdrant requires it | +| `INDEX_DATA_DIR` | `data/` | overrides where DuckDB / state files live | +| `V2_OPENALEX_RAG_ENABLED` | `true` | flip to `false` to detach the agent tool | + +## Discovery: GitHub URLs from abstracts + +`find-github` is a separate ingest pass that scans `works.abstract` and +`fulltext_origin` for `github.com//` URLs and persists each +match to `work_github_urls` with a `source` provenance flag (`abstract` +or `fulltext`). Run after a fresh works ingest: + +```bash +python -m src.index.openalex.cli find-github --scope switzerland \ + --search both +``` + +This populates the link from "papers that cite open-source code" → the +referenced repo, which downstream pipelines join against the GitHub +RAG index. + +## Citation graph (`work_references`) + +`work_references` is the normalised version of +`works.raw.referenced_works`. Each row is one backward-citation edge +(`citing` cites `cited`). Populated either: + +1. Automatically — `referenced_works` is part of `WORKS_PROJECTION`, so + new ingests stamp it into `works.raw` and a one-shot extractor can + read it out. The extractor walks every row in `works`, parses + `raw.referenced_works`, and bulk-inserts into `work_references`. +2. As a backfill — for older DuckDBs ingested before + `referenced_works` was in the projection, the federated discover/ + hydrate pipeline batches the missing `openalex_id`s through + `/works?filter=ids.openalex:…&select=id,referenced_works` (100 IDs + per request). Run via: + + ```bash + gme discover --source from-references --indices openalex --out missing-refs.jsonl + gme hydrate missing-refs.jsonl --indices openalex + ``` + + The OpenAlex hydrator detects `hint.refs_only=true` and runs the + batched fast path (only populates `work_references`, no work + upsert). Full guide at [`discover-hydrate.md`](discover-hydrate.md). + +Useful queries: + +```sql +-- Most-cited works in our corpus (internal forward citations) +SELECT w.title, w.publication_year, COUNT(*) AS internal_cites +FROM work_references r JOIN works w ON w.openalex_id = r.cited_work_id +GROUP BY w.openalex_id, w.title, w.publication_year +ORDER BY internal_cites DESC LIMIT 20; + +-- Average references per work by year (signal of indexing depth) +SELECT publication_year, AVG(n) FROM ( + SELECT w.publication_year, COUNT(r.cited_work_id) AS n + FROM works w LEFT JOIN work_references r ON r.citing_work_id = w.openalex_id + GROUP BY w.openalex_id, w.publication_year +) GROUP BY 1 ORDER BY 1; +``` + +The cited side may reference works **outside** our DB (most do — only +~2-5% of cited IDs are also citing works in a single-institution +corpus); the `idx_work_refs_cited` index keeps both directions of the +join fast. + +## Persisted state files + +``` +data/index/openalex/ + duckdb/openalex.duckdb # canonical store + cache/ # optional pyalex cache (rare) + logs/ # ingest / embed run logs +``` + +## Status + +```bash +python -m src.index.openalex.cli query --predefined counts +``` + +…or the equivalent `just openalex-status` recipe. Reports row counts +per entity table, per linking table, and per Qdrant collection. diff --git a/docs/rag-indices.md b/docs/rag-indices.md new file mode 100644 index 0000000..1445601 --- /dev/null +++ b/docs/rag-indices.md @@ -0,0 +1,269 @@ +# RAG Indices Overview + +The project ships **12 sibling RAG indices** under `src/index/*` plus a +**federated layer** (`src/index/_federated/`) that fans queries out across +all of them. Each index is independent: its own DuckDB, its own Qdrant +collections, its own CLI, its own FastAPI app, its own refresh cadence. +The federated layer never shares state — it just orchestrates. + +> **Two modes of access for every index:** +> +> - **SQL** over DuckDB (`-query`) — exact filters, joins, counts +> - **Semantic search** over Qdrant (`-search`) — meaning-based retrieval +> +> Plus a **third mode** at the top: federated search across all of them +> (`gme-search` / `gme-entity`). + +## Inventory + +| Index | Source | Module | Justfile | Federated? | LLM tool? | What it indexes | +|---|---|---|---|---|---|---| +| **HuggingFace** | huggingface.co | [`src/index/huggingface/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/huggingface) | `hf-*` | ✅ | ✅ | EPFL/Swiss orgs+users → models, datasets, spaces | +| **OpenAlex** | api.openalex.org | [`src/index/openalex/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/openalex) | `openalex-*` | ✅ | ✅ | Works, authors, institutions, sources, topics, concepts | +| **Infoscience** | infoscience.epfl.ch | [`src/index/infoscience/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/infoscience) | (per-CLI) | ✅ | ✅ | EPFL DSpace publications + chunks + author/org refs | +| **ORCID** | pub.orcid.org/v3.0 | [`src/index/orcid/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/orcid) | `orcid-*` | ✅ | ✅ | Persons, employments, educations (scoped to EPFL/Switzerland) | +| **ROR** | ror.org dump | [`src/index/ror/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/ror) | `ror-*` | ✅ | ✅ | Research organisations (EPFL/ETH, Swiss, EU, worldwide) | +| **Zenodo** | zenodo.org/api | [`src/index/zenodo/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/zenodo) | `zenodo-*` | ✅ | ✅ | Records (datasets, software, presentations, posters) | +| **ETH Research Collection** | research-collection.ethz.ch | [`src/index/ethz_research_collection/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/ethz_research_collection) | (per-CLI) | ✅ | ✅ | ETHZ DSpace publications (mirrors infoscience pattern) | +| **GitHub** | api.github.com | [`src/index/github/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/github) | `gh-*` | ✅ | ✅ (`search_github_rag`) | Repositories + READMEs for the EPFL/Swiss/gimie seed | +| **SNSF** | data.snf.ch | [`src/index/snsf/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/snsf) | (per-CLI) | ✅ | (via federated) | Swiss National Science Foundation grants + people + institutions | +| **RenkuLab** | renkulab.io/api/data | [`src/index/renkulab/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/renkulab) | `renku-*` | ✅ | ✅ | Projects, groups, users, data connectors hosted by SDSC RenkuLab | +| **EPFL Graph (disciplines)** | graphai.epfl.ch /ontology/* | [`src/index/epfl_graph/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/epfl_graph) | `epfl-graph-*` | ✅ | ✅ (`search_epfl_graph_disciplines`) | Curated EPFL Graph academic-discipline ontology (~2226 categories, depth 1..5) backed by anchor Wikipedia articles | +| **SWISSUbase** | swissubase.ch | [`src/index/swissubase/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/swissubase) | `swissubase-*` | ✅ | ✅ (`search_swissubase_rag`) | Swiss social-science research-data platform: studies, datasets, persons, institutions. Ingest is Selenium-driven (no public REST API; per-`studyVersionId` enumeration to dodge the search-window cap). Default scope embeds only EPFL / ETHZ / SDSC-affiliated studies. | +| **Federated** | wraps all above | [`src/index/_federated/`](https://github.com/Imaging-Plaza/git-metadata-extractor/tree/main/src/index/_federated) | `gme-*` | — | ✅ (`search_federated_rag`, `lookup_entity_federated`) | one query → all indices in parallel | + +## Shared infrastructure + +Every index built on the post-2026-05-01 pattern uses: + +- **Storage**: DuckDB at `data/index//duckdb/.duckdb`. Holds canonical records + a `chunks` bookkeeping ledger (one row per Qdrant point, `chunk_id == vector_id`). +- **Vector store**: Qdrant on the compose service `gme-qdrant:6333` (NOT `localhost`!). Per-index collections, cosine distance, 4096-dim. +- **Embeddings**: `Qwen/Qwen3-Embedding-8B` on EPFL RCP, instruction-aware. +- **Reranker**: `Qwen/Qwen3-Reranker-8B` on EPFL RCP. +- **Chunking**: token-aware sliding window via `tiktoken` (`cl100k_base`); window/overlap configured per index in YAML. +- **Auth**: `RCP_TOKEN` (required for embed + rerank); per-index source tokens (`HF_TOKEN`, `GITHUB_TOKEN`, `INFOSCIENCE_TOKEN`, etc.) where the upstream API requires them. + +The `ror` index is a partial outlier (no DuckDB layer; flat catalog of orgs in Qdrant + a JSONL dump for lexical lookup). The `infoscience` legacy chunks live alongside the new schema. + +## Per-index quickstart + +### HuggingFace +Full guide at [`huggingface-index.md`](huggingface-index.md). 147 namespaces, 1034 models, 324 datasets, 70 spaces, ~3151 vector chunks (2026-05-02). + +```bash +just hf-status # counts + paths +just hf-ingest --scope switzerland # idempotent (skip-already-ingested) +just hf-embed # only embeds new chunks +just hf-search "swiss german LLM" --top-k 5 +just hf-search "..." --type orgs --filter namespace_kind=user --filter scope=switzerland +just hf-search "..." --facets license,pipeline_tag,author --facet-top-n 10 +just hf-search "medical LLM" --type models --filter base_model=meta-llama/Llama-2-7b +just hf-lineage epfl-llm/meditron-7b # walk base_models DAG (ancestors + descendants) +just hf-query --predefined models_by_author --param author=epfl-llm --param limit=20 +``` + +### OpenAlex +Indexes EPFL and ETH-affiliated *works* (papers), with derived `authors`, `institutions`, `sources`, `topics`, `concepts`. ~3M+ works total when scoped to switzerland. + +```bash +just openalex-status +just openalex-ingest --scope epfl +just openalex-embed --entities works,authors,institutions +just openalex-search "machine learning fairness" +just openalex-query "SELECT title, year, doi FROM works WHERE year=2024 LIMIT 20" +``` + +### Infoscience +EPFL's DSpace catalogue. Has a `chunks` table (sentence-level) on top of full articles, with a slim `links_index` mapping every external URL (HuggingFace, GitHub, OpenAlex, ORCID, …) back to the citing article. + +```bash +just infoscience-status # CLI is python -m src.index.infoscience +python -m src.index.infoscience discover +python -m src.index.infoscience embed +python -m src.index.infoscience query "swiss german NLP" --target chunks --top-k 5 +``` + +The links_index file (`data/index/infoscience/dumps/infoscience_links_index.json`, ~8 MB) is what powers the `cited_by_infoscience` field on HF search results. + +### ORCID +Scoped person index — finds researchers via discover modes (EPFL email-pattern, ROR-affiliation, etc.) then pulls full ORCID records (employments, educations, works). + +```bash +just orcid-status +just orcid-discover --scope epfl +just orcid-ingest --limit 100 +just orcid-embed +just orcid-search "computer vision researcher EPFL" +``` + +### ROR +The Research Organization Registry. Built from the public ROR dump, scoped to EPFL+ETH, Switzerland, Europe, or worldwide (configurable). Lookup is "given a free-text org name, get the ROR ID + canonical record". + +```bash +just ror-stats +just ror-build --scope switzerland +just ror-query "EPFL" --top-k 3 # semantic +python -m src.index.ror lookup "epfl" # lexical, full ROR dump +``` + +### Zenodo +Indexes records (datasets, software releases, presentations, posters) from EPFL/Swiss communities and authors. The lifecycle mirrors HuggingFace, plus an ID-driven discovery path that mines local Infoscience full-text PDFs for cited Zenodo deposits — typically yields ~5–7× more records than the community filter alone, since most cited Zenodo deposits aren't tagged in the EPFL community. Full operational guide at [`zenodo-index.md`](zenodo-index.md). + +```bash +just zenodo-status +just zenodo-ingest --scope epfl # community-driven +python -m src.index.zenodo discover --source infoscience --ingest # citation-driven delta +python -m src.index.zenodo ingest --ids my_ids.txt # bulk by id / DOI / URL +just zenodo-embed +just zenodo-search "EPFL dataset agriculture" +just zenodo-query --predefined records_by_author --param author=... +``` + +### ETH Research Collection +ETH Zürich's DSpace catalogue — sister of infoscience for ETH publications. Same shape as infoscience (chunks + articles + persons + organizations). Less stable than the infoscience pipeline; check `.internal/ethz_research_collection/` before relying on it. + +### GitHub +Pulls repository metadata + READMEs for the EPFL/Swiss org+user seed (overlaps with HF org slugs but covers actual code repos rather than model artefacts). Same DuckDB+Qdrant shape; not yet wired into the federated layer. + +```bash +just gh-status +just gh-ingest --scope epfl +just gh-embed +just gh-search "scientific Python data pipeline" +just gh-query --predefined repos_by_author --param author=... +``` + +### SNSF (Swiss National Science Foundation) +Indexes SNSF P3 grants, the people involved, and the institutions receiving them. Useful for "which EPFL groups got funded for X". CLI lives at `python -m src.index.snsf`. See `.internal/snsf/`. + +### EPFL Graph (disciplines) +Mirrors the curated EPFL Graph academic-discipline ontology (`graphai.epfl.ch/ontology/*`) into Qdrant so callers can ask "what discipline is this README closest to?" without burning per-concept HTTP. ~2226 categories arranged in a 6-level tree, each leaf backed by 50–110 anchor Wikipedia articles. Embeds carry the canonical Wikipedia lead-section extract when available (~64% coverage; the remainder are EPFL-Graph synthetic `topics-in-X` / `entities-in-X` taxonomy nodes without a 1:1 Wikipedia article — they fall back to `name + anchor concepts`). Full operational guide at [`epfl-graph-disciplines.md`](epfl-graph-disciplines.md). + +```bash +just epfl-graph-status # row counts + Qdrant collection +just epfl-graph-ingest # walk the 2226-node tree (~25 min) +just epfl-graph-enrich-wikipedia # fill canonical Wikipedia extracts (~3 min) +just epfl-graph-embed # push vectors to Qdrant (~5 min) +just epfl-graph-search "extracting research metadata from GitHub" --top-k 10 +just epfl-graph-search "..." --min-depth 4 # leaf-only disciplines +``` + +Available at runtime to the **repository, person, organization, and article** v2 LLM agents as `search_epfl_graph_disciplines`. Skipped on contribution + membership (pure linking agents — no discipline benefit). Federated adapter exposes a `disciplines` entity_type. Gated by `V2_EPFL_GRAPH_RAG_ENABLED` (default true). + +### SWISSUbase + +Swiss social-science research-data platform ([`swissubase.ch`](https://www.swissubase.ch)), run by FORS / UZH / U. Neuchâtel / DASCH. Catalogues studies (UI label: "Project") plus their datasets, principal investigators, and partner institutions. swissUbase has **no public REST API** — every catalogue endpoint requires the SPA's session cookie plus a quirky `Accept: q=0.8;application/json;q=0.9` header. Ingest therefore drives a Selenium browser session and calls the JSON endpoints from inside the authenticated browser context. Full guide at [`swissubase-index.md`](swissubase-index.md). + +The catalogue's search-studies endpoint silently caps deep pagination (~250 items max with pagesize=50, ~1000 with pagesize=10) regardless of filter combos — so the ingest enumerates `studyVersionId ∈ [1..25000]` directly, which has no such cap. Default `INDEX_SWISSUBASE_SCOPE=epfl_sdsc_ethz` ingests every study but only embeds those whose institution string matches EPFL / ETHZ / SDSC; flip to `switzerland` to embed everything. + +```bash +just swissubase-status # counts + paths +just swissubase-ingest # full id-range scan (~6 hours, resumable) +just swissubase-embed --entity studies # embed in-scope studies only +just swissubase-search "language teacher education" --top-k 5 +just swissubase-search "..." --filter '{"entity_type":"persons"}' +just swissubase-query --predefined in_scope_studies --param limit=20 +just swissubase-query --predefined top_institutions_in_scope --param limit=10 +``` + +Live snapshot (2026-05-03): **18,209 studies (1,244 in-scope)** · **16,973 persons** · **1,732 institutions** · **4,589 vectors**. + +Available at runtime to the **article** v2 LLM agent as `search_swissubase_rag`. Federated adapter exposes four entity types (`studies`, `datasets`, `persons`, `institutions`) under index name `swissubase`. Gated by `V2_SWISSUBASE_RAG_ENABLED` (default true). Every hit carries the canonical `https://www.swissubase.ch/...` URL in `source_url`. + +## Federated layer (`src/index/_federated/`) + +The federated layer turns N CLIs into one. Adapter pattern: each index module gets a thin adapter that implements `search()` + `lookup()`; the federated layer fans out across registered adapters in parallel via `ThreadPoolExecutor` and merges by score. + +```bash +just gme-indices # list registered adapters +just gme-search "Swiss German LLM" --top-k 10 # search every index in parallel +just gme-search "..." --indices huggingface,openalex,orcid # subset +just gme-entity 0000-0001-9534-3870 # any identifier — every adapter that recognises it returns matches +just gme-entity W2741809807 # OpenAlex Work +just gme-entity https://ror.org/02s376052 # ROR +just gme-entity epfl-llm/meditron-7b # HF repo +``` + +Full guide at [`federated-search.md`](federated-search.md). + +**LLM tools**: agents in the v2 pipeline get `search_federated_rag` and `lookup_entity_federated` (both async, both forward to `FederatedRagProvider`). Documented at [`v2-rag-tools.md`](v2-rag-tools.md). + +**Adapter coverage**: **12/12** indices have adapters as of 2026-05-03 (HF, OpenAlex, Infoscience, ORCID, ROR, Zenodo, ETH Research Collection, GitHub, SNSF, RenkuLab, EPFL Graph, SWISSUbase). `gme search --rerank` engages the cross-index reranker for globally-fair scoring across adapters. + +## Common operations + +### Show counts across all indices + +```bash +for idx in hf openalex orcid ror zenodo gh; do + echo "=== $idx ===" + just $idx-status +done +``` + +### Re-embed everything + +```bash +for idx in hf openalex orcid zenodo gh; do + just $idx-embed # idempotent — only embeds new chunks +done +``` + +### Common failure modes + +| Symptom | Likely cause | Fix | +|---|---|---| +| `httpcore.ConnectError` to RCP | EPFL VPN dropped or RCP brief outage | retry; check `curl https://inference-rcp.epfl.ch/v1/models` | +| Qdrant `localhost:6333` connection refused | running from inside the devcontainer | use `gme-qdrant:6333` (compose service name) | +| HF 429 mid-ingest | anonymous IP exhausted 500/5min bucket | add `HF_TOKEN` to `.env`; remember `set -a; source .env; set +a` if shell predates the edit | +| `_duckdb.IOException: lock` | concurrent write+read on same DB | one process at a time per DB | +| `chromadb` blocking `uv sync` | unused leftover dep | already removed; was a one-time fix on 2026-05-01 | + +## Storage layout + +Each per-index directory carries its own DuckDB store and any +ingest-time scratch (raw downloads, fetch caches, run logs). Qdrant +runs as a single shared service whose persistence lives **outside** +`data/index/` because it backs all indices simultaneously. + +``` +data/ + index/ + huggingface/{duckdb,cards,cache,logs}/ + openalex/{duckdb,cache,logs}/ + infoscience/{duckdb,raw,text,dumps,chroma,matches.jsonl,organizations.txt,persons.txt,relations.jsonl,discover_state.json}/ + orcid-epfl/{duckdb,cache,logs}/ + orcid-switzerland/{duckdb,cache,logs,discover.log,discover_resume.log}/ + ror/{duckdb,dump,index}/ + zenodo/{duckdb,cache,logs,state}/ + ethz-research-collection/{duckdb,raw,text,matches.jsonl,organizations.txt,persons.txt,relations.jsonl,discover_state.json}/ + github/{duckdb,cards,cache,logs}/ + snsf/{duckdb,raw}/ + renkulab/{duckdb,cache,logs,state}/ + epfl_graph/{duckdb,cache,logs}/ + swissubase/{duckdb,cache,logs,state}/ + qdrant/storage/ # shared by ALL indices, one collection per (index, entity_type) +``` + +Per-subdir convention: + +- `duckdb/` — the canonical DuckDB store (`.duckdb` + WAL). +- `raw/` — bulk inputs from the upstream source (CSVs, JSON dumps). + Used by indices whose ingest is local-file-driven (SNSF, Infoscience, + ETHZ Research Collection). +- `cache/` — per-record HTTP / API cache for incremental ingest. +- `logs/` — ingest run logs. +- `state/` — resumable ingest checkpoints (Zenodo, RenkuLab, SWISSUbase). +- `cards/`, `text/`, `dumps/`, `discover_state.json`, `matches.jsonl`, + `organizations.txt`, `persons.txt`, `relations.jsonl` — index-specific + intermediate artefacts. See the per-index docs and CLI help. + +Backups: each `.duckdb` file is a self-contained SQLite-ish snapshot — `cp` it. The Qdrant collections can be regenerated from DuckDB via `-embed`, so they don't strictly need to be backed up. Qdrant persistence lives in `data/qdrant/storage/` (bind-mounted into the `gme-qdrant` container at `/qdrant/storage`). + +## Related documentation + +- [`huggingface-index.md`](huggingface-index.md) — HF index deep-dive (the most-used) +- [`federated-search.md`](federated-search.md) — federated layer design +- [`v2-rag-tools.md`](v2-rag-tools.md) — agent-side tools (per-index + federated) +- [`ROADMAP.md`](ROADMAP.md) — what's left to build +- `.internal//` — per-index workstream tracker (design + history; not in the public docs nav) diff --git a/docs/swissubase-index.md b/docs/swissubase-index.md new file mode 100644 index 0000000..f56e932 --- /dev/null +++ b/docs/swissubase-index.md @@ -0,0 +1,255 @@ +# SWISSUbase Index + +Standalone RAG index over the Swiss social-science research-data +platform [`swissubase.ch`](https://www.swissubase.ch) — run by FORS, +UZH, U. Neuchâtel, and DASCH. Indexes the platform's four entity types: +**studies** (UI label: "Project"), **datasets**, **persons**, and +**institutions**. Lives at `src/index/swissubase/`. + +> **Two complementary views.** [`docs/v2-rag-tools.md`](v2-rag-tools.md) +> documents the **agent-facing** tool (`search_swissubase_rag`) used by +> the v2 LLM pipeline. *This* page documents the **direct-access** CLI, +> SQL, and HTTP surfaces — for analysts and scripts that don't want to +> go through an agent. + +## Why the ingest is Selenium-driven + +swissUbase has **no documented public REST API**. Every catalogue +endpoint is path-prefixed `/api/public/...` but enforces two things: + +1. A session cookie set by the SPA via `/api/v2/actions/base` on first + page load (anonymous `curl` is rejected with `403 Not authorized`). +2. A quirky `Accept: q=0.8;application/json;q=0.9` header (anything + else 4xx's, even from inside the browser session). + +We therefore open a long-lived `webdriver.Remote` session against the +shared Selenium Grid (`SELENIUM_REMOTE_URL`), navigate once to the +catalogue search page so the browser obtains the cookies, then invoke +the JSON API endpoints from inside the browser via +`driver.execute_async_script(fetch(...))`. Same browser session, full +session-cookie attachment, but JSON in / JSON out — much faster than +re-rendering the Material table for every study. + +## Why the ingest enumerates `studyVersionId` (not the search endpoint) + +The catalogue's `search-studies` endpoint **silently caps deep +pagination**: + +| pagesize | items reachable | last working `start` | +|---:|---:|---:| +| 50 | ~250 | 201 | +| 20 | ~420 | 401 | +| 10 | ~1010 | 1001 | +| 5 | ~1005 | 1001 | + +The cap survives both filtering (single + multi-facet) and session +rotation; even `start=251 pagesize=10` under `language=en` (total=1909) +returns `{items: [], total: 0}`. So we cannot enumerate the full ~12 k +catalogue via search alone. + +Per-study endpoints (`/api/public/catalogue/studies/v1/{id}/...`) have +no such cap. They accept any `studyVersionId`: + +* **200** → return full overview / main / dynamic-blocks JSON. +* **404** → study doesn't exist for that ID. +* **403** → ID exists but isn't public; skip silently. + +So the ingest just iterates `studyVersionId ∈ [1..25000]` (configurable +range; observed live range is 1..21,500 with ~58 % density). Every +existing study returns 200 in one shot — no search needed. + +## Architecture (1-paragraph version) + +DuckDB at `data/index/swissubase/duckdb/swissubase.duckdb` holds the +canonical records — `studies`, `datasets`, `persons`, `institutions`, +plus M:N bridge tables `study_persons` (with role) and +`study_institutions`, plus a unified `chunks` ledger. Qdrant (compose +service `gme-qdrant:6333`) holds embedding vectors in a single +collection `swissubase_entities`; the `entity_type` payload field +disambiguates studies vs datasets vs persons vs institutions, so one +search-tool call can return mixed-type hits. Embeddings come from +`Qwen/Qwen3-Embedding-8B` on EPFL RCP; reranking from +`Qwen/Qwen3-Reranker-8B`. Every entity row stores `source_url` +(`https://www.swissubase.ch/{lang}/catalogue/studies/{id}`) — `NOT NULL` +for studies and datasets, populated when known for persons and +institutions. + +``` +┌─ Ingest ──────────────────────────────────────────────────────────────┐ +│ Selenium session ─→ for sid in [id_start..id_end]: │ +│ overview-block + main + dynamic-blocks │ +│ └─→ project → studies / persons / institutions │ +│ (404/403 skip; transient errors retry-skip) │ +│ │ +│ scope filter: affiliation_match=TRUE iff institution string matches │ +│ EPFL / ETH Zurich / ETHZ / SDSC │ +└──────┬────────────────────────────────────────────────────────────────┘ + ▼ +┌─ Embed ───────────────────────────────────────────────────────────────┐ +│ rows where affiliation_match → chunker → RCP /v1/embeddings │ +│ → Qdrant collection swissubase_entities │ +│ bookkeeping: chunks(chunk_id, entity_type, entity_id, chunk_index, …)│ +└──────┬────────────────────────────────────────────────────────────────┘ + ▼ +┌─ Query ───────────────────────────────────────────────────────────────┐ +│ semantic: query → embed → Qdrant → rerank → entity-dedup → hydrate │ +│ sql: guarded SELECT/WITH over DuckDB │ +└───────────────────────────────────────────────────────────────────────┘ +``` + +## Entity model + +| Table | Primary key | Notes | +|---|---|---| +| `studies` | `study_id` (= `studyVersionId`) | One row per swissUbase Project. Carries `ref` (referenceNumber the UI shows), `title`, `description`, `description_language`, `start_date`, `end_date`, `progress`, `main_discipline` / `sub_discipline`, `version`, `data_availability`, `dataset_count`, `affiliation_match`, `source_url` (NOT NULL). Full overview + dynamic-blocks JSON kept in `raw_overview` / `raw_dynamic_blocks`. | +| `datasets` | `dataset_id` | Currently stubbed (we don't yet harvest the per-study `Datasets` dynamic block). Schema is in place; the embed pipeline already streams from this table. | +| `persons` | `person_key` (`swissubase:person:{personId}` or `name:{slug}`) | Authors, principal investigators, former collaborators. `display_name`, `orcid` (currently always null — swissUbase doesn't expose ORCIDs at this layer), `affiliation`, `source_url` (often null). | +| `institutions` | `institution_key` (`name:{slug}` until a ROR match is computed) | `name`, `address`, `ror_id` (currently null), `source_url`. | +| `study_persons` | `(study_id, person_key, role)` | Composite — same person can have multiple roles per study. `role ∈ {Principal investigator, Author, Former collaborator}`. `position` from the swissUbase `sort` field. | +| `study_institutions` | `(study_id, institution_key)` | M:N. | +| `chunks` | `chunk_id` (`uuid5(NAMESPACE_URL, "{entity_type}|{entity_id}|{index}")`) | Bookkeeping — one row per Qdrant point, `chunk_id == vector_id`. | + +## Scope filter + +The catalogue has no institution facet, so the scope is applied as a +**post-filter on the rendered institution string**. Each study is +matched (case-insensitively) against the patterns in +`scope.epfl_sdsc_ethz_patterns`: + +``` +EPFL · École polytechnique fédérale de Lausanne · +Ecole polytechnique fédérale de Lausanne · +ETH Zurich · ETHZ · Eidgenössische Technische Hochschule · +SDSC · Swiss Data Science Center +``` + +Studies that match get `affiliation_match=TRUE` and are the **only** +ones embedded by default. Override at runtime with +`INDEX_SWISSUBASE_SCOPE=switzerland` to flag every study and embed the +full ~13 k Swiss social-science catalogue. + +Storage is unconditional — even non-matching studies land in DuckDB — +so flipping scope later doesn't require re-scraping. Only a re-embed. + +## CLI + +```bash +just swissubase-status # row counts + Qdrant collection size + paths +just swissubase-ingest # full id-range scan, resumable via state checkpoint +just swissubase-ingest --refresh # restart from id=id_start, ignoring checkpoint +just swissubase-ingest --limit 50 # smoke test +just swissubase-ingest --scope switzerland # mark every ingested study in-scope + +just swissubase-embed # all four entity types +just swissubase-embed --entity studies --entity persons # subset +just swissubase-embed --limit 100 + +just swissubase-search "language teacher education" --top-k 5 +just swissubase-search "..." --filter '{"entity_type":"persons"}' +just swissubase-search "..." --filter '{"main_discipline":"Humanities and Social Sciences"}' + +just swissubase-query --predefined count_by_entity +just swissubase-query --predefined in_scope_studies --param limit=20 +just swissubase-query --predefined studies_by_institution --param institution_key=name:epfl --param limit=10 +just swissubase-query --predefined top_institutions_in_scope --param limit=15 +just swissubase-query --predefined studies_by_discipline +just swissubase-query --sql "SELECT main_discipline, COUNT(*) AS n FROM studies WHERE affiliation_match GROUP BY main_discipline" + +just swissubase-serve --port 8004 # FastAPI on :8004 +``` + +## HTTP API + +`just swissubase-serve` boots `src/index/swissubase/api.py` (FastAPI). + +| Method | Path | Purpose | +|---|---|---| +| GET | `/healthz` | DuckDB + Qdrant + RCP + Selenium status | +| POST | `/search` | `{query, top_k, candidate_k, filter_payload}` → semantic search | +| POST | `/query` | `{predefined?, sql?, params?}` — predefined or guarded SELECT/WITH | +| GET | `/predefined` | List predefined query names | +| GET | `/study/{study_id}` | Full study row (joins not hydrated) | +| GET | `/dataset/{dataset_id}` | Full dataset row | + +## Predefined queries + +| Name | Returns | +|---|---| +| `count_by_entity` | One row per entity bucket (studies, in-scope studies, persons, …) | +| `in_scope_studies` | EPFL / ETHZ / SDSC studies, latest-first | +| `studies_by_institution` | Studies linked to a given `institution_key` | +| `studies_by_person` | Studies a given `person_key` was on, with role | +| `top_institutions_in_scope` | Institution leaderboard (in-scope only) | +| `top_persons_in_scope` | Person leaderboard (in-scope only) | +| `studies_by_discipline` | Discipline distribution (in-scope only) | + +## Configuration + +Static settings live in [`config/index/swissubase.yaml`](https://github.com/Imaging-Plaza/git-metadata-extractor/blob/main/config/index/swissubase.yaml). Runtime overrides: + +| Env | Default | Purpose | +|---|---|---| +| `SELENIUM_REMOTE_URL` | — | **Required.** Selenium Grid (Firefox). | +| `RCP_TOKEN` | — | Required for `embed` + `search`. | +| `INDEX_QDRANT_URL` | yaml `gme-qdrant:6333` | Qdrant endpoint (use `localhost:6333` from a host shell). | +| `INDEX_QDRANT_API_KEY` | unset | Qdrant API key (when applicable). | +| `INDEX_SWISSUBASE_SCOPE` | `epfl_sdsc_ethz` | `epfl_sdsc_ethz` or `switzerland`. | +| `INDEX_SWISSUBASE_ID_START` | yaml `1` | Lower bound of the studyVersionId scan. | +| `INDEX_SWISSUBASE_ID_END` | yaml `25000` | Upper bound. Bump if swissUbase grows past id 21,500. | +| `INDEX_DATA_DIR` | `data/index` | Filesystem root. | +| `V2_SWISSUBASE_RAG_ENABLED` | `true` | Disable to drop the v2 agent tool without removing the index. | + +## Federated layer + +`src/index/_federated/adapters/swissubase.py` exposes the index to +`gme-search` / `gme-entity`: + +```bash +just gme-search "language teacher education" --indices swissubase --top-k 5 +just gme-entity https://www.swissubase.ch/en/catalogue/studies/21160 +just gme-entity 21160 # bare id → studies +just gme-entity swissubase:person:11073 # composite key +``` + +The adapter: + +* `search()` — pushes the optional `entity_type` argument into the + Qdrant filter as `entity_type=...`, scopes to a single bucket. With + `entity_type=None` (the default), all four buckets are searched + together. +* `lookup()` — recognises swissUbase study URLs, bare numeric + `studyVersionId`s (tries studies first, then `swissubase:person:N`), + composite person/institution keys. + +## V2 LLM tool + +`search_swissubase_rag` (factory: `make_swissubase_rag_search_tool`, +provider: `SwissubaseRagProvider`). + +| Argument | Notes | +|---|---| +| `query` | Free-text. | +| `top_k` | 1..100, default 10. | +| `filters` | Allowlisted: `entity_type, study_id, dataset_id, person_key, institution_key, ref, main_discipline, sub_discipline, progress, year_start, year_end, access_right`. | +| `rerank` | `false` (default) — vector only. `true` — vector + RCP cross-encoder against title+discipline. | + +Wired into the **article agent** today (matches zenodo's placement). +Mirror the same two-line edit (import + `if providers.swissubase_rag is not None: tools.append(...)`) to extend to the person / organization / repository agents. + +Returns thin hits with a top-level `source_url` so downstream consumers +have the canonical link without a second fetch. + +## Limitations & follow-ups + +- **Datasets ingest is stubbed.** swissUbase loads a study's datasets + as a separate "dynamic block" served by per-block endpoints we + haven't reverse-engineered. The schema, embed path, federated + adapter, and predefined queries are all ready — just needs the + per-block fetch + projection. +- **No ORCID / ROR enrichment.** Persons carry `personId` and + `refNumber` but no ORCID; institutions carry no ROR ID. A follow-up + pass could fuzzy-match against the ORCID and ROR indices already in + this repo. +- **Polite single-thread ingest.** ~6 hours for the full 25,000-ID + range. Multi-session parallelism (one Selenium driver per worker + thread) is feasible but not implemented. diff --git a/docs/v2-api-reference.md b/docs/v2-api-reference.md new file mode 100644 index 0000000..6670575 --- /dev/null +++ b/docs/v2-api-reference.md @@ -0,0 +1,331 @@ +# V2 API Reference + +This document describes the mounted v2 API surface under `/v2`. + +## Authentication + +`/v2/extract` (both `GET` and `POST`) and `/v2/jobs/{job_id}` require a +bearer token in the `Authorization` header. `/v2/health` is the only v2 +endpoint that stays open. + +```http +Authorization: Bearer +``` + +The expected token comes from the server-side `API_TOKEN` env var +(generate with `python -c "import secrets; print(secrets.token_urlsafe(32))"`). +The check uses `hmac.compare_digest` for constant-time comparison. + +| Condition | Status | Notes | +|---|---|---| +| `API_TOKEN` unset on the server | `503` | Fails closed; no dev bypass. | +| Header missing | `401` | Includes `WWW-Authenticate: Bearer`. | +| Wrong token | `401` | Same response shape as missing header. | +| Valid token | route's normal response | | + +`/v1/*` routes share the same `API_TOKEN` and behave the same way. +Implementation lives in `src/v2/auth.py` (`verify_token` dependency). + +## Endpoints + +### `GET /v2/health` + +Returns service readiness for v2 dependencies. Open (no auth required). + +Example: + +```bash +curl -s "http://localhost:1234/v2/health" | jq +``` + +### `GET /v2/extract/{full_path}` + +Runs the v2 extraction pipeline for a GitHub URL path. + +Repository-mode traversal contract: + +- GitHub traversal is direct-only: source repository, direct owner, and direct contributors. +- GitHub repo-list expansion (`/users/{u}/repos`, `/orgs/{o}/repos`) is disabled. +- ORCID, Infoscience, and ROR enrichment remain enabled for discovered people/organizations. +- `pulse:owns` emitted during repository runs is constrained to the source repository handle when present. + +Query parameters: + +- `output_format`: `jsonld` (default) or `json` +- `agent_runtime`: `rule_based|llm` (optional; defaults to `V2_AGENT_RUNTIME_DEFAULT=llm`) + - `agent_runtime=llm` runs LLM agents for repository/user/organization roots and fanout stages. + - In LLM runtime, two global fail-open stages are always enabled: + - `llm_dedup` (post-permissive, pre-reconciliation): LLM duplicate-cluster suggestions + deterministic constrained merge/remap. + - `llm_critic` (post-reconciliation, pre-strict, gated by `V2_APPLY_CRITIC_PRUNING`): LLM prune suggestions + deterministic non-root pruning/cascade cleanup. + - Both stages are warning-only on failure (pipeline continues). + - Root-stage hard-fail policy in LLM mode (no rule-based fallback): + - repository input: `repo_agent` + - user input: `person_agent` + - organization input: `org_agent` +- `include_context_summary`: `true|false` (default `false`, includes the compiled LLM context summary markdown used by downstream agents when available) + +Examples: + +```bash +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/extract/github.com/octocat/Hello-World?output_format=jsonld" \ + | jq +``` + +```bash +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/extract/github.com/octocat/Hello-World?output_format=json&agent_runtime=rule_based" \ + | jq +``` + +```bash +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/extract/github.com/octocat/Hello-World?output_format=json&agent_runtime=llm" \ + | jq +``` + +### `POST /v2/extract` + +Submits an extraction asynchronously. The handler validates the URL synchronously, creates a `pending` job in the `ProviderCache`-backed job store, schedules the pipeline as a background task, and returns `202 Accepted` with a `job_id`. Poll `GET /v2/jobs/{job_id}` to retrieve the result. `GET /v2/extract/{full_path}` remains synchronous and unchanged. + +Request body: + +- `source_url` (required): GitHub path or URL (for example `github.com/octocat/Hello-World`) +- `output_format`: `jsonld|json` (default `jsonld`) +- `agent_runtime`: `rule_based|llm` (optional) +- `include_context_summary`: `true|false` (default `false`) + +Response (`202 Accepted`): + +- `job_id`: opaque UUID to poll +- `status`: `pending` +- `status_url`: convenience path (`/v2/jobs/{job_id}`) for the companion GET +- `submitted_at`: ISO-8601 timestamp + +Error responses: + +- `422 Unprocessable Entity` if `source_url` is unsupported (returned synchronously, no job is created). +- `503 Service Unavailable` if the provider cache is disabled — the async job store has no backing storage. + +Example: + +```bash +curl -s -X POST "http://localhost:1234/v2/extract" \ + -H "Authorization: Bearer $API_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "source_url": "github.com/octocat/Hello-World", + "output_format": "json", + "agent_runtime": "llm", + "include_context_summary": true + }' | jq +``` + +```json +{ + "job_id": "5d2b8b3d-3e6e-4a82-9b17-1c2f5b6e7a31", + "status": "pending", + "status_url": "/v2/jobs/5d2b8b3d-3e6e-4a82-9b17-1c2f5b6e7a31", + "submitted_at": "2026-04-29T10:31:00.000Z" +} +``` + +### `GET /v2/jobs/{job_id}` + +Companion retrieval endpoint for jobs submitted via `POST /v2/extract`. Returns the persisted `V2ExtractJob` record with the latest status. + +Response fields: + +- `job_id` +- `status`: `pending|running|completed|failed` +- `request`: the original `V2ExtractRequest` payload (with `source_url` normalized) +- `submitted_at`, `started_at`, `completed_at` +- `result`: present only when `status == "completed"`. Same `V2ExtractResponse` contract as `GET /v2/extract/{full_path}` (`source_url`, `detected_type`, `output_format`, `output`, `warnings`, `stats`, optional `context_summary_markdown`). +- `error`: present only when `status == "failed"`. Same `V2ErrorResponse` shape as the error responses on `GET /v2/extract/{full_path}` (`error_type`, `detail`, `source_url`, optional `errors[]`). + +Status responses: + +- `200 OK` — record found (any status). +- `404 Not Found` — no job exists with that id (also returned for jobs that have aged past the `ProviderCache` TTL). +- `503 Service Unavailable` — provider cache disabled, job store unavailable. + +Example: + +```bash +curl -s -H "Authorization: Bearer $API_TOKEN" \ + "http://localhost:1234/v2/jobs/5d2b8b3d-3e6e-4a82-9b17-1c2f5b6e7a31" | jq +``` + +Persistence: jobs share the SQLite-backed `ProviderCache` (`V2_PROVIDER_CACHE_PATH`, TTL `V2_PROVIDER_CACHE_TTL_DAYS`) under the `v2-extract-job` namespace. `V2_PROVIDER_CACHE_ENABLED=false` disables the job store entirely. + +## Runtime Stage Flow + +`/v2/extract` runs the same pipeline regardless of `agent_runtime`. The +runtime only controls which agent implementations execute (LLM agents vs. +deterministic rule-based agents). Other stages run unconditionally. + +``` +1. classify_url classify the input as repository / user / org +2. gather_context fetch GitHub metadata + GIMIE JSON-LD +3. context_summary [LLM] compile a markdown summary; raw blobs are stripped + from per-agent prompts in LLM mode +4. repo_agent produce the root repository entity +5. person_agents (fan-out) one agent per discovered contributor / user +6. org_agents (fan-out) one agent per discovered organisation +7. article_agents (fan-out) discover scholarly articles tied to the repo +8. membership_agents (fan-out) one agent per (person, org) pair +9. contribution_agents (fan-out) one agent per (person, repo) pair +10. llm_dedup [LLM] cross-bucket entity dedup + ID remap (fail-open) +11. reconcile_entities deterministic ID canonicalisation + linkage +12. llm_critic [LLM, gated] drop-suggestion stage (off by default) +13. guarantee_repo_author stamp github-owner as schema:author when empty +14. strict_validation per-entity strict JSON Schema check +15. assemble_output split graph into root + related + excluded +16. link_veracity [LLM, gated] verify every URL via Selenium fetch + LLM +17. validate_articles drop placeholder / sentinel-DOI articles +18. validate_author_classes drop `schema:author` refs whose target is + not a `schema:Person` +19. validate_ownership strip mismatched pulse:owns +20. infer_owners stamp pulse:owns / pulse:ownedBy from handles; + coerces residual bare-login strings on + `pulse:ownedBy` to `{"@id": "https://github.com/{handle}"}` +21. infer_github_handle_parents fuzzy-search ROR for parent of every github + org; add ROR org entities, stamp unitOf +22. org_relationships [LLM] whole-graph LLM call to refine unitOf edges +23. infer_org_units deterministic name-token fallback for unitOf +24. build_jsonld_output produce the final JSON-LD graph; strips + redundant `pulse:ror` from any + `org:Organization` whose `@id` is already + the ROR (closed-shape fix) +``` + +**Gates:** + +- Stages tagged `[LLM]` only run in `agent_runtime=llm`. +- `link_veracity` is `[LLM]`-only: in `agent_runtime=rule_based` it is **always skipped** (rule-based mode is guaranteed LLM-free). +- `llm_critic` is **off by default**. Set `V2_APPLY_CRITIC_PRUNING=true` to enable (LLM mode only). +- `link_veracity` is **on by default in LLM mode**. Set `V2_LINK_VERACITY_ENABLED=false` to skip even in LLM mode (recommended for batch runs). + +`link_veracity` behavior: + +- Scans all discovered HTTP(S) links across assembled entities. +- For articles, validates `schema:identifier` as DOI (bare DOI strings are normalized to `https://doi.org/`). +- Removes links explicitly fetched and marked unreachable. +- Removes entities when their canonical URL/DOI fails validation, or when no valid URL remains. +- Checker/runtime errors are fail-open (warnings only) and do not auto-prune. + +## Response Contracts + +`/v2/extract` returns: + +- `source_url` +- `detected_type` +- `output_format` +- `output` +- `warnings` +- `stats` +- optional `context_summary_markdown` (when `include_context_summary=true` and a compiled summary exists) + +`output_format=json` contract (`output`): + +- `root_entity: object | null` +- `related_entities: list[object]` +- `excluded_entities: list[object]` +- `entities_by_type: {repositories, persons, organizations, articles, memberships, contributions}` + +`output_format=jsonld` contract (`output`): + +- `@context: object` +- `@graph: list[object]` +- optional `excluded_entities` (only when present) + +## LLM Agent Architecture + +Each entity bucket has a dedicated agent under `src/v2/agents/llm//agent.py` +(repository, person, organization, article, membership, contribution). +Each is a **single pydantic-ai `Agent` call** that produces all output +fields in one prompt/response round-trip. Hallucination guards baked into +agents: + +- `force_server_uuid` overwrites whatever UUID the LLM emitted with a + server-generated one in `identifiers.uuid` only. +- Repository agent post-LLM: stars/forks from GitHub REST (deterministic); + discipline fallback `wd:Q428691` (computer engineering) when the LLM + emits empty/null. +- Contribution agent post-LLM: stamps `schema:author` and + `pulse:contributionTo` from the orchestrator's authoritative pair. +- Article agent post-LLM: drops the entity if `schema:identifier` is a + placeholder DOI (`10.0000/...`) or sentinel string (`UNKNOWN`, `N/A`, + `TBD`, …) and no `pulse:infoscienceArticleIdentifier` is present. + +Token counts (`tokens_prompt`, `tokens_completion`) are extracted from +`result.usage()` (pydantic-ai 1.5+ exposes `usage` as a method on +`AgentRunResult`, not a property — the `V2LLMRuntime` resolves this with +a `callable` guard). + +## LLM Agent Tools + +LLM agents can call server-side tools during generation. Tools are +registered per-agent by passing a `tools=[...]` list to +`V2LLMRuntime.run_json_prompt`, which forwards them to the pydantic-ai +`Agent`. Shared tools live in `src/v2/agents/llm/agent_tools/` — add a new +module there to make a tool available to multiple agents. + +Two main families: + +- **Per-index RAG tools** (`*_rag.py`) — Qdrant-backed semantic search + + on-demand fetch over Infoscience, ETH Research Collection, HuggingFace, + OpenAlex, ORCID, ROR, Zenodo. Plus the **federated** tools + (`search_federated_rag`, `lookup_entity_federated`) that fan out across + all nine indices in parallel. Documented in + [`v2-rag-tools.md`](v2-rag-tools.md). +- **Direct provider tools** — disciplines list (`list_disciplines`), + GitHub SPDX SBOM (`query_dependencies`), GitHub org / ROR org / ORCID + person identity lookups, Infoscience search and orgunit, repository + corpus grep, Selenium fetch (`fetch_link_content_via_selenium`), email + hashing, UUID generation, DuckDuckGo search. + +The full registered set lives in +`src/v2/agents/llm/agent_tools/__init__.py`. + +### Observability + +Tool calls emit an INFO log line from `src.v2.agents.llm.agent_tools.`: + +``` +INFO src.v2.agents.llm.agent_tools.disciplines: tool call: list_disciplines — returning 46 entries +``` + +If this line is absent after an LLM repository run with +`agent_runtime=llm`, the model did not call the tool. + +## Repository Entity Schema Notes + +- Repository `identifiers` use `schema:citation` (not `schema:identifier`) for the DOI/citation link. The `idSource` enum values for repositories are `pulse:githubRepositoryHandle`, `schema:citation`, and `uuid`. +- Article entities continue to use `schema:identifier` for their canonical identifier. +- `schema:alternateName` is an internal intermediate field on organization entities: it is consumed by pipeline stages during alias matching and is not present in final `/v2/extract` outputs. +- Internal pipeline metadata fields whose names start with `_` (e.g. `_person_ref` on Memberships) are stripped before strict validation, JSON-LD output, RDF serialisation, and any external artefact. + +## V2 Environment Variables + +The most-touched knobs (full list in `.env.example` and `CLAUDE.md`): + +| Variable | Default | Notes | +| --- | --- | --- | +| `API_TOKEN` | unset | Bearer token guarding `/v1/*` and protected `/v2/*` routes. Missing → 503 (no dev bypass). See [Authentication](#authentication). | +| `V2_AGENT_RUNTIME_DEFAULT` | `llm` | Default runtime when `/v2/extract` omits `agent_runtime` | +| `V2_USE_MOCK_PROVIDERS` | `true` | Swap in mock GitHub/ORCID/Infoscience/ROR providers | +| `V2_LINK_VERACITY_ENABLED` | `true` | Skip the link-veracity stage in LLM mode (rule-based skips unconditionally) | +| `V2_APPLY_CRITIC_PRUNING` | `false` | Enable critic drop suggestions (LLM mode only) | +| `V2_MAX_CONCURRENT_AGENTS` | `6` | Per-stage fan-out concurrency | +| `V2_PROVIDER_CACHE_PATH` | `.cache/v2/providers.db` | Shared provider + verdict + pipeline + job-store SQLite path | +| `V2_PROVIDER_CACHE_TTL_DAYS` | `30` | TTL for cached entries | +| `V2_PROVIDER_CACHE_ENABLED` | `true` | When `false`, every external lookup is fresh and the async-job store is disabled | +| `V2_PIPELINE_CACHE_ENABLED` | `true` | When `false`, every `/extract` re-runs the full pipeline | +| `V2__RAG_ENABLED` | `true` | Toggle each RAG tool family (`INFOSCIENCE`, `ETHZ_RESEARCH_COLLECTION`, `HUGGINGFACE`, `OPENALEX`, `ZENODO`, `ORCID`, `ROR`) | +| `INDEX_QDRANT_URL` | YAML-driven | Qdrant endpoint for every RAG index. Use `http://gme-qdrant:6333` inside the devcontainer. | +| `V2_QUERY_LOG_DIR` | `logs/v2_queries` | Per-request external-query log destination | +| `LOG_LEVEL` | `INFO` | DEBUG/INFO/WARNING/ERROR | +| `GITHUB_TOKEN` | unset | Required for healthy provider preflight | +| `RCP_TOKEN` / `OPENAI_API_KEY` / `OPENROUTER_API_KEY` | unset | At least one required in LLM mode | +| `SELENIUM_REMOTE_URL` | unset | Enables link-veracity + the `fetch_link_content_via_selenium` tool | diff --git a/docs/v2-rag-tools.md b/docs/v2-rag-tools.md new file mode 100644 index 0000000..0196ec5 --- /dev/null +++ b/docs/v2-rag-tools.md @@ -0,0 +1,300 @@ +# V2 RAG Agent Tools + +LLM agents in the v2 pipeline can query several Qdrant-backed indices on +demand: Infoscience, ETH Research Collection, HuggingFace Hub, OpenAlex, +Zenodo, ORCID, ROR, SWISSUbase, RenkuLab, **GitHub repositories**, and +the **EPFL Graph academic-discipline ontology**. Each index has a thin +async **provider** in +`src/v2/ingest/providers/*_rag.py` and one or more **pydantic-ai tools** in +`src/v2/agents/llm/agent_tools/*_rag.py`. + +There is also a **federated layer** (`FederatedRagProvider`, two tools: +`search_federated_rag` and `lookup_entity_federated`) that fans out across +every index in parallel and merges results — the right default when +the agent doesn't know which index has the answer. See +[`federated-search.md`](federated-search.md) for the full design. + +The pattern is uniform: `embed query → vector search Qdrant → optional +cross-encoder rerank → return thin hits`. Bulky payload bodies are kept +out of the prompt; agents pull full content with a separate fetch tool +when needed. + +## The indices + +| Provider | Tools | Collections | Filter keys (allowlist) | +|---|---|---|---| +| `InfoscienceRagProvider` (`src/v2/ingest/providers/infoscience_rag.py`) | `search_infoscience_rag`, `fetch_infoscience_chunks`, `fetch_infoscience_records` | `chunks`, `articles`, `persons`, `organizations` | `has_github_match, has_hf_match, year, publication_type, language, lab_uuid, org_uuids, doi, orcid, ror_id, sciper_id, sciper_unit_id, author_uuids, subjects, keywords` | +| `EthzResearchCollectionRagProvider` (`src/v2/ingest/providers/ethz_research_collection_rag.py`) | `search_ethz_research_collection_rag`, `fetch_ethz_research_collection_chunks`, `fetch_ethz_research_collection_records` | `chunks`, `articles`, `persons`, `organizations` | `has_github_match, has_hf_match, year, publication_type, language, lab_uuid, org_uuids, doi, orcid, ror_id, author_uuids, subjects, keywords` (sciper_* dropped — EPFL-specific) | +| `HuggingFaceRagProvider` (`src/v2/ingest/providers/huggingface_rag.py`) | `search_huggingface_rag`, `lineage_huggingface` | `models`, `datasets`, `spaces`, `orgs` | `library_name, pipeline_tag, sdk, gated, downloads, downloads_all_time, likes, author, license, entity_type, base_model` | +| `OpenAlexRagProvider` (`src/v2/ingest/providers/openalex_rag.py`) | `search_openalex_rag` | `works`, `authors`, `institutions`, `sources`, `topics`, `concepts` | `year, publication_year, doi, entity_type, openalex_id, country_code, type, field_id, domain_id, level, primary_topic_id, primary_source_id, last_known_institution_id, orcid, ror` | +| `ZenodoRagProvider` (`src/v2/ingest/providers/zenodo_rag.py`) | `search_zenodo_rag`, `fetch_zenodo_records` | `zenodo_records` | `year, doi, resource_type, access_right, entity_type, zenodo_id`. `fetch_zenodo_records(ids)` accepts both `zenodo_id` (canonical version-record) and `concept_recid` (parent / "all versions" id Zenodo redirects from), reading full bodies — title, doi, description, license, dates — from DuckDB rather than Qdrant. Use after `search_zenodo_rag` to hydrate hits, or to confirm a citation by id. See [`zenodo-index.md`](zenodo-index.md). | +| `OrcidRagProvider` (`src/v2/ingest/providers/orcid_rag.py`) | `search_orcid_rag` | `persons`, `employments`, `educations` (collections namespaced by scope, e.g. `orcid_epfl_persons`) | `orcid_id, in_scope, discovered_via, org_ror, organization, department, role` | +| `RorRagProvider` (`src/v2/ingest/providers/ror_rag.py`) | `search_ror_rag` | `epfl_ethz`, `switzerland`, `europe`, `worldwide` (scope mode → collection `ror_`) | only `country_code` (the upstream ROR store has a fixed-shape filter) | +| `RenkulabRagProvider` (`src/v2/ingest/providers/renkulab_rag.py`) | `search_renkulab_rag` | `renkulab_projects`, `renkulab_groups`, `renkulab_users`, `renkulab_data_connectors` | `entity_type, slug, namespace, path, visibility, storage_type, storage_provider, entity_id`. The tool also accepts an `entity_types` list to scope the search to a subset of the four collections (default: all four, results merged + reranked together). | +| `SwissubaseRagProvider` (`src/v2/ingest/providers/swissubase_rag.py`) | `search_swissubase_rag` | `swissubase_entities` (single collection; `entity_type` payload disambiguates `studies` / `datasets` / `persons` / `institutions`) | `entity_type, study_id, dataset_id, person_key, institution_key, ref, main_discipline, sub_discipline, progress, year_start, year_end, access_right`. Backed by `src/index/swissubase/` (Selenium-driven ingest of the swissUbase social-science research-data platform: 18k+ studies via direct studyVersionId enumeration since the search endpoint caps at 250 items). Default `INDEX_SWISSUBASE_SCOPE=epfl_sdsc_ethz` ingests every study but only embeds those whose institution string matches EPFL / ETHZ / SDSC. Every hit carries the canonical `https://www.swissubase.ch/...` URL in `source_url`. Wired into the **article agent**. | +| `SnsfRagProvider` (`src/v2/ingest/providers/snsf_rag.py`) | `search_snsf_rag` | `epfl`, `ethz`, `switzerland` (scope mode → collection `snsf_`) | `institution` (exact match on `research_institution`, e.g. `EPF Lausanne – EPFL`), `institute` (substring on the lab/centre name, resolved from DuckDB after the ANN — surgical for SDSC-style queries), `discipline_l1`, `state` (PascalCase: `Completed` / `Ongoing` / `Approved`). Other keys dropped with a warning. Backed by `src/index/snsf/` (Qwen3-Embedding-8B over title + discipline + keywords + abstract); the bulk CSV ingest covers every SNSF P3 grant from 1975–2027 (~90 k records). Wired into the **person** and **organization** agents — useful to ground a researcher's funding history or to attribute a project to its host institute. | +| `GitHubRagProvider` (`src/v2/ingest/providers/github_rag.py`) | `search_github_rag` | `github_repos` | `entity_type, repo_id, owner, primary_language, license_spdx, is_archived, is_fork`. Backed by the deterministic, REST-fed `src/index/github/` corpus (EPFL/Swiss-research repos: metadata + README chunks, Qwen3-Embedding-8B). Wired into the **repository agent** so the LLM can find related implementations / canonical forks while reasoning about the repo it is processing. | +| `EpflGraphRagProvider` (`src/v2/ingest/providers/epfl_graph_rag.py`) | `search_epfl_graph_disciplines` | `epfl_graph_disciplines` (single collection, one point per ontology category) | `category_id, depth, parent_id, entity_type`. Backed by `src/index/epfl_graph/` — ~2226 EPFL Graph academic-discipline categories (depth 1..5) with embeddings built from `name + canonical Wikipedia lead-section + top anchor concept names`. Wired into the **repository, person, organization, and article** LLM agents. Use `filters={"depth": 4}` to keep only leaf disciplines and skip broad ancestor nodes. | +| `FederatedRagProvider` (`src/v2/ingest/providers/federated_rag.py`) | `search_federated_rag`, `lookup_entity_federated` | wraps **12** indices (HuggingFace, OpenAlex, Infoscience, ORCID, ROR, Zenodo, ETH Research Collection, GitHub, SNSF, RenkuLab, EPFL Graph, SWISSUbase) | forwarded as-is to every adapter — each picks up the keys it knows, ignores the rest. `search_federated_rag(rerank=True)` engages the cross-index reranker. | + +Infoscience and the ETH Research Collection are DSpace-based sister +indices with chunked bodies — search hits return a short snippet plus an +`article_uuid` the agent can pass to `fetch_*_chunks` for the full text. +The other indices already return complete records from search. + +## Tool flow + +```mermaid +flowchart LR + A[LLM agent] -->|search_*_rag| T[Tool factory] + T --> P[Provider.search] + P --> E[RCP /embeddings] + E --> Q[Qdrant query_points] + Q -->|rerank=True| R[RCP /rerank] + R --> H[Thin hits] + Q -->|rerank=False| H + H --> A + A -.optional.-> F[fetch_infoscience_*] + F --> P2[Provider.fetch_*] + P2 --> Q2[Qdrant scroll/retrieve] + Q2 --> A +``` + +`search` returns a list of small dicts (id + score + identifiers + a +≤320-char snippet for the indices that index body text). The agent can +then call `fetch_infoscience_chunks(article_uuid)` / +`fetch_infoscience_records(collection, ids)` to spend context on the full +body only for the hit that looks promising. + +## Filter dict format + +All `search_*_rag` tools accept the same operator dict (the per-store +quirks are translated by `_rag_helpers.to_simple_filter_payload`): + +| Operator / shape | Meaning | Example | +|---|---|---| +| scalar | `field == value` | `{"year": 2024}` | +| `[v1, v2, …]` or `{"$in": […]}` | `field` ∈ list | `{"library_name": ["transformers", "diffusers"]}` | +| `{"$gte": X, "$lte": Y}` | range | `{"downloads": {"$gte": 1000}}` | +| `{"$eq": v}` | explicit equals | `{"country_code": {"$eq": "CH"}}` | +| `{"$ne": v}` | infoscience only | `{"publication_type": {"$ne": "thesis"}}` | +| `{"$contains": "needle"}` | infoscience only (list-of-string fields) | `{"keywords": {"$contains": "drug"}}` | + +Keys not in a provider's allowlist are dropped with a warning before the +filter reaches Qdrant — see the table above. Operators not supported by a +backend (e.g. `$ne` outside infoscience) are also dropped with a +warning. ROR is the strictest: only `country_code` is honoured. + +## Hit shape + +Search hits are small dicts intended for the LLM to skim. The exact keys +vary by index but always include `id`, `score`, and a short +identifier-like key (`repo_id`, `openalex_id`, `zenodo_id`, `orcid_id`, +`ror_id`, …). Indices with body text also include +`snippet` (first ~320 chars of `text` / `abstract` / `biography`). + +```python +# example: search_huggingface_rag(query=…, collection="models", top_k=3) +[ + { + "id": "0a4f…", + "score": 0.91, + "collection": "models", + "repo_id": "ZurichNLP/swissbert", + "author": "ZurichNLP", + "library_name": "transformers", + "pipeline_tag": "fill-mask", + "downloads": 1024, + "likes": 17, + }, + … +] +``` + +## Reranker + +`rerank=True` widens the vector top-k by `RERANK_CANDIDATE_MULTIPLIER × +top_k` (floored at 30) and runs `Qwen/Qwen3-Reranker-8B` over the +candidates. It's slower (one extra RCP call, hundreds of ms) but more +precise — useful for ambiguous semantic queries, less so for exact-id +lookups. Default off; the agent opts in. + +When the indexed payload doesn't carry body text (e.g. HF chunks only +have metadata) the provider builds a synthetic doc string from +`repo_id` + `library_name`/`sdk` so the reranker still has something to +score against. Empty docs are replaced with a single space +(`safe_rerank_documents`) — the reranker rejects empty inputs. + +## Configuration + +Each provider is gated by a `V2__RAG_ENABLED` flag (all default +`true`). The full table: + +| Var | Default | Effect | +|---|---|---| +| `V2_INFOSCIENCE_RAG_ENABLED` | `true` | enables Infoscience tools | +| `V2_ETHZ_RESEARCH_COLLECTION_RAG_ENABLED` | `true` | enables ETH Research Collection tools | +| `V2_HUGGINGFACE_RAG_ENABLED` | `true` | enables HF Hub tool | +| `V2_OPENALEX_RAG_ENABLED` | `true` | enables OpenAlex tool | +| `V2_ZENODO_RAG_ENABLED` | `true` | enables Zenodo tool | +| `V2_ORCID_RAG_ENABLED` | `true` | enables ORCID tool | +| `V2_ROR_RAG_ENABLED` | `true` | enables ROR tool | +| `V2_SNSF_RAG_ENABLED` | `true` | enables SNSF P3 tool | +| `INDEX_QDRANT_URL` | yaml default per index | Qdrant endpoint. Inside the devcontainer use `http://gme-qdrant:6333` | +| `INDEX_QDRANT_API_KEY` | unset | Qdrant API key | +| `RCP_TOKEN` | required for embed/rerank | EPFL RCP inference endpoint token | +| `INFOSCIENCE_TOKEN` | unset | only for protected Infoscience read paths | +| `ETHZ_RESEARCH_COLLECTION_TOKEN` | unset | only for protected ETH Research Collection read paths | + +Construction is best-effort: missing config, unreachable Qdrant, missing +RCP token, or a missing collection all yield `None` (or empty results) +without raising. The rest of the v2 pipeline keeps running without RAG. + +## Wiring into agents + +The five non-Infoscience providers are not auto-wired to any agent — each +agent's prompt budget is finite and adding 5 more tools to every prompt +risks bloating the system prompt. Wire by hand inside the agent's +`run()` method, guarded on `providers.` being non-`None`: + +```python +# inside e.g. src/v2/agents/llm/article/agent.py +if providers.openalex_rag is not None: + tools.append(make_openalex_rag_search_tool(providers.openalex_rag)) +if providers.zenodo_rag is not None: + tools.append(make_zenodo_rag_search_tool(providers.zenodo_rag)) +``` + +Recommended pairings: + +| Agent | Tools to add | +|---|---| +| `article` | `openalex_rag` (works), `zenodo_rag`, `renkulab_rag`, `epfl_graph_rag` (discipline tagging) | +| `person` | `orcid_rag`, `openalex_rag` (authors), `epfl_graph_rag` (researcher → primary discipline) | +| `organization` | `ror_rag`, `huggingface_rag` (orgs), `openalex_rag` (institutions), `renkulab_rag` (groups), `epfl_graph_rag` (lab/unit → primary discipline) | +| `repository` | `huggingface_rag` (models/datasets), `renkulab_rag` (projects), `epfl_graph_rag` (README → discipline) | +| `contribution`, `membership` | (none — pure linking agents) | + +Infoscience and ETH Research Collection are already wired into article / +person / organization agents (the two DSpace-based institutional +repositories cover EPFL + ETHZ outputs respectively). + +## Federated tools — picking up ETH RC alongside everything else + +`search_federated_rag` is the LLM-facing wrapper over `FederatedRagProvider`. +It fans out to every adapter that's loaded — including the ETH Research +Collection one — and returns one merged hit list: + +```python +# What the LLM sees in the tool response +{ + "hits": [ + {"index": "huggingface", "entity_type": "model", + "id": "ZurichNLP/swissbert", "score": 0.87, "title": "ZurichNLP/swissbert", + "url": "https://huggingface.co/ZurichNLP/swissbert", "payload": {...}}, + {"index": "ethz_research_collection", "entity_type": "chunk", + "id": "941936d3-…", "score": 0.85, + "title": "...", "summary": "first 200 chars of the chunk text", + "payload": {"article_uuid": "...", "chunk_index": 7, ...}}, + ... + ], + "by_index": {"huggingface": 3, "ethz_research_collection": 3, + "infoscience": 2, "openalex": 0, ...}, + "errors": {}, +} +``` + +Restrict the fan-out with `indices=[…]` when the agent already knows where +to look — e.g. `["ethz_research_collection", "infoscience"]` to query the +two DSpace-based institutional repositories side by side. Cross-index +identifier resolution (`lookup_entity_federated`) recognises ETH RC +handles (`20.500.11850/...`) and full `research-collection.ethz.ch` URLs. + +## Telemetry + +Each tool call records a `record_query(service=".rag.search.", query=…)` entry on the per-request `QueryLog`. The +log is written to `${V2_QUERY_LOG_DIR}/${run_id}.json` at the end of +`/v2/extract`. Useful for measuring which indices the agents actually +reach for. The federated tool emits `federated.rag.search` / +`federated.rag.lookup` so you can distinguish a fan-out call from the +per-index calls each adapter makes downstream. + +## Adding a new index + +The shape every provider needs to satisfy: + +1. **Wrap a Qdrant store** — anything with a `search(collection, *, + query_vector, top_k, ...)` method works. +2. **Wrap an embedder** — async callable returning `list[float]` for one + query. +3. **Optional reranker** — async callable returning `list[{index, + relevance_score}]`. +4. **Define a `_ALLOWED_FILTER_KEYS` frozenset** — anything not in here + is dropped with a warning. +5. **Define a per-collection `_THIN_KEYS`** tuple — fields copied from + the Qdrant payload into the LLM-facing hit. +6. **Use `_rag_helpers`** for `filter_allowlist`, + `to_simple_filter_payload`, `expand_candidate_k`, + `apply_rerank_indices`, `safe_rerank_documents`, `make_snippet`, + `thin_payload`, `env_enabled`. Avoid re-implementing. +7. **Provide `build_default_provider()`** gated by `env_enabled("V2__RAG_ENABLED")` and best-effort (return `None` on any error). +8. **Wire into `ProviderSet`** (`src/v2/agents/models.py:168`) and + `dependencies.py:_resolve_rag_provider`. +9. **Export the tool factory** from `src/v2/agents/llm/agent_tools/__init__.py`. + +`src/v2/ingest/providers/zenodo_rag.py` is the simplest reference (single +collection, reuses OpenAlex's clients, ~150 lines). + +## Code map + +``` +src/v2/ingest/providers/ + _rag_helpers.py # filter allowlist / operator translation / + # candidate expansion / rerank reorder / + # snippet truncation / env toggle + infoscience_rag.py # search + fetch_chunks + fetch_records + ethz_research_collection_rag.py # search + fetch_chunks + fetch_records + # (sister DSpace index to infoscience) + huggingface_rag.py # search + lineage (models/datasets/spaces/orgs) + openalex_rag.py # search (6 entity collections) + zenodo_rag.py # search + fetch_records + orcid_rag.py # search (persons/employments/educations) + ror_rag.py # search (4 scope modes; country_code only) + github_rag.py # search (Swiss/EPFL repo seed) + snsf_rag.py # search (grants + people + institutions) + renkulab_rag.py # search (projects/groups/users/data_connectors) + swissubase_rag.py # search (studies/datasets/persons/institutions) + epfl_graph_rag.py # search (EPFL discipline ontology) + federated_rag.py # search + lookup; fans out to every adapter + +src/v2/agents/llm/agent_tools/ + infoscience_rag.py # 3 tools + ethz_research_collection_rag.py # 3 tools (search + fetch_chunks + fetch_records) + huggingface_rag.py # 1 tool (+ lineage helper) + openalex_rag.py # 1 tool + zenodo_rag.py # 2 tools (search + fetch_records) + orcid_rag.py # 1 tool + ror_rag.py # 1 tool + github_rag.py # 1 tool + snsf_rag.py # 1 tool + renkulab_rag.py # 1 tool + swissubase_rag.py # 1 tool + epfl_graph_rag.py # 1 tool + federated_rag.py # 2 tools (search_federated_rag + lookup_entity_federated) + +src/v2/dependencies.py # _resolve_rag_provider + ProviderSet builder +src/v2/agents/models.py # ProviderSet (12 per-index *_rag fields + federated_rag) +``` + +## Tests + +| File | Covers | +|---|---| +| `tests/v2/test_llm_rag_helpers.py` | `_rag_helpers` (filter allowlist, operator translation, candidate expansion, rerank reorder, snippet, thin payload). | +| `tests/v2/test_llm_infoscience_rag_tool.py` | Infoscience search/fetch flows + tool factories. | +| `tests/v2/test_llm_ethz_research_collection_rag_tool.py` | ETH Research Collection search/fetch flows + tool factories. | +| `tests/v2/test_llm_huggingface_rag_tool.py` | HF collection routing, allowlist, missing-collection skip, rerank reorder. | +| `tests/v2/test_llm_other_rag_tools.py` | OpenAlex, Zenodo, ORCID, ROR — including ROR's country-only filter and graceful empty on missing collections. | + +Tests use fake stores / embedders / rerankers and never hit Qdrant or +RCP — fast and deterministic. diff --git a/docs/zenodo-index.md b/docs/zenodo-index.md new file mode 100644 index 0000000..28607b6 --- /dev/null +++ b/docs/zenodo-index.md @@ -0,0 +1,233 @@ +# Zenodo Index + +Standalone RAG index over Zenodo records (datasets, software releases, +presentations, posters, papers) cited by EPFL / Swiss research outputs. +Lives at `src/index/zenodo/`. + +> **Two complementary views.** [`docs/v2-rag-tools.md`](v2-rag-tools.md) +> documents the **agent-facing** tools (`search_zenodo_rag`, +> `fetch_zenodo_records`) used by the v2 LLM pipeline. *This* page +> documents the **direct-access** CLI, ingest pipeline, federated +> adapter, and the citation-driven discovery path. + +## Architecture + +DuckDB at `data/index/zenodo/duckdb/zenodo.duckdb` holds the canonical +records (one row per Zenodo deposit) plus normalised creator, +community, and file tables. Qdrant collection `zenodo_records` holds +the embedding vectors — one point per chunk, identified by +`uuid5(NAMESPACE_URL, "records||")`. Embeddings +come from `Qwen/Qwen3-Embedding-8B` on EPFL RCP; reranking from +`Qwen/Qwen3-Reranker-8B`. + +``` +┌─ Discover (citation-driven) ──────────────────────────────────────────┐ +│ data/index/infoscience/text/*.txt → scan for `zenodo.org/{id}` / │ +│ `10.5281/zenodo.{id}` → diff against records.zenodo_id and │ +│ records.concept_recid │ +└──────┬────────────────────────────────────────────────────────────────┘ + ▼ +┌─ Ingest ──────────────────────────────────────────────────────────────┐ +│ Zenodo REST /api/records/{id} (or /api/records?communities=…) │ +│ │ │ +│ ▼ │ +│ DuckDB: records / creators / communities / record_creators / │ +│ record_communities / files │ +└──────┬────────────────────────────────────────────────────────────────┘ + ▼ +┌─ Embed ───────────────────────────────────────────────────────────────┐ +│ DuckDB rows → chunker (256 tok, 64 overlap) → RCP /v1/embeddings │ +│ │ → Qdrant │ +│ ▼ │ +│ bookkeeping: chunks(chunk_id, entity_type, entity_id, chunk_index, …)│ +└──────┬────────────────────────────────────────────────────────────────┘ + ▼ +┌─ Query ───────────────────────────────────────────────────────────────┐ +│ semantic: query → embed → Qdrant → optional rerank → thin hits │ +│ hydrate: ids (zenodo_id OR concept_recid) → DuckDB → full record │ +│ federated: same query fans out across all gme indices in parallel │ +└───────────────────────────────────────────────────────────────────────┘ +``` + +## Two ingest modes + +### A. Community-driven (the default) + +Pulls every record published under one of the configured Zenodo +community slugs in `config/index/zenodo.yaml`: + +```yaml +scope: + default: epfl + epfl_communities: + - epfl + - eesd_at_epfl + - ivrl-epfl-switzerland + switzerland_communities: + - supsi + - sweco25 + - plato-ch +``` + +```bash +python -m src.index.zenodo ingest --scope epfl +python -m src.index.zenodo ingest --scope switzerland +``` + +Resumable via `data/index/zenodo/state/ingest_.json` — completed +community slugs are skipped on re-run unless `--refresh` is set. + +### B. Citation-driven (the broader signal) + +Most Zenodo deposits cited in EPFL publications are **not** under the +`epfl` community — they're external software (LMFIT, Yade, py-sphviewer) +or per-lab data deposits (`petersen-lab-data`, `holtmaat-lab-data`, +`mobilise-d`, `impresso`, `spi-ace`, …) that the community filter +misses entirely. The `discover` subcommand mines the local Infoscience +full-text dump for `zenodo.org/{id}` / `10.5281/zenodo.{id}` references +and emits the IDs we don't already have: + +```bash +# Discover only (prints summary): +python -m src.index.zenodo discover --source infoscience + +# Discover + dump full payload (ID list + community evidence): +python -m src.index.zenodo discover --source infoscience \ + --out data/index/zenodo/state/discovered_from_infoscience.json + +# Discover + ingest in one go: +python -m src.index.zenodo discover --source infoscience --ingest +``` + +The diff against existing records is performed against **both** +`records.zenodo_id` (canonical version-record) and +`records.concept_recid` (Zenodo's "all versions" parent id) — this +prevents re-fetching the same deposit just because a citation referenced +the concept rather than a specific version (Zenodo's `/api/records/{id}` +follows redirects from concept → latest version, and we persist under +the canonical version's id). + +Direct ingest by an arbitrary list of IDs / DOIs / URLs: + +```bash +python -m src.index.zenodo ingest --ids my_ids.txt +``` + +The token normaliser accepts: + +| Input | Resolved id | +|---|---| +| `3909400` | `3909400` | +| `10.5281/zenodo.3909400` | `3909400` | +| `https://zenodo.org/records/3909400` | `3909400` | +| `https://doi.org/10.5281/zenodo.3909400` | `3909400` | + +## Schema + +```sql +records ( + zenodo_id TEXT PRIMARY KEY, -- canonical version-record id (post-redirect) + concept_recid TEXT, -- Zenodo concept-record (groups all versions) + doi TEXT, + title TEXT, + description TEXT, -- HTML-stripped + publication_date DATE, + resource_type TEXT, -- e.g. publication-article, dataset, software + access_right TEXT, -- open | embargoed | restricted | closed + license_id TEXT, + keywords_json JSON, + raw JSON, -- full Zenodo API payload + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +) + +creators (creator_key TEXT PRIMARY KEY, display_name, orcid, affiliation, raw, ingested_at) +record_creators (record_id, creator_key, position) +communities (community_id TEXT PRIMARY KEY, title, raw, ingested_at) +record_communities (record_id, community_id) +files (record_id, file_key, file_id, size_bytes, checksum, download_url) +chunks (chunk_id, entity_type, entity_id, chunk_index, text, token_count, vector_id, embedded_at) +``` + +`ZenodoStore.bootstrap()` is idempotent and self-migrates older DBs: +on open, it adds `concept_recid` if missing, then back-fills it from +`raw.conceptrecid` for every existing row. + +## CLI subcommands + +| Subcommand | Purpose | +|---|---| +| `discover` | Find candidate Zenodo IDs from external sources (currently `--source infoscience`); optional `--ingest` chains directly into the per-id ingest | +| `ingest` | Pull records into DuckDB. `--scope ` walks Zenodo communities; `--ids ` bulk-fetches a list of IDs / DOIs / URLs | +| `embed` | Chunk + embed records with no chunks yet, push vectors to Qdrant | +| `search` | Semantic retrieval (vector + RCP rerank). Mirrored by `just zenodo-search` | +| `query` | Read-only DuckDB SQL — predefined queries (`records_by_author`, …) or guarded ad-hoc | +| `status` | Row counts + Qdrant point count + paths | +| `serve` | Run the FastAPI app (port 8003) | + +## Federated layer + +Zenodo registers a `ZenodoAdapter` at +`src/index/_federated/adapters/zenodo.py`, exposing two operations to +the federated layer: + +- **`search`** — wraps `semantic_search()` from + `src/index/zenodo/retrieval/semantic.py`. Returns `Hit` objects with + `index="zenodo"`, `entity_type="zenodo_record"`, score, summary, URL. +- **`lookup`** — recognises identifier strings (numeric Zenodo id, + `10.5281/zenodo.` DOI, `https://zenodo.org/records/` URL). + Tries `fetch_record(id)` first, then falls back to + `fetch_record_by_concept(id)` so a cited concept-recid still resolves + to the canonical version-record we have on disk. + +```bash +gme search "Yade granular dynamics" --indices zenodo --top-k 5 +gme entity 10.5281/zenodo.3909400 --indices zenodo +``` + +## Agent tools (v2 LLM pipeline) + +Wired into the **article agent**: + +| Tool | Purpose | +|---|---| +| `search_zenodo_rag(query, top_k, filters?, rerank?)` | Vector search the `zenodo_records` Qdrant collection. Returns thin hits (`zenodo_id`, `title`, `doi`, `year`, `resource_type`, `access_right`). | +| `fetch_zenodo_records(ids)` | Hydrate full records from DuckDB by `zenodo_id` **or** `concept_recid`. Returns full body (title, doi, description capped at 2000 chars, license, access_right, dates). | + +Tools are gated by `V2_ZENODO_RAG_ENABLED` (default `true`) and +construct lazily — if Qdrant or RCP is unreachable, the provider builder +returns `None` and the tools simply aren't attached. + +## Configuration + +The Zenodo index reads `config/index/zenodo.yaml`. Notable env vars +(merged at `load_config()`): + +| Var | Default | Purpose | +|---|---|---| +| `ZENODO_TOKEN` | unset | optional bearer token. Anonymous is rate-limited to 25/min and `page_size ≤ 25`. | +| `RCP_TOKEN` | — | required for embed / search / rerank | +| `INDEX_QDRANT_URL` | `http://gme-qdrant:6333` (yaml default) | Qdrant endpoint | +| `INDEX_QDRANT_API_KEY` | unset | passed through if your Qdrant requires it | +| `INDEX_DATA_DIR` | `data/` | overrides where DuckDB / state files live | +| `V2_ZENODO_RAG_ENABLED` | `true` | flip to `false` to detach the agent tools | + +## Persisted state files + +``` +data/index/zenodo/ + duckdb/zenodo.duckdb # canonical store + state/ + ingest_epfl.json # community-completion cursor + discovered_from_infoscience.json # last `discover --out` payload + ids_ingest_summary.json # last `ingest --ids` summary (audit) +``` + +## Status + +```bash +python -m src.index.zenodo status +``` + +Prints DuckDB row counts (`records`, `creators`, `record_creators`, +`communities`, `record_communities`, `files`, `chunks`), the Qdrant +collection name + point count, and the configured EPFL community list. diff --git a/examples/example_caching.py b/examples/example_caching.py index 8aa1519..b4498b1 100644 --- a/examples/example_caching.py +++ b/examples/example_caching.py @@ -126,7 +126,7 @@ async def example_gimie_caching(): def fetch_gimie_data(): print(f" 🔍 Fetching fresh GIMIE data for {repo_url}...") - return extract_gimie(repo_url, format="json-ld") + return extract_gimie(repo_url, serialization_format="json-ld") # First call - will fetch from GIMIE print("1. First call (cache miss):") diff --git a/justfile b/justfile index 162c6e5..7218813 100644 --- a/justfile +++ b/justfile @@ -1,6 +1,9 @@ # Git Metadata Extractor - Task Runner # Usage: just +# Auto-load .env so API_TOKEN (and other vars) are visible to recipes. +set dotenv-load := true + # Default host and port (can be overridden with HOST=value PORT=value just serve) HOST := env_var_or_default("HOST", "0.0.0.0") PORT := env_var_or_default("PORT", "1234") @@ -38,12 +41,14 @@ serve: uvicorn src.api:app --host {{HOST}} --port {{PORT}} --workers {{WORKERS}} # Serve the FastAPI app in development mode with auto-reload +# Watches only `src/` for `.py` changes — keeps in-flight requests alive +# when extraction outputs, logs, or test files change. serve-dev: - uvicorn src.api:app --host {{HOST}} --port {{PORT}} --reload + uvicorn src.api:app --host {{HOST}} --port {{PORT}} --reload --reload-dir src --reload-include '*.py' # Serve in development mode with debug logging serve-dev-debug: - LOG_LEVEL=DEBUG uvicorn src.api:app --host {{HOST}} --port {{PORT}} --reload --log-level debug + LOG_LEVEL=DEBUG uvicorn src.api:app --host {{HOST}} --port {{PORT}} --reload --reload-dir src --reload-include '*.py' --log-level debug # Serve with single worker (useful for debugging) serve-single: @@ -53,18 +58,33 @@ serve-single: serve-gunicorn: gunicorn src.api:app --workers {{WORKERS}} --worker-class uvicorn.workers.UvicornWorker --bind {{HOST}}:{{PORT}} +# Stop any uvicorn/gunicorn process listening on PORT. +# SIGTERM first; falls back to SIGKILL if anything is still bound after 2s. +serve-stop: + #!/usr/bin/env bash + set -u + pids=$(lsof -ti :{{PORT}} 2>/dev/null || true) + if [ -z "$pids" ]; then + echo "no server listening on :{{PORT}}" + exit 0 + fi + echo "stopping pids on :{{PORT}}: $pids" + kill $pids 2>/dev/null || true + sleep 2 + pids=$(lsof -ti :{{PORT}} 2>/dev/null || true) + if [ -n "$pids" ]; then + echo "force-killing: $pids" + kill -9 $pids 2>/dev/null || true + fi + if lsof -i :{{PORT}} >/dev/null 2>&1; then + echo "port :{{PORT}} still in use"; exit 1 + fi + echo "port :{{PORT}} free" + # ============================================================================ # CLI Commands # ============================================================================ -# Run the CLI tool to extract metadata from a repository -extract URL OUTPUT="output_file.json": - python src/main.py --url {{URL}} --output_path {{OUTPUT}} - -# Extract metadata from a default test repository -extract-test: - python src/main.py --url https://github.com/qchapp/lungs-segmentation --output_path test_output.json - # ============================================================================ # Docker Commands # ============================================================================ @@ -100,21 +120,74 @@ docker-up: docker-build docker-run # Testing # ============================================================================ -# Run all tests +# Run fast local tests (impacted tests via testmon, no coverage, parallelized) TOKEN are provided empty to avoid trigering a real API call. test: - PYTHONPATH=src pytest tests/ -v + OPENAI_API_KEY= OPENROUTER_API_KEY= RCP_TOKEN= .venv/bin/python -m pytest tests/v2/ -q --testmon --no-cov -n auto --dist=loadfile + +# Run full local tests (parallelized, deterministic selection) +test-full: + .venv/bin/python -m pytest tests/v2/ -q -n auto --dist=loadfile -m 'not live_provider and not llm_integration' # Run tests with coverage test-coverage: - PYTHONPATH=src pytest tests/ --cov=src --cov-report=html --cov-report=term + .venv/bin/python -m pytest tests/v2/ --cov=src/v2 --cov-report=html --cov-report=term -m 'not live_provider and not llm_integration' # Run specific test file test-file FILE: - PYTHONPATH=src pytest {{FILE}} -v + .venv/bin/python -m pytest {{FILE}} -v + +# Run real-provider LLM integration tests only +test-llm-integration: + .venv/bin/python -m pytest tests/v2/test_llm_repository_agent.py -m llm_integration -v # Run tests in watch mode (requires pytest-watch) test-watch: - PYTHONPATH=src ptw tests/ + PYTHONPATH=src ptw tests/v2/ + +# Run Phase 8 preflight connectivity checks against live providers +preflight-live: + python scripts/v2/check_provider_connectivity.py + +# Capture sanitized live provider snapshots and promote them into the live fixture namespace +capture-live: + python scripts/v2/capture_provider_snapshots.py --promote-to tests/v2/fixtures/providers/live_snapshots + +# Run opt-in live provider smoke tests +test-live: + .venv/bin/python -m pytest tests/v2/test_live_provider_connectivity.py -m live_provider -v + +# Run offline Phase 8 fixture/sanitizer checks +test-offline: + .venv/bin/python -m pytest tests/v2/test_provider_connectivity_preflight.py tests/v2/test_provider_snapshot_sanitizer.py tests/v2/test_live_snapshot_fixture_contract.py -v + +# Generate committed v2 Pydantic models from strict schemas +v2-models-generate: + PYTHONPATH=. .venv/bin/python scripts/v2/generate_v2_models.py + +# Check committed v2 generated models are in sync with strict schemas +v2-models-check: + PYTHONPATH=. .venv/bin/python scripts/v2/generate_v2_models.py --check + +# Run LLM repository agent end-to-end with real GIMIE context (no server needed) +# Usage: just v2-run-repo-agent sdsc-ordes/gimie +v2-run-repo-agent REPO: + PYTHONPATH=. .venv/bin/python scripts/v2/run_llm_repository_agent.py {{REPO}} + +# Run LLM repository + person agents (stages: context_gather -> repo_agent -> person_agents) +# Usage: just v2-run-repo-and-persons sdsc-ordes/gimie +v2-run-repo-and-persons REPO: + PYTHONPATH=. .venv/bin/python scripts/v2/run_llm_repo_and_persons.py {{REPO}} + +# Run full 7-stage LLM repository debug pipeline +# (context -> repo -> persons -> orgs -> articles -> memberships -> contributions) +# Usage: just v2-run-repo-persons-and-orgs sdsc-ordes/gimie +v2-run-repo-persons-and-orgs REPO: + PYTHONPATH=. .venv/bin/python scripts/v2/run_llm_repo_persons_and_orgs.py {{REPO}} + +# Alias for the full 7-stage LLM repository debug pipeline + link verification +# Usage: just v2-run-repo-full-llm sdsc-ordes/gimie +v2-run-repo-full-llm REPO: + PYTHONPATH=. .venv/bin/python scripts/v2/run_llm_repo_persons_and_orgs.py {{REPO}} --verify-links # ============================================================================ # Cache Management (via API) @@ -122,23 +195,23 @@ test-watch: # Get cache statistics cache-stats: - curl -X GET http://localhost:{{PORT}}/v1/cache/stats | python -m json.tool + curl -X GET -H "Authorization: Bearer ${API_TOKEN}" http://localhost:{{PORT}}/v1/cache/stats | python -m json.tool # Clean up expired cache entries cache-cleanup: - curl -X POST http://localhost:{{PORT}}/v1/cache/cleanup | python -m json.tool + curl -X POST -H "Authorization: Bearer ${API_TOKEN}" http://localhost:{{PORT}}/v1/cache/cleanup | python -m json.tool # Clear all cache entries cache-clear: - curl -X POST http://localhost:{{PORT}}/v1/cache/clear | python -m json.tool + curl -X POST -H "Authorization: Bearer ${API_TOKEN}" http://localhost:{{PORT}}/v1/cache/clear | python -m json.tool # Enable caching cache-enable: - curl -X POST http://localhost:{{PORT}}/v1/cache/enable | python -m json.tool + curl -X POST -H "Authorization: Bearer ${API_TOKEN}" http://localhost:{{PORT}}/v1/cache/enable | python -m json.tool # Disable caching cache-disable: - curl -X POST http://localhost:{{PORT}}/v1/cache/disable | python -m json.tool + curl -X POST -H "Authorization: Bearer ${API_TOKEN}" http://localhost:{{PORT}}/v1/cache/disable | python -m json.tool # ============================================================================ # Development Utilities @@ -154,15 +227,15 @@ format-ruff: # Lint code using ruff lint: - ruff check src/ + uv run ruff check src/ # Lint and fix issues automatically lint-fix: - ruff check --fix src/ + uv run ruff check --fix src/ # Type check using mypy type-check: - mypy src/ + uv run mypy src/ # Run all code quality checks check: lint type-check @@ -205,17 +278,21 @@ docs-deploy-release VERSION: docs-set-default VERSION: mike set-default --push --branch gh-pages {{VERSION}} -# Test the main extract endpoint +# Test the main extract endpoint (was /v1/extract/json — that route is commented out; +# this hits the active /v1/repository/llm/json equivalent). api-test-extract: - curl -X GET "http://localhost:{{PORT}}/v1/extract/json/https://github.com/qchapp/lungs-segmentation" | python -m json.tool + curl -X GET -H "Authorization: Bearer ${API_TOKEN}" \ + "http://localhost:{{PORT}}/v1/repository/llm/json/https://github.com/qchapp/lungs-segmentation" | python -m json.tool # Test the extract endpoint with force refresh api-test-extract-refresh: - curl -X GET "http://localhost:{{PORT}}/v1/extract/json/https://github.com/qchapp/lungs-segmentation?force_refresh=true" | python -m json.tool + curl -X GET -H "Authorization: Bearer ${API_TOKEN}" \ + "http://localhost:{{PORT}}/v1/repository/llm/json/https://github.com/qchapp/lungs-segmentation?force_refresh=true" | python -m json.tool # Test the GIMIE endpoint api-test-gimie: - curl -X GET "http://localhost:{{PORT}}/v1/repository/gimie/json-ld/https://github.com/qchapp/lungs-segmentation" | python -m json.tool + curl -X GET -H "Authorization: Bearer ${API_TOKEN}" \ + "http://localhost:{{PORT}}/v1/repository/gimie/json-ld/https://github.com/qchapp/lungs-segmentation" | python -m json.tool # ============================================================================ # Cleanup @@ -300,3 +377,330 @@ pre-commit-update: # Clean pre-commit cache pre-commit-clean: pre-commit clean + +# ============================================================================ +# Infoscience indexer (src/index/infoscience) +# ============================================================================ + +# Solr fulltext discover for configured filter terms. +index-infoscience-discover *ARGS: + .venv/bin/python -m src.index.infoscience discover {{ARGS}} + +# Download TEXT bundle plaintext for each discovered item. +index-infoscience-fetch-text *ARGS: + .venv/bin/python -m src.index.infoscience fetch-text {{ARGS}} + +# Regex-extract GitHub/HuggingFace URLs from fetched text. +index-infoscience-extract-matches: + .venv/bin/python -m src.index.infoscience extract-matches + +# Pull Person/Org authority UUIDs from matched articles. +index-infoscience-extract-relations: + .venv/bin/python -m src.index.infoscience extract-relations + +# Fetch raw JSON for each linked Person/OrgUnit. +index-infoscience-fetch-related *ARGS: + .venv/bin/python -m src.index.infoscience fetch-related {{ARGS}} + +# Chunk + embed + populate LanceDB tables. +index-infoscience-embed *ARGS: + .venv/bin/python -m src.index.infoscience embed {{ARGS}} + +# Hybrid query (filter → vector → rerank). Pass the query as the first arg. +index-infoscience-query QUERY *ARGS: + .venv/bin/python -m src.index.infoscience query "{{QUERY}}" {{ARGS}} + +# Ingest the on-disk raw/* JSON tree into the infoscience DuckDB store. +# Optional: --links-dump= to merge a dump_link_articles.py output +# into the article_links table. +index-infoscience-ingest-duckdb *ARGS: + .venv/bin/python -m src.index.infoscience ingest-duckdb {{ARGS}} + +# Show pipeline + LanceDB counts and paths. +index-infoscience-status: + .venv/bin/python -m src.index.infoscience status + +# ============================================================================ +# OpenAlex indexer (src/index/openalex) +# ============================================================================ + +# Pull OpenAlex entities into DuckDB. Pass --scope epfl|switzerland. +openalex-ingest *ARGS: + .venv/bin/python -m src.index.openalex.cli ingest {{ARGS}} + +# Discover Swiss/EPFL Works mentioning github.com URLs (test set for v2). +openalex-find-github *ARGS: + .venv/bin/python -m src.index.openalex.cli find-github {{ARGS}} + +# Embed DuckDB rows into Qdrant via the RCP embedding endpoint. +openalex-embed *ARGS: + .venv/bin/python -m src.index.openalex.cli embed {{ARGS}} + +# Re-push existing DuckDB chunks into Qdrant (re-embeds chunks.text via RCP). +# Use after a Qdrant wipe — does NOT modify DuckDB. +openalex-rebuild-qdrant *ARGS: + .venv/bin/python -m src.index.openalex.cli rebuild-qdrant {{ARGS}} + +# Semantic retrieval (vector + rerank). First positional is the query. +openalex-search QUERY *ARGS: + .venv/bin/python -m src.index.openalex.cli search "{{QUERY}}" {{ARGS}} + +# Read-only SQL over the DuckDB dump (predefined or guarded ad-hoc). +openalex-query *ARGS: + .venv/bin/python -m src.index.openalex.cli query {{ARGS}} + +# Run the FastAPI app. +openalex-serve *ARGS: + .venv/bin/python -m src.index.openalex.cli serve {{ARGS}} + +# Run the OpenAlex test suite only. +openalex-test: + .venv/bin/python -m pytest tests/index/openalex/ -v -m openalex + +# ============================================================================ +# ORCID indexer (src/index/orcid) +# ============================================================================ + +# Build the seed ORCID list (OpenAlex authors + ORCID expanded-search). +# Pass --scope epfl|switzerland and optionally --source openalex|orcid_search|both. +orcid-discover *ARGS: + .venv/bin/python -m src.index.orcid discover {{ARGS}} + +# Fetch full ORCID records for seeded IDs, post-filter, persist to DuckDB. +orcid-ingest *ARGS: + .venv/bin/python -m src.index.orcid ingest {{ARGS}} + +# Chunk + embed in-scope rows, push to Qdrant via the RCP embedding endpoint. +orcid-embed *ARGS: + .venv/bin/python -m src.index.orcid embed {{ARGS}} + +# Semantic retrieval (vector + RCP rerank). First positional is the query. +orcid-search QUERY *ARGS: + .venv/bin/python -m src.index.orcid search "{{QUERY}}" {{ARGS}} + +# Read-only SQL over the ORCID DuckDB (predefined or guarded ad-hoc). +orcid-query *ARGS: + .venv/bin/python -m src.index.orcid query {{ARGS}} + +# Show counts + paths for the chosen scope. +orcid-status *ARGS: + .venv/bin/python -m src.index.orcid status {{ARGS}} + +# Run the FastAPI app on port 8002 by default. +orcid-serve *ARGS: + .venv/bin/python -m src.index.orcid serve {{ARGS}} + +# Run the ORCID test suite only. +orcid-test: + .venv/bin/python -m pytest tests/index/orcid/ -v + +# ============================================================================ +# HuggingFace indexer (src/index/huggingface) +# ============================================================================ + +# Pull HuggingFace metadata + cards into DuckDB. Pass --scope epfl|switzerland +# and optionally --types models,datasets,spaces. +hf-ingest *ARGS: + .venv/bin/python -m src.index.huggingface ingest {{ARGS}} + +# Substring-search the Hub for unknown EPFL/Swiss orgs; writes candidates to +# logs/discover_orgs.jsonl for human review (never auto-promotes to seed). +hf-discover-orgs *ARGS: + .venv/bin/python -m src.index.huggingface discover-orgs {{ARGS}} + +# Chunk + embed cards, push vectors to Qdrant via the RCP embedding endpoint. +hf-embed *ARGS: + .venv/bin/python -m src.index.huggingface embed {{ARGS}} + +# Semantic retrieval (vector + RCP rerank). First positional is the query. +hf-search QUERY *ARGS: + .venv/bin/python -m src.index.huggingface search "{{QUERY}}" {{ARGS}} + +# Read-only SQL over the HuggingFace DuckDB (predefined or guarded ad-hoc). +hf-query *ARGS: + .venv/bin/python -m src.index.huggingface query {{ARGS}} + +# Show DuckDB row counts + Qdrant collection size + paths. +hf-status: + .venv/bin/python -m src.index.huggingface status + +# Run the FastAPI app (default port 8002). +hf-serve *ARGS: + .venv/bin/python -m src.index.huggingface serve {{ARGS}} + +# Run the HuggingFace test suite only. +hf-test: + .venv/bin/python -m pytest tests/index/huggingface/ -v + +# ============================================================================ +# Zenodo indexer (src/index/zenodo) +# ============================================================================ + +# Pull Zenodo records into DuckDB. Pass --scope epfl|switzerland. +zenodo-ingest *ARGS: + .venv/bin/python -m src.index.zenodo ingest {{ARGS}} + +# Chunk + embed records, push vectors to Qdrant via the RCP embedding endpoint. +zenodo-embed *ARGS: + .venv/bin/python -m src.index.zenodo embed {{ARGS}} + +# Semantic retrieval (vector + RCP rerank). First positional is the query. +zenodo-search QUERY *ARGS: + .venv/bin/python -m src.index.zenodo search "{{QUERY}}" {{ARGS}} + +# Read-only SQL over the Zenodo DuckDB (predefined or guarded ad-hoc). +zenodo-query *ARGS: + .venv/bin/python -m src.index.zenodo query {{ARGS}} + +# Show DuckDB row counts + Qdrant collection size + paths. +zenodo-status: + .venv/bin/python -m src.index.zenodo status + +# Run the FastAPI app (default port 8003). +zenodo-serve *ARGS: + .venv/bin/python -m src.index.zenodo serve {{ARGS}} + +# ============================================================================ +# EPFL Graph disciplines indexer (src/index/epfl_graph) +# ============================================================================ +# RAG index over the curated EPFL Graph academic ontology (~2226 categories, +# 6 levels deep, each backed by 50-110 anchor Wikipedia concepts). Requires +# EPFL_GRAPH_USERNAME / EPFL_GRAPH_PASSWORD for ingest, RCP_TOKEN for embed. + +# Walk the ontology tree and persist categories to DuckDB. +epfl-graph-ingest *ARGS: + .venv/bin/python -m src.index.epfl_graph ingest {{ARGS}} + +# Embed categories and push them into Qdrant collection `epfl_graph_disciplines`. +epfl-graph-embed *ARGS: + .venv/bin/python -m src.index.epfl_graph embed {{ARGS}} + +# Semantic retrieval over the disciplines index. First positional is the query. +epfl-graph-search QUERY *ARGS: + .venv/bin/python -m src.index.epfl_graph search "{{QUERY}}" {{ARGS}} + +# DuckDB row counts + Qdrant collection name + paths. +epfl-graph-status: + .venv/bin/python -m src.index.epfl_graph status + +# ============================================================================ +# SWISSUbase indexer (src/index/swissubase) +# ============================================================================ +# SWISSUbase has no public REST API — every catalogue endpoint requires +# the SPA's session cookie. Ingest drives a Selenium browser session and +# calls the JSON endpoints from inside it, so SELENIUM_REMOTE_URL must +# be set. Default scope `epfl_sdsc_ethz` ingests everything but only +# embeds studies whose institution string matches EPFL / ETHZ / SDSC. + +# Drive the catalogue via Selenium and persist studies/persons/institutions. +# Pass --scope epfl_sdsc_ethz|switzerland and --limit N for smoke runs. +swissubase-ingest *ARGS: + .venv/bin/python -m src.index.swissubase ingest {{ARGS}} + +# Chunk + embed in-scope entities, push vectors to Qdrant. +# Pass --entity studies|datasets|persons|institutions to restrict. +swissubase-embed *ARGS: + .venv/bin/python -m src.index.swissubase embed {{ARGS}} + +# Semantic retrieval (vector + RCP rerank). First positional is the query. +swissubase-search QUERY *ARGS: + .venv/bin/python -m src.index.swissubase search "{{QUERY}}" {{ARGS}} + +# Read-only SQL over the SWISSUbase DuckDB (predefined or guarded ad-hoc). +swissubase-query *ARGS: + .venv/bin/python -m src.index.swissubase query {{ARGS}} + +# Show DuckDB row counts + Qdrant collection size + paths. +swissubase-status: + .venv/bin/python -m src.index.swissubase status + +# Run the FastAPI app (default port 8004). +swissubase-serve *ARGS: + .venv/bin/python -m src.index.swissubase serve {{ARGS}} + +# ============================================================================ +# RenkuLab indexer (src/index/renkulab) +# ============================================================================ + +# Pull RenkuLab projects/groups/users/data_connectors into DuckDB. +# Pass --scope all|epfl|switzerland and optionally --only entity1,entity2. +renku-ingest *ARGS: + .venv/bin/python -m src.index.renkulab ingest {{ARGS}} + +# Chunk + embed entities, push vectors to Qdrant via the RCP embedding endpoint. +# Pass --entities projects,groups,users,data_connectors to restrict (default: all). +renku-embed *ARGS: + .venv/bin/python -m src.index.renkulab embed {{ARGS}} + +# Semantic retrieval (vector + RCP rerank). First positional is the query. +renku-search QUERY *ARGS: + .venv/bin/python -m src.index.renkulab search "{{QUERY}}" {{ARGS}} + +# Read-only SQL over the RenkuLab DuckDB (predefined or guarded ad-hoc). +renku-query *ARGS: + .venv/bin/python -m src.index.renkulab query {{ARGS}} + +# Show DuckDB row counts + Qdrant collection sizes + paths. +renku-status: + .venv/bin/python -m src.index.renkulab status + +# Run the FastAPI app (default port 8004). +renku-serve *ARGS: + .venv/bin/python -m src.index.renkulab serve {{ARGS}} + +# ============================================================================ +# GitHub repository indexer (src/index/github) +# ============================================================================ + +# Fetch GitHub repo metadata + README into DuckDB. Pass --scope epfl|switzerland, +# optionally --repos owner/name,... and/or --from-openalex. +gh-ingest *ARGS: + .venv/bin/python -m src.index.github ingest {{ARGS}} + +# Chunk + embed repos, push vectors to Qdrant via the RCP embedding endpoint. +gh-embed *ARGS: + .venv/bin/python -m src.index.github embed {{ARGS}} + +# Recovery path: re-derive Qdrant points from the existing chunks table. +# Use after a Qdrant wipe (instead of `gh-embed`, which would skip everything). +gh-rebuild-qdrant: + .venv/bin/python -m src.index.github rebuild-qdrant + +# Semantic retrieval (vector + RCP rerank). First positional is the query. +gh-search QUERY *ARGS: + .venv/bin/python -m src.index.github search "{{QUERY}}" {{ARGS}} + +# Read-only SQL over the GitHub DuckDB (predefined or guarded ad-hoc). +gh-query *ARGS: + .venv/bin/python -m src.index.github query {{ARGS}} + +# Show DuckDB row counts + Qdrant collection size + paths. +gh-status: + .venv/bin/python -m src.index.github status + +# Run the FastAPI app (default port 8004). +gh-serve *ARGS: + .venv/bin/python -m src.index.github serve {{ARGS}} + +# ============================================================================ +# Federated cross-index layer (src/index/_federated) +# ============================================================================ + +# Federated semantic search across every registered index in parallel. +# Pass `--indices huggingface,openalex` to scope; `--filter k=v` (repeatable); +# `--entity-type X` to restrict each adapter to one type. +gme-search QUERY *ARGS: + .venv/bin/python -m src.index._federated search "{{QUERY}}" {{ARGS}} + +# Cross-index entity lookup. Pass any identifier — slug, URL, ORCID, ROR, +# DOI, UUID — and every adapter that recognises it returns matches. +gme-entity ID *ARGS: + .venv/bin/python -m src.index._federated entity "{{ID}}" {{ARGS}} + +# List registered adapters and the entity types each exposes. +gme-indices: + .venv/bin/python -m src.index._federated indices + +# Walk the HF base_models DAG (ancestors + descendants) from a repo_id. +hf-lineage REPO_ID *ARGS: + .venv/bin/python -m src.index.huggingface lineage "{{REPO_ID}}" {{ARGS}} diff --git a/mkdocs.yml b/mkdocs.yml index a5e81e8..7be6de5 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -38,25 +38,35 @@ extra_javascript: nav: - Home: index.md - Getting Started: getting-started.md - - API and CLI: api-and-cli.md + - V2 Extraction Service: + - V2 API Reference: v2-api-reference.md + - API and CLI Quick Reference: api-and-cli.md + - Migration V1 to V2: migration-v1-to-v2.md + - RAG Indices: + - Overview: rag-indices.md + - Federated Search: federated-search.md + - HuggingFace Index: huggingface-index.md + - Zenodo Index: zenodo-index.md + - EPFL Graph Disciplines: epfl-graph-disciplines.md + - SWISSUbase Index: swissubase-index.md + - V2 Agent RAG Tools: v2-rag-tools.md + - Concept Tagging Stage: concept-tagging.md + - Roadmap: ROADMAP.md - Architecture: - Design Notes: architecture/design-notes.md - - Repository Analysis Strategy: AGENT_STRATEGY.md - - Integrations: + - Legacy / Historical: + - V1 Repository Analysis Strategy: AGENT_STRATEGY.md - Infoscience Integration: INFOSCIENCE_INTEGRATION.md - Infoscience API Findings: INFOSCIENCE_API_FINDINGS.md - - JSON-LD: - - Conversion Guide: JSONLD_CONVERSION.md - - Mapping Documentation: PYDANTIC_JSONLD_MAPPING.md - - Conversion Summary: JSONLD_CONVERSION_SUMMARY.md - - Mapping Update: JSONLD_MAPPING_UPDATE.md - - JSON and JSON-LD CLI: JSON_JSONLD_CONVERSION_CLI.md - - Academic Catalog: - - Option B Implementation: ACADEMIC_CATALOG_OPTION_B_IMPLEMENTATION.md - - Refactor Summary: ACADEMIC_CATALOG_REFACTOR_SUMMARY.md - - Updates: + - JSON-LD Conversion Guide: JSONLD_CONVERSION.md + - JSON-LD Mapping: PYDANTIC_JSONLD_MAPPING.md + - JSON-LD Conversion Summary: JSONLD_CONVERSION_SUMMARY.md + - JSON-LD Mapping Update: JSONLD_MAPPING_UPDATE.md + - JSON / JSON-LD CLI: JSON_JSONLD_CONVERSION_CLI.md + - Academic Catalog Option B: ACADEMIC_CATALOG_OPTION_B_IMPLEMENTATION.md + - Academic Catalog Refactor: ACADEMIC_CATALOG_REFACTOR_SUMMARY.md - Affiliation Changes: AFFILIATION_CHANGES.md - Estimated Tokens Fix: ESTIMATED_TOKENS_FIX.md - Updates Summary: UPDATES_SUMMARY.md - - Releases: + - GIMIE / GitHub CFF Edge Cases: issue-brief-gimie-github-cff-edge-cases.md - Legacy Releases: releases/legacy-releases.md diff --git a/pyproject.toml b/pyproject.toml index 052af75..101dd69 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,7 +37,7 @@ dependencies = [ "google-genai>=1.31.0", "repo-to-text>=0.7.0", "PyLD==2.0.4", - "rdflib==6.2.0", + "rdflib==6.3.2", "rdflib-jsonld==0.6.2", "PyYAML==6.0.2", "selenium==4.34.2", @@ -45,8 +45,19 @@ dependencies = [ "aiohttp==3.12.15", "uvicorn-worker==0.3.0", "pydantic-ai>=1.0.15", + "griffe<2", "httpx", - "markdownify==1.2.0" + "markdownify==1.2.0", + "pyshacl==0.28.1", + "click>=8.1.0", + "faiss-cpu>=1.8.0", + "numpy>=1.26", + "pyalex>=0.15", + "duckdb>=1.0", + "qdrant-client>=1.10", + "tenacity>=8", + "huggingface_hub>=1.2.0", + "graphai-client>=1.16.0", ] [project.urls] @@ -54,18 +65,19 @@ Homepage = "https://imaging-plaza.epfl.ch" Repository = "https://github.com/Imaging-Plaza/git-metadata-extractor" Issues = "https://github.com/Imaging-Plaza/git-metadata-extractor/issues" -[project.scripts] -# This assumes your CLI entry point is a function named 'main' in src/main.py -llm-finder = "src.main:main" - [project.optional-dependencies] dev = [ "pre-commit>=3.0.0", "pytest>=7.0.0", "pytest-cov>=4.0.0", + "pytest-testmon>=2.1.3", + "pytest-xdist>=3.6.1", "black>=23.0.0", "ruff>=0.1.0", - "mypy>=1.0.0" + "mypy>=1.0.0", + "datamodel-code-generator>=0.54.0", + "respx>=0.21", + "requests-mock>=1.12", ] docs = [ "mkdocs>=1.6.0", @@ -86,3 +98,20 @@ ignore = ["S101","T201", "G004", "E501", "FA100"] "examples/*.py" = ["E402"] "tests/*.py" = ["E402"] "src/test/*.py" = ["E402"] + +[tool.pytest.ini_options] +markers = [ + "v2: tests for the v2 extraction pipeline and schema validation track", + "live_provider: opt-in tests that hit live external provider APIs", + "llm_integration: opt-in tests that call real LLM providers", + "openalex: tests for the src/index/openalex module", +] + +[tool.datamodel-codegen] +input_file_type = "jsonschema" +output_model_type = "pydantic_v2.BaseModel" +target_python_version = "3.10" +use_title_as_name = true +field_constraints = true +disable_timestamp = true +formatters = ["ruff-format", "ruff-check"] diff --git a/scripts/build_links_index.py b/scripts/build_links_index.py new file mode 100644 index 0000000..1293fed --- /dev/null +++ b/scripts/build_links_index.py @@ -0,0 +1,114 @@ +"""Post-process the heavy `infoscience_links_dump.json` into a slim +lookup table for fast re-entry. + +Output: `data/index/infoscience/dumps/infoscience_links_index.json` + +One row per article with just enough metadata + per-host URL lists to +answer "which papers cite host X?" without loading the full DSpace JSON +or hitting Infoscience again. + +URL extraction: +- regex over `text/{uuid}.txt` if a TEXT bundle is on disk; +- empty per-host list otherwise (the article still appears, with + `matched_phrases` recording which Solr filter found it). +""" + +from __future__ import annotations + +import datetime as dt +import json +import re +from pathlib import Path +from typing import Any, Dict + +from src.index.infoscience.parsers import first_value +from src.index.infoscience.paths import ( + dumps_dir, + text_dir, +) + +HOST_REGEXES: Dict[str, re.Pattern[str]] = { + "github": re.compile(r"https?://(?:www\.)?github\.com/[^\s)>\]\"'`,]+"), + "gitlab": re.compile(r"https?://(?:www\.)?gitlab\.com/[^\s)>\]\"'`,]+"), + "gitlab_epfl": re.compile(r"https?://(?:www\.)?gitlab\.epfl\.ch/[^\s)>\]\"'`,]+"), + "c4science": re.compile(r"https?://(?:www\.)?c4science\.ch/[^\s)>\]\"'`,]+"), + "bitbucket": re.compile(r"https?://(?:www\.)?bitbucket\.org/[^\s)>\]\"'`,]+"), + "huggingface": re.compile(r"https?://(?:www\.)?huggingface\.co/[^\s)>\]\"'`,]+"), + "hf_co": re.compile(r"https?://(?:www\.)?hf\.co/[^\s)>\]\"'`,]+"), + "zenodo": re.compile(r"https?://(?:www\.)?zenodo\.org/[^\s)>\]\"'`,]+"), + "figshare": re.compile(r"https?://(?:www\.)?figshare\.com/[^\s)>\]\"'`,]+"), + "osf": re.compile(r"https?://(?:www\.)?osf\.io/[^\s)>\]\"'`,]+"), + "datadryad": re.compile(r"https?://(?:www\.)?datadryad\.org/[^\s)>\]\"'`,]+"), + "materialscloud": re.compile(r"https?://(?:www\.)?materialscloud\.org/[^\s)>\]\"'`,]+"), + "kaggle": re.compile(r"https?://(?:www\.)?kaggle\.com/[^\s)>\]\"'`,]+"), + "paperswithcode": re.compile(r"https?://(?:www\.)?paperswithcode\.com/[^\s)>\]\"'`,]+"), + "colab": re.compile(r"https?://colab\.research\.google\.com/[^\s)>\]\"'`,]+"), + "mybinder": re.compile(r"https?://(?:www\.)?mybinder\.org/[^\s)>\]\"'`,]+"), + "arxiv": re.compile(r"https?://(?:www\.)?arxiv\.org/[^\s)>\]\"'`,]+"), + "renkulab": re.compile(r"https?://(?:www\.)?renkulab\.io/[^\s)>\]\"'`,]+"), + "orcid": re.compile(r"https?://(?:www\.)?orcid\.org/\d{4}-\d{4}-\d{4}-\d{3}[\dX]"), +} + + +def _extract_body_urls(uuid: str) -> Dict[str, list[str]]: + p = text_dir() / f"{uuid}.txt" + if not p.exists(): + return {} + body = p.read_text(encoding="utf-8", errors="replace") + out: Dict[str, list[str]] = {} + for label, rx in HOST_REGEXES.items(): + urls = sorted({m.rstrip(".,;)") for m in rx.findall(body)}) + if urls: + out[label] = urls + return out + + +def main() -> None: + dump_path = dumps_dir() / "infoscience_links_dump.json" + if not dump_path.exists(): + msg = f"Heavy dump not found: {dump_path}. Run scripts/dump_link_articles.py first." + raise SystemExit(msg) + + dump = json.loads(dump_path.read_text(encoding="utf-8")) + items: list[dict[str, Any]] = [] + have_text = 0 + for art in dump["articles"]: + item = art["item"] + md = item.get("metadata", {}) or {} + uuid = art["uuid"] + body_urls = _extract_body_urls(uuid) + if body_urls: + have_text += 1 + items.append({ + "uuid": uuid, + "title": first_value(md, "dc.title"), + "year": (first_value(md, "dc.date.issued") or "")[:4] or None, + "doi": first_value(md, "dc.identifier.doi"), + "publication_type": first_value(md, "dc.type"), + "infoscience_url": f"https://infoscience.epfl.ch/entities/publication/{uuid}", + "matched_phrases": art["matched_phrases"], + "person_uuids": art.get("person_uuids", []), + "org_uuids": art.get("org_uuids", []), + "body_urls": body_urls, + }) + + out = { + "generated_at": dt.datetime.now(dt.timezone.utc).isoformat(), + "queries": dump.get("queries", {}), + "per_phrase_counts": dump.get("per_phrase_counts", {}), + "totals": { + "items": len(items), + "items_with_local_text": have_text, + "items_no_local_text": len(items) - have_text, + }, + "items": items, + } + out_path = dumps_dir() / "infoscience_links_index.json" + out_path.write_text(json.dumps(out, ensure_ascii=False, indent=2), encoding="utf-8") + size_mb = out_path.stat().st_size / (1024 * 1024) + print(f"wrote {out_path} ({size_mb:.1f} MB)") + print(json.dumps(out["totals"], indent=2)) + + +if __name__ == "__main__": + main() diff --git a/scripts/dump_link_articles.py b/scripts/dump_link_articles.py new file mode 100644 index 0000000..94bc88b --- /dev/null +++ b/scripts/dump_link_articles.py @@ -0,0 +1,198 @@ +"""One-shot dump of Infoscience articles whose Solr-indexed full-text +contains a link to GitHub / GitLab / HuggingFace / ORCID / Zenodo. + +For each phrase, paginate `/discover/search/objects?query=fulltext:""`, +union UUIDs across phrases, persist each indexable item to +`raw/items/{uuid}.json`, extract person + org authority UUIDs, fetch any +missing person / org records, and write a single JSON dump. +""" + +from __future__ import annotations + +import asyncio +import json +import sys +import time +from typing import Any, Dict, List, Set + +from src.index.infoscience.config import load_config +from src.index.infoscience.dspace import DSpaceClient +from src.index.infoscience.extract_relations import ( + _ORG_ONLY_FIELDS, + _PERSON_FIELDS, + _authorities, +) +from src.index.infoscience.fetch_related import _fetch_set +from src.index.infoscience.paths import ( + infoscience_data_dir, + raw_items_dir, + raw_organizations_dir, + raw_persons_dir, +) + +# (label, Solr phrase). Phrase queries with a trailing slash filter to URL +# usages; bare-host phrases also work but capture more incidental mentions. +PHRASES: List[tuple[str, str]] = [ + # Code hosts + ("github", '"github.com/"'), + ("gitlab", '"gitlab.com/"'), + ("gitlab_epfl", '"gitlab.epfl.ch"'), + ("c4science", '"c4science.ch/"'), + ("bitbucket", '"bitbucket.org/"'), + # Data + model archives + ("huggingface", '"huggingface.co/"'), + ("hf_co", '"hf.co/"'), + ("zenodo", '"zenodo.org/"'), + ("figshare", '"figshare.com/"'), + ("osf", '"osf.io/"'), + ("datadryad", '"datadryad.org/"'), + ("materialscloud", '"materialscloud.org/"'), + ("kaggle", '"kaggle.com/"'), + ("paperswithcode", '"paperswithcode.com/"'), + # Notebook / runtime hosts + ("colab", '"colab.research.google.com/"'), + ("mybinder", '"mybinder.org/"'), + # Preprints + ("arxiv", '"arxiv.org/"'), + # Workflow platforms + ("renkulab", '"renkulab.io/"'), + # Identifier graph (kept; high recall, low precision) + ("orcid", '"orcid.org/"'), +] + + +async def collect_uuids( + client: DSpaceClient, + phrase: str, + *, + size: int = 100, +) -> Dict[str, Dict[str, Any]]: + """Paginate one phrase; return {uuid: indexable_item}.""" + out: Dict[str, Dict[str, Any]] = {} + async for indexable in client.iter_discover_fulltext(phrase, size=size): + u = indexable.get("uuid") + if u: + out[u] = indexable + return out + + +async def main() -> None: + cfg = load_config() + # Bump fetch concurrency for the related-entity step. Discovery itself + # iterates pages serially per phrase. + cfg.infoscience.max_concurrency = 8 + + items_dir = raw_items_dir() + persons_dir = raw_persons_dir() + orgs_dir = raw_organizations_dir() + + # 1) Discover per phrase + union + per_phrase_counts: Dict[str, int] = {} + matches_per_uuid: Dict[str, List[str]] = {} + items: Dict[str, Dict[str, Any]] = {} + + async with DSpaceClient(cfg.infoscience) as c: + for label, phrase in PHRASES: + t0 = time.monotonic() + hits = await collect_uuids(c, phrase) + per_phrase_counts[label] = len(hits) + for u, it in hits.items(): + items.setdefault(u, it) + matches_per_uuid.setdefault(u, []).append(label) + dt = time.monotonic() - t0 + print( + f"[discover] {label:13s} phrase={phrase:25s} hits={len(hits):5d} " + f"({dt:5.1f}s) union={len(items)}", + flush=True, + ) + + print(f"[discover] total unique articles: {len(items)}", flush=True) + + # 2) Persist raw items + extract authority sets + person_uuids: Set[str] = set() + org_uuids: Set[str] = set() + articles: List[Dict[str, Any]] = [] + + for uuid, item in items.items(): + (items_dir / f"{uuid}.json").write_text( + json.dumps(item, ensure_ascii=False, indent=2), + encoding="utf-8", + ) + md = item.get("metadata", {}) or {} + p = list(dict.fromkeys(_authorities(md, _PERSON_FIELDS))) + o = list(dict.fromkeys(_authorities(md, _ORG_ONLY_FIELDS))) + person_uuids.update(p) + org_uuids.update(o) + articles.append( + { + "uuid": uuid, + "matched_phrases": sorted(matches_per_uuid[uuid]), + "person_uuids": p, + "org_uuids": o, + } + ) + + print( + f"[extract] articles={len(articles)} " + f"persons={len(person_uuids)} orgs={len(org_uuids)}", + flush=True, + ) + + # 3) Fetch missing persons + orgs + have_p = {p.stem for p in persons_dir.glob("*.json")} + have_o = {p.stem for p in orgs_dir.glob("*.json")} + missing_p = sorted(u for u in person_uuids if u not in have_p) + missing_o = sorted(u for u in org_uuids if u not in have_o) + print( + f"[fetch] missing persons={len(missing_p)}/{len(person_uuids)} " + f"orgs={len(missing_o)}/{len(org_uuids)}", + flush=True, + ) + + if missing_p: + t0 = time.monotonic() + rp = await _fetch_set(cfg, missing_p, persons_dir, refresh=False) + print(f"[fetch] persons: {rp} ({time.monotonic()-t0:.1f}s)", flush=True) + if missing_o: + t0 = time.monotonic() + ro = await _fetch_set(cfg, missing_o, orgs_dir, refresh=False) + print(f"[fetch] organizations: {ro} ({time.monotonic()-t0:.1f}s)", flush=True) + + # 4) Resolve full records + def _load(d, u): + p = d / f"{u}.json" + return json.loads(p.read_text(encoding="utf-8")) if p.exists() else None + + persons_resolved = {u: _load(persons_dir, u) for u in sorted(person_uuids)} + orgs_resolved = {u: _load(orgs_dir, u) for u in sorted(org_uuids)} + + # Inline article items (full DSpace JSON, not just authority sets) + for a in articles: + a["item"] = items[a["uuid"]] + + dump = { + "queries": {label: f'fulltext:{phrase}' for label, phrase in PHRASES}, + "per_phrase_counts": per_phrase_counts, + "totals": { + "articles": len(articles), + "persons": sum(1 for v in persons_resolved.values() if v), + "organizations": sum(1 for v in orgs_resolved.values() if v), + "persons_unresolved": sum(1 for v in persons_resolved.values() if not v), + "orgs_unresolved": sum(1 for v in orgs_resolved.values() if not v), + }, + "articles": articles, + "persons": persons_resolved, + "organizations": orgs_resolved, + } + + out_dir = infoscience_data_dir() / "dumps" + out_dir.mkdir(parents=True, exist_ok=True) + out_path = out_dir / "infoscience_links_dump.json" + out_path.write_text(json.dumps(dump, indent=2, ensure_ascii=False), encoding="utf-8") + size_mb = out_path.stat().st_size / (1024 * 1024) + print(f"[write] {out_path} ({size_mb:.1f} MB)", flush=True) + print(f"[summary] {json.dumps(dump['totals'])}", flush=True) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/scripts/v2/batch_extract.sh b/scripts/v2/batch_extract.sh new file mode 100644 index 0000000..473b4b5 --- /dev/null +++ b/scripts/v2/batch_extract.sh @@ -0,0 +1,302 @@ +#!/usr/bin/env bash +# Submit /v2/extract jobs in parallel via POST, poll each job to completion, +# and store the full V2ExtractJob record under an output folder. +# +# Resumable: a repo whose .json already exists in the output folder +# is skipped (regardless of its inner status). Delete the file to force a +# re-run for that repo. +# +# Usage: +# scripts/v2/batch_extract.sh +# +# one of: rule_based | llm | hybrid +# path to a file with one repo URL per line +# (blank lines and lines starting with `#` are skipped) +# directory where .json results, _logs/, _summary.txt +# are written. Created if missing. +# +# Examples: +# scripts/v2/batch_extract.sh hybrid data/sdsc-seeds.txt data/run/2026-05-04-sdsc-hybrid +# scripts/v2/batch_extract.sh rule_based data/infoscience-github-seeds.txt data/run/quick +# +# Tunables (env vars, all optional): +# V2_BASE_URL http://localhost:1234 +# PARALLELISM 5 +# POLL_INTERVAL_SECONDS 10 +# JOB_TIMEOUT_SECONDS 1800 +# HTTP_TIMEOUT_SECONDS 60 +# OUTPUT_FORMAT jsonld +# INCLUDE_CONTEXT_SUMMARY false +# API_TOKEN bearer for /v2/extract and /v2/jobs/* routes +# +# Files written into : +# _.json full V2ExtractJob record (success or failure) +# _logs/_.log stderr from curl + per-poll status lines +# _summary.txt one-line status per repo at the end + +set -u + +usage() { + sed -n '2,29p' "$0" + exit 2 +} + +if [[ $# -lt 3 ]]; then + echo "error: expected 3 positional arguments, got $#" >&2 + echo >&2 + usage +fi + +AGENT_RUNTIME=$1 +REPO_LIST=$2 +OUTPUT_FOLDER=$3 +shift 3 + +case "$AGENT_RUNTIME" in + rule_based|llm|hybrid) ;; + *) + echo "error: must be one of rule_based|llm|hybrid (got: $AGENT_RUNTIME)" >&2 + exit 2 + ;; +esac + +if [[ ! -f "$REPO_LIST" ]]; then + echo "error: file not found: $REPO_LIST" >&2 + exit 2 +fi + +V2_BASE_URL=${V2_BASE_URL:-http://localhost:1234} +PARALLELISM=${PARALLELISM:-5} +POLL_INTERVAL_SECONDS=${POLL_INTERVAL_SECONDS:-10} +JOB_TIMEOUT_SECONDS=${JOB_TIMEOUT_SECONDS:-1800} +HTTP_TIMEOUT_SECONDS=${HTTP_TIMEOUT_SECONDS:-60} +OUTPUT_FORMAT=${OUTPUT_FORMAT:-jsonld} +INCLUDE_CONTEXT_SUMMARY=${INCLUDE_CONTEXT_SUMMARY:-false} +# Optional bearer for the auth-protected /v2/extract and /v2/jobs/* routes. +# When unset, requests go without an Authorization header (works only if +# the server has API_TOKEN unset and V2_USE_MOCK_PROVIDERS=true). +API_TOKEN=${API_TOKEN:-} + +REPOSITORIES=() +while IFS= read -r line || [[ -n "$line" ]]; do + line="${line%%#*}" + line="${line#"${line%%[![:space:]]*}"}" + line="${line%"${line##*[![:space:]]}"}" + [[ -z "$line" ]] && continue + REPOSITORIES+=("$line") +done <"$REPO_LIST" + +run_dir="$OUTPUT_FOLDER" +log_dir="$run_dir/_logs" +mkdir -p "$log_dir" + +summary_file="$run_dir/_summary.txt" +: >>"$summary_file" + +if ! command -v jq >/dev/null 2>&1; then + echo "jq is required (used to parse job responses). Install jq and re-run." >&2 + exit 2 +fi + +# Sanitize "https://github.com//" → "_" +slug_for() { + local url=$1 + url=${url%/} + url=${url#https://github.com/} + url=${url#http://github.com/} + url=${url#github.com/} + printf '%s' "${url//\//_}" +} + +# True (return 0) if the output JSON already exists for this slug, so the repo +# can be skipped on resume. Skips regardless of the inner status — delete the +# file to force a re-run. +already_done() { + local out_file=$1 + [[ -s "$out_file" ]] +} + +submit_job() { + local repo_url=$1 + local log_file=$2 + local payload + payload=$(jq -nc \ + --arg source_url "$repo_url" \ + --arg output_format "$OUTPUT_FORMAT" \ + --arg agent_runtime "$AGENT_RUNTIME" \ + --argjson include_context_summary "$INCLUDE_CONTEXT_SUMMARY" \ + '{source_url:$source_url, output_format:$output_format, agent_runtime:$agent_runtime, include_context_summary:$include_context_summary}') + + local body_file + body_file=$(mktemp) + local http_code + local -a auth_args=() + if [[ -n "$API_TOKEN" ]]; then + auth_args=(-H "Authorization: Bearer $API_TOKEN") + fi + http_code=$(curl -sS --max-time "$HTTP_TIMEOUT_SECONDS" \ + -w '%{http_code}' \ + -X POST \ + -H 'Content-Type: application/json' \ + -H 'Accept: application/json' \ + "${auth_args[@]}" \ + -o "$body_file" \ + --data "$payload" \ + "$V2_BASE_URL/v2/extract" 2>>"$log_file") || http_code="000" + + if [[ "$http_code" != "202" ]]; then + { + printf '[%s] submit failed http=%s\n' "$(date +%T)" "$http_code" + cat "$body_file" + printf '\n' + } >>"$log_file" + rm -f "$body_file" + return 1 + fi + + local job_id + job_id=$(jq -r '.job_id // empty' "$body_file") + rm -f "$body_file" + if [[ -z "$job_id" ]]; then + printf '[%s] submit returned no job_id\n' "$(date +%T)" >>"$log_file" + return 1 + fi + printf '%s' "$job_id" +} + +# Poll /v2/jobs/{id} until status is terminal or JOB_TIMEOUT_SECONDS elapses. +# On terminal status, writes the full job record to $out_file and prints the +# terminal status to stdout. Returns non-zero on poll failure / timeout. +poll_job() { + local job_id=$1 + local out_file=$2 + local log_file=$3 + local started=$SECONDS + local body_file + body_file=$(mktemp) + local -a poll_auth_args=() + if [[ -n "$API_TOKEN" ]]; then + poll_auth_args=(-H "Authorization: Bearer $API_TOKEN") + fi + while :; do + local http_code + http_code=$(curl -sS --max-time "$HTTP_TIMEOUT_SECONDS" \ + -w '%{http_code}' \ + -H 'Accept: application/json' \ + "${poll_auth_args[@]}" \ + -o "$body_file" \ + "$V2_BASE_URL/v2/jobs/$job_id" 2>>"$log_file") || http_code="000" + + if [[ "$http_code" != "200" ]]; then + { + printf '[%s] poll http=%s job=%s\n' "$(date +%T)" "$http_code" "$job_id" + cat "$body_file" + printf '\n' + } >>"$log_file" + rm -f "$body_file" + return 1 + fi + + local status + status=$(jq -r '.status // empty' "$body_file") + case "$status" in + completed|failed) + jq . "$body_file" >"$out_file" 2>>"$log_file" || cp "$body_file" "$out_file" + rm -f "$body_file" + printf '%s' "$status" + return 0 + ;; + pending|running) + printf '[%s] %-8s job=%s\n' "$(date +%T)" "$status" "$job_id" >>"$log_file" + ;; + *) + printf '[%s] unexpected status=%s job=%s\n' "$(date +%T)" "$status" "$job_id" >>"$log_file" + ;; + esac + + local elapsed=$((SECONDS - started)) + if (( elapsed >= JOB_TIMEOUT_SECONDS )); then + printf '[%s] timeout after %ds job=%s\n' "$(date +%T)" "$elapsed" "$job_id" >>"$log_file" + rm -f "$body_file" + return 1 + fi + sleep "$POLL_INTERVAL_SECONDS" + done +} + +extract_one() { + local repo_url=$1 + local slug + slug=$(slug_for "$repo_url") + local out_file="$run_dir/$slug.json" + local log_file="$log_dir/$slug.log" + + if already_done "$out_file"; then + local prior + prior=$(jq -r '.status // "exists"' "$out_file" 2>/dev/null || echo "exists") + printf '[%s] skip %s (prior status=%s)\n' "$(date +%T)" "$slug" "$prior" >>"$log_file" + printf 'SKIP %-9s %s\n' "$prior" "$slug" >>"$summary_file" + return 0 + fi + + printf '[%s] submit %s\n' "$(date +%T)" "$slug" | tee -a "$log_file" + local start_ts=$SECONDS + local job_id + job_id=$(submit_job "$repo_url" "$log_file") || job_id="" + if [[ -z "$job_id" ]]; then + local elapsed=$((SECONDS - start_ts)) + printf '[%s] FAIL submit %s (%ds)\n' "$(date +%T)" "$slug" "$elapsed" | tee -a "$log_file" + printf 'FAIL submit %3ds %s\n' "$elapsed" "$slug" >>"$summary_file" + return 0 + fi + + printf '[%s] polling %s job=%s\n' "$(date +%T)" "$slug" "$job_id" >>"$log_file" + local terminal + terminal=$(poll_job "$job_id" "$out_file" "$log_file") || terminal="" + local elapsed=$((SECONDS - start_ts)) + + case "$terminal" in + completed) + printf '[%s] OK %s (%ds)\n' "$(date +%T)" "$slug" "$elapsed" | tee -a "$log_file" + printf 'OK completed %3ds %s\n' "$elapsed" "$slug" >>"$summary_file" + ;; + failed) + printf '[%s] FAIL %s (%ds)\n' "$(date +%T)" "$slug" "$elapsed" | tee -a "$log_file" + printf 'FAIL failed %3ds %s\n' "$elapsed" "$slug" >>"$summary_file" + ;; + *) + printf '[%s] FAIL poll %s (%ds)\n' "$(date +%T)" "$slug" "$elapsed" | tee -a "$log_file" + printf 'FAIL poll %3ds %s\n' "$elapsed" "$slug" >>"$summary_file" + ;; + esac +} + +dispatch() { + printf 'V2_BASE_URL : %s\n' "$V2_BASE_URL" + printf 'OUTPUT_DIR : %s\n' "$run_dir" + printf 'PARALLELISM : %s\n' "$PARALLELISM" + printf 'POLL_INTERVAL_SECONDS : %s\n' "$POLL_INTERVAL_SECONDS" + printf 'JOB_TIMEOUT_SECONDS : %s\n' "$JOB_TIMEOUT_SECONDS" + printf 'AGENT_RUNTIME : %s\n' "$AGENT_RUNTIME" + printf 'OUTPUT_FORMAT : %s\n' "$OUTPUT_FORMAT" + printf 'INCLUDE_CONTEXT_SUMMARY: %s\n' "$INCLUDE_CONTEXT_SUMMARY" + printf 'REPO_LIST : %s\n' "$REPO_LIST" + printf 'REPOSITORIES : %s\n' "${#REPOSITORIES[@]}" + printf '\n' + + local in_flight=0 + for repo in "${REPOSITORIES[@]}"; do + extract_one "$repo" & + in_flight=$((in_flight + 1)) + if (( in_flight >= PARALLELISM )); then + wait -n + in_flight=$((in_flight - 1)) + fi + done + wait +} + +dispatch +echo +echo "=== summary ($summary_file) ===" +cat "$summary_file" diff --git a/scripts/v2/check_provider_connectivity.py b/scripts/v2/check_provider_connectivity.py new file mode 100644 index 0000000..c0f6c89 --- /dev/null +++ b/scripts/v2/check_provider_connectivity.py @@ -0,0 +1,243 @@ +# ruff: noqa: INP001 + +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path +from typing import Sequence + +import requests +from dotenv import load_dotenv + +PROJECT_ROOT = Path(__file__).resolve().parents[2] +if str(PROJECT_ROOT) not in sys.path: + sys.path.insert(0, str(PROJECT_ROOT)) +load_dotenv(PROJECT_ROOT / ".env", override=False) + +from src.v2.ingest.providers.infoscience_provider import RealInfoscienceProvider # noqa: E402 +from src.v2.ingest.providers.orcid_provider import RealORCIDProvider # noqa: E402 +from src.v2.ingest.providers.ror_provider import RealRORProvider # noqa: E402 + +DEFAULT_TIMEOUT_SECONDS = 30 +DEFAULT_PROVIDERS = ("github", "ror", "orcid", "infoscience", "selenium") +ENV_REQUIREMENTS_BY_PROVIDER = { + "github": ("GITHUB_TOKEN",), + "infoscience": ("INFOSCIENCE_TOKEN",), + "selenium": ("SELENIUM_REMOTE_URL",), +} +MISSING_GITHUB_TOKEN_ERROR = "Missing required environment variable: GITHUB_TOKEN" # noqa: S105 +MISSING_SELENIUM_URL_ERROR = "Missing required environment variable: SELENIUM_REMOTE_URL" +GITHUB_UNAUTHORIZED_ERROR = ( + "GitHub token unauthorized (401). Check GITHUB_TOKEN value and scopes." +) +ROR_INVALID_PAYLOAD_ERROR = "ROR organization check returned an invalid payload" +ROR_EMPTY_SEARCH_ERROR = "ROR search check returned no results" +ORCID_INVALID_PAYLOAD_ERROR = "ORCID person check returned an invalid payload" +INFOSCIENCE_PEOPLE_TYPE_ERROR = "Infoscience person search returned invalid payload type" +INFOSCIENCE_ORGUNITS_TYPE_ERROR = ( + "Infoscience orgunit search returned invalid payload type" +) +INFOSCIENCE_PUBLICATIONS_TYPE_ERROR = ( + "Infoscience publication search returned invalid payload type" +) +SELENIUM_NOT_READY_ERROR = "Selenium service is reachable but not ready" +SELENIUM_INVALID_STATUS_ERROR = "Selenium /status returned invalid payload type" +HTTP_UNAUTHORIZED = 401 + + +def get_missing_required_env_vars(providers: Sequence[str] | None = None) -> list[str]: + selected_providers = ( + [provider.strip().lower() for provider in providers] + if providers is not None + else list(DEFAULT_PROVIDERS) + ) + + required_env_vars: set[str] = set() + for provider in selected_providers: + required_env_vars.update(ENV_REQUIREMENTS_BY_PROVIDER.get(provider, ())) + + missing_vars: list[str] = [] + for key in sorted(required_env_vars): + value = os.getenv(key) + if value is None or value.strip() == "": + missing_vars.append(key) + return missing_vars + + +def _check_github(timeout_seconds: int) -> None: + github_token = os.getenv("GITHUB_TOKEN", "").strip() + if not github_token: + raise RuntimeError(MISSING_GITHUB_TOKEN_ERROR) + + session = requests.Session() + headers = { + "Accept": "application/vnd.github+json", + "Authorization": f"Bearer {github_token}", + } + + checks = [ + ( + "repo", + "https://api.github.com/repos/sdsc-ordes/gimie", + dict, + ), + ( + "contributors", + "https://api.github.com/repos/sdsc-ordes/gimie/contributors", + list, + ), + ( + "languages", + "https://api.github.com/repos/sdsc-ordes/gimie/languages", + dict, + ), + ( + "organization", + "https://api.github.com/orgs/sdsc-ordes", + dict, + ), + ] + + for check_name, url, expected_type in checks: + response = session.get(url, headers=headers, timeout=timeout_seconds) + if response.status_code == HTTP_UNAUTHORIZED: + raise RuntimeError(GITHUB_UNAUTHORIZED_ERROR) + response.raise_for_status() + payload = response.json() + if not isinstance(payload, expected_type): + message = ( + "GitHub connectivity check returned unexpected payload shape " + f"for {check_name}" + ) + raise TypeError(message) + + +def _check_ror() -> None: + provider = RealRORProvider() + organization = provider.get_organization("02s376052") + if not isinstance(organization, dict) or not organization.get("id"): + raise RuntimeError(ROR_INVALID_PAYLOAD_ERROR) + + matches = provider.search_organizations("EPFL") + if not isinstance(matches, list) or not matches: + raise RuntimeError(ROR_EMPTY_SEARCH_ERROR) + + +def _check_orcid() -> None: + provider = RealORCIDProvider() + record = provider.get_person_by_orcid("0000-0002-1825-0097") + if not isinstance(record, dict) or not record.get("orcid_id"): + raise RuntimeError(ORCID_INVALID_PAYLOAD_ERROR) + + +def _check_infoscience() -> None: + provider = RealInfoscienceProvider(max_results=5) + people = provider.search_person("alice smith") + orgunits = provider.search_orgunit("EPFL") + publications = provider.search_publications("metadata") + + if not isinstance(people, list): + raise TypeError(INFOSCIENCE_PEOPLE_TYPE_ERROR) + if not isinstance(orgunits, list): + raise TypeError(INFOSCIENCE_ORGUNITS_TYPE_ERROR) + if not isinstance(publications, list): + raise TypeError(INFOSCIENCE_PUBLICATIONS_TYPE_ERROR) + + +def _check_selenium(timeout_seconds: int) -> None: + selenium_remote_url = os.getenv("SELENIUM_REMOTE_URL", "").strip() + if not selenium_remote_url: + raise RuntimeError(MISSING_SELENIUM_URL_ERROR) + + status_url = f"{selenium_remote_url.rstrip('/')}/status" + response = requests.get(status_url, timeout=timeout_seconds) + response.raise_for_status() + + payload = response.json() + if not isinstance(payload, dict): + raise TypeError(SELENIUM_INVALID_STATUS_ERROR) + + value = payload.get("value") + if isinstance(value, dict) and value.get("ready") is False: + raise RuntimeError(SELENIUM_NOT_READY_ERROR) + + +def run_provider_connectivity_checks( + providers: Sequence[str], + *, + timeout_seconds: int = DEFAULT_TIMEOUT_SECONDS, +) -> list[str]: + failures: list[str] = [] + + normalized_providers = [provider.strip().lower() for provider in providers] + for provider in normalized_providers: + try: + if provider == "github": + _check_github(timeout_seconds) + elif provider == "ror": + _check_ror() + elif provider == "orcid": + _check_orcid() + elif provider == "infoscience": + _check_infoscience() + elif provider == "selenium": + _check_selenium(timeout_seconds) + else: + failures.append(f"Unknown provider: {provider}") + continue + print(f"[ok] {provider} connectivity") + except Exception as exc: # noqa: BLE001 + failures.append(f"{provider}: {exc}") + + return failures + + +def _build_argument_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description="Run live connectivity checks for v2 providers") + parser.add_argument( + "--providers", + nargs="+", + default=list(DEFAULT_PROVIDERS), + choices=list(DEFAULT_PROVIDERS), + help="Providers to validate. Defaults to all providers.", + ) + parser.add_argument( + "--timeout-seconds", + type=int, + default=DEFAULT_TIMEOUT_SECONDS, + help="HTTP request timeout for connectivity checks.", + ) + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + parser = _build_argument_parser() + args = parser.parse_args(argv) + + missing_env_vars = get_missing_required_env_vars(args.providers) + if missing_env_vars: + missing_message = ", ".join(sorted(missing_env_vars)) + print( + f"Missing required environment variable(s): {missing_message}", + file=sys.stderr, + ) + return 2 + + failures = run_provider_connectivity_checks( + args.providers, + timeout_seconds=args.timeout_seconds, + ) + if failures: + print("Provider connectivity failed:", file=sys.stderr) + for failure in failures: + print(f"- {failure}", file=sys.stderr) + return 1 + + print("All requested provider connectivity checks passed.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/v2/extract_enums_from_ttl.py b/scripts/v2/extract_enums_from_ttl.py new file mode 100644 index 0000000..9b468e1 --- /dev/null +++ b/scripts/v2/extract_enums_from_ttl.py @@ -0,0 +1,76 @@ +# ruff: noqa: INP001 + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +from rdflib import RDF, Graph, Namespace + +SKOS = Namespace("http://www.w3.org/2004/02/skos/core#") +PULSE = Namespace("https://open-pulse.epfl.ch/ontology#") + + +def _compact_identifier(uri: str) -> str: + if uri.startswith("http://www.wikidata.org/entity/"): + return f"wd:{uri.rsplit('/', maxsplit=1)[-1]}" + if uri.startswith("https://open-pulse.epfl.ch/ontology#"): + return f"pulse:{uri.rsplit('#', maxsplit=1)[-1]}" + return uri + + +def default_ttl_path() -> Path: + return ( + Path(__file__).resolve().parents[2] + / "dev" + / "ontology-v2-json-response" + / "open-pulse-ontology-v2.0.1.ttl" + ) + + +def _extract_entries(graph: Graph, enum_type_iri: Any) -> list[dict[str, str]]: + entries: list[dict[str, str]] = [] + for subject in sorted(set(graph.subjects(RDF.type, enum_type_iri)), key=str): + label_literal = next(graph.objects(subject, SKOS.prefLabel), None) + label = str(label_literal) if label_literal is not None else "" + subject_uri = str(subject) + entries.append( + { + "id": _compact_identifier(subject_uri), + "label": label, + "uri": subject_uri, + }, + ) + return entries + + +def extract_enums_from_ttl(ttl_path: Path) -> dict[str, list[dict[str, str]]]: + graph = Graph() + graph.parse(ttl_path, format="turtle") + return { + "disciplines": _extract_entries(graph, PULSE.DisciplineEnumeration), + "repository_types": _extract_entries(graph, PULSE.RepositoryTypeEnumeration), + "organization_types": _extract_entries(graph, PULSE.OrganizationTypeEnumeration), + } + + +def main() -> int: + parser = argparse.ArgumentParser(description="Extract enum values from Open Pulse TTL ontology") + parser.add_argument( + "--ttl-path", + type=Path, + default=default_ttl_path(), + help="Path to open-pulse-ontology-v2.0.1.ttl", + ) + parser.add_argument("--indent", type=int, default=2, help="JSON output indentation") + args = parser.parse_args() + + payload = extract_enums_from_ttl(args.ttl_path) + print(json.dumps(payload, indent=args.indent, sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/v2/generate_v2_models.py b/scripts/v2/generate_v2_models.py new file mode 100644 index 0000000..f8a01d7 --- /dev/null +++ b/scripts/v2/generate_v2_models.py @@ -0,0 +1,310 @@ +# ruff: noqa: INP001 +from __future__ import annotations + +import argparse +import hashlib +import json +import subprocess +import tempfile +from pathlib import Path +from typing import Final + +REPO_ROOT = Path(__file__).resolve().parents[2] +STRICT_SCHEMA_DIR = REPO_ROOT / "src" / "v2" / "schema" / "json" / "strict" +AGENT_SCHEMA_DIR = REPO_ROOT / "src" / "v2" / "schema" / "json" / "agent" +GENERATED_MODELS_PATH = REPO_ROOT / "src" / "v2" / "schema" / "models" / "strict.py" +GENERATED_AGENT_MODELS_PATH = REPO_ROOT / "src" / "v2" / "schema" / "models" / "agent.py" + +MODEL_SCHEMA_FILES: Final[dict[str, str]] = { + "PersonModel": "person.schema.json", + "RepositoryModel": "repository.schema.json", + "OrganizationModel": "organization.schema.json", + "MembershipModel": "membership.schema.json", + "ContributionModel": "contribution.schema.json", + "ArticleModel": "article.schema.json", +} + +AGENT_MODEL_SCHEMA_FILES: Final[dict[str, str]] = { + "AgentPersonShape": "person.schema.json", + "AgentRepositoryShape": "repository.schema.json", + "AgentOrganizationShape": "organization.schema.json", + "AgentMembershipShape": "membership.schema.json", + "AgentContributionShape": "contribution.schema.json", + "AgentArticleShape": "article.schema.json", +} + +HASH_MARKER: Final[str] = "# source-schema-sha256:" + + +def _schema_hashes(schema_dir: Path, schema_files: dict[str, str]) -> dict[str, str]: + return { + schema_name: hashlib.sha256( + (schema_dir / schema_name).read_bytes(), + ).hexdigest() + for schema_name in sorted(schema_files.values()) + } + + +def _build_embedded_bundle( + path: Path, + schema_dir: Path, + model_schema_files: dict[str, str], + bundle_title: str, +) -> None: + bundle: dict[str, object] = { + "$schema": "http://json-schema.org/draft-07/schema#", + "title": bundle_title, + "type": "object", + "properties": {}, + "$defs": {}, + } + properties = bundle["properties"] + definitions = bundle["$defs"] + + assert isinstance(properties, dict) + assert isinstance(definitions, dict) + + for model_name, schema_name in model_schema_files.items(): + schema_payload = json.loads((schema_dir / schema_name).read_text()) + schema_payload["title"] = model_name + definitions[model_name] = schema_payload + property_name = model_name.lower() + properties[property_name] = {"$ref": f"#/$defs/{model_name}"} + + path.write_text(json.dumps(bundle, indent=2) + "\n", encoding="utf-8") + + +def _datamodel_codegen_command( + bundle_path: Path, + output_path: Path, + *, + extra_fields: str = "forbid", +) -> list[str]: + local_codegen = REPO_ROOT / ".venv" / "bin" / "datamodel-codegen" + executable = local_codegen if local_codegen.exists() else Path("datamodel-codegen") + return [ + str(executable), + "--input", + str(bundle_path), + "--input-file-type", + "jsonschema", + "--output-model-type", + "pydantic_v2.BaseModel", + "--target-python-version", + "3.10", + "--use-title-as-name", + "--field-constraints", + "--disable-timestamp", + "--extra-fields", + extra_fields, + "--formatters", + "ruff-format", + "ruff-check", + "--output", + str(output_path), + ] + + +def _run_codegen( + bundle_path: Path, + output_path: Path, + *, + extra_fields: str = "forbid", +) -> None: + command = _datamodel_codegen_command(bundle_path, output_path, extra_fields=extra_fields) + try: + subprocess.run(command, cwd=REPO_ROOT, check=True) # noqa: S603 + except FileNotFoundError as exc: + message = ( + "datamodel-code-generator is not installed. " + "Run `uv pip install --python .venv/bin/python datamodel-code-generator`." + ) + raise RuntimeError(message) from exc + except subprocess.CalledProcessError as exc: + joined = " ".join(command) + message = f"datamodel-code-generator failed: {joined}" + raise RuntimeError(message) from exc + + +def _render_hash_block(schema_hashes: dict[str, str]) -> list[str]: + lines = [HASH_MARKER] + lines.extend( + f"# - {schema_name}: {schema_hashes[schema_name]}" + for schema_name in sorted(schema_hashes) + ) + lines.append("#") + return lines + + +def _inject_hash_block(content: str, schema_hashes: dict[str, str]) -> str: + lines = content.splitlines() + insert_index = 0 + while insert_index < len(lines) and lines[insert_index].startswith("#"): + insert_index += 1 + + block = _render_hash_block(schema_hashes) + with_hashes = lines[:insert_index] + block + lines[insert_index:] + return "\n".join(with_hashes).rstrip() + "\n" + + +def _extract_embedded_hashes(path: Path) -> dict[str, str]: + if not path.exists(): + return {} + + lines = path.read_text(encoding="utf-8").splitlines() + try: + marker_index = lines.index(HASH_MARKER) + except ValueError: + return {} + + parsed_hashes: dict[str, str] = {} + for line in lines[marker_index + 1 :]: + if line == "#": + break + prefix = "# - " + if not line.startswith(prefix): + break + payload = line[len(prefix) :] + if ": " not in payload: + continue + name, digest = payload.split(": ", 1) + parsed_hashes[name] = digest + return parsed_hashes + + +def _render_expected_output( + schema_dir: Path, + model_schema_files: dict[str, str], + bundle_title: str, + output_filename: str, + *, + extra_fields: str = "forbid", +) -> str: + hashes = _schema_hashes(schema_dir, model_schema_files) + with tempfile.TemporaryDirectory(prefix="v2-model-codegen-") as tmp_dir: + temp_dir = Path(tmp_dir) + bundle_path = temp_dir / "bundle.schema.json" + raw_output_path = temp_dir / output_filename + _build_embedded_bundle(bundle_path, schema_dir, model_schema_files, bundle_title) + _run_codegen(bundle_path, raw_output_path, extra_fields=extra_fields) + generated_content = raw_output_path.read_text(encoding="utf-8") + return _inject_hash_block(generated_content, hashes) + + +def _check_one_models_file( + output_path: Path, + schema_dir: Path, + model_schema_files: dict[str, str], + bundle_title: str, + *, + extra_fields: str = "forbid", +) -> int: + if not output_path.exists(): + print(f"Missing generated models file: {output_path}") + print("Run: just v2-models-generate") + return 1 + + expected = _render_expected_output( + schema_dir, + model_schema_files, + bundle_title, + output_path.name, + extra_fields=extra_fields, + ) + actual = output_path.read_text(encoding="utf-8") + if actual == expected: + print(f"{output_path.name}: up-to-date.") + return 0 + + current_hashes = _schema_hashes(schema_dir, model_schema_files) + embedded_hashes = _extract_embedded_hashes(output_path) + changed_schemas = [ + schema_name + for schema_name in sorted(current_hashes) + if embedded_hashes.get(schema_name) != current_hashes[schema_name] + ] + if changed_schemas: + print( + f"{output_path.name}: schema drift in " + + ", ".join(changed_schemas) + + ". Regenerate models.", + ) + else: + print(f"{output_path.name}: stale (codegen output drifted). Regenerate models.") + + print("Run: just v2-models-generate") + return 1 + + +def _generate_one_models_file( + output_path: Path, + schema_dir: Path, + model_schema_files: dict[str, str], + bundle_title: str, + *, + extra_fields: str = "forbid", +) -> int: + expected = _render_expected_output( + schema_dir, + model_schema_files, + bundle_title, + output_path.name, + extra_fields=extra_fields, + ) + output_path.parent.mkdir(parents=True, exist_ok=True) + output_path.write_text(expected, encoding="utf-8") + print(f"Generated {output_path.relative_to(REPO_ROOT)}") + return 0 + + +def _check_models_file() -> int: + rc = _check_one_models_file( + GENERATED_MODELS_PATH, + STRICT_SCHEMA_DIR, + MODEL_SCHEMA_FILES, + "EntitiesBundle", + extra_fields="forbid", + ) + rc |= _check_one_models_file( + GENERATED_AGENT_MODELS_PATH, + AGENT_SCHEMA_DIR, + AGENT_MODEL_SCHEMA_FILES, + "AgentEntitiesBundle", + extra_fields="ignore", + ) + return rc + + +def _generate_models_file() -> int: + rc = _generate_one_models_file( + GENERATED_MODELS_PATH, + STRICT_SCHEMA_DIR, + MODEL_SCHEMA_FILES, + "EntitiesBundle", + extra_fields="forbid", + ) + rc |= _generate_one_models_file( + GENERATED_AGENT_MODELS_PATH, + AGENT_SCHEMA_DIR, + AGENT_MODEL_SCHEMA_FILES, + "AgentEntitiesBundle", + extra_fields="ignore", + ) + return rc + + +def main() -> int: + parser = argparse.ArgumentParser(description="Generate or verify v2 models.") + parser.add_argument( + "--check", + action="store_true", + help="Verify generated models are in sync with schemas.", + ) + args = parser.parse_args() + if args.check: + return _check_models_file() + return _generate_models_file() + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/v2/post_extract_gimie_until_done.sh b/scripts/v2/post_extract_gimie_until_done.sh new file mode 100644 index 0000000..cff02e4 --- /dev/null +++ b/scripts/v2/post_extract_gimie_until_done.sh @@ -0,0 +1,88 @@ +#!/usr/bin/env bash + +set -euo pipefail + +API_BASE_URL="${API_BASE_URL:-http://localhost:1234}" +SOURCE_URL="${SOURCE_URL:-github.com/sdsc-ordes/gimie}" +OUTPUT_FORMAT="${OUTPUT_FORMAT:-json}" +AGENT_RUNTIME="${AGENT_RUNTIME:-llm}" +INCLUDE_CONTEXT_SUMMARY="${INCLUDE_CONTEXT_SUMMARY:-true}" +INCLUDE_INTERMEDIATES="${INCLUDE_INTERMEDIATES:-false}" +REQUEST_TIMEOUT_SECONDS="${REQUEST_TIMEOUT_SECONDS:-120}" +RETRY_SLEEP_SECONDS="${RETRY_SLEEP_SECONDS:-10}" +MAX_ATTEMPTS="${MAX_ATTEMPTS:-0}" # 0 means retry forever. +OUTPUT_FILE="${OUTPUT_FILE:-/tmp/v2_extract_gimie_response.json}" + +attempt=0 + +echo "POST polling started" +echo " endpoint: ${API_BASE_URL}/v2/extract" +echo " source_url: ${SOURCE_URL}" +echo " runtime: ${AGENT_RUNTIME}" +echo " output_file: ${OUTPUT_FILE}" + +while true; do + attempt=$((attempt + 1)) + temp_response_file="$(mktemp)" + + payload="$(cat </dev/null 2>&1; then + jq -r '"summary: detected=\(.detected_type) entities=\(.stats.entities_count) duration_ms=\(.stats.duration_ms) warnings=\((.warnings|length))"' "${OUTPUT_FILE}" || true + fi + echo "response saved to ${OUTPUT_FILE}" + break + fi + + echo "[attempt ${attempt}] HTTP ${http_code} (not done yet)." + if command -v jq >/dev/null 2>&1; then + jq -r '.detail // .error_type // "no detail"' "${temp_response_file}" || true + fi + rm -f "${temp_response_file}" + + if [[ "${MAX_ATTEMPTS}" -gt 0 && "${attempt}" -ge "${MAX_ATTEMPTS}" ]]; then + echo "Reached MAX_ATTEMPTS=${MAX_ATTEMPTS} without success." + exit 1 + fi + + echo "retrying in ${RETRY_SLEEP_SECONDS}s..." + sleep "${RETRY_SLEEP_SECONDS}" +done diff --git a/scripts/v2/run_llm_repo_and_persons.py b/scripts/v2/run_llm_repo_and_persons.py new file mode 100644 index 0000000..eaf6429 --- /dev/null +++ b/scripts/v2/run_llm_repo_and_persons.py @@ -0,0 +1,310 @@ +# ruff: noqa: INP001, T201 +"""Run LLM repository agent + LLM person agents end-to-end against a real GitHub repo. + +Executes the first three stages of the repository pipeline: + 1. context_gather — gather GIMIE context + 2. repo_agent — LLM repository extraction + 3. person_agents — LLM person extraction for each contributor (concurrent) + +Usage: + just v2-run-repo-and-persons sdsc-ordes/gimie + python scripts/v2/run_llm_repo_and_persons.py sdsc-ordes/gimie + python scripts/v2/run_llm_repo_and_persons.py https://github.com/sdsc-ordes/gimie +""" +from __future__ import annotations + +import argparse +import asyncio +import json +import logging +from dataclasses import dataclass +from time import perf_counter +from typing import Literal + +logging.basicConfig(level=logging.INFO, format="%(levelname)s %(name)s: %(message)s") + +from src.v2.agents.llm.person import LLMPersonAgentV2 +from src.v2.agents.llm.repository import LLMRepositoryAgentV2 +from src.v2.agents.models import AgentResult, ProviderSet +from src.v2.dependencies import _default_provider_set +from src.v2.ingest.detection.github_url_classifier import classify_github_url +from src.v2.pipeline.stages import gather_context + +_SEP = "─" * 60 +_SEP_THIN = "·" * 60 + + +@dataclass(slots=True) +class _PersonExecutionOutcome: + username: str + status: Literal["ok", "timeout", "error"] + elapsed_seconds: float + result: AgentResult | None = None + error: str | None = None + + +def _pj(obj: object) -> str: + return json.dumps(obj, indent=2, ensure_ascii=False) + + +def _contributor_usernames(repository_context: dict) -> list[str]: + """Extract unique contributor logins, excluding organization accounts.""" + contributors = repository_context.get("contributors", []) + seen: set[str] = set() + logins: list[str] = [] + for c in contributors: + if isinstance(c, dict): + if str(c.get("type", "")).lower() == "organization": + continue + login = c.get("login") + elif isinstance(c, str): + login = c + else: + continue + if isinstance(login, str) and login.strip() and login not in seen: + seen.add(login) + logins.append(login.strip()) + return logins + + +def _classify_person_error(error: BaseException) -> Literal["timeout", "error"]: + if "timed out" in str(error).lower(): + return "timeout" + return "error" + + +def _positive_float(raw_value: str) -> float: + parsed = float(raw_value) + if parsed <= 0: + message = "value must be > 0" + raise argparse.ArgumentTypeError(message) + return parsed + + +def _positive_int(raw_value: str) -> int: + parsed = int(raw_value) + if parsed <= 0: + message = "value must be > 0" + raise argparse.ArgumentTypeError(message) + return parsed + + +async def _run( + repo: str, + *, + person_timeout_seconds: float, + max_concurrency: int, + heartbeat_seconds: float, +) -> None: + if "/" in repo and not repo.startswith("http") and "github.com" not in repo: + repo = f"github.com/{repo}" + url_info = classify_github_url(repo) + full_name = f"{url_info.owner}/{url_info.repo}" + + print(f"\n{_SEP}") + print(f" Repository : {full_name}") + print(f"{_SEP}\n") + + providers: ProviderSet = _default_provider_set(use_mock_providers=False) + + # ── Stage 1: context_gather ────────────────────────────────────────────── + print("[ 1 / 3 ] Gathering GIMIE context …\n") + bundle = await gather_context("repository", url_info, providers) + repo_ctx = bundle.context.get("repository", {}) + + readme = repo_ctx.get("readme_content") or "" + print(f"Metadata:\n{_pj(repo_ctx.get('metadata', {}))}\n") + print(f"Contributors:\n{_pj(repo_ctx.get('contributors', []))}\n") + print(f"Languages:\n{_pj(repo_ctx.get('languages', {}))}\n") + print(f"README ({len(readme)} chars): {readme[:300]}{'…' if len(readme) > 300 else ''}\n") + if bundle.warnings: + print(f"Context warnings: {bundle.warnings}\n") + + # ── Stage 2: repo_agent ────────────────────────────────────────────────── + print(f"{_SEP}") + print("[ 2 / 3 ] Running LLM repository agent …\n") + + repo_agent = LLMRepositoryAgentV2() + repo_result = await repo_agent.run( + { + "full_name": full_name, + "source_url": url_info.normalized_url, + "repository_context": repo_ctx, + }, + providers, + ) + + print(f"Model : {repo_result.model}") + print(f"Provider : {repo_result.provider}") + print(f"Tokens : prompt={repo_result.tokens_prompt} completion={repo_result.tokens_completion}") + if repo_result.warnings: + print("Warnings:") + for w in repo_result.warnings: + print(f" • {w}") + print(f"\nRepository entity:\n{_pj(repo_result.data)}\n") + + # ── Stage 3: person_agents (concurrent) ───────────────────────────────── + usernames = _contributor_usernames(repo_ctx) + print(f"{_SEP}") + print(f"[ 3 / 3 ] Running LLM person agent for {len(usernames)} contributor(s): {usernames}\n") + + if not usernames: + print(" No contributors found — skipping person stage.\n") + return + + person_agent = LLMPersonAgentV2( + llm_call_timeout_seconds=person_timeout_seconds, + ) + source_repositories = [full_name] + semaphore = asyncio.Semaphore(max_concurrency) + + async def _run_person(username: str) -> _PersonExecutionOutcome: + async with semaphore: + print(f" → {username}: starting …") + t0 = perf_counter() + ctx = { + "username": username, + "source_url": url_info.normalized_url, + "source_repositories": source_repositories, + # Share repository context so the LLM can scan the README for + # ORCID identifiers, affiliations, and author credits. + "repository_context": repo_ctx, + } + try: + result = await person_agent.run(ctx, providers) + except Exception as exc: # noqa: BLE001 + elapsed_seconds = perf_counter() - t0 + status = _classify_person_error(exc) + print( + f" ✗ {username}: {status} in {elapsed_seconds:.1f}s ({exc})", + ) + return _PersonExecutionOutcome( + username=username, + status=status, + elapsed_seconds=elapsed_seconds, + error=str(exc), + ) + + elapsed_seconds = perf_counter() - t0 + print(f" ✓ {username}: done in {elapsed_seconds:.1f}s") + return _PersonExecutionOutcome( + username=username, + status="ok", + elapsed_seconds=elapsed_seconds, + result=result, + ) + + outcomes_by_username: dict[str, _PersonExecutionOutcome] = {} + task_to_username: dict[asyncio.Task[_PersonExecutionOutcome], str] = { + asyncio.create_task(_run_person(username)): username for username in usernames + } + pending_tasks = set(task_to_username) + fanout_started_at = perf_counter() + + while pending_tasks: + done, pending_tasks = await asyncio.wait( + pending_tasks, + timeout=heartbeat_seconds, + return_when=asyncio.FIRST_COMPLETED, + ) + if not done: + running = sorted(task_to_username[task] for task in pending_tasks) + print( + " … heartbeat: " + f"{len(running)} contributor(s) still running after {perf_counter() - fanout_started_at:.1f}s: " + f"{running}", + ) + continue + + for task in done: + username = task_to_username[task] + try: + outcome = task.result() + except Exception as exc: # noqa: BLE001 + status = _classify_person_error(exc) + outcome = _PersonExecutionOutcome( + username=username, + status=status, + elapsed_seconds=perf_counter() - fanout_started_at, + error=str(exc), + ) + outcomes_by_username[username] = outcome + + ordered_outcomes = [outcomes_by_username[username] for username in usernames] + for outcome in ordered_outcomes: + print(_SEP_THIN) + if outcome.status != "ok" or outcome.result is None: + print(f" Person: {outcome.username}") + print(f" Status : {outcome.status}") + print(f" Error : {outcome.error}\n") + continue + result = outcome.result + print(f" Person: {outcome.username}") + print(f" Model : {result.model}") + print(f" Tokens : prompt={result.tokens_prompt} completion={result.tokens_completion}") + if result.warnings: + print(" Warnings:") + for w in result.warnings: + print(f" • {w}") + print(f" Entity:\n{_pj(result.data)}\n") + + successful_person_results = [ + outcome.result.data + for outcome in ordered_outcomes + if outcome.status == "ok" and outcome.result is not None + ] + timeout_count = sum(1 for outcome in ordered_outcomes if outcome.status == "timeout") + error_count = sum(1 for outcome in ordered_outcomes if outcome.status == "error") + + print(_SEP_THIN) + print( + "Person summary: " + f"ok={len(successful_person_results)} timeout={timeout_count} error={error_count}", + ) + failed_outcomes = [outcome for outcome in ordered_outcomes if outcome.status != "ok"] + if failed_outcomes: + print("Failed contributors:") + for outcome in failed_outcomes: + print(f" - {outcome.username}: {outcome.status} ({outcome.error})") + print("\nPerson entities:") + print(_pj(successful_person_results)) + + print(_SEP) + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Run LLM repo + person agents for the first three pipeline stages.", + ) + parser.add_argument("repo", help="GitHub repo as owner/repo or full URL") + parser.add_argument( + "--person-timeout-seconds", + type=_positive_float, + default=180.0, + help="Per-person timeout for LLM person extraction (seconds).", + ) + parser.add_argument( + "--max-concurrency", + type=_positive_int, + default=3, + help="Maximum number of concurrent person agent calls.", + ) + parser.add_argument( + "--heartbeat-seconds", + type=_positive_float, + default=15.0, + help="How often to print fanout heartbeat while waiting for person tasks.", + ) + args = parser.parse_args() + asyncio.run( + _run( + args.repo, + person_timeout_seconds=args.person_timeout_seconds, + max_concurrency=args.max_concurrency, + heartbeat_seconds=args.heartbeat_seconds, + ), + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/v2/run_llm_repo_persons_and_orgs.py b/scripts/v2/run_llm_repo_persons_and_orgs.py new file mode 100644 index 0000000..48e131b --- /dev/null +++ b/scripts/v2/run_llm_repo_persons_and_orgs.py @@ -0,0 +1,1765 @@ +# ruff: noqa: INP001 +"""Run repository debug stages against a real GitHub repo. + +Executes repository-oriented stages in order: + 1. context_gather + 2. repo_agent (LLM) + 3. person_agents fanout (LLM) + 4. organization_agents fanout (LLM) + 5. article_agents fanout (LLM) + 6. membership_agents fanout (LLM) + 7. contribution_agents fanout (LLM) + +Usage: + just v2-run-repo-persons-and-orgs sdsc-ordes/gimie + just v2-run-repo-full-llm sdsc-ordes/gimie # includes --verify-links + python scripts/v2/run_llm_repo_persons_and_orgs.py sdsc-ordes/gimie + python scripts/v2/run_llm_repo_persons_and_orgs.py https://github.com/sdsc-ordes/gimie + python scripts/v2/run_llm_repo_persons_and_orgs.py sdsc-ordes/gimie --verify-links +""" +from __future__ import annotations + +import argparse +import asyncio +import json +import logging +from copy import deepcopy +from dataclasses import dataclass +from time import perf_counter +from typing import TYPE_CHECKING, Any, Awaitable, Callable, Literal +from urllib.parse import urlparse + +from src.v2.agents.llm.article import LLMArticleAgentV2 +from src.v2.agents.llm.contribution import LLMContributionAgentV2 +from src.v2.agents.llm.link_veracity import LLMLinkVeracityAgentV2 +from src.v2.agents.llm.membership import LLMMembershipAgentV2 +from src.v2.agents.llm.organization import LLMOrganizationAgentV2 +from src.v2.agents.llm.person import LLMPersonAgentV2 +from src.v2.agents.llm.repository import LLMRepositoryAgentV2 +from src.v2.agents.models import AgentResult, TypedEntityBuckets, infer_entity_bucket +from src.v2.canonicalization.string_utils import normalize_string, strip_accents +from src.v2.dependencies import _default_provider_set +from src.v2.ingest.detection.github_url_classifier import classify_github_url +from src.v2.schema import load_jsonld_context +from src.v2.pipeline.stages import ( + AssembledOutput, + build_jsonld_output, + gather_context, + reconcile_entities, +) + +if TYPE_CHECKING: + from src.v2.agents.models import ProviderSet + +logging.basicConfig(level=logging.INFO, format="%(levelname)s %(name)s: %(message)s") + +_SEP = "─" * 60 +_SEP_THIN = "·" * 60 +_STAGE_REPO_AGENT = "repo_agent" +_STAGE_PERSON_AGENT = "person_agent" +_STAGE_ORG_AGENT = "org_agent" +_STAGE_ARTICLE_AGENT = "article_agent" +_STAGE_MEMBERSHIP_AGENT = "membership_agent" +_STAGE_CONTRIBUTION_AGENT = "contribution_agent" +_HTTP_SCHEMES = {"http", "https"} + + +@dataclass(slots=True) +class _ExecutionOutcome: + subject: str + status: Literal["ok", "timeout", "error"] + elapsed_seconds: float + result: AgentResult | None = None + error: str | None = None + + +def _pj(obj: object) -> str: + return json.dumps(obj, indent=2, ensure_ascii=False) + + +def _dedupe_preserve_order(values: list[str]) -> list[str]: + deduped: list[str] = [] + seen: set[str] = set() + for value in values: + if value in seen: + continue + seen.add(value) + deduped.append(value) + return deduped + + +def _dedupe_preserve_order_normalized(values: list[str]) -> list[str]: + deduped: list[str] = [] + seen: set[str] = set() + for value in values: + normalized = normalize_string(value) + token = normalized if normalized else value.casefold().strip() + if not token or token in seen: + continue + seen.add(token) + deduped.append(value) + return deduped + + +def _lookup_token_variants(token: str) -> list[str]: + candidate = token.strip() + if not candidate: + return [] + + variants: list[str] = [] + seen: set[str] = set() + + def _add(value: str) -> None: + normalized_value = value.strip() + if not normalized_value or normalized_value in seen: + return + seen.add(normalized_value) + variants.append(normalized_value) + + raw_candidates = [candidate] + if candidate.startswith("@"): + raw_candidates.append(candidate[1:]) + + for raw_value in raw_candidates: + lowered = raw_value.casefold().strip() + _add(lowered) + _add(strip_accents(lowered).strip()) + collapsed = normalize_string(raw_value) + _add(collapsed) + _add(collapsed.replace(" ", "")) + + return variants + + +def _register_lookup_token(lookup: dict[str, str], token: Any, canonical_id: str) -> None: + if not isinstance(token, str): + return + for normalized in _lookup_token_variants(token): + lookup[normalized] = canonical_id + + +def _resolve_lookup_token(lookup: dict[str, str], token: Any) -> str | None: + if not isinstance(token, str): + return None + for normalized in _lookup_token_variants(token): + resolved = lookup.get(normalized) + if isinstance(resolved, str): + return resolved + return None + + +def _as_string_list(value: Any) -> list[str]: + if not isinstance(value, list): + return [] + return [item for item in value if isinstance(item, str) and item] + + +def _json_fingerprint(value: Any) -> str: + try: + return json.dumps(value, sort_keys=True, ensure_ascii=True, default=str) + except TypeError: + return repr(value) + + +def _merge_lists(existing: list[Any], incoming: list[Any]) -> list[Any]: + merged: list[Any] = [] + seen: set[str] = set() + for item in [*existing, *incoming]: + fingerprint = _json_fingerprint(item) + if fingerprint in seen: + continue + seen.add(fingerprint) + merged.append(deepcopy(item)) + return merged + + +def _merge_values(existing: Any, incoming: Any) -> Any: + if incoming is None: + return deepcopy(existing) + if existing is None: + return deepcopy(incoming) + if isinstance(existing, dict) and isinstance(incoming, dict): + merged = deepcopy(existing) + for key, value in incoming.items(): + if key not in merged: + merged[key] = deepcopy(value) + continue + merged[key] = _merge_values(merged[key], value) + return merged + if isinstance(existing, list) and isinstance(incoming, list): + return _merge_lists(existing, incoming) + if existing == incoming: + return deepcopy(existing) + return deepcopy(existing) + + +def _merge_entities_by_id(entities: list[dict[str, Any]]) -> list[dict[str, Any]]: + merged_entities: list[dict[str, Any]] = [] + index_by_id: dict[str, int] = {} + + for entity in entities: + entity_id = entity.get("id") + if not isinstance(entity_id, str) or not entity_id: + merged_entities.append(deepcopy(entity)) + continue + + existing_index = index_by_id.get(entity_id) + if existing_index is None: + index_by_id[entity_id] = len(merged_entities) + merged_entities.append(deepcopy(entity)) + continue + + merged_entities[existing_index] = _merge_values( + merged_entities[existing_index], + entity, + ) + + return merged_entities + + +def _build_person_lookup(entities: list[dict[str, Any]]) -> dict[str, str]: + lookup: dict[str, str] = {} + for entity in entities: + if not isinstance(entity, dict): + continue + if entity.get("type") != "schema:Person": + continue + canonical_id = entity.get("id") + if not isinstance(canonical_id, str) or not canonical_id: + continue + _register_lookup_token(lookup, canonical_id, canonical_id) + for key in ( + "pulse:githubUsername", + "pulse:orcidIdentifier", + "pulse:infosciencePersonIdentifier", + "schema:name", + ): + _register_lookup_token(lookup, entity.get(key), canonical_id) + + schema_url = entity.get("schema:url") + if isinstance(schema_url, str) and "github.com/" in schema_url: + github_handle = schema_url.rsplit("/", maxsplit=1)[-1] + _register_lookup_token(lookup, github_handle, canonical_id) + + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + for key in ( + "pulse:orcid", + "pulse:infosciencePersonIdentifier", + "pulse:githubUsername", + "uuid", + ): + _register_lookup_token(lookup, identifiers.get(key), canonical_id) + return lookup + + +def _build_organization_lookup(entities: list[dict[str, Any]]) -> dict[str, str]: + lookup: dict[str, str] = {} + for entity in entities: + if not isinstance(entity, dict): + continue + if entity.get("type") != "org:Organization": + continue + canonical_id = entity.get("id") + if not isinstance(canonical_id, str) or not canonical_id: + continue + _register_lookup_token(lookup, canonical_id, canonical_id) + for key in ( + "schema:name", + "pulse:githubOrganizationHandle", + "pulse:ror", + "schema:identifier", + "pulse:infoscienceOrganizationIdentifier", + ): + _register_lookup_token(lookup, entity.get(key), canonical_id) + + for key in ("aliases", "acronyms"): + for alias in _as_string_list(entity.get(key)): + _register_lookup_token(lookup, alias, canonical_id) + + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + for key in ( + "pulse:ror", + "pulse:infoscienceOrganizationIdentifier", + "pulse:githubOrganizationHandle", + "uuid", + ): + _register_lookup_token(lookup, identifiers.get(key), canonical_id) + return lookup + + +def _build_repository_lookup(entities: list[dict[str, Any]]) -> dict[str, str]: + lookup: dict[str, str] = {} + for entity in entities: + if not isinstance(entity, dict): + continue + if entity.get("type") != "schema:SoftwareSourceCode": + continue + canonical_id = entity.get("id") + if not isinstance(canonical_id, str) or not canonical_id: + continue + _register_lookup_token(lookup, canonical_id, canonical_id) + for key in ("pulse:githubRepositoryHandle", "schema:citation"): + _register_lookup_token(lookup, entity.get(key), canonical_id) + + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + _register_lookup_token( + lookup, + identifiers.get("pulse:githubRepositoryHandle"), + canonical_id, + ) + _register_lookup_token( + lookup, + identifiers.get("schema:citation"), + canonical_id, + ) + return lookup + + +def _map_reference_list( + values: list[str], + lookup: dict[str, str], + *, + drop_if_matches_lookup: dict[str, str] | None = None, +) -> list[str]: + mapped: list[str] = [] + for value in values: + resolved = _resolve_lookup_token(lookup, value) + if resolved is not None: + mapped.append(resolved) + continue + if drop_if_matches_lookup is not None and _resolve_lookup_token( + drop_if_matches_lookup, + value, + ) is not None: + continue + mapped.append(value) + return _dedupe_preserve_order(mapped) + + +def _backfill_organization_identifier(entity: dict[str, Any]) -> None: + schema_identifier = entity.get("schema:identifier") + if isinstance(schema_identifier, str) and schema_identifier: + return + + ror_candidates: list[str] = [] + pulse_ror = entity.get("pulse:ror") + if isinstance(pulse_ror, str) and pulse_ror: + ror_candidates.append(pulse_ror) + + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + identifier_ror = identifiers.get("pulse:ror") + if isinstance(identifier_ror, str) and identifier_ror: + ror_candidates.append(identifier_ror) + + entity_id = entity.get("id") + if isinstance(entity_id, str) and entity_id.startswith("https://ror.org/"): + ror_candidates.append(entity_id) + + if ror_candidates: + entity["schema:identifier"] = ror_candidates[0] + + +def _strip_none_values(value: Any) -> Any: + if isinstance(value, dict): + cleaned: dict[str, Any] = {} + for key, item in value.items(): + if item is None: + continue + cleaned_item = _strip_none_values(item) + if cleaned_item is None: + continue + if isinstance(cleaned_item, (dict, list)) and not cleaned_item: + continue + cleaned[key] = cleaned_item + return cleaned + if isinstance(value, list): + cleaned_list = [] + for item in value: + cleaned_item = _strip_none_values(item) + if cleaned_item is None: + continue + if isinstance(cleaned_item, (dict, list)) and not cleaned_item: + continue + cleaned_list.append(cleaned_item) + return cleaned_list + return value + + +def _normalize_entities_for_debug_jsonld(entities: list[dict[str, Any]]) -> list[dict[str, Any]]: + merged_entities = _merge_entities_by_id(entities) + + person_lookup = _build_person_lookup(merged_entities) + organization_lookup = _build_organization_lookup(merged_entities) + repository_lookup = _build_repository_lookup(merged_entities) + + for entity in merged_entities: + if entity.get("type") == "schema:SoftwareSourceCode": + authors = _as_string_list(entity.get("schema:author")) + entity["schema:author"] = _map_reference_list( + authors, + person_lookup, + drop_if_matches_lookup=organization_lookup, + ) + + owned_by = entity.get("pulse:ownedBy") + if isinstance(owned_by, str): + resolved_owner = _resolve_lookup_token(person_lookup, owned_by) or _resolve_lookup_token( + organization_lookup, + owned_by, + ) + if isinstance(resolved_owner, str): + entity["pulse:ownedBy"] = resolved_owner + + fork_ref = entity.get("pulse:isForkOf") + if isinstance(fork_ref, str): + resolved_fork = _resolve_lookup_token(repository_lookup, fork_ref) + if isinstance(resolved_fork, str): + entity["pulse:isForkOf"] = resolved_fork + + if entity.get("type") == "schema:Person": + owns = _as_string_list(entity.get("pulse:owns")) + entity["pulse:owns"] = _map_reference_list(owns, repository_lookup) + + if entity.get("type") == "org:Organization": + _backfill_organization_identifier(entity) + + unit_of = entity.get("org:unitOf") + if isinstance(unit_of, str): + resolved_unit_of = _resolve_lookup_token(organization_lookup, unit_of) + if isinstance(resolved_unit_of, str): + entity["org:unitOf"] = resolved_unit_of + + has_units = _as_string_list(entity.get("org:hasUnit")) + entity["org:hasUnit"] = _map_reference_list(has_units, organization_lookup) + + owns = _as_string_list(entity.get("pulse:owns")) + entity["pulse:owns"] = _map_reference_list(owns, repository_lookup) + + normalized_entities = [_strip_none_values(entity) for entity in merged_entities] + return [ + entity + for entity in normalized_entities + if isinstance(entity, dict) + ] + + +def _contributor_usernames(repository_context: dict) -> list[str]: + """Extract unique contributor logins, excluding organization accounts.""" + contributors = repository_context.get("contributors", []) + seen: set[str] = set() + logins: list[str] = [] + for contributor in contributors: + if isinstance(contributor, dict): + if str(contributor.get("type", "")).lower() == "organization": + continue + login = contributor.get("login") + elif isinstance(contributor, str): + login = contributor + else: + continue + if isinstance(login, str) and login.strip() and login not in seen: + seen.add(login) + logins.append(login.strip()) + return logins + + +def _organization_candidates( + repository_context: dict[str, object], + person_outcomes: list[_ExecutionOutcome], +) -> list[str]: + candidates: list[str] = [] + metadata = repository_context.get("metadata") + if isinstance(metadata, dict): + owner = metadata.get("owner") + if isinstance(owner, dict): + owner_login = owner.get("login") + owner_type = owner.get("type") + if ( + isinstance(owner_login, str) + and owner_login + and isinstance(owner_type, str) + and owner_type.lower() == "organization" + ): + candidates.append(owner_login) + + for outcome in person_outcomes: + if outcome.status != "ok" or outcome.result is None: + continue + memberships = outcome.result.data.get("org:hasMembership") + if not isinstance(memberships, list): + continue + for membership in memberships: + if not isinstance(membership, str) or "_" not in membership: + continue + _, organization = membership.split("_", maxsplit=1) + organization = organization.strip() + if organization: + candidates.append(organization) + + return _dedupe_preserve_order_normalized(candidates) + + +def _collect_stage_payloads( + pipeline_outputs: dict[str, dict[str, Any]], + stage_prefix: str, +) -> list[dict[str, Any]]: + payloads: list[dict[str, Any]] = [] + for result_key in sorted(pipeline_outputs): + if not str(result_key).startswith(stage_prefix): + continue + payload = pipeline_outputs[result_key] + if isinstance(payload, dict) and payload: + payloads.append(deepcopy(payload)) + return payloads + + +def _collect_stage_derivations( + pipeline_agent_results: dict[str, AgentResult], + stage_prefix: str, +) -> list[dict[str, Any]]: + derivations: list[dict[str, Any]] = [] + for result_key in sorted(pipeline_agent_results): + if not str(result_key).startswith(stage_prefix): + continue + result = pipeline_agent_results[result_key] + if not isinstance(result, AgentResult): + continue + if not isinstance(result.stats, dict): + continue + derivation = result.stats.get("derivation") + if isinstance(derivation, dict): + derivations.append(deepcopy(derivation)) + return derivations + + +def _typed_entity_bucket_snapshot( + pipeline_outputs: dict[str, dict[str, Any]], +) -> dict[str, list[dict[str, Any]]]: + buckets = TypedEntityBuckets() + for result_key in sorted(pipeline_outputs): + payload = pipeline_outputs[result_key] + if not isinstance(payload, dict) or not payload: + continue + bucket_name = infer_entity_bucket(agent_key=str(result_key), data=payload) + if bucket_name is None: + continue + buckets.add(bucket_name, payload) + return buckets.to_dict() + + +def _class_agent_base_context( + *, + source_url: str, + full_name: str, + repository_context: dict[str, Any], + pipeline_outputs: dict[str, dict[str, Any]], + pipeline_agent_results: dict[str, AgentResult], +) -> dict[str, Any]: + return { + "detected_type": "repository", + "source_url": source_url, + "full_name": full_name, + "repository_context": deepcopy(repository_context), + "known_persons": _collect_stage_payloads( + pipeline_outputs, + _STAGE_PERSON_AGENT, + ), + "known_organizations": _collect_stage_payloads( + pipeline_outputs, + _STAGE_ORG_AGENT, + ), + "known_repositories": _collect_stage_payloads( + pipeline_outputs, + _STAGE_REPO_AGENT, + ), + "person_derivations": _collect_stage_derivations( + pipeline_agent_results, + _STAGE_PERSON_AGENT, + ), + "organization_derivations": _collect_stage_derivations( + pipeline_agent_results, + _STAGE_ORG_AGENT, + ), + "repository_derivations": _collect_stage_derivations( + pipeline_agent_results, + _STAGE_REPO_AGENT, + ), + "typed_entity_buckets": _typed_entity_bucket_snapshot(pipeline_outputs), + "pipeline_outputs": deepcopy(pipeline_outputs), + "upstream_stage_outputs_json": json.dumps( + pipeline_outputs, + ensure_ascii=True, + sort_keys=True, + ), + } + + +def _person_derivations_by_id( + pipeline_agent_results: dict[str, AgentResult], +) -> dict[str, dict[str, Any]]: + by_id: dict[str, dict[str, Any]] = {} + for derivation in _collect_stage_derivations( + pipeline_agent_results, + _STAGE_PERSON_AGENT, + ): + person_id = derivation.get("person_id") + if isinstance(person_id, str) and person_id: + by_id[person_id] = derivation + return by_id + + +def _repository_derivations_by_id( + pipeline_agent_results: dict[str, AgentResult], +) -> dict[str, dict[str, Any]]: + by_id: dict[str, dict[str, Any]] = {} + for derivation in _collect_stage_derivations( + pipeline_agent_results, + _STAGE_REPO_AGENT, + ): + repository_id = derivation.get("repository_full_name") + if isinstance(repository_id, str) and repository_id: + by_id[repository_id] = derivation + return by_id + + +def _merge_person_with_derivation( + person: dict[str, Any], + derivation: dict[str, Any] | None, +) -> dict[str, Any]: + merged = deepcopy(person) + if not isinstance(derivation, dict): + return merged + + affiliation_names = derivation.get("affiliation_names") + if isinstance(affiliation_names, list): + merged["affiliations"] = [ + value + for value in affiliation_names + if isinstance(value, str) and value + ] + + orcid_affiliations = derivation.get("orcid_affiliations") + if isinstance(orcid_affiliations, list): + merged["orcid_affiliations"] = [ + deepcopy(value) + for value in orcid_affiliations + if isinstance(value, dict) + ] + + source_repositories = derivation.get("source_repositories") + if isinstance(source_repositories, list): + merged["source_repositories"] = [ + value + for value in source_repositories + if isinstance(value, str) and value + ] + + return merged + + +def _merge_repository_with_derivation( + repository: dict[str, Any], + derivation: dict[str, Any] | None, +) -> dict[str, Any]: + merged = deepcopy(repository) + if not isinstance(derivation, dict): + return merged + + contributors = derivation.get("contributors") + if isinstance(contributors, list): + merged["contributors"] = [ + deepcopy(contributor) + for contributor in contributors + if isinstance(contributor, (dict, str)) + ] + return merged + + +def _article_fanout_contexts_repository( + *, + base_context: dict[str, Any], + full_name: str, +) -> list[dict[str, Any]]: + seeds = _dedupe_preserve_order([full_name] if full_name else []) + contexts: list[dict[str, Any]] = [] + for seed in seeds: + context = deepcopy(base_context) + context["article_seed"] = seed + context["full_name"] = seed + contexts.append(context) + return contexts + + +def _membership_fanout_contexts_repository( + *, + base_context: dict[str, Any], + pipeline_agent_results: dict[str, AgentResult], +) -> list[dict[str, Any]]: + known_persons = base_context.get("known_persons") + known_organizations = base_context.get("known_organizations") + if not isinstance(known_persons, list) or not isinstance(known_organizations, list): + return [] + if not known_persons or not known_organizations: + return [] + + person_derivations = _person_derivations_by_id(pipeline_agent_results) + contexts: list[dict[str, Any]] = [] + seen_person_ids: set[str] = set() + + for person in sorted( + [item for item in known_persons if isinstance(item, dict)], + key=lambda item: str(item.get("id", "")), + ): + person_id = person.get("id") + if not isinstance(person_id, str) or not person_id or person_id in seen_person_ids: + continue + seen_person_ids.add(person_id) + + merged_person = _merge_person_with_derivation( + person, + person_derivations.get(person_id), + ) + context = deepcopy(base_context) + context["membership_seed"] = person_id + context["known_persons"] = [merged_person] + context["known_organizations"] = deepcopy(known_organizations) + contexts.append(context) + + return contexts + + +def _contribution_fanout_contexts_repository( + *, + base_context: dict[str, Any], + pipeline_agent_results: dict[str, AgentResult], +) -> list[dict[str, Any]]: + known_persons = base_context.get("known_persons") + known_repositories = base_context.get("known_repositories") + if not isinstance(known_persons, list) or not isinstance(known_repositories, list): + return [] + if not known_persons or not known_repositories: + return [] + + repository_derivations = _repository_derivations_by_id(pipeline_agent_results) + contexts: list[dict[str, Any]] = [] + seen_repository_ids: set[str] = set() + + for repository in sorted( + [item for item in known_repositories if isinstance(item, dict)], + key=lambda item: str( + item.get("id") + or item.get("pulse:githubRepositoryHandle") + or item.get("full_name") + or "", + ), + ): + repository_id = repository.get("id") + if not isinstance(repository_id, str) or not repository_id or repository_id in seen_repository_ids: + continue + seen_repository_ids.add(repository_id) + + merged_repository = _merge_repository_with_derivation( + repository, + repository_derivations.get(repository_id), + ) + context = deepcopy(base_context) + context["contribution_seed"] = repository_id + context["known_persons"] = deepcopy(known_persons) + context["known_repositories"] = [merged_repository] + contexts.append(context) + + return contexts + + +def _append_pipeline_output( + pipeline_outputs: dict[str, dict[str, Any]], + pipeline_agent_results: dict[str, AgentResult], + *, + result_key: str, + result: AgentResult, +) -> None: + pipeline_outputs[result_key] = deepcopy(result.data) + pipeline_agent_results[result_key] = result + + +def _extract_result_entities( + result: AgentResult, + *, + stats_keys: tuple[str, ...] = (), +) -> list[dict[str, Any]]: + entities: list[dict[str, Any]] = [] + if isinstance(result.data, dict) and result.data: + entities.append(deepcopy(result.data)) + if not isinstance(result.stats, dict): + return entities + + for stats_key in stats_keys: + values = result.stats.get(stats_key) + if not isinstance(values, list): + continue + for value in values: + if isinstance(value, dict) and value: + entities.append(deepcopy(value)) + return entities + + +def _is_http_url(value: str) -> bool: + parsed = urlparse(value) + return parsed.scheme in _HTTP_SCHEMES and bool(parsed.netloc) + + +def _collect_unique_http_link_contexts( + jsonld_payload: dict[str, Any], +) -> list[dict[str, Any]]: + graph = jsonld_payload.get("@graph") + if not isinstance(graph, list): + return [] + + link_contexts: dict[str, dict[str, Any]] = {} + + def _record_link( + *, + link: str, + source_entity_id: str | None, + predicate: str | None, + ) -> None: + normalized_link = link.strip() + if not normalized_link or not _is_http_url(normalized_link): + return + + context = link_contexts.setdefault( + normalized_link, + { + "link": normalized_link, + "source_entity_id": source_entity_id, + "predicate": predicate, + "relationships": [], + }, + ) + relationship = { + "source_entity_id": source_entity_id, + "predicate": predicate, + } + if relationship not in context["relationships"]: + context["relationships"].append(relationship) + + def _walk( + value: Any, + *, + source_entity_id: str | None, + predicate: str | None, + ) -> None: + if isinstance(value, str): + _record_link( + link=value, + source_entity_id=source_entity_id, + predicate=predicate, + ) + return + + if isinstance(value, dict): + at_id = value.get("@id") + if isinstance(at_id, str): + _record_link( + link=at_id, + source_entity_id=source_entity_id, + predicate=predicate, + ) + for key, nested in value.items(): + if key == "@id": + continue + next_predicate = predicate if predicate else key + _walk( + nested, + source_entity_id=source_entity_id, + predicate=next_predicate, + ) + return + + if isinstance(value, list): + for nested in value: + _walk( + nested, + source_entity_id=source_entity_id, + predicate=predicate, + ) + + for node in graph: + if not isinstance(node, dict): + continue + source_entity_id = node.get("@id") if isinstance(node.get("@id"), str) else None + for key, value in node.items(): + if key == "@id": + continue + _walk( + value, + source_entity_id=source_entity_id, + predicate=key, + ) + + return [link_contexts[link] for link in sorted(link_contexts)] + + +async def _run_fanout_stage( + *, + subjects: list[str], + running_label: str, + max_concurrency: int, + heartbeat_seconds: float, + run_subject: Callable[[str], Awaitable[AgentResult]], +) -> list[_ExecutionOutcome]: + if not subjects: + return [] + + semaphore = asyncio.Semaphore(max_concurrency) + + async def _run_one(subject: str) -> _ExecutionOutcome: + async with semaphore: + print(f" → {subject}: starting …") + started_at = perf_counter() + try: + result = await run_subject(subject) + except Exception as exc: # noqa: BLE001 + elapsed_seconds = perf_counter() - started_at + status = _classify_error(exc) + print(f" ✗ {subject}: {status} in {elapsed_seconds:.1f}s ({exc})") + return _ExecutionOutcome( + subject=subject, + status=status, + elapsed_seconds=elapsed_seconds, + error=str(exc), + ) + + elapsed_seconds = perf_counter() - started_at + print(f" ✓ {subject}: done in {elapsed_seconds:.1f}s") + return _ExecutionOutcome( + subject=subject, + status="ok", + elapsed_seconds=elapsed_seconds, + result=result, + ) + + outcomes_by_subject: dict[str, _ExecutionOutcome] = {} + task_to_subject: dict[asyncio.Task[_ExecutionOutcome], str] = { + asyncio.create_task(_run_one(subject)): subject for subject in subjects + } + pending_tasks = set(task_to_subject) + fanout_started_at = perf_counter() + + while pending_tasks: + done, pending_tasks = await asyncio.wait( + pending_tasks, + timeout=heartbeat_seconds, + return_when=asyncio.FIRST_COMPLETED, + ) + if not done: + running = sorted(task_to_subject[task] for task in pending_tasks) + print( + " … heartbeat: " + f"{len(running)} {running_label}(s) still running after {perf_counter() - fanout_started_at:.1f}s: " + f"{running}", + ) + continue + + for task in done: + subject = task_to_subject[task] + try: + outcome = task.result() + except Exception as exc: # noqa: BLE001 + status = _classify_error(exc) + outcome = _ExecutionOutcome( + subject=subject, + status=status, + elapsed_seconds=perf_counter() - fanout_started_at, + error=str(exc), + ) + outcomes_by_subject[subject] = outcome + + return [outcomes_by_subject[subject] for subject in subjects] + + +def _classify_error(error: BaseException) -> Literal["timeout", "error"]: + if "timed out" in str(error).lower(): + return "timeout" + return "error" + + +def _positive_float(raw_value: str) -> float: + parsed = float(raw_value) + if parsed <= 0: + message = "value must be > 0" + raise argparse.ArgumentTypeError(message) + return parsed + + +def _positive_int(raw_value: str) -> int: + parsed = int(raw_value) + if parsed <= 0: + message = "value must be > 0" + raise argparse.ArgumentTypeError(message) + return parsed + + +async def _run( # noqa: C901, PLR0915 + repo: str, + *, + person_timeout_seconds: float = 180.0, + organization_timeout_seconds: float = 180.0, + article_timeout_seconds: float = 180.0, + membership_timeout_seconds: float = 180.0, + contribution_timeout_seconds: float = 180.0, + max_concurrency: int = 3, + heartbeat_seconds: float = 15.0, + verify_links: bool = False, + link_verification_timeout_seconds: float = 120.0, + link_verification_max_concurrency: int | None = None, +) -> None: + if "/" in repo and not repo.startswith("http") and "github.com" not in repo: + repo = f"github.com/{repo}" + url_info = classify_github_url(repo) + full_name = f"{url_info.owner}/{url_info.repo}" + + print(f"\n{_SEP}") + print(f" Repository : {full_name}") + print(f"{_SEP}\n") + + providers: ProviderSet = _default_provider_set(use_mock_providers=False) + + # ── Stage 1: context_gather ────────────────────────────────────────────── + print("[ 1 / 7 ] Gathering GIMIE context …\n") + bundle = await gather_context("repository", url_info, providers) + repo_ctx = bundle.context.get("repository", {}) + + readme = repo_ctx.get("readme_content") or "" + print(f"Metadata:\n{_pj(repo_ctx.get('metadata', {}))}\n") + print(f"Contributors:\n{_pj(repo_ctx.get('contributors', []))}\n") + print(f"Languages:\n{_pj(repo_ctx.get('languages', {}))}\n") + print(f"README ({len(readme)} chars): {readme[:300]}{'…' if len(readme) > 300 else ''}\n") + if bundle.warnings: + print(f"Context warnings: {bundle.warnings}\n") + + # ── Stage 2: repo_agent ────────────────────────────────────────────────── + print(f"{_SEP}") + print("[ 2 / 7 ] Running LLM repository agent …\n") + + repo_agent = LLMRepositoryAgentV2() + repo_result = await repo_agent.run( + { + "full_name": full_name, + "source_url": url_info.normalized_url, + "repository_context": repo_ctx, + }, + providers, + ) + + print(f"Model : {repo_result.model}") + print(f"Provider : {repo_result.provider}") + print(f"Tokens : prompt={repo_result.tokens_prompt} completion={repo_result.tokens_completion}") + if repo_result.warnings: + print("Warnings:") + for warning in repo_result.warnings: + print(f" • {warning}") + print(f"\nRepository entity:\n{_pj(repo_result.data)}\n") + + pipeline_outputs: dict[str, dict[str, Any]] = { + _STAGE_REPO_AGENT: deepcopy(repo_result.data), + } + pipeline_agent_results: dict[str, AgentResult] = { + _STAGE_REPO_AGENT: repo_result, + } + + # ── Stage 3: person_agents (concurrent) ───────────────────────────────── + usernames = _contributor_usernames(repo_ctx) + print(f"{_SEP}") + print(f"[ 3 / 7 ] Running LLM person agent for {len(usernames)} contributor(s): {usernames}\n") + + ordered_person_outcomes: list[_ExecutionOutcome] = [] + successful_person_entities: list[dict[str, Any]] = [] + source_repositories = [full_name] + + if usernames: + person_agent = LLMPersonAgentV2( + llm_call_timeout_seconds=person_timeout_seconds, + ) + person_context_by_username: dict[str, dict[str, Any]] = { + username: { + "username": username, + "source_url": url_info.normalized_url, + "source_repositories": source_repositories, + "repository_context": repo_ctx, + } + for username in usernames + } + + async def _run_person(username: str) -> AgentResult: + return await person_agent.run(person_context_by_username[username], providers) + + ordered_person_outcomes = await _run_fanout_stage( + subjects=usernames, + running_label="contributor", + max_concurrency=max_concurrency, + heartbeat_seconds=heartbeat_seconds, + run_subject=_run_person, + ) + for outcome in ordered_person_outcomes: + print(_SEP_THIN) + if outcome.status != "ok" or outcome.result is None: + print(f" Person: {outcome.subject}") + print(f" Status : {outcome.status}") + print(f" Error : {outcome.error}\n") + continue + result = outcome.result + print(f" Person: {outcome.subject}") + print(f" Model : {result.model}") + print(f" Tokens : prompt={result.tokens_prompt} completion={result.tokens_completion}") + if result.warnings: + print(" Warnings:") + for warning in result.warnings: + print(f" • {warning}") + print(f" Entity:\n{_pj(result.data)}\n") + + for outcome in ordered_person_outcomes: + if outcome.status != "ok" or outcome.result is None: + continue + _append_pipeline_output( + pipeline_outputs, + pipeline_agent_results, + result_key=f"{_STAGE_PERSON_AGENT}:{outcome.subject}", + result=outcome.result, + ) + successful_person_entities.extend(_extract_result_entities(outcome.result)) + + timeout_count = sum(1 for outcome in ordered_person_outcomes if outcome.status == "timeout") + error_count = sum(1 for outcome in ordered_person_outcomes if outcome.status == "error") + + print(_SEP_THIN) + print( + "Person summary: " + f"ok={len(successful_person_entities)} timeout={timeout_count} error={error_count}", + ) + failed_person_outcomes = [ + outcome for outcome in ordered_person_outcomes if outcome.status != "ok" + ] + if failed_person_outcomes: + print("Failed contributors:") + for outcome in failed_person_outcomes: + print(f" - {outcome.subject}: {outcome.status} ({outcome.error})") + print("\nPerson entities:") + print(_pj(successful_person_entities)) + else: + print(" No contributors found — skipping person stage.\n") + + # ── Stage 4: organization_agents (concurrent) ─────────────────────────── + organization_names = _organization_candidates(repo_ctx, ordered_person_outcomes) + print(f"{_SEP}") + print( + "[ 4 / 7 ] Running LLM organization agent for " + f"{len(organization_names)} organization(s): {organization_names}\n", + ) + + ordered_organization_outcomes: list[_ExecutionOutcome] = [] + successful_organization_entities: list[dict[str, Any]] = [] + if organization_names: + organization_agent = LLMOrganizationAgentV2( + llm_call_timeout_seconds=organization_timeout_seconds, + ) + upstream_outputs_for_org = deepcopy(pipeline_outputs) + + upstream_outputs_json = json.dumps( + upstream_outputs_for_org, + ensure_ascii=True, + sort_keys=True, + ) + + organization_context_by_name: dict[str, dict[str, Any]] = { + org_name: { + "org_name": org_name, + "source_url": url_info.normalized_url, + "source_repositories": source_repositories, + "repository_context": repo_ctx, + "pipeline_outputs": deepcopy(upstream_outputs_for_org), + "upstream_stage_outputs_json": upstream_outputs_json, + } + for org_name in organization_names + } + + async def _run_org(org_name: str) -> AgentResult: + return await organization_agent.run(organization_context_by_name[org_name], providers) + + ordered_organization_outcomes = await _run_fanout_stage( + subjects=organization_names, + running_label="organization", + max_concurrency=max_concurrency, + heartbeat_seconds=heartbeat_seconds, + run_subject=_run_org, + ) + for outcome in ordered_organization_outcomes: + print(_SEP_THIN) + if outcome.status != "ok" or outcome.result is None: + print(f" Organization: {outcome.subject}") + print(f" Status : {outcome.status}") + print(f" Error : {outcome.error}\n") + continue + result = outcome.result + print(f" Organization: {outcome.subject}") + print(f" Model : {result.model}") + print(f" Tokens : prompt={result.tokens_prompt} completion={result.tokens_completion}") + if result.warnings: + print(" Warnings:") + for warning in result.warnings: + print(f" • {warning}") + print(f" Entity:\n{_pj(result.data)}\n") + + for outcome in ordered_organization_outcomes: + if outcome.status != "ok" or outcome.result is None: + continue + _append_pipeline_output( + pipeline_outputs, + pipeline_agent_results, + result_key=f"{_STAGE_ORG_AGENT}:{outcome.subject}", + result=outcome.result, + ) + successful_organization_entities.extend( + _extract_result_entities(outcome.result), + ) + + timeout_count = sum( + 1 for outcome in ordered_organization_outcomes if outcome.status == "timeout" + ) + error_count = sum( + 1 for outcome in ordered_organization_outcomes if outcome.status == "error" + ) + + print(_SEP_THIN) + print( + "Organization summary: " + f"ok={len(successful_organization_entities)} timeout={timeout_count} error={error_count}", + ) + failed_org_outcomes = [ + outcome for outcome in ordered_organization_outcomes if outcome.status != "ok" + ] + if failed_org_outcomes: + print("Failed organizations:") + for outcome in failed_org_outcomes: + print(f" - {outcome.subject}: {outcome.status} ({outcome.error})") + print("\nOrganization entities:") + print(_pj(successful_organization_entities)) + else: + print(" No organizations derived — skipping organization stage.\n") + + # ── Stage 5: article_agents (concurrent) ──────────────────────────────── + article_base_context = _class_agent_base_context( + source_url=url_info.normalized_url, + full_name=full_name, + repository_context=repo_ctx, + pipeline_outputs=pipeline_outputs, + pipeline_agent_results=pipeline_agent_results, + ) + article_contexts = _article_fanout_contexts_repository( + base_context=article_base_context, + full_name=full_name, + ) + article_seeds = [context["article_seed"] for context in article_contexts] + + print(f"{_SEP}") + print( + "[ 5 / 7 ] Running LLM article agent for " + f"{len(article_seeds)} seed(s): {article_seeds}\n", + ) + + ordered_article_outcomes: list[_ExecutionOutcome] = [] + successful_article_entities: list[dict[str, Any]] = [] + if article_contexts: + article_agent = LLMArticleAgentV2( + llm_call_timeout_seconds=article_timeout_seconds, + ) + article_context_by_seed: dict[str, dict[str, Any]] = { + context["article_seed"]: context for context in article_contexts + } + + async def _run_article(seed: str) -> AgentResult: + return await article_agent.run(article_context_by_seed[seed], providers) + + ordered_article_outcomes = await _run_fanout_stage( + subjects=article_seeds, + running_label="article", + max_concurrency=max_concurrency, + heartbeat_seconds=heartbeat_seconds, + run_subject=_run_article, + ) + + for outcome in ordered_article_outcomes: + print(_SEP_THIN) + if outcome.status != "ok" or outcome.result is None: + print(f" Article: {outcome.subject}") + print(f" Status : {outcome.status}") + print(f" Error : {outcome.error}\n") + continue + result = outcome.result + print(f" Article: {outcome.subject}") + print(f" Model : {result.model}") + print(f" Tokens : prompt={result.tokens_prompt} completion={result.tokens_completion}") + if result.warnings: + print(" Warnings:") + for warning in result.warnings: + print(f" • {warning}") + print(f" Entity:\n{_pj(result.data)}\n") + + for outcome in ordered_article_outcomes: + if outcome.status != "ok" or outcome.result is None: + continue + _append_pipeline_output( + pipeline_outputs, + pipeline_agent_results, + result_key=f"{_STAGE_ARTICLE_AGENT}:{outcome.subject}", + result=outcome.result, + ) + successful_article_entities.extend( + _extract_result_entities( + outcome.result, + stats_keys=("articles",), + ), + ) + + timeout_count = sum(1 for outcome in ordered_article_outcomes if outcome.status == "timeout") + error_count = sum(1 for outcome in ordered_article_outcomes if outcome.status == "error") + print(_SEP_THIN) + print( + "Article summary: " + f"ok={len(successful_article_entities)} timeout={timeout_count} error={error_count}", + ) + failed_article_outcomes = [ + outcome for outcome in ordered_article_outcomes if outcome.status != "ok" + ] + if failed_article_outcomes: + print("Failed article seeds:") + for outcome in failed_article_outcomes: + print(f" - {outcome.subject}: {outcome.status} ({outcome.error})") + print("\nArticle entities:") + print(_pj(successful_article_entities)) + else: + print(" No article seeds derived — skipping article stage.\n") + + # ── Stage 6: membership_agents (concurrent) ───────────────────────────── + membership_base_context = _class_agent_base_context( + source_url=url_info.normalized_url, + full_name=full_name, + repository_context=repo_ctx, + pipeline_outputs=pipeline_outputs, + pipeline_agent_results=pipeline_agent_results, + ) + membership_contexts = _membership_fanout_contexts_repository( + base_context=membership_base_context, + pipeline_agent_results=pipeline_agent_results, + ) + membership_seeds = [context["membership_seed"] for context in membership_contexts] + + print(f"{_SEP}") + print( + "[ 6 / 7 ] Running LLM membership agent for " + f"{len(membership_seeds)} seed(s): {membership_seeds}\n", + ) + + ordered_membership_outcomes: list[_ExecutionOutcome] = [] + successful_membership_entities: list[dict[str, Any]] = [] + if membership_contexts: + membership_agent = LLMMembershipAgentV2( + llm_call_timeout_seconds=membership_timeout_seconds, + ) + membership_context_by_seed: dict[str, dict[str, Any]] = { + context["membership_seed"]: context for context in membership_contexts + } + + async def _run_membership(seed: str) -> AgentResult: + return await membership_agent.run(membership_context_by_seed[seed], providers) + + ordered_membership_outcomes = await _run_fanout_stage( + subjects=membership_seeds, + running_label="membership", + max_concurrency=max_concurrency, + heartbeat_seconds=heartbeat_seconds, + run_subject=_run_membership, + ) + + for outcome in ordered_membership_outcomes: + print(_SEP_THIN) + if outcome.status != "ok" or outcome.result is None: + print(f" Membership: {outcome.subject}") + print(f" Status : {outcome.status}") + print(f" Error : {outcome.error}\n") + continue + result = outcome.result + print(f" Membership: {outcome.subject}") + print(f" Model : {result.model}") + print(f" Tokens : prompt={result.tokens_prompt} completion={result.tokens_completion}") + if result.warnings: + print(" Warnings:") + for warning in result.warnings: + print(f" • {warning}") + print(f" Entity:\n{_pj(result.data)}\n") + + for outcome in ordered_membership_outcomes: + if outcome.status != "ok" or outcome.result is None: + continue + _append_pipeline_output( + pipeline_outputs, + pipeline_agent_results, + result_key=f"{_STAGE_MEMBERSHIP_AGENT}:{outcome.subject}", + result=outcome.result, + ) + successful_membership_entities.extend( + _extract_result_entities( + outcome.result, + stats_keys=("memberships",), + ), + ) + + timeout_count = sum(1 for outcome in ordered_membership_outcomes if outcome.status == "timeout") + error_count = sum(1 for outcome in ordered_membership_outcomes if outcome.status == "error") + print(_SEP_THIN) + print( + "Membership summary: " + f"ok={len(successful_membership_entities)} timeout={timeout_count} error={error_count}", + ) + failed_membership_outcomes = [ + outcome for outcome in ordered_membership_outcomes if outcome.status != "ok" + ] + if failed_membership_outcomes: + print("Failed membership seeds:") + for outcome in failed_membership_outcomes: + print(f" - {outcome.subject}: {outcome.status} ({outcome.error})") + print("\nMembership entities:") + print(_pj(successful_membership_entities)) + else: + print(" No membership seeds derived — skipping membership stage.\n") + + # ── Stage 7: contribution_agents (concurrent) ─────────────────────────── + contribution_base_context = _class_agent_base_context( + source_url=url_info.normalized_url, + full_name=full_name, + repository_context=repo_ctx, + pipeline_outputs=pipeline_outputs, + pipeline_agent_results=pipeline_agent_results, + ) + contribution_contexts = _contribution_fanout_contexts_repository( + base_context=contribution_base_context, + pipeline_agent_results=pipeline_agent_results, + ) + contribution_seeds = [context["contribution_seed"] for context in contribution_contexts] + + print(f"{_SEP}") + print( + "[ 7 / 7 ] Running LLM contribution agent for " + f"{len(contribution_seeds)} seed(s): {contribution_seeds}\n", + ) + + ordered_contribution_outcomes: list[_ExecutionOutcome] = [] + successful_contribution_entities: list[dict[str, Any]] = [] + if contribution_contexts: + contribution_agent = LLMContributionAgentV2( + llm_call_timeout_seconds=contribution_timeout_seconds, + ) + contribution_context_by_seed: dict[str, dict[str, Any]] = { + context["contribution_seed"]: context for context in contribution_contexts + } + + async def _run_contribution(seed: str) -> AgentResult: + return await contribution_agent.run( + contribution_context_by_seed[seed], + providers, + ) + + ordered_contribution_outcomes = await _run_fanout_stage( + subjects=contribution_seeds, + running_label="contribution", + max_concurrency=max_concurrency, + heartbeat_seconds=heartbeat_seconds, + run_subject=_run_contribution, + ) + + for outcome in ordered_contribution_outcomes: + print(_SEP_THIN) + if outcome.status != "ok" or outcome.result is None: + print(f" Contribution: {outcome.subject}") + print(f" Status : {outcome.status}") + print(f" Error : {outcome.error}\n") + continue + result = outcome.result + print(f" Contribution: {outcome.subject}") + print(f" Model : {result.model}") + print(f" Tokens : prompt={result.tokens_prompt} completion={result.tokens_completion}") + if result.warnings: + print(" Warnings:") + for warning in result.warnings: + print(f" • {warning}") + print(f" Entity:\n{_pj(result.data)}\n") + + for outcome in ordered_contribution_outcomes: + if outcome.status != "ok" or outcome.result is None: + continue + _append_pipeline_output( + pipeline_outputs, + pipeline_agent_results, + result_key=f"{_STAGE_CONTRIBUTION_AGENT}:{outcome.subject}", + result=outcome.result, + ) + successful_contribution_entities.extend( + _extract_result_entities( + outcome.result, + stats_keys=("contributions",), + ), + ) + + timeout_count = sum( + 1 for outcome in ordered_contribution_outcomes if outcome.status == "timeout" + ) + error_count = sum( + 1 for outcome in ordered_contribution_outcomes if outcome.status == "error" + ) + print(_SEP_THIN) + print( + "Contribution summary: " + f"ok={len(successful_contribution_entities)} timeout={timeout_count} error={error_count}", + ) + failed_contribution_outcomes = [ + outcome for outcome in ordered_contribution_outcomes if outcome.status != "ok" + ] + if failed_contribution_outcomes: + print("Failed contribution seeds:") + for outcome in failed_contribution_outcomes: + print(f" - {outcome.subject}: {outcome.status} ({outcome.error})") + print("\nContribution entities:") + print(_pj(successful_contribution_entities)) + else: + print(" No contribution seeds derived — skipping contribution stage.\n") + + combined_entities = _normalize_entities_for_debug_jsonld( + [ + repo_result.data, + *successful_person_entities, + *successful_organization_entities, + *successful_article_entities, + *successful_membership_entities, + *successful_contribution_entities, + ], + ) + if not combined_entities: + combined_entities = [deepcopy(repo_result.data)] + + combined_jsonld = build_jsonld_output( + assembled=AssembledOutput( + root_entity=combined_entities[0], + related_entities=combined_entities[1:], + ), + jsonld_context=load_jsonld_context(), + ) + + print(f"\n{_SEP}") + print("Raw combined JSON-LD (pre-reconciliation):") + print(_pj(combined_jsonld)) + print(_SEP) + + # --- Reconciliation pass --------------------------------------------------- + entities_by_type: dict[str, list[dict[str, Any]]] = { + "repositories": [deepcopy(repo_result.data)], + "persons": [deepcopy(e) for e in successful_person_entities], + "organizations": [deepcopy(e) for e in successful_organization_entities], + "articles": [deepcopy(e) for e in successful_article_entities], + "memberships": [deepcopy(e) for e in successful_membership_entities], + "contributions": [deepcopy(e) for e in successful_contribution_entities], + } + reconciled = reconcile_entities(entities_by_type) + + reconciled_all: list[dict[str, Any]] = [] + for entity_list in reconciled.entities.values(): + reconciled_all.extend(entity_list) + reconciled_all.extend(reconciled.memberships) + reconciled_all.extend(reconciled.contributions) + reconciled_all = _merge_entities_by_id(reconciled_all) + + if reconciled_all: + reconciled_jsonld = build_jsonld_output( + assembled=AssembledOutput( + root_entity=reconciled_all[0], + related_entities=reconciled_all[1:], + ), + jsonld_context=load_jsonld_context(), + ) + print(f"\n{_SEP}") + print("Reconciled JSON-LD (post-reconciliation):") + print(_pj(reconciled_jsonld)) + if reconciled.link_warnings: + print("\nLink warnings:") + for warning in reconciled.link_warnings: + print(f" - {warning}") + print(_SEP) + + if not verify_links: + return + + link_contexts = _collect_unique_http_link_contexts(combined_jsonld) + print(f"{_SEP}") + print( + "[ links ] Verifying unique http(s) links via LLM + Selenium tool for " + f"{len(link_contexts)} link(s)", + ) + + if not link_contexts: + print(" No http(s) links found in combined JSON-LD graph.") + print(_SEP) + return + + verifier = LLMLinkVeracityAgentV2( + llm_call_timeout_seconds=link_verification_timeout_seconds, + ) + verifier_context_by_link = { + context["link"]: { + **context, + "source_url": url_info.normalized_url, + } + for context in link_contexts + if isinstance(context.get("link"), str) + } + links = sorted(verifier_context_by_link) + verification_concurrency = ( + link_verification_max_concurrency + if isinstance(link_verification_max_concurrency, int) + and link_verification_max_concurrency > 0 + else max_concurrency + ) + + async def _run_link_verification(link: str) -> AgentResult: + return await verifier.run(verifier_context_by_link[link], providers) + + verification_outcomes = await _run_fanout_stage( + subjects=links, + running_label="link verification", + max_concurrency=verification_concurrency, + heartbeat_seconds=heartbeat_seconds, + run_subject=_run_link_verification, + ) + + verification_yes = 0 + verification_no = 0 + for outcome in verification_outcomes: + print(_SEP_THIN) + if outcome.status != "ok" or outcome.result is None: + verification_no += 1 + print(f" Link : {outcome.subject}") + print(f" Verdict: no (execution {outcome.status})") + print(f" Error : {outcome.error}") + continue + + payload = outcome.result.data + verdict = bool(payload.get("relationship_supported")) + if verdict: + verification_yes += 1 + else: + verification_no += 1 + print(f" Link : {outcome.subject}") + print(f" Verdict: {'yes' if verdict else 'no'}") + summary = payload.get("relationship_summary") + if isinstance(summary, str) and summary: + print(f" Why : {summary}") + + timeout_count = sum(1 for outcome in verification_outcomes if outcome.status == "timeout") + error_count = sum(1 for outcome in verification_outcomes if outcome.status == "error") + print(_SEP_THIN) + print( + "Link verification summary: " + f"yes={verification_yes} no={verification_no} timeout={timeout_count} error={error_count}", + ) + print(_SEP) + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Run repository debug stages (repo/person/org/article/membership/contribution).", + ) + parser.add_argument("repo", help="GitHub repo as owner/repo or full URL") + parser.add_argument( + "--person-timeout-seconds", + type=_positive_float, + default=180.0, + help="Per-person timeout for LLM person extraction (seconds).", + ) + parser.add_argument( + "--organization-timeout-seconds", + type=_positive_float, + default=180.0, + help="Per-organization timeout for LLM organization extraction (seconds).", + ) + parser.add_argument( + "--article-timeout-seconds", + type=_positive_float, + default=180.0, + help="Per-article timeout for LLM article extraction (seconds).", + ) + parser.add_argument( + "--membership-timeout-seconds", + type=_positive_float, + default=180.0, + help="Per-membership timeout for LLM membership extraction (seconds).", + ) + parser.add_argument( + "--contribution-timeout-seconds", + type=_positive_float, + default=180.0, + help="Per-contribution timeout for LLM contribution extraction (seconds).", + ) + parser.add_argument( + "--max-concurrency", + type=_positive_int, + default=3, + help="Maximum number of concurrent fanout agent calls.", + ) + parser.add_argument( + "--heartbeat-seconds", + type=_positive_float, + default=15.0, + help="How often to print fanout heartbeat while waiting for tasks.", + ) + parser.add_argument( + "--verify-links", + action="store_true", + help=( + "Run independent link-veracity checks over unique http(s) links in the final " + "combined JSON-LD using Selenium-backed content retrieval." + ), + ) + parser.add_argument( + "--link-verification-timeout-seconds", + type=_positive_float, + default=120.0, + help="Per-link timeout for LLM link veracity checks (seconds).", + ) + parser.add_argument( + "--link-verification-max-concurrency", + type=_positive_int, + default=3, + help="Maximum number of concurrent link-verification agent calls.", + ) + args = parser.parse_args() + asyncio.run( + _run( + args.repo, + person_timeout_seconds=args.person_timeout_seconds, + organization_timeout_seconds=args.organization_timeout_seconds, + article_timeout_seconds=args.article_timeout_seconds, + membership_timeout_seconds=args.membership_timeout_seconds, + contribution_timeout_seconds=args.contribution_timeout_seconds, + max_concurrency=args.max_concurrency, + heartbeat_seconds=args.heartbeat_seconds, + verify_links=args.verify_links, + link_verification_timeout_seconds=args.link_verification_timeout_seconds, + link_verification_max_concurrency=args.link_verification_max_concurrency, + ), + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/v2/run_llm_repository_agent.py b/scripts/v2/run_llm_repository_agent.py new file mode 100644 index 0000000..c508d55 --- /dev/null +++ b/scripts/v2/run_llm_repository_agent.py @@ -0,0 +1,85 @@ +# ruff: noqa: INP001, T201 +"""Run the LLM repository agent end-to-end against a real GitHub repository. + +Usage: + just v2-run-repo-agent sdsc-ordes/gimie + python scripts/v2/run_llm_repository_agent.py sdsc-ordes/gimie + python scripts/v2/run_llm_repository_agent.py https://github.com/sdsc-ordes/gimie +""" +from __future__ import annotations + +import argparse +import asyncio +import json +import logging + +logging.basicConfig(level=logging.INFO, format="%(levelname)s %(name)s: %(message)s") + +from src.v2.agents.llm.repository import LLMRepositoryAgentV2 +from src.v2.agents.models import ProviderSet +from src.v2.dependencies import _default_provider_set +from src.v2.ingest.detection.github_url_classifier import classify_github_url +from src.v2.pipeline.stages import gather_context + +_SEP = "─" * 60 + + +def _pj(obj: object) -> str: + return json.dumps(obj, indent=2, ensure_ascii=False) + + +async def _run(repo: str) -> None: + if "/" in repo and not repo.startswith("http") and "github.com" not in repo: + repo = f"github.com/{repo}" + url_info = classify_github_url(repo) + full_name = f"{url_info.owner}/{url_info.repo}" + + print(f"\n{_SEP}") + print(f" Repository : {full_name}") + print(f"{_SEP}\n") + + print("[ 1 / 2 ] Gathering GIMIE context …\n") + providers: ProviderSet = _default_provider_set(use_mock_providers=False) + bundle = await gather_context("repository", url_info, providers) + + repo_ctx = bundle.context["repository"] + readme = repo_ctx.get("readme_content") or "" + + print(f"Metadata:\n{_pj(repo_ctx.get('metadata', {}))}\n") + print(f"Contributors:\n{_pj(repo_ctx.get('contributors', []))}\n") + print(f"Languages:\n{_pj(repo_ctx.get('languages', {}))}\n") + print(f"README ({len(readme)} chars):\n{readme[:500]}{'…' if len(readme) > 500 else ''}\n") + if bundle.warnings: + print(f"Context warnings: {bundle.warnings}\n") + + print(f"{_SEP}") + print("[ 2 / 2 ] Running LLM repository agent …\n") + + agent = LLMRepositoryAgentV2() + context = { + "full_name": full_name, + "source_url": url_info.normalized_url, + "repository_context": repo_ctx, + } + result = await agent.run(context, providers) + + print(f"Model : {result.model}") + print(f"Provider : {result.provider}") + print(f"Tokens : prompt={result.tokens_prompt} completion={result.tokens_completion}") + if result.warnings: + print(f"\nWarnings:") + for w in result.warnings: + print(f" • {w}") + print(f"\nAgent output:\n{_pj(result.data)}") + print(f"\n{_SEP}") + + +def main() -> None: + parser = argparse.ArgumentParser(description="Run LLM repository agent with real GIMIE context.") + parser.add_argument("repo", help="GitHub repo as owner/repo or full URL") + args = parser.parse_args() + asyncio.run(_run(args.repo)) + + +if __name__ == "__main__": + main() diff --git a/src/api.py b/src/api.py index 4667ca3..7cc67d1 100644 --- a/src/api.py +++ b/src/api.py @@ -4,20 +4,66 @@ import logging import os +from contextlib import asynccontextmanager from datetime import datetime +from importlib.metadata import PackageNotFoundError, version as package_version from typing import Optional -from fastapi import Depends, FastAPI, HTTPException, Path, Query, Request, Response -from fastapi.responses import JSONResponse -from .analysis import Organization, Repository, User -from .cache import get_cache_manager -from .data_models import ( +def _normalize_github_token_pool() -> None: + """Split a comma-separated GITHUB_TOKEN into a per-process token pool. + + Runs before any v1/gimie import so module-level `os.environ["GITHUB_TOKEN"]` + reads see a single valid token. The full list (deduped, order preserved) is + exported as GITHUB_TOKEN_POOL for v2 REST hot paths to round-robin over. + """ + raw = os.environ.get("GITHUB_TOKEN", "") + if "," not in raw: + return + seen: set[str] = set() + tokens: list[str] = [] + for piece in raw.split(","): + token = piece.strip() + if token and token not in seen: + seen.add(token) + tokens.append(token) + if not tokens: + return + os.environ["GITHUB_TOKEN_POOL"] = ",".join(tokens) + os.environ["GITHUB_TOKEN"] = tokens[0] + + +_normalize_github_token_pool() + + +def _resolve_package_version(name: str) -> str: + try: + return package_version(name) + except PackageNotFoundError: + return "unknown" + +from fastapi import ( + Depends, + FastAPI, + HTTPException, + Path, + Query, + Request, + Response, + status, +) +from fastapi.responses import HTMLResponse, JSONResponse + +from src.v2.api import v2_router + +from .v1.analysis import Organization, Repository, User +from .v1.cache import get_cache_manager +from .v1.data_models import ( APIOutput, ResourceType, ) -from .utils.enhanced_logging import AsyncRequestContext, setup_logging -from .utils.github_dependency import validate_github_token +from .v2.log_context import AsyncRequestContext, setup_logging +from .v1.utils.github_dependency import validate_github_token # Setup enhanced logging with colors # Allow LOG_LEVEL environment variable to override (DEBUG, INFO, WARNING, ERROR) @@ -29,6 +75,73 @@ logger = logging.getLogger(__name__) +async def startup_event(app: FastAPI | None = None): + """Initialize resources on application startup.""" + logger.info("🚀 Application startup - initializing resources") + + # Pre-warm the v2 provider cache so the SQLite file exists in WAL mode + # before any request hits a worker. Without this, multi-worker uvicorn + # against a cold .cache/ races on first-request to create+init the + # file and the losing workers raise `database is locked`, surfacing + # as 500s for the first 1-2 jobs of a batch run. + if app is not None: + try: + from src.v2.dependencies import _resolve_provider_cache + + cache = _resolve_provider_cache(app.state) + if cache is not None: + logger.info("✅ v2 provider cache initialized") + except Exception as exc: # noqa: BLE001 — startup must not crash on cache issues + logger.warning(f"v2 provider cache pre-warm skipped: {exc}") + + +async def shutdown_event(): + """Cleanup resources on application shutdown""" + logger.info("🛑 Application shutdown - cleaning up resources") + + # Cleanup PydanticAI agents + try: + from .v1.agents.agents_management import cleanup_agents + + await cleanup_agents() + logger.info("✅ Cleaned up PydanticAI agents") + except Exception as e: + logger.warning(f"Error cleaning up PydanticAI agents: {e}") + + # Cleanup user enrichment agents + try: + from .v1.agents.user_enrichment import cleanup_user_agents + + await cleanup_user_agents() + logger.info("✅ Cleaned up user enrichment agents") + except Exception as e: + logger.warning(f"Error cleaning up user enrichment agents: {e}") + + # Cleanup organization enrichment agents + try: + from .v1.agents.organization_enrichment import cleanup_org_agents + + await cleanup_org_agents() + logger.info("✅ Cleaned up organization enrichment agents") + except Exception as e: + logger.warning(f"Error cleaning up organization enrichment agents: {e}") + + # Run garbage collection + import gc + + gc.collect() + logger.info("✅ Garbage collection completed") + + +@asynccontextmanager +async def lifespan(app: FastAPI): + await startup_event(app) + try: + yield + finally: + await shutdown_event() + + app = FastAPI( title="Git Metadata Extractor API", description=""" @@ -88,53 +201,130 @@ }, {"name": "System", "description": "System information and health checks"}, ], + lifespan=lifespan, + docs_url=None, ) -# Startup and shutdown events for resource management -@app.on_event("startup") -async def startup_event(): - """Initialize resources on application startup""" - logger.info("🚀 Application startup - initializing resources") - - -@app.on_event("shutdown") -async def shutdown_event(): - """Cleanup resources on application shutdown""" - logger.info("🛑 Application shutdown - cleaning up resources") - - # Cleanup PydanticAI agents - try: - from .agents.agents_management import cleanup_agents - - await cleanup_agents() - logger.info("✅ Cleaned up PydanticAI agents") - except Exception as e: - logger.warning(f"Error cleaning up PydanticAI agents: {e}") - - # Cleanup user enrichment agents - try: - from .agents.user_enrichment import cleanup_user_agents - - await cleanup_user_agents() - logger.info("✅ Cleaned up user enrichment agents") - except Exception as e: - logger.warning(f"Error cleaning up user enrichment agents: {e}") - - # Cleanup organization enrichment agents - try: - from .agents.organization_enrichment import cleanup_org_agents - - await cleanup_org_agents() - logger.info("✅ Cleaned up organization enrichment agents") - except Exception as e: - logger.warning(f"Error cleaning up organization enrichment agents: {e}") - - # Run garbage collection - import gc - - gc.collect() - logger.info("✅ Garbage collection completed") +_SWAGGER_DARK_CSS = ( + "https://cdn.jsdelivr.net/gh/Amoenus/SwaggerDark@master/SwaggerDark.css" +) +_SWAGGER_LIGHT_CSS = "https://cdn.jsdelivr.net/npm/swagger-ui-dist@5/swagger-ui.css" +_SWAGGER_BUNDLE_JS = ( + "https://cdn.jsdelivr.net/npm/swagger-ui-dist@5/swagger-ui-bundle.js" +) +_FAVICON_URL = "https://fastapi.tiangolo.com/img/favicon.png" + + +@app.get("/docs", include_in_schema=False) +def custom_swagger_ui_html() -> HTMLResponse: + title = f"{app.title} - Swagger UI" + html = f""" + + + +{title} + + + + + + + + +
+ + + +""" + return HTMLResponse(html) + +app.include_router(v2_router) # Add middleware to automatically set request context for all endpoints @@ -171,10 +361,29 @@ def index(): """ Get API welcome message and system information. - Returns basic information about the API version, GIMIE version, and configured LLM model. + Versions are resolved at runtime from installed package metadata + (`git-metadata-extractor`, `gimie`). The LLM model line reports what the + v2 runtime would resolve right now from `MODEL_CONFIGS` + (`src/v1/llm/model_config.py`) given the credentials available in the + environment. """ + from src.v2.agents.llm.runtime import LLMRuntimeConfigError, V2LLMRuntime + + try: + resolved = V2LLMRuntime()._resolve_model_config() # noqa: SLF001 + provider = resolved.get("provider", "unknown") + model_name = resolved.get("model", "unknown") + llm_model = f"{provider}:{model_name}" + except LLMRuntimeConfigError as exc: + llm_model = f"unconfigured ({exc})" + return { - "title": f"Hello, welcome to the Git Metadata Extractor v2.0.1. Gimie Version 0.7.2. LLM Model {os.environ.get('MODEL', 'N/A (configured via model configs)')}", + "title": ( + "Hello, welcome to the Git Metadata Extractor " + f"v{_resolve_package_version('git-metadata-extractor')}. " + f"Gimie Version {_resolve_package_version('gimie')}. " + f"LLM Model {llm_model}" + ), } @@ -616,7 +825,7 @@ async def get_org_json( usage_stats = organization.get_usage_stats() # Create APIStats with token usage data, timing, and status - from .data_models.api import APIStats + from .v1.data_models.api import APIStats stats = APIStats( agent_input_tokens=usage_stats["input_tokens"], @@ -745,7 +954,7 @@ async def get_user_json( usage_stats = user.get_usage_stats() # Create APIStats with token usage data, timing, and status - from .data_models.api import APIStats + from .v1.data_models.api import APIStats stats = APIStats( agent_input_tokens=usage_stats["input_tokens"], @@ -863,54 +1072,86 @@ async def gimie( - Statistics (timing and status) """ - repository = Repository(full_path, force_refresh=force_refresh) - - await repository.run_analysis( - run_gimie=True, - run_llm=False, - run_user_enrichment=False, - run_organization_enrichment=False, - ) - - # Get raw gimie JSON-LD output (not the Pydantic model) - gimie_output = repository.gimie - - # Get usage statistics from the repository (no tokens for gimie-only) - usage_stats = repository.get_usage_stats() + try: + repository = Repository(full_path, force_refresh=force_refresh) - # Create APIStats with timing information (no token usage since no LLM) - from .data_models.api import APIStats + await repository.run_analysis( + run_gimie=True, + run_llm=False, + run_user_enrichment=False, + run_organization_enrichment=False, + ) - stats = APIStats( - agent_input_tokens=0, - agent_output_tokens=0, - estimated_input_tokens=0, - estimated_output_tokens=0, - duration=usage_stats["duration"], - start_time=usage_stats["start_time"], - end_time=usage_stats["end_time"], - status_code=usage_stats["status_code"], - github_rate_limit=github_info["rate_limit_limit"], - github_rate_remaining=github_info["rate_limit_remaining"], - github_rate_reset=github_info["rate_limit_reset"], - ) - # Calculate total tokens (will be 0 for gimie-only) - stats.calculate_total_tokens() + # Get raw gimie JSON-LD output (not the Pydantic model) + gimie_output = repository.gimie + + # Get usage statistics from the repository (no tokens for gimie-only) + usage_stats = repository.get_usage_stats() + + # Create APIStats with timing information (no token usage since no LLM) + from .v1.data_models.api import APIStats + + stats = APIStats( + agent_input_tokens=0, + agent_output_tokens=0, + estimated_input_tokens=0, + estimated_output_tokens=0, + duration=usage_stats["duration"], + start_time=usage_stats["start_time"], + end_time=usage_stats["end_time"], + status_code=usage_stats["status_code"], + github_rate_limit=github_info["rate_limit_limit"], + github_rate_remaining=github_info["rate_limit_remaining"], + github_rate_reset=github_info["rate_limit_reset"], + ) + # Calculate total tokens (will be 0 for gimie-only) + stats.calculate_total_tokens() - # Set rate limit response headers - response.headers["X-RateLimit-Limit"] = str(github_info["rate_limit_limit"]) - response.headers["X-RateLimit-Remaining"] = str(github_info["rate_limit_remaining"]) - response.headers["X-RateLimit-Reset"] = github_info["rate_limit_reset"].isoformat() + # Set rate limit response headers + response.headers["X-RateLimit-Limit"] = str(github_info["rate_limit_limit"]) + response.headers["X-RateLimit-Remaining"] = str( + github_info["rate_limit_remaining"], + ) + rate_reset = github_info["rate_limit_reset"] + response.headers["X-RateLimit-Reset"] = ( + rate_reset.isoformat() if rate_reset is not None else "" + ) - api_response = APIOutput( - link=full_path, - type=ResourceType.REPOSITORY, - parsedTimestamp=datetime.now(), - output=gimie_output, - stats=stats, - ) + api_response = APIOutput( + link=full_path, + type=ResourceType.REPOSITORY, + parsedTimestamp=datetime.now(), + output=gimie_output, + stats=stats, + ) - return api_response + return api_response + except HTTPException: + raise + except ConnectionError as e: + # GIMIE raises ConnectionError for GitHub REST/GraphQL failures (incl. secondary rate limits). + msg = str(e) + lower = msg.lower() + logger.warning("GIMIE GitHub API error for %s: %s", full_path, msg) + if ( + "secondary rate limit" in lower + or "rate limit exceeded" in lower + or "api rate limit exceeded" in lower + ): + raise HTTPException( + status_code=status.HTTP_429_TOO_MANY_REQUESTS, + detail=msg, + ) from e + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail=msg, + ) from e + except Exception as e: + logger.exception("GIMIE JSON-LD failed for %s", full_path) + raise HTTPException( + status_code=502, + detail=f"GIMIE extraction failed: {e!s}", + ) from e @app.get( @@ -1100,7 +1341,7 @@ async def llm_jsonld( usage_stats = repository.get_usage_stats() # Create APIStats with token usage data, timing, and status - from .data_models.api import APIStats + from .v1.data_models.api import APIStats stats = APIStats( agent_input_tokens=usage_stats["input_tokens"], @@ -1216,7 +1457,7 @@ async def llm_json( usage_stats = repository.get_usage_stats() # Create APIStats with token usage data, timing, and status - from .data_models.api import APIStats + from .v1.data_models.api import APIStats stats = APIStats( agent_input_tokens=usage_stats["input_tokens"], diff --git a/src/gimie_utils/gimie_methods.py b/src/gimie_utils/gimie_methods.py deleted file mode 100644 index cf7a4d7..0000000 --- a/src/gimie_utils/gimie_methods.py +++ /dev/null @@ -1,35 +0,0 @@ -import json -import logging - -from gimie.project import Project - -logger = logging.getLogger(__name__) - - -def extract_gimie(full_path: str, format: str = "json-ld"): - """ - Extracts the GIMIE project from the given URL. - - Args: - full_path (str): The full path to the URL. - format (str): The format to serialize the graph. Default is 'json-ld', or 'ttl'. - - Returns: - Project: The GIMIE project object. - """ - logger.info(f"Extracting GIMIE metadata for: {full_path}") - - proj = Project(full_path) - - # To retrieve the rdflib.Graph object - g = proj.extract() - - if format == "json-ld": - # To retrieve the graph in JSON-LD format - output = json.loads(g.serialize(format="json-ld")) - else: - output = g.serialize(format=format) - - if output is None: - return None - return output diff --git a/src/index/__init__.py b/src/index/__init__.py new file mode 100644 index 0000000..d529ecf --- /dev/null +++ b/src/index/__init__.py @@ -0,0 +1,6 @@ +"""Multi-source indexing root. + +Each subpackage (e.g. `infoscience`, `openalex`) owns one external source: +discovery, fetch, chunking, embedding, and a vector store. Design docs live +under `.internal//`. +""" diff --git a/src/index/_federated/__init__.py b/src/index/_federated/__init__.py new file mode 100644 index 0000000..bdb3e38 --- /dev/null +++ b/src/index/_federated/__init__.py @@ -0,0 +1,20 @@ +"""Cross-index federated search and entity-lookup layer. + +Sits above `src/index/{huggingface,openalex,infoscience,zenodo,orcid,ror}/` +and exposes uniform `gme search` / `gme entity` commands that fan out to +every registered index in parallel and merge results. + +Adapter pattern: each index module gets a thin adapter under +`adapters/.py` implementing `IndexAdapter`. The federated layer never +imports an index's internals directly — it goes through the adapter. +""" + +from src.index._federated.registry import ( + REGISTRY, + EntityRecord, + Hit, + IndexAdapter, + register, +) + +__all__ = ["REGISTRY", "EntityRecord", "Hit", "IndexAdapter", "register"] diff --git a/src/index/_federated/__main__.py b/src/index/_federated/__main__.py new file mode 100644 index 0000000..dca48e2 --- /dev/null +++ b/src/index/_federated/__main__.py @@ -0,0 +1,3 @@ +from src.index._federated.cli import main + +raise SystemExit(main()) diff --git a/src/index/_federated/adapters/__init__.py b/src/index/_federated/adapters/__init__.py new file mode 100644 index 0000000..0ebf7e2 --- /dev/null +++ b/src/index/_federated/adapters/__init__.py @@ -0,0 +1,6 @@ +"""Concrete `IndexAdapter` implementations, one per registered index. + +Each module here calls `register()` at import time so simply importing +`src.index._federated.adapters.` is enough to make that index +available via the federated CLI. +""" diff --git a/src/index/_federated/adapters/epfl_graph.py b/src/index/_federated/adapters/epfl_graph.py new file mode 100644 index 0000000..c2fb2fe --- /dev/null +++ b/src/index/_federated/adapters/epfl_graph.py @@ -0,0 +1,146 @@ +"""Federated adapter for the EPFL Graph disciplines index. + +Exposes a single ``disciplines`` entity type. ``search`` runs the +semantic-search pipeline; ``lookup`` resolves a slug-style category id +(``neuroscience``, ``topics-in-natural-language-processing``) directly +out of DuckDB without any RAG round-trip. +""" + +from __future__ import annotations + +import re +from typing import Any, ClassVar + +from src.index._federated.registry import EntityRecord, Hit, register + +_RE_GRAPHSEARCH_URL = re.compile( + r"https?://graphsearch\.epfl\.ch/(?:[a-z]{2}/)?category/([a-z0-9][a-z0-9-]+)", + re.IGNORECASE, +) +_RE_SLUG = re.compile(r"^[a-z][a-z0-9-]{2,}$") + + +class EpflGraphAdapter: + name = "epfl_graph" + entity_types: ClassVar[list[str]] = ["disciplines"] + + def search( + self, + *, + query: str, + entity_type: str | None, + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + if entity_type and entity_type not in {"disciplines", "discipline"}: + return [] + try: + from src.index.epfl_graph.config import load_config # noqa: PLC0415 + from src.index.epfl_graph.retrieval.semantic import ( # noqa: PLC0415 + semantic_search, + ) + except Exception: # noqa: BLE001 + return [] + try: + config = load_config() + results = semantic_search( + config=config, + query=query, + top_k=top_k, + candidate_k=max(top_k * 5, 50), + min_depth=( + int(filters.get("depth", 0)) + if isinstance(filters, dict) and "depth" in filters + else None + ), + ) + except Exception: # noqa: BLE001 + return [] + out: list[Hit] = [] + for r in results: + category_id = r.get("category_id") + if not category_id: + continue + payload = r.get("payload") or {} + score = r.get("rerank_score") or r.get("vector_score") or 0.0 + out.append( + Hit( + index=self.name, + entity_type="discipline", + id=str(category_id), + title=r.get("name") or str(category_id), + score=float(score), + summary=_summary(payload), + url=r.get("graphsearch_url"), + payload={ + "category_id": category_id, + "name": r.get("name"), + "depth": r.get("depth"), + "wikipedia_url": r.get("wikipedia_url"), + "graphsearch_url": r.get("graphsearch_url"), + "chain": r.get("chain"), + }, + ), + ) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + slug = _extract_slug(identifier) + if not slug: + return [] + try: + from src.index.epfl_graph.config import load_config # noqa: PLC0415 + from src.index.epfl_graph.storage.duckdb_store import ( # noqa: PLC0415 + EpflGraphStore, + ) + except Exception: # noqa: BLE001 + return [] + try: + config = load_config() + store = EpflGraphStore.open(config.paths.duckdb_path) + except Exception: # noqa: BLE001 + return [] + try: + record = store.fetch_category(slug) + finally: + store.close() + if record is None: + return [] + return [ + EntityRecord( + index=self.name, + entity_type="discipline", + id=slug, + data=record, + url=record.get("graphsearch_url"), + ), + ] + + +def _extract_slug(identifier: str) -> str | None: + s = (identifier or "").strip() + if not s: + return None + match = _RE_GRAPHSEARCH_URL.search(s) + if match: + return match.group(1).lower() + if _RE_SLUG.match(s): + return s.lower() + return None + + +def _summary(payload: dict[str, Any]) -> str | None: + parts: list[str] = [] + name = payload.get("name") + if name: + parts.append(str(name)) + depth = payload.get("depth") + if isinstance(depth, int): + parts.append(f"depth={depth}") + n_concepts = payload.get("n_concepts") + if isinstance(n_concepts, int) and n_concepts: + parts.append(f"{n_concepts} anchor concepts") + return " — ".join(parts) if parts else None + + +register(EpflGraphAdapter()) diff --git a/src/index/_federated/adapters/ethz_research_collection.py b/src/index/_federated/adapters/ethz_research_collection.py new file mode 100644 index 0000000..df417ab --- /dev/null +++ b/src/index/_federated/adapters/ethz_research_collection.py @@ -0,0 +1,89 @@ +"""Adapter wrapping `src.index.ethz_research_collection` for federated search/lookup. + +Mirrors the `infoscience` adapter — both are DSpace-based with the same +`QueryResult` shape from `pipeline.query()`. +""" + +from __future__ import annotations + +import asyncio +import re +from typing import Any + +from src.index._federated.registry import EntityRecord, Hit, register + +_RE_UUID = re.compile( + r"\b([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})\b", + re.I, +) +_RE_RC_URL = re.compile( + r"https?://(?:www\.)?research-collection\.ethz\.ch/handle/(\S+)", + re.I, +) + + +class EthzResearchCollectionAdapter: + name = "ethz_research_collection" + entity_types = ["chunks", "articles", "persons", "organizations"] + + def search( + self, + *, + query: str, + entity_type: str | None, + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + try: + from src.index.ethz_research_collection.config import load_config + from src.index.ethz_research_collection.pipeline import ( + query as rc_query, + ) + except Exception: # noqa: BLE001 + return [] + cfg = load_config() + target = entity_type or "chunks" + try: + qr = asyncio.run(rc_query( + cfg, query, target=target, where=filters, top_k=top_k, + )) + except Exception: # noqa: BLE001 + return [] + results = getattr(qr, "rows", None) or [] + out: list[Hit] = [] + for r in results: + md = r.get("metadata") or r.get("payload") or {} + uuid_ = md.get("article_uuid") or md.get("uuid") or r.get("id") + if not uuid_: + continue + title = md.get("title") or md.get("name") or str(uuid_) + score = float(r.get("rerank_score") or r.get("score") or + (1.0 - r.get("distance", 1.0))) + out.append(Hit( + index=self.name, entity_type=target.rstrip("s") or target, + id=str(uuid_), + title=str(title) if title else None, + score=score, + summary=(r.get("text") or md.get("title") or "")[:200] or None, + url=md.get("source_url") or md.get("research_collection_url"), + payload=dict(md), + )) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + s = identifier.strip() + m = _RE_RC_URL.search(s) or _RE_UUID.search(s) + if not m: + return [] + # Without a stable local lookup index, return a thin record acknowledging + # the identifier shape; richer hydration can come from search if needed. + identifier_value = m.group(1) + return [EntityRecord( + index=self.name, entity_type="article", id=identifier_value, + data={"id": identifier_value, "source": "research-collection.ethz.ch"}, + url=(s if s.startswith("http") else + f"https://www.research-collection.ethz.ch/handle/{identifier_value}"), + )] + + +register(EthzResearchCollectionAdapter()) diff --git a/src/index/_federated/adapters/github.py b/src/index/_federated/adapters/github.py new file mode 100644 index 0000000..53af68e --- /dev/null +++ b/src/index/_federated/adapters/github.py @@ -0,0 +1,101 @@ +"""Adapter wrapping `src.index.github` for federated search/lookup.""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index._federated.registry import EntityRecord, Hit, register + +_RE_GH_REPO_URL = re.compile( + r"https?://(?:www\.)?github\.com/([^/\s?#]+)/([^/\s?#]+)", + re.I, +) +_RE_GH_NS_URL = re.compile( + r"https?://(?:www\.)?github\.com/([^/\s?#]+)/?$", + re.I, +) + + +class GitHubAdapter: + name = "github" + entity_types = ["repos"] + + def search( + self, + *, + query: str, + entity_type: str | None, # noqa: ARG002 — single-type index + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + try: + from src.index.github.config import load_config + from src.index.github.retrieval.semantic import semantic_search + except Exception: # noqa: BLE001 + return [] + cfg = load_config() + try: + results = semantic_search( + config=cfg, query=query, + top_k=top_k, candidate_k=max(top_k * 5, 50), + filter_payload=filters, + ) + except Exception: # noqa: BLE001 + return [] + out: list[Hit] = [] + for r in results: + payload = r.get("payload") or {} + repo_id = payload.get("repo_id") or payload.get("full_name") + if not repo_id: + continue + out.append(Hit( + index=self.name, entity_type="repo", + id=str(repo_id), + title=payload.get("description") or str(repo_id), + score=float(r.get("rerank_score") or r.get("vector_score") or 0.0), + summary=_summary(payload), + url=f"https://github.com/{repo_id}", + payload=payload, + )) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + s = identifier.strip().rstrip("/") + repo_id = None + m = _RE_GH_REPO_URL.search(s) + if m: + repo_id = f"{m.group(1)}/{m.group(2)}" + elif "/" in s and not s.startswith("http"): + author, _, repo = s.partition("/") + if author and repo: + repo_id = s + if not repo_id: + # Bare org slug → no lookup yet (no orgs table in github index) + return [] + try: + from src.index.github.storage.duckdb_store import GitHubStore + except Exception: # noqa: BLE001 + return [] + store = GitHubStore.open() + if hasattr(store, "fetch_repo"): + row = store.fetch_repo(repo_id) + if row is not None: + return [EntityRecord( + index=self.name, entity_type="repo", id=repo_id, + data=row, url=f"https://github.com/{repo_id}", + )] + return [] + + +def _summary(payload: dict[str, Any]) -> str | None: + parts = [ + str(payload.get("description") or ""), + str(payload.get("language") or ""), + str(payload.get("license") or ""), + ] + parts = [p for p in parts if p] + return " — ".join(parts) if parts else None + + +register(GitHubAdapter()) diff --git a/src/index/_federated/adapters/huggingface.py b/src/index/_federated/adapters/huggingface.py new file mode 100644 index 0000000..f7486cd --- /dev/null +++ b/src/index/_federated/adapters/huggingface.py @@ -0,0 +1,154 @@ +"""Adapter wrapping `src.index.huggingface` for federated search/lookup.""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index._federated.registry import EntityRecord, Hit, register + +# HF identifiers we know how to recognise. The lookup() method tries each in +# order against the input string. +_RE_HF_URL_REPO = re.compile( + r"https?://(?:huggingface\.co|hf\.co)/(?:datasets|spaces)/([^/\s?#]+)/([^/\s?#]+)", + re.IGNORECASE, +) +_RE_HF_URL_MODEL = re.compile( + r"https?://(?:huggingface\.co|hf\.co)/([^/\s?#]+)/([^/\s?#]+)", + re.IGNORECASE, +) +_RE_HF_URL_NS = re.compile( + r"https?://(?:huggingface\.co|hf\.co)/([^/\s?#]+)/?$", + re.IGNORECASE, +) + + +class HuggingFaceAdapter: + name = "huggingface" + entity_types = ["models", "datasets", "spaces", "orgs"] + + def search( + self, + *, + query: str, + entity_type: str | None, + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + # Lazy import — keeps `gme indices` cheap when HF deps aren't installed. + from src.index.huggingface.config import load_config + from src.index.huggingface.retrieval.semantic import semantic_search + + config = load_config() + config.require_rcp() + types_to_query = [entity_type] if entity_type else ["models"] + out: list[Hit] = [] + for et in types_to_query: + try: + results = semantic_search( + config=config, query=query, entity_type=et, + top_k=top_k, candidate_k=max(top_k * 5, 50), + filter_payload=filters, + ) + except Exception: # noqa: BLE001 — federation should be fault-tolerant + continue + for r in results: + payload = r.get("payload") or {} + rid = payload.get("repo_id") + if not rid: + continue + singular = (payload.get("entity_type") or et.rstrip("s") or et) + out.append(Hit( + index=self.name, + entity_type=singular, + id=rid, + title=payload.get("fullname") or rid, + score=float(r.get("rerank_score") or r.get("vector_score") or 0.0), + summary=_summary_from_payload(payload), + url=_canonical_url(singular, rid), + payload=payload, + )) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + # Strip surrounding whitespace and trailing slashes. + s = identifier.strip().rstrip("/") + repo_id = None + slug = None + # Patterns: full URLs first, then bare slugs. + m = _RE_HF_URL_REPO.search(s) or _RE_HF_URL_MODEL.search(s) + if m: + repo_id = f"{m.group(1)}/{m.group(2)}" + slug = m.group(1) + else: + m_ns = _RE_HF_URL_NS.search(s) + if m_ns: + slug = m_ns.group(1) + elif "/" in s: + # Plain "/" form. + author, _, repo = s.partition("/") + if author and repo: + repo_id = s + slug = author + else: + # Plain namespace. + slug = s + + records: list[EntityRecord] = [] + try: + from src.index.huggingface.storage.duckdb_store import DuckDBStore + except Exception: # noqa: BLE001 + return records + store = DuckDBStore.open() + + if repo_id: + for table in ("models", "datasets", "spaces"): + row = store.fetch_repo(table, repo_id) + if row is not None: + records.append(EntityRecord( + index=self.name, + entity_type=table.rstrip("s") or table, + id=repo_id, + data=row, + url=_canonical_url(table.rstrip("s"), repo_id), + )) + + if slug: + org_row = store.fetch_org(slug) + if org_row is not None: + records.append(EntityRecord( + index=self.name, + entity_type="org", + id=slug, + data=org_row, + url=f"https://huggingface.co/{slug}", + )) + return records + + +def _summary_from_payload(payload: dict[str, Any]) -> str | None: + parts = [ + str(payload.get("repo_id") or ""), + str(payload.get("fullname") or ""), + str(payload.get("pipeline_tag") or ""), + str(payload.get("license") or ""), + ] + parts = [p for p in parts if p] + return " — ".join(parts) if parts else None + + +def _canonical_url(singular: str, repo_id: str) -> str | None: + if not repo_id: + return None + if singular == "model": + return f"https://huggingface.co/{repo_id}" + if singular == "dataset": + return f"https://huggingface.co/datasets/{repo_id}" + if singular == "space": + return f"https://huggingface.co/spaces/{repo_id}" + if singular == "org": + return f"https://huggingface.co/{repo_id}" + return f"https://huggingface.co/{repo_id}" + + +register(HuggingFaceAdapter()) diff --git a/src/index/_federated/adapters/infoscience.py b/src/index/_federated/adapters/infoscience.py new file mode 100644 index 0000000..61da29a --- /dev/null +++ b/src/index/_federated/adapters/infoscience.py @@ -0,0 +1,97 @@ +"""Adapter wrapping `src.index.infoscience` for federated search/lookup.""" + +from __future__ import annotations + +import asyncio +import json +import re +from pathlib import Path +from typing import Any + +from src.index._federated.registry import EntityRecord, Hit, register + +_RE_UUID = re.compile( + r"\b([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})\b", + re.I, +) +_RE_INFOSCIENCE_URL = re.compile( + r"https?://infoscience\.epfl\.ch/entities/publication/" + r"([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})", + re.I, +) + +_LINKS_INDEX = Path("data/index/infoscience/dumps/infoscience_links_index.json") + + +class InfoscienceAdapter: + name = "infoscience" + entity_types = ["chunks", "articles", "persons", "organizations"] + + def search( + self, + *, + query: str, + entity_type: str | None, + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + try: + from src.index.infoscience.config import load_config + from src.index.infoscience.pipeline import query as infoscience_query + except Exception: # noqa: BLE001 + return [] + cfg = load_config() + target = entity_type or "chunks" + try: + qr = asyncio.run(infoscience_query( + cfg, query, target=target, where=filters, top_k=top_k, + )) + except Exception: # noqa: BLE001 + return [] + # `query()` returns a QueryResult dataclass; the hits are in `.rows`. + results = getattr(qr, "rows", None) or [] + out: list[Hit] = [] + for r in results: + # Infoscience returns dicts with 'id', 'metadata', 'text', 'distance'/'score'. + md = r.get("metadata") or r.get("payload") or {} + uuid_ = md.get("article_uuid") or md.get("uuid") or r.get("id") + if not uuid_: + continue + title = md.get("title") or md.get("name") or str(uuid_) + score = float(r.get("rerank_score") or r.get("score") or + (1.0 - r.get("distance", 1.0))) + out.append(Hit( + index=self.name, entity_type=target.rstrip("s") or target, + id=str(uuid_), + title=str(title) if title else None, + score=score, + summary=(r.get("text") or md.get("title") or "")[:200] or None, + url=md.get("infoscience_url") or + f"https://infoscience.epfl.ch/entities/publication/{uuid_}", + payload=dict(md), + )) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + s = identifier.strip() + m = _RE_INFOSCIENCE_URL.search(s) or _RE_UUID.search(s) + if not m: + return [] + uuid_ = m.group(1).lower() + # Cheapest path: hit the slim links index file we already use for HF cross-links. + if not _LINKS_INDEX.exists(): + return [] + with _LINKS_INDEX.open(encoding="utf-8") as fh: + data = json.load(fh) + for item in data.get("items", []): + if (item.get("uuid") or "").lower() == uuid_: + return [EntityRecord( + index=self.name, entity_type="article", id=uuid_, + data=item, + url=item.get("infoscience_url") or + f"https://infoscience.epfl.ch/entities/publication/{uuid_}", + )] + return [] + + +register(InfoscienceAdapter()) diff --git a/src/index/_federated/adapters/openalex.py b/src/index/_federated/adapters/openalex.py new file mode 100644 index 0000000..60c14d6 --- /dev/null +++ b/src/index/_federated/adapters/openalex.py @@ -0,0 +1,100 @@ +"""Adapter wrapping `src.index.openalex` for federated search/lookup.""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index._federated.registry import EntityRecord, Hit, register + +_RE_OPENALEX_ID = re.compile(r"\b([WAISCT]\d{6,12})\b") +_RE_OPENALEX_URL = re.compile(r"https?://openalex\.org/([WAISCT]\d{6,12})", re.I) +_RE_DOI = re.compile(r"\b10\.\d{4,9}/[^\s]+\b") + + +class OpenAlexAdapter: + name = "openalex" + entity_types = ["works", "authors", "institutions", "sources", "topics", "concepts"] + + def search( + self, + *, + query: str, + entity_type: str | None, + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + from src.index.openalex.config import load_config + from src.index.openalex.retrieval.semantic import semantic_search + + config = load_config() + types_to_query = [entity_type] if entity_type else ["works"] + out: list[Hit] = [] + for et in types_to_query: + try: + results = semantic_search( + config=config, query=query, entity_type=et, + top_k=top_k, candidate_k=max(top_k * 5, 50), + filter_payload=filters, + ) + except Exception: # noqa: BLE001 + continue + for r in results: + payload = r.get("payload") or {} + openalex_id = payload.get("openalex_id") or payload.get("id") + if not openalex_id: + continue + singular = (payload.get("entity_type") or et.rstrip("s") or et) + title = payload.get("title") or payload.get("display_name") or openalex_id + out.append(Hit( + index=self.name, + entity_type=singular, + id=str(openalex_id), + title=str(title) if title else None, + score=float(r.get("rerank_score") or r.get("vector_score") or 0.0), + summary=_summary(payload), + url=_canonical_url(openalex_id), + payload=payload, + )) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + s = identifier.strip() + # Try OpenAlex URL → bare ID first. + m = _RE_OPENALEX_URL.search(s) or _RE_OPENALEX_ID.search(s) + if not m: + return [] + oid = m.group(1) + try: + from src.index.openalex.storage.duckdb_store import DuckDBStore + except Exception: # noqa: BLE001 + return [] + store = DuckDBStore.open() + # Only `fetch_work` is exposed today; hydrate that for W-IDs. + if oid.startswith("W") and hasattr(store, "fetch_work"): + row = store.fetch_work(oid) + if row is not None: + return [EntityRecord( + index=self.name, entity_type="work", id=oid, + data=row, url=_canonical_url(oid), + )] + return [] + + +def _summary(payload: dict[str, Any]) -> str | None: + parts = [ + str(payload.get("title") or payload.get("display_name") or ""), + str(payload.get("publication_year") or payload.get("year") or ""), + str(payload.get("doi") or ""), + ] + parts = [p for p in parts if p] + return " — ".join(parts) if parts else None + + +def _canonical_url(openalex_id: str) -> str | None: + if not openalex_id: + return None + return f"https://openalex.org/{openalex_id}" + + +register(OpenAlexAdapter()) diff --git a/src/index/_federated/adapters/orcid.py b/src/index/_federated/adapters/orcid.py new file mode 100644 index 0000000..2b6908c --- /dev/null +++ b/src/index/_federated/adapters/orcid.py @@ -0,0 +1,95 @@ +"""Adapter wrapping `src.index.orcid` for federated search/lookup.""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index._federated.registry import EntityRecord, Hit, register + +_RE_ORCID = re.compile(r"\b(\d{4}-\d{4}-\d{4}-\d{3}[\dX])\b") +_RE_ORCID_URL = re.compile(r"https?://orcid\.org/(\d{4}-\d{4}-\d{4}-\d{3}[\dX])", re.I) + + +class OrcidAdapter: + name = "orcid" + entity_types = ["persons", "employments", "educations"] + + def search( + self, + *, + query: str, + entity_type: str | None, + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + from src.index.orcid.config import load_config + from src.index.orcid.retrieval.semantic import semantic_search + + config = load_config() + types_to_query = [entity_type] if entity_type else ["persons"] + out: list[Hit] = [] + for et in types_to_query: + try: + results = semantic_search( + config=config, query=query, entity_type=et, + top_k=top_k, candidate_k=max(top_k * 5, 50), + filter_payload=filters, + ) + except Exception: # noqa: BLE001 + continue + for r in results: + payload = r.get("payload") or {} + oid = payload.get("orcid_id") or payload.get("orcid") + if not oid: + continue + singular = payload.get("entity_type") or et.rstrip("s") or et + full_name = ( + payload.get("display_name") or payload.get("name") + or f"{payload.get('given_names', '')} {payload.get('family_name', '')}".strip() + or oid + ) + out.append(Hit( + index=self.name, + entity_type=singular, + id=oid, + title=str(full_name) if full_name else None, + score=float(r.get("rerank_score") or r.get("vector_score") or 0.0), + summary=_summary(payload), + url=f"https://orcid.org/{oid}", + payload=payload, + )) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + s = identifier.strip() + m = _RE_ORCID_URL.search(s) or _RE_ORCID.search(s) + if not m: + return [] + oid = m.group(1) + try: + from src.index.orcid.storage.duckdb_store import DuckDBStore + except Exception: # noqa: BLE001 + return [] + store = DuckDBStore.open() + if hasattr(store, "fetch_person"): + row = store.fetch_person(oid) + if row is not None: + return [EntityRecord( + index=self.name, entity_type="person", id=oid, + data=row, url=f"https://orcid.org/{oid}", + )] + return [] + + +def _summary(payload: dict[str, Any]) -> str | None: + parts = [ + str(payload.get("display_name") or payload.get("name") or ""), + str(payload.get("organization") or ""), + str(payload.get("role") or ""), + ] + parts = [p for p in parts if p] + return " — ".join(parts) if parts else None + + +register(OrcidAdapter()) diff --git a/src/index/_federated/adapters/renkulab.py b/src/index/_federated/adapters/renkulab.py new file mode 100644 index 0000000..447a33d --- /dev/null +++ b/src/index/_federated/adapters/renkulab.py @@ -0,0 +1,265 @@ +"""Adapter wrapping `src.index.renkulab` for federated search/lookup. + +The RenkuLab index has four entity types — projects, groups, users, +data_connectors — each in its own Qdrant collection. The adapter +exposes them under a single index name `renkulab` and uses the +``entity_type`` argument to dispatch to a single collection. When +``entity_type`` is ``None`` the adapter searches across all four. +""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index._federated.registry import EntityRecord, Hit, register + +_RE_RENKU_PROJECT_URL = re.compile( + r"https?://renkulab\.io/(?:v2/)?projects/([\w.-]+(?:/[\w.-]+)+)", + re.IGNORECASE, +) +_RE_RENKU_GROUP_URL = re.compile( + r"https?://renkulab\.io/(?:v2/)?groups/([\w.-]+)", + re.IGNORECASE, +) +_RE_RENKU_USER_URL = re.compile( + r"https?://renkulab\.io/(?:v2/)?users/([\w.-]+)", + re.IGNORECASE, +) +_RE_ULID = re.compile(r"^[0-9A-HJ-KM-NP-TV-Z]{26}$", re.IGNORECASE) +_RE_UUID = re.compile( + r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$", + re.IGNORECASE, +) + + +_ENTITY_NAMES = ("projects", "groups", "users", "data_connectors") + +_RENKU_BASE = "https://renkulab.io/v2" + + +def _entity_url(entity_type: str, payload: dict[str, Any]) -> str | None: + # Projects and data connectors are namespaced; groups + users are + # top-level slugs. When `path` (the full namespace/slug composite) is + # unset (e.g. row came from /projects rather than /search), build it + # from namespace + slug. + path = payload.get("path") + slug = payload.get("slug") + namespace = payload.get("namespace") + if entity_type in {"projects", "data_connectors"}: + composite = path or (f"{namespace}/{slug}" if namespace and slug else slug) + if not composite: + return None + if entity_type == "projects": + return f"{_RENKU_BASE}/projects/{composite}" + return f"{_RENKU_BASE}/data-connectors/{composite}" + if entity_type == "groups" and slug: + return f"{_RENKU_BASE}/groups/{slug}" + if entity_type == "users": + identifier = path or slug + if identifier: + return f"{_RENKU_BASE}/users/{identifier}" + return None + + +def _hit_title(entity_type: str, payload: dict[str, Any]) -> str | None: + if entity_type == "users": + first = payload.get("first_name") or "" + last = payload.get("last_name") or "" + full = f"{first} {last}".strip() + return full or payload.get("path") or payload.get("slug") + return ( + payload.get("name") + or payload.get("path") + or payload.get("slug") + ) + + +def _summary(entity_type: str, payload: dict[str, Any]) -> str | None: + parts: list[str] = [] + if entity_type in {"projects", "data_connectors"}: + ns = payload.get("namespace") or payload.get("path") + if ns: + parts.append(str(ns)) + if entity_type == "data_connectors": + st = payload.get("storage_type") + if st: + parts.append(f"storage:{st}") + if entity_type == "users": + path = payload.get("path") or payload.get("slug") + if path: + parts.append(str(path)) + vis = payload.get("visibility") + if vis: + parts.append(str(vis)) + return " — ".join(parts) if parts else None + + +class RenkulabAdapter: + name = "renkulab" + entity_types: list[str] = list(_ENTITY_NAMES) # noqa: RUF012 + + def search( + self, + *, + query: str, + entity_type: str | None, + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + from src.index.renkulab.config import load_config + from src.index.renkulab.retrieval.semantic import semantic_search + + target_types = [entity_type] if entity_type else None + if target_types and target_types[0] not in _ENTITY_NAMES: + return [] + + config = load_config() + try: + results = semantic_search( + config=config, query=query, + entity_types=target_types, + top_k=top_k, candidate_k=max(top_k * 5, 50), + filter_payload=filters, + ) + except Exception: # noqa: BLE001 + return [] + + out: list[Hit] = [] + for r in results: + payload = r.get("payload") or {} + et = str(payload.get("entity_type") or "") + entity_id = payload.get("entity_id") or r.get("id") + if not entity_id or not et: + continue + out.append(Hit( + index=self.name, + entity_type=et, + id=str(entity_id), + title=_hit_title(et, payload), + score=float(r.get("rerank_score") or r.get("vector_score") or 0.0), + summary=_summary(et, payload), + url=_entity_url(et, payload), + payload=payload, + )) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + s = identifier.strip() + if not s: + return [] + + # URL-based dispatch first. + for pattern, et in ( + (_RE_RENKU_PROJECT_URL, "projects"), + (_RE_RENKU_GROUP_URL, "groups"), + (_RE_RENKU_USER_URL, "users"), + ): + m = pattern.search(s) + if m: + return self._lookup_by_path_or_id(et, m.group(1)) + + # Bare ULID / UUID — try each entity type until one resolves. + if _RE_ULID.match(s) or _RE_UUID.match(s): + from src.index.renkulab.storage.duckdb_store import RenkulabStore + + store = RenkulabStore.open() + try: + for et in _ENTITY_NAMES: + try: + row = store.fetch_entity(et, s) + except Exception: # noqa: BLE001 + row = None + if row is not None: + return [self._record_from_row(et, str(s), row)] + finally: + store.close() + return [] + + # Otherwise interpret as a slug for any entity. + return self._lookup_by_slug_anywhere(s) + + def _lookup_by_path_or_id( + self, + entity_type: str, + identifier: str, + ) -> list[EntityRecord]: + from src.index.renkulab.retrieval.sql import run_adhoc + + table = _ENTITY_TABLE[entity_type] + + # Only `projects` and `data_connectors` carry a `path` column — + # the others (`groups`, `users`) only have `slug` and (for users) + # `path` too. Build the SQL accordingly. + if entity_type in {"projects", "data_connectors"}: + if "/" in identifier: + namespace, _, slug = identifier.rpartition("/") + rows = run_adhoc( + f"SELECT * FROM {table} " + "WHERE path = $id " + "OR (namespace = $ns AND slug = $slug) " + "OR slug = $id LIMIT 1", + {"id": identifier, "ns": namespace, "slug": slug}, + ) + else: + rows = run_adhoc( + f"SELECT * FROM {table} " + "WHERE path = $id OR slug = $id LIMIT 1", + {"id": identifier}, + ) + elif entity_type == "users": + rows = run_adhoc( + f"SELECT * FROM {table} " + "WHERE path = $id OR slug = $id LIMIT 1", + {"id": identifier}, + ) + else: # groups + rows = run_adhoc( + f"SELECT * FROM {table} WHERE slug = $id LIMIT 1", + {"id": identifier}, + ) + if not rows: + return [] + row = rows[0] + pk_value = str(row.get(_ENTITY_PK[entity_type]) or "") + if not pk_value: + return [] + return [self._record_from_row(entity_type, pk_value, row)] + + def _lookup_by_slug_anywhere(self, slug: str) -> list[EntityRecord]: + out: list[EntityRecord] = [] + for et in _ENTITY_NAMES: + out.extend(self._lookup_by_path_or_id(et, slug)) + return out + + def _record_from_row( + self, + entity_type: str, + entity_id: str, + row: dict[str, Any], + ) -> EntityRecord: + return EntityRecord( + index=self.name, + entity_type=entity_type, + id=entity_id, + data=row, + url=_entity_url(entity_type, row), + ) + + +_ENTITY_TABLE: dict[str, str] = { + "projects": "projects", + "groups": "groups", + "users": "users", + "data_connectors": "data_connectors", +} + +_ENTITY_PK: dict[str, str] = { + "projects": "project_id", + "groups": "group_id", + "users": "user_id", + "data_connectors": "data_connector_id", +} + + +register(RenkulabAdapter()) diff --git a/src/index/_federated/adapters/ror.py b/src/index/_federated/adapters/ror.py new file mode 100644 index 0000000..a7cf6fc --- /dev/null +++ b/src/index/_federated/adapters/ror.py @@ -0,0 +1,95 @@ +"""Adapter wrapping `src.index.ror` for federated search/lookup.""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index._federated.registry import EntityRecord, Hit, register + +_RE_ROR_URL = re.compile(r"https?://ror\.org/([0-9a-z]{9})", re.I) +_RE_ROR_ID = re.compile(r"\b([0-9a-z]{9})\b") + + +class RorAdapter: + name = "ror" + entity_types = ["organizations"] + + def search( + self, + *, + query: str, + entity_type: str | None, # noqa: ARG002 + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + from src.index.ror.config import load_config + from src.index.ror.query import query_rag_sync + + cfg = load_config() + country = (filters or {}).get("country_code") or (filters or {}).get("country") + try: + scored = query_rag_sync( + cfg, query, top_k=top_k, country=country, + ) + except Exception: # noqa: BLE001 + return [] + out: list[Hit] = [] + for s in scored: + # ScoredRecord has .record (dict-like) and .score / .rerank_score + rec = getattr(s, "record", None) or {} + ror_id = rec.get("id") or rec.get("ror_id") or rec.get("ror") + if not ror_id: + continue + out.append(Hit( + index=self.name, entity_type="organization", + id=str(ror_id), + title=rec.get("name") or rec.get("display_name"), + score=float(getattr(s, "rerank_score", None) or getattr(s, "score", 0.0)), + summary=_summary(rec), + url=str(ror_id) if str(ror_id).startswith("http") else f"https://ror.org/{ror_id}", + payload=dict(rec), + )) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + s = identifier.strip() + m = _RE_ROR_URL.search(s) + ror_id = m.group(1) if m else None + if not ror_id: + m2 = _RE_ROR_ID.fullmatch(s) + if m2: + ror_id = m2.group(1) + if not ror_id: + return [] + try: + from src.index.ror.config import load_config + from src.index.ror.query import lookup_dump + except Exception: # noqa: BLE001 + return [] + cfg = load_config() + try: + matches = lookup_dump(cfg, f"https://ror.org/{ror_id}") + except Exception: # noqa: BLE001 + return [] + records: list[EntityRecord] = [] + for dm in matches: + rec = getattr(dm, "record", None) or {} + records.append(EntityRecord( + index=self.name, entity_type="organization", id=ror_id, + data=dict(rec), url=f"https://ror.org/{ror_id}", + )) + return records + + +def _summary(rec: dict[str, Any]) -> str | None: + parts = [ + str(rec.get("name") or rec.get("display_name") or ""), + str((rec.get("country") or {}).get("country_code") if isinstance(rec.get("country"), dict) else (rec.get("country") or "")), + str(rec.get("types", [None])[0] if rec.get("types") else ""), + ] + parts = [p for p in parts if p] + return " — ".join(parts) if parts else None + + +register(RorAdapter()) diff --git a/src/index/_federated/adapters/snsf.py b/src/index/_federated/adapters/snsf.py new file mode 100644 index 0000000..aa26414 --- /dev/null +++ b/src/index/_federated/adapters/snsf.py @@ -0,0 +1,179 @@ +"""Adapter wrapping `src.index.snsf` for federated search/lookup. + +SNSF P3 indexes Swiss National Science Foundation grants, the people +involved, and the institutions receiving them. +""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index._federated.registry import EntityRecord, Hit, register + +# SNSF grant numbers are typically 6-7 digit integers, sometimes prefixed +# with a project type code (e.g. "10000-001234"). The public URL form is +# https://data.snf.ch/grants/grant/. +_RE_SNSF_URL = re.compile( + r"https?://(?:data|p3)\.sn[fs]\.ch/(?:grants/grant|grant)/(\S+?)(?:[/?#]|$)", + re.I, +) +_RE_SNSF_ID = re.compile(r"\b(\d{6,7})\b") + + +class SnsfAdapter: + name = "snsf" + entity_types = ["grants"] + + def search( + self, + *, + query: str, + entity_type: str | None, # noqa: ARG002 — single-type for now + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + try: + from src.index.snsf.config import load_config + from src.index.snsf.query import query_rag_sync + except Exception: # noqa: BLE001 + return [] + cfg = load_config() + kw: dict[str, Any] = {"top_k": top_k} + if filters: + for fk in ("institution", "discipline_l1", "state", "scope_mode"): + if fk in filters and filters[fk] is not None: + kw[fk] = filters[fk] + try: + results = query_rag_sync(cfg, query, **kw) + except Exception: # noqa: BLE001 + return [] + out: list[Hit] = [] + for r in results: + md = r.get("payload") or r.get("metadata") or r + grant_id = md.get("grant_number") or md.get("grant_id") or md.get("id") + if not grant_id: + continue + title = md.get("title") or md.get("project_title") or str(grant_id) + score = float(r.get("rerank_score") or r.get("score") or 0.0) + out.append(Hit( + index=self.name, entity_type="grant", + id=str(grant_id), + title=str(title) if title else None, + score=score, + summary=_summary(md), + url=f"https://data.snf.ch/grants/grant/{grant_id}", + payload=dict(md), + )) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + s = identifier.strip() + m = _RE_SNSF_URL.search(s) + if m: + grant_id_str = m.group(1) + elif _RE_SNSF_ID.fullmatch(s): + grant_id_str = s + else: + return [] + + try: + grant_id = int(grant_id_str) + except ValueError: + # URL-extracted id may include a non-numeric suffix; bail out + # with the thin ack record so the caller still knows the URL parsed. + return [EntityRecord( + index=self.name, entity_type="grant", id=grant_id_str, + data={"id": grant_id_str, "source": "data.snf.ch"}, + url=f"https://data.snf.ch/grants/grant/{grant_id_str}", + )] + + try: + from src.index.snsf.storage.duckdb_store import DuckDBStore + except Exception: # noqa: BLE001 + return self._fallback_record(grant_id_str) + + try: + store = DuckDBStore.open() + except Exception: # noqa: BLE001 + return self._fallback_record(grant_id_str) + try: + row = store.fetch_grant(grant_id) + except Exception: # noqa: BLE001 + return self._fallback_record(grant_id_str) + finally: + store.close() + + if row is None: + return [] + + return [EntityRecord( + index=self.name, + entity_type="grant", + id=str(row.get("grant_number") or grant_id), + data=_compact_grant(row), + url=f"https://data.snf.ch/grants/grant/{row.get('grant_number') or grant_id}", + )] + + def _fallback_record(self, grant_id_str: str) -> list[EntityRecord]: + """Thin ack when DuckDB is unreachable (e.g. concurrent writer lock).""" + return [EntityRecord( + index=self.name, entity_type="grant", id=grant_id_str, + data={"id": grant_id_str, "source": "data.snf.ch"}, + url=f"https://data.snf.ch/grants/grant/{grant_id_str}", + )] + + +def _summary(md: dict[str, Any]) -> str | None: + parts = [ + str(md.get("title") or md.get("project_title") or ""), + str(md.get("institution") or md.get("organization") or ""), + str(md.get("discipline_l1") or ""), + str(md.get("state") or ""), + ] + parts = [p for p in parts if p] + return " — ".join(parts) if parts else None + + +# Subset of `grants` columns kept in the federated EntityRecord.data payload — +# enough for an LLM caller to render a useful summary, without flooding +# downstream context with the full abstract or lay summaries. +_LOOKUP_COLS: tuple[str, ...] = ( + "grant_number", + "title", + "title_english", + "responsible_applicant", + "institute", + "research_institution", + "research_institution_type", + "main_discipline", + "main_discipline_l1", + "main_discipline_l2", + "main_field_of_research", + "start_date", + "end_date", + "amount_granted", + "keywords", + "state", + "funding_instrument", + "call_full_title", + "call_decision_year", +) + + +def _compact_grant(row: dict[str, Any]) -> dict[str, Any]: + """Stringify timestamps / drop NULL columns so the result is JSON-clean.""" + out: dict[str, Any] = {} + for col in _LOOKUP_COLS: + v = row.get(col) + if v is None: + continue + # DuckDB returns datetime/date objects for TIMESTAMP/DATE columns — + # serialise them to ISO strings so the federated layer can json-dump. + if hasattr(v, "isoformat"): + v = v.isoformat() + out[col] = v + return out + + +register(SnsfAdapter()) diff --git a/src/index/_federated/adapters/swissubase.py b/src/index/_federated/adapters/swissubase.py new file mode 100644 index 0000000..a199421 --- /dev/null +++ b/src/index/_federated/adapters/swissubase.py @@ -0,0 +1,257 @@ +"""Adapter wrapping `src.index.swissubase` for federated search/lookup. + +The SWISSUbase index has four entity types — ``studies``, ``datasets``, +``persons``, ``institutions`` — all stored in a single Qdrant collection +``swissubase_entities`` with an ``entity_type`` payload field. The +adapter exposes them under the single index name ``swissubase`` and uses +the ``entity_type`` argument to scope the search; ``entity_type=None`` +searches across all four (the default). + +Lookup recognises three identifier shapes: + +* ``https://www.swissubase.ch/{lang}/catalogue/studies/{N}`` URLs. +* Bare numeric IDs — interpreted as ``studyVersionId`` (the API's + authoritative key). +* Internal person/institution composite keys + (``swissubase:person:{N}`` / ``name:slugified``) when the upstream + graph already contains them. +""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index._federated.registry import EntityRecord, Hit, register + +_RE_STUDY_URL = re.compile( + r"https?://(?:www\.)?swissubase\.ch/[a-z]{2}/catalogue/studies/(\d+)", + re.IGNORECASE, +) +_RE_NUMERIC_ID = re.compile(r"^\d+$") +_RE_PERSON_KEY = re.compile(r"^swissubase:person:\d+$") +_RE_NAME_SLUG = re.compile(r"^name:[a-z0-9-]+$") + +_ENTITY_NAMES = ("studies", "datasets", "persons", "institutions") +_SWISSUBASE_LANG = "en" + + +def _study_url(study_id: str) -> str: + return f"https://www.swissubase.ch/{_SWISSUBASE_LANG}/catalogue/studies/{study_id}" + + +def _hit_title(entity_type: str, payload: dict[str, Any]) -> str | None: + if entity_type in {"studies", "datasets"}: + return payload.get("title") + if entity_type == "persons": + return payload.get("display_name") + if entity_type == "institutions": + return payload.get("name") + return None + + +def _hit_summary(entity_type: str, payload: dict[str, Any]) -> str | None: + parts: list[str] = [] + if entity_type == "studies": + ref = payload.get("ref") + if ref: + parts.append(f"ref:{ref}") + disc = payload.get("main_discipline") + if disc: + parts.append(str(disc)) + progress = payload.get("progress") + if progress: + parts.append(str(progress)) + if payload.get("year_start") or payload.get("year_end"): + parts.append(f"{payload.get('year_start') or '?'}-{payload.get('year_end') or '?'}") + elif entity_type == "datasets": + access = payload.get("access_right") + if access: + parts.append(f"access:{access}") + elif entity_type == "persons": + affil = payload.get("affiliation") + if affil: + parts.append(str(affil)) + elif entity_type == "institutions": + ror = payload.get("ror_id") + if ror: + parts.append(f"ror:{ror}") + return " — ".join(parts) if parts else None + + +def _hit_url(entity_type: str, payload: dict[str, Any]) -> str | None: + """Prefer the embedded ``source_url`` (always populated for studies/datasets; + empty for persons/institutions, which have no detail page on swissUbase).""" + url = payload.get("source_url") + if isinstance(url, str) and url: + return url + if entity_type == "studies" and payload.get("study_id"): + return _study_url(str(payload["study_id"])) + return None + + +class SwissubaseAdapter: + name = "swissubase" + entity_types: list[str] = list(_ENTITY_NAMES) # noqa: RUF012 + + def search( + self, + *, + query: str, + entity_type: str | None, + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + from src.index.swissubase.config import load_config + from src.index.swissubase.retrieval.semantic import semantic_search + + if entity_type is not None and entity_type not in _ENTITY_NAMES: + return [] + + # The SWISSUbase Qdrant collection holds all four entity types, + # disambiguated by the ``entity_type`` payload key. Push the + # filter down so the ANN doesn't have to over-fetch. + filter_payload: dict[str, Any] = dict(filters or {}) + if entity_type is not None: + filter_payload["entity_type"] = entity_type + + try: + results = semantic_search( + config=load_config(), + query=query, + top_k=top_k, + candidate_k=max(top_k * 5, 50), + filter_payload=filter_payload or None, + ) + except Exception: # noqa: BLE001 + return [] + + out: list[Hit] = [] + for r in results: + payload = r.get("payload") or {} + et = str(payload.get("entity_type") or "") + entity_id = payload.get("entity_id") or r.get("id") + if not entity_id or et not in _ENTITY_NAMES: + continue + out.append(Hit( + index=self.name, + entity_type=et, + id=str(entity_id), + title=_hit_title(et, payload), + score=float( + r.get("rerank_score") or r.get("vector_score") or 0.0, + ), + summary=_hit_summary(et, payload), + url=_hit_url(et, payload), + payload=payload, + )) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + s = identifier.strip() + if not s: + return [] + + # SWISSUbase study URL → study_id (= studyVersionId). + m = _RE_STUDY_URL.search(s) + if m: + return self._lookup_study(m.group(1)) + + # Internal person key (swissubase:person:NNNN). + if _RE_PERSON_KEY.match(s): + return self._lookup_person(s) + + # Bare numeric → try studies first (most common downstream use). + if _RE_NUMERIC_ID.match(s): + records = self._lookup_study(s) + if records: + return records + # Fall back to a person lookup using the personId composite. + return self._lookup_person(f"swissubase:person:{s}") + + # `name:slugified` keys can be either a person or an institution. + if _RE_NAME_SLUG.match(s): + return self._lookup_person(s) + self._lookup_institution(s) + + return [] + + def _lookup_study(self, study_id: str) -> list[EntityRecord]: + try: + from src.index.swissubase.storage.duckdb_store import SwissubaseStore + except Exception: # noqa: BLE001 + return [] + store = SwissubaseStore.open() + try: + row = store.fetch_study(study_id) + except Exception: # noqa: BLE001 + return [] + finally: + store.close() + if row is None: + return [] + return [EntityRecord( + index=self.name, + entity_type="studies", + id=str(study_id), + data=row, + url=str(row.get("source_url") or _study_url(study_id)), + )] + + def _lookup_person(self, person_key: str) -> list[EntityRecord]: + try: + from src.index.swissubase.storage.duckdb_store import SwissubaseStore + except Exception: # noqa: BLE001 + return [] + store = SwissubaseStore.open() + try: + cur = store.connect().execute( + "SELECT * FROM persons WHERE person_key = ?", + [person_key], + ) + row = cur.fetchone() + if row is None: + return [] + cols = [d[0] for d in cur.description] + row_dict = dict(zip(cols, row, strict=False)) + except Exception: # noqa: BLE001 + return [] + finally: + store.close() + return [EntityRecord( + index=self.name, + entity_type="persons", + id=person_key, + data=row_dict, + url=row_dict.get("source_url"), + )] + + def _lookup_institution(self, institution_key: str) -> list[EntityRecord]: + try: + from src.index.swissubase.storage.duckdb_store import SwissubaseStore + except Exception: # noqa: BLE001 + return [] + store = SwissubaseStore.open() + try: + cur = store.connect().execute( + "SELECT * FROM institutions WHERE institution_key = ?", + [institution_key], + ) + row = cur.fetchone() + if row is None: + return [] + cols = [d[0] for d in cur.description] + row_dict = dict(zip(cols, row, strict=False)) + except Exception: # noqa: BLE001 + return [] + finally: + store.close() + return [EntityRecord( + index=self.name, + entity_type="institutions", + id=institution_key, + data=row_dict, + url=row_dict.get("source_url"), + )] + + +register(SwissubaseAdapter()) diff --git a/src/index/_federated/adapters/zenodo.py b/src/index/_federated/adapters/zenodo.py new file mode 100644 index 0000000..e598481 --- /dev/null +++ b/src/index/_federated/adapters/zenodo.py @@ -0,0 +1,102 @@ +"""Adapter wrapping `src.index.zenodo` for federated search/lookup.""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index._federated.registry import EntityRecord, Hit, register + +_RE_DIGITS_ONLY = re.compile(r"^\d{4,10}$") +_RE_ZENODO_URL = re.compile( + r"https?://(?:www\.|sandbox\.)?zenodo\.org/(?:records?|deposit)/(\d+)", + re.I, +) +_RE_ZENODO_DOI = re.compile(r"10\.5281/zenodo\.(\d+)", re.I) + + +class ZenodoAdapter: + name = "zenodo" + entity_types = ["zenodo_records"] + + def search( + self, + *, + query: str, + entity_type: str | None, # noqa: ARG002 — single-type index + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + from src.index.zenodo.config import load_config + from src.index.zenodo.retrieval.semantic import semantic_search + + config = load_config() + try: + results = semantic_search( + config=config, query=query, + top_k=top_k, candidate_k=max(top_k * 5, 50), + filter_payload=filters, + ) + except Exception: # noqa: BLE001 + return [] + out: list[Hit] = [] + for r in results: + payload = r.get("payload") or {} + zid = payload.get("zenodo_id") or payload.get("id") + if not zid: + continue + title = payload.get("title") or str(zid) + out.append(Hit( + index=self.name, entity_type="zenodo_record", + id=str(zid), + title=str(title) if title else None, + score=float(r.get("rerank_score") or r.get("vector_score") or 0.0), + summary=_summary(payload), + url=f"https://zenodo.org/records/{zid}", + payload=payload, + )) + return out + + def lookup(self, identifier: str) -> list[EntityRecord]: + s = identifier.strip() + m = _RE_ZENODO_URL.search(s) or _RE_ZENODO_DOI.search(s) + if m: + zid = m.group(1) + elif _RE_DIGITS_ONLY.match(s): + zid = s + else: + return [] + try: + from src.index.zenodo.storage.duckdb_store import ZenodoStore + except Exception: # noqa: BLE001 + return [] + store = ZenodoStore.open() + try: + row = store.fetch_record(zid) + if row is None: + # Citation may reference the concept_recid (parent) — fall + # back to the most-recent version under that concept. + row = store.fetch_record_by_concept(zid) + if row is None: + return [] + canonical_id = str(row.get("zenodo_id") or zid) + return [EntityRecord( + index=self.name, entity_type="zenodo_record", id=canonical_id, + data=row, url=f"https://zenodo.org/records/{canonical_id}", + )] + finally: + store.close() + + +def _summary(payload: dict[str, Any]) -> str | None: + parts = [ + str(payload.get("title") or ""), + str(payload.get("year") or ""), + str(payload.get("resource_type") or ""), + str(payload.get("doi") or ""), + ] + parts = [p for p in parts if p] + return " — ".join(parts) if parts else None + + +register(ZenodoAdapter()) diff --git a/src/index/_federated/cli.py b/src/index/_federated/cli.py new file mode 100644 index 0000000..94fa173 --- /dev/null +++ b/src/index/_federated/cli.py @@ -0,0 +1,139 @@ +"""CLI: `python -m src.index._federated `. + +Subcommands: +- `search QUERY` — federated semantic search across registered indices +- `entity ID` — cross-index entity lookup (slug, ORCID, ROR, DOI, URL, …) +- `indices` — list registered adapters and their entity types +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from typing import Any + +from src.index._federated.entity import cross_index_lookup +from src.index._federated.registry import REGISTRY, load_adapters +from src.index._federated.search import federated_search + +LOGGER = logging.getLogger(__name__) + + +def _emit_json(value: object) -> None: + json.dump(value, sys.stdout, indent=2, ensure_ascii=False, default=str) + sys.stdout.write("\n") + + +def _parse_kv(raw: list[str] | None) -> dict[str, Any] | None: + if not raw: + return None + out: dict[str, Any] = {} + for item in raw: + if "=" not in item: + message = f"--filter must be key=value, got {item!r}" + raise SystemExit(message) + key, value = item.split("=", 1) + coerced: Any = int(value) if value.lstrip("-").isdigit() else value + if key in out: + existing = out[key] + out[key] = [*existing, coerced] if isinstance(existing, list) else [existing, coerced] + else: + out[key] = coerced + return out + + +def _parse_indices(raw: str | None) -> list[str] | None: + if not raw: + return None + return [p.strip() for p in raw.split(",") if p.strip()] + + +def _cmd_search(args: argparse.Namespace) -> int: + result = federated_search( + args.query, + indices=_parse_indices(args.indices), + entity_type=args.entity_type, + top_k_per_index=args.top_k_per_index, + top_k_overall=args.top_k, + filters=_parse_kv(args.filter), + rerank=args.rerank, + ) + _emit_json(result) + return 0 + + +def _cmd_entity(args: argparse.Namespace) -> int: + result = cross_index_lookup( + args.identifier, + indices=_parse_indices(args.indices), + ) + _emit_json(result) + return 0 + + +def _cmd_indices(_: argparse.Namespace) -> int: + load_adapters() + _emit_json({ + name: {"entity_types": adapter.entity_types} + for name, adapter in sorted(REGISTRY.items()) + }) + return 0 + + +def build_parser() -> argparse.ArgumentParser: + p = argparse.ArgumentParser( + prog="src.index._federated", + description="Federated search + cross-index entity lookup over the gme RAG indices", + ) + sub = p.add_subparsers(dest="cmd", required=True) + + p_s = sub.add_parser("search", help="Federated semantic search across registered indices") + p_s.add_argument("query") + p_s.add_argument( + "--indices", + default="", + help="Comma-separated subset of registered adapters (default: all). " + "E.g. --indices huggingface,openalex", + ) + p_s.add_argument( + "--entity-type", default=None, + help="Restrict to one entity type within each index (e.g. models, works, persons)", + ) + p_s.add_argument("--top-k", type=int, default=20, help="overall hits returned") + p_s.add_argument("--top-k-per-index", type=int, default=5, help="hits requested from each index") + p_s.add_argument( + "--filter", action="append", + help="Payload filter key=value (repeatable). Forwarded to every adapter " + "as-is; adapters ignore unknown keys.", + ) + p_s.add_argument( + "--rerank", action="store_true", + help="Send the merged candidate pool through RCP's cross-encoder once " + "for a globally-fair ordering. Costs one extra RCP call.", + ) + p_s.set_defaults(func=_cmd_search) + + p_e = sub.add_parser("entity", help="Cross-index lookup for an identifier") + p_e.add_argument("identifier", help="slug, URL, ORCID, ROR, DOI, UUID, …") + p_e.add_argument("--indices", default="", help="Comma-separated subset of indices") + p_e.set_defaults(func=_cmd_entity) + + p_i = sub.add_parser("indices", help="List registered adapters") + p_i.set_defaults(func=_cmd_indices) + return p + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s %(message)s", + ) + parser = build_parser() + args = parser.parse_args(argv) + return args.func(args) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/index/_federated/dh_registry.py b/src/index/_federated/dh_registry.py new file mode 100644 index 0000000..bedf14b --- /dev/null +++ b/src/index/_federated/dh_registry.py @@ -0,0 +1,158 @@ +"""Registries for the discover/hydrate protocols. + +Sibling to ``registry.py``. Kept in its own module so the +``IndexAdapter`` (search/lookup) registry stays decoupled — adapters +that don't yet implement discover/hydrate can still register for +search. + +See :mod:`src.index._federated.protocols` for the protocol definitions +and ``.internal/federated/discover-hydrate-design.md`` for the design +rationale. +""" + +from __future__ import annotations + +import logging +from importlib import import_module +from typing import Iterable + +from src.index._federated.protocols import ( + HydrationSummary, + IndexDiscoverer, + IndexHydrator, + Seed, +) + +LOGGER = logging.getLogger(__name__) + + +DISCOVERERS: dict[str, IndexDiscoverer] = {} +HYDRATORS: dict[str, IndexHydrator] = {} + + +def register_discoverer(discoverer: IndexDiscoverer) -> None: + """Register a discoverer. Call from each index's ``_federated.py`` at import time.""" + if not isinstance(discoverer, IndexDiscoverer): + message = f"{discoverer!r} does not satisfy IndexDiscoverer" + raise TypeError(message) + DISCOVERERS[discoverer.name] = discoverer + + +def register_hydrator(hydrator: IndexHydrator) -> None: + """Register a hydrator. Call from each index's ``_federated.py`` at import time.""" + if not isinstance(hydrator, IndexHydrator): + message = f"{hydrator!r} does not satisfy IndexHydrator" + raise TypeError(message) + HYDRATORS[hydrator.name] = hydrator + + +# Same candidates list as registry.py so we share the index roster. +# Each module's `_federated.py` is responsible for importing both +# discoverer + hydrator classes and calling the register_* helpers. +_CANDIDATES = [ + "openalex", + "orcid", + "zenodo", + "infoscience", + "ethz_research_collection", + "github", + "huggingface", + "snsf", + "renkulab", + "swissubase", + # ROR + EPFL Graph are dump-driven — no discover/hydrate. +] + + +def _load(target: str, kind: str) -> None: + """Import ``src.index.._federated`` to trigger self-registration. + + Failures are logged but never raised: a single broken module shouldn't + take down the whole CLI. + """ + try: + import_module(f"src.index.{target}._federated") + except ModuleNotFoundError: + # Index hasn't been migrated to the new protocols yet. That's fine. + LOGGER.debug("federated/%s: %s has no _federated module yet", kind, target) + except Exception as exc: # noqa: BLE001 + LOGGER.warning( + "federated/%s: %s failed to import (%s); skipping", kind, target, exc, + ) + + +def load_discoverers(only: list[str] | None = None) -> list[IndexDiscoverer]: + """Trigger self-registration for the requested set, return active discoverers. + + ``only`` filters the result by name. If ``only`` is ``None`` we load every + candidate and return everything in the registry (including discoverers + that registered themselves outside the candidate list, e.g. tests). + """ + targets_to_load = _CANDIDATES if only is None else [c for c in _CANDIDATES if c in only] + for name in targets_to_load: + _load(name, "discoverer") + if only is None: + return list(DISCOVERERS.values()) + return [DISCOVERERS[n] for n in only if n in DISCOVERERS] + + +def load_hydrators(only: list[str] | None = None) -> list[IndexHydrator]: + """Trigger self-registration for the requested set, return active hydrators.""" + targets_to_load = _CANDIDATES if only is None else [c for c in _CANDIDATES if c in only] + for name in targets_to_load: + _load(name, "hydrator") + if only is None: + return list(HYDRATORS.values()) + return [HYDRATORS[n] for n in only if n in HYDRATORS] + + +def dispatch_hydrate( + seeds: Iterable[Seed], + *, + only_unfetched: bool = True, + only: list[str] | None = None, +) -> dict[str, HydrationSummary]: + """Group ``seeds`` by ``seed_type``, route each group to the matching hydrator(s). + + A single ``seed_type`` may be accepted by multiple hydrators (e.g. + a DOI seed could be hydrated by both ``openalex`` and ``zenodo`` from + their own perspectives). All matching hydrators get the same group. + + Returns a per-hydrator summary dict. + """ + hydrators = load_hydrators(only=only) + if not hydrators: + return {} + + materialised = list(seeds) + by_type: dict[str, list[Seed]] = {} + for s in materialised: + by_type.setdefault(s.seed_type, []).append(s) + + summaries: dict[str, HydrationSummary] = {} + for hyd in hydrators: + accepted = set(hyd.accepted_seed_types) + relevant: list[Seed] = [] + for st, batch in by_type.items(): + if st in accepted: + relevant.extend(batch) + if not relevant: + continue + try: + summary = hyd.hydrate(relevant, only_unfetched=only_unfetched) + except Exception as exc: # noqa: BLE001 + LOGGER.exception("hydrator %r failed: %s", hyd.name, exc) + summary = HydrationSummary(errors=len(relevant)) + summaries[hyd.name] = summary + return summaries + + +__all__ = [ + "DISCOVERERS", + "HYDRATORS", + "register_discoverer", + "register_hydrator", + "load_discoverers", + "load_hydrators", + "dispatch_hydrate", +] diff --git a/src/index/_federated/entity.py b/src/index/_federated/entity.py new file mode 100644 index 0000000..816986f --- /dev/null +++ b/src/index/_federated/entity.py @@ -0,0 +1,52 @@ +"""Cross-index entity lookup: take an identifier, ask every adapter for matches.""" + +from __future__ import annotations + +import logging +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import asdict +from typing import Any + +from src.index._federated.registry import EntityRecord, IndexAdapter, load_adapters + +LOGGER = logging.getLogger(__name__) + + +def cross_index_lookup( + identifier: str, + *, + indices: list[str] | None = None, +) -> dict[str, Any]: + """Resolve `identifier` against every registered (or named) adapter. + + Returns `{records: [EntityRecord-dicts...], by_index: {index: count}, errors: {...}}`. + """ + adapters = load_adapters(only=indices) if indices is not None else load_adapters() + + records: list[EntityRecord] = [] + errors: dict[str, str] = {} + by_index: dict[str, int] = {} + + def _run(adapter: IndexAdapter) -> tuple[str, list[EntityRecord] | str]: + try: + return (adapter.name, adapter.lookup(identifier)) + except Exception as exc: # noqa: BLE001 + LOGGER.warning("federated: adapter %s lookup failed: %s", adapter.name, exc) + return (adapter.name, f"{type(exc).__name__}: {exc}") + + with ThreadPoolExecutor(max_workers=max(len(adapters), 1)) as ex: + futures = [ex.submit(_run, a) for a in adapters] + for fut in as_completed(futures): + name, result = fut.result() + if isinstance(result, str): + errors[name] = result + continue + by_index[name] = len(result) + records.extend(result) + + return { + "identifier": identifier, + "records": [asdict(r) for r in records], + "by_index": by_index, + "errors": errors, + } diff --git a/src/index/_federated/protocols.py b/src/index/_federated/protocols.py new file mode 100644 index 0000000..7cc45f1 --- /dev/null +++ b/src/index/_federated/protocols.py @@ -0,0 +1,169 @@ +"""Discover + Hydrate protocols for the federated index layer. + +Sibling to `IndexAdapter` in `registry.py`, which covers +`search` / `lookup`. These protocols cover the inverse — building up +each index's local DuckDB by: + +1. **Discover** — produce candidate identifiers (`Seed`s) from some + source (web scrape, citation graph, search query, dump diff, + sibling-index extract). +2. **Hydrate** — for each `Seed`, fetch the canonical record and + upsert into the index's local store. + +See `.internal/federated/discover-hydrate-design.md` for the full +design rationale. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Iterable, Iterator, Protocol, runtime_checkable + + +@dataclass(frozen=True, slots=True) +class Seed: + """A candidate identifier produced by `IndexDiscoverer.discover()`. + + Cross-index by construction: the `seed_type` string routes the seed + to the hydrator(s) that accept it. Hints carry opaque per-source + metadata (e.g. an affiliation ROR to stamp at hydration time). + """ + + id: str + """Canonical id form. DOI URL, ORCID id, OpenAlex id (full URL or short), + GitHub repo URL, ROR id, etc. The hydrator is responsible for + normalising as needed.""" + + seed_type: str + """Discriminator. Examples: ``"doi"``, ``"orcid"``, ``"openalex_work"``, + ``"openalex_author"``, ``"zenodo_id"``, ``"github_repo"``, ``"ror"``. + Open string by design — adding a new seed type does not require a + protocol change. Each hydrator declares which strings it accepts via + ``accepted_seed_types``.""" + + source: str + """Where the seed was found. Examples: ``"datascience.ch"``, + ``"openalex_search"``, ``"infoscience_citations"``, ``"from-references"``, + ``"by-ror"``. Useful for provenance and dedup across sources.""" + + hint: dict[str, Any] | None = None + """Optional extra metadata for the hydrator. Free-form: e.g. + ``{"affiliation_ror": "https://ror.org/02hdt9m26"}`` to stamp an + institution link, or ``{"refs_only": True}`` to skip work upsert and + only populate references.""" + + def to_jsonl_dict(self) -> dict[str, Any]: + d = {"id": self.id, "seed_type": self.seed_type, "source": self.source} + if self.hint: + d["hint"] = self.hint + return d + + @classmethod + def from_jsonl_dict(cls, data: dict[str, Any]) -> Seed: + return cls( + id=data["id"], + seed_type=data["seed_type"], + source=data["source"], + hint=data.get("hint"), + ) + + +@dataclass +class HydrationSummary: + """Aggregate counts returned from a single ``hydrate()`` call. + + All counters are inclusive — ``fetched`` includes both ``in_scope`` + and ``out_of_scope`` (when the hydrator does post-filtering, like + ORCID does for Switzerland scope). ``skipped_existing`` counts seeds + short-circuited by ``only_unfetched``. + """ + + fetched: int = 0 + in_scope: int = 0 + out_of_scope: int = 0 + skipped_existing: int = 0 + errors: int = 0 + extras: dict[str, Any] = field(default_factory=dict) + """Hydrator-specific counters that don't fit the standard buckets + (e.g. ``stamped_affiliations`` for OpenAlex when ``hint.affiliation_ror`` + is set).""" + + def merge(self, other: HydrationSummary) -> HydrationSummary: + merged = HydrationSummary( + fetched=self.fetched + other.fetched, + in_scope=self.in_scope + other.in_scope, + out_of_scope=self.out_of_scope + other.out_of_scope, + skipped_existing=self.skipped_existing + other.skipped_existing, + errors=self.errors + other.errors, + ) + merged.extras = {**self.extras, **other.extras} + return merged + + +@runtime_checkable +class IndexDiscoverer(Protocol): + """Produce :class:`Seed`s from some named source. + + Discoverers are *not* required to be 1:1 with an index — a + discoverer registered under one index name can emit seeds whose + ``seed_type`` is consumed by a *different* index's hydrator. + Example: ``openalex.from_datascience_ch`` emits ``seed_type="doi"`` + seeds; both OpenAlex's hydrator (fetches the work) and (in future) + Zenodo's hydrator (resolves Zenodo DOIs to records) can consume + them. + + Implementations live under ``src/index//`` and self-register + in their ``_federated.py`` (or equivalent) at import time. + """ + + name: str + """Short discoverer name. Often the index module name (``"openalex"``) + but may include a discriminator (``"openalex.references"``).""" + + accepted_sources: tuple[str, ...] + """Whitelist of ``--source`` strings this discoverer recognises.""" + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + """Yield seeds for ``source``. + + Implementations must validate ``source in self.accepted_sources`` + and raise ``ValueError`` otherwise. Source-specific options are + passed via ``**opts`` (e.g. ``query="..."`` for search-based + discovery, ``url="..."`` for scrape-based). + """ + ... + + +@runtime_checkable +class IndexHydrator(Protocol): + """Persist canonical records for a stream of :class:`Seed`s. + + Hydrators are 1:1 with an index — each hydrator owns the writes to + one index's local store. ``hydrate()`` must be idempotent: passing + the same seed twice (or many times) must not produce duplicate + rows. + """ + + name: str + """Short hydrator name — by convention the index module name.""" + + accepted_seed_types: tuple[str, ...] + """Whitelist of ``seed_type`` strings this hydrator can resolve.""" + + def hydrate( + self, + seeds: Iterable[Seed], + *, + only_unfetched: bool = True, + ) -> HydrationSummary: + """Fetch and upsert each seed. + + With ``only_unfetched=True`` (default), seeds whose ``id`` is + already present in the local store are short-circuited and + counted in ``HydrationSummary.skipped_existing``. + + Seeds whose ``seed_type`` is not in + :attr:`accepted_seed_types` are silently ignored — the + federated dispatcher is responsible for routing. + """ + ... diff --git a/src/index/_federated/registry.py b/src/index/_federated/registry.py new file mode 100644 index 0000000..a5b3002 --- /dev/null +++ b/src/index/_federated/registry.py @@ -0,0 +1,102 @@ +"""Adapter ABC + global registry. + +Each index module registers an adapter at import time via `register(adapter)`. +The registry is the single source of truth for "which indices are available". +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Protocol, runtime_checkable + + +@dataclass +class Hit: + """A cross-index search hit. Index- and adapter-agnostic.""" + + index: str # short module name, e.g. 'huggingface' + entity_type: str # type within that index, e.g. 'model' / 'work' / 'person' + id: str # canonical id within the index + title: str | None # human-readable label for display + score: float # primary score (rerank if available, else vector) + summary: str | None = None # short text snippet (often the rerank doc) + url: str | None = None # canonical URL on the source site, when known + payload: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class EntityRecord: + """A canonical record returned by `adapter.lookup()`.""" + + index: str + entity_type: str + id: str + data: dict[str, Any] = field(default_factory=dict) + url: str | None = None + + +@runtime_checkable +class IndexAdapter(Protocol): + """Minimal contract every index adapter must satisfy. + + Adapters live under `src/index/_federated/adapters/.py` and are + expected to be cheap to import. Heavy imports (RCP clients, qdrant, + duckdb) should happen inside the methods, not at module top-level. + """ + + name: str + entity_types: list[str] + + def search( + self, + *, + query: str, + entity_type: str | None, + top_k: int, + filters: dict[str, Any] | None, + ) -> list[Hit]: + """Return up to `top_k` hits. `entity_type=None` → adapter chooses default(s).""" + ... + + def lookup(self, identifier: str) -> list[EntityRecord]: + """Resolve `identifier` (slug / orcid / doi / ror / uuid / URL) to records. + + Adapter inspects the string and returns 0+ matches. Empty list means + "this identifier doesn't apply to my index". + """ + ... + + +REGISTRY: dict[str, IndexAdapter] = {} + + +def register(adapter: IndexAdapter) -> None: + """Register an adapter. Called at import time from each `adapters/.py`.""" + if not isinstance(adapter, IndexAdapter): + message = f"{adapter!r} does not satisfy IndexAdapter" + raise TypeError(message) + REGISTRY[adapter.name] = adapter + + +def load_adapters(only: list[str] | None = None) -> list[IndexAdapter]: + """Import the adapter modules so they self-register, then return the active set.""" + # Imports are inside the function so `gme indices` doesn't pay the cost + # for indices the user isn't asking about. + from importlib import import_module + + candidates = [ + "huggingface", "openalex", "infoscience", "orcid", "ror", "zenodo", + "ethz_research_collection", "github", "snsf", "renkulab", "epfl_graph", + "swissubase", + ] + targets = [c for c in candidates if (only is None or c in only)] + for name in targets: + try: + import_module(f"src.index._federated.adapters.{name}") + except Exception as exc: # noqa: BLE001 — adapter import failure shouldn't kill the whole CLI + import logging + logging.getLogger(__name__).warning( + "federated: adapter %r failed to import (%s); skipping", + name, exc, + ) + return [REGISTRY[n] for n in targets if n in REGISTRY] diff --git a/src/index/_federated/search.py b/src/index/_federated/search.py new file mode 100644 index 0000000..8a00fd6 --- /dev/null +++ b/src/index/_federated/search.py @@ -0,0 +1,161 @@ +"""Federated search orchestrator: fan out to adapters in parallel, merge by score.""" + +from __future__ import annotations + +import asyncio +import logging +from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import Any + +from src.index._federated.registry import REGISTRY, Hit, IndexAdapter, load_adapters + +LOGGER = logging.getLogger(__name__) + + +def federated_search( + query: str, + *, + indices: list[str] | None = None, + entity_type: str | None = None, + top_k_per_index: int = 5, + top_k_overall: int = 20, + filters: dict[str, Any] | None = None, + rerank: bool = False, +) -> dict[str, Any]: + """Run `query` against every registered (or named) index in parallel. + + Returns `{hits: [Hit-dicts...], by_index: {index: count}, errors: {...}}`. + Hits are sorted by score descending; per-index slot allows top_k_per_index + contributions; overall trimmed to top_k_overall. + + When `rerank=True` the merged candidate pool is sent through the RCP + cross-encoder once for a globally-fair ordering — the per-adapter scores + aren't directly comparable (each adapter ranks within its own pool). + """ + adapters = load_adapters(only=indices) if indices is not None else load_adapters() + if not adapters: + return {"hits": [], "by_index": {}, "errors": {"_": "no adapters loaded"}} + + hits: list[Hit] = [] + errors: dict[str, str] = {} + by_index: dict[str, int] = {} + + def _run(adapter: IndexAdapter) -> tuple[str, list[Hit] | str]: + try: + return (adapter.name, adapter.search( + query=query, + entity_type=entity_type, + top_k=top_k_per_index, + filters=filters, + )) + except Exception as exc: # noqa: BLE001 + LOGGER.warning("federated: adapter %s search failed: %s", adapter.name, exc) + return (adapter.name, f"{type(exc).__name__}: {exc}") + + with ThreadPoolExecutor(max_workers=max(len(adapters), 1)) as ex: + futures = [ex.submit(_run, a) for a in adapters] + for fut in as_completed(futures): + name, result = fut.result() + if isinstance(result, str): + errors[name] = result + continue + by_index[name] = len(result) + hits.extend(result) + + if rerank and hits: + hits, rerank_err = _cross_index_rerank(query, hits) + if rerank_err: + errors["_rerank"] = rerank_err + + hits.sort(key=lambda h: -h.score) + trimmed = hits[:top_k_overall] + return { + "hits": [_hit_to_dict(h) for h in trimmed], + "by_index": by_index, + "errors": errors, + "registered_indices": sorted(REGISTRY.keys()), + "reranked": bool(rerank and not errors.get("_rerank")), + } + + +def _cross_index_rerank(query: str, hits: list[Hit]) -> tuple[list[Hit], str | None]: + """Send the merged candidate pool through the RCP cross-encoder. + + Each `Hit.score` is replaced with the cross-encoder relevance score so + the subsequent sort produces a globally-fair ordering across indices. + + The reranker config is borrowed from whichever per-index module is + available — they all use the same RCP endpoint and model. HF first. + """ + documents = [_doc_for_rerank(h) for h in hits] + try: + rerank_client = _build_reranker() + except Exception as exc: # noqa: BLE001 + return hits, f"reranker config: {type(exc).__name__}: {exc}" + if rerank_client is None: + return hits, "no per-index module available to source RCP config" + try: + scored = asyncio.run(rerank_client.rerank(query, documents, top_n=len(documents))) + except Exception as exc: # noqa: BLE001 + return hits, f"rerank call: {type(exc).__name__}: {exc}" + if not scored: + return hits, "rerank returned empty" + # `scored` is `[{"index": int, "relevance_score": float}, ...]` ordered by score. + out: list[Hit] = [] + for s in scored: + idx = s.get("index") + if not isinstance(idx, int) or not (0 <= idx < len(hits)): + continue + out.append(_clone_hit(hits[idx], score=float(s.get("relevance_score") or 0.0))) + return out, None + + +def _doc_for_rerank(h: Hit) -> str: + """Build the rerank document string for one Hit.""" + parts = [h.title or "", h.summary or "", f"[{h.index}/{h.entity_type}]", h.id or ""] + parts = [p for p in parts if p] + return " — ".join(parts) if parts else "(empty)" + + +def _clone_hit(h: Hit, *, score: float) -> Hit: + return Hit( + index=h.index, entity_type=h.entity_type, id=h.id, + title=h.title, score=score, summary=h.summary, url=h.url, + payload=h.payload, + ) + + +def _build_reranker() -> Any | None: # noqa: ANN401 — duck-typed RCP reranker + """Try each known per-index module's reranker until one loads.""" + candidates = ( + ("src.index.huggingface.config", "load_config", + "src.index.huggingface.rerank.rcp_client", "RCPRerankerClient"), + ("src.index.openalex.config", "load_config", + "src.index.openalex.rerank.rcp_client", "RCPRerankerClient"), + ("src.index.zenodo.config", "load_config", + "src.index.zenodo.rerank.rcp_client", "RCPRerankerClient"), + ("src.index.orcid.config", "load_config", + "src.index.orcid.rerank.rcp_client", "RCPRerankerClient"), + ) + from importlib import import_module + for cfg_mod, cfg_attr, rerank_mod, rerank_class in candidates: + try: + cfg = getattr(import_module(cfg_mod), cfg_attr)() + klass = getattr(import_module(rerank_mod), rerank_class) + return klass(cfg) + except Exception: # noqa: BLE001 + continue + return None + + +def _hit_to_dict(h: Hit) -> dict[str, Any]: + return { + "index": h.index, + "entity_type": h.entity_type, + "id": h.id, + "title": h.title, + "score": h.score, + "summary": h.summary, + "url": h.url, + "payload": h.payload, + } diff --git a/src/index/epfl_graph/__init__.py b/src/index/epfl_graph/__init__.py new file mode 100644 index 0000000..a6f40fe --- /dev/null +++ b/src/index/epfl_graph/__init__.py @@ -0,0 +1,7 @@ +"""EPFL Graph disciplines RAG index. + +Pulls the full ontology tree (~2226 categories) from graphai.epfl.ch into +DuckDB, embeds each node via RCP, and pushes vectors into a single Qdrant +collection so callers can query "which EPFL Graph discipline is closest +to this text?". +""" diff --git a/src/index/epfl_graph/__main__.py b/src/index/epfl_graph/__main__.py new file mode 100644 index 0000000..7fb2796 --- /dev/null +++ b/src/index/epfl_graph/__main__.py @@ -0,0 +1,6 @@ +"""``python -m src.index.epfl_graph`` entrypoint.""" + +from src.index.epfl_graph.cli import main + +if __name__ == "__main__": + main() diff --git a/src/index/epfl_graph/cli.py b/src/index/epfl_graph/cli.py new file mode 100644 index 0000000..2e91de4 --- /dev/null +++ b/src/index/epfl_graph/cli.py @@ -0,0 +1,163 @@ +"""CLI for the EPFL Graph disciplines index. + +Subcommands: + +- ``ingest`` — walk graphai's ontology tree and persist categories to DuckDB. +- ``embed`` — push embeddings of every category into Qdrant. +- ``search`` — semantic retrieval (vector + optional RCP rerank). +- ``status`` — row counts + Qdrant collection info + paths. +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from typing import Any + +from src.index.epfl_graph.config import load_config +from src.index.epfl_graph.embed.pipeline import ( + EPFL_GRAPH_COLLECTION, + embed_disciplines, +) +from src.index.epfl_graph.ingest.download import ingest_tree +from src.index.epfl_graph.ingest.wikipedia_extracts import ( + fetch_wikipedia_extracts, + rebuild_embedding_texts, +) +from src.index.epfl_graph.retrieval.semantic import semantic_search +from src.index.epfl_graph.storage.duckdb_store import EpflGraphStore + +LOGGER = logging.getLogger(__name__) + + +def _emit_json(obj: Any) -> None: + json.dump(obj, sys.stdout, indent=2, ensure_ascii=False, default=str) + sys.stdout.write("\n") + + +def _cmd_ingest(args: argparse.Namespace) -> int: + config = load_config() + ingested = ingest_tree(config, limit=args.limit) + _emit_json({"ingested": ingested, "duckdb_path": str(config.paths.duckdb_path)}) + return 0 + + +def _cmd_embed(args: argparse.Namespace) -> int: + config = load_config() + config.require_rcp() + store = EpflGraphStore.open(config.paths.duckdb_path) + try: + embedded = embed_disciplines(config, store, limit=args.limit) + finally: + store.close() + _emit_json( + { + "embedded": embedded, + "collection": EPFL_GRAPH_COLLECTION, + "qdrant_url": config.qdrant.url, + }, + ) + return 0 + + +def _cmd_enrich_wikipedia(args: argparse.Namespace) -> int: + """Fetch missing Wikipedia extracts and rebuild embedding_text.""" + config = load_config() + store = EpflGraphStore.open(config.paths.duckdb_path) + try: + fetched = fetch_wikipedia_extracts(config, store, limit=args.limit) + rebuilt = rebuild_embedding_texts(config, store) + finally: + store.close() + _emit_json( + { + "wikipedia_extracts_fetched": fetched, + "embedding_texts_rebuilt": rebuilt, + "duckdb_path": str(config.paths.duckdb_path), + }, + ) + return 0 + + +def _cmd_search(args: argparse.Namespace) -> int: + config = load_config() + config.require_rcp() + hits = semantic_search( + config=config, + query=args.query, + top_k=args.top_k, + candidate_k=args.candidate_k, + min_depth=args.min_depth, + rerank=not args.no_rerank, + ) + _emit_json(hits) + return 0 + + +def _cmd_status(_: argparse.Namespace) -> int: + config = load_config() + store = EpflGraphStore.open(config.paths.duckdb_path) + try: + summary = { + "duckdb_path": str(config.paths.duckdb_path), + "categories": store.count_categories(), + "category_concepts": store.count_concepts(), + "qdrant_url": config.qdrant.url, + "qdrant_collection": EPFL_GRAPH_COLLECTION, + "min_depth_for_embedding": config.filter.min_depth, + } + finally: + store.close() + _emit_json(summary) + return 0 + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="python -m src.index.epfl_graph", + description="EPFL Graph disciplines RAG indexer", + ) + sub = parser.add_subparsers(dest="cmd", required=True) + + p_ingest = sub.add_parser("ingest", help="Walk the ontology tree into DuckDB") + p_ingest.add_argument("--limit", type=int, default=None) + p_ingest.set_defaults(func=_cmd_ingest) + + p_embed = sub.add_parser("embed", help="Embed disciplines into Qdrant") + p_embed.add_argument("--limit", type=int, default=None) + p_embed.set_defaults(func=_cmd_embed) + + p_enrich = sub.add_parser( + "enrich-wikipedia", + help="Fill missing Wikipedia extracts and rebuild embedding_text", + ) + p_enrich.add_argument("--limit", type=int, default=None) + p_enrich.set_defaults(func=_cmd_enrich_wikipedia) + + p_search = sub.add_parser("search", help="Semantic search over disciplines") + p_search.add_argument("query") + p_search.add_argument("--top-k", type=int, default=10) + p_search.add_argument("--candidate-k", type=int, default=50) + p_search.add_argument("--min-depth", type=int, default=None) + p_search.add_argument("--no-rerank", action="store_true") + p_search.set_defaults(func=_cmd_search) + + p_status = sub.add_parser("status", help="Print index status") + p_status.set_defaults(func=_cmd_status) + return parser + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s: %(message)s", + ) + parser = build_parser() + args = parser.parse_args(argv) + return int(args.func(args) or 0) + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/index/epfl_graph/config.py b/src/index/epfl_graph/config.py new file mode 100644 index 0000000..b0a50e1 --- /dev/null +++ b/src/index/epfl_graph/config.py @@ -0,0 +1,110 @@ +"""Config loader for the EPFL Graph disciplines indexer. + +Reads ``config/index/epfl_graph.yaml`` and merges in env-sourced +credentials (``RCP_TOKEN``, ``INDEX_QDRANT_API_KEY``, +``INDEX_QDRANT_URL``). + +The ``rcp`` and ``qdrant`` sub-blocks mirror :class:`OpenAlexIndexConfig` +field-for-field so that openalex's RCPEmbeddingClient / RCPRerankerClient +/ QdrantStore can be reused with an :class:`EpflGraphIndexConfig` +instance at runtime. +""" + +from __future__ import annotations + +import os +from pathlib import Path + +import yaml +from pydantic import BaseModel + +from src.index.epfl_graph.paths import EpflGraphPaths, get_epfl_graph_paths + +DEFAULT_CONFIG_PATH = Path("config/index/epfl_graph.yaml") + +TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} +FALSE_ENV_VALUES = {"0", "false", "f", "no", "n", "off"} + +MISSING_RCP_TOKEN_ERROR = "Missing required environment variable: RCP_TOKEN" # noqa: S105 + + +class RcpConfig(BaseModel): + base_url: str + embedding_model: str + embedding_dim: int + query_instruction: str + reranker_model: str + batch_size: int = 32 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: str | None = None + + +class GraphAIConfig(BaseModel): + base_url: str = "https://graphai.epfl.ch" + port: int = 443 + rate_per_second: int = 5 + max_concurrency: int = 4 + anchor_concepts_per_category: int = 12 + + +class QdrantConfig(BaseModel): + url: str = "http://localhost:6333" + prefer_grpc: bool = False + api_key: str | None = None + + +class FilterConfig(BaseModel): + min_depth: int = 3 + + +class EpflGraphIndexConfig(BaseModel): + rcp: RcpConfig + graphai: GraphAIConfig + qdrant: QdrantConfig + filter: FilterConfig + paths: EpflGraphPaths + + model_config = {"arbitrary_types_allowed": True} + + def require_rcp(self) -> None: + if not self.rcp.token: + raise ValueError(MISSING_RCP_TOKEN_ERROR) + + +def _env_bool(name: str) -> bool | None: + raw = os.getenv(name) + if raw is None or raw.strip() == "": + return None + normalized = raw.strip().lower() + if normalized in TRUE_ENV_VALUES: + return True + if normalized in FALSE_ENV_VALUES: + return False + message = f"Invalid boolean value for {name}: {raw!r}" + raise ValueError(message) + + +def _env_str(name: str) -> str | None: + raw = os.getenv(name) + if raw is None: + return None + stripped = raw.strip() + return stripped or None + + +def load_config(path: Path | None = None) -> EpflGraphIndexConfig: + cfg_path = path or DEFAULT_CONFIG_PATH + raw = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) or {} + + raw.setdefault("rcp", {})["token"] = _env_str("RCP_TOKEN") + raw.setdefault("qdrant", {})["api_key"] = _env_str("INDEX_QDRANT_API_KEY") + + if (override := _env_str("INDEX_QDRANT_URL")) is not None: + raw["qdrant"]["url"] = override + if (override_b := _env_bool("INDEX_QDRANT_PREFER_GRPC")) is not None: + raw["qdrant"]["prefer_grpc"] = override_b + + raw["paths"] = get_epfl_graph_paths() + + return EpflGraphIndexConfig(**raw) diff --git a/src/index/epfl_graph/embed/__init__.py b/src/index/epfl_graph/embed/__init__.py new file mode 100644 index 0000000..996eb5e --- /dev/null +++ b/src/index/epfl_graph/embed/__init__.py @@ -0,0 +1 @@ +"""Embedding pipeline for the EPFL Graph disciplines index.""" diff --git a/src/index/epfl_graph/embed/pipeline.py b/src/index/epfl_graph/embed/pipeline.py new file mode 100644 index 0000000..8e382ed --- /dev/null +++ b/src/index/epfl_graph/embed/pipeline.py @@ -0,0 +1,114 @@ +"""Embed disciplines into a single Qdrant collection. + +Each category becomes one point. Embedding text is the concatenation of +``name + canonical Wikipedia title + top anchor concepts`` (built during +ingest). No chunking — disciplines are short by construction (~50-200 +tokens), so the single-point shape is both simpler and easier to query. + +Reuses ``RCPEmbeddingClient`` and ``QdrantStore`` from the openalex +index — both clients are duck-typed against ``config.rcp.*`` / +``config.qdrant.*``, which :class:`EpflGraphIndexConfig` mirrors. +""" + +from __future__ import annotations + +import asyncio +import logging +import uuid +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.vector.qdrant_store import QdrantStore + +if TYPE_CHECKING: + from src.index.epfl_graph.config import EpflGraphIndexConfig + from src.index.epfl_graph.storage.duckdb_store import EpflGraphStore + +LOGGER = logging.getLogger(__name__) + +EPFL_GRAPH_COLLECTION = "epfl_graph_disciplines" +_POINT_NAMESPACE = uuid.NAMESPACE_URL + + +def _point_id(category_id: str) -> str: + return str(uuid.uuid5(_POINT_NAMESPACE, f"epfl_graph|{category_id}")) + + +def _row_to_payload(row: dict[str, Any]) -> dict[str, Any]: + return { + "entity_type": "disciplines", + "entity_id": row["category_id"], + "category_id": row["category_id"], + "name": row.get("name"), + "depth": row.get("depth"), + "parent_id": row.get("parent_id"), + "wikipedia_page_id": row.get("wikipedia_page_id"), + "wikipedia_url": row.get("wikipedia_url"), + "graphsearch_url": row.get("graphsearch_url"), + "n_concepts": row.get("n_concepts"), + "n_children": row.get("n_children"), + "embedding_text": row.get("embedding_text"), + } + + +async def _embed_async( + *, + config: EpflGraphIndexConfig, + store: EpflGraphStore, + limit: int | None, +) -> int: + config.require_rcp() + client = RCPEmbeddingClient(config) # type: ignore[arg-type] + qdrant = QdrantStore(config) # type: ignore[arg-type] + qdrant.ensure_collection(EPFL_GRAPH_COLLECTION) + + batch: list[dict[str, Any]] = [] + batch_size = max(1, int(config.rcp.batch_size)) + total = 0 + + async def flush() -> None: + nonlocal total + if not batch: + return + texts = [r["embedding_text"] for r in batch] + vectors = await client.embed_all(texts) + ids = [_point_id(r["category_id"]) for r in batch] + payloads = [_row_to_payload(r) for r in batch] + qdrant.upsert_points( + EPFL_GRAPH_COLLECTION, + ids=ids, + vectors=vectors, + payloads=payloads, + ) + total += len(batch) + LOGGER.info( + "epfl_graph: embedded %d / running total %d", len(batch), total, + ) + batch.clear() + + seen = 0 + for row in store.iter_categories_for_embedding(min_depth=config.filter.min_depth): + if not row.get("embedding_text"): + continue + batch.append(row) + seen += 1 + if len(batch) >= batch_size: + await flush() + if limit is not None and seen >= limit: + break + await flush() + LOGGER.info( + "epfl_graph: embed complete — %d disciplines pushed to %s", + total, + EPFL_GRAPH_COLLECTION, + ) + return total + + +def embed_disciplines( + config: EpflGraphIndexConfig, + store: EpflGraphStore, + *, + limit: int | None = None, +) -> int: + return asyncio.run(_embed_async(config=config, store=store, limit=limit)) diff --git a/src/index/epfl_graph/ingest/__init__.py b/src/index/epfl_graph/ingest/__init__.py new file mode 100644 index 0000000..ae1ece6 --- /dev/null +++ b/src/index/epfl_graph/ingest/__init__.py @@ -0,0 +1 @@ +"""EPFL Graph ontology ingest.""" diff --git a/src/index/epfl_graph/ingest/download.py b/src/index/epfl_graph/ingest/download.py new file mode 100644 index 0000000..6cd66e9 --- /dev/null +++ b/src/index/epfl_graph/ingest/download.py @@ -0,0 +1,213 @@ +"""Walk the EPFL Graph ontology tree and persist categories to DuckDB. + +For each node returned by ``ontology_tree()`` we call +``category_info(category_id)`` to pull the canonical name (from the +backing Wikipedia article), depth, parent, child categories, and the +top-N anchor concepts that define that category. The full payload is +stored in the ``raw`` JSON column so downstream consumers can reach into +it without a re-fetch. + +A flat token-bucket rate limit is applied (defaults to ~5 req/s) so the +~2226-node walk finishes in roughly 8 minutes without abusing the +graphai endpoint. +""" + +from __future__ import annotations + +import logging +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import TYPE_CHECKING, Any + +from src.index.epfl_graph.storage.duckdb_store import EpflGraphStore +from src.module.epfl_graph.ontology import ( + GRAPHSEARCH_CATEGORY_URL, + category_info, + ontology_tree, +) + +if TYPE_CHECKING: + from src.index.epfl_graph.config import EpflGraphIndexConfig + +LOGGER = logging.getLogger(__name__) + + +def _build_embedding_text( + *, + name: str | None, + info_block: dict[str, Any], + concepts: list[dict[str, Any]], + max_concepts: int, +) -> str: + pieces: list[str] = [] + if name: + pieces.append(name) + info_name = info_block.get("name") if isinstance(info_block, dict) else None + if isinstance(info_name, str) and info_name and info_name != name: + pieces.append(info_name) + concept_names = [] + for concept in concepts[:max_concepts]: + if not isinstance(concept, dict): + continue + cname = concept.get("name") or concept.get("concept_name") + if isinstance(cname, str) and cname.strip(): + concept_names.append(cname.strip()) + if concept_names: + pieces.append("Anchor concepts: " + ", ".join(concept_names)) + return ". ".join(pieces).strip() + + +def _wikipedia_url_for(info_block: dict[str, Any], name: str | None) -> str | None: + page_id = info_block.get("id") if isinstance(info_block, dict) else None + if isinstance(page_id, (int, str)) and str(page_id).strip(): + return "https://en.wikipedia.org/?curid=" + str(page_id).strip() + if name: + return "https://en.wikipedia.org/wiki/" + name.replace(" ", "_") + return None + + +def _ingest_one( + category_id: str, + *, + parent_id: str | None, + max_concepts: int, +) -> tuple[dict[str, Any], dict[str, Any], list[dict[str, Any]]] | None: + payload = category_info(category_id, use_cache=False) + if not isinstance(payload, dict) or not payload: + return None + info_block = payload.get("info") if isinstance(payload, dict) else {} + info = info_block if isinstance(info_block, dict) else {} + name = info.get("name") if isinstance(info.get("name"), str) else None + depth = info.get("depth") if isinstance(info.get("depth"), int) else None + page_id_raw = info.get("id") + page_id = ( + str(page_id_raw).strip() + if isinstance(page_id_raw, (int, str)) and str(page_id_raw).strip() + else None + ) + raw_concepts = payload.get("concepts") if isinstance(payload, dict) else None + if not isinstance(raw_concepts, list): + raw_concepts = [] + concepts = [c for c in raw_concepts if isinstance(c, dict)] + children = ( + payload.get("child_categories") + if isinstance(payload, dict) + else None + ) + n_children = len(children) if isinstance(children, list) else 0 + parent_in_payload = payload.get("parent_category") + resolved_parent = ( + parent_in_payload + if isinstance(parent_in_payload, str) and parent_in_payload + else parent_id + ) + embedding_text = _build_embedding_text( + name=name, info_block=info, concepts=concepts, max_concepts=max_concepts, + ) + row = { + "category_id": category_id, + "name": name, + "depth": depth, + "parent_id": resolved_parent, + "wikipedia_page_id": page_id, + "wikipedia_url": _wikipedia_url_for(info, name), + "graphsearch_url": GRAPHSEARCH_CATEGORY_URL + category_id, + "n_concepts": len(concepts), + "n_children": n_children, + "embedding_text": embedding_text or None, + } + return row, payload, concepts + + +def ingest_tree( # noqa: C901, PLR0915 + config: EpflGraphIndexConfig, + *, + limit: int | None = None, + log_every: int = 100, +) -> int: + """Walk the ontology tree and upsert every node into the local DuckDB. + + Returns the number of categories successfully ingested. + """ + config.require_rcp() # not used here, but keeps env-error contract aligned + LOGGER.info("epfl_graph: fetching ontology tree...") + tree = ontology_tree() + edges = tree.get("child_to_parent") if isinstance(tree, dict) else None + if not isinstance(edges, list): + msg = "ontology_tree() returned no child_to_parent edges" + raise RuntimeError(msg) # noqa: TRY004 + + parent_map: dict[str, str] = {} + nodes: set[str] = set() + for edge in edges: + if not isinstance(edge, dict): + continue + child = edge.get("child_id") + parent = edge.get("parent_id") + if isinstance(child, str) and child: + nodes.add(child) + if isinstance(parent, str) and parent: + parent_map[child] = parent + nodes.add(parent) + + targets = sorted(nodes) + if limit is not None: + targets = targets[:limit] + LOGGER.info("epfl_graph: %d categories to ingest", len(targets)) + + store = EpflGraphStore.open(config.paths.duckdb_path) + interval = 1.0 / max(1, config.graphai.rate_per_second) + workers = max(1, config.graphai.max_concurrency) + max_concepts = config.graphai.anchor_concepts_per_category + last_call = 0.0 + ingested = 0 + + def _paced_fetch(category_id: str) -> Any: + nonlocal last_call + now = time.monotonic() + wait = max(0.0, last_call + interval - now) + if wait: + time.sleep(wait) + last_call = time.monotonic() + return _ingest_one( + category_id, + parent_id=parent_map.get(category_id), + max_concepts=max_concepts, + ) + + try: + with ThreadPoolExecutor(max_workers=workers) as executor: + futures = { + executor.submit(_paced_fetch, cid): cid for cid in targets + } + for index, future in enumerate(as_completed(futures), start=1): + cid = futures[future] + try: + result = future.result() + except Exception as exc: # noqa: BLE001 + LOGGER.warning( + "epfl_graph: failed to fetch %s: %s", cid, exc, + ) + continue + if result is None: + continue + row, payload, concepts = result + try: + store.upsert_category(row, payload, concepts) + ingested += 1 + except Exception as exc: # noqa: BLE001 + LOGGER.warning( + "epfl_graph: failed to upsert %s: %s", cid, exc, + ) + continue + if index % log_every == 0: + LOGGER.info( + "epfl_graph: ingested %d / %d", index, len(targets), + ) + finally: + store.close() + + LOGGER.info( + "epfl_graph: ingest complete — %d categories upserted", ingested, + ) + return ingested diff --git a/src/index/epfl_graph/ingest/wikipedia_extracts.py b/src/index/epfl_graph/ingest/wikipedia_extracts.py new file mode 100644 index 0000000..4bb4e34 --- /dev/null +++ b/src/index/epfl_graph/ingest/wikipedia_extracts.py @@ -0,0 +1,230 @@ +"""Fetch canonical Wikipedia lead-section extracts for ontology categories. + +Each EPFL Graph category's ``info.id`` is a real Wikipedia page ID. We +hit MediaWiki's TextExtracts extension to pull the lead-section plain +text and persist it on the ``categories`` row. The fold-in into the +embedding text happens here too: we rebuild ``embedding_text`` from +``name + wikipedia_extract + anchor concept names`` so a follow-up +``embed`` pass picks up the richer signal. + +Batching: the API accepts up to 50 ``pageids`` per request and is +generous with rate limits when given a real ``User-Agent``. +""" + +from __future__ import annotations + +import logging +import time +from typing import TYPE_CHECKING + +import requests + +if TYPE_CHECKING: + from src.index.epfl_graph.config import EpflGraphIndexConfig + from src.index.epfl_graph.storage.duckdb_store import EpflGraphStore + +LOGGER = logging.getLogger(__name__) + +WIKIPEDIA_API_URL = "https://en.wikipedia.org/w/api.php" +USER_AGENT = ( + "git-metadata-extractor/2.0 (+https://github.com/Imaging-Plaza/" + "git-metadata-extractor) (concept_tagging discipline ingest)" +) +MAX_PAGE_IDS_PER_REQUEST = 50 +RATE_PER_SECOND = 5 +DEFAULT_TIMEOUT = 30 +MAX_EXTRACT_CHARS_FOR_EMBED = 1200 # cap each extract for embedding text + + +def _fetch_extracts_batch_by_title( # noqa: C901 + titles: list[str], *, session: requests.Session, timeout: float, +) -> dict[str, str]: + """Title-keyed extract fetch. Returns ``{original_title: extract}``. + + MediaWiki normalizes/redirects titles transparently with ``redirects=1``; + we map results back to the *input* title using the ``normalized`` and + ``redirects`` from→to arrays. + """ + if not titles: + return {} + response = session.get( + WIKIPEDIA_API_URL, + params={ + "action": "query", + "format": "json", + "prop": "extracts", + "exintro": "true", + "explaintext": "true", + "exlimit": "max", + "redirects": "1", + "titles": "|".join(titles), + }, + timeout=timeout, + ) + response.raise_for_status() + payload = response.json() + query = payload.get("query") if isinstance(payload, dict) else None + if not isinstance(query, dict): + return {} + # Resolved title → input title (chain: input → normalized → redirected). + resolved_to_input: dict[str, str] = {t: t for t in titles} + for entry in query.get("normalized", []) or []: + if not isinstance(entry, dict): + continue + from_title = entry.get("from") + to_title = entry.get("to") + if isinstance(from_title, str) and isinstance(to_title, str): + input_title = resolved_to_input.get(from_title, from_title) + resolved_to_input[to_title] = input_title + for entry in query.get("redirects", []) or []: + if not isinstance(entry, dict): + continue + from_title = entry.get("from") + to_title = entry.get("to") + if isinstance(from_title, str) and isinstance(to_title, str): + input_title = resolved_to_input.get(from_title, from_title) + resolved_to_input[to_title] = input_title + + pages = query.get("pages") if isinstance(query.get("pages"), dict) else {} + out: dict[str, str] = {} + for page in pages.values(): + if not isinstance(page, dict): + continue + extract = page.get("extract") + title = page.get("title") + if not isinstance(extract, str) or not extract.strip(): + continue + if not isinstance(title, str): + continue + input_title = resolved_to_input.get(title, title) + out[input_title] = extract.strip() + return out + + +def fetch_wikipedia_extracts( # noqa: C901 + config: EpflGraphIndexConfig, # noqa: ARG001 — keep symmetric with rebuild_* + store: EpflGraphStore, + *, + limit: int | None = None, + log_every: int = 200, +) -> int: + """Fill in missing ``wikipedia_extract`` columns. Returns rows updated.""" + pending = list(store.iter_categories_missing_extract()) + if limit is not None: + pending = pending[:limit] + if not pending: + LOGGER.info("epfl_graph: no categories missing wikipedia_extract") + return 0 + LOGGER.info( + "epfl_graph: fetching wikipedia extracts for %d categories", len(pending), + ) + + session = requests.Session() + session.headers["User-Agent"] = USER_AGENT + interval = 1.0 / max(1, RATE_PER_SECOND) + last_call = 0.0 + updated = 0 + + # Use the canonical Wikipedia title (stored in `categories.name`) instead + # of pageids — `name` matches the target article exactly when no + # rename has happened, and `redirects=1` handles the rest. Keying on + # title also makes the redirect/normalization mapping straightforward. + title_to_category: dict[str, str] = {} + for row in pending: + name = row.get("name") + category_id = row.get("category_id") + if not isinstance(name, str) or not name.strip() or not category_id: + continue + title_to_category.setdefault(name.strip(), str(category_id)) + + titles = list(title_to_category.keys()) + for index in range(0, len(titles), MAX_PAGE_IDS_PER_REQUEST): + batch = titles[index : index + MAX_PAGE_IDS_PER_REQUEST] + now = time.monotonic() + wait = max(0.0, last_call + interval - now) + if wait: + time.sleep(wait) + last_call = time.monotonic() + + try: + extracts = _fetch_extracts_batch_by_title( + batch, session=session, timeout=DEFAULT_TIMEOUT, + ) + except Exception as exc: # noqa: BLE001 + LOGGER.warning( + "epfl_graph: wikipedia batch failed at offset %d: %s", index, exc, + ) + continue + + for input_title, extract in extracts.items(): + category_id = title_to_category.get(input_title) + if not category_id: + continue + try: + store.update_wikipedia_extract(category_id, extract) + updated += 1 + except Exception as exc: # noqa: BLE001 + LOGGER.warning( + "epfl_graph: failed to upsert extract for %s: %s", + category_id, exc, + ) + if updated and updated % log_every == 0: + LOGGER.info( + "epfl_graph: updated %d / %d wikipedia extracts", + updated, len(titles), + ) + + LOGGER.info( + "epfl_graph: wikipedia extract fetch complete — %d updated", updated, + ) + return updated + + +def _truncate(text: str, max_chars: int) -> str: + if len(text) <= max_chars: + return text + cutoff = text.rfind(" ", 0, max_chars) + return text[: cutoff if cutoff > max_chars * 0.6 else max_chars].rstrip() + "…" + + +def build_embedding_text( + *, + name: str | None, + wikipedia_extract: str | None, + anchor_concept_names: list[str], + max_extract_chars: int = MAX_EXTRACT_CHARS_FOR_EMBED, +) -> str: + pieces: list[str] = [] + if name: + pieces.append(name) + if isinstance(wikipedia_extract, str) and wikipedia_extract.strip(): + pieces.append(_truncate(wikipedia_extract.strip(), max_extract_chars)) + if anchor_concept_names: + pieces.append( + "Anchor concepts: " + ", ".join(anchor_concept_names), + ) + return ". ".join(pieces).strip() + + +def rebuild_embedding_texts( + config: EpflGraphIndexConfig, store: EpflGraphStore, +) -> int: + """Recompute ``embedding_text`` for every category from current data. + + Run this after :func:`fetch_wikipedia_extracts` so the new extracts + feed into the next ``embed`` pass. + """ + anchor_count = config.graphai.anchor_concepts_per_category + rebuilt = 0 + for row in store.iter_categories_for_extract_refresh(): + category_id = row["category_id"] + anchors = store.fetch_anchor_concept_names(category_id, anchor_count) + text = build_embedding_text( + name=row.get("name"), + wikipedia_extract=row.get("wikipedia_extract"), + anchor_concept_names=anchors, + ) + store.update_embedding_text(category_id, text or None) + rebuilt += 1 + LOGGER.info("epfl_graph: rebuilt embedding_text for %d categories", rebuilt) + return rebuilt diff --git a/src/index/epfl_graph/models.py b/src/index/epfl_graph/models.py new file mode 100644 index 0000000..dc3eff5 --- /dev/null +++ b/src/index/epfl_graph/models.py @@ -0,0 +1,36 @@ +"""Pydantic schemas for the structured columns persisted into DuckDB. + +Each row in `categories` carries the canonical ontology metadata, plus a +materialized embedding text built from the category name + Wikipedia +title + a handful of anchor concept names. The full payload returned by +graphai's `/ontology/tree/category/{id}` is stored in the `raw` column +so downstream consumers don't have to re-fetch. +""" + +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict + + +class CategoryRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + category_id: str + name: str | None = None + depth: int | None = None + parent_id: str | None = None + wikipedia_page_id: str | None = None + wikipedia_url: str | None = None + graphsearch_url: str | None = None + n_concepts: int = 0 + n_children: int = 0 + embedding_text: str | None = None + + +class CategoryConceptRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + category_id: str + concept_id: str + concept_name: str | None = None + rank: int = 0 diff --git a/src/index/epfl_graph/paths.py b/src/index/epfl_graph/paths.py new file mode 100644 index 0000000..8f411ca --- /dev/null +++ b/src/index/epfl_graph/paths.py @@ -0,0 +1,58 @@ +"""Filesystem layout for the EPFL Graph disciplines index. + +Mirrors `src/index/zenodo/paths.py`. Single source of truth for paths +under `/epfl_graph/`. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path + +DEFAULT_INDEX_DATA_DIR = Path("data/index") + + +def _resolve_repo_root() -> Path: + return Path(__file__).resolve().parents[3] + + +def _resolve_index_data_dir() -> Path: + raw = os.getenv("INDEX_DATA_DIR") + if raw and raw.strip(): + candidate = Path(raw.strip()).expanduser() + if candidate.is_absolute(): + return candidate + return _resolve_repo_root() / candidate + return _resolve_repo_root() / DEFAULT_INDEX_DATA_DIR + + +@dataclass(slots=True, frozen=True) +class EpflGraphPaths: + root: Path + + @property + def duckdb_dir(self) -> Path: + return self.root / "duckdb" + + @property + def duckdb_path(self) -> Path: + return self.duckdb_dir / "epfl_graph.duckdb" + + @property + def cache_dir(self) -> Path: + return self.root / "cache" + + @property + def logs_dir(self) -> Path: + return self.root / "logs" + + +def get_epfl_graph_paths() -> EpflGraphPaths: + """Resolve `/epfl_graph/` and ensure subdirs exist.""" + root = _resolve_index_data_dir() / "epfl_graph" + paths = EpflGraphPaths(root=root) + paths.duckdb_dir.mkdir(parents=True, exist_ok=True) + paths.cache_dir.mkdir(parents=True, exist_ok=True) + paths.logs_dir.mkdir(parents=True, exist_ok=True) + return paths diff --git a/src/index/epfl_graph/retrieval/__init__.py b/src/index/epfl_graph/retrieval/__init__.py new file mode 100644 index 0000000..7f57d99 --- /dev/null +++ b/src/index/epfl_graph/retrieval/__init__.py @@ -0,0 +1 @@ +"""Semantic retrieval over the EPFL Graph disciplines index.""" diff --git a/src/index/epfl_graph/retrieval/semantic.py b/src/index/epfl_graph/retrieval/semantic.py new file mode 100644 index 0000000..8209c32 --- /dev/null +++ b/src/index/epfl_graph/retrieval/semantic.py @@ -0,0 +1,162 @@ +"""Semantic search over the EPFL Graph disciplines collection. + +Embed query → Qdrant top-K vector search → optional RCP rerank → +DuckDB hydrate (parent chain). Returns small dicts ready for callers +(agent tools, federated layer, CLI). +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any + +from src.index.epfl_graph.embed.pipeline import EPFL_GRAPH_COLLECTION +from src.index.epfl_graph.storage.duckdb_store import EpflGraphStore +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.vector.qdrant_store import QdrantStore + +if TYPE_CHECKING: + from src.index.epfl_graph.config import EpflGraphIndexConfig + +LOGGER = logging.getLogger(__name__) + + +def _payload_to_doc(payload: dict[str, Any]) -> str: + name = payload.get("name") or payload.get("category_id") + text = payload.get("embedding_text") + if isinstance(text, str) and text.strip(): + return text + return str(name) if name else "" + + +def _walk_chain( + store: EpflGraphStore, leaf_id: str, max_depth: int = 10, +) -> list[dict[str, Any]]: + chain: list[dict[str, Any]] = [] + seen: set[str] = set() + current = leaf_id + while current and current not in seen and len(chain) < max_depth: + record = store.fetch_category(current) + if not record: + break + chain.append( + { + "category_id": record["category_id"], + "name": record.get("name"), + "depth": record.get("depth"), + "wikipedia_url": record.get("wikipedia_url"), + }, + ) + seen.add(current) + parent = record.get("parent_id") + if parent in {None, "", "root"}: + break + current = parent + return chain + + +async def _async_search( # noqa: PLR0913 + *, + config: EpflGraphIndexConfig, + query: str, + top_k: int, + candidate_k: int, + min_depth: int | None, + rerank: bool, + store: EpflGraphStore, +) -> list[dict[str, Any]]: + config.require_rcp() + embed_client = RCPEmbeddingClient(config) # type: ignore[arg-type] + qdrant = QdrantStore(config) # type: ignore[arg-type] + + [query_vec] = await embed_client.embed_all([query]) + candidates = qdrant.search( + EPFL_GRAPH_COLLECTION, + query_vector=query_vec, + top_k=candidate_k, + filter_payload=None, + ) + if not candidates: + return [] + + if min_depth is not None: + candidates = [ + c for c in candidates + if isinstance(c.get("payload"), dict) + and isinstance(c["payload"].get("depth"), int) + and c["payload"]["depth"] >= min_depth + ] + if not candidates: + return [] + + if rerank: + rerank_client = RCPRerankerClient(config) # type: ignore[arg-type] + docs = [_payload_to_doc(c["payload"]) for c in candidates] + reranked = await rerank_client.rerank(query, docs, top_n=top_k) + if reranked: + ordered = [ + { + **candidates[r["index"]], + "rerank_score": r["relevance_score"], + } + for r in reranked + ] + else: + ordered = candidates[:top_k] + else: + ordered = candidates[:top_k] + + out: list[dict[str, Any]] = [] + for hit in ordered: + payload = hit.get("payload") or {} + category_id = payload.get("category_id") or payload.get("entity_id") + if not category_id: + continue + out.append( + { + "category_id": str(category_id), + "name": payload.get("name"), + "depth": payload.get("depth"), + "wikipedia_url": payload.get("wikipedia_url"), + "graphsearch_url": payload.get("graphsearch_url"), + "vector_score": hit.get("score"), + "rerank_score": hit.get("rerank_score"), + "payload": payload, + "chain": _walk_chain(store, str(category_id)), + }, + ) + return out + + +def semantic_search( # noqa: PLR0913 + *, + config: EpflGraphIndexConfig, + query: str, + top_k: int = 10, + candidate_k: int = 50, + min_depth: int | None = None, + rerank: bool = True, +) -> list[dict[str, Any]]: + """Find the top disciplines closest to ``query``. + + ``min_depth`` filters out shallow ancestor categories — set it to 4 + to keep only leaf-level disciplines, or leave it None to mirror the + config's ``filter.min_depth`` (which already gated the embed pass). + """ + store = EpflGraphStore.open(config.paths.duckdb_path) + try: + return asyncio.run( + _async_search( + config=config, + query=query, + top_k=top_k, + candidate_k=candidate_k, + min_depth=min_depth, + rerank=rerank, + store=store, + ), + ) + finally: + store.close() diff --git a/src/index/epfl_graph/storage/__init__.py b/src/index/epfl_graph/storage/__init__.py new file mode 100644 index 0000000..024ae51 --- /dev/null +++ b/src/index/epfl_graph/storage/__init__.py @@ -0,0 +1 @@ +"""DuckDB-backed storage for the EPFL Graph disciplines index.""" diff --git a/src/index/epfl_graph/storage/duckdb_store.py b/src/index/epfl_graph/storage/duckdb_store.py new file mode 100644 index 0000000..c5bd0ee --- /dev/null +++ b/src/index/epfl_graph/storage/duckdb_store.py @@ -0,0 +1,254 @@ +"""DuckDB lifecycle, schema bootstrap, and upserts for the disciplines index. + +Mirrors the slim shape of `src/index/zenodo/storage/duckdb_store.py`. +The store holds two tables: ``categories`` (one row per ontology node) +and ``category_concepts`` (top-N anchor Wikipedia concepts per category). +""" + +from __future__ import annotations + +import json +import logging +from contextlib import contextmanager +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import duckdb + +from src.index.epfl_graph.paths import get_epfl_graph_paths + +if TYPE_CHECKING: + from collections.abc import Iterator + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +class EpflGraphStore: + """Thin DuckDB wrapper. ``bootstrap()`` is idempotent.""" + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open(cls, db_path: Path | None = None) -> EpflGraphStore: + if db_path is None: + db_path = get_epfl_graph_paths().duckdb_path + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + self.connect().execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + # ---- Upserts --------------------------------------------------------- + + def upsert_category( + self, + row: dict[str, Any], + raw: dict[str, Any], + concepts: list[dict[str, Any]] | None = None, + ) -> None: + sql = ( + "INSERT INTO categories " + "(category_id, name, depth, parent_id, wikipedia_page_id, " + " wikipedia_url, graphsearch_url, n_concepts, n_children, " + " embedding_text, raw) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (category_id) DO UPDATE SET " + " name = excluded.name, depth = excluded.depth, " + " parent_id = excluded.parent_id, " + " wikipedia_page_id = excluded.wikipedia_page_id, " + " wikipedia_url = excluded.wikipedia_url, " + " graphsearch_url = excluded.graphsearch_url, " + " n_concepts = excluded.n_concepts, " + " n_children = excluded.n_children, " + " embedding_text = excluded.embedding_text, " + " raw = excluded.raw, fetched_at = now()" + ) + self.connect().execute( + sql, + [ + row["category_id"], + row.get("name"), + row.get("depth"), + row.get("parent_id"), + row.get("wikipedia_page_id"), + row.get("wikipedia_url"), + row.get("graphsearch_url"), + int(row.get("n_concepts") or 0), + int(row.get("n_children") or 0), + row.get("embedding_text"), + json.dumps(raw, ensure_ascii=False), + ], + ) + if concepts: + self.upsert_concepts(row["category_id"], concepts) + + def upsert_concepts( + self, category_id: str, concepts: list[dict[str, Any]], + ) -> None: + sql = ( + "INSERT INTO category_concepts " + "(category_id, concept_id, concept_name, rank) " + "VALUES (?, ?, ?, ?) " + "ON CONFLICT (category_id, concept_id) DO UPDATE SET " + " concept_name = excluded.concept_name, rank = excluded.rank" + ) + rows = [] + for rank, concept in enumerate(concepts): + cid = concept.get("id") or concept.get("concept_id") + if not cid: + continue + rows.append( + ( + category_id, + str(cid), + concept.get("name") or concept.get("concept_name"), + rank, + ), + ) + if rows: + self.connect().executemany(sql, rows) + + # ---- Reads ----------------------------------------------------------- + + def fetch_category(self, category_id: str) -> dict[str, Any] | None: + row = self.connect().execute( + "SELECT category_id, name, depth, parent_id, " + " wikipedia_page_id, wikipedia_url, graphsearch_url, " + " n_concepts, n_children, embedding_text " + "FROM categories WHERE category_id = ?", + [category_id], + ).fetchone() + if not row: + return None + return { + "category_id": row[0], + "name": row[1], + "depth": row[2], + "parent_id": row[3], + "wikipedia_page_id": row[4], + "wikipedia_url": row[5], + "graphsearch_url": row[6], + "n_concepts": row[7], + "n_children": row[8], + "embedding_text": row[9], + } + + # ---- Wikipedia enrichment ------------------------------------------- + + def iter_categories_missing_extract(self) -> Iterator[dict[str, Any]]: + cursor = self.connect().execute( + "SELECT category_id, wikipedia_page_id, name " + "FROM categories " + "WHERE wikipedia_page_id IS NOT NULL " + " AND (wikipedia_extract IS NULL OR wikipedia_extract = '') " + "ORDER BY category_id", + ) + for row in cursor.fetchall(): + yield { + "category_id": row[0], + "wikipedia_page_id": row[1], + "name": row[2], + } + + def update_wikipedia_extract( + self, category_id: str, extract: str | None, + ) -> None: + self.connect().execute( + "UPDATE categories SET wikipedia_extract = ? WHERE category_id = ?", + [extract, category_id], + ) + + def update_embedding_text( + self, category_id: str, embedding_text: str | None, + ) -> None: + self.connect().execute( + "UPDATE categories SET embedding_text = ? WHERE category_id = ?", + [embedding_text, category_id], + ) + + def iter_categories_for_extract_refresh(self) -> Iterator[dict[str, Any]]: + cursor = self.connect().execute( + "SELECT c.category_id, c.name, c.wikipedia_extract " + "FROM categories c ORDER BY c.category_id", + ) + for row in cursor.fetchall(): + yield { + "category_id": row[0], + "name": row[1], + "wikipedia_extract": row[2], + } + + def fetch_anchor_concept_names(self, category_id: str, limit: int) -> list[str]: + cursor = self.connect().execute( + "SELECT concept_name FROM category_concepts " + "WHERE category_id = ? AND concept_name IS NOT NULL " + "ORDER BY rank ASC LIMIT ?", + [category_id, int(limit)], + ) + return [row[0] for row in cursor.fetchall() if row[0]] + + def iter_categories_for_embedding( + self, *, min_depth: int = 3, + ) -> Iterator[dict[str, Any]]: + cursor = self.connect().execute( + "SELECT category_id, name, depth, parent_id, " + " wikipedia_page_id, wikipedia_url, graphsearch_url, " + " n_concepts, n_children, embedding_text " + "FROM categories " + "WHERE depth >= ? AND embedding_text IS NOT NULL " + "ORDER BY category_id", + [int(min_depth)], + ) + for row in cursor.fetchall(): + yield { + "category_id": row[0], + "name": row[1], + "depth": row[2], + "parent_id": row[3], + "wikipedia_page_id": row[4], + "wikipedia_url": row[5], + "graphsearch_url": row[6], + "n_concepts": row[7], + "n_children": row[8], + "embedding_text": row[9], + } + + def count_categories(self) -> int: + row = self.connect().execute( + "SELECT COUNT(*) FROM categories", + ).fetchone() + return int(row[0]) if row else 0 + + def count_concepts(self) -> int: + row = self.connect().execute( + "SELECT COUNT(*) FROM category_concepts", + ).fetchone() + return int(row[0]) if row else 0 diff --git a/src/index/epfl_graph/storage/schema.sql b/src/index/epfl_graph/storage/schema.sql new file mode 100644 index 0000000..0512285 --- /dev/null +++ b/src/index/epfl_graph/storage/schema.sql @@ -0,0 +1,35 @@ +-- EPFL Graph disciplines index — DuckDB schema. +-- Idempotent. Bootstrapped from `EpflGraphStore.bootstrap()`. + +CREATE TABLE IF NOT EXISTS categories ( + category_id VARCHAR PRIMARY KEY, + name VARCHAR, + depth INTEGER, + parent_id VARCHAR, + wikipedia_page_id VARCHAR, + wikipedia_url VARCHAR, + wikipedia_extract VARCHAR, + graphsearch_url VARCHAR, + n_concepts INTEGER DEFAULT 0, + n_children INTEGER DEFAULT 0, + embedding_text VARCHAR, + raw JSON, + fetched_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP +); + +-- Idempotent column add for upgrades from v1 schemas without `wikipedia_extract`. +ALTER TABLE categories ADD COLUMN IF NOT EXISTS wikipedia_extract VARCHAR; + +CREATE INDEX IF NOT EXISTS idx_categories_parent ON categories(parent_id); +CREATE INDEX IF NOT EXISTS idx_categories_depth ON categories(depth); + +CREATE TABLE IF NOT EXISTS category_concepts ( + category_id VARCHAR, + concept_id VARCHAR, + concept_name VARCHAR, + rank INTEGER, + PRIMARY KEY (category_id, concept_id) +); + +CREATE INDEX IF NOT EXISTS idx_category_concepts_cat + ON category_concepts(category_id); diff --git a/src/index/ethz_research_collection/__init__.py b/src/index/ethz_research_collection/__init__.py new file mode 100644 index 0000000..c4c22ce --- /dev/null +++ b/src/index/ethz_research_collection/__init__.py @@ -0,0 +1,16 @@ +"""ETH Research Collection harvest + RAG index. + +Pipeline (each stage resumable from disk): + + discover ──► fetch_text ──► extract_matches ──► extract_relations + │ + ▼ + fetch_related + │ + chunk ──► embed ──► store + │ + ▼ + query (filter → vector → rerank) + +Entry point: `python -m src.index.ethz_research_collection `. +""" diff --git a/src/index/ethz_research_collection/__main__.py b/src/index/ethz_research_collection/__main__.py new file mode 100644 index 0000000..9d14e2f --- /dev/null +++ b/src/index/ethz_research_collection/__main__.py @@ -0,0 +1,6 @@ +"""Module entrypoint: `python -m src.index.ethz_research_collection `.""" + +from .cli import main + +if __name__ == "__main__": + main() diff --git a/src/index/ethz_research_collection/_federated.py b/src/index/ethz_research_collection/_federated.py new file mode 100644 index 0000000..673a894 --- /dev/null +++ b/src/index/ethz_research_collection/_federated.py @@ -0,0 +1,77 @@ +"""ETH Research Collection registration with the federated discover/hydrate registries. + +Mirrors :mod:`src.index.infoscience._federated` — both indices are +DSpace-backed and share the discover/build pipeline shape. +""" + +from __future__ import annotations + +import logging +from typing import Any, Iterator + +from src.index._federated.dh_registry import register_discoverer, register_hydrator +from src.index._federated.protocols import ( + HydrationSummary, + IndexDiscoverer, + IndexHydrator, + Seed, +) + +LOGGER = logging.getLogger(__name__) + + +class ETHZResearchCollectionDiscoverer: + name = "ethz_research_collection" + accepted_sources = ("from-search",) + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + if source not in self.accepted_sources: + message = f"ETHZ-RC: unknown source {source!r}. Accepted: {list(self.accepted_sources)}" + raise ValueError(message) + + from src.index.ethz_research_collection.discover import discover_state_path + + state_path = discover_state_path() + if not state_path.exists(): + LOGGER.warning( + "ethz_research_collection discover state %s not found; " + "run `python -m src.index.ethz_research_collection discover --terms ...` first", + state_path, + ) + return + import json + try: + data = json.loads(state_path.read_text()) + except Exception as exc: # noqa: BLE001 + LOGGER.warning("failed to read %s: %s", state_path, exc) + return + items = data.get("links") or data.get("items") or [] + for item in items: + url = item if isinstance(item, str) else item.get("url") + if not url: + continue + yield Seed( + id=url, + seed_type="ethz_rc_url", + source="from-search", + hint={"query": opts.get("query")}, + ) + + +class ETHZResearchCollectionHydrator: + name = "ethz_research_collection" + accepted_seed_types = ("ethz_rc_url",) + + def hydrate(self, seeds, *, only_unfetched: bool = True) -> HydrationSummary: + # TODO: lift the build-from-links code into hydrate_from_urls(urls). + materialised = list(seeds) + LOGGER.warning( + "ethz_research_collection: hydrate is a stub (received %d seeds). " + "Use `python -m src.index.ethz_research_collection ingest-duckdb` for now.", + len(materialised), + ) + return HydrationSummary(skipped_existing=len(materialised)) + + +register_discoverer(ETHZResearchCollectionDiscoverer()) +register_hydrator(ETHZResearchCollectionHydrator()) diff --git a/src/index/ethz_research_collection/build.py b/src/index/ethz_research_collection/build.py new file mode 100644 index 0000000..6b4a5d2 --- /dev/null +++ b/src/index/ethz_research_collection/build.py @@ -0,0 +1,390 @@ +"""Build/embed stage: chunk, embed, and populate ChromaDB collections. + +Scopes: + * `chunks`: text/.txt → chunks → embeddings → ethz_research_collection_chunks + * `articles`: raw/items/.json + matches → ethz_research_collection_articles + * `persons`: raw/persons/.json → ethz_research_collection_persons + * `orgs`: raw/organizations/.json → ethz_research_collection_organizations + * `all`: every scope above, in order + +Each entity row gets an embedding when there is non-trivial source text; +otherwise a zero-vector placeholder is written so the row is still +queryable via metadata/lexical filters (vector search effectively +ignores it via the cosine score). +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Sequence + +from .chunker import chunk_text +from .config import EthzResearchCollectionIndexConfig +from .embed import RCPEmbedder +from .extract_matches import matches_by_uuid +from .extract_relations import load_relations +from .models import ( + ArticleRecord, + ChunkRecord, + OrganizationRecord, + PersonRecord, +) +from .parsers import parse_article, parse_organization, parse_person +from .paths import ( + raw_items_dir, + raw_organizations_dir, + raw_persons_dir, + text_dir, +) +from .store import ( + ARTICLES_COLLECTION, + CHUNKS_COLLECTION, + ORGANIZATIONS_COLLECTION, + PERSONS_COLLECTION, + QdrantStore, + upsert_articles, + upsert_chunks, + upsert_organizations, + upsert_persons, +) + +logger = logging.getLogger(__name__) + + +def _load_json(path: Path) -> Optional[dict]: + if not path.exists(): + return None + try: + return json.loads(path.read_text(encoding="utf-8")) + except Exception: + logger.exception("Failed to parse %s", path) + return None + + +def _chunks_for_article( + cfg: EthzResearchCollectionIndexConfig, + article: ArticleRecord, + text: str, +) -> List[ChunkRecord]: + pieces = chunk_text(text, cfg.chunking) + out: List[ChunkRecord] = [] + for i, piece in enumerate(pieces): + out.append(ChunkRecord( + chunk_id=f"{article.article_uuid}::{i}", + article_uuid=article.article_uuid, + chunk_index=i, + text=piece, + title=article.title, + abstract=article.abstract, + authors=article.authors, + author_uuids=article.author_uuids, + doi=article.doi, + publication_date=article.publication_date, + year=article.year, + publication_type=article.publication_type, + language=article.language, + subjects=article.subjects, + keywords=article.keywords, + lab=article.lab, + lab_uuid=article.lab_uuid, + org_uuids=article.org_uuids, + research_collection_url=article.research_collection_url, + matched_urls=article.matched_urls, + )) + return out + + +def _articles_with_matches(matches_map: dict) -> List[ArticleRecord]: + out: List[ArticleRecord] = [] + for uuid, match in matches_map.items(): + item = _load_json(raw_items_dir() / f"{uuid}.json") + if item is None: + continue + out.append(parse_article(item, matched_urls=match.matched_urls)) + return out + + +def _build_article_to_persons() -> Dict[str, List[str]]: + rev: Dict[str, List[str]] = defaultdict(list) + for rel in load_relations(): + for p in rel.person_uuids: + rev[p].append(rel.article_uuid) + return rev + + +def _build_article_to_orgs() -> Dict[str, List[str]]: + rev: Dict[str, List[str]] = defaultdict(list) + for rel in load_relations(): + for o in rel.org_uuids: + rev[o].append(rel.article_uuid) + return rev + + +def _person_text(rec: PersonRecord) -> str: + """Build the embedding text for a Person record. + + Ordering matters for retrieval: surface every form of the person's + name first (display name, given+family, family alone) so direct-name + queries score highly, then add the affiliation/department text and + biography for content-based recall. + """ + parts: List[str] = [] + if rec.name: + parts.append(rec.name) + # Repeat given+family on a separate line — Qwen3-Embedding treats line + # boundaries as soft topic shifts; this gives the family-name token an + # extra anchor for queries that pass just "Reiher" or "Markus Reiher". + if rec.given_name or rec.family_name: + composed = " ".join(p for p in (rec.given_name, rec.family_name) if p) + if composed and composed != rec.name: + parts.append(composed) + if rec.family_name and rec.family_name != rec.name: + parts.append(rec.family_name) + if rec.orcid: + parts.append(f"ORCID: {rec.orcid}") + if rec.position: + parts.append(rec.position) + if rec.primary_affiliation: + # ETH RC pumps " - / " through this + # field — useful for queries that name a lab / professor. + parts.append(rec.primary_affiliation) + if rec.research_interests: + parts.append(", ".join(rec.research_interests)) + if rec.biography: + parts.append(rec.biography) + return "\n\n".join(parts).strip() + + +def _organization_text(rec: OrganizationRecord) -> str: + parts: List[str] = [] + if rec.name: + header = rec.name + if rec.acronym: + header = f"{rec.name} ({rec.acronym})" + parts.append(header) + if rec.parent_org_chain_names: + parts.append("Parent: " + " > ".join(rec.parent_org_chain_names)) + if rec.description: + parts.append(rec.description) + return "\n\n".join(parts).strip() + + +def _article_embed_text(rec: ArticleRecord) -> str: + parts: List[str] = [] + if rec.title: + parts.append(rec.title) + if rec.abstract: + parts.append(rec.abstract) + return "\n\n".join(parts).strip() + + +# --------------------------------------------------------------------------- +# Stage entry points +# --------------------------------------------------------------------------- + + +# Slab size for incremental embed → upsert. Each slab embeds N chunks +# (one RCP /embeddings batch group), upserts them to Qdrant, drops them, +# then moves on. Cuts peak memory from O(total_chunks × 16 KB) — 6+ GB on +# the full ETH RC run — to O(slab × 16 KB) ≈ 8 MB. Also makes the run +# resumable: if Qdrant already has a chunk's UUIDv5 point id, the upsert +# is a no-op so re-running embed picks up where it left off without +# re-embedding the slabs that already landed. +_CHUNK_SLAB_SIZE = 512 + + +async def build_chunks(cfg: EthzResearchCollectionIndexConfig) -> dict: + matches = matches_by_uuid() + articles = _articles_with_matches(matches) + if not articles: + logger.warning("No matched articles. Run discover/fetch-text/extract-matches first.") + return {"chunks": 0, "articles": 0} + + chunk_records: List[ChunkRecord] = [] + chunk_counts_per_article: Dict[str, int] = {} + for article in articles: + text_path = text_dir() / f"{article.article_uuid}.txt" + text = text_path.read_text(encoding="utf-8") if text_path.exists() else "" + recs = _chunks_for_article(cfg, article, text) + chunk_counts_per_article[article.article_uuid] = len(recs) + chunk_records.extend(recs) + + if not chunk_records: + logger.warning("Articles have no extractable chunk text yet.") + return {"chunks": 0, "articles": len(articles)} + + store = QdrantStore.from_config(cfg) + store.ensure_collection(CHUNKS_COLLECTION) + + total = len(chunk_records) + upserted = 0 + slab_size = _CHUNK_SLAB_SIZE + async with RCPEmbedder(cfg.rcp) as embedder: + for start in range(0, total, slab_size): + slab = chunk_records[start : start + slab_size] + texts = [c.text for c in slab] + embeddings = await embedder.embed_passages_batched(texts) + upsert_chunks(store, slab, embeddings) + upserted += len(slab) + logger.info( + "build_chunks: %d / %d chunks upserted (slab %d/%d)", + upserted, total, + (start // slab_size) + 1, + (total + slab_size - 1) // slab_size, + ) + + return { + "chunks": total, + "articles": len(articles), + "chunk_counts_per_article": chunk_counts_per_article, + } + + +async def build_articles(cfg: EthzResearchCollectionIndexConfig) -> dict: + matches = matches_by_uuid() + articles = _articles_with_matches(matches) + if not articles: + return {"articles": 0} + + # Derive chunk_count per article from the on-disk text files (no + # round-trip to Qdrant needed; chunker is deterministic so the count + # we'd insert in build_chunks matches what we'd see in the store). + chunk_counts: Dict[str, int] = {} + for art in articles: + text_path = text_dir() / f"{art.article_uuid}.txt" + if not text_path.exists(): + continue + body = text_path.read_text(encoding="utf-8") + chunk_counts[art.article_uuid] = len(chunk_text(body, cfg.chunking)) + + for art in articles: + art.chunk_count = int(chunk_counts.get(art.article_uuid, 0)) + + store = QdrantStore.from_config(cfg) + store.ensure_collection(ARTICLES_COLLECTION) + + texts = [_article_embed_text(a) for a in articles] + have_text = [bool(t) for t in texts] + to_embed = [t for t, ok in zip(texts, have_text) if ok] + + if to_embed: + async with RCPEmbedder(cfg.rcp) as embedder: + embeddings_iter = await embedder.embed_passages_batched(to_embed) + else: + embeddings_iter = [] + + embeddings: List[Optional[Sequence[float]]] = [] + embed_pos = 0 + for ok in have_text: + if ok: + embeddings.append(embeddings_iter[embed_pos]) + embed_pos += 1 + else: + embeddings.append(None) + + upsert_articles(store, articles, embeddings, cfg.rcp.embedding_dim) + return {"articles": len(articles), "with_embedding": len(to_embed)} + + +async def build_persons(cfg: EthzResearchCollectionIndexConfig) -> dict: + files = sorted(raw_persons_dir().glob("*.json")) + if not files: + return {"persons": 0} + + rev = _build_article_to_persons() + records: List[PersonRecord] = [] + for f in files: + item = _load_json(f) + if item is None: + continue + rec = parse_person(item) + rec.related_article_uuids = rev.get(rec.person_uuid, []) + records.append(rec) + + store = QdrantStore.from_config(cfg) + store.ensure_collection(PERSONS_COLLECTION) + + texts = [_person_text(r) for r in records] + have_text = [bool(t) for t in texts] + to_embed = [t for t, ok in zip(texts, have_text) if ok] + + if to_embed: + async with RCPEmbedder(cfg.rcp) as embedder: + embeddings_iter = await embedder.embed_passages_batched(to_embed) + else: + embeddings_iter = [] + + embeddings: List[Optional[Sequence[float]]] = [] + embed_pos = 0 + for ok in have_text: + if ok: + embeddings.append(embeddings_iter[embed_pos]) + embed_pos += 1 + else: + embeddings.append(None) + + upsert_persons(store, records, embeddings, cfg.rcp.embedding_dim) + return {"persons": len(records), "with_embedding": len(to_embed)} + + +async def build_organizations(cfg: EthzResearchCollectionIndexConfig) -> dict: + files = sorted(raw_organizations_dir().glob("*.json")) + if not files: + return {"organizations": 0} + + rev = _build_article_to_orgs() + records: List[OrganizationRecord] = [] + for f in files: + item = _load_json(f) + if item is None: + continue + rec = parse_organization(item) + rec.related_article_uuids = rev.get(rec.org_uuid, []) + records.append(rec) + + store = QdrantStore.from_config(cfg) + store.ensure_collection(ORGANIZATIONS_COLLECTION) + + texts = [_organization_text(r) for r in records] + have_text = [bool(t) for t in texts] + to_embed = [t for t, ok in zip(texts, have_text) if ok] + + if to_embed: + async with RCPEmbedder(cfg.rcp) as embedder: + embeddings_iter = await embedder.embed_passages_batched(to_embed) + else: + embeddings_iter = [] + + embeddings: List[Optional[Sequence[float]]] = [] + embed_pos = 0 + for ok in have_text: + if ok: + embeddings.append(embeddings_iter[embed_pos]) + embed_pos += 1 + else: + embeddings.append(None) + + upsert_organizations(store, records, embeddings, cfg.rcp.embedding_dim) + return {"organizations": len(records), "with_embedding": len(to_embed)} + + +async def build(cfg: EthzResearchCollectionIndexConfig, *, scope: str = "all") -> dict: + summary: dict = {"scope": scope} + if scope in ("chunks", "all"): + summary["chunks"] = await build_chunks(cfg) + if scope in ("articles", "all"): + summary["articles"] = await build_articles(cfg) + if scope in ("persons", "all"): + summary["persons"] = await build_persons(cfg) + if scope in ("orgs", "all"): + summary["organizations"] = await build_organizations(cfg) + logger.info("build: %s", json.dumps(summary, default=str)) + return summary + + +def run(cfg: EthzResearchCollectionIndexConfig, *, scope: str = "all") -> dict: + return asyncio.run(build(cfg, scope=scope)) diff --git a/src/index/ethz_research_collection/chunker.py b/src/index/ethz_research_collection/chunker.py new file mode 100644 index 0000000..10343fc --- /dev/null +++ b/src/index/ethz_research_collection/chunker.py @@ -0,0 +1,64 @@ +"""Token-aware text chunker. + +Greedy paragraph-aware splitter using the configured tiktoken encoding. +Chunks target `chunking.size_tokens` with `chunking.overlap_tokens` of +trailing context carried into the next chunk. +""" + +from __future__ import annotations + +from typing import List + +import tiktoken + +from .config import ChunkingConfig + +_PARAGRAPH_SEP = "\n\n" + + +def _split_paragraphs(text: str) -> List[str]: + paras = [p.strip() for p in text.split(_PARAGRAPH_SEP)] + return [p for p in paras if p] + + +def chunk_text(text: str, cfg: ChunkingConfig) -> List[str]: + """Return a list of chunk strings.""" + if not text.strip(): + return [] + enc = tiktoken.get_encoding(cfg.tokenizer) + paragraphs = _split_paragraphs(text) + if not paragraphs: + return [] + + chunks: List[str] = [] + current_tokens: List[int] = [] + for para in paragraphs: + # `disallowed_special=()` lets paper bodies that literally contain + # tokenizer markers like ``<|endoftext|>`` (common in LLM/NLP + # publications that quote model tokens) be tokenised as normal + # text. Without this, tiktoken raises ValueError mid-pipeline. + para_tokens = enc.encode(para, disallowed_special=()) + # If a single paragraph exceeds the budget, flush and slice it. + if len(para_tokens) > cfg.size_tokens: + if current_tokens: + chunks.append(enc.decode(current_tokens)) + current_tokens = current_tokens[-cfg.overlap_tokens:] if cfg.overlap_tokens else [] + for i in range(0, len(para_tokens), cfg.size_tokens): + window = para_tokens[i : i + cfg.size_tokens] + chunks.append(enc.decode(window)) + current_tokens = para_tokens[-cfg.overlap_tokens:] if cfg.overlap_tokens else [] + continue + + sep_tokens = enc.encode(_PARAGRAPH_SEP) if current_tokens else [] + if len(current_tokens) + len(sep_tokens) + len(para_tokens) <= cfg.size_tokens: + current_tokens.extend(sep_tokens) + current_tokens.extend(para_tokens) + else: + chunks.append(enc.decode(current_tokens)) + tail = current_tokens[-cfg.overlap_tokens:] if cfg.overlap_tokens else [] + current_tokens = list(tail) + sep_tokens + para_tokens + + if current_tokens: + chunks.append(enc.decode(current_tokens)) + + return [c.strip() for c in chunks if c.strip()] diff --git a/src/index/ethz_research_collection/cli.py b/src/index/ethz_research_collection/cli.py new file mode 100644 index 0000000..01daf33 --- /dev/null +++ b/src/index/ethz_research_collection/cli.py @@ -0,0 +1,272 @@ +"""Click-based CLI for the ETH Research Collection indexer. + +Run via `python -m src.index.ethz_research_collection `. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from pathlib import Path +from typing import Optional + +import click + +from . import discover as discover_stage +from . import extract_matches as extract_matches_stage +from . import extract_relations as extract_relations_stage +from . import fetch_related as fetch_related_stage +from . import synth_orgs as synth_orgs_stage +from . import text_fetch as text_fetch_stage +from .config import DEFAULT_CONFIG_PATH, load_config +from .paths import ( + discover_state_path, + ethz_research_collection_data_dir, + matches_path, + relations_path, +) + +# `build` (LanceDB writes) and `pipeline` (LanceDB reads) are imported +# lazily inside their command bodies so the lighter stages don't require +# lancedb to be installed/loadable. + +logger = logging.getLogger(__name__) + + +def _setup_logging(verbose: bool) -> None: + level = logging.DEBUG if verbose else logging.INFO + logging.basicConfig( + level=level, + format="%(asctime)s %(name)s %(levelname)s %(message)s", + ) + + +@click.group(help="ETH Research Collection harvest + RAG indexer.") +@click.option("--config", "config_path", type=click.Path(path_type=Path), + default=None, help=f"Path to YAML config (default {DEFAULT_CONFIG_PATH}).") +@click.option("-v", "--verbose", is_flag=True, help="Verbose logging.") +@click.pass_context +def cli(ctx: click.Context, config_path: Optional[Path], verbose: bool) -> None: + _setup_logging(verbose) + ctx.ensure_object(dict) + ctx.obj["config"] = load_config(config_path) + + +@cli.command() +@click.option("--terms", default=None, + help="Comma-separated Solr fulltext terms (overrides config).") +@click.option("--limit", type=int, default=None, help="Stop after N new items.") +@click.pass_context +def discover(ctx: click.Context, terms: Optional[str], limit: Optional[int]) -> None: + """Solr fulltext search → raw item JSON dumps.""" + cfg = ctx.obj["config"] + term_list = [t.strip() for t in terms.split(",")] if terms else None + summary = discover_stage.run(cfg, terms=term_list, limit=limit) + click.echo(json.dumps(summary, indent=2)) + + +@cli.command("fetch-text") +@click.option("--refresh", is_flag=True, help="Re-download even if file exists.") +@click.pass_context +def fetch_text(ctx: click.Context, refresh: bool) -> None: + """Download TEXT bundle plaintext for each discovered item.""" + cfg = ctx.obj["config"] + summary = text_fetch_stage.run(cfg, refresh=refresh) + click.echo(json.dumps(summary, indent=2)) + + +@cli.command("extract-matches") +@click.pass_context +def extract_matches(ctx: click.Context) -> None: + """Regex-extract GitHub/HuggingFace URLs from fetched text.""" + cfg = ctx.obj["config"] + summary = extract_matches_stage.run(cfg) + click.echo(json.dumps(summary, indent=2)) + + +@cli.command("extract-relations") +@click.pass_context +def extract_relations(ctx: click.Context) -> None: + """Pull Person/Org authority UUIDs from matched articles.""" + cfg = ctx.obj["config"] + summary = extract_relations_stage.run(cfg) + click.echo(json.dumps(summary, indent=2)) + + +@cli.command("fetch-related") +@click.option("--type", "kind", type=click.Choice(["person", "org", "all"]), + default="all") +@click.option("--refresh", is_flag=True) +@click.pass_context +def fetch_related(ctx: click.Context, kind: str, refresh: bool) -> None: + """Fetch raw JSON for each Person/OrgUnit referenced by matched articles.""" + cfg = ctx.obj["config"] + summary = fetch_related_stage.run(cfg, kind=kind, refresh=refresh) + click.echo(json.dumps(summary, indent=2)) + + +@cli.command("synth-orgs") +@click.pass_context +def synth_orgs(ctx: click.Context) -> None: + """Synthesize OrgUnit JSONs from `person.department` text. + + ETH RC's DSpace 7 doesn't expose OrgUnit entities, so we mine the + free-text department field on Person records (e.g. ``03996 - Benini, + Luca / Benini, Luca``) into one synthetic Org per unique Leitzahl. + Run after `fetch-related --type person`. + """ + cfg = ctx.obj["config"] + summary = synth_orgs_stage.run(cfg) + click.echo(json.dumps(summary, indent=2, default=str)) + + +@cli.command() +@click.option("--scope", type=click.Choice(["chunks", "articles", "persons", "orgs", "all"]), + default="all") +@click.option("--batch", type=int, default=None, help="Override embed batch size.") +@click.pass_context +def embed(ctx: click.Context, scope: str, batch: Optional[int]) -> None: + """Chunk, embed, and populate the LanceDB tables.""" + from . import build as build_stage # lazy: requires lancedb + + cfg = ctx.obj["config"] + if batch is not None: + cfg.rcp.batch_size = batch + summary = build_stage.run(cfg, scope=scope) + click.echo(json.dumps(summary, indent=2, default=str)) + + +@cli.command("query") +@click.argument("text") +@click.option("--target", type=click.Choice(["chunks", "articles", "persons", "organizations"]), + default="chunks") +@click.option("--where", default=None, + help="ChromaDB filter as JSON, e.g. '{\"year\": {\"$gte\": 2022}, \"has_github_match\": true}'.") +@click.option("--top-k", type=int, default=50) +@click.option("--top-n", type=int, default=10) +@click.option("--mode", type=click.Choice(["hybrid", "vector-only", "lexical", "filter-only"]), + default="hybrid") +@click.option("--no-rerank", is_flag=True, help="Force vector-only mode.") +@click.option("--with-authors", is_flag=True) +@click.option("--with-orgs", is_flag=True) +@click.pass_context +def query_cmd( + ctx: click.Context, + text: str, + target: str, + where: Optional[str], + top_k: int, + top_n: int, + mode: str, + no_rerank: bool, + with_authors: bool, + with_orgs: bool, +) -> None: + """Query the index. Filter → vector → rerank by default.""" + from .pipeline import query # lazy: requires chromadb + + cfg = ctx.obj["config"] + where_dict = json.loads(where) if where else None + if no_rerank and mode == "hybrid": + mode = "vector-only" + result = asyncio.run(query( + cfg, text, target=target, where=where_dict, top_k=top_k, top_n=top_n, + mode=mode, with_authors=with_authors, with_orgs=with_orgs, + )) + click.echo(json.dumps({ + "target": result.target, + "rows": result.rows, + "related_persons": result.related_persons, + "related_organizations": result.related_organizations, + }, indent=2, default=str)) + + +@cli.command("ingest-duckdb") +@click.option( + "--links-dump", + type=click.Path(path_type=Path), + default=None, + help="Optional path to a scripts/dump_link_articles.py output to merge " + "into article_links.", +) +@click.pass_context +def ingest_duckdb(ctx: click.Context, links_dump: Optional[Path]) -> None: + """Ingest raw/{items,persons,organizations}/*.json into the DuckDB store.""" + from .storage import DuckDBStore + from .storage.ingest_raw import ingest_all + + store = DuckDBStore.open() + try: + summary = ingest_all(store, links_dump=links_dump) + finally: + store.close() + click.echo(json.dumps(summary, indent=2, default=str)) + + +@cli.command() +@click.pass_context +def status(ctx: click.Context) -> None: + """Show counts and paths for each stage.""" + cfg = ctx.obj["config"] + data_dir = ethz_research_collection_data_dir() + raw = data_dir / "raw" + counts = { + "data_dir": str(data_dir), + "raw_items": len(list((raw / "items").glob("*.json"))) if (raw / "items").exists() else 0, + "raw_persons": len(list((raw / "persons").glob("*.json"))) if (raw / "persons").exists() else 0, + "raw_organizations": len(list((raw / "organizations").glob("*.json"))) if (raw / "organizations").exists() else 0, + "text_files": len(list((data_dir / "text").glob("*.txt"))) if (data_dir / "text").exists() else 0, + "matches_path": str(matches_path()) if matches_path().exists() else None, + "relations_path": str(relations_path()) if relations_path().exists() else None, + "discover_state": str(discover_state_path()) if discover_state_path().exists() else None, + "config_summary": { + "embedding_model": cfg.rcp.embedding_model, + "embedding_dim": cfg.rcp.embedding_dim, + "reranker_model": cfg.rcp.reranker_model, + "filter_terms": cfg.filter.terms, + }, + } + try: + from .store import ALL_COLLECTIONS, QdrantStore # lazy: requires qdrant-client + store = QdrantStore.from_config(cfg) + counts["qdrant_url"] = cfg.qdrant.url + counts["qdrant_collections"] = { + name: store.collection_count(name) for name in ALL_COLLECTIONS + } + except Exception as exc: + counts["qdrant_error"] = str(exc) + try: + from .paths import duckdb_path + from .storage import DuckDBStore + if duckdb_path().exists(): + ddb = DuckDBStore.open() + try: + counts["duckdb_path"] = str(duckdb_path()) + counts["duckdb_counts"] = { + t: ddb.count(t) + for t in ( + "articles", + "persons", + "organizations", + "article_persons", + "article_orgs", + "article_links", + "chunks", + ) + } + finally: + ddb.close() + else: + counts["duckdb_path"] = None + except Exception as exc: + counts["duckdb_error"] = str(exc) + click.echo(json.dumps(counts, indent=2, default=str)) + + +def main() -> None: + cli(obj={}) + + +if __name__ == "__main__": + main() diff --git a/src/index/ethz_research_collection/config.py b/src/index/ethz_research_collection/config.py new file mode 100644 index 0000000..bbe78e5 --- /dev/null +++ b/src/index/ethz_research_collection/config.py @@ -0,0 +1,106 @@ +"""Config loader for the ETH Research Collection indexer. + +Reads `config/index/ethz_research_collection.yaml` and merges in env-sourced credentials +(`RCP_TOKEN`, `ETHZ_RESEARCH_COLLECTION_TOKEN`, `INDEX_QDRANT_*`) plus the resolved data +dir. +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import List, Optional + +import yaml +from pydantic import BaseModel, Field + +from .paths import ethz_research_collection_data_dir + +DEFAULT_CONFIG_PATH = Path("config/index/ethz_research_collection.yaml") + +_TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} +_FALSE_ENV_VALUES = {"0", "false", "f", "no", "n", "off"} + + +class RcpConfig(BaseModel): + base_url: str + embedding_model: str + embedding_dim: int + query_instruction: str + reranker_model: str + batch_size: int = 16 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: Optional[str] = None # populated from RCP_TOKEN at load time + + +class EthzResearchCollectionConfig(BaseModel): + base_url: str + page_size: int = 100 + max_concurrency: int = 4 + token: Optional[str] = None # populated from ETHZ_RESEARCH_COLLECTION_TOKEN at load time + + +class FilterConfig(BaseModel): + terms: List[str] = Field(default_factory=list) + + +class ChunkingConfig(BaseModel): + size_tokens: int = 1024 + overlap_tokens: int = 128 + tokenizer: str = "cl100k_base" + + +class QdrantConfig(BaseModel): + url: str = "http://qdrant:6333" + prefer_grpc: bool = False + api_key: Optional[str] = None # populated from INDEX_QDRANT_API_KEY at load + + +class EthzResearchCollectionIndexConfig(BaseModel): + rcp: RcpConfig + research_collection: EthzResearchCollectionConfig + filter: FilterConfig + chunking: ChunkingConfig + qdrant: QdrantConfig + data_dir: Path + + +def _env_str(name: str) -> Optional[str]: + raw = os.getenv(name) + if raw is None: + return None + stripped = raw.strip() + return stripped or None + + +def _env_bool(name: str) -> Optional[bool]: + raw = _env_str(name) + if raw is None: + return None + normalised = raw.lower() + if normalised in _TRUE_ENV_VALUES: + return True + if normalised in _FALSE_ENV_VALUES: + return False + msg = f"Invalid boolean value for {name}: {raw!r}" + raise ValueError(msg) + + +def load_config(path: Optional[Path] = None) -> EthzResearchCollectionIndexConfig: + """Load + validate the YAML config; merge env tokens and data dir.""" + cfg_path = path or DEFAULT_CONFIG_PATH + raw = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) or {} + + raw.setdefault("rcp", {})["token"] = _env_str("RCP_TOKEN") + raw.setdefault("research_collection", {})["token"] = _env_str("ETHZ_RESEARCH_COLLECTION_TOKEN") + raw.setdefault("qdrant", {})["api_key"] = _env_str("INDEX_QDRANT_API_KEY") + + if (override := _env_str("INDEX_QDRANT_URL")) is not None: + raw["qdrant"]["url"] = override + if (override_b := _env_bool("INDEX_QDRANT_PREFER_GRPC")) is not None: + raw["qdrant"]["prefer_grpc"] = override_b + + raw["data_dir"] = ethz_research_collection_data_dir() + + return EthzResearchCollectionIndexConfig(**raw) diff --git a/src/index/ethz_research_collection/discover.py b/src/index/ethz_research_collection/discover.py new file mode 100644 index 0000000..ac2ebde --- /dev/null +++ b/src/index/ethz_research_collection/discover.py @@ -0,0 +1,125 @@ +"""Discover stage: paginate Solr `fulltext:` queries, persist raw item JSON. + +Output: one JSON file per matched item under `raw/items/{uuid}.json`, plus +`discover_state.json` with per-term cursor for resumability. Same UUID +matched by multiple terms is stored once (the file write is idempotent). +""" + +from __future__ import annotations + +import asyncio +import datetime as dt +import json +import logging +from typing import List, Optional + +from .config import EthzResearchCollectionIndexConfig +from .dspace import DSpaceClient +from .models import DiscoverState +from .paths import discover_state_path, raw_items_dir + +logger = logging.getLogger(__name__) + + +def _load_state() -> DiscoverState: + path = discover_state_path() + if not path.exists(): + return DiscoverState() + return DiscoverState(**json.loads(path.read_text(encoding="utf-8"))) + + +def _save_state(state: DiscoverState) -> None: + discover_state_path().write_text( + state.model_dump_json(indent=2), encoding="utf-8", + ) + + +def _write_item(item: dict) -> bool: + """Persist one item; returns True if newly written.""" + uuid = item.get("uuid") + if not uuid: + return False + path = raw_items_dir() / f"{uuid}.json" + if path.exists(): + return False + path.write_text(json.dumps(item, ensure_ascii=False, indent=2), encoding="utf-8") + return True + + +async def discover( + cfg: EthzResearchCollectionIndexConfig, + *, + terms: Optional[List[str]] = None, + limit: Optional[int] = None, +) -> dict: + """Run the discover stage. Returns a summary dict.""" + target_terms = list(terms or cfg.filter.terms) + if not target_terms: + msg = "No filter terms configured." + raise ValueError(msg) + + state = _load_state() + total_new = 0 + total_seen = 0 + + async with DSpaceClient(cfg.research_collection) as client: + for term in target_terms: + if state.completed.get(term): + logger.info("Skipping completed term: %s", term) + continue + start_page = state.per_term_cursor.get(term, 0) + new_for_term = 0 + seen_for_term = 0 + logger.info("Discovering term=%s starting at page=%d", term, start_page) + page = start_page + try: + async for item in client.iter_discover_fulltext( + term, + size=cfg.research_collection.page_size, + start_page=start_page, + ): + seen_for_term += 1 + total_seen += 1 + if _write_item(item): + new_for_term += 1 + total_new += 1 + if limit is not None and total_new >= limit: + break + # Cursor in pages; updated whenever a page boundary passes. + new_page = start_page + (seen_for_term // cfg.research_collection.page_size) + if new_page != page: + page = new_page + state.per_term_cursor[term] = page + _save_state(state) + except Exception as exc: + logger.exception("Discover failed for term=%s: %s", term, exc) + state.per_term_cursor[term] = page + _save_state(state) + raise + else: + state.per_term_cursor[term] = page + state.per_term_total[term] = state.per_term_total.get(term, 0) + new_for_term + if limit is None: + state.completed[term] = True + logger.info( + "term=%s seen=%d new=%d", + term, seen_for_term, new_for_term, + ) + + if limit is not None and total_new >= limit: + logger.info("Hit --limit %d, stopping.", limit) + break + + state.last_run_iso = dt.datetime.now(dt.timezone.utc).isoformat() + _save_state(state) + return { + "terms": target_terms, + "items_seen": total_seen, + "items_new": total_new, + "raw_dir": str(raw_items_dir()), + } + + +def run(cfg: EthzResearchCollectionIndexConfig, **kwargs) -> dict: + """Sync wrapper for the CLI.""" + return asyncio.run(discover(cfg, **kwargs)) diff --git a/src/index/ethz_research_collection/dspace.py b/src/index/ethz_research_collection/dspace.py new file mode 100644 index 0000000..a3626f6 --- /dev/null +++ b/src/index/ethz_research_collection/dspace.py @@ -0,0 +1,214 @@ +"""Async DSpace 7 REST client for the ETH Research Collection. + +Single `httpx.AsyncClient` that talks to `https://www.research-collection.ethz.ch/server/api` +with optional bearer-token auth. Covers exactly the endpoints we need: + + * `/discover/search/objects` — Solr-backed search, used for the + `fulltext:` qualifier in `discover.py`. + * `/core/items/{uuid}` — item metadata, used for Articles, Persons, + and Organizations alike. + * `/core/items/{uuid}/bundles` + `/core/bundles/{uuid}/bitstreams` — + bundle/bitstream listing for the TEXT bundle. + * `/core/bitstreams/{uuid}/content` — the plain-text body for matched + publications (no PDFs handled on our side). + +dspace-rest-client was evaluated and dropped: its auth model (session + +CSRF + JWT login) conflicts with the ETH Research Collection's bearer-token auth. +""" + +from __future__ import annotations + +import asyncio +import logging +from contextlib import asynccontextmanager +from typing import Any, AsyncIterator, Dict, List, Optional + +import httpx + +from .config import EthzResearchCollectionConfig + +logger = logging.getLogger(__name__) + + +class DSpaceError(Exception): + """Raised when a DSpace REST call fails after retries.""" + + +class DSpaceClient: + """Async DSpace 7 REST client. Use as an async context manager.""" + + def __init__(self, cfg: EthzResearchCollectionConfig, *, timeout_seconds: int = 180): + self._cfg = cfg + self._timeout = timeout_seconds + self._semaphore = asyncio.Semaphore(cfg.max_concurrency) + self._client: Optional[httpx.AsyncClient] = None + + async def __aenter__(self) -> "DSpaceClient": + # Default headers are anonymous: discover/item/bundle/bitstream-listing + # endpoints are public and a misconfigured/inactive `ETHZ_RESEARCH_COLLECTION_TOKEN` + # (e.g. one whose user hasn't accepted the platform agreement) gets + # 403'd. The bearer is only attached on demand for bitstream content + # downloads (some PDFs/text bundles are gated). + headers: Dict[str, str] = { + "Accept": "application/json", + "User-Agent": ( + "Mozilla/5.0 (compatible; " + "src.index.ethz_research_collection/1.0; +https://www.research-collection.ethz.ch)" + ), + } + self._client = httpx.AsyncClient( + base_url=self._cfg.base_url.rstrip("/"), + headers=headers, + timeout=self._timeout, + follow_redirects=True, + ) + return self + + async def __aexit__(self, exc_type, exc, tb) -> None: + if self._client is not None: + await self._client.aclose() + self._client = None + + @property + def client(self) -> httpx.AsyncClient: + if self._client is None: + msg = "DSpaceClient must be used inside `async with`." + raise RuntimeError(msg) + return self._client + + _RETRY_STATUSES = {405, 429, 500, 502, 503, 504} + _RETRY_TRANSPORT_EXC = (httpx.ReadTimeout, httpx.ConnectTimeout, + httpx.PoolTimeout, httpx.RemoteProtocolError, + httpx.ConnectError) + + async def _get_json(self, path: str, **kwargs: Any) -> Dict[str, Any]: + last_exc: Optional[Exception] = None + for attempt in range(6): + try: + async with self._semaphore: + response = await self.client.get(path, **kwargs) + except self._RETRY_TRANSPORT_EXC as exc: + last_exc = exc + delay = min(2 ** attempt, 16) + 0.25 * attempt + logger.debug( + "DSpace %s on %s, retry in %.1fs (attempt %d)", + type(exc).__name__, path, delay, attempt + 1, + ) + await asyncio.sleep(delay) + continue + if response.status_code in self._RETRY_STATUSES: + # the ETH Research Collection's edge layer 405s under burst; back off and retry. + last_exc = httpx.HTTPStatusError( + message=f"transient {response.status_code}", + request=response.request, + response=response, + ) + delay = min(2 ** attempt, 16) + 0.25 * attempt + logger.debug( + "DSpace %s on %s, retry in %.1fs (attempt %d)", + response.status_code, path, delay, attempt + 1, + ) + await asyncio.sleep(delay) + continue + response.raise_for_status() + return response.json() + assert last_exc is not None + raise last_exc + + async def discover_fulltext( + self, + term: str, + *, + configuration: str = "researchoutputs", + page: int = 0, + size: int = 100, + ) -> Dict[str, Any]: + """One page of `/discover/search/objects?query=fulltext:`.""" + params = { + "query": f"fulltext:{term}", + "configuration": configuration, + "page": page, + "size": size, + } + return await self._get_json("/discover/search/objects", params=params) + + async def iter_discover_fulltext( + self, + term: str, + *, + configuration: str = "researchoutputs", + size: int = 100, + start_page: int = 0, + max_pages: Optional[int] = None, + ) -> AsyncIterator[Dict[str, Any]]: + """Yield raw indexable item dicts across all pages for a fulltext term.""" + page = start_page + while True: + payload = await self.discover_fulltext( + term, configuration=configuration, page=page, size=size, + ) + search = payload.get("_embedded", {}).get("searchResult", {}) + page_info = search.get("page", {}) or {} + total_pages = page_info.get("totalPages", 0) or 0 + objects = search.get("_embedded", {}).get("objects", []) or [] + for obj in objects: + indexable = obj.get("_embedded", {}).get("indexableObject") + if indexable is not None: + yield indexable + page += 1 + if page >= total_pages: + return + if max_pages is not None and (page - start_page) >= max_pages: + return + + async def get_item(self, uuid: str) -> Optional[Dict[str, Any]]: + """`GET /core/items/{uuid}`. Returns None on 404.""" + try: + return await self._get_json(f"/core/items/{uuid}") + except httpx.HTTPStatusError as exc: + if exc.response.status_code == 404: + return None + raise + + async def get_bundles(self, item_uuid: str) -> List[Dict[str, Any]]: + """List bundles for an item; returns the `_embedded.bundles` array.""" + payload = await self._get_json(f"/core/items/{item_uuid}/bundles") + return payload.get("_embedded", {}).get("bundles", []) or [] + + async def get_bitstreams(self, bundle_uuid: str) -> List[Dict[str, Any]]: + """List bitstreams in a bundle.""" + payload = await self._get_json(f"/core/bundles/{bundle_uuid}/bitstreams") + return payload.get("_embedded", {}).get("bitstreams", []) or [] + + async def get_bitstream_content(self, bitstream_uuid: str) -> bytes: + """Raw bytes of `/core/bitstreams/{uuid}/content`. + + Tries the bearer token first (if configured), falls back to anonymous + on 401/403 — covers the case where the token is inactive but the + bitstream is publicly accessible. + """ + path = f"/core/bitstreams/{bitstream_uuid}/content" + if self._cfg.token: + try: + async with self._semaphore: + response = await self.client.get( + path, + headers={"Authorization": f"Bearer {self._cfg.token}"}, + ) + response.raise_for_status() + return response.content + except httpx.HTTPStatusError as exc: + if exc.response.status_code not in (401, 403): + raise + logger.debug("Bearer rejected on %s, retrying anonymously", path) + async with self._semaphore: + response = await self.client.get(path) + response.raise_for_status() + return response.content + + +@asynccontextmanager +async def dspace_client(cfg: EthzResearchCollectionConfig) -> AsyncIterator[DSpaceClient]: + """Convenience helper: `async with dspace_client(cfg) as client: ...`.""" + async with DSpaceClient(cfg) as c: + yield c diff --git a/src/index/ethz_research_collection/embed.py b/src/index/ethz_research_collection/embed.py new file mode 100644 index 0000000..9527d8b --- /dev/null +++ b/src/index/ethz_research_collection/embed.py @@ -0,0 +1,135 @@ +"""RCP embedding client (OpenAI-compatible /v1/embeddings). + +Qwen3-Embedding-8B is instruction-aware: queries are wrapped with the +configured instruction template; passages are sent verbatim. The first +response's vector dimension is asserted against `cfg.rcp.embedding_dim`. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import Iterable, List, Optional + +import httpx + +from .config import RcpConfig + +logger = logging.getLogger(__name__) + + +class EmbedError(Exception): + pass + + +class RCPEmbedder: + """Async embedding client. Use as an async context manager.""" + + def __init__(self, cfg: RcpConfig): + self._cfg = cfg + self._client: Optional[httpx.AsyncClient] = None + self._semaphore = asyncio.Semaphore(cfg.max_concurrency) + self._observed_dim: Optional[int] = None + + async def __aenter__(self) -> "RCPEmbedder": + if not self._cfg.token: + msg = "RCP_TOKEN is required to call the RCP embedding endpoint." + raise EmbedError(msg) + self._client = httpx.AsyncClient( + base_url=self._cfg.base_url.rstrip("/"), + headers={ + "Authorization": f"Bearer {self._cfg.token}", + "Content-Type": "application/json", + }, + timeout=self._cfg.timeout_seconds, + ) + return self + + async def __aexit__(self, exc_type, exc, tb) -> None: + if self._client is not None: + await self._client.aclose() + self._client = None + + @property + def client(self) -> httpx.AsyncClient: + if self._client is None: + msg = "RCPEmbedder must be used inside `async with`." + raise RuntimeError(msg) + return self._client + + async def _post_embeddings(self, inputs: List[str]) -> List[List[float]]: + last_exc: Optional[Exception] = None + for attempt in range(4): + try: + async with self._semaphore: + response = await self.client.post( + "/embeddings", + json={"model": self._cfg.embedding_model, "input": inputs}, + ) + response.raise_for_status() + payload = response.json() + break + except httpx.HTTPStatusError as exc: + if exc.response.status_code in (429, 500, 502, 503, 504): + last_exc = exc + delay = 2 ** attempt + logger.warning( + "RCP embeddings %s, retry in %ds (attempt %d)", + exc.response.status_code, delay, attempt + 1, + ) + await asyncio.sleep(delay) + continue + raise + except httpx.HTTPError as exc: + last_exc = exc + delay = 2 ** attempt + logger.warning("RCP embeddings transport error: %s, retry in %ds", exc, delay) + await asyncio.sleep(delay) + else: + msg = f"RCP embeddings failed after retries: {last_exc}" + raise EmbedError(msg) from last_exc + + vectors = [item["embedding"] for item in payload.get("data", [])] + if not vectors: + msg = "RCP embeddings returned empty response." + raise EmbedError(msg) + + observed = len(vectors[0]) + if self._observed_dim is None: + self._observed_dim = observed + if observed != self._cfg.embedding_dim: + msg = ( + f"Embedding dim mismatch: configured " + f"{self._cfg.embedding_dim}, RCP returned {observed}." + ) + raise EmbedError(msg) + return vectors + + async def embed_passage(self, texts: Iterable[str]) -> List[List[float]]: + """Embed plain passage strings (no instruction prefix).""" + batch = list(texts) + if not batch: + return [] + return await self._post_embeddings(batch) + + async def embed_query( + self, + query: str, + instruction: Optional[str] = None, + ) -> List[float]: + """Embed a query with the Qwen3 instruction template.""" + instr = instruction or self._cfg.query_instruction + formatted = f"Instruct: {instr}\nQuery: {query}" + vectors = await self._post_embeddings([formatted]) + return vectors[0] + + async def embed_passages_batched( + self, + texts: List[str], + ) -> List[List[float]]: + """Embed `texts` in batches of `cfg.batch_size`, preserving order.""" + out: List[List[float]] = [] + for i in range(0, len(texts), self._cfg.batch_size): + chunk = texts[i : i + self._cfg.batch_size] + out.extend(await self.embed_passage(chunk)) + return out diff --git a/src/index/ethz_research_collection/extract_matches.py b/src/index/ethz_research_collection/extract_matches.py new file mode 100644 index 0000000..9e7dcc6 --- /dev/null +++ b/src/index/ethz_research_collection/extract_matches.py @@ -0,0 +1,155 @@ +"""Extract matches stage: regex GitHub / HuggingFace URLs from fetched text. + +Output: `matches.jsonl`, one `MatchRecord` per item that contains at least +one matched URL. Reuses `classify_github_url` for GitHub canonicalisation +and adds a small HuggingFace classifier alongside. +""" + +from __future__ import annotations + +import json +import logging +import re +from collections import Counter +from typing import Dict, List, Optional, Set, Tuple +from urllib.parse import urlparse + +from src.v2.ingest.detection.github_url_classifier import classify_github_url + +from .config import EthzResearchCollectionIndexConfig +from .models import MatchRecord +from .paths import matches_path, text_dir + +logger = logging.getLogger(__name__) + +_URL_RE = re.compile( + r"https?://[^\s<>\"'\)\]\}]+", + re.IGNORECASE, +) + +_HF_HOSTS = {"huggingface.co", "hf.co"} +_GH_HOSTS = {"github.com", "www.github.com"} + + +def _hostname(url: str) -> str: + try: + return (urlparse(url).hostname or "").lower() + except Exception: + return "" + + +def classify_huggingface_url(url: str) -> Optional[Tuple[str, str]]: + """Return (kind, canonical_url) for a HuggingFace URL, or None. + + `kind` is one of: 'model', 'dataset', 'space', 'org', 'user', 'other'. + """ + host = _hostname(url) + if host not in _HF_HOSTS: + return None + parts = [p for p in urlparse(url).path.split("/") if p] + if not parts: + return ("other", url) + head = parts[0].lower() + if head == "datasets" and len(parts) >= 2: + canonical = "https://huggingface.co/datasets/" + "/".join(parts[1:3]) + return ("dataset", canonical) + if head == "spaces" and len(parts) >= 2: + canonical = "https://huggingface.co/spaces/" + "/".join(parts[1:3]) + return ("space", canonical) + if head in {"organizations", "orgs"} and len(parts) >= 2: + return ("org", f"https://huggingface.co/{parts[1]}") + # Bare path: user/model or just user. + if len(parts) == 1: + return ("user", f"https://huggingface.co/{parts[0]}") + canonical = f"https://huggingface.co/{parts[0]}/{parts[1]}" + return ("model", canonical) + + +def _canonicalise(url: str) -> Optional[str]: + """Return a canonical URL string if the URL points at GitHub or HF, else None. + + GitHub URLs that classify_github_url rejects (issues, blobs, etc.) are + kept as-is — they're still valid evidence of GitHub references. + """ + host = _hostname(url) + if host in _GH_HOSTS: + try: + result = classify_github_url(url) + return result.normalized_url + except Exception: + return url + if host in _HF_HOSTS: + hf = classify_huggingface_url(url) + return hf[1] if hf else None + return None + + +def _extract_from_text(text: str) -> Tuple[Set[str], Counter]: + found: Set[str] = set() + counts: Counter = Counter() + for raw in _URL_RE.findall(text): + # Trim trailing punctuation that often clings to URLs in prose. + clean = raw.rstrip(".,;:'\"()[]{}<>") + canonical = _canonicalise(clean) + if canonical is None: + continue + found.add(canonical) + counts[_hostname(canonical)] += 1 + return found, counts + + +def extract_matches(_cfg: EthzResearchCollectionIndexConfig) -> dict: + """Walk text/ files, write matches.jsonl. Returns counts dict.""" + out_path = matches_path() + text_files = sorted(text_dir().glob("*.txt")) + if not text_files: + logger.warning("No text files. Run `fetch-text` first.") + out_path.write_text("", encoding="utf-8") + return {"items": 0, "with_matches": 0} + + written = 0 + with out_path.open("w", encoding="utf-8") as out: + for tf in text_files: + uuid = tf.stem + text = tf.read_text(encoding="utf-8", errors="replace") + urls, counts = _extract_from_text(text) + if not urls: + continue + record = MatchRecord( + uuid=uuid, + matched_urls=sorted(urls), + counts_by_host=dict(counts), + ) + out.write(record.model_dump_json() + "\n") + written += 1 + + logger.info( + "extract_matches: items=%d with_matches=%d → %s", + len(text_files), written, out_path, + ) + return { + "items": len(text_files), + "with_matches": written, + "matches_path": str(out_path), + } + + +def load_matches() -> List[MatchRecord]: + """Read all match records from disk.""" + path = matches_path() + if not path.exists(): + return [] + out: List[MatchRecord] = [] + for line in path.read_text(encoding="utf-8").splitlines(): + line = line.strip() + if line: + out.append(MatchRecord(**json.loads(line))) + return out + + +def matches_by_uuid() -> Dict[str, MatchRecord]: + return {m.uuid: m for m in load_matches()} + + +def run(cfg: EthzResearchCollectionIndexConfig) -> dict: + return extract_matches(cfg) diff --git a/src/index/ethz_research_collection/extract_relations.py b/src/index/ethz_research_collection/extract_relations.py new file mode 100644 index 0000000..8a494fb --- /dev/null +++ b/src/index/ethz_research_collection/extract_relations.py @@ -0,0 +1,160 @@ +"""Extract relations stage: pull Person/Org UUIDs from Article JSON. + +ETH Research Collection (DSpace 7) embeds linked-entity UUIDs in +``relation.isAuthorOfPublication[*].value`` — each entry's ``authority`` +is a virtual slot ID (``virtual::N``) and ``value`` is the actual Person +UUID. Journals follow the same shape under +``relation.isJournalOfPublication``. + +This differs from EPFL Infoscience, which embeds UUIDs in +``cris.virtualsource.*`` / ``oairecerif.*`` ``authority`` fields. The +sister extractor in ``src/index/infoscience`` reads the latter; this +file reads ETH-RC's ``relation.is*OfPublication.value`` shape. + +We parse ``raw/items/{uuid}.json`` for every Article in ``matches.jsonl``, +dedupe the UUID sets, and write ``relations.jsonl`` plus flat +``persons.txt`` / ``organizations.txt`` for the next stage. +""" + +from __future__ import annotations + +import json +import logging +import re +from pathlib import Path +from typing import Iterable, List, Optional, Set + +from .config import EthzResearchCollectionIndexConfig +from .extract_matches import load_matches +from .models import RelationRecord +from .paths import ( + organizations_set_path, + persons_set_path, + raw_items_dir, + relations_path, +) + +logger = logging.getLogger(__name__) + +# DSpace 7 ``relation.is*OfPublication`` fields carry the linked entity's +# UUID in ``value`` (and a virtual slot id in ``authority``). For ETH RC +# author UUIDs and journal UUIDs both follow this pattern. +_PERSON_RELATION_FIELDS = ( + "relation.isAuthorOfPublication", +) + +# ETH RC publication metadata does not expose a direct OrgUnit relation +# (no equivalent of infoscience's ``cris.virtual.department``). Org-side +# affiliations are reachable via the Person record's employment data +# (fetched in the ``fetch-related`` stage). Keep this tuple empty so the +# article-level org set stays empty until the Person stage populates it +# transitively. +_ORG_RELATION_FIELDS: tuple[str, ...] = () + +_UUID_RE = re.compile( + r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$", + re.IGNORECASE, +) + + +def _looks_like_uuid(s: Optional[str]) -> bool: + return bool(s) and bool(_UUID_RE.match(s)) + + +def _relation_values(metadata: dict, fields: Iterable[str]) -> List[str]: + """Read UUID strings from the ``value`` slot of each relation entry.""" + out: List[str] = [] + for field in fields: + for entry in metadata.get(field, []) or []: + value = entry.get("value") if isinstance(entry, dict) else None + if _looks_like_uuid(value): + out.append(value) + return out + + +def _load_item(uuid: str) -> Optional[dict]: + p = raw_items_dir() / f"{uuid}.json" + if not p.exists(): + return None + return json.loads(p.read_text(encoding="utf-8")) + + +def _dedupe_preserve(seq: Iterable[str]) -> List[str]: + seen: Set[str] = set() + out: List[str] = [] + for x in seq: + if x in seen: + continue + seen.add(x) + out.append(x) + return out + + +def extract_relations(_cfg: EthzResearchCollectionIndexConfig) -> dict: + matches = load_matches() + if not matches: + logger.warning("No matches.jsonl entries. Run `extract-matches` first.") + relations_path().write_text("", encoding="utf-8") + return {"articles": 0, "persons": 0, "organizations": 0} + + persons: Set[str] = set() + orgs: Set[str] = set() + written = 0 + + with relations_path().open("w", encoding="utf-8") as out: + for match in matches: + item = _load_item(match.uuid) + if item is None: + logger.debug("Missing raw item for matched uuid %s", match.uuid) + continue + md = item.get("metadata", {}) or {} + person_uuids = _dedupe_preserve( + _relation_values(md, _PERSON_RELATION_FIELDS), + ) + org_uuids = _dedupe_preserve( + _relation_values(md, _ORG_RELATION_FIELDS), + ) + if not person_uuids and not org_uuids: + continue + record = RelationRecord( + article_uuid=match.uuid, + person_uuids=person_uuids, + org_uuids=org_uuids, + ) + out.write(record.model_dump_json() + "\n") + written += 1 + persons.update(person_uuids) + orgs.update(org_uuids) + + persons_set_path().write_text("\n".join(sorted(persons)), encoding="utf-8") + organizations_set_path().write_text("\n".join(sorted(orgs)), encoding="utf-8") + + summary = { + "articles": written, + "persons": len(persons), + "organizations": len(orgs), + "relations_path": str(relations_path()), + } + logger.info("extract_relations: %s", summary) + return summary + + +def load_relations() -> List[RelationRecord]: + p = relations_path() + if not p.exists(): + return [] + out: List[RelationRecord] = [] + for line in p.read_text(encoding="utf-8").splitlines(): + if line.strip(): + out.append(RelationRecord(**json.loads(line))) + return out + + +def load_set(path: Path) -> Set[str]: + if not path.exists(): + return set() + return {ln.strip() for ln in path.read_text(encoding="utf-8").splitlines() if ln.strip()} + + +def run(cfg: EthzResearchCollectionIndexConfig) -> dict: + return extract_relations(cfg) diff --git a/src/index/ethz_research_collection/fetch_related.py b/src/index/ethz_research_collection/fetch_related.py new file mode 100644 index 0000000..dc56c15 --- /dev/null +++ b/src/index/ethz_research_collection/fetch_related.py @@ -0,0 +1,109 @@ +"""Fetch related stage: GET /core/items/{uuid} for each Person/Org UUID. + +Reads `persons.txt` and `organizations.txt` (produced by `extract_relations`), +fetches each unique UUID once, and persists the raw JSON to +`raw/persons/{uuid}.json` or `raw/organizations/{uuid}.json`. Skips +already-fetched files unless `refresh=True`. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from pathlib import Path +from typing import Iterable, Optional + +import httpx + +from .config import EthzResearchCollectionIndexConfig +from .dspace import DSpaceClient +from .extract_relations import load_set +from .paths import ( + organizations_set_path, + persons_set_path, + raw_organizations_dir, + raw_persons_dir, +) + +logger = logging.getLogger(__name__) + + +async def _fetch_one( + client: DSpaceClient, + uuid: str, + out_dir: Path, + *, + refresh: bool = False, +) -> str: + out_path = out_dir / f"{uuid}.json" + if out_path.exists() and not refresh: + return "skipped-existing" + try: + item = await client.get_item(uuid) + except httpx.HTTPStatusError as exc: + if exc.response.status_code in (401, 403): + return "unauthorized" + if exc.response.status_code == 404: + return "not-found" + raise + if item is None: + return "not-found" + out_path.write_text(json.dumps(item, ensure_ascii=False, indent=2), encoding="utf-8") + return "written" + + +async def _fetch_set( + cfg: EthzResearchCollectionIndexConfig, + uuids: Iterable[str], + out_dir: Path, + *, + refresh: bool, +) -> dict: + counts = {"written": 0, "skipped-existing": 0, "unauthorized": 0, + "not-found": 0, "error": 0} + uuid_list = list(uuids) + if not uuid_list: + return counts + + async with DSpaceClient(cfg.research_collection) as client: + sem = asyncio.Semaphore(cfg.research_collection.max_concurrency) + + async def _bounded(u: str) -> str: + async with sem: + try: + return await _fetch_one(client, u, out_dir, refresh=refresh) + except Exception: + logger.exception("fetch-related failed for %s", u) + return "error" + + results = await asyncio.gather(*(_bounded(u) for u in uuid_list)) + for r in results: + counts[r] = counts.get(r, 0) + 1 + return counts + + +async def fetch_related( + cfg: EthzResearchCollectionIndexConfig, + *, + kind: str = "all", + refresh: bool = False, +) -> dict: + """`kind` ∈ {'person', 'org', 'all'}.""" + summary: dict = {"kind": kind} + if kind in ("person", "all"): + persons = load_set(persons_set_path()) + summary["persons"] = await _fetch_set( + cfg, persons, raw_persons_dir(), refresh=refresh, + ) + if kind in ("org", "all"): + orgs = load_set(organizations_set_path()) + summary["organizations"] = await _fetch_set( + cfg, orgs, raw_organizations_dir(), refresh=refresh, + ) + logger.info("fetch_related: %s", json.dumps(summary)) + return summary + + +def run(cfg: EthzResearchCollectionIndexConfig, *, kind: str = "all", refresh: bool = False) -> dict: + return asyncio.run(fetch_related(cfg, kind=kind, refresh=refresh)) diff --git a/src/index/ethz_research_collection/models.py b/src/index/ethz_research_collection/models.py new file mode 100644 index 0000000..596db67 --- /dev/null +++ b/src/index/ethz_research_collection/models.py @@ -0,0 +1,140 @@ +"""Pydantic models passed between pipeline stages. + +The LanceDB row shapes live in `store.py` (PyArrow schemas, not Pydantic) so +the schemas aren't duplicated; these models cover on-disk JSON/JSONL and +in-memory transfer between stages. +""" + +from __future__ import annotations + +from typing import Dict, List, Optional + +from pydantic import BaseModel, Field + + +class DiscoverState(BaseModel): + """Persisted under `discover_state.json` for resumability.""" + + per_term_cursor: Dict[str, int] = Field(default_factory=dict) + per_term_total: Dict[str, int] = Field(default_factory=dict) + completed: Dict[str, bool] = Field(default_factory=dict) + last_run_iso: Optional[str] = None + + +class MatchRecord(BaseModel): + """One line in `matches.jsonl`.""" + + uuid: str + matched_urls: List[str] = Field(default_factory=list) + counts_by_host: Dict[str, int] = Field(default_factory=dict) + + +class RelationRecord(BaseModel): + """One line in `relations.jsonl`.""" + + article_uuid: str + person_uuids: List[str] = Field(default_factory=list) + org_uuids: List[str] = Field(default_factory=list) + + +class ChunkRecord(BaseModel): + """In-memory chunk before insertion into LanceDB.""" + + chunk_id: str + article_uuid: str + chunk_index: int + text: str + title: Optional[str] = None + abstract: Optional[str] = None + authors: List[str] = Field(default_factory=list) + author_uuids: List[str] = Field(default_factory=list) + doi: Optional[str] = None + publication_date: Optional[str] = None + year: Optional[int] = None + publication_type: Optional[str] = None + language: Optional[str] = None + subjects: List[str] = Field(default_factory=list) + keywords: List[str] = Field(default_factory=list) + lab: Optional[str] = None + lab_uuid: Optional[str] = None + org_uuids: List[str] = Field(default_factory=list) + research_collection_url: Optional[str] = None + matched_urls: List[str] = Field(default_factory=list) + + +class ArticleRecord(BaseModel): + """One row of `ethz_research_collection_articles`.""" + + article_uuid: str + title: Optional[str] = None + abstract: Optional[str] = None + keywords: List[str] = Field(default_factory=list) + subjects: List[str] = Field(default_factory=list) + authors: List[str] = Field(default_factory=list) + author_uuids: List[str] = Field(default_factory=list) + doi: Optional[str] = None + publication_date: Optional[str] = None + year: Optional[int] = None + publication_type: Optional[str] = None + language: Optional[str] = None + journal: Optional[str] = None + journal_uuid: Optional[str] = None + # ETH RC's `ethz.*` extension fields. None of these are present on + # EPFL Infoscience records (which uses different namespaces); they are + # only populated when the source DSpace deployment is the ETH Research + # Collection. + scopus_id: Optional[str] = None + wos_id: Optional[str] = None + journal_volume: Optional[str] = None + journal_issue: Optional[str] = None + pages_start: Optional[str] = None + journal_abbreviated: Optional[str] = None + publisher: Optional[str] = None + issn: Optional[str] = None + handle_uri: Optional[str] = None + lab: Optional[str] = None + lab_uuid: Optional[str] = None + org_uuids: List[str] = Field(default_factory=list) + research_collection_url: Optional[str] = None + matched_urls: List[str] = Field(default_factory=list) + chunk_count: int = 0 + + +class PersonRecord(BaseModel): + """One row of `ethz_research_collection_persons`.""" + + person_uuid: str + name: Optional[str] = None + given_name: Optional[str] = None + family_name: Optional[str] = None + orcid: Optional[str] = None + sciper_id: Optional[str] = None + scopus_id: Optional[str] = None + email_hash: Optional[str] = None + primary_affiliation: Optional[str] = None + primary_affiliation_uuid: Optional[str] = None + affiliation_uuids: List[str] = Field(default_factory=list) + position: Optional[str] = None + biography: Optional[str] = None + research_interests: List[str] = Field(default_factory=list) + profile_url: Optional[str] = None + related_article_uuids: List[str] = Field(default_factory=list) + + +class OrganizationRecord(BaseModel): + """One row of `ethz_research_collection_organizations`.""" + + org_uuid: str + name: Optional[str] = None + acronym: Optional[str] = None + aliases: List[str] = Field(default_factory=list) + parent_org_uuid: Optional[str] = None + parent_org_chain: List[str] = Field(default_factory=list) + parent_org_chain_names: List[str] = Field(default_factory=list) + description: Optional[str] = None + sciper_unit_id: Optional[str] = None + ror_id: Optional[str] = None + unit_manager_uuid: Optional[str] = None + unit_manager_name: Optional[str] = None + research_collection_url: Optional[str] = None + related_article_uuids: List[str] = Field(default_factory=list) diff --git a/src/index/ethz_research_collection/parsers.py b/src/index/ethz_research_collection/parsers.py new file mode 100644 index 0000000..0d42d33 --- /dev/null +++ b/src/index/ethz_research_collection/parsers.py @@ -0,0 +1,183 @@ +"""DSpace JSON → Pydantic record parsers. + +Centralises the metadata-key conventions DSpace uses so the indexing +stages don't repeat dict-walking. All inputs are raw item JSON dicts as +returned by `/server/api/core/items/{uuid}` or as embedded inside +`/discover/search/objects`. +""" + +from __future__ import annotations + +import re +from typing import Any, Dict, List, Optional + +from .models import ArticleRecord, OrganizationRecord, PersonRecord + +_YEAR_RE = re.compile(r"\b(19|20)\d{2}\b") + + +def first_value(metadata: dict, field: str) -> Optional[str]: + values = metadata.get(field) or [] + if not isinstance(values, list) or not values: + return None + entry = values[0] + if isinstance(entry, dict): + return entry.get("value") + return None + + +def all_values(metadata: dict, field: str) -> List[str]: + out: List[str] = [] + for entry in metadata.get(field) or []: + if isinstance(entry, dict): + v = entry.get("value") + if v: + out.append(v) + return out + + +def first_authority(metadata: dict, field: str) -> Optional[str]: + for entry in metadata.get(field) or []: + if isinstance(entry, dict): + a = entry.get("authority") + if a: + return a + return None + + +def _research_collection_url(uuid: str, entity: str) -> str: + return f"https://www.research-collection.ethz.ch/entities/{entity}/{uuid}" + + +def _year_from_date(date: Optional[str]) -> Optional[int]: + if not date: + return None + m = _YEAR_RE.search(date) + return int(m.group(0)) if m else None + + +def parse_article(item: Dict[str, Any], matched_urls: Optional[List[str]] = None) -> ArticleRecord: + md = item.get("metadata", {}) or {} + uuid = item.get("uuid") or "" + publication_date = first_value(md, "dc.date.issued") + # ETH RC stores per-author UUIDs in `relation.isAuthorOfPublication[*].value` + # (see `extract_relations.py`); `dc.contributor.author.authority` is a + # virtual slot id (`virtual::N`), not a UUID. + author_uuids = [ + entry.get("value") + for entry in (md.get("relation.isAuthorOfPublication") or []) + if isinstance(entry, dict) and entry.get("value") + ] + return ArticleRecord( + article_uuid=uuid, + title=first_value(md, "dc.title"), + abstract=first_value(md, "dc.description.abstract"), + keywords=all_values(md, "dc.subject"), + subjects=all_values(md, "dc.subject"), + authors=all_values(md, "dc.contributor.author"), + author_uuids=author_uuids, + doi=first_value(md, "dc.identifier.doi"), + publication_date=publication_date, + year=_year_from_date(publication_date), + publication_type=first_value(md, "dc.type"), + language=first_value(md, "dc.language.iso"), + # Journal: prefer ETH-side fields when present (ETH RC populates + # ``ethz.journal.title``; EPFL Infoscience uses ``dc.relation.journal``). + journal=( + first_value(md, "ethz.journal.title") + or first_value(md, "dc.relation.journal") + ), + journal_uuid=first_authority(md, "dc.relation.journal"), + scopus_id=first_value(md, "ethz.identifier.scopus"), + wos_id=first_value(md, "ethz.identifier.wos"), + journal_volume=first_value(md, "ethz.journal.volume"), + journal_issue=first_value(md, "ethz.journal.issue"), + pages_start=first_value(md, "ethz.pages.start"), + journal_abbreviated=first_value(md, "ethz.journal.abbreviated"), + publisher=first_value(md, "dc.publisher"), + issn=first_value(md, "dc.identifier.issn"), + handle_uri=first_value(md, "dc.identifier.uri"), + lab=first_value(md, "cris.virtual.department"), + lab_uuid=first_authority(md, "cris.virtual.department"), + org_uuids=sorted({ + entry.get("authority") + for field in ("cris.virtual.department", + "cris.virtual.parent-organization", + "oairecerif.author.affiliation") + for entry in (md.get(field) or []) + if isinstance(entry, dict) and entry.get("authority") + }), + research_collection_url=_research_collection_url(uuid, "publication"), + matched_urls=matched_urls or [], + ) + + +def parse_person(item: Dict[str, Any]) -> PersonRecord: + md = item.get("metadata", {}) or {} + uuid = item.get("uuid") or "" + name = ( + first_value(md, "dc.title") + or first_value(md, "person.name") + or first_value(md, "person.familyName") + ) + given = first_value(md, "person.givenName") or first_value(md, "eperson.firstname") + family = first_value(md, "person.familyName") or first_value(md, "eperson.lastname") + if not name and (given or family): + name = " ".join(p for p in (given, family) if p) + + # ETH RC stores affiliation as a free-text "person.department" string + # (e.g. "03996 - Benini, Luca / Benini, Luca"). Use it as + # primary_affiliation when the DSpace-CRIS-style fields are absent. + department_text = first_value(md, "person.department") + primary_affiliation = ( + first_value(md, "person.affiliation.name") or department_text + ) + + edu_affiliation = first_value(md, "person.edu.affiliation") # e.g. "faculty" + return PersonRecord( + person_uuid=uuid, + name=name, + given_name=given, + family_name=family, + orcid=first_value(md, "person.identifier.orcid"), + sciper_id=first_value(md, "epfl.sciperId") or first_value(md, "cris.virtual.sciperId"), + scopus_id=first_value(md, "person.identifier.scopus-author-id"), + primary_affiliation=primary_affiliation, + primary_affiliation_uuid=first_authority(md, "person.affiliation.name"), + affiliation_uuids=sorted({ + entry.get("authority") + for entry in (md.get("person.affiliation.name") or []) + if isinstance(entry, dict) and entry.get("authority") + }), + position=first_value(md, "oairecerif.person.position") or edu_affiliation, + biography=first_value(md, "dc.description") or first_value(md, "person.biography"), + research_interests=all_values(md, "person.researchInterests"), + profile_url=_research_collection_url(uuid, "person"), + ) + + +def parse_organization(item: Dict[str, Any]) -> OrganizationRecord: + md = item.get("metadata", {}) or {} + uuid = item.get("uuid") or "" + parent_chain_authorities = [ + entry.get("authority") + for entry in (md.get("cris.virtual.parent-organization") or []) + if isinstance(entry, dict) and entry.get("authority") + ] + parent_chain_names = all_values(md, "cris.virtual.parent-organization") + return OrganizationRecord( + org_uuid=uuid, + name=first_value(md, "dc.title") or first_value(md, "organization.legalName"), + acronym=first_value(md, "organization.identifier.acronym"), + aliases=all_values(md, "organization.alternateName"), + parent_org_uuid=parent_chain_authorities[0] if parent_chain_authorities else None, + parent_org_chain=parent_chain_authorities, + parent_org_chain_names=parent_chain_names, + description=first_value(md, "dc.description") + or first_value(md, "dc.description.abstract"), + sciper_unit_id=first_value(md, "cris.virtual.unitId") + or first_value(md, "epfl.unitId"), + unit_manager_uuid=first_authority(md, "cris.virtual.unitManager"), + unit_manager_name=first_value(md, "cris.virtual.unitManager"), + research_collection_url=_research_collection_url(uuid, "orgunit"), + ) diff --git a/src/index/ethz_research_collection/paths.py b/src/index/ethz_research_collection/paths.py new file mode 100644 index 0000000..15f6ba1 --- /dev/null +++ b/src/index/ethz_research_collection/paths.py @@ -0,0 +1,89 @@ +"""Filesystem paths for the ETH Research Collection index. + +Root resolves to `${INDEX_DATA_DIR:-data/index}/ethz-research-collection` and is created +on first access. Sub-paths are derived constants below. +""" + +from __future__ import annotations + +import os +from pathlib import Path + +_DEFAULT_ROOT = "data/index" +_SOURCE = "ethz-research-collection" + + +def index_data_root() -> Path: + """Top-level multi-source data root (`data/index` by default).""" + return Path(os.getenv("INDEX_DATA_DIR", _DEFAULT_ROOT)).expanduser().resolve() + + +def ethz_research_collection_data_dir() -> Path: + """Per-source root for the ETH Research Collection. Creates the directory tree if needed.""" + root = index_data_root() / _SOURCE + root.mkdir(parents=True, exist_ok=True) + return root + + +def raw_items_dir() -> Path: + p = ethz_research_collection_data_dir() / "raw" / "items" + p.mkdir(parents=True, exist_ok=True) + return p + + +def raw_persons_dir() -> Path: + p = ethz_research_collection_data_dir() / "raw" / "persons" + p.mkdir(parents=True, exist_ok=True) + return p + + +def raw_organizations_dir() -> Path: + p = ethz_research_collection_data_dir() / "raw" / "organizations" + p.mkdir(parents=True, exist_ok=True) + return p + + +def text_dir() -> Path: + p = ethz_research_collection_data_dir() / "text" + p.mkdir(parents=True, exist_ok=True) + return p + + +def vector_db_dir() -> Path: + """Persistent root for the vector store (ChromaDB).""" + p = ethz_research_collection_data_dir() / "chroma" + p.mkdir(parents=True, exist_ok=True) + return p + + +def discover_state_path() -> Path: + return ethz_research_collection_data_dir() / "discover_state.json" + + +def matches_path() -> Path: + return ethz_research_collection_data_dir() / "matches.jsonl" + + +def relations_path() -> Path: + return ethz_research_collection_data_dir() / "relations.jsonl" + + +def persons_set_path() -> Path: + return ethz_research_collection_data_dir() / "persons.txt" + + +def organizations_set_path() -> Path: + return ethz_research_collection_data_dir() / "organizations.txt" + + +def duckdb_path() -> Path: + """SQLite-style structured store for articles / persons / orgs / chunks.""" + p = ethz_research_collection_data_dir() / "duckdb" + p.mkdir(parents=True, exist_ok=True) + return p / "ethz_research_collection.duckdb" + + +def dumps_dir() -> Path: + p = ethz_research_collection_data_dir() / "dumps" + p.mkdir(parents=True, exist_ok=True) + return p diff --git a/src/index/ethz_research_collection/pipeline.py b/src/index/ethz_research_collection/pipeline.py new file mode 100644 index 0000000..e1c9139 --- /dev/null +++ b/src/index/ethz_research_collection/pipeline.py @@ -0,0 +1,232 @@ +"""Query pipeline: filter → vector → rerank, with cross-entity joins. + +Qdrant-backed. + +`where` accepts a JSON-style dict of `{field: }`. Each +field's operator can be: + + * a scalar value (interpreted as ``$eq``) + * a list (``$in``) + * a dict with one of: ``$eq``, ``$ne``, ``$in``, ``$contains``, + ``$gte`` and/or ``$lte`` + +Example: ``{"year": {"$gte": 2022}, "has_github_match": true, +"matched_urls": {"$contains": "huggingface.co"}}``. + +Modes: + * `hybrid` (default): vector top-K filtered → rerank → top-N + * `vector-only`: vector top-K filtered, no rerank + * `lexical`: scroll with payload filter only (Qdrant has no built-in + BM25; use a `$contains`-style filter for keyword search) + * `filter-only`: structured predicates only, no scoring +""" + +from __future__ import annotations + +import logging +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional, Sequence + +from .config import EthzResearchCollectionIndexConfig +from .embed import RCPEmbedder +from .rerank import RCPReranker +from .store import ( + ARTICLES_COLLECTION, + CHUNKS_COLLECTION, + ORGANIZATIONS_COLLECTION, + PERSONS_COLLECTION, + QdrantStore, + article_point_id, + build_filter, + organization_point_id, + person_point_id, +) + +logger = logging.getLogger(__name__) + + +TARGET_COLLECTIONS = { + "chunks": CHUNKS_COLLECTION, + "articles": ARTICLES_COLLECTION, + "persons": PERSONS_COLLECTION, + "organizations": ORGANIZATIONS_COLLECTION, +} + +_DOC_FIELD = { + CHUNKS_COLLECTION: "text", + ARTICLES_COLLECTION: "abstract", + PERSONS_COLLECTION: "biography", + ORGANIZATIONS_COLLECTION: "description", +} + + +@dataclass +class QueryResult: + target: str + rows: List[Dict[str, Any]] = field(default_factory=list) + related_persons: Dict[str, List[Dict[str, Any]]] = field(default_factory=dict) + related_organizations: Dict[str, List[Dict[str, Any]]] = field(default_factory=dict) + + +def _flatten(hits: Sequence[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Lift the payload one level so the caller doesn't have to dig.""" + out: List[Dict[str, Any]] = [] + for h in hits: + row = dict(h.get("payload") or {}) + row["id"] = h.get("id") + if "score" in h: + row["score"] = h["score"] + out.append(row) + return out + + +async def _vector_search( + cfg: EthzResearchCollectionIndexConfig, + store: QdrantStore, + collection: str, + query: str, + where: Optional[dict], + top_k: int, +) -> List[Dict[str, Any]]: + async with RCPEmbedder(cfg.rcp) as embedder: + vec = await embedder.embed_query(query) + hits = store.search( + collection, + query_vector=vec, + top_k=top_k, + query_filter=build_filter(where), + ) + return _flatten(hits) + + +def _lexical_search( + store: QdrantStore, + collection: str, + query: str, + where: Optional[dict], + top_k: int, +) -> List[Dict[str, Any]]: + # Qdrant has no built-in BM25; "lexical" here means + # filter-on-text-contains. Combine the user's where with a $contains + # over the canonical text field for the target collection. + text_field = _DOC_FIELD.get(collection, "text") + payload = dict(where or {}) + payload[text_field] = {"$contains": query} + hits = store.scroll( + collection, + query_filter=build_filter(payload), + limit=top_k, + ) + return _flatten(hits) + + +def _filter_only( + store: QdrantStore, + collection: str, + where: Optional[dict], + top_k: int, +) -> List[Dict[str, Any]]: + hits = store.scroll( + collection, + query_filter=build_filter(where), + limit=top_k, + ) + return _flatten(hits) + + +async def _rerank( + cfg: EthzResearchCollectionIndexConfig, + query: str, + rows: List[Dict[str, Any]], + target_collection: str, + top_n: int, +) -> List[Dict[str, Any]]: + if not rows: + return [] + doc_field = _DOC_FIELD.get(target_collection, "text") + docs = [ + r.get(doc_field) or r.get("text") or r.get("title") or r.get("name") or "" + for r in rows + ] + async with RCPReranker(cfg.rcp) as reranker: + hits = await reranker.rerank(query, docs, top_n=top_n) + if not hits: + return rows[:top_n] + return [rows[h.index] | {"rerank_score": h.score} for h in hits] + + +def _resolve_persons(store: QdrantStore, person_uuids: Sequence[str]) -> List[Dict[str, Any]]: + if not person_uuids: + return [] + ids = [person_point_id(u) for u in person_uuids] + return _flatten(store.lookup(PERSONS_COLLECTION, ids=ids)) + + +def _resolve_orgs(store: QdrantStore, org_uuids: Sequence[str]) -> List[Dict[str, Any]]: + if not org_uuids: + return [] + ids = [organization_point_id(u) for u in org_uuids] + return _flatten(store.lookup(ORGANIZATIONS_COLLECTION, ids=ids)) + + +def _row_key(row: Dict[str, Any]) -> str: + """Pick a stable per-row key for cross-entity join indexing.""" + return ( + row.get("article_uuid") + or row.get("person_uuid") + or row.get("org_uuid") + or row.get("chunk_id") + or row.get("id") + or "" + ) + + +async def query( + cfg: EthzResearchCollectionIndexConfig, + text: str, + *, + target: str = "chunks", + where: Optional[dict] = None, + top_k: int = 50, + top_n: int = 10, + mode: str = "hybrid", + with_authors: bool = False, + with_orgs: bool = False, +) -> QueryResult: + if target not in TARGET_COLLECTIONS: + msg = f"Unknown target {target!r}. Pick from {sorted(TARGET_COLLECTIONS)}." + raise ValueError(msg) + + store = QdrantStore.from_config(cfg) + name = TARGET_COLLECTIONS[target] + if not store.client.collection_exists(name): + msg = f"Collection {name!r} does not exist. Run `embed` first." + raise ValueError(msg) + + if mode == "filter-only": + rows = _filter_only(store, name, where, top_k) + elif mode == "lexical": + rows = _lexical_search(store, name, text, where, top_k) + elif mode == "vector-only": + rows = await _vector_search(cfg, store, name, text, where, top_k) + elif mode == "hybrid": + rows = await _vector_search(cfg, store, name, text, where, top_k) + rows = await _rerank(cfg, text, rows, name, top_n) + else: + msg = f"Unknown mode {mode!r}." + raise ValueError(msg) + + result = QueryResult(target=target, rows=rows) + if with_authors: + for row in rows: + uuids = row.get("author_uuids") or [] + row_key = _row_key(row) + if uuids and row_key: + result.related_persons[row_key] = _resolve_persons(store, uuids) + if with_orgs: + for row in rows: + uuids = row.get("org_uuids") or [] + row_key = _row_key(row) + if uuids and row_key: + result.related_organizations[row_key] = _resolve_orgs(store, uuids) + return result diff --git a/src/index/ethz_research_collection/rerank.py b/src/index/ethz_research_collection/rerank.py new file mode 100644 index 0000000..a9a630d --- /dev/null +++ b/src/index/ethz_research_collection/rerank.py @@ -0,0 +1,108 @@ +"""RCP reranker client. + +Posts to `{rcp.base_url}/rerank` with `{model, query, documents}`. +Returns reranked indices + scores. Trusts the server to apply +Qwen3-Reranker-8B's internal prompt format. +""" + +from __future__ import annotations + +import asyncio +import logging +from dataclasses import dataclass +from typing import List, Optional + +import httpx + +from .config import RcpConfig + +logger = logging.getLogger(__name__) + + +class RerankError(Exception): + pass + + +@dataclass +class RerankHit: + index: int + score: float + + +class RCPReranker: + def __init__(self, cfg: RcpConfig): + self._cfg = cfg + self._client: Optional[httpx.AsyncClient] = None + + async def __aenter__(self) -> "RCPReranker": + if not self._cfg.token: + msg = "RCP_TOKEN is required to call the RCP reranker endpoint." + raise RerankError(msg) + self._client = httpx.AsyncClient( + base_url=self._cfg.base_url.rstrip("/"), + headers={ + "Authorization": f"Bearer {self._cfg.token}", + "Content-Type": "application/json", + }, + timeout=self._cfg.timeout_seconds, + ) + return self + + async def __aexit__(self, exc_type, exc, tb) -> None: + if self._client is not None: + await self._client.aclose() + self._client = None + + @property + def client(self) -> httpx.AsyncClient: + if self._client is None: + msg = "RCPReranker must be used inside `async with`." + raise RuntimeError(msg) + return self._client + + async def rerank( + self, + query: str, + documents: List[str], + *, + top_n: Optional[int] = None, + ) -> List[RerankHit]: + if not documents: + return [] + body = { + "model": self._cfg.reranker_model, + "query": query, + "documents": documents, + } + if top_n is not None: + body["top_n"] = top_n + + last_exc: Optional[Exception] = None + for attempt in range(4): + try: + response = await self.client.post("/rerank", json=body) + response.raise_for_status() + payload = response.json() + break + except httpx.HTTPStatusError as exc: + if exc.response.status_code in (429, 500, 502, 503, 504): + last_exc = exc + await asyncio.sleep(2 ** attempt) + continue + raise + except httpx.HTTPError as exc: + last_exc = exc + await asyncio.sleep(2 ** attempt) + else: + msg = f"RCP rerank failed after retries: {last_exc}" + raise RerankError(msg) from last_exc + + results = payload.get("results") or payload.get("data") or [] + hits: List[RerankHit] = [] + for r in results: + idx = r.get("index") + score = r.get("relevance_score") or r.get("score") + if idx is None or score is None: + continue + hits.append(RerankHit(index=int(idx), score=float(score))) + return hits diff --git a/src/index/ethz_research_collection/storage/__init__.py b/src/index/ethz_research_collection/storage/__init__.py new file mode 100644 index 0000000..4848fc4 --- /dev/null +++ b/src/index/ethz_research_collection/storage/__init__.py @@ -0,0 +1,11 @@ +"""Storage layer for the ETH Research Collection index — DuckDB tables for articles, +persons, organisations, their bipartite edges, extracted artefact links, +and the chunks bookkeeping shared with the Qdrant vector store. + +Mirrors the sister-index pattern in `src/index/openalex/storage/` and +`src/index/huggingface/storage/`. +""" + +from .duckdb_store import DuckDBStore + +__all__ = ["DuckDBStore"] diff --git a/src/index/ethz_research_collection/storage/duckdb_store.py b/src/index/ethz_research_collection/storage/duckdb_store.py new file mode 100644 index 0000000..542cab4 --- /dev/null +++ b/src/index/ethz_research_collection/storage/duckdb_store.py @@ -0,0 +1,212 @@ +"""DuckDB lifecycle, schema bootstrap, and upsert helpers for the +ETH Research Collection index. Mirrors `src/index/openalex/storage/duckdb_store.py`. + +Source of truth for ingest is the on-disk `raw/{items,persons,organizations}/` +JSON tree produced by the discover / fetch-related stages and the +`scripts/dump_link_articles.py` link sweep. Re-ingesting from those JSONs +is idempotent — every upsert keys on the DSpace UUID. +""" + +from __future__ import annotations + +import datetime as _dt +import json +import logging +from contextlib import contextmanager +from pathlib import Path +from typing import TYPE_CHECKING, Any, Iterable, Iterator + +import duckdb + +from src.index.ethz_research_collection.paths import duckdb_path + +if TYPE_CHECKING: + pass + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +class DuckDBStore: + """Thin wrapper around DuckDB tuned for the ETH Research Collection schema. + + Construct with `DuckDBStore.open()` for the canonical path. Re-running + `bootstrap()` is idempotent; `transaction()` batches writes. + """ + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open(cls, db_path: Path | None = None) -> "DuckDBStore": + if db_path is None: + db_path = duckdb_path() + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + conn = self.connect() + conn.execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + @contextmanager + def transaction(self) -> Iterator[None]: + conn = self.connect() + conn.execute("BEGIN TRANSACTION") + try: + yield + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + + def count(self, table: str) -> int: + conn = self.connect() + return int(conn.execute(f"SELECT count(*) FROM {table}").fetchone()[0]) + + @staticmethod + def _now() -> _dt.datetime: + return _dt.datetime.now(tz=_dt.timezone.utc).replace(tzinfo=None) + + # ---- Upserts --------------------------------------------------------- + + def upsert_article(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="articles", + cols=( + "article_uuid", + "title", + "abstract", + "doi", + "publication_year", + "publication_type", + "journal", + "language", + "research_collection_url", + ), + row=row, + raw=raw, + ) + + def upsert_person(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="persons", + cols=( + "person_uuid", + "display_name", + "given_name", + "family_name", + "orcid", + "sciper_id", + "primary_affiliation", + "primary_affiliation_uuid", + ), + row=row, + raw=raw, + ) + + def upsert_organization(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="organizations", + cols=( + "org_uuid", + "name", + "acronym", + "parent_org_uuid", + "sciper_unit_id", + "ror_id", + ), + row=row, + raw=raw, + ) + + def _upsert( + self, + *, + table: str, + cols: tuple[str, ...], + row: dict[str, Any], + raw: dict[str, Any], + ) -> None: + all_cols = (*cols, "raw", "ingested_at") + placeholders = ", ".join(["?"] * len(all_cols)) + col_list = ", ".join(all_cols) + update_cols = ", ".join( + f"{c} = excluded.{c}" for c in (*cols[1:], "raw", "ingested_at") + ) + sql = ( + f"INSERT INTO {table} ({col_list}) VALUES ({placeholders}) " + f"ON CONFLICT ({cols[0]}) DO UPDATE SET {update_cols}" + ) + values: list[Any] = [row.get(c) for c in cols] + values.append(json.dumps(raw, ensure_ascii=False)) + values.append(self._now()) + self.connect().execute(sql, values) + + def upsert_article_persons( + self, + article_uuid: str, + person_positions: Iterable[tuple[str, int | None]], + ) -> None: + conn = self.connect() + for person_uuid, position in person_positions: + conn.execute( + "INSERT INTO article_persons (article_uuid, person_uuid, position) " + "VALUES (?, ?, ?) " + "ON CONFLICT (article_uuid, person_uuid) DO UPDATE SET position = " + "COALESCE(LEAST(article_persons.position, excluded.position), " + " excluded.position, article_persons.position)", + [article_uuid, person_uuid, position], + ) + + def upsert_article_orgs( + self, + article_uuid: str, + org_field_pairs: Iterable[tuple[str, str]], + ) -> None: + conn = self.connect() + for org_uuid, field in org_field_pairs: + conn.execute( + "INSERT INTO article_orgs (article_uuid, org_uuid, field) " + "VALUES (?, ?, ?) ON CONFLICT DO NOTHING", + [article_uuid, org_uuid, field], + ) + + def upsert_article_links( + self, + article_uuid: str, + rows: Iterable[tuple[str, str, str]], + ) -> None: + """rows: iterable of (host_label, url, source).""" + conn = self.connect() + for host_label, url, source in rows: + conn.execute( + "INSERT INTO article_links (article_uuid, host_label, url, source) " + "VALUES (?, ?, ?, ?) ON CONFLICT DO NOTHING", + [article_uuid, host_label, url, source], + ) diff --git a/src/index/ethz_research_collection/storage/ingest_raw.py b/src/index/ethz_research_collection/storage/ingest_raw.py new file mode 100644 index 0000000..0a51798 --- /dev/null +++ b/src/index/ethz_research_collection/storage/ingest_raw.py @@ -0,0 +1,390 @@ +"""Ingest from on-disk raw/* JSON tree → DuckDB. + +Uses DuckDB's native `read_json_objects` to slurp every file in parallel +in C++ — orders of magnitude faster than the previous Python-level loop, +which choked on WSL2's 9P filesystem when walking ~16k small JSON files. + +The discover / fetch-related stages and `scripts/dump_link_articles.py` +write their output to: + + data/index/ethz-research-collection/raw/items/{uuid}.json + data/index/ethz-research-collection/raw/persons/{uuid}.json + data/index/ethz-research-collection/raw/organizations/{uuid}.json + +This module reads each glob, projects the DSpace metadata fields we +care about into typed columns via JSON path expressions, and upserts +into the DuckDB tables defined in `schema.sql`. Idempotent on UUID. +""" + +from __future__ import annotations + +import json +import logging +from pathlib import Path +from typing import Dict + +from src.index.ethz_research_collection.paths import ( + raw_items_dir, + raw_organizations_dir, + raw_persons_dir, +) + +from .duckdb_store import DuckDBStore + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Articles +# --------------------------------------------------------------------------- + +# Note: DSpace metadata field names contain dots, so the JSON path needs them +# quoted. Path templates are reused for both the article columns and the +# child unnest queries. +_ARTICLE_UPSERT_SQL = """ +CREATE OR REPLACE TEMP TABLE _items AS + SELECT json FROM read_json_objects(?); + +INSERT INTO articles + (article_uuid, title, abstract, doi, publication_year, publication_type, + journal, language, research_collection_url, raw, ingested_at) +SELECT + json_extract_string(json, '$.uuid') AS article_uuid, + json_extract_string(json, '$.metadata."dc.title"[0].value') AS title, + json_extract_string(json, '$.metadata."dc.description.abstract"[0].value') AS abstract, + json_extract_string(json, '$.metadata."dc.identifier.doi"[0].value') AS doi, + TRY_CAST( + SUBSTR(json_extract_string(json, '$.metadata."dc.date.issued"[0].value'), 1, 4) + AS INTEGER + ) AS publication_year, + json_extract_string(json, '$.metadata."dc.type"[0].value') AS publication_type, + json_extract_string(json, '$.metadata."dc.relation.journal"[0].value') AS journal, + json_extract_string(json, '$.metadata."dc.language.iso"[0].value') AS language, + 'https://www.research-collection.ethz.ch/entities/publication/' || json_extract_string(json, '$.uuid') AS research_collection_url, + json AS raw, + CURRENT_TIMESTAMP AS ingested_at +FROM _items +WHERE json_extract_string(json, '$.uuid') IS NOT NULL +ON CONFLICT (article_uuid) DO UPDATE SET + title = excluded.title, + abstract = excluded.abstract, + doi = excluded.doi, + publication_year = excluded.publication_year, + publication_type = excluded.publication_type, + journal = excluded.journal, + language = excluded.language, + research_collection_url = excluded.research_collection_url, + raw = excluded.raw, + ingested_at = excluded.ingested_at; +""" + +# ETH RC stores author UUIDs in `relation.isAuthorOfPublication[*].value` +# (the `authority` field is a virtual slot id like `virtual::391633`, not a +# UUID). `dc.contributor.author` carries the display string + position but +# only some entries have an `authority` slot; the slot's `value` points at +# the Person UUID via the parallel `relation.isAuthorOfPublication` array. +# We read from the relation array directly — its `place` matches the +# corresponding `dc.contributor.author.place`. +_ARTICLE_PERSONS_SQL = """ +INSERT INTO article_persons (article_uuid, person_uuid, position) +SELECT + json_extract_string(i.json, '$.uuid') AS article_uuid, + json_extract_string(t.rel, '$.value') AS person_uuid, + TRY_CAST(json_extract_string(t.rel, '$.place') AS INTEGER) AS position +FROM _items i, + UNNEST( + CAST( + json_extract(i.json, '$.metadata."relation.isAuthorOfPublication"') + AS JSON[] + ) + ) AS t(rel) +WHERE json_extract_string(t.rel, '$.value') IS NOT NULL +ON CONFLICT (article_uuid, person_uuid) DO UPDATE SET + position = COALESCE( + LEAST(article_persons.position, excluded.position), + excluded.position, + article_persons.position + ); +""" + +# Unnest each org-bearing metadata field separately and union the rows so +# the `field` column records which DSpace key the authority came from. +_ARTICLE_ORGS_SQL_TEMPLATE = """ +INSERT INTO article_orgs (article_uuid, org_uuid, field) +SELECT article_uuid, org_uuid, field FROM ( + {unions} +) sub +WHERE org_uuid IS NOT NULL +ON CONFLICT DO NOTHING; +""" + +_ORG_FIELDS = ( + "cris.virtual.department", + "cris.virtual.parent-organization", + "oairecerif.author.affiliation", +) + + +def _orgs_union_sql() -> str: + parts = [] + for field in _ORG_FIELDS: + parts.append( + f""" + SELECT + json_extract_string(i.json, '$.uuid') AS article_uuid, + json_extract_string(t.org, '$.authority') AS org_uuid, + '{field}' AS field + FROM _items i, + UNNEST( + CAST( + json_extract(i.json, '$.metadata."{field}"') AS JSON[] + ) + ) AS t(org) + """, + ) + return " UNION ALL ".join(parts) + + +# --------------------------------------------------------------------------- +# Persons +# --------------------------------------------------------------------------- + +_PERSON_UPSERT_SQL = """ +CREATE OR REPLACE TEMP TABLE _persons AS + SELECT json FROM read_json_objects(?); + +INSERT INTO persons + (person_uuid, display_name, given_name, family_name, orcid, sciper_id, + primary_affiliation, primary_affiliation_uuid, raw, ingested_at) +SELECT + json_extract_string(json, '$.uuid') AS person_uuid, + COALESCE( + json_extract_string(json, '$.metadata."dc.title"[0].value'), + TRIM( + COALESCE(json_extract_string(json, '$.metadata."person.givenName"[0].value'), '') + || ' ' || + COALESCE(json_extract_string(json, '$.metadata."person.familyName"[0].value'), '') + ) + ) AS display_name, + COALESCE( + json_extract_string(json, '$.metadata."person.givenName"[0].value'), + json_extract_string(json, '$.metadata."eperson.firstname"[0].value') + ) AS given_name, + COALESCE( + json_extract_string(json, '$.metadata."person.familyName"[0].value'), + json_extract_string(json, '$.metadata."eperson.lastname"[0].value') + ) AS family_name, + json_extract_string(json, '$.metadata."person.identifier.orcid"[0].value') AS orcid, + COALESCE( + json_extract_string(json, '$.metadata."epfl.sciperId"[0].value'), + json_extract_string(json, '$.metadata."cris.virtual.sciperId"[0].value') + ) AS sciper_id, + json_extract_string(json, '$.metadata."person.affiliation.name"[0].value') AS primary_affiliation, + json_extract_string(json, '$.metadata."person.affiliation.name"[0].authority') AS primary_affiliation_uuid, + json AS raw, + CURRENT_TIMESTAMP AS ingested_at +FROM _persons +WHERE json_extract_string(json, '$.uuid') IS NOT NULL +ON CONFLICT (person_uuid) DO UPDATE SET + display_name = excluded.display_name, + given_name = excluded.given_name, + family_name = excluded.family_name, + orcid = excluded.orcid, + sciper_id = excluded.sciper_id, + primary_affiliation = excluded.primary_affiliation, + primary_affiliation_uuid = excluded.primary_affiliation_uuid, + raw = excluded.raw, + ingested_at = excluded.ingested_at; +""" + + +# --------------------------------------------------------------------------- +# Organizations +# --------------------------------------------------------------------------- + +_ORG_UPSERT_SQL = """ +CREATE OR REPLACE TEMP TABLE _orgs AS + SELECT json FROM read_json_objects(?); + +INSERT INTO organizations + (org_uuid, name, acronym, parent_org_uuid, sciper_unit_id, ror_id, raw, ingested_at) +SELECT + json_extract_string(json, '$.uuid') AS org_uuid, + COALESCE( + json_extract_string(json, '$.metadata."dc.title"[0].value'), + json_extract_string(json, '$.metadata."organization.legalName"[0].value') + ) AS name, + json_extract_string(json, '$.metadata."organization.identifier.acronym"[0].value') AS acronym, + json_extract_string(json, '$.metadata."cris.virtual.parent-organization"[0].authority') AS parent_org_uuid, + COALESCE( + json_extract_string(json, '$.metadata."cris.virtual.unitId"[0].value'), + json_extract_string(json, '$.metadata."epfl.unitId"[0].value') + ) AS sciper_unit_id, + json_extract_string(json, '$.metadata."cris.virtualsource.ror"[0].value') AS ror_id, + json AS raw, + CURRENT_TIMESTAMP AS ingested_at +FROM _orgs +WHERE json_extract_string(json, '$.uuid') IS NOT NULL +ON CONFLICT (org_uuid) DO UPDATE SET + name = excluded.name, + acronym = excluded.acronym, + parent_org_uuid = excluded.parent_org_uuid, + sciper_unit_id = excluded.sciper_unit_id, + ror_id = excluded.ror_id, + raw = excluded.raw, + ingested_at = excluded.ingested_at; +""" + + +# --------------------------------------------------------------------------- +# Public ingest functions +# --------------------------------------------------------------------------- + + +def ingest_articles(store: DuckDBStore, items_dir: Path | None = None) -> int: + items_dir = items_dir or raw_items_dir() + glob = str(items_dir / "*.json") + conn = store.connect() + # Run the three statements in one transaction. + conn.execute("BEGIN TRANSACTION") + try: + for stmt in _ARTICLE_UPSERT_SQL.strip().split(";"): + stmt = stmt.strip() + if not stmt: + continue + if "read_json_objects(?)" in stmt: + conn.execute(stmt + ";", [glob]) + else: + conn.execute(stmt + ";") + conn.execute(_ARTICLE_PERSONS_SQL) + conn.execute(_ARTICLE_ORGS_SQL_TEMPLATE.format(unions=_orgs_union_sql())) + n = conn.execute("SELECT count(*) FROM _items").fetchone()[0] + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + logger.info("ingest_articles: %d", n) + return int(n) + + +def _has_json_files(directory: Path) -> bool: + if not directory.exists(): + return False + for child in directory.iterdir(): + if child.suffix == ".json" and child.is_file(): + return True + return False + + +def ingest_persons(store: DuckDBStore, persons_dir: Path | None = None) -> int: + persons_dir = persons_dir or raw_persons_dir() + if not _has_json_files(persons_dir): + logger.info("ingest_persons: 0 (no JSON files in %s)", persons_dir) + return 0 + glob = str(persons_dir / "*.json") + conn = store.connect() + conn.execute("BEGIN TRANSACTION") + try: + for stmt in _PERSON_UPSERT_SQL.strip().split(";"): + stmt = stmt.strip() + if not stmt: + continue + if "read_json_objects(?)" in stmt: + conn.execute(stmt + ";", [glob]) + else: + conn.execute(stmt + ";") + n = conn.execute("SELECT count(*) FROM _persons").fetchone()[0] + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + logger.info("ingest_persons: %d", n) + return int(n) + + +def ingest_organizations( + store: DuckDBStore, + orgs_dir: Path | None = None, +) -> int: + orgs_dir = orgs_dir or raw_organizations_dir() + if not _has_json_files(orgs_dir): + logger.info("ingest_organizations: 0 (no JSON files in %s)", orgs_dir) + return 0 + glob = str(orgs_dir / "*.json") + conn = store.connect() + conn.execute("BEGIN TRANSACTION") + try: + for stmt in _ORG_UPSERT_SQL.strip().split(";"): + stmt = stmt.strip() + if not stmt: + continue + if "read_json_objects(?)" in stmt: + conn.execute(stmt + ";", [glob]) + else: + conn.execute(stmt + ";") + n = conn.execute("SELECT count(*) FROM _orgs").fetchone()[0] + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + logger.info("ingest_organizations: %d", n) + return int(n) + + +def ingest_links_dump(store: DuckDBStore, dump_path: Path) -> int: + """Read a `scripts/dump_link_articles.py` output and upsert article_links. + + The heavy dump is a single ~400 MB JSON. Read once with DuckDB's JSON + loader so the whole `articles` array becomes a row set we can unnest + in pure SQL. + """ + dump_path = Path(dump_path) + if not dump_path.exists(): + return 0 + + # Pull only the metadata we need (queries map + articles array) so we + # don't materialise the full ~400 MB persons/organizations sub-objects. + raw = json.loads(dump_path.read_text(encoding="utf-8")) + queries = raw.get("queries", {}) + articles = raw.get("articles", []) + + rows: list[tuple[str, str, str, str]] = [] + for art in articles: + uuid = art.get("uuid") + if not uuid: + continue + for label in art.get("matched_phrases", []) or []: + phrase = queries.get(label, "") + rows.append((uuid, label, phrase, "phrase_match")) + for label, urls in (art.get("body_urls") or {}).items(): + for url in urls or []: + rows.append((uuid, label, url, "body_text")) + + if not rows: + return 0 + + conn = store.connect() + conn.execute("BEGIN TRANSACTION") + try: + conn.executemany( + "INSERT INTO article_links (article_uuid, host_label, url, source) " + "VALUES (?, ?, ?, ?) ON CONFLICT DO NOTHING", + rows, + ) + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + logger.info("ingest_links_dump: %d link rows", len(rows)) + return len(rows) + + +def ingest_all(store: DuckDBStore, *, links_dump: Path | None = None) -> Dict[str, int]: + summary = { + "articles": ingest_articles(store), + "persons": ingest_persons(store), + "organizations": ingest_organizations(store), + } + if links_dump: + summary["link_rows"] = ingest_links_dump(store, links_dump) + return summary diff --git a/src/index/ethz_research_collection/storage/schema.sql b/src/index/ethz_research_collection/storage/schema.sql new file mode 100644 index 0000000..75a70f4 --- /dev/null +++ b/src/index/ethz_research_collection/storage/schema.sql @@ -0,0 +1,94 @@ +-- Canonical DuckDB schema for the infoscience index module. +-- Idempotent: every statement uses IF NOT EXISTS so re-runs are safe. +-- Mirrors the openalex / huggingface sister-index pattern. + +CREATE TABLE IF NOT EXISTS articles ( + article_uuid TEXT PRIMARY KEY, + title TEXT, + abstract TEXT, + doi TEXT, + publication_year INTEGER, + publication_type TEXT, + journal TEXT, + language TEXT, + research_collection_url TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS persons ( + person_uuid TEXT PRIMARY KEY, + display_name TEXT, + given_name TEXT, + family_name TEXT, + orcid TEXT, + sciper_id TEXT, + primary_affiliation TEXT, + primary_affiliation_uuid TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS organizations ( + org_uuid TEXT PRIMARY KEY, + name TEXT, + acronym TEXT, + parent_org_uuid TEXT, + sciper_unit_id TEXT, + ror_id TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +-- Bipartite article ↔ person, ordered by author position. +CREATE TABLE IF NOT EXISTS article_persons ( + article_uuid TEXT NOT NULL, + person_uuid TEXT NOT NULL, + position INTEGER, + PRIMARY KEY (article_uuid, person_uuid) +); + +-- Bipartite article ↔ organisation. `field` records which DSpace field +-- the authority came from (department / parent-organization / affiliation). +CREATE TABLE IF NOT EXISTS article_orgs ( + article_uuid TEXT NOT NULL, + org_uuid TEXT NOT NULL, + field TEXT NOT NULL, + PRIMARY KEY (article_uuid, org_uuid, field) +); + +-- Every artefact-host URL extracted from an article body, plus the Solr +-- phrase that matched it. Populated by the link sweep +-- (scripts/dump_link_articles.py) and refreshed on subsequent runs. +CREATE TABLE IF NOT EXISTS article_links ( + article_uuid TEXT NOT NULL, + host_label TEXT NOT NULL, -- 'github' | 'arxiv' | 'orcid' | ... + url TEXT NOT NULL, + source TEXT NOT NULL CHECK (source IN ('phrase_match', 'body_text')), + PRIMARY KEY (article_uuid, host_label, url, source) +); + +-- chunk_id is deterministic: uuid5(NAMESPACE_URL, "||") +-- so the primary key alone provides the (entity_type, entity_id, chunk_index) +-- uniqueness guarantee. See `embed/pipeline.py:_chunk_id` for the canonical helper +-- (or build/store.py if it lives there for infoscience). +CREATE TABLE IF NOT EXISTS chunks ( + chunk_id TEXT PRIMARY KEY, + entity_type TEXT NOT NULL, + entity_id TEXT NOT NULL, + chunk_index INTEGER NOT NULL, + text TEXT NOT NULL, + token_count INTEGER NOT NULL, + vector_id TEXT NOT NULL, + embedded_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS idx_articles_year ON articles (publication_year); +CREATE INDEX IF NOT EXISTS idx_articles_doi ON articles (doi); +CREATE INDEX IF NOT EXISTS idx_persons_orcid ON persons (orcid); +CREATE INDEX IF NOT EXISTS idx_persons_sciper ON persons (sciper_id); +CREATE INDEX IF NOT EXISTS idx_orgs_ror ON organizations (ror_id); +CREATE INDEX IF NOT EXISTS idx_orgs_parent ON organizations (parent_org_uuid); +CREATE INDEX IF NOT EXISTS idx_article_links_host ON article_links (host_label); +CREATE INDEX IF NOT EXISTS idx_article_links_url ON article_links (url); +CREATE INDEX IF NOT EXISTS idx_chunks_entity ON chunks (entity_type, entity_id); diff --git a/src/index/ethz_research_collection/store.py b/src/index/ethz_research_collection/store.py new file mode 100644 index 0000000..5b9e297 --- /dev/null +++ b/src/index/ethz_research_collection/store.py @@ -0,0 +1,477 @@ +"""Qdrant client wrapper for the ETH Research Collection index. + +Per-entity collections following the canonical pattern in this repo: + + * ``ethz_research_collection_chunks`` — publication body fragments + * ``ethz_research_collection_articles`` — one row per matched publication + * ``ethz_research_collection_persons`` — Person entities resolved from author + authority IDs + * ``ethz_research_collection_organizations`` — OrgUnit entities + +See `.internal/ror/qdrant-setup.md` for the broader convention. + +Qdrant is a server-side store; configuration comes from +`config.qdrant` (URL, gRPC toggle, optional API key) which respects the +`INDEX_QDRANT_*` env-var overrides at config-load time. + +Point IDs are deterministic UUIDv5 over a stable string key: + + * chunks: ``"{article_uuid}::{chunk_index}"`` + * articles: ``article_uuid`` + * persons: ``person_uuid`` + * organizations: ``org_uuid`` + +Payloads carry the full entity record so structured filters work natively +(no list-flattening tricks needed). +""" + +from __future__ import annotations + +import json +import logging +import uuid +from typing import Any, Iterable, List, Optional, Sequence + +from qdrant_client import QdrantClient, models + +from .config import EthzResearchCollectionIndexConfig, QdrantConfig +from .models import ( + ArticleRecord, + ChunkRecord, + OrganizationRecord, + PersonRecord, +) + +logger = logging.getLogger(__name__) + +CHUNKS_COLLECTION = "ethz_research_collection_chunks" +ARTICLES_COLLECTION = "ethz_research_collection_articles" +PERSONS_COLLECTION = "ethz_research_collection_persons" +ORGANIZATIONS_COLLECTION = "ethz_research_collection_organizations" + +ALL_COLLECTIONS: tuple[str, ...] = ( + CHUNKS_COLLECTION, + ARTICLES_COLLECTION, + PERSONS_COLLECTION, + ORGANIZATIONS_COLLECTION, +) + +# Stable namespace for UUIDv5 point IDs scoped to this index. Distinct +# from the infoscience namespace so points can't collide if both indices +# ever share a Qdrant DB. +_INDEX_NAMESPACE = uuid.UUID("8d49bc05-751a-457f-bd25-6630ea5fdac4") + +_GH_HOSTS = ("github.com",) +_HF_HOSTS = ("huggingface.co", "hf.co") + + +def _has_host(urls: Sequence[str], hosts: Iterable[str]) -> bool: + if not urls: + return False + needles = tuple(hosts) + return any(any(h in u for h in needles) for u in urls) + + +def _point_id(key: str) -> str: + return str(uuid.uuid5(_INDEX_NAMESPACE, key)) + + +def _drop_none(payload: dict[str, Any]) -> dict[str, Any]: + return {k: v for k, v in payload.items() if v is not None} + + +# --------------------------------------------------------------------------- +# Payloads +# --------------------------------------------------------------------------- + + +def chunk_payload(rec: ChunkRecord) -> dict[str, Any]: + return _drop_none({ + "chunk_id": rec.chunk_id, + "article_uuid": rec.article_uuid, + "chunk_index": rec.chunk_index, + "text": rec.text, + "title": rec.title, + "abstract": rec.abstract, + "authors": rec.authors or None, + "author_uuids": rec.author_uuids or None, + "doi": rec.doi, + "publication_date": rec.publication_date, + "year": rec.year, + "publication_type": rec.publication_type, + "language": rec.language, + "subjects": rec.subjects or None, + "keywords": rec.keywords or None, + "lab": rec.lab, + "lab_uuid": rec.lab_uuid, + "org_uuids": rec.org_uuids or None, + "research_collection_url": rec.research_collection_url, + "matched_urls": rec.matched_urls or None, + "has_github_match": _has_host(rec.matched_urls, _GH_HOSTS), + "has_hf_match": _has_host(rec.matched_urls, _HF_HOSTS), + }) + + +def article_payload(rec: ArticleRecord) -> dict[str, Any]: + return _drop_none({ + "article_uuid": rec.article_uuid, + "title": rec.title, + "abstract": rec.abstract, + "keywords": rec.keywords or None, + "subjects": rec.subjects or None, + "authors": rec.authors or None, + "author_uuids": rec.author_uuids or None, + "doi": rec.doi, + "publication_date": rec.publication_date, + "year": rec.year, + "publication_type": rec.publication_type, + "language": rec.language, + "journal": rec.journal, + "journal_uuid": rec.journal_uuid, + "scopus_id": rec.scopus_id, + "wos_id": rec.wos_id, + "journal_volume": rec.journal_volume, + "journal_issue": rec.journal_issue, + "pages_start": rec.pages_start, + "journal_abbreviated": rec.journal_abbreviated, + "publisher": rec.publisher, + "issn": rec.issn, + "handle_uri": rec.handle_uri, + "lab": rec.lab, + "lab_uuid": rec.lab_uuid, + "org_uuids": rec.org_uuids or None, + "research_collection_url": rec.research_collection_url, + "matched_urls": rec.matched_urls or None, + "chunk_count": rec.chunk_count, + "has_github_match": _has_host(rec.matched_urls, _GH_HOSTS), + "has_hf_match": _has_host(rec.matched_urls, _HF_HOSTS), + }) + + +def person_payload(rec: PersonRecord) -> dict[str, Any]: + return _drop_none({ + "person_uuid": rec.person_uuid, + "name": rec.name, + "given_name": rec.given_name, + "family_name": rec.family_name, + "orcid": rec.orcid, + "sciper_id": rec.sciper_id, + "scopus_id": rec.scopus_id, + "email_hash": rec.email_hash, + "primary_affiliation": rec.primary_affiliation, + "primary_affiliation_uuid": rec.primary_affiliation_uuid, + "affiliation_uuids": rec.affiliation_uuids or None, + "position": rec.position, + "biography": rec.biography, + "research_interests": rec.research_interests or None, + "profile_url": rec.profile_url, + "related_article_uuids": rec.related_article_uuids or None, + }) + + +def organization_payload(rec: OrganizationRecord) -> dict[str, Any]: + return _drop_none({ + "org_uuid": rec.org_uuid, + "name": rec.name, + "acronym": rec.acronym, + "aliases": rec.aliases or None, + "parent_org_uuid": rec.parent_org_uuid, + "parent_org_chain": rec.parent_org_chain or None, + "parent_org_chain_names": rec.parent_org_chain_names or None, + "description": rec.description, + "sciper_unit_id": rec.sciper_unit_id, + "ror_id": rec.ror_id, + "unit_manager_uuid": rec.unit_manager_uuid, + "unit_manager_name": rec.unit_manager_name, + "research_collection_url": rec.research_collection_url, + "related_article_uuids": rec.related_article_uuids or None, + }) + + +# --------------------------------------------------------------------------- +# Store wrapper +# --------------------------------------------------------------------------- + +# Qdrant default body limit is 32 MB; 64 × ~40 KB vector + payload ≈ 3 MB. +_UPSERT_BATCH_SIZE = 64 + + +class QdrantStore: + """Per-entity collection bootstrap + upsert + filtered search.""" + + def __init__(self, qcfg: QdrantConfig, *, vector_size: int): + self._qcfg = qcfg + self._dim = vector_size + # qdrant-client's default 5s timeout is too tight for 64-point + # batches of 4096-dim vectors with payload (~3 MB body) — large + # ingest runs hit ResponseHandlingException("timed out") under + # load. 120s mirrors the ROR store's tuning. + self._client = QdrantClient( + url=qcfg.url, + api_key=qcfg.api_key, + prefer_grpc=qcfg.prefer_grpc, + timeout=120, + ) + + @classmethod + def from_config(cls, cfg: EthzResearchCollectionIndexConfig) -> "QdrantStore": + return cls(cfg.qdrant, vector_size=cfg.rcp.embedding_dim) + + @property + def client(self) -> QdrantClient: + return self._client + + def ensure_collection(self, name: str) -> None: + if self._client.collection_exists(name): + return + self._client.create_collection( + collection_name=name, + vectors_config=models.VectorParams( + size=self._dim, + distance=models.Distance.COSINE, + ), + ) + logger.info("created qdrant collection %s (dim=%d)", name, self._dim) + + def collection_count(self, name: str) -> int: + if not self._client.collection_exists(name): + return 0 + return int(self._client.count(collection_name=name, exact=True).count) + + def upsert_points( + self, + collection: str, + *, + ids: Sequence[str], + vectors: Sequence[Sequence[float]], + payloads: Sequence[dict[str, Any]], + ) -> int: + if not (len(ids) == len(vectors) == len(payloads)): + msg = "ids/vectors/payloads must be the same length" + raise ValueError(msg) + if not ids: + return 0 + self.ensure_collection(collection) + points = [ + models.PointStruct(id=pid, vector=list(vec), payload=payload) + for pid, vec, payload in zip(ids, vectors, payloads, strict=False) + ] + # Qdrant rejects request bodies above 32 MB. A 4096-dim float vector + # serialises to ~40 KB, so a chunks payload with body text easily + # crosses the cap in one shot — split into sub-batches. + for start in range(0, len(points), _UPSERT_BATCH_SIZE): + self._client.upsert( + collection_name=collection, + points=points[start : start + _UPSERT_BATCH_SIZE], + ) + return len(points) + + def search( + self, + collection: str, + *, + query_vector: Sequence[float], + top_k: int = 50, + query_filter: Optional[models.Filter] = None, + ) -> List[dict[str, Any]]: + if not self._client.collection_exists(collection): + return [] + hits = self._client.query_points( + collection_name=collection, + query=list(query_vector), + limit=top_k, + query_filter=query_filter, + with_payload=True, + ).points + return [ + {"id": str(p.id), "score": float(p.score), "payload": p.payload or {}} + for p in hits + ] + + def lookup( + self, + collection: str, + *, + ids: Sequence[str], + ) -> List[dict[str, Any]]: + """Read-only retrieval by point IDs.""" + if not ids or not self._client.collection_exists(collection): + return [] + records = self._client.retrieve( + collection_name=collection, + ids=list(ids), + with_payload=True, + ) + return [ + {"id": str(r.id), "payload": r.payload or {}} + for r in records + ] + + def scroll( + self, + collection: str, + *, + query_filter: Optional[models.Filter] = None, + limit: int = 50, + ) -> List[dict[str, Any]]: + """Filter-only listing (no vector); returns up to `limit` points.""" + if not self._client.collection_exists(collection): + return [] + records, _next = self._client.scroll( + collection_name=collection, + scroll_filter=query_filter, + limit=limit, + with_payload=True, + ) + return [ + {"id": str(r.id), "payload": r.payload or {}} + for r in records + ] + + +# --------------------------------------------------------------------------- +# Filter builder +# --------------------------------------------------------------------------- + + +def build_filter(payload: Optional[dict[str, Any]]) -> Optional[models.Filter]: + """Translate a JSON-style filter dict into a Qdrant ``Filter``. + + Supported operators per key: + * ``{"$eq": value}`` (or scalar shorthand) + * ``{"$ne": value}`` + * ``{"$gte": v, "$lte": v}`` (range) + * ``{"$in": [v, ...]}`` (any of) + * ``{"$contains": "needle"}`` for list-of-string fields + * raw ``[...]`` shorthand → ``MatchAny`` + """ + if not payload: + return None + must: list[models.Condition] = [] + must_not: list[models.Condition] = [] + for key, value in payload.items(): + if isinstance(value, dict): + if "$ne" in value: + must_not.append(models.FieldCondition( + key=key, match=models.MatchValue(value=value["$ne"]), + )) + continue + if "$in" in value: + must.append(models.FieldCondition( + key=key, match=models.MatchAny(any=list(value["$in"])), + )) + continue + if "$contains" in value: + must.append(models.FieldCondition( + key=key, match=models.MatchValue(value=value["$contains"]), + )) + continue + if "$gte" in value or "$lte" in value: + must.append(models.FieldCondition( + key=key, + range=models.Range( + gte=value.get("$gte"), lte=value.get("$lte"), + ), + )) + continue + if "$eq" in value: + must.append(models.FieldCondition( + key=key, match=models.MatchValue(value=value["$eq"]), + )) + continue + msg = f"Unsupported operator dict for {key!r}: {value!r}" + raise ValueError(msg) + if isinstance(value, list): + must.append(models.FieldCondition( + key=key, match=models.MatchAny(any=value), + )) + continue + must.append(models.FieldCondition( + key=key, match=models.MatchValue(value=value), + )) + return models.Filter( + must=must or None, + must_not=must_not or None, + ) + + +# --------------------------------------------------------------------------- +# Convenience upsert helpers for each entity type +# --------------------------------------------------------------------------- + + +def upsert_chunks( + store: QdrantStore, + records: Sequence[ChunkRecord], + embeddings: Sequence[Sequence[float]], +) -> int: + if not records: + return 0 + ids = [_point_id(r.chunk_id) for r in records] + payloads = [chunk_payload(r) for r in records] + return store.upsert_points( + CHUNKS_COLLECTION, ids=ids, vectors=list(embeddings), payloads=payloads, + ) + + +def upsert_articles( + store: QdrantStore, + records: Sequence[ArticleRecord], + embeddings: Sequence[Optional[Sequence[float]]], + dim: int, +) -> int: + if not records: + return 0 + placeholder = [0.0] * dim + ids = [_point_id(r.article_uuid) for r in records] + vectors = [list(e) if e is not None else placeholder for e in embeddings] + payloads = [article_payload(r) for r in records] + return store.upsert_points( + ARTICLES_COLLECTION, ids=ids, vectors=vectors, payloads=payloads, + ) + + +def upsert_persons( + store: QdrantStore, + records: Sequence[PersonRecord], + embeddings: Sequence[Optional[Sequence[float]]], + dim: int, +) -> int: + if not records: + return 0 + placeholder = [0.0] * dim + ids = [_point_id(r.person_uuid) for r in records] + vectors = [list(e) if e is not None else placeholder for e in embeddings] + payloads = [person_payload(r) for r in records] + return store.upsert_points( + PERSONS_COLLECTION, ids=ids, vectors=vectors, payloads=payloads, + ) + + +def upsert_organizations( + store: QdrantStore, + records: Sequence[OrganizationRecord], + embeddings: Sequence[Optional[Sequence[float]]], + dim: int, +) -> int: + if not records: + return 0 + placeholder = [0.0] * dim + ids = [_point_id(r.org_uuid) for r in records] + vectors = [list(e) if e is not None else placeholder for e in embeddings] + payloads = [organization_payload(r) for r in records] + return store.upsert_points( + ORGANIZATIONS_COLLECTION, ids=ids, vectors=vectors, payloads=payloads, + ) + + +def article_point_id(article_uuid: str) -> str: + return _point_id(article_uuid) + + +def person_point_id(person_uuid: str) -> str: + return _point_id(person_uuid) + + +def organization_point_id(org_uuid: str) -> str: + return _point_id(org_uuid) diff --git a/src/index/ethz_research_collection/synth_orgs.py b/src/index/ethz_research_collection/synth_orgs.py new file mode 100644 index 0000000..29d3f01 --- /dev/null +++ b/src/index/ethz_research_collection/synth_orgs.py @@ -0,0 +1,182 @@ +"""Synthesize OrgUnit records from `person.department` text. + +ETH Research Collection's DSpace 7 deployment does not expose OrgUnit +entities with stable UUIDs (the ``/relationships`` subresource on Person +items is empty, and the ``/leitzahl`` HAL endpoint returns no body). The +only organisational signal on a Person record is the free-text +``person.department`` field, which follows a consistent pattern: + + "<5-digit-leitzahl> - [/ ]" + +Examples observed in pilot data: + + "03996 - Benini, Luca / Benini, Luca" + "08686 - Gruppe Strassenverkehrstechnik" + "03736 - Reiher, Markus / Reiher, Markus" + +This module scans ``raw/persons/*.json`` for the unique set of +``person.department`` values, parses each into ``(leitzahl, lab_name, +head_name)``, and writes one synthetic Org JSON per leitzahl into +``raw/organizations/`` plus the leitzahl→uuid set into ``organizations.txt`` +so the downstream ``ingest-duckdb`` and ``embed`` stages can process them +without further changes. + +The synthetic UUID is ``uuid5(_LEITZAHL_NAMESPACE, leitzahl)`` so the +mapping is stable across rebuilds. +""" + +from __future__ import annotations + +import json +import logging +import re +import uuid +from typing import Optional + +from .config import EthzResearchCollectionIndexConfig +from .paths import ( + organizations_set_path, + raw_organizations_dir, + raw_persons_dir, +) + +logger = logging.getLogger(__name__) + +# Stable namespace for OrgUnit synth UUIDs derived from ETH leitzahl codes. +# Distinct from src/index/ethz_research_collection/store.py's index namespace +# so we don't collide with any future real OrgUnit ingestion. +_LEITZAHL_NAMESPACE = uuid.UUID("c1e2d3f4-5a6b-7c8d-9e0f-112233445566") + +# Strict ETH leitzahl: 5 digits, hyphen, rest of name. Some entries lack +# a "/ " tail, so the head capture is optional. +_DEPT_RE = re.compile( + r""" + ^\s* + (?P\d{5}) # 5-digit Leitzahl + \s*-\s* + (?P[^/]+?) # lab name (everything up to optional ' / ') + (?:\s*/\s*(?P.+?))? + \s*$ + """, + re.VERBOSE, +) + + +def _synth_uuid(leitzahl: str) -> str: + return str(uuid.uuid5(_LEITZAHL_NAMESPACE, f"ethz-leitzahl-{leitzahl}")) + + +def _parse_department(text: str) -> Optional[dict]: + """Return ``{leitzahl, name, head}`` or ``None`` if the text is malformed.""" + if not isinstance(text, str): + return None + m = _DEPT_RE.match(text.strip()) + if not m: + return None + leitzahl = m.group("leitzahl") + name = (m.group("name") or "").strip() + head = (m.group("head") or "").strip() or None + if not name: + return None + return {"leitzahl": leitzahl, "name": name, "head": head} + + +def _build_synthetic_item(parsed: dict) -> dict: + """Build a DSpace-shaped Org JSON from a parsed department row.""" + leitzahl = parsed["leitzahl"] + name = parsed["name"] + head = parsed.get("head") + description_parts = [f"ETH Zürich Leitzahl {leitzahl}"] + if head and head != name: + description_parts.append(f"Head: {head}") + if head and head == name: + # When the lab is named after a single PI, the head and lab name + # collapse to one string; mention the role explicitly. + description_parts.append(f"Professorship of {name}") + description = "; ".join(description_parts) + + return { + "uuid": _synth_uuid(leitzahl), + "name": name, + "type": "item", + "_synthetic": True, + "_synthetic_source": "ethz_leitzahl_from_person.department", + "metadata": { + "dc.title": [{"value": name, "language": None, + "authority": None, "confidence": -1, "place": 0}], + "organization.identifier.acronym": [{ + "value": leitzahl, "language": None, + "authority": None, "confidence": -1, "place": 0, + }], + "dc.description": [{"value": description, "language": None, + "authority": None, "confidence": -1, "place": 0}], + "epfl.unitId": [{"value": leitzahl, "language": None, + "authority": None, "confidence": -1, "place": 0}], + "dspace.entity.type": [{"value": "OrgUnit", "language": None, + "authority": None, "confidence": -1, "place": 0}], + }, + } + + +def synthesize_organizations(_cfg: EthzResearchCollectionIndexConfig) -> dict: + """Mine ``person.department`` text → write synthetic Org JSONs.""" + persons_dir = raw_persons_dir() + orgs_dir = raw_organizations_dir() + orgs_dir.mkdir(parents=True, exist_ok=True) + + seen_dept_text: set[str] = set() + written = 0 + skipped_existing = 0 + malformed: list[str] = [] + leitzahls: dict[str, str] = {} # leitzahl → synth uuid + + for person_path in sorted(persons_dir.glob("*.json")): + try: + person = json.loads(person_path.read_text(encoding="utf-8")) + except Exception as exc: # noqa: BLE001 + logger.warning("synth_orgs: failed to read %s — %s", person_path.name, exc) + continue + md = person.get("metadata", {}) or {} + for entry in md.get("person.department", []) or []: + text = entry.get("value") if isinstance(entry, dict) else None + if not text or text in seen_dept_text: + continue + seen_dept_text.add(text) + parsed = _parse_department(text) + if parsed is None: + malformed.append(text) + continue + org = _build_synthetic_item(parsed) + leitzahls[parsed["leitzahl"]] = org["uuid"] + target = orgs_dir / f"{org['uuid']}.json" + if target.exists(): + skipped_existing += 1 + continue + target.write_text( + json.dumps(org, ensure_ascii=False, indent=2), + encoding="utf-8", + ) + written += 1 + + organizations_set_path().write_text( + "\n".join(sorted(leitzahls.values())), + encoding="utf-8", + ) + + summary = { + "distinct_department_texts": len(seen_dept_text), + "distinct_leitzahls": len(leitzahls), + "orgs_written": written, + "orgs_skipped_existing": skipped_existing, + "malformed_department_texts": malformed[:5] + (["…"] if len(malformed) > 5 else []), + "raw_orgs_dir": str(orgs_dir), + } + logger.info("synth_orgs: %s", summary) + return summary + + +def run(cfg: EthzResearchCollectionIndexConfig) -> dict: + return synthesize_organizations(cfg) + + +__all__ = ["run", "synthesize_organizations"] diff --git a/src/index/ethz_research_collection/text_fetch.py b/src/index/ethz_research_collection/text_fetch.py new file mode 100644 index 0000000..7d8aed2 --- /dev/null +++ b/src/index/ethz_research_collection/text_fetch.py @@ -0,0 +1,130 @@ +"""Text fetch stage: download the TEXT bundle's plaintext bitstream per item. + +For each UUID in `raw/items/`, locate the bundle named `TEXT`, find a +`.txt` / `text/plain` bitstream inside, download it, and save verbatim to +`text/{uuid}.txt`. Skips items already on disk and items with no TEXT +bundle. We never touch PDFs. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from pathlib import Path +from typing import Optional + +import httpx + +from .config import EthzResearchCollectionIndexConfig +from .dspace import DSpaceClient +from .paths import raw_items_dir, text_dir + +logger = logging.getLogger(__name__) + +_TEXT_BUNDLE_NAME = "TEXT" + + +def _pick_text_bitstream(bitstreams: list) -> Optional[dict]: + """Pick the first bitstream that looks like extracted plain text.""" + for bs in bitstreams: + name = (bs.get("name") or "").lower() + mime = (bs.get("metadata", {}).get("dc.format.mimetype", [{}]) or [{}])[0] + mime_value = (mime.get("value") if isinstance(mime, dict) else "") or "" + if name.endswith(".txt") or mime_value == "text/plain": + return bs + return bitstreams[0] if bitstreams else None + + +async def _fetch_one( + client: DSpaceClient, + uuid: str, + out_dir: Path, + *, + refresh: bool = False, +) -> str: + """Returns one of: 'written', 'skipped-existing', 'no-text-bundle', + 'no-bitstream', 'unauthorized', 'not-found', 'error'.""" + out_path = out_dir / f"{uuid}.txt" + if out_path.exists() and not refresh: + return "skipped-existing" + try: + bundles = await client.get_bundles(uuid) + except httpx.HTTPStatusError as exc: + if exc.response.status_code == 404: + return "not-found" + if exc.response.status_code in (401, 403): + return "unauthorized" + raise + + text_bundle = next( + (b for b in bundles if (b.get("name") or "").upper() == _TEXT_BUNDLE_NAME), + None, + ) + if text_bundle is None: + return "no-text-bundle" + + bitstreams = await client.get_bitstreams(text_bundle["uuid"]) + bs = _pick_text_bitstream(bitstreams) + if bs is None: + return "no-bitstream" + + try: + body = await client.get_bitstream_content(bs["uuid"]) + except httpx.HTTPStatusError as exc: + if exc.response.status_code in (401, 403): + return "unauthorized" + if exc.response.status_code == 404: + return "not-found" + raise + + text = body.decode("utf-8", errors="replace") + out_path.write_text(text, encoding="utf-8") + return "written" + + +async def fetch_text( + cfg: EthzResearchCollectionIndexConfig, + *, + refresh: bool = False, +) -> dict: + """Fetch text for every UUID found in raw/items/.""" + out_dir = text_dir() + item_files = sorted(raw_items_dir().glob("*.json")) + if not item_files: + logger.warning("No items in raw/items/. Run `discover` first.") + return {"items": 0} + + counts = { + "written": 0, + "skipped-existing": 0, + "no-text-bundle": 0, + "no-bitstream": 0, + "unauthorized": 0, + "not-found": 0, + "error": 0, + } + + async with DSpaceClient(cfg.research_collection) as client: + sem = asyncio.Semaphore(cfg.research_collection.max_concurrency) + + async def _bounded(uuid: str) -> str: + async with sem: + try: + return await _fetch_one(client, uuid, out_dir, refresh=refresh) + except Exception: + logger.exception("text-fetch failed for %s", uuid) + return "error" + + uuids = [p.stem for p in item_files] + results = await asyncio.gather(*(_bounded(u) for u in uuids)) + + for r in results: + counts[r] = counts.get(r, 0) + 1 + + logger.info("fetch_text: %s", json.dumps(counts)) + return {"items": len(uuids), **counts, "text_dir": str(out_dir)} + + +def run(cfg: EthzResearchCollectionIndexConfig, **kwargs) -> dict: + return asyncio.run(fetch_text(cfg, **kwargs)) diff --git a/src/index/github/__init__.py b/src/index/github/__init__.py new file mode 100644 index 0000000..e9f801f --- /dev/null +++ b/src/index/github/__init__.py @@ -0,0 +1,19 @@ +"""GitHub repository RAG indexer. + +Pipeline shape (mirrors `src/index/zenodo`): + + ingest → fetches GitHub repo metadata + README via REST (deterministic, + rule-based; no LLM), upserts into DuckDB and writes the + README to `cards/{owner}/{name}/README.md`. + embed → chunks full_name + description + topics + README, embeds + via RCP, upserts into Qdrant. Re-runs skip repos whose + chunks are already present. + rebuild-qdrant → re-derives Qdrant points from the existing `chunks` table + (used after a Qdrant wipe; does not touch DuckDB). + search → vector + RCP rerank + DuckDB hydrate. + query → predefined or guarded ad-hoc SQL over DuckDB. + +Phase 1 default scope: `epfl` (curated YAML seed). Phase 2 extends to +`switzerland` by extending the YAML seed (no code changes needed); the +OpenAlex `work_github_urls` table can bootstrap new candidates. +""" diff --git a/src/index/github/__main__.py b/src/index/github/__main__.py new file mode 100644 index 0000000..32dc1e4 --- /dev/null +++ b/src/index/github/__main__.py @@ -0,0 +1,8 @@ +"""Entry point: `python -m src.index.github`.""" + +from __future__ import annotations + +from src.index.github.cli import main + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/index/github/_federated.py b/src/index/github/_federated.py new file mode 100644 index 0000000..d331e3b --- /dev/null +++ b/src/index/github/_federated.py @@ -0,0 +1,103 @@ +"""GitHub registration with the federated discover/hydrate registries. + +Discover sources +---------------- + +- ``dependents`` — scrape ``github.com///network/dependents`` + to discover repos that depend on a given target (uses the existing + module under ``src/module/dependents/``). +- ``from-references`` — placeholder for future "GitHub URLs found in + works abstracts" cross-index discovery (currently produced by + ``openalex find-github`` populating ``work_github_urls``). + +Hydrate seed types +------------------ + +- ``github_repo`` — fetch repo metadata via ``ingest_repos`` (delegates + to GitHub REST + commit / contributor enrichment). +""" + +from __future__ import annotations + +import logging +from typing import Any, Iterator + +from src.index._federated.dh_registry import register_discoverer, register_hydrator +from src.index._federated.protocols import ( + HydrationSummary, + IndexDiscoverer, + IndexHydrator, + Seed, +) + +LOGGER = logging.getLogger(__name__) + + +class GitHubDiscoverer: + name = "github" + accepted_sources = ("dependents", "from-references") + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + if source not in self.accepted_sources: + message = f"GitHub: unknown source {source!r}. Accepted: {list(self.accepted_sources)}" + raise ValueError(message) + + if source == "dependents": + target = opts.get("target") + if not target: + message = "GitHub.dependents requires --opt target=owner/repo" + raise ValueError(message) + try: + from src.module.dependents import scrape_dependents + except ImportError: + LOGGER.warning("src.module.dependents not importable; cannot run dependents discover") + return + for entry in scrape_dependents(target): + yield Seed( + id=f"https://github.com/{entry}", + seed_type="github_repo", + source=f"dependents:{target}", + hint={"target": target}, + ) + return + + # source == "from-references" + # Pull github URLs that OpenAlex's find-github discovered into work_github_urls. + try: + from src.index.openalex.storage.duckdb_store import DuckDBStore as OAStore + except ImportError: + LOGGER.warning("OpenAlex DB not available for from-references discover") + return + store = OAStore.open() + cur = store.connect() + rows = cur.execute( + "SELECT DISTINCT normalized_url FROM work_github_urls " + "WHERE normalized_url IS NOT NULL" + ).fetchall() + for (url,) in rows: + yield Seed( + id=url, + seed_type="github_repo", + source="from-references", + ) + + +class GitHubHydrator: + name = "github" + accepted_seed_types = ("github_repo",) + + def hydrate(self, seeds, *, only_unfetched: bool = True) -> HydrationSummary: + # TODO: lift `ingest_repos` into a function that accepts a list of + # github URLs / owner-repo pairs. Currently it operates on a config- + # driven list. v1 returns a stub summary. + materialised = list(seeds) + LOGGER.warning( + "github: hydrate is a stub (received %d seeds). " + "Wire to ingest_repos in a follow-up.", + len(materialised), + ) + return HydrationSummary(skipped_existing=len(materialised)) + + +register_discoverer(GitHubDiscoverer()) +register_hydrator(GitHubHydrator()) diff --git a/src/index/github/api.py b/src/index/github/api.py new file mode 100644 index 0000000..e6be249 --- /dev/null +++ b/src/index/github/api.py @@ -0,0 +1,105 @@ +"""FastAPI app exposing the GitHub index dual query surface.""" + +from __future__ import annotations + +import logging +from typing import Any + +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel, Field + +from src.index.github.config import load_config +from src.index.github.embed.pipeline import GITHUB_COLLECTION +from src.index.github.retrieval.semantic import semantic_search +from src.index.github.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) +from src.index.github.storage.duckdb_store import GitHubStore +from src.index.openalex.vector.qdrant_store import QdrantStore + +LOGGER = logging.getLogger(__name__) + +app = FastAPI(title="GitHub Index") + + +class SearchRequest(BaseModel): + query: str + top_k: int = Field(default=10, ge=1, le=100) + candidate_k: int = Field(default=50, ge=1, le=500) + filter_payload: dict[str, Any] | None = None + + +class QueryRequest(BaseModel): + sql: str | None = None + predefined: str | None = None + params: dict[str, Any] | None = None + + +@app.get("/healthz") +def healthz() -> dict[str, Any]: + config = load_config() + duck_status = "ok" + qdrant_status = "ok" + try: + GitHubStore.open().count("repos") + except Exception as exc: # noqa: BLE001 + duck_status = f"error: {exc}" + try: + QdrantStore(config).count(GITHUB_COLLECTION) # type: ignore[arg-type] + except Exception as exc: # noqa: BLE001 + qdrant_status = f"error: {exc}" + return { + "duckdb": duck_status, + "qdrant": qdrant_status, + "rcp_configured": bool(config.rcp.token), + "github_token_configured": bool(config.github.token), + } + + +@app.post("/search") +def search(req: SearchRequest) -> list[dict[str, Any]]: + config = load_config() + try: + config.require_rcp() + except ValueError as exc: + raise HTTPException(status_code=503, detail=str(exc)) from exc + return semantic_search( + config=config, + query=req.query, + top_k=req.top_k, + candidate_k=req.candidate_k, + filter_payload=req.filter_payload, + ) + + +@app.post("/query") +def query(req: QueryRequest) -> list[dict[str, Any]]: + if req.predefined and req.sql: + raise HTTPException( + status_code=400, + detail="Provide either `predefined` or `sql`, not both", + ) + try: + if req.predefined: + return run_predefined(req.predefined, req.params) + if req.sql: + return run_adhoc(req.sql, req.params) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + raise HTTPException(status_code=400, detail="Pass `predefined` or `sql`") + + +@app.get("/predefined") +def list_predefined() -> dict[str, list[str]]: + return {"predefined": sorted(PREDEFINED_QUERIES)} + + +@app.get("/repo/{owner}/{name}") +def get_repo(owner: str, name: str) -> dict[str, Any]: + store = GitHubStore.open() + repo = store.fetch_repo(f"{owner}/{name}") + if repo is None: + raise HTTPException(status_code=404, detail="not found") + return repo diff --git a/src/index/github/cli.py b/src/index/github/cli.py new file mode 100644 index 0000000..ed8deb1 --- /dev/null +++ b/src/index/github/cli.py @@ -0,0 +1,271 @@ +"""CLI for the GitHub index module. + +Subcommands: + +- `ingest` — fetch GitHub repo metadata + README into DuckDB. +- `embed` — chunk + embed unembedded repos, push vectors to Qdrant. +- `rebuild-qdrant` — re-derive Qdrant points from the existing `chunks` table + (recovery path after a Qdrant wipe; does not touch DuckDB). +- `search` — semantic retrieval (vector + RCP rerank). +- `query` — read-only SQL over DuckDB (predefined or guarded ad-hoc). +- `status` — print row counts + Qdrant collection size + paths. +- `serve` — run the FastAPI app. +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from typing import Any + +from src.index.github.config import load_config +from src.index.github.embed.pipeline import ( + GITHUB_COLLECTION, + embed_repos, + rebuild_qdrant_from_chunks, +) +from src.index.github.ingest.repos import ingest_repos +from src.index.github.ingest.scope import merge_openalex_repos, resolve_scope +from src.index.github.retrieval.semantic import semantic_search +from src.index.github.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) +from src.index.github.storage.duckdb_store import GitHubStore + +LOGGER = logging.getLogger(__name__) + + +def _emit_json(obj: Any) -> None: + json.dump(obj, sys.stdout, indent=2, ensure_ascii=False, default=str) + sys.stdout.write("\n") + + +def _cmd_ingest(args: argparse.Namespace) -> int: + config = load_config() + config.require_github() + scope = resolve_scope(args.scope, config) + if args.repos: + # CLI-supplied repos extend the scope without touching the YAML seed. + scope.repos = list(dict.fromkeys([*scope.repos, *args.repos])) + if args.repos_file: + from pathlib import Path + + path = Path(args.repos_file) + lines = [ + ln.strip() + for ln in path.read_text(encoding="utf-8").splitlines() + if ln.strip() and not ln.lstrip().startswith("#") + ] + scope.repos = list(dict.fromkeys([*scope.repos, *lines])) + if args.from_openalex: + from src.index.openalex.paths import get_openalex_paths + + scope = merge_openalex_repos( + scope, + openalex_db_path=get_openalex_paths().duckdb_path, + ) + if not scope.repos: + message = ( + f"scope={scope.name} resolved to zero repos. " + "Edit config/index/github.yaml under `scope.seeds.` " + "or pass --repos / --repos-file." + ) + raise SystemExit(message) + store = GitHubStore.open() + try: + summary = ingest_repos( + config=config, + store=store, + scope=scope, + limit=args.limit, + ) + finally: + store.close() + _emit_json({"scope": scope.name, "repos_in_scope": len(scope.repos), **summary}) + return 0 + + +def _cmd_embed(args: argparse.Namespace) -> int: + config = load_config() + config.require_rcp() + store = GitHubStore.open() + try: + summary = embed_repos(config=config, store=store, limit=args.limit) + finally: + store.close() + _emit_json(summary) + return 0 + + +def _cmd_rebuild_qdrant(args: argparse.Namespace) -> int: + del args + config = load_config() + config.require_rcp() + store = GitHubStore.open() + try: + summary = rebuild_qdrant_from_chunks(config=config, store=store) + finally: + store.close() + _emit_json(summary) + return 0 + + +def _cmd_search(args: argparse.Namespace) -> int: + config = load_config() + config.require_rcp() + filter_payload = json.loads(args.filter) if args.filter else None + hits = semantic_search( + config=config, + query=args.query, + top_k=args.top_k, + candidate_k=args.candidate_k, + filter_payload=filter_payload, + ) + _emit_json(hits) + return 0 + + +def _cmd_query(args: argparse.Namespace) -> int: + if args.predefined and args.sql: + message = "Pass either --predefined or --sql, not both" + raise SystemExit(message) + params: dict[str, Any] = {} + for kv in args.param or []: + if "=" not in kv: + message = f"--param expects key=value, got {kv!r}" + raise SystemExit(message) + key, value = kv.split("=", 1) + try: + params[key] = int(value) + except ValueError: + try: + params[key] = float(value) + except ValueError: + params[key] = value + if args.predefined: + rows = run_predefined(args.predefined, params) + elif args.sql: + rows = run_adhoc(args.sql, params) + else: + _emit_json({"predefined": sorted(PREDEFINED_QUERIES)}) + return 0 + _emit_json(rows) + return 0 + + +def _cmd_status(args: argparse.Namespace) -> int: + del args + config = load_config() + store = GitHubStore.open() + try: + counts = {t: store.count(t) for t in ("repos", "chunks")} + finally: + store.close() + qdrant_count: int | str + try: + from src.index.openalex.vector.qdrant_store import QdrantStore + + qdrant_count = QdrantStore(config).count(GITHUB_COLLECTION) # type: ignore[arg-type] + except Exception as exc: # noqa: BLE001 + qdrant_count = f"error: {exc}" + _emit_json( + { + "duckdb_path": str(config.paths.duckdb_path), + "duckdb_counts": counts, + "qdrant_collection": GITHUB_COLLECTION, + "qdrant_points": qdrant_count, + "scope_active": config.scope.active, + "scope_seed_sizes": {k: len(v) for k, v in config.scope.seeds.items()}, + }, + ) + return 0 + + +def _cmd_serve(args: argparse.Namespace) -> int: + import uvicorn + + uvicorn.run( + "src.index.github.api:app", + host=args.host, + port=args.port, + reload=args.reload, + ) + return 0 + + +def _split_repos(raw: str) -> list[str]: + return [r.strip() for r in raw.split(",") if r.strip()] + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(prog="src.index.github") + sub = parser.add_subparsers(dest="cmd", required=True) + + p_ingest = sub.add_parser("ingest", help="Fetch GitHub repo metadata + README into DuckDB") + p_ingest.add_argument("--scope", default="epfl", help="Scope name (key in scope.seeds)") + p_ingest.add_argument( + "--repos", + type=_split_repos, + default=None, + help="Extra owner/name list, comma-separated (extends the scope without editing YAML)", + ) + p_ingest.add_argument( + "--repos-file", + default=None, + help="Path to a newline-delimited file of owner/name entries (# comments + blank lines ignored)", + ) + p_ingest.add_argument( + "--from-openalex", + action="store_true", + help="Augment the scope with distinct repos from openalex.work_github_urls", + ) + p_ingest.add_argument("--limit", type=int, default=None, help="Cap repos processed this run") + p_ingest.set_defaults(func=_cmd_ingest) + + p_embed = sub.add_parser("embed", help="Chunk + embed repos, push to Qdrant") + p_embed.add_argument("--limit", type=int, default=None) + p_embed.set_defaults(func=_cmd_embed) + + p_rebuild = sub.add_parser( + "rebuild-qdrant", + help="Re-derive Qdrant points from the existing chunks table (post-wipe recovery)", + ) + p_rebuild.set_defaults(func=_cmd_rebuild_qdrant) + + p_search = sub.add_parser("search", help="Semantic retrieval (vector + rerank)") + p_search.add_argument("query", help="Natural-language query") + p_search.add_argument("--top-k", type=int, default=10) + p_search.add_argument("--candidate-k", type=int, default=50) + p_search.add_argument("--filter", default=None, help="JSON dict for Qdrant payload filter") + p_search.set_defaults(func=_cmd_search) + + p_query = sub.add_parser("query", help="Read-only SQL (predefined or guarded ad-hoc)") + p_query.add_argument("--predefined", default=None, help=f"One of: {sorted(PREDEFINED_QUERIES)}") + p_query.add_argument("--sql", default=None, help="Ad-hoc SELECT/WITH query") + p_query.add_argument("--param", action="append", help="Repeatable key=value param") + p_query.set_defaults(func=_cmd_query) + + p_status = sub.add_parser("status", help="Show DuckDB + Qdrant counts and paths") + p_status.set_defaults(func=_cmd_status) + + p_serve = sub.add_parser("serve", help="Run the GitHub FastAPI app") + p_serve.add_argument("--host", default="0.0.0.0") + p_serve.add_argument("--port", type=int, default=8004) + p_serve.add_argument("--reload", action="store_true") + p_serve.set_defaults(func=_cmd_serve) + + return parser + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s — %(message)s", + ) + parser = _build_parser() + args = parser.parse_args(argv) + return int(args.func(args) or 0) diff --git a/src/index/github/config.py b/src/index/github/config.py new file mode 100644 index 0000000..4366162 --- /dev/null +++ b/src/index/github/config.py @@ -0,0 +1,128 @@ +"""Config loader for the GitHub indexer. + +Reads `config/index/github.yaml` and merges in env-sourced credentials +(`RCP_TOKEN`, `GITHUB_TOKEN`, `INDEX_QDRANT_API_KEY`) plus the resolved +data dir. + +The `rcp` and `qdrant` sub-blocks mirror `OpenAlexIndexConfig` +field-for-field so that the openalex RCP / Qdrant clients can be +reused at runtime — they only TYPE_CHECK against the OpenAlex config and +never import it at runtime. +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Optional + +import yaml +from pydantic import BaseModel + +from src.index.github.paths import GitHubPaths, get_github_paths + +DEFAULT_CONFIG_PATH = Path("config/index/github.yaml") + +TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} +FALSE_ENV_VALUES = {"0", "false", "f", "no", "n", "off"} + +MISSING_RCP_TOKEN_ERROR = "Missing required environment variable: RCP_TOKEN" +MISSING_GITHUB_TOKEN_ERROR = "Missing required environment variable: GITHUB_TOKEN" + + +class RcpConfig(BaseModel): + base_url: str + embedding_model: str + embedding_dim: int + query_instruction: str + reranker_model: str + batch_size: int = 32 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: Optional[str] = None + + +class GitHubConfig(BaseModel): + api_base: str = "https://api.github.com" + per_page: int = 100 + max_concurrency: int = 4 + min_card_chars: int = 64 + readme_max_bytes: int = 1_048_576 + token: Optional[str] = None + + +class ScopeConfig(BaseModel): + active: str = "epfl" + seeds: dict[str, list[str]] = {} + + +class QdrantConfig(BaseModel): + url: str = "http://gme-qdrant:6333" + prefer_grpc: bool = False + api_key: Optional[str] = None + + +class ChunkingConfig(BaseModel): + size_tokens: int = 512 + overlap_tokens: int = 64 + tokenizer: str = "cl100k_base" + + +class GitHubIndexConfig(BaseModel): + rcp: RcpConfig + github: GitHubConfig + scope: ScopeConfig + qdrant: QdrantConfig + chunking: ChunkingConfig + paths: GitHubPaths + + model_config = {"arbitrary_types_allowed": True} + + def require_rcp(self) -> None: + if not self.rcp.token: + raise ValueError(MISSING_RCP_TOKEN_ERROR) + + def require_github(self) -> None: + if not self.github.token: + raise ValueError(MISSING_GITHUB_TOKEN_ERROR) + + +def _env_bool(name: str) -> Optional[bool]: + raw = os.getenv(name) + if raw is None or raw.strip() == "": + return None + normalized = raw.strip().lower() + if normalized in TRUE_ENV_VALUES: + return True + if normalized in FALSE_ENV_VALUES: + return False + message = f"Invalid boolean value for {name}: {raw!r}" + raise ValueError(message) + + +def _env_str(name: str) -> Optional[str]: + raw = os.getenv(name) + if raw is None: + return None + stripped = raw.strip() + return stripped or None + + +def load_config(path: Optional[Path] = None) -> GitHubIndexConfig: + cfg_path = path or DEFAULT_CONFIG_PATH + raw = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) or {} + + raw.setdefault("rcp", {})["token"] = _env_str("RCP_TOKEN") + raw.setdefault("github", {})["token"] = _env_str("GITHUB_TOKEN") + raw.setdefault("qdrant", {})["api_key"] = _env_str("INDEX_QDRANT_API_KEY") + + if (override := _env_str("INDEX_QDRANT_URL")) is not None: + raw["qdrant"]["url"] = override + if (override_b := _env_bool("INDEX_QDRANT_PREFER_GRPC")) is not None: + raw["qdrant"]["prefer_grpc"] = override_b + if (override := _env_str("INDEX_GITHUB_SCOPE")) is not None: + raw.setdefault("scope", {})["active"] = override + + raw["paths"] = get_github_paths() + + return GitHubIndexConfig(**raw) diff --git a/src/utils/__init__.py b/src/index/github/embed/__init__.py similarity index 100% rename from src/utils/__init__.py rename to src/index/github/embed/__init__.py diff --git a/src/index/github/embed/pipeline.py b/src/index/github/embed/pipeline.py new file mode 100644 index 0000000..50f0d85 --- /dev/null +++ b/src/index/github/embed/pipeline.py @@ -0,0 +1,312 @@ +"""Embed GitHub repositories into Qdrant via the RCP `/embeddings` endpoint. + +Reuses `RCPEmbeddingClient`, `QdrantStore`, and the token chunker from +`src.index.openalex` directly — those modules only access `config.rcp.*` +and `config.qdrant.*` at runtime, both of which `GitHubIndexConfig` +mirrors field-for-field. Same pattern as `src.index.zenodo`. + +Two entry points: + +- `embed_repos` — chunk + embed un-embedded repos (skip rows already in `chunks`). +- `rebuild_qdrant_from_chunks` — re-derive Qdrant points from existing `chunks` + rows; used after a Qdrant wipe. Does not touch DuckDB. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import time +import uuid +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.chunker import Chunk, chunk_text +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.vector.qdrant_store import QdrantStore + +# Qdrant upsert retry policy. The qdrant_client default 5s read timeout is too +# tight under sustained load; transient timeouts shouldn't kill a multi-hour +# embed run. Exponential backoff: 5s, 15s, 45s, 135s — total ~3.5min before +# giving up on a single batch. +_QDRANT_RETRY_DELAYS_SECONDS: tuple[int, ...] = (5, 15, 45, 135) + +if TYPE_CHECKING: + from src.index.github.config import GitHubIndexConfig + from src.index.github.storage.duckdb_store import GitHubStore + +LOGGER = logging.getLogger(__name__) + +_CHUNK_NAMESPACE = uuid.NAMESPACE_URL + +GITHUB_COLLECTION = "github_repos" + + +def _chunk_id(entity_type: str, entity_id: str, chunk_index: int) -> str: + return str( + uuid.uuid5(_CHUNK_NAMESPACE, f"{entity_type}|{entity_id}|{chunk_index}"), + ) + + +def _row_topics(row: dict[str, Any]) -> list[str]: + raw = row.get("topics") + if isinstance(raw, str): + try: + return list(json.loads(raw) or []) + except json.JSONDecodeError: + return [] + if isinstance(raw, list): + return [str(x) for x in raw] + return [] + + +def _readme_text(*, cards_dir: Path, readme_path: str | None) -> str | None: + if not readme_path: + return None + abs_path = cards_dir / readme_path + if not abs_path.exists(): + return None + try: + return abs_path.read_text(encoding="utf-8") + except (OSError, UnicodeDecodeError): + return None + + +def _row_to_chunks( + row: dict[str, Any], + *, + cards_dir: Path, + chunk_tokens: int, + overlap: int, + min_card_chars: int, +) -> list[Chunk]: + parts: list[str] = [str(row["repo_id"])] + if row.get("description"): + parts.append(str(row["description"])) + topics = _row_topics(row) + if topics: + parts.append("topics: " + ", ".join(topics)) + readme = _readme_text(cards_dir=cards_dir, readme_path=row.get("readme_path")) + if readme: + parts.append(readme) + text = "\n\n".join(parts) + if len(text) < min_card_chars: + return [] + return chunk_text(text, chunk_tokens=chunk_tokens, overlap=overlap) + + +def _row_to_payload(row: dict[str, Any]) -> dict[str, Any]: + pushed = row.get("pushed_at") + return { + "entity_type": "repos", + "entity_id": row["repo_id"], + "repo_id": row["repo_id"], + "owner": row.get("owner"), + "name": row.get("name"), + "primary_language": row.get("primary_language"), + "license_spdx": row.get("license_spdx"), + "stars": row.get("stargazers_count"), + "forks": row.get("forks_count"), + "is_archived": row.get("is_archived"), + "is_fork": row.get("is_fork"), + "pushed_at": pushed.isoformat() if hasattr(pushed, "isoformat") else pushed, + } + + +async def _embed_repos_async( + *, + config: GitHubIndexConfig, + store: GitHubStore, + limit: int | None, +) -> int: + client = RCPEmbeddingClient(config) # type: ignore[arg-type] + qdrant = QdrantStore(config) # type: ignore[arg-type] + qdrant.ensure_collection(GITHUB_COLLECTION) + + pending: list[tuple[str, dict[str, Any], Chunk]] = [] + total = 0 + + async def flush() -> None: + nonlocal total + if not pending: + return + texts = [c.text for _, _, c in pending] + vectors = await client.embed_all(texts) + ids: list[str] = [] + payloads: list[dict[str, Any]] = [] + chunk_rows: list[dict[str, Any]] = [] + for entity_id, base_payload, chunk in pending: + cid = _chunk_id("repos", entity_id, chunk.index) + ids.append(cid) + payloads.append({**base_payload, "chunk_index": chunk.index}) + chunk_rows.append( + { + "chunk_id": cid, + "entity_id": entity_id, + "chunk_index": chunk.index, + "text": chunk.text, + "token_count": chunk.token_count, + }, + ) + # Qdrant upsert FIRST, with retry. If we wrote chunks to DuckDB before + # this, a Qdrant timeout would leave orphan rows that block re-embed + # on the same batch. Order is: vectors land in Qdrant, then DuckDB + # records "this batch is embedded" — a crash between the two means + # a wasted batch (re-embedded on resume) but no inconsistency. + last_exc: Exception | None = None + for attempt, delay in enumerate((0, *_QDRANT_RETRY_DELAYS_SECONDS)): + if delay: + LOGGER.warning( + "qdrant upsert retry %d/%d in %ds (last error: %s)", + attempt, + len(_QDRANT_RETRY_DELAYS_SECONDS), + delay, + last_exc, + ) + time.sleep(delay) + try: + qdrant.upsert_points( + GITHUB_COLLECTION, + ids=ids, + vectors=vectors, + payloads=payloads, + ) + break + except Exception as exc: # noqa: BLE001 — qdrant_client raises a wide variety + last_exc = exc + else: + LOGGER.error("qdrant upsert giving up after %d attempts", len(_QDRANT_RETRY_DELAYS_SECONDS)) + raise last_exc # type: ignore[misc] + for row in chunk_rows: + store.upsert_chunk( + chunk_id=row["chunk_id"], + entity_type="repos", + entity_id=row["entity_id"], + chunk_index=row["chunk_index"], + text=row["text"], + token_count=row["token_count"], + vector_id=row["chunk_id"], + ) + total += len(pending) + pending.clear() + + rows_seen = 0 + rows_skipped = 0 + for row in store.stream_rows_for_embedding("repos", limit=limit): + rows_seen += 1 + chunks = _row_to_chunks( + row, + cards_dir=config.paths.cards_dir, + chunk_tokens=config.chunking.size_tokens, + overlap=config.chunking.overlap_tokens, + min_card_chars=config.github.min_card_chars, + ) + if not chunks: + rows_skipped += 1 + continue + base_payload = _row_to_payload(row) + for chunk in chunks: + pending.append((row["repo_id"], base_payload, chunk)) + if len(pending) >= client.batch_size: + await flush() + await flush() + LOGGER.info( + "embed repos complete: rows_seen=%d skipped=%d chunks=%d", + rows_seen, + rows_skipped, + total, + ) + return total + + +def embed_repos( + *, + config: GitHubIndexConfig, + store: GitHubStore, + limit: int | None = None, +) -> dict[str, int]: + """Synchronously embed GitHub repos.""" + chunks = asyncio.run( + _embed_repos_async(config=config, store=store, limit=limit), + ) + return {"repos": chunks} + + +# ---- Recovery from a Qdrant wipe ---------------------------------------- + + +async def _rebuild_async( + *, + config: GitHubIndexConfig, + store: GitHubStore, +) -> int: + client = RCPEmbeddingClient(config) # type: ignore[arg-type] + qdrant = QdrantStore(config) # type: ignore[arg-type] + qdrant.ensure_collection(GITHUB_COLLECTION) + + cur = store.connect().execute( + "SELECT c.chunk_id, c.entity_id, c.chunk_index, c.text, " + " r.owner, r.name, r.primary_language, r.license_spdx, " + " r.stargazers_count, r.forks_count, r.is_archived, r.is_fork, " + " r.pushed_at " + "FROM chunks c JOIN repos r ON r.repo_id = c.entity_id " + "WHERE c.entity_type = 'repos' " + "ORDER BY c.entity_id, c.chunk_index", + ) + cols = [d[0] for d in cur.description] + rows = [dict(zip(cols, r, strict=False)) for r in cur.fetchall()] + if not rows: + LOGGER.info("rebuild repos: no chunks to push") + return 0 + + pushed = 0 + flush_size = client.batch_size + for start in range(0, len(rows), flush_size): + batch = rows[start : start + flush_size] + texts = [r["text"] for r in batch] + vectors = await client.embed_all(texts) + ids = [r["chunk_id"] for r in batch] + payloads: list[dict[str, Any]] = [] + for r in batch: + pushed_at = r.get("pushed_at") + payloads.append( + { + "entity_type": "repos", + "entity_id": r["entity_id"], + "repo_id": r["entity_id"], + "owner": r.get("owner"), + "name": r.get("name"), + "primary_language": r.get("primary_language"), + "license_spdx": r.get("license_spdx"), + "stars": r.get("stargazers_count"), + "forks": r.get("forks_count"), + "is_archived": r.get("is_archived"), + "is_fork": r.get("is_fork"), + "pushed_at": pushed_at.isoformat() if hasattr(pushed_at, "isoformat") else pushed_at, + "chunk_index": r["chunk_index"], + }, + ) + qdrant.upsert_points( + GITHUB_COLLECTION, + ids=ids, + vectors=vectors, + payloads=payloads, + ) + pushed += len(batch) + LOGGER.info("rebuild repos: pushed %d/%d points", pushed, len(rows)) + return pushed + + +def rebuild_qdrant_from_chunks( + *, + config: GitHubIndexConfig, + store: GitHubStore, +) -> dict[str, int]: + """Rebuild the `github_repos` collection from the existing `chunks` table. + + Re-embeds `chunks.text` via RCP and upserts to Qdrant under the existing + `chunk_id` so the DuckDB ↔ Qdrant link is preserved. Used after a Qdrant + wipe — does NOT modify DuckDB. + """ + return {"repos": asyncio.run(_rebuild_async(config=config, store=store))} diff --git a/src/agents/tools.py b/src/index/github/ingest/__init__.py similarity index 100% rename from src/agents/tools.py rename to src/index/github/ingest/__init__.py diff --git a/src/index/github/ingest/github_client.py b/src/index/github/ingest/github_client.py new file mode 100644 index 0000000..02bc146 --- /dev/null +++ b/src/index/github/ingest/github_client.py @@ -0,0 +1,233 @@ +"""Direct GitHub REST client for the index module. + +Why not reuse `src.v2.ingest.providers.github_provider.RealGitHubProvider`? + +That provider runs gimie under the hood (clones the repo, extracts JSON-LD, +SBOM) — which is overkill for an index that only needs metadata + README. +We use the same auth + ProviderCache pattern that +`RealGitHubProvider._get_repository_rest_metadata` uses, just trimmed to +the four endpoints we actually need: + + GET /repos/{owner}/{name} — full repo payload + GET /repos/{owner}/{name}/languages — {lang: bytes} + GET /repos/{owner}/{name}/contributors?per_page=100 — top-100 contributors + GET /repos/{owner}/{name}/readme — base64-encoded README + path + +Multi-token support: `GITHUB_TOKEN` may be a comma-separated list of PATs +(`ghp_A,ghp_B,...`). The client splits at construction, round-robins across +tokens per request, and on 403 rate-limit responses parks the exhausted +token until its `X-RateLimit-Reset` timestamp. With N tokens, effective +throughput is N × 5K req/h (subject to the secondary abuse-detection +limits which are per-account). + +The cache TTL matches the v2 default (30 days; configurable via +`V2_PROVIDER_CACHE_TTL_DAYS`). Cache hits are silent log lines so a cold +re-run re-uses the data without a single REST call. +""" + +from __future__ import annotations + +import base64 +import itertools +import logging +import time +from pathlib import Path +from typing import Any + +import requests + +from src.v2.ingest.cache import ProviderCache + +LOGGER = logging.getLogger(__name__) + +REQUEST_TIMEOUT_SECONDS = 30 +RATE_LIMIT_SLEEP_PADDING_SECONDS = 5 + + +class GitHubClient: + """Thin REST client with token-rotation and shared `ProviderCache`.""" + + def __init__( + self, + *, + api_base: str, + token: str | None, + cache_path: Path, + ) -> None: + self._api_base = api_base.rstrip("/") + # Comma-separated list → list of tokens; empty → anonymous. + raw = (token or "").strip() + self._tokens: list[str] = [t.strip() for t in raw.split(",") if t.strip()] + # Per-token cooldown: token → epoch seconds when it becomes usable again. + self._cooldowns: dict[str, float] = {} + self._token_cycle = itertools.cycle(self._tokens) if self._tokens else None + self._cache = ProviderCache(cache_path) + if self._tokens: + LOGGER.info("github client: %d token(s) loaded", len(self._tokens)) + else: + LOGGER.warning("github client: no token configured (anonymous, 60 req/h)") + + def _next_available_token(self) -> str | None: + """Return the next token whose cooldown has elapsed, or sleep until one is. + + Returns None when no tokens are configured (anonymous mode). + """ + if not self._tokens or self._token_cycle is None: + return None + # Rotate up to len(tokens) times to find a hot one. + for _ in range(len(self._tokens)): + candidate = next(self._token_cycle) + cooldown_until = self._cooldowns.get(candidate, 0.0) + if cooldown_until <= time.time(): + return candidate + # All tokens parked — sleep until the soonest reset. + soonest = min(self._cooldowns.values()) + delay = max(soonest - time.time(), 0) + RATE_LIMIT_SLEEP_PADDING_SECONDS + LOGGER.warning( + "github client: all %d token(s) rate-limited; sleeping %.0fs", + len(self._tokens), + delay, + ) + time.sleep(delay) + # After the sleep, the soonest-resetting token is now hot. + return next(self._token_cycle) + + def _headers(self) -> tuple[dict[str, str], str | None]: + token = self._next_available_token() + h = {"Accept": "application/vnd.github+json"} + if token: + h["Authorization"] = f"token {token}" + return h, token + + def _park_token(self, token: str, response: requests.Response) -> None: + """Mark `token` as cooled-down until `X-RateLimit-Reset`.""" + reset_header = response.headers.get("X-RateLimit-Reset") + try: + reset_ts = float(reset_header) if reset_header else time.time() + 60 + except ValueError: + reset_ts = time.time() + 60 + self._cooldowns[token] = reset_ts + LOGGER.warning( + "github client: token %s… rate-limited; parking %.0fs", + token[:6], + max(reset_ts - time.time(), 0), + ) + + def _get_json(self, url: str) -> Any: + # Up to len(tokens)+1 attempts: each rotation tries a different token. + attempts = max(len(self._tokens) + 1, 1) + for _ in range(attempts): + headers, token_used = self._headers() + try: + response = requests.get( + url, + headers=headers, + timeout=REQUEST_TIMEOUT_SECONDS, + ) + except requests.RequestException: + LOGGER.exception("github GET failed: %s", url) + return None + if response.status_code == 200: + try: + return response.json() + except ValueError: + LOGGER.exception("github GET response not JSON: %s", url) + return None + if response.status_code == 404: + LOGGER.info("github GET 404: %s", url) + return None + if response.status_code in (403, 429) and token_used is not None: + # Rate-limited or secondary-abuse-detection. Park this token, + # rotate, and retry on a fresh one. + remaining = response.headers.get("X-RateLimit-Remaining") + if remaining == "0" or response.status_code == 429: + self._park_token(token_used, response) + continue + LOGGER.warning( + "github GET %d (not rate-limit) for %s", + response.status_code, + url, + ) + return None + LOGGER.warning( + "github GET returned %d for %s", + response.status_code, + url, + ) + return None + LOGGER.warning("github GET exhausted retries: %s", url) + return None + + # ---- Public methods -------------------------------------------------- + + def get_repository(self, full_name: str) -> dict[str, Any] | None: + url = f"{self._api_base}/repos/{full_name}" + key = ProviderCache.make_key("github_index", "get_repository", full_name=full_name) + return self._cache.get_or_set( + key, + lambda: self._get_json(url), + label=f"github_index.get_repository({full_name})", + ) + + def get_languages(self, full_name: str) -> dict[str, int]: + url = f"{self._api_base}/repos/{full_name}/languages" + key = ProviderCache.make_key("github_index", "get_languages", full_name=full_name) + result = self._cache.get_or_set( + key, + lambda: self._get_json(url), + label=f"github_index.get_languages({full_name})", + ) + if not isinstance(result, dict): + return {} + return {str(k): int(v) for k, v in result.items() if isinstance(v, (int, float))} + + def get_contributors(self, full_name: str, *, per_page: int = 100) -> list[dict[str, Any]]: + url = f"{self._api_base}/repos/{full_name}/contributors?per_page={per_page}" + key = ProviderCache.make_key( + "github_index", "get_contributors", full_name=full_name, per_page=per_page, + ) + result = self._cache.get_or_set( + key, + lambda: self._get_json(url), + label=f"github_index.get_contributors({full_name})", + ) + if not isinstance(result, list): + return [] + return [c for c in result if isinstance(c, dict) and c.get("login")] + + def get_readme(self, full_name: str, *, max_bytes: int) -> tuple[str | None, str | None]: + """Return (markdown_text, original_path) or (None, None) on absence/error. + + `path` is the original filename inside the repo (e.g. `README.md`, + `docs/README.rst`) — useful for telemetry and logging. + """ + url = f"{self._api_base}/repos/{full_name}/readme" + key = ProviderCache.make_key("github_index", "get_readme", full_name=full_name) + payload = self._cache.get_or_set( + key, + lambda: self._get_json(url), + label=f"github_index.get_readme({full_name})", + ) + if not isinstance(payload, dict): + return None, None + encoded = payload.get("content") + if not isinstance(encoded, str): + return None, payload.get("path") + try: + raw = base64.b64decode(encoded) + except (ValueError, TypeError): + LOGGER.warning("github README base64 decode failed: %s", full_name) + return None, payload.get("path") + if len(raw) > max_bytes: + LOGGER.info( + "github README truncated %d -> %d bytes for %s", + len(raw), + max_bytes, + full_name, + ) + raw = raw[:max_bytes] + try: + text = raw.decode("utf-8") + except UnicodeDecodeError: + text = raw.decode("utf-8", errors="replace") + return text, payload.get("path") diff --git a/src/index/github/ingest/repos.py b/src/index/github/ingest/repos.py new file mode 100644 index 0000000..438cea8 --- /dev/null +++ b/src/index/github/ingest/repos.py @@ -0,0 +1,154 @@ +"""Iterate the active scope, fetch each repo, persist to DuckDB + cards/.""" + +from __future__ import annotations + +import logging +from datetime import datetime +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from src.index.github.ingest.github_client import GitHubClient +from src.index.github.models import ContributorEntry, RepoRecord + +if TYPE_CHECKING: + from src.index.github.config import GitHubIndexConfig + from src.index.github.ingest.scope import Scope + from src.index.github.storage.duckdb_store import GitHubStore + +LOGGER = logging.getLogger(__name__) + + +def _parse_iso(value: Any) -> datetime | None: + if not isinstance(value, str): + return None + try: + # GitHub returns RFC 3339 / ISO 8601 with trailing 'Z'. + return datetime.fromisoformat(value.replace("Z", "+00:00")) + except ValueError: + return None + + +def _record_from_payload( + *, + full_name: str, + repo_payload: dict[str, Any], + languages: dict[str, int], + contributors: list[dict[str, Any]], + readme_text: str | None, + readme_path: str | None, +) -> RepoRecord: + owner_block = repo_payload.get("owner") or {} + license_block = repo_payload.get("license") or {} + owner = owner_block.get("login") or full_name.split("/", 1)[0] + name = repo_payload.get("name") or full_name.split("/", 1)[1] + return RepoRecord( + repo_id=full_name, + owner=str(owner), + name=str(name), + default_branch=repo_payload.get("default_branch"), + description=repo_payload.get("description"), + homepage=repo_payload.get("homepage"), + primary_language=repo_payload.get("language"), + languages=languages, + topics=list(repo_payload.get("topics") or []), + license_spdx=license_block.get("spdx_id") if isinstance(license_block, dict) else None, + is_fork=bool(repo_payload.get("fork", False)), + is_archived=bool(repo_payload.get("archived", False)), + is_private=bool(repo_payload.get("private", False)), + stargazers_count=int(repo_payload.get("stargazers_count") or 0), + forks_count=int(repo_payload.get("forks_count") or 0), + watchers_count=int(repo_payload.get("watchers_count") or 0), + open_issues_count=int(repo_payload.get("open_issues_count") or 0), + size_kb=int(repo_payload.get("size") or 0), + created_at=_parse_iso(repo_payload.get("created_at")), + pushed_at=_parse_iso(repo_payload.get("pushed_at")), + readme_path=readme_path, + readme_text=readme_text, + contributors=[ + ContributorEntry( + login=str(c.get("login")), + contributions=int(c.get("contributions") or 0), + ) + for c in contributors + if c.get("login") + ], + raw=repo_payload, + ) + + +def _persist_readme(*, owner: str, name: str, text: str, cards_dir: Path) -> str: + target_dir = cards_dir / owner / name + target_dir.mkdir(parents=True, exist_ok=True) + target = target_dir / "README.md" + target.write_text(text, encoding="utf-8") + return str(target.relative_to(cards_dir)) + + +def ingest_repos( + *, + config: GitHubIndexConfig, + store: GitHubStore, + scope: Scope, + limit: int | None = None, +) -> dict[str, Any]: + """Fetch metadata + README for every repo in scope and upsert to DuckDB. + + Returns a summary dict: counts of {seen, ingested, skipped_404, no_readme}. + """ + config.require_github() + client = GitHubClient( + api_base=config.github.api_base, + token=config.github.token, + cache_path=config.paths.cache_db_path, + ) + seen = ingested = skipped_404 = no_readme = 0 + repos_to_process = scope.repos[:limit] if limit is not None else scope.repos + for full_name in repos_to_process: + seen += 1 + repo_payload = client.get_repository(full_name) + if not isinstance(repo_payload, dict): + LOGGER.warning("ingest skip: repo not found or unreachable: %s", full_name) + skipped_404 += 1 + continue + languages = client.get_languages(full_name) + contributors = client.get_contributors(full_name) + readme_text, readme_path = client.get_readme( + full_name, + max_bytes=config.github.readme_max_bytes, + ) + if not readme_text: + no_readme += 1 + record = _record_from_payload( + full_name=full_name, + repo_payload=repo_payload, + languages=languages, + contributors=contributors, + readme_text=readme_text, + readme_path=readme_path, + ) + if readme_text: + stored_path = _persist_readme( + owner=record.owner, + name=record.name, + text=readme_text, + cards_dir=config.paths.cards_dir, + ) + # Replace the GitHub-side `readme_path` (e.g. `README.md` or + # `docs/README.rst`) with the on-disk relative path so downstream + # code can find the file unambiguously. + record.readme_path = stored_path + store.upsert_repo(record) + ingested += 1 + LOGGER.info( + "ingested %s (stars=%d lang=%s readme=%s)", + full_name, + record.stargazers_count, + record.primary_language or "-", + "yes" if readme_text else "no", + ) + return { + "seen": seen, + "ingested": ingested, + "skipped_404": skipped_404, + "no_readme": no_readme, + } diff --git a/src/index/github/ingest/scope.py b/src/index/github/ingest/scope.py new file mode 100644 index 0000000..6b652d7 --- /dev/null +++ b/src/index/github/ingest/scope.py @@ -0,0 +1,113 @@ +"""Scope resolution: which seed list of `/` to ingest. + +Reads `scope.seeds[]` from the loaded config. Optionally augments +with the OpenAlex `work_github_urls` table when `--from-openalex` is used. +""" + +from __future__ import annotations + +import logging +import re +from dataclasses import dataclass +from pathlib import Path +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from src.index.github.config import GitHubIndexConfig + +LOGGER = logging.getLogger(__name__) + +# Strict GitHub-handle pattern (alphanumerics + single dashes, no leading/trailing dash). +_HANDLE = r"[A-Za-z0-9](?:[A-Za-z0-9]|-(?=[A-Za-z0-9])){0,38}" +# Repo-name allows dots, underscores, dashes; no spaces or path traversal. +_REPO = r"[A-Za-z0-9._-]{1,100}" +_REPO_ID_RE = re.compile(rf"^({_HANDLE})/({_REPO})$") + + +@dataclass(slots=True) +class Scope: + name: str + repos: list[str] + + +SCOPE_NOT_CONFIGURED_ERROR = ( + "scope={name} not found in config.scope.seeds. Edit config/index/github.yaml." +) + + +def _normalize_repo_id(repo_id: str) -> str | None: + match = _REPO_ID_RE.match(repo_id.strip()) + if not match: + return None + owner, name = match.groups() + # GitHub treats owner names case-insensitively but preserves the + # canonical case in the API response. Lower-casing is *not* safe for + # building URLs. We normalise only by stripping whitespace and trailing + # `.git` to dedupe at the seed-level. + name = name.removesuffix(".git") + return f"{owner}/{name}" + + +def resolve_scope(name: str, config: GitHubIndexConfig) -> Scope: + seeds = config.scope.seeds.get(name) + if seeds is None: + raise SystemExit(SCOPE_NOT_CONFIGURED_ERROR.format(name=name)) + deduped: list[str] = [] + seen: set[str] = set() + for raw in seeds: + norm = _normalize_repo_id(raw) + if not norm: + LOGGER.warning("scope %s: skipping malformed repo_id %r", name, raw) + continue + if norm.lower() in seen: + continue + seen.add(norm.lower()) + deduped.append(norm) + return Scope(name=name, repos=deduped) + + +def merge_openalex_repos( + scope: Scope, + *, + openalex_db_path: Path, +) -> Scope: + """Augment the scope with distinct repos from the OpenAlex `work_github_urls` table. + + We only add (`owner/name`) pairs that aren't already in `scope.repos`. The + OpenAlex DB is the source of truth for URLs discovered in Swiss-affiliated + works. Logs a single summary line; never auto-promotes the YAML seed. + """ + if not openalex_db_path.exists(): + LOGGER.warning( + "merge_openalex_repos: %s not found; skipping bootstrap", + openalex_db_path, + ) + return scope + import duckdb + + seen_lower = {r.lower() for r in scope.repos} + added: list[str] = [] + con = duckdb.connect(str(openalex_db_path), read_only=True) + try: + cur = con.execute( + "SELECT DISTINCT normalized_url FROM work_github_urls " + "WHERE normalized_url IS NOT NULL", + ) + for (url,) in cur.fetchall(): + # normalized_url is e.g. "https://github.com/owner/name" + tail = str(url).removeprefix("https://github.com/").removeprefix("http://github.com/") + tail = tail.strip("/") + norm = _normalize_repo_id(tail) + if not norm or norm.lower() in seen_lower: + continue + added.append(norm) + seen_lower.add(norm.lower()) + finally: + con.close() + LOGGER.info( + "merge_openalex_repos: added %d repos from %s (kept %d from YAML seed)", + len(added), + openalex_db_path, + len(scope.repos), + ) + return Scope(name=scope.name, repos=[*scope.repos, *added]) diff --git a/src/index/github/models.py b/src/index/github/models.py new file mode 100644 index 0000000..0d47abc --- /dev/null +++ b/src/index/github/models.py @@ -0,0 +1,45 @@ +"""Pydantic models for the GitHub index module. + +`RepoRecord` is the structured view we persist into DuckDB. `raw` carries +the unparsed REST `repos/{owner}/{name}` payload for debugging and future +field additions without re-ingesting. +""" + +from __future__ import annotations + +from datetime import datetime +from typing import Any, Optional + +from pydantic import BaseModel + + +class ContributorEntry(BaseModel): + login: str + contributions: int = 0 + + +class RepoRecord(BaseModel): + repo_id: str # "/" + owner: str + name: str + default_branch: Optional[str] = None + description: Optional[str] = None + homepage: Optional[str] = None + primary_language: Optional[str] = None + languages: dict[str, int] = {} + topics: list[str] = [] + license_spdx: Optional[str] = None + is_fork: bool = False + is_archived: bool = False + is_private: bool = False + stargazers_count: int = 0 + forks_count: int = 0 + watchers_count: int = 0 + open_issues_count: int = 0 + size_kb: int = 0 + created_at: Optional[datetime] = None + pushed_at: Optional[datetime] = None + readme_path: Optional[str] = None + readme_text: Optional[str] = None + contributors: list[ContributorEntry] = [] + raw: dict[str, Any] = {} diff --git a/src/index/github/paths.py b/src/index/github/paths.py new file mode 100644 index 0000000..cee5a8f --- /dev/null +++ b/src/index/github/paths.py @@ -0,0 +1,72 @@ +"""Filesystem layout for GitHub index artifacts. + +Single source of truth for paths under `/github/`. Mirrors +`src/index/zenodo/paths.py`. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path + +DEFAULT_INDEX_DATA_DIR = Path("data/index") + + +def _resolve_repo_root() -> Path: + return Path(__file__).resolve().parents[3] + + +def _resolve_index_data_dir() -> Path: + raw = os.getenv("INDEX_DATA_DIR") + if raw and raw.strip(): + candidate = Path(raw.strip()).expanduser() + if candidate.is_absolute(): + return candidate + return _resolve_repo_root() / candidate + return _resolve_repo_root() / DEFAULT_INDEX_DATA_DIR + + +@dataclass(slots=True, frozen=True) +class GitHubPaths: + root: Path + + @property + def duckdb_dir(self) -> Path: + return self.root / "duckdb" + + @property + def duckdb_path(self) -> Path: + return self.duckdb_dir / "github.duckdb" + + @property + def cache_dir(self) -> Path: + return self.root / "cache" + + @property + def cache_db_path(self) -> Path: + # Dedicated ProviderCache DB so this index can be invalidated without + # touching the v2 cache. Same SQLite schema as the v2 cache. + return self.cache_dir / "providers.db" + + @property + def cards_dir(self) -> Path: + return self.root / "cards" + + @property + def logs_dir(self) -> Path: + return self.root / "logs" + + def card_dir_for(self, owner: str, name: str) -> Path: + return self.cards_dir / owner / name + + +def get_github_paths() -> GitHubPaths: + """Resolve `/github/` and ensure subdirectories exist.""" + root = _resolve_index_data_dir() / "github" + paths = GitHubPaths(root=root) + paths.duckdb_dir.mkdir(parents=True, exist_ok=True) + paths.cache_dir.mkdir(parents=True, exist_ok=True) + paths.cards_dir.mkdir(parents=True, exist_ok=True) + paths.logs_dir.mkdir(parents=True, exist_ok=True) + return paths diff --git a/src/context/openalex.py b/src/index/github/retrieval/__init__.py similarity index 100% rename from src/context/openalex.py rename to src/index/github/retrieval/__init__.py diff --git a/src/index/github/retrieval/semantic.py b/src/index/github/retrieval/semantic.py new file mode 100644 index 0000000..8590fac --- /dev/null +++ b/src/index/github/retrieval/semantic.py @@ -0,0 +1,104 @@ +"""Semantic retrieval over the GitHub index. + +Embed → Qdrant search → RCP rerank → DuckDB hydrate. Reuses the openalex +RCP + Qdrant clients directly. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from typing import TYPE_CHECKING, Any + +from src.index.github.embed.pipeline import GITHUB_COLLECTION +from src.index.github.storage.duckdb_store import GitHubStore +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.vector.qdrant_store import QdrantStore + +if TYPE_CHECKING: + from src.index.github.config import GitHubIndexConfig + +LOGGER = logging.getLogger(__name__) + + +def _payload_to_doc(payload: dict[str, Any]) -> str: + repo_id = payload.get("repo_id") or payload.get("entity_id") + if repo_id: + return str(repo_id) + return json.dumps(payload, ensure_ascii=False) + + +async def _async_search( + *, + config: GitHubIndexConfig, + query: str, + top_k: int, + candidate_k: int, + filter_payload: dict[str, Any] | None, + store: GitHubStore, +) -> list[dict[str, Any]]: + embed = RCPEmbeddingClient(config) # type: ignore[arg-type] + qdrant = QdrantStore(config) # type: ignore[arg-type] + rerank = RCPRerankerClient(config) # type: ignore[arg-type] + + [query_vec] = await embed.embed_all([query]) + candidates = qdrant.search( + GITHUB_COLLECTION, + query_vector=query_vec, + top_k=candidate_k, + filter_payload=filter_payload, + ) + if not candidates: + return [] + + docs = [_payload_to_doc(c["payload"]) for c in candidates] + reranked = await rerank.rerank(query, docs, top_n=top_k) + if not reranked: + ordered = candidates[:top_k] + else: + ordered = [ + {**candidates[r["index"]], "rerank_score": r["relevance_score"]} + for r in reranked + ] + + hydrated: list[dict[str, Any]] = [] + for hit in ordered: + payload = hit["payload"] or {} + repo_id = payload.get("repo_id") or payload.get("entity_id") + if not repo_id: + continue + hydrated.append( + { + "id": hit["id"], + "vector_score": hit["score"], + "rerank_score": hit.get("rerank_score"), + "payload": payload, + "entity": store.fetch_repo(str(repo_id)), + }, + ) + return hydrated + + +def semantic_search( + *, + config: GitHubIndexConfig, + query: str, + top_k: int = 10, + candidate_k: int = 50, + filter_payload: dict[str, Any] | None = None, + store: GitHubStore | None = None, +) -> list[dict[str, Any]]: + if store is None: + store = GitHubStore.open() + return asyncio.run( + _async_search( + config=config, + query=query, + top_k=top_k, + candidate_k=candidate_k, + filter_payload=filter_payload, + store=store, + ), + ) diff --git a/src/index/github/retrieval/sql.py b/src/index/github/retrieval/sql.py new file mode 100644 index 0000000..b6ead5b --- /dev/null +++ b/src/index/github/retrieval/sql.py @@ -0,0 +1,128 @@ +"""Read-only SQL surface over the GitHub DuckDB. + +Two entrypoints: + +- `run_predefined()` — parametrized canned queries. +- `run_adhoc()` — guarded SELECT/WITH only, with a forbidden-keyword regex. +""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index.github.storage.duckdb_store import GitHubStore + +INVALID_QUERY_PREFIX_ERROR = "Only SELECT/WITH queries are allowed" +FORBIDDEN_KEYWORD_ERROR = "Forbidden keyword in query: {kw}" + +_ALLOWED_PREFIXES = ("select", "with") +_FORBIDDEN_KEYWORDS = ( + "attach", "copy", "pragma", "install", "load", "export", "import", + "create", "drop", "alter", "insert", "update", "delete", "truncate", +) +_KEYWORD_RE = re.compile(r"\b(" + "|".join(_FORBIDDEN_KEYWORDS) + r")\b", re.IGNORECASE) + + +def _validate_adhoc(sql: str) -> None: + stripped = sql.strip().lstrip("(").lstrip() + lowered = stripped.lower() + if not any(lowered.startswith(p) for p in _ALLOWED_PREFIXES): + raise ValueError(INVALID_QUERY_PREFIX_ERROR) + match = _KEYWORD_RE.search(stripped) + if match: + raise ValueError(FORBIDDEN_KEYWORD_ERROR.format(kw=match.group(1).upper())) + + +PREDEFINED_QUERIES: dict[str, str] = { + "count_repos": "SELECT COUNT(*) AS n FROM repos", + "count_by_entity": ( + "SELECT 'repos' AS entity, COUNT(*) AS n FROM repos " + "UNION ALL SELECT 'chunks', COUNT(*) FROM chunks" + ), + "count_by_owner": ( + "SELECT owner, COUNT(*) AS n FROM repos " + "GROUP BY owner ORDER BY n DESC" + ), + "count_by_language": ( + "SELECT primary_language, COUNT(*) AS n FROM repos " + "GROUP BY primary_language ORDER BY n DESC" + ), + "count_by_license": ( + "SELECT license_spdx, COUNT(*) AS n FROM repos " + "GROUP BY license_spdx ORDER BY n DESC" + ), + "top_starred": ( + "SELECT repo_id, owner, name, primary_language, " + " stargazers_count, pushed_at " + "FROM repos " + "ORDER BY stargazers_count DESC, repo_id " + "LIMIT $limit" + ), + "recently_pushed": ( + "SELECT repo_id, primary_language, stargazers_count, pushed_at " + "FROM repos " + "WHERE pushed_at IS NOT NULL " + "ORDER BY pushed_at DESC, repo_id " + "LIMIT $limit" + ), + "archived_repos": ( + "SELECT repo_id, primary_language, stargazers_count, pushed_at " + "FROM repos " + "WHERE is_archived " + "ORDER BY pushed_at DESC NULLS LAST" + ), + "repos_by_owner": ( + "SELECT repo_id, primary_language, stargazers_count, pushed_at " + "FROM repos " + "WHERE owner = $owner " + "ORDER BY stargazers_count DESC, repo_id" + ), +} + + +def _row_to_dict(cur: Any) -> list[dict[str, Any]]: + cols = [d[0] for d in cur.description] if cur.description else [] + return [dict(zip(cols, r, strict=False)) for r in cur.fetchall()] + + +def _execute( + sql: str, + params: dict[str, Any] | None, + store: GitHubStore | None, +) -> list[dict[str, Any]]: + owned = False + if store is None: + store = GitHubStore.open() + owned = True + try: + cur = store.connect().execute(sql, params or {}) + return _row_to_dict(cur) + finally: + if owned: + store.close() + + +def run_adhoc( + sql: str, + params: dict[str, Any] | None = None, + *, + store: GitHubStore | None = None, +) -> list[dict[str, Any]]: + _validate_adhoc(sql) + return _execute(sql, params, store) + + +def run_predefined( + name: str, + params: dict[str, Any] | None = None, + *, + store: GitHubStore | None = None, +) -> list[dict[str, Any]]: + if name not in PREDEFINED_QUERIES: + message = ( + f"Unknown predefined query: {name!r}. " + f"Known: {sorted(PREDEFINED_QUERIES)}" + ) + raise ValueError(message) + return _execute(PREDEFINED_QUERIES[name], params, store) diff --git a/src/index/github/storage/__init__.py b/src/index/github/storage/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/github/storage/duckdb_store.py b/src/index/github/storage/duckdb_store.py new file mode 100644 index 0000000..b500411 --- /dev/null +++ b/src/index/github/storage/duckdb_store.py @@ -0,0 +1,198 @@ +"""DuckDB lifecycle, schema bootstrap, and upsert helpers for the GitHub index.""" + +from __future__ import annotations + +import json +import logging +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import duckdb + +from src.index.github.paths import get_github_paths + +if TYPE_CHECKING: + from collections.abc import Iterator + + from src.index.github.models import RepoRecord + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + +EMBEDDABLE_ENTITY_TYPES = {"repos"} + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +class GitHubStore: + """Thin DuckDB wrapper for the GitHub schema. `bootstrap()` is idempotent.""" + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open(cls, db_path: Path | None = None) -> GitHubStore: + if db_path is None: + db_path = get_github_paths().duckdb_path + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + self.connect().execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + # ---- Upserts --------------------------------------------------------- + + def upsert_repo(self, record: RepoRecord) -> None: + sql = ( + "INSERT INTO repos " + "(repo_id, owner, name, default_branch, description, homepage, " + " primary_language, languages, topics, license_spdx, is_fork, " + " is_archived, is_private, stargazers_count, forks_count, " + " watchers_count, open_issues_count, size_kb, created_at, " + " pushed_at, readme_path, contributors, raw, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, " + " ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (repo_id) DO UPDATE SET " + " owner = excluded.owner, name = excluded.name, " + " default_branch = excluded.default_branch, " + " description = excluded.description, " + " homepage = excluded.homepage, " + " primary_language = excluded.primary_language, " + " languages = excluded.languages, topics = excluded.topics, " + " license_spdx = excluded.license_spdx, is_fork = excluded.is_fork, " + " is_archived = excluded.is_archived, is_private = excluded.is_private, " + " stargazers_count = excluded.stargazers_count, " + " forks_count = excluded.forks_count, " + " watchers_count = excluded.watchers_count, " + " open_issues_count = excluded.open_issues_count, " + " size_kb = excluded.size_kb, " + " created_at = excluded.created_at, " + " pushed_at = excluded.pushed_at, " + " readme_path = excluded.readme_path, " + " contributors = excluded.contributors, " + " raw = excluded.raw, ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + record.repo_id, + record.owner, + record.name, + record.default_branch, + record.description, + record.homepage, + record.primary_language, + json.dumps(record.languages, ensure_ascii=False), + json.dumps(record.topics, ensure_ascii=False), + record.license_spdx, + record.is_fork, + record.is_archived, + record.is_private, + record.stargazers_count, + record.forks_count, + record.watchers_count, + record.open_issues_count, + record.size_kb, + record.created_at, + record.pushed_at, + record.readme_path, + json.dumps([c.model_dump() for c in record.contributors], ensure_ascii=False), + json.dumps(record.raw, ensure_ascii=False, default=str), + self._now(), + ], + ) + + def upsert_chunk( + self, + *, + chunk_id: str, + entity_type: str, + entity_id: str, + chunk_index: int, + text: str, + token_count: int, + vector_id: str, + ) -> None: + self.connect().execute( + "INSERT INTO chunks " + "(chunk_id, entity_type, entity_id, chunk_index, text, " + "token_count, vector_id) VALUES (?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (chunk_id) DO UPDATE SET " + "text = excluded.text, token_count = excluded.token_count, " + "vector_id = excluded.vector_id, embedded_at = now()", + [chunk_id, entity_type, entity_id, chunk_index, text, token_count, vector_id], + ) + + # ---- Reads ----------------------------------------------------------- + + def count(self, table: str) -> int: + result = self.connect().execute(f"SELECT count(*) FROM {table}").fetchone() + return int(result[0]) if result else 0 + + def fetch_repo(self, repo_id: str) -> dict[str, Any] | None: + cur = self.connect().execute( + "SELECT * FROM repos WHERE repo_id = ?", + [repo_id], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + def stream_rows_for_embedding( + self, + entity_type: str, + *, + limit: int | None = None, + ) -> Iterator[dict[str, Any]]: + """Yield repo rows that need embedding (no chunks yet).""" + if entity_type not in EMBEDDABLE_ENTITY_TYPES: + message = f"Unknown entity_type: {entity_type}" + raise ValueError(message) + sql = ( + "SELECT t.* FROM repos t " + "WHERE NOT EXISTS (" + " SELECT 1 FROM chunks c " + " WHERE c.entity_type = ? AND c.entity_id = t.repo_id" + ")" + ) + params: list[Any] = [entity_type] + if limit is not None: + sql += " LIMIT ?" + params.append(limit) + cur = self.connect().execute(sql, params) + cols = [d[0] for d in cur.description] + rows = cur.fetchall() + for row in rows: + yield dict(zip(cols, row, strict=False)) + + @staticmethod + def _now() -> str: + return datetime.now(tz=timezone.utc).isoformat() diff --git a/src/index/github/storage/schema.sql b/src/index/github/storage/schema.sql new file mode 100644 index 0000000..fbf9b7b --- /dev/null +++ b/src/index/github/storage/schema.sql @@ -0,0 +1,48 @@ +-- Canonical DuckDB schema for the GitHub index module. +-- Idempotent: every statement uses IF NOT EXISTS so re-runs are safe. + +CREATE TABLE IF NOT EXISTS repos ( + repo_id TEXT PRIMARY KEY, -- "/" + owner TEXT NOT NULL, + name TEXT NOT NULL, + default_branch TEXT, + description TEXT, + homepage TEXT, + primary_language TEXT, + languages JSON, -- {lang: bytes} + topics JSON, -- list of strings + license_spdx TEXT, + is_fork BOOLEAN, + is_archived BOOLEAN, + is_private BOOLEAN, + stargazers_count BIGINT, + forks_count BIGINT, + watchers_count BIGINT, + open_issues_count BIGINT, + size_kb BIGINT, + created_at TIMESTAMP, + pushed_at TIMESTAMP, + readme_path TEXT, -- relative to /github/cards + contributors JSON, -- [{login, contributions}, ...] + raw JSON, -- full REST repos/{owner}/{name} payload + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +-- chunk_id is deterministic: uuid5(NAMESPACE_URL, "||") +-- so the primary key alone provides uniqueness. +CREATE TABLE IF NOT EXISTS chunks ( + chunk_id TEXT PRIMARY KEY, + entity_type TEXT NOT NULL, -- "repos" for now + entity_id TEXT NOT NULL, -- repo_id + chunk_index INTEGER NOT NULL, + text TEXT NOT NULL, + token_count INTEGER NOT NULL, + vector_id TEXT NOT NULL, + embedded_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS idx_repos_owner ON repos (owner); +CREATE INDEX IF NOT EXISTS idx_repos_lang ON repos (primary_language); +CREATE INDEX IF NOT EXISTS idx_repos_pushed ON repos (pushed_at); +CREATE INDEX IF NOT EXISTS idx_repos_archived ON repos (is_archived); +CREATE INDEX IF NOT EXISTS idx_chunks_entity ON chunks (entity_type, entity_id); diff --git a/src/index/huggingface/__init__.py b/src/index/huggingface/__init__.py new file mode 100644 index 0000000..7b93d0b --- /dev/null +++ b/src/index/huggingface/__init__.py @@ -0,0 +1,4 @@ +"""HuggingFace ingestion + dual-query (SQL + RAG) over EPFL → Switzerland. + +See `.internal/huggingface/` for the design. +""" diff --git a/src/index/huggingface/__main__.py b/src/index/huggingface/__main__.py new file mode 100644 index 0000000..731e6bd --- /dev/null +++ b/src/index/huggingface/__main__.py @@ -0,0 +1,6 @@ +"""Entry point for `python -m src.index.huggingface`.""" + +from src.index.huggingface.cli import main + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/index/huggingface/_federated.py b/src/index/huggingface/_federated.py new file mode 100644 index 0000000..879062e --- /dev/null +++ b/src/index/huggingface/_federated.py @@ -0,0 +1,79 @@ +"""HuggingFace registration with the federated discover/hydrate registries. + +Discover sources +---------------- + +- ``orgs`` — discover model/dataset/space owners via the HF Hub search. +- ``from-search`` — generic full-text search yielding model/dataset/space IDs. + +Hydrate seed types +------------------ + +- ``hf_model`` / ``hf_dataset`` / ``hf_space`` / ``hf_org`` — bulk fetch by ID. + +v1 only registers the Discoverer + Hydrator surface; the ingest helpers +are wrapped in follow-up work. +""" + +from __future__ import annotations + +import logging +from typing import Any, Iterator + +from src.index._federated.dh_registry import register_discoverer, register_hydrator +from src.index._federated.protocols import ( + HydrationSummary, + IndexDiscoverer, + IndexHydrator, + Seed, +) + +LOGGER = logging.getLogger(__name__) + + +class HuggingFaceDiscoverer: + name = "huggingface" + accepted_sources = ("orgs", "from-search") + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + if source not in self.accepted_sources: + message = f"HF: unknown source {source!r}. Accepted: {list(self.accepted_sources)}" + raise ValueError(message) + + if source == "orgs": + try: + from src.index.huggingface.ingest.orgs_ingest import discover_orgs + except ImportError: + LOGGER.warning("hf discover_orgs not importable") + return + query = opts.get("query") or "" + for org in discover_orgs(query=query): # type: ignore[call-arg] + yield Seed( + id=org if isinstance(org, str) else org.get("id"), + seed_type="hf_org", + source="orgs", + hint={"query": query}, + ) + return + + # source == "from-search" — placeholder until we wrap a search call + LOGGER.warning("hf from-search discover is a stub") + return + + +class HuggingFaceHydrator: + name = "huggingface" + accepted_seed_types = ("hf_model", "hf_dataset", "hf_space", "hf_org") + + def hydrate(self, seeds, *, only_unfetched: bool = True) -> HydrationSummary: + # TODO: route by seed_type to ingest_models / ingest_datasets / ingest_spaces / ingest_orgs. + materialised = list(seeds) + LOGGER.warning( + "huggingface: hydrate is a stub (received %d seeds).", + len(materialised), + ) + return HydrationSummary(skipped_existing=len(materialised)) + + +register_discoverer(HuggingFaceDiscoverer()) +register_hydrator(HuggingFaceHydrator()) diff --git a/src/index/huggingface/api.py b/src/index/huggingface/api.py new file mode 100644 index 0000000..d240863 --- /dev/null +++ b/src/index/huggingface/api.py @@ -0,0 +1,114 @@ +"""FastAPI app exposing the HuggingFace index dual query surface.""" + +from __future__ import annotations + +import logging +from typing import Any + +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel, Field + +from src.index.huggingface.config import load_config +from src.index.huggingface.retrieval.semantic import semantic_search +from src.index.huggingface.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) +from src.index.huggingface.storage.duckdb_store import ENTITY_TABLES, DuckDBStore +from src.index.huggingface.vector.qdrant_store import COLLECTION_FOR_TABLE, QdrantStore + +LOGGER = logging.getLogger(__name__) + +app = FastAPI(title="HuggingFace Index") + + +class SearchRequest(BaseModel): + query: str + entity_type: str = "models" + top_k: int = Field(default=10, ge=1, le=100) + candidate_k: int = Field(default=50, ge=1, le=500) + filter_payload: dict[str, Any] | None = None + + +class QueryRequest(BaseModel): + sql: str | None = None + predefined: str | None = None + params: dict[str, Any] | None = None + + +@app.get("/healthz") +def healthz() -> dict[str, Any]: + """Probe DuckDB + Qdrant. RCP probe deferred to first /search call.""" + config = load_config() + duck_status = "ok" + qdrant_status: dict[str, Any] = {} + try: + DuckDBStore.open().count("models") + except Exception as exc: # noqa: BLE001 + duck_status = f"error: {exc}" + try: + store = QdrantStore(config) + for collection in COLLECTION_FOR_TABLE.values(): + qdrant_status[collection] = store.count(collection) + except Exception as exc: # noqa: BLE001 + qdrant_status = {"error": str(exc)} + return { + "duckdb": duck_status, + "qdrant": qdrant_status, + "rcp_configured": bool(config.rcp.token), + "hf_token_configured": bool(config.huggingface.token), + } + + +@app.post("/search") +def search(req: SearchRequest) -> list[dict[str, Any]]: + config = load_config() + try: + config.require_rcp() + except ValueError as exc: + raise HTTPException(status_code=503, detail=str(exc)) from exc + return semantic_search( + config=config, + query=req.query, + entity_type=req.entity_type, + top_k=req.top_k, + candidate_k=req.candidate_k, + filter_payload=req.filter_payload, + ) + + +@app.post("/query") +def query(req: QueryRequest) -> list[dict[str, Any]]: + if req.predefined and req.sql: + raise HTTPException( + status_code=400, + detail="Provide either `predefined` or `sql`, not both", + ) + try: + if req.predefined: + return run_predefined(req.predefined, req.params) + if req.sql: + return run_adhoc(req.sql, req.params) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + raise HTTPException(status_code=400, detail="Pass `predefined` or `sql`") + + +@app.get("/predefined") +def list_predefined() -> dict[str, list[str]]: + return {"predefined": sorted(PREDEFINED_QUERIES)} + + +@app.get("/entity/{entity_table}/{repo_id:path}") +def get_entity(entity_table: str, repo_id: str) -> dict[str, Any]: + if entity_table not in ENTITY_TABLES: + raise HTTPException( + status_code=404, + detail=f"unknown entity table: {entity_table}", + ) + store = DuckDBStore.open() + row = store.fetch_repo(entity_table, repo_id) + if row is None: + raise HTTPException(status_code=404, detail="not found") + return row diff --git a/src/index/huggingface/cli.py b/src/index/huggingface/cli.py new file mode 100644 index 0000000..11cbf4b --- /dev/null +++ b/src/index/huggingface/cli.py @@ -0,0 +1,374 @@ +"""CLI for the HuggingFace index module. + +Subcommands: + +- `ingest` — fetch metadata + READMEs for the configured seed. +- `discover-orgs` — search-based org expansion → JSONL log for human review. +- `embed` — embed DuckDB rows into Qdrant via RCP. +- `search` — semantic retrieval (vector + rerank). +- `query` — read-only SQL over the DuckDB dump. +- `status` — counts + paths summary. +- `serve` — run the FastAPI app on a chosen port. +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys + +from src.index.huggingface.config import load_config +from src.index.huggingface.ingest.datasets_ingest import ingest_datasets +from src.index.huggingface.ingest.discover_orgs import discover_orgs +from src.index.huggingface.ingest.hf_client import HFClient +from src.index.huggingface.ingest.models_ingest import ingest_models +from src.index.huggingface.ingest.orgs_ingest import ingest_orgs +from src.index.huggingface.ingest.scope import resolve_scope +from src.index.huggingface.ingest.spaces_ingest import ingest_spaces +from src.index.huggingface.models import ALL_EMBEDDABLE_TYPES, ALL_ENTITY_TYPES +from src.index.huggingface.retrieval.sql import run_adhoc, run_predefined +from src.index.huggingface.storage.duckdb_store import DuckDBStore +from src.index.huggingface.vector.qdrant_store import COLLECTION_FOR_TABLE, QdrantStore + +LOGGER = logging.getLogger(__name__) + +ENTITY_INGESTERS = { + "models": ingest_models, + "datasets": ingest_datasets, + "spaces": ingest_spaces, + "orgs": ingest_orgs, +} + +# Embed accepts everything ingest accepts (orgs included). +EMBEDDABLE_TABLES = set(ALL_EMBEDDABLE_TYPES) + + +def _split_entities(raw: str, *, valid: set[str] | None = None) -> list[str]: + parts = [p.strip() for p in raw.split(",") if p.strip()] + allowed = valid if valid is not None else set(ENTITY_INGESTERS) + unknown = [p for p in parts if p not in allowed] + if unknown: + message = f"Unknown entity types: {unknown}. Known: {sorted(allowed)}" + raise SystemExit(message) + return parts + + +def _emit_json(value: object) -> None: + json.dump(value, sys.stdout, indent=2, ensure_ascii=False, default=str) + sys.stdout.write("\n") + + +def _cmd_ingest(args: argparse.Namespace) -> int: + config = load_config() + scope = resolve_scope(args.scope, config) + entities = _split_entities(args.types) + client = HFClient(config) + store = DuckDBStore.open() + summary: dict[str, int] = {} + for entity in entities: + ingester = ENTITY_INGESTERS[entity] + summary[entity] = ingester( + config=config, + client=client, + store=store, + scope=scope, + limit=args.limit, + ) + _emit_json({"scope": args.scope, "ingested": summary}) + return 0 + + +def _cmd_discover(args: argparse.Namespace) -> int: + config = load_config() + client = HFClient(config) + candidates = discover_orgs( + config=config, + client=client, + scope_name=args.scope, + per_term_limit=args.per_term_limit, + ) + _emit_json( + { + "scope": args.scope, + "candidates": len(candidates), + "log_path": str(config.paths.logs_dir / "discover_orgs.jsonl"), + "new_namespaces": [c["namespace"] for c in candidates if not c["in_seed"]], + }, + ) + return 0 + + +def _cmd_embed(args: argparse.Namespace) -> int: + from src.index.huggingface.embed.pipeline import embed_entities + + entities = _split_entities(args.types, valid=EMBEDDABLE_TABLES) + config = load_config() + config.require_rcp() + store = DuckDBStore.open() + summary = embed_entities( + config=config, + store=store, + entity_tables=entities, + limit=args.limit, + ) + _emit_json({"embedded": summary}) + return 0 + + +def _cmd_search(args: argparse.Namespace) -> int: + from src.index.huggingface.retrieval.semantic import ( + semantic_search, + semantic_search_with_facets, + ) + + config = load_config() + config.require_rcp() + filter_payload = _parse_filter(args.filter or []) + facet_keys: tuple[str, ...] = tuple( + k.strip() for k in (args.facets or "").split(",") if k.strip() + ) + if facet_keys: + hits, facets = semantic_search_with_facets( + config=config, + query=args.query, + entity_type=args.type, + top_k=args.top_k, + candidate_k=args.candidate_k, + filter_payload=filter_payload, + facet_keys=facet_keys, + facet_top_n=args.facet_top_n, + ) + _emit_json({"hits": hits, "facets": facets}) + else: + hits = semantic_search( + config=config, + query=args.query, + entity_type=args.type, + top_k=args.top_k, + candidate_k=args.candidate_k, + filter_payload=filter_payload, + ) + _emit_json(hits) + return 0 + + +def _parse_filter(raw: list[str]) -> dict[str, object] | None: + """Parse `key=value` flags into a Qdrant payload filter dict. + + Repeated keys collapse to a list (Qdrant's `MatchAny`). Values that look + like ints are coerced; everything else is kept as a string. + """ + if not raw: + return None + parsed: dict[str, object] = {} + for item in raw: + if "=" not in item: + message = f"--filter must be key=value, got {item!r}" + raise SystemExit(message) + key, value = item.split("=", 1) + coerced: object = int(value) if value.lstrip("-").isdigit() else value + if key in parsed: + existing = parsed[key] + parsed[key] = [*existing, coerced] if isinstance(existing, list) else [existing, coerced] + else: + parsed[key] = coerced + return parsed + + +def _cmd_query(args: argparse.Namespace) -> int: + params: dict[str, object] = {} + for raw in args.param or []: + if "=" not in raw: + message = f"--param must be key=value, got {raw!r}" + raise SystemExit(message) + key, value = raw.split("=", 1) + if value.isdigit(): + params[key] = int(value) + else: + params[key] = value + if args.predefined: + rows = run_predefined(args.predefined, params) + elif args.sql: + rows = run_adhoc(args.sql, params) + else: + raise SystemExit("Pass --predefined NAME or a positional SQL string") + _emit_json(rows) + return 0 + + +def _cmd_lineage(args: argparse.Namespace) -> int: + from src.index.huggingface.retrieval.lineage import compute_lineage + + store = DuckDBStore.open() + result = compute_lineage(args.repo_id, store=store, depth=args.depth) + _emit_json(result) + return 0 + + +def _cmd_backfill_payloads(_: argparse.Namespace) -> int: + from src.index.huggingface.embed.pipeline import backfill_model_base_payloads + + config = load_config() + store = DuckDBStore.open() + n = backfill_model_base_payloads(config=config, store=store) + _emit_json({"chunks_updated": n}) + return 0 + + +def _cmd_status(_: argparse.Namespace) -> int: + config = load_config() + store = DuckDBStore.open() + counts = { + "orgs": store.count("orgs"), + "models": store.count("models"), + "datasets": store.count("datasets"), + "spaces": store.count("spaces"), + "chunks": store.count("chunks"), + } + qdrant_counts: dict[str, int] = {} + try: + qdrant = QdrantStore(config) + for collection in sorted(COLLECTION_FOR_TABLE.values()): + qdrant_counts[collection] = qdrant.count(collection) + except Exception as exc: # noqa: BLE001 + qdrant_counts = {"error": str(exc)} + _emit_json( + { + "duckdb_path": str(config.paths.duckdb_path), + "cards_dir": str(config.paths.cards_dir), + "logs_dir": str(config.paths.logs_dir), + "duckdb_counts": counts, + "qdrant_counts": qdrant_counts, + "active_scope": config.scope.active, + "rcp_configured": bool(config.rcp.token), + "hf_token_configured": bool(config.huggingface.token), + }, + ) + return 0 + + +def _cmd_serve(args: argparse.Namespace) -> int: + import uvicorn + + uvicorn.run( + "src.index.huggingface.api:app", + host=args.host, + port=args.port, + reload=args.reload, + ) + return 0 + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="src.index.huggingface", + description="HuggingFace ingestion + RAG over EPFL/Switzerland", + ) + sub = parser.add_subparsers(dest="cmd", required=True) + + p_ingest = sub.add_parser("ingest", help="Fetch metadata + READMEs into DuckDB") + p_ingest.add_argument("--scope", choices=["epfl", "switzerland"], required=True) + p_ingest.add_argument( + "--types", + default=",".join(ALL_ENTITY_TYPES), + help="Comma-separated subset of: models, datasets, spaces, orgs", + ) + p_ingest.add_argument( + "--limit", + type=int, + default=None, + help="Stop after this many repos per org (per-type).", + ) + p_ingest.set_defaults(func=_cmd_ingest) + + p_disc = sub.add_parser( + "discover-orgs", + help="Substring-search the Hub for unknown EPFL/Swiss namespaces", + ) + p_disc.add_argument("--scope", choices=["epfl", "switzerland"], required=True) + p_disc.add_argument("--per-term-limit", type=int, default=200) + p_disc.set_defaults(func=_cmd_discover) + + p_e = sub.add_parser("embed", help="Embed DuckDB rows into Qdrant via RCP") + p_e.add_argument( + "--types", + default=",".join(ALL_EMBEDDABLE_TYPES), + help="Comma-separated subset of: models, datasets, spaces, orgs", + ) + p_e.add_argument("--limit", type=int, default=None) + p_e.set_defaults(func=_cmd_embed) + + p_s = sub.add_parser("search", help="Semantic retrieval (vector + rerank)") + p_s.add_argument("query") + p_s.add_argument( + "--type", + default="models", + help="Entity type or table (models|datasets|spaces|orgs).", + ) + p_s.add_argument("--top-k", type=int, default=10) + p_s.add_argument("--candidate-k", type=int, default=50) + p_s.add_argument( + "--filter", + action="append", + help="Qdrant payload filter, key=value (repeatable). " + "E.g. --filter namespace_kind=user --filter scope=switzerland", + ) + p_s.add_argument( + "--facets", + default="", + help="Comma-separated payload keys to aggregate alongside hits. " + "E.g. --facets license,pipeline_tag,author. " + "Counts entities (deduped on repo_id) over the candidate pool.", + ) + p_s.add_argument("--facet-top-n", type=int, default=10) + p_s.set_defaults(func=_cmd_search) + + p_q = sub.add_parser("query", help="Read-only SQL over the DuckDB dump") + p_q.add_argument("sql", nargs="?", help="Ad-hoc SELECT/WITH (omit if --predefined)") + p_q.add_argument("--predefined", help="Run a predefined named query") + p_q.add_argument( + "--param", + action="append", + help="key=value for predefined queries (repeatable)", + ) + p_q.set_defaults(func=_cmd_query) + + p_st = sub.add_parser("status", help="Show counts + paths") + p_st.set_defaults(func=_cmd_status) + + p_bf = sub.add_parser( + "backfill-payloads", + help="One-shot: push base_model payload to existing Qdrant points without re-embedding", + ) + p_bf.set_defaults(func=_cmd_backfill_payloads) + + p_l = sub.add_parser( + "lineage", + help="Walk the base_models DAG from a repo_id (ancestors + descendants).", + ) + p_l.add_argument("repo_id", help="e.g. epfl-llm/meditron-7b") + p_l.add_argument("--depth", type=int, default=3) + p_l.set_defaults(func=_cmd_lineage) + + p_v = sub.add_parser("serve", help="Run the FastAPI app") + p_v.add_argument("--host", default="0.0.0.0") # noqa: S104 + p_v.add_argument("--port", type=int, default=8002) + p_v.add_argument("--reload", action="store_true") + p_v.set_defaults(func=_cmd_serve) + + return parser + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s %(message)s", + ) + parser = build_parser() + args = parser.parse_args(argv) + return args.func(args) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/index/huggingface/config.py b/src/index/huggingface/config.py new file mode 100644 index 0000000..143dced --- /dev/null +++ b/src/index/huggingface/config.py @@ -0,0 +1,145 @@ +"""Config loader for the HuggingFace indexer. + +Reads `config/index/huggingface.yaml` and merges in env-sourced credentials +(`RCP_TOKEN`, `HF_TOKEN`, `INDEX_QDRANT_API_KEY`) plus the resolved data dir. +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Optional + +import yaml +from pydantic import BaseModel + +from src.index.huggingface.paths import HuggingFacePaths, get_huggingface_paths + +DEFAULT_CONFIG_PATH = Path("config/index/huggingface.yaml") + +TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} +FALSE_ENV_VALUES = {"0", "false", "f", "no", "n", "off"} + +MISSING_RCP_TOKEN_ERROR = "Missing required environment variable: RCP_TOKEN" +UNKNOWN_SCOPE_ERROR = "Unknown scope: {name}. Known: {known}" + + +class RcpConfig(BaseModel): + base_url: str + embedding_model: str + embedding_dim: int + query_instruction: str + reranker_model: str + batch_size: int = 32 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: Optional[str] = None # populated from RCP_TOKEN at load time + + +class HuggingFaceConfig(BaseModel): + api_base: str = "https://huggingface.co" + per_page: int = 100 + max_concurrency: int = 4 + full_cards: bool = False + min_card_chars: int = 64 + token: Optional[str] = None # populated from HF_TOKEN at load time + + +class ScopeConfig(BaseModel): + active: str = "epfl" + seeds: dict[str, list[str]] + + +class DiscoveryConfig(BaseModel): + search_terms: dict[str, list[str]] + + +class QdrantConfig(BaseModel): + url: str = "http://localhost:6333" + prefer_grpc: bool = False + api_key: Optional[str] = None # populated from INDEX_QDRANT_API_KEY at load time + + +class ChunkingConfig(BaseModel): + size_tokens: int = 512 + overlap_tokens: int = 64 + tokenizer: str = "cl100k_base" + + +class HuggingFaceIndexConfig(BaseModel): + rcp: RcpConfig + huggingface: HuggingFaceConfig + scope: ScopeConfig + discovery: DiscoveryConfig + qdrant: QdrantConfig + chunking: ChunkingConfig + paths: HuggingFacePaths + + model_config = {"arbitrary_types_allowed": True} + + def require_rcp(self) -> None: + """Validate config required to call the RCP embedding/rerank endpoints.""" + if not self.rcp.token: + raise ValueError(MISSING_RCP_TOKEN_ERROR) + + def seed_for(self, scope_name: str) -> list[str]: + if scope_name not in self.scope.seeds: + raise ValueError( + UNKNOWN_SCOPE_ERROR.format( + name=scope_name, + known=sorted(self.scope.seeds), + ), + ) + return list(self.scope.seeds[scope_name]) + + def search_terms_for(self, scope_name: str) -> list[str]: + if scope_name not in self.discovery.search_terms: + raise ValueError( + UNKNOWN_SCOPE_ERROR.format( + name=scope_name, + known=sorted(self.discovery.search_terms), + ), + ) + return list(self.discovery.search_terms[scope_name]) + + +def _env_bool(name: str) -> Optional[bool]: + raw = os.getenv(name) + if raw is None or raw.strip() == "": + return None + normalized = raw.strip().lower() + if normalized in TRUE_ENV_VALUES: + return True + if normalized in FALSE_ENV_VALUES: + return False + message = f"Invalid boolean value for {name}: {raw!r}" + raise ValueError(message) + + +def _env_str(name: str) -> Optional[str]: + raw = os.getenv(name) + if raw is None: + return None + stripped = raw.strip() + return stripped or None + + +def load_config(path: Optional[Path] = None) -> HuggingFaceIndexConfig: + """Load + validate the YAML config; merge env tokens, env overrides, paths.""" + cfg_path = path or DEFAULT_CONFIG_PATH + raw = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) or {} + + raw.setdefault("rcp", {})["token"] = _env_str("RCP_TOKEN") + raw.setdefault("huggingface", {})["token"] = _env_str("HF_TOKEN") + raw.setdefault("qdrant", {})["api_key"] = _env_str("INDEX_QDRANT_API_KEY") + + if (override := _env_str("INDEX_QDRANT_URL")) is not None: + raw["qdrant"]["url"] = override + if (override_b := _env_bool("INDEX_QDRANT_PREFER_GRPC")) is not None: + raw["qdrant"]["prefer_grpc"] = override_b + if (override := _env_str("INDEX_HUGGINGFACE_SCOPE")) is not None: + raw.setdefault("scope", {})["active"] = override + + raw["paths"] = get_huggingface_paths() + + return HuggingFaceIndexConfig(**raw) diff --git a/src/index/huggingface/embed/__init__.py b/src/index/huggingface/embed/__init__.py new file mode 100644 index 0000000..70e1be6 --- /dev/null +++ b/src/index/huggingface/embed/__init__.py @@ -0,0 +1 @@ +"""Chunk + embed cards into Qdrant via the RCP embedding endpoint.""" diff --git a/src/index/huggingface/embed/chunker.py b/src/index/huggingface/embed/chunker.py new file mode 100644 index 0000000..2432ff6 --- /dev/null +++ b/src/index/huggingface/embed/chunker.py @@ -0,0 +1,99 @@ +# TODO: dedupe with src/index/openalex/embed/chunker.py — kept as a copy +# while the user defers extracting a shared chunker module. +"""Token-based sliding-window chunker built on tiktoken. + +Encoding-agnostic — `cl100k_base` is the default since it's a reasonable +proxy for many modern tokenizers. The Qwen3 tokenizer differs but for +window *sizing* the proxy is fine: we err on the side of slightly smaller +chunks, which is safer for context limits. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +import tiktoken + +DEFAULT_ENCODING = "cl100k_base" + + +@dataclass(slots=True, frozen=True) +class Chunk: + index: int + text: str + token_count: int + + +def _get_encoder(name: str = DEFAULT_ENCODING) -> tiktoken.Encoding: + return tiktoken.get_encoding(name) + + +def chunk_text( + text: str, + *, + chunk_tokens: int, + overlap: int, + encoding_name: str = DEFAULT_ENCODING, +) -> list[Chunk]: + if chunk_tokens <= 0: + message = "chunk_tokens must be positive" + raise ValueError(message) + if overlap < 0 or overlap >= chunk_tokens: + message = "overlap must be in [0, chunk_tokens)" + raise ValueError(message) + if not text: + return [] + enc = _get_encoder(encoding_name) + tokens = enc.encode(text) + if not tokens: + return [] + if len(tokens) <= chunk_tokens: + return [Chunk(index=0, text=text, token_count=len(tokens))] + chunks: list[Chunk] = [] + step = chunk_tokens - overlap + start = 0 + idx = 0 + while start < len(tokens): + window = tokens[start : start + chunk_tokens] + if not window: + break + chunks.append( + Chunk(index=idx, text=enc.decode(window), token_count=len(window)), + ) + if start + chunk_tokens >= len(tokens): + break + start += step + idx += 1 + return chunks + + +def chunk_for_card( + *, + title: str, + tags: list[str] | None, + description: str | None, + readme: str | None, + chunk_tokens: int, + overlap: int, + encoding_name: str = DEFAULT_ENCODING, +) -> list[Chunk]: + """Build the embedding text for a HuggingFace card and chunk it. + + Layout: title (line 1) → comma-joined tags (line 2 if any) → description + (paragraph if any) → README body (rest). The first chunk thus always + leads with the title + tags, which is what the embedding model sees first. + """ + parts: list[str] = [title] + if tags: + parts.append("Tags: " + ", ".join(tags)) + if description: + parts.append(description.strip()) + if readme: + parts.append(readme.strip()) + text = "\n\n".join(parts) + return chunk_text( + text, + chunk_tokens=chunk_tokens, + overlap=overlap, + encoding_name=encoding_name, + ) diff --git a/src/index/huggingface/embed/pipeline.py b/src/index/huggingface/embed/pipeline.py new file mode 100644 index 0000000..adfefa1 --- /dev/null +++ b/src/index/huggingface/embed/pipeline.py @@ -0,0 +1,442 @@ +"""Stream DuckDB rows → chunk card → embed → upsert into Qdrant. + +Idempotent: rows whose card has already been embedded are skipped via +`DuckDBStore.stream_rows_for_embedding`. + +The text fed to the embedder is built per-row from: + + - Title (always — `repo_id`), + - Comma-joined tags, + - Description from `card_data` (when present), + - The README markdown. + +Rows whose composite text is shorter than `huggingface.min_card_chars` are +skipped — many spaces ship with one-line placeholder READMEs that would +otherwise pollute the vector space. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import uuid +from typing import TYPE_CHECKING, Any + +from src.index.huggingface.embed.chunker import Chunk, chunk_for_card +from src.index.huggingface.embed.rcp_client import RCPEmbeddingClient +from src.index.huggingface.ingest._common import description_from_card_data +from src.index.huggingface.models import ENTITY_TYPE_SINGULAR +from src.index.huggingface.vector.qdrant_store import COLLECTION_FOR_TABLE, QdrantStore + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + from src.index.huggingface.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + +_CHUNK_NAMESPACE = uuid.NAMESPACE_URL + + +def _chunk_id(entity_type_singular: str, repo_id: str, chunk_index: int) -> str: + return str( + uuid.uuid5( + _CHUNK_NAMESPACE, + f"{entity_type_singular}|{repo_id}|{chunk_index}", + ), + ) + + +def _readme_for(config: HuggingFaceIndexConfig, *, entity_table: str, repo_id: str) -> str | None: + path = config.paths.cards_path_for(entity_table, repo_id) / "README.md" + if not path.exists(): + return None + try: + return path.read_text(encoding="utf-8") + except (OSError, UnicodeDecodeError): + return None + + +def _row_to_chunks( + *, + entity_table: str, + row: dict[str, Any], + config: HuggingFaceIndexConfig, + chunk_tokens: int, + overlap: int, +) -> list[Chunk]: + repo_id = row["repo_id"] + tags = _coerce_tags(row.get("tags")) + card_data = _coerce_json(row.get("card_data")) + description = description_from_card_data(card_data) + readme = _readme_for(config, entity_table=entity_table, repo_id=repo_id) + + composite_chars = sum( + len(s) + for s in ( + repo_id, + ", ".join(tags or []), + description or "", + readme or "", + ) + ) + if composite_chars < config.huggingface.min_card_chars: + return [] + return chunk_for_card( + title=repo_id, + tags=tags, + description=description, + readme=readme, + chunk_tokens=chunk_tokens, + overlap=overlap, + encoding_name=config.chunking.tokenizer, + ) + + +def _coerce_tags(value: Any) -> list[str] | None: + if value is None: + return None + if isinstance(value, list): + return [str(t) for t in value if t] + if isinstance(value, str): + try: + parsed = json.loads(value) + except (TypeError, ValueError): + return None + if isinstance(parsed, list): + return [str(t) for t in parsed if t] + return None + + +def _coerce_json(value: Any) -> dict | None: + if value is None: + return None + if isinstance(value, dict): + return value + if isinstance(value, str): + try: + parsed = json.loads(value) + except (TypeError, ValueError): + return None + return parsed if isinstance(parsed, dict) else None + return None + + +def _row_to_payload(*, entity_type_singular: str, row: dict[str, Any]) -> dict[str, Any]: + payload: dict[str, Any] = { + "entity_type": entity_type_singular, + "repo_id": row["repo_id"], + "author": row.get("author"), + "license": row.get("license"), + "downloads": row.get("downloads"), + } + last_modified = row.get("last_modified") + if last_modified is not None: + payload["last_modified"] = ( + last_modified.isoformat() if hasattr(last_modified, "isoformat") else str(last_modified) + ) + if entity_type_singular == "model": + payload["pipeline_tag"] = row.get("pipeline_tag") + payload["library_name"] = row.get("library_name") + # Surface base_models as a payload list so `--filter base_model=X` can + # match via Qdrant's MatchAny semantics for "fine-tuned from X" queries. + base_models = _coerce_base_models(row.get("base_models")) + if base_models: + payload["base_model"] = base_models + if entity_type_singular == "space": + payload["sdk"] = row.get("sdk") + return payload + + +def _coerce_base_models(value: Any) -> list[str] | None: + if value is None: + return None + if isinstance(value, list): + return [str(v) for v in value if v] + if isinstance(value, str): + try: + parsed = json.loads(value) + except (TypeError, ValueError): + return None + if isinstance(parsed, list): + return [str(v) for v in parsed if v] + return None + + +async def _embed_table_async( + *, + config: HuggingFaceIndexConfig, + store: DuckDBStore, + entity_table: str, + limit: int | None, +) -> int: + client = RCPEmbeddingClient(config) + qdrant = QdrantStore(config) + collection = COLLECTION_FOR_TABLE[entity_table] + qdrant.ensure_collection(collection) + singular = ENTITY_TYPE_SINGULAR[entity_table] # type: ignore[index] + + pending: list[tuple[str, dict[str, Any], Chunk]] = [] + total_chunks = 0 + + async def flush() -> None: + nonlocal total_chunks + if not pending: + return + texts = [c.text for _, _, c in pending] + vectors = await client.embed_all(texts) + ids: list[str] = [] + payloads: list[dict[str, Any]] = [] + for repo_id, base_payload, chunk in pending: + cid = _chunk_id(singular, repo_id, chunk.index) + ids.append(cid) + payloads.append({**base_payload, "chunk_index": chunk.index}) + store.upsert_chunk( + chunk_id=cid, + entity_type=singular, + repo_id=repo_id, + chunk_index=chunk.index, + text=chunk.text, + token_count=chunk.token_count, + vector_id=cid, + ) + qdrant.upsert_points( + collection, + ids=ids, + vectors=vectors, + payloads=payloads, + ) + total_chunks += len(pending) + pending.clear() + + rows_seen = 0 + for row in store.stream_rows_for_embedding(entity_table, limit=limit): + rows_seen += 1 + chunks = _row_to_chunks( + entity_table=entity_table, + row=row, + config=config, + chunk_tokens=config.chunking.size_tokens, + overlap=config.chunking.overlap_tokens, + ) + if not chunks: + continue + base_payload = _row_to_payload(entity_type_singular=singular, row=row) + for chunk in chunks: + pending.append((row["repo_id"], base_payload, chunk)) + if len(pending) >= client.batch_size: + await flush() + await flush() + LOGGER.info( + "embed %s complete: rows_seen=%d chunks=%d", + entity_table, + rows_seen, + total_chunks, + ) + return total_chunks + + +def _org_to_chunks( + *, + org_row: dict[str, Any], + repos_by_table: dict[str, list[dict[str, Any]]], + config: HuggingFaceIndexConfig, + chunk_tokens: int, + overlap: int, +) -> list[Chunk]: + """Build the embed text for a namespace from its overview + repo titles/tags.""" + slug = org_row["slug"] + fullname = org_row.get("fullname") or "" + details = org_row.get("details") or "" + kind = org_row.get("namespace_kind") or "org" + + sections: list[str] = [] + for table, repos in repos_by_table.items(): + if not repos: + continue + lines: list[str] = [] + for repo in repos: + tags = _coerce_tags(repo.get("tags")) or [] + card_data = _coerce_json(repo.get("card_data")) or {} + description = description_from_card_data(card_data) or "" + tag_str = ", ".join(tags[:8]) # cap to keep signal:noise high + line = repo["repo_id"] + if tag_str: + line += f" [{tag_str}]" + if description: + line += f" — {description[:160]}" + lines.append(line) + sections.append(f"{table.capitalize()}:\n" + "\n".join(lines)) + + description = details if details else fullname + readme = "\n\n".join(sections) if sections else None + + composite_chars = sum( + len(s) for s in (slug, fullname, details, readme or "") + ) + if composite_chars < config.huggingface.min_card_chars: + return [] + title = f"{slug} ({kind})" if not fullname else f"{slug} — {fullname} ({kind})" + return chunk_for_card( + title=title, + tags=None, + description=description or None, + readme=readme, + chunk_tokens=chunk_tokens, + overlap=overlap, + encoding_name=config.chunking.tokenizer, + ) + + +def _org_to_payload(*, org_row: dict[str, Any]) -> dict[str, Any]: + return { + "entity_type": "org", + "repo_id": org_row["slug"], # keep payload key consistent across collections + "slug": org_row["slug"], + "namespace_kind": org_row.get("namespace_kind"), + "scope": org_row.get("scope"), + "fullname": org_row.get("fullname"), + "num_models": org_row.get("num_models"), + "num_datasets": org_row.get("num_datasets"), + "num_spaces": org_row.get("num_spaces"), + "num_followers": org_row.get("num_followers"), + } + + +async def _embed_orgs_async( + *, + config: HuggingFaceIndexConfig, + store: DuckDBStore, + limit: int | None, +) -> int: + client = RCPEmbeddingClient(config) + qdrant = QdrantStore(config) + collection = COLLECTION_FOR_TABLE["orgs"] + qdrant.ensure_collection(collection) + + pending: list[tuple[str, dict[str, Any], Chunk]] = [] + total_chunks = 0 + + async def flush() -> None: + nonlocal total_chunks + if not pending: + return + texts = [c.text for _, _, c in pending] + vectors = await client.embed_all(texts) + ids: list[str] = [] + payloads: list[dict[str, Any]] = [] + for slug, base_payload, chunk in pending: + cid = _chunk_id("org", slug, chunk.index) + ids.append(cid) + payloads.append({**base_payload, "chunk_index": chunk.index}) + store.upsert_chunk( + chunk_id=cid, + entity_type="org", + repo_id=slug, + chunk_index=chunk.index, + text=chunk.text, + token_count=chunk.token_count, + vector_id=cid, + ) + qdrant.upsert_points(collection, ids=ids, vectors=vectors, payloads=payloads) + total_chunks += len(pending) + pending.clear() + + rows_seen = 0 + for row in store.stream_orgs_for_embedding(limit=limit): + rows_seen += 1 + repos_by_table = store.list_repo_titles_for_org(row["slug"]) + chunks = _org_to_chunks( + org_row=row, + repos_by_table=repos_by_table, + config=config, + chunk_tokens=config.chunking.size_tokens, + overlap=config.chunking.overlap_tokens, + ) + if not chunks: + continue + base_payload = _org_to_payload(org_row=row) + for chunk in chunks: + pending.append((row["slug"], base_payload, chunk)) + if len(pending) >= client.batch_size: + await flush() + await flush() + LOGGER.info("embed orgs complete: rows_seen=%d chunks=%d", rows_seen, total_chunks) + return total_chunks + + +def embed_entities( + *, + config: HuggingFaceIndexConfig, + store: DuckDBStore, + entity_tables: list[str], + limit: int | None = None, +) -> dict[str, int]: + """Synchronously embed across the requested entity tables.""" + summary: dict[str, int] = {} + for table in entity_tables: + if table == "orgs": + summary[table] = asyncio.run( + _embed_orgs_async(config=config, store=store, limit=limit), + ) + else: + summary[table] = asyncio.run( + _embed_table_async( + config=config, + store=store, + entity_table=table, + limit=limit, + ), + ) + return summary + + +def backfill_model_base_payloads( + *, + config: HuggingFaceIndexConfig, + store: DuckDBStore, +) -> int: + """One-shot: for every model in DuckDB with non-empty `base_models`, push the + `base_model` payload field to its existing Qdrant points without re-embedding. + + Used to retrofit chunks created before `base_model` was added to the + payload builder. Cheap — pure metadata update, no RCP calls. + """ + qdrant = QdrantStore(config) + collection = COLLECTION_FOR_TABLE["models"] + cur = store.connect().execute( + "SELECT m.repo_id, m.base_models, c.chunk_id " + "FROM models m JOIN chunks c " + " ON c.entity_type='model' AND c.repo_id = m.repo_id " + "WHERE m.base_models IS NOT NULL " + " AND m.base_models != 'null' AND m.base_models != '[]'", + ) + rows = cur.fetchall() + by_repo: dict[str, dict[str, Any]] = {} + for repo_id, base_json, chunk_id in rows: + bm = _coerce_base_models(base_json) + if not bm: + continue + bucket = by_repo.setdefault(repo_id, {"base_model": bm, "chunk_ids": []}) + bucket["chunk_ids"].append(chunk_id) + + updated = 0 + for repo_id, info in by_repo.items(): + try: + qdrant.client.set_payload( + collection_name=collection, + payload={"base_model": info["base_model"]}, + points=info["chunk_ids"], + ) + updated += len(info["chunk_ids"]) + except Exception as exc: # noqa: BLE001 + LOGGER.warning("backfill payload failed for %s: %s", repo_id, exc) + LOGGER.info( + "backfill: %d models, %d chunk payloads updated", + len(by_repo), + updated, + ) + return updated + + +__all__ = ["embed_entities", "_chunk_id", "backfill_model_base_payloads"] diff --git a/src/index/huggingface/embed/rcp_client.py b/src/index/huggingface/embed/rcp_client.py new file mode 100644 index 0000000..98fa2ab --- /dev/null +++ b/src/index/huggingface/embed/rcp_client.py @@ -0,0 +1,119 @@ +# TODO: dedupe with src/index/openalex/embed/rcp_client.py — kept as a copy +# while the user defers extracting a shared `src/index/_rcp/` module. +"""Async OpenAI-compatible client for the RCP `/embeddings` endpoint.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +import httpx +from tenacity import ( + retry, + retry_if_exception_type, + stop_after_attempt, + wait_exponential, +) + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + +LOGGER = logging.getLogger(__name__) + +DEFAULT_TIMEOUT_S = 60.0 +DEFAULT_BATCH_SIZE = 32 + +_RETRYABLE_STATUS = {429, 500, 502, 503, 504} + + +class RCPEmbeddingError(RuntimeError): + pass + + +def _is_retryable(exc: BaseException) -> bool: + if isinstance(exc, httpx.TimeoutException): + return True + if isinstance(exc, httpx.HTTPStatusError): + return exc.response.status_code in _RETRYABLE_STATUS + return False + + +class RCPEmbeddingClient: + """Thin async wrapper around the RCP `/embeddings` endpoint.""" + + def __init__( + self, + config: HuggingFaceIndexConfig, + *, + batch_size: int | None = None, + timeout_s: float | None = None, + ) -> None: + config.require_rcp() + self._config = config + self._batch_size = batch_size or config.rcp.batch_size + self._timeout = httpx.Timeout(timeout_s or config.rcp.timeout_seconds) + self._url = f"{config.rcp.base_url.rstrip('/')}/embeddings" + self._headers = { + "Authorization": f"Bearer {config.rcp.token}", + "Content-Type": "application/json", + } + + @property + def batch_size(self) -> int: + return self._batch_size + + @property + def model(self) -> str: + return self._config.rcp.embedding_model + + async def embed_batch( + self, + client: httpx.AsyncClient, + inputs: list[str], + ) -> list[list[float]]: + @retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=1, min=2, max=30), + retry=retry_if_exception_type((httpx.TimeoutException, httpx.HTTPStatusError)), + reraise=True, + ) + async def _call() -> list[list[float]]: + response = await client.post( + self._url, + headers=self._headers, + json={ + "model": self._config.rcp.embedding_model, + "input": inputs, + "encoding_format": "float", + }, + timeout=self._timeout, + ) + try: + response.raise_for_status() + except httpx.HTTPStatusError as exc: + if not _is_retryable(exc): + body = response.text[:500] + LOGGER.error("RCP embed non-retryable %d: %s", response.status_code, body) + raise + payload: dict[str, Any] = response.json() + data = payload.get("data") or [] + if len(data) != len(inputs): + message = ( + f"RCP returned {len(data)} embeddings for {len(inputs)} inputs" + ) + raise RCPEmbeddingError(message) + return [item["embedding"] for item in data] + + return await _call() + + async def embed_all(self, inputs: list[str]) -> list[list[float]]: + """Embed a list of inputs, batching internally.""" + if not inputs: + return [] + out: list[list[float]] = [] + async with httpx.AsyncClient() as client: + for start in range(0, len(inputs), self._batch_size): + batch = inputs[start : start + self._batch_size] + vecs = await self.embed_batch(client, batch) + out.extend(vecs) + return out diff --git a/src/index/huggingface/ingest/__init__.py b/src/index/huggingface/ingest/__init__.py new file mode 100644 index 0000000..f02366c --- /dev/null +++ b/src/index/huggingface/ingest/__init__.py @@ -0,0 +1 @@ +"""HuggingFace Hub ingestion pipeline.""" diff --git a/src/index/huggingface/ingest/_common.py b/src/index/huggingface/ingest/_common.py new file mode 100644 index 0000000..071ebcd --- /dev/null +++ b/src/index/huggingface/ingest/_common.py @@ -0,0 +1,110 @@ +"""Shared helpers for the per-entity ingest modules.""" + +from __future__ import annotations + +import json +import logging +from datetime import datetime +from typing import Any + +LOGGER = logging.getLogger(__name__) + + +def info_to_dict(info: Any) -> dict[str, Any]: + """Convert a huggingface_hub `ModelInfo` / `DatasetInfo` / `SpaceInfo` + object to a plain JSON-serialisable dict. + + The SDK objects don't expose a stable `.to_dict()` across versions; we + iterate over the object's `__dict__` and fall back to `str()` for any + non-JSON-native value. + """ + if info is None: + return {} + raw = getattr(info, "__dict__", None) + if raw is None: + return {} + out: dict[str, Any] = {} + for key, value in raw.items(): + if key.startswith("_"): + continue + out[key] = _json_safe(value) + return out + + +def _json_safe(value: Any) -> Any: + if value is None or isinstance(value, (bool, int, float, str)): + return value + if isinstance(value, datetime): + return value.isoformat() + if isinstance(value, dict): + return {str(k): _json_safe(v) for k, v in value.items()} + if isinstance(value, (list, tuple, set)): + return [_json_safe(v) for v in value] + raw = getattr(value, "__dict__", None) + if raw: + return {k: _json_safe(v) for k, v in raw.items() if not k.startswith("_")} + try: + json.dumps(value) + except (TypeError, ValueError): + return str(value) + return value + + +def normalise_dt(value: Any) -> datetime | None: + if value is None: + return None + if isinstance(value, datetime): + return value + if isinstance(value, str): + try: + return datetime.fromisoformat(value.replace("Z", "+00:00")) + except ValueError: + return None + return None + + +def author_from_repo_id(repo_id: str) -> str | None: + if "/" not in repo_id: + return None + return repo_id.split("/", 1)[0] + + +def card_data_to_dict(card_data: Any) -> dict[str, Any] | None: + """Coerce huggingface_hub's `CardData` object into a plain dict.""" + if card_data is None: + return None + for attr in ("to_dict", "__dict__"): + candidate = getattr(card_data, attr, None) + if callable(candidate): + try: + return _json_safe(candidate()) + except Exception: # noqa: BLE001 - SDK quirks; fall through + continue + if isinstance(candidate, dict) and candidate: + return {k: _json_safe(v) for k, v in candidate.items() if not k.startswith("_")} + if isinstance(card_data, dict): + return _json_safe(card_data) + return None + + +def base_models_from_card_data(card_data: dict[str, Any] | None) -> list[str] | None: + if not card_data: + return None + raw = card_data.get("base_model") or card_data.get("base_models") + if raw is None: + return None + if isinstance(raw, str): + return [raw] + if isinstance(raw, list): + return [str(b) for b in raw if b] + return None + + +def description_from_card_data(card_data: dict[str, Any] | None) -> str | None: + if not card_data: + return None + for key in ("description", "summary", "short_description"): + value = card_data.get(key) + if isinstance(value, str) and value.strip(): + return value.strip() + return None diff --git a/src/index/huggingface/ingest/cards.py b/src/index/huggingface/ingest/cards.py new file mode 100644 index 0000000..42d8245 --- /dev/null +++ b/src/index/huggingface/ingest/cards.py @@ -0,0 +1,58 @@ +"""Persist fetched README + (optional) card-adjacent files to disk. + +Layout: `/huggingface/cards///`. +The README is always written (when present); the rest of the card files +only appear when `huggingface.full_cards` is enabled. +""" + +from __future__ import annotations + +import logging +from pathlib import Path +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + from src.index.huggingface.ingest.hf_client import HFClient + +LOGGER = logging.getLogger(__name__) + + +def card_dir_for( + config: HuggingFaceIndexConfig, + *, + entity_type: str, + repo_id: str, +) -> Path: + return config.paths.cards_path_for(entity_type, repo_id) + + +def write_readme( + config: HuggingFaceIndexConfig, + *, + entity_type: str, + repo_id: str, + readme: str, +) -> Path: + target_dir = card_dir_for(config, entity_type=entity_type, repo_id=repo_id) + target_dir.mkdir(parents=True, exist_ok=True) + target = target_dir / "README.md" + target.write_text(readme, encoding="utf-8") + return target + + +def maybe_snapshot_full_card( + *, + config: HuggingFaceIndexConfig, + client: HFClient, + entity_type: str, + repo_id: str, + repo_type: str, +) -> None: + """If full_cards is enabled, pull README/JSON/YAML/MD files into the + repo's local card dir. Weight files are always ignored.""" + if not config.huggingface.full_cards: + return + target_dir = card_dir_for(config, entity_type=entity_type, repo_id=repo_id) + target_dir.mkdir(parents=True, exist_ok=True) + client.snapshot_card_files(repo_id, repo_type=repo_type, local_dir=target_dir) diff --git a/src/index/huggingface/ingest/datasets_ingest.py b/src/index/huggingface/ingest/datasets_ingest.py new file mode 100644 index 0000000..580511e --- /dev/null +++ b/src/index/huggingface/ingest/datasets_ingest.py @@ -0,0 +1,137 @@ +"""Ingest HuggingFace datasets for the configured seed orgs.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +from src.index.huggingface.ingest._common import ( + author_from_repo_id, + card_data_to_dict, + info_to_dict, + normalise_dt, +) +from src.index.huggingface.ingest.cards import maybe_snapshot_full_card, write_readme +from src.index.huggingface.models import DATASET_EXPAND_FIELDS + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + from src.index.huggingface.ingest.hf_client import HFClient + from src.index.huggingface.ingest.scope import Scope + from src.index.huggingface.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def ingest_datasets( + *, + config: HuggingFaceIndexConfig, + client: HFClient, + store: DuckDBStore, + scope: Scope, + limit: int | None = None, +) -> int: + upserted = 0 + skipped = 0 + for slug in scope.seeds: + store.upsert_org(slug=slug, scope=scope.name, source="seed") + listed = list(client.list_datasets(slug, limit=limit)) + LOGGER.info("datasets: %s → %d listed", slug, len(listed)) + for stub in listed: + repo_id = getattr(stub, "id", None) or getattr(stub, "datasetId", None) + if not repo_id: + continue + stub_sha = getattr(stub, "sha", None) + if stub_sha and store.repo_sha("datasets", repo_id) == stub_sha: + skipped += 1 + continue + info = client.dataset_info(repo_id, expand=DATASET_EXPAND_FIELDS) + if info is None: + continue + row = _dataset_row(repo_id, info) + raw = info_to_dict(info) + store.upsert_dataset(row, raw) + + readme = client.fetch_readme(repo_id, repo_type="dataset") + if readme: + write_readme( + config, + entity_type="datasets", + repo_id=repo_id, + readme=readme, + ) + maybe_snapshot_full_card( + config=config, + client=client, + entity_type="datasets", + repo_id=repo_id, + repo_type="dataset", + ) + upserted += 1 + if limit is not None and upserted >= limit * len(scope.seeds): + return upserted + LOGGER.info("datasets: ingest summary upserted=%d skipped_unchanged=%d", upserted, skipped) + return upserted + + +def _dataset_row(repo_id: str, info: object) -> dict[str, object | None]: + card_data = card_data_to_dict(getattr(info, "card_data", None) or getattr(info, "cardData", None)) + tags = list(getattr(info, "tags", None) or []) + dataset_info_payload = getattr(info, "dataset_info", None) or getattr(info, "datasetInfo", None) + return { + "repo_id": repo_id, + "author": getattr(info, "author", None) or author_from_repo_id(repo_id), + "sha": getattr(info, "sha", None), + "license": _license_from(card_data, getattr(info, "license", None)), + "downloads": _coerce_int(getattr(info, "downloads", None)), + "downloads_all_time": _coerce_int( + getattr(info, "downloads_all_time", None) + or getattr(info, "downloadsAllTime", None), + ), + "likes": _coerce_int(getattr(info, "likes", None)), + "gated": _coerce_bool(getattr(info, "gated", None)), + "private": _coerce_bool(getattr(info, "private", None)), + "created_at": normalise_dt(getattr(info, "created_at", None) or getattr(info, "createdAt", None)), + "last_modified": normalise_dt(getattr(info, "last_modified", None) or getattr(info, "lastModified", None)), + "tags": tags, + "card_data": card_data, + "dataset_info": _coerce_dict(dataset_info_payload), + } + + +def _license_from(card_data: dict | None, fallback: object) -> str | None: + if card_data and isinstance(card_data.get("license"), str): + return card_data["license"] + if isinstance(fallback, str): + return fallback + return None + + +def _coerce_int(value: object) -> int | None: + if value is None: + return None + try: + return int(value) + except (TypeError, ValueError): + return None + + +def _coerce_bool(value: object) -> bool | None: + if value is None: + return None + if isinstance(value, bool): + return value + if isinstance(value, str): + return value.strip().lower() in {"true", "1", "yes"} + return bool(value) + + +def _coerce_dict(value: object) -> dict | None: + if value is None: + return None + if isinstance(value, dict): + return value + raw = getattr(value, "__dict__", None) + if isinstance(raw, dict): + return {k: v for k, v in raw.items() if not k.startswith("_")} + return None diff --git a/src/index/huggingface/ingest/discover_orgs.py b/src/index/huggingface/ingest/discover_orgs.py new file mode 100644 index 0000000..099377d --- /dev/null +++ b/src/index/huggingface/ingest/discover_orgs.py @@ -0,0 +1,93 @@ +"""Search-based org discovery — surfaces unknown namespaces for human review. + +Substring-searches the Hub via `list_models(search=term)` / +`list_datasets(search=term)` for each term in +`config.discovery.search_terms[scope]`, groups hits by namespace, and +writes candidates to `/huggingface/logs/discover_orgs.jsonl`. + +Never auto-promotes namespaces into the seed — the user reviews and edits +`config/index/huggingface.yaml` by hand. This is the human-in-the-loop +safeguard against false positives like the personal `huggingface.co/EPFL` +account. +""" + +from __future__ import annotations + +import json +import logging +from collections import defaultdict +from typing import TYPE_CHECKING, Iterable + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + from src.index.huggingface.ingest.hf_client import HFClient + from src.index.huggingface.models import DiscoveryCandidate + +LOGGER = logging.getLogger(__name__) + +DEFAULT_PER_TERM_LIMIT = 200 +MAX_SAMPLES_PER_NAMESPACE = 5 +LOG_FILENAME = "discover_orgs.jsonl" + + +def discover_orgs( + *, + config: HuggingFaceIndexConfig, + client: HFClient, + scope_name: str, + per_term_limit: int = DEFAULT_PER_TERM_LIMIT, +) -> list[dict]: + """Run discovery. Returns the candidate list and writes the JSONL log.""" + seed = set(config.seed_for(scope_name)) + terms = config.search_terms_for(scope_name) + + hits_by_ns: dict[str, dict[str, set[str]]] = defaultdict( + lambda: {"models": set(), "datasets": set(), "matched_terms": set()}, + ) + + for term in terms: + for kind, lister in ( + ("models", client.search_models), + ("datasets", client.search_datasets), + ): + for stub in _iter_with_limit(lister(term, limit=per_term_limit), per_term_limit): + repo_id = getattr(stub, "id", None) + if not repo_id or "/" not in repo_id: + continue + namespace, _ = repo_id.split("/", 1) + bucket = hits_by_ns[namespace] + bucket[kind].add(repo_id) + bucket["matched_terms"].add(term) + + candidates: list[dict] = [] + for namespace, bucket in sorted(hits_by_ns.items()): + sample_pool = sorted(bucket["models"]) + sorted(bucket["datasets"]) + candidates.append( + { + "namespace": namespace, + "hits_models": len(bucket["models"]), + "hits_datasets": len(bucket["datasets"]), + "hits_total": len(bucket["models"]) + len(bucket["datasets"]), + "sample_repo_ids": sample_pool[:MAX_SAMPLES_PER_NAMESPACE], + "matched_terms": sorted(bucket["matched_terms"]), + "in_seed": namespace in seed, + }, + ) + + log_path = config.paths.logs_dir / LOG_FILENAME + log_path.parent.mkdir(parents=True, exist_ok=True) + with log_path.open("w", encoding="utf-8") as fh: + for cand in candidates: + fh.write(json.dumps(cand, ensure_ascii=False) + "\n") + LOGGER.info("discover_orgs: wrote %d candidates → %s", len(candidates), log_path) + return candidates + + +def _iter_with_limit(iterable: Iterable, limit: int) -> Iterable: + """huggingface_hub's `limit=` is sometimes ignored on search; enforce it.""" + yielded = 0 + for item in iterable: + if yielded >= limit: + return + yielded += 1 + yield item diff --git a/src/index/huggingface/ingest/hf_client.py b/src/index/huggingface/ingest/hf_client.py new file mode 100644 index 0000000..d7f77a4 --- /dev/null +++ b/src/index/huggingface/ingest/hf_client.py @@ -0,0 +1,235 @@ +"""Thin wrapper around `huggingface_hub.HfApi`. + +We only expose the calls the ingest pipeline needs (list/info/download +README) and centralise the auth + base-url config in one place. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any, Iterable + +from huggingface_hub import HfApi +from huggingface_hub.utils import ( + EntryNotFoundError, + GatedRepoError, + HfHubHTTPError, + RepositoryNotFoundError, +) + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + +LOGGER = logging.getLogger(__name__) + +# Files we never want to download even when full_cards is True. Keeping +# this list tight avoids accidentally pulling multi-GB weight files. +WEIGHT_PATTERNS: tuple[str, ...] = ( + "*.bin", + "*.safetensors", + "*.pt", + "*.ckpt", + "*.gguf", + "*.onnx", + "*.h5", + "*.msgpack", + "*.tflite", + "*.pkl", + "*.npz", +) + +# Files we keep when full_cards is True. +CARD_PATTERNS: tuple[str, ...] = ( + "README.md", + "README", + "*.json", + "*.yaml", + "*.yml", + "*.md", +) + +# Common non-README aliases on HF; tried in order. +README_FILENAME_FALLBACKS: tuple[str, ...] = ("README.md", "README", "Readme.md") + + +class HFClient: + """Lifecycle + auth + thin HfApi wrapper. + + All methods are synchronous — `huggingface_hub` is a sync client and we + don't gain much from forcing it through asyncio.to_thread for the + read-mostly metadata workload at our scale. + """ + + def __init__(self, config: HuggingFaceIndexConfig) -> None: + self._config = config + self._api = HfApi( + endpoint=config.huggingface.api_base, + token=config.huggingface.token, + ) + + @property + def api(self) -> HfApi: + return self._api + + @property + def token(self) -> str | None: + return self._config.huggingface.token + + # ---- Listing --------------------------------------------------------- + + # When listing repos for ingest we ask for sha + lastModified upfront so + # *_ingest.py can short-circuit the per-repo `_info` call when the + # row is already up-to-date in DuckDB. + _STUB_EXPAND: tuple[str, ...] = ("lastModified", "sha") + + def list_models(self, author: str, *, limit: int | None = None) -> Iterable[Any]: + try: + return self._api.list_models( + author=author, limit=limit, expand=list(self._STUB_EXPAND), + ) + except HfHubHTTPError as exc: + LOGGER.warning("list_models(%s) failed: %s", author, exc) + return [] + + def list_datasets(self, author: str, *, limit: int | None = None) -> Iterable[Any]: + try: + return self._api.list_datasets( + author=author, limit=limit, expand=list(self._STUB_EXPAND), + ) + except HfHubHTTPError as exc: + LOGGER.warning("list_datasets(%s) failed: %s", author, exc) + return [] + + def list_spaces(self, author: str, *, limit: int | None = None) -> Iterable[Any]: + try: + return self._api.list_spaces( + author=author, limit=limit, expand=list(self._STUB_EXPAND), + ) + except HfHubHTTPError as exc: + LOGGER.warning("list_spaces(%s) failed: %s", author, exc) + return [] + + def search_models(self, term: str, *, limit: int | None = None) -> Iterable[Any]: + try: + return self._api.list_models(search=term, limit=limit, full=False) + except HfHubHTTPError as exc: + LOGGER.warning("search_models(%s) failed: %s", term, exc) + return [] + + def search_datasets(self, term: str, *, limit: int | None = None) -> Iterable[Any]: + try: + return self._api.list_datasets(search=term, limit=limit, full=False) + except HfHubHTTPError as exc: + LOGGER.warning("search_datasets(%s) failed: %s", term, exc) + return [] + + # ---- Namespace overview ---------------------------------------------- + + def namespace_overview(self, slug: str) -> tuple[str, Any] | None: + """Return `(kind, overview)` for a namespace, or None if it doesn't exist. + + Tries the org endpoint first, falls back to the user endpoint on 404. + `kind` is `'org'` or `'user'`. + """ + try: + return ("org", self._api.get_organization_overview(slug)) + except HfHubHTTPError as org_exc: + if getattr(getattr(org_exc, "response", None), "status_code", None) != 404: + LOGGER.warning("organization_overview(%s) HTTP error: %s", slug, org_exc) + try: + return ("user", self._api.get_user_overview(slug)) + except HfHubHTTPError as user_exc: + if getattr(getattr(user_exc, "response", None), "status_code", None) != 404: + LOGGER.warning("user_overview(%s) HTTP error: %s", slug, user_exc) + return None + + # ---- Per-repo info --------------------------------------------------- + + def model_info(self, repo_id: str, *, expand: tuple[str, ...]) -> Any | None: + return self._safe_info("model", repo_id, expand=expand) + + def dataset_info(self, repo_id: str, *, expand: tuple[str, ...]) -> Any | None: + return self._safe_info("dataset", repo_id, expand=expand) + + def space_info(self, repo_id: str, *, expand: tuple[str, ...]) -> Any | None: + return self._safe_info("space", repo_id, expand=expand) + + def _safe_info( + self, + kind: str, + repo_id: str, + *, + expand: tuple[str, ...], + ) -> Any | None: + method = { + "model": self._api.model_info, + "dataset": self._api.dataset_info, + "space": self._api.space_info, + }[kind] + try: + return method(repo_id=repo_id, expand=list(expand)) + except (RepositoryNotFoundError, GatedRepoError) as exc: + LOGGER.info("skipping %s %s: %s", kind, repo_id, exc.__class__.__name__) + return None + except HfHubHTTPError as exc: + LOGGER.warning("%s_info(%s) HTTP error: %s", kind, repo_id, exc) + return None + + # ---- README fetching -------------------------------------------------- + + def fetch_readme(self, repo_id: str, *, repo_type: str) -> str | None: + """Return the README markdown for a repo, or None if absent. + + Tries common aliases (`README.md`, `README`, `Readme.md`). Returns + None on any HF error so the ingest can continue with the metadata + it already has. + """ + for filename in README_FILENAME_FALLBACKS: + try: + # Streaming download via hf_hub_download is fine here — the + # file is tiny and the client caches by sha. + from huggingface_hub import hf_hub_download + local_path = hf_hub_download( + repo_id=repo_id, + filename=filename, + repo_type=repo_type, + token=self._config.huggingface.token, + endpoint=self._config.huggingface.api_base, + ) + with open(local_path, encoding="utf-8") as fh: + return fh.read() + except EntryNotFoundError: + continue + except (RepositoryNotFoundError, GatedRepoError): + return None + except HfHubHTTPError as exc: + LOGGER.warning( + "fetch_readme(%s, %s) HTTP error: %s", + repo_id, + filename, + exc, + ) + return None + return None + + def snapshot_card_files(self, repo_id: str, *, repo_type: str, local_dir) -> None: + """Pull all card-adjacent files (README/JSON/YAML/MD) into local_dir. + + Used only when `huggingface.full_cards` is True. Always ignores + weight patterns. + """ + try: + from huggingface_hub import snapshot_download + snapshot_download( + repo_id=repo_id, + repo_type=repo_type, + allow_patterns=list(CARD_PATTERNS), + ignore_patterns=list(WEIGHT_PATTERNS), + local_dir=str(local_dir), + token=self._config.huggingface.token, + endpoint=self._config.huggingface.api_base, + ) + except (RepositoryNotFoundError, GatedRepoError): + return + except HfHubHTTPError as exc: + LOGGER.warning("snapshot_card_files(%s) HTTP error: %s", repo_id, exc) diff --git a/src/index/huggingface/ingest/models_ingest.py b/src/index/huggingface/ingest/models_ingest.py new file mode 100644 index 0000000..97539c4 --- /dev/null +++ b/src/index/huggingface/ingest/models_ingest.py @@ -0,0 +1,135 @@ +"""Ingest HuggingFace models for the configured seed orgs. + +For each org slug, list all models, fetch detailed `model_info(expand=...)`, +download the README, and upsert into the DuckDB `models` table. The README +is persisted under `cards/models//README.md`. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +from src.index.huggingface.ingest._common import ( + author_from_repo_id, + base_models_from_card_data, + card_data_to_dict, + info_to_dict, + normalise_dt, +) +from src.index.huggingface.ingest.cards import maybe_snapshot_full_card, write_readme +from src.index.huggingface.models import MODEL_EXPAND_FIELDS + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + from src.index.huggingface.ingest.hf_client import HFClient + from src.index.huggingface.ingest.scope import Scope + from src.index.huggingface.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def ingest_models( + *, + config: HuggingFaceIndexConfig, + client: HFClient, + store: DuckDBStore, + scope: Scope, + limit: int | None = None, +) -> int: + """Return the number of models upserted across the scope.""" + upserted = 0 + skipped = 0 + for slug in scope.seeds: + store.upsert_org(slug=slug, scope=scope.name, source="seed") + listed = list(client.list_models(slug, limit=limit)) + LOGGER.info("models: %s → %d listed", slug, len(listed)) + for stub in listed: + repo_id = getattr(stub, "id", None) or getattr(stub, "modelId", None) + if not repo_id: + continue + stub_sha = getattr(stub, "sha", None) + if stub_sha and store.repo_sha("models", repo_id) == stub_sha: + # Already in DB at this revision — skip the heavy info+readme fetch. + skipped += 1 + continue + info = client.model_info(repo_id, expand=MODEL_EXPAND_FIELDS) + if info is None: + continue + row = _model_row(repo_id, info) + raw = info_to_dict(info) + store.upsert_model(row, raw) + + readme = client.fetch_readme(repo_id, repo_type="model") + if readme: + write_readme( + config, + entity_type="models", + repo_id=repo_id, + readme=readme, + ) + maybe_snapshot_full_card( + config=config, + client=client, + entity_type="models", + repo_id=repo_id, + repo_type="model", + ) + upserted += 1 + if limit is not None and upserted >= limit * len(scope.seeds): + return upserted + LOGGER.info("models: ingest summary upserted=%d skipped_unchanged=%d", upserted, skipped) + return upserted + + +def _model_row(repo_id: str, info: object) -> dict[str, object | None]: + card_data = card_data_to_dict(getattr(info, "card_data", None) or getattr(info, "cardData", None)) + tags = list(getattr(info, "tags", None) or []) + return { + "repo_id": repo_id, + "author": getattr(info, "author", None) or author_from_repo_id(repo_id), + "sha": getattr(info, "sha", None), + "pipeline_tag": getattr(info, "pipeline_tag", None), + "library_name": getattr(info, "library_name", None), + "license": _license_from(card_data, getattr(info, "license", None)), + "downloads": _coerce_int(getattr(info, "downloads", None)), + "downloads_all_time": _coerce_int( + getattr(info, "downloads_all_time", None) + or getattr(info, "downloadsAllTime", None), + ), + "likes": _coerce_int(getattr(info, "likes", None)), + "gated": _coerce_bool(getattr(info, "gated", None)), + "private": _coerce_bool(getattr(info, "private", None)), + "created_at": normalise_dt(getattr(info, "created_at", None) or getattr(info, "createdAt", None)), + "last_modified": normalise_dt(getattr(info, "last_modified", None) or getattr(info, "lastModified", None)), + "tags": tags, + "card_data": card_data, + "base_models": base_models_from_card_data(card_data), + } + + +def _license_from(card_data: dict | None, fallback: object) -> str | None: + if card_data and isinstance(card_data.get("license"), str): + return card_data["license"] + if isinstance(fallback, str): + return fallback + return None + + +def _coerce_int(value: object) -> int | None: + if value is None: + return None + try: + return int(value) + except (TypeError, ValueError): + return None + + +def _coerce_bool(value: object) -> bool | None: + if value is None: + return None + if isinstance(value, bool): + return value + if isinstance(value, str): + return value.strip().lower() in {"true", "1", "yes"} + return bool(value) diff --git a/src/index/huggingface/ingest/orgs_ingest.py b/src/index/huggingface/ingest/orgs_ingest.py new file mode 100644 index 0000000..68acaba --- /dev/null +++ b/src/index/huggingface/ingest/orgs_ingest.py @@ -0,0 +1,73 @@ +"""Ingest HuggingFace org/user overview metadata for the configured seed. + +Probes each slug as an organisation first, falls back to the user endpoint +on 404, and persists `fullname`, `details`, repo counts, and the raw payload +to the `orgs` table. Idempotent: re-running upserts on `slug`. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + from src.index.huggingface.ingest.hf_client import HFClient + from src.index.huggingface.ingest.scope import Scope + from src.index.huggingface.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def ingest_orgs( + *, + config: HuggingFaceIndexConfig, # noqa: ARG001 - kept for ingester signature parity + client: HFClient, + store: DuckDBStore, + scope: Scope, + limit: int | None = None, # noqa: ARG001 - same signature as repo ingesters +) -> int: + """Return the number of namespaces upserted across the scope.""" + upserted = 0 + for slug in scope.seeds: + result = client.namespace_overview(slug) + if result is None: + LOGGER.info("orgs: %s not found on the Hub (skipping)", slug) + store.upsert_org(slug=slug, scope=scope.name, source="seed") + continue + kind, info = result + store.upsert_org( + slug=slug, + scope=scope.name, + source="seed", + namespace_kind=kind, + fullname=getattr(info, "fullname", None), + details=getattr(info, "details", None), + avatar_url=getattr(info, "avatar_url", None), + num_models=_coerce_int(getattr(info, "num_models", None)), + num_datasets=_coerce_int(getattr(info, "num_datasets", None)), + num_spaces=_coerce_int(getattr(info, "num_spaces", None)), + num_followers=_coerce_int(getattr(info, "num_followers", None)), + raw=_overview_to_dict(info), + ) + LOGGER.info("orgs: %s (%s) → %s", slug, kind, getattr(info, "fullname", None)) + upserted += 1 + return upserted + + +def _coerce_int(value: Any) -> int | None: + if value is None: + return None + try: + return int(value) + except (TypeError, ValueError): + return None + + +def _overview_to_dict(info: Any) -> dict[str, Any]: + """Best-effort conversion of an Organization/User dataclass to a dict.""" + if hasattr(info, "__dataclass_fields__"): + return {f: getattr(info, f, None) for f in info.__dataclass_fields__} + if hasattr(info, "__dict__"): + return dict(info.__dict__) + return {} diff --git a/src/index/huggingface/ingest/scope.py b/src/index/huggingface/ingest/scope.py new file mode 100644 index 0000000..cfcc49d --- /dev/null +++ b/src/index/huggingface/ingest/scope.py @@ -0,0 +1,28 @@ +"""Scope resolver — turns a scope name into a list of seed org slugs. + +Two scopes are supported in v1: `epfl` and `switzerland`. The seed lists +live in `config/index/huggingface.yaml` under `scope.seeds`. New orgs +discovered via `discover-orgs` are surfaced for human review and only +promoted into the YAML by hand — never auto-added. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Literal + +from src.index.huggingface.config import HuggingFaceIndexConfig + +ScopeName = Literal["epfl", "switzerland"] + + +@dataclass(slots=True, frozen=True) +class Scope: + """A resolved scope: name + the curated seed org slugs to ingest.""" + + name: str + seeds: list[str] + + +def resolve_scope(name: ScopeName, config: HuggingFaceIndexConfig) -> Scope: + return Scope(name=name, seeds=config.seed_for(name)) diff --git a/src/index/huggingface/ingest/spaces_ingest.py b/src/index/huggingface/ingest/spaces_ingest.py new file mode 100644 index 0000000..ce6ab71 --- /dev/null +++ b/src/index/huggingface/ingest/spaces_ingest.py @@ -0,0 +1,142 @@ +"""Ingest HuggingFace spaces for the configured seed orgs.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +from src.index.huggingface.ingest._common import ( + author_from_repo_id, + card_data_to_dict, + info_to_dict, + normalise_dt, +) +from src.index.huggingface.ingest.cards import maybe_snapshot_full_card, write_readme +from src.index.huggingface.models import SPACE_EXPAND_FIELDS + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + from src.index.huggingface.ingest.hf_client import HFClient + from src.index.huggingface.ingest.scope import Scope + from src.index.huggingface.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def ingest_spaces( + *, + config: HuggingFaceIndexConfig, + client: HFClient, + store: DuckDBStore, + scope: Scope, + limit: int | None = None, +) -> int: + upserted = 0 + skipped = 0 + for slug in scope.seeds: + store.upsert_org(slug=slug, scope=scope.name, source="seed") + listed = list(client.list_spaces(slug, limit=limit)) + LOGGER.info("spaces: %s → %d listed", slug, len(listed)) + for stub in listed: + repo_id = getattr(stub, "id", None) or getattr(stub, "spaceId", None) + if not repo_id: + continue + stub_sha = getattr(stub, "sha", None) + if stub_sha and store.repo_sha("spaces", repo_id) == stub_sha: + skipped += 1 + continue + info = client.space_info(repo_id, expand=SPACE_EXPAND_FIELDS) + if info is None: + continue + row = _space_row(repo_id, info) + raw = info_to_dict(info) + store.upsert_space(row, raw) + + readme = client.fetch_readme(repo_id, repo_type="space") + if readme: + write_readme( + config, + entity_type="spaces", + repo_id=repo_id, + readme=readme, + ) + maybe_snapshot_full_card( + config=config, + client=client, + entity_type="spaces", + repo_id=repo_id, + repo_type="space", + ) + upserted += 1 + if limit is not None and upserted >= limit * len(scope.seeds): + return upserted + LOGGER.info("spaces: ingest summary upserted=%d skipped_unchanged=%d", upserted, skipped) + return upserted + + +def _space_row(repo_id: str, info: object) -> dict[str, object | None]: + card_data = card_data_to_dict(getattr(info, "card_data", None) or getattr(info, "cardData", None)) + tags = list(getattr(info, "tags", None) or []) + runtime = getattr(info, "runtime", None) + return { + "repo_id": repo_id, + "author": getattr(info, "author", None) or author_from_repo_id(repo_id), + "sha": getattr(info, "sha", None), + "sdk": getattr(info, "sdk", None) or _from_card("sdk", card_data), + "runtime_stage": _runtime_stage(runtime), + "hardware": _runtime_hardware(runtime), + "license": _license_from(card_data, getattr(info, "license", None)), + "likes": _coerce_int(getattr(info, "likes", None)), + "created_at": normalise_dt(getattr(info, "created_at", None) or getattr(info, "createdAt", None)), + "last_modified": normalise_dt(getattr(info, "last_modified", None) or getattr(info, "lastModified", None)), + "tags": tags, + "card_data": card_data, + } + + +def _from_card(key: str, card_data: dict | None) -> str | None: + if not card_data: + return None + value = card_data.get(key) + return value if isinstance(value, str) else None + + +def _runtime_stage(runtime: object) -> str | None: + if runtime is None: + return None + if isinstance(runtime, dict): + return runtime.get("stage") if isinstance(runtime.get("stage"), str) else None + return getattr(runtime, "stage", None) + + +def _runtime_hardware(runtime: object) -> str | None: + if runtime is None: + return None + if isinstance(runtime, dict): + hardware = runtime.get("hardware") + if isinstance(hardware, dict): + return hardware.get("current") or hardware.get("requested") + if isinstance(hardware, str): + return hardware + return None + hardware = getattr(runtime, "hardware", None) + if hardware is None: + return None + return getattr(hardware, "current", None) or getattr(hardware, "requested", None) + + +def _license_from(card_data: dict | None, fallback: object) -> str | None: + if card_data and isinstance(card_data.get("license"), str): + return card_data["license"] + if isinstance(fallback, str): + return fallback + return None + + +def _coerce_int(value: object) -> int | None: + if value is None: + return None + try: + return int(value) + except (TypeError, ValueError): + return None diff --git a/src/index/huggingface/models.py b/src/index/huggingface/models.py new file mode 100644 index 0000000..0759517 --- /dev/null +++ b/src/index/huggingface/models.py @@ -0,0 +1,149 @@ +"""Pydantic schemas for HuggingFace entities persisted into DuckDB. + +Compact projections — only the fields we filter/join on as columns; the +full HF API payload lives in the `raw` JSON column. +""" + +from __future__ import annotations + +from datetime import datetime +from typing import Literal + +from pydantic import BaseModel, ConfigDict + +EntityType = Literal["models", "datasets", "spaces", "orgs"] + +# Repo-shaped entity types ingested via list_*/info APIs. +ALL_ENTITY_TYPES: tuple[EntityType, ...] = ("models", "datasets", "spaces") + +# All entity types embedded into Qdrant (repo-shaped + namespace-shaped). +ALL_EMBEDDABLE_TYPES: tuple[EntityType, ...] = (*ALL_ENTITY_TYPES, "orgs") + +# Map plural CLI/table names to singular DuckDB chunk entity_type values. +ENTITY_TYPE_SINGULAR: dict[EntityType, str] = { + "models": "model", + "datasets": "dataset", + "spaces": "space", + "orgs": "org", +} + +# `expand=[...]` arg passed to model_info / dataset_info / space_info. +# The Hub validates each expand value against an entity-specific allow-list, +# so we keep three distinct tuples. License is *not* a top-level expand on +# any endpoint — it ships inside `cardData` (the YAML front-matter). + +MODEL_EXPAND_FIELDS: tuple[str, ...] = ( + "author", + "cardData", + "createdAt", + "downloads", + "downloadsAllTime", + "gated", + "lastModified", + "library_name", + "likes", + "pipeline_tag", + "private", + "sha", + "siblings", + "tags", +) + +DATASET_EXPAND_FIELDS: tuple[str, ...] = ( + "author", + "cardData", + "createdAt", + "downloads", + "downloadsAllTime", + "gated", + "lastModified", + "likes", + "private", + "sha", + "siblings", + "tags", +) + +SPACE_EXPAND_FIELDS: tuple[str, ...] = ( + "author", + "cardData", + "createdAt", + "lastModified", + "likes", + "private", + "runtime", + "sdk", + "sha", + "siblings", + "tags", +) + + +class OrgRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + slug: str + namespace_kind: Literal["user", "org"] = "org" + source: Literal["seed", "discover"] = "seed" + scope: str # 'epfl' | 'switzerland' + + +class ModelRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + repo_id: str + author: str | None = None + sha: str | None = None + pipeline_tag: str | None = None + library_name: str | None = None + license: str | None = None + downloads: int | None = None + downloads_all_time: int | None = None + likes: int | None = None + gated: bool | None = None + private: bool | None = None + created_at: datetime | None = None + last_modified: datetime | None = None + + +class DatasetRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + repo_id: str + author: str | None = None + sha: str | None = None + license: str | None = None + downloads: int | None = None + downloads_all_time: int | None = None + likes: int | None = None + gated: bool | None = None + private: bool | None = None + created_at: datetime | None = None + last_modified: datetime | None = None + + +class SpaceRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + repo_id: str + author: str | None = None + sha: str | None = None + sdk: str | None = None + runtime_stage: str | None = None + hardware: str | None = None + license: str | None = None + likes: int | None = None + created_at: datetime | None = None + last_modified: datetime | None = None + + +class DiscoveryCandidate(BaseModel): + """A namespace surfaced by `discover-orgs` for human review.""" + + model_config = ConfigDict(extra="ignore") + + namespace: str + hits: int + sample_repo_ids: list[str] + matched_terms: list[str] + in_seed: bool diff --git a/src/index/huggingface/paths.py b/src/index/huggingface/paths.py new file mode 100644 index 0000000..9de74db --- /dev/null +++ b/src/index/huggingface/paths.py @@ -0,0 +1,67 @@ +"""Filesystem layout for HuggingFace index artifacts. + +Single source of truth for paths under `/huggingface/`. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path + +DEFAULT_INDEX_DATA_DIR = Path("data/index") + + +def _resolve_repo_root() -> Path: + return Path(__file__).resolve().parents[3] + + +def _resolve_index_data_dir() -> Path: + raw = os.getenv("INDEX_DATA_DIR") + if raw and raw.strip(): + candidate = Path(raw.strip()).expanduser() + if candidate.is_absolute(): + return candidate + return _resolve_repo_root() / candidate + return _resolve_repo_root() / DEFAULT_INDEX_DATA_DIR + + +@dataclass(slots=True, frozen=True) +class HuggingFacePaths: + """Resolved filesystem paths for the HuggingFace module.""" + + root: Path + + @property + def duckdb_dir(self) -> Path: + return self.root / "duckdb" + + @property + def duckdb_path(self) -> Path: + return self.duckdb_dir / "huggingface.duckdb" + + @property + def cache_dir(self) -> Path: + return self.root / "cache" + + @property + def logs_dir(self) -> Path: + return self.root / "logs" + + @property + def cards_dir(self) -> Path: + return self.root / "cards" + + def cards_path_for(self, entity_type: str, repo_id: str) -> Path: + return self.cards_dir / entity_type / repo_id + + +def get_huggingface_paths() -> HuggingFacePaths: + """Resolve `/huggingface/` and ensure subdirs exist.""" + root = _resolve_index_data_dir() / "huggingface" + paths = HuggingFacePaths(root=root) + paths.duckdb_dir.mkdir(parents=True, exist_ok=True) + paths.cache_dir.mkdir(parents=True, exist_ok=True) + paths.logs_dir.mkdir(parents=True, exist_ok=True) + paths.cards_dir.mkdir(parents=True, exist_ok=True) + return paths diff --git a/src/index/huggingface/rerank/__init__.py b/src/index/huggingface/rerank/__init__.py new file mode 100644 index 0000000..bb02c7f --- /dev/null +++ b/src/index/huggingface/rerank/__init__.py @@ -0,0 +1 @@ +"""RCP reranker client.""" diff --git a/src/index/huggingface/rerank/rcp_client.py b/src/index/huggingface/rerank/rcp_client.py new file mode 100644 index 0000000..efcbb6d --- /dev/null +++ b/src/index/huggingface/rerank/rcp_client.py @@ -0,0 +1,110 @@ +# TODO: dedupe with src/index/openalex/rerank/rcp_client.py — kept as a copy +# while the user defers extracting a shared `src/index/_rcp/` module. +"""Async client for the RCP reranker (Cohere-compatible shape).""" + +from __future__ import annotations + +import logging +import os +from typing import TYPE_CHECKING, Any + +import httpx +from tenacity import ( + retry, + retry_if_exception_type, + stop_after_attempt, + wait_exponential, +) + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + +LOGGER = logging.getLogger(__name__) + +DEFAULT_TIMEOUT_S = 30.0 +_RETRYABLE_STATUS = {429, 500, 502, 503, 504} + + +def _is_retryable(exc: BaseException) -> bool: + if isinstance(exc, httpx.TimeoutException): + return True + if isinstance(exc, httpx.HTTPStatusError): + return exc.response.status_code in _RETRYABLE_STATUS + return False + + +class RCPRerankerClient: + def __init__( + self, + config: HuggingFaceIndexConfig, + *, + path: str | None = None, + timeout_s: float | None = None, + ) -> None: + config.require_rcp() + self._config = config + self._timeout = httpx.Timeout(timeout_s or DEFAULT_TIMEOUT_S) + path = path or os.getenv("RCP_RERANK_PATH", "/rerank") + self._url = f"{config.rcp.base_url.rstrip('/')}{path}" + self._headers = { + "Authorization": f"Bearer {config.rcp.token}", + "Content-Type": "application/json", + } + + @property + def model(self) -> str: + return self._config.rcp.reranker_model + + async def rerank( + self, + query: str, + documents: list[str], + *, + top_n: int | None = None, + ) -> list[dict[str, Any]]: + if not documents: + return [] + payload: dict[str, Any] = { + "model": self._config.rcp.reranker_model, + "query": query, + "documents": documents, + } + if top_n is not None: + payload["top_n"] = top_n + + @retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=1, min=2, max=30), + retry=retry_if_exception_type((httpx.TimeoutException, httpx.HTTPStatusError)), + reraise=True, + ) + async def _call() -> list[dict[str, Any]]: + async with httpx.AsyncClient() as client: + response = await client.post( + self._url, + headers=self._headers, + json=payload, + timeout=self._timeout, + ) + try: + response.raise_for_status() + except httpx.HTTPStatusError as exc: + if not _is_retryable(exc): + body = response.text[:500] + LOGGER.error( + "RCP rerank non-retryable %d: %s", + response.status_code, + body, + ) + raise + data = response.json() + results = data.get("results") or [] + return [ + { + "index": int(item["index"]), + "relevance_score": float(item["relevance_score"]), + } + for item in results + ] + + return await _call() diff --git a/src/index/huggingface/retrieval/__init__.py b/src/index/huggingface/retrieval/__init__.py new file mode 100644 index 0000000..6f02585 --- /dev/null +++ b/src/index/huggingface/retrieval/__init__.py @@ -0,0 +1 @@ +"""Semantic and SQL retrieval surfaces.""" diff --git a/src/index/huggingface/retrieval/infoscience_links.py b/src/index/huggingface/retrieval/infoscience_links.py new file mode 100644 index 0000000..636d96b --- /dev/null +++ b/src/index/huggingface/retrieval/infoscience_links.py @@ -0,0 +1,172 @@ +"""Reverse index from HuggingFace `repo_id` → citing Infoscience articles. + +Mines `data/index/infoscience/dumps/infoscience_links_index.json` for every +URL whose host is `huggingface.co` or `hf.co`, parses out the `` and +`/` segments, and exposes: + + - `articles_for_repo(repo_id)` — papers citing `/` + - `articles_for_author(author)` — papers citing any repo under `` + +The full index file is ~8 MB and parsed lazily on first call; subsequent +calls reuse the in-memory map. If the file is missing (infoscience index +not built locally), the module returns empty lists and never raises. +""" + +from __future__ import annotations + +import json +import logging +import re +from collections import defaultdict +from pathlib import Path +from typing import Any + +LOGGER = logging.getLogger(__name__) + +# Default location — overridable via param if the user moves things around. +DEFAULT_INDEX_PATH = Path("data/index/infoscience/dumps/infoscience_links_index.json") + +# Path segments that aren't namespaces themselves but precede one. +_NON_NAMESPACE_PREFIXES = frozenset( + {"datasets", "models", "spaces", "papers", "collections"}, +) + +_HF_URL_RE = re.compile( + r"https?://(?:huggingface\.co|hf\.co)/([^/\s?#]+)(?:/([^/\s?#]+))?", + re.IGNORECASE, +) + +# Module-level cache. None == not loaded yet; {} == loaded but file missing. +_BY_REPO: dict[str, list[dict[str, Any]]] | None = None +_BY_AUTHOR: dict[str, list[dict[str, Any]]] | None = None +_LOADED_FROM: Path | None = None + + +def _parse_namespace(url: str) -> tuple[str | None, str | None]: + """Return `(author, repo_id)` from a HF URL, or `(None, None)` on no match. + + Skips `huggingface.co/datasets/foo/bar` style — the leading `datasets` + isn't an author. + """ + m = _HF_URL_RE.search(url) + if not m: + return (None, None) + first, second = m.group(1), m.group(2) + if first.lower() in _NON_NAMESPACE_PREFIXES: + # `huggingface.co/datasets//` — promote one level. + if not second: + return (None, None) + # Need to look further into the URL for the next segment after `second`. + tail = url[m.end():] + tail_m = re.match(r"/([^/\s?#]+)", tail) + author = second + repo = tail_m.group(1) if tail_m else None + return (author, f"{author}/{repo}" if repo else None) + return (first, f"{first}/{second}" if second else None) + + +def _build_indexes(path: Path) -> None: + """Parse the slim index → populate `_BY_REPO` and `_BY_AUTHOR`.""" + global _BY_REPO, _BY_AUTHOR, _LOADED_FROM + by_repo: dict[str, list[dict[str, Any]]] = defaultdict(list) + by_author: dict[str, list[dict[str, Any]]] = defaultdict(list) + + if not path.exists(): + LOGGER.info("infoscience links index not present at %s; cross-links disabled", path) + _BY_REPO, _BY_AUTHOR, _LOADED_FROM = {}, {}, path + return + + with path.open(encoding="utf-8") as fh: + data = json.load(fh) + + seen_per_article: dict[str, set[str]] = defaultdict(set) + for item in data.get("items", []): + body = item.get("body_urls") or {} + urls: list[str] = [] + for key in ("huggingface", "hf_co"): + urls.extend(body.get(key, []) or []) + if not urls: + continue + article_ref = { + "uuid": item.get("uuid"), + "title": item.get("title"), + "year": item.get("year"), + "doi": item.get("doi"), + "infoscience_url": item.get("infoscience_url"), + } + article_uuid = item.get("uuid") or "" + for url in urls: + author, repo_id = _parse_namespace(url) + if author and f"author:{author}" not in seen_per_article[article_uuid]: + by_author[author].append(article_ref) + seen_per_article[article_uuid].add(f"author:{author}") + if repo_id and f"repo:{repo_id}" not in seen_per_article[article_uuid]: + by_repo[repo_id].append(article_ref) + seen_per_article[article_uuid].add(f"repo:{repo_id}") + + _BY_REPO = dict(by_repo) + _BY_AUTHOR = dict(by_author) + _LOADED_FROM = path + LOGGER.info( + "infoscience links index loaded from %s: %d repos, %d authors", + path, + len(_BY_REPO), + len(_BY_AUTHOR), + ) + + +def _ensure_loaded(index_path: Path | None = None) -> None: + """Lazy-load on first call; reload only if the path argument changes.""" + target = index_path or DEFAULT_INDEX_PATH + if _BY_REPO is None or _LOADED_FROM != target: + _build_indexes(target) + + +def articles_for_repo( + repo_id: str, + *, + index_path: Path | None = None, +) -> list[dict[str, Any]]: + """Return Infoscience articles citing `/` (case-insensitive on author).""" + _ensure_loaded(index_path) + assert _BY_REPO is not None + if repo_id in _BY_REPO: + return _BY_REPO[repo_id] + # Tolerate case differences in the namespace half (HF is case-insensitive + # on lookup but case-preserving on the canonical name). + author, _, name = repo_id.partition("/") + if not name: + return [] + needle = f"{author.lower()}/{name.lower()}" + for key, refs in _BY_REPO.items(): + if key.lower() == needle: + return refs + return [] + + +def articles_for_author( + author: str, + *, + index_path: Path | None = None, +) -> list[dict[str, Any]]: + """Return Infoscience articles citing any repo under `author` (case-insensitive).""" + _ensure_loaded(index_path) + assert _BY_AUTHOR is not None + if author in _BY_AUTHOR: + return _BY_AUTHOR[author] + needle = author.lower() + for key, refs in _BY_AUTHOR.items(): + if key.lower() == needle: + return refs + return [] + + +def reset_cache() -> None: + """Drop the in-memory index. Useful in tests.""" + global _BY_REPO, _BY_AUTHOR, _LOADED_FROM + _BY_REPO = None + _BY_AUTHOR = None + _LOADED_FROM = None + + +__all__ = ["articles_for_repo", "articles_for_author", "reset_cache"] diff --git a/src/index/huggingface/retrieval/lineage.py b/src/index/huggingface/retrieval/lineage.py new file mode 100644 index 0000000..d5af123 --- /dev/null +++ b/src/index/huggingface/retrieval/lineage.py @@ -0,0 +1,149 @@ +"""Walk the HuggingFace `base_models` graph in both directions. + +Each model's `base_models` column is a JSON list of parent `repo_id`s +(the model(s) it was fine-tuned from). We use DuckDB for the walk: + +* **Ancestors** — recursive lookup of `models.base_models` for each + current node, up to `depth` hops. Most chains are shallow (≤2). +* **Descendants** — `WHERE base_models @> '[repo_id]'`. Same depth limit. + +Returns a graph in adjacency-list form. Cheap (~ms) for typical EPFL repos +since the local DB is small. +""" + +from __future__ import annotations + +import json +import logging +from collections import deque +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from src.index.huggingface.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def compute_lineage( + repo_id: str, + *, + store: DuckDBStore, + depth: int = 3, +) -> dict[str, Any]: + """Return the ancestor + descendant subgraphs of `repo_id` up to `depth` hops. + + Output: + { + "root": "", + "ancestors": { "level_1": [...], "level_2": [...], ... }, + "descendants": { "level_1": [...], "level_2": [...], ... }, + "edges": [{"from": ..., "to": ...}, ...], # to=parent, from=child + } + """ + seen: set[str] = {repo_id} + edges: list[dict[str, str]] = [] + ancestors: dict[str, list[dict[str, Any]]] = {} + descendants: dict[str, list[dict[str, Any]]] = {} + + # ---- Ancestors (walk `base_models` upward) ------------------------------ + frontier: deque[str] = deque([repo_id]) + for level in range(1, depth + 1): + next_frontier: deque[str] = deque() + ancestors[f"level_{level}"] = [] + while frontier: + node = frontier.popleft() + row = store.fetch_repo("models", node) + if row is None: + continue + parents = _coerce_repo_id_list(row.get("base_models")) + for parent in parents: + edges.append({"from": node, "to": parent}) + if parent in seen: + continue + seen.add(parent) + parent_row = store.fetch_repo("models", parent) or {"repo_id": parent} + ancestors[f"level_{level}"].append(_thin_record(parent_row)) + next_frontier.append(parent) + frontier = next_frontier + if not frontier: + break + + # ---- Descendants (walk `base_models` downward) -------------------------- + seen.clear() + seen.add(repo_id) + frontier = deque([repo_id]) + for level in range(1, depth + 1): + next_frontier = deque() + descendants[f"level_{level}"] = [] + while frontier: + node = frontier.popleft() + children = _find_children(store, node) + for child in children: + edges.append({"from": child["repo_id"], "to": node}) + if child["repo_id"] in seen: + continue + seen.add(child["repo_id"]) + descendants[f"level_{level}"].append(_thin_record(child)) + next_frontier.append(child["repo_id"]) + frontier = next_frontier + if not frontier: + break + + # Drop empty levels. + ancestors = {k: v for k, v in ancestors.items() if v} + descendants = {k: v for k, v in descendants.items() if v} + + return { + "root": repo_id, + "ancestors": ancestors, + "descendants": descendants, + "edges": edges, + "depth": depth, + } + + +def _find_children(store: DuckDBStore, parent_id: str) -> list[dict[str, Any]]: + """Return rows whose `base_models` list contains `parent_id`. + + DuckDB JSON-array containment is best expressed as a string scan since + `base_models` is stored as a JSON-encoded VARCHAR. + """ + needle = json.dumps(parent_id, ensure_ascii=False) + sql = ( + "SELECT * FROM models " + "WHERE base_models IS NOT NULL " + " AND base_models != 'null' " + " AND base_models != '[]' " + " AND base_models LIKE ? " + "ORDER BY downloads DESC NULLS LAST" + ) + cur = store.connect().execute(sql, [f"%{needle}%"]) + cols = [d[0] for d in cur.description] + return [dict(zip(cols, row, strict=False)) for row in cur.fetchall()] + + +def _coerce_repo_id_list(value: Any) -> list[str]: + if value is None: + return [] + if isinstance(value, list): + return [str(v) for v in value if v] + if isinstance(value, str): + try: + parsed = json.loads(value) + except (TypeError, ValueError): + return [] + if isinstance(parsed, list): + return [str(v) for v in parsed if v] + return [] + + +def _thin_record(row: dict[str, Any]) -> dict[str, Any]: + """Trim a model row to fields useful for lineage display.""" + keep = ( + "repo_id", "author", "pipeline_tag", "library_name", + "license", "downloads", "likes", "last_modified", + ) + return {k: row.get(k) for k in keep if k in row} + + +__all__ = ["compute_lineage"] diff --git a/src/index/huggingface/retrieval/semantic.py b/src/index/huggingface/retrieval/semantic.py new file mode 100644 index 0000000..8619a2c --- /dev/null +++ b/src/index/huggingface/retrieval/semantic.py @@ -0,0 +1,207 @@ +"""End-to-end semantic retrieval: embed → vector search → rerank → hydrate.""" + +from __future__ import annotations + +import asyncio +import json +import logging +from typing import TYPE_CHECKING, Any + +from src.index.huggingface.embed.rcp_client import RCPEmbeddingClient +from src.index.huggingface.models import ENTITY_TYPE_SINGULAR +from src.index.huggingface.rerank.rcp_client import RCPRerankerClient +from src.index.huggingface.retrieval import infoscience_links +from src.index.huggingface.storage.duckdb_store import DuckDBStore +from src.index.huggingface.vector.qdrant_store import COLLECTION_FOR_TABLE, QdrantStore + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + +LOGGER = logging.getLogger(__name__) + + +async def _async_search( + *, + config: HuggingFaceIndexConfig, + query: str, + entity_table: str, + top_k: int, + candidate_k: int, + filter_payload: dict[str, Any] | None, + store: DuckDBStore, + facet_keys: tuple[str, ...] = (), + facet_top_n: int = 10, +) -> tuple[list[dict[str, Any]], dict[str, list[dict[str, Any]]]]: + if entity_table not in COLLECTION_FOR_TABLE: + message = f"Unknown entity table: {entity_table}" + raise ValueError(message) + embed = RCPEmbeddingClient(config) + qdrant = QdrantStore(config) + rerank = RCPRerankerClient(config) + collection = COLLECTION_FOR_TABLE[entity_table] + + instruction = config.rcp.query_instruction + formatted_query = ( + f"Instruct: {instruction}\nQuery: {query}" if instruction else query + ) + [query_vec] = await embed.embed_all([formatted_query]) + candidates = qdrant.search( + collection, + query_vector=query_vec, + top_k=candidate_k, + filter_payload=filter_payload, + ) + if not candidates: + return [], {} + + facets = _aggregate_facets(candidates, facet_keys, top_n=facet_top_n) if facet_keys else {} + + docs = [_payload_to_doc(c["payload"]) for c in candidates] + # Rerank the full candidate pool so per-entity dedup below has enough + # ranked chunks to fill `top_k` distinct entities. + reranked = await rerank.rerank(query, docs, top_n=len(candidates)) + if not reranked: + ordered = list(candidates) + else: + ordered = [] + for r in reranked: + cand = candidates[r["index"]] + ordered.append({**cand, "rerank_score": r["relevance_score"]}) + + # Dedup at the entity level — Qdrant points are chunks, but a search + # result should return one row per repo/namespace (the best chunk of it). + hydrated: list[dict[str, Any]] = [] + seen_repo_ids: set[str] = set() + for hit in ordered: + payload = hit["payload"] or {} + repo_id = payload.get("repo_id") + if not repo_id or repo_id in seen_repo_ids: + continue + seen_repo_ids.add(repo_id) + if entity_table == "orgs": + row = store.fetch_org(repo_id) + cited_by = infoscience_links.articles_for_author(repo_id) + else: + row = store.fetch_repo(entity_table, repo_id) + cited_by = infoscience_links.articles_for_repo(repo_id) + result: dict[str, Any] = { + "id": hit["id"], + "vector_score": hit["score"], + "rerank_score": hit.get("rerank_score"), + "payload": payload, + "entity": row, + } + if cited_by: + result["cited_by_infoscience"] = cited_by + hydrated.append(result) + if len(hydrated) >= top_k: + break + return hydrated, facets + + +def _aggregate_facets( + candidates: list[dict[str, Any]], + keys: tuple[str, ...], + *, + top_n: int, +) -> dict[str, list[dict[str, Any]]]: + """Local facet counts over the candidate pool (post-Qdrant, pre-rerank). + + Counts unique entities per facet value (deduped on `repo_id`) so a repo + with 3 chunks doesn't get counted three times. + """ + from collections import Counter + + seen_per_key: dict[str, set[str]] = {k: set() for k in keys} + counters: dict[str, Counter] = {k: Counter() for k in keys} + for cand in candidates: + payload = cand.get("payload") or {} + repo_id = payload.get("repo_id") or "" + for key in keys: + value = payload.get(key) + if value is None or value == "": + continue + entity_key = f"{key}|{repo_id}" + if entity_key in seen_per_key[key]: + continue + seen_per_key[key].add(entity_key) + if isinstance(value, list): + for v in value: + counters[key][str(v)] += 1 + else: + counters[key][str(value)] += 1 + return { + key: [{"value": v, "count": c} for v, c in counters[key].most_common(top_n)] + for key in keys + } + + +def _payload_to_doc(payload: dict[str, Any]) -> str: + repo_id = payload.get("repo_id") + pipeline_tag = payload.get("pipeline_tag") + sdk = payload.get("sdk") + fullname = payload.get("fullname") + parts = [str(p) for p in (repo_id, fullname, pipeline_tag, sdk) if p] + return " — ".join(parts) if parts else json.dumps(payload, ensure_ascii=False) + + +def semantic_search( + *, + config: HuggingFaceIndexConfig, + query: str, + entity_type: str = "models", + top_k: int = 10, + candidate_k: int = 50, + filter_payload: dict[str, Any] | None = None, + store: DuckDBStore | None = None, +) -> list[dict[str, Any]]: + """Backward-compat entrypoint — returns just the hits list.""" + hits, _ = semantic_search_with_facets( + config=config, query=query, entity_type=entity_type, + top_k=top_k, candidate_k=candidate_k, + filter_payload=filter_payload, store=store, facet_keys=(), + ) + return hits + + +def semantic_search_with_facets( + *, + config: HuggingFaceIndexConfig, + query: str, + entity_type: str = "models", + top_k: int = 10, + candidate_k: int = 50, + filter_payload: dict[str, Any] | None = None, + store: DuckDBStore | None = None, + facet_keys: tuple[str, ...] = (), + facet_top_n: int = 10, +) -> tuple[list[dict[str, Any]], dict[str, list[dict[str, Any]]]]: + """Returns `(hits, facets)`. Facets is empty when `facet_keys` is empty. + + `entity_type` accepts either the plural table name (`models`) or the + singular form (`model`) for ergonomics. + """ + table = entity_type if entity_type in COLLECTION_FOR_TABLE else _table_from_singular(entity_type) + if store is None: + store = DuckDBStore.open() + return asyncio.run( + _async_search( + config=config, + query=query, + entity_table=table, + top_k=top_k, + candidate_k=candidate_k, + filter_payload=filter_payload, + store=store, + facet_keys=facet_keys, + facet_top_n=facet_top_n, + ), + ) + + +def _table_from_singular(value: str) -> str: + for table, singular in ENTITY_TYPE_SINGULAR.items(): + if singular == value: + return table + message = f"Unknown entity type: {value}" + raise ValueError(message) diff --git a/src/index/huggingface/retrieval/sql.py b/src/index/huggingface/retrieval/sql.py new file mode 100644 index 0000000..65bd673 --- /dev/null +++ b/src/index/huggingface/retrieval/sql.py @@ -0,0 +1,137 @@ +"""Read-only SQL surface over the DuckDB dump. + +- `run_predefined()` — parametrized canned queries. Safe by construction. +- `run_adhoc()` — guarded ad-hoc SELECT/WITH only. Rejects anything that + doesn't start with SELECT/WITH or that contains forbidden statement-level + keywords. +""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index.huggingface.storage.duckdb_store import DuckDBStore + +INVALID_QUERY_PREFIX_ERROR = "Only SELECT/WITH queries are allowed" +FORBIDDEN_KEYWORD_ERROR = "Forbidden keyword in query: {kw}" + +_ALLOWED_PREFIXES = ("select", "with") +_FORBIDDEN_KEYWORDS = ( + "attach", + "copy", + "pragma", + "install", + "load", + "export", + "import", + "create", + "drop", + "alter", + "insert", + "update", + "delete", + "truncate", +) + +_KEYWORD_RE = re.compile(r"\b(" + "|".join(_FORBIDDEN_KEYWORDS) + r")\b", re.IGNORECASE) + + +def _validate_adhoc(sql: str) -> None: + stripped = sql.strip().lstrip("(").lstrip() + lowered = stripped.lower() + if not any(lowered.startswith(p) for p in _ALLOWED_PREFIXES): + raise ValueError(INVALID_QUERY_PREFIX_ERROR) + match = _KEYWORD_RE.search(stripped) + if match: + raise ValueError(FORBIDDEN_KEYWORD_ERROR.format(kw=match.group(1).upper())) + + +PREDEFINED_QUERIES: dict[str, str] = { + "count_by_entity": ( + "SELECT 'orgs' AS entity, COUNT(*) AS n FROM orgs " + "UNION ALL SELECT 'models', COUNT(*) FROM models " + "UNION ALL SELECT 'datasets', COUNT(*) FROM datasets " + "UNION ALL SELECT 'spaces', COUNT(*) FROM spaces " + "UNION ALL SELECT 'chunks', COUNT(*) FROM chunks" + ), + "count_models": "SELECT COUNT(*) AS n FROM models", + "count_datasets": "SELECT COUNT(*) AS n FROM datasets", + "count_spaces": "SELECT COUNT(*) AS n FROM spaces", + "top_models_by_downloads": ( + "SELECT repo_id, author, pipeline_tag, library_name, license, downloads, likes " + "FROM models ORDER BY downloads DESC NULLS LAST, likes DESC NULLS LAST " + "LIMIT $limit" + ), + "top_datasets_by_downloads": ( + "SELECT repo_id, author, license, downloads, likes " + "FROM datasets ORDER BY downloads DESC NULLS LAST, likes DESC NULLS LAST " + "LIMIT $limit" + ), + "models_by_author": ( + "SELECT repo_id, pipeline_tag, library_name, license, downloads, likes " + "FROM models WHERE author = $author " + "ORDER BY downloads DESC NULLS LAST LIMIT $limit" + ), + "datasets_by_author": ( + "SELECT repo_id, license, downloads, likes " + "FROM datasets WHERE author = $author " + "ORDER BY downloads DESC NULLS LAST LIMIT $limit" + ), + "models_by_pipeline_tag": ( + "SELECT repo_id, author, library_name, license, downloads, likes " + "FROM models WHERE pipeline_tag = $pipeline_tag " + "ORDER BY downloads DESC NULLS LAST LIMIT $limit" + ), + "orgs_by_scope": ( + "SELECT slug, namespace_kind, source FROM orgs " + "WHERE scope = $scope ORDER BY slug" + ), +} + + +def _row_to_dict(cur: Any) -> list[dict[str, Any]]: + cols = [d[0] for d in cur.description] if cur.description else [] + return [dict(zip(cols, r, strict=False)) for r in cur.fetchall()] + + +def _execute( + sql: str, + params: dict[str, Any] | None, + store: DuckDBStore | None, +) -> list[dict[str, Any]]: + owned = False + if store is None: + store = DuckDBStore.open() + owned = True + try: + cur = store.connect().execute(sql, params or {}) + return _row_to_dict(cur) + finally: + if owned: + store.close() + + +def run_adhoc( + sql: str, + params: dict[str, Any] | None = None, + *, + store: DuckDBStore | None = None, +) -> list[dict[str, Any]]: + _validate_adhoc(sql) + return _execute(sql, params, store) + + +def run_predefined( + name: str, + params: dict[str, Any] | None = None, + *, + store: DuckDBStore | None = None, +) -> list[dict[str, Any]]: + if name not in PREDEFINED_QUERIES: + message = ( + f"Unknown predefined query: {name!r}. " + f"Known: {sorted(PREDEFINED_QUERIES)}" + ) + raise ValueError(message) + return _execute(PREDEFINED_QUERIES[name], params, store) diff --git a/src/index/huggingface/storage/__init__.py b/src/index/huggingface/storage/__init__.py new file mode 100644 index 0000000..0d9db2f --- /dev/null +++ b/src/index/huggingface/storage/__init__.py @@ -0,0 +1 @@ +"""DuckDB storage for HuggingFace metadata.""" diff --git a/src/index/huggingface/storage/duckdb_store.py b/src/index/huggingface/storage/duckdb_store.py new file mode 100644 index 0000000..10f2c89 --- /dev/null +++ b/src/index/huggingface/storage/duckdb_store.py @@ -0,0 +1,343 @@ +"""DuckDB lifecycle, schema bootstrap, and upsert helpers.""" + +from __future__ import annotations + +import json +import logging +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import duckdb + +from src.index.huggingface.paths import get_huggingface_paths + +if TYPE_CHECKING: + from collections.abc import Iterator + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + +ENTITY_TABLES: tuple[str, ...] = ("models", "datasets", "spaces") + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +class DuckDBStore: + """Thin wrapper around DuckDB tuned for the HuggingFace schema. + + Construct with `DuckDBStore.open()` for the default repo path. + `bootstrap()` is idempotent. + """ + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open(cls, db_path: Path | None = None) -> DuckDBStore: + if db_path is None: + db_path = get_huggingface_paths().duckdb_path + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + conn = self.connect() + conn.execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + # ---- Upserts --------------------------------------------------------- + + def upsert_org( + self, + *, + slug: str, + scope: str, + namespace_kind: str = "org", + source: str = "seed", + fullname: str | None = None, + details: str | None = None, + avatar_url: str | None = None, + num_models: int | None = None, + num_datasets: int | None = None, + num_spaces: int | None = None, + num_followers: int | None = None, + raw: dict[str, Any] | None = None, + ) -> None: + raw_json = json.dumps(raw, ensure_ascii=False, default=str) if raw is not None else None + self.connect().execute( + "INSERT INTO orgs (slug, namespace_kind, source, scope, fullname, " + "details, avatar_url, num_models, num_datasets, num_spaces, " + "num_followers, raw, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (slug) DO UPDATE SET " + "namespace_kind = excluded.namespace_kind, " + "source = excluded.source, scope = excluded.scope, " + "fullname = COALESCE(excluded.fullname, orgs.fullname), " + "details = COALESCE(excluded.details, orgs.details), " + "avatar_url = COALESCE(excluded.avatar_url, orgs.avatar_url), " + "num_models = COALESCE(excluded.num_models, orgs.num_models), " + "num_datasets = COALESCE(excluded.num_datasets, orgs.num_datasets), " + "num_spaces = COALESCE(excluded.num_spaces, orgs.num_spaces), " + "num_followers = COALESCE(excluded.num_followers, orgs.num_followers), " + "raw = COALESCE(excluded.raw, orgs.raw), " + "ingested_at = excluded.ingested_at", + [ + slug, namespace_kind, source, scope, fullname, + details, avatar_url, num_models, num_datasets, num_spaces, + num_followers, raw_json, self._now(), + ], + ) + + def fetch_org(self, slug: str) -> dict[str, Any] | None: + cur = self.connect().execute("SELECT * FROM orgs WHERE slug = ?", [slug]) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + def stream_orgs_for_embedding( + self, + *, + limit: int | None = None, + ) -> Iterator[dict[str, Any]]: + """Yield org rows whose namespace text hasn't been embedded yet.""" + sql = ( + "SELECT t.* FROM orgs t WHERE NOT EXISTS (" + " SELECT 1 FROM chunks c " + " WHERE c.entity_type = 'org' AND c.repo_id = t.slug" + ")" + ) + params: list[Any] = [] + if limit is not None: + sql += " LIMIT ?" + params.append(limit) + cur = self.connect().execute(sql, params) + cols = [d[0] for d in cur.description] + rows = cur.fetchall() + for row in rows: + yield dict(zip(cols, row, strict=False)) + + def list_repo_titles_for_org(self, slug: str) -> dict[str, list[dict[str, Any]]]: + """Return per-table compact repo info used to compose the org embed text.""" + out: dict[str, list[dict[str, Any]]] = {} + for table in ENTITY_TABLES: + cur = self.connect().execute( + f"SELECT repo_id, tags, card_data FROM {table} WHERE author = ?", # noqa: S608 + [slug], + ) + cols = [d[0] for d in cur.description] + out[table] = [dict(zip(cols, r, strict=False)) for r in cur.fetchall()] + return out + + def upsert_model(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert_repo( + table="models", + cols=( + "repo_id", + "author", + "sha", + "pipeline_tag", + "library_name", + "license", + "downloads", + "downloads_all_time", + "likes", + "gated", + "private", + "created_at", + "last_modified", + ), + json_cols=("tags", "card_data", "base_models"), + row=row, + raw=raw, + ) + + def upsert_dataset(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert_repo( + table="datasets", + cols=( + "repo_id", + "author", + "sha", + "license", + "downloads", + "downloads_all_time", + "likes", + "gated", + "private", + "created_at", + "last_modified", + ), + json_cols=("tags", "card_data", "dataset_info"), + row=row, + raw=raw, + ) + + def upsert_space(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert_repo( + table="spaces", + cols=( + "repo_id", + "author", + "sha", + "sdk", + "runtime_stage", + "hardware", + "license", + "likes", + "created_at", + "last_modified", + ), + json_cols=("tags", "card_data"), + row=row, + raw=raw, + ) + + def _upsert_repo( + self, + *, + table: str, + cols: tuple[str, ...], + json_cols: tuple[str, ...], + row: dict[str, Any], + raw: dict[str, Any], + ) -> None: + all_cols = (*cols, *json_cols, "raw", "ingested_at") + placeholders = ", ".join(["?"] * len(all_cols)) + col_list = ", ".join(all_cols) + update_cols = ", ".join( + f"{c} = excluded.{c}" for c in (*cols[1:], *json_cols, "raw", "ingested_at") + ) + sql = ( + f"INSERT INTO {table} ({col_list}) VALUES ({placeholders}) " + f"ON CONFLICT ({cols[0]}) DO UPDATE SET {update_cols}" + ) + values: list[Any] = [row.get(c) for c in cols] + for jc in json_cols: + payload = row.get(jc) + values.append(json.dumps(payload, ensure_ascii=False, default=str) if payload is not None else None) + values.append(json.dumps(raw, ensure_ascii=False, default=str)) + values.append(self._now()) + self.connect().execute(sql, values) + + def upsert_chunk( + self, + *, + chunk_id: str, + entity_type: str, + repo_id: str, + chunk_index: int, + text: str, + token_count: int, + vector_id: str, + ) -> None: + self.connect().execute( + "INSERT INTO chunks " + "(chunk_id, entity_type, repo_id, chunk_index, text, " + "token_count, vector_id) VALUES (?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (chunk_id) DO UPDATE SET " + "text = excluded.text, token_count = excluded.token_count, " + "vector_id = excluded.vector_id, embedded_at = now()", + [ + chunk_id, + entity_type, + repo_id, + chunk_index, + text, + token_count, + vector_id, + ], + ) + + # ---- Reads ----------------------------------------------------------- + + def count(self, table: str) -> int: + result = self.connect().execute(f"SELECT count(*) FROM {table}").fetchone() + return int(result[0]) if result else 0 + + def stream_rows_for_embedding( + self, + entity_table: str, + *, + limit: int | None = None, + ) -> Iterator[dict[str, Any]]: + """Yield repo rows whose card hasn't been embedded yet.""" + if entity_table not in ENTITY_TABLES: + message = f"Unknown entity table: {entity_table}" + raise ValueError(message) + # Map plural → singular for chunks.entity_type comparison. + singular = {"models": "model", "datasets": "dataset", "spaces": "space"}[ + entity_table + ] + sql = ( + f"SELECT t.* FROM {entity_table} t " # noqa: S608 - table guarded above + "WHERE NOT EXISTS (" + " SELECT 1 FROM chunks c " + " WHERE c.entity_type = ? AND c.repo_id = t.repo_id" + ")" + ) + params: list[Any] = [singular] + if limit is not None: + sql += " LIMIT ?" + params.append(limit) + # Materialize upfront: the embed pipeline calls `upsert_chunk` on the + # same connection inside `flush()`, which replaces the connection's + # active cursor and clobbers our SELECT result set. + cur = self.connect().execute(sql, params) + cols = [d[0] for d in cur.description] + rows = cur.fetchall() + for row in rows: + yield dict(zip(cols, row, strict=False)) + + def repo_sha(self, entity_table: str, repo_id: str) -> str | None: + """Return the stored `sha` for a repo, or None if the repo isn't in the table.""" + if entity_table not in ENTITY_TABLES: + return None + cur = self.connect().execute( + f"SELECT sha FROM {entity_table} WHERE repo_id = ?", # noqa: S608 + [repo_id], + ) + row = cur.fetchone() + return row[0] if row else None + + def fetch_repo(self, entity_table: str, repo_id: str) -> dict[str, Any] | None: + if entity_table not in ENTITY_TABLES: + return None + cur = self.connect().execute( + f"SELECT * FROM {entity_table} WHERE repo_id = ?", # noqa: S608 + [repo_id], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + @staticmethod + def _now() -> str: + return datetime.now(tz=timezone.utc).isoformat() diff --git a/src/index/huggingface/storage/schema.sql b/src/index/huggingface/storage/schema.sql new file mode 100644 index 0000000..ddf327b --- /dev/null +++ b/src/index/huggingface/storage/schema.sql @@ -0,0 +1,112 @@ +-- Canonical DuckDB schema for the HuggingFace index module. +-- Idempotent: every statement uses IF NOT EXISTS so re-runs are safe. +-- See .internal/huggingface/PLAN.md → "Storage schema" for column rationale. + +CREATE TABLE IF NOT EXISTS orgs ( + slug TEXT PRIMARY KEY, + namespace_kind TEXT NOT NULL DEFAULT 'org', -- 'user' | 'org' + source TEXT NOT NULL DEFAULT 'seed', -- 'seed' | 'discover' + scope TEXT NOT NULL, -- 'epfl' | 'switzerland' + fullname TEXT, -- HF display name + details TEXT, -- HF org/user bio (often empty) + avatar_url TEXT, + num_models BIGINT, + num_datasets BIGINT, + num_spaces BIGINT, + num_followers BIGINT, + raw JSON, -- full HF overview payload + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +-- Additive migrations for DBs created before the orgs overview columns existed. +-- DuckDB silently no-ops `ADD COLUMN IF NOT EXISTS` when the column is already there. +ALTER TABLE orgs ADD COLUMN IF NOT EXISTS fullname TEXT; +ALTER TABLE orgs ADD COLUMN IF NOT EXISTS details TEXT; +ALTER TABLE orgs ADD COLUMN IF NOT EXISTS avatar_url TEXT; +ALTER TABLE orgs ADD COLUMN IF NOT EXISTS num_models BIGINT; +ALTER TABLE orgs ADD COLUMN IF NOT EXISTS num_datasets BIGINT; +ALTER TABLE orgs ADD COLUMN IF NOT EXISTS num_spaces BIGINT; +ALTER TABLE orgs ADD COLUMN IF NOT EXISTS num_followers BIGINT; +ALTER TABLE orgs ADD COLUMN IF NOT EXISTS raw JSON; + +CREATE TABLE IF NOT EXISTS models ( + repo_id TEXT PRIMARY KEY, + author TEXT, + sha TEXT, + pipeline_tag TEXT, + library_name TEXT, + license TEXT, + downloads BIGINT, + downloads_all_time BIGINT, + likes BIGINT, + gated BOOLEAN, + private BOOLEAN, + created_at TIMESTAMP, + last_modified TIMESTAMP, + tags JSON, + card_data JSON, + base_models JSON, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS datasets ( + repo_id TEXT PRIMARY KEY, + author TEXT, + sha TEXT, + license TEXT, + downloads BIGINT, + downloads_all_time BIGINT, + likes BIGINT, + gated BOOLEAN, + private BOOLEAN, + created_at TIMESTAMP, + last_modified TIMESTAMP, + tags JSON, + card_data JSON, + dataset_info JSON, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS spaces ( + repo_id TEXT PRIMARY KEY, + author TEXT, + sha TEXT, + sdk TEXT, + runtime_stage TEXT, + hardware TEXT, + license TEXT, + likes BIGINT, + created_at TIMESTAMP, + last_modified TIMESTAMP, + tags JSON, + card_data JSON, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +-- chunk_id is deterministic: uuid5(NAMESPACE_URL, "||") +-- so the primary key alone provides the (entity_type, repo_id, chunk_index) +-- uniqueness guarantee. See `embed/pipeline.py:_chunk_id`. +CREATE TABLE IF NOT EXISTS chunks ( + chunk_id TEXT PRIMARY KEY, + entity_type TEXT NOT NULL, -- 'model' | 'dataset' | 'space' + repo_id TEXT NOT NULL, + chunk_index INTEGER NOT NULL, + text TEXT NOT NULL, + token_count INTEGER NOT NULL, + vector_id TEXT NOT NULL, + embedded_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS idx_models_author ON models (author); +CREATE INDEX IF NOT EXISTS idx_models_pipeline_tag ON models (pipeline_tag); +CREATE INDEX IF NOT EXISTS idx_models_library_name ON models (library_name); +CREATE INDEX IF NOT EXISTS idx_models_license ON models (license); +CREATE INDEX IF NOT EXISTS idx_datasets_author ON datasets (author); +CREATE INDEX IF NOT EXISTS idx_datasets_license ON datasets (license); +CREATE INDEX IF NOT EXISTS idx_spaces_author ON spaces (author); +CREATE INDEX IF NOT EXISTS idx_spaces_sdk ON spaces (sdk); +CREATE INDEX IF NOT EXISTS idx_orgs_scope ON orgs (scope); +CREATE INDEX IF NOT EXISTS idx_chunks_entity ON chunks (entity_type, repo_id); diff --git a/src/index/huggingface/vector/__init__.py b/src/index/huggingface/vector/__init__.py new file mode 100644 index 0000000..5a5d2f3 --- /dev/null +++ b/src/index/huggingface/vector/__init__.py @@ -0,0 +1 @@ +"""Qdrant vector storage for HuggingFace cards.""" diff --git a/src/index/huggingface/vector/qdrant_store.py b/src/index/huggingface/vector/qdrant_store.py new file mode 100644 index 0000000..94adb5c --- /dev/null +++ b/src/index/huggingface/vector/qdrant_store.py @@ -0,0 +1,124 @@ +"""Qdrant client wrapper: collection bootstrap, upsert, search. + +Per-entity collections: hf_models, hf_datasets, hf_spaces, hf_orgs. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from qdrant_client import QdrantClient, models + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + +LOGGER = logging.getLogger(__name__) + +# Qdrant collection names per HuggingFace entity table. +COLLECTION_FOR_TABLE: dict[str, str] = { + "models": "hf_models", + "datasets": "hf_datasets", + "spaces": "hf_spaces", + "orgs": "hf_orgs", +} + + +class QdrantStore: + """Per-entity collection bootstrap + upsert + filtered search.""" + + def __init__(self, config: HuggingFaceIndexConfig) -> None: + self._config = config + self._client = QdrantClient( + url=config.qdrant.url, + api_key=config.qdrant.api_key, + prefer_grpc=config.qdrant.prefer_grpc, + ) + self._dim = config.rcp.embedding_dim + + @property + def client(self) -> QdrantClient: + return self._client + + def ensure_collection(self, name: str) -> None: + if self._client.collection_exists(name): + return + self._client.create_collection( + collection_name=name, + vectors_config=models.VectorParams( + size=self._dim, + distance=models.Distance.COSINE, + ), + ) + LOGGER.info("created qdrant collection %s (dim=%d)", name, self._dim) + + def upsert_points( + self, + collection: str, + *, + ids: list[str], + vectors: list[list[float]], + payloads: list[dict[str, Any]], + ) -> None: + if not (len(ids) == len(vectors) == len(payloads)): + message = "ids/vectors/payloads must be the same length" + raise ValueError(message) + if not ids: + return + self.ensure_collection(collection) + points = [ + models.PointStruct(id=pid, vector=vec, payload=payload) + for pid, vec, payload in zip(ids, vectors, payloads, strict=False) + ] + self._client.upsert(collection_name=collection, points=points) + + def search( + self, + collection: str, + *, + query_vector: list[float], + top_k: int = 50, + filter_payload: dict[str, Any] | None = None, + ) -> list[dict[str, Any]]: + self.ensure_collection(collection) + qdrant_filter = self._build_filter(filter_payload) if filter_payload else None + hits = self._client.query_points( + collection_name=collection, + query=query_vector, + limit=top_k, + query_filter=qdrant_filter, + with_payload=True, + ).points + return [ + {"id": str(p.id), "score": float(p.score), "payload": p.payload or {}} + for p in hits + ] + + def count(self, collection: str) -> int: + if not self._client.collection_exists(collection): + return 0 + return int(self._client.count(collection_name=collection, exact=True).count) + + @staticmethod + def _build_filter(payload: dict[str, Any]) -> models.Filter: + must: list[models.Condition] = [] + for key, value in payload.items(): + if isinstance(value, dict) and ("gte" in value or "lte" in value): + must.append( + models.FieldCondition( + key=key, + range=models.Range( + gte=value.get("gte"), + lte=value.get("lte"), + ), + ), + ) + elif isinstance(value, list): + must.append( + models.FieldCondition(key=key, match=models.MatchAny(any=value)), + ) + else: + must.append( + models.FieldCondition(key=key, match=models.MatchValue(value=value)), + ) + return models.Filter(must=must) diff --git a/src/index/infoscience/__init__.py b/src/index/infoscience/__init__.py new file mode 100644 index 0000000..465eb35 --- /dev/null +++ b/src/index/infoscience/__init__.py @@ -0,0 +1,16 @@ +"""Infoscience EPFL harvest + RAG index. + +Pipeline (each stage resumable from disk): + + discover ──► fetch_text ──► extract_matches ──► extract_relations + │ + ▼ + fetch_related + │ + chunk ──► embed ──► store + │ + ▼ + query (filter → vector → rerank) + +Entry point: `python -m src.index.infoscience `. +""" diff --git a/src/index/infoscience/__main__.py b/src/index/infoscience/__main__.py new file mode 100644 index 0000000..b58274b --- /dev/null +++ b/src/index/infoscience/__main__.py @@ -0,0 +1,6 @@ +"""Module entrypoint: `python -m src.index.infoscience `.""" + +from .cli import main + +if __name__ == "__main__": + main() diff --git a/src/index/infoscience/_embed_worker.py b/src/index/infoscience/_embed_worker.py new file mode 100644 index 0000000..faaf022 --- /dev/null +++ b/src/index/infoscience/_embed_worker.py @@ -0,0 +1,89 @@ +"""Subprocess worker for the streaming chunks-embed pipeline. + +Runs one group's worth of (article_uuids → chunks → embed → upsert) and +exits, so the OS reclaims every byte of Python heap, RCP connection +buffers, and Qdrant client state. Used by `build_chunks` when +`INFOSCIENCE_EMBED_WORKER_MODE=subprocess` is set. + +Protocol: +- stdin: JSON list of article UUIDs. +- stdout (last line): JSON `{"upserted": int, "chunks": int, "skipped_no_text": int}`. +- exit 0 on success; non-zero on any error (parent re-raises). + +Why a worker instead of just `gc.collect() + malloc_trim(0)`: +- `gc + malloc_trim` reclaim ~30-50% of free pages but won't undo + fragmentation accumulated across many groups. +- A worker's RSS dies with `os.exit`. Predictable, bounded peak. +- Cost: ~3-5 s per-group startup (importing tiktoken, httpx, + qdrant_client, pydantic). For ~80 groups, that's ~7 min added — + acceptable to avoid 14+ GB RSS slowly creeping toward swap. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import sys +from typing import List + +from .build import _articles_with_matches, _chunks_for_article +from .config import load_config +from .embed import RCPEmbedder +from .extract_matches import matches_by_uuid +from .paths import text_dir +from .store import CHUNKS_COLLECTION, QdrantStore, upsert_chunks + + +async def _run(uuids: List[str]) -> dict: + cfg = load_config() + matches = matches_by_uuid() + matches_subset = {u: matches[u] for u in uuids if u in matches} + articles = _articles_with_matches(matches_subset) + + chunk_records = [] + skipped_no_text = 0 + for article in articles: + tp = text_dir() / f"{article.article_uuid}.txt" + if not tp.exists(): + skipped_no_text += 1 + continue + text = tp.read_text(encoding="utf-8", errors="replace") + chunk_records.extend(_chunks_for_article(cfg, article, text)) + + if not chunk_records: + return {"upserted": 0, "chunks": 0, "skipped_no_text": skipped_no_text} + + store = QdrantStore.from_config(cfg) + store.ensure_collection(CHUNKS_COLLECTION) + + async with RCPEmbedder(cfg.rcp) as embedder: + texts = [c.text for c in chunk_records] + embeddings = await embedder.embed_passages_batched(texts) + + upserted = upsert_chunks(store, chunk_records, embeddings) + return { + "upserted": upserted, + "chunks": len(chunk_records), + "skipped_no_text": skipped_no_text, + } + + +def main() -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s %(message)s", + ) + payload = sys.stdin.read() + uuids = json.loads(payload) if payload.strip() else [] + if not isinstance(uuids, list): + sys.stderr.write("expected a JSON list of article UUIDs on stdin\n") + return 2 + summary = asyncio.run(_run([str(u) for u in uuids])) + # Last line of stdout = JSON summary (parent reads only the last line). + print(json.dumps(summary)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/index/infoscience/_federated.py b/src/index/infoscience/_federated.py new file mode 100644 index 0000000..e339180 --- /dev/null +++ b/src/index/infoscience/_federated.py @@ -0,0 +1,87 @@ +"""Infoscience registration with the federated discover/hydrate registries. + +Thin wrapper. The underlying ``infoscience`` module already supports +``discover`` (terms-based async search, persists state to disk) and +``ingest-duckdb`` (build the DuckDB store from discovered links). v1 +exposes the search-driven discover path; hydrate by UUID/handle is a +TODO (current ingest is bulk-only). +""" + +from __future__ import annotations + +import logging +from typing import Any, Iterator + +from src.index._federated.dh_registry import register_discoverer, register_hydrator +from src.index._federated.protocols import ( + HydrationSummary, + IndexDiscoverer, + IndexHydrator, + Seed, +) + +LOGGER = logging.getLogger(__name__) + + +class InfoscienceDiscoverer: + name = "infoscience" + accepted_sources = ("from-search",) + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + if source not in self.accepted_sources: + message = f"Infoscience: unknown source {source!r}. Accepted: {list(self.accepted_sources)}" + raise ValueError(message) + + # Re-use the existing async discover entrypoint. Returns Infoscience + # UUIDs / handle URLs the user could feed back into hydrate later. + # For v1 we yield seeds based on the existing on-disk state file + # so that re-discoveries are cached. + from src.index.infoscience.discover import discover_state_path + + state_path = discover_state_path() + if not state_path.exists(): + LOGGER.warning( + "infoscience discover state %s not found; " + "run `python -m src.index.infoscience discover --terms ...` first", + state_path, + ) + return + # Yield each link captured in the state file as a seed. + import json + try: + data = json.loads(state_path.read_text()) + except Exception as exc: # noqa: BLE001 + LOGGER.warning("failed to read %s: %s", state_path, exc) + return + items = data.get("links") or data.get("items") or [] + for item in items: + url = item if isinstance(item, str) else item.get("url") + if not url: + continue + yield Seed( + id=url, + seed_type="infoscience_url", + source="from-search", + hint={"query": opts.get("query")}, + ) + + +class InfoscienceHydrator: + name = "infoscience" + accepted_seed_types = ("infoscience_url",) + + def hydrate(self, seeds, *, only_unfetched: bool = True) -> HydrationSummary: + # TODO: lift the existing build-from-links code into a callable + # `hydrate_from_urls(urls)` so we can route Seeds here. v1 returns + # a no-op summary. + materialised = list(seeds) + LOGGER.warning( + "infoscience: hydrate is a stub (received %d seeds). " + "Use `python -m src.index.infoscience ingest-duckdb` for now.", + len(materialised), + ) + return HydrationSummary(skipped_existing=len(materialised)) + + +register_discoverer(InfoscienceDiscoverer()) +register_hydrator(InfoscienceHydrator()) diff --git a/src/index/infoscience/build.py b/src/index/infoscience/build.py new file mode 100644 index 0000000..e403e31 --- /dev/null +++ b/src/index/infoscience/build.py @@ -0,0 +1,487 @@ +"""Build/embed stage: chunk, embed, and populate ChromaDB collections. + +Scopes: + * `chunks`: text/.txt → chunks → embeddings → infoscience_chunks + * `articles`: raw/items/.json + matches → infoscience_articles + * `persons`: raw/persons/.json → infoscience_persons + * `orgs`: raw/organizations/.json → infoscience_organizations + * `all`: every scope above, in order + +Each entity row gets an embedding when there is non-trivial source text; +otherwise a zero-vector placeholder is written so the row is still +queryable via metadata/lexical filters (vector search effectively +ignores it via the cosine score). +""" + +from __future__ import annotations + +import asyncio +import gc +import json +import logging +import os +import subprocess +import sys +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Sequence + +from .chunker import chunk_text +from .config import InfoscienceIndexConfig +from .embed import RCPEmbedder +from .extract_matches import matches_by_uuid +from .extract_relations import load_relations +from .models import ( + ArticleRecord, + ChunkRecord, + OrganizationRecord, + PersonRecord, +) +from .parsers import parse_article, parse_organization, parse_person +from .paths import ( + raw_items_dir, + raw_organizations_dir, + raw_persons_dir, + text_dir, +) +from .store import ( + ARTICLES_COLLECTION, + CHUNKS_COLLECTION, + ORGANIZATIONS_COLLECTION, + PERSONS_COLLECTION, + QdrantStore, + upsert_articles, + upsert_chunks, + upsert_organizations, + upsert_persons, +) + +logger = logging.getLogger(__name__) + + +def _release_memory() -> None: + """Force CPython to drop freed pages back to the OS. + + `gc.collect()` on its own only collects unreachable objects; it does + not return arena pages to glibc. The follow-up `malloc_trim(0)` + asks glibc to release any free pages above the heap top. Together + they keep RSS bounded across the long streaming chunks loop. + """ + gc.collect() + try: + import ctypes + ctypes.CDLL("libc.so.6").malloc_trim(0) + except Exception: # noqa: BLE001 (Linux/glibc-specific; ignore elsewhere) + pass + + +def _load_json(path: Path) -> Optional[dict]: + if not path.exists(): + return None + try: + return json.loads(path.read_text(encoding="utf-8")) + except Exception: + logger.exception("Failed to parse %s", path) + return None + + +def _chunks_for_article( + cfg: InfoscienceIndexConfig, + article: ArticleRecord, + text: str, +) -> List[ChunkRecord]: + pieces = chunk_text(text, cfg.chunking) + out: List[ChunkRecord] = [] + for i, piece in enumerate(pieces): + out.append(ChunkRecord( + chunk_id=f"{article.article_uuid}::{i}", + article_uuid=article.article_uuid, + chunk_index=i, + text=piece, + title=article.title, + abstract=article.abstract, + authors=article.authors, + author_uuids=article.author_uuids, + doi=article.doi, + publication_date=article.publication_date, + year=article.year, + publication_type=article.publication_type, + language=article.language, + subjects=article.subjects, + keywords=article.keywords, + lab=article.lab, + lab_uuid=article.lab_uuid, + org_uuids=article.org_uuids, + infoscience_url=article.infoscience_url, + matched_urls=article.matched_urls, + )) + return out + + +def _articles_with_matches(matches_map: dict) -> List[ArticleRecord]: + out: List[ArticleRecord] = [] + for uuid, match in matches_map.items(): + item = _load_json(raw_items_dir() / f"{uuid}.json") + if item is None: + continue + out.append(parse_article(item, matched_urls=match.matched_urls)) + return out + + +def _build_article_to_persons() -> Dict[str, List[str]]: + rev: Dict[str, List[str]] = defaultdict(list) + for rel in load_relations(): + for p in rel.person_uuids: + rev[p].append(rel.article_uuid) + return rev + + +def _build_article_to_orgs() -> Dict[str, List[str]]: + rev: Dict[str, List[str]] = defaultdict(list) + for rel in load_relations(): + for o in rel.org_uuids: + rev[o].append(rel.article_uuid) + return rev + + +def _person_text(rec: PersonRecord) -> str: + parts: List[str] = [] + if rec.name: + parts.append(rec.name) + if rec.position: + parts.append(rec.position) + if rec.primary_affiliation: + parts.append(rec.primary_affiliation) + if rec.research_interests: + parts.append(", ".join(rec.research_interests)) + if rec.biography: + parts.append(rec.biography) + return "\n\n".join(parts).strip() + + +def _organization_text(rec: OrganizationRecord) -> str: + parts: List[str] = [] + if rec.name: + header = rec.name + if rec.acronym: + header = f"{rec.name} ({rec.acronym})" + parts.append(header) + if rec.parent_org_chain_names: + parts.append("Parent: " + " > ".join(rec.parent_org_chain_names)) + if rec.description: + parts.append(rec.description) + return "\n\n".join(parts).strip() + + +def _article_embed_text(rec: ArticleRecord) -> str: + parts: List[str] = [] + if rec.title: + parts.append(rec.title) + if rec.abstract: + parts.append(rec.abstract) + return "\n\n".join(parts).strip() + + +# --------------------------------------------------------------------------- +# Stage entry points +# --------------------------------------------------------------------------- + + +async def build_chunks(cfg: InfoscienceIndexConfig) -> dict: + """Streaming embed: process articles in groups, embed-then-upsert each + group, free memory, repeat. Resumable across crashes — if Qdrant + already has any chunk for an article, the article is skipped on the + next run. + + The previous all-at-once design buffered ~225k chunks in memory + (peaked at ~21 GB RSS and was killed when an RCP DNS hiccup raised + EmbedError mid-run, losing the entire embedding pass). + """ + matches = matches_by_uuid() + articles = _articles_with_matches(matches) + if not articles: + logger.warning("No matched articles. Run discover/fetch-text/extract-matches first.") + return {"chunks": 0, "articles": 0} + + store = QdrantStore.from_config(cfg) + store.ensure_collection(CHUNKS_COLLECTION) + + # Resume: skip articles that already have any chunk in Qdrant. + already_done: set[str] = set() + try: + next_offset = None + while True: + points, next_offset = store.client.scroll( + collection_name=CHUNKS_COLLECTION, + limit=4096, + with_payload=["article_uuid"], + with_vectors=False, + offset=next_offset, + ) + for p in points: + au = (p.payload or {}).get("article_uuid") + if au: + already_done.add(au) + if next_offset is None: + break + logger.info("build_chunks resume: %d articles already in qdrant", len(already_done)) + except Exception as exc: # noqa: BLE001 + logger.warning("build_chunks resume scroll failed (will reprocess all): %s", exc) + already_done = set() + + todo = [a for a in articles if a.article_uuid not in already_done] + logger.info( + "build_chunks: %d articles total, %d todo (%d skipped as already-embedded)", + len(articles), len(todo), len(articles) - len(todo), + ) + + group_size = max(1, int(getattr(cfg.chunking, "stream_group_size", 100) or 100)) + total_chunks = 0 + total_upserted = 0 + skipped_no_text = 0 + chunk_counts_per_article: Dict[str, int] = {} + worker_mode = os.getenv("INFOSCIENCE_EMBED_WORKER_MODE", "inproc").lower() + n_groups = (len(todo) + group_size - 1) // group_size + + if worker_mode == "subprocess": + # OS-level reclaim: each group runs in a fresh subprocess and dies + # cleanly, returning all heap pages to the kernel. Adds ~3-5 s of + # interpreter+import startup per group. + for start in range(0, len(todo), group_size): + group = todo[start : start + group_size] + uuids_payload = json.dumps([a.article_uuid for a in group]) + try: + proc = subprocess.run( + [sys.executable, "-m", "src.index.infoscience._embed_worker"], + input=uuids_payload, + capture_output=True, + text=True, + timeout=1800, + check=False, + env={**os.environ, "PYTHONPATH": os.environ.get("PYTHONPATH", ".")}, + ) + except subprocess.TimeoutExpired: + logger.exception( + "build_chunks: worker group %d timed out (will retry next run)", + (start // group_size) + 1, + ) + raise + if proc.returncode != 0: + logger.error( + "build_chunks: worker group %d failed (rc=%d)\n--stderr tail--\n%s", + (start // group_size) + 1, proc.returncode, + "\n".join(proc.stderr.splitlines()[-30:]), + ) + raise RuntimeError(f"embed worker failed (rc={proc.returncode})") + # Last non-empty stdout line is the JSON summary. + summary_line = next( + (ln for ln in reversed(proc.stdout.splitlines()) if ln.strip()), + "{}", + ) + try: + summary = json.loads(summary_line) + except json.JSONDecodeError: + logger.warning("worker stdout was not JSON: %r", summary_line) + summary = {} + upserted = int(summary.get("upserted") or 0) + chunks_in_group = int(summary.get("chunks") or 0) + skipped_no_text += int(summary.get("skipped_no_text") or 0) + total_chunks += chunks_in_group + total_upserted += upserted + logger.info( + "build_chunks[worker]: group %d/%d (%d articles) -> %d chunks upserted (cum %d)", + (start // group_size) + 1, n_groups, + len(group), chunks_in_group, total_chunks, + ) + else: + async with RCPEmbedder(cfg.rcp) as embedder: + for start in range(0, len(todo), group_size): + group = todo[start : start + group_size] + chunk_records: List[ChunkRecord] = [] + for article in group: + text_path = text_dir() / f"{article.article_uuid}.txt" + if not text_path.exists(): + skipped_no_text += 1 + continue + text = text_path.read_text(encoding="utf-8", errors="replace") + recs = _chunks_for_article(cfg, article, text) + chunk_counts_per_article[article.article_uuid] = len(recs) + chunk_records.extend(recs) + if not chunk_records: + continue + texts = [c.text for c in chunk_records] + try: + embeddings = await embedder.embed_passages_batched(texts) + except Exception: + logger.exception( + "build_chunks: embed failed at group %d-%d (will be retried on next run)", + start, start + len(group), + ) + raise + upserted = upsert_chunks(store, chunk_records, embeddings) + total_chunks += len(chunk_records) + total_upserted += upserted + logger.info( + "build_chunks: group %d/%d (%d articles) -> %d chunks upserted (cum %d)", + (start // group_size) + 1, n_groups, + len(group), len(chunk_records), total_chunks, + ) + # Free per-group state explicitly to keep the working set bounded. + del chunk_records, texts, embeddings + _release_memory() + + return { + "chunks": total_chunks, + "chunks_upserted": total_upserted, + "articles_total": len(articles), + "articles_skipped_already_embedded": len(articles) - len(todo), + "articles_skipped_no_text": skipped_no_text, + "articles_processed": len(todo) - skipped_no_text, + "worker_mode": worker_mode, + } + + +async def build_articles(cfg: InfoscienceIndexConfig) -> dict: + matches = matches_by_uuid() + articles = _articles_with_matches(matches) + if not articles: + return {"articles": 0} + + # Derive chunk_count per article from the on-disk text files (no + # round-trip to Qdrant needed; chunker is deterministic so the count + # we'd insert in build_chunks matches what we'd see in the store). + chunk_counts: Dict[str, int] = {} + for art in articles: + text_path = text_dir() / f"{art.article_uuid}.txt" + if not text_path.exists(): + continue + body = text_path.read_text(encoding="utf-8") + chunk_counts[art.article_uuid] = len(chunk_text(body, cfg.chunking)) + + for art in articles: + art.chunk_count = int(chunk_counts.get(art.article_uuid, 0)) + + store = QdrantStore.from_config(cfg) + store.ensure_collection(ARTICLES_COLLECTION) + + texts = [_article_embed_text(a) for a in articles] + have_text = [bool(t) for t in texts] + to_embed = [t for t, ok in zip(texts, have_text) if ok] + + if to_embed: + async with RCPEmbedder(cfg.rcp) as embedder: + embeddings_iter = await embedder.embed_passages_batched(to_embed) + else: + embeddings_iter = [] + + embeddings: List[Optional[Sequence[float]]] = [] + embed_pos = 0 + for ok in have_text: + if ok: + embeddings.append(embeddings_iter[embed_pos]) + embed_pos += 1 + else: + embeddings.append(None) + + upsert_articles(store, articles, embeddings, cfg.rcp.embedding_dim) + return {"articles": len(articles), "with_embedding": len(to_embed)} + + +async def build_persons(cfg: InfoscienceIndexConfig) -> dict: + files = sorted(raw_persons_dir().glob("*.json")) + if not files: + return {"persons": 0} + + rev = _build_article_to_persons() + records: List[PersonRecord] = [] + for f in files: + item = _load_json(f) + if item is None: + continue + rec = parse_person(item) + rec.related_article_uuids = rev.get(rec.person_uuid, []) + records.append(rec) + + store = QdrantStore.from_config(cfg) + store.ensure_collection(PERSONS_COLLECTION) + + texts = [_person_text(r) for r in records] + have_text = [bool(t) for t in texts] + to_embed = [t for t, ok in zip(texts, have_text) if ok] + + if to_embed: + async with RCPEmbedder(cfg.rcp) as embedder: + embeddings_iter = await embedder.embed_passages_batched(to_embed) + else: + embeddings_iter = [] + + embeddings: List[Optional[Sequence[float]]] = [] + embed_pos = 0 + for ok in have_text: + if ok: + embeddings.append(embeddings_iter[embed_pos]) + embed_pos += 1 + else: + embeddings.append(None) + + upsert_persons(store, records, embeddings, cfg.rcp.embedding_dim) + return {"persons": len(records), "with_embedding": len(to_embed)} + + +async def build_organizations(cfg: InfoscienceIndexConfig) -> dict: + files = sorted(raw_organizations_dir().glob("*.json")) + if not files: + return {"organizations": 0} + + rev = _build_article_to_orgs() + records: List[OrganizationRecord] = [] + for f in files: + item = _load_json(f) + if item is None: + continue + rec = parse_organization(item) + rec.related_article_uuids = rev.get(rec.org_uuid, []) + records.append(rec) + + store = QdrantStore.from_config(cfg) + store.ensure_collection(ORGANIZATIONS_COLLECTION) + + texts = [_organization_text(r) for r in records] + have_text = [bool(t) for t in texts] + to_embed = [t for t, ok in zip(texts, have_text) if ok] + + if to_embed: + async with RCPEmbedder(cfg.rcp) as embedder: + embeddings_iter = await embedder.embed_passages_batched(to_embed) + else: + embeddings_iter = [] + + embeddings: List[Optional[Sequence[float]]] = [] + embed_pos = 0 + for ok in have_text: + if ok: + embeddings.append(embeddings_iter[embed_pos]) + embed_pos += 1 + else: + embeddings.append(None) + + upsert_organizations(store, records, embeddings, cfg.rcp.embedding_dim) + return {"organizations": len(records), "with_embedding": len(to_embed)} + + +async def build(cfg: InfoscienceIndexConfig, *, scope: str = "all") -> dict: + summary: dict = {"scope": scope} + if scope in ("chunks", "all"): + summary["chunks"] = await build_chunks(cfg) + if scope in ("articles", "all"): + summary["articles"] = await build_articles(cfg) + if scope in ("persons", "all"): + summary["persons"] = await build_persons(cfg) + if scope in ("orgs", "all"): + summary["organizations"] = await build_organizations(cfg) + logger.info("build: %s", json.dumps(summary, default=str)) + return summary + + +def run(cfg: InfoscienceIndexConfig, *, scope: str = "all") -> dict: + return asyncio.run(build(cfg, scope=scope)) diff --git a/src/index/infoscience/chunker.py b/src/index/infoscience/chunker.py new file mode 100644 index 0000000..33acc65 --- /dev/null +++ b/src/index/infoscience/chunker.py @@ -0,0 +1,60 @@ +"""Token-aware text chunker. + +Greedy paragraph-aware splitter using the configured tiktoken encoding. +Chunks target `chunking.size_tokens` with `chunking.overlap_tokens` of +trailing context carried into the next chunk. +""" + +from __future__ import annotations + +from typing import List + +import tiktoken + +from .config import ChunkingConfig + +_PARAGRAPH_SEP = "\n\n" + + +def _split_paragraphs(text: str) -> List[str]: + paras = [p.strip() for p in text.split(_PARAGRAPH_SEP)] + return [p for p in paras if p] + + +def chunk_text(text: str, cfg: ChunkingConfig) -> List[str]: + """Return a list of chunk strings.""" + if not text.strip(): + return [] + enc = tiktoken.get_encoding(cfg.tokenizer) + paragraphs = _split_paragraphs(text) + if not paragraphs: + return [] + + chunks: List[str] = [] + current_tokens: List[int] = [] + for para in paragraphs: + para_tokens = enc.encode(para) + # If a single paragraph exceeds the budget, flush and slice it. + if len(para_tokens) > cfg.size_tokens: + if current_tokens: + chunks.append(enc.decode(current_tokens)) + current_tokens = current_tokens[-cfg.overlap_tokens:] if cfg.overlap_tokens else [] + for i in range(0, len(para_tokens), cfg.size_tokens): + window = para_tokens[i : i + cfg.size_tokens] + chunks.append(enc.decode(window)) + current_tokens = para_tokens[-cfg.overlap_tokens:] if cfg.overlap_tokens else [] + continue + + sep_tokens = enc.encode(_PARAGRAPH_SEP) if current_tokens else [] + if len(current_tokens) + len(sep_tokens) + len(para_tokens) <= cfg.size_tokens: + current_tokens.extend(sep_tokens) + current_tokens.extend(para_tokens) + else: + chunks.append(enc.decode(current_tokens)) + tail = current_tokens[-cfg.overlap_tokens:] if cfg.overlap_tokens else [] + current_tokens = list(tail) + sep_tokens + para_tokens + + if current_tokens: + chunks.append(enc.decode(current_tokens)) + + return [c.strip() for c in chunks if c.strip()] diff --git a/src/index/infoscience/cli.py b/src/index/infoscience/cli.py new file mode 100644 index 0000000..471af73 --- /dev/null +++ b/src/index/infoscience/cli.py @@ -0,0 +1,256 @@ +"""Click-based CLI for the Infoscience indexer. + +Run via `python -m src.index.infoscience `. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from pathlib import Path +from typing import Optional + +import click + +from . import discover as discover_stage +from . import extract_matches as extract_matches_stage +from . import extract_relations as extract_relations_stage +from . import fetch_related as fetch_related_stage +from . import text_fetch as text_fetch_stage +from .config import DEFAULT_CONFIG_PATH, load_config +from .paths import ( + discover_state_path, + infoscience_data_dir, + matches_path, + relations_path, +) + +# `build` (LanceDB writes) and `pipeline` (LanceDB reads) are imported +# lazily inside their command bodies so the lighter stages don't require +# lancedb to be installed/loadable. + +logger = logging.getLogger(__name__) + + +def _setup_logging(verbose: bool) -> None: + level = logging.DEBUG if verbose else logging.INFO + logging.basicConfig( + level=level, + format="%(asctime)s %(name)s %(levelname)s %(message)s", + ) + + +@click.group(help="Infoscience EPFL harvest + RAG indexer.") +@click.option("--config", "config_path", type=click.Path(path_type=Path), + default=None, help=f"Path to YAML config (default {DEFAULT_CONFIG_PATH}).") +@click.option("-v", "--verbose", is_flag=True, help="Verbose logging.") +@click.pass_context +def cli(ctx: click.Context, config_path: Optional[Path], verbose: bool) -> None: + _setup_logging(verbose) + ctx.ensure_object(dict) + ctx.obj["config"] = load_config(config_path) + + +@cli.command() +@click.option("--terms", default=None, + help="Comma-separated Solr fulltext terms (overrides config).") +@click.option("--limit", type=int, default=None, help="Stop after N new items.") +@click.pass_context +def discover(ctx: click.Context, terms: Optional[str], limit: Optional[int]) -> None: + """Solr fulltext search → raw item JSON dumps.""" + cfg = ctx.obj["config"] + term_list = [t.strip() for t in terms.split(",")] if terms else None + summary = discover_stage.run(cfg, terms=term_list, limit=limit) + click.echo(json.dumps(summary, indent=2)) + + +@cli.command("fetch-text") +@click.option("--refresh", is_flag=True, help="Re-download even if file exists.") +@click.pass_context +def fetch_text(ctx: click.Context, refresh: bool) -> None: + """Download TEXT bundle plaintext for each discovered item.""" + cfg = ctx.obj["config"] + summary = text_fetch_stage.run(cfg, refresh=refresh) + click.echo(json.dumps(summary, indent=2)) + + +@cli.command("extract-matches") +@click.pass_context +def extract_matches(ctx: click.Context) -> None: + """Regex-extract GitHub/HuggingFace URLs from fetched text.""" + cfg = ctx.obj["config"] + summary = extract_matches_stage.run(cfg) + click.echo(json.dumps(summary, indent=2)) + + +@cli.command("extract-relations") +@click.pass_context +def extract_relations(ctx: click.Context) -> None: + """Pull Person/Org authority UUIDs from matched articles.""" + cfg = ctx.obj["config"] + summary = extract_relations_stage.run(cfg) + click.echo(json.dumps(summary, indent=2)) + + +@cli.command("fetch-related") +@click.option("--type", "kind", type=click.Choice(["person", "org", "all"]), + default="all") +@click.option("--refresh", is_flag=True) +@click.pass_context +def fetch_related(ctx: click.Context, kind: str, refresh: bool) -> None: + """Fetch raw JSON for each Person/OrgUnit referenced by matched articles.""" + cfg = ctx.obj["config"] + summary = fetch_related_stage.run(cfg, kind=kind, refresh=refresh) + click.echo(json.dumps(summary, indent=2)) + + +@cli.command() +@click.option("--scope", type=click.Choice(["chunks", "articles", "persons", "orgs", "all"]), + default="all") +@click.option("--batch", type=int, default=None, help="Override embed batch size.") +@click.pass_context +def embed(ctx: click.Context, scope: str, batch: Optional[int]) -> None: + """Chunk, embed, and populate the LanceDB tables.""" + from . import build as build_stage # lazy: requires lancedb + + cfg = ctx.obj["config"] + if batch is not None: + cfg.rcp.batch_size = batch + summary = build_stage.run(cfg, scope=scope) + click.echo(json.dumps(summary, indent=2, default=str)) + + +@cli.command("query") +@click.argument("text") +@click.option("--target", type=click.Choice(["chunks", "articles", "persons", "organizations"]), + default="chunks") +@click.option("--where", default=None, + help="ChromaDB filter as JSON, e.g. '{\"year\": {\"$gte\": 2022}, \"has_github_match\": true}'.") +@click.option("--top-k", type=int, default=50) +@click.option("--top-n", type=int, default=10) +@click.option("--mode", type=click.Choice(["hybrid", "vector-only", "lexical", "filter-only"]), + default="hybrid") +@click.option("--no-rerank", is_flag=True, help="Force vector-only mode.") +@click.option("--with-authors", is_flag=True) +@click.option("--with-orgs", is_flag=True) +@click.pass_context +def query_cmd( + ctx: click.Context, + text: str, + target: str, + where: Optional[str], + top_k: int, + top_n: int, + mode: str, + no_rerank: bool, + with_authors: bool, + with_orgs: bool, +) -> None: + """Query the index. Filter → vector → rerank by default.""" + from .pipeline import query # lazy: requires chromadb + + cfg = ctx.obj["config"] + where_dict = json.loads(where) if where else None + if no_rerank and mode == "hybrid": + mode = "vector-only" + result = asyncio.run(query( + cfg, text, target=target, where=where_dict, top_k=top_k, top_n=top_n, + mode=mode, with_authors=with_authors, with_orgs=with_orgs, + )) + click.echo(json.dumps({ + "target": result.target, + "rows": result.rows, + "related_persons": result.related_persons, + "related_organizations": result.related_organizations, + }, indent=2, default=str)) + + +@cli.command("ingest-duckdb") +@click.option( + "--links-dump", + type=click.Path(path_type=Path), + default=None, + help="Optional path to a scripts/dump_link_articles.py output to merge " + "into article_links.", +) +@click.pass_context +def ingest_duckdb(ctx: click.Context, links_dump: Optional[Path]) -> None: + """Ingest raw/{items,persons,organizations}/*.json into the DuckDB store.""" + from .storage import DuckDBStore + from .storage.ingest_raw import ingest_all + + store = DuckDBStore.open() + try: + summary = ingest_all(store, links_dump=links_dump) + finally: + store.close() + click.echo(json.dumps(summary, indent=2, default=str)) + + +@cli.command() +@click.pass_context +def status(ctx: click.Context) -> None: + """Show counts and paths for each stage.""" + cfg = ctx.obj["config"] + data_dir = infoscience_data_dir() + raw = data_dir / "raw" + counts = { + "data_dir": str(data_dir), + "raw_items": len(list((raw / "items").glob("*.json"))) if (raw / "items").exists() else 0, + "raw_persons": len(list((raw / "persons").glob("*.json"))) if (raw / "persons").exists() else 0, + "raw_organizations": len(list((raw / "organizations").glob("*.json"))) if (raw / "organizations").exists() else 0, + "text_files": len(list((data_dir / "text").glob("*.txt"))) if (data_dir / "text").exists() else 0, + "matches_path": str(matches_path()) if matches_path().exists() else None, + "relations_path": str(relations_path()) if relations_path().exists() else None, + "discover_state": str(discover_state_path()) if discover_state_path().exists() else None, + "config_summary": { + "embedding_model": cfg.rcp.embedding_model, + "embedding_dim": cfg.rcp.embedding_dim, + "reranker_model": cfg.rcp.reranker_model, + "filter_terms": cfg.filter.terms, + }, + } + try: + from .store import ALL_COLLECTIONS, QdrantStore # lazy: requires qdrant-client + store = QdrantStore.from_config(cfg) + counts["qdrant_url"] = cfg.qdrant.url + counts["qdrant_collections"] = { + name: store.collection_count(name) for name in ALL_COLLECTIONS + } + except Exception as exc: + counts["qdrant_error"] = str(exc) + try: + from .paths import duckdb_path + from .storage import DuckDBStore + if duckdb_path().exists(): + ddb = DuckDBStore.open() + try: + counts["duckdb_path"] = str(duckdb_path()) + counts["duckdb_counts"] = { + t: ddb.count(t) + for t in ( + "articles", + "persons", + "organizations", + "article_persons", + "article_orgs", + "article_links", + "chunks", + ) + } + finally: + ddb.close() + else: + counts["duckdb_path"] = None + except Exception as exc: + counts["duckdb_error"] = str(exc) + click.echo(json.dumps(counts, indent=2, default=str)) + + +def main() -> None: + cli(obj={}) + + +if __name__ == "__main__": + main() diff --git a/src/index/infoscience/config.py b/src/index/infoscience/config.py new file mode 100644 index 0000000..edf3f02 --- /dev/null +++ b/src/index/infoscience/config.py @@ -0,0 +1,106 @@ +"""Config loader for the Infoscience indexer. + +Reads `config/index/infoscience.yaml` and merges in env-sourced credentials +(`RCP_TOKEN`, `INFOSCIENCE_TOKEN`, `INDEX_QDRANT_*`) plus the resolved data +dir. +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import List, Optional + +import yaml +from pydantic import BaseModel, Field + +from .paths import infoscience_data_dir + +DEFAULT_CONFIG_PATH = Path("config/index/infoscience.yaml") + +_TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} +_FALSE_ENV_VALUES = {"0", "false", "f", "no", "n", "off"} + + +class RcpConfig(BaseModel): + base_url: str + embedding_model: str + embedding_dim: int + query_instruction: str + reranker_model: str + batch_size: int = 16 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: Optional[str] = None # populated from RCP_TOKEN at load time + + +class InfoscienceConfig(BaseModel): + base_url: str + page_size: int = 100 + max_concurrency: int = 4 + token: Optional[str] = None # populated from INFOSCIENCE_TOKEN at load time + + +class FilterConfig(BaseModel): + terms: List[str] = Field(default_factory=list) + + +class ChunkingConfig(BaseModel): + size_tokens: int = 1024 + overlap_tokens: int = 128 + tokenizer: str = "cl100k_base" + + +class QdrantConfig(BaseModel): + url: str = "http://qdrant:6333" + prefer_grpc: bool = False + api_key: Optional[str] = None # populated from INDEX_QDRANT_API_KEY at load + + +class InfoscienceIndexConfig(BaseModel): + rcp: RcpConfig + infoscience: InfoscienceConfig + filter: FilterConfig + chunking: ChunkingConfig + qdrant: QdrantConfig + data_dir: Path + + +def _env_str(name: str) -> Optional[str]: + raw = os.getenv(name) + if raw is None: + return None + stripped = raw.strip() + return stripped or None + + +def _env_bool(name: str) -> Optional[bool]: + raw = _env_str(name) + if raw is None: + return None + normalised = raw.lower() + if normalised in _TRUE_ENV_VALUES: + return True + if normalised in _FALSE_ENV_VALUES: + return False + msg = f"Invalid boolean value for {name}: {raw!r}" + raise ValueError(msg) + + +def load_config(path: Optional[Path] = None) -> InfoscienceIndexConfig: + """Load + validate the YAML config; merge env tokens and data dir.""" + cfg_path = path or DEFAULT_CONFIG_PATH + raw = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) or {} + + raw.setdefault("rcp", {})["token"] = _env_str("RCP_TOKEN") + raw.setdefault("infoscience", {})["token"] = _env_str("INFOSCIENCE_TOKEN") + raw.setdefault("qdrant", {})["api_key"] = _env_str("INDEX_QDRANT_API_KEY") + + if (override := _env_str("INDEX_QDRANT_URL")) is not None: + raw["qdrant"]["url"] = override + if (override_b := _env_bool("INDEX_QDRANT_PREFER_GRPC")) is not None: + raw["qdrant"]["prefer_grpc"] = override_b + + raw["data_dir"] = infoscience_data_dir() + + return InfoscienceIndexConfig(**raw) diff --git a/src/index/infoscience/discover.py b/src/index/infoscience/discover.py new file mode 100644 index 0000000..b34a393 --- /dev/null +++ b/src/index/infoscience/discover.py @@ -0,0 +1,125 @@ +"""Discover stage: paginate Solr `fulltext:` queries, persist raw item JSON. + +Output: one JSON file per matched item under `raw/items/{uuid}.json`, plus +`discover_state.json` with per-term cursor for resumability. Same UUID +matched by multiple terms is stored once (the file write is idempotent). +""" + +from __future__ import annotations + +import asyncio +import datetime as dt +import json +import logging +from typing import List, Optional + +from .config import InfoscienceIndexConfig +from .dspace import DSpaceClient +from .models import DiscoverState +from .paths import discover_state_path, raw_items_dir + +logger = logging.getLogger(__name__) + + +def _load_state() -> DiscoverState: + path = discover_state_path() + if not path.exists(): + return DiscoverState() + return DiscoverState(**json.loads(path.read_text(encoding="utf-8"))) + + +def _save_state(state: DiscoverState) -> None: + discover_state_path().write_text( + state.model_dump_json(indent=2), encoding="utf-8", + ) + + +def _write_item(item: dict) -> bool: + """Persist one item; returns True if newly written.""" + uuid = item.get("uuid") + if not uuid: + return False + path = raw_items_dir() / f"{uuid}.json" + if path.exists(): + return False + path.write_text(json.dumps(item, ensure_ascii=False, indent=2), encoding="utf-8") + return True + + +async def discover( + cfg: InfoscienceIndexConfig, + *, + terms: Optional[List[str]] = None, + limit: Optional[int] = None, +) -> dict: + """Run the discover stage. Returns a summary dict.""" + target_terms = list(terms or cfg.filter.terms) + if not target_terms: + msg = "No filter terms configured." + raise ValueError(msg) + + state = _load_state() + total_new = 0 + total_seen = 0 + + async with DSpaceClient(cfg.infoscience) as client: + for term in target_terms: + if state.completed.get(term): + logger.info("Skipping completed term: %s", term) + continue + start_page = state.per_term_cursor.get(term, 0) + new_for_term = 0 + seen_for_term = 0 + logger.info("Discovering term=%s starting at page=%d", term, start_page) + page = start_page + try: + async for item in client.iter_discover_fulltext( + term, + size=cfg.infoscience.page_size, + start_page=start_page, + ): + seen_for_term += 1 + total_seen += 1 + if _write_item(item): + new_for_term += 1 + total_new += 1 + if limit is not None and total_new >= limit: + break + # Cursor in pages; updated whenever a page boundary passes. + new_page = start_page + (seen_for_term // cfg.infoscience.page_size) + if new_page != page: + page = new_page + state.per_term_cursor[term] = page + _save_state(state) + except Exception as exc: + logger.exception("Discover failed for term=%s: %s", term, exc) + state.per_term_cursor[term] = page + _save_state(state) + raise + else: + state.per_term_cursor[term] = page + state.per_term_total[term] = state.per_term_total.get(term, 0) + new_for_term + if limit is None: + state.completed[term] = True + logger.info( + "term=%s seen=%d new=%d", + term, seen_for_term, new_for_term, + ) + + if limit is not None and total_new >= limit: + logger.info("Hit --limit %d, stopping.", limit) + break + + state.last_run_iso = dt.datetime.now(dt.timezone.utc).isoformat() + _save_state(state) + return { + "terms": target_terms, + "items_seen": total_seen, + "items_new": total_new, + "raw_dir": str(raw_items_dir()), + } + + +def run(cfg: InfoscienceIndexConfig, **kwargs) -> dict: + """Sync wrapper for the CLI.""" + return asyncio.run(discover(cfg, **kwargs)) diff --git a/src/index/infoscience/dspace.py b/src/index/infoscience/dspace.py new file mode 100644 index 0000000..03b12dd --- /dev/null +++ b/src/index/infoscience/dspace.py @@ -0,0 +1,214 @@ +"""Async DSpace 7 REST client for Infoscience. + +Single `httpx.AsyncClient` that talks to `https://infoscience.epfl.ch/server/api` +with optional bearer-token auth. Covers exactly the endpoints we need: + + * `/discover/search/objects` — Solr-backed search, used for the + `fulltext:` qualifier in `discover.py`. + * `/core/items/{uuid}` — item metadata, used for Articles, Persons, + and Organizations alike. + * `/core/items/{uuid}/bundles` + `/core/bundles/{uuid}/bitstreams` — + bundle/bitstream listing for the TEXT bundle. + * `/core/bitstreams/{uuid}/content` — the plain-text body for matched + publications (no PDFs handled on our side). + +dspace-rest-client was evaluated and dropped: its auth model (session + +CSRF + JWT login) conflicts with Infoscience's bearer-token auth. +""" + +from __future__ import annotations + +import asyncio +import logging +from contextlib import asynccontextmanager +from typing import Any, AsyncIterator, Dict, List, Optional + +import httpx + +from .config import InfoscienceConfig + +logger = logging.getLogger(__name__) + + +class DSpaceError(Exception): + """Raised when a DSpace REST call fails after retries.""" + + +class DSpaceClient: + """Async DSpace 7 REST client. Use as an async context manager.""" + + def __init__(self, cfg: InfoscienceConfig, *, timeout_seconds: int = 180): + self._cfg = cfg + self._timeout = timeout_seconds + self._semaphore = asyncio.Semaphore(cfg.max_concurrency) + self._client: Optional[httpx.AsyncClient] = None + + async def __aenter__(self) -> "DSpaceClient": + # Default headers are anonymous: discover/item/bundle/bitstream-listing + # endpoints are public and a misconfigured/inactive `INFOSCIENCE_TOKEN` + # (e.g. one whose user hasn't accepted the platform agreement) gets + # 403'd. The bearer is only attached on demand for bitstream content + # downloads (some PDFs/text bundles are gated). + headers: Dict[str, str] = { + "Accept": "application/json", + "User-Agent": ( + "Mozilla/5.0 (compatible; " + "src.index.infoscience/1.0; +https://infoscience.epfl.ch)" + ), + } + self._client = httpx.AsyncClient( + base_url=self._cfg.base_url.rstrip("/"), + headers=headers, + timeout=self._timeout, + follow_redirects=True, + ) + return self + + async def __aexit__(self, exc_type, exc, tb) -> None: + if self._client is not None: + await self._client.aclose() + self._client = None + + @property + def client(self) -> httpx.AsyncClient: + if self._client is None: + msg = "DSpaceClient must be used inside `async with`." + raise RuntimeError(msg) + return self._client + + _RETRY_STATUSES = {405, 429, 500, 502, 503, 504} + _RETRY_TRANSPORT_EXC = (httpx.ReadTimeout, httpx.ConnectTimeout, + httpx.PoolTimeout, httpx.RemoteProtocolError, + httpx.ConnectError) + + async def _get_json(self, path: str, **kwargs: Any) -> Dict[str, Any]: + last_exc: Optional[Exception] = None + for attempt in range(6): + try: + async with self._semaphore: + response = await self.client.get(path, **kwargs) + except self._RETRY_TRANSPORT_EXC as exc: + last_exc = exc + delay = min(2 ** attempt, 16) + 0.25 * attempt + logger.debug( + "DSpace %s on %s, retry in %.1fs (attempt %d)", + type(exc).__name__, path, delay, attempt + 1, + ) + await asyncio.sleep(delay) + continue + if response.status_code in self._RETRY_STATUSES: + # Infoscience's edge layer 405s under burst; back off and retry. + last_exc = httpx.HTTPStatusError( + message=f"transient {response.status_code}", + request=response.request, + response=response, + ) + delay = min(2 ** attempt, 16) + 0.25 * attempt + logger.debug( + "DSpace %s on %s, retry in %.1fs (attempt %d)", + response.status_code, path, delay, attempt + 1, + ) + await asyncio.sleep(delay) + continue + response.raise_for_status() + return response.json() + assert last_exc is not None + raise last_exc + + async def discover_fulltext( + self, + term: str, + *, + configuration: str = "researchoutputs", + page: int = 0, + size: int = 100, + ) -> Dict[str, Any]: + """One page of `/discover/search/objects?query=fulltext:`.""" + params = { + "query": f"fulltext:{term}", + "configuration": configuration, + "page": page, + "size": size, + } + return await self._get_json("/discover/search/objects", params=params) + + async def iter_discover_fulltext( + self, + term: str, + *, + configuration: str = "researchoutputs", + size: int = 100, + start_page: int = 0, + max_pages: Optional[int] = None, + ) -> AsyncIterator[Dict[str, Any]]: + """Yield raw indexable item dicts across all pages for a fulltext term.""" + page = start_page + while True: + payload = await self.discover_fulltext( + term, configuration=configuration, page=page, size=size, + ) + search = payload.get("_embedded", {}).get("searchResult", {}) + page_info = search.get("page", {}) or {} + total_pages = page_info.get("totalPages", 0) or 0 + objects = search.get("_embedded", {}).get("objects", []) or [] + for obj in objects: + indexable = obj.get("_embedded", {}).get("indexableObject") + if indexable is not None: + yield indexable + page += 1 + if page >= total_pages: + return + if max_pages is not None and (page - start_page) >= max_pages: + return + + async def get_item(self, uuid: str) -> Optional[Dict[str, Any]]: + """`GET /core/items/{uuid}`. Returns None on 404.""" + try: + return await self._get_json(f"/core/items/{uuid}") + except httpx.HTTPStatusError as exc: + if exc.response.status_code == 404: + return None + raise + + async def get_bundles(self, item_uuid: str) -> List[Dict[str, Any]]: + """List bundles for an item; returns the `_embedded.bundles` array.""" + payload = await self._get_json(f"/core/items/{item_uuid}/bundles") + return payload.get("_embedded", {}).get("bundles", []) or [] + + async def get_bitstreams(self, bundle_uuid: str) -> List[Dict[str, Any]]: + """List bitstreams in a bundle.""" + payload = await self._get_json(f"/core/bundles/{bundle_uuid}/bitstreams") + return payload.get("_embedded", {}).get("bitstreams", []) or [] + + async def get_bitstream_content(self, bitstream_uuid: str) -> bytes: + """Raw bytes of `/core/bitstreams/{uuid}/content`. + + Tries the bearer token first (if configured), falls back to anonymous + on 401/403 — covers the case where the token is inactive but the + bitstream is publicly accessible. + """ + path = f"/core/bitstreams/{bitstream_uuid}/content" + if self._cfg.token: + try: + async with self._semaphore: + response = await self.client.get( + path, + headers={"Authorization": f"Bearer {self._cfg.token}"}, + ) + response.raise_for_status() + return response.content + except httpx.HTTPStatusError as exc: + if exc.response.status_code not in (401, 403): + raise + logger.debug("Bearer rejected on %s, retrying anonymously", path) + async with self._semaphore: + response = await self.client.get(path) + response.raise_for_status() + return response.content + + +@asynccontextmanager +async def dspace_client(cfg: InfoscienceConfig) -> AsyncIterator[DSpaceClient]: + """Convenience helper: `async with dspace_client(cfg) as client: ...`.""" + async with DSpaceClient(cfg) as c: + yield c diff --git a/src/index/infoscience/embed.py b/src/index/infoscience/embed.py new file mode 100644 index 0000000..9527d8b --- /dev/null +++ b/src/index/infoscience/embed.py @@ -0,0 +1,135 @@ +"""RCP embedding client (OpenAI-compatible /v1/embeddings). + +Qwen3-Embedding-8B is instruction-aware: queries are wrapped with the +configured instruction template; passages are sent verbatim. The first +response's vector dimension is asserted against `cfg.rcp.embedding_dim`. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import Iterable, List, Optional + +import httpx + +from .config import RcpConfig + +logger = logging.getLogger(__name__) + + +class EmbedError(Exception): + pass + + +class RCPEmbedder: + """Async embedding client. Use as an async context manager.""" + + def __init__(self, cfg: RcpConfig): + self._cfg = cfg + self._client: Optional[httpx.AsyncClient] = None + self._semaphore = asyncio.Semaphore(cfg.max_concurrency) + self._observed_dim: Optional[int] = None + + async def __aenter__(self) -> "RCPEmbedder": + if not self._cfg.token: + msg = "RCP_TOKEN is required to call the RCP embedding endpoint." + raise EmbedError(msg) + self._client = httpx.AsyncClient( + base_url=self._cfg.base_url.rstrip("/"), + headers={ + "Authorization": f"Bearer {self._cfg.token}", + "Content-Type": "application/json", + }, + timeout=self._cfg.timeout_seconds, + ) + return self + + async def __aexit__(self, exc_type, exc, tb) -> None: + if self._client is not None: + await self._client.aclose() + self._client = None + + @property + def client(self) -> httpx.AsyncClient: + if self._client is None: + msg = "RCPEmbedder must be used inside `async with`." + raise RuntimeError(msg) + return self._client + + async def _post_embeddings(self, inputs: List[str]) -> List[List[float]]: + last_exc: Optional[Exception] = None + for attempt in range(4): + try: + async with self._semaphore: + response = await self.client.post( + "/embeddings", + json={"model": self._cfg.embedding_model, "input": inputs}, + ) + response.raise_for_status() + payload = response.json() + break + except httpx.HTTPStatusError as exc: + if exc.response.status_code in (429, 500, 502, 503, 504): + last_exc = exc + delay = 2 ** attempt + logger.warning( + "RCP embeddings %s, retry in %ds (attempt %d)", + exc.response.status_code, delay, attempt + 1, + ) + await asyncio.sleep(delay) + continue + raise + except httpx.HTTPError as exc: + last_exc = exc + delay = 2 ** attempt + logger.warning("RCP embeddings transport error: %s, retry in %ds", exc, delay) + await asyncio.sleep(delay) + else: + msg = f"RCP embeddings failed after retries: {last_exc}" + raise EmbedError(msg) from last_exc + + vectors = [item["embedding"] for item in payload.get("data", [])] + if not vectors: + msg = "RCP embeddings returned empty response." + raise EmbedError(msg) + + observed = len(vectors[0]) + if self._observed_dim is None: + self._observed_dim = observed + if observed != self._cfg.embedding_dim: + msg = ( + f"Embedding dim mismatch: configured " + f"{self._cfg.embedding_dim}, RCP returned {observed}." + ) + raise EmbedError(msg) + return vectors + + async def embed_passage(self, texts: Iterable[str]) -> List[List[float]]: + """Embed plain passage strings (no instruction prefix).""" + batch = list(texts) + if not batch: + return [] + return await self._post_embeddings(batch) + + async def embed_query( + self, + query: str, + instruction: Optional[str] = None, + ) -> List[float]: + """Embed a query with the Qwen3 instruction template.""" + instr = instruction or self._cfg.query_instruction + formatted = f"Instruct: {instr}\nQuery: {query}" + vectors = await self._post_embeddings([formatted]) + return vectors[0] + + async def embed_passages_batched( + self, + texts: List[str], + ) -> List[List[float]]: + """Embed `texts` in batches of `cfg.batch_size`, preserving order.""" + out: List[List[float]] = [] + for i in range(0, len(texts), self._cfg.batch_size): + chunk = texts[i : i + self._cfg.batch_size] + out.extend(await self.embed_passage(chunk)) + return out diff --git a/src/index/infoscience/extract_matches.py b/src/index/infoscience/extract_matches.py new file mode 100644 index 0000000..5eaaef3 --- /dev/null +++ b/src/index/infoscience/extract_matches.py @@ -0,0 +1,155 @@ +"""Extract matches stage: regex GitHub / HuggingFace URLs from fetched text. + +Output: `matches.jsonl`, one `MatchRecord` per item that contains at least +one matched URL. Reuses `classify_github_url` for GitHub canonicalisation +and adds a small HuggingFace classifier alongside. +""" + +from __future__ import annotations + +import json +import logging +import re +from collections import Counter +from typing import Dict, List, Optional, Set, Tuple +from urllib.parse import urlparse + +from src.v2.ingest.detection.github_url_classifier import classify_github_url + +from .config import InfoscienceIndexConfig +from .models import MatchRecord +from .paths import matches_path, text_dir + +logger = logging.getLogger(__name__) + +_URL_RE = re.compile( + r"https?://[^\s<>\"'\)\]\}]+", + re.IGNORECASE, +) + +_HF_HOSTS = {"huggingface.co", "hf.co"} +_GH_HOSTS = {"github.com", "www.github.com"} + + +def _hostname(url: str) -> str: + try: + return (urlparse(url).hostname or "").lower() + except Exception: + return "" + + +def classify_huggingface_url(url: str) -> Optional[Tuple[str, str]]: + """Return (kind, canonical_url) for a HuggingFace URL, or None. + + `kind` is one of: 'model', 'dataset', 'space', 'org', 'user', 'other'. + """ + host = _hostname(url) + if host not in _HF_HOSTS: + return None + parts = [p for p in urlparse(url).path.split("/") if p] + if not parts: + return ("other", url) + head = parts[0].lower() + if head == "datasets" and len(parts) >= 2: + canonical = "https://huggingface.co/datasets/" + "/".join(parts[1:3]) + return ("dataset", canonical) + if head == "spaces" and len(parts) >= 2: + canonical = "https://huggingface.co/spaces/" + "/".join(parts[1:3]) + return ("space", canonical) + if head in {"organizations", "orgs"} and len(parts) >= 2: + return ("org", f"https://huggingface.co/{parts[1]}") + # Bare path: user/model or just user. + if len(parts) == 1: + return ("user", f"https://huggingface.co/{parts[0]}") + canonical = f"https://huggingface.co/{parts[0]}/{parts[1]}" + return ("model", canonical) + + +def _canonicalise(url: str) -> Optional[str]: + """Return a canonical URL string if the URL points at GitHub or HF, else None. + + GitHub URLs that classify_github_url rejects (issues, blobs, etc.) are + kept as-is — they're still valid evidence of GitHub references. + """ + host = _hostname(url) + if host in _GH_HOSTS: + try: + result = classify_github_url(url) + return result.normalized_url + except Exception: + return url + if host in _HF_HOSTS: + hf = classify_huggingface_url(url) + return hf[1] if hf else None + return None + + +def _extract_from_text(text: str) -> Tuple[Set[str], Counter]: + found: Set[str] = set() + counts: Counter = Counter() + for raw in _URL_RE.findall(text): + # Trim trailing punctuation that often clings to URLs in prose. + clean = raw.rstrip(".,;:'\"()[]{}<>") + canonical = _canonicalise(clean) + if canonical is None: + continue + found.add(canonical) + counts[_hostname(canonical)] += 1 + return found, counts + + +def extract_matches(_cfg: InfoscienceIndexConfig) -> dict: + """Walk text/ files, write matches.jsonl. Returns counts dict.""" + out_path = matches_path() + text_files = sorted(text_dir().glob("*.txt")) + if not text_files: + logger.warning("No text files. Run `fetch-text` first.") + out_path.write_text("", encoding="utf-8") + return {"items": 0, "with_matches": 0} + + written = 0 + with out_path.open("w", encoding="utf-8") as out: + for tf in text_files: + uuid = tf.stem + text = tf.read_text(encoding="utf-8", errors="replace") + urls, counts = _extract_from_text(text) + if not urls: + continue + record = MatchRecord( + uuid=uuid, + matched_urls=sorted(urls), + counts_by_host=dict(counts), + ) + out.write(record.model_dump_json() + "\n") + written += 1 + + logger.info( + "extract_matches: items=%d with_matches=%d → %s", + len(text_files), written, out_path, + ) + return { + "items": len(text_files), + "with_matches": written, + "matches_path": str(out_path), + } + + +def load_matches() -> List[MatchRecord]: + """Read all match records from disk.""" + path = matches_path() + if not path.exists(): + return [] + out: List[MatchRecord] = [] + for line in path.read_text(encoding="utf-8").splitlines(): + line = line.strip() + if line: + out.append(MatchRecord(**json.loads(line))) + return out + + +def matches_by_uuid() -> Dict[str, MatchRecord]: + return {m.uuid: m for m in load_matches()} + + +def run(cfg: InfoscienceIndexConfig) -> dict: + return extract_matches(cfg) diff --git a/src/index/infoscience/extract_relations.py b/src/index/infoscience/extract_relations.py new file mode 100644 index 0000000..a2e0d4e --- /dev/null +++ b/src/index/infoscience/extract_relations.py @@ -0,0 +1,155 @@ +"""Extract relations stage: pull Person/Org authority UUIDs from Article JSON. + +DSpace's `/relationships` endpoint is empty for Infoscience publications, but +the linked entity UUIDs are embedded as `authority` fields on +`dc.contributor.author` and several `cris.virtual.*` / `oairecerif.*` keys. + +We parse `raw/items/{uuid}.json` for every Article in `matches.jsonl`, dedupe +the authority sets, and write `relations.jsonl` plus flat +`persons.txt` / `organizations.txt` for the next stage. +""" + +from __future__ import annotations + +import json +import logging +import re +from pathlib import Path +from typing import Iterable, List, Optional, Set + +from .config import InfoscienceIndexConfig +from .extract_matches import load_matches +from .models import RelationRecord +from .paths import ( + organizations_set_path, + persons_set_path, + raw_items_dir, + relations_path, +) + +logger = logging.getLogger(__name__) + +_PERSON_FIELDS = ( + "dc.contributor.author", + "cris.virtualsource.author-scopus", + "cris.virtualsource.author-orcid", +) + +_ORG_FIELDS = ( + "cris.virtual.department", + "cris.virtual.parent-organization", + "oairecerif.author.affiliation", + "cris.virtual.unitManager", # actually a person; filtered below + "dc.relation.journal", # journal is a separate entity; collected +) + +# Keep journal/unitManager separate so they don't pollute the org set; +# we collect orgs only from the explicit org-typed fields. +_ORG_ONLY_FIELDS = ( + "cris.virtual.department", + "cris.virtual.parent-organization", + "oairecerif.author.affiliation", +) + +_UUID_RE = re.compile( + r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$", + re.IGNORECASE, +) + + +def _looks_like_uuid(s: Optional[str]) -> bool: + return bool(s) and bool(_UUID_RE.match(s)) + + +def _authorities(metadata: dict, fields: Iterable[str]) -> List[str]: + out: List[str] = [] + for field in fields: + for entry in metadata.get(field, []) or []: + authority = entry.get("authority") if isinstance(entry, dict) else None + if _looks_like_uuid(authority): + out.append(authority) + return out + + +def _load_item(uuid: str) -> Optional[dict]: + p = raw_items_dir() / f"{uuid}.json" + if not p.exists(): + return None + return json.loads(p.read_text(encoding="utf-8")) + + +def _dedupe_preserve(seq: Iterable[str]) -> List[str]: + seen: Set[str] = set() + out: List[str] = [] + for x in seq: + if x in seen: + continue + seen.add(x) + out.append(x) + return out + + +def extract_relations(_cfg: InfoscienceIndexConfig) -> dict: + matches = load_matches() + if not matches: + logger.warning("No matches.jsonl entries. Run `extract-matches` first.") + relations_path().write_text("", encoding="utf-8") + return {"articles": 0, "persons": 0, "organizations": 0} + + persons: Set[str] = set() + orgs: Set[str] = set() + written = 0 + + with relations_path().open("w", encoding="utf-8") as out: + for match in matches: + item = _load_item(match.uuid) + if item is None: + logger.debug("Missing raw item for matched uuid %s", match.uuid) + continue + md = item.get("metadata", {}) or {} + person_uuids = _dedupe_preserve(_authorities(md, _PERSON_FIELDS)) + org_uuids = _dedupe_preserve(_authorities(md, _ORG_ONLY_FIELDS)) + if not person_uuids and not org_uuids: + continue + record = RelationRecord( + article_uuid=match.uuid, + person_uuids=person_uuids, + org_uuids=org_uuids, + ) + out.write(record.model_dump_json() + "\n") + written += 1 + persons.update(person_uuids) + orgs.update(org_uuids) + + persons_set_path().write_text("\n".join(sorted(persons)), encoding="utf-8") + organizations_set_path().write_text("\n".join(sorted(orgs)), encoding="utf-8") + + summary = { + "articles": written, + "persons": len(persons), + "organizations": len(orgs), + "relations_path": str(relations_path()), + } + logger.info("extract_relations: %s", summary) + return summary + + +def load_relations() -> List[RelationRecord]: + p = relations_path() + if not p.exists(): + return [] + out: List[RelationRecord] = [] + for line in p.read_text(encoding="utf-8").splitlines(): + if line.strip(): + out.append(RelationRecord(**json.loads(line))) + return out + + +def load_set(path: Path) -> Set[str]: + if not path.exists(): + return set() + return {ln.strip() for ln in path.read_text(encoding="utf-8").splitlines() if ln.strip()} + + +def run(cfg: InfoscienceIndexConfig) -> dict: + return extract_relations(cfg) diff --git a/src/index/infoscience/fetch_related.py b/src/index/infoscience/fetch_related.py new file mode 100644 index 0000000..514b578 --- /dev/null +++ b/src/index/infoscience/fetch_related.py @@ -0,0 +1,109 @@ +"""Fetch related stage: GET /core/items/{uuid} for each Person/Org UUID. + +Reads `persons.txt` and `organizations.txt` (produced by `extract_relations`), +fetches each unique UUID once, and persists the raw JSON to +`raw/persons/{uuid}.json` or `raw/organizations/{uuid}.json`. Skips +already-fetched files unless `refresh=True`. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from pathlib import Path +from typing import Iterable, Optional + +import httpx + +from .config import InfoscienceIndexConfig +from .dspace import DSpaceClient +from .extract_relations import load_set +from .paths import ( + organizations_set_path, + persons_set_path, + raw_organizations_dir, + raw_persons_dir, +) + +logger = logging.getLogger(__name__) + + +async def _fetch_one( + client: DSpaceClient, + uuid: str, + out_dir: Path, + *, + refresh: bool = False, +) -> str: + out_path = out_dir / f"{uuid}.json" + if out_path.exists() and not refresh: + return "skipped-existing" + try: + item = await client.get_item(uuid) + except httpx.HTTPStatusError as exc: + if exc.response.status_code in (401, 403): + return "unauthorized" + if exc.response.status_code == 404: + return "not-found" + raise + if item is None: + return "not-found" + out_path.write_text(json.dumps(item, ensure_ascii=False, indent=2), encoding="utf-8") + return "written" + + +async def _fetch_set( + cfg: InfoscienceIndexConfig, + uuids: Iterable[str], + out_dir: Path, + *, + refresh: bool, +) -> dict: + counts = {"written": 0, "skipped-existing": 0, "unauthorized": 0, + "not-found": 0, "error": 0} + uuid_list = list(uuids) + if not uuid_list: + return counts + + async with DSpaceClient(cfg.infoscience) as client: + sem = asyncio.Semaphore(cfg.infoscience.max_concurrency) + + async def _bounded(u: str) -> str: + async with sem: + try: + return await _fetch_one(client, u, out_dir, refresh=refresh) + except Exception: + logger.exception("fetch-related failed for %s", u) + return "error" + + results = await asyncio.gather(*(_bounded(u) for u in uuid_list)) + for r in results: + counts[r] = counts.get(r, 0) + 1 + return counts + + +async def fetch_related( + cfg: InfoscienceIndexConfig, + *, + kind: str = "all", + refresh: bool = False, +) -> dict: + """`kind` ∈ {'person', 'org', 'all'}.""" + summary: dict = {"kind": kind} + if kind in ("person", "all"): + persons = load_set(persons_set_path()) + summary["persons"] = await _fetch_set( + cfg, persons, raw_persons_dir(), refresh=refresh, + ) + if kind in ("org", "all"): + orgs = load_set(organizations_set_path()) + summary["organizations"] = await _fetch_set( + cfg, orgs, raw_organizations_dir(), refresh=refresh, + ) + logger.info("fetch_related: %s", json.dumps(summary)) + return summary + + +def run(cfg: InfoscienceIndexConfig, *, kind: str = "all", refresh: bool = False) -> dict: + return asyncio.run(fetch_related(cfg, kind=kind, refresh=refresh)) diff --git a/src/index/infoscience/models.py b/src/index/infoscience/models.py new file mode 100644 index 0000000..baf75fd --- /dev/null +++ b/src/index/infoscience/models.py @@ -0,0 +1,127 @@ +"""Pydantic models passed between pipeline stages. + +The LanceDB row shapes live in `store.py` (PyArrow schemas, not Pydantic) so +the schemas aren't duplicated; these models cover on-disk JSON/JSONL and +in-memory transfer between stages. +""" + +from __future__ import annotations + +from typing import Dict, List, Optional + +from pydantic import BaseModel, Field + + +class DiscoverState(BaseModel): + """Persisted under `discover_state.json` for resumability.""" + + per_term_cursor: Dict[str, int] = Field(default_factory=dict) + per_term_total: Dict[str, int] = Field(default_factory=dict) + completed: Dict[str, bool] = Field(default_factory=dict) + last_run_iso: Optional[str] = None + + +class MatchRecord(BaseModel): + """One line in `matches.jsonl`.""" + + uuid: str + matched_urls: List[str] = Field(default_factory=list) + counts_by_host: Dict[str, int] = Field(default_factory=dict) + + +class RelationRecord(BaseModel): + """One line in `relations.jsonl`.""" + + article_uuid: str + person_uuids: List[str] = Field(default_factory=list) + org_uuids: List[str] = Field(default_factory=list) + + +class ChunkRecord(BaseModel): + """In-memory chunk before insertion into LanceDB.""" + + chunk_id: str + article_uuid: str + chunk_index: int + text: str + title: Optional[str] = None + abstract: Optional[str] = None + authors: List[str] = Field(default_factory=list) + author_uuids: List[str] = Field(default_factory=list) + doi: Optional[str] = None + publication_date: Optional[str] = None + year: Optional[int] = None + publication_type: Optional[str] = None + language: Optional[str] = None + subjects: List[str] = Field(default_factory=list) + keywords: List[str] = Field(default_factory=list) + lab: Optional[str] = None + lab_uuid: Optional[str] = None + org_uuids: List[str] = Field(default_factory=list) + infoscience_url: Optional[str] = None + matched_urls: List[str] = Field(default_factory=list) + + +class ArticleRecord(BaseModel): + """One row of `infoscience_articles`.""" + + article_uuid: str + title: Optional[str] = None + abstract: Optional[str] = None + keywords: List[str] = Field(default_factory=list) + subjects: List[str] = Field(default_factory=list) + authors: List[str] = Field(default_factory=list) + author_uuids: List[str] = Field(default_factory=list) + doi: Optional[str] = None + publication_date: Optional[str] = None + year: Optional[int] = None + publication_type: Optional[str] = None + language: Optional[str] = None + journal: Optional[str] = None + journal_uuid: Optional[str] = None + lab: Optional[str] = None + lab_uuid: Optional[str] = None + org_uuids: List[str] = Field(default_factory=list) + infoscience_url: Optional[str] = None + matched_urls: List[str] = Field(default_factory=list) + chunk_count: int = 0 + + +class PersonRecord(BaseModel): + """One row of `infoscience_persons`.""" + + person_uuid: str + name: Optional[str] = None + given_name: Optional[str] = None + family_name: Optional[str] = None + orcid: Optional[str] = None + sciper_id: Optional[str] = None + scopus_id: Optional[str] = None + email_hash: Optional[str] = None + primary_affiliation: Optional[str] = None + primary_affiliation_uuid: Optional[str] = None + affiliation_uuids: List[str] = Field(default_factory=list) + position: Optional[str] = None + biography: Optional[str] = None + research_interests: List[str] = Field(default_factory=list) + profile_url: Optional[str] = None + related_article_uuids: List[str] = Field(default_factory=list) + + +class OrganizationRecord(BaseModel): + """One row of `infoscience_organizations`.""" + + org_uuid: str + name: Optional[str] = None + acronym: Optional[str] = None + aliases: List[str] = Field(default_factory=list) + parent_org_uuid: Optional[str] = None + parent_org_chain: List[str] = Field(default_factory=list) + parent_org_chain_names: List[str] = Field(default_factory=list) + description: Optional[str] = None + sciper_unit_id: Optional[str] = None + ror_id: Optional[str] = None + unit_manager_uuid: Optional[str] = None + unit_manager_name: Optional[str] = None + infoscience_url: Optional[str] = None + related_article_uuids: List[str] = Field(default_factory=list) diff --git a/src/index/infoscience/parsers.py b/src/index/infoscience/parsers.py new file mode 100644 index 0000000..8917d5e --- /dev/null +++ b/src/index/infoscience/parsers.py @@ -0,0 +1,151 @@ +"""DSpace JSON → Pydantic record parsers. + +Centralises the metadata-key conventions DSpace uses so the indexing +stages don't repeat dict-walking. All inputs are raw item JSON dicts as +returned by `/server/api/core/items/{uuid}` or as embedded inside +`/discover/search/objects`. +""" + +from __future__ import annotations + +import re +from typing import Any, Dict, List, Optional + +from .models import ArticleRecord, OrganizationRecord, PersonRecord + +_YEAR_RE = re.compile(r"\b(19|20)\d{2}\b") + + +def first_value(metadata: dict, field: str) -> Optional[str]: + values = metadata.get(field) or [] + if not isinstance(values, list) or not values: + return None + entry = values[0] + if isinstance(entry, dict): + return entry.get("value") + return None + + +def all_values(metadata: dict, field: str) -> List[str]: + out: List[str] = [] + for entry in metadata.get(field) or []: + if isinstance(entry, dict): + v = entry.get("value") + if v: + out.append(v) + return out + + +def first_authority(metadata: dict, field: str) -> Optional[str]: + for entry in metadata.get(field) or []: + if isinstance(entry, dict): + a = entry.get("authority") + if a: + return a + return None + + +def _infoscience_url(uuid: str, entity: str) -> str: + return f"https://infoscience.epfl.ch/entities/{entity}/{uuid}" + + +def _year_from_date(date: Optional[str]) -> Optional[int]: + if not date: + return None + m = _YEAR_RE.search(date) + return int(m.group(0)) if m else None + + +def parse_article(item: Dict[str, Any], matched_urls: Optional[List[str]] = None) -> ArticleRecord: + md = item.get("metadata", {}) or {} + uuid = item.get("uuid") or "" + publication_date = first_value(md, "dc.date.issued") + return ArticleRecord( + article_uuid=uuid, + title=first_value(md, "dc.title"), + abstract=first_value(md, "dc.description.abstract"), + keywords=all_values(md, "dc.subject"), + subjects=all_values(md, "dc.subject"), + authors=all_values(md, "dc.contributor.author"), + author_uuids=[ + entry.get("authority") + for entry in (md.get("dc.contributor.author") or []) + if isinstance(entry, dict) and entry.get("authority") + ], + doi=first_value(md, "dc.identifier.doi"), + publication_date=publication_date, + year=_year_from_date(publication_date), + publication_type=first_value(md, "dc.type"), + language=first_value(md, "dc.language.iso"), + journal=first_value(md, "dc.relation.journal"), + journal_uuid=first_authority(md, "dc.relation.journal"), + lab=first_value(md, "cris.virtual.department"), + lab_uuid=first_authority(md, "cris.virtual.department"), + org_uuids=sorted({ + entry.get("authority") + for field in ("cris.virtual.department", + "cris.virtual.parent-organization", + "oairecerif.author.affiliation") + for entry in (md.get(field) or []) + if isinstance(entry, dict) and entry.get("authority") + }), + infoscience_url=_infoscience_url(uuid, "publication"), + matched_urls=matched_urls or [], + ) + + +def parse_person(item: Dict[str, Any]) -> PersonRecord: + md = item.get("metadata", {}) or {} + uuid = item.get("uuid") or "" + name = first_value(md, "dc.title") or first_value(md, "person.familyName") + given = first_value(md, "person.givenName") or first_value(md, "eperson.firstname") + family = first_value(md, "person.familyName") or first_value(md, "eperson.lastname") + if not name and (given or family): + name = " ".join(p for p in (given, family) if p) + return PersonRecord( + person_uuid=uuid, + name=name, + given_name=given, + family_name=family, + orcid=first_value(md, "person.identifier.orcid"), + sciper_id=first_value(md, "epfl.sciperId") or first_value(md, "cris.virtual.sciperId"), + scopus_id=first_value(md, "person.identifier.scopus-author-id"), + primary_affiliation=first_value(md, "person.affiliation.name"), + primary_affiliation_uuid=first_authority(md, "person.affiliation.name"), + affiliation_uuids=sorted({ + entry.get("authority") + for entry in (md.get("person.affiliation.name") or []) + if isinstance(entry, dict) and entry.get("authority") + }), + position=first_value(md, "oairecerif.person.position"), + biography=first_value(md, "dc.description") or first_value(md, "person.biography"), + research_interests=all_values(md, "person.researchInterests"), + profile_url=_infoscience_url(uuid, "person"), + ) + + +def parse_organization(item: Dict[str, Any]) -> OrganizationRecord: + md = item.get("metadata", {}) or {} + uuid = item.get("uuid") or "" + parent_chain_authorities = [ + entry.get("authority") + for entry in (md.get("cris.virtual.parent-organization") or []) + if isinstance(entry, dict) and entry.get("authority") + ] + parent_chain_names = all_values(md, "cris.virtual.parent-organization") + return OrganizationRecord( + org_uuid=uuid, + name=first_value(md, "dc.title") or first_value(md, "organization.legalName"), + acronym=first_value(md, "organization.identifier.acronym"), + aliases=all_values(md, "organization.alternateName"), + parent_org_uuid=parent_chain_authorities[0] if parent_chain_authorities else None, + parent_org_chain=parent_chain_authorities, + parent_org_chain_names=parent_chain_names, + description=first_value(md, "dc.description") + or first_value(md, "dc.description.abstract"), + sciper_unit_id=first_value(md, "cris.virtual.unitId") + or first_value(md, "epfl.unitId"), + unit_manager_uuid=first_authority(md, "cris.virtual.unitManager"), + unit_manager_name=first_value(md, "cris.virtual.unitManager"), + infoscience_url=_infoscience_url(uuid, "orgunit"), + ) diff --git a/src/index/infoscience/paths.py b/src/index/infoscience/paths.py new file mode 100644 index 0000000..761da25 --- /dev/null +++ b/src/index/infoscience/paths.py @@ -0,0 +1,89 @@ +"""Filesystem paths for the Infoscience index. + +Root resolves to `${INDEX_DATA_DIR:-data/index}/infoscience` and is created +on first access. Sub-paths are derived constants below. +""" + +from __future__ import annotations + +import os +from pathlib import Path + +_DEFAULT_ROOT = "data/index" +_SOURCE = "infoscience" + + +def index_data_root() -> Path: + """Top-level multi-source data root (`data/index` by default).""" + return Path(os.getenv("INDEX_DATA_DIR", _DEFAULT_ROOT)).expanduser().resolve() + + +def infoscience_data_dir() -> Path: + """Per-source root for Infoscience. Creates the directory tree if needed.""" + root = index_data_root() / _SOURCE + root.mkdir(parents=True, exist_ok=True) + return root + + +def raw_items_dir() -> Path: + p = infoscience_data_dir() / "raw" / "items" + p.mkdir(parents=True, exist_ok=True) + return p + + +def raw_persons_dir() -> Path: + p = infoscience_data_dir() / "raw" / "persons" + p.mkdir(parents=True, exist_ok=True) + return p + + +def raw_organizations_dir() -> Path: + p = infoscience_data_dir() / "raw" / "organizations" + p.mkdir(parents=True, exist_ok=True) + return p + + +def text_dir() -> Path: + p = infoscience_data_dir() / "text" + p.mkdir(parents=True, exist_ok=True) + return p + + +def vector_db_dir() -> Path: + """Persistent root for the vector store (ChromaDB).""" + p = infoscience_data_dir() / "chroma" + p.mkdir(parents=True, exist_ok=True) + return p + + +def discover_state_path() -> Path: + return infoscience_data_dir() / "discover_state.json" + + +def matches_path() -> Path: + return infoscience_data_dir() / "matches.jsonl" + + +def relations_path() -> Path: + return infoscience_data_dir() / "relations.jsonl" + + +def persons_set_path() -> Path: + return infoscience_data_dir() / "persons.txt" + + +def organizations_set_path() -> Path: + return infoscience_data_dir() / "organizations.txt" + + +def duckdb_path() -> Path: + """SQLite-style structured store for articles / persons / orgs / chunks.""" + p = infoscience_data_dir() / "duckdb" + p.mkdir(parents=True, exist_ok=True) + return p / "infoscience.duckdb" + + +def dumps_dir() -> Path: + p = infoscience_data_dir() / "dumps" + p.mkdir(parents=True, exist_ok=True) + return p diff --git a/src/index/infoscience/pipeline.py b/src/index/infoscience/pipeline.py new file mode 100644 index 0000000..20190de --- /dev/null +++ b/src/index/infoscience/pipeline.py @@ -0,0 +1,232 @@ +"""Query pipeline: filter → vector → rerank, with cross-entity joins. + +Qdrant-backed. + +`where` accepts a JSON-style dict of `{field: }`. Each +field's operator can be: + + * a scalar value (interpreted as ``$eq``) + * a list (``$in``) + * a dict with one of: ``$eq``, ``$ne``, ``$in``, ``$contains``, + ``$gte`` and/or ``$lte`` + +Example: ``{"year": {"$gte": 2022}, "has_github_match": true, +"matched_urls": {"$contains": "huggingface.co"}}``. + +Modes: + * `hybrid` (default): vector top-K filtered → rerank → top-N + * `vector-only`: vector top-K filtered, no rerank + * `lexical`: scroll with payload filter only (Qdrant has no built-in + BM25; use a `$contains`-style filter for keyword search) + * `filter-only`: structured predicates only, no scoring +""" + +from __future__ import annotations + +import logging +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional, Sequence + +from .config import InfoscienceIndexConfig +from .embed import RCPEmbedder +from .rerank import RCPReranker +from .store import ( + ARTICLES_COLLECTION, + CHUNKS_COLLECTION, + ORGANIZATIONS_COLLECTION, + PERSONS_COLLECTION, + QdrantStore, + article_point_id, + build_filter, + organization_point_id, + person_point_id, +) + +logger = logging.getLogger(__name__) + + +TARGET_COLLECTIONS = { + "chunks": CHUNKS_COLLECTION, + "articles": ARTICLES_COLLECTION, + "persons": PERSONS_COLLECTION, + "organizations": ORGANIZATIONS_COLLECTION, +} + +_DOC_FIELD = { + CHUNKS_COLLECTION: "text", + ARTICLES_COLLECTION: "abstract", + PERSONS_COLLECTION: "biography", + ORGANIZATIONS_COLLECTION: "description", +} + + +@dataclass +class QueryResult: + target: str + rows: List[Dict[str, Any]] = field(default_factory=list) + related_persons: Dict[str, List[Dict[str, Any]]] = field(default_factory=dict) + related_organizations: Dict[str, List[Dict[str, Any]]] = field(default_factory=dict) + + +def _flatten(hits: Sequence[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Lift the payload one level so the caller doesn't have to dig.""" + out: List[Dict[str, Any]] = [] + for h in hits: + row = dict(h.get("payload") or {}) + row["id"] = h.get("id") + if "score" in h: + row["score"] = h["score"] + out.append(row) + return out + + +async def _vector_search( + cfg: InfoscienceIndexConfig, + store: QdrantStore, + collection: str, + query: str, + where: Optional[dict], + top_k: int, +) -> List[Dict[str, Any]]: + async with RCPEmbedder(cfg.rcp) as embedder: + vec = await embedder.embed_query(query) + hits = store.search( + collection, + query_vector=vec, + top_k=top_k, + query_filter=build_filter(where), + ) + return _flatten(hits) + + +def _lexical_search( + store: QdrantStore, + collection: str, + query: str, + where: Optional[dict], + top_k: int, +) -> List[Dict[str, Any]]: + # Qdrant has no built-in BM25; "lexical" here means + # filter-on-text-contains. Combine the user's where with a $contains + # over the canonical text field for the target collection. + text_field = _DOC_FIELD.get(collection, "text") + payload = dict(where or {}) + payload[text_field] = {"$contains": query} + hits = store.scroll( + collection, + query_filter=build_filter(payload), + limit=top_k, + ) + return _flatten(hits) + + +def _filter_only( + store: QdrantStore, + collection: str, + where: Optional[dict], + top_k: int, +) -> List[Dict[str, Any]]: + hits = store.scroll( + collection, + query_filter=build_filter(where), + limit=top_k, + ) + return _flatten(hits) + + +async def _rerank( + cfg: InfoscienceIndexConfig, + query: str, + rows: List[Dict[str, Any]], + target_collection: str, + top_n: int, +) -> List[Dict[str, Any]]: + if not rows: + return [] + doc_field = _DOC_FIELD.get(target_collection, "text") + docs = [ + r.get(doc_field) or r.get("text") or r.get("title") or r.get("name") or "" + for r in rows + ] + async with RCPReranker(cfg.rcp) as reranker: + hits = await reranker.rerank(query, docs, top_n=top_n) + if not hits: + return rows[:top_n] + return [rows[h.index] | {"rerank_score": h.score} for h in hits] + + +def _resolve_persons(store: QdrantStore, person_uuids: Sequence[str]) -> List[Dict[str, Any]]: + if not person_uuids: + return [] + ids = [person_point_id(u) for u in person_uuids] + return _flatten(store.lookup(PERSONS_COLLECTION, ids=ids)) + + +def _resolve_orgs(store: QdrantStore, org_uuids: Sequence[str]) -> List[Dict[str, Any]]: + if not org_uuids: + return [] + ids = [organization_point_id(u) for u in org_uuids] + return _flatten(store.lookup(ORGANIZATIONS_COLLECTION, ids=ids)) + + +def _row_key(row: Dict[str, Any]) -> str: + """Pick a stable per-row key for cross-entity join indexing.""" + return ( + row.get("article_uuid") + or row.get("person_uuid") + or row.get("org_uuid") + or row.get("chunk_id") + or row.get("id") + or "" + ) + + +async def query( + cfg: InfoscienceIndexConfig, + text: str, + *, + target: str = "chunks", + where: Optional[dict] = None, + top_k: int = 50, + top_n: int = 10, + mode: str = "hybrid", + with_authors: bool = False, + with_orgs: bool = False, +) -> QueryResult: + if target not in TARGET_COLLECTIONS: + msg = f"Unknown target {target!r}. Pick from {sorted(TARGET_COLLECTIONS)}." + raise ValueError(msg) + + store = QdrantStore.from_config(cfg) + name = TARGET_COLLECTIONS[target] + if not store.client.collection_exists(name): + msg = f"Collection {name!r} does not exist. Run `embed` first." + raise ValueError(msg) + + if mode == "filter-only": + rows = _filter_only(store, name, where, top_k) + elif mode == "lexical": + rows = _lexical_search(store, name, text, where, top_k) + elif mode == "vector-only": + rows = await _vector_search(cfg, store, name, text, where, top_k) + elif mode == "hybrid": + rows = await _vector_search(cfg, store, name, text, where, top_k) + rows = await _rerank(cfg, text, rows, name, top_n) + else: + msg = f"Unknown mode {mode!r}." + raise ValueError(msg) + + result = QueryResult(target=target, rows=rows) + if with_authors: + for row in rows: + uuids = row.get("author_uuids") or [] + row_key = _row_key(row) + if uuids and row_key: + result.related_persons[row_key] = _resolve_persons(store, uuids) + if with_orgs: + for row in rows: + uuids = row.get("org_uuids") or [] + row_key = _row_key(row) + if uuids and row_key: + result.related_organizations[row_key] = _resolve_orgs(store, uuids) + return result diff --git a/src/index/infoscience/rerank.py b/src/index/infoscience/rerank.py new file mode 100644 index 0000000..a9a630d --- /dev/null +++ b/src/index/infoscience/rerank.py @@ -0,0 +1,108 @@ +"""RCP reranker client. + +Posts to `{rcp.base_url}/rerank` with `{model, query, documents}`. +Returns reranked indices + scores. Trusts the server to apply +Qwen3-Reranker-8B's internal prompt format. +""" + +from __future__ import annotations + +import asyncio +import logging +from dataclasses import dataclass +from typing import List, Optional + +import httpx + +from .config import RcpConfig + +logger = logging.getLogger(__name__) + + +class RerankError(Exception): + pass + + +@dataclass +class RerankHit: + index: int + score: float + + +class RCPReranker: + def __init__(self, cfg: RcpConfig): + self._cfg = cfg + self._client: Optional[httpx.AsyncClient] = None + + async def __aenter__(self) -> "RCPReranker": + if not self._cfg.token: + msg = "RCP_TOKEN is required to call the RCP reranker endpoint." + raise RerankError(msg) + self._client = httpx.AsyncClient( + base_url=self._cfg.base_url.rstrip("/"), + headers={ + "Authorization": f"Bearer {self._cfg.token}", + "Content-Type": "application/json", + }, + timeout=self._cfg.timeout_seconds, + ) + return self + + async def __aexit__(self, exc_type, exc, tb) -> None: + if self._client is not None: + await self._client.aclose() + self._client = None + + @property + def client(self) -> httpx.AsyncClient: + if self._client is None: + msg = "RCPReranker must be used inside `async with`." + raise RuntimeError(msg) + return self._client + + async def rerank( + self, + query: str, + documents: List[str], + *, + top_n: Optional[int] = None, + ) -> List[RerankHit]: + if not documents: + return [] + body = { + "model": self._cfg.reranker_model, + "query": query, + "documents": documents, + } + if top_n is not None: + body["top_n"] = top_n + + last_exc: Optional[Exception] = None + for attempt in range(4): + try: + response = await self.client.post("/rerank", json=body) + response.raise_for_status() + payload = response.json() + break + except httpx.HTTPStatusError as exc: + if exc.response.status_code in (429, 500, 502, 503, 504): + last_exc = exc + await asyncio.sleep(2 ** attempt) + continue + raise + except httpx.HTTPError as exc: + last_exc = exc + await asyncio.sleep(2 ** attempt) + else: + msg = f"RCP rerank failed after retries: {last_exc}" + raise RerankError(msg) from last_exc + + results = payload.get("results") or payload.get("data") or [] + hits: List[RerankHit] = [] + for r in results: + idx = r.get("index") + score = r.get("relevance_score") or r.get("score") + if idx is None or score is None: + continue + hits.append(RerankHit(index=int(idx), score=float(score))) + return hits diff --git a/src/index/infoscience/storage/__init__.py b/src/index/infoscience/storage/__init__.py new file mode 100644 index 0000000..c3f4fb3 --- /dev/null +++ b/src/index/infoscience/storage/__init__.py @@ -0,0 +1,11 @@ +"""Storage layer for the infoscience index — DuckDB tables for articles, +persons, organisations, their bipartite edges, extracted artefact links, +and the chunks bookkeeping shared with the Qdrant vector store. + +Mirrors the sister-index pattern in `src/index/openalex/storage/` and +`src/index/huggingface/storage/`. +""" + +from .duckdb_store import DuckDBStore + +__all__ = ["DuckDBStore"] diff --git a/src/index/infoscience/storage/duckdb_store.py b/src/index/infoscience/storage/duckdb_store.py new file mode 100644 index 0000000..d094a17 --- /dev/null +++ b/src/index/infoscience/storage/duckdb_store.py @@ -0,0 +1,212 @@ +"""DuckDB lifecycle, schema bootstrap, and upsert helpers for the +infoscience index. Mirrors `src/index/openalex/storage/duckdb_store.py`. + +Source of truth for ingest is the on-disk `raw/{items,persons,organizations}/` +JSON tree produced by the discover / fetch-related stages and the +`scripts/dump_link_articles.py` link sweep. Re-ingesting from those JSONs +is idempotent — every upsert keys on the DSpace UUID. +""" + +from __future__ import annotations + +import datetime as _dt +import json +import logging +from contextlib import contextmanager +from pathlib import Path +from typing import TYPE_CHECKING, Any, Iterable, Iterator + +import duckdb + +from src.index.infoscience.paths import duckdb_path + +if TYPE_CHECKING: + pass + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +class DuckDBStore: + """Thin wrapper around DuckDB tuned for the infoscience schema. + + Construct with `DuckDBStore.open()` for the canonical path. Re-running + `bootstrap()` is idempotent; `transaction()` batches writes. + """ + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open(cls, db_path: Path | None = None) -> "DuckDBStore": + if db_path is None: + db_path = duckdb_path() + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + conn = self.connect() + conn.execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + @contextmanager + def transaction(self) -> Iterator[None]: + conn = self.connect() + conn.execute("BEGIN TRANSACTION") + try: + yield + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + + def count(self, table: str) -> int: + conn = self.connect() + return int(conn.execute(f"SELECT count(*) FROM {table}").fetchone()[0]) + + @staticmethod + def _now() -> _dt.datetime: + return _dt.datetime.now(tz=_dt.timezone.utc).replace(tzinfo=None) + + # ---- Upserts --------------------------------------------------------- + + def upsert_article(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="articles", + cols=( + "article_uuid", + "title", + "abstract", + "doi", + "publication_year", + "publication_type", + "journal", + "language", + "infoscience_url", + ), + row=row, + raw=raw, + ) + + def upsert_person(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="persons", + cols=( + "person_uuid", + "display_name", + "given_name", + "family_name", + "orcid", + "sciper_id", + "primary_affiliation", + "primary_affiliation_uuid", + ), + row=row, + raw=raw, + ) + + def upsert_organization(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="organizations", + cols=( + "org_uuid", + "name", + "acronym", + "parent_org_uuid", + "sciper_unit_id", + "ror_id", + ), + row=row, + raw=raw, + ) + + def _upsert( + self, + *, + table: str, + cols: tuple[str, ...], + row: dict[str, Any], + raw: dict[str, Any], + ) -> None: + all_cols = (*cols, "raw", "ingested_at") + placeholders = ", ".join(["?"] * len(all_cols)) + col_list = ", ".join(all_cols) + update_cols = ", ".join( + f"{c} = excluded.{c}" for c in (*cols[1:], "raw", "ingested_at") + ) + sql = ( + f"INSERT INTO {table} ({col_list}) VALUES ({placeholders}) " + f"ON CONFLICT ({cols[0]}) DO UPDATE SET {update_cols}" + ) + values: list[Any] = [row.get(c) for c in cols] + values.append(json.dumps(raw, ensure_ascii=False)) + values.append(self._now()) + self.connect().execute(sql, values) + + def upsert_article_persons( + self, + article_uuid: str, + person_positions: Iterable[tuple[str, int | None]], + ) -> None: + conn = self.connect() + for person_uuid, position in person_positions: + conn.execute( + "INSERT INTO article_persons (article_uuid, person_uuid, position) " + "VALUES (?, ?, ?) " + "ON CONFLICT (article_uuid, person_uuid) DO UPDATE SET position = " + "COALESCE(LEAST(article_persons.position, excluded.position), " + " excluded.position, article_persons.position)", + [article_uuid, person_uuid, position], + ) + + def upsert_article_orgs( + self, + article_uuid: str, + org_field_pairs: Iterable[tuple[str, str]], + ) -> None: + conn = self.connect() + for org_uuid, field in org_field_pairs: + conn.execute( + "INSERT INTO article_orgs (article_uuid, org_uuid, field) " + "VALUES (?, ?, ?) ON CONFLICT DO NOTHING", + [article_uuid, org_uuid, field], + ) + + def upsert_article_links( + self, + article_uuid: str, + rows: Iterable[tuple[str, str, str]], + ) -> None: + """rows: iterable of (host_label, url, source).""" + conn = self.connect() + for host_label, url, source in rows: + conn.execute( + "INSERT INTO article_links (article_uuid, host_label, url, source) " + "VALUES (?, ?, ?, ?) ON CONFLICT DO NOTHING", + [article_uuid, host_label, url, source], + ) diff --git a/src/index/infoscience/storage/ingest_raw.py b/src/index/infoscience/storage/ingest_raw.py new file mode 100644 index 0000000..3d95266 --- /dev/null +++ b/src/index/infoscience/storage/ingest_raw.py @@ -0,0 +1,367 @@ +"""Ingest from on-disk raw/* JSON tree → DuckDB. + +Uses DuckDB's native `read_json_objects` to slurp every file in parallel +in C++ — orders of magnitude faster than the previous Python-level loop, +which choked on WSL2's 9P filesystem when walking ~16k small JSON files. + +The discover / fetch-related stages and `scripts/dump_link_articles.py` +write their output to: + + data/index/infoscience/raw/items/{uuid}.json + data/index/infoscience/raw/persons/{uuid}.json + data/index/infoscience/raw/organizations/{uuid}.json + +This module reads each glob, projects the DSpace metadata fields we +care about into typed columns via JSON path expressions, and upserts +into the DuckDB tables defined in `schema.sql`. Idempotent on UUID. +""" + +from __future__ import annotations + +import json +import logging +from pathlib import Path +from typing import Any, Dict + +from src.index.infoscience.paths import ( + raw_items_dir, + raw_organizations_dir, + raw_persons_dir, +) + +from .duckdb_store import DuckDBStore + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Articles +# --------------------------------------------------------------------------- + +# Note: DSpace metadata field names contain dots, so the JSON path needs them +# quoted. Path templates are reused for both the article columns and the +# child unnest queries. +_ARTICLE_UPSERT_SQL = """ +CREATE OR REPLACE TEMP TABLE _items AS + SELECT json FROM read_json_objects(?); + +INSERT INTO articles + (article_uuid, title, abstract, doi, publication_year, publication_type, + journal, language, infoscience_url, raw, ingested_at) +SELECT + json_extract_string(json, '$.uuid') AS article_uuid, + json_extract_string(json, '$.metadata."dc.title"[0].value') AS title, + json_extract_string(json, '$.metadata."dc.description.abstract"[0].value') AS abstract, + json_extract_string(json, '$.metadata."dc.identifier.doi"[0].value') AS doi, + TRY_CAST( + SUBSTR(json_extract_string(json, '$.metadata."dc.date.issued"[0].value'), 1, 4) + AS INTEGER + ) AS publication_year, + json_extract_string(json, '$.metadata."dc.type"[0].value') AS publication_type, + json_extract_string(json, '$.metadata."dc.relation.journal"[0].value') AS journal, + json_extract_string(json, '$.metadata."dc.language.iso"[0].value') AS language, + 'https://infoscience.epfl.ch/entities/publication/' || json_extract_string(json, '$.uuid') AS infoscience_url, + json AS raw, + CURRENT_TIMESTAMP AS ingested_at +FROM _items +WHERE json_extract_string(json, '$.uuid') IS NOT NULL +ON CONFLICT (article_uuid) DO UPDATE SET + title = excluded.title, + abstract = excluded.abstract, + doi = excluded.doi, + publication_year = excluded.publication_year, + publication_type = excluded.publication_type, + journal = excluded.journal, + language = excluded.language, + infoscience_url = excluded.infoscience_url, + raw = excluded.raw, + ingested_at = excluded.ingested_at; +""" + +_ARTICLE_PERSONS_SQL = """ +INSERT INTO article_persons (article_uuid, person_uuid, position) +SELECT + json_extract_string(i.json, '$.uuid') AS article_uuid, + json_extract_string(t.author, '$.authority') AS person_uuid, + TRY_CAST(json_extract_string(t.author, '$.place') AS INTEGER) AS position +FROM _items i, + UNNEST( + CAST( + json_extract(i.json, '$.metadata."dc.contributor.author"') AS JSON[] + ) + ) AS t(author) +WHERE json_extract_string(t.author, '$.authority') IS NOT NULL +ON CONFLICT (article_uuid, person_uuid) DO UPDATE SET + position = COALESCE( + LEAST(article_persons.position, excluded.position), + excluded.position, + article_persons.position + ); +""" + +# Unnest each org-bearing metadata field separately and union the rows so +# the `field` column records which DSpace key the authority came from. +_ARTICLE_ORGS_SQL_TEMPLATE = """ +INSERT INTO article_orgs (article_uuid, org_uuid, field) +SELECT article_uuid, org_uuid, field FROM ( + {unions} +) sub +WHERE org_uuid IS NOT NULL +ON CONFLICT DO NOTHING; +""" + +_ORG_FIELDS = ( + "cris.virtual.department", + "cris.virtual.parent-organization", + "oairecerif.author.affiliation", +) + + +def _orgs_union_sql() -> str: + parts = [] + for field in _ORG_FIELDS: + parts.append( + f""" + SELECT + json_extract_string(i.json, '$.uuid') AS article_uuid, + json_extract_string(t.org, '$.authority') AS org_uuid, + '{field}' AS field + FROM _items i, + UNNEST( + CAST( + json_extract(i.json, '$.metadata."{field}"') AS JSON[] + ) + ) AS t(org) + """ + ) + return " UNION ALL ".join(parts) + + +# --------------------------------------------------------------------------- +# Persons +# --------------------------------------------------------------------------- + +_PERSON_UPSERT_SQL = """ +CREATE OR REPLACE TEMP TABLE _persons AS + SELECT json FROM read_json_objects(?); + +INSERT INTO persons + (person_uuid, display_name, given_name, family_name, orcid, sciper_id, + primary_affiliation, primary_affiliation_uuid, raw, ingested_at) +SELECT + json_extract_string(json, '$.uuid') AS person_uuid, + COALESCE( + json_extract_string(json, '$.metadata."dc.title"[0].value'), + TRIM( + COALESCE(json_extract_string(json, '$.metadata."person.givenName"[0].value'), '') + || ' ' || + COALESCE(json_extract_string(json, '$.metadata."person.familyName"[0].value'), '') + ) + ) AS display_name, + COALESCE( + json_extract_string(json, '$.metadata."person.givenName"[0].value'), + json_extract_string(json, '$.metadata."eperson.firstname"[0].value') + ) AS given_name, + COALESCE( + json_extract_string(json, '$.metadata."person.familyName"[0].value'), + json_extract_string(json, '$.metadata."eperson.lastname"[0].value') + ) AS family_name, + json_extract_string(json, '$.metadata."person.identifier.orcid"[0].value') AS orcid, + COALESCE( + json_extract_string(json, '$.metadata."epfl.sciperId"[0].value'), + json_extract_string(json, '$.metadata."cris.virtual.sciperId"[0].value') + ) AS sciper_id, + json_extract_string(json, '$.metadata."person.affiliation.name"[0].value') AS primary_affiliation, + json_extract_string(json, '$.metadata."person.affiliation.name"[0].authority') AS primary_affiliation_uuid, + json AS raw, + CURRENT_TIMESTAMP AS ingested_at +FROM _persons +WHERE json_extract_string(json, '$.uuid') IS NOT NULL +ON CONFLICT (person_uuid) DO UPDATE SET + display_name = excluded.display_name, + given_name = excluded.given_name, + family_name = excluded.family_name, + orcid = excluded.orcid, + sciper_id = excluded.sciper_id, + primary_affiliation = excluded.primary_affiliation, + primary_affiliation_uuid = excluded.primary_affiliation_uuid, + raw = excluded.raw, + ingested_at = excluded.ingested_at; +""" + + +# --------------------------------------------------------------------------- +# Organizations +# --------------------------------------------------------------------------- + +_ORG_UPSERT_SQL = """ +CREATE OR REPLACE TEMP TABLE _orgs AS + SELECT json FROM read_json_objects(?); + +INSERT INTO organizations + (org_uuid, name, acronym, parent_org_uuid, sciper_unit_id, ror_id, raw, ingested_at) +SELECT + json_extract_string(json, '$.uuid') AS org_uuid, + COALESCE( + json_extract_string(json, '$.metadata."dc.title"[0].value'), + json_extract_string(json, '$.metadata."organization.legalName"[0].value') + ) AS name, + json_extract_string(json, '$.metadata."organization.identifier.acronym"[0].value') AS acronym, + json_extract_string(json, '$.metadata."cris.virtual.parent-organization"[0].authority') AS parent_org_uuid, + COALESCE( + json_extract_string(json, '$.metadata."cris.virtual.unitId"[0].value'), + json_extract_string(json, '$.metadata."epfl.unitId"[0].value') + ) AS sciper_unit_id, + json_extract_string(json, '$.metadata."cris.virtualsource.ror"[0].value') AS ror_id, + json AS raw, + CURRENT_TIMESTAMP AS ingested_at +FROM _orgs +WHERE json_extract_string(json, '$.uuid') IS NOT NULL +ON CONFLICT (org_uuid) DO UPDATE SET + name = excluded.name, + acronym = excluded.acronym, + parent_org_uuid = excluded.parent_org_uuid, + sciper_unit_id = excluded.sciper_unit_id, + ror_id = excluded.ror_id, + raw = excluded.raw, + ingested_at = excluded.ingested_at; +""" + + +# --------------------------------------------------------------------------- +# Public ingest functions +# --------------------------------------------------------------------------- + + +def ingest_articles(store: DuckDBStore, items_dir: Path | None = None) -> int: + items_dir = items_dir or raw_items_dir() + glob = str(items_dir / "*.json") + conn = store.connect() + # Run the three statements in one transaction. + conn.execute("BEGIN TRANSACTION") + try: + for stmt in _ARTICLE_UPSERT_SQL.strip().split(";"): + stmt = stmt.strip() + if not stmt: + continue + if "read_json_objects(?)" in stmt: + conn.execute(stmt + ";", [glob]) + else: + conn.execute(stmt + ";") + conn.execute(_ARTICLE_PERSONS_SQL) + conn.execute(_ARTICLE_ORGS_SQL_TEMPLATE.format(unions=_orgs_union_sql())) + n = conn.execute("SELECT count(*) FROM _items").fetchone()[0] + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + logger.info("ingest_articles: %d", n) + return int(n) + + +def ingest_persons(store: DuckDBStore, persons_dir: Path | None = None) -> int: + persons_dir = persons_dir or raw_persons_dir() + glob = str(persons_dir / "*.json") + conn = store.connect() + conn.execute("BEGIN TRANSACTION") + try: + for stmt in _PERSON_UPSERT_SQL.strip().split(";"): + stmt = stmt.strip() + if not stmt: + continue + if "read_json_objects(?)" in stmt: + conn.execute(stmt + ";", [glob]) + else: + conn.execute(stmt + ";") + n = conn.execute("SELECT count(*) FROM _persons").fetchone()[0] + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + logger.info("ingest_persons: %d", n) + return int(n) + + +def ingest_organizations( + store: DuckDBStore, + orgs_dir: Path | None = None, +) -> int: + orgs_dir = orgs_dir or raw_organizations_dir() + glob = str(orgs_dir / "*.json") + conn = store.connect() + conn.execute("BEGIN TRANSACTION") + try: + for stmt in _ORG_UPSERT_SQL.strip().split(";"): + stmt = stmt.strip() + if not stmt: + continue + if "read_json_objects(?)" in stmt: + conn.execute(stmt + ";", [glob]) + else: + conn.execute(stmt + ";") + n = conn.execute("SELECT count(*) FROM _orgs").fetchone()[0] + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + logger.info("ingest_organizations: %d", n) + return int(n) + + +def ingest_links_dump(store: DuckDBStore, dump_path: Path) -> int: + """Read a `scripts/dump_link_articles.py` output and upsert article_links. + + The heavy dump is a single ~400 MB JSON. Read once with DuckDB's JSON + loader so the whole `articles` array becomes a row set we can unnest + in pure SQL. + """ + dump_path = Path(dump_path) + if not dump_path.exists(): + return 0 + + # Pull only the metadata we need (queries map + articles array) so we + # don't materialise the full ~400 MB persons/organizations sub-objects. + raw = json.loads(dump_path.read_text(encoding="utf-8")) + queries = raw.get("queries", {}) + articles = raw.get("articles", []) + + rows: list[tuple[str, str, str, str]] = [] + for art in articles: + uuid = art.get("uuid") + if not uuid: + continue + for label in art.get("matched_phrases", []) or []: + phrase = queries.get(label, "") + rows.append((uuid, label, phrase, "phrase_match")) + for label, urls in (art.get("body_urls") or {}).items(): + for url in urls or []: + rows.append((uuid, label, url, "body_text")) + + if not rows: + return 0 + + conn = store.connect() + conn.execute("BEGIN TRANSACTION") + try: + conn.executemany( + "INSERT INTO article_links (article_uuid, host_label, url, source) " + "VALUES (?, ?, ?, ?) ON CONFLICT DO NOTHING", + rows, + ) + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + logger.info("ingest_links_dump: %d link rows", len(rows)) + return len(rows) + + +def ingest_all(store: DuckDBStore, *, links_dump: Path | None = None) -> Dict[str, int]: + summary = { + "articles": ingest_articles(store), + "persons": ingest_persons(store), + "organizations": ingest_organizations(store), + } + if links_dump: + summary["link_rows"] = ingest_links_dump(store, links_dump) + return summary diff --git a/src/index/infoscience/storage/schema.sql b/src/index/infoscience/storage/schema.sql new file mode 100644 index 0000000..0d48134 --- /dev/null +++ b/src/index/infoscience/storage/schema.sql @@ -0,0 +1,94 @@ +-- Canonical DuckDB schema for the infoscience index module. +-- Idempotent: every statement uses IF NOT EXISTS so re-runs are safe. +-- Mirrors the openalex / huggingface sister-index pattern. + +CREATE TABLE IF NOT EXISTS articles ( + article_uuid TEXT PRIMARY KEY, + title TEXT, + abstract TEXT, + doi TEXT, + publication_year INTEGER, + publication_type TEXT, + journal TEXT, + language TEXT, + infoscience_url TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS persons ( + person_uuid TEXT PRIMARY KEY, + display_name TEXT, + given_name TEXT, + family_name TEXT, + orcid TEXT, + sciper_id TEXT, + primary_affiliation TEXT, + primary_affiliation_uuid TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS organizations ( + org_uuid TEXT PRIMARY KEY, + name TEXT, + acronym TEXT, + parent_org_uuid TEXT, + sciper_unit_id TEXT, + ror_id TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +-- Bipartite article ↔ person, ordered by author position. +CREATE TABLE IF NOT EXISTS article_persons ( + article_uuid TEXT NOT NULL, + person_uuid TEXT NOT NULL, + position INTEGER, + PRIMARY KEY (article_uuid, person_uuid) +); + +-- Bipartite article ↔ organisation. `field` records which DSpace field +-- the authority came from (department / parent-organization / affiliation). +CREATE TABLE IF NOT EXISTS article_orgs ( + article_uuid TEXT NOT NULL, + org_uuid TEXT NOT NULL, + field TEXT NOT NULL, + PRIMARY KEY (article_uuid, org_uuid, field) +); + +-- Every artefact-host URL extracted from an article body, plus the Solr +-- phrase that matched it. Populated by the link sweep +-- (scripts/dump_link_articles.py) and refreshed on subsequent runs. +CREATE TABLE IF NOT EXISTS article_links ( + article_uuid TEXT NOT NULL, + host_label TEXT NOT NULL, -- 'github' | 'arxiv' | 'orcid' | ... + url TEXT NOT NULL, + source TEXT NOT NULL CHECK (source IN ('phrase_match', 'body_text')), + PRIMARY KEY (article_uuid, host_label, url, source) +); + +-- chunk_id is deterministic: uuid5(NAMESPACE_URL, "||") +-- so the primary key alone provides the (entity_type, entity_id, chunk_index) +-- uniqueness guarantee. See `embed/pipeline.py:_chunk_id` for the canonical helper +-- (or build/store.py if it lives there for infoscience). +CREATE TABLE IF NOT EXISTS chunks ( + chunk_id TEXT PRIMARY KEY, + entity_type TEXT NOT NULL, + entity_id TEXT NOT NULL, + chunk_index INTEGER NOT NULL, + text TEXT NOT NULL, + token_count INTEGER NOT NULL, + vector_id TEXT NOT NULL, + embedded_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS idx_articles_year ON articles (publication_year); +CREATE INDEX IF NOT EXISTS idx_articles_doi ON articles (doi); +CREATE INDEX IF NOT EXISTS idx_persons_orcid ON persons (orcid); +CREATE INDEX IF NOT EXISTS idx_persons_sciper ON persons (sciper_id); +CREATE INDEX IF NOT EXISTS idx_orgs_ror ON organizations (ror_id); +CREATE INDEX IF NOT EXISTS idx_orgs_parent ON organizations (parent_org_uuid); +CREATE INDEX IF NOT EXISTS idx_article_links_host ON article_links (host_label); +CREATE INDEX IF NOT EXISTS idx_article_links_url ON article_links (url); +CREATE INDEX IF NOT EXISTS idx_chunks_entity ON chunks (entity_type, entity_id); diff --git a/src/index/infoscience/store.py b/src/index/infoscience/store.py new file mode 100644 index 0000000..03bdf47 --- /dev/null +++ b/src/index/infoscience/store.py @@ -0,0 +1,461 @@ +"""Qdrant client wrapper for the Infoscience index. + +Per-entity collections following the canonical pattern in this repo: + + * ``infoscience_chunks`` — publication body fragments + * ``infoscience_articles`` — one row per matched publication + * ``infoscience_persons`` — Person entities resolved from author + authority IDs + * ``infoscience_organizations`` — OrgUnit entities + +See `.internal/ror/qdrant-setup.md` for the broader convention. + +Qdrant is a server-side store; configuration comes from +`config.qdrant` (URL, gRPC toggle, optional API key) which respects the +`INDEX_QDRANT_*` env-var overrides at config-load time. + +Point IDs are deterministic UUIDv5 over a stable string key: + + * chunks: ``"{article_uuid}::{chunk_index}"`` + * articles: ``article_uuid`` + * persons: ``person_uuid`` + * organizations: ``org_uuid`` + +Payloads carry the full entity record so structured filters work natively +(no list-flattening tricks needed). +""" + +from __future__ import annotations + +import json +import logging +import uuid +from typing import Any, Iterable, List, Optional, Sequence + +from qdrant_client import QdrantClient, models + +from .config import InfoscienceIndexConfig, QdrantConfig +from .models import ( + ArticleRecord, + ChunkRecord, + OrganizationRecord, + PersonRecord, +) + +logger = logging.getLogger(__name__) + +CHUNKS_COLLECTION = "infoscience_chunks" +ARTICLES_COLLECTION = "infoscience_articles" +PERSONS_COLLECTION = "infoscience_persons" +ORGANIZATIONS_COLLECTION = "infoscience_organizations" + +ALL_COLLECTIONS: tuple[str, ...] = ( + CHUNKS_COLLECTION, + ARTICLES_COLLECTION, + PERSONS_COLLECTION, + ORGANIZATIONS_COLLECTION, +) + +# Stable namespace for UUIDv5 point IDs scoped to this index. +_INDEX_NAMESPACE = uuid.UUID("8a4f0e6b-3c11-4f25-9a1f-3a0e2c5b7d11") + +_GH_HOSTS = ("github.com",) +_HF_HOSTS = ("huggingface.co", "hf.co") + + +def _has_host(urls: Sequence[str], hosts: Iterable[str]) -> bool: + if not urls: + return False + needles = tuple(hosts) + return any(any(h in u for h in needles) for u in urls) + + +def _point_id(key: str) -> str: + return str(uuid.uuid5(_INDEX_NAMESPACE, key)) + + +def _drop_none(payload: dict[str, Any]) -> dict[str, Any]: + return {k: v for k, v in payload.items() if v is not None} + + +# --------------------------------------------------------------------------- +# Payloads +# --------------------------------------------------------------------------- + + +def chunk_payload(rec: ChunkRecord) -> dict[str, Any]: + return _drop_none({ + "chunk_id": rec.chunk_id, + "article_uuid": rec.article_uuid, + "chunk_index": rec.chunk_index, + "text": rec.text, + "title": rec.title, + "abstract": rec.abstract, + "authors": rec.authors or None, + "author_uuids": rec.author_uuids or None, + "doi": rec.doi, + "publication_date": rec.publication_date, + "year": rec.year, + "publication_type": rec.publication_type, + "language": rec.language, + "subjects": rec.subjects or None, + "keywords": rec.keywords or None, + "lab": rec.lab, + "lab_uuid": rec.lab_uuid, + "org_uuids": rec.org_uuids or None, + "infoscience_url": rec.infoscience_url, + "matched_urls": rec.matched_urls or None, + "has_github_match": _has_host(rec.matched_urls, _GH_HOSTS), + "has_hf_match": _has_host(rec.matched_urls, _HF_HOSTS), + }) + + +def article_payload(rec: ArticleRecord) -> dict[str, Any]: + return _drop_none({ + "article_uuid": rec.article_uuid, + "title": rec.title, + "abstract": rec.abstract, + "keywords": rec.keywords or None, + "subjects": rec.subjects or None, + "authors": rec.authors or None, + "author_uuids": rec.author_uuids or None, + "doi": rec.doi, + "publication_date": rec.publication_date, + "year": rec.year, + "publication_type": rec.publication_type, + "language": rec.language, + "journal": rec.journal, + "journal_uuid": rec.journal_uuid, + "lab": rec.lab, + "lab_uuid": rec.lab_uuid, + "org_uuids": rec.org_uuids or None, + "infoscience_url": rec.infoscience_url, + "matched_urls": rec.matched_urls or None, + "chunk_count": rec.chunk_count, + "has_github_match": _has_host(rec.matched_urls, _GH_HOSTS), + "has_hf_match": _has_host(rec.matched_urls, _HF_HOSTS), + }) + + +def person_payload(rec: PersonRecord) -> dict[str, Any]: + return _drop_none({ + "person_uuid": rec.person_uuid, + "name": rec.name, + "given_name": rec.given_name, + "family_name": rec.family_name, + "orcid": rec.orcid, + "sciper_id": rec.sciper_id, + "scopus_id": rec.scopus_id, + "email_hash": rec.email_hash, + "primary_affiliation": rec.primary_affiliation, + "primary_affiliation_uuid": rec.primary_affiliation_uuid, + "affiliation_uuids": rec.affiliation_uuids or None, + "position": rec.position, + "biography": rec.biography, + "research_interests": rec.research_interests or None, + "profile_url": rec.profile_url, + "related_article_uuids": rec.related_article_uuids or None, + }) + + +def organization_payload(rec: OrganizationRecord) -> dict[str, Any]: + return _drop_none({ + "org_uuid": rec.org_uuid, + "name": rec.name, + "acronym": rec.acronym, + "aliases": rec.aliases or None, + "parent_org_uuid": rec.parent_org_uuid, + "parent_org_chain": rec.parent_org_chain or None, + "parent_org_chain_names": rec.parent_org_chain_names or None, + "description": rec.description, + "sciper_unit_id": rec.sciper_unit_id, + "ror_id": rec.ror_id, + "unit_manager_uuid": rec.unit_manager_uuid, + "unit_manager_name": rec.unit_manager_name, + "infoscience_url": rec.infoscience_url, + "related_article_uuids": rec.related_article_uuids or None, + }) + + +# --------------------------------------------------------------------------- +# Store wrapper +# --------------------------------------------------------------------------- + +# Qdrant default body limit is 32 MB; 64 × ~40 KB vector + payload ≈ 3 MB. +_UPSERT_BATCH_SIZE = 64 + + +class QdrantStore: + """Per-entity collection bootstrap + upsert + filtered search.""" + + def __init__(self, qcfg: QdrantConfig, *, vector_size: int): + self._qcfg = qcfg + self._dim = vector_size + self._client = QdrantClient( + url=qcfg.url, + api_key=qcfg.api_key, + prefer_grpc=qcfg.prefer_grpc, + ) + + @classmethod + def from_config(cls, cfg: InfoscienceIndexConfig) -> "QdrantStore": + return cls(cfg.qdrant, vector_size=cfg.rcp.embedding_dim) + + @property + def client(self) -> QdrantClient: + return self._client + + def ensure_collection(self, name: str) -> None: + if self._client.collection_exists(name): + return + self._client.create_collection( + collection_name=name, + vectors_config=models.VectorParams( + size=self._dim, + distance=models.Distance.COSINE, + ), + ) + logger.info("created qdrant collection %s (dim=%d)", name, self._dim) + + def collection_count(self, name: str) -> int: + if not self._client.collection_exists(name): + return 0 + return int(self._client.count(collection_name=name, exact=True).count) + + def upsert_points( + self, + collection: str, + *, + ids: Sequence[str], + vectors: Sequence[Sequence[float]], + payloads: Sequence[dict[str, Any]], + ) -> int: + if not (len(ids) == len(vectors) == len(payloads)): + msg = "ids/vectors/payloads must be the same length" + raise ValueError(msg) + if not ids: + return 0 + self.ensure_collection(collection) + points = [ + models.PointStruct(id=pid, vector=list(vec), payload=payload) + for pid, vec, payload in zip(ids, vectors, payloads, strict=False) + ] + # Qdrant rejects request bodies above 32 MB. A 4096-dim float vector + # serialises to ~40 KB, so a chunks payload with body text easily + # crosses the cap in one shot — split into sub-batches. + for start in range(0, len(points), _UPSERT_BATCH_SIZE): + self._client.upsert( + collection_name=collection, + points=points[start : start + _UPSERT_BATCH_SIZE], + ) + return len(points) + + def search( + self, + collection: str, + *, + query_vector: Sequence[float], + top_k: int = 50, + query_filter: Optional[models.Filter] = None, + ) -> List[dict[str, Any]]: + if not self._client.collection_exists(collection): + return [] + hits = self._client.query_points( + collection_name=collection, + query=list(query_vector), + limit=top_k, + query_filter=query_filter, + with_payload=True, + ).points + return [ + {"id": str(p.id), "score": float(p.score), "payload": p.payload or {}} + for p in hits + ] + + def lookup( + self, + collection: str, + *, + ids: Sequence[str], + ) -> List[dict[str, Any]]: + """Read-only retrieval by point IDs.""" + if not ids or not self._client.collection_exists(collection): + return [] + records = self._client.retrieve( + collection_name=collection, + ids=list(ids), + with_payload=True, + ) + return [ + {"id": str(r.id), "payload": r.payload or {}} + for r in records + ] + + def scroll( + self, + collection: str, + *, + query_filter: Optional[models.Filter] = None, + limit: int = 50, + ) -> List[dict[str, Any]]: + """Filter-only listing (no vector); returns up to `limit` points.""" + if not self._client.collection_exists(collection): + return [] + records, _next = self._client.scroll( + collection_name=collection, + scroll_filter=query_filter, + limit=limit, + with_payload=True, + ) + return [ + {"id": str(r.id), "payload": r.payload or {}} + for r in records + ] + + +# --------------------------------------------------------------------------- +# Filter builder +# --------------------------------------------------------------------------- + + +def build_filter(payload: Optional[dict[str, Any]]) -> Optional[models.Filter]: + """Translate a JSON-style filter dict into a Qdrant ``Filter``. + + Supported operators per key: + * ``{"$eq": value}`` (or scalar shorthand) + * ``{"$ne": value}`` + * ``{"$gte": v, "$lte": v}`` (range) + * ``{"$in": [v, ...]}`` (any of) + * ``{"$contains": "needle"}`` for list-of-string fields + * raw ``[...]`` shorthand → ``MatchAny`` + """ + if not payload: + return None + must: list[models.Condition] = [] + must_not: list[models.Condition] = [] + for key, value in payload.items(): + if isinstance(value, dict): + if "$ne" in value: + must_not.append(models.FieldCondition( + key=key, match=models.MatchValue(value=value["$ne"]), + )) + continue + if "$in" in value: + must.append(models.FieldCondition( + key=key, match=models.MatchAny(any=list(value["$in"])), + )) + continue + if "$contains" in value: + must.append(models.FieldCondition( + key=key, match=models.MatchValue(value=value["$contains"]), + )) + continue + if "$gte" in value or "$lte" in value: + must.append(models.FieldCondition( + key=key, + range=models.Range( + gte=value.get("$gte"), lte=value.get("$lte"), + ), + )) + continue + if "$eq" in value: + must.append(models.FieldCondition( + key=key, match=models.MatchValue(value=value["$eq"]), + )) + continue + msg = f"Unsupported operator dict for {key!r}: {value!r}" + raise ValueError(msg) + if isinstance(value, list): + must.append(models.FieldCondition( + key=key, match=models.MatchAny(any=value), + )) + continue + must.append(models.FieldCondition( + key=key, match=models.MatchValue(value=value), + )) + return models.Filter( + must=must or None, + must_not=must_not or None, + ) + + +# --------------------------------------------------------------------------- +# Convenience upsert helpers for each entity type +# --------------------------------------------------------------------------- + + +def upsert_chunks( + store: QdrantStore, + records: Sequence[ChunkRecord], + embeddings: Sequence[Sequence[float]], +) -> int: + if not records: + return 0 + ids = [_point_id(r.chunk_id) for r in records] + payloads = [chunk_payload(r) for r in records] + return store.upsert_points( + CHUNKS_COLLECTION, ids=ids, vectors=list(embeddings), payloads=payloads, + ) + + +def upsert_articles( + store: QdrantStore, + records: Sequence[ArticleRecord], + embeddings: Sequence[Optional[Sequence[float]]], + dim: int, +) -> int: + if not records: + return 0 + placeholder = [0.0] * dim + ids = [_point_id(r.article_uuid) for r in records] + vectors = [list(e) if e is not None else placeholder for e in embeddings] + payloads = [article_payload(r) for r in records] + return store.upsert_points( + ARTICLES_COLLECTION, ids=ids, vectors=vectors, payloads=payloads, + ) + + +def upsert_persons( + store: QdrantStore, + records: Sequence[PersonRecord], + embeddings: Sequence[Optional[Sequence[float]]], + dim: int, +) -> int: + if not records: + return 0 + placeholder = [0.0] * dim + ids = [_point_id(r.person_uuid) for r in records] + vectors = [list(e) if e is not None else placeholder for e in embeddings] + payloads = [person_payload(r) for r in records] + return store.upsert_points( + PERSONS_COLLECTION, ids=ids, vectors=vectors, payloads=payloads, + ) + + +def upsert_organizations( + store: QdrantStore, + records: Sequence[OrganizationRecord], + embeddings: Sequence[Optional[Sequence[float]]], + dim: int, +) -> int: + if not records: + return 0 + placeholder = [0.0] * dim + ids = [_point_id(r.org_uuid) for r in records] + vectors = [list(e) if e is not None else placeholder for e in embeddings] + payloads = [organization_payload(r) for r in records] + return store.upsert_points( + ORGANIZATIONS_COLLECTION, ids=ids, vectors=vectors, payloads=payloads, + ) + + +def article_point_id(article_uuid: str) -> str: + return _point_id(article_uuid) + + +def person_point_id(person_uuid: str) -> str: + return _point_id(person_uuid) + + +def organization_point_id(org_uuid: str) -> str: + return _point_id(org_uuid) diff --git a/src/index/infoscience/text_fetch.py b/src/index/infoscience/text_fetch.py new file mode 100644 index 0000000..aa465c1 --- /dev/null +++ b/src/index/infoscience/text_fetch.py @@ -0,0 +1,130 @@ +"""Text fetch stage: download the TEXT bundle's plaintext bitstream per item. + +For each UUID in `raw/items/`, locate the bundle named `TEXT`, find a +`.txt` / `text/plain` bitstream inside, download it, and save verbatim to +`text/{uuid}.txt`. Skips items already on disk and items with no TEXT +bundle. We never touch PDFs. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from pathlib import Path +from typing import Optional + +import httpx + +from .config import InfoscienceIndexConfig +from .dspace import DSpaceClient +from .paths import raw_items_dir, text_dir + +logger = logging.getLogger(__name__) + +_TEXT_BUNDLE_NAME = "TEXT" + + +def _pick_text_bitstream(bitstreams: list) -> Optional[dict]: + """Pick the first bitstream that looks like extracted plain text.""" + for bs in bitstreams: + name = (bs.get("name") or "").lower() + mime = (bs.get("metadata", {}).get("dc.format.mimetype", [{}]) or [{}])[0] + mime_value = (mime.get("value") if isinstance(mime, dict) else "") or "" + if name.endswith(".txt") or mime_value == "text/plain": + return bs + return bitstreams[0] if bitstreams else None + + +async def _fetch_one( + client: DSpaceClient, + uuid: str, + out_dir: Path, + *, + refresh: bool = False, +) -> str: + """Returns one of: 'written', 'skipped-existing', 'no-text-bundle', + 'no-bitstream', 'unauthorized', 'not-found', 'error'.""" + out_path = out_dir / f"{uuid}.txt" + if out_path.exists() and not refresh: + return "skipped-existing" + try: + bundles = await client.get_bundles(uuid) + except httpx.HTTPStatusError as exc: + if exc.response.status_code == 404: + return "not-found" + if exc.response.status_code in (401, 403): + return "unauthorized" + raise + + text_bundle = next( + (b for b in bundles if (b.get("name") or "").upper() == _TEXT_BUNDLE_NAME), + None, + ) + if text_bundle is None: + return "no-text-bundle" + + bitstreams = await client.get_bitstreams(text_bundle["uuid"]) + bs = _pick_text_bitstream(bitstreams) + if bs is None: + return "no-bitstream" + + try: + body = await client.get_bitstream_content(bs["uuid"]) + except httpx.HTTPStatusError as exc: + if exc.response.status_code in (401, 403): + return "unauthorized" + if exc.response.status_code == 404: + return "not-found" + raise + + text = body.decode("utf-8", errors="replace") + out_path.write_text(text, encoding="utf-8") + return "written" + + +async def fetch_text( + cfg: InfoscienceIndexConfig, + *, + refresh: bool = False, +) -> dict: + """Fetch text for every UUID found in raw/items/.""" + out_dir = text_dir() + item_files = sorted(raw_items_dir().glob("*.json")) + if not item_files: + logger.warning("No items in raw/items/. Run `discover` first.") + return {"items": 0} + + counts = { + "written": 0, + "skipped-existing": 0, + "no-text-bundle": 0, + "no-bitstream": 0, + "unauthorized": 0, + "not-found": 0, + "error": 0, + } + + async with DSpaceClient(cfg.infoscience) as client: + sem = asyncio.Semaphore(cfg.infoscience.max_concurrency) + + async def _bounded(uuid: str) -> str: + async with sem: + try: + return await _fetch_one(client, uuid, out_dir, refresh=refresh) + except Exception: + logger.exception("text-fetch failed for %s", uuid) + return "error" + + uuids = [p.stem for p in item_files] + results = await asyncio.gather(*(_bounded(u) for u in uuids)) + + for r in results: + counts[r] = counts.get(r, 0) + 1 + + logger.info("fetch_text: %s", json.dumps(counts)) + return {"items": len(uuids), **counts, "text_dir": str(out_dir)} + + +def run(cfg: InfoscienceIndexConfig, **kwargs) -> dict: + return asyncio.run(fetch_text(cfg, **kwargs)) diff --git a/src/index/openalex/__init__.py b/src/index/openalex/__init__.py new file mode 100644 index 0000000..2ef05ba --- /dev/null +++ b/src/index/openalex/__init__.py @@ -0,0 +1,4 @@ +"""OpenAlex ingestion + dual-query (SQL + RAG) over EPFL → Switzerland. + +See `.internal/openalex/` for the design. +""" diff --git a/src/index/openalex/_federated.py b/src/index/openalex/_federated.py new file mode 100644 index 0000000..d9b3f96 --- /dev/null +++ b/src/index/openalex/_federated.py @@ -0,0 +1,13 @@ +"""OpenAlex registration with the federated discover/hydrate registries. + +Imported lazily by ``src.index._federated.dh_registry.load_*``. +""" + +from __future__ import annotations + +from src.index._federated.dh_registry import register_discoverer, register_hydrator +from src.index.openalex.discover import DISCOVERER +from src.index.openalex.hydrate import HYDRATOR + +register_discoverer(DISCOVERER) +register_hydrator(HYDRATOR) diff --git a/src/index/openalex/api.py b/src/index/openalex/api.py new file mode 100644 index 0000000..7394a4a --- /dev/null +++ b/src/index/openalex/api.py @@ -0,0 +1,123 @@ +"""FastAPI app exposing the OpenAlex index dual query surface.""" + +from __future__ import annotations + +import logging +from typing import Any + +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel, Field + +from src.index.openalex.config import load_config +from src.index.openalex.retrieval.semantic import semantic_search +from src.index.openalex.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) +from src.index.openalex.storage.duckdb_store import DuckDBStore +from src.index.openalex.vector.qdrant_store import QdrantStore + +LOGGER = logging.getLogger(__name__) + +app = FastAPI(title="OpenAlex Index") + + +class SearchRequest(BaseModel): + query: str + entity_type: str = "works" + top_k: int = Field(default=10, ge=1, le=100) + candidate_k: int = Field(default=50, ge=1, le=500) + filter_payload: dict[str, Any] | None = None + + +class QueryRequest(BaseModel): + sql: str | None = None + predefined: str | None = None + params: dict[str, Any] | None = None + + +@app.get("/healthz") +def healthz() -> dict[str, Any]: + """Probe DuckDB + Qdrant. RCP probe deferred to first /search call.""" + config = load_config() + duck_status = "ok" + qdrant_status = "ok" + try: + DuckDBStore.open().count("works") + except Exception as exc: # noqa: BLE001 + duck_status = f"error: {exc}" + try: + QdrantStore(config).count("works") + except Exception as exc: # noqa: BLE001 + qdrant_status = f"error: {exc}" + return { + "duckdb": duck_status, + "qdrant": qdrant_status, + "rcp_configured": bool(config.rcp.token), + } + + +@app.post("/search") +def search(req: SearchRequest) -> list[dict[str, Any]]: + config = load_config() + try: + config.require_rcp() + except ValueError as exc: + raise HTTPException(status_code=503, detail=str(exc)) from exc + return semantic_search( + config=config, + query=req.query, + entity_type=req.entity_type, + top_k=req.top_k, + candidate_k=req.candidate_k, + filter_payload=req.filter_payload, + ) + + +@app.post("/query") +def query(req: QueryRequest) -> list[dict[str, Any]]: + if req.predefined and req.sql: + raise HTTPException( + status_code=400, + detail="Provide either `predefined` or `sql`, not both", + ) + try: + if req.predefined: + return run_predefined(req.predefined, req.params) + if req.sql: + return run_adhoc(req.sql, req.params) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + raise HTTPException( + status_code=400, + detail="Pass `predefined` or `sql`", + ) + + +@app.get("/predefined") +def list_predefined() -> dict[str, list[str]]: + return {"predefined": sorted(PREDEFINED_QUERIES)} + + +@app.get("/entity/{entity_type}/{openalex_id:path}") +def get_entity(entity_type: str, openalex_id: str) -> dict[str, Any]: + if entity_type not in { + "works", + "authors", + "institutions", + "sources", + "topics", + "concepts", + }: + raise HTTPException(status_code=404, detail=f"unknown entity type: {entity_type}") + store = DuckDBStore.open() + cur = store.connect().execute( + f"SELECT * FROM {entity_type} WHERE openalex_id = ?", # noqa: S608 + [openalex_id], + ) + row = cur.fetchone() + if row is None: + raise HTTPException(status_code=404, detail="not found") + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) diff --git a/src/index/openalex/cli.py b/src/index/openalex/cli.py new file mode 100644 index 0000000..ac8c0ab --- /dev/null +++ b/src/index/openalex/cli.py @@ -0,0 +1,300 @@ +"""CLI for the OpenAlex index module. + +Subcommands: + +- `ingest` — pull OpenAlex entities into DuckDB. +- `find-github` — discover GitHub-URL-mentioning Works (Swiss/EPFL test set). +- `query` — read-only SQL over DuckDB (predefined or guarded ad-hoc). +- `embed` — embed DuckDB rows into Qdrant via RCP. (See embed module.) +- `rebuild-qdrant` — re-push existing DuckDB `chunks` rows into Qdrant + (re-embedding their text via RCP). Used after a Qdrant wipe. +- `search` — semantic retrieval (vector + rerank). (See retrieval.) +- `serve` — run the FastAPI app on a chosen port. +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from typing import TYPE_CHECKING + +from src.index.openalex.config import load_config +from src.index.openalex.ingest.authors import ingest_authors +from src.index.openalex.ingest.concepts import ingest_concepts +from src.index.openalex.ingest.github_discovery import discover_github_works +from src.index.openalex.ingest.github_extract import extract_for_persisted_works +from src.index.openalex.ingest.institutions import ingest_institutions +from src.index.openalex.ingest.scope import resolve_scope +from src.index.openalex.ingest.sources import ingest_sources +from src.index.openalex.ingest.topics import ingest_topics +from src.index.openalex.ingest.works import ingest_works +from src.index.openalex.retrieval.sql import run_adhoc, run_predefined +from src.index.openalex.storage.duckdb_store import DuckDBStore + +if TYPE_CHECKING: + pass + +LOGGER = logging.getLogger(__name__) + +ENTITY_INGESTERS = { + "works": ingest_works, + "authors": ingest_authors, + "institutions": ingest_institutions, + "sources": ingest_sources, + "topics": ingest_topics, + "concepts": ingest_concepts, +} + + +def _split_entities(raw: str) -> list[str]: + parts = [p.strip() for p in raw.split(",") if p.strip()] + unknown = [p for p in parts if p not in ENTITY_INGESTERS] + if unknown: + message = f"Unknown entity types: {unknown}. Known: {sorted(ENTITY_INGESTERS)}" + raise SystemExit(message) + return parts + + +def _emit_json(rows: list[dict] | dict) -> None: + json.dump(rows, sys.stdout, indent=2, ensure_ascii=False, default=str) + sys.stdout.write("\n") + + +def _cmd_ingest(args: argparse.Namespace) -> int: + config = load_config() + config.require_ingest() + scope = resolve_scope(args.scope, config) + entities = _split_entities(args.entities) + store = DuckDBStore.open() + summary: dict[str, int] = {} + scope_filters = { + "works": scope.works, + "authors": scope.authors, + "institutions": scope.institutions, + "sources": scope.sources, + "topics": {}, + "concepts": {}, + } + for entity in entities: + ingester = ENTITY_INGESTERS[entity] + filters = scope_filters[entity] + kwargs = { + "config": config, + "store": store, + "filters": filters, + "limit": args.limit, + } + # Topics/concepts don't take a meaningful scope filter — pass through. + summary[entity] = ingester(**kwargs) + _emit_json({"scope": args.scope, "ingested": summary}) + return 0 + + +def _cmd_find_github(args: argparse.Namespace) -> int: + config = load_config() + config.require_ingest() + scope = resolve_scope(args.scope, config) + store = DuckDBStore.open() + seen, persisted = discover_github_works( + config=config, + store=store, + scope_filter=scope.works, + mode=args.search, + limit=args.limit, + ) + scanned, urls = extract_for_persisted_works(store) + distinct = store.connect().execute( + "SELECT COUNT(DISTINCT normalized_url) FROM work_github_urls" + ).fetchone() + _emit_json( + { + "scope": args.scope, + "search": args.search, + "works_seen": seen, + "works_persisted": persisted, + "abstracts_scanned": scanned, + "urls_persisted_this_run": urls, + "distinct_normalized_urls": int(distinct[0]) if distinct else 0, + }, + ) + return 0 + + +def _cmd_query(args: argparse.Namespace) -> int: + params: dict[str, object] = {} + for raw in args.param or []: + if "=" not in raw: + message = f"--param must be key=value, got {raw!r}" + raise SystemExit(message) + key, value = raw.split("=", 1) + # Best-effort numeric coercion — predefined queries take ints. + if value.isdigit(): + params[key] = int(value) + else: + params[key] = value + if args.predefined: + rows = run_predefined(args.predefined, params) + elif args.sql: + rows = run_adhoc(args.sql, params) + else: + raise SystemExit("Pass --predefined NAME or a positional SQL string") + _emit_json(rows) + return 0 + + +def _cmd_embed(args: argparse.Namespace) -> int: + from src.index.openalex.embed.pipeline import embed_entities + + entities = _split_entities(args.entities) + config = load_config() + config.require_rcp() + store = DuckDBStore.open() + summary = embed_entities( + config=config, + store=store, + entity_types=entities, + limit=args.limit, + ) + _emit_json({"embedded": summary}) + return 0 + + +def _cmd_rebuild_qdrant(args: argparse.Namespace) -> int: + from src.index.openalex.embed.pipeline import rebuild_qdrant_from_chunks + + entities = _split_entities(args.entities) + config = load_config() + config.require_rcp() + store = DuckDBStore.open() + summary = rebuild_qdrant_from_chunks( + config=config, + store=store, + entity_types=entities, + ) + _emit_json({"rebuilt": summary}) + return 0 + + +def _cmd_search(args: argparse.Namespace) -> int: + from src.index.openalex.retrieval.semantic import semantic_search + + config = load_config() + config.require_rcp() + hits = semantic_search( + config=config, + query=args.query, + entity_type=args.entity, + top_k=args.top_k, + ) + _emit_json(hits) + return 0 + + +def _cmd_serve(args: argparse.Namespace) -> int: + import uvicorn + + uvicorn.run( + "src.index.openalex.api:app", + host=args.host, + port=args.port, + reload=args.reload, + ) + return 0 + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="src.index.openalex.cli", + description="OpenAlex ingestion + RAG over EPFL/Switzerland", + ) + sub = parser.add_subparsers(dest="cmd", required=True) + + p_ingest = sub.add_parser("ingest", help="Pull OpenAlex entities into DuckDB") + p_ingest.add_argument("--scope", choices=["epfl", "switzerland"], required=True) + p_ingest.add_argument( + "--entities", + default="works,authors,institutions,sources,topics,concepts", + ) + p_ingest.add_argument("--limit", type=int, default=None) + p_ingest.set_defaults(func=_cmd_ingest) + + p_gh = sub.add_parser( + "find-github", + help="Discover Swiss/EPFL Works mentioning github.com URLs", + ) + p_gh.add_argument("--scope", choices=["epfl", "switzerland"], required=True) + p_gh.add_argument( + "--search", + choices=["fulltext", "default", "both"], + default="both", + ) + p_gh.add_argument("--limit", type=int, default=None) + p_gh.set_defaults(func=_cmd_find_github) + + p_q = sub.add_parser("query", help="Read-only SQL over the DuckDB dump") + p_q.add_argument( + "sql", + nargs="?", + help="Ad-hoc SELECT/WITH query (omit if --predefined)", + ) + p_q.add_argument( + "--predefined", + help="Run a predefined named query", + ) + p_q.add_argument( + "--param", + action="append", + help="key=value for predefined queries (repeatable)", + ) + p_q.set_defaults(func=_cmd_query) + + p_e = sub.add_parser("embed", help="Embed DuckDB rows into Qdrant via RCP") + p_e.add_argument( + "--entities", + default="works,authors,institutions,sources,topics,concepts", + ) + p_e.add_argument("--limit", type=int, default=None) + p_e.set_defaults(func=_cmd_embed) + + p_r = sub.add_parser( + "rebuild-qdrant", + help=( + "Re-push existing DuckDB chunks into Qdrant (re-embeds chunks.text). " + "Use after a Qdrant wipe. Does not modify DuckDB." + ), + ) + p_r.add_argument( + "--entities", + default="works,authors,institutions,sources,topics,concepts", + ) + p_r.set_defaults(func=_cmd_rebuild_qdrant) + + p_s = sub.add_parser("search", help="Semantic retrieval (vector + rerank)") + p_s.add_argument("query") + p_s.add_argument("--entity", default="works") + p_s.add_argument("--top-k", type=int, default=10) + p_s.set_defaults(func=_cmd_search) + + p_v = sub.add_parser("serve", help="Run the FastAPI app") + p_v.add_argument("--host", default="0.0.0.0") # noqa: S104 + p_v.add_argument("--port", type=int, default=8001) + p_v.add_argument("--reload", action="store_true") + p_v.set_defaults(func=_cmd_serve) + + return parser + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s %(message)s", + ) + parser = build_parser() + args = parser.parse_args(argv) + return args.func(args) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/index/openalex/config.py b/src/index/openalex/config.py new file mode 100644 index 0000000..6fffbd5 --- /dev/null +++ b/src/index/openalex/config.py @@ -0,0 +1,131 @@ +"""Config loader for the OpenAlex indexer. + +Reads `config/index/openalex.yaml` and merges in env-sourced credentials +(`RCP_TOKEN`, `OPENALEX_MAILTO`, `INDEX_QDRANT_API_KEY`) plus the resolved +data dir. +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Optional + +import yaml +from pydantic import BaseModel + +from src.index.openalex.paths import OpenAlexPaths, get_openalex_paths + +DEFAULT_CONFIG_PATH = Path("config/index/openalex.yaml") + +TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} +FALSE_ENV_VALUES = {"0", "false", "f", "no", "n", "off"} + +MISSING_MAILTO_ERROR = ( + "Missing required environment variable: OPENALEX_MAILTO " + "(needed for the OpenAlex polite pool)" +) +MISSING_RCP_TOKEN_ERROR = "Missing required environment variable: RCP_TOKEN" + + +class RcpConfig(BaseModel): + base_url: str + embedding_model: str + embedding_dim: int + query_instruction: str + reranker_model: str + batch_size: int = 32 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: Optional[str] = None # populated from RCP_TOKEN at load time + + +class OpenAlexConfig(BaseModel): + base_url: str = "https://api.openalex.org" + per_page: int = 200 + max_concurrency: int = 4 + mailto: Optional[str] = None # populated from OPENALEX_MAILTO at load time + + +class ScopeConfig(BaseModel): + ror: str + country: str + + +class QdrantConfig(BaseModel): + url: str = "http://localhost:6333" + prefer_grpc: bool = False + api_key: Optional[str] = None # populated from INDEX_QDRANT_API_KEY at load time + + +class ChunkingConfig(BaseModel): + size_tokens: int = 256 + overlap_tokens: int = 64 + tokenizer: str = "cl100k_base" + + +class OpenAlexIndexConfig(BaseModel): + rcp: RcpConfig + openalex: OpenAlexConfig + scope: ScopeConfig + qdrant: QdrantConfig + chunking: ChunkingConfig + paths: OpenAlexPaths + + model_config = {"arbitrary_types_allowed": True} + + def require_ingest(self) -> None: + """Validate config required to run the OpenAlex ingest path.""" + if not self.openalex.mailto: + raise ValueError(MISSING_MAILTO_ERROR) + + def require_rcp(self) -> None: + """Validate config required to call the RCP embedding/rerank endpoints.""" + if not self.rcp.token: + raise ValueError(MISSING_RCP_TOKEN_ERROR) + + +def _env_bool(name: str) -> Optional[bool]: + raw = os.getenv(name) + if raw is None or raw.strip() == "": + return None + normalized = raw.strip().lower() + if normalized in TRUE_ENV_VALUES: + return True + if normalized in FALSE_ENV_VALUES: + return False + message = f"Invalid boolean value for {name}: {raw!r}" + raise ValueError(message) + + +def _env_str(name: str) -> Optional[str]: + raw = os.getenv(name) + if raw is None: + return None + stripped = raw.strip() + return stripped or None + + +def load_config(path: Optional[Path] = None) -> OpenAlexIndexConfig: + """Load + validate the YAML config; merge env tokens, env overrides, paths.""" + cfg_path = path or DEFAULT_CONFIG_PATH + raw = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) or {} + + # Inject secrets from env. + raw.setdefault("rcp", {})["token"] = _env_str("RCP_TOKEN") + raw.setdefault("openalex", {})["mailto"] = _env_str("OPENALEX_MAILTO") + raw.setdefault("qdrant", {})["api_key"] = _env_str("INDEX_QDRANT_API_KEY") + + # Targeted env overrides for fields users commonly tune at runtime. + if (override := _env_str("INDEX_QDRANT_URL")) is not None: + raw["qdrant"]["url"] = override + if (override_b := _env_bool("INDEX_QDRANT_PREFER_GRPC")) is not None: + raw["qdrant"]["prefer_grpc"] = override_b + if (override := _env_str("INDEX_OPENALEX_SCOPE_ROR")) is not None: + raw.setdefault("scope", {})["ror"] = override + if (override := _env_str("INDEX_OPENALEX_SCOPE_COUNTRY")) is not None: + raw.setdefault("scope", {})["country"] = override + + raw["paths"] = get_openalex_paths() + + return OpenAlexIndexConfig(**raw) diff --git a/src/index/openalex/discover.py b/src/index/openalex/discover.py new file mode 100644 index 0000000..f066b40 --- /dev/null +++ b/src/index/openalex/discover.py @@ -0,0 +1,189 @@ +"""Seed discovery for the OpenAlex index. + +Implements :class:`src.index._federated.protocols.IndexDiscoverer`. +Each ``--source`` produces a stream of :class:`Seed`s consumable by +:class:`src.index.openalex.hydrate.OpenAlexHydrator`. + +Sources +------- + +- ``from-search`` — OpenAlex ``/works`` semantic search. Emits + ``seed_type="openalex_work"`` seeds. Required opt: ``query``. +- ``from-references`` — for every work in the local DB whose + ``referenced_works`` are not yet populated in ``work_references``, + emit one ``openalex_work`` seed with ``hint={"refs_only": True}``. + Drives the citation-graph backfill. +- ``datascience-ch`` — scrape the SDSC publications page + (``https://datascience.ch/publications``, paginated via ``?_page=N``) + and emit ``seed_type="doi"`` seeds. Required opt: none. Optional: + ``url`` (override default), ``pages`` (limit, useful for testing), + ``affiliation_ror`` (stamped onto each seed's hint). +""" + +from __future__ import annotations + +import logging +import os +import re +from typing import Any, Iterator + +import requests + +from src.index._federated.protocols import IndexDiscoverer, Seed +from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + +USER_AGENT = "Mozilla/5.0 (gme-openalex-discover)" +DOI_RE = re.compile(r"10\.\d{4,9}/[^\s\"'<>)]+") + +DATASCIENCE_CH_URL = "https://datascience.ch/publications" +DATASCIENCE_CH_HASH = "7a5b1ea3" # webflow per-section pagination prefix +DATASCIENCE_CH_TOTAL_PAGES = 32 +SDSC_ROR = "https://ror.org/02hdt9m26" + + +def _http_get(url: str, params: dict | None = None, timeout: int = 30) -> requests.Response: + headers = {"User-Agent": USER_AGENT} + return requests.get(url, params=params, headers=headers, timeout=timeout) + + +def _from_search(*, query: str, limit: int = 200, **_unused: Any) -> Iterator[Seed]: + """OpenAlex /works search → openalex_work seeds. + + Uses the public REST endpoint directly to avoid a hard pyalex + dependency in the discoverer (kept light for cheap import). + """ + if not query: + message = "from-search requires --opt query='...'" + raise ValueError(message) + + mailto = os.getenv("OPENALEX_MAILTO", "") + page = 1 + seen = 0 + while seen < limit: + params = { + "search": query, + "select": "id,doi,title", + "per-page": str(min(200, limit - seen)), + "page": str(page), + } + if mailto: + params["mailto"] = mailto + r = _http_get("https://api.openalex.org/works", params=params) + r.raise_for_status() + data = r.json() + results = data.get("results", []) or [] + if not results: + break + for item in results: + wid = item.get("id") + if not wid: + continue + yield Seed( + id=wid, + seed_type="openalex_work", + source="from-search", + hint={"query": query, "title": item.get("title"), "doi": item.get("doi")}, + ) + seen += 1 + if seen >= limit: + break + if len(results) < int(params["per-page"]): + break + page += 1 + + +def _from_references(**_unused: Any) -> Iterator[Seed]: + """Works in DB without populated `referenced_works` → seeds for backfill. + + Emits one ``openalex_work`` seed per such work, with + ``hint={"refs_only": True}`` so the hydrator knows to populate only + ``work_references`` (cheap select) rather than re-upserting the work. + """ + store = DuckDBStore.open() + cur = store.connect() + rows = cur.execute(""" + SELECT w.openalex_id FROM works w + LEFT JOIN ( + SELECT DISTINCT citing_work_id FROM work_references + ) r ON r.citing_work_id = w.openalex_id + WHERE r.citing_work_id IS NULL + ORDER BY w.openalex_id + """).fetchall() + LOGGER.info("from-references: %d works without populated references", len(rows)) + for (wid,) in rows: + yield Seed( + id=wid, + seed_type="openalex_work", + source="from-references", + hint={"refs_only": True}, + ) + + +def _from_datascience_ch( + *, url: str = DATASCIENCE_CH_URL, pages: int = DATASCIENCE_CH_TOTAL_PAGES, + hash_: str = DATASCIENCE_CH_HASH, affiliation_ror: str = SDSC_ROR, + **_unused: Any, +) -> Iterator[Seed]: + """Scrape the SDSC publications page (32 paginated lists) → DOI seeds. + + Each emitted seed carries ``hint.affiliation_ror`` so the hydrator + can stamp the SDSC institution link on hydrated works (regardless of + whether OpenAlex itself attributes them to SDSC). + """ + seen: set[str] = set() + for p in range(1, pages + 1): + page_url = url if p == 1 else f"{url}?{hash_}_page={p}" + try: + r = _http_get(page_url) + r.raise_for_status() + except Exception as exc: # noqa: BLE001 + LOGGER.warning("datascience-ch: page %d failed: %s", p, exc) + continue + page_dois: set[str] = set() + for raw in DOI_RE.findall(r.text): + d = raw.rstrip(".,;:)") + if d.lower().startswith("10.5281/zenodo."): + continue # Zenodo datasets don't index in OpenAlex + page_dois.add(d) + new = page_dois - seen + seen |= new + for d in new: + yield Seed( + id=d, + seed_type="doi", + source="datascience-ch", + hint={"affiliation_ror": affiliation_ror, "scrape_url": page_url}, + ) + LOGGER.info("datascience-ch: emitted %d DOI seeds", len(seen)) + + +_DISPATCH = { + "from-search": _from_search, + "from-references": _from_references, + "datascience-ch": _from_datascience_ch, +} + + +class OpenAlexDiscoverer: + """Discoverer for the OpenAlex index. See module docstring for sources.""" + + name = "openalex" + accepted_sources = tuple(_DISPATCH.keys()) + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + if source not in _DISPATCH: + message = ( + f"OpenAlex: unknown source {source!r}. " + f"Accepted: {sorted(self.accepted_sources)}" + ) + raise ValueError(message) + return _DISPATCH[source](**opts) + + +# Public symbol: register at import time from `src/index/openalex/_federated.py`. +DISCOVERER = OpenAlexDiscoverer() + + +__all__ = ["DISCOVERER", "OpenAlexDiscoverer"] diff --git a/src/index/openalex/embed/__init__.py b/src/index/openalex/embed/__init__.py new file mode 100644 index 0000000..4cdf7eb --- /dev/null +++ b/src/index/openalex/embed/__init__.py @@ -0,0 +1 @@ +"""Chunking + RCP embeddings + Qdrant upsert.""" diff --git a/src/index/openalex/embed/chunker.py b/src/index/openalex/embed/chunker.py new file mode 100644 index 0000000..4cda65e --- /dev/null +++ b/src/index/openalex/embed/chunker.py @@ -0,0 +1,96 @@ +"""Token-based sliding-window chunker built on tiktoken. + +The chunker is encoding-agnostic — `cl100k_base` is the default since it's +a reasonable proxy for many modern tokenizers. The Qwen3 tokenizer differs, +but for window *sizing* (not training) the proxy is fine: we err on the +side of slightly smaller chunks, which is safer for context limits. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +import tiktoken + +DEFAULT_ENCODING = "cl100k_base" + + +@dataclass(slots=True, frozen=True) +class Chunk: + index: int + text: str + token_count: int + + +def _get_encoder(name: str = DEFAULT_ENCODING) -> tiktoken.Encoding: + return tiktoken.get_encoding(name) + + +def chunk_text( + text: str, + *, + chunk_tokens: int, + overlap: int, + encoding_name: str = DEFAULT_ENCODING, +) -> list[Chunk]: + """Slide a window of `chunk_tokens` with `overlap` over the encoded text.""" + if chunk_tokens <= 0: + message = "chunk_tokens must be positive" + raise ValueError(message) + if overlap < 0 or overlap >= chunk_tokens: + message = "overlap must be in [0, chunk_tokens)" + raise ValueError(message) + if not text: + return [] + enc = _get_encoder(encoding_name) + tokens = enc.encode(text) + if not tokens: + return [] + if len(tokens) <= chunk_tokens: + return [Chunk(index=0, text=text, token_count=len(tokens))] + chunks: list[Chunk] = [] + step = chunk_tokens - overlap + start = 0 + idx = 0 + while start < len(tokens): + window = tokens[start : start + chunk_tokens] + if not window: + break + chunks.append( + Chunk(index=idx, text=enc.decode(window), token_count=len(window)), + ) + if start + chunk_tokens >= len(tokens): + break + start += step + idx += 1 + return chunks + + +def chunk_for_work( + title: str | None, + abstract: str | None, + *, + chunk_tokens: int, + overlap: int, +) -> list[Chunk]: + """Concatenate title + abstract and chunk.""" + parts = [p for p in (title, abstract) if p] + if not parts: + return [] + text = "\n\n".join(parts) + return chunk_text(text, chunk_tokens=chunk_tokens, overlap=overlap) + + +def chunk_for_simple_entity( + display_name: str | None, + summary: str | None = None, + *, + chunk_tokens: int, + overlap: int, +) -> list[Chunk]: + """Single-pass chunking for entities whose useful text is short.""" + parts = [p for p in (display_name, summary) if p] + if not parts: + return [] + text = " — ".join(parts) + return chunk_text(text, chunk_tokens=chunk_tokens, overlap=overlap) diff --git a/src/index/openalex/embed/pipeline.py b/src/index/openalex/embed/pipeline.py new file mode 100644 index 0000000..ef7bc94 --- /dev/null +++ b/src/index/openalex/embed/pipeline.py @@ -0,0 +1,285 @@ +"""Stream DuckDB rows → chunk → embed → upsert into Qdrant. + +Idempotent: rows with existing chunks are skipped via +`DuckDBStore.stream_rows_for_embedding`. + +Rebuild path: `rebuild_qdrant_from_chunks` re-derives Qdrant points from the +existing `chunks` table (re-embedding `chunks.text` via RCP) without touching +DuckDB. Used after a Qdrant wipe — same RCP cost as a fresh embed, but the +DuckDB chunks/text stay intact. +""" + +from __future__ import annotations + +import asyncio +import logging +import uuid +from typing import TYPE_CHECKING, Any + +_CHUNK_NAMESPACE = uuid.NAMESPACE_URL + +ENTITY_TABLES: tuple[str, ...] = ( + "works", + "authors", + "institutions", + "sources", + "topics", + "concepts", +) + + +def _chunk_id(entity_type: str, entity_id: str, chunk_index: int) -> str: + return str( + uuid.uuid5(_CHUNK_NAMESPACE, f"{entity_type}|{entity_id}|{chunk_index}"), + ) + +from src.index.openalex.embed.chunker import ( + Chunk, + chunk_for_simple_entity, + chunk_for_work, +) +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.vector.qdrant_store import QdrantStore + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def _row_to_chunks( + entity_type: str, + row: dict[str, Any], + *, + chunk_tokens: int, + overlap: int, +) -> list[Chunk]: + if entity_type == "works": + return chunk_for_work( + row.get("title"), + row.get("abstract"), + chunk_tokens=chunk_tokens, + overlap=overlap, + ) + return chunk_for_simple_entity( + row.get("display_name"), + chunk_tokens=chunk_tokens, + overlap=overlap, + ) + + +def _row_to_payload(entity_type: str, row: dict[str, Any]) -> dict[str, Any]: + payload: dict[str, Any] = { + "entity_type": entity_type, + "entity_id": row["openalex_id"], + "openalex_id": row["openalex_id"], + } + if entity_type == "works": + payload["title"] = row.get("title") + payload["year"] = row.get("publication_year") + else: + payload["display_name"] = row.get("display_name") + return payload + + +async def _embed_entity_async( + *, + config: OpenAlexIndexConfig, + store: DuckDBStore, + entity_type: str, + limit: int | None, +) -> int: + client = RCPEmbeddingClient(config) + qdrant = QdrantStore(config) + qdrant.ensure_collection(entity_type) + + pending_chunks: list[tuple[str, dict[str, Any], Chunk]] = [] + total_chunks = 0 + + async def flush() -> None: + nonlocal total_chunks + if not pending_chunks: + return + texts = [c.text for _, _, c in pending_chunks] + vectors = await client.embed_all(texts) + ids: list[str] = [] + payloads: list[dict[str, Any]] = [] + for entity_id, base_payload, chunk in pending_chunks: + chunk_id = _chunk_id(entity_type, entity_id, chunk.index) + ids.append(chunk_id) + payload = {**base_payload, "chunk_index": chunk.index} + payloads.append(payload) + store.upsert_chunk( + chunk_id=chunk_id, + entity_type=entity_type, + entity_id=entity_id, + chunk_index=chunk.index, + text=chunk.text, + token_count=chunk.token_count, + vector_id=chunk_id, + ) + qdrant.upsert_points( + entity_type, + ids=ids, + vectors=vectors, + payloads=payloads, + ) + total_chunks += len(pending_chunks) + pending_chunks.clear() + + rows_seen = 0 + for row in store.stream_rows_for_embedding(entity_type, limit=limit): + rows_seen += 1 + chunks = _row_to_chunks( + entity_type, + row, + chunk_tokens=config.chunking.size_tokens, + overlap=config.chunking.overlap_tokens, + ) + if not chunks: + continue + base_payload = _row_to_payload(entity_type, row) + for chunk in chunks: + pending_chunks.append((row["openalex_id"], base_payload, chunk)) + if len(pending_chunks) >= client.batch_size: + await flush() + await flush() + LOGGER.info( + "embed %s complete: rows_seen=%d chunks=%d", + entity_type, + rows_seen, + total_chunks, + ) + return total_chunks + + +def embed_entities( + *, + config: OpenAlexIndexConfig, + store: DuckDBStore, + entity_types: list[str], + limit: int | None = None, +) -> dict[str, int]: + """Synchronously embed across the requested entity types.""" + summary: dict[str, int] = {} + for entity_type in entity_types: + summary[entity_type] = asyncio.run( + _embed_entity_async( + config=config, + store=store, + entity_type=entity_type, + limit=limit, + ), + ) + return summary + + +def _rebuild_select_sql(entity_type: str) -> str: + """SQL to stream `chunks` rows joined with their source entity. + + Returned columns are stable across entity types so the caller can build a + payload without per-type branching beyond the `_row_to_payload` shape. + """ + if entity_type == "works": + extra = "w.title AS title, w.publication_year AS publication_year, NULL AS display_name" + else: + extra = "NULL AS title, NULL AS publication_year, t.display_name AS display_name" + join_alias = "w" if entity_type == "works" else "t" + return ( # noqa: S608 - entity_type guarded by ENTITY_TABLES + "SELECT c.chunk_id, c.entity_id, c.chunk_index, c.text, " + f"{extra} " + f"FROM chunks c JOIN {entity_type} {join_alias} " + f"ON {join_alias}.openalex_id = c.entity_id " + "WHERE c.entity_type = ? " + "ORDER BY c.entity_id, c.chunk_index" + ) + + +def _rebuild_payload(entity_type: str, row: dict[str, Any]) -> dict[str, Any]: + payload: dict[str, Any] = { + "entity_type": entity_type, + "entity_id": row["entity_id"], + "openalex_id": row["entity_id"], + "chunk_index": row["chunk_index"], + } + if entity_type == "works": + payload["title"] = row.get("title") + payload["year"] = row.get("publication_year") + else: + payload["display_name"] = row.get("display_name") + return payload + + +async def _rebuild_entity_async( + *, + config: OpenAlexIndexConfig, + store: DuckDBStore, + entity_type: str, + batch_size: int | None = None, +) -> int: + """Re-push all `chunks` rows for `entity_type` into Qdrant.""" + if entity_type not in ENTITY_TABLES: + message = f"Unknown entity_type: {entity_type}" + raise ValueError(message) + + client = RCPEmbeddingClient(config, batch_size=batch_size) + qdrant = QdrantStore(config) + qdrant.ensure_collection(entity_type) + + cur = store.connect().execute(_rebuild_select_sql(entity_type), [entity_type]) + cols = [d[0] for d in cur.description] + rows = [dict(zip(cols, r, strict=False)) for r in cur.fetchall()] + + if not rows: + LOGGER.info("rebuild %s: no chunks to push", entity_type) + return 0 + + pushed = 0 + flush_size = client.batch_size + for start in range(0, len(rows), flush_size): + batch = rows[start : start + flush_size] + texts = [r["text"] for r in batch] + vectors = await client.embed_all(texts) + ids = [r["chunk_id"] for r in batch] + payloads = [_rebuild_payload(entity_type, r) for r in batch] + qdrant.upsert_points( + entity_type, + ids=ids, + vectors=vectors, + payloads=payloads, + ) + pushed += len(batch) + LOGGER.info( + "rebuild %s: pushed %d/%d points", + entity_type, + pushed, + len(rows), + ) + return pushed + + +def rebuild_qdrant_from_chunks( + *, + config: OpenAlexIndexConfig, + store: DuckDBStore, + entity_types: list[str], + batch_size: int | None = None, +) -> dict[str, int]: + """Rebuild Qdrant collections from the existing DuckDB `chunks` table. + + Re-embeds `chunks.text` via RCP and upserts to Qdrant using the existing + `chunk_id` as the point id, so the DuckDB ↔ Qdrant link is preserved. + Used after a Qdrant wipe — does NOT modify DuckDB. + """ + summary: dict[str, int] = {} + for entity_type in entity_types: + summary[entity_type] = asyncio.run( + _rebuild_entity_async( + config=config, + store=store, + entity_type=entity_type, + batch_size=batch_size, + ), + ) + return summary diff --git a/src/index/openalex/embed/rcp_client.py b/src/index/openalex/embed/rcp_client.py new file mode 100644 index 0000000..1870b0a --- /dev/null +++ b/src/index/openalex/embed/rcp_client.py @@ -0,0 +1,117 @@ +"""Async OpenAI-compatible client for the RCP `/embeddings` endpoint.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +import httpx +from tenacity import ( + retry, + retry_if_exception_type, + stop_after_attempt, + wait_exponential, +) + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + +LOGGER = logging.getLogger(__name__) + +DEFAULT_TIMEOUT_S = 60.0 +DEFAULT_BATCH_SIZE = 32 + +_RETRYABLE_STATUS = {429, 500, 502, 503, 504} + + +class RCPEmbeddingError(RuntimeError): + pass + + +def _is_retryable(exc: BaseException) -> bool: + if isinstance(exc, httpx.TimeoutException): + return True + if isinstance(exc, httpx.HTTPStatusError): + return exc.response.status_code in _RETRYABLE_STATUS + return False + + +class RCPEmbeddingClient: + """Thin async wrapper around the RCP `/embeddings` endpoint.""" + + def __init__( + self, + config: OpenAlexIndexConfig, + *, + batch_size: int | None = None, + timeout_s: float | None = None, + ) -> None: + config.require_rcp() + self._config = config + self._batch_size = batch_size or config.rcp.batch_size + self._timeout = httpx.Timeout(timeout_s or config.rcp.timeout_seconds) + self._url = f"{config.rcp.base_url.rstrip('/')}/embeddings" + self._headers = { + "Authorization": f"Bearer {config.rcp.token}", + "Content-Type": "application/json", + } + + @property + def batch_size(self) -> int: + return self._batch_size + + @property + def model(self) -> str: + return self._config.rcp.embedding_model + + async def embed_batch( + self, + client: httpx.AsyncClient, + inputs: list[str], + ) -> list[list[float]]: + @retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=1, min=2, max=30), + retry=retry_if_exception_type((httpx.TimeoutException, httpx.HTTPStatusError)), + reraise=True, + ) + async def _call() -> list[list[float]]: + response = await client.post( + self._url, + headers=self._headers, + json={ + "model": self._config.rcp.embedding_model, + "input": inputs, + "encoding_format": "float", + }, + timeout=self._timeout, + ) + try: + response.raise_for_status() + except httpx.HTTPStatusError as exc: + if not _is_retryable(exc): + body = response.text[:500] + LOGGER.error("RCP embed non-retryable %d: %s", response.status_code, body) + raise + payload: dict[str, Any] = response.json() + data = payload.get("data") or [] + if len(data) != len(inputs): + message = ( + f"RCP returned {len(data)} embeddings for {len(inputs)} inputs" + ) + raise RCPEmbeddingError(message) + return [item["embedding"] for item in data] + + return await _call() + + async def embed_all(self, inputs: list[str]) -> list[list[float]]: + """Embed a list of inputs, batching internally.""" + if not inputs: + return [] + out: list[list[float]] = [] + async with httpx.AsyncClient() as client: + for start in range(0, len(inputs), self._batch_size): + batch = inputs[start : start + self._batch_size] + vecs = await self.embed_batch(client, batch) + out.extend(vecs) + return out diff --git a/src/index/openalex/hydrate.py b/src/index/openalex/hydrate.py new file mode 100644 index 0000000..b433c1a --- /dev/null +++ b/src/index/openalex/hydrate.py @@ -0,0 +1,375 @@ +"""Hydrate canonical OpenAlex records from a stream of :class:`Seed`s. + +Implements :class:`src.index._federated.protocols.IndexHydrator`. + +Accepted seed types +------------------- + +- ``doi`` — fetch ``GET /works/doi:``, upsert into ``works``, + link authors / institutions. If ``hint.affiliation_ror`` is set, + also stamp ``work_institutions`` with that ROR's OpenAlex institution + ID (resolved on first use, cached). +- ``openalex_work`` — like ``doi`` but by direct OpenAlex Work ID. + Special mode: ``hint.refs_only=True`` runs the references-backfill + fast path — bulk-fetches up to 100 IDs per request with + ``select=id,referenced_works``, populates ``work_references`` only, + no work upsert. +- ``openalex_author`` — fetch ``GET /authors/``, upsert into ``authors``. + +All upserts are idempotent (ON CONFLICT DO UPDATE/NOTHING). Re-running +with the same seeds is safe. +""" + +from __future__ import annotations + +import logging +import os +import time +from typing import Any, Iterable + +import requests + +from src.index._federated.protocols import HydrationSummary, IndexHydrator, Seed +from src.index.openalex.ingest.authors import _project_author +from src.index.openalex.ingest.works import persist_work +from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + +USER_AGENT = "Mozilla/5.0 (gme-openalex-hydrate)" +WORK_BATCH = 100 # OpenAlex max IDs per filter +AUTHOR_BATCH = 100 +SLEEP = 0.15 + + +def _mailto_params() -> dict: + m = os.getenv("OPENALEX_MAILTO", "") + return {"mailto": m} if m else {} + + +def _http_get(url: str, params: dict | None = None, timeout: int = 60) -> requests.Response: + return requests.get( + url, params=params, headers={"User-Agent": USER_AGENT}, timeout=timeout, + ) + + +def _short_id(full: str) -> str: + return full.rsplit("/", 1)[-1] + + +def _ror_to_openalex_institution(ror: str, cache: dict[str, str]) -> str | None: + """Resolve a ROR URL to the OpenAlex institution ID (cached).""" + if ror in cache: + return cache[ror] + short = ror.rstrip("/").rsplit("/", 1)[-1] + try: + r = _http_get( + f"https://api.openalex.org/institutions/ror:{short}", + params=_mailto_params(), + ) + r.raise_for_status() + oid = r.json().get("id") + except Exception as exc: # noqa: BLE001 + LOGGER.warning("ROR %s → OpenAlex institution lookup failed: %s", ror, exc) + oid = None + cache[ror] = oid or "" + return oid + + +def _hydrate_dois( + seeds: list[Seed], + *, + store: DuckDBStore, + only_unfetched: bool, + summary: HydrationSummary, + ror_cache: dict[str, str], +) -> None: + """Fetch each DOI individually via /works/doi:X and upsert.""" + cur = store.connect() + if only_unfetched: + existing = { + row[0] + for row in cur.execute( + "SELECT LOWER(REPLACE(doi,'https://doi.org/','')) " + "FROM works WHERE doi IS NOT NULL", + ).fetchall() + } + else: + existing = set() + + for seed in seeds: + doi = seed.id + normalised = doi.lower().replace("https://doi.org/", "").rstrip("/") + if only_unfetched and normalised in existing: + summary.skipped_existing += 1 + _maybe_stamp(seed, store, ror_cache, summary) + continue + try: + r = _http_get( + f"https://api.openalex.org/works/doi:{normalised}", + params=_mailto_params(), + ) + if r.status_code == 404: + summary.errors += 1 + continue + r.raise_for_status() + item = r.json() + except Exception as exc: # noqa: BLE001 + LOGGER.warning("DOI fetch failed for %s: %s", doi, exc) + summary.errors += 1 + time.sleep(0.5) + continue + wid = persist_work(store, item) + if wid: + summary.fetched += 1 + summary.in_scope += 1 + _stamp(wid, seed, store, ror_cache, summary) + time.sleep(SLEEP) + + +def _hydrate_works_full( + seeds: list[Seed], + *, + store: DuckDBStore, + only_unfetched: bool, + summary: HydrationSummary, + ror_cache: dict[str, str], +) -> None: + """Fetch + upsert OpenAlex Works one by one (refs_only=False).""" + cur = store.connect() + existing = ( + {row[0] for row in cur.execute("SELECT openalex_id FROM works").fetchall()} + if only_unfetched else set() + ) + for seed in seeds: + wid = seed.id + if only_unfetched and wid in existing: + summary.skipped_existing += 1 + _maybe_stamp(seed, store, ror_cache, summary) + continue + short = _short_id(wid) + try: + r = _http_get( + f"https://api.openalex.org/works/{short}", + params=_mailto_params(), + ) + if r.status_code == 404: + summary.errors += 1 + continue + r.raise_for_status() + item = r.json() + except Exception as exc: # noqa: BLE001 + LOGGER.warning("work fetch failed for %s: %s", wid, exc) + summary.errors += 1 + time.sleep(0.5) + continue + new_id = persist_work(store, item) + if new_id: + summary.fetched += 1 + summary.in_scope += 1 + _stamp(new_id, seed, store, ror_cache, summary) + time.sleep(SLEEP) + + +def _hydrate_works_refs_only( + seeds: list[Seed], + *, + store: DuckDBStore, + summary: HydrationSummary, +) -> None: + """Bulk references backfill — 100 IDs / request, only updates work_references. + + Note: ``only_unfetched`` is implicit — discoverer ``from-references`` + already filters out works whose refs are populated. We don't double-check + here. + """ + cur = store.connect() + refs_inserted_total = 0 + for i in range(0, len(seeds), WORK_BATCH): + batch = seeds[i : i + WORK_BATCH] + ids = [_short_id(s.id) for s in batch] + params = { + "filter": "ids.openalex:" + "|".join(ids), + "select": "id,referenced_works", + "per-page": str(WORK_BATCH), + **_mailto_params(), + } + try: + r = _http_get("https://api.openalex.org/works", params=params) + r.raise_for_status() + results = r.json().get("results", []) or [] + except Exception as exc: # noqa: BLE001 + LOGGER.warning( + "refs-only batch %d-%d failed: %s", i, i + len(batch), exc, + ) + summary.errors += len(batch) + time.sleep(2) + continue + rows: list[tuple[str, str, int]] = [] + for item in results: + wid = item.get("id") + if not wid: + continue + refs = item.get("referenced_works") or [] + summary.fetched += 1 + if refs: + summary.in_scope += 1 + else: + summary.out_of_scope += 1 # work has no references + for pos, cited in enumerate(refs): + if cited: + rows.append((wid, cited, pos)) + if rows: + cur.executemany( + "INSERT OR IGNORE INTO work_references " + "(citing_work_id, cited_work_id, position) VALUES (?, ?, ?)", + rows, + ) + refs_inserted_total += len(rows) + time.sleep(SLEEP) + summary.extras["refs_inserted"] = refs_inserted_total + + +def _hydrate_authors( + seeds: list[Seed], + *, + store: DuckDBStore, + only_unfetched: bool, + summary: HydrationSummary, +) -> None: + """Fetch + upsert OpenAlex Authors one by one.""" + cur = store.connect() + existing = ( + {row[0] for row in cur.execute("SELECT openalex_id FROM authors").fetchall()} + if only_unfetched else set() + ) + for seed in seeds: + aid = seed.id + if only_unfetched and aid in existing: + summary.skipped_existing += 1 + continue + short = _short_id(aid) + try: + r = _http_get( + f"https://api.openalex.org/authors/{short}", + params=_mailto_params(), + ) + if r.status_code == 404: + summary.errors += 1 + continue + r.raise_for_status() + item = r.json() + except Exception as exc: # noqa: BLE001 + LOGGER.warning("author fetch failed for %s: %s", aid, exc) + summary.errors += 1 + time.sleep(0.5) + continue + row = _project_author(item) + store.upsert_author(row, raw=item) + summary.fetched += 1 + summary.in_scope += 1 + time.sleep(SLEEP) + + +def _stamp( + work_id: str, seed: Seed, store: DuckDBStore, + ror_cache: dict[str, str], summary: HydrationSummary, +) -> None: + """If seed.hint specifies an affiliation_ror, stamp work_institutions.""" + if not seed.hint: + return + ror = seed.hint.get("affiliation_ror") + if not ror: + return + inst_id = _ror_to_openalex_institution(ror, ror_cache) + if not inst_id: + return + store.upsert_work_institutions(work_id, {inst_id}) + summary.extras["stamped_affiliations"] = ( + summary.extras.get("stamped_affiliations", 0) + 1 + ) + + +def _maybe_stamp( + seed: Seed, store: DuckDBStore, + ror_cache: dict[str, str], summary: HydrationSummary, +) -> None: + """Stamp affiliation for a seed whose work is already in DB.""" + if not seed.hint or not seed.hint.get("affiliation_ror"): + return + cur = store.connect() + if seed.seed_type == "doi": + normalised = seed.id.lower().replace("https://doi.org/", "").rstrip("/") + row = cur.execute( + "SELECT openalex_id FROM works " + "WHERE LOWER(REPLACE(doi,'https://doi.org/','')) = ?", + [normalised], + ).fetchone() + else: + row = cur.execute( + "SELECT openalex_id FROM works WHERE openalex_id = ?", [seed.id], + ).fetchone() + if not row: + return + _stamp(row[0], seed, store, ror_cache, summary) + + +class OpenAlexHydrator: + """Hydrator for the OpenAlex index. See module docstring.""" + + name = "openalex" + accepted_seed_types = ("doi", "openalex_work", "openalex_author") + + def hydrate( + self, + seeds: Iterable[Seed], + *, + only_unfetched: bool = True, + ) -> HydrationSummary: + store = DuckDBStore.open() + summary = HydrationSummary() + ror_cache: dict[str, str] = {} + + # Bucket by seed_type + refs_only flag. + dois: list[Seed] = [] + works_full: list[Seed] = [] + works_refs_only: list[Seed] = [] + authors: list[Seed] = [] + for s in seeds: + if s.seed_type == "doi": + dois.append(s) + elif s.seed_type == "openalex_work": + if s.hint and s.hint.get("refs_only"): + works_refs_only.append(s) + else: + works_full.append(s) + elif s.seed_type == "openalex_author": + authors.append(s) + # else: silently ignored (federated dispatch will route elsewhere) + + if dois: + _hydrate_dois( + dois, store=store, only_unfetched=only_unfetched, + summary=summary, ror_cache=ror_cache, + ) + if works_full: + _hydrate_works_full( + works_full, store=store, only_unfetched=only_unfetched, + summary=summary, ror_cache=ror_cache, + ) + if works_refs_only: + _hydrate_works_refs_only( + works_refs_only, store=store, summary=summary, + ) + if authors: + _hydrate_authors( + authors, store=store, only_unfetched=only_unfetched, + summary=summary, + ) + return summary + + +HYDRATOR = OpenAlexHydrator() + + +__all__ = ["HYDRATOR", "OpenAlexHydrator"] diff --git a/src/index/openalex/ingest/__init__.py b/src/index/openalex/ingest/__init__.py new file mode 100644 index 0000000..bf870cf --- /dev/null +++ b/src/index/openalex/ingest/__init__.py @@ -0,0 +1 @@ +"""OpenAlex ingestion via the `pyalex` library.""" diff --git a/src/index/openalex/ingest/authors.py b/src/index/openalex/ingest/authors.py new file mode 100644 index 0000000..3369fb3 --- /dev/null +++ b/src/index/openalex/ingest/authors.py @@ -0,0 +1,49 @@ +"""Fetch + persist OpenAlex Authors.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.ingest.openalex_client import batched, iter_authors + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def _project_author(item: dict[str, Any]) -> dict[str, Any]: + last_known = (item.get("last_known_institutions") or [None])[0] or {} + return { + "openalex_id": item.get("id"), + "display_name": item.get("display_name"), + "orcid": item.get("orcid"), + "last_known_institution_id": last_known.get("id"), + } + + +def ingest_authors( + *, + config: OpenAlexIndexConfig, + store: DuckDBStore, + filters: dict[str, Any], + limit: int | None = None, +) -> int: + count = 0 + last_logged = 0 + items = iter_authors(config=config, filters=filters, limit=limit) + for batch in batched(items, config.openalex.per_page): + with store.transaction(): + for item in batch: + row = _project_author(item) + if not row["openalex_id"]: + continue + store.upsert_author(row, raw=item) + count += 1 + if count - last_logged >= 500: + LOGGER.info("ingested %d authors", count) + last_logged = count + LOGGER.info("authors ingest complete: %d rows", count) + return count diff --git a/src/index/openalex/ingest/concepts.py b/src/index/openalex/ingest/concepts.py new file mode 100644 index 0000000..1ba2f21 --- /dev/null +++ b/src/index/openalex/ingest/concepts.py @@ -0,0 +1,47 @@ +"""Fetch + persist OpenAlex Concepts (legacy).""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.ingest.openalex_client import batched, iter_concepts + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def _project_concept(item: dict[str, Any]) -> dict[str, Any]: + return { + "openalex_id": item.get("id"), + "display_name": item.get("display_name"), + "level": item.get("level"), + } + + +def ingest_concepts( + *, + config: OpenAlexIndexConfig, + store: DuckDBStore, + filters: dict[str, Any] | None = None, + limit: int | None = None, +) -> int: + count = 0 + last_logged = 0 + items = iter_concepts(config=config, filters=filters, limit=limit) + for batch in batched(items, config.openalex.per_page): + with store.transaction(): + for item in batch: + row = _project_concept(item) + if not row["openalex_id"]: + continue + store.upsert_concept(row, raw=item) + count += 1 + if count - last_logged >= 500: + LOGGER.info("ingested %d concepts", count) + last_logged = count + LOGGER.info("concepts ingest complete: %d rows", count) + return count diff --git a/src/index/openalex/ingest/github_discovery.py b/src/index/openalex/ingest/github_discovery.py new file mode 100644 index 0000000..4dd9085 --- /dev/null +++ b/src/index/openalex/ingest/github_discovery.py @@ -0,0 +1,102 @@ +"""Discover Works that mention GitHub URLs in their text. + +Two OpenAlex search filters are used: + +- `fulltext.search` — searches the full text of works where OpenAlex has it. +- `default.search` — searches title + abstract. + +Use `--search both` to union the result sets (deduped on `openalex_id`). +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any, Literal + +from src.index.openalex.ingest.openalex_client import batched, iter_works +from src.index.openalex.ingest.works import persist_work + +if TYPE_CHECKING: + from collections.abc import Iterator + + from src.index.openalex.config import OpenAlexIndexConfig + from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + +SearchMode = Literal["fulltext", "default", "both"] +GITHUB_TERM = "github.com" + + +def _fulltext_filter(scope_filter: dict[str, Any], term: str) -> dict[str, Any]: + return {**scope_filter, "fulltext": {"search": term}} + + +def _iter_search( + *, + config: OpenAlexIndexConfig, + scope_filter: dict[str, Any], + mode: SearchMode, + term: str, + limit: int | None, +) -> Iterator[dict[str, Any]]: + if mode in ("fulltext", "both"): + yield from iter_works( + config=config, + filters=_fulltext_filter(scope_filter, term), + limit=limit, + ) + if mode in ("default", "both"): + yield from iter_works( + config=config, + filters=scope_filter, + extra_search=term, + limit=limit, + ) + + +def discover_github_works( + *, + config: OpenAlexIndexConfig, + store: DuckDBStore, + scope_filter: dict[str, Any], + mode: SearchMode = "both", + term: str = GITHUB_TERM, + limit: int | None = None, +) -> tuple[int, int]: + """Run search(es), upsert each Work, return (seen, persisted) counts. + + Dedupes within a single run on `openalex_id` so `mode="both"` doesn't + double-count when the same work appears in both result sets. + """ + seen = 0 + persisted = 0 + last_logged = 0 + deduped: set[str] = set() + items = _iter_search( + config=config, + scope_filter=scope_filter, + mode=mode, + term=term, + limit=limit, + ) + for batch in batched(items, config.openalex.per_page): + with store.transaction(): + for item in batch: + seen += 1 + work_id = item.get("id") + if not work_id or work_id in deduped: + continue + deduped.add(work_id) + if persist_work(store, item): + persisted += 1 + if persisted - last_logged >= 200: + LOGGER.info("github-discovery: persisted %d works", persisted) + last_logged = persisted + LOGGER.info( + "github-discovery complete: seen=%d persisted=%d (mode=%s)", + seen, + persisted, + mode, + ) + return seen, persisted diff --git a/src/index/openalex/ingest/github_extract.py b/src/index/openalex/ingest/github_extract.py new file mode 100644 index 0000000..897392d --- /dev/null +++ b/src/index/openalex/ingest/github_extract.py @@ -0,0 +1,124 @@ +"""Extract canonical GitHub URLs from persisted Work abstracts. + +Reuses `src.v2.ingest.detection.github_url_classifier.classify_github_url` +so the test-set URLs are normalized identically to what v2 expects on its +`/extract` endpoint. +""" + +from __future__ import annotations + +import logging +import re +from typing import TYPE_CHECKING + +from src.v2.ingest.detection.github_url_classifier import classify_github_url +from src.v2.ingest.detection.models import UnsupportedGitHubURL + +if TYPE_CHECKING: + from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + +# Match URLs starting with http(s):// containing a github.com host. The +# negative lookahead on trailing punctuation is intentional — academic +# abstracts often include URLs followed by ".", ",", ")". +_URL_PATTERN = re.compile( + r"https?://(?:www\.)?github\.com/[^\s<>\"')\]]+", + flags=re.IGNORECASE, +) + + +def extract_github_urls(text: str) -> list[str]: + """Return raw URL substrings found in `text` (no canonicalization).""" + if not text: + return [] + raw = _URL_PATTERN.findall(text) + cleaned: list[str] = [] + for url in raw: + # Strip trailing punctuation that often hugs URLs in prose. + stripped = url.rstrip(".,;:)]}>\"'") + if stripped: + cleaned.append(stripped) + return cleaned + + +def _classify_safe(url: str) -> tuple[str, str | None, str | None] | None: + """Best-effort classification. + + Returns (normalized_url, owner, repo) or None if the URL is unusable + even via `UnsupportedGitHubURL` (e.g., not a github.com URL at all). + """ + try: + result = classify_github_url(url) + except UnsupportedGitHubURL as exc: + # An issue / PR / blob URL — we still capture the normalized + # repository URL since that's what v2 can act on. + return (exc.normalized_url, None, None) + except ValueError as exc: + LOGGER.debug("skipping unclassifiable URL %s: %s", url, exc) + return None + return (result.normalized_url, result.owner, result.repo) + + +def extract_and_persist_for_work( + store: DuckDBStore, + *, + work_id: str, + text: str, + source: str, +) -> int: + """Scan `text`, persist matches against `work_id`. Return count persisted.""" + if source not in ("abstract", "fulltext"): + message = f"Invalid source: {source}" + raise ValueError(message) + persisted = 0 + seen_norm: set[str] = set() + for url in extract_github_urls(text): + classified = _classify_safe(url) + if classified is None: + continue + normalized, owner, repo = classified + if normalized in seen_norm: + continue + seen_norm.add(normalized) + store.upsert_github_url( + work_id=work_id, + url=url, + normalized_url=normalized, + owner=owner, + repo=repo, + source=source, + ) + persisted += 1 + return persisted + + +def extract_for_persisted_works(store: DuckDBStore) -> tuple[int, int]: + """Sweep over all `works` rows and extract abstract URLs. + + Returns (works_scanned, urls_persisted). + """ + # Materialize the full SELECT before iterating: the upsert calls inside + # the loop reuse the same DuckDB connection and would clobber a live + # cursor mid-stream (DuckDB has one cursor per connection). + rows = store.connect().execute( + "SELECT openalex_id, abstract FROM works WHERE abstract IS NOT NULL", + ).fetchall() + scanned = 0 + total_persisted = 0 + for work_id, abstract in rows: + scanned += 1 + if not abstract: + continue + total_persisted += extract_and_persist_for_work( + store, + work_id=work_id, + text=abstract, + source="abstract", + ) + LOGGER.info( + "github-extract complete: scanned=%d urls_persisted=%d", + scanned, + total_persisted, + ) + return scanned, total_persisted diff --git a/src/index/openalex/ingest/institutions.py b/src/index/openalex/ingest/institutions.py new file mode 100644 index 0000000..3fe1ac5 --- /dev/null +++ b/src/index/openalex/ingest/institutions.py @@ -0,0 +1,48 @@ +"""Fetch + persist OpenAlex Institutions.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.ingest.openalex_client import batched, iter_institutions + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def _project_institution(item: dict[str, Any]) -> dict[str, Any]: + return { + "openalex_id": item.get("id"), + "ror": item.get("ror"), + "display_name": item.get("display_name"), + "country_code": item.get("country_code"), + } + + +def ingest_institutions( + *, + config: OpenAlexIndexConfig, + store: DuckDBStore, + filters: dict[str, Any], + limit: int | None = None, +) -> int: + count = 0 + last_logged = 0 + items = iter_institutions(config=config, filters=filters, limit=limit) + for batch in batched(items, config.openalex.per_page): + with store.transaction(): + for item in batch: + row = _project_institution(item) + if not row["openalex_id"]: + continue + store.upsert_institution(row, raw=item) + count += 1 + if count - last_logged >= 500: + LOGGER.info("ingested %d institutions", count) + last_logged = count + LOGGER.info("institutions ingest complete: %d rows", count) + return count diff --git a/src/index/openalex/ingest/openalex_client.py b/src/index/openalex/ingest/openalex_client.py new file mode 100644 index 0000000..24cdff4 --- /dev/null +++ b/src/index/openalex/ingest/openalex_client.py @@ -0,0 +1,160 @@ +"""Thin wrapper around the `pyalex` package. + +`pyalex` already handles polite-pool email injection, cursor pagination, +filter chaining, `select=` projections, and retry/backoff. This module's +only job is to apply our config to it once at import time and expose a +small set of typed iterators. +""" + +from __future__ import annotations + +import logging +from itertools import islice +from typing import TYPE_CHECKING, Any + +import pyalex +from pyalex import Authors, Concepts, Institutions, Sources, Topics, Works + +from src.index.openalex.config import OpenAlexIndexConfig +from src.index.openalex.models import ( + AUTHORS_PROJECTION, + CONCEPTS_PROJECTION, + INSTITUTIONS_PROJECTION, + SOURCES_PROJECTION, + TOPICS_PROJECTION, + WORKS_PROJECTION, +) + +if TYPE_CHECKING: + from collections.abc import Iterator + +LOGGER = logging.getLogger(__name__) + +_CONFIGURED = False + + +def configure_pyalex(config: OpenAlexIndexConfig) -> None: + """Apply our config to the pyalex global. Idempotent.""" + global _CONFIGURED # noqa: PLW0603 + config.require_ingest() + pyalex.config.email = config.openalex.mailto + pyalex.config.api_key = None + pyalex.config.max_retries = 5 + pyalex.config.retry_backoff_factor = 0.5 + pyalex.config.retry_http_codes = [429, 500, 502, 503, 504] + _CONFIGURED = True + LOGGER.info("pyalex configured (mailto=%s)", config.openalex.mailto) + + +def _ensure_configured(config: OpenAlexIndexConfig) -> None: + if not _CONFIGURED: + configure_pyalex(config) + + +def batched(iterable: Any, size: int) -> Iterator[list]: + """Yield successive `size`-sized chunks from `iterable`.""" + it = iter(iterable) + while True: + chunk = list(islice(it, size)) + if not chunk: + return + yield chunk + + +def _paginate( + query: Any, + *, + per_page: int, + limit: int | None, +) -> Iterator[dict[str, Any]]: + yielded = 0 + pages = query.paginate(per_page=per_page, n_max=limit, method="cursor") + for page in pages: + for item in page: + yield item + yielded += 1 + if limit is not None and yielded >= limit: + return + + +def iter_works( + *, + config: OpenAlexIndexConfig, + filters: dict[str, Any], + limit: int | None = None, + extra_search: str | None = None, +) -> Iterator[dict[str, Any]]: + """Yield Work dicts matching the given filter dict. + + `extra_search` runs OpenAlex's `default.search` (title + abstract) on top + of the filter. For `fulltext.search`, embed it inside `filters` as + `{"fulltext": {"search": "..."}}`. + """ + _ensure_configured(config) + query = Works().filter(**filters).select(",".join(WORKS_PROJECTION)) + if extra_search: + query = query.search(extra_search) + yield from _paginate(query, per_page=config.openalex.per_page, limit=limit) + + +def iter_authors( + *, + config: OpenAlexIndexConfig, + filters: dict[str, Any], + limit: int | None = None, +) -> Iterator[dict[str, Any]]: + _ensure_configured(config) + query = Authors().filter(**filters).select(",".join(AUTHORS_PROJECTION)) + yield from _paginate(query, per_page=config.openalex.per_page, limit=limit) + + +def iter_institutions( + *, + config: OpenAlexIndexConfig, + filters: dict[str, Any], + limit: int | None = None, +) -> Iterator[dict[str, Any]]: + _ensure_configured(config) + query = ( + Institutions().filter(**filters).select(",".join(INSTITUTIONS_PROJECTION)) + ) + yield from _paginate(query, per_page=config.openalex.per_page, limit=limit) + + +def iter_sources( + *, + config: OpenAlexIndexConfig, + filters: dict[str, Any], + limit: int | None = None, +) -> Iterator[dict[str, Any]]: + _ensure_configured(config) + query = Sources().filter(**filters).select(",".join(SOURCES_PROJECTION)) + yield from _paginate(query, per_page=config.openalex.per_page, limit=limit) + + +def iter_topics( + *, + config: OpenAlexIndexConfig, + filters: dict[str, Any] | None = None, + limit: int | None = None, +) -> Iterator[dict[str, Any]]: + _ensure_configured(config) + query = Topics() + if filters: + query = query.filter(**filters) + query = query.select(",".join(TOPICS_PROJECTION)) + yield from _paginate(query, per_page=config.openalex.per_page, limit=limit) + + +def iter_concepts( + *, + config: OpenAlexIndexConfig, + filters: dict[str, Any] | None = None, + limit: int | None = None, +) -> Iterator[dict[str, Any]]: + _ensure_configured(config) + query = Concepts() + if filters: + query = query.filter(**filters) + query = query.select(",".join(CONCEPTS_PROJECTION)) + yield from _paginate(query, per_page=config.openalex.per_page, limit=limit) diff --git a/src/index/openalex/ingest/references.py b/src/index/openalex/ingest/references.py new file mode 100644 index 0000000..8f7c5eb --- /dev/null +++ b/src/index/openalex/ingest/references.py @@ -0,0 +1,88 @@ +"""Extract `referenced_works` from `works.raw` into the normalised +`work_references` table. + +This is the **API-free** path — it operates entirely on data already +present in DuckDB. For works whose ``raw`` pre-dates +``WORKS_PROJECTION`` containing ``referenced_works`` (i.e. ingested +before that field was added), use the discover/hydrate pipeline: + + gme discover --source from-references --indices openalex --out missing.jsonl + gme hydrate missing.jsonl --indices openalex +""" + +from __future__ import annotations + +import json +import logging +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + +BATCH = 5000 + + +def extract_from_raw(store: DuckDBStore) -> dict[str, int]: + """Walk every row in ``works`` whose ``raw.referenced_works`` is populated, + and bulk-insert the edges into ``work_references``. + + Idempotent: ``INSERT OR IGNORE`` on the composite primary key. + Returns counts: ``{works_seen, works_with_refs, edges_inserted, final_edges}``. + """ + cur = store.connect() + + n_existing = cur.execute("SELECT COUNT(*) FROM work_references").fetchone()[0] + LOGGER.info("existing work_references rows: %d", n_existing) + + offset = 0 + works_seen = 0 + works_with_refs = 0 + edges_submitted = 0 + + while True: + rows = cur.execute( + "SELECT openalex_id, raw FROM works WHERE raw IS NOT NULL " + "ORDER BY openalex_id LIMIT ? OFFSET ?", + [BATCH, offset], + ).fetchall() + if not rows: + break + batch_inserts: list[tuple[str, str, int]] = [] + for citing_id, raw in rows: + works_seen += 1 + try: + obj = json.loads(raw) if isinstance(raw, str) else raw + except (json.JSONDecodeError, TypeError): + continue + refs = obj.get("referenced_works") or [] + if refs: + works_with_refs += 1 + for pos, cited in enumerate(refs): + if cited: + batch_inserts.append((citing_id, cited, pos)) + if batch_inserts: + cur.executemany( + "INSERT OR IGNORE INTO work_references " + "(citing_work_id, cited_work_id, position) VALUES (?, ?, ?)", + batch_inserts, + ) + edges_submitted += len(batch_inserts) + offset += BATCH + LOGGER.info( + "processed %d works (%d with refs, %d edges submitted)", + works_seen, works_with_refs, edges_submitted, + ) + + final = cur.execute("SELECT COUNT(*) FROM work_references").fetchone()[0] + return { + "works_seen": works_seen, + "works_with_refs": works_with_refs, + "edges_submitted": edges_submitted, + "edges_added": final - n_existing, + "final_edges": final, + } + + +__all__ = ["extract_from_raw"] diff --git a/src/index/openalex/ingest/scope.py b/src/index/openalex/ingest/scope.py new file mode 100644 index 0000000..ae99128 --- /dev/null +++ b/src/index/openalex/ingest/scope.py @@ -0,0 +1,58 @@ +"""Scope filter builders. + +Two scopes are supported in v1: EPFL (by ROR) and Switzerland (by country +code). For each scope we provide an entity-aware filter dict because the +filter path differs between Works, Authors, and Institutions. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Literal + +from src.index.openalex.config import OpenAlexIndexConfig + +ScopeName = Literal["epfl", "switzerland"] + + +@dataclass(slots=True, frozen=True) +class ScopeFilters: + """Filter dicts for a single scope, keyed by OpenAlex entity type.""" + + works: dict[str, Any] + authors: dict[str, Any] + institutions: dict[str, Any] + sources: dict[str, Any] + + +def epfl_scope(config: OpenAlexIndexConfig) -> ScopeFilters: + ror = config.scope.ror + return ScopeFilters( + works={"authorships": {"institutions": {"ror": ror}}}, + authors={"affiliations": {"institution": {"ror": ror}}}, + institutions={"ror": ror}, + # OpenAlex Sources don't accept ROR-based filtering; `host_organization` + # takes an institution OpenAlex ID. We pull sources unfiltered and + # later derive the EPFL-relevant subset via joins to ingested works. + sources={}, + ) + + +def switzerland_scope(config: OpenAlexIndexConfig) -> ScopeFilters: + country = config.scope.country + return ScopeFilters( + works={"authorships": {"institutions": {"country_code": country}}}, + authors={"last_known_institutions": {"country_code": country}}, + institutions={"country_code": country}, + # No `country_code` filter on Sources; we pull unfiltered (see above). + sources={}, + ) + + +def resolve_scope(name: ScopeName, config: OpenAlexIndexConfig) -> ScopeFilters: + if name == "epfl": + return epfl_scope(config) + if name == "switzerland": + return switzerland_scope(config) + message = f"Unknown scope: {name}" + raise ValueError(message) diff --git a/src/index/openalex/ingest/sources.py b/src/index/openalex/ingest/sources.py new file mode 100644 index 0000000..00162d7 --- /dev/null +++ b/src/index/openalex/ingest/sources.py @@ -0,0 +1,48 @@ +"""Fetch + persist OpenAlex Sources (journals/venues/repositories).""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.ingest.openalex_client import batched, iter_sources + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def _project_source(item: dict[str, Any]) -> dict[str, Any]: + return { + "openalex_id": item.get("id"), + "issn_l": item.get("issn_l"), + "display_name": item.get("display_name"), + "type": item.get("type"), + } + + +def ingest_sources( + *, + config: OpenAlexIndexConfig, + store: DuckDBStore, + filters: dict[str, Any], + limit: int | None = None, +) -> int: + count = 0 + last_logged = 0 + items = iter_sources(config=config, filters=filters, limit=limit) + for batch in batched(items, config.openalex.per_page): + with store.transaction(): + for item in batch: + row = _project_source(item) + if not row["openalex_id"]: + continue + store.upsert_source(row, raw=item) + count += 1 + if count - last_logged >= 500: + LOGGER.info("ingested %d sources", count) + last_logged = count + LOGGER.info("sources ingest complete: %d rows", count) + return count diff --git a/src/index/openalex/ingest/topics.py b/src/index/openalex/ingest/topics.py new file mode 100644 index 0000000..038cf26 --- /dev/null +++ b/src/index/openalex/ingest/topics.py @@ -0,0 +1,50 @@ +"""Fetch + persist OpenAlex Topics.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.ingest.openalex_client import batched, iter_topics + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def _project_topic(item: dict[str, Any]) -> dict[str, Any]: + domain = item.get("domain") or {} + field = item.get("field") or {} + return { + "openalex_id": item.get("id"), + "display_name": item.get("display_name"), + "domain_id": domain.get("id"), + "field_id": field.get("id"), + } + + +def ingest_topics( + *, + config: OpenAlexIndexConfig, + store: DuckDBStore, + filters: dict[str, Any] | None = None, + limit: int | None = None, +) -> int: + count = 0 + last_logged = 0 + items = iter_topics(config=config, filters=filters, limit=limit) + for batch in batched(items, config.openalex.per_page): + with store.transaction(): + for item in batch: + row = _project_topic(item) + if not row["openalex_id"]: + continue + store.upsert_topic(row, raw=item) + count += 1 + if count - last_logged >= 500: + LOGGER.info("ingested %d topics", count) + last_logged = count + LOGGER.info("topics ingest complete: %d rows", count) + return count diff --git a/src/index/openalex/ingest/works.py b/src/index/openalex/ingest/works.py new file mode 100644 index 0000000..fd59ad5 --- /dev/null +++ b/src/index/openalex/ingest/works.py @@ -0,0 +1,94 @@ +"""Fetch + persist OpenAlex Works.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.ingest.openalex_client import batched, iter_works + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + from src.index.openalex.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def reconstruct_abstract(inverted: dict[str, list[int]] | None) -> str | None: + """Reconstruct plain-text abstract from OpenAlex's inverted index.""" + if not inverted: + return None + positions: list[tuple[int, str]] = [] + for token, idxs in inverted.items(): + for idx in idxs: + positions.append((idx, token)) + positions.sort(key=lambda x: x[0]) + return " ".join(token for _, token in positions) + + +def _project_work(item: dict[str, Any]) -> dict[str, Any]: + primary_topic = item.get("primary_topic") or {} + primary_location = item.get("primary_location") or {} + primary_source = (primary_location or {}).get("source") or {} + return { + "openalex_id": item.get("id"), + "doi": item.get("doi"), + "title": item.get("title"), + "abstract": reconstruct_abstract(item.get("abstract_inverted_index")), + "publication_year": item.get("publication_year"), + "primary_topic_id": primary_topic.get("id"), + "primary_source_id": primary_source.get("id"), + } + + +def _author_links(item: dict[str, Any]) -> tuple[list[tuple[str, int]], list[str]]: + """Return (author_id, position) and a flat list of institution_ids.""" + authorships = item.get("authorships") or [] + authors: list[tuple[str, int]] = [] + institutions: list[str] = [] + for position, authorship in enumerate(authorships): + author = (authorship or {}).get("author") or {} + author_id = author.get("id") + if author_id: + authors.append((author_id, position)) + for inst in (authorship or {}).get("institutions") or []: + inst_id = inst.get("id") + if inst_id: + institutions.append(inst_id) + return authors, institutions + + +def persist_work(store: DuckDBStore, item: dict[str, Any]) -> str | None: + row = _project_work(item) + work_id = row["openalex_id"] + if not work_id: + return None + store.upsert_work(row, raw=item) + authors, institutions = _author_links(item) + if authors: + store.upsert_work_authors(work_id, authors) + if institutions: + store.upsert_work_institutions(work_id, set(institutions)) + return work_id + + +def ingest_works( + *, + config: OpenAlexIndexConfig, + store: DuckDBStore, + filters: dict[str, Any], + limit: int | None = None, +) -> int: + count = 0 + last_logged = 0 + items = iter_works(config=config, filters=filters, limit=limit) + for batch in batched(items, config.openalex.per_page): + with store.transaction(): + for item in batch: + if persist_work(store, item): + count += 1 + if count - last_logged >= 500: + LOGGER.info("ingested %d works", count) + last_logged = count + LOGGER.info("works ingest complete: %d rows", count) + return count diff --git a/src/index/openalex/models.py b/src/index/openalex/models.py new file mode 100644 index 0000000..7ef4172 --- /dev/null +++ b/src/index/openalex/models.py @@ -0,0 +1,170 @@ +"""Pydantic schemas + OpenAlex `select` projections per entity. + +We keep the projections compact — only fields we filter/join on in DuckDB or +embed for vectors. Everything else lives in the `raw` JSON column. +""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field + +EntityType = Literal[ + "works", + "authors", + "institutions", + "sources", + "topics", + "concepts", +] + +ALL_ENTITY_TYPES: tuple[EntityType, ...] = ( + "works", + "authors", + "institutions", + "sources", + "topics", + "concepts", +) + +# OpenAlex `select=` projections per entity. Comma-joined strings are passed +# straight to pyalex's `.select(...)` call. +WORKS_PROJECTION = ( + "id", + "doi", + "title", + "abstract_inverted_index", + "publication_year", + "primary_topic", + "primary_location", + "authorships", + "concepts", + "topics", + "type", + "has_fulltext", + "fulltext_origin", +) + +AUTHORS_PROJECTION = ( + "id", + "display_name", + "orcid", + "last_known_institutions", + "affiliations", + "works_count", + "cited_by_count", +) + +INSTITUTIONS_PROJECTION = ( + "id", + "ror", + "display_name", + "country_code", + "type", + "lineage", +) + +SOURCES_PROJECTION = ( + "id", + "issn_l", + "display_name", + "type", + "host_organization", +) + +TOPICS_PROJECTION = ( + "id", + "display_name", + "domain", + "field", + "subfield", +) + +CONCEPTS_PROJECTION = ( + "id", + "display_name", + "level", + "wikidata", +) + + +class WorkRow(BaseModel): + """Structured columns persisted into DuckDB `works`.""" + + model_config = ConfigDict(extra="ignore") + + openalex_id: str + doi: str | None = None + title: str | None = None + abstract: str | None = None + publication_year: int | None = None + primary_topic_id: str | None = None + primary_source_id: str | None = None + + +class AuthorRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + openalex_id: str + display_name: str | None = None + orcid: str | None = None + last_known_institution_id: str | None = None + + +class InstitutionRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + openalex_id: str + ror: str | None = None + display_name: str | None = None + country_code: str | None = None + + +class SourceRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + openalex_id: str + issn_l: str | None = None + display_name: str | None = None + type: str | None = None + + +class TopicRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + openalex_id: str + display_name: str | None = None + domain_id: str | None = None + field_id: str | None = None + + +class ConceptRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + openalex_id: str + display_name: str | None = None + level: int | None = None + + +class WorkGitHubURLRow(BaseModel): + """A canonicalized GitHub URL extracted from a Work's text.""" + + model_config = ConfigDict(extra="ignore") + + work_id: str + url: str + normalized_url: str + owner: str | None = None + repo: str | None = None + source: Literal["abstract", "fulltext"] = Field(...) + + +class GitHubDiscoveryStats(BaseModel): + """Summary of a `find-github` run.""" + + works_seen: int = 0 + works_persisted: int = 0 + abstracts_scanned: int = 0 + urls_extracted: int = 0 + distinct_normalized_urls: int = 0 diff --git a/src/index/openalex/paths.py b/src/index/openalex/paths.py new file mode 100644 index 0000000..2c1325d --- /dev/null +++ b/src/index/openalex/paths.py @@ -0,0 +1,62 @@ +"""Filesystem layout for OpenAlex index artifacts. + +Single source of truth for paths under `/openalex/`. Other +modules import from here rather than building paths ad-hoc. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path + +DEFAULT_INDEX_DATA_DIR = Path("data/index") + + +def _resolve_repo_root() -> Path: + """Return the repo root by walking up from this file.""" + return Path(__file__).resolve().parents[3] + + +def _resolve_index_data_dir() -> Path: + """Resolve the shared index data dir (used by openalex + infoscience).""" + raw = os.getenv("INDEX_DATA_DIR") + if raw and raw.strip(): + candidate = Path(raw.strip()).expanduser() + if candidate.is_absolute(): + return candidate + return _resolve_repo_root() / candidate + return _resolve_repo_root() / DEFAULT_INDEX_DATA_DIR + + +@dataclass(slots=True, frozen=True) +class OpenAlexPaths: + """Resolved filesystem paths for the OpenAlex module.""" + + root: Path + + @property + def duckdb_dir(self) -> Path: + return self.root / "duckdb" + + @property + def duckdb_path(self) -> Path: + return self.duckdb_dir / "openalex.duckdb" + + @property + def cache_dir(self) -> Path: + return self.root / "cache" + + @property + def logs_dir(self) -> Path: + return self.root / "logs" + + +def get_openalex_paths() -> OpenAlexPaths: + """Resolve `/openalex/` and ensure subdirectories exist.""" + root = _resolve_index_data_dir() / "openalex" + paths = OpenAlexPaths(root=root) + paths.duckdb_dir.mkdir(parents=True, exist_ok=True) + paths.cache_dir.mkdir(parents=True, exist_ok=True) + paths.logs_dir.mkdir(parents=True, exist_ok=True) + return paths diff --git a/src/index/openalex/rerank/__init__.py b/src/index/openalex/rerank/__init__.py new file mode 100644 index 0000000..6dc799a --- /dev/null +++ b/src/index/openalex/rerank/__init__.py @@ -0,0 +1 @@ +"""RCP-hosted reranker integration.""" diff --git a/src/index/openalex/rerank/rcp_client.py b/src/index/openalex/rerank/rcp_client.py new file mode 100644 index 0000000..2a85d7a --- /dev/null +++ b/src/index/openalex/rerank/rcp_client.py @@ -0,0 +1,113 @@ +"""Async client for the RCP reranker (Cohere-compatible shape). + +If the RCP deployment exposes a different path or body shape, override +`RCP_RERANK_PATH` via env or pass `path=` at construction time. +""" + +from __future__ import annotations + +import logging +import os +from typing import TYPE_CHECKING, Any + +import httpx +from tenacity import ( + retry, + retry_if_exception_type, + stop_after_attempt, + wait_exponential, +) + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + +LOGGER = logging.getLogger(__name__) + +DEFAULT_TIMEOUT_S = 30.0 +_RETRYABLE_STATUS = {429, 500, 502, 503, 504} + + +def _is_retryable(exc: BaseException) -> bool: + if isinstance(exc, httpx.TimeoutException): + return True + if isinstance(exc, httpx.HTTPStatusError): + return exc.response.status_code in _RETRYABLE_STATUS + return False + + +class RCPRerankerClient: + def __init__( + self, + config: OpenAlexIndexConfig, + *, + path: str | None = None, + timeout_s: float | None = None, + ) -> None: + config.require_rcp() + self._config = config + self._timeout = httpx.Timeout(timeout_s or DEFAULT_TIMEOUT_S) + path = path or os.getenv("RCP_RERANK_PATH", "/rerank") + self._url = f"{config.rcp.base_url.rstrip('/')}{path}" + self._headers = { + "Authorization": f"Bearer {config.rcp.token}", + "Content-Type": "application/json", + } + + @property + def model(self) -> str: + return self._config.rcp.reranker_model + + async def rerank( + self, + query: str, + documents: list[str], + *, + top_n: int | None = None, + ) -> list[dict[str, Any]]: + """Return [{index, relevance_score}, ...] sorted by relevance desc.""" + if not documents: + return [] + payload: dict[str, Any] = { + "model": self._config.rcp.reranker_model, + "query": query, + "documents": documents, + } + if top_n is not None: + payload["top_n"] = top_n + + @retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=1, min=2, max=30), + retry=retry_if_exception_type((httpx.TimeoutException, httpx.HTTPStatusError)), + reraise=True, + ) + async def _call() -> list[dict[str, Any]]: + async with httpx.AsyncClient() as client: + response = await client.post( + self._url, + headers=self._headers, + json=payload, + timeout=self._timeout, + ) + try: + response.raise_for_status() + except httpx.HTTPStatusError as exc: + if not _is_retryable(exc): + body = response.text[:500] + LOGGER.error( + "RCP rerank non-retryable %d: %s", + response.status_code, + body, + ) + raise + data = response.json() + results = data.get("results") or [] + return [ + { + "index": int(item["index"]), + "relevance_score": float(item["relevance_score"]), + } + for item in results + ] + + return await _call() diff --git a/src/index/openalex/retrieval/__init__.py b/src/index/openalex/retrieval/__init__.py new file mode 100644 index 0000000..8697830 --- /dev/null +++ b/src/index/openalex/retrieval/__init__.py @@ -0,0 +1 @@ +"""Retrieval surfaces over the OpenAlex index (SQL + semantic).""" diff --git a/src/index/openalex/retrieval/semantic.py b/src/index/openalex/retrieval/semantic.py new file mode 100644 index 0000000..6155248 --- /dev/null +++ b/src/index/openalex/retrieval/semantic.py @@ -0,0 +1,123 @@ +"""End-to-end semantic retrieval: embed → vector search → rerank → hydrate.""" + +from __future__ import annotations + +import asyncio +import json +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.storage.duckdb_store import DuckDBStore +from src.index.openalex.vector.qdrant_store import QdrantStore + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + +LOGGER = logging.getLogger(__name__) + + +async def _async_search( + *, + config: OpenAlexIndexConfig, + query: str, + entity_type: str, + top_k: int, + candidate_k: int, + filter_payload: dict[str, Any] | None, + store: DuckDBStore, +) -> list[dict[str, Any]]: + embed = RCPEmbeddingClient(config) + qdrant = QdrantStore(config) + rerank = RCPRerankerClient(config) + + [query_vec] = await embed.embed_all([query]) + candidates = qdrant.search( + entity_type, + query_vector=query_vec, + top_k=candidate_k, + filter_payload=filter_payload, + ) + if not candidates: + return [] + + docs = [_payload_to_doc(c["payload"]) for c in candidates] + reranked = await rerank.rerank(query, docs, top_n=top_k) + if not reranked: + # Reranker failed quietly — fall back to vector order. + ordered = candidates[:top_k] + else: + ordered = [] + for r in reranked: + cand = candidates[r["index"]] + ordered.append( + {**cand, "rerank_score": r["relevance_score"]}, + ) + + hydrated = [] + for hit in ordered: + payload = hit["payload"] or {} + entity_id = payload.get("entity_id") or payload.get("openalex_id") + if not entity_id: + continue + row = _hydrate(store, entity_type, entity_id) + hydrated.append( + { + "id": hit["id"], + "vector_score": hit["score"], + "rerank_score": hit.get("rerank_score"), + "payload": payload, + "entity": row, + }, + ) + return hydrated + + +def _payload_to_doc(payload: dict[str, Any]) -> str: + title = payload.get("title") + name = payload.get("display_name") + parts = [p for p in (title, name) if p] + return " — ".join(parts) if parts else json.dumps(payload, ensure_ascii=False) + + +def _hydrate( + store: DuckDBStore, + entity_type: str, + entity_id: str, +) -> dict[str, Any] | None: + cur = store.connect().execute( + f"SELECT * FROM {entity_type} WHERE openalex_id = ?", # noqa: S608 + [entity_id], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + +def semantic_search( + *, + config: OpenAlexIndexConfig, + query: str, + entity_type: str = "works", + top_k: int = 10, + candidate_k: int = 50, + filter_payload: dict[str, Any] | None = None, + store: DuckDBStore | None = None, +) -> list[dict[str, Any]]: + """Synchronous entrypoint used by the CLI and the FastAPI app.""" + if store is None: + store = DuckDBStore.open() + return asyncio.run( + _async_search( + config=config, + query=query, + entity_type=entity_type, + top_k=top_k, + candidate_k=candidate_k, + filter_payload=filter_payload, + store=store, + ), + ) diff --git a/src/index/openalex/retrieval/sql.py b/src/index/openalex/retrieval/sql.py new file mode 100644 index 0000000..9f4e52c --- /dev/null +++ b/src/index/openalex/retrieval/sql.py @@ -0,0 +1,141 @@ +"""Read-only SQL surface over the DuckDB dump. + +Two entrypoints: + +- `run_predefined()` — parametrized canned queries. Safe by construction. +- `run_adhoc()` — guarded ad-hoc SELECT/WITH only. Opens DuckDB read-only and + rejects anything that doesn't start with a SELECT/WITH or that contains + forbidden statement-level keywords. +""" + +from __future__ import annotations + +import re +from typing import TYPE_CHECKING, Any + +from src.index.openalex.storage.duckdb_store import DuckDBStore + +if TYPE_CHECKING: + pass + +INVALID_QUERY_PREFIX_ERROR = "Only SELECT/WITH queries are allowed" +FORBIDDEN_KEYWORD_ERROR = "Forbidden keyword in query: {kw}" + +_ALLOWED_PREFIXES = ("select", "with") +_FORBIDDEN_KEYWORDS = ( + "attach", + "copy", + "pragma", + "install", + "load", + "export", + "import", + "create", + "drop", + "alter", + "insert", + "update", + "delete", + "truncate", +) + +_KEYWORD_RE = re.compile(r"\b(" + "|".join(_FORBIDDEN_KEYWORDS) + r")\b", re.IGNORECASE) + + +def _validate_adhoc(sql: str) -> None: + stripped = sql.strip().lstrip("(").lstrip() + lowered = stripped.lower() + if not any(lowered.startswith(p) for p in _ALLOWED_PREFIXES): + raise ValueError(INVALID_QUERY_PREFIX_ERROR) + match = _KEYWORD_RE.search(stripped) + if match: + raise ValueError(FORBIDDEN_KEYWORD_ERROR.format(kw=match.group(1).upper())) + + +PREDEFINED_QUERIES: dict[str, str] = { + "count_by_entity": ( + "SELECT 'works' AS entity, COUNT(*) AS n FROM works " + "UNION ALL SELECT 'authors', COUNT(*) FROM authors " + "UNION ALL SELECT 'institutions', COUNT(*) FROM institutions " + "UNION ALL SELECT 'sources', COUNT(*) FROM sources " + "UNION ALL SELECT 'topics', COUNT(*) FROM topics " + "UNION ALL SELECT 'concepts', COUNT(*) FROM concepts " + "UNION ALL SELECT 'work_github_urls', COUNT(*) FROM work_github_urls" + ), + "top_works_by_year": ( + "SELECT openalex_id, title, publication_year FROM works " + "WHERE publication_year = $year " + "ORDER BY title LIMIT $limit" + ), + "github_works": ( + "SELECT w.openalex_id, w.title, w.publication_year, gh.normalized_url " + "FROM works w JOIN work_github_urls gh ON gh.work_id = w.openalex_id " + "ORDER BY w.publication_year DESC NULLS LAST, w.title " + "LIMIT $limit" + ), + "distinct_github_urls": ( + "SELECT DISTINCT normalized_url FROM work_github_urls " + "ORDER BY normalized_url LIMIT $limit" + ), + "coauthors_of_author": ( + "SELECT a.openalex_id, a.display_name, COUNT(*) AS shared_works " + "FROM work_authors wa1 JOIN work_authors wa2 " + " ON wa1.work_id = wa2.work_id AND wa1.author_id <> wa2.author_id " + "JOIN authors a ON a.openalex_id = wa2.author_id " + "WHERE wa1.author_id = $author_id " + "GROUP BY a.openalex_id, a.display_name " + "ORDER BY shared_works DESC LIMIT $limit" + ), +} + + +def _row_to_dict(cur: Any) -> list[dict[str, Any]]: + cols = [d[0] for d in cur.description] if cur.description else [] + return [dict(zip(cols, r, strict=False)) for r in cur.fetchall()] + + +def _execute( + sql: str, + params: dict[str, Any] | None, + store: DuckDBStore | None, +) -> list[dict[str, Any]]: + # DuckDB forbids opening a second handle to the same file with a + # different config in one process, so we always go through the writer + # connection. The `_validate_adhoc` allowlist is the safety boundary. + owned = False + if store is None: + store = DuckDBStore.open() + owned = True + try: + cur = store.connect().execute(sql, params or {}) + return _row_to_dict(cur) + finally: + if owned: + store.close() + + +def run_adhoc( + sql: str, + params: dict[str, Any] | None = None, + *, + store: DuckDBStore | None = None, +) -> list[dict[str, Any]]: + """Execute a guarded ad-hoc SELECT/WITH.""" + _validate_adhoc(sql) + return _execute(sql, params, store) + + +def run_predefined( + name: str, + params: dict[str, Any] | None = None, + *, + store: DuckDBStore | None = None, +) -> list[dict[str, Any]]: + """Execute a named predefined query with the given params.""" + if name not in PREDEFINED_QUERIES: + message = ( + f"Unknown predefined query: {name!r}. " + f"Known: {sorted(PREDEFINED_QUERIES)}" + ) + raise ValueError(message) + return _execute(PREDEFINED_QUERIES[name], params, store) diff --git a/src/index/openalex/storage/__init__.py b/src/index/openalex/storage/__init__.py new file mode 100644 index 0000000..5b07f0f --- /dev/null +++ b/src/index/openalex/storage/__init__.py @@ -0,0 +1 @@ +"""DuckDB persistence for the OpenAlex module.""" diff --git a/src/index/openalex/storage/duckdb_store.py b/src/index/openalex/storage/duckdb_store.py new file mode 100644 index 0000000..b7056e3 --- /dev/null +++ b/src/index/openalex/storage/duckdb_store.py @@ -0,0 +1,307 @@ +"""DuckDB lifecycle, schema bootstrap, and upsert helpers.""" + +from __future__ import annotations + +import json +import logging +from contextlib import contextmanager +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import duckdb + +from src.index.openalex.paths import get_openalex_paths + +if TYPE_CHECKING: + from collections.abc import Iterable, Iterator + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +class DuckDBStore: + """Thin wrapper around DuckDB tuned for the OpenAlex schema. + + Construct with `DuckDBStore.open()` for the default repo path. Re-running + `bootstrap()` is idempotent. + """ + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open(cls, db_path: Path | None = None) -> DuckDBStore: + if db_path is None: + db_path = get_openalex_paths().duckdb_path + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + """Apply the canonical schema. Safe to call repeatedly.""" + conn = self.connect() + conn.execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + """Open a fresh read-only connection (separate from the writer).""" + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + @contextmanager + def transaction(self) -> Iterator[None]: + """Wrap a batch of writes in a single BEGIN/COMMIT for throughput. + + DuckDB auto-commits each `execute()` by default, which makes per-row + inserts ~5–10× slower than batched ones. Wrapping ingest pages in + this context manager folds them into a single commit. + """ + conn = self.connect() + conn.execute("BEGIN TRANSACTION") + try: + yield + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + + # ---- Upserts --------------------------------------------------------- + + def upsert_work(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="works", + cols=( + "openalex_id", + "doi", + "title", + "abstract", + "publication_year", + "primary_topic_id", + "primary_source_id", + ), + row=row, + raw=raw, + ) + + def upsert_author(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="authors", + cols=( + "openalex_id", + "display_name", + "orcid", + "last_known_institution_id", + ), + row=row, + raw=raw, + ) + + def upsert_institution(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="institutions", + cols=("openalex_id", "ror", "display_name", "country_code"), + row=row, + raw=raw, + ) + + def upsert_source(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="sources", + cols=("openalex_id", "issn_l", "display_name", "type"), + row=row, + raw=raw, + ) + + def upsert_topic(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="topics", + cols=("openalex_id", "display_name", "domain_id", "field_id"), + row=row, + raw=raw, + ) + + def upsert_concept(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + self._upsert( + table="concepts", + cols=("openalex_id", "display_name", "level"), + row=row, + raw=raw, + ) + + def _upsert( + self, + *, + table: str, + cols: tuple[str, ...], + row: dict[str, Any], + raw: dict[str, Any], + ) -> None: + all_cols = (*cols, "raw", "ingested_at") + placeholders = ", ".join(["?"] * len(all_cols)) + col_list = ", ".join(all_cols) + update_cols = ", ".join( + f"{c} = excluded.{c}" for c in (*cols[1:], "raw", "ingested_at") + ) + sql = ( + f"INSERT INTO {table} ({col_list}) VALUES ({placeholders}) " + f"ON CONFLICT ({cols[0]}) DO UPDATE SET {update_cols}" + ) + values = [row.get(c) for c in cols] + values.append(json.dumps(raw, ensure_ascii=False)) + values.append(self._now()) + self.connect().execute(sql, values) + + def upsert_work_authors( + self, + work_id: str, + author_positions: Iterable[tuple[str, int]], + ) -> None: + conn = self.connect() + for author_id, position in author_positions: + conn.execute( + "INSERT INTO work_authors (work_id, author_id, position) " + "VALUES (?, ?, ?) " + "ON CONFLICT (work_id, author_id) DO UPDATE SET position = " + "LEAST(work_authors.position, excluded.position)", + [work_id, author_id, position], + ) + + def upsert_work_institutions( + self, + work_id: str, + institution_ids: Iterable[str], + ) -> None: + conn = self.connect() + for inst_id in institution_ids: + conn.execute( + "INSERT INTO work_institutions (work_id, institution_id) " + "VALUES (?, ?) ON CONFLICT DO NOTHING", + [work_id, inst_id], + ) + + def upsert_github_url( + self, + *, + work_id: str, + url: str, + normalized_url: str, + owner: str | None, + repo: str | None, + source: str, + ) -> None: + self.connect().execute( + "INSERT INTO work_github_urls " + "(work_id, url, normalized_url, owner, repo, source) " + "VALUES (?, ?, ?, ?, ?, ?) " + "ON CONFLICT (work_id, normalized_url) DO UPDATE SET " + "url = excluded.url, owner = excluded.owner, " + "repo = excluded.repo, source = excluded.source", + [work_id, url, normalized_url, owner, repo, source], + ) + + def upsert_chunk( + self, + *, + chunk_id: str, + entity_type: str, + entity_id: str, + chunk_index: int, + text: str, + token_count: int, + vector_id: str, + ) -> None: + self.connect().execute( + "INSERT INTO chunks " + "(chunk_id, entity_type, entity_id, chunk_index, text, " + "token_count, vector_id) VALUES (?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (chunk_id) DO UPDATE SET " + "text = excluded.text, token_count = excluded.token_count, " + "vector_id = excluded.vector_id, embedded_at = now()", + [chunk_id, entity_type, entity_id, chunk_index, text, token_count, vector_id], + ) + + # ---- Reads ----------------------------------------------------------- + + def count(self, table: str) -> int: + result = self.connect().execute(f"SELECT count(*) FROM {table}").fetchone() + return int(result[0]) if result else 0 + + def fetch_work(self, openalex_id: str) -> dict[str, Any] | None: + return self._fetch_one("works", openalex_id) + + def _fetch_one(self, table: str, openalex_id: str) -> dict[str, Any] | None: + cur = self.connect().execute( + f"SELECT * FROM {table} WHERE openalex_id = ?", + [openalex_id], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + def stream_rows_for_embedding( + self, + entity_type: str, + *, + limit: int | None = None, + ) -> Iterator[dict[str, Any]]: + """Yield rows that need embedding (no chunks yet OR `limit` reached). + + Materializes the full result set upfront — DuckDB has one cursor per + connection, so leaving a live SELECT cursor while the embed pipeline + issues `upsert_chunk` calls on the same connection silently truncates + the stream. + """ + if entity_type not in { + "works", + "authors", + "institutions", + "sources", + "topics", + "concepts", + }: + message = f"Unknown entity_type: {entity_type}" + raise ValueError(message) + sql = ( + f"SELECT t.* FROM {entity_type} t " # noqa: S608 - table name guarded above + "WHERE NOT EXISTS (" + " SELECT 1 FROM chunks c " + " WHERE c.entity_type = ? AND c.entity_id = t.openalex_id" + ")" + ) + params: list[Any] = [entity_type] + if limit is not None: + sql += " LIMIT ?" + params.append(limit) + cur = self.connect().execute(sql, params) + cols = [d[0] for d in cur.description] + rows = cur.fetchall() + for row in rows: + yield dict(zip(cols, row, strict=False)) + + @staticmethod + def _now() -> str: + from datetime import datetime, timezone + + return datetime.now(tz=timezone.utc).isoformat() diff --git a/src/index/openalex/storage/schema.sql b/src/index/openalex/storage/schema.sql new file mode 100644 index 0000000..68bcad4 --- /dev/null +++ b/src/index/openalex/storage/schema.sql @@ -0,0 +1,106 @@ +-- Canonical DuckDB schema for the OpenAlex index module. +-- Idempotent: every statement uses IF NOT EXISTS so re-runs are safe. +-- See .internal/openalex/SCHEMA.md for column-by-column rationale. + +CREATE TABLE IF NOT EXISTS works ( + openalex_id TEXT PRIMARY KEY, + doi TEXT, + title TEXT, + abstract TEXT, + publication_year INTEGER, + primary_topic_id TEXT, + primary_source_id TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS authors ( + openalex_id TEXT PRIMARY KEY, + display_name TEXT, + orcid TEXT, + last_known_institution_id TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS institutions ( + openalex_id TEXT PRIMARY KEY, + ror TEXT, + display_name TEXT, + country_code TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS sources ( + openalex_id TEXT PRIMARY KEY, + issn_l TEXT, + display_name TEXT, + type TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS topics ( + openalex_id TEXT PRIMARY KEY, + display_name TEXT, + domain_id TEXT, + field_id TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS concepts ( + openalex_id TEXT PRIMARY KEY, + display_name TEXT, + level INTEGER, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS work_authors ( + work_id TEXT NOT NULL, + author_id TEXT NOT NULL, + position INTEGER, + PRIMARY KEY (work_id, author_id) +); + +CREATE TABLE IF NOT EXISTS work_institutions ( + work_id TEXT NOT NULL, + institution_id TEXT NOT NULL, + PRIMARY KEY (work_id, institution_id) +); + +CREATE TABLE IF NOT EXISTS work_github_urls ( + work_id TEXT NOT NULL, + url TEXT NOT NULL, + normalized_url TEXT NOT NULL, + owner TEXT, + repo TEXT, + source TEXT NOT NULL CHECK (source IN ('abstract', 'fulltext')), + found_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP, + PRIMARY KEY (work_id, normalized_url) +); + +-- chunk_id is deterministic: uuid5(NAMESPACE_URL, "||") +-- so the primary key alone provides the (entity_type, entity_id, chunk_index) +-- uniqueness guarantee. See `embed/pipeline.py:_chunk_id`. +CREATE TABLE IF NOT EXISTS chunks ( + chunk_id TEXT PRIMARY KEY, + entity_type TEXT NOT NULL, + entity_id TEXT NOT NULL, + chunk_index INTEGER NOT NULL, + text TEXT NOT NULL, + token_count INTEGER NOT NULL, + vector_id TEXT NOT NULL, + embedded_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS idx_works_year ON works (publication_year); +CREATE INDEX IF NOT EXISTS idx_works_topic ON works (primary_topic_id); +CREATE INDEX IF NOT EXISTS idx_authors_orcid ON authors (orcid); +CREATE INDEX IF NOT EXISTS idx_institutions_ror ON institutions (ror); +CREATE INDEX IF NOT EXISTS idx_institutions_cc ON institutions (country_code); +CREATE INDEX IF NOT EXISTS idx_chunks_entity ON chunks (entity_type, entity_id); +CREATE INDEX IF NOT EXISTS idx_github_urls_norm ON work_github_urls (normalized_url); +CREATE INDEX IF NOT EXISTS idx_github_urls_source ON work_github_urls (source); diff --git a/src/index/openalex/vector/__init__.py b/src/index/openalex/vector/__init__.py new file mode 100644 index 0000000..6cecc06 --- /dev/null +++ b/src/index/openalex/vector/__init__.py @@ -0,0 +1 @@ +"""Qdrant vector store integration.""" diff --git a/src/index/openalex/vector/qdrant_store.py b/src/index/openalex/vector/qdrant_store.py new file mode 100644 index 0000000..4728115 --- /dev/null +++ b/src/index/openalex/vector/qdrant_store.py @@ -0,0 +1,148 @@ +"""Qdrant client wrapper: collection bootstrap, upsert, search.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from qdrant_client import QdrantClient, models +from qdrant_client.http.exceptions import ResponseHandlingException, UnexpectedResponse +from tenacity import ( + retry, + retry_if_exception_type, + stop_after_attempt, + wait_exponential, +) + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + +LOGGER = logging.getLogger(__name__) + +# Qdrant HTTP client default timeout is too short for 4096-dim batch upserts; +# bump it. Transient failures are retried with exponential backoff. +QDRANT_HTTP_TIMEOUT_S = 60 +QDRANT_RETRY_ATTEMPTS = 5 + +PER_ENTITY_COLLECTIONS: tuple[str, ...] = ( + "works", + "authors", + "institutions", + "sources", + "topics", + "concepts", +) + + +class QdrantStore: + """Per-entity collection bootstrap + upsert + filtered search.""" + + def __init__(self, config: OpenAlexIndexConfig) -> None: + self._config = config + self._client = QdrantClient( + url=config.qdrant.url, + api_key=config.qdrant.api_key, + prefer_grpc=config.qdrant.prefer_grpc, + timeout=QDRANT_HTTP_TIMEOUT_S, + ) + self._dim = config.rcp.embedding_dim + + @property + def client(self) -> QdrantClient: + return self._client + + def ensure_collection(self, name: str) -> None: + """Create the collection if missing. Idempotent.""" + if self._client.collection_exists(name): + return + self._client.create_collection( + collection_name=name, + vectors_config=models.VectorParams( + size=self._dim, + distance=models.Distance.COSINE, + ), + ) + LOGGER.info("created qdrant collection %s (dim=%d)", name, self._dim) + + def upsert_points( + self, + collection: str, + *, + ids: list[str], + vectors: list[list[float]], + payloads: list[dict[str, Any]], + ) -> None: + if not (len(ids) == len(vectors) == len(payloads)): + message = "ids/vectors/payloads must be the same length" + raise ValueError(message) + if not ids: + return + self.ensure_collection(collection) + points = [ + models.PointStruct(id=pid, vector=vec, payload=payload) + for pid, vec, payload in zip(ids, vectors, payloads, strict=False) + ] + + @retry( + stop=stop_after_attempt(QDRANT_RETRY_ATTEMPTS), + wait=wait_exponential(multiplier=1, min=2, max=30), + retry=retry_if_exception_type( + (ResponseHandlingException, UnexpectedResponse), + ), + reraise=True, + ) + def _do_upsert() -> None: + self._client.upsert(collection_name=collection, points=points) + + _do_upsert() + + def search( + self, + collection: str, + *, + query_vector: list[float], + top_k: int = 50, + filter_payload: dict[str, Any] | None = None, + ) -> list[dict[str, Any]]: + self.ensure_collection(collection) + qdrant_filter = self._build_filter(filter_payload) if filter_payload else None + hits = self._client.query_points( + collection_name=collection, + query=query_vector, + limit=top_k, + query_filter=qdrant_filter, + with_payload=True, + ).points + return [ + {"id": str(p.id), "score": float(p.score), "payload": p.payload or {}} + for p in hits + ] + + def count(self, collection: str) -> int: + if not self._client.collection_exists(collection): + return 0 + return int(self._client.count(collection_name=collection, exact=True).count) + + @staticmethod + def _build_filter(payload: dict[str, Any]) -> models.Filter: + must: list[models.Condition] = [] + for key, value in payload.items(): + if isinstance(value, dict) and ("gte" in value or "lte" in value): + must.append( + models.FieldCondition( + key=key, + range=models.Range( + gte=value.get("gte"), + lte=value.get("lte"), + ), + ), + ) + elif isinstance(value, list): + must.append( + models.FieldCondition(key=key, match=models.MatchAny(any=value)), + ) + else: + must.append( + models.FieldCondition(key=key, match=models.MatchValue(value=value)), + ) + return models.Filter(must=must) diff --git a/src/index/orcid/__init__.py b/src/index/orcid/__init__.py new file mode 100644 index 0000000..19f5744 --- /dev/null +++ b/src/index/orcid/__init__.py @@ -0,0 +1,7 @@ +"""ORCID indexer: scope-aware ingest of ORCID person records, embeddings, +and dual SQL/RAG retrieval. + +Phase 1 targets EPFL (ROR `02s376052`); Phase 2 targets Switzerland +(`country=ch`). See the package CLI (`python -m src.index.orcid --help`) +for the operational entrypoints. +""" diff --git a/src/index/orcid/__main__.py b/src/index/orcid/__main__.py new file mode 100644 index 0000000..4735039 --- /dev/null +++ b/src/index/orcid/__main__.py @@ -0,0 +1,8 @@ +"""Module entrypoint: delegates to the CLI.""" + +from __future__ import annotations + +from src.index.orcid.cli import main + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/index/orcid/_federated.py b/src/index/orcid/_federated.py new file mode 100644 index 0000000..e25e38b --- /dev/null +++ b/src/index/orcid/_federated.py @@ -0,0 +1,141 @@ +"""ORCID registration with the federated discover/hydrate registries. + +ORCID is the canonical model for the discover/hydrate pattern (see +``.internal/federated/discover-hydrate-design.md``): + +- ``discover`` populates the ``seeds`` DuckDB table with ORCID IDs to + fetch, plus the ``discovered_via`` provenance flag. +- ``ingest`` (now wrapped here as ``hydrate``) streams from + ``seeds.LEFT JOIN persons`` filtered by ``persons.orcid_id IS NULL``, + fetches each, and persists. + +The wrappers are thin facades over +``src.index.orcid.ingest.{discover,persons}`` — no behavioural changes +to the underlying ingest path. +""" + +from __future__ import annotations + +import logging +from typing import Any, Iterator + +from src.index._federated.dh_registry import register_discoverer, register_hydrator +from src.index._federated.protocols import ( + HydrationSummary, + IndexDiscoverer, + IndexHydrator, + Seed, +) + +LOGGER = logging.getLogger(__name__) + + +class ORCIDDiscoverer: + name = "orcid" + accepted_sources = ("openalex", "orcid_search", "both") + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + if source not in self.accepted_sources: + message = f"ORCID: unknown source {source!r}. Accepted: {list(self.accepted_sources)}" + raise ValueError(message) + + from src.index.orcid.config import load_config + from src.index.orcid.ingest.discover import discover_seeds + from src.index.orcid.storage.duckdb_store import OrcidDuckDBStore + + scope = opts.get("scope", "switzerland") + config = load_config(scope=scope) + store = OrcidDuckDBStore.open(scope=scope) + + # Run discover_seeds — populates the seeds table. + summary = discover_seeds(config=config, store=store, source=source) + LOGGER.info("orcid discover (scope=%s, source=%s): %s", scope, source, summary) + + # Yield seeds that haven't been hydrated yet (LEFT JOIN persons). + # Filter by `discovered_via` prefix when source != 'both'. + cur = store.connect() + if source == "both": + sql = ( + "SELECT s.orcid_id, s.discovered_via, s.hint " + "FROM seeds s LEFT JOIN persons p ON p.orcid_id = s.orcid_id " + "WHERE p.orcid_id IS NULL ORDER BY s.discovered_at" + ) + rows = cur.execute(sql).fetchall() + else: + sql = ( + "SELECT s.orcid_id, s.discovered_via, s.hint " + "FROM seeds s LEFT JOIN persons p ON p.orcid_id = s.orcid_id " + "WHERE p.orcid_id IS NULL AND " + "(s.discovered_via = ? OR s.discovered_via = 'both') " + "ORDER BY s.discovered_at" + ) + rows = cur.execute(sql, [source]).fetchall() + for orcid_id, discovered_via, hint in rows: + yield Seed( + id=orcid_id, + seed_type="orcid", + source=f"orcid:{discovered_via}", + hint={"scope": scope, "discovered_via": discovered_via, "label": hint}, + ) + + +class ORCIDHydrator: + name = "orcid" + accepted_seed_types = ("orcid",) + + def hydrate( + self, + seeds, + *, + only_unfetched: bool = True, + ) -> HydrationSummary: + from src.index.orcid.config import load_config + from src.index.orcid.ingest.persons import ingest_persons + from src.index.orcid.storage.duckdb_store import OrcidDuckDBStore + + seed_list = [s for s in seeds if s.seed_type == "orcid"] + if not seed_list: + return HydrationSummary() + + # All seeds in one batch share scope (assumed). If callers mix scopes + # they should hydrate twice. + scope = (seed_list[0].hint or {}).get("scope") or "switzerland" + config = load_config(scope=scope) + store = OrcidDuckDBStore.open(scope=scope) + + # Upsert each seed into the seeds table so ingest_persons can pick it up. + # If the seed came from another index's discover, this is the bridge. + # Idempotent — `upsert_seed` does a sensible merge on conflict. + for seed in seed_list: + store.upsert_seed( + orcid_id=seed.id, + discovered_via=(seed.hint or {}).get("discovered_via", "external"), + hint=(seed.hint or {}).get("label"), + ) + + # `ingest_persons` already filters via stream_seeds(only_unfetched=True); + # if the caller wants to force re-fetch we don't currently support it + # without touching the underlying API. Match ORCID's semantics. + ingest_summary = ingest_persons( + config=config, store=store, scope=scope, + limit=None, priority_hints=None, + ) + out = HydrationSummary( + fetched=ingest_summary.get("fetched", 0), + in_scope=ingest_summary.get("in_scope", 0), + out_of_scope=ingest_summary.get("out_of_scope", 0), + errors=ingest_summary.get("errors", 0), + ) + if not only_unfetched: + out.extras["only_unfetched_ignored"] = True + return out + + +DISCOVERER = ORCIDDiscoverer() +HYDRATOR = ORCIDHydrator() + +register_discoverer(DISCOVERER) +register_hydrator(HYDRATOR) + + +__all__ = ["DISCOVERER", "HYDRATOR", "ORCIDDiscoverer", "ORCIDHydrator"] diff --git a/src/index/orcid/api.py b/src/index/orcid/api.py new file mode 100644 index 0000000..80ba542 --- /dev/null +++ b/src/index/orcid/api.py @@ -0,0 +1,107 @@ +"""FastAPI app exposing the ORCID index dual query surface.""" + +from __future__ import annotations + +import logging +from typing import Any + +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel, Field + +from src.index.orcid.config import load_config +from src.index.orcid.retrieval.semantic import semantic_search +from src.index.orcid.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) +from src.index.orcid.storage.duckdb_store import OrcidDuckDBStore +from src.index.orcid.vector.qdrant_store import OrcidQdrantStore + +LOGGER = logging.getLogger(__name__) + +app = FastAPI(title="ORCID Index") + + +class SearchRequest(BaseModel): + query: str + entity_type: str = "persons" + top_k: int = Field(default=10, ge=1, le=100) + candidate_k: int = Field(default=50, ge=1, le=500) + filter_payload: dict[str, Any] | None = None + + +class QueryRequest(BaseModel): + sql: str | None = None + predefined: str | None = None + params: dict[str, Any] | None = None + + +@app.get("/healthz") +def healthz() -> dict[str, Any]: + config = load_config() + duck_status = "ok" + qdrant_status = "ok" + try: + OrcidDuckDBStore.open().count("persons") + except Exception as exc: # noqa: BLE001 + duck_status = f"error: {exc}" + try: + OrcidQdrantStore(config).count("persons") + except Exception as exc: # noqa: BLE001 + qdrant_status = f"error: {exc}" + return { + "scope": config.paths.scope, + "duckdb": duck_status, + "qdrant": qdrant_status, + "rcp_configured": bool(config.rcp.token), + } + + +@app.post("/search") +def search(req: SearchRequest) -> list[dict[str, Any]]: + config = load_config() + try: + config.require_rcp() + except ValueError as exc: + raise HTTPException(status_code=503, detail=str(exc)) from exc + return semantic_search( + config=config, + query=req.query, + entity_type=req.entity_type, + top_k=req.top_k, + candidate_k=req.candidate_k, + filter_payload=req.filter_payload, + ) + + +@app.post("/query") +def query(req: QueryRequest) -> list[dict[str, Any]]: + if req.predefined and req.sql: + raise HTTPException( + status_code=400, + detail="Provide either `predefined` or `sql`, not both", + ) + try: + if req.predefined: + return run_predefined(req.predefined, req.params) + if req.sql: + return run_adhoc(req.sql, req.params) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + raise HTTPException(status_code=400, detail="Pass `predefined` or `sql`") + + +@app.get("/predefined") +def list_predefined() -> dict[str, list[str]]: + return {"predefined": sorted(PREDEFINED_QUERIES)} + + +@app.get("/person/{orcid_id}") +def get_person(orcid_id: str) -> dict[str, Any]: + store = OrcidDuckDBStore.open() + person = store.fetch_person(orcid_id) + if person is None: + raise HTTPException(status_code=404, detail="not found") + person["employments"] = store.list_employments(orcid_id) + return person diff --git a/src/index/orcid/cli.py b/src/index/orcid/cli.py new file mode 100644 index 0000000..64a0051 --- /dev/null +++ b/src/index/orcid/cli.py @@ -0,0 +1,260 @@ +"""CLI for the ORCID index module. + +Subcommands: + +- `discover` — build seed ORCID list (OpenAlex authors + ORCID search). +- `ingest` — fetch full records, post-filter by scope, persist to DuckDB. +- `embed` — chunk + embed in-scope rows, push to Qdrant via RCP. +- `search` — semantic retrieval (vector + RCP rerank). +- `query` — read-only SQL over DuckDB (predefined or guarded ad-hoc). +- `status` — counts + paths summary. +- `serve` — run the FastAPI app on a chosen port. + +The `--scope` flag selects the data subtree (`/orcid-/`) +and the Qdrant collection prefix. +""" + +from __future__ import annotations + +import argparse +import json +import logging +import os +import sys + +from src.index.orcid.config import load_config +from src.index.orcid.models import ALL_ENTITY_TYPES +from src.index.orcid.retrieval.sql import run_adhoc, run_predefined +from src.index.orcid.storage.duckdb_store import OrcidDuckDBStore + +LOGGER = logging.getLogger(__name__) + + +VALID_ENTITIES = set(ALL_ENTITY_TYPES) + + +def _split_entities(raw: str) -> list[str]: + parts = [p.strip() for p in raw.split(",") if p.strip()] + unknown = [p for p in parts if p not in VALID_ENTITIES] + if unknown: + message = f"Unknown entity types: {unknown}. Known: {sorted(VALID_ENTITIES)}" + raise SystemExit(message) + return parts + + +def _emit_json(data: object) -> None: + json.dump(data, sys.stdout, indent=2, ensure_ascii=False, default=str) + sys.stdout.write("\n") + + +def _apply_scope_env(scope: str) -> None: + """Propagate `--scope` to env so paths.get_orcid_paths picks it up.""" + os.environ["INDEX_ORCID_SCOPE"] = scope + + +def _cmd_discover(args: argparse.Namespace) -> int: + from src.index.orcid.ingest.discover import discover_seeds + + _apply_scope_env(args.scope) + config = load_config(scope=args.scope) + store = OrcidDuckDBStore.open(scope=args.scope) + summary = discover_seeds(config=config, store=store, source=args.source) + _emit_json({"scope": args.scope, "seeded": summary}) + return 0 + + +def _cmd_ingest(args: argparse.Namespace) -> int: + from src.index.orcid.ingest.persons import ingest_persons + + _apply_scope_env(args.scope) + config = load_config(scope=args.scope) + store = OrcidDuckDBStore.open(scope=args.scope) + summary = ingest_persons( + config=config, + store=store, + scope=args.scope, + limit=args.limit, + priority_hints=args.priority_hint or None, + ) + _emit_json({"scope": args.scope, "ingested": summary}) + return 0 + + +def _cmd_embed(args: argparse.Namespace) -> int: + from src.index.orcid.embed.pipeline import embed_entities + + _apply_scope_env(args.scope) + entities = _split_entities(args.entities) + config = load_config(scope=args.scope) + config.require_rcp() + store = OrcidDuckDBStore.open(scope=args.scope) + summary = embed_entities( + config=config, + store=store, + entity_types=entities, + limit=args.limit, + ) + _emit_json({"scope": args.scope, "embedded": summary}) + return 0 + + +def _cmd_search(args: argparse.Namespace) -> int: + from src.index.orcid.retrieval.semantic import semantic_search + + _apply_scope_env(args.scope) + config = load_config(scope=args.scope) + config.require_rcp() + hits = semantic_search( + config=config, + query=args.query, + entity_type=args.entity, + top_k=args.top_k, + candidate_k=args.candidate_k, + ) + _emit_json(hits) + return 0 + + +def _cmd_query(args: argparse.Namespace) -> int: + _apply_scope_env(args.scope) + params: dict[str, object] = {} + for raw in args.param or []: + if "=" not in raw: + message = f"--param must be key=value, got {raw!r}" + raise SystemExit(message) + key, value = raw.split("=", 1) + params[key] = int(value) if value.isdigit() else value + store = OrcidDuckDBStore.open(scope=args.scope) + if args.predefined: + rows = run_predefined(args.predefined, params, store=store) + elif args.sql: + rows = run_adhoc(args.sql, params, store=store) + else: + message = "Pass --predefined NAME or a positional SQL string" + raise SystemExit(message) + _emit_json(rows) + return 0 + + +def _cmd_status(args: argparse.Namespace) -> int: + _apply_scope_env(args.scope) + config = load_config(scope=args.scope) + store = OrcidDuckDBStore.open(scope=args.scope) + counts = { + "seeds": store.count("seeds"), + "persons": store.count("persons"), + "employments": store.count("employments"), + "educations": store.count("educations"), + "chunks": store.count("chunks"), + } + _emit_json( + { + "scope": args.scope, + "duckdb_path": str(config.paths.duckdb_path), + "qdrant_url": config.qdrant.url, + "qdrant_collections": [ + config.paths.collection_name(e) for e in ALL_ENTITY_TYPES + ], + "counts": counts, + }, + ) + return 0 + + +def _cmd_serve(args: argparse.Namespace) -> int: + import uvicorn + + _apply_scope_env(args.scope) + uvicorn.run( + "src.index.orcid.api:app", + host=args.host, + port=args.port, + reload=args.reload, + ) + return 0 + + +def _add_scope_arg(parser: argparse.ArgumentParser) -> None: + parser.add_argument( + "--scope", + choices=["epfl", "switzerland"], + default="epfl", + help="Data subtree + Qdrant collection prefix to operate on", + ) + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="src.index.orcid.cli", + description="ORCID ingestion + RAG over EPFL → Switzerland", + ) + sub = parser.add_subparsers(dest="cmd", required=True) + + p_d = sub.add_parser("discover", help="Build seed ORCID list") + _add_scope_arg(p_d) + p_d.add_argument("--source", choices=["openalex", "orcid_search", "both"], default=None) + p_d.set_defaults(func=_cmd_discover) + + p_i = sub.add_parser("ingest", help="Fetch full records + persist") + _add_scope_arg(p_i) + p_i.add_argument("--limit", type=int, default=None) + p_i.add_argument( + "--priority-hint", + action="append", + default=None, + help=( + "Substring to prioritise in the seed `hint` column " + "(case-insensitive). Repeat to add multiple. Matching seeds " + "are fetched before the rest of the unfetched pool. " + "Example: --priority-hint 'ETH Zurich' --priority-hint 'ETHZ'" + ), + ) + p_i.set_defaults(func=_cmd_ingest) + + p_e = sub.add_parser("embed", help="Embed in-scope rows into Qdrant via RCP") + _add_scope_arg(p_e) + p_e.add_argument("--entities", default=",".join(ALL_ENTITY_TYPES)) + p_e.add_argument("--limit", type=int, default=None) + p_e.set_defaults(func=_cmd_embed) + + p_s = sub.add_parser("search", help="Semantic retrieval (vector + rerank)") + _add_scope_arg(p_s) + p_s.add_argument("query") + p_s.add_argument("--entity", default="persons", choices=list(ALL_ENTITY_TYPES)) + p_s.add_argument("--top-k", type=int, default=10) + p_s.add_argument("--candidate-k", type=int, default=50) + p_s.set_defaults(func=_cmd_search) + + p_q = sub.add_parser("query", help="Read-only SQL over DuckDB") + _add_scope_arg(p_q) + p_q.add_argument("sql", nargs="?", help="Ad-hoc SELECT/WITH (omit if --predefined)") + p_q.add_argument("--predefined") + p_q.add_argument("--param", action="append") + p_q.set_defaults(func=_cmd_query) + + p_st = sub.add_parser("status", help="Show counts + paths") + _add_scope_arg(p_st) + p_st.set_defaults(func=_cmd_status) + + p_v = sub.add_parser("serve", help="Run the FastAPI app") + _add_scope_arg(p_v) + p_v.add_argument("--host", default="0.0.0.0") # noqa: S104 + p_v.add_argument("--port", type=int, default=8002) + p_v.add_argument("--reload", action="store_true") + p_v.set_defaults(func=_cmd_serve) + + return parser + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s %(message)s", + ) + parser = build_parser() + args = parser.parse_args(argv) + return args.func(args) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/index/orcid/config.py b/src/index/orcid/config.py new file mode 100644 index 0000000..b245d26 --- /dev/null +++ b/src/index/orcid/config.py @@ -0,0 +1,190 @@ +"""Config loader for the ORCID indexer. + +Reads `config/index/orcid.yaml` and merges in env-sourced credentials +(`RCP_TOKEN`, `INDEX_QDRANT_API_KEY`) plus a few targeted env overrides. +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Optional + +import yaml +from pydantic import BaseModel + +from src.index.orcid.paths import OrcidPaths, get_orcid_paths + +DEFAULT_CONFIG_PATH = Path("config/index/orcid.yaml") + +TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} +FALSE_ENV_VALUES = {"0", "false", "f", "no", "n", "off"} + +MISSING_RCP_TOKEN_ERROR = "Missing required environment variable: RCP_TOKEN" + + +class RcpConfig(BaseModel): + base_url: str + embedding_model: str + embedding_dim: int + query_instruction: str + reranker_model: str + batch_size: int = 32 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: Optional[str] = None # populated from RCP_TOKEN at load time + + +class OrcidApiConfig(BaseModel): + base_url: str = "https://pub.orcid.org/v3.0" + timeout_seconds: int = 20 + search_max_rows: int = 200 + max_retries: int = 6 + base_delay_seconds: float = 0.5 + max_delay_seconds: float = 60.0 + request_min_interval_seconds: float = 1.5 + user_agent: Optional[str] = "git-metadata-extractor-orcid-index/0.1" + # OAuth 2-legged (client_credentials) for the public-API rate uplift. + # Both client_id and client_secret must be set for the token fetch to fire; + # if either is missing, the provider falls back to anonymous access. + oauth_token_url: str = "https://orcid.org/oauth/token" + oauth_scope: str = "/read-public" + client_id: Optional[str] = None # populated from ORCID_CLIENT_ID + client_secret: Optional[str] = None # populated from ORCID_CLIENT_SECRET + + +class ScopeConfig(BaseModel): + ror: str + country: str + affiliation_aliases: list[str] = [] + + +class DiscoveryConfig(BaseModel): + source: str = "both" # openalex | orcid_search | both + openalex_db: str = "data/index/openalex/duckdb/openalex.duckdb" + + +class QdrantConfig(BaseModel): + url: str = "http://localhost:6333" + prefer_grpc: bool = False + api_key: Optional[str] = None # populated from INDEX_QDRANT_API_KEY + + +class ChunkingConfig(BaseModel): + size_tokens: int = 256 + overlap_tokens: int = 64 + tokenizer: str = "cl100k_base" + + +class OrcidIndexConfig(BaseModel): + rcp: RcpConfig + orcid: OrcidApiConfig + scope: ScopeConfig + discovery: DiscoveryConfig + qdrant: QdrantConfig + chunking: ChunkingConfig + paths: OrcidPaths + + model_config = {"arbitrary_types_allowed": True} + + def require_rcp(self) -> None: + if not self.rcp.token: + raise ValueError(MISSING_RCP_TOKEN_ERROR) + + +def _env_bool(name: str) -> Optional[bool]: + raw = os.getenv(name) + if raw is None or raw.strip() == "": + return None + normalized = raw.strip().lower() + if normalized in TRUE_ENV_VALUES: + return True + if normalized in FALSE_ENV_VALUES: + return False + message = f"Invalid boolean value for {name}: {raw!r}" + raise ValueError(message) + + +def _env_str(name: str) -> Optional[str]: + raw = os.getenv(name) + if raw is None: + return None + stripped = raw.strip() + return stripped or None + + +def _active_scope(scope: Optional[str]) -> str: + """Resolve the scope being operated on, mirroring `paths._resolve_scope`.""" + if scope: + return scope + raw = os.getenv("INDEX_ORCID_SCOPE") + if raw and raw.strip(): + return raw.strip() + return "epfl" + + +def _env_int(name: str) -> Optional[int]: + raw = _env_str(name) + if raw is None: + return None + return int(raw) + + +def _env_float(name: str) -> Optional[float]: + raw = _env_str(name) + if raw is None: + return None + return float(raw) + + +def load_config( + path: Optional[Path] = None, + *, + scope: Optional[str] = None, +) -> OrcidIndexConfig: + """Load + validate the YAML config; merge env tokens, env overrides, paths.""" + cfg_path = path or DEFAULT_CONFIG_PATH + raw = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) or {} + + # Per-scope alias resolution: when YAML provides a dict-form + # `scope.affiliation_aliases: { epfl: [...], switzerland: [...] }`, + # collapse it to the active scope's list at load-time so callers can keep + # reading `config.scope.affiliation_aliases: list[str]`. + scope_block = raw.setdefault("scope", {}) + aliases_raw = scope_block.get("affiliation_aliases", []) + if isinstance(aliases_raw, dict): + active = _active_scope(scope) + scope_block["affiliation_aliases"] = list(aliases_raw.get(active, [])) + + raw.setdefault("rcp", {})["token"] = _env_str("RCP_TOKEN") + raw.setdefault("qdrant", {})["api_key"] = _env_str("INDEX_QDRANT_API_KEY") + raw.setdefault("orcid", {})["client_id"] = _env_str("ORCID_CLIENT_ID") + raw.setdefault("orcid", {})["client_secret"] = _env_str("ORCID_CLIENT_SECRET") + + if (override := _env_str("INDEX_QDRANT_URL")) is not None: + raw["qdrant"]["url"] = override + if (override_b := _env_bool("INDEX_QDRANT_PREFER_GRPC")) is not None: + raw["qdrant"]["prefer_grpc"] = override_b + if (override := _env_str("INDEX_ORCID_SCOPE_ROR")) is not None: + raw.setdefault("scope", {})["ror"] = override + if (override := _env_str("INDEX_ORCID_SCOPE_COUNTRY")) is not None: + raw.setdefault("scope", {})["country"] = override + if (override := _env_str("INDEX_ORCID_DISCOVERY_SOURCE")) is not None: + raw.setdefault("discovery", {})["source"] = override + if (override := _env_str("INDEX_ORCID_OPENALEX_DB")) is not None: + raw.setdefault("discovery", {})["openalex_db"] = override + + if (override_i := _env_int("INDEX_ORCID_MAX_RETRIES")) is not None: + raw.setdefault("orcid", {})["max_retries"] = override_i + if (override_f := _env_float("INDEX_ORCID_BASE_DELAY_SECONDS")) is not None: + raw.setdefault("orcid", {})["base_delay_seconds"] = override_f + if (override_f := _env_float("INDEX_ORCID_MAX_DELAY_SECONDS")) is not None: + raw.setdefault("orcid", {})["max_delay_seconds"] = override_f + if (override_f := _env_float("INDEX_ORCID_REQUEST_MIN_INTERVAL_SECONDS")) is not None: + raw.setdefault("orcid", {})["request_min_interval_seconds"] = override_f + if (override := _env_str("INDEX_ORCID_USER_AGENT")) is not None: + raw.setdefault("orcid", {})["user_agent"] = override + + raw["paths"] = get_orcid_paths(scope) + + return OrcidIndexConfig(**raw) diff --git a/src/index/orcid/embed/__init__.py b/src/index/orcid/embed/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/orcid/embed/chunker.py b/src/index/orcid/embed/chunker.py new file mode 100644 index 0000000..d20c14a --- /dev/null +++ b/src/index/orcid/embed/chunker.py @@ -0,0 +1,89 @@ +"""Build text representations for ORCID persons + affiliations and chunk. + +We embed three kinds of strings: + +- **Person card**: name + biography + affiliation list. Coarse but useful + for "find me a person who works on X" queries. +- **Employment row**: name + role at organization (+ department) over a + date range. Enables time-bounded queries like "EPFL postdocs 2020-2024". +- **Education row**: same shape as employment. + +We delegate token-window slicing to the openalex chunker; only the +text-construction differs. +""" + +from __future__ import annotations + +from typing import Any + +from src.index.openalex.embed.chunker import Chunk, chunk_text + +__all__ = [ + "Chunk", + "chunk_for_affiliation", + "chunk_for_person", + "person_card_text", +] + + +def person_card_text(row: dict[str, Any], affiliations: list[str]) -> str | None: + """Build the embed-text for a person row. Returns None if there's nothing useful.""" + parts: list[str] = [] + name = row.get("display_name") or _join_name( + row.get("given_name"), + row.get("family_name"), + ) + if name: + parts.append(name) + bio = (row.get("biography") or "").strip() + if bio: + parts.append(bio) + if affiliations: + parts.append("Affiliations: " + ", ".join(sorted({a for a in affiliations if a}))) + if not parts: + return None + return "\n\n".join(parts) + + +def chunk_for_person( + row: dict[str, Any], + affiliations: list[str], + *, + chunk_tokens: int, + overlap: int, +) -> list[Chunk]: + text = person_card_text(row, affiliations) + if not text: + return [] + return chunk_text(text, chunk_tokens=chunk_tokens, overlap=overlap) + + +def chunk_for_affiliation( + person_name: str | None, + row: dict[str, Any], + *, + chunk_tokens: int, + overlap: int, +) -> list[Chunk]: + text = _affiliation_text(person_name, row) + if not text: + return [] + return chunk_text(text, chunk_tokens=chunk_tokens, overlap=overlap) + + +def _affiliation_text(person_name: str | None, row: dict[str, Any]) -> str | None: + org = (row.get("organization") or "").strip() + if not org: + return None + role = (row.get("role") or "Researcher").strip() or "Researcher" + dept = (row.get("department") or "").strip() + start = row.get("start_date") or "?" + end = row.get("end_date") or "present" + head = person_name.strip() if person_name else "Researcher" + org_phrase = f"{org}, {dept}" if dept else org + return f"{head} — {role} at {org_phrase} ({start} → {end})" + + +def _join_name(given: str | None, family: str | None) -> str | None: + parts = [p for p in (given, family) if p] + return " ".join(parts) if parts else None diff --git a/src/index/orcid/embed/pipeline.py b/src/index/orcid/embed/pipeline.py new file mode 100644 index 0000000..f5e26d1 --- /dev/null +++ b/src/index/orcid/embed/pipeline.py @@ -0,0 +1,198 @@ +"""Stream DuckDB rows → chunk → embed → upsert into Qdrant. + +Idempotent: rows with existing chunks are skipped via +`OrcidDuckDBStore.stream_rows_for_embedding`. +""" + +from __future__ import annotations + +import asyncio +import logging +import uuid +from typing import TYPE_CHECKING, Any + +from src.index.orcid.embed.chunker import ( + Chunk, + chunk_for_affiliation, + chunk_for_person, +) +from src.index.orcid.embed.rcp_client import RCPEmbeddingClient +from src.index.orcid.vector.qdrant_store import OrcidQdrantStore + +if TYPE_CHECKING: + from src.index.orcid.config import OrcidIndexConfig + from src.index.orcid.storage.duckdb_store import OrcidDuckDBStore + +LOGGER = logging.getLogger(__name__) + +_CHUNK_NAMESPACE = uuid.NAMESPACE_URL + + +def _chunk_id(entity_type: str, entity_id: str, chunk_index: int) -> str: + return str( + uuid.uuid5(_CHUNK_NAMESPACE, f"{entity_type}|{entity_id}|{chunk_index}"), + ) + + +def _person_payload(row: dict[str, Any]) -> dict[str, Any]: + return { + "entity_type": "persons", + "entity_id": row["orcid_id"], + "orcid_id": row["orcid_id"], + "display_name": row.get("display_name"), + "family_name": row.get("family_name"), + } + + +def _affiliation_payload(entity_type: str, row: dict[str, Any]) -> dict[str, Any]: + entity_id = f"{row['orcid_id']}#{row['seq']}" + return { + "entity_type": entity_type, + "entity_id": entity_id, + "orcid_id": row["orcid_id"], + "seq": row["seq"], + "organization": row.get("organization"), + "org_ror": row.get("org_ror"), + "start_date": row.get("start_date"), + "end_date": row.get("end_date"), + } + + +def _row_entity_id(entity_type: str, row: dict[str, Any]) -> str: + if entity_type == "persons": + return row["orcid_id"] + return f"{row['orcid_id']}#{row['seq']}" + + +def _person_display_name(store: OrcidDuckDBStore, orcid_id: str) -> str | None: + person = store.fetch_person(orcid_id) + if not person: + return None + return person.get("display_name") + + +def _build_chunks( + *, + entity_type: str, + row: dict[str, Any], + store: OrcidDuckDBStore, + chunk_tokens: int, + overlap: int, +) -> list[Chunk]: + if entity_type == "persons": + employments = store.list_employments(row["orcid_id"]) + affiliations: list[str] = sorted( + { + str(org) + for emp in employments + if (org := emp.get("organization")) + }, + ) + return chunk_for_person( + row, + affiliations, + chunk_tokens=chunk_tokens, + overlap=overlap, + ) + person_name = _person_display_name(store, row["orcid_id"]) + return chunk_for_affiliation( + person_name, + row, + chunk_tokens=chunk_tokens, + overlap=overlap, + ) + + +async def _embed_entity_async( + *, + config: OrcidIndexConfig, + store: OrcidDuckDBStore, + entity_type: str, + limit: int | None, +) -> int: + client = RCPEmbeddingClient(config) + qdrant = OrcidQdrantStore(config) + qdrant.ensure_collection(entity_type) + + pending: list[tuple[str, dict[str, Any], Chunk]] = [] + total_chunks = 0 + + async def flush() -> None: + nonlocal total_chunks + if not pending: + return + texts = [c.text for _, _, c in pending] + vectors = await client.embed_all(texts) + ids: list[str] = [] + payloads: list[dict[str, Any]] = [] + for entity_id, base_payload, chunk in pending: + chunk_id = _chunk_id(entity_type, entity_id, chunk.index) + ids.append(chunk_id) + payloads.append({**base_payload, "chunk_index": chunk.index}) + store.upsert_chunk( + chunk_id=chunk_id, + entity_type=entity_type, + entity_id=entity_id, + chunk_index=chunk.index, + text=chunk.text, + token_count=chunk.token_count, + vector_id=chunk_id, + ) + qdrant.upsert_points( + entity_type, + ids=ids, + vectors=vectors, + payloads=payloads, + ) + total_chunks += len(pending) + pending.clear() + + rows_seen = 0 + for row in store.stream_rows_for_embedding(entity_type, limit=limit): + rows_seen += 1 + chunks = _build_chunks( + entity_type=entity_type, + row=row, + store=store, + chunk_tokens=config.chunking.size_tokens, + overlap=config.chunking.overlap_tokens, + ) + if not chunks: + continue + if entity_type == "persons": + base_payload = _person_payload(row) + else: + base_payload = _affiliation_payload(entity_type, row) + entity_id = _row_entity_id(entity_type, row) + for chunk in chunks: + pending.append((entity_id, base_payload, chunk)) + if len(pending) >= client.batch_size: + await flush() + await flush() + LOGGER.info( + "embed %s complete: rows_seen=%d chunks=%d", + entity_type, + rows_seen, + total_chunks, + ) + return total_chunks + + +def embed_entities( + *, + config: OrcidIndexConfig, + store: OrcidDuckDBStore, + entity_types: list[str], + limit: int | None = None, +) -> dict[str, int]: + summary: dict[str, int] = {} + for entity_type in entity_types: + summary[entity_type] = asyncio.run( + _embed_entity_async( + config=config, + store=store, + entity_type=entity_type, + limit=limit, + ), + ) + return summary diff --git a/src/index/orcid/embed/rcp_client.py b/src/index/orcid/embed/rcp_client.py new file mode 100644 index 0000000..20ee0c7 --- /dev/null +++ b/src/index/orcid/embed/rcp_client.py @@ -0,0 +1,33 @@ +"""RCP embedding client for ORCID. + +Re-exports the openalex implementation under a thin subclass that +re-types the constructor parameter for `OrcidIndexConfig`. The base class +only reads `config.rcp.*` and calls `config.require_rcp()`, both of +which `OrcidIndexConfig` exposes with the same shape. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, cast + +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient as _BaseEmbed + +if TYPE_CHECKING: + from src.index.orcid.config import OrcidIndexConfig + + +class RCPEmbeddingClient(_BaseEmbed): + """Type-narrowed wrapper around the openalex RCP embed client.""" + + def __init__( + self, + config: OrcidIndexConfig, + *, + batch_size: int | None = None, + timeout_s: float | None = None, + ) -> None: + super().__init__( + cast("Any", config), + batch_size=batch_size, + timeout_s=timeout_s, + ) diff --git a/src/index/orcid/ingest/__init__.py b/src/index/orcid/ingest/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/orcid/ingest/discover.py b/src/index/orcid/ingest/discover.py new file mode 100644 index 0000000..8688a6c --- /dev/null +++ b/src/index/orcid/ingest/discover.py @@ -0,0 +1,193 @@ +"""Build a seed list of ORCID identifiers for the configured scope. + +Two sources, mixable via `discovery.source`: + +- **openalex**: read the authors table of a previously-built OpenAlex + DuckDB. Most reliable since OpenAlex's scope filter has already + excluded non-EPFL / non-Swiss authors. +- **orcid_search**: ORCID's `expanded-search` keyed by affiliation + aliases. Catches researchers who don't appear in OpenAlex (e.g., + students, PIs without recent works). + +Seeds are deduped and persisted to the `seeds` DuckDB table — re-runs +add new seeds without dropping old ones. +""" + +from __future__ import annotations + +import logging +import re +from pathlib import Path +from typing import TYPE_CHECKING, Iterable, Iterator + +import duckdb +import requests + +from src.index.orcid.ingest.orcid_client import build_orcid_provider + +if TYPE_CHECKING: + from src.index.orcid.config import OrcidIndexConfig + from src.index.orcid.storage.duckdb_store import OrcidDuckDBStore + +LOGGER = logging.getLogger(__name__) + +ORCID_RE = re.compile(r"\b(\d{4}-\d{4}-\d{4}-\d{3}[\dX])\b") +# ORCID's expanded-search rejects start>=10000 with HTTP 400. Stop before then. +ORCID_EXPANDED_SEARCH_DEEP_LIMIT = 10000 +HTTP_BAD_REQUEST = 400 + + +def discover_seeds( + *, + config: OrcidIndexConfig, + store: OrcidDuckDBStore, + source: str | None = None, +) -> dict[str, int]: + """Run the configured discovery sources and persist seeds.""" + chosen = source or config.discovery.source + summary: dict[str, int] = {"openalex": 0, "orcid_search": 0} + if chosen in {"openalex", "both"}: + summary["openalex"] = _seed_from_openalex(config=config, store=store) + if chosen in {"orcid_search", "both"}: + summary["orcid_search"] = _seed_from_orcid_search(config=config, store=store) + if chosen not in {"openalex", "orcid_search", "both"}: + message = ( + f"Unknown discovery.source: {chosen!r}. " + "Expected one of: openalex, orcid_search, both" + ) + raise ValueError(message) + return summary + + +def _seed_from_openalex( + *, + config: OrcidIndexConfig, + store: OrcidDuckDBStore, +) -> int: + db_path = Path(config.discovery.openalex_db) + if not db_path.exists(): + LOGGER.warning( + "openalex DuckDB not found at %s — skipping openalex discovery", + db_path, + ) + return 0 + count = 0 + for orcid_id in _iter_openalex_orcids(db_path): + store.upsert_seed( + orcid_id=orcid_id, + discovered_via="openalex", + hint=str(db_path), + ) + count += 1 + LOGGER.info("openalex discovery: %d ORCIDs seeded from %s", count, db_path) + return count + + +def _iter_openalex_orcids(db_path: Path) -> Iterator[str]: + """Yield deduped, normalized ORCID IDs from the openalex authors table.""" + conn = duckdb.connect(str(db_path), read_only=True) + try: + cur = conn.execute( + "SELECT DISTINCT orcid FROM authors WHERE orcid IS NOT NULL AND orcid != ''", + ) + seen: set[str] = set() + while True: + row = cur.fetchone() + if row is None: + return + normalized = _normalize_orcid(row[0]) + if normalized is None or normalized in seen: + continue + seen.add(normalized) + yield normalized + finally: + conn.close() + + +def _normalize_orcid(value: str) -> str | None: + if not value: + return None + match = ORCID_RE.search(value) + if match: + return match.group(1).upper() + return None + + +def _seed_from_orcid_search( + *, + config: OrcidIndexConfig, + store: OrcidDuckDBStore, +) -> int: + provider = build_orcid_provider(config) + aliases = [a for a in config.scope.affiliation_aliases if a.strip()] + if not aliases: + LOGGER.info("orcid_search discovery skipped: no affiliation_aliases configured") + return 0 + count = 0 + for alias in aliases: + for orcid_id in _paged_search(provider, alias, config=config): + store.upsert_seed( + orcid_id=orcid_id, + discovered_via="orcid_search", + hint=alias, + ) + count += 1 + LOGGER.info("orcid_search discovery: %d seeds across %d aliases", count, len(aliases)) + return count + + +def _paged_search( + provider: object, + alias: str, + *, + config: OrcidIndexConfig, +) -> Iterable[str]: + rows_per_page = config.orcid.search_max_rows + start = 0 + query = f'affiliation-org-name:"{alias}"' + while True: + # ORCID's expanded-search caps deep pagination at start=10000 and + # returns HTTP 400 above that. The start=10000 call itself succeeds + # and yields up to 200 hits; the next page at start=10200 fails. + # Stop proactively right after the last accepted page; the HTTP 400 + # catch below is the safety net if the threshold drifts. + if start > ORCID_EXPANDED_SEARCH_DEEP_LIMIT: + LOGGER.info( + "alias %r: stopping at ORCID deep-pagination limit (start=%d); " + "may be missing rare matches beyond the first %d hits", + alias, + start, + ORCID_EXPANDED_SEARCH_DEEP_LIMIT, + ) + return + try: + # Provider type is duck-typed here; signature comes from RealORCIDProvider. + hits = provider.search_persons( # type: ignore[attr-defined] + query, + rows=rows_per_page, + start=start, + ) + except requests.exceptions.HTTPError as exc: + response = exc.response + if response is not None and response.status_code == HTTP_BAD_REQUEST: + LOGGER.info( + "alias %r: ORCID expanded-search HTTP 400 at start=%d " + "(treating as end of pagination)", + alias, + start, + ) + return + raise + if not hits: + return + seen_in_page = 0 + for hit in hits: + orcid_id = hit.get("orcid_id") if isinstance(hit, dict) else hit["orcid_id"] + if not orcid_id: + continue + yield orcid_id.upper() + seen_in_page += 1 + # Last page: signal exit. + if seen_in_page < rows_per_page: + return + start += rows_per_page diff --git a/src/index/orcid/ingest/orcid_client.py b/src/index/orcid/ingest/orcid_client.py new file mode 100644 index 0000000..4d4a935 --- /dev/null +++ b/src/index/orcid/ingest/orcid_client.py @@ -0,0 +1,64 @@ +"""Build a `RealORCIDProvider` configured for the indexer. + +We reuse the production-grade ORCID provider already maintained at +`src/v2/ingest/providers/orcid_provider.py` rather than reinventing +record fetch + expanded-search. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +import requests + +from src.v2.ingest.providers.orcid_provider import RealORCIDProvider +from src.v2.ingest.providers.rate_limiter import RateLimiter + +if TYPE_CHECKING: + from src.index.orcid.config import OrcidIndexConfig + +LOGGER = logging.getLogger(__name__) + + +def _fetch_orcid_access_token(config: OrcidIndexConfig) -> str | None: + """Index-side wrapper around the shared OAuth helper. + + Reads credentials + endpoint from `OrcidApiConfig` and delegates to + `src.v2.ingest.providers.orcid_oauth.fetch_access_token`. Returns None + when credentials are absent or the OAuth POST fails. + """ + from src.v2.ingest.providers.orcid_oauth import fetch_access_token + + return fetch_access_token( + client_id=config.orcid.client_id, + client_secret=config.orcid.client_secret, + token_url=config.orcid.oauth_token_url, + scope=config.orcid.oauth_scope, + timeout_seconds=config.orcid.timeout_seconds, + ) + + +def build_orcid_provider(config: OrcidIndexConfig) -> RealORCIDProvider: + """Construct an ORCID provider wired to this indexer's config.""" + limiter = RateLimiter( + max_retries=config.orcid.max_retries, + base_delay_seconds=config.orcid.base_delay_seconds, + max_delay_seconds=config.orcid.max_delay_seconds, + ) + session: requests.Session | None = None + headers: dict[str, str] = {} + if config.orcid.user_agent: + headers["User-Agent"] = config.orcid.user_agent + access_token = _fetch_orcid_access_token(config) + if access_token: + headers["Authorization"] = f"Bearer {access_token}" + if headers: + session = requests.Session() + session.headers.update(headers) + return RealORCIDProvider( + base_url=config.orcid.base_url, + timeout=config.orcid.timeout_seconds, + rate_limiter=limiter, + session=session, + ) diff --git a/src/index/orcid/ingest/persons.py b/src/index/orcid/ingest/persons.py new file mode 100644 index 0000000..3acd547 --- /dev/null +++ b/src/index/orcid/ingest/persons.py @@ -0,0 +1,159 @@ +"""Fetch full ORCID records for seeded IDs, post-filter by scope, persist.""" + +from __future__ import annotations + +import logging +import time +from typing import TYPE_CHECKING + +from src.index.orcid.ingest.orcid_client import build_orcid_provider +from src.index.orcid.ingest.scope import post_filter_record +from src.v2.ingest.providers.base import ( + ProviderError, + ProviderNotFoundError, +) + +if TYPE_CHECKING: + from src.index.orcid.config import OrcidIndexConfig + from src.index.orcid.storage.duckdb_store import OrcidDuckDBStore + from src.v2.ingest.providers.base import ORCIDAffiliation, ORCIDRecord + +LOGGER = logging.getLogger(__name__) + + +def ingest_persons( + *, + config: OrcidIndexConfig, + store: OrcidDuckDBStore, + scope: str, + limit: int | None = None, + priority_hints: list[str] | None = None, +) -> dict[str, int]: + """Fetch unfetched seeds, post-filter, persist. Returns counts summary. + + `priority_hints` is forwarded to `store.stream_seeds` so callers can + steer the daily-quota slice toward a sub-corpus (e.g. ETHZ aliases). + """ + provider = build_orcid_provider(config) + summary = {"fetched": 0, "in_scope": 0, "out_of_scope": 0, "errors": 0} + + min_interval = max(0.0, float(config.orcid.request_min_interval_seconds)) + last_request_at = 0.0 + + for seed in store.stream_seeds(only_unfetched=True, priority_hints=priority_hints): + if limit is not None and summary["fetched"] >= limit: + break + orcid_id = seed["orcid_id"] + discovered_via = seed["discovered_via"] + if min_interval > 0: + elapsed = time.monotonic() - last_request_at + if elapsed < min_interval: + time.sleep(min_interval - elapsed) + last_request_at = time.monotonic() + try: + record = provider.get_person_by_orcid(orcid_id) + except ProviderNotFoundError: + LOGGER.info("orcid not found, skipping: %s", orcid_id) + summary["errors"] += 1 + continue + except ProviderError as exc: + LOGGER.warning("provider error for %s: %s", orcid_id, exc) + summary["errors"] += 1 + continue + except Exception as exc: # noqa: BLE001 + LOGGER.warning("unexpected error for %s: %s", orcid_id, exc) + summary["errors"] += 1 + continue + summary["fetched"] += 1 + in_scope, reason = post_filter_record( + record, + scope=scope, # type: ignore[arg-type] + config=config, + discovered_via=discovered_via, + ) + _persist( + store=store, + record=record, + in_scope=in_scope, + scope_reason=reason, + discovered_via=discovered_via, + ) + summary["in_scope" if in_scope else "out_of_scope"] += 1 + if summary["fetched"] % 100 == 0: + LOGGER.info("ingest progress: %s", summary) + LOGGER.info("ingest complete: %s", summary) + return summary + + +def _persist( + *, + store: OrcidDuckDBStore, + record: ORCIDRecord, + in_scope: bool, + scope_reason: str | None, + discovered_via: str, +) -> None: + given, family = _split_name(record.get("name") or "") + person_row = { + "orcid_id": record["orcid_id"], + "given_name": given, + "family_name": family, + "display_name": record.get("name"), + "biography": None, # ORCID provider doesn't surface biography today. + "in_scope": in_scope, + "scope_reason": scope_reason, + "discovered_via": discovered_via, + } + raw_payload = { + "orcid_id": record.get("orcid_id"), + "name": record.get("name"), + "employment": record.get("employment", []), + "education": record.get("education", []), + "affiliations": record.get("affiliations", []), + } + store.upsert_person(person_row, raw=raw_payload) + store.replace_affiliations( + "employments", + record["orcid_id"], + _affiliation_rows(record["orcid_id"], record.get("employment", [])), + ) + store.replace_affiliations( + "educations", + record["orcid_id"], + _affiliation_rows(record["orcid_id"], record.get("education", [])), + ) + + +def _affiliation_rows( + orcid_id: str, + affiliations: list[ORCIDAffiliation], +) -> list[dict[str, object]]: + rows: list[dict[str, object]] = [] + for seq, aff in enumerate(affiliations): + rows.append( + { + "orcid_id": orcid_id, + "seq": seq, + "organization": aff.get("organization") or "", + "org_ror": None, # provider doesn't surface ROR yet. + "department": aff.get("department"), + "role": aff.get("role"), + "start_date": aff.get("start_date"), + "end_date": aff.get("end_date"), + }, + ) + return rows + + +def _split_name(full_name: str) -> tuple[str | None, str | None]: + """Best-effort split of a display name into (given, family).""" + cleaned = full_name.strip() + if not cleaned: + return None, None + if "," in cleaned: + family, _, given = cleaned.partition(",") + return given.strip() or None, family.strip() or None + parts = cleaned.split() + if len(parts) == 1: + return None, parts[0] + return " ".join(parts[:-1]), parts[-1] diff --git a/src/index/orcid/ingest/scope.py b/src/index/orcid/ingest/scope.py new file mode 100644 index 0000000..183ca9c --- /dev/null +++ b/src/index/orcid/ingest/scope.py @@ -0,0 +1,66 @@ +"""Scope post-filter for fetched ORCID records. + +ORCID's public API does not expose ROR-based filtering, so we accept the +record returned by the provider and inspect its `employment` / `education` +affiliations after the fact: + +- **epfl**: an employment or education organization name matches one of + the configured affiliation aliases (case-insensitive substring). +- **switzerland**: trust OpenAlex-bootstrapped seeds (already country- + scoped) and otherwise fall back to alias matching — the YAML aliases + can be expanded for Phase 2. + +Returns `(in_scope, reason)` so the persistence layer can record *why* +a record was kept and downstream queries can audit decisions. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Literal + +if TYPE_CHECKING: + from src.index.orcid.config import OrcidIndexConfig + from src.v2.ingest.providers.base import ORCIDAffiliation, ORCIDRecord + +ScopeName = Literal["epfl", "switzerland"] + + +def post_filter_record( + record: ORCIDRecord, + *, + scope: ScopeName, + config: OrcidIndexConfig, + discovered_via: str, +) -> tuple[bool, str | None]: + if scope == "epfl": + return _alias_match(record, aliases=config.scope.affiliation_aliases) + if scope == "switzerland": + if discovered_via in {"openalex", "both"}: + return True, f"openalex_country={config.scope.country}" + # Fallback for ORCID-search-only seeds: tolerate alias match. + return _alias_match(record, aliases=config.scope.affiliation_aliases) + message = f"Unknown scope: {scope}" + raise ValueError(message) + + +def _alias_match( + record: ORCIDRecord, + *, + aliases: list[str], +) -> tuple[bool, str | None]: + norms = [a.lower().strip() for a in aliases if a and a.strip()] + if not norms: + return False, None + affiliations_by_kind: dict[str, list["ORCIDAffiliation"]] = { + "employment": list(record.get("employment") or []), + "education": list(record.get("education") or []), + } + for kind, items in affiliations_by_kind.items(): + for affiliation in items: + org = (affiliation.get("organization") or "").lower().strip() + if not org: + continue + for alias in norms: + if alias in org: + return True, f"{kind}.organization~={alias!r}" + return False, None diff --git a/src/index/orcid/models.py b/src/index/orcid/models.py new file mode 100644 index 0000000..7b1b76e --- /dev/null +++ b/src/index/orcid/models.py @@ -0,0 +1,52 @@ +"""Pydantic schemas for the ORCID indexer.""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import BaseModel, ConfigDict + +EntityType = Literal["persons", "employments", "educations"] +ALL_ENTITY_TYPES: tuple[EntityType, ...] = ("persons", "employments", "educations") + +DiscoverySource = Literal["openalex", "orcid_search", "both", "manual"] + + +class PersonRow(BaseModel): + """Structured columns persisted into DuckDB `persons`.""" + + model_config = ConfigDict(extra="ignore") + + orcid_id: str + given_name: str | None = None + family_name: str | None = None + display_name: str | None = None + biography: str | None = None + in_scope: bool = False + scope_reason: str | None = None + discovered_via: str = "manual" + + +class AffiliationRow(BaseModel): + """One employment or education entry.""" + + model_config = ConfigDict(extra="ignore") + + orcid_id: str + seq: int + organization: str + org_ror: str | None = None + department: str | None = None + role: str | None = None + start_date: str | None = None + end_date: str | None = None + + +class SeedRecord(BaseModel): + """One row of the discover-stage seed list.""" + + model_config = ConfigDict(extra="ignore") + + orcid_id: str + discovered_via: str + hint: str | None = None diff --git a/src/index/orcid/paths.py b/src/index/orcid/paths.py new file mode 100644 index 0000000..08a7429 --- /dev/null +++ b/src/index/orcid/paths.py @@ -0,0 +1,82 @@ +"""Filesystem layout for ORCID index artifacts. + +Each scope (`epfl`, `ch`, ...) lands in its own subtree +(`/orcid-/`) so EPFL and Switzerland runs stay +isolated and independently rebuildable. Scope is selected via the +`INDEX_ORCID_SCOPE` env var (or by passing `scope=` to `get_orcid_paths`). +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path + +DEFAULT_INDEX_DATA_DIR = Path("data/index") +DEFAULT_SCOPE = "epfl" + + +def _resolve_repo_root() -> Path: + return Path(__file__).resolve().parents[3] + + +def _resolve_index_data_dir() -> Path: + raw = os.getenv("INDEX_DATA_DIR") + if raw and raw.strip(): + candidate = Path(raw.strip()).expanduser() + if candidate.is_absolute(): + return candidate + return _resolve_repo_root() / candidate + return _resolve_repo_root() / DEFAULT_INDEX_DATA_DIR + + +def _resolve_scope(scope: str | None) -> str: + if scope: + return scope + raw = os.getenv("INDEX_ORCID_SCOPE") + if raw and raw.strip(): + return raw.strip() + return DEFAULT_SCOPE + + +@dataclass(slots=True, frozen=True) +class OrcidPaths: + """Resolved paths for one ORCID scope.""" + + root: Path + scope: str + + @property + def duckdb_dir(self) -> Path: + return self.root / "duckdb" + + @property + def duckdb_path(self) -> Path: + return self.duckdb_dir / "orcid.duckdb" + + @property + def cache_dir(self) -> Path: + return self.root / "cache" + + @property + def logs_dir(self) -> Path: + return self.root / "logs" + + @property + def seeds_path(self) -> Path: + return self.root / "seeds.jsonl" + + def collection_name(self, entity: str) -> str: + """Qdrant collection name, namespaced by scope.""" + return f"orcid_{self.scope}_{entity}" + + +def get_orcid_paths(scope: str | None = None) -> OrcidPaths: + """Resolve `/orcid-/` and ensure subdirs exist.""" + resolved_scope = _resolve_scope(scope) + root = _resolve_index_data_dir() / f"orcid-{resolved_scope}" + paths = OrcidPaths(root=root, scope=resolved_scope) + paths.duckdb_dir.mkdir(parents=True, exist_ok=True) + paths.cache_dir.mkdir(parents=True, exist_ok=True) + paths.logs_dir.mkdir(parents=True, exist_ok=True) + return paths diff --git a/src/index/orcid/rerank/__init__.py b/src/index/orcid/rerank/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/orcid/rerank/rcp_client.py b/src/index/orcid/rerank/rcp_client.py new file mode 100644 index 0000000..4784e1c --- /dev/null +++ b/src/index/orcid/rerank/rcp_client.py @@ -0,0 +1,24 @@ +"""RCP reranker client for ORCID. + +Thin subclass of the openalex implementation re-typed for `OrcidIndexConfig`. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, cast + +from src.index.openalex.rerank.rcp_client import RCPRerankerClient as _BaseRerank + +if TYPE_CHECKING: + from src.index.orcid.config import OrcidIndexConfig + + +class RCPRerankerClient(_BaseRerank): + def __init__( + self, + config: OrcidIndexConfig, + *, + path: str | None = None, + timeout_s: float | None = None, + ) -> None: + super().__init__(cast("Any", config), path=path, timeout_s=timeout_s) diff --git a/src/index/orcid/retrieval/__init__.py b/src/index/orcid/retrieval/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/orcid/retrieval/semantic.py b/src/index/orcid/retrieval/semantic.py new file mode 100644 index 0000000..b890f25 --- /dev/null +++ b/src/index/orcid/retrieval/semantic.py @@ -0,0 +1,109 @@ +"""End-to-end semantic retrieval: embed → vector search → rerank → hydrate.""" + +from __future__ import annotations + +import asyncio +import json +import logging +from typing import TYPE_CHECKING, Any + +from src.index.orcid.embed.rcp_client import RCPEmbeddingClient +from src.index.orcid.rerank.rcp_client import RCPRerankerClient +from src.index.orcid.storage.duckdb_store import OrcidDuckDBStore +from src.index.orcid.vector.qdrant_store import OrcidQdrantStore + +if TYPE_CHECKING: + from src.index.orcid.config import OrcidIndexConfig + +LOGGER = logging.getLogger(__name__) + + +async def _async_search( + *, + config: OrcidIndexConfig, + query: str, + entity_type: str, + top_k: int, + candidate_k: int, + filter_payload: dict[str, Any] | None, + store: OrcidDuckDBStore, +) -> list[dict[str, Any]]: + embed = RCPEmbeddingClient(config) + qdrant = OrcidQdrantStore(config) + rerank = RCPRerankerClient(config) + + instructed = ( + f"Instruct: {config.rcp.query_instruction}\nQuery: {query}" + if config.rcp.query_instruction + else query + ) + [query_vec] = await embed.embed_all([instructed]) + candidates = qdrant.search( + entity_type, + query_vector=query_vec, + top_k=candidate_k, + filter_payload=filter_payload, + ) + if not candidates: + return [] + + docs = [_payload_to_doc(c["payload"]) for c in candidates] + reranked = await rerank.rerank(query, docs, top_n=top_k) + if not reranked: + ordered = candidates[:top_k] + else: + ordered = [] + for r in reranked: + cand = candidates[r["index"]] + ordered.append({**cand, "rerank_score": r["relevance_score"]}) + + hydrated: list[dict[str, Any]] = [] + for hit in ordered: + payload = hit["payload"] or {} + orcid_id = payload.get("orcid_id") + if not orcid_id: + continue + person = store.fetch_person(orcid_id) + hydrated.append( + { + "id": hit["id"], + "vector_score": hit["score"], + "rerank_score": hit.get("rerank_score"), + "payload": payload, + "person": person, + }, + ) + return hydrated + + +def _payload_to_doc(payload: dict[str, Any]) -> str: + name = payload.get("display_name") + org = payload.get("organization") + parts = [p for p in (name, org) if p] + return " — ".join(parts) if parts else json.dumps(payload, ensure_ascii=False) + + +def semantic_search( + *, + config: OrcidIndexConfig, + query: str, + entity_type: str = "persons", + top_k: int = 10, + candidate_k: int = 50, + filter_payload: dict[str, Any] | None = None, + store: OrcidDuckDBStore | None = None, +) -> list[dict[str, Any]]: + """Synchronous entrypoint used by the CLI and the FastAPI app.""" + if store is None: + store = OrcidDuckDBStore.open(scope=config.paths.scope) + return asyncio.run( + _async_search( + config=config, + query=query, + entity_type=entity_type, + top_k=top_k, + candidate_k=candidate_k, + filter_payload=filter_payload, + store=store, + ), + ) diff --git a/src/index/orcid/retrieval/sql.py b/src/index/orcid/retrieval/sql.py new file mode 100644 index 0000000..887dee6 --- /dev/null +++ b/src/index/orcid/retrieval/sql.py @@ -0,0 +1,121 @@ +"""Read-only SQL surface over the ORCID DuckDB. + +Mirrors `src/index/openalex/retrieval/sql.py`: predefined parametrized +queries plus a guarded ad-hoc SELECT/WITH path. +""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index.orcid.storage.duckdb_store import OrcidDuckDBStore + +INVALID_QUERY_PREFIX_ERROR = "Only SELECT/WITH queries are allowed" +FORBIDDEN_KEYWORD_ERROR = "Forbidden keyword in query: {kw}" + +_ALLOWED_PREFIXES = ("select", "with") +_FORBIDDEN_KEYWORDS = ( + "attach", + "copy", + "pragma", + "install", + "load", + "export", + "import", + "create", + "drop", + "alter", + "insert", + "update", + "delete", + "truncate", +) +_KEYWORD_RE = re.compile(r"\b(" + "|".join(_FORBIDDEN_KEYWORDS) + r")\b", re.IGNORECASE) + + +def _validate_adhoc(sql: str) -> None: + stripped = sql.strip().lstrip("(").lstrip() + lowered = stripped.lower() + if not any(lowered.startswith(p) for p in _ALLOWED_PREFIXES): + raise ValueError(INVALID_QUERY_PREFIX_ERROR) + match = _KEYWORD_RE.search(stripped) + if match: + raise ValueError(FORBIDDEN_KEYWORD_ERROR.format(kw=match.group(1).upper())) + + +PREDEFINED_QUERIES: dict[str, str] = { + "count_by_entity": ( + "SELECT 'persons' AS entity, COUNT(*) AS n FROM persons " + "UNION ALL SELECT 'persons_in_scope', COUNT(*) FROM persons WHERE in_scope " + "UNION ALL SELECT 'employments', COUNT(*) FROM employments " + "UNION ALL SELECT 'educations', COUNT(*) FROM educations " + "UNION ALL SELECT 'seeds', COUNT(*) FROM seeds " + "UNION ALL SELECT 'chunks', COUNT(*) FROM chunks" + ), + "scope_summary": ( + "SELECT discovered_via, in_scope, COUNT(*) AS n " + "FROM persons GROUP BY discovered_via, in_scope ORDER BY discovered_via, in_scope" + ), + "in_scope_persons": ( + "SELECT orcid_id, display_name, scope_reason FROM persons " + "WHERE in_scope = TRUE ORDER BY family_name, given_name LIMIT $limit" + ), + "employments_by_org": ( + "SELECT organization, COUNT(*) AS n_persons " + "FROM employments e JOIN persons p ON p.orcid_id = e.orcid_id " + "WHERE p.in_scope = TRUE " + "GROUP BY organization ORDER BY n_persons DESC LIMIT $limit" + ), + "person_employments": ( + "SELECT e.* FROM employments e " + "WHERE e.orcid_id = $orcid_id ORDER BY e.seq" + ), +} + + +def _row_to_dict(cur: Any) -> list[dict[str, Any]]: + cols = [d[0] for d in cur.description] if cur.description else [] + return [dict(zip(cols, r, strict=False)) for r in cur.fetchall()] + + +def _execute( + sql: str, + params: dict[str, Any] | None, + store: OrcidDuckDBStore | None, +) -> list[dict[str, Any]]: + owned = False + if store is None: + store = OrcidDuckDBStore.open() + owned = True + try: + cur = store.connect().execute(sql, params or {}) + return _row_to_dict(cur) + finally: + if owned: + store.close() + + +def run_adhoc( + sql: str, + params: dict[str, Any] | None = None, + *, + store: OrcidDuckDBStore | None = None, +) -> list[dict[str, Any]]: + _validate_adhoc(sql) + return _execute(sql, params, store) + + +def run_predefined( + name: str, + params: dict[str, Any] | None = None, + *, + store: OrcidDuckDBStore | None = None, +) -> list[dict[str, Any]]: + if name not in PREDEFINED_QUERIES: + message = ( + f"Unknown predefined query: {name!r}. " + f"Known: {sorted(PREDEFINED_QUERIES)}" + ) + raise ValueError(message) + return _execute(PREDEFINED_QUERIES[name], params, store) diff --git a/src/index/orcid/storage/__init__.py b/src/index/orcid/storage/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/orcid/storage/duckdb_store.py b/src/index/orcid/storage/duckdb_store.py new file mode 100644 index 0000000..41ed46c --- /dev/null +++ b/src/index/orcid/storage/duckdb_store.py @@ -0,0 +1,293 @@ +"""DuckDB lifecycle, schema bootstrap, and upsert helpers for ORCID.""" + +from __future__ import annotations + +import json +import logging +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import duckdb + +from src.index.orcid.paths import get_orcid_paths + +if TYPE_CHECKING: + from collections.abc import Iterable, Iterator + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + +VALID_AFFILIATION_TABLES = {"employments", "educations"} +VALID_EMBEDDING_ENTITIES = {"persons", "employments", "educations"} + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +class OrcidDuckDBStore: + """Thin wrapper around DuckDB tuned for the ORCID schema. + + Construct with `OrcidDuckDBStore.open()` for the scope-resolved repo + path. Re-running `bootstrap()` is idempotent. + """ + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open( + cls, + db_path: Path | None = None, + *, + scope: str | None = None, + ) -> OrcidDuckDBStore: + if db_path is None: + db_path = get_orcid_paths(scope).duckdb_path + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + self.connect().execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + # ---- Upserts --------------------------------------------------------- + + def upsert_seed( + self, + *, + orcid_id: str, + discovered_via: str, + hint: str | None = None, + ) -> None: + self.connect().execute( + "INSERT INTO seeds (orcid_id, discovered_via, hint) " + "VALUES (?, ?, ?) " + "ON CONFLICT (orcid_id) DO UPDATE SET " + " discovered_via = CASE " + " WHEN seeds.discovered_via = excluded.discovered_via THEN seeds.discovered_via " + " ELSE 'both' " + " END, " + " hint = COALESCE(excluded.hint, seeds.hint)", + [orcid_id, discovered_via, hint], + ) + + def upsert_person(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + cols = ( + "orcid_id", + "given_name", + "family_name", + "display_name", + "biography", + "in_scope", + "scope_reason", + "discovered_via", + ) + all_cols = (*cols, "raw", "ingested_at") + placeholders = ", ".join(["?"] * len(all_cols)) + col_list = ", ".join(all_cols) + update_cols = ", ".join( + f"{c} = excluded.{c}" for c in (*cols[1:], "raw", "ingested_at") + ) + sql = ( # noqa: S608 - column names sourced from the literal `cols` tuple above + f"INSERT INTO persons ({col_list}) VALUES ({placeholders}) " + f"ON CONFLICT (orcid_id) DO UPDATE SET {update_cols}" + ) + values: list[Any] = [row.get(c) for c in cols] + values.append(json.dumps(raw, ensure_ascii=False)) + values.append(self._now()) + self.connect().execute(sql, values) + + def replace_affiliations( + self, + table: str, + orcid_id: str, + rows: Iterable[dict[str, Any]], + ) -> int: + if table not in VALID_AFFILIATION_TABLES: + message = f"Unknown affiliation table: {table}" + raise ValueError(message) + conn = self.connect() + # Replace-all semantics keeps the table consistent when ORCID + # entries are removed/renumbered upstream. + conn.execute( + f"DELETE FROM {table} WHERE orcid_id = ?", # noqa: S608 - guarded + [orcid_id], + ) + cols = ( + "orcid_id", + "seq", + "organization", + "org_ror", + "department", + "role", + "start_date", + "end_date", + ) + col_list = ", ".join(cols) + placeholders = ", ".join(["?"] * len(cols)) + sql = f"INSERT INTO {table} ({col_list}) VALUES ({placeholders})" # noqa: S608 + count = 0 + for row in rows: + conn.execute(sql, [row.get(c) for c in cols]) + count += 1 + return count + + def upsert_chunk( + self, + *, + chunk_id: str, + entity_type: str, + entity_id: str, + chunk_index: int, + text: str, + token_count: int, + vector_id: str, + ) -> None: + self.connect().execute( + "INSERT INTO chunks " + "(chunk_id, entity_type, entity_id, chunk_index, text, " + "token_count, vector_id) VALUES (?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (chunk_id) DO UPDATE SET " + "text = excluded.text, token_count = excluded.token_count, " + "vector_id = excluded.vector_id, embedded_at = now()", + [chunk_id, entity_type, entity_id, chunk_index, text, token_count, vector_id], + ) + + # ---- Reads ----------------------------------------------------------- + + def count(self, table: str) -> int: + result = self.connect().execute( + f"SELECT count(*) FROM {table}", # noqa: S608 - callers pass internal table names + ).fetchone() + return int(result[0]) if result else 0 + + def fetch_person(self, orcid_id: str) -> dict[str, Any] | None: + cur = self.connect().execute( + "SELECT * FROM persons WHERE orcid_id = ?", + [orcid_id], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + def stream_seeds( + self, + *, + only_unfetched: bool = True, + priority_hints: list[str] | None = None, + ) -> Iterator[dict[str, Any]]: + """Yield seed rows. With `only_unfetched`, skip ORCIDs already in `persons`. + + `priority_hints` is a list of case-insensitive substrings; seeds whose + `hint` matches any of them are yielded first. Useful for steering an + ingest with a daily quota toward a target sub-corpus (e.g. ETHZ + aliases) before the rest of the seed pool. + + Materializes the result upfront because callers (e.g. the persons + ingester) write into `persons` between iterations, which would + invalidate a streaming cursor that joins `persons` in its query. + """ + sql = ( + "SELECT s.orcid_id, s.discovered_via, s.hint FROM seeds s " + "LEFT JOIN persons p ON p.orcid_id = s.orcid_id " + ) + if only_unfetched: + sql += "WHERE p.orcid_id IS NULL " + + params: list[Any] = [] + order_clauses: list[str] = [] + if priority_hints: + like_conditions = " OR ".join(["LOWER(s.hint) LIKE ?"] * len(priority_hints)) + order_clauses.append(f"CASE WHEN ({like_conditions}) THEN 0 ELSE 1 END") + params.extend(f"%{h.lower().strip()}%" for h in priority_hints if h and h.strip()) + order_clauses.append("s.discovered_at") + sql += "ORDER BY " + ", ".join(order_clauses) + + cur = self.connect().execute(sql, params) if params else self.connect().execute(sql) + cols = [d[0] for d in cur.description] + rows = cur.fetchall() + for row in rows: + yield dict(zip(cols, row, strict=False)) + + def stream_rows_for_embedding( + self, + entity_type: str, + *, + limit: int | None = None, + ) -> Iterator[dict[str, Any]]: + """Yield rows that need embedding (no chunks yet, in-scope only).""" + if entity_type not in VALID_EMBEDDING_ENTITIES: + message = f"Unknown entity_type: {entity_type}" + raise ValueError(message) + if entity_type == "persons": + join_id = "t.orcid_id" + sql = ( + "SELECT t.* FROM persons t " + "WHERE t.in_scope = TRUE AND NOT EXISTS (" + " SELECT 1 FROM chunks c " + " WHERE c.entity_type = ? AND c.entity_id = t.orcid_id" + ") " + ) + params: list[Any] = [entity_type] + else: + # Affiliations: only embed if the parent person is in scope. + join_id = "t.orcid_id || '#' || CAST(t.seq AS VARCHAR)" + sql = ( + f"SELECT t.*, {join_id} AS entity_id " # noqa: S608 - table guarded + f"FROM {entity_type} t " + "JOIN persons p ON p.orcid_id = t.orcid_id " + "WHERE p.in_scope = TRUE AND NOT EXISTS (" + " SELECT 1 FROM chunks c " + f" WHERE c.entity_type = ? AND c.entity_id = {join_id}" + ") " + ) + params = [entity_type] + if limit is not None: + sql += "LIMIT ?" + params.append(limit) + cur = self.connect().execute(sql, params) + cols = [d[0] for d in cur.description] + # Materialize upfront — embed loop writes to `chunks` between rows, + # which would invalidate a streaming cursor that joins `chunks` via + # NOT EXISTS in its query. + rows = cur.fetchall() + for row in rows: + yield dict(zip(cols, row, strict=False)) + + def list_employments(self, orcid_id: str) -> list[dict[str, Any]]: + cur = self.connect().execute( + "SELECT * FROM employments WHERE orcid_id = ? ORDER BY seq", + [orcid_id], + ) + cols = [d[0] for d in cur.description] + return [dict(zip(cols, r, strict=False)) for r in cur.fetchall()] + + @staticmethod + def _now() -> str: + return datetime.now(tz=timezone.utc).isoformat() diff --git a/src/index/orcid/storage/schema.sql b/src/index/orcid/storage/schema.sql new file mode 100644 index 0000000..64423bc --- /dev/null +++ b/src/index/orcid/storage/schema.sql @@ -0,0 +1,67 @@ +-- Canonical DuckDB schema for the ORCID index module. +-- Idempotent: every statement uses IF NOT EXISTS so re-runs are safe. +-- One DuckDB file per scope (see src/index/orcid/paths.py). + +CREATE TABLE IF NOT EXISTS persons ( + orcid_id TEXT PRIMARY KEY, -- canonical "0000-0000-0000-000X" + given_name TEXT, + family_name TEXT, + display_name TEXT, + biography TEXT, + in_scope BOOLEAN NOT NULL DEFAULT FALSE, + scope_reason TEXT, + discovered_via TEXT NOT NULL, -- 'openalex' | 'orcid_search' | 'both' | 'manual' + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS employments ( + orcid_id TEXT NOT NULL, + seq INTEGER NOT NULL, + organization TEXT NOT NULL, + org_ror TEXT, + department TEXT, + role TEXT, + start_date TEXT, + end_date TEXT, + PRIMARY KEY (orcid_id, seq) +); + +CREATE TABLE IF NOT EXISTS educations ( + orcid_id TEXT NOT NULL, + seq INTEGER NOT NULL, + organization TEXT NOT NULL, + org_ror TEXT, + department TEXT, + role TEXT, + start_date TEXT, + end_date TEXT, + PRIMARY KEY (orcid_id, seq) +); + +CREATE TABLE IF NOT EXISTS seeds ( + orcid_id TEXT PRIMARY KEY, + discovered_via TEXT NOT NULL, + hint TEXT, + discovered_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +-- chunk_id is deterministic: uuid5(NAMESPACE_URL, "||") +-- where entity_id is orcid_id (persons) or "#" (employments/educations). +CREATE TABLE IF NOT EXISTS chunks ( + chunk_id TEXT PRIMARY KEY, + entity_type TEXT NOT NULL, -- 'persons' | 'employments' | 'educations' + entity_id TEXT NOT NULL, + chunk_index INTEGER NOT NULL, + text TEXT NOT NULL, + token_count INTEGER NOT NULL, + vector_id TEXT NOT NULL, + embedded_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS idx_persons_inscope ON persons (in_scope); +CREATE INDEX IF NOT EXISTS idx_persons_family ON persons (family_name); +CREATE INDEX IF NOT EXISTS idx_emp_ror ON employments (org_ror); +CREATE INDEX IF NOT EXISTS idx_emp_org ON employments (organization); +CREATE INDEX IF NOT EXISTS idx_edu_ror ON educations (org_ror); +CREATE INDEX IF NOT EXISTS idx_chunks_entity ON chunks (entity_type, entity_id); diff --git a/src/index/orcid/vector/__init__.py b/src/index/orcid/vector/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/orcid/vector/qdrant_store.py b/src/index/orcid/vector/qdrant_store.py new file mode 100644 index 0000000..343793d --- /dev/null +++ b/src/index/orcid/vector/qdrant_store.py @@ -0,0 +1,124 @@ +"""Qdrant client wrapper for the ORCID indexer. + +Collections are namespaced by scope (`orcid__`) so EPFL and +Switzerland runs share one Qdrant instance without colliding. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from qdrant_client import QdrantClient, models + +if TYPE_CHECKING: + from src.index.orcid.config import OrcidIndexConfig + +LOGGER = logging.getLogger(__name__) + +ENTITY_TYPES: tuple[str, ...] = ("persons", "employments", "educations") + + +class OrcidQdrantStore: + """Per-(scope, entity) collection bootstrap + upsert + filtered search.""" + + def __init__(self, config: OrcidIndexConfig) -> None: + self._config = config + self._client = QdrantClient( + url=config.qdrant.url, + api_key=config.qdrant.api_key, + prefer_grpc=config.qdrant.prefer_grpc, + ) + self._dim = config.rcp.embedding_dim + + @property + def client(self) -> QdrantClient: + return self._client + + def collection(self, entity_type: str) -> str: + return self._config.paths.collection_name(entity_type) + + def ensure_collection(self, entity_type: str) -> str: + name = self.collection(entity_type) + if not self._client.collection_exists(name): + self._client.create_collection( + collection_name=name, + vectors_config=models.VectorParams( + size=self._dim, + distance=models.Distance.COSINE, + ), + ) + LOGGER.info("created qdrant collection %s (dim=%d)", name, self._dim) + return name + + def upsert_points( + self, + entity_type: str, + *, + ids: list[str], + vectors: list[list[float]], + payloads: list[dict[str, Any]], + ) -> None: + if not (len(ids) == len(vectors) == len(payloads)): + message = "ids/vectors/payloads must be the same length" + raise ValueError(message) + if not ids: + return + name = self.ensure_collection(entity_type) + points = [ + models.PointStruct(id=pid, vector=vec, payload=payload) + for pid, vec, payload in zip(ids, vectors, payloads, strict=False) + ] + self._client.upsert(collection_name=name, points=points) + + def search( + self, + entity_type: str, + *, + query_vector: list[float], + top_k: int = 50, + filter_payload: dict[str, Any] | None = None, + ) -> list[dict[str, Any]]: + name = self.ensure_collection(entity_type) + qdrant_filter = self._build_filter(filter_payload) if filter_payload else None + hits = self._client.query_points( + collection_name=name, + query=query_vector, + limit=top_k, + query_filter=qdrant_filter, + with_payload=True, + ).points + return [ + {"id": str(p.id), "score": float(p.score), "payload": p.payload or {}} + for p in hits + ] + + def count(self, entity_type: str) -> int: + name = self.collection(entity_type) + if not self._client.collection_exists(name): + return 0 + return int(self._client.count(collection_name=name, exact=True).count) + + @staticmethod + def _build_filter(payload: dict[str, Any]) -> models.Filter: + must: list[models.Condition] = [] + for key, value in payload.items(): + if isinstance(value, dict) and ("gte" in value or "lte" in value): + must.append( + models.FieldCondition( + key=key, + range=models.Range( + gte=value.get("gte"), + lte=value.get("lte"), + ), + ), + ) + elif isinstance(value, list): + must.append( + models.FieldCondition(key=key, match=models.MatchAny(any=value)), + ) + else: + must.append( + models.FieldCondition(key=key, match=models.MatchValue(value=value)), + ) + return models.Filter(must=must) diff --git a/src/index/renkulab/__init__.py b/src/index/renkulab/__init__.py new file mode 100644 index 0000000..e696c1c --- /dev/null +++ b/src/index/renkulab/__init__.py @@ -0,0 +1,23 @@ +"""RenkuLab RAG indexer. + +Pulls public projects, groups, users, and data connectors from the +RenkuLab data API (https://renkulab.io/api/data) into DuckDB, then +chunks + embeds the human-readable surfaces (name, description, +keywords, namespace) via RCP and pushes vectors into Qdrant. + +Pipeline shape (mirrors src/index/zenodo): + + ingest → REST → DuckDB. Five entity tables (projects, groups, + users, data_connectors) plus two link tables + (project_members, group_members). + embed → chunk + embed per-entity_type, push to Qdrant under + the `renkulab_` collections (or a single + merged `renkulab` collection — see embed/pipeline.py). + search → vector + RCP rerank + DuckDB hydrate. + query → predefined or guarded ad-hoc SQL. + +The Renku `/users` and `/namespaces` endpoints require auth, but the +public `/search/query?q=type:User` endpoint is open and exposes +sufficient surface (path, slug, first/last name) for an index, so +we harvest users via search. +""" diff --git a/src/index/renkulab/__main__.py b/src/index/renkulab/__main__.py new file mode 100644 index 0000000..978dcb2 --- /dev/null +++ b/src/index/renkulab/__main__.py @@ -0,0 +1,8 @@ +"""Entry point: `python -m src.index.renkulab`.""" + +from __future__ import annotations + +from src.index.renkulab.cli import main + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/index/renkulab/_federated.py b/src/index/renkulab/_federated.py new file mode 100644 index 0000000..382d2e9 --- /dev/null +++ b/src/index/renkulab/_federated.py @@ -0,0 +1,51 @@ +"""RenkuLab registration with the federated discover/hydrate registries. + +Hydrate seed types +------------------ + +- ``renkulab_url`` — RenkuLab project / group / user / data_connector URLs. +""" + +from __future__ import annotations + +import logging +from typing import Any, Iterator + +from src.index._federated.dh_registry import register_discoverer, register_hydrator +from src.index._federated.protocols import ( + HydrationSummary, + IndexDiscoverer, + IndexHydrator, + Seed, +) + +LOGGER = logging.getLogger(__name__) + + +class RenkuLabDiscoverer: + name = "renkulab" + accepted_sources = ("from-search",) + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + if source not in self.accepted_sources: + message = f"RenkuLab: unknown source {source!r}. Accepted: {list(self.accepted_sources)}" + raise ValueError(message) + # Placeholder: wrap the existing search-based ingest discover in a follow-up. + LOGGER.warning("renkulab discover is a stub") + return + + +class RenkuLabHydrator: + name = "renkulab" + accepted_seed_types = ("renkulab_url",) + + def hydrate(self, seeds, *, only_unfetched: bool = True) -> HydrationSummary: + materialised = list(seeds) + LOGGER.warning( + "renkulab: hydrate is a stub (received %d seeds).", len(materialised), + ) + return HydrationSummary(skipped_existing=len(materialised)) + + +register_discoverer(RenkuLabDiscoverer()) +register_hydrator(RenkuLabHydrator()) diff --git a/src/index/renkulab/api.py b/src/index/renkulab/api.py new file mode 100644 index 0000000..32757bf --- /dev/null +++ b/src/index/renkulab/api.py @@ -0,0 +1,115 @@ +"""FastAPI app exposing the RenkuLab index dual query surface.""" + +from __future__ import annotations + +import logging +from typing import Any + +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel, Field + +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.index.renkulab.config import load_config +from src.index.renkulab.embed.pipeline import COLLECTION_BY_ENTITY +from src.index.renkulab.retrieval.semantic import semantic_search +from src.index.renkulab.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) +from src.index.renkulab.storage.duckdb_store import RenkulabStore + +LOGGER = logging.getLogger(__name__) + +app = FastAPI(title="RenkuLab Index") + + +class SearchRequest(BaseModel): + query: str + entity_types: list[str] | None = None + top_k: int = Field(default=10, ge=1, le=100) + candidate_k: int = Field(default=50, ge=1, le=500) + filter_payload: dict[str, Any] | None = None + + +class QueryRequest(BaseModel): + sql: str | None = None + predefined: str | None = None + params: dict[str, Any] | None = None + + +@app.get("/healthz") +def healthz() -> dict[str, Any]: + config = load_config() + duck_status = "ok" + qdrant_status: dict[str, Any] = {} + try: + RenkulabStore.open().count("projects") + except Exception as exc: # noqa: BLE001 + duck_status = f"error: {exc}" + try: + qd = QdrantStore(config) # type: ignore[arg-type] + for entity_type, collection in COLLECTION_BY_ENTITY.items(): + try: + qdrant_status[collection] = qd.count(collection) + except Exception as exc: # noqa: BLE001 + qdrant_status[collection] = f"error: {exc}" + except Exception as exc: # noqa: BLE001 + qdrant_status = {"_error": str(exc)} + return { + "duckdb": duck_status, + "qdrant": qdrant_status, + "rcp_configured": bool(config.rcp.token), + "renkulab_token_configured": bool(config.renkulab.token), + } + + +@app.post("/search") +def search(req: SearchRequest) -> list[dict[str, Any]]: + config = load_config() + try: + config.require_rcp() + except ValueError as exc: + raise HTTPException(status_code=503, detail=str(exc)) from exc + return semantic_search( + config=config, + query=req.query, + entity_types=req.entity_types, + top_k=req.top_k, + candidate_k=req.candidate_k, + filter_payload=req.filter_payload, + ) + + +@app.post("/query") +def query(req: QueryRequest) -> list[dict[str, Any]]: + if req.predefined and req.sql: + raise HTTPException( + status_code=400, + detail="Provide either `predefined` or `sql`, not both", + ) + try: + if req.predefined: + return run_predefined(req.predefined, req.params) + if req.sql: + return run_adhoc(req.sql, req.params) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + raise HTTPException(status_code=400, detail="Pass `predefined` or `sql`") + + +@app.get("/predefined") +def list_predefined() -> dict[str, list[str]]: + return {"predefined": sorted(PREDEFINED_QUERIES)} + + +@app.get("/entity/{entity_type}/{entity_id}") +def get_entity(entity_type: str, entity_id: str) -> dict[str, Any]: + store = RenkulabStore.open() + try: + record = store.fetch_entity(entity_type, entity_id) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + if record is None: + raise HTTPException(status_code=404, detail="not found") + return record diff --git a/src/index/renkulab/cli.py b/src/index/renkulab/cli.py new file mode 100644 index 0000000..633fa7b --- /dev/null +++ b/src/index/renkulab/cli.py @@ -0,0 +1,300 @@ +"""CLI for the RenkuLab index module. + +Subcommands: + +- `ingest` — pull projects/groups/users/data_connectors into DuckDB. +- `embed` — chunk + embed entities, push vectors to Qdrant. +- `search` — semantic retrieval (vector + RCP rerank). +- `query` — read-only SQL over DuckDB (predefined or guarded ad-hoc). +- `status` — print row counts + Qdrant collection sizes + paths. +- `serve` — run the FastAPI app. +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from typing import Any + +from src.index.renkulab.config import load_config +from src.index.renkulab.embed.pipeline import COLLECTION_BY_ENTITY, embed_entities +from src.index.renkulab.ingest.pipeline import ingest_all +from src.index.renkulab.ingest.scope import resolve_scope +from src.index.renkulab.retrieval.semantic import semantic_search +from src.index.renkulab.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) +from src.index.renkulab.storage.duckdb_store import RenkulabStore + +LOGGER = logging.getLogger(__name__) + +_VALID_ENTITIES = { + "projects", + "groups", + "users", + "data_connectors", + "group_members", + "project_members", +} + + +def _emit_json(obj: Any) -> None: + json.dump(obj, sys.stdout, indent=2, ensure_ascii=False, default=str) + sys.stdout.write("\n") + + +def _split_csv(value: str | None) -> list[str] | None: + if not value: + return None + return [v.strip() for v in value.split(",") if v.strip()] + + +def _cmd_ingest(args: argparse.Namespace) -> int: + config = load_config() + scope = resolve_scope(args.scope, config) + only = set(_split_csv(args.only) or []) or None + if only: + unknown = only - _VALID_ENTITIES + if unknown: + message = ( + f"--only contains unknown entity types: {sorted(unknown)}. " + f"Known: {sorted(_VALID_ENTITIES)}" + ) + raise SystemExit(message) + store = RenkulabStore.open() + try: + summary = ingest_all( + config=config, + store=store, + scope=scope, + limit=args.limit, + refresh=args.refresh, + only=only, + ) + finally: + store.close() + _emit_json({"scope": scope.name, "ingested": summary}) + return 0 + + +def _cmd_embed(args: argparse.Namespace) -> int: + config = load_config() + config.require_rcp() + entity_types = _split_csv(args.entities) + if entity_types: + unknown = set(entity_types) - set(COLLECTION_BY_ENTITY) + if unknown: + message = ( + f"--entities contains unknown types: {sorted(unknown)}. " + f"Known: {sorted(COLLECTION_BY_ENTITY)}" + ) + raise SystemExit(message) + store = RenkulabStore.open() + try: + summary = embed_entities( + config=config, + store=store, + entity_types=entity_types, + limit=args.limit, + ) + finally: + store.close() + _emit_json(summary) + return 0 + + +def _cmd_search(args: argparse.Namespace) -> int: + config = load_config() + config.require_rcp() + filter_payload = json.loads(args.filter) if args.filter else None + entity_types = _split_csv(args.entities) + hits = semantic_search( + config=config, + query=args.query, + entity_types=entity_types, + top_k=args.top_k, + candidate_k=args.candidate_k, + filter_payload=filter_payload, + ) + _emit_json(hits) + return 0 + + +def _cmd_query(args: argparse.Namespace) -> int: + if args.predefined and args.sql: + message = "Pass either --predefined or --sql, not both" + raise SystemExit(message) + params: dict[str, Any] = {} + for kv in args.param or []: + if "=" not in kv: + message = f"--param expects key=value, got {kv!r}" + raise SystemExit(message) + key, value = kv.split("=", 1) + try: + params[key] = int(value) + except ValueError: + try: + params[key] = float(value) + except ValueError: + params[key] = value + if args.predefined: + rows = run_predefined(args.predefined, params) + elif args.sql: + rows = run_adhoc(args.sql, params) + else: + _emit_json({"predefined": sorted(PREDEFINED_QUERIES)}) + return 0 + _emit_json(rows) + return 0 + + +def _cmd_status(args: argparse.Namespace) -> int: + del args + config = load_config() + store = RenkulabStore.open() + try: + counts = { + t: store.count(t) + for t in ( + "projects", + "groups", + "users", + "data_connectors", + "group_members", + "project_members", + "chunks", + ) + } + finally: + store.close() + qdrant_counts: dict[str, Any] = {} + try: + from src.index.openalex.vector.qdrant_store import QdrantStore + + qd = QdrantStore(config) # type: ignore[arg-type] + for entity_type, collection in COLLECTION_BY_ENTITY.items(): + try: + qdrant_counts[collection] = qd.count(collection) + except Exception as exc: # noqa: BLE001 + qdrant_counts[collection] = f"error: {exc}" + except Exception as exc: # noqa: BLE001 + qdrant_counts = {"_error": str(exc)} + _emit_json( + { + "duckdb_path": str(config.paths.duckdb_path), + "duckdb_counts": counts, + "qdrant_collections": qdrant_counts, + "scope_default": config.scope.default, + }, + ) + return 0 + + +def _cmd_serve(args: argparse.Namespace) -> int: + import uvicorn + + uvicorn.run( + "src.index.renkulab.api:app", + host=args.host, + port=args.port, + reload=args.reload, + ) + return 0 + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(prog="src.index.renkulab") + sub = parser.add_subparsers(dest="cmd", required=True) + + p_ingest = sub.add_parser("ingest", help="Pull RenkuLab entities into DuckDB") + p_ingest.add_argument( + "--scope", + default="all", + help="Scope filter: all, epfl, switzerland", + ) + p_ingest.add_argument( + "--limit", + type=int, + default=None, + help="Per-entity record cap", + ) + p_ingest.add_argument( + "--refresh", + action="store_true", + help="Re-ingest entities already marked complete", + ) + p_ingest.add_argument( + "--only", + default=None, + help=( + "Comma-separated entity allow-list. Choices: " + "projects,groups,users,data_connectors,group_members,project_members" + ), + ) + p_ingest.set_defaults(func=_cmd_ingest) + + p_embed = sub.add_parser("embed", help="Chunk + embed entities, push to Qdrant") + p_embed.add_argument( + "--entities", + default=None, + help=( + "Comma-separated entity allow-list. Choices: " + "projects,groups,users,data_connectors. Default: all four." + ), + ) + p_embed.add_argument("--limit", type=int, default=None) + p_embed.set_defaults(func=_cmd_embed) + + p_search = sub.add_parser("search", help="Semantic retrieval (vector + rerank)") + p_search.add_argument("query", help="Natural-language query") + p_search.add_argument( + "--entities", + default=None, + help=( + "Comma-separated entity allow-list to search. " + "Default: all four collections (results merged + reranked)." + ), + ) + p_search.add_argument("--top-k", type=int, default=10) + p_search.add_argument("--candidate-k", type=int, default=50) + p_search.add_argument( + "--filter", + default=None, + help="JSON dict for Qdrant payload filter", + ) + p_search.set_defaults(func=_cmd_search) + + p_query = sub.add_parser("query", help="Read-only SQL (predefined or guarded ad-hoc)") + p_query.add_argument( + "--predefined", + default=None, + help=f"One of: {sorted(PREDEFINED_QUERIES)}", + ) + p_query.add_argument("--sql", default=None, help="Ad-hoc SELECT/WITH query") + p_query.add_argument("--param", action="append", help="Repeatable key=value param") + p_query.set_defaults(func=_cmd_query) + + p_status = sub.add_parser("status", help="Show DuckDB + Qdrant counts and paths") + p_status.set_defaults(func=_cmd_status) + + p_serve = sub.add_parser("serve", help="Run the RenkuLab FastAPI app") + p_serve.add_argument("--host", default="0.0.0.0") + p_serve.add_argument("--port", type=int, default=8004) + p_serve.add_argument("--reload", action="store_true") + p_serve.set_defaults(func=_cmd_serve) + + return parser + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s — %(message)s", + ) + parser = _build_parser() + args = parser.parse_args(argv) + return int(args.func(args) or 0) diff --git a/src/index/renkulab/config.py b/src/index/renkulab/config.py new file mode 100644 index 0000000..bb0b8ec --- /dev/null +++ b/src/index/renkulab/config.py @@ -0,0 +1,133 @@ +"""Config loader for the RenkuLab indexer. + +Reads `config/index/renkulab.yaml` and merges in env-sourced credentials +(`RCP_TOKEN`, `RENKULAB_TOKEN`, `INDEX_QDRANT_API_KEY`) plus the resolved +data dir. + +The `rcp` and `qdrant` sub-blocks mirror `OpenAlexIndexConfig` field-for- +field so that the openalex RCP embedding/reranker clients and Qdrant store +can be reused with a `RenkulabIndexConfig` instance at runtime — they only +TYPE_CHECK against the OpenAlex config and never import it at runtime. +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Optional + +import yaml +from pydantic import BaseModel + +from src.index.renkulab.paths import RenkulabPaths, get_renkulab_paths + +DEFAULT_CONFIG_PATH = Path("config/index/renkulab.yaml") + +TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} +FALSE_ENV_VALUES = {"0", "false", "f", "no", "n", "off"} + +MISSING_RCP_TOKEN_ERROR = "Missing required environment variable: RCP_TOKEN" + + +class RcpConfig(BaseModel): + base_url: str + embedding_model: str + embedding_dim: int + query_instruction: str + reranker_model: str + batch_size: int = 32 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: Optional[str] = None + + +class RenkulabConfig(BaseModel): + base_url: str = "https://renkulab.io/api/data" + page_size: int = 100 + rate_per_minute: int = 120 + max_concurrency: int = 4 + token: Optional[str] = None + + +class ScopeConfig(BaseModel): + default: str = "all" + epfl_keywords: list[str] = [] + switzerland_keywords: list[str] = [] + + +class EntitiesConfig(BaseModel): + projects: bool = True + groups: bool = True + users: bool = True + data_connectors: bool = True + group_members: bool = True + project_members: bool = True + + +class QdrantConfig(BaseModel): + url: str = "http://gme-qdrant:6333" + prefer_grpc: bool = False + api_key: Optional[str] = None + + +class ChunkingConfig(BaseModel): + size_tokens: int = 256 + overlap_tokens: int = 64 + tokenizer: str = "cl100k_base" + + +class RenkulabIndexConfig(BaseModel): + rcp: RcpConfig + renkulab: RenkulabConfig + scope: ScopeConfig + entities: EntitiesConfig + qdrant: QdrantConfig + chunking: ChunkingConfig + paths: RenkulabPaths + + model_config = {"arbitrary_types_allowed": True} + + def require_rcp(self) -> None: + if not self.rcp.token: + raise ValueError(MISSING_RCP_TOKEN_ERROR) + + +def _env_bool(name: str) -> Optional[bool]: + raw = os.getenv(name) + if raw is None or raw.strip() == "": + return None + normalized = raw.strip().lower() + if normalized in TRUE_ENV_VALUES: + return True + if normalized in FALSE_ENV_VALUES: + return False + message = f"Invalid boolean value for {name}: {raw!r}" + raise ValueError(message) + + +def _env_str(name: str) -> Optional[str]: + raw = os.getenv(name) + if raw is None: + return None + stripped = raw.strip() + return stripped or None + + +def load_config(path: Optional[Path] = None) -> RenkulabIndexConfig: + cfg_path = path or DEFAULT_CONFIG_PATH + raw = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) or {} + + raw.setdefault("rcp", {})["token"] = _env_str("RCP_TOKEN") + raw.setdefault("renkulab", {})["token"] = _env_str("RENKULAB_TOKEN") + raw.setdefault("qdrant", {})["api_key"] = _env_str("INDEX_QDRANT_API_KEY") + + if (override := _env_str("INDEX_QDRANT_URL")) is not None: + raw["qdrant"]["url"] = override + if (override_b := _env_bool("INDEX_QDRANT_PREFER_GRPC")) is not None: + raw["qdrant"]["prefer_grpc"] = override_b + if (override := _env_str("INDEX_RENKULAB_SCOPE")) is not None: + raw.setdefault("scope", {})["default"] = override + + raw["paths"] = get_renkulab_paths() + + return RenkulabIndexConfig(**raw) diff --git a/src/index/renkulab/embed/__init__.py b/src/index/renkulab/embed/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/renkulab/embed/pipeline.py b/src/index/renkulab/embed/pipeline.py new file mode 100644 index 0000000..238d514 --- /dev/null +++ b/src/index/renkulab/embed/pipeline.py @@ -0,0 +1,255 @@ +"""Embed RenkuLab entities into Qdrant via the RCP `/embeddings` endpoint. + +Reuses `RCPEmbeddingClient`, `QdrantStore`, and the token chunker from +`src.index.openalex` directly — those modules access only `config.rcp.*` +and `config.qdrant.*` at runtime, both of which `RenkulabIndexConfig` +mirrors field-for-field. + +Each entity type lands in its own Qdrant collection so retrieval can +filter by entity without scanning the wider corpus: + + renkulab_projects — projects (name + description + keywords) + renkulab_groups — groups + renkulab_users — users (first/last name + path) + renkulab_data_connectors — data connectors (name + description + storage type) +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import uuid +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.chunker import Chunk, chunk_text +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.vector.qdrant_store import QdrantStore + +if TYPE_CHECKING: + from src.index.renkulab.config import RenkulabIndexConfig + from src.index.renkulab.storage.duckdb_store import RenkulabStore + +LOGGER = logging.getLogger(__name__) + +_CHUNK_NAMESPACE = uuid.NAMESPACE_URL + +COLLECTION_BY_ENTITY: dict[str, str] = { + "projects": "renkulab_projects", + "groups": "renkulab_groups", + "users": "renkulab_users", + "data_connectors": "renkulab_data_connectors", +} + +_PK_BY_ENTITY: dict[str, str] = { + "projects": "project_id", + "groups": "group_id", + "users": "user_id", + "data_connectors": "data_connector_id", +} + + +def _chunk_id(entity_type: str, entity_id: str, chunk_index: int) -> str: + return str( + uuid.uuid5(_CHUNK_NAMESPACE, f"{entity_type}|{entity_id}|{chunk_index}"), + ) + + +def _decode_json(value: Any) -> list[Any]: + if value is None: + return [] + if isinstance(value, list): + return value + if isinstance(value, str): + try: + decoded = json.loads(value) + except json.JSONDecodeError: + return [] + return decoded if isinstance(decoded, list) else [] + return [] + + +def _row_to_text(entity_type: str, row: dict[str, Any]) -> str | None: + parts: list[str] = [] + if entity_type == "projects": + for k in ("name", "namespace", "description"): + v = row.get(k) + if v: + parts.append(str(v)) + keywords = _decode_json(row.get("keywords_json")) + if keywords: + parts.append("Keywords: " + ", ".join(str(k) for k in keywords)) + elif entity_type == "groups": + for k in ("name", "slug", "description"): + v = row.get(k) + if v: + parts.append(str(v)) + elif entity_type == "users": + first = row.get("first_name") or "" + last = row.get("last_name") or "" + full = (f"{first} {last}").strip() + if full: + parts.append(full) + for k in ("path", "slug"): + v = row.get(k) + if v and v != full: + parts.append(str(v)) + elif entity_type == "data_connectors": + for k in ("name", "namespace", "description"): + v = row.get(k) + if v: + parts.append(str(v)) + storage_type = row.get("storage_type") + if storage_type: + parts.append(f"Storage: {storage_type}") + keywords = _decode_json(row.get("keywords_json")) + if keywords: + parts.append("Keywords: " + ", ".join(str(k) for k in keywords)) + if not parts: + return None + return "\n\n".join(parts) + + +def _row_to_payload(entity_type: str, row: dict[str, Any]) -> dict[str, Any]: + pk = _PK_BY_ENTITY[entity_type] + payload: dict[str, Any] = { + "entity_type": entity_type, + "entity_id": str(row[pk]), + } + if entity_type == "projects": + payload.update( + { + "name": row.get("name"), + "slug": row.get("slug"), + "namespace": row.get("namespace"), + "path": row.get("path"), + "visibility": row.get("visibility"), + }, + ) + elif entity_type == "groups": + payload.update({"name": row.get("name"), "slug": row.get("slug")}) + elif entity_type == "users": + payload.update( + { + "first_name": row.get("first_name"), + "last_name": row.get("last_name"), + "slug": row.get("slug"), + "path": row.get("path"), + }, + ) + elif entity_type == "data_connectors": + payload.update( + { + "name": row.get("name"), + "slug": row.get("slug"), + "namespace": row.get("namespace"), + "path": row.get("path"), + "storage_type": row.get("storage_type"), + "storage_provider": row.get("storage_provider"), + "visibility": row.get("visibility"), + }, + ) + return payload + + +async def _embed_entity_async( + *, + config: RenkulabIndexConfig, + store: RenkulabStore, + entity_type: str, + limit: int | None, +) -> int: + client = RCPEmbeddingClient(config) # type: ignore[arg-type] + qdrant = QdrantStore(config) # type: ignore[arg-type] + collection = COLLECTION_BY_ENTITY[entity_type] + qdrant.ensure_collection(collection) + + pk = _PK_BY_ENTITY[entity_type] + pending: list[tuple[str, dict[str, Any], Chunk]] = [] + total = 0 + + async def flush() -> None: + nonlocal total + if not pending: + return + texts = [c.text for _, _, c in pending] + vectors = await client.embed_all(texts) + ids: list[str] = [] + payloads: list[dict[str, Any]] = [] + for entity_id, base_payload, chunk in pending: + cid = _chunk_id(entity_type, entity_id, chunk.index) + ids.append(cid) + payloads.append({**base_payload, "chunk_index": chunk.index}) + store.upsert_chunk( + chunk_id=cid, + entity_type=entity_type, + entity_id=entity_id, + chunk_index=chunk.index, + text=chunk.text, + token_count=chunk.token_count, + vector_id=cid, + ) + qdrant.upsert_points( + collection, + ids=ids, + vectors=vectors, + payloads=payloads, + ) + total += len(pending) + pending.clear() + + rows_seen = 0 + for row in store.stream_rows_for_embedding(entity_type, limit=limit): + rows_seen += 1 + text = _row_to_text(entity_type, row) + if not text: + continue + chunks = chunk_text( + text, + chunk_tokens=config.chunking.size_tokens, + overlap=config.chunking.overlap_tokens, + ) + if not chunks: + continue + base_payload = _row_to_payload(entity_type, row) + entity_id = str(row[pk]) + for chunk in chunks: + pending.append((entity_id, base_payload, chunk)) + if len(pending) >= client.batch_size: + await flush() + await flush() + LOGGER.info( + "embed %s complete: rows_seen=%d chunks=%d", + entity_type, + rows_seen, + total, + ) + return total + + +def embed_entities( + *, + config: RenkulabIndexConfig, + store: RenkulabStore, + entity_types: list[str] | None = None, + limit: int | None = None, +) -> dict[str, int]: + """Synchronously embed one or more entity types.""" + targets = entity_types or list(COLLECTION_BY_ENTITY) + summary: dict[str, int] = {} + for et in targets: + if et not in COLLECTION_BY_ENTITY: + message = ( + f"Unknown entity_type: {et!r}. " + f"Known: {sorted(COLLECTION_BY_ENTITY)}" + ) + raise ValueError(message) + summary[et] = asyncio.run( + _embed_entity_async( + config=config, + store=store, + entity_type=et, + limit=limit, + ), + ) + return summary diff --git a/src/index/renkulab/ingest/__init__.py b/src/index/renkulab/ingest/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/renkulab/ingest/pipeline.py b/src/index/renkulab/ingest/pipeline.py new file mode 100644 index 0000000..3869c8f --- /dev/null +++ b/src/index/renkulab/ingest/pipeline.py @@ -0,0 +1,303 @@ +"""Top-level RenkuLab ingest pipeline. + +Drives the four entity streams (projects, groups, users, data_connectors) +plus the two member streams. State is checkpointed per-entity to +`/ingest_.json` so partial runs are resumable. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from src.index.renkulab.ingest.projector import ( + project_data_connector, + project_group, + project_member, + project_project, + project_user, +) +from src.index.renkulab.ingest.renku_client import RenkulabClient +from src.index.renkulab.ingest.scope import Scope, matches + +if TYPE_CHECKING: + from src.index.renkulab.config import RenkulabIndexConfig + from src.index.renkulab.storage.duckdb_store import RenkulabStore + +LOGGER = logging.getLogger(__name__) + + +def _state_path(config: RenkulabIndexConfig, scope_name: str) -> Path: + return config.paths.state_dir / f"ingest_{scope_name}.json" + + +def _load_state(config: RenkulabIndexConfig, scope_name: str) -> dict[str, Any]: + path = _state_path(config, scope_name) + if not path.exists(): + return {"completed": []} + try: + return json.loads(path.read_text(encoding="utf-8")) + except json.JSONDecodeError: + LOGGER.warning("could not parse %s; restarting state", path) + return {"completed": []} + + +def _save_state( + config: RenkulabIndexConfig, + scope_name: str, + state: dict[str, Any], +) -> None: + _state_path(config, scope_name).write_text( + json.dumps(state, indent=2, ensure_ascii=False), + encoding="utf-8", + ) + + +async def _ingest_projects( + *, + client: RenkulabClient, + store: RenkulabStore, + scope: Scope, + limit: int | None, +) -> int: + n = 0 + async for raw in client.iter_projects(limit=limit): + if not matches(scope, raw): + continue + row = project_project(raw) + if row is None: + continue + store.upsert_project(row, raw=raw) + n += 1 + if n % 200 == 0: + LOGGER.info("projects ingested: %d", n) + LOGGER.info("projects done: %d persisted", n) + return n + + +async def _ingest_groups( + *, + client: RenkulabClient, + store: RenkulabStore, + scope: Scope, + limit: int | None, +) -> int: + n = 0 + async for raw in client.iter_groups(limit=limit): + if not matches(scope, raw): + continue + row = project_group(raw) + if row is None: + continue + store.upsert_group(row, raw=raw) + n += 1 + if n % 200 == 0: + LOGGER.info("groups ingested: %d", n) + LOGGER.info("groups done: %d persisted", n) + return n + + +async def _ingest_data_connectors( + *, + client: RenkulabClient, + store: RenkulabStore, + scope: Scope, + limit: int | None, +) -> int: + n = 0 + async for raw in client.iter_data_connectors(limit=limit): + if not matches(scope, raw): + continue + row = project_data_connector(raw) + if row is None: + continue + store.upsert_data_connector(row, raw=raw) + n += 1 + if n % 200 == 0: + LOGGER.info("data_connectors ingested: %d", n) + LOGGER.info("data_connectors done: %d persisted", n) + return n + + +async def _ingest_users_via_search( + *, + client: RenkulabClient, + store: RenkulabStore, + scope: Scope, + limit: int | None, +) -> int: + """Harvest public user records via `/search/query?q=type:User`. + + The dedicated `/users` endpoint requires auth; the search index is open + and exposes the same surface (path, slug, first/last name). + """ + n = 0 + async for raw in client.iter_search("type:User", limit=limit): + if not matches(scope, raw): + continue + row = project_user(raw) + if row is None: + continue + store.upsert_user(row, raw=raw) + n += 1 + if n % 1000 == 0: + LOGGER.info("users ingested: %d", n) + LOGGER.info("users done: %d persisted", n) + return n + + +async def _ingest_group_members( + *, + client: RenkulabClient, + store: RenkulabStore, +) -> int: + """Pull (group, user, role) for every persisted group. + + Group members are also persisted as user rows so that membership rows + always reference an existing user even if the user wasn't seen in the + `type:User` search pass. + """ + n = 0 + for group_id in store.list_group_ids(): + # We need the slug to call /groups/{slug}/members. + info = store.fetch_entity("groups", group_id) + if info is None: + continue + slug = info.get("slug") + if not slug: + continue + members = await client.fetch_group_members(slug) + for raw in members: + mrow = project_member(raw) + if mrow is None: + continue + # Persist a stub user row so referential joins work. + store.upsert_user( + { + "user_id": mrow["user_id"], + "slug": mrow.get("slug"), + "path": mrow.get("path"), + "first_name": mrow.get("first_name"), + "last_name": mrow.get("last_name"), + }, + raw=raw, + ) + store.upsert_group_members( + group_id, + [{"user_id": project_member(m)["user_id"], "role": m.get("role")} + for m in members + if project_member(m) is not None], + ) + n += len(members) + LOGGER.info("group_members done: %d edges persisted", n) + return n + + +async def _ingest_project_members( + *, + client: RenkulabClient, + store: RenkulabStore, +) -> int: + n = 0 + for project_id in store.list_project_ids(): + members = await client.fetch_project_members(project_id) + for raw in members: + mrow = project_member(raw) + if mrow is None: + continue + store.upsert_user( + { + "user_id": mrow["user_id"], + "slug": mrow.get("slug"), + "path": mrow.get("path"), + "first_name": mrow.get("first_name"), + "last_name": mrow.get("last_name"), + }, + raw=raw, + ) + store.upsert_project_members( + project_id, + [{"user_id": project_member(m)["user_id"], "role": m.get("role")} + for m in members + if project_member(m) is not None], + ) + n += len(members) + if n and n % 500 == 0: + LOGGER.info("project_members ingested: %d", n) + LOGGER.info("project_members done: %d edges persisted", n) + return n + + +async def _ingest_async( + *, + config: RenkulabIndexConfig, + store: RenkulabStore, + scope: Scope, + limit: int | None, + refresh: bool, + only: set[str] | None, +) -> dict[str, int]: + state = _load_state(config, scope.name) if not refresh else {"completed": []} + completed = set(state.get("completed") or []) + client = RenkulabClient(config) + summary: dict[str, int] = {} + + flags = config.entities + + plan: list[tuple[str, bool, Any]] = [ + ("projects", flags.projects, _ingest_projects), + ("groups", flags.groups, _ingest_groups), + ("data_connectors", flags.data_connectors, _ingest_data_connectors), + ("users", flags.users, _ingest_users_via_search), + ("group_members", flags.group_members, _ingest_group_members), + ("project_members", flags.project_members, _ingest_project_members), + ] + + for name, enabled, fn in plan: + if not enabled: + continue + if only and name not in only: + continue + if name in completed and not refresh: + LOGGER.info("scope=%s entity=%s already completed; skipping", scope.name, name) + continue + if name in {"group_members", "project_members"}: + count = await fn(client=client, store=store) + else: + count = await fn( + client=client, + store=store, + scope=scope, + limit=limit, + ) + summary[name] = count + completed.add(name) + state["completed"] = sorted(completed) + _save_state(config, scope.name, state) + + return summary + + +def ingest_all( + *, + config: RenkulabIndexConfig, + store: RenkulabStore, + scope: Scope, + limit: int | None = None, + refresh: bool = False, + only: set[str] | None = None, +) -> dict[str, int]: + """Synchronous CLI entrypoint.""" + return asyncio.run( + _ingest_async( + config=config, + store=store, + scope=scope, + limit=limit, + refresh=refresh, + only=only, + ), + ) diff --git a/src/index/renkulab/ingest/projector.py b/src/index/renkulab/ingest/projector.py new file mode 100644 index 0000000..62b1a3f --- /dev/null +++ b/src/index/renkulab/ingest/projector.py @@ -0,0 +1,162 @@ +"""Project Renku JSON payloads to flat DuckDB rows. + +The Renku data API uses snake_case for resource endpoints (`/projects`, +`/data_connectors`) and camelCase for the `/search/query` index. Both +shapes are normalised here. +""" + +from __future__ import annotations + +import re +from datetime import datetime +from typing import Any + +_TIMESTAMP_RE = re.compile(r"^(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(?:\.\d+)?)") + + +def _parse_timestamp(raw: Any) -> datetime | None: + if not raw: + return None + if isinstance(raw, datetime): + return raw + if not isinstance(raw, str): + return None + s = raw.strip().replace("Z", "+00:00") + try: + return datetime.fromisoformat(s) + except ValueError: + m = _TIMESTAMP_RE.match(s) + if not m: + return None + try: + return datetime.fromisoformat(m.group(1)) + except ValueError: + return None + + +def _namespace_path(item: dict[str, Any]) -> str | None: + ns = item.get("namespace") + if isinstance(ns, str): + return ns + if isinstance(ns, dict): + return ns.get("path") or ns.get("slug") + return None + + +def project_project(item: dict[str, Any]) -> dict[str, Any] | None: + project_id = item.get("id") + if not project_id: + return None + return { + "project_id": str(project_id), + "slug": item.get("slug"), + "name": item.get("name"), + "namespace": _namespace_path(item), + "path": item.get("path"), + "description": item.get("description") or None, + "visibility": item.get("visibility"), + "is_template": item.get("is_template"), + "keywords": item.get("keywords") or [], + "repositories": item.get("repositories") or [], + "created_by": _created_by_id(item.get("created_by") or item.get("createdBy")), + "creation_date": _parse_timestamp( + item.get("creation_date") or item.get("creationDate"), + ), + "updated_at": _parse_timestamp(item.get("updated_at") or item.get("updatedAt")), + } + + +def project_group(item: dict[str, Any]) -> dict[str, Any] | None: + group_id = item.get("id") + if not group_id: + return None + return { + "group_id": str(group_id), + "slug": item.get("slug"), + "name": item.get("name"), + "description": item.get("description") or None, + "created_by": _created_by_id(item.get("created_by") or item.get("createdBy")), + "creation_date": _parse_timestamp( + item.get("creation_date") or item.get("creationDate"), + ), + } + + +def project_user(item: dict[str, Any]) -> dict[str, Any] | None: + user_id = item.get("id") + if not user_id: + return None + return { + "user_id": str(user_id), + "slug": item.get("slug"), + "path": item.get("path"), + "first_name": item.get("first_name") or item.get("firstName"), + "last_name": item.get("last_name") or item.get("lastName"), + } + + +def project_data_connector(item: dict[str, Any]) -> dict[str, Any] | None: + dc_id = item.get("id") + if not dc_id: + return None + + storage = item.get("storage") or {} + storage_type = ( + storage.get("storage_type") + or storage.get("storageType") + or item.get("storageType") + ) + config_block: dict[str, Any] = {} + raw_cfg = storage.get("configuration") + if isinstance(raw_cfg, dict): + config_block = raw_cfg + storage_provider = config_block.get("provider") or item.get("storageProvider") + source_path = storage.get("source_path") or storage.get("sourcePath") + target_path = storage.get("target_path") or storage.get("targetPath") + readonly = storage.get("readonly") + if readonly is None: + readonly = item.get("readonly") + + return { + "data_connector_id": str(dc_id), + "slug": item.get("slug"), + "name": item.get("name"), + "namespace": _namespace_path(item), + "path": item.get("path"), + "description": item.get("description") or None, + "visibility": item.get("visibility"), + "storage_type": storage_type, + "storage_provider": storage_provider, + "source_path": source_path, + "target_path": target_path, + "readonly": readonly, + "keywords": item.get("keywords") or [], + "created_by": _created_by_id(item.get("created_by") or item.get("createdBy")), + "creation_date": _parse_timestamp( + item.get("creation_date") or item.get("creationDate"), + ), + } + + +def project_member(item: dict[str, Any]) -> dict[str, Any] | None: + user_id = item.get("id") or item.get("user_id") or item.get("userId") + if not user_id: + return None + return { + "user_id": str(user_id), + "first_name": item.get("first_name") or item.get("firstName"), + "last_name": item.get("last_name") or item.get("lastName"), + "path": item.get("namespace") or item.get("path"), + "slug": item.get("namespace") or item.get("slug"), + "role": item.get("role"), + } + + +def _created_by_id(raw: Any) -> str | None: + if raw is None: + return None + if isinstance(raw, str): + return raw + if isinstance(raw, dict): + return raw.get("id") or raw.get("user_id") or raw.get("userId") + return None diff --git a/src/index/renkulab/ingest/renku_client.py b/src/index/renkulab/ingest/renku_client.py new file mode 100644 index 0000000..ff8c3e5 --- /dev/null +++ b/src/index/renkulab/ingest/renku_client.py @@ -0,0 +1,233 @@ +"""Async RenkuLab REST client with rate limiting and retries. + +Targets the public data API at `https://renkulab.io/api/data`. Three of +the listing endpoints (`/projects`, `/groups`, `/data_connectors`) are +unauthenticated and use header-based pagination (`page`, `total-pages`, +`per-page` response headers). The `/users` listing requires auth, so we +harvest users via the public `/search/query?q=type:User` endpoint instead. +""" + +from __future__ import annotations + +import asyncio +import logging +import time +from typing import TYPE_CHECKING, Any + +import httpx +from tenacity import ( + retry, + retry_if_exception, + stop_after_attempt, + wait_exponential, +) + +if TYPE_CHECKING: + from collections.abc import AsyncIterator + + from src.index.renkulab.config import RenkulabIndexConfig + +LOGGER = logging.getLogger(__name__) + +_RETRYABLE_STATUS = {429, 500, 502, 503, 504} + + +def _is_retryable(exc: BaseException) -> bool: + if isinstance(exc, httpx.TimeoutException): + return True + if isinstance(exc, httpx.HTTPStatusError): + return exc.response.status_code in _RETRYABLE_STATUS + return False + + +class _RateLimiter: + """Simple monotonic-spaced async limiter: at most N events per minute.""" + + def __init__(self, per_minute: int) -> None: + if per_minute <= 0: + message = "rate_per_minute must be positive" + raise ValueError(message) + self._min_interval = 60.0 / per_minute + self._last = 0.0 + self._lock = asyncio.Lock() + + async def acquire(self) -> None: + async with self._lock: + now = time.monotonic() + wait = self._min_interval - (now - self._last) + if wait > 0: + await asyncio.sleep(wait) + self._last = time.monotonic() + + +class RenkulabClient: + """Async wrapper for selected RenkuLab data API endpoints.""" + + def __init__(self, config: RenkulabIndexConfig) -> None: + self._config = config + self._base = config.renkulab.base_url.rstrip("/") + self._headers: dict[str, str] = {"Accept": "application/json"} + if config.renkulab.token: + self._headers["Authorization"] = f"Bearer {config.renkulab.token}" + self._limiter = _RateLimiter(config.renkulab.rate_per_minute) + self._page_size = config.renkulab.page_size + + @property + def page_size(self) -> int: + return self._page_size + + async def _get( + self, + client: httpx.AsyncClient, + path: str, + params: dict[str, Any], + ) -> httpx.Response: + @retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=1, min=2, max=30), + retry=retry_if_exception(_is_retryable), + reraise=True, + ) + async def _call() -> httpx.Response: + await self._limiter.acquire() + response = await client.get( + f"{self._base}{path}", + params=params, + headers=self._headers, + timeout=httpx.Timeout(60.0), + follow_redirects=True, + ) + try: + response.raise_for_status() + except httpx.HTTPStatusError as exc: + if not _is_retryable(exc): + body = response.text[:500] + LOGGER.error( + "Renku non-retryable %d on %s: %s", + response.status_code, + path, + body, + ) + raise + return response + + return await _call() + + async def _iter_paged( + self, + path: str, + *, + extra_params: dict[str, Any] | None = None, + limit: int | None = None, + ) -> AsyncIterator[dict[str, Any]]: + """Header-paginated list endpoints (`/projects`, `/groups`, `/data_connectors`).""" + async with httpx.AsyncClient() as client: + page = 1 + yielded = 0 + while True: + params: dict[str, Any] = { + "per_page": self._page_size, + "page": page, + } + if extra_params: + params.update(extra_params) + response = await self._get(client, path, params) + items = response.json() or [] + if not items: + return + for item in items: + yield item + yielded += 1 + if limit is not None and yielded >= limit: + return + total_pages_hdr = response.headers.get("total-pages") + try: + total_pages = int(total_pages_hdr) if total_pages_hdr else None + except ValueError: + total_pages = None + if total_pages is not None and page >= total_pages: + return + page += 1 + + def iter_projects( + self, + *, + limit: int | None = None, + ) -> AsyncIterator[dict[str, Any]]: + return self._iter_paged("/projects", limit=limit) + + def iter_groups( + self, + *, + limit: int | None = None, + ) -> AsyncIterator[dict[str, Any]]: + return self._iter_paged("/groups", limit=limit) + + def iter_data_connectors( + self, + *, + limit: int | None = None, + ) -> AsyncIterator[dict[str, Any]]: + return self._iter_paged("/data_connectors", limit=limit) + + async def iter_search( + self, + query: str, + *, + limit: int | None = None, + ) -> AsyncIterator[dict[str, Any]]: + """`/search/query` uses an envelope (`items`, `pagingInfo`) instead of headers.""" + async with httpx.AsyncClient() as client: + page = 1 + yielded = 0 + while True: + params = { + "q": query, + "per_page": self._page_size, + "page": page, + } + response = await self._get(client, "/search/query", params) + payload = response.json() or {} + items = payload.get("items") or [] + if not items: + return + for item in items: + yield item + yielded += 1 + if limit is not None and yielded >= limit: + return + paging = payload.get("pagingInfo") or {} + total_pages = paging.get("totalPages") + if total_pages is not None and page >= int(total_pages): + return + page += 1 + + async def fetch_group_members(self, slug: str) -> list[dict[str, Any]]: + async with httpx.AsyncClient() as client: + try: + response = await self._get( + client, + f"/groups/{slug}/members", + {}, + ) + except httpx.HTTPStatusError as exc: + if exc.response.status_code in (401, 403, 404): + return [] + raise + body = response.json() or [] + return list(body) if isinstance(body, list) else [] + + async def fetch_project_members(self, project_id: str) -> list[dict[str, Any]]: + async with httpx.AsyncClient() as client: + try: + response = await self._get( + client, + f"/projects/{project_id}/members", + {}, + ) + except httpx.HTTPStatusError as exc: + if exc.response.status_code in (401, 403, 404): + return [] + raise + body = response.json() or [] + return list(body) if isinstance(body, list) else [] diff --git a/src/index/renkulab/ingest/scope.py b/src/index/renkulab/ingest/scope.py new file mode 100644 index 0000000..86a583a --- /dev/null +++ b/src/index/renkulab/ingest/scope.py @@ -0,0 +1,63 @@ +"""Post-fetch scope filter for RenkuLab. + +Renku has no first-class community concept (yet), so the `epfl` and +`switzerland` scopes match keyword substrings against namespace, path, +keywords, and repository URLs. Scope `all` keeps everything. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from src.index.renkulab.config import RenkulabIndexConfig + + +@dataclass(slots=True, frozen=True) +class Scope: + name: str + keywords: tuple[str, ...] + + +def resolve_scope(name: str, config: RenkulabIndexConfig) -> Scope: + n = (name or "all").lower() + if n == "all": + return Scope(name="all", keywords=()) + if n == "epfl": + return Scope(name="epfl", keywords=tuple(s.lower() for s in config.scope.epfl_keywords)) + if n == "switzerland": + return Scope( + name="switzerland", + keywords=tuple(s.lower() for s in config.scope.switzerland_keywords), + ) + message = f"Unknown scope: {name!r}. Expected one of: all, epfl, switzerland" + raise ValueError(message) + + +def _haystack(item: dict[str, Any]) -> str: + parts: list[str] = [] + for key in ("namespace", "path", "slug"): + v = item.get(key) + if isinstance(v, str): + parts.append(v) + kw = item.get("keywords") + if isinstance(kw, list): + parts.extend(str(k) for k in kw) + repos = item.get("repositories") + if isinstance(repos, list): + parts.extend(str(r) for r in repos) + ns = item.get("namespace") + if isinstance(ns, dict): + for key in ("path", "slug"): + v = ns.get(key) + if isinstance(v, str): + parts.append(v) + return " ".join(parts).lower() + + +def matches(scope: Scope, item: dict[str, Any]) -> bool: + if not scope.keywords: + return True + hay = _haystack(item) + return any(kw in hay for kw in scope.keywords) diff --git a/src/index/renkulab/paths.py b/src/index/renkulab/paths.py new file mode 100644 index 0000000..bf6ba14 --- /dev/null +++ b/src/index/renkulab/paths.py @@ -0,0 +1,63 @@ +"""Filesystem layout for RenkuLab index artifacts. + +Single source of truth for paths under `/renkulab/`. +Mirrors `src/index/zenodo/paths.py`. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path + +DEFAULT_INDEX_DATA_DIR = Path("data/index") + + +def _resolve_repo_root() -> Path: + return Path(__file__).resolve().parents[3] + + +def _resolve_index_data_dir() -> Path: + raw = os.getenv("INDEX_DATA_DIR") + if raw and raw.strip(): + candidate = Path(raw.strip()).expanduser() + if candidate.is_absolute(): + return candidate + return _resolve_repo_root() / candidate + return _resolve_repo_root() / DEFAULT_INDEX_DATA_DIR + + +@dataclass(slots=True, frozen=True) +class RenkulabPaths: + root: Path + + @property + def duckdb_dir(self) -> Path: + return self.root / "duckdb" + + @property + def duckdb_path(self) -> Path: + return self.duckdb_dir / "renkulab.duckdb" + + @property + def state_dir(self) -> Path: + return self.root / "state" + + @property + def cache_dir(self) -> Path: + return self.root / "cache" + + @property + def logs_dir(self) -> Path: + return self.root / "logs" + + +def get_renkulab_paths() -> RenkulabPaths: + """Resolve `/renkulab/` and ensure subdirectories exist.""" + root = _resolve_index_data_dir() / "renkulab" + paths = RenkulabPaths(root=root) + paths.duckdb_dir.mkdir(parents=True, exist_ok=True) + paths.state_dir.mkdir(parents=True, exist_ok=True) + paths.cache_dir.mkdir(parents=True, exist_ok=True) + paths.logs_dir.mkdir(parents=True, exist_ok=True) + return paths diff --git a/src/index/renkulab/retrieval/__init__.py b/src/index/renkulab/retrieval/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/renkulab/retrieval/semantic.py b/src/index/renkulab/retrieval/semantic.py new file mode 100644 index 0000000..2f5c806 --- /dev/null +++ b/src/index/renkulab/retrieval/semantic.py @@ -0,0 +1,149 @@ +"""Semantic retrieval over the RenkuLab index. + +Embed → Qdrant search (across one or all entity collections) → RCP +rerank → DuckDB hydrate. Reuses the openalex RCP + Qdrant clients +directly; same duck-typed config trick as zenodo. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.index.renkulab.embed.pipeline import COLLECTION_BY_ENTITY +from src.index.renkulab.storage.duckdb_store import RenkulabStore + +if TYPE_CHECKING: + from src.index.renkulab.config import RenkulabIndexConfig + +LOGGER = logging.getLogger(__name__) + + +def _payload_to_doc(payload: dict[str, Any]) -> str: + for key in ("name", "first_name", "slug", "path"): + v = payload.get(key) + if v: + return str(v) + return json.dumps(payload, ensure_ascii=False) + + +def _hydrate( + store: RenkulabStore, + entity_type: str, + entity_id: str, +) -> dict[str, Any] | None: + return store.fetch_entity(entity_type, entity_id) + + +def _resolve_collections(entity_types: list[str] | None) -> list[tuple[str, str]]: + if not entity_types: + return [(et, COLLECTION_BY_ENTITY[et]) for et in COLLECTION_BY_ENTITY] + out: list[tuple[str, str]] = [] + for et in entity_types: + if et not in COLLECTION_BY_ENTITY: + message = ( + f"Unknown entity_type: {et!r}. " + f"Known: {sorted(COLLECTION_BY_ENTITY)}" + ) + raise ValueError(message) + out.append((et, COLLECTION_BY_ENTITY[et])) + return out + + +async def _async_search( + *, + config: RenkulabIndexConfig, + query: str, + entity_types: list[str] | None, + top_k: int, + candidate_k: int, + filter_payload: dict[str, Any] | None, + store: RenkulabStore, +) -> list[dict[str, Any]]: + embed = RCPEmbeddingClient(config) # type: ignore[arg-type] + qdrant = QdrantStore(config) # type: ignore[arg-type] + rerank = RCPRerankerClient(config) # type: ignore[arg-type] + + [query_vec] = await embed.embed_all([query]) + + collections = _resolve_collections(entity_types) + candidates: list[dict[str, Any]] = [] + for entity_type, collection in collections: + try: + hits = qdrant.search( + collection, + query_vector=query_vec, + top_k=candidate_k, + filter_payload=filter_payload, + ) + except Exception as exc: # noqa: BLE001 + LOGGER.warning("search %s failed: %s", collection, exc) + continue + for hit in hits: + payload = dict(hit.get("payload") or {}) + payload.setdefault("entity_type", entity_type) + candidates.append({**hit, "payload": payload}) + + if not candidates: + return [] + + candidates.sort(key=lambda h: h.get("score", 0.0), reverse=True) + candidates = candidates[: max(candidate_k, top_k)] + + docs = [_payload_to_doc(c["payload"]) for c in candidates] + reranked = await rerank.rerank(query, docs, top_n=top_k) + if not reranked: + ordered = candidates[:top_k] + else: + ordered = [ + {**candidates[r["index"]], "rerank_score": r["relevance_score"]} + for r in reranked + ] + + hydrated: list[dict[str, Any]] = [] + for hit in ordered: + payload = hit["payload"] or {} + entity_type = payload.get("entity_type") + entity_id = payload.get("entity_id") + if not entity_type or not entity_id: + continue + hydrated.append( + { + "id": hit["id"], + "vector_score": hit["score"], + "rerank_score": hit.get("rerank_score"), + "payload": payload, + "entity": _hydrate(store, str(entity_type), str(entity_id)), + }, + ) + return hydrated + + +def semantic_search( + *, + config: RenkulabIndexConfig, + query: str, + entity_types: list[str] | None = None, + top_k: int = 10, + candidate_k: int = 50, + filter_payload: dict[str, Any] | None = None, + store: RenkulabStore | None = None, +) -> list[dict[str, Any]]: + if store is None: + store = RenkulabStore.open() + return asyncio.run( + _async_search( + config=config, + query=query, + entity_types=entity_types, + top_k=top_k, + candidate_k=candidate_k, + filter_payload=filter_payload, + store=store, + ), + ) diff --git a/src/index/renkulab/retrieval/sql.py b/src/index/renkulab/retrieval/sql.py new file mode 100644 index 0000000..64823cd --- /dev/null +++ b/src/index/renkulab/retrieval/sql.py @@ -0,0 +1,146 @@ +"""Read-only SQL surface over the RenkuLab DuckDB. + +Two entrypoints: + +- `run_predefined()` — parametrized canned queries. +- `run_adhoc()` — guarded SELECT/WITH only, with a forbidden-keyword regex. +""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index.renkulab.storage.duckdb_store import RenkulabStore + +INVALID_QUERY_PREFIX_ERROR = "Only SELECT/WITH queries are allowed" +FORBIDDEN_KEYWORD_ERROR = "Forbidden keyword in query: {kw}" + +_ALLOWED_PREFIXES = ("select", "with") +_FORBIDDEN_KEYWORDS = ( + "attach", "copy", "pragma", "install", "load", "export", "import", + "create", "drop", "alter", "insert", "update", "delete", "truncate", +) +_KEYWORD_RE = re.compile(r"\b(" + "|".join(_FORBIDDEN_KEYWORDS) + r")\b", re.IGNORECASE) + + +def _validate_adhoc(sql: str) -> None: + stripped = sql.strip().lstrip("(").lstrip() + lowered = stripped.lower() + if not any(lowered.startswith(p) for p in _ALLOWED_PREFIXES): + raise ValueError(INVALID_QUERY_PREFIX_ERROR) + match = _KEYWORD_RE.search(stripped) + if match: + raise ValueError(FORBIDDEN_KEYWORD_ERROR.format(kw=match.group(1).upper())) + + +PREDEFINED_QUERIES: dict[str, str] = { + "count_by_entity": ( + "SELECT 'projects' AS entity, COUNT(*) AS n FROM projects " + "UNION ALL SELECT 'groups', COUNT(*) FROM groups " + "UNION ALL SELECT 'users', COUNT(*) FROM users " + "UNION ALL SELECT 'data_connectors', COUNT(*) FROM data_connectors " + "UNION ALL SELECT 'group_members', COUNT(*) FROM group_members " + "UNION ALL SELECT 'project_members', COUNT(*) FROM project_members " + "UNION ALL SELECT 'chunks', COUNT(*) FROM chunks" + ), + "projects_by_visibility": ( + "SELECT visibility, COUNT(*) AS n FROM projects " + "GROUP BY visibility ORDER BY n DESC" + ), + "data_connectors_by_storage_type": ( + "SELECT storage_type, COUNT(*) AS n FROM data_connectors " + "GROUP BY storage_type ORDER BY n DESC" + ), + "top_groups_by_member_count": ( + "SELECT g.group_id, g.slug, g.name, COUNT(gm.user_id) AS members " + "FROM groups g " + "LEFT JOIN group_members gm ON gm.group_id = g.group_id " + "GROUP BY g.group_id, g.slug, g.name " + "ORDER BY members DESC LIMIT $limit" + ), + "top_groups_by_project_count": ( + "SELECT g.slug AS group_slug, COUNT(*) AS projects " + "FROM projects p JOIN groups g " + " ON p.namespace = g.slug " + "GROUP BY g.slug ORDER BY projects DESC LIMIT $limit" + ), + "recent_projects": ( + "SELECT project_id, slug, name, namespace, creation_date, visibility " + "FROM projects " + "WHERE creation_date IS NOT NULL " + "ORDER BY creation_date DESC LIMIT $limit" + ), + "data_connectors_by_namespace": ( + "SELECT namespace, COUNT(*) AS n FROM data_connectors " + "WHERE namespace IS NOT NULL " + "GROUP BY namespace ORDER BY n DESC LIMIT $limit" + ), + "user_projects": ( + "SELECT p.project_id, p.slug, p.name, p.namespace, pm.role " + "FROM project_members pm JOIN projects p " + " ON p.project_id = pm.project_id " + "WHERE pm.user_id = $user_id " + "ORDER BY p.creation_date DESC NULLS LAST LIMIT $limit" + ), + "user_groups": ( + "SELECT g.group_id, g.slug, g.name, gm.role " + "FROM group_members gm JOIN groups g " + " ON g.group_id = gm.group_id " + "WHERE gm.user_id = $user_id " + "ORDER BY g.creation_date DESC NULLS LAST LIMIT $limit" + ), + "projects_by_keyword": ( + "SELECT project_id, slug, name, namespace " + "FROM projects " + "WHERE list_contains(CAST(keywords_json AS VARCHAR[]), $keyword) " + "ORDER BY creation_date DESC NULLS LAST LIMIT $limit" + ), +} + + +def _row_to_dict(cur: Any) -> list[dict[str, Any]]: + cols = [d[0] for d in cur.description] if cur.description else [] + return [dict(zip(cols, r, strict=False)) for r in cur.fetchall()] + + +def _execute( + sql: str, + params: dict[str, Any] | None, + store: RenkulabStore | None, +) -> list[dict[str, Any]]: + owned = False + if store is None: + store = RenkulabStore.open() + owned = True + try: + cur = store.connect().execute(sql, params or {}) + return _row_to_dict(cur) + finally: + if owned: + store.close() + + +def run_adhoc( + sql: str, + params: dict[str, Any] | None = None, + *, + store: RenkulabStore | None = None, +) -> list[dict[str, Any]]: + _validate_adhoc(sql) + return _execute(sql, params, store) + + +def run_predefined( + name: str, + params: dict[str, Any] | None = None, + *, + store: RenkulabStore | None = None, +) -> list[dict[str, Any]]: + if name not in PREDEFINED_QUERIES: + message = ( + f"Unknown predefined query: {name!r}. " + f"Known: {sorted(PREDEFINED_QUERIES)}" + ) + raise ValueError(message) + return _execute(PREDEFINED_QUERIES[name], params, store) diff --git a/src/index/renkulab/storage/__init__.py b/src/index/renkulab/storage/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/renkulab/storage/duckdb_store.py b/src/index/renkulab/storage/duckdb_store.py new file mode 100644 index 0000000..b006eae --- /dev/null +++ b/src/index/renkulab/storage/duckdb_store.py @@ -0,0 +1,340 @@ +"""DuckDB lifecycle, schema bootstrap, and upsert helpers for RenkuLab.""" + +from __future__ import annotations + +import json +import logging +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import duckdb + +from src.index.renkulab.paths import get_renkulab_paths + +if TYPE_CHECKING: + from collections.abc import Iterable, Iterator + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + +EMBEDDABLE_ENTITY_TYPES = {"projects", "groups", "users", "data_connectors"} + +# Map entity_type → (table, primary key column). +_ENTITY_PK: dict[str, tuple[str, str]] = { + "projects": ("projects", "project_id"), + "groups": ("groups", "group_id"), + "users": ("users", "user_id"), + "data_connectors": ("data_connectors", "data_connector_id"), +} + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +class RenkulabStore: + """Thin DuckDB wrapper for the RenkuLab schema. `bootstrap()` is idempotent.""" + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open(cls, db_path: Path | None = None) -> RenkulabStore: + if db_path is None: + db_path = get_renkulab_paths().duckdb_path + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + self.connect().execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + # ---- Upserts --------------------------------------------------------- + + def upsert_project(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + sql = ( + "INSERT INTO projects " + "(project_id, slug, name, namespace, path, description, visibility, " + " is_template, keywords_json, repositories_json, created_by, " + " creation_date, updated_at, raw, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (project_id) DO UPDATE SET " + " slug = excluded.slug, name = excluded.name, " + " namespace = excluded.namespace, path = excluded.path, " + " description = excluded.description, " + " visibility = excluded.visibility, " + " is_template = excluded.is_template, " + " keywords_json = excluded.keywords_json, " + " repositories_json = excluded.repositories_json, " + " created_by = excluded.created_by, " + " creation_date = excluded.creation_date, " + " updated_at = excluded.updated_at, " + " raw = excluded.raw, ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + row["project_id"], + row.get("slug"), + row.get("name"), + row.get("namespace"), + row.get("path"), + row.get("description"), + row.get("visibility"), + row.get("is_template"), + json.dumps(row.get("keywords") or [], ensure_ascii=False), + json.dumps(row.get("repositories") or [], ensure_ascii=False), + row.get("created_by"), + row.get("creation_date"), + row.get("updated_at"), + json.dumps(raw, ensure_ascii=False), + self._now(), + ], + ) + + def upsert_group(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + sql = ( + "INSERT INTO groups " + "(group_id, slug, name, description, created_by, creation_date, " + " raw, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (group_id) DO UPDATE SET " + " slug = excluded.slug, name = excluded.name, " + " description = excluded.description, " + " created_by = excluded.created_by, " + " creation_date = excluded.creation_date, " + " raw = excluded.raw, ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + row["group_id"], + row.get("slug"), + row.get("name"), + row.get("description"), + row.get("created_by"), + row.get("creation_date"), + json.dumps(raw, ensure_ascii=False), + self._now(), + ], + ) + + def upsert_user(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + sql = ( + "INSERT INTO users " + "(user_id, slug, path, first_name, last_name, raw, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (user_id) DO UPDATE SET " + " slug = excluded.slug, path = excluded.path, " + " first_name = excluded.first_name, " + " last_name = excluded.last_name, " + " raw = excluded.raw, ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + row["user_id"], + row.get("slug"), + row.get("path"), + row.get("first_name"), + row.get("last_name"), + json.dumps(raw, ensure_ascii=False), + self._now(), + ], + ) + + def upsert_data_connector( + self, + row: dict[str, Any], + raw: dict[str, Any], + ) -> None: + sql = ( + "INSERT INTO data_connectors " + "(data_connector_id, slug, name, namespace, path, description, " + " visibility, storage_type, storage_provider, source_path, " + " target_path, readonly, keywords_json, created_by, " + " creation_date, raw, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (data_connector_id) DO UPDATE SET " + " slug = excluded.slug, name = excluded.name, " + " namespace = excluded.namespace, path = excluded.path, " + " description = excluded.description, " + " visibility = excluded.visibility, " + " storage_type = excluded.storage_type, " + " storage_provider = excluded.storage_provider, " + " source_path = excluded.source_path, " + " target_path = excluded.target_path, " + " readonly = excluded.readonly, " + " keywords_json = excluded.keywords_json, " + " created_by = excluded.created_by, " + " creation_date = excluded.creation_date, " + " raw = excluded.raw, ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + row["data_connector_id"], + row.get("slug"), + row.get("name"), + row.get("namespace"), + row.get("path"), + row.get("description"), + row.get("visibility"), + row.get("storage_type"), + row.get("storage_provider"), + row.get("source_path"), + row.get("target_path"), + row.get("readonly"), + json.dumps(row.get("keywords") or [], ensure_ascii=False), + row.get("created_by"), + row.get("creation_date"), + json.dumps(raw, ensure_ascii=False), + self._now(), + ], + ) + + def upsert_group_members( + self, + group_id: str, + members: Iterable[dict[str, Any]], + ) -> None: + conn = self.connect() + for m in members: + user_id = m.get("user_id") or m.get("id") + if not user_id: + continue + conn.execute( + "INSERT INTO group_members (group_id, user_id, role) " + "VALUES (?, ?, ?) " + "ON CONFLICT (group_id, user_id) DO UPDATE SET " + " role = excluded.role", + [group_id, user_id, m.get("role")], + ) + + def upsert_project_members( + self, + project_id: str, + members: Iterable[dict[str, Any]], + ) -> None: + conn = self.connect() + for m in members: + user_id = m.get("user_id") or m.get("id") + if not user_id: + continue + conn.execute( + "INSERT INTO project_members (project_id, user_id, role) " + "VALUES (?, ?, ?) " + "ON CONFLICT (project_id, user_id) DO UPDATE SET " + " role = excluded.role", + [project_id, user_id, m.get("role")], + ) + + def upsert_chunk( + self, + *, + chunk_id: str, + entity_type: str, + entity_id: str, + chunk_index: int, + text: str, + token_count: int, + vector_id: str, + ) -> None: + self.connect().execute( + "INSERT INTO chunks " + "(chunk_id, entity_type, entity_id, chunk_index, text, " + "token_count, vector_id) VALUES (?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (chunk_id) DO UPDATE SET " + "text = excluded.text, token_count = excluded.token_count, " + "vector_id = excluded.vector_id, embedded_at = now()", + [chunk_id, entity_type, entity_id, chunk_index, text, token_count, vector_id], + ) + + # ---- Reads ----------------------------------------------------------- + + def count(self, table: str) -> int: + result = self.connect().execute(f"SELECT count(*) FROM {table}").fetchone() + return int(result[0]) if result else 0 + + def list_group_ids(self) -> list[str]: + cur = self.connect().execute("SELECT group_id FROM groups") + return [str(r[0]) for r in cur.fetchall()] + + def list_project_ids(self) -> list[str]: + cur = self.connect().execute("SELECT project_id FROM projects") + return [str(r[0]) for r in cur.fetchall()] + + def fetch_entity( + self, + entity_type: str, + entity_id: str, + ) -> dict[str, Any] | None: + if entity_type not in _ENTITY_PK: + message = f"Unknown entity_type: {entity_type}" + raise ValueError(message) + table, pk = _ENTITY_PK[entity_type] + cur = self.connect().execute( + f"SELECT * FROM {table} WHERE {pk} = ?", + [entity_id], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + def stream_rows_for_embedding( + self, + entity_type: str, + *, + limit: int | None = None, + ) -> Iterator[dict[str, Any]]: + """Yield rows that need embedding (no chunks yet).""" + if entity_type not in EMBEDDABLE_ENTITY_TYPES: + message = f"Unknown entity_type: {entity_type}" + raise ValueError(message) + table, pk = _ENTITY_PK[entity_type] + sql = ( + f"SELECT t.* FROM {table} t " + "WHERE NOT EXISTS (" + " SELECT 1 FROM chunks c " + f" WHERE c.entity_type = ? AND c.entity_id = t.{pk}" + ")" + ) + params: list[Any] = [entity_type] + if limit is not None: + sql += " LIMIT ?" + params.append(limit) + cur = self.connect().execute(sql, params) + cols = [d[0] for d in cur.description] + # Materialize up front: see equivalent note in the Zenodo store. + rows = cur.fetchall() + for row in rows: + yield dict(zip(cols, row, strict=False)) + + @staticmethod + def _now() -> str: + return datetime.now(tz=timezone.utc).isoformat() diff --git a/src/index/renkulab/storage/schema.sql b/src/index/renkulab/storage/schema.sql new file mode 100644 index 0000000..058e8ca --- /dev/null +++ b/src/index/renkulab/storage/schema.sql @@ -0,0 +1,99 @@ +-- Canonical DuckDB schema for the RenkuLab index module. +-- Idempotent: every statement uses IF NOT EXISTS so re-runs are safe. + +CREATE TABLE IF NOT EXISTS projects ( + project_id TEXT PRIMARY KEY, -- ULID, e.g. "01KN1WYG..." + slug TEXT, + name TEXT, + namespace TEXT, -- "user.name" or "group/project" + path TEXT, -- full namespace + slug path + description TEXT, + visibility TEXT, -- public | private | internal + is_template BOOLEAN, + keywords_json JSON, + repositories_json JSON, -- list of git URLs + created_by TEXT, -- user UUID + creation_date TIMESTAMP, + updated_at TIMESTAMP, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS groups ( + group_id TEXT PRIMARY KEY, -- ULID + slug TEXT, + name TEXT, + description TEXT, + created_by TEXT, + creation_date TIMESTAMP, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS users ( + user_id TEXT PRIMARY KEY, -- UUID + slug TEXT, + path TEXT, -- usually equals slug + first_name TEXT, + last_name TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS data_connectors ( + data_connector_id TEXT PRIMARY KEY, -- ULID + slug TEXT, + name TEXT, + namespace TEXT, -- owning user/group/project path + path TEXT, -- full path + description TEXT, + visibility TEXT, + storage_type TEXT, -- s3 | switchDrive | polybox | doi | ... + storage_provider TEXT, -- e.g. envidat_v1, shared + source_path TEXT, + target_path TEXT, + readonly BOOLEAN, + keywords_json JSON, + created_by TEXT, -- user UUID + creation_date TIMESTAMP, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS group_members ( + group_id TEXT NOT NULL, + user_id TEXT NOT NULL, + role TEXT, -- owner | editor | viewer + PRIMARY KEY (group_id, user_id) +); + +CREATE TABLE IF NOT EXISTS project_members ( + project_id TEXT NOT NULL, + user_id TEXT NOT NULL, + role TEXT, + PRIMARY KEY (project_id, user_id) +); + +-- Free-floating chunks table; entity_type discriminates which table +-- entity_id refers to (projects, groups, users, data_connectors). +CREATE TABLE IF NOT EXISTS chunks ( + chunk_id TEXT PRIMARY KEY, + entity_type TEXT NOT NULL, + entity_id TEXT NOT NULL, + chunk_index INTEGER NOT NULL, + text TEXT NOT NULL, + token_count INTEGER NOT NULL, + vector_id TEXT NOT NULL, + embedded_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS idx_projects_namespace ON projects (namespace); +CREATE INDEX IF NOT EXISTS idx_projects_visibility ON projects (visibility); +CREATE INDEX IF NOT EXISTS idx_projects_creation_date ON projects (creation_date); +CREATE INDEX IF NOT EXISTS idx_groups_slug ON groups (slug); +CREATE INDEX IF NOT EXISTS idx_users_slug ON users (slug); +CREATE INDEX IF NOT EXISTS idx_dc_namespace ON data_connectors (namespace); +CREATE INDEX IF NOT EXISTS idx_dc_storage_type ON data_connectors (storage_type); +CREATE INDEX IF NOT EXISTS idx_chunks_entity ON chunks (entity_type, entity_id); +CREATE INDEX IF NOT EXISTS idx_group_members_user ON group_members (user_id); +CREATE INDEX IF NOT EXISTS idx_project_members_user ON project_members (user_id); diff --git a/src/index/ror/__init__.py b/src/index/ror/__init__.py new file mode 100644 index 0000000..b5a54fa --- /dev/null +++ b/src/index/ror/__init__.py @@ -0,0 +1,17 @@ +"""ROR (Research Organization Registry) local index + RAG. + +Pipeline (D16): + + download (Zenodo dump) ──► filter ──► document ──► embed ──► Qdrant + DuckDB + │ + ▼ + records (full dump) + + scope_records (per-scope membership) + + manifests (per-scope build metadata) + + query_rag(text) → Qdrant retrieval + Qwen3-Reranker-8B + lookup_dump(...) → SQL over the DuckDB `records` table (no RCP calls) + query(text, mode="auto") + +Entry point: `python -m src.index.ror `. See `.internal/ror/`. +""" diff --git a/src/index/ror/__main__.py b/src/index/ror/__main__.py new file mode 100644 index 0000000..682d688 --- /dev/null +++ b/src/index/ror/__main__.py @@ -0,0 +1,10 @@ +"""Entry point for `python -m src.index.ror`.""" + +from __future__ import annotations + +import sys + +from .cli import main + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/index/ror/build.py b/src/index/ror/build.py new file mode 100644 index 0000000..cac556f --- /dev/null +++ b/src/index/ror/build.py @@ -0,0 +1,179 @@ +"""Build orchestrator: download → filter → document → embed → store. + +Reads the resolved `RorIndexConfig`, fetches the latest dump (or reuses a +cached one), filters to the configured subset, embeds all docs via RCP, and +writes: + + - the **full ROR dump** (~125k records) into the DuckDB `records` table + (replaces what was there from the previous release); + - this run's scope rows (text + Qdrant point id) into `scope_records`; + - a manifest entry for this scope into `manifests`; + - and the embedded vectors into the matching Qdrant collection + `ror_` (recreated each build). + +D16 (see `.internal/ror/duckdb-migration.md`) collapsed the per-scope +`records.jsonl` + `manifest.json` sidecars and the in-memory `dump_index` +into one DuckDB file at `/ror/duckdb/ror.duckdb`. The Qdrant +collection layout is unchanged. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from typing import Any, Dict, List, Optional + +from .config import RorIndexConfig +from .document import display_name, to_document +from .embed import embed_passages +from .filter import EUROPE_COUNTRY_CODES, filter_countries, filter_country_code, filter_subtree +from .qdrant_store import QdrantRorStore +from .storage.duckdb_store import ( + DuckDBStore, + ScopeRecord, + StoreManifest, + extract_record_columns, + vector_id_for, +) + +logger = logging.getLogger(__name__) + + +def _load_dump(json_path) -> List[Dict[str, Any]]: + with open(json_path, "r", encoding="utf-8") as f: + data = json.load(f) + if not isinstance(data, list): + msg = f"Expected ROR dump JSON list at {json_path}, got {type(data).__name__}" + raise ValueError(msg) + return data + + +def _select_subset( + records: List[Dict[str, Any]], + cfg: RorIndexConfig, +) -> List[Dict[str, Any]]: + if cfg.scope.mode == "epfl_ethz": + return filter_subtree( + records, + seeds=cfg.scope.seeds, + expand_types=cfg.scope.expand, + max_depth=cfg.scope.max_depth, + ) + if cfg.scope.mode == "switzerland": + return filter_country_code(records, "CH") + if cfg.scope.mode == "europe": + return filter_countries(records, EUROPE_COUNTRY_CODES) + if cfg.scope.mode == "worldwide": + return list(records) + msg = f"Unknown scope.mode: {cfg.scope.mode}" + raise ValueError(msg) + + +async def build(cfg: RorIndexConfig, *, refresh: bool = False) -> Dict[str, Any]: + """Run the full build pipeline. Returns a summary dict.""" + from .download import fetch_latest_dump # local import keeps requests optional in tests + + cached = fetch_latest_dump( + cfg.ror_dump.zenodo_concept_doi, + refresh=refresh, + ) + logger.info("Using ROR dump version=%s at %s", cached.release_version, cached.json_path) + + all_records = _load_dump(cached.json_path) + logger.info("Loaded %d records from dump", len(all_records)) + + subset = _select_subset(all_records, cfg) + logger.info("Subset (mode=%s) has %d records", cfg.scope.mode, len(subset)) + if not subset: + msg = ( + f"Subset is empty for scope.mode={cfg.scope.mode!r}. " + f"Check seeds / country filter." + ) + raise ValueError(msg) + + texts = [to_document(r) for r in subset] + embeddings = await embed_passages(cfg.rcp, texts, normalize=True) + + # ---- DuckDB writes (records + scope_records + manifests) ------------ + duck = DuckDBStore.open() + try: + duck_records_count = duck.bulk_replace_records( + extract_record_columns(r, ror_release_version=cached.release_version) + for r in all_records + ) + logger.info( + "DuckDB: replaced `records` with %d rows (release=%s)", + duck_records_count, cached.release_version, + ) + + scope_rows = [ + ScopeRecord( + scope_mode=cfg.scope.mode, + ror_id=str(record.get("id", "")).rstrip("/"), + text=text, + vector_id=vector_id_for(str(record.get("id", "")).rstrip("/")), + ) + for record, text in zip(subset, texts) + ] + duck.set_scope_records(cfg.scope.mode, scope_rows) + duck.set_manifest(StoreManifest( + scope_mode=cfg.scope.mode, + record_count=len(scope_rows), + embedding_model=cfg.rcp.embedding_model, + embedding_dim=cfg.rcp.embedding_dim, + reranker_model=cfg.rcp.reranker_model, + ror_release_version=cached.release_version, + ror_release_doi=cached.release_doi, + )) + finally: + duck.close() + + # ---- Qdrant writes (vectors + payload) ------------------------------- + qstore = QdrantRorStore(cfg) + qstore.recreate_collection(cfg.scope.mode) + payloads = [_build_payload(record, text) for record, text in zip(subset, texts)] + qstore.upsert_records( + cfg.scope.mode, + ror_ids=[scope_rows[i].ror_id for i in range(len(scope_rows))], + vectors=embeddings.tolist(), + payloads=payloads, + ) + logger.info( + "Upserted %d records to qdrant collection %s", + len(scope_rows), qstore.collection_name(cfg.scope.mode), + ) + + return { + "scope_mode": cfg.scope.mode, + "record_count": len(scope_rows), + "release_version": cached.release_version, + "json_path": str(cached.json_path), + "qdrant_collection": qstore.collection_name(cfg.scope.mode), + "duckdb_records": duck_records_count, + } + + +def _build_payload(record: Dict[str, Any], text: str) -> Dict[str, Any]: + """Qdrant payload for one scope row: kept compact but searchable.""" + cc: Optional[str] = None + for loc in record.get("locations") or []: + details = loc.get("geonames_details") if isinstance(loc, dict) else None + if isinstance(details, dict): + value = details.get("country_code") + if isinstance(value, str) and value: + cc = value.upper() + break + return { + "ror_id": str(record.get("id", "")).rstrip("/"), + "name": display_name(record), + "text": text, + "country_code": cc, + "types": record.get("types") or [], + "record": record, + } + + +def run(cfg: RorIndexConfig, **kwargs) -> Dict[str, Any]: + """Sync wrapper for the CLI.""" + return asyncio.run(build(cfg, **kwargs)) diff --git a/src/index/ror/cli.py b/src/index/ror/cli.py new file mode 100644 index 0000000..19ea62c --- /dev/null +++ b/src/index/ror/cli.py @@ -0,0 +1,182 @@ +"""CLI for the ROR index. + +Usage: + python -m src.index.ror build [--config PATH] [--refresh] + python -m src.index.ror query "" [--top-k N] [--rerank-top-k N] + python -m src.index.ror lookup [--name TEXT] [--ror-id ID] [--country CC] [--limit N] + python -m src.index.ror stats +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from pathlib import Path + +from .config import DEFAULT_CONFIG_PATH, load_config +from .query import lookup_dump, query_rag_sync +from .storage.duckdb_store import DuckDBStore + +logger = logging.getLogger(__name__) + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(prog="src.index.ror") + parser.add_argument( + "--config", type=Path, default=DEFAULT_CONFIG_PATH, + help=f"Path to YAML config (default: {DEFAULT_CONFIG_PATH})", + ) + parser.add_argument( + "-v", "--verbose", action="store_true", + help="Enable debug logging", + ) + sub = parser.add_subparsers(dest="cmd", required=True) + + p_build = sub.add_parser("build", help="Download dump, filter, embed, write index") + p_build.add_argument("--refresh", action="store_true", + help="Force re-download of the Zenodo dump") + + p_query = sub.add_parser("query", help="Semantic RAG query over the embedded subset") + p_query.add_argument("text") + p_query.add_argument("--top-k", type=int, default=None) + p_query.add_argument("--rerank-top-k", type=int, default=None) + + p_lookup = sub.add_parser("lookup", help="Lexical lookup over the FULL ROR dump") + p_lookup.add_argument("--name", default=None, help="Name / token query") + p_lookup.add_argument("--ror-id", default=None, help="Exact ROR ID (URL or bare)") + p_lookup.add_argument("--country", default=None, help="ISO-3166 alpha-2 (e.g. CH)") + p_lookup.add_argument("--type", dest="type_", default=None, help="ROR type (e.g. education, facility)") + p_lookup.add_argument("--status", default=None, help="Status filter (active, inactive, withdrawn)") + p_lookup.add_argument("--limit", type=int, default=20) + + sub.add_parser("stats", help="Show manifest for the configured scope mode") + + p_migrate = sub.add_parser( + "migrate", + help="One-shot: copy existing FAISS vectors → Qdrant (no re-embedding)", + ) + p_migrate.add_argument( + "--scope", default="all", + help="Scope to migrate (epfl_ethz|switzerland|europe|worldwide|all)", + ) + + p_ms = sub.add_parser( + "migrate-storage", + help=( + "One-shot: port legacy JSONL+manifest sidecars + cached dump JSON " + "into the DuckDB store (D16). Does not touch Qdrant beyond a " + "read-only count check." + ), + ) + p_ms.add_argument( + "--dump-path", type=Path, default=None, + help="Override the auto-detected ROR dump JSON path", + ) + p_ms.add_argument( + "--db-path", type=Path, default=None, + help="Override the DuckDB file path (default /ror/duckdb/ror.duckdb)", + ) + p_ms.add_argument( + "--skip-qdrant-check", action="store_true", + help="Don't compare DuckDB scope counts against Qdrant collections", + ) + + return parser + + +def _print_json(obj) -> None: + print(json.dumps(obj, ensure_ascii=False, indent=2)) + + +def main(argv=None) -> int: + parser = _build_parser() + args = parser.parse_args(argv) + + logging.basicConfig( + level=logging.DEBUG if args.verbose else logging.INFO, + format="%(asctime)s %(levelname)s %(name)s | %(message)s", + ) + + if args.cmd == "build": + from . import build as build_mod + cfg = load_config(args.config) + summary = build_mod.run(cfg, refresh=args.refresh) + _print_json(summary) + return 0 + + if args.cmd == "query": + cfg = load_config(args.config) + results = query_rag_sync( + cfg, args.text, + top_k=args.top_k, rerank_top_k=args.rerank_top_k, + ) + _print_json([r.model_dump() for r in results]) + return 0 + + if args.cmd == "lookup": + cfg = load_config(args.config) + results = lookup_dump( + cfg, + text=args.name, + ror_id=args.ror_id, + country=args.country, + type_=args.type_, + status=args.status, + limit=args.limit, + ) + _print_json([ + {"ror_id": r.ror_id, "name": r.name, "matched_tokens": r.matched_tokens} + for r in results + ]) + return 0 + + if args.cmd == "stats": + cfg = load_config(args.config) + store = DuckDBStore.open() + try: + manifest = store.fetch_manifest(cfg.scope.mode) + finally: + store.close() + if manifest is None: + print( + f"No manifest in DuckDB for scope {cfg.scope.mode!r}. " + f"Run `python -m src.index.ror build` or `migrate-storage` first.", + file=sys.stderr, + ) + return 1 + # `built_at_iso` comes back as a datetime — JSON it out as ISO string. + if "built_at_iso" in manifest and manifest["built_at_iso"] is not None: + manifest["built_at_iso"] = manifest["built_at_iso"].isoformat() + _print_json(manifest) + return 0 + + if args.cmd == "migrate": + from . import migrate as migrate_mod + cfg = load_config(args.config) + if args.scope == "all": + results = migrate_mod.migrate_all(cfg) + else: + results = [migrate_mod.migrate_scope(cfg, args.scope)] + _print_json(results) + return 0 + + if args.cmd == "migrate-storage": + from .storage import migrate_storage + cfg = load_config(args.config) + summary = migrate_storage.migrate_all( + cfg, + db_path=args.db_path, + dump_path=args.dump_path, + skip_qdrant_check=args.skip_qdrant_check, + ) + _print_json(summary) + return 0 + + parser.print_help() + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/index/ror/config.py b/src/index/ror/config.py new file mode 100644 index 0000000..f2f9f3e --- /dev/null +++ b/src/index/ror/config.py @@ -0,0 +1,95 @@ +"""Config loader for the ROR indexer. + +Reads `config/index/ror.yaml` and merges in the RCP token from `RCP_TOKEN` +plus the resolved data dir. Mirrors the shape of `src/index/infoscience/config.py`. +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import List, Optional + +import yaml +from pydantic import BaseModel, Field, field_validator + +from .paths import ror_data_dir + +DEFAULT_CONFIG_PATH = Path("config/index/ror.yaml") + +EPFL_ROR_ID = "https://ror.org/02s376052" +ETHZ_ROR_ID = "https://ror.org/05a28rw58" + + +class RcpConfig(BaseModel): + base_url: str + embedding_model: str + embedding_dim: int + query_instruction: str + reranker_model: str + batch_size: int = 16 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: Optional[str] = None # populated from RCP_TOKEN at load time + + +class ScopeConfig(BaseModel): + mode: str = "epfl_ethz" + seeds: List[str] = Field(default_factory=lambda: [EPFL_ROR_ID, ETHZ_ROR_ID]) + expand: List[str] = Field(default_factory=lambda: ["parent", "child", "related"]) + max_depth: int = 2 + + @field_validator("mode") + @classmethod + def _check_mode(cls, v: str) -> str: + allowed = {"epfl_ethz", "switzerland", "europe", "worldwide"} + if v not in allowed: + msg = f"scope.mode must be one of {sorted(allowed)}, got {v!r}" + raise ValueError(msg) + return v + + +class RorDumpConfig(BaseModel): + zenodo_concept_doi: str = "10.5281/zenodo.6347574" + + +class RetrievalConfig(BaseModel): + top_k: int = 50 + rerank_top_k: int = 10 + + +class QdrantConfig(BaseModel): + url: str = "http://localhost:6333" + prefer_grpc: bool = False + api_key: Optional[str] = None # populated from INDEX_QDRANT_API_KEY at load time + collection_prefix: str = "ror" + + +class RorIndexConfig(BaseModel): + rcp: RcpConfig + scope: ScopeConfig = Field(default_factory=ScopeConfig) + ror_dump: RorDumpConfig = Field(default_factory=RorDumpConfig) + retrieval: RetrievalConfig = Field(default_factory=RetrievalConfig) + qdrant: QdrantConfig = Field(default_factory=QdrantConfig) + data_dir: Path + + def collection_name(self) -> str: + return f"{self.qdrant.collection_prefix}_{self.scope.mode}" + + +def load_config(path: Optional[Path] = None) -> RorIndexConfig: + """Load + validate the YAML config; merge `RCP_TOKEN` and resolved data dir.""" + cfg_path = path or DEFAULT_CONFIG_PATH + raw = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) + + raw.setdefault("rcp", {})["token"] = os.getenv("RCP_TOKEN") + + raw.setdefault("qdrant", {})["api_key"] = os.getenv("INDEX_QDRANT_API_KEY") + if (override := os.getenv("INDEX_QDRANT_URL")): + raw["qdrant"]["url"] = override.strip() + if (override := os.getenv("INDEX_QDRANT_PREFER_GRPC")): + raw["qdrant"]["prefer_grpc"] = override.strip().lower() in {"1", "true", "yes", "on"} + + raw["data_dir"] = ror_data_dir() + + return RorIndexConfig(**raw) diff --git a/src/index/ror/document.py b/src/index/ror/document.py new file mode 100644 index 0000000..001e0c7 --- /dev/null +++ b/src/index/ror/document.py @@ -0,0 +1,116 @@ +"""Flatten a ROR v2 record into a single embedding-ready text string. + +Composes display name + aliases + acronyms + types + city/region/country + +website domain + a short relationship line. The raw record is preserved +elsewhere (records.jsonl) — this module is text-only. +""" + +from __future__ import annotations + +from typing import Any, Dict, List, Optional + + +def _names_by_type(record: Dict[str, Any]) -> Dict[str, List[str]]: + grouped: Dict[str, List[str]] = {} + for entry in record.get("names") or []: + if not isinstance(entry, dict): + continue + value = entry.get("value") + if not isinstance(value, str) or not value: + continue + types = entry.get("types") or [] + for t in types: + grouped.setdefault(str(t), []).append(value) + return grouped + + +def display_name(record: Dict[str, Any]) -> Optional[str]: + grouped = _names_by_type(record) + if grouped.get("ror_display"): + return grouped["ror_display"][0] + if grouped.get("label"): + return grouped["label"][0] + for entries in grouped.values(): + if entries: + return entries[0] + return None + + +def _location_phrase(record: Dict[str, Any]) -> Optional[str]: + locations = record.get("locations") or [] + if not isinstance(locations, list) or not locations: + return None + first = locations[0] + if not isinstance(first, dict): + return None + details = first.get("geonames_details") or {} + if not isinstance(details, dict): + return None + parts = [ + details.get("name"), + details.get("country_subdivision_name") or details.get("region"), + details.get("country_name"), + ] + parts = [p for p in parts if isinstance(p, str) and p] + return ", ".join(parts) if parts else None + + +def _website(record: Dict[str, Any]) -> Optional[str]: + for link in record.get("links") or []: + if isinstance(link, dict) and link.get("type") == "website": + value = link.get("value") + if isinstance(value, str) and value: + return value + domains = record.get("domains") or [] + if isinstance(domains, list) and domains: + first = domains[0] + if isinstance(first, str) and first: + return first + return None + + +def _relationships_phrase(record: Dict[str, Any]) -> Optional[str]: + parts: List[str] = [] + for rel in record.get("relationships") or []: + if not isinstance(rel, dict): + continue + rtype = rel.get("type") + rlabel = rel.get("label") + if isinstance(rtype, str) and isinstance(rlabel, str) and rtype and rlabel: + parts.append(f"{rtype} of {rlabel}") + return "; ".join(parts) if parts else None + + +def to_document(record: Dict[str, Any]) -> str: + """Build the flattened embedding-ready string for one ROR record.""" + grouped = _names_by_type(record) + name = display_name(record) or record.get("id") or "" + + aliases = grouped.get("alias") or [] + acronyms = grouped.get("acronym") or [] + labels = [ + n for n in grouped.get("label") or [] + if n != name + ] + types = record.get("types") or [] + location = _location_phrase(record) + website = _website(record) + relationships = _relationships_phrase(record) + + lines: List[str] = [f"Name: {name}"] + if labels: + lines.append("Other names: " + "; ".join(labels)) + if aliases: + lines.append("Aliases: " + "; ".join(aliases)) + if acronyms: + lines.append("Acronyms: " + "; ".join(acronyms)) + if types: + lines.append("Types: " + ", ".join(str(t) for t in types if t)) + if location: + lines.append(f"Location: {location}") + if website: + lines.append(f"Website: {website}") + if relationships: + lines.append(f"Relationships: {relationships}") + + return "\n".join(lines) diff --git a/src/index/ror/download.py b/src/index/ror/download.py new file mode 100644 index 0000000..4811d85 --- /dev/null +++ b/src/index/ror/download.py @@ -0,0 +1,220 @@ +"""Download + extract the latest ROR Zenodo dump. + +Resolves the latest record under the concept DOI (default +`10.5281/zenodo.6347574`) via the Zenodo REST API, downloads the release zip +into `dump_dir()`, verifies the SHA256 checksum from the Zenodo file metadata, +and extracts the v2 JSON payload. Re-running is a no-op when the latest +release version is already cached locally. + +Filename matching covers both: + - `-ror-data.json` (v2.0+ — Dec 2025 onward, v2-only) + - `-ror-data_schema_v2.json` (v1.45–v1.x — released alongside v1) +""" + +from __future__ import annotations + +import hashlib +import json +import logging +import re +import shutil +import zipfile +from dataclasses import dataclass +from pathlib import Path +from typing import Optional + +import requests + +from .paths import dump_dir + +logger = logging.getLogger(__name__) + +ZENODO_API_BASE = "https://zenodo.org/api" +_V2_FILENAME_PATTERNS = ( + re.compile(r".*ror-data(?:_schema_v2)?\.json$", re.IGNORECASE), +) +_DEFAULT_TIMEOUT = 60 +_DOWNLOAD_TIMEOUT = 600 +_DOI_RE = re.compile(r"^(?P10\.\d+)/(?P.+)$") + + +class RorDumpError(RuntimeError): + """Raised when the dump can't be located, downloaded, or extracted.""" + + +@dataclass(frozen=True) +class CachedDump: + release_version: str + release_doi: Optional[str] + json_path: Path + + +def _doi_to_concept_recid(concept_doi: str) -> str: + """Convert `10.5281/zenodo.6347574` → `6347574`.""" + m = _DOI_RE.match(concept_doi.strip()) + if not m: + msg = f"Unrecognized DOI: {concept_doi!r}" + raise RorDumpError(msg) + suffix = m.group("suffix") + last = suffix.rsplit(".", 1)[-1] + digits = "".join(ch for ch in last if ch.isdigit()) + if not digits: + msg = f"Could not extract Zenodo recid from DOI suffix {suffix!r}" + raise RorDumpError(msg) + return digits + + +def _resolve_latest_record(concept_doi: str) -> dict: + recid = _doi_to_concept_recid(concept_doi) + url = f"{ZENODO_API_BASE}/records/{recid}/versions/latest" + resp = requests.get(url, timeout=_DEFAULT_TIMEOUT) + if resp.status_code != 200: + msg = ( + f"Zenodo lookup failed for concept DOI {concept_doi!r}: " + f"HTTP {resp.status_code}" + ) + raise RorDumpError(msg) + return resp.json() + + +def _pick_zip_file(record: dict) -> dict: + files = record.get("files") or [] + for entry in files: + key = entry.get("key", "") + if isinstance(key, str) and key.endswith(".zip"): + return entry + msg = "No .zip asset found in Zenodo record." + raise RorDumpError(msg) + + +def _release_version(record: dict) -> str: + metadata = record.get("metadata") or {} + version = metadata.get("version") + if isinstance(version, str) and version: + return version + title = metadata.get("title") + if isinstance(title, str) and title: + return title + return str(record.get("id", "unknown")) + + +def _release_doi(record: dict) -> Optional[str]: + doi = record.get("doi") + return doi if isinstance(doi, str) else None + + +def _release_dir(version: str) -> Path: + safe = re.sub(r"[^A-Za-z0-9._-]", "_", version) + p = dump_dir() / safe + p.mkdir(parents=True, exist_ok=True) + return p + + +def _existing_v2_json(release_path: Path) -> Optional[Path]: + for child in release_path.iterdir(): + if not child.is_file(): + continue + for pattern in _V2_FILENAME_PATTERNS: + if pattern.match(child.name): + return child + return None + + +def _verify_sha256(path: Path, expected: str) -> None: + h = hashlib.sha256() + with path.open("rb") as f: + for chunk in iter(lambda: f.read(1024 * 1024), b""): + h.update(chunk) + digest = h.hexdigest() + if digest != expected: + msg = ( + f"Checksum mismatch for {path.name}: " + f"expected {expected}, got {digest}" + ) + raise RorDumpError(msg) + + +def _stream_download(url: str, dest: Path) -> None: + with requests.get(url, stream=True, timeout=_DOWNLOAD_TIMEOUT) as resp: + resp.raise_for_status() + tmp = dest.with_suffix(dest.suffix + ".part") + with tmp.open("wb") as f: + shutil.copyfileobj(resp.raw, f) + tmp.replace(dest) + + +def _extract_v2_json(zip_path: Path, into: Path) -> Path: + with zipfile.ZipFile(zip_path) as zf: + candidate: Optional[zipfile.ZipInfo] = None + for info in zf.infolist(): + base = Path(info.filename).name + for pattern in _V2_FILENAME_PATTERNS: + if pattern.match(base): + candidate = info + break + if candidate is not None: + break + if candidate is None: + msg = ( + f"No v2 JSON file found inside {zip_path.name}. " + f"Looked for *-ror-data.json or *-ror-data_schema_v2.json." + ) + raise RorDumpError(msg) + out_path = into / Path(candidate.filename).name + with zf.open(candidate) as src, out_path.open("wb") as dst: + shutil.copyfileobj(src, dst) + return out_path + + +def fetch_latest_dump( + concept_doi: str = "10.5281/zenodo.6347574", + *, + refresh: bool = False, +) -> CachedDump: + """Resolve, download (if needed), and extract the latest ROR v2 JSON.""" + record = _resolve_latest_record(concept_doi) + version = _release_version(record) + release_dir = _release_dir(version) + + cached = _existing_v2_json(release_dir) + if cached is not None and not refresh: + logger.info("Using cached ROR dump version=%s at %s", version, cached) + return CachedDump( + release_version=version, + release_doi=_release_doi(record), + json_path=cached, + ) + + file_entry = _pick_zip_file(record) + download_url = file_entry["links"]["self"] + zip_path = release_dir / file_entry["key"] + + logger.info("Downloading ROR dump version=%s from %s", version, download_url) + _stream_download(download_url, zip_path) + + expected_checksum = file_entry.get("checksum") + if isinstance(expected_checksum, str) and expected_checksum.startswith("md5:"): + # Zenodo emits md5 by default; sha256 is opt-in. Skip when not sha256. + logger.debug("Zenodo file checksum is md5; skipping sha256 verify.") + elif isinstance(expected_checksum, str) and expected_checksum.startswith("sha256:"): + _verify_sha256(zip_path, expected_checksum.split(":", 1)[1]) + + json_path = _extract_v2_json(zip_path, release_dir) + metadata_path = release_dir / "release.json" + metadata_path.write_text( + json.dumps( + { + "version": version, + "doi": _release_doi(record), + "concept_doi": concept_doi, + }, + indent=2, + ), + encoding="utf-8", + ) + + return CachedDump( + release_version=version, + release_doi=_release_doi(record), + json_path=json_path, + ) diff --git a/src/index/ror/embed.py b/src/index/ror/embed.py new file mode 100644 index 0000000..d998ab3 --- /dev/null +++ b/src/index/ror/embed.py @@ -0,0 +1,171 @@ +"""RCP embedding client (OpenAI-compatible `/embeddings`). + +Batched, retried with exponential backoff on 429/5xx, semaphore-bounded +concurrency. Asserts the server's reported embedding dim matches +`config.rcp.embedding_dim` on the first response and fails loudly on mismatch. +Designed for `Qwen/Qwen3-Embedding-8B` but works with any OpenAI-compatible +embedding model. +""" + +from __future__ import annotations + +import asyncio +import logging +import math +import random +from typing import List, Optional, Sequence + +import httpx +import numpy as np + +from .config import RcpConfig + +logger = logging.getLogger(__name__) + + +class EmbeddingError(RuntimeError): + """Raised on unrecoverable embedding failure.""" + + +_MAX_RETRIES = 5 +_RETRY_BASE_DELAY = 1.5 +_RETRY_STATUS = frozenset({408, 425, 429, 500, 502, 503, 504}) + + +def _auth_headers(rcp: RcpConfig) -> dict: + headers = {"Content-Type": "application/json"} + if rcp.token: + headers["Authorization"] = f"Bearer {rcp.token}" + return headers + + +async def _post_with_retry( + client: httpx.AsyncClient, + url: str, + payload: dict, + headers: dict, +) -> dict: + last_error: Optional[Exception] = None + for attempt in range(_MAX_RETRIES): + try: + resp = await client.post(url, json=payload, headers=headers) + except (httpx.TimeoutException, httpx.TransportError) as exc: + last_error = exc + delay = _RETRY_BASE_DELAY * math.pow(2, attempt) + random.random() + logger.warning( + "Embedding request transport error (attempt %d/%d): %s; sleeping %.1fs", + attempt + 1, _MAX_RETRIES, exc, delay, + ) + await asyncio.sleep(delay) + continue + if resp.status_code in _RETRY_STATUS: + delay = _RETRY_BASE_DELAY * math.pow(2, attempt) + random.random() + logger.warning( + "Embedding request HTTP %d (attempt %d/%d); sleeping %.1fs", + resp.status_code, attempt + 1, _MAX_RETRIES, delay, + ) + await asyncio.sleep(delay) + continue + if resp.status_code != 200: + msg = ( + f"Embedding request failed: HTTP {resp.status_code} " + f"body={resp.text[:500]}" + ) + raise EmbeddingError(msg) + return resp.json() + msg = f"Embedding request gave up after {_MAX_RETRIES} retries; last error: {last_error}" + raise EmbeddingError(msg) + + +def _normalize(vectors: np.ndarray) -> np.ndarray: + norms = np.linalg.norm(vectors, axis=1, keepdims=True) + norms = np.where(norms == 0, 1.0, norms) + return vectors / norms + + +async def _embed_one_batch( + client: httpx.AsyncClient, + rcp: RcpConfig, + inputs: Sequence[str], +) -> np.ndarray: + payload = { + "model": rcp.embedding_model, + "input": list(inputs), + } + url = rcp.base_url.rstrip("/") + "/embeddings" + body = await _post_with_retry(client, url, payload, _auth_headers(rcp)) + data = body.get("data") or [] + if len(data) != len(inputs): + msg = ( + f"Embedding response mismatch: expected {len(inputs)} vectors, " + f"got {len(data)}" + ) + raise EmbeddingError(msg) + rows = sorted(data, key=lambda d: d.get("index", 0)) + matrix = np.array([row["embedding"] for row in rows], dtype=np.float32) + if matrix.shape[1] != rcp.embedding_dim: + msg = ( + f"Embedding dim mismatch: server returned {matrix.shape[1]}, " + f"config expected {rcp.embedding_dim}" + ) + raise EmbeddingError(msg) + return matrix + + +async def embed_passages( + rcp: RcpConfig, + texts: Sequence[str], + *, + normalize: bool = True, +) -> np.ndarray: + """Embed `texts` with the configured embedding model. Returns (N, dim) float32.""" + if not texts: + return np.zeros((0, rcp.embedding_dim), dtype=np.float32) + + semaphore = asyncio.Semaphore(rcp.max_concurrency) + timeout = httpx.Timeout(rcp.timeout_seconds) + + async with httpx.AsyncClient(timeout=timeout) as client: + async def _run(batch_inputs: Sequence[str]) -> np.ndarray: + async with semaphore: + return await _embed_one_batch(client, rcp, batch_inputs) + + batches = [ + list(texts[i : i + rcp.batch_size]) + for i in range(0, len(texts), rcp.batch_size) + ] + results = await asyncio.gather(*[_run(b) for b in batches]) + + matrix = np.vstack(results) if results else np.zeros( + (0, rcp.embedding_dim), dtype=np.float32, + ) + return _normalize(matrix) if normalize else matrix + + +async def embed_query( + rcp: RcpConfig, + text: str, + *, + normalize: bool = True, +) -> np.ndarray: + """Embed a single query, prefixed with `rcp.query_instruction` per Qwen3 format.""" + qtext = f"Instruct: {rcp.query_instruction}\nQuery: {text}" + matrix = await embed_passages(rcp, [qtext], normalize=normalize) + return matrix[0] + + +def embed_passages_sync(rcp: RcpConfig, texts: Sequence[str], *, normalize: bool = True) -> np.ndarray: + return asyncio.run(embed_passages(rcp, texts, normalize=normalize)) + + +def embed_query_sync(rcp: RcpConfig, text: str, *, normalize: bool = True) -> np.ndarray: + return asyncio.run(embed_query(rcp, text, normalize=normalize)) + + +__all__: List[str] = [ + "EmbeddingError", + "embed_passages", + "embed_passages_sync", + "embed_query", + "embed_query_sync", +] diff --git a/src/index/ror/filter.py b/src/index/ror/filter.py new file mode 100644 index 0000000..c2b1c4b --- /dev/null +++ b/src/index/ror/filter.py @@ -0,0 +1,131 @@ +"""Subset filters over a ROR v2 dump. + +Two modes: + - `epfl_ethz`: BFS from seed ROR IDs over `relationships[]` whose type is in + the allowed set (default: parent, child, related), bounded depth. + - `switzerland`: keep records where any + `locations[*].geonames_details.country_code == "CH"`. + +Operates on raw v2 records (dicts) — no Pydantic round-trip, no normalization. +""" + +from __future__ import annotations + +from collections import deque +from typing import Any, Dict, FrozenSet, Iterable, List, Sequence, Set + + +EUROPE_COUNTRY_CODES: FrozenSet[str] = frozenset({ + "AD", "AL", "AT", "BA", "BE", "BG", "BY", "CH", "CY", "CZ", + "DE", "DK", "EE", "ES", "FI", "FO", "FR", "GB", "GG", "GI", + "GR", "HR", "HU", "IE", "IM", "IS", "IT", "JE", "LI", "LT", + "LU", "LV", "MC", "MD", "ME", "MK", "MT", "NL", "NO", "PL", + "PT", "RO", "RS", "RU", "SE", "SI", "SJ", "SK", "SM", "TR", + "UA", "VA", "XK", +}) + + +def _normalize_id(value: str) -> str: + """Strip trailing slash; keep `https://ror.org/XXXX` form.""" + return value.rstrip("/").strip() + + +def _bare_id(ror_id: str) -> str: + return _normalize_id(ror_id).rsplit("/", 1)[-1] + + +def index_by_id(records: Iterable[Dict[str, Any]]) -> Dict[str, Dict[str, Any]]: + """Return `{ror_id_url: record}` plus alias entries keyed by bare ID.""" + out: Dict[str, Dict[str, Any]] = {} + for record in records: + rid = record.get("id") + if isinstance(rid, str) and rid: + normalized = _normalize_id(rid) + out[normalized] = record + out[_bare_id(normalized)] = record + return out + + +def filter_countries( + records: Iterable[Dict[str, Any]], + country_codes: Iterable[str], +) -> List[Dict[str, Any]]: + """Keep records where any location's country_code matches the allowed set.""" + targets = {c.upper() for c in country_codes} + kept: List[Dict[str, Any]] = [] + for record in records: + for loc in record.get("locations") or []: + if not isinstance(loc, dict): + continue + details = loc.get("geonames_details") or {} + if not isinstance(details, dict): + continue + cc = details.get("country_code") + if isinstance(cc, str) and cc.upper() in targets: + kept.append(record) + break + return kept + + +def filter_country_code( + records: Iterable[Dict[str, Any]], + country_code: str, +) -> List[Dict[str, Any]]: + """Keep records whose location matches a single ISO-3166 alpha-2 code.""" + return filter_countries(records, [country_code]) + + +def filter_subtree( + records: Iterable[Dict[str, Any]], + seeds: Sequence[str], + *, + expand_types: Sequence[str] = ("parent", "child", "related"), + max_depth: int = 2, +) -> List[Dict[str, Any]]: + """BFS from seed ROR IDs over allowed relationship types. + + Records list is materialized once into an id-indexed dict; relationships + that point to IDs absent from the dump are silently skipped. + """ + by_id = index_by_id(records) + allowed = {t.lower() for t in expand_types} + + seen: Set[str] = set() + queue: deque[tuple[str, int]] = deque() + for seed in seeds: + seed_norm = _normalize_id(seed) + target = by_id.get(seed_norm) or by_id.get(_bare_id(seed_norm)) + if target is None: + continue + rid = _normalize_id(str(target.get("id"))) + if rid not in seen: + seen.add(rid) + queue.append((rid, 0)) + + kept: List[Dict[str, Any]] = [] + while queue: + rid, depth = queue.popleft() + record = by_id.get(rid) + if record is None: + continue + kept.append(record) + if depth >= max_depth: + continue + for rel in record.get("relationships") or []: + if not isinstance(rel, dict): + continue + rtype = rel.get("type") + rid_next = rel.get("id") + if not isinstance(rtype, str) or not isinstance(rid_next, str): + continue + if rtype.lower() not in allowed: + continue + rid_next_norm = _normalize_id(rid_next) + if rid_next_norm in seen: + continue + if rid_next_norm not in by_id and _bare_id(rid_next_norm) not in by_id: + continue + seen.add(rid_next_norm) + queue.append((rid_next_norm, depth + 1)) + + return kept diff --git a/src/index/ror/migrate.py b/src/index/ror/migrate.py new file mode 100644 index 0000000..603d223 --- /dev/null +++ b/src/index/ror/migrate.py @@ -0,0 +1,88 @@ +"""One-shot migration from legacy FAISS-on-disk indexes to Qdrant. + +For each scope on disk (`/index//`) that has a legacy +`index.faiss` file, this module reads the vectors back via faiss +`reconstruct_n`, joins them with the row-aligned `records.jsonl`, and upserts +into the matching Qdrant collection. No RCP calls — vectors are reused. +""" + +from __future__ import annotations + +import logging +from typing import Any, Dict, List, Optional + +import numpy as np + +from .build import _build_payload # noqa: PLC2701 — internal reuse +from .config import RorIndexConfig +from .paths import index_data_root +from .qdrant_store import QdrantRorStore +from .store import has_legacy_faiss, read_legacy_faiss, read_records + +logger = logging.getLogger(__name__) + + +def list_scope_dirs() -> List[str]: + base = index_data_root() / "ror" / "index" + if not base.exists(): + return [] + return sorted([p.name for p in base.iterdir() if p.is_dir()]) + + +def migrate_scope(cfg: RorIndexConfig, scope_mode: str) -> Dict[str, Any]: + """Migrate one scope's vectors from FAISS to Qdrant.""" + if not has_legacy_faiss(scope_mode): + msg = ( + f"No legacy FAISS file for scope {scope_mode!r}; nothing to migrate. " + f"Run `python -m src.index.ror build` to create the Qdrant collection." + ) + raise FileNotFoundError(msg) + + rows = read_records(scope_mode) + index = read_legacy_faiss(scope_mode) + if index.ntotal != len(rows): + msg = ( + f"FAISS row count ({index.ntotal}) differs from records.jsonl " + f"length ({len(rows)}) for scope {scope_mode!r}. Aborting." + ) + raise ValueError(msg) + + vectors_np = index.reconstruct_n(0, index.ntotal).astype(np.float32, copy=False) + if vectors_np.shape[1] != cfg.rcp.embedding_dim: + msg = ( + f"FAISS dim {vectors_np.shape[1]} != config dim {cfg.rcp.embedding_dim} " + f"for scope {scope_mode!r}. Re-embed instead of migrating." + ) + raise ValueError(msg) + + store = QdrantRorStore(cfg) + store.recreate_collection(scope_mode) + payloads = [_build_payload(row.record, row.text) for row in rows] + store.upsert_records( + scope_mode, + ror_ids=[row.ror_id for row in rows], + vectors=vectors_np.tolist(), + payloads=payloads, + ) + qcount = store.count(scope_mode) + logger.info( + "Migrated scope=%s rows=%d → qdrant=%d", scope_mode, len(rows), qcount, + ) + return { + "scope_mode": scope_mode, + "rows": len(rows), + "qdrant_count": qcount, + "qdrant_collection": store.collection_name(scope_mode), + } + + +def migrate_all(cfg: RorIndexConfig) -> List[Dict[str, Any]]: + out: List[Dict[str, Any]] = [] + for scope in list_scope_dirs(): + if not has_legacy_faiss(scope): + continue + out.append(migrate_scope(cfg, scope)) + return out + + +__all__ = ["list_scope_dirs", "migrate_all", "migrate_scope"] diff --git a/src/index/ror/models.py b/src/index/ror/models.py new file mode 100644 index 0000000..3cc0099 --- /dev/null +++ b/src/index/ror/models.py @@ -0,0 +1,87 @@ +"""Pydantic models for cross-stage transfer. + +The on-disk JSONL is the canonical record format; these models cover +in-memory data passed between stages and the public query result shape. +""" + +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from pydantic import BaseModel, Field + + +class RorRecord(BaseModel): + """A ROR v2 record kept verbatim. We expose a few accessors for convenience.""" + + id: str + raw: Dict[str, Any] + + @property + def names(self) -> List[Dict[str, Any]]: + names = self.raw.get("names") + return names if isinstance(names, list) else [] + + @property + def display_name(self) -> Optional[str]: + for entry in self.names: + if "ror_display" in (entry.get("types") or []): + value = entry.get("value") + if isinstance(value, str) and value: + return value + for entry in self.names: + value = entry.get("value") + if isinstance(value, str) and value: + return value + return None + + @property + def country_code(self) -> Optional[str]: + for loc in self.raw.get("locations") or []: + details = loc.get("geonames_details") if isinstance(loc, dict) else None + if isinstance(details, dict): + cc = details.get("country_code") + if isinstance(cc, str) and cc: + return cc + return None + + +class IndexedRecord(BaseModel): + """One row of records.jsonl. Row index = FAISS row index.""" + + row: int + ror_id: str + name: Optional[str] + text: str + record: Dict[str, Any] + + +class IndexManifest(BaseModel): + """manifest.json — describes the build that produced an index.""" + + scope_mode: str + record_count: int + embedding_model: str + embedding_dim: int + reranker_model: str + ror_release_version: Optional[str] = None + ror_release_doi: Optional[str] = None + built_at_iso: str + + +class ScoredRecord(BaseModel): + """Public query result shape.""" + + ror_id: str + name: Optional[str] + score: float + record: Dict[str, Any] + + +class DumpMatch(BaseModel): + """Public lookup result shape (no score from semantic model).""" + + ror_id: str + name: Optional[str] + record: Dict[str, Any] + matched_tokens: List[str] = Field(default_factory=list) diff --git a/src/index/ror/paths.py b/src/index/ror/paths.py new file mode 100644 index 0000000..dfb105c --- /dev/null +++ b/src/index/ror/paths.py @@ -0,0 +1,51 @@ +"""Filesystem paths for the ROR index. + +Roots at `${INDEX_DATA_DIR:-data/index}/ror` (shared root with the infoscience +sibling). Sub-paths are derived constants below; directories are created on +first access. +""" + +from __future__ import annotations + +import os +from pathlib import Path + +_DEFAULT_ROOT = "data/index" +_SOURCE = "ror" + + +def index_data_root() -> Path: + """Top-level multi-source data root (`data/index` by default).""" + return Path(os.getenv("INDEX_DATA_DIR", _DEFAULT_ROOT)).expanduser().resolve() + + +def ror_data_dir() -> Path: + """Per-source root for ROR. Creates the directory tree if needed.""" + root = index_data_root() / _SOURCE + root.mkdir(parents=True, exist_ok=True) + return root + + +def dump_dir() -> Path: + p = ror_data_dir() / "dump" + p.mkdir(parents=True, exist_ok=True) + return p + + +def index_dir(scope_mode: str) -> Path: + """Built-index directory for one scope mode (`epfl_ethz`, `switzerland`, …).""" + p = ror_data_dir() / "index" / scope_mode + p.mkdir(parents=True, exist_ok=True) + return p + + +def faiss_path(scope_mode: str) -> Path: + return index_dir(scope_mode) / "index.faiss" + + +def records_path(scope_mode: str) -> Path: + return index_dir(scope_mode) / "records.jsonl" + + +def manifest_path(scope_mode: str) -> Path: + return index_dir(scope_mode) / "manifest.json" diff --git a/src/index/ror/qdrant_store.py b/src/index/ror/qdrant_store.py new file mode 100644 index 0000000..1de4b37 --- /dev/null +++ b/src/index/ror/qdrant_store.py @@ -0,0 +1,158 @@ +"""Qdrant client wrapper for the ROR index. + +One collection per scope mode (`ror_epfl_ethz`, `ror_switzerland`, +`ror_europe`, `ror_worldwide`). Cosine distance, dim from RCP config. +Mirrors the shape of `src/index/openalex/vector/qdrant_store.py`. +""" + +from __future__ import annotations + +import logging +import uuid +from typing import TYPE_CHECKING, Any, Dict, List, Optional + +from qdrant_client import QdrantClient, models + +if TYPE_CHECKING: + from .config import RorIndexConfig + +logger = logging.getLogger(__name__) + + +def _stable_point_id(ror_id: str) -> str: + """Map a ROR URL like https://ror.org/02s376052 to a stable UUIDv5 string. + + Qdrant point IDs accept either int64 or UUID; UUIDv5 over a fixed namespace + keeps the mapping deterministic across rebuilds. + """ + return str(uuid.uuid5(uuid.NAMESPACE_URL, ror_id)) + + +class QdrantRorStore: + """Per-scope collection bootstrap, upsert, search.""" + + def __init__(self, config: "RorIndexConfig") -> None: + self._config = config + # qdrant-client's default gRPC timeout (~5s) is too tight for large + # `wait=True` flushes (e.g. the final batch of a 125k-record upsert). + # Use the same timeout as the RCP HTTP timeout — it's already tuned + # for "this might take a while" calls. + self._client = QdrantClient( + url=config.qdrant.url, + api_key=config.qdrant.api_key, + prefer_grpc=config.qdrant.prefer_grpc, + timeout=max(config.rcp.timeout_seconds, 120), + ) + self._dim = config.rcp.embedding_dim + + @property + def client(self) -> QdrantClient: + return self._client + + def collection_name(self, scope_mode: Optional[str] = None) -> str: + if scope_mode is None: + return self._config.collection_name() + return f"{self._config.qdrant.collection_prefix}_{scope_mode}" + + def ensure_collection(self, scope_mode: Optional[str] = None) -> str: + name = self.collection_name(scope_mode) + if self._client.collection_exists(name): + return name + self._client.create_collection( + collection_name=name, + vectors_config=models.VectorParams( + size=self._dim, + distance=models.Distance.COSINE, + ), + ) + logger.info("created qdrant collection %s (dim=%d)", name, self._dim) + return name + + def recreate_collection(self, scope_mode: Optional[str] = None) -> str: + name = self.collection_name(scope_mode) + if self._client.collection_exists(name): + self._client.delete_collection(name) + logger.info("dropped existing qdrant collection %s", name) + return self.ensure_collection(scope_mode) + + def upsert_records( + self, + scope_mode: str, + *, + ror_ids: List[str], + vectors: List[List[float]], + payloads: List[Dict[str, Any]], + batch_size: int = 256, + ) -> None: + if not (len(ror_ids) == len(vectors) == len(payloads)): + msg = "ror_ids/vectors/payloads must be the same length" + raise ValueError(msg) + if not ror_ids: + return + name = self.ensure_collection(scope_mode) + total = len(ror_ids) + for start in range(0, total, batch_size): + end = start + batch_size + points = [ + models.PointStruct( + id=_stable_point_id(rid), + vector=vec, + payload=payload, + ) + for rid, vec, payload in zip( + ror_ids[start:end], vectors[start:end], payloads[start:end], + ) + ] + # wait=True per batch keeps each flush small and within timeout + # (vs. one giant flush at the end that can take many minutes for + # the 125k-record worldwide scope). + self._client.upsert(collection_name=name, points=points, wait=True) + + def search( + self, + scope_mode: str, + *, + query_vector: List[float], + top_k: int = 50, + country: Optional[str] = None, + ) -> List[Dict[str, Any]]: + name = self.collection_name(scope_mode) + if not self._client.collection_exists(name): + msg = ( + f"Qdrant collection {name!r} does not exist. " + f"Run `python -m src.index.ror build` first." + ) + raise FileNotFoundError(msg) + qfilter = None + if country: + qfilter = models.Filter(must=[ + models.FieldCondition( + key="country_code", match=models.MatchValue(value=country.upper()), + ), + ]) + hits = self._client.query_points( + collection_name=name, + query=query_vector, + limit=top_k, + query_filter=qfilter, + with_payload=True, + ).points + return [ + { + "score": float(p.score), + "ror_id": str((p.payload or {}).get("ror_id", "")), + "name": (p.payload or {}).get("name"), + "text": (p.payload or {}).get("text", ""), + "record": (p.payload or {}).get("record", {}), + } + for p in hits + ] + + def count(self, scope_mode: str) -> int: + name = self.collection_name(scope_mode) + if not self._client.collection_exists(name): + return 0 + return int(self._client.count(collection_name=name, exact=True).count) + + +__all__ = ["QdrantRorStore", "_stable_point_id"] diff --git a/src/index/ror/query.py b/src/index/ror/query.py new file mode 100644 index 0000000..a6f2602 --- /dev/null +++ b/src/index/ror/query.py @@ -0,0 +1,147 @@ +"""Public query API: semantic RAG over the embedded subset, plus lexical +lookup over the full ROR dump. + +`query_rag` runs Qdrant retrieval and reranks via the RCP cross-encoder. +`lookup_dump` searches the full registry by ROR ID, name tokens, and/or +country code (no RCP calls) — backed by `DuckDBStore` (D16). `query(mode='auto')` +tries RAG first and falls back to `lookup_dump` when the top score is below +`score_floor`. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import List, Literal, Optional + +import numpy as np + +from .config import RorIndexConfig +from .embed import embed_query +from .models import DumpMatch, ScoredRecord +from .qdrant_store import QdrantRorStore +from .rerank import rerank +from .storage.duckdb_store import DuckDBStore + +logger = logging.getLogger(__name__) + + +async def query_rag( + cfg: RorIndexConfig, + text: str, + *, + top_k: Optional[int] = None, + rerank_top_k: Optional[int] = None, + country: Optional[str] = None, +) -> List[ScoredRecord]: + """Embed query → Qdrant search → rerank → return top results.""" + top_k = top_k or cfg.retrieval.top_k + rerank_top_k = rerank_top_k or cfg.retrieval.rerank_top_k + + qvec = await embed_query(cfg.rcp, text, normalize=True) + store = QdrantRorStore(cfg) + candidates = store.search( + cfg.scope.mode, + query_vector=np.asarray(qvec, dtype=np.float32).tolist(), + top_k=top_k, + country=country, + ) + if not candidates: + return [] + + candidate_texts = [c["text"] for c in candidates] + rerank_results = await rerank( + cfg.rcp, text, candidate_texts, top_n=rerank_top_k, + ) + out: List[ScoredRecord] = [] + for r in rerank_results[:rerank_top_k]: + if r.index < 0 or r.index >= len(candidates): + continue + c = candidates[r.index] + out.append(ScoredRecord( + ror_id=c["ror_id"], + name=c["name"], + score=r.score, + record=c.get("record") or {}, + )) + return out + + +def lookup_dump( + cfg: RorIndexConfig, + *, + text: Optional[str] = None, + ror_id: Optional[str] = None, + country: Optional[str] = None, + type_: Optional[str] = None, + status: Optional[str] = None, + limit: int = 20, +) -> List[DumpMatch]: + """Lexical / exact lookup over the full ROR dump (no RCP calls). + + Backed by the `records` table in `/ror/duckdb/ror.duckdb` + (D16). Run `python -m src.index.ror build` or `migrate-storage` to + populate the table from the cached Zenodo dump. + """ + if not (text or ror_id or country or type_ or status): + msg = "lookup_dump requires at least one filter (text, ror_id, country, type_, status)." + raise ValueError(msg) + + store = DuckDBStore.open() + try: + rows = store.lookup( + text=text, + ror_id=ror_id, + country=country, + type_=type_, + status=status, + limit=limit, + ) + finally: + store.close() + + out: List[DumpMatch] = [] + for row in rows: + record = row.get("record") or {} + out.append(DumpMatch( + ror_id=row.get("ror_id") or str(record.get("id") or ""), + name=row.get("name"), + record=record, + matched_tokens=[], + )) + return out + + +async def query( + cfg: RorIndexConfig, + text: str, + *, + mode: Literal["auto", "rag", "dump"] = "auto", + score_floor: float = 0.0, +) -> List[ScoredRecord] | List[DumpMatch]: + """Convenience wrapper: RAG if `mode!="dump"` and there's a hit above floor.""" + if mode == "dump": + return lookup_dump(cfg, text=text) + rag_hits = await query_rag(cfg, text) + if mode == "rag": + return rag_hits + if rag_hits and rag_hits[0].score > score_floor: + return rag_hits + return lookup_dump(cfg, text=text) + + +def query_rag_sync(cfg: RorIndexConfig, text: str, **kwargs) -> List[ScoredRecord]: + return asyncio.run(query_rag(cfg, text, **kwargs)) + + +def query_sync(cfg: RorIndexConfig, text: str, **kwargs): + return asyncio.run(query(cfg, text, **kwargs)) + + +__all__: List[str] = [ + "lookup_dump", + "query", + "query_rag", + "query_rag_sync", + "query_sync", +] diff --git a/src/index/ror/rerank.py b/src/index/ror/rerank.py new file mode 100644 index 0000000..dccfa24 --- /dev/null +++ b/src/index/ror/rerank.py @@ -0,0 +1,76 @@ +"""RCP reranker client (Cohere-compatible `/rerank`). + +Posts `{model, query, documents}` and reads back `{results: [{index, relevance_score}, ...]}`. +This shape is what vLLM, Infinity, and most modern inference gateways expose +for cross-encoder / Qwen3-Reranker-style models. If RCP later switches the +schema, only this module needs updating. +""" + +from __future__ import annotations + +import logging +from typing import List, Sequence + +import httpx +from pydantic import BaseModel + +from .config import RcpConfig +from .embed import _auth_headers, _post_with_retry # noqa: PLC2701 + +logger = logging.getLogger(__name__) + + +class RerankError(RuntimeError): + """Raised on unrecoverable rerank failure.""" + + +class RerankResult(BaseModel): + index: int + score: float + + +async def rerank( + rcp: RcpConfig, + query: str, + documents: Sequence[str], + *, + top_n: int | None = None, +) -> List[RerankResult]: + """Rerank `documents` by relevance to `query`. Returns descending score order.""" + if not documents: + return [] + + payload = { + "model": rcp.reranker_model, + "query": query, + "documents": list(documents), + } + if top_n is not None: + payload["top_n"] = top_n + + url = rcp.base_url.rstrip("/") + "/rerank" + timeout = httpx.Timeout(rcp.timeout_seconds) + + async with httpx.AsyncClient(timeout=timeout) as client: + body = await _post_with_retry(client, url, payload, _auth_headers(rcp)) + + raw_results = body.get("results") + if not isinstance(raw_results, list): + msg = f"Rerank response missing 'results' list; got keys {list(body.keys())}" + raise RerankError(msg) + + out: List[RerankResult] = [] + for entry in raw_results: + if not isinstance(entry, dict): + continue + idx = entry.get("index") + score = entry.get("relevance_score", entry.get("score")) + if not isinstance(idx, int) or not isinstance(score, (int, float)): + continue + out.append(RerankResult(index=idx, score=float(score))) + + out.sort(key=lambda r: r.score, reverse=True) + return out + + +__all__: List[str] = ["RerankError", "RerankResult", "rerank"] diff --git a/src/index/ror/storage/__init__.py b/src/index/ror/storage/__init__.py new file mode 100644 index 0000000..0563854 --- /dev/null +++ b/src/index/ror/storage/__init__.py @@ -0,0 +1,20 @@ +"""DuckDB-backed metadata store for the ROR index. + +See `.internal/ror/duckdb-migration.md` (D16) for the design rationale. +""" + +from src.index.ror.storage.duckdb_store import ( + DuckDBStore, + ScopeRecord, + build_search_blob, + extract_record_columns, + fold_for_search, +) + +__all__ = [ + "DuckDBStore", + "ScopeRecord", + "build_search_blob", + "extract_record_columns", + "fold_for_search", +] diff --git a/src/index/ror/storage/duckdb_store.py b/src/index/ror/storage/duckdb_store.py new file mode 100644 index 0000000..24fbbff --- /dev/null +++ b/src/index/ror/storage/duckdb_store.py @@ -0,0 +1,755 @@ +"""DuckDB lifecycle, schema bootstrap, and upsert helpers for the ROR index. + +Mirrors the shape of `src/index/openalex/storage/duckdb_store.py` so a future +shared `src/index/_shared/` factor-up is mechanical. ROR-specific bits: + + - One DuckDB file per repo, three tables: `records` (full v2 dump), + `scope_records` (per-scope membership + Qdrant point id), `manifests`. + - `search_blob` is the lexical-lookup column — pre-folded (lowercase + + accent-stripped) concatenation of names/aliases/acronyms — replacing + `dump_index.py`'s in-memory inverted index with a `LIKE` over an + already-normalised string. +""" + +from __future__ import annotations + +import csv +import datetime as dt +import json +import logging +import os +import re +import tempfile +import unicodedata +import uuid +from contextlib import contextmanager +from pathlib import Path +from typing import TYPE_CHECKING, Any, Optional + +import duckdb +from pydantic import BaseModel + +from src.index.ror.paths import ror_data_dir + +if TYPE_CHECKING: + from collections.abc import Iterable, Iterator + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +def default_db_path() -> Path: + """`/ror/duckdb/ror.duckdb` — single-file shared by all scopes.""" + return ror_data_dir() / "duckdb" / "ror.duckdb" + + +# --------------------------------------------------------------------------- +# Search-blob helpers — also used by the migrator and tests. +# --------------------------------------------------------------------------- + +_TOKEN_RE = re.compile(r"[A-Za-z0-9]+") + + +def fold_for_search(text: str) -> str: + """Lowercase + drop combining marks (NFKD then drop Mn category). + + Matches the semantics of `dump_index.py:_fold`, so a record built into the + DuckDB store is searchable by the same query strings the in-memory index + accepted (e.g. `Universität` ↔ `Universitat`, `École` ↔ `Ecole`). + """ + nfkd = unicodedata.normalize("NFKD", text) + return "".join(ch for ch in nfkd if not unicodedata.combining(ch)).lower() + + +def _names_grouped(record: dict[str, Any]) -> dict[str, list[str]]: + grouped: dict[str, list[str]] = {} + for entry in record.get("names") or []: + if not isinstance(entry, dict): + continue + value = entry.get("value") + if not isinstance(value, str) or not value: + continue + for t in entry.get("types") or []: + grouped.setdefault(str(t), []).append(value) + return grouped + + +def build_search_blob(record: dict[str, Any]) -> str: + """Concatenate names + aliases + acronyms + label, return folded string.""" + parts: list[str] = [] + for entry in record.get("names") or []: + if isinstance(entry, dict): + value = entry.get("value") + if isinstance(value, str) and value: + parts.append(value) + return " | ".join(fold_for_search(p) for p in parts) + + +_NAMESPACE_URL = uuid.NAMESPACE_URL + + +def vector_id_for(ror_id: str) -> str: + """Deterministic UUIDv5 over the ROR URL — matches the existing Qdrant point id.""" + return str(uuid.uuid5(_NAMESPACE_URL, ror_id)) + + +def _bare_id(value: str) -> str: + return value.rstrip("/").rsplit("/", 1)[-1] + + +def _display_name(record: dict[str, Any]) -> Optional[str]: + grouped = _names_grouped(record) + if grouped.get("ror_display"): + return grouped["ror_display"][0] + if grouped.get("label"): + return grouped["label"][0] + for values in grouped.values(): + if values: + return values[0] + return None + + +def _first_location(record: dict[str, Any]) -> dict[str, Any]: + locations = record.get("locations") or [] + if isinstance(locations, list) and locations and isinstance(locations[0], dict): + details = locations[0].get("geonames_details") or {} + if isinstance(details, dict): + return details + return {} + + +def _first_website(record: dict[str, Any]) -> Optional[str]: + for link in record.get("links") or []: + if isinstance(link, dict) and link.get("type") == "website": + value = link.get("value") + if isinstance(value, str) and value: + return value + return None + + +def extract_record_columns( + record: dict[str, Any], + *, + ror_release_version: Optional[str] = None, +) -> dict[str, Any]: + """Turn a raw ROR v2 record into the column dict accepted by `upsert_record`. + + Pure: no I/O, no DB calls. The migrator and the future `build.py` both + funnel through this. + """ + rid = record.get("id") + if not isinstance(rid, str) or not rid: + msg = "ROR record is missing a string `id`" + raise ValueError(msg) + rid = rid.rstrip("/") + grouped = _names_grouped(record) + location = _first_location(record) + types_list = [str(t) for t in (record.get("types") or []) if t] + return { + "ror_id": rid, + "ror_id_short": _bare_id(rid), + "name": _display_name(record), + "search_blob": build_search_blob(record), + "status": record.get("status"), + "country_code": location.get("country_code"), + "country_name": location.get("country_name"), + "city": location.get("name"), + "region": location.get("country_subdivision_name") or location.get("region"), + "established": record.get("established"), + "website": _first_website(record), + "types_json": types_list, + "domains_json": record.get("domains") or [], + "names_json": record.get("names") or [], + "aliases_json": grouped.get("alias") or [], + "acronyms_json": grouped.get("acronym") or [], + "external_ids_json": record.get("external_ids") or [], + "relationships_json": record.get("relationships") or [], + "record": record, + "ror_release_version": ror_release_version, + } + + +# --------------------------------------------------------------------------- +# Pydantic carriers +# --------------------------------------------------------------------------- + + +class ScopeRecord(BaseModel): + """One row of `scope_records`. Bridge to a Qdrant point.""" + + scope_mode: str + ror_id: str + text: str + vector_id: str + embedded_at: Optional[str] = None + + +class StoreManifest(BaseModel): + """One row of `manifests`. Mirrors `models.IndexManifest` minus the dual + representation — kept separate so the storage layer doesn't import the + sidecar-era models. + """ + + scope_mode: str + record_count: int + embedding_model: str + embedding_dim: int + reranker_model: str + ror_release_version: Optional[str] = None + ror_release_doi: Optional[str] = None + built_at_iso: Optional[str] = None + + +# --------------------------------------------------------------------------- +# Store +# --------------------------------------------------------------------------- + + +_RECORD_COLS: tuple[str, ...] = ( + "ror_id", + "ror_id_short", + "name", + "search_blob", + "status", + "country_code", + "country_name", + "city", + "region", + "established", + "website", + "types_json", + "domains_json", + "names_json", + "aliases_json", + "acronyms_json", + "external_ids_json", + "relationships_json", + "record", + "ror_release_version", +) + +_JSON_COLS: frozenset[str] = frozenset( + { + "types_json", + "domains_json", + "names_json", + "aliases_json", + "acronyms_json", + "external_ids_json", + "relationships_json", + "record", + }, +) + + +def _build_records_upsert_sql() -> str: + cols = ", ".join(_RECORD_COLS) + placeholders = ", ".join(["?"] * len(_RECORD_COLS)) + update_cols = ", ".join(f"{c} = excluded.{c}" for c in _RECORD_COLS[1:]) + return ( + f"INSERT INTO records ({cols}) VALUES ({placeholders}) " + f"ON CONFLICT (ror_id) DO UPDATE SET " + f"{update_cols}, ingested_at = now()" + ) + + +# Cached at import time — the column list never changes, so repeating the +# string concat per row in a 125k-record load is wasted work. +_RECORDS_UPSERT_SQL: str = _build_records_upsert_sql() + +# Chunk size for batched ingest. Tuned so each transaction's undo log stays +# well under 100 MB even with the full record JSON in `record`. +_DEFAULT_CHUNK_SIZE: int = 2000 + + +class DuckDBStore: + """Thin wrapper around DuckDB tuned for the ROR schema. + + Construct with `DuckDBStore.open()` for the default repo path. Re-running + `bootstrap()` is idempotent. + """ + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open(cls, db_path: Path | None = None) -> DuckDBStore: + if db_path is None: + db_path = default_db_path() + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + """Apply the canonical schema. Safe to call repeatedly.""" + conn = self.connect() + conn.execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + """Open a fresh read-only connection (separate from the writer).""" + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + @contextmanager + def transaction(self) -> Iterator[None]: + """Wrap a batch of writes in a single BEGIN/COMMIT for throughput. + + DuckDB auto-commits each `execute()` by default, which makes per-row + inserts ~5–10× slower than batched ones. Wrapping ingest in this + context manager folds them into a single commit — important for the + ~125k-record full-dump load. + """ + conn = self.connect() + conn.execute("BEGIN TRANSACTION") + try: + yield + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + + # ---- Writes ---------------------------------------------------------- + + def upsert_record(self, columns: dict[str, Any]) -> None: + """Upsert one row into `records`. `columns` shape from `extract_record_columns`.""" + self.connect().execute(_RECORDS_UPSERT_SQL, _row_values(columns)) + + def upsert_records(self, rows: Iterable[dict[str, Any]]) -> int: + """Bulk upsert. Returns the number of rows written. + + Caller controls the transaction boundary — wrap a small batch in + `transaction()` for atomicity, or call `upsert_records_chunked()` for + a 125k-row full-dump load (chunked commits keep memory bounded). + """ + n = 0 + conn = self.connect() + for columns in rows: + conn.execute(_RECORDS_UPSERT_SQL, _row_values(columns)) + n += 1 + return n + + def bulk_replace_records( + self, + rows: Iterable[dict[str, Any]], + *, + csv_chunk_size: int = 50_000, + progress: Optional[Any] = None, + ) -> int: + """Replace the `records` table via streaming `COPY FROM CSV`. + + The `INSERT … ON CONFLICT` path through DuckDB's Python binding caps + out at ~18 rec/sec on this 20-column schema (measured), which would + make the 125k-row full-dump load take ~2 hours. `COPY FROM` runs the + whole load entirely inside DuckDB's vectorised executor and is + ~230× faster on the same data — 125k rows in ~30 s. + + Workflow per call: + 1. `DELETE FROM records` — truncates atomically inside a transaction. + 2. Stream `rows` through a temp CSV file in chunks of + `csv_chunk_size`, COPY each chunk, then delete the temp file. + 3. Commit. + + `progress`, if supplied, is called as `progress(rows_so_far)` after + every successful COPY. + + Use this path for the full-dump load. For one-off / incremental + writes (e.g. updating one record from `build.py`), use + `upsert_record` / `upsert_records_chunked` — the slow path is fine + when the volume is small. + """ + if csv_chunk_size <= 0: + msg = f"csv_chunk_size must be > 0, got {csv_chunk_size}" + raise ValueError(msg) + + conn = self.connect() + col_list = ", ".join(_RECORD_COLS) + copy_sql = ( + f"COPY records ({col_list}) FROM ? " + f"(FORMAT CSV, HEADER, DELIMITER ',', QUOTE '\"', ESCAPE '\"', NULLSTR '\\N', STRICT_MODE FALSE, PARALLEL FALSE)" + ) + + n = 0 + conn.execute("BEGIN TRANSACTION") + try: + conn.execute("DELETE FROM records") + buffer: list[dict[str, Any]] = [] + for columns in rows: + buffer.append(columns) + if len(buffer) >= csv_chunk_size: + self._copy_records_chunk(conn, buffer, copy_sql) + n += len(buffer) + buffer.clear() + if progress is not None: + progress(n) + if buffer: + self._copy_records_chunk(conn, buffer, copy_sql) + n += len(buffer) + if progress is not None: + progress(n) + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + return n + + @staticmethod + def _copy_records_chunk( + conn: duckdb.DuckDBPyConnection, + chunk: list[dict[str, Any]], + copy_sql: str, + ) -> None: + # Materialise the chunk as a temp CSV, then run a single COPY. + # `\N` is the NULL sentinel (matches the COPY clause above) — keeps + # NULL distinguishable from the empty string for nullable columns + # like `established`. + fd, path = tempfile.mkstemp(prefix="ror_records_", suffix=".csv", dir=tempfile.gettempdir()) + try: + with os.fdopen(fd, "w", encoding="utf-8", newline="") as f: + writer = csv.writer(f, quoting=csv.QUOTE_ALL) + writer.writerow(_RECORD_COLS) + for columns in chunk: + writer.writerow(_csv_row(columns)) + conn.execute(copy_sql, [path]) + finally: + try: + os.unlink(path) + except OSError: + pass + + def upsert_records_chunked( + self, + rows: Iterable[dict[str, Any]], + *, + chunk_size: int = _DEFAULT_CHUNK_SIZE, + progress: Optional[Any] = None, + ) -> int: + """Bulk upsert with per-chunk transactions and optional progress hook. + + Each chunk is its own BEGIN/COMMIT — this caps DuckDB's in-memory + undo log to one chunk's worth of rows, which is what makes a + 125k-record load fit in normal RAM. + + `progress`, if supplied, is called as `progress(rows_so_far)` after + every commit. + """ + if chunk_size <= 0: + msg = f"chunk_size must be > 0, got {chunk_size}" + raise ValueError(msg) + conn = self.connect() + n = 0 + buffer: list[dict[str, Any]] = [] + for columns in rows: + buffer.append(columns) + if len(buffer) >= chunk_size: + self._flush_records_chunk(conn, buffer) + n += len(buffer) + buffer.clear() + if progress is not None: + progress(n) + if buffer: + self._flush_records_chunk(conn, buffer) + n += len(buffer) + if progress is not None: + progress(n) + return n + + @staticmethod + def _flush_records_chunk( + conn: duckdb.DuckDBPyConnection, + chunk: list[dict[str, Any]], + ) -> None: + # Single multi-row VALUES INSERT instead of N single-row inserts — + # DuckDB's Python `executemany` was measured at ~18 rec/sec for the + # ON CONFLICT upsert; this collapses N rows into one statement and + # drops the per-row planning overhead. + if not chunk: + return + n_cols = len(_RECORD_COLS) + row_placeholder = "(" + ", ".join(["?"] * n_cols) + ")" + values = ", ".join([row_placeholder] * len(chunk)) + col_list = ", ".join(_RECORD_COLS) + update_cols = ", ".join(f"{c} = excluded.{c}" for c in _RECORD_COLS[1:]) + sql = ( + f"INSERT INTO records ({col_list}) VALUES {values} " + f"ON CONFLICT (ror_id) DO UPDATE SET " + f"{update_cols}, ingested_at = now()" + ) + params: list[Any] = [] + for columns in chunk: + params.extend(_row_values(columns)) + conn.execute("BEGIN TRANSACTION") + try: + conn.execute(sql, params) + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + + def set_scope_records( + self, scope_mode: str, rows: Iterable[ScopeRecord], + ) -> int: + """Replace all rows for `scope_mode` via streaming COPY FROM CSV. + + Same bulk path as `bulk_replace_records` — necessary because the + worldwide scope is 125k rows and the per-row INSERT path saturates + at ~18 rec/sec on this binding. + + Returns the row count written. + """ + conn = self.connect() + embedded_default = _now_iso() + conn.execute("BEGIN TRANSACTION") + try: + conn.execute( + "DELETE FROM scope_records WHERE scope_mode = ?", [scope_mode], + ) + buffer: list[ScopeRecord] = [] + n = 0 + for row in rows: + buffer.append(row) + if len(buffer) >= 50_000: + self._copy_scope_records_chunk(conn, buffer, embedded_default) + n += len(buffer) + buffer.clear() + if buffer: + self._copy_scope_records_chunk(conn, buffer, embedded_default) + n += len(buffer) + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + return n + + @staticmethod + def _copy_scope_records_chunk( + conn: duckdb.DuckDBPyConnection, + chunk: list[ScopeRecord], + embedded_default: str, + ) -> None: + fd, path = tempfile.mkstemp(prefix="ror_scope_", suffix=".csv", dir=tempfile.gettempdir()) + try: + with os.fdopen(fd, "w", encoding="utf-8", newline="") as f: + writer = csv.writer(f, quoting=csv.QUOTE_ALL) + writer.writerow(["scope_mode", "ror_id", "text", "vector_id", "embedded_at"]) + for row in chunk: + writer.writerow([ + row.scope_mode, + row.ror_id, + row.text, + row.vector_id, + row.embedded_at or embedded_default, + ]) + conn.execute( + "COPY scope_records (scope_mode, ror_id, text, vector_id, embedded_at) " + "FROM ? (FORMAT CSV, HEADER, DELIMITER ',', QUOTE '\"', ESCAPE '\"', NULLSTR '\\N', STRICT_MODE FALSE, PARALLEL FALSE)", + [path], + ) + finally: + try: + os.unlink(path) + except OSError: + pass + + def set_manifest(self, manifest: StoreManifest) -> None: + sql = ( + "INSERT INTO manifests " + "(scope_mode, record_count, embedding_model, embedding_dim, " + " reranker_model, ror_release_version, ror_release_doi, built_at_iso) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (scope_mode) DO UPDATE SET " + "record_count = excluded.record_count, " + "embedding_model = excluded.embedding_model, " + "embedding_dim = excluded.embedding_dim, " + "reranker_model = excluded.reranker_model, " + "ror_release_version = excluded.ror_release_version, " + "ror_release_doi = excluded.ror_release_doi, " + "built_at_iso = excluded.built_at_iso" + ) + self.connect().execute( + sql, + [ + manifest.scope_mode, + manifest.record_count, + manifest.embedding_model, + manifest.embedding_dim, + manifest.reranker_model, + manifest.ror_release_version, + manifest.ror_release_doi, + manifest.built_at_iso or _now_iso(), + ], + ) + + # ---- Reads ----------------------------------------------------------- + + def count_records(self) -> int: + result = self.connect().execute("SELECT count(*) FROM records").fetchone() + return int(result[0]) if result else 0 + + def count_scope_records(self, scope_mode: str) -> int: + result = self.connect().execute( + "SELECT count(*) FROM scope_records WHERE scope_mode = ?", + [scope_mode], + ).fetchone() + return int(result[0]) if result else 0 + + def fetch_record(self, ror_id: str) -> Optional[dict[str, Any]]: + """Exact match on ROR URL or bare id (`02s376052`). Returns the full + record JSON merged with structured columns, or None.""" + rid = ror_id.strip().rstrip("/") + bare = _bare_id(rid) + cur = self.connect().execute( + "SELECT * FROM records WHERE ror_id = ? OR ror_id_short = ? LIMIT 1", + [rid, bare], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return _hydrate_row(dict(zip(cols, row, strict=False))) + + def fetch_manifest(self, scope_mode: str) -> Optional[dict[str, Any]]: + cur = self.connect().execute( + "SELECT * FROM manifests WHERE scope_mode = ?", [scope_mode], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + def lookup( + self, + *, + text: Optional[str] = None, + ror_id: Optional[str] = None, + country: Optional[str] = None, + type_: Optional[str] = None, + status: Optional[str] = None, + limit: int = 20, + ) -> list[dict[str, Any]]: + """Lexical/exact lookup over the full `records` table. + + Returns the full hydrated rows (record JSON + structured columns). + Mirrors `dump_index.DumpIndex.search` semantics: + + - `ror_id` (URL or bare): single hit or []. + - `text`: tokenized, accent-folded; ranked by number of distinct + tokens that appear in `search_blob`. + - `country` / `type_` / `status`: payload filters; combinable. + """ + conn = self.connect() + + if ror_id: + hit = self.fetch_record(ror_id) + return [hit] if hit else [] + + where: list[str] = [] + params: list[Any] = [] + score_expr = "0" + + if text: + tokens = _TOKEN_RE.findall(fold_for_search(text)) + if not tokens: + return [] + score_terms: list[str] = [] + or_terms: list[str] = [] + for tok in tokens: + like = f"%{tok}%" + score_terms.append("(CASE WHEN search_blob LIKE ? THEN 1 ELSE 0 END)") + params.append(like) + or_terms.append("search_blob LIKE ?") + # SCORE params first, then WHERE params. + params.extend([f"%{t}%" for t in tokens]) + score_expr = " + ".join(score_terms) + where.append("(" + " OR ".join(or_terms) + ")") + + if country: + where.append("country_code = ?") + params.append(country.upper()) + if type_: + where.append("list_contains(CAST(types_json AS VARCHAR[]), ?)") + params.append(type_) + if status: + where.append("status = ?") + params.append(status) + + sql = f"SELECT *, ({score_expr}) AS _score FROM records" + if where: + sql += " WHERE " + " AND ".join(where) + sql += " ORDER BY _score DESC, ror_id ASC LIMIT ?" + params.append(int(limit)) + + cur = conn.execute(sql, params) + cols = [d[0] for d in cur.description] + rows = cur.fetchall() + return [_hydrate_row(dict(zip(cols, r, strict=False))) for r in rows] + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _row_values(columns: dict[str, Any]) -> list[Any]: + out: list[Any] = [] + for col in _RECORD_COLS: + v = columns.get(col) + if col in _JSON_COLS: + out.append(json.dumps(v if v is not None else None, ensure_ascii=False)) + else: + out.append(v) + return out + + +_NULL_SENTINEL = "\\N" + + +def _csv_row(columns: dict[str, Any]) -> list[str]: + """CSV-encode one row. `None` renders as `\\N` (matches `NULLSTR '\\N'`).""" + out: list[str] = [] + for col in _RECORD_COLS: + v = columns.get(col) + if v is None: + out.append(_NULL_SENTINEL) + elif col in _JSON_COLS: + out.append(json.dumps(v, ensure_ascii=False)) + else: + out.append(str(v)) + return out + + +def _hydrate_row(row: dict[str, Any]) -> dict[str, Any]: + """Parse JSON-typed columns into Python values for caller convenience.""" + out = dict(row) + for col in _JSON_COLS: + if col in out and isinstance(out[col], str): + try: + out[col] = json.loads(out[col]) + except (TypeError, ValueError): + pass + return out + + +def _now_iso() -> str: + return dt.datetime.now(dt.timezone.utc).isoformat() diff --git a/src/index/ror/storage/migrate_storage.py b/src/index/ror/storage/migrate_storage.py new file mode 100644 index 0000000..00c75be --- /dev/null +++ b/src/index/ror/storage/migrate_storage.py @@ -0,0 +1,291 @@ +"""One-shot porter: legacy JSONL+manifest sidecars → DuckDB (D16, PR 3). + +Walks `/ror/index//` for each scope and folds the +existing `records.jsonl` + `manifest.json` into the DuckDB store. Also loads +the cached ROR v2 dump JSON into the `records` table so the lexical-lookup +path has the full ~125k records to query. + +**Does not** call RCP and **does not** touch Qdrant beyond a read-only count +check at the end. Vectors stay where they are; the deterministic UUIDv5 +`vector_id` we write into `scope_records` matches the existing Qdrant point +ids (built the same way at upsert time in `qdrant_store.py`). + +Idempotent: re-running upserts records with `ON CONFLICT DO UPDATE` and +replaces `scope_records` for each scope as a unit. +""" + +from __future__ import annotations + +import json +import logging +import re +from pathlib import Path +from typing import Any, Iterable, Iterator, Optional + +from src.index.ror.config import RorIndexConfig +from src.index.ror.paths import dump_dir, index_data_root, manifest_path, records_path +from src.index.ror.qdrant_store import QdrantRorStore +from src.index.ror.storage.duckdb_store import ( + DuckDBStore, + ScopeRecord, + StoreManifest, + extract_record_columns, + vector_id_for, +) + +LOGGER = logging.getLogger(__name__) + +_VERSION_DIR_RE = re.compile(r"^v?\d+(\.\d+)*([_.-].*)?$") + + +# --------------------------------------------------------------------------- +# Discovery helpers +# --------------------------------------------------------------------------- + + +def list_scope_dirs() -> list[str]: + base = index_data_root() / "ror" / "index" + if not base.exists(): + return [] + return sorted(p.name for p in base.iterdir() if p.is_dir()) + + +def find_cached_dump_json() -> Optional[Path]: + """Locate the most recent cached ROR v2 dump JSON. + + Returns the highest-version `*-ror-data.json` (or `_schema_v2.json`) + under `/ror/dump/`. Returns None if no dump has been + downloaded — caller should run `python -m src.index.ror build` first. + """ + base = dump_dir() + if not base.exists(): + return None + versions = sorted( + (p for p in base.iterdir() if p.is_dir() and _VERSION_DIR_RE.match(p.name)), + key=lambda p: p.name, + reverse=True, + ) + for vdir in versions: + for child in vdir.iterdir(): + if child.is_file() and child.name.endswith(".json") and "ror-data" in child.name: + return child + return None + + +def read_release_metadata(json_path: Path) -> dict[str, Any]: + """Load `release.json` next to the dump JSON. Returns empty dict if absent.""" + meta = json_path.parent / "release.json" + if not meta.exists(): + return {} + try: + return json.loads(meta.read_text(encoding="utf-8")) + except (OSError, ValueError): + return {} + + +# --------------------------------------------------------------------------- +# Loaders (pure, no DB writes) +# --------------------------------------------------------------------------- + + +def iter_dump_records(json_path: Path) -> Iterator[dict[str, Any]]: + """Stream the ROR v2 dump records. + + The dump is a single JSON array on disk. DuckDB can read it with + `read_json_auto` for ingest, but we go through Python because we want + `extract_record_columns` to compute the `search_blob` and structured + columns deterministically (and to reuse the same shape `build.py` will + use). + """ + with json_path.open("r", encoding="utf-8") as f: + data = json.load(f) + if not isinstance(data, list): + msg = f"Expected ROR dump JSON to be a list at the top level: {json_path}" + raise ValueError(msg) + for record in data: + if isinstance(record, dict): + yield record + + +def iter_jsonl_records(jsonl_path: Path) -> Iterator[dict[str, Any]]: + with jsonl_path.open("r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + yield json.loads(line) + + +# --------------------------------------------------------------------------- +# Migration steps +# --------------------------------------------------------------------------- + + +def populate_full_dump( + store: DuckDBStore, + json_path: Path, + *, + release_version: Optional[str] = None, + csv_chunk_size: int = 50_000, +) -> int: + """Load all records from the cached ROR dump JSON into the `records` table. + + Uses `DuckDBStore.bulk_replace_records` (COPY FROM CSV) — ~230× faster + than the per-row INSERT/UPSERT path (measured 4170 rec/sec vs 18 rec/sec + on this schema). The `records` table is fully replaced inside a single + transaction, so a partial run leaves the previous state intact. + """ + LOGGER.info("Loading ROR dump from %s", json_path) + + def _columns_iter(): + for record in iter_dump_records(json_path): + try: + yield extract_record_columns(record, ror_release_version=release_version) + except ValueError: + LOGGER.warning("Skipping ROR record without id: %r", record.get("id")) + continue + + n = store.bulk_replace_records( + _columns_iter(), + csv_chunk_size=csv_chunk_size, + progress=lambda done: LOGGER.info( + "Loaded %d records into `records` (COPY FROM CSV)", done, + ), + ) + LOGGER.info("Done — %d records in `records`", n) + return n + + +def populate_scope(store: DuckDBStore, scope_mode: str) -> dict[str, Any]: + """Port one scope's `records.jsonl` + `manifest.json` into DuckDB. + + Returns a summary dict including the row count written and the manifest. + Raises `FileNotFoundError` if the scope's sidecar files are missing. + """ + rp = records_path(scope_mode) + mp = manifest_path(scope_mode) + if not rp.exists(): + msg = f"records.jsonl not found for scope {scope_mode!r}: {rp}" + raise FileNotFoundError(msg) + if not mp.exists(): + msg = f"manifest.json not found for scope {scope_mode!r}: {mp}" + raise FileNotFoundError(msg) + + manifest = json.loads(mp.read_text(encoding="utf-8")) + + rows: list[ScopeRecord] = [] + for entry in iter_jsonl_records(rp): + rid = entry.get("ror_id") + text = entry.get("text") or "" + if not isinstance(rid, str) or not rid: + continue + rows.append( + ScopeRecord( + scope_mode=scope_mode, + ror_id=rid.rstrip("/"), + text=text, + vector_id=vector_id_for(rid.rstrip("/")), + ), + ) + + n = store.set_scope_records(scope_mode, rows) + store.set_manifest( + StoreManifest( + scope_mode=scope_mode, + record_count=int(manifest.get("record_count", n)), + embedding_model=str(manifest.get("embedding_model", "")), + embedding_dim=int(manifest.get("embedding_dim", 0)), + reranker_model=str(manifest.get("reranker_model", "")), + ror_release_version=manifest.get("ror_release_version"), + ror_release_doi=manifest.get("ror_release_doi"), + built_at_iso=manifest.get("built_at_iso"), + ), + ) + LOGGER.info("Migrated scope=%s rows=%d (jsonl→duckdb)", scope_mode, n) + return {"scope_mode": scope_mode, "rows": n, "manifest": manifest} + + +def verify_against_qdrant( + cfg: RorIndexConfig, store: DuckDBStore, scope_mode: str, +) -> dict[str, Any]: + """Read-only Qdrant point count + DuckDB scope_records count, compared.""" + duck_count = store.count_scope_records(scope_mode) + qstore = QdrantRorStore(cfg) + try: + qdrant_count = qstore.count(scope_mode) + except Exception as exc: # noqa: BLE001 + LOGGER.warning( + "Could not read Qdrant count for scope=%s: %s. " + "Migration of DuckDB succeeded; Qdrant comparison skipped.", + scope_mode, exc, + ) + qdrant_count = None + match = (qdrant_count is not None) and (duck_count == qdrant_count) + return { + "scope_mode": scope_mode, + "duckdb_count": duck_count, + "qdrant_count": qdrant_count, + "match": match, + } + + +def migrate_all( + cfg: RorIndexConfig, + *, + db_path: Optional[Path] = None, + dump_path: Optional[Path] = None, + skip_qdrant_check: bool = False, +) -> dict[str, Any]: + """Run the full storage migration. Returns a summary suitable for printing.""" + store = DuckDBStore.open(db_path) + + json_path = dump_path or find_cached_dump_json() + if json_path is None: + msg = ( + "No cached ROR dump JSON found under /ror/dump/. " + "Run `python -m src.index.ror build` once to download a dump first." + ) + raise FileNotFoundError(msg) + release_meta = read_release_metadata(json_path) + release_version = release_meta.get("version") or _guess_version_from_path(json_path) + record_count = populate_full_dump(store, json_path, release_version=release_version) + + scopes_summary: list[dict[str, Any]] = [] + for scope in list_scope_dirs(): + try: + scopes_summary.append(populate_scope(store, scope)) + except FileNotFoundError as exc: + LOGGER.warning("Skipping scope %s: %s", scope, exc) + + checks: list[dict[str, Any]] = [] + if not skip_qdrant_check: + for entry in scopes_summary: + checks.append(verify_against_qdrant(cfg, store, entry["scope_mode"])) + + store.close() + return { + "dump_path": str(json_path), + "release_version": release_version, + "records_loaded": record_count, + "scopes": scopes_summary, + "qdrant_checks": checks, + } + + +def _guess_version_from_path(json_path: Path) -> Optional[str]: + """Fallback when `release.json` is missing — read the parent dir name.""" + parent = json_path.parent.name + return parent if _VERSION_DIR_RE.match(parent) else None + + +__all__ = [ + "find_cached_dump_json", + "iter_dump_records", + "iter_jsonl_records", + "list_scope_dirs", + "migrate_all", + "populate_full_dump", + "populate_scope", + "read_release_metadata", + "verify_against_qdrant", +] diff --git a/src/index/ror/storage/schema.sql b/src/index/ror/storage/schema.sql new file mode 100644 index 0000000..be2b0e7 --- /dev/null +++ b/src/index/ror/storage/schema.sql @@ -0,0 +1,53 @@ +-- Canonical DuckDB schema for the ROR index module (D16). +-- Idempotent: every statement uses IF NOT EXISTS so re-runs are safe. +-- See .internal/ror/duckdb-migration.md for column-by-column rationale. + +CREATE TABLE IF NOT EXISTS records ( + ror_id TEXT PRIMARY KEY, + ror_id_short TEXT UNIQUE, + name TEXT, + search_blob TEXT, + status TEXT, + country_code TEXT, + country_name TEXT, + city TEXT, + region TEXT, + established INTEGER, + website TEXT, + types_json JSON, + domains_json JSON, + names_json JSON, + aliases_json JSON, + acronyms_json JSON, + external_ids_json JSON, + relationships_json JSON, + record JSON, + ror_release_version TEXT, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS records_country_idx ON records(country_code); +CREATE INDEX IF NOT EXISTS records_status_idx ON records(status); + +CREATE TABLE IF NOT EXISTS scope_records ( + scope_mode TEXT NOT NULL, + ror_id TEXT NOT NULL, + text TEXT, + vector_id TEXT, + embedded_at TIMESTAMP, + PRIMARY KEY (scope_mode, ror_id) +); + +CREATE INDEX IF NOT EXISTS scope_records_vector_idx ON scope_records(vector_id); +CREATE INDEX IF NOT EXISTS scope_records_ror_idx ON scope_records(ror_id); + +CREATE TABLE IF NOT EXISTS manifests ( + scope_mode TEXT PRIMARY KEY, + record_count INTEGER, + embedding_model TEXT, + embedding_dim INTEGER, + reranker_model TEXT, + ror_release_version TEXT, + ror_release_doi TEXT, + built_at_iso TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); diff --git a/src/index/ror/store.py b/src/index/ror/store.py new file mode 100644 index 0000000..d7d18d3 --- /dev/null +++ b/src/index/ror/store.py @@ -0,0 +1,83 @@ +"""Legacy sidecar readers for the ROR index. + +After D16 (see `.internal/ror/duckdb-migration.md`), all writes go through +`storage.duckdb_store.DuckDBStore`. This module survives only to support: + + - reading existing `records.jsonl` / `manifest.json` files left on disk + by pre-D16 builds (used by the `migrate-storage` porter and the + one-shot D15 FAISS→Qdrant migrator), + - the `read_legacy_faiss()` helper that opens an `index.faiss` file when + present (D15). + +New code should not call `write_sidecar` — it has been removed. New builds +write to DuckDB only. +""" + +from __future__ import annotations + +import datetime as dt +import json +import logging +from typing import List + +from .models import IndexedRecord, IndexManifest +from .paths import faiss_path, manifest_path, records_path + +logger = logging.getLogger(__name__) + + +def now_iso() -> str: + return dt.datetime.now(dt.timezone.utc).isoformat() + + +def read_records(scope_mode: str) -> List[IndexedRecord]: + rp = records_path(scope_mode) + if not rp.exists(): + msg = f"Records file not found: {rp}. Run `build` first." + raise FileNotFoundError(msg) + out: List[IndexedRecord] = [] + with rp.open("r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + out.append(IndexedRecord(**json.loads(line))) + return out + + +def read_manifest(scope_mode: str) -> IndexManifest: + mp = manifest_path(scope_mode) + if not mp.exists(): + msg = f"Manifest not found: {mp}. Run `build` first." + raise FileNotFoundError(msg) + return IndexManifest(**json.loads(mp.read_text(encoding="utf-8"))) + + +def has_legacy_faiss(scope_mode: str) -> bool: + return faiss_path(scope_mode).exists() + + +def read_legacy_faiss(scope_mode: str): + """Open a legacy FAISS index for migration. Lazily imports faiss-cpu.""" + fp = faiss_path(scope_mode) + if not fp.exists(): + msg = f"Legacy FAISS index not found: {fp}" + raise FileNotFoundError(msg) + try: + import faiss + except ImportError as exc: + msg = ( + "faiss-cpu is required to migrate legacy ROR indexes. " + "Install with `pip install faiss-cpu`." + ) + raise RuntimeError(msg) from exc + return faiss.read_index(str(fp)) + + +__all__: List[str] = [ + "has_legacy_faiss", + "now_iso", + "read_legacy_faiss", + "read_manifest", + "read_records", +] diff --git a/src/index/snsf/__init__.py b/src/index/snsf/__init__.py new file mode 100644 index 0000000..6c614f9 --- /dev/null +++ b/src/index/snsf/__init__.py @@ -0,0 +1,14 @@ +"""SNSF P3 (Swiss National Science Foundation) local index. + +Pipeline (Phase 1): + + POST /api/grants/export ──► paginate ──► DuckDB `grants` table + POST /api/grants/search ──► (optional --enrich) ──► same row, _source columns + + snsf.lookup(...) → SQL over `grants` (count, filter, group-by) + +Phase 2 will add embedding (Qwen3-Embedding-8B via EPFL RCP) and Qdrant +`snsf_` collections to mirror the openalex/ror sibling shape. + +Entry point: `python -m src.index.snsf `. See `.internal/snsf/`. +""" diff --git a/src/index/snsf/__main__.py b/src/index/snsf/__main__.py new file mode 100644 index 0000000..acf428e --- /dev/null +++ b/src/index/snsf/__main__.py @@ -0,0 +1,9 @@ +from __future__ import annotations + +import sys + +from src.index.snsf.cli import main + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/index/snsf/_federated.py b/src/index/snsf/_federated.py new file mode 100644 index 0000000..117a21a --- /dev/null +++ b/src/index/snsf/_federated.py @@ -0,0 +1,41 @@ +"""SNSF registration with the federated discover/hydrate registries. + +v1 stub. SNSF is a small index — just registering the surface so it +shows up in ``gme indices``. Real discover/hydrate to follow. +""" + +from __future__ import annotations + +import logging +from typing import Any, Iterator + +from src.index._federated.dh_registry import register_discoverer, register_hydrator +from src.index._federated.protocols import ( + HydrationSummary, + IndexDiscoverer, + IndexHydrator, + Seed, +) + +LOGGER = logging.getLogger(__name__) + + +class SNSFDiscoverer: + name = "snsf" + accepted_sources: tuple[str, ...] = () + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + message = "SNSF discover not yet implemented" + raise ValueError(message) + + +class SNSFHydrator: + name = "snsf" + accepted_seed_types: tuple[str, ...] = () + + def hydrate(self, seeds, *, only_unfetched: bool = True) -> HydrationSummary: + return HydrationSummary() + + +register_discoverer(SNSFDiscoverer()) +register_hydrator(SNSFHydrator()) diff --git a/src/index/snsf/cli.py b/src/index/snsf/cli.py new file mode 100644 index 0000000..9ba8132 --- /dev/null +++ b/src/index/snsf/cli.py @@ -0,0 +1,242 @@ +"""CLI for the SNSF P3 index (Phase 1). + +Usage: + python -m src.index.snsf load-local [--source-dir PATH] [--scope SCOPE] + [--db-path PATH] [--skip-persons] + [--skip-disciplines] + python -m src.index.snsf stats [--scope SCOPE] + python -m src.index.snsf lookup --grant-number 123456 +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from pathlib import Path + +from src.index.snsf.config import DEFAULT_CONFIG_PATH, load_config +from src.index.snsf.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(prog="src.index.snsf") + parser.add_argument( + "--config", type=Path, default=DEFAULT_CONFIG_PATH, + help=f"Path to YAML config (default: {DEFAULT_CONFIG_PATH})", + ) + parser.add_argument( + "-v", "--verbose", action="store_true", help="Enable debug logging", + ) + sub = parser.add_subparsers(dest="cmd", required=True) + + p_load = sub.add_parser( + "load-local", + help=( + "Ingest the SNSF bulk CSVs from a local directory into DuckDB. " + "User must download the CSVs first — see .internal/snsf/README.md." + ), + ) + p_load.add_argument( + "--source-dir", type=Path, default=None, + help="Directory holding the CSV set (default: data/index/snsf/raw/)", + ) + p_load.add_argument( + "--db-path", type=Path, default=None, + help="Override DuckDB file path (default: /snsf/duckdb/snsf.duckdb)", + ) + p_load.add_argument( + "--scope", default=None, + help="Override active scope mode (epfl|ethz|eth_domain|switzerland)", + ) + p_load.add_argument( + "--skip-persons", action="store_true", + help="Don't load persons.csv", + ) + p_load.add_argument( + "--skip-disciplines", action="store_true", + help="Don't load SNF_field_of_research_disciplines.csv", + ) + p_load.add_argument( + "--skip-outputs", action="store_true", + help="Don't load output_data_*.csv (publications/datasets/etc.)", + ) + + p_stats = sub.add_parser("stats", help="Show manifest + scope counts from DuckDB") + p_stats.add_argument( + "--scope", default=None, + help="Scope mode to report (default: cfg.scope.active)", + ) + + p_lookup = sub.add_parser("lookup", help="Fetch a single grant row by GrantNumber") + p_lookup.add_argument("--grant-number", type=int, required=True) + + p_embed = sub.add_parser( + "embed", + help="Embed all scope grants via RCP + upsert to Qdrant snsf_", + ) + p_embed.add_argument( + "--scope", default=None, + help="Scope to embed (default: cfg.scope.active)", + ) + p_embed.add_argument( + "--keep-existing", action="store_true", + help="Keep the existing Qdrant collection (default: drop+recreate)", + ) + + p_query = sub.add_parser("query", help="Semantic search via Qdrant + RCP rerank") + p_query.add_argument("text") + p_query.add_argument("--top-k", type=int, default=None) + p_query.add_argument("--rerank-top-k", type=int, default=None) + p_query.add_argument("--scope", default=None) + p_query.add_argument("--institution", default=None, + help="Filter by research_institution payload field (exact match)") + p_query.add_argument("--institute", default=None, + help=( + "Post-filter by `institute` (specific lab/centre name) " + "via DuckDB lookup. Substring match, case-insensitive. " + "Example: --institute 'Swiss Data Science Center'." + )) + p_query.add_argument("--discipline-l1", default=None, + help="Filter by main_discipline_l1 payload field") + p_query.add_argument("--state", default=None, + help="Filter by state payload field (e.g. 'completed', 'ongoing')") + + p_link = sub.add_parser( + "orcid-link", + help=( + "Look up an ORCID in both SNSF persons and OpenAlex authors. " + "Returns the person's SNSF grants + OpenAlex works." + ), + ) + p_link.add_argument("--orcid", required=True, help="ORCID (any format)") + p_link.add_argument("--snsf-scope", default=None, + help="Limit SNSF grants to this scope (epfl|ethz|...|switzerland)") + p_link.add_argument("--grant-limit", type=int, default=50) + p_link.add_argument("--work-limit", type=int, default=50) + + p_cov = sub.add_parser( + "orcid-coverage", + help="Report how many SNSF persons (in scope) link to OpenAlex authors via ORCID.", + ) + p_cov.add_argument("--snsf-scope", default=None, + help="SNSF scope to count over (default: all)") + + return parser + + +def _print_json(obj) -> None: + print(json.dumps(obj, ensure_ascii=False, indent=2, default=str)) + + +def main(argv=None) -> int: + parser = _build_parser() + args = parser.parse_args(argv) + logging.basicConfig( + level=logging.DEBUG if args.verbose else logging.INFO, + format="%(asctime)s %(levelname)s %(name)s | %(message)s", + ) + + if args.cmd == "load-local": + from src.index.snsf.ingest import local_ingest + cfg = load_config(args.config) + summary = local_ingest.run( + cfg, + source_dir=args.source_dir, + db_path=args.db_path, + skip_persons=args.skip_persons, + skip_disciplines=args.skip_disciplines, + skip_outputs=args.skip_outputs, + scope_mode=args.scope, + ) + _print_json(summary.model_dump()) + return 0 + + if args.cmd == "stats": + cfg = load_config(args.config) + scope = args.scope or cfg.scope.active + store = DuckDBStore.open() + try: + counts = { + "grants_total": store.count_grants(), + "persons_total": store.count_persons(), + "disciplines_total": store.count_disciplines(), + "scope": scope, + "scope_grants": store.count_scope_records(scope), + "manifest": store.fetch_manifest(scope), + } + finally: + store.close() + if counts["manifest"] is None: + print( + f"No manifest in DuckDB for scope {scope!r}. " + f"Run `python -m src.index.snsf load-local` first.", + file=sys.stderr, + ) + _print_json(counts) + return 0 if counts["manifest"] is not None else 1 + + if args.cmd == "lookup": + store = DuckDBStore.open() + try: + row = store.fetch_grant(args.grant_number) + finally: + store.close() + if row is None: + print(f"GrantNumber {args.grant_number} not found", file=sys.stderr) + return 1 + _print_json(row) + return 0 + + if args.cmd == "embed": + from src.index.snsf import embed_pipeline + cfg = load_config(args.config) + summary = embed_pipeline.run_sync( + cfg, + scope_mode=args.scope, + recreate=not args.keep_existing, + ) + _print_json(summary) + return 0 + + if args.cmd == "orcid-link": + from src.index.snsf.orcid_link import link_by_orcid + result = link_by_orcid( + args.orcid, + snsf_scope=args.snsf_scope, + grant_limit=args.grant_limit, + work_limit=args.work_limit, + ) + _print_json(result) + return 0 + + if args.cmd == "orcid-coverage": + from src.index.snsf.orcid_link import coverage_report + _print_json(coverage_report(args.snsf_scope)) + return 0 + + if args.cmd == "query": + from src.index.snsf.query import query_rag_sync + cfg = load_config(args.config) + results = query_rag_sync( + cfg, args.text, + top_k=args.top_k, + rerank_top_k=args.rerank_top_k, + institution=args.institution, + institute=args.institute, + discipline_l1=args.discipline_l1, + state=args.state, + scope_mode=args.scope, + ) + _print_json(results) + return 0 + + parser.print_help() + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/index/snsf/config.py b/src/index/snsf/config.py new file mode 100644 index 0000000..e9badd5 --- /dev/null +++ b/src/index/snsf/config.py @@ -0,0 +1,119 @@ +"""Pydantic config for the SNSF P3 index. + +Resolved by `load_config()` from `config/index/snsf.yaml` and overlayed +with env vars: `RCP_TOKEN`, `INDEX_QDRANT_URL`, `INDEX_QDRANT_API_KEY`, +`INDEX_QDRANT_PREFER_GRPC`. +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Any, Dict, List, Literal, Optional + +import yaml +from pydantic import BaseModel, Field + +DEFAULT_CONFIG_PATH = Path(__file__).resolve().parents[3] / "config" / "index" / "snsf.yaml" + + +ScopeMode = Literal["epfl", "ethz", "eth_domain", "switzerland"] + + +class SnsfApiConfig(BaseModel): + base_url: str = "https://data.snf.ch" + request_timeout_s: float = 60.0 + page_size: int = 1000 + inter_request_sleep_s: float = 0.2 + user_agent: str = ( + "git-metadata-extractor/snsf (polite scraper; email if issues)" + ) + + +class ScopeConfig(BaseModel): + active: ScopeMode = "epfl" + filters: Dict[str, Dict[str, List[str]]] = Field(default_factory=dict) + + def filter_for_active_scope(self) -> Dict[str, List[str]]: + return dict(self.filters.get(self.active, {})) + + +class RcpConfig(BaseModel): + """EPFL RCP OpenAI-compatible inference gateway.""" + + base_url: str = "https://inference-rcp.epfl.ch/v1" + embedding_model: str = "Qwen/Qwen3-Embedding-8B" + embedding_dim: int = 4096 + query_instruction: str = ( + "Given a query, retrieve SNSF grant titles and abstracts that match" + ) + reranker_model: str = "Qwen/Qwen3-Reranker-8B" + batch_size: int = 32 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: Optional[str] = None + + +class QdrantConfig(BaseModel): + url: str = "http://gme-qdrant:6333" + api_key: Optional[str] = None + prefer_grpc: bool = False + collection_prefix: str = "snsf" + + +class RetrievalConfig(BaseModel): + top_k: int = 50 + rerank_top_k: int = 10 + + +class SnsfIndexConfig(BaseModel): + snsf: SnsfApiConfig = Field(default_factory=SnsfApiConfig) + scope: ScopeConfig = Field(default_factory=ScopeConfig) + rcp: RcpConfig = Field(default_factory=RcpConfig) + qdrant: QdrantConfig = Field(default_factory=QdrantConfig) + retrieval: RetrievalConfig = Field(default_factory=RetrievalConfig) + + def collection_name(self) -> str: + return f"{self.qdrant.collection_prefix}_{self.scope.active}" + + +def _env_override(cfg: SnsfIndexConfig) -> SnsfIndexConfig: + """Apply env-var overrides post-load. Same shape as the ror sibling.""" + rcp_token = os.environ.get("RCP_TOKEN", "").strip() + if rcp_token: + cfg.rcp.token = rcp_token + + qurl = os.environ.get("INDEX_QDRANT_URL", "").strip() + if qurl: + cfg.qdrant.url = qurl + qkey = os.environ.get("INDEX_QDRANT_API_KEY", "").strip() + if qkey: + cfg.qdrant.api_key = qkey + qgrpc = os.environ.get("INDEX_QDRANT_PREFER_GRPC", "").strip().lower() + if qgrpc in {"1", "true", "yes", "on"}: + cfg.qdrant.prefer_grpc = True + elif qgrpc in {"0", "false", "no", "off"}: + cfg.qdrant.prefer_grpc = False + + return cfg + + +def load_config(path: Path | None = None) -> SnsfIndexConfig: + src = Path(path) if path else DEFAULT_CONFIG_PATH + raw: Dict[str, Any] = {} + if src.exists(): + raw = yaml.safe_load(src.read_text(encoding="utf-8")) or {} + return _env_override(SnsfIndexConfig(**raw)) + + +__all__ = [ + "DEFAULT_CONFIG_PATH", + "QdrantConfig", + "RcpConfig", + "RetrievalConfig", + "ScopeConfig", + "ScopeMode", + "SnsfApiConfig", + "SnsfIndexConfig", + "load_config", +] diff --git a/src/index/snsf/document.py b/src/index/snsf/document.py new file mode 100644 index 0000000..cf97e1c --- /dev/null +++ b/src/index/snsf/document.py @@ -0,0 +1,50 @@ +"""Build the embedding-ready text for one SNSF grant row. + +Composes title (preferring English) + keywords + main discipline + the +technical Abstract. Skips the lay summaries — those use simpler language +and would dilute the technical signal we're embedding for. +""" + +from __future__ import annotations + +from typing import Any, Dict + + +def _coalesce(*values: Any) -> str: + for v in values: + if isinstance(v, str) and v.strip(): + return v.strip() + return "" + + +def to_document(grant: Dict[str, Any]) -> str: + """Flatten a `grants` row dict into a single embedding-ready string. + + `grant` is the dict shape returned by `DuckDBStore.fetch_grant`; columns + are snake_case (per `storage/schema.sql`). + """ + title = _coalesce(grant.get("title_english"), grant.get("title")) + keywords = _coalesce(grant.get("keywords")) + discipline = _coalesce(grant.get("main_discipline")) + field = _coalesce(grant.get("main_field_of_research")) + abstract = _coalesce(grant.get("abstract")) + + lines = [] + if title: + lines.append(f"Title: {title}") + if discipline: + lines.append(f"Discipline: {discipline}") + if field and field != discipline: + lines.append(f"Field: {field}") + if keywords: + lines.append(f"Keywords: {keywords}") + if abstract: + lines.append(f"Abstract: {abstract}") + return "\n".join(lines) + + +def short_label(grant: Dict[str, Any]) -> str: + """Used for log lines + reranker fallback when abstract is missing.""" + title = _coalesce(grant.get("title_english"), grant.get("title")) + inst = _coalesce(grant.get("research_institution")) + return f"{title} ({inst})" if inst else title diff --git a/src/index/snsf/embed.py b/src/index/snsf/embed.py new file mode 100644 index 0000000..1a7a883 --- /dev/null +++ b/src/index/snsf/embed.py @@ -0,0 +1,171 @@ +"""RCP embedding client (OpenAI-compatible `/embeddings`). + +Batched, retried with exponential backoff on 429/5xx, semaphore-bounded +concurrency. Asserts the server's reported embedding dim matches +`config.rcp.embedding_dim` on the first response and fails loudly on mismatch. +Designed for `Qwen/Qwen3-Embedding-8B` but works with any OpenAI-compatible +embedding model. +""" + +from __future__ import annotations + +import asyncio +import logging +import math +import random +from typing import List, Optional, Sequence + +import httpx +import numpy as np + +from src.index.snsf.config import RcpConfig + +logger = logging.getLogger(__name__) + + +class EmbeddingError(RuntimeError): + """Raised on unrecoverable embedding failure.""" + + +_MAX_RETRIES = 5 +_RETRY_BASE_DELAY = 1.5 +_RETRY_STATUS = frozenset({408, 425, 429, 500, 502, 503, 504}) + + +def _auth_headers(rcp: RcpConfig) -> dict: + headers = {"Content-Type": "application/json"} + if rcp.token: + headers["Authorization"] = f"Bearer {rcp.token}" + return headers + + +async def _post_with_retry( + client: httpx.AsyncClient, + url: str, + payload: dict, + headers: dict, +) -> dict: + last_error: Optional[Exception] = None + for attempt in range(_MAX_RETRIES): + try: + resp = await client.post(url, json=payload, headers=headers) + except (httpx.TimeoutException, httpx.TransportError) as exc: + last_error = exc + delay = _RETRY_BASE_DELAY * math.pow(2, attempt) + random.random() + logger.warning( + "Embedding request transport error (attempt %d/%d): %s; sleeping %.1fs", + attempt + 1, _MAX_RETRIES, exc, delay, + ) + await asyncio.sleep(delay) + continue + if resp.status_code in _RETRY_STATUS: + delay = _RETRY_BASE_DELAY * math.pow(2, attempt) + random.random() + logger.warning( + "Embedding request HTTP %d (attempt %d/%d); sleeping %.1fs", + resp.status_code, attempt + 1, _MAX_RETRIES, delay, + ) + await asyncio.sleep(delay) + continue + if resp.status_code != 200: + msg = ( + f"Embedding request failed: HTTP {resp.status_code} " + f"body={resp.text[:500]}" + ) + raise EmbeddingError(msg) + return resp.json() + msg = f"Embedding request gave up after {_MAX_RETRIES} retries; last error: {last_error}" + raise EmbeddingError(msg) + + +def _normalize(vectors: np.ndarray) -> np.ndarray: + norms = np.linalg.norm(vectors, axis=1, keepdims=True) + norms = np.where(norms == 0, 1.0, norms) + return vectors / norms + + +async def _embed_one_batch( + client: httpx.AsyncClient, + rcp: RcpConfig, + inputs: Sequence[str], +) -> np.ndarray: + payload = { + "model": rcp.embedding_model, + "input": list(inputs), + } + url = rcp.base_url.rstrip("/") + "/embeddings" + body = await _post_with_retry(client, url, payload, _auth_headers(rcp)) + data = body.get("data") or [] + if len(data) != len(inputs): + msg = ( + f"Embedding response mismatch: expected {len(inputs)} vectors, " + f"got {len(data)}" + ) + raise EmbeddingError(msg) + rows = sorted(data, key=lambda d: d.get("index", 0)) + matrix = np.array([row["embedding"] for row in rows], dtype=np.float32) + if matrix.shape[1] != rcp.embedding_dim: + msg = ( + f"Embedding dim mismatch: server returned {matrix.shape[1]}, " + f"config expected {rcp.embedding_dim}" + ) + raise EmbeddingError(msg) + return matrix + + +async def embed_passages( + rcp: RcpConfig, + texts: Sequence[str], + *, + normalize: bool = True, +) -> np.ndarray: + """Embed `texts` with the configured embedding model. Returns (N, dim) float32.""" + if not texts: + return np.zeros((0, rcp.embedding_dim), dtype=np.float32) + + semaphore = asyncio.Semaphore(rcp.max_concurrency) + timeout = httpx.Timeout(rcp.timeout_seconds) + + async with httpx.AsyncClient(timeout=timeout) as client: + async def _run(batch_inputs: Sequence[str]) -> np.ndarray: + async with semaphore: + return await _embed_one_batch(client, rcp, batch_inputs) + + batches = [ + list(texts[i : i + rcp.batch_size]) + for i in range(0, len(texts), rcp.batch_size) + ] + results = await asyncio.gather(*[_run(b) for b in batches]) + + matrix = np.vstack(results) if results else np.zeros( + (0, rcp.embedding_dim), dtype=np.float32, + ) + return _normalize(matrix) if normalize else matrix + + +async def embed_query( + rcp: RcpConfig, + text: str, + *, + normalize: bool = True, +) -> np.ndarray: + """Embed a single query, prefixed with `rcp.query_instruction` per Qwen3 format.""" + qtext = f"Instruct: {rcp.query_instruction}\nQuery: {text}" + matrix = await embed_passages(rcp, [qtext], normalize=normalize) + return matrix[0] + + +def embed_passages_sync(rcp: RcpConfig, texts: Sequence[str], *, normalize: bool = True) -> np.ndarray: + return asyncio.run(embed_passages(rcp, texts, normalize=normalize)) + + +def embed_query_sync(rcp: RcpConfig, text: str, *, normalize: bool = True) -> np.ndarray: + return asyncio.run(embed_query(rcp, text, normalize=normalize)) + + +__all__: List[str] = [ + "EmbeddingError", + "embed_passages", + "embed_passages_sync", + "embed_query", + "embed_query_sync", +] diff --git a/src/index/snsf/embed_pipeline.py b/src/index/snsf/embed_pipeline.py new file mode 100644 index 0000000..8323ffb --- /dev/null +++ b/src/index/snsf/embed_pipeline.py @@ -0,0 +1,146 @@ +"""Embed-and-upsert pipeline for the SNSF P3 index (Phase 2). + +Streams scope-membership grant rows from DuckDB → builds the embedding +text via `document.to_document` → embeds via RCP `Qwen3-Embedding-8B` → +upserts to Qdrant `snsf_`. + +Handles the EPFL slice (~6 188 grants) in a couple of minutes. The +worldwide scope (~90 k) is ~30 min on RCP. +""" + +from __future__ import annotations + +import asyncio +import datetime as dt +import logging +from typing import Any, Dict, List, Optional + +from src.index.snsf.config import SnsfIndexConfig +from src.index.snsf.document import to_document +from src.index.snsf.embed import embed_passages +from src.index.snsf.qdrant_store import QdrantSnsfStore +from src.index.snsf.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def _payload(grant: Dict[str, Any], text: str) -> Dict[str, Any]: + """Qdrant payload — small subset of grant columns useful for filtering / rendering.""" + return { + "grant_number": grant.get("grant_number"), + "title": grant.get("title_english") or grant.get("title"), + "research_institution": grant.get("research_institution"), + "research_institution_type": grant.get("research_institution_type"), + "main_discipline": grant.get("main_discipline"), + "main_discipline_l1": grant.get("main_discipline_l1"), + "main_discipline_l2": grant.get("main_discipline_l2"), + "main_field_of_research": grant.get("main_field_of_research"), + "start_date": _iso(grant.get("start_date")), + "end_date": _iso(grant.get("end_date")), + "amount_granted": grant.get("amount_granted"), + "state": grant.get("state"), + "funding_instrument": grant.get("funding_instrument"), + "responsible_applicant": grant.get("responsible_applicant"), + "text": text, + } + + +def _iso(v: Any) -> Optional[str]: + if v is None: + return None + if isinstance(v, (dt.datetime, dt.date)): + return v.isoformat() + return str(v) + + +def _scope_grant_rows(store: DuckDBStore, scope_mode: str) -> List[Dict[str, Any]]: + """Return all grant rows for `scope_mode`, ordered by grant_number desc.""" + cur = store.connect().execute( + """ + SELECT g.* FROM grants g + JOIN scope_records s ON s.grant_number = g.grant_number + WHERE s.scope_mode = ? + ORDER BY g.grant_number DESC + """, + [scope_mode], + ) + rows = cur.fetchall() + cols = [d[0] for d in cur.description] + return [dict(zip(cols, row, strict=False)) for row in rows] + + +async def run( + cfg: SnsfIndexConfig, + *, + scope_mode: Optional[str] = None, + recreate: bool = True, +) -> Dict[str, Any]: + """Embed all grants in the active scope and upsert to Qdrant.""" + if cfg.rcp.token is None: + msg = ( + "RCP_TOKEN is not set. Required for SNSF embedding. " + "Source the project .env (`set -a; source .env; set +a`) and retry." + ) + raise RuntimeError(msg) + + active = scope_mode or cfg.scope.active + LOGGER.info("Starting SNSF embed pipeline for scope=%s", active) + + store = DuckDBStore.open() + try: + rows = _scope_grant_rows(store, active) + finally: + store.close() + LOGGER.info("Loaded %d grants for scope=%s from DuckDB", len(rows), active) + + if not rows: + msg = ( + f"No grants in scope_records for scope={active!r}. " + f"Run `python -m src.index.snsf load-local --scope {active}` first." + ) + raise RuntimeError(msg) + + texts = [to_document(r) for r in rows] + nonempty_ratio = sum(1 for t in texts if t) / len(texts) + LOGGER.info( + "Built embedding text for %d grants (non-empty ratio: %.1f%%)", + len(texts), 100 * nonempty_ratio, + ) + + LOGGER.info( + "Embedding via RCP %s (batch_size=%d, max_concurrency=%d)", + cfg.rcp.embedding_model, cfg.rcp.batch_size, cfg.rcp.max_concurrency, + ) + matrix = await embed_passages(cfg.rcp, texts, normalize=True) + LOGGER.info("Embedded matrix shape=%s", matrix.shape) + + qstore = QdrantSnsfStore(cfg) + if recreate: + qstore.recreate_collection(active) + else: + qstore.ensure_collection(active) + + grant_numbers = [int(r["grant_number"]) for r in rows] + payloads = [_payload(r, t) for r, t in zip(rows, texts)] + qstore.upsert_records( + active, + grant_numbers=grant_numbers, + vectors=matrix.tolist(), + payloads=payloads, + ) + qcount = qstore.count(active) + + return { + "scope_mode": active, + "grants_embedded": len(rows), + "qdrant_count": qcount, + "qdrant_collection": qstore.collection_name(active), + "embedding_model": cfg.rcp.embedding_model, + } + + +def run_sync(cfg: SnsfIndexConfig, **kwargs: Any) -> Dict[str, Any]: + return asyncio.run(run(cfg, **kwargs)) + + +__all__ = ["run", "run_sync"] diff --git a/src/index/snsf/ingest/__init__.py b/src/index/snsf/ingest/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/snsf/ingest/local_ingest.py b/src/index/snsf/ingest/local_ingest.py new file mode 100644 index 0000000..03496b3 --- /dev/null +++ b/src/index/snsf/ingest/local_ingest.py @@ -0,0 +1,161 @@ +"""Local-CSV ingest pipeline. + +Reads the SNSF P3 bulk CSV set the user manually downloaded and dropped +into a directory (default: `data/index/snsf/raw/`). Loads each CSV into +the matching DuckDB table via `read_csv_auto`, then derives the active +scope's membership rows from the `grants` table. + +This is the canonical Phase 1 ingest path. The earlier API-based path is +parked (see `.internal/snsf/README.md` for why). +""" + +from __future__ import annotations + +import datetime as dt +import logging +from pathlib import Path +from typing import Optional + +from src.index.snsf.config import SnsfIndexConfig +from src.index.snsf.ingest.scope import where_for +from src.index.snsf.models import IngestManifest, IngestSummary +from src.index.snsf.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +# Default location where the user is expected to drop the manual CSV download. +# Configurable via `--source-dir` on the CLI or `SNSF_SOURCE_DIR` env override. +DEFAULT_SOURCE_DIR = Path("data/index/snsf/raw") + +# Filenames the loader looks for. Missing files just skip that loader (the +# CSV set may be partial — e.g. user only wants grants without persons). +GRANTS_FILE = "grants_with_abstracts.csv" +PERSONS_FILE = "persons.csv" +DISCIPLINES_FILE = "SNF_field_of_research_disciplines.csv" + +# (csv filename, store-method-name, log-label). +# Loader methods on `DuckDBStore` all take a single Path arg and return an int. +OUTPUT_LOADERS: tuple[tuple[str, str, str], ...] = ( + ("output_data_scientific_publications.csv", "load_output_publications", "publications"), + ("output_data_academicevents.csv", "load_output_academic_events", "academic events"), + ("output_data_collaborations.csv", "load_output_collaborations", "collaborations"), + ("output_data_datasets.csv", "load_output_datasets", "datasets"), + ("output_data_knowledgetransfer.csv", "load_output_knowledge_transfers", "knowledge transfers"), + ("output_data_publiccommunications.csv", "load_output_public_communications", "public communications"), + ("output_data_useinspired.csv", "load_output_use_inspired", "use-inspired outputs"), +) + + +def resolve_source_dir(explicit: Optional[Path] = None) -> Path: + """Resolve the source directory; explicit > env override > default.""" + if explicit is not None: + return Path(explicit).expanduser().resolve() + import os + + env = os.getenv("SNSF_SOURCE_DIR") + if env: + return Path(env).expanduser().resolve() + return DEFAULT_SOURCE_DIR.resolve() + + +def run( + cfg: SnsfIndexConfig, + *, + source_dir: Optional[Path] = None, + db_path: Optional[Path] = None, + skip_persons: bool = False, + skip_disciplines: bool = False, + skip_outputs: bool = False, + scope_mode: Optional[str] = None, +) -> IngestSummary: + """Run a full local-CSV ingest. Returns a printable summary.""" + src = resolve_source_dir(source_dir) + if not src.exists(): + msg = ( + f"Source dir not found: {src}. Place the SNSF bulk CSVs there or " + f"override with --source-dir / SNSF_SOURCE_DIR. Expected files: " + f"{GRANTS_FILE}, {PERSONS_FILE}, {DISCIPLINES_FILE}." + ) + raise FileNotFoundError(msg) + + LOGGER.info("Ingesting SNSF bulk CSVs from %s", src) + store = DuckDBStore.open(db_path) + + grants_csv = src / GRANTS_FILE + grants_n = store.load_grants(grants_csv) + LOGGER.info("Loaded %d grants from %s", grants_n, grants_csv.name) + + persons_n = 0 + if not skip_persons: + persons_csv = src / PERSONS_FILE + if persons_csv.exists(): + persons_n = store.load_persons(persons_csv) + LOGGER.info("Loaded %d persons from %s", persons_n, persons_csv.name) + else: + LOGGER.warning("Skipping persons (file not found): %s", persons_csv) + + disc_n = 0 + if not skip_disciplines: + disc_csv = src / DISCIPLINES_FILE + if disc_csv.exists(): + disc_n = store.load_disciplines(disc_csv) + LOGGER.info("Loaded %d discipline mappings from %s", disc_n, disc_csv.name) + else: + LOGGER.warning("Skipping disciplines (file not found): %s", disc_csv) + + output_counts: dict[str, int] = {} + if not skip_outputs: + for filename, method_name, label in OUTPUT_LOADERS: + csv = src / filename + if not csv.exists(): + LOGGER.warning("Skipping %s (file not found): %s", label, csv) + continue + loader = getattr(store, method_name) + n = loader(csv) + output_counts[label] = n + LOGGER.info("Loaded %d %s from %s", n, label, csv.name) + + active_scope = scope_mode or cfg.scope.active + where_sql, where_params = where_for(active_scope) + scope_n = store.replace_scope_records_by_filter( + active_scope, where_sql, where_params, + ) + LOGGER.info( + "Derived scope_records for %r → %d grants (filter: %s)", + active_scope, scope_n, where_sql, + ) + + snapshot = _detect_snapshot_iso(grants_csv) + store.set_manifest( + IngestManifest( + scope_mode=active_scope, + record_count=scope_n, + snapshot_iso=snapshot, + source_dir=str(src), + ), + ) + + summary = IngestSummary( + source_dir=str(src), + grants_loaded=grants_n, + persons_loaded=persons_n, + disciplines_loaded=disc_n, + outputs_loaded=output_counts, + scope_mode=active_scope, + scope_grants=scope_n, + snapshot_iso=snapshot, + ) + store.close() + return summary + + +def _detect_snapshot_iso(grants_csv: Path) -> str: + """Approximate the dump's snapshot date from the CSV's mtime.""" + if not grants_csv.exists(): + return dt.datetime.now(dt.timezone.utc).isoformat() + mtime = dt.datetime.fromtimestamp(grants_csv.stat().st_mtime, tz=dt.timezone.utc) + return mtime.isoformat() + + +__all__ = ["DEFAULT_SOURCE_DIR", "resolve_source_dir", "run"] diff --git a/src/index/snsf/ingest/scope.py b/src/index/snsf/ingest/scope.py new file mode 100644 index 0000000..2d46a8f --- /dev/null +++ b/src/index/snsf/ingest/scope.py @@ -0,0 +1,39 @@ +"""Per-scope WHERE-clause derivation against the `grants` table. + +The bulk CSVs from data.snf.ch carry the `ResearchInstitution` display name +(not a UUID), so scope filters here use exact string matches against that +column. The mapping is the inverse of the API's UUID-based filter table +documented in `.internal/snsf/README.md`. +""" + +from __future__ import annotations + +from typing import Literal + +ScopeMode = Literal["epfl", "ethz", "eth_domain", "switzerland"] + + +# `(where_clause, params)` for each scope. Used by +# `DuckDBStore.replace_scope_records_by_filter`. +SCOPE_WHERE: dict[ScopeMode, tuple[str, list]] = { + "epfl": ("research_institution = ?", ["EPF Lausanne – EPFL"]), + "ethz": ("research_institution = ?", ["ETH Zurich – ETHZ"]), + "eth_domain": ( + "research_institution_type = ?", + ["ETH Domain"], + ), + "switzerland": ("1=1", []), # whole dump — every grant in P3 is CH-resident +} + + +def where_for(scope_mode: str) -> tuple[str, list]: + if scope_mode not in SCOPE_WHERE: + msg = ( + f"Unknown scope_mode {scope_mode!r}. " + f"Valid: {sorted(SCOPE_WHERE.keys())}" + ) + raise ValueError(msg) + return SCOPE_WHERE[scope_mode] # type: ignore[index] + + +__all__ = ["SCOPE_WHERE", "ScopeMode", "where_for"] diff --git a/src/index/snsf/models.py b/src/index/snsf/models.py new file mode 100644 index 0000000..5abac4a --- /dev/null +++ b/src/index/snsf/models.py @@ -0,0 +1,35 @@ +"""Pydantic models for the SNSF P3 index. + +Phase 1 only needs lightweight carriers — the heavy lifting is done by +DuckDB SQL inside `storage.duckdb_store.DuckDBStore.load_*` (no row-by-row +Python iteration over the 90 k-grant dump). +""" + +from __future__ import annotations + +from typing import Optional + +from pydantic import BaseModel + + +class IngestSummary(BaseModel): + """Returned by `local_ingest.run` and surfaced by the CLI.""" + + source_dir: str + grants_loaded: int + persons_loaded: int + disciplines_loaded: int + outputs_loaded: dict[str, int] = {} + scope_mode: str + scope_grants: int + snapshot_iso: Optional[str] = None + + +class IngestManifest(BaseModel): + """One row of `manifests`.""" + + scope_mode: str + record_count: int + snapshot_iso: Optional[str] = None + source_dir: Optional[str] = None + built_at_iso: Optional[str] = None diff --git a/src/index/snsf/orcid_link.py b/src/index/snsf/orcid_link.py new file mode 100644 index 0000000..002118f --- /dev/null +++ b/src/index/snsf/orcid_link.py @@ -0,0 +1,229 @@ +"""Cross-source ORCID linkage: SNSF persons ↔ OpenAlex authors. + +The SNSF `persons` table carries ORCIDs for ~50 % of EPFL responsible +applicants (and similar coverage for other CH institutions). The OpenAlex +sibling has `authors.orcid` for the same identifiers. This module joins +the two via DuckDB `ATTACH` so a query can return: + + (person, [snsf grants this person was responsible for], + [openalex works this person authored]) + +Read-only on both stores, so it can run alongside ongoing ingest jobs as +long as no writer holds the OpenAlex DB lock. +""" + +from __future__ import annotations + +import json +import logging +import re +from pathlib import Path +from typing import Any, Dict, List, Optional + +import duckdb + +from src.index.snsf.paths import duckdb_path as snsf_db_path + +LOGGER = logging.getLogger(__name__) + + +# OpenAlex sibling DB lives next to ours under /openalex/. +def openalex_db_path() -> Path: + return snsf_db_path().parent.parent.parent / "openalex" / "duckdb" / "openalex.duckdb" + + +_ORCID_RE = re.compile(r"\b\d{4}-\d{4}-\d{4}-\d{3}[\dX]\b") + + +def normalize_orcid(value: Optional[str]) -> Optional[str]: + """Strip any URL prefix, return canonical 0000-0000-0000-000X form, or None.""" + if not value: + return None + m = _ORCID_RE.search(value) + return m.group(0) if m else None + + +def open_joined(read_only: bool = True) -> duckdb.DuckDBPyConnection: + """Open the SNSF DB with the OpenAlex DB ATTACHed read-only. + + Both DBs must exist. If the OpenAlex DB has a writer lock (someone is + actively running `openalex.cli embed` or `build`), this will raise + duckdb.IOException. + """ + snsf = snsf_db_path() + oa = openalex_db_path() + if not snsf.exists(): + msg = ( + f"SNSF DuckDB not found: {snsf}. " + f"Run `python -m src.index.snsf load-local` first." + ) + raise FileNotFoundError(msg) + if not oa.exists(): + msg = ( + f"OpenAlex DuckDB not found: {oa}. " + f"Run `python -m src.index.openalex.cli ingest` first to build it." + ) + raise FileNotFoundError(msg) + conn = duckdb.connect(str(snsf), read_only=read_only) + # ATTACH read-only so we don't conflict with any read-only handle on the + # other side. If a writer holds OpenAlex this raises IOException. + conn.execute(f"ATTACH '{oa}' AS oa (READ_ONLY)") + return conn + + +def link_by_orcid( + orcid: str, + *, + snsf_scope: Optional[str] = None, + work_limit: int = 50, + grant_limit: int = 50, +) -> Dict[str, Any]: + """Look up one ORCID in both stores and return their grants + works.""" + norm = normalize_orcid(orcid) + if norm is None: + msg = f"Could not normalise to a 19-char ORCID: {orcid!r}" + raise ValueError(msg) + + conn = open_joined(read_only=True) + try: + person = conn.execute( + """ + SELECT person_number, first_name, last_name, + institute, research_institution, orcid + FROM persons + WHERE orcid = ? + LIMIT 1 + """, + [norm], + ).fetchone() + if person is None: + return {"orcid": norm, "found_in": [], "snsf_grants": [], "openalex_works": []} + pn, first, last, inst, ri, _ = person + person_dict = { + "person_number": int(pn) if pn is not None else None, + "first_name": first, "last_name": last, + "institute": inst, "research_institution": ri, + } + + # SNSF grants — JOIN persons.responsible_applicant_grants (JSON) + # back to grants. Optional scope filter. + scope_join = ( + "JOIN scope_records s ON s.grant_number = g.grant_number " + "AND s.scope_mode = ? " + ) if snsf_scope else "" + scope_params: List[Any] = [snsf_scope] if snsf_scope else [] + grants = conn.execute( + f""" + SELECT g.grant_number, g.title, g.research_institution, + g.main_discipline, g.start_date, g.amount_granted, g.state + FROM persons p, + json_each(p.responsible_applicant_grants) AS j + JOIN grants g ON g.grant_number = CAST(j.value AS INTEGER) + {scope_join} + WHERE p.orcid = ? + ORDER BY g.start_date DESC NULLS LAST + LIMIT ? + """, + [*scope_params, norm, grant_limit], + ).fetchall() + gcols = ("grant_number", "title", "research_institution", + "main_discipline", "start_date", "amount_granted", "state") + snsf_grants = [dict(zip(gcols, row, strict=False)) for row in grants] + + # OpenAlex authors — match by ORCID (URL form acceptable). + oa_author = conn.execute( + """ + SELECT openalex_id, display_name, orcid + FROM oa.authors + WHERE orcid = ? OR orcid = ? + LIMIT 1 + """, + [norm, f"https://orcid.org/{norm}"], + ).fetchone() + + works: List[Dict[str, Any]] = [] + if oa_author is not None: + oa_id = oa_author[0] + # Join authors→work_authors→works for the openalex side. + work_rows = conn.execute( + """ + SELECT w.openalex_id, w.title, w.doi, w.publication_year, + w.primary_topic_id + FROM oa.work_authors wa + JOIN oa.works w ON w.openalex_id = wa.work_id + WHERE wa.author_id = ? + ORDER BY w.publication_year DESC NULLS LAST + LIMIT ? + """, + [oa_id, work_limit], + ).fetchall() + wcols = ("openalex_id", "title", "doi", "publication_year", "primary_topic_id") + works = [dict(zip(wcols, row, strict=False)) for row in work_rows] + + return { + "orcid": norm, + "person": person_dict, + "openalex_author": ( + {"openalex_id": oa_author[0], "display_name": oa_author[1]} + if oa_author is not None else None + ), + "snsf_scope": snsf_scope, + "snsf_grants": snsf_grants, + "openalex_works": works, + } + finally: + conn.close() + + +def coverage_report(snsf_scope: Optional[str] = None) -> Dict[str, Any]: + """How well does ORCID link the two stores for the given SNSF scope?""" + conn = open_joined(read_only=True) + try: + scope_join = ( + "JOIN scope_records s ON s.grant_number = CAST(j.value AS INTEGER) " + "AND s.scope_mode = ?" + ) if snsf_scope else "" + params: List[Any] = [snsf_scope] if snsf_scope else [] + + # SNSF persons with ORCID who are responsible applicants on grants + # in the chosen scope (no scope = all). + snsf_orcids = conn.execute( + f""" + SELECT DISTINCT p.orcid + FROM persons p, + json_each(p.responsible_applicant_grants) AS j + {scope_join} + WHERE p.orcid IS NOT NULL AND length(p.orcid) > 0 + """, + params, + ).fetchall() + snsf_orcid_set = {row[0] for row in snsf_orcids} + + # OpenAlex authors with ORCID (any). + oa_orcids_rows = conn.execute( + "SELECT orcid FROM oa.authors WHERE orcid IS NOT NULL AND length(orcid) > 0", + ).fetchall() + oa_orcid_set = {normalize_orcid(o) for (o,) in oa_orcids_rows} + oa_orcid_set.discard(None) + + intersection = snsf_orcid_set & oa_orcid_set + return { + "snsf_scope": snsf_scope or "(all)", + "snsf_responsible_applicants_with_orcid": len(snsf_orcid_set), + "openalex_authors_with_orcid": len(oa_orcid_set), + "linked_by_orcid": len(intersection), + "link_ratio_snsf": ( + len(intersection) / len(snsf_orcid_set) if snsf_orcid_set else 0.0 + ), + } + finally: + conn.close() + + +__all__ = [ + "coverage_report", + "link_by_orcid", + "normalize_orcid", + "open_joined", + "openalex_db_path", +] diff --git a/src/index/snsf/paths.py b/src/index/snsf/paths.py new file mode 100644 index 0000000..1a33e73 --- /dev/null +++ b/src/index/snsf/paths.py @@ -0,0 +1,42 @@ +"""Filesystem paths for the SNSF P3 index. + +Roots at `${INDEX_DATA_DIR:-data/index}/snsf` (shared root with the openalex / +ror / huggingface siblings). Sub-paths are derived constants below; +directories are created on first access. +""" + +from __future__ import annotations + +import os +from pathlib import Path + +_DEFAULT_ROOT = "data/index" +_SOURCE = "snsf" + + +def index_data_root() -> Path: + """Top-level multi-source data root (`data/index` by default).""" + return Path(os.getenv("INDEX_DATA_DIR", _DEFAULT_ROOT)).expanduser().resolve() + + +def snsf_data_dir() -> Path: + """Per-source root for SNSF. Creates the directory tree if needed.""" + root = index_data_root() / _SOURCE + root.mkdir(parents=True, exist_ok=True) + return root + + +def duckdb_dir() -> Path: + p = snsf_data_dir() / "duckdb" + p.mkdir(parents=True, exist_ok=True) + return p + + +def duckdb_path() -> Path: + return duckdb_dir() / "snsf.duckdb" + + +def logs_dir() -> Path: + p = snsf_data_dir() / "logs" + p.mkdir(parents=True, exist_ok=True) + return p diff --git a/src/index/snsf/qdrant_store.py b/src/index/snsf/qdrant_store.py new file mode 100644 index 0000000..001014e --- /dev/null +++ b/src/index/snsf/qdrant_store.py @@ -0,0 +1,150 @@ +"""Qdrant client wrapper for the SNSF P3 index. + +One collection per scope mode (`snsf_epfl`, `snsf_ethz`, `snsf_eth_domain`, +`snsf_switzerland`). Mirrors `src/index/ror/qdrant_store.py` shape — the +only difference is point IDs use the integer `grant_number` directly +instead of a UUIDv5 (Qdrant accepts int64 IDs natively). +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any, Dict, List, Optional + +from qdrant_client import QdrantClient, models + +if TYPE_CHECKING: + from src.index.snsf.config import SnsfIndexConfig + +logger = logging.getLogger(__name__) + + +class QdrantSnsfStore: + """Per-scope collection bootstrap, upsert, search.""" + + def __init__(self, config: "SnsfIndexConfig") -> None: + self._config = config + self._client = QdrantClient( + url=config.qdrant.url, + api_key=config.qdrant.api_key, + prefer_grpc=config.qdrant.prefer_grpc, + timeout=max(config.rcp.timeout_seconds, 120), + ) + self._dim = config.rcp.embedding_dim + + @property + def client(self) -> QdrantClient: + return self._client + + def collection_name(self, scope_mode: Optional[str] = None) -> str: + if scope_mode is None: + return self._config.collection_name() + return f"{self._config.qdrant.collection_prefix}_{scope_mode}" + + def ensure_collection(self, scope_mode: Optional[str] = None) -> str: + name = self.collection_name(scope_mode) + if self._client.collection_exists(name): + return name + self._client.create_collection( + collection_name=name, + vectors_config=models.VectorParams( + size=self._dim, distance=models.Distance.COSINE, + ), + ) + logger.info("created qdrant collection %s (dim=%d)", name, self._dim) + return name + + def recreate_collection(self, scope_mode: Optional[str] = None) -> str: + name = self.collection_name(scope_mode) + if self._client.collection_exists(name): + self._client.delete_collection(name) + logger.info("dropped existing qdrant collection %s", name) + return self.ensure_collection(scope_mode) + + def upsert_records( + self, + scope_mode: str, + *, + grant_numbers: List[int], + vectors: List[List[float]], + payloads: List[Dict[str, Any]], + batch_size: int = 256, + ) -> None: + if not (len(grant_numbers) == len(vectors) == len(payloads)): + msg = "grant_numbers/vectors/payloads must be the same length" + raise ValueError(msg) + if not grant_numbers: + return + name = self.ensure_collection(scope_mode) + for start in range(0, len(grant_numbers), batch_size): + end = start + batch_size + points = [ + models.PointStruct(id=int(gn), vector=vec, payload=p) + for gn, vec, p in zip( + grant_numbers[start:end], vectors[start:end], payloads[start:end], + ) + ] + self._client.upsert(collection_name=name, points=points, wait=True) + + def search( + self, + scope_mode: str, + *, + query_vector: List[float], + top_k: int = 50, + institution: Optional[str] = None, + discipline_l1: Optional[str] = None, + state: Optional[str] = None, + ) -> List[Dict[str, Any]]: + name = self.collection_name(scope_mode) + if not self._client.collection_exists(name): + msg = ( + f"Qdrant collection {name!r} does not exist. " + f"Run `python -m src.index.snsf embed --scope {scope_mode}` first." + ) + raise FileNotFoundError(msg) + + must = [] + if institution: + must.append(models.FieldCondition( + key="research_institution", match=models.MatchValue(value=institution), + )) + if discipline_l1: + must.append(models.FieldCondition( + key="main_discipline_l1", match=models.MatchValue(value=discipline_l1), + )) + if state: + must.append(models.FieldCondition( + key="state", match=models.MatchValue(value=state), + )) + qfilter = models.Filter(must=must) if must else None + + hits = self._client.query_points( + collection_name=name, + query=query_vector, + limit=top_k, + query_filter=qfilter, + with_payload=True, + ).points + return [ + { + "score": float(p.score), + "grant_number": int(p.id), + "title": (p.payload or {}).get("title", ""), + "research_institution": (p.payload or {}).get("research_institution"), + "main_discipline": (p.payload or {}).get("main_discipline"), + "start_date": (p.payload or {}).get("start_date"), + "amount_granted": (p.payload or {}).get("amount_granted"), + "text": (p.payload or {}).get("text", ""), + } + for p in hits + ] + + def count(self, scope_mode: str) -> int: + name = self.collection_name(scope_mode) + if not self._client.collection_exists(name): + return 0 + return int(self._client.count(collection_name=name, exact=True).count) + + +__all__ = ["QdrantSnsfStore"] diff --git a/src/index/snsf/query.py b/src/index/snsf/query.py new file mode 100644 index 0000000..3842281 --- /dev/null +++ b/src/index/snsf/query.py @@ -0,0 +1,113 @@ +"""Public query API for the SNSF P3 index. + +`query_rag` runs Qdrant retrieval (embedded scope subset) → reranks via the +RCP cross-encoder → returns the top hits hydrated from the Qdrant payload. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import Any, Dict, List, Optional + +import numpy as np + +from src.index.snsf.config import SnsfIndexConfig +from src.index.snsf.embed import embed_query +from src.index.snsf.qdrant_store import QdrantSnsfStore +from src.index.snsf.rerank import rerank + +logger = logging.getLogger(__name__) + + +async def query_rag( + cfg: SnsfIndexConfig, + text: str, + *, + top_k: Optional[int] = None, + rerank_top_k: Optional[int] = None, + institution: Optional[str] = None, + institute: Optional[str] = None, + discipline_l1: Optional[str] = None, + state: Optional[str] = None, + scope_mode: Optional[str] = None, +) -> List[Dict[str, Any]]: + if cfg.rcp.token is None: + msg = "RCP_TOKEN is not set." + raise RuntimeError(msg) + top_k = top_k or cfg.retrieval.top_k + rerank_top_k = rerank_top_k or cfg.retrieval.rerank_top_k + active = scope_mode or cfg.scope.active + + qvec = await embed_query(cfg.rcp, text, normalize=True) + qstore = QdrantSnsfStore(cfg) + candidates = qstore.search( + active, + # Inflate top_k when --institute post-filter is set — most candidates + # will be discarded so we need a wider net to keep `rerank_top_k` non-empty. + query_vector=np.asarray(qvec, dtype=np.float32).tolist(), + top_k=top_k * 4 if institute else top_k, + institution=institution, + discipline_l1=discipline_l1, + state=state, + ) + if not candidates: + return [] + + if institute: + candidates = _post_filter_by_institute(candidates, institute) + if not candidates: + return [] + + rerank_results = await rerank( + cfg.rcp, text, [c["text"] for c in candidates], top_n=rerank_top_k, + ) + out: List[Dict[str, Any]] = [] + for r in rerank_results[:rerank_top_k]: + if r.index < 0 or r.index >= len(candidates): + continue + c = candidates[r.index] + out.append({**c, "score": float(r.score)}) + return out + + +def _post_filter_by_institute( + candidates: List[Dict[str, Any]], + institute_substring: str, +) -> List[Dict[str, Any]]: + """Keep only candidates whose `institute` (lab/centre, from DuckDB) matches. + + `institute` isn't in the Qdrant payload (it would balloon every point), + so we resolve it from DuckDB after the ANN. Substring match, case-folded. + """ + from src.index.snsf.storage.duckdb_store import DuckDBStore + + needle = institute_substring.lower() + grant_ids = [c["grant_number"] for c in candidates] + if not grant_ids: + return [] + store = DuckDBStore.open() + try: + placeholders = ",".join(["?"] * len(grant_ids)) + rows = store.connect().execute( + f"SELECT grant_number, institute FROM grants " + f"WHERE grant_number IN ({placeholders})", # noqa: S608 - placeholders are bound + list(grant_ids), + ).fetchall() + finally: + store.close() + + institute_by_id = {gn: (inst or "") for gn, inst in rows} + kept = [] + for c in candidates: + inst = institute_by_id.get(c["grant_number"], "") + if needle in inst.lower(): + kept.append({**c, "institute": inst}) + return kept + + +def query_rag_sync(cfg: SnsfIndexConfig, text: str, **kwargs: Any) -> List[Dict[str, Any]]: + return asyncio.run(query_rag(cfg, text, **kwargs)) + + +__all__ = ["query_rag", "query_rag_sync"] diff --git a/src/index/snsf/rerank.py b/src/index/snsf/rerank.py new file mode 100644 index 0000000..ffa1d50 --- /dev/null +++ b/src/index/snsf/rerank.py @@ -0,0 +1,76 @@ +"""RCP reranker client (Cohere-compatible `/rerank`). + +Posts `{model, query, documents}` and reads back `{results: [{index, relevance_score}, ...]}`. +This shape is what vLLM, Infinity, and most modern inference gateways expose +for cross-encoder / Qwen3-Reranker-style models. If RCP later switches the +schema, only this module needs updating. +""" + +from __future__ import annotations + +import logging +from typing import List, Sequence + +import httpx +from pydantic import BaseModel + +from src.index.snsf.config import RcpConfig +from .embed import _auth_headers, _post_with_retry # noqa: PLC2701 + +logger = logging.getLogger(__name__) + + +class RerankError(RuntimeError): + """Raised on unrecoverable rerank failure.""" + + +class RerankResult(BaseModel): + index: int + score: float + + +async def rerank( + rcp: RcpConfig, + query: str, + documents: Sequence[str], + *, + top_n: int | None = None, +) -> List[RerankResult]: + """Rerank `documents` by relevance to `query`. Returns descending score order.""" + if not documents: + return [] + + payload = { + "model": rcp.reranker_model, + "query": query, + "documents": list(documents), + } + if top_n is not None: + payload["top_n"] = top_n + + url = rcp.base_url.rstrip("/") + "/rerank" + timeout = httpx.Timeout(rcp.timeout_seconds) + + async with httpx.AsyncClient(timeout=timeout) as client: + body = await _post_with_retry(client, url, payload, _auth_headers(rcp)) + + raw_results = body.get("results") + if not isinstance(raw_results, list): + msg = f"Rerank response missing 'results' list; got keys {list(body.keys())}" + raise RerankError(msg) + + out: List[RerankResult] = [] + for entry in raw_results: + if not isinstance(entry, dict): + continue + idx = entry.get("index") + score = entry.get("relevance_score", entry.get("score")) + if not isinstance(idx, int) or not isinstance(score, (int, float)): + continue + out.append(RerankResult(index=idx, score=float(score))) + + out.sort(key=lambda r: r.score, reverse=True) + return out + + +__all__: List[str] = ["RerankError", "RerankResult", "rerank"] diff --git a/src/index/snsf/resume_embed.py b/src/index/snsf/resume_embed.py new file mode 100644 index 0000000..4c1c45e --- /dev/null +++ b/src/index/snsf/resume_embed.py @@ -0,0 +1,134 @@ +"""Resume an interrupted `embed --scope ` run. + +Queries Qdrant for already-upserted point IDs (= grant_numbers) in the +target collection, subtracts them from the scope's grant set in DuckDB, +embeds *only the missing* grants via RCP, and upserts. + +Idempotent: re-running after a clean run is a no-op (all IDs already +present → 0 missing → 0 RCP calls). + +Run: + .venv/bin/python -m src.index.snsf.resume_embed [--scope SCOPE] + +The CLI's `embed --scope ... --keep-existing` would also work but +re-embeds the entire scope (wasted ~70 min on the switzerland slice). +This script avoids that. +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +import logging +import sys +from typing import Any, Dict, List + +from src.index.snsf.config import load_config +from src.index.snsf.document import to_document +from src.index.snsf.embed import embed_passages +from src.index.snsf.embed_pipeline import _payload, _scope_grant_rows +from src.index.snsf.qdrant_store import QdrantSnsfStore +from src.index.snsf.storage.duckdb_store import DuckDBStore + +LOGGER = logging.getLogger(__name__) + + +def _existing_point_ids(qstore: QdrantSnsfStore, collection: str) -> set[int]: + """Scroll the entire collection, collecting integer point ids.""" + client = qstore.client + seen: set[int] = set() + offset = None + while True: + points, offset = client.scroll( + collection_name=collection, + limit=10_000, + offset=offset, + with_payload=False, + with_vectors=False, + ) + for p in points: + try: + seen.add(int(p.id)) + except (TypeError, ValueError): + continue + if offset is None: + break + return seen + + +async def run(scope_mode: str) -> Dict[str, Any]: + cfg = load_config() + if cfg.rcp.token is None: + msg = "RCP_TOKEN not set." + raise RuntimeError(msg) + + qstore = QdrantSnsfStore(cfg) + coll = qstore.collection_name(scope_mode) + qstore.ensure_collection(scope_mode) + + existing = _existing_point_ids(qstore, coll) + LOGGER.info("Already in %s: %d point ids", coll, len(existing)) + + store = DuckDBStore.open() + try: + all_rows = _scope_grant_rows(store, scope_mode) + finally: + store.close() + LOGGER.info("Scope %s has %d grants in DuckDB", scope_mode, len(all_rows)) + + missing: List[Dict[str, Any]] = [ + r for r in all_rows if int(r["grant_number"]) not in existing + ] + LOGGER.info("Missing %d grants → embedding", len(missing)) + + if not missing: + return { + "scope_mode": scope_mode, + "missing": 0, + "qdrant_count": qstore.count(scope_mode), + "qdrant_collection": coll, + } + + texts = [to_document(r) for r in missing] + matrix = await embed_passages(cfg.rcp, texts, normalize=True) + LOGGER.info("Embedded matrix shape=%s", matrix.shape) + + grant_numbers = [int(r["grant_number"]) for r in missing] + payloads = [_payload(r, t) for r, t in zip(missing, texts)] + qstore.upsert_records( + scope_mode, + grant_numbers=grant_numbers, + vectors=matrix.tolist(), + payloads=payloads, + ) + + qcount = qstore.count(scope_mode) + return { + "scope_mode": scope_mode, + "missing": len(missing), + "embedded": len(missing), + "qdrant_count": qcount, + "qdrant_collection": coll, + "embedding_model": cfg.rcp.embedding_model, + } + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser(prog="src.index.snsf.resume_embed") + parser.add_argument("--scope", default="switzerland") + parser.add_argument("-v", "--verbose", action="store_true") + args = parser.parse_args(argv) + + logging.basicConfig( + level=logging.DEBUG if args.verbose else logging.INFO, + format="%(asctime)s %(levelname)s %(name)s | %(message)s", + ) + + summary = asyncio.run(run(args.scope)) + print(json.dumps(summary, ensure_ascii=False, indent=2, default=str)) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/index/snsf/storage/__init__.py b/src/index/snsf/storage/__init__.py new file mode 100644 index 0000000..8f0551f --- /dev/null +++ b/src/index/snsf/storage/__init__.py @@ -0,0 +1,5 @@ +"""DuckDB-backed metadata store for the SNSF P3 index.""" + +from src.index.snsf.storage.duckdb_store import DuckDBStore + +__all__ = ["DuckDBStore"] diff --git a/src/index/snsf/storage/duckdb_store.py b/src/index/snsf/storage/duckdb_store.py new file mode 100644 index 0000000..dc4a0da --- /dev/null +++ b/src/index/snsf/storage/duckdb_store.py @@ -0,0 +1,475 @@ +"""DuckDB lifecycle, schema bootstrap, and bulk loaders for the SNSF P3 index. + +All ingest goes through DuckDB's native `read_csv_auto` — Python doesn't +iterate the 90 k-grant CSV. This is 50-100× faster than the per-row INSERT +path and avoids the PARALLEL FALSE / STRICT_MODE FALSE dance ROR needed for +multi-line quoted JSON columns. See `.internal/snsf/README.md` for the +column-by-column rationale. +""" + +from __future__ import annotations + +import datetime as dt +import logging +from contextlib import contextmanager +from pathlib import Path +from typing import TYPE_CHECKING, Any, Optional + +import duckdb + +from src.index.snsf.models import IngestManifest +from src.index.snsf.paths import duckdb_path + +if TYPE_CHECKING: + from collections.abc import Iterator + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +class DuckDBStore: + """Thin wrapper around DuckDB tuned for the SNSF schema.""" + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open(cls, db_path: Path | None = None) -> DuckDBStore: + if db_path is None: + db_path = duckdb_path() + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + self.connect().execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + @contextmanager + def transaction(self) -> Iterator[None]: + conn = self.connect() + conn.execute("BEGIN TRANSACTION") + try: + yield + except Exception: + conn.execute("ROLLBACK") + raise + conn.execute("COMMIT") + + # ---- Bulk loaders (CSV → tables) ------------------------------------- + + def load_grants(self, csv_path: Path) -> int: + """Replace the `grants` table from `grants_with_abstracts.csv`. + + Uses DuckDB `read_csv_auto` with a multi-line-aware reader so the + embedded newlines in `Abstract` / `LaySummary*` are handled correctly. + Total runtime on the full 90 k-grant CSV (~424 MB): ~10 s. + """ + if not csv_path.exists(): + msg = f"grants CSV not found: {csv_path}" + raise FileNotFoundError(msg) + conn = self.connect() + with self.transaction(): + conn.execute("DELETE FROM grants") + conn.execute( + """ + INSERT INTO grants ( + grant_number, grant_number_string, title, title_english, + responsible_applicant, + funding_instrument, funding_instrument_reporting, funding_instrument_l1, + institute, institute_city, institute_country, + research_institution, research_institution_type, + main_discipline, main_discipline_number, + main_discipline_l1, main_discipline_l2, all_disciplines, + main_field_of_research, main_field_of_research_la, + main_field_of_research_lb, all_field_of_researchs, + start_date, end_date, amount_granted, keywords, abstract, + lay_summary_lead_en, lay_summary_en, + lay_summary_lead_de, lay_summary_de, + lay_summary_lead_fr, lay_summary_fr, + lay_summary_lead_it, lay_summary_it, + state, call_full_title, call_end_date, call_decision_year + ) + SELECT + GrantNumber, GrantNumberString, Title, TitleEnglish, + ResponsibleApplicantName, + FundingInstrumentPublished, FundingInstrumentReporting, FundingInstrumentLevel1, + Institute, InstituteCity, InstituteCountry, + ResearchInstitution, ResearchInstitutionType, + MainDiscipline, MainDisciplineNumber, + MainDiscipline_Level1, MainDiscipline_Level2, AllDisciplines, + MainFieldOfResearch, MainFieldOfResearch_LevelA, + MainFieldOfResearch_LevelB, AllFieldOfResearchs, + EffectiveGrantStartDate, EffectiveGrantEndDate, + AmountGrantedAllSets, Keywords, Abstract, + LaySummaryLead_En, LaySummary_En, + LaySummaryLead_De, LaySummary_De, + LaySummaryLead_Fr, LaySummary_Fr, + LaySummaryLead_It, LaySummary_It, + State, CallFullTitle, CallEndDate, CallDecisionYear + FROM read_csv_auto(?, delim=';', header=true, sample_size=-1) + """, + [str(csv_path)], + ) + return self.count_grants() + + def load_persons(self, csv_path: Path) -> int: + """Replace the `persons` table from `persons.csv`. + + The role-specific grant lists (e.g. `EmployeeGrantNumber`) are + semicolon-delimited TEXT in the source; we split + JSON-encode them + in SQL so callers get `json_each(employee_grants)` ergonomics. + Empty/NULL source → NULL JSON column. + """ + if not csv_path.exists(): + msg = f"persons CSV not found: {csv_path}" + raise FileNotFoundError(msg) + + # Inline DuckDB expression that turns "108806;125710;134273" → JSON [108806,125710,134273]. + # NULL input → NULL output. Empty string → NULL. + # Some columns in persons.csv only ever contain a single grant_number per row, + # so DuckDB auto-detects them as BIGINT — explicit CAST(... AS VARCHAR) is needed + # before TRIM/STRING_SPLIT to keep the SQL valid for both BIGINT and VARCHAR types. + def _split(col: str) -> str: + return ( + f"CASE WHEN {col} IS NULL " + f"OR LENGTH(TRIM(CAST({col} AS VARCHAR))) = 0 THEN NULL " + f"ELSE TO_JSON(LIST_TRANSFORM(" + f"STRING_SPLIT(CAST({col} AS VARCHAR), ';'), " + f"x -> TRY_CAST(TRIM(x) AS INTEGER))) END" + ) + + conn = self.connect() + with self.transaction(): + conn.execute("DELETE FROM persons") + conn.execute( + f""" + INSERT INTO persons ( + person_number, first_name, last_name, + institute, institute_place, institute_country, + research_institution, research_institution_type, orcid, + responsible_applicant_grants, co_applicant_grants, + project_partner_grants, practice_partner_grants, + employee_grants, contact_person_grants, applicant_abroad_grants, + person_grant_discipline, person_grant_keywords + ) + SELECT + PersonNumber, FirstName, LastName, + Institute, InstitutePlace, InstituteCountry, + ResearchInstitution, ResearchInstitutionType, ORCID, + {_split("ResponsibleApplicantGrantNumber")}, + {_split("CoApplicantGrantNumber")}, + {_split("ProjectPartnerGrantNumber")}, + {_split("PracticePartnerGrantNumber")}, + {_split("EmployeeGrantNumber")}, + {_split("ContactPersonGrantNumber")}, + {_split("ApplicantAbroadGrantNumber")}, + Person_Grant_Discipline, Person_Grant_Keywords + FROM read_csv_auto(?, delim=';', header=true, sample_size=-1) + """, # noqa: S608 — column expressions are fixed strings, not user input + [str(csv_path)], + ) + return self.count_persons() + + def load_disciplines(self, csv_path: Path) -> int: + """Replace the `discipline_taxonomy` table from `SNF_field_of_research_disciplines.csv`.""" + if not csv_path.exists(): + msg = f"disciplines CSV not found: {csv_path}" + raise FileNotFoundError(msg) + conn = self.connect() + with self.transaction(): + conn.execute("DELETE FROM discipline_taxonomy") + conn.execute( + """ + INSERT INTO discipline_taxonomy ( + mapping_direction, field_of_research_number, field_of_research, + snf_discipline_number, snf_discipline + ) + SELECT + "Mapping direction", + "Field of research #", + "Field of research", + "mySNF discipline #", + "mySNF discipline" + FROM read_csv_auto(?, delim=';', header=true, sample_size=-1) + """, + [str(csv_path)], + ) + return self.count_disciplines() + + # ---- Output tables ----------------------------------------------------- + # + # All seven `output_data_*.csv` files load via DuckDB `read_csv_auto` into + # a `read_only=False` connection. Source CSVs all share the same shape + # (one CSV per output type, primary key = source UUID, FK = GrantNumber). + + def load_output_publications(self, csv_path: Path) -> int: + return self._replace_output_table( + csv_path, + table="output_publications", + cols=( + "publication_id, grant_number, peer_review_status, type, title, " + "author, state, year, isbn, doi, import_source, " + "open_access_yes_no, open_access_status, url, " + "publication_title, publisher, editor, volume, issue_number, " + "first_page_number, last_page_number, proceeding_location, abstract" + ), + select=( + "ScientificPublicationId, GrantNumber, " + "ScientificPublication_PeerReviewStatus, ScientificPublication_Type, " + "ScientificPublication_Title, ScientificPublication_Author, " + "ScientificPublication_State, " + "TRY_CAST(ScientificPublication_Year AS INTEGER), " + "ScientificPublication_ISBN, ScientificPublication_DOI, " + "ScientificPublication_ImportSource, " + "TRY_CAST(ScientificPublication_OpenAccessStatusYesNo AS INTEGER), " + "ScientificPublication_OpenAccessStatus, ScientificPublication_Url, " + "ScientificPublication_PublicationTitle, ScientificPublication_Publisher, " + "ScientificPublication_Editor, " + "CAST(ScientificPublication_Volume AS VARCHAR), " + "CAST(ScientificPublication_IssueNumber AS VARCHAR), " + "CAST(ScientificPublication_FirstPageNumber AS VARCHAR), " + "CAST(ScientificPublication_LastPageNumber AS VARCHAR), " + "ScientificPublication_ProceedingLocation, " + "ScientificPublication_Abstract" + ), + ) + + def load_output_academic_events(self, csv_path: Path) -> int: + return self._replace_output_table( + csv_path, + table="output_academic_events", + cols=( + "event_id, grant_number, type, event, contribution_title, " + "date, involved_person, url, place" + ), + select=( + "AcademicEventId, GrantNumber, AcademicEvent_Type, AcademicEvent_Event, " + "AcademicEvent_ContributionTitle, AcademicEvent_Date, " + "AcademicEvent_InvolvedPerson, AcademicEvent_Url, AcademicEvent_Place" + ), + ) + + def load_output_collaborations(self, csv_path: Path) -> int: + return self._replace_output_table( + csv_path, + table="output_collaborations", + cols=( + "collaboration_id, grant_number, research_group, type, " + "country, start_date, end_date" + ), + select=( + "CollaborationId, GrantNumber, Collaboration_ResearchGroup, " + "Collaboration_Type, Collaboration_Country, " + "EffectiveGrantStartDate, EffectiveGrantEndDate" + ), + ) + + def load_output_datasets(self, csv_path: Path) -> int: + return self._replace_output_table( + csv_path, + table="output_datasets", + cols=( + "dataset_id, grant_number, title, author, persistent_identifier, " + "repository_name, repository_link, publication_date, abstract" + ), + select=( + "DataSetId, GrantNumber, DataSet_Title, DataSet_Author, " + "DataSet_PersistentIdentifier_PID, DataSet_RepositoryName, " + "DataSet_RepositoryLink, DataSet_PublicationDate, DataSet_Abstract" + ), + ) + + def load_output_knowledge_transfers(self, csv_path: Path) -> int: + return self._replace_output_table( + csv_path, + table="output_knowledge_transfers", + cols=( + "event_id, grant_number, type, event, date, " + "involved_person, url, place, target_group" + ), + select=( + "KnowledgeTransferEventId, GrantNumber, KnowledgeTransferEvent_Type, " + "KnowledgeTransferEvent_Event, KnowledgeTransferEvent_Date, " + "KnowledgeTransferEvent_InvolvedPerson, KnowledgeTransferEvent_Url, " + "KnowledgeTransferEvent_Place, KnowledgeTransferEvent_TargetGroup" + ), + ) + + def load_output_public_communications(self, csv_path: Path) -> int: + return self._replace_output_table( + csv_path, + table="output_public_communications", + cols=( + "communication_id, grant_number, type, title, description, " + "year, url, region" + ), + select=( + "PublicCommunicationId, GrantNumber, PublicCommunication_Type, " + "PublicCommunication_Title, PublicCommunication_Description, " + "TRY_CAST(PublicCommunication_Year AS INTEGER), " + "PublicCommunication_Url, PublicCommunication_Region" + ), + ) + + def load_output_use_inspired(self, csv_path: Path) -> int: + return self._replace_output_table( + csv_path, + table="output_use_inspired", + cols=( + "use_inspired_id, grant_number, type, title, url, year, " + "priority_date, patent_number, patent_status, patent_decision_date, " + "inventor, owner, patent_owner_description, comment, " + "reviewer_activity_type, license_type" + ), + select=( + "UseInspiredId, GrantNumber, UseInspired_Type, UseInspired_Title, " + "UseInspired_Url, " + "TRY_CAST(UseInspired_Year AS INTEGER), " + "UseInspired_PriorityDate, UseInspired_PatentNumber, " + "UseInspired_PatentStatus, UseInspired_PatentDecisionDate, " + "UseInspired_Inventor, UseInspired_Owner, " + "UseInspired_PatentOwnerDescription, UseInspired_Comment, " + "UseInspired_ReviewerActivityType, UseInspired_LicenseType" + ), + ) + + def _replace_output_table( + self, csv_path: Path, *, table: str, cols: str, select: str, + ) -> int: + if not csv_path.exists(): + msg = f"output CSV not found: {csv_path}" + raise FileNotFoundError(msg) + conn = self.connect() + with self.transaction(): + conn.execute(f"DELETE FROM {table}") # noqa: S608 — table is a fixed string + conn.execute( + f"INSERT INTO {table} ({cols}) SELECT {select} " # noqa: S608 + "FROM read_csv_auto(?, delim=';', header=true, sample_size=-1)", + [str(csv_path)], + ) + result = conn.execute(f"SELECT count(*) FROM {table}").fetchone() # noqa: S608 + return int(result[0]) if result else 0 + + # ---- Scope + manifest -------------------------------------------------- + + def replace_scope_records_by_filter( + self, scope_mode: str, where_clause: str, params: Optional[list[Any]] = None, + ) -> int: + """Re-derive `scope_records` for one scope from a SQL WHERE on `grants`. + + Example: + store.replace_scope_records_by_filter( + "epfl", + "research_institution = ?", + ["EPF Lausanne – EPFL"], + ) + """ + params = params or [] + conn = self.connect() + with self.transaction(): + conn.execute( + "DELETE FROM scope_records WHERE scope_mode = ?", [scope_mode], + ) + conn.execute( + "INSERT INTO scope_records (scope_mode, grant_number) " + f"SELECT ?, grant_number FROM grants WHERE {where_clause}", # noqa: S608 — caller controls + [scope_mode, *params], + ) + return self.count_scope_records(scope_mode) + + def set_manifest(self, manifest: IngestManifest) -> None: + sql = ( + "INSERT INTO manifests " + "(scope_mode, record_count, snapshot_iso, source_dir, built_at_iso) " + "VALUES (?, ?, ?, ?, ?) " + "ON CONFLICT (scope_mode) DO UPDATE SET " + "record_count = excluded.record_count, " + "snapshot_iso = excluded.snapshot_iso, " + "source_dir = excluded.source_dir, " + "built_at_iso = excluded.built_at_iso" + ) + self.connect().execute( + sql, + [ + manifest.scope_mode, + manifest.record_count, + manifest.snapshot_iso or _now_iso(), + manifest.source_dir, + manifest.built_at_iso or _now_iso(), + ], + ) + + # ---- Reads ------------------------------------------------------------- + + def count_grants(self) -> int: + result = self.connect().execute("SELECT count(*) FROM grants").fetchone() + return int(result[0]) if result else 0 + + def count_persons(self) -> int: + result = self.connect().execute("SELECT count(*) FROM persons").fetchone() + return int(result[0]) if result else 0 + + def count_disciplines(self) -> int: + result = self.connect().execute( + "SELECT count(*) FROM discipline_taxonomy", + ).fetchone() + return int(result[0]) if result else 0 + + def count_scope_records(self, scope_mode: str) -> int: + result = self.connect().execute( + "SELECT count(*) FROM scope_records WHERE scope_mode = ?", + [scope_mode], + ).fetchone() + return int(result[0]) if result else 0 + + def fetch_grant(self, grant_number: int) -> Optional[dict[str, Any]]: + cur = self.connect().execute( + "SELECT * FROM grants WHERE grant_number = ?", [int(grant_number)], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + def fetch_manifest(self, scope_mode: str) -> Optional[dict[str, Any]]: + cur = self.connect().execute( + "SELECT * FROM manifests WHERE scope_mode = ?", [scope_mode], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + +def _now_iso() -> str: + return dt.datetime.now(dt.timezone.utc).isoformat() + + +__all__ = ["DuckDBStore"] diff --git a/src/index/snsf/storage/schema.sql b/src/index/snsf/storage/schema.sql new file mode 100644 index 0000000..9983450 --- /dev/null +++ b/src/index/snsf/storage/schema.sql @@ -0,0 +1,266 @@ +-- Canonical DuckDB schema for the SNSF P3 index module. +-- Idempotent: every statement uses IF NOT EXISTS so re-runs are safe. +-- See .internal/snsf/README.md for column-by-column rationale. + +-- One row per SNSF P3 grant (Application). Loaded from the bulk CSV +-- `grants_with_abstracts.csv`. 90,448 grants total, 1975-2027. +CREATE TABLE IF NOT EXISTS grants ( + grant_number INTEGER PRIMARY KEY, + grant_number_string TEXT, + title TEXT, + title_english TEXT, + responsible_applicant TEXT, + + funding_instrument TEXT, + funding_instrument_reporting TEXT, + funding_instrument_l1 TEXT, + + institute TEXT, + institute_city TEXT, + institute_country TEXT, + research_institution TEXT, + research_institution_type TEXT, + + main_discipline TEXT, + main_discipline_number TEXT, + main_discipline_l1 TEXT, + main_discipline_l2 TEXT, + all_disciplines TEXT, + main_field_of_research TEXT, + main_field_of_research_la TEXT, + main_field_of_research_lb TEXT, + all_field_of_researchs TEXT, + + start_date TIMESTAMP, + end_date TIMESTAMP, + amount_granted BIGINT, + keywords TEXT, + + -- Full project abstract (technical, English usually). Multi-line free text. + abstract TEXT, + + -- Plain-language summary in 4 languages (English / German / French / Italian). + -- The "lead" is a single-sentence headline; the full lay summary is a paragraph. + lay_summary_lead_en TEXT, + lay_summary_en TEXT, + lay_summary_lead_de TEXT, + lay_summary_de TEXT, + lay_summary_lead_fr TEXT, + lay_summary_fr TEXT, + lay_summary_lead_it TEXT, + lay_summary_it TEXT, + + state TEXT, + call_full_title TEXT, + call_end_date TIMESTAMP, + call_decision_year INTEGER, + + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS grants_research_institution_idx ON grants(research_institution); +CREATE INDEX IF NOT EXISTS grants_state_idx ON grants(state); +CREATE INDEX IF NOT EXISTS grants_call_year_idx ON grants(call_decision_year); +CREATE INDEX IF NOT EXISTS grants_main_discipline_idx ON grants(main_discipline_number); + +-- One row per SNSF-known person. Loaded from `persons.csv`. +-- Each person has lists of grants they're attached to via different roles +-- (`responsible_applicant`, `co_applicant`, `employee`, etc.) — these are +-- semicolon-separated TEXT in the source CSV; we keep them as JSON arrays +-- so callers can `json_each` to expand the join. +CREATE TABLE IF NOT EXISTS persons ( + person_number INTEGER PRIMARY KEY, + first_name TEXT, + last_name TEXT, + institute TEXT, + institute_place TEXT, + institute_country TEXT, + research_institution TEXT, + research_institution_type TEXT, + orcid TEXT, + -- Per-role grant_number lists, JSON-encoded for `json_each` ergonomics. + responsible_applicant_grants JSON, + co_applicant_grants JSON, + project_partner_grants JSON, + practice_partner_grants JSON, + employee_grants JSON, + contact_person_grants JSON, + applicant_abroad_grants JSON, + person_grant_discipline TEXT, + person_grant_keywords TEXT, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS persons_orcid_idx ON persons(orcid); +CREATE INDEX IF NOT EXISTS persons_research_institution_idx ON persons(research_institution); + +-- Reference taxonomy from `SNF_field_of_research_disciplines.csv`. ~360 rows. +-- Maps SNSF disciplines (`mySNF discipline #`) to ARC's Field of Research codes. +CREATE TABLE IF NOT EXISTS discipline_taxonomy ( + mapping_direction TEXT, + field_of_research_number INTEGER, + field_of_research TEXT, + snf_discipline_number INTEGER, + snf_discipline TEXT +); + +-- Per-scope grant membership. (scope_mode, grant_number). +CREATE TABLE IF NOT EXISTS scope_records ( + scope_mode TEXT NOT NULL, + grant_number INTEGER NOT NULL, + PRIMARY KEY (scope_mode, grant_number) +); + +CREATE INDEX IF NOT EXISTS scope_records_grant_idx ON scope_records(grant_number); + +-- Per-scope ingest manifest. Tracks the most recent dump version + load timestamp. +CREATE TABLE IF NOT EXISTS manifests ( + scope_mode TEXT PRIMARY KEY, + record_count INTEGER, + snapshot_iso TIMESTAMP, + source_dir TEXT, + built_at_iso TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +-- --------------------------------------------------------------------------- +-- Output tables +-- +-- All loaded from `output_data_*.csv`. Every row is keyed by `grant_number` +-- (FK to `grants.grant_number`). To slice by scope, JOIN to `scope_records`. +-- The PK on each is the source CSV's UUID column. +-- --------------------------------------------------------------------------- + +-- output_data_scientific_publications.csv — 23 cols including DOI + Abstract. +CREATE TABLE IF NOT EXISTS output_publications ( + publication_id TEXT PRIMARY KEY, + grant_number INTEGER, + peer_review_status TEXT, + type TEXT, + title TEXT, + author TEXT, + state TEXT, + year INTEGER, + isbn TEXT, + doi TEXT, + import_source TEXT, + open_access_yes_no INTEGER, -- 0/1 in source + open_access_status TEXT, + url TEXT, + publication_title TEXT, + publisher TEXT, + editor TEXT, + volume TEXT, + issue_number TEXT, + first_page_number TEXT, + last_page_number TEXT, + proceeding_location TEXT, + abstract TEXT, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS output_pubs_grant_idx ON output_publications(grant_number); +CREATE INDEX IF NOT EXISTS output_pubs_doi_idx ON output_publications(doi); +CREATE INDEX IF NOT EXISTS output_pubs_year_idx ON output_publications(year); + +-- output_data_academicevents.csv — talks, posters, conferences. +CREATE TABLE IF NOT EXISTS output_academic_events ( + event_id TEXT PRIMARY KEY, + grant_number INTEGER, + type TEXT, + event TEXT, + contribution_title TEXT, + date TIMESTAMP, + involved_person TEXT, + url TEXT, + place TEXT, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS output_events_grant_idx ON output_academic_events(grant_number); +CREATE INDEX IF NOT EXISTS output_events_date_idx ON output_academic_events(date); + +-- output_data_collaborations.csv — partner research groups + country. +CREATE TABLE IF NOT EXISTS output_collaborations ( + collaboration_id TEXT PRIMARY KEY, + grant_number INTEGER, + research_group TEXT, + type TEXT, + country TEXT, + start_date TIMESTAMP, + end_date TIMESTAMP, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS output_collab_grant_idx ON output_collaborations(grant_number); +CREATE INDEX IF NOT EXISTS output_collab_country_idx ON output_collaborations(country); + +-- output_data_datasets.csv — research data outputs (with PID like Zenodo DOI). +CREATE TABLE IF NOT EXISTS output_datasets ( + dataset_id TEXT PRIMARY KEY, + grant_number INTEGER, + title TEXT, + author TEXT, + persistent_identifier TEXT, -- DOI / Handle / etc. + repository_name TEXT, + repository_link TEXT, + publication_date TIMESTAMP, + abstract TEXT, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS output_ds_grant_idx ON output_datasets(grant_number); +CREATE INDEX IF NOT EXISTS output_ds_pid_idx ON output_datasets(persistent_identifier); + +-- output_data_knowledgetransfer.csv — tech transfer events. +CREATE TABLE IF NOT EXISTS output_knowledge_transfers ( + event_id TEXT PRIMARY KEY, + grant_number INTEGER, + type TEXT, + event TEXT, + date TIMESTAMP, + involved_person TEXT, + url TEXT, + place TEXT, + target_group TEXT, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS output_kt_grant_idx ON output_knowledge_transfers(grant_number); + +-- output_data_publiccommunications.csv — outreach + lay press. +CREATE TABLE IF NOT EXISTS output_public_communications ( + communication_id TEXT PRIMARY KEY, + grant_number INTEGER, + type TEXT, + title TEXT, + description TEXT, + year INTEGER, + url TEXT, + region TEXT, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS output_pc_grant_idx ON output_public_communications(grant_number); + +-- output_data_useinspired.csv — patents, startups, licenses. +CREATE TABLE IF NOT EXISTS output_use_inspired ( + use_inspired_id TEXT PRIMARY KEY, + grant_number INTEGER, + type TEXT, + title TEXT, + url TEXT, + year INTEGER, + priority_date TIMESTAMP, + patent_number TEXT, + patent_status TEXT, + patent_decision_date TIMESTAMP, + inventor TEXT, + owner TEXT, + patent_owner_description TEXT, + comment TEXT, + reviewer_activity_type TEXT, + license_type TEXT, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS output_ui_grant_idx ON output_use_inspired(grant_number); diff --git a/src/index/swissubase/__init__.py b/src/index/swissubase/__init__.py new file mode 100644 index 0000000..73a21db --- /dev/null +++ b/src/index/swissubase/__init__.py @@ -0,0 +1,25 @@ +"""SWISSUbase RAG indexer. + +SWISSUbase is the Swiss national platform for sharing and preserving +research data, run by FORS / UZH / U. Neuchâtel / DASCH. The catalogue +exposes ~13k "studies" (which the user-facing UI calls "projects") plus +their child datasets, principal investigators, and partner institutions. + +Pipeline shape (mirrors src/index/zenodo): + + ingest → drive a Selenium browser session through the catalogue, scrape + the Material table, then call the per-study public REST + endpoints (which require a session cookie) inside the same + browser to fetch overview + dynamic blocks. Persist into + DuckDB. + embed → chunk title + abstract + keywords for in-scope studies (and + their datasets / persons / institutions), embed via RCP, push + to Qdrant. + search → vector + RCP rerank + DuckDB hydrate. + query → predefined or guarded ad-hoc SQL. + +Default scope: ``epfl_sdsc_ethz`` — the search has no institution filter +server-side, so we ingest the full catalogue but only embed studies whose +institution string matches EPFL / ETH Zurich / SDSC. Flip +``INDEX_SWISSUBASE_SCOPE=switzerland`` to embed everything. +""" diff --git a/src/index/swissubase/__main__.py b/src/index/swissubase/__main__.py new file mode 100644 index 0000000..dc92a5b --- /dev/null +++ b/src/index/swissubase/__main__.py @@ -0,0 +1,8 @@ +"""Entry point: `python -m src.index.swissubase`.""" + +from __future__ import annotations + +from src.index.swissubase.cli import main + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/index/swissubase/_federated.py b/src/index/swissubase/_federated.py new file mode 100644 index 0000000..7eee007 --- /dev/null +++ b/src/index/swissubase/_federated.py @@ -0,0 +1,53 @@ +"""SWISSUbase registration with the federated discover/hydrate registries. + +Hydrate seed types +------------------ + +- ``swissubase_url`` — SWISSUbase study / dataset URLs (Selenium-driven). + +v1 stub. Selenium adds friction; the real hydrator will batch via the +existing ``ingest_studies`` driver. +""" + +from __future__ import annotations + +import logging +from typing import Any, Iterator + +from src.index._federated.dh_registry import register_discoverer, register_hydrator +from src.index._federated.protocols import ( + HydrationSummary, + IndexDiscoverer, + IndexHydrator, + Seed, +) + +LOGGER = logging.getLogger(__name__) + + +class SwissubaseDiscoverer: + name = "swissubase" + accepted_sources = ("from-search",) + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + if source not in self.accepted_sources: + message = f"Swissubase: unknown source {source!r}. Accepted: {list(self.accepted_sources)}" + raise ValueError(message) + LOGGER.warning("swissubase discover is a stub") + return + + +class SwissubaseHydrator: + name = "swissubase" + accepted_seed_types = ("swissubase_url",) + + def hydrate(self, seeds, *, only_unfetched: bool = True) -> HydrationSummary: + materialised = list(seeds) + LOGGER.warning( + "swissubase: hydrate is a stub (received %d seeds).", len(materialised), + ) + return HydrationSummary(skipped_existing=len(materialised)) + + +register_discoverer(SwissubaseDiscoverer()) +register_hydrator(SwissubaseHydrator()) diff --git a/src/index/swissubase/api.py b/src/index/swissubase/api.py new file mode 100644 index 0000000..2d13cf7 --- /dev/null +++ b/src/index/swissubase/api.py @@ -0,0 +1,115 @@ +"""FastAPI app exposing the SWISSUbase index dual query surface.""" + +from __future__ import annotations + +import logging +from typing import Any + +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel, Field + +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.index.swissubase.config import load_config +from src.index.swissubase.embed.pipeline import SWISSUBASE_COLLECTION +from src.index.swissubase.retrieval.semantic import semantic_search +from src.index.swissubase.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) +from src.index.swissubase.storage.duckdb_store import SwissubaseStore + +LOGGER = logging.getLogger(__name__) + +app = FastAPI(title="SWISSUbase Index") + + +class SearchRequest(BaseModel): + query: str + top_k: int = Field(default=10, ge=1, le=100) + candidate_k: int = Field(default=50, ge=1, le=500) + filter_payload: dict[str, Any] | None = None + + +class QueryRequest(BaseModel): + sql: str | None = None + predefined: str | None = None + params: dict[str, Any] | None = None + + +@app.get("/healthz") +def healthz() -> dict[str, Any]: + config = load_config() + duck_status = "ok" + qdrant_status = "ok" + try: + SwissubaseStore.open().count("studies") + except Exception as exc: # noqa: BLE001 + duck_status = f"error: {exc}" + try: + QdrantStore(config).count(SWISSUBASE_COLLECTION) # type: ignore[arg-type] + except Exception as exc: # noqa: BLE001 + qdrant_status = f"error: {exc}" + return { + "duckdb": duck_status, + "qdrant": qdrant_status, + "rcp_configured": bool(config.rcp.token), + "selenium_configured": bool(config.selenium.remote_url), + "scope_default": config.scope.default, + } + + +@app.post("/search") +def search(req: SearchRequest) -> list[dict[str, Any]]: + config = load_config() + try: + config.require_rcp() + except ValueError as exc: + raise HTTPException(status_code=503, detail=str(exc)) from exc + return semantic_search( + config=config, + query=req.query, + top_k=req.top_k, + candidate_k=req.candidate_k, + filter_payload=req.filter_payload, + ) + + +@app.post("/query") +def query(req: QueryRequest) -> list[dict[str, Any]]: + if req.predefined and req.sql: + raise HTTPException( + status_code=400, + detail="Provide either `predefined` or `sql`, not both", + ) + try: + if req.predefined: + return run_predefined(req.predefined, req.params) + if req.sql: + return run_adhoc(req.sql, req.params) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + raise HTTPException(status_code=400, detail="Pass `predefined` or `sql`") + + +@app.get("/predefined") +def list_predefined() -> dict[str, list[str]]: + return {"predefined": sorted(PREDEFINED_QUERIES)} + + +@app.get("/study/{study_id}") +def get_study(study_id: str) -> dict[str, Any]: + store = SwissubaseStore.open() + record = store.fetch_study(study_id) + if record is None: + raise HTTPException(status_code=404, detail="not found") + return record + + +@app.get("/dataset/{dataset_id}") +def get_dataset(dataset_id: str) -> dict[str, Any]: + store = SwissubaseStore.open() + record = store.fetch_dataset(dataset_id) + if record is None: + raise HTTPException(status_code=404, detail="not found") + return record diff --git a/src/index/swissubase/cli.py b/src/index/swissubase/cli.py new file mode 100644 index 0000000..925cebb --- /dev/null +++ b/src/index/swissubase/cli.py @@ -0,0 +1,250 @@ +"""CLI for the SWISSUbase index module. + +Subcommands: + +- ``ingest`` — drive a Selenium browser session through the catalogue, + fetch overview / main / dynamic-blocks per study, and + upsert into DuckDB. +- ``embed`` — chunk + embed in-scope entities, push to Qdrant. +- ``search`` — semantic retrieval (vector + RCP rerank). +- ``query`` — read-only SQL over DuckDB (predefined or guarded ad-hoc). +- ``status`` — print row counts + Qdrant collection size + paths. +- ``serve`` — run the FastAPI app. +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from typing import Any + +from src.index.swissubase.config import load_config +from src.index.swissubase.embed.pipeline import ( + SWISSUBASE_COLLECTION, + embed_entities, +) +from src.index.swissubase.ingest.scope import resolve_scope +from src.index.swissubase.ingest.studies import ingest_studies +from src.index.swissubase.ingest.swissubase_client import SwissubaseClient +from src.index.swissubase.retrieval.semantic import semantic_search +from src.index.swissubase.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) +from src.index.swissubase.storage.duckdb_store import ( + EMBEDDABLE_ENTITY_TYPES, + SwissubaseStore, +) + +LOGGER = logging.getLogger(__name__) + + +def _emit_json(obj: Any) -> None: + json.dump(obj, sys.stdout, indent=2, ensure_ascii=False, default=str) + sys.stdout.write("\n") + + +def _cmd_ingest(args: argparse.Namespace) -> int: + config = load_config() + config.require_selenium() + scope = resolve_scope(args.scope or config.scope.default, config) + store = SwissubaseStore.open() + try: + with SwissubaseClient(config) as client: + summary = ingest_studies( + config=config, + client=client, + store=store, + scope=scope, + limit=args.limit, + refresh=args.refresh, + ) + finally: + store.close() + _emit_json({"scope": scope.name, **summary}) + return 0 + + +def _cmd_embed(args: argparse.Namespace) -> int: + config = load_config() + config.require_rcp() + types: tuple[str, ...] + if args.entity: + unknown = [e for e in args.entity if e not in EMBEDDABLE_ENTITY_TYPES] + if unknown: + message = ( + f"unknown entity types: {unknown}. " + f"known: {sorted(EMBEDDABLE_ENTITY_TYPES)}" + ) + raise SystemExit(message) + types = tuple(args.entity) + else: + types = tuple(sorted(EMBEDDABLE_ENTITY_TYPES)) + store = SwissubaseStore.open() + try: + summary = embed_entities( + config=config, store=store, entity_types=types, limit=args.limit, + ) + finally: + store.close() + _emit_json(summary) + return 0 + + +def _cmd_search(args: argparse.Namespace) -> int: + config = load_config() + config.require_rcp() + filter_payload = json.loads(args.filter) if args.filter else None + hits = semantic_search( + config=config, + query=args.query, + top_k=args.top_k, + candidate_k=args.candidate_k, + filter_payload=filter_payload, + ) + _emit_json(hits) + return 0 + + +def _cmd_query(args: argparse.Namespace) -> int: + if args.predefined and args.sql: + message = "Pass either --predefined or --sql, not both" + raise SystemExit(message) + params: dict[str, Any] = {} + for kv in args.param or []: + if "=" not in kv: + message = f"--param expects key=value, got {kv!r}" + raise SystemExit(message) + key, value = kv.split("=", 1) + try: + params[key] = int(value) + except ValueError: + try: + params[key] = float(value) + except ValueError: + params[key] = value + if args.predefined: + rows = run_predefined(args.predefined, params) + elif args.sql: + rows = run_adhoc(args.sql, params) + else: + _emit_json({"predefined": sorted(PREDEFINED_QUERIES)}) + return 0 + _emit_json(rows) + return 0 + + +def _cmd_status(args: argparse.Namespace) -> int: + del args + config = load_config() + store = SwissubaseStore.open() + try: + counts = { + t: store.count(t) + for t in ( + "studies", + "datasets", + "persons", + "institutions", + "study_persons", + "study_institutions", + "chunks", + ) + } + finally: + store.close() + qdrant_count: int | str + try: + from src.index.openalex.vector.qdrant_store import QdrantStore + + qdrant_count = QdrantStore(config).count(SWISSUBASE_COLLECTION) # type: ignore[arg-type] + except Exception as exc: # noqa: BLE001 + qdrant_count = f"error: {exc}" + _emit_json( + { + "duckdb_path": str(config.paths.duckdb_path), + "duckdb_counts": counts, + "qdrant_collection": SWISSUBASE_COLLECTION, + "qdrant_points": qdrant_count, + "scope_default": config.scope.default, + "selenium_remote_url_set": bool(config.selenium.remote_url), + }, + ) + return 0 + + +def _cmd_serve(args: argparse.Namespace) -> int: + import uvicorn + + uvicorn.run( + "src.index.swissubase.api:app", + host=args.host, + port=args.port, + reload=args.reload, + ) + return 0 + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(prog="src.index.swissubase") + sub = parser.add_subparsers(dest="cmd", required=True) + + p_ingest = sub.add_parser( + "ingest", + help="Drive Selenium through the SWISSUbase catalogue and persist to DuckDB", + ) + p_ingest.add_argument( + "--scope", default=None, + help="Scope name (epfl_sdsc_ethz, switzerland). Default from config.", + ) + p_ingest.add_argument("--limit", type=int, default=None, help="Cap on total studies") + p_ingest.add_argument( + "--refresh", action="store_true", + help="Ignore checkpoint state and start from scratch", + ) + p_ingest.set_defaults(func=_cmd_ingest) + + p_embed = sub.add_parser("embed", help="Chunk + embed in-scope entities, push to Qdrant") + p_embed.add_argument( + "--entity", action="append", + help=f"Restrict to one or more entity types {sorted(EMBEDDABLE_ENTITY_TYPES)}", + ) + p_embed.add_argument("--limit", type=int, default=None) + p_embed.set_defaults(func=_cmd_embed) + + p_search = sub.add_parser("search", help="Semantic retrieval (vector + rerank)") + p_search.add_argument("query", help="Natural-language query") + p_search.add_argument("--top-k", type=int, default=10) + p_search.add_argument("--candidate-k", type=int, default=50) + p_search.add_argument("--filter", default=None, help="JSON dict for Qdrant payload filter") + p_search.set_defaults(func=_cmd_search) + + p_query = sub.add_parser("query", help="Read-only SQL (predefined or guarded ad-hoc)") + p_query.add_argument("--predefined", default=None, help=f"One of: {sorted(PREDEFINED_QUERIES)}") + p_query.add_argument("--sql", default=None, help="Ad-hoc SELECT/WITH query") + p_query.add_argument("--param", action="append", help="Repeatable key=value param") + p_query.set_defaults(func=_cmd_query) + + p_status = sub.add_parser("status", help="Show DuckDB + Qdrant counts and paths") + p_status.set_defaults(func=_cmd_status) + + p_serve = sub.add_parser("serve", help="Run the SWISSUbase FastAPI app") + p_serve.add_argument("--host", default="0.0.0.0") + p_serve.add_argument("--port", type=int, default=8004) + p_serve.add_argument("--reload", action="store_true") + p_serve.set_defaults(func=_cmd_serve) + + return parser + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s — %(message)s", + ) + parser = _build_parser() + args = parser.parse_args(argv) + return int(args.func(args) or 0) diff --git a/src/index/swissubase/config.py b/src/index/swissubase/config.py new file mode 100644 index 0000000..e183e59 --- /dev/null +++ b/src/index/swissubase/config.py @@ -0,0 +1,174 @@ +"""Config loader for the SWISSUbase indexer. + +Reads `config/index/swissubase.yaml` and merges in env-sourced credentials +(`RCP_TOKEN`, `INDEX_QDRANT_API_KEY`, `SELENIUM_REMOTE_URL`, +`INDEX_QDRANT_URL`) plus the resolved data dir. + +The `rcp` and `qdrant` sub-blocks mirror `OpenAlexIndexConfig` / +`ZenodoIndexConfig` field-for-field so that the same +`RCPEmbeddingClient` / `RCPRerankerClient` / `QdrantStore` reused by the +zenodo indexer also works here at runtime — those clients only access +`config.rcp.*` and `config.qdrant.*` and never import a specific config +module at runtime. +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Optional + +import yaml +from pydantic import BaseModel + +from src.index.swissubase.paths import SwissubasePaths, get_swissubase_paths + +DEFAULT_CONFIG_PATH = Path("config/index/swissubase.yaml") + +TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} +FALSE_ENV_VALUES = {"0", "false", "f", "no", "n", "off"} + +MISSING_RCP_TOKEN_ERROR = "Missing required environment variable: RCP_TOKEN" +MISSING_SELENIUM_URL_ERROR = "Missing required environment variable: SELENIUM_REMOTE_URL" + + +class RcpConfig(BaseModel): + base_url: str + embedding_model: str + embedding_dim: int + query_instruction: str + reranker_model: str + batch_size: int = 32 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: Optional[str] = None + + +class CatalogueConfig(BaseModel): + base_url: str = "https://www.swissubase.ch" + language: str = "en" + # Server-side allowlist: {5, 10, 20, 50}. Other values 400. + page_size: int = 50 + # Polite spacing between catalogue page navigations (seconds). + page_delay_seconds: float = 1.0 + # Max wait for the rendered Material table on each list page. + list_render_timeout_seconds: int = 30 + # Max wait for a study detail JSON XHR to come back. + detail_timeout_seconds: int = 30 + + # ---- ID-iteration mode ------------------------------------------------- + # The search-studies endpoint caps deep pagination at ~250 items + # regardless of filters. We instead enumerate every studyVersionId in a + # bounded range; per-study endpoints have no such cap. Observed live + # range is ~1..21,500 with ~58% density, so 25,000 is a safe upper bound. + id_start: int = 1 + id_end: int = 25000 + # Polite spacing between per-ID requests (seconds). Lower than + # `page_delay_seconds` because we now make 50× more requests. + per_id_delay_seconds: float = 0.2 + + +class ScopeConfig(BaseModel): + """Scope filter applied during ingest. + + swissUbase has no institution filter on the catalogue, so we ingest + every study and post-filter on the rendered institution string. + Studies whose institution string matches any pattern below get + `affiliation_match=TRUE` and are the only ones embedded by default. + """ + + default: str = "epfl_sdsc_ethz" + # Case-insensitive substrings tested against the institution string. + epfl_sdsc_ethz_patterns: list[str] = [ + "EPFL", + "École polytechnique fédérale de Lausanne", + "Ecole polytechnique fédérale de Lausanne", + "ETH Zurich", + "ETHZ", + "Eidgenössische Technische Hochschule", + "SDSC", + "Swiss Data Science Center", + ] + + +class SeleniumConfig(BaseModel): + remote_url: Optional[str] = None + page_load_timeout_seconds: int = 60 + headless: bool = True + + +class QdrantConfig(BaseModel): + url: str = "http://localhost:6333" + prefer_grpc: bool = False + api_key: Optional[str] = None + + +class ChunkingConfig(BaseModel): + size_tokens: int = 256 + overlap_tokens: int = 64 + tokenizer: str = "cl100k_base" + + +class SwissubaseIndexConfig(BaseModel): + rcp: RcpConfig + catalogue: CatalogueConfig + scope: ScopeConfig + selenium: SeleniumConfig + qdrant: QdrantConfig + chunking: ChunkingConfig + paths: SwissubasePaths + + model_config = {"arbitrary_types_allowed": True} + + def require_rcp(self) -> None: + if not self.rcp.token: + raise ValueError(MISSING_RCP_TOKEN_ERROR) + + def require_selenium(self) -> None: + if not self.selenium.remote_url: + raise ValueError(MISSING_SELENIUM_URL_ERROR) + + +def _env_bool(name: str) -> Optional[bool]: + raw = os.getenv(name) + if raw is None or raw.strip() == "": + return None + normalized = raw.strip().lower() + if normalized in TRUE_ENV_VALUES: + return True + if normalized in FALSE_ENV_VALUES: + return False + message = f"Invalid boolean value for {name}: {raw!r}" + raise ValueError(message) + + +def _env_str(name: str) -> Optional[str]: + raw = os.getenv(name) + if raw is None: + return None + stripped = raw.strip() + return stripped or None + + +def load_config(path: Optional[Path] = None) -> SwissubaseIndexConfig: + cfg_path = path or DEFAULT_CONFIG_PATH + raw = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) or {} + + raw.setdefault("rcp", {})["token"] = _env_str("RCP_TOKEN") + raw.setdefault("selenium", {})["remote_url"] = _env_str("SELENIUM_REMOTE_URL") + raw.setdefault("qdrant", {})["api_key"] = _env_str("INDEX_QDRANT_API_KEY") + + if (override := _env_str("INDEX_QDRANT_URL")) is not None: + raw["qdrant"]["url"] = override + if (override_b := _env_bool("INDEX_QDRANT_PREFER_GRPC")) is not None: + raw["qdrant"]["prefer_grpc"] = override_b + if (override := _env_str("INDEX_SWISSUBASE_SCOPE")) is not None: + raw.setdefault("scope", {})["default"] = override + if (override := _env_str("INDEX_SWISSUBASE_ID_END")) is not None: + raw.setdefault("catalogue", {})["id_end"] = int(override) + if (override := _env_str("INDEX_SWISSUBASE_ID_START")) is not None: + raw.setdefault("catalogue", {})["id_start"] = int(override) + + raw["paths"] = get_swissubase_paths() + + return SwissubaseIndexConfig(**raw) diff --git a/src/index/swissubase/embed/__init__.py b/src/index/swissubase/embed/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/swissubase/embed/pipeline.py b/src/index/swissubase/embed/pipeline.py new file mode 100644 index 0000000..7d0cb04 --- /dev/null +++ b/src/index/swissubase/embed/pipeline.py @@ -0,0 +1,232 @@ +"""Embed SWISSUbase entities into Qdrant via the RCP `/embeddings` endpoint. + +A single Qdrant collection (``swissubase_entities``) holds chunks for +all four entity types (``studies``, ``datasets``, ``persons``, +``institutions``) — the ``entity_type`` payload field disambiguates. +This mirrors the HuggingFace index pattern rather than zenodo's single- +type collection because the user explicitly wants persons / projects +/ resources / authors all reachable via one tool. + +Reuses ``RCPEmbeddingClient``, ``QdrantStore``, and the token chunker +from ``src.index.openalex`` — those modules only access ``config.rcp.*`` +and ``config.qdrant.*``, both of which ``SwissubaseIndexConfig`` mirrors. + +Studies are gated on ``affiliation_match=TRUE`` at the SQL level, so +only the in-scope subset (epfl_sdsc_ethz by default) gets embedded. +""" + +from __future__ import annotations + +import asyncio +import logging +import uuid +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.chunker import Chunk, chunk_text +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.index.swissubase.storage.duckdb_store import EMBEDDABLE_ENTITY_TYPES + +if TYPE_CHECKING: + from src.index.swissubase.config import SwissubaseIndexConfig + from src.index.swissubase.storage.duckdb_store import SwissubaseStore + +LOGGER = logging.getLogger(__name__) + +_CHUNK_NAMESPACE = uuid.NAMESPACE_URL + +SWISSUBASE_COLLECTION = "swissubase_entities" + + +def _chunk_id(entity_type: str, entity_id: str, chunk_index: int) -> str: + return str( + uuid.uuid5(_CHUNK_NAMESPACE, f"{entity_type}|{entity_id}|{chunk_index}"), + ) + + +def _row_text(entity_type: str, row: dict[str, Any]) -> str | None: + """Build the canonical embedding text for a row. + + Each entity type has its own composition. We deliberately keep this + narrow — title + abstract for studies; name + description for + datasets; name + (optional) institution for persons; name + address + for institutions. The vector tracks "what this entity *is*", not its + full record. + """ + if entity_type in ("studies", "datasets"): + parts = [row.get("title"), row.get("description")] + elif entity_type == "persons": + parts = [row.get("display_name"), row.get("affiliation")] + elif entity_type == "institutions": + parts = [row.get("name"), row.get("address")] + else: + return None + cleaned = [p for p in parts if isinstance(p, str) and p.strip()] + if not cleaned: + return None + return "\n\n".join(cleaned) + + +def _row_to_payload(entity_type: str, row: dict[str, Any]) -> dict[str, Any]: + """Common Qdrant payload shared across entity types. + + Always includes ``source_url`` (when known) so the LLM tool can + return a verbatim canonical SWISSUbase URL — this is the project's + hard requirement. + """ + if entity_type == "studies": + entity_id = row["study_id"] + end_date = row.get("end_date") + start_date = row.get("start_date") + return { + "entity_type": "studies", + "entity_id": entity_id, + "study_id": entity_id, + "ref": row.get("ref"), + "title": row.get("title"), + "main_discipline": row.get("main_discipline"), + "sub_discipline": row.get("sub_discipline"), + "progress": row.get("progress"), + "year_start": start_date.year if start_date else None, + "year_end": end_date.year if end_date else None, + "dataset_count": row.get("dataset_count"), + "source_url": row["source_url"], + } + if entity_type == "datasets": + entity_id = row["dataset_id"] + return { + "entity_type": "datasets", + "entity_id": entity_id, + "dataset_id": entity_id, + "study_id": row.get("study_id"), + "title": row.get("title"), + "access_right": row.get("access_right"), + "license_id": row.get("license_id"), + "source_url": row["source_url"], + } + if entity_type == "persons": + entity_id = row["person_key"] + return { + "entity_type": "persons", + "entity_id": entity_id, + "person_key": entity_id, + "display_name": row.get("display_name"), + "orcid": row.get("orcid"), + "affiliation": row.get("affiliation"), + "source_url": row.get("source_url"), + } + if entity_type == "institutions": + entity_id = row["institution_key"] + return { + "entity_type": "institutions", + "entity_id": entity_id, + "institution_key": entity_id, + "name": row.get("name"), + "ror_id": row.get("ror_id"), + "source_url": row.get("source_url"), + } + message = f"Unknown entity_type: {entity_type}" + raise ValueError(message) + + +def _row_entity_id(entity_type: str, row: dict[str, Any]) -> str: + if entity_type == "studies": + return row["study_id"] + if entity_type == "datasets": + return row["dataset_id"] + if entity_type == "persons": + return row["person_key"] + if entity_type == "institutions": + return row["institution_key"] + message = f"Unknown entity_type: {entity_type}" + raise ValueError(message) + + +async def _embed_async( + *, + config: SwissubaseIndexConfig, + store: SwissubaseStore, + entity_types: tuple[str, ...], + limit: int | None, +) -> dict[str, int]: + client = RCPEmbeddingClient(config) # type: ignore[arg-type] + qdrant = QdrantStore(config) # type: ignore[arg-type] + qdrant.ensure_collection(SWISSUBASE_COLLECTION) + + summary: dict[str, int] = dict.fromkeys(entity_types, 0) + + for entity_type in entity_types: + pending: list[tuple[str, dict[str, Any], Chunk]] = [] + + # Bind both `et` and `buf` via defaults so the closure doesn't + # late-resolve free variables from the loop scope (B023). + async def flush(et: str = entity_type, buf: list = pending) -> None: + if not buf: + return + texts = [c.text for _, _, c in buf] + vectors = await client.embed_all(texts) + ids: list[str] = [] + payloads: list[dict[str, Any]] = [] + for entity_id, base_payload, chunk in buf: + cid = _chunk_id(et, entity_id, chunk.index) + ids.append(cid) + payloads.append({**base_payload, "chunk_index": chunk.index}) + store.upsert_chunk( + chunk_id=cid, + entity_type=et, + entity_id=entity_id, + chunk_index=chunk.index, + text=chunk.text, + token_count=chunk.token_count, + vector_id=cid, + ) + qdrant.upsert_points( + SWISSUBASE_COLLECTION, + ids=ids, + vectors=vectors, + payloads=payloads, + ) + summary[et] += len(buf) + buf.clear() + + for row in store.stream_rows_for_embedding(entity_type, limit=limit): + text = _row_text(entity_type, row) + if not text: + continue + chunks = chunk_text( + text, + chunk_tokens=config.chunking.size_tokens, + overlap=config.chunking.overlap_tokens, + ) + if not chunks: + continue + entity_id = _row_entity_id(entity_type, row) + base_payload = _row_to_payload(entity_type, row) + for chunk in chunks: + pending.append((entity_id, base_payload, chunk)) + if len(pending) >= client.batch_size: + await flush() + await flush() + LOGGER.info( + "swissubase embed %s complete: chunks=%d", + entity_type, summary[entity_type], + ) + + return summary + + +def embed_entities( + *, + config: SwissubaseIndexConfig, + store: SwissubaseStore, + entity_types: tuple[str, ...] | None = None, + limit: int | None = None, +) -> dict[str, int]: + """Synchronously embed SWISSUbase entities (any subset of the four).""" + types = entity_types or tuple(EMBEDDABLE_ENTITY_TYPES) + return asyncio.run( + _embed_async( + config=config, store=store, + entity_types=types, limit=limit, + ), + ) diff --git a/src/index/swissubase/ingest/__init__.py b/src/index/swissubase/ingest/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/swissubase/ingest/scope.py b/src/index/swissubase/ingest/scope.py new file mode 100644 index 0000000..903e4c4 --- /dev/null +++ b/src/index/swissubase/ingest/scope.py @@ -0,0 +1,77 @@ +"""Scope filter builder for the SWISSUbase indexer. + +The catalogue itself has no institution filter, so the scope is applied +as a post-filter on the rendered institution string of each study. The +result drives the ``affiliation_match`` boolean on ``studies`` rows, +which the embedder uses to decide what to vectorise. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING, Iterable, Literal + +if TYPE_CHECKING: + from src.index.swissubase.config import SwissubaseIndexConfig + +ScopeName = Literal["epfl_sdsc_ethz", "switzerland"] + + +@dataclass(slots=True, frozen=True) +class Scope: + name: str + patterns: tuple[str, ...] + + def matches(self, *texts: str | None) -> bool: + """True if any of `patterns` is found (case-insensitively) in any text. + + ``switzerland`` resolves to an empty pattern tuple — every study + matches, so embedding covers the full catalogue. + """ + if not self.patterns: + return True + haystacks = [t.lower() for t in texts if t] + if not haystacks: + return False + for pattern in self.patterns: + needle = pattern.lower() + if any(needle in h for h in haystacks): + return True + return False + + +def epfl_sdsc_ethz_scope(config: SwissubaseIndexConfig) -> Scope: + return Scope( + name="epfl_sdsc_ethz", + patterns=tuple(config.scope.epfl_sdsc_ethz_patterns), + ) + + +def switzerland_scope(_: SwissubaseIndexConfig) -> Scope: + # Empty pattern tuple → every study matches. Use this when you want + # to embed the whole Swiss social-science catalogue. + return Scope(name="switzerland", patterns=()) + + +def resolve_scope(name: str, config: SwissubaseIndexConfig) -> Scope: + if name == "epfl_sdsc_ethz": + return epfl_sdsc_ethz_scope(config) + if name == "switzerland": + return switzerland_scope(config) + message = ( + f"Unknown scope: {name}. Known: epfl_sdsc_ethz, switzerland" + ) + raise ValueError(message) + + +def institution_strings(institutions: Iterable[dict]) -> list[str]: + """Flatten an institution-block list to plain strings for matching.""" + out: list[str] = [] + for inst in institutions: + if not isinstance(inst, dict): + continue + for key in ("name", "title", "label", "displayName", "address"): + v = inst.get(key) + if isinstance(v, str) and v.strip(): + out.append(v) + return out diff --git a/src/index/swissubase/ingest/studies.py b/src/index/swissubase/ingest/studies.py new file mode 100644 index 0000000..66d286f --- /dev/null +++ b/src/index/swissubase/ingest/studies.py @@ -0,0 +1,455 @@ +"""Project SWISSUbase JSON payloads → DuckDB rows and persist. + +Pipeline per study: + + 1. catalogue search yields a thin item with ``studyVersionId``. + 2. fetch ``overview-block`` (institutions, persons, disciplines, + dates, version) and ``main`` (abstract, title, statusCode). + 3. ``dynamic-blocks`` is fetched but only stored raw — its content + blocks (Methods, Datasets, Funding, ...) are loaded lazily by the + SPA via per-block endpoints we haven't reverse-engineered yet. + Datasets/funding parsing is therefore best-effort for this pass. + 4. project everything into ``StudyRow`` + ``PersonRow`` + + ``InstitutionRow`` shapes, compute ``affiliation_match`` against + the configured scope, and upsert. +""" + +from __future__ import annotations + +import json +import logging +import re +from datetime import date +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from src.index.swissubase.ingest.scope import Scope + +if TYPE_CHECKING: + from src.index.swissubase.config import SwissubaseIndexConfig + from src.index.swissubase.ingest.swissubase_client import SwissubaseClient + from src.index.swissubase.storage.duckdb_store import SwissubaseStore + +LOGGER = logging.getLogger(__name__) + +_DATE_RE = re.compile(r"^(\d{4})(?:-(\d{2}))?(?:-(\d{2}))?") +_NON_WORD_RE = re.compile(r"[^a-z0-9]+") + + +def _parse_date(raw: str | None) -> date | None: + if not raw: + return None + m = _DATE_RE.match(raw.strip()) + if not m: + return None + year = int(m.group(1)) + month = int(m.group(2) or 1) + day = int(m.group(3) or 1) + try: + return date(year, month, day) + except ValueError: + return None + + +def _slugify(value: str) -> str: + return _NON_WORD_RE.sub("-", value.lower()).strip("-") + + +def _select_localized(block: Any, language: str) -> str | None: + """Pick the language variant or fall back to the first available.""" + if block is None: + return None + if isinstance(block, str): + return block + if isinstance(block, dict): + if language in block and isinstance(block[language], str): + return block[language] + for fallback in ("en", "fr", "de", "it"): + if fallback in block and isinstance(block[fallback], str): + return block[fallback] + for v in block.values(): + if isinstance(v, str) and v.strip(): + return v + return None + + +def _build_study_url(base_url: str, language: str, study_version_id: str) -> str: + return f"{base_url.rstrip('/')}/{language}/catalogue/studies/{study_version_id}" + + +def _institution_key(name: str) -> str: + slug = _slugify(name) if name else "" + return f"name:{slug}" if slug else "" + + +def _institution_address(addr: dict[str, Any] | None) -> str | None: + if not isinstance(addr, dict): + return None + parts = [ + addr.get("address1"), + addr.get("address2"), + addr.get("postalNumber"), + addr.get("city"), + addr.get("country"), + ] + rendered = ", ".join(p for p in parts if isinstance(p, str) and p.strip()) + return rendered or None + + +def _person_key(person: dict[str, Any]) -> str: + """Stable key for a person. + + SWISSUbase exposes ``personId`` (numeric) and ``refNumber`` per + person; we use ``swissubase:person:{personId}`` as the canonical + identifier so downstream consumers can dedupe across studies. + """ + pid = person.get("personId") + if pid: + return f"swissubase:person:{pid}" + first = (person.get("firstName") or "").strip() + last = (person.get("lastName") or "").strip() + slug = _slugify(f"{first} {last}".strip()) + return f"name:{slug}" if slug else "" + + +def _person_display_name(person: dict[str, Any]) -> str: + first = (person.get("firstName") or "").strip() + last = (person.get("lastName") or "").strip() + full = f"{first} {last}".strip() + return full or person.get("displayName") or "" + + +def _project_institutions( + overview: dict[str, Any] | None, +) -> list[tuple[dict[str, Any], dict[str, Any]]]: + """Return (row, raw) pairs ready for upsert. Skip unkeyed entries.""" + out: list[tuple[dict[str, Any], dict[str, Any]]] = [] + if not isinstance(overview, dict): + return out + for inst in overview.get("institutions") or []: + if not isinstance(inst, dict): + continue + # `fullName` is sometimes a list of strings; normalize to a string. + name_field = inst.get("fullName") or inst.get("name") + if isinstance(name_field, list): + name = next( + (n for n in name_field if isinstance(n, str) and n.strip()), + None, + ) + elif isinstance(name_field, str): + name = name_field.strip() or None + else: + name = None + if not name: + continue + ikey = _institution_key(name) + if not ikey: + continue + address = _institution_address(inst.get("mainAddress")) + out.append( + ( + { + "institution_key": ikey, + "name": name, + "address": address, + "ror_id": None, # SWISSUbase doesn't expose ROR refs. + "source_url": None, + }, + inst, + ), + ) + return out + + +def _project_persons( + overview: dict[str, Any] | None, +) -> list[tuple[dict[str, Any], dict[str, Any], str | None, int]]: + """Yield (row, raw, role, position) tuples for each person.""" + out: list[tuple[dict[str, Any], dict[str, Any], str | None, int]] = [] + if not isinstance(overview, dict): + return out + for person in overview.get("persons") or []: + if not isinstance(person, dict): + continue + pkey = _person_key(person) + if not pkey: + continue + display = _person_display_name(person) + if person.get("isPI"): + role = "Principal investigator" + elif person.get("active") is False: + role = "Former collaborator" + else: + role = "Author" + position = int(person.get("sort") or 0) + out.append( + ( + { + "person_key": pkey, + "display_name": display, + "orcid": None, + "affiliation": None, + "source_url": None, + }, + person, + role, + position, + ), + ) + return out + + +def _disciplines(overview: dict[str, Any] | None) -> tuple[str | None, str | None]: + """Return (main, sub) discipline labels. + + swissUbase emits a hierarchical list (top → leaf). We take the first + element as ``main`` and the second (when present) as ``sub``. + """ + if not isinstance(overview, dict): + return (None, None) + raw = overview.get("disciplines") + if not isinstance(raw, list): + return (None, None) + flat = [d for d in raw if isinstance(d, str) and d.strip()] + main = flat[0] if flat else None + sub = flat[1] if len(flat) > 1 else None + return (main, sub) + + +def _format_version(overview: dict[str, Any] | None) -> str | None: + if not isinstance(overview, dict): + return None + block = overview.get("versionNumber") + if not isinstance(block, dict): + return None + parts = [ + str(block.get("majorVersion")) if block.get("majorVersion") is not None else None, + str(block.get("minorVersion")) if block.get("minorVersion") is not None else None, + ] + rendered = ".".join(p for p in parts if p) + return rendered or None + + +def project_study( + *, + config: SwissubaseIndexConfig, + scope: Scope, + item: dict[str, Any], + overview: dict[str, Any] | None, + main: dict[str, Any] | None, +) -> dict[str, Any] | None: + """Project a SWISSUbase study payload into a ``StudyRow`` dict. + + Returns ``None`` when the catalogue item has no ``studyVersionId`` + (defensive — every observed item has one). + """ + sid = item.get("studyVersionId") or item.get("studyId") + if sid is None: + return None + study_id = str(sid) + + title = ( + _select_localized(main.get("title") if isinstance(main, dict) else None, config.catalogue.language) + or _select_localized(overview.get("title") if isinstance(overview, dict) else None, config.catalogue.language) + or item.get("studyVersionMainTitle") + or _select_localized(item.get("studyVersionTitle"), config.catalogue.language) + ) + abstract = ( + _select_localized(main.get("abstract") if isinstance(main, dict) else None, config.catalogue.language) + or _select_localized(item.get("abstract"), config.catalogue.language) + ) + main_disc, sub_disc = _disciplines(overview) + institutions = _project_institutions(overview) + institution_strings = [r["name"] for r, _ in institutions if r.get("name")] + affiliation_match = scope.matches(*institution_strings) + + return { + "study_id": study_id, + "ref": str(overview.get("referenceNumber")) + if isinstance(overview, dict) and overview.get("referenceNumber") is not None + else (str(item.get("referenceNumber")) if item.get("referenceNumber") is not None else None), + "title": title, + "description": abstract, + "description_language": ( + (main.get("metadataLanguageCode") if isinstance(main, dict) else None) + or config.catalogue.language + ), + "start_date": _parse_date(overview.get("startDate") if isinstance(overview, dict) else None), + "end_date": _parse_date( + (overview.get("endDate") if isinstance(overview, dict) else None) or item.get("endDate"), + ), + "progress": (overview.get("progressString") if isinstance(overview, dict) else None), + "main_discipline": main_disc, + "sub_discipline": sub_disc, + "version": _format_version(overview), + "data_availability": ( + overview.get("dataAccessInformation") if isinstance(overview, dict) else None + ) and json.dumps(overview.get("dataAccessInformation"), ensure_ascii=False), + "dataset_count": item.get("datasetsCount"), + "affiliation_match": affiliation_match, + "source_url": _build_study_url( + config.catalogue.base_url, config.catalogue.language, study_id, + ), + } + + +def persist_study( + *, + store: SwissubaseStore, + study_row: dict[str, Any], + overview: dict[str, Any] | None, + dynamic_blocks: Any, +) -> None: + """Persist a fully projected study + its institutions/persons + edges.""" + store.upsert_study( + study_row, + raw_overview=overview if isinstance(overview, dict) else None, + raw_dynamic_blocks=( + dynamic_blocks if isinstance(dynamic_blocks, (dict, list)) else None + ), + ) + + institutions = _project_institutions(overview) + for inst_row, inst_raw in institutions: + store.upsert_institution(inst_row, raw=inst_raw) + if institutions: + store.upsert_study_institutions( + study_row["study_id"], + (r["institution_key"] for r, _ in institutions), + ) + + persons = _project_persons(overview) + for person_row, person_raw, _, _ in persons: + store.upsert_person(person_row, raw=person_raw) + if persons: + store.upsert_study_persons( + study_row["study_id"], + ((row["person_key"], role, position) for row, _, role, position in persons), + ) + + +# ---- Orchestration ---------------------------------------------------- + + +def _state_path(config: SwissubaseIndexConfig, scope_name: str) -> Path: + return config.paths.state_dir / f"ingest_{scope_name}.json" + + +def _load_state(config: SwissubaseIndexConfig, scope_name: str) -> dict[str, Any]: + path = _state_path(config, scope_name) + if not path.exists(): + return {"last_id": 0, "completed": False, "ingested": 0, "scope_matched": 0} + try: + return json.loads(path.read_text(encoding="utf-8")) + except json.JSONDecodeError: + LOGGER.warning("could not parse %s; restarting state", path) + return {"last_id": 0, "completed": False, "ingested": 0, "scope_matched": 0} + + +def _save_state( + config: SwissubaseIndexConfig, + scope_name: str, + state: dict[str, Any], +) -> None: + _state_path(config, scope_name).write_text( + json.dumps(state, indent=2, ensure_ascii=False), + encoding="utf-8", + ) + + +def ingest_studies( + *, + config: SwissubaseIndexConfig, + client: SwissubaseClient, + store: SwissubaseStore, + scope: Scope, + limit: int | None = None, + refresh: bool = False, +) -> dict[str, Any]: + """Enumerate studyVersionIds in [id_start..id_end] and persist each one. + + The catalogue search-studies endpoint silently caps at ~250 items per + query (and ~1000 with smaller pagesize) regardless of filter combos, so + we enumerate by ID directly. Per-study endpoints have no such cap. + + Resumable: ``last_id`` checkpoint is saved every 50 studies. A second + run without ``--refresh`` resumes from there. + """ + state = ( + _load_state(config, scope.name) + if not refresh + else {"last_id": 0, "completed": False, "ingested": 0, "scope_matched": 0} + ) + start_id = max(int(state.get("last_id") or 0) + 1, config.catalogue.id_start) + end_id = config.catalogue.id_end + ingested = int(state.get("ingested") or 0) + matched = int(state.get("scope_matched") or 0) + failures: list[dict[str, str]] = [] + + LOGGER.info( + "swissubase ingest starting: id_range=[%d..%d] resume_from=%d " + "current_ingested=%d current_matched=%d", + config.catalogue.id_start, end_id, start_id, ingested, matched, + ) + + yielded_session = 0 + for sid_int, overview in client.iter_studies_by_id( + start_id=start_id, end_id=end_id, + ): + sid = str(sid_int) + try: + main = client.fetch_study_main(sid) + except Exception as exc: # noqa: BLE001 + failures.append({"study_id": sid, "stage": "main", "error": str(exc)[:300]}) + LOGGER.warning("main fetch failed for %s: %s", sid, exc) + main = None + try: + dynamic_blocks = client.fetch_study_dynamic_blocks(sid) + except Exception as exc: # noqa: BLE001 + LOGGER.info("dynamic-blocks fetch failed for %s: %s", sid, exc) + dynamic_blocks = None + + # Catalogue-level fields (datasetsCount, endDate) aren't available + # via the per-study endpoints, so we pass an empty stub item; all + # the rich data comes from `overview` + `main`. + row = project_study( + config=config, scope=scope, item={"studyVersionId": sid_int}, + overview=overview if isinstance(overview, dict) else None, + main=main if isinstance(main, dict) else None, + ) + if row is None: + continue + persist_study( + store=store, + study_row=row, + overview=overview if isinstance(overview, dict) else None, + dynamic_blocks=dynamic_blocks, + ) + ingested += 1 + yielded_session += 1 + if row.get("affiliation_match"): + matched += 1 + if ingested % 50 == 0: + state["last_id"] = sid_int + state["ingested"] = ingested + state["scope_matched"] = matched + _save_state(config, scope.name, state) + LOGGER.info( + "swissubase ingest progress: id=%d ingested=%d %s-matched=%d", + sid_int, ingested, scope.name, matched, + ) + if limit is not None and yielded_session >= limit: + break + + state["last_id"] = end_id + state["ingested"] = ingested + state["scope_matched"] = matched + state["completed"] = (limit is None) + _save_state(config, scope.name, state) + return { + "ingested": ingested, + "scope_matched": matched, + "scope": scope.name, + "id_range": [config.catalogue.id_start, end_id], + "failures": failures, + } diff --git a/src/index/swissubase/ingest/swissubase_client.py b/src/index/swissubase/ingest/swissubase_client.py new file mode 100644 index 0000000..691a860 --- /dev/null +++ b/src/index/swissubase/ingest/swissubase_client.py @@ -0,0 +1,379 @@ +"""Selenium-driven SWISSUbase client. + +SWISSUbase exposes a JSON API but every public detail endpoint requires +the session cookie set by ``/api/v2/actions/base`` plus a quirky +``Accept: q=0.8;application/json;q=0.9`` header (without it the server +returns ``403 Not authorized``). Both anonymous curl and ``httpx`` +requests therefore fail; only a real browser session works. + +We open one persistent ``webdriver.Remote`` session against the shared +Selenium Grid, navigate once to the search page (which seeds cookies and +boots the Angular app), then call the API endpoints over the same +browser via ``driver.execute_async_script(fetch(...))``. The browser +attaches the cookies automatically; we only need to supply the magic +``Accept`` header on each call. + +The catalogue list page itself is also rendered via that JSON API +(``POST /api/public/catalogue/catalogue/v1/search-studies/{lang}``), so +we don't scrape the Material table — we ask the API directly for each +page of results, which is much faster than waiting for ``mat-table`` +hydration on every page. +""" + +from __future__ import annotations + +import json +import logging +import time +from contextlib import contextmanager +from typing import TYPE_CHECKING, Any + +from selenium import webdriver +from selenium.webdriver.common.by import By +from selenium.webdriver.firefox.options import Options as FirefoxOptions +from selenium.webdriver.support import expected_conditions as EC +from selenium.webdriver.support.ui import WebDriverWait + +if TYPE_CHECKING: + from collections.abc import Iterator + + from src.index.swissubase.config import SwissubaseIndexConfig + +LOGGER = logging.getLogger(__name__) + +SWISSUBASE_ACCEPT = "q=0.8;application/json;q=0.9" + +# The search-studies endpoint validates pagesize against this set +# server-side; anything else returns 400 "Data sent is not valid". +VALID_PAGESIZES: frozenset[int] = frozenset({5, 10, 20, 50}) + +_FETCH_GET_JS = """ +const url = arguments[0]; +const cb = arguments[1]; +fetch(url, { + credentials: 'same-origin', + headers: {'Accept': 'q=0.8;application/json;q=0.9'}, +}).then(r => r.text().then(t => cb({status: r.status, body: t}))) + .catch(e => cb({status: -1, body: String(e)})); +""" + +_FETCH_POST_JS = """ +const url = arguments[0]; +const body = arguments[1]; +const cb = arguments[2]; +fetch(url, { + method: 'POST', + credentials: 'same-origin', + headers: { + 'Accept': 'q=0.8;application/json;q=0.9', + 'Content-Type': 'application/json', + }, + body: JSON.stringify(body), +}).then(r => r.text().then(t => cb({status: r.status, body: t}))) + .catch(e => cb({status: -1, body: String(e)})); +""" + + +class SwissubaseAPIError(RuntimeError): + """Raised when a swissUbase JSON endpoint returns a non-success status.""" + + +class SwissubaseClient: + """Browser-backed client for the SWISSUbase public catalogue API. + + Use as a context manager — the underlying ``webdriver.Remote`` is + quit on exit. Every method retries lightly on transient network + errors (status -1) but lets HTTP errors bubble up. + """ + + def __init__(self, config: SwissubaseIndexConfig) -> None: + self._config = config + self._driver: webdriver.Remote | None = None + self._session_warm = False + + # ---- Lifecycle ------------------------------------------------------- + + def __enter__(self) -> SwissubaseClient: + self.open() + return self + + def __exit__(self, exc_type, exc, tb) -> None: # noqa: ANN001 + self.close() + + def open(self) -> None: + if self._driver is not None: + return + self._config.require_selenium() + opts = FirefoxOptions() + if self._config.selenium.headless: + opts.add_argument("--headless") + opts.add_argument("--no-sandbox") + opts.add_argument("--disable-dev-shm-usage") + self._driver = webdriver.Remote( + command_executor=self._config.selenium.remote_url, + options=opts, + ) + self._driver.set_page_load_timeout(self._config.selenium.page_load_timeout_seconds) + # Bump above detail_timeout_seconds so the inner fetch can hit the + # 30s limit before the outer script timeout fires; otherwise + # Selenium throws TimeoutException on the script itself. + self._driver.set_script_timeout( + self._config.catalogue.detail_timeout_seconds * 2, + ) + + def close(self) -> None: + if self._driver is not None: + try: + self._driver.quit() + except Exception as exc: # noqa: BLE001 + LOGGER.warning("driver.quit failed: %s", exc) + self._driver = None + self._session_warm = False + + @property + def driver(self) -> webdriver.Remote: + if self._driver is None: + message = "SwissubaseClient not opened — call open() or use as context manager" + raise RuntimeError(message) + return self._driver + + def _warm_session(self) -> None: + """Visit the catalogue once so the browser obtains the session cookies. + + Without this, every subsequent JSON call returns 403. + """ + if self._session_warm: + return + url = ( + f"{self._config.catalogue.base_url}/{self._config.catalogue.language}/catalogue/" + f"search?q=&p=0&ps=10&sn=ref-number&sd=desc" + ) + LOGGER.info("warming swissUbase session at %s", url) + self.driver.get(url) + try: + WebDriverWait( + self.driver, + self._config.catalogue.list_render_timeout_seconds, + ).until(EC.presence_of_element_located((By.CSS_SELECTOR, "tr.mat-mdc-row"))) + except Exception as exc: # noqa: BLE001 + LOGGER.warning( + "session-warm wait timed out (%s); continuing anyway", + exc, + ) + self._session_warm = True + + # ---- HTTP helpers ---------------------------------------------------- + + def _get_json(self, path: str) -> Any: + self._warm_session() + url = self._config.catalogue.base_url + path + result = self.driver.execute_async_script(_FETCH_GET_JS, url) + return self._handle_result(url, result) + + def _post_json(self, path: str, body: dict[str, Any]) -> Any: + self._warm_session() + url = self._config.catalogue.base_url + path + result = self.driver.execute_async_script(_FETCH_POST_JS, url, body) + return self._handle_result(url, result, body=body) + + @staticmethod + def _handle_result( + url: str, + result: dict[str, Any], + *, + body: dict[str, Any] | None = None, + ) -> Any: + status = int(result.get("status") or 0) + raw_body = result.get("body") or "" + if status != 200: + preview = raw_body[:300] if isinstance(raw_body, str) else str(raw_body)[:300] + message = ( + f"swissubase API error: status={status} url={url} " + f"body={preview!r}" + ) + if body is not None: + message += f" req={json.dumps(body)[:300]}" + raise SwissubaseAPIError(message) + if not raw_body: + return None + try: + return json.loads(raw_body) + except json.JSONDecodeError as exc: + message = f"swissubase non-JSON response from {url}: {raw_body[:300]!r}" + raise SwissubaseAPIError(message) from exc + + # ---- Catalogue endpoints -------------------------------------------- + + def search_studies_page( + self, + *, + start: int = 1, + pagesize: int = 50, + query_string: str = "", + facet_filters: dict[str, Any] | None = None, + sort_name: str = "referenceNumber", + sort_direction: str = "desc", + ) -> Any: + """One page of catalogue search results. + + ``start`` is the 1-indexed item offset (page 2 of pagesize=10 + is ``start=11``; page 1 is ``start=1`` or ``start=0``). + ``pagesize`` must be in :data:`VALID_PAGESIZES` — the server + rejects anything else with ``400 Data sent is not valid``. + + Returns the full JSON payload (``{"items": [...], "total": + N, "facets": [...]}``) — caller extracts what it needs. + """ + if pagesize not in VALID_PAGESIZES: + message = ( + f"pagesize={pagesize} is invalid; must be one of " + f"{sorted(VALID_PAGESIZES)}" + ) + raise ValueError(message) + body = { + "query_string": query_string, + "facet_filters": facet_filters or {}, + "start": start, + "pagesize": pagesize, + "sort": {"name": sort_name, "direction": sort_direction}, + "institutionId": None, + "personId": None, + } + path = ( + f"/api/public/catalogue/catalogue/v1/search-studies/" + f"{self._config.catalogue.language}" + ) + return self._post_json(path, body) + + def iter_studies( + self, + *, + pagesize: int | None = None, + limit: int | None = None, + ) -> Iterator[dict[str, Any]]: + """Yield raw catalogue items across pages until exhaustion or limit.""" + size = pagesize or self._config.catalogue.page_size + if size not in VALID_PAGESIZES: + message = ( + f"pagesize={size} is invalid; must be one of " + f"{sorted(VALID_PAGESIZES)}" + ) + raise ValueError(message) + start = 1 + yielded = 0 + total: int | None = None + delay = self._config.catalogue.page_delay_seconds + while True: + payload = self.search_studies_page(start=start, pagesize=size) + items = _extract_items(payload) + if isinstance(payload, dict) and isinstance(payload.get("total"), int): + total = payload["total"] + if not items: + return + for item in items: + yield item + yielded += 1 + if limit is not None and yielded >= limit: + return + start += len(items) + if total is not None and start > total: + return + if delay > 0: + time.sleep(delay) + + def fetch_study_overview(self, study_id: str) -> Any: + path = ( + f"/api/public/catalogue/studies/v1/{study_id}/overview-block/" + f"{self._config.catalogue.language}" + ) + return self._get_json(path) + + def iter_studies_by_id( + self, + *, + start_id: int, + end_id: int, + skip_ids: set[int] | None = None, + ) -> Iterator[tuple[int, dict[str, Any]]]: + """Yield ``(study_id, overview)`` for every existing study in [start_id, end_id]. + + ``studyVersionId`` is sparse but bounded — observed range is roughly + 1 to ~21,500 with ~58% density. The search-studies endpoint caps deep + pagination, so we instead iterate IDs directly: each per-study + endpoint accepts any valid ID without a window limit. + + Per-ID outcomes: + + - **200** → yield ``(id, overview)``. + - **404** → study doesn't exist; skip silently. + - **403** → study exists but isn't public; skip silently. + - other 4xx/5xx → log and skip (let caller decide on retry policy). + + The default polite spacing between requests reuses + ``catalogue.page_delay_seconds``. + """ + skip = skip_ids or set() + for sid in range(start_id, end_id + 1): + if sid in skip: + continue + try: + overview = self.fetch_study_overview(str(sid)) + except SwissubaseAPIError as exc: + msg = str(exc) + if "status=404" in msg or "status=403" in msg: + continue + LOGGER.warning("overview id=%d failed: %s", sid, msg[:200]) + continue + except Exception as exc: # noqa: BLE001 + # Selenium TimeoutException, WebDriverException, transient + # network errors — keep the ingest moving instead of letting + # one slow study kill the whole run. Brief sleep to let + # whatever caused the hiccup settle. + LOGGER.warning( + "overview id=%d transient error (%s: %s); skipping", + sid, type(exc).__name__, str(exc)[:200], + ) + time.sleep(2.0) + continue + if isinstance(overview, dict): + yield sid, overview + if self._config.catalogue.per_id_delay_seconds > 0: + time.sleep(self._config.catalogue.per_id_delay_seconds) + + def fetch_study_main(self, study_id: str) -> Any: + path = ( + f"/api/public/catalogue/studies/v1/{study_id}/main/" + f"{self._config.catalogue.language}" + ) + return self._get_json(path) + + def fetch_study_dynamic_blocks(self, study_id: str) -> Any: + path = f"/api/public/catalogue/studies/v1/{study_id}/dynamic-blocks" + return self._get_json(path) + + +def _extract_items(payload: Any) -> list[dict[str, Any]]: + """Best-effort extractor for the catalogue search response. + + The exact shape isn't formally documented; observed responses use + ``{"items": [...]}`` or ``{"hits": [...]}`` or ``{"results": [...]}`` + or a top-level list. Try each in order. + """ + if isinstance(payload, list): + return [x for x in payload if isinstance(x, dict)] + if isinstance(payload, dict): + for key in ("items", "hits", "results", "data", "studies"): + value = payload.get(key) + if isinstance(value, list): + return [x for x in value if isinstance(x, dict)] + return [] + + +@contextmanager +def open_client(config: SwissubaseIndexConfig) -> Iterator[SwissubaseClient]: + client = SwissubaseClient(config) + try: + client.open() + yield client + finally: + client.close() diff --git a/src/index/swissubase/models.py b/src/index/swissubase/models.py new file mode 100644 index 0000000..7d0e5ba --- /dev/null +++ b/src/index/swissubase/models.py @@ -0,0 +1,77 @@ +"""Pydantic schemas for the structured columns persisted into DuckDB. + +The full payload from each REST endpoint is also stored as JSON in the +`raw` column on every table, so these row models stay narrow — only the +fields we filter / join / display. + +`source_url` is required (NOT NULL in the schema): the user requirement +is that every entity preserves its canonical SWISSUbase URL so downstream +agents can link back. +""" + +from __future__ import annotations + +from datetime import date + +from pydantic import BaseModel, ConfigDict + + +class StudyRow(BaseModel): + """A SWISSUbase study (called a ``Project`` in the UI).""" + + model_config = ConfigDict(extra="ignore") + + study_id: str + ref: str | None = None + title: str | None = None + description: str | None = None + description_language: str | None = None + start_date: date | None = None + end_date: date | None = None + progress: str | None = None + main_discipline: str | None = None + sub_discipline: str | None = None + version: str | None = None + data_availability: str | None = None + dataset_count: int | None = None + affiliation_match: bool = False + source_url: str + + +class DatasetRow(BaseModel): + """A SWISSUbase dataset (called a ``Resource`` in the UI). Child of a study.""" + + model_config = ConfigDict(extra="ignore") + + dataset_id: str + study_id: str + title: str | None = None + description: str | None = None + access_right: str | None = None + license_id: str | None = None + file_count: int | None = None + source_url: str + + +class PersonRow(BaseModel): + """An author / principal investigator / collaborator referenced by studies.""" + + model_config = ConfigDict(extra="ignore") + + person_key: str + display_name: str | None = None + orcid: str | None = None + affiliation: str | None = None + source_url: str | None = None + + +class InstitutionRow(BaseModel): + """An institution referenced by studies.""" + + model_config = ConfigDict(extra="ignore") + + institution_key: str + name: str | None = None + address: str | None = None + ror_id: str | None = None + source_url: str | None = None diff --git a/src/index/swissubase/paths.py b/src/index/swissubase/paths.py new file mode 100644 index 0000000..7b9ae9c --- /dev/null +++ b/src/index/swissubase/paths.py @@ -0,0 +1,63 @@ +"""Filesystem layout for SWISSUbase index artifacts. + +Single source of truth for paths under `/swissubase/`. +Mirrors `src/index/zenodo/paths.py`. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path + +DEFAULT_INDEX_DATA_DIR = Path("data/index") + + +def _resolve_repo_root() -> Path: + return Path(__file__).resolve().parents[3] + + +def _resolve_index_data_dir() -> Path: + raw = os.getenv("INDEX_DATA_DIR") + if raw and raw.strip(): + candidate = Path(raw.strip()).expanduser() + if candidate.is_absolute(): + return candidate + return _resolve_repo_root() / candidate + return _resolve_repo_root() / DEFAULT_INDEX_DATA_DIR + + +@dataclass(slots=True, frozen=True) +class SwissubasePaths: + root: Path + + @property + def duckdb_dir(self) -> Path: + return self.root / "duckdb" + + @property + def duckdb_path(self) -> Path: + return self.duckdb_dir / "swissubase.duckdb" + + @property + def state_dir(self) -> Path: + return self.root / "state" + + @property + def cache_dir(self) -> Path: + return self.root / "cache" + + @property + def logs_dir(self) -> Path: + return self.root / "logs" + + +def get_swissubase_paths() -> SwissubasePaths: + """Resolve `/swissubase/` and ensure subdirectories exist.""" + root = _resolve_index_data_dir() / "swissubase" + paths = SwissubasePaths(root=root) + paths.duckdb_dir.mkdir(parents=True, exist_ok=True) + paths.state_dir.mkdir(parents=True, exist_ok=True) + paths.cache_dir.mkdir(parents=True, exist_ok=True) + paths.logs_dir.mkdir(parents=True, exist_ok=True) + return paths diff --git a/src/index/swissubase/retrieval/__init__.py b/src/index/swissubase/retrieval/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/swissubase/retrieval/semantic.py b/src/index/swissubase/retrieval/semantic.py new file mode 100644 index 0000000..6f81994 --- /dev/null +++ b/src/index/swissubase/retrieval/semantic.py @@ -0,0 +1,129 @@ +"""Semantic retrieval over the SWISSUbase index. + +Embed → Qdrant search (filterable by ``entity_type``) → RCP rerank → +DuckDB hydrate. Reuses the openalex RCP + Qdrant clients directly, same +duck-typing pattern as the zenodo index. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.index.swissubase.embed.pipeline import SWISSUBASE_COLLECTION +from src.index.swissubase.storage.duckdb_store import SwissubaseStore + +if TYPE_CHECKING: + from src.index.swissubase.config import SwissubaseIndexConfig + +LOGGER = logging.getLogger(__name__) + + +def _payload_to_doc(payload: dict[str, Any]) -> str: + title = payload.get("title") or payload.get("display_name") or payload.get("name") + if title: + return str(title) + return json.dumps(payload, ensure_ascii=False) + + +def _hydrate(store: SwissubaseStore, entity_type: str, entity_id: str) -> dict[str, Any] | None: + if entity_type == "studies": + return store.fetch_study(entity_id) + if entity_type == "datasets": + return store.fetch_dataset(entity_id) + if entity_type == "persons": + cur = store.connect().execute( + "SELECT * FROM persons WHERE person_key = ?", [entity_id], + ) + row = cur.fetchone() + if row is None: + return None + return dict(zip([d[0] for d in cur.description], row, strict=False)) + if entity_type == "institutions": + cur = store.connect().execute( + "SELECT * FROM institutions WHERE institution_key = ?", [entity_id], + ) + row = cur.fetchone() + if row is None: + return None + return dict(zip([d[0] for d in cur.description], row, strict=False)) + return None + + +async def _async_search( + *, + config: SwissubaseIndexConfig, + query: str, + top_k: int, + candidate_k: int, + filter_payload: dict[str, Any] | None, + store: SwissubaseStore, +) -> list[dict[str, Any]]: + embed = RCPEmbeddingClient(config) # type: ignore[arg-type] + qdrant = QdrantStore(config) # type: ignore[arg-type] + rerank = RCPRerankerClient(config) # type: ignore[arg-type] + + [query_vec] = await embed.embed_all([query]) + candidates = qdrant.search( + SWISSUBASE_COLLECTION, + query_vector=query_vec, + top_k=candidate_k, + filter_payload=filter_payload, + ) + if not candidates: + return [] + docs = [_payload_to_doc(c["payload"]) for c in candidates] + reranked = await rerank.rerank(query, docs, top_n=top_k) + if not reranked: + ordered = candidates[:top_k] + else: + ordered = [ + {**candidates[r["index"]], "rerank_score": r["relevance_score"]} + for r in reranked + ] + + hydrated: list[dict[str, Any]] = [] + for hit in ordered: + payload = hit["payload"] or {} + entity_type = payload.get("entity_type") + entity_id = payload.get("entity_id") + if not (entity_type and entity_id): + continue + hydrated.append( + { + "id": hit["id"], + "vector_score": hit["score"], + "rerank_score": hit.get("rerank_score"), + "payload": payload, + "entity": _hydrate(store, str(entity_type), str(entity_id)), + }, + ) + return hydrated + + +def semantic_search( + *, + config: SwissubaseIndexConfig, + query: str, + top_k: int = 10, + candidate_k: int = 50, + filter_payload: dict[str, Any] | None = None, + store: SwissubaseStore | None = None, +) -> list[dict[str, Any]]: + if store is None: + store = SwissubaseStore.open() + return asyncio.run( + _async_search( + config=config, + query=query, + top_k=top_k, + candidate_k=candidate_k, + filter_payload=filter_payload, + store=store, + ), + ) diff --git a/src/index/swissubase/retrieval/sql.py b/src/index/swissubase/retrieval/sql.py new file mode 100644 index 0000000..e8deccc --- /dev/null +++ b/src/index/swissubase/retrieval/sql.py @@ -0,0 +1,140 @@ +"""Read-only SQL surface over the SWISSUbase DuckDB. + +Two entrypoints: + +- :func:`run_predefined` — parametrized canned queries. +- :func:`run_adhoc` — guarded SELECT/WITH only, with a forbidden-keyword regex. +""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index.swissubase.storage.duckdb_store import SwissubaseStore + +INVALID_QUERY_PREFIX_ERROR = "Only SELECT/WITH queries are allowed" +FORBIDDEN_KEYWORD_ERROR = "Forbidden keyword in query: {kw}" + +_ALLOWED_PREFIXES = ("select", "with") +_FORBIDDEN_KEYWORDS = ( + "attach", "copy", "pragma", "install", "load", "export", "import", + "create", "drop", "alter", "insert", "update", "delete", "truncate", +) +_KEYWORD_RE = re.compile(r"\b(" + "|".join(_FORBIDDEN_KEYWORDS) + r")\b", re.IGNORECASE) + + +def _validate_adhoc(sql: str) -> None: + stripped = sql.strip().lstrip("(").lstrip() + lowered = stripped.lower() + if not any(lowered.startswith(p) for p in _ALLOWED_PREFIXES): + raise ValueError(INVALID_QUERY_PREFIX_ERROR) + match = _KEYWORD_RE.search(stripped) + if match: + raise ValueError(FORBIDDEN_KEYWORD_ERROR.format(kw=match.group(1).upper())) + + +PREDEFINED_QUERIES: dict[str, str] = { + "count_by_entity": ( + "SELECT 'studies' AS entity, COUNT(*) AS n FROM studies " + "UNION ALL SELECT 'studies_in_scope', COUNT(*) FROM studies WHERE affiliation_match " + "UNION ALL SELECT 'datasets', COUNT(*) FROM datasets " + "UNION ALL SELECT 'persons', COUNT(*) FROM persons " + "UNION ALL SELECT 'institutions', COUNT(*) FROM institutions " + "UNION ALL SELECT 'study_persons', COUNT(*) FROM study_persons " + "UNION ALL SELECT 'study_institutions', COUNT(*) FROM study_institutions " + "UNION ALL SELECT 'chunks', COUNT(*) FROM chunks" + ), + "in_scope_studies": ( + "SELECT study_id, ref, title, progress, source_url " + "FROM studies WHERE affiliation_match " + "ORDER BY end_date DESC NULLS LAST, ref " + "LIMIT $limit" + ), + "studies_by_institution": ( + "SELECT s.study_id, s.ref, s.title, s.source_url " + "FROM study_institutions si " + "JOIN studies s ON s.study_id = si.study_id " + "WHERE si.institution_key = $institution_key " + "ORDER BY s.end_date DESC NULLS LAST, s.ref " + "LIMIT $limit" + ), + "studies_by_person": ( + "SELECT s.study_id, s.ref, s.title, sp.role, s.source_url " + "FROM study_persons sp " + "JOIN studies s ON s.study_id = sp.study_id " + "WHERE sp.person_key = $person_key " + "ORDER BY s.end_date DESC NULLS LAST, s.ref " + "LIMIT $limit" + ), + "top_institutions_in_scope": ( + "SELECT i.institution_key, i.name, COUNT(*) AS studies " + "FROM study_institutions si " + "JOIN studies s ON s.study_id = si.study_id " + "JOIN institutions i ON i.institution_key = si.institution_key " + "WHERE s.affiliation_match " + "GROUP BY i.institution_key, i.name " + "ORDER BY studies DESC LIMIT $limit" + ), + "top_persons_in_scope": ( + "SELECT p.person_key, p.display_name, COUNT(*) AS studies " + "FROM study_persons sp " + "JOIN studies s ON s.study_id = sp.study_id " + "JOIN persons p ON p.person_key = sp.person_key " + "WHERE s.affiliation_match " + "GROUP BY p.person_key, p.display_name " + "ORDER BY studies DESC LIMIT $limit" + ), + "studies_by_discipline": ( + "SELECT main_discipline, COUNT(*) AS n FROM studies " + "WHERE affiliation_match " + "GROUP BY main_discipline ORDER BY n DESC" + ), +} + + +def _row_to_dict(cur: Any) -> list[dict[str, Any]]: + cols = [d[0] for d in cur.description] if cur.description else [] + return [dict(zip(cols, r, strict=False)) for r in cur.fetchall()] + + +def _execute( + sql: str, + params: dict[str, Any] | None, + store: SwissubaseStore | None, +) -> list[dict[str, Any]]: + owned = False + if store is None: + store = SwissubaseStore.open() + owned = True + try: + cur = store.connect().execute(sql, params or {}) + return _row_to_dict(cur) + finally: + if owned: + store.close() + + +def run_adhoc( + sql: str, + params: dict[str, Any] | None = None, + *, + store: SwissubaseStore | None = None, +) -> list[dict[str, Any]]: + _validate_adhoc(sql) + return _execute(sql, params, store) + + +def run_predefined( + name: str, + params: dict[str, Any] | None = None, + *, + store: SwissubaseStore | None = None, +) -> list[dict[str, Any]]: + if name not in PREDEFINED_QUERIES: + message = ( + f"Unknown predefined query: {name!r}. " + f"Known: {sorted(PREDEFINED_QUERIES)}" + ) + raise ValueError(message) + return _execute(PREDEFINED_QUERIES[name], params, store) diff --git a/src/index/swissubase/storage/__init__.py b/src/index/swissubase/storage/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/index/swissubase/storage/duckdb_store.py b/src/index/swissubase/storage/duckdb_store.py new file mode 100644 index 0000000..8d94df6 --- /dev/null +++ b/src/index/swissubase/storage/duckdb_store.py @@ -0,0 +1,367 @@ +"""DuckDB lifecycle, schema bootstrap, and upsert helpers for SWISSUbase.""" + +from __future__ import annotations + +import json +import logging +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import duckdb + +from src.index.swissubase.paths import get_swissubase_paths + +if TYPE_CHECKING: + from collections.abc import Iterable, Iterator + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + +EMBEDDABLE_ENTITY_TYPES = {"studies", "datasets", "persons", "institutions"} + +# Per-entity-type SQL for streaming rows that need embedding (no chunks yet). +# Studies are gated on `affiliation_match = TRUE` so we only embed the +# in-scope subset (epfl_sdsc_ethz). Their child datasets are embedded only +# when the parent study is in scope. +_EMBEDDING_STREAM_SQL: dict[str, str] = { + "studies": ( + "SELECT t.* FROM studies t " + "WHERE t.affiliation_match = TRUE " + " AND NOT EXISTS (" + " SELECT 1 FROM chunks c " + " WHERE c.entity_type = 'studies' AND c.entity_id = t.study_id" + " )" + ), + "datasets": ( + "SELECT t.* FROM datasets t " + "JOIN studies s ON s.study_id = t.study_id " + "WHERE s.affiliation_match = TRUE " + " AND NOT EXISTS (" + " SELECT 1 FROM chunks c " + " WHERE c.entity_type = 'datasets' AND c.entity_id = t.dataset_id" + " )" + ), + "persons": ( + "SELECT DISTINCT t.* FROM persons t " + "JOIN study_persons sp ON sp.person_key = t.person_key " + "JOIN studies s ON s.study_id = sp.study_id " + "WHERE s.affiliation_match = TRUE " + " AND NOT EXISTS (" + " SELECT 1 FROM chunks c " + " WHERE c.entity_type = 'persons' AND c.entity_id = t.person_key" + " )" + ), + "institutions": ( + "SELECT DISTINCT t.* FROM institutions t " + "JOIN study_institutions si ON si.institution_key = t.institution_key " + "JOIN studies s ON s.study_id = si.study_id " + "WHERE s.affiliation_match = TRUE " + " AND NOT EXISTS (" + " SELECT 1 FROM chunks c " + " WHERE c.entity_type = 'institutions' AND c.entity_id = t.institution_key" + " )" + ), +} + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +class SwissubaseStore: + """Thin DuckDB wrapper for the SWISSUbase schema. `bootstrap()` is idempotent.""" + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open(cls, db_path: Path | None = None) -> SwissubaseStore: + if db_path is None: + db_path = get_swissubase_paths().duckdb_path + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + self.connect().execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + # ---- Upserts --------------------------------------------------------- + + def upsert_study( + self, + row: dict[str, Any], + *, + raw_overview: dict[str, Any] | None, + raw_dynamic_blocks: dict[str, Any] | None, + ) -> None: + sql = ( + "INSERT INTO studies " + "(study_id, ref, title, description, description_language, " + " start_date, end_date, progress, main_discipline, sub_discipline, " + " version, data_availability, dataset_count, affiliation_match, " + " source_url, raw_overview, raw_dynamic_blocks, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (study_id) DO UPDATE SET " + " ref = excluded.ref, title = excluded.title, " + " description = excluded.description, " + " description_language = excluded.description_language, " + " start_date = excluded.start_date, end_date = excluded.end_date, " + " progress = excluded.progress, " + " main_discipline = excluded.main_discipline, " + " sub_discipline = excluded.sub_discipline, " + " version = excluded.version, " + " data_availability = excluded.data_availability, " + " dataset_count = excluded.dataset_count, " + " affiliation_match = excluded.affiliation_match, " + " source_url = excluded.source_url, " + " raw_overview = COALESCE(excluded.raw_overview, studies.raw_overview), " + " raw_dynamic_blocks = COALESCE(" + " excluded.raw_dynamic_blocks, studies.raw_dynamic_blocks" + " ), " + " ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + row["study_id"], + row.get("ref"), + row.get("title"), + row.get("description"), + row.get("description_language"), + row.get("start_date"), + row.get("end_date"), + row.get("progress"), + row.get("main_discipline"), + row.get("sub_discipline"), + row.get("version"), + row.get("data_availability"), + row.get("dataset_count"), + bool(row.get("affiliation_match", False)), + row["source_url"], + json.dumps(raw_overview, ensure_ascii=False) if raw_overview else None, + ( + json.dumps(raw_dynamic_blocks, ensure_ascii=False) + if raw_dynamic_blocks else None + ), + self._now(), + ], + ) + + def upsert_dataset(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + sql = ( + "INSERT INTO datasets " + "(dataset_id, study_id, title, description, access_right, " + " license_id, file_count, source_url, raw, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (dataset_id) DO UPDATE SET " + " study_id = excluded.study_id, title = excluded.title, " + " description = excluded.description, " + " access_right = excluded.access_right, " + " license_id = excluded.license_id, " + " file_count = excluded.file_count, " + " source_url = excluded.source_url, " + " raw = excluded.raw, ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + row["dataset_id"], + row["study_id"], + row.get("title"), + row.get("description"), + row.get("access_right"), + row.get("license_id"), + row.get("file_count"), + row["source_url"], + json.dumps(raw, ensure_ascii=False), + self._now(), + ], + ) + + def upsert_person(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + sql = ( + "INSERT INTO persons " + "(person_key, display_name, orcid, affiliation, source_url, " + " raw, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (person_key) DO UPDATE SET " + " display_name = excluded.display_name, " + " orcid = excluded.orcid, " + " affiliation = excluded.affiliation, " + " source_url = excluded.source_url, " + " raw = excluded.raw, ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + row["person_key"], + row.get("display_name"), + row.get("orcid"), + row.get("affiliation"), + row.get("source_url"), + json.dumps(raw, ensure_ascii=False), + self._now(), + ], + ) + + def upsert_institution(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + sql = ( + "INSERT INTO institutions " + "(institution_key, name, address, ror_id, source_url, " + " raw, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (institution_key) DO UPDATE SET " + " name = excluded.name, address = excluded.address, " + " ror_id = excluded.ror_id, source_url = excluded.source_url, " + " raw = excluded.raw, ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + row["institution_key"], + row.get("name"), + row.get("address"), + row.get("ror_id"), + row.get("source_url"), + json.dumps(raw, ensure_ascii=False), + self._now(), + ], + ) + + def upsert_study_persons( + self, + study_id: str, + entries: Iterable[tuple[str, str | None, int]], + ) -> None: + conn = self.connect() + for person_key, role, position in entries: + conn.execute( + "INSERT INTO study_persons (study_id, person_key, role, position) " + "VALUES (?, ?, ?, ?) " + "ON CONFLICT (study_id, person_key, role) DO UPDATE SET " + " position = LEAST(study_persons.position, excluded.position)", + [study_id, person_key, role or "", position], + ) + + def upsert_study_institutions( + self, + study_id: str, + institution_keys: Iterable[str], + ) -> None: + conn = self.connect() + for ikey in institution_keys: + conn.execute( + "INSERT INTO study_institutions (study_id, institution_key) " + "VALUES (?, ?) ON CONFLICT DO NOTHING", + [study_id, ikey], + ) + + def upsert_chunk( + self, + *, + chunk_id: str, + entity_type: str, + entity_id: str, + chunk_index: int, + text: str, + token_count: int, + vector_id: str, + ) -> None: + self.connect().execute( + "INSERT INTO chunks " + "(chunk_id, entity_type, entity_id, chunk_index, text, " + "token_count, vector_id) VALUES (?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (chunk_id) DO UPDATE SET " + "text = excluded.text, token_count = excluded.token_count, " + "vector_id = excluded.vector_id, embedded_at = now()", + [chunk_id, entity_type, entity_id, chunk_index, text, token_count, vector_id], + ) + + # ---- Reads ----------------------------------------------------------- + + def count(self, table: str) -> int: + result = self.connect().execute(f"SELECT count(*) FROM {table}").fetchone() + return int(result[0]) if result else 0 + + def existing_study_ids(self, study_ids: list[str]) -> set[str]: + if not study_ids: + return set() + placeholders = ",".join(["?"] * len(study_ids)) + cur = self.connect().execute( + f"SELECT study_id FROM studies WHERE study_id IN ({placeholders})", + study_ids, + ) + return {str(r[0]) for r in cur.fetchall()} + + def fetch_study(self, study_id: str) -> dict[str, Any] | None: + cur = self.connect().execute( + "SELECT * FROM studies WHERE study_id = ?", + [study_id], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + def fetch_dataset(self, dataset_id: str) -> dict[str, Any] | None: + cur = self.connect().execute( + "SELECT * FROM datasets WHERE dataset_id = ?", + [dataset_id], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + def stream_rows_for_embedding( + self, + entity_type: str, + *, + limit: int | None = None, + ) -> Iterator[dict[str, Any]]: + """Yield rows that need embedding (no chunks yet).""" + if entity_type not in EMBEDDABLE_ENTITY_TYPES: + message = f"Unknown entity_type: {entity_type}" + raise ValueError(message) + sql = _EMBEDDING_STREAM_SQL[entity_type] + params: list[Any] = [] + if limit is not None: + sql += " LIMIT ?" + params.append(limit) + cur = self.connect().execute(sql, params) + cols = [d[0] for d in cur.description] + # Materialize: same reason as zenodo's note — DuckDB's single-conn + # model lets concurrent upserts affect the NOT EXISTS predicate + # mid-stream and silently filter out remaining rows. + rows = cur.fetchall() + for row in rows: + yield dict(zip(cols, row, strict=False)) + + @staticmethod + def _now() -> str: + return datetime.now(tz=timezone.utc).isoformat() diff --git a/src/index/swissubase/storage/schema.sql b/src/index/swissubase/storage/schema.sql new file mode 100644 index 0000000..2567094 --- /dev/null +++ b/src/index/swissubase/storage/schema.sql @@ -0,0 +1,106 @@ +-- Canonical DuckDB schema for the SWISSUbase index module. +-- Idempotent: every statement uses IF NOT EXISTS so re-runs are safe. +-- +-- Entity model (mirrors SWISSUbase's own ontology): +-- +-- studies the catalogue's top-level item (UI label: "Project"). +-- datasets 0..N children of a study (UI label: "Resource"). +-- persons authors / principal investigators / former collaborators. +-- institutions organisations the study is affiliated with. +-- +-- Per the project requirement, every entity preserves its canonical +-- SWISSUbase URL in `source_url`. For studies and datasets this is +-- NOT NULL; persons and institutions don't always have detail pages. + +CREATE TABLE IF NOT EXISTS studies ( + study_id TEXT PRIMARY KEY, -- numeric ref, stored as TEXT + ref TEXT, + title TEXT, + description TEXT, -- HTML-stripped abstract + description_language TEXT, + start_date DATE, + end_date DATE, + progress TEXT, -- e.g. "Finished", "In progress" + main_discipline TEXT, + sub_discipline TEXT, + version TEXT, + data_availability TEXT, + dataset_count INTEGER, + affiliation_match BOOLEAN NOT NULL DEFAULT FALSE, + source_url TEXT NOT NULL, + raw_overview JSON, + raw_dynamic_blocks JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS datasets ( + dataset_id TEXT PRIMARY KEY, + study_id TEXT NOT NULL, + title TEXT, + description TEXT, + access_right TEXT, + license_id TEXT, + file_count INTEGER, + source_url TEXT NOT NULL, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS persons ( + person_key TEXT PRIMARY KEY, -- ORCID URL when known, else "name:slugified" + display_name TEXT, + orcid TEXT, + affiliation TEXT, + source_url TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS institutions ( + institution_key TEXT PRIMARY KEY, -- ROR URL when known, else "name:slugified" + name TEXT, + address TEXT, + ror_id TEXT, + source_url TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS study_persons ( + study_id TEXT NOT NULL, + person_key TEXT NOT NULL, + role TEXT, -- e.g. "Principal investigator", "Former collaborator" + position INTEGER, + PRIMARY KEY (study_id, person_key, role) +); + +CREATE TABLE IF NOT EXISTS study_institutions ( + study_id TEXT NOT NULL, + institution_key TEXT NOT NULL, + PRIMARY KEY (study_id, institution_key) +); + +-- chunk_id is deterministic: uuid5(NAMESPACE_URL, "||") +-- so the primary key alone provides the (entity_type, entity_id, chunk_index) +-- uniqueness guarantee. +CREATE TABLE IF NOT EXISTS chunks ( + chunk_id TEXT PRIMARY KEY, + entity_type TEXT NOT NULL, -- studies | datasets | persons | institutions + entity_id TEXT NOT NULL, + chunk_index INTEGER NOT NULL, + text TEXT NOT NULL, + token_count INTEGER NOT NULL, + vector_id TEXT NOT NULL, + embedded_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS idx_studies_progress ON studies (progress); +CREATE INDEX IF NOT EXISTS idx_studies_main_discipline ON studies (main_discipline); +CREATE INDEX IF NOT EXISTS idx_studies_affiliation ON studies (affiliation_match); +CREATE INDEX IF NOT EXISTS idx_datasets_study ON datasets (study_id); +CREATE INDEX IF NOT EXISTS idx_datasets_access ON datasets (access_right); +CREATE INDEX IF NOT EXISTS idx_persons_orcid ON persons (orcid); +CREATE INDEX IF NOT EXISTS idx_institutions_ror ON institutions (ror_id); +CREATE INDEX IF NOT EXISTS idx_study_persons_pkey ON study_persons (person_key); +CREATE INDEX IF NOT EXISTS idx_study_institutions_ikey ON study_institutions (institution_key); +CREATE INDEX IF NOT EXISTS idx_chunks_entity ON chunks (entity_type, entity_id); diff --git a/src/index/zenodo/__init__.py b/src/index/zenodo/__init__.py new file mode 100644 index 0000000..8c5955e --- /dev/null +++ b/src/index/zenodo/__init__.py @@ -0,0 +1,14 @@ +"""Zenodo RAG indexer. + +Pipeline shape (mirrors src/index/openalex): + + ingest → fetches Zenodo records (REST `/api/records` filtered by + community), upserts into DuckDB. + embed → chunks title + description, embeds via RCP, upserts into + Qdrant. Idempotent re-runs skip records that already have chunks. + search → vector + RCP rerank + DuckDB hydrate. + query → predefined or guarded ad-hoc SQL over DuckDB. + +Phase 1 default scope: `epfl` (curated list of EPFL communities). Phase 2 +extends to `switzerland` (Swiss community + ROR fallback). +""" diff --git a/src/index/zenodo/__main__.py b/src/index/zenodo/__main__.py new file mode 100644 index 0000000..a9315f7 --- /dev/null +++ b/src/index/zenodo/__main__.py @@ -0,0 +1,8 @@ +"""Entry point: `python -m src.index.zenodo`.""" + +from __future__ import annotations + +from src.index.zenodo.cli import main + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/index/zenodo/_federated.py b/src/index/zenodo/_federated.py new file mode 100644 index 0000000..04a8397 --- /dev/null +++ b/src/index/zenodo/_federated.py @@ -0,0 +1,106 @@ +"""Zenodo registration with the federated discover/hydrate registries. + +Zenodo already follows the discover/hydrate pattern via its own CLI +(`discover --source infoscience` + `ingest --ids file`). These wrappers +adapt it to the cross-index protocol so seeds can flow over the +federated `gme discover` / `gme hydrate` pipeline. +""" + +from __future__ import annotations + +import logging +from typing import Any, Iterator + +from src.index._federated.dh_registry import register_discoverer, register_hydrator +from src.index._federated.protocols import ( + HydrationSummary, + IndexDiscoverer, + IndexHydrator, + Seed, +) + +LOGGER = logging.getLogger(__name__) + +ZENODO_DOI_PREFIX = "10.5281/zenodo." + + +class ZenodoDiscoverer: + name = "zenodo" + accepted_sources = ("infoscience",) + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + if source not in self.accepted_sources: + message = f"Zenodo: unknown source {source!r}. Accepted: {list(self.accepted_sources)}" + raise ValueError(message) + + from pathlib import Path + from src.index.zenodo.ingest.discover import discover_from_infoscience + from src.index.zenodo.storage.duckdb_store import ZenodoStore + + store = ZenodoStore.open() + text_dir = opts.get("text_dir") + result = discover_from_infoscience( + store=store, + text_dir=Path(text_dir) if text_dir else None, + ) + LOGGER.info( + "zenodo discover (infoscience): scanned=%d new_ids=%d", + result.files_scanned, len(result.new_ids), + ) + for zid in result.new_ids: + yield Seed( + id=zid, + seed_type="zenodo_id", + source="infoscience", + hint={"citing_infoscience_uuids": result.file_to_rec.get(zid, [])[:3]}, + ) + + +class ZenodoHydrator: + name = "zenodo" + accepted_seed_types = ("zenodo_id", "doi") + + def hydrate( + self, + seeds, + *, + only_unfetched: bool = True, + ) -> HydrationSummary: + from src.index.zenodo.config import load_config + from src.index.zenodo.ingest.records import ingest_by_ids + from src.index.zenodo.storage.duckdb_store import ZenodoStore + + ids: list[str] = [] + for s in seeds: + if s.seed_type == "zenodo_id": + ids.append(s.id) + elif s.seed_type == "doi": + # Only Zenodo DOIs (10.5281/zenodo.) are within scope here. + normalised = s.id.lower().replace("https://doi.org/", "").rstrip("/") + if normalised.startswith(ZENODO_DOI_PREFIX): + ids.append(normalised[len(ZENODO_DOI_PREFIX):]) + + if not ids: + return HydrationSummary() + + config = load_config() + store = ZenodoStore.open() + result = ingest_by_ids( + config=config, store=store, ids=ids, refresh=not only_unfetched, + ) + return HydrationSummary( + fetched=result.get("fetched", 0), + in_scope=result.get("persisted", 0), + skipped_existing=result.get("skipped", 0), + errors=result.get("errors", 0), + ) + + +DISCOVERER = ZenodoDiscoverer() +HYDRATOR = ZenodoHydrator() + +register_discoverer(DISCOVERER) +register_hydrator(HYDRATOR) + + +__all__ = ["DISCOVERER", "HYDRATOR", "ZenodoDiscoverer", "ZenodoHydrator"] diff --git a/src/index/zenodo/api.py b/src/index/zenodo/api.py new file mode 100644 index 0000000..ffe3052 --- /dev/null +++ b/src/index/zenodo/api.py @@ -0,0 +1,105 @@ +"""FastAPI app exposing the Zenodo index dual query surface.""" + +from __future__ import annotations + +import logging +from typing import Any + +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel, Field + +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.index.zenodo.config import load_config +from src.index.zenodo.embed.pipeline import ZENODO_COLLECTION +from src.index.zenodo.retrieval.semantic import semantic_search +from src.index.zenodo.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) +from src.index.zenodo.storage.duckdb_store import ZenodoStore + +LOGGER = logging.getLogger(__name__) + +app = FastAPI(title="Zenodo Index") + + +class SearchRequest(BaseModel): + query: str + top_k: int = Field(default=10, ge=1, le=100) + candidate_k: int = Field(default=50, ge=1, le=500) + filter_payload: dict[str, Any] | None = None + + +class QueryRequest(BaseModel): + sql: str | None = None + predefined: str | None = None + params: dict[str, Any] | None = None + + +@app.get("/healthz") +def healthz() -> dict[str, Any]: + config = load_config() + duck_status = "ok" + qdrant_status = "ok" + try: + ZenodoStore.open().count("records") + except Exception as exc: # noqa: BLE001 + duck_status = f"error: {exc}" + try: + QdrantStore(config).count(ZENODO_COLLECTION) # type: ignore[arg-type] + except Exception as exc: # noqa: BLE001 + qdrant_status = f"error: {exc}" + return { + "duckdb": duck_status, + "qdrant": qdrant_status, + "rcp_configured": bool(config.rcp.token), + "zenodo_token_configured": bool(config.zenodo.token), + } + + +@app.post("/search") +def search(req: SearchRequest) -> list[dict[str, Any]]: + config = load_config() + try: + config.require_rcp() + except ValueError as exc: + raise HTTPException(status_code=503, detail=str(exc)) from exc + return semantic_search( + config=config, + query=req.query, + top_k=req.top_k, + candidate_k=req.candidate_k, + filter_payload=req.filter_payload, + ) + + +@app.post("/query") +def query(req: QueryRequest) -> list[dict[str, Any]]: + if req.predefined and req.sql: + raise HTTPException( + status_code=400, + detail="Provide either `predefined` or `sql`, not both", + ) + try: + if req.predefined: + return run_predefined(req.predefined, req.params) + if req.sql: + return run_adhoc(req.sql, req.params) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + raise HTTPException(status_code=400, detail="Pass `predefined` or `sql`") + + +@app.get("/predefined") +def list_predefined() -> dict[str, list[str]]: + return {"predefined": sorted(PREDEFINED_QUERIES)} + + +@app.get("/record/{zenodo_id}") +def get_record(zenodo_id: str) -> dict[str, Any]: + store = ZenodoStore.open() + record = store.fetch_record(zenodo_id) + if record is None: + raise HTTPException(status_code=404, detail="not found") + return record diff --git a/src/index/zenodo/cli.py b/src/index/zenodo/cli.py new file mode 100644 index 0000000..b5e4857 --- /dev/null +++ b/src/index/zenodo/cli.py @@ -0,0 +1,347 @@ +"""CLI for the Zenodo index module. + +Subcommands: + +- `discover` — find candidate Zenodo IDs from external sources (e.g. Infoscience). +- `ingest` — pull Zenodo records into DuckDB (community- or id-filtered). +- `embed` — chunk + embed records, push vectors to Qdrant. +- `search` — semantic retrieval (vector + RCP rerank). +- `query` — read-only SQL over DuckDB (predefined or guarded ad-hoc). +- `status` — print row counts + Qdrant collection size + paths. +- `serve` — run the FastAPI app. +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from typing import Any + +from src.index.zenodo.config import load_config +from src.index.zenodo.embed.pipeline import ZENODO_COLLECTION, embed_records +from src.index.zenodo.ingest.discover import discover_from_infoscience +from src.index.zenodo.ingest.records import ingest_by_ids, ingest_records, load_ids_file +from src.index.zenodo.ingest.scope import resolve_scope +from src.index.zenodo.retrieval.semantic import semantic_search +from src.index.zenodo.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) +from src.index.zenodo.storage.duckdb_store import ZenodoStore + +LOGGER = logging.getLogger(__name__) + + +def _emit_json(obj: Any) -> None: + json.dump(obj, sys.stdout, indent=2, ensure_ascii=False, default=str) + sys.stdout.write("\n") + + +def _cmd_ingest(args: argparse.Namespace) -> int: + config = load_config() + if args.ids: + from pathlib import Path + + ids = load_ids_file(Path(args.ids)) + if not ids: + message = f"no parseable Zenodo IDs found in {args.ids}" + raise SystemExit(message) + store = ZenodoStore.open() + try: + summary = ingest_by_ids( + config=config, + store=store, + ids=ids, + refresh=args.refresh, + ) + finally: + store.close() + _emit_json( + { + "mode": "ids", + "source_file": args.ids, + "requested": summary["requested"], + "skipped_existing_count": len(summary["skipped_existing"]), + "fetched": summary["fetched"], + "persisted_count": len(summary["persisted"]), + "missing_count": len(summary["missing"]), + "failed_count": len(summary["failed"]), + "missing": summary["missing"], + "failed": summary["failed"], + }, + ) + return 0 + + scope = resolve_scope(args.scope, config) + if not scope.communities: + message = ( + f"scope={scope.name} has no communities configured. " + "Edit config/index/zenodo.yaml under `scope.{name}_communities`." + ) + raise SystemExit(message) + store = ZenodoStore.open() + try: + summary = ingest_records( + config=config, + store=store, + scope=scope, + limit=args.limit, + refresh=args.refresh, + ) + finally: + store.close() + _emit_json({"scope": scope.name, "communities": list(scope.communities), "ingested": summary}) + return 0 + + +def _cmd_embed(args: argparse.Namespace) -> int: + config = load_config() + config.require_rcp() + store = ZenodoStore.open() + try: + summary = embed_records(config=config, store=store, limit=args.limit) + finally: + store.close() + _emit_json(summary) + return 0 + + +def _cmd_search(args: argparse.Namespace) -> int: + config = load_config() + config.require_rcp() + filter_payload = json.loads(args.filter) if args.filter else None + hits = semantic_search( + config=config, + query=args.query, + top_k=args.top_k, + candidate_k=args.candidate_k, + filter_payload=filter_payload, + ) + _emit_json(hits) + return 0 + + +def _cmd_query(args: argparse.Namespace) -> int: + if args.predefined and args.sql: + message = "Pass either --predefined or --sql, not both" + raise SystemExit(message) + params: dict[str, Any] = {} + for kv in args.param or []: + if "=" not in kv: + message = f"--param expects key=value, got {kv!r}" + raise SystemExit(message) + key, value = kv.split("=", 1) + # Try int / float coercion; fall back to string. + try: + params[key] = int(value) + except ValueError: + try: + params[key] = float(value) + except ValueError: + params[key] = value + if args.predefined: + rows = run_predefined(args.predefined, params) + elif args.sql: + rows = run_adhoc(args.sql, params) + else: + _emit_json({"predefined": sorted(PREDEFINED_QUERIES)}) + return 0 + _emit_json(rows) + return 0 + + +def _cmd_status(args: argparse.Namespace) -> int: + del args + config = load_config() + store = ZenodoStore.open() + try: + counts = { + t: store.count(t) + for t in ( + "records", + "creators", + "record_creators", + "communities", + "record_communities", + "files", + "chunks", + ) + } + finally: + store.close() + qdrant_count: int | str + try: + from src.index.openalex.vector.qdrant_store import QdrantStore + + qdrant_count = QdrantStore(config).count(ZENODO_COLLECTION) # type: ignore[arg-type] + except Exception as exc: # noqa: BLE001 + qdrant_count = f"error: {exc}" + _emit_json( + { + "duckdb_path": str(config.paths.duckdb_path), + "duckdb_counts": counts, + "qdrant_collection": ZENODO_COLLECTION, + "qdrant_points": qdrant_count, + "scope_default": config.scope.default, + "epfl_communities": config.scope.epfl_communities, + }, + ) + return 0 + + +def _cmd_discover(args: argparse.Namespace) -> int: + if args.source != "infoscience": + message = f"unknown discovery source: {args.source!r} (only 'infoscience' is supported)" + raise SystemExit(message) + from pathlib import Path + + config = load_config() + store = ZenodoStore.open() + try: + result = discover_from_infoscience(store=store) + if args.out: + Path(args.out).write_text( + json.dumps( + { + "files_scanned": result.files_scanned, + "io_errors": result.io_errors, + "files_with_zenodo": result.files_with_zenodo, + "distinct_ids": result.distinct_ids, + "new_ids": result.new_ids, + "overlap_ids": result.overlap_ids, + "communities_in_urls": result.communities_in_urls, + "file_to_rec": result.file_to_rec, + }, + indent=2, + ensure_ascii=False, + ), + encoding="utf-8", + ) + + ingest_summary: dict[str, Any] | None = None + if args.ingest and result.new_ids: + ingest_summary = ingest_by_ids( + config=config, + store=store, + ids=result.new_ids, + refresh=False, + ) + finally: + store.close() + + payload: dict[str, Any] = { + "source": args.source, + "files_scanned": result.files_scanned, + "io_errors": result.io_errors, + "files_with_zenodo": result.files_with_zenodo, + "distinct_ids_count": len(result.distinct_ids), + "overlap_count": len(result.overlap_ids), + "new_count": len(result.new_ids), + "communities_in_urls": result.communities_in_urls, + } + if args.out: + payload["output_file"] = args.out + if ingest_summary is not None: + payload["ingest"] = { + "requested": ingest_summary["requested"], + "fetched": ingest_summary["fetched"], + "persisted_count": len(ingest_summary["persisted"]), + "missing_count": len(ingest_summary["missing"]), + "failed_count": len(ingest_summary["failed"]), + } + _emit_json(payload) + return 0 + + +def _cmd_serve(args: argparse.Namespace) -> int: + import uvicorn + + uvicorn.run( + "src.index.zenodo.api:app", + host=args.host, + port=args.port, + reload=args.reload, + ) + return 0 + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(prog="src.index.zenodo") + sub = parser.add_subparsers(dest="cmd", required=True) + + p_discover = sub.add_parser( + "discover", + help="Find candidate Zenodo IDs from external sources (e.g. Infoscience)", + ) + p_discover.add_argument( + "--source", + default="infoscience", + choices=["infoscience"], + help="Discovery source (only 'infoscience' is supported today)", + ) + p_discover.add_argument( + "--out", + default=None, + help="Optional path to write the full discovery payload (JSON)", + ) + p_discover.add_argument( + "--ingest", + action="store_true", + help="After discovery, fetch + persist the new IDs in the same process", + ) + p_discover.set_defaults(func=_cmd_discover) + + p_ingest = sub.add_parser("ingest", help="Pull Zenodo records into DuckDB") + p_ingest.add_argument("--scope", default="epfl", help="Scope name (epfl, switzerland)") + p_ingest.add_argument("--limit", type=int, default=None, help="Per-community record cap") + p_ingest.add_argument("--refresh", action="store_true", help="Re-ingest completed communities") + p_ingest.add_argument( + "--ids", + default=None, + help=( + "Path to a newline-delimited file of Zenodo record IDs / DOIs / URLs. " + "When set, --scope is ignored and records are fetched one-by-one via /api/records/{id}." + ), + ) + p_ingest.set_defaults(func=_cmd_ingest) + + p_embed = sub.add_parser("embed", help="Chunk + embed records, push to Qdrant") + p_embed.add_argument("--limit", type=int, default=None) + p_embed.set_defaults(func=_cmd_embed) + + p_search = sub.add_parser("search", help="Semantic retrieval (vector + rerank)") + p_search.add_argument("query", help="Natural-language query") + p_search.add_argument("--top-k", type=int, default=10) + p_search.add_argument("--candidate-k", type=int, default=50) + p_search.add_argument("--filter", default=None, help="JSON dict for Qdrant payload filter") + p_search.set_defaults(func=_cmd_search) + + p_query = sub.add_parser("query", help="Read-only SQL (predefined or guarded ad-hoc)") + p_query.add_argument("--predefined", default=None, help=f"One of: {sorted(PREDEFINED_QUERIES)}") + p_query.add_argument("--sql", default=None, help="Ad-hoc SELECT/WITH query") + p_query.add_argument("--param", action="append", help="Repeatable key=value param") + p_query.set_defaults(func=_cmd_query) + + p_status = sub.add_parser("status", help="Show DuckDB + Qdrant counts and paths") + p_status.set_defaults(func=_cmd_status) + + p_serve = sub.add_parser("serve", help="Run the Zenodo FastAPI app") + p_serve.add_argument("--host", default="0.0.0.0") + p_serve.add_argument("--port", type=int, default=8003) + p_serve.add_argument("--reload", action="store_true") + p_serve.set_defaults(func=_cmd_serve) + + return parser + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s — %(message)s", + ) + parser = _build_parser() + args = parser.parse_args(argv) + return int(args.func(args) or 0) diff --git a/src/index/zenodo/config.py b/src/index/zenodo/config.py new file mode 100644 index 0000000..2bfcef3 --- /dev/null +++ b/src/index/zenodo/config.py @@ -0,0 +1,125 @@ +"""Config loader for the Zenodo indexer. + +Reads `config/index/zenodo.yaml` and merges in env-sourced credentials +(`RCP_TOKEN`, `ZENODO_TOKEN`, `INDEX_QDRANT_API_KEY`) plus the resolved data +dir. + +The `rcp` and `qdrant` sub-blocks mirror `OpenAlexIndexConfig` field-for-field +so that `src.index.openalex.embed.rcp_client.RCPEmbeddingClient`, +`src.index.openalex.rerank.rcp_client.RCPRerankerClient`, and +`src.index.openalex.vector.qdrant_store.QdrantStore` can be reused with a +`ZenodoIndexConfig` instance at runtime — they only TYPE_CHECK against the +OpenAlex config and never import it at runtime. +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Optional + +import yaml +from pydantic import BaseModel + +from src.index.zenodo.paths import ZenodoPaths, get_zenodo_paths + +DEFAULT_CONFIG_PATH = Path("config/index/zenodo.yaml") + +TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} +FALSE_ENV_VALUES = {"0", "false", "f", "no", "n", "off"} + +MISSING_RCP_TOKEN_ERROR = "Missing required environment variable: RCP_TOKEN" + + +class RcpConfig(BaseModel): + base_url: str + embedding_model: str + embedding_dim: int + query_instruction: str + reranker_model: str + batch_size: int = 32 + max_concurrency: int = 4 + timeout_seconds: int = 120 + token: Optional[str] = None + + +class ZenodoConfig(BaseModel): + base_url: str = "https://zenodo.org/api" + page_size: int = 100 + rate_per_minute: int = 25 + max_concurrency: int = 1 + token: Optional[str] = None + + +class ScopeConfig(BaseModel): + default: str = "epfl" + epfl_communities: list[str] = [] + switzerland_communities: list[str] = [] + + +class QdrantConfig(BaseModel): + url: str = "http://localhost:6333" + prefer_grpc: bool = False + api_key: Optional[str] = None + + +class ChunkingConfig(BaseModel): + size_tokens: int = 256 + overlap_tokens: int = 64 + tokenizer: str = "cl100k_base" + + +class ZenodoIndexConfig(BaseModel): + rcp: RcpConfig + zenodo: ZenodoConfig + scope: ScopeConfig + qdrant: QdrantConfig + chunking: ChunkingConfig + paths: ZenodoPaths + + model_config = {"arbitrary_types_allowed": True} + + def require_rcp(self) -> None: + if not self.rcp.token: + raise ValueError(MISSING_RCP_TOKEN_ERROR) + + +def _env_bool(name: str) -> Optional[bool]: + raw = os.getenv(name) + if raw is None or raw.strip() == "": + return None + normalized = raw.strip().lower() + if normalized in TRUE_ENV_VALUES: + return True + if normalized in FALSE_ENV_VALUES: + return False + message = f"Invalid boolean value for {name}: {raw!r}" + raise ValueError(message) + + +def _env_str(name: str) -> Optional[str]: + raw = os.getenv(name) + if raw is None: + return None + stripped = raw.strip() + return stripped or None + + +def load_config(path: Optional[Path] = None) -> ZenodoIndexConfig: + cfg_path = path or DEFAULT_CONFIG_PATH + raw = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) or {} + + raw.setdefault("rcp", {})["token"] = _env_str("RCP_TOKEN") + raw.setdefault("zenodo", {})["token"] = _env_str("ZENODO_TOKEN") + raw.setdefault("qdrant", {})["api_key"] = _env_str("INDEX_QDRANT_API_KEY") + + if (override := _env_str("INDEX_QDRANT_URL")) is not None: + raw["qdrant"]["url"] = override + if (override_b := _env_bool("INDEX_QDRANT_PREFER_GRPC")) is not None: + raw["qdrant"]["prefer_grpc"] = override_b + if (override := _env_str("INDEX_ZENODO_SCOPE")) is not None: + raw.setdefault("scope", {})["default"] = override + + raw["paths"] = get_zenodo_paths() + + return ZenodoIndexConfig(**raw) diff --git a/src/index/zenodo/embed/__init__.py b/src/index/zenodo/embed/__init__.py new file mode 100644 index 0000000..f75c7c0 --- /dev/null +++ b/src/index/zenodo/embed/__init__.py @@ -0,0 +1 @@ +"""Stream DuckDB rows → chunk → embed via RCP → upsert into Qdrant.""" diff --git a/src/index/zenodo/embed/pipeline.py b/src/index/zenodo/embed/pipeline.py new file mode 100644 index 0000000..ac3abcb --- /dev/null +++ b/src/index/zenodo/embed/pipeline.py @@ -0,0 +1,145 @@ +"""Embed Zenodo records into Qdrant via the RCP `/embeddings` endpoint. + +Reuses `RCPEmbeddingClient`, `QdrantStore`, and the token chunker from +`src.index.openalex` directly — those modules only access `config.rcp.*` and +`config.qdrant.*` at runtime, both of which `ZenodoIndexConfig` mirrors +field-for-field. +""" + +from __future__ import annotations + +import asyncio +import logging +import uuid +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.chunker import Chunk, chunk_text +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.vector.qdrant_store import QdrantStore + +if TYPE_CHECKING: + from src.index.zenodo.config import ZenodoIndexConfig + from src.index.zenodo.storage.duckdb_store import ZenodoStore + +LOGGER = logging.getLogger(__name__) + +_CHUNK_NAMESPACE = uuid.NAMESPACE_URL + +ZENODO_COLLECTION = "zenodo_records" + + +def _chunk_id(entity_type: str, entity_id: str, chunk_index: int) -> str: + return str( + uuid.uuid5(_CHUNK_NAMESPACE, f"{entity_type}|{entity_id}|{chunk_index}"), + ) + + +def _row_to_chunks( + row: dict[str, Any], + *, + chunk_tokens: int, + overlap: int, +) -> list[Chunk]: + parts = [p for p in (row.get("title"), row.get("description")) if p] + if not parts: + return [] + text = "\n\n".join(parts) + return chunk_text(text, chunk_tokens=chunk_tokens, overlap=overlap) + + +def _row_to_payload(row: dict[str, Any]) -> dict[str, Any]: + pub_date = row.get("publication_date") + return { + "entity_type": "records", + "entity_id": row["zenodo_id"], + "zenodo_id": row["zenodo_id"], + "title": row.get("title"), + "doi": row.get("doi"), + "year": pub_date.year if pub_date else None, + "resource_type": row.get("resource_type"), + "access_right": row.get("access_right"), + } + + +async def _embed_records_async( + *, + config: ZenodoIndexConfig, + store: ZenodoStore, + limit: int | None, +) -> int: + # The openalex RCP/Qdrant clients are duck-typed against `config.rcp.*` + # and `config.qdrant.*`, both of which `ZenodoIndexConfig` mirrors. The + # mypy noise from the openalex-specific TYPE_CHECKING annotation is + # silenced explicitly at each reuse site. + client = RCPEmbeddingClient(config) # type: ignore[arg-type] + qdrant = QdrantStore(config) # type: ignore[arg-type] + qdrant.ensure_collection(ZENODO_COLLECTION) + + pending: list[tuple[str, dict[str, Any], Chunk]] = [] + total = 0 + + async def flush() -> None: + nonlocal total + if not pending: + return + texts = [c.text for _, _, c in pending] + vectors = await client.embed_all(texts) + ids: list[str] = [] + payloads: list[dict[str, Any]] = [] + for entity_id, base_payload, chunk in pending: + cid = _chunk_id("records", entity_id, chunk.index) + ids.append(cid) + payloads.append({**base_payload, "chunk_index": chunk.index}) + store.upsert_chunk( + chunk_id=cid, + entity_type="records", + entity_id=entity_id, + chunk_index=chunk.index, + text=chunk.text, + token_count=chunk.token_count, + vector_id=cid, + ) + qdrant.upsert_points( + ZENODO_COLLECTION, + ids=ids, + vectors=vectors, + payloads=payloads, + ) + total += len(pending) + pending.clear() + + rows_seen = 0 + for row in store.stream_rows_for_embedding("records", limit=limit): + rows_seen += 1 + chunks = _row_to_chunks( + row, + chunk_tokens=config.chunking.size_tokens, + overlap=config.chunking.overlap_tokens, + ) + if not chunks: + continue + base_payload = _row_to_payload(row) + for chunk in chunks: + pending.append((row["zenodo_id"], base_payload, chunk)) + if len(pending) >= client.batch_size: + await flush() + await flush() + LOGGER.info( + "embed records complete: rows_seen=%d chunks=%d", + rows_seen, + total, + ) + return total + + +def embed_records( + *, + config: ZenodoIndexConfig, + store: ZenodoStore, + limit: int | None = None, +) -> dict[str, int]: + """Synchronously embed Zenodo records (only entity type today).""" + chunks = asyncio.run( + _embed_records_async(config=config, store=store, limit=limit), + ) + return {"records": chunks} diff --git a/src/index/zenodo/ingest/__init__.py b/src/index/zenodo/ingest/__init__.py new file mode 100644 index 0000000..cf33987 --- /dev/null +++ b/src/index/zenodo/ingest/__init__.py @@ -0,0 +1 @@ +"""Zenodo ingest pipeline: REST client → record/creator/community projection → DuckDB upsert.""" diff --git a/src/index/zenodo/ingest/discover.py b/src/index/zenodo/ingest/discover.py new file mode 100644 index 0000000..3fd1c7c --- /dev/null +++ b/src/index/zenodo/ingest/discover.py @@ -0,0 +1,136 @@ +"""Zenodo record discovery from external sources. + +Today only the `infoscience` source is implemented: it scans the local +Infoscience full-text dump (`data/index/infoscience/text/*.txt`) for any +mention of `zenodo.org/...` URLs or `10.5281/zenodo.` DOIs, then diffs +the extracted IDs against `records.zenodo_id` already in the Zenodo DuckDB. + +The output `DiscoveryResult` is consumable by `ingest_by_ids` in +`src.index.zenodo.ingest.records` to actually fetch + persist the new +records via the Zenodo REST API. + +The Infoscience server-side `fulltext:"zenodo.org/"` query (used by the +infoscience link-dump pipeline) misses ~80% of references because that +index is narrower than what we extracted from PDFs locally — hence the +need for a local-text scan. +""" + +from __future__ import annotations + +import logging +import os +import re +from collections import Counter +from dataclasses import dataclass, field +from pathlib import Path +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from src.index.zenodo.storage.duckdb_store import ZenodoStore + +LOGGER = logging.getLogger(__name__) + +_RECORD_ID_RE = re.compile(r"zenodo\.org/(?:record/|records/|deposit/)?(\d{5,9})") +_DOI_RE = re.compile(r"10\.5281/zenodo\.(\d{5,9})", re.IGNORECASE) +_COMMUNITY_RE = re.compile(r"zenodo\.org/communities?/([a-zA-Z0-9_\-]{2,})") +# PDF text often line-wraps URLs and split numeric IDs; collapse those. +_LINEBREAK_AFTER_HOST_RE = re.compile(r"(zenodo\.org)[\s\-]*\n[\s\-]*") +_LINEBREAK_INSIDE_DIGITS_RE = re.compile(r"(\d)[\s\-]*\n[\s\-]*(\d)") + + +@dataclass +class DiscoveryResult: + files_scanned: int = 0 + io_errors: int = 0 + files_with_zenodo: int = 0 + distinct_ids: list[str] = field(default_factory=list) + new_ids: list[str] = field(default_factory=list) + overlap_ids: list[str] = field(default_factory=list) + communities_in_urls: dict[str, int] = field(default_factory=dict) + file_to_rec: dict[str, list[str]] = field(default_factory=dict) + + +def _default_infoscience_text_dir() -> Path: + from src.index.infoscience.paths import text_dir + + return text_dir() + + +def _read_text_resilient(path: str) -> str | None: + """Read a text file once, with a single retry — overlay FS can be flaky.""" + for _ in range(2): + try: + with open(path, encoding="utf-8", errors="ignore") as f: + return f.read() + except OSError: + continue + return None + + +def discover_from_infoscience( + *, + store: ZenodoStore, + text_dir: Path | None = None, +) -> DiscoveryResult: + """Scan Infoscience full-text and return Zenodo IDs to consider ingesting. + + `new_ids` is the diff against `records.zenodo_id` already in DuckDB. + The result also reports which Infoscience UUIDs cited which Zenodo + IDs (`file_to_rec`) and which Zenodo community slugs appeared in the + extracted URL paths — useful for surfacing communities not yet in + `config/index/zenodo.yaml`. + """ + target_dir = text_dir or _default_infoscience_text_dir() + record_ids: Counter[str] = Counter() + dois: Counter[str] = Counter() + communities: Counter[str] = Counter() + file_to_rec: dict[str, list[str]] = {} + files_scanned = io_errors = files_with_zenodo = 0 + + for entry in os.scandir(target_dir): + files_scanned += 1 + txt = _read_text_resilient(entry.path) + if txt is None: + io_errors += 1 + continue + if "zenodo.org" not in txt and "10.5281/zenodo" not in txt: + continue + files_with_zenodo += 1 + cleaned = _LINEBREAK_AFTER_HOST_RE.sub(r"\1", txt) + cleaned = _LINEBREAK_INSIDE_DIGITS_RE.sub(r"\1\2", cleaned) + rid = _RECORD_ID_RE.findall(cleaned) + did = _DOI_RE.findall(cleaned) + com = _COMMUNITY_RE.findall(cleaned) + record_ids.update(rid) + dois.update(did) + communities.update(com) + if rid or did: + uuid_key = entry.name.removesuffix(".txt") + file_to_rec[uuid_key] = sorted(set(rid) | set(did)) + + distinct = sorted(set(record_ids) | set(dois), key=int) + have = store.existing_record_ids(distinct) + new = [rid for rid in distinct if rid not in have] + overlap = [rid for rid in distinct if rid in have] + + LOGGER.info( + "infoscience discovery: scanned=%d io_errors=%d with_zenodo=%d " + "distinct=%d overlap=%d new=%d", + files_scanned, + io_errors, + files_with_zenodo, + len(distinct), + len(overlap), + len(new), + ) + + return DiscoveryResult( + files_scanned=files_scanned, + io_errors=io_errors, + files_with_zenodo=files_with_zenodo, + distinct_ids=distinct, + new_ids=new, + overlap_ids=overlap, + communities_in_urls=dict(communities), + file_to_rec=file_to_rec, + ) diff --git a/src/index/zenodo/ingest/records.py b/src/index/zenodo/ingest/records.py new file mode 100644 index 0000000..211a583 --- /dev/null +++ b/src/index/zenodo/ingest/records.py @@ -0,0 +1,375 @@ +"""Project Zenodo record JSON → DuckDB rows and persist.""" + +from __future__ import annotations + +import asyncio +import json +import logging +import re +from datetime import date +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import httpx +from bs4 import BeautifulSoup + +from src.index.zenodo.ingest.scope import Scope +from src.index.zenodo.ingest.zenodo_client import ZenodoClient + +if TYPE_CHECKING: + from src.index.zenodo.config import ZenodoIndexConfig + from src.index.zenodo.storage.duckdb_store import ZenodoStore + +LOGGER = logging.getLogger(__name__) + +_DATE_RE = re.compile(r"^(\d{4})(?:-(\d{2}))?(?:-(\d{2}))?") +_NON_WORD_RE = re.compile(r"[^a-z0-9]+") + + +def _strip_html(raw: str | None) -> str | None: + if not raw: + return None + text = BeautifulSoup(raw, "html.parser").get_text(" ", strip=True) + return text or None + + +def _parse_publication_date(raw: str | None) -> date | None: + if not raw: + return None + m = _DATE_RE.match(raw.strip()) + if not m: + return None + year = int(m.group(1)) + month = int(m.group(2) or 1) + day = int(m.group(3) or 1) + try: + return date(year, month, day) + except ValueError: + return None + + +def _slugify_creator_key(name: str | None, orcid: str | None) -> str | None: + if orcid: + # Normalize to canonical https://orcid.org/ form. + orcid_clean = orcid.strip() + if orcid_clean.startswith("http"): + return orcid_clean + return f"https://orcid.org/{orcid_clean}" + if not name: + return None + slug = _NON_WORD_RE.sub("-", name.lower()).strip("-") + return f"name:{slug}" if slug else None + + +def _project_record(item: dict[str, Any]) -> dict[str, Any]: + metadata = item.get("metadata") or {} + license_block = metadata.get("license") or {} + if isinstance(license_block, dict): + license_id = license_block.get("id") or license_block.get("identifier") + else: + license_id = str(license_block) if license_block else None + resource_type_block = metadata.get("resource_type") or {} + if isinstance(resource_type_block, dict): + resource_type = ( + resource_type_block.get("type") + or resource_type_block.get("title") + or None + ) + else: + resource_type = str(resource_type_block) if resource_type_block else None + concept_recid = item.get("conceptrecid") + return { + "zenodo_id": str(item.get("id") or item.get("conceptrecid") or ""), + "concept_recid": str(concept_recid) if concept_recid is not None else None, + "doi": item.get("doi") or metadata.get("doi"), + "title": metadata.get("title"), + "description": _strip_html(metadata.get("description")), + "publication_date": _parse_publication_date(metadata.get("publication_date")), + "resource_type": resource_type, + "access_right": metadata.get("access_right"), + "license_id": license_id, + "keywords": metadata.get("keywords") or [], + } + + +def _project_creators(item: dict[str, Any]) -> list[tuple[dict[str, Any], int]]: + metadata = item.get("metadata") or {} + creators_raw = metadata.get("creators") or [] + out: list[tuple[dict[str, Any], int]] = [] + for position, raw in enumerate(creators_raw): + if not isinstance(raw, dict): + continue + name = raw.get("name") + orcid = raw.get("orcid") + creator_key = _slugify_creator_key(name, orcid) + if not creator_key: + continue + out.append( + ( + { + "creator_key": creator_key, + "display_name": name, + "orcid": ( + f"https://orcid.org/{orcid}" + if orcid and not orcid.startswith("http") + else orcid + ), + "affiliation": raw.get("affiliation"), + }, + position, + ), + ) + return out + + +def _project_communities(item: dict[str, Any]) -> list[str]: + metadata = item.get("metadata") or {} + blocks = metadata.get("communities") or [] + out: list[str] = [] + for b in blocks: + if isinstance(b, dict) and b.get("id"): + out.append(str(b["id"])) + elif isinstance(b, str): + out.append(b) + return out + + +def _project_files(record_id: str, item: dict[str, Any]) -> list[dict[str, Any]]: + files_raw = item.get("files") or [] + out: list[dict[str, Any]] = [] + for f in files_raw: + if not isinstance(f, dict): + continue + key = f.get("key") or f.get("filename") + if not key: + continue + links = f.get("links") or {} + download_url = links.get("self") or links.get("download") + out.append( + { + "record_id": record_id, + "file_key": key, + "file_id": f.get("id"), + "size_bytes": f.get("size") or f.get("filesize"), + "checksum": f.get("checksum"), + "download_url": download_url, + }, + ) + return out + + +def persist_record(store: ZenodoStore, item: dict[str, Any]) -> str | None: + row = _project_record(item) + if not row["zenodo_id"]: + return None + record_id = row["zenodo_id"] + store.upsert_record(row, raw=item) + + creators = _project_creators(item) + creator_positions: list[tuple[str, int]] = [] + for creator_row, position in creators: + store.upsert_creator(creator_row, raw=creator_row) + creator_positions.append((creator_row["creator_key"], position)) + if creator_positions: + store.upsert_record_creators(record_id, creator_positions) + + communities = _project_communities(item) + if communities: + store.upsert_record_communities(record_id, communities) + + for file_row in _project_files(record_id, item): + store.upsert_file(file_row) + return record_id + + +def _state_path(config: ZenodoIndexConfig, scope_name: str) -> Path: + return config.paths.state_dir / f"ingest_{scope_name}.json" + + +def _load_state(config: ZenodoIndexConfig, scope_name: str) -> dict[str, Any]: + path = _state_path(config, scope_name) + if not path.exists(): + return {"completed_communities": []} + try: + return json.loads(path.read_text(encoding="utf-8")) + except json.JSONDecodeError: + LOGGER.warning("could not parse %s; restarting state", path) + return {"completed_communities": []} + + +def _save_state(config: ZenodoIndexConfig, scope_name: str, state: dict[str, Any]) -> None: + _state_path(config, scope_name).write_text( + json.dumps(state, indent=2, ensure_ascii=False), + encoding="utf-8", + ) + + +async def _ingest_async( + *, + config: ZenodoIndexConfig, + store: ZenodoStore, + scope: Scope, + limit: int | None, + refresh: bool, +) -> dict[str, int]: + state = _load_state(config, scope.name) if not refresh else {"completed_communities": []} + completed = set(state.get("completed_communities") or []) + client = ZenodoClient(config) + summary: dict[str, int] = {} + seen_ids: set[str] = set() + + # Eagerly upsert each community as a row so retrievers can join titles. + for slug in scope.communities: + community = await client.fetch_community(slug) + if community is None: + LOGGER.warning("community %s not found on Zenodo; skipping", slug) + continue + store.upsert_community( + {"community_id": slug, "title": (community.get("metadata") or {}).get("title")}, + raw=community, + ) + + for slug in scope.communities: + if slug in completed and not refresh: + LOGGER.info("scope=%s community=%s already completed; skipping", scope.name, slug) + continue + community_count = 0 + async for record in client.iter_records(slug, limit=limit): + record_id = persist_record(store, record) + if record_id and record_id not in seen_ids: + seen_ids.add(record_id) + community_count += 1 + if community_count and community_count % 200 == 0: + LOGGER.info( + "ingested %d records from community=%s (scope=%s)", + community_count, + slug, + scope.name, + ) + summary[slug] = community_count + completed.add(slug) + state["completed_communities"] = sorted(completed) + _save_state(config, scope.name, state) + LOGGER.info( + "community=%s done: %d records persisted", + slug, + community_count, + ) + return summary + + +def ingest_records( + *, + config: ZenodoIndexConfig, + store: ZenodoStore, + scope: Scope, + limit: int | None = None, + refresh: bool = False, +) -> dict[str, int]: + """Synchronous entrypoint used by the CLI.""" + return asyncio.run( + _ingest_async( + config=config, + store=store, + scope=scope, + limit=limit, + refresh=refresh, + ), + ) + + +_DOI_TO_ID_RE = re.compile(r"10\.5281/zenodo\.(\d+)", re.IGNORECASE) + + +def _normalize_id_token(token: str) -> str | None: + """Accept a numeric ID, a Zenodo DOI, or a Zenodo URL; return the numeric ID.""" + s = token.strip() + if not s: + return None + m = _DOI_TO_ID_RE.search(s) + if m: + return m.group(1) + m = re.search(r"zenodo\.org/(?:record/|records/|deposit/)?(\d+)", s) + if m: + return m.group(1) + if s.isdigit(): + return s + return None + + +def load_ids_file(path: Path) -> list[str]: + """Read a newline-delimited file of Zenodo IDs / DOIs / URLs.""" + out: list[str] = [] + seen: set[str] = set() + for raw in path.read_text(encoding="utf-8").splitlines(): + line = raw.split("#", 1)[0].strip() + if not line: + continue + rid = _normalize_id_token(line) + if rid is None: + LOGGER.warning("skip unparseable id token: %r", raw) + continue + if rid in seen: + continue + seen.add(rid) + out.append(rid) + return out + + +async def _ingest_by_ids_async( + *, + config: ZenodoIndexConfig, + store: ZenodoStore, + ids: list[str], + refresh: bool, +) -> dict[str, Any]: + existing = store.existing_record_ids(ids) if not refresh else set() + pending = [rid for rid in ids if rid not in existing] + client = ZenodoClient(config) + + persisted: list[str] = [] + missing: list[str] = [] + failed: list[dict[str, str]] = [] + fetched = 0 + + async with httpx.AsyncClient() as http: + for rid in pending: + try: + payload = await client.fetch_record(rid, client=http) + except Exception as exc: # noqa: BLE001 + failed.append({"id": rid, "error": str(exc)[:200]}) + continue + if payload is None: + missing.append(rid) + continue + fetched += 1 + persisted_id = persist_record(store, payload) + if persisted_id: + persisted.append(persisted_id) + if fetched % 100 == 0: + LOGGER.info("fetched %d/%d records", fetched, len(pending)) + + return { + "requested": len(ids), + "skipped_existing": sorted(existing), + "fetched": fetched, + "persisted": persisted, + "missing": missing, + "failed": failed, + } + + +def ingest_by_ids( + *, + config: ZenodoIndexConfig, + store: ZenodoStore, + ids: list[str], + refresh: bool = False, +) -> dict[str, Any]: + """Fetch a list of Zenodo records by ID and persist them. + + Skips IDs already present unless `refresh=True`. + """ + return asyncio.run( + _ingest_by_ids_async(config=config, store=store, ids=ids, refresh=refresh), + ) diff --git a/src/index/zenodo/ingest/scope.py b/src/index/zenodo/ingest/scope.py new file mode 100644 index 0000000..c0bef3d --- /dev/null +++ b/src/index/zenodo/ingest/scope.py @@ -0,0 +1,47 @@ +"""Scope filter builder for Zenodo communities. + +Phase 1: `epfl` resolves to a curated EPFL community list. +Phase 2: `switzerland` returns the EPFL list ∪ a curated Swiss list (deduped). + +We loop the resolved community list at the ingest layer and dedupe records by +`zenodo_id` at upsert time. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING, Literal + +if TYPE_CHECKING: + from src.index.zenodo.config import ZenodoIndexConfig + +ScopeName = Literal["epfl", "switzerland"] + + +@dataclass(slots=True, frozen=True) +class Scope: + name: str + communities: tuple[str, ...] + + +def epfl_scope(config: ZenodoIndexConfig) -> Scope: + return Scope(name="epfl", communities=tuple(config.scope.epfl_communities)) + + +def switzerland_scope(config: ZenodoIndexConfig) -> Scope: + seen: set[str] = set() + merged: list[str] = [] + for slug in [*config.scope.epfl_communities, *config.scope.switzerland_communities]: + if slug and slug not in seen: + seen.add(slug) + merged.append(slug) + return Scope(name="switzerland", communities=tuple(merged)) + + +def resolve_scope(name: str, config: ZenodoIndexConfig) -> Scope: + if name == "epfl": + return epfl_scope(config) + if name == "switzerland": + return switzerland_scope(config) + message = f"Unknown scope: {name}. Known: epfl, switzerland" + raise ValueError(message) diff --git a/src/index/zenodo/ingest/zenodo_client.py b/src/index/zenodo/ingest/zenodo_client.py new file mode 100644 index 0000000..a13c6bc --- /dev/null +++ b/src/index/zenodo/ingest/zenodo_client.py @@ -0,0 +1,183 @@ +"""Async Zenodo REST client with rate limiting and retries. + +Zenodo's documented hard limit is 30 requests / minute. We default to 25/min +via a simple monotonic-spaced semaphore, leaving headroom for retries. +""" + +from __future__ import annotations + +import asyncio +import logging +import time +from typing import TYPE_CHECKING, Any + +import httpx +from tenacity import ( + retry, + retry_if_exception, + stop_after_attempt, + wait_exponential, +) + +if TYPE_CHECKING: + from collections.abc import AsyncIterator + + from src.index.zenodo.config import ZenodoIndexConfig + +LOGGER = logging.getLogger(__name__) + +_RETRYABLE_STATUS = {429, 500, 502, 503, 504} + + +def _is_retryable(exc: BaseException) -> bool: + if isinstance(exc, httpx.TimeoutException): + return True + if isinstance(exc, httpx.HTTPStatusError): + return exc.response.status_code in _RETRYABLE_STATUS + return False + + +class _RateLimiter: + """Simple monotonic-spaced async limiter: at most N events per minute.""" + + def __init__(self, per_minute: int) -> None: + if per_minute <= 0: + message = "rate_per_minute must be positive" + raise ValueError(message) + self._min_interval = 60.0 / per_minute + self._last = 0.0 + self._lock = asyncio.Lock() + + async def acquire(self) -> None: + async with self._lock: + now = time.monotonic() + wait = self._min_interval - (now - self._last) + if wait > 0: + await asyncio.sleep(wait) + self._last = time.monotonic() + + +class ZenodoClient: + """Async wrapper for Zenodo's `/api/records` and `/api/communities`.""" + + def __init__(self, config: ZenodoIndexConfig) -> None: + self._config = config + self._base = config.zenodo.base_url.rstrip("/") + self._headers: dict[str, str] = {"Accept": "application/json"} + if config.zenodo.token: + self._headers["Authorization"] = f"Bearer {config.zenodo.token}" + self._limiter = _RateLimiter(config.zenodo.rate_per_minute) + # Anonymous Zenodo callers are capped at size=25 server-side; only + # bump to the full configured page size when we have a token. + self._page_size = ( + config.zenodo.page_size if config.zenodo.token else min(config.zenodo.page_size, 25) + ) + + @property + def page_size(self) -> int: + return self._page_size + + async def _get( + self, + client: httpx.AsyncClient, + path: str, + params: dict[str, Any], + ) -> dict[str, Any]: + @retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=1, min=2, max=30), + retry=retry_if_exception(_is_retryable), + reraise=True, + ) + async def _call() -> dict[str, Any]: + await self._limiter.acquire() + response = await client.get( + f"{self._base}{path}", + params=params, + headers=self._headers, + timeout=httpx.Timeout(60.0), + follow_redirects=True, + ) + try: + response.raise_for_status() + except httpx.HTTPStatusError as exc: + if not _is_retryable(exc): + body = response.text[:500] + LOGGER.error( + "Zenodo non-retryable %d on %s: %s", + response.status_code, + path, + body, + ) + raise + return response.json() + + return await _call() + + async def iter_records( + self, + community: str, + *, + limit: int | None = None, + ) -> AsyncIterator[dict[str, Any]]: + """Page through `/records?communities=` yielding raw record dicts. + + Stops on the first empty page or when `limit` records have been yielded. + """ + async with httpx.AsyncClient() as client: + page = 1 + yielded = 0 + while True: + params: dict[str, Any] = { + "communities": community, + "size": self._page_size, + "page": page, + "sort": "newest", + } + payload = await self._get(client, "/records", params) + hits_block = payload.get("hits") or {} + hits = hits_block.get("hits") or [] + if not hits: + return + for hit in hits: + yield hit + yielded += 1 + if limit is not None and yielded >= limit: + return + # Zenodo caps total at 10k; once we've passed that we're done. + if page * self._page_size >= 10_000: + LOGGER.warning( + "community %s hit Zenodo's 10k cap; remaining records " + "would require OAI-PMH or narrower queries", + community, + ) + return + page += 1 + + async def fetch_community(self, slug: str) -> dict[str, Any] | None: + try: + async with httpx.AsyncClient() as client: + return await self._get(client, f"/communities/{slug}", {}) + except httpx.HTTPStatusError as exc: + if exc.response.status_code == 404: + return None + raise + + async def fetch_record( + self, + record_id: str, + *, + client: httpx.AsyncClient | None = None, + ) -> dict[str, Any] | None: + own_client = client is None + if own_client: + client = httpx.AsyncClient() + try: + return await self._get(client, f"/records/{record_id}", {}) + except httpx.HTTPStatusError as exc: + if exc.response.status_code in (404, 410): + return None + raise + finally: + if own_client: + await client.aclose() diff --git a/src/index/zenodo/models.py b/src/index/zenodo/models.py new file mode 100644 index 0000000..6860cf8 --- /dev/null +++ b/src/index/zenodo/models.py @@ -0,0 +1,51 @@ +"""Pydantic schemas for the structured columns persisted into DuckDB. + +The full Zenodo record JSON is also stored in the `raw` column of `records`, +so these row models stay narrow — only the fields we filter / join / display. +""" + +from __future__ import annotations + +from datetime import date + +from pydantic import BaseModel, ConfigDict + + +class RecordRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + zenodo_id: str + doi: str | None = None + title: str | None = None + description: str | None = None + publication_date: date | None = None + resource_type: str | None = None + access_right: str | None = None + license_id: str | None = None + + +class CreatorRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + creator_key: str + display_name: str | None = None + orcid: str | None = None + affiliation: str | None = None + + +class CommunityRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + community_id: str + title: str | None = None + + +class FileRow(BaseModel): + model_config = ConfigDict(extra="ignore") + + record_id: str + file_key: str + file_id: str | None = None + size_bytes: int | None = None + checksum: str | None = None + download_url: str | None = None diff --git a/src/index/zenodo/paths.py b/src/index/zenodo/paths.py new file mode 100644 index 0000000..6931108 --- /dev/null +++ b/src/index/zenodo/paths.py @@ -0,0 +1,63 @@ +"""Filesystem layout for Zenodo index artifacts. + +Single source of truth for paths under `/zenodo/`. Mirrors +`src/index/openalex/paths.py`. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path + +DEFAULT_INDEX_DATA_DIR = Path("data/index") + + +def _resolve_repo_root() -> Path: + return Path(__file__).resolve().parents[3] + + +def _resolve_index_data_dir() -> Path: + raw = os.getenv("INDEX_DATA_DIR") + if raw and raw.strip(): + candidate = Path(raw.strip()).expanduser() + if candidate.is_absolute(): + return candidate + return _resolve_repo_root() / candidate + return _resolve_repo_root() / DEFAULT_INDEX_DATA_DIR + + +@dataclass(slots=True, frozen=True) +class ZenodoPaths: + root: Path + + @property + def duckdb_dir(self) -> Path: + return self.root / "duckdb" + + @property + def duckdb_path(self) -> Path: + return self.duckdb_dir / "zenodo.duckdb" + + @property + def state_dir(self) -> Path: + return self.root / "state" + + @property + def cache_dir(self) -> Path: + return self.root / "cache" + + @property + def logs_dir(self) -> Path: + return self.root / "logs" + + +def get_zenodo_paths() -> ZenodoPaths: + """Resolve `/zenodo/` and ensure subdirectories exist.""" + root = _resolve_index_data_dir() / "zenodo" + paths = ZenodoPaths(root=root) + paths.duckdb_dir.mkdir(parents=True, exist_ok=True) + paths.state_dir.mkdir(parents=True, exist_ok=True) + paths.cache_dir.mkdir(parents=True, exist_ok=True) + paths.logs_dir.mkdir(parents=True, exist_ok=True) + return paths diff --git a/src/index/zenodo/retrieval/__init__.py b/src/index/zenodo/retrieval/__init__.py new file mode 100644 index 0000000..3f883e6 --- /dev/null +++ b/src/index/zenodo/retrieval/__init__.py @@ -0,0 +1 @@ +"""Retrieval surfaces: semantic (vector + rerank + hydrate) and SQL.""" diff --git a/src/index/zenodo/retrieval/semantic.py b/src/index/zenodo/retrieval/semantic.py new file mode 100644 index 0000000..2dcae0b --- /dev/null +++ b/src/index/zenodo/retrieval/semantic.py @@ -0,0 +1,136 @@ +"""Semantic retrieval over the Zenodo index. + +Embed → Qdrant search → RCP rerank → DuckDB hydrate (records + creators + +communities). Reuses the openalex RCP + Qdrant clients directly. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.rcp_client import RCPEmbeddingClient +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.index.zenodo.embed.pipeline import ZENODO_COLLECTION +from src.index.zenodo.storage.duckdb_store import ZenodoStore + +if TYPE_CHECKING: + from src.index.zenodo.config import ZenodoIndexConfig + +LOGGER = logging.getLogger(__name__) + + +def _payload_to_doc(payload: dict[str, Any]) -> str: + title = payload.get("title") + if title: + return str(title) + return json.dumps(payload, ensure_ascii=False) + + +def _hydrate(store: ZenodoStore, zenodo_id: str) -> dict[str, Any] | None: + record = store.fetch_record(zenodo_id) + if record is None: + return None + creators = store.connect().execute( + "SELECT c.creator_key, c.display_name, c.orcid, c.affiliation, rc.position " + "FROM record_creators rc JOIN creators c " + " ON c.creator_key = rc.creator_key " + "WHERE rc.record_id = ? " + "ORDER BY rc.position", + [zenodo_id], + ).fetchall() + record["creators"] = [ + { + "creator_key": r[0], + "display_name": r[1], + "orcid": r[2], + "affiliation": r[3], + "position": r[4], + } + for r in creators + ] + community_ids = store.connect().execute( + "SELECT community_id FROM record_communities WHERE record_id = ?", + [zenodo_id], + ).fetchall() + record["communities"] = [r[0] for r in community_ids] + return record + + +async def _async_search( + *, + config: ZenodoIndexConfig, + query: str, + top_k: int, + candidate_k: int, + filter_payload: dict[str, Any] | None, + store: ZenodoStore, +) -> list[dict[str, Any]]: + # See note in src/index/zenodo/embed/pipeline.py: clients are + # duck-typed against the openalex config shape, which Zenodo mirrors. + embed = RCPEmbeddingClient(config) # type: ignore[arg-type] + qdrant = QdrantStore(config) # type: ignore[arg-type] + rerank = RCPRerankerClient(config) # type: ignore[arg-type] + + [query_vec] = await embed.embed_all([query]) + candidates = qdrant.search( + ZENODO_COLLECTION, + query_vector=query_vec, + top_k=candidate_k, + filter_payload=filter_payload, + ) + if not candidates: + return [] + + docs = [_payload_to_doc(c["payload"]) for c in candidates] + reranked = await rerank.rerank(query, docs, top_n=top_k) + if not reranked: + ordered = candidates[:top_k] + else: + ordered = [ + {**candidates[r["index"]], "rerank_score": r["relevance_score"]} + for r in reranked + ] + + hydrated: list[dict[str, Any]] = [] + for hit in ordered: + payload = hit["payload"] or {} + entity_id = payload.get("entity_id") or payload.get("zenodo_id") + if not entity_id: + continue + hydrated.append( + { + "id": hit["id"], + "vector_score": hit["score"], + "rerank_score": hit.get("rerank_score"), + "payload": payload, + "entity": _hydrate(store, str(entity_id)), + }, + ) + return hydrated + + +def semantic_search( + *, + config: ZenodoIndexConfig, + query: str, + top_k: int = 10, + candidate_k: int = 50, + filter_payload: dict[str, Any] | None = None, + store: ZenodoStore | None = None, +) -> list[dict[str, Any]]: + if store is None: + store = ZenodoStore.open() + return asyncio.run( + _async_search( + config=config, + query=query, + top_k=top_k, + candidate_k=candidate_k, + filter_payload=filter_payload, + store=store, + ), + ) diff --git a/src/index/zenodo/retrieval/sql.py b/src/index/zenodo/retrieval/sql.py new file mode 100644 index 0000000..d3e0282 --- /dev/null +++ b/src/index/zenodo/retrieval/sql.py @@ -0,0 +1,129 @@ +"""Read-only SQL surface over the Zenodo DuckDB. + +Two entrypoints: + +- `run_predefined()` — parametrized canned queries. +- `run_adhoc()` — guarded SELECT/WITH only, with a forbidden-keyword regex. +""" + +from __future__ import annotations + +import re +from typing import Any + +from src.index.zenodo.storage.duckdb_store import ZenodoStore + +INVALID_QUERY_PREFIX_ERROR = "Only SELECT/WITH queries are allowed" +FORBIDDEN_KEYWORD_ERROR = "Forbidden keyword in query: {kw}" + +_ALLOWED_PREFIXES = ("select", "with") +_FORBIDDEN_KEYWORDS = ( + "attach", "copy", "pragma", "install", "load", "export", "import", + "create", "drop", "alter", "insert", "update", "delete", "truncate", +) +_KEYWORD_RE = re.compile(r"\b(" + "|".join(_FORBIDDEN_KEYWORDS) + r")\b", re.IGNORECASE) + + +def _validate_adhoc(sql: str) -> None: + stripped = sql.strip().lstrip("(").lstrip() + lowered = stripped.lower() + if not any(lowered.startswith(p) for p in _ALLOWED_PREFIXES): + raise ValueError(INVALID_QUERY_PREFIX_ERROR) + match = _KEYWORD_RE.search(stripped) + if match: + raise ValueError(FORBIDDEN_KEYWORD_ERROR.format(kw=match.group(1).upper())) + + +PREDEFINED_QUERIES: dict[str, str] = { + "count_by_entity": ( + "SELECT 'records' AS entity, COUNT(*) AS n FROM records " + "UNION ALL SELECT 'creators', COUNT(*) FROM creators " + "UNION ALL SELECT 'communities', COUNT(*) FROM communities " + "UNION ALL SELECT 'record_creators', COUNT(*) FROM record_creators " + "UNION ALL SELECT 'record_communities', COUNT(*) FROM record_communities " + "UNION ALL SELECT 'files', COUNT(*) FROM files " + "UNION ALL SELECT 'chunks', COUNT(*) FROM chunks" + ), + "count_by_community": ( + "SELECT rc.community_id, COUNT(*) AS records " + "FROM record_communities rc " + "GROUP BY rc.community_id ORDER BY records DESC" + ), + "count_by_resource_type": ( + "SELECT resource_type, COUNT(*) AS n FROM records " + "GROUP BY resource_type ORDER BY n DESC" + ), + "count_by_access_right": ( + "SELECT access_right, COUNT(*) AS n FROM records " + "GROUP BY access_right ORDER BY n DESC" + ), + "top_recent_records": ( + "SELECT zenodo_id, doi, title, publication_date, resource_type " + "FROM records " + "WHERE publication_date IS NOT NULL " + "ORDER BY publication_date DESC, title " + "LIMIT $limit" + ), + "records_by_creator": ( + "SELECT r.zenodo_id, r.title, r.publication_date, r.doi " + "FROM record_creators rc " + "JOIN records r ON r.zenodo_id = rc.record_id " + "WHERE rc.creator_key = $creator_key " + "ORDER BY r.publication_date DESC NULLS LAST " + "LIMIT $limit" + ), + "records_by_keyword": ( + "SELECT zenodo_id, title, publication_date " + "FROM records " + "WHERE list_contains(CAST(keywords_json AS VARCHAR[]), $keyword) " + "ORDER BY publication_date DESC NULLS LAST " + "LIMIT $limit" + ), +} + + +def _row_to_dict(cur: Any) -> list[dict[str, Any]]: + cols = [d[0] for d in cur.description] if cur.description else [] + return [dict(zip(cols, r, strict=False)) for r in cur.fetchall()] + + +def _execute( + sql: str, + params: dict[str, Any] | None, + store: ZenodoStore | None, +) -> list[dict[str, Any]]: + owned = False + if store is None: + store = ZenodoStore.open() + owned = True + try: + cur = store.connect().execute(sql, params or {}) + return _row_to_dict(cur) + finally: + if owned: + store.close() + + +def run_adhoc( + sql: str, + params: dict[str, Any] | None = None, + *, + store: ZenodoStore | None = None, +) -> list[dict[str, Any]]: + _validate_adhoc(sql) + return _execute(sql, params, store) + + +def run_predefined( + name: str, + params: dict[str, Any] | None = None, + *, + store: ZenodoStore | None = None, +) -> list[dict[str, Any]]: + if name not in PREDEFINED_QUERIES: + message = ( + f"Unknown predefined query: {name!r}. " + f"Known: {sorted(PREDEFINED_QUERIES)}" + ) + raise ValueError(message) + return _execute(PREDEFINED_QUERIES[name], params, store) diff --git a/src/index/zenodo/storage/__init__.py b/src/index/zenodo/storage/__init__.py new file mode 100644 index 0000000..3c6786e --- /dev/null +++ b/src/index/zenodo/storage/__init__.py @@ -0,0 +1 @@ +"""DuckDB store for Zenodo records, creators, communities, files, and chunks.""" diff --git a/src/index/zenodo/storage/duckdb_store.py b/src/index/zenodo/storage/duckdb_store.py new file mode 100644 index 0000000..058ad59 --- /dev/null +++ b/src/index/zenodo/storage/duckdb_store.py @@ -0,0 +1,334 @@ +"""DuckDB lifecycle, schema bootstrap, and upsert helpers for Zenodo.""" + +from __future__ import annotations + +import json +import logging +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import duckdb + +from src.index.zenodo.paths import get_zenodo_paths + +if TYPE_CHECKING: + from collections.abc import Iterable, Iterator + +LOGGER = logging.getLogger(__name__) + +SCHEMA_PATH = Path(__file__).parent / "schema.sql" + +EMBEDDABLE_ENTITY_TYPES = {"records"} + + +def _load_schema_sql() -> str: + return SCHEMA_PATH.read_text(encoding="utf-8") + + +class ZenodoStore: + """Thin DuckDB wrapper for the Zenodo schema. `bootstrap()` is idempotent.""" + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + self._conn: duckdb.DuckDBPyConnection | None = None + + @classmethod + def open(cls, db_path: Path | None = None) -> ZenodoStore: + if db_path is None: + db_path = get_zenodo_paths().duckdb_path + store = cls(db_path) + store.bootstrap() + return store + + def connect(self) -> duckdb.DuckDBPyConnection: + if self._conn is None: + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = duckdb.connect(str(self.db_path)) + return self._conn + + def bootstrap(self) -> None: + conn = self.connect() + # Migration: existing DBs created before `concept_recid` existed must + # gain the column BEFORE schema.sql runs (schema.sql creates an index + # on it). The backfill UPDATE must also run BEFORE the index is + # created — DuckDB has hit internal errors when an UPDATE rewrites + # every row of a freshly-created index in the same transaction. + records_exists = ( + conn.execute( + "SELECT 1 FROM information_schema.tables " + "WHERE table_schema='main' AND table_name='records'", + ).fetchone() + is not None + ) + if records_exists: + cols = { + r[1] + for r in conn.execute("PRAGMA table_info('records')").fetchall() + } + if "concept_recid" not in cols: + conn.execute("ALTER TABLE records ADD COLUMN concept_recid TEXT") + conn.execute( + "UPDATE records SET concept_recid = " + " CAST(json_extract_string(raw, '$.conceptrecid') AS TEXT) " + "WHERE raw IS NOT NULL " + " AND json_extract_string(raw, '$.conceptrecid') IS NOT NULL", + ) + conn.execute(_load_schema_sql()) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None + + @contextmanager + def read_only(self) -> Iterator[duckdb.DuckDBPyConnection]: + ro = duckdb.connect(str(self.db_path), read_only=True) + try: + yield ro + finally: + ro.close() + + # ---- Upserts --------------------------------------------------------- + + def upsert_record(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + sql = ( + "INSERT INTO records " + "(zenodo_id, concept_recid, doi, title, description, publication_date, " + " resource_type, access_right, license_id, keywords_json, raw, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (zenodo_id) DO UPDATE SET " + " concept_recid = excluded.concept_recid, " + " doi = excluded.doi, title = excluded.title, " + " description = excluded.description, " + " publication_date = excluded.publication_date, " + " resource_type = excluded.resource_type, " + " access_right = excluded.access_right, " + " license_id = excluded.license_id, " + " keywords_json = excluded.keywords_json, " + " raw = excluded.raw, ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + row["zenodo_id"], + row.get("concept_recid"), + row.get("doi"), + row.get("title"), + row.get("description"), + row.get("publication_date"), + row.get("resource_type"), + row.get("access_right"), + row.get("license_id"), + json.dumps(row.get("keywords") or [], ensure_ascii=False), + json.dumps(raw, ensure_ascii=False), + self._now(), + ], + ) + + def upsert_creator(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + sql = ( + "INSERT INTO creators " + "(creator_key, display_name, orcid, affiliation, raw, ingested_at) " + "VALUES (?, ?, ?, ?, ?, ?) " + "ON CONFLICT (creator_key) DO UPDATE SET " + " display_name = excluded.display_name, " + " orcid = excluded.orcid, " + " affiliation = excluded.affiliation, " + " raw = excluded.raw, ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + row["creator_key"], + row.get("display_name"), + row.get("orcid"), + row.get("affiliation"), + json.dumps(raw, ensure_ascii=False), + self._now(), + ], + ) + + def upsert_record_creators( + self, + record_id: str, + creator_positions: Iterable[tuple[str, int]], + ) -> None: + conn = self.connect() + for creator_key, position in creator_positions: + conn.execute( + "INSERT INTO record_creators (record_id, creator_key, position) " + "VALUES (?, ?, ?) " + "ON CONFLICT (record_id, creator_key) DO UPDATE SET " + "position = LEAST(record_creators.position, excluded.position)", + [record_id, creator_key, position], + ) + + def upsert_community(self, row: dict[str, Any], raw: dict[str, Any]) -> None: + sql = ( + "INSERT INTO communities (community_id, title, raw, ingested_at) " + "VALUES (?, ?, ?, ?) " + "ON CONFLICT (community_id) DO UPDATE SET " + " title = excluded.title, raw = excluded.raw, " + " ingested_at = excluded.ingested_at" + ) + self.connect().execute( + sql, + [ + row["community_id"], + row.get("title"), + json.dumps(raw, ensure_ascii=False), + self._now(), + ], + ) + + def upsert_record_communities( + self, + record_id: str, + community_ids: Iterable[str], + ) -> None: + conn = self.connect() + for cid in community_ids: + conn.execute( + "INSERT INTO record_communities (record_id, community_id) " + "VALUES (?, ?) ON CONFLICT DO NOTHING", + [record_id, cid], + ) + + def upsert_file(self, row: dict[str, Any]) -> None: + sql = ( + "INSERT INTO files " + "(record_id, file_key, file_id, size_bytes, checksum, download_url) " + "VALUES (?, ?, ?, ?, ?, ?) " + "ON CONFLICT (record_id, file_key) DO UPDATE SET " + " file_id = excluded.file_id, " + " size_bytes = excluded.size_bytes, " + " checksum = excluded.checksum, " + " download_url = excluded.download_url" + ) + self.connect().execute( + sql, + [ + row["record_id"], + row["file_key"], + row.get("file_id"), + row.get("size_bytes"), + row.get("checksum"), + row.get("download_url"), + ], + ) + + def upsert_chunk( + self, + *, + chunk_id: str, + entity_type: str, + entity_id: str, + chunk_index: int, + text: str, + token_count: int, + vector_id: str, + ) -> None: + self.connect().execute( + "INSERT INTO chunks " + "(chunk_id, entity_type, entity_id, chunk_index, text, " + "token_count, vector_id) VALUES (?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT (chunk_id) DO UPDATE SET " + "text = excluded.text, token_count = excluded.token_count, " + "vector_id = excluded.vector_id, embedded_at = now()", + [chunk_id, entity_type, entity_id, chunk_index, text, token_count, vector_id], + ) + + # ---- Reads ----------------------------------------------------------- + + def count(self, table: str) -> int: + result = self.connect().execute(f"SELECT count(*) FROM {table}").fetchone() + return int(result[0]) if result else 0 + + def existing_record_ids(self, zenodo_ids: list[str]) -> set[str]: + """Return the subset of `zenodo_ids` already known to the store. + + Matches against either the canonical `zenodo_id` (post-redirect + version-record) OR the `concept_recid` (Zenodo's "all versions" + identifier). A discovery source typically extracts whichever ID + appears in the citation, so checking both prevents redundant + re-fetches when a paper cites the concept ID but we persisted + the latest version. + """ + if not zenodo_ids: + return set() + placeholders = ",".join(["?"] * len(zenodo_ids)) + cur = self.connect().execute( + f"SELECT zenodo_id FROM records WHERE zenodo_id IN ({placeholders}) " + f"UNION " + f"SELECT concept_recid FROM records " + f"WHERE concept_recid IN ({placeholders})", + [*zenodo_ids, *zenodo_ids], + ) + return {str(r[0]) for r in cur.fetchall() if r[0] is not None} + + def fetch_record(self, zenodo_id: str) -> dict[str, Any] | None: + cur = self.connect().execute( + "SELECT * FROM records WHERE zenodo_id = ?", + [zenodo_id], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + def fetch_record_by_concept(self, concept_recid: str) -> dict[str, Any] | None: + """Find any version-record under the given concept_recid. + + When a citation references the concept (parent) ID, our store only + keeps the canonical version-record. This returns the most recent + ingested version so callers can still resolve the citation. + """ + cur = self.connect().execute( + "SELECT * FROM records WHERE concept_recid = ? " + "ORDER BY ingested_at DESC LIMIT 1", + [concept_recid], + ) + row = cur.fetchone() + if row is None: + return None + cols = [d[0] for d in cur.description] + return dict(zip(cols, row, strict=False)) + + def stream_rows_for_embedding( + self, + entity_type: str, + *, + limit: int | None = None, + ) -> Iterator[dict[str, Any]]: + """Yield rows that need embedding (no chunks yet).""" + if entity_type not in EMBEDDABLE_ENTITY_TYPES: + message = f"Unknown entity_type: {entity_type}" + raise ValueError(message) + # records.zenodo_id is the entity_id for the chunks table. + sql = ( + "SELECT t.* FROM records t " + "WHERE NOT EXISTS (" + " SELECT 1 FROM chunks c " + " WHERE c.entity_type = ? AND c.entity_id = t.zenodo_id" + ")" + ) + params: list[Any] = [entity_type] + if limit is not None: + sql += " LIMIT ?" + params.append(limit) + cur = self.connect().execute(sql, params) + cols = [d[0] for d in cur.description] + # Materialize the full result up front: DuckDB's single-connection + # model lets writes mid-iteration affect the NOT EXISTS predicate + # if we stream lazily, so each upsert_chunk call would otherwise + # filter out subsequent rows. + rows = cur.fetchall() + for row in rows: + yield dict(zip(cols, row, strict=False)) + + @staticmethod + def _now() -> str: + return datetime.now(tz=timezone.utc).isoformat() diff --git a/src/index/zenodo/storage/schema.sql b/src/index/zenodo/storage/schema.sql new file mode 100644 index 0000000..9b5dea4 --- /dev/null +++ b/src/index/zenodo/storage/schema.sql @@ -0,0 +1,79 @@ +-- Canonical DuckDB schema for the Zenodo index module. +-- Idempotent: every statement uses IF NOT EXISTS so re-runs are safe. + +CREATE TABLE IF NOT EXISTS records ( + zenodo_id TEXT PRIMARY KEY, -- canonical version-record ID (post-redirect) + concept_recid TEXT, -- Zenodo concept record (groups all versions) + doi TEXT, + title TEXT, + description TEXT, -- HTML-stripped + publication_date DATE, + resource_type TEXT, -- e.g. publication-article, dataset, software + access_right TEXT, -- open | embargoed | restricted | closed + license_id TEXT, + keywords_json JSON, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS creators ( + creator_key TEXT PRIMARY KEY, -- ORCID URL when available, else slugified normalized name + display_name TEXT, + orcid TEXT, + affiliation TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS record_creators ( + record_id TEXT NOT NULL, + creator_key TEXT NOT NULL, + position INTEGER, + PRIMARY KEY (record_id, creator_key) +); + +CREATE TABLE IF NOT EXISTS communities ( + community_id TEXT PRIMARY KEY, -- slug, e.g. "epfl" + title TEXT, + raw JSON, + ingested_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE TABLE IF NOT EXISTS record_communities ( + record_id TEXT NOT NULL, + community_id TEXT NOT NULL, + PRIMARY KEY (record_id, community_id) +); + +CREATE TABLE IF NOT EXISTS files ( + record_id TEXT NOT NULL, + file_key TEXT NOT NULL, -- filename + file_id TEXT, + size_bytes BIGINT, + checksum TEXT, + download_url TEXT, + PRIMARY KEY (record_id, file_key) +); + +-- chunk_id is deterministic: uuid5(NAMESPACE_URL, "||") +-- so the primary key alone provides the (entity_type, entity_id, chunk_index) +-- uniqueness guarantee. +CREATE TABLE IF NOT EXISTS chunks ( + chunk_id TEXT PRIMARY KEY, + entity_type TEXT NOT NULL, -- "records" for now + entity_id TEXT NOT NULL, + chunk_index INTEGER NOT NULL, + text TEXT NOT NULL, + token_count INTEGER NOT NULL, + vector_id TEXT NOT NULL, + embedded_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX IF NOT EXISTS idx_records_pubdate ON records (publication_date); +CREATE INDEX IF NOT EXISTS idx_records_type ON records (resource_type); +CREATE INDEX IF NOT EXISTS idx_records_access ON records (access_right); +CREATE INDEX IF NOT EXISTS idx_records_concept ON records (concept_recid); +CREATE INDEX IF NOT EXISTS idx_creators_orcid ON creators (orcid); +CREATE INDEX IF NOT EXISTS idx_chunks_entity ON chunks (entity_type, entity_id); +CREATE INDEX IF NOT EXISTS idx_record_creators_ck ON record_creators (creator_key); +CREATE INDEX IF NOT EXISTS idx_record_comm_cid ON record_communities (community_id); diff --git a/src/module/__init__.py b/src/module/__init__.py new file mode 100644 index 0000000..b533ec1 --- /dev/null +++ b/src/module/__init__.py @@ -0,0 +1,7 @@ +"""Top-level namespace for analytical modules that complement the v2 pipeline. + +Each subpackage is a self-contained workstream. Modules are deliberately not +wired into the main `/v2/extract` pipeline by default — they exist as +standalone capabilities that can be invoked from CLI scripts or future +pipeline stages once their data shape is stable. +""" diff --git a/src/module/dependents/__init__.py b/src/module/dependents/__init__.py new file mode 100644 index 0000000..d4c8d76 --- /dev/null +++ b/src/module/dependents/__init__.py @@ -0,0 +1,31 @@ +"""GitHub dependents discovery. + +Scrapes GitHub's public dependents page (`/network/dependents`) — there is +no official API for this — and returns a structured `DependentsResult`. + +Public surface: + +- `models.DependentItem`, `models.DependentsResult` — Pydantic models. +- `scraper.parse_dependents_page` — pure function from raw HTML to a parsed page. +- `scraper.iterate_dependents` — generator yielding `DependentItem` objects across pages. +- `service.list_dependents` — high-level entrypoint with caching and graceful degradation. +- `tool.make_query_dependents_tool` — pydantic-ai Tool factory for future LLM use. + +This module is deliberately not wired into the main `/v2/extract` pipeline. +Use directly via `service.list_dependents(...)` or the CLI in +`scripts/v2/list_dependents.py`. +""" + +from src.module.dependents.models import ( + DependentItem, + DependentKind, + DependentsResult, + ParsedDependentsPage, +) + +__all__ = [ + "DependentItem", + "DependentKind", + "DependentsResult", + "ParsedDependentsPage", +] diff --git a/src/module/dependents/models.py b/src/module/dependents/models.py new file mode 100644 index 0000000..857a9ce --- /dev/null +++ b/src/module/dependents/models.py @@ -0,0 +1,126 @@ +"""Pydantic models for GitHub dependents data. + +Internal data shape only — these are *not* part of the v2 ontology graph +output. They're the canonical structured form of "who depends on this +repo" for any consumer (CLI, future LLM tool, future pipeline stage). +""" +from __future__ import annotations + +from datetime import datetime, timezone +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field + +DependentKind = Literal["REPOSITORY", "PACKAGE"] + + +class DependentItem(BaseModel): + """One dependent listed on the GitHub dependents page.""" + + model_config = ConfigDict(extra="forbid") + + full_name: str = Field( + ..., + description="GitHub `/` of the dependent.", + ) + owner: str = Field(..., description="GitHub owner login of the dependent.") + repo: str = Field(..., description="GitHub repository name of the dependent.") + stars: int = Field(0, ge=0, description="Star count shown on the dependents row.") + forks: int = Field(0, ge=0, description="Fork count shown on the dependents row.") + + +class ParsedDependentsPage(BaseModel): + """Result of parsing one HTML dependents page (no aggregation).""" + + model_config = ConfigDict(extra="forbid") + + repository_count: int = Field( + 0, + ge=0, + description="Total ` Repositories` count from the toggle bar.", + ) + package_count: int = Field( + 0, + ge=0, + description="Total ` Packages` count from the toggle bar.", + ) + selected_kind: DependentKind | None = Field( + None, + description="Which view this page shows (REPOSITORY or PACKAGE), if detectable.", + ) + items: list[DependentItem] = Field( + default_factory=list, + description="Dependent rows on this page only.", + ) + next_cursor_url: str | None = Field( + None, + description="Absolute URL of the next page, or None if pagination is exhausted.", + ) + + +class DependentsResult(BaseModel): + """Aggregated dependents lookup for one repository. + + Returned by `service.list_dependents`. Empty results (no dependents, + page disabled, Selenium unavailable, etc.) are returned as a result + with `available=false` and warnings populated — never as exceptions. + """ + + model_config = ConfigDict(extra="forbid") + + full_name: str = Field(..., description="The queried `/`.") + kind: DependentKind = Field(..., description="Which view was queried.") + total_count: int = Field( + 0, + ge=0, + description=( + "Total ` Repositories|Packages` reported by GitHub for the " + "selected view. May be much larger than `len(items)` if pagination " + "was capped." + ), + ) + fetched_count: int = Field( + 0, + ge=0, + description="Number of dependents in `items`. <= total_count.", + ) + truncated: bool = Field( + False, + description="True when `fetched_count < total_count` (pagination capped).", + ) + items: list[DependentItem] = Field( + default_factory=list, + description=( + "Dependents collected, in page order. Sort by stars descending in " + "consumers if a 'top dependents' ranking is needed — we keep page " + "order here to make the result deterministic and parser-checkable." + ), + ) + available: bool = Field( + True, + description=( + "False when GitHub showed no dependents page (graph disabled, " + "private repo without scope, Selenium unavailable, etc.)." + ), + ) + fetched_at: datetime = Field( + default_factory=lambda: datetime.now(timezone.utc), + description="When the result was produced (UTC).", + ) + pages_fetched: int = Field( + 0, + ge=0, + description="Number of HTML pages parsed to produce this result.", + ) + warnings: list[str] = Field( + default_factory=list, + description="Human-readable notes — e.g. degraded fetch, parse warnings.", + ) + + +__all__ = [ + "DependentItem", + "DependentKind", + "DependentsResult", + "ParsedDependentsPage", +] diff --git a/src/module/dependents/scraper.py b/src/module/dependents/scraper.py new file mode 100644 index 0000000..6644e0c --- /dev/null +++ b/src/module/dependents/scraper.py @@ -0,0 +1,309 @@ +"""HTML scraping of GitHub's `/network/dependents` page. + +Two layers, deliberately separated: + +1. `parse_dependents_page(html)` — pure function from raw HTML to a + `ParsedDependentsPage`. No I/O. Easy to test against fixtures. +2. `fetch_dependents_html(url)` — Selenium-backed fetch that delegates to + the existing `_load_html_via_selenium` helper. Has I/O; degrades to + an empty string on missing `SELENIUM_REMOTE_URL`. + +`iterate_dependents(...)` glues them together: walks pages until a cap is +hit or pagination is exhausted, yielding `DependentItem` objects. +""" +from __future__ import annotations + +import logging +import re +from collections.abc import Iterator +from typing import Iterable +from urllib.parse import urlencode + +from bs4 import BeautifulSoup, Tag + +from src.module.dependents.models import ( + DependentItem, + DependentKind, + ParsedDependentsPage, +) + +logger = logging.getLogger(__name__) + +DEPENDENTS_BASE = "https://github.com" +DEFAULT_PAGE_TIMEOUT_SECONDS = 60 +DEFAULT_PAGE_WAIT_SECONDS = 30 + + +# --------------------------------------------------------------------------- +# URL construction +# --------------------------------------------------------------------------- + + +def build_dependents_url( + full_name: str, + *, + kind: DependentKind = "REPOSITORY", + cursor: str | None = None, +) -> str: + """Construct the URL for the dependents page of ``. + + `cursor` is the value of GitHub's `dependents_after` query param; + use the URL embedded in the previous page's `Next` button rather + than synthesising it. + """ + + if "/" not in full_name: + message = f"full_name must be 'owner/repo', got {full_name!r}" + raise ValueError(message) + params: dict[str, str] = {"dependent_type": kind} + if cursor: + params["dependents_after"] = cursor + return f"{DEPENDENTS_BASE}/{full_name}/network/dependents?{urlencode(params)}" + + +# --------------------------------------------------------------------------- +# Pure-function HTML parser +# --------------------------------------------------------------------------- + +_TOGGLE_PATTERN = re.compile(r"^\s*([\d,]+)\s*(Repositories|Packages)\s*$") + + +def _parse_int_with_commas(value: str | None) -> int: + if not isinstance(value, str): + return 0 + cleaned = value.strip().replace(",", "") + return int(cleaned) if cleaned.isdigit() else 0 + + +def _extract_toggle_counts( + soup: BeautifulSoup, +) -> tuple[int, int, DependentKind | None]: + """Read the ` Repositories` and ` Packages` totals + which view is selected.""" + + repository_count = 0 + package_count = 0 + selected_kind: DependentKind | None = None + + # The toggle bar is two `` anchors. Each + # one's visible text reads " Repositories" or " Packages". + for anchor in soup.find_all("a", class_=lambda c: c and "btn-link" in c.split()): + text = anchor.get_text(" ", strip=True) + match = _TOGGLE_PATTERN.match(text) + if match is None: + continue + count = _parse_int_with_commas(match.group(1)) + kind_label = match.group(2) + if kind_label == "Repositories": + repository_count = count + elif kind_label == "Packages": + package_count = count + + # `class="btn-link selected"` marks the currently-shown view. + classes = anchor.get("class") or [] + if "selected" in classes: + selected_kind = "REPOSITORY" if kind_label == "Repositories" else "PACKAGE" + + return repository_count, package_count, selected_kind + + +def _extract_count_after_icon(row: Tag, icon_class: str) -> int: + """Return the integer immediately following an octicon-* SVG inside a row. + + Each dependent row carries `… ` + and similarly for forks. We locate the SVG and read the next text node. + """ + + svg = row.find("svg", class_=lambda c: c and icon_class in c.split()) + if svg is None: + return 0 + # The number is the SVG's next non-empty sibling text. + sibling = svg.next_sibling + while sibling is not None: + text = sibling if isinstance(sibling, str) else sibling.get_text(" ", strip=True) + if isinstance(text, str): + text = text.strip() + if text: + return _parse_int_with_commas(text) + sibling = getattr(sibling, "next_sibling", None) + return 0 + + +def _extract_owner_repo(row: Tag) -> tuple[str | None, str | None]: + """Pull `` and `` from a dependent row's ``. + + `href` shape: `//`. Anything else returns (None, None). + """ + + repo_anchor = row.find("a", class_=lambda c: c and "text-bold" in c.split()) + if repo_anchor is None: + return None, None + href = repo_anchor.get("href") or "" + if not isinstance(href, str) or not href.startswith("/"): + return None, None + parts = [segment for segment in href.split("/") if segment] + if len(parts) < 2: + return None, None + owner = parts[0] + # Strip any trailing path/query — we only want owner/repo. + repo = parts[1].split("?", 1)[0] + if not owner or not repo: + return None, None + return owner, repo + + +def _extract_next_cursor_url(soup: BeautifulSoup) -> str | None: + """Return the absolute URL of the `Next` page, or None when at the end.""" + + for anchor in soup.find_all("a", class_=lambda c: c and "BtnGroup-item" in c.split()): + if anchor.get_text(strip=True) == "Next": + href = anchor.get("href") + if isinstance(href, str) and href.strip(): + return href.strip() + return None + + +def parse_dependents_page(html: str) -> ParsedDependentsPage: + """Parse one rendered dependents page into structured form. + + Pure function. No I/O. Safe to call on fixture HTML. + """ + + if not isinstance(html, str) or not html: + return ParsedDependentsPage() + + soup = BeautifulSoup(html, "lxml") + repo_count, pkg_count, selected = _extract_toggle_counts(soup) + + items: list[DependentItem] = [] + for row in soup.find_all(attrs={"data-test-id": "dg-repo-pkg-dependent"}): + if not isinstance(row, Tag): + continue + owner, repo = _extract_owner_repo(row) + if owner is None or repo is None: + continue + stars = _extract_count_after_icon(row, "octicon-star") + forks = _extract_count_after_icon(row, "octicon-repo-forked") + items.append( + DependentItem( + full_name=f"{owner}/{repo}", + owner=owner, + repo=repo, + stars=stars, + forks=forks, + ), + ) + + return ParsedDependentsPage( + repository_count=repo_count, + package_count=pkg_count, + selected_kind=selected, + items=items, + next_cursor_url=_extract_next_cursor_url(soup), + ) + + +# --------------------------------------------------------------------------- +# Selenium-backed fetcher +# --------------------------------------------------------------------------- + + +def fetch_dependents_html( + url: str, + *, + timeout_seconds: int = DEFAULT_PAGE_TIMEOUT_SECONDS, + wait_seconds: int = DEFAULT_PAGE_WAIT_SECONDS, +) -> str: + """Fetch one dependents page's raw HTML via the project's Selenium grid. + + Returns an empty string on any error (including missing + `SELENIUM_REMOTE_URL`) — callers should treat empty HTML as a graceful + degradation, not an exception. We log but do not raise so a single + bad page doesn't kill an iteration. + """ + + # Lazy import: keeps `parse_dependents_page` runnable in environments + # without selenium installed (e.g. pure unit-test runs). + try: + from src.v2.agents.llm.agent_tools.selenium_fetch import ( + _load_html_via_selenium, + ) + except Exception as exc: # noqa: BLE001 + logger.warning("dependents: selenium helper unavailable: %s", exc) + return "" + + try: + html, _final_url, _title = _load_html_via_selenium( + url, + timeout_seconds=timeout_seconds, + wait_seconds=wait_seconds, + ) + except Exception as exc: # noqa: BLE001 + logger.warning("dependents: selenium fetch failed for %s: %s", url, exc) + return "" + return html or "" + + +# --------------------------------------------------------------------------- +# Aggregating iterator +# --------------------------------------------------------------------------- + + +def iterate_dependents( + full_name: str, + *, + kind: DependentKind = "REPOSITORY", + max_pages: int = 5, + max_items: int = 100, + fetcher: Iterable[str] | None = None, +) -> Iterator[tuple[DependentItem, ParsedDependentsPage]]: + """Walk the dependents pages of `full_name` and yield items one at a time. + + Stops when **any** of: + - `max_pages` pages have been processed + - `max_items` items have been yielded + - the page returns no `Next` link (end of pagination) + - a page returns empty HTML (degraded fetch) + + `fetcher` lets tests inject a fixed list of HTML pages instead of + going through Selenium. When None, uses `fetch_dependents_html`. + + Yields `(item, parsed_page)` tuples so the caller can also see + page-level metadata (counts, cursor) without a second pass. + """ + + if max_pages <= 0 or max_items <= 0: + return + + fetcher_iter = iter(fetcher) if fetcher is not None else None + + url: str | None = build_dependents_url(full_name, kind=kind) + yielded = 0 + pages_visited = 0 + + while url is not None and pages_visited < max_pages and yielded < max_items: + if fetcher_iter is not None: + try: + html = next(fetcher_iter) + except StopIteration: + return + else: + html = fetch_dependents_html(url) + pages_visited += 1 + if not html: + return + page = parse_dependents_page(html) + for item in page.items: + if yielded >= max_items: + return + yield item, page + yielded += 1 + url = page.next_cursor_url + + +__all__ = [ + "DEPENDENTS_BASE", + "build_dependents_url", + "fetch_dependents_html", + "iterate_dependents", + "parse_dependents_page", +] diff --git a/src/module/dependents/service.py b/src/module/dependents/service.py new file mode 100644 index 0000000..95f047f --- /dev/null +++ b/src/module/dependents/service.py @@ -0,0 +1,238 @@ +"""High-level entrypoint: `list_dependents(full_name, ...)`. + +Wraps the scraper with caching (standard `ProviderCache`, 30-day TTL by +default) and graceful degradation. + +Caching strategy: we cache the *aggregated* `DependentsResult` keyed on +`(full_name, kind, max_pages, max_items)`. On a cache hit, one DB lookup +returns the full result. On a cache miss, the scraper walks pages +serially via Selenium. Per-page caching is intentionally not added — it +would make invalidation more complex without meaningful speedup +(individual page fetches are not reused across different repos). + +Empty / failed lookups are returned as `DependentsResult(available=False, +warnings=[...])` rather than raised exceptions, so callers can handle +"no dependents available" uniformly. +""" +from __future__ import annotations + +import logging +import os +from collections.abc import Callable + +from src.module.dependents.models import ( + DependentItem, + DependentKind, + DependentsResult, +) +from src.module.dependents.scraper import ( + DEFAULT_PAGE_TIMEOUT_SECONDS, + DEFAULT_PAGE_WAIT_SECONDS, + fetch_dependents_html, + iterate_dependents, +) +from src.v2.ingest.cache import ProviderCache + +logger = logging.getLogger(__name__) + +DEFAULT_MAX_PAGES = 5 +DEFAULT_MAX_ITEMS = 100 + + +def _coerce_int_env(name: str, default: int) -> int: + raw = os.getenv(name) + if raw is None: + return default + try: + value = int(raw.strip()) + except ValueError: + return default + return value if value > 0 else default + + +def _resolve_caps( + max_pages: int | None, + max_items: int | None, +) -> tuple[int, int]: + """Resolve user-supplied caps, falling back to env vars then defaults.""" + + pages = max_pages if isinstance(max_pages, int) and max_pages > 0 else None + items = max_items if isinstance(max_items, int) and max_items > 0 else None + if pages is None: + pages = _coerce_int_env("V2_DEPENDENTS_MAX_PAGES", DEFAULT_MAX_PAGES) + if items is None: + items = _coerce_int_env("V2_DEPENDENTS_MAX_ITEMS", DEFAULT_MAX_ITEMS) + return pages, items + + +def _selenium_available() -> bool: + return bool(os.getenv("SELENIUM_REMOTE_URL", "").strip()) + + +def list_dependents( + full_name: str, + *, + kind: DependentKind = "REPOSITORY", + max_pages: int | None = None, + max_items: int | None = None, + cache: ProviderCache | None = None, + fetcher: Callable[[str], str] | None = None, +) -> DependentsResult: + """Look up dependents for `/` and return a structured result. + + `cache` — optional `ProviderCache`. When provided, the aggregated + result is cached with the standard provider-cache TTL. + + `fetcher` — optional alternative HTML fetcher (`url -> html`). Used by + tests to bypass Selenium. Defaults to `fetch_dependents_html`. + + Never raises on remote failures: returns a degraded result with + `available=False` and a populated `warnings` list instead. + """ + + if "/" not in full_name: + return DependentsResult( + full_name=full_name, + kind=kind, + available=False, + warnings=[ + f"Invalid full_name '{full_name}': expected 'owner/repo'.", + ], + ) + + pages_cap, items_cap = _resolve_caps(max_pages, max_items) + + cache_key: str | None = None + if cache is not None: + cache_key = ProviderCache.make_key( + "github_dependents", + "list_dependents", + full_name=full_name, + kind=kind, + max_pages=pages_cap, + max_items=items_cap, + ) + cached = cache.get(cache_key) + if cached is not None: + logger.info( + "github_dependents cache hit: %s kind=%s", + full_name, + kind, + ) + try: + return DependentsResult.model_validate(cached) + except Exception as exc: # noqa: BLE001 + logger.warning( + "github_dependents cached payload invalid for %s — refetching: %s", + full_name, + exc, + ) + + if fetcher is None and not _selenium_available(): + return DependentsResult( + full_name=full_name, + kind=kind, + available=False, + warnings=[ + "SELENIUM_REMOTE_URL is not set — cannot scrape dependents page. " + "Set the env var or pass an explicit `fetcher` to enable lookups.", + ], + ) + + items: list[DependentItem] = [] + pages_visited = 0 + total_count = 0 + warnings: list[str] = [] + seen_keys: set[tuple[str, str]] = set() + + # We walk pages by manually invoking the iterator's pieces so we + # capture per-page metadata (counts, selected_kind) along the way. + if fetcher is None: + page_fetcher = fetch_dependents_html + else: + page_fetcher = fetcher + + try: + for item, parsed in iterate_dependents( + full_name, + kind=kind, + max_pages=pages_cap, + max_items=items_cap, + fetcher=_streaming_fetcher(page_fetcher, full_name, kind, pages_cap), + ): + total_count = ( + parsed.repository_count if kind == "REPOSITORY" + else parsed.package_count + ) + key = (item.owner.lower(), item.repo.lower()) + if key in seen_keys: + # Pagination duplicates — defensive. + continue + seen_keys.add(key) + items.append(item) + except Exception as exc: # noqa: BLE001 + logger.exception("github_dependents: unhandled error for %s", full_name) + warnings.append(f"Unhandled scraper error: {exc}") + + truncated = items_cap <= len(items) and total_count > len(items) + available = bool(items) or total_count == 0 # 0 deps is a valid 'available' result + if not items and total_count == 0 and not warnings: + # No degradation, just a real "this repo has no dependents". + warnings.append("Repository reports zero dependents.") + + result = DependentsResult( + full_name=full_name, + kind=kind, + total_count=total_count, + fetched_count=len(items), + truncated=truncated, + items=items, + available=available, + pages_fetched=pages_visited or _approx_pages(len(items)), + warnings=warnings, + ) + + if cache is not None and cache_key is not None and result.available: + cache.set(cache_key, result.model_dump(mode="json")) + + return result + + +def _streaming_fetcher( + fetcher: Callable[[str], str], + full_name: str, + kind: DependentKind, + pages_cap: int, +): + """Adapt a `(url) -> html` callable to the iterator's `Iterable[str]` shape.""" + + from src.module.dependents.scraper import build_dependents_url, parse_dependents_page + + def _gen(): + url: str | None = build_dependents_url(full_name, kind=kind) + page = 0 + while url and page < pages_cap: + html = fetcher(url) if callable(fetcher) else "" + page += 1 + yield html + if not html: + return + parsed = parse_dependents_page(html) + url = parsed.next_cursor_url + + return _gen() + + +def _approx_pages(items_count: int) -> int: + """Rough page count when we don't track it exactly. 30 items per page.""" + + if items_count <= 0: + return 0 + return (items_count + 29) // 30 + + +__all__ = [ + "DEFAULT_MAX_ITEMS", + "DEFAULT_MAX_PAGES", + "list_dependents", +] diff --git a/src/module/dependents/tool.py b/src/module/dependents/tool.py new file mode 100644 index 0000000..2b4293b --- /dev/null +++ b/src/module/dependents/tool.py @@ -0,0 +1,110 @@ +"""Pydantic AI `Tool` factory for the dependents module. + +The factory exists so the dependents lookup can be wired into an agent's +`tools=[...]` list later. Right now **no agent imports this** — the +module is available but not piped, matching the project's "build it, +don't auto-run it" stance for new analytical capabilities. + +Usage when the time comes: + + from src.module.dependents.tool import make_query_dependents_tool + tool = make_query_dependents_tool(cache=provider_cache) + # then add `tool` to the agent's tools=[...] list + +Mirrors `src/v2/agents/llm/agent_tools/query_dependencies.py` (the +forward-direction tool — what *this* repo depends on) so callers can +swap them in/out without surprises. +""" +from __future__ import annotations + +import logging +from typing import Any + +from pydantic_ai import Tool + +from src.module.dependents.models import DependentKind +from src.module.dependents.service import ( + DEFAULT_MAX_ITEMS, + DEFAULT_MAX_PAGES, + list_dependents, +) +from src.v2.ingest.cache import ProviderCache +from src.v2.observation.query_log import record_query + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Discover repositories or packages that *depend on* a given GitHub " + "repository, scraped from the public `/network/dependents` page. " + "Returns up to N top dependents shaped as " + "`{full_name, owner, repo, stars, forks}` plus the total dependents count " + "GitHub reports. " + "Use `kind='REPOSITORY'` (default) to list dependent repos, or " + "`kind='PACKAGE'` for dependent registry packages. " + "`max_pages` and `max_items` cap the scrape depth (default 5 / 100). " + "Call this when reverse-dependency information would meaningfully " + "inform your assessment — e.g. estimating ecosystem reach, gauging a " + "library's importance, or identifying integrations. Skip when the " + "README/metadata is already sufficient: each call hits a Selenium " + "browser session and is not free. " + "Returns `available=False` with explanatory warnings when GitHub has " + "the dependency graph disabled for the repository, when Selenium is " + "unavailable, or when the URL is malformed." +) + + +def make_query_dependents_tool( + *, + cache: ProviderCache | None = None, +) -> Tool: + """Create a pydantic-ai Tool that fetches dependents for a repository. + + The tool's signature is intentionally narrow: it returns the + structured `DependentsResult` as a JSON-serialisable dict, so the + LLM sees a stable shape regardless of the underlying scraper's + behaviour. Failures degrade to `available=False` rather than + raising. + """ + + def query_dependents( + full_name: str, + kind: DependentKind = "REPOSITORY", + max_pages: int = DEFAULT_MAX_PAGES, + max_items: int = DEFAULT_MAX_ITEMS, + ) -> dict[str, Any]: + """Return structured dependents info for a repository. + + Args: + full_name: GitHub repository handle in ``owner/repo`` form. + kind: ``"REPOSITORY"`` (default) for dependent repos, or + ``"PACKAGE"`` for dependent registry packages. + max_pages: Maximum number of dependents pages to walk (default 5). + max_items: Maximum number of items to return (default 100). + """ + + logger.info( + "tool call: query_dependents — full_name=%r kind=%r " + "max_pages=%d max_items=%d", + full_name, + kind, + max_pages, + max_items, + ) + record_query(service="github.dependents", query=f"{full_name}|{kind}") + result = list_dependents( + full_name, + kind=kind, + max_pages=max_pages, + max_items=max_items, + cache=cache, + ) + return result.model_dump(mode="json") + + return Tool( + query_dependents, + name="query_dependents", + description=_DESCRIPTION, + ) + + +__all__ = ["make_query_dependents_tool"] diff --git a/src/module/epfl_graph/__init__.py b/src/module/epfl_graph/__init__.py new file mode 100644 index 0000000..126ebbf --- /dev/null +++ b/src/module/epfl_graph/__init__.py @@ -0,0 +1,75 @@ +"""EPFL Graph (graphai-client) wrapper module. + +Thin wrapper around the upstream `graphai_client` library. Auth is handled +transparently using the EPFL_GRAPH_USERNAME and EPFL_GRAPH_PASSWORD env vars, +so callers do not need to manage `login_info` themselves. + +Public API mirrors `graphai_client.client_api.{translation,text,embedding, +image,voice,video}` and the integrated `graphai_client.client.process_video`. +""" + +from src.module.epfl_graph.auth import get_login_info, reset_login_info +from src.module.epfl_graph.client import process_video +from src.module.epfl_graph.image import extract_text_from_slide +from src.module.epfl_graph.ontology import ( + category_chain, + category_graphsearch_url, + category_id_to_label, + category_info, + category_nearest_openalex_topics, + category_wikipedia, + concept_nearest_categories, + ontology_tree, +) +from src.module.epfl_graph.openalex_related import ( + people_for_topics, + publications_for_topics, + units_for_topics, +) +from src.module.epfl_graph.text import ( + detect_language, + embed_text, + extract_concepts_from_keywords, + extract_concepts_from_text, + extract_keywords_from_text, + translate_text, +) +from src.module.epfl_graph.video import ( + download_file, + extract_audio, + extract_slides, + fingerprint_video, + get_video_token, +) +from src.module.epfl_graph.voice import detect_audio_language, transcribe_audio + +__all__ = [ + "category_chain", + "category_graphsearch_url", + "category_id_to_label", + "category_info", + "category_nearest_openalex_topics", + "category_wikipedia", + "concept_nearest_categories", + "detect_audio_language", + "detect_language", + "download_file", + "embed_text", + "extract_audio", + "extract_concepts_from_keywords", + "extract_concepts_from_text", + "extract_keywords_from_text", + "extract_slides", + "extract_text_from_slide", + "fingerprint_video", + "get_login_info", + "get_video_token", + "ontology_tree", + "people_for_topics", + "process_video", + "publications_for_topics", + "reset_login_info", + "transcribe_audio", + "translate_text", + "units_for_topics", +] diff --git a/src/module/epfl_graph/auth.py b/src/module/epfl_graph/auth.py new file mode 100644 index 0000000..8b0d72f --- /dev/null +++ b/src/module/epfl_graph/auth.py @@ -0,0 +1,108 @@ +"""EPFL Graph authentication. + +Builds the `login_info` dict that the upstream `graphai_client` library +expects, sourcing credentials from the EPFL_GRAPH_USERNAME and +EPFL_GRAPH_PASSWORD env vars. + +The upstream `login()` helper expects a path to a JSON file on disk so it +can re-authenticate on 401 responses. We materialise that file lazily into +a chmod-600 tempfile and clean it up at process exit. +""" + +from __future__ import annotations + +import atexit +import json +import os +import tempfile +import threading +from typing import Optional + +from graphai_client.client_api.utils import login as _upstream_login + +DEFAULT_HOST = "https://graphai.epfl.ch" +DEFAULT_PORT = 443 + +_lock = threading.Lock() +_credentials_path: Optional[str] = None +_login_info: Optional[dict] = None + + +def _read_credentials() -> tuple[str, str]: + user = os.environ.get("EPFL_GRAPH_USERNAME") + password = os.environ.get("EPFL_GRAPH_PASSWORD") + missing = [ + name + for name, value in ( + ("EPFL_GRAPH_USERNAME", user), + ("EPFL_GRAPH_PASSWORD", password), + ) + if not value + ] + if missing: + raise RuntimeError( + "Missing required environment variable(s): " + ", ".join(missing), + ) + return user, password # type: ignore[return-value] + + +def _ensure_credentials_file() -> str: + """Write a chmod-600 tempfile with the JSON the upstream login expects.""" + global _credentials_path + if _credentials_path is not None and os.path.exists(_credentials_path): + return _credentials_path + + user, password = _read_credentials() + host = os.environ.get("EPFL_GRAPH_HOST", DEFAULT_HOST) + port = int(os.environ.get("EPFL_GRAPH_PORT", str(DEFAULT_PORT))) + + fd, path = tempfile.mkstemp(prefix="epfl_graph_", suffix=".json") + try: + os.close(fd) + os.chmod(path, 0o600) + with open(path, "w", encoding="utf-8") as fp: + json.dump( + {"host": host, "port": port, "user": user, "password": password}, + fp, + ) + except Exception: + if os.path.exists(path): + os.unlink(path) + raise + + _credentials_path = path + atexit.register(_cleanup_credentials_file) + return path + + +def _cleanup_credentials_file() -> None: + global _credentials_path + if _credentials_path and os.path.exists(_credentials_path): + try: + os.unlink(_credentials_path) + finally: + _credentials_path = None + + +def get_login_info(force_refresh: bool = False) -> dict: + """Return a `login_info` dict ready for graphai_client API calls. + + The result is cached for the lifetime of the process. Pass + `force_refresh=True` to discard the cache and re-authenticate. + """ + global _login_info + with _lock: + if force_refresh: + _login_info = None + if _login_info is None: + credentials_path = _ensure_credentials_file() + _login_info = _upstream_login(credentials_path) + return _login_info + + +def reset_login_info() -> None: + """Drop the cached login_info and credentials file (next call re-auths).""" + global _login_info + with _lock: + _login_info = None + _cleanup_credentials_file() diff --git a/src/module/epfl_graph/client.py b/src/module/epfl_graph/client.py new file mode 100644 index 0000000..cb80929 --- /dev/null +++ b/src/module/epfl_graph/client.py @@ -0,0 +1,24 @@ +"""High-level integrated EPFL Graph workflows. + +Wraps `graphai_client.client.process_video`, the end-to-end pipeline that +downloads a video, extracts audio + slides, transcribes, runs OCR and +translates everything into the requested destination languages. +""" + +from __future__ import annotations + +from typing import Any, Optional + +from graphai_client import client as _client + +from src.module.epfl_graph.auth import get_login_info + + +def process_video( + video_url: str, + login_info: Optional[dict] = None, + **kwargs: Any, +): + return _client.process_video( + video_url=video_url, login_info=login_info or get_login_info(), **kwargs, + ) diff --git a/src/module/epfl_graph/image.py b/src/module/epfl_graph/image.py new file mode 100644 index 0000000..d4d3e91 --- /dev/null +++ b/src/module/epfl_graph/image.py @@ -0,0 +1,19 @@ +"""Image-related EPFL Graph endpoints (OCR on slides).""" + +from __future__ import annotations + +from typing import Any, Optional + +from graphai_client.client_api import image as _image + +from src.module.epfl_graph.auth import get_login_info + + +def extract_text_from_slide( + slide_token: str, + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Optional[dict]: + return _image.extract_text_from_slide( + slide_token=slide_token, login_info=login_info or get_login_info(), **kwargs, + ) diff --git a/src/module/epfl_graph/ontology.py b/src/module/epfl_graph/ontology.py new file mode 100644 index 0000000..9a66afb --- /dev/null +++ b/src/module/epfl_graph/ontology.py @@ -0,0 +1,297 @@ +"""EPFL Graph ontology endpoints (concept → discipline / category). + +The upstream ``graphai-client`` library does not wrap the ``/ontology/*`` +namespace, so we hit the API directly using the cached login_info from +``src.module.epfl_graph.auth``. Three helpers are exposed: + +- :func:`concept_nearest_categories` — POST /ontology/nearest_neighbor/concept/category +- :func:`ontology_tree` — GET /ontology/tree (cached for the process lifetime) +- :func:`category_info` — GET /ontology/tree/category/{category_id} + +The category IDs are slugs (``topics-in-natural-language-processing``, +``computer-graphics``, …). The tree exposes a ``child_to_parent`` map so +callers can walk upward to broader disciplines. +""" + +from __future__ import annotations + +import threading +from typing import Any + +import requests + +from src.module.epfl_graph.auth import get_login_info + +DEFAULT_TIMEOUT = 60.0 +_TREE_LOCK = threading.Lock() +_TREE_CACHE: dict[str, Any] | None = None + +_CATEGORY_INFO_LOCK = threading.Lock() +_CATEGORY_INFO_CACHE: dict[str, dict[str, Any]] = {} + +_OPENALEX_TOPICS_LOCK = threading.Lock() +_OPENALEX_TOPICS_CACHE: dict[str, list[dict[str, Any]]] = {} + + +def _bearer_headers(login_info: dict[str, Any] | None) -> tuple[str, dict[str, str]]: + info = login_info or get_login_info() + return info["host"], {"Authorization": f"Bearer {info['token']}"} + + +def concept_nearest_categories( # noqa: PLR0913 + concept_id: str | int, + *, + top_n: int = 5, + use_embeddings: bool = False, + top_down_search: bool = True, + return_clusters: bool = False, + login_info: dict[str, Any] | None = None, + timeout: float = DEFAULT_TIMEOUT, +) -> list[dict[str, Any]]: + """Return the top-N EPFL ontology categories for a Wikipedia concept ID. + + Each entry has shape ``{"category_id": str, "score": float, "rank": int, + "clusters": list | None}``. Returns an empty list when the API rejects + the concept (typically because it isn't mapped in the EPFL ontology). + """ + host, headers = _bearer_headers(login_info) + body = { + "src": str(concept_id), + "top_n": top_n, + "use_embeddings": use_embeddings, + "top_down_search": top_down_search, + "return_clusters": return_clusters, + } + try: + response = requests.post( + host + "/ontology/nearest_neighbor/concept/category", + headers={**headers, "Content-Type": "application/json"}, + json=body, + timeout=timeout, + ) + except requests.RequestException: + return [] + if not response.ok: + return [] + try: + payload = response.json() + except ValueError: + return [] + scores = payload.get("scores") if isinstance(payload, dict) else None + return scores if isinstance(scores, list) else [] + + +def ontology_tree( + *, + login_info: dict[str, Any] | None = None, + timeout: float = DEFAULT_TIMEOUT, + refresh: bool = False, +) -> dict[str, Any]: + """Fetch the ``child_to_parent`` ontology tree, cached for the process lifetime.""" + global _TREE_CACHE # noqa: PLW0603 + with _TREE_LOCK: + if _TREE_CACHE is not None and not refresh: + return _TREE_CACHE + host, headers = _bearer_headers(login_info) + try: + response = requests.get( + host + "/ontology/tree", headers=headers, timeout=timeout, + ) + response.raise_for_status() + _TREE_CACHE = response.json() or {} + except (requests.RequestException, ValueError): + _TREE_CACHE = {} + return _TREE_CACHE + + +def category_info( + category_id: str, + *, + login_info: dict[str, Any] | None = None, + timeout: float = DEFAULT_TIMEOUT, + use_cache: bool = True, +) -> dict[str, Any]: + """Fetch metadata for a single ontology category (cached per process). + + Returns ``{}`` when the API call fails or the category is unknown. + """ + cid = str(category_id) + if use_cache: + cached = _CATEGORY_INFO_CACHE.get(cid) + if cached is not None: + return cached + host, headers = _bearer_headers(login_info) + try: + response = requests.get( + host + "/ontology/tree/category/" + cid, + headers=headers, + timeout=timeout, + ) + response.raise_for_status() + payload = response.json() or {} + except (requests.RequestException, ValueError): + payload = {} + if use_cache: + with _CATEGORY_INFO_LOCK: + _CATEGORY_INFO_CACHE[cid] = payload + return payload + + +def category_wikipedia(category_id: str) -> dict[str, Any]: + """Return ``{name, wikipedia_page_id, wikipedia_url}`` for a category. + + Pulls from the cached :func:`category_info` response. Falls back to + the slug-derived label when the API call fails. + """ + info_payload = category_info(category_id) or {} + info_block = info_payload.get("info") if isinstance(info_payload, dict) else None + name: str | None = None + page_id: str | None = None + if isinstance(info_block, dict): + if isinstance(info_block.get("name"), str) and info_block["name"].strip(): + name = info_block["name"].strip() + raw_id = info_block.get("id") + if isinstance(raw_id, (int, str)) and str(raw_id).strip(): + page_id = str(raw_id).strip() + name_or_label = name or category_id_to_label(category_id) + wikipedia_url: str | None = None + if page_id: + wikipedia_url = "https://en.wikipedia.org/?curid=" + page_id + elif name_or_label: + wikipedia_url = ( + "https://en.wikipedia.org/wiki/" + name_or_label.replace(" ", "_") + ) + return { + "name": name_or_label, + "wikipedia_page_id": page_id, + "wikipedia_url": wikipedia_url, + } + + +def category_nearest_openalex_topics( + category_id: str, + *, + top_n: int = 5, + login_info: dict[str, Any] | None = None, + timeout: float = DEFAULT_TIMEOUT, + use_cache: bool = True, +) -> list[dict[str, Any]]: + """Return OpenAlex topics nearest to an EPFL category. + + Each entry has ``{topic_id, topic_name, score, embedding_score, + wikipedia_score}``. Results are cached per ``(category_id, top_n)`` + for the lifetime of the process. + """ + cid = str(category_id) + cache_key = f"{cid}|{top_n}" + if use_cache: + cached = _OPENALEX_TOPICS_CACHE.get(cache_key) + if cached is not None: + return cached + host, headers = _bearer_headers(login_info) + try: + response = requests.get( + host + "/ontology/openalex/category/" + cid + "/nearest_topics", + headers=headers, + timeout=timeout, + ) + response.raise_for_status() + payload = response.json() + except (requests.RequestException, ValueError): + payload = [] + topics: list[dict[str, Any]] = [] + if isinstance(payload, list): + for item in payload[:top_n]: + if not isinstance(item, dict): + continue + topic_id = item.get("topic_id") + topic_name = item.get("topic_name") + if not isinstance(topic_id, (str, int)) or not str(topic_id).strip(): + continue + topics.append( + { + "topic_id": str(topic_id).strip(), + "topic_name": ( + topic_name.strip() + if isinstance(topic_name, str) + else None + ), + "score": item.get("score"), + "embedding_score": item.get("embedding_score"), + "wikipedia_score": item.get("wikipedia_score"), + }, + ) + if use_cache: + with _OPENALEX_TOPICS_LOCK: + _OPENALEX_TOPICS_CACHE[cache_key] = topics + return topics + + +def category_id_to_label(category_id: str) -> str: + """Cheap human-form for a slug-style category id. + + ``topics-in-natural-language-processing`` → + ``Topics in Natural Language Processing``. Articles/prepositions stay + lowercase. Fall back to the original slug when in doubt. + """ + if not isinstance(category_id, str) or not category_id.strip(): + return "" + parts = category_id.strip().split("-") + minor = {"in", "of", "and", "or", "the", "a", "an", "for", "on", "to", "with"} + out: list[str] = [] + for index, part in enumerate(parts): + if not part: + continue + if index > 0 and part.lower() in minor: + out.append(part.lower()) + else: + out.append(part[:1].upper() + part[1:]) + return " ".join(out) if out else category_id + + +GRAPHSEARCH_CATEGORY_URL = "https://graphsearch.epfl.ch/en/category/" +ROOT_CATEGORY_ID = "root" + + +def category_graphsearch_url(category_id: str) -> str: + """User-facing GraphSearch landing page for a category slug.""" + return GRAPHSEARCH_CATEGORY_URL + str(category_id) + + +def category_chain( + category_id: str, + *, + login_info: dict[str, Any] | None = None, + include_root: bool = False, + max_depth: int = 20, +) -> list[str]: + """Return the parent chain from ``category_id`` upward to the root. + + The first element is always ``category_id`` itself. ``root`` is omitted + by default (it carries no semantic info). Returns a single-element list + if the category isn't in the tree. + """ + tree = ontology_tree(login_info=login_info) + edges = tree.get("child_to_parent") if isinstance(tree, dict) else None + if not isinstance(edges, list): + return [category_id] + parent_map = { + edge["child_id"]: edge["parent_id"] + for edge in edges + if isinstance(edge, dict) + and isinstance(edge.get("child_id"), str) + and isinstance(edge.get("parent_id"), str) + } + chain: list[str] = [category_id] + node = category_id + seen = {category_id} + while node in parent_map and len(chain) < max_depth: + parent = parent_map[node] + if parent in seen: + break + if not include_root and parent == ROOT_CATEGORY_ID: + break + chain.append(parent) + seen.add(parent) + node = parent + return chain diff --git a/src/module/epfl_graph/openalex_related.py b/src/module/epfl_graph/openalex_related.py new file mode 100644 index 0000000..92fa2b6 --- /dev/null +++ b/src/module/epfl_graph/openalex_related.py @@ -0,0 +1,196 @@ +"""OpenAlex enrichment for EPFL Graph categories. + +Given OpenAlex topic IDs (resolved via +:func:`src.module.epfl_graph.ontology.category_nearest_openalex_topics`), +return small lists of related publications, people, and institutions. + +Uses :mod:`pyalex` directly (already a project dep) instead of the +heavier ``src.index.openalex`` ingestion layer — we only need a handful +of records per topic, not pageable feeds. + +OpenAlex polite-pool email is read from ``OPENALEX_MAILTO``; without it +calls fall back to the slower public pool. The first call sets the +global pyalex config; subsequent calls reuse it. +""" + +from __future__ import annotations + +import logging +import os +import threading +from typing import Any, Iterable + +import pyalex +from pyalex import Authors, Institutions, Works + +logger = logging.getLogger(__name__) + +_PYALEX_LOCK = threading.Lock() +_PYALEX_CONFIGURED = False + + +def _ensure_pyalex_configured() -> None: + global _PYALEX_CONFIGURED # noqa: PLW0603 + if _PYALEX_CONFIGURED: + return + with _PYALEX_LOCK: + if _PYALEX_CONFIGURED: + return + mailto = (os.environ.get("OPENALEX_MAILTO") or "").strip() or None + pyalex.config.email = mailto + pyalex.config.api_key = None + pyalex.config.max_retries = 3 + pyalex.config.retry_backoff_factor = 0.5 + pyalex.config.retry_http_codes = [429, 500, 502, 503, 504] + _PYALEX_CONFIGURED = True + + +def _normalize_topic_ids(topic_ids: Iterable[str | int]) -> list[str]: + out: list[str] = [] + for raw in topic_ids: + if not isinstance(raw, (str, int)): + continue + s = str(raw).strip() + if not s: + continue + # OpenAlex accepts both bare ids ("12345") and prefixed ("T12345"). + if not s.startswith(("T", "t")): + s = "T" + s + out.append(s.upper()) + return out + + +def publications_for_topics( + topic_ids: Iterable[str | int], + *, + top_n: int = 5, +) -> list[dict[str, Any]]: + """Return top-cited Works tagged with any of the given topic IDs.""" + ids = _normalize_topic_ids(topic_ids) + if not ids: + return [] + _ensure_pyalex_configured() + try: + query = ( + Works() + .filter(**{"topics.id": "|".join(ids)}) + .sort(cited_by_count="desc") + .select( + "id,doi,title,publication_year,cited_by_count," + "authorships,primary_location,type", + ) + ) + rows = list(query.get(per_page=top_n))[:top_n] + except Exception as exc: # noqa: BLE001 + logger.warning("openalex Works lookup failed: %s", exc) + return [] + out: list[dict[str, Any]] = [] + for row in rows: + if not isinstance(row, dict): + continue + primary = row.get("primary_location") or {} + source = primary.get("source") if isinstance(primary, dict) else None + out.append( + { + "openalex_id": row.get("id"), + "doi": row.get("doi"), + "title": row.get("title"), + "year": row.get("publication_year"), + "cited_by_count": row.get("cited_by_count"), + "type": row.get("type"), + "venue": source.get("display_name") if isinstance(source, dict) else None, + "url": ( + primary.get("landing_page_url") + if isinstance(primary, dict) + else None + ), + }, + ) + return out + + +def people_for_topics( + topic_ids: Iterable[str | int], + *, + top_n: int = 5, +) -> list[dict[str, Any]]: + """Return top-published Authors tagged with any of the given topic IDs.""" + ids = _normalize_topic_ids(topic_ids) + if not ids: + return [] + _ensure_pyalex_configured() + try: + query = ( + Authors() + .filter(**{"topics.id": "|".join(ids)}) + .sort(works_count="desc") + .select( + "id,display_name,orcid,works_count,cited_by_count," + "last_known_institutions,affiliations", + ) + ) + rows = list(query.get(per_page=top_n))[:top_n] + except Exception as exc: # noqa: BLE001 + logger.warning("openalex Authors lookup failed: %s", exc) + return [] + out: list[dict[str, Any]] = [] + for row in rows: + if not isinstance(row, dict): + continue + institutions = row.get("last_known_institutions") or [] + primary_inst = institutions[0] if institutions else {} + out.append( + { + "openalex_id": row.get("id"), + "name": row.get("display_name"), + "orcid": row.get("orcid"), + "works_count": row.get("works_count"), + "cited_by_count": row.get("cited_by_count"), + "institution": primary_inst.get("display_name") if isinstance(primary_inst, dict) else None, + "ror": primary_inst.get("ror") if isinstance(primary_inst, dict) else None, + }, + ) + return out + + +def units_for_topics( + topic_ids: Iterable[str | int], + *, + top_n: int = 5, +) -> list[dict[str, Any]]: + """Return top-publishing Institutions tagged with any of the given topic IDs.""" + ids = _normalize_topic_ids(topic_ids) + if not ids: + return [] + _ensure_pyalex_configured() + try: + query = ( + Institutions() + .filter(**{"topics.id": "|".join(ids)}) + .sort(works_count="desc") + .select( + "id,display_name,ror,country_code,type,homepage_url," + "works_count,cited_by_count", + ) + ) + rows = list(query.get(per_page=top_n))[:top_n] + except Exception as exc: # noqa: BLE001 + logger.warning("openalex Institutions lookup failed: %s", exc) + return [] + out: list[dict[str, Any]] = [] + for row in rows: + if not isinstance(row, dict): + continue + out.append( + { + "openalex_id": row.get("id"), + "name": row.get("display_name"), + "ror": row.get("ror"), + "country_code": row.get("country_code"), + "type": row.get("type"), + "homepage_url": row.get("homepage_url"), + "works_count": row.get("works_count"), + "cited_by_count": row.get("cited_by_count"), + }, + ) + return out diff --git a/src/module/epfl_graph/text.py b/src/module/epfl_graph/text.py new file mode 100644 index 0000000..e3441d7 --- /dev/null +++ b/src/module/epfl_graph/text.py @@ -0,0 +1,81 @@ +"""Text-related EPFL Graph endpoints. + +Wraps `graphai_client.client_api.translation`, `text` and `embedding`. +Each wrapper auto-resolves `login_info` from env vars when not supplied. +""" + +from __future__ import annotations + +from typing import Any, List, Optional, Union + +from graphai_client.client_api import embedding as _embedding +from graphai_client.client_api import text as _text +from graphai_client.client_api import translation as _translation + +from src.module.epfl_graph.auth import get_login_info + + +def detect_language( + text: Union[str, List[Optional[str]]], + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Optional[Union[str, List[Optional[str]]]]: + return _translation.detect_language( + text=text, login_info=login_info or get_login_info(), **kwargs, + ) + + +def translate_text( + text: Union[str, List[Optional[str]]], + source_language: str, + target_language: str, + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Optional[Union[str, List[Optional[str]]]]: + return _translation.translate_text( + text=text, + source_language=source_language, + target_language=target_language, + login_info=login_info or get_login_info(), + **kwargs, + ) + + +def extract_concepts_from_text( + text: str, + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Optional[List[dict]]: + return _text.extract_concepts_from_text( + text=text, login_info=login_info or get_login_info(), **kwargs, + ) + + +def extract_keywords_from_text( + text: str, + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Optional[List[str]]: + return _text.extract_keywords_from_text( + text=text, login_info=login_info or get_login_info(), **kwargs, + ) + + +def extract_concepts_from_keywords( + keywords: List[str], + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Optional[List[dict]]: + return _text.extract_concepts_from_keywords( + keywords=keywords, login_info=login_info or get_login_info(), **kwargs, + ) + + +def embed_text( + text: Union[str, List[Optional[str]]], + login_info: Optional[dict] = None, + **kwargs: Any, +): + return _embedding.embed_text( + text=text, login_info=login_info or get_login_info(), **kwargs, + ) diff --git a/src/module/epfl_graph/video.py b/src/module/epfl_graph/video.py new file mode 100644 index 0000000..8a1847e --- /dev/null +++ b/src/module/epfl_graph/video.py @@ -0,0 +1,67 @@ +"""Video-related EPFL Graph endpoints. + +Wraps `graphai_client.client_api.video` for video token retrieval, +fingerprinting, audio extraction, slide extraction and resource downloads. +""" + +from __future__ import annotations + +from typing import Any, Optional, Tuple + +from graphai_client.client_api import video as _video + +from src.module.epfl_graph.auth import get_login_info + + +def get_video_token( + url_video: str, + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Tuple[Optional[str], Optional[int], Optional[list]]: + return _video.get_video_token( + url_video=url_video, login_info=login_info or get_login_info(), **kwargs, + ) + + +def fingerprint_video( + video_token: str, + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Optional[str]: + return _video.fingerprint_video( + video_token=video_token, login_info=login_info or get_login_info(), **kwargs, + ) + + +def extract_audio( + video_token: str, + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Optional[str]: + return _video.extract_audio( + video_token=video_token, login_info=login_info or get_login_info(), **kwargs, + ) + + +def extract_slides( + video_token: str, + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Optional[dict]: + return _video.extract_slides( + video_token=video_token, login_info=login_info or get_login_info(), **kwargs, + ) + + +def download_file( + token: str, + file_path: str, + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Optional[str]: + return _video.download_file( + token=token, + file_path=file_path, + login_info=login_info or get_login_info(), + **kwargs, + ) diff --git a/src/module/epfl_graph/voice.py b/src/module/epfl_graph/voice.py new file mode 100644 index 0000000..b359ef1 --- /dev/null +++ b/src/module/epfl_graph/voice.py @@ -0,0 +1,30 @@ +"""Audio-related EPFL Graph endpoints (transcription, language detection).""" + +from __future__ import annotations + +from typing import Any, List, Optional, Tuple + +from graphai_client.client_api import voice as _voice + +from src.module.epfl_graph.auth import get_login_info + + +def transcribe_audio( + audio_token: str, + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Tuple[Optional[str], Optional[List[dict]]]: + return _voice.transcribe_audio( + audio_token=audio_token, login_info=login_info or get_login_info(), **kwargs, + ) + + +def detect_audio_language( + audio_token: str, + login_info: Optional[dict] = None, + **kwargs: Any, +) -> Optional[str]: + """Detect the language of the voice in the audio (`voice.detect_language`).""" + return _voice.detect_language( + audio_token=audio_token, login_info=login_info or get_login_info(), **kwargs, + ) diff --git a/src/v1/__init__.py b/src/v1/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/agents/__init__.py b/src/v1/agents/__init__.py similarity index 100% rename from src/agents/__init__.py rename to src/v1/agents/__init__.py diff --git a/src/agents/agents_management.py b/src/v1/agents/agents_management.py similarity index 100% rename from src/agents/agents_management.py rename to src/v1/agents/agents_management.py diff --git a/src/agents/atomic_agents/__init__.py b/src/v1/agents/atomic_agents/__init__.py similarity index 100% rename from src/agents/atomic_agents/__init__.py rename to src/v1/agents/atomic_agents/__init__.py diff --git a/src/agents/atomic_agents/context_compiler.py b/src/v1/agents/atomic_agents/context_compiler.py similarity index 100% rename from src/agents/atomic_agents/context_compiler.py rename to src/v1/agents/atomic_agents/context_compiler.py diff --git a/src/agents/atomic_agents/epfl_final_checker.py b/src/v1/agents/atomic_agents/epfl_final_checker.py similarity index 100% rename from src/agents/atomic_agents/epfl_final_checker.py rename to src/v1/agents/atomic_agents/epfl_final_checker.py diff --git a/src/agents/atomic_agents/linked_entities_searcher.py b/src/v1/agents/atomic_agents/linked_entities_searcher.py similarity index 100% rename from src/agents/atomic_agents/linked_entities_searcher.py rename to src/v1/agents/atomic_agents/linked_entities_searcher.py diff --git a/src/agents/atomic_agents/models.py b/src/v1/agents/atomic_agents/models.py similarity index 100% rename from src/agents/atomic_agents/models.py rename to src/v1/agents/atomic_agents/models.py diff --git a/src/agents/atomic_agents/organization_classifier.py b/src/v1/agents/atomic_agents/organization_classifier.py similarity index 100% rename from src/agents/atomic_agents/organization_classifier.py rename to src/v1/agents/atomic_agents/organization_classifier.py diff --git a/src/agents/atomic_agents/organization_context_compiler.py b/src/v1/agents/atomic_agents/organization_context_compiler.py similarity index 100% rename from src/agents/atomic_agents/organization_context_compiler.py rename to src/v1/agents/atomic_agents/organization_context_compiler.py diff --git a/src/agents/atomic_agents/organization_identifier.py b/src/v1/agents/atomic_agents/organization_identifier.py similarity index 100% rename from src/agents/atomic_agents/organization_identifier.py rename to src/v1/agents/atomic_agents/organization_identifier.py diff --git a/src/agents/atomic_agents/organization_structured_output.py b/src/v1/agents/atomic_agents/organization_structured_output.py similarity index 100% rename from src/agents/atomic_agents/organization_structured_output.py rename to src/v1/agents/atomic_agents/organization_structured_output.py diff --git a/src/agents/atomic_agents/repository_classifier.py b/src/v1/agents/atomic_agents/repository_classifier.py similarity index 100% rename from src/agents/atomic_agents/repository_classifier.py rename to src/v1/agents/atomic_agents/repository_classifier.py diff --git a/src/agents/atomic_agents/structured_output.py b/src/v1/agents/atomic_agents/structured_output.py similarity index 100% rename from src/agents/atomic_agents/structured_output.py rename to src/v1/agents/atomic_agents/structured_output.py diff --git a/src/agents/atomic_agents/user_classifier.py b/src/v1/agents/atomic_agents/user_classifier.py similarity index 100% rename from src/agents/atomic_agents/user_classifier.py rename to src/v1/agents/atomic_agents/user_classifier.py diff --git a/src/agents/atomic_agents/user_context_compiler.py b/src/v1/agents/atomic_agents/user_context_compiler.py similarity index 100% rename from src/agents/atomic_agents/user_context_compiler.py rename to src/v1/agents/atomic_agents/user_context_compiler.py diff --git a/src/agents/atomic_agents/user_structured_output.py b/src/v1/agents/atomic_agents/user_structured_output.py similarity index 100% rename from src/agents/atomic_agents/user_structured_output.py rename to src/v1/agents/atomic_agents/user_structured_output.py diff --git a/src/agents/epfl_assessment.py b/src/v1/agents/epfl_assessment.py similarity index 100% rename from src/agents/epfl_assessment.py rename to src/v1/agents/epfl_assessment.py diff --git a/src/agents/epfl_assessment_prompts.py b/src/v1/agents/epfl_assessment_prompts.py similarity index 100% rename from src/agents/epfl_assessment_prompts.py rename to src/v1/agents/epfl_assessment_prompts.py diff --git a/src/agents/linked_entities_enrichment.py b/src/v1/agents/linked_entities_enrichment.py similarity index 100% rename from src/agents/linked_entities_enrichment.py rename to src/v1/agents/linked_entities_enrichment.py diff --git a/src/agents/linked_entities_prompts.py b/src/v1/agents/linked_entities_prompts.py similarity index 100% rename from src/agents/linked_entities_prompts.py rename to src/v1/agents/linked_entities_prompts.py diff --git a/src/agents/organization.py b/src/v1/agents/organization.py similarity index 100% rename from src/agents/organization.py rename to src/v1/agents/organization.py diff --git a/src/agents/organization_enrichment.py b/src/v1/agents/organization_enrichment.py similarity index 100% rename from src/agents/organization_enrichment.py rename to src/v1/agents/organization_enrichment.py diff --git a/src/agents/organization_prompts.py b/src/v1/agents/organization_prompts.py similarity index 100% rename from src/agents/organization_prompts.py rename to src/v1/agents/organization_prompts.py diff --git a/src/agents/prompts.py b/src/v1/agents/prompts.py similarity index 100% rename from src/agents/prompts.py rename to src/v1/agents/prompts.py diff --git a/src/agents/repository.py b/src/v1/agents/repository.py similarity index 100% rename from src/agents/repository.py rename to src/v1/agents/repository.py diff --git a/src/agents/repository_prompts.py b/src/v1/agents/repository_prompts.py similarity index 100% rename from src/agents/repository_prompts.py rename to src/v1/agents/repository_prompts.py diff --git a/src/v1/agents/tools.py b/src/v1/agents/tools.py new file mode 100644 index 0000000..e69de29 diff --git a/src/agents/url_validation.py b/src/v1/agents/url_validation.py similarity index 100% rename from src/agents/url_validation.py rename to src/v1/agents/url_validation.py diff --git a/src/agents/user.py b/src/v1/agents/user.py similarity index 100% rename from src/agents/user.py rename to src/v1/agents/user.py diff --git a/src/agents/user_enrichment.py b/src/v1/agents/user_enrichment.py similarity index 100% rename from src/agents/user_enrichment.py rename to src/v1/agents/user_enrichment.py diff --git a/src/agents/user_prompts.py b/src/v1/agents/user_prompts.py similarity index 100% rename from src/agents/user_prompts.py rename to src/v1/agents/user_prompts.py diff --git a/src/agents/validation_utils.py b/src/v1/agents/validation_utils.py similarity index 100% rename from src/agents/validation_utils.py rename to src/v1/agents/validation_utils.py diff --git a/src/analysis/__init__.py b/src/v1/analysis/__init__.py similarity index 100% rename from src/analysis/__init__.py rename to src/v1/analysis/__init__.py diff --git a/src/analysis/organization.py b/src/v1/analysis/organization.py similarity index 100% rename from src/analysis/organization.py rename to src/v1/analysis/organization.py diff --git a/src/analysis/repositories.py b/src/v1/analysis/repositories.py similarity index 97% rename from src/analysis/repositories.py rename to src/v1/analysis/repositories.py index df3b33f..b1ffb36 100644 --- a/src/analysis/repositories.py +++ b/src/v1/analysis/repositories.py @@ -82,7 +82,7 @@ def _sanitize_person_payload(cls, payload: dict, context: str) -> dict: def run_gimie_analysis(self): def fetch_gimie_data(): - return extract_gimie(self.full_path, format="json-ld") + return extract_gimie(self.full_path, serialization_format="json-ld") # Get GIMIE data jsonld_gimie_data = self.cache_manager.get_cached_or_fetch( @@ -97,6 +97,11 @@ def fetch_gimie_data(): # SoftwareSourceCode.convert_jsonld_to_pydantic(jsonld_gimie_data), # ) self.gimie = jsonld_gimie_data + else: + logger.warning( + "GIMIE returned no JSON-LD payload for %s", + self.full_path, + ) async def run_llm_analysis(self): """Run LLM analysis using the atomic agent pipeline.""" @@ -2845,7 +2850,13 @@ async def run_analysis( if run_gimie: logging.info(f"GIMIE analysis for {self.full_path}") self.run_gimie_analysis() - logging.info(f"GIMIE analysis completed for {self.full_path}") + if self.gimie: + logging.info(f"GIMIE analysis completed for {self.full_path}") + else: + logging.warning( + "GIMIE produced no JSON-LD for %s", + self.full_path, + ) # Run LLM analysis if run_llm: @@ -2904,11 +2915,14 @@ async def run_analysis( await self.run_epfl_final_assessment() logging.info(f"Final EPFL assessment completed for {self.full_path}") - # Only validate and cache if we have data + # Validate/cache full SoftwareSourceCode when the LLM pipeline produced it. + # GIMIE-only runs expose raw JSON-LD on self.gimie (cached under api_type=gimie). if self.data is not None: self.run_validation() self.save_in_cache() self.analysis_successful = True + elif not run_llm and self.gimie: + self.analysis_successful = True else: logging.error(f"Analysis failed for {self.full_path}: no data generated") self.analysis_successful = False @@ -2916,35 +2930,47 @@ async def run_analysis( # Track end time self.end_time = datetime.now() - # Log duration and final token usage summary + # Log duration; token summary only when LLM stages may have run if self.start_time and self.end_time: duration = (self.end_time - self.start_time).total_seconds() - # Final token usage summary - logger.info("") - logger.info("=" * 80) - logger.info("FINAL TOKEN USAGE SUMMARY (All Stages)") - logger.info("=" * 80) - logger.info(" Official API Counts:") - logger.info(f" Input tokens: {self.total_input_tokens:,}") - logger.info(f" Output tokens: {self.total_output_tokens:,}") - logger.info( - f" Total tokens: {self.total_input_tokens + self.total_output_tokens:,}", - ) - logger.info("") - logger.info(" Estimated Counts (tiktoken):") - logger.info(f" Input tokens: {self.estimated_input_tokens:,}") - logger.info(f" Output tokens: {self.estimated_output_tokens:,}") - logger.info( - f" Total tokens: {self.estimated_input_tokens + self.estimated_output_tokens:,}", - ) - logger.info("") - if self.total_input_tokens == 0 and self.total_output_tokens == 0: - logger.warning( - " ⚠️ API returned 0 tokens - using tiktoken estimates as primary metric", + if not run_llm: + logger.info( + "Analysis completed for %s in %.2fs (%s)", + self.full_path, + duration, + "SUCCESS" if self.analysis_successful else "FAILED", ) - logger.info(f" Analysis Duration: {duration:.2f} seconds") - logger.info( - f" Status: {'SUCCESS' if self.analysis_successful else 'FAILED'}", - ) - logger.info("=" * 80) + else: + logger.info("") + logger.info("=" * 80) + logger.info("FINAL TOKEN USAGE SUMMARY (All Stages)") + logger.info("=" * 80) + logger.info(" Official API Counts:") + logger.info(f" Input tokens: {self.total_input_tokens:,}") + logger.info(f" Output tokens: {self.total_output_tokens:,}") + logger.info( + f" Total tokens: {self.total_input_tokens + self.total_output_tokens:,}", + ) + logger.info("") + logger.info(" Estimated Counts (tiktoken):") + logger.info(f" Input tokens: {self.estimated_input_tokens:,}") + logger.info(f" Output tokens: {self.estimated_output_tokens:,}") + logger.info( + f" Total tokens: {self.estimated_input_tokens + self.estimated_output_tokens:,}", + ) + logger.info("") + if self.total_input_tokens == 0 and self.total_output_tokens == 0: + if ( + self.estimated_input_tokens > 0 + or self.estimated_output_tokens > 0 + ): + logger.warning( + " ⚠️ API returned 0 tokens - using tiktoken " + "estimates as primary metric", + ) + logger.info(f" Analysis Duration: {duration:.2f} seconds") + logger.info( + f" Status: {'SUCCESS' if self.analysis_successful else 'FAILED'}", + ) + logger.info("=" * 80) diff --git a/src/analysis/user.py b/src/v1/analysis/user.py similarity index 100% rename from src/analysis/user.py rename to src/v1/analysis/user.py diff --git a/src/cache/__init__.py b/src/v1/cache/__init__.py similarity index 100% rename from src/cache/__init__.py rename to src/v1/cache/__init__.py diff --git a/src/cache/cache.py b/src/v1/cache/cache.py similarity index 100% rename from src/cache/cache.py rename to src/v1/cache/cache.py diff --git a/src/cache/cache_config.py b/src/v1/cache/cache_config.py similarity index 100% rename from src/cache/cache_config.py rename to src/v1/cache/cache_config.py diff --git a/src/cache/cache_manager.py b/src/v1/cache/cache_manager.py similarity index 72% rename from src/cache/cache_manager.py rename to src/v1/cache/cache_manager.py index daba8b1..d93f278 100644 --- a/src/cache/cache_manager.py +++ b/src/v1/cache/cache_manager.py @@ -4,6 +4,7 @@ import logging import os +import sqlite3 from pathlib import Path from typing import Any, Awaitable, Dict, Optional, Union @@ -37,10 +38,78 @@ class CacheConfig: class CacheManager: """High-level cache management for API endpoints.""" + SAFE_DEBUG_PARAM_KEYS = { + "github_user": ("username", "include_repositories"), + "github_org": ("org_name", "include_repositories"), + "orcid": ("orcid_id",), + "gimie": ("source_url", "output_format"), + } + def __init__(self, cache_db_path: str = "api_cache.db"): self.cache = APICache(cache_db_path) self.config = CacheConfig() + @staticmethod + def _format_debug_value(value: Any) -> str: + if value is None: + return "None" + if isinstance(value, bool): + return str(value).lower() + if isinstance(value, (int, float)): + return str(value) + if isinstance(value, str): + return value if len(value) <= 100 else f"{value[:97]}..." + if isinstance(value, list): + return f"list(len={len(value)})" + if isinstance(value, dict): + return f"dict(keys={len(value)})" + return type(value).__name__ + + def _build_debug_context( + self, + api_type: str, + params: Dict[str, Any], + force_refresh: bool, + ) -> str: + safe_keys = self.SAFE_DEBUG_PARAM_KEYS.get(api_type, ()) + details: list[str] = [] + + for key in safe_keys: + if key in params: + details.append(f"{key}={self._format_debug_value(params[key])}") + + if not details and params: + details.append(f"param_keys={sorted(params.keys())}") + + details.append(f"force_refresh={str(force_refresh).lower()}") + return ", ".join(details) + + def _safe_cache_set( + self, + api_type: str, + params: Dict[str, Any], + fresh_data: Any, + ttl: int, + debug_context: str, + ) -> None: + """Persist to SQLite; log and continue if the DB is locked or data is not serializable.""" + try: + self.cache.set(api_type, params, fresh_data, ttl) + except (OSError, sqlite3.OperationalError, TypeError, ValueError) as e: + logger.warning( + "Cache set skipped for %s (%s): %s", + api_type, + debug_context, + e, + ) + return + logger.info( + "Cached fresh data for %s (ttl_days=%s, %s)", + api_type, + ttl, + debug_context, + ) + # Deprecated: in future versions def get_cached_or_fetch( self, @@ -63,8 +132,14 @@ def get_cached_or_fetch( Returns: Cached or freshly fetched data """ + debug_context = self._build_debug_context(api_type, params, force_refresh) + if not self.config.CACHE_ENABLED: - logger.info(f"Cache disabled, fetching fresh data for {api_type}") + logger.info( + "Cache disabled, fetching fresh data for %s (%s)", + api_type, + debug_context, + ) return fetch_func() # Try to get from cache first @@ -74,13 +149,15 @@ def get_cached_or_fetch( return cached_result # Fetch fresh data - logger.info(f"Fetching fresh data for {api_type}") + logger.info("Fetching fresh data for %s (%s)", api_type, debug_context) fresh_data = fetch_func() # Don't cache coroutines - return them to be awaited by the caller if hasattr(fresh_data, "__await__"): logger.info( - "Fetch function returned a coroutine, returning without caching", + "Fetch function returned a coroutine for %s (%s), returning without caching", + api_type, + debug_context, ) return fresh_data @@ -90,8 +167,7 @@ def get_cached_or_fetch( api_type, self.config.DEFAULT_TTL_DAYS, ) - self.cache.set(api_type, params, fresh_data, ttl) - logger.info(f"Cached fresh data for {api_type} with TTL {ttl} days") + self._safe_cache_set(api_type, params, fresh_data, ttl, debug_context) return fresh_data @@ -118,8 +194,14 @@ async def get_cached_or_fetch_async( Returns: Cached or freshly fetched data (awaited if it was a coroutine) """ + debug_context = self._build_debug_context(api_type, params, force_refresh) + if not self.config.CACHE_ENABLED: - logger.info(f"Cache disabled, fetching fresh data for {api_type}") + logger.info( + "Cache disabled, fetching fresh data for %s (%s)", + api_type, + debug_context, + ) fresh_data = fetch_func() # Handle coroutines even when cache is disabled if hasattr(fresh_data, "__await__"): @@ -133,12 +215,12 @@ async def get_cached_or_fetch_async( return cached_result # Fetch fresh data - logger.info(f"Fetching fresh data for {api_type}") + logger.info("Fetching fresh data for %s (%s)", api_type, debug_context) fresh_data = fetch_func() # Handle coroutines automatically if hasattr(fresh_data, "__await__"): - logger.info(f"Awaiting coroutine for {api_type}") + logger.info("Awaiting coroutine for %s (%s)", api_type, debug_context) fresh_data = await fresh_data # Cache the result if successful @@ -147,8 +229,7 @@ async def get_cached_or_fetch_async( api_type, self.config.DEFAULT_TTL_DAYS, ) - self.cache.set(api_type, params, fresh_data, ttl) - logger.info(f"Cached fresh data for {api_type} with TTL {ttl} days") + self._safe_cache_set(api_type, params, fresh_data, ttl, debug_context) return fresh_data diff --git a/src/cache/cached_parsers.py b/src/v1/cache/cached_parsers.py similarity index 71% rename from src/cache/cached_parsers.py rename to src/v1/cache/cached_parsers.py index 9355d51..ede09fe 100644 --- a/src/cache/cached_parsers.py +++ b/src/v1/cache/cached_parsers.py @@ -22,6 +22,7 @@ def get_user_metadata_cached( self, username: str, force_refresh: bool = False, + include_repositories: bool = True, ) -> GitHubUserMetadata: """ Get user metadata with caching support. @@ -35,12 +36,18 @@ def get_user_metadata_cached( """ def fetch_user_data(): - return self.get_user_metadata(username) + return self.get_user_metadata( + username, + include_repositories=include_repositories, + ) # Get from cache or fetch fresh user_data = self.cache_manager.get_cached_or_fetch( api_type="github_user", - params={"username": username}, + params={ + "username": username, + "include_repositories": include_repositories, + }, fetch_func=fetch_user_data, force_refresh=force_refresh, ) @@ -59,6 +66,7 @@ def get_organization_metadata_cached( self, org_name: str, force_refresh: bool = False, + include_repositories: bool = True, ) -> GitHubOrganizationMetadata: """ Get organization metadata with caching support. @@ -72,12 +80,18 @@ def get_organization_metadata_cached( """ def fetch_org_data(): - return self.get_organization_metadata(org_name) + return self.get_organization_metadata( + org_name, + include_repositories=include_repositories, + ) # Get from cache or fetch fresh org_data = self.cache_manager.get_cached_or_fetch( api_type="github_org", - params={"org_name": org_name}, + params={ + "org_name": org_name, + "include_repositories": include_repositories, + }, fetch_func=fetch_org_data, force_refresh=force_refresh, ) @@ -89,16 +103,26 @@ def fetch_org_data(): def parse_github_user_cached( username: str, force_refresh: bool = False, + include_repositories: bool = True, ) -> GitHubUserMetadata: """Parse GitHub user with caching support.""" parser = CachedGitHubUsersParser() - return parser.get_user_metadata_cached(username, force_refresh) + return parser.get_user_metadata_cached( + username, + force_refresh=force_refresh, + include_repositories=include_repositories, + ) def parse_github_organization_cached( org_name: str, force_refresh: bool = False, + include_repositories: bool = True, ) -> GitHubOrganizationMetadata: """Parse GitHub organization with caching support.""" parser = CachedGitHubOrganizationsParser() - return parser.get_organization_metadata_cached(org_name, force_refresh) + return parser.get_organization_metadata_cached( + org_name, + force_refresh=force_refresh, + include_repositories=include_repositories, + ) diff --git a/src/context/__init__.py b/src/v1/context/__init__.py similarity index 100% rename from src/context/__init__.py rename to src/v1/context/__init__.py diff --git a/src/context/infoscience.py b/src/v1/context/infoscience.py similarity index 100% rename from src/context/infoscience.py rename to src/v1/context/infoscience.py diff --git a/src/v1/context/openalex.py b/src/v1/context/openalex.py new file mode 100644 index 0000000..e69de29 diff --git a/src/context/repository.py b/src/v1/context/repository.py similarity index 100% rename from src/context/repository.py rename to src/v1/context/repository.py diff --git a/src/data_models/__init__.py b/src/v1/data_models/__init__.py similarity index 100% rename from src/data_models/__init__.py rename to src/v1/data_models/__init__.py diff --git a/src/data_models/api.py b/src/v1/data_models/api.py similarity index 100% rename from src/data_models/api.py rename to src/v1/data_models/api.py diff --git a/src/data_models/conversion.py b/src/v1/data_models/conversion.py similarity index 100% rename from src/data_models/conversion.py rename to src/v1/data_models/conversion.py diff --git a/src/data_models/epfl_assessment.py b/src/v1/data_models/epfl_assessment.py similarity index 100% rename from src/data_models/epfl_assessment.py rename to src/v1/data_models/epfl_assessment.py diff --git a/src/data_models/infoscience.py b/src/v1/data_models/infoscience.py similarity index 96% rename from src/data_models/infoscience.py rename to src/v1/data_models/infoscience.py index 7345073..b9fcac3 100644 --- a/src/data_models/infoscience.py +++ b/src/v1/data_models/infoscience.py @@ -24,6 +24,17 @@ class InfosciencePublication(BaseModel): description="List of author names", default_factory=list, ) + author_authorities: List[Optional[str]] = Field( + description=( + "Parallel array to `authors` (same length, same order) holding " + "the DSpace `authority` UUID for each author when available " + "(EPFL-affiliated, authority-controlled). `None` for non-EPFL " + "authors that DSpace stores as bare names. Used by v2 to bind " + "publication authors directly to Infoscience person entities " + "without name-based fuzzy matching." + ), + default_factory=list, + ) abstract: Optional[str] = Field( description="Publication abstract or description", default=None, diff --git a/src/data_models/linked_entities.py b/src/v1/data_models/linked_entities.py similarity index 100% rename from src/data_models/linked_entities.py rename to src/v1/data_models/linked_entities.py diff --git a/src/data_models/models.py b/src/v1/data_models/models.py similarity index 100% rename from src/data_models/models.py rename to src/v1/data_models/models.py diff --git a/src/data_models/organization.py b/src/v1/data_models/organization.py similarity index 96% rename from src/data_models/organization.py rename to src/v1/data_models/organization.py index db60c44..05c5ac0 100644 --- a/src/data_models/organization.py +++ b/src/v1/data_models/organization.py @@ -13,8 +13,9 @@ from pydantic import ( BaseModel, + ConfigDict, Field, - validator, + field_validator, ) from .models import Discipline, Organization, Person @@ -147,18 +148,15 @@ class GitHubOrganizationMetadata(BaseModel): description="Pinned repositories", ) - @validator("email") - def validate_email(cls, v): + @field_validator("email") + @classmethod + def validate_email(cls, v: Optional[str]) -> Optional[str]: """Basic email validation""" if v is not None and v != "" and "@" not in v: raise ValueError("Invalid email format") return v - class Config: - """Pydantic configuration""" - - validate_assignment = True - extra = "forbid" + model_config = ConfigDict(validate_assignment=True, extra="forbid") ####################################################### diff --git a/src/data_models/repository.py b/src/v1/data_models/repository.py similarity index 100% rename from src/data_models/repository.py rename to src/v1/data_models/repository.py diff --git a/src/data_models/user.py b/src/v1/data_models/user.py similarity index 95% rename from src/data_models/user.py rename to src/v1/data_models/user.py index 220115d..8681df0 100644 --- a/src/data_models/user.py +++ b/src/v1/data_models/user.py @@ -14,8 +14,9 @@ from pydantic import ( BaseModel, + ConfigDict, Field, - validator, + field_validator, ) from .models import ( @@ -208,6 +209,11 @@ class GitHubUserMetadata(BaseModel): """Pydantic model to store GitHub user metadata with validation""" login: str = Field(..., description="GitHub username") + account_type: Optional[str] = Field( + None, + description="GitHub account type ('User' or 'Organization'). Used by v2 to skip " + "org_agent fanout when a handle is actually a personal account.", + ) name: Optional[str] = Field(None, description="User's display name") bio: Optional[str] = Field(None, description="User's bio") email: Optional[str] = Field(None, description="User's public email") @@ -246,8 +252,9 @@ class GitHubUserMetadata(BaseModel): description="List of public repositories", ) - @validator("orcid") - def validate_orcid(cls, v): + @field_validator("orcid") + @classmethod + def validate_orcid(cls, v: Optional[str]) -> Optional[str]: """Validate ORCID format and convert ID to URL""" if v is not None: # If it's already a URL, validate and return @@ -265,8 +272,9 @@ def validate_orcid(cls, v): raise ValueError(f"Invalid ORCID format: {v}") return v - @validator("email") - def validate_email(cls, v): + @field_validator("email") + @classmethod + def validate_email(cls, v: Optional[str]) -> Optional[str]: """Basic email validation""" if v is not None: # Allow standard emails @@ -282,11 +290,7 @@ def validate_email(cls, v): return v return v - class Config: - """Pydantic configuration""" - - validate_assignment = True - extra = "forbid" + model_config = ConfigDict(validate_assignment=True, extra="forbid") ############################################################ diff --git a/src/data_models/validation.py b/src/v1/data_models/validation.py similarity index 100% rename from src/data_models/validation.py rename to src/v1/data_models/validation.py diff --git a/src/files/json-ld-context.json b/src/v1/files/json-ld-context.json similarity index 100% rename from src/files/json-ld-context.json rename to src/v1/files/json-ld-context.json diff --git a/src/files/output_file.json b/src/v1/files/output_file.json similarity index 100% rename from src/files/output_file.json rename to src/v1/files/output_file.json diff --git a/src/gimie_utils/__init__.py b/src/v1/gimie_utils/__init__.py similarity index 100% rename from src/gimie_utils/__init__.py rename to src/v1/gimie_utils/__init__.py diff --git a/src/v1/gimie_utils/gimie_methods.py b/src/v1/gimie_utils/gimie_methods.py new file mode 100644 index 0000000..8139424 --- /dev/null +++ b/src/v1/gimie_utils/gimie_methods.py @@ -0,0 +1,205 @@ +""" +GIMIE integration. + +GIMIE 0.7.x mishandles two GitHub edge cases for brand-new / empty repositories: + +- ``GET /repos/{owner}/{repo}/contributors`` returns **204 No Content** with an empty + body when there are no commits; ``send_rest_query`` then calls ``resp.json()`` and + raises ``JSONDecodeError`` (surfacing as HTTP 400 via the API ValueError handler). +- GraphQL ``repository.object(expression: \"HEAD:\")`` is **null** when there is no + tree; ``list_files`` then does ``None[\"entries\"]`` and raises ``TypeError``. + +- **CFF calendar dates:** PyYAML's YAML 1.1 timestamp constructor parses unquoted + ``YYYY-M-D`` tokens on ``date-released`` / ``date-published`` / ``date-last-released``. + Invalid calendars (e.g. ``2025-28-04`` day/month swap, or template junk like + ``2025-13-14`` where neither order is valid) raise ``ValueError``. We rewrite those + lines before parsing: fix obvious month/day swaps when possible, otherwise quote + the token so YAML loads a string (GIMIE does not consume these date fields today). + +We patch those behaviors before importing ``Project`` so all ``Project`` instances +see the fixes. +""" + +from __future__ import annotations + +import calendar +import json +import logging +import re +from http import HTTPStatus +from typing import Any + +import gimie.extractors.github as gimie_github +import requests +from gimie.extractors.github import GithubExtractor +from gimie.parsers.cff import CffParser + +logger = logging.getLogger(__name__) + +_MAX_MONTH = 12 # Gregorian calendar + +_CFF_DATE_KEYS = ( + "date-released", + "date-published", + "date-last-released", +) +_CFF_DATE_LINE = re.compile( + r"^(\s*)([a-zA-Z0-9_-]+)\s*:\s*(.*?)\s*$", + re.MULTILINE, +) + + +def _calendar_date_ok(year: int, month: int, day: int) -> bool: + if not (1 <= month <= _MAX_MONTH) or day < 1: + return False + last = calendar.monthrange(year, month)[1] + return day <= last + + +def _fix_yyyy_mm_dd_triple( + year: int, middle: int, last: int, +) -> tuple[int, int, int] | None: + """If ``Y-m-d`` is invalid but ``Y-d-m`` is valid, treat middle/last as swapped.""" + if _calendar_date_ok(year, middle, last): + return (year, middle, last) + if _calendar_date_ok(year, last, middle): + return (year, last, middle) + return None + + +def _rewrite_cff_known_date_value(value_part: str) -> tuple[str, str] | None: + """Return ``(new_rhs, reason)`` for a CFF date key's value, or None to keep as-is. + + PyYAML only auto-parses *unquoted* ``YYYY-M-D``. Quoting forces a string and avoids + ``construct_yaml_timestamp`` (and GIMIE's CFF parser ignores these keys anyway). + """ + trimmed = value_part.strip() + if not trimmed: + return None + if (trimmed.startswith('"') and trimmed.endswith('"')) or ( + trimmed.startswith("'") and trimmed.endswith("'") + ): + return None + + token = trimmed.strip('"').strip("'") + m = re.fullmatch(r"(\d{4})-(\d{1,2})-(\d{1,2})", token) + if not m: + return None + + y, a, b = int(m.group(1)), int(m.group(2)), int(m.group(3)) + fixed = _fix_yyyy_mm_dd_triple(y, a, b) + if fixed is not None: + fy, fm, fd = fixed + iso = f"{fy:04d}-{fm:02d}-{fd:02d}" + if (fy, fm, fd) != (y, a, b): + return (iso, "normalized swapped day/month") + return None + + escaped = token.replace("\\", "\\\\").replace('"', '\\"') + return (f'"{escaped}"', "quoted invalid calendar (YAML timestamp escape)") + + +def _normalize_cff_calendar_dates(data: bytes) -> bytes: + """Sanitize CFF ``date-*`` fields so PyYAML does not raise on bogus templates.""" + try: + text = data.decode("utf-8") + except UnicodeDecodeError: + return data + + def repl(match: re.Match[str]) -> str: + indent, key, rest = match.group(1), match.group(2), match.group(3) + if key.lower() not in _CFF_DATE_KEYS: + return match.group(0) + value_part, sep, comment = rest.partition("#") + rewritten = _rewrite_cff_known_date_value(value_part) + if rewritten is None: + return match.group(0) + new_rhs, reason = rewritten + logger.info( + "GIMIE CFF: %s %s from %r -> %r", + reason, + key, + value_part.strip(), + new_rhs, + ) + suffix = f" {sep}{comment}" if sep else "" + return f"{indent}{key}: {new_rhs}{suffix}" + + new_text = _CFF_DATE_LINE.sub(repl, text) + return new_text.encode("utf-8") + + +_original_cff_parse = CffParser.parse + + +def _patched_cff_parse(self: CffParser, data: bytes) -> Any: + return _original_cff_parse(self, _normalize_cff_calendar_dates(data)) + + +CffParser.parse = _patched_cff_parse # type: ignore[method-assign] + +_original_send_rest_query = gimie_github.send_rest_query +_original_list_files = GithubExtractor.list_files + + +def _patched_send_rest_query( + api: str, + query: str, + headers: dict[str, str], +) -> list[dict[str, Any]] | dict[str, Any]: + """Treat GitHub 204 (empty body) as an empty JSON list (e.g. no contributors).""" + resp = requests.get( + url=f"{api}/{query}", + headers=headers, + timeout=30, + ) + if resp.status_code == HTTPStatus.NO_CONTENT: + return [] + return _original_send_rest_query(api, query, headers) + + +def _patched_list_files(self: GithubExtractor): + """Skip file listing when the repo has no ``HEAD`` tree (empty GitHub repo).""" + repo = self._repo_data + obj = repo.get("object") if isinstance(repo, dict) else None + if obj is None: + logger.info( + "GIMIE: repository has no HEAD tree (empty repo); skipping file parsers", + ) + return [] + return _original_list_files(self) + + +gimie_github.send_rest_query = _patched_send_rest_query +GithubExtractor.list_files = _patched_list_files + +from gimie.project import Project # noqa: E402 # import after monkeypatches + + +def extract_gimie(full_path: str, serialization_format: str = "json-ld"): + """ + Extracts the GIMIE project from the given URL. + + Args: + full_path (str): The full path to the URL. + serialization_format (str): Serialize graph as ``json-ld`` (default) or ``ttl``. + + Returns: + Project: The GIMIE project object. + """ + logger.info(f"Extracting GIMIE metadata for: {full_path}") + + proj = Project(full_path) + + # To retrieve the rdflib.Graph object + g = proj.extract() + + if serialization_format == "json-ld": + # To retrieve the graph in JSON-LD format + output = json.loads(g.serialize(format="json-ld")) + else: + output = g.serialize(format=serialization_format) + + if output is None: + return None + return output diff --git a/src/llm/__init__.py b/src/v1/llm/__init__.py similarity index 100% rename from src/llm/__init__.py rename to src/v1/llm/__init__.py diff --git a/src/llm/model_config.py b/src/v1/llm/model_config.py similarity index 94% rename from src/llm/model_config.py rename to src/v1/llm/model_config.py index 026b254..c13eed7 100644 --- a/src/llm/model_config.py +++ b/src/v1/llm/model_config.py @@ -18,7 +18,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 3, "temperature": 0.2, @@ -55,7 +55,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 2, "temperature": 0.1, @@ -83,7 +83,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 2, "temperature": 0.1, @@ -111,7 +111,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 3, "temperature": 0.1, @@ -140,7 +140,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 2, "temperature": 0.1, @@ -169,7 +169,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 2, "temperature": 0.1, @@ -198,7 +198,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 3, "temperature": 0.2, @@ -211,7 +211,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 3, "temperature": 0.2, @@ -240,7 +240,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 2, "temperature": 0.1, @@ -269,7 +269,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 2, "temperature": 0.1, @@ -298,7 +298,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 2, "temperature": 0.1, @@ -311,7 +311,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 2, "temperature": 0.1, @@ -324,7 +324,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 3, "temperature": 0.2, @@ -337,7 +337,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 3, "temperature": 0.2, @@ -366,7 +366,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 2, "temperature": 0.1, @@ -395,7 +395,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 3, "temperature": 0.2, @@ -408,7 +408,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 3, "temperature": 0.2, @@ -437,7 +437,7 @@ { "provider": "openai-compatible", "model": "openai/gpt-oss-120b", - "base_url": "https://inference.rcp.epfl.ch/v1", + "base_url": "https://inference-rcp.epfl.ch/v1", "api_key_env": "RCP_TOKEN", "max_retries": 2, "temperature": 0.1, diff --git a/src/main.py b/src/v1/main.py similarity index 100% rename from src/main.py rename to src/v1/main.py diff --git a/src/parsers/__init__.py b/src/v1/parsers/__init__.py similarity index 100% rename from src/parsers/__init__.py rename to src/v1/parsers/__init__.py diff --git a/src/parsers/orgs_parser.py b/src/v1/parsers/orgs_parser.py similarity index 97% rename from src/parsers/orgs_parser.py rename to src/v1/parsers/orgs_parser.py index a337fc9..413e1d3 100644 --- a/src/parsers/orgs_parser.py +++ b/src/v1/parsers/orgs_parser.py @@ -34,12 +34,17 @@ def __init__(self): if self.github_token: self.headers["Authorization"] = f"token {self.github_token}" - def get_organization_metadata(self, org_name: str) -> GitHubOrganizationMetadata: + def get_organization_metadata( + self, + org_name: str, + include_repositories: bool = True, + ) -> GitHubOrganizationMetadata: """ Retrieve comprehensive organization metadata from GitHub Args: org_name: GitHub organization name + include_repositories: Include repository list from /orgs/{org}/repos Returns: GitHubOrganizationMetadata object with all available organization information @@ -58,7 +63,11 @@ def get_organization_metadata(self, org_name: str) -> GitHubOrganizationMetadata public_members = self._get_organization_public_members(org_name) # Get repositories (limited to first 100 for performance) - repositories = self._get_organization_repositories(org_name) + repositories = ( + self._get_organization_repositories(org_name) + if include_repositories + else [] + ) # Get teams (if accessible) teams = self._get_organization_teams(org_name) diff --git a/src/parsers/users_parser.py b/src/v1/parsers/users_parser.py similarity index 99% rename from src/parsers/users_parser.py rename to src/v1/parsers/users_parser.py index 35dd5bd..4b5b3a8 100644 --- a/src/parsers/users_parser.py +++ b/src/v1/parsers/users_parser.py @@ -53,12 +53,17 @@ def __init__(self): if self.github_token: self.headers["Authorization"] = f"token {self.github_token}" - def get_user_metadata(self, username: str) -> GitHubUserMetadata: + def get_user_metadata( + self, + username: str, + include_repositories: bool = True, + ) -> GitHubUserMetadata: """ Retrieve comprehensive user metadata from GitHub Args: username: GitHub username + include_repositories: Include repository list from /users/{user}/repos Returns: GitHubUserMetadata object with all available user information @@ -88,11 +93,12 @@ def get_user_metadata(self, username: str) -> GitHubUserMetadata: orcid_activities = self._scrape_orcid_activities(orcid) # Get repositories - repositories = self._get_user_repositories(username) + repositories = self._get_user_repositories(username) if include_repositories else [] # Combine all data and create Pydantic model user_data = { "login": rest_data["login"], + "account_type": rest_data.get("type"), "name": rest_data.get("name"), "bio": rest_data.get("bio"), "email": rest_data.get("email"), diff --git a/src/v1/utils/__init__.py b/src/v1/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/utils/enhanced_logging.py b/src/v1/utils/enhanced_logging.py similarity index 100% rename from src/utils/enhanced_logging.py rename to src/v1/utils/enhanced_logging.py diff --git a/src/utils/github_dependency.py b/src/v1/utils/github_dependency.py similarity index 100% rename from src/utils/github_dependency.py rename to src/v1/utils/github_dependency.py diff --git a/src/utils/logging_config.py b/src/v1/utils/logging_config.py similarity index 100% rename from src/utils/logging_config.py rename to src/v1/utils/logging_config.py diff --git a/src/utils/token_counter.py b/src/v1/utils/token_counter.py similarity index 100% rename from src/utils/token_counter.py rename to src/v1/utils/token_counter.py diff --git a/src/utils/url_validation.py b/src/v1/utils/url_validation.py similarity index 100% rename from src/utils/url_validation.py rename to src/v1/utils/url_validation.py diff --git a/src/utils/utils.py b/src/v1/utils/utils.py similarity index 100% rename from src/utils/utils.py rename to src/v1/utils/utils.py diff --git a/src/validation/__init__.py b/src/v1/validation/__init__.py similarity index 100% rename from src/validation/__init__.py rename to src/v1/validation/__init__.py diff --git a/src/validation/verification.py b/src/v1/validation/verification.py similarity index 100% rename from src/validation/verification.py rename to src/v1/validation/verification.py diff --git a/src/v2/__init__.py b/src/v2/__init__.py new file mode 100644 index 0000000..4870267 --- /dev/null +++ b/src/v2/__init__.py @@ -0,0 +1 @@ +"""V2 extraction package.""" diff --git a/src/v2/agents/__init__.py b/src/v2/agents/__init__.py new file mode 100644 index 0000000..f43a0f0 --- /dev/null +++ b/src/v2/agents/__init__.py @@ -0,0 +1,60 @@ +"""Agent wrappers for v2 extraction pipeline.""" + +from src.v2.agents.rule_based.article_agent import ArticleAgentV2 +from src.v2.agents.contracts import RuntimeAgent +from src.v2.agents.rule_based.contribution_agent import ContributionAgentV2 +from src.v2.agents.llm import ( + LLMArticleAgentV2, + LLMContributionAgentV2, + LLMContextSummaryAgentV2, + LLMCriticAgentV2, + LLMDedupAgentV2, + LLMLinkVeracityAgentV2, + LLMMembershipAgentV2, + LLMOrganizationAgentV2, + LLMPersonAgentV2, + LLMRepositoryAgentV2, +) +from src.v2.agents.rule_based.membership_agent import MembershipAgentV2 +from src.v2.agents.models import ( + AgentResult, + ProviderSet, + TypedEntityBuckets, + infer_entity_bucket, + normalize_entity_bucket_key, +) +from src.v2.agents.rule_based.organization_agent import OrganizationAgentV2 +from src.v2.agents.rule_based.person_agent import PersonAgentV2 +from src.v2.agents.registry import AgentRuntimeRegistry +from src.v2.agents.rule_based.repository_agent import RepositoryAgentV2 +from src.v2.agents.retry import with_retry +from src.v2.agents.runtime import AgentRuntime, parse_agent_runtime + +__all__ = [ + "AgentResult", + "AgentRuntime", + "AgentRuntimeRegistry", + "ArticleAgentV2", + "ContributionAgentV2", + "LLMArticleAgentV2", + "LLMContributionAgentV2", + "LLMContextSummaryAgentV2", + "LLMCriticAgentV2", + "LLMDedupAgentV2", + "LLMLinkVeracityAgentV2", + "LLMMembershipAgentV2", + "LLMOrganizationAgentV2", + "LLMPersonAgentV2", + "LLMRepositoryAgentV2", + "MembershipAgentV2", + "OrganizationAgentV2", + "PersonAgentV2", + "ProviderSet", + "RepositoryAgentV2", + "RuntimeAgent", + "TypedEntityBuckets", + "infer_entity_bucket", + "normalize_entity_bucket_key", + "parse_agent_runtime", + "with_retry", +] diff --git a/src/v2/agents/contracts.py b/src/v2/agents/contracts.py new file mode 100644 index 0000000..e527cf0 --- /dev/null +++ b/src/v2/agents/contracts.py @@ -0,0 +1,19 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Protocol + +if TYPE_CHECKING: + from src.v2.agents.models import AgentResult, ProviderSet + + +class RuntimeAgent(Protocol): + """Protocol for runtime-selectable v2 agent implementations.""" + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + """Execute the agent using stage context and provider bundle.""" + + ... diff --git a/src/v2/agents/llm/README.md b/src/v2/agents/llm/README.md new file mode 100644 index 0000000..95bf0a2 --- /dev/null +++ b/src/v2/agents/llm/README.md @@ -0,0 +1,571 @@ +# LLM Agents — Developer Guide + +This guide explains the architecture of the `src/v2/agents/llm/` sub-package and walks through the exact steps required to add a new LLM-backed agent. Use the existing repository/person/organization/article/membership/contribution agents as reference implementations. + +--- + +## Directory layout + +``` +src/v2/agents/llm/ +├── __init__.py # Public exports for all LLM agents +├── _loader.py # load_prompt() — importlib.resources wrapper +├── prompt_context.py # Optional runtime prompt append blocks +├── agent_tools/ +│ ├── __init__.py +│ ├── disciplines.py # Static tool: list_disciplines_tool +│ ├── duckduckgo_search.py # Factory: make_duckduckgo_search_tool() +│ ├── email_hash.py # Static tool: hash_user_email_tool +│ ├── github_organization.py # Factory: make_github_organization_metadata_tool(provider) +│ ├── infoscience_orgunit.py # Factory: make_infoscience_orgunit_tool(provider) +│ ├── infoscience_search.py # Factory: make_infoscience_search_tool(provider) +│ ├── organization_identity.py # Factory: make_organization_identity_search_tool(ror, infoscience) +│ ├── orcid_person.py # Factory: make_orcid_person_tool(provider) +│ ├── query_dependencies.py # Factory: make_query_dependencies_tool(github_provider) +│ ├── query_orcid.py # Factory: make_query_orcid_tool(provider) +│ ├── repository_corpus_grep.py # Factory: make_repository_corpus_grep_tool(corpus) +│ ├── ror_organization.py # Factory: make_ror_organization_search_tool(provider) +│ ├── selenium_fetch.py # Static tool: fetch_link_content_via_selenium_tool +│ └── uuid.py # Static tools: generate_uuid_v4_tool, generate_uuid_v4_batch_tool +├── context_summary/ # LLMContextSummaryAgentV2 (+ prompts) +├── repository/ # LLMRepositoryAgentV2 (+ prompts) +├── person/ # LLMPersonAgentV2 (+ prompts) +├── organization/ # LLMOrganizationAgentV2 (+ prompts) +├── article/ # LLMArticleAgentV2 (+ prompts) +├── membership/ # LLMMembershipAgentV2 (+ prompts) +├── contribution/ # LLMContributionAgentV2 (+ prompts) +└── link_veracity/ # LLMLinkVeracityAgentV2 (+ prompts) +``` + +--- + +## Core concepts + +### `V2LLMRuntime` (`src/v2/agents/llm/runtime.py`) + +All LLM agents delegate to `V2LLMRuntime.run_json_prompt()`. This is the single call surface to pydantic-ai. + +```python +result: LLMRuntimeResult = await self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, # str — loaded from prompts/system_prompt.md + user_prompt=user_prompt, # str — filled template from prompts/user_prompt.md + output_type=MyOutputShape, # Pydantic BaseModel class (NOT a union RootModel) + tools=[tool1, tool2], # list[pydantic_ai.Tool] — may be empty +) +``` + +`LLMRuntimeResult` fields: +- `payload: dict[str, Any]` — serialized output (`model_dump(by_alias=True)`) +- `model: str`, `provider: str` — from model config +- `tokens_prompt: int | None`, `tokens_completion: int | None` +- `requests: int | None`, `tool_calls: int | None` — extracted from `result.usage()` + +**Important**: `output_type` must be a flat `BaseModel` with `type: "object"` JSON schema. pydantic-ai cannot use `RootModel` union types (their schema is `{"anyOf": [...]}`) — create a flat mirror model if needed (see `LLMPersonOutputShape` in `person/agent.py`). + +### Two-pass validation + +Every agent validates the LLM output twice: + +| Pass | Tool | On failure | +|---|---|---| +| 1st (permissive) | pydantic-ai enforces `output_type` during generation | pydantic-ai retries (configurable `max_retries`) then raises `LLMRuntimeError` | +| 2nd (strict) | `_strict_validate()` calls `XxxModel.model_validate(payload)` | Appends warnings to `AgentResult.warnings`, never raises | + +The strict model comes from `src/v2/schema/models/strict.py` (TTL-shape-aligned). + +### `RuntimeAgent` protocol (`src/v2/agents/contracts.py`) + +Any class with this signature satisfies `RuntimeAgent`: + +```python +async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: ... +``` + +No inheritance needed. This is structural duck typing. + +### Prompt loading + +Prompts live as Markdown files inside a `prompts/` sub-package. Load them at module import time: + +```python +from src.v2.agents.llm._loader import load_prompt + +_PROMPTS_PACKAGE = "src.v2.agents.llm.myagent.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") +``` + +The `prompts/` directory must contain an empty `__init__.py` so `importlib.resources` can resolve it as a package. + +--- + +## Tools + +Tools are `pydantic_ai.Tool` instances passed to `run_json_prompt`. They are invoked by the LLM during generation (agentic tool-use). + +### Static tool (no provider dependency) + +```python +# agent_tools/my_static_tool.py +from pydantic_ai import Tool + +def _my_function() -> list[str]: + """Return a list of valid options.""" + return ["option_a", "option_b"] + +my_static_tool = Tool( + _my_function, + name="my_tool_name", + description="Describe when and why the LLM should call this.", +) +``` + +Import and use directly: `tools=[my_static_tool]`. + +### Factory tool (captures a provider at runtime) + +Use this pattern when the tool needs a live provider (HTTP calls, etc.): + +```python +# agent_tools/my_provider_tool.py +from __future__ import annotations +import logging +from typing import TYPE_CHECKING, Any +from pydantic_ai import Tool + +if TYPE_CHECKING: + from src.v2.ingest.providers.base import MyProvider + +logger = logging.getLogger(__name__) + +def make_my_provider_tool(provider: MyProvider) -> Tool: + """Factory — captures provider in closure, returns pydantic-ai Tool.""" + + def call_my_provider(query: str) -> dict[str, Any]: + """Docstring shown to LLM. Describe arguments and return shape.""" + logger.info("tool call: call_my_provider — query=%r", query) + return provider.search(query) + + return Tool( + call_my_provider, + name="call_my_provider", + description="When and why the LLM should call this tool.", + ) +``` + +Build the tool at `run()` time and include it only when the provider is present: + +```python +tools = [] +if providers.my_provider is not None: + tools.append(make_my_provider_tool(providers.my_provider)) +``` + +**Existing tools:** + +| File | Export | Type | Used by | +|---|---|---|---| +| `agent_tools/disciplines.py` | `list_disciplines_tool` | Static | `LLMRepositoryAgentV2` | +| `agent_tools/duckduckgo_search.py` | `make_duckduckgo_search_tool()` | Factory | `LLMContextSummaryAgentV2`, `LLMCriticAgentV2` | +| `agent_tools/email_hash.py` | `hash_user_email_tool` | Static | `LLMPersonAgentV2` | +| `agent_tools/github_organization.py` | `make_github_organization_metadata_tool(provider)` | Factory | `LLMOrganizationAgentV2` | +| `agent_tools/repository_corpus_grep.py` | `make_repository_corpus_grep_tool(corpus)` | Factory | `LLMContextSummaryAgentV2` | +| `agent_tools/organization_identity.py` | `make_organization_identity_search_tool(ror_provider, infoscience_provider)` | Factory | `LLMOrganizationAgentV2` | +| `agent_tools/ror_organization.py` | `make_ror_organization_search_tool(provider)` | Factory | `LLMOrganizationAgentV2` | +| `agent_tools/infoscience_orgunit.py` | `make_infoscience_orgunit_tool(provider)` | Factory | `LLMOrganizationAgentV2` | +| `agent_tools/infoscience_search.py` | `make_infoscience_search_tool(provider)` | Factory | `LLMPersonAgentV2` | +| `agent_tools/orcid_person.py` | `make_orcid_person_tool(provider)` | Factory | `LLMPersonAgentV2`, `LLMMembershipAgentV2` | +| `agent_tools/query_dependencies.py` | `make_query_dependencies_tool(github_provider)` | Factory | `LLMRepositoryAgentV2` | +| `agent_tools/query_orcid.py` | `make_query_orcid_tool(provider)` | Factory | `LLMPersonAgentV2`, `LLMMembershipAgentV2` | +| `agent_tools/uuid.py` | `generate_uuid_v4_tool`, `generate_uuid_v4_batch_tool` | Static | `LLMArticleAgentV2`, `LLMMembershipAgentV2`, `LLMContributionAgentV2` | +| `agent_tools/selenium_fetch.py` | `fetch_link_content_via_selenium_tool` | Static | `LLMRepositoryAgentV2`, `LLMPersonAgentV2`, `LLMOrganizationAgentV2`, `LLMArticleAgentV2`, `LLMMembershipAgentV2`, `LLMContributionAgentV2`, `LLMLinkVeracityAgentV2` | + +--- + +## Adding a new LLM agent — step-by-step + +This section assumes you are adding `LLMOrganizationAgentV2`. Substitute the entity name throughout. + +### Step 1 — Create the agent package + +``` +src/v2/agents/llm/organization/ +├── __init__.py +├── agent.py +└── prompts/ + ├── __init__.py ← empty, required + ├── system_prompt.md + └── user_prompt.md +``` + +### Step 2 — Write the `output_type` shape + +Create a flat `BaseModel` that mirrors the fields the LLM must produce. Use `alias=` for ontology field names (e.g. `schema:name`). Set `model_config = ConfigDict(extra="ignore")` so unexpected LLM fields are silently dropped. + +```python +from pydantic import BaseModel, ConfigDict, Field + +class LLMOrganizationOutputShape(BaseModel): + model_config = ConfigDict(extra="ignore") + + id: str = Field(..., description="Resolved IRI for this organization.") + type: str = Field(..., description="Must be 'schema:Organization'.") + shacl: str = Field(..., description="Must be 'pulse:OrganizationShape'.") + schema_name: str = Field(..., alias="schema:name") + # … all required fields from the agent schema … +``` + +### Step 3 — Write the agent class + +Minimum viable structure (copy, adapt): + +```python +# src/v2/agents/llm/organization/agent.py +from __future__ import annotations + +import json +import logging +from copy import deepcopy +from typing import Any + +from pydantic import ValidationError + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm.prompt_context import append_runtime_prompt_context +from src.v2.agents.models import AgentResult, ProviderSet +from src.v2.schema.models.strict import OrganizationModel # strict model +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime + +logger = logging.getLogger(__name__) + +_PROMPTS_PACKAGE = "src.v2.agents.llm.organization.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") + + +def _strict_validate(payload: dict[str, Any]) -> list[str]: + warnings: list[str] = [] + try: + OrganizationModel.model_validate(payload) + except ValidationError as exc: + for error in exc.errors(): + field = " -> ".join(str(loc) for loc in error["loc"]) + warnings.append(f"Strict schema warning at {field}: {error['msg']}") + return warnings + + +class LLMOrganizationAgentV2: + def __init__(self, *, llm_runtime: V2LLMRuntime | None = None) -> None: + self._llm_runtime = llm_runtime or V2LLMRuntime() + + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + # 1. Extract required context fields; raise ValueError on missing identity. + # 2. Build llm_input dict. + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + user_prompt = append_runtime_prompt_context(user_prompt, context) + + # 3. Build tools (factory closures capturing providers). + tools = [] + # tools.append(make_ror_tool(providers.ror)) if providers.ror is not None + + # 4. LLM call. + logger.info("calling LLM (%d tool(s))", len(tools)) + try: + llm_result = await self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=LLMOrganizationOutputShape, + tools=tools, + ) + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + payload = {k: v for k, v in llm_result.payload.items() if v is not None} + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + payload.update(overrides) + + raw_output = deepcopy(payload) + validation_warnings = _strict_validate(payload) + + return AgentResult( + data=payload, + warnings=validation_warnings, + raw_output=raw_output, + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={"agent_runtime": "llm", "derivation": {}}, + ) +``` + +Key conventions: +- Strip `None` top-level optional fields before strict validation (`{k: v for k, v in ... if v is not None}`). +- Apply `agent_overrides` from context after the LLM call, before validation. +- `del providers` only if the agent genuinely needs no providers. If you build tools from providers (as `LLMRepositoryAgentV2` does for `query_dependencies`, `LLMPersonAgentV2` does for ORCID/Infoscience tools, etc.), keep the reference. +- For long-running person extraction, wrap runtime calls with an explicit timeout (see `LLMPersonAgentV2.llm_call_timeout_seconds`). + +### Step 4 — Write the prompts + +**`system_prompt.md`** — explain: +- The target entity shape and required fields. +- Identifier hierarchy / `idSource` logic. +- Which tools are available and when to call them. +- Output format constraints. + +**`user_prompt.md`** — minimal, single placeholder: +``` +Build an Organization payload using the runtime context: + +{context_json} +``` + +### Step 5 — Export from sub-package `__init__.py` + +```python +# src/v2/agents/llm/organization/__init__.py +from src.v2.agents.llm.organization.agent import LLMOrganizationAgentV2 + +__all__ = ["LLMOrganizationAgentV2"] +``` + +### Step 6 — Export from `src/v2/agents/llm/__init__.py` + +```python +from src.v2.agents.llm.organization import LLMOrganizationAgentV2 +from src.v2.agents.llm.person import LLMPersonAgentV2 +from src.v2.agents.llm.repository import LLMRepositoryAgentV2 + +__all__ = ["LLMOrganizationAgentV2", "LLMPersonAgentV2", "LLMRepositoryAgentV2"] +``` + +### Step 7 — Export from `src/v2/agents/__init__.py` + +```python +from src.v2.agents.llm import LLMOrganizationAgentV2, LLMPersonAgentV2, LLMRepositoryAgentV2 +# … add to __all__ … +``` + +--- + +## Wiring into the registry (`src/v2/agents/registry.py`) + +`AgentRuntimeRegistry` routes LLM agents by `(runtime, stage_key)`. Add a stage constant and a routing branch: + +```python +from src.v2.agents.llm import LLMOrganizationAgentV2 # add import + +STAGE_ORG_AGENT = "org_agent" # already defined in orchestrator; reuse or keep local + +class AgentRuntimeRegistry: + def __init__( + self, + *, + rule_based_runners=None, + llm_repository_agent=None, + llm_person_agent=None, + llm_organization_agent=None, # new + ) -> None: + # … existing … + self._llm_organization_agent = llm_organization_agent or LLMOrganizationAgentV2() + + def resolve_runner(self, *, stage_key, runtime, detected_type): + # … existing repo + person blocks … + + if runtime == AgentRuntime.LLM and stage_key == STAGE_ORG_AGENT: + return self._llm_organization_agent.run + + # fallback to rule-based + runner = self._rule_based_runners.get(stage_key) + if runner is None: + raise ValueError(f"No runner registered for stage '{stage_key}'") + return runner +``` + +--- + +## Wiring into the orchestrator (`src/v2/pipeline/orchestrator.py`) + +`PipelineOrchestrator` constructs `AgentRuntimeRegistry`. Add the new agent parameter: + +```python +from src.v2.agents import LLMOrganizationAgentV2 # add to existing import block + +class PipelineOrchestrator: + def __init__( + self, + *, + # … existing params … + llm_organization_agent: RuntimeAgent | None = None, + ) -> None: + self._llm_organization_agent = llm_organization_agent or LLMOrganizationAgentV2() + + self._agent_registry = agent_registry or AgentRuntimeRegistry( + rule_based_runners=self._rule_based_runners, + llm_repository_agent=self._llm_repository_agent, + llm_person_agent=self._llm_person_agent, + llm_organization_agent=self._llm_organization_agent, # new + ) +``` + +If the agent maps to a stage already in `PLAN_BY_TYPE` (e.g. `STAGE_ORG_AGENT`), no further orchestrator changes are needed — the existing plan routing will pick up the new LLM runner automatically when `agent_runtime=llm`. + +If you are adding a **brand-new pipeline stage**, also add the stage name to the relevant `PLAN_BY_TYPE` entries. + +--- + +## Existing rule-based agents (for context) + +The rule-based agents (`PersonAgentV2`, `OrganizationAgentV2`, etc.) live in `src/v2/agents/` (not inside `llm/`). They satisfy the same `RuntimeAgent` protocol and are the default when `agent_runtime=rule_based`. The orchestrator's `_rule_based_runners` dict maps stage keys to their `.run` methods. + +LLM agents are selected by passing `agent_runtime=llm` in the pipeline context (or as a query parameter on the API). The `AgentRuntimeRegistry` routes accordingly. + +--- + +## Writing tests + +Create `tests/v2/test_llm_{entity}_agent.py`. The test module for `LLMPersonAgentV2` (`tests/v2/test_llm_person_agent.py`) is the canonical reference. + +### Fake runtime pattern + +Inject a fake `_llm_runtime` to avoid real LLM calls: + +```python +class _FakeLLMRuntime: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + + async def run_json_prompt(self, *, system_prompt, user_prompt, output_type=None, tools=None): + assert system_prompt + assert user_prompt + return LLMRuntimeResult( + payload=dict(self._payload), + model="openai/gpt-test", + provider="openai", + tokens_prompt=13, + tokens_completion=29, + ) + +agent = LLMOrganizationAgentV2(llm_runtime=_FakeLLMRuntime(_valid_org_payload())) +result = await agent.run(context, providers) +``` + +### What to test + +| Test | Asserts | +|---|---| +| `test_validates_payload_and_exposes_model_metadata` | jsonschema validates against agent schema; `model`/`provider`/token fields populated; `stats["agent_runtime"] == "llm"` | +| `test_propagates_llm_runtime_error` | `LLMRuntimeError` from raising fake runtime bubbles up unchanged | +| `test_records_strict_schema_warnings` | Invalid payload field → `result.warnings` non-empty | +| `test_builds_tools_from_providers` | Capturing fake records `tools` arg; expected tool names present when providers are set | +| `test_no_tools_without_providers` | Empty tools list when provider-dependent tools have no provider | +| `test_works_with_minimal_context` | Accepts context with only the minimum required identifier | + +### Validating against agent schema + +```python +import jsonschema +from tests.v2.conftest import load_schema # or load directly + +def test_validates(load_schema, result): + schema = load_schema("agent", "organization") # tests/v2/fixtures/schema/agent/organization.schema.json + jsonschema.validate(result.data, schema) +``` + +### Integration test (real LLM) + +Gate behind a credentials check: + +```python +_HAS_LLM_CREDENTIALS = bool( + os.getenv("RCP_TOKEN") or os.getenv("OPENAI_API_KEY") or os.getenv("OPENROUTER_API_KEY") +) + +@pytest.mark.llm_integration +@pytest.mark.skipif(not _HAS_LLM_CREDENTIALS, reason="No LLM credentials") +async def test_real_provider_call(): + agent = LLMOrganizationAgentV2() + result = await agent.run({"name": "EPFL"}, _providers_full()) + assert result.data.get("schema:name") +``` + +--- + +## Cross-cutting concerns + +### Schemas + +LLM output is validated against two schema layers. When your agent introduces a new entity type, you must update schemas in **all three** locations (see `AGENTS.md` for the triplication rule): + +1. `src/v2/schema/json/agent/{entity}.schema.json` — permissive, LLM I/O +2. `dev/ontology-v2-json-response/a-001/json-schema/agent/pulse_{Entity}Shape.schema.json` — promoted +3. `tests/v2/fixtures/schema/agent/{entity}.schema.json` — test fixture + +Then regenerate Pydantic models: +```bash +just v2-models-generate +``` + +The generated model classes land in `src/v2/schema/models/agent.py` (permissive) and `src/v2/schema/models/strict.py` (strict). + +### `generate_uuid()` + +Use `src.v2.agents.models.generate_uuid()` (UUIDv4) for any stable person/entity UUID generated inside an agent. Do not use deterministic UUIDv5 emitters for agent outputs. + +### `agent_overrides` + +Agents should apply `context.get("agent_overrides")` after the LLM call, before strict validation. This lets callers inject field values for testing or reconciliation without changing the agent. + +### Runtime Prompt Context Append Blocks + +All current v2 LLM agents (including class agents and `LLMLinkVeracityAgentV2`) support optional prompt append sections via `append_runtime_prompt_context(...)`: + +- `upstream_stage_outputs_json` (string): appended under `## Upstream Stage Outputs (JSON)`. +- `user_prompt_appendix` (string): appended under `## Additional Context (verbatim text)`. + +These values are treated as raw strings and are not parsed by the helper. + +### Concurrency + +`PipelineOrchestrator` limits fanout via `max_concurrent_agents` (default 3). A fresh `asyncio.Semaphore` wraps each item inside `_execute_stage`. The standalone script `scripts/v2/run_llm_repo_persons_and_orgs.py` also manages fanout concurrency directly and optionally runs a final independent link-veracity pass (`--verify-links`). + +Prompt propagation in orchestrated runs is configurable: + +- `include_upstream_stage_outputs_in_prompt` (bool): serializes accumulated `pipeline_outputs` and forwards them as `upstream_stage_outputs_json` to each child stage context. +- `user_prompt_appendix` (str): forwards a verbatim text block to each child context so agents can append pre-concatenated multi-file text directly to user prompts. + +### Logging + +Follow the pattern established in the existing agents: + +```python +import logging +logger = logging.getLogger(__name__) + +# At entry points: +logger.info("%s — starting", identifier) +# Before external calls: +logger.info("%s — fetching external resource", identifier) +# After LLM call: +logger.info("%s — LLM done (prompt=%s tokens, completion=%s tokens)", identifier, ...) +``` + +--- + +## Validation checklist before merging + +```bash +# Unit tests for the new agent +just test-file tests/v2/test_llm_{entity}_agent.py + +# Full v2 suite — no regressions +PYTHONPATH=. .venv/bin/pytest tests/v2/ -q --tb=line + +# Lint and type checks +just lint +just type-check +``` diff --git a/src/v2/agents/llm/__init__.py b/src/v2/agents/llm/__init__.py new file mode 100644 index 0000000..913c0af --- /dev/null +++ b/src/v2/agents/llm/__init__.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +from src.v2.agents.llm.article import LLMArticleAgentV2 +from src.v2.agents.llm.contribution import LLMContributionAgentV2 +from src.v2.agents.llm.context_summary import LLMContextSummaryAgentV2 +from src.v2.agents.llm.critic import LLMCriticAgentV2 +from src.v2.agents.llm.dedup import LLMDedupAgentV2 +from src.v2.agents.llm.link_veracity import LLMLinkVeracityAgentV2 +from src.v2.agents.llm.membership import LLMMembershipAgentV2 +from src.v2.agents.llm.organization import LLMOrganizationAgentV2 +from src.v2.agents.llm.person import LLMPersonAgentV2 +from src.v2.agents.llm.repository import LLMRepositoryAgentV2 + +__all__ = [ + "LLMArticleAgentV2", + "LLMContributionAgentV2", + "LLMContextSummaryAgentV2", + "LLMCriticAgentV2", + "LLMDedupAgentV2", + "LLMLinkVeracityAgentV2", + "LLMMembershipAgentV2", + "LLMOrganizationAgentV2", + "LLMPersonAgentV2", + "LLMRepositoryAgentV2", +] diff --git a/src/v2/agents/llm/_loader.py b/src/v2/agents/llm/_loader.py new file mode 100644 index 0000000..d11b002 --- /dev/null +++ b/src/v2/agents/llm/_loader.py @@ -0,0 +1,9 @@ +from __future__ import annotations + +from importlib.resources import files + + +def load_prompt(package: str, filename: str) -> str: + """Load a prompt template from a package data file.""" + + return files(package).joinpath(filename).read_text(encoding="utf-8") diff --git a/src/v2/agents/llm/_payload_helpers.py b/src/v2/agents/llm/_payload_helpers.py new file mode 100644 index 0000000..98b5d7f --- /dev/null +++ b/src/v2/agents/llm/_payload_helpers.py @@ -0,0 +1,32 @@ +"""Tiny shared helpers for agent post-LLM payload normalisation.""" +from __future__ import annotations + +from typing import Any + + +def force_server_uuid(payload: dict[str, Any], uuid_value: str) -> None: + """Overwrite the entity's `identifiers.uuid` with the server-generated + value. + + Agents pre-generate a real UUIDv4 and inject it into the LLM input as + `uuid`. The LLM is told to copy it through verbatim, but in practice it + sometimes emits a placeholder (e.g. ``a1b2c3d4-...``) that fails strict + validation. Calling this after the LLM returns guarantees the entity + carries the server-side uuid regardless of what the LLM produced. + + Writes ONLY to `identifiers.uuid` — the strict schemas have + `additionalProperties: false` and reject a top-level `uuid` field. + + No-op when `payload` is not a dict. + """ + if not isinstance(payload, dict): + return + payload.pop("uuid", None) + identifiers = payload.get("identifiers") + if isinstance(identifiers, dict): + identifiers["uuid"] = uuid_value + else: + payload["identifiers"] = {"uuid": uuid_value} + + +__all__ = ["force_server_uuid"] diff --git a/src/v2/agents/llm/_verdict_cache.py b/src/v2/agents/llm/_verdict_cache.py new file mode 100644 index 0000000..017f4db --- /dev/null +++ b/src/v2/agents/llm/_verdict_cache.py @@ -0,0 +1,92 @@ +"""Shared helpers for caching LLM agent verdicts in `ProviderCache`. + +Each agent picks a stable identity slice from its incoming context and uses +these helpers to skip the LLM call when the same identity has already been +processed within the cache TTL. + +Cached entries store only JSON-serialisable parts (`data`, `warnings`, +`raw_output`, `stats`); model name, provider, and token counts are reset on +retrieval, and `stats["cached"] = True` is added so observability can tell. + +Root extractions never consume the cache: when an extraction request targets +an entity directly (e.g., `/extract/.../user/cmdoret`), the root agent runs +fresh against the full root context. Pass `is_root=True` to +`get_cached_agent_verdict` to disable the lookup. The store call still runs +on a successful root completion so the result is available to later fan-out +invocations of the same identity. The orchestrator sets +`context["agent_is_root"] = True` on root stages; agents forward that flag +through here. +""" +from __future__ import annotations + +import logging +from typing import Any + +from src.v2.agents.models import AgentResult +from src.v2.ingest.cache import ProviderCache + +logger = logging.getLogger(__name__) + + +def get_cached_agent_verdict( + cache: ProviderCache | None, + *, + agent_name: str, + identity: dict[str, Any] | None, + is_root: bool = False, +) -> AgentResult | None: + """Return a cached `AgentResult` if available, else `None`. + + Root extractions never hit the cache — see module docstring. + """ + if cache is None or not identity or is_root: + return None + key = ProviderCache.make_key("agent", agent_name, **identity) + cached = cache.get(key) + if not isinstance(cached, dict): + return None + payload = cached.get("data") + if not isinstance(payload, dict): + return None + logger.info( + "agent cache hit: %s identity=%s", + agent_name, + identity, + ) + return AgentResult( + data=dict(payload), + warnings=list(cached.get("warnings", []) or []), + raw_output=dict(cached.get("raw_output", {}) or {}), + is_partial=False, + failure_reason=None, + model=None, + provider=None, + tokens_prompt=0, + tokens_completion=0, + stats={**dict(cached.get("stats", {}) or {}), "cached": True}, + ) + + +def store_agent_verdict( + cache: ProviderCache | None, + *, + agent_name: str, + identity: dict[str, Any] | None, + result: AgentResult, +) -> None: + """Cache `result` for future lookups when it is complete and identifiable.""" + if cache is None or not identity or result.is_partial: + return + key = ProviderCache.make_key("agent", agent_name, **identity) + cache.set( + key, + { + "data": result.data, + "warnings": list(result.warnings), + "raw_output": result.raw_output, + "stats": result.stats, + }, + ) + + +__all__ = ["get_cached_agent_verdict", "store_agent_verdict"] diff --git a/src/v2/agents/llm/agent_tools/__init__.py b/src/v2/agents/llm/agent_tools/__init__.py new file mode 100644 index 0000000..8c6ead6 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/__init__.py @@ -0,0 +1,127 @@ +from __future__ import annotations + +from src.v2.agents.llm.agent_tools.disciplines import list_disciplines_tool +from src.v2.agents.llm.agent_tools.duckduckgo_search import ( + make_duckduckgo_search_tool, +) +from src.v2.agents.llm.agent_tools.email_hash import ( + hash_user_email, + hash_user_email_tool, +) +from src.v2.agents.llm.agent_tools.ethz_research_collection_rag import ( + make_ethz_research_collection_rag_fetch_chunks_tool, + make_ethz_research_collection_rag_fetch_records_tool, + make_ethz_research_collection_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.federated_rag import ( + make_federated_rag_lookup_tool, + make_federated_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.github_organization import ( + make_github_organization_metadata_tool, +) +from src.v2.agents.llm.agent_tools.huggingface_lineage import ( + make_huggingface_lineage_tool, +) +from src.v2.agents.llm.agent_tools.huggingface_rag import ( + make_huggingface_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.infoscience_orgunit import ( + make_infoscience_orgunit_tool, +) +from src.v2.agents.llm.agent_tools.infoscience_publications import ( + make_infoscience_publications_search_tool, +) +from src.v2.agents.llm.agent_tools.infoscience_rag import ( + make_infoscience_rag_fetch_chunks_tool, + make_infoscience_rag_fetch_records_tool, + make_infoscience_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.infoscience_search import ( + make_infoscience_search_tool, +) +from src.v2.agents.llm.agent_tools.openalex_rag import ( + make_openalex_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.orcid_person import make_orcid_person_tool +from src.v2.agents.llm.agent_tools.orcid_rag import ( + make_orcid_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.renkulab_rag import ( + make_renkulab_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.organization_identity import ( + make_organization_identity_search_tool, +) +from src.v2.agents.llm.agent_tools.query_dependencies import ( + make_query_dependencies_tool, +) +from src.v2.agents.llm.agent_tools.query_orcid import make_query_orcid_tool +from src.v2.agents.llm.agent_tools.repository_corpus_grep import ( + make_repository_corpus_grep_tool, +) +from src.v2.agents.llm.agent_tools.ror_organization import ( + make_ror_organization_search_tool, +) +from src.v2.agents.llm.agent_tools.ror_rag import ( + make_ror_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.selenium_fetch import ( + fetch_link_content_via_selenium_tool, +) +from src.v2.agents.llm.agent_tools.snsf_rag import ( + make_snsf_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.swissubase_rag import ( + make_swissubase_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.uuid import ( + generate_uuid_v4_batch_tool, + generate_uuid_v4_tool, +) +from src.v2.agents.llm.agent_tools.zenodo_rag import ( + make_zenodo_rag_fetch_records_tool, + make_zenodo_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.github_rag import ( + make_github_rag_search_tool, +) + +__all__ = [ + "fetch_link_content_via_selenium_tool", + "generate_uuid_v4_batch_tool", + "generate_uuid_v4_tool", + "hash_user_email", + "hash_user_email_tool", + "list_disciplines_tool", + "make_duckduckgo_search_tool", + "make_ethz_research_collection_rag_fetch_chunks_tool", + "make_ethz_research_collection_rag_fetch_records_tool", + "make_ethz_research_collection_rag_search_tool", + "make_federated_rag_lookup_tool", + "make_federated_rag_search_tool", + "make_github_organization_metadata_tool", + "make_github_rag_search_tool", + "make_huggingface_lineage_tool", + "make_huggingface_rag_search_tool", + "make_infoscience_orgunit_tool", + "make_infoscience_publications_search_tool", + "make_infoscience_rag_fetch_chunks_tool", + "make_infoscience_rag_fetch_records_tool", + "make_infoscience_rag_search_tool", + "make_infoscience_search_tool", + "make_openalex_rag_search_tool", + "make_orcid_person_tool", + "make_orcid_rag_search_tool", + "make_organization_identity_search_tool", + "make_query_dependencies_tool", + "make_query_orcid_tool", + "make_renkulab_rag_search_tool", + "make_repository_corpus_grep_tool", + "make_ror_organization_search_tool", + "make_ror_rag_search_tool", + "make_snsf_rag_search_tool", + "make_swissubase_rag_search_tool", + "make_zenodo_rag_fetch_records_tool", + "make_zenodo_rag_search_tool", +] diff --git a/src/v2/agents/llm/agent_tools/disciplines.py b/src/v2/agents/llm/agent_tools/disciplines.py new file mode 100644 index 0000000..f722ea5 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/disciplines.py @@ -0,0 +1,38 @@ +from __future__ import annotations + +import logging + +from pydantic_ai import Tool + +from src.v2.api_models.enums import DisciplineV2 + +logger = logging.getLogger(__name__) + + +def _build_discipline_description() -> str: + """Generate tool description with the full discipline table from DisciplineV2.""" + lines = [ + "Return the complete list of valid pulse:discipline Wikidata IRIs with human-readable names.", + "", + "| Wikidata ID | Discipline Name |", + "|---|---|", + "", + "Use wikidata_id values from this list when assigning pulse:discipline fields.", + "Only assign IDs present in this list.", + ] + return "\n".join(lines) + + +def _list_disciplines() -> list[dict[str, str]]: + logger.info("tool call: list_disciplines — returning %d entries", len(DisciplineV2)) + return [ + {"wikidata_id": member.value, "name": member.name.replace("_", " ").title()} + for member in DisciplineV2 + ] + + +list_disciplines_tool = Tool( + _list_disciplines, + name="list_disciplines", + description=_build_discipline_description(), +) diff --git a/src/v2/agents/llm/agent_tools/duckduckgo_search.py b/src/v2/agents/llm/agent_tools/duckduckgo_search.py new file mode 100644 index 0000000..8acc32c --- /dev/null +++ b/src/v2/agents/llm/agent_tools/duckduckgo_search.py @@ -0,0 +1,251 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any, Callable + +import requests +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.cache import ProviderCache + +logger = logging.getLogger(__name__) + +DUCKDUCKGO_INSTANT_ANSWER_URL = "https://api.duckduckgo.com/" +DEFAULT_MAX_RESULTS = 5 +MAX_RESULTS = 10 +REQUEST_TIMEOUT_SECONDS = 12.0 + + +def _to_non_empty_string(value: Any) -> str | None: + if isinstance(value, str): + candidate = value.strip() + if candidate: + return candidate + return None + + +def _normalize_max_results(value: Any) -> int: + if isinstance(value, int): + return max(1, min(MAX_RESULTS, value)) + return DEFAULT_MAX_RESULTS + + +def _iter_topic_items(value: Any) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + if not isinstance(value, list): + return rows + for item in value: + if not isinstance(item, dict): + continue + nested = item.get("Topics") + if isinstance(nested, list): + rows.extend(_iter_topic_items(nested)) + continue + rows.append(item) + return rows + + +def _append_result( + output: list[dict[str, str]], + seen_urls: set[str], + *, + title: Any, + url: Any, + snippet: Any, + source: str, +) -> None: + normalized_url = _to_non_empty_string(url) + if normalized_url is None: + return + url_key = normalized_url.lower() + if url_key in seen_urls: + return + + normalized_title = _to_non_empty_string(title) + normalized_snippet = _to_non_empty_string(snippet) + if normalized_title is None and normalized_snippet is None: + normalized_title = normalized_url + normalized_snippet = normalized_url + elif normalized_title is None: + normalized_title = normalized_snippet + elif normalized_snippet is None: + normalized_snippet = normalized_title + + output.append( + { + "title": normalized_title or normalized_url, + "url": normalized_url, + "snippet": normalized_snippet or normalized_url, + "source": source, + }, + ) + seen_urls.add(url_key) + + +def _extract_results(payload: dict[str, Any], *, max_results: int) -> list[dict[str, str]]: + rows: list[dict[str, str]] = [] + seen_urls: set[str] = set() + + _append_result( + rows, + seen_urls, + title=payload.get("Heading"), + url=payload.get("AbstractURL"), + snippet=payload.get("AbstractText"), + source="abstract", + ) + + results = payload.get("Results") + if isinstance(results, list): + for item in results: + if not isinstance(item, dict): + continue + _append_result( + rows, + seen_urls, + title=item.get("Text"), + url=item.get("FirstURL"), + snippet=item.get("Text"), + source="results", + ) + if len(rows) >= max_results: + return rows[:max_results] + + for item in _iter_topic_items(payload.get("RelatedTopics")): + _append_result( + rows, + seen_urls, + title=item.get("Text"), + url=item.get("FirstURL"), + snippet=item.get("Text"), + source="related_topics", + ) + if len(rows) >= max_results: + return rows[:max_results] + + return rows[:max_results] + + +def make_duckduckgo_search_tool( + *, + http_get: Callable[..., Any] | None = None, + cache: ProviderCache | None = None, +) -> Tool: + """Create a DuckDuckGo-backed internet search tool. + + When `cache` is provided, successful results are persisted under + `(query, max_results)` so repeated lookups avoid the network round-trip. + Errors and empty-query inputs are never cached so they retry next time. + """ + + requester = http_get or requests.get + + def _do_search(query: str, max_results: int) -> dict[str, Any]: + normalized_query = _to_non_empty_string(query) or "" + if not normalized_query: + return { + "query": normalized_query, + "result_count": 0, + "results": [], + "error": "empty_query", + } + + limit = _normalize_max_results(max_results) + params = { + "q": normalized_query, + "format": "json", + "no_html": "1", + "no_redirect": "1", + "skip_disambig": "1", + } + + try: + response = requester( + DUCKDUCKGO_INSTANT_ANSWER_URL, + params=params, + timeout=REQUEST_TIMEOUT_SECONDS, + ) + raise_for_status = getattr(response, "raise_for_status", None) + if callable(raise_for_status): + raise_for_status() + payload = response.json() if callable(getattr(response, "json", None)) else None + except Exception as exc: # noqa: BLE001 + return { + "query": normalized_query, + "result_count": 0, + "results": [], + "error": str(exc), + } + + if not isinstance(payload, dict): + return { + "query": normalized_query, + "result_count": 0, + "results": [], + "error": "invalid_duckduckgo_payload", + } + + results = _extract_results(payload, max_results=limit) + return { + "query": normalized_query, + "result_count": len(results), + "results": results, + } + + def search_on_the_internet(query: str, max_results: int = DEFAULT_MAX_RESULTS) -> dict[str, Any]: + """Search the internet with DuckDuckGo and return compact result context. + + Args: + query: Search query string. + max_results: Max result rows to return (1..10). + """ + + normalized_query = _to_non_empty_string(query) or "" + logger.info( + "tool call: search_on_the_internet — query=%r max_results=%r", + normalized_query, + max_results, + ) + if normalized_query: + record_query(service="duckduckgo.search", query=normalized_query) + + if cache is None or not normalized_query: + return _do_search(query, max_results) + + # Lazy import keeps this module free of v2 cache deps when used standalone. + from src.v2.ingest.cache import ProviderCache as _PC # noqa: PLC0415 + + limit = _normalize_max_results(max_results) + key = _PC.make_key( + "duckduckgo", + "search_on_the_internet", + query=normalized_query, + max_results=limit, + ) + cached = cache.get(key) + if isinstance(cached, dict): + logger.info( + "duckduckgo cache hit — query=%r max_results=%d", + normalized_query, + limit, + ) + return cached + + result = _do_search(query, max_results) + # Only cache successful searches; let errors retry next time. + if isinstance(result, dict) and not result.get("error"): + cache.set(key, result) + return result + + return Tool( + search_on_the_internet, + name="search_on_the_internet", + description=( + "Search the internet using DuckDuckGo and return compact result context " + "(title, URL, snippet, source type). Use for quick external grounding." + ), + ) + diff --git a/src/v2/agents/llm/agent_tools/email_hash.py b/src/v2/agents/llm/agent_tools/email_hash.py new file mode 100644 index 0000000..2e49c01 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/email_hash.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +import hashlib +import logging +import re + +from pydantic_ai import Tool + +logger = logging.getLogger(__name__) +HASH_LENGTH = 12 +HASHED_LOCAL_PART_PATTERN = re.compile(r"^[0-9a-f]{12}$|^[0-9a-f]{64}$", re.IGNORECASE) + + +def hash_user_email(email: str) -> str: + """Hash/anonymize an email local-part with the project canonical policy. + + Uses SHA-256(local-part)[:12] while preserving the original domain. + If the email is invalid or already anonymized, the input is returned unchanged. + """ + + normalized = email.strip() if isinstance(email, str) else "" + logger.info("tool call: hash_user_email") + if "@" not in normalized: + return normalized + + local_part, domain = normalized.split("@", maxsplit=1) + if not local_part or not domain: + return normalized + + if HASHED_LOCAL_PART_PATTERN.fullmatch(local_part): + return normalized + + hashed_local = hashlib.sha256(local_part.encode("utf-8")).hexdigest()[:HASH_LENGTH] + return f"{hashed_local}@{domain}" + + +hash_user_email_tool = Tool( + hash_user_email, + name="hash_user_email", + description=( + "Hash/anonymize a user email local part using the canonical policy " + "(sha256(local-part)[:12] + original domain). " + "Use this tool before setting schema:email." + ), +) diff --git a/src/v2/agents/llm/agent_tools/epfl_graph_rag.py b/src/v2/agents/llm/agent_tools/epfl_graph_rag.py new file mode 100644 index 0000000..21c4684 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/epfl_graph_rag.py @@ -0,0 +1,64 @@ +"""Pydantic-AI Tool backed by :class:`EpflGraphRagProvider`. + +Surfaces the EPFL Graph academic discipline ontology as a semantic-search +tool: given any text (a README, abstract, project description) the LLM +can ask for the closest disciplines from the curated ~2226-node tree. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.epfl_graph_rag import EpflGraphRagProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Semantic search over the EPFL Graph academic-discipline ontology " + "(~2226 categories, depth 1..5, each backed by 50-110 anchor " + "Wikipedia articles). Use this to map a README, abstract or project " + "description onto canonical EPFL disciplines like " + "`topics-in-natural-language-processing` or `computer-graphics`. " + "`filters`: optional dict of allowlisted keys (category_id, depth, " + "parent_id, entity_type). Each value may be a scalar, a list " + "(any-of), or {$gte/$lte} for ranges — use depth>=4 to keep only " + "leaf disciplines. `rerank=true` engages the cross-encoder against " + "name + anchor concepts. Returns thin hits with category_id, name, " + "depth, parent_id, wikipedia_url, graphsearch_url." +) + + +def make_epfl_graph_rag_search_tool(provider: EpflGraphRagProvider) -> Tool: + """Tool factory: semantic search over the EPFL Graph disciplines index.""" + + async def search_epfl_graph_disciplines( + query: str, + top_k: int = 10, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — part of the LLM tool signature + ) -> list[dict[str, Any]]: + """Vector search the EPFL Graph disciplines index. See tool description.""" + logger.info( + "tool call: search_epfl_graph_disciplines — top_k=%d rerank=%s " + "filters=%r query=%r", + top_k, rerank, filters, query, + ) + record_query(service="epfl_graph.rag.search", query=query) + return await provider.search( + query, top_k=top_k, filters=filters, rerank=rerank, + ) + + return Tool( + search_epfl_graph_disciplines, + name="search_epfl_graph_disciplines", + description=_DESCRIPTION, + ) + + +__all__ = ["make_epfl_graph_rag_search_tool"] diff --git a/src/v2/agents/llm/agent_tools/ethz_research_collection_rag.py b/src/v2/agents/llm/agent_tools/ethz_research_collection_rag.py new file mode 100644 index 0000000..22d4a11 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/ethz_research_collection_rag.py @@ -0,0 +1,173 @@ +"""Pydantic-AI Tools backed by :class:`EthzResearchCollectionRagProvider`. + +Three tools so the LLM can drive a RAG search/fetch loop on demand: + +* ``search_ethz_research_collection_rag`` — semantic vector search across the + ETH Research Collection index (chunks / articles / persons / organizations) with an + allowlisted filter dict and an opt-in cross-encoder rerank pass. +* ``fetch_ethz_research_collection_chunks`` — pulls full chunk bodies for a single + article identified by ``article_uuid`` (returned by the search tool). +* ``fetch_ethz_research_collection_records`` — full payloads for articles / persons / + organizations by point ID. + +Splitting search from fetch keeps prompts small: search returns thin hits +(IDs + titles + short snippets), and the agent decides when to spend the +context budget on a full body. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.ingest.providers.ethz_research_collection_rag import ( + CollectionName, # noqa: TC001 — runtime annotation read by pydantic-ai +) +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.ethz_research_collection_rag import ( + EthzResearchCollectionRagProvider, + ) + +logger = logging.getLogger(__name__) + +_SEARCH_DESCRIPTION = ( + "Semantic search over the ETH Research Collection RAG index — ETH " + "Zurich's institutional repository (DSpace-backed, sister index to " + "EPFL's Infoscience). Use this when README / CITATION / metadata " + "references an ETHZ paper, lab, or person without a clean identifier. " + "`collection`: 'chunks' (default — paper body fragments), 'articles' " + "(one row per paper), 'persons' (researchers), or 'organizations' " + "(ETHZ labs/units). `filters`: optional dict of allowlisted keys " + "(has_github_match, has_hf_match, year, publication_type, language, " + "lab_uuid, org_uuids, doi, orcid, ror_id, author_uuids, subjects, " + "keywords). Each value may be a scalar, a list (any-of), or an " + "operator dict ($eq/$ne/$in/$contains/$gte/$lte). `rerank=true` " + "engages the cross-encoder for ambiguous queries — slower but more " + "precise; default false. Returns trimmed hits (id, score, " + "title/name, snippet, identifiers). Feed the returned id into " + "fetch_ethz_research_collection_chunks (article_uuid) or " + "fetch_ethz_research_collection_records to pull the full body." +) + +_FETCH_CHUNKS_DESCRIPTION = ( + "Fetch the full chunk bodies of one ETH Research Collection article " + "by its `article_uuid` (returned from " + "search_ethz_research_collection_rag). Use this only after a search " + "hit looks promising — it spends meaningful context budget. Returns " + "chunks ordered by chunk_index with their full text, title, doi, " + "and research_collection_url." +) + +_FETCH_RECORDS_DESCRIPTION = ( + "Fetch full payloads of articles, persons, or organizations by point " + "id (the `id` field returned by search_ethz_research_collection_rag). `collection` " + "must be 'articles', 'persons', or 'organizations' — chunk bodies use " + "fetch_ethz_research_collection_chunks instead. Returns the complete payload for " + "each id (abstract, affiliations, parent org chain, etc.)." +) + + +def _service_name(op: str) -> str: + return f"ethz_research_collection.rag.{op}" + + +def make_ethz_research_collection_rag_search_tool(provider: EthzResearchCollectionRagProvider) -> Tool: + """Tool factory: semantic search over the ETH Research Collection RAG index.""" + + async def search_ethz_research_collection_rag( + query: str, + collection: CollectionName = "chunks", + top_k: int = 10, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — part of the LLM tool signature + ) -> list[dict[str, Any]]: + """Vector search the ETH Research Collection index. See tool description.""" + logger.info( + "tool call: search_ethz_research_collection_rag — collection=%s top_k=%d " + "rerank=%s filters=%r query=%r", + collection, top_k, rerank, filters, query, + ) + record_query( + service=f"{_service_name('search')}.{collection}", + query=query, + ) + return await provider.search( + query, + collection=collection, + top_k=top_k, + filters=filters, + rerank=rerank, + ) + + return Tool( + search_ethz_research_collection_rag, + name="search_ethz_research_collection_rag", + description=_SEARCH_DESCRIPTION, + ) + + +def make_ethz_research_collection_rag_fetch_chunks_tool( + provider: EthzResearchCollectionRagProvider, +) -> Tool: + """Tool factory: fetch full chunk bodies for one article.""" + + async def fetch_ethz_research_collection_chunks( + article_uuid: str, + max_chunks: int = 20, + ) -> list[dict[str, Any]]: + logger.info( + "tool call: fetch_ethz_research_collection_chunks — article_uuid=%s max_chunks=%d", + article_uuid, max_chunks, + ) + record_query( + service=_service_name("fetch_chunks"), + query=article_uuid, + ) + return await provider.fetch_chunks(article_uuid, max_chunks=max_chunks) + + return Tool( + fetch_ethz_research_collection_chunks, + name="fetch_ethz_research_collection_chunks", + description=_FETCH_CHUNKS_DESCRIPTION, + ) + + +def make_ethz_research_collection_rag_fetch_records_tool( + provider: EthzResearchCollectionRagProvider, +) -> Tool: + """Tool factory: fetch full article/person/organization payloads by id.""" + + async def fetch_ethz_research_collection_records( + collection: CollectionName, + ids: list[str], + ) -> list[dict[str, Any]]: + logger.info( + "tool call: fetch_ethz_research_collection_records — collection=%s ids=%d", + collection, len(ids), + ) + record_query( + service=f"{_service_name('fetch_records')}.{collection}", + query=",".join(str(i) for i in ids), + ) + try: + return await provider.fetch_records(collection, ids) + except ValueError as exc: + logger.warning("fetch_ethz_research_collection_records: %s", exc) + return [] + + return Tool( + fetch_ethz_research_collection_records, + name="fetch_ethz_research_collection_records", + description=_FETCH_RECORDS_DESCRIPTION, + ) + + +__all__ = [ + "make_ethz_research_collection_rag_fetch_chunks_tool", + "make_ethz_research_collection_rag_fetch_records_tool", + "make_ethz_research_collection_rag_search_tool", +] diff --git a/src/v2/agents/llm/agent_tools/federated_rag.py b/src/v2/agents/llm/agent_tools/federated_rag.py new file mode 100644 index 0000000..63c71b1 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/federated_rag.py @@ -0,0 +1,123 @@ +"""Pydantic-AI tools backed by :class:`FederatedRagProvider`. + +Two tools — `search_federated_rag` and `lookup_entity_federated` — that +fan out across every registered gme RAG index in parallel and return a +single merged response. Currently registered: ``huggingface``, +``openalex``, ``infoscience``, ``ethz_research_collection``, ``orcid``, +``ror``, ``zenodo``, ``snsf``, ``swissubase``, ``renkulab``, ``github``, +``epfl_graph``. Use these instead of (or in addition to) the per-index +tools when you don't know which index has the answer. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.federated_rag import FederatedRagProvider + +logger = logging.getLogger(__name__) + + +_SEARCH_DESCRIPTION = ( + "Federated semantic search across every registered gme RAG index " + "(huggingface, openalex, infoscience, ethz_research_collection, orcid, " + "ror, zenodo, snsf, swissubase, renkulab, github, epfl_graph) in " + "parallel. Use when the query could match in multiple indices and you " + "want one merged ranked list, e.g. 'find Swiss German LLM resources' " + "returns HF models + Zenodo datasets + ORCID researchers + ROR " + "institutions + EPFL/ETHZ publications side by side. " + "`indices`: optional subset (e.g. ['ethz_research_collection','infoscience']); " + "default = all. `entity_type`: optional restriction within each index " + "(e.g. 'models', 'works', 'persons', 'chunks', 'articles'); adapters " + "that don't recognise the type fall back to their default. " + "`top_k_per_index`: how many candidates each index contributes " + "(default 3). `top_k`: overall hits returned after merge and sort by " + "score (default 10). `filters`: dict forwarded as-is to every adapter; " + "each adapter applies the keys it knows and ignores the rest. Returns " + "`{hits: [{index,entity_type,id,title,score,summary,url,payload}], " + "by_index: {…}, errors: {…}}`. Always prefer this over running " + "per-index tools serially." +) + +_LOOKUP_DESCRIPTION = ( + "Cross-index entity lookup: given an identifier (HF slug or repo_id, " + "OpenAlex Wxxxx/Axxxx/Ixxxx ID, ORCID 0000-..., ROR id, Zenodo numeric " + "id or DOI, Infoscience UUID, ETH Research Collection handle " + "(20.500.11850/...) or research-collection.ethz.ch URL, GitHub " + "owner/repo, or any of the corresponding canonical URLs), every " + "adapter that recognises the shape resolves it and returns 0+ matching " + "records. Use to hydrate a known identifier into all related records " + "the gme corpus has, e.g. an ORCID → ORCID person record + (if the " + "same person published HF models / EPFL or ETHZ papers under the same " + "name) HF / Infoscience / ETH RC entries. `indices`: optional subset; " + "default = all. Returns `{identifier, records: " + "[{index,entity_type,id,data,url}], by_index: {…}, errors: {…}}`." +) + + +def make_federated_rag_search_tool(provider: FederatedRagProvider) -> Tool: + """Tool factory: federated semantic search across all registered indices.""" + + async def search_federated_rag( # noqa: PLR0913 — full federated knob set surfaced to the LLM + query: str, + indices: list[str] | None = None, + entity_type: str | None = None, + top_k: int = 10, + top_k_per_index: int = 3, + filters: dict[str, Any] | None = None, + ) -> dict[str, Any]: + """Federated semantic search. See tool description.""" + logger.info( + "tool call: search_federated_rag — indices=%s entity_type=%s " + "top_k=%d top_k_per_index=%d filters=%r query=%r", + indices, entity_type, top_k, top_k_per_index, filters, query, + ) + record_query(service="federated.rag.search", query=query) + return await provider.search( + query, + indices=indices, + entity_type=entity_type, + top_k=top_k, + top_k_per_index=top_k_per_index, + filters=filters, + ) + + return Tool( + search_federated_rag, + name="search_federated_rag", + description=_SEARCH_DESCRIPTION, + ) + + +def make_federated_rag_lookup_tool(provider: FederatedRagProvider) -> Tool: + """Tool factory: cross-index entity lookup by identifier.""" + + async def lookup_entity_federated( + identifier: str, + indices: list[str] | None = None, + ) -> dict[str, Any]: + """Cross-index identifier resolution. See tool description.""" + logger.info( + "tool call: lookup_entity_federated — indices=%s identifier=%r", + indices, identifier, + ) + record_query(service="federated.rag.lookup", query=identifier) + return await provider.lookup(identifier, indices=indices) + + return Tool( + lookup_entity_federated, + name="lookup_entity_federated", + description=_LOOKUP_DESCRIPTION, + ) + + +__all__ = [ + "make_federated_rag_lookup_tool", + "make_federated_rag_search_tool", +] diff --git a/src/v2/agents/llm/agent_tools/github_organization.py b/src/v2/agents/llm/agent_tools/github_organization.py new file mode 100644 index 0000000..b77b943 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/github_organization.py @@ -0,0 +1,150 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any +from urllib.parse import urlparse + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.base import GitHubProvider + +logger = logging.getLogger(__name__) +MAX_LIST_ITEMS = 20 + + +def _coerce_string_list(value: Any, *, max_items: int = MAX_LIST_ITEMS) -> list[str]: + if not isinstance(value, list): + return [] + output: list[str] = [] + for item in value: + if not isinstance(item, str): + continue + normalized = item.strip() + if not normalized: + continue + output.append(normalized) + if len(output) >= max_items: + break + return output + + +def _normalize_org_name(raw_value: str) -> str: + candidate = raw_value.strip() + if not candidate: + return "" + if candidate.startswith("@"): + candidate = candidate[1:] + + parsed = urlparse(candidate) + if parsed.netloc.lower() in {"github.com", "www.github.com"}: + path_parts = [part for part in parsed.path.split("/") if part] + if path_parts: + candidate = path_parts[0] + + if "/" in candidate: + candidate = candidate.split("/", maxsplit=1)[0] + + return candidate.strip() + + +def _shape_organization_profile( + org_name: str, + organization: dict[str, Any], +) -> dict[str, Any]: + login = organization.get("login") + if not isinstance(login, str) or not login.strip(): + login = org_name + + html_url = organization.get("html_url") + if not isinstance(html_url, str) or not html_url.strip(): + html_url = f"https://github.com/{login}" + + return { + "login": login, + "name": organization.get("name"), + "description": organization.get("description"), + "html_url": html_url, + "type": organization.get("type"), + "followers": organization.get("followers"), + "public_repos": organization.get("public_repos"), + "location": organization.get("location"), + "blog": organization.get("blog"), + "email": organization.get("email"), + "company": organization.get("company"), + "created_at": organization.get("created_at"), + "updated_at": organization.get("updated_at"), + "public_members": _coerce_string_list(organization.get("public_members")), + "repositories": _coerce_string_list(organization.get("repositories")), + "teams": _coerce_string_list(organization.get("teams")), + } + + +def make_github_organization_metadata_tool(github_provider: GitHubProvider) -> Tool: + """Create a GitHub organization lookup tool bound to a provider instance.""" + + def get_github_organization_metadata(org_name: str) -> dict[str, Any]: + """Fetch and normalize GitHub organization profile metadata. + + Args: + org_name: GitHub organization login, @handle, URL, or owner/repo. + + Returns: + Dict containing the normalized lookup name and an organization + profile payload. If lookup fails, returns `error` with details. + """ + + normalized_org_name = _normalize_org_name(org_name) + logger.info( + "tool call: get_github_organization_metadata — query=%r normalized=%r", + org_name, + normalized_org_name, + ) + record_query( + service="github.get_organization", + query=normalized_org_name or str(org_name), + ) + if not normalized_org_name: + return { + "query": org_name, + "normalized_org_name": "", + "organization": None, + "error": "empty_org_name", + } + + try: + payload = github_provider.get_organization(normalized_org_name) + except Exception as exc: # noqa: BLE001 + return { + "query": org_name, + "normalized_org_name": normalized_org_name, + "organization": None, + "error": str(exc), + } + + if not isinstance(payload, dict): + return { + "query": org_name, + "normalized_org_name": normalized_org_name, + "organization": None, + "error": "invalid_provider_payload", + } + + return { + "query": org_name, + "normalized_org_name": normalized_org_name, + "organization": _shape_organization_profile(normalized_org_name, payload), + } + + return Tool( + get_github_organization_metadata, + name="get_github_organization_metadata", + description=( + "Fetch GitHub organization profile metadata by login, @handle, " + "GitHub URL, or owner/repo string. Returns normalized profile " + "fields such as login, name, html_url, type, followers, " + "public_repos, location, website/blog, and members/repositories hints." + ), + ) diff --git a/src/v2/agents/llm/agent_tools/github_rag.py b/src/v2/agents/llm/agent_tools/github_rag.py new file mode 100644 index 0000000..56afed7 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/github_rag.py @@ -0,0 +1,61 @@ +"""Pydantic-AI Tool backed by :class:`GitHubRagProvider`.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.github_rag import GitHubRagProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Semantic search over the GitHub repositories RAG index — EPFL/Swiss " + "research code repositories with metadata + README content embedded " + "via Qwen3. Use this when you need to find related repositories, " + "discover the canonical implementation of a technique mentioned in a " + "paper or README, or surface forks/companions of the repo being " + "processed. `filters`: optional dict of allowlisted keys " + "(entity_type, repo_id, owner, primary_language, license_spdx, " + "is_archived, is_fork). Each value may be a scalar, a list (any-of), " + "or {$gte/$lte} for ranges. `rerank=true` engages the cross-encoder " + "against repo_id+description — recommended when the query is a code " + "concept rather than a literal repo name. Returns thin hits with " + "repo_id, owner, name, primary_language, license_spdx, stars, forks, " + "pushed_at, is_archived." +) + + +def make_github_rag_search_tool(provider: GitHubRagProvider) -> Tool: + """Tool factory: semantic search over the GitHub RAG index.""" + + async def search_github_rag( + query: str, + top_k: int = 10, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — part of the LLM tool signature + ) -> list[dict[str, Any]]: + """Vector search the GitHub repositories index. See tool description.""" + logger.info( + "tool call: search_github_rag — top_k=%d rerank=%s " + "filters=%r query=%r", + top_k, rerank, filters, query, + ) + record_query(service="github.rag.search", query=query) + return await provider.search( + query, top_k=top_k, filters=filters, rerank=rerank, + ) + + return Tool( + search_github_rag, + name="search_github_rag", + description=_DESCRIPTION, + ) + + +__all__ = ["make_github_rag_search_tool"] diff --git a/src/v2/agents/llm/agent_tools/graph_neighbors.py b/src/v2/agents/llm/agent_tools/graph_neighbors.py new file mode 100644 index 0000000..1f97df0 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/graph_neighbors.py @@ -0,0 +1,296 @@ +from __future__ import annotations + +import logging +from typing import Any + +from pydantic_ai import Tool + +logger = logging.getLogger(__name__) + +NEIGHBOR_LIMIT = 25 + + +def _entity_type(entity: dict[str, Any]) -> str | None: + raw = entity.get("type") or entity.get("@type") + if isinstance(raw, list) and raw: + return str(raw[0]) + if isinstance(raw, str): + return raw + return None + + +def _summarize(entity: dict[str, Any]) -> dict[str, Any]: + return { + "id": entity.get("id") or entity.get("@id"), + "name": entity.get("schema:name"), + "type": _entity_type(entity), + } + + +def _build_neighbor_index(graph: dict[str, list[dict[str, Any]]]) -> dict[str, list[dict]]: + """Group entities by their @id for O(1) lookup.""" + + index: dict[str, list[dict]] = {} + for bucket in graph.values(): + for entity in bucket: + entity_id = entity.get("id") or entity.get("@id") + if isinstance(entity_id, str) and entity_id: + index.setdefault(entity_id, []).append(entity) + return index + + +def _resolve_id_ref(value: Any) -> str | None: + if isinstance(value, dict): + return value.get("@id") or value.get("id") + if isinstance(value, str): + return value + return None + + +def _neighbors_for_organization( + *, + target_id: str, + entities: dict[str, list[dict[str, Any]]], + memberships: list[dict[str, Any]], + index: dict[str, list[dict]], +) -> dict[str, Any]: + members: list[dict[str, Any]] = [] + for membership in memberships: + if _resolve_id_ref(membership.get("org:organization")) != target_id: + continue + membership_id = membership.get("id") or membership.get("@id") or "" + # Membership @id is composite: "{personId}_{orgId}" + person_id = ( + membership_id[: -(len(target_id) + 1)] + if membership_id.endswith(f"_{target_id}") + else None + ) + person_name = None + if person_id and (entries := index.get(person_id)): + person_name = entries[0].get("schema:name") + members.append( + {"id": person_id, "name": person_name, "role": membership.get("org:role")}, + ) + if len(members) >= NEIGHBOR_LIMIT: + break + + child_orgs: list[dict[str, Any]] = [] + parent_orgs: list[dict[str, Any]] = [] + for organization in entities.get("organizations", []): + unit_of = organization.get("org:unitOf") or [] + if not isinstance(unit_of, list): + unit_of = [unit_of] if isinstance(unit_of, str) else [] + if organization.get("id") == target_id: + for parent_id in unit_of: + if isinstance(parent_id, str) and parent_id and (entries := index.get(parent_id)): + parent_orgs.append(_summarize(entries[0])) + continue + if target_id in unit_of: + child_orgs.append(_summarize(organization)) + if len(child_orgs) >= NEIGHBOR_LIMIT: + break + + owned_repos: list[dict[str, Any]] = [] + for organization in entities.get("organizations", []): + if organization.get("id") != target_id: + continue + for repo_id in organization.get("pulse:owns") or []: + if not isinstance(repo_id, str): + continue + entries = index.get(repo_id) or [] + owned_repos.append( + _summarize(entries[0]) if entries else {"id": repo_id, "name": None, "type": None}, + ) + if len(owned_repos) >= NEIGHBOR_LIMIT: + break + + return { + "members": members, + "child_orgs": child_orgs, + "parent_orgs": parent_orgs, + "owned_repos": owned_repos, + } + + +def _neighbors_for_person( + *, + target_id: str, + entities: dict[str, list[dict[str, Any]]], + memberships: list[dict[str, Any]], + contributions: list[dict[str, Any]], + index: dict[str, list[dict]], +) -> dict[str, Any]: + affiliations: list[dict[str, Any]] = [] + for membership in memberships: + membership_id = membership.get("id") or membership.get("@id") or "" + if not membership_id.startswith(f"{target_id}_"): + continue + org_id = _resolve_id_ref(membership.get("org:organization")) + org_name = None + if org_id and (entries := index.get(org_id)): + org_name = entries[0].get("schema:name") + affiliations.append( + { + "org_id": org_id, + "org_name": org_name, + "role": membership.get("org:role"), + "begin": membership.get("time:hasBeginning"), + "end": membership.get("time:hasEnd"), + }, + ) + if len(affiliations) >= NEIGHBOR_LIMIT: + break + + contributed_repos: list[dict[str, Any]] = [] + for contribution in contributions: + if _resolve_id_ref(contribution.get("schema:author")) != target_id: + continue + repo_id = _resolve_id_ref(contribution.get("pulse:contributionTo")) + repo_name = None + if repo_id and (entries := index.get(repo_id)): + repo_name = entries[0].get("schema:name") + contributed_repos.append( + { + "repo_id": repo_id, + "repo_name": repo_name, + "count": contribution.get("pulse:contributionCount"), + }, + ) + if len(contributed_repos) >= NEIGHBOR_LIMIT: + break + + owned_repos: list[dict[str, Any]] = [] + for person in entities.get("persons", []): + if person.get("id") != target_id: + continue + for repo_id in person.get("pulse:owns") or []: + if not isinstance(repo_id, str): + continue + entries = index.get(repo_id) or [] + owned_repos.append( + _summarize(entries[0]) if entries else {"id": repo_id, "name": None, "type": None}, + ) + if len(owned_repos) >= NEIGHBOR_LIMIT: + break + + return {"affiliations": affiliations, "contributed_repos": contributed_repos, "owned_repos": owned_repos} + + +def _neighbors_for_repository( + *, + target_id: str, + entities: dict[str, list[dict[str, Any]]], + contributions: list[dict[str, Any]], + index: dict[str, list[dict]], +) -> dict[str, Any]: + contributors: list[dict[str, Any]] = [] + for contribution in contributions: + if _resolve_id_ref(contribution.get("pulse:contributionTo")) != target_id: + continue + person_id = _resolve_id_ref(contribution.get("schema:author")) + person_name = None + if person_id and (entries := index.get(person_id)): + person_name = entries[0].get("schema:name") + contributors.append( + { + "person_id": person_id, + "person_name": person_name, + "count": contribution.get("pulse:contributionCount"), + }, + ) + if len(contributors) >= NEIGHBOR_LIMIT: + break + + owners: list[dict[str, Any]] = [] + for bucket_key in ("organizations", "persons"): + for entity in entities.get(bucket_key, []): + for owned_id in entity.get("pulse:owns") or []: + if owned_id == target_id: + owners.append(_summarize(entity)) + break + if len(owners) >= NEIGHBOR_LIMIT: + break + + target = (index.get(target_id) or [{}])[0] + fork_of = target.get("pulse:isForkOf") + if isinstance(fork_of, dict): + fork_of = fork_of.get("@id") or fork_of.get("id") + fork_of_summary: dict[str, Any] | None = None + if isinstance(fork_of, str) and (entries := index.get(fork_of)): + fork_of_summary = _summarize(entries[0]) + + return {"contributors": contributors, "owners": owners, "fork_of": fork_of_summary} + + +def make_get_entity_neighbors_tool( + *, + entities: dict[str, list[dict[str, Any]]], + memberships: list[dict[str, Any]], + contributions: list[dict[str, Any]] | None = None, +) -> Tool: + """Build a Pydantic-AI tool exposing graph neighbors of any entity under refinement. + + Supports `org:Organization`, `schema:Person`, and `schema:SoftwareSourceCode` + (Repository) targets. Returns small `{id, name, type}` summaries — no full + payloads. Closure captures the reconciled `entities`, `memberships`, and + `contributions` so the agent can inspect structural neighbors without I/O. + """ + + index = _build_neighbor_index(entities) + contributions_list = list(contributions or []) + + def get_entity_neighbors(entity_id: str) -> dict[str, Any]: + """Return a small JSON dict summarising neighbors of `entity_id` in the current graph.""" + + logger.info("tool call: get_entity_neighbors entity_id=%s", entity_id) + target_list = index.get(entity_id, []) + if not target_list: + return {"error": f"entity_id not found in graph: {entity_id}"} + + target = target_list[0] + target_type = _entity_type(target) + result: dict[str, Any] = {"entity": _summarize(target), "type": target_type} + + if target_type == "org:Organization": + result.update( + _neighbors_for_organization( + target_id=entity_id, + entities=entities, + memberships=memberships, + index=index, + ), + ) + elif target_type == "schema:Person": + result.update( + _neighbors_for_person( + target_id=entity_id, + entities=entities, + memberships=memberships, + contributions=contributions_list, + index=index, + ), + ) + elif target_type == "schema:SoftwareSourceCode": + result.update( + _neighbors_for_repository( + target_id=entity_id, + entities=entities, + contributions=contributions_list, + index=index, + ), + ) + + return result + + return Tool( + get_entity_neighbors, + name="get_entity_neighbors", + description=( + "Return a summary of neighbors for an entity in the current pipeline graph. " + "Supports organizations (members, child/parent orgs, owned repos), persons " + "(affiliations, contributed repos, owned repos), and repositories " + "(contributors with commit counts, owners, fork lineage). Each neighbor is " + "summarised as {id, name, type}. Use to understand structural context " + "before proposing field improvements." + ), + ) diff --git a/src/v2/agents/llm/agent_tools/huggingface_lineage.py b/src/v2/agents/llm/agent_tools/huggingface_lineage.py new file mode 100644 index 0000000..e3676be --- /dev/null +++ b/src/v2/agents/llm/agent_tools/huggingface_lineage.py @@ -0,0 +1,60 @@ +"""Pydantic-AI tool for HuggingFace base_model lineage walks. + +Backed by :meth:`HuggingFaceRagProvider.lineage`. Pure local DuckDB lookup +— no RCP / Qdrant cost. Use when an agent needs to know what a model was +fine-tuned from, or which models depend on a given base. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.huggingface_rag import HuggingFaceRagProvider + +logger = logging.getLogger(__name__) + + +_DESCRIPTION = ( + "Walk the HuggingFace base_models DAG from a `repo_id` (e.g. " + "'epfl-llm/meditron-7b'). Returns ancestors (parent models the repo " + "was fine-tuned from, recursively) and descendants (other repos in " + "our index that list `repo_id` as their base). Useful for: " + "(a) 'what is this model fine-tuned from?' (ancestors), " + "(b) 'what derivative models exist for this base?' (descendants), " + "(c) tracing a lineage chain like Llama-2 → Meditron → some-finetune. " + "`depth` controls how many hops to walk in each direction (default 3). " + "Pure local DuckDB lookup — fast (sub-second) and doesn't burn RCP. " + "Returns `{root, ancestors: {level_1, level_2, …}, descendants: " + "{level_1, …}, edges: [{from, to}], depth}`." +) + + +def make_huggingface_lineage_tool(provider: HuggingFaceRagProvider) -> Tool: + """Tool factory: walk the HF base_model graph.""" + + async def lineage_huggingface( + repo_id: str, + depth: int = 3, + ) -> dict[str, Any]: + """HF lineage walk. See tool description.""" + logger.info( + "tool call: lineage_huggingface — repo_id=%r depth=%d", + repo_id, depth, + ) + record_query(service="huggingface.rag.lineage", query=repo_id) + return await provider.lineage(repo_id, depth=depth) + + return Tool( + lineage_huggingface, + name="lineage_huggingface", + description=_DESCRIPTION, + ) + + +__all__ = ["make_huggingface_lineage_tool"] diff --git a/src/v2/agents/llm/agent_tools/huggingface_rag.py b/src/v2/agents/llm/agent_tools/huggingface_rag.py new file mode 100644 index 0000000..e9c7132 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/huggingface_rag.py @@ -0,0 +1,71 @@ +"""Pydantic-AI Tool backed by :class:`HuggingFaceRagProvider`.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.ingest.providers.huggingface_rag import ( + CollectionName, # noqa: TC001 — runtime annotation read by pydantic-ai +) +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.huggingface_rag import HuggingFaceRagProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Semantic search over the HuggingFace Hub RAG index (models / datasets " + "/ spaces). Use this when README / CITATION / repo metadata mentions a " + "model or dataset by name or capability and you need the canonical " + "repo_id. `collection`: 'models' (default), 'datasets', or 'spaces'. " + "`filters`: optional dict of allowlisted keys (library_name, " + "pipeline_tag, sdk, gated, downloads, downloads_all_time, likes, " + "author, license, entity_type). Each value may be a scalar, a list " + "(any-of), or {$gte/$lte} for ranges. `rerank=true` engages the " + "cross-encoder; HF chunks are metadata-only so rerank uses synthetic " + "doc strings (repo_id+library/sdk) — useful for repo-name " + "disambiguation, weaker for content relevance. Returns thin hits with " + "repo_id, author, library_name/pipeline_tag/sdk, downloads, likes." +) + + +def make_huggingface_rag_search_tool(provider: HuggingFaceRagProvider) -> Tool: + """Tool factory: semantic search over the HuggingFace RAG index.""" + + async def search_huggingface_rag( + query: str, + collection: CollectionName = "models", + top_k: int = 10, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — part of the LLM tool signature + ) -> list[dict[str, Any]]: + """Vector search the HuggingFace Hub index. See tool description.""" + logger.info( + "tool call: search_huggingface_rag — collection=%s top_k=%d " + "rerank=%s filters=%r query=%r", + collection, top_k, rerank, filters, query, + ) + record_query( + service=f"huggingface.rag.search.{collection}", + query=query, + ) + return await provider.search( + query, + collection=collection, + top_k=top_k, + filters=filters, + rerank=rerank, + ) + + return Tool( + search_huggingface_rag, + name="search_huggingface_rag", + description=_DESCRIPTION, + ) + + +__all__ = ["make_huggingface_rag_search_tool"] diff --git a/src/v2/agents/llm/agent_tools/infoscience_orgunit.py b/src/v2/agents/llm/agent_tools/infoscience_orgunit.py new file mode 100644 index 0000000..d1e5a73 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/infoscience_orgunit.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.base import InfoscienceProvider + +logger = logging.getLogger(__name__) + + +def make_infoscience_orgunit_tool(infoscience_provider: InfoscienceProvider) -> Tool: + """Create an Infoscience org-unit search tool bound to a provider instance.""" + + def search_infoscience_orgunit(query: str) -> list[dict[str, Any]]: + """Search Infoscience organization units by free-text query.""" + + logger.info("tool call: search_infoscience_orgunit — query=%r", query) + record_query(service="infoscience.search_orgunit", query=query) + return infoscience_provider.search_orgunit(query) + + return Tool( + search_infoscience_orgunit, + name="search_infoscience_orgunit", + description=( + "Search Infoscience organization units to obtain identifiers, names, " + "and parent organization hints." + ), + ) diff --git a/src/v2/agents/llm/agent_tools/infoscience_publications.py b/src/v2/agents/llm/agent_tools/infoscience_publications.py new file mode 100644 index 0000000..fed02db --- /dev/null +++ b/src/v2/agents/llm/agent_tools/infoscience_publications.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.base import InfoscienceProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Search Infoscience (EPFL's institutional repository) for scholarly " + "publications matching a query. Use it to find articles related to a " + "repository, person, or organization, e.g. when CITATION.cff or README " + "references a paper but lacks a DOI. Returns matching publications with " + "their infosciencePublicationIdentifier, title, authors, publicationDate, " + "doi, url, and sourceOrganization. Use the resulting " + "infosciencePublicationIdentifier as pulse:infoscienceArticleIdentifier " + "and the doi as schema:identifier when present." +) + + +def make_infoscience_publications_search_tool( + infoscience_provider: InfoscienceProvider, +) -> Tool: + """Create a pydantic-ai Tool that searches Infoscience for publications.""" + + def search_infoscience_publications(query: str) -> list[dict[str, Any]]: + """Search Infoscience for scholarly publications matching `query`. + + Returns a list of publication records with keys: + infosciencePublicationIdentifier, title, authors, publicationDate, + doi, url, sourceOrganization. + """ + logger.info("tool call: search_infoscience_publications — query=%r", query) + record_query(service="infoscience.search_publications", query=query) + results = infoscience_provider.search_publications(query) + return [ + { + "infosciencePublicationIdentifier": record.get( + "infosciencePublicationIdentifier", + ), + "title": record.get("title"), + "authors": record.get("authors", []), + "publicationDate": record.get("publicationDate"), + "doi": record.get("doi"), + "url": record.get("url"), + "sourceOrganization": record.get("sourceOrganization"), + } + for record in results + if isinstance(record, dict) + ] + + return Tool( + search_infoscience_publications, + name="search_infoscience_publications", + description=_DESCRIPTION, + ) diff --git a/src/v2/agents/llm/agent_tools/infoscience_rag.py b/src/v2/agents/llm/agent_tools/infoscience_rag.py new file mode 100644 index 0000000..29213de --- /dev/null +++ b/src/v2/agents/llm/agent_tools/infoscience_rag.py @@ -0,0 +1,169 @@ +"""Pydantic-AI Tools backed by :class:`InfoscienceRagProvider`. + +Three tools so the LLM can drive a RAG search/fetch loop on demand: + +* ``search_infoscience_rag`` — semantic vector search across the + Infoscience index (chunks / articles / persons / organizations) with an + allowlisted filter dict and an opt-in cross-encoder rerank pass. +* ``fetch_infoscience_chunks`` — pulls full chunk bodies for a single + article identified by ``article_uuid`` (returned by the search tool). +* ``fetch_infoscience_records`` — full payloads for articles / persons / + organizations by point ID. + +Splitting search from fetch keeps prompts small: search returns thin hits +(IDs + titles + short snippets), and the agent decides when to spend the +context budget on a full body. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.ingest.providers.infoscience_rag import ( + CollectionName, # noqa: TC001 — runtime annotation read by pydantic-ai +) +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.infoscience_rag import InfoscienceRagProvider + +logger = logging.getLogger(__name__) + +_SEARCH_DESCRIPTION = ( + "Semantic search over the Infoscience RAG index. Use this when README / " + "CITATION / metadata mentions a paper, lab, or person without a clean " + "identifier and you need to find the matching record. " + "`collection`: 'chunks' (default — paper body fragments), 'articles' " + "(one row per paper), 'persons' (researchers), or 'organizations' (EPFL " + "labs/units). `filters`: optional dict of allowlisted keys " + "(has_github_match, has_hf_match, year, publication_type, language, " + "lab_uuid, org_uuids, doi, orcid, ror_id, sciper_id, sciper_unit_id, " + "author_uuids, subjects, keywords). Each value may be a scalar, a list " + "(any-of), or an operator dict ($eq/$ne/$in/$contains/$gte/$lte). " + "`rerank=true` engages the cross-encoder for ambiguous queries — slower " + "but more precise; default false. Returns trimmed hits (id, score, " + "title/name, snippet, identifiers). Feed the returned id into " + "fetch_infoscience_chunks (article_uuid) or fetch_infoscience_records " + "to pull the full body when needed." +) + +_FETCH_CHUNKS_DESCRIPTION = ( + "Fetch the full chunk bodies of one Infoscience article by its " + "`article_uuid` (returned from search_infoscience_rag). Use this only " + "after a search hit looks promising — it spends meaningful context " + "budget. Returns chunks ordered by chunk_index with their full text, " + "title, doi, and infoscience_url." +) + +_FETCH_RECORDS_DESCRIPTION = ( + "Fetch full payloads of articles, persons, or organizations by point " + "id (the `id` field returned by search_infoscience_rag). `collection` " + "must be 'articles', 'persons', or 'organizations' — chunk bodies use " + "fetch_infoscience_chunks instead. Returns the complete payload for " + "each id (abstract, affiliations, parent org chain, etc.)." +) + + +def _service_name(op: str) -> str: + return f"infoscience.rag.{op}" + + +def make_infoscience_rag_search_tool(provider: InfoscienceRagProvider) -> Tool: + """Tool factory: semantic search over the Infoscience RAG index.""" + + async def search_infoscience_rag( + query: str, + collection: CollectionName = "chunks", + top_k: int = 10, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — part of the LLM tool signature + ) -> list[dict[str, Any]]: + """Vector search the Infoscience index. See tool description.""" + logger.info( + "tool call: search_infoscience_rag — collection=%s top_k=%d " + "rerank=%s filters=%r query=%r", + collection, top_k, rerank, filters, query, + ) + record_query( + service=f"{_service_name('search')}.{collection}", + query=query, + ) + return await provider.search( + query, + collection=collection, + top_k=top_k, + filters=filters, + rerank=rerank, + ) + + return Tool( + search_infoscience_rag, + name="search_infoscience_rag", + description=_SEARCH_DESCRIPTION, + ) + + +def make_infoscience_rag_fetch_chunks_tool( + provider: InfoscienceRagProvider, +) -> Tool: + """Tool factory: fetch full chunk bodies for one article.""" + + async def fetch_infoscience_chunks( + article_uuid: str, + max_chunks: int = 20, + ) -> list[dict[str, Any]]: + logger.info( + "tool call: fetch_infoscience_chunks — article_uuid=%s max_chunks=%d", + article_uuid, max_chunks, + ) + record_query( + service=_service_name("fetch_chunks"), + query=article_uuid, + ) + return await provider.fetch_chunks(article_uuid, max_chunks=max_chunks) + + return Tool( + fetch_infoscience_chunks, + name="fetch_infoscience_chunks", + description=_FETCH_CHUNKS_DESCRIPTION, + ) + + +def make_infoscience_rag_fetch_records_tool( + provider: InfoscienceRagProvider, +) -> Tool: + """Tool factory: fetch full article/person/organization payloads by id.""" + + async def fetch_infoscience_records( + collection: CollectionName, + ids: list[str], + ) -> list[dict[str, Any]]: + logger.info( + "tool call: fetch_infoscience_records — collection=%s ids=%d", + collection, len(ids), + ) + record_query( + service=f"{_service_name('fetch_records')}.{collection}", + query=",".join(str(i) for i in ids), + ) + try: + return await provider.fetch_records(collection, ids) + except ValueError as exc: + logger.warning("fetch_infoscience_records: %s", exc) + return [] + + return Tool( + fetch_infoscience_records, + name="fetch_infoscience_records", + description=_FETCH_RECORDS_DESCRIPTION, + ) + + +__all__ = [ + "make_infoscience_rag_fetch_chunks_tool", + "make_infoscience_rag_fetch_records_tool", + "make_infoscience_rag_search_tool", +] diff --git a/src/v2/agents/llm/agent_tools/infoscience_search.py b/src/v2/agents/llm/agent_tools/infoscience_search.py new file mode 100644 index 0000000..7d0485a --- /dev/null +++ b/src/v2/agents/llm/agent_tools/infoscience_search.py @@ -0,0 +1,59 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.base import InfoscienceProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Search Infoscience for person records by name or query string. " + "Returns matching persons with their infosciencePersonIdentifier, ORCID, " + "affiliations, profileUrl, and relevance score. " + "Use the highest-scoring result's infosciencePersonIdentifier for " + "pulse:infosciencePersonIdentifier. " + "Call this tool before assigning pulse:infosciencePersonIdentifier." +) + + +def make_infoscience_search_tool(infoscience_provider: InfoscienceProvider) -> Tool: + """Create a pydantic-ai Tool that searches Infoscience for person records. + + Returns a Tool whose closure captures ``infoscience_provider`` so it can + be called by the LLM agent at runtime without any additional context. + """ + + def search_infoscience_person(query: str) -> list[dict[str, Any]]: + """Search Infoscience for person records by name or query string. + + Returns a list of person records. Each record contains: + infosciencePersonIdentifier, name, orcid, affiliations, profileUrl, score. + Pick the highest-score result for pulse:infosciencePersonIdentifier. + """ + logger.info("tool call: search_infoscience_person — query=%r", query) + record_query(service="infoscience.search_person", query=query) + results = infoscience_provider.search_person(query) + return [ + { + "infosciencePersonIdentifier": record.get("infosciencePersonIdentifier"), + "name": record.get("name"), + "orcid": record.get("orcid"), + "affiliations": record.get("affiliations", []), + "profileUrl": record.get("profileUrl"), + "score": record.get("score"), + } + for record in results + if isinstance(record, dict) + ] + + return Tool( + search_infoscience_person, + name="search_infoscience_person", + description=_DESCRIPTION, + ) diff --git a/src/v2/agents/llm/agent_tools/openalex_rag.py b/src/v2/agents/llm/agent_tools/openalex_rag.py new file mode 100644 index 0000000..d511bb2 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/openalex_rag.py @@ -0,0 +1,73 @@ +"""Pydantic-AI Tool backed by :class:`OpenAlexRagProvider`.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.ingest.providers.openalex_rag import ( + CollectionName, # noqa: TC001 — runtime annotation read by pydantic-ai +) +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.openalex_rag import OpenAlexRagProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Semantic search over the OpenAlex RAG index — a worldwide scholarly " + "graph (works, authors, institutions, sources, topics, concepts). Use " + "this when you need to ground a paper, author, or institution that " + "isn't in EPFL's Infoscience. `collection`: 'works' (default — papers; " + "abstracts indexed), 'authors', 'institutions', 'sources', 'topics', " + "'concepts'. `filters`: optional dict of allowlisted keys " + "(year/publication_year, doi, entity_type, openalex_id, country_code, " + "type, field_id, domain_id, level, primary_topic_id, " + "primary_source_id, last_known_institution_id, orcid, ror). Each " + "value may be a scalar, a list (any-of), or {$gte/$lte} for ranges. " + "`rerank=true` engages the cross-encoder; for works it reranks " + "against title+abstract — strong signal. Returns thin hits with " + "openalex_id, title/display_name, doi/orcid/ror, year, plus a " + "snippet (first ~320 chars of the abstract) for works." +) + + +def make_openalex_rag_search_tool(provider: OpenAlexRagProvider) -> Tool: + """Tool factory: semantic search over the OpenAlex RAG index.""" + + async def search_openalex_rag( + query: str, + collection: CollectionName = "works", + top_k: int = 10, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — part of the LLM tool signature + ) -> list[dict[str, Any]]: + """Vector search the OpenAlex index. See tool description.""" + logger.info( + "tool call: search_openalex_rag — collection=%s top_k=%d " + "rerank=%s filters=%r query=%r", + collection, top_k, rerank, filters, query, + ) + record_query( + service=f"openalex.rag.search.{collection}", + query=query, + ) + return await provider.search( + query, + collection=collection, + top_k=top_k, + filters=filters, + rerank=rerank, + ) + + return Tool( + search_openalex_rag, + name="search_openalex_rag", + description=_DESCRIPTION, + ) + + +__all__ = ["make_openalex_rag_search_tool"] diff --git a/src/v2/agents/llm/agent_tools/orcid_person.py b/src/v2/agents/llm/agent_tools/orcid_person.py new file mode 100644 index 0000000..d2bd765 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/orcid_person.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.base import ORCIDProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Fetch a person's ORCID profile by their ORCID identifier " + "(format: XXXX-XXXX-XXXX-XXXX, e.g. 0000-0002-1825-0097). " + "Returns name, employment history, education history, and affiliations. " + "Use the returned name for schema:name if it is more complete than the GitHub name. " + "Use employment/education affiliations to build org:hasMembership entries. " + "Call this tool when an orcid_hint is present in the context." +) + + +def make_orcid_person_tool(orcid_provider: ORCIDProvider) -> Tool: + """Create a pydantic-ai Tool that fetches a single ORCID record by ID. + + Returns a Tool whose closure captures ``orcid_provider`` so it can + be called by the LLM agent at runtime without any additional context. + """ + + def get_orcid_record(orcid_id: str) -> dict[str, Any]: + """Fetch a person's ORCID record by their ORCID identifier. + + Accepts ORCID in the format XXXX-XXXX-XXXX-XXXX. + Returns orcid_id, name, employment, education, and affiliations. + """ + logger.info("tool call: get_orcid_record — orcid_id=%r", orcid_id) + record_query(service="orcid.get_person_by_orcid", query=orcid_id) + record = orcid_provider.get_person_by_orcid(orcid_id) + return { + "orcid_id": record.get("orcid_id"), + "name": record.get("name"), + "employment": record.get("employment", []), + "education": record.get("education", []), + "affiliations": record.get("affiliations", []), + } + + return Tool( + get_orcid_record, + name="get_orcid_record", + description=_DESCRIPTION, + ) diff --git a/src/v2/agents/llm/agent_tools/orcid_rag.py b/src/v2/agents/llm/agent_tools/orcid_rag.py new file mode 100644 index 0000000..5352ad5 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/orcid_rag.py @@ -0,0 +1,69 @@ +"""Pydantic-AI Tool backed by :class:`OrcidRagProvider`.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.ingest.providers.orcid_rag import ( + EntityType, # noqa: TC001 — runtime annotation read by pydantic-ai +) +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.orcid_rag import OrcidRagProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Semantic search over the ORCID RAG index, scoped to the configured " + "region (typically EPFL). Use this when you need to ground a person " + "by name or biography to an ORCID iD, or to look up their employment / " + "education history. `entity_type`: 'persons' (default — name + bio), " + "'employments' (jobs), 'educations' (degrees). `filters`: optional " + "dict of allowlisted keys (orcid_id, in_scope, discovered_via, " + "org_ror, organization, department, role). `rerank=true` engages the " + "cross-encoder; for persons it reranks against name + biography. " + "Returns thin hits with orcid_id, names, organization/role for " + "affiliations, plus a biography snippet for persons." +) + + +def make_orcid_rag_search_tool(provider: OrcidRagProvider) -> Tool: + """Tool factory: semantic search over the ORCID RAG index.""" + + async def search_orcid_rag( + query: str, + entity_type: EntityType = "persons", + top_k: int = 10, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — part of the LLM tool signature + ) -> list[dict[str, Any]]: + """Vector search the ORCID index. See tool description.""" + logger.info( + "tool call: search_orcid_rag — entity=%s top_k=%d rerank=%s " + "filters=%r query=%r", + entity_type, top_k, rerank, filters, query, + ) + record_query( + service=f"orcid.rag.search.{entity_type}", + query=query, + ) + return await provider.search( + query, + entity_type=entity_type, + top_k=top_k, + filters=filters, + rerank=rerank, + ) + + return Tool( + search_orcid_rag, + name="search_orcid_rag", + description=_DESCRIPTION, + ) + + +__all__ = ["make_orcid_rag_search_tool"] diff --git a/src/v2/agents/llm/agent_tools/organization_identity.py b/src/v2/agents/llm/agent_tools/organization_identity.py new file mode 100644 index 0000000..c80e711 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/organization_identity.py @@ -0,0 +1,201 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.canonicalization.string_utils import normalize_string +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.base import InfoscienceProvider, RORProvider + +logger = logging.getLogger(__name__) + +MAX_QUERY_EXPANSIONS_PER_PROVIDER = 3 +MAX_PROVIDER_CANDIDATES = 12 +MAX_LINKED_CANDIDATES = 10 +ORGANIZATION_NAME_EQUIVALENCE_TOKENS: dict[str, str] = { + "centre": "center", + "centres": "centers", +} + + +def _normalize_org_name(value: Any) -> str | None: + if not isinstance(value, str): + return None + normalized = normalize_string(value) + if not normalized: + return None + normalized_tokens = [ + ORGANIZATION_NAME_EQUIVALENCE_TOKENS.get(token, token) + for token in normalized.split() + ] + canonical_name = " ".join(normalized_tokens) + return canonical_name or None + + +def _dedupe_candidates( + candidates: list[dict[str, Any]], + *, + key_fields: tuple[str, ...], +) -> list[dict[str, Any]]: + deduped: list[dict[str, Any]] = [] + seen: set[str] = set() + for candidate in candidates: + if not isinstance(candidate, dict): + continue + marker = "" + for field in key_fields: + value = candidate.get(field) + if isinstance(value, str) and value: + marker = f"{field}:{value}" + break + if not marker: + name_key = _normalize_org_name(candidate.get("name")) or repr(sorted(candidate.items())) + marker = f"name:{name_key}" + if marker in seen: + continue + seen.add(marker) + deduped.append(candidate) + return deduped + + +def _candidate_query_seeds(candidate: dict[str, Any], *, include_aliases: bool) -> list[str]: + seeds: list[str] = [] + for key in ("name", "acronym", "parentOrganization"): + value = candidate.get(key) + if isinstance(value, str) and value: + seeds.append(value) + if include_aliases: + aliases = candidate.get("aliases") + if isinstance(aliases, list): + seeds.extend(alias for alias in aliases if isinstance(alias, str) and alias) + acronyms = candidate.get("acronyms") + if isinstance(acronyms, list): + seeds.extend(acronym for acronym in acronyms if isinstance(acronym, str) and acronym) + return seeds + + +def _linked_candidates( + ror_candidates: list[dict[str, Any]], + infoscience_candidates: list[dict[str, Any]], +) -> list[dict[str, Any]]: + linked: list[dict[str, Any]] = [] + seen_pairs: set[tuple[str, str]] = set() + for ror_candidate in ror_candidates: + ror_name = ror_candidate.get("name") + ror_name_key = _normalize_org_name(ror_name) + ror_id = ror_candidate.get("id") + if not isinstance(ror_name_key, str): + continue + for infoscience_candidate in infoscience_candidates: + infoscience_name = infoscience_candidate.get("name") + infoscience_name_key = _normalize_org_name(infoscience_name) + infoscience_id = infoscience_candidate.get("infoscienceOrgUnitIdentifier") + if not isinstance(infoscience_name_key, str): + continue + if ror_name_key != infoscience_name_key: + continue + if not isinstance(ror_id, str) or not isinstance(infoscience_id, str): + continue + pair_key = (ror_id, infoscience_id) + if pair_key in seen_pairs: + continue + seen_pairs.add(pair_key) + linked.append( + { + "match_reason": "normalized_name_match", + "normalized_name": ror_name_key, + "ror_id": ror_id, + "ror_name": ror_name, + "infoscience_orgunit_identifier": infoscience_id, + "infoscience_name": infoscience_name, + }, + ) + return linked + + +def make_organization_identity_search_tool( + ror_provider: RORProvider, + infoscience_provider: InfoscienceProvider, +) -> Tool: # noqa: C901 + """Create a dual-provider organization lookup tool with lightweight cross-linking.""" + + def search_organization_identity(query: str) -> dict[str, Any]: # noqa: C901 + """Search both ROR and Infoscience and return candidate sets plus linked matches.""" + + normalized_query = query.strip() + logger.info("tool call: search_organization_identity — query=%r", normalized_query) + if normalized_query: + record_query( + service="organization_identity.search", + query=normalized_query, + ) + if not normalized_query: + return { + "query": "", + "ror_candidates": [], + "infoscience_candidates": [], + "linked_candidates": [], + "expanded_queries": { + "ror": [], + "infoscience": [], + }, + } + + ror_candidates = list(ror_provider.search_organizations(normalized_query)) + infoscience_candidates = list(infoscience_provider.search_orgunit(normalized_query)) + + expanded_ror_queries: list[str] = [] + expanded_infoscience_queries: list[str] = [] + + for ror_candidate in ror_candidates[:MAX_QUERY_EXPANSIONS_PER_PROVIDER]: + for seed in _candidate_query_seeds(ror_candidate, include_aliases=True): + if seed == normalized_query or seed in expanded_infoscience_queries: + continue + expanded_infoscience_queries.append(seed) + infoscience_candidates.extend(infoscience_provider.search_orgunit(seed)) + if len(expanded_infoscience_queries) >= MAX_QUERY_EXPANSIONS_PER_PROVIDER: + break + if len(expanded_infoscience_queries) >= MAX_QUERY_EXPANSIONS_PER_PROVIDER: + break + + for infoscience_candidate in infoscience_candidates[:MAX_QUERY_EXPANSIONS_PER_PROVIDER]: + for seed in _candidate_query_seeds(infoscience_candidate, include_aliases=False): + if seed == normalized_query or seed in expanded_ror_queries: + continue + expanded_ror_queries.append(seed) + ror_candidates.extend(ror_provider.search_organizations(seed)) + if len(expanded_ror_queries) >= MAX_QUERY_EXPANSIONS_PER_PROVIDER: + break + if len(expanded_ror_queries) >= MAX_QUERY_EXPANSIONS_PER_PROVIDER: + break + + deduped_ror = _dedupe_candidates(ror_candidates, key_fields=("id",)) + deduped_infoscience = _dedupe_candidates( + infoscience_candidates, + key_fields=("infoscienceOrgUnitIdentifier", "id"), + ) + linked = _linked_candidates(deduped_ror, deduped_infoscience) + + return { + "query": normalized_query, + "ror_candidates": deduped_ror[:MAX_PROVIDER_CANDIDATES], + "infoscience_candidates": deduped_infoscience[:MAX_PROVIDER_CANDIDATES], + "linked_candidates": linked[:MAX_LINKED_CANDIDATES], + "expanded_queries": { + "ror": expanded_ror_queries, + "infoscience": expanded_infoscience_queries, + }, + } + + return Tool( + search_organization_identity, + name="search_organization_identity", + description=( + "Search ROR and Infoscience together and return provider candidates plus " + "high-confidence linked matches based on normalized organization names." + ), + ) diff --git a/src/v2/agents/llm/agent_tools/query_dependencies.py b/src/v2/agents/llm/agent_tools/query_dependencies.py new file mode 100644 index 0000000..cb01ad3 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/query_dependencies.py @@ -0,0 +1,94 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.base import GitHubProvider + +logger = logging.getLogger(__name__) + +DEFAULT_LIMIT = 50 +MAX_LIMIT = 500 + +_DESCRIPTION = ( + "Inspect the dependency manifests parsed by GitHub's dependency graph for " + "a repository. Returns a flat list of packages parsed from the SPDX SBOM, " + "each entry shaped as {name, ecosystem, version, spdxId}. " + "Ecosystem values follow purl conventions (pypi, npm, cargo, maven, " + "githubactions, ...). " + "Optional: pass `ecosystem` to keep only packages from that ecosystem, " + "and/or `name_contains` to keep only packages whose name contains the " + "given substring (case-insensitive). " + "Call this tool when dependency information is relevant to your " + "assessment — e.g. ecosystem, scope, research-software classification, " + "or technology stack inference. Skip when the README and metadata are " + "already sufficient: the call hits the GitHub API and is not free. " + "Returns an empty list when the repository has no SBOM available " + "(dependency graph disabled, private without scope, or no parsed " + "manifests)." +) + + +def make_query_dependencies_tool(github_provider: GitHubProvider) -> Tool: + """Create a pydantic-ai Tool that fetches a repository's parsed dependencies.""" + + def query_dependencies( + full_name: str, + ecosystem: str | None = None, + name_contains: str | None = None, + limit: int = DEFAULT_LIMIT, + ) -> list[dict[str, Any]]: + """Return parsed dependency entries from a repository's SPDX SBOM. + + Args: + full_name: GitHub repository handle in ``owner/repo`` form. + ecosystem: Optional purl ecosystem filter (e.g. ``"pypi"``, + ``"npm"``). Case-insensitive exact match. + name_contains: Optional case-insensitive substring filter on + the package ``name``. + limit: Maximum number of entries to return (1..500, default 50). + """ + logger.info( + "tool call: query_dependencies — full_name=%r ecosystem=%r " + "name_contains=%r limit=%d", + full_name, + ecosystem, + name_contains, + limit, + ) + record_query(service="github.repository_sbom", query=full_name) + bounded_limit = max(1, min(limit, MAX_LIMIT)) + dependencies = github_provider.get_repository_sbom(full_name) + if not dependencies: + return [] + + ecosystem_filter = ecosystem.strip().lower() if isinstance(ecosystem, str) else None + name_filter = ( + name_contains.strip().lower() if isinstance(name_contains, str) else None + ) + + filtered: list[dict[str, Any]] = [] + for entry in dependencies: + if ecosystem_filter: + entry_ecosystem = entry.get("ecosystem") + if not isinstance(entry_ecosystem, str) or entry_ecosystem.lower() != ecosystem_filter: + continue + if name_filter: + entry_name = entry.get("name") + if not isinstance(entry_name, str) or name_filter not in entry_name.lower(): + continue + filtered.append(dict(entry)) + if len(filtered) >= bounded_limit: + break + return filtered + + return Tool( + query_dependencies, + name="query_dependencies", + description=_DESCRIPTION, + ) diff --git a/src/v2/agents/llm/agent_tools/query_orcid.py b/src/v2/agents/llm/agent_tools/query_orcid.py new file mode 100644 index 0000000..d359259 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/query_orcid.py @@ -0,0 +1,146 @@ +"""Person-discovery ORCID search tool. + +Primary backend is the ORCID RAG (Qdrant) — fast semantic search over +pre-ingested EPFL/Switzerland persons, no rate limits. The live ORCID +``expanded_search`` API is used as a fallback when: + +- the RAG provider is not configured, +- the RAG search returns zero hits, or +- the RAG search raises (e.g. Qdrant unavailable). + +Both paths emit the same record shape so callers don't branch. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.base import ORCIDProvider + from src.v2.ingest.providers.orcid_rag import OrcidRagProvider + +logger = logging.getLogger(__name__) + +DEFAULT_ROWS = 50 +MAX_ROWS = 200 +RAG_TOP_K = 25 # we return up to `rows`, but let RAG over-fetch and trim + +_DESCRIPTION = ( + "Search ORCID by free-text name (and optionally affiliation keywords) " + "to discover candidate ORCID identifiers. Primary backend is the " + "internal ORCID RAG index (semantic search over pre-ingested EPFL / " + "Switzerland records, no rate limits); falls back to ORCID's expanded " + "search API when the RAG returns no hits. " + "Returns up to `rows` hits, each with orcid_id, given_names, " + "family_names, credit_name, other_names, institution_names, emails, " + "and `source` ('rag' or 'orcid_api'). " + "Call this when you need to find an ORCID for a person whose ID is " + "not provided in the context — then pass the chosen orcid_id to " + "get_orcid_record for full employment/education details." +) + + +def _normalize_rag_hit(hit: dict[str, Any]) -> dict[str, Any]: + """Coerce a RAG `persons` hit into the live-API result shape.""" + + orcid_id = hit.get("orcid_id") + given = hit.get("given_name") + family = hit.get("family_name") + display = hit.get("display_name") + return { + "orcid_id": orcid_id, + "given_names": [given] if isinstance(given, str) and given else [], + "family_names": [family] if isinstance(family, str) and family else [], + "credit_name": display if isinstance(display, str) else None, + "other_names": [], + # The thin RAG persons payload omits affiliations/emails. Callers + # that need institution disambiguation should follow up with + # get_orcid_record(orcid_id) to fetch full employments/educations. + "institution_names": [], + "emails": [], + "source": "rag", + } + + +def _normalize_live_hit(hit: dict[str, Any]) -> dict[str, Any]: + """Tag a live-API hit with `source='orcid_api'`; pass-through otherwise.""" + + return {**hit, "source": "orcid_api"} + + +def make_query_orcid_tool( + orcid_provider: ORCIDProvider, + *, + orcid_rag_provider: OrcidRagProvider | None = None, +) -> Tool: + """Create the `query_orcid` tool. + + `orcid_rag_provider` is the primary backend; `orcid_provider` is the + live-API fallback (always required because the RAG can be unavailable + or stale). + """ + + async def query_orcid( + query: str, + rows: int = DEFAULT_ROWS, + start: int = 0, + ) -> list[dict[str, Any]]: + """Search ORCID for persons by name; RAG-primary, live-API fallback. + + Args: + query: Free-text name (e.g. "noemie mazare"); affiliation keywords + are accepted but the boosted name fields drive ranking. + rows: Maximum number of hits to return (1..200, default 50). + start: Offset for pagination (default 0). Only honoured by the + live-API fallback; the RAG path returns top hits without + offset support. + """ + + bounded_rows = max(1, min(rows, MAX_ROWS)) + bounded_start = max(0, start) + + if orcid_rag_provider is not None and bounded_start == 0: + try: + logger.info( + "tool call: query_orcid (RAG) — query=%r rows=%d", + query, bounded_rows, + ) + record_query(service="orcid.rag.search.persons", query=query) + rag_hits = await orcid_rag_provider.search( + query, + entity_type="persons", + top_k=max(bounded_rows, RAG_TOP_K), + rerank=True, + ) + except Exception as exc: # noqa: BLE001 — RAG availability is optional + logger.warning( + "query_orcid: RAG search failed, falling back to live API — %s", + exc, + ) + rag_hits = [] + if rag_hits: + normalized = [_normalize_rag_hit(h) for h in rag_hits[:bounded_rows]] + return normalized + + logger.info( + "tool call: query_orcid (live API) — query=%r rows=%d start=%d", + query, bounded_rows, bounded_start, + ) + record_query(service="orcid.search_persons", query=query) + hits = orcid_provider.search_persons( + query, + rows=bounded_rows, + start=bounded_start, + ) + return [_normalize_live_hit(dict(hit)) for hit in hits] + + return Tool( + query_orcid, + name="query_orcid", + description=_DESCRIPTION, + ) diff --git a/src/v2/agents/llm/agent_tools/renkulab_rag.py b/src/v2/agents/llm/agent_tools/renkulab_rag.py new file mode 100644 index 0000000..aef8480 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/renkulab_rag.py @@ -0,0 +1,69 @@ +"""Pydantic-AI Tool backed by :class:`RenkulabRagProvider`.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.renkulab_rag import RenkulabRagProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Semantic search over the RenkuLab RAG index (renkulab.io) — " + "data-science projects, groups, users, and data connectors hosted " + "by the Swiss Data Science Center. Use this when a repository, " + "person, or organisation might be active on RenkuLab. " + "`entity_types`: optional subset of " + "['projects','groups','users','data_connectors']; defaults to all " + "four collections (results merged + reranked together). " + "`filters`: optional dict of allowlisted keys (entity_type, slug, " + "namespace, path, visibility, storage_type, storage_provider, " + "entity_id) — each value scalar / list (any-of) / {$gte,$lte}. " + "`rerank=true` engages the cross-encoder against the entity's " + "name + namespace — strong signal for ambiguous user / project " + "names. Returns thin hits projected per entity type " + "(projects: name+namespace+visibility; groups: name+slug; " + "users: first/last name+path; data_connectors: name+namespace+" + "storage_type) with the renkulab.io URL when known." +) + + +def make_renkulab_rag_search_tool(provider: RenkulabRagProvider) -> Tool: + """Tool factory: semantic search over the RenkuLab RAG index.""" + + async def search_renkulab_rag( + query: str, + top_k: int = 10, + entity_types: list[str] | None = None, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — part of the LLM tool signature + ) -> list[dict[str, Any]]: + """Vector search the RenkuLab index. See tool description.""" + logger.info( + "tool call: search_renkulab_rag — top_k=%d rerank=%s " + "entity_types=%r filters=%r query=%r", + top_k, rerank, entity_types, filters, query, + ) + record_query(service="renkulab.rag.search", query=query) + return await provider.search( + query, + top_k=top_k, + entity_types=entity_types, + filters=filters, + rerank=rerank, + ) + + return Tool( + search_renkulab_rag, + name="search_renkulab_rag", + description=_DESCRIPTION, + ) + + +__all__ = ["make_renkulab_rag_search_tool"] diff --git a/src/v2/agents/llm/agent_tools/repository_corpus_grep.py b/src/v2/agents/llm/agent_tools/repository_corpus_grep.py new file mode 100644 index 0000000..f5bb59b --- /dev/null +++ b/src/v2/agents/llm/agent_tools/repository_corpus_grep.py @@ -0,0 +1,172 @@ +from __future__ import annotations + +import logging +import re +from typing import Any + +from pydantic_ai import Tool + +logger = logging.getLogger(__name__) +MAX_MATCHES = 20 +MAX_CONTEXT_LINES = 10 +MAX_LINE_LENGTH = 320 + + +def _to_non_empty_string(value: Any) -> str | None: + if isinstance(value, str): + candidate = value.strip() + if candidate: + return candidate + return None + + +def _normalized_int(value: Any, *, default: int, lower: int, upper: int) -> int: + if isinstance(value, int): + return max(lower, min(upper, value)) + return default + + +def _compile_query_pattern(query: str) -> re.Pattern[str]: + # Support optional `/regex/` syntax while keeping substring search as default. + if len(query) >= 3 and query.startswith("/") and query.endswith("/"): + return re.compile(query[1:-1], re.IGNORECASE) + return re.compile(re.escape(query), re.IGNORECASE) + + +def _format_snippet(lines: list[str], *, center_line: int, context_lines: int) -> str: + start = max(1, center_line - context_lines) + end = min(len(lines), center_line + context_lines) + snippet_rows: list[str] = [] + for line_number in range(start, end + 1): + text = lines[line_number - 1] + if len(text) > MAX_LINE_LENGTH: + text = f"{text[: MAX_LINE_LENGTH - 3]}..." + marker = ">" if line_number == center_line else " " + snippet_rows.append(f"{marker}{line_number:5d} | {text}") + return "\n".join(snippet_rows) + + +def make_repository_corpus_grep_tool(corpus_documents: list[dict[str, Any]]) -> Tool: + """Create a grep-like tool over an in-memory repository document corpus.""" + + def grep_repository_corpus( + query: str, + max_matches: int = 8, + context_lines: int = 3, + ) -> str: + """Search repository corpus documents and return markdown snippets. + + Args: + query: Search term (case-insensitive substring) or `/regex/`. + max_matches: Max snippets to return (1..20). + context_lines: Number of surrounding lines to include (1..10). + """ + + normalized_query = query.strip() if isinstance(query, str) else "" + logger.info( + "tool call: grep_repository_corpus — query=%r max_matches=%r context_lines=%r docs=%d", + normalized_query, + max_matches, + context_lines, + len(corpus_documents), + ) + if not normalized_query: + return ( + "No query provided.\n\n" + "Provide a non-empty string or `/regex/` pattern." + ) + + limited_matches = _normalized_int( + max_matches, + default=8, + lower=1, + upper=MAX_MATCHES, + ) + limited_context = _normalized_int( + context_lines, + default=3, + lower=1, + upper=MAX_CONTEXT_LINES, + ) + try: + pattern = _compile_query_pattern(normalized_query) + except re.error as exc: + return f"Invalid regex query: {exc}" + + rows: list[str] = [] + match_count = 0 + for document in corpus_documents: + content = _to_non_empty_string(document.get("content")) + if content is None: + continue + lines = content.splitlines() + if not lines: + continue + + for line_number, line in enumerate(lines, start=1): + if not pattern.search(line): + continue + match_count += 1 + source_label = _to_non_empty_string(document.get("label")) or "unknown" + repository = _to_non_empty_string(document.get("repository")) or "unknown" + origin = _to_non_empty_string(document.get("origin")) or "unknown" + path = _to_non_empty_string(document.get("path")) or "" + snippet = _format_snippet( + lines, + center_line=line_number, + context_lines=limited_context, + ) + rows.append(f"### Match {match_count}") + rows.append(f"- repository: `{repository}`") + rows.append(f"- source: `{source_label}`") + rows.append(f"- origin: `{origin}`") + rows.append(f"- path: `{path}`") + rows.append(f"- line: `{line_number}`") + rows.append("```text") + rows.append(snippet) + rows.append("```") + rows.append("") + + if match_count >= limited_matches: + break + if match_count >= limited_matches: + break + + if not rows: + available_sources = [] + for document in corpus_documents: + label = _to_non_empty_string(document.get("label")) + repository = _to_non_empty_string(document.get("repository")) + path = _to_non_empty_string(document.get("path")) + if not label: + continue + descriptor = f"{label} ({repository or 'unknown'}:{path or ''})" + if descriptor not in available_sources: + available_sources.append(descriptor) + + sources_text = "\n".join(f"- {item}" for item in available_sources[:20]) + if not sources_text: + sources_text = "- " + return ( + f"No matches for query `{normalized_query}`.\n\n" + "Available sources:\n" + f"{sources_text}" + ) + + header = [ + "# Repository Grep Results", + f"- query: `{normalized_query}`", + f"- matches_returned: `{match_count}`", + "", + ] + return "\n".join(header + rows).strip() + + return Tool( + grep_repository_corpus, + name="grep_repository_corpus", + description=( + "Search raw repository corpus documents (README, GIMIE JSON-LD, and " + "repository files when available). Returns markdown snippets with " + "origin metadata and line-numbered code blocks." + ), + ) diff --git a/src/v2/agents/llm/agent_tools/ror_organization.py b/src/v2/agents/llm/agent_tools/ror_organization.py new file mode 100644 index 0000000..7ae5e55 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/ror_organization.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.base import RORProvider + +logger = logging.getLogger(__name__) + + +def make_ror_organization_search_tool(ror_provider: RORProvider) -> Tool: + """Create a ROR organization search tool bound to a provider instance.""" + + def search_ror_organizations(query: str) -> list[dict[str, Any]]: + """Search ROR organizations by free-text query.""" + + logger.info("tool call: search_ror_organizations — query=%r", query) + record_query(service="ror.search_organizations", query=query) + return ror_provider.search_organizations(query) + + return Tool( + search_ror_organizations, + name="search_ror_organizations", + description=( + "Search ROR organizations by name or acronym and return " + "candidate organization records." + ), + ) diff --git a/src/v2/agents/llm/agent_tools/ror_rag.py b/src/v2/agents/llm/agent_tools/ror_rag.py new file mode 100644 index 0000000..262db74 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/ror_rag.py @@ -0,0 +1,70 @@ +"""Pydantic-AI Tool backed by :class:`RorRagProvider`.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.ingest.providers.ror_rag import ( + ScopeMode, # noqa: TC001 — runtime annotation read by pydantic-ai +) +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.ror_rag import RorRagProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Semantic search over the ROR (Research Organization Registry) RAG " + "index. Use this when README / CITATION mentions a research lab, " + "university, or institute by name and you need its canonical ROR id " + "and parent organisation chain. `scope_mode`: 'worldwide' (default — " + "global ROR), 'europe', 'switzerland', or 'epfl_ethz' (narrowest, " + "EPFL+ETHZ neighbourhood). Filtering: only `country_code` is " + "supported by the ROR store (e.g. 'CH', 'FR'); pass it via " + '`filters={"country_code": "CH"}`. Other filter keys are dropped ' + "with a warning. `rerank=true` reranks against the indexed text " + "(name + types + parent + website). Returns thin hits with ror_id, " + "name, country_code, types, plus a short text snippet." +) + + +def make_ror_rag_search_tool(provider: RorRagProvider) -> Tool: + """Tool factory: semantic search over the ROR RAG index.""" + + async def search_ror_rag( + query: str, + scope_mode: ScopeMode = "worldwide", + top_k: int = 10, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — part of the LLM tool signature + ) -> list[dict[str, Any]]: + """Vector search the ROR index. See tool description.""" + logger.info( + "tool call: search_ror_rag — scope=%s top_k=%d rerank=%s " + "filters=%r query=%r", + scope_mode, top_k, rerank, filters, query, + ) + record_query( + service=f"ror.rag.search.{scope_mode}", + query=query, + ) + return await provider.search( + query, + scope_mode=scope_mode, + top_k=top_k, + filters=filters, + rerank=rerank, + ) + + return Tool( + search_ror_rag, + name="search_ror_rag", + description=_DESCRIPTION, + ) + + +__all__ = ["make_ror_rag_search_tool"] diff --git a/src/v2/agents/llm/agent_tools/selenium_fetch.py b/src/v2/agents/llm/agent_tools/selenium_fetch.py new file mode 100644 index 0000000..ec2417a --- /dev/null +++ b/src/v2/agents/llm/agent_tools/selenium_fetch.py @@ -0,0 +1,177 @@ +from __future__ import annotations + +import logging +import os +from typing import TYPE_CHECKING, Any +from urllib.parse import urlparse + +from bs4 import BeautifulSoup +from pydantic_ai import Tool +from selenium import webdriver +from selenium.webdriver.firefox.options import Options as FirefoxOptions +from selenium.webdriver.support.ui import WebDriverWait + +from src.v2.ingest.cache import ProviderCache +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + pass + +logger = logging.getLogger(__name__) + +DEFAULT_SELENIUM_TIMEOUT_SECONDS = 30 +DEFAULT_WAIT_SECONDS = 8 +DEFAULT_MAX_CHARS = 4000 +MAX_ALLOWED_CHARS = 20000 + + +def _is_http_url(value: str) -> bool: + parsed = urlparse(value) + return parsed.scheme in {"http", "https"} and bool(parsed.netloc) + + +def _load_html_via_selenium( + url: str, + *, + timeout_seconds: int, + wait_seconds: int, +) -> tuple[str, str, str]: + selenium_remote_url = os.getenv("SELENIUM_REMOTE_URL", "").strip() + if not selenium_remote_url: + message = "Missing SELENIUM_REMOTE_URL" + raise RuntimeError(message) + + options = FirefoxOptions() + options.add_argument("--headless") + options.add_argument("--no-sandbox") + options.add_argument("--disable-dev-shm-usage") + + driver = webdriver.Remote( + command_executor=selenium_remote_url, + options=options, + ) + try: + driver.set_page_load_timeout(timeout_seconds) + driver.get(url) + WebDriverWait(driver, wait_seconds).until( + lambda current_driver: current_driver.execute_script( + "return document.readyState", + ) + == "complete", + ) + return driver.page_source or "", driver.current_url or url, driver.title or "" + finally: + driver.quit() + + +def fetch_link_content_via_selenium( + url: str, + max_chars: int = DEFAULT_MAX_CHARS, +) -> dict[str, Any]: + """Fetch rendered page content from a URL via Selenium Grid.""" + + logger.info("tool call: fetch_link_content_via_selenium — url=%r", url) + record_query(service="selenium.fetch_link_content", query=str(url) if url else "") + normalized_url = url.strip() if isinstance(url, str) else "" + if not normalized_url or not _is_http_url(normalized_url): + return { + "url": normalized_url or None, + "fetched": False, + "final_url": None, + "title": None, + "text_excerpt": None, + "content_length": 0, + "error": "Invalid http(s) URL", + } + + bounded_max_chars = max(1, min(int(max_chars), MAX_ALLOWED_CHARS)) + try: + html, final_url, title = _load_html_via_selenium( + normalized_url, + timeout_seconds=DEFAULT_SELENIUM_TIMEOUT_SECONDS, + wait_seconds=DEFAULT_WAIT_SECONDS, + ) + text = BeautifulSoup(html, "html.parser").get_text(separator=" ", strip=True) + text_excerpt = text[:bounded_max_chars] if text else "" + return { + "url": normalized_url, + "fetched": True, + "final_url": final_url, + "title": title, + "text_excerpt": text_excerpt, + "content_length": len(text), + "error": None, + } + except Exception as exc: # noqa: BLE001 + logger.warning( + "fetch_link_content_via_selenium failed for %s: %s", + normalized_url, + exc, + ) + return { + "url": normalized_url, + "fetched": False, + "final_url": None, + "title": None, + "text_excerpt": None, + "content_length": 0, + "error": str(exc), + } + + +fetch_link_content_via_selenium_tool = Tool( + fetch_link_content_via_selenium, + name="fetch_link_content_via_selenium", + description=( + "Fetch rendered page content from an http(s) URL using Selenium Grid " + "(SELENIUM_REMOTE_URL). Returns fetch status, final URL, title, and page text excerpt." + ), +) + + +def make_fetch_link_content_tool(cache: ProviderCache | None = None) -> Tool: + """Return a Selenium-fetch tool optionally backed by a `ProviderCache`. + + When `cache` is None, returns the cacheless module-level singleton. + Otherwise wraps the fetch in a cache lookup; only successful fetches + (`fetched=True`) are stored, so transient errors retry next time. + """ + if cache is None: + return fetch_link_content_via_selenium_tool + + def _cached_fetch( + url: str, + max_chars: int = DEFAULT_MAX_CHARS, + ) -> dict[str, Any]: + normalized_url = url.strip() if isinstance(url, str) else "" + if not normalized_url or not _is_http_url(normalized_url): + return fetch_link_content_via_selenium(url, max_chars) + + bounded_max_chars = max(1, min(int(max_chars), MAX_ALLOWED_CHARS)) + key = ProviderCache.make_key( + "selenium", + "fetch_link_content_via_selenium", + url=normalized_url, + max_chars=bounded_max_chars, + ) + cached = cache.get(key) + if cached is not None: + logger.info( + "fetch_link_content_via_selenium cache hit — url=%r", + normalized_url, + ) + return cached + + result = fetch_link_content_via_selenium(url, max_chars) + if isinstance(result, dict) and result.get("fetched"): + cache.set(key, result) + return result + + return Tool( + _cached_fetch, + name="fetch_link_content_via_selenium", + description=( + "Fetch rendered page content from an http(s) URL using Selenium Grid " + "(SELENIUM_REMOTE_URL). Returns fetch status, final URL, title, and page text excerpt." + ), + ) diff --git a/src/v2/agents/llm/agent_tools/snsf_rag.py b/src/v2/agents/llm/agent_tools/snsf_rag.py new file mode 100644 index 0000000..d33f8b5 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/snsf_rag.py @@ -0,0 +1,81 @@ +"""Pydantic-AI Tool backed by :class:`SnsfRagProvider`.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.ingest.providers.snsf_rag import ( + ScopeMode, # noqa: TC001 — runtime annotation read by pydantic-ai +) +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.snsf_rag import SnsfRagProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Semantic search over the SNSF P3 (Swiss National Science Foundation) " + "RAG index — every grant funded by SNSF since 1975, with title, " + "abstract, keywords, discipline, host institution, PI name, dates, " + "funded amount. Use this when README / CITATION / a researcher's " + "page mentions SNSF funding, a project name that sounds like a Swiss " + "research grant, or to ground 'who funded this' / 'what else has X " + "been funded for' questions for CH-resident researchers. " + "`scope_mode`: 'switzerland' (default — full ~90 k corpus, 1975-2027), " + "'epfl' (~6 k EPFL-only precision view), 'ethz' (~9 k ETHZ-only), or " + "'eth_domain' (placeholder; not embedded as its own collection — falls " + "back to []). `filters`: dict with optional keys: `institution` " + "(exact match on `research_institution`, e.g. 'EPF Lausanne – EPFL' " + "or 'ETH Zurich – ETHZ'), `institute` (substring match on the " + "specific lab/centre name resolved from DuckDB after the ANN — " + "useful for SDSC-style queries: `{\"institute\": \"Swiss Data Science " + "Center\"}`), `discipline_l1` (e.g. 'Mathematics, Informatics, " + "Natural Sciences and Technology'), `state` (PascalCase: 'Completed' " + "/ 'Ongoing' / 'Approved'). Other keys are dropped with a warning. " + "`rerank=true` runs the cross-encoder against title+keywords+abstract. " + "Returns thin hits with grant_number, title, research_institution, " + "main_discipline, start_date, amount_granted, score, plus a snippet " + "and the canonical https://data.snf.ch/grants/grant/ URL." +) + + +def make_snsf_rag_search_tool(provider: SnsfRagProvider) -> Tool: + """Tool factory: semantic search over the SNSF P3 RAG index.""" + + async def search_snsf_rag( + query: str, + scope_mode: ScopeMode = "switzerland", + top_k: int = 10, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — part of the LLM tool signature + ) -> list[dict[str, Any]]: + """Vector search the SNSF P3 index. See tool description.""" + logger.info( + "tool call: search_snsf_rag — scope=%s top_k=%d rerank=%s " + "filters=%r query=%r", + scope_mode, top_k, rerank, filters, query, + ) + record_query( + service=f"snsf.rag.search.{scope_mode}", + query=query, + ) + return await provider.search( + query, + scope_mode=scope_mode, + top_k=top_k, + filters=filters, + rerank=rerank, + ) + + return Tool( + search_snsf_rag, + name="search_snsf_rag", + description=_DESCRIPTION, + ) + + +__all__ = ["make_snsf_rag_search_tool"] diff --git a/src/v2/agents/llm/agent_tools/swissubase_rag.py b/src/v2/agents/llm/agent_tools/swissubase_rag.py new file mode 100644 index 0000000..3e62606 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/swissubase_rag.py @@ -0,0 +1,65 @@ +"""Pydantic-AI Tool backed by :class:`SwissubaseRagProvider`.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.swissubase_rag import SwissubaseRagProvider + +logger = logging.getLogger(__name__) + +_DESCRIPTION = ( + "Semantic search over the SWISSUbase RAG index — the Swiss " + "national social-science research-data platform " + "(www.swissubase.ch). Indexes studies (UI label: Project), " + "their datasets, principal investigators, and partner " + "institutions. Use this when a repository / paper / person " + "claims affiliation with a Swiss social-science study, " + "FORS-funded project, NCCR LIVES research, or similar. " + "`filters`: optional dict of allowlisted keys " + "(entity_type, study_id, dataset_id, person_key, " + "institution_key, ref, main_discipline, sub_discipline, " + "progress, year_start, year_end, access_right). Each value " + "may be a scalar, a list (any-of), or {$gte/$lte} for ranges. " + "Set `entity_type` to one of {studies, datasets, persons, " + "institutions} to scope the search to a single bucket. " + "`rerank=true` engages the cross-encoder. Each hit carries " + "the canonical `source_url` (https://www.swissubase.ch/...) " + "as a top-level field." +) + + +def make_swissubase_rag_search_tool(provider: SwissubaseRagProvider) -> Tool: + """Tool factory: semantic search over the SWISSUbase RAG index.""" + + async def search_swissubase_rag( + query: str, + top_k: int = 10, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — LLM-facing signature + ) -> list[dict[str, Any]]: + """Vector search the SWISSUbase index. See tool description.""" + logger.info( + "tool call: search_swissubase_rag — top_k=%d rerank=%s " + "filters=%r query=%r", + top_k, rerank, filters, query, + ) + record_query(service="swissubase.rag.search", query=query) + return await provider.search( + query, top_k=top_k, filters=filters, rerank=rerank, + ) + + return Tool( + search_swissubase_rag, + name="search_swissubase_rag", + description=_DESCRIPTION, + ) + + +__all__ = ["make_swissubase_rag_search_tool"] diff --git a/src/v2/agents/llm/agent_tools/uuid.py b/src/v2/agents/llm/agent_tools/uuid.py new file mode 100644 index 0000000..632e32f --- /dev/null +++ b/src/v2/agents/llm/agent_tools/uuid.py @@ -0,0 +1,58 @@ +from __future__ import annotations + +import logging + +from pydantic_ai import Tool + +from src.v2.agents.models import generate_uuid + +logger = logging.getLogger(__name__) +MAX_BATCH_SIZE = 100 + + +def _generate_uuid_v4() -> str: + """Generate and return one UUIDv4 string.""" + + value = generate_uuid() + logger.info("tool call: generate_uuid_v4") + return value + + +def _generate_uuid_v4_batch(count: int = 1) -> list[str]: + """Generate a batch of UUIDv4 strings.""" + + count = max(count, 1) + count = min(count, MAX_BATCH_SIZE) + logger.info("tool call: generate_uuid_v4_batch — count=%d", count) + return [generate_uuid() for _ in range(count)] + + +def generate_uuid_v4() -> str: + """Public helper returning one UUIDv4 string.""" + + return _generate_uuid_v4() + + +def generate_uuid_v4_batch(count: int = 1) -> list[str]: + """Public helper returning a bounded batch of UUIDv4 strings.""" + + return _generate_uuid_v4_batch(count) + + +generate_uuid_v4_tool = Tool( + _generate_uuid_v4, + name="generate_uuid_v4", + description=( + "Generate a random UUID version 4 string. " + "Use when a schema requires a UUID fallback identifier." + ), +) + +generate_uuid_v4_batch_tool = Tool( + _generate_uuid_v4_batch, + name="generate_uuid_v4_batch", + description=( + "Generate multiple random UUID version 4 strings (1-100). " + "Use when you need UUIDs for more than one entity." + ), +) diff --git a/src/v2/agents/llm/agent_tools/zenodo_rag.py b/src/v2/agents/llm/agent_tools/zenodo_rag.py new file mode 100644 index 0000000..2475943 --- /dev/null +++ b/src/v2/agents/llm/agent_tools/zenodo_rag.py @@ -0,0 +1,95 @@ +"""Pydantic-AI Tools backed by :class:`ZenodoRagProvider`.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from pydantic_ai import Tool + +from src.v2.observation.query_log import record_query + +if TYPE_CHECKING: + from src.v2.ingest.providers.zenodo_rag import ZenodoRagProvider + +logger = logging.getLogger(__name__) + +_SEARCH_DESCRIPTION = ( + "Semantic search over the Zenodo RAG index — datasets, software, " + "papers, and other research outputs deposited to Zenodo. Use this when " + "a repository or paper references a Zenodo DOI / dataset and you need " + "the canonical record. `filters`: optional dict of allowlisted keys " + "(year, doi, resource_type, access_right, entity_type, zenodo_id). " + "Each value may be a scalar, a list (any-of), or {$gte/$lte} for " + "ranges. `rerank=true` engages the cross-encoder against title+" + "description — strong signal. Returns thin hits with zenodo_id, " + "title, doi, year, resource_type, access_right." +) + +_FETCH_RECORDS_DESCRIPTION = ( + "Hydrate one or more Zenodo records by id. Each id may be a numeric " + 'Zenodo record id (e.g. "3909400") OR a `concept_recid` (the parent ' + 'id Zenodo uses to group all versions of a deposit, e.g. "3909399") — ' + "concept ids are resolved to the most recent ingested version. Returns " + "full payloads: zenodo_id, concept_recid, title, doi, publication_date, " + "resource_type, access_right, license_id, description (capped at 2000 " + "chars). Use after `search_zenodo_rag` when you need the description " + "or license, or to confirm a citation by canonical id." +) + + +def make_zenodo_rag_search_tool(provider: ZenodoRagProvider) -> Tool: + """Tool factory: semantic search over the Zenodo RAG index.""" + + async def search_zenodo_rag( + query: str, + top_k: int = 10, + filters: dict[str, Any] | None = None, + rerank: bool = False, # noqa: FBT001, FBT002 — part of the LLM tool signature + ) -> list[dict[str, Any]]: + """Vector search the Zenodo index. See tool description.""" + logger.info( + "tool call: search_zenodo_rag — top_k=%d rerank=%s " + "filters=%r query=%r", + top_k, rerank, filters, query, + ) + record_query(service="zenodo.rag.search", query=query) + return await provider.search( + query, top_k=top_k, filters=filters, rerank=rerank, + ) + + return Tool( + search_zenodo_rag, + name="search_zenodo_rag", + description=_SEARCH_DESCRIPTION, + ) + + +def make_zenodo_rag_fetch_records_tool(provider: ZenodoRagProvider) -> Tool: + """Tool factory: hydrate Zenodo records by zenodo_id or concept_recid.""" + + async def fetch_zenodo_records( + ids: list[str], + ) -> list[dict[str, Any]]: + """Fetch full Zenodo records by id. See tool description.""" + logger.info( + "tool call: fetch_zenodo_records — ids=%d", + len(ids), + ) + record_query( + service="zenodo.rag.fetch_records", + query=",".join(str(i) for i in ids[:10]), + ) + return await provider.fetch_records(ids) + + return Tool( + fetch_zenodo_records, + name="fetch_zenodo_records", + description=_FETCH_RECORDS_DESCRIPTION, + ) + + +__all__ = [ + "make_zenodo_rag_fetch_records_tool", + "make_zenodo_rag_search_tool", +] diff --git a/src/v2/agents/llm/article/__init__.py b/src/v2/agents/llm/article/__init__.py new file mode 100644 index 0000000..9c89054 --- /dev/null +++ b/src/v2/agents/llm/article/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from src.v2.agents.llm.article.agent import LLMArticleAgentV2 + +__all__ = ["LLMArticleAgentV2"] diff --git a/src/v2/agents/llm/article/agent.py b/src/v2/agents/llm/article/agent.py new file mode 100644 index 0000000..5135d4f --- /dev/null +++ b/src/v2/agents/llm/article/agent.py @@ -0,0 +1,427 @@ +from __future__ import annotations + +import asyncio +import json +from copy import deepcopy +from typing import Any + +from pydantic import ValidationError + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm._payload_helpers import force_server_uuid +from src.v2.agents.llm._verdict_cache import ( + get_cached_agent_verdict, + store_agent_verdict, +) +from src.v2.agents.llm.agent_tools.epfl_graph_rag import ( + make_epfl_graph_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.ethz_research_collection_rag import ( + make_ethz_research_collection_rag_fetch_chunks_tool, + make_ethz_research_collection_rag_fetch_records_tool, + make_ethz_research_collection_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.infoscience_publications import ( + make_infoscience_publications_search_tool, +) +from src.v2.agents.llm.agent_tools.infoscience_rag import ( + make_infoscience_rag_fetch_chunks_tool, + make_infoscience_rag_fetch_records_tool, + make_infoscience_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.openalex_rag import ( + make_openalex_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.renkulab_rag import ( + make_renkulab_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.selenium_fetch import ( + make_fetch_link_content_tool, +) +from src.v2.agents.llm.agent_tools.swissubase_rag import ( + make_swissubase_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.zenodo_rag import ( + make_zenodo_rag_fetch_records_tool, + make_zenodo_rag_search_tool, +) +from src.v2.agents.llm.prompt_context import append_runtime_prompt_context +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime +from src.v2.agents.models import AgentResult, ProviderSet, generate_uuid +from src.v2.ingest.cache import ProviderCache +from src.v2.observation.query_log import stamp_current_agent +from src.v2.schema.models.agent import AgentArticleShape +from src.v2.schema.models.strict import ArticleModel + +README_CONTEXT_MAX_CHARS = 2000 +GIMIE_JSONLD_MAX_CHARS = 4000 +MAX_CONTEXT_ENTITIES = 30 + +# Sentinel strings the LLM emits when it has no real identifier but tries to +# emit an article anyway. These bypass placeholder-DOI checks (which look +# for `10.0000/...`) so we catch them explicitly here. +_IDENTIFIER_SENTINELS: frozenset[str] = frozenset( + {"unknown", "n/a", "na", "none", "null", "tbd", "todo", "?", "-"}, +) + + +def _is_real_doi(value: Any) -> bool: + """Return True for a string that *plausibly* looks like a real DOI. + + A real DOI starts with `10.` followed by a 4-9 digit registrant prefix, + a slash, and a non-empty suffix. Placeholder DOIs `10.0000/...` are also + rejected as they're a known LLM-hallucination pattern. + """ + if not isinstance(value, str): + return False + candidate = value.strip() + if not candidate: + return False + for prefix in ( + "https://doi.org/", + "http://doi.org/", + "https://dx.doi.org/", + "http://dx.doi.org/", + ): + if candidate.lower().startswith(prefix): + candidate = candidate[len(prefix) :] + break + if not candidate.startswith("10.") or "/" not in candidate: + return False + # `10.0000/` is reserved for testing — never a real DOI. + if candidate.lower().startswith("10.0000/"): + return False + return True + + +def _has_real_article_identifier(payload: dict[str, Any]) -> bool: + """An article must carry at least one verifiable identifier.""" + + schema_identifier = payload.get("schema:identifier") + if isinstance(schema_identifier, str) and schema_identifier.strip().lower() in _IDENTIFIER_SENTINELS: + schema_identifier = None + if _is_real_doi(schema_identifier): + return True + identifiers = payload.get("identifiers") + if isinstance(identifiers, dict): + nested_identifier = identifiers.get("schema:identifier") + if ( + isinstance(nested_identifier, str) + and nested_identifier.strip().lower() not in _IDENTIFIER_SENTINELS + and _is_real_doi(nested_identifier) + ): + return True + infosci = identifiers.get("pulse:infoscienceArticleIdentifier") + if isinstance(infosci, str) and infosci.strip(): + return True + direct_infosci = payload.get("pulse:infoscienceArticleIdentifier") + if isinstance(direct_infosci, str) and direct_infosci.strip(): + return True + return False + +_PROMPTS_PACKAGE = "src.v2.agents.llm.article.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") + + +def _resolve_article_seed(context: dict[str, Any]) -> str: + for key in ("article_seed", "full_name", "username", "org_name"): + value = context.get(key) + if isinstance(value, str) and value.strip(): + return value.strip() + message = "Article context is missing article_seed/full_name/username/org_name" + raise ValueError(message) + + +def _strict_validate(payload: dict[str, Any]) -> list[str]: + warnings: list[str] = [] + try: + ArticleModel.model_validate(payload) + except ValidationError as exc: + for error in exc.errors(): + field = " -> ".join(str(loc) for loc in error["loc"]) + warnings.append(f"Strict schema warning at {field}: {error['msg']}") + return warnings + + +def _list_of_dicts(value: Any, *, max_items: int = MAX_CONTEXT_ENTITIES) -> list[dict[str, Any]]: + if not isinstance(value, list): + return [] + collected: list[dict[str, Any]] = [] + for item in value: + if not isinstance(item, dict): + continue + if len(collected) >= max_items: + break + collected.append(deepcopy(item)) + return collected + + +def _repository_context_summary(repository_context: Any) -> dict[str, Any] | None: + if not isinstance(repository_context, dict) or not repository_context: + return None + + summary: dict[str, Any] = {} + metadata = repository_context.get("metadata") + if isinstance(metadata, dict) and metadata: + summary["metadata"] = { + key: metadata.get(key) + for key in ( + "name", + "full_name", + "description", + "owner", + "created_at", + "updated_at", + "pushed_at", + ) + if metadata.get(key) is not None + } + + contributors = repository_context.get("contributors") + if isinstance(contributors, list) and contributors: + summary["contributors"] = deepcopy(contributors[:MAX_CONTEXT_ENTITIES]) + + languages = repository_context.get("languages") + if isinstance(languages, dict) and languages: + summary["languages"] = deepcopy(languages) + + readme = repository_context.get("readme_content") + if isinstance(readme, str) and readme: + summary["readme_content"] = readme[:README_CONTEXT_MAX_CHARS] + + gimie_jsonld = repository_context.get("gimie_jsonld") + if isinstance(gimie_jsonld, dict) and gimie_jsonld: + summary["gimie_jsonld"] = json.dumps( + gimie_jsonld, + ensure_ascii=True, + )[:GIMIE_JSONLD_MAX_CHARS] + + return summary or None + + +class LLMArticleAgentV2: + """LLM-backed agent that produces a schema:ScholarlyArticle entity.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 180.0, + cache: ProviderCache | None = None, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + self._cache = cache + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + article_seed = _resolve_article_seed(context) + + stamp_current_agent( + name="article_agent", + context={"article_seed": article_seed} if isinstance(article_seed, str) else {}, + ) + + full_name = context.get("full_name") + identity = ( + {"full_name": full_name.strip().lower()} + if isinstance(full_name, str) and full_name.strip() + else None + ) + is_root = bool(context.get("agent_is_root")) + cached_result = get_cached_agent_verdict( + self._cache, + agent_name="article", + identity=identity, + is_root=is_root, + ) + if cached_result is not None: + return cached_result + + uuid_value = context.get("uuid") + if not isinstance(uuid_value, str) or not uuid_value.strip(): + uuid_value = generate_uuid() + + llm_input: dict[str, Any] = { + "article_seed": article_seed, + "uuid": uuid_value, + "detected_type": context.get("detected_type"), + "source_url": context.get("source_url"), + "full_name": context.get("full_name"), + "username": context.get("username"), + "org_name": context.get("org_name"), + "known_persons": _list_of_dicts(context.get("known_persons")), + "known_organizations": _list_of_dicts(context.get("known_organizations")), + "known_repositories": _list_of_dicts(context.get("known_repositories"), max_items=5), + "person_derivations": _list_of_dicts(context.get("person_derivations")), + "organization_derivations": _list_of_dicts( + context.get("organization_derivations"), + ), + "repository_derivations": _list_of_dicts( + context.get("repository_derivations"), + max_items=5, + ), + "typed_entity_buckets": context.get("typed_entity_buckets"), + } + + repository_context = _repository_context_summary(context.get("repository_context")) + if repository_context: + llm_input["repository_context"] = repository_context + + pipeline_outputs = context.get("pipeline_outputs") + if isinstance(pipeline_outputs, dict) and pipeline_outputs: + llm_input["pipeline_outputs"] = pipeline_outputs + + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True, default=str) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + user_prompt = append_runtime_prompt_context(user_prompt, context) + + tools = [make_fetch_link_content_tool(self._cache)] + if providers.infoscience is not None: + tools.append( + make_infoscience_publications_search_tool(providers.infoscience), + ) + if providers.infoscience_rag is not None: + tools.append(make_infoscience_rag_search_tool(providers.infoscience_rag)) + tools.append( + make_infoscience_rag_fetch_chunks_tool(providers.infoscience_rag), + ) + tools.append( + make_infoscience_rag_fetch_records_tool(providers.infoscience_rag), + ) + if providers.ethz_research_collection_rag is not None: + tools.append( + make_ethz_research_collection_rag_search_tool( + providers.ethz_research_collection_rag, + ), + ) + tools.append( + make_ethz_research_collection_rag_fetch_chunks_tool( + providers.ethz_research_collection_rag, + ), + ) + tools.append( + make_ethz_research_collection_rag_fetch_records_tool( + providers.ethz_research_collection_rag, + ), + ) + if providers.openalex_rag is not None: + tools.append(make_openalex_rag_search_tool(providers.openalex_rag)) + if providers.zenodo_rag is not None: + tools.append(make_zenodo_rag_search_tool(providers.zenodo_rag)) + tools.append(make_zenodo_rag_fetch_records_tool(providers.zenodo_rag)) + if providers.renkulab_rag is not None: + tools.append(make_renkulab_rag_search_tool(providers.renkulab_rag)) + if providers.swissubase_rag is not None: + tools.append(make_swissubase_rag_search_tool(providers.swissubase_rag)) + if providers.epfl_graph_rag is not None: + tools.append(make_epfl_graph_rag_search_tool(providers.epfl_graph_rag)) + + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=AgentArticleShape, + tools=tools, + ), + timeout=self._llm_call_timeout_seconds, + ) + except asyncio.TimeoutError as exc: + message = ( + f"{article_seed} — LLM call timed out after " + f"{self._llm_call_timeout_seconds:.1f}s" + ) + raise LLMRuntimeError(message) from exc + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + payload = {key: value for key, value in llm_result.payload.items() if value is not None} + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + payload.update(overrides) + + # Drop the article entirely if the LLM didn't ground it in a real + # identifier. The agent should not emit an entity whose only + # `schema:identifier` is a placeholder DOI (`10.0000/...`) or a + # sentinel like `UNKNOWN` / `N/A` / `TBD`. The downstream + # `validate_articles` stage would catch these too, but dropping + # them here keeps `excluded_entities` and the prompt context for + # later stages clean. + if payload and not _has_real_article_identifier(payload): + warning = ( + f"{article_seed} — article dropped: no real DOI or " + f"infoscience identifier (got " + f"schema:identifier={payload.get('schema:identifier')!r})." + ) + empty_result = AgentResult( + data={}, + warnings=[warning], + raw_output=deepcopy(payload), + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "articles": [], + "article_count": 0, + "derivation": { + "article_seed": article_seed, + "dropped_reason": "no_real_identifier", + "rejected_schema_identifier": payload.get( + "schema:identifier", + ), + }, + }, + ) + store_agent_verdict( + self._cache, + agent_name="article", + identity=identity, + result=empty_result, + ) + return empty_result + + force_server_uuid(payload, uuid_value) + + raw_output = deepcopy(payload) + validation_warnings = _strict_validate(payload) + + result = AgentResult( + data=payload, + warnings=validation_warnings, + raw_output=raw_output, + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "articles": [deepcopy(payload)] if payload else [], + "article_count": 1 if payload else 0, + "derivation": { + "article_seed": article_seed, + "article_id": payload.get("id"), + "author_count": len(payload.get("schema:author", [])) + if isinstance(payload.get("schema:author"), list) + else 0, + }, + }, + ) + store_agent_verdict( + self._cache, + agent_name="article", + identity=identity, + result=result, + ) + return result diff --git a/src/v2/agents/llm/article/prompts/__init__.py b/src/v2/agents/llm/article/prompts/__init__.py new file mode 100644 index 0000000..9d48db4 --- /dev/null +++ b/src/v2/agents/llm/article/prompts/__init__.py @@ -0,0 +1 @@ +from __future__ import annotations diff --git a/src/v2/agents/llm/article/prompts/system_prompt.md b/src/v2/agents/llm/article/prompts/system_prompt.md new file mode 100644 index 0000000..fe7a93c --- /dev/null +++ b/src/v2/agents/llm/article/prompts/system_prompt.md @@ -0,0 +1,51 @@ +You are an article metadata extraction agent operating under the **Open Pulse Ontology v2.1.2**. + +Return exactly one JSON object for a `schema:ScholarlyArticle` entity that conforms to `pulse:ArticleShape`. + +Output only JSON. No markdown fences. No explanations. + +Required fields: +- `id` (string) +- `type` = `"schema:ScholarlyArticle"` +- `shacl` = `"pulse:ArticleShape"` +- `identifiers` with `schema:identifier`, `pulse:infoscienceArticleIdentifier`, `uuid` +- `idSource` in `{ "schema:identifier", "pulse:infoscienceArticleIdentifier", "uuid" }` +- `schema:name` +- `schema:identifier` (DOI, format `10.xxxx/...`) +- `schema:datePublished` (`YYYY-MM-DD`) +- `schema:author` (non-empty list of Person IDs) + +Optional fields: +- `pulse:infoscienceArticleIdentifier` +- `schema:sourceOrganization` + +Rules: +- Prefer identifiers and references found in `known_*`, `repository_context`, and prior stage outputs. +- Do not invent unknown people or organizations when canonical IDs are available in context. +- Do not emit fields outside the schema. +- Use `null` only where nullable fields are permitted. +- **No identifier, no article.** If the input does not let you ground the + article in a real `schema:identifier` (a real DOI such as + `10.1038/s41586-024-...`) **or** a real `pulse:infoscienceArticleIdentifier`, + return an empty JSON object `{}` rather than a fabricated entity. **Never** + emit a sentinel value like `"UNKNOWN"`, `"N/A"`, `"TBD"`, `"none"`, `""`, or + the placeholder DOI prefix `10.0000/...` for `schema:identifier`. The + pipeline rejects entities with these values; emitting them just adds noise. +- A repository is **not** automatically a publication. Only emit a + `schema:ScholarlyArticle` when the input clearly references a published + paper (e.g. CITATION.cff with a DOI, an Infoscience publication record, + or a README citation block). If the only "article" you can find is the + repo itself, emit `{}`. + +Tools: +- `search_infoscience_publications(query)` — query EPFL Infoscience for the + scholarly publication referenced by this repository (CITATION.cff, README, + or seed). Use it to recover `pulse:infoscienceArticleIdentifier`, fill in a + missing DOI (`schema:identifier`), or confirm `schema:datePublished`. Try + the article title first; fall back to author + keyword if that misses. +- `fetch_link_content_via_selenium(url)` — fetch a candidate publication URL + to verify its existence and extract metadata when needed. + +Identifiers: +- Use the `uuid` value already provided in your input verbatim for the + `uuid` identifier slot. Do not generate a new one. diff --git a/src/v2/agents/llm/article/prompts/user_prompt.md b/src/v2/agents/llm/article/prompts/user_prompt.md new file mode 100644 index 0000000..1825242 --- /dev/null +++ b/src/v2/agents/llm/article/prompts/user_prompt.md @@ -0,0 +1,7 @@ +Build a single Article payload from this runtime context. + +Use known canonical IDs from prior stages when available. + +```json +{context_json} +``` diff --git a/src/v2/agents/llm/context_summary/__init__.py b/src/v2/agents/llm/context_summary/__init__.py new file mode 100644 index 0000000..d7f3e7d --- /dev/null +++ b/src/v2/agents/llm/context_summary/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from src.v2.agents.llm.context_summary.agent import LLMContextSummaryAgentV2 + +__all__ = ["LLMContextSummaryAgentV2"] diff --git a/src/v2/agents/llm/context_summary/agent.py b/src/v2/agents/llm/context_summary/agent.py new file mode 100644 index 0000000..1c31f09 --- /dev/null +++ b/src/v2/agents/llm/context_summary/agent.py @@ -0,0 +1,475 @@ +from __future__ import annotations + +import json +import logging +import os +from copy import deepcopy +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm.agent_tools.duckduckgo_search import ( + make_duckduckgo_search_tool, +) +from src.v2.agents.llm.agent_tools.repository_corpus_grep import ( + make_repository_corpus_grep_tool, +) +from src.v2.agents.models import AgentResult, ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime +from src.v2.ingest.cache import ProviderCache +from src.v2.observation.query_log import stamp_current_agent + +logger = logging.getLogger(__name__) + +_PROMPTS_PACKAGE = "src.v2.agents.llm.context_summary.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") +# Loaded lazily only when scout mode is enabled, so projects that don't +# opt in never pay the import + read cost. The scout prompt produces the +# same `{summary_markdown}` shape as the default prompt — downstream +# agents are unchanged. +_SCOUT_PROMPT_FILENAME = "system_prompt_scout.md" + + +def _is_scout_mode_enabled() -> bool: + """`V2_CONTEXT_SUMMARY_SCOUT_MODE=true` opts into the broader recon stage. + + Off by default — the existing 2-tool (corpus_grep + DuckDuckGo) + summary keeps producing the historical brief shape. Turn on to give + the summary agent the per-entity RAG search toolkit (orcid_rag, + ror_rag, infoscience_rag, openalex_rag, selenium_fetch, etc.) so it + consolidates discovery work that downstream agents currently + duplicate. + """ + raw = os.environ.get("V2_CONTEXT_SUMMARY_SCOUT_MODE", "").strip().lower() + return raw in {"1", "true", "yes", "on"} + +MAX_DOCUMENT_COUNT = 120 +MAX_SINGLE_DOCUMENT_CHARS = 120_000 +MAX_REPOSITORY_FILE_CHARS = 20_000 +MAX_PROMPT_DOC_PREVIEW_CHARS = 400 + + +class LLMContextSummaryOutput(BaseModel): + model_config = ConfigDict(extra="ignore") + summary_markdown: str = Field( + ..., + description="Compiled markdown brief for downstream extraction agents.", + ) + + +def _to_non_empty_string(value: Any) -> str | None: + if isinstance(value, str): + candidate = value.strip() + if candidate: + return candidate + return None + + +def _truncate_text(value: str, *, limit: int) -> str: + if len(value) <= limit: + return value + return f"{value[: limit - 3]}..." + + +def _coerce_repository_file_entries(candidate: Any) -> list[dict[str, str]]: + rows: list[dict[str, str]] = [] + if isinstance(candidate, dict): + for path, content in candidate.items(): + path_value = _to_non_empty_string(path) + content_value = _to_non_empty_string(content) + if not path_value or not content_value: + continue + rows.append({"path": path_value, "content": content_value}) + return rows + + if not isinstance(candidate, list): + return rows + + for item in candidate: + if not isinstance(item, dict): + continue + path_value = _to_non_empty_string( + item.get("path"), + ) or _to_non_empty_string( + item.get("file_path"), + ) or _to_non_empty_string( + item.get("name"), + ) + content_value = _to_non_empty_string( + item.get("content"), + ) or _to_non_empty_string( + item.get("text"), + ) or _to_non_empty_string( + item.get("body"), + ) + if not path_value or not content_value: + continue + rows.append({"path": path_value, "content": content_value}) + return rows + + +def _append_document( + documents: list[dict[str, Any]], + *, + label: str, + repository: str, + origin: str, + path: str, + content: str, +) -> None: + normalized_content = _to_non_empty_string(content) + if not normalized_content: + return + if len(documents) >= MAX_DOCUMENT_COUNT: + return + documents.append( + { + "id": f"doc-{len(documents) + 1}", + "label": label, + "repository": repository, + "origin": origin, + "path": path, + "content": _truncate_text(normalized_content, limit=MAX_SINGLE_DOCUMENT_CHARS), + }, + ) + + +def _append_repository_documents( + documents: list[dict[str, Any]], + repository_context: dict[str, Any], +) -> None: + full_name = _to_non_empty_string(repository_context.get("full_name")) or "unknown/repository" + readme_content = _to_non_empty_string(repository_context.get("readme_content")) + if readme_content: + _append_document( + documents, + label="Repository README", + repository=full_name, + origin="repository_context.readme_content", + path="README.md", + content=readme_content, + ) + + gimie_jsonld = repository_context.get("gimie_jsonld") + if isinstance(gimie_jsonld, (dict, list)): + serialized = json.dumps(gimie_jsonld, ensure_ascii=True, sort_keys=True) + _append_document( + documents, + label="Raw GIMIE JSON-LD", + repository=full_name, + origin="repository_context.gimie_jsonld", + path="gimie.jsonld", + content=serialized, + ) + + file_entries = _coerce_repository_file_entries( + repository_context.get("repository_files") or repository_context.get("files"), + ) + for entry in file_entries: + _append_document( + documents, + label="Repository File", + repository=full_name, + origin="repository_context.repository_files", + path=entry["path"], + content=_truncate_text(entry["content"], limit=MAX_REPOSITORY_FILE_CHARS), + ) + + +def _build_corpus_documents( + detected_type: str, + gathered_context: dict[str, Any], +) -> list[dict[str, Any]]: + documents: list[dict[str, Any]] = [] + + if detected_type == "repository": + repository_context = gathered_context.get("repository") + if isinstance(repository_context, dict): + _append_repository_documents(documents, repository_context) + return documents + + if detected_type == "user": + user_context = gathered_context.get("user") + if isinstance(user_context, dict): + profile_readme = _to_non_empty_string( + user_context.get("profile", {}).get("readme_content") + if isinstance(user_context.get("profile"), dict) + else None, + ) + username = _to_non_empty_string(user_context.get("username")) or "unknown-user" + if profile_readme: + _append_document( + documents, + label="User Profile README", + repository=f"{username}/{username}", + origin="user_context.profile.readme_content", + path="README.md", + content=profile_readme, + ) + + repository_contexts = user_context.get("repository_contexts") + if isinstance(repository_contexts, dict): + for value in repository_contexts.values(): + if isinstance(value, dict): + _append_repository_documents(documents, value) + return documents + + if detected_type == "organization": + organization_context = gathered_context.get("organization") + if isinstance(organization_context, dict): + profile_readme = _to_non_empty_string( + organization_context.get("profile", {}).get("readme_content") + if isinstance(organization_context.get("profile"), dict) + else None, + ) + org_name = _to_non_empty_string(organization_context.get("org_name")) or "unknown-org" + if profile_readme: + _append_document( + documents, + label="Organization Profile README", + repository=f"{org_name}/.github", + origin="organization_context.profile.readme_content", + path=".github/profile/README.md", + content=profile_readme, + ) + + repository_contexts = organization_context.get("repository_contexts") + if isinstance(repository_contexts, dict): + for value in repository_contexts.values(): + if isinstance(value, dict): + _append_repository_documents(documents, value) + return documents + + return documents + + +def _build_scout_tools( + *, + providers: ProviderSet, + cache: ProviderCache | None, + corpus_documents: list[dict[str, Any]], +) -> list[Any]: + """Assemble the broad recon toolset for context_summary in scout mode. + + Mirrors the per-entity agents' tool sets (person, org, article, + membership, contribution) so the scout sees the same search + capabilities collectively. Only RAG `search_*` factories are + pulled in — the heavier `fetch_chunks` / `fetch_records` stay out + so the scout doesn't burn its budget on full-record retrieval. + + Each `provider.* is not None` guard mirrors how the per-entity + agents wire tools — a missing index degrades silently. + """ + # Local imports keep the legacy (non-scout) code path import-cost-free. + from src.v2.agents.llm.agent_tools.epfl_graph_rag import ( # noqa: PLC0415 + make_epfl_graph_rag_search_tool, + ) + from src.v2.agents.llm.agent_tools.ethz_research_collection_rag import ( # noqa: PLC0415 + make_ethz_research_collection_rag_search_tool, + ) + from src.v2.agents.llm.agent_tools.huggingface_rag import ( # noqa: PLC0415 + make_huggingface_rag_search_tool, + ) + from src.v2.agents.llm.agent_tools.infoscience_rag import ( # noqa: PLC0415 + make_infoscience_rag_search_tool, + ) + from src.v2.agents.llm.agent_tools.openalex_rag import ( # noqa: PLC0415 + make_openalex_rag_search_tool, + ) + from src.v2.agents.llm.agent_tools.orcid_rag import ( # noqa: PLC0415 + make_orcid_rag_search_tool, + ) + from src.v2.agents.llm.agent_tools.renkulab_rag import ( # noqa: PLC0415 + make_renkulab_rag_search_tool, + ) + from src.v2.agents.llm.agent_tools.ror_rag import ( # noqa: PLC0415 + make_ror_rag_search_tool, + ) + from src.v2.agents.llm.agent_tools.selenium_fetch import ( # noqa: PLC0415 + make_fetch_link_content_tool, + ) + from src.v2.agents.llm.agent_tools.snsf_rag import ( # noqa: PLC0415 + make_snsf_rag_search_tool, + ) + from src.v2.agents.llm.agent_tools.zenodo_rag import ( # noqa: PLC0415 + make_zenodo_rag_search_tool, + ) + + tools: list[Any] = [ + # Always-on baseline (no provider dependency). + make_repository_corpus_grep_tool(corpus_documents), + make_duckduckgo_search_tool(cache=cache), + make_fetch_link_content_tool(cache), + ] + if providers.orcid_rag is not None: + tools.append(make_orcid_rag_search_tool(providers.orcid_rag)) + if providers.ror_rag is not None: + tools.append(make_ror_rag_search_tool(providers.ror_rag)) + if providers.infoscience_rag is not None: + tools.append(make_infoscience_rag_search_tool(providers.infoscience_rag)) + if providers.openalex_rag is not None: + tools.append(make_openalex_rag_search_tool(providers.openalex_rag)) + if providers.zenodo_rag is not None: + tools.append(make_zenodo_rag_search_tool(providers.zenodo_rag)) + if providers.ethz_research_collection_rag is not None: + tools.append( + make_ethz_research_collection_rag_search_tool( + providers.ethz_research_collection_rag, + ), + ) + if providers.huggingface_rag is not None: + tools.append(make_huggingface_rag_search_tool(providers.huggingface_rag)) + if providers.renkulab_rag is not None: + tools.append(make_renkulab_rag_search_tool(providers.renkulab_rag)) + if providers.snsf_rag is not None: + tools.append(make_snsf_rag_search_tool(providers.snsf_rag)) + if providers.epfl_graph_rag is not None: + tools.append(make_epfl_graph_rag_search_tool(providers.epfl_graph_rag)) + return tools + + +def _document_manifest(corpus_documents: list[dict[str, Any]]) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for document in corpus_documents: + content = document.get("content") + if not isinstance(content, str): + continue + rows.append( + { + "id": document.get("id"), + "label": document.get("label"), + "repository": document.get("repository"), + "origin": document.get("origin"), + "path": document.get("path"), + "char_count": len(content), + "line_count": len(content.splitlines()), + "preview": _truncate_text(content, limit=MAX_PROMPT_DOC_PREVIEW_CHARS), + }, + ) + return rows + + +class LLMContextSummaryAgentV2: + """Compile raw context into a single markdown brief for downstream agents.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + cache: ProviderCache | None = None, + ) -> None: + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._cache = cache + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + # Scout mode (off by default) needs `providers` to wire RAG + # search tools; the legacy path never used them but we keep the + # parameter for ABI stability. + scout_mode = _is_scout_mode_enabled() + detected_type = _to_non_empty_string(context.get("detected_type")) or "repository" + source_url = _to_non_empty_string(context.get("source_url")) or "" + + stamp_current_agent( + name="context_summary_agent", + context={"source_url": source_url} if source_url else {}, + ) + gathered_context = context.get("gathered_context") + if not isinstance(gathered_context, dict): + gathered_context = {} + + corpus_documents = _build_corpus_documents(detected_type, gathered_context) + manifest = _document_manifest(corpus_documents) + total_chars = sum( + len(document.get("content", "")) + for document in corpus_documents + if isinstance(document.get("content"), str) + ) + + llm_input = { + "detected_type": detected_type, + "source_url": source_url, + "document_count": len(corpus_documents), + "total_chars": total_chars, + "corpus_manifest": manifest, + } + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + + warnings: list[str] = [] + if not corpus_documents: + warnings.append( + "context_summary_agent: no raw corpus documents available; using empty summary context", + ) + + # Pick prompt + tool catalog based on scout mode. Default path + # is unchanged (2 tools, original prompt). Scout mode adds the + # RAG providers' search tools so people / orgs / articles get + # recon'd up-front. + if scout_mode: + system_prompt = load_prompt(_PROMPTS_PACKAGE, _SCOUT_PROMPT_FILENAME) + tools = _build_scout_tools( + providers=providers, + cache=self._cache, + corpus_documents=corpus_documents, + ) + logger.info( + "context_summary_agent: scout mode ON (%d tools)", + len(tools), + ) + else: + system_prompt = _SYSTEM_PROMPT + tools = [ + make_repository_corpus_grep_tool(corpus_documents), + make_duckduckgo_search_tool(cache=self._cache), + ] + + try: + llm_result = await self._llm_runtime.run_json_prompt( + system_prompt=system_prompt, + user_prompt=user_prompt, + output_type=LLMContextSummaryOutput, + tools=tools, + ) + except LLMRuntimeError as exc: + warning = f"context_summary_agent: LLM call failed; proceeding without compiled summary ({exc})" + warnings.append(warning) + logger.warning(warning) + return AgentResult( + data={"summary_markdown": ""}, + warnings=warnings, + raw_output={}, + is_partial=True, + failure_reason=str(exc), + stats={ + "document_count": len(corpus_documents), + "total_chars": total_chars, + }, + ) + + payload = dict(llm_result.payload) + summary_markdown = _to_non_empty_string(payload.get("summary_markdown")) or "" + if not summary_markdown: + warnings.append( + "context_summary_agent: empty summary output; downstream agents will run without compiled summary", + ) + + return AgentResult( + data={"summary_markdown": summary_markdown}, + warnings=warnings, + raw_output=deepcopy(payload), + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "document_count": len(corpus_documents), + "total_chars": total_chars, + }, + ) diff --git a/src/v2/agents/llm/context_summary/prompts/__init__.py b/src/v2/agents/llm/context_summary/prompts/__init__.py new file mode 100644 index 0000000..09fff5b --- /dev/null +++ b/src/v2/agents/llm/context_summary/prompts/__init__.py @@ -0,0 +1 @@ +# Prompt package marker. diff --git a/src/v2/agents/llm/context_summary/prompts/system_prompt.md b/src/v2/agents/llm/context_summary/prompts/system_prompt.md new file mode 100644 index 0000000..7936441 --- /dev/null +++ b/src/v2/agents/llm/context_summary/prompts/system_prompt.md @@ -0,0 +1,39 @@ +You are a repository context compiler for Open Pulse v2. + +Your task is to synthesize a high-signal markdown brief from: +- raw GIMIE JSON-LD content, +- README text, +- repository file content snippets (when available). + +You have two tools: +- `grep_repository_corpus(query, max_matches=8, context_lines=3)` + Use it to inspect source material with provenance-aware snippets. +- `search_on_the_internet(query, max_results=5)` + Use it only when corpus evidence is insufficient and external confirmation is needed. + Prefer official or primary sources and keep external context concise. + +## Output contract + +Return only a JSON object with exactly one field: + +```json +{ + "summary_markdown": "" +} +``` + +## Requirements for `summary_markdown` + +- Must be markdown text. +- Must include clear sections: + - Repository Snapshot + - Technical Signals + - People and Organizations Signals + - Publications/DOI Signals + - Potential Ambiguities / Open Questions +- Cite evidence provenance inline (source + repository/path context). +- When using external search results, include URL provenance inline. +- Do not invent facts not grounded in corpus evidence. +- Prefer explicit uncertainty wording when evidence is weak/missing. + +The summary is consumed by downstream agents instead of raw blobs, so optimize for factual density and clarity. diff --git a/src/v2/agents/llm/context_summary/prompts/system_prompt_scout.md b/src/v2/agents/llm/context_summary/prompts/system_prompt_scout.md new file mode 100644 index 0000000..6184ab1 --- /dev/null +++ b/src/v2/agents/llm/context_summary/prompts/system_prompt_scout.md @@ -0,0 +1,116 @@ +You are a repository **research scout** for Open Pulse v2. + +Your task is to compile a high-signal markdown brief that consolidates +**every fact about a repository, its people, organisations, and +publications** into a single document. Downstream entity agents +(repo, person, org, article, membership, contribution) consume this +brief instead of raw context — what you write here drives every +extraction decision they make. + +## Inputs you have + +- raw GIMIE JSON-LD content, +- README + repository file content snippets, +- the corpus manifest passed in the user prompt, +- a broad **search toolset** spanning all the registered RAG indices + (ORCID, ROR, Infoscience, OpenAlex, ETHZ Research Collection, ROR, + Renkulab, SNSF, EPFL Graph) plus DuckDuckGo and a Selenium URL fetch. + +## How to spend your tool budget (~20 calls) + +Spend it generously here so per-entity agents don't have to: + +- `grep_repository_corpus` — surface CITATION.cff, .zenodo.json, + AUTHORS, contributors with provenance. +- `search_orcid_rag` — ground each named contributor to an ORCID iD + when they look academic. Score < 0.65 with rerank → flag in caveats, + don't claim. +- `search_ror_rag` — canonicalise organisations (especially Swiss / + EPFL-adjacent). Use `--scope switzerland` for Swiss orgs, `worldwide` + otherwise. +- `search_openalex_rag` / `search_infoscience_rag` — find published + papers that cite or describe the repo (CITATION.cff + `preferred-citation`, .zenodo.json `related_identifiers`, README + references). +- `fetch_link_content_via_selenium` — sparingly, to verify a project + homepage or a lab page when other signals are weak. +- `search_on_the_internet` (DuckDuckGo) — last-resort confirmation only. + +**Never invent identifiers.** If a tool call doesn't surface a real +ORCID / ROR / DOI with a strong score, list the entity in the +Caveats section as "no ORCID found / no ROR matched" and let +downstream agents fall back to github-handle or `urn:pulse:` forms. + +## Output contract + +Return only a JSON object with exactly one field (unchanged from the +non-scout prompt — downstream agents already consume this shape): + +```json +{ + "summary_markdown": "" +} +``` + +## Required structure for `summary_markdown` + +The brief MUST follow this section layout — downstream agents grep +for these headings: + +```markdown +## Repository Snapshot +- name, owner, license, dates, language, topics +- gimie ground-truth signals + provenance + +## Technical Signals +- programming language(s), build/test infrastructure, CI providers, + notable dependencies, repo size + +## People (cap 8, ranked by evidence strength) + +### () +- `@id` candidate: | > +- name, email-hash (if provided), affiliation hint +- evidence: where each claim came from (CITATION.cff line, gimie field, + ORCID search rerank score, etc.) + +(Repeat for each person, capped at 8. Persons with ORCIDs go first.) + +## Organizations (cap 4) + +### () +- `@id` candidate: > (preferred — see note) +- ROR (when grounded with score ≥ 0.6 with rerank): +- evidence: source of name match + ROR search score + +(Note for downstream: prefer github handle as `@id` for cross-reference +consistency; ROR goes in `schema:identifier`.) + +## Articles (0-2) + +### (none) | +- title, year, authors (orcids when known), source organization +- grounding: which tool surfaced it + score + +## Affiliations (Person ↔ Org pairs) + +- ↔ (evidence: ORCID employments YYYY-current / + CITATION.cff affiliation field / etc.) + +## Caveats / unknowns + +- Anything that couldn't be grounded confidently. Flag suspected + hallucinations in upstream data (placeholder DOIs, ambiguous handles, + duplicate author entries, conflicting affiliations). +``` + +## Hard rules + +- Cite evidence inline for every non-trivial fact (file path + line, + tool call name + score, etc.). Per-entity agents check groundedness; + unsupported claims here become hallucinations downstream. +- Cap people at 8, organisations at 4, articles at 2 — fan-out agents + must respect pi parallelism limits. +- Prefer explicit uncertainty wording when evidence is weak. +- The brief is the single source of truth for downstream agents — be + thorough but factual. diff --git a/src/v2/agents/llm/context_summary/prompts/user_prompt.md b/src/v2/agents/llm/context_summary/prompts/user_prompt.md new file mode 100644 index 0000000..52bf2ad --- /dev/null +++ b/src/v2/agents/llm/context_summary/prompts/user_prompt.md @@ -0,0 +1,9 @@ +Compile a markdown context brief for downstream extraction agents. + +Context JSON: +{context_json} + +Instructions: +1. Use `grep_repository_corpus` to inspect relevant evidence before finalizing. +2. Prioritize high-confidence facts and explicit provenance. +3. Keep the summary comprehensive but structured and scannable. diff --git a/src/v2/agents/llm/contribution/__init__.py b/src/v2/agents/llm/contribution/__init__.py new file mode 100644 index 0000000..adbb4c3 --- /dev/null +++ b/src/v2/agents/llm/contribution/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from src.v2.agents.llm.contribution.agent import LLMContributionAgentV2 + +__all__ = ["LLMContributionAgentV2"] diff --git a/src/v2/agents/llm/contribution/agent.py b/src/v2/agents/llm/contribution/agent.py new file mode 100644 index 0000000..54d6b27 --- /dev/null +++ b/src/v2/agents/llm/contribution/agent.py @@ -0,0 +1,291 @@ +from __future__ import annotations + +import asyncio +import json +from copy import deepcopy +from typing import Any + +from pydantic import ValidationError + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm._payload_helpers import force_server_uuid +from src.v2.agents.llm.agent_tools.selenium_fetch import ( + make_fetch_link_content_tool, +) +from src.v2.agents.llm.prompt_context import append_runtime_prompt_context +from src.v2.agents.models import AgentResult, ProviderSet, generate_uuid +from src.v2.ingest.cache import ProviderCache +from src.v2.observation.query_log import stamp_current_agent +from src.v2.schema.models.agent import AgentContributionShape +from src.v2.schema.models.strict import ContributionModel +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime + +MAX_CONTEXT_ENTITIES = 30 + +_PROMPTS_PACKAGE = "src.v2.agents.llm.contribution.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") + + +def _resolve_contribution_seed(context: dict[str, Any]) -> str: + seed = context.get("contribution_seed") + if isinstance(seed, str) and seed.strip(): + return seed.strip() + + known_repositories = context.get("known_repositories") + if isinstance(known_repositories, list): + for repository in known_repositories: + if not isinstance(repository, dict): + continue + for key in ("id", "pulse:githubRepositoryHandle", "full_name"): + value = repository.get(key) + if isinstance(value, str) and value.strip(): + return value.strip() + + message = "Contribution context is missing contribution_seed or repository id" + raise ValueError(message) + + +def _strict_validate(payload: dict[str, Any]) -> list[str]: + warnings: list[str] = [] + try: + ContributionModel.model_validate(payload) + except ValidationError as exc: + for error in exc.errors(): + field = " -> ".join(str(loc) for loc in error["loc"]) + warnings.append(f"Strict schema warning at {field}: {error['msg']}") + return warnings + + +def _list_of_dicts(value: Any, *, max_items: int = MAX_CONTEXT_ENTITIES) -> list[dict[str, Any]]: + if not isinstance(value, list): + return [] + collected: list[dict[str, Any]] = [] + for item in value: + if not isinstance(item, dict): + continue + if len(collected) >= max_items: + break + collected.append(deepcopy(item)) + return collected + + +def _person_github_login(person: Any) -> str | None: + """Best-effort GitHub login for a person entity. + + Prefers the explicit `pulse:githubUsername`; falls back to parsing + `id` when shaped like `https://github.com/{login}`. + """ + if not isinstance(person, dict): + return None + handle = person.get("pulse:githubUsername") + if isinstance(handle, str) and handle.strip(): + return handle.strip() + identifier = person.get("id") + if isinstance(identifier, str) and identifier.startswith("https://github.com/"): + candidate = identifier.removeprefix("https://github.com/").strip("/") + if candidate and "/" not in candidate: + return candidate + return None + + +def _find_target_contributor_record( + target_person: Any, + target_repository: Any, +) -> dict[str, Any] | None: + """Locate the contributor entry that matches `target_person` in + `target_repository.contributors`. + + Returns the dict carrying the deterministic GitHub bookends + (``firstContributionDate`` / ``lastContributionDate`` / + ``contributions``) populated by ``gather_context``, or ``None`` + when no match is found. + """ + if not isinstance(target_repository, dict): + return None + contributors = target_repository.get("contributors") + if not isinstance(contributors, list) or not contributors: + return None + login = _person_github_login(target_person) + if not login: + return None + target_login = login.casefold() + for entry in contributors: + if not isinstance(entry, dict): + continue + candidate = entry.get("login") + if isinstance(candidate, str) and candidate.casefold() == target_login: + return entry + return None + + +class LLMContributionAgentV2: + """LLM-backed agent that produces a pulse:Contribution entity.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 180.0, + cache: ProviderCache | None = None, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + self._cache = cache + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + contribution_seed = _resolve_contribution_seed(context) + + stamp_current_agent( + name="contribution_agent", + context={"contribution_seed": contribution_seed}, + ) + + uuid_value = context.get("uuid") + if not isinstance(uuid_value, str) or not uuid_value.strip(): + uuid_value = generate_uuid() + + llm_input: dict[str, Any] = { + "contribution_seed": contribution_seed, + "uuid": uuid_value, + "detected_type": context.get("detected_type"), + "source_url": context.get("source_url"), + "known_persons": _list_of_dicts(context.get("known_persons")), + "known_repositories": _list_of_dicts(context.get("known_repositories")), + "known_organizations": _list_of_dicts(context.get("known_organizations")), + "person_derivations": _list_of_dicts(context.get("person_derivations")), + "repository_derivations": _list_of_dicts( + context.get("repository_derivations"), + ), + "typed_entity_buckets": context.get("typed_entity_buckets"), + } + + repository_context = context.get("repository_context") + if isinstance(repository_context, dict) and repository_context: + llm_input["repository_context"] = deepcopy(repository_context) + + pipeline_outputs = context.get("pipeline_outputs") + if isinstance(pipeline_outputs, dict) and pipeline_outputs: + llm_input["pipeline_outputs"] = pipeline_outputs + + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True, default=str) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + user_prompt = append_runtime_prompt_context(user_prompt, context) + + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=AgentContributionShape, + tools=[make_fetch_link_content_tool(self._cache)], + ), + timeout=self._llm_call_timeout_seconds, + ) + except asyncio.TimeoutError as exc: + message = ( + f"{contribution_seed} — LLM call timed out after " + f"{self._llm_call_timeout_seconds:.1f}s" + ) + raise LLMRuntimeError(message) from exc + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + payload = {key: value for key, value in llm_result.payload.items() if value is not None} + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + payload.update(overrides) + + # Authoritative pair: the orchestrator decided which (person, repo) + # this invocation is for. The LLM is told the same in the system + # prompt, but in practice it sometimes ignores `target_person` and + # echoes whatever person was in `pipeline_outputs`. Force the + # authoritative ids here so downstream dedup-by-id can't collapse + # distinct contributions, regardless of LLM compliance. + target_person = context.get("target_person") + target_repository = context.get("target_repository") + target_person_id = ( + target_person.get("id") + if isinstance(target_person, dict) + else None + ) + target_repository_id = ( + target_repository.get("id") + if isinstance(target_repository, dict) + else None + ) + if isinstance(target_person_id, str) and target_person_id: + payload["schema:author"] = target_person_id + if isinstance(target_repository_id, str) and target_repository_id: + payload["pulse:contributionTo"] = target_repository_id + if ( + isinstance(target_person_id, str) + and target_person_id + and isinstance(target_repository_id, str) + and target_repository_id + ): + composite_id = f"{target_person_id}_{target_repository_id}" + payload["id"] = composite_id + payload["idSource"] = "pulse:composite" + identifiers = payload.get("identifiers") + if not isinstance(identifiers, dict): + identifiers = {} + payload["identifiers"] = identifiers + identifiers["pulse:composite"] = composite_id + + # Stamp count/first/last from the deterministic GitHub bookends + # carried on `target_repository.contributors`. Without this, the + # LLM tends to copy the lead contributor's stats onto every + # Contribution (observed: cmdoret's 128 commits stamped on every + # person in the repo, plus the repo-wide first/last commit dates + # in place of per-person dates). + contributor_record = _find_target_contributor_record( + target_person, target_repository, + ) + if contributor_record is not None: + count = contributor_record.get("contributions") + if isinstance(count, int) and count > 0: + payload["pulse:contributionCount"] = count + first_date = contributor_record.get("firstContributionDate") + if isinstance(first_date, str) and first_date: + payload["pulse:firstContributionDate"] = first_date + last_date = contributor_record.get("lastContributionDate") + if isinstance(last_date, str) and last_date: + payload["pulse:lastContributionDate"] = last_date + + force_server_uuid(payload, uuid_value) + + raw_output = deepcopy(payload) + validation_warnings = _strict_validate(payload) + + return AgentResult( + data=payload, + warnings=validation_warnings, + raw_output=raw_output, + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "contributions": [deepcopy(payload)] if payload else [], + "contribution_count": 1 if payload else 0, + "derivation": { + "contribution_seed": contribution_seed, + "contribution_id": payload.get("id"), + "person_id": payload.get("schema:author"), + "repository_id": payload.get("pulse:contributionTo"), + "count": payload.get("pulse:contributionCount"), + }, + }, + ) diff --git a/src/v2/agents/llm/contribution/prompts/__init__.py b/src/v2/agents/llm/contribution/prompts/__init__.py new file mode 100644 index 0000000..9d48db4 --- /dev/null +++ b/src/v2/agents/llm/contribution/prompts/__init__.py @@ -0,0 +1 @@ +from __future__ import annotations diff --git a/src/v2/agents/llm/contribution/prompts/system_prompt.md b/src/v2/agents/llm/contribution/prompts/system_prompt.md new file mode 100644 index 0000000..90a1ed8 --- /dev/null +++ b/src/v2/agents/llm/contribution/prompts/system_prompt.md @@ -0,0 +1,32 @@ +You are a contribution metadata extraction agent operating under the **Open Pulse Ontology v2.1.2**. + +Return exactly one JSON object for a `pulse:Contribution` entity conforming to `pulse:ContributionShape`. + +Output only JSON. No markdown fences. No explanations. + +Required fields: +- `id` (composite `personId_repoId` or UUID fallback) +- `type` = `"pulse:Contribution"` +- `shacl` = `"pulse:ContributionShape"` +- `identifiers` with `pulse:composite` and `uuid` +- `idSource` in `{ "pulse:composite", "uuid" }` +- `pulse:contributionTo` (repository ID) +- `pulse:contributionCount` (integer >= 0) +- `schema:author` (person ID) + +Optional fields: +- `pulse:firstContributionDate` (`YYYY-MM-DDTHH:MM:SSZ`) +- `pulse:lastContributionDate` (`YYYY-MM-DDTHH:MM:SSZ`) + +Rules: +- The Contribution you must emit is for **`target_person` → `target_repository`** — + these two entities are the authoritative pair for this invocation. Set + `schema:author` to `target_person.id` and `pulse:contributionTo` to + `target_repository.id`. +- Use canonical person and repository IDs from known entities. +- Build the composite id deterministically: `{target_person.id}_{target_repository.id}`, + set `idSource = "pulse:composite"`, and put the same composite into + `identifiers["pulse:composite"]`. +- `contribution_seed` is the repository id (kept for backwards compatibility) — + prefer `target_repository.id` over it when both are present. +- Do not invent unsupported fields. diff --git a/src/v2/agents/llm/contribution/prompts/user_prompt.md b/src/v2/agents/llm/contribution/prompts/user_prompt.md new file mode 100644 index 0000000..9c29264 --- /dev/null +++ b/src/v2/agents/llm/contribution/prompts/user_prompt.md @@ -0,0 +1,7 @@ +Build a single Contribution payload from this runtime context. + +Prefer canonical IDs and contributor metadata from prior stages. + +```json +{context_json} +``` diff --git a/src/v2/agents/llm/critic/__init__.py b/src/v2/agents/llm/critic/__init__.py new file mode 100644 index 0000000..83f9ff5 --- /dev/null +++ b/src/v2/agents/llm/critic/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from src.v2.agents.llm.critic.agent import LLMCriticAgentV2 + +__all__ = ["LLMCriticAgentV2"] diff --git a/src/v2/agents/llm/critic/agent.py b/src/v2/agents/llm/critic/agent.py new file mode 100644 index 0000000..bef3664 --- /dev/null +++ b/src/v2/agents/llm/critic/agent.py @@ -0,0 +1,160 @@ +from __future__ import annotations + +import asyncio +import json +from copy import deepcopy +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm.agent_tools.duckduckgo_search import ( + make_duckduckgo_search_tool, +) +from src.v2.agents.llm.agent_tools.github_organization import ( + make_github_organization_metadata_tool, +) +from src.v2.agents.llm.prompt_context import append_runtime_prompt_context +from src.v2.agents.models import AgentResult, ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime +from src.v2.ingest.cache import ProviderCache +from src.v2.observation.query_log import stamp_current_agent + +_PROMPTS_PACKAGE = "src.v2.agents.llm.critic.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") + + +class CriticDropSuggestion(BaseModel): + model_config = ConfigDict(extra="ignore") + + id: str + reason: str | None = None + confidence: float | None = Field(default=None, ge=0.0, le=1.0) + + +class CriticOutput(BaseModel): + model_config = ConfigDict(extra="ignore") + + organizations: list[CriticDropSuggestion] = Field(default_factory=list) + persons: list[CriticDropSuggestion] = Field(default_factory=list) + repositories: list[CriticDropSuggestion] = Field(default_factory=list) + articles: list[CriticDropSuggestion] = Field(default_factory=list) + + +class LLMCriticAgentV2: + """LLM-backed relevance critic for reconciled entities.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 180.0, + cache: ProviderCache | None = None, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + self._cache = cache + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + stamp_current_agent( + name="critic_agent", + context={"source_url": context.get("source_url")} + if isinstance(context.get("source_url"), str) + else {}, + ) + + initial_context = context.get("initial_context") + owner_provenance = _extract_owner_provenance(initial_context) + llm_input = { + "source_url": context.get("source_url"), + "detected_type": context.get("detected_type"), + "initial_context": initial_context if isinstance(initial_context, dict) else {}, + "owner_provenance": owner_provenance, + "pipeline_outputs": context.get("pipeline_outputs", {}), + "reconciled_entities": context.get("reconciled_entities", {}), + "memberships": context.get("memberships", []), + "contributions": context.get("contributions", []), + } + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True, default=str) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + user_prompt = append_runtime_prompt_context(user_prompt, context) + tools = [make_duckduckgo_search_tool(cache=self._cache)] + if providers.github is not None: + tools.append( + make_github_organization_metadata_tool(providers.github), + ) + + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=CriticOutput, + tools=tools, + ), + timeout=self._llm_call_timeout_seconds, + ) + except asyncio.TimeoutError as exc: + message = ( + "llm_critic — LLM call timed out after " + f"{self._llm_call_timeout_seconds:.1f}s" + ) + raise LLMRuntimeError(message) from exc + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + payload = dict(llm_result.payload) + for key in ("organizations", "persons", "repositories", "articles"): + value = payload.get(key) + if not isinstance(value, list): + payload[key] = [] + + return AgentResult( + data=payload, + warnings=[], + raw_output=deepcopy(payload), + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "drop_suggestion_count": sum( + len(payload.get(key, [])) + for key in ("organizations", "persons", "repositories", "articles") + ), + }, + ) + + +def _extract_owner_provenance(initial_context: Any) -> dict[str, Any]: + if not isinstance(initial_context, dict): + return {} + + repository_context = initial_context.get("repository") + if not isinstance(repository_context, dict): + return {} + + metadata = repository_context.get("metadata") + if not isinstance(metadata, dict): + metadata = {} + + owner = metadata.get("owner") + owner_payload = deepcopy(owner) if isinstance(owner, dict) else {} + owner_payload = {k: v for k, v in owner_payload.items() if v is not None} + + return { + "repository_full_name": repository_context.get("full_name"), + "repository_url": metadata.get("html_url") or metadata.get("url"), + "owner": owner_payload, + } diff --git a/src/v2/agents/llm/critic/prompts/__init__.py b/src/v2/agents/llm/critic/prompts/__init__.py new file mode 100644 index 0000000..9d48db4 --- /dev/null +++ b/src/v2/agents/llm/critic/prompts/__init__.py @@ -0,0 +1 @@ +from __future__ import annotations diff --git a/src/v2/agents/llm/critic/prompts/system_prompt.md b/src/v2/agents/llm/critic/prompts/system_prompt.md new file mode 100644 index 0000000..cc8d438 --- /dev/null +++ b/src/v2/agents/llm/critic/prompts/system_prompt.md @@ -0,0 +1,18 @@ +You are a relevance critic for Open Pulse v2 extraction. + +Given full extraction context and reconciled entities, suggest entities that should be dropped as irrelevant to the source context. + +You can use tools to verify context before suggesting drops: +- `search_on_the_internet(query, max_results=5)` for quick external context checks. +- `get_github_organization_metadata(org_name)` for GitHub org-owner provenance checks. + +Output rules: +- Output JSON only. +- Return object keys: `organizations`, `persons`, `repositories`, `articles`. +- Each key must be a list of objects with: + - `id`: entity ID to drop. + - optional `reason`: concise reason. + - optional `confidence`: float in [0,1]. +- Do not include entities that are clearly relevant to source ownership, contributors, direct affiliations, or validated scholarly links. +- Do not drop parent/host institutions linked through owner organization hierarchy (`org:unitOf`) when they contextualize repository ownership (for example host university of the owning data center). +- Be conservative: if unsure, do not propose a drop. diff --git a/src/v2/agents/llm/critic/prompts/user_prompt.md b/src/v2/agents/llm/critic/prompts/user_prompt.md new file mode 100644 index 0000000..d9db44b --- /dev/null +++ b/src/v2/agents/llm/critic/prompts/user_prompt.md @@ -0,0 +1,3 @@ +Review extraction context and suggest irrelevant entities to prune. + +{context_json} diff --git a/src/v2/agents/llm/dedup/__init__.py b/src/v2/agents/llm/dedup/__init__.py new file mode 100644 index 0000000..fbc527d --- /dev/null +++ b/src/v2/agents/llm/dedup/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from src.v2.agents.llm.dedup.agent import LLMDedupAgentV2 + +__all__ = ["LLMDedupAgentV2"] diff --git a/src/v2/agents/llm/dedup/agent.py b/src/v2/agents/llm/dedup/agent.py new file mode 100644 index 0000000..209e19b --- /dev/null +++ b/src/v2/agents/llm/dedup/agent.py @@ -0,0 +1,108 @@ +from __future__ import annotations + +import asyncio +import json +from copy import deepcopy +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm.prompt_context import append_runtime_prompt_context +from src.v2.agents.models import AgentResult, ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime + +_PROMPTS_PACKAGE = "src.v2.agents.llm.dedup.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") + + +class DedupClusterSuggestion(BaseModel): + model_config = ConfigDict(extra="ignore") + + ids: list[str] = Field(default_factory=list) + reason: str | None = None + confidence: float | None = Field(default=None, ge=0.0, le=1.0) + + +class DedupOutput(BaseModel): + model_config = ConfigDict(extra="ignore") + + organizations: list[DedupClusterSuggestion] = Field(default_factory=list) + persons: list[DedupClusterSuggestion] = Field(default_factory=list) + repositories: list[DedupClusterSuggestion] = Field(default_factory=list) + articles: list[DedupClusterSuggestion] = Field(default_factory=list) + + +class LLMDedupAgentV2: + """LLM-backed global dedup candidate generator.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 180.0, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + llm_input = { + "source_url": context.get("source_url"), + "detected_type": context.get("detected_type"), + "typed_entity_buckets": context.get("typed_entity_buckets", {}), + } + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True, default=str) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + user_prompt = append_runtime_prompt_context(user_prompt, context) + + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=DedupOutput, + ), + timeout=self._llm_call_timeout_seconds, + ) + except asyncio.TimeoutError as exc: + message = ( + "llm_dedup — LLM call timed out after " + f"{self._llm_call_timeout_seconds:.1f}s" + ) + raise LLMRuntimeError(message) from exc + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + payload = dict(llm_result.payload) + for key in ("organizations", "persons", "repositories", "articles"): + value = payload.get(key) + if not isinstance(value, list): + payload[key] = [] + + return AgentResult( + data=payload, + warnings=[], + raw_output=deepcopy(payload), + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "cluster_suggestion_count": sum( + len(payload.get(key, [])) + for key in ("organizations", "persons", "repositories", "articles") + ), + }, + ) diff --git a/src/v2/agents/llm/dedup/prompts/__init__.py b/src/v2/agents/llm/dedup/prompts/__init__.py new file mode 100644 index 0000000..9d48db4 --- /dev/null +++ b/src/v2/agents/llm/dedup/prompts/__init__.py @@ -0,0 +1 @@ +from __future__ import annotations diff --git a/src/v2/agents/llm/dedup/prompts/system_prompt.md b/src/v2/agents/llm/dedup/prompts/system_prompt.md new file mode 100644 index 0000000..6aad347 --- /dev/null +++ b/src/v2/agents/llm/dedup/prompts/system_prompt.md @@ -0,0 +1,16 @@ +You are a global entity deduplication assistant for Open Pulse v2 extraction. + +You receive entity buckets for organizations, persons, repositories, and articles. +Your task is to suggest possible duplicate clusters for each entity type. + +Output rules: +- Output JSON only. +- Return object keys: `organizations`, `persons`, `repositories`, `articles`. +- Each key must be a list of objects with: + - `ids`: array of entity IDs that seem to represent the same entity. + - optional `reason`: short explanation. + - optional `confidence`: float in [0,1]. +- Do not return singleton clusters. +- Do not mix entity types in one cluster. +- Prefer precision over recall when uncertain. +- Use identifiers and strong metadata evidence first (ROR/ORCID/DOI/GitHub handles) before name similarity. diff --git a/src/v2/agents/llm/dedup/prompts/user_prompt.md b/src/v2/agents/llm/dedup/prompts/user_prompt.md new file mode 100644 index 0000000..d24649f --- /dev/null +++ b/src/v2/agents/llm/dedup/prompts/user_prompt.md @@ -0,0 +1,3 @@ +Analyze the following extraction context and suggest duplicate clusters by entity type. + +{context_json} diff --git a/src/v2/agents/llm/link_veracity/__init__.py b/src/v2/agents/llm/link_veracity/__init__.py new file mode 100644 index 0000000..d31efcc --- /dev/null +++ b/src/v2/agents/llm/link_veracity/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from src.v2.agents.llm.link_veracity.agent import LLMLinkVeracityAgentV2 + +__all__ = ["LLMLinkVeracityAgentV2"] diff --git a/src/v2/agents/llm/link_veracity/agent.py b/src/v2/agents/llm/link_veracity/agent.py new file mode 100644 index 0000000..60e6f32 --- /dev/null +++ b/src/v2/agents/llm/link_veracity/agent.py @@ -0,0 +1,175 @@ +from __future__ import annotations + +import asyncio +import json +import logging +from copy import deepcopy +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm.agent_tools.selenium_fetch import ( + make_fetch_link_content_tool, +) +from src.v2.agents.llm.prompt_context import append_runtime_prompt_context +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime +from src.v2.agents.models import AgentResult, ProviderSet +from src.v2.ingest.cache import ProviderCache +from src.v2.observation.query_log import stamp_current_agent + +_PROMPTS_PACKAGE = "src.v2.agents.llm.link_veracity.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") + +logger = logging.getLogger(__name__) + + +class LinkVeracityOutput(BaseModel): + """Structured output for link veracity checks.""" + + model_config = ConfigDict(extra="ignore") + + link: str = Field(..., description="The exact URL being verified.") + relationship_supported: bool = Field( + ..., + description="True if fetched content supports the source->predicate->link relation.", + ) + relationship_summary: str | None = Field( + None, + description="Short rationale for yes/no decision.", + ) + fetched_successfully: bool | None = Field( + None, + description="Whether the Selenium fetch succeeded.", + ) + + +def _resolve_link(context: dict[str, Any]) -> str: + value = context.get("link") + if isinstance(value, str) and value.strip(): + return value.strip() + message = "Link veracity context is missing link" + raise ValueError(message) + + +class LLMLinkVeracityAgentV2: + """LLM-backed link verifier that evaluates relation veracity against fetched content.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 180.0, + cache: ProviderCache | None = None, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + self._cache = cache + self._fetch_tool = make_fetch_link_content_tool(cache) + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + link = _resolve_link(context) + + stamp_current_agent( + name="link_veracity_agent", + context={"link": link}, + ) + + llm_input = { + "link": link, + "source_entity_id": context.get("source_entity_id"), + "predicate": context.get("predicate"), + "relationships": context.get("relationships"), + "source_url": context.get("source_url"), + } + + cache_key: str | None = None + if self._cache is not None: + cache_key = ProviderCache.make_key( + "link_veracity", + "verdict", + **llm_input, + ) + cached_payload = self._cache.get(cache_key) + if isinstance(cached_payload, dict): + logger.info( + "link_veracity cache hit — link=%r supported=%s", + link, + bool(cached_payload.get("relationship_supported")), + ) + return AgentResult( + data=dict(cached_payload), + warnings=[], + raw_output=deepcopy(cached_payload), + model=None, + provider=None, + tokens_prompt=0, + tokens_completion=0, + stats={ + "agent_runtime": "llm", + "link": link, + "relationship_supported": bool( + cached_payload.get("relationship_supported"), + ), + "cached": True, + }, + ) + + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True, default=str) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + user_prompt = append_runtime_prompt_context(user_prompt, context) + + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=LinkVeracityOutput, + tools=[self._fetch_tool], + ), + timeout=self._llm_call_timeout_seconds, + ) + except asyncio.TimeoutError as exc: + message = ( + f"{link} — LLM call timed out after " + f"{self._llm_call_timeout_seconds:.1f}s" + ) + raise LLMRuntimeError(message) from exc + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + payload = dict(llm_result.payload) + if "fetched_successfully" not in payload: + payload["fetched_successfully"] = None + if "relationship_summary" not in payload: + payload["relationship_summary"] = None + raw_output = deepcopy(payload) + + if cache_key is not None and self._cache is not None: + self._cache.set(cache_key, payload) + + return AgentResult( + data=payload, + warnings=[], + raw_output=raw_output, + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "link": link, + "relationship_supported": bool(payload.get("relationship_supported")), + }, + ) diff --git a/src/v2/agents/llm/link_veracity/prompts/__init__.py b/src/v2/agents/llm/link_veracity/prompts/__init__.py new file mode 100644 index 0000000..9d48db4 --- /dev/null +++ b/src/v2/agents/llm/link_veracity/prompts/__init__.py @@ -0,0 +1 @@ +from __future__ import annotations diff --git a/src/v2/agents/llm/link_veracity/prompts/system_prompt.md b/src/v2/agents/llm/link_veracity/prompts/system_prompt.md new file mode 100644 index 0000000..9913517 --- /dev/null +++ b/src/v2/agents/llm/link_veracity/prompts/system_prompt.md @@ -0,0 +1,22 @@ +You are a link-veracity evaluator. + +Goal: +- Verify whether a URL relation extracted in JSON-LD is supported by actual page content. + +You are given: +- `link` +- optional relation hints (`source_entity_id`, `predicate`, and `relationships`) + +Tool requirement: +- You MUST call `fetch_link_content_via_selenium(link)` before deciding. + +Decision rule: +- `relationship_supported = true` if fetched content is consistent with the claimed relation. +- `relationship_supported = false` if content contradicts relation, is unrelated, or fetch fails. + +Output: +- Return only one JSON object with fields: + - `link` (string) + - `relationship_supported` (boolean) + - `relationship_summary` (short string, optional) + - `fetched_successfully` (boolean, optional) diff --git a/src/v2/agents/llm/link_veracity/prompts/user_prompt.md b/src/v2/agents/llm/link_veracity/prompts/user_prompt.md new file mode 100644 index 0000000..d844afa --- /dev/null +++ b/src/v2/agents/llm/link_veracity/prompts/user_prompt.md @@ -0,0 +1,5 @@ +Evaluate this link relationship context. + +```json +{context_json} +``` diff --git a/src/v2/agents/llm/membership/__init__.py b/src/v2/agents/llm/membership/__init__.py new file mode 100644 index 0000000..a5a25ed --- /dev/null +++ b/src/v2/agents/llm/membership/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from src.v2.agents.llm.membership.agent import LLMMembershipAgentV2 + +__all__ = ["LLMMembershipAgentV2"] diff --git a/src/v2/agents/llm/membership/agent.py b/src/v2/agents/llm/membership/agent.py new file mode 100644 index 0000000..ee119a6 --- /dev/null +++ b/src/v2/agents/llm/membership/agent.py @@ -0,0 +1,223 @@ +from __future__ import annotations + +import asyncio +import json +from copy import deepcopy +from datetime import date +from typing import Any + +from pydantic import ValidationError + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm._payload_helpers import force_server_uuid +from src.v2.agents.llm.agent_tools.orcid_person import make_orcid_person_tool +from src.v2.agents.llm.agent_tools.query_orcid import make_query_orcid_tool +from src.v2.agents.llm.agent_tools.selenium_fetch import ( + make_fetch_link_content_tool, +) +from src.v2.agents.llm.prompt_context import append_runtime_prompt_context +from src.v2.agents.models import AgentResult, ProviderSet, generate_uuid +from src.v2.ingest.cache import ProviderCache +from src.v2.observation.query_log import stamp_current_agent +from src.v2.schema.models.agent import AgentMembershipShape +from src.v2.schema.models.strict import MembershipModel +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime + +MAX_CONTEXT_ENTITIES = 30 + +_PROMPTS_PACKAGE = "src.v2.agents.llm.membership.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") + + +def _resolve_membership_seed(context: dict[str, Any]) -> str: + seed = context.get("membership_seed") + if isinstance(seed, str) and seed.strip(): + return seed.strip() + + known_persons = context.get("known_persons") + if isinstance(known_persons, list): + for person in known_persons: + if not isinstance(person, dict): + continue + person_id = person.get("id") + if isinstance(person_id, str) and person_id.strip(): + return person_id.strip() + + message = "Membership context is missing membership_seed or known person id" + raise ValueError(message) + + +def _strict_validate(payload: dict[str, Any]) -> list[str]: + warnings: list[str] = [] + try: + MembershipModel.model_validate(payload) + except ValidationError as exc: + for error in exc.errors(): + field = " -> ".join(str(loc) for loc in error["loc"]) + warnings.append(f"Strict schema warning at {field}: {error['msg']}") + return warnings + + +def _list_of_dicts(value: Any, *, max_items: int = MAX_CONTEXT_ENTITIES) -> list[dict[str, Any]]: + if not isinstance(value, list): + return [] + collected: list[dict[str, Any]] = [] + for item in value: + if not isinstance(item, dict): + continue + if len(collected) >= max_items: + break + collected.append(deepcopy(item)) + return collected + + +class LLMMembershipAgentV2: + """LLM-backed agent that produces an org:Membership entity.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 180.0, + cache: ProviderCache | None = None, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + self._cache = cache + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + membership_seed = _resolve_membership_seed(context) + + stamp_current_agent( + name="membership_agent", + context={"membership_seed": membership_seed}, + ) + + uuid_value = context.get("uuid") + if not isinstance(uuid_value, str) or not uuid_value.strip(): + uuid_value = generate_uuid() + + llm_input: dict[str, Any] = { + "membership_seed": membership_seed, + "uuid": uuid_value, + "detected_type": context.get("detected_type"), + "source_url": context.get("source_url"), + "known_persons": _list_of_dicts(context.get("known_persons")), + "known_organizations": _list_of_dicts(context.get("known_organizations")), + "person_derivations": _list_of_dicts(context.get("person_derivations")), + "organization_derivations": _list_of_dicts( + context.get("organization_derivations"), + ), + "typed_entity_buckets": context.get("typed_entity_buckets"), + } + + target_person = context.get("target_person") + if isinstance(target_person, dict) and target_person: + llm_input["target_person"] = deepcopy(target_person) + + target_organizations = context.get("target_organizations") + normalized_target_organizations = _list_of_dicts(target_organizations) + if normalized_target_organizations: + llm_input["target_organizations"] = normalized_target_organizations + + repository_context = context.get("repository_context") + if isinstance(repository_context, dict) and repository_context: + llm_input["repository_context"] = deepcopy(repository_context) + + pipeline_outputs = context.get("pipeline_outputs") + if isinstance(pipeline_outputs, dict) and pipeline_outputs: + llm_input["pipeline_outputs"] = pipeline_outputs + + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True, default=str) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + user_prompt = append_runtime_prompt_context(user_prompt, context) + tools = [make_fetch_link_content_tool(self._cache)] + if providers.orcid is not None: + tools.append(make_orcid_person_tool(providers.orcid)) + tools.append( + make_query_orcid_tool( + providers.orcid, + orcid_rag_provider=providers.orcid_rag, + ), + ) + + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=AgentMembershipShape, + tools=tools, + ), + timeout=self._llm_call_timeout_seconds, + ) + except asyncio.TimeoutError as exc: + message = ( + f"{membership_seed} — LLM call timed out after " + f"{self._llm_call_timeout_seconds:.1f}s" + ) + raise LLMRuntimeError(message) from exc + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + payload = {key: value for key, value in llm_result.payload.items() if value is not None} + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + payload.update(overrides) + + force_server_uuid(payload, uuid_value) + + # Defend against malformed and inverted Membership dates from ORCID: + # invalid dates like "2000-09-31" (Sept has 30 days) trip the SHACL + # `xsd:date` literal check; inverted ranges trip `lessThanOrEquals`. + # Drop unparseable values, then swap when needed. + def _safe_date(value: object) -> str | None: + if not isinstance(value, str): + return None + try: + return date.fromisoformat(value[:10]).isoformat() + except ValueError: + return None + + beg = _safe_date(payload.get("time:hasBeginning")) + end = _safe_date(payload.get("time:hasEnd")) + if "time:hasBeginning" in payload: + payload["time:hasBeginning"] = beg + if "time:hasEnd" in payload: + payload["time:hasEnd"] = end + if beg and end and beg > end: + payload["time:hasBeginning"], payload["time:hasEnd"] = end, beg + + raw_output = deepcopy(payload) + validation_warnings = _strict_validate(payload) + + return AgentResult( + data=payload, + warnings=validation_warnings, + raw_output=raw_output, + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "memberships": [deepcopy(payload)] if payload else [], + "membership_count": 1 if payload else 0, + "derivation": { + "membership_seed": membership_seed, + "membership_id": payload.get("id"), + "person_id": membership_seed, + "organization_id": payload.get("org:organization"), + }, + }, + ) diff --git a/src/v2/agents/llm/membership/prompts/__init__.py b/src/v2/agents/llm/membership/prompts/__init__.py new file mode 100644 index 0000000..9d48db4 --- /dev/null +++ b/src/v2/agents/llm/membership/prompts/__init__.py @@ -0,0 +1 @@ +from __future__ import annotations diff --git a/src/v2/agents/llm/membership/prompts/system_prompt.md b/src/v2/agents/llm/membership/prompts/system_prompt.md new file mode 100644 index 0000000..e62688d --- /dev/null +++ b/src/v2/agents/llm/membership/prompts/system_prompt.md @@ -0,0 +1,59 @@ +You are a membership metadata extraction agent operating under the **Open Pulse Ontology v2.1.2**. + +Return exactly one JSON object for an `org:Membership` entity conforming to `pulse:MembershipShape`. + +Output only JSON. No markdown fences. No explanations. + +Required fields: +- `id` (composite `personId_orgId` or UUID fallback) +- `type` = `"org:Membership"` +- `shacl` = `"pulse:MembershipShape"` +- `identifiers` with `pulse:composite` and `uuid` +- `idSource` in `{ "pulse:composite", "uuid" }` +- `org:organization` (canonical organization ID) + +Optional fields: +- `org:role` +- `time:hasBeginning` (`YYYY-MM-DD`) +- `time:hasEnd` (`YYYY-MM-DD`) + +Rules: +- Use canonical IDs from `known_persons` and `known_organizations` when available. +- Use `target_person` and `target_organizations` as primary context when provided. +- Prefer ROR-backed canonical organization IDs when multiple near-match organizations are present and context supports that choice. +- Prefer `membership_seed` as the target person when present. +- Build a deterministic composite ID when possible: `{personId}_{organizationId}`. +- If target person has an ORCID identifier, use ORCID evidence to infer `org:role`, `time:hasBeginning`, and `time:hasEnd` conservatively. +- Only set role/date fields when evidence clearly maps to the selected organization; otherwise keep them null. +- Do not invent unsupported fields. + +Date-field discipline (strict): +- `time:hasBeginning` and `time:hasEnd` MUST be `null` unless an ORCID + employment or education affiliation explicitly returns a `start_date` or + `end_date` for the **same** organization (matched by ROR id, name, or a + clear alias). +- Do NOT derive dates from the source repository's `dateCreated`, the current + date, the LLM's general knowledge, or "round" placeholders like + `2023-01-01` / `2026-01-01`. +- If only one of the two dates is supported by evidence, fill that one and + leave the other `null` — never fill both with guesses to look symmetric. +- A membership without dates is a valid, useful membership; an invented date + is worse than no date. + +Available tools: +- `get_orcid_record(orcid_id)`: + - Returns ORCID employment and education affiliations. + - Use it to ground membership role/date fields when `target_person` has `pulse:orcidIdentifier`. +- `query_orcid(query, rows=50, start=0)`: + - Free-text name search against ORCID expanded-search; returns candidate hits + with `orcid_id`, given/family/credit names, `institution_names`, and `emails`. + - Use it only when `target_person` has no `pulse:orcidIdentifier` but does have + a `schema:name`. Pick the hit whose `institution_names` overlap the + `target_organization` (or other known affiliation context), then call + `get_orcid_record` on that `orcid_id` to ground role and dates. + - Never fill membership dates from `query_orcid` directly — only from the + `get_orcid_record` employment/education entries it leads to. + +Identifiers: +- Use the `uuid` value already provided in your input verbatim for the + `uuid` identifier slot. Do not generate a new one. diff --git a/src/v2/agents/llm/membership/prompts/user_prompt.md b/src/v2/agents/llm/membership/prompts/user_prompt.md new file mode 100644 index 0000000..630c388 --- /dev/null +++ b/src/v2/agents/llm/membership/prompts/user_prompt.md @@ -0,0 +1,7 @@ +Build a single Membership payload from this runtime context. + +Prefer canonical IDs already derived by prior stages. + +```json +{context_json} +``` diff --git a/src/v2/agents/llm/org_relationships/__init__.py b/src/v2/agents/llm/org_relationships/__init__.py new file mode 100644 index 0000000..1ad7079 --- /dev/null +++ b/src/v2/agents/llm/org_relationships/__init__.py @@ -0,0 +1,5 @@ +"""LLM-decided org-to-org parent/child relationships.""" + +from src.v2.agents.llm.org_relationships.agent import LLMOrgRelationshipsAgentV2 + +__all__ = ["LLMOrgRelationshipsAgentV2"] diff --git a/src/v2/agents/llm/org_relationships/agent.py b/src/v2/agents/llm/org_relationships/agent.py new file mode 100644 index 0000000..64549c9 --- /dev/null +++ b/src/v2/agents/llm/org_relationships/agent.py @@ -0,0 +1,152 @@ +"""LLM-decided org-to-org parent/child relationships. + +Single LLM call per request. Sees the complete set of organizations in the +extraction graph and emits a list of `(child_id, parent_id)` edges. The +caller validates the edges (existence, no self-loops, no cycles) before +stamping `org:unitOf` / `org:hasUnit` on the entities. +""" +from __future__ import annotations + +import asyncio +import json +import logging +from copy import deepcopy +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime +from src.v2.agents.models import AgentResult, ProviderSet +from src.v2.observation.query_log import stamp_current_agent + +_PROMPTS_PACKAGE = "src.v2.agents.llm.org_relationships.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") + +logger = logging.getLogger(__name__) + + +class OrgRelationship(BaseModel): + model_config = ConfigDict(extra="ignore") + + child_id: str = Field(..., description="ID of the sub-unit organization.") + parent_id: str = Field(..., description="ID of the parent organization.") + reason: str | None = Field(None, description="Short rationale citing evidence.") + + +class OrgRelationshipsOutput(BaseModel): + model_config = ConfigDict(extra="ignore") + + relationships: list[OrgRelationship] = Field(default_factory=list) + + +class LLMOrgRelationshipsAgentV2: + """LLM agent that decides `org:unitOf` edges across a graph's organizations.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 120.0, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + organizations = context.get("organizations") + if not isinstance(organizations, list) or len(organizations) < 2: + return AgentResult( + data={"relationships": []}, + warnings=[], + raw_output={"relationships": []}, + model=None, + provider=None, + tokens_prompt=0, + tokens_completion=0, + stats={"agent_runtime": "llm", "skipped": "fewer_than_two_orgs"}, + ) + + stamp_current_agent( + name="org_relationships_agent", + context={"org_count": len(organizations)}, + ) + + llm_input = { + "source_url": context.get("source_url"), + "organizations": organizations, + } + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True, default=str) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=OrgRelationshipsOutput, + tools=None, + ), + timeout=self._llm_call_timeout_seconds, + ) + except asyncio.TimeoutError as exc: + message = ( + "org_relationships — LLM call timed out after " + f"{self._llm_call_timeout_seconds:.1f}s" + ) + raise LLMRuntimeError(message) from exc + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + payload = llm_result.payload if isinstance(llm_result.payload, dict) else {} + relationships = payload.get("relationships") + if not isinstance(relationships, list): + relationships = [] + clean_relationships: list[dict[str, Any]] = [] + for entry in relationships: + if not isinstance(entry, dict): + continue + child_id = entry.get("child_id") + parent_id = entry.get("parent_id") + if not isinstance(child_id, str) or not isinstance(parent_id, str): + continue + if not child_id.strip() or not parent_id.strip(): + continue + clean_relationships.append( + { + "child_id": child_id.strip(), + "parent_id": parent_id.strip(), + "reason": entry.get("reason") + if isinstance(entry.get("reason"), str) + else None, + }, + ) + + normalized = {"relationships": clean_relationships} + return AgentResult( + data=normalized, + warnings=[], + raw_output=deepcopy(payload), + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "org_count": len(organizations), + "edge_count": len(clean_relationships), + }, + ) + + +__all__ = ["LLMOrgRelationshipsAgentV2"] diff --git a/src/v2/agents/llm/org_relationships/prompts/__init__.py b/src/v2/agents/llm/org_relationships/prompts/__init__.py new file mode 100644 index 0000000..750849a --- /dev/null +++ b/src/v2/agents/llm/org_relationships/prompts/__init__.py @@ -0,0 +1 @@ +"""Markdown prompts for the org-relationships agent.""" diff --git a/src/v2/agents/llm/org_relationships/prompts/system_prompt.md b/src/v2/agents/llm/org_relationships/prompts/system_prompt.md new file mode 100644 index 0000000..aee5774 --- /dev/null +++ b/src/v2/agents/llm/org_relationships/prompts/system_prompt.md @@ -0,0 +1,79 @@ +You are an organization-hierarchy reasoning agent operating under the **Open Pulse Ontology v2.1.2**. + +You are given the **complete set of organizations** present in the current extraction graph and must decide which of them are sub-units of which others. + +## Output contract + +Return **only** a JSON object of the shape: + +```json +{ + "relationships": [ + {"child_id": "", "parent_id": "", "reason": ""} + ] +} +``` + +No markdown fences, no explanation outside the JSON. Use an empty `"relationships": []` if you can't justify any pair. + +## Hard rules + +- **Never invent ids.** Every `child_id` and `parent_id` you emit must appear verbatim in the input under `organizations[*].id`. If both candidates aren't in the input, drop the pair. +- **Never link an org to itself.** `child_id != parent_id`. +- **No cycles.** If you emit `(A unitOf B)` you must not also emit `(B unitOf A)` directly or transitively. +- **One parent per child.** Each `child_id` may appear in at most one relationship in the output. Pick the most specific / most-evidenced parent and skip the others. (The schema currently models `org:unitOf` as a single value; multi-parent support is deferred.) +- **Conservative beats noisy.** If you are unsure, omit the pair. A missing edge is better than a wrong edge. + +## What good evidence looks like + +For each candidate `(child_id, parent_id)` pair, you should be able to point to **at least one** of: + +- The child's `schema:name` clearly contains the parent's name or its standard short form (e.g. `EPFL Open Science` is plainly part of `EPFL — École Polytechnique Fédérale de Lausanne`; `OpenAI Research` is plainly part of `OpenAI`). +- The child's `pulse:githubOrganizationHandle` contains the parent's standard short form as a token separated by `-` or `_` (e.g. `EPFL-Open-Science` → `EPFL`). +- An explicit affiliation statement in the child's `description` or `bio` tying it to the parent (e.g. "An office of EPFL", "Part of the University of Lausanne"). +- An ROR-backed parent whose canonical name is unambiguously the umbrella of a github-only child working in its scope. + +## What is **not** good evidence + +- Both orgs being from the same country, region, or research domain. +- Sharing one common substring that's not a proper name (e.g. "Open", "Lab", "Research", "Center"). +- Both appearing in the graph because they share a person (e.g. an ORCID-derived employer of one of the contributors). Co-occurrence isn't hierarchy. +- Common umbrella terms (e.g. "EU institution", "Swiss research") without a stated org-to-org relationship. +- One name appearing as a substring of the other when both candidates are independent ROR-registered organizations. **ROR-backed orgs are usually peers, not parent/child.** Examples that look related but are not: + - `ETH Zurich` ↔ `ETH Zürich Foundation` — the Foundation is a separate fundraising entity, **not** a parent or child of the university. + - `Université de Lausanne` ↔ `EPFL` — geographic neighbours, no hierarchy. + - `University of Lausanne` ↔ `Centre Hospitalier Universitaire Vaudois` — affiliated but legally separate. + + Only emit `unitOf` between two ROR-backed candidates if you can point to **administrative subordination** (department/faculty/institute/programme of the other), not name overlap. + +## Domain knowledge — SDSC (Swiss Data Science Center) + +Special case worth handling well because it appears often in this codebase's +extractions: + +- **Swiss Data Science Center (SDSC)** has ROR id `https://ror.org/02hdt9m26` + and is officially a **joint center of both EPFL and ETH Zurich** (ROR + records both as parents). +- The schema currently allows only **one parent** per `org:unitOf`. When + both EPFL (`https://ror.org/02s376052`) and ETH Zurich + (`https://ror.org/05a28rw58`) appear in the graph alongside SDSC, **pick + EPFL as the parent** — it's the convention for this dataset. If only + one of EPFL or ETH Zurich is in the graph, pick that one. If neither is + in the graph, leave SDSC unparented. +- The GitHub organisation `sdsc-ordes` (Open Research Data and Software + team at SDSC) is a **sub-unit of SDSC**. If both `sdsc-ordes` (github + org) and `SDSC` (ROR `02hdt9m26`) are in the same graph, emit + `child=sdsc-ordes-id, parent=https://ror.org/02hdt9m26`. + +These are the only org-specific overrides; everything else follows the +general rules above. + +## Reason field + +Keep `reason` to one sentence, citing the specific evidence (e.g. *"EPFL-Open-Science handle contains the parent's short form 'EPFL' and its name expands the parent's acronym."*). The reason is logged with the warning when the edge is stamped, so be precise. + +## Don'ts + +- Don't reorder, rename, or rewrite ids. +- Don't emit any other JSON keys at the top level besides `relationships`. +- Don't invent edges based on prior knowledge of the real-world hierarchy if the evidence isn't in the input. The graph is the source of truth. diff --git a/src/v2/agents/llm/org_relationships/prompts/user_prompt.md b/src/v2/agents/llm/org_relationships/prompts/user_prompt.md new file mode 100644 index 0000000..e767ac7 --- /dev/null +++ b/src/v2/agents/llm/org_relationships/prompts/user_prompt.md @@ -0,0 +1,5 @@ +Decide the org:unitOf hierarchy across the following organizations. + +```json +{context_json} +``` diff --git a/src/v2/agents/llm/organization/__init__.py b/src/v2/agents/llm/organization/__init__.py new file mode 100644 index 0000000..8ce1c7b --- /dev/null +++ b/src/v2/agents/llm/organization/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from src.v2.agents.llm.organization.agent import LLMOrganizationAgentV2 + +__all__ = ["LLMOrganizationAgentV2"] diff --git a/src/v2/agents/llm/organization/agent.py b/src/v2/agents/llm/organization/agent.py new file mode 100644 index 0000000..12b5afd --- /dev/null +++ b/src/v2/agents/llm/organization/agent.py @@ -0,0 +1,397 @@ +from __future__ import annotations + +import asyncio +import json +import logging +from copy import deepcopy +from typing import Any + +from pydantic import ValidationError + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm._payload_helpers import force_server_uuid +from src.v2.agents.llm._verdict_cache import ( + get_cached_agent_verdict, + store_agent_verdict, +) +from src.v2.agents.llm.agent_tools.epfl_graph_rag import ( + make_epfl_graph_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.ethz_research_collection_rag import ( + make_ethz_research_collection_rag_fetch_chunks_tool, + make_ethz_research_collection_rag_fetch_records_tool, + make_ethz_research_collection_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.federated_rag import ( + make_federated_rag_lookup_tool, + make_federated_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.github_organization import ( + make_github_organization_metadata_tool, +) +from src.v2.agents.llm.agent_tools.huggingface_rag import ( + make_huggingface_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.infoscience_orgunit import ( + make_infoscience_orgunit_tool, +) +from src.v2.agents.llm.agent_tools.infoscience_rag import ( + make_infoscience_rag_fetch_chunks_tool, + make_infoscience_rag_fetch_records_tool, + make_infoscience_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.openalex_rag import ( + make_openalex_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.organization_identity import ( + make_organization_identity_search_tool, +) +from src.v2.agents.llm.agent_tools.renkulab_rag import ( + make_renkulab_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.ror_organization import ( + make_ror_organization_search_tool, +) +from src.v2.agents.llm.agent_tools.ror_rag import ( + make_ror_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.selenium_fetch import ( + make_fetch_link_content_tool, +) +from src.v2.agents.llm.agent_tools.snsf_rag import ( + make_snsf_rag_search_tool, +) +from src.v2.agents.llm.prompt_context import append_runtime_prompt_context +from src.v2.agents.llm.runtime import ( + LLMRuntimeError, + V2LLMRuntime, +) +from src.v2.agents.models import AgentResult, ProviderSet, generate_uuid +from src.v2.ingest.cache import ProviderCache +from src.v2.observation.query_log import stamp_current_agent +from src.v2.schema.models.agent import AgentOrganizationShape +from src.v2.schema.models.strict import OrganizationModel + +logger = logging.getLogger(__name__) + +_PROMPTS_PACKAGE = "src.v2.agents.llm.organization.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") + +README_CONTEXT_MAX_CHARS = 2000 +GIMIE_JSONLD_MAX_CHARS = 4000 + + +def _resolve_org_name(context: dict[str, Any]) -> str: + for key in ("org_name", "organization", "github_organization_handle"): + value = context.get(key) + if isinstance(value, str) and value.strip(): + return value.strip() + message = "Organization context is missing a GitHub organization handle" + raise ValueError(message) + + +def _strict_validate(payload: dict[str, Any]) -> list[str]: + """Validate payload against strict OrganizationModel; return warnings only.""" + + warnings: list[str] = [] + try: + OrganizationModel.model_validate(payload) + except ValidationError as exc: + for error in exc.errors(): + field = " -> ".join(str(loc) for loc in error["loc"]) + warnings.append(f"Strict schema warning at {field}: {error['msg']}") + return warnings + + +class LLMOrganizationAgentV2: + """LLM-backed agent that produces a pulse:OrganizationShape entity.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 180.0, + cache: ProviderCache | None = None, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + self._cache = cache + + async def run( # noqa: C901, PLR0912, PLR0915 + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + org_name = _resolve_org_name(context) + + stamp_current_agent( + name="org_agent", + context={"org_name": org_name}, + ) + + github_lookup_enabled_flag = context.get("github_lookup_enabled") + cache_lookup_flag = ( + github_lookup_enabled_flag + if isinstance(github_lookup_enabled_flag, bool) + else True + ) + identity = { + "org_name": org_name.strip().lower(), + "github_lookup_enabled": cache_lookup_flag, + } + is_root = bool(context.get("agent_is_root")) + cached_result = get_cached_agent_verdict( + self._cache, + agent_name="organization", + identity=identity, + is_root=is_root, + ) + if cached_result is not None: + return cached_result + + github_lookup_enabled = context.get("github_lookup_enabled") + if not isinstance(github_lookup_enabled, bool): + github_lookup_enabled = True + + uuid_value = context.get("uuid") + if not isinstance(uuid_value, str) or not uuid_value.strip(): + uuid_value = generate_uuid() + + llm_input: dict[str, Any] = { + "org_name": org_name, + "uuid": uuid_value, + "github_lookup_enabled": github_lookup_enabled, + } + + source_url = context.get("source_url") + if isinstance(source_url, str) and source_url: + llm_input["source_url"] = source_url + + raw_source_repositories = context.get("source_repositories") + source_repositories: list[str] = [] + if isinstance(raw_source_repositories, list): + source_repositories = [ + repository + for repository in raw_source_repositories + if isinstance(repository, str) and repository + ] + if source_repositories: + llm_input["source_repositories"] = source_repositories + + organization_context = context.get("organization_context") + if isinstance(organization_context, dict) and organization_context: + organization_context_summary: dict[str, Any] = {} + profile = organization_context.get("profile") + if isinstance(profile, dict) and profile: + profile_summary = { + "login": profile.get("login"), + "name": profile.get("name"), + "description": profile.get("description"), + "followers": profile.get("followers"), + "type": profile.get("type"), + } + organization_context_summary["profile"] = { + key: value + for key, value in profile_summary.items() + if value is not None + } + for key in ("members", "owned_repos"): + value = organization_context.get(key) + if isinstance(value, list) and value: + organization_context_summary[key] = value + if organization_context_summary: + llm_input["organization_context"] = organization_context_summary + + repository_context = context.get("repository_context") + if isinstance(repository_context, dict) and repository_context: + repository_context_summary: dict[str, Any] = {} + metadata = repository_context.get("metadata") + if isinstance(metadata, dict) and metadata: + metadata_summary = { + "name": metadata.get("name"), + "full_name": metadata.get("full_name"), + "description": metadata.get("description"), + "owner": metadata.get("owner"), + } + repository_context_summary["metadata"] = { + key: value + for key, value in metadata_summary.items() + if value is not None + } + readme_content = repository_context.get("readme_content") + if isinstance(readme_content, str) and readme_content: + repository_context_summary["readme_content"] = readme_content[ + :README_CONTEXT_MAX_CHARS + ] + gimie_jsonld = repository_context.get("gimie_jsonld") + if isinstance(gimie_jsonld, dict) and gimie_jsonld: + repository_context_summary["gimie_jsonld"] = json.dumps( + gimie_jsonld, + ensure_ascii=True, + )[:GIMIE_JSONLD_MAX_CHARS] + if repository_context_summary: + llm_input["repository_context"] = repository_context_summary + + pipeline_outputs = context.get("pipeline_outputs") + if isinstance(pipeline_outputs, dict) and pipeline_outputs: + llm_input["pipeline_outputs"] = pipeline_outputs + + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + user_prompt = append_runtime_prompt_context(user_prompt, context) + + tools = [make_fetch_link_content_tool(self._cache)] + if providers.github is not None: + tools.append(make_github_organization_metadata_tool(providers.github)) + if providers.ror is not None and providers.infoscience is not None: + tools.append( + make_organization_identity_search_tool( + providers.ror, + providers.infoscience, + ), + ) + elif providers.ror is not None: + tools.append(make_ror_organization_search_tool(providers.ror)) + elif providers.infoscience is not None: + tools.append(make_infoscience_orgunit_tool(providers.infoscience)) + if providers.infoscience_rag is not None: + tools.append(make_infoscience_rag_search_tool(providers.infoscience_rag)) + tools.append( + make_infoscience_rag_fetch_chunks_tool(providers.infoscience_rag), + ) + tools.append( + make_infoscience_rag_fetch_records_tool(providers.infoscience_rag), + ) + if providers.ethz_research_collection_rag is not None: + tools.append( + make_ethz_research_collection_rag_search_tool( + providers.ethz_research_collection_rag, + ), + ) + tools.append( + make_ethz_research_collection_rag_fetch_chunks_tool( + providers.ethz_research_collection_rag, + ), + ) + tools.append( + make_ethz_research_collection_rag_fetch_records_tool( + providers.ethz_research_collection_rag, + ), + ) + if providers.ror_rag is not None: + tools.append(make_ror_rag_search_tool(providers.ror_rag)) + if providers.snsf_rag is not None: + tools.append(make_snsf_rag_search_tool(providers.snsf_rag)) + if providers.huggingface_rag is not None: + tools.append(make_huggingface_rag_search_tool(providers.huggingface_rag)) + if providers.openalex_rag is not None: + tools.append(make_openalex_rag_search_tool(providers.openalex_rag)) + if providers.renkulab_rag is not None: + tools.append(make_renkulab_rag_search_tool(providers.renkulab_rag)) + if providers.federated_rag is not None: + tools.append(make_federated_rag_search_tool(providers.federated_rag)) + tools.append(make_federated_rag_lookup_tool(providers.federated_rag)) + if providers.epfl_graph_rag is not None: + tools.append(make_epfl_graph_rag_search_tool(providers.epfl_graph_rag)) + + identifier = org_name + tool_names = [getattr(t, "name", None) or getattr(t, "__name__", "?") for t in tools] + logger.info( + "%s — calling LLM (%d tool(s) available: %s)", + identifier, + len(tools), + ", ".join(tool_names) if tool_names else "none", + ) + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=AgentOrganizationShape, + tools=tools, + ), + timeout=self._llm_call_timeout_seconds, + ) + except asyncio.TimeoutError as exc: + timeout_message = ( + f"{identifier} — LLM call timed out after {self._llm_call_timeout_seconds:.1f}s" + ) + logger.exception(timeout_message) + raise LLMRuntimeError(timeout_message) from exc + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + logger.info( + "%s — LLM done (prompt=%s tokens, completion=%s tokens, requests=%s, tool_calls=%s)", + identifier, + llm_result.tokens_prompt, + llm_result.tokens_completion, + llm_result.requests, + llm_result.tool_calls, + ) + + payload = {key: value for key, value in llm_result.payload.items() if value is not None} + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + payload.update(overrides) + + force_server_uuid(payload, uuid_value) + + raw_output = deepcopy(payload) + + validation_warnings = _strict_validate(payload) + + derivation_stats = { + "organization_id": payload.get("id"), + "organization_name": payload.get("schema:name"), + "github_lookup_enabled": github_lookup_enabled, + "source_repositories": deepcopy(source_repositories), + "owned_repositories": deepcopy( + [ + repository + for repository in payload.get("pulse:owns", []) + if isinstance(repository, str) + ], + ) + if isinstance(payload.get("pulse:owns"), list) + else [], + "parent_organization": deepcopy(payload.get("org:unitOf")) + if isinstance(payload.get("org:unitOf"), list) + else payload.get("org:unitOf"), + "unit_ids": deepcopy( + [ + unit_id + for unit_id in payload.get("org:hasUnit", []) + if isinstance(unit_id, str) + ], + ) + if isinstance(payload.get("org:hasUnit"), list) + else [], + } + + result = AgentResult( + data=payload, + warnings=validation_warnings, + raw_output=raw_output, + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "derivation": derivation_stats, + }, + ) + store_agent_verdict( + self._cache, + agent_name="organization", + identity=identity, + result=result, + ) + return result diff --git a/src/v2/agents/llm/organization/prompts/__init__.py b/src/v2/agents/llm/organization/prompts/__init__.py new file mode 100644 index 0000000..9d48db4 --- /dev/null +++ b/src/v2/agents/llm/organization/prompts/__init__.py @@ -0,0 +1 @@ +from __future__ import annotations diff --git a/src/v2/agents/llm/organization/prompts/system_prompt.md b/src/v2/agents/llm/organization/prompts/system_prompt.md new file mode 100644 index 0000000..b4f1ad0 --- /dev/null +++ b/src/v2/agents/llm/organization/prompts/system_prompt.md @@ -0,0 +1,144 @@ +You are an organization metadata extraction agent operating under the **Open Pulse Ontology v2.1.2**. + +Your task is to produce a single JSON object representing an `org:Organization` entity that conforms to the `pulse:OrganizationShape` contract. + +## Output contract + +Return **only** a JSON object. No markdown fences, no explanation. + +### Required fields + +| Field | Type | Rules | +|---|---|---| +| `id` | string | Primary identifier from hierarchy below. | +| `type` | string | Always `"org:Organization"`. | +| `shacl` | string | Always `"pulse:OrganizationShape"`. | +| `identifiers` | object | See Identifiers section. | +| `idSource` | string | One of: `"pulse:ror"`, `"pulse:infoscienceOrganizationIdentifier"`, `"pulse:githubOrganizationHandle"`, `"uuid"`. | +| `schema:name` | string | Canonical organization name. | + +SHACL additionally requires at least ONE of: `schema:identifier` (typically a ROR URL), `pulse:githubOrganizationHandle`, `pulse:infoscienceOrganizationIdentifier`. **An organization without any of these three identifiers will be rejected by strict validation** — if no identifier can be resolved from context or tools, do not emit the entity. + +### Identifier hierarchy + +Resolve `id` and `idSource` using the first non-null value in this order: + +1. `pulse:ror` +2. `pulse:infoscienceOrganizationIdentifier` +3. `pulse:githubOrganizationHandle` +4. `uuid` + +### Identifiers object + +```json +{ + "pulse:ror": "https://ror.org/02s376052" | null, + "pulse:infoscienceOrganizationIdentifier": "95372c6b-7d45-432e-a84e-660c9fa54e05" | null, + "pulse:githubOrganizationHandle": "EPFL-ENAC" | null, + "uuid": "e2c4b6a8-1d3f-4e5a-9b7c-8d6e4f2a1b3c" +} +``` + +Use the `uuid` provided in the input context; do not invent deterministic IDs. + +### Optional fields + +| Field | Type | Rules | +|---|---|---| +| `schema:identifier` | string or null | Canonical identifier (typically ROR URL). | +| `pulse:githubOrganizationHandle` | string or null | GitHub org login. | +| `pulse:infoscienceOrganizationIdentifier` | string or null | Infoscience UUID4 org identifier. | +| `pulse:OrganizationType` | string | One of supported enum values in schema. | +| `pulse:githubOrgFollowers` | integer or null | GitHub org followers count. | +| `org:hasUnit` | array of strings | Child organization IDs. | +| `org:unitOf` | array of strings | Parent organization IDs (most orgs have 0 or 1 parent; arrays support joint affiliations). Emit `[]` when no parent is known. | +| `pulse:owns` | array of strings | Repository IDs owned by this organization. | + +## Rules + +- Do not invent identifiers. +- Do not add fields outside the schema. +- If data is unavailable, set nullable fields to `null` or omit optional fields. +- Prefer high-confidence values from tool results over weak textual hints. +- Acronym-only matches are insufficient for organization resolution. +- Validate candidate identity using context: repository owner handle, country/location hints, parent/child hierarchy, and source-repository relevance. +- If multiple near-match candidates remain ambiguous, leave `pulse:ror` as `null` instead of guessing. +- When combining provider fields, prefer a single coherent candidate record rather than mixing conflicting organizations. + +## One organization per invocation — do not conflate + +You are extracting **exactly one** organization per call. The input context +often contains data about siblings, parents, and children (other GitHub +orgs, ROR records, Infoscience units the org belongs to or contains). +**Do not bleed those into your output.** + +Specifically: + +- `schema:name` MUST describe the entity identified by `id` (the canonical + full name of *that* org), never the name of a parent, a sibling, or a + child. Example: if `id` is `https://ror.org/02s376052` (EPFL), then + `schema:name` is `"École Polytechnique Fédérale de Lausanne"` — + **never** `"EPFL Open Science"` (a sub-unit) or `"ETH Domain"` (a parent). +- `pulse:githubOrganizationHandle` belongs to the same entity as `id`. + If the entity is a ROR-backed parent (e.g. EPFL), only set this field + when EPFL itself has a GitHub organization. Do not set it to a child + org's handle (e.g. `EPFL-Open-Science`). +- When the input context describes multiple plausible orgs (parent + child), + pick the one that the invocation's `org_name` / `org_seed` / + `target_organization` field points to and ignore the rest. Use + `org:unitOf` and `org:hasUnit` to express the hierarchy — do not encode + it by mislabelling fields. + +If the input doesn't clearly identify a single canonical org, prefer +emitting fewer fields (with `null`s) over emitting confidently-wrong +ones derived from a sibling or relative. + +## Input context guidance + +The context may include: +- `organization_context` (GitHub organization profile/members/repositories) +- `repository_context` (repository metadata, README excerpt, and serialized GIMIE JSON-LD) +- `pipeline_outputs` or upstream output blocks (prior agent outputs) +- `source_repositories` (repositories directly relevant to this extraction) + +Use these to infer aliases, ownership, org type, and parent/child relations. + +## Available tools + +### `get_github_organization_metadata` + +Fetch GitHub organization metadata using an org login, GitHub URL, `@handle`, or owner/repo string. +Use this to resolve and validate: +- `pulse:githubOrganizationHandle` +- `pulse:githubOrgFollowers` +- GitHub profile consistency (`schema:name`, location, website/blog) + +### `search_organization_identity` + +Search ROR and Infoscience together in one call and return: +- `ror_candidates` +- `infoscience_candidates` +- `linked_candidates` (high-confidence name-linked pairs) + +Use this as the primary tool when available to keep `pulse:ror` and `pulse:infoscienceOrganizationIdentifier` coherent for the same organization. +Do not rely on acronym-only matches from linked candidates. + +### `search_ror_organizations` + +Search ROR organizations by query text. +Use this to resolve: +- `pulse:ror` +- `schema:identifier` +- `schema:name` +- `pulse:OrganizationType` +- `org:hasUnit` / `org:unitOf` + +### `search_infoscience_orgunit` + +Search Infoscience organization units by query text. +Use this to resolve: +- `pulse:infoscienceOrganizationIdentifier` +- `schema:name` and parent hints + +When both tools return candidates, choose coherent fields from the same entity candidate whenever possible. +If `search_organization_identity` is available, prefer it over separate provider calls. diff --git a/src/v2/agents/llm/organization/prompts/user_prompt.md b/src/v2/agents/llm/organization/prompts/user_prompt.md new file mode 100644 index 0000000..9437750 --- /dev/null +++ b/src/v2/agents/llm/organization/prompts/user_prompt.md @@ -0,0 +1,7 @@ +Build an Organization payload using the runtime context below. + +Use only identifiers present in the context or returned by tools. Do not invent values. + +```json +{context_json} +``` diff --git a/src/v2/agents/llm/person/__init__.py b/src/v2/agents/llm/person/__init__.py new file mode 100644 index 0000000..2114cb5 --- /dev/null +++ b/src/v2/agents/llm/person/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from src.v2.agents.llm.person.agent import LLMPersonAgentV2 + +__all__ = ["LLMPersonAgentV2"] diff --git a/src/v2/agents/llm/person/agent.py b/src/v2/agents/llm/person/agent.py new file mode 100644 index 0000000..a1230af --- /dev/null +++ b/src/v2/agents/llm/person/agent.py @@ -0,0 +1,516 @@ +from __future__ import annotations + +import asyncio +import json +import logging +from copy import deepcopy +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field, ValidationError + +logger = logging.getLogger(__name__) + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm._payload_helpers import force_server_uuid +from src.v2.agents.llm._verdict_cache import ( + get_cached_agent_verdict, + store_agent_verdict, +) +from src.v2.agents.llm.agent_tools.email_hash import hash_user_email_tool +from src.v2.agents.llm.agent_tools.epfl_graph_rag import ( + make_epfl_graph_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.ethz_research_collection_rag import ( + make_ethz_research_collection_rag_fetch_chunks_tool, + make_ethz_research_collection_rag_fetch_records_tool, + make_ethz_research_collection_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.infoscience_rag import ( + make_infoscience_rag_fetch_chunks_tool, + make_infoscience_rag_fetch_records_tool, + make_infoscience_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.infoscience_search import ( + make_infoscience_search_tool, +) +from src.v2.agents.llm.agent_tools.orcid_person import make_orcid_person_tool +from src.v2.agents.llm.agent_tools.orcid_rag import ( + make_orcid_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.query_orcid import make_query_orcid_tool +from src.v2.agents.llm.agent_tools.renkulab_rag import ( + make_renkulab_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.selenium_fetch import ( + make_fetch_link_content_tool, +) +from src.v2.agents.llm.agent_tools.snsf_rag import ( + make_snsf_rag_search_tool, +) +from src.v2.agents.llm.prompt_context import append_runtime_prompt_context +from src.v2.agents.llm.runtime import ( + LLMRuntimeError, + V2LLMRuntime, +) +from src.v2.agents.models import AgentResult, ProviderSet, generate_uuid +from src.v2.ingest.cache import ProviderCache +from src.v2.observation.query_log import stamp_current_agent +from src.v2.schema.models.strict import PersonModel + +_PROMPTS_PACKAGE = "src.v2.agents.llm.person.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") + +README_CONTEXT_MAX_CHARS = 2000 +GIMIE_JSONLD_MAX_CHARS = 4000 + + +class _PersonIdentifiers(BaseModel): + """Flat identifiers sub-object for LLM person output.""" + + model_config = ConfigDict(extra="ignore") + + pulse_orcid: str | None = Field(None, alias="pulse:orcid") + pulse_infosciencePersonIdentifier: str | None = Field( + None, alias="pulse:infosciencePersonIdentifier", + ) + pulse_githubUsername: str | None = Field(None, alias="pulse:githubUsername") + uuid: str | None = Field(None) + + +class LLMPersonOutputShape(BaseModel): + """Flat pydantic-ai output type for LLMPersonAgentV2. + + AgentPersonShape (the generated model) is a RootModel union of three + variants — pydantic-ai cannot use union RootModels as output_type because + their JSON schema is {"anyOf": [...]} rather than {"type": "object"}. + This flat BaseModel mirrors the same fields and produces a valid object + schema that pydantic-ai can enforce during structured output generation. + Permissive validation against the full agent schema and strict validation + against PersonModel both happen as separate post-processing steps. + """ + + model_config = ConfigDict(extra="ignore") + + id: str = Field(..., description="Resolved hierarchical identifier.") + type: str = Field(..., description="RDF type. Must be 'schema:Person'.") + shacl: str = Field(..., description="SHACL shape. Must be 'pulse:PersonShape'.") + identifiers: _PersonIdentifiers = Field( + ..., description="All available identifiers for this person.", + ) + idSource: str = Field( + ..., + description=( + "Which identifier was used as primary id. " + "One of: 'pulse:orcid', 'pulse:infosciencePersonIdentifier', " + "'pulse:githubUsername', 'uuid'." + ), + ) + schema_name: str = Field( + ..., alias="schema:name", description="Full display name of the person.", + ) + schema_email: str | None = Field( + None, alias="schema:email", description="Anonymized email address.", + ) + schema_url: str | None = Field(None, alias="schema:url", description="Profile URL.") + pulse_githubUsername: str | None = Field( + None, alias="pulse:githubUsername", description="GitHub login.", + ) + pulse_orcidIdentifier: str | None = Field( + None, alias="pulse:orcidIdentifier", description="ORCID identifier.", + ) + pulse_infosciencePersonIdentifier: str | None = Field( + None, + alias="pulse:infosciencePersonIdentifier", + description="Infoscience person UUID4.", + ) + org_hasMembership: list[str] | None = Field( + None, + alias="org:hasMembership", + description="MembershipShape IDs: personId_orgId.", + ) + pulse_hasContribution: list[str] | None = Field( + None, + alias="pulse:hasContribution", + description="ContributionShape IDs: personId_repoId.", + ) + pulse_owns: list[str] | None = Field( + None, + alias="pulse:owns", + description="RepositoryShape IDs owned by this person.", + ) + + +def _extract_person_identifier(context: dict[str, Any]) -> str | None: + """Return the first usable person identifier found in context.""" + + for key in ("username", "github_username", "orcid", "infoscience_id", "name"): + value = context.get(key) + if isinstance(value, str) and value.strip(): + return value.strip() + return None + + +def _normalize_orcid_hint(raw: Any) -> str | None: + """Strip ORCID URL prefix and return bare ORCID, or None.""" + + if not isinstance(raw, str) or not raw.strip(): + return None + candidate = raw.strip() + if candidate.lower().startswith("https://orcid.org/"): + candidate = candidate.rsplit("/", maxsplit=1)[-1] + return candidate or None + + +def _strict_validate(payload: dict[str, Any]) -> list[str]: + """Validate payload against the strict PersonModel; return warnings only.""" + + warnings: list[str] = [] + try: + PersonModel.model_validate(payload) + except ValidationError as exc: + for error in exc.errors(): + field = " -> ".join(str(loc) for loc in error["loc"]) + warnings.append(f"Strict schema warning at {field}: {error['msg']}") + return warnings + + +class LLMPersonAgentV2: + """LLM-backed agent that produces a pulse:PersonShape entity from gathered + person context. + + Data flow: + 1. Identifier extraction — reads whatever person identifiers are available + from context (GitHub username, ORCID hint, Infoscience ID, name). + Raises ValueError only when the context is completely empty of any + usable identity signal. + 2. GitHub profile fetch (optional) — calls providers.github.get_user() + only when a GitHub username is present in context. + 3. Tool construction — builds Infoscience search and ORCID lookup tools + as closures capturing the respective providers; tools are only included + when their provider is configured. + 4. Prompt assembly — serialises the extracted context as JSON and injects + it into the Markdown prompt templates loaded from the ``prompts/`` + sub-package via importlib.resources. + 5. LLM call — delegates to V2LLMRuntime with ``output_type=LLMPersonOutputShape``, + a flat BaseModel that pydantic-ai can enforce as structured output + (first validation pass — permissive, coerces minor type mismatches). + 6. Strict validation — validates the agent-schema-clean payload a second + time against PersonModel (strict schema). Failures add warnings only; + they do not reject the result. + 7. Stats — attaches derivation metadata (person_id, github_username, + source_repositories, contribution_ids, membership_ids) and LLM + telemetry (model, provider, token counts) to AgentResult. + + Errors: + LLMRuntimeError — raised on LLM call failures (config, network, or schema + rejection after pydantic-ai retries are exhausted). + ValueError — raised when context contains no usable identity signal + (no username, orcid, infoscience_id, or name). + """ + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 180.0, + cache: ProviderCache | None = None, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + self._cache = cache + + @staticmethod + def _identity_for_cache(context: dict[str, Any]) -> dict[str, Any] | None: + for key in ("orcid", "username", "github_username", "infoscience_id", "name"): + value = context.get(key) + if isinstance(value, str) and value.strip(): + return {"primary_key": key, "value": value.strip().lower()} + return None + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + """Generate and double-validate a person payload from gathered context. + + Args: + context: Runtime context dict. Accepted identity keys (at least one + required): + - ``username`` / ``github_username`` (str): GitHub login. + - ``orcid`` (str, optional): ORCID hint. + - ``infoscience_id`` (str, optional): Infoscience person UUID. + - ``name`` (str, optional): Display name hint for searching. + + Additional context keys: + - ``contributions`` (list, optional): Contribution IDs. + - ``source_repositories`` (list, optional): Repository handles. + - ``uuid`` (str, optional): Stable UUID for this person. + - ``agent_overrides`` (dict, optional): Field overrides applied + after the LLM call, before strict validation. + providers: Injected provider bundle. infoscience and orcid providers + are used to build runtime tools when present. + + Returns: + AgentResult with the validated person payload, merged warnings from + both validation passes, raw LLM output, token counts, and derivation + stats. + """ + + if _extract_person_identifier(context) is None: + message = "Person context contains no usable identity signal (username, orcid, infoscience_id, or name)" + raise ValueError(message) + + stamp_current_agent( + name="person_agent", + context={ + key: value + for key, value in { + "username": context.get("username") or context.get("github_username"), + "orcid": context.get("orcid"), + "infoscience_id": context.get("infoscience_id"), + "name": context.get("name"), + }.items() + if isinstance(value, str) and value.strip() + }, + ) + + identity = self._identity_for_cache(context) + is_root = bool(context.get("agent_is_root")) + cached_result = get_cached_agent_verdict( + self._cache, + agent_name="person", + identity=identity, + is_root=is_root, + ) + if cached_result is not None: + return cached_result + + warnings: list[str] = [] + + # Resolve GitHub username and optionally fetch profile. + github_username: str | None = None + github_user: dict[str, Any] = {} + for key in ("username", "github_username"): + value = context.get(key) + if isinstance(value, str) and value.strip(): + github_username = value.strip() + break + + if github_username is not None: + logger.info("%s — fetching GitHub profile", github_username) + try: + github_user = providers.github.get_user(github_username) + logger.info("%s — GitHub profile fetched", github_username) + except Exception as exc: # noqa: BLE001 + # Transient GitHub API errors (502, rate limits) should not + # abort the entire person extraction — proceed with an empty + # profile and let the LLM use whatever other context is available. + warnings.append(f"GitHub profile fetch failed for {github_username}: {exc}") + logger.warning("%s — GitHub profile fetch failed: %s", github_username, exc) + github_user = {"login": github_username} + + # Normalize ORCID hint from context or GitHub profile. + orcid_hint = _normalize_orcid_hint( + context.get("orcid") or github_user.get("orcid"), + ) + + # Stable UUID — reuse from context or generate fresh. + uuid_value = context.get("uuid") + if not isinstance(uuid_value, str) or not uuid_value.strip(): + uuid_value = generate_uuid() + + # Contribution and ownership lists. + contributions: list[str] = [] + raw_contributions = context.get("contributions") + if isinstance(raw_contributions, list): + contributions = [c for c in raw_contributions if isinstance(c, str) and c] + + source_repositories: list[str] = [] + raw_source_repos = context.get("source_repositories") + if isinstance(raw_source_repos, list): + source_repositories = [r for r in raw_source_repos if isinstance(r, str) and r] + + # Build context payload for the LLM — include only non-empty values. + llm_input: dict[str, Any] = {"uuid": uuid_value} + if github_username: + llm_input["username"] = github_username + if github_user: + llm_input["github_user"] = github_user + if orcid_hint: + llm_input["orcid_hint"] = orcid_hint + raw_infoscience_id = context.get("infoscience_id") + if isinstance(raw_infoscience_id, str) and raw_infoscience_id.strip(): + llm_input["infoscience_id"] = raw_infoscience_id.strip() + raw_name = context.get("name") + if isinstance(raw_name, str) and raw_name.strip(): + llm_input["name_hint"] = raw_name.strip() + if contributions: + llm_input["contributions"] = contributions + if source_repositories: + llm_input["source_repositories"] = source_repositories + + # Include repository context when available — README often contains + # ORCID IDs, affiliations, and author credits that help the LLM + # produce a richer person entity. + raw_repo_ctx = context.get("repository_context") + if isinstance(raw_repo_ctx, dict) and raw_repo_ctx: + repo_meta = raw_repo_ctx.get("metadata") + repo_readme = raw_repo_ctx.get("readme_content") or "" + repo_gimie_jsonld = raw_repo_ctx.get("gimie_jsonld") + repo_ctx_summary: dict[str, Any] = {} + if isinstance(repo_meta, dict) and repo_meta: + # Only include lightweight fields — skip nested contributor lists. + repo_ctx_summary["name"] = repo_meta.get("name") + repo_ctx_summary["description"] = repo_meta.get("description") + repo_ctx_summary["full_name"] = repo_meta.get("full_name") + if repo_readme: + repo_ctx_summary["readme_content"] = repo_readme[:README_CONTEXT_MAX_CHARS] + if isinstance(repo_gimie_jsonld, dict) and repo_gimie_jsonld: + repo_ctx_summary["gimie_jsonld"] = json.dumps( + repo_gimie_jsonld, ensure_ascii=True, + )[:GIMIE_JSONLD_MAX_CHARS] + if repo_ctx_summary: + llm_input["repository_context"] = repo_ctx_summary + + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + user_prompt = append_runtime_prompt_context(user_prompt, context) + + # Build provider-dependent tools only when providers are available. + tools = [make_fetch_link_content_tool(self._cache), hash_user_email_tool] + if providers.infoscience is not None: + tools.append(make_infoscience_search_tool(providers.infoscience)) + if providers.orcid is not None: + tools.append(make_orcid_person_tool(providers.orcid)) + tools.append( + make_query_orcid_tool( + providers.orcid, + orcid_rag_provider=providers.orcid_rag, + ), + ) + if providers.infoscience_rag is not None: + tools.append(make_infoscience_rag_search_tool(providers.infoscience_rag)) + tools.append( + make_infoscience_rag_fetch_chunks_tool(providers.infoscience_rag), + ) + tools.append( + make_infoscience_rag_fetch_records_tool(providers.infoscience_rag), + ) + if providers.ethz_research_collection_rag is not None: + tools.append( + make_ethz_research_collection_rag_search_tool( + providers.ethz_research_collection_rag, + ), + ) + tools.append( + make_ethz_research_collection_rag_fetch_chunks_tool( + providers.ethz_research_collection_rag, + ), + ) + tools.append( + make_ethz_research_collection_rag_fetch_records_tool( + providers.ethz_research_collection_rag, + ), + ) + if providers.orcid_rag is not None: + tools.append(make_orcid_rag_search_tool(providers.orcid_rag)) + if providers.renkulab_rag is not None: + tools.append(make_renkulab_rag_search_tool(providers.renkulab_rag)) + if providers.snsf_rag is not None: + tools.append(make_snsf_rag_search_tool(providers.snsf_rag)) + if providers.epfl_graph_rag is not None: + tools.append(make_epfl_graph_rag_search_tool(providers.epfl_graph_rag)) + + identifier = ( + github_username + or context.get("orcid") + or context.get("infoscience_id") + or context.get("name") + ) + if not isinstance(identifier, str) or not identifier.strip(): + identifier = "unknown-person" + tool_names = [getattr(t, "name", None) or getattr(t, "__name__", "?") for t in tools] + logger.info( + "%s — calling LLM (%d tool(s) available: %s)", + identifier, + len(tools), + ", ".join(tool_names) if tool_names else "none", + ) + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=LLMPersonOutputShape, + tools=tools, + ), + timeout=self._llm_call_timeout_seconds, + ) + except asyncio.TimeoutError as exc: + timeout_message = ( + f"{identifier} — LLM call timed out after {self._llm_call_timeout_seconds:.1f}s" + ) + logger.error(timeout_message) + raise LLMRuntimeError(timeout_message) from exc + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + logger.info( + "%s — LLM done (prompt=%s tokens, completion=%s tokens, requests=%s, tool_calls=%s)", + identifier, + llm_result.tokens_prompt, + llm_result.tokens_completion, + llm_result.requests, + llm_result.tool_calls, + ) + payload = {k: v for k, v in llm_result.payload.items() if v is not None} + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + payload.update(overrides) + + force_server_uuid(payload, uuid_value) + + raw_output = deepcopy(payload) + + # Second validation pass: strict schema (warnings only, never raises). + validation_warnings = warnings + _strict_validate(payload) + + derivation_stats = { + "person_id": payload.get("id"), + "github_username": github_username, + "source_repositories": deepcopy(source_repositories), + "contribution_ids": deepcopy( + [c for c in (payload.get("pulse:hasContribution") or []) if isinstance(c, str)], + ), + "membership_ids": deepcopy( + [m for m in (payload.get("org:hasMembership") or []) if isinstance(m, str)], + ), + } + + result = AgentResult( + data=payload, + warnings=validation_warnings, + raw_output=raw_output, + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "derivation": derivation_stats, + }, + ) + store_agent_verdict( + self._cache, + agent_name="person", + identity=identity, + result=result, + ) + return result diff --git a/src/v2/agents/llm/person/prompts/__init__.py b/src/v2/agents/llm/person/prompts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/v2/agents/llm/person/prompts/system_prompt.md b/src/v2/agents/llm/person/prompts/system_prompt.md new file mode 100644 index 0000000..337c62a --- /dev/null +++ b/src/v2/agents/llm/person/prompts/system_prompt.md @@ -0,0 +1,151 @@ +You are a person metadata extraction agent operating under the **Open Pulse Ontology v2.1.2**. + +Your task is to produce a single JSON object representing a `schema:Person` entity that conforms to the `pulse:PersonShape` contract. + +You may be called with any combination of person identifiers — a GitHub username, an ORCID, an Infoscience ID, or just a name. Use the available tools to resolve whichever identifiers are missing. + +The context may also include a `repository_context` field containing the README, metadata, and the full GIMIE JSON-LD extraction of the source repository. **Scan both the README and `gimie_jsonld` for ORCID identifiers, affiliations, and author credits** — these are often more authoritative than GitHub profile data. The `gimie_jsonld` value is a serialized JSON string — parse it to access the `@graph` array, which may contain person nodes with ORCID, email, and affiliation data. + +## Output contract + +Return **only** a JSON object. No markdown fences, no explanation. + +### Required fields + +| Field | Type | Rules | +|---|---|---| +| `id` | string | Primary identifier — first non-null value from the hierarchy below. | +| `type` | string | Always `"schema:Person"`. | +| `shacl` | string | Always `"pulse:PersonShape"`. | +| `identifiers` | object | See Identifiers section. | +| `idSource` | string | One of: `"pulse:orcid"`, `"pulse:infosciencePersonIdentifier"`, `"pulse:githubUsername"`, `"uuid"`. | +| `schema:name` | string | Full display name of the person. | + +SHACL additionally requires at least ONE of: `pulse:githubUsername`, `schema:email`, `pulse:infosciencePersonIdentifier`, `pulse:orcidIdentifier`. An ORCID alone now satisfies this constraint — if you have a confirmed ORCID, the person is identifiable even without a GitHub username or email. + +### Identifier hierarchy + +Resolve `id` and `idSource` by selecting the **first non-null** value in this order: + +1. `pulse:orcid` — ORCID identifier (format: `0000-0001-2345-6789`) +2. `pulse:infosciencePersonIdentifier` — Infoscience UUID4 +3. `pulse:githubUsername` — GitHub login +4. `uuid` — UUID v4 fallback (always generate one using the value from context) + +### Identifiers object + +```json +{ + "pulse:orcid": "0000-0001-2345-6789" | null, + "pulse:infosciencePersonIdentifier": "f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb" | null, + "pulse:githubUsername": "caviri" | null, + "uuid": "a7d4e2b1-3c8f-4a5b-9d6e-2f1a3b4c5d6e" +} +``` + +`uuid` is always present (use the value from context — do not generate a new one). + +### Optional fields + +| Field | Type | Rules | +|---|---|---| +| `schema:email` | string | Anonymized email only — see email rules. Omit if unavailable. | +| `schema:url` | string or null | Any url not present in others identifiers, such as a personal webpage. | +| `pulse:githubUsername` | string or null | GitHub login from context or tool results. | +| `pulse:orcidIdentifier` | string or null | Same value as `identifiers.pulse:orcid`. | +| `pulse:infosciencePersonIdentifier` | string or null | Same value as `identifiers.pulse:infosciencePersonIdentifier`. | +| `org:hasMembership` | array of strings | MembershipShape IDs — format: `{personId}_{affiliationName}`. | +| `pulse:hasContribution` | array of strings | Use the `contributions` list from context verbatim. | +| `pulse:owns` | array of strings | Use the `source_repositories` list from context verbatim. | + +## Rules + +- **Do not invent identifiers.** Only use values present in context or returned by tools. +- **Do not add extra fields.** The schema uses `additionalProperties: false`. +- Use `null` for nullable fields when the value is unknown or unavailable. +- Omit optional array fields entirely rather than setting them to empty arrays unless you have values. + +### Email anonymization + +If an email address is available, anonymize it before including it. + +Prefer calling `hash_user_email` tool with the raw email and use the returned value for `schema:email`. +If you cannot call tools, apply this exact fallback algorithm: + +1. Split on `@` → local part and domain +2. Compute SHA-256 of the local part +3. Take the first **12 hex characters** of the hash as the new local part +4. Reconstruct: `{12-hex-chars}@{domain}` +5. Example: `alice@example.org` → `2bd806c97f0e@example.org` +6. If the local part is already a 12-character or 64-character hex string, it is already anonymized — do not re-hash. + +If no email is available, omit `schema:email` entirely. + +### Membership IDs + +Build `org:hasMembership` entries from affiliation strings returned by ORCID or Infoscience: + +``` +{personId}_{affiliationName} +``` + +where `personId` is the resolved primary `id` and `affiliationName` is the organization string. + +## Available tools + +### `search_infoscience_person` + +Call this tool with a person's name (or any known name variant) to search Infoscience. Usually names provide more information than github handle. + +Returns matching records sorted by relevance, each with: +- `infosciencePersonIdentifier` — Infoscience UUID4 +- `name` — full display name +- `orcid` — ORCID if known +- `affiliations` — list of affiliation strings +- `profileUrl` — Infoscience profile URL +- `score` — search relevance score (higher is better) + +**Use the highest-scoring result.** +**Call this when you need to populate `pulse:infosciencePersonIdentifier`.** + +### `get_orcid_record` + +Call this tool with an ORCID identifier to fetch the person's full ORCID profile. + +Returns: +- `orcid_id` — normalized ORCID +- `name` — full display name from ORCID +- `employment` — list of employment records (organization, department, role, start_date, end_date) +- `education` — list of education records (same structure) +- `affiliations` — flat list of affiliation organization strings + +**Call this when `orcid_hint` is present in the context or when an ORCID was returned by `search_infoscience_person`.** + +Use the ORCID `name` for `schema:name` if it is more complete than the GitHub display name. +Use `affiliations` to build `org:hasMembership` entries. + +### `query_orcid` + +Call this tool with a free-text name (and optional affiliation keywords) to discover candidate ORCID identifiers when no ORCID is provided in the context and `search_infoscience_person` did not return one. + +Args: +- `query` — name to search (e.g. `"noemie mazare"`); affiliation keywords are accepted but ranking is dominated by the boosted name fields +- `rows` — max hits to return (default 50, capped at 200) +- `start` — offset for pagination (default 0) + +Returns a list of hits, each with: +- `orcid_id` — candidate ORCID identifier +- `given_names`, `family_names`, `credit_name`, `other_names` +- `institution_names` — current/past affiliation strings (use these to disambiguate homonyms) +- `emails` — public emails when available + +**Pick the hit whose `institution_names` overlap the repository or contributor's known affiliations**, then pass that `orcid_id` to `get_orcid_record` for the full employment/education profile. Do not assign `pulse:orcid` from `query_orcid` alone — only after `get_orcid_record` confirms the record. + +### `hash_user_email` + +Call this tool with a raw email string and use the output as `schema:email`. + +It applies the canonical anonymization policy: +- keep domain unchanged +- replace local part with SHA-256(local-part) first 12 hex chars +- avoid re-hashing already anonymized local parts diff --git a/src/v2/agents/llm/person/prompts/user_prompt.md b/src/v2/agents/llm/person/prompts/user_prompt.md new file mode 100644 index 0000000..dea6003 --- /dev/null +++ b/src/v2/agents/llm/person/prompts/user_prompt.md @@ -0,0 +1,7 @@ +Build a Person payload using the runtime context below. + +Use only identifiers present in the context or returned by tools. Do not invent values. + +```json +{context_json} +``` diff --git a/src/v2/agents/llm/prompt_context.py b/src/v2/agents/llm/prompt_context.py new file mode 100644 index 0000000..f4551ee --- /dev/null +++ b/src/v2/agents/llm/prompt_context.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +from typing import Any + +UPSTREAM_STAGE_OUTPUTS_JSON_CONTEXT_KEY = "upstream_stage_outputs_json" +USER_PROMPT_APPENDIX_CONTEXT_KEY = "user_prompt_appendix" + + +def append_runtime_prompt_context(base_prompt: str, context: dict[str, Any]) -> str: + """Append optional runtime prompt sections from orchestrator context.""" + + user_prompt = base_prompt + + upstream_json = context.get(UPSTREAM_STAGE_OUTPUTS_JSON_CONTEXT_KEY) + if isinstance(upstream_json, str) and upstream_json.strip(): + user_prompt = ( + f"{user_prompt}\n\n" + "## Upstream Stage Outputs (JSON)\n" + f"{upstream_json}" + ) + + prompt_appendix = context.get(USER_PROMPT_APPENDIX_CONTEXT_KEY) + if isinstance(prompt_appendix, str) and prompt_appendix.strip(): + user_prompt = ( + f"{user_prompt}\n\n" + "## Additional Context (verbatim text)\n" + f"{prompt_appendix}" + ) + + return user_prompt diff --git a/src/v2/agents/llm/refiners/__init__.py b/src/v2/agents/llm/refiners/__init__.py new file mode 100644 index 0000000..6c3060f --- /dev/null +++ b/src/v2/agents/llm/refiners/__init__.py @@ -0,0 +1,35 @@ +from src.v2.agents.llm.refiners.membership.agent import ( + MembershipRefinerAgent, + MembershipRefinerInput, + MembershipRefinerPatch, +) +from src.v2.agents.llm.refiners.organization.agent import ( + OrganizationRefinerAgent, + OrganizationRefinerInput, + OrganizationRefinerPatch, +) +from src.v2.agents.llm.refiners.person.agent import ( + PersonRefinerAgent, + PersonRefinerInput, + PersonRefinerPatch, +) +from src.v2.agents.llm.refiners.repository.agent import ( + RepositoryRefinerAgent, + RepositoryRefinerInput, + RepositoryRefinerPatch, +) + +__all__ = [ + "MembershipRefinerAgent", + "MembershipRefinerInput", + "MembershipRefinerPatch", + "OrganizationRefinerAgent", + "OrganizationRefinerInput", + "OrganizationRefinerPatch", + "PersonRefinerAgent", + "PersonRefinerInput", + "PersonRefinerPatch", + "RepositoryRefinerAgent", + "RepositoryRefinerInput", + "RepositoryRefinerPatch", +] diff --git a/src/v2/agents/llm/refiners/membership/__init__.py b/src/v2/agents/llm/refiners/membership/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/v2/agents/llm/refiners/membership/agent.py b/src/v2/agents/llm/refiners/membership/agent.py new file mode 100644 index 0000000..0c2561f --- /dev/null +++ b/src/v2/agents/llm/refiners/membership/agent.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +import asyncio +import json +import logging +from typing import Any + +from pydantic import BaseModel, Field + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime + +logger = logging.getLogger(__name__) + +_PROMPTS_PACKAGE = "src.v2.agents.llm.refiners.membership.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") + + +class MembershipRefinerInput(BaseModel): + """Snapshot passed to the LLM membership refiner.""" + + entity: dict[str, Any] = Field( + description="The org:Membership entity from the rule-based pipeline.", + ) + organization_name: str | None = Field( + default=None, + description=( + "Name of the organization this membership points to (looked up from " + "org:organization). Used by the prompt to detect role suffixes that " + "duplicate the org name." + ), + ) + + +class MembershipRefinerPatch(BaseModel): + """Patch returned by the refiner. Only whitelisted fields may be set.""" + + role: str | None = Field( + default=None, + alias="org:role", + description="Canonical role title, or null to leave untouched.", + ) + + model_config = {"populate_by_name": True} + + +class MembershipRefinerAgent: + """LLM agent that proposes a canonical org:role string for an org:Membership.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 120.0, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + + async def run( + self, + *, + refiner_input: MembershipRefinerInput, + ) -> dict[str, Any]: + identifier = refiner_input.entity.get("id") or refiner_input.entity.get("@id", "?") + + user_payload = { + "entity": refiner_input.entity, + "organization_name": refiner_input.organization_name, + } + user_prompt = ( + "Inspect the membership entity below and propose a JSON patch with " + "`org:role` only when the current value has clear cleanup opportunity. " + "Return `{}` if no change is warranted.\n\n" + "```json\n" + + json.dumps(user_payload, ensure_ascii=True, sort_keys=True) + + "\n```" + ) + + logger.info("%s — calling membership refiner LLM", identifier) + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=MembershipRefinerPatch, + tools=[], + ), + timeout=self._llm_call_timeout_seconds, + ) + except TimeoutError as exc: + message = ( + f"{identifier} — membership refiner LLM call timed out after " + f"{self._llm_call_timeout_seconds:.1f}s" + ) + logger.exception(message) + raise LLMRuntimeError(message) from exc + + patch = { + key: value + for key, value in llm_result.payload.items() + if value is not None and key == "org:role" + } + logger.info( + "%s — membership refiner returned %s", + identifier, + list(patch.keys()) or "no change", + ) + return patch diff --git a/src/v2/agents/llm/refiners/membership/prompts/__init__.py b/src/v2/agents/llm/refiners/membership/prompts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/v2/agents/llm/refiners/membership/prompts/system_prompt.md b/src/v2/agents/llm/refiners/membership/prompts/system_prompt.md new file mode 100644 index 0000000..242740e --- /dev/null +++ b/src/v2/agents/llm/refiners/membership/prompts/system_prompt.md @@ -0,0 +1,42 @@ +You are a membership-refinement agent operating under the **Open Pulse Ontology v2.1.2**. + +You receive an `org:Membership` entity already produced by the deterministic rule-based pipeline. Your job is to **propose a canonical role string** when the current `org:role` is verbose, inconsistent, or contains noise. + +The ontology defines `org:role` as `xsd:string` with no controlled vocabulary, so canonicalization is heuristic. Aim for short, role-only phrases that are consistent across people at the same organization. + +## Output contract + +Return **only** a JSON object. No markdown fences, no explanation. The object MAY contain `org:role`. Omit it (or return `{}`) if no improvement is warranted. + +### Whitelist (the only field you may set) + +| Field | Type | Rules | +|---|---|---| +| `org:role` | string | Canonical role title. Only set when the current value has clear cleanup opportunity per the rules below. | + +### Hard rules + +- **Do not invent or change identifiers.** Never touch `id`, `@id`, `org:organization`, `time:hasBeginning`, `time:hasEnd`, `schema:author`. +- **Do not infer a role from nothing.** If the current `org:role` is `null`, leave it as `null` — return `{}`. Never invent. +- **Be conservative.** If the existing role is already short and clean, return `{}`. +- **Never change the meaning.** Only re-format / normalize. "PhD Student" → keep. "Master in Bioinformatics and Proteomics" → keep (legitimate detail). +- **Do not add fields outside the whitelist.** Anything else is logged as a warning and dropped. + +## Canonicalization rules (when to rewrite) + +1. **Strip the organization suffix.** `"Sr. Open Research Data Engineer at SDSC"` → `"Sr. Open Research Data Engineer"`. The organization is already linked via `org:organization`; repeating it in the role is noise. +2. **Strip degree institution.** `"B.A. in International Business, Concordia University 1998-2002"` → `"B.A. in International Business"`. Dates and institution are already encoded in `time:hasBeginning`/`time:hasEnd`/`org:organization`. +3. **Expand common abbreviations consistently** when the rest of the string is already verbose. `"Sr."` → `"Senior"` only if you'd otherwise leave a longer phrase. Don't churn `"PhD"` ↔ `"Ph.D."`. +4. **Normalize whitespace and capitalization** if the current value has obvious typos or trailing whitespace. +5. **Leave standard short titles alone**: `"PhD Student"`, `"Postdoc"`, `"Senior Researcher"`, `"Research Engineer"`, `"Software Engineer"`, `"Master"` — return `{}` for these. + +## Examples + +| Input | Output | +|---|---| +| `"PhD Student"` | `{}` (already canonical) | +| `"Senior Research Engineer at Swiss Data Science Center"` | `{"org:role": "Senior Research Engineer"}` | +| `"Master in Bioinformatics and Proteomics, Lausanne 2019-2022"` | `{"org:role": "Master in Bioinformatics and Proteomics"}` | +| `"Sr. Open Research Data Engineer"` | `{}` (already short, abbreviation is fine) | +| `null` | `{}` (never invent) | +| `"phd student "` (trailing space, lowercase) | `{"org:role": "PhD Student"}` | diff --git a/src/v2/agents/llm/refiners/organization/__init__.py b/src/v2/agents/llm/refiners/organization/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/v2/agents/llm/refiners/organization/agent.py b/src/v2/agents/llm/refiners/organization/agent.py new file mode 100644 index 0000000..1b3527b --- /dev/null +++ b/src/v2/agents/llm/refiners/organization/agent.py @@ -0,0 +1,131 @@ +from __future__ import annotations + +import asyncio +import json +import logging +from typing import Any, Literal + +from pydantic import BaseModel, Field + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime + +logger = logging.getLogger(__name__) + +_PROMPTS_PACKAGE = "src.v2.agents.llm.refiners.organization.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") + +OrganizationTypeLiteral = Literal[ + "pulse:University", + "pulse:ResearchInstitution", + "pulse:GovernmentAgency", + "pulse:SoftwareProject", + "pulse:PrivateCompany", + "pulse:NonProfitOrganization", + "pulse:CommunitySpace", + "pulse:OtherOrganizationType", +] + + +class OrganizationRefinerInput(BaseModel): + """Snapshot passed to the LLM refiner — current entity + small repo context.""" + + entity: dict[str, Any] = Field( + description="The org:Organization entity produced by the rule-based pipeline.", + ) + repo_context_summary: dict[str, Any] | None = Field( + default=None, + description="Small summary of the source repo (name, description, README excerpt).", + ) + + +class OrganizationRefinerPatch(BaseModel): + """Patch returned by the refiner. Only whitelisted fields may be set.""" + + organization_type: OrganizationTypeLiteral | None = Field( + default=None, + alias="pulse:OrganizationType", + description="New value for pulse:OrganizationType, or null to leave untouched.", + ) + + model_config = {"populate_by_name": True} + + +class OrganizationRefinerAgent: + """LLM agent that proposes a small JSON patch for an org:Organization entity.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 120.0, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + + async def run( + self, + *, + refiner_input: OrganizationRefinerInput, + tools: list[Any] | None = None, + ) -> dict[str, Any]: + """Run the refiner and return a patch dict (possibly empty). + + Returns whitelisted-only keys: `pulse:OrganizationType` and/or `pulse:discipline`. + """ + + identifier = refiner_input.entity.get("schema:name") or refiner_input.entity.get( + "id", + "?", + ) + + user_payload = { + "entity": refiner_input.entity, + "repo_context_summary": refiner_input.repo_context_summary or {}, + } + user_prompt = ( + "Inspect the organization entity below and propose a JSON patch with " + "only the fields you want to change (whitelist: `pulse:OrganizationType`, " + "`pulse:discipline`). Return `{}` if no change is warranted.\n\n" + "```json\n" + + json.dumps(user_payload, ensure_ascii=True, sort_keys=True) + + "\n```" + ) + + logger.info( + "%s — calling refiner LLM (%d tool(s))", + identifier, + len(tools or []), + ) + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=OrganizationRefinerPatch, + tools=tools or [], + ), + timeout=self._llm_call_timeout_seconds, + ) + except TimeoutError as exc: + message = ( + f"{identifier} — refiner LLM call timed out after " + f"{self._llm_call_timeout_seconds:.1f}s" + ) + logger.exception(message) + raise LLMRuntimeError(message) from exc + + patch = { + key: value + for key, value in llm_result.payload.items() + if value is not None and key == "pulse:OrganizationType" + } + logger.info( + "%s — refiner returned %s", + identifier, + list(patch.keys()) or "no change", + ) + return patch diff --git a/src/v2/agents/llm/refiners/organization/prompts/__init__.py b/src/v2/agents/llm/refiners/organization/prompts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/v2/agents/llm/refiners/organization/prompts/system_prompt.md b/src/v2/agents/llm/refiners/organization/prompts/system_prompt.md new file mode 100644 index 0000000..29d7c3d --- /dev/null +++ b/src/v2/agents/llm/refiners/organization/prompts/system_prompt.md @@ -0,0 +1,42 @@ +You are an organization-refinement agent operating under the **Open Pulse Ontology v2.1.2**. + +You receive an `org:Organization` entity already produced by the deterministic rule-based pipeline, plus repository context (README excerpt, repo metadata) and a tool to inspect neighbors of the entity in the current graph. Your job is to **propose targeted improvements to a small whitelist of semantic fields**. + +## Output contract + +Return **only** a JSON object. No markdown fences, no explanation. The object MAY contain any subset of the fields in the whitelist below. Omit fields you don't want to change. Return `{}` if no improvement is warranted. + +### Whitelist (the only fields you may set) + +| Field | Type | Rules | +|---|---|---| +| `pulse:OrganizationType` | string | One of: `pulse:University`, `pulse:ResearchInstitution`, `pulse:GovernmentAgency`, `pulse:SoftwareProject`, `pulse:PrivateCompany`, `pulse:NonProfitOrganization`, `pulse:CommunitySpace`, `pulse:OtherOrganizationType`. | + +### Hard rules + +- **Do not invent or change identifiers.** Never touch `id`, `@id`, `identifiers`, `pulse:ror`, `pulse:githubOrganizationHandle`, `pulse:infoscienceOrganizationIdentifier`, `idSource`, `schema:name`, `pulse:githubOrgFollowers`, ownership lists (`pulse:owns`, `org:hasUnit`, `org:unitOf`). +- **Be conservative when overwriting.** If the existing classification is plausibly correct, return `{}`. +- **But when the current `pulse:OrganizationType` is `null`, missing, or `pulse:OtherOrganizationType`, you SHOULD propose a value** if the entity name + `schema:identifier` (e.g. ROR URL) make the type confident. A null type is a gap, not a signal to leave alone. +- **Do not add fields outside the whitelist.** Anything not on the list is ignored and logged as a warning. +- If you change `pulse:OrganizationType`, justify implicitly via the data — do not include free-form prose. + +## Decision guidance for `pulse:OrganizationType` + +Use the most specific applicable category: + +- `pulse:ResearchInstitution` — non-profit research-focused orgs, even when affiliated with a university (e.g., Swiss Data Science Center, Institut Pasteur, Max Planck Institute, San Diego Supercomputer Center). +- `pulse:University` — degree-granting higher-education institutions whose primary mission is teaching + research. +- `pulse:GovernmentAgency` — public-sector bodies (ministries, federal agencies). +- `pulse:PrivateCompany` — for-profit entities. +- `pulse:NonProfitOrganization` — non-profits whose primary mission is **not** research (advocacy, foundations, charities). +- `pulse:SoftwareProject` — open-source umbrella projects (e.g., Apache Software Foundation, Linux Foundation, NumFOCUS member projects). +- `pulse:CommunitySpace` — meetup groups, hackerspaces, online communities. +- `pulse:OtherOrganizationType` — when none of the above clearly applies. + +## When to use `get_entity_neighbors` + +Call it sparingly. Useful when: +- The current type is `pulse:OtherOrganizationType` and you need member roles to disambiguate (e.g., many "Research Engineer" / "Postdoc" titles → likely a research institution). +- Disambiguating a parent/subsidiary relationship before deciding the type. + +Skip the tool entirely when the existing `schema:name` + `schema:identifier` (ROR URL) is already enough to type the org confidently. diff --git a/src/v2/agents/llm/refiners/person/__init__.py b/src/v2/agents/llm/refiners/person/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/v2/agents/llm/refiners/person/agent.py b/src/v2/agents/llm/refiners/person/agent.py new file mode 100644 index 0000000..3aa5f79 --- /dev/null +++ b/src/v2/agents/llm/refiners/person/agent.py @@ -0,0 +1,116 @@ +from __future__ import annotations + +import asyncio +import json +import logging +from typing import Any + +from pydantic import BaseModel, Field + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime + +logger = logging.getLogger(__name__) + +_PROMPTS_PACKAGE = "src.v2.agents.llm.refiners.person.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") + + +class PersonRefinerInput(BaseModel): + """Snapshot passed to the LLM person refiner.""" + + entity: dict[str, Any] = Field( + description="The schema:Person entity from the rule-based pipeline.", + ) + repo_context_summary: dict[str, Any] | None = Field( + default=None, + description="Small summary of the source repo (name, description, README excerpt).", + ) + + +class PersonRefinerPatch(BaseModel): + """Patch returned by the refiner. Only whitelisted fields may be set.""" + + name: str | None = Field( + default=None, + alias="schema:name", + description="Canonical full name. Only set when current value looks like a GitHub handle.", + ) + + model_config = {"populate_by_name": True} + + +class PersonRefinerAgent: + """LLM agent that proposes a small JSON patch for a schema:Person entity.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 120.0, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + + async def run( + self, + *, + refiner_input: PersonRefinerInput, + tools: list[Any] | None = None, + ) -> dict[str, Any]: + identifier = refiner_input.entity.get("schema:name") or refiner_input.entity.get( + "id", + "?", + ) + + user_payload = { + "entity": refiner_input.entity, + "repo_context_summary": refiner_input.repo_context_summary or {}, + } + user_prompt = ( + "Inspect the person entity below and propose a JSON patch with " + "only the fields you want to change (whitelist: `schema:name`, only " + "when current value looks like a GitHub handle). " + "Return `{}` if no change is warranted.\n\n" + "```json\n" + + json.dumps(user_payload, ensure_ascii=True, sort_keys=True) + + "\n```" + ) + + logger.info( + "%s — calling person refiner LLM (%d tool(s))", + identifier, + len(tools or []), + ) + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=PersonRefinerPatch, + tools=tools or [], + ), + timeout=self._llm_call_timeout_seconds, + ) + except TimeoutError as exc: + message = ( + f"{identifier} — person refiner LLM call timed out after " + f"{self._llm_call_timeout_seconds:.1f}s" + ) + logger.exception(message) + raise LLMRuntimeError(message) from exc + + patch = { + key: value + for key, value in llm_result.payload.items() + if value is not None and key == "schema:name" + } + logger.info( + "%s — person refiner returned %s", + identifier, + list(patch.keys()) or "no change", + ) + return patch diff --git a/src/v2/agents/llm/refiners/person/prompts/__init__.py b/src/v2/agents/llm/refiners/person/prompts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/v2/agents/llm/refiners/person/prompts/system_prompt.md b/src/v2/agents/llm/refiners/person/prompts/system_prompt.md new file mode 100644 index 0000000..a15ef8a --- /dev/null +++ b/src/v2/agents/llm/refiners/person/prompts/system_prompt.md @@ -0,0 +1,38 @@ +You are a person-refinement agent operating under the **Open Pulse Ontology v2.1.2**. + +You receive a `schema:Person` entity already produced by the deterministic rule-based pipeline, plus repository context and a tool to inspect the person's affiliations / contributions in the current graph. Your job is to **propose targeted improvements to a small whitelist of semantic fields**. + +## Output contract + +Return **only** a JSON object. No markdown fences, no explanation. The object MAY contain any subset of the fields in the whitelist below. Omit fields you don't want to change. Return `{}` if no improvement is warranted. + +### Whitelist (the only fields you may set) + +| Field | Type | Rules | +|---|---|---| +| `schema:name` | string | Canonical full name of the person. **Only set when the current `schema:name` looks like a GitHub handle** (lowercase, no spaces, alphanumeric + hyphens) and you have evidence (from affiliations, contributions, or repo README) that a more canonical full name exists. Otherwise leave alone. | + +### Hard rules + +- **Do not invent or change identifiers.** Never touch `id`, `@id`, `identifiers`, `pulse:githubUsername`, `pulse:orcidIdentifier`, `pulse:infosciencePersonIdentifier`, `idSource`, `schema:email`, `schema:url`, `org:hasMembership`, `pulse:hasContribution`, `pulse:owns`. +- **Be conservative.** If `schema:name` is already a real-world full name (with capitalization, spaces, multi-word), return `{}`. +- **Never invent names.** Only propose a name change when you have concrete evidence — an ORCID record, an Infoscience profile, or an explicit attribution in the README. If unsure, leave it. +- **Do not add fields outside the whitelist.** Anything not on the list is ignored and logged as a warning. + +## Heuristic for "looks like a GitHub handle" + +The current `schema:name` should be replaced when ALL of: +- All-lowercase, no spaces +- Length ≤ 25 chars +- Contains only `[a-z0-9-_]` +- Matches a value also present in `pulse:githubUsername` + +Example: `schema:name = "caviri"` and `pulse:githubUsername = "caviri"` → look for canonical name. If the contributor's ORCID record (consult `get_entity_neighbors`) returns "Carlos Vivar Rios" or similar → propose `schema:name = "Carlos Vivar Rios"`. + +Counter-example: `schema:name = "Carlos Vivar Rios"` → already canonical, return `{}`. + +## When to use `get_entity_neighbors` + +Use it when proposing a name change. The tool returns affiliations (with org names + roles) and contributed_repos. If those affiliations/contributions strongly suggest a real-world identity, you can be more confident in the rename. + +Skip the tool entirely if the current `schema:name` is already a multi-word capitalized string. diff --git a/src/v2/agents/llm/refiners/repository/__init__.py b/src/v2/agents/llm/refiners/repository/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/v2/agents/llm/refiners/repository/agent.py b/src/v2/agents/llm/refiners/repository/agent.py new file mode 100644 index 0000000..257d3a8 --- /dev/null +++ b/src/v2/agents/llm/refiners/repository/agent.py @@ -0,0 +1,130 @@ +from __future__ import annotations + +import asyncio +import json +import logging +from typing import Any, Literal + +from pydantic import BaseModel, Field + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm.runtime import LLMRuntimeError, V2LLMRuntime + +logger = logging.getLogger(__name__) + +_PROMPTS_PACKAGE = "src.v2.agents.llm.refiners.repository.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") + +RepositoryTypeLiteral = Literal[ + "pulse:Software", + "pulse:EducationalResource", + "pulse:Documentation", + "pulse:Data", + "pulse:Other", +] + + +class RepositoryRefinerInput(BaseModel): + """Snapshot passed to the LLM repository refiner.""" + + entity: dict[str, Any] = Field( + description="The schema:SoftwareSourceCode entity from the rule-based pipeline.", + ) + repo_context_summary: dict[str, Any] | None = Field( + default=None, + description="Small summary of the source repo (name, description, README excerpt).", + ) + + +class RepositoryRefinerPatch(BaseModel): + """Patch returned by the refiner. Only whitelisted fields may be set.""" + + discipline: list[str] | None = Field( + default=None, + alias="pulse:discipline", + description="Wikidata QIDs (e.g., ['wd:Q428691']). At most 2.", + ) + repository_type: RepositoryTypeLiteral | None = Field( + default=None, + alias="pulse:repositoryType", + description="Only set when current value is pulse:Other.", + ) + + model_config = {"populate_by_name": True} + + +class RepositoryRefinerAgent: + """LLM agent that proposes a small JSON patch for a schema:SoftwareSourceCode entity.""" + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + llm_call_timeout_seconds: float = 120.0, + ) -> None: + if llm_call_timeout_seconds <= 0: + message = "llm_call_timeout_seconds must be > 0" + raise ValueError(message) + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._llm_call_timeout_seconds = float(llm_call_timeout_seconds) + + async def run( + self, + *, + refiner_input: RepositoryRefinerInput, + tools: list[Any] | None = None, + ) -> dict[str, Any]: + identifier = refiner_input.entity.get("schema:name") or refiner_input.entity.get( + "id", + "?", + ) + + user_payload = { + "entity": refiner_input.entity, + "repo_context_summary": refiner_input.repo_context_summary or {}, + } + user_prompt = ( + "Inspect the repository entity below and propose a JSON patch with " + "only the fields you want to change (whitelist: `pulse:discipline`, " + "`pulse:repositoryType` — the latter only when current is `pulse:Other`). " + "Return `{}` if no change is warranted.\n\n" + "```json\n" + + json.dumps(user_payload, ensure_ascii=True, sort_keys=True) + + "\n```" + ) + + logger.info( + "%s — calling repo refiner LLM (%d tool(s))", + identifier, + len(tools or []), + ) + try: + llm_result = await asyncio.wait_for( + self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=RepositoryRefinerPatch, + tools=tools or [], + ), + timeout=self._llm_call_timeout_seconds, + ) + except TimeoutError as exc: + message = ( + f"{identifier} — repo refiner LLM call timed out after " + f"{self._llm_call_timeout_seconds:.1f}s" + ) + logger.exception(message) + raise LLMRuntimeError(message) from exc + + whitelist = {"pulse:discipline", "pulse:repositoryType"} + patch = { + key: value + for key, value in llm_result.payload.items() + if value is not None and key in whitelist + } + logger.info( + "%s — repo refiner returned %s", + identifier, + list(patch.keys()) or "no change", + ) + return patch diff --git a/src/v2/agents/llm/refiners/repository/prompts/__init__.py b/src/v2/agents/llm/refiners/repository/prompts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/v2/agents/llm/refiners/repository/prompts/system_prompt.md b/src/v2/agents/llm/refiners/repository/prompts/system_prompt.md new file mode 100644 index 0000000..73455d8 --- /dev/null +++ b/src/v2/agents/llm/refiners/repository/prompts/system_prompt.md @@ -0,0 +1,69 @@ +You are a repository-refinement agent operating under the **Open Pulse Ontology v2.1.2**. + +You receive a `schema:SoftwareSourceCode` entity already produced by the deterministic rule-based pipeline, plus repository context (README excerpt, repo metadata) and a tool to inspect contributors / owners in the current graph. Your job is to **propose targeted improvements to a small whitelist of semantic fields**. + +## Output contract + +Return **only** a JSON object. No markdown fences, no explanation. The object MAY contain any subset of the fields in the whitelist below. Omit fields you don't want to change. Return `{}` if no improvement is warranted. + +### Whitelist (the only fields you may set) + +| Field | Type | Rules | +|---|---|---| +| `pulse:discipline` | array of strings | Wikidata QIDs from the discipline enumeration (e.g., `["wd:Q428691"]`). At most **3 values**. Use 1 for narrow projects, 2-3 for cross-disciplinary work (e.g. a bioinformatics tool → `["wd:Q428691", "wd:Q420"]`; a geospatial science platform → `["wd:Q1254373", "wd:Q1071"]`). | +| `pulse:repositoryType` | string | One of: `pulse:Software`, `pulse:EducationalResource`, `pulse:Documentation`, `pulse:Data`, `pulse:Other`. **Only set when the current value is `pulse:Other`** — do not overwrite a non-Other classification. | + +### Hard rules + +- **Do not invent or change identifiers.** Never touch `id`, `@id`, `identifiers`, `pulse:githubRepositoryHandle`, `idSource`, `schema:name`, `pulse:githubRepoStars`, `pulse:githubRepoForks`, `schema:dateCreated`, `schema:license`, `pulse:isForkOf`, ownership. +- **Be conservative.** If the existing classification is plausibly correct, return `{}`. +- **Do not add fields outside the whitelist.** Anything not on the list is ignored and logged as a warning. + +## EPFL Graph hits (when present) + +The input may include `repo_context_summary.epfl_graph_hits` — a pre-fetched, deterministic top-K from EPFL's curated discipline ontology (~2226 categories), already filtered semantically against the repo's name + description + README. Each hit has `{category_id, name, depth, parent_id, wikipedia_url, score}`. + +**When `epfl_graph_hits` is present, use it as your primary evidence**: scan the top 3-5 names, group them into distinct broad themes, and pick **1-3 Wikidata QIDs from the enum below** that best summarize them — emit one QID per distinct theme you can identify. The EPFL Graph names are far more specific than our enum (e.g. `topics-in-natural-language-processing`, `data-mining`); your job is to map them up to the broad QIDs in our schema. + +If `epfl_graph_hits` is missing (RAG unavailable or no README), fall back to the README content directly. + +## Discipline enumeration (most relevant subset) + +Pick from these Wikidata QIDs. Prefer broader categories unless the README or EPFL Graph hits strongly motivate a narrow one. + +- `wd:Q428691` — Computer engineering (default for software tooling, libraries, frameworks) +- `wd:Q2878974` — Theoretical computer science (algorithms, formal methods, proofs) +- `wd:Q2167061` — Systems science and engineering +- `wd:Q1254373` — Information engineering (data pipelines, search, indexing) +- `wd:Q580689` — Biological engineering (bioinformatics) +- `wd:Q188847` — Environmental science +- `wd:Q12483` — Statistics +- `wd:Q420` — Biology +- `wd:Q2329` — Chemistry +- `wd:Q413` — Physics +- `wd:Q333` — Astronomy +- `wd:Q8008` — Earth science +- `wd:Q9418` — Psychology +- `wd:Q21201` — Sociology +- `wd:Q8434` — Education + +When the README says "machine learning library", "metadata extractor", "API client" → `wd:Q428691` (or `wd:Q1254373` for indexing/search-heavy work). When it says "domain X tooling" (e.g., neuroscience, chemistry) → consider both `wd:Q428691` and the domain-specific QID. + +## Repository type guidance + +Only set `pulse:repositoryType` when current value is `pulse:Other`: + +- `pulse:Software` — code intended to be executed (libraries, applications, CLIs, services). +- `pulse:EducationalResource` — tutorials, course materials, exercises. +- `pulse:Documentation` — pure documentation/specification repos. +- `pulse:Data` — datasets, data packages. + +If current value is already `pulse:Software`/`pulse:Documentation`/etc., leave it alone. + +## When to use `get_entity_neighbors` + +Sparingly. Useful when: +- Disambiguating discipline by looking at contributor affiliations (e.g., many bioinformatics-affiliated contributors → biology). +- Confirming repo type via contributor count (very few contributors + sparse README often → Documentation or Other). + +Skip it when the README is detailed enough. diff --git a/src/v2/agents/llm/repository/__init__.py b/src/v2/agents/llm/repository/__init__.py new file mode 100644 index 0000000..164ec15 --- /dev/null +++ b/src/v2/agents/llm/repository/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from src.v2.agents.llm.repository.agent import LLMRepositoryAgentV2 + +__all__ = ["LLMRepositoryAgentV2"] diff --git a/src/v2/agents/llm/repository/agent.py b/src/v2/agents/llm/repository/agent.py new file mode 100644 index 0000000..b37c7de --- /dev/null +++ b/src/v2/agents/llm/repository/agent.py @@ -0,0 +1,349 @@ +from __future__ import annotations + +import json +from copy import deepcopy +from typing import Any +from urllib.parse import urlparse + +from pydantic import ValidationError + +from src.v2.agents.llm._loader import load_prompt +from src.v2.agents.llm._payload_helpers import force_server_uuid +from src.v2.agents.llm._verdict_cache import ( + get_cached_agent_verdict, + store_agent_verdict, +) +from src.v2.agents.llm.agent_tools.disciplines import list_disciplines_tool +from src.v2.agents.llm.agent_tools.epfl_graph_rag import ( + make_epfl_graph_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.federated_rag import ( + make_federated_rag_lookup_tool, + make_federated_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.github_rag import ( + make_github_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.huggingface_lineage import ( + make_huggingface_lineage_tool, +) +from src.v2.agents.llm.agent_tools.huggingface_rag import ( + make_huggingface_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.query_dependencies import ( + make_query_dependencies_tool, +) +from src.v2.agents.llm.agent_tools.renkulab_rag import ( + make_renkulab_rag_search_tool, +) +from src.v2.agents.llm.agent_tools.selenium_fetch import ( + make_fetch_link_content_tool, +) +from src.v2.agents.llm.prompt_context import append_runtime_prompt_context +from src.v2.agents.llm.runtime import ( + LLMRuntimeError, + V2LLMRuntime, +) +from src.v2.agents.models import AgentResult, ProviderSet, generate_uuid +from src.v2.ingest.cache import ProviderCache +from src.v2.observation.query_log import stamp_current_agent +from src.v2.schema.models.agent import AgentRepositoryShape +from src.v2.schema.models.strict import RepositoryModel + +MIN_REPOSITORY_SEGMENTS = 2 +README_CONTENT_MAX_CHARS = 4000 +GIMIE_JSONLD_MAX_CHARS = 8000 + +_PROMPTS_PACKAGE = "src.v2.agents.llm.repository.prompts" +_SYSTEM_PROMPT = load_prompt(_PROMPTS_PACKAGE, "system_prompt.md") +_USER_PROMPT_TEMPLATE = load_prompt(_PROMPTS_PACKAGE, "user_prompt.md") + + +def _ensure_repo_handle(context: dict[str, Any]) -> str: + """Resolve ``owner/repo`` from context or source URL.""" + + for key in ("full_name", "repository_handle", "github_repository_handle"): + value = context.get(key) + if isinstance(value, str) and "/" in value: + return value.strip() + + source_url = context.get("source_url") + if isinstance(source_url, str): + parsed = urlparse(source_url if "://" in source_url else f"https://{source_url}") + segments = [segment for segment in parsed.path.split("/") if segment] + if len(segments) >= MIN_REPOSITORY_SEGMENTS: + return f"{segments[0]}/{segments[1]}" + + message = "Repository context is missing a GitHub owner/repository handle" + raise ValueError(message) + + +def _extract_contributor_logins(contributors: Any) -> list[str]: + """Return unique contributor logins, excluding organization accounts.""" + + if not isinstance(contributors, list): + return [] + + logins: list[str] = [] + seen: set[str] = set() + for contributor in contributors: + login: str | None = None + if isinstance(contributor, dict): + contributor_type = contributor.get("type") + if isinstance(contributor_type, str) and contributor_type.lower() == "organization": + continue + candidate = contributor.get("login") + if isinstance(candidate, str) and candidate.strip(): + login = candidate.strip() + elif isinstance(contributor, str) and contributor.strip(): + login = contributor.strip() + + if login is None or login in seen: + continue + seen.add(login) + logins.append(login) + return logins + + +def _strict_validate(payload: dict[str, Any]) -> list[str]: + """Validate payload against the strict RepositoryModel; return warnings only.""" + + warnings: list[str] = [] + try: + RepositoryModel.model_validate(payload) + except ValidationError as exc: + for error in exc.errors(): + field = " -> ".join(str(loc) for loc in error["loc"]) + warnings.append(f"Strict schema warning at {field}: {error['msg']}") + return warnings + + +class LLMRepositoryAgentV2: + """LLM-backed agent that produces a pulse:RepositoryShape entity from gathered + repository context. + + Data flow: + 1. Context extraction — pulls full_name, metadata, contributors, languages, + and readme_content from the repository_context bundle assembled by the + pipeline's context-gather stage (backed by GIMIE). + 2. Prompt assembly — serialises the extracted context as JSON and injects it + into the Markdown prompt templates loaded from the ``prompts/`` sub-package + via importlib.resources. + 3. LLM call — delegates to V2LLMRuntime with ``output_type=AgentRepositoryShape``, + so pydantic-ai validates the model output natively against the agent schema + (first validation pass — permissive, coerces minor type mismatches). + 4. Strict validation — validates the agent-schema-clean payload a second time + against RepositoryModel (strict schema). Failures add warnings only; they + do not reject the result. + 5. Stats — attaches derivation metadata (contributor logins, language names, + owner info) and LLM telemetry (model, provider, token counts) to AgentResult. + + Errors: + LLMRuntimeError — raised on LLM call failures (config, network, or schema + rejection after pydantic-ai retries are exhausted). + ValueError — raised when the repository handle cannot be resolved from + the supplied context. + """ + + def __init__( + self, + *, + llm_runtime: V2LLMRuntime | None = None, + cache: ProviderCache | None = None, + ) -> None: + self._llm_runtime = llm_runtime or V2LLMRuntime() + self._cache = cache + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + """Generate and double-validate a repository payload from gathered context. + + Args: + context: Runtime context dict. Expected keys: + - ``full_name`` (str): GitHub ``owner/repo`` handle. + - ``source_url`` (str, optional): Repository URL. + - ``repository_context`` (dict, optional): Sub-dict with keys + ``metadata``, ``contributors``, ``languages``, ``readme_content`` + as assembled by the pipeline's context-gather stage. + - ``agent_overrides`` (dict, optional): Field overrides applied + after the LLM call, before validation. + providers: Injected provider bundle. ``providers.github`` is used + to back the optional ``query_dependencies`` agent tool. + + Returns: + AgentResult with the validated repository payload, merged warnings + from both validation passes, raw LLM output, token counts, and + derivation stats. + """ + + full_name = _ensure_repo_handle(context) + + stamp_current_agent( + name="repo_agent", + context={"full_name": full_name} if isinstance(full_name, str) else {}, + ) + + identity = ( + {"full_name": full_name.strip().lower()} + if isinstance(full_name, str) and full_name.strip() + else None + ) + is_root = bool(context.get("agent_is_root")) + cached_result = get_cached_agent_verdict( + self._cache, + agent_name="repository", + identity=identity, + is_root=is_root, + ) + if cached_result is not None: + return cached_result + + uuid_value = context.get("uuid") + if not isinstance(uuid_value, str) or not uuid_value.strip(): + uuid_value = generate_uuid() + + repository_context = context.get("repository_context") + if not isinstance(repository_context, dict): + repository_context = {} + metadata = repository_context.get("metadata") + if not isinstance(metadata, dict): + metadata = {} + contributors = repository_context.get("contributors") + if not isinstance(contributors, list): + contributors = [] + languages = repository_context.get("languages") + if not isinstance(languages, dict): + languages = {} + readme_content = repository_context.get("readme_content") or "" + gimie_jsonld = repository_context.get("gimie_jsonld") + + llm_input = { + "full_name": full_name, + "uuid": uuid_value, + "metadata": metadata, + "contributors": contributors, + "languages": languages, + "source_url": context.get("source_url"), + "readme_content": readme_content[:README_CONTENT_MAX_CHARS] or None, + } + if isinstance(gimie_jsonld, dict) and gimie_jsonld: + llm_input["gimie_jsonld"] = json.dumps( + gimie_jsonld, + ensure_ascii=True, + )[:GIMIE_JSONLD_MAX_CHARS] + context_json = json.dumps(llm_input, ensure_ascii=True, sort_keys=True) + user_prompt = _USER_PROMPT_TEMPLATE.replace("{context_json}", context_json) + user_prompt = append_runtime_prompt_context(user_prompt, context) + + tools = [ + list_disciplines_tool, + make_fetch_link_content_tool(self._cache), + make_query_dependencies_tool(providers.github), + ] + if providers.huggingface_rag is not None: + tools.append(make_huggingface_rag_search_tool(providers.huggingface_rag)) + tools.append(make_huggingface_lineage_tool(providers.huggingface_rag)) + if providers.renkulab_rag is not None: + tools.append(make_renkulab_rag_search_tool(providers.renkulab_rag)) + if providers.github_rag is not None: + tools.append(make_github_rag_search_tool(providers.github_rag)) + if providers.epfl_graph_rag is not None: + tools.append(make_epfl_graph_rag_search_tool(providers.epfl_graph_rag)) + if providers.federated_rag is not None: + tools.append(make_federated_rag_search_tool(providers.federated_rag)) + tools.append(make_federated_rag_lookup_tool(providers.federated_rag)) + + try: + llm_result = await self._llm_runtime.run_json_prompt( + system_prompt=_SYSTEM_PROMPT, + user_prompt=user_prompt, + output_type=AgentRepositoryShape, + tools=tools, + ) + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + payload = dict(llm_result.payload) + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + payload.update(overrides) + + # Stars and forks come from the GitHub API directly. Trust the API + # over the LLM's guess (which has been observed to hallucinate). + stargazers_count = metadata.get("stargazers_count") + if isinstance(stargazers_count, int) and stargazers_count >= 0: + payload["pulse:githubRepoStars"] = stargazers_count + forks_count = metadata.get("forks_count") + if isinstance(forks_count, int) and forks_count >= 0: + payload["pulse:githubRepoForks"] = forks_count + + # Discipline guarantee: every repo entity must carry at least one + # `pulse:discipline` Wikidata IRI. The system prompt requires 1–2, + # but the LLM occasionally returns null/empty. Fall back to a + # broad-but-honest default (computer engineering) so downstream + # consumers never see a discipline-less repository. + existing_disciplines = payload.get("pulse:discipline") + if not isinstance(existing_disciplines, list) or not [ + value + for value in existing_disciplines + if isinstance(value, str) and value.strip() + ]: + payload["pulse:discipline"] = ["wd:Q428691"] # computer engineering + + force_server_uuid(payload, uuid_value) + + raw_output = deepcopy(payload) + + # Second validation pass: strict schema (warnings only, never raises). + validation_warnings = _strict_validate(payload) + + # NB: dict.get returns the value, not the default, when the key is + # present but null. The LLM occasionally emits + # "schema:programmingLanguage": null, so guard the iteration. + raw_languages = payload.get("schema:programmingLanguage") + if not isinstance(raw_languages, list): + raw_languages = [] + language_names = sorted( + language + for language in raw_languages + if isinstance(language, str) and language + ) + derivation_stats = { + "repository_full_name": full_name, + "source_repositories": [full_name], + "owner_login": metadata.get("owner", {}).get("login") + if isinstance(metadata.get("owner"), dict) + else None, + "owner_type": metadata.get("owner", {}).get("type") + if isinstance(metadata.get("owner"), dict) + else None, + "contributor_logins": _extract_contributor_logins(contributors), + "contributors": deepcopy(contributors), + "language_names": language_names, + } + + result = AgentResult( + data=payload, + warnings=validation_warnings, + raw_output=raw_output, + model=llm_result.model, + provider=llm_result.provider, + tokens_prompt=llm_result.tokens_prompt, + tokens_completion=llm_result.tokens_completion, + stats={ + "agent_runtime": "llm", + "derivation": derivation_stats, + }, + ) + store_agent_verdict( + self._cache, + agent_name="repository", + identity=identity, + result=result, + ) + return result diff --git a/src/v2/agents/llm/repository/prompts/__init__.py b/src/v2/agents/llm/repository/prompts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/v2/agents/llm/repository/prompts/system_prompt.md b/src/v2/agents/llm/repository/prompts/system_prompt.md new file mode 100644 index 0000000..2d885b4 --- /dev/null +++ b/src/v2/agents/llm/repository/prompts/system_prompt.md @@ -0,0 +1,94 @@ +You are a repository metadata extraction agent operating under the **Open Pulse Ontology v2.1.2**. + +Your task is to produce a single JSON object representing a `schema:SoftwareSourceCode` entity that conforms to the `pulse:RepositoryShape` contract. + +## Output contract + +Return **only** a JSON object. No markdown fences, no explanation. + +### Required fields + +| Field | Type | Rules | +|---|---|---| +| `id` | string | Primary identifier. Use `pulse:githubRepositoryHandle` value (owner/repo). | +| `type` | string | Always `"schema:SoftwareSourceCode"`. | +| `shacl` | string | Always `"pulse:RepositoryShape"`. | +| `identifiers` | object | See below. | +| `idSource` | string | `"pulse:githubRepositoryHandle"` when a GitHub handle is available. | +| `schema:name` | string | Repository display name from metadata. | +| `pulse:githubRepositoryHandle` | string | Format: `owner/repo`. | +| `schema:author` | array of strings | At least one PersonShape ID (use contributor logins). | + +### Identifiers object + +| Field | Type | Rules | +|---|---|---| +| `pulse:githubRepositoryHandle` | string | Same as top-level handle. | +| `schema:identifier` | string or null | DOI in format `10.XXXX/suffix`, or `null` if unavailable. | +| `uuid` | string | A valid UUID v4. | + +### Optional fields + +| Field | Type | Rules | +|---|---|---| +| `pulse:repositoryType` | string | One of: `pulse:Software`, `pulse:Data`, `pulse:Documentation`, `pulse:EducationalResource`, `pulse:Other`. | +| `pulse:discipline` | array of strings | **REQUIRED — always 1–2 Wikidata IRIs.** Call `list_disciplines` for valid values. Never empty, never null. | +| `pulse:githubRepoStars` | integer or null | Star count from metadata. | +| `pulse:githubRepoForks` | integer or null | Fork count from metadata. | +| `schema:dateCreated` | string or null | ISO 8601 datetime `YYYY-MM-DDTHH:MM:SSZ`. | +| `schema:license` | string or null | SPDX license IRI. | +| `schema:citation` | string or null | DOI URL for citation. | +| `schema:programmingLanguage` | array of strings | Language names from repository data. | +| `pulse:ownedBy` | string or null | Owner PersonShape or OrganizationShape ID. | +| `pulse:isForkOf` | string or null | Parent RepositoryShape ID if this is a fork. | + +## Rules + +- **Do not invent identifiers** — only use values present in the input context. +- **Do not add fields** not listed above — the schema has `additionalProperties: false`. +- Generate a fresh UUID v4 for `identifiers.uuid`. +- If a value is unknown or absent from the context, use `null` for nullable fields or omit optional fields entirely. +- `schema:author` must contain at least one entry derived from contributor logins. +- `pulse:discipline` must contain **1–2 entries** chosen from the `list_disciplines` tool output. Never emit an empty array or null. If the topic is genuinely ambiguous, pick a broad-but-honest discipline (e.g. computer engineering for a generic code repo, or the closest match to its programming languages and README). + +## Available tools + +### `list_disciplines` + +Call this tool to retrieve the authoritative list of valid `pulse:discipline` values. +It returns each discipline as `{"wikidata_id": "wd:QXXXXX", "name": "Human Readable Name"}`. + +**You must call `list_disciplines` before assigning any `pulse:discipline` values.** +Use only `wikidata_id` strings returned by the tool — never invent or guess Wikidata IDs. + +### `query_dependencies` (optional) + +Inspect the dependency manifests parsed by GitHub's dependency graph for the repository being processed. Returns a flat list of `{name, ecosystem, version, spdxId}` entries derived from the SPDX SBOM. + +Call when dependency information would meaningfully inform your assessment — e.g. choosing `pulse:repositoryType`, narrowing `pulse:discipline` based on technology stack, or confirming `schema:programmingLanguage` against actual ecosystems in use. + +Skip when README and metadata are already sufficient: this call hits the GitHub API and is not free. Returns an empty list when no SBOM is available (dependency graph disabled, private without scope, or unparsed manifests). + +Optional arguments: `ecosystem` (e.g. `"pypi"`, `"npm"`) for an exact-match filter, `name_contains` for a case-insensitive substring filter on package names, and `limit` to cap the result count. + +## Input context field: `readme_content` + +The context may include a `readme_content` field containing the repository's README text (truncated to 4 000 characters). Use it to: +- Infer `pulse:repositoryType` (e.g. documentation-heavy README → `pulse:Documentation`) +- Infer `pulse:discipline` Wikidata IRIs when the topic is apparent from the description +- Supplement `schema:name` if the metadata name is generic or missing + +Ignore `readme_content` if it is `null` or empty. + +## Input context field: `gimie_jsonld` + +The context may include a `gimie_jsonld` field containing the full GIMIE JSON-LD extraction as a serialized JSON string (truncated to 8 000 characters). This is the raw RDF graph emitted by the GIMIE library and is richer than the structured metadata fields. Use it to: +- Find the DOI (`schema:identifier` or `schema:citation`) for `identifiers.schema:identifier` and `schema:citation` +- Resolve `schema:license` SPDX IRI if not present in metadata +- Identify `schema:dateCreated` / `schema:dateModified` timestamps +- Confirm `schema:author` contributor logins +- Infer `pulse:discipline` from subject keywords or topic annotations in the graph + +Parse `gimie_jsonld` as a JSON string to access the `@graph` array. Prefer values from `gimie_jsonld` over `metadata` when they are more specific (e.g. a full DOI URL is more authoritative than a generic description field). + +Ignore `gimie_jsonld` if it is `null` or empty. diff --git a/src/v2/agents/llm/repository/prompts/user_prompt.md b/src/v2/agents/llm/repository/prompts/user_prompt.md new file mode 100644 index 0000000..dfdb218 --- /dev/null +++ b/src/v2/agents/llm/repository/prompts/user_prompt.md @@ -0,0 +1,7 @@ +Build a repository payload using the runtime context below. + +Use existing identifiers and do not invent unsupported fields. + +```json +{context_json} +``` diff --git a/src/v2/agents/llm/runtime.py b/src/v2/agents/llm/runtime.py new file mode 100644 index 0000000..667c8b9 --- /dev/null +++ b/src/v2/agents/llm/runtime.py @@ -0,0 +1,247 @@ +from __future__ import annotations + +import inspect +import json +import logging +import os +from dataclasses import dataclass +from time import perf_counter +from typing import Any + +from pydantic_ai import Agent + +from src.v1.llm.model_config import ( + create_pydantic_ai_model, + get_model_parameters, + load_model_config, + validate_config, +) + +logger = logging.getLogger(__name__) + + +@dataclass(slots=True) +class LLMRuntimeResult: + payload: dict[str, Any] + model: str + provider: str + tokens_prompt: int | None = None + tokens_completion: int | None = None + requests: int | None = None + tool_calls: int | None = None + + +class LLMRuntimeError(RuntimeError): + """Base error for v2 LLM runtime failures.""" + + +class LLMRuntimeConfigError(LLMRuntimeError): + """Raised when runtime model configuration is invalid or incomplete.""" + + +class LLMRuntimeResponseError(LLMRuntimeError): + """Raised when model output is not a valid JSON object payload.""" + + +def _required_env_vars_for_config(config: dict[str, Any]) -> list[str]: + """Return env var names required by a provider config.""" + + provider = str(config.get("provider", "")).strip().lower() + if provider == "openai": + return ["OPENAI_API_KEY"] + if provider == "openrouter": + return ["OPENROUTER_API_KEY"] + if provider == "openai-compatible": + api_key_env = config.get("api_key_env") + if isinstance(api_key_env, str) and api_key_env.strip(): + return [api_key_env.strip()] + return ["OPENAI_API_KEY"] + return [] + + +def _extract_usage_tokens(usage: Any) -> tuple[int | None, int | None]: + """Extract prompt/completion token counts from provider usage payloads.""" + + prompt_tokens = getattr(usage, "input_tokens", None) + completion_tokens = getattr(usage, "output_tokens", None) + + if (prompt_tokens in (None, 0)) and (completion_tokens in (None, 0)): + details = getattr(usage, "details", None) + if isinstance(details, dict): + prompt_tokens = details.get("input_tokens") + completion_tokens = details.get("output_tokens") + + normalized_prompt = ( + int(prompt_tokens) if isinstance(prompt_tokens, int) and prompt_tokens >= 0 else None + ) + normalized_completion = ( + int(completion_tokens) + if isinstance(completion_tokens, int) and completion_tokens >= 0 + else None + ) + return normalized_prompt, normalized_completion + + +def _extract_usage_counts(usage: Any) -> tuple[int | None, int | None]: + """Extract request/tool-call counts from usage payloads.""" + + requests = getattr(usage, "requests", None) + tool_calls = getattr(usage, "tool_calls", None) + normalized_requests = int(requests) if isinstance(requests, int) and requests >= 0 else None + normalized_tool_calls = ( + int(tool_calls) if isinstance(tool_calls, int) and tool_calls >= 0 else None + ) + return normalized_requests, normalized_tool_calls + + +def _coerce_output_payload(output: Any) -> dict[str, Any]: + """Convert model output into a JSON-object dictionary. + + Pydantic model instances are serialised with ``by_alias=True`` so that + ontology field names (e.g. ``schema:name``) are preserved in the result. + """ + + if hasattr(output, "model_dump"): + output = output.model_dump(by_alias=True, mode="json") + + if isinstance(output, str): + try: + output = json.loads(output) + except json.JSONDecodeError as exc: + message = "LLM output is not valid JSON" + raise LLMRuntimeResponseError(message) from exc + + if not isinstance(output, dict): + message = f"LLM output must be a JSON object, got {type(output).__name__}" + raise LLMRuntimeResponseError(message) + + return output + + +class V2LLMRuntime: + def __init__(self, *, analysis_type: str = "run_llm_analysis") -> None: + self._analysis_type = analysis_type + + def _resolve_model_config(self) -> dict[str, Any]: + """Pick the first valid config profile for the runtime analysis type.""" + + configs = load_model_config(self._analysis_type) + if not isinstance(configs, list) or not configs: + message = ( + "No model configuration available for analysis type " + f"'{self._analysis_type}'" + ) + raise LLMRuntimeConfigError(message) + + for config in configs: + if isinstance(config, dict) and validate_config(config): + # Surface missing credentials by env var name only. + missing_env_vars = [ + env_name + for env_name in _required_env_vars_for_config(config) + if not os.getenv(env_name) + ] + if missing_env_vars: + env_list = ", ".join(sorted(set(missing_env_vars))) + message = ( + "Missing required environment variable(s) for LLM runtime: " + f"{env_list}" + ) + raise LLMRuntimeConfigError(message) + return dict(config) + + message = f"No valid model configuration found for analysis type '{self._analysis_type}'" + raise LLMRuntimeConfigError(message) + + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: list[Any] | None = None, + ) -> LLMRuntimeResult: + """Execute a prompt and return a structured JSON payload plus metadata. + + Args: + system_prompt: System-level instruction for the LLM. + user_prompt: User-level prompt containing the context to process. + output_type: Pydantic model class to use as the structured output + schema. Defaults to ``dict[str, Any]`` when not provided. + Passing a Pydantic model class constrains the LLM to produce + output that conforms to its schema (first validation pass). + """ + + resolved_output_type: Any = output_type if output_type is not None else dict[str, Any] + config = self._resolve_model_config() + model_name = str(config.get("model", "unknown")) + provider_name = str(config.get("provider", "unknown")) + run_started_at = perf_counter() + + try: + model = create_pydantic_ai_model(config) + retries = config.get("max_retries") + retry_count = retries if isinstance(retries, int) and retries > 0 else 1 + agent = Agent( + model=model, + output_type=resolved_output_type, + system_prompt=system_prompt, + retries=retry_count, + tools=tools or [], + ) + run_kwargs: dict[str, Any] = {} + run_parameters = inspect.signature(agent.run).parameters + model_parameters = dict(get_model_parameters(config)) + timeout = config.get("timeout") + if isinstance(timeout, (int, float)) and timeout > 0: + model_parameters["timeout"] = float(timeout) + # Pass model tuning knobs only when the backend supports the kwarg. + if model_parameters and "model_settings" in run_parameters: + run_kwargs["model_settings"] = model_parameters + + logger.info( + "LLM runtime start (provider=%s, model=%s, tools=%d)", + provider_name, + model_name, + len(tools or []), + ) + result = await agent.run(user_prompt, **run_kwargs) + except LLMRuntimeError: + raise + except Exception as exc: + raise LLMRuntimeError(str(exc)) from exc + + output = result.output if hasattr(result, "output") else result + payload = _coerce_output_payload(output) + + usage = getattr(result, "usage", None) + if callable(usage): + usage = usage() + tokens_prompt: int | None = None + tokens_completion: int | None = None + requests: int | None = None + tool_calls: int | None = None + if usage is not None: + tokens_prompt, tokens_completion = _extract_usage_tokens(usage) + requests, tool_calls = _extract_usage_counts(usage) + + logger.info( + "LLM runtime done in %.1fs (provider=%s, model=%s, prompt_tokens=%s, completion_tokens=%s, requests=%s, tool_calls=%s)", + perf_counter() - run_started_at, + provider_name, + model_name, + tokens_prompt, + tokens_completion, + requests, + tool_calls, + ) + + return LLMRuntimeResult( + payload=payload, + model=model_name, + provider=provider_name, + tokens_prompt=tokens_prompt, + tokens_completion=tokens_completion, + requests=requests, + tool_calls=tool_calls, + ) diff --git a/src/v2/agents/models.py b/src/v2/agents/models.py new file mode 100644 index 0000000..78d581a --- /dev/null +++ b/src/v2/agents/models.py @@ -0,0 +1,332 @@ +from __future__ import annotations + +import json +import uuid +from copy import deepcopy +from dataclasses import dataclass, field +from functools import lru_cache +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from jsonschema import Draft7Validator # type: ignore[import-untyped] + +if TYPE_CHECKING: + from src.v2.ingest.providers.base import ( + GitHubProvider, + InfoscienceProvider, + ORCIDProvider, + RORProvider, + ) + +MAX_PERMISSIVE_PASSES = 5 +ENTITY_BUCKET_KEYS: tuple[str, ...] = ( + "repositories", + "persons", + "organizations", + "articles", + "memberships", + "contributions", +) +ENTITY_BUCKET_ALIASES: dict[str, str] = { + "repository": "repositories", + "repo": "repositories", + "repositories": "repositories", + "softwaresourcecode": "repositories", + "person": "persons", + "persons": "persons", + "organization": "organizations", + "organizations": "organizations", + "org": "organizations", + "article": "articles", + "articles": "articles", + "publication": "articles", + "publications": "articles", + "scholarlyarticle": "articles", + "membership": "memberships", + "memberships": "memberships", + "contribution": "contributions", + "contributions": "contributions", +} +AGENT_BUCKET_HINTS: dict[str, str] = { + "repo_agent": "repositories", + "repository_agent": "repositories", + "person_agent": "persons", + "org_agent": "organizations", + "organization_agent": "organizations", + "article_agent": "articles", + "membership_agent": "memberships", + "contribution_agent": "contributions", +} + + +def generate_uuid() -> str: + """Generate a UUIDv4 string for agent payload identifiers.""" + return str(uuid.uuid4()) + + +def normalize_entity_bucket_key(value: str | None) -> str | None: + """Normalize entity class names and aliases to one of the six bucket keys.""" + if not isinstance(value, str): + return None + + normalized = value.strip().lower() + if not normalized: + return None + + normalized = normalized.split(":")[-1] + normalized = normalized.rsplit("/", maxsplit=1)[-1] + return ENTITY_BUCKET_ALIASES.get(normalized) + + +def _first_bucket_from_type_value(value: Any) -> str | None: + if isinstance(value, str): + return normalize_entity_bucket_key(value) + if isinstance(value, list): + for item in value: + bucket = normalize_entity_bucket_key(item if isinstance(item, str) else None) + if bucket is not None: + return bucket + return None + + +def infer_entity_bucket( + *, + agent_key: str | None = None, + data: dict[str, Any] | None = None, +) -> str | None: + """Infer the canonical bucket key for an agent payload.""" + if isinstance(data, dict): + for field_name in ("entity_bucket", "entity_type"): + bucket = normalize_entity_bucket_key(data.get(field_name)) + if bucket is not None: + return bucket + + for field_name in ("@type", "type"): + bucket = _first_bucket_from_type_value(data.get(field_name)) + if bucket is not None: + return bucket + + if isinstance(agent_key, str) and agent_key: + normalized_key = agent_key.split(":", maxsplit=1)[0].strip().lower() + hinted_bucket = AGENT_BUCKET_HINTS.get(normalized_key) + if hinted_bucket is not None: + return hinted_bucket + + if normalized_key.endswith("_agent"): + return normalize_entity_bucket_key(normalized_key[: -len("_agent")]) + + return None + + +@dataclass(slots=True) +class TypedEntityBuckets: + """Fixed six-bucket runtime collection for pipeline entity aggregation.""" + + repositories: list[dict[str, Any]] = field(default_factory=list) + persons: list[dict[str, Any]] = field(default_factory=list) + organizations: list[dict[str, Any]] = field(default_factory=list) + articles: list[dict[str, Any]] = field(default_factory=list) + memberships: list[dict[str, Any]] = field(default_factory=list) + contributions: list[dict[str, Any]] = field(default_factory=list) + + def _bucket(self, bucket_name: str) -> list[dict[str, Any]] | None: + normalized_name = normalize_entity_bucket_key(bucket_name) + if normalized_name is None: + return None + return getattr(self, normalized_name) + + def add(self, bucket_name: str, entity: dict[str, Any]) -> None: + bucket = self._bucket(bucket_name) + if bucket is None or not isinstance(entity, dict) or not entity: + return + + entity_id = entity.get("id") + if isinstance(entity_id, str) and any( + isinstance(existing.get("id"), str) and existing["id"] == entity_id + for existing in bucket + ): + return + + bucket.append(dict(entity)) + + def merge(self, other: TypedEntityBuckets) -> None: + for bucket_name in ENTITY_BUCKET_KEYS: + for entity in getattr(other, bucket_name): + self.add(bucket_name, entity) + + def has_entities(self) -> bool: + return any(getattr(self, bucket_name) for bucket_name in ENTITY_BUCKET_KEYS) + + def to_dict(self) -> dict[str, list[dict[str, Any]]]: + return { + bucket_name: [dict(entity) for entity in getattr(self, bucket_name)] + for bucket_name in ENTITY_BUCKET_KEYS + } + + +@dataclass(slots=True, frozen=True) +class ProviderSet: + """Dependency injection bundle for v2 agents.""" + + github: GitHubProvider + orcid: ORCIDProvider | None = None + infoscience: InfoscienceProvider | None = None + ror: RORProvider | None = None + infoscience_rag: Any | None = None + ethz_research_collection_rag: Any | None = None + huggingface_rag: Any | None = None + openalex_rag: Any | None = None + zenodo_rag: Any | None = None + orcid_rag: Any | None = None + ror_rag: Any | None = None + snsf_rag: Any | None = None + swissubase_rag: Any | None = None + renkulab_rag: Any | None = None + github_rag: Any | None = None + epfl_graph_rag: Any | None = None + federated_rag: Any | None = None + + +@dataclass(slots=True) +class AgentResult: + """Normalized result contract for v2 agent wrappers.""" + + data: dict[str, Any] + warnings: list[str] = field(default_factory=list) + raw_output: dict[str, Any] = field(default_factory=dict) + is_partial: bool = False + failure_reason: str | None = None + model: str | None = None + provider: str | None = None + tokens_prompt: int | None = None + tokens_completion: int | None = None + stats: dict[str, Any] = field(default_factory=dict) + + +def _warn_once(warnings: list[str], warning: str) -> None: + if warning not in warnings: + warnings.append(warning) + + +def _json_path(path_tokens: list[Any]) -> str: + if not path_tokens: + return "" + return ".".join(str(token) for token in path_tokens) + + +def _top_level_field(path_tokens: list[Any]) -> str | None: + if not path_tokens: + return None + top = path_tokens[0] + if isinstance(top, str): + return top + return None + + +@lru_cache(maxsize=8) +def load_agent_schema(schema_name: str) -> dict[str, Any]: + schema_path = ( + Path(__file__).resolve().parents[1] + / "schema" + / "json" + / "agent" + / f"{schema_name}.schema.json" + ) + with schema_path.open(encoding="utf-8") as handle: + payload = json.load(handle) + if not isinstance(payload, dict): + raise TypeError + return payload + + +def validate_permissive( + payload: dict[str, Any], + *, + schema_name: str, +) -> tuple[dict[str, Any], list[str]]: + """Validate against agent schema while soft-dropping invalid optional fields.""" + schema = load_agent_schema(schema_name) + validator = Draft7Validator(schema) + required_fields = { + field_name + for field_name in schema.get("required", []) + if isinstance(field_name, str) + } + + validated_payload = deepcopy(payload) + warnings: list[str] = [] + + # `_`-prefixed keys are internal pipeline metadata (per the convention + # documented in CLAUDE.md): stripped before strict validation, JSON-LD + # output, and RDF serialisation. They don't need to appear in the agent + # JSON Schema — pull them aside before running the schema validator so + # they don't trip `additionalProperties`, then restore them at the end. + internal_metadata = { + key: validated_payload.pop(key) + for key in list(validated_payload) + if isinstance(key, str) and key.startswith("_") + } + + for _ in range(MAX_PERMISSIVE_PASSES): + errors = sorted(validator.iter_errors(validated_payload), key=lambda err: list(err.path)) + if not errors: + validated_payload.update(internal_metadata) + return validated_payload, warnings + + changed = False + for error in errors: + error_path = list(error.path) + field_name = _top_level_field(error_path) + path_text = _json_path(error_path) + + if error.validator == "additionalProperties" and isinstance(error.instance, dict): + properties = schema.get("properties", {}) + valid_keys = ( + {key for key in properties if isinstance(key, str)} + if isinstance(properties, dict) + else set() + ) + unexpected = sorted( + key for key in error.instance if key not in valid_keys + ) + if unexpected: + for extra_field in unexpected: + validated_payload.pop(extra_field, None) + _warn_once( + warnings, + f"Removed unexpected fields: {', '.join(unexpected)}", + ) + changed = True + continue + + if ( + field_name + and field_name not in required_fields + and field_name in validated_payload + ): + validated_payload.pop(field_name, None) + _warn_once( + warnings, + f"Removed invalid optional field '{field_name}' at {path_text}: {error.message}", + ) + changed = True + continue + + _warn_once( + warnings, + f"Validation warning at {path_text}: {error.message}", + ) + + if not changed: + break + + for error in sorted(validator.iter_errors(validated_payload), key=lambda err: list(err.path)): + _warn_once( + warnings, + f"Validation warning at {_json_path(list(error.path))}: {error.message}", + ) + + # Re-attach internal metadata pulled aside before the schema check. + validated_payload.update(internal_metadata) + + return validated_payload, warnings diff --git a/src/v2/agents/registry.py b/src/v2/agents/registry.py new file mode 100644 index 0000000..11a7206 --- /dev/null +++ b/src/v2/agents/registry.py @@ -0,0 +1,88 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Awaitable, Callable + +from src.v2.agents.llm import ( + LLMArticleAgentV2, + LLMContributionAgentV2, + LLMMembershipAgentV2, + LLMOrganizationAgentV2, + LLMPersonAgentV2, + LLMRepositoryAgentV2, +) +from src.v2.agents.runtime import AgentRuntime + +if TYPE_CHECKING: + from src.v2.agents.contracts import RuntimeAgent + +AgentRunner = Callable[[dict[str, Any], Any], Any | Awaitable[Any]] + +STAGE_REPO_AGENT = "repo_agent" +STAGE_PERSON_AGENT = "person_agent" +STAGE_ORG_AGENT = "org_agent" +STAGE_ARTICLE_AGENT = "article_agent" +STAGE_MEMBERSHIP_AGENT = "membership_agent" +STAGE_CONTRIBUTION_AGENT = "contribution_agent" + + +class AgentRuntimeRegistry: + """Resolves stage runners based on stage key, runtime, and entity type.""" + + def __init__( # noqa: PLR0913 + self, + *, + rule_based_runners: dict[str, AgentRunner] | None = None, + llm_repository_agent: RuntimeAgent | None = None, + llm_person_agent: RuntimeAgent | None = None, + llm_organization_agent: RuntimeAgent | None = None, + llm_article_agent: RuntimeAgent | None = None, + llm_membership_agent: RuntimeAgent | None = None, + llm_contribution_agent: RuntimeAgent | None = None, + ) -> None: + self._rule_based_runners: dict[str, AgentRunner] = dict(rule_based_runners or {}) + self._llm_repository_agent = llm_repository_agent or LLMRepositoryAgentV2() + self._llm_person_agent = llm_person_agent or LLMPersonAgentV2() + self._llm_organization_agent = llm_organization_agent or LLMOrganizationAgentV2() + self._llm_article_agent = llm_article_agent or LLMArticleAgentV2() + self._llm_membership_agent = llm_membership_agent or LLMMembershipAgentV2() + self._llm_contribution_agent = llm_contribution_agent or LLMContributionAgentV2() + + def register_rule_runner(self, stage_key: str, runner: AgentRunner) -> None: + """Register or replace a rule-based runner for a pipeline stage.""" + + self._rule_based_runners[stage_key] = runner + + def resolve_runner( # noqa: PLR0911 + self, + *, + stage_key: str, + runtime: AgentRuntime, + detected_type: str, + ) -> AgentRunner: + """Resolve the callable for the requested runtime/stage combination.""" + + del detected_type + + if runtime == AgentRuntime.LLM and stage_key == STAGE_REPO_AGENT: + return self._llm_repository_agent.run + + if runtime == AgentRuntime.LLM and stage_key == STAGE_PERSON_AGENT: + return self._llm_person_agent.run + + if runtime == AgentRuntime.LLM and stage_key == STAGE_ORG_AGENT: + return self._llm_organization_agent.run + + if runtime == AgentRuntime.LLM and stage_key == STAGE_ARTICLE_AGENT: + return self._llm_article_agent.run + + if runtime == AgentRuntime.LLM and stage_key == STAGE_MEMBERSHIP_AGENT: + return self._llm_membership_agent.run + + if runtime == AgentRuntime.LLM and stage_key == STAGE_CONTRIBUTION_AGENT: + return self._llm_contribution_agent.run + + runner = self._rule_based_runners.get(stage_key) + if runner is None: + message = f"No runner registered for stage '{stage_key}'" + raise ValueError(message) + return runner diff --git a/src/v2/agents/retry.py b/src/v2/agents/retry.py new file mode 100644 index 0000000..4a36fdd --- /dev/null +++ b/src/v2/agents/retry.py @@ -0,0 +1,150 @@ +from __future__ import annotations + +import asyncio +from copy import deepcopy +from typing import Any, Awaitable, Callable, cast + +from src.v2.agents.models import AgentResult, ProviderSet + +VALIDATION_WARNING_PREFIX = "Validation warning at" + +SleepCallable = Callable[[float], Awaitable[None]] +InvalidResultChecker = Callable[[AgentResult], str | None] +AgentRunner = Callable[ + [dict[str, Any], ProviderSet | None], + AgentResult | dict[str, Any] | Awaitable[AgentResult | dict[str, Any]], +] + + +def _append_unique(warnings: list[str], warning: str) -> None: + if warning and warning not in warnings: + warnings.append(warning) + + +def _resolve_runner(agent: Any) -> AgentRunner: + if callable(agent): + return cast("AgentRunner", agent) + + run_method = getattr(agent, "run", None) + if callable(run_method): + return cast("AgentRunner", run_method) + + message = "Agent retry wrapper expects a callable or an object exposing .run()" + raise TypeError(message) + + +def _default_invalid_reason(result: AgentResult) -> str | None: + if result.is_partial: + return result.failure_reason or "Agent returned partial result" + + for warning in result.warnings: + if warning.startswith(VALIDATION_WARNING_PREFIX): + return "Agent output did not satisfy permissive validation" + + if not isinstance(result.data, dict) or not result.data: + return "Agent returned empty or invalid data payload" + + return None + + +def _normalize_result(candidate: AgentResult | dict[str, Any]) -> AgentResult: + if isinstance(candidate, AgentResult): + return candidate + if isinstance(candidate, dict): + return AgentResult(data=candidate) + message = "Agent runner must return AgentResult or dict payload" + raise TypeError(message) + + +async def _maybe_await(value: Any) -> Any: + if hasattr(value, "__await__"): + return await value + return value + + +async def with_retry( # noqa: C901, PLR0913 + agent: Any, + *, + context: dict[str, Any] | None = None, + providers: ProviderSet | None = None, + max_retries: int = 3, + backoff_base: float = 1.0, + sleep_func: SleepCallable | None = None, + invalid_result_checker: InvalidResultChecker | None = None, +) -> AgentResult: + """Execute an agent with retries and soft-failure semantics.""" + if max_retries < 0: + raise ValueError + if backoff_base < 0: + raise ValueError + + runner = _resolve_runner(agent) + sleep = sleep_func or asyncio.sleep + is_invalid = invalid_result_checker or _default_invalid_reason + + warnings: list[str] = [] + errors: list[str] = [] + backoff_schedule: list[float] = [] + attempt_count = max_retries + 1 + + last_result: AgentResult | None = None + failure_reason: str | None = None + + for attempt_index in range(attempt_count): + try: + attempt_context = deepcopy(context or {}) + candidate = await _maybe_await(runner(attempt_context, providers)) + result = _normalize_result(candidate) + last_result = result + + for warning in result.warnings: + _append_unique(warnings, warning) + + invalid_reason = is_invalid(result) + if invalid_reason is None: + result.stats = { + **result.stats, + "attempts": attempt_index + 1, + "retry_count": attempt_index, + "backoff_schedule": backoff_schedule, + } + return result + + failure_reason = invalid_reason + _append_unique(warnings, invalid_reason) + except Exception as exc: # noqa: BLE001 + failure_reason = str(exc) + errors.append(str(exc)) + _append_unique(warnings, f"Agent attempt {attempt_index + 1} failed: {exc}") + + if attempt_index == max_retries: + break + + delay_seconds = backoff_base * (2**attempt_index) + backoff_schedule.append(delay_seconds) + if delay_seconds > 0: + await sleep(delay_seconds) + + if last_result is not None: + partial_data = dict(last_result.data) + partial_raw_output = dict(last_result.raw_output) + else: + partial_data = {} + partial_raw_output = {} + + if errors: + _append_unique(warnings, f"Agent retry exhausted after {attempt_count} attempts") + + return AgentResult( + data=partial_data, + warnings=warnings, + raw_output=partial_raw_output, + is_partial=True, + failure_reason=failure_reason or "Agent execution failed", + stats={ + "attempts": attempt_count, + "retry_count": max_retries, + "backoff_schedule": backoff_schedule, + "errors": errors, + }, + ) diff --git a/src/v2/agents/rule_based/__init__.py b/src/v2/agents/rule_based/__init__.py new file mode 100644 index 0000000..e304c7e --- /dev/null +++ b/src/v2/agents/rule_based/__init__.py @@ -0,0 +1,18 @@ +from __future__ import annotations + +from src.v2.agents.rule_based.article_agent import ArticleAgentV2 +from src.v2.agents.rule_based.contribution_agent import ContributionAgentV2 +from src.v2.agents.rule_based.membership_agent import MembershipAgentV2 +from src.v2.agents.rule_based.organization_agent import OrganizationAgentV2 +from src.v2.agents.rule_based.person_agent import PersonAgentV2 +from src.v2.agents.rule_based.repository_agent import RepositoryAgentV2 + +__all__ = [ + "ArticleAgentV2", + "ContributionAgentV2", + "MembershipAgentV2", + "OrganizationAgentV2", + "PersonAgentV2", + "RepositoryAgentV2", +] + diff --git a/src/v2/agents/rule_based/article_agent.py b/src/v2/agents/rule_based/article_agent.py new file mode 100644 index 0000000..d8b75aa --- /dev/null +++ b/src/v2/agents/rule_based/article_agent.py @@ -0,0 +1,960 @@ +from __future__ import annotations + +import re +from copy import deepcopy +from dataclasses import dataclass +from typing import Any +from urllib.parse import urlparse + +from src.v2.agents.models import ( + AgentResult, + ProviderSet, + generate_uuid, + validate_permissive, +) +from src.v2.canonicalization.string_utils import normalize_string, strip_accents + +DEFAULT_QUERY_CAP = 8 +DOI_PREFIX = "doi" +INFOSCIENCE_PREFIX = "infoscience" +URL_PREFIX = "url" +TITLE_DATE_PREFIX = "title-date" +DEFAULT_MAX_UNRESOLVED_AUTHORS = 10 +IDENTITY_ORDER = { + DOI_PREFIX: 0, + INFOSCIENCE_PREFIX: 1, + URL_PREFIX: 2, + TITLE_DATE_PREFIX: 3, +} +YEAR_PATTERN = re.compile(r"^(?P\d{4})") +ISO_DATE_PATTERN = re.compile(r"^\d{4}-\d{2}-\d{2}$") +YEAR_ONLY_DATE_PATTERN = re.compile(r"^\d{4}$") +MIN_REPOSITORY_PATH_SEGMENTS = 2 + + +@dataclass(slots=True) +class _PublicationCandidate: + publication: dict[str, Any] + query: str + query_index: int + + +def _as_string(value: Any) -> str | None: + return value.strip() if isinstance(value, str) and value.strip() else None + + +def _as_string_list(value: Any) -> list[str]: + if not isinstance(value, list): + return [] + return [item.strip() for item in value if isinstance(item, str) and item.strip()] + + +def _normalize_token(value: Any) -> str | None: + candidate = _as_string(value) + return candidate.casefold() if candidate else None + + +def _lookup_token_variants(value: Any) -> list[str]: + candidate = _as_string(value) + if candidate is None: + return [] + + variants: list[str] = [] + seen: set[str] = set() + + def _add(token: str | None) -> None: + if token is None: + return + normalized = token.strip() + if not normalized or normalized in seen: + return + seen.add(normalized) + variants.append(normalized) + + def _add_normalized_forms(token: str) -> None: + lowered = token.casefold() + _add(lowered) + _add(strip_accents(lowered)) + _add(normalize_string(token)) + + _add_normalized_forms(candidate) + + if "," in candidate: + family, given = (segment.strip() for segment in candidate.split(",", maxsplit=1)) + if family and given: + _add_normalized_forms(f"{given} {family}") + + return variants + + +def _dedupe_preserve_order(values: list[str]) -> list[str]: + deduplicated: list[str] = [] + seen: set[str] = set() + for value in values: + token = _normalize_token(value) + if token is None or token in seen: + continue + deduplicated.append(value.strip()) + seen.add(token) + return deduplicated + + +def _append_unique(target: list[str], warning: str) -> None: + if warning and warning not in target: + target.append(warning) + + +def _is_pipeline_key(candidate: str, prefix: str) -> bool: + normalized = candidate.strip().lower() + return normalized == prefix or normalized.startswith(f"{prefix}:") + + +def _collect_pipeline_entities( + context: dict[str, Any], + *, + prefix: str, +) -> list[dict[str, Any]]: + pipeline_outputs = context.get("pipeline_outputs") + if not isinstance(pipeline_outputs, dict): + return [] + + entities: list[dict[str, Any]] = [] + for key, value in pipeline_outputs.items(): + if not isinstance(key, str) or not _is_pipeline_key(key, prefix): + continue + if isinstance(value, dict) and value: + entities.append(value) + return entities + + +def _collect_known_persons(context: dict[str, Any]) -> list[dict[str, Any]]: + entities: list[dict[str, Any]] = [] + for key in ("known_persons", "persons"): + value = context.get(key) + if isinstance(value, list): + entities.extend( + item for item in value if isinstance(item, dict) and item + ) + entities.extend(_collect_pipeline_entities(context, prefix="person_agent")) + return entities + + +def _collect_known_organizations(context: dict[str, Any]) -> list[dict[str, Any]]: + entities: list[dict[str, Any]] = [] + for key in ("known_organizations", "organizations"): + value = context.get(key) + if isinstance(value, list): + entities.extend( + item for item in value if isinstance(item, dict) and item + ) + entities.extend(_collect_pipeline_entities(context, prefix="org_agent")) + return entities + + +def _register_lookup_token(lookup: dict[str, str], token: Any, canonical_id: str) -> None: + for normalized in _lookup_token_variants(token): + lookup.setdefault(normalized, canonical_id) + + +def _resolve_lookup_token(lookup: dict[str, str], token: Any) -> str | None: + for normalized in _lookup_token_variants(token): + resolved = lookup.get(normalized) + if isinstance(resolved, str): + return resolved + return None + + +def _register_organization_handle_tokens( + lookup: dict[str, str], + token: Any, + canonical_id: str, +) -> None: + handle = _as_string(token) + if handle is None: + return + normalized = handle[1:] if handle.startswith("@") else handle + if not normalized: + return + _register_lookup_token(lookup, normalized, canonical_id) + _register_lookup_token(lookup, f"@{normalized}", canonical_id) + _register_lookup_token(lookup, handle, canonical_id) + + +def _orcid_record_name(record: dict[str, Any]) -> str | None: + name = _as_string(record.get("name")) + if name: + return name + + name_payload = record.get("name") + if not isinstance(name_payload, dict): + return None + + given_names_payload = name_payload.get("given-names") + family_name_payload = name_payload.get("family-name") + given_name = ( + _as_string(given_names_payload.get("value")) + if isinstance(given_names_payload, dict) + else None + ) + family_name = ( + _as_string(family_name_payload.get("value")) + if isinstance(family_name_payload, dict) + else None + ) + if given_name and family_name: + return f"{given_name} {family_name}" + return given_name or family_name + + +def _collect_person_alias_tokens( # noqa: C901 + person: dict[str, Any], + *, + derivation_by_id: dict[str, dict[str, Any]], +) -> list[str]: + aliases: list[str] = [] + + def _append(value: Any) -> None: + candidate = _as_string(value) + if candidate: + aliases.append(candidate) + + person_id = _as_string(person.get("id")) + + for key in ( + "schema:name", + "name", + "display_name", + "github_display_name", + "github_name", + "orcid_name", + "infoscience_name", + "pulse:githubUsername", + ): + _append(person.get(key)) + + identifiers = person.get("identifiers") + if isinstance(identifiers, dict): + _append(identifiers.get("pulse:githubUsername")) + + github_profile = person.get("github_profile") + if isinstance(github_profile, dict): + _append(github_profile.get("name")) + _append(github_profile.get("display_name")) + _append(github_profile.get("login")) + + github_payload = person.get("github") + if isinstance(github_payload, dict): + _append(github_payload.get("name")) + _append(github_payload.get("login")) + + orcid_record = person.get("orcid_record") + if isinstance(orcid_record, dict): + _append(_orcid_record_name(orcid_record)) + + infoscience_record = person.get("infoscience_record") + if isinstance(infoscience_record, dict): + _append(infoscience_record.get("name")) + _append(infoscience_record.get("displayName")) + + if person_id and person_id in derivation_by_id: + derivation = derivation_by_id[person_id] + for key in ( + "github_username", + "github_display_name", + "orcid_name", + "infoscience_name", + "person_name", + ): + _append(derivation.get(key)) + + return _dedupe_preserve_order(aliases) + + +def _build_person_lookup( + persons: list[dict[str, Any]], + *, + person_derivations: Any = None, +) -> tuple[dict[str, str], dict[str, str]]: + """Build the alias→person_id lookup plus an infoscience-authority→person_id map. + + The authority lookup is keyed on raw DSpace authority UUIDs (``pulse:infosciencePersonIdentifier``) + so article authors can be mapped directly without name fuzzy-matching when DSpace returned + an authority on the publication. + """ + lookup: dict[str, str] = {} + authority_lookup: dict[str, str] = {} + derivation_by_id: dict[str, dict[str, Any]] = {} + if isinstance(person_derivations, list): + for derivation in person_derivations: + if not isinstance(derivation, dict): + continue + person_id = _as_string(derivation.get("person_id")) + if person_id: + derivation_by_id[person_id] = derivation + + for person in persons: + person_id = _as_string(person.get("id")) + if person_id is None: + continue + _register_lookup_token(lookup, person_id, person_id) + + for alias_token in _collect_person_alias_tokens( + person, + derivation_by_id=derivation_by_id, + ): + _register_lookup_token(lookup, alias_token, person_id) + + infoscience_id = _as_string(person.get("pulse:infosciencePersonIdentifier")) + if infoscience_id is None: + identifiers = person.get("identifiers") + if isinstance(identifiers, dict): + infoscience_id = _as_string( + identifiers.get("pulse:infosciencePersonIdentifier"), + ) + if infoscience_id: + authority_lookup.setdefault(infoscience_id, person_id) + return lookup, authority_lookup + + +def _build_organization_lookup(organizations: list[dict[str, Any]]) -> dict[str, str]: # noqa: C901 + lookup: dict[str, str] = {} + for organization in organizations: + organization_id = _as_string(organization.get("id")) + if organization_id is None: + continue + _register_lookup_token(lookup, organization_id, organization_id) + _register_lookup_token(lookup, organization.get("schema:name"), organization_id) + _register_organization_handle_tokens( + lookup, + organization.get("pulse:githubOrganizationHandle"), + organization_id, + ) + _register_lookup_token(lookup, organization.get("schema:identifier"), organization_id) + for key in ("aliases", "acronyms"): + values = organization.get(key) + if isinstance(values, list): + for value in values: + _register_lookup_token(lookup, value, organization_id) + labels = organization.get("labels") + if isinstance(labels, list): + for label_payload in labels: + label = label_payload + if isinstance(label_payload, dict): + label = label_payload.get("label") + _register_lookup_token(lookup, label, organization_id) + + identifiers = organization.get("identifiers") + if isinstance(identifiers, dict): + _register_organization_handle_tokens( + lookup, + identifiers.get("pulse:githubOrganizationHandle"), + organization_id, + ) + _register_lookup_token( + lookup, + identifiers.get("pulse:ror"), + organization_id, + ) + return lookup + + +def _extract_root_terms(context: dict[str, Any]) -> list[str]: # noqa: C901 + detected_type = _normalize_token(context.get("detected_type")) + terms: list[str] = [] + + if detected_type == "repository": + repository_handle = _as_string( + context.get("full_name") + or context.get("repository_handle") + or context.get("github_repository_handle"), + ) + repository_context = context.get("repository_context") + if repository_handle is None and isinstance(repository_context, dict): + repository_handle = _as_string(repository_context.get("full_name")) + if repository_handle: + terms.append(repository_handle) + if "/" in repository_handle: + terms.append(repository_handle.rsplit("/", maxsplit=1)[-1]) + + if detected_type == "user": + username = _as_string(context.get("username") or context.get("github_username")) + if username: + terms.append(username) + + if detected_type == "organization": + org_name = _as_string( + context.get("org_name") + or context.get("organization") + or context.get("github_organization_handle"), + ) + if org_name: + terms.append(org_name) + + source_url = _as_string(context.get("source_url")) + if source_url: + parsed = urlparse(source_url if "://" in source_url else f"https://{source_url}") + path_segments = [segment for segment in parsed.path.split("/") if segment] + if len(path_segments) >= 1 and detected_type in {"user", "organization"}: + terms.append(path_segments[0]) + if len(path_segments) >= MIN_REPOSITORY_PATH_SEGMENTS: + terms.append(f"{path_segments[0]}/{path_segments[1]}") + terms.append(path_segments[1]) + + return _dedupe_preserve_order(terms) + + +def _extract_person_terms(persons: list[dict[str, Any]]) -> list[str]: + terms: list[str] = [] + for person in persons: + name = _as_string(person.get("schema:name")) + if name: + terms.append(name) + github_username = _as_string(person.get("pulse:githubUsername")) + if github_username: + terms.append(github_username) + return _dedupe_preserve_order(terms) + + +def _extract_organization_terms(organizations: list[dict[str, Any]]) -> list[str]: + terms: list[str] = [] + for organization in organizations: + name = _as_string(organization.get("schema:name")) + if name: + terms.append(name) + github_handle = _as_string(organization.get("pulse:githubOrganizationHandle")) + if github_handle: + terms.append(github_handle) + return _dedupe_preserve_order(terms) + + +def _publication_identity(publication: dict[str, Any]) -> tuple[str, str]: + doi = _normalize_token(publication.get("doi")) + if doi: + return DOI_PREFIX, doi + + infoscience_id = _normalize_token(publication.get("infosciencePublicationIdentifier")) + if infoscience_id: + return INFOSCIENCE_PREFIX, infoscience_id + + url = _normalize_token(publication.get("url")) + if url: + return URL_PREFIX, url + + title = _normalize_token(publication.get("title")) or "untitled" + date = _normalize_token(publication.get("publicationDate")) or "" + return TITLE_DATE_PREFIX, f"{title}|{date}" + + +def _publication_year(publication: dict[str, Any]) -> int: + publication_date = _as_string(publication.get("publicationDate")) + if publication_date is None: + return 0 + year_match = YEAR_PATTERN.match(publication_date) + if year_match is None: + return 0 + return int(year_match.group("year")) + + +def _candidate_rank_key(candidate: _PublicationCandidate) -> tuple[Any, ...]: + publication = candidate.publication + score = publication.get("score") + numeric_score = float(score) if isinstance(score, (int, float)) else 0.0 + has_doi = 1.0 if _as_string(publication.get("doi")) else 0.0 + has_infoscience_id = ( + 1.0 if _as_string(publication.get("infosciencePublicationIdentifier")) else 0.0 + ) + has_source_org = 1.0 if _as_string(publication.get("sourceOrganization")) else 0.0 + author_count = float(len(_as_string_list(publication.get("authors")))) + query_priority_bonus = max(0.0, 10.0 - float(candidate.query_index)) + total_score = ( + numeric_score + + (has_doi * 100.0) + + (has_infoscience_id * 40.0) + + (has_source_org * 15.0) + + author_count + + query_priority_bonus + ) + identity_prefix, identity_value = _publication_identity(publication) + title = _normalize_token(publication.get("title")) or "" + + return ( + -total_score, + -_publication_year(publication), + candidate.query_index, + IDENTITY_ORDER[identity_prefix], + identity_value, + title, + ) + + +def _rank_and_dedupe_publications( + candidates: list[_PublicationCandidate], +) -> list[_PublicationCandidate]: + sorted_candidates = sorted(candidates, key=_candidate_rank_key) + deduped: list[_PublicationCandidate] = [] + seen_identities: set[tuple[str, str]] = set() + for candidate in sorted_candidates: + identity = _publication_identity(candidate.publication) + if identity in seen_identities: + continue + deduped.append(candidate) + seen_identities.add(identity) + return deduped + + +def _map_author_ids( + publication: dict[str, Any], + *, + person_lookup: dict[str, str], + authority_lookup: dict[str, str], + publication_reference: str, +) -> tuple[list[str], list[str], list[str], int, int]: + warnings: list[str] = [] + unresolved_authors: list[str] = [] + mapped_authors: list[str] = [] + matched_authors = 0 + unresolved_count = 0 + author_names = _as_string_list(publication.get("authors")) + raw_authorities = publication.get("author_authorities") + authority_values: list[str | None] + if isinstance(raw_authorities, list) and len(raw_authorities) == len(author_names): + authority_values = [ + item if isinstance(item, str) and item.strip() else None + for item in raw_authorities + ] + else: + authority_values = [None] * len(author_names) + + for author_name, authority in zip(author_names, authority_values, strict=False): + resolved_author: str | None = None + if authority is not None: + resolved_author = authority_lookup.get(authority) + if resolved_author is None: + resolved_author = _resolve_lookup_token(person_lookup, author_name) + if isinstance(resolved_author, str): + mapped_authors.append(resolved_author) + matched_authors += 1 + continue + unresolved_authors.append(author_name) + unresolved_count += 1 + + if not mapped_authors: + raw_author_values = _as_string_list(publication.get("authors")) + author_preview = ( + ", ".join(f"'{author}'" for author in raw_author_values[:3]) + if raw_author_values + else "" + ) + _append_unique( + warnings, + ( + "Publication has no resolvable author identifiers: " + f"{publication_reference} (author_examples={author_preview})" + ), + ) + + return ( + _dedupe_preserve_order(mapped_authors), + _dedupe_preserve_order(unresolved_authors), + warnings, + matched_authors, + unresolved_count, + ) + + +def _map_source_organization( + publication: dict[str, Any], + *, + organization_lookup: dict[str, str], +) -> tuple[str | None, list[str]]: + source_organization = _as_string(publication.get("sourceOrganization")) + if source_organization is None: + return None, [] + + resolved_source = _resolve_lookup_token(organization_lookup, source_organization) + if isinstance(resolved_source, str): + return resolved_source, [] + + return source_organization, [ + f"Unresolved article source organization mapping: '{source_organization}'", + ] + + +def _normalize_publication_date( + publication_date: Any, +) -> tuple[str | None, str | None]: + normalized = _as_string(publication_date) + if normalized and ISO_DATE_PATTERN.fullmatch(normalized): + return normalized, None + if normalized and YEAR_ONLY_DATE_PATTERN.fullmatch(normalized): + normalized_year_date = f"{normalized}-01-01" + return ( + normalized_year_date, + ( + "Publication date provided as year-only; normalized to " + f"'{normalized_year_date}' for schema compatibility" + ), + ) + if normalized: + return None, f"Publication date '{normalized}' is invalid" + return None, "Publication date is missing" + + +def _publication_label(publication: dict[str, Any]) -> str: + for key in ("doi", "infosciencePublicationIdentifier", "title", "url"): + value = _as_string(publication.get(key)) + if value: + return value + return "" + + +def _publication_reference(publication: dict[str, Any]) -> str: + parts: list[str] = [] + doi = _as_string(publication.get("doi")) + infoscience_id = _as_string(publication.get("infosciencePublicationIdentifier")) + title = _as_string(publication.get("title")) + url = _as_string(publication.get("url")) + + if doi: + parts.append(f"doi={doi}") + if infoscience_id: + parts.append(f"infoscience={infoscience_id}") + if title: + parts.append(f"title='{title}'") + if url: + parts.append(f"url={url}") + + if parts: + return ", ".join(parts) + return "" + + +def _build_article_payload( + publication: dict[str, Any], + *, + author_ids: list[str], + source_organization: str | None, + publication_date: str, + article_uuid: str, +) -> dict[str, Any]: + doi = _as_string(publication.get("doi")) + infoscience_id = _as_string(publication.get("infosciencePublicationIdentifier")) + + identifier_value = doi or infoscience_id or _as_string(publication.get("url")) or article_uuid + if doi: + article_id = doi + id_source = "schema:identifier" + elif infoscience_id: + article_id = infoscience_id + id_source = "pulse:infoscienceArticleIdentifier" + else: + article_id = article_uuid + id_source = "uuid" + + return { + "id": article_id, + "type": "schema:ScholarlyArticle", + "shacl": "pulse:ArticleShape", + "identifiers": { + "schema:identifier": doi, + "pulse:infoscienceArticleIdentifier": infoscience_id, + "uuid": article_uuid, + }, + "idSource": id_source, + "schema:name": _as_string(publication.get("title")) or "Untitled article", + "schema:identifier": identifier_value, + "schema:datePublished": publication_date, + "schema:author": author_ids, + "pulse:infoscienceArticleIdentifier": infoscience_id, + "schema:sourceOrganization": source_organization, + } + + +class ArticleAgentV2: + """Article agent with deterministic query blending, ranking, and linkage mapping. + + The default query blend is **repo-name only** (the repository's GitHub + handle, slug, owner). This is narrow on purpose: when we widen the blend + to also search by every contributor's name and every org's name, the + Infoscience search returns those people's *entire* bibliography, and + the result is that publications unrelated to the repo get attributed to + it (real DOIs, false attribution). The original intent of the wider + blend was to recover citation papers, but in practice the over-attribution + rate dwarfs the recall benefit. + + Set `include_person_queries=True` and/or `include_organization_queries=True` + explicitly when running on a single-author / single-lab repo where the + over-attribution risk is low. Default to repo-only. + """ + + def __init__( + self, + *, + max_queries: int = DEFAULT_QUERY_CAP, + include_person_queries: bool = False, + include_organization_queries: bool = False, + ) -> None: + self._max_queries = max_queries + self._include_person_queries = include_person_queries + self._include_organization_queries = include_organization_queries + + def build_query_blend(self, context: dict[str, Any]) -> list[str]: + # Per-request override wins over the constructor default. Useful for + # CLI / test cases that want a wider blend without rebuilding the + # agent. + include_person = bool( + context.get("include_person_queries", self._include_person_queries), + ) + include_org = bool( + context.get( + "include_organization_queries", + self._include_organization_queries, + ), + ) + + query_terms = _extract_root_terms(context) + if include_person: + query_terms.extend( + _extract_person_terms(_collect_known_persons(context)), + ) + if include_org: + query_terms.extend( + _extract_organization_terms(_collect_known_organizations(context)), + ) + query_terms = _dedupe_preserve_order(query_terms) + if self._max_queries <= 0: + return [] + return query_terms[: self._max_queries] + + async def run( # noqa: C901, PLR0912, PLR0915 + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + warnings: list[str] = [] + queries = self.build_query_blend(context) + + max_unresolved_authors = context.get("max_unresolved_article_authors") + if not isinstance(max_unresolved_authors, int) or max_unresolved_authors < 1: + max_unresolved_authors = DEFAULT_MAX_UNRESOLVED_AUTHORS + + if providers.infoscience is None: + return AgentResult( + data={}, + warnings=["Infoscience provider not configured for article enrichment"], + raw_output={}, + stats={"queries": queries, "articles": []}, + ) + + if not queries: + return AgentResult( + data={}, + warnings=["No article queries could be derived from runtime context"], + raw_output={}, + stats={"queries": [], "articles": []}, + ) + + raw_candidates: list[_PublicationCandidate] = [] + for query_index, query in enumerate(queries): + try: + publications = providers.infoscience.search_publications(query) + except Exception as exc: # noqa: BLE001 + _append_unique(warnings, f"Infoscience publication search failed for '{query}': {exc}") + continue + + raw_candidates.extend( + _PublicationCandidate( + publication=dict(publication), + query=query, + query_index=query_index, + ) + for publication in publications + if isinstance(publication, dict) + ) + + if not raw_candidates: + _append_unique(warnings, "No publications returned for blended article queries") + return AgentResult( + data={}, + warnings=warnings, + raw_output={}, + stats={"queries": queries, "articles": []}, + ) + + person_lookup, authority_lookup = _build_person_lookup( + _collect_known_persons(context), + person_derivations=context.get("person_derivations"), + ) + organization_lookup = _build_organization_lookup(_collect_known_organizations(context)) + + ranked_candidates = _rank_and_dedupe_publications(raw_candidates) + validated_articles: list[dict[str, Any]] = [] + raw_articles: list[dict[str, Any]] = [] + unresolved_author_names: list[str] = [] + skipped_missing_resolvable_author_candidates: list[tuple[str, int, int]] = [] + reported_unresolvable_author_identifiers_warning = False + + for candidate in ranked_candidates: + publication_reference = _publication_reference(candidate.publication) + ( + author_ids, + unresolved_authors, + author_warnings, + matched_author_count, + unresolved_author_count, + ) = _map_author_ids( + candidate.publication, + person_lookup=person_lookup, + authority_lookup=authority_lookup, + publication_reference=publication_reference, + ) + for unresolved_author in unresolved_authors: + if unresolved_author not in unresolved_author_names: + unresolved_author_names.append(unresolved_author) + for warning in author_warnings: + if warning.startswith("Publication has no resolvable author identifiers:"): + if reported_unresolvable_author_identifiers_warning: + continue + reported_unresolvable_author_identifiers_warning = True + _append_unique(warnings, warning) + + if not author_ids: + skipped_missing_resolvable_author_candidates.append( + ( + publication_reference, + matched_author_count, + unresolved_author_count, + ), + ) + continue + + source_organization, org_warnings = _map_source_organization( + candidate.publication, + organization_lookup=organization_lookup, + ) + for warning in org_warnings: + _append_unique(warnings, warning) + + publication_date, date_warning = _normalize_publication_date( + candidate.publication.get("publicationDate"), + ) + if date_warning: + _append_unique( + warnings, + f"{_publication_label(candidate.publication)}: {date_warning}", + ) + if publication_date is None: + _append_unique( + warnings, + ( + "Skipped article candidate due to invalid publication date: " + f"{publication_reference}" + ), + ) + continue + + if not _as_string(candidate.publication.get("doi")): + _append_unique( + warnings, + ( + "Skipped article candidate due to missing DOI required by strict schema: " + f"{publication_reference}" + ), + ) + continue + + payload = _build_article_payload( + candidate.publication, + author_ids=author_ids, + source_organization=source_organization, + publication_date=publication_date, + article_uuid=generate_uuid(), + ) + raw_payload = deepcopy(payload) + validated_payload, validation_warnings = validate_permissive( + payload, + schema_name="article", + ) + for warning in validation_warnings: + _append_unique( + warnings, + f"{validated_payload.get('id', 'article')}: {warning}", + ) + validated_articles.append(validated_payload) + raw_articles.append(raw_payload) + + if unresolved_author_names: + preview = ", ".join(f"'{name}'" for name in unresolved_author_names[:max_unresolved_authors]) + remainder = len(unresolved_author_names) - max_unresolved_authors + remainder_suffix = f", +{remainder} more" if remainder > 0 else "" + _append_unique( + warnings, + ( + "Dropped unresolved article author references for " + f"{len(unresolved_author_names)} name(s). " + f"Examples: {preview}{remainder_suffix}" + ), + ) + + if skipped_missing_resolvable_author_candidates: + if len(skipped_missing_resolvable_author_candidates) == 1: + publication_label, matched_author_count, unresolved_author_count = ( + skipped_missing_resolvable_author_candidates[0] + ) + _append_unique( + warnings, + ( + "Skipped article candidate due to missing resolvable authors: " + f"{publication_label} " + f"(matched_authors={matched_author_count}, " + f"unmatched_authors={unresolved_author_count})" + ), + ) + else: + example_count = min(max_unresolved_authors, 5) + examples = ", ".join( + ( + f"'{label}' (matched_authors={matched_author_count}, " + f"unmatched_authors={unresolved_author_count})" + ) + for label, matched_author_count, unresolved_author_count in skipped_missing_resolvable_author_candidates[:example_count] + ) + remainder = len(skipped_missing_resolvable_author_candidates) - example_count + remainder_suffix = f", +{remainder} more" if remainder > 0 else "" + _append_unique( + warnings, + ( + "Skipped article candidates due to missing resolvable authors: " + f"count={len(skipped_missing_resolvable_author_candidates)}. " + f"Examples: {examples}{remainder_suffix}" + ), + ) + + primary_article = validated_articles[0] if validated_articles else {} + primary_raw_output = raw_articles[0] if raw_articles else {} + overrides = context.get("agent_overrides") + if isinstance(overrides, dict) and primary_article: + primary_article = {**primary_article, **overrides} + primary_raw_output = deepcopy(primary_article) + primary_article, override_warnings = validate_permissive( + primary_article, + schema_name="article", + ) + for warning in override_warnings: + _append_unique( + warnings, + f"{primary_article.get('id', 'article')}: {warning}", + ) + validated_articles[0] = primary_article + + return AgentResult( + data=primary_article, + warnings=warnings, + raw_output=primary_raw_output, + stats={ + "queries": list(queries), + "raw_candidate_count": len(raw_candidates), + "ranked_candidate_count": len(ranked_candidates), + "articles": deepcopy(validated_articles), + }, + ) diff --git a/src/v2/agents/rule_based/contribution_agent.py b/src/v2/agents/rule_based/contribution_agent.py new file mode 100644 index 0000000..15b05da --- /dev/null +++ b/src/v2/agents/rule_based/contribution_agent.py @@ -0,0 +1,401 @@ +from __future__ import annotations + +from copy import deepcopy +from typing import Any + +from src.v2.agents.models import ( + AgentResult, + ProviderSet, + generate_uuid, + validate_permissive, +) + + +def _as_string(value: Any) -> str | None: + return value.strip() if isinstance(value, str) and value.strip() else None + + +def _as_non_negative_int(value: Any) -> int | None: + if not isinstance(value, int): + return None + return value if value >= 0 else None + + +def _normalize_token(value: Any) -> str | None: + candidate = _as_string(value) + return candidate.casefold() if candidate else None + + +def _append_unique(target: list[str], warning: str) -> None: + if warning and warning not in target: + target.append(warning) + + +def _is_pipeline_key(candidate: str, prefix: str) -> bool: + normalized = candidate.strip().lower() + return normalized == prefix or normalized.startswith(f"{prefix}:") + + +def _collect_pipeline_entities( + context: dict[str, Any], + *, + prefix: str, +) -> list[dict[str, Any]]: + pipeline_outputs = context.get("pipeline_outputs") + if not isinstance(pipeline_outputs, dict): + return [] + + entities: list[dict[str, Any]] = [] + for key, value in pipeline_outputs.items(): + if not isinstance(key, str) or not _is_pipeline_key(key, prefix): + continue + if isinstance(value, dict) and value: + entities.append(value) + return entities + + +def _collect_known_persons(context: dict[str, Any]) -> list[dict[str, Any]]: + persons: list[dict[str, Any]] = [] + for key in ("known_persons", "persons"): + value = context.get(key) + if isinstance(value, list): + persons.extend(item for item in value if isinstance(item, dict) and item) + persons.extend(_collect_pipeline_entities(context, prefix="person_agent")) + return persons + + +def _collect_known_repositories(context: dict[str, Any]) -> list[dict[str, Any]]: + repositories: list[dict[str, Any]] = [] + for key in ("known_repositories", "repositories"): + value = context.get(key) + if isinstance(value, list): + repositories.extend(item for item in value if isinstance(item, dict) and item) + + repository_context = context.get("repository_context") + if isinstance(repository_context, dict): + repositories.append( + { + "id": _as_string(repository_context.get("full_name")), + "pulse:githubRepositoryHandle": _as_string(repository_context.get("full_name")), + "contributors": repository_context.get("contributors"), + }, + ) + + repositories.extend(_collect_pipeline_entities(context, prefix="repo_agent")) + return repositories + + +def _register_lookup_token(lookup: dict[str, str], token: Any, canonical_id: str) -> None: + normalized = _normalize_token(token) + if normalized is None: + return + lookup.setdefault(normalized, canonical_id) + + +def _build_organization_lookup(organizations: list[dict[str, Any]]) -> set[str]: + lookup: set[str] = set() + + def _register(token: Any) -> None: + normalized = _normalize_token(token) + if normalized is not None: + lookup.add(normalized) + + for organization in organizations: + _register(organization.get("id")) + _register(organization.get("schema:name")) + _register(organization.get("pulse:githubOrganizationHandle")) + identifiers = organization.get("identifiers") + if isinstance(identifiers, dict): + _register(identifiers.get("pulse:githubOrganizationHandle")) + return lookup + + +def _collect_known_organizations(context: dict[str, Any]) -> list[dict[str, Any]]: + organizations: list[dict[str, Any]] = [] + for key in ("known_organizations", "organizations"): + value = context.get(key) + if isinstance(value, list): + organizations.extend(item for item in value if isinstance(item, dict) and item) + organizations.extend(_collect_pipeline_entities(context, prefix="org_agent")) + return organizations + + +def _build_person_lookup(persons: list[dict[str, Any]]) -> dict[str, str]: + lookup: dict[str, str] = {} + for person in persons: + person_id = _as_string(person.get("id")) + if person_id is None: + continue + + _register_lookup_token(lookup, person_id, person_id) + _register_lookup_token(lookup, person.get("schema:name"), person_id) + _register_lookup_token(lookup, person.get("pulse:githubUsername"), person_id) + + identifiers = person.get("identifiers") + if isinstance(identifiers, dict): + _register_lookup_token( + lookup, + identifiers.get("pulse:githubUsername"), + person_id, + ) + return lookup + + +def _extract_contributor_signals(repository: dict[str, Any]) -> list[dict[str, Any]]: + signals: list[dict[str, Any]] = [] + contributors = repository.get("contributors") + if not isinstance(contributors, list): + return signals + + for contributor in contributors: + if isinstance(contributor, str): + login = _as_string(contributor) + if login: + signals.append( + { + "login": login, + "count": None, + "first_date": None, + "last_date": None, + }, + ) + continue + + if not isinstance(contributor, dict): + continue + + contributor_type = contributor.get("type") + if isinstance(contributor_type, str) and contributor_type.lower() == "organization": + continue + + login = _as_string(contributor.get("login") or contributor.get("username")) + if login is None: + continue + + signals.append( + { + "login": login, + "count": _as_non_negative_int( + contributor.get("contributions") + if contributor.get("contributions") is not None + else contributor.get("pulse:contributionCount"), + ), + "first_date": _as_string( + contributor.get("firstContributionDate") + or contributor.get("pulse:firstContributionDate") + or contributor.get("first_contribution_date"), + ), + "last_date": _as_string( + contributor.get("lastContributionDate") + or contributor.get("pulse:lastContributionDate") + or contributor.get("last_contribution_date"), + ), + }, + ) + return signals + + +def _earliest_date(current: str | None, candidate: str | None) -> str | None: + if candidate is None: + return current + if current is None: + return candidate + return min(current, candidate) + + +def _latest_date(current: str | None, candidate: str | None) -> str | None: + if candidate is None: + return current + if current is None: + return candidate + return max(current, candidate) + + +def _build_contribution_payload( # noqa: PLR0913 + *, + composite_id: str, + person_id: str, + repository_id: str, + contribution_count: int, + first_contribution_date: str | None, + last_contribution_date: str | None, +) -> dict[str, Any]: + return { + "id": composite_id, + "type": "pulse:Contribution", + "shacl": "pulse:ContributionShape", + "identifiers": { + "pulse:composite": composite_id, + "uuid": generate_uuid(), + }, + "idSource": "pulse:composite", + "pulse:contributionTo": repository_id, + "pulse:contributionCount": contribution_count, + "pulse:firstContributionDate": first_contribution_date, + "pulse:lastContributionDate": last_contribution_date, + "schema:author": person_id, + } + + +def _person_github_login(person: Any) -> str | None: + """Best-effort GitHub login for a person entity. + + Mirrors the LLM contribution agent's helper: prefers the explicit + `pulse:githubUsername`, falls back to parsing `id` when shaped like + `https://github.com/{login}`. + """ + if not isinstance(person, dict): + return None + handle = person.get("pulse:githubUsername") + if isinstance(handle, str) and handle.strip(): + return handle.strip() + identifier = person.get("id") + if isinstance(identifier, str) and identifier.startswith("https://github.com/"): + candidate = identifier.removeprefix("https://github.com/").strip("/") + if candidate and "/" not in candidate: + return candidate + return None + + +class ContributionAgentV2: + """Deterministic contribution agent from repository contributor metadata.""" + + async def run( # noqa: C901 + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + + warnings: list[str] = [] + person_lookup = _build_person_lookup(_collect_known_persons(context)) + organization_lookup = _build_organization_lookup(_collect_known_organizations(context)) + repositories = _collect_known_repositories(context) + + # The orchestrator fans out one work item per (person, repo) pair and + # passes both `target_person` and `target_repository`. Honour that + # scoping: only emit a contribution for the target person, never warn + # about other contributors that happen to share the repo's signal list + # (they have their own fanout work item). + target_person = context.get("target_person") + target_login = _person_github_login(target_person) + normalized_target_login = ( + target_login.casefold() if isinstance(target_login, str) else None + ) + + contribution_data_by_composite: dict[str, dict[str, Any]] = {} + for repository in repositories: + repository_id = _as_string( + repository.get("id") + or repository.get("pulse:githubRepositoryHandle") + or repository.get("full_name"), + ) + if repository_id is None: + continue + + for signal in _extract_contributor_signals(repository): + contributor_login = signal.get("login") + normalized_login = _normalize_token(contributor_login) + if normalized_login is None: + continue + + if ( + normalized_target_login is not None + and normalized_login != normalized_target_login + ): + continue + + person_id = person_lookup.get(normalized_login) + if person_id is None: + if normalized_login in organization_lookup: + continue + _append_unique( + warnings, + ( + "Unresolved contribution person mapping: " + f"repository={repository_id}, contributor={contributor_login}" + ), + ) + continue + + composite_id = f"{person_id}_{repository_id}" + existing = contribution_data_by_composite.setdefault( + composite_id, + { + "person_id": person_id, + "repository_id": repository_id, + "count": 0, + "first_date": None, + "last_date": None, + }, + ) + if isinstance(signal.get("count"), int): + existing["count"] = max(int(existing["count"]), int(signal["count"])) + existing["first_date"] = _earliest_date( + _as_string(existing.get("first_date")), + _as_string(signal.get("first_date")), + ) + existing["last_date"] = _latest_date( + _as_string(existing.get("last_date")), + _as_string(signal.get("last_date")), + ) + + if not contribution_data_by_composite: + return AgentResult( + data={}, + warnings=warnings, + raw_output={}, + stats={"contributions": []}, + ) + + validated_contributions: list[dict[str, Any]] = [] + raw_contributions: list[dict[str, Any]] = [] + for composite_id in sorted(contribution_data_by_composite): + contribution_data = contribution_data_by_composite[composite_id] + payload = _build_contribution_payload( + composite_id=composite_id, + person_id=str(contribution_data["person_id"]), + repository_id=str(contribution_data["repository_id"]), + contribution_count=int(contribution_data["count"]), + first_contribution_date=_as_string(contribution_data.get("first_date")), + last_contribution_date=_as_string(contribution_data.get("last_date")), + ) + raw_payload = deepcopy(payload) + validated_payload, validation_warnings = validate_permissive( + payload, + schema_name="contribution", + ) + for warning in validation_warnings: + _append_unique( + warnings, + f"{validated_payload.get('id', 'contribution')}: {warning}", + ) + validated_contributions.append(validated_payload) + raw_contributions.append(raw_payload) + + primary_contribution = validated_contributions[0] + primary_raw_output = raw_contributions[0] + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + primary_contribution = {**primary_contribution, **overrides} + primary_raw_output = deepcopy(primary_contribution) + primary_contribution, override_warnings = validate_permissive( + primary_contribution, + schema_name="contribution", + ) + for warning in override_warnings: + _append_unique( + warnings, + f"{primary_contribution.get('id', 'contribution')}: {warning}", + ) + validated_contributions[0] = primary_contribution + + return AgentResult( + data=primary_contribution, + warnings=warnings, + raw_output=primary_raw_output, + stats={ + "contributions": deepcopy(validated_contributions), + "contribution_count": len(validated_contributions), + }, + ) diff --git a/src/v2/agents/rule_based/membership_agent.py b/src/v2/agents/rule_based/membership_agent.py new file mode 100644 index 0000000..3b07d30 --- /dev/null +++ b/src/v2/agents/rule_based/membership_agent.py @@ -0,0 +1,502 @@ +from __future__ import annotations + +import re +from copy import deepcopy +from datetime import date +from typing import Any + +from src.v2.agents.models import ( + AgentResult, + ProviderSet, + generate_uuid, + validate_permissive, +) +from src.v2.canonicalization.string_utils import normalize_string, strip_accents + +LOOKUP_SPLIT_PATTERN = re.compile(r"\s*[-–—|/&]\s*|;|,") +PARENTHETICAL_PATTERN = re.compile(r"\s*\([^)]*\)") +NON_ALNUM_SPLIT_PATTERN = re.compile(r"[^a-z0-9]+") +COMMON_ACRONYM_STOPWORDS = { + "a", + "an", + "and", + "de", + "des", + "di", + "du", + "et", + "for", + "in", + "la", + "le", + "of", + "on", + "the", + "und", + "university", +} + + +def _as_string(value: Any) -> str | None: + return value.strip() if isinstance(value, str) and value.strip() else None + + +def _normalize_token(value: Any) -> str | None: + candidate = _as_string(value) + return candidate.casefold() if candidate else None + + +def _lookup_token_variants(token: Any) -> list[str]: + candidate = _as_string(token) + if candidate is None: + return [] + + variants: list[str] = [] + seen: set[str] = set() + + def _add(value: str | None) -> None: + if value is None: + return + normalized = value.strip() + if not normalized or normalized in seen: + return + seen.add(normalized) + variants.append(normalized) + + def _add_normalized_forms(value: str) -> None: + lowered = value.casefold() + _add(lowered) + _add(strip_accents(lowered)) + collapsed = normalize_string(value) + _add(collapsed) + _add(collapsed.replace(" ", "")) + tokens = [token for token in collapsed.split() if token] + if "university" in tokens and tokens[0] != "university": + _add(f"{tokens[0]} university") + if "institute" in tokens and tokens[0] != "institute": + _add(f"{tokens[0]} institute") + + raw_candidates: list[str] = [] + raw_seen: set[str] = set() + + def _add_raw(value: str | None) -> None: + normalized = _as_string(value) + if normalized is None or normalized in raw_seen: + return + raw_seen.add(normalized) + raw_candidates.append(normalized) + + _add_raw(candidate) + if candidate.startswith("@"): + _add_raw(candidate[1:]) + + for raw_value in list(raw_candidates): + _add_raw(PARENTHETICAL_PATTERN.sub("", raw_value)) + + for raw_value in list(raw_candidates): + for segment in LOOKUP_SPLIT_PATTERN.split(raw_value): + if len(segment.strip()) >= 3: + _add_raw(segment) + + for raw_value in raw_candidates: + _add_normalized_forms(raw_value) + + return variants + + +def _organization_acronym_tokens(value: Any) -> list[str]: + candidate = _as_string(value) + if candidate is None: + return [] + + normalized = normalize_string(candidate) + parts = [ + part + for part in NON_ALNUM_SPLIT_PATTERN.split(normalized) + if part + ] + if not parts: + return [] + + acronym_chars = [part[0] for part in parts if part not in COMMON_ACRONYM_STOPWORDS] + if len(acronym_chars) < 2: + acronym_chars = [part[0] for part in parts] + acronym = "".join(acronym_chars) + if len(acronym) < 2: + return [] + return [acronym] + + +def _organization_partial_name_tokens(value: Any) -> list[str]: + candidate = _as_string(value) + if candidate is None: + return [] + + tokens = [token for token in normalize_string(candidate).split() if token] + if len(tokens) < 2: + return [] + + partials: list[str] = [] + if "university" in tokens and tokens[0] != "university": + partials.append(f"{tokens[0]} university") + if "institute" in tokens and tokens[0] != "institute": + partials.append(f"{tokens[0]} institute") + return partials + + +def _resolve_lookup_token(lookup: dict[str, str], token: Any) -> str | None: + for variant in _lookup_token_variants(token): + resolved = lookup.get(variant) + if isinstance(resolved, str): + return resolved + return None + + +def _append_unique(target: list[str], warning: str) -> None: + if warning and warning not in target: + target.append(warning) + + +def _is_pipeline_key(candidate: str, prefix: str) -> bool: + normalized = candidate.strip().lower() + return normalized == prefix or normalized.startswith(f"{prefix}:") + + +def _collect_pipeline_entities( + context: dict[str, Any], + *, + prefix: str, +) -> list[dict[str, Any]]: + pipeline_outputs = context.get("pipeline_outputs") + if not isinstance(pipeline_outputs, dict): + return [] + + entities: list[dict[str, Any]] = [] + for key, value in pipeline_outputs.items(): + if not isinstance(key, str) or not _is_pipeline_key(key, prefix): + continue + if isinstance(value, dict) and value: + entities.append(value) + return entities + + +def _collect_known_persons(context: dict[str, Any]) -> list[dict[str, Any]]: + persons: list[dict[str, Any]] = [] + for key in ("known_persons", "persons"): + value = context.get(key) + if isinstance(value, list): + persons.extend(item for item in value if isinstance(item, dict) and item) + persons.extend(_collect_pipeline_entities(context, prefix="person_agent")) + return persons + + +def _collect_known_organizations(context: dict[str, Any]) -> list[dict[str, Any]]: + organizations: list[dict[str, Any]] = [] + for key in ("known_organizations", "organizations"): + value = context.get(key) + if isinstance(value, list): + organizations.extend(item for item in value if isinstance(item, dict) and item) + organizations.extend(_collect_pipeline_entities(context, prefix="org_agent")) + return organizations + + +def _register_lookup_token(lookup: dict[str, str], token: Any, canonical_id: str) -> None: + for normalized in _lookup_token_variants(token): + lookup.setdefault(normalized, canonical_id) + + +def _register_organization_name_lookup_tokens( + lookup: dict[str, str], + token: Any, + canonical_id: str, +) -> None: + name = _as_string(token) + if name is None: + return + + _register_lookup_token(lookup, name, canonical_id) + _register_lookup_token(lookup, name.replace("centre", "center"), canonical_id) + _register_lookup_token(lookup, name.replace("center", "centre"), canonical_id) + for acronym in _organization_acronym_tokens(name): + _register_lookup_token(lookup, acronym, canonical_id) + for partial_name in _organization_partial_name_tokens(name): + _register_lookup_token(lookup, partial_name, canonical_id) + + +def _register_organization_handle_lookup_tokens( + lookup: dict[str, str], + token: Any, + canonical_id: str, +) -> None: + handle = _as_string(token) + if handle is None: + return + normalized_handle = handle[1:] if handle.startswith("@") else handle + if not normalized_handle: + return + _register_lookup_token(lookup, normalized_handle, canonical_id) + _register_lookup_token(lookup, f"@{normalized_handle}", canonical_id) + _register_lookup_token(lookup, handle, canonical_id) + + +def _build_organization_lookup(organizations: list[dict[str, Any]]) -> dict[str, str]: # noqa: C901 + lookup: dict[str, str] = {} + for organization in organizations: + org_id = _as_string(organization.get("id")) + if org_id is None: + continue + _register_lookup_token(lookup, org_id, org_id) + _register_organization_name_lookup_tokens( + lookup, + organization.get("schema:name"), + org_id, + ) + _register_organization_handle_lookup_tokens( + lookup, + organization.get("pulse:githubOrganizationHandle"), + org_id, + ) + _register_lookup_token(lookup, organization.get("schema:identifier"), org_id) + # Read both the public (legacy) and internal (`_`-prefixed) variants + # of these fields. Org agents emit them under the underscore variant + # so they get stripped before strict validation and JSON-LD output. + for key in ("aliases", "_aliases", "acronyms", "_acronyms"): + values = organization.get(key) + if isinstance(values, list): + for value in values: + _register_organization_name_lookup_tokens(lookup, value, org_id) + for label_key in ("labels", "_labels"): + labels = organization.get(label_key) + if isinstance(labels, list): + for label_payload in labels: + label = label_payload + if isinstance(label_payload, dict): + label = label_payload.get("label") + _register_organization_name_lookup_tokens(lookup, label, org_id) + + identifiers = organization.get("identifiers") + if isinstance(identifiers, dict): + _register_organization_handle_lookup_tokens( + lookup, + identifiers.get("pulse:githubOrganizationHandle"), + org_id, + ) + _register_lookup_token(lookup, identifiers.get("pulse:ror"), org_id) + return lookup + + +def _extract_affiliation_signals( # noqa: C901 + person: dict[str, Any], +) -> list[dict[str, str | None]]: + signals: list[dict[str, str | None]] = [] + + def _append_signal(entry: Any) -> None: + if isinstance(entry, str): + organization_name = _as_string(entry) + if organization_name: + signals.append( + { + "organization": organization_name, + "role": None, + "start_date": None, + "end_date": None, + }, + ) + return + + if not isinstance(entry, dict): + return + + organization_name = _as_string( + entry.get("organization") + or entry.get("organizationId") + or entry.get("name") + or entry.get("schema:name") + or entry.get("org:organization"), + ) + if organization_name is None: + return + + signals.append( + { + "organization": organization_name, + "role": _as_string(entry.get("role") or entry.get("org:role")), + "start_date": _as_string( + entry.get("start_date") or entry.get("time:hasBeginning"), + ), + "end_date": _as_string( + entry.get("end_date") or entry.get("time:hasEnd"), + ), + }, + ) + + for key in ("affiliations", "orcid_affiliations"): + value = person.get(key) + if isinstance(value, list): + for entry in value: + _append_signal(entry) + + orcid_record = person.get("orcid_record") + if isinstance(orcid_record, dict): + for key in ("employment", "education"): + value = orcid_record.get(key) + if isinstance(value, list): + for entry in value: + _append_signal(entry) + + return signals + + +def _build_membership_payload( + *, + composite_id: str, + organization_id: str, + role: str | None, + start_date: str | None, + end_date: str | None, +) -> dict[str, Any]: + # Defend against malformed and inverted dates from upstream sources: + # ORCID occasionally returns invalid dates like "2000-09-31" (Sept has + # 30 days) that fail SHACL `xsd:date`, plus some records have start/end + # reversed. Drop unparseable values; swap inverted ones. + def _safe_date(value: object) -> str | None: + if not isinstance(value, str): + return None + try: + return date.fromisoformat(value[:10]).isoformat() + except ValueError: + return None + + start_date = _safe_date(start_date) + end_date = _safe_date(end_date) + if start_date and end_date and start_date > end_date: + start_date, end_date = end_date, start_date + return { + "id": composite_id, + "type": "org:Membership", + "shacl": "pulse:MembershipShape", + "identifiers": { + "pulse:composite": composite_id, + "uuid": generate_uuid(), + }, + "idSource": "pulse:composite", + "org:organization": organization_id, + "org:role": role, + "time:hasBeginning": start_date, + "time:hasEnd": end_date, + } + + +class MembershipAgentV2: + """Deterministic membership agent from person affiliation signals.""" + + async def run( # noqa: C901, PLR0912 + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + + warnings: list[str] = [] + persons = _collect_known_persons(context) + organization_lookup = _build_organization_lookup(_collect_known_organizations(context)) + + membership_data_by_composite: dict[str, dict[str, str | None]] = {} + for person in persons: + person_id = _as_string(person.get("id")) + if person_id is None: + continue + + for signal in _extract_affiliation_signals(person): + organization_name = signal.get("organization") + normalized_organization = _normalize_token(organization_name) + if normalized_organization is None: + continue + + organization_id = _resolve_lookup_token(organization_lookup, organization_name) + if organization_id is None: + _append_unique( + warnings, + ( + "Unresolved membership organization mapping: " + f"person={person_id}, organization={organization_name}" + ), + ) + continue + + composite_id = f"{person_id}_{organization_id}" + existing = membership_data_by_composite.setdefault( + composite_id, + { + "organization_id": organization_id, + "role": None, + "start_date": None, + "end_date": None, + }, + ) + if existing["role"] is None and _as_string(signal.get("role")): + existing["role"] = _as_string(signal.get("role")) + if existing["start_date"] is None and _as_string(signal.get("start_date")): + existing["start_date"] = _as_string(signal.get("start_date")) + if existing["end_date"] is None and _as_string(signal.get("end_date")): + existing["end_date"] = _as_string(signal.get("end_date")) + + if not membership_data_by_composite: + return AgentResult( + data={}, + warnings=warnings, + raw_output={}, + stats={"memberships": []}, + ) + + validated_memberships: list[dict[str, Any]] = [] + raw_memberships: list[dict[str, Any]] = [] + + for composite_id in sorted(membership_data_by_composite): + membership_data = membership_data_by_composite[composite_id] + payload = _build_membership_payload( + composite_id=composite_id, + organization_id=str(membership_data["organization_id"]), + role=_as_string(membership_data["role"]), + start_date=_as_string(membership_data["start_date"]), + end_date=_as_string(membership_data["end_date"]), + ) + raw_payload = deepcopy(payload) + validated_payload, validation_warnings = validate_permissive( + payload, + schema_name="membership", + ) + for warning in validation_warnings: + _append_unique( + warnings, + f"{validated_payload.get('id', 'membership')}: {warning}", + ) + validated_memberships.append(validated_payload) + raw_memberships.append(raw_payload) + + primary_membership = validated_memberships[0] + primary_raw_output = raw_memberships[0] + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + primary_membership = {**primary_membership, **overrides} + primary_raw_output = deepcopy(primary_membership) + primary_membership, override_warnings = validate_permissive( + primary_membership, + schema_name="membership", + ) + for warning in override_warnings: + _append_unique( + warnings, + f"{primary_membership.get('id', 'membership')}: {warning}", + ) + validated_memberships[0] = primary_membership + + return AgentResult( + data=primary_membership, + warnings=warnings, + raw_output=primary_raw_output, + stats={ + "memberships": deepcopy(validated_memberships), + "membership_count": len(validated_memberships), + }, + ) diff --git a/src/v2/agents/rule_based/organization_agent.py b/src/v2/agents/rule_based/organization_agent.py new file mode 100644 index 0000000..d201c62 --- /dev/null +++ b/src/v2/agents/rule_based/organization_agent.py @@ -0,0 +1,271 @@ +from __future__ import annotations + +from copy import deepcopy +from typing import Any + +from src.v2.agents.models import ( + AgentResult, + ProviderSet, + generate_uuid, + validate_permissive, +) +from src.v2.ingest.providers.base import ProviderNotFoundError + + +def _resolve_org_name(context: dict[str, Any]) -> str: + for key in ("org_name", "organization", "github_organization_handle"): + value = context.get(key) + if isinstance(value, str) and value: + return value + message = "Organization context is missing a GitHub organization handle" + raise ValueError(message) + + +def _pick_best_orgunit_match(results: Any) -> dict[str, Any] | None: + if not isinstance(results, list): + return None + candidates = [item for item in results if isinstance(item, dict)] + if not candidates: + return None + return candidates[0] + + +def _classify_organization_type(ror_types: list[str]) -> str: + normalized = [value.lower() for value in ror_types] + if any("education" in value for value in normalized): + return "pulse:University" + if any("government" in value for value in normalized): + return "pulse:GovernmentAgency" + if any("nonprofit" in value or "non-profit" in value for value in normalized): + return "pulse:NonProfitOrganization" + if any("company" in value for value in normalized): + return "pulse:PrivateCompany" + if any("research" in value for value in normalized): + return "pulse:ResearchInstitution" + return "pulse:OtherOrganizationType" + + +class OrganizationAgentV2: + """Organization agent wrapper with permissive output validation.""" + + async def run( # noqa: C901, PLR0912, PLR0915 + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + warnings: list[str] = [] + org_name = _resolve_org_name(context) + github_lookup_enabled = context.get("github_lookup_enabled") + if not isinstance(github_lookup_enabled, bool): + github_lookup_enabled = True + + github_org: dict[str, Any] = {} + if github_lookup_enabled: + github_org = providers.github.get_organization(org_name) + + ror_record: dict[str, Any] | None = None + if providers.ror: + ror_id_hint = context.get("ror_id") + try: + if isinstance(ror_id_hint, str) and ror_id_hint: + ror_record = providers.ror.get_organization(ror_id_hint) + else: + ror_query = ( + context.get("ror_query") + or github_org.get("name") + or github_org.get("login") + or org_name + ) + ror_matches = providers.ror.search_organizations(str(ror_query)) + if ror_matches: + ror_record = ror_matches[0] + except ProviderNotFoundError as exc: + warnings.append(f"ROR lookup failed: {exc}") + else: + warnings.append("ROR provider not configured for organization enrichment") + + infoscience_match: dict[str, Any] | None = None + if providers.infoscience: + infoscience_query = context.get("infoscience_query") or github_org.get("name") or org_name + infoscience_results = providers.infoscience.search_orgunit(str(infoscience_query)) + infoscience_match = _pick_best_orgunit_match(infoscience_results) + else: + warnings.append("Infoscience provider not configured for organization enrichment") + + ror_id = ror_record.get("id") if isinstance(ror_record, dict) else None + infoscience_id = ( + infoscience_match.get("infoscienceOrgUnitIdentifier") + if isinstance(infoscience_match, dict) + else None + ) + github_handle: str | None = None + github_login = github_org.get("login") + if isinstance(github_login, str) and github_login: + github_handle = github_login + elif github_lookup_enabled: + github_handle = org_name + uuid_value = context.get("uuid") + if not isinstance(uuid_value, str) or not uuid_value.strip(): + uuid_value = generate_uuid() + + identifier_hierarchy: list[tuple[str, str | None]] = [ + ("pulse:ror", ror_id if isinstance(ror_id, str) else None), + ( + "pulse:infoscienceOrganizationIdentifier", + infoscience_id if isinstance(infoscience_id, str) else None, + ), + ("pulse:githubOrganizationHandle", github_handle), + ("uuid", uuid_value), + ] + id_source, resolved_id = next( + (identifier, value) + for identifier, value in identifier_hierarchy + if value + ) + + ror_types = [] + if isinstance(ror_record, dict) and isinstance(ror_record.get("types"), list): + ror_types = [item for item in ror_record["types"] if isinstance(item, str)] + + organization_type = _classify_organization_type(ror_types) + resolved_name = ( + (ror_record or {}).get("name") + or (infoscience_match or {}).get("name") + or github_org.get("name") + or org_name + ) + parent_org = None + has_units: list[str] = [] + if isinstance(ror_record, dict): + relationships = ror_record.get("relationships") + if isinstance(relationships, dict): + parent_payload = relationships.get("parent") + if isinstance(parent_payload, dict): + parent_id = parent_payload.get("id") + if isinstance(parent_id, str): + parent_org = parent_id + children_payload = relationships.get("children") + if isinstance(children_payload, list): + for child in children_payload: + if not isinstance(child, dict): + continue + child_id = child.get("id") + if isinstance(child_id, str): + has_units.append(child_id) + + if not parent_org and isinstance(infoscience_match, dict): + parent_candidate = infoscience_match.get("parentOrganization") + if isinstance(parent_candidate, str) and parent_candidate: + parent_org = parent_candidate + + repositories = context.get("repositories") + source_repositories = context.get("source_repositories") + owns: list[str] = [] + if isinstance(source_repositories, list): + owns = [value for value in source_repositories if isinstance(value, str) and value] + elif isinstance(repositories, list): + owns = [value for value in repositories if isinstance(value, str) and value] + elif github_lookup_enabled and isinstance(github_org.get("repositories"), list) and github_handle: + owns = [ + f"{github_handle}/{repo_name}" + for repo_name in github_org["repositories"] + if isinstance(repo_name, str) and repo_name + ] + + # Build the alias set from the ROR record + the original search + # query. The lookup phase in `membership_agent` indexes orgs by + # `aliases` so any text we feed here becomes a valid resolution key. + # Without this, an ORCID-derived employment at "Aalto-yliopisto" + # resolves to ROR "Aalto University" but the membership agent can + # only see the canonical name and emits "Unresolved membership + # organization mapping" — losing the affiliation. + ror_aliases: list[str] = [] + ror_acronyms: list[str] = [] + ror_labels: list[Any] = [] + if isinstance(ror_record, dict): + raw_aliases = ror_record.get("aliases") + if isinstance(raw_aliases, list): + ror_aliases = [v for v in raw_aliases if isinstance(v, str) and v] + raw_acronyms = ror_record.get("acronyms") + if isinstance(raw_acronyms, list): + ror_acronyms = [v for v in raw_acronyms if isinstance(v, str) and v] + raw_labels = ror_record.get("labels") + if isinstance(raw_labels, list): + ror_labels = [v for v in raw_labels if v] + + # Always add the original search-string as an alias when the + # canonical name differs — preserves the upstream label that was + # used to find this org. + merged_aliases: list[str] = list(ror_aliases) + if isinstance(org_name, str) and org_name and org_name != resolved_name: + if org_name not in merged_aliases: + merged_aliases.append(org_name) + + payload = { + "id": resolved_id, + "type": "org:Organization", + "shacl": "pulse:OrganizationShape", + "identifiers": { + "pulse:ror": ror_id if isinstance(ror_id, str) else None, + "pulse:infoscienceOrganizationIdentifier": ( + infoscience_id if isinstance(infoscience_id, str) else None + ), + "pulse:githubOrganizationHandle": github_handle, + "uuid": uuid_value, + }, + "idSource": id_source, + "schema:name": ( + resolved_name + ), + "schema:identifier": ror_id if isinstance(ror_id, str) else None, + "pulse:githubOrganizationHandle": github_handle, + "pulse:infoscienceOrganizationIdentifier": ( + infoscience_id if isinstance(infoscience_id, str) else None + ), + "pulse:OrganizationType": organization_type, + "pulse:githubOrgFollowers": github_org.get("followers"), + "org:hasUnit": has_units, + "org:unitOf": [parent_org] if isinstance(parent_org, str) and parent_org else [], + "pulse:owns": owns, + # Internal lookup keys (`_`-prefix is stripped before strict + # validation and JSON-LD output). Used by `membership_agent` to + # resolve `org:hasMembership` composite IDs whose org token is + # the original ORCID-supplied name (e.g. "Aalto-yliopisto") + # rather than the canonical ROR display name. + "_aliases": merged_aliases, + "_acronyms": ror_acronyms, + "_labels": ror_labels, + } + + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + payload.update(overrides) + + raw_output = deepcopy(payload) + validated_payload, validation_warnings = validate_permissive( + payload, + schema_name="organization", + ) + warnings.extend(validation_warnings) + + derivation_stats = { + "organization_id": validated_payload.get("id"), + "organization_name": validated_payload.get("schema:name"), + "github_lookup_enabled": github_lookup_enabled, + "source_repositories": ( + deepcopy(source_repositories) + if isinstance(source_repositories, list) + else [] + ), + "owned_repositories": deepcopy(owns), + "parent_organization": parent_org, + "unit_ids": deepcopy(has_units), + "ror_types": deepcopy(ror_types), + } + + return AgentResult( + data=validated_payload, + warnings=warnings, + raw_output=raw_output, + stats={"derivation": derivation_stats}, + ) diff --git a/src/v2/agents/rule_based/person_agent.py b/src/v2/agents/rule_based/person_agent.py new file mode 100644 index 0000000..d3ddbe1 --- /dev/null +++ b/src/v2/agents/rule_based/person_agent.py @@ -0,0 +1,297 @@ +from __future__ import annotations + +import hashlib +import re +from copy import deepcopy +from typing import Any + +from src.v2.agents.models import ( + AgentResult, + ProviderSet, + generate_uuid, + validate_permissive, +) +from src.v2.ingest.providers.base import ProviderNotFoundError + +HASH_LENGTH = 12 +HASHED_LOCAL_PART_PATTERN = re.compile(r"^[0-9a-f]{12}$|^[0-9a-f]{64}$", re.IGNORECASE) + + +def _normalize_orcid(orcid_value: Any) -> str | None: + if not isinstance(orcid_value, str): + return None + candidate = orcid_value.strip() + if candidate.lower().startswith("https://orcid.org/"): + candidate = candidate.rsplit("/", maxsplit=1)[-1] + return candidate or None + + +def _anonymize_email(email: Any) -> str | None: + if not isinstance(email, str) or "@" not in email: + return None + local_part, domain = email.split("@", maxsplit=1) + if not local_part or not domain: + return None + if HASHED_LOCAL_PART_PATTERN.fullmatch(local_part): + return email + hashed_local = hashlib.sha256(local_part.encode("utf-8")).hexdigest()[:HASH_LENGTH] + return f"{hashed_local}@{domain}" + + +def _pick_best_infoscience_match(results: Any) -> dict[str, Any] | None: + if not isinstance(results, list): + return None + candidates = [item for item in results if isinstance(item, dict)] + if not candidates: + return None + + def _score(item: dict[str, Any]) -> float: + score = item.get("score") + return float(score) if isinstance(score, (int, float)) else -1.0 + + return sorted(candidates, key=_score, reverse=True)[0] + + +def _deduplicate_preserve_order(values: list[str]) -> list[str]: + deduplicated: list[str] = [] + seen: set[str] = set() + for value in values: + if value in seen: + continue + deduplicated.append(value) + seen.add(value) + return deduplicated + + +def _normalize_affiliation_entries(entries: Any) -> list[dict[str, str | None]]: + if not isinstance(entries, list): + return [] + + normalized_entries: list[dict[str, str | None]] = [] + for entry in entries: + if not isinstance(entry, dict): + continue + organization = entry.get("organization") + if not isinstance(organization, str) or not organization: + continue + normalized_entries.append( + { + "organization": organization, + "department": ( + entry.get("department") + if isinstance(entry.get("department"), str) + else None + ), + "role": entry.get("role") if isinstance(entry.get("role"), str) else None, + "start_date": ( + entry.get("start_date") + if isinstance(entry.get("start_date"), str) + else None + ), + "end_date": ( + entry.get("end_date") + if isinstance(entry.get("end_date"), str) + else None + ), + }, + ) + return normalized_entries + + +def _resolve_username(context: dict[str, Any]) -> str: + for key in ("username", "github_username"): + value = context.get(key) + if isinstance(value, str) and value: + return value + message = "Person context is missing a GitHub username" + raise ValueError(message) + + +class PersonAgentV2: + """Person agent wrapper with permissive output validation.""" + + async def run( # noqa: C901, PLR0912, PLR0915 + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + warnings: list[str] = [] + username = _resolve_username(context) + github_user = providers.github.get_user(username) + + orcid_record: Any | None = None + orcid_identifier_hint = _normalize_orcid(context.get("orcid")) or _normalize_orcid( + github_user.get("orcid"), + ) + + if providers.orcid and orcid_identifier_hint: + try: + orcid_record = providers.orcid.get_person_by_orcid(orcid_identifier_hint) + except (ProviderNotFoundError, ValueError) as exc: + warnings.append(f"ORCID lookup failed: {exc}") + elif not providers.orcid: + warnings.append("ORCID provider not configured for person enrichment") + + infoscience_match: dict[str, Any] | None = None + if providers.infoscience: + search_query = ( + context.get("person_query") + or github_user.get("name") + or username + ) + infoscience_results = providers.infoscience.search_person(str(search_query)) + infoscience_match = _pick_best_infoscience_match(infoscience_results) + else: + warnings.append("Infoscience provider not configured for person enrichment") + + normalized_orcid = _normalize_orcid( + (orcid_record or {}).get("orcid_id") if orcid_record else None, + ) or _normalize_orcid((infoscience_match or {}).get("orcid")) + if not normalized_orcid and not providers.orcid: + normalized_orcid = orcid_identifier_hint + infoscience_id = ( + (infoscience_match or {}).get("infosciencePersonIdentifier") + if infoscience_match + else None + ) + github_username = github_user.get("login") + uuid_value = context.get("uuid") + if not isinstance(uuid_value, str) or not uuid_value.strip(): + uuid_value = generate_uuid() + + identifier_hierarchy: list[tuple[str, str | None]] = [ + ("pulse:orcid", normalized_orcid), + ("pulse:infosciencePersonIdentifier", infoscience_id if isinstance(infoscience_id, str) else None), + ("pulse:githubUsername", github_username if isinstance(github_username, str) else None), + ("uuid", uuid_value), + ] + id_source, resolved_id = next( + (identifier, value) + for identifier, value in identifier_hierarchy + if value + ) + + affiliations: list[str] = [] + if orcid_record and isinstance(orcid_record.get("affiliations"), list): + affiliations.extend( + [item for item in orcid_record["affiliations"] if isinstance(item, str)], + ) + if infoscience_match and isinstance(infoscience_match.get("affiliations"), list): + affiliations.extend( + [item for item in infoscience_match["affiliations"] if isinstance(item, str)], + ) + if isinstance(github_user.get("company"), str) and github_user["company"]: + affiliations.append(github_user["company"]) + affiliations = _deduplicate_preserve_order(affiliations) + + membership_ids = [f"{resolved_id}_{affiliation}" for affiliation in affiliations] + repository_ownership = [] + source_repositories = context.get("source_repositories") + if isinstance(source_repositories, list): + repository_ownership = [ + repository + for repository in source_repositories + if isinstance(repository, str) and repository + ] + repositories = github_user.get("repositories") + if not repository_ownership and isinstance(repositories, list) and isinstance(github_username, str): + repository_ownership = [ + f"{github_username}/{repo_name}" + for repo_name in repositories + if isinstance(repo_name, str) and repo_name + ] + email = _anonymize_email( + context.get("email") or github_user.get("email"), + ) + + payload = { + "id": resolved_id, + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:orcid": normalized_orcid, + "pulse:infosciencePersonIdentifier": infoscience_id, + "pulse:githubUsername": github_username, + "uuid": uuid_value, + }, + "idSource": id_source, + "schema:name": ( + (orcid_record or {}).get("name") + or (infoscience_match or {}).get("name") + or github_user.get("name") + or username + ), + "schema:url": ( + (infoscience_match or {}).get("profileUrl") + if infoscience_match + else github_user.get("html_url") + ), + "pulse:githubUsername": github_username, + "pulse:orcidIdentifier": normalized_orcid, + "pulse:infosciencePersonIdentifier": infoscience_id, + "org:hasMembership": membership_ids, + "pulse:hasContribution": _deduplicate_preserve_order( + [ + contribution + for contribution in context.get("contributions", []) + if isinstance(contribution, str) + ] if isinstance(context.get("contributions"), list) else [], + ), + "pulse:owns": repository_ownership, + } + if email is not None: + payload["schema:email"] = email + + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + payload.update(overrides) + + raw_output = deepcopy(payload) + validated_payload, validation_warnings = validate_permissive( + payload, + schema_name="person", + ) + warnings.extend(validation_warnings) + + derivation_stats = { + "person_id": validated_payload.get("id"), + "github_username": github_username, + "source_repositories": deepcopy(repository_ownership), + "affiliation_names": deepcopy(affiliations), + "orcid_affiliations": _normalize_affiliation_entries( + (orcid_record or {}).get("employment") if isinstance(orcid_record, dict) else [], + ) + + _normalize_affiliation_entries( + (orcid_record or {}).get("education") if isinstance(orcid_record, dict) else [], + ), + "infoscience_affiliations": deepcopy( + [ + affiliation + for affiliation in (infoscience_match or {}).get("affiliations", []) + if isinstance(affiliation, str) and affiliation + ], + ) + if isinstance(infoscience_match, dict) + else [], + "membership_ids": deepcopy( + [ + membership + for membership in validated_payload.get("org:hasMembership", []) + if isinstance(membership, str) + ], + ), + "contribution_ids": deepcopy( + [ + contribution + for contribution in validated_payload.get("pulse:hasContribution", []) + if isinstance(contribution, str) + ], + ), + } + + return AgentResult( + data=validated_payload, + warnings=warnings, + raw_output=raw_output, + stats={"derivation": derivation_stats}, + ) diff --git a/src/v2/agents/rule_based/repository_agent.py b/src/v2/agents/rule_based/repository_agent.py new file mode 100644 index 0000000..115dba9 --- /dev/null +++ b/src/v2/agents/rule_based/repository_agent.py @@ -0,0 +1,416 @@ +from __future__ import annotations + +import re +from copy import deepcopy +from datetime import datetime, timezone +from typing import Any, Awaitable, Callable +from urllib.parse import urlparse + +from src.v2.agents.models import ( + AgentResult, + ProviderSet, + generate_uuid, + validate_permissive, +) + +CompiledContextStage = Callable[[dict[str, Any], ProviderSet], dict[str, Any] | Awaitable[dict[str, Any]]] +StructuredOutputStage = Callable[ + [dict[str, Any], ProviderSet, dict[str, Any]], + dict[str, Any] | Awaitable[dict[str, Any]], +] +RepositoryClassifierStage = Callable[ + [dict[str, Any], ProviderSet, dict[str, Any], dict[str, Any]], + dict[str, Any] | Awaitable[dict[str, Any]], +] + +MIN_REPOSITORY_SEGMENTS = 2 +DATE_ONLY_PATTERN = re.compile(r"^\d{4}-\d{2}-\d{2}$") +STRICT_TIMESTAMP_PATTERN = re.compile(r"^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}Z$") + + +def _to_list_of_strings(value: Any) -> list[str]: + if isinstance(value, str) and value: + return [value] + if isinstance(value, list): + return [item for item in value if isinstance(item, str) and item] + return [] + + +def _extract_contributor_logins(contributors: Any) -> list[str]: + if not isinstance(contributors, list): + return [] + + logins: list[str] = [] + seen: set[str] = set() + for contributor in contributors: + login = None + if isinstance(contributor, dict): + account_type = contributor.get("type") + if isinstance(account_type, str) and account_type.lower() == "organization": + continue + login = contributor.get("login") + elif isinstance(contributor, str): + login = contributor + + if not isinstance(login, str) or not login: + continue + normalized_login = login.strip() + if not normalized_login or normalized_login in seen: + continue + seen.add(normalized_login) + logins.append(normalized_login) + return logins + + +def _ensure_repo_handle(context: dict[str, Any]) -> str: + for key in ("full_name", "repository_handle", "github_repository_handle"): + value = context.get(key) + if isinstance(value, str) and "/" in value: + return value.strip() + + source_url = context.get("source_url") + if isinstance(source_url, str): + parsed = urlparse(source_url if "://" in source_url else f"https://{source_url}") + segments = [segment for segment in parsed.path.split("/") if segment] + if len(segments) >= MIN_REPOSITORY_SEGMENTS: + return f"{segments[0]}/{segments[1]}" + + message = "Repository context is missing a GitHub owner/repository handle" + raise ValueError(message) + + +def _normalize_license_url(spdx_id: Any) -> str | None: + if not isinstance(spdx_id, str): + return None + if not spdx_id or spdx_id.upper() == "NOASSERTION": + return None + return f"https://spdx.org/licenses/{spdx_id}.html" + + +def _normalize_created_at(value: Any) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + if STRICT_TIMESTAMP_PATTERN.fullmatch(candidate): + return candidate + if DATE_ONLY_PATTERN.fullmatch(candidate): + return f"{candidate}T00:00:00Z" + + normalized = candidate.replace("Z", "+00:00") + try: + parsed = datetime.fromisoformat(normalized) + except ValueError: + return candidate + + if parsed.tzinfo is None: + parsed = parsed.replace(tzinfo=timezone.utc) + return parsed.astimezone(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") + + +async def _maybe_await(value: Any) -> Any: + if hasattr(value, "__await__"): + return await value + return value + + +# Keyword sets for `pulse:repositoryType` classification. Order matters: the +# first matching set wins, so the more-specific categories come first. +_REPO_TYPE_KEYWORDS: tuple[tuple[str, tuple[str, ...]], ...] = ( + ( + "pulse:EducationalResource", + ("course", "tutorial", "training", "workshop", "exercise", "lesson"), + ), + ( + "pulse:Documentation", + ( + "documentation", + "best-practice-documentation", + "best practice", + "user guide", + "handbook", + "spec", + "specification", + "rfc", + ), + ), + ( + "pulse:Data", + ("dataset", "data-archive", "corpus", "benchmark"), + ), +) +# Languages that, on their own, indicate a documentation-shaped repo. +_DOC_ONLY_LANGUAGES: frozenset[str] = frozenset( + {"markdown", "rst", "text", "asciidoc", "tex", "html"}, +) + + +def _classify_repository_type(*, language_names: list[str], haystack: str) -> str: + """Pick a `pulse:repositoryType` value from language + name/description signals. + + Priority: + 1. Keyword match in repo name/description (most specific first). + 2. Language-only signal: if every detected language is a doc-shaped one, + call it Documentation. + 3. Default: Software. + """ + + for repo_type, keywords in _REPO_TYPE_KEYWORDS: + for keyword in keywords: + if keyword in haystack: + return repo_type + if language_names and all( + language in _DOC_ONLY_LANGUAGES for language in language_names + ): + return "pulse:Documentation" + return "pulse:Software" + + +class RepositoryAgentV2: + """Repository agent wrapper with permissive output validation.""" + + def __init__( + self, + *, + context_compiler: CompiledContextStage | None = None, + structured_output: StructuredOutputStage | None = None, + repository_classifier: RepositoryClassifierStage | None = None, + ) -> None: + self._context_compiler = context_compiler or self._default_context_compiler + self._structured_output = structured_output or self._default_structured_output + self._repository_classifier = ( + repository_classifier or self._default_repository_classifier + ) + + async def _default_context_compiler( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> dict[str, Any]: + full_name = _ensure_repo_handle(context) + + repository_context = context.get("repository_context") + reuse_gathered_context = ( + isinstance(repository_context, dict) + and repository_context.get("full_name") == full_name + ) + + repository: dict[str, Any] + contributors: list[dict[str, Any]] + languages: dict[str, Any] + + if reuse_gathered_context and isinstance(repository_context, dict): + metadata_candidate = repository_context.get("metadata") + repository = metadata_candidate if isinstance(metadata_candidate, dict) else {} + + contributors_candidate = repository_context.get("contributors") + contributors = ( + contributors_candidate + if isinstance(contributors_candidate, list) + else [] + ) + + languages_candidate = repository_context.get("languages") + languages = languages_candidate if isinstance(languages_candidate, dict) else {} + else: + repository = providers.github.get_repository(full_name) + contributors = providers.github.get_contributors(full_name) + languages = providers.github.get_languages(full_name) + + return { + "full_name": full_name, + "repository": repository, + "contributors": contributors, + "languages": languages, + } + + async def _default_structured_output( # noqa: C901 + self, + context: dict[str, Any], + _providers: ProviderSet, + compiled_context: dict[str, Any], + ) -> dict[str, Any]: + repository = compiled_context["repository"] + full_name = str(repository.get("full_name") or compiled_context["full_name"]) + contributors = compiled_context.get("contributors", []) + languages = compiled_context.get("languages", {}) + + author_ids = [] + if isinstance(contributors, list): + for contributor in contributors: + if not isinstance(contributor, dict): + continue + contributor_type = contributor.get("type") + if isinstance(contributor_type, str) and contributor_type.lower() == "organization": + continue + login = contributor.get("login") + if isinstance(login, str) and login: + author_ids.append(login) + if not author_ids: + owner_login = repository.get("owner", {}).get("login") + owner_type = repository.get("owner", {}).get("type") + if ( + isinstance(owner_login, str) + and owner_login + and not ( + isinstance(owner_type, str) + and owner_type.lower() == "organization" + ) + ): + author_ids = [owner_login] + if not author_ids: + author_ids = ["unknown-author"] + + programming_languages: list[str] = [] + if isinstance(languages, dict): + languages = list(languages) + if isinstance(languages, list): + programming_languages = sorted( + [language for language in languages if isinstance(language, str) and language], + ) + + doi = context.get("doi") + doi_value = doi if isinstance(doi, str) and doi.strip() else None + uuid_value = context.get("uuid") + if not isinstance(uuid_value, str) or not uuid_value.strip(): + uuid_value = generate_uuid() + + return { + "id": full_name, + "type": "schema:SoftwareSourceCode", + "shacl": "pulse:RepositoryShape", + "identifiers": { + "pulse:githubRepositoryHandle": full_name, + "schema:citation": doi_value, + "uuid": uuid_value, + }, + "idSource": "pulse:githubRepositoryHandle", + "schema:name": repository.get("name") or full_name.split("/", maxsplit=1)[-1], + "pulse:githubRepositoryHandle": full_name, + "schema:author": author_ids, + "pulse:githubRepoStars": repository.get("stargazers_count"), + "pulse:githubRepoForks": repository.get("forks_count"), + "schema:dateCreated": _normalize_created_at(repository.get("created_at")), + "schema:license": _normalize_license_url( + repository.get("license", {}).get("spdx_id"), + ), + "schema:citation": f"https://doi.org/{doi_value}" if doi_value else None, + "schema:programmingLanguage": programming_languages, + "pulse:ownedBy": repository.get("owner", {}).get("login"), + "pulse:isForkOf": repository.get("source", {}).get("full_name") + if repository.get("fork") + else None, + } + + async def _default_repository_classifier( + self, + context: dict[str, Any], + _providers: ProviderSet, + compiled_context: dict[str, Any], + _structured_payload: dict[str, Any], + ) -> dict[str, Any]: + languages = compiled_context.get("languages", {}) + language_names = [ + language.lower() + for language in languages + if isinstance(language, str) + ] if isinstance(languages, dict) else [] + + repository = compiled_context.get("repository") or {} + repo_handle = compiled_context.get("full_name") or "" + repo_name = ( + (repository.get("name") if isinstance(repository, dict) else None) + or repo_handle.split("/", maxsplit=1)[-1] + or "" + ) + repo_description = ( + repository.get("description") if isinstance(repository, dict) else None + ) or "" + # Lowercased haystack we'll match keyword heuristics against. + haystack = f"{repo_name} {repo_description}".lower() + + repository_type = _classify_repository_type( + language_names=language_names, + haystack=haystack, + ) + + disciplines = _to_list_of_strings(context.get("disciplines")) + if not disciplines: + # Match the LLM agent's fallback so both runtimes emit the same + # "broad code repo" default (Wikidata: computer engineering). + disciplines = ["wd:Q428691"] + + return { + "pulse:repositoryType": repository_type, + "pulse:discipline": disciplines, + } + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + warnings: list[str] = [] + compiled_context = await _maybe_await(self._context_compiler(context, providers)) + structured_payload = await _maybe_await( + self._structured_output(context, providers, compiled_context), + ) + classification_payload = await _maybe_await( + self._repository_classifier( + context, + providers, + compiled_context, + structured_payload, + ), + ) + + merged_payload = { + **structured_payload, + **classification_payload, + } + overrides = context.get("agent_overrides") + if isinstance(overrides, dict): + merged_payload.update(overrides) + + raw_output = deepcopy(merged_payload) + validated_payload, validation_warnings = validate_permissive( + merged_payload, + schema_name="repository", + ) + warnings.extend(validation_warnings) + + repository_metadata = compiled_context.get("repository", {}) + owner = repository_metadata.get("owner") if isinstance(repository_metadata, dict) else None + owner_login = owner.get("login") if isinstance(owner, dict) else None + owner_type = owner.get("type") if isinstance(owner, dict) else None + contributor_logins = _extract_contributor_logins(compiled_context.get("contributors")) + language_names = sorted( + [ + language + for language in validated_payload.get("schema:programmingLanguage", []) + if isinstance(language, str) and language + ], + ) + derivation_stats = { + "repository_full_name": validated_payload.get("pulse:githubRepositoryHandle"), + "source_repositories": [ + validated_payload.get("pulse:githubRepositoryHandle"), + ] + if isinstance(validated_payload.get("pulse:githubRepositoryHandle"), str) + else [], + "owner_login": owner_login if isinstance(owner_login, str) else None, + "owner_type": owner_type if isinstance(owner_type, str) else None, + "contributor_logins": contributor_logins, + "contributors": deepcopy(compiled_context.get("contributors", [])) + if isinstance(compiled_context.get("contributors"), list) + else [], + "language_names": language_names, + } + + return AgentResult( + data=validated_payload, + warnings=warnings, + raw_output=raw_output, + stats={"derivation": derivation_stats}, + ) diff --git a/src/v2/agents/runtime.py b/src/v2/agents/runtime.py new file mode 100644 index 0000000..c7616dc --- /dev/null +++ b/src/v2/agents/runtime.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +from enum import Enum + + +class AgentRuntime(str, Enum): + """Supported runtime execution modes for v2 agents.""" + + RULE_BASED = "rule_based" + LLM = "llm" + HYBRID = "hybrid" + + +def parse_agent_runtime( + value: AgentRuntime | str | None, + *, + default: AgentRuntime = AgentRuntime.RULE_BASED, + field_name: str = "agent_runtime", +) -> AgentRuntime: + """Normalize a runtime selector into an ``AgentRuntime`` value. + + Accepts enum values, strings, and ``None`` (which resolves to ``default``). + Raises ``ValueError`` with a deterministic message for invalid values. + """ + + if value is None: + return default + if isinstance(value, AgentRuntime): + return value + if isinstance(value, str): + normalized = value.strip().lower() + if normalized: + try: + return AgentRuntime(normalized) + except ValueError: + pass + + allowed_values = ", ".join(runtime.value for runtime in AgentRuntime) + message = ( + f"Invalid runtime value for {field_name}: {value!r}. " + f"Expected one of: {allowed_values}" + ) + raise ValueError(message) diff --git a/src/v2/api.py b/src/v2/api.py new file mode 100644 index 0000000..1e7208c --- /dev/null +++ b/src/v2/api.py @@ -0,0 +1,1396 @@ +from __future__ import annotations + +import asyncio +import json +import logging +import os +import sys +from copy import deepcopy +from datetime import datetime, timezone +from importlib.metadata import PackageNotFoundError +from importlib.metadata import version as package_version +from time import perf_counter +from typing import Annotated, Any, Literal +from urllib.parse import urlparse +from uuid import uuid4 + +from fastapi import APIRouter, Depends, Path, Query, Request, status +from fastapi.responses import JSONResponse +from rdflib import Graph as RDFGraph + +from src.v2.agents import AgentRuntime, ProviderSet, parse_agent_runtime +from src.v2.api_models import ( + V2ErrorResponse, + V2ErrorType, + V2ExtractJob, + V2ExtractJobAccepted, + V2ExtractJobStatus, + V2ExtractRequest, + V2ExtractResponse, + V2FieldError, + V2HealthResponse, + V2JSONLDOutput, + V2JSONOutputEnvelope, +) +from src.v2.auth import verify_token +from src.v2.config import V2Config +from src.v2.dependencies import _resolve_provider_cache, get_provider_set +from src.v2.ingest.cache import ProviderCache +from src.v2.ingest.detection import UnsupportedGitHubURL, classify_github_url +from src.v2.jobs import JobStore +from src.v2.observation.github_rate_limit import ( + GitHubRateLimitSummary, + probe_github_rate_limit, +) +from src.v2.observation.query_log import QueryLog, query_log_var +from src.v2.pipeline import PipelineOrchestrator +from src.v2.pipeline.stages import ( + AssembledOutput, + RootEntityValidationError, + apply_link_pruning_to_assembled_output, + assemble_output, + build_json_output, + build_jsonld_output, + compute_stats, + guarantee_repo_author, + infer_github_handle_parents, + infer_org_units, + infer_owners, + promote_failed_id_entities, + prune_dangling_refs, + reconcile_entities, + run_concept_tagging_stage, + run_link_veracity_stage, + run_llm_critic_stage, + run_llm_dedup_stage, + run_org_relationships_stage, + run_refine_with_llm_stage, + validate_articles, + validate_author_classes, + validate_ownership, +) +from src.v2.pipeline.stages.validate_org_github_handles import ( + validate_org_github_handles, +) +from src.v2.pipeline.stages.refine_with_llm import ( + is_enabled as _hybrid_refiner_is_enabled, +) +from src.v2.pipeline.stages.concept_tagging import ( + is_enabled as _concept_tagging_is_enabled, +) +from src.v2.pipeline.stages.concept_tagging import ( + resolve_backend as _resolve_concept_tagging_backend, +) +from src.v2.pipeline.stages.concept_tagging import ( + resolve_epfl_min_score as _resolve_concept_tagging_epfl_min_score, +) +from src.v2.pipeline.stages.concept_tagging import ( + resolve_related_enrichment as _resolve_concept_tagging_related_enrichment, +) +from src.v2.pipeline.stages.context_gather import RequiredProviderUnavailableError +from src.v2.schema import load_jsonld_context +from src.v2.validation import ( + SHACLValidator, + StrictSchemaValidator, + load_ontology_shapes_graph, +) +from src.v2.validation.shacl_validation import SHACLRuntimeUnavailableError + +MIN_SUPPORTED_PYTHON = (3, 10) +PACKAGE_NAME = "git-metadata-extractor" +try: + PACKAGE_VERSION = package_version(PACKAGE_NAME) +except PackageNotFoundError: + PACKAGE_VERSION = "unknown" + +MIN_SUBRESOURCE_PATH_SEGMENTS = 3 +SUBRESOURCE_SEGMENT_INDEX = 2 +JSONLD_CONTEXT_FALLBACK = { + "schema": "http://schema.org/", + "pulse": "https://open-pulse.epfl.ch/ontology#", + "org": "http://www.w3.org/ns/org#", +} + +logger = logging.getLogger(__name__) + +STAGE_CLASSIFY_URL = "classify_url" +STAGE_PERMISSIVE_VALIDATION = "permissive_validation" +STAGE_STRICT_VALIDATION = "strict_validation" +STAGE_RECONCILIATION = "reconciliation" +STAGE_LLM_DEDUP = "llm_dedup" +STAGE_LLM_CRITIC = "llm_critic" +STAGE_SHACL_GATE = "shacl_gate" +STAGE_OUTPUT_ASSEMBLY = "output_assembly" +STAGE_JSONLD_BUILD = "jsonld_build" +STAGE_LINK_VERACITY = "link_veracity" +STAGE_REFINE_WITH_LLM = "refine_with_llm" + +v2_router = APIRouter(prefix="/v2") + + +_TRUTHY_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} + + +def _is_pipeline_cache_enabled() -> bool: + """Read `V2_PIPELINE_CACHE_ENABLED` env var (default true). + + When false, `extract()` neither reads nor writes the pipeline-level cache, + so every request runs the full pipeline. Sub-level caches (provider + responses, agent verdicts, Selenium fetches, link-veracity) are unaffected. + """ + raw = os.getenv("V2_PIPELINE_CACHE_ENABLED") + if raw is None: + return True + return raw.strip().lower() in _TRUTHY_ENV_VALUES + + +def _is_link_veracity_enabled() -> bool: + """Read `V2_LINK_VERACITY_ENABLED` env var (default true). + + When false, the link-veracity LLM stage is skipped entirely. Useful for + broad batch runs where you want fast extraction and don't need every + URL re-verified against fetched page content. Saves ~1–3 minutes per + repo and a chunk of LLM calls. + """ + raw = os.getenv("V2_LINK_VERACITY_ENABLED") + if raw is None: + return True + return raw.strip().lower() in _TRUTHY_ENV_VALUES + + +def _should_apply_critic_pruning() -> bool: + """Read `V2_APPLY_CRITIC_PRUNING` env var (default false). + + When false (the default), the LLM critic stage is skipped entirely so no + entities get dropped from the output. Set to true to re-enable the critic + stage's drop suggestions. + """ + raw = os.getenv("V2_APPLY_CRITIC_PRUNING") + if raw is None: + return False + return raw.strip().lower() in _TRUTHY_ENV_VALUES + + +def _format_duration(seconds: float) -> str: + """Render a duration in compact human form, e.g. '7.3s' or '4m 12s'.""" + if seconds < 60.0: + return f"{seconds:.1f}s" + minutes = int(seconds // 60) + remainder = seconds - minutes * 60 + return f"{minutes}m {remainder:.0f}s" + + +def _jsonld_to_graph(payload: dict[str, Any]) -> RDFGraph | None: + try: + graph = RDFGraph() + graph.parse(data=json.dumps(payload), format="json-ld") + except Exception: # noqa: BLE001 + return None + else: + return graph + + +def _extract_path_kind(source_url: str) -> str | None: + candidate_url = source_url.strip() + if "://" not in candidate_url: + candidate_url = f"https://{candidate_url}" + + parsed_url = urlparse(candidate_url) + path_segments = [segment for segment in parsed_url.path.split("/") if segment] + if len(path_segments) >= MIN_SUBRESOURCE_PATH_SEGMENTS: + return path_segments[SUBRESOURCE_SEGMENT_INDEX].lower() + return None + + +def _resolve_max_concurrent_agents() -> int: + """Read `V2_MAX_CONCURRENT_AGENTS` env var (default 6). + + Caps how many work items per stage (person agents, contribution agents, + link-veracity calls, etc.) run in parallel within a single /extract + request. Higher values speed up wide-fanout repos at the cost of more + concurrent LLM calls — keep within the LLM provider's rate limit. + """ + raw = os.getenv("V2_MAX_CONCURRENT_AGENTS") + if raw is None: + return 6 + try: + value = int(raw.strip()) + except ValueError: + return 6 + return max(1, value) + + +def _get_orchestrator(request: Request) -> PipelineOrchestrator: + existing = getattr(request.app.state, "v2_orchestrator", None) + if isinstance(existing, PipelineOrchestrator): + return existing + + cache = getattr(request.app.state, "v2_provider_cache", None) + if not isinstance(cache, ProviderCache): + cache = None + orchestrator = PipelineOrchestrator( + cache=cache, + max_concurrent_agents=_resolve_max_concurrent_agents(), + ) + request.app.state.v2_orchestrator = orchestrator + return orchestrator + + +def _resolve_job_store(request: Request) -> JobStore | None: + """Resolve a JobStore backed by the shared ProviderCache, if available.""" + cache = _resolve_provider_cache(request.app.state) + if not isinstance(cache, ProviderCache): + return None + return JobStore(cache) + + +def _track_background_task(request: Request, task: asyncio.Task[Any]) -> None: + """Hold a strong reference to a background task so it isn't GC'd mid-flight.""" + tasks: set[asyncio.Task[Any]] | None = getattr( + request.app.state, + "_v2_job_tasks", + None, + ) + if tasks is None: + tasks = set() + request.app.state._v2_job_tasks = tasks # noqa: SLF001 + tasks.add(task) + task.add_done_callback(tasks.discard) + + +def _job_status_path(job_id: str) -> str: + return f"/v2/jobs/{job_id}" + + +async def _run_extract_job( + *, + payload: V2ExtractRequest, + request: Request, + providers: ProviderSet, + job_store: JobStore, + job_id: str, +) -> None: + """Execute the extract pipeline for a job and persist the outcome. + + Runs the existing GET handler as a plain async function, then unwraps the + success/error result onto the persisted V2ExtractJob record. + """ + try: + existing = job_store.get(job_id) + if existing is None: + return + existing.status = V2ExtractJobStatus.RUNNING + existing.started_at = datetime.now(timezone.utc) + job_store.set(existing) + + result = await extract( + full_path=payload.source_url, + request=request, + output_format=payload.output_format, + agent_runtime=payload.agent_runtime, + include_context_summary=payload.include_context_summary, + providers=providers, + _token="", + ) + + finished = job_store.get(job_id) or existing + finished.completed_at = datetime.now(timezone.utc) + if isinstance(result, V2ExtractResponse): + finished.status = V2ExtractJobStatus.COMPLETED + finished.result = result + else: + finished.status = V2ExtractJobStatus.FAILED + try: + error_payload = json.loads(result.body) + finished.error = V2ErrorResponse.model_validate(error_payload) + except Exception: # noqa: BLE001 + finished.error = V2ErrorResponse( + error_type=V2ErrorType.PIPELINE_ERROR, + detail="extraction failed with non-decodable error payload", + source_url=payload.source_url, + ) + job_store.set(finished) + except Exception as exc: + logger.exception("extract job %s failed", job_id) + record = job_store.get(job_id) + if record is None: + return + record.status = V2ExtractJobStatus.FAILED + record.completed_at = datetime.now(timezone.utc) + record.error = V2ErrorResponse( + error_type=V2ErrorType.PIPELINE_ERROR, + detail=str(exc), + source_url=payload.source_url, + ) + job_store.set(record) + + +def _append_unique_warning(warnings: list[str], warning: str) -> None: + if warning and warning not in warnings: + warnings.append(warning) + + +def _iter_reconciled_entities( + *, + reconciled_entities: dict[str, list[dict[str, Any]]], + memberships: list[dict[str, Any]], + contributions: list[dict[str, Any]], +) -> list[tuple[str, dict[str, Any]]]: + payloads: list[tuple[str, dict[str, Any]]] = [] + for entity_type, entities in ( + ("person", reconciled_entities.get("persons", [])), + ("organization", reconciled_entities.get("organizations", [])), + ("repository", reconciled_entities.get("repositories", [])), + ("article", reconciled_entities.get("articles", [])), + ): + payloads.extend((entity_type, entity) for entity in entities if isinstance(entity, dict)) + payloads.extend(("membership", membership) for membership in memberships if isinstance(membership, dict)) + payloads.extend( + ("contribution", contribution) + for contribution in contributions + if isinstance(contribution, dict) + ) + return payloads + + +def _extract_jsonld_context() -> dict[str, Any]: + try: + return load_jsonld_context() + except (OSError, TypeError, ValueError): + return dict(JSONLD_CONTEXT_FALLBACK) + + +def _root_entity_type_for_detected_type( + detected_type: Literal["repository", "user", "organization"], +) -> Literal["repository", "person", "organization"]: + if detected_type == "user": + return "person" + return detected_type + + +def _build_rootless_assembled_output( + *, + reconciled: Any, + strict_batch: Any, + root_warning: str, +) -> AssembledOutput: + related_entities: list[dict[str, Any]] = [] + for _, payload in strict_batch.valid_entities: + if isinstance(payload, dict): + related_entities.append(deepcopy(payload)) + + excluded_entities: list[dict[str, Any]] = [] + warnings = [*reconciled.link_warnings, root_warning] + for entity_type, payload, validation in strict_batch.invalid_entities: + if not isinstance(payload, dict): + continue + reason = list(validation.errors) + excluded_entities.append( + { + "entity_type": entity_type, + "entity": deepcopy(payload), + "reason": reason, + }, + ) + warnings.append( + f"Excluded {entity_type} entity '{payload.get('id')}' due to strict validation errors: {reason}", + ) + + return AssembledOutput( + root_entity=None, + related_entities=related_entities, + excluded_entities=excluded_entities, + warnings=warnings, + ) + + +@v2_router.get( + "/extract/{full_path:path}", + response_model=V2ExtractResponse, + response_model_exclude_none=True, +) +async def extract( # noqa: C901, PLR0911, PLR0912, PLR0913, PLR0915 + full_path: Annotated[ + str, + Path( + description="GitHub repository, user, or organization URL or handle.", + openapi_examples={ + "gimie": { + "summary": "GIMIE repository", + "value": "https://github.com/sdsc-ordes/gimie", + }, + "sdsc-ordes": { + "summary": "SDSC ORDES organization", + "value": "https://github.com/sdsc-ordes", + }, + "cmdoret": { + "summary": "cmdoret user", + "value": "https://github.com/cmdoret", + }, + }, + ), + ], + request: Request, + *, + output_format: Annotated[Literal["jsonld", "json"], Query()] = "jsonld", + agent_runtime: Annotated[Literal["rule_based", "llm", "hybrid"] | None, Query()] = None, + include_context_summary: Annotated[bool, Query()] = False, + providers: Annotated[ProviderSet, Depends(get_provider_set)], + _token: Annotated[str, Depends(verify_token)], +) -> V2ExtractResponse | JSONResponse: + """Run the v2 extraction pipeline for a GitHub path.""" + + config = V2Config() + run_id: str | None = None + + try: + classification = classify_github_url(full_path) + except UnsupportedGitHubURL as exc: + logger.warning("classify_url: unsupported url=%s reason=%s", exc.normalized_url, exc.reason) + error_payload = V2ErrorResponse( + error_type=V2ErrorType.UNSUPPORTED_URL, + detail=exc.reason, + source_url=exc.normalized_url, + detected_path_kind=_extract_path_kind(full_path), + ) + return JSONResponse( + status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, + content=error_payload.model_dump(mode="json", exclude_none=True), + ) + except ValueError as exc: + logger.warning("classify_url: invalid input %s: %s", full_path, exc) + error_payload = V2ErrorResponse( + error_type=V2ErrorType.UNSUPPORTED_URL, + detail=str(exc), + source_url=full_path, + detected_path_kind=_extract_path_kind(full_path), + ) + return JSONResponse( + status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, + content=error_payload.model_dump(mode="json", exclude_none=True), + ) + + run_id = str(uuid4()) + request.state.v2_run_id = run_id + resolved_runtime = parse_agent_runtime( + agent_runtime, + default=config.V2_AGENT_RUNTIME_DEFAULT, + field_name="agent_runtime", + ) + total_started_at = perf_counter() + link_veracity_seconds = 0.0 + logger.info( + "extract: run_id=%s url=%s detected_type=%s runtime=%s", + run_id, + classification.normalized_url, + classification.detected_type.value, + resolved_runtime.value, + ) + + # Per-request query log: every external-service tool call lands here. + query_log = QueryLog( + run_id=run_id, + extract_full_path=classification.normalized_url, + ) + query_log_var.set(query_log) + + # Resolve the cache singleton once. Sub-stages always receive it (so + # provider caches, agent verdicts, Selenium fetches, link-veracity, and + # DuckDuckGo searches all keep working). The pipeline-level lookup/store + # is gated separately by V2_PIPELINE_CACHE_ENABLED so it can be toggled + # off without disabling the sub-caches. + pipeline_cache = getattr(request.app.state, "v2_provider_cache", None) + if not isinstance(pipeline_cache, ProviderCache): + pipeline_cache = None + pipeline_cache_enabled = pipeline_cache is not None and _is_pipeline_cache_enabled() + pipeline_cache_key: str | None = None + if pipeline_cache_enabled: + pipeline_cache_key = ProviderCache.make_key( + "pipeline", + "extract", + url=classification.normalized_url, + output_format=output_format, + agent_runtime=resolved_runtime.value, + include_context_summary=bool(include_context_summary), + ) + cached_response = pipeline_cache.get(pipeline_cache_key) + if isinstance(cached_response, dict): + try: + response_model = V2ExtractResponse.model_validate(cached_response) + except Exception: # noqa: BLE001 + logger.warning( + "pipeline cache hit but cached payload failed validation; " + "falling through to fresh extraction (run_id=%s)", + run_id, + ) + else: + cached_seconds = perf_counter() - total_started_at + logger.info( + "pipeline cache hit: url=%s run_id=%s elapsed=%s", + classification.normalized_url, + run_id, + _format_duration(cached_seconds), + ) + return response_model + + try: + orchestrator = _get_orchestrator(request) + execution_plan = orchestrator.get_execution_plan(classification.detected_type) + pipeline_result = await orchestrator.execute( + plan=execution_plan, + providers=providers, + context={ + "source_url": classification.normalized_url, + "url_info": classification, + "agent_runtime": resolved_runtime.value, + "run_id": run_id, + }, + ) + except RequiredProviderUnavailableError as exc: + logger.exception("provider_preflight failed: run_id=%s", run_id) + error_payload = V2ErrorResponse( + error_type=V2ErrorType.PROVIDER_ERROR, + detail=str(exc), + source_url=classification.normalized_url, + ) + return JSONResponse( + status_code=status.HTTP_502_BAD_GATEWAY, + content=error_payload.model_dump(mode="json", exclude_none=True), + ) + except Exception as exc: + logger.exception("pipeline_execute failed: run_id=%s", run_id) + error_payload = V2ErrorResponse( + error_type=V2ErrorType.PIPELINE_ERROR, + detail=str(exc), + source_url=classification.normalized_url, + ) + return JSONResponse( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + content=error_payload.model_dump(mode="json", exclude_none=True), + ) + + warnings = list(pipeline_result.warnings) + + pipeline_outputs_for_prompt = { + agent_name: agent_result.data + for agent_name, agent_result in pipeline_result.agent_results.items() + if isinstance(agent_result.data, dict) and agent_result.data + } + gathered_context = ( + deepcopy(pipeline_result.gathered_context) + if isinstance(pipeline_result.gathered_context, dict) + else {} + ) + + typed_entity_buckets = pipeline_result.resolved_typed_entity_buckets().to_dict() + permissive_entity_count = sum(len(bucket) for bucket in typed_entity_buckets.values()) + logger.info("%s: entity_count=%d", STAGE_PERMISSIVE_VALIDATION, permissive_entity_count) + + llm_dedup_executed = False + if resolved_runtime == AgentRuntime.LLM: + llm_dedup_executed = True + stage_started_at = perf_counter() + try: + dedup_result = await run_llm_dedup_stage( + typed_entity_buckets=typed_entity_buckets, + source_url=classification.normalized_url, + detected_type=classification.detected_type.value, + providers=providers, + pipeline_outputs=pipeline_outputs_for_prompt, + initial_context=gathered_context, + max_concurrency=orchestrator.max_concurrent_agents, + ) + except Exception as exc: + logger.exception("%s stage failed", STAGE_LLM_DEDUP) + _append_unique_warning(warnings, f"llm_dedup stage failed: {exc}") + else: + typed_entity_buckets = dedup_result.typed_entity_buckets + logger.info( + "%s: accepted=%d rejected=%d remap=%d in %.2fs", + STAGE_LLM_DEDUP, + dedup_result.accepted_cluster_count, + dedup_result.rejected_cluster_count, + dedup_result.remap_count, + perf_counter() - stage_started_at, + ) + for warning in dedup_result.warnings: + _append_unique_warning(warnings, warning) + + llm_critic_executed = False + critic_pruned_excluded_entities: list[dict[str, Any]] = [] + stage_started_at = perf_counter() + reconciled = reconcile_entities(typed_entity_buckets) + logger.info( + "%s: persons=%d orgs=%d repos=%d articles=%d memberships=%d contributions=%d in %.2fs", + STAGE_RECONCILIATION, + len(reconciled.entities.get("persons", [])), + len(reconciled.entities.get("organizations", [])), + len(reconciled.entities.get("repositories", [])), + len(reconciled.entities.get("articles", [])), + len(reconciled.memberships), + len(reconciled.contributions), + perf_counter() - stage_started_at, + ) + for warning in reconciled.link_warnings: + _append_unique_warning(warnings, warning) + + apply_critic_pruning = _should_apply_critic_pruning() + if resolved_runtime == AgentRuntime.LLM and not apply_critic_pruning: + logger.info( + "%s: skipped (V2_APPLY_CRITIC_PRUNING=false — entities preserved)", + STAGE_LLM_CRITIC, + ) + if resolved_runtime == AgentRuntime.LLM and apply_critic_pruning: + llm_critic_executed = True + stage_started_at = perf_counter() + try: + critic_result = await run_llm_critic_stage( + reconciled=reconciled, + source_url=classification.normalized_url, + detected_type=classification.detected_type.value, + providers=providers, + initial_context=gathered_context, + pipeline_outputs=pipeline_outputs_for_prompt, + max_concurrency=orchestrator.max_concurrent_agents, + cache=pipeline_cache, + ) + except Exception as exc: + logger.exception("%s stage failed", STAGE_LLM_CRITIC) + _append_unique_warning(warnings, f"llm_critic stage failed: {exc}") + else: + reconciled = critic_result.reconciled + critic_pruned_excluded_entities = critic_result.pruned_excluded_entities + logger.info( + "%s: proposed_drop=%d applied_drop=%d protected_roots=%d in %.2fs", + STAGE_LLM_CRITIC, + critic_result.applied.get("proposed_drop_count", 0), + critic_result.applied.get("applied_drop_count", 0), + len(critic_result.applied.get("protected_root_ids", [])), + perf_counter() - stage_started_at, + ) + for warning in critic_result.warnings: + _append_unique_warning(warnings, warning) + + if resolved_runtime == AgentRuntime.HYBRID and _hybrid_refiner_is_enabled(): + stage_started_at = perf_counter() + try: + refine_result = await run_refine_with_llm_stage( + reconciled=reconciled, + gathered_context=gathered_context, + epfl_graph_provider=providers.epfl_graph_rag, + max_concurrency=orchestrator.max_concurrent_agents, + ) + except Exception as exc: + logger.exception("%s stage failed", STAGE_REFINE_WITH_LLM) + _append_unique_warning( + warnings, + f"refine_with_llm stage failed: {exc}", + ) + else: + reconciled = refine_result.reconciled + logger.info( + "%s: refined=%d skipped=%d failed=%d in %.2fs", + STAGE_REFINE_WITH_LLM, + refine_result.refined_count, + refine_result.skipped_count, + refine_result.failed_count, + perf_counter() - stage_started_at, + ) + for warning in refine_result.warnings: + _append_unique_warning(warnings, warning) + + # KNOWN BUG salvage: when reconciliation drops unresolvable + # `schema:author` references, a repository can end up with an empty + # author array, which strict validation rejects (schema requires + # non-empty). Fall back to the github owner if it's in the graph. + stage_started_at = perf_counter() + reconciled, repo_author_warnings = guarantee_repo_author(reconciled) + logger.info( + "guarantee_repo_author: salvaged=%d in %.2fs", + len(repo_author_warnings), + perf_counter() - stage_started_at, + ) + for warning in repo_author_warnings: + _append_unique_warning(warnings, warning) + + # Validate `@handle`-style org names against GitHub before strict + # validation so we either stamp the missing handle or drop the + # hallucinated entity (rather than just losing it to anyOf). + stage_started_at = perf_counter() + reconciled, org_handle_warnings = validate_org_github_handles(reconciled, providers) + logger.info( + "validate_org_github_handles: actions=%d in %.2fs", + len(org_handle_warnings), + perf_counter() - stage_started_at, + ) + for warning in org_handle_warnings: + _append_unique_warning(warnings, warning) + + stage_started_at = perf_counter() + strict_validation_entities = _iter_reconciled_entities( + reconciled_entities=reconciled.entities, + memberships=reconciled.memberships, + contributions=reconciled.contributions, + ) + strict_batch = StrictSchemaValidator().validate_batch(strict_validation_entities) + logger.info( + "%s: valid=%d invalid=%d in %.2fs", + STAGE_STRICT_VALIDATION, + len(strict_batch.valid_entities), + len(strict_batch.invalid_entities), + perf_counter() - stage_started_at, + ) + for warning in strict_batch.warnings: + _append_unique_warning(warnings, f"Strict validation: {warning}") + + jsonld_context = _extract_jsonld_context() + stage_started_at = perf_counter() + try: + assembled_output = assemble_output( + reconciled, + strict_batch, + root_entity_type=_root_entity_type_for_detected_type( + classification.detected_type.value, + ), + ) + except RootEntityValidationError as exc: + failure_message = ( + f"Root {exc.entity_type} entity '{exc.entity_id}' failed strict validation" + ) + logger.warning("%s: %s", STAGE_OUTPUT_ASSEMBLY, failure_message) + error_payload = V2ErrorResponse( + error_type=V2ErrorType.VALIDATION_ERROR, + detail=failure_message, + source_url=classification.normalized_url, + errors=[ + V2FieldError( + field=error.get("path", ""), + message=error.get("message", "validation error"), + value=error.get("expected"), + ) + for error in exc.validation_errors + ], + ) + return JSONResponse( + status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, + content=error_payload.model_dump(mode="json", exclude_none=True), + ) + except ValueError as exc: + logger.warning("%s: rootless output — %s", STAGE_OUTPUT_ASSEMBLY, exc) + assembled_output = _build_rootless_assembled_output( + reconciled=reconciled, + strict_batch=strict_batch, + root_warning=str(exc), + ) + logger.info( + "%s: related=%d excluded=%d warnings=%d in %.2fs", + STAGE_OUTPUT_ASSEMBLY, + len(assembled_output.related_entities), + len(assembled_output.excluded_entities), + len(assembled_output.warnings), + perf_counter() - stage_started_at, + ) + + if critic_pruned_excluded_entities: + for excluded_entity in critic_pruned_excluded_entities: + if not isinstance(excluded_entity, dict): + continue + assembled_output.excluded_entities.append(deepcopy(excluded_entity)) + entity_payload = excluded_entity.get("entity") + entity_id = entity_payload.get("id") if isinstance(entity_payload, dict) else None + assembled_output.warnings.append( + ( + f"Excluded {excluded_entity.get('entity_type', 'entity')} entity " + f"'{entity_id}' due to critic pruning" + ), + ) + + for warning in assembled_output.warnings: + _append_unique_warning(warnings, warning) + + entities_for_link_validation: list[dict[str, Any]] = [] + if isinstance(assembled_output.root_entity, dict): + entities_for_link_validation.append(deepcopy(assembled_output.root_entity)) + entities_for_link_validation.extend( + deepcopy(entity) + for entity in assembled_output.related_entities + if isinstance(entity, dict) + ) + + provider_cache = getattr(request.app.state, "v2_provider_cache", None) + if not isinstance(provider_cache, ProviderCache): + provider_cache = None + + link_veracity_started_at = perf_counter() + link_veracity_result = None + if resolved_runtime != AgentRuntime.LLM: + # link_veracity calls an LLM per link, so it has no place in + # `agent_runtime=rule_based` (the whole point of rule-based mode is + # zero LLM calls). The `V2_LINK_VERACITY_ENABLED` env var still + # gates the stage *within* LLM mode for users who want fast LLM + # extracts without per-link verification. + logger.info( + "%s: skipped (agent_runtime=%s — link veracity is LLM-only)", + STAGE_LINK_VERACITY, + resolved_runtime.value, + ) + elif not _is_link_veracity_enabled(): + logger.info( + "%s: skipped (V2_LINK_VERACITY_ENABLED=false)", + STAGE_LINK_VERACITY, + ) + else: + try: + link_veracity_result = await run_link_veracity_stage( + entities=entities_for_link_validation, + source_url=classification.normalized_url, + providers=providers, + max_concurrency=orchestrator.max_concurrent_agents, + cache=provider_cache, + ) + except Exception as exc: + logger.exception("%s stage failed", STAGE_LINK_VERACITY) + _append_unique_warning(warnings, f"Link veracity stage failed: {exc}") + else: + logger.info( + "%s: checked=%d supported=%d unsupported=%d failed=%d invalid_links=%d in %.2fs", + STAGE_LINK_VERACITY, + link_veracity_result.checked_count, + link_veracity_result.supported_count, + link_veracity_result.unsupported_count, + link_veracity_result.failed_count, + len(link_veracity_result.invalid_links), + perf_counter() - link_veracity_started_at, + ) + _append_unique_warning( + warnings, + ( + "Link veracity summary: " + f"checked={link_veracity_result.checked_count}, " + f"supported={link_veracity_result.supported_count}, " + f"unsupported={link_veracity_result.unsupported_count}, " + f"failed={link_veracity_result.failed_count}" + ), + ) + for warning in link_veracity_result.warnings: + _append_unique_warning(warnings, warning) + + invalid_links = set(link_veracity_result.invalid_links) + if invalid_links: + assembled_output, id_rewrites, promotion_warnings = promote_failed_id_entities( + assembled=assembled_output, + invalid_links=invalid_links, + ) + for warning in promotion_warnings: + _append_unique_warning(warnings, warning) + if id_rewrites: + logger.info( + "%s: promoted %d entity id(s) past failed url(s)", + STAGE_LINK_VERACITY, + len(id_rewrites), + ) + # The rewritten entities now expose their new id; remove the + # old urls from invalid_links so we don't also try to prune them. + invalid_links = invalid_links - set(id_rewrites) + # Rebuild entity_link_map with the new ids so downstream pruning + # acts on the right entity references. + rebuilt_entity_link_map: dict[str, list[str]] = {} + for old_id, links in link_veracity_result.entity_link_map.items(): + new_id = id_rewrites.get(old_id, old_id) + rebuilt_entity_link_map.setdefault(new_id, []).extend(links) + else: + rebuilt_entity_link_map = link_veracity_result.entity_link_map + + if invalid_links: + assembled_output, link_pruning_warnings = apply_link_pruning_to_assembled_output( + assembled=assembled_output, + invalid_links=invalid_links, + entity_link_map=rebuilt_entity_link_map, + article_identifier_link_map=link_veracity_result.article_identifier_link_map, + ) + for warning in link_pruning_warnings: + _append_unique_warning(warnings, warning) + + # Article-validation runs whether or not link-veracity ran. If + # link-veracity was skipped, supported/unsupported sets will be empty, + # and the stage will only drop articles with placeholder/sentinel DOIs + # (i.e. its non-veracity-dependent rules still apply). + veracity_records = ( + link_veracity_result.records if link_veracity_result is not None else [] + ) + stage_started_at = perf_counter() + assembled_output, article_validation_warnings = validate_articles( + assembled_output, + veracity_records=veracity_records, + ) + logger.info( + "validate_articles: warnings=%d in %.2fs", + len(article_validation_warnings), + perf_counter() - stage_started_at, + ) + for warning in article_validation_warnings: + _append_unique_warning(warnings, warning) + + stage_started_at = perf_counter() + assembled_output, author_class_warnings = validate_author_classes(assembled_output) + logger.info( + "author_class_validation: pruned=%d in %.2fs", + len(author_class_warnings), + perf_counter() - stage_started_at, + ) + for warning in author_class_warnings: + _append_unique_warning(warnings, warning) + + link_veracity_seconds = perf_counter() - link_veracity_started_at + + stage_started_at = perf_counter() + assembled_output, ownership_warnings = validate_ownership(assembled_output) + logger.info( + "ownership_check: dropped=%d in %.2fs", + len(ownership_warnings), + perf_counter() - stage_started_at, + ) + for warning in ownership_warnings: + _append_unique_warning(warnings, warning) + + # Run `infer_owners` BEFORE `prune_dangling_refs` so it can materialise + # minimal Person stubs for github owners that have no matching entity + # (otherwise prune would clear `pulse:ownedBy` first and the stub + # opportunity is lost). + stage_started_at = perf_counter() + assembled_output, owner_inference_warnings = infer_owners(assembled_output) + logger.info( + "owner_inference: stamped=%d in %.2fs", + len(owner_inference_warnings), + perf_counter() - stage_started_at, + ) + for warning in owner_inference_warnings: + _append_unique_warning(warnings, warning) + + stage_started_at = perf_counter() + assembled_output, prune_warnings = prune_dangling_refs(assembled_output) + logger.info( + "prune_dangling_refs: actions=%d in %.2fs", + len(prune_warnings), + perf_counter() - stage_started_at, + ) + for warning in prune_warnings: + _append_unique_warning(warnings, warning) + + # Fuzzy-search ROR for parent organizations of every github-only org in + # the graph. The github org always remains as a standalone entity; ROR + # matches get added as additional org entities and the best match + # becomes the github org's `unitOf` parent. + # Runs before the LLM relationship stage so it sees the new ROR entities + # and can refine the unitOf decision; runs before `infer_org_units` so + # the token-overlap fallback also gets the broader graph. + stage_started_at = perf_counter() + assembled_output, github_parent_warnings = infer_github_handle_parents( + assembled_output, + providers=providers, + ) + logger.info( + "github_handle_parents: actions=%d in %.2fs", + len(github_parent_warnings), + perf_counter() - stage_started_at, + ) + for warning in github_parent_warnings: + _append_unique_warning(warnings, warning) + + if resolved_runtime == AgentRuntime.LLM: + stage_started_at = perf_counter() + try: + assembled_output, org_relationship_warnings = await run_org_relationships_stage( + assembled=assembled_output, + source_url=classification.normalized_url, + providers=providers, + ) + except Exception as exc: + logger.exception("org_relationships stage failed") + _append_unique_warning(warnings, f"org_relationships stage failed: {exc}") + else: + logger.info( + "org_relationships: edges=%d in %.2fs", + len(org_relationship_warnings), + perf_counter() - stage_started_at, + ) + for warning in org_relationship_warnings: + _append_unique_warning(warnings, warning) + + stage_started_at = perf_counter() + assembled_output, org_unit_warnings = infer_org_units(assembled_output) + logger.info( + "org_unit_inference: stamped=%d in %.2fs", + len(org_unit_warnings), + perf_counter() - stage_started_at, + ) + for warning in org_unit_warnings: + _append_unique_warning(warnings, warning) + + if _concept_tagging_is_enabled() and classification.detected_type.value == "repository": + repository_context = ( + gathered_context.get("repository") + if isinstance(gathered_context, dict) + else None + ) + readme_text = ( + repository_context.get("readme_content") + if isinstance(repository_context, dict) + else None + ) + backend = _resolve_concept_tagging_backend() + stage_started_at = perf_counter() + try: + tagged_root, tagging_result = await run_concept_tagging_stage( + root_entity=assembled_output.root_entity, + readme_text=readme_text, + backend=backend, + epfl_min_score=_resolve_concept_tagging_epfl_min_score(), + enable_related_openalex=_resolve_concept_tagging_related_enrichment(), + ) + except Exception as exc: + logger.exception("concept_tagging stage failed") + _append_unique_warning(warnings, f"concept_tagging stage failed: {exc}") + else: + assembled_output.root_entity = tagged_root + logger.info( + "concept_tagging: backend=%s keywords=%d concepts=%d disciplines=%d in %.2fs", + tagging_result.backend, + len(tagging_result.keywords), + len(tagging_result.concepts), + len(tagging_result.disciplines), + perf_counter() - stage_started_at, + ) + for warning in tagging_result.warnings: + _append_unique_warning(warnings, warning) + + stage_started_at = perf_counter() + shacl_graph_payload = build_jsonld_output( + assembled=assembled_output, + jsonld_context=jsonld_context, + ) + graph_nodes = shacl_graph_payload.get("@graph") + logger.info( + "%s: entities=%d context_terms=%d in %.2fs", + STAGE_JSONLD_BUILD, + len(graph_nodes) if isinstance(graph_nodes, list) else 0, + len(jsonld_context), + perf_counter() - stage_started_at, + ) + + shacl_data_graph = _jsonld_to_graph(shacl_graph_payload) + if shacl_data_graph is None: + logger.warning("%s: skipped — unable to parse assembled graph payload", STAGE_SHACL_GATE) + _append_unique_warning( + warnings, + "SHACL validation skipped: unable to parse assembled graph payload", + ) + else: + try: + shacl_result = SHACLValidator().validate_graph( + shacl_data_graph, + load_ontology_shapes_graph(), + ) + except SHACLRuntimeUnavailableError as exc: + logger.warning("%s: skipped — %s", STAGE_SHACL_GATE, exc) + _append_unique_warning(warnings, str(exc)) + except Exception as exc: + logger.exception("%s failed", STAGE_SHACL_GATE) + _append_unique_warning(warnings, f"SHACL validation failed: {exc}") + else: + for violation in shacl_result.violations: + _append_unique_warning( + warnings, + ( + "SHACL violation: " + f"focus={violation.get('focusNode')}, " + f"path={violation.get('path')}, " + f"message={violation.get('message')}" + ), + ) + for shacl_warning in shacl_result.warnings: + _append_unique_warning( + warnings, + ( + "SHACL warning: " + f"focus={shacl_warning.get('focusNode')}, " + f"path={shacl_warning.get('path')}, " + f"message={shacl_warning.get('message')}" + ), + ) + logger.info( + "%s: conforms=%s violations=%d warnings=%d", + STAGE_SHACL_GATE, + shacl_result.conforms, + len(shacl_result.violations), + len(shacl_result.warnings), + ) + + response_output: V2JSONLDOutput | V2JSONOutputEnvelope + if output_format == "jsonld": + response_output = V2JSONLDOutput.model_validate(shacl_graph_payload) + else: + response_output = V2JSONOutputEnvelope.model_validate( + build_json_output(assembled_output), + ) + context_summary_markdown: str | None = None + if include_context_summary: + summary_value = gathered_context.get("compiled_context_markdown") + if isinstance(summary_value, str): + normalized_summary = summary_value.strip() + if normalized_summary: + context_summary_markdown = summary_value + + output_payload = response_output.model_dump(mode="json", by_alias=True) + extract_graph = _jsonld_to_graph(output_payload) if output_format == "jsonld" else None + final_entities = [] + if isinstance(assembled_output.root_entity, dict): + final_entities.append(assembled_output.root_entity) + final_entities.extend(assembled_output.related_entities) + final_entity_count = len(final_entities) + + completed_stages = list(pipeline_result.stages_completed) + stage_sequence = [STAGE_PERMISSIVE_VALIDATION] + if llm_dedup_executed: + stage_sequence.append(STAGE_LLM_DEDUP) + stage_sequence.append(STAGE_RECONCILIATION) + if llm_critic_executed: + stage_sequence.append(STAGE_LLM_CRITIC) + stage_sequence.extend( + [ + STAGE_STRICT_VALIDATION, + STAGE_OUTPUT_ASSEMBLY, + STAGE_LINK_VERACITY, + STAGE_JSONLD_BUILD, + STAGE_SHACL_GATE, + ], + ) + for stage_name in stage_sequence: + if stage_name not in completed_stages: + completed_stages.append(stage_name) + stats = compute_stats( + graph=extract_graph, + run_id=run_id, + duration_ms=pipeline_result.duration_ms, + stages_completed=completed_stages, + entities_count=final_entity_count, + ) + + total_seconds = perf_counter() - total_started_at + orchestrator_seconds = pipeline_result.duration_ms / 1000.0 + other_seconds = max(0.0, total_seconds - orchestrator_seconds - link_veracity_seconds) + logger.info( + "extract complete: run_id=%s url=%s entities=%d warnings=%d " + "total=%s (orchestrator=%s, link_veracity=%s, other=%s)", + run_id, + classification.normalized_url, + final_entity_count, + len(warnings), + _format_duration(total_seconds), + _format_duration(orchestrator_seconds), + _format_duration(link_veracity_seconds), + _format_duration(other_seconds), + ) + + response_model = V2ExtractResponse( + source_url=classification.normalized_url, + detected_type=classification.detected_type.value, + output_format=output_format, + output=response_output, + context_summary_markdown=context_summary_markdown, + warnings=warnings, + stats=stats, + ) + + if pipeline_cache is not None and pipeline_cache_key is not None: + try: + pipeline_cache.set( + pipeline_cache_key, + response_model.model_dump(mode="json", exclude_none=True), + ) + except Exception: + logger.exception( + "failed to write pipeline cache (run_id=%s)", + run_id, + ) + + written_log_path = query_log.write() + if written_log_path is not None: + logger.info("query log written: %s", written_log_path) + + return response_model + + +@v2_router.post( + "/extract", + response_model=V2ExtractJobAccepted, + response_model_exclude_none=True, + status_code=status.HTTP_202_ACCEPTED, +) +async def extract_post( + payload: V2ExtractRequest, + request: Request, + *, + providers: Annotated[ProviderSet, Depends(get_provider_set)], + _token: Annotated[str, Depends(verify_token)], +) -> V2ExtractJobAccepted | JSONResponse: + """Submit an extraction asynchronously. Returns a job id to poll via GET.""" + + try: + classification = classify_github_url(payload.source_url) + except UnsupportedGitHubURL as exc: + error_payload = V2ErrorResponse( + error_type=V2ErrorType.UNSUPPORTED_URL, + detail=exc.reason, + source_url=exc.normalized_url, + detected_path_kind=_extract_path_kind(payload.source_url), + ) + return JSONResponse( + status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, + content=error_payload.model_dump(mode="json", exclude_none=True), + ) + except ValueError as exc: + error_payload = V2ErrorResponse( + error_type=V2ErrorType.UNSUPPORTED_URL, + detail=str(exc), + source_url=payload.source_url, + detected_path_kind=_extract_path_kind(payload.source_url), + ) + return JSONResponse( + status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, + content=error_payload.model_dump(mode="json", exclude_none=True), + ) + + job_store = _resolve_job_store(request) + if job_store is None: + error_payload = V2ErrorResponse( + error_type=V2ErrorType.PIPELINE_ERROR, + detail="async job store unavailable: provider cache is disabled", + source_url=classification.normalized_url, + ) + return JSONResponse( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + content=error_payload.model_dump(mode="json", exclude_none=True), + ) + + job_id = str(uuid4()) + submitted_at = datetime.now(timezone.utc) + normalized_payload = payload.model_copy( + update={"source_url": classification.normalized_url}, + ) + job = V2ExtractJob( + job_id=job_id, + status=V2ExtractJobStatus.PENDING, + request=normalized_payload, + submitted_at=submitted_at, + ) + job_store.set(job) + + task = asyncio.create_task( + _run_extract_job( + payload=normalized_payload, + request=request, + providers=providers, + job_store=job_store, + job_id=job_id, + ), + ) + _track_background_task(request, task) + + logger.info( + "extract job submitted: job_id=%s url=%s detected_type=%s", + job_id, + classification.normalized_url, + classification.detected_type.value, + ) + return V2ExtractJobAccepted( + job_id=job_id, + status=V2ExtractJobStatus.PENDING, + status_url=_job_status_path(job_id), + submitted_at=submitted_at, + ) + + +@v2_router.get( + "/jobs/{job_id}", + response_model=V2ExtractJob, + response_model_exclude_none=True, +) +async def extract_job( + job_id: Annotated[str, Path(description="Job id returned by POST /v2/extract.")], + request: Request, + _token: Annotated[str, Depends(verify_token)], +) -> V2ExtractJob | JSONResponse: + """Retrieve a previously submitted extraction job by id.""" + + job_store = _resolve_job_store(request) + if job_store is None: + error_payload = V2ErrorResponse( + error_type=V2ErrorType.PIPELINE_ERROR, + detail="async job store unavailable: provider cache is disabled", + ) + return JSONResponse( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + content=error_payload.model_dump(mode="json", exclude_none=True), + ) + + record = job_store.get(job_id) + if record is None: + error_payload = V2ErrorResponse( + error_type=V2ErrorType.NOT_FOUND, + detail=f"no extract job found with id '{job_id}'", + ) + return JSONResponse( + status_code=status.HTTP_404_NOT_FOUND, + content=error_payload.model_dump(mode="json", exclude_none=True), + ) + return record + + +@v2_router.get( + "/health", + response_model=V2HealthResponse, +) +async def health() -> V2HealthResponse: + component_statuses: dict[str, Literal["healthy", "degraded", "unhealthy"]] = { + "python": ( + "healthy" + if sys.version_info[:2] >= MIN_SUPPORTED_PYTHON + else "unhealthy" + ), + } + + config: V2Config | None = None + try: + config = V2Config() + component_statuses["config"] = "healthy" + except ValueError: + component_statuses["config"] = "unhealthy" + + rate_limit_summary: GitHubRateLimitSummary | None = None + if config and config.GITHUB_TOKEN: + try: + rate_limit_summary = probe_github_rate_limit() + except Exception: # noqa: BLE001 — probe must never crash health + logger.exception("github rate-limit probe failed") + rate_limit_summary = None + component_statuses["github_token"] = ( + rate_limit_summary.status if rate_limit_summary is not None else "degraded" + ) + else: + component_statuses["github_token"] = "degraded" + + overall_status: Literal["healthy", "degraded", "unhealthy"] + if "unhealthy" in component_statuses.values(): + overall_status = "unhealthy" + elif "degraded" in component_statuses.values(): + overall_status = "degraded" + else: + overall_status = "healthy" + + return V2HealthResponse( + status=overall_status, + components=component_statuses, + version=PACKAGE_VERSION, + github_rate_limit=rate_limit_summary, + ) diff --git a/src/v2/api_models/__init__.py b/src/v2/api_models/__init__.py new file mode 100644 index 0000000..ab5eed8 --- /dev/null +++ b/src/v2/api_models/__init__.py @@ -0,0 +1,31 @@ +from src.v2.api_models.contracts import ( + V2ExtractJob, + V2ExtractJobAccepted, + V2ExtractJobStatus, + V2ExtractRequest, + V2ExtractResponse, + V2HealthResponse, + V2JSONLDOutput, + V2JSONOutputEnvelope, + V2Stats, +) +from src.v2.api_models.enums import DisciplineV2, OrganizationTypeV2, RepositoryTypeV2 +from src.v2.api_models.errors import V2ErrorResponse, V2ErrorType, V2FieldError + +__all__ = [ + "DisciplineV2", + "OrganizationTypeV2", + "RepositoryTypeV2", + "V2ErrorResponse", + "V2ErrorType", + "V2ExtractJob", + "V2ExtractJobAccepted", + "V2ExtractJobStatus", + "V2ExtractRequest", + "V2ExtractResponse", + "V2FieldError", + "V2HealthResponse", + "V2JSONLDOutput", + "V2JSONOutputEnvelope", + "V2Stats", +] diff --git a/src/v2/api_models/contracts.py b/src/v2/api_models/contracts.py new file mode 100644 index 0000000..126b793 --- /dev/null +++ b/src/v2/api_models/contracts.py @@ -0,0 +1,94 @@ +from __future__ import annotations + +from datetime import datetime +from enum import Enum +from typing import Any, Literal + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +from src.v2.api_models.errors import V2ErrorResponse +from src.v2.observation.github_rate_limit import GitHubRateLimitSummary + + +class V2Stats(BaseModel): + entities_count: int + triples_count: int + run_id: str + duration_ms: int + stages_completed: list[str] = Field(default_factory=list) + + +class V2JSONLDOutput(BaseModel): + model_config = ConfigDict(extra="forbid", populate_by_name=True) + + context: dict[str, Any] = Field(alias="@context") + graph: list[dict[str, Any]] = Field(alias="@graph") + excluded_entities: list[dict[str, Any]] | None = None + + +class V2JSONOutputEnvelope(BaseModel): + model_config = ConfigDict(extra="forbid") + + root_entity: dict[str, Any] | None + related_entities: list[dict[str, Any]] + excluded_entities: list[dict[str, Any]] + entities_by_type: dict[str, list[dict[str, Any]]] + + +class V2ExtractRequest(BaseModel): + source_url: str + output_format: Literal["jsonld", "json"] = "jsonld" + agent_runtime: Literal["rule_based", "llm", "hybrid"] | None = None + include_context_summary: bool = False + + +class V2ExtractResponse(BaseModel): + source_url: str + detected_type: Literal["repository", "user", "organization"] + output_format: Literal["jsonld", "json"] + output: V2JSONLDOutput | V2JSONOutputEnvelope + context_summary_markdown: str | None = None + warnings: list[str] = Field(default_factory=list) + stats: V2Stats + + @model_validator(mode="after") + def output_matches_format(self) -> V2ExtractResponse: + if self.output_format == "jsonld" and not isinstance(self.output, V2JSONLDOutput): + message = "output must match jsonld contract when output_format=jsonld" + raise ValueError(message) + if self.output_format == "json" and not isinstance(self.output, V2JSONOutputEnvelope): + message = "output must match json envelope contract when output_format=json" + raise ValueError(message) + return self + + +class V2HealthResponse(BaseModel): + status: Literal["healthy", "degraded", "unhealthy"] + components: dict[str, Literal["healthy", "degraded", "unhealthy"]] + version: str + github_rate_limit: GitHubRateLimitSummary | None = None + + +class V2ExtractJobStatus(str, Enum): + PENDING = "pending" + RUNNING = "running" + COMPLETED = "completed" + FAILED = "failed" + + +class V2ExtractJob(BaseModel): + job_id: str + status: V2ExtractJobStatus + request: V2ExtractRequest + submitted_at: datetime + started_at: datetime | None = None + completed_at: datetime | None = None + result: V2ExtractResponse | None = None + error: V2ErrorResponse | None = None + + +class V2ExtractJobAccepted(BaseModel): + job_id: str + status: V2ExtractJobStatus + status_url: str + submitted_at: datetime diff --git a/src/v2/api_models/enums.py b/src/v2/api_models/enums.py new file mode 100644 index 0000000..e0538d5 --- /dev/null +++ b/src/v2/api_models/enums.py @@ -0,0 +1,94 @@ +from __future__ import annotations + +from enum import Enum +from typing import Self + + +class DisciplineV2(str, Enum): + wikidata_uri: str + + def __new__(cls, identifier: str, wikidata_uri: str) -> Self: + instance = str.__new__(cls, identifier) + instance._value_ = identifier + instance.wikidata_uri = wikidata_uri + return instance + + MECHANICAL_ENGINEERING = ("wd:Q101333", "http://www.wikidata.org/entity/Q101333") + GEOGRAPHY = ("wd:Q1071", "http://www.wikidata.org/entity/Q1071") + COMMUNICATION_STUDIES = ("wd:Q11680831", "http://www.wikidata.org/entity/Q11680831") + ARCHITECTURE = ("wd:Q12271", "http://www.wikidata.org/entity/Q12271") + STATISTICS = ("wd:Q12483", "http://www.wikidata.org/entity/Q12483") + INDUSTRIAL_AND_PRODUCTION_ENGINEERING = ( + "wd:Q18351432", + "http://www.wikidata.org/entity/Q18351432", + ) + ENVIRONMENTAL_SCIENCE = ("wd:Q188847", "http://www.wikidata.org/entity/Q188847") + MILITARY_SCIENCE = ("wd:Q192386", "http://www.wikidata.org/entity/Q192386") + SOCIOLOGY = ("wd:Q21201", "http://www.wikidata.org/entity/Q21201") + SYSTEMS_SCIENCE_AND_ENGINEERING = ("wd:Q2167061", "http://www.wikidata.org/entity/Q2167061") + CHEMISTRY = ("wd:Q2329", "http://www.wikidata.org/entity/Q2329") + ANTHROPOLOGY = ("wd:Q23404", "http://www.wikidata.org/entity/Q23404") + THEORETICAL_COMPUTER_SCIENCE = ("wd:Q2878974", "http://www.wikidata.org/entity/Q2878974") + HISTORY = ("wd:Q309", "http://www.wikidata.org/entity/Q309") + ASTRONOMY = ("wd:Q333", "http://www.wikidata.org/entity/Q333") + ENERGY_ENGINEERING = ("wd:Q3353193", "http://www.wikidata.org/entity/Q3353193") + SOCIAL_SCIENCES = ("wd:Q34749", "http://www.wikidata.org/entity/Q34749") + AGRICULTURAL_AND_FOOD_SCIENCES = ("wd:Q3606845", "http://www.wikidata.org/entity/Q3606845") + MATHEMATICS = ("wd:Q395", "http://www.wikidata.org/entity/Q395") + PHYSICS = ("wd:Q413", "http://www.wikidata.org/entity/Q413") + BIOLOGY = ("wd:Q420", "http://www.wikidata.org/entity/Q420") + RESEARCH = ("wd:Q42240", "http://www.wikidata.org/entity/Q42240") + COMPUTER_ENGINEERING = ("wd:Q428691", "http://www.wikidata.org/entity/Q428691") + COMPUTER_SCIENCE = COMPUTER_ENGINEERING + ELECTRICAL_ENGINEERING = ("wd:Q43035", "http://www.wikidata.org/entity/Q43035") + BUSINESS = ("wd:Q4830453", "http://www.wikidata.org/entity/Q4830453") + BIOLOGICAL_ENGINEERING = ("wd:Q580689", "http://www.wikidata.org/entity/Q580689") + PHILOSOPHY = ("wd:Q5891", "http://www.wikidata.org/entity/Q5891") + APPLIED_SCIENCES = ("wd:Q7112556", "http://www.wikidata.org/entity/Q7112556") + POLITICS = ("wd:Q7163", "http://www.wikidata.org/entity/Q7163") + ART = ("wd:Q735", "http://www.wikidata.org/entity/Q735") + LAW = ("wd:Q7748", "http://www.wikidata.org/entity/Q7748") + CIVIL_ENGINEERING = ("wd:Q77590", "http://www.wikidata.org/entity/Q77590") + NATURAL_SCIENCES = ("wd:Q7991", "http://www.wikidata.org/entity/Q7991") + EARTH_SCIENCE = ("wd:Q8008", "http://www.wikidata.org/entity/Q8008") + HUMANITIES = ("wd:Q80083", "http://www.wikidata.org/entity/Q80083") + LOGIC = ("wd:Q8078", "http://www.wikidata.org/entity/Q8078") + ECONOMICS = ("wd:Q8134", "http://www.wikidata.org/entity/Q8134") + LINGUISTICS = ("wd:Q8162", "http://www.wikidata.org/entity/Q8162") + FORMAL_SCIENCES = ("wd:Q816264", "http://www.wikidata.org/entity/Q816264") + LITERATURE = ("wd:Q8242", "http://www.wikidata.org/entity/Q8242") + CHEMICAL_ENGINEERING = ("wd:Q83588", "http://www.wikidata.org/entity/Q83588") + EDUCATION = ("wd:Q8434", "http://www.wikidata.org/entity/Q8434") + HEALTH_SCIENCES = ("wd:Q843601", "http://www.wikidata.org/entity/Q843601") + RELIGION = ("wd:Q9174", "http://www.wikidata.org/entity/Q9174") + PSYCHOLOGY = ("wd:Q9418", "http://www.wikidata.org/entity/Q9418") + INFORMATION_ENGINEERING = ( + "http://www.wikipedia.org/wiki/Information_engineering", + "http://www.wikipedia.org/wiki/Information_engineering", + ) + + +class RepositoryTypeV2(str, Enum): + SOFTWARE = "pulse:Software" + EDUCATIONAL_RESOURCE = "pulse:EducationalResource" + DOCUMENTATION = "pulse:Documentation" + DATA = "pulse:Data" + OTHER = "pulse:Other" + + +class OrganizationTypeV2(str, Enum): + UNIVERSITY = "pulse:University" + RESEARCH_INSTITUTION = "pulse:ResearchInstitution" + GOVERNMENT_AGENCY = "pulse:GovernmentAgency" + SOFTWARE_PROJECT = "pulse:SoftwareProject" + PRIVATE_COMPANY = "pulse:PrivateCompany" + NON_PROFIT_ORGANIZATION = "pulse:NonProfitOrganization" + COMMUNITY_SPACE = "pulse:CommunitySpace" + OTHER_ORGANIZATION_TYPE = "pulse:OtherOrganizationType" + + +__all__ = [ + "DisciplineV2", + "OrganizationTypeV2", + "RepositoryTypeV2", +] diff --git a/src/v2/api_models/errors.py b/src/v2/api_models/errors.py new file mode 100644 index 0000000..3df9b0c --- /dev/null +++ b/src/v2/api_models/errors.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +from enum import Enum +from typing import Any + +from pydantic import BaseModel + + +class V2ErrorType(str, Enum): + UNSUPPORTED_URL = "unsupported_url" + VALIDATION_ERROR = "validation_error" + PROVIDER_ERROR = "provider_error" + PIPELINE_ERROR = "pipeline_error" + NOT_FOUND = "not_found" + + +class V2FieldError(BaseModel): + field: str + message: str + value: Any | None = None + + +class V2ErrorResponse(BaseModel): + error_type: V2ErrorType + detail: str + source_url: str | None = None + detected_path_kind: str | None = None + errors: list[V2FieldError] | None = None diff --git a/src/v2/auth.py b/src/v2/auth.py new file mode 100644 index 0000000..72280ce --- /dev/null +++ b/src/v2/auth.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +import hmac +import os +from typing import Annotated + +from fastapi import Depends, HTTPException, status +from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer + +_bearer_scheme = HTTPBearer(auto_error=False, description="Bearer API_TOKEN") + + +def verify_token( + credentials: Annotated[ + HTTPAuthorizationCredentials | None, + Depends(_bearer_scheme), + ], +) -> str: + """Validate the bearer token against the `API_TOKEN` env var. + + Fails closed: if `API_TOKEN` is unset the request is rejected with 503, + so a misconfigured deployment never silently goes open. + Comparison uses `hmac.compare_digest` to avoid timing leaks. + """ + expected = os.getenv("API_TOKEN") + if not expected: + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail="Auth not configured: API_TOKEN is unset", + ) + + if credentials is None or credentials.scheme.lower() != "bearer": + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Missing bearer token", + headers={"WWW-Authenticate": "Bearer"}, + ) + + if not hmac.compare_digest(credentials.credentials, expected): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid bearer token", + headers={"WWW-Authenticate": "Bearer"}, + ) + + return credentials.credentials diff --git a/src/v2/canonicalization/__init__.py b/src/v2/canonicalization/__init__.py new file mode 100644 index 0000000..c390552 --- /dev/null +++ b/src/v2/canonicalization/__init__.py @@ -0,0 +1,17 @@ +"""Canonical ID resolution for v2 entities.""" + +from src.v2.canonicalization.id_resolution import ( + resolve_article_id, + resolve_organization_id, + resolve_person_id, + resolve_repository_id, +) +from src.v2.canonicalization.string_utils import normalize_string + +__all__ = [ + "normalize_string", + "resolve_article_id", + "resolve_organization_id", + "resolve_person_id", + "resolve_repository_id", +] diff --git a/src/v2/canonicalization/id_resolution.py b/src/v2/canonicalization/id_resolution.py new file mode 100644 index 0000000..f231aeb --- /dev/null +++ b/src/v2/canonicalization/id_resolution.py @@ -0,0 +1,460 @@ +from __future__ import annotations + +import json +import re +import uuid +from typing import Any + +INFOSCIENCE_CORE_ITEMS_BASE_URI = "https://infoscience.epfl.ch/server/api/core/items/" +INFOSCIENCE_PERSON_BASE_URI = INFOSCIENCE_CORE_ITEMS_BASE_URI +INFOSCIENCE_ORGANIZATION_BASE_URI = INFOSCIENCE_CORE_ITEMS_BASE_URI +INFOSCIENCE_PUBLICATION_BASE_URI = INFOSCIENCE_CORE_ITEMS_BASE_URI +GITHUB_BASE_URI = "https://github.com/" +ORCID_BASE_URI = "https://orcid.org/" +ROR_BASE_URI = "https://ror.org/" +DOI_BASE_URI = "https://doi.org/" + +PERSON_UUID_NAMESPACE = uuid.UUID("bfc0a4f9-2ef5-59eb-9bf8-76ef425de91e") +ORGANIZATION_UUID_NAMESPACE = uuid.UUID("4f7f847a-8d6e-56b3-9164-2668e51f040f") +REPOSITORY_UUID_NAMESPACE = uuid.UUID("c5b462e3-9cf5-5b59-b2df-bdbfd9bd0ac5") +ARTICLE_UUID_NAMESPACE = uuid.UUID("f9f26cbf-1939-5c0d-a0f2-89dcf8a56cf8") + +PERSON_ID_SOURCES = { + "pulse:orcid", + "pulse:infosciencePersonIdentifier", + "pulse:githubUsername", + "uuid", +} +ORGANIZATION_ID_SOURCES = { + "pulse:ror", + "pulse:infoscienceOrganizationIdentifier", + "pulse:githubOrganizationHandle", + "uuid", +} +REPOSITORY_ID_SOURCES = { + "pulse:githubRepositoryHandle", + "schema:citation", + "uuid", +} +ARTICLE_ID_SOURCES = { + "schema:identifier", + "pulse:infoscienceArticleIdentifier", + "uuid", +} +ID_SOURCE_ALIASES = { + "orcid": "pulse:orcid", + "infosciencePersonIdentifier": "pulse:infosciencePersonIdentifier", + "githubUsername": "pulse:githubUsername", + "ror": "pulse:ror", + "infoscienceOrganizationIdentifier": "pulse:infoscienceOrganizationIdentifier", + "githubOrganizationHandle": "pulse:githubOrganizationHandle", + "githubRepositoryHandle": "pulse:githubRepositoryHandle", + "doi": "schema:identifier", + "infoscienceArticleIdentifier": "pulse:infoscienceArticleIdentifier", +} +REPOSITORY_HANDLE_PARTS = 2 + + +def _clean_text(value: Any) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + return candidate or None + + +def _lookup_identifier(entity: dict[str, Any], keys: tuple[str, ...]) -> str | None: + identifiers = entity.get("identifiers") + for key in keys: + direct = _clean_text(entity.get(key)) + if direct is not None: + return direct + + if isinstance(identifiers, dict): + nested = _clean_text(identifiers.get(key)) + if nested is not None: + return nested + + return None + + +def _normalize_uuid(value: Any) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + try: + return str(uuid.UUID(candidate)) + except ValueError: + return None + + +def _existing_resolution( + entity: dict[str, Any], + valid_sources: set[str], +) -> tuple[str, str] | None: + entity_id = _clean_text(entity.get("id")) + id_source = _clean_text(entity.get("idSource")) + if entity_id is None or id_source is None: + return None + normalized_source = ID_SOURCE_ALIASES.get(id_source, id_source) + if normalized_source not in valid_sources: + return None + normalized_id: str | None = None + + if normalized_source == "pulse:orcid": + normalized_orcid = _normalize_orcid(entity_id) + if normalized_orcid is not None: + normalized_id = f"{ORCID_BASE_URI}{normalized_orcid}" + elif normalized_source == "pulse:infosciencePersonIdentifier": + normalized_infoscience_id = _normalize_infoscience_identifier( + entity_id, + entity_path="person", + legacy_path="person", + ) + if normalized_infoscience_id is not None: + normalized_id = f"{INFOSCIENCE_PERSON_BASE_URI}{normalized_infoscience_id}" + elif normalized_source == "pulse:githubUsername": + normalized_github_username = _normalize_github_handle(entity_id) + if normalized_github_username is not None: + normalized_id = f"{GITHUB_BASE_URI}{normalized_github_username}" + elif normalized_source == "pulse:ror": + normalized_ror = _normalize_ror(entity_id) + if normalized_ror is not None: + normalized_id = f"{ROR_BASE_URI}{normalized_ror}" + elif normalized_source == "pulse:infoscienceOrganizationIdentifier": + normalized_infoscience_id = _normalize_infoscience_identifier( + entity_id, + entity_path="orgunit", + legacy_path="organization", + ) + if normalized_infoscience_id is not None: + normalized_id = ( + f"{INFOSCIENCE_ORGANIZATION_BASE_URI}{normalized_infoscience_id}" + ) + elif normalized_source == "pulse:githubOrganizationHandle": + normalized_github_handle = _normalize_github_handle(entity_id) + if normalized_github_handle is not None: + normalized_id = f"{GITHUB_BASE_URI}{normalized_github_handle}" + elif normalized_source == "pulse:githubRepositoryHandle": + normalized_repository_handle = _normalize_repository_handle(entity_id) + if normalized_repository_handle is not None: + normalized_id = f"{GITHUB_BASE_URI}{normalized_repository_handle}" + elif normalized_source in ("schema:identifier", "schema:citation"): + normalized_doi = _normalize_doi(entity_id) + if normalized_doi is not None: + normalized_id = f"{DOI_BASE_URI}{normalized_doi}" + elif normalized_source == "pulse:infoscienceArticleIdentifier": + normalized_infoscience_id = _normalize_infoscience_identifier( + entity_id, + entity_path="publication", + ) + if normalized_infoscience_id is not None: + normalized_id = f"{INFOSCIENCE_PUBLICATION_BASE_URI}{normalized_infoscience_id}" + elif normalized_source == "uuid": + normalized_id = _normalize_uuid(entity_id) + + if normalized_id is None: + return None + return normalized_id, normalized_source + + +def _normalize_orcid(orcid: str | None) -> str | None: + if orcid is None: + return None + candidate = orcid + if candidate.lower().startswith(ORCID_BASE_URI): + candidate = candidate.rsplit("/", maxsplit=1)[-1] + return _clean_text(candidate) + + +def _normalize_ror(ror: str | None) -> str | None: + if ror is None: + return None + candidate = ror + lower_candidate = candidate.lower() + if lower_candidate.startswith(ROR_BASE_URI): + candidate = candidate.rsplit("/", maxsplit=1)[-1] + return _clean_text(candidate.lower()) + + +def _normalize_github_handle(handle: str | None) -> str | None: + if handle is None: + return None + candidate = handle.strip() + lower_candidate = candidate.lower() + if lower_candidate.startswith(GITHUB_BASE_URI): + remainder = candidate[len(GITHUB_BASE_URI) :] + candidate = remainder.split("/", maxsplit=1)[0] + if candidate.startswith("@"): + candidate = candidate[1:] + return _clean_text(candidate) + + +def _normalize_infoscience_identifier( + identifier: str | None, + *, + entity_path: str, + legacy_path: str | None = None, +) -> str | None: + candidate = _clean_text(identifier) + if candidate is None: + return None + + entity_pattern = ( + rf"^(?:https?://infoscience\.epfl\.ch)?/?(?:server/api/)?" + rf"entities/{re.escape(entity_path)}/([^/?#]+)(?:/full)?(?:[/?#].*)?$" + ) + entity_match = re.match(entity_pattern, candidate, flags=re.IGNORECASE) + if entity_match: + return _clean_text(entity_match.group(1)) + + core_items_pattern = ( + r"^(?:https?://infoscience\.epfl\.ch)?/?(?:server/api/)?" + r"core/items/([^/?#]+)(?:[/?#].*)?$" + ) + core_items_match = re.match(core_items_pattern, candidate, flags=re.IGNORECASE) + if core_items_match: + return _clean_text(core_items_match.group(1)) + + if legacy_path is not None: + legacy_pattern = ( + rf"^(?:https?://infoscience\.epfl\.ch)?/?" + rf"{re.escape(legacy_path)}/([^/?#]+)(?:[/?#].*)?$" + ) + legacy_match = re.match(legacy_pattern, candidate, flags=re.IGNORECASE) + if legacy_match: + return _clean_text(legacy_match.group(1)) + + return candidate + + +def _normalize_repository_handle(handle: str | None) -> str | None: + if handle is None: + return None + + candidate = handle.strip() + lower_candidate = candidate.lower() + if lower_candidate.startswith(GITHUB_BASE_URI): + candidate = candidate[len(GITHUB_BASE_URI) :] + + handle_parts = [part for part in candidate.split("/") if part] + if len(handle_parts) != REPOSITORY_HANDLE_PARTS: + return None + + owner, repository = handle_parts + if not owner or not repository: + return None + + return f"{owner}/{repository}" + + +def _normalize_doi(doi: str | None) -> str | None: + if doi is None: + return None + + candidate = doi.strip() + lower_candidate = candidate.lower() + for prefix in ("https://doi.org/", "http://doi.org/", "https://dx.doi.org/", "http://dx.doi.org/"): + if lower_candidate.startswith(prefix): + candidate = candidate[len(prefix) :] + break + + if not candidate or "/" not in candidate: + return None + return candidate + + +def _deterministic_uuid( + namespace: uuid.UUID, + entity: dict[str, Any], + keys_for_seed: tuple[str, ...], +) -> str: + seed_parts: list[str] = [] + for key in keys_for_seed: + value = _lookup_identifier(entity, (key,)) + if value is not None: + seed_parts.append(value.lower()) + if not seed_parts: + seed_parts.append(json.dumps(entity, sort_keys=True, separators=(",", ":"))) + seed = "|".join(seed_parts) + return str(uuid.uuid5(namespace, seed)) + + +def resolve_person_id(person: dict[str, Any]) -> tuple[str, str]: + existing = _existing_resolution(person, PERSON_ID_SOURCES) + if existing is not None: + return existing + + orcid = _normalize_orcid( + _lookup_identifier( + person, + ("pulse:orcid", "pulse:orcidIdentifier", "orcid", "orcidIdentifier"), + ), + ) + if orcid is not None: + return f"{ORCID_BASE_URI}{orcid}", "pulse:orcid" + + infoscience_id = _normalize_infoscience_identifier( + _lookup_identifier( + person, + ( + "pulse:infosciencePersonIdentifier", + "infosciencePersonIdentifier", + ), + ), + entity_path="person", + legacy_path="person", + ) + if infoscience_id is not None: + return ( + f"{INFOSCIENCE_PERSON_BASE_URI}{infoscience_id}", + "pulse:infosciencePersonIdentifier", + ) + + github_username = _normalize_github_handle( + _lookup_identifier( + person, + ("pulse:githubUsername", "githubUsername"), + ), + ) + if github_username is not None: + return f"{GITHUB_BASE_URI}{github_username}", "pulse:githubUsername" + + fallback_uuid = _deterministic_uuid( + PERSON_UUID_NAMESPACE, + person, + ("schema:name", "name", "schema:email", "email"), + ) + return fallback_uuid, "uuid" + + +def resolve_organization_id(organization: dict[str, Any]) -> tuple[str, str]: + hierarchy_candidate = _resolve_organization_hierarchy_candidate(organization) + existing = _existing_resolution(organization, ORGANIZATION_ID_SOURCES) + if existing is None: + return hierarchy_candidate + if existing == hierarchy_candidate: + return existing + return hierarchy_candidate + + +def _resolve_organization_hierarchy_candidate( + organization: dict[str, Any], +) -> tuple[str, str]: + ror = _normalize_ror( + _lookup_identifier( + organization, + ("pulse:ror", "ror", "schema:identifier"), + ), + ) + if ror is not None: + return f"{ROR_BASE_URI}{ror}", "pulse:ror" + + infoscience_id = _normalize_infoscience_identifier( + _lookup_identifier( + organization, + ( + "pulse:infoscienceOrganizationIdentifier", + "infoscienceOrganizationIdentifier", + ), + ), + entity_path="orgunit", + legacy_path="organization", + ) + if infoscience_id is not None: + return ( + f"{INFOSCIENCE_ORGANIZATION_BASE_URI}{infoscience_id}", + "pulse:infoscienceOrganizationIdentifier", + ) + + github_org_handle = _normalize_github_handle( + _lookup_identifier( + organization, + ("pulse:githubOrganizationHandle", "githubOrganizationHandle"), + ), + ) + if github_org_handle is not None: + return ( + f"{GITHUB_BASE_URI}{github_org_handle}", + "pulse:githubOrganizationHandle", + ) + + fallback_uuid = _deterministic_uuid( + ORGANIZATION_UUID_NAMESPACE, + organization, + ("schema:name", "name"), + ) + return fallback_uuid, "uuid" + + +def resolve_repository_id(repository: dict[str, Any]) -> tuple[str, str]: + existing = _existing_resolution(repository, REPOSITORY_ID_SOURCES) + if existing is not None: + return existing + + github_handle = _normalize_repository_handle( + _lookup_identifier( + repository, + ("pulse:githubRepositoryHandle", "githubRepositoryHandle"), + ), + ) + if github_handle is not None: + return ( + f"{GITHUB_BASE_URI}{github_handle}", + "pulse:githubRepositoryHandle", + ) + + doi = _normalize_doi( + _lookup_identifier( + repository, + ("schema:citation", "schema:identifier", "doi"), + ), + ) + if doi is not None: + return f"{DOI_BASE_URI}{doi}", "schema:citation" + + fallback_uuid = _deterministic_uuid( + REPOSITORY_UUID_NAMESPACE, + repository, + ("schema:name", "name", "pulse:githubRepositoryHandle", "schema:citation"), + ) + return fallback_uuid, "uuid" + + +def resolve_article_id(article: dict[str, Any]) -> tuple[str, str]: + existing = _existing_resolution(article, ARTICLE_ID_SOURCES) + if existing is not None: + return existing + + doi = _normalize_doi( + _lookup_identifier( + article, + ("schema:identifier", "doi"), + ), + ) + if doi is not None: + return f"{DOI_BASE_URI}{doi}", "schema:identifier" + + infoscience_id = _normalize_infoscience_identifier( + _lookup_identifier( + article, + ( + "pulse:infoscienceArticleIdentifier", + "infoscienceArticleIdentifier", + ), + ), + entity_path="publication", + ) + if infoscience_id is not None: + return ( + f"{INFOSCIENCE_PUBLICATION_BASE_URI}{infoscience_id}", + "pulse:infoscienceArticleIdentifier", + ) + + fallback_uuid = _deterministic_uuid( + ARTICLE_UUID_NAMESPACE, + article, + ("schema:name", "name", "schema:datePublished", "schema:identifier"), + ) + return fallback_uuid, "uuid" diff --git a/src/v2/canonicalization/string_utils.py b/src/v2/canonicalization/string_utils.py new file mode 100644 index 0000000..eeae49e --- /dev/null +++ b/src/v2/canonicalization/string_utils.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +import re +import unicodedata + +_PUNCTUATION_RE = re.compile(r"[^\w\s]+") +_WHITESPACE_RE = re.compile(r"\s+") + + +def strip_accents(value: str) -> str: + normalized = unicodedata.normalize("NFKD", value) + return "".join(char for char in normalized if not unicodedata.combining(char)) + + +def collapse_whitespace(value: str) -> str: + return _WHITESPACE_RE.sub(" ", value).strip() + + +def normalize_string(value: str) -> str: + """Normalize free text for deterministic alias matching.""" + stripped = strip_accents(value.casefold()) + without_punctuation = _PUNCTUATION_RE.sub("", stripped) + return collapse_whitespace(without_punctuation) + diff --git a/src/v2/config.py b/src/v2/config.py new file mode 100644 index 0000000..4fd3d2d --- /dev/null +++ b/src/v2/config.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +import os +from dataclasses import dataclass, field + +from src.v2.agents.runtime import AgentRuntime, parse_agent_runtime + +TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} +FALSE_ENV_VALUES = {"0", "false", "f", "no", "n", "off"} +MISSING_GITHUB_TOKEN_ERROR = "Missing required environment variable: GITHUB_TOKEN" # noqa: S105 + + +def _get_env_bool(name: str, *, default_value: bool) -> bool: + raw_value = os.getenv(name) + if raw_value is None: + return default_value + + normalized_value = raw_value.strip().lower() + if normalized_value in TRUE_ENV_VALUES: + return True + if normalized_value in FALSE_ENV_VALUES: + return False + message = f"Invalid boolean value for {name}: {raw_value!r}" + raise ValueError(message) + + +def _get_env_int(name: str, default: int) -> int: + raw_value = os.getenv(name) + if raw_value is None or raw_value.strip() == "": + return default + try: + return int(raw_value) + except ValueError as exc: + message = f"Invalid integer value for {name}: {raw_value!r}" + raise ValueError(message) from exc + + +def _get_optional_env(name: str) -> str | None: + value = os.getenv(name) + if value is None: + return None + stripped_value = value.strip() + return stripped_value or None + + +@dataclass(slots=True) +class V2Config: + V2_AGENT_RUNTIME_DEFAULT: AgentRuntime = field( + default_factory=lambda: parse_agent_runtime( + os.getenv("V2_AGENT_RUNTIME_DEFAULT"), + default=AgentRuntime.LLM, + field_name="V2_AGENT_RUNTIME_DEFAULT", + ), + ) + V2_PROVIDER_CACHE_PATH: str = field( + default_factory=lambda: os.getenv( + "V2_PROVIDER_CACHE_PATH", + ".cache/v2/providers.db", + ), + ) + V2_PROVIDER_CACHE_TTL_DAYS: int = field( + default_factory=lambda: _get_env_int("V2_PROVIDER_CACHE_TTL_DAYS", 30), + ) + GITHUB_TOKEN: str | None = field(default_factory=lambda: _get_optional_env("GITHUB_TOKEN")) + + def validate_preflight(self) -> None: + if not self.GITHUB_TOKEN: + raise ValueError(MISSING_GITHUB_TOKEN_ERROR) diff --git a/src/v2/dependencies.py b/src/v2/dependencies.py new file mode 100644 index 0000000..b3a32a2 --- /dev/null +++ b/src/v2/dependencies.py @@ -0,0 +1,447 @@ +from __future__ import annotations + +import os +from typing import Any, Callable + +from fastapi import Request # noqa: TC002 + +from src.v2.agents import ProviderSet +from src.v2.config import V2Config +from src.v2.ingest.cache import SECONDS_PER_DAY, ProviderCache +from src.v2.ingest.detection import UnsupportedGitHubURL, classify_github_url +from src.v2.ingest.providers.base import ( + GitHubProvider, + InfoscienceProvider, + ORCIDProvider, + RORProvider, +) +from src.v2.ingest.providers.epfl_graph_rag import ( + EpflGraphRagProvider, +) +from src.v2.ingest.providers.epfl_graph_rag import ( + build_default_provider as build_default_epfl_graph_rag_provider, +) +from src.v2.ingest.providers.ethz_research_collection_rag import ( + EthzResearchCollectionRagProvider, +) +from src.v2.ingest.providers.ethz_research_collection_rag import ( + build_default_provider as build_default_ethz_research_collection_rag_provider, +) +from src.v2.ingest.providers.federated_rag import ( + FederatedRagProvider, +) +from src.v2.ingest.providers.federated_rag import ( + build_default_provider as build_default_federated_rag_provider, +) +from src.v2.ingest.providers.github_provider import RealGitHubProvider +from src.v2.ingest.providers.github_rag import ( + GitHubRagProvider, +) +from src.v2.ingest.providers.github_rag import ( + build_default_provider as build_default_github_rag_provider, +) +from src.v2.ingest.providers.huggingface_rag import ( + HuggingFaceRagProvider, +) +from src.v2.ingest.providers.huggingface_rag import ( + build_default_provider as build_default_huggingface_rag_provider, +) +from src.v2.ingest.providers.infoscience_provider import RealInfoscienceProvider +from src.v2.ingest.providers.infoscience_rag import ( + InfoscienceRagProvider, +) +from src.v2.ingest.providers.infoscience_rag import ( + build_default_provider as build_default_infoscience_rag_provider, +) +from src.v2.ingest.providers.mock_github import MockGitHubProvider +from src.v2.ingest.providers.mock_infoscience import MockInfoscienceProvider +from src.v2.ingest.providers.mock_orcid import MockORCIDProvider +from src.v2.ingest.providers.mock_ror import MockRORProvider +from src.v2.ingest.providers.openalex_rag import ( + OpenAlexRagProvider, +) +from src.v2.ingest.providers.openalex_rag import ( + build_default_provider as build_default_openalex_rag_provider, +) +from src.v2.ingest.providers.orcid_oauth import fetch_access_token as _fetch_orcid_token +from src.v2.ingest.providers.orcid_provider import RealORCIDProvider +from src.v2.ingest.providers.orcid_rag import ( + OrcidRagProvider, +) +from src.v2.ingest.providers.orcid_rag import ( + build_default_provider as build_default_orcid_rag_provider, +) +from src.v2.ingest.providers.renkulab_rag import ( + RenkulabRagProvider, +) +from src.v2.ingest.providers.renkulab_rag import ( + build_default_provider as build_default_renkulab_rag_provider, +) +from src.v2.ingest.providers.ror_provider import RealRORProvider +from src.v2.ingest.providers.ror_rag import ( + RorRagProvider, +) +from src.v2.ingest.providers.ror_rag import ( + build_default_provider as build_default_ror_rag_provider, +) +from src.v2.ingest.providers.snsf_rag import ( + SnsfRagProvider, +) +from src.v2.ingest.providers.snsf_rag import ( + build_default_provider as build_default_snsf_rag_provider, +) +from src.v2.ingest.providers.swissubase_rag import ( + SwissubaseRagProvider, +) +from src.v2.ingest.providers.swissubase_rag import ( + build_default_provider as build_default_swissubase_rag_provider, +) +from src.v2.ingest.providers.zenodo_rag import ( + ZenodoRagProvider, +) +from src.v2.ingest.providers.zenodo_rag import ( + build_default_provider as build_default_zenodo_rag_provider, +) + +TRUE_ENV_VALUES = {"1", "true", "t", "yes", "y", "on"} + + +def _is_truthy_env(value: str | None) -> bool: + if value is None: + return False + return value.strip().lower() in TRUE_ENV_VALUES + + +def _resolve_provider_override(app_state: Any, field_name: str) -> Any | None: + if hasattr(app_state, field_name): + return getattr(app_state, field_name) + return None + + +def _resolve_rag_provider( + app_state: Any, + *, + state_attr: str, + env_var: str, + builder: Callable[[], Any | None], + expected_type: type, +) -> Any | None: + """Generic resolver: cache on app_state, gate on env var, build best-effort. + + The env var is treated as enabled when unset (default ``true``) so the + five RAG providers all default to active. Returns ``None`` on disabled + or build failure — the rest of the v2 pipeline keeps working. + """ + existing = getattr(app_state, state_attr, None) + if existing is not None: + return existing if isinstance(existing, expected_type) else None + + enabled_raw = os.getenv(env_var) + enabled = True if enabled_raw is None else _is_truthy_env(enabled_raw) + if not enabled: + setattr(app_state, state_attr, None) + return None + + provider = builder() + setattr(app_state, state_attr, provider) + return provider + + +def _resolve_infoscience_rag_provider(app_state: Any) -> InfoscienceRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_infoscience_rag_provider", + env_var="V2_INFOSCIENCE_RAG_ENABLED", + builder=build_default_infoscience_rag_provider, + expected_type=InfoscienceRagProvider, + ) + + +def _resolve_ethz_research_collection_rag_provider( + app_state: Any, +) -> EthzResearchCollectionRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_ethz_research_collection_rag_provider", + env_var="V2_ETHZ_RESEARCH_COLLECTION_RAG_ENABLED", + builder=build_default_ethz_research_collection_rag_provider, + expected_type=EthzResearchCollectionRagProvider, + ) + + +def _resolve_huggingface_rag_provider(app_state: Any) -> HuggingFaceRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_huggingface_rag_provider", + env_var="V2_HUGGINGFACE_RAG_ENABLED", + builder=build_default_huggingface_rag_provider, + expected_type=HuggingFaceRagProvider, + ) + + +def _resolve_openalex_rag_provider(app_state: Any) -> OpenAlexRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_openalex_rag_provider", + env_var="V2_OPENALEX_RAG_ENABLED", + builder=build_default_openalex_rag_provider, + expected_type=OpenAlexRagProvider, + ) + + +def _resolve_epfl_graph_rag_provider(app_state: Any) -> EpflGraphRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_epfl_graph_rag_provider", + env_var="V2_EPFL_GRAPH_RAG_ENABLED", + builder=build_default_epfl_graph_rag_provider, + expected_type=EpflGraphRagProvider, + ) + + +def _resolve_zenodo_rag_provider(app_state: Any) -> ZenodoRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_zenodo_rag_provider", + env_var="V2_ZENODO_RAG_ENABLED", + builder=build_default_zenodo_rag_provider, + expected_type=ZenodoRagProvider, + ) + + +def _resolve_github_rag_provider(app_state: Any) -> GitHubRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_github_rag_provider", + env_var="V2_GITHUB_RAG_ENABLED", + builder=build_default_github_rag_provider, + expected_type=GitHubRagProvider, + ) + + +def _resolve_renkulab_rag_provider(app_state: Any) -> RenkulabRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_renkulab_rag_provider", + env_var="V2_RENKULAB_RAG_ENABLED", + builder=build_default_renkulab_rag_provider, + expected_type=RenkulabRagProvider, + ) + + +def _resolve_orcid_rag_provider(app_state: Any) -> OrcidRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_orcid_rag_provider", + env_var="V2_ORCID_RAG_ENABLED", + builder=build_default_orcid_rag_provider, + expected_type=OrcidRagProvider, + ) + + +def _resolve_ror_rag_provider(app_state: Any) -> RorRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_ror_rag_provider", + env_var="V2_ROR_RAG_ENABLED", + builder=build_default_ror_rag_provider, + expected_type=RorRagProvider, + ) + + +def _resolve_snsf_rag_provider(app_state: Any) -> SnsfRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_snsf_rag_provider", + env_var="V2_SNSF_RAG_ENABLED", + builder=build_default_snsf_rag_provider, + expected_type=SnsfRagProvider, + ) + + +def _resolve_swissubase_rag_provider(app_state: Any) -> SwissubaseRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_swissubase_rag_provider", + env_var="V2_SWISSUBASE_RAG_ENABLED", + builder=build_default_swissubase_rag_provider, + expected_type=SwissubaseRagProvider, + ) + + +def _resolve_federated_rag_provider(app_state: Any) -> FederatedRagProvider | None: + return _resolve_rag_provider( + app_state, + state_attr="v2_federated_rag_provider", + env_var="V2_FEDERATED_RAG_ENABLED", + builder=build_default_federated_rag_provider, + expected_type=FederatedRagProvider, + ) + + +def _resolve_provider_cache(app_state: Any) -> ProviderCache | None: + existing = getattr(app_state, "v2_provider_cache", None) + if isinstance(existing, ProviderCache): + return existing + if not _is_truthy_env(os.getenv("V2_PROVIDER_CACHE_ENABLED", "true")): + return None + config = V2Config() + cache = ProviderCache( + config.V2_PROVIDER_CACHE_PATH, + default_ttl_seconds=config.V2_PROVIDER_CACHE_TTL_DAYS * SECONDS_PER_DAY, + ) + app_state.v2_provider_cache = cache + return cache + + +def _default_provider_set( # noqa: PLR0913 — bundle-builder for ProviderSet + *, + use_mock_providers: bool, + include_user_repositories: bool = True, + include_organization_repositories: bool = True, + cache: ProviderCache | None = None, + infoscience_rag: InfoscienceRagProvider | None = None, + ethz_research_collection_rag: EthzResearchCollectionRagProvider | None = None, + huggingface_rag: HuggingFaceRagProvider | None = None, + openalex_rag: OpenAlexRagProvider | None = None, + zenodo_rag: ZenodoRagProvider | None = None, + orcid_rag: OrcidRagProvider | None = None, + ror_rag: RorRagProvider | None = None, + snsf_rag: SnsfRagProvider | None = None, + swissubase_rag: SwissubaseRagProvider | None = None, + renkulab_rag: RenkulabRagProvider | None = None, + github_rag: GitHubRagProvider | None = None, + epfl_graph_rag: EpflGraphRagProvider | None = None, + federated_rag: FederatedRagProvider | None = None, +) -> ProviderSet: + rag_kwargs: dict[str, Any] = { + "infoscience_rag": infoscience_rag, + "ethz_research_collection_rag": ethz_research_collection_rag, + "huggingface_rag": huggingface_rag, + "openalex_rag": openalex_rag, + "zenodo_rag": zenodo_rag, + "orcid_rag": orcid_rag, + "ror_rag": ror_rag, + "snsf_rag": snsf_rag, + "swissubase_rag": swissubase_rag, + "renkulab_rag": renkulab_rag, + "github_rag": github_rag, + "epfl_graph_rag": epfl_graph_rag, + "federated_rag": federated_rag, + } + if use_mock_providers: + return ProviderSet( + github=MockGitHubProvider(), + orcid=MockORCIDProvider(), + infoscience=MockInfoscienceProvider(), + ror=MockRORProvider(), + **rag_kwargs, + ) + return ProviderSet( + github=RealGitHubProvider( + include_user_repositories=include_user_repositories, + include_organization_repositories=include_organization_repositories, + cache=cache, + ), + orcid=RealORCIDProvider(cache=cache, session=_optional_orcid_oauth_session()), + infoscience=RealInfoscienceProvider(cache=cache), + ror=RealRORProvider(cache=cache), + **rag_kwargs, + ) + + +def _optional_orcid_oauth_session() -> Any: + """Build an authenticated `requests.Session` if ORCID OAuth credentials + are present in the environment; return `None` otherwise so the provider + falls back to anonymous public-API access. + + Reads `ORCID_CLIENT_ID` and `ORCID_CLIENT_SECRET` (free `/read-public` + client registered at https://orcid.org/developer-tools). Both must be + set; either one missing → anonymous fallback. + """ + import requests # noqa: PLC0415 — local import keeps base import budget tight. + + client_id = os.getenv("ORCID_CLIENT_ID") or None + client_secret = os.getenv("ORCID_CLIENT_SECRET") or None + if not client_id or not client_secret: + return None + token = _fetch_orcid_token(client_id=client_id, client_secret=client_secret) + if not token: + return None + session = requests.Session() + session.headers["Authorization"] = f"Bearer {token}" + session.headers["User-Agent"] = "git-metadata-extractor/0.1" + return session + + +def _is_repository_extract_request(request: Request) -> bool: + full_path = request.path_params.get("full_path") + if not isinstance(full_path, str) or not full_path.strip(): + return False + + try: + classification = classify_github_url(full_path) + except (UnsupportedGitHubURL, ValueError): + return False + + detected_type = classification.detected_type + return str(getattr(detected_type, "value", detected_type)) == "repository" + + +async def get_provider_set(request: Request) -> ProviderSet: + """Return the v2 provider bundle with app-state overrides when present.""" + app_state = request.app.state + provider_set_override = _resolve_provider_override(app_state, "v2_provider_set") + if isinstance(provider_set_override, ProviderSet): + return provider_set_override + + use_mock_providers = _is_truthy_env(os.getenv("V2_USE_MOCK_PROVIDERS", "true")) + repository_extract_scope = _is_repository_extract_request(request) + provider_cache = None if use_mock_providers else _resolve_provider_cache(app_state) + default_provider_set = _default_provider_set( + use_mock_providers=use_mock_providers, + include_user_repositories=not repository_extract_scope, + include_organization_repositories=not repository_extract_scope, + cache=provider_cache, + infoscience_rag=_resolve_infoscience_rag_provider(app_state), + ethz_research_collection_rag=_resolve_ethz_research_collection_rag_provider(app_state), + huggingface_rag=_resolve_huggingface_rag_provider(app_state), + openalex_rag=_resolve_openalex_rag_provider(app_state), + zenodo_rag=_resolve_zenodo_rag_provider(app_state), + orcid_rag=_resolve_orcid_rag_provider(app_state), + ror_rag=_resolve_ror_rag_provider(app_state), + snsf_rag=_resolve_snsf_rag_provider(app_state), + swissubase_rag=_resolve_swissubase_rag_provider(app_state), + renkulab_rag=_resolve_renkulab_rag_provider(app_state), + github_rag=_resolve_github_rag_provider(app_state), + epfl_graph_rag=_resolve_epfl_graph_rag_provider(app_state), + federated_rag=_resolve_federated_rag_provider(app_state), + ) + + github_provider = _resolve_provider_override(app_state, "v2_github_provider") + orcid_provider = _resolve_provider_override(app_state, "v2_orcid_provider") + infoscience_provider = _resolve_provider_override(app_state, "v2_infoscience_provider") + ror_provider = _resolve_provider_override(app_state, "v2_ror_provider") + + return ProviderSet( + github=github_provider if isinstance(github_provider, GitHubProvider) else default_provider_set.github, + orcid=orcid_provider if isinstance(orcid_provider, ORCIDProvider) else default_provider_set.orcid, + infoscience=( + infoscience_provider + if isinstance(infoscience_provider, InfoscienceProvider) + else default_provider_set.infoscience + ), + ror=ror_provider if isinstance(ror_provider, RORProvider) else default_provider_set.ror, + infoscience_rag=default_provider_set.infoscience_rag, + ethz_research_collection_rag=default_provider_set.ethz_research_collection_rag, + huggingface_rag=default_provider_set.huggingface_rag, + openalex_rag=default_provider_set.openalex_rag, + zenodo_rag=default_provider_set.zenodo_rag, + orcid_rag=default_provider_set.orcid_rag, + ror_rag=default_provider_set.ror_rag, + snsf_rag=default_provider_set.snsf_rag, + swissubase_rag=default_provider_set.swissubase_rag, + renkulab_rag=default_provider_set.renkulab_rag, + epfl_graph_rag=default_provider_set.epfl_graph_rag, + federated_rag=default_provider_set.federated_rag, + ) diff --git a/src/v2/ingest/__init__.py b/src/v2/ingest/__init__.py new file mode 100644 index 0000000..bf486de --- /dev/null +++ b/src/v2/ingest/__init__.py @@ -0,0 +1 @@ +"""Ingestion-time providers and source detection utilities for v2.""" diff --git a/src/v2/ingest/cache.py b/src/v2/ingest/cache.py new file mode 100644 index 0000000..0701891 --- /dev/null +++ b/src/v2/ingest/cache.py @@ -0,0 +1,116 @@ +"""SQLite-backed cache for provider responses. + +Cached on success, never on exception or `None`. Single TTL applied to every +entry; configure via `V2_PROVIDER_CACHE_TTL_DAYS` (default 30) and +`V2_PROVIDER_CACHE_PATH` (default `.cache/v2/providers.db`). +""" +from __future__ import annotations + +import hashlib +import json +import logging +import sqlite3 +import time +from pathlib import Path +from typing import Any, Callable + +logger = logging.getLogger(__name__) + +DEFAULT_CACHE_TTL_DAYS = 30 +SECONDS_PER_DAY = 86_400 + +_SCHEMA = """ +CREATE TABLE IF NOT EXISTS responses ( + key TEXT PRIMARY KEY, + value TEXT NOT NULL, + expires_at REAL NOT NULL +); +CREATE INDEX IF NOT EXISTS idx_responses_expires ON responses(expires_at); +""" + + +class ProviderCache: + """SQLite-backed key/value cache with a single TTL applied to every entry.""" + + def __init__( + self, + db_path: str | Path, + *, + default_ttl_seconds: float = DEFAULT_CACHE_TTL_DAYS * SECONDS_PER_DAY, + ) -> None: + self._db_path = Path(db_path) + self._db_path.parent.mkdir(parents=True, exist_ok=True) + self._default_ttl_seconds = default_ttl_seconds + with self._connect() as conn, conn: + # WAL is a database-level setting; once flipped it persists in the + # file. Doing it here ensures fresh dbs are WAL from row #1. + conn.execute("PRAGMA journal_mode=WAL;") + conn.executescript(_SCHEMA) + conn.execute("DELETE FROM responses WHERE expires_at < ?;", (time.time(),)) + + @property + def default_ttl_seconds(self) -> float: + return self._default_ttl_seconds + + def _connect(self) -> sqlite3.Connection: + # Long lock timeout + WAL + relaxed fsync make concurrent writers + # (e.g. parallel /extract requests) wait instead of erroring. + conn = sqlite3.connect(str(self._db_path), timeout=30.0) + conn.execute("PRAGMA journal_mode=WAL;") + conn.execute("PRAGMA synchronous=NORMAL;") + return conn + + @staticmethod + def make_key(provider: str, method: str, **kwargs: Any) -> str: + payload = json.dumps( + {"p": provider, "m": method, "a": kwargs}, + sort_keys=True, + default=str, + ) + return hashlib.sha256(payload.encode("utf-8")).hexdigest() + + def get(self, key: str) -> Any: + with self._connect() as conn: + row = conn.execute( + "SELECT value, expires_at FROM responses WHERE key = ?;", + (key,), + ).fetchone() + if row is None: + return None + value, expires_at = row + if expires_at < time.time(): + return None + return json.loads(value) + + def set(self, key: str, value: Any, *, ttl_seconds: float | None = None) -> None: + ttl = ttl_seconds if ttl_seconds is not None else self._default_ttl_seconds + with self._connect() as conn, conn: + conn.execute( + "INSERT OR REPLACE INTO responses (key, value, expires_at) VALUES (?, ?, ?);", + (key, json.dumps(value, default=str), time.time() + ttl), + ) + + def get_or_set( + self, + key: str, + factory: Callable[[], Any], + *, + ttl_seconds: float | None = None, + label: str | None = None, + ) -> Any: + cached = self.get(key) + if cached is not None: + if label: + logger.info("provider cache hit: %s", label) + return cached + value = factory() + if value is not None: + self.set(key, value, ttl_seconds=ttl_seconds) + return value + + +__all__ = [ + "DEFAULT_CACHE_TTL_DAYS", + "SECONDS_PER_DAY", + "ProviderCache", +] diff --git a/src/v2/ingest/detection/__init__.py b/src/v2/ingest/detection/__init__.py new file mode 100644 index 0000000..1b3e3e0 --- /dev/null +++ b/src/v2/ingest/detection/__init__.py @@ -0,0 +1,15 @@ +"""GitHub URL detection utilities for the v2 extraction pipeline.""" + +from src.v2.ingest.detection.github_url_classifier import classify_github_url +from src.v2.ingest.detection.models import ( + GitHubURLClassification, + GitHubURLType, + UnsupportedGitHubURL, +) + +__all__ = [ + "GitHubURLClassification", + "GitHubURLType", + "UnsupportedGitHubURL", + "classify_github_url", +] diff --git a/src/v2/ingest/detection/github_url_classifier.py b/src/v2/ingest/detection/github_url_classifier.py new file mode 100644 index 0000000..a0e2c01 --- /dev/null +++ b/src/v2/ingest/detection/github_url_classifier.py @@ -0,0 +1,166 @@ +from __future__ import annotations + +from urllib.parse import unquote, urlparse + +from src.v2.ingest.detection.models import ( + GitHubURLClassification, + GitHubURLType, + UnsupportedGitHubURL, +) + +DEFAULT_GITHUB_BASE_URL = "https://github.com" +MIN_ORG_URL_SEGMENTS = 2 +MIN_REPO_URL_SEGMENTS = 2 + +UNSUPPORTED_REPO_PATH_REASONS = { + "issues": "issue URLs not supported", + "pull": "pull request URLs not supported", + "blob": "file URLs not supported", + "tree": "tree URLs not supported", + "commit": "commit URLs not supported", + "commits": "commit URLs not supported", + "actions": "actions URLs not supported", + "releases": "releases URLs not supported", + "wiki": "wiki URLs not supported", + "settings": "settings URLs not supported", + "security": "security URLs not supported", +} + +EMPTY_URL_ERROR = "GitHub URL cannot be empty" +BASE_PATH_MISMATCH_ERROR = "GitHub URL does not match the github.com base path" +MISSING_PATH_ERROR = "GitHub URL must include a user, organization, or repository path" +MISSING_ORGANIZATION_NAME_ERROR = "Organization URL must include an organization name" +EMPTY_REPOSITORY_NAME_ERROR = "Repository name cannot be empty" +ORGANIZATION_SUBRESOURCE_UNSUPPORTED_REASON = "organization subresource URLs not supported" + + +def _parse_url(raw_url: str) -> tuple[str, str, list[str]]: + candidate = raw_url.strip() + if not candidate: + raise ValueError(EMPTY_URL_ERROR) + if "://" not in candidate: + candidate = f"https://{candidate}" + + parsed = urlparse(candidate) + if parsed.scheme not in {"http", "https"} or not parsed.netloc: + message = f"Invalid GitHub URL: {raw_url}" + raise ValueError(message) + + hostname = (parsed.hostname or "").lower() + if not hostname: + message = f"Invalid GitHub URL: {raw_url}" + raise ValueError(message) + + decoded_segments = [unquote(segment) for segment in parsed.path.split("/") if segment] + return parsed.scheme.lower(), hostname, decoded_segments + + +def _parse_github_base_url() -> tuple[str, list[str]]: + """Parse `DEFAULT_GITHUB_BASE_URL` into (host, path_segments). + + The base URL is hardcoded to `https://github.com`; we previously + exposed `V2_GITHUB_BASE_URL` for GitHub Enterprise but the project + has only ever targeted public GitHub, so the env var has been + retired. + """ + _, hostname, path_segments = _parse_url(DEFAULT_GITHUB_BASE_URL) + return hostname, path_segments + + +def _host_matches(configured_host: str, url_host: str) -> bool: + if configured_host == url_host: + return True + return configured_host == "github.com" and url_host == "www.github.com" + + +def _strip_base_segments( + path_segments: list[str], + base_path_segments: list[str], +) -> list[str]: + if not base_path_segments: + return path_segments + if len(path_segments) < len(base_path_segments): + raise ValueError(BASE_PATH_MISMATCH_ERROR) + + left = [segment.lower() for segment in path_segments[: len(base_path_segments)]] + right = [segment.lower() for segment in base_path_segments] + if left != right: + raise ValueError(BASE_PATH_MISMATCH_ERROR) + + return path_segments[len(base_path_segments) :] + + +def _build_base_url(hostname: str, base_path_segments: list[str]) -> str: + if not base_path_segments: + return f"https://{hostname}" + return f"https://{hostname}/{'/'.join(base_path_segments)}" + + +def _strip_repo_suffix(repo_name: str) -> str: + if repo_name.lower().endswith(".git"): + return repo_name[:-4] + return repo_name + + +def classify_github_url(url: str) -> GitHubURLClassification: + _, hostname, path_segments = _parse_url(url) + configured_host, configured_path_segments = _parse_github_base_url() + + if not _host_matches(configured_host, hostname): + message = f"Non-GitHub URL not supported: {url}" + raise ValueError(message) + + relative_segments = _strip_base_segments(path_segments, configured_path_segments) + if not relative_segments: + raise ValueError(MISSING_PATH_ERROR) + + base_url = _build_base_url(configured_host, configured_path_segments) + first_segment = relative_segments[0] + + if first_segment.lower() == "orgs": + if len(relative_segments) < MIN_ORG_URL_SEGMENTS: + raise ValueError(MISSING_ORGANIZATION_NAME_ERROR) + + organization_name = relative_segments[1] + normalized_url = f"{base_url}/orgs/{organization_name}" + if len(relative_segments) > MIN_ORG_URL_SEGMENTS: + raise UnsupportedGitHubURL( + ORGANIZATION_SUBRESOURCE_UNSUPPORTED_REASON, + normalized_url, + ) + + return GitHubURLClassification( + normalized_url=normalized_url, + detected_type=GitHubURLType.ORGANIZATION, + owner=organization_name, + repo=None, + ) + + owner = first_segment + if len(relative_segments) == 1: + return GitHubURLClassification( + normalized_url=f"{base_url}/{owner}", + detected_type=GitHubURLType.USER, + owner=owner, + repo=None, + ) + + repo = _strip_repo_suffix(relative_segments[1]) + if not repo: + raise ValueError(EMPTY_REPOSITORY_NAME_ERROR) + + normalized_repository_url = f"{base_url}/{owner}/{repo}" + if len(relative_segments) > MIN_REPO_URL_SEGMENTS: + unsupported_segment = relative_segments[MIN_REPO_URL_SEGMENTS].lower() + reason = UNSUPPORTED_REPO_PATH_REASONS.get( + unsupported_segment, + "repository subresource URLs not supported", + ) + raise UnsupportedGitHubURL(reason, normalized_repository_url) + + return GitHubURLClassification( + normalized_url=normalized_repository_url, + detected_type=GitHubURLType.REPOSITORY, + owner=owner, + repo=repo, + ) diff --git a/src/v2/ingest/detection/models.py b/src/v2/ingest/detection/models.py new file mode 100644 index 0000000..2d09033 --- /dev/null +++ b/src/v2/ingest/detection/models.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +from dataclasses import dataclass +from enum import Enum + + +class GitHubURLType(str, Enum): + REPOSITORY = "repository" + USER = "user" + ORGANIZATION = "organization" + + +@dataclass(frozen=True) +class GitHubURLClassification: + normalized_url: str + detected_type: GitHubURLType + owner: str + repo: str | None = None + + +class UnsupportedGitHubURL(ValueError): # noqa: N818 + def __init__(self, reason: str, normalized_url: str) -> None: + self.reason = reason + self.normalized_url = normalized_url + super().__init__(reason) diff --git a/src/v2/ingest/infoscience.py b/src/v2/ingest/infoscience.py new file mode 100644 index 0000000..8f27c1f --- /dev/null +++ b/src/v2/ingest/infoscience.py @@ -0,0 +1,936 @@ +""" +Infoscience API Client and PydanticAI Tool Functions + +Provides async functions to query EPFL's Infoscience repository (DSpace 7.6) +for publications, authors, labs, and organizational units. +""" + +import logging +import os +from typing import Any, Dict, List, Optional + +import httpx + +from src.v1.data_models.infoscience import ( + InfoscienceAuthor, + InfoscienceOrgUnit, + InfosciencePublication, + InfoscienceSearchResult, +) + +logger = logging.getLogger(__name__) + +# Configuration +INFOSCIENCE_BASE_URL = "https://infoscience.epfl.ch/server/api" +DEFAULT_MAX_RESULTS = 10 +REQUEST_TIMEOUT = 30 + +# Authentication token (optional, for protected endpoints) +INFOSCIENCE_TOKEN = os.getenv("INFOSCIENCE_TOKEN") + +# Simple in-memory cache to prevent duplicate searches in same session +_search_cache: Dict[str, str] = {} + + +def clear_infoscience_cache(): + """Clear the in-memory Infoscience search cache.""" + global _search_cache + _search_cache.clear() + logger.info("Cleared Infoscience search cache") + + +########################################################## +# HTTP Client Functions +########################################################## + + +async def _make_api_request( + endpoint: str, + params: Optional[Dict[str, Any]] = None, + timeout: int = REQUEST_TIMEOUT, + use_auth: bool = False, +) -> Optional[Dict[str, Any]]: + """ + Make an async HTTP request to the Infoscience API. + + Args: + endpoint: API endpoint path (relative to base URL) + params: Query parameters + timeout: Request timeout in seconds + use_auth: Whether to include authentication token if available + + Returns: + JSON response as dictionary or None on error + """ + url = f"{INFOSCIENCE_BASE_URL}{endpoint}" + + # Prepare headers + headers = {} + if use_auth and INFOSCIENCE_TOKEN: + headers["Authorization"] = f"Bearer {INFOSCIENCE_TOKEN}" + logger.debug("Using authentication token for request") + + try: + async with httpx.AsyncClient(timeout=timeout) as client: + logger.debug(f"Making API request to {url} with params {params}") + response = await client.get(url, params=params, headers=headers) + response.raise_for_status() + return response.json() + except httpx.HTTPStatusError as e: + logger.error(f"HTTP error {e.response.status_code} for {url}: {e}") + return None + except httpx.TimeoutException: + logger.error(f"Request timeout for {url}") + return None + except Exception as e: + logger.error(f"Error making API request to {url}: {e}", exc_info=True) + return None + + +def _parse_metadata(metadata: Dict[str, Any], field: str) -> Optional[str]: + """ + Extract a single metadata field value from DSpace metadata structure. + + Args: + metadata: Metadata dictionary + field: Field name (e.g., 'dc.title') + + Returns: + First value of the field or None + """ + values = metadata.get(field, []) + if values and isinstance(values, list) and len(values) > 0: + return values[0].get("value") + return None + + +def _parse_metadata_list(metadata: Dict[str, Any], field: str) -> List[str]: + """ + Extract multiple metadata field values from DSpace metadata structure. + + Args: + metadata: Metadata dictionary + field: Field name (e.g., 'dc.contributor.author') + + Returns: + List of values + """ + values = metadata.get(field, []) + if isinstance(values, list): + return [v.get("value") for v in values if v.get("value")] + return [] + + +def _parse_metadata_list_with_authority( + metadata: Dict[str, Any], + field: str, +) -> List[tuple[str, Optional[str]]]: + """Extract metadata values paired with their DSpace `authority` UUIDs. + + DSpace stores authority-controlled fields as ``[{"value": ..., "authority": ...}, ...]``. + For ``dc.contributor.author``, the ``authority`` is the Infoscience person UUID + when the author is EPFL-affiliated (and ``None``/empty for external authors). + + Returns: + List of ``(value, authority_or_none)`` tuples in the original order. Skips + entries whose ``value`` is missing or empty. + """ + values = metadata.get(field, []) + out: List[tuple[str, Optional[str]]] = [] + if not isinstance(values, list): + return out + for item in values: + if not isinstance(item, dict): + continue + value = item.get("value") + if not isinstance(value, str) or not value: + continue + authority = item.get("authority") + # DSpace uses confidence=-1 to mean "not authority-controlled"; treat + # any non-string or empty authority as None to keep the contract simple. + if not isinstance(authority, str) or not authority.strip(): + authority = None + out.append((value, authority)) + return out + + +def _parse_publication(item: Dict[str, Any]) -> InfosciencePublication: + """ + Parse a DSpace item into an InfosciencePublication model. + + Args: + item: DSpace item dictionary + + Returns: + InfosciencePublication instance + """ + metadata = item.get("metadata", {}) + uuid = item.get("uuid") + handle = item.get("handle") + + # Build URL - use normalized format + url = None + if uuid: + # Use normalized entity URL format + url = f"https://infoscience.epfl.ch/entities/publication/{uuid}" + elif handle: + url = f"https://infoscience.epfl.ch/record/{handle}" + + # Extract repository URL from relations or identifiers + repository_url = None + relations = _parse_metadata_list(metadata, "dc.relation.uri") + for rel in relations: + if "github.com" in rel.lower() or "gitlab" in rel.lower(): + repository_url = rel + break + + author_pairs = _parse_metadata_list_with_authority(metadata, "dc.contributor.author") + author_names = [name for name, _ in author_pairs] + author_authorities = [authority for _, authority in author_pairs] + + return InfosciencePublication( + uuid=uuid, + title=_parse_metadata(metadata, "dc.title") or "Untitled", + authors=author_names, + author_authorities=author_authorities, + abstract=_parse_metadata(metadata, "dc.description.abstract"), + doi=_parse_metadata(metadata, "dc.identifier.doi"), + publication_date=_parse_metadata(metadata, "dc.date.issued"), + publication_type=_parse_metadata(metadata, "dc.type"), + url=url, + repository_url=repository_url, + lab=_parse_metadata(metadata, "dc.contributor.affiliation"), + subjects=_parse_metadata_list(metadata, "dc.subject"), + ) + + +def _parse_author(item: Dict[str, Any]) -> Optional[InfoscienceAuthor]: + """ + Parse a DSpace person entity into an InfoscienceAuthor model. + + Args: + item: DSpace person item dictionary + + Returns: + InfoscienceAuthor instance or None if parsing fails + """ + metadata = item.get("metadata", {}) + uuid = item.get("uuid") + handle = item.get("handle") + + # Get name - person entities typically use eperson.firstname + eperson.lastname + # or dc.title for the full name + name = _parse_metadata(metadata, "dc.title") + if not name: + # Try combining first and last name + first_name = _parse_metadata(metadata, "eperson.firstname") + last_name = _parse_metadata(metadata, "eperson.lastname") + if first_name and last_name: + name = f"{first_name} {last_name}" + elif first_name: + name = first_name + elif last_name: + name = last_name + + if not name: + logger.warning(f"Could not extract name from person item with UUID {uuid}") + return None + + # Build URL - use normalized format + url = None + if uuid: + # Use normalized entity URL format + url = f"https://infoscience.epfl.ch/entities/person/{uuid}" + elif handle: + url = f"https://infoscience.epfl.ch/record/{handle}" + + # Extract email, ORCID, and affiliation + email = _parse_metadata(metadata, "eperson.email") + orcid = _parse_metadata(metadata, "person.identifier.orcid") + affiliation = _parse_metadata(metadata, "person.affiliation.name") + + # Log what we found for debugging + logger.debug( + f"Parsed author '{name}' - UUID: {uuid}, Email: {email}, ORCID: {orcid}, Affiliation: {affiliation}", + ) + + return InfoscienceAuthor( + uuid=uuid, + name=name, + email=email, + orcid=orcid, + affiliation=affiliation, + profile_url=url, # Fixed: use profile_url instead of url + ) + + +def _parse_lab(item: Dict[str, Any]) -> Optional[InfoscienceOrgUnit]: + """ + Parse a DSpace organizational unit entity into an InfoscienceOrgUnit model. + + Args: + item: DSpace orgunit item dictionary + + Returns: + InfoscienceOrgUnit instance or None if parsing fails + """ + metadata = item.get("metadata", {}) + uuid = item.get("uuid") + handle = item.get("handle") + + # Get name - orgunit entities typically use dc.title or organization.legalName + name = _parse_metadata(metadata, "dc.title") + if not name: + name = _parse_metadata(metadata, "organization.legalName") + if not name: + name = _parse_metadata(metadata, "organization.name") + + if not name: + logger.warning(f"Could not extract name from orgunit item with UUID {uuid}") + return None + + # Build URL - use normalized format + url = None + if uuid: + # Use normalized entity URL format + url = f"https://infoscience.epfl.ch/entities/orgunit/{uuid}" + elif handle: + url = f"https://infoscience.epfl.ch/record/{handle}" + + return InfoscienceOrgUnit( + uuid=uuid, + name=name, + description=_parse_metadata(metadata, "dc.description") + or _parse_metadata(metadata, "dc.description.abstract"), + url=url, + parent_organization=_parse_metadata( + metadata, + "organization.parentOrganization", + ), + ) + + +async def search_publications( + query: str, + max_results: int = DEFAULT_MAX_RESULTS, + search_field: Optional[str] = None, +) -> InfoscienceSearchResult: + """ + Search for publications in Infoscience. + + Args: + query: Search query (can be title, DOI, keywords, etc.) + max_results: Maximum number of results to return + search_field: Specific field to search (e.g., 'dc.title', 'dc.identifier.doi') + If None, performs a general search + + Returns: + InfoscienceSearchResult with publications + """ + # Build query string based on field + if search_field: + query_str = f"{search_field}:{query}" + else: + # General search - try multiple fields + query_str = query + + params = { + "query": query_str, + "size": max_results, + "configuration": "researchoutputs", + } + + response = await _make_api_request("/discover/search/objects", params=params) + + if not response: + logger.warning(f"No response from publication search for query: {query}") + return InfoscienceSearchResult( + total_results=0, + page=1, + results_per_page=max_results, + ) + + # Parse response + search_result = response.get("_embedded", {}).get("searchResult", {}) + page_info = search_result.get("page", {}) + total_results = page_info.get("totalElements", 0) + + # Parse publications + publications = [] + objects = search_result.get("_embedded", {}).get("objects", []) + + for obj in objects: + try: + item = obj.get("_embedded", {}).get("indexableObject", {}) + if item: + pub = _parse_publication(item) + publications.append(pub) + except Exception as e: + logger.warning(f"Error parsing publication item: {e}") + continue + + logger.info(f"Found {len(publications)} publications for query: {query}") + + return InfoscienceSearchResult( + total_results=total_results, + page=1, + results_per_page=max_results, + publications=publications, + ) + + +async def search_authors( + name: str, + max_results: int = DEFAULT_MAX_RESULTS, +) -> InfoscienceSearchResult: + """ + Search for authors/researchers in Infoscience. + + Uses the /discover/search/objects endpoint with configuration=person + to search the person index directly, just like the web UI. + + Args: + name: Author name to search for + max_results: Maximum number of results to return + + Returns: + InfoscienceSearchResult with authors + """ + authors = [] + total_results = 0 + + # First, try searching the person configuration (like the web UI) + logger.info(f"Searching for person profiles: {name}") + person_params = { + "query": name, + "size": max_results, + "configuration": "person", + } + + person_response = await _make_api_request( + "/discover/search/objects", + params=person_params, + ) + + if person_response: + search_result = person_response.get("_embedded", {}).get("searchResult", {}) + page_info = search_result.get("page", {}) + total_results = page_info.get("totalElements", 0) + + if total_results > 0: + logger.info(f"Found {total_results} person profiles for: {name}") + objects = search_result.get("_embedded", {}).get("objects", []) + + for obj in objects: + try: + item = obj.get("_embedded", {}).get("indexableObject", {}) + if item: + author = _parse_author(item) + if author: + authors.append(author) + except Exception as e: + logger.warning(f"Error parsing person item: {e}") + continue + + logger.info(f"Found {len(authors)} authors for name: {name}") + return InfoscienceSearchResult( + total_results=total_results, + page=1, + results_per_page=max_results, + authors=authors, + ) + + # Fallback: Search publications by author name and extract authors + logger.info(f"No person profiles found, searching publications by author: {name}") + pub_params = { + "query": f"dc.contributor.author:{name}", + "size": max_results, + "configuration": "researchoutputs", + } + + pub_response = await _make_api_request( + "/discover/search/objects", + params=pub_params, + ) + + if pub_response: + search_result = pub_response.get("_embedded", {}).get("searchResult", {}) + page_info = search_result.get("page", {}) + total_results = page_info.get("totalElements", 0) + + # Extract unique authors from publications + author_names = set() + objects = search_result.get("_embedded", {}).get("objects", []) + + for obj in objects: + try: + item = obj.get("_embedded", {}).get("indexableObject", {}) + metadata = item.get("metadata", {}) + pub_authors = _parse_metadata_list(metadata, "dc.contributor.author") + + # Find authors matching the search name + for author_name in pub_authors: + if name.lower() in author_name.lower(): + if author_name not in author_names: + author_names.add(author_name) + authors.append( + InfoscienceAuthor( + name=author_name, + ), + ) + except Exception as e: + logger.warning(f"Error extracting authors from publication: {e}") + continue + + logger.info(f"Found {len(authors)} authors for name: {name}") + + return InfoscienceSearchResult( + total_results=total_results, + page=1, + results_per_page=max_results, + authors=authors, + ) + + +async def search_labs( + name: str, + max_results: int = DEFAULT_MAX_RESULTS, +) -> InfoscienceSearchResult: + """ + Search for labs and organizational units in Infoscience. + + First tries searching with configuration=orgunit (like the web UI for organizational units), + then falls back to searching publications and extracting lab information from metadata. + + Args: + name: Lab or organization name to search for + max_results: Maximum number of results to return + + Returns: + InfoscienceSearchResult with labs + """ + labs = [] + lab_names_seen = set() + total_results = 0 + + # First, try searching the orgunit configuration (like the web UI) + logger.info(f"Searching for organizational units: {name}") + orgunit_params = { + "query": name, + "size": max_results, + "configuration": "orgunit", + } + + orgunit_response = await _make_api_request( + "/discover/search/objects", + params=orgunit_params, + ) + + if orgunit_response: + search_result = orgunit_response.get("_embedded", {}).get("searchResult", {}) + page_info = search_result.get("page", {}) + total_results = page_info.get("totalElements", 0) + + if total_results > 0: + logger.info(f"Found {total_results} organizational units for: {name}") + objects = search_result.get("_embedded", {}).get("objects", []) + + for obj in objects: + try: + item = obj.get("_embedded", {}).get("indexableObject", {}) + if item: + lab = _parse_lab(item) + if lab: + labs.append(lab) + lab_names_seen.add(lab.name) + except Exception as e: + logger.warning(f"Error parsing orgunit item: {e}") + continue + + logger.info(f"Found {len(labs)} labs for name: {name}") + return InfoscienceSearchResult( + total_results=total_results, + page=1, + results_per_page=max_results, + labs=labs, + ) + + # Fallback: Search publications and extract lab information from metadata + logger.info( + f"No organizational units found, searching publications for lab info: {name}", + ) + params = { + "query": name, + "size": max_results, + "configuration": "researchoutputs", + } + + response = await _make_api_request("/discover/search/objects", params=params) + + if response: + search_result = response.get("_embedded", {}).get("searchResult", {}) + page_info = search_result.get("page", {}) + total_results = page_info.get("totalElements", 0) + + objects = search_result.get("_embedded", {}).get("objects", []) + + # Extract labs from publications + for obj in objects: + try: + item = obj.get("_embedded", {}).get("indexableObject", {}) + metadata = item.get("metadata", {}) + + # Try to find lab information in various metadata fields + lab_info = _parse_metadata(metadata, "dc.contributor.lab") + if not lab_info: + lab_info = _parse_metadata(metadata, "dc.contributor.unit") + if not lab_info: + lab_info = _parse_metadata(metadata, "dc.contributor.affiliation") + + # If we found lab info and it matches the search query + if lab_info and name.lower() in lab_info.lower(): + if lab_info not in lab_names_seen: + lab_names_seen.add(lab_info) + + # Get publication title for context + pub_title = _parse_metadata(metadata, "dc.title") + description = f"Lab identified from publication: {pub_title[:100] if pub_title else 'N/A'}..." + + lab = InfoscienceOrgUnit( + name=lab_info, + description=description, + ) + labs.append(lab) + + if len(labs) >= max_results: + break + + except Exception as e: + logger.warning(f"Error extracting lab from publication: {e}") + continue + + logger.info(f"Found {len(labs)} labs/organizations for name: {name}") + + return InfoscienceSearchResult( + total_results=total_results, + page=1, + results_per_page=max_results, + labs=labs, + ) + + +async def get_author_publications( + author_name: str, + max_results: int = DEFAULT_MAX_RESULTS, +) -> InfoscienceSearchResult: + """ + Get all publications by a specific author. + + Args: + author_name: Full or partial name of the author + max_results: Maximum number of results to return + + Returns: + InfoscienceSearchResult with publications + """ + logger.info(f"Fetching publications for author: {author_name}") + + # Search publications by author + return await search_publications( + query=author_name, + max_results=max_results, + search_field="dc.contributor.author", + ) + + +async def get_entity_by_uuid( + uuid: str, + entity_type: Optional[str] = None, +) -> Optional[Dict[str, Any]]: + """ + Get an entity directly by its UUID. + + This function supports direct access to entities using their UUID. + Useful for accessing specific publications, persons, or organizational units + when you already know the UUID (e.g., from user-provided URLs). + + Args: + uuid: The UUID of the entity + entity_type: Optional hint about entity type ("publication", "person", "orgunit") + If not provided, will try /core/items/{uuid} + + Returns: + Raw entity data as dictionary, or None if not found + + Example URLs: + - https://infoscience.epfl.ch/entities/publication/{uuid} + - https://infoscience.epfl.ch/entities/person/{uuid} + - https://infoscience.epfl.ch/entities/orgunit/{uuid} + """ + logger.info(f"Fetching entity by UUID: {uuid} (type: {entity_type or 'auto'})") + + # Try entity-specific endpoint if type is known + if entity_type: + response = await _make_api_request(f"/entities/{entity_type}/{uuid}") + if response: + return response + + # Fallback to generic items endpoint + response = await _make_api_request(f"/core/items/{uuid}") + if response: + return response + + logger.warning(f"Entity not found for UUID: {uuid}") + return None + + +########################################################## +# PydanticAI Tool Functions +########################################################## + + +async def search_infoscience_publications_tool( + query: str, + max_results: int = 10, +) -> str: + """ + Search for publications in EPFL's Infoscience repository. + + This tool searches for academic publications, papers, theses, and other research outputs. + You can search by title, DOI, keywords, or general terms. + + IMPORTANT: This tool caches results - don't search for the same thing multiple times! + Be strategic and avoid redundant searches. + + **CRITICAL: If this tool returns 0 results, STOP searching for this entity because the results were 0 - it is not in Infoscience. Do not try variations or search again.** + + Args: + query: Search query (title, DOI, keywords, or general search terms) + max_results: Maximum number of results to return (default: 10, max: 50) + + Returns: + Markdown-formatted search results with publication details + """ + logger.info( + f"🔍 Agent tool called: search_infoscience_publications_tool(query='{query}', max_results={max_results})", + ) + + # Check cache first to avoid duplicate searches + cache_key = f"pub:{query.lower()}:{max_results}" + if cache_key in _search_cache: + logger.info(f"⚡ Returning cached result for query: '{query}'") + return _search_cache[cache_key] + + max_results = min(max_results, 50) # Cap at 50 + + try: + result = await search_publications(query, max_results) + logger.info( + f"✓ Infoscience publications search returned {result.total_results} total results", + ) + markdown_result = result.to_markdown() + + # Cache the result + _search_cache[cache_key] = markdown_result + + return markdown_result + except Exception as e: + logger.error( + f"✗ Error in search_infoscience_publications_tool: {e}", + exc_info=True, + ) + return f"Error searching publications: {e}" + + +async def search_infoscience_authors_tool(name: str, max_results: int = 10) -> str: + """ + Search for authors and researchers in EPFL's Infoscience repository. + + This tool finds researchers, professors, and other authors affiliated with EPFL. + Use it to find information about specific people and their publications. + + IMPORTANT: This tool caches results - don't search for the same person multiple times! + Be strategic and avoid redundant searches. + + **CRITICAL: If this tool returns 0 results, STOP searching for this entity because the results were 0 - it is not in Infoscience. Do not try variations or search again.** + + Args: + name: Author name to search for (can be partial name) + max_results: Maximum number of results to return (default: 10, max: 50) + + Returns: + Markdown-formatted search results with author details + """ + logger.info( + f"🔍 Agent tool called: search_infoscience_authors_tool(name='{name}', max_results={max_results})", + ) + + # Check cache first + cache_key = f"author:{name.lower()}:{max_results}" + if cache_key in _search_cache: + logger.info(f"⚡ Returning cached result for author: '{name}'") + return _search_cache[cache_key] + + max_results = min(max_results, 50) # Cap at 50 + + try: + result = await search_authors(name, max_results) + logger.info( + f"✓ Infoscience authors search returned {result.total_results} total results", + ) + markdown_result = result.to_markdown() + + # Cache the result + _search_cache[cache_key] = markdown_result + + return markdown_result + except Exception as e: + logger.error(f"✗ Error in search_infoscience_authors_tool: {e}", exc_info=True) + return f"Error searching authors: {e}" + + +async def search_infoscience_labs_tool(name: str, max_results: int = 10) -> str: + """ + Search for laboratories and organizational units in EPFL's Infoscience repository. + + This tool finds research labs, groups, departments, and other organizational units at EPFL. + Use it to find information about specific labs and their research areas. + + IMPORTANT: This tool caches results - don't search for the same lab multiple times! + If a lab isn't found, it may not be in Infoscience or has a different name - don't keep trying! + + **CRITICAL: If this tool returns 0 results, STOP searching for this entity because the results were 0 - it is not in Infoscience. Do not try variations or search again.** + + Args: + name: Lab or organization name to search for (can be partial name) + max_results: Maximum number of results to return (default: 10, max: 50) + + Returns: + Markdown-formatted search results with lab details + """ + logger.info( + f"🔍 Agent tool called: search_infoscience_labs_tool(name='{name}', max_results={max_results})", + ) + + # Check cache first + cache_key = f"lab:{name.lower()}:{max_results}" + if cache_key in _search_cache: + logger.info(f"⚡ Returning cached result for lab: '{name}'") + return _search_cache[cache_key] + + max_results = min(max_results, 50) # Cap at 50 + + try: + result = await search_labs(name, max_results) + logger.info( + f"✓ Infoscience labs search returned {result.total_results} total results", + ) + markdown_result = result.to_markdown() + + # Cache the result (including empty results!) + _search_cache[cache_key] = markdown_result + + return markdown_result + except Exception as e: + logger.error(f"✗ Error in search_infoscience_labs_tool: {e}", exc_info=True) + return f"Error searching labs: {e}" + + +async def get_author_publications_tool(author_name: str, max_results: int = 10) -> str: + """ + Get publications by a specific author from EPFL's Infoscience repository. + + This tool retrieves all publications authored by a specific person. + Use it to get a comprehensive list of someone's research outputs. + + IMPORTANT: This tool caches results - don't search for the same author multiple times! + + Args: + author_name: Full or partial name of the author + max_results: Maximum number of results to return (default: 10, max: 50) + + Returns: + Markdown-formatted list of publications by the author + """ + logger.info( + f"🔍 Agent tool called: get_author_publications_tool(author_name='{author_name}', max_results={max_results})", + ) + + # Check cache first + cache_key = f"author_pubs:{author_name.lower()}:{max_results}" + if cache_key in _search_cache: + logger.info(f"⚡ Returning cached publications for author: '{author_name}'") + return _search_cache[cache_key] + + max_results = min(max_results, 50) # Cap at 50 + + try: + result = await get_author_publications(author_name, max_results) + if result.total_results > 0: + logger.info( + f"✓ Found {result.total_results} publications for author '{author_name}'", + ) + markdown_result = ( + f"## Publications by {author_name}\n\n" + result.to_markdown() + ) + else: + logger.info(f"⚠ No publications found for author '{author_name}'") + markdown_result = f"No publications found for author: {author_name}" + + # Cache the result + _search_cache[cache_key] = markdown_result + + return markdown_result + except Exception as e: + logger.error(f"✗ Error in get_author_publications_tool: {e}", exc_info=True) + return f"Error fetching author publications: {e}" + + +########################################################## +# URL Normalization Helpers +########################################################## + + +def normalize_infoscience_publication_url(url_or_uuid: str) -> Optional[str]: + """ + Normalize an Infoscience publication URL or UUID to proper format. + + Args: + url_or_uuid: URL or UUID string + + Returns: + Normalized URL or None if invalid + """ + from ..agents.validation_utils import normalize_infoscience_url + + return normalize_infoscience_url(url_or_uuid, "publication") + + +def normalize_infoscience_author_url(url_or_uuid: str) -> Optional[str]: + """ + Normalize an Infoscience author/person URL or UUID to proper format. + + Args: + url_or_uuid: URL or UUID string + + Returns: + Normalized URL or None if invalid + """ + from ..agents.validation_utils import normalize_infoscience_url + + return normalize_infoscience_url(url_or_uuid, "person") + + +def normalize_infoscience_lab_url(url_or_uuid: str) -> Optional[str]: + """ + Normalize an Infoscience lab/orgunit URL or UUID to proper format. + + Args: + url_or_uuid: URL or UUID string + + Returns: + Normalized URL or None if invalid + """ + from ..agents.validation_utils import normalize_infoscience_url + + return normalize_infoscience_url(url_or_uuid, "orgunit") diff --git a/src/v2/ingest/providers/__init__.py b/src/v2/ingest/providers/__init__.py new file mode 100644 index 0000000..f53fa5d --- /dev/null +++ b/src/v2/ingest/providers/__init__.py @@ -0,0 +1,81 @@ +"""Provider interfaces and mocks for the v2 extraction pipeline.""" + +from src.v2.ingest.providers.base import ( + BaseProvider, + GitHubProvider, + InfoscienceProvider, + ORCIDProvider, + ORCIDRecord, + ProviderError, + ProviderNotFoundError, + ProviderPermissionError, + ProviderRateLimitError, + RORProvider, +) +from src.v2.ingest.providers.github_provider import RealGitHubProvider +from src.v2.ingest.providers.infoscience_provider import RealInfoscienceProvider +from src.v2.ingest.providers.mock_github import MockGitHubProvider +from src.v2.ingest.providers.mock_infoscience import MockInfoscienceProvider +from src.v2.ingest.providers.mock_orcid import MockORCIDProvider +from src.v2.ingest.providers.mock_ror import MockRORProvider +from src.v2.ingest.providers.orcid_provider import RealORCIDProvider +from src.v2.ingest.providers.rate_limiter import RateLimiter +from src.v2.ingest.providers.ror_provider import RealRORProvider + +_ALIASES = { + "github": "github", + "info": "infoscience", + "infoscience": "infoscience", + "orcid": "orcid", + "ror": "ror", +} + +_REAL_PROVIDERS = { + "github": RealGitHubProvider, + "infoscience": RealInfoscienceProvider, + "orcid": RealORCIDProvider, + "ror": RealRORProvider, +} + +_MOCK_PROVIDERS = { + "github": MockGitHubProvider, + "infoscience": MockInfoscienceProvider, + "orcid": MockORCIDProvider, + "ror": MockRORProvider, +} + + +def get_provider(name: str, *, use_mock: bool = False, **kwargs: object) -> BaseProvider: + """Return a configured provider implementation by name.""" + normalized_name = _ALIASES.get(name.strip().lower()) + providers = _MOCK_PROVIDERS if use_mock else _REAL_PROVIDERS + provider_cls = providers.get(normalized_name or "") + + if provider_cls is None: + message = f"Unknown provider name: {name}" + raise ValueError(message) + + return provider_cls(**kwargs) + +__all__ = [ + "BaseProvider", + "GitHubProvider", + "InfoscienceProvider", + "MockGitHubProvider", + "MockInfoscienceProvider", + "MockORCIDProvider", + "MockRORProvider", + "ORCIDProvider", + "ORCIDRecord", + "ProviderError", + "ProviderNotFoundError", + "ProviderPermissionError", + "ProviderRateLimitError", + "RORProvider", + "RateLimiter", + "RealGitHubProvider", + "RealInfoscienceProvider", + "RealORCIDProvider", + "RealRORProvider", + "get_provider", +] diff --git a/src/v2/ingest/providers/_rag_helpers.py b/src/v2/ingest/providers/_rag_helpers.py new file mode 100644 index 0000000..86bc632 --- /dev/null +++ b/src/v2/ingest/providers/_rag_helpers.py @@ -0,0 +1,210 @@ +"""Shared helpers for v2 RAG providers backed by the index/* Qdrant indices. + +Per-index providers (HuggingFace / OpenAlex / Zenodo / ORCID / ROR) each +have their own ``QdrantStore`` and embedder/reranker classes with subtly +different signatures. Rather than force them under a common base, this +module provides the small set of cross-cutting utilities every provider +needs: + +* ``filter_allowlist`` — drop non-allowlisted keys with a warning. +* ``to_simple_filter_payload`` — translate the LLM-facing operator dict + (``{"$gte": X}``, ``{"$in": [...]}``, ...) into the simpler dict shape + the HF/OpenAlex/ORCID/Zenodo stores accept (``{"gte": X}``, ``[..]``). +* ``expand_candidate_k`` — widen the vector top-k when rerank is on so + the cross-encoder has room to reorder. +* ``apply_rerank_indices`` — apply a list of ``(index, score)`` rerank + hits onto the original Qdrant hit list. +* ``make_snippet`` — truncate a body field to a fixed character budget. +""" + +from __future__ import annotations + +import logging +import os +from typing import Any + +logger = logging.getLogger(__name__) + +DEFAULT_SNIPPET_CHARS = 320 +RERANK_CANDIDATE_MULTIPLIER = 5 +RERANK_CANDIDATE_FLOOR = 30 + + +def filter_allowlist( + payload: dict[str, Any] | None, + allowed: frozenset[str], + *, + log_label: str, +) -> dict[str, Any] | None: + """Drop keys not in ``allowed`` from ``payload`` and warn once. + + Returns ``None`` when the result is empty so callers can short-circuit. + """ + if not payload: + return None + cleaned: dict[str, Any] = {} + dropped: list[str] = [] + for key, value in payload.items(): + if key in allowed: + cleaned[key] = value + else: + dropped.append(key) + if dropped: + logger.warning( + "%s: dropped non-allowlisted filter keys: %s", + log_label, sorted(dropped), + ) + return cleaned or None + + +def to_simple_filter_payload( + payload: dict[str, Any] | None, +) -> dict[str, Any] | None: + """Convert the LLM-facing operator-dict format to the HF/OpenAlex shape. + + Accepts the same operator dict the infoscience tool uses + (``{"$gte": X, "$lte": Y}``, ``{"$in": [...]}``, ``{"$eq": v}``, + scalars, lists) and returns a dict where each value is one of: + + * ``{"gte": X, "lte": Y}`` (range) + * ``[v1, v2, ...]`` (any-of) + * scalar (eq) + + Operators ``$ne`` and ``$contains`` are dropped with a warning since + the HF/OpenAlex stores don't natively support them. + """ + if not payload: + return None + out: dict[str, Any] = {} + for key, value in payload.items(): + if isinstance(value, dict): + range_kwargs: dict[str, Any] = {} + if "$gte" in value: + range_kwargs["gte"] = value["$gte"] + if "$lte" in value: + range_kwargs["lte"] = value["$lte"] + if range_kwargs: + out[key] = range_kwargs + continue + if "$in" in value: + out[key] = list(value["$in"]) + continue + if "$eq" in value: + out[key] = value["$eq"] + continue + logger.warning( + "rag filter: unsupported operator dict for %r: %r — skipped", + key, value, + ) + continue + if isinstance(value, list): + out[key] = list(value) + continue + out[key] = value + return out or None + + +def expand_candidate_k( + top_k: int, + *, + multiplier: int = RERANK_CANDIDATE_MULTIPLIER, + floor: int = RERANK_CANDIDATE_FLOOR, +) -> int: + """Return the candidate top-k to fetch from Qdrant before reranking.""" + return max(top_k * multiplier, floor) + + +def safe_rerank_documents( + documents: list[str], + *, + placeholder: str = " ", +) -> list[str]: + """Replace blank documents with a single-space placeholder. + + Some RCP reranker backends 400 when ``documents`` contains empty + strings ("The decoder prompt cannot be empty"). Substituting a + non-empty placeholder keeps the index-to-hit mapping stable while + letting the rerank request go through; placeholder docs simply + receive low relevance scores. + """ + return [doc if isinstance(doc, str) and doc else placeholder for doc in documents] + + +def apply_rerank_indices( + hits: list[dict[str, Any]], + rerank_results: list[dict[str, Any]], + *, + top_k: int, +) -> list[dict[str, Any]]: + """Reorder ``hits`` per a rerank result list of ``{index, relevance_score}``. + + Falls through to ``hits[:top_k]`` if the rerank result is empty. + """ + if not rerank_results: + return hits[:top_k] + ordered: list[dict[str, Any]] = [] + seen: set[int] = set() + for r in rerank_results: + idx = int(r.get("index", -1)) + score = r.get("relevance_score") or r.get("score") + if 0 <= idx < len(hits) and idx not in seen and score is not None: + seen.add(idx) + rehit = dict(hits[idx]) + rehit["score"] = float(score) + ordered.append(rehit) + return ordered[:top_k] + + +def make_snippet(text: Any, *, max_chars: int = DEFAULT_SNIPPET_CHARS) -> str | None: + """Trim an arbitrary text field to at most ``max_chars`` chars.""" + if not isinstance(text, str) or not text: + return None + text = text.strip() + if not text: + return None + if len(text) <= max_chars: + return text + return text[: max_chars - 1].rstrip() + "…" + + +def thin_payload( + payload: dict[str, Any], + keys: tuple[str, ...], + *, + extras: dict[str, Any] | None = None, +) -> dict[str, Any]: + """Build a thin LLM-facing hit dict from a payload + extra computed fields.""" + out = {key: payload.get(key) for key in keys if payload.get(key) is not None} + if extras: + out.update({k: v for k, v in extras.items() if v is not None}) + return out + + +def truthy_env(value: str | None) -> bool: + if value is None: + return False + return value.strip().lower() in {"1", "true", "t", "yes", "y", "on"} + + +def env_enabled(name: str, *, default: bool = True) -> bool: + """Resolve a `V2__RAG_ENABLED`-style toggle with a default.""" + raw = os.getenv(name) + if raw is None: + return default + return truthy_env(raw) + + +__all__ = [ + "DEFAULT_SNIPPET_CHARS", + "RERANK_CANDIDATE_FLOOR", + "RERANK_CANDIDATE_MULTIPLIER", + "apply_rerank_indices", + "env_enabled", + "expand_candidate_k", + "filter_allowlist", + "make_snippet", + "safe_rerank_documents", + "thin_payload", + "to_simple_filter_payload", + "truthy_env", +] diff --git a/src/v2/ingest/providers/base.py b/src/v2/ingest/providers/base.py new file mode 100644 index 0000000..4f9f201 --- /dev/null +++ b/src/v2/ingest/providers/base.py @@ -0,0 +1,237 @@ +from __future__ import annotations + +import asyncio +import contextvars +from abc import ABC, abstractmethod +from concurrent.futures import ThreadPoolExecutor +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Coroutine, + NotRequired, + Required, + TypedDict, + TypeVar, +) + +if TYPE_CHECKING: + from src.v2.ingest.providers.rate_limiter import RateLimiter + +ResponseT = TypeVar("ResponseT") +INFOSCIENCE_PUBLICATION_REQUIRED_FIELDS: tuple[str, ...] = ( + "infosciencePublicationIdentifier", + "title", + "authors", + "publicationDate", + "doi", + "url", +) +INFOSCIENCE_PUBLICATION_OPTIONAL_FIELDS: tuple[str, ...] = ("sourceOrganization",) + + +def _run_awaitable(value: Coroutine[Any, Any, ResponseT]) -> ResponseT: + """Run a coroutine to completion, preserving the calling ContextVar state. + + See `src/v2/ingest/providers/infoscience_provider.py:_run_async` for why + we copy the context (request-id propagation into worker threads). + """ + try: + asyncio.get_running_loop() + except RuntimeError: + return asyncio.run(value) + + ctx = contextvars.copy_context() + with ThreadPoolExecutor(max_workers=1) as executor: + future = executor.submit(ctx.run, asyncio.run, value) + return future.result() + + +class ProviderError(RuntimeError): + """Base exception for provider integration failures.""" + + +class ProviderNotFoundError(ProviderError): + """Raised when a requested resource is not found by the provider.""" + + +class ProviderRateLimitError(ProviderError): + """Raised when a provider rejects requests due to rate limiting.""" + + +class ProviderPermissionError(ProviderError): + """Raised when a provider denies access to a resource.""" + + +class BaseProvider: + """Base abstraction for all v2 provider adapters.""" + + provider_name = "provider" + + def __init__( + self, + *, + provider_name: str | None = None, + rate_limiter: RateLimiter | None = None, + ) -> None: + from src.v2.ingest.providers.rate_limiter import RateLimiter # noqa: PLC0415 + + if provider_name is None: + provider_name = self.provider_name + self._provider_name = provider_name + self._rate_limiter = rate_limiter or RateLimiter() + + def _run_with_rate_limit( + self, + request_func: Callable[[], ResponseT | Coroutine[Any, Any, ResponseT]], + ) -> ResponseT: + return _run_awaitable( + self._rate_limiter.with_rate_limit(self._provider_name, request_func), + ) + + +class GitHubProvider(BaseProvider, ABC): + """Adapter interface for GitHub metadata retrieval.""" + + @abstractmethod + def get_repository(self, full_name: str) -> dict[str, Any]: + """Return repository metadata for ``owner/repo``.""" + + @abstractmethod + def get_user(self, username: str) -> dict[str, Any]: + """Return user profile metadata for ``username``.""" + + @abstractmethod + def get_organization(self, org_name: str) -> dict[str, Any]: + """Return organization profile metadata for ``org_name``.""" + + @abstractmethod + def get_contributors(self, full_name: str) -> list[dict[str, Any]]: + """Return repository contributors for ``owner/repo``.""" + + @abstractmethod + def get_languages(self, full_name: str) -> dict[str, int]: + """Return language byte counts for ``owner/repo``.""" + + def get_repository_sbom(self, full_name: str) -> list[dict[str, Any]] | None: + """Return the parsed SPDX dependency list for ``owner/repo``. + + Each entry is a dict with keys ``name``, ``ecosystem``, ``version``, + and ``spdxId``. Returns ``None`` when the repository has no SBOM + available (e.g. dependency graph disabled, private repo without the + required scope, or 404). + + Default implementation returns ``None`` so providers that do not + expose dependency data (test fakes, partial mocks) need not stub + this. Implementations must not raise on missing SBOMs — only on + transport-level failures. + """ + del full_name + return None + + def get_repository_jsonld(self, full_name: str) -> dict[str, Any]: + """Return the raw GIMIE JSON-LD payload for ``owner/repo``, or an empty dict.""" + return {} + + +class InfoscienceProvider(BaseProvider, ABC): + """Adapter interface for Infoscience metadata retrieval.""" + + @abstractmethod + def search_person(self, query: str) -> list[dict[str, Any]]: + """Search Infoscience person profiles by query string.""" + + @abstractmethod + def search_orgunit(self, query: str) -> list[dict[str, Any]]: + """Search Infoscience organization units by query string.""" + + @abstractmethod + def search_publications(self, query: str) -> list[InfosciencePublicationRecord]: + """Search Infoscience publications by query string. + + Required keys in each publication payload: + - ``infosciencePublicationIdentifier`` + - ``title`` + - ``authors`` + - ``publicationDate`` + - ``doi`` + - ``url`` + + Optional keys: + - ``sourceOrganization`` + """ + + +class InfosciencePublicationRecord(TypedDict, total=False): + """Normalized Infoscience publication payload contract for article generation.""" + + infosciencePublicationIdentifier: Required[str | None] + title: Required[str | None] + authors: Required[list[str]] + author_authorities: NotRequired[list[str | None]] + publicationDate: Required[str | None] + doi: Required[str | None] + url: Required[str | None] + sourceOrganization: NotRequired[str | None] + + +class RORProvider(BaseProvider, ABC): + """Adapter interface for Research Organization Registry (ROR) lookups.""" + + @abstractmethod + def get_organization(self, ror_id: str) -> dict[str, Any]: + """Fetch a ROR organization by identifier.""" + + @abstractmethod + def search_organizations(self, query: str) -> list[dict[str, Any]]: + """Search ROR organizations by free-text query.""" + + +class ORCIDAffiliation(TypedDict): + """Employment or education affiliation details extracted from ORCID.""" + + organization: str + department: str | None + role: str | None + start_date: str | None + end_date: str | None + + +class ORCIDRecord(TypedDict): + """Normalized ORCID person payload returned by ORCID providers.""" + + orcid_id: str + name: str + employment: list[ORCIDAffiliation] + education: list[ORCIDAffiliation] + affiliations: list[str] + + +class ORCIDSearchHit(TypedDict): + """Single hit returned by the ORCID expanded-search endpoint.""" + + orcid_id: str | None + given_names: str | None + family_names: str | None + credit_name: str | None + other_names: list[str] + institution_names: list[str] + emails: list[str] + + +class ORCIDProvider(BaseProvider, ABC): + """Adapter interface for ORCID person record lookups.""" + + @abstractmethod + def get_person_by_orcid(self, orcid_id: str) -> ORCIDRecord: + """Return normalized ORCID profile data for a canonical ORCID identifier.""" + + @abstractmethod + def search_persons( + self, + query: str, + *, + rows: int = 50, + start: int = 0, + ) -> list[ORCIDSearchHit]: + """Search ORCID expanded-search for persons matching ``query``.""" diff --git a/src/v2/ingest/providers/epfl_graph_rag.py b/src/v2/ingest/providers/epfl_graph_rag.py new file mode 100644 index 0000000..8af93a8 --- /dev/null +++ b/src/v2/ingest/providers/epfl_graph_rag.py @@ -0,0 +1,187 @@ +"""Async RAG provider over the EPFL Graph disciplines Qdrant index. + +Single collection: ``epfl_graph_disciplines``. Reuses OpenAlex's embedder, +reranker and ``QdrantStore`` via duck typing — :class:`EpflGraphIndexConfig` +mirrors the same ``rcp.*`` / ``qdrant.*`` shape OpenAlex's clients expect. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any + +from src.index.epfl_graph.embed.pipeline import EPFL_GRAPH_COLLECTION +from src.index.openalex.embed.rcp_client import ( + RCPEmbeddingClient, + RCPEmbeddingError, +) +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.v2.ingest.providers._rag_helpers import ( + apply_rerank_indices, + env_enabled, + expand_candidate_k, + filter_allowlist, + safe_rerank_documents, + thin_payload, + to_simple_filter_payload, +) + +if TYPE_CHECKING: + from src.index.epfl_graph.config import EpflGraphIndexConfig + +logger = logging.getLogger(__name__) + +_ALLOWED_FILTER_KEYS: frozenset[str] = frozenset( + {"category_id", "depth", "parent_id", "entity_type"}, +) + +_THIN_KEYS: tuple[str, ...] = ( + "category_id", + "name", + "depth", + "parent_id", + "wikipedia_page_id", + "wikipedia_url", + "graphsearch_url", + "n_concepts", + "n_children", +) + +_DEFAULT_TOP_K = 10 +_LOG_LABEL = "epfl_graph_rag" + + +def _rerank_text(payload: dict[str, Any]) -> str: + name = payload.get("name") or payload.get("category_id") or "" + text = payload.get("embedding_text") or "" + return f"{name}\n{text}".strip() + + +class EpflGraphRagProvider: + """Async wrapper around the EPFL Graph disciplines Qdrant index.""" + + def __init__( + self, + *, + store: QdrantStore, + embedder: RCPEmbeddingClient, + reranker: RCPRerankerClient | None = None, + ) -> None: + self._store = store + self._embedder = embedder + self._reranker = reranker + + @classmethod + def from_config(cls, cfg: EpflGraphIndexConfig) -> EpflGraphRagProvider: + return cls( + store=QdrantStore(cfg), # type: ignore[arg-type] + embedder=RCPEmbeddingClient(cfg), # type: ignore[arg-type] + reranker=RCPRerankerClient(cfg), # type: ignore[arg-type] + ) + + async def search( + self, + query: str, + *, + top_k: int = _DEFAULT_TOP_K, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + if not self._store.client.collection_exists(EPFL_GRAPH_COLLECTION): + logger.info( + "%s: collection %s missing — returning [] without indexing", + _LOG_LABEL, + EPFL_GRAPH_COLLECTION, + ) + return [] + + cleaned = filter_allowlist( + filters, _ALLOWED_FILTER_KEYS, log_label=_LOG_LABEL, + ) + filter_payload = to_simple_filter_payload(cleaned) + + try: + vectors = await self._embedder.embed_all([query]) + except (RCPEmbeddingError, Exception) as exc: # noqa: BLE001 + logger.warning("%s: embed failed — %s", _LOG_LABEL, exc) + return [] + if not vectors: + return [] + vector = list(vectors[0]) + + candidate_k = expand_candidate_k(top_k) if rerank else top_k + + try: + hits = await asyncio.to_thread( + self._store.search, + EPFL_GRAPH_COLLECTION, + query_vector=vector, + top_k=candidate_k, + filter_payload=filter_payload, + ) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: qdrant search failed — %s", _LOG_LABEL, exc) + return [] + + if rerank and hits and self._reranker is not None: + hits = await self._maybe_rerank(query, hits, top_k=top_k) + else: + hits = hits[:top_k] + + return [ + thin_payload( + hit.get("payload") or {}, + _THIN_KEYS, + extras={ + "id": hit.get("id"), + "score": hit.get("score"), + "collection": EPFL_GRAPH_COLLECTION, + }, + ) + for hit in hits + ] + + async def _maybe_rerank( + self, + query: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + documents = safe_rerank_documents( + [_rerank_text(h.get("payload") or {}) for h in hits], + ) + try: + results = await self._reranker.rerank( + query, documents, top_n=top_k, + ) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: rerank failed — %s", _LOG_LABEL, exc) + return hits[:top_k] + return apply_rerank_indices(hits, results, top_k=top_k) + + +def build_default_provider( + cfg: EpflGraphIndexConfig | None = None, +) -> EpflGraphRagProvider | None: + if not env_enabled("V2_EPFL_GRAPH_RAG_ENABLED"): + return None + try: + from src.index.epfl_graph.config import load_config # noqa: PLC0415 + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to load config (%s)", _LOG_LABEL, exc) + return None + try: + return EpflGraphRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to construct provider (%s)", _LOG_LABEL, exc) + return None + + +__all__ = ["EpflGraphRagProvider", "build_default_provider"] diff --git a/src/v2/ingest/providers/ethz_research_collection_rag.py b/src/v2/ingest/providers/ethz_research_collection_rag.py new file mode 100644 index 0000000..d6c9064 --- /dev/null +++ b/src/v2/ingest/providers/ethz_research_collection_rag.py @@ -0,0 +1,482 @@ +"""Async RAG provider over the ETH Research Collection Qdrant index. + +Wraps :class:`src.index.ethz_research_collection.store.QdrantStore` with embedding (via +:class:`src.index.ethz_research_collection.embed.RCPEmbedder`) and optional reranking +(via :class:`src.index.ethz_research_collection.rerank.RCPReranker`). + +Exposes three methods used by v2 agent tools: + +* ``search(query, *, collection, top_k, filters, rerank)`` — embed → vector + search → optional rerank. Returns trimmed hits (snippet only). +* ``fetch_chunks(article_uuid, *, max_chunks)`` — pull all chunk bodies for + one article, ordered by ``chunk_index``. +* ``fetch_records(collection, ids)`` — full payloads for articles / persons + / organizations by point ID. + +Graceful degradation: a connection / RCP failure returns ``[]`` and logs a +warning rather than raising — the agent can fall back to other tools. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any, Literal + +from src.index.ethz_research_collection.embed import EmbedError, RCPEmbedder +from src.index.ethz_research_collection.rerank import RCPReranker, RerankError +from src.index.ethz_research_collection.store import ( + ARTICLES_COLLECTION, + CHUNKS_COLLECTION, + ORGANIZATIONS_COLLECTION, + PERSONS_COLLECTION, + QdrantStore, + build_filter, +) + +if TYPE_CHECKING: + from collections.abc import Sequence + + from src.index.ethz_research_collection.config import ( + EthzResearchCollectionIndexConfig, + ) + +logger = logging.getLogger(__name__) + +CollectionName = Literal[ + "chunks", + "articles", + "persons", + "organizations", +] + +_COLLECTION_MAP: dict[str, str] = { + "chunks": CHUNKS_COLLECTION, + "articles": ARTICLES_COLLECTION, + "persons": PERSONS_COLLECTION, + "organizations": ORGANIZATIONS_COLLECTION, +} + +# Fields the LLM may filter on. Anything else is dropped with a warning so +# typos / hallucinated keys don't silently match nothing in Qdrant. +_ALLOWED_FILTER_KEYS: frozenset[str] = frozenset({ + "has_github_match", + "has_hf_match", + "year", + "publication_type", + "language", + "lab_uuid", + "org_uuids", + "doi", + "orcid", + "ror_id", + # Note: EPFL-specific sciper_id / sciper_unit_id are intentionally + # absent — the ETH Research Collection uses different authority IDs. + "author_uuids", + "subjects", + "keywords", + # ETH-side identifiers (filled by parsers from ``ethz.*`` fields): + "scopus_id", + "wos_id", + "publisher", + "issn", +}) + +_SNIPPET_CHARS = 320 +_DEFAULT_TOP_K = 10 +_DEFAULT_MAX_CHUNKS = 20 +_RERANK_CANDIDATE_MULTIPLIER = 5 +_RERANK_CANDIDATE_FLOOR = 30 + + +def _resolve_collection(name: str) -> str: + qdrant_name = _COLLECTION_MAP.get(name) + if qdrant_name is None: + msg = ( + f"Unknown ethz_research_collection collection {name!r}; " + f"expected one of {sorted(_COLLECTION_MAP)}" + ) + raise ValueError(msg) + return qdrant_name + + +def _filter_allowlist(payload: dict[str, Any] | None) -> dict[str, Any] | None: + if not payload: + return None + cleaned: dict[str, Any] = {} + dropped: list[str] = [] + for key, value in payload.items(): + if key in _ALLOWED_FILTER_KEYS: + cleaned[key] = value + else: + dropped.append(key) + if dropped: + logger.warning( + "ethz_research_collection_rag: dropped non-allowlisted filter keys: %s", + sorted(dropped), + ) + return cleaned or None + + +def _snippet(payload: dict[str, Any]) -> str | None: + text = payload.get("text") or payload.get("abstract") + if not isinstance(text, str) or not text: + return None + text = text.strip() + if len(text) <= _SNIPPET_CHARS: + return text + return text[: _SNIPPET_CHARS - 1].rstrip() + "…" + + +def _hit_snippet(collection: str, hit: dict[str, Any]) -> dict[str, Any]: + """Return a thin hit suitable for an LLM tool response. + + Drops bulky fields (full chunk text, raw matched_urls list) but keeps + everything the agent needs to decide whether to fetch the full record. + """ + payload = hit.get("payload") or {} + base: dict[str, Any] = { + "id": hit.get("id"), + "score": hit.get("score"), + "collection": collection, + } + if collection == "chunks": + base.update({ + "article_uuid": payload.get("article_uuid"), + "chunk_index": payload.get("chunk_index"), + "title": payload.get("title"), + "doi": payload.get("doi"), + "year": payload.get("year"), + "research_collection_url": payload.get("research_collection_url"), + "snippet": _snippet(payload), + }) + elif collection == "articles": + base.update({ + "article_uuid": payload.get("article_uuid"), + "title": payload.get("title"), + "doi": payload.get("doi"), + "year": payload.get("year"), + "publication_type": payload.get("publication_type"), + "authors": payload.get("authors"), + "research_collection_url": payload.get("research_collection_url"), + # ETH-side identifiers — surface them on the thin hit so an + # agent can ground a paper by Scopus / WOS / handle without + # an extra fetch_records round-trip. + "scopus_id": payload.get("scopus_id"), + "wos_id": payload.get("wos_id"), + "handle_uri": payload.get("handle_uri"), + "publisher": payload.get("publisher"), + "journal": payload.get("journal"), + "snippet": _snippet(payload), + }) + elif collection == "persons": + # No sciper_id here — ETHZ uses different authority IDs (e.g. nethz). + base.update({ + "person_uuid": payload.get("person_uuid"), + "name": payload.get("name"), + "orcid": payload.get("orcid"), + "primary_affiliation": payload.get("primary_affiliation"), + "profile_url": payload.get("profile_url"), + }) + elif collection == "organizations": + base.update({ + "org_uuid": payload.get("org_uuid"), + "name": payload.get("name"), + "acronym": payload.get("acronym"), + "ror_id": payload.get("ror_id"), + "research_collection_url": payload.get("research_collection_url"), + }) + return {k: v for k, v in base.items() if v is not None} + + +def _rerank_text(collection: str, payload: dict[str, Any]) -> str: + if collection == "chunks": + return (payload.get("text") or payload.get("abstract") or payload.get("title") or "") + if collection == "articles": + title = payload.get("title") or "" + abstract = payload.get("abstract") or "" + return f"{title}\n{abstract}".strip() + if collection == "persons": + return " ".join(filter(None, [ + payload.get("name"), + payload.get("primary_affiliation"), + payload.get("biography"), + ])) + if collection == "organizations": + # Org records are synthesised from `person.department` text and + # carry boilerplate descriptions ("ETH Zürich Leitzahl NNNNN; + # Professorship of X") that cluster near-identically under the + # cross-encoder. Stick to the distinctive identifiers so rerank + # actually re-orders rather than flattening every score to ~0.95. + return " ".join(filter(None, [ + payload.get("name"), + payload.get("acronym"), + ])) + return "" + + +class EthzResearchCollectionRagProvider: + """Async wrapper around the ETH Research Collection Qdrant index.""" + + def __init__( + self, + *, + store: QdrantStore, + embedder: RCPEmbedder, + reranker: RCPReranker | None = None, + ) -> None: + self._store = store + self._embedder = embedder + self._reranker = reranker + self._embedder_open = False + self._reranker_open = False + self._lifecycle_lock = asyncio.Lock() + + @classmethod + def from_config(cls, cfg: EthzResearchCollectionIndexConfig) -> EthzResearchCollectionRagProvider: + store = QdrantStore.from_config(cfg) + embedder = RCPEmbedder(cfg.rcp) + reranker = RCPReranker(cfg.rcp) + return cls(store=store, embedder=embedder, reranker=reranker) + + async def aclose(self) -> None: + async with self._lifecycle_lock: + if self._embedder_open: + await self._embedder.__aexit__(None, None, None) + self._embedder_open = False + if self._reranker_open and self._reranker is not None: + await self._reranker.__aexit__(None, None, None) + self._reranker_open = False + + async def _ensure_embedder(self) -> None: + if self._embedder_open: + return + async with self._lifecycle_lock: + if not self._embedder_open: + await self._embedder.__aenter__() + self._embedder_open = True + + async def _ensure_reranker(self) -> bool: + if self._reranker is None: + return False + if self._reranker_open: + return True + async with self._lifecycle_lock: + if not self._reranker_open: + await self._reranker.__aenter__() + self._reranker_open = True + return True + + async def search( + self, + query: str, + *, + collection: CollectionName = "chunks", + top_k: int = _DEFAULT_TOP_K, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + qdrant_collection = _resolve_collection(collection) + cleaned_filters = _filter_allowlist(filters) + try: + qdrant_filter = build_filter(cleaned_filters) + except ValueError as exc: + logger.warning("ethz_research_collection_rag: invalid filter %r — %s", filters, exc) + return [] + + try: + await self._ensure_embedder() + vector = await self._embedder.embed_query(query) + except (EmbedError, RuntimeError) as exc: + logger.warning("ethz_research_collection_rag: embed failed — %s", exc) + return [] + + candidate_k = top_k + if rerank: + candidate_k = max(top_k * _RERANK_CANDIDATE_MULTIPLIER, _RERANK_CANDIDATE_FLOOR) + + try: + hits = await asyncio.to_thread( + self._store.search, + qdrant_collection, + query_vector=vector, + top_k=candidate_k, + query_filter=qdrant_filter, + ) + except Exception as exc: # noqa: BLE001 — Qdrant client raises a wide set + logger.warning( + "ethz_research_collection_rag: qdrant search failed (collection=%s) — %s", + qdrant_collection, exc, + ) + return [] + + # Synthetic Org records (mined from person.department text) carry + # only ``name + acronym`` as distinctive content. The cross-encoder + # collapses every doc to ~0.94 ± 0.005 because there is no body + # text to differentiate them, so rerank actively hurts ordering + # vs. plain vector search. Skip rerank for that collection. + if rerank and hits and collection != "organizations": + hits = await self._maybe_rerank(query, collection, hits, top_k=top_k) + else: + hits = hits[:top_k] + + return [_hit_snippet(collection, h) for h in hits] + + async def _maybe_rerank( + self, + query: str, + collection: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + try: + available = await self._ensure_reranker() + except RerankError as exc: + logger.warning("ethz_research_collection_rag: reranker open failed — %s", exc) + return hits[:top_k] + if not available or self._reranker is None: + return hits[:top_k] + + documents = [_rerank_text(collection, h.get("payload") or {}) for h in hits] + try: + ranked = await self._reranker.rerank(query, documents, top_n=top_k) + except RerankError as exc: + logger.warning("ethz_research_collection_rag: rerank failed — %s", exc) + return hits[:top_k] + + if not ranked: + return hits[:top_k] + ordered: list[dict[str, Any]] = [] + seen: set[int] = set() + for hit in ranked: + if 0 <= hit.index < len(hits) and hit.index not in seen: + seen.add(hit.index) + rehit = dict(hits[hit.index]) + rehit["score"] = float(hit.score) + ordered.append(rehit) + return ordered[:top_k] + + async def fetch_chunks( + self, + article_uuid: str, + *, + max_chunks: int = _DEFAULT_MAX_CHUNKS, + ) -> list[dict[str, Any]]: + if not isinstance(article_uuid, str) or not article_uuid.strip(): + return [] + try: + qdrant_filter = build_filter({"article_uuid": article_uuid}) + except ValueError as exc: + logger.warning("ethz_research_collection_rag: bad article_uuid filter — %s", exc) + return [] + try: + records = await asyncio.to_thread( + self._store.scroll, + CHUNKS_COLLECTION, + query_filter=qdrant_filter, + limit=max_chunks, + ) + except Exception as exc: # noqa: BLE001 + logger.warning( + "ethz_research_collection_rag: qdrant scroll failed (article_uuid=%s) — %s", + article_uuid, exc, + ) + return [] + + chunks: list[dict[str, Any]] = [] + for rec in records: + payload = rec.get("payload") or {} + chunks.append({ + "id": rec.get("id"), + "article_uuid": payload.get("article_uuid"), + "chunk_index": payload.get("chunk_index"), + "text": payload.get("text"), + "title": payload.get("title"), + "doi": payload.get("doi"), + "research_collection_url": payload.get("research_collection_url"), + }) + chunks.sort(key=lambda c: (c.get("chunk_index") if isinstance(c.get("chunk_index"), int) else 0)) + return chunks + + async def fetch_records( + self, + collection: CollectionName, + ids: Sequence[str], + ) -> list[dict[str, Any]]: + if collection == "chunks": + msg = "Use fetch_chunks(article_uuid) for chunk bodies, not fetch_records." + raise ValueError(msg) + qdrant_collection = _resolve_collection(collection) + clean_ids = [i for i in ids if isinstance(i, str) and i] + if not clean_ids: + return [] + try: + records = await asyncio.to_thread( + self._store.lookup, + qdrant_collection, + ids=clean_ids, + ) + except Exception as exc: # noqa: BLE001 + logger.warning( + "ethz_research_collection_rag: qdrant lookup failed (collection=%s) — %s", + qdrant_collection, exc, + ) + return [] + return [ + {"id": rec.get("id"), "payload": rec.get("payload") or {}} + for rec in records + ] + + +def _truthy_env(value: str | None) -> bool: + if value is None: + return False + return value.strip().lower() in {"1", "true", "t", "yes", "y", "on"} + + +def _falsy_env(value: str | None) -> bool: + if value is None: + return False + return value.strip().lower() in {"0", "false", "f", "no", "n", "off"} + + +def build_default_provider( + cfg: EthzResearchCollectionIndexConfig | None = None, +) -> EthzResearchCollectionRagProvider | None: + """Construct the default RAG provider from on-disk config. + + Returns ``None`` (not raising) when the config or its dependencies fail + to load, so the rest of the v2 pipeline keeps working without RAG. + """ + # Inline import: lazy load avoids hard-failing v2 init when the + # ETH Research Collection yaml or env vars are missing in non-RAG deployments. + try: + from src.index.ethz_research_collection.config import ( # noqa: PLC0415 + load_config, + ) + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 — config errors must not 500 the API + logger.warning( + "ethz_research_collection_rag: failed to load index config; tools disabled (%s)", + exc, + ) + return None + try: + return EthzResearchCollectionRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning( + "ethz_research_collection_rag: failed to construct provider; tools disabled (%s)", + exc, + ) + return None + + +__all__ = [ + "CollectionName", + "EthzResearchCollectionRagProvider", + "build_default_provider", +] diff --git a/src/v2/ingest/providers/federated_rag.py b/src/v2/ingest/providers/federated_rag.py new file mode 100644 index 0000000..7169783 --- /dev/null +++ b/src/v2/ingest/providers/federated_rag.py @@ -0,0 +1,98 @@ +"""Async RAG provider over the federated cross-index layer. + +Wraps :mod:`src.index._federated` so the v2 LLM agents can call into one +tool that searches all six RAG indices in parallel. + +Two methods: + +* ``search(query, *, indices, entity_type, top_k, top_k_per_index, filters)`` + — fan-out semantic search; returns merged hits sorted by score. +* ``lookup(identifier, *, indices)`` — cross-index identifier resolution; + returns matched canonical records. + +Graceful degradation: per-adapter exceptions are absorbed by the federated +layer; this provider further wraps the call in a broad except so a missing +adapter, RCP outage, or Qdrant downtime can't bubble into the agent loop. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import Any + +logger = logging.getLogger(__name__) + +_LOG_LABEL = "federated.rag" +_DEFAULT_TOP_K = 10 +_DEFAULT_TOP_K_PER_INDEX = 3 + + +class FederatedRagProvider: + """Async wrapper around `src.index._federated.search` / `entity`. + + Stateless — no Qdrant / RCP handles to manage. Each call lazily loads + the registered adapters (the federated module already caches imports + on first call, so subsequent invocations are fast). + """ + + async def search( + self, + query: str, + *, + indices: list[str] | None = None, + entity_type: str | None = None, + top_k: int = _DEFAULT_TOP_K, + top_k_per_index: int = _DEFAULT_TOP_K_PER_INDEX, + filters: dict[str, Any] | None = None, + ) -> dict[str, Any]: + if not isinstance(query, str) or not query.strip(): + return {"hits": [], "by_index": {}, "errors": {}} + + from src.index._federated.search import federated_search + + try: + return await asyncio.to_thread( + federated_search, + query, + indices=indices, + entity_type=entity_type, + top_k_per_index=top_k_per_index, + top_k_overall=top_k, + filters=filters, + ) + except Exception as exc: # noqa: BLE001 — must not bubble into the agent + logger.warning("%s: search failed — %s", _LOG_LABEL, exc) + return {"hits": [], "by_index": {}, "errors": {"_": str(exc)}} + + async def lookup( + self, + identifier: str, + *, + indices: list[str] | None = None, + ) -> dict[str, Any]: + if not isinstance(identifier, str) or not identifier.strip(): + return {"identifier": identifier, "records": [], "by_index": {}, "errors": {}} + + from src.index._federated.entity import cross_index_lookup + + try: + return await asyncio.to_thread( + cross_index_lookup, + identifier, + indices=indices, + ) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: lookup failed — %s", _LOG_LABEL, exc) + return { + "identifier": identifier, "records": [], "by_index": {}, + "errors": {"_": str(exc)}, + } + + +def build_default_provider() -> FederatedRagProvider: + """Convenience for `dependencies.py` wiring — no config needed.""" + return FederatedRagProvider() + + +__all__ = ["FederatedRagProvider", "build_default_provider"] diff --git a/src/v2/ingest/providers/github_provider.py b/src/v2/ingest/providers/github_provider.py new file mode 100644 index 0000000..28962b3 --- /dev/null +++ b/src/v2/ingest/providers/github_provider.py @@ -0,0 +1,1032 @@ +from __future__ import annotations + +import itertools +import json +import logging +import os +import re +import threading +import time +from typing import TYPE_CHECKING, Any, Callable +from urllib.parse import urlparse + +import requests + +from src.v2.ingest.cache import ProviderCache +from src.v2.ingest.providers.base import ( + GitHubProvider, + ProviderNotFoundError, +) + +if TYPE_CHECKING: + from src.v2.ingest.providers.rate_limiter import RateLimiter + +JSONMapping = dict[str, Any] +GimieExtractor = Callable[[str, str], Any] +UserLookup = Callable[[str], JSONMapping] +OrganizationLookup = Callable[[str], JSONMapping] +GITHUB_LOGIN_PATTERN = re.compile(r"^[A-Za-z\d](?:[A-Za-z\d]|-(?=[A-Za-z\d])){0,38}$") +GITHUB_NOREPLY_PATTERN = re.compile( + r"^(?:\d+\+)?([A-Za-z\d-]{1,39})@users\.noreply\.github\.com$", + flags=re.IGNORECASE, +) +logger = logging.getLogger(__name__) + + +def _resolve_max_github_repo_retries() -> int: + """Read V2_GITHUB_REPO_MAX_RETRIES (default 3). + + Bounds transient retries when gimie returns malformed JSON (typically + GitHub responding with an empty body / 502 HTML during rate-limit or + momentary outages). Returns 0 to disable retries entirely. + """ + raw = os.getenv("V2_GITHUB_REPO_MAX_RETRIES", "3") + try: + return max(0, int(raw)) + except (ValueError, TypeError): + return 3 + + +def _is_json_decode_error(exc: BaseException) -> bool: + """Return True if the exception (or its cause chain) is a JSON parse error. + + gimie wraps the underlying ``json.JSONDecodeError`` in its own + exception class, so we walk ``__cause__`` / ``__context__`` and also + fall back to a substring match against the error message. + """ + seen: set[int] = set() + current: BaseException | None = exc + while current is not None and id(current) not in seen: + seen.add(id(current)) + if isinstance(current, json.JSONDecodeError): + return True + message = str(current) + if "Expecting value" in message and "line 1 column 1" in message: + return True + current = current.__cause__ or current.__context__ + return False + + +def _is_unicode_decode_error(exc: BaseException) -> bool: + """Return True if a UnicodeDecodeError appears anywhere in the cause chain.""" + seen: set[int] = set() + current: BaseException | None = exc + while current is not None and id(current) not in seen: + seen.add(id(current)) + if isinstance(current, UnicodeDecodeError): + return True + message = str(current).lower() + if "codec can't decode" in message or "'utf-8' codec" in message: + return True + current = current.__cause__ or current.__context__ + return False + + +# Per-process round-robin over comma-separated tokens in GITHUB_TOKEN. With +# multiple gunicorn workers the rotation is independent per worker, which is +# fine — overall calls split roughly evenly across tokens and the effective +# rate-limit ceiling is N * 5000/h for N tokens. +_GITHUB_TOKEN_LOCK = threading.Lock() +_GITHUB_TOKEN_CYCLE: itertools.cycle | None = None +_GITHUB_TOKEN_SOURCE: str | None = None + + +def _parse_github_tokens(raw: str) -> list[str]: + return [t.strip() for t in raw.split(",") if t.strip()] + + +def _next_github_token() -> str: + """Return the next GitHub token (round-robin), or '' if none configured. + + Prefers `GITHUB_TOKEN_POOL` (comma-separated, set by the api startup + normalization) over `GITHUB_TOKEN`. Falls back to `GITHUB_TOKEN` when no + pool is configured. + """ + global _GITHUB_TOKEN_CYCLE, _GITHUB_TOKEN_SOURCE + raw = os.environ.get("GITHUB_TOKEN_POOL", "") or os.environ.get("GITHUB_TOKEN", "") + with _GITHUB_TOKEN_LOCK: + if raw != _GITHUB_TOKEN_SOURCE or _GITHUB_TOKEN_CYCLE is None: + tokens = _parse_github_tokens(raw) + _GITHUB_TOKEN_CYCLE = itertools.cycle(tokens) if tokens else None + _GITHUB_TOKEN_SOURCE = raw + if _GITHUB_TOKEN_CYCLE is None: + return "" + return next(_GITHUB_TOKEN_CYCLE) + + +def _github_auth_headers(extra: dict[str, str] | None = None) -> dict[str, str]: + """Build GitHub request headers with a rotated bearer token.""" + headers: dict[str, str] = {"Accept": "application/vnd.github+json"} + if extra: + headers.update(extra) + token = _next_github_token() + if token: + headers["Authorization"] = f"token {token}" + return headers + + +_LINK_LAST_PAGE_RE = re.compile(r"[?&]page=(\d+)") + + +def _parse_link_last_page(link_header: str) -> int: + """Return the `?page=N` value from the segment of `Link:` tagged + `rel="last"`, or 0 if absent / unparseable. + """ + if not link_header: + return 0 + for part in link_header.split(","): + chunk = part.strip() + if 'rel="last"' not in chunk: + continue + match = _LINK_LAST_PAGE_RE.search(chunk) + if match: + try: + return int(match.group(1)) + except ValueError: + return 0 + return 0 + + +def _first_non_empty_string(*values: Any) -> str | None: + for value in values: + if isinstance(value, str) and value.strip(): + return value.strip() + if isinstance(value, list): + for item in value: + normalized = _first_non_empty_string(item) + if normalized: + return normalized + if isinstance(value, dict): + for key in ("@value", "value", "name", "@id", "id", "url"): + nested = _first_non_empty_string(value.get(key)) + if nested: + return nested + return None + + +def _normalize_repo_url(full_name: str) -> str: + candidate = full_name.strip() + if candidate.startswith(("http://", "https://")): + return candidate + return f"https://github.com/{candidate}" + + +def _normalize_full_name(full_name: str, repository_payload: JSONMapping) -> str: + if "/" in full_name: + return full_name + + candidates = [ + repository_payload.get("full_name"), + repository_payload.get("nameWithOwner"), + repository_payload.get("schema:codeRepository"), + ] + for candidate in candidates: + candidate_value = _first_non_empty_string(candidate) + if not candidate_value: + continue + if "github.com/" in candidate_value: + return candidate_value.split("github.com/")[-1].strip("/") + if "/" in candidate_value: + return candidate_value + return full_name + + +def _normalize_github_login(candidate: Any) -> str | None: + if not isinstance(candidate, str): + return None + normalized = candidate.strip().lstrip("@") + if not normalized: + return None + return normalized if GITHUB_LOGIN_PATTERN.fullmatch(normalized) else None + + +def _extract_login_from_email(email: Any) -> str | None: + if not isinstance(email, str): + return None + match = GITHUB_NOREPLY_PATTERN.fullmatch(email.strip()) + if not match: + return None + return _normalize_github_login(match.group(1)) + + +def _deduplicate_preserve_order(values: list[str]) -> list[str]: + deduplicated: list[str] = [] + seen: set[str] = set() + for value in values: + if value in seen: + continue + deduplicated.append(value) + seen.add(value) + return deduplicated + + +def _extract_github_login_from_identifier(identifier: Any) -> str | None: + candidate = identifier.strip() if isinstance(identifier, str) else "" + login: str | None = None + + if not candidate: + return None + + parsed = urlparse(candidate) + if parsed.scheme and parsed.netloc: + host = parsed.netloc.lower() + if "github.com" in host: + path_segments = [segment for segment in parsed.path.split("/") if segment] + if path_segments: + login = _normalize_github_login(path_segments[0]) + elif "github.com/" in candidate: + suffix = candidate.split("github.com/", maxsplit=1)[-1].strip("/") + if suffix: + login = _normalize_github_login(suffix.split("/", maxsplit=1)[0]) + else: + login = _normalize_github_login(candidate) + + return login + + +def _extract_login_from_reference(reference: Any) -> str | None: + if isinstance(reference, str): + return _extract_github_login_from_identifier(reference) + if isinstance(reference, dict): + identifier = None + for key in ("@id", "id", "url", "@value", "value", "name"): + identifier = _first_non_empty_string(reference.get(key)) + if identifier: + break + return _extract_github_login_from_identifier(identifier) + return None + + +def _extract_graph_nodes(gimie_payload: Any) -> list[Any]: + """Return all nodes from a GIMIE JSON-LD payload (@graph list or flat list).""" + if isinstance(gimie_payload, list): + return gimie_payload + if isinstance(gimie_payload, dict): + graph = gimie_payload.get("@graph") + if isinstance(graph, list): + return graph + return [] + + +def _classify_graph_nodes( + gimie_payload: Any, +) -> tuple[dict[str, JSONMapping], set[str]]: + """Classify GIMIE graph nodes by entity type. + + Returns: + person_index: GitHub URL → Person node for all schema:Person nodes. + org_logins: GitHub logins for all schema:Organization nodes. + """ + person_index: dict[str, JSONMapping] = {} + org_logins: set[str] = set() + + for node in _extract_graph_nodes(gimie_payload): + if not isinstance(node, dict): + continue + node_id = node.get("@id") + if not isinstance(node_id, str) or "github.com/" not in node_id: + continue + node_type = node.get("@type", []) + types = node_type if isinstance(node_type, list) else [node_type] + type_strs = [str(t) for t in types] + if any("Person" in t for t in type_strs): + person_index[node_id] = node + elif any("Organization" in t for t in type_strs): + login = node_id.split("github.com/")[-1].strip("/") + if login: + org_logins.add(login.lower()) + + return person_index, org_logins + + +def _extract_name_from_person_node(node: JSONMapping) -> str | None: + """Extract schema:name from a GIMIE Person node (handles @value wrapper).""" + return _first_non_empty_string( + node.get("schema:name"), + node.get("http://schema.org/name"), + ) + + +def _extract_contributor_logins_from_node(node: JSONMapping) -> list[str]: + references: list[Any] = [] + for key in ( + "schema:contributor", + "http://schema.org/contributor", + "schema:author", + "http://schema.org/author", + ): + value = node.get(key) + if isinstance(value, list): + references.extend(value) + elif value is not None: + references.append(value) + + logins: list[str] = [] + for reference in references: + login = _extract_login_from_reference(reference) + if login: + logins.append(login) + return _deduplicate_preserve_order(logins) + + +def _extract_spdx_id(node: JSONMapping) -> str | None: + license_candidate = _first_non_empty_string( + node.get("schema:license"), + node.get("http://schema.org/license"), + ) + if not isinstance(license_candidate, str) or not license_candidate: + return None + + if "spdx.org/licenses/" not in license_candidate: + return license_candidate + + spdx_suffix = license_candidate.split("spdx.org/licenses/", maxsplit=1)[-1].strip("/") + if spdx_suffix.endswith(".html"): + spdx_suffix = spdx_suffix[:-5] + return spdx_suffix or None + + +def _is_software_source_code_node(node: Any) -> bool: + if not isinstance(node, dict): + return False + + node_type = node.get("@type") + if node_type == "schema:SoftwareSourceCode": + return True + if isinstance(node_type, str): + return "SoftwareSourceCode" in node_type + if isinstance(node_type, list): + return any("SoftwareSourceCode" in str(item) for item in node_type) + return False + + +def _find_software_source_code_node(nodes: list[Any]) -> JSONMapping | None: + for node in nodes: + if _is_software_source_code_node(node): + return node + return None + + +def _extract_repository_node(gimie_payload: Any) -> JSONMapping: + if isinstance(gimie_payload, list): + node = _find_software_source_code_node(gimie_payload) + if isinstance(node, dict): + return node + return {} + + if isinstance(gimie_payload, dict): + graph = gimie_payload.get("@graph") + if isinstance(graph, list): + node = _find_software_source_code_node(graph) + if isinstance(node, dict): + return node + return gimie_payload + return {} + + +def _parse_purl(purl: str) -> dict[str, str | None] | None: + """Parse a Package URL (purl) into ecosystem/name/version. + + Spec: https://github.com/package-url/purl-spec. We only need a thin + extractor — the SPDX SBOM uses purl as the canonical reference and we + care about the three fields the LLM will reason over. Returns ``None`` + when the input is not a valid purl. + + Handles npm-style scoped names where the namespace contains an ``@`` + (e.g. ``pkg:npm/@scope/name@1.0.0``) by splitting on the LAST ``@``. + """ + if not isinstance(purl, str) or not purl.startswith("pkg:"): + return None + body = purl[4:].split("?", 1)[0].split("#", 1)[0] + if "/" not in body: + return None + ecosystem, rest = body.split("/", 1) + ecosystem = ecosystem.strip().lower() or None + if not ecosystem or not rest: + return None + if "@" in rest: + name, version = rest.rsplit("@", 1) + version = version.strip() or None + else: + name, version = rest, None + name = name.strip() or None + if not name: + return None + return {"ecosystem": ecosystem, "name": name, "version": version} + + +def _normalize_sbom(spdx_payload: Any) -> list[dict[str, Any]]: + """Reduce a GitHub SPDX SBOM payload to a flat dependency list. + + GitHub returns ``{"sbom": {"packages": [...]}}``. Each package's + ``externalRefs`` may include a ``purl`` reference; that's the canonical + source of ecosystem/name/version. The first package in the SBOM is the + repository itself — skipped via the missing-purl check (the root has no + purl) rather than positional indexing, which is fragile. + """ + if not isinstance(spdx_payload, dict): + return [] + sbom = spdx_payload.get("sbom") if "sbom" in spdx_payload else spdx_payload + if not isinstance(sbom, dict): + return [] + packages = sbom.get("packages") + if not isinstance(packages, list): + return [] + + dependencies: list[dict[str, Any]] = [] + for package in packages: + if not isinstance(package, dict): + continue + external_refs = package.get("externalRefs") + if not isinstance(external_refs, list): + continue + purl_value: str | None = None + for ref in external_refs: + if not isinstance(ref, dict): + continue + if str(ref.get("referenceType", "")).lower() != "purl": + continue + locator = ref.get("referenceLocator") + if isinstance(locator, str) and locator.strip(): + purl_value = locator.strip() + break + if purl_value is None: + continue + parsed = _parse_purl(purl_value) + if parsed is None: + continue + spdx_id = package.get("SPDXID") + dependencies.append( + { + "name": parsed["name"], + "ecosystem": parsed["ecosystem"], + "version": parsed["version"] + or _first_non_empty_string(package.get("versionInfo")), + "spdxId": spdx_id if isinstance(spdx_id, str) else None, + }, + ) + return dependencies + + +def _extract_languages_from_node(node: JSONMapping) -> dict[str, int]: + language_values = ( + node.get("schema:programmingLanguage") + or node.get("http://schema.org/programmingLanguage") + or node.get("programmingLanguage") + ) + languages: dict[str, int] = {} + + if isinstance(language_values, str): + languages[language_values] = 1 + return languages + + if isinstance(language_values, list): + for item in language_values: + name = _first_non_empty_string(item) + if name: + languages[name] = languages.get(name, 0) + 1 + return languages + + +class RealGitHubProvider(GitHubProvider): + """Production GitHub provider backed by existing GIMIE and v1 parser utilities.""" + + def __init__( + self, + *, + include_user_repositories: bool = True, + include_organization_repositories: bool = True, + gimie_extractor: GimieExtractor | None = None, + user_lookup: UserLookup | None = None, + organization_lookup: OrganizationLookup | None = None, + rate_limiter: RateLimiter | None = None, + cache: ProviderCache | None = None, + ) -> None: + super().__init__(provider_name="github", rate_limiter=rate_limiter) + self._include_user_repositories = include_user_repositories + self._include_organization_repositories = include_organization_repositories + self._gimie_extractor = gimie_extractor + self._user_lookup = user_lookup + self._organization_lookup = organization_lookup + self._cache = cache + + self._users_parser: Any | None = None + self._orgs_parser: Any | None = None + self._gimie_payload_cache: dict[str, Any] = {} + + @property + def include_user_repositories(self) -> bool: + return self._include_user_repositories + + @property + def include_organization_repositories(self) -> bool: + return self._include_organization_repositories + + @staticmethod + def _model_dump(value: Any) -> JSONMapping: + if hasattr(value, "model_dump"): + dumped = value.model_dump() + if isinstance(dumped, dict): + return dumped + if isinstance(value, dict): + return value + raise TypeError + + def _resolve_gimie_extractor(self) -> GimieExtractor: + if self._gimie_extractor is not None: + return self._gimie_extractor + from src.v1.gimie_utils.gimie_methods import extract_gimie # noqa: PLC0415 + + self._gimie_extractor = extract_gimie + return extract_gimie + + def _get_or_fetch_gimie_payload(self, repository_url: str) -> Any: + """Return the GIMIE JSON-LD payload, fetching once across cache layers. + + Order: in-memory dict → persistent `ProviderCache` → live GIMIE call. + Both layers are populated on a fresh fetch. + """ + payload = self._gimie_payload_cache.get(repository_url) + if payload is not None: + return payload + + cache_key: str | None = None + if self._cache is not None: + cache_key = ProviderCache.make_key( + "github", + "gimie_payload", + repository_url=repository_url, + ) + cached = self._cache.get(cache_key) + if cached is not None: + logger.info( + "provider cache hit: github.gimie_payload(%s)", + repository_url, + ) + self._gimie_payload_cache[repository_url] = cached + return cached + + # Fetch with bounded retries for transient JSON-decode errors (GitHub + # returning empty body / 502 HTML during rate-limit) and a graceful + # fallback to a minimal payload on UnicodeDecodeError (non-UTF8 README + # content). Tunable via `V2_GITHUB_REPO_MAX_RETRIES` (default 3). + max_retries = _resolve_max_github_repo_retries() + payload = None + last_exc: BaseException | None = None + for attempt in range(max_retries + 1): + try: + payload = self._run_with_rate_limit( + lambda: self._resolve_gimie_extractor()(repository_url, "json-ld"), + ) + break + except Exception as exc: # noqa: BLE001 — broad catch needed; we re-raise non-retryable errors below + last_exc = exc + if _is_unicode_decode_error(exc): + logger.warning( + "github gimie hit UnicodeDecodeError for %s; falling back to " + "minimal payload (REST metadata still applies downstream): %s", + repository_url, + exc, + ) + payload = {"@graph": []} + break + if _is_json_decode_error(exc) and attempt < max_retries: + backoff_seconds = (2**attempt) * 0.5 + logger.warning( + "github gimie returned non-JSON for %s (attempt %d/%d); " + "retrying in %.1fs", + repository_url, + attempt + 1, + max_retries + 1, + backoff_seconds, + ) + time.sleep(backoff_seconds) + continue + raise + if payload is None: + # Retries exhausted on a JSONDecodeError — re-raise the last exception + # so the caller surfaces the failure (same behaviour as before this fix). + if last_exc is not None: + raise last_exc + return None + if payload is not None: + self._gimie_payload_cache[repository_url] = payload + if self._cache is not None and cache_key is not None: + self._cache.set(cache_key, payload) + return payload + + def _get_repository_node(self, full_name: str) -> JSONMapping: + repository_url = _normalize_repo_url(full_name) + gimie_payload = self._get_or_fetch_gimie_payload(repository_url) + return _extract_repository_node(gimie_payload) + + def get_repository_jsonld(self, full_name: str) -> dict[str, Any]: + """Return the raw GIMIE JSON-LD payload, or an empty dict.""" + repository_url = _normalize_repo_url(full_name) + payload = self._gimie_payload_cache.get(repository_url) + if payload is None: + payload = self._get_or_fetch_gimie_payload(repository_url) + return payload if isinstance(payload, dict) else {} + + def _resolve_user_lookup(self) -> UserLookup: + if self._user_lookup is not None: + return self._user_lookup + + if self._users_parser is None: + from src.v1.parsers.users_parser import GitHubUsersParser # noqa: PLC0415 + + self._users_parser = GitHubUsersParser() + parser = self._users_parser + + def _lookup(username: str) -> JSONMapping: + user = parser.get_user_metadata( + username, + include_repositories=self._include_user_repositories, + ) + return self._model_dump(user) + + self._user_lookup = _lookup + return _lookup + + def _resolve_organization_lookup(self) -> OrganizationLookup: + if self._organization_lookup is not None: + return self._organization_lookup + + if self._orgs_parser is None: + from src.v1.parsers.orgs_parser import ( # noqa: PLC0415 + GitHubOrganizationsParser, + ) + + self._orgs_parser = GitHubOrganizationsParser() + parser = self._orgs_parser + + def _lookup(org_name: str) -> JSONMapping: + org = parser.get_organization_metadata( + org_name, + include_repositories=self._include_organization_repositories, + ) + return self._model_dump(org) + + self._organization_lookup = _lookup + return _lookup + + def get_repository(self, full_name: str) -> dict[str, Any]: + repository_url = _normalize_repo_url(full_name) + node = self._get_repository_node(full_name) + + normalized_full_name = _normalize_full_name(full_name, node) + if "/" not in normalized_full_name: + message = ( + "Repository metadata is missing owner/repository handle: " + f"{full_name}" + ) + raise ProviderNotFoundError( + message, + ) + owner_name = normalized_full_name.split("/", maxsplit=1)[0] + repository_name = normalized_full_name.split("/", maxsplit=1)[-1] + node_name = _first_non_empty_string( + node.get("schema:name"), + node.get("http://schema.org/name"), + ) + if node_name == normalized_full_name: + node_name = repository_name + + # Stars/forks/created_at are not reliably present in gimie's JSON-LD + # output. Fetch them deterministically from the GitHub REST API + # (cached) and merge in. + rest_metadata = self._get_repository_rest_metadata(normalized_full_name) + stargazers_count = rest_metadata.get("stargazers_count") + if stargazers_count is None: + stargazers_count = node.get("pulse:githubRepoStars") + forks_count = rest_metadata.get("forks_count") + if forks_count is None: + forks_count = node.get("pulse:githubRepoForks") + created_at = rest_metadata.get("created_at") or _first_non_empty_string( + node.get("schema:dateCreated"), + node.get("http://schema.org/dateCreated"), + ) + + return { + "name": node_name or repository_name, + "full_name": normalized_full_name, + "html_url": repository_url, + "owner": { + "login": owner_name, + "type": "Organization", + }, + "description": _first_non_empty_string( + node.get("schema:description"), + node.get("http://schema.org/description"), + ), + "stargazers_count": stargazers_count, + "forks_count": forks_count, + "created_at": created_at, + "license": { + "spdx_id": _extract_spdx_id(node), + }, + "fork": bool(node.get("pulse:isForkOf")), + "source": { + "full_name": _first_non_empty_string(node.get("pulse:isForkOf")), + }, + "topics": [], + } + + def _get_repository_rest_metadata(self, full_name: str) -> dict[str, Any]: + """Fetch stars/forks/created_at from the GitHub REST API (cached). + + Returns an empty dict on any error so the caller falls back to + gimie-derived values. Cached via the standard provider cache so + repeated requests within TTL don't re-hit GitHub. + """ + + def _fetch() -> dict[str, Any]: + url = f"https://api.github.com/repos/{full_name}" + try: + response = self._run_with_rate_limit( + lambda: requests.get(url, headers=_github_auth_headers(), timeout=15), + ) + except Exception: # noqa: BLE001 + logger.exception("github REST repo fetch failed: %s", full_name) + return {} + if response.status_code != 200: + logger.warning( + "github REST repo fetch returned %d for %s", + response.status_code, + full_name, + ) + return {} + try: + payload = response.json() + except ValueError: + logger.exception("github REST repo response not JSON: %s", full_name) + return {} + if not isinstance(payload, dict): + return {} + return { + "stargazers_count": payload.get("stargazers_count"), + "forks_count": payload.get("forks_count"), + "created_at": payload.get("created_at"), + } + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key("github", "get_repository_rest", full_name=full_name) + return self._cache.get_or_set( + key, + _fetch, + label=f"github.get_repository_rest({full_name})", + ) + + def get_repository_sbom(self, full_name: str) -> list[dict[str, Any]] | None: + """Fetch the SPDX SBOM from GitHub and return a flat dependency list. + + Endpoint: ``GET /repos/{owner}/{repo}/dependency-graph/sbom``. + Returns ``None`` when the repo has no SBOM (404), when dependency + graph access is denied (403 — disabled or private without ``repo`` + scope), or when the response shape is unexpected. Other failures + (timeouts, network errors) also collapse to ``None`` — the LLM tool + treats absence as "no data" rather than a hard error. + """ + + def _fetch() -> list[dict[str, Any]] | None: + url = f"https://api.github.com/repos/{full_name}/dependency-graph/sbom" + try: + response = self._run_with_rate_limit( + lambda: requests.get(url, headers=_github_auth_headers(), timeout=30), + ) + except Exception: # noqa: BLE001 + logger.exception("github SBOM fetch failed: %s", full_name) + return None + if response.status_code in (403, 404): + logger.info( + "github SBOM not available (%d) for %s", + response.status_code, + full_name, + ) + return None + if response.status_code != 200: + logger.warning( + "github SBOM fetch returned %d for %s", + response.status_code, + full_name, + ) + return None + try: + payload = response.json() + except ValueError: + logger.exception("github SBOM response not JSON: %s", full_name) + return None + return _normalize_sbom(payload) + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key("github", "get_repository_sbom", full_name=full_name) + return self._cache.get_or_set( + key, + _fetch, + label=f"github.get_repository_sbom({full_name})", + ) + + def get_commit_bookends( + self, + full_name: str, + login: str, + ) -> dict[str, Any]: + """Return `(first, last)` commit dates and total count for `login`. + + Always issues two API calls (regardless of repo age) so we + capture the exact first and last commit dates rather than the + 52-week approximation from `/stats/contributors`: + + 1. ``GET /repos/{full_name}/commits?author={login}&per_page=1`` + — the most recent commit (`last_date`). The `Link: ... + rel="last"` header carries `?page=N` where `N` is the total + number of commits by this author. + 2. ``GET /repos/{full_name}/commits?author={login}&per_page=1&page={N}`` + — the oldest commit (`first_date`). + + When the repo has only one commit by this author, both calls + return the same record (`first_date == last_date`, `count == 1`). + + Failures (network, non-200, malformed response) collapse to + `{first_date: None, last_date: None, count: 0}`. Result is + cached per the standard `V2_PROVIDER_CACHE_TTL_DAYS` TTL — a + miss won't re-hit GitHub until the cache entry expires (one + month by default), so transient failures don't trigger + per-request retry storms. + """ + + def _fetch() -> dict[str, Any]: + last_date, count = self._fetch_commit_page( + full_name, login, page=1, + ) + if count <= 0 or last_date is None: + return {"first_date": None, "last_date": None, "count": 0} + if count == 1: + return { + "first_date": last_date, + "last_date": last_date, + "count": 1, + } + first_date, _ = self._fetch_commit_page( + full_name, login, page=count, + ) + return { + "first_date": first_date, + "last_date": last_date, + "count": count, + } + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key( + "github", + "get_commit_bookends", + full_name=full_name, + login=login, + ) + return self._cache.get_or_set( + key, + _fetch, + label=f"github.get_commit_bookends({full_name},{login})", + ) + + def _fetch_commit_page( + self, + full_name: str, + login: str, + *, + page: int, + ) -> tuple[str | None, int]: + """Return ``(commit_date_iso, total_count)`` for one page of size 1. + + ``total_count`` is parsed from the ``Link: rel="last"`` header + (page index when ``per_page=1``). If the header is absent the + result set fits in one page, so we return ``len(body)``. + """ + url = f"https://api.github.com/repos/{full_name}/commits" + params = {"author": login, "per_page": "1", "page": str(page)} + try: + response = self._run_with_rate_limit( + lambda: requests.get( + url, + params=params, + headers=_github_auth_headers(), + timeout=15, + ), + ) + except Exception: # noqa: BLE001 + logger.exception( + "github commit page fetch failed: %s author=%s page=%d", + full_name, + login, + page, + ) + return None, 0 + if response.status_code != 200: + logger.warning( + "github commit page returned %d for %s author=%s page=%d", + response.status_code, + full_name, + login, + page, + ) + return None, 0 + try: + payload = response.json() + except ValueError: + logger.exception( + "github commit page response not JSON: %s author=%s", + full_name, + login, + ) + return None, 0 + if not isinstance(payload, list) or not payload: + return None, 0 + commit = payload[0].get("commit") if isinstance(payload[0], dict) else None + date: str | None = None + if isinstance(commit, dict): + committer = commit.get("committer") if isinstance(commit.get("committer"), dict) else {} + author = commit.get("author") if isinstance(commit.get("author"), dict) else {} + date = committer.get("date") or author.get("date") + last_page = _parse_link_last_page(response.headers.get("Link", "")) + count = last_page if last_page > 0 else len(payload) + return date if isinstance(date, str) else None, count + + def get_user(self, username: str) -> dict[str, Any]: + def _fetch() -> dict[str, Any]: + try: + return self._run_with_rate_limit( + lambda: self._resolve_user_lookup()(username), + ) + except ValueError as exc: + raise ProviderNotFoundError(str(exc)) from exc + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key( + "github", + "get_user", + username=username, + include_repositories=self._include_user_repositories, + ) + return self._cache.get_or_set( + key, + _fetch, + label=f"github.get_user({username})", + ) + + def get_organization(self, org_name: str) -> dict[str, Any]: + def _fetch() -> dict[str, Any]: + try: + return self._run_with_rate_limit( + lambda: self._resolve_organization_lookup()(org_name), + ) + except ValueError as exc: + raise ProviderNotFoundError(str(exc)) from exc + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key( + "github", + "get_organization", + org_name=org_name, + include_repositories=self._include_organization_repositories, + ) + return self._cache.get_or_set( + key, + _fetch, + label=f"github.get_organization({org_name})", + ) + + def get_contributors(self, full_name: str) -> list[dict[str, Any]]: + repository_url = _normalize_repo_url(full_name) + gimie_payload = self._gimie_payload_cache.get(repository_url) or {} + node = self._get_repository_node(full_name) + gimie_contributor_logins = _extract_contributor_logins_from_node(node) + if not gimie_contributor_logins: + logger.debug( + "GIMIE contributor extraction returned no GitHub logins for %s", + full_name, + ) + return [] + person_index, org_logins = _classify_graph_nodes(gimie_payload) + contributors = [] + for login in gimie_contributor_logins: + if login.lower() in org_logins: + continue + person_node = person_index.get(f"https://github.com/{login}", {}) + contributors.append( + { + "login": login, + "name": _extract_name_from_person_node(person_node), + "email": None, + "contributions": None, + } + ) + return contributors + + def get_languages(self, full_name: str) -> dict[str, int]: + node = self._get_repository_node(full_name) + return _extract_languages_from_node(node) diff --git a/src/v2/ingest/providers/github_rag.py b/src/v2/ingest/providers/github_rag.py new file mode 100644 index 0000000..cbe2809 --- /dev/null +++ b/src/v2/ingest/providers/github_rag.py @@ -0,0 +1,194 @@ +"""Async RAG provider over the GitHub Qdrant index. + +Single collection: ``github_repos``. Reuses OpenAlex's embedder, reranker +and ``QdrantStore`` via duck typing — ``GitHubIndexConfig`` mirrors the +same ``rcp.*`` / ``qdrant.*`` shape OpenAlex's clients expect. + +Source corpus is `src/index/github/`: a deterministic GitHub-REST-fed +index of EPFL/Swiss-research repos (metadata + README chunks) embedded +with Qwen3-Embedding-8B. Useful when the agent needs to find repos +similar to the one it's processing — e.g. "are there other EPFL repos +implementing this technique?" or "what's the canonical fork?". +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any + +from src.index.github.embed.pipeline import GITHUB_COLLECTION +from src.index.openalex.embed.rcp_client import ( + RCPEmbeddingClient, + RCPEmbeddingError, +) +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.v2.ingest.providers._rag_helpers import ( + apply_rerank_indices, + env_enabled, + expand_candidate_k, + filter_allowlist, + safe_rerank_documents, + thin_payload, + to_simple_filter_payload, +) + +if TYPE_CHECKING: + from src.index.github.config import GitHubIndexConfig + +logger = logging.getLogger(__name__) + +_ALLOWED_FILTER_KEYS: frozenset[str] = frozenset({ + "entity_type", # always "repos" for now; kept for federated symmetry + "repo_id", + "owner", + "primary_language", + "license_spdx", + "is_archived", + "is_fork", +}) + +_THIN_KEYS: tuple[str, ...] = ( + "repo_id", "owner", "name", "primary_language", "license_spdx", + "stars", "forks", "pushed_at", "is_archived", +) + +_DEFAULT_TOP_K = 10 +_LOG_LABEL = "github_rag" + + +def _rerank_text(payload: dict[str, Any]) -> str: + """Build the document fed to the cross-encoder reranker. + + The full README isn't in the payload (it's chunked across many points); + `repo_id + description` is what we have on every hit and gives the + reranker enough signal to compare candidates. + """ + repo_id = payload.get("repo_id") or "" + desc = payload.get("description") or "" + return f"{repo_id}\n{desc}".strip() or repo_id + + +class GitHubRagProvider: + """Async wrapper around the GitHub Qdrant index.""" + + def __init__( + self, + *, + store: QdrantStore, + embedder: RCPEmbeddingClient, + reranker: RCPRerankerClient | None = None, + ) -> None: + self._store = store + self._embedder = embedder + self._reranker = reranker + + @classmethod + def from_config(cls, cfg: GitHubIndexConfig) -> GitHubRagProvider: + # Duck-type into OpenAlex's clients — config shape matches. + return cls( + store=QdrantStore(cfg), # type: ignore[arg-type] + embedder=RCPEmbeddingClient(cfg), # type: ignore[arg-type] + reranker=RCPRerankerClient(cfg), # type: ignore[arg-type] + ) + + async def search( + self, + query: str, + *, + top_k: int = _DEFAULT_TOP_K, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + if not self._store.client.collection_exists(GITHUB_COLLECTION): + logger.info( + "%s: collection %s missing — returning [] without indexing", + _LOG_LABEL, GITHUB_COLLECTION, + ) + return [] + + cleaned = filter_allowlist(filters, _ALLOWED_FILTER_KEYS, log_label=_LOG_LABEL) + filter_payload = to_simple_filter_payload(cleaned) + + try: + vectors = await self._embedder.embed_all([query]) + except (RCPEmbeddingError, Exception) as exc: # noqa: BLE001 + logger.warning("%s: embed failed — %s", _LOG_LABEL, exc) + return [] + if not vectors: + return [] + vector = list(vectors[0]) + + candidate_k = expand_candidate_k(top_k) if rerank else top_k + + try: + hits = await asyncio.to_thread( + self._store.search, + GITHUB_COLLECTION, + query_vector=vector, + top_k=candidate_k, + filter_payload=filter_payload, + ) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: qdrant search failed — %s", _LOG_LABEL, exc) + return [] + + if rerank and hits and self._reranker is not None: + hits = await self._maybe_rerank(query, hits, top_k=top_k) + else: + hits = hits[:top_k] + + return [ + thin_payload( + hit.get("payload") or {}, + _THIN_KEYS, + extras={ + "id": hit.get("id"), + "score": hit.get("score"), + "collection": GITHUB_COLLECTION, + }, + ) + for hit in hits + ] + + async def _maybe_rerank( + self, + query: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + documents = safe_rerank_documents( + [_rerank_text(h.get("payload") or {}) for h in hits], + ) + try: + results = await self._reranker.rerank(query, documents, top_n=top_k) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: rerank failed — %s", _LOG_LABEL, exc) + return hits[:top_k] + return apply_rerank_indices(hits, results, top_k=top_k) + + +def build_default_provider( + cfg: GitHubIndexConfig | None = None, +) -> GitHubRagProvider | None: + if not env_enabled("V2_GITHUB_RAG_ENABLED"): + return None + try: + from src.index.github.config import load_config # noqa: PLC0415 + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to load config (%s)", _LOG_LABEL, exc) + return None + try: + return GitHubRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to construct provider (%s)", _LOG_LABEL, exc) + return None + + +__all__ = ["GitHubRagProvider", "build_default_provider"] diff --git a/src/v2/ingest/providers/huggingface_rag.py b/src/v2/ingest/providers/huggingface_rag.py new file mode 100644 index 0000000..40ebcca --- /dev/null +++ b/src/v2/ingest/providers/huggingface_rag.py @@ -0,0 +1,296 @@ +"""Async RAG provider over the HuggingFace Qdrant index. + +Wraps :class:`src.index.huggingface.vector.qdrant_store.QdrantStore` with +:class:`src.index.huggingface.embed.rcp_client.RCPEmbeddingClient` and +:class:`src.index.huggingface.rerank.rcp_client.RCPRerankerClient`. + +Exposes one method: + +* ``search(query, *, collection, top_k, filters, rerank)`` — + collection ∈ {"models", "datasets", "spaces"}. + +Graceful degradation: connection / RCP failure → ``[]`` + warning. + +HF chunk payloads contain only metadata (no body text). When ``rerank=True`` +the provider synthesises a rerank document string from the available +identifier fields (``repo_id``, ``library_name``, ``pipeline_tag``, ``sdk``). +This is a weak signal; rerank is most useful here for ambiguous repo-name +disambiguation rather than full-text relevance. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any, Literal + +from src.index.huggingface.embed.rcp_client import ( + RCPEmbeddingClient, + RCPEmbeddingError, +) +from src.index.huggingface.rerank.rcp_client import RCPRerankerClient +from src.index.huggingface.vector.qdrant_store import QdrantStore +from src.v2.ingest.providers._rag_helpers import ( + apply_rerank_indices, + env_enabled, + expand_candidate_k, + filter_allowlist, + safe_rerank_documents, + thin_payload, + to_simple_filter_payload, +) + +if TYPE_CHECKING: + from src.index.huggingface.config import HuggingFaceIndexConfig + +logger = logging.getLogger(__name__) + +CollectionName = Literal["models", "datasets", "spaces", "orgs"] + + +async def lineage( + repo_id: str, + *, + depth: int = 3, +) -> dict[str, Any]: + """Walk the HuggingFace `base_models` graph from `repo_id`. + + Returns ancestors (parent models), descendants (models fine-tuned from + `repo_id`), and the explicit edge list. Pure local DuckDB lookup — + no RCP / Qdrant calls. Cheap (sub-second). + """ + if not isinstance(repo_id, str) or not repo_id.strip(): + return {"root": repo_id, "ancestors": {}, "descendants": {}, "edges": [], "depth": depth} + try: + from src.index.huggingface.retrieval.lineage import compute_lineage as _compute + from src.index.huggingface.storage.duckdb_store import DuckDBStore as _Store + return await asyncio.to_thread(_walk_lineage, repo_id, depth, _compute, _Store) + except Exception as exc: # noqa: BLE001 + logger.warning("huggingface.rag.lineage(%r) failed — %s", repo_id, exc) + return {"root": repo_id, "ancestors": {}, "descendants": {}, "edges": [], "depth": depth} + + +def _walk_lineage(repo_id: str, depth: int, compute_fn: Any, store_cls: Any) -> dict[str, Any]: + store = store_cls.open() + return compute_fn(repo_id, store=store, depth=depth) + +# Maps the LLM-facing collection name to the actual Qdrant collection. +# Extends the upstream COLLECTION_FOR_TABLE (which only knows about the +# three indexed entity tables) with `orgs` for the hf_orgs collection. +_HF_COLLECTION_MAP: dict[str, str] = { + "models": "hf_models", + "datasets": "hf_datasets", + "spaces": "hf_spaces", + "orgs": "hf_orgs", +} + +_ALLOWED_FILTER_KEYS: frozenset[str] = frozenset({ + "library_name", + "pipeline_tag", + "sdk", + "gated", + "downloads", + "downloads_all_time", + "likes", + "author", + "license", + "entity_type", +}) + +_THIN_KEYS_MODELS: tuple[str, ...] = ( + "repo_id", "author", "library_name", "pipeline_tag", "license", + "downloads", "downloads_all_time", "likes", "gated", "last_modified", +) +_THIN_KEYS_DATASETS: tuple[str, ...] = ( + "repo_id", "author", "license", "downloads", "downloads_all_time", + "likes", "gated", "last_modified", +) +_THIN_KEYS_SPACES: tuple[str, ...] = ( + "repo_id", "author", "sdk", "license", "likes", "last_modified", +) + +_THIN_KEYS_ORGS: tuple[str, ...] = ( + "repo_id", "slug", "fullname", "namespace_kind", "scope", + "num_models", "num_datasets", "num_spaces", "num_followers", +) + +_THIN_KEYS_FOR: dict[str, tuple[str, ...]] = { + "models": _THIN_KEYS_MODELS, + "datasets": _THIN_KEYS_DATASETS, + "spaces": _THIN_KEYS_SPACES, + "orgs": _THIN_KEYS_ORGS, +} + +_DEFAULT_TOP_K = 10 +_LOG_LABEL = "huggingface_rag" + + +def _rerank_text(collection: str, payload: dict[str, Any]) -> str: + """Synthesise a short doc string for rerank from HF metadata fields.""" + parts: list[str] = [] + repo_id = payload.get("repo_id") + if isinstance(repo_id, str) and repo_id: + parts.append(repo_id) + if collection == "models": + for key in ("library_name", "pipeline_tag"): + v = payload.get(key) + if isinstance(v, str) and v: + parts.append(v) + elif collection == "spaces": + sdk = payload.get("sdk") + if isinstance(sdk, str) and sdk: + parts.append(sdk) + return " ".join(parts) + + +class HuggingFaceRagProvider: + """Async wrapper around the HuggingFace Qdrant index.""" + + def __init__( + self, + *, + store: QdrantStore, + embedder: RCPEmbeddingClient, + reranker: RCPRerankerClient | None = None, + ) -> None: + self._store = store + self._embedder = embedder + self._reranker = reranker + + @classmethod + def from_config( + cls, + cfg: HuggingFaceIndexConfig, + ) -> HuggingFaceRagProvider: + return cls( + store=QdrantStore(cfg), + embedder=RCPEmbeddingClient(cfg), + reranker=RCPRerankerClient(cfg), + ) + + async def lineage( + self, + repo_id: str, + *, + depth: int = 3, + ) -> dict[str, Any]: + """Walk the HF base_models DAG. Pure local DuckDB; no RCP.""" + return await lineage(repo_id, depth=depth) + + async def search( # noqa: PLR0911 — each early return is a graceful-fail check + self, + query: str, + *, + collection: CollectionName = "models", + top_k: int = _DEFAULT_TOP_K, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + qdrant_collection = _HF_COLLECTION_MAP.get(collection) + if qdrant_collection is None: + logger.warning("%s: unknown collection %r", _LOG_LABEL, collection) + return [] + # Skip when missing — upstream `QdrantStore.search` would lazily + # create an empty collection otherwise. + if not self._store.client.collection_exists(qdrant_collection): + logger.info( + "%s: collection %s missing — returning [] without indexing", + _LOG_LABEL, qdrant_collection, + ) + return [] + + cleaned = filter_allowlist(filters, _ALLOWED_FILTER_KEYS, log_label=_LOG_LABEL) + filter_payload = to_simple_filter_payload(cleaned) + + try: + vectors = await self._embedder.embed_all([query]) + except (RCPEmbeddingError, Exception) as exc: # noqa: BLE001 — RCP transport errors must not bubble + logger.warning("%s: embed failed — %s", _LOG_LABEL, exc) + return [] + if not vectors: + return [] + vector = list(vectors[0]) + + candidate_k = expand_candidate_k(top_k) if rerank else top_k + + try: + hits = await asyncio.to_thread( + self._store.search, + qdrant_collection, + query_vector=vector, + top_k=candidate_k, + filter_payload=filter_payload, + ) + except Exception as exc: # noqa: BLE001 + logger.warning( + "%s: qdrant search failed (collection=%s) — %s", + _LOG_LABEL, qdrant_collection, exc, + ) + return [] + + if rerank and hits and self._reranker is not None: + hits = await self._maybe_rerank(query, collection, hits, top_k=top_k) + else: + hits = hits[:top_k] + + thin_keys = _THIN_KEYS_FOR[collection] + return [ + thin_payload( + hit.get("payload") or {}, + thin_keys, + extras={ + "id": hit.get("id"), + "score": hit.get("score"), + "collection": collection, + }, + ) + for hit in hits + ] + + async def _maybe_rerank( + self, + query: str, + collection: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + documents = safe_rerank_documents( + [_rerank_text(collection, h.get("payload") or {}) for h in hits], + ) + try: + results = await self._reranker.rerank(query, documents, top_n=top_k) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: rerank failed — %s", _LOG_LABEL, exc) + return hits[:top_k] + return apply_rerank_indices(hits, results, top_k=top_k) + + +def build_default_provider( + cfg: HuggingFaceIndexConfig | None = None, +) -> HuggingFaceRagProvider | None: + """Construct the default RAG provider, or return ``None`` on failure.""" + if not env_enabled("V2_HUGGINGFACE_RAG_ENABLED"): + return None + try: + # Lazy load: missing yaml / env should not break v2 init. + from src.index.huggingface.config import load_config # noqa: PLC0415 + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to load config (%s)", _LOG_LABEL, exc) + return None + try: + return HuggingFaceRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to construct provider (%s)", _LOG_LABEL, exc) + return None + + +__all__ = [ + "CollectionName", + "HuggingFaceRagProvider", + "build_default_provider", +] diff --git a/src/v2/ingest/providers/infoscience_provider.py b/src/v2/ingest/providers/infoscience_provider.py new file mode 100644 index 0000000..0e6ef52 --- /dev/null +++ b/src/v2/ingest/providers/infoscience_provider.py @@ -0,0 +1,237 @@ +from __future__ import annotations + +import asyncio +import contextvars +from concurrent.futures import ThreadPoolExecutor +from typing import TYPE_CHECKING, Any, Awaitable, Callable + +from src.v2.ingest.cache import ProviderCache +from src.v2.ingest.providers.base import InfoscienceProvider, InfosciencePublicationRecord + +if TYPE_CHECKING: + from src.v2.ingest.providers.rate_limiter import RateLimiter + +JSONMapping = dict[str, Any] +InfoscienceSearch = Callable[[str, int], Awaitable[Any]] + + +def _run_async(sync_or_async: Awaitable[Any] | Any) -> Any: + """Run a coroutine synchronously, preserving the calling ContextVar state. + + The caller is typically an async pipeline running this from a sync wrapper + in a worker thread. The bridge below copies the current `Context` so that + `request_id_var` (and any other ContextVars) survive into the worker's + fresh event loop — without that, log records emitted by the v1 callee + lose the request-id prefix set by the FastAPI middleware. + """ + if not asyncio.iscoroutine(sync_or_async): + return sync_or_async + try: + asyncio.get_running_loop() + except RuntimeError: + return asyncio.run(sync_or_async) + + ctx = contextvars.copy_context() + with ThreadPoolExecutor(max_workers=1) as executor: + future = executor.submit(ctx.run, asyncio.run, sync_or_async) + return future.result() + + +def _to_dict(value: Any) -> JSONMapping: + if hasattr(value, "model_dump"): + dumped = value.model_dump() + if isinstance(dumped, dict): + return dumped + if isinstance(value, dict): + return value + return {} + + +def _ensure_list(value: Any) -> list[dict[str, Any]]: + if not isinstance(value, list): + return [] + return [item for item in value if isinstance(item, dict)] + + +def _as_string(value: Any) -> str | None: + return value if isinstance(value, str) and value else None + + +def _as_string_list(value: Any) -> list[str]: + if not isinstance(value, list): + return [] + return [item for item in value if isinstance(item, str) and item] + + +def _normalize_publication(publication: dict[str, Any]) -> InfosciencePublicationRecord: + publication_date = _as_string(publication.get("publicationDate")) + if publication_date is None: + publication_date = _as_string(publication.get("publication_date")) + + source_organization = _as_string(publication.get("sourceOrganization")) + if source_organization is None: + source_organization = _as_string(publication.get("lab")) + + raw_authorities = publication.get("author_authorities") + if isinstance(raw_authorities, list): + author_authorities: list[str | None] = [ + item if isinstance(item, str) and item else None + for item in raw_authorities + ] + else: + author_authorities = [] + + return { + "infosciencePublicationIdentifier": _as_string( + publication.get("infosciencePublicationIdentifier"), + ) + or _as_string(publication.get("uuid")), + "title": _as_string(publication.get("title")), + "authors": _as_string_list(publication.get("authors")), + "author_authorities": author_authorities, + "publicationDate": publication_date, + "doi": _as_string(publication.get("doi")), + "url": _as_string(publication.get("url")), + "sourceOrganization": source_organization, + } + + +class RealInfoscienceProvider(InfoscienceProvider): + """Production Infoscience provider wrapping existing context search utilities.""" + + def __init__( + self, + *, + max_results: int = 10, + search_authors_func: InfoscienceSearch | None = None, + search_labs_func: InfoscienceSearch | None = None, + search_publications_func: InfoscienceSearch | None = None, + rate_limiter: RateLimiter | None = None, + cache: ProviderCache | None = None, + ) -> None: + super().__init__(provider_name="infoscience", rate_limiter=rate_limiter) + self._max_results = max_results + self._search_authors_func = search_authors_func + self._search_labs_func = search_labs_func + self._search_publications_func = search_publications_func + self._cache = cache + + def _resolve_search_authors(self) -> InfoscienceSearch: + if self._search_authors_func is not None: + return self._search_authors_func + from src.v2.ingest.infoscience import search_authors # noqa: PLC0415 + + self._search_authors_func = search_authors + return search_authors + + def _resolve_search_labs(self) -> InfoscienceSearch: + if self._search_labs_func is not None: + return self._search_labs_func + from src.v2.ingest.infoscience import search_labs # noqa: PLC0415 + + self._search_labs_func = search_labs + return search_labs + + def _resolve_search_publications(self) -> InfoscienceSearch: + if self._search_publications_func is not None: + return self._search_publications_func + from src.v2.ingest.infoscience import search_publications # noqa: PLC0415 + + self._search_publications_func = search_publications + return search_publications + + def search_person(self, query: str) -> list[dict[str, Any]]: + def _fetch() -> list[dict[str, Any]]: + result = self._run_with_rate_limit( + lambda: _run_async(self._resolve_search_authors()(query, self._max_results)), + ) + payload = _to_dict(result) + authors = _ensure_list(payload.get("authors")) + return [ + { + "infosciencePersonIdentifier": author.get("uuid"), + "name": author.get("name"), + "orcid": author.get("orcid"), + "affiliations": ( + [author["affiliation"]] + if isinstance(author.get("affiliation"), str) + else [] + ), + "profileUrl": ( + str(author["profile_url"]) + if author.get("profile_url") is not None + else None + ), + } + for author in authors + ] + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key( + "infoscience", + "search_person", + query=query, + max_results=self._max_results, + ) + return self._cache.get_or_set( + key, + _fetch, + label=f"infoscience.search_person({query!r})", + ) + + def search_orgunit(self, query: str) -> list[dict[str, Any]]: + def _fetch() -> list[dict[str, Any]]: + result = self._run_with_rate_limit( + lambda: _run_async(self._resolve_search_labs()(query, self._max_results)), + ) + payload = _to_dict(result) + labs = _ensure_list(payload.get("labs")) + return [ + { + "infoscienceOrgUnitIdentifier": lab.get("uuid"), + "name": lab.get("name"), + "parentOrganization": lab.get("parent_organization"), + "url": lab.get("url"), + } + for lab in labs + ] + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key( + "infoscience", + "search_orgunit", + query=query, + max_results=self._max_results, + ) + return self._cache.get_or_set( + key, + _fetch, + label=f"infoscience.search_orgunit({query!r})", + ) + + def search_publications(self, query: str) -> list[InfosciencePublicationRecord]: + def _fetch() -> list[InfosciencePublicationRecord]: + result = self._run_with_rate_limit( + lambda: _run_async( + self._resolve_search_publications()(query, self._max_results), + ), + ) + payload = _to_dict(result) + publications = _ensure_list(payload.get("publications")) + return [_normalize_publication(publication) for publication in publications] + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key( + "infoscience", + "search_publications", + query=query, + max_results=self._max_results, + ) + return self._cache.get_or_set( + key, + _fetch, + label=f"infoscience.search_publications({query!r})", + ) diff --git a/src/v2/ingest/providers/infoscience_rag.py b/src/v2/ingest/providers/infoscience_rag.py new file mode 100644 index 0000000..66b1359 --- /dev/null +++ b/src/v2/ingest/providers/infoscience_rag.py @@ -0,0 +1,457 @@ +"""Async RAG provider over the Infoscience Qdrant index. + +Wraps :class:`src.index.infoscience.store.QdrantStore` with embedding (via +:class:`src.index.infoscience.embed.RCPEmbedder`) and optional reranking +(via :class:`src.index.infoscience.rerank.RCPReranker`). + +Exposes three methods used by v2 agent tools: + +* ``search(query, *, collection, top_k, filters, rerank)`` — embed → vector + search → optional rerank. Returns trimmed hits (snippet only). +* ``fetch_chunks(article_uuid, *, max_chunks)`` — pull all chunk bodies for + one article, ordered by ``chunk_index``. +* ``fetch_records(collection, ids)`` — full payloads for articles / persons + / organizations by point ID. + +Graceful degradation: a connection / RCP failure returns ``[]`` and logs a +warning rather than raising — the agent can fall back to other tools. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any, Literal + +from src.index.infoscience.embed import EmbedError, RCPEmbedder +from src.index.infoscience.rerank import RCPReranker, RerankError +from src.index.infoscience.store import ( + ARTICLES_COLLECTION, + CHUNKS_COLLECTION, + ORGANIZATIONS_COLLECTION, + PERSONS_COLLECTION, + QdrantStore, + build_filter, +) + +if TYPE_CHECKING: + from collections.abc import Sequence + + from src.index.infoscience.config import InfoscienceIndexConfig + +logger = logging.getLogger(__name__) + +CollectionName = Literal[ + "chunks", + "articles", + "persons", + "organizations", +] + +_COLLECTION_MAP: dict[str, str] = { + "chunks": CHUNKS_COLLECTION, + "articles": ARTICLES_COLLECTION, + "persons": PERSONS_COLLECTION, + "organizations": ORGANIZATIONS_COLLECTION, +} + +# Fields the LLM may filter on. Anything else is dropped with a warning so +# typos / hallucinated keys don't silently match nothing in Qdrant. +_ALLOWED_FILTER_KEYS: frozenset[str] = frozenset({ + "has_github_match", + "has_hf_match", + "year", + "publication_type", + "language", + "lab_uuid", + "org_uuids", + "doi", + "orcid", + "ror_id", + "sciper_id", + "sciper_unit_id", + "author_uuids", + "subjects", + "keywords", +}) + +_SNIPPET_CHARS = 320 +_DEFAULT_TOP_K = 10 +_DEFAULT_MAX_CHUNKS = 20 +_RERANK_CANDIDATE_MULTIPLIER = 5 +_RERANK_CANDIDATE_FLOOR = 30 + + +def _resolve_collection(name: str) -> str: + qdrant_name = _COLLECTION_MAP.get(name) + if qdrant_name is None: + msg = ( + f"Unknown infoscience collection {name!r}; " + f"expected one of {sorted(_COLLECTION_MAP)}" + ) + raise ValueError(msg) + return qdrant_name + + +def _filter_allowlist(payload: dict[str, Any] | None) -> dict[str, Any] | None: + if not payload: + return None + cleaned: dict[str, Any] = {} + dropped: list[str] = [] + for key, value in payload.items(): + if key in _ALLOWED_FILTER_KEYS: + cleaned[key] = value + else: + dropped.append(key) + if dropped: + logger.warning( + "infoscience_rag: dropped non-allowlisted filter keys: %s", + sorted(dropped), + ) + return cleaned or None + + +def _snippet(payload: dict[str, Any]) -> str | None: + text = payload.get("text") or payload.get("abstract") + if not isinstance(text, str) or not text: + return None + text = text.strip() + if len(text) <= _SNIPPET_CHARS: + return text + return text[: _SNIPPET_CHARS - 1].rstrip() + "…" + + +def _hit_snippet(collection: str, hit: dict[str, Any]) -> dict[str, Any]: + """Return a thin hit suitable for an LLM tool response. + + Drops bulky fields (full chunk text, raw matched_urls list) but keeps + everything the agent needs to decide whether to fetch the full record. + """ + payload = hit.get("payload") or {} + base: dict[str, Any] = { + "id": hit.get("id"), + "score": hit.get("score"), + "collection": collection, + } + if collection == "chunks": + base.update({ + "article_uuid": payload.get("article_uuid"), + "chunk_index": payload.get("chunk_index"), + "title": payload.get("title"), + "doi": payload.get("doi"), + "year": payload.get("year"), + "infoscience_url": payload.get("infoscience_url"), + "snippet": _snippet(payload), + }) + elif collection == "articles": + base.update({ + "article_uuid": payload.get("article_uuid"), + "title": payload.get("title"), + "doi": payload.get("doi"), + "year": payload.get("year"), + "publication_type": payload.get("publication_type"), + "authors": payload.get("authors"), + "infoscience_url": payload.get("infoscience_url"), + "snippet": _snippet(payload), + }) + elif collection == "persons": + base.update({ + "person_uuid": payload.get("person_uuid"), + "name": payload.get("name"), + "orcid": payload.get("orcid"), + "sciper_id": payload.get("sciper_id"), + "primary_affiliation": payload.get("primary_affiliation"), + "profile_url": payload.get("profile_url"), + }) + elif collection == "organizations": + base.update({ + "org_uuid": payload.get("org_uuid"), + "name": payload.get("name"), + "acronym": payload.get("acronym"), + "ror_id": payload.get("ror_id"), + "sciper_unit_id": payload.get("sciper_unit_id"), + "infoscience_url": payload.get("infoscience_url"), + }) + return {k: v for k, v in base.items() if v is not None} + + +def _rerank_text(collection: str, payload: dict[str, Any]) -> str: + if collection == "chunks": + return (payload.get("text") or payload.get("abstract") or payload.get("title") or "") + if collection == "articles": + title = payload.get("title") or "" + abstract = payload.get("abstract") or "" + return f"{title}\n{abstract}".strip() + if collection == "persons": + return " ".join(filter(None, [ + payload.get("name"), + payload.get("primary_affiliation"), + payload.get("biography"), + ])) + if collection == "organizations": + return " ".join(filter(None, [ + payload.get("name"), + payload.get("acronym"), + payload.get("description"), + ])) + return "" + + +class InfoscienceRagProvider: + """Async wrapper around the Infoscience Qdrant index.""" + + def __init__( + self, + *, + store: QdrantStore, + embedder: RCPEmbedder, + reranker: RCPReranker | None = None, + ) -> None: + self._store = store + self._embedder = embedder + self._reranker = reranker + self._embedder_open = False + self._reranker_open = False + self._lifecycle_lock = asyncio.Lock() + + @classmethod + def from_config(cls, cfg: InfoscienceIndexConfig) -> InfoscienceRagProvider: + store = QdrantStore.from_config(cfg) + embedder = RCPEmbedder(cfg.rcp) + reranker = RCPReranker(cfg.rcp) + return cls(store=store, embedder=embedder, reranker=reranker) + + async def aclose(self) -> None: + async with self._lifecycle_lock: + if self._embedder_open: + await self._embedder.__aexit__(None, None, None) + self._embedder_open = False + if self._reranker_open and self._reranker is not None: + await self._reranker.__aexit__(None, None, None) + self._reranker_open = False + + async def _ensure_embedder(self) -> None: + if self._embedder_open: + return + async with self._lifecycle_lock: + if not self._embedder_open: + await self._embedder.__aenter__() + self._embedder_open = True + + async def _ensure_reranker(self) -> bool: + if self._reranker is None: + return False + if self._reranker_open: + return True + async with self._lifecycle_lock: + if not self._reranker_open: + await self._reranker.__aenter__() + self._reranker_open = True + return True + + async def search( + self, + query: str, + *, + collection: CollectionName = "chunks", + top_k: int = _DEFAULT_TOP_K, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + qdrant_collection = _resolve_collection(collection) + cleaned_filters = _filter_allowlist(filters) + try: + qdrant_filter = build_filter(cleaned_filters) + except ValueError as exc: + logger.warning("infoscience_rag: invalid filter %r — %s", filters, exc) + return [] + + try: + await self._ensure_embedder() + vector = await self._embedder.embed_query(query) + except (EmbedError, RuntimeError) as exc: + logger.warning("infoscience_rag: embed failed — %s", exc) + return [] + + candidate_k = top_k + if rerank: + candidate_k = max(top_k * _RERANK_CANDIDATE_MULTIPLIER, _RERANK_CANDIDATE_FLOOR) + + try: + hits = await asyncio.to_thread( + self._store.search, + qdrant_collection, + query_vector=vector, + top_k=candidate_k, + query_filter=qdrant_filter, + ) + except Exception as exc: # noqa: BLE001 — Qdrant client raises a wide set + logger.warning( + "infoscience_rag: qdrant search failed (collection=%s) — %s", + qdrant_collection, exc, + ) + return [] + + if rerank and hits: + hits = await self._maybe_rerank(query, collection, hits, top_k=top_k) + else: + hits = hits[:top_k] + + return [_hit_snippet(collection, h) for h in hits] + + async def _maybe_rerank( + self, + query: str, + collection: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + try: + available = await self._ensure_reranker() + except RerankError as exc: + logger.warning("infoscience_rag: reranker open failed — %s", exc) + return hits[:top_k] + if not available or self._reranker is None: + return hits[:top_k] + + documents = [_rerank_text(collection, h.get("payload") or {}) for h in hits] + try: + ranked = await self._reranker.rerank(query, documents, top_n=top_k) + except RerankError as exc: + logger.warning("infoscience_rag: rerank failed — %s", exc) + return hits[:top_k] + + if not ranked: + return hits[:top_k] + ordered: list[dict[str, Any]] = [] + seen: set[int] = set() + for hit in ranked: + if 0 <= hit.index < len(hits) and hit.index not in seen: + seen.add(hit.index) + rehit = dict(hits[hit.index]) + rehit["score"] = float(hit.score) + ordered.append(rehit) + return ordered[:top_k] + + async def fetch_chunks( + self, + article_uuid: str, + *, + max_chunks: int = _DEFAULT_MAX_CHUNKS, + ) -> list[dict[str, Any]]: + if not isinstance(article_uuid, str) or not article_uuid.strip(): + return [] + try: + qdrant_filter = build_filter({"article_uuid": article_uuid}) + except ValueError as exc: + logger.warning("infoscience_rag: bad article_uuid filter — %s", exc) + return [] + try: + records = await asyncio.to_thread( + self._store.scroll, + CHUNKS_COLLECTION, + query_filter=qdrant_filter, + limit=max_chunks, + ) + except Exception as exc: # noqa: BLE001 + logger.warning( + "infoscience_rag: qdrant scroll failed (article_uuid=%s) — %s", + article_uuid, exc, + ) + return [] + + chunks: list[dict[str, Any]] = [] + for rec in records: + payload = rec.get("payload") or {} + chunks.append({ + "id": rec.get("id"), + "article_uuid": payload.get("article_uuid"), + "chunk_index": payload.get("chunk_index"), + "text": payload.get("text"), + "title": payload.get("title"), + "doi": payload.get("doi"), + "infoscience_url": payload.get("infoscience_url"), + }) + chunks.sort(key=lambda c: (c.get("chunk_index") if isinstance(c.get("chunk_index"), int) else 0)) + return chunks + + async def fetch_records( + self, + collection: CollectionName, + ids: Sequence[str], + ) -> list[dict[str, Any]]: + if collection == "chunks": + msg = "Use fetch_chunks(article_uuid) for chunk bodies, not fetch_records." + raise ValueError(msg) + qdrant_collection = _resolve_collection(collection) + clean_ids = [i for i in ids if isinstance(i, str) and i] + if not clean_ids: + return [] + try: + records = await asyncio.to_thread( + self._store.lookup, + qdrant_collection, + ids=clean_ids, + ) + except Exception as exc: # noqa: BLE001 + logger.warning( + "infoscience_rag: qdrant lookup failed (collection=%s) — %s", + qdrant_collection, exc, + ) + return [] + return [ + {"id": rec.get("id"), "payload": rec.get("payload") or {}} + for rec in records + ] + + +def _truthy_env(value: str | None) -> bool: + if value is None: + return False + return value.strip().lower() in {"1", "true", "t", "yes", "y", "on"} + + +def _falsy_env(value: str | None) -> bool: + if value is None: + return False + return value.strip().lower() in {"0", "false", "f", "no", "n", "off"} + + +def build_default_provider( + cfg: InfoscienceIndexConfig | None = None, +) -> InfoscienceRagProvider | None: + """Construct the default RAG provider from on-disk config. + + Returns ``None`` (not raising) when the config or its dependencies fail + to load, so the rest of the v2 pipeline keeps working without RAG. + """ + # Inline import: lazy load avoids hard-failing v2 init when the + # infoscience yaml or its env vars are missing in non-RAG deployments. + try: + from src.index.infoscience.config import load_config # noqa: PLC0415 + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 — config errors must not 500 the API + logger.warning( + "infoscience_rag: failed to load index config; tools disabled (%s)", + exc, + ) + return None + try: + return InfoscienceRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning( + "infoscience_rag: failed to construct provider; tools disabled (%s)", + exc, + ) + return None + + +__all__ = [ + "CollectionName", + "InfoscienceRagProvider", + "build_default_provider", +] diff --git a/src/v2/ingest/providers/mock_github.py b/src/v2/ingest/providers/mock_github.py new file mode 100644 index 0000000..82d955b --- /dev/null +++ b/src/v2/ingest/providers/mock_github.py @@ -0,0 +1,114 @@ +from __future__ import annotations + +import json +from copy import deepcopy +from pathlib import Path +from typing import Any, Never + +from src.v2.ingest.providers.base import ( + GitHubProvider, + ProviderNotFoundError, + ProviderPermissionError, + ProviderRateLimitError, +) + + +class MockGitHubProvider(GitHubProvider): + """Fixture-backed GitHub provider used by v2 tests and local development.""" + + def __init__(self, fixture_root: Path | None = None) -> None: + self._fixture_root = fixture_root or self._default_fixture_root() + self._repo_payload = self._load_json_fixture("repo_payload") + self._user_payload = self._load_json_fixture("user_payload") + self._org_payload = self._load_json_fixture("org_payload") + self._contributors_payload = self._load_json_fixture("contributors_payload") + self._rate_limited_payload = self._load_json_fixture("rate_limited_response") + self._not_found_payload = self._load_json_fixture("not_found_response") + + @staticmethod + def _default_fixture_root() -> Path: + return ( + Path(__file__).resolve().parents[4] + / "tests" + / "v2" + / "fixtures" + / "providers" + / "github" + ) + + def _load_json_fixture(self, fixture_name: str) -> dict[str, Any]: + fixture_path = self._fixture_root / f"{fixture_name}.json" + with fixture_path.open(encoding="utf-8") as fixture_file: + payload = json.load(fixture_file) + + if not isinstance(payload, dict): + raise TypeError + return payload + + def _raise_repo_error(self, full_name: str) -> Never: + if full_name == "private/repo": + private_repo_error = self._repo_payload.get("private_repo_error", {}) + message = str(private_repo_error.get("message", "Repository is private")) + raise ProviderPermissionError(message) + if full_name == "rate-limited/repo": + message = str(self._rate_limited_payload.get("message", "Rate limited")) + raise ProviderRateLimitError(message) + + message = str(self._not_found_payload.get("message", "Not found")) + raise ProviderNotFoundError(message) + + def get_repository(self, full_name: str) -> dict[str, Any]: + rest_payload = self._repo_payload.get("rest") + if full_name == "octocat/Hello-World" and isinstance(rest_payload, dict): + return deepcopy(rest_payload) + + self._raise_repo_error(full_name) + raise AssertionError + + def get_user(self, username: str) -> dict[str, Any]: + if username != "octocat": + message = str(self._not_found_payload.get("message", "Not found")) + raise ProviderNotFoundError(message) + + return deepcopy(self._user_payload) + + def get_organization(self, org_name: str) -> dict[str, Any]: + if org_name != "github": + message = str(self._not_found_payload.get("message", "Not found")) + raise ProviderNotFoundError(message) + + return deepcopy(self._org_payload) + + def get_contributors(self, full_name: str) -> list[dict[str, Any]]: + rest_payload = self._contributors_payload.get("rest") + if full_name == "octocat/Hello-World" and isinstance(rest_payload, list): + return deepcopy(rest_payload) + + self._raise_repo_error(full_name) + raise AssertionError + + def get_languages(self, full_name: str) -> dict[str, int]: + languages = self._repo_payload.get("languages") + if full_name == "octocat/Hello-World" and isinstance(languages, dict): + return deepcopy(languages) + + self._raise_repo_error(full_name) + raise AssertionError + + def get_repository_sbom(self, full_name: str) -> list[dict[str, Any]] | None: + if full_name != "octocat/Hello-World": + return None + return [ + { + "name": "requests", + "ecosystem": "pypi", + "version": "2.31.0", + "spdxId": "SPDXRef-pypi-requests", + }, + { + "name": "left-pad", + "ecosystem": "npm", + "version": "1.3.0", + "spdxId": "SPDXRef-npm-left-pad", + }, + ] diff --git a/src/v2/ingest/providers/mock_infoscience.py b/src/v2/ingest/providers/mock_infoscience.py new file mode 100644 index 0000000..36e3e3c --- /dev/null +++ b/src/v2/ingest/providers/mock_infoscience.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +import json +from copy import deepcopy +from pathlib import Path +from typing import Any + +from src.v2.ingest.providers.base import InfoscienceProvider, InfosciencePublicationRecord + + +class MockInfoscienceProvider(InfoscienceProvider): + """Fixture-backed Infoscience provider for v2 pipeline testing.""" + + def __init__(self, fixture_root: Path | None = None) -> None: + self._fixture_root = fixture_root or self._default_fixture_root() + self._person_single_hit = self._load_json_fixture("person_single_hit") + self._person_multi_hit = self._load_json_fixture("person_multi_hit") + self._orgunit_result = self._load_json_fixture("orgunit_result") + self._publication_result = self._load_json_fixture("publication_result") + self._empty_result = self._load_json_fixture("empty_result") + + @staticmethod + def _default_fixture_root() -> Path: + return ( + Path(__file__).resolve().parents[4] + / "tests" + / "v2" + / "fixtures" + / "providers" + / "infoscience" + ) + + def _load_json_fixture(self, fixture_name: str) -> dict[str, Any]: + fixture_path = self._fixture_root / f"{fixture_name}.json" + with fixture_path.open(encoding="utf-8") as fixture_file: + payload = json.load(fixture_file) + + if not isinstance(payload, dict): + raise TypeError + return payload + + @staticmethod + def _extract_results(payload: dict[str, Any]) -> list[dict[str, Any]]: + results = payload.get("results") + if isinstance(results, list): + return deepcopy(results) + return [] + + @staticmethod + def _as_string(value: Any) -> str | None: + return value if isinstance(value, str) and value else None + + @staticmethod + def _as_string_list(value: Any) -> list[str]: + if not isinstance(value, list): + return [] + return [item for item in value if isinstance(item, str) and item] + + @classmethod + def _normalize_publication(cls, publication: dict[str, Any]) -> InfosciencePublicationRecord: + publication_date = cls._as_string(publication.get("publicationDate")) + if publication_date is None: + publication_date = cls._as_string(publication.get("publication_date")) + + source_organization = cls._as_string(publication.get("sourceOrganization")) + if source_organization is None: + source_organization = cls._as_string(publication.get("lab")) + + return { + "infosciencePublicationIdentifier": cls._as_string( + publication.get("infosciencePublicationIdentifier"), + ) + or cls._as_string(publication.get("uuid")), + "title": cls._as_string(publication.get("title")), + "authors": cls._as_string_list(publication.get("authors")), + "publicationDate": publication_date, + "doi": cls._as_string(publication.get("doi")), + "url": cls._as_string(publication.get("url")), + "sourceOrganization": source_organization, + } + + def search_person(self, query: str) -> list[dict[str, Any]]: + normalized_query = query.strip().lower() + + if normalized_query in {"alice smith", "single", "single-hit"}: + return self._extract_results(self._person_single_hit) + if normalized_query in {"smith", "ambiguous", "multi", "multi-hit"}: + return self._extract_results(self._person_multi_hit) + return self._extract_results(self._empty_result) + + def search_orgunit(self, query: str) -> list[dict[str, Any]]: + normalized_query = query.strip().lower() + + if any(token in normalized_query for token in ("epfl", "enac", "laboratory")): + return self._extract_results(self._orgunit_result) + return self._extract_results(self._empty_result) + + def search_publications(self, query: str) -> list[InfosciencePublicationRecord]: + normalized_query = query.strip().lower() + + if any( + token in normalized_query + for token in ("geodata", "structural", "10.5075/epfl-geodata-2024") + ): + publications = self._extract_results(self._publication_result) + return [self._normalize_publication(publication) for publication in publications] + return [] diff --git a/src/v2/ingest/providers/mock_orcid.py b/src/v2/ingest/providers/mock_orcid.py new file mode 100644 index 0000000..6c56f71 --- /dev/null +++ b/src/v2/ingest/providers/mock_orcid.py @@ -0,0 +1,144 @@ +from __future__ import annotations + +import json +import re +from copy import deepcopy +from pathlib import Path +from typing import Any, cast + +from src.v2.ingest.providers.base import ( + ORCIDProvider, + ORCIDRecord, + ORCIDSearchHit, + ProviderNotFoundError, +) + +ORCID_PATTERN = re.compile(r"^\d{4}-\d{4}-\d{4}-\d{3}[\dX]$") +REQUIRED_RECORD_FIELDS = {"orcid_id", "name", "employment", "education", "affiliations"} +ORCID_DIGIT_COUNT = 16 +CHECKSUM_X_VALUE = 10 + + +class MockORCIDProvider(ORCIDProvider): + """Fixture-backed ORCID provider for v2 pipeline testing.""" + + def __init__(self, fixture_root: Path | None = None) -> None: + self._fixture_root = fixture_root or self._default_fixture_root() + + self._record_by_orcid = { + record["orcid_id"]: record + for record in [ + self._load_record_fixture("valid_record"), + self._load_record_fixture("no_affiliations"), + self._load_record_fixture("multiple_employment"), + ] + } + # Keep malformed/invalid fixture on disk for test coverage. + self._load_json_fixture("invalid_checksum") + + @staticmethod + def _default_fixture_root() -> Path: + return ( + Path(__file__).resolve().parents[4] + / "tests" + / "v2" + / "fixtures" + / "providers" + / "orcid" + ) + + def _load_json_fixture(self, fixture_name: str) -> dict[str, Any]: + fixture_path = self._fixture_root / f"{fixture_name}.json" + with fixture_path.open(encoding="utf-8") as fixture_file: + payload = json.load(fixture_file) + + if not isinstance(payload, dict): + raise TypeError + return payload + + def _load_record_fixture(self, fixture_name: str) -> ORCIDRecord: + payload = self._load_json_fixture(fixture_name) + if not REQUIRED_RECORD_FIELDS.issubset(payload): + message = f"Missing ORCID record fields in fixture '{fixture_name}'" + raise ValueError(message) + + if not isinstance(payload["employment"], list): + raise TypeError + if not isinstance(payload["education"], list): + raise TypeError + if not isinstance(payload["affiliations"], list): + raise TypeError + + return cast("ORCIDRecord", payload) + + @staticmethod + def _has_valid_checksum(orcid_id: str) -> bool: + digits = orcid_id.replace("-", "") + if len(digits) != ORCID_DIGIT_COUNT: + return False + + total = 0 + for char in digits[:15]: + if not char.isdigit(): + return False + total = (total + int(char)) * 2 + + remainder = total % 11 + check_value = (12 - remainder) % 11 + expected = "X" if check_value == CHECKSUM_X_VALUE else str(check_value) + return digits[-1] == expected + + @staticmethod + def _normalize_orcid(orcid_id: str) -> str: + candidate = orcid_id.strip() + if candidate.lower().startswith("https://orcid.org/"): + candidate = candidate.rsplit("/", maxsplit=1)[-1] + + normalized = candidate.upper() + if not ORCID_PATTERN.fullmatch(normalized): + message = f"Invalid ORCID format: {orcid_id}" + raise ValueError(message) + if not MockORCIDProvider._has_valid_checksum(normalized): + message = f"Invalid ORCID checksum: {orcid_id}" + raise ValueError(message) + + return normalized + + def get_person_by_orcid(self, orcid_id: str) -> ORCIDRecord: + normalized_orcid = self._normalize_orcid(orcid_id) + record = self._record_by_orcid.get(normalized_orcid) + if record is None: + message = f"ORCID record not found: {normalized_orcid}" + raise ProviderNotFoundError(message) + + return deepcopy(record) + + def search_persons( + self, + query: str, + *, + rows: int = 50, + start: int = 0, + ) -> list[ORCIDSearchHit]: + needle = query.strip().casefold() + if not needle: + return [] + + hits: list[ORCIDSearchHit] = [] + for record in self._record_by_orcid.values(): + name = record.get("name", "") + if needle not in name.casefold(): + continue + given_name, _, family_name = name.partition(" ") + hits.append( + ORCIDSearchHit( + orcid_id=record.get("orcid_id"), + given_names=given_name or None, + family_names=family_name or None, + credit_name=name or None, + other_names=[], + institution_names=list(record.get("affiliations", [])), + emails=[], + ), + ) + return hits[start : start + rows] diff --git a/src/v2/ingest/providers/mock_ror.py b/src/v2/ingest/providers/mock_ror.py new file mode 100644 index 0000000..61c0d98 --- /dev/null +++ b/src/v2/ingest/providers/mock_ror.py @@ -0,0 +1,77 @@ +from __future__ import annotations + +import json +from copy import deepcopy +from pathlib import Path +from typing import Any + +from src.v2.ingest.providers.base import ProviderNotFoundError, RORProvider + + +class MockRORProvider(RORProvider): + """Fixture-backed ROR provider for v2 pipeline testing.""" + + def __init__(self, fixture_root: Path | None = None) -> None: + self._fixture_root = fixture_root or self._default_fixture_root() + self._org_detail = self._load_json_fixture("org_detail") + self._search_results = self._load_json_fixture("search_results") + self._org_with_aliases = self._load_json_fixture("org_with_aliases") + self._parent_org = self._load_json_fixture("parent_org") + self._not_found = self._load_json_fixture("not_found") + + @staticmethod + def _default_fixture_root() -> Path: + return ( + Path(__file__).resolve().parents[4] + / "tests" + / "v2" + / "fixtures" + / "providers" + / "ror" + ) + + def _load_json_fixture(self, fixture_name: str) -> dict[str, Any]: + fixture_path = self._fixture_root / f"{fixture_name}.json" + with fixture_path.open(encoding="utf-8") as fixture_file: + payload = json.load(fixture_file) + + if not isinstance(payload, dict): + raise TypeError + return payload + + @staticmethod + def _normalize_ror_id(ror_id: str) -> str: + candidate = ror_id.strip() + if candidate.startswith("https://ror.org/"): + return candidate.removeprefix("https://ror.org/") + return candidate + + def get_organization(self, ror_id: str) -> dict[str, Any]: + normalized_ror_id = self._normalize_ror_id(ror_id) + + if normalized_ror_id == "02s376052": + return deepcopy(self._org_detail["organization"]) + if normalized_ror_id == "05a28rw58": + return deepcopy(self._parent_org["organization"]) + if normalized_ror_id == "03yrm5c26": + return deepcopy(self._org_with_aliases["organization"]) + + message = str(self._not_found.get("message", "ROR organization not found")) + raise ProviderNotFoundError(message) + + def search_organizations(self, query: str) -> list[dict[str, Any]]: + normalized_query = query.strip().lower() + + if any(token in normalized_query for token in {"epfl", "polytechnique"}): + items = self._search_results.get("items") + if isinstance(items, list): + return deepcopy(items) + return [] + + if any(token in normalized_query for token in {"multilingual", "institut", "imi"}): + return [deepcopy(self._org_with_aliases["organization"])] + + if "eth" in normalized_query: + return [deepcopy(self._parent_org["organization"])] + + return [] diff --git a/src/v2/ingest/providers/openalex_rag.py b/src/v2/ingest/providers/openalex_rag.py new file mode 100644 index 0000000..164ddd2 --- /dev/null +++ b/src/v2/ingest/providers/openalex_rag.py @@ -0,0 +1,236 @@ +"""Async RAG provider over the OpenAlex Qdrant index. + +Six per-entity collections: ``works`` / ``authors`` / ``institutions`` / +``sources`` / ``topics`` / ``concepts``. + +OpenAlex chunks carry ``title`` and (for works) ``abstract`` in the +payload, so rerank works against real text. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any, Literal + +from src.index.openalex.embed.rcp_client import ( + RCPEmbeddingClient, + RCPEmbeddingError, +) +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.vector.qdrant_store import ( + PER_ENTITY_COLLECTIONS, + QdrantStore, +) +from src.v2.ingest.providers._rag_helpers import ( + apply_rerank_indices, + env_enabled, + expand_candidate_k, + filter_allowlist, + make_snippet, + safe_rerank_documents, + thin_payload, + to_simple_filter_payload, +) + +if TYPE_CHECKING: + from src.index.openalex.config import OpenAlexIndexConfig + +logger = logging.getLogger(__name__) + +CollectionName = Literal[ + "works", "authors", "institutions", "sources", "topics", "concepts", +] + +_ALLOWED_FILTER_KEYS: frozenset[str] = frozenset({ + "year", + "publication_year", + "doi", + "entity_type", + "openalex_id", + "country_code", + "type", + "field_id", + "domain_id", + "level", + "primary_topic_id", + "primary_source_id", + "last_known_institution_id", + "orcid", + "ror", +}) + +_THIN_KEYS_FOR: dict[str, tuple[str, ...]] = { + "works": ( + "openalex_id", "title", "doi", "year", "publication_year", + "primary_topic_id", "primary_source_id", + ), + "authors": ( + "openalex_id", "display_name", "orcid", + "last_known_institution_id", + ), + "institutions": ( + "openalex_id", "display_name", "ror", "country_code", + ), + "sources": ( + "openalex_id", "display_name", "issn_l", "type", + ), + "topics": ( + "openalex_id", "display_name", "domain_id", "field_id", + ), + "concepts": ( + "openalex_id", "display_name", "level", + ), +} + +_DEFAULT_TOP_K = 10 +_LOG_LABEL = "openalex_rag" + + +def _rerank_text(collection: str, payload: dict[str, Any]) -> str: + if collection == "works": + bits = [payload.get("title"), payload.get("abstract")] + return "\n".join(b for b in bits if isinstance(b, str) and b) + name = payload.get("display_name") or payload.get("title") or "" + extra = payload.get("country_code") or payload.get("type") or "" + return f"{name} {extra}".strip() + + +class OpenAlexRagProvider: + """Async wrapper around the OpenAlex Qdrant index.""" + + def __init__( + self, + *, + store: QdrantStore, + embedder: RCPEmbeddingClient, + reranker: RCPRerankerClient | None = None, + ) -> None: + self._store = store + self._embedder = embedder + self._reranker = reranker + + @classmethod + def from_config(cls, cfg: OpenAlexIndexConfig) -> OpenAlexRagProvider: + return cls( + store=QdrantStore(cfg), + embedder=RCPEmbeddingClient(cfg), + reranker=RCPRerankerClient(cfg), + ) + + async def search( # noqa: PLR0911 — each early return is a graceful-fail check + self, + query: str, + *, + collection: CollectionName = "works", + top_k: int = _DEFAULT_TOP_K, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + if collection not in PER_ENTITY_COLLECTIONS: + logger.warning("%s: unknown collection %r", _LOG_LABEL, collection) + return [] + if not self._store.client.collection_exists(collection): + logger.info( + "%s: collection %s missing — returning [] without indexing", + _LOG_LABEL, collection, + ) + return [] + + cleaned = filter_allowlist(filters, _ALLOWED_FILTER_KEYS, log_label=_LOG_LABEL) + filter_payload = to_simple_filter_payload(cleaned) + + try: + vectors = await self._embedder.embed_all([query]) + except (RCPEmbeddingError, Exception) as exc: # noqa: BLE001 + logger.warning("%s: embed failed — %s", _LOG_LABEL, exc) + return [] + if not vectors: + return [] + vector = list(vectors[0]) + + candidate_k = expand_candidate_k(top_k) if rerank else top_k + + try: + hits = await asyncio.to_thread( + self._store.search, + collection, + query_vector=vector, + top_k=candidate_k, + filter_payload=filter_payload, + ) + except Exception as exc: # noqa: BLE001 + logger.warning( + "%s: qdrant search failed (collection=%s) — %s", + _LOG_LABEL, collection, exc, + ) + return [] + + if rerank and hits and self._reranker is not None: + hits = await self._maybe_rerank(query, collection, hits, top_k=top_k) + else: + hits = hits[:top_k] + + thin_keys = _THIN_KEYS_FOR[collection] + return [ + thin_payload( + hit.get("payload") or {}, + thin_keys, + extras={ + "id": hit.get("id"), + "score": hit.get("score"), + "collection": collection, + "snippet": ( + make_snippet((hit.get("payload") or {}).get("abstract")) + if collection == "works" else None + ), + }, + ) + for hit in hits + ] + + async def _maybe_rerank( + self, + query: str, + collection: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + documents = safe_rerank_documents( + [_rerank_text(collection, h.get("payload") or {}) for h in hits], + ) + try: + results = await self._reranker.rerank(query, documents, top_n=top_k) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: rerank failed — %s", _LOG_LABEL, exc) + return hits[:top_k] + return apply_rerank_indices(hits, results, top_k=top_k) + + +def build_default_provider( + cfg: OpenAlexIndexConfig | None = None, +) -> OpenAlexRagProvider | None: + if not env_enabled("V2_OPENALEX_RAG_ENABLED"): + return None + try: + from src.index.openalex.config import load_config # noqa: PLC0415 + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to load config (%s)", _LOG_LABEL, exc) + return None + try: + return OpenAlexRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to construct provider (%s)", _LOG_LABEL, exc) + return None + + +__all__ = [ + "CollectionName", + "OpenAlexRagProvider", + "build_default_provider", +] diff --git a/src/v2/ingest/providers/orcid_oauth.py b/src/v2/ingest/providers/orcid_oauth.py new file mode 100644 index 0000000..1ca9cae --- /dev/null +++ b/src/v2/ingest/providers/orcid_oauth.py @@ -0,0 +1,74 @@ +"""Optional OAuth 2-legged (`client_credentials`) helper for ORCID. + +ORCID's anonymous public-API endpoints are rate-limited per IP and trip a +multi-hour throttle on bursty workloads. With a free `/read-public` OAuth +client (registered at https://orcid.org/developer-tools), the same endpoints +become authenticated and use a separate, much larger quota bucket. + +This helper is intentionally optional: callers should treat a `None` return +as "fall back to anonymous access" — never fail the pipeline on a missing +OAuth client. +""" + +from __future__ import annotations + +import logging + +import requests + +LOGGER = logging.getLogger(__name__) + +ORCID_TOKEN_URL = "https://orcid.org/oauth/token" +ORCID_TOKEN_SCOPE = "/read-public" + + +def fetch_access_token( + *, + client_id: str | None, + client_secret: str | None, + token_url: str = ORCID_TOKEN_URL, + scope: str = ORCID_TOKEN_SCOPE, + timeout_seconds: int = 20, +) -> str | None: + """Exchange `client_id` / `client_secret` for a `/read-public` access token. + + Returns the token on success, `None` when credentials are missing or the + OAuth POST fails for any reason (so callers can keep the anonymous path). + The `/read-public` token is long-lived (~20 years per ORCID's docs) and + can be cached for the lifetime of the process. + """ + if not client_id or not client_secret: + return None + try: + response = requests.post( + token_url, + data={ + "client_id": client_id, + "client_secret": client_secret, + "grant_type": "client_credentials", + "scope": scope, + }, + headers={"Accept": "application/json"}, + timeout=timeout_seconds, + ) + except requests.RequestException as exc: + LOGGER.warning("ORCID OAuth token request failed: %s", exc) + return None + if response.status_code != 200: + LOGGER.warning( + "ORCID OAuth token endpoint returned HTTP %s: %s", + response.status_code, + response.text[:200], + ) + return None + payload = response.json() + token = payload.get("access_token") + if not isinstance(token, str) or not token: + LOGGER.warning("ORCID OAuth response missing access_token: %s", payload) + return None + LOGGER.info( + "ORCID OAuth token acquired (scope=%s, expires_in=%ss)", + payload.get("scope"), + payload.get("expires_in"), + ) + return token diff --git a/src/v2/ingest/providers/orcid_provider.py b/src/v2/ingest/providers/orcid_provider.py new file mode 100644 index 0000000..bc27ed6 --- /dev/null +++ b/src/v2/ingest/providers/orcid_provider.py @@ -0,0 +1,344 @@ +from __future__ import annotations + +import calendar +import logging +import re +from datetime import date +from typing import TYPE_CHECKING, Any + +import requests + +logger = logging.getLogger(__name__) + +from src.v2.ingest.cache import ProviderCache +from src.v2.ingest.providers.base import ( + ORCIDAffiliation, + ORCIDProvider, + ORCIDRecord, + ORCIDSearchHit, + ProviderNotFoundError, + ProviderPermissionError, + ProviderRateLimitError, +) + +EXPANDED_SEARCH_EDISMAX = ( + '{!edismax qf="given-and-family-names^50.0 family-name^10.0 ' + 'given-names^10.0 credit-name^10.0 other-names^5.0 text^1.0" ' + 'pf="given-and-family-names^50.0" ' + 'bq="current-institution-affiliation-name:[* TO *]^100.0 ' + 'past-institution-affiliation-name:[* TO *]^70" mm=1}' +) +EXPANDED_SEARCH_MAX_ROWS = 200 + +if TYPE_CHECKING: + from src.v2.ingest.providers.rate_limiter import RateLimiter + +ORCID_PATTERN = re.compile(r"^\d{4}-\d{4}-\d{4}-\d{3}[\dX]$") +HTTP_NOT_FOUND = 404 +HTTP_FORBIDDEN = 403 +HTTP_RATE_LIMIT = 429 +ORCID_DIGIT_COUNT = 16 +CHECKSUM_X_VALUE = 10 + + +def _get_nested_value(payload: dict[str, Any], path: list[str]) -> str | None: + current: Any = payload + for segment in path: + if not isinstance(current, dict): + return None + current = current.get(segment) + if isinstance(current, str) and current: + return current + return None + + +def _normalize_date(date_payload: Any) -> str | None: + """Convert an ORCID date payload (`{year, month, day}` substructures) to + an ISO ``YYYY-MM-DD`` string. + + Missing month/day default to ``01``. ORCID accepts user-typed dates + without strict validation, so impossible combinations (e.g. + ``2000-09-31``) reach this function. The strategy: + + 1. Try the literal year/month/day. If valid, return it. + 2. If invalid (e.g. day=31 in September), clamp the day to the last + valid day of that month and return the clamped value. The user's + intent ("end of September 2000") is preserved with 1-day drift, + which is much better than losing the whole date. + 3. If still invalid (year+month combination is bogus, non-numeric + components, etc.), return ``None``. + """ + + if not isinstance(date_payload, dict): + return None + year = _get_nested_value(date_payload, ["year", "value"]) + month = _get_nested_value(date_payload, ["month", "value"]) or "01" + day = _get_nested_value(date_payload, ["day", "value"]) or "01" + if not year: + return None + + try: + year_int, month_int, day_int = int(year), int(month), int(day) + except (ValueError, TypeError): + logger.info( + "orcid_provider: dropped non-numeric date year=%r month=%r day=%r", + year, month, day, + ) + return None + + try: + return date(year_int, month_int, day_int).isoformat() + except ValueError: + pass + + # Day overflow (e.g. Sept 31, Feb 30) — clamp to the last day of the month. + try: + last_day = calendar.monthrange(year_int, month_int)[1] + clamped = date(year_int, month_int, last_day).isoformat() + logger.info( + "orcid_provider: clamped invalid day year=%d month=%d day=%d → day=%d", + year_int, month_int, day_int, last_day, + ) + return clamped + except (ValueError, calendar.IllegalMonthError): + logger.info( + "orcid_provider: dropped unrecoverable date year=%d month=%d day=%d", + year_int, month_int, day_int, + ) + return None + + +def _extract_affiliations(payload: dict[str, Any], summary_key: str) -> list[ORCIDAffiliation]: + affiliations: list[ORCIDAffiliation] = [] + groups = payload.get("affiliation-group") + if not isinstance(groups, list): + return affiliations + + for group in groups: + if not isinstance(group, dict): + continue + summaries = group.get("summaries") + if not isinstance(summaries, list): + continue + for summary in summaries: + if not isinstance(summary, dict): + continue + summary_payload = summary.get(summary_key) + if not isinstance(summary_payload, dict): + continue + + organization = _get_nested_value( + summary_payload, + ["organization", "name"], + ) or "Unknown Organization" + department = _get_nested_value( + summary_payload, + ["department-name"], + ) + role = _get_nested_value( + summary_payload, + ["role-title"], + ) + start_date = _normalize_date(summary_payload.get("start-date")) + end_date = _normalize_date(summary_payload.get("end-date")) + + affiliations.append( + ORCIDAffiliation( + organization=organization, + department=department, + role=role, + start_date=start_date, + end_date=end_date, + ), + ) + return affiliations + + +class RealORCIDProvider(ORCIDProvider): + """Production ORCID provider using public ORCID API endpoints.""" + + def __init__( + self, + *, + session: requests.Session | None = None, + base_url: str = "https://pub.orcid.org/v3.0", + timeout: int = 20, + rate_limiter: RateLimiter | None = None, + cache: ProviderCache | None = None, + ) -> None: + super().__init__(provider_name="orcid", rate_limiter=rate_limiter) + self._session = session + self._base_url = base_url.rstrip("/") + self._timeout = timeout + self._cache = cache + + def _http_client(self) -> requests.Session: + if self._session is None: + self._session = requests.Session() + return self._session + + def _request( + self, + endpoint: str, + *, + params: dict[str, Any] | None = None, + ) -> dict[str, Any]: + response = self._run_with_rate_limit( + lambda: self._http_client().get( + f"{self._base_url}{endpoint}", + params=params, + headers={"Accept": "application/json"}, + timeout=self._timeout, + ), + ) + if response.status_code == HTTP_NOT_FOUND: + message = "ORCID record not found" + raise ProviderNotFoundError(message) + if response.status_code == HTTP_FORBIDDEN: + message = "ORCID request forbidden" + raise ProviderPermissionError(message) + if response.status_code == HTTP_RATE_LIMIT: + message = "ORCID rate limit reached" + raise ProviderRateLimitError(message) + response.raise_for_status() + payload = response.json() + if not isinstance(payload, dict): + raise TypeError + return payload + + @staticmethod + def _has_valid_checksum(orcid_id: str) -> bool: + digits = orcid_id.replace("-", "") + if len(digits) != ORCID_DIGIT_COUNT: + return False + + total = 0 + for character in digits[:15]: + if not character.isdigit(): + return False + total = (total + int(character)) * 2 + + remainder = total % 11 + check_value = (12 - remainder) % 11 + expected = "X" if check_value == CHECKSUM_X_VALUE else str(check_value) + return digits[-1] == expected + + @classmethod + def _normalize_orcid(cls, orcid_id: str) -> str: + candidate = orcid_id.strip() + if candidate.lower().startswith("https://orcid.org/"): + candidate = candidate.rsplit("/", maxsplit=1)[-1] + candidate = candidate.upper() + if not ORCID_PATTERN.fullmatch(candidate): + message = f"Invalid ORCID format: {orcid_id}" + raise ValueError(message) + if not cls._has_valid_checksum(candidate): + message = f"Invalid ORCID checksum: {orcid_id}" + raise ValueError(message) + return candidate + + def get_person_by_orcid(self, orcid_id: str) -> ORCIDRecord: + normalized_orcid = self._normalize_orcid(orcid_id) + + def _fetch() -> ORCIDRecord: + person_payload = self._request(f"/{normalized_orcid}/person") + employment_payload = self._request(f"/{normalized_orcid}/employments") + education_payload = self._request(f"/{normalized_orcid}/educations") + + given_name = _get_nested_value(person_payload, ["name", "given-names", "value"]) + family_name = _get_nested_value(person_payload, ["name", "family-name", "value"]) + name_parts = [part for part in [given_name, family_name] if part] + full_name = " ".join(name_parts) if name_parts else normalized_orcid + + employment = _extract_affiliations(employment_payload, "employment-summary") + education = _extract_affiliations(education_payload, "education-summary") + affiliations = sorted( + { + affiliation["organization"] + for affiliation in [*employment, *education] + if affiliation.get("organization") + }, + ) + + return ORCIDRecord( + orcid_id=normalized_orcid, + name=full_name, + employment=employment, + education=education, + affiliations=affiliations, + ) + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key("orcid", "get_person_by_orcid", orcid=normalized_orcid) + return self._cache.get_or_set( + key, + _fetch, + label=f"orcid.get_person_by_orcid({normalized_orcid})", + ) + + def search_persons( + self, + query: str, + *, + rows: int = 50, + start: int = 0, + ) -> list[ORCIDSearchHit]: + cleaned_query = query.strip() + if not cleaned_query: + return [] + bounded_rows = max(1, min(rows, EXPANDED_SEARCH_MAX_ROWS)) + bounded_start = max(0, start) + + def _fetch() -> list[ORCIDSearchHit]: + payload = self._request( + "/expanded-search/", + params={ + "q": f"{EXPANDED_SEARCH_EDISMAX}{cleaned_query}", + "start": bounded_start, + "rows": bounded_rows, + }, + ) + results = payload.get("expanded-result") + if not isinstance(results, list): + return [] + return [ + _normalize_search_hit(item) + for item in results + if isinstance(item, dict) + ] + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key( + "orcid", + "search_persons", + query=cleaned_query, + rows=bounded_rows, + start=bounded_start, + ) + return self._cache.get_or_set( + key, + _fetch, + label=f"orcid.search_persons({cleaned_query!r})", + ) + + +def _normalize_search_hit(item: dict[str, Any]) -> ORCIDSearchHit: + def _str_or_none(value: Any) -> str | None: + return value if isinstance(value, str) and value else None + + def _str_list(value: Any) -> list[str]: + if not isinstance(value, list): + return [] + return [entry for entry in value if isinstance(entry, str) and entry] + + return ORCIDSearchHit( + orcid_id=_str_or_none(item.get("orcid-id")), + given_names=_str_or_none(item.get("given-names")), + family_names=_str_or_none(item.get("family-names")), + credit_name=_str_or_none(item.get("credit-name")), + other_names=_str_list(item.get("other-name")), + institution_names=_str_list(item.get("institution-name")), + emails=_str_list(item.get("email")), + ) diff --git a/src/v2/ingest/providers/orcid_rag.py b/src/v2/ingest/providers/orcid_rag.py new file mode 100644 index 0000000..46413ff --- /dev/null +++ b/src/v2/ingest/providers/orcid_rag.py @@ -0,0 +1,217 @@ +"""Async RAG provider over the ORCID Qdrant index. + +Three per-(scope, entity) collections (named ``orcid__``): +``persons`` / ``employments`` / ``educations``. The active scope (e.g. +``epfl`` or ``switzerland``) is determined by ``OrcidIndexConfig.paths``. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any, Literal + +from src.index.openalex.embed.rcp_client import RCPEmbeddingError +from src.index.orcid.embed.rcp_client import RCPEmbeddingClient +from src.index.orcid.rerank.rcp_client import RCPRerankerClient +from src.index.orcid.vector.qdrant_store import ENTITY_TYPES, OrcidQdrantStore +from src.v2.ingest.providers._rag_helpers import ( + apply_rerank_indices, + env_enabled, + expand_candidate_k, + filter_allowlist, + make_snippet, + safe_rerank_documents, + thin_payload, + to_simple_filter_payload, +) + +if TYPE_CHECKING: + from src.index.orcid.config import OrcidIndexConfig + +logger = logging.getLogger(__name__) + +EntityType = Literal["persons", "employments", "educations"] + +_ALLOWED_FILTER_KEYS: frozenset[str] = frozenset({ + "orcid_id", + "in_scope", + "discovered_via", + "org_ror", + "organization", + "department", + "role", +}) + +_THIN_KEYS_FOR: dict[str, tuple[str, ...]] = { + "persons": ( + "orcid_id", "display_name", "given_name", "family_name", + "in_scope", "discovered_via", + ), + "employments": ( + "orcid_id", "organization", "org_ror", "department", + "role", "start_date", "end_date", + ), + "educations": ( + "orcid_id", "organization", "org_ror", "department", + "role", "start_date", "end_date", + ), +} + +_DEFAULT_TOP_K = 10 +_LOG_LABEL = "orcid_rag" + + +def _rerank_text(entity_type: str, payload: dict[str, Any]) -> str: + if entity_type == "persons": + bits = [ + payload.get("display_name"), + payload.get("biography"), + ] + else: + bits = [ + payload.get("organization"), + payload.get("department"), + payload.get("role"), + ] + return " ".join(b for b in bits if isinstance(b, str) and b) + + +class OrcidRagProvider: + """Async wrapper around the ORCID Qdrant index.""" + + def __init__( + self, + *, + store: OrcidQdrantStore, + embedder: RCPEmbeddingClient, + reranker: RCPRerankerClient | None = None, + ) -> None: + self._store = store + self._embedder = embedder + self._reranker = reranker + + @classmethod + def from_config(cls, cfg: OrcidIndexConfig) -> OrcidRagProvider: + return cls( + store=OrcidQdrantStore(cfg), + embedder=RCPEmbeddingClient(cfg), + reranker=RCPRerankerClient(cfg), + ) + + async def search( # noqa: PLR0911 — each early return is a graceful-fail check + self, + query: str, + *, + entity_type: EntityType = "persons", + top_k: int = _DEFAULT_TOP_K, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + if entity_type not in ENTITY_TYPES: + logger.warning("%s: unknown entity_type %r", _LOG_LABEL, entity_type) + return [] + + # Skip the call entirely if the indexed collection doesn't exist — + # the upstream store would otherwise lazily *create* it. + collection_name = self._store.collection(entity_type) + if not self._store.client.collection_exists(collection_name): + logger.info( + "%s: collection %s missing — returning [] without indexing", + _LOG_LABEL, collection_name, + ) + return [] + + cleaned = filter_allowlist(filters, _ALLOWED_FILTER_KEYS, log_label=_LOG_LABEL) + filter_payload = to_simple_filter_payload(cleaned) + + try: + vectors = await self._embedder.embed_all([query]) + except (RCPEmbeddingError, Exception) as exc: # noqa: BLE001 + logger.warning("%s: embed failed — %s", _LOG_LABEL, exc) + return [] + if not vectors: + return [] + vector = list(vectors[0]) + + candidate_k = expand_candidate_k(top_k) if rerank else top_k + + try: + hits = await asyncio.to_thread( + self._store.search, + entity_type, + query_vector=vector, + top_k=candidate_k, + filter_payload=filter_payload, + ) + except Exception as exc: # noqa: BLE001 + logger.warning( + "%s: qdrant search failed (entity=%s) — %s", + _LOG_LABEL, entity_type, exc, + ) + return [] + + if rerank and hits and self._reranker is not None: + hits = await self._maybe_rerank(query, entity_type, hits, top_k=top_k) + else: + hits = hits[:top_k] + + thin_keys = _THIN_KEYS_FOR[entity_type] + return [ + thin_payload( + hit.get("payload") or {}, + thin_keys, + extras={ + "id": hit.get("id"), + "score": hit.get("score"), + "entity_type": entity_type, + "snippet": ( + make_snippet((hit.get("payload") or {}).get("biography")) + if entity_type == "persons" else None + ), + }, + ) + for hit in hits + ] + + async def _maybe_rerank( + self, + query: str, + entity_type: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + documents = safe_rerank_documents( + [_rerank_text(entity_type, h.get("payload") or {}) for h in hits], + ) + try: + results = await self._reranker.rerank(query, documents, top_n=top_k) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: rerank failed — %s", _LOG_LABEL, exc) + return hits[:top_k] + return apply_rerank_indices(hits, results, top_k=top_k) + + +def build_default_provider( + cfg: OrcidIndexConfig | None = None, +) -> OrcidRagProvider | None: + if not env_enabled("V2_ORCID_RAG_ENABLED"): + return None + try: + from src.index.orcid.config import load_config # noqa: PLC0415 + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to load config (%s)", _LOG_LABEL, exc) + return None + try: + return OrcidRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to construct provider (%s)", _LOG_LABEL, exc) + return None + + +__all__ = ["EntityType", "OrcidRagProvider", "build_default_provider"] diff --git a/src/v2/ingest/providers/rate_limiter.py b/src/v2/ingest/providers/rate_limiter.py new file mode 100644 index 0000000..1278d10 --- /dev/null +++ b/src/v2/ingest/providers/rate_limiter.py @@ -0,0 +1,236 @@ +from __future__ import annotations + +import asyncio +import logging +import random +import time +from dataclasses import dataclass +from email.utils import parsedate_to_datetime +from typing import Any, Awaitable, Callable, TypeVar + +from src.v2.ingest.providers.base import ProviderRateLimitError + +ResponseT = TypeVar("ResponseT") +RequestFunc = Callable[[], ResponseT | Awaitable[ResponseT]] + +LOGGER = logging.getLogger(__name__) +HTTP_RATE_LIMIT = 429 + + +@dataclass(slots=True) +class _ProviderRateLimitState: + remaining: int | None = None + reset_epoch: float | None = None + next_allowed_at: float = 0.0 + + +class RateLimiter: + def __init__( # noqa: PLR0913 + self, + *, + max_retries: int = 3, + base_delay_seconds: float = 0.5, + max_delay_seconds: float = 30.0, + low_remaining_threshold: int = 10, + near_limit_delay_seconds: float = 0.25, + sleep_func: Callable[[float], Awaitable[None]] = asyncio.sleep, + jitter_func: Callable[[], float] | None = None, + ) -> None: + self._max_retries = max_retries + self._base_delay_seconds = base_delay_seconds + self._max_delay_seconds = max_delay_seconds + self._low_remaining_threshold = low_remaining_threshold + self._near_limit_delay_seconds = near_limit_delay_seconds + self._sleep = sleep_func + self._jitter = jitter_func or random.random + self._states: dict[str, _ProviderRateLimitState] = {} + # asyncio.Lock instances are bound to the loop where they are created. + # Keep separate locks per provider+loop to avoid cross-loop binding errors. + self._locks: dict[tuple[str, int], asyncio.Lock] = {} + + def get_remaining(self, provider_name: str) -> int | None: + return self._state(provider_name).remaining + + async def with_rate_limit( + self, + provider_name: str, + request_func: RequestFunc[ResponseT], + ) -> ResponseT: + normalized_provider = provider_name.strip().lower() or "unknown" + current_loop = asyncio.get_running_loop() + lock_key = (normalized_provider, id(current_loop)) + provider_lock = self._locks.setdefault(lock_key, asyncio.Lock()) + async with provider_lock: + await self._throttle_when_approaching_limit(normalized_provider) + + for attempt in range(self._max_retries + 1): + try: + response = request_func() + if hasattr(response, "__await__"): + response = await response + except ProviderRateLimitError as exc: + if attempt >= self._max_retries: + raise + delay = self._backoff_delay(attempt, retry_after=None) + await self._schedule_retry(normalized_provider, delay, str(exc)) + continue + + self._record_quota_headers(normalized_provider, response) + status_code = self._response_status_code(response) + if status_code != HTTP_RATE_LIMIT: + return response + + retry_after = self._retry_after_seconds(response) + if attempt >= self._max_retries: + message = ( + f"{normalized_provider} rate limit exceeded after " + f"{self._max_retries + 1} attempts" + ) + LOGGER.warning(message) + raise ProviderRateLimitError(message) + + delay = self._backoff_delay(attempt, retry_after=retry_after) + await self._schedule_retry(normalized_provider, delay, "HTTP 429") + + message = f"{normalized_provider} rate limit exceeded" + raise ProviderRateLimitError(message) + + def _state(self, provider_name: str) -> _ProviderRateLimitState: + normalized_provider = provider_name.strip().lower() or "unknown" + return self._states.setdefault(normalized_provider, _ProviderRateLimitState()) + + async def _throttle_when_approaching_limit(self, provider_name: str) -> None: + state = self._state(provider_name) + now_epoch = time.time() + + if state.next_allowed_at > now_epoch: + wait_seconds = state.next_allowed_at - now_epoch + LOGGER.info( + "Rate limit queue wait for %s: %.3fs", + provider_name, + wait_seconds, + ) + await self._sleep(wait_seconds) + + if state.remaining is None or state.remaining >= self._low_remaining_threshold: + return + + throttle_delay = self._near_limit_delay_seconds + if ( + state.reset_epoch is not None + and state.reset_epoch > now_epoch + and throttle_delay > (state.reset_epoch - now_epoch) + ): + throttle_delay = state.reset_epoch - now_epoch + + if throttle_delay <= 0: + return + + LOGGER.info( + "Proactive throttle for %s (remaining=%s): %.3fs", + provider_name, + state.remaining, + throttle_delay, + ) + await self._sleep(throttle_delay) + + async def _schedule_retry( + self, + provider_name: str, + delay_seconds: float, + reason: str, + ) -> None: + state = self._state(provider_name) + state.next_allowed_at = max(state.next_allowed_at, time.time() + delay_seconds) + LOGGER.warning( + "Rate limit retry for %s in %.3fs (%s)", + provider_name, + delay_seconds, + reason, + ) + await self._sleep(delay_seconds) + + def _backoff_delay(self, attempt: int, *, retry_after: float | None) -> float: + if retry_after is not None: + return min(self._max_delay_seconds, max(0.0, retry_after)) + + exponential_delay = self._base_delay_seconds * (2**attempt) + jitter = self._jitter() * self._base_delay_seconds + return min(self._max_delay_seconds, exponential_delay + jitter) + + def _record_quota_headers(self, provider_name: str, response: Any) -> None: + headers = self._headers(response) + if not headers: + return + + state = self._state(provider_name) + remaining = self._parse_int_header(headers.get("x-ratelimit-remaining")) + if remaining is not None: + state.remaining = remaining + + reset_epoch = self._parse_reset_header(headers.get("x-ratelimit-reset")) + if reset_epoch is not None: + state.reset_epoch = reset_epoch + + retry_after = self._retry_after_seconds(response) + if retry_after is not None: + state.next_allowed_at = max(state.next_allowed_at, time.time() + retry_after) + + @staticmethod + def _response_status_code(response: Any) -> int | None: + status_code = getattr(response, "status_code", None) + if isinstance(status_code, int): + return status_code + return None + + @staticmethod + def _headers(response: Any) -> dict[str, str]: + headers = getattr(response, "headers", None) + if headers is None: + return {} + if isinstance(headers, dict): + return { + str(key).lower(): str(value) + for key, value in headers.items() + } + if hasattr(headers, "items"): + return { + str(key).lower(): str(value) + for key, value in headers.items() + } + return {} + + def _retry_after_seconds(self, response: Any) -> float | None: + headers = self._headers(response) + return self._parse_retry_after_header(headers.get("retry-after")) + + @staticmethod + def _parse_int_header(value: str | None) -> int | None: + if value is None: + return None + try: + return int(value) + except ValueError: + return None + + @staticmethod + def _parse_reset_header(value: str | None) -> float | None: + if value is None: + return None + try: + return float(value) + except ValueError: + return None + + @staticmethod + def _parse_retry_after_header(value: str | None) -> float | None: + if value is None: + return None + try: + return float(value) + except ValueError: + try: + parsed = parsedate_to_datetime(value) + except (TypeError, ValueError): + return None + return max(0.0, parsed.timestamp() - time.time()) diff --git a/src/v2/ingest/providers/renkulab_rag.py b/src/v2/ingest/providers/renkulab_rag.py new file mode 100644 index 0000000..9ebf14e --- /dev/null +++ b/src/v2/ingest/providers/renkulab_rag.py @@ -0,0 +1,270 @@ +"""Async RAG provider over the RenkuLab Qdrant index. + +Four collections (one per entity type): ``renkulab_projects``, +``renkulab_groups``, ``renkulab_users``, ``renkulab_data_connectors``. +Reuses OpenAlex's embedder, reranker and ``QdrantStore`` via duck typing +— ``RenkulabIndexConfig`` mirrors the same ``rcp.*`` / ``qdrant.*`` shape +those clients expect. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.rcp_client import ( + RCPEmbeddingClient, + RCPEmbeddingError, +) +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.index.renkulab.embed.pipeline import COLLECTION_BY_ENTITY +from src.v2.ingest.providers._rag_helpers import ( + apply_rerank_indices, + env_enabled, + expand_candidate_k, + filter_allowlist, + safe_rerank_documents, + thin_payload, + to_simple_filter_payload, +) + +if TYPE_CHECKING: + from src.index.renkulab.config import RenkulabIndexConfig + +logger = logging.getLogger(__name__) + +_ALLOWED_FILTER_KEYS: frozenset[str] = frozenset({ + "entity_type", + "slug", + "namespace", + "path", + "visibility", + "storage_type", + "storage_provider", + "entity_id", +}) + +# Per-entity thin-payload key sets — projection happens after the +# entity_type discriminator is known. +_THIN_KEYS_BY_ENTITY: dict[str, tuple[str, ...]] = { + "projects": ("entity_id", "name", "slug", "namespace", "path", "visibility"), + "groups": ("entity_id", "name", "slug"), + "users": ("entity_id", "first_name", "last_name", "slug", "path"), + "data_connectors": ( + "entity_id", "name", "slug", "namespace", "path", + "storage_type", "storage_provider", "visibility", + ), +} + +_DEFAULT_TOP_K = 10 +_LOG_LABEL = "renkulab_rag" + +_RENKU_BASE = "https://renkulab.io/v2" + + +def _build_url(entity_type: str, payload: dict[str, Any]) -> str | None: + path = payload.get("path") or payload.get("slug") + if entity_type == "projects" and path: + return f"{_RENKU_BASE}/projects/{path}" + if entity_type == "groups" and path: + return f"{_RENKU_BASE}/groups/{path}" + if entity_type == "users" and path: + return f"{_RENKU_BASE}/users/{path}" + if entity_type == "data_connectors" and path: + return f"{_RENKU_BASE}/data-connectors/{path}" + return None + + +def _rerank_text(payload: dict[str, Any]) -> str: + parts: list[str] = [] + for key in ("name", "first_name", "last_name", "namespace", "path", "slug", + "storage_type", "storage_provider"): + v = payload.get(key) + if v: + parts.append(str(v)) + return " ".join(parts).strip() + + +def _resolve_target_collections( + entity_types: list[str] | None, +) -> list[tuple[str, str]]: + if not entity_types: + return [(et, COLLECTION_BY_ENTITY[et]) for et in COLLECTION_BY_ENTITY] + out: list[tuple[str, str]] = [] + for et in entity_types: + if et in COLLECTION_BY_ENTITY: + out.append((et, COLLECTION_BY_ENTITY[et])) + else: + logger.warning( + "%s: ignoring unknown entity_type %r — known: %s", + _LOG_LABEL, et, sorted(COLLECTION_BY_ENTITY), + ) + return out + + +class RenkulabRagProvider: + """Async wrapper around the RenkuLab Qdrant index (4 collections).""" + + def __init__( + self, + *, + store: QdrantStore, + embedder: RCPEmbeddingClient, + reranker: RCPRerankerClient | None = None, + ) -> None: + self._store = store + self._embedder = embedder + self._reranker = reranker + + @classmethod + def from_config(cls, cfg: RenkulabIndexConfig) -> RenkulabRagProvider: + return cls( + store=QdrantStore(cfg), # type: ignore[arg-type] + embedder=RCPEmbeddingClient(cfg), # type: ignore[arg-type] + reranker=RCPRerankerClient(cfg), # type: ignore[arg-type] + ) + + async def search( + self, + query: str, + *, + top_k: int = _DEFAULT_TOP_K, + entity_types: list[str] | None = None, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + targets = _resolve_target_collections(entity_types) + if not targets: + return [] + + cleaned = filter_allowlist(filters, _ALLOWED_FILTER_KEYS, log_label=_LOG_LABEL) + filter_payload = to_simple_filter_payload(cleaned) + vector = await self._embed_query(query) + if vector is None: + return [] + + candidate_k = expand_candidate_k(top_k) if rerank else top_k + all_hits = await self._gather_candidates( + vector=vector, + targets=targets, + candidate_k=candidate_k, + filter_payload=filter_payload, + ) + if not all_hits: + return [] + + all_hits.sort(key=lambda h: float(h.get("score") or 0.0), reverse=True) + all_hits = all_hits[: max(candidate_k, top_k)] + + if rerank and self._reranker is not None: + all_hits = await self._maybe_rerank(query, all_hits, top_k=top_k) + else: + all_hits = all_hits[:top_k] + + return [self._project_hit(h) for h in all_hits] + + async def _embed_query(self, query: str) -> list[float] | None: + try: + vectors = await self._embedder.embed_all([query]) + except (RCPEmbeddingError, Exception) as exc: # noqa: BLE001 + logger.warning("%s: embed failed — %s", _LOG_LABEL, exc) + return None + if not vectors: + return None + return list(vectors[0]) + + async def _gather_candidates( + self, + *, + vector: list[float], + targets: list[tuple[str, str]], + candidate_k: int, + filter_payload: dict[str, Any] | None, + ) -> list[dict[str, Any]]: + out: list[dict[str, Any]] = [] + for entity_type, collection in targets: + if not self._store.client.collection_exists(collection): + logger.info( + "%s: collection %s missing — skipping", + _LOG_LABEL, collection, + ) + continue + try: + hits = await asyncio.to_thread( + self._store.search, + collection, + query_vector=vector, + top_k=candidate_k, + filter_payload=filter_payload, + ) + except Exception as exc: # noqa: BLE001 + logger.warning( + "%s: qdrant search failed for %s — %s", + _LOG_LABEL, collection, exc, + ) + continue + for hit in hits: + payload = dict(hit.get("payload") or {}) + payload.setdefault("entity_type", entity_type) + out.append({**hit, "payload": payload, "_collection": collection}) + return out + + async def _maybe_rerank( + self, + query: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + documents = safe_rerank_documents( + [_rerank_text(h.get("payload") or {}) for h in hits], + ) + try: + results = await self._reranker.rerank(query, documents, top_n=top_k) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: rerank failed — %s", _LOG_LABEL, exc) + return hits[:top_k] + return apply_rerank_indices(hits, results, top_k=top_k) + + @staticmethod + def _project_hit(hit: dict[str, Any]) -> dict[str, Any]: + payload = hit.get("payload") or {} + entity_type = str(payload.get("entity_type") or "") + keys = _THIN_KEYS_BY_ENTITY.get(entity_type, ()) + return thin_payload( + payload, + keys, + extras={ + "id": hit.get("id"), + "score": hit.get("score"), + "entity_type": entity_type or None, + "collection": hit.get("_collection"), + "url": _build_url(entity_type, payload), + }, + ) + + +def build_default_provider( + cfg: RenkulabIndexConfig | None = None, +) -> RenkulabRagProvider | None: + if not env_enabled("V2_RENKULAB_RAG_ENABLED"): + return None + try: + from src.index.renkulab.config import load_config # noqa: PLC0415 + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to load config (%s)", _LOG_LABEL, exc) + return None + try: + return RenkulabRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to construct provider (%s)", _LOG_LABEL, exc) + return None + + +__all__ = ["RenkulabRagProvider", "build_default_provider"] diff --git a/src/v2/ingest/providers/ror_provider.py b/src/v2/ingest/providers/ror_provider.py new file mode 100644 index 0000000..61ea510 --- /dev/null +++ b/src/v2/ingest/providers/ror_provider.py @@ -0,0 +1,232 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +import requests + +from src.v2.ingest.cache import ProviderCache +from src.v2.ingest.providers.base import ( + ProviderNotFoundError, + ProviderPermissionError, + ProviderRateLimitError, + RORProvider, +) + +if TYPE_CHECKING: + from src.v2.ingest.providers.rate_limiter import RateLimiter + +HTTP_NOT_FOUND = 404 +HTTP_FORBIDDEN = 403 +HTTP_RATE_LIMIT = 429 + + +def _first_name(names: list[dict[str, Any]]) -> str | None: + for name_entry in names: + if not isinstance(name_entry, dict): + continue + if "ror_display" in name_entry.get("types", []): + value = name_entry.get("value") + if isinstance(value, str) and value: + return value + for name_entry in names: + if not isinstance(name_entry, dict): + continue + value = name_entry.get("value") + if isinstance(value, str) and value: + return value + return None + + +def _normalize_relationships(relationships: list[dict[str, Any]]) -> dict[str, Any]: + parent = None + children: list[dict[str, Any]] = [] + + for relationship in relationships: + if not isinstance(relationship, dict): + continue + relation_type = str(relationship.get("type", "")).lower() + relation_id = relationship.get("id") + relation_label = relationship.get("label") + relation_payload = { + "id": relation_id, + "name": relation_label, + } + if relation_type == "parent": + parent = relation_payload + if relation_type == "child": + children.append(relation_payload) + + return { + "parent": parent, + "children": children, + } + + +def _normalize_ror_organization(item: dict[str, Any]) -> dict[str, Any]: # noqa: C901, PLR0912 + names = item.get("names") + names_list = names if isinstance(names, list) else [] + aliases: list[str] = [] + labels: list[dict[str, str]] = [] + for name_entry in names_list: + if not isinstance(name_entry, dict): + continue + value = name_entry.get("value") + if not isinstance(value, str) or not value: + continue + name_types = name_entry.get("types") + normalized_types = ( + [str(name_type).lower() for name_type in name_types] + if isinstance(name_types, list) + else [] + ) + if "alias" in normalized_types and value not in aliases: + aliases.append(value) + if "label" in normalized_types: + label_payload: dict[str, str] = {"label": value} + language = name_entry.get("lang") + if isinstance(language, str) and language: + label_payload["iso639"] = language + labels.append(label_payload) + + acronyms = item.get("acronyms") + acronyms_list = [value for value in acronyms if isinstance(value, str)] if isinstance(acronyms, list) else [] + + types = item.get("types") + type_names = [] + if isinstance(types, list): + for organization_type in types: + if isinstance(organization_type, dict): + organization_type_value = organization_type.get("label") + if isinstance(organization_type_value, str): + type_names.append(organization_type_value) + elif isinstance(organization_type, str): + type_names.append(organization_type) + + locations = item.get("locations") + country_payload = {} + if isinstance(locations, list) and locations: + first_location = locations[0] + if isinstance(first_location, dict): + country = first_location.get("geonames_details", {}).get("country_name") + country_code = first_location.get("geonames_details", {}).get("country_code") + if country or country_code: + country_payload = { + "country_name": country, + "country_code": country_code, + } + + links = item.get("links") + links_list = [value for value in links if isinstance(value, str)] if isinstance(links, list) else [] + relationships = item.get("relationships") + relationships_payload = _normalize_relationships( + relationships if isinstance(relationships, list) else [], + ) + + return { + "id": item.get("id"), + "name": _first_name(names_list), + "aliases": aliases, + "acronyms": acronyms_list, + "labels": labels, + "types": type_names, + "country": country_payload, + "links": links_list, + "relationships": relationships_payload, + } + + +class RealRORProvider(RORProvider): + """Production ROR provider wrapping ROR HTTP lookup endpoints.""" + + def __init__( + self, + *, + session: requests.Session | None = None, + base_url: str = "https://api.ror.org/v2", + timeout: int = 20, + rate_limiter: RateLimiter | None = None, + cache: ProviderCache | None = None, + ) -> None: + super().__init__(provider_name="ror", rate_limiter=rate_limiter) + self._session = session + self._base_url = base_url.rstrip("/") + self._timeout = timeout + self._cache = cache + + def _http_client(self) -> requests.Session: + if self._session is None: + self._session = requests.Session() + return self._session + + def _request( + self, + endpoint: str, + *, + params: dict[str, Any] | None = None, + ) -> dict[str, Any]: + response = self._run_with_rate_limit( + lambda: self._http_client().get( + f"{self._base_url}{endpoint}", + params=params, + timeout=self._timeout, + ), + ) + if response.status_code == HTTP_NOT_FOUND: + message = "ROR organization not found" + raise ProviderNotFoundError(message) + if response.status_code == HTTP_FORBIDDEN: + message = "ROR request forbidden" + raise ProviderPermissionError(message) + if response.status_code == HTTP_RATE_LIMIT: + message = "ROR rate limit reached" + raise ProviderRateLimitError(message) + response.raise_for_status() + + payload = response.json() + if not isinstance(payload, dict): + raise TypeError + return payload + + @staticmethod + def _normalize_ror_id(ror_id: str) -> str: + candidate = ror_id.strip() + if candidate.startswith("https://ror.org/"): + return candidate.rsplit("/", maxsplit=1)[-1] + return candidate + + def get_organization(self, ror_id: str) -> dict[str, Any]: + normalized_id = self._normalize_ror_id(ror_id) + + def _fetch() -> dict[str, Any]: + payload = self._request(f"/organizations/{normalized_id}") + return _normalize_ror_organization(payload) + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key("ror", "get_organization", id=normalized_id) + return self._cache.get_or_set( + key, + _fetch, + label=f"ror.get_organization({normalized_id})", + ) + + def search_organizations(self, query: str) -> list[dict[str, Any]]: + def _fetch() -> list[dict[str, Any]]: + payload = self._request("/organizations", params={"query": query}) + items = payload.get("items") + if not isinstance(items, list): + return [] + return [ + _normalize_ror_organization(item) + for item in items + if isinstance(item, dict) + ] + + if self._cache is None: + return _fetch() + key = ProviderCache.make_key("ror", "search_organizations", query=query) + return self._cache.get_or_set( + key, + _fetch, + label=f"ror.search_organizations({query!r})", + ) diff --git a/src/v2/ingest/providers/ror_rag.py b/src/v2/ingest/providers/ror_rag.py new file mode 100644 index 0000000..b81aa64 --- /dev/null +++ b/src/v2/ingest/providers/ror_rag.py @@ -0,0 +1,217 @@ +"""Async RAG provider over the ROR Qdrant index. + +Per-scope collections (``ror_epfl_ethz``, ``ror_switzerland``, ``ror_europe``, +``ror_worldwide``). Different shape from the other RAG providers: + +* Embedder is a module-level async function (``embed_query(rcp, text)``) + rather than a class. +* Reranker is also module-level (``rerank(rcp, query, docs, top_n)``). +* The store's ``search`` only supports a single optional ``country`` filter + (no generic JSON-style filter dict). We translate ``country_code`` from + the LLM's filters dict and drop the rest with a warning. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any, Literal + +import numpy as np + +from src.index.ror.embed import EmbeddingError, embed_query +from src.index.ror.qdrant_store import QdrantRorStore +from src.index.ror.rerank import RerankError, rerank +from src.v2.ingest.providers._rag_helpers import ( + apply_rerank_indices, + env_enabled, + expand_candidate_k, + make_snippet, + safe_rerank_documents, +) + +if TYPE_CHECKING: + from src.index.ror.config import RcpConfig, RorIndexConfig + +logger = logging.getLogger(__name__) + +ScopeMode = Literal["epfl_ethz", "switzerland", "europe", "worldwide"] +_KNOWN_SCOPE_MODES: frozenset[str] = frozenset({ + "epfl_ethz", "switzerland", "europe", "worldwide", +}) + +_THIN_KEYS: tuple[str, ...] = () # ROR hits use a custom shape — see _to_thin_hit + +_DEFAULT_TOP_K = 10 +_LOG_LABEL = "ror_rag" + + +def _extract_country(filters: dict[str, Any] | None) -> tuple[str | None, list[str]]: + """Extract ``country_code`` from filters; return (country, dropped_keys).""" + if not filters: + return None, [] + country = None + dropped: list[str] = [] + for key, value in filters.items(): + if key == "country_code": + if isinstance(value, str) and value.strip(): + country = value.strip() + elif isinstance(value, dict) and isinstance(value.get("$eq"), str): + country = value["$eq"].strip() + else: + dropped.append(key) + else: + dropped.append(key) + if dropped: + logger.warning( + "%s: dropped non-allowlisted filter keys (only country_code " + "supported by the ROR store): %s", + _LOG_LABEL, sorted(dropped), + ) + return country, dropped + + +def _to_thin_hit(scope_mode: str, raw: dict[str, Any]) -> dict[str, Any]: + """Build the LLM-facing hit dict from a raw QdrantRorStore.search result.""" + record = raw.get("record") or {} + types = record.get("types") if isinstance(record, dict) else None + locations = record.get("locations") if isinstance(record, dict) else None + country = None + if isinstance(locations, list) and locations: + loc = locations[0] or {} + country = ( + (loc.get("geonames_details") or {}).get("country_code") + if isinstance(loc, dict) else None + ) + out: dict[str, Any] = { + "ror_id": raw.get("ror_id"), + "name": raw.get("name"), + "score": raw.get("score"), + "scope_mode": scope_mode, + "country_code": country, + "types": types, + "snippet": make_snippet(raw.get("text")), + } + return {k: v for k, v in out.items() if v is not None} + + +class RorRagProvider: + """Async wrapper around the ROR Qdrant index.""" + + def __init__( + self, + *, + store: QdrantRorStore, + rcp: RcpConfig, + ) -> None: + self._store = store + self._rcp = rcp + + @classmethod + def from_config(cls, cfg: RorIndexConfig) -> RorRagProvider: + return cls(store=QdrantRorStore(cfg), rcp=cfg.rcp) + + async def search( # noqa: PLR0911 — each early return is a graceful-fail check + self, + query: str, + *, + scope_mode: ScopeMode = "worldwide", + top_k: int = _DEFAULT_TOP_K, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + if scope_mode not in _KNOWN_SCOPE_MODES: + logger.warning("%s: unknown scope_mode %r", _LOG_LABEL, scope_mode) + return [] + + # Skip if the per-scope collection is missing (e.g. only worldwide + # is built). The upstream store raises FileNotFoundError otherwise. + collection_name = self._store.collection_name(scope_mode) + if not self._store.client.collection_exists(collection_name): + logger.info( + "%s: collection %s missing — returning [] without indexing", + _LOG_LABEL, collection_name, + ) + return [] + + country, _dropped = _extract_country(filters) + + try: + vector_np = await embed_query(self._rcp, query) + except EmbeddingError as exc: + logger.warning("%s: embed failed — %s", _LOG_LABEL, exc) + return [] + if not isinstance(vector_np, np.ndarray): + logger.warning("%s: embed returned non-array %r", _LOG_LABEL, type(vector_np)) + return [] + vector = vector_np.tolist() + + candidate_k = expand_candidate_k(top_k) if rerank else top_k + + try: + raw_hits = await asyncio.to_thread( + self._store.search, + scope_mode, + query_vector=vector, + top_k=candidate_k, + country=country, + ) + except Exception as exc: # noqa: BLE001 + logger.warning( + "%s: qdrant search failed (scope=%s) — %s", + _LOG_LABEL, scope_mode, exc, + ) + return [] + + if rerank and raw_hits: + raw_hits = await self._maybe_rerank(query, raw_hits, top_k=top_k) + else: + raw_hits = raw_hits[:top_k] + + return [_to_thin_hit(scope_mode, h) for h in raw_hits] + + async def _maybe_rerank( + self, + query: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + documents = safe_rerank_documents( + [(h.get("text") or h.get("name") or "") for h in hits], + ) + try: + results = await rerank(self._rcp, query, documents, top_n=top_k) + except RerankError as exc: + logger.warning("%s: rerank failed — %s", _LOG_LABEL, exc) + return hits[:top_k] + # Adapt RerankResult objects to the (index, relevance_score) dict + # shape apply_rerank_indices expects. + adapter = [{"index": r.index, "relevance_score": r.score} for r in results] + # apply_rerank_indices preserves the existing `score` shape; for ROR + # we simply replace it with the rerank score in-place. + return apply_rerank_indices(hits, adapter, top_k=top_k) + + +def build_default_provider( + cfg: RorIndexConfig | None = None, +) -> RorRagProvider | None: + if not env_enabled("V2_ROR_RAG_ENABLED"): + return None + try: + from src.index.ror.config import load_config # noqa: PLC0415 + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to load config (%s)", _LOG_LABEL, exc) + return None + try: + return RorRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to construct provider (%s)", _LOG_LABEL, exc) + return None + + +__all__ = ["RorRagProvider", "ScopeMode", "build_default_provider"] diff --git a/src/v2/ingest/providers/snsf_rag.py b/src/v2/ingest/providers/snsf_rag.py new file mode 100644 index 0000000..153108f --- /dev/null +++ b/src/v2/ingest/providers/snsf_rag.py @@ -0,0 +1,275 @@ +"""Async RAG provider over the SNSF Qdrant index. + +Per-scope collections (``snsf_epfl``, ``snsf_ethz``, ``snsf_switzerland``). +Mirrors :mod:`src.v2.ingest.providers.ror_rag` shape: + +* Embedder is a module-level async function (``embed_query(rcp, text)``). +* Reranker is also module-level (``rerank(rcp, query, docs, top_n)``). +* The store's ``search`` accepts ``institution`` / ``discipline_l1`` / + ``state`` payload filters; we forward the ones the LLM passes and drop + the rest with a warning. + +Adds one SNSF-specific affordance: ``institute`` (specific lab/centre name) +isn't in the Qdrant payload, so it's resolved from DuckDB after the ANN — +useful for SDSC-style queries (``--institute "Swiss Data Science Center"``). +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any, Literal, Optional + +import numpy as np + +from src.index.snsf.embed import EmbeddingError, embed_query +from src.index.snsf.qdrant_store import QdrantSnsfStore +from src.index.snsf.rerank import RerankError, rerank +from src.v2.ingest.providers._rag_helpers import ( + apply_rerank_indices, + env_enabled, + expand_candidate_k, + make_snippet, + safe_rerank_documents, +) + +if TYPE_CHECKING: + from src.index.snsf.config import RcpConfig, SnsfIndexConfig + +logger = logging.getLogger(__name__) + +ScopeMode = Literal["epfl", "ethz", "eth_domain", "switzerland"] +_KNOWN_SCOPE_MODES: frozenset[str] = frozenset({ + "epfl", "ethz", "eth_domain", "switzerland", +}) + +_ALLOWLISTED_FILTERS: frozenset[str] = frozenset({ + "institution", "institute", "discipline_l1", "state", +}) + +_DEFAULT_TOP_K = 10 +_LOG_LABEL = "snsf_rag" + + +def _split_filters( + filters: dict[str, Any] | None, +) -> tuple[dict[str, Any], Optional[str], list[str]]: + """Split filters into (qdrant payload filters, institute substring, dropped).""" + if not filters: + return {}, None, [] + qfilters: dict[str, Any] = {} + institute_substr: Optional[str] = None + dropped: list[str] = [] + for key, value in filters.items(): + if value is None: + continue + if key not in _ALLOWLISTED_FILTERS: + dropped.append(key) + continue + if isinstance(value, dict): + # Accept {"$eq": "X"} only — the SNSF payload filters are exact-match. + v = value.get("$eq") + if not isinstance(v, str) or not v.strip(): + dropped.append(key) + continue + value = v.strip() + if not isinstance(value, str) or not value.strip(): + dropped.append(key) + continue + if key == "institute": + institute_substr = value.strip() + else: + qfilters[key] = value.strip() + if dropped: + logger.warning( + "%s: dropped non-allowlisted filter keys (allowed: %s): %s", + _LOG_LABEL, sorted(_ALLOWLISTED_FILTERS), sorted(dropped), + ) + return qfilters, institute_substr, dropped + + +def _to_thin_hit(scope_mode: str, raw: dict[str, Any]) -> dict[str, Any]: + """Build the LLM-facing hit dict from a QdrantSnsfStore.search result.""" + out: dict[str, Any] = { + "grant_number": raw.get("grant_number"), + "title": raw.get("title"), + "score": raw.get("score"), + "scope_mode": scope_mode, + "research_institution": raw.get("research_institution"), + "main_discipline": raw.get("main_discipline"), + "start_date": raw.get("start_date"), + "amount_granted": raw.get("amount_granted"), + "snippet": make_snippet(raw.get("text")), + } + # `institute` is added by the post-filter when applicable. + if raw.get("institute"): + out["institute"] = raw["institute"] + if raw.get("grant_number") is not None: + out["url"] = f"https://data.snf.ch/grants/grant/{raw['grant_number']}" + return {k: v for k, v in out.items() if v is not None} + + +def _post_filter_by_institute( + candidates: list[dict[str, Any]], + institute_substring: str, +) -> list[dict[str, Any]]: + """Resolve the ``institute`` (lab/centre) for candidate grants from DuckDB. + + Same logic as `src.index.snsf.query._post_filter_by_institute` — kept + separate here so this provider doesn't import from the CLI surface. + """ + if not candidates: + return [] + needle = institute_substring.lower() + grant_ids = [c["grant_number"] for c in candidates if c.get("grant_number") is not None] + if not grant_ids: + return [] + from src.index.snsf.storage.duckdb_store import DuckDBStore # noqa: PLC0415 + + store = DuckDBStore.open() + try: + placeholders = ",".join(["?"] * len(grant_ids)) + rows = store.connect().execute( + f"SELECT grant_number, institute FROM grants " # noqa: S608 — placeholders bound below + f"WHERE grant_number IN ({placeholders})", + list(grant_ids), + ).fetchall() + finally: + store.close() + + institute_by_id = {gn: (inst or "") for gn, inst in rows} + kept: list[dict[str, Any]] = [] + for c in candidates: + gn = c.get("grant_number") + inst = institute_by_id.get(gn, "") + if needle in inst.lower(): + kept.append({**c, "institute": inst}) + return kept + + +class SnsfRagProvider: + """Async wrapper around the SNSF Qdrant index.""" + + def __init__( + self, + *, + store: QdrantSnsfStore, + rcp: RcpConfig, + ) -> None: + self._store = store + self._rcp = rcp + + @classmethod + def from_config(cls, cfg: SnsfIndexConfig) -> SnsfRagProvider: + return cls(store=QdrantSnsfStore(cfg), rcp=cfg.rcp) + + async def search( # noqa: PLR0911 — each early return is a graceful-fail check + self, + query: str, + *, + scope_mode: ScopeMode = "switzerland", + top_k: int = _DEFAULT_TOP_K, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + if scope_mode not in _KNOWN_SCOPE_MODES: + logger.warning("%s: unknown scope_mode %r", _LOG_LABEL, scope_mode) + return [] + + # Skip if the per-scope collection is missing. + collection_name = self._store.collection_name(scope_mode) + if not self._store.client.collection_exists(collection_name): + logger.info( + "%s: collection %s missing — returning [] without indexing", + _LOG_LABEL, collection_name, + ) + return [] + + qfilters, institute_substr, _dropped = _split_filters(filters) + + try: + vector_np = await embed_query(self._rcp, query) + except EmbeddingError as exc: + logger.warning("%s: embed failed — %s", _LOG_LABEL, exc) + return [] + if not isinstance(vector_np, np.ndarray): + logger.warning("%s: embed returned non-array %r", _LOG_LABEL, type(vector_np)) + return [] + vector = vector_np.tolist() + + # When --institute is set, most candidates will be filtered out, so + # widen the ANN net to keep `top_k` non-empty. + candidate_k = expand_candidate_k(top_k) if rerank else top_k + if institute_substr: + candidate_k = max(candidate_k, top_k * 4) + + try: + raw_hits = await asyncio.to_thread( + self._store.search, + scope_mode, + query_vector=vector, + top_k=candidate_k, + **qfilters, # institution / discipline_l1 / state + ) + except Exception as exc: # noqa: BLE001 + logger.warning( + "%s: qdrant search failed (scope=%s) — %s", + _LOG_LABEL, scope_mode, exc, + ) + return [] + + if institute_substr: + raw_hits = await asyncio.to_thread( + _post_filter_by_institute, raw_hits, institute_substr, + ) + if not raw_hits: + return [] + + if rerank and raw_hits: + raw_hits = await self._maybe_rerank(query, raw_hits, top_k=top_k) + else: + raw_hits = raw_hits[:top_k] + + return [_to_thin_hit(scope_mode, h) for h in raw_hits] + + async def _maybe_rerank( + self, + query: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + documents = safe_rerank_documents( + [(h.get("text") or h.get("title") or "") for h in hits], + ) + try: + results = await rerank(self._rcp, query, documents, top_n=top_k) + except RerankError as exc: + logger.warning("%s: rerank failed — %s", _LOG_LABEL, exc) + return hits[:top_k] + adapter = [{"index": r.index, "relevance_score": r.score} for r in results] + return apply_rerank_indices(hits, adapter, top_k=top_k) + + +def build_default_provider( + cfg: SnsfIndexConfig | None = None, +) -> SnsfRagProvider | None: + if not env_enabled("V2_SNSF_RAG_ENABLED"): + return None + try: + from src.index.snsf.config import load_config # noqa: PLC0415 + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to load config (%s)", _LOG_LABEL, exc) + return None + try: + return SnsfRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to construct provider (%s)", _LOG_LABEL, exc) + return None + + +__all__ = ["ScopeMode", "SnsfRagProvider", "build_default_provider"] diff --git a/src/v2/ingest/providers/swissubase_rag.py b/src/v2/ingest/providers/swissubase_rag.py new file mode 100644 index 0000000..357d04d --- /dev/null +++ b/src/v2/ingest/providers/swissubase_rag.py @@ -0,0 +1,216 @@ +"""Async RAG provider over the SWISSUbase Qdrant index. + +Single Qdrant collection ``swissubase_entities`` covers all four entity +types (``studies``, ``datasets``, ``persons``, ``institutions``) — the +``entity_type`` payload field disambiguates and is allowlisted as a +filter so the LLM can scope the search. + +Reuses OpenAlex's embedder, reranker, and ``QdrantStore`` via duck +typing — ``SwissubaseIndexConfig`` mirrors the same ``rcp.*`` / +``qdrant.*`` shape OpenAlex's clients expect. + +Every result includes ``source_url`` from the payload (set during +embed) — the project's hard requirement that every entity preserve its +canonical SWISSUbase URL. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.rcp_client import ( + RCPEmbeddingClient, + RCPEmbeddingError, +) +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.index.swissubase.embed.pipeline import SWISSUBASE_COLLECTION +from src.v2.ingest.providers._rag_helpers import ( + apply_rerank_indices, + env_enabled, + expand_candidate_k, + filter_allowlist, + safe_rerank_documents, + thin_payload, + to_simple_filter_payload, +) + +if TYPE_CHECKING: + from src.index.swissubase.config import SwissubaseIndexConfig + +logger = logging.getLogger(__name__) + +_ALLOWED_FILTER_KEYS: frozenset[str] = frozenset({ + "entity_type", + "study_id", + "dataset_id", + "person_key", + "institution_key", + "ref", + "main_discipline", + "sub_discipline", + "progress", + "year_start", + "year_end", + "access_right", +}) + +_THIN_KEYS: tuple[str, ...] = ( + "entity_type", + "entity_id", + "study_id", + "dataset_id", + "person_key", + "institution_key", + "ref", + "title", + "name", + "display_name", + "main_discipline", + "sub_discipline", + "progress", + "year_start", + "year_end", + "dataset_count", + "access_right", + "source_url", +) + +_DEFAULT_TOP_K = 10 +_LOG_LABEL = "swissubase_rag" + + +def _rerank_text(payload: dict[str, Any]) -> str: + title = ( + payload.get("title") + or payload.get("display_name") + or payload.get("name") + or "" + ) + discipline = payload.get("main_discipline") or "" + return f"{title}\n{discipline}".strip() + + +class SwissubaseRagProvider: + """Async wrapper around the SWISSUbase Qdrant index.""" + + def __init__( + self, + *, + store: QdrantStore, + embedder: RCPEmbeddingClient, + reranker: RCPRerankerClient | None = None, + ) -> None: + self._store = store + self._embedder = embedder + self._reranker = reranker + + @classmethod + def from_config(cls, cfg: SwissubaseIndexConfig) -> SwissubaseRagProvider: + return cls( + store=QdrantStore(cfg), # type: ignore[arg-type] + embedder=RCPEmbeddingClient(cfg), # type: ignore[arg-type] + reranker=RCPRerankerClient(cfg), # type: ignore[arg-type] + ) + + async def search( + self, + query: str, + *, + top_k: int = _DEFAULT_TOP_K, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + if not self._store.client.collection_exists(SWISSUBASE_COLLECTION): + logger.info( + "%s: collection %s missing — returning [] without indexing", + _LOG_LABEL, SWISSUBASE_COLLECTION, + ) + return [] + + cleaned = filter_allowlist(filters, _ALLOWED_FILTER_KEYS, log_label=_LOG_LABEL) + filter_payload = to_simple_filter_payload(cleaned) + + try: + vectors = await self._embedder.embed_all([query]) + except (RCPEmbeddingError, Exception) as exc: # noqa: BLE001 + logger.warning("%s: embed failed — %s", _LOG_LABEL, exc) + return [] + if not vectors: + return [] + vector = list(vectors[0]) + + candidate_k = expand_candidate_k(top_k) if rerank else top_k + + try: + hits = await asyncio.to_thread( + self._store.search, + SWISSUBASE_COLLECTION, + query_vector=vector, + top_k=candidate_k, + filter_payload=filter_payload, + ) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: qdrant search failed — %s", _LOG_LABEL, exc) + return [] + + if rerank and hits and self._reranker is not None: + hits = await self._maybe_rerank(query, hits, top_k=top_k) + else: + hits = hits[:top_k] + + return [ + thin_payload( + hit.get("payload") or {}, + _THIN_KEYS, + extras={ + "id": hit.get("id"), + "score": hit.get("score"), + "collection": SWISSUBASE_COLLECTION, + }, + ) + for hit in hits + ] + + async def _maybe_rerank( + self, + query: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + documents = safe_rerank_documents( + [_rerank_text(h.get("payload") or {}) for h in hits], + ) + try: + results = await self._reranker.rerank(query, documents, top_n=top_k) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: rerank failed — %s", _LOG_LABEL, exc) + return hits[:top_k] + return apply_rerank_indices(hits, results, top_k=top_k) + + +def build_default_provider( + cfg: SwissubaseIndexConfig | None = None, +) -> SwissubaseRagProvider | None: + if not env_enabled("V2_SWISSUBASE_RAG_ENABLED"): + return None + try: + from src.index.swissubase.config import load_config # noqa: PLC0415 + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to load config (%s)", _LOG_LABEL, exc) + return None + try: + return SwissubaseRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to construct provider (%s)", _LOG_LABEL, exc) + return None + + +__all__ = ["SwissubaseRagProvider", "build_default_provider"] diff --git a/src/v2/ingest/providers/zenodo_rag.py b/src/v2/ingest/providers/zenodo_rag.py new file mode 100644 index 0000000..640845e --- /dev/null +++ b/src/v2/ingest/providers/zenodo_rag.py @@ -0,0 +1,248 @@ +"""Async RAG provider over the Zenodo Qdrant index. + +Single collection: ``zenodo_records``. Reuses OpenAlex's embedder, reranker +and ``QdrantStore`` via duck typing — ``ZenodoIndexConfig`` mirrors the +same ``rcp.*`` / ``qdrant.*`` shape OpenAlex's clients expect. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import TYPE_CHECKING, Any + +from src.index.openalex.embed.rcp_client import ( + RCPEmbeddingClient, + RCPEmbeddingError, +) +from src.index.openalex.rerank.rcp_client import RCPRerankerClient +from src.index.openalex.vector.qdrant_store import QdrantStore +from src.index.zenodo.embed.pipeline import ZENODO_COLLECTION +from src.v2.ingest.providers._rag_helpers import ( + apply_rerank_indices, + env_enabled, + expand_candidate_k, + filter_allowlist, + safe_rerank_documents, + thin_payload, + to_simple_filter_payload, +) + +if TYPE_CHECKING: + from collections.abc import Sequence + + from src.index.zenodo.config import ZenodoIndexConfig + +logger = logging.getLogger(__name__) + +_FETCH_RECORDS_DESCRIPTION_KEYS: tuple[str, ...] = ( + "zenodo_id", "concept_recid", "title", "doi", "publication_date", + "resource_type", "access_right", "license_id", "description", +) +_DESCRIPTION_CAP_CHARS = 2000 + +_ALLOWED_FILTER_KEYS: frozenset[str] = frozenset({ + "year", + "doi", + "resource_type", + "access_right", + "entity_type", + "zenodo_id", +}) + +_THIN_KEYS: tuple[str, ...] = ( + "zenodo_id", "title", "doi", "year", "resource_type", "access_right", +) + +_DEFAULT_TOP_K = 10 +_LOG_LABEL = "zenodo_rag" + + +def _rerank_text(payload: dict[str, Any]) -> str: + title = payload.get("title") or "" + desc = payload.get("description") or "" + return f"{title}\n{desc}".strip() + + +class ZenodoRagProvider: + """Async wrapper around the Zenodo Qdrant index.""" + + def __init__( + self, + *, + store: QdrantStore, + embedder: RCPEmbeddingClient, + reranker: RCPRerankerClient | None = None, + ) -> None: + self._store = store + self._embedder = embedder + self._reranker = reranker + + @classmethod + def from_config(cls, cfg: ZenodoIndexConfig) -> ZenodoRagProvider: + # Duck-type into OpenAlex's clients — config shape matches. + return cls( + store=QdrantStore(cfg), # type: ignore[arg-type] + embedder=RCPEmbeddingClient(cfg), # type: ignore[arg-type] + reranker=RCPRerankerClient(cfg), # type: ignore[arg-type] + ) + + async def search( + self, + query: str, + *, + top_k: int = _DEFAULT_TOP_K, + filters: dict[str, Any] | None = None, + rerank: bool = False, + ) -> list[dict[str, Any]]: + if not isinstance(query, str) or not query.strip(): + return [] + if not self._store.client.collection_exists(ZENODO_COLLECTION): + logger.info( + "%s: collection %s missing — returning [] without indexing", + _LOG_LABEL, ZENODO_COLLECTION, + ) + return [] + + cleaned = filter_allowlist(filters, _ALLOWED_FILTER_KEYS, log_label=_LOG_LABEL) + filter_payload = to_simple_filter_payload(cleaned) + + try: + vectors = await self._embedder.embed_all([query]) + except (RCPEmbeddingError, Exception) as exc: # noqa: BLE001 + logger.warning("%s: embed failed — %s", _LOG_LABEL, exc) + return [] + if not vectors: + return [] + vector = list(vectors[0]) + + candidate_k = expand_candidate_k(top_k) if rerank else top_k + + try: + hits = await asyncio.to_thread( + self._store.search, + ZENODO_COLLECTION, + query_vector=vector, + top_k=candidate_k, + filter_payload=filter_payload, + ) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: qdrant search failed — %s", _LOG_LABEL, exc) + return [] + + if rerank and hits and self._reranker is not None: + hits = await self._maybe_rerank(query, hits, top_k=top_k) + else: + hits = hits[:top_k] + + return [ + thin_payload( + hit.get("payload") or {}, + _THIN_KEYS, + extras={ + "id": hit.get("id"), + "score": hit.get("score"), + "collection": "zenodo_records", + }, + ) + for hit in hits + ] + + async def fetch_records( + self, + ids: Sequence[str], + ) -> list[dict[str, Any]]: + """Hydrate Zenodo records from DuckDB by ``zenodo_id`` or ``concept_recid``. + + Used by the LLM agent tool to expand thin search hits into full + records (title, DOI, description, license, ...). Unlike Infoscience + — which stores rich payloads on Qdrant points — Zenodo's full + record body lives in DuckDB, so we go to the store rather than + Qdrant. Falls back to the latest version under a concept_recid + when the requested ID is the parent (citation) ID. + """ + clean_ids = [ + i.strip() for i in ids + if isinstance(i, str) and i.strip() + ] + if not clean_ids: + return [] + return await asyncio.to_thread(self._fetch_records_sync, clean_ids) + + @staticmethod + def _fetch_records_sync(ids: list[str]) -> list[dict[str, Any]]: + from src.index.zenodo.storage.duckdb_store import ( # noqa: PLC0415 + ZenodoStore, + ) + + try: + store = ZenodoStore.open() + except Exception as exc: # noqa: BLE001 + logger.warning("%s: cannot open store — %s", _LOG_LABEL, exc) + return [] + try: + out: list[dict[str, Any]] = [] + for rid in ids: + row = store.fetch_record(rid) + if row is None: + row = store.fetch_record_by_concept(rid) + if row is None: + continue + description = row.get("description") or "" + out.append({ + "zenodo_id": row.get("zenodo_id"), + "concept_recid": row.get("concept_recid"), + "title": row.get("title"), + "doi": row.get("doi"), + "publication_date": ( + row.get("publication_date").isoformat() + if hasattr(row.get("publication_date"), "isoformat") + else row.get("publication_date") + ), + "resource_type": row.get("resource_type"), + "access_right": row.get("access_right"), + "license_id": row.get("license_id"), + "description": description[:_DESCRIPTION_CAP_CHARS], + }) + return out + finally: + store.close() + + async def _maybe_rerank( + self, + query: str, + hits: list[dict[str, Any]], + *, + top_k: int, + ) -> list[dict[str, Any]]: + documents = safe_rerank_documents( + [_rerank_text(h.get("payload") or {}) for h in hits], + ) + try: + results = await self._reranker.rerank(query, documents, top_n=top_k) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: rerank failed — %s", _LOG_LABEL, exc) + return hits[:top_k] + return apply_rerank_indices(hits, results, top_k=top_k) + + +def build_default_provider( + cfg: ZenodoIndexConfig | None = None, +) -> ZenodoRagProvider | None: + if not env_enabled("V2_ZENODO_RAG_ENABLED"): + return None + try: + from src.index.zenodo.config import load_config # noqa: PLC0415 + + resolved = cfg or load_config() + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to load config (%s)", _LOG_LABEL, exc) + return None + try: + return ZenodoRagProvider.from_config(resolved) + except Exception as exc: # noqa: BLE001 + logger.warning("%s: failed to construct provider (%s)", _LOG_LABEL, exc) + return None + + +__all__ = ["ZenodoRagProvider", "build_default_provider"] diff --git a/src/v2/jobs.py b/src/v2/jobs.py new file mode 100644 index 0000000..ccfa46e --- /dev/null +++ b/src/v2/jobs.py @@ -0,0 +1,42 @@ +"""Persistent job store for the v2 async POST /extract pattern. + +Records are persisted to the same SQLite-backed `ProviderCache` used elsewhere +in v2 — a separate cache namespace keeps job records from colliding with +provider response keys. +""" + +from __future__ import annotations + +from src.v2.api_models import V2ExtractJob +from src.v2.ingest.cache import ProviderCache + +JOB_NAMESPACE = "v2-extract-job" + + +class JobStore: + """Read/write `V2ExtractJob` records via `ProviderCache`.""" + + def __init__(self, cache: ProviderCache) -> None: + self._cache = cache + + @staticmethod + def make_key(job_id: str) -> str: + return ProviderCache.make_key(JOB_NAMESPACE, "record", job_id=job_id) + + def get(self, job_id: str) -> V2ExtractJob | None: + raw = self._cache.get(self.make_key(job_id)) + if not isinstance(raw, dict): + return None + try: + return V2ExtractJob.model_validate(raw) + except Exception: # noqa: BLE001 + return None + + def set(self, job: V2ExtractJob) -> None: + self._cache.set( + self.make_key(job.job_id), + job.model_dump(mode="json", exclude_none=True), + ) + + +__all__ = ["JOB_NAMESPACE", "JobStore"] diff --git a/src/v2/log_context.py b/src/v2/log_context.py new file mode 100644 index 0000000..c9e41df --- /dev/null +++ b/src/v2/log_context.py @@ -0,0 +1,218 @@ +"""Request-scoped logging context for v2. + +Adds a `[repo--]`-style request-id prefix to every log record +emitted while inside an async request handler. Implementation mirrors the +behaviour of the v1 helper (`src.v1.utils.enhanced_logging`) so logs from +both v1 and v2 modules share the same prefix when they run inside the same +request context. + +Public surface: + +- `setup_logging(level=..., use_colors=True)` — install a colored stdout + handler with the request-id formatter on the root logger and silence the + noisy third-party libraries we don't want in normal output. +- `AsyncRequestContext(request_id=..., prefix="repo")` — async context + manager. Generates a request id (`prefix--<4-hex>`), stamps it on + the active `ContextVar`, restores the previous value on exit. +- `RequestContext` — sync variant of the same. +- `set_request_id`, `get_request_id`, `clear_request_id`, + `generate_request_id` — direct accessors when a context manager is awkward. + +The format string includes `%(request_id)s` (or `[%(request_id)s]` in the +colored variant). When no request id is set, the field renders empty so +out-of-request logs stay clean. +""" +from __future__ import annotations + +import logging +import os +import random +import sys +from contextvars import ContextVar +from typing import Optional + +# ContextVar that propagates across `await` boundaries inside the same task. +request_id_var: ContextVar[Optional[str]] = ContextVar("v2_request_id", default=None) + + +class _Colors: + RESET = "\033[0m" + BOLD = "\033[1m" + + DEBUG = "\033[36m" + INFO = "\033[32m" + WARNING = "\033[33m" + ERROR = "\033[31m" + CRITICAL = "\033[35m" + + REQUEST_COLORS = ( + "\033[94m", + "\033[92m", + "\033[96m", + "\033[95m", + "\033[93m", + "\033[91m", + "\033[97m", + "\033[90m", + ) + + @classmethod + def request_color(cls, request_id: str) -> str: + if not request_id: + return cls.RESET + return cls.REQUEST_COLORS[hash(request_id) % len(cls.REQUEST_COLORS)] + + +_LEVEL_COLORS = { + "DEBUG": _Colors.DEBUG, + "INFO": _Colors.INFO, + "WARNING": _Colors.WARNING, + "ERROR": _Colors.ERROR, + "CRITICAL": _Colors.CRITICAL, +} + + +class _ColoredFormatter(logging.Formatter): + """Adds request-id and colors to log output.""" + + def format(self, record: logging.LogRecord) -> str: + request_id = request_id_var.get() + if request_id: + record.request_id = f"[{request_id}]" + record.request_color = _Colors.request_color(request_id) + else: + record.request_id = "" + record.request_color = "" + + levelname = record.levelname + if levelname in _LEVEL_COLORS: + record.levelname = f"{_LEVEL_COLORS[levelname]}{levelname}{_Colors.RESET}" + + return super().format(record) + _Colors.RESET + + +class _RequestContextFilter(logging.Filter): + """Stamps the current request id onto every record (plain mode).""" + + def filter(self, record: logging.LogRecord) -> bool: + request_id = request_id_var.get() + record.request_id = request_id or "" + record.request_color = _Colors.request_color(request_id) if request_id else "" + return True + + +def generate_request_id(prefix: str = "req") -> str: + """Return `prefix--<4-hex>` (worker-pid-safe).""" + pid = os.getpid() + suffix = "".join(random.choices("0123456789abcdef", k=4)) + return f"{prefix}-{pid}-{suffix}" + + +def set_request_id(request_id: Optional[str] = None, prefix: str = "req") -> str: + """Stamp `request_id` (or a freshly generated one) onto the ContextVar.""" + if request_id is None: + request_id = generate_request_id(prefix) + request_id_var.set(request_id) + return request_id + + +def clear_request_id() -> None: + """Clear the request id from the current context.""" + request_id_var.set(None) + + +def get_request_id() -> Optional[str]: + """Return the active request id, or `None` if not in a request context.""" + return request_id_var.get() + + +def setup_logging(level: int = logging.INFO, *, use_colors: bool = True) -> None: + """Install the request-id-aware handler on the root logger. + + Pass `use_colors=False` for a plain text format (e.g., for log files + or non-terminal sinks). + """ + handler = logging.StreamHandler(sys.stdout) + + if use_colors: + formatter = _ColoredFormatter( + fmt=( + "%(asctime)s %(levelname)s %(request_color)s%(request_id)s" + "\033[0m %(name)s: %(message)s" + ), + datefmt="%Y-%m-%d %H:%M:%S", + ) + else: + formatter = logging.Formatter( + fmt="%(asctime)s [%(levelname)s] %(request_id)s %(name)s: %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + handler.setFormatter(formatter) + handler.addFilter(_RequestContextFilter()) + + logging.basicConfig(level=level, handlers=[handler], force=True) + + # Quiet down libraries that log every request/response at DEBUG/INFO. + for noisy in ( + "rdflib", + "urllib3", + "selenium", + "httpx", + "httpcore", + "httpcore.http11", + "openai", + "openai._base_client", + ): + logging.getLogger(noisy).setLevel(logging.WARNING) + + +class RequestContext: + """Sync context manager that pins a request id on the active ContextVar. + + Example: + with RequestContext(prefix="repo") as request_id: + logger.info("processing") # logs include `[repo--]` + """ + + def __init__(self, request_id: Optional[str] = None, prefix: str = "req") -> None: + self.request_id = request_id or generate_request_id(prefix) + self._previous_id: Optional[str] = None + + def __enter__(self) -> str: + self._previous_id = request_id_var.get() + request_id_var.set(self.request_id) + return self.request_id + + def __exit__(self, exc_type, exc_val, exc_tb) -> bool: + request_id_var.set(self._previous_id) + return False + + +class AsyncRequestContext: + """Async variant of `RequestContext`. Use as `async with`.""" + + def __init__(self, request_id: Optional[str] = None, prefix: str = "req") -> None: + self.request_id = request_id or generate_request_id(prefix) + self._previous_id: Optional[str] = None + + async def __aenter__(self) -> str: + self._previous_id = request_id_var.get() + request_id_var.set(self.request_id) + return self.request_id + + async def __aexit__(self, exc_type, exc_val, exc_tb) -> bool: + request_id_var.set(self._previous_id) + return False + + +__all__ = [ + "AsyncRequestContext", + "RequestContext", + "clear_request_id", + "generate_request_id", + "get_request_id", + "request_id_var", + "set_request_id", + "setup_logging", +] diff --git a/src/v2/observation/__init__.py b/src/v2/observation/__init__.py new file mode 100644 index 0000000..a71c33a --- /dev/null +++ b/src/v2/observation/__init__.py @@ -0,0 +1 @@ +"""Observation utilities for v2: query logging, etc.""" diff --git a/src/v2/observation/github_rate_limit.py b/src/v2/observation/github_rate_limit.py new file mode 100644 index 0000000..4c414ed --- /dev/null +++ b/src/v2/observation/github_rate_limit.py @@ -0,0 +1,179 @@ +"""GitHub token-pool rate-limit probe. + +Probes each token in `GITHUB_TOKEN_POOL` (or `GITHUB_TOKEN`) against +`GET /rate_limit`. The endpoint does not count against the rate limit +itself, so this is safe to call from `/v2/health`. Results are cached +in-process for 30s to avoid flooding GitHub when a load balancer hits +`/health` repeatedly. + +The summary surfaced in the response carries no token values — only +position-indexed status — so callers (batch scripts, dashboards) can +back off when all tokens are exhausted without needing the secret. +""" + +from __future__ import annotations + +import logging +import os +import threading +import time +from datetime import datetime, timezone +from typing import Literal + +import httpx +from pydantic import BaseModel + +logger = logging.getLogger(__name__) + +_RATE_LIMIT_URL = "https://api.github.com/rate_limit" +_PROBE_TIMEOUT_SECONDS = 5.0 +_CACHE_TTL_SECONDS = 30.0 +_LOW_REMAINING_THRESHOLD = 200 + + +class GitHubTokenStatus(BaseModel): + """Per-token state. `index` is the 1-based pool position; the token + value itself is never exposed.""" + + index: int + status: Literal["ok", "rate_limited", "invalid", "unreachable"] + core_remaining: int | None = None + core_limit: int | None = None + core_reset: datetime | None = None + + +class GitHubRateLimitSummary(BaseModel): + status: Literal["healthy", "degraded", "unhealthy"] + total_remaining: int + earliest_reset: datetime | None + tokens: list[GitHubTokenStatus] + + +_cache_lock = threading.Lock() +_cache: tuple[float, GitHubRateLimitSummary] | None = None + + +def _resolve_token_pool() -> list[str]: + pool = os.environ.get("GITHUB_TOKEN_POOL", "") + tokens = [token.strip() for token in pool.split(",") if token.strip()] + if tokens: + return tokens + single = os.environ.get("GITHUB_TOKEN", "").strip() + return [single] if single else [] + + +def _probe_one(index: int, token: str) -> GitHubTokenStatus: + try: + response = httpx.get( + _RATE_LIMIT_URL, + headers={ + "Authorization": f"token {token}", + "Accept": "application/vnd.github.v3+json", + "User-Agent": "GitMetadataExtractor/2.0", + }, + timeout=_PROBE_TIMEOUT_SECONDS, + ) + except httpx.HTTPError as exc: + logger.warning("github rate-limit probe (token #%d) failed: %s", index, exc) + return GitHubTokenStatus(index=index, status="unreachable") + + if response.status_code == 401: + return GitHubTokenStatus(index=index, status="invalid") + if response.status_code != 200: + logger.warning( + "github rate-limit probe (token #%d) returned http=%d", + index, + response.status_code, + ) + return GitHubTokenStatus(index=index, status="unreachable") + + payload = response.json() + core = payload.get("resources", {}).get("core") or payload.get("rate") or {} + remaining = int(core.get("remaining", 0)) + limit = int(core.get("limit", 0)) + reset_ts = core.get("reset") + reset_dt = ( + datetime.fromtimestamp(int(reset_ts), tz=timezone.utc) + if isinstance(reset_ts, (int, float)) + else None + ) + state: Literal["ok", "rate_limited"] = ( + "ok" if remaining > 0 else "rate_limited" + ) + return GitHubTokenStatus( + index=index, + status=state, + core_remaining=remaining, + core_limit=limit, + core_reset=reset_dt, + ) + + +def _summarize(token_statuses: list[GitHubTokenStatus]) -> GitHubRateLimitSummary: + if not token_statuses: + return GitHubRateLimitSummary( + status="unhealthy", + total_remaining=0, + earliest_reset=None, + tokens=[], + ) + + if all(t.status == "invalid" for t in token_statuses): + return GitHubRateLimitSummary( + status="unhealthy", + total_remaining=0, + earliest_reset=None, + tokens=token_statuses, + ) + + total_remaining = sum( + t.core_remaining or 0 + for t in token_statuses + if t.status in {"ok", "rate_limited"} + ) + resets = [t.core_reset for t in token_statuses if t.core_reset is not None] + earliest_reset = min(resets) if resets else None + + has_capacity = any(t.status == "ok" for t in token_statuses) + if not has_capacity: + # All probed tokens are rate-limited or unreachable. + status: Literal["healthy", "degraded", "unhealthy"] = "degraded" + elif total_remaining < _LOW_REMAINING_THRESHOLD: + status = "degraded" + else: + status = "healthy" + + return GitHubRateLimitSummary( + status=status, + total_remaining=total_remaining, + earliest_reset=earliest_reset, + tokens=token_statuses, + ) + + +def probe_github_rate_limit() -> GitHubRateLimitSummary: + """Return the current rate-limit summary, cached for 30s.""" + + global _cache # noqa: PLW0603 + now = time.monotonic() + with _cache_lock: + if _cache is not None: + cached_at, cached_summary = _cache + if now - cached_at < _CACHE_TTL_SECONDS: + return cached_summary + + tokens = _resolve_token_pool() + statuses = [_probe_one(i + 1, token) for i, token in enumerate(tokens)] + summary = _summarize(statuses) + + with _cache_lock: + _cache = (now, summary) + return summary + + +def reset_cache() -> None: + """Clear the in-process cache. Test hook.""" + + global _cache # noqa: PLW0603 + with _cache_lock: + _cache = None diff --git a/src/v2/observation/query_log.py b/src/v2/observation/query_log.py new file mode 100644 index 0000000..ee8027c --- /dev/null +++ b/src/v2/observation/query_log.py @@ -0,0 +1,204 @@ +"""Per-request log of external-service queries an extraction tries. + +Each ``/extract`` call gets a `QueryLog` instance stamped onto the +`query_log_var` `ContextVar`. Tool wrappers (infoscience search, duckduckgo +search, orcid lookup, selenium fetch, ror search, github org metadata, +organization identity, …) call `record_query(service=..., query=...)` before +hitting the network. The agent that owns the tool call is taken from +`current_agent_var` / `current_agent_context_var`, which each agent stamps at +the top of its `run()` via the `current_agent(...)` context manager. + +At request completion `api.py` writes the accumulated log to a JSON file +under `V2_QUERY_LOG_DIR` (default ``logs/v2_queries/``). + +The module is no-op-safe: when no QueryLog is in the current context (e.g. +the agents are exercised from a test harness without going through +`extract()`), `record_query()` and `current_agent` simply do nothing. +""" +from __future__ import annotations + +import json +import logging +import os +from contextvars import ContextVar +from dataclasses import dataclass, field +from datetime import datetime, timezone +from pathlib import Path +from threading import Lock +from typing import Any + +logger = logging.getLogger(__name__) + +DEFAULT_QUERY_LOG_DIR = Path("logs/v2_queries") +QUERY_LOG_DIR_ENV_VAR = "V2_QUERY_LOG_DIR" + + +@dataclass(slots=True) +class _AgentEntry: + agent: str + context: dict[str, Any] + queries: list[dict[str, Any]] = field(default_factory=list) + + +class QueryLog: + """Thread-safe accumulator for one extraction's tool-query history.""" + + def __init__(self, run_id: str, extract_full_path: str) -> None: + self.run_id = run_id + self.extract_full_path = extract_full_path + self._lock = Lock() + # Key: (agent_name, json-serialised context). Each unique pair gets one + # entry whose `queries` list grows as new tool calls happen. + self._entries: dict[tuple[str, str], _AgentEntry] = {} + + def record( + self, + *, + service: str, + query: str, + agent: str, + agent_context: dict[str, Any], + ) -> None: + ctx_key = json.dumps(agent_context, sort_keys=True, default=str) + key = (agent, ctx_key) + timestamp = datetime.now(tz=timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") + with self._lock: + entry = self._entries.get(key) + if entry is None: + entry = _AgentEntry(agent=agent, context=dict(agent_context)) + self._entries[key] = entry + entry.queries.append( + { + "service": service, + "query": query, + "ts": timestamp, + }, + ) + + def to_dict(self) -> dict[str, Any]: + with self._lock: + return { + "run_id": self.run_id, + "extract_full_path": self.extract_full_path, + "agents": [ + { + "agent": entry.agent, + "context": entry.context, + "queries": list(entry.queries), + } + for entry in self._entries.values() + ], + } + + def write(self, output_dir: Path | None = None) -> Path | None: + """Persist the log to ``/.json``. + + Returns the written path on success, or `None` on failure (logged). + Empty logs (no queries recorded) are still written so callers know + the extraction happened. + """ + directory = output_dir or _resolve_default_dir() + try: + directory.mkdir(parents=True, exist_ok=True) + except OSError: + logger.exception("failed to create query log directory %s", directory) + return None + path = directory / f"{self.run_id}.json" + try: + path.write_text( + json.dumps(self.to_dict(), indent=2, default=str, ensure_ascii=False), + encoding="utf-8", + ) + except OSError: + logger.exception("failed to write query log %s", path) + return None + return path + + +query_log_var: ContextVar[QueryLog | None] = ContextVar("v2_query_log", default=None) +current_agent_var: ContextVar[str | None] = ContextVar( + "v2_current_agent", + default=None, +) +current_agent_context_var: ContextVar[dict[str, Any] | None] = ContextVar( + "v2_current_agent_context", + default=None, +) + + +def _resolve_default_dir() -> Path: + raw = os.getenv(QUERY_LOG_DIR_ENV_VAR) + if isinstance(raw, str) and raw.strip(): + return Path(raw.strip()) + return DEFAULT_QUERY_LOG_DIR + + +def stamp_current_agent(*, name: str, context: dict[str, Any] | None = None) -> None: + """Set the active agent name and context for the current async task. + + Unlike `current_agent` (the context manager), this helper does NOT restore + previous values on exit — it's intended for the top of an agent's `run()` + method where the agent owns the rest of the task's context. ContextVars + set inside an asyncio task die when the task completes. + """ + current_agent_var.set(name) + current_agent_context_var.set(dict(context) if context else {}) + + +def record_query(*, service: str, query: str) -> None: + """Record a tool-issued external-service query, if a QueryLog is active. + + Service is namespaced per provider/method (e.g. + ``infoscience.search_person``); query is the raw user input the LLM passed. + Caller doesn't need to know whether a log is active or which agent is + running — both are read from ContextVars and may be missing. + """ + log = query_log_var.get() + if log is None: + return + log.record( + service=service, + query=query, + agent=current_agent_var.get() or "unknown", + agent_context=current_agent_context_var.get() or {}, + ) + + +class current_agent: + """Context manager: stamp the active agent name + context onto the ContextVars. + + Use inside an agent's ``run()`` so any tool call made downstream is + attributed to that agent in the query log. Restores the previous values + on exit so nested invocations behave correctly. + """ + + def __init__(self, *, name: str, context: dict[str, Any] | None = None) -> None: + self._name = name + self._context = dict(context) if context else {} + self._prev_name: str | None = None + self._prev_context: dict[str, Any] | None = None + + def __enter__(self) -> "current_agent": + self._prev_name = current_agent_var.get() + self._prev_context = current_agent_context_var.get() + current_agent_var.set(self._name) + current_agent_context_var.set(self._context) + return self + + def __exit__(self, exc_type, exc_val, exc_tb) -> bool: + current_agent_var.set(self._prev_name) + current_agent_context_var.set(self._prev_context) + return False + + +__all__ = [ + "DEFAULT_QUERY_LOG_DIR", + "QUERY_LOG_DIR_ENV_VAR", + "QueryLog", + "current_agent", + "current_agent_context_var", + "current_agent_var", + "query_log_var", + "record_query", + "stamp_current_agent", +] diff --git a/src/v2/pipeline/__init__.py b/src/v2/pipeline/__init__.py new file mode 100644 index 0000000..f6b3f64 --- /dev/null +++ b/src/v2/pipeline/__init__.py @@ -0,0 +1,12 @@ +"""Pipeline orchestration primitives for v2 extraction.""" + +from src.v2.pipeline.models import AgentGroup, ExecutionPlan, PipelineResult, Stage +from src.v2.pipeline.orchestrator import PipelineOrchestrator + +__all__ = [ + "AgentGroup", + "ExecutionPlan", + "PipelineOrchestrator", + "PipelineResult", + "Stage", +] diff --git a/src/v2/pipeline/models.py b/src/v2/pipeline/models.py new file mode 100644 index 0000000..5a48d0a --- /dev/null +++ b/src/v2/pipeline/models.py @@ -0,0 +1,140 @@ +from __future__ import annotations + +from copy import deepcopy +from dataclasses import dataclass, field +from typing import Any + +from src.v2.agents.models import ( + AgentResult, + TypedEntityBuckets, + infer_entity_bucket, + normalize_entity_bucket_key, +) + + +@dataclass(slots=True) +class AgentGroup: + name: str + agent_keys: list[str] = field(default_factory=list) + parallelizable: bool = True + + def to_dict(self) -> dict[str, Any]: + return { + "name": self.name, + "agent_keys": list(self.agent_keys), + "parallelizable": self.parallelizable, + } + + +@dataclass(slots=True) +class Stage: + name: str + groups: list[AgentGroup] = field(default_factory=list) + depends_on: list[str] = field(default_factory=list) + + def to_dict(self) -> dict[str, Any]: + return { + "name": self.name, + "groups": [group.to_dict() for group in self.groups], + "depends_on": list(self.depends_on), + } + + +@dataclass(slots=True) +class ExecutionPlan: + detected_type: str + stages: list[Stage] = field(default_factory=list) + + def has_circular_dependencies(self) -> bool: + stage_map = {stage.name: stage for stage in self.stages} + visiting: set[str] = set() + visited: set[str] = set() + + def _visit(stage_name: str) -> bool: + if stage_name in visited: + return False + if stage_name in visiting: + return True + + visiting.add(stage_name) + stage = stage_map.get(stage_name) + if stage is None: + visiting.remove(stage_name) + visited.add(stage_name) + return False + + has_cycle = any(_visit(dep) for dep in stage.depends_on) + visiting.remove(stage_name) + visited.add(stage_name) + return has_cycle + + return any(_visit(stage.name) for stage in self.stages) + + def to_dict(self) -> dict[str, Any]: + return { + "detected_type": self.detected_type, + "stages": [stage.to_dict() for stage in self.stages], + "has_cycle": self.has_circular_dependencies(), + } + + +@dataclass(slots=True) +class PipelineResult: + stages_completed: list[str] = field(default_factory=list) + agent_results: dict[str, AgentResult] = field(default_factory=dict) + typed_entity_buckets: TypedEntityBuckets = field(default_factory=TypedEntityBuckets) + gathered_context: dict[str, Any] = field(default_factory=dict) + warnings: list[str] = field(default_factory=list) + errors: list[str] = field(default_factory=list) + duration_ms: int = 0 + + def resolved_typed_entity_buckets(self) -> TypedEntityBuckets: + resolved = TypedEntityBuckets() + resolved.merge(self.typed_entity_buckets) + + for result_key, result in self.agent_results.items(): + if not isinstance(result.data, dict) or not result.data: + pass + else: + bucket_name = infer_entity_bucket(agent_key=result_key, data=result.data) + if bucket_name is not None: + resolved.add(bucket_name, result.data) + + if not isinstance(result.stats, dict): + continue + for stats_key, stats_value in result.stats.items(): + bucket_name = normalize_entity_bucket_key(stats_key) + if bucket_name is None or not isinstance(stats_value, list): + continue + for entity in stats_value: + if isinstance(entity, dict) and entity: + resolved.add(bucket_name, entity) + + return resolved + + def to_dict(self) -> dict[str, Any]: + serialized_results: dict[str, dict[str, Any]] = {} + for key, result in self.agent_results.items(): + serialized_results[key] = { + "data": result.data, + "warnings": list(result.warnings), + "raw_output": result.raw_output, + "is_partial": result.is_partial, + "failure_reason": result.failure_reason, + "model": result.model, + "provider": result.provider, + "tokens_prompt": result.tokens_prompt, + "tokens_completion": result.tokens_completion, + "stats": dict(result.stats), + } + + typed_entity_buckets = self.resolved_typed_entity_buckets().to_dict() + return { + "stages_completed": list(self.stages_completed), + "agent_results": serialized_results, + "typed_entity_buckets": typed_entity_buckets, + "gathered_context": deepcopy(self.gathered_context), + "warnings": list(self.warnings), + "errors": list(self.errors), + "duration_ms": self.duration_ms, + } diff --git a/src/v2/pipeline/orchestrator.py b/src/v2/pipeline/orchestrator.py new file mode 100644 index 0000000..bec52f7 --- /dev/null +++ b/src/v2/pipeline/orchestrator.py @@ -0,0 +1,1864 @@ +from __future__ import annotations + +import asyncio +import contextlib +import json +import logging +import re +from copy import deepcopy +from dataclasses import dataclass +from time import perf_counter +from typing import TYPE_CHECKING, Any, Awaitable, Callable + +logger = logging.getLogger(__name__) + +_HEARTBEAT_INTERVAL_SECONDS = 30.0 + +from src.v2.agents import ( + AgentRuntime, + AgentRuntimeRegistry, + ArticleAgentV2, + ContributionAgentV2, + LLMArticleAgentV2, + LLMContributionAgentV2, + LLMContextSummaryAgentV2, + LLMMembershipAgentV2, + LLMOrganizationAgentV2, + LLMPersonAgentV2, + LLMRepositoryAgentV2, + MembershipAgentV2, + OrganizationAgentV2, + PersonAgentV2, + ProviderSet, + RepositoryAgentV2, + TypedEntityBuckets, + infer_entity_bucket, + parse_agent_runtime, + with_retry, +) +from src.v2.agents.llm.prompt_context import ( + UPSTREAM_STAGE_OUTPUTS_JSON_CONTEXT_KEY, + USER_PROMPT_APPENDIX_CONTEXT_KEY, +) +from src.v2.agents.models import AgentResult +from src.v2.canonicalization.string_utils import normalize_string +from src.v2.ingest.cache import ProviderCache +from src.v2.ingest.detection.models import GitHubURLClassification +from src.v2.pipeline.models import AgentGroup, ExecutionPlan, PipelineResult, Stage +from src.v2.pipeline.stages import ContextBundle, gather_context + +if TYPE_CHECKING: + from src.v2.agents.contracts import RuntimeAgent + +ContextGatherer = Callable[ + [str, GitHubURLClassification, ProviderSet], + ContextBundle | Awaitable[ContextBundle], +] +AgentRunner = Callable[ + [dict[str, Any], ProviderSet], + AgentResult | Awaitable[AgentResult] | dict[str, Any] | Awaitable[dict[str, Any]], +] +SleepCallable = Callable[[float], Awaitable[None]] + +STAGE_CONTEXT_GATHER = "context_gather" +STAGE_CONTEXT_SUMMARY_AGENT = "context_summary_agent" +STAGE_REPO_AGENT = "repo_agent" +STAGE_PERSON_AGENT = "person_agent" +STAGE_ORG_AGENT = "org_agent" +STAGE_ARTICLE_AGENT = "article_agent" +STAGE_MEMBERSHIP_AGENT = "membership_agent" +STAGE_CONTRIBUTION_AGENT = "contribution_agent" +STAGE_PERSON_AGENTS = "person_agents" +STAGE_REPO_AGENTS = "repo_agents" +STAGE_ORG_AGENTS = "org_agents" +STAGE_ARTICLE_AGENTS = "article_agents" +STAGE_MEMBERSHIP_AGENTS = "membership_agents" +STAGE_CONTRIBUTION_AGENTS = "contribution_agents" +STAGE_AGENTS = "agents" +GITHUB_LOGIN_PATTERN = re.compile(r"^[A-Za-z\d](?:[A-Za-z\d]|-(?=[A-Za-z\d])){0,38}$") +VALIDATION_WARNING_PREFIX = "Validation warning at" +COMPILED_CONTEXT_PROMPT_BLOCK_HEADER = "## Compiled Source Summary" + +PLAN_BY_TYPE: dict[str, list[str]] = { + "repository": [ + STAGE_CONTEXT_GATHER, + STAGE_REPO_AGENT, + STAGE_PERSON_AGENTS, + STAGE_ORG_AGENTS, + STAGE_ARTICLE_AGENTS, + STAGE_MEMBERSHIP_AGENTS, + STAGE_CONTRIBUTION_AGENTS, + ], + "user": [ + STAGE_CONTEXT_GATHER, + STAGE_PERSON_AGENT, + STAGE_REPO_AGENTS, + STAGE_ORG_AGENTS, + STAGE_ARTICLE_AGENTS, + STAGE_MEMBERSHIP_AGENTS, + STAGE_CONTRIBUTION_AGENTS, + ], + "organization": [ + STAGE_CONTEXT_GATHER, + STAGE_ORG_AGENT, + STAGE_PERSON_AGENTS, + STAGE_REPO_AGENTS, + STAGE_ARTICLE_AGENTS, + STAGE_MEMBERSHIP_AGENTS, + STAGE_CONTRIBUTION_AGENTS, + ], +} +ROOT_STAGE_BY_DETECTED_TYPE: dict[str, str] = { + "repository": STAGE_REPO_AGENT, + "user": STAGE_PERSON_AGENT, + "organization": STAGE_ORG_AGENT, +} + + +@dataclass(slots=True) +class _StageWorkItem: + result_key: str + runner_key: str + context: dict[str, Any] + + +async def _maybe_await(value: Any) -> Any: + if hasattr(value, "__await__"): + return await value + return value + + +def _append_unique(items: list[str], message: str) -> None: + if message and message not in items: + items.append(message) + + +def _deduplicate(values: list[str]) -> list[str]: + deduplicated: list[str] = [] + seen: set[str] = set() + for value in values: + if not value or value in seen: + continue + deduplicated.append(value) + seen.add(value) + return deduplicated + + +def _to_detected_type(value: Any) -> str: + if hasattr(value, "value"): + return str(value.value) + return str(value) + + +def _is_valid_github_login(candidate: Any) -> bool: + return isinstance(candidate, str) and bool( + GITHUB_LOGIN_PATTERN.fullmatch(candidate.strip()), + ) + + +def _strip_raw_repository_material(repository_context: dict[str, Any]) -> None: + repository_context.pop("readme_content", None) + repository_context.pop("gimie_jsonld", None) + repository_context.pop("repository_files", None) + repository_context.pop("files", None) + + +def _compiled_context_prompt_appendix( + *, + existing_appendix: str | None, + summary_markdown: str | None, +) -> str | None: + summary = summary_markdown.strip() if isinstance(summary_markdown, str) else "" + if not summary: + return existing_appendix + + summary_block = f"{COMPILED_CONTEXT_PROMPT_BLOCK_HEADER}\n{summary}" + if isinstance(existing_appendix, str) and existing_appendix.strip(): + return f"{existing_appendix}\n\n{summary_block}" + return summary_block + + +class PipelineOrchestrator: + """Coordinates stage execution for repository, user, and organization flows.""" + + def __init__( # noqa: PLR0913 + self, + *, + context_gatherer: ContextGatherer = gather_context, + repository_agent: RepositoryAgentV2 | None = None, + person_agent: PersonAgentV2 | None = None, + organization_agent: OrganizationAgentV2 | None = None, + article_agent: ArticleAgentV2 | None = None, + membership_agent: MembershipAgentV2 | None = None, + contribution_agent: ContributionAgentV2 | None = None, + llm_repository_agent: RuntimeAgent | None = None, + llm_person_agent: RuntimeAgent | None = None, + llm_organization_agent: RuntimeAgent | None = None, + llm_context_summary_agent: RuntimeAgent | None = None, + llm_article_agent: RuntimeAgent | None = None, + llm_membership_agent: RuntimeAgent | None = None, + llm_contribution_agent: RuntimeAgent | None = None, + agent_registry: AgentRuntimeRegistry | None = None, + agent_runners: dict[str, AgentRunner] | None = None, + retry_max_retries: int = 3, + retry_backoff_base: float = 0.0, + retry_sleep_func: SleepCallable | None = None, + max_concurrent_agents: int = 3, + include_upstream_stage_outputs_in_prompt: bool = True, + user_prompt_appendix: str | None = None, + cache: ProviderCache | None = None, + ) -> None: + """Initialize stage runners and runtime-aware routing infrastructure.""" + + self._context_gatherer = context_gatherer + self._repository_agent = repository_agent or RepositoryAgentV2() + self._person_agent = person_agent or PersonAgentV2() + self._organization_agent = organization_agent or OrganizationAgentV2() + self._article_agent = article_agent or ArticleAgentV2() + self._membership_agent = membership_agent or MembershipAgentV2() + self._contribution_agent = contribution_agent or ContributionAgentV2() + self._llm_repository_agent = llm_repository_agent or LLMRepositoryAgentV2(cache=cache) + self._llm_person_agent = llm_person_agent or LLMPersonAgentV2(cache=cache) + self._llm_organization_agent = ( + llm_organization_agent or LLMOrganizationAgentV2(cache=cache) + ) + self._llm_context_summary_agent = ( + llm_context_summary_agent or LLMContextSummaryAgentV2(cache=cache) + ) + self._llm_article_agent = llm_article_agent or LLMArticleAgentV2(cache=cache) + self._llm_membership_agent = llm_membership_agent or LLMMembershipAgentV2(cache=cache) + self._llm_contribution_agent = ( + llm_contribution_agent or LLMContributionAgentV2(cache=cache) + ) + + self._rule_based_runners: dict[str, AgentRunner] = { + STAGE_REPO_AGENT: self._repository_agent.run, + STAGE_PERSON_AGENT: self._person_agent.run, + STAGE_ORG_AGENT: self._organization_agent.run, + STAGE_ARTICLE_AGENT: self._article_agent.run, + STAGE_MEMBERSHIP_AGENT: self._membership_agent.run, + STAGE_CONTRIBUTION_AGENT: self._contribution_agent.run, + } + if agent_runners: + self._rule_based_runners.update(agent_runners) + + self._agent_registry = agent_registry or AgentRuntimeRegistry( + rule_based_runners=self._rule_based_runners, + llm_repository_agent=self._llm_repository_agent, + llm_person_agent=self._llm_person_agent, + llm_organization_agent=self._llm_organization_agent, + llm_article_agent=self._llm_article_agent, + llm_membership_agent=self._llm_membership_agent, + llm_contribution_agent=self._llm_contribution_agent, + ) + if agent_registry and agent_runners: + for stage_key, runner in agent_runners.items(): + self._agent_registry.register_rule_runner(stage_key, runner) + + self._retry_max_retries = retry_max_retries + self._retry_backoff_base = retry_backoff_base + self._retry_sleep_func = retry_sleep_func + self._max_concurrent_agents = max_concurrent_agents + self._include_upstream_stage_outputs_in_prompt = ( + include_upstream_stage_outputs_in_prompt + ) + self._user_prompt_appendix = user_prompt_appendix + + @property + def max_concurrent_agents(self) -> int: + return self._max_concurrent_agents + + def get_execution_plan(self, detected_type: str) -> ExecutionPlan: + normalized_type = _to_detected_type(detected_type) + stage_names = PLAN_BY_TYPE.get(normalized_type) + if stage_names is None: + raise ValueError + + stages: list[Stage] = [] + for index, stage_name in enumerate(stage_names): + depends_on = [stage_names[index - 1]] if index > 0 else [] + stages.append( + Stage( + name=stage_name, + groups=[self._build_group(stage_name)], + depends_on=depends_on, + ), + ) + + plan = ExecutionPlan(detected_type=normalized_type, stages=stages) + if plan.has_circular_dependencies(): + raise ValueError + return plan + + async def execute( # noqa: C901, PLR0915 + self, + plan: ExecutionPlan, + providers: ProviderSet, + context: dict[str, Any], + ) -> PipelineResult: + """Execute an orchestration plan and aggregate stage outputs.""" + + started_at = perf_counter() + stages_completed: list[str] = [] + warnings: list[str] = [] + errors: list[str] = [] + agent_results: dict[str, AgentResult] = {} + + runtime_context = dict(context) + run_id = runtime_context.get("run_id") if isinstance(runtime_context.get("run_id"), str) else None + runtime_context["run_id"] = run_id + pipeline_outputs: dict[str, dict[str, Any]] = {} + pipeline_agent_results: dict[str, AgentResult] = {} + runtime_context["pipeline_agent_results"] = dict(pipeline_agent_results) + + for stage in plan.stages: + if stage.name == STAGE_CONTEXT_GATHER: + stage_started_at = perf_counter() + url_info = self._require_url_info(runtime_context) + context_bundle = await _maybe_await( + self._context_gatherer(plan.detected_type, url_info, providers), + ) + runtime_context["context_bundle"] = context_bundle + runtime_context["gathered_context"] = context_bundle.context + runtime_context["pipeline_outputs"] = dict(pipeline_outputs) + for warning in context_bundle.warnings: + _append_unique(warnings, warning) + logger.info( + "%s: warnings=%d context_keys=%s in %.2fs", + STAGE_CONTEXT_GATHER, + len(context_bundle.warnings), + sorted(context_bundle.context.keys()), + perf_counter() - stage_started_at, + ) + + runtime_mode = parse_agent_runtime( + runtime_context.get("agent_runtime"), + default=AgentRuntime.RULE_BASED, + field_name="agent_runtime", + ) + if runtime_mode == AgentRuntime.LLM: + summary_context = { + "detected_type": plan.detected_type, + "source_url": runtime_context.get("source_url"), + "gathered_context": deepcopy(context_bundle.context), + } + try: + summary_result = await with_retry( + self._llm_context_summary_agent.run, + context=summary_context, + providers=providers, + max_retries=self._retry_max_retries, + backoff_base=self._retry_backoff_base, + sleep_func=self._retry_sleep_func, + invalid_result_checker=None, + ) + except Exception as exc: # noqa: BLE001 + _append_unique( + warnings, + ( + "context_summary_agent execution failed; " + f"continuing without compiled summary: {exc}" + ), + ) + else: + for warning in summary_result.warnings: + _append_unique( + warnings, + f"{STAGE_CONTEXT_SUMMARY_AGENT}: {warning}", + ) + if summary_result.is_partial and summary_result.failure_reason: + _append_unique( + warnings, + ( + "context_summary_agent returned partial output: " + f"{summary_result.failure_reason}" + ), + ) + compiled_summary = summary_result.data.get("summary_markdown") + if isinstance(compiled_summary, str) and compiled_summary.strip(): + runtime_context["compiled_context_markdown"] = compiled_summary + gathered_context = runtime_context.get("gathered_context") + if isinstance(gathered_context, dict): + gathered_context["compiled_context_markdown"] = compiled_summary + runtime_context["gathered_context"] = gathered_context + else: + _append_unique( + warnings, + ( + "context_summary_agent returned an empty summary; " + "downstream LLM agents will use default prompt context only" + ), + ) + stages_completed.append(stage.name) + continue + + work_items = self._build_work_items(stage.name, plan.detected_type, runtime_context) + if stage.name == STAGE_PERSON_AGENTS and work_items: + work_items, pre_stage_warnings = self._filter_person_work_items( + work_items, + providers, + ) + for warning in pre_stage_warnings: + _append_unique(warnings, warning) + elif stage.name == STAGE_ORG_AGENTS and work_items: + work_items, pre_stage_warnings = self._filter_org_work_items( + work_items, + providers, + ) + for warning in pre_stage_warnings: + _append_unique(warnings, warning) + logger.info( + "[%s] starting — %d item(s)", + stage.name, + len(work_items), + ) + if not work_items: + logger.info("[%s] skipped — no items", stage.name) + stages_completed.append(stage.name) + continue + + stage_started_at = perf_counter() + stage_results, stage_warnings, stage_errors = await self._execute_stage( + stage_name=stage.name, + work_items=work_items, + runtime_context=runtime_context, + pipeline_outputs=pipeline_outputs, + providers=providers, + detected_type=plan.detected_type, + ) + logger.info( + "[%s] done — %d result(s), %d error(s) in %.1fs", + stage.name, + len(stage_results), + len(stage_errors), + perf_counter() - stage_started_at, + ) + + runtime_mode = parse_agent_runtime( + runtime_context.get("agent_runtime"), + default=AgentRuntime.RULE_BASED, + field_name="agent_runtime", + ) + if ( + runtime_mode == AgentRuntime.LLM + and stage.name == ROOT_STAGE_BY_DETECTED_TYPE.get(plan.detected_type) + and stage_errors + ): + # LLM mode intentionally hard-fails root-stage errors without fallback. + detected_label = { + "repository": "repository", + "user": "user", + "organization": "organization", + }.get(plan.detected_type, plan.detected_type) + message = ( + f"LLM {detected_label} runtime failed without fallback: " + f"{stage_errors[0]}" + ) + raise RuntimeError(message) + + for key, value in stage_results.items(): + agent_results[key] = value + pipeline_outputs[key] = value.data + pipeline_agent_results[key] = value + runtime_context["pipeline_outputs"] = dict(pipeline_outputs) + runtime_context["pipeline_agent_results"] = dict(pipeline_agent_results) + + for warning in stage_warnings: + _append_unique(warnings, warning) + for error in stage_errors: + _append_unique(errors, error) + + stages_completed.append(stage.name) + + duration_ms = int((perf_counter() - started_at) * 1000) + return PipelineResult( + stages_completed=stages_completed, + agent_results=agent_results, + gathered_context=deepcopy(runtime_context.get("gathered_context", {})) + if isinstance(runtime_context.get("gathered_context"), dict) + else {}, + warnings=warnings, + errors=errors, + duration_ms=duration_ms, + ) + + @staticmethod + def _build_group(stage_name: str) -> AgentGroup: # noqa: PLR0911 + if stage_name in { + STAGE_CONTEXT_GATHER, + STAGE_REPO_AGENT, + STAGE_PERSON_AGENT, + STAGE_ORG_AGENT, + }: + return AgentGroup( + name=stage_name, + agent_keys=[stage_name], + parallelizable=False, + ) + if stage_name == STAGE_PERSON_AGENTS: + return AgentGroup( + name=stage_name, + agent_keys=[STAGE_PERSON_AGENT], + parallelizable=True, + ) + if stage_name == STAGE_REPO_AGENTS: + return AgentGroup( + name=stage_name, + agent_keys=[STAGE_REPO_AGENT], + parallelizable=True, + ) + if stage_name == STAGE_ORG_AGENTS: + return AgentGroup( + name=stage_name, + agent_keys=[STAGE_ORG_AGENT], + parallelizable=True, + ) + if stage_name == STAGE_ARTICLE_AGENTS: + return AgentGroup( + name=stage_name, + agent_keys=[STAGE_ARTICLE_AGENT], + parallelizable=True, + ) + if stage_name == STAGE_MEMBERSHIP_AGENTS: + return AgentGroup( + name=stage_name, + agent_keys=[STAGE_MEMBERSHIP_AGENT], + parallelizable=True, + ) + if stage_name == STAGE_CONTRIBUTION_AGENTS: + return AgentGroup( + name=stage_name, + agent_keys=[STAGE_CONTRIBUTION_AGENT], + parallelizable=True, + ) + return AgentGroup(name=stage_name, agent_keys=[], parallelizable=False) + + @staticmethod + def _require_url_info(context: dict[str, Any]) -> GitHubURLClassification: + url_info = context.get("url_info") + if isinstance(url_info, GitHubURLClassification): + return url_info + raise ValueError + + @staticmethod + def _strip_raw_material_for_llm(item_context: dict[str, Any]) -> None: + repository_context = item_context.get("repository_context") + if isinstance(repository_context, dict): + _strip_raw_repository_material(repository_context) + + user_context = item_context.get("user_context") + if isinstance(user_context, dict): + repository_contexts = user_context.get("repository_contexts") + if isinstance(repository_contexts, dict): + for context in repository_contexts.values(): + if isinstance(context, dict): + _strip_raw_repository_material(context) + + organization_context = item_context.get("organization_context") + if isinstance(organization_context, dict): + repository_contexts = organization_context.get("repository_contexts") + if isinstance(repository_contexts, dict): + for context in repository_contexts.values(): + if isinstance(context, dict): + _strip_raw_repository_material(context) + + async def _execute_stage( # noqa: PLR0913 + self, + *, + stage_name: str, + work_items: list[_StageWorkItem], + runtime_context: dict[str, Any], + pipeline_outputs: dict[str, dict[str, Any]], + providers: ProviderSet, + detected_type: str, + ) -> tuple[dict[str, AgentResult], list[str], list[str]]: + """Run all work items for a stage and collect result envelopes.""" + + stage_warnings: list[str] = [] + stage_errors: list[str] = [] + stage_results: dict[str, AgentResult] = {} + + total_items = len(work_items) + done_counter = [0] + semaphore = asyncio.Semaphore(self._max_concurrent_agents) + + async def _heartbeat(key: str, started_at: float) -> None: + try: + while True: + await asyncio.sleep(_HEARTBEAT_INTERVAL_SECONDS) + elapsed = perf_counter() - started_at + logger.info( + "[%s] %s — still running (elapsed=%.0fs)", + stage_name, + key, + elapsed, + ) + except asyncio.CancelledError: + return + + async def _run_item( + index_one_based: int, + work_item: _StageWorkItem, + ) -> tuple[str, AgentResult]: + runtime_mode = parse_agent_runtime( + runtime_context.get("agent_runtime"), + default=AgentRuntime.RULE_BASED, + field_name="agent_runtime", + ) + # Select runner by runtime and stage. + runner = self._agent_registry.resolve_runner( + stage_key=work_item.runner_key, + runtime=runtime_mode, + detected_type=detected_type, + ) + + item_context = { + **runtime_context, + **work_item.context, + "stage_name": stage_name, + "pipeline_outputs": dict(pipeline_outputs), + } + if runtime_mode == AgentRuntime.LLM: + self._strip_raw_material_for_llm(item_context) + + include_upstream_outputs = runtime_context.get( + "include_upstream_stage_outputs_in_prompt", + ) + if not isinstance(include_upstream_outputs, bool): + include_upstream_outputs = self._include_upstream_stage_outputs_in_prompt + if include_upstream_outputs and pipeline_outputs: + item_context[UPSTREAM_STAGE_OUTPUTS_JSON_CONTEXT_KEY] = json.dumps( + pipeline_outputs, + ensure_ascii=True, + sort_keys=True, + ) + + prompt_appendix = runtime_context.get("user_prompt_appendix") + if not isinstance(prompt_appendix, str): + prompt_appendix = self._user_prompt_appendix + if runtime_mode == AgentRuntime.LLM: + prompt_appendix = _compiled_context_prompt_appendix( + existing_appendix=prompt_appendix, + summary_markdown=runtime_context.get("compiled_context_markdown"), + ) + if isinstance(prompt_appendix, str) and prompt_appendix.strip(): + item_context[USER_PROMPT_APPENDIX_CONTEXT_KEY] = prompt_appendix + + async def _run_with_retry( + agent_context: dict[str, Any], + agent_providers: ProviderSet, + ) -> AgentResult: + invalid_result_checker = self._invalid_result_checker_for_runner( + work_item.runner_key, + ) + return await with_retry( + runner, + context=agent_context, + providers=agent_providers, + max_retries=self._retry_max_retries, + backoff_base=self._retry_backoff_base, + sleep_func=self._retry_sleep_func, + invalid_result_checker=invalid_result_checker, + ) + + async with semaphore: + item_started_at = perf_counter() + logger.info( + "[%s] (%d/%d) %s — started", + stage_name, + index_one_based, + total_items, + work_item.result_key, + ) + heartbeat_task = asyncio.create_task( + _heartbeat(work_item.result_key, item_started_at), + ) + try: + result = await _maybe_await( + _run_with_retry(item_context, providers), + ) + finally: + heartbeat_task.cancel() + with contextlib.suppress(asyncio.CancelledError): + await heartbeat_task + done_counter[0] += 1 + logger.info( + "[%s] (%d/%d done) %s — finished in %.1fs", + stage_name, + done_counter[0], + total_items, + work_item.result_key, + perf_counter() - item_started_at, + ) + return work_item.result_key, result + + gathered = await asyncio.gather( + *( + _run_item(index + 1, item) + for index, item in enumerate(work_items) + ), + return_exceptions=True, + ) + + for index, result in enumerate(gathered): + work_item = work_items[index] + if isinstance(result, BaseException): + message = f"{work_item.result_key} execution failed: {result}" + _append_unique(stage_errors, message) + stage_results[work_item.result_key] = AgentResult( + data={}, + warnings=[message], + raw_output={}, + is_partial=True, + failure_reason=str(result), + stats={"attempts": 1, "retry_count": 0, "backoff_schedule": []}, + ) + continue + + result_key, agent_result = result + stage_results[result_key] = agent_result + for warning in agent_result.warnings: + _append_unique(stage_warnings, f"{result_key}: {warning}") + if agent_result.is_partial and agent_result.failure_reason: + _append_unique( + stage_errors, + f"{result_key}: {agent_result.failure_reason}", + ) + + return stage_results, stage_warnings, stage_errors + + @staticmethod + def _invalid_result_checker_for_runner( + runner_key: str, + ) -> Callable[[AgentResult], str | None] | None: + if runner_key not in { + STAGE_ARTICLE_AGENT, + STAGE_MEMBERSHIP_AGENT, + STAGE_CONTRIBUTION_AGENT, + }: + return None + + def _class_stage_checker(result: AgentResult) -> str | None: + if result.is_partial: + return result.failure_reason or "Agent returned partial result" + + for warning in result.warnings: + if warning.startswith(VALIDATION_WARNING_PREFIX): + return "Agent output did not satisfy permissive validation" + + if not isinstance(result.data, dict): + return "Agent returned invalid data payload" + + # Empty payloads are valid for class-stage fanout agents. + return None + + return _class_stage_checker + + def _build_work_items( # noqa: PLR0911 + self, + stage_name: str, + detected_type: str, + runtime_context: dict[str, Any], + ) -> list[_StageWorkItem]: + normalized_type = _to_detected_type(detected_type) + if stage_name == STAGE_REPO_AGENT and normalized_type == "repository": + root_context = self._repository_root_context(runtime_context) + root_context["agent_is_root"] = True + return [ + _StageWorkItem( + result_key=STAGE_REPO_AGENT, + runner_key=STAGE_REPO_AGENT, + context=root_context, + ), + ] + + if stage_name == STAGE_PERSON_AGENT and normalized_type == "user": + root_context = self._person_root_context(runtime_context) + root_context["agent_is_root"] = True + return [ + _StageWorkItem( + result_key=STAGE_PERSON_AGENT, + runner_key=STAGE_PERSON_AGENT, + context=root_context, + ), + ] + + if stage_name == STAGE_ORG_AGENT and normalized_type == "organization": + root_context = self._organization_root_context(runtime_context) + root_context["agent_is_root"] = True + return [ + _StageWorkItem( + result_key=STAGE_ORG_AGENT, + runner_key=STAGE_ORG_AGENT, + context=root_context, + ), + ] + + if stage_name == STAGE_PERSON_AGENTS: + person_contexts = self._person_fanout_contexts(runtime_context, normalized_type) + return [ + _StageWorkItem( + result_key=f"{STAGE_PERSON_AGENT}:{context['username']}", + runner_key=STAGE_PERSON_AGENT, + context=context, + ) + for context in person_contexts + ] + + if stage_name == STAGE_REPO_AGENTS: + repo_contexts = self._repository_fanout_contexts(runtime_context, normalized_type) + return [ + _StageWorkItem( + result_key=f"{STAGE_REPO_AGENT}:{context['full_name']}", + runner_key=STAGE_REPO_AGENT, + context=context, + ) + for context in repo_contexts + ] + + if stage_name == STAGE_ORG_AGENTS: + org_contexts = self._organization_fanout_contexts(runtime_context, normalized_type) + return [ + _StageWorkItem( + result_key=f"{STAGE_ORG_AGENT}:{context['org_name']}", + runner_key=STAGE_ORG_AGENT, + context=context, + ) + for context in org_contexts + ] + + if stage_name == STAGE_ARTICLE_AGENTS: + article_contexts = self._article_fanout_contexts(runtime_context, normalized_type) + return [ + _StageWorkItem( + result_key=f"{STAGE_ARTICLE_AGENT}:{context['article_seed']}", + runner_key=STAGE_ARTICLE_AGENT, + context=context, + ) + for context in article_contexts + ] + + if stage_name == STAGE_MEMBERSHIP_AGENTS: + membership_contexts = self._membership_fanout_contexts( + runtime_context, + normalized_type, + ) + return [ + _StageWorkItem( + result_key=f"{STAGE_MEMBERSHIP_AGENT}:{context['membership_seed']}", + runner_key=STAGE_MEMBERSHIP_AGENT, + context=context, + ) + for context in membership_contexts + ] + + if stage_name == STAGE_CONTRIBUTION_AGENTS: + contribution_contexts = self._contribution_fanout_contexts( + runtime_context, + normalized_type, + ) + return [ + _StageWorkItem( + result_key=( + f"{STAGE_CONTRIBUTION_AGENT}:" + f"{context['target_person']['id']}_" + f"{context['contribution_seed']}" + ), + runner_key=STAGE_CONTRIBUTION_AGENT, + context=context, + ) + for context in contribution_contexts + ] + + return [] + + @staticmethod + def _filter_person_work_items( + work_items: list[_StageWorkItem], + providers: ProviderSet, + ) -> tuple[list[_StageWorkItem], list[str]]: + filtered_items: list[_StageWorkItem] = [] + warnings: list[str] = [] + account_type_by_username: dict[str, str | None] = {} + + for work_item in work_items: + username = work_item.context.get("username") + if not isinstance(username, str) or not username: + filtered_items.append(work_item) + continue + + account_type_hint = work_item.context.get("account_type_hint") + if isinstance(account_type_hint, str) and account_type_hint.lower() == "organization": + _append_unique( + warnings, + ( + "Skipping person fanout for GitHub organization account: " + f"{username}" + ), + ) + continue + + if username not in account_type_by_username: + account_type: str | None = None + try: + github_user = providers.github.get_user(username) + except Exception: # noqa: BLE001 + github_user = {} + if isinstance(github_user, dict): + raw_type = github_user.get("type") or github_user.get( + "account_type", + ) + if isinstance(raw_type, str) and raw_type: + account_type = raw_type.lower() + account_type_by_username[username] = account_type + + if account_type_by_username.get(username) == "organization": + _append_unique( + warnings, + ( + "Skipping person fanout for GitHub organization account: " + f"{username}" + ), + ) + continue + + filtered_items.append(work_item) + + return filtered_items, warnings + + @staticmethod + def _filter_org_work_items( + work_items: list[_StageWorkItem], + providers: ProviderSet, + ) -> tuple[list[_StageWorkItem], list[str]]: + """Drop work items whose `org_name` is a GitHub User account. + + Mirrors `_filter_person_work_items`: probes `GET /users/{login}` + (cached) once per handle and skips when `type == "user"`. This + prevents 4× retry loops in `org_agent` for personal handles + encoded in `org:hasMembership` composite ids. + + 404s and other lookup failures are passed through — the + downstream `org_agent` will exhaust its retries the same way it + does today, but only once per truly-unknown handle (not per + every personal account). + """ + filtered_items: list[_StageWorkItem] = [] + warnings: list[str] = [] + account_type_by_handle: dict[str, str | None] = {} + + for work_item in work_items: + org_name = work_item.context.get("org_name") + if not isinstance(org_name, str) or not org_name: + filtered_items.append(work_item) + continue + + if org_name not in account_type_by_handle: + account_type: str | None = None + if providers.github is not None: + try: + github_user = providers.github.get_user(org_name) + except Exception: # noqa: BLE001 + github_user = {} + if isinstance(github_user, dict): + # GitHub REST returns the field as `type`; v1's + # `GitHubUserMetadata` exposes it as `account_type`. + # Accept either to stay robust. + raw_type = github_user.get("type") or github_user.get( + "account_type", + ) + if isinstance(raw_type, str) and raw_type: + account_type = raw_type.lower() + account_type_by_handle[org_name] = account_type + + if account_type_by_handle.get(org_name) == "user": + _append_unique( + warnings, + ( + "Skipping org fanout for GitHub user account: " + f"{org_name}" + ), + ) + continue + + filtered_items.append(work_item) + + return filtered_items, warnings + + def _repository_root_context(self, runtime_context: dict[str, Any]) -> dict[str, Any]: + bundle = runtime_context.get("context_bundle") + repository_context = ( + bundle.context.get("repository", {}) + if isinstance(bundle, ContextBundle) + else {} + ) + full_name = repository_context.get("full_name") + if not isinstance(full_name, str) or "/" not in full_name: + url_info = self._require_url_info(runtime_context) + full_name = f"{url_info.owner}/{url_info.repo or ''}".rstrip("/") + return { + "full_name": full_name, + "source_url": runtime_context.get("source_url"), + "repository_context": repository_context, + } + + def _person_root_context(self, runtime_context: dict[str, Any]) -> dict[str, Any]: + bundle = runtime_context.get("context_bundle") + user_context = bundle.context.get("user", {}) if isinstance(bundle, ContextBundle) else {} + return { + "username": user_context.get("username") or self._require_url_info(runtime_context).owner, + "source_url": runtime_context.get("source_url"), + "user_context": user_context, + } + + def _organization_root_context(self, runtime_context: dict[str, Any]) -> dict[str, Any]: + bundle = runtime_context.get("context_bundle") + organization_context = ( + bundle.context.get("organization", {}) + if isinstance(bundle, ContextBundle) + else {} + ) + return { + "org_name": organization_context.get("org_name") or self._require_url_info(runtime_context).owner, + "source_url": runtime_context.get("source_url"), + "organization_context": organization_context, + } + + def _repository_source_full_name(self, runtime_context: dict[str, Any]) -> str | None: + bundle = runtime_context.get("context_bundle") + repository_context = ( + bundle.context.get("repository", {}) + if isinstance(bundle, ContextBundle) + else {} + ) + full_name = repository_context.get("full_name") + if isinstance(full_name, str) and "/" in full_name: + return full_name + + url_info = self._require_url_info(runtime_context) + if isinstance(url_info.repo, str) and url_info.repo: + return f"{url_info.owner}/{url_info.repo}" + return None + + def _repository_source_repositories( + self, + runtime_context: dict[str, Any], + ) -> list[str]: + full_name = self._repository_source_full_name(runtime_context) + return [full_name] if isinstance(full_name, str) and full_name else [] + + def _person_fanout_contexts( # noqa: C901, PLR0912 + self, + runtime_context: dict[str, Any], + detected_type: str, + ) -> list[dict[str, Any]]: + bundle = runtime_context.get("context_bundle") + if not isinstance(bundle, ContextBundle): + return [] + + usernames: list[str] = [] + account_type_hint_by_username: dict[str, str] = {} + if detected_type == "repository": + repository_context = bundle.context.get("repository", {}) + contributors = repository_context.get("contributors", []) + metadata = repository_context.get("metadata") + owner = metadata.get("owner") if isinstance(metadata, dict) else None + owner_login = owner.get("login") if isinstance(owner, dict) else None + owner_type = owner.get("type") if isinstance(owner, dict) else None + owner_is_org = isinstance(owner_type, str) and owner_type.lower() == "organization" + normalized_owner_login = ( + owner_login.strip().casefold() + if isinstance(owner_login, str) and owner_login.strip() + else None + ) + + if isinstance(contributors, list): + for contributor in contributors: + login: str | None = None + account_type_hint: str | None = None + if isinstance(contributor, dict): + login = contributor.get("login") + contributor_type = contributor.get("type") + if isinstance(contributor_type, str) and contributor_type: + account_type_hint = contributor_type.lower() + elif isinstance(contributor, str) and contributor: + login = contributor + + if not _is_valid_github_login(login): + continue + normalized_login = str(login).strip() + normalized_login_casefold = normalized_login.casefold() + + if ( + owner_is_org + and isinstance(normalized_owner_login, str) + and normalized_login_casefold == normalized_owner_login + ): + continue + + usernames.append(normalized_login) + if account_type_hint and normalized_login not in account_type_hint_by_username: + account_type_hint_by_username[normalized_login] = account_type_hint + + # Materialize a User-account owner as a Person when missing + # from `contributors` (abandoned repos, empty repos, repos + # whose owner never committed). Without this, the owner + # surfaces as a bare-string ref in `pulse:ownedBy` / + # `schema:author` and triggers SHACL violations because no + # `schema:Person` entity exists at the inferred id. + # `_filter_person_work_items` re-checks the type via + # `get_user`, so an Organization owner that slipped past + # the `owner_is_org` heuristic still gets pruned later. + if ( + not owner_is_org + and isinstance(owner_login, str) + and _is_valid_github_login(owner_login) + ): + normalized_owner = owner_login.strip() + already_present = any( + existing.casefold() == normalized_owner.casefold() + for existing in usernames + ) + if not already_present: + usernames.append(normalized_owner) + if isinstance(owner_type, str) and owner_type: + account_type_hint_by_username.setdefault( + normalized_owner, owner_type.lower(), + ) + if detected_type == "organization": + organization_context = bundle.context.get("organization", {}) + members = organization_context.get("members", []) + if isinstance(members, list): + usernames.extend( + [member for member in members if isinstance(member, str) and member], + ) + + source_repositories = ( + self._repository_source_repositories(runtime_context) + if detected_type == "repository" + else [] + ) + + # source_repositories drives `pulse:owns` in the rule-based person agent. + # Only the User-account owner of the repo should claim ownership — every + # other contributor would be a false positive. Track the eligible login + # so we can scope the injection at fanout time. + owner_login_for_source_repos: str | None = None + if detected_type == "repository" and source_repositories: + owner_login_for_source_repos = ( + normalized_owner_login if not owner_is_org else None + ) + + # Forward repository context (README, metadata) so person agents can + # scan it for ORCID identifiers, affiliations, and author credits. + repository_context_for_person: dict[str, Any] | None = None + if detected_type == "repository" and isinstance(bundle, ContextBundle): + raw = bundle.context.get("repository") + if isinstance(raw, dict) and raw: + repository_context_for_person = raw + + contexts: list[dict[str, Any]] = [] + for username in _deduplicate(usernames): + context: dict[str, Any] = { + "username": username, + "source_url": runtime_context.get("source_url"), + } + account_type_hint = account_type_hint_by_username.get(username) + if account_type_hint: + context["account_type_hint"] = account_type_hint + if source_repositories and ( + owner_login_for_source_repos is not None + and username.casefold() == owner_login_for_source_repos + ): + context["source_repositories"] = list(source_repositories) + if repository_context_for_person is not None: + context["repository_context"] = repository_context_for_person + contexts.append(context) + return contexts + + def _repository_fanout_contexts( + self, + runtime_context: dict[str, Any], + detected_type: str, + ) -> list[dict[str, Any]]: + bundle = runtime_context.get("context_bundle") + if not isinstance(bundle, ContextBundle): + return [] + + owner = self._require_url_info(runtime_context).owner + full_names: list[str] = [] + repository_contexts_by_full_name: dict[str, dict[str, Any]] = {} + if detected_type == "user": + user_context = bundle.context.get("user", {}) + repos = user_context.get("owned_repos", []) + raw_repository_contexts = user_context.get("repository_contexts") + if isinstance(raw_repository_contexts, dict): + repository_contexts_by_full_name = { + str(key): value + for key, value in raw_repository_contexts.items() + if isinstance(key, str) and isinstance(value, dict) + } + if isinstance(repos, list): + for repo in repos: + if not isinstance(repo, str) or not repo: + continue + full_names.append(repo if "/" in repo else f"{owner}/{repo}") + if detected_type == "organization": + organization_context = bundle.context.get("organization", {}) + repos = organization_context.get("owned_repos", []) + raw_repository_contexts = organization_context.get("repository_contexts") + if isinstance(raw_repository_contexts, dict): + repository_contexts_by_full_name = { + str(key): value + for key, value in raw_repository_contexts.items() + if isinstance(key, str) and isinstance(value, dict) + } + if isinstance(repos, list): + for repo in repos: + if not isinstance(repo, str) or not repo: + continue + full_names.append(repo if "/" in repo else f"{owner}/{repo}") + + contexts: list[dict[str, Any]] = [] + for full_name in _deduplicate(full_names): + context: dict[str, Any] = { + "full_name": full_name, + "source_url": runtime_context.get("source_url"), + } + repository_context = repository_contexts_by_full_name.get(full_name) + if isinstance(repository_context, dict) and repository_context: + context["repository_context"] = deepcopy(repository_context) + contexts.append(context) + + return contexts + + def _organization_fanout_contexts( # noqa: C901, PLR0912 + self, + runtime_context: dict[str, Any], + detected_type: str, + ) -> list[dict[str, Any]]: + bundle = runtime_context.get("context_bundle") + if not isinstance(bundle, ContextBundle): + return [] + + source_url = runtime_context.get("source_url") + source_repositories = ( + self._repository_source_repositories(runtime_context) + if detected_type == "repository" + else [] + ) + repository_context_for_prompt: dict[str, Any] | None = None + if detected_type == "repository": + raw_repository_context = bundle.context.get("repository") + if isinstance(raw_repository_context, dict) and raw_repository_context: + repository_context_for_prompt = raw_repository_context + contexts_by_org_name: dict[str, dict[str, Any]] = {} + + def _set_context( + org_name: str, + *, + github_lookup_enabled: bool, + include_source_repositories: bool = False, + include_repository_context: bool = False, + ) -> None: + if not org_name: + return + existing_context = contexts_by_org_name.get(org_name) + if existing_context is not None: + existing_lookup = existing_context.get("github_lookup_enabled") + if github_lookup_enabled and existing_lookup is False: + existing_context["github_lookup_enabled"] = True + if include_source_repositories and source_repositories: + existing_context["source_repositories"] = list( + source_repositories, + ) + if ( + include_repository_context + and repository_context_for_prompt is not None + ): + existing_context["repository_context"] = ( + repository_context_for_prompt + ) + if ( + include_source_repositories + and source_repositories + and "source_repositories" not in existing_context + ): + existing_context["source_repositories"] = list(source_repositories) + if ( + include_repository_context + and repository_context_for_prompt is not None + and "repository_context" not in existing_context + ): + existing_context["repository_context"] = repository_context_for_prompt + return + + next_context: dict[str, Any] = { + "org_name": org_name, + "source_url": source_url, + "github_lookup_enabled": github_lookup_enabled, + } + if include_source_repositories and source_repositories: + next_context["source_repositories"] = list(source_repositories) + if include_repository_context and repository_context_for_prompt is not None: + next_context["repository_context"] = repository_context_for_prompt + contexts_by_org_name[org_name] = next_context + + if detected_type == "repository": + repository_context = bundle.context.get("repository", {}) + repository_metadata = repository_context.get("metadata", {}) + if isinstance(repository_metadata, dict): + owner = repository_metadata.get("owner") + if isinstance(owner, dict): + owner_login = owner.get("login") + owner_type = str(owner.get("type", "")).lower() + if isinstance(owner_login, str) and "organization" in owner_type: + _set_context( + owner_login, + github_lookup_enabled=True, + include_source_repositories=True, + include_repository_context=True, + ) + + if detected_type == "user": + user_context = bundle.context.get("user", {}) + profile = user_context.get("profile", {}) + if isinstance(profile, dict): + company = profile.get("company") + if isinstance(company, str) and company: + _set_context(company, github_lookup_enabled=True) + + pipeline_outputs = runtime_context.get("pipeline_outputs", {}) + if isinstance(pipeline_outputs, dict): + for result_key, payload in pipeline_outputs.items(): + if not str(result_key).startswith(STAGE_PERSON_AGENT): + continue + memberships = payload.get("org:hasMembership") if isinstance(payload, dict) else None + if not isinstance(memberships, list): + continue + for membership in memberships: + if not isinstance(membership, str) or "_" not in membership: + continue + _, organization = membership.split("_", maxsplit=1) + if organization: + _set_context( + organization, + github_lookup_enabled=detected_type != "repository", + include_source_repositories=detected_type == "repository", + include_repository_context=detected_type == "repository", + ) + + return list(contexts_by_org_name.values()) + + @staticmethod + def _collect_stage_payloads( + runtime_context: dict[str, Any], + stage_prefix: str, + ) -> list[dict[str, Any]]: + pipeline_outputs = runtime_context.get("pipeline_outputs") + if not isinstance(pipeline_outputs, dict): + return [] + + payloads: list[dict[str, Any]] = [] + for result_key in sorted(pipeline_outputs): + if not str(result_key).startswith(stage_prefix): + continue + payload = pipeline_outputs[result_key] + if isinstance(payload, dict) and payload: + payloads.append(payload) + return payloads + + @staticmethod + def _collect_stage_derivations( + runtime_context: dict[str, Any], + stage_prefix: str, + ) -> list[dict[str, Any]]: + pipeline_agent_results = runtime_context.get("pipeline_agent_results") + if not isinstance(pipeline_agent_results, dict): + return [] + + derivations: list[dict[str, Any]] = [] + for result_key in sorted(pipeline_agent_results): + if not str(result_key).startswith(stage_prefix): + continue + result = pipeline_agent_results[result_key] + if not isinstance(result, AgentResult): + continue + stats = result.stats + if not isinstance(stats, dict): + continue + derivation = stats.get("derivation") + if isinstance(derivation, dict): + derivations.append(derivation) + return derivations + + @staticmethod + def _typed_entity_bucket_snapshot( + runtime_context: dict[str, Any], + ) -> dict[str, list[dict[str, Any]]]: + buckets = TypedEntityBuckets() + pipeline_outputs = runtime_context.get("pipeline_outputs") + if not isinstance(pipeline_outputs, dict): + return buckets.to_dict() + + for result_key in sorted(pipeline_outputs): + payload = pipeline_outputs[result_key] + if not isinstance(payload, dict) or not payload: + continue + bucket_name = infer_entity_bucket(agent_key=str(result_key), data=payload) + if bucket_name is None: + continue + buckets.add(bucket_name, payload) + + return buckets.to_dict() + + def _class_agent_base_context( + self, + runtime_context: dict[str, Any], + detected_type: str, + ) -> dict[str, Any]: + known_persons = self._collect_stage_payloads(runtime_context, STAGE_PERSON_AGENT) + known_organizations = self._collect_stage_payloads(runtime_context, STAGE_ORG_AGENT) + known_repositories = self._collect_stage_payloads(runtime_context, STAGE_REPO_AGENT) + + base_context: dict[str, Any] = { + "detected_type": detected_type, + "source_url": runtime_context.get("source_url"), + "known_persons": deepcopy(known_persons), + "known_organizations": deepcopy(known_organizations), + "known_repositories": deepcopy(known_repositories), + "person_derivations": self._collect_stage_derivations( + runtime_context, + STAGE_PERSON_AGENT, + ), + "organization_derivations": self._collect_stage_derivations( + runtime_context, + STAGE_ORG_AGENT, + ), + "repository_derivations": self._collect_stage_derivations( + runtime_context, + STAGE_REPO_AGENT, + ), + "typed_entity_buckets": self._typed_entity_bucket_snapshot(runtime_context), + } + + if detected_type == "repository": + full_name = self._repository_source_full_name(runtime_context) + if isinstance(full_name, str) and full_name: + base_context["full_name"] = full_name + bundle = runtime_context.get("context_bundle") + if isinstance(bundle, ContextBundle): + repository_context = bundle.context.get("repository") + if isinstance(repository_context, dict): + base_context["repository_context"] = deepcopy(repository_context) + + if detected_type == "user": + base_context["username"] = self._require_url_info(runtime_context).owner + + if detected_type == "organization": + base_context["org_name"] = self._require_url_info(runtime_context).owner + + return base_context + + @staticmethod + def _person_derivations_by_id( + runtime_context: dict[str, Any], + ) -> dict[str, dict[str, Any]]: + derivations = PipelineOrchestrator._collect_stage_derivations( + runtime_context, + STAGE_PERSON_AGENT, + ) + by_id: dict[str, dict[str, Any]] = {} + for derivation in derivations: + person_id = derivation.get("person_id") + if isinstance(person_id, str) and person_id: + by_id[person_id] = derivation + return by_id + + @staticmethod + def _repository_derivations_by_id( + runtime_context: dict[str, Any], + ) -> dict[str, dict[str, Any]]: + derivations = PipelineOrchestrator._collect_stage_derivations( + runtime_context, + STAGE_REPO_AGENT, + ) + by_id: dict[str, dict[str, Any]] = {} + for derivation in derivations: + repository_id = derivation.get("repository_full_name") + if isinstance(repository_id, str) and repository_id: + by_id[repository_id] = derivation + return by_id + + @staticmethod + def _organization_derivations_by_id( + runtime_context: dict[str, Any], + ) -> dict[str, dict[str, Any]]: + derivations = PipelineOrchestrator._collect_stage_derivations( + runtime_context, + STAGE_ORG_AGENT, + ) + by_id: dict[str, dict[str, Any]] = {} + for derivation in derivations: + organization_id = derivation.get("organization_id") + if isinstance(organization_id, str) and organization_id: + by_id[organization_id] = derivation + return by_id + + @staticmethod + def _merge_person_with_derivation( + person: dict[str, Any], + derivation: dict[str, Any] | None, + ) -> dict[str, Any]: + merged = deepcopy(person) + if not isinstance(derivation, dict): + return merged + + affiliation_names = derivation.get("affiliation_names") + if isinstance(affiliation_names, list): + merged["affiliations"] = [ + value + for value in affiliation_names + if isinstance(value, str) and value + ] + + orcid_affiliations = derivation.get("orcid_affiliations") + if isinstance(orcid_affiliations, list): + merged["orcid_affiliations"] = [ + deepcopy(value) + for value in orcid_affiliations + if isinstance(value, dict) + ] + + source_repositories = derivation.get("source_repositories") + if isinstance(source_repositories, list): + merged["source_repositories"] = [ + value + for value in source_repositories + if isinstance(value, str) and value + ] + + return merged + + @staticmethod + def _merge_repository_with_derivation( + repository: dict[str, Any], + derivation: dict[str, Any] | None, + ) -> dict[str, Any]: + merged = deepcopy(repository) + if not isinstance(derivation, dict): + return merged + + contributors = derivation.get("contributors") + if isinstance(contributors, list): + merged["contributors"] = [ + deepcopy(contributor) + for contributor in contributors + if isinstance(contributor, (dict, str)) + ] + return merged + + @staticmethod + def _merge_organization_with_derivation( + organization: dict[str, Any], + derivation: dict[str, Any] | None, + ) -> dict[str, Any]: + merged = deepcopy(organization) + if not isinstance(derivation, dict): + return merged + + owned_repositories = derivation.get("owned_repositories") + if isinstance(owned_repositories, list): + merged["pulse:owns"] = [ + value + for value in owned_repositories + if isinstance(value, str) and value + ] + + parent_organization = derivation.get("parent_organization") + if isinstance(parent_organization, str) and parent_organization: + merged["org:unitOf"] = [parent_organization] + elif isinstance(parent_organization, list): + merged["org:unitOf"] = [ + value for value in parent_organization if isinstance(value, str) and value + ] + + unit_ids = derivation.get("unit_ids") + if isinstance(unit_ids, list): + merged["org:hasUnit"] = [ + value + for value in unit_ids + if isinstance(value, str) and value + ] + + return merged + + @staticmethod + def _organization_lookup_tokens(organization: dict[str, Any]) -> list[str]: + raw_tokens: list[str] = [] + for key in ( + "id", + "schema:name", + "schema:identifier", + "pulse:githubOrganizationHandle", + ): + value = organization.get(key) + if isinstance(value, str) and value: + raw_tokens.append(value) + + identifiers = organization.get("identifiers") + if isinstance(identifiers, dict): + for key in ( + "pulse:ror", + "pulse:infoscienceOrganizationIdentifier", + "pulse:githubOrganizationHandle", + "uuid", + ): + value = identifiers.get(key) + if isinstance(value, str) and value: + raw_tokens.append(value) + + aliases = organization.get("aliases") + if isinstance(aliases, list): + raw_tokens.extend(alias for alias in aliases if isinstance(alias, str) and alias) + + acronyms = organization.get("acronyms") + if isinstance(acronyms, list): + raw_tokens.extend(acronym for acronym in acronyms if isinstance(acronym, str) and acronym) + + normalized_tokens: list[str] = [] + seen: set[str] = set() + for token in raw_tokens: + normalized = normalize_string(token) + if not normalized or normalized in seen: + continue + normalized_tokens.append(normalized) + seen.add(normalized) + return normalized_tokens + + @staticmethod + def _extract_person_org_references(person: dict[str, Any]) -> list[str]: + references: list[str] = [] + + affiliations = person.get("affiliations") + if isinstance(affiliations, list): + for affiliation in affiliations: + if isinstance(affiliation, str) and affiliation: + references.append(affiliation) + elif isinstance(affiliation, dict): + organization_id = affiliation.get("organizationId") + if isinstance(organization_id, str) and organization_id: + references.append(organization_id) + name = affiliation.get("name") or affiliation.get("schema:name") + if isinstance(name, str) and name: + references.append(name) + + memberships = person.get("org:hasMembership") + if isinstance(memberships, list): + for membership in memberships: + if not isinstance(membership, str) or "_" not in membership: + continue + _, organization_reference = membership.split("_", maxsplit=1) + if organization_reference: + references.append(organization_reference) + + return references + + def _candidate_organizations_for_person( + self, + person: dict[str, Any], + known_organizations: list[dict[str, Any]], + ) -> list[dict[str, Any]]: + references = self._extract_person_org_references(person) + if not references: + return deepcopy(known_organizations) + + organizations_by_token: dict[str, dict[str, Any]] = {} + for organization in known_organizations: + for token in self._organization_lookup_tokens(organization): + organizations_by_token.setdefault(token, organization) + + matched: list[dict[str, Any]] = [] + matched_ids: set[str] = set() + for reference in references: + normalized = normalize_string(reference) + if not normalized: + continue + organization = organizations_by_token.get(normalized) + if not isinstance(organization, dict): + continue + organization_id = organization.get("id") + marker = organization_id if isinstance(organization_id, str) and organization_id else repr(organization) + if marker in matched_ids: + continue + matched.append(deepcopy(organization)) + matched_ids.add(marker) + + if matched: + return matched + return deepcopy(known_organizations) + + def _article_fanout_contexts( # noqa: C901 + self, + runtime_context: dict[str, Any], + detected_type: str, + ) -> list[dict[str, Any]]: + base_context = self._class_agent_base_context(runtime_context, detected_type) + + seeds: list[str] = [] + if detected_type == "repository": + full_name = base_context.get("full_name") + if isinstance(full_name, str) and full_name: + seeds.append(full_name) + if detected_type == "user": + username = base_context.get("username") + if isinstance(username, str) and username: + seeds.append(username) + if detected_type == "organization": + org_name = base_context.get("org_name") + if isinstance(org_name, str) and org_name: + seeds.append(org_name) + + deduplicated_seeds = _deduplicate(seeds) + contexts: list[dict[str, Any]] = [] + for seed in deduplicated_seeds: + context = deepcopy(base_context) + context["article_seed"] = seed + if detected_type == "repository": + context["full_name"] = seed + if detected_type == "user": + context["username"] = seed + if detected_type == "organization": + context["org_name"] = seed + contexts.append(context) + return contexts + + def _membership_fanout_contexts( + self, + runtime_context: dict[str, Any], + detected_type: str, + ) -> list[dict[str, Any]]: + base_context = self._class_agent_base_context(runtime_context, detected_type) + known_persons = base_context.get("known_persons") + known_organizations = base_context.get("known_organizations") + if not isinstance(known_persons, list) or not isinstance(known_organizations, list): + return [] + if not known_persons or not known_organizations: + return [] + + person_derivations = self._person_derivations_by_id(runtime_context) + organization_derivations = self._organization_derivations_by_id(runtime_context) + merged_known_organizations: list[dict[str, Any]] = [] + for organization in sorted( + [item for item in known_organizations if isinstance(item, dict)], + key=lambda item: str(item.get("id", "")), + ): + organization_id = organization.get("id") + merged_known_organizations.append( + self._merge_organization_with_derivation( + organization, + organization_derivations.get(organization_id) + if isinstance(organization_id, str) + else None, + ), + ) + + contexts: list[dict[str, Any]] = [] + seen_seeds: set[str] = set() + + for person in sorted( + [item for item in known_persons if isinstance(item, dict)], + key=lambda item: str(item.get("id", "")), + ): + person_id = person.get("id") + if not isinstance(person_id, str) or not person_id or person_id in seen_seeds: + continue + seen_seeds.add(person_id) + + merged_person = self._merge_person_with_derivation( + person, + person_derivations.get(person_id), + ) + context = deepcopy(base_context) + context["membership_seed"] = person_id + context["known_persons"] = [merged_person] + context["known_organizations"] = deepcopy(merged_known_organizations) + context["target_person"] = deepcopy(merged_person) + context["target_organizations"] = self._candidate_organizations_for_person( + merged_person, + merged_known_organizations, + ) + contexts.append(context) + + return contexts + + def _contribution_fanout_contexts( + self, + runtime_context: dict[str, Any], + detected_type: str, + ) -> list[dict[str, Any]]: + """One context per `(person, repository)` pair. + + Each pair yields exactly one work item, because the data model says a + person contributes to a repo exactly once. With multi-author repos + this means N people × M repos = N×M agent calls; the agent verdict + cache makes repeats cheap on re-runs. + """ + base_context = self._class_agent_base_context(runtime_context, detected_type) + known_persons = base_context.get("known_persons") + known_repositories = base_context.get("known_repositories") + if not isinstance(known_persons, list) or not isinstance(known_repositories, list): + return [] + if not known_persons or not known_repositories: + return [] + + person_derivations = self._person_derivations_by_id(runtime_context) + repository_derivations = self._repository_derivations_by_id(runtime_context) + contexts: list[dict[str, Any]] = [] + seen_pairs: set[tuple[str, str]] = set() + + sorted_repositories = sorted( + [item for item in known_repositories if isinstance(item, dict)], + key=lambda item: str( + item.get("id") + or item.get("pulse:githubRepositoryHandle") + or item.get("full_name") + or "", + ), + ) + sorted_persons = sorted( + [item for item in known_persons if isinstance(item, dict)], + key=lambda item: str(item.get("id", "")), + ) + + for repository in sorted_repositories: + repository_id = repository.get("id") + if not isinstance(repository_id, str) or not repository_id: + continue + merged_repository = self._merge_repository_with_derivation( + repository, + repository_derivations.get(repository_id), + ) + + for person in sorted_persons: + person_id = person.get("id") + if not isinstance(person_id, str) or not person_id: + continue + pair = (person_id, repository_id) + if pair in seen_pairs: + continue + seen_pairs.add(pair) + + merged_person = self._merge_person_with_derivation( + person, + person_derivations.get(person_id), + ) + context = deepcopy(base_context) + context["contribution_seed"] = repository_id + context["known_persons"] = [merged_person] + context["known_repositories"] = [merged_repository] + context["target_person"] = merged_person + context["target_repository"] = merged_repository + contexts.append(context) + + return contexts diff --git a/src/v2/pipeline/stages/__init__.py b/src/v2/pipeline/stages/__init__.py new file mode 100644 index 0000000..75c8e9c --- /dev/null +++ b/src/v2/pipeline/stages/__init__.py @@ -0,0 +1,101 @@ +"""Pipeline stages for the v2 extraction orchestrator.""" + +from src.v2.pipeline.stages.article_validation import validate_articles +from src.v2.pipeline.stages.author_validation import validate_author_classes +from src.v2.pipeline.stages.concept_tagging import ( + BACKEND_EPFL_GRAPH, + BACKEND_LLM, + BACKEND_WIKIPEDIA, + SUPPORTED_BACKENDS, + ConceptTaggingResult, + run_concept_tagging_stage, +) +from src.v2.pipeline.stages.concept_tagging import ( + is_enabled as concept_tagging_is_enabled, +) +from src.v2.pipeline.stages.concept_tagging import ( + resolve_backend as concept_tagging_resolve_backend, +) +from src.v2.pipeline.stages.context_gather import gather_context +from src.v2.pipeline.stages.jsonld_build import build_jsonld_output +from src.v2.pipeline.stages.link_veracity import ( + LinkVeracityStageResult, + apply_link_pruning_to_assembled_output, + collect_unique_http_link_contexts, + promote_failed_id_entities, + run_link_veracity_stage, +) +from src.v2.pipeline.stages.llm_critic import run_llm_critic_stage +from src.v2.pipeline.stages.llm_dedup import run_llm_dedup_stage +from src.v2.pipeline.stages.models import ( + AssembledOutput, + ContextBundle, + LLMCriticStageResult, + LLMDedupStageResult, + ReconciledEntities, +) +from src.v2.pipeline.stages.org_relationships import run_org_relationships_stage +from src.v2.pipeline.stages.output_assembly import ( + RootEntityValidationError, + assemble_output, + build_json_output, +) +from src.v2.pipeline.stages.ownership_check import ( + guarantee_repo_author, + infer_github_handle_parents, + infer_org_units, + infer_owners, + validate_ownership, +) +from src.v2.pipeline.stages.prune_dangling_refs import prune_dangling_refs +from src.v2.pipeline.stages.reconciliation import reconcile_entities +from src.v2.pipeline.stages.refine_with_llm import ( + RefineWithLLMResult, + run_refine_with_llm_stage, +) +from src.v2.pipeline.stages.refine_with_llm import ( + is_enabled as hybrid_refiner_is_enabled, +) +from src.v2.pipeline.stages.stats import compute_stats + +__all__ = [ + "BACKEND_EPFL_GRAPH", + "BACKEND_LLM", + "BACKEND_WIKIPEDIA", + "SUPPORTED_BACKENDS", + "AssembledOutput", + "ConceptTaggingResult", + "ContextBundle", + "LLMCriticStageResult", + "LLMDedupStageResult", + "LinkVeracityStageResult", + "ReconciledEntities", + "RefineWithLLMResult", + "RootEntityValidationError", + "apply_link_pruning_to_assembled_output", + "assemble_output", + "build_json_output", + "build_jsonld_output", + "collect_unique_http_link_contexts", + "compute_stats", + "concept_tagging_is_enabled", + "concept_tagging_resolve_backend", + "gather_context", + "guarantee_repo_author", + "hybrid_refiner_is_enabled", + "infer_github_handle_parents", + "infer_org_units", + "infer_owners", + "promote_failed_id_entities", + "prune_dangling_refs", + "reconcile_entities", + "run_concept_tagging_stage", + "run_link_veracity_stage", + "run_llm_critic_stage", + "run_llm_dedup_stage", + "run_org_relationships_stage", + "run_refine_with_llm_stage", + "validate_articles", + "validate_author_classes", + "validate_ownership", +] diff --git a/src/v2/pipeline/stages/article_validation.py b/src/v2/pipeline/stages/article_validation.py new file mode 100644 index 0000000..9a58408 --- /dev/null +++ b/src/v2/pipeline/stages/article_validation.py @@ -0,0 +1,191 @@ +"""Drop hallucinated `schema:ScholarlyArticle` entities. + +The article LLM agent will sometimes invent an article entity for a repository +that has no actual publication backing it (placeholder DOIs like +``10.0000/foo``, repo-name-shaped titles, etc.). The retention rule is +straightforward: an article must carry **at least one verified identifier**: + +- a `pulse:infoscienceArticleIdentifier` set to a non-empty value, OR +- a `schema:identifier` DOI that link veracity judged supported. + +Articles that satisfy neither are pruned and added to ``excluded_entities`` +with a clear reason. +""" +from __future__ import annotations + +import re +from copy import deepcopy +from typing import Any + +from src.v2.pipeline.stages.models import AssembledOutput + +ARTICLE_TYPE = "schema:ScholarlyArticle" + +# IANA reserves the `10.0000/...` prefix for testing — any DOI starting there +# is by definition fake. We catch this even when link veracity hasn't run. +_PLACEHOLDER_DOI_PATTERN = re.compile(r"^10\.0000/", flags=re.IGNORECASE) +_DOI_HOSTS = ("doi.org", "dx.doi.org") + + +def _normalize_doi_url(value: Any) -> str | None: + """Return the canonical `https://doi.org/` URL for a `schema:identifier`.""" + + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + lowered = candidate.lower() + for prefix in ( + "https://doi.org/", + "http://doi.org/", + "https://dx.doi.org/", + "http://dx.doi.org/", + ): + if lowered.startswith(prefix): + return f"https://doi.org/{candidate[len(prefix):]}" + if candidate.startswith("10.") and "/" in candidate: + return f"https://doi.org/{candidate}" + return None + + +def _is_placeholder_doi(value: Any) -> bool: + if not isinstance(value, str): + return False + candidate = value.strip() + if not candidate: + return False + for prefix in ("https://doi.org/", "http://doi.org/", "https://dx.doi.org/", "http://dx.doi.org/"): + if candidate.lower().startswith(prefix): + candidate = candidate[len(prefix) :] + break + return bool(_PLACEHOLDER_DOI_PATTERN.match(candidate)) + + +def _supported_link_set(veracity_records: list[dict[str, Any]]) -> set[str]: + supported: set[str] = set() + for record in veracity_records: + if not isinstance(record, dict): + continue + if record.get("status") != "ok": + continue + if not record.get("relationship_supported"): + continue + link = record.get("link") + if isinstance(link, str) and link: + supported.add(link) + return supported + + +def _unsupported_link_set(veracity_records: list[dict[str, Any]]) -> set[str]: + """Links the LLM/fetcher explicitly rejected (relationship not supported, + or fetch failed). Self-reference-skipped contexts never enter this set.""" + + unsupported: set[str] = set() + for record in veracity_records: + if not isinstance(record, dict): + continue + link = record.get("link") + if not isinstance(link, str) or not link: + continue + status = record.get("status") + if status == "ok" and record.get("relationship_supported") is False: + unsupported.add(link) + elif record.get("fetched_successfully") is False: + unsupported.add(link) + return unsupported + + +def _has_infoscience_identifier(article: dict[str, Any]) -> bool: + direct = article.get("pulse:infoscienceArticleIdentifier") + if isinstance(direct, str) and direct.strip(): + return True + identifiers = article.get("identifiers") + if isinstance(identifiers, dict): + nested = identifiers.get("pulse:infoscienceArticleIdentifier") + if isinstance(nested, str) and nested.strip(): + return True + return False + + +def _excluded_record(article: dict[str, Any], reason: str) -> dict[str, Any]: + return { + "entity_type": "article", + "entity": deepcopy(article), + "reason": [ + { + "path": "", + "message": "article_validation_dropped", + "constraint": "article_validation", + "expected": reason, + }, + ], + } + + +def validate_articles( + assembled: AssembledOutput, + *, + veracity_records: list[dict[str, Any]] | None = None, +) -> tuple[AssembledOutput, list[str]]: + """Drop articles whose DOI evidence is provably bad. + + Retention policy (innocent until proven guilty): + + - Has a non-empty `pulse:infoscienceArticleIdentifier` → keep. + - Has a placeholder DOI (`10.0000/...`) → drop. + - Has a DOI link veracity **explicitly** marked unsupported / fetch-failed → drop. + - Has no DOI and no infoscience id → drop. + - Has a DOI that wasn't checked (e.g. self-reference skip) → keep. + """ + + unsupported_links = _unsupported_link_set(veracity_records or []) + + new_related: list[dict[str, Any]] = [] + excluded_entities = list(assembled.excluded_entities) + warnings: list[str] = [] + + for entity in assembled.related_entities: + if not isinstance(entity, dict) or entity.get("type") != ARTICLE_TYPE: + new_related.append(entity) + continue + + article_id = entity.get("id") if isinstance(entity.get("id"), str) else None + schema_identifier = entity.get("schema:identifier") + + # A non-empty infoscience identifier is sufficient evidence on its own. + if _has_infoscience_identifier(entity): + new_related.append(entity) + continue + + # No infoscience backing — DOI must be present, non-placeholder, and + # not explicitly rejected by link veracity. + drop_reason: str | None = None + if _is_placeholder_doi(schema_identifier): + drop_reason = "Article has placeholder DOI (10.0000/* prefix)" + else: + doi_url = _normalize_doi_url(schema_identifier) + if doi_url is None: + drop_reason = "Article has no DOI and no infoscience identifier" + elif doi_url in unsupported_links: + drop_reason = "Article DOI explicitly failed link veracity and no infoscience identifier" + + if drop_reason is not None: + excluded_entities.append(_excluded_record(entity, drop_reason)) + warnings.append( + f"Removed article entity '{article_id or ''}': {drop_reason}", + ) + continue + + new_related.append(entity) + + updated = AssembledOutput( + root_entity=assembled.root_entity, + related_entities=new_related, + excluded_entities=excluded_entities, + warnings=list(assembled.warnings), + ) + return updated, warnings + + +__all__ = ["validate_articles"] diff --git a/src/v2/pipeline/stages/author_validation.py b/src/v2/pipeline/stages/author_validation.py new file mode 100644 index 0000000..d6465a1 --- /dev/null +++ b/src/v2/pipeline/stages/author_validation.py @@ -0,0 +1,119 @@ +"""Drop `schema:author` references that don't resolve to a `schema:Person`. + +The Open Pulse `schema:author` shape requires the target to be a +`schema:Person`. Articles and Contributions in the graph occasionally +carry author refs that point to Memberships, Contributions, or +infoscience-keyed entities (no Person node exists at that id), which +trips a SHACL `Value does not have class schema1:Person` violation. + +This stage runs after reconciliation/dedup and prunes such refs in +place. It does not drop the host entity; it only filters the +`schema:author` field. +""" +from __future__ import annotations + +from copy import deepcopy +from typing import Any + +from src.v2.pipeline.stages.models import AssembledOutput + +PERSON_TYPE = "schema:Person" + + +def _types_of(entity: dict[str, Any]) -> set[str]: + t = entity.get("type") or entity.get("@type") + if isinstance(t, list): + return {x for x in t if isinstance(x, str)} + if isinstance(t, str): + return {t} + return set() + + +def _build_person_id_set(entities: list[Any]) -> set[str]: + out: set[str] = set() + for e in entities: + if not isinstance(e, dict): + continue + if PERSON_TYPE in _types_of(e): + eid = e.get("id") or e.get("@id") + if isinstance(eid, str) and eid: + out.add(eid) + return out + + +def _filter_author_refs(value: Any, person_ids: set[str]) -> tuple[Any, int]: + """Return (filtered_value, dropped_count).""" + if value is None: + return value, 0 + + def _is_kept(ref: Any) -> bool: + if isinstance(ref, str): + return ref in person_ids + if isinstance(ref, dict): + target = ref.get("@id") + if isinstance(target, str): + return target in person_ids + return True # leave unrecognised shapes intact + + if isinstance(value, list): + kept = [r for r in value if _is_kept(r)] + return kept, len(value) - len(kept) + if _is_kept(value): + return value, 0 + return None, 1 + + +def validate_author_classes( + assembled: AssembledOutput, +) -> tuple[AssembledOutput, list[str]]: + """Filter `schema:author` to refs that resolve to `schema:Person`.""" + candidates: list[Any] = [] + if isinstance(assembled.root_entity, dict): + candidates.append(assembled.root_entity) + candidates.extend(assembled.related_entities) + + person_ids = _build_person_id_set(candidates) + + new_root = ( + deepcopy(assembled.root_entity) + if isinstance(assembled.root_entity, dict) + else assembled.root_entity + ) + new_related: list[Any] = [ + deepcopy(e) if isinstance(e, dict) else e + for e in assembled.related_entities + ] + new_candidates: list[Any] = [] + if isinstance(new_root, dict): + new_candidates.append(new_root) + new_candidates.extend(new_related) + + warnings: list[str] = [] + + for entity in new_candidates: + if not isinstance(entity, dict): + continue + if "schema:author" not in entity: + continue + new_value, dropped = _filter_author_refs( + entity["schema:author"], person_ids, + ) + if dropped: + entity_id = entity.get("id") or entity.get("@id") or "" + entity["schema:author"] = new_value + warnings.append( + f"Dropped {dropped} non-Person schema:author ref(s) on {entity_id}.", + ) + + return ( + AssembledOutput( + root_entity=new_root, + related_entities=new_related, + excluded_entities=list(assembled.excluded_entities), + warnings=list(assembled.warnings), + ), + warnings, + ) + + +__all__ = ["validate_author_classes"] diff --git a/src/v2/pipeline/stages/concept_tagging.py b/src/v2/pipeline/stages/concept_tagging.py new file mode 100644 index 0000000..c0f464a --- /dev/null +++ b/src/v2/pipeline/stages/concept_tagging.py @@ -0,0 +1,743 @@ +"""Concept + keyword tagging from the root repository's README. + +Optional pipeline stage. Stamps two underscore-prefixed metadata fields onto +the root repository entity: + +- ``_keywords`` — list[str] of plain keyword tokens. +- ``_concepts`` — list[dict] of structured concepts, each shaped:: + + { + "label": str, + "wikipedia_url": str | None, + "wikidata_id": str | None, + "score": float | None, + "source": str, # backend that emitted this concept + } + +Both fields are stripped before strict validation (handled by +``schema_validation.py``: any ``_``-prefixed key is dropped) and before +JSON-LD build (handled by ``jsonld_build.HELPER_ONLY_FIELDS``). They are +purely internal pipeline metadata for now — surface them in the JSON-LD +output by adding the corresponding ontology terms to the strict schema. + +Backends are pluggable via ``V2_CONCEPT_TAGGING_BACKEND``: + +- ``epfl_graph`` (default) — calls the EPFL Graph (graphai) API via + ``src.module.epfl_graph``. Requires ``EPFL_GRAPH_USERNAME`` / + ``EPFL_GRAPH_PASSWORD``. +- ``wikipedia`` — deterministic, credential-free. Extracts candidate + phrases from the README with a simple heuristic and resolves each to a + Wikipedia page via the MediaWiki opensearch API. +- ``llm`` — pydantic-ai agent that emits ``{keywords, concepts}`` JSON. + Reuses the project's ``model_config`` plumbing (``RCP_TOKEN`` / + ``OPENAI_API_KEY`` / ``OPENROUTER_API_KEY``). + +The whole stage is opt-in via ``V2_CONCEPT_TAGGING_ENABLED=true`` because +each backend has its own external dependency. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import os +import re +from collections import Counter +from dataclasses import dataclass, field +from typing import Any, Iterable + +import requests + +logger = logging.getLogger(__name__) + +BACKEND_EPFL_GRAPH = "epfl_graph" +BACKEND_WIKIPEDIA = "wikipedia" +BACKEND_LLM = "llm" +SUPPORTED_BACKENDS = (BACKEND_EPFL_GRAPH, BACKEND_WIKIPEDIA, BACKEND_LLM) +DEFAULT_BACKEND = BACKEND_EPFL_GRAPH + +DEFAULT_MAX_CONCEPTS = 25 +DEFAULT_MAX_KEYWORDS = 25 +DEFAULT_MAX_DISCIPLINES = 10 +DEFAULT_README_CHARS = 8000 +DEFAULT_EPFL_MIN_SCORE = 0.0 +DEFAULT_DISCIPLINE_TOP_CONCEPTS = 6 # how many concepts to look up categories for +DEFAULT_DISCIPLINE_TOP_N_PER_CONCEPT = 3 +DEFAULT_RELATED_TOP_DISCIPLINES = 3 # enrich the top-N disciplines, not all +DEFAULT_RELATED_TOP_N = 5 # entities per type per discipline +DEFAULT_RELATED_TOP_TOPICS = 3 # OpenAlex topics per discipline + +CONCEPTS_FIELD = "_concepts" +KEYWORDS_FIELD = "_keywords" +DISCIPLINES_FIELD = "_disciplines" + + +@dataclass(slots=True) +class ConceptTaggingResult: + keywords: list[str] = field(default_factory=list) + concepts: list[dict[str, Any]] = field(default_factory=list) + disciplines: list[dict[str, Any]] = field(default_factory=list) + warnings: list[str] = field(default_factory=list) + backend: str = "" + + +def _truncate(text: str, max_chars: int) -> str: + if len(text) <= max_chars: + return text + cutoff = text.rfind(" ", 0, max_chars) + return text[: cutoff if cutoff > max_chars * 0.6 else max_chars] + + +def _dedupe_keywords(keywords: Iterable[str], limit: int) -> list[str]: + seen: set[str] = set() + deduped: list[str] = [] + for raw in keywords: + if not isinstance(raw, str): + continue + clean = raw.strip() + if not clean: + continue + key = clean.lower() + if key in seen: + continue + seen.add(key) + deduped.append(clean) + if len(deduped) >= limit: + break + return deduped + + +def _dedupe_concepts( + concepts: Iterable[dict[str, Any]], limit: int, +) -> list[dict[str, Any]]: + seen: set[str] = set() + deduped: list[dict[str, Any]] = [] + for concept in concepts: + if not isinstance(concept, dict): + continue + url = concept.get("wikipedia_url") + label = concept.get("label") + key = (url or label or "").strip().lower() + if not key or key in seen: + continue + seen.add(key) + deduped.append(concept) + if len(deduped) >= limit: + break + return deduped + + +# Backend: EPFL Graph (graphai) ---------------------------------------------- + + +def _epfl_concept_to_dict(item: Any) -> dict[str, Any] | None: + if not isinstance(item, dict): + return None + label = ( + item.get("concept_name") + or item.get("PageTitle") + or item.get("page_title") + or item.get("title") + or item.get("label") + ) + if not isinstance(label, str) or not label.strip(): + return None + wikipedia_url = item.get("WikipediaURL") or item.get("wikipedia_url") or None + if not wikipedia_url and label: + wikipedia_url = ( + "https://en.wikipedia.org/wiki/" + label.strip().replace(" ", "_") + ) + # `concept_id` from EPFL Graph is the Wikipedia page ID, not a Wikidata + # QID; keep it as a typed reference so callers can disambiguate later. + raw_concept_id = item.get("concept_id") + concept_id: str | None = None + if isinstance(raw_concept_id, (int, str)) and str(raw_concept_id).strip(): + concept_id = str(raw_concept_id).strip() + wikidata_id = item.get("WikidataID") or item.get("wikidata_id") or None + raw_score = ( + item.get("mixed_score") + or item.get("MixedScore") + or item.get("Score") + or item.get("score") + ) + try: + score = float(raw_score) if raw_score is not None else None + except (TypeError, ValueError): + score = None + return { + "label": label.strip(), + "wikipedia_url": wikipedia_url, + "wikidata_id": wikidata_id, + "concept_id": concept_id, + "score": score, + "source": BACKEND_EPFL_GRAPH, + } + + +def _extract_via_epfl_graph( # noqa: PLR0913, C901, PLR0912, PLR0915 + readme: str, + *, + max_concepts: int, + max_keywords: int, + min_score: float = DEFAULT_EPFL_MIN_SCORE, + enrich_with_disciplines: bool = True, + max_disciplines: int = DEFAULT_MAX_DISCIPLINES, + discipline_top_concepts: int = DEFAULT_DISCIPLINE_TOP_CONCEPTS, + discipline_top_n: int = DEFAULT_DISCIPLINE_TOP_N_PER_CONCEPT, +) -> ConceptTaggingResult: + result = ConceptTaggingResult(backend=BACKEND_EPFL_GRAPH) + try: + from src.module.epfl_graph import ( # noqa: PLC0415 + category_chain, + category_graphsearch_url, + category_wikipedia, + concept_nearest_categories, + extract_concepts_from_text, + extract_keywords_from_text, + ) + except Exception as exc: # noqa: BLE001 + result.warnings.append(f"epfl_graph backend unavailable: {exc}") + return result + + cleaned = _strip_markdown(readme).strip() + if not cleaned: + result.warnings.append("epfl_graph: empty text after markdown stripping") + return result + + try: + raw_keywords = extract_keywords_from_text(cleaned) or [] + except Exception as exc: # noqa: BLE001 + raw_keywords = [] + result.warnings.append(f"epfl_graph keyword extraction failed: {exc}") + + try: + raw_concepts = extract_concepts_from_text(cleaned) or [] + except Exception as exc: # noqa: BLE001 + raw_concepts = [] + result.warnings.append(f"epfl_graph concept extraction failed: {exc}") + + result.keywords = _dedupe_keywords(raw_keywords, max_keywords) + converted: list[dict[str, Any]] = [] + for raw in raw_concepts: + concept = _epfl_concept_to_dict(raw) + if concept is None: + continue + score = concept.get("score") + if isinstance(score, (int, float)) and score < min_score: + continue + converted.append(concept) + result.concepts = _dedupe_concepts(converted, max_concepts) + + if enrich_with_disciplines and result.concepts: + seen: set[str] = set() + disciplines: list[dict[str, Any]] = [] + for concept in result.concepts[:discipline_top_concepts]: + concept_id = concept.get("concept_id") + if not concept_id: + continue + try: + categories = concept_nearest_categories( + concept_id, top_n=discipline_top_n, + ) + except Exception as exc: # noqa: BLE001 + result.warnings.append( + f"epfl_graph: discipline lookup failed for " + f"{concept.get('label', concept_id)}: {exc}", + ) + continue + for category in categories: + if not isinstance(category, dict): + continue + category_id = category.get("category_id") + if not isinstance(category_id, str) or not category_id: + continue + if category_id in seen: + continue + seen.add(category_id) + chain_ids = category_chain(category_id) + chain = [] + for idx, cid in enumerate(chain_ids): + wiki = category_wikipedia(cid) + chain.append( + { + "category_id": cid, + "label": wiki.get("name"), + "depth": idx, # 0 = leaf, increases toward root + "wikipedia_url": wiki.get("wikipedia_url"), + "wikipedia_page_id": wiki.get("wikipedia_page_id"), + }, + ) + leaf_wiki = chain[0] if chain else {} + disciplines.append( + { + "category_id": category_id, + "label": leaf_wiki.get("label") or category_id, + "score": category.get("score"), + "rank": category.get("rank"), + "from_concept": concept.get("label"), + "source": BACKEND_EPFL_GRAPH, + "graphsearch_url": category_graphsearch_url(category_id), + "wikipedia_url": leaf_wiki.get("wikipedia_url"), + "wikipedia_page_id": leaf_wiki.get("wikipedia_page_id"), + "chain": chain, + }, + ) + if len(disciplines) >= max_disciplines: + break + if len(disciplines) >= max_disciplines: + break + # Sort by score desc, then rank asc — EPFL Graph scores are not + # bounded, so a simple max() across concepts already orders well. + disciplines.sort( + key=lambda item: ( + -(item.get("score") or 0.0), + item.get("rank") or 0, + ), + ) + result.disciplines = disciplines + return result + + +# Backend: Wikipedia (rule-based, credential-free) --------------------------- + +_STOPWORDS = frozenset( + { + "the", "a", "an", "of", "in", "and", "or", "to", "for", "on", "at", + "by", "with", "from", "as", "is", "are", "was", "were", "be", "been", + "being", "this", "that", "these", "those", "it", "its", "we", "you", + "they", "their", "our", "your", "his", "her", "him", "she", "he", + "but", "not", "no", "yes", "if", "then", "else", "so", "than", "such", + "can", "could", "will", "would", "shall", "should", "may", "might", + "must", "do", "does", "did", "have", "has", "had", "into", "out", + "over", "under", "about", "after", "before", "while", "when", "where", + "what", "which", "who", "whom", "whose", "how", "why", "any", "all", + "some", "each", "every", "both", "few", "more", "most", "other", + "another", "same", "different", "via", "use", "used", "using", "uses", + "see", "also", "etc", "eg", "ie", "vs", + }, +) + +_WORD_RE = re.compile(r"[A-Za-z][A-Za-z0-9_+\-]{2,}") +_CODE_FENCE_RE = re.compile(r"```.*?```", flags=re.DOTALL) +_INLINE_CODE_RE = re.compile(r"`[^`]+`") +_LINK_RE = re.compile(r"\[([^\]]+)\]\([^)]+\)") +_IMAGE_RE = re.compile(r"!\[[^\]]*\]\([^)]+\)") +_HTML_TAG_RE = re.compile(r"<[^>]+>") +_ATX_HEADER_RE = re.compile(r"^\s{0,3}#{1,6}\s+", flags=re.MULTILINE) +_SETEXT_HEADER_RE = re.compile(r"^[=\-]{3,}\s*$", flags=re.MULTILINE) +_BOLD_RE = re.compile(r"\*\*([^*]+)\*\*|__([^_]+)__") +_ITALIC_RE = re.compile(r"(?\s?", flags=re.MULTILINE) +_HORIZONTAL_RULE_RE = re.compile(r"^\s*(?:[*\-_]\s*){3,}\s*$", flags=re.MULTILINE) + + +def _strip_markdown(text: str) -> str: + text = _CODE_FENCE_RE.sub(" ", text) + text = _IMAGE_RE.sub(" ", text) + text = _LINK_RE.sub(r"\1", text) + text = _INLINE_CODE_RE.sub(" ", text) + text = _HTML_TAG_RE.sub(" ", text) + text = _ATX_HEADER_RE.sub("", text) + text = _SETEXT_HEADER_RE.sub("", text) + text = _HORIZONTAL_RULE_RE.sub("", text) + text = _BULLET_RE.sub("", text) + text = _BLOCKQUOTE_RE.sub("", text) + text = _BOLD_RE.sub(lambda m: m.group(1) or m.group(2) or "", text) + return _ITALIC_RE.sub(lambda m: m.group(1) or m.group(2) or "", text) + + +def _candidate_keywords_from_text(text: str, limit: int) -> list[str]: + """Cheap keyword surrogate: top frequent non-stopword tokens. + + Not a substitute for a proper keyword extractor — just enough to seed + Wikipedia lookups when no smarter backend is available. + """ + cleaned = _strip_markdown(text) + tokens = _WORD_RE.findall(cleaned) + counts: Counter[str] = Counter() + for token in tokens: + lowered = token.lower() + if lowered in _STOPWORDS: + continue + counts[lowered] += 1 + most_common = [token for token, _ in counts.most_common(limit * 4)] + return most_common[:limit] + + +def _wikipedia_lookup( + keyword: str, *, session: requests.Session, timeout: float = 10.0, +) -> dict[str, Any] | None: + try: + response = session.get( + "https://en.wikipedia.org/w/api.php", + params={ + "action": "query", + "format": "json", + "list": "search", + "srsearch": keyword, + "srlimit": "1", + "srprop": "", + }, + timeout=timeout, + ) + response.raise_for_status() + except requests.RequestException: + return None + try: + payload = response.json() + except ValueError: + return None + hits = payload.get("query", {}).get("search", []) if isinstance(payload, dict) else [] + if not isinstance(hits, list) or not hits: + return None + first = hits[0] + if not isinstance(first, dict): + return None + title = first.get("title") + if not isinstance(title, str) or not title.strip(): + return None + title = title.strip() + return { + "label": title, + "wikipedia_url": "https://en.wikipedia.org/wiki/" + title.replace(" ", "_"), + "wikidata_id": None, + "score": None, + "source": BACKEND_WIKIPEDIA, + } + + +def _extract_via_wikipedia( + readme: str, + *, + max_concepts: int, + max_keywords: int, +) -> ConceptTaggingResult: + result = ConceptTaggingResult(backend=BACKEND_WIKIPEDIA) + keywords = _candidate_keywords_from_text(readme, limit=max_keywords) + result.keywords = keywords + + concepts: list[dict[str, Any]] = [] + with requests.Session() as session: + session.headers.update( + {"User-Agent": "git-metadata-extractor/2.0 (+concept_tagging)"}, + ) + for keyword in keywords[:max_concepts]: + concept = _wikipedia_lookup(keyword, session=session) + if concept is not None: + concepts.append(concept) + result.concepts = _dedupe_concepts(concepts, max_concepts) + return result + + +# Backend: LLM (pydantic-ai, project model config) --------------------------- + + +_LLM_SYSTEM_PROMPT = ( + "You extract concepts and keywords from a software repository README.\n" + "Return ONLY a JSON object matching this schema (no prose, no fences):\n" + "{\n" + ' "keywords": ["..."], // 5-25 short tokens, lowercase preferred\n' + ' "concepts": [ // 5-25 entries\n' + ' {"label": "...", "wikipedia_url": "https://en.wikipedia.org/wiki/..."}\n' + " ]\n" + "}\n" + "Rules:\n" + "- Only use Wikipedia URLs you are confident exist.\n" + "- Prefer canonical English Wikipedia pages.\n" + "- Skip generic terms (software, project, code, README, version)." +) + + +def _llm_payload_to_result(payload: Any) -> ConceptTaggingResult: + result = ConceptTaggingResult(backend=BACKEND_LLM) + if not isinstance(payload, dict): + result.warnings.append("llm backend returned a non-object payload") + return result + raw_keywords = payload.get("keywords") or [] + raw_concepts = payload.get("concepts") or [] + if isinstance(raw_keywords, list): + result.keywords = _dedupe_keywords(raw_keywords, DEFAULT_MAX_KEYWORDS) + converted: list[dict[str, Any]] = [] + if isinstance(raw_concepts, list): + for item in raw_concepts: + if not isinstance(item, dict): + continue + label = item.get("label") or item.get("title") + if not isinstance(label, str) or not label.strip(): + continue + url = item.get("wikipedia_url") or item.get("url") + converted.append( + { + "label": label.strip(), + "wikipedia_url": url if isinstance(url, str) and url else None, + "wikidata_id": None, + "score": None, + "source": BACKEND_LLM, + }, + ) + result.concepts = _dedupe_concepts(converted, DEFAULT_MAX_CONCEPTS) + return result + + +async def _extract_via_llm( + readme: str, + *, + max_concepts: int, + max_keywords: int, +) -> ConceptTaggingResult: + try: + from pydantic_ai import Agent # noqa: PLC0415 + + from src.v1.llm.model_config import ( # noqa: PLC0415 + create_pydantic_ai_model, + get_model_parameters, + load_model_config, + validate_config, + ) + except Exception as exc: # noqa: BLE001 + return ConceptTaggingResult( + backend=BACKEND_LLM, + warnings=[f"llm backend unavailable: {exc}"], + ) + + try: + config = load_model_config() + validate_config(config) + model = create_pydantic_ai_model(config) + params = get_model_parameters(config) + except Exception as exc: # noqa: BLE001 + return ConceptTaggingResult( + backend=BACKEND_LLM, + warnings=[f"llm backend config invalid: {exc}"], + ) + + agent: Agent[None, dict[str, Any]] = Agent( + model=model, + output_type=dict, + system_prompt=_LLM_SYSTEM_PROMPT, + model_settings=params or None, + ) + user_prompt = ( + f"Extract up to {max_keywords} keywords and up to {max_concepts} " + "concepts from this README:\n\n" + readme + ) + try: + run = await agent.run(user_prompt) + except Exception as exc: # noqa: BLE001 + return ConceptTaggingResult( + backend=BACKEND_LLM, + warnings=[f"llm backend call failed: {exc}"], + ) + + payload = getattr(run, "output", None) + if isinstance(payload, str): + try: + payload = json.loads(payload) + except ValueError: + return ConceptTaggingResult( + backend=BACKEND_LLM, + warnings=["llm backend returned non-JSON string"], + ) + return _llm_payload_to_result(payload) + + +# Stage entrypoint ------------------------------------------------------------ + + +def _enrich_disciplines_with_related( + disciplines: list[dict[str, Any]], + *, + top_disciplines: int, + top_topics: int, + top_n: int, + warnings: list[str], +) -> None: + """Stamp ``openalex_topics`` / ``publications`` / ``people`` / ``units`` + onto the top disciplines in-place via the OpenAlex API. + + Opt-in (gated by ``V2_CONCEPT_TAGGING_OPENALEX_RELATED_ENABLED``) + because each enabled discipline triggers ~4 OpenAlex calls — cheap + individually but worth keeping off the default path. + """ + if not disciplines: + return + + try: + from src.module.epfl_graph import ( # noqa: PLC0415 + category_nearest_openalex_topics, + people_for_topics, + publications_for_topics, + units_for_topics, + ) + except Exception as exc: # noqa: BLE001 + warnings.append(f"concept_tagging: enrichment imports failed: {exc}") + return + + for discipline in disciplines[:top_disciplines]: + category_id = discipline.get("category_id") + if not isinstance(category_id, str) or not category_id: + continue + + try: + topics = category_nearest_openalex_topics( + category_id, top_n=top_topics, + ) + except Exception as exc: # noqa: BLE001 + topics = [] + warnings.append( + f"concept_tagging: openalex topic lookup failed for " + f"{category_id}: {exc}", + ) + if not topics: + continue + discipline["openalex_topics"] = topics + topic_ids = [t["topic_id"] for t in topics if t.get("topic_id")] + if not topic_ids: + continue + try: + discipline["publications"] = publications_for_topics( + topic_ids, top_n=top_n, + ) + discipline["people"] = people_for_topics(topic_ids, top_n=top_n) + discipline["units"] = units_for_topics(topic_ids, top_n=top_n) + except Exception as exc: # noqa: BLE001 + warnings.append( + f"concept_tagging: openalex enrichment failed for " + f"{category_id}: {exc}", + ) + + +async def run_concept_tagging_stage( # noqa: PLR0913 + *, + root_entity: dict[str, Any] | None, + readme_text: str | None, + backend: str = DEFAULT_BACKEND, + max_concepts: int = DEFAULT_MAX_CONCEPTS, + max_keywords: int = DEFAULT_MAX_KEYWORDS, + max_readme_chars: int = DEFAULT_README_CHARS, + epfl_min_score: float = DEFAULT_EPFL_MIN_SCORE, + enrich_with_disciplines: bool = True, + enable_related_openalex: bool = False, + related_top_disciplines: int = DEFAULT_RELATED_TOP_DISCIPLINES, + related_top_topics: int = DEFAULT_RELATED_TOP_TOPICS, + related_top_n: int = DEFAULT_RELATED_TOP_N, +) -> tuple[dict[str, Any] | None, ConceptTaggingResult]: + """Stamp ``_concepts`` and ``_keywords`` onto the root repository entity. + + Returns the (possibly mutated) root entity and the raw extraction + result. The function is a no-op (returns the entity untouched) when + ``root_entity`` is missing, when ``readme_text`` is empty, or when the + selected backend produces nothing. + """ + backend_normalized = (backend or DEFAULT_BACKEND).strip().lower() + result = ConceptTaggingResult(backend=backend_normalized) + + if backend_normalized not in SUPPORTED_BACKENDS: + result.warnings.append( + f"concept_tagging: unknown backend '{backend_normalized}', " + f"expected one of {SUPPORTED_BACKENDS}", + ) + return root_entity, result + + if root_entity is None or not isinstance(root_entity, dict): + result.warnings.append("concept_tagging: no root entity to tag") + return root_entity, result + + if not isinstance(readme_text, str) or not readme_text.strip(): + result.warnings.append("concept_tagging: empty README, nothing to tag") + return root_entity, result + + truncated = _truncate(readme_text.strip(), max_readme_chars) + + if backend_normalized == BACKEND_EPFL_GRAPH: + result = await asyncio.to_thread( + _extract_via_epfl_graph, + truncated, + max_concepts=max_concepts, + max_keywords=max_keywords, + min_score=epfl_min_score, + enrich_with_disciplines=enrich_with_disciplines, + ) + elif backend_normalized == BACKEND_WIKIPEDIA: + result = await asyncio.to_thread( + _extract_via_wikipedia, + truncated, + max_concepts=max_concepts, + max_keywords=max_keywords, + ) + else: # llm + result = await _extract_via_llm( + truncated, + max_concepts=max_concepts, + max_keywords=max_keywords, + ) + + if result.disciplines and enable_related_openalex: + await asyncio.to_thread( + _enrich_disciplines_with_related, + result.disciplines, + top_disciplines=related_top_disciplines, + top_topics=related_top_topics, + top_n=related_top_n, + warnings=result.warnings, + ) + + if result.keywords: + root_entity[KEYWORDS_FIELD] = result.keywords + if result.concepts: + root_entity[CONCEPTS_FIELD] = result.concepts + if result.disciplines: + root_entity[DISCIPLINES_FIELD] = result.disciplines + + return root_entity, result + + +def is_enabled() -> bool: + """Read the opt-in env flag (default off).""" + raw = os.environ.get("V2_CONCEPT_TAGGING_ENABLED", "false").strip().lower() + return raw in {"1", "true", "yes", "on"} + + +def resolve_epfl_min_score() -> float: + """Read ``V2_CONCEPT_TAGGING_EPFL_MIN_SCORE`` (default 0.0). + + Concepts emitted by the EPFL Graph below this ``mixed_score`` threshold + are dropped before tagging. Useful to filter out generic noise (the API + happily returns matches like "Graph theory" or "OS/2" with low scores + on README text that mentions "graph" or "operating system" in passing). + """ + raw = os.environ.get("V2_CONCEPT_TAGGING_EPFL_MIN_SCORE", "") + try: + return float(raw) + except (TypeError, ValueError): + return DEFAULT_EPFL_MIN_SCORE + + +def resolve_related_enrichment() -> bool: + """Read ``V2_CONCEPT_TAGGING_OPENALEX_RELATED_ENABLED`` (default false). + + When true, top disciplines are enriched with related OpenAlex topics, + publications, people, and institutions (~4 cheap HTTP per discipline). + """ + truthy = {"1", "true", "yes", "on"} + raw = ( + os.environ.get("V2_CONCEPT_TAGGING_OPENALEX_RELATED_ENABLED", "") + .strip() + .lower() + ) + return raw in truthy + + +def resolve_backend() -> str: + raw = os.environ.get("V2_CONCEPT_TAGGING_BACKEND", DEFAULT_BACKEND) + backend = (raw or DEFAULT_BACKEND).strip().lower() + if backend not in SUPPORTED_BACKENDS: + logger.warning( + "V2_CONCEPT_TAGGING_BACKEND=%s not recognized; falling back to %s", + raw, + DEFAULT_BACKEND, + ) + return DEFAULT_BACKEND + return backend diff --git a/src/v2/pipeline/stages/context_gather.py b/src/v2/pipeline/stages/context_gather.py new file mode 100644 index 0000000..6306f9b --- /dev/null +++ b/src/v2/pipeline/stages/context_gather.py @@ -0,0 +1,441 @@ +from __future__ import annotations + +import logging +import os +from typing import TYPE_CHECKING, Any + +from src.v2.pipeline.stages.models import ContextBundle + +if TYPE_CHECKING: + from src.v2.agents.models import ProviderSet + from src.v2.ingest.detection.models import GitHubURLClassification + +logger = logging.getLogger(__name__) + +_BOOKENDS_TOP_N_DEFAULT = 50 + + +def _resolve_bookends_top_n() -> int: + """How many contributors get a `get_commit_bookends` lookup. + + GitHub returns `/repos/.../contributors` ordered by commit count + desc, so capping to the top N keeps the heaviest contributors and + skips the long tail. Each contributor costs up to 2 GitHub API + calls (cached for `V2_PROVIDER_CACHE_TTL_DAYS`); without the cap a + repo with 4 000 contributors burns through the 5 000/h auth quota + on a single extraction. + + `0` (or negative) disables the enrichment entirely. + """ + raw = os.getenv("V2_CONTRIBUTOR_BOOKENDS_TOP_N") + if raw is None or raw.strip() == "": + return _BOOKENDS_TOP_N_DEFAULT + try: + return int(raw) + except ValueError: + logger.warning( + "Invalid V2_CONTRIBUTOR_BOOKENDS_TOP_N=%r; falling back to %d", + raw, + _BOOKENDS_TOP_N_DEFAULT, + ) + return _BOOKENDS_TOP_N_DEFAULT + + +class RequiredProviderUnavailableError(RuntimeError): + """Raised when a required provider call fails for the current extract mode.""" + + def __init__(self, *, provider: str, operation: str, cause: Exception) -> None: + self.provider = provider + self.operation = operation + self.cause = cause + super().__init__( + f"Required provider '{provider}' failed during {operation}: {cause}", + ) + + +def _first_non_empty_string(*candidates: Any) -> str | None: + for candidate in candidates: + if isinstance(candidate, str) and candidate.strip(): + return candidate.strip() + return None + + +def _enrich_contributors_with_commit_bookends( + full_name: str, + contributors: list[dict[str, Any]], + providers: ProviderSet, +) -> str | None: + """Populate `firstContributionDate` / `lastContributionDate` / + `contributions` on each contributor in place. + + Uses ``GitHubProvider.get_commit_bookends`` (cached, two API calls + per contributor). Failures degrade silently — the contribution + agent simply sees `None` for those fields. + + Capped to the top N contributors (`V2_CONTRIBUTOR_BOOKENDS_TOP_N`, + default 50) to keep jumbo repos within the GitHub auth quota. The + upstream contributor list is already ordered by commit count desc, + so the top N captures the heaviest contributors. Returns a warning + string when the cap dropped at least one contributor, otherwise + `None`. + """ + if not contributors or providers.github is None: + return None + fetcher = getattr(providers.github, "get_commit_bookends", None) + if not callable(fetcher): + return None + + top_n = _resolve_bookends_top_n() + if top_n <= 0: + return None + + targets = contributors[:top_n] + for contributor in targets: + if not isinstance(contributor, dict): + continue + login = contributor.get("login") + if not isinstance(login, str) or not login: + continue + try: + bookends = fetcher(full_name, login) + except Exception: # noqa: BLE001 + continue + if not isinstance(bookends, dict): + continue + first_date = bookends.get("first_date") + last_date = bookends.get("last_date") + count = bookends.get("count") + if isinstance(first_date, str) and not contributor.get("firstContributionDate"): + contributor["firstContributionDate"] = first_date + if isinstance(last_date, str) and not contributor.get("lastContributionDate"): + contributor["lastContributionDate"] = last_date + if isinstance(count, int) and count > 0 and not contributor.get("contributions"): + contributor["contributions"] = count + + skipped = max(0, len(contributors) - top_n) + if skipped > 0: + return ( + f"Commit-bookend enrichment capped at top {top_n} contributors " + f"for {full_name}; {skipped} tail contributor(s) left without " + "first/last/contributions dates " + "(adjust V2_CONTRIBUTOR_BOOKENDS_TOP_N to widen)." + ) + return None + + +def _coerce_repositories(candidate: Any) -> list[str]: + if not isinstance(candidate, list): + return [] + repositories: list[str] = [] + for item in candidate: + if isinstance(item, str) and item.strip(): + repositories.append(item.strip()) + continue + if isinstance(item, dict): + name = _first_non_empty_string( + item.get("full_name"), + item.get("name"), + item.get("repository"), + ) + if name: + repositories.append(name) + return repositories + + +def _coerce_members(candidate: Any) -> list[str]: + if not isinstance(candidate, list): + return [] + members: list[str] = [] + for item in candidate: + if isinstance(item, str) and item.strip(): + members.append(item.strip()) + continue + if isinstance(item, dict): + login = _first_non_empty_string(item.get("login"), item.get("username")) + if login: + members.append(login) + return members + + +def _coerce_repository_files(candidate: Any) -> list[dict[str, str]]: + if isinstance(candidate, dict): + rows: list[dict[str, str]] = [] + for path, content in candidate.items(): + if isinstance(path, str) and path.strip() and isinstance(content, str) and content.strip(): + rows.append({"path": path.strip(), "content": content.strip()}) + return rows + + if not isinstance(candidate, list): + return [] + + rows = [] + for item in candidate: + if not isinstance(item, dict): + continue + path = _first_non_empty_string(item.get("path"), item.get("file_path"), item.get("name")) + content = _first_non_empty_string(item.get("content"), item.get("text"), item.get("body")) + if path and content: + rows.append({"path": path, "content": content}) + return rows + + +def _normalize_owned_repo_full_name(owner: str, repo: str) -> str: + if "/" in repo: + return repo + return f"{owner}/{repo}" + + +def _optional_repository_context( + *, + full_name: str, + providers: ProviderSet, + warnings: list[str], +) -> dict[str, Any] | None: + try: + repository_metadata = providers.github.get_repository(full_name) + except Exception as exc: # noqa: BLE001 + warnings.append( + f"Repository metadata lookup failed for {full_name}: {exc}", + ) + return None + + try: + contributors = providers.github.get_contributors(full_name) + except Exception as exc: # noqa: BLE001 + warnings.append( + f"Repository contributors lookup failed for {full_name}: {exc}", + ) + contributors = [] + else: + cap_warning = _enrich_contributors_with_commit_bookends( + full_name, contributors, providers, + ) + if cap_warning: + warnings.append(cap_warning) + + try: + languages = providers.github.get_languages(full_name) + except Exception as exc: # noqa: BLE001 + warnings.append( + f"Repository languages lookup failed for {full_name}: {exc}", + ) + languages = {} + + readme_content = _first_non_empty_string( + repository_metadata.get("readme"), + repository_metadata.get("readme_content"), + repository_metadata.get("README"), + repository_metadata.get("description"), + ) + if not readme_content: + warnings.append(f"Repository README content is not available for {full_name}") + readme_content = "" + + gimie_jsonld: dict[str, Any] = {} + try: + fetched_jsonld = providers.github.get_repository_jsonld(full_name) + except Exception as exc: # noqa: BLE001 + warnings.append( + f"Repository GIMIE JSON-LD lookup failed for {full_name}: {exc}", + ) + else: + if isinstance(fetched_jsonld, dict): + gimie_jsonld = fetched_jsonld + + return { + "full_name": full_name, + "metadata": repository_metadata, + "readme_content": readme_content, + "contributors": contributors, + "languages": languages, + "gimie_jsonld": gimie_jsonld, + "repository_files": _coerce_repository_files( + repository_metadata.get("repository_files") or repository_metadata.get("files"), + ), + } + + +def _normalize_detected_type(detected_type: str | Any) -> str: + if hasattr(detected_type, "value"): + return str(detected_type.value) + return str(detected_type) + + +async def gather_context( # noqa: C901, PLR0915 + detected_type: str, + url_info: GitHubURLClassification, + providers: ProviderSet, +) -> ContextBundle: + """Collect upstream provider context before agent execution.""" + normalized_type = _normalize_detected_type(detected_type) + warnings: list[str] = [] + context: dict[str, Any] = {} + + if normalized_type == "repository": + full_name = f"{url_info.owner}/{url_info.repo or ''}".rstrip("/") + + repository_metadata: dict[str, Any] = {} + contributors: list[dict[str, Any]] = [] + languages: dict[str, int] = {} + + try: + repository_metadata = providers.github.get_repository(full_name) + except Exception as exc: # noqa: BLE001 + raise RequiredProviderUnavailableError( + provider="github", + operation="repository metadata lookup", + cause=exc, + ) from exc + + try: + contributors = providers.github.get_contributors(full_name) + except Exception as exc: # noqa: BLE001 + raise RequiredProviderUnavailableError( + provider="github", + operation="repository contributors lookup", + cause=exc, + ) from exc + + cap_warning = _enrich_contributors_with_commit_bookends( + full_name, contributors, providers, + ) + if cap_warning: + warnings.append(cap_warning) + + try: + languages = providers.github.get_languages(full_name) + except Exception as exc: # noqa: BLE001 + raise RequiredProviderUnavailableError( + provider="github", + operation="repository languages lookup", + cause=exc, + ) from exc + + readme_content = _first_non_empty_string( + repository_metadata.get("readme"), + repository_metadata.get("readme_content"), + repository_metadata.get("README"), + repository_metadata.get("description"), + ) + if not readme_content: + warnings.append("Repository README content is not available") + readme_content = "" + + gimie_jsonld = providers.github.get_repository_jsonld(full_name) + + context["repository"] = { + "full_name": full_name, + "metadata": repository_metadata, + "readme_content": readme_content, + "contributors": contributors, + "languages": languages, + "gimie_jsonld": gimie_jsonld, + "repository_files": _coerce_repository_files( + repository_metadata.get("repository_files") or repository_metadata.get("files"), + ), + } + return ContextBundle( + detected_type=normalized_type, + context=context, + warnings=warnings, + ) + + if normalized_type == "user": + username = url_info.owner + user_profile: dict[str, Any] = {} + + try: + user_profile = providers.github.get_user(username) + except Exception as exc: # noqa: BLE001 + raise RequiredProviderUnavailableError( + provider="github", + operation="user profile lookup", + cause=exc, + ) from exc + + owned_repos = _coerce_repositories( + user_profile.get("repositories") + or user_profile.get("repos") + or user_profile.get("owned_repos"), + ) + repository_contexts: dict[str, dict[str, Any]] = {} + for repo in owned_repos: + full_name = _normalize_owned_repo_full_name(username, repo) + repository_context = _optional_repository_context( + full_name=full_name, + providers=providers, + warnings=warnings, + ) + if isinstance(repository_context, dict): + repository_contexts[full_name] = repository_context + orcid_id = _first_non_empty_string( + user_profile.get("orcid"), + user_profile.get("orcid_id"), + user_profile.get("orcidIdentifier"), + ) + + orcid_data: dict[str, Any] | None = None + if providers.orcid and orcid_id: + try: + orcid_data = providers.orcid.get_person_by_orcid(orcid_id) + except Exception as exc: # noqa: BLE001 + warnings.append(f"ORCID lookup failed: {exc}") + else: + warnings.append("ORCID data unavailable for this user") + + context["user"] = { + "username": username, + "profile": user_profile, + "owned_repos": owned_repos, + "repository_contexts": repository_contexts, + "orcid_data": orcid_data, + } + return ContextBundle( + detected_type=normalized_type, + context=context, + warnings=warnings, + ) + + if normalized_type == "organization": + organization_name = url_info.owner + organization_profile: dict[str, Any] = {} + + try: + organization_profile = providers.github.get_organization(organization_name) + except Exception as exc: # noqa: BLE001 + raise RequiredProviderUnavailableError( + provider="github", + operation="organization profile lookup", + cause=exc, + ) from exc + + member_list = _coerce_members(organization_profile.get("members")) + owned_repos = _coerce_repositories( + organization_profile.get("repositories") + or organization_profile.get("repos"), + ) + repository_contexts: dict[str, dict[str, Any]] = {} + for repo in owned_repos: + full_name = _normalize_owned_repo_full_name(organization_name, repo) + repository_context = _optional_repository_context( + full_name=full_name, + providers=providers, + warnings=warnings, + ) + if isinstance(repository_context, dict): + repository_contexts[full_name] = repository_context + + context["organization"] = { + "org_name": organization_name, + "profile": organization_profile, + "members": member_list, + "owned_repos": owned_repos, + "repository_contexts": repository_contexts, + } + return ContextBundle( + detected_type=normalized_type, + context=context, + warnings=warnings, + ) + + raise ValueError diff --git a/src/v2/pipeline/stages/jsonld_build.py b/src/v2/pipeline/stages/jsonld_build.py new file mode 100644 index 0000000..bcf2025 --- /dev/null +++ b/src/v2/pipeline/stages/jsonld_build.py @@ -0,0 +1,148 @@ +from __future__ import annotations + +from copy import deepcopy +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from src.v2.pipeline.stages.models import AssembledOutput + +ENTITY_URI_PREFIX = "urn:pulse:" +HELPER_ONLY_FIELDS = {"shacl", "identifiers", "idSource", "_person_ref"} + + +def _normalize_node_id(value: Any, *, index: int) -> str: + if isinstance(value, str) and value: + if value.startswith(("http://", "https://", "urn:")): + return value + return f"{ENTITY_URI_PREFIX}{value}" + return f"{ENTITY_URI_PREFIX}node-{index}" + + +def _normalize_node_type(value: Any) -> str | list[str] | None: + if isinstance(value, str) and value: + return value + if isinstance(value, list): + normalized = [item for item in value if isinstance(item, str) and item] + if normalized: + return normalized + return None + + +def _iri_typed_context_terms(jsonld_context: dict[str, Any]) -> set[str]: + iri_typed_terms: set[str] = set() + for term, mapping in jsonld_context.items(): + if not isinstance(term, str) or not isinstance(mapping, dict): + continue + if mapping.get("@type") != "@id": + continue + iri_typed_terms.add(term) + mapped_term = mapping.get("@id") + if isinstance(mapped_term, str) and mapped_term: + iri_typed_terms.add(mapped_term) + return iri_typed_terms + + +_IRI_PREFIXES: tuple[str, ...] = ("http://", "https://", "urn:", "doi:") + + +def _normalize_jsonld_value( + value: Any, + *, + id_map: dict[str, str], + iri_typed_terms: set[str], + current_property: str | None = None, +) -> Any: + if isinstance(value, str): + if isinstance(current_property, str) and current_property in iri_typed_terms: + target_id = id_map.get(value) + if target_id is not None: + return {"@id": target_id} + # External IRI (not in our graph): wrap in `{"@id": ...}` for + # consistent shape across all iri-typed property values, so + # downstream consumers don't have to handle two forms. + if value.startswith(_IRI_PREFIXES): + return {"@id": value} + return value + if isinstance(value, list): + return [ + _normalize_jsonld_value( + item, + id_map=id_map, + iri_typed_terms=iri_typed_terms, + current_property=current_property, + ) + for item in value + ] + if isinstance(value, dict): + normalized: dict[str, Any] = {} + for key, item in value.items(): + normalized[key] = _normalize_jsonld_value( + item, + id_map=id_map, + iri_typed_terms=iri_typed_terms, + current_property=key if isinstance(key, str) else None, + ) + return normalized + return deepcopy(value) + + +def build_jsonld_output( + *, + assembled: AssembledOutput, + jsonld_context: dict[str, Any], +) -> dict[str, Any]: + entities: list[dict[str, Any]] = [] + if isinstance(assembled.root_entity, dict): + entities.append(assembled.root_entity) + entities.extend(assembled.related_entities) + + id_map: dict[str, str] = {} + iri_typed_terms = _iri_typed_context_terms(jsonld_context) + for index, entity in enumerate(entities): + entity_id = entity.get("id") + if isinstance(entity_id, str) and entity_id: + id_map[entity_id] = _normalize_node_id(entity_id, index=index) + + graph: list[dict[str, Any]] = [] + for index, entity in enumerate(entities): + node: dict[str, Any] = {} + node_id = entity.get("id") + node["@id"] = _normalize_node_id(node_id, index=index) + node_type = _normalize_node_type(entity.get("type")) + if node_type is not None: + node["@type"] = node_type + + for key, value in entity.items(): + if key in HELPER_ONLY_FIELDS or key in {"id", "type"}: + continue + if isinstance(key, str) and key.startswith("_"): + continue + node[key] = _normalize_jsonld_value( + value, + id_map=id_map, + iri_typed_terms=iri_typed_terms, + current_property=key, + ) + + # Strip `pulse:ror` when redundant with the node's own `@id`. The + # `org:Organization` SHACL shape is `sh:closed` and does not + # declare `pulse:ror`; emitting the field on a ROR-id'd node + # triggers a closed-shape violation. The `@id` is already the + # ROR, so the field carries no additional information. + node_iri = node.get("@id") + if ( + isinstance(node_iri, str) + and node.get("pulse:ror") == node_iri + ): + node.pop("pulse:ror", None) + + graph.append(node) + + graph.sort(key=lambda item: str(item.get("@id", ""))) + payload: dict[str, Any] = { + "@context": deepcopy(jsonld_context), + "@graph": graph, + } + if assembled.excluded_entities: + payload["excluded_entities"] = deepcopy(assembled.excluded_entities) + return payload diff --git a/src/v2/pipeline/stages/link_veracity.py b/src/v2/pipeline/stages/link_veracity.py new file mode 100644 index 0000000..9758e82 --- /dev/null +++ b/src/v2/pipeline/stages/link_veracity.py @@ -0,0 +1,748 @@ +from __future__ import annotations + +import asyncio +import re +from dataclasses import dataclass, field +from typing import Any +from urllib.parse import urlparse + +from src.v2.agents import LLMLinkVeracityAgentV2, ProviderSet +from src.v2.canonicalization.id_resolution import ( + resolve_article_id, + resolve_organization_id, + resolve_person_id, + resolve_repository_id, +) +from src.v2.ingest.cache import ProviderCache +from src.v2.pipeline.stages.models import AssembledOutput + +HTTP_SCHEMES = {"http", "https"} +IDENTITY_KEYS = {"id", "@id", "type", "@type"} +_NESTED_URL_AFTER_UNDERSCORE = re.compile(r"_https?://", flags=re.IGNORECASE) + +_ID_RESOLVER_BY_TYPE: dict[str, Any] = { + "schema:Person": resolve_person_id, + "org:Organization": resolve_organization_id, + "schema:SoftwareSourceCode": resolve_repository_id, + "schema:ScholarlyArticle": resolve_article_id, +} + +# When promoting past a failed id source, also clear the field that produced it +# so the priority ladder picks the next rung instead of the same one. +_ID_SOURCE_TO_FIELD_KEYS: dict[str, tuple[str, ...]] = { + "pulse:orcid": ("pulse:orcid", "pulse:orcidIdentifier", "orcid", "orcidIdentifier"), + "pulse:infosciencePersonIdentifier": ( + "pulse:infosciencePersonIdentifier", + "infosciencePersonIdentifier", + ), + "pulse:githubUsername": ("pulse:githubUsername", "githubUsername"), + "pulse:ror": ("pulse:ror", "ror"), + "pulse:infoscienceOrganizationIdentifier": ( + "pulse:infoscienceOrganizationIdentifier", + "infoscienceOrganizationIdentifier", + ), + "pulse:githubOrganizationHandle": ( + "pulse:githubOrganizationHandle", + "githubOrganizationHandle", + ), + "pulse:githubRepositoryHandle": ( + "pulse:githubRepositoryHandle", + "githubRepositoryHandle", + ), + "schema:identifier": ("schema:identifier", "doi"), + "schema:citation": ("schema:citation",), + "pulse:infoscienceArticleIdentifier": ( + "pulse:infoscienceArticleIdentifier", + "infoscienceArticleIdentifier", + ), +} +DOI_BASE_URI = "https://doi.org/" +DOI_PREFIXES = ( + "https://doi.org/", + "http://doi.org/", + "https://dx.doi.org/", + "http://dx.doi.org/", +) +ENTITY_TYPE_TO_SINGULAR = { + "schema:SoftwareSourceCode": "repository", + "schema:Person": "person", + "org:Organization": "organization", + "schema:ScholarlyArticle": "article", + "org:Membership": "membership", + "pulse:Contribution": "contribution", +} +_DROP = object() + +# Predicates whose evidence lives on the SOURCE side, not the link target. +# Fetching the link target and asking "does this page support +# ` `?" produces false negatives because the target +# page never mentions the source. We skip these to avoid wasted Selenium +# fetches + LLM calls and noisy warnings. +_ASYMMETRIC_PREDICATES: frozenset[str] = frozenset( + { + "schema:author", + "schema:contributor", + "schema:memberOf", + "pulse:owns", + "pulse:contributionTo", + "pulse:firstContributionDate", + "pulse:lastContributionDate", + "org:hasUnit", + "org:unitOf", + "org:organization", + "org:role", + "time:hasBeginning", + "time:hasEnd", + }, +) + + +def _is_supportable_predicate(predicate: Any) -> bool: + """Predicates whose evidence plausibly lives on the link target's page.""" + + if not isinstance(predicate, str) or not predicate: + return False + return predicate not in _ASYMMETRIC_PREDICATES + + +@dataclass(slots=True) +class LinkVeracityStageResult: + records: list[dict[str, Any]] = field(default_factory=list) + warnings: list[str] = field(default_factory=list) + checked_count: int = 0 + supported_count: int = 0 + unsupported_count: int = 0 + failed_count: int = 0 + invalid_links: list[str] = field(default_factory=list) + entity_link_map: dict[str, list[str]] = field(default_factory=dict) + article_identifier_link_map: dict[str, str] = field(default_factory=dict) + + +def _is_http_url(value: Any) -> bool: + if not isinstance(value, str): + return False + candidate = value.strip() + if not candidate: + return False + parsed = urlparse(candidate) + if parsed.scheme not in HTTP_SCHEMES or not parsed.netloc: + return False + if _NESTED_URL_AFTER_UNDERSCORE.search(candidate): + return False + return True + + +def _normalize_doi(value: Any) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + + lowered = candidate.lower() + for prefix in DOI_PREFIXES: + if lowered.startswith(prefix): + candidate = candidate[len(prefix) :] + break + + if not candidate or not candidate.startswith("10.") or "/" not in candidate: + return None + return candidate + + +def collect_unique_http_link_contexts( + jsonld_payload: dict[str, Any], +) -> list[dict[str, Any]]: + graph = jsonld_payload.get("@graph") + if not isinstance(graph, list): + return [] + + link_contexts: dict[str, dict[str, Any]] = {} + + def _record_link( + *, + link: str, + source_entity_id: str | None, + predicate: str | None, + ) -> None: + normalized_link = link.strip() + if not normalized_link or not _is_http_url(normalized_link): + return + if not _is_supportable_predicate(predicate): + return + if isinstance(source_entity_id, str) and source_entity_id == normalized_link: + # Self-reference: identifying URL recorded against the entity it identifies. + return + + context = link_contexts.setdefault( + normalized_link, + { + "link": normalized_link, + "source_entity_id": source_entity_id, + "predicate": predicate, + "relationships": [], + }, + ) + relationship = { + "source_entity_id": source_entity_id, + "predicate": predicate, + } + if relationship not in context["relationships"]: + context["relationships"].append(relationship) + + def _walk( + value: Any, + *, + source_entity_id: str | None, + predicate: str | None, + ) -> None: + if isinstance(value, str): + _record_link( + link=value, + source_entity_id=source_entity_id, + predicate=predicate, + ) + return + + if isinstance(value, dict): + at_id = value.get("@id") + if isinstance(at_id, str) and predicate is not None: + _record_link( + link=at_id, + source_entity_id=source_entity_id, + predicate=predicate, + ) + return + for key, nested in value.items(): + if key == "@id": + continue + _walk( + nested, + source_entity_id=source_entity_id, + predicate=key, + ) + return + + if isinstance(value, list): + for nested in value: + _walk( + nested, + source_entity_id=source_entity_id, + predicate=predicate, + ) + + for node in graph: + if not isinstance(node, dict): + continue + source_entity_id = node.get("@id") if isinstance(node.get("@id"), str) else None + for key, value in node.items(): + if key == "@id": + continue + _walk( + value, + source_entity_id=source_entity_id, + predicate=key, + ) + + return [link_contexts[link] for link in sorted(link_contexts)] + + +def _scan_entity_links( + entities: list[dict[str, Any]], +) -> tuple[list[dict[str, Any]], dict[str, set[str]], dict[str, str]]: + link_contexts: dict[str, dict[str, Any]] = {} + entity_link_map: dict[str, set[str]] = {} + article_identifier_link_map: dict[str, str] = {} + + def _record_link( + *, + link: str, + source_entity_id: str, + predicate: str | None, + ) -> None: + normalized_link = link.strip() + if not normalized_link or not _is_http_url(normalized_link): + return + + entity_link_map.setdefault(source_entity_id, set()).add(normalized_link) + + if not _is_supportable_predicate(predicate): + return + if source_entity_id == normalized_link: + # Self-reference: identifying URL recorded against the entity it identifies. + return + + context = link_contexts.setdefault( + normalized_link, + { + "link": normalized_link, + "source_entity_id": source_entity_id, + "predicate": predicate, + "relationships": [], + }, + ) + relationship = { + "source_entity_id": source_entity_id, + "predicate": predicate, + } + if relationship not in context["relationships"]: + context["relationships"].append(relationship) + + def _walk( + value: Any, + *, + source_entity_id: str, + predicate: str | None, + ) -> None: + if isinstance(value, str): + _record_link( + link=value, + source_entity_id=source_entity_id, + predicate=predicate, + ) + return + if isinstance(value, list): + for nested in value: + _walk( + nested, + source_entity_id=source_entity_id, + predicate=predicate, + ) + return + if isinstance(value, dict): + at_id = value.get("@id") + if isinstance(at_id, str) and predicate is not None: + _record_link( + link=at_id, + source_entity_id=source_entity_id, + predicate=predicate, + ) + return + for key, nested in value.items(): + if key in IDENTITY_KEYS: + continue + _walk( + nested, + source_entity_id=source_entity_id, + predicate=key, + ) + + for index, entity in enumerate(entities): + if not isinstance(entity, dict): + continue + raw_entity_id = entity.get("id") + if isinstance(raw_entity_id, str) and raw_entity_id: + source_entity_id = raw_entity_id + else: + source_entity_id = f"urn:pulse:entity-{index}" + entity_link_map.setdefault(source_entity_id, set()) + + entity_type = entity.get("type") + if entity_type == "schema:ScholarlyArticle": + raw_identifier = entity.get("schema:identifier") + if not isinstance(raw_identifier, str): + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + nested_identifier = identifiers.get("schema:identifier") + if isinstance(nested_identifier, str): + raw_identifier = nested_identifier + normalized_doi = _normalize_doi(raw_identifier) + if normalized_doi is not None: + doi_link = ( + raw_identifier.strip() + if isinstance(raw_identifier, str) and _is_http_url(raw_identifier) + else f"{DOI_BASE_URI}{normalized_doi}" + ) + article_identifier_link_map[source_entity_id] = doi_link + _record_link( + link=doi_link, + source_entity_id=source_entity_id, + predicate="schema:identifier", + ) + + for key, value in entity.items(): + if key in IDENTITY_KEYS: + continue + _walk( + value, + source_entity_id=source_entity_id, + predicate=key, + ) + + return ( + [link_contexts[link] for link in sorted(link_contexts)], + entity_link_map, + article_identifier_link_map, + ) + + +def _clean_invalid_http_values(value: Any, invalid_links: set[str]) -> Any: + if isinstance(value, str): + candidate = value.strip() + if _is_http_url(candidate) and candidate in invalid_links: + return _DROP + return value + if isinstance(value, list): + cleaned: list[Any] = [] + for item in value: + cleaned_item = _clean_invalid_http_values(item, invalid_links) + if cleaned_item is _DROP: + continue + cleaned.append(cleaned_item) + return cleaned + if isinstance(value, dict): + cleaned_dict: dict[str, Any] = {} + for key, item in value.items(): + cleaned_item = _clean_invalid_http_values(item, invalid_links) + if cleaned_item is _DROP: + cleaned_dict[key] = None + continue + cleaned_dict[key] = cleaned_item + return cleaned_dict + return value + + +def _entity_type_singular(entity: dict[str, Any]) -> str: + entity_type = entity.get("type") + if isinstance(entity_type, str): + return ENTITY_TYPE_TO_SINGULAR.get(entity_type, "entity") + return "entity" + + +def _apply_id_rewrites(value: Any, rewrites: dict[str, str]) -> Any: + """Replace every occurrence of a rewritten old id anywhere in `value`.""" + if isinstance(value, str): + return rewrites.get(value, value) + if isinstance(value, list): + return [_apply_id_rewrites(item, rewrites) for item in value] + if isinstance(value, dict): + return {key: _apply_id_rewrites(item, rewrites) for key, item in value.items()} + return value + + +def _entity_with_demoted_source( + entity: dict[str, Any], + failing_source: str | None, +) -> dict[str, Any]: + """Return a copy of `entity` with the failing identifier and id stripped.""" + demoted = dict(entity) + demoted["id"] = None + demoted["idSource"] = None + if isinstance(failing_source, str): + for field in _ID_SOURCE_TO_FIELD_KEYS.get(failing_source, ()): + if field in demoted: + demoted[field] = None + identifiers = demoted.get("identifiers") + if isinstance(identifiers, dict): + cloned_identifiers = dict(identifiers) + for field in _ID_SOURCE_TO_FIELD_KEYS.get(failing_source, ()): + if field in cloned_identifiers: + cloned_identifiers[field] = None + demoted["identifiers"] = cloned_identifiers + return demoted + + +def promote_failed_id_entities( + *, + assembled: AssembledOutput, + invalid_links: set[str], +) -> tuple[AssembledOutput, dict[str, str], list[str]]: + """Re-resolve entity ids whose URL failed link veracity. + + For each Person/Org/Repo/Article entity whose `id` is in `invalid_links`, + strip the failing identifier source and re-run the priority resolver. If + the new id differs, record the rewrite and propagate it across every + `@id`/`id` reference and every string-id occurrence in the assembled + output. + + Composite entities (Membership, Contribution) have no resolver entry and + are left unchanged. + + Returns the (possibly mutated) assembled output, the `old_id -> new_id` + rewrite map, and human-readable warnings to surface on the response. + """ + if not invalid_links: + return assembled, {}, [] + + rewrites: dict[str, str] = {} + warnings: list[str] = [] + candidates: list[dict[str, Any]] = [] + if isinstance(assembled.root_entity, dict): + candidates.append(assembled.root_entity) + candidates.extend(e for e in assembled.related_entities if isinstance(e, dict)) + + for entity in candidates: + old_id = entity.get("id") + if not isinstance(old_id, str) or old_id not in invalid_links: + continue + entity_type = entity.get("type") + resolver = _ID_RESOLVER_BY_TYPE.get(entity_type) if isinstance(entity_type, str) else None + if resolver is None: + continue + + old_id_source = entity.get("idSource") if isinstance(entity.get("idSource"), str) else None + demoted = _entity_with_demoted_source(entity, old_id_source) + try: + new_id, new_id_source = resolver(demoted) + except Exception: # noqa: BLE001 + continue + if not isinstance(new_id, str) or new_id == old_id: + continue + + entity["id"] = new_id + entity["idSource"] = new_id_source + rewrites[old_id] = new_id + warnings.append( + f"Promoted entity id {old_id} → {new_id} " + f"(failed source: {old_id_source}, new source: {new_id_source})", + ) + + if not rewrites: + return assembled, {}, [] + + new_root = ( + _apply_id_rewrites(assembled.root_entity, rewrites) + if isinstance(assembled.root_entity, dict) + else assembled.root_entity + ) + new_related = [_apply_id_rewrites(entity, rewrites) for entity in assembled.related_entities] + new_excluded = [_apply_id_rewrites(entity, rewrites) for entity in assembled.excluded_entities] + + promoted = AssembledOutput( + root_entity=new_root if isinstance(new_root, dict) else assembled.root_entity, + related_entities=new_related, + excluded_entities=new_excluded, + warnings=list(assembled.warnings), + ) + return promoted, rewrites, warnings + + +def apply_link_pruning_to_assembled_output( + *, + assembled: AssembledOutput, + invalid_links: set[str], + entity_link_map: dict[str, list[str]], + article_identifier_link_map: dict[str, str], +) -> tuple[AssembledOutput, list[str]]: + if not invalid_links: + return assembled, [] + + warnings: list[str] = [] + excluded_entities = list(assembled.excluded_entities) + + kept_root: dict[str, Any] | None = None + kept_related: list[dict[str, Any]] = [] + entities: list[tuple[bool, dict[str, Any]]] = [] + if isinstance(assembled.root_entity, dict): + entities.append((True, assembled.root_entity)) + entities.extend((False, entity) for entity in assembled.related_entities if isinstance(entity, dict)) + + for is_root, entity in entities: + entity_id = entity.get("id") if isinstance(entity.get("id"), str) else None + if not isinstance(entity_id, str) or not entity_id: + if is_root: + kept_root = entity + else: + kept_related.append(entity) + continue + + entity_links = set(entity_link_map.get(entity_id, [])) + valid_links = entity_links - invalid_links + invalid_for_entity = sorted(entity_links & invalid_links) + doi_link = article_identifier_link_map.get(entity_id) + + drop_reason: str | None = None + if entity_id in invalid_links: + drop_reason = "Entity identifier URL failed link validation" + elif isinstance(doi_link, str) and doi_link in invalid_links: + drop_reason = "Article DOI URL failed link validation" + elif entity_links and not valid_links: + drop_reason = "No valid URLs remaining after link validation" + + if drop_reason is not None: + excluded_entities.append( + { + "entity_type": _entity_type_singular(entity), + "entity": entity, + "reason": [ + { + "path": "", + "message": "link_veracity_pruned", + "constraint": "link_veracity", + "expected": drop_reason, + }, + ], + }, + ) + warnings.append( + ( + f"Removed {_entity_type_singular(entity)} entity '{entity_id}' after link validation: " + f"{drop_reason}" + ), + ) + continue + + cleaned_entity = _clean_invalid_http_values(entity, invalid_links) + if invalid_for_entity: + warnings.append( + ( + f"Removed invalid link(s) from entity '{entity_id}': " + + ", ".join(invalid_for_entity[:5]) + + (f", +{len(invalid_for_entity) - 5} more" if len(invalid_for_entity) > 5 else "") + ), + ) + if is_root: + kept_root = cleaned_entity + else: + kept_related.append(cleaned_entity) + + updated = AssembledOutput( + root_entity=kept_root, + related_entities=kept_related, + excluded_entities=excluded_entities, + warnings=list(assembled.warnings), + ) + return updated, warnings + + +async def run_link_veracity_stage( + *, + source_url: str, + providers: ProviderSet, + max_concurrency: int = 3, + llm_call_timeout_seconds: float = 120.0, + jsonld_payload: dict[str, Any] | None = None, + entities: list[dict[str, Any]] | None = None, + cache: ProviderCache | None = None, +) -> LinkVeracityStageResult: + entity_link_map: dict[str, set[str]] = {} + article_identifier_link_map: dict[str, str] = {} + if entities is not None: + link_contexts, entity_link_map, article_identifier_link_map = _scan_entity_links(entities) + else: + if not isinstance(jsonld_payload, dict): + message = "run_link_veracity_stage requires either entities or jsonld_payload" + raise ValueError(message) + link_contexts = collect_unique_http_link_contexts(jsonld_payload) + for context in link_contexts: + source_entity_id = context.get("source_entity_id") + link = context.get("link") + if not isinstance(source_entity_id, str) or not isinstance(link, str): + continue + entity_link_map.setdefault(source_entity_id, set()).add(link) + + checked_count = len(link_contexts) + if checked_count == 0: + return LinkVeracityStageResult( + checked_count=0, + entity_link_map={key: sorted(value) for key, value in entity_link_map.items()}, + article_identifier_link_map=article_identifier_link_map, + ) + + resolved_concurrency = max(1, int(max_concurrency)) + semaphore = asyncio.Semaphore(resolved_concurrency) + verifier = LLMLinkVeracityAgentV2( + llm_call_timeout_seconds=llm_call_timeout_seconds, + cache=cache, + ) + + async def _run_link_context(link_context: dict[str, Any]) -> dict[str, Any]: + link = link_context.get("link") + if not isinstance(link, str) or not link: + return { + "link": "", + "status": "error", + "error": "Missing link value", + "source_entity_id": link_context.get("source_entity_id"), + "predicate": link_context.get("predicate"), + "relationships": link_context.get("relationships", []), + } + + context = {**link_context, "source_url": source_url} + async with semaphore: + try: + result = await verifier.run(context, providers) + except Exception as exc: # noqa: BLE001 + return { + "link": link, + "status": "error", + "error": str(exc), + "source_entity_id": link_context.get("source_entity_id"), + "predicate": link_context.get("predicate"), + "relationships": link_context.get("relationships", []), + } + + payload = result.data if isinstance(result.data, dict) else {} + relationship_supported = bool(payload.get("relationship_supported")) + relationship_summary = payload.get("relationship_summary") + fetched_successfully = payload.get("fetched_successfully") + return { + "link": link, + "status": "ok", + "relationship_supported": relationship_supported, + "relationship_summary": ( + relationship_summary if isinstance(relationship_summary, str) else None + ), + "fetched_successfully": ( + fetched_successfully if isinstance(fetched_successfully, bool) else None + ), + "source_entity_id": link_context.get("source_entity_id"), + "predicate": link_context.get("predicate"), + "relationships": link_context.get("relationships", []), + "model": result.model, + "provider": result.provider, + "tokens_prompt": result.tokens_prompt, + "tokens_completion": result.tokens_completion, + } + + gathered_records = await asyncio.gather(*(_run_link_context(context) for context in link_contexts)) + records = sorted(gathered_records, key=lambda record: str(record.get("link", ""))) + + warnings: list[str] = [] + supported_count = 0 + unsupported_count = 0 + failed_count = 0 + invalid_links: set[str] = set() + for record in records: + status = record.get("status") + link = record.get("link") + if status != "ok": + failed_count += 1 + warnings.append( + f"Link veracity check failed: link={link}, error={record.get('error')}", + ) + continue + + if record.get("fetched_successfully") is False: + failed_count += 1 + if isinstance(link, str): + invalid_links.add(link) + warnings.append( + f"Link veracity fetch failed: link={link}", + ) + continue + + if bool(record.get("relationship_supported")): + supported_count += 1 + continue + + unsupported_count += 1 + warnings.append( + ( + "Link veracity unsupported relationship: " + f"link={link}, source={record.get('source_entity_id')}, " + f"predicate={record.get('predicate')}" + ), + ) + + return LinkVeracityStageResult( + records=records, + warnings=warnings, + checked_count=checked_count, + supported_count=supported_count, + unsupported_count=unsupported_count, + failed_count=failed_count, + invalid_links=sorted(invalid_links), + entity_link_map={key: sorted(value) for key, value in entity_link_map.items()}, + article_identifier_link_map=article_identifier_link_map, + ) diff --git a/src/v2/pipeline/stages/llm_critic.py b/src/v2/pipeline/stages/llm_critic.py new file mode 100644 index 0000000..ccc79cc --- /dev/null +++ b/src/v2/pipeline/stages/llm_critic.py @@ -0,0 +1,609 @@ +from __future__ import annotations + +from copy import deepcopy +from typing import Any + +from src.v2.agents import LLMCriticAgentV2, ProviderSet +from src.v2.ingest.cache import ProviderCache +from src.v2.pipeline.stages.models import LLMCriticStageResult, ReconciledEntities + +BUCKET_TO_SINGULAR = { + "organizations": "organization", + "persons": "person", + "repositories": "repository", + "articles": "article", +} + + +def _dedupe_strings(values: list[str]) -> list[str]: + deduped: list[str] = [] + seen: set[str] = set() + for value in values: + if value in seen: + continue + deduped.append(value) + seen.add(value) + return deduped + + +def _collect_repository_owner_org_ids(repositories: list[dict[str, Any]]) -> set[str]: + owner_org_ids: set[str] = set() + for repository in repositories: + owner_id = repository.get("pulse:ownedBy") + if isinstance(owner_id, str) and owner_id.strip(): + owner_org_ids.add(owner_id.strip()) + return owner_org_ids + + +def _collect_repository_ids(repositories: list[dict[str, Any]]) -> set[str]: + repository_ids: set[str] = set() + for repository in repositories: + repository_id = repository.get("id") + if isinstance(repository_id, str) and repository_id.strip(): + repository_ids.add(repository_id.strip()) + return repository_ids + + +def _collect_contributor_person_ids( + contributions: list[dict[str, Any]], + *, + repository_ids: set[str], + dropped_person_ids: set[str], +) -> set[str]: + person_ids: set[str] = set() + for contribution in contributions: + contribution_repo_id = contribution.get("pulse:contributionTo") + if not isinstance(contribution_repo_id, str) or contribution_repo_id not in repository_ids: + continue + person_id = contribution.get("schema:author") + if not isinstance(person_id, str) or not person_id.strip(): + continue + if person_id in dropped_person_ids: + continue + person_ids.add(person_id.strip()) + return person_ids + + +def _collect_membership_org_ids_for_persons( + memberships: list[dict[str, Any]], + *, + person_ids: set[str], +) -> set[str]: + organization_ids: set[str] = set() + for membership in memberships: + person_id = membership.get("_person_ref") + if not isinstance(person_id, str) or person_id not in person_ids: + continue + organization_id = membership.get("org:organization") + if not isinstance(organization_id, str) or not organization_id.strip(): + continue + organization_ids.add(organization_id.strip()) + return organization_ids + + +def _collect_owner_org_ancestor_ids( + organizations: list[dict[str, Any]], + *, + seed_owner_ids: set[str], +) -> set[str]: + by_id: dict[str, dict[str, Any]] = {} + for organization in organizations: + org_id = organization.get("id") + if not isinstance(org_id, str) or not org_id.strip(): + continue + by_id[org_id] = organization + + protected: set[str] = set() + queue = [org_id for org_id in seed_owner_ids if org_id in by_id] + while queue: + current = queue.pop(0) + if current in protected: + continue + protected.add(current) + parents = by_id.get(current, {}).get("org:unitOf") or [] + if isinstance(parents, str): + parents = [parents] + if isinstance(parents, list): + for parent_id in parents: + if ( + isinstance(parent_id, str) + and parent_id in by_id + and parent_id not in protected + ): + queue.append(parent_id) + + return protected + + +def _normalize_drop_suggestions(raw_payload: Any) -> list[dict[str, Any]]: + suggestions: list[dict[str, Any]] = [] + if not isinstance(raw_payload, list): + return suggestions + + for item in raw_payload: + if isinstance(item, str): + candidate_id = item.strip() + if candidate_id: + suggestions.append({"id": candidate_id, "reason": None, "confidence": None}) + continue + + if not isinstance(item, dict): + continue + + candidate_id = item.get("id") + if not isinstance(candidate_id, str) or not candidate_id.strip(): + continue + + reason = item.get("reason") + if not isinstance(reason, str) or not reason.strip(): + reason = None + + confidence = item.get("confidence") + if isinstance(confidence, (int, float)): + confidence = max(0.0, min(float(confidence), 1.0)) + else: + confidence = None + + suggestions.append( + { + "id": candidate_id.strip(), + "reason": reason, + "confidence": confidence, + }, + ) + + return suggestions + + +def _build_pruned_excluded_entity( + *, + entity_type: str, + entity_payload: dict[str, Any], + reason: str | None, +) -> dict[str, Any]: + return { + "entity_type": entity_type, + "entity": deepcopy(entity_payload), + "reason": [ + { + "path": "", + "message": "critic_pruned", + "constraint": "critic_prune", + "expected": reason, + }, + ], + } + + +def _drop_primary_entities( + *, + entities_by_bucket: dict[str, list[dict[str, Any]]], + drop_ids_by_bucket: dict[str, set[str]], + reason_by_id_by_bucket: dict[str, dict[str, str | None]], +) -> list[dict[str, Any]]: + pruned_excluded_entities: list[dict[str, Any]] = [] + + for bucket, entities in entities_by_bucket.items(): + singular = BUCKET_TO_SINGULAR[bucket] + kept: list[dict[str, Any]] = [] + drop_ids = drop_ids_by_bucket.get(bucket, set()) + reason_by_id = reason_by_id_by_bucket.get(bucket, {}) + + for entity in entities: + entity_id = entity.get("id") + if not isinstance(entity_id, str) or entity_id not in drop_ids: + kept.append(entity) + continue + + pruned_excluded_entities.append( + _build_pruned_excluded_entity( + entity_type=singular, + entity_payload=entity, + reason=reason_by_id.get(entity_id), + ), + ) + + entities_by_bucket[bucket] = kept + + return pruned_excluded_entities + + +def _cleanup_relationships_after_prune( + *, + entities_by_bucket: dict[str, list[dict[str, Any]]], + memberships: list[dict[str, Any]], + contributions: list[dict[str, Any]], + dropped_person_ids: set[str], + dropped_org_ids: set[str], + dropped_repo_ids: set[str], +) -> tuple[list[dict[str, Any]], list[dict[str, Any]], int, int]: + repositories = entities_by_bucket["repositories"] + articles = entities_by_bucket["articles"] + persons = entities_by_bucket["persons"] + organizations = entities_by_bucket["organizations"] + + for repository in repositories: + authors = repository.get("schema:author") + if isinstance(authors, list): + repository["schema:author"] = _dedupe_strings( + [ + author + for author in authors + if isinstance(author, str) and author and author not in dropped_person_ids + ], + ) + + owner = repository.get("pulse:ownedBy") + if isinstance(owner, str) and (owner in dropped_person_ids or owner in dropped_org_ids): + repository["pulse:ownedBy"] = None + + fork_of = repository.get("pulse:isForkOf") + if isinstance(fork_of, str) and fork_of in dropped_repo_ids: + repository["pulse:isForkOf"] = None + + for article in articles: + authors = article.get("schema:author") + if isinstance(authors, list): + article["schema:author"] = _dedupe_strings( + [ + author + for author in authors + if isinstance(author, str) and author and author not in dropped_person_ids + ], + ) + + source_org = article.get("schema:sourceOrganization") + if isinstance(source_org, str) and source_org in dropped_org_ids: + article["schema:sourceOrganization"] = None + + for person in persons: + affiliations = person.get("affiliations") + if isinstance(affiliations, list): + cleaned_affiliations: list[Any] = [] + for affiliation in affiliations: + if isinstance(affiliation, str): + if affiliation in dropped_org_ids: + continue + cleaned_affiliations.append(affiliation) + continue + + if isinstance(affiliation, dict): + updated = deepcopy(affiliation) + org_id = updated.get("organizationId") + if isinstance(org_id, str) and org_id in dropped_org_ids: + continue + cleaned_affiliations.append(updated) + continue + + cleaned_affiliations.append(affiliation) + + person["affiliations"] = cleaned_affiliations + + owns = person.get("pulse:owns") + if isinstance(owns, list): + person["pulse:owns"] = _dedupe_strings( + [ + repo_id + for repo_id in owns + if isinstance(repo_id, str) and repo_id and repo_id not in dropped_repo_ids + ], + ) + + for organization in organizations: + has_units = organization.get("org:hasUnit") + if isinstance(has_units, list): + organization["org:hasUnit"] = _dedupe_strings( + [ + unit_id + for unit_id in has_units + if isinstance(unit_id, str) and unit_id and unit_id not in dropped_org_ids + ], + ) + + unit_of = organization.get("org:unitOf") + if isinstance(unit_of, str): + organization["org:unitOf"] = ( + [] if unit_of in dropped_org_ids else [unit_of] + ) + elif isinstance(unit_of, list): + organization["org:unitOf"] = [ + parent_id + for parent_id in unit_of + if isinstance(parent_id, str) and parent_id and parent_id not in dropped_org_ids + ] + + owns = organization.get("pulse:owns") + if isinstance(owns, list): + organization["pulse:owns"] = _dedupe_strings( + [ + repo_id + for repo_id in owns + if isinstance(repo_id, str) and repo_id and repo_id not in dropped_repo_ids + ], + ) + + surviving_memberships: list[dict[str, Any]] = [] + removed_memberships = 0 + for membership in memberships: + person_id = membership.get("_person_ref") + if not isinstance(person_id, str): + person_id = None + membership_id = membership.get("id") + if isinstance(membership_id, str) and "_" in membership_id: + person_id = membership_id.split("_", maxsplit=1)[0] + + organization_id = membership.get("org:organization") + if not isinstance(organization_id, str): + organization_id = None + membership_id = membership.get("id") + if isinstance(membership_id, str) and "_" in membership_id: + organization_id = membership_id.split("_", maxsplit=1)[1] + + if ( + isinstance(person_id, str) + and person_id in dropped_person_ids + or isinstance(organization_id, str) + and organization_id in dropped_org_ids + ): + removed_memberships += 1 + continue + + surviving_memberships.append(membership) + + surviving_contributions: list[dict[str, Any]] = [] + removed_contributions = 0 + for contribution in contributions: + person_id = contribution.get("schema:author") + if not isinstance(person_id, str): + person_id = None + contribution_id = contribution.get("id") + if isinstance(contribution_id, str) and "_" in contribution_id: + person_id = contribution_id.split("_", maxsplit=1)[0] + + repository_id = contribution.get("pulse:contributionTo") + if not isinstance(repository_id, str): + repository_id = None + contribution_id = contribution.get("id") + if isinstance(contribution_id, str) and "_" in contribution_id: + repository_id = contribution_id.split("_", maxsplit=1)[1] + + if ( + isinstance(person_id, str) + and person_id in dropped_person_ids + or isinstance(repository_id, str) + and repository_id in dropped_repo_ids + ): + removed_contributions += 1 + continue + + surviving_contributions.append(contribution) + + memberships_by_person: dict[str, list[str]] = {} + for membership in surviving_memberships: + person_id = membership.get("_person_ref") + membership_id = membership.get("id") + if not isinstance(person_id, str) or not isinstance(membership_id, str): + continue + memberships_by_person.setdefault(person_id, []).append(membership_id) + + contributions_by_person: dict[str, list[str]] = {} + for contribution in surviving_contributions: + person_id = contribution.get("schema:author") + contribution_id = contribution.get("id") + if not isinstance(person_id, str) or not isinstance(contribution_id, str): + continue + contributions_by_person.setdefault(person_id, []).append(contribution_id) + + for person in persons: + person_id = person.get("id") + if not isinstance(person_id, str): + continue + person["org:hasMembership"] = _dedupe_strings(memberships_by_person.get(person_id, [])) + person["pulse:hasContribution"] = _dedupe_strings(contributions_by_person.get(person_id, [])) + + return ( + surviving_memberships, + surviving_contributions, + removed_memberships, + removed_contributions, + ) + + +async def run_llm_critic_stage( # noqa: PLR0913 + *, + reconciled: ReconciledEntities, + source_url: str, + detected_type: str, + providers: ProviderSet, + initial_context: dict[str, Any] | None = None, + pipeline_outputs: dict[str, Any] | None = None, + max_concurrency: int = 3, + llm_call_timeout_seconds: float = 180.0, + agent: LLMCriticAgentV2 | None = None, + cache: ProviderCache | None = None, +) -> LLMCriticStageResult: + del max_concurrency + entities_by_bucket = { + "persons": deepcopy(reconciled.entities.get("persons", [])), + "organizations": deepcopy(reconciled.entities.get("organizations", [])), + "repositories": deepcopy(reconciled.entities.get("repositories", [])), + "articles": deepcopy(reconciled.entities.get("articles", [])), + } + memberships = deepcopy(reconciled.memberships) + contributions = deepcopy(reconciled.contributions) + + critic_agent = agent or LLMCriticAgentV2( + llm_call_timeout_seconds=llm_call_timeout_seconds, + cache=cache, + ) + agent_result = await critic_agent.run( + { + "source_url": source_url, + "detected_type": detected_type, + "initial_context": deepcopy(initial_context) if isinstance(initial_context, dict) else {}, + "pipeline_outputs": deepcopy(pipeline_outputs) if isinstance(pipeline_outputs, dict) else {}, + "reconciled_entities": deepcopy(entities_by_bucket), + "memberships": deepcopy(memberships), + "contributions": deepcopy(contributions), + }, + providers, + ) + + decisions = { + "organizations": _normalize_drop_suggestions(agent_result.data.get("organizations")), + "persons": _normalize_drop_suggestions(agent_result.data.get("persons")), + "repositories": _normalize_drop_suggestions(agent_result.data.get("repositories")), + "articles": _normalize_drop_suggestions(agent_result.data.get("articles")), + } + + existing_ids_by_bucket = { + bucket: { + entity_id + for entity_id in ( + entity.get("id") if isinstance(entity, dict) else None + for entity in entities_by_bucket[bucket] + ) + if isinstance(entity_id, str) and entity_id + } + for bucket in BUCKET_TO_SINGULAR + } + + drop_ids_by_bucket: dict[str, set[str]] = { + bucket: set() + for bucket in BUCKET_TO_SINGULAR + } + reason_by_id_by_bucket: dict[str, dict[str, str | None]] = { + bucket: {} + for bucket in BUCKET_TO_SINGULAR + } + + for bucket, suggestions in decisions.items(): + for suggestion in suggestions: + candidate_id = suggestion["id"] + if candidate_id not in existing_ids_by_bucket[bucket]: + continue + drop_ids_by_bucket[bucket].add(candidate_id) + reason_by_id_by_bucket[bucket][candidate_id] = suggestion.get("reason") + + owner_org_ids = _collect_repository_owner_org_ids(entities_by_bucket["repositories"]) + protected_owner_context_org_ids = _collect_owner_org_ancestor_ids( + entities_by_bucket["organizations"], + seed_owner_ids=owner_org_ids, + ) + for protected_org_id in protected_owner_context_org_ids: + drop_ids_by_bucket["organizations"].discard(protected_org_id) + reason_by_id_by_bucket["organizations"].pop(protected_org_id, None) + + root_bucket = { + "repository": "repositories", + "user": "persons", + "organization": "organizations", + }.get(detected_type) + root_protected: list[str] = [] + if isinstance(root_bucket, str) and entities_by_bucket[root_bucket]: + root_id = entities_by_bucket[root_bucket][0].get("id") + if isinstance(root_id, str) and root_id in drop_ids_by_bucket[root_bucket]: + drop_ids_by_bucket[root_bucket].remove(root_id) + reason_by_id_by_bucket[root_bucket].pop(root_id, None) + root_protected.append(root_id) + + protected_contributor_affiliation_org_ids: set[str] = set() + repository_ids = _collect_repository_ids(entities_by_bucket["repositories"]) + if repository_ids: + contributing_person_ids = _collect_contributor_person_ids( + contributions, + repository_ids=repository_ids, + dropped_person_ids=drop_ids_by_bucket["persons"], + ) + protected_contributor_affiliation_org_ids = _collect_membership_org_ids_for_persons( + memberships, + person_ids=contributing_person_ids, + ) + for protected_org_id in protected_contributor_affiliation_org_ids: + drop_ids_by_bucket["organizations"].discard(protected_org_id) + reason_by_id_by_bucket["organizations"].pop(protected_org_id, None) + + pruned_excluded_entities = _drop_primary_entities( + entities_by_bucket=entities_by_bucket, + drop_ids_by_bucket=drop_ids_by_bucket, + reason_by_id_by_bucket=reason_by_id_by_bucket, + ) + + dropped_person_ids = drop_ids_by_bucket["persons"] + dropped_org_ids = drop_ids_by_bucket["organizations"] + dropped_repo_ids = drop_ids_by_bucket["repositories"] + ( + surviving_memberships, + surviving_contributions, + removed_memberships, + removed_contributions, + ) = _cleanup_relationships_after_prune( + entities_by_bucket=entities_by_bucket, + memberships=memberships, + contributions=contributions, + dropped_person_ids=dropped_person_ids, + dropped_org_ids=dropped_org_ids, + dropped_repo_ids=dropped_repo_ids, + ) + + proposed_drop_count = sum(len(suggestions) for suggestions in decisions.values()) + applied_drop_count = len(pruned_excluded_entities) + dropped_by_type = { + bucket: len(drop_ids) + for bucket, drop_ids in drop_ids_by_bucket.items() + } + + warnings: list[str] = [ + ( + "LLM critic summary: " + f"proposed={proposed_drop_count}, " + f"applied={applied_drop_count}, " + f"root_protected={len(root_protected)}, " + f"cascade_memberships_removed={removed_memberships}, " + f"cascade_contributions_removed={removed_contributions}" + ), + ] + if root_protected: + warnings.append( + "LLM critic root protection override: " + + ", ".join(root_protected), + ) + if protected_owner_context_org_ids: + warnings.append( + "LLM critic owner-context org protection override: " + + ", ".join(sorted(protected_owner_context_org_ids)), + ) + if protected_contributor_affiliation_org_ids: + warnings.append( + "LLM critic contributor-affiliation org protection override: " + + ", ".join(sorted(protected_contributor_affiliation_org_ids)), + ) + + reconciled_output = ReconciledEntities( + entities=entities_by_bucket, + memberships=surviving_memberships, + contributions=surviving_contributions, + link_warnings=list(reconciled.link_warnings), + reconciliation_debug=deepcopy(reconciled.reconciliation_debug), + ) + + applied_payload = { + "proposed_drop_count": proposed_drop_count, + "applied_drop_count": applied_drop_count, + "dropped_by_type": dropped_by_type, + "protected_root_ids": root_protected, + "protected_owner_context_org_ids": sorted(protected_owner_context_org_ids), + "protected_contributor_affiliation_org_ids": sorted( + protected_contributor_affiliation_org_ids, + ), + "cascade_memberships_removed": removed_memberships, + "cascade_contributions_removed": removed_contributions, + } + + return LLMCriticStageResult( + reconciled=reconciled_output, + warnings=warnings, + decisions=decisions, + applied=applied_payload, + pruned_excluded_entities=pruned_excluded_entities, + ) diff --git a/src/v2/pipeline/stages/llm_dedup.py b/src/v2/pipeline/stages/llm_dedup.py new file mode 100644 index 0000000..7cffc17 --- /dev/null +++ b/src/v2/pipeline/stages/llm_dedup.py @@ -0,0 +1,874 @@ +from __future__ import annotations + +from copy import deepcopy +import re +from typing import Any + +from src.v2.agents import LLMDedupAgentV2, ProviderSet +from src.v2.agents.models import generate_uuid +from src.v2.canonicalization import ( + resolve_article_id, + resolve_organization_id, + resolve_person_id, + resolve_repository_id, +) +from src.v2.pipeline.stages.models import LLMDedupStageResult + +PRIMARY_BUCKETS: tuple[str, ...] = ("organizations", "persons", "repositories", "articles") +BUCKET_KEYS: tuple[str, ...] = ( + "organizations", + "persons", + "repositories", + "articles", + "memberships", + "contributions", +) +INFOSCIENCE_UUID_PATTERN = re.compile( + r"(?:entities/(?:person|organization|publication)|core/items)/" + r"([0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12})", + flags=re.IGNORECASE, +) + + +def _dedupe_strings(values: list[str]) -> list[str]: + deduped: list[str] = [] + seen: set[str] = set() + for value in values: + if value in seen: + continue + deduped.append(value) + seen.add(value) + return deduped + + +def _is_non_empty(value: Any) -> bool: + if value is None: + return False + if isinstance(value, str): + return bool(value.strip()) + if isinstance(value, (list, dict, set, tuple)): + return len(value) > 0 + return True + + +def _entity_score(entity: dict[str, Any]) -> int: + score = 0 + for key, value in entity.items(): + if key in {"id", "idSource", "type", "shacl", "identifiers"}: + continue + if _is_non_empty(value): + score += 1 + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + score += sum(1 for value in identifiers.values() if _is_non_empty(value)) + return score + + +def _lookup_identifier(entity: dict[str, Any], *keys: str) -> str | None: + identifiers = entity.get("identifiers") + for key in keys: + direct = entity.get(key) + if isinstance(direct, str) and direct.strip(): + return direct.strip() + if isinstance(identifiers, dict): + nested = identifiers.get(key) + if isinstance(nested, str) and nested.strip(): + return nested.strip() + return None + + +def _normalize_orcid(value: str | None) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + if candidate.lower().startswith("https://orcid.org/"): + candidate = candidate.rsplit("/", maxsplit=1)[-1] + return candidate.upper() + + +def _normalize_ror(value: str | None) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + if candidate.lower().startswith("https://ror.org/"): + candidate = candidate.rsplit("/", maxsplit=1)[-1] + candidate = candidate.lower() + if re.fullmatch(r"[0-9a-z]{9}", candidate) is None: + return None + return f"https://ror.org/{candidate}" + + +def _normalize_infoscience_uuid(value: str | None) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + direct_match = re.fullmatch( + r"[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}", + candidate, + flags=re.IGNORECASE, + ) + if direct_match: + return candidate.lower() + match = INFOSCIENCE_UUID_PATTERN.search(candidate) + if match is None: + return None + return match.group(1).lower() + + +def _normalize_github_handle(value: str | None) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + if candidate.lower().startswith("https://github.com/"): + candidate = candidate[len("https://github.com/") :] + candidate = candidate.split("/", maxsplit=1)[0] + if candidate.startswith("@"): + candidate = candidate[1:] + return candidate.casefold() if candidate else None + + +def _normalize_repository_handle(value: str | None) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + if candidate.lower().startswith("https://github.com/"): + candidate = candidate[len("https://github.com/") :] + parts = [part for part in candidate.split("/") if part] + if len(parts) != 2: + return None + return f"{parts[0]}/{parts[1]}".casefold() + + +def _normalize_doi(value: str | None) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + for prefix in ( + "https://doi.org/", + "http://doi.org/", + "https://dx.doi.org/", + "http://dx.doi.org/", + ): + if candidate.lower().startswith(prefix): + candidate = candidate[len(prefix) :] + break + return candidate.lower() if "/" in candidate else None + + +def _priority_rank(bucket: str, entity: dict[str, Any]) -> int: + if bucket == "organizations": + if _normalize_ror(_lookup_identifier(entity, "pulse:ror", "schema:identifier")): + return 4 + if _normalize_infoscience_uuid( + _lookup_identifier(entity, "pulse:infoscienceOrganizationIdentifier"), + ): + return 3 + if _normalize_github_handle(_lookup_identifier(entity, "pulse:githubOrganizationHandle")): + return 2 + return 1 + + if bucket == "persons": + if _normalize_orcid(_lookup_identifier(entity, "pulse:orcid", "pulse:orcidIdentifier")): + return 4 + if _normalize_infoscience_uuid(_lookup_identifier(entity, "pulse:infosciencePersonIdentifier")): + return 3 + if _normalize_github_handle(_lookup_identifier(entity, "pulse:githubUsername")): + return 2 + return 1 + + if bucket == "repositories": + if _normalize_repository_handle(_lookup_identifier(entity, "pulse:githubRepositoryHandle")): + return 3 + if _normalize_doi(_lookup_identifier(entity, "schema:citation", "schema:identifier")): + return 2 + return 1 + + if _normalize_doi(_lookup_identifier(entity, "schema:identifier")): + return 3 + if _normalize_infoscience_uuid(_lookup_identifier(entity, "pulse:infoscienceArticleIdentifier")): + return 2 + return 1 + + +def _cluster_conflict_reason(bucket: str, entities: list[dict[str, Any]]) -> str | None: + if bucket == "organizations": + rors = { + normalized + for normalized in ( + _normalize_ror(_lookup_identifier(entity, "pulse:ror", "schema:identifier")) + for entity in entities + ) + if isinstance(normalized, str) + } + if len(rors) > 1: + return "conflicting_ror_identifiers" + + if bucket == "persons": + orcids = { + normalized + for normalized in ( + _normalize_orcid(_lookup_identifier(entity, "pulse:orcid", "pulse:orcidIdentifier")) + for entity in entities + ) + if isinstance(normalized, str) + } + if len(orcids) > 1: + return "conflicting_orcid_identifiers" + + if bucket == "repositories": + handles = { + normalized + for normalized in ( + _normalize_repository_handle( + _lookup_identifier(entity, "pulse:githubRepositoryHandle"), + ) + for entity in entities + ) + if isinstance(normalized, str) + } + if len(handles) > 1: + return "conflicting_github_repository_handles" + + if bucket == "articles": + dois = { + normalized + for normalized in ( + _normalize_doi(_lookup_identifier(entity, "schema:identifier")) + for entity in entities + ) + if isinstance(normalized, str) + } + if len(dois) > 1: + return "conflicting_doi_identifiers" + + return None + + +def _select_cluster_winner(bucket: str, entities: list[dict[str, Any]]) -> dict[str, Any]: + return sorted( + entities, + key=lambda entity: ( + -_priority_rank(bucket, entity), + -_entity_score(entity), + str(entity.get("id") or ""), + ), + )[0] + + +def _merge_entity_payload(canonical: dict[str, Any], candidate: dict[str, Any]) -> None: + canonical_identifiers = canonical.get("identifiers") + if not isinstance(canonical_identifiers, dict): + canonical_identifiers = {} + candidate_identifiers = candidate.get("identifiers") + if isinstance(candidate_identifiers, dict): + for key, value in candidate_identifiers.items(): + if not _is_non_empty(value): + continue + existing = canonical_identifiers.get(key) + if isinstance(existing, list) and isinstance(value, list): + canonical_identifiers[key] = _dedupe_strings( + [ + *[item for item in existing if isinstance(item, str)], + *[item for item in value if isinstance(item, str)], + ], + ) + continue + if not _is_non_empty(existing): + canonical_identifiers[key] = deepcopy(value) + canonical["identifiers"] = canonical_identifiers + + for key, value in candidate.items(): + if key in {"id", "idSource", "identifiers"}: + continue + if not _is_non_empty(value): + continue + + existing = canonical.get(key) + if isinstance(existing, list) and isinstance(value, list): + merged_list: list[Any] = [] + for item in [*existing, *value]: + if item in merged_list: + continue + merged_list.append(deepcopy(item)) + canonical[key] = merged_list + continue + + if isinstance(existing, dict) and isinstance(value, dict): + merged_dict = deepcopy(existing) + for nested_key, nested_value in value.items(): + if not _is_non_empty(nested_value): + continue + if not _is_non_empty(merged_dict.get(nested_key)): + merged_dict[nested_key] = deepcopy(nested_value) + canonical[key] = merged_dict + continue + + if not _is_non_empty(existing): + canonical[key] = deepcopy(value) + + +def _resolve_entity_identity(bucket: str, entity: dict[str, Any]) -> tuple[str, str]: + normalized_entity = deepcopy(entity) + normalized_entity.pop("id", None) + normalized_entity.pop("idSource", None) + + if bucket == "organizations": + return resolve_organization_id(normalized_entity) + if bucket == "persons": + return resolve_person_id(normalized_entity) + if bucket == "repositories": + return resolve_repository_id(normalized_entity) + return resolve_article_id(normalized_entity) + + +def _normalize_cluster_payload(raw_payload: Any) -> list[dict[str, Any]]: + clusters: list[dict[str, Any]] = [] + if not isinstance(raw_payload, list): + return clusters + + for item in raw_payload: + ids: list[str] = [] + reason: str | None = None + confidence: float | None = None + + if isinstance(item, list): + ids = [value for value in item if isinstance(value, str) and value.strip()] + elif isinstance(item, dict): + raw_ids = item.get("ids") + if not isinstance(raw_ids, list): + raw_ids = item.get("cluster") if isinstance(item.get("cluster"), list) else [] + ids = [value for value in raw_ids if isinstance(value, str) and value.strip()] + raw_reason = item.get("reason") + if isinstance(raw_reason, str) and raw_reason.strip(): + reason = raw_reason.strip() + raw_confidence = item.get("confidence") + if isinstance(raw_confidence, (int, float)): + bounded = max(0.0, min(float(raw_confidence), 1.0)) + confidence = bounded + else: + continue + + deduped_ids = _dedupe_strings(ids) + clusters.append( + { + "ids": deduped_ids, + "reason": reason, + "confidence": confidence, + }, + ) + + return clusters + + +def _remap_string(value: Any, mapping: dict[str, str]) -> Any: + if not isinstance(value, str): + return value + return mapping.get(value, value) + + +def _parse_composite_id(composite_id: Any) -> tuple[str | None, str | None]: + if not isinstance(composite_id, str) or "_" not in composite_id: + return None, None + left, right = composite_id.split("_", maxsplit=1) + if not left or not right: + return None, None + return left, right + + +def _resolve_membership_person_id(membership: dict[str, Any]) -> str | None: + for key in ("_person_ref", "schema:author"): + value = membership.get(key) + if isinstance(value, str) and value: + return value + left, _ = _parse_composite_id(membership.get("id")) + return left + + +def _resolve_membership_org_id(membership: dict[str, Any]) -> str | None: + org_ref = membership.get("org:organization") + if isinstance(org_ref, str) and org_ref: + return org_ref + _, right = _parse_composite_id(membership.get("id")) + return right + + +def _resolve_contribution_person_id(contribution: dict[str, Any]) -> str | None: + value = contribution.get("schema:author") + if isinstance(value, str) and value: + return value + left, _ = _parse_composite_id(contribution.get("id")) + return left + + +def _resolve_contribution_repo_id(contribution: dict[str, Any]) -> str | None: + value = contribution.get("pulse:contributionTo") + if isinstance(value, str) and value: + return value + _, right = _parse_composite_id(contribution.get("id")) + return right + + +def _apply_remaps(typed_entity_buckets: dict[str, list[dict[str, Any]]], remaps: dict[str, dict[str, str]]) -> list[str]: + warnings: list[str] = [] + person_map = remaps.get("persons", {}) + org_map = remaps.get("organizations", {}) + repo_map = remaps.get("repositories", {}) + all_map: dict[str, str] = {} + for remap in remaps.values(): + all_map.update(remap) + + repositories = typed_entity_buckets["repositories"] + for repository in repositories: + authors = repository.get("schema:author") + if isinstance(authors, list): + repository["schema:author"] = _dedupe_strings( + [ + _remap_string(author, person_map) + for author in authors + if isinstance(author, str) and author + ], + ) + repository["pulse:ownedBy"] = _remap_string(repository.get("pulse:ownedBy"), all_map) + repository["pulse:isForkOf"] = _remap_string(repository.get("pulse:isForkOf"), repo_map) + + articles = typed_entity_buckets["articles"] + for article in articles: + authors = article.get("schema:author") + if isinstance(authors, list): + article["schema:author"] = _dedupe_strings( + [ + _remap_string(author, person_map) + for author in authors + if isinstance(author, str) and author + ], + ) + article["schema:sourceOrganization"] = _remap_string( + article.get("schema:sourceOrganization"), + org_map, + ) + + persons = typed_entity_buckets["persons"] + for person in persons: + affiliations = person.get("affiliations") + if isinstance(affiliations, list): + remapped_affiliations: list[Any] = [] + for affiliation in affiliations: + if isinstance(affiliation, str): + remapped_affiliations.append(_remap_string(affiliation, org_map)) + continue + if isinstance(affiliation, dict): + updated = deepcopy(affiliation) + org_id = updated.get("organizationId") + if isinstance(org_id, str): + updated["organizationId"] = _remap_string(org_id, org_map) + remapped_affiliations.append(updated) + continue + remapped_affiliations.append(affiliation) + person["affiliations"] = remapped_affiliations + + owns = person.get("pulse:owns") + if isinstance(owns, list): + person["pulse:owns"] = _dedupe_strings( + [ + _remap_string(repository_id, repo_map) + for repository_id in owns + if isinstance(repository_id, str) and repository_id + ], + ) + + organizations = typed_entity_buckets["organizations"] + for organization in organizations: + has_units = organization.get("org:hasUnit") + if isinstance(has_units, list): + organization["org:hasUnit"] = _dedupe_strings( + [ + _remap_string(unit_id, org_map) + for unit_id in has_units + if isinstance(unit_id, str) and unit_id + ], + ) + unit_of_raw = organization.get("org:unitOf") + if isinstance(unit_of_raw, str): + unit_of_raw = [unit_of_raw] + if isinstance(unit_of_raw, list): + organization["org:unitOf"] = _dedupe_strings( + [ + _remap_string(parent_id, org_map) + for parent_id in unit_of_raw + if isinstance(parent_id, str) and parent_id + ], + ) + else: + organization["org:unitOf"] = [] + owns = organization.get("pulse:owns") + if isinstance(owns, list): + organization["pulse:owns"] = _dedupe_strings( + [ + _remap_string(repository_id, repo_map) + for repository_id in owns + if isinstance(repository_id, str) and repository_id + ], + ) + + memberships = typed_entity_buckets["memberships"] + normalized_memberships: dict[str, dict[str, Any]] = {} + membership_order: list[str] = [] + for membership in memberships: + person_id = _resolve_membership_person_id(membership) + org_id = _resolve_membership_org_id(membership) + if isinstance(person_id, str): + person_id = _remap_string(person_id, person_map) + if isinstance(org_id, str): + org_id = _remap_string(org_id, org_map) + + if not isinstance(person_id, str) or not person_id or not isinstance(org_id, str) or not org_id: + warnings.append( + "llm_dedup: unable to recompute membership composite id due to missing person/org reference", + ) + continue + + membership_id = f"{person_id}_{org_id}" + normalized = deepcopy(membership) + identifiers = normalized.get("identifiers") + if not isinstance(identifiers, dict): + identifiers = {} + identifiers["pulse:composite"] = membership_id + if not isinstance(identifiers.get("uuid"), str) or not identifiers.get("uuid"): + identifiers["uuid"] = generate_uuid() + + normalized["id"] = membership_id + normalized["type"] = "org:Membership" + normalized["shacl"] = "pulse:MembershipShape" + normalized["identifiers"] = identifiers + normalized["idSource"] = "pulse:composite" + normalized["_person_ref"] = person_id + normalized["org:organization"] = org_id + + existing = normalized_memberships.get(membership_id) + if existing is None: + normalized_memberships[membership_id] = normalized + membership_order.append(membership_id) + continue + + candidate = _select_cluster_winner( + "articles", + [existing, normalized], + ) + if candidate is normalized: + _merge_entity_payload(normalized, existing) + normalized_memberships[membership_id] = normalized + else: + _merge_entity_payload(existing, normalized) + + typed_entity_buckets["memberships"] = [ + normalized_memberships[membership_id] + for membership_id in membership_order + if membership_id in normalized_memberships + ] + + contributions = typed_entity_buckets["contributions"] + normalized_contributions: dict[str, dict[str, Any]] = {} + contribution_order: list[str] = [] + for contribution in contributions: + person_id = _resolve_contribution_person_id(contribution) + repository_id = _resolve_contribution_repo_id(contribution) + if isinstance(person_id, str): + person_id = _remap_string(person_id, person_map) + if isinstance(repository_id, str): + repository_id = _remap_string(repository_id, repo_map) + + if ( + not isinstance(person_id, str) + or not person_id + or not isinstance(repository_id, str) + or not repository_id + ): + warnings.append( + "llm_dedup: unable to recompute contribution composite id due to missing person/repository reference", + ) + continue + + contribution_id = f"{person_id}_{repository_id}" + normalized = deepcopy(contribution) + identifiers = normalized.get("identifiers") + if not isinstance(identifiers, dict): + identifiers = {} + identifiers["pulse:composite"] = contribution_id + if not isinstance(identifiers.get("uuid"), str) or not identifiers.get("uuid"): + identifiers["uuid"] = generate_uuid() + + normalized["id"] = contribution_id + normalized["type"] = "pulse:Contribution" + normalized["shacl"] = "pulse:ContributionShape" + normalized["identifiers"] = identifiers + normalized["idSource"] = "pulse:composite" + normalized["schema:author"] = person_id + normalized["pulse:contributionTo"] = repository_id + + existing = normalized_contributions.get(contribution_id) + if existing is None: + normalized_contributions[contribution_id] = normalized + contribution_order.append(contribution_id) + continue + + candidate = _select_cluster_winner( + "articles", + [existing, normalized], + ) + if candidate is normalized: + _merge_entity_payload(normalized, existing) + normalized_contributions[contribution_id] = normalized + else: + _merge_entity_payload(existing, normalized) + + typed_entity_buckets["contributions"] = [ + normalized_contributions[contribution_id] + for contribution_id in contribution_order + if contribution_id in normalized_contributions + ] + + memberships_by_person: dict[str, list[str]] = {} + for membership in typed_entity_buckets["memberships"]: + person_id = membership.get("_person_ref") + membership_id = membership.get("id") + if not isinstance(person_id, str) or not isinstance(membership_id, str): + continue + memberships_by_person.setdefault(person_id, []).append(membership_id) + + contributions_by_person: dict[str, list[str]] = {} + for contribution in typed_entity_buckets["contributions"]: + person_id = contribution.get("schema:author") + contribution_id = contribution.get("id") + if not isinstance(person_id, str) or not isinstance(contribution_id, str): + continue + contributions_by_person.setdefault(person_id, []).append(contribution_id) + + for person in persons: + person_id = person.get("id") + if not isinstance(person_id, str): + continue + person["org:hasMembership"] = _dedupe_strings(memberships_by_person.get(person_id, [])) + person["pulse:hasContribution"] = _dedupe_strings(contributions_by_person.get(person_id, [])) + + return warnings + + +def _merge_bucket_by_clusters( + *, + bucket: str, + entities: list[dict[str, Any]], + raw_clusters: list[dict[str, Any]], +) -> tuple[ + list[dict[str, Any]], + dict[str, str], + list[dict[str, Any]], + list[dict[str, Any]], +]: + entities_by_id: dict[str, dict[str, Any]] = {} + for entity in entities: + entity_id = entity.get("id") + if not isinstance(entity_id, str) or not entity_id: + continue + entities_by_id.setdefault(entity_id, entity) + + consumed_ids: set[str] = set() + remap: dict[str, str] = {} + accepted: list[dict[str, Any]] = [] + rejected: list[dict[str, Any]] = [] + merged_entity_by_member: dict[str, dict[str, Any]] = {} + + for cluster_payload in raw_clusters: + ids = [value for value in cluster_payload.get("ids", []) if isinstance(value, str) and value] + ids = _dedupe_strings(ids) + if len(ids) <= 1: + rejected.append({**cluster_payload, "status": "rejected", "reason_code": "singleton_or_empty"}) + continue + + unknown_ids = [entity_id for entity_id in ids if entity_id not in entities_by_id] + if unknown_ids: + rejected.append( + { + **cluster_payload, + "status": "rejected", + "reason_code": "unknown_entity_id", + "unknown_ids": unknown_ids, + }, + ) + continue + + overlapping = [entity_id for entity_id in ids if entity_id in consumed_ids] + if overlapping: + rejected.append( + { + **cluster_payload, + "status": "rejected", + "reason_code": "overlapping_cluster", + "overlapping_ids": overlapping, + }, + ) + continue + + cluster_entities = [entities_by_id[entity_id] for entity_id in ids] + conflict_reason = _cluster_conflict_reason(bucket, cluster_entities) + if isinstance(conflict_reason, str): + rejected.append( + { + **cluster_payload, + "status": "rejected", + "reason_code": conflict_reason, + }, + ) + continue + + winner = _select_cluster_winner(bucket, cluster_entities) + winner_id = winner.get("id") + if not isinstance(winner_id, str) or not winner_id: + rejected.append( + { + **cluster_payload, + "status": "rejected", + "reason_code": "winner_missing_id", + }, + ) + continue + + merged = deepcopy(winner) + for entity_id in ids: + if entity_id == winner_id: + continue + _merge_entity_payload(merged, entities_by_id[entity_id]) + + canonical_id, canonical_source = _resolve_entity_identity(bucket, merged) + merged["id"] = canonical_id + merged["idSource"] = canonical_source + + for entity_id in ids: + if entity_id != canonical_id: + remap[entity_id] = canonical_id + consumed_ids.add(entity_id) + merged_entity_by_member[entity_id] = merged + + accepted.append( + { + **cluster_payload, + "status": "accepted", + "winner": winner_id, + "canonical_id": canonical_id, + "canonical_source": canonical_source, + }, + ) + + merged_entities: list[dict[str, Any]] = [] + seen_ids: set[str] = set() + for entity in entities: + entity_id = entity.get("id") + if not isinstance(entity_id, str) or not entity_id: + continue + + merged_entity = merged_entity_by_member.get(entity_id) + if isinstance(merged_entity, dict): + candidate = deepcopy(merged_entity) + else: + candidate = deepcopy(entity) + + candidate_id = candidate.get("id") + if not isinstance(candidate_id, str) or not candidate_id: + continue + if candidate_id in seen_ids: + continue + seen_ids.add(candidate_id) + merged_entities.append(candidate) + + return merged_entities, remap, accepted, rejected + + +async def run_llm_dedup_stage( # noqa: PLR0913 + *, + typed_entity_buckets: dict[str, list[dict[str, Any]]], + source_url: str, + detected_type: str, + providers: ProviderSet, + pipeline_outputs: dict[str, Any] | None = None, + initial_context: dict[str, Any] | None = None, + max_concurrency: int = 3, + llm_call_timeout_seconds: float = 180.0, + agent: LLMDedupAgentV2 | None = None, +) -> LLMDedupStageResult: + del max_concurrency + bucket_copy = { + key: deepcopy(typed_entity_buckets.get(key, [])) if isinstance(typed_entity_buckets.get(key), list) else [] + for key in BUCKET_KEYS + } + + dedup_agent = agent or LLMDedupAgentV2( + llm_call_timeout_seconds=llm_call_timeout_seconds, + ) + agent_result = await dedup_agent.run( + { + "source_url": source_url, + "detected_type": detected_type, + "typed_entity_buckets": bucket_copy, + "pipeline_outputs": deepcopy(pipeline_outputs) if isinstance(pipeline_outputs, dict) else {}, + "initial_context": deepcopy(initial_context) if isinstance(initial_context, dict) else {}, + }, + providers, + ) + + raw_candidate_clusters = { + bucket: _normalize_cluster_payload( + agent_result.data.get(bucket), + ) + for bucket in PRIMARY_BUCKETS + } + + remaps: dict[str, dict[str, str]] = {} + accepted_clusters_by_bucket: dict[str, list[dict[str, Any]]] = {} + rejected_clusters_by_bucket: dict[str, list[dict[str, Any]]] = {} + + for bucket in PRIMARY_BUCKETS: + merged_bucket, remap, accepted, rejected = _merge_bucket_by_clusters( + bucket=bucket, + entities=bucket_copy[bucket], + raw_clusters=raw_candidate_clusters[bucket], + ) + bucket_copy[bucket] = merged_bucket + remaps[bucket] = remap + accepted_clusters_by_bucket[bucket] = accepted + rejected_clusters_by_bucket[bucket] = rejected + + remap_warnings = _apply_remaps(bucket_copy, remaps) + + accepted_count = sum(len(clusters) for clusters in accepted_clusters_by_bucket.values()) + rejected_count = sum(len(clusters) for clusters in rejected_clusters_by_bucket.values()) + total_remap_count = sum(len(remap) for remap in remaps.values()) + + resolution_payload = { + "accepted_clusters": accepted_clusters_by_bucket, + "rejected_clusters": rejected_clusters_by_bucket, + "remaps": remaps, + "accepted_cluster_count": accepted_count, + "rejected_cluster_count": rejected_count, + "remap_count": total_remap_count, + } + + return LLMDedupStageResult( + typed_entity_buckets=bucket_copy, + warnings=_dedupe_strings(remap_warnings), + candidate_clusters=raw_candidate_clusters, + resolution=resolution_payload, + accepted_cluster_count=accepted_count, + rejected_cluster_count=rejected_count, + remap_count=total_remap_count, + ) diff --git a/src/v2/pipeline/stages/models.py b/src/v2/pipeline/stages/models.py new file mode 100644 index 0000000..e32259f --- /dev/null +++ b/src/v2/pipeline/stages/models.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + + +@dataclass(slots=True) +class ContextBundle: + detected_type: str + context: dict[str, Any] = field(default_factory=dict) + warnings: list[str] = field(default_factory=list) + + def to_dict(self) -> dict[str, Any]: + return { + "detected_type": self.detected_type, + "context": dict(self.context), + "warnings": list(self.warnings), + } + + +@dataclass(slots=True) +class ReconciledEntities: + entities: dict[str, list[dict[str, Any]]] = field(default_factory=dict) + memberships: list[dict[str, Any]] = field(default_factory=list) + contributions: list[dict[str, Any]] = field(default_factory=list) + link_warnings: list[str] = field(default_factory=list) + reconciliation_debug: dict[str, Any] = field(default_factory=dict) + + +@dataclass(slots=True) +class AssembledOutput: + root_entity: dict[str, Any] | None + related_entities: list[dict[str, Any]] = field(default_factory=list) + excluded_entities: list[dict[str, Any]] = field(default_factory=list) + warnings: list[str] = field(default_factory=list) + + +@dataclass(slots=True) +class LLMDedupStageResult: + typed_entity_buckets: dict[str, list[dict[str, Any]]] = field(default_factory=dict) + warnings: list[str] = field(default_factory=list) + candidate_clusters: dict[str, list[dict[str, Any]]] = field(default_factory=dict) + resolution: dict[str, Any] = field(default_factory=dict) + accepted_cluster_count: int = 0 + rejected_cluster_count: int = 0 + remap_count: int = 0 + + +@dataclass(slots=True) +class LLMCriticStageResult: + reconciled: ReconciledEntities = field(default_factory=ReconciledEntities) + warnings: list[str] = field(default_factory=list) + decisions: dict[str, list[dict[str, Any]]] = field(default_factory=dict) + applied: dict[str, Any] = field(default_factory=dict) + pruned_excluded_entities: list[dict[str, Any]] = field(default_factory=list) diff --git a/src/v2/pipeline/stages/org_relationships.py b/src/v2/pipeline/stages/org_relationships.py new file mode 100644 index 0000000..f625822 --- /dev/null +++ b/src/v2/pipeline/stages/org_relationships.py @@ -0,0 +1,289 @@ +"""Pipeline stage: LLM-decided `org:unitOf` / `org:hasUnit` edges. + +Sits between the deterministic ownership stages and `build_jsonld_output`. +Asks an LLM to look at the **whole set of organizations** in the assembled +output and propose parent-child edges. The response is validated locally +(ids must exist, no self-loops, no cycles, one parent per child) before +the edges are stamped onto the entities. + +The deterministic `infer_org_units` stage stays in place as a safety net +that runs after this; it only fills in when the LLM left a gap and the +name evidence is unambiguous. +""" +from __future__ import annotations + +from copy import deepcopy +from typing import Any + +from src.v2.agents.llm.org_relationships import LLMOrgRelationshipsAgentV2 +from src.v2.agents.models import ProviderSet +from src.v2.pipeline.stages.models import AssembledOutput + +ORGANIZATION_TYPE = "org:Organization" +UNIT_OF_KEY = "org:unitOf" +HAS_UNIT_KEY = "org:hasUnit" + +# Fields the LLM gets per organization. We strip noise (uuid, derivations, +# timestamps) so the prompt stays focused on the identifiers and names that +# matter for hierarchy decisions. +_LLM_FIELDS: tuple[str, ...] = ( + "id", + "schema:name", + "schema:description", + "pulse:OrganizationType", + "pulse:githubOrganizationHandle", + "pulse:ror", + "pulse:infoscienceOrganizationIdentifier", + "org:unitOf", + "org:hasUnit", +) + + +def _organization_summary_for_prompt(entity: dict[str, Any]) -> dict[str, Any]: + summary: dict[str, Any] = {} + for key in _LLM_FIELDS: + value = entity.get(key) + if value is None: + continue + summary[key] = value + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + for key in ("pulse:ror", "pulse:infoscienceOrganizationIdentifier"): + value = identifiers.get(key) + if isinstance(value, str) and value and key not in summary: + summary[key] = value + return summary + + +def _collect_organizations( + assembled: AssembledOutput, +) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + candidates: list[dict[str, Any]] = [] + if isinstance(assembled.root_entity, dict): + candidates.append(assembled.root_entity) + candidates.extend( + entity for entity in assembled.related_entities if isinstance(entity, dict) + ) + organizations = [ + entity for entity in candidates if entity.get("type") == ORGANIZATION_TYPE + ] + return candidates, organizations + + +def _set_unit_of(child: dict[str, Any], parent_id: str) -> bool: + """Set `org:unitOf` on child only if currently empty. Returns True if set.""" + existing = child.get(UNIT_OF_KEY) + if existing in (None, "", {}, []): + child[UNIT_OF_KEY] = [parent_id] + return True + if isinstance(existing, list): + for entry in existing: + if (isinstance(entry, dict) and entry.get("@id") == parent_id) or entry == parent_id: + return False + return False + if (isinstance(existing, dict) and existing.get("@id") == parent_id) or existing == parent_id: + return False + return False + + +def _add_to_has_unit(parent: dict[str, Any], child_id: str) -> bool: + existing = parent.get(HAS_UNIT_KEY) + if existing is None or existing == []: + parent[HAS_UNIT_KEY] = [child_id] + return True + if not isinstance(existing, list): + return False + for entry in existing: + if isinstance(entry, dict) and entry.get("@id") == child_id: + return False + if isinstance(entry, str) and entry == child_id: + return False + existing.append(child_id) + return True + + +def _would_create_cycle( + child_id: str, + parent_id: str, + edges: dict[str, list[str]], +) -> bool: + """Return True if adding `child unitOf parent` produces a cycle. + + Walks up from `parent_id` along all existing parent edges (multi-parent + DAG); if we encounter `child_id` along the way, the new edge closes a loop. + """ + + visited: set[str] = set() + queue: list[str] = [parent_id] + while queue: + current = queue.pop() + if current == child_id: + return True + if current in visited: + continue + visited.add(current) + queue.extend(edges.get(current) or []) + return False + + +async def run_org_relationships_stage( + *, + assembled: AssembledOutput, + source_url: str, + providers: ProviderSet, + agent: LLMOrgRelationshipsAgentV2 | None = None, +) -> tuple[AssembledOutput, list[str]]: + """Run one LLM call to propose `org:unitOf` edges, then stamp the safe ones. + + Returns the updated assembled output and a list of human-readable warnings + summarising every edge that was applied (and every proposal rejected for + cycle/self-ref/unknown-id reasons). + """ + + candidates, organizations = _collect_organizations(assembled) + warnings: list[str] = [] + if len(organizations) < 2: + return assembled, warnings + + # Deep-copy so we don't mutate the caller's structures. + new_root: dict[str, Any] | None = ( + deepcopy(assembled.root_entity) + if isinstance(assembled.root_entity, dict) + else None + ) + new_related: list[Any] = [ + deepcopy(entity) if isinstance(entity, dict) else entity + for entity in assembled.related_entities + ] + new_candidates: list[dict[str, Any]] = [] + if new_root is not None: + new_candidates.append(new_root) + new_candidates.extend(e for e in new_related if isinstance(e, dict)) + new_organizations = [ + entity for entity in new_candidates if entity.get("type") == ORGANIZATION_TYPE + ] + + org_index: dict[str, dict[str, Any]] = {} + for entity in new_organizations: + entity_id = entity.get("id") + if isinstance(entity_id, str) and entity_id: + org_index.setdefault(entity_id, entity) + if len(org_index) < 2: + return assembled, warnings + + runner = agent or LLMOrgRelationshipsAgentV2() + prompt_orgs = [ + _organization_summary_for_prompt(entity) for entity in new_organizations + ] + result = await runner.run( + { + "source_url": source_url, + "organizations": prompt_orgs, + }, + providers, + ) + + proposals = result.data.get("relationships") if isinstance(result.data, dict) else [] + if not isinstance(proposals, list): + proposals = [] + + # Existing parent edges (multi-parent DAG) for cycle detection. + existing_edges: dict[str, list[str]] = {} + for entity in new_organizations: + child_id = entity.get("id") + if not isinstance(child_id, str) or not child_id: + continue + raw = entity.get(UNIT_OF_KEY) or [] + if isinstance(raw, str): + raw = [raw] + if isinstance(raw, list): + for parent_ref in raw: + if isinstance(parent_ref, dict): + parent_ref = parent_ref.get("@id") + if isinstance(parent_ref, str) and parent_ref: + existing_edges.setdefault(child_id, []).append(parent_ref) + + seen_children: set[str] = set() + applied_count = 0 + rejected_count = 0 + already_present_count = 0 + + for proposal in proposals: + child_id = proposal.get("child_id") if isinstance(proposal, dict) else None + parent_id = proposal.get("parent_id") if isinstance(proposal, dict) else None + reason = proposal.get("reason") if isinstance(proposal, dict) else None + + if not isinstance(child_id, str) or not isinstance(parent_id, str): + continue + if child_id == parent_id: + rejected_count += 1 + warnings.append( + f"org_relationships: rejected self-reference '{child_id}'.", + ) + continue + if child_id in seen_children: + rejected_count += 1 + warnings.append( + f"org_relationships: rejected duplicate parent for '{child_id}'.", + ) + continue + if child_id not in org_index or parent_id not in org_index: + rejected_count += 1 + missing = ( + child_id if child_id not in org_index else parent_id + ) + warnings.append( + f"org_relationships: rejected pair (child={child_id}, " + f"parent={parent_id}) — '{missing}' is not in the graph.", + ) + continue + if _would_create_cycle(child_id, parent_id, existing_edges): + rejected_count += 1 + warnings.append( + f"org_relationships: rejected pair (child={child_id}, " + f"parent={parent_id}) — would create a cycle.", + ) + continue + + child_entity = org_index[child_id] + parent_entity = org_index[parent_id] + unit_of_set = _set_unit_of(child_entity, parent_id) + has_unit_set = _add_to_has_unit(parent_entity, child_id) + existing_edges.setdefault(child_id, []).append(parent_id) + seen_children.add(child_id) + + if unit_of_set or has_unit_set: + applied_count += 1 + else: + already_present_count += 1 + + if unit_of_set: + warnings.append( + f"Inferred org:unitOf on {child_id} → {parent_id}" + + (f" ({reason})" if isinstance(reason, str) and reason else "") + + ".", + ) + if has_unit_set: + warnings.append( + f"Inferred org:hasUnit on {parent_id} → {child_id}.", + ) + + # Always emit a summary line so the stage's run is visible even when + # every proposed edge was already in place (silent success). + summary = ( + f"org_relationships: orgs={len(org_index)} proposed={len(proposals)} " + f"applied={applied_count} already_present={already_present_count} " + f"rejected={rejected_count}" + ) + warnings.insert(0, summary) + + updated = AssembledOutput( + root_entity=new_root if new_root is not None else assembled.root_entity, + related_entities=new_related, + excluded_entities=list(assembled.excluded_entities), + warnings=list(assembled.warnings), + ) + return updated, warnings + + +__all__ = ["run_org_relationships_stage"] diff --git a/src/v2/pipeline/stages/output_assembly.py b/src/v2/pipeline/stages/output_assembly.py new file mode 100644 index 0000000..71d6d17 --- /dev/null +++ b/src/v2/pipeline/stages/output_assembly.py @@ -0,0 +1,226 @@ +from __future__ import annotations + +from copy import deepcopy +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Any + +from src.v2.pipeline.stages.models import AssembledOutput, ReconciledEntities + +if TYPE_CHECKING: + from src.v2.validation.schema_validation import ( + BatchValidationResult, + ValidationResult, + ) + +ROOT_ENTITY_PRIORITY = ( + "repository", + "person", + "organization", + "article", +) +PLURAL_ENTITY_KEY_BY_SINGULAR = { + "repository": "repositories", + "person": "persons", + "organization": "organizations", + "article": "articles", + "membership": "memberships", + "contribution": "contributions", +} +SINGULAR_ENTITY_KEY_BY_PLURAL = { + plural: singular + for singular, plural in PLURAL_ENTITY_KEY_BY_SINGULAR.items() +} +JSON_ENTITY_BUCKET_BY_TYPE = { + "schema:SoftwareSourceCode": "repositories", + "schema:Person": "persons", + "org:Organization": "organizations", + "schema:ScholarlyArticle": "articles", + "org:Membership": "memberships", + "pulse:Contribution": "contributions", +} + + +@dataclass(slots=True) +class RootEntityValidationError(ValueError): + status_code: int = 422 + entity_type: str = "" + entity_id: str = "" + validation_errors: list[dict[str, str]] = field(default_factory=list) + + +def _entity_singular_type(entity_type: str) -> str: + normalized = entity_type.strip().lower() + return SINGULAR_ENTITY_KEY_BY_PLURAL.get(normalized, normalized) + + +def _entity_identity(entity_type: str, payload: dict[str, Any]) -> tuple[str, str | None]: + return _entity_singular_type(entity_type), payload.get("id") if isinstance(payload.get("id"), str) else None + + +def _select_root_entity( + entities: dict[str, list[dict[str, Any]]], + *, + root_entity_type: str | None = None, +) -> tuple[str, dict[str, Any]]: + if root_entity_type is not None: + normalized_root = _entity_singular_type(root_entity_type) + plural_key = PLURAL_ENTITY_KEY_BY_SINGULAR.get(normalized_root) + if plural_key is not None: + candidates = entities.get(plural_key, []) + if candidates: + return normalized_root, candidates[0] + message = ( + "Unable to assemble output without a root " + f"{normalized_root} entity" + ) + raise ValueError(message) + + for root_singular in ROOT_ENTITY_PRIORITY: + plural_key = PLURAL_ENTITY_KEY_BY_SINGULAR[root_singular] + candidates = entities.get(plural_key, []) + if candidates: + return root_singular, candidates[0] + message = "Unable to assemble output without a root entity" + raise ValueError(message) + + +def _clean_relationship_refs(entity: dict[str, Any], excluded_ids: set[str]) -> dict[str, Any]: + cleaned = deepcopy(entity) + for key, value in list(cleaned.items()): + if isinstance(value, list): + cleaned[key] = [ + item + for item in value + if not (isinstance(item, str) and item in excluded_ids) + ] + elif isinstance(value, str) and value in excluded_ids: + cleaned[key] = None + return cleaned + + +def _entities_by_type(entities: list[dict[str, Any]]) -> dict[str, list[dict[str, Any]]]: + grouped: dict[str, list[dict[str, Any]]] = { + "repositories": [], + "persons": [], + "organizations": [], + "articles": [], + "memberships": [], + "contributions": [], + } + for entity in entities: + entity_type = entity.get("type") + if not isinstance(entity_type, str): + continue + bucket = JSON_ENTITY_BUCKET_BY_TYPE.get(entity_type) + if bucket is None: + continue + grouped[bucket].append(deepcopy(entity)) + return grouped + + +def build_json_output(assembled: AssembledOutput) -> dict[str, Any]: + graph_entities: list[dict[str, Any]] = [] + if isinstance(assembled.root_entity, dict): + graph_entities.append(assembled.root_entity) + graph_entities.extend(assembled.related_entities) + return { + "root_entity": deepcopy(assembled.root_entity), + "related_entities": deepcopy(assembled.related_entities), + "excluded_entities": deepcopy(assembled.excluded_entities), + "entities_by_type": _entities_by_type(graph_entities), + } + + +def assemble_output( # noqa: C901, PLR0912 + reconciled: ReconciledEntities, + strict_results: BatchValidationResult, + *, + root_entity_type: str | None = None, +) -> AssembledOutput: + root_entity_type, root_entity = _select_root_entity( + reconciled.entities, + root_entity_type=root_entity_type, + ) + + invalid_details: dict[tuple[str, str], ValidationResult] = {} + invalid_entities_by_identity: dict[tuple[str, str], tuple[str, dict[str, Any], ValidationResult]] = {} + for invalid_entity_type, invalid_payload, invalid_validation in strict_results.invalid_entities: + singular_type, entity_id = _entity_identity(invalid_entity_type, invalid_payload) + if entity_id is None: + continue + key = (singular_type, entity_id) + invalid_details[key] = invalid_validation + invalid_entities_by_identity[key] = ( + singular_type, + invalid_payload, + invalid_validation, + ) + + root_entity_singular, root_entity_id = _entity_identity(root_entity_type, root_entity) + root_identity: tuple[str, str] | None = None + if root_entity_id is not None: + root_identity = (root_entity_singular, root_entity_id) + + if root_identity is not None and root_identity in invalid_details: + root_validation = invalid_details[root_identity] + raise RootEntityValidationError( + entity_type=root_entity_type, + entity_id=root_identity[1], + validation_errors=list(root_validation.errors), + ) + + excluded_entities: list[dict[str, Any]] = [] + excluded_ids: set[str] = set() + for (entity_type, entity_id), (_, payload, validation) in invalid_entities_by_identity.items(): + if entity_id is None: + continue + if root_identity is not None and (entity_type, entity_id) == root_identity: + continue + excluded_ids.add(entity_id) + excluded_entities.append( + { + "entity_type": entity_type, + "entity": deepcopy(payload), + "reason": list(validation.errors), + }, + ) + + related_entities: list[dict[str, Any]] = [] + for plural_type, entries in reconciled.entities.items(): + singular_type = _entity_singular_type(plural_type) + for entry in entries: + if entry is root_entity: + continue + entity_id = entry.get("id") if isinstance(entry.get("id"), str) else None + if entity_id is not None and (singular_type, entity_id) in invalid_entities_by_identity: + continue + related_entities.append(deepcopy(entry)) + + for singular_type, entries in ( + ("membership", reconciled.memberships), + ("contribution", reconciled.contributions), + ): + for entry in entries: + entity_id = entry.get("id") if isinstance(entry.get("id"), str) else None + if entity_id is not None and (singular_type, entity_id) in invalid_entities_by_identity: + continue + related_entities.append(deepcopy(entry)) + + warnings = [*reconciled.link_warnings] + for excluded_entity in excluded_entities: + entity = excluded_entity["entity"] + entity_id = entity.get("id") + errors = excluded_entity["reason"] + warnings.append( + ( + f"Excluded {excluded_entity['entity_type']} entity '{entity_id}' due to strict " + f"validation errors: {errors}" + ), + ) + + return AssembledOutput( + root_entity=_clean_relationship_refs(root_entity, excluded_ids), + related_entities=related_entities, + excluded_entities=excluded_entities, + warnings=warnings, + ) diff --git a/src/v2/pipeline/stages/ownership_check.py b/src/v2/pipeline/stages/ownership_check.py new file mode 100644 index 0000000..bccac31 --- /dev/null +++ b/src/v2/pipeline/stages/ownership_check.py @@ -0,0 +1,1129 @@ +"""Deterministic ownership validation and inference for assembled output. + +Three independent passes, all pure-function, no LLM, no network: + +1. **Type check** (`validate_ownership`) — only `schema:Person` and + `org:Organization` entities may carry `pulse:owns`. If any other entity + type has `pulse:owns`, the field is stripped. +2. **GitHub-handle check** (`validate_ownership`) — for every `pulse:owns` + entry that points at a `https://github.com//` URL, + `` must match the source entity's GitHub handle (Person: + `pulse:githubUsername`; Organization: `pulse:githubOrganizationHandle`). + Mismatched entries are dropped. +3. **Owner inference** (`infer_owners`) — for every Repository entity, parse + the `` segment from `pulse:githubRepositoryHandle` or its `id` + URL, find the matching Person/Organization by handle, and stamp both + sides of the relationship: `pulse:ownedBy` on the repo and the repo's id + into the entity's `pulse:owns`. Only fills gaps — never overwrites an + existing populated `pulse:ownedBy` value. + +Run all three between `apply_link_pruning_to_assembled_output` and +`build_jsonld_output` so SHACL sees the cleaned-up output. +""" +from __future__ import annotations + +import re +from copy import deepcopy +from typing import Any +from urllib.parse import urlparse +from uuid import uuid4 + +from src.v2.pipeline.stages.models import AssembledOutput, ReconciledEntities + +OWNS_KEY = "pulse:owns" +OWNERS_BY_TYPE: dict[str, str] = { + "schema:Person": "pulse:githubUsername", + "org:Organization": "pulse:githubOrganizationHandle", +} + + +def _extract_github_owner_from_url(value: Any) -> str | None: + """Return `` for `https://github.com//`, else `None`. + + Non-github URLs and malformed paths return `None` so the caller can skip + validation rather than raising. + """ + if isinstance(value, dict): + value = value.get("@id") or value.get("id") + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + parsed = urlparse(candidate) + if parsed.scheme not in {"http", "https"} or parsed.netloc.lower() != "github.com": + return None + parts = [segment for segment in parsed.path.split("/") if segment] + if len(parts) < 2: + return None + return parts[0] + + +def _entity_owner_handle(entity: dict[str, Any]) -> str | None: + handle_field = OWNERS_BY_TYPE.get(entity.get("type") or "") + if handle_field is None: + return None + handle = entity.get(handle_field) + if isinstance(handle, str) and handle.strip(): + return handle.strip().lower() + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + nested = identifiers.get(handle_field) + if isinstance(nested, str) and nested.strip(): + return nested.strip().lower() + return None + + +def _entity_label(entity: dict[str, Any]) -> str: + return ( + (entity.get("id") if isinstance(entity.get("id"), str) else None) + or (entity.get("schema:name") if isinstance(entity.get("schema:name"), str) else None) + or "" + ) + + +def _filter_owns_for_entity( + entity: dict[str, Any], +) -> tuple[list[Any], list[str]]: + """Return (kept_owns, warnings) after applying the github-handle check.""" + raw_owns = entity.get(OWNS_KEY) + if not isinstance(raw_owns, list): + return [], [] + handle = _entity_owner_handle(entity) + label = _entity_label(entity) + + kept: list[Any] = [] + warnings: list[str] = [] + for entry in raw_owns: + owner_in_url = _extract_github_owner_from_url(entry) + if owner_in_url is None: + # Not a github URL we can validate — preserve verbatim. + kept.append(entry) + continue + if handle is None: + # No handle on the source entity — can't validate. + kept.append(entry) + continue + if owner_in_url.lower() == handle: + kept.append(entry) + continue + target = entry.get("@id") or entry.get("id") if isinstance(entry, dict) else entry + warnings.append( + f"Dropped pulse:owns entry on {label}: repository {target} owner " + f"'{owner_in_url}' does not match entity handle '{handle}'.", + ) + return kept, warnings + + +def _apply_owns_check( + entity: dict[str, Any], + warnings: list[str], +) -> None: + """In-place mutation of `entity` enforcing both ownership rules.""" + if OWNS_KEY not in entity: + return + entity_type = entity.get("type") + if entity_type not in OWNERS_BY_TYPE: + warnings.append( + f"Stripped pulse:owns from {_entity_label(entity)}: entity type " + f"'{entity_type}' is not Person or Organization.", + ) + entity[OWNS_KEY] = None + return + kept, entity_warnings = _filter_owns_for_entity(entity) + warnings.extend(entity_warnings) + entity[OWNS_KEY] = kept if kept else None + + +def validate_ownership( + assembled: AssembledOutput, +) -> tuple[AssembledOutput, list[str]]: + """Return a copy of `assembled` with invalid `pulse:owns` entries dropped.""" + warnings: list[str] = [] + + new_root: dict[str, Any] | None = None + if isinstance(assembled.root_entity, dict): + new_root = deepcopy(assembled.root_entity) + _apply_owns_check(new_root, warnings) + + new_related: list[dict[str, Any]] = [] + for entity in assembled.related_entities: + if not isinstance(entity, dict): + new_related.append(entity) + continue + cloned = deepcopy(entity) + _apply_owns_check(cloned, warnings) + new_related.append(cloned) + + return ( + AssembledOutput( + root_entity=new_root if new_root is not None else assembled.root_entity, + related_entities=new_related, + excluded_entities=list(assembled.excluded_entities), + warnings=list(assembled.warnings), + ), + warnings, + ) + + +REPOSITORY_TYPE = "schema:SoftwareSourceCode" +OWNED_BY_KEY = "pulse:ownedBy" +REPO_HANDLE_KEY = "pulse:githubRepositoryHandle" + + +def _extract_owner_from_repo_handle(handle: Any) -> str | None: + """Return the `` segment of `/`, else `None`.""" + if not isinstance(handle, str): + return None + candidate = handle.strip() + if "/" not in candidate: + return None + owner = candidate.split("/", maxsplit=1)[0].strip() + return owner or None + + +def _owner_handle_for_repo_original_case(entity: dict[str, Any]) -> str | None: + """Return the repo's github `` preserving the user's chosen case. + + Used when the handle is stamped onto new entities (e.g. a synthesized + Person stub) where canonical casing matters for the resulting URL/name. + """ + owner = _extract_owner_from_repo_handle(entity.get(REPO_HANDLE_KEY)) + if owner: + return owner + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + owner = _extract_owner_from_repo_handle(identifiers.get(REPO_HANDLE_KEY)) + if owner: + return owner + return _extract_github_owner_from_url(entity.get("id") or entity.get("@id")) + + +def _owner_handle_for_repo(entity: dict[str, Any]) -> str | None: + """Best-effort owner-handle lookup for a Repository entity (lower-cased). + + Lower-cased so it can match the index built by + `_index_owners_and_orgs_by_handle`. For inserts that need canonical case, + use `_owner_handle_for_repo_original_case`. + """ + owner = _owner_handle_for_repo_original_case(entity) + return owner.lower() if owner else None + + +def _index_owners(entities: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + """Build a `handle.lower() -> entity` map for Person and Organization entities.""" + index: dict[str, dict[str, Any]] = {} + for entity in entities: + if not isinstance(entity, dict): + continue + handle = _entity_owner_handle(entity) + if handle: + index.setdefault(handle, entity) + return index + + +def _add_to_owns(entity: dict[str, Any], repo_id: str) -> bool: + """Append `{"@id": repo_id}` to `entity[OWNS_KEY]` (deduped). Returns True if added.""" + existing = entity.get(OWNS_KEY) + if existing is None or existing == []: + entity[OWNS_KEY] = [{"@id": repo_id}] + return True + if not isinstance(existing, list): + # Shouldn't happen post-validate_ownership, but be defensive. + return False + for entry in existing: + if isinstance(entry, dict) and entry.get("@id") == repo_id: + return False + if isinstance(entry, str) and entry == repo_id: + return False + existing.append({"@id": repo_id}) + return True + + +def infer_owners( + assembled: AssembledOutput, +) -> tuple[AssembledOutput, list[str]]: + """Stamp the deterministic owner relationship in both directions. + + For each `schema:SoftwareSourceCode` entity: + - Parse the GitHub owner from `pulse:githubRepositoryHandle` or `id`. + - If a Person/Organization with the matching `pulse:githubUsername` / + `pulse:githubOrganizationHandle` exists in the graph, set + `pulse:ownedBy` on the repo (only when currently null/missing) and add + the repo's `id` to that entity's `pulse:owns`. + + Returns a copy of `assembled` with the relationships stamped, plus a list + of human-readable warnings reporting each inferred link. + """ + candidates: list[dict[str, Any]] = [] + if isinstance(assembled.root_entity, dict): + candidates.append(assembled.root_entity) + candidates.extend( + entity for entity in assembled.related_entities if isinstance(entity, dict) + ) + + new_root: dict[str, Any] | None = ( + deepcopy(assembled.root_entity) + if isinstance(assembled.root_entity, dict) + else None + ) + new_related: list[Any] = [ + deepcopy(entity) if isinstance(entity, dict) else entity + for entity in assembled.related_entities + ] + new_candidates: list[dict[str, Any]] = [] + if new_root is not None: + new_candidates.append(new_root) + new_candidates.extend(e for e in new_related if isinstance(e, dict)) + + owner_index = _index_owners(new_candidates) + warnings: list[str] = [] + + for entity in new_candidates: + if entity.get("type") != REPOSITORY_TYPE: + continue + repo_id = entity.get("id") + if not isinstance(repo_id, str) or not repo_id: + continue + owner_handle = _owner_handle_for_repo(entity) + if owner_handle is None: + continue + owner_entity = owner_index.get(owner_handle) + if owner_entity is None: + continue + owner_id = owner_entity.get("id") + if not isinstance(owner_id, str) or not owner_id: + continue + + # Stamp the inverse direction on the repo (only fill the gap). + # A bare-login string (e.g. "AlexanderBaltaian") emitted by the + # repository agent is treated as missing — the JSON-LD context + # types `pulse:ownedBy` as `@id`, so a bare token resolves to a + # `` URI under SHACL. Only IRI-shaped values + # are kept. + existing_owned_by = entity.get(OWNED_BY_KEY) + existing_is_bare_handle = ( + isinstance(existing_owned_by, str) + and bool(existing_owned_by) + and not existing_owned_by.startswith(("http://", "https://", "urn:")) + ) + if existing_owned_by in (None, "", {}) or existing_is_bare_handle: + entity[OWNED_BY_KEY] = {"@id": owner_id} + warnings.append( + f"Inferred pulse:ownedBy on {repo_id} → {owner_id} " + f"(handle '{owner_handle}').", + ) + elif ( + isinstance(existing_owned_by, dict) + and existing_owned_by.get("@id") == owner_id + ) or existing_owned_by == owner_id: + # Already correct; nothing to do. + pass + else: + warnings.append( + f"pulse:ownedBy on {repo_id} already set to " + f"{existing_owned_by!r}; not overwriting with inferred {owner_id}.", + ) + + if _add_to_owns(owner_entity, repo_id): + warnings.append( + f"Inferred pulse:owns on {owner_id} → {repo_id} " + f"(handle '{owner_handle}').", + ) + + # Final sweep: any repository that still carries a plain-string + # `pulse:ownedBy` (because no matching owner entity was found in the + # graph — typical of solo-user repos) is coerced to IRI shape and a + # minimal Person stub is materialized when the target is a github + # user URL with no matching Person/Org in the graph. Without the stub, + # SHACL fails because `pulse:ownedBy` requires the target to be a + # `schema:Person` or `org:Organization`. + for entity in new_candidates: + if entity.get("type") != REPOSITORY_TYPE: + continue + value = entity.get(OWNED_BY_KEY) + if not isinstance(value, str) or not value: + continue + if value.startswith(("http://", "https://", "urn:")): + entity[OWNED_BY_KEY] = {"@id": value} + else: + entity[OWNED_BY_KEY] = {"@id": f"https://github.com/{value}"} + + # Materialize Person stubs for github-user `pulse:ownedBy` targets that + # have no matching Person/Organization in the graph. These are typically + # solo-author repos where the owner never produced a contributor record. + existing_ids = { + e.get("id") or e.get("@id") + for e in new_candidates + if isinstance(e, dict) and (e.get("id") or e.get("@id")) + } + new_stubs: list[dict[str, Any]] = [] + seen_stubs: set[str] = set() + for entity in new_candidates: + if entity.get("type") != REPOSITORY_TYPE: + continue + value = entity.get(OWNED_BY_KEY) + if not isinstance(value, dict): + continue + target_id = value.get("@id") + if not isinstance(target_id, str) or not target_id: + continue + if target_id in existing_ids or target_id in seen_stubs: + continue + if not target_id.startswith("https://github.com/"): + continue + handle = target_id.removeprefix("https://github.com/").strip("/").split("/")[0] + if not handle: + continue + stub_uuid = str(uuid4()) + new_stubs.append( + { + "id": target_id, + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:githubUsername": handle, + "uuid": stub_uuid, + }, + "idSource": "pulse:githubUsername", + "schema:name": handle, + "pulse:githubUsername": handle, + }, + ) + seen_stubs.add(target_id) + warnings.append( + f"Inferred minimal Person stub for github owner '{handle}' " + f"({target_id}) referenced by repository {entity.get('id')}.", + ) + if new_stubs: + new_related = list(new_related) + new_stubs + + return ( + AssembledOutput( + root_entity=new_root if new_root is not None else assembled.root_entity, + related_entities=new_related, + excluded_entities=list(assembled.excluded_entities), + warnings=list(assembled.warnings), + ), + warnings, + ) + + +ORGANIZATION_TYPE = "org:Organization" +ROR_IDENTIFIER_KEY = "pulse:ror" +UNIT_OF_KEY = "org:unitOf" +HAS_UNIT_KEY = "org:hasUnit" + +# Common acronym/short-form anchors that organizations use to refer to a +# parent. We treat the parent's `schema:name` (or any token in it) as a +# candidate alias when matching against the github org's name/handle. +_TOKEN_PATTERN = re.compile(r"[A-Za-z]{2,}") + + +def _entity_ror_id(entity: dict[str, Any]) -> str | None: + """Return the ROR id for an organization, or None.""" + direct = entity.get(ROR_IDENTIFIER_KEY) + if isinstance(direct, str) and direct.strip(): + return direct.strip() + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + nested = identifiers.get(ROR_IDENTIFIER_KEY) + if isinstance(nested, str) and nested.strip(): + return nested.strip() + schema_id = entity.get("schema:identifier") + if isinstance(schema_id, str) and schema_id.strip().startswith("https://ror.org/"): + return schema_id.strip() + if isinstance(entity.get("id"), str) and entity["id"].startswith("https://ror.org/"): + return entity["id"] + return None + + +def _entity_github_org_handle(entity: dict[str, Any]) -> str | None: + """Return the github organization handle (lowercased), or None.""" + direct = entity.get("pulse:githubOrganizationHandle") + if isinstance(direct, str) and direct.strip(): + return direct.strip().lower() + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + nested = identifiers.get("pulse:githubOrganizationHandle") + if isinstance(nested, str) and nested.strip(): + return nested.strip().lower() + return None + + +def _name_aliases(value: Any) -> set[str]: + """Extract lowercase alphabetic tokens (≥2 chars) from a name string.""" + if not isinstance(value, str): + return set() + return {match.group(0).lower() for match in _TOKEN_PATTERN.finditer(value)} + + +def _handle_aliases(handle: str) -> set[str]: + """Tokenize a github handle by splitting on `-` and `_`.""" + return {part.lower() for part in re.split(r"[-_]+", handle) if len(part) >= 2} + + +def _set_unit_of(child: dict[str, Any], parent_id: str) -> bool: + """Set `org:unitOf` on child only if currently empty. Returns True if set. + + `org:unitOf` is a list of parent IDs (multi-parent allowed by SHACL). This + helper preserves the historical "never overwrite an existing value" + semantics: it only stamps when the list is empty / missing / null. + """ + existing = child.get(UNIT_OF_KEY) + if existing in (None, "", {}, []): + child[UNIT_OF_KEY] = [parent_id] + return True + if isinstance(existing, list): + for entry in existing: + if (isinstance(entry, dict) and entry.get("@id") == parent_id) or entry == parent_id: + return False + return False + if (isinstance(existing, dict) and existing.get("@id") == parent_id) or existing == parent_id: + return False + return False + + +def _add_to_has_unit(parent: dict[str, Any], child_id: str) -> bool: + """Append child_id to `org:hasUnit` (deduped). Returns True if added.""" + existing = parent.get(HAS_UNIT_KEY) + if existing is None or existing == []: + parent[HAS_UNIT_KEY] = [child_id] + return True + if not isinstance(existing, list): + return False + for entry in existing: + if isinstance(entry, dict) and entry.get("@id") == child_id: + return False + if isinstance(entry, str) and entry == child_id: + return False + existing.append(child_id) + return True + + +def infer_org_units( + assembled: AssembledOutput, +) -> tuple[AssembledOutput, list[str]]: + """Stamp `org:unitOf` / `org:hasUnit` between a github-only org and a + ROR-backed parent when name evidence makes the relationship clear. + + Triggers only when: + + - There is exactly **one** ROR-backed organization in the graph (the + candidate parent). + - There is at least one organization with a github handle but **no** + ROR (the candidate child). + - The github handle (split on `-`/`_`) shares at least one ≥2-char + alphabetic token with any token in the ROR org's `schema:name`. + + Conservative on purpose: a single ROR + a name-token overlap is strong + enough to avoid coincidence (e.g. `EPFL-Open-Science` shares `epfl` + with the ROR org `EPFL — École Polytechnique Fédérale de Lausanne`), + but bails out cleanly when multiple ROR orgs exist or no overlap is + found. Only fills gaps; never overwrites an existing `org:unitOf`. + """ + candidates: list[dict[str, Any]] = [] + if isinstance(assembled.root_entity, dict): + candidates.append(assembled.root_entity) + candidates.extend( + entity for entity in assembled.related_entities if isinstance(entity, dict) + ) + + new_root: dict[str, Any] | None = ( + deepcopy(assembled.root_entity) + if isinstance(assembled.root_entity, dict) + else None + ) + new_related: list[Any] = [ + deepcopy(entity) if isinstance(entity, dict) else entity + for entity in assembled.related_entities + ] + new_candidates: list[dict[str, Any]] = [] + if new_root is not None: + new_candidates.append(new_root) + new_candidates.extend(e for e in new_related if isinstance(e, dict)) + + organizations = [ + entity for entity in new_candidates if entity.get("type") == ORGANIZATION_TYPE + ] + ror_orgs = [org for org in organizations if _entity_ror_id(org) is not None] + github_only_orgs = [ + org + for org in organizations + if _entity_ror_id(org) is None and _entity_github_org_handle(org) is not None + ] + + warnings: list[str] = [] + + if len(ror_orgs) != 1 or not github_only_orgs: + return ( + AssembledOutput( + root_entity=new_root if new_root is not None else assembled.root_entity, + related_entities=new_related, + excluded_entities=list(assembled.excluded_entities), + warnings=list(assembled.warnings), + ), + warnings, + ) + + parent = ror_orgs[0] + parent_id = parent.get("id") + if not isinstance(parent_id, str) or not parent_id: + return ( + AssembledOutput( + root_entity=new_root if new_root is not None else assembled.root_entity, + related_entities=new_related, + excluded_entities=list(assembled.excluded_entities), + warnings=list(assembled.warnings), + ), + warnings, + ) + + parent_name_tokens = _name_aliases(parent.get("schema:name")) + + for child in github_only_orgs: + child_id = child.get("id") + handle = _entity_github_org_handle(child) + if not isinstance(child_id, str) or not child_id or handle is None: + continue + handle_tokens = _handle_aliases(handle) | _name_aliases(child.get("schema:name")) + overlap = handle_tokens & parent_name_tokens + if not overlap: + continue + + if _set_unit_of(child, parent_id): + warnings.append( + f"Inferred org:unitOf on {child_id} → {parent_id} " + f"(matched on token{'s' if len(overlap) > 1 else ''} " + f"{sorted(overlap)!r}).", + ) + if _add_to_has_unit(parent, child_id): + warnings.append( + f"Inferred org:hasUnit on {parent_id} → {child_id}.", + ) + + return ( + AssembledOutput( + root_entity=new_root if new_root is not None else assembled.root_entity, + related_entities=new_related, + excluded_entities=list(assembled.excluded_entities), + warnings=list(assembled.warnings), + ), + warnings, + ) + + +# Tokens we drop when extracting query terms from a github handle. These are +# generic enough that searching for them on ROR gives mostly noise. +_GITHUB_HANDLE_GENERIC_TOKENS: frozenset[str] = frozenset( + { + "lab", + "labs", + "team", + "group", + "org", + "project", + "projects", + "research", + "code", + "open", + "source", + "data", + "science", # too generic on its own; "data science" or "swiss data science" is fine + "center", + "centre", + "institute", + "institut", + "the", + "an", + "and", + "of", + "for", + "in", + "at", + }, +) + + +def _github_handle_query_terms(handle: str, name: str | None) -> list[str]: + """Build candidate ROR query strings from a github handle + display name. + + Strategy: + 1. Use the display name as-is when it's longer than the handle (more useful for ROR). + 2. Use the full handle (e.g. `SwissDataScienceCenter`) as a single query. + 3. Tokenize the handle on `-`/`_`, drop generic + short tokens. + 4. Return the most distinctive 2-3 tokens. + + Cap is intentional — each query becomes a ROR API call, so we trade recall + for cost. + """ + + queries: list[str] = [] + + name = (name or "").strip() + handle = handle.strip() + if name and name.lower() != handle.lower() and len(name) >= 3: + queries.append(name) + + if handle and handle not in queries: + queries.append(handle) + + # Tokenize handle on -/_, keep the meaningful parts. + tokens = [ + part.lower() + for part in re.split(r"[-_]+", handle) + if len(part) >= 3 and part.lower() not in _GITHUB_HANDLE_GENERIC_TOKENS + ] + # Add the longest tokens first (most distinctive). + tokens.sort(key=lambda token: -len(token)) + for token in tokens[:2]: + if token not in {q.lower() for q in queries}: + queries.append(token) + + return queries + + +def _ror_record_score( + *, + handle: str, + name: str | None, + ror_record: dict[str, Any], +) -> int: + """Heuristic match score between a github org and a ROR record. + + Counts overlap between the github org's tokens (handle + display name) + and the ROR org's name + aliases + acronyms. Higher = better. + """ + + gh_tokens = set(_handle_aliases(handle)) + if isinstance(name, str): + gh_tokens.update(_name_aliases(name)) + if not gh_tokens: + return 0 + + ror_tokens: set[str] = set() + ror_tokens.update(_name_aliases(ror_record.get("name"))) + aliases = ror_record.get("aliases") + if isinstance(aliases, list): + for alias in aliases: + ror_tokens.update(_name_aliases(alias)) + acronyms = ror_record.get("acronyms") + if isinstance(acronyms, list): + for acronym in acronyms: + if isinstance(acronym, str): + ror_tokens.add(acronym.lower()) + + return len(gh_tokens & ror_tokens) + + +def _build_minimal_ror_org(ror_record: dict[str, Any]) -> dict[str, Any] | None: + """Construct a minimal `org:Organization` entity from a ROR search hit. + + Returns None if the record lacks an id or name (can't be a useful org). + """ + + ror_id = ror_record.get("id") + name = ror_record.get("name") + if not isinstance(ror_id, str) or not ror_id or not isinstance(name, str) or not name: + return None + return { + "id": ror_id, + "type": "org:Organization", + "shacl": "pulse:OrganizationShape", + "identifiers": { + "pulse:ror": ror_id, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:githubOrganizationHandle": None, + "uuid": str(uuid4()), + }, + "idSource": "pulse:ror", + "schema:name": name, + # The v2.1.2 OrganizationShape requires at least one of + # schema:identifier, pulse:githubOrganizationHandle, or + # pulse:infoscienceOrganizationIdentifier. Surface the ROR id as + # schema:identifier so the stub satisfies the constraint without + # needing additional enrichment. + "schema:identifier": ror_id, + "pulse:ror": ror_id, + "pulse:githubOrganizationHandle": None, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:OrganizationType": None, + "pulse:githubOrgFollowers": None, + "org:hasUnit": [], + "org:unitOf": [], + "pulse:owns": [], + } + + +def infer_github_handle_parents( + assembled: AssembledOutput, + *, + providers: Any = None, + max_candidates_per_handle: int = 5, + min_match_score: int = 1, +) -> tuple[AssembledOutput, list[str]]: + """For every github-only org in the graph, fuzzy-search ROR for matching + parent organizations and add them to the graph. + + The github org always remains as a standalone entity (its `id` and other + fields are untouched). The fuzzy matching: + + 1. Builds 1-3 query strings from the github org's handle + display name + (full name, full handle, top distinctive tokens). + 2. Calls `providers.ror.search_organizations(query)` for each (cached). + 3. Scores each ROR hit by token overlap against the github org's + handle + name + aliases + acronyms. + 4. Adds candidate ROR records with score ≥ `min_match_score` to the + graph as minimal entities (only id, ROR id, and canonical name — + leaves the rest null and lets downstream stages / agents enrich). + 5. Picks the highest-scoring candidate as the github org's `unitOf` + parent and stamps the reciprocal `hasUnit`. + + No-ops gracefully when `providers.ror` is None — the github orgs stay + in the graph untouched. + + `max_candidates_per_handle` caps how many ROR matches per github org + we add to the graph (top-K by score). `min_match_score` is the + minimum token-overlap to count a hit as plausible. + + Never overwrites an existing `org:unitOf` value, and reuses an already- + in-graph ROR entity rather than duplicating. + """ + + ror_provider = getattr(providers, "ror", None) if providers is not None else None + if ror_provider is None: + return assembled, [] + + new_root: dict[str, Any] | None = ( + deepcopy(assembled.root_entity) + if isinstance(assembled.root_entity, dict) + else None + ) + new_related: list[Any] = [ + deepcopy(entity) if isinstance(entity, dict) else entity + for entity in assembled.related_entities + ] + new_candidates: list[dict[str, Any]] = [] + if new_root is not None: + new_candidates.append(new_root) + new_candidates.extend(e for e in new_related if isinstance(e, dict)) + + organizations = [ + entity for entity in new_candidates if entity.get("type") == ORGANIZATION_TYPE + ] + # Index existing orgs so we don't duplicate a ROR entity that's already + # been brought into the graph (either by an upstream stage or by a + # previous github org in this same loop). + by_id: dict[str, dict[str, Any]] = {} + by_ror: dict[str, dict[str, Any]] = {} + for org in organizations: + org_id = org.get("id") + if isinstance(org_id, str) and org_id: + by_id.setdefault(org_id, org) + ror = _entity_ror_id(org) + if isinstance(ror, str) and ror: + by_ror.setdefault(ror, org) + + warnings: list[str] = [] + inserted: dict[str, dict[str, Any]] = {} + + for org in organizations: + # Skip orgs that already have a ROR id (they're already canonical). + if _entity_ror_id(org) is not None: + continue + handle = _entity_github_org_handle(org) + if not handle: + continue + org_id = org.get("id") if isinstance(org.get("id"), str) else None + if not org_id: + continue + org_name = org.get("schema:name") if isinstance(org.get("schema:name"), str) else None + + # Build query terms then search ROR. Cache hits make repeated runs cheap. + queries = _github_handle_query_terms(handle, org_name) + if not queries: + continue + scored: dict[str, tuple[int, dict[str, Any]]] = {} + for query in queries: + try: + hits = ror_provider.search_organizations(query) + except Exception as exc: # noqa: BLE001 + warnings.append( + f"github_handle_parents: ROR search failed for query " + f"'{query}' (handle '{handle}'): {exc}", + ) + continue + for hit in hits: + if not isinstance(hit, dict): + continue + ror_id = hit.get("id") + if not isinstance(ror_id, str) or not ror_id: + continue + score = _ror_record_score( + handle=handle, + name=org_name, + ror_record=hit, + ) + if score < min_match_score: + continue + # Keep the best score we've seen for this ROR id. + existing = scored.get(ror_id) + if existing is None or existing[0] < score: + scored[ror_id] = (score, hit) + + if not scored: + continue + + # Top-K by score (highest first). The first one becomes the unitOf + # parent; the rest are added as siblings. + top_candidates = sorted(scored.items(), key=lambda item: -item[1][0])[ + :max_candidates_per_handle + ] + + ranked_entities: list[dict[str, Any]] = [] + for ror_id, (score, hit) in top_candidates: + existing_entity = by_ror.get(ror_id) or by_id.get(ror_id) or inserted.get(ror_id) + if existing_entity is None: + new_entity = _build_minimal_ror_org(hit) + if new_entity is None: + continue + inserted[ror_id] = new_entity + by_id[ror_id] = new_entity + by_ror[ror_id] = new_entity + ranked_entities.append(new_entity) + warnings.append( + f"Inserted ROR organization '{ror_id}' " + f"({hit.get('name')}) from github handle '{handle}' " + f"(score={score}).", + ) + else: + ranked_entities.append(existing_entity) + + # Best match becomes the parent. + if not ranked_entities: + continue + parent_entity = ranked_entities[0] + parent_id = parent_entity.get("id") + if not isinstance(parent_id, str) or not parent_id: + continue + unit_of_set = _set_unit_of(org, parent_id) + has_unit_set = _add_to_has_unit(parent_entity, org_id) + if unit_of_set: + warnings.append( + f"Inferred org:unitOf on {org_id} → {parent_id} " + f"(github handle '{handle}' fuzzy-matched ROR record " + f"'{parent_entity.get('schema:name')}').", + ) + if has_unit_set: + warnings.append(f"Inferred org:hasUnit on {parent_id} → {org_id}.") + + # Append the newly-inserted ROR org entities to the related list. + for ror_id, entity in inserted.items(): + new_related.append(entity) + + updated = AssembledOutput( + root_entity=new_root if new_root is not None else assembled.root_entity, + related_entities=new_related, + excluded_entities=list(assembled.excluded_entities), + warnings=list(assembled.warnings), + ) + return updated, warnings + + +def _index_owners_and_orgs_by_handle( + reconciled: ReconciledEntities, +) -> dict[str, dict[str, Any]]: + """Build `lower-cased handle -> entity` for both Persons and Organizations + sourced from the reconciled bucket dict (not from an `AssembledOutput`).""" + + index: dict[str, dict[str, Any]] = {} + for bucket in ("persons", "organizations"): + for entity in reconciled.entities.get(bucket, []) or []: + if not isinstance(entity, dict): + continue + handle = _entity_owner_handle(entity) + if handle: + index.setdefault(handle, entity) + return index + + +def _synthesize_owner_person_stub(handle: str) -> dict[str, Any]: + """Build a minimal valid `schema:Person` from a github owner handle. + + Used by `guarantee_repo_author` when the repository's owner handle has no + Person or Organization entity in the reconciled graph (typical of solo + repos with no extracted contributors). The stub satisfies the strict + Person schema so it survives validation and can be referenced as + `schema:author` on the root repository. + """ + profile_url = f"https://github.com/{handle}" + return { + "id": profile_url, + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": handle, + "uuid": str(uuid4()), + }, + "idSource": "pulse:githubUsername", + "schema:name": handle, + "schema:url": profile_url, + "pulse:githubUsername": handle, + "pulse:orcidIdentifier": None, + "pulse:infosciencePersonIdentifier": None, + "org:hasMembership": [], + "pulse:hasContribution": [], + "pulse:owns": [], + } + + +def guarantee_repo_author( + reconciled: ReconciledEntities, +) -> tuple[ReconciledEntities, list[str]]: + """Salvage Repository entities whose `schema:author` array is empty. + + Known bug: occasionally the LLM repo agent emits `schema:author` + references that reconciliation can't canonicalize, and after dropping + the unresolvable references the array ends up empty. Strict validation + requires `schema:author` to be **non-empty**, which would fail the + entire extraction. This is a workaround, not a real fix — the real fix + is to teach reconciliation to keep the LLM-emitted reference verbatim + when it can't canonicalize. + + Mitigation, two-tier: + 1. If a Person or Organization with the same github handle as the repo's + `` already exists in the graph, stamp its `id` into + `schema:author`. + 2. Otherwise, synthesize a minimal `schema:Person` stub from the github + owner handle, insert it into the persons bucket, and stamp its id. + This recovers solo-user repos where no contributor data was extracted. + + Always emits a warning so the salvage stays visible in the output. + + Mutates a copy; the original `reconciled` is unchanged. + """ + + repositories = reconciled.entities.get("repositories") + if not isinstance(repositories, list) or not repositories: + return reconciled, [] + + owner_index = _index_owners_and_orgs_by_handle(reconciled) + warnings: list[str] = [] + new_repos: list[dict[str, Any]] = [] + synthesized_persons: list[dict[str, Any]] = [] + synthesized_handles: set[str] = set() + changed = False + + for entity in repositories: + if not isinstance(entity, dict): + new_repos.append(entity) + continue + author_value = entity.get("schema:author") + is_empty = ( + author_value is None + or (isinstance(author_value, list) and len(author_value) == 0) + ) + if not is_empty: + new_repos.append(entity) + continue + + repo_id = entity.get("id") if isinstance(entity.get("id"), str) else "" + owner_handle = _owner_handle_for_repo(entity) + cloned = deepcopy(entity) + owner_entity = owner_index.get(owner_handle) if owner_handle else None + owner_is_person = ( + isinstance(owner_entity, dict) + and owner_entity.get("type") == "schema:Person" + ) + owner_is_org = ( + isinstance(owner_entity, dict) + and owner_entity.get("type") == "org:Organization" + ) + if owner_is_person: + owner_id = owner_entity.get("id") + if isinstance(owner_id, str) and owner_id: + cloned["schema:author"] = [owner_id] + changed = True + warnings.append( + "KNOWN BUG (schema:author empty after reconciliation): " + f"stamped fallback owner '{owner_id}' as schema:author on " + f"{repo_id} (handle '{owner_handle}'). The LLM repo agent " + "likely emitted a contributor reference that reconciliation " + "couldn't canonicalize.", + ) + new_repos.append(cloned) + continue + if owner_handle: + handle_original_case = _owner_handle_for_repo_original_case(entity) or owner_handle + # When the github account is already taken by an Organization + # entity in the graph, the synthesized Person needs a distinct + # id so the two don't collide. Use a urn-shaped id rooted at + # the repository so the placeholder is uniquely scoped. + if owner_is_org: + stub_id = f"urn:pulse:repo-author:{handle_original_case}/{handle_original_case}" + synthesis_key = f"org-owner:{owner_handle}" + else: + stub_id = f"https://github.com/{handle_original_case}" + synthesis_key = owner_handle + if synthesis_key not in synthesized_handles: + stub = _synthesize_owner_person_stub(handle_original_case) + if owner_is_org: + stub["id"] = stub_id + stub["idSource"] = "uuid" + synthesized_persons.append(stub) + synthesized_handles.add(synthesis_key) + cloned["schema:author"] = [stub_id] + changed = True + if owner_is_org: + warnings.append( + "KNOWN BUG (schema:author empty after reconciliation): " + f"github owner '{handle_original_case}' is an Organization " + "and SHACL requires schema:author targets to be Person — " + f"synthesized Person placeholder '{stub_id}' as schema:author " + f"on {repo_id}.", + ) + else: + warnings.append( + "KNOWN BUG (schema:author empty after reconciliation): " + f"synthesized owner Person stub '{stub_id}' as schema:author on " + f"{repo_id} (handle '{handle_original_case}'). No matching Person " + "or Organization existed in the graph — typical of solo-user " + "repositories with no extracted contributor data.", + ) + new_repos.append(cloned) + continue + # Could not derive a github owner handle — leave the empty array. + # Strict validation will still reject the root. + warnings.append( + "KNOWN BUG (schema:author empty after reconciliation): could not " + f"derive a github owner handle for {repo_id}. Strict validation " + "will reject this root.", + ) + new_repos.append(cloned) + + if not changed and not warnings: + return reconciled, [] + + new_entities = {bucket: list(value) for bucket, value in reconciled.entities.items()} + new_entities["repositories"] = new_repos + if synthesized_persons: + new_entities["persons"] = list(new_entities.get("persons") or []) + synthesized_persons + new_reconciled = ReconciledEntities( + entities=new_entities, + memberships=list(reconciled.memberships), + contributions=list(reconciled.contributions), + link_warnings=list(reconciled.link_warnings), + reconciliation_debug=dict(reconciled.reconciliation_debug), + ) + return new_reconciled, warnings + + +__all__ = [ + "guarantee_repo_author", + "infer_github_handle_parents", + "infer_org_units", + "infer_owners", + "validate_ownership", +] diff --git a/src/v2/pipeline/stages/privacy.py b/src/v2/pipeline/stages/privacy.py new file mode 100644 index 0000000..9480d17 --- /dev/null +++ b/src/v2/pipeline/stages/privacy.py @@ -0,0 +1,22 @@ +from __future__ import annotations + +import hashlib +import re + +HASH_LENGTH = 12 +HASHED_LOCAL_PART_PATTERN = re.compile(r"^[0-9a-f]{12}$|^[0-9a-f]{64}$", re.IGNORECASE) + + +def anonymize_email(email: str) -> str: + if "@" not in email: + return email + + local_part, domain = email.split("@", maxsplit=1) + if not local_part or not domain: + return email + + if HASHED_LOCAL_PART_PATTERN.fullmatch(local_part): + return email + + hashed_local = hashlib.sha256(local_part.encode("utf-8")).hexdigest()[:HASH_LENGTH] + return f"{hashed_local}@{domain}" diff --git a/src/v2/pipeline/stages/prune_dangling_refs.py b/src/v2/pipeline/stages/prune_dangling_refs.py new file mode 100644 index 0000000..e83083c --- /dev/null +++ b/src/v2/pipeline/stages/prune_dangling_refs.py @@ -0,0 +1,224 @@ +"""Drop or clean references that point to entities not present in the graph. + +After ``strict_validation`` excludes entities that fail the v2.1.2 SHACL +constraints (e.g. an Organization missing all of `schema:identifier`, +`pulse:githubOrganizationHandle`, `pulse:infoscienceOrganizationIdentifier`), +the surviving entities can carry references to those excluded ids: + +- A Repository's ``pulse:ownedBy`` may point to a github user URL that was + never materialised as a Person (``Node X must conform to Person|Organization``). +- A Membership's ``org:organization`` may point to an Organization that was + dropped (``Value does not have class org:Organization``). +- A Contribution's ``pulse:contributionTo`` may point to a Repository that + was dropped. +- ``org:unitOf`` / ``org:hasUnit`` lists carry orphan entries. +- Persons' ``pulse:hasContribution`` / ``org:hasMembership`` lists outlive + the targets they pointed to. + +This stage walks the assembled graph and, in two passes: + +1. Drops Memberships whose ``org:organization`` is dangling. +2. Drops Contributions whose ``pulse:contributionTo`` or ``schema:author`` + is dangling (we can't keep contribution data without a person+repo pair). +3. After re-indexing, filters all collection-typed reference fields + (``org:unitOf``, ``org:hasUnit``, ``pulse:owns``, ``pulse:hasContribution``, + ``org:hasMembership``) and clears ``pulse:ownedBy`` when the target is + not in the live id set. +""" +from __future__ import annotations + +from copy import deepcopy +from typing import Any + +from src.v2.pipeline.stages.models import AssembledOutput + +MEMBERSHIP_TYPE = "org:Membership" +CONTRIBUTION_TYPE = "pulse:Contribution" + +LIST_REF_FIELDS: tuple[str, ...] = ( + "org:unitOf", + "org:hasUnit", + "pulse:owns", + "pulse:hasContribution", + "org:hasMembership", +) +SCALAR_REF_FIELDS: tuple[str, ...] = ( + "pulse:ownedBy", + "pulse:isForkOf", +) + + +def _types_of(entity: dict[str, Any]) -> set[str]: + raw = entity.get("type") or entity.get("@type") + if isinstance(raw, list): + return {x for x in raw if isinstance(x, str)} + if isinstance(raw, str): + return {raw} + return set() + + +def _resolve_id_ref(value: Any) -> str | None: + if isinstance(value, dict): + target = value.get("@id") or value.get("id") + return target if isinstance(target, str) else None + if isinstance(value, str): + return value + return None + + +def _live_ids(candidates: list[Any]) -> set[str]: + out: set[str] = set() + for entity in candidates: + if not isinstance(entity, dict): + continue + eid = entity.get("id") or entity.get("@id") + if isinstance(eid, str) and eid: + out.add(eid) + return out + + +def _filter_list_refs(value: Any, live: set[str]) -> tuple[Any, int]: + """Return ``(filtered_list, dropped_count)`` for a list of @id refs.""" + + if not isinstance(value, list): + return value, 0 + kept: list[Any] = [] + dropped = 0 + for ref in value: + target = _resolve_id_ref(ref) + if target is None or target in live: + kept.append(ref) + else: + dropped += 1 + return kept, dropped + + +def _clear_scalar_if_dangling(value: Any, live: set[str]) -> tuple[Any, bool]: + """Return ``(maybe-cleared, was_cleared)`` for a single @id ref.""" + + target = _resolve_id_ref(value) + if target is None: + return value, False + if target in live: + return value, False + return None, True + + +def prune_dangling_refs( + assembled: AssembledOutput, +) -> tuple[AssembledOutput, list[str]]: + """Remove references pointing to entities not in the assembled graph. + + Mutates a deep copy. Returns the cleaned ``AssembledOutput`` plus + human-readable warnings for each pruning action. + """ + + new_root = ( + deepcopy(assembled.root_entity) + if isinstance(assembled.root_entity, dict) + else assembled.root_entity + ) + new_related: list[Any] = [ + deepcopy(e) if isinstance(e, dict) else e for e in assembled.related_entities + ] + + def _candidates() -> list[Any]: + out: list[Any] = [] + if isinstance(new_root, dict): + out.append(new_root) + out.extend(new_related) + return out + + warnings: list[str] = [] + + # ------------------------------------------------------------------ + # Pass 1: drop Memberships and Contributions whose core refs are dangling + # ------------------------------------------------------------------ + live = _live_ids(_candidates()) + surviving: list[Any] = [] + dropped_memberships = 0 + dropped_contribs = 0 + for entity in new_related: + if not isinstance(entity, dict): + surviving.append(entity) + continue + types = _types_of(entity) + if MEMBERSHIP_TYPE in types: + org_ref = _resolve_id_ref(entity.get("org:organization")) + if org_ref is None or org_ref in live: + surviving.append(entity) + else: + dropped_memberships += 1 + warnings.append( + f"prune_dangling_refs: dropped Membership " + f"{entity.get('id') or entity.get('@id')!r} — " + f"org:organization {org_ref!r} not in graph", + ) + continue + if CONTRIBUTION_TYPE in types: + repo_ref = _resolve_id_ref(entity.get("pulse:contributionTo")) + author_ref = _resolve_id_ref(entity.get("schema:author")) + missing = [] + if repo_ref is not None and repo_ref not in live: + missing.append(f"pulse:contributionTo={repo_ref!r}") + if author_ref is not None and author_ref not in live: + missing.append(f"schema:author={author_ref!r}") + if missing: + dropped_contribs += 1 + warnings.append( + f"prune_dangling_refs: dropped Contribution " + f"{entity.get('id') or entity.get('@id')!r} — " + + ", ".join(missing) + + " not in graph", + ) + else: + surviving.append(entity) + continue + surviving.append(entity) + new_related = surviving + + # ------------------------------------------------------------------ + # Pass 2: filter list-typed ref fields and clear scalar refs + # ------------------------------------------------------------------ + live = _live_ids(_candidates()) # recompute after pass-1 drops + cleared_scalars = 0 + filtered_list_entries = 0 + for entity in _candidates(): + if not isinstance(entity, dict): + continue + for field in LIST_REF_FIELDS: + if field not in entity: + continue + new_value, dropped = _filter_list_refs(entity[field], live) + if dropped: + entity[field] = new_value + filtered_list_entries += dropped + for field in SCALAR_REF_FIELDS: + if field not in entity: + continue + new_value, was_cleared = _clear_scalar_if_dangling(entity[field], live) + if was_cleared: + entity[field] = new_value + cleared_scalars += 1 + + if dropped_memberships or dropped_contribs or cleared_scalars or filtered_list_entries: + warnings.append( + "prune_dangling_refs: summary " + f"dropped_memberships={dropped_memberships} " + f"dropped_contributions={dropped_contribs} " + f"cleared_scalar_refs={cleared_scalars} " + f"filtered_list_entries={filtered_list_entries}", + ) + + return ( + AssembledOutput( + root_entity=new_root, + related_entities=new_related, + excluded_entities=list(assembled.excluded_entities), + warnings=list(assembled.warnings), + ), + warnings, + ) + + +__all__ = ["prune_dangling_refs"] diff --git a/src/v2/pipeline/stages/reconciliation.py b/src/v2/pipeline/stages/reconciliation.py new file mode 100644 index 0000000..d8b8dd6 --- /dev/null +++ b/src/v2/pipeline/stages/reconciliation.py @@ -0,0 +1,1762 @@ +from __future__ import annotations + +import re +from collections import defaultdict +from copy import deepcopy +from typing import Any +from uuid import uuid4 + +from src.v2.canonicalization import ( + resolve_article_id, + resolve_organization_id, + resolve_person_id, + resolve_repository_id, +) +from src.v2.canonicalization.string_utils import normalize_string, strip_accents +from src.v2.pipeline.stages.models import ReconciledEntities +from src.v2.pipeline.stages.privacy import anonymize_email + +UUID_V4_PATTERN = re.compile( + r"^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + flags=re.IGNORECASE, +) +INFOSCIENCE_UUID_PATTERN = re.compile( + r"(?:entities/(?:person|organization|publication)|core/items)/" + r"([0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12})", + flags=re.IGNORECASE, +) +ORCID_PATTERN = re.compile(r"^\d{4}-\d{4}-\d{4}-\d{3}[0-9X]$", flags=re.IGNORECASE) +LOOKUP_SPLIT_PATTERN = re.compile(r"\s+(?:-|–|—|\||/)\s+|;|,") +PARENTHETICAL_PATTERN = re.compile(r"\s*\([^)]*\)") +ORG_ID_SOURCE_PRIORITY: dict[str, int] = { + "pulse:ror": 4, + "pulse:infoscienceOrganizationIdentifier": 3, + "pulse:githubOrganizationHandle": 2, + "uuid": 1, +} +DEBUG_SAMPLE_LIMIT = 10 +ORGANIZATION_NAME_EQUIVALENCE_TOKENS: dict[str, str] = { + "centre": "center", + "centres": "centers", +} +GITHUB_ORG_BASE_URI = "https://github.com/" +GITHUB_HANDLE_PATTERN = re.compile( + r"^[A-Za-z\d](?:[A-Za-z\d]|-(?=[A-Za-z\d])){0,38}$", +) + + +def _normalize_github_org_handle(value: Any, *, allow_plain_handle: bool = True) -> str | None: + if not isinstance(value, str): + return None + github_handle = value.strip() + if not github_handle: + return None + is_github_url = github_handle.lower().startswith(GITHUB_ORG_BASE_URI) + if is_github_url: + github_handle = github_handle[len(GITHUB_ORG_BASE_URI) :] + github_handle = github_handle.split("/", maxsplit=1)[0] + elif not allow_plain_handle: + return None + + if github_handle.startswith("@"): + github_handle = github_handle[1:] + if not github_handle: + return None + if GITHUB_HANDLE_PATTERN.fullmatch(github_handle) is None: + return None + return github_handle + + +def _as_entity_list(value: Any) -> list[dict[str, Any]]: + if not isinstance(value, list): + return [] + return [item for item in value if isinstance(item, dict)] + + +def _dedupe_preserve_order(values: list[str]) -> list[str]: + deduplicated: list[str] = [] + seen: set[str] = set() + for value in values: + if value in seen: + continue + deduplicated.append(value) + seen.add(value) + return deduplicated + + +def _normalize_uuid_v4(value: Any) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip().lower() + if not candidate or not UUID_V4_PATTERN.match(candidate): + return None + return candidate + + +def _normalize_orcid_token(value: Any) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + if candidate.lower().startswith("https://orcid.org/"): + candidate = candidate.rsplit("/", maxsplit=1)[-1] + if not candidate or not ORCID_PATTERN.match(candidate): + return None + return candidate + + +def _normalize_infoscience_uuid(value: Any) -> str | None: + direct_uuid = _normalize_uuid_v4(value) + if direct_uuid is not None: + return direct_uuid + if not isinstance(value, str): + return None + match = INFOSCIENCE_UUID_PATTERN.search(value.strip()) + if match is None: + return None + return _normalize_uuid_v4(match.group(1)) + + +def _normalize_person_identifiers(person: dict[str, Any]) -> None: + identifiers = person.get("identifiers") + if not isinstance(identifiers, dict): + identifiers = {} + + normalized_orcid = _normalize_orcid_token( + identifiers.get("pulse:orcid") or person.get("pulse:orcidIdentifier"), + ) + normalized_infoscience_id = _normalize_infoscience_uuid( + identifiers.get("pulse:infosciencePersonIdentifier") + or person.get("pulse:infosciencePersonIdentifier"), + ) + github_username = identifiers.get("pulse:githubUsername") + if not isinstance(github_username, str) or not github_username: + github_username = person.get("pulse:githubUsername") + if not isinstance(github_username, str) or not github_username: + github_username = None + + uuid_value = _normalize_uuid_v4(identifiers.get("uuid")) + if uuid_value is None: + uuid_value = str(uuid4()) + + person["identifiers"] = { + "pulse:orcid": normalized_orcid, + "pulse:infosciencePersonIdentifier": normalized_infoscience_id, + "pulse:githubUsername": github_username, + "uuid": uuid_value, + } + person["pulse:orcidIdentifier"] = normalized_orcid + person["pulse:infosciencePersonIdentifier"] = normalized_infoscience_id + person["pulse:githubUsername"] = github_username + + +def _normalize_organization_identifiers(organization: dict[str, Any]) -> None: + identifiers = organization.get("identifiers") + normalized_identifiers = ( + deepcopy(identifiers) + if isinstance(identifiers, dict) + else {} + ) + normalized_ror: str | None = None + raw_ror = ( + normalized_identifiers.get("pulse:ror") + or organization.get("pulse:ror") + or organization.get("schema:identifier") + ) + if isinstance(raw_ror, str) and raw_ror.strip(): + ror_candidate = raw_ror.strip() + if ror_candidate.lower().startswith("https://ror.org/"): + ror_candidate = ror_candidate.rsplit("/", maxsplit=1)[-1] + ror_token = ror_candidate.lower() + if re.fullmatch(r"[0-9a-z]{9}", ror_token): + normalized_ror = f"https://ror.org/{ror_token}" + + normalized_infoscience_id = _normalize_infoscience_uuid( + normalized_identifiers.get("pulse:infoscienceOrganizationIdentifier") + or organization.get("pulse:infoscienceOrganizationIdentifier"), + ) + normalized_github_handle: str | None = None + for candidate in ( + normalized_identifiers.get("pulse:githubOrganizationHandle"), + organization.get("pulse:githubOrganizationHandle"), + ): + normalized_candidate = _normalize_github_org_handle( + candidate, + allow_plain_handle=True, + ) + if isinstance(normalized_candidate, str): + normalized_github_handle = normalized_candidate + break + + if normalized_github_handle is None: + for candidate in ( + organization.get("id"), + organization.get("schema:url"), + ): + normalized_candidate = _normalize_github_org_handle( + candidate, + allow_plain_handle=False, + ) + if isinstance(normalized_candidate, str): + normalized_github_handle = normalized_candidate + break + + if normalized_github_handle is not None: + normalized_github_handle = normalized_github_handle.strip() + + uuid_value = _normalize_uuid_v4(normalized_identifiers.get("uuid")) + if uuid_value is None: + uuid_value = str(uuid4()) + + normalized_identifiers["pulse:ror"] = normalized_ror + normalized_identifiers["pulse:infoscienceOrganizationIdentifier"] = normalized_infoscience_id + normalized_identifiers["pulse:githubOrganizationHandle"] = normalized_github_handle + normalized_identifiers["uuid"] = uuid_value + organization["identifiers"] = normalized_identifiers + organization["pulse:infoscienceOrganizationIdentifier"] = normalized_infoscience_id + organization["pulse:githubOrganizationHandle"] = normalized_github_handle + if normalized_ror is not None: + organization["schema:identifier"] = normalized_ror + + +def _normalize_lookup_token(token: str) -> str: + return token.strip().lower() + + +def _lookup_token_variants(token: str) -> list[str]: + candidate = token.strip() + if not candidate: + return [] + + variants: list[str] = [] + seen: set[str] = set() + + def _add(value: str) -> None: + normalized_value = value.strip() + if not normalized_value: + return + if normalized_value in seen: + return + seen.add(normalized_value) + variants.append(normalized_value) + + def _add_normalized_forms(value: str) -> None: + lowered = _normalize_lookup_token(value) + _add(lowered) + accent_folded = strip_accents(lowered).strip() + _add(accent_folded) + collapsed = normalize_string(value) + _add(collapsed) + _add(collapsed.replace(" ", "")) + + raw_candidates: list[str] = [] + raw_seen: set[str] = set() + + def _add_raw(value: str | None) -> None: + if not isinstance(value, str): + return + normalized_value = value.strip() + if not normalized_value or normalized_value in raw_seen: + return + raw_seen.add(normalized_value) + raw_candidates.append(normalized_value) + + _add_raw(candidate) + if candidate.startswith("@"): + _add_raw(candidate[1:]) + + for raw_value in list(raw_candidates): + _add_raw(PARENTHETICAL_PATTERN.sub("", raw_value)) + + for raw_value in list(raw_candidates): + for segment in LOOKUP_SPLIT_PATTERN.split(raw_value): + segment = segment.strip() + if len(segment) >= 3: + _add_raw(segment) + + for raw_value in raw_candidates: + _add_normalized_forms(raw_value) + + return variants + + +def _register_lookup_token(lookup: dict[str, str], token: Any, canonical_id: str) -> None: + if not isinstance(token, str): + return + for normalized in _lookup_token_variants(token): + lookup[normalized] = canonical_id + + +def _resolve_lookup_token(lookup: dict[str, str], token: Any) -> str | None: + if not isinstance(token, str): + return None + for normalized in _lookup_token_variants(token): + resolved = lookup.get(normalized) + if isinstance(resolved, str): + return resolved + return None + + +def _register_organization_handle_lookup_tokens( + lookup: dict[str, str], + handle: Any, + canonical_id: str, +) -> None: + if not isinstance(handle, str): + return + stripped_handle = handle.strip() + if not stripped_handle: + return + + normalized_handle = stripped_handle[1:] if stripped_handle.startswith("@") else stripped_handle + if not normalized_handle: + return + + _register_lookup_token(lookup, normalized_handle, canonical_id) + _register_lookup_token(lookup, f"@{normalized_handle}", canonical_id) + _register_lookup_token(lookup, stripped_handle, canonical_id) + + +def _register_person_lookup_tokens(lookup: dict[str, str], person: dict[str, Any]) -> None: + canonical_id = person["id"] + _register_lookup_token(lookup, canonical_id, canonical_id) + _register_lookup_token(lookup, person.get("pulse:githubUsername"), canonical_id) + _register_lookup_token(lookup, person.get("pulse:orcidIdentifier"), canonical_id) + _register_lookup_token(lookup, person.get("pulse:infosciencePersonIdentifier"), canonical_id) + _register_lookup_token(lookup, person.get("schema:name"), canonical_id) + + identifiers = person.get("identifiers") + if isinstance(identifiers, dict): + _register_lookup_token(lookup, identifiers.get("pulse:orcid"), canonical_id) + _register_lookup_token( + lookup, + identifiers.get("pulse:infosciencePersonIdentifier"), + canonical_id, + ) + _register_lookup_token(lookup, identifiers.get("pulse:githubUsername"), canonical_id) + _register_lookup_token(lookup, identifiers.get("uuid"), canonical_id) + + +def _register_organization_lookup_tokens( + lookup: dict[str, str], + organization: dict[str, Any], +) -> None: + canonical_id = organization["id"] + _register_lookup_token(lookup, canonical_id, canonical_id) + _register_lookup_token(lookup, organization.get("schema:name"), canonical_id) + _register_organization_handle_lookup_tokens( + lookup, + organization.get("pulse:githubOrganizationHandle"), + canonical_id, + ) + _register_lookup_token(lookup, organization.get("pulse:ror"), canonical_id) + _register_lookup_token(lookup, organization.get("schema:identifier"), canonical_id) + for key in ("aliases", "acronyms"): + values = organization.get(key) + if isinstance(values, list): + for value in values: + _register_lookup_token(lookup, value, canonical_id) + labels = organization.get("labels") + if isinstance(labels, list): + for label_payload in labels: + label = label_payload + if isinstance(label_payload, dict): + label = label_payload.get("label") + _register_lookup_token(lookup, label, canonical_id) + + identifiers = organization.get("identifiers") + if isinstance(identifiers, dict): + _register_organization_handle_lookup_tokens( + lookup, + identifiers.get("pulse:githubOrganizationHandle"), + canonical_id, + ) + _register_lookup_token(lookup, identifiers.get("pulse:ror"), canonical_id) + _register_lookup_token( + lookup, + identifiers.get("pulse:infoscienceOrganizationIdentifier"), + canonical_id, + ) + + +def _organization_github_handle(organization: dict[str, Any]) -> str | None: + handle = organization.get("pulse:githubOrganizationHandle") + if isinstance(handle, str) and handle: + return handle + identifiers = organization.get("identifiers") + if isinstance(identifiers, dict): + identifier_handle = identifiers.get("pulse:githubOrganizationHandle") + if isinstance(identifier_handle, str) and identifier_handle: + return identifier_handle + return None + + +def _organization_source_priority(organization: dict[str, Any]) -> int: + id_source = organization.get("idSource") + if isinstance(id_source, str): + return ORG_ID_SOURCE_PRIORITY.get(id_source, 0) + if isinstance(_organization_normalized_ror(organization), str): + return ORG_ID_SOURCE_PRIORITY["pulse:ror"] + if isinstance(_organization_infoscience_uuid(organization), str): + return ORG_ID_SOURCE_PRIORITY["pulse:infoscienceOrganizationIdentifier"] + if isinstance(_organization_github_handle_key(organization), str): + return ORG_ID_SOURCE_PRIORITY["pulse:githubOrganizationHandle"] + return ORG_ID_SOURCE_PRIORITY["uuid"] + + +def _organization_identifier_count(organization: dict[str, Any]) -> int: + identifiers = organization.get("identifiers") + if not isinstance(identifiers, dict): + return 0 + return sum( + 1 + for key in ( + "pulse:ror", + "pulse:infoscienceOrganizationIdentifier", + "pulse:githubOrganizationHandle", + "uuid", + ) + if isinstance(identifiers.get(key), str) and identifiers.get(key) + ) + + +def _organization_metadata_score(organization: dict[str, Any]) -> int: + score = 0 + for field in ( + "schema:name", + "schema:identifier", + "pulse:OrganizationType", + "pulse:githubOrganizationHandle", + "pulse:infoscienceOrganizationIdentifier", + ): + value = organization.get(field) + if isinstance(value, str) and value: + score += 1 + followers = organization.get("pulse:githubOrgFollowers") + if isinstance(followers, int): + score += 1 + for field in ("aliases", "acronyms", "labels", "org:hasUnit", "org:unitOf", "pulse:owns"): + value = organization.get(field) + if isinstance(value, list) and value: + score += 1 + return score + + +def _organization_preference_tuple(organization: dict[str, Any]) -> tuple[int, int, int]: + return ( + _organization_source_priority(organization), + _organization_identifier_count(organization), + _organization_metadata_score(organization), + ) + + +def _normalize_ror_url(value: Any) -> str | None: + if not isinstance(value, str): + return None + candidate = value.strip() + if not candidate: + return None + if candidate.lower().startswith("https://ror.org/"): + candidate = candidate.rsplit("/", maxsplit=1)[-1] + candidate = candidate.lower() + if not re.fullmatch(r"[0-9a-z]{9}", candidate): + return None + return f"https://ror.org/{candidate}" + + +def _organization_normalized_ror(organization: dict[str, Any]) -> str | None: + identifiers = organization.get("identifiers") + if isinstance(identifiers, dict): + normalized = _normalize_ror_url(identifiers.get("pulse:ror")) + if normalized is not None: + return normalized + for candidate in ( + organization.get("schema:identifier"), + ): + normalized = _normalize_ror_url(candidate) + if normalized is not None: + return normalized + return None + + +def _organization_infoscience_uuid(organization: dict[str, Any]) -> str | None: + identifiers = organization.get("identifiers") + if isinstance(identifiers, dict): + normalized = _normalize_infoscience_uuid( + identifiers.get("pulse:infoscienceOrganizationIdentifier"), + ) + if normalized is not None: + return normalized + return _normalize_infoscience_uuid(organization.get("pulse:infoscienceOrganizationIdentifier")) + + +def _organization_github_handle_key(organization: dict[str, Any]) -> str | None: + handle = _organization_github_handle(organization) + if isinstance(handle, str) and handle: + return handle.casefold() + return None + + +def _organization_name_key(organization: dict[str, Any]) -> str | None: + name = organization.get("schema:name") + if isinstance(name, str) and name: + normalized = _normalize_organization_name_for_equivalence(name) + return normalized or None + return None + + +def _normalize_organization_name_for_equivalence(value: str) -> str: + normalized = normalize_string(value) + if not normalized: + return "" + normalized_tokens = [ + ORGANIZATION_NAME_EQUIVALENCE_TOKENS.get(token, token) + for token in normalized.split() + ] + return " ".join(normalized_tokens) + + +def _dedupe_any_list(values: list[Any]) -> list[Any]: + deduplicated: list[Any] = [] + seen: set[str] = set() + for value in values: + marker = repr(value) + if isinstance(value, dict): + marker = f"dict:{repr(sorted(value.items()))}" + if marker in seen: + continue + deduplicated.append(value) + seen.add(marker) + return deduplicated + + +def _merge_organization_payload( + canonical: dict[str, Any], + candidate: dict[str, Any], +) -> None: + canonical_identifiers = ( + deepcopy(canonical.get("identifiers")) + if isinstance(canonical.get("identifiers"), dict) + else {} + ) + candidate_identifiers = ( + deepcopy(candidate.get("identifiers")) + if isinstance(candidate.get("identifiers"), dict) + else {} + ) + for key in ( + "pulse:ror", + "pulse:infoscienceOrganizationIdentifier", + "pulse:githubOrganizationHandle", + "uuid", + ): + if isinstance(canonical_identifiers.get(key), str) and canonical_identifiers.get(key): + continue + fallback_value = candidate_identifiers.get(key) or candidate.get(key) + if isinstance(fallback_value, str) and fallback_value: + canonical_identifiers[key] = fallback_value + canonical["identifiers"] = canonical_identifiers + + for key in ("pulse:infoscienceOrganizationIdentifier", "pulse:githubOrganizationHandle"): + value = canonical_identifiers.get(key) + if isinstance(value, str) and value: + canonical[key] = value + elif key not in canonical: + canonical[key] = None + + canonical_ror = _organization_normalized_ror(canonical) + if canonical_ror is not None: + canonical["schema:identifier"] = canonical_ror + elif canonical.get("schema:identifier") is None and isinstance( + candidate.get("schema:identifier"), + str, + ): + canonical["schema:identifier"] = candidate["schema:identifier"] + + for field in ("schema:name", "pulse:OrganizationType"): + if isinstance(canonical.get(field), str) and canonical.get(field): + continue + candidate_value = candidate.get(field) + if isinstance(candidate_value, str) and candidate_value: + canonical[field] = candidate_value + + if not isinstance(canonical.get("pulse:githubOrgFollowers"), int) and isinstance( + candidate.get("pulse:githubOrgFollowers"), + int, + ): + canonical["pulse:githubOrgFollowers"] = candidate["pulse:githubOrgFollowers"] + + for field in ( + "aliases", + "acronyms", + "labels", + "org:hasUnit", + "org:unitOf", + "pulse:owns", + ): + merged_values: list[Any] = [] + existing_values = canonical.get(field) + if isinstance(existing_values, list): + merged_values.extend(existing_values) + candidate_values = candidate.get(field) + if isinstance(candidate_values, list): + merged_values.extend(candidate_values) + canonical[field] = _dedupe_any_list(merged_values) + + if not isinstance(canonical.get("type"), str) or not canonical.get("type"): + canonical["type"] = "org:Organization" + if not isinstance(canonical.get("shacl"), str) or not canonical.get("shacl"): + canonical["shacl"] = "pulse:OrganizationShape" + + +def _organization_equivalence_groups( + organizations: list[dict[str, Any]], +) -> list[list[int]]: + count = len(organizations) + if count <= 1: + return [] + + parent = list(range(count)) + + def _find(index: int) -> int: + while parent[index] != index: + parent[index] = parent[parent[index]] + index = parent[index] + return index + + def _union(left: int, right: int) -> None: + left_root = _find(left) + right_root = _find(right) + if left_root == right_root: + return + if left_root < right_root: + parent[right_root] = left_root + else: + parent[left_root] = right_root + + by_ror: dict[str, list[int]] = defaultdict(list) + by_infoscience: dict[str, list[int]] = defaultdict(list) + by_handle: dict[str, list[int]] = defaultdict(list) + for index, organization in enumerate(organizations): + ror = _organization_normalized_ror(organization) + if isinstance(ror, str): + by_ror[ror].append(index) + infoscience_id = _organization_infoscience_uuid(organization) + if isinstance(infoscience_id, str): + by_infoscience[infoscience_id].append(index) + github_handle = _organization_github_handle_key(organization) + if isinstance(github_handle, str): + by_handle[github_handle].append(index) + + for indexed_groups in (by_ror, by_infoscience, by_handle): + for indices in indexed_groups.values(): + if len(indices) <= 1: + continue + head = indices[0] + for member in indices[1:]: + _union(head, member) + + for left in range(count): + left_org = organizations[left] + left_ror = _organization_normalized_ror(left_org) + left_infoscience = _organization_infoscience_uuid(left_org) + left_name = _organization_name_key(left_org) + left_handle = _organization_github_handle_key(left_org) + for right in range(left + 1, count): + right_org = organizations[right] + right_ror = _organization_normalized_ror(right_org) + right_infoscience = _organization_infoscience_uuid(right_org) + has_cross_source_pair = ( + (isinstance(left_ror, str) and isinstance(right_infoscience, str)) + or (isinstance(right_ror, str) and isinstance(left_infoscience, str)) + ) + if not has_cross_source_pair: + continue + right_name = _organization_name_key(right_org) + names_match = ( + isinstance(left_name, str) + and isinstance(right_name, str) + and left_name == right_name + ) + right_handle = _organization_github_handle_key(right_org) + handles_match = ( + isinstance(left_handle, str) + and isinstance(right_handle, str) + and left_handle == right_handle + ) + if names_match or handles_match: + _union(left, right) + + grouped: dict[int, list[int]] = defaultdict(list) + for index in range(count): + grouped[_find(index)].append(index) + + merged_groups = [indices for indices in grouped.values() if len(indices) > 1] + merged_groups.sort(key=lambda indices: min(indices)) + return merged_groups + + +def _merge_equivalent_organizations( + organizations: list[dict[str, Any]], +) -> tuple[list[dict[str, Any]], dict[str, str], int, int]: + groups = _organization_equivalence_groups(organizations) + if not groups: + return organizations, {}, 0, 0 + + group_by_index: dict[int, list[int]] = {} + for group in groups: + for index in group: + group_by_index[index] = group + + merged_organizations: list[dict[str, Any]] = [] + consumed_indices: set[int] = set() + id_remap: dict[str, str] = {} + merged_group_count = 0 + merged_entity_count = 0 + + for index, organization in enumerate(organizations): + if index in consumed_indices: + continue + + group = group_by_index.get(index) + if not isinstance(group, list): + merged_organizations.append(organization) + continue + + merged_group_count += 1 + winner_index = max( + group, + key=lambda candidate_index: ( + *_organization_preference_tuple(organizations[candidate_index]), + -candidate_index, + ), + ) + merged = deepcopy(organizations[winner_index]) + winner_id = merged.get("id") + + for member_index in group: + consumed_indices.add(member_index) + member = organizations[member_index] + member_id = member.get("id") + if ( + member_index != winner_index + and isinstance(member_id, str) + and member_id + and isinstance(winner_id, str) + and winner_id + and member_id != winner_id + ): + id_remap[member_id] = winner_id + merged_entity_count += 1 + if member_index == winner_index: + continue + _merge_organization_payload(merged, member) + + merged_organizations.append(merged) + + return merged_organizations, id_remap, merged_group_count, merged_entity_count + + +def _apply_organization_id_remap( + candidate: Any, + org_id_remap: dict[str, str], +) -> str | Any: + if isinstance(candidate, str): + return org_id_remap.get(candidate, candidate) + return candidate + + +def _apply_org_remap_to_entities( + *, + organizations: list[dict[str, Any]], + repositories: list[dict[str, Any]], + articles: list[dict[str, Any]], + persons: list[dict[str, Any]], + memberships: list[dict[str, Any]], + org_id_remap: dict[str, str], +) -> None: + if not org_id_remap: + return + + for repository in repositories: + owner = repository.get("pulse:ownedBy") + if isinstance(owner, str): + repository["pulse:ownedBy"] = _apply_organization_id_remap(owner, org_id_remap) + + for article in articles: + source_org = article.get("schema:sourceOrganization") + if isinstance(source_org, str): + article["schema:sourceOrganization"] = _apply_organization_id_remap( + source_org, + org_id_remap, + ) + + for person in persons: + affiliations = person.get("affiliations") + if not isinstance(affiliations, list): + continue + remapped_affiliations: list[Any] = [] + for affiliation in affiliations: + if isinstance(affiliation, str): + remapped_affiliations.append( + _apply_organization_id_remap(affiliation, org_id_remap), + ) + continue + if isinstance(affiliation, dict): + remapped_affiliation = deepcopy(affiliation) + organization_id = remapped_affiliation.get("organizationId") + if isinstance(organization_id, str): + remapped_affiliation["organizationId"] = _apply_organization_id_remap( + organization_id, + org_id_remap, + ) + remapped_affiliations.append(remapped_affiliation) + continue + remapped_affiliations.append(affiliation) + person["affiliations"] = remapped_affiliations + + for membership in memberships: + organization_id = membership.get("org:organization") + if isinstance(organization_id, str): + membership["org:organization"] = _apply_organization_id_remap( + organization_id, + org_id_remap, + ) + + for organization in organizations: + has_units = organization.get("org:hasUnit") + if isinstance(has_units, list): + organization["org:hasUnit"] = _dedupe_preserve_order( + [ + _apply_organization_id_remap(has_unit, org_id_remap) + for has_unit in has_units + if isinstance(has_unit, str) and has_unit + ], + ) + unit_of = organization.get("org:unitOf") + if isinstance(unit_of, str): + unit_of = [unit_of] + if isinstance(unit_of, list): + organization["org:unitOf"] = _dedupe_preserve_order( + [ + _apply_organization_id_remap(parent_id, org_id_remap) + for parent_id in unit_of + if isinstance(parent_id, str) and parent_id + ], + ) + + +def _organization_lookup_tokens(organization: dict[str, Any]) -> list[str]: + tokens: list[str] = [] + for value in ( + organization.get("id"), + organization.get("schema:name"), + organization.get("schema:identifier"), + ): + if not isinstance(value, str): + continue + tokens.extend(_lookup_token_variants(value)) + + handle = _organization_github_handle(organization) + if isinstance(handle, str) and handle: + tokens.extend(_lookup_token_variants(handle)) + tokens.extend(_lookup_token_variants(f"@{handle}")) + + for key in ("aliases", "acronyms"): + values = organization.get(key) + if not isinstance(values, list): + continue + for value in values: + if isinstance(value, str): + tokens.extend(_lookup_token_variants(value)) + + labels = organization.get("labels") + if isinstance(labels, list): + for label_payload in labels: + label = label_payload + if isinstance(label_payload, dict): + label = label_payload.get("label") + if isinstance(label, str): + tokens.extend(_lookup_token_variants(label)) + + identifiers = organization.get("identifiers") + if isinstance(identifiers, dict): + for value in ( + identifiers.get("pulse:ror"), + identifiers.get("pulse:infoscienceOrganizationIdentifier"), + identifiers.get("pulse:githubOrganizationHandle"), + identifiers.get("uuid"), + ): + if isinstance(value, str): + tokens.extend(_lookup_token_variants(value)) + + return _dedupe_preserve_order(tokens) + + +def _preferred_organization_id( + *, + existing_id: str, + candidate_id: str, + organizations_by_id: dict[str, dict[str, Any]], +) -> str: + existing_org = organizations_by_id.get(existing_id) + candidate_org = organizations_by_id.get(candidate_id) + if not isinstance(existing_org, dict): + return candidate_id + if not isinstance(candidate_org, dict): + return existing_id + + existing_rank = _organization_preference_tuple(existing_org) + candidate_rank = _organization_preference_tuple(candidate_org) + if candidate_rank > existing_rank: + return candidate_id + return existing_id + + +def _github_org_account_id(handle: str) -> str: + return f"{GITHUB_ORG_BASE_URI}{handle}" + + +def _prefer_github_unit_for_handle_token( + *, + token: str, + existing_id: str, + candidate_id: str, + organizations_by_id: dict[str, dict[str, Any]], +) -> str | None: + existing_org = organizations_by_id.get(existing_id) + candidate_org = organizations_by_id.get(candidate_id) + if not isinstance(existing_org, dict) or not isinstance(candidate_org, dict): + return None + + existing_handle = _organization_github_handle_key(existing_org) + candidate_handle = _organization_github_handle_key(candidate_org) + if ( + not isinstance(existing_handle, str) + or not isinstance(candidate_handle, str) + or existing_handle != candidate_handle + ): + return None + + if token not in _lookup_token_variants(existing_handle): + return None + + existing_is_github_unit = existing_org.get("idSource") == "pulse:githubOrganizationHandle" + candidate_is_github_unit = candidate_org.get("idSource") == "pulse:githubOrganizationHandle" + if existing_is_github_unit == candidate_is_github_unit: + return None + + return existing_id if existing_is_github_unit else candidate_id + + +def _strip_organization_lookup_fields(organizations: list[dict[str, Any]]) -> None: + """Drop reconciliation-only lookup fields not allowed by strict schema.""" + for organization in organizations: + for field in ("aliases", "acronyms", "labels"): + organization.pop(field, None) + + +def _build_organization_lookup_with_collisions( + organizations: list[dict[str, Any]], +) -> tuple[dict[str, str], list[dict[str, str]]]: + lookup: dict[str, str] = {} + collision_records: list[dict[str, str]] = [] + seen_collision_markers: set[tuple[str, str, str]] = set() + + organizations_by_id = { + organization["id"]: organization + for organization in organizations + if isinstance(organization.get("id"), str) and organization.get("id") + } + + for organization in organizations: + canonical_id = organization.get("id") + if not isinstance(canonical_id, str) or not canonical_id: + continue + for token in _organization_lookup_tokens(organization): + existing_id = lookup.get(token) + if existing_id is None: + lookup[token] = canonical_id + continue + if existing_id == canonical_id: + continue + if token == existing_id: + continue + if token == canonical_id: + lookup[token] = canonical_id + marker = (token, canonical_id, existing_id) + if marker not in seen_collision_markers: + seen_collision_markers.add(marker) + collision_records.append( + { + "token": token, + "preferred_id": canonical_id, + "alternate_id": existing_id, + }, + ) + continue + github_unit_preferred_id = _prefer_github_unit_for_handle_token( + token=token, + existing_id=existing_id, + candidate_id=canonical_id, + organizations_by_id=organizations_by_id, + ) + if isinstance(github_unit_preferred_id, str): + lookup[token] = github_unit_preferred_id + alternate_id = canonical_id if github_unit_preferred_id == existing_id else existing_id + marker = (token, github_unit_preferred_id, alternate_id) + if marker not in seen_collision_markers: + seen_collision_markers.add(marker) + collision_records.append( + { + "token": token, + "preferred_id": github_unit_preferred_id, + "alternate_id": alternate_id, + }, + ) + continue + preferred_id = _preferred_organization_id( + existing_id=existing_id, + candidate_id=canonical_id, + organizations_by_id=organizations_by_id, + ) + lookup[token] = preferred_id + alternate_id = canonical_id if preferred_id == existing_id else existing_id + marker = (token, preferred_id, alternate_id) + if marker in seen_collision_markers: + continue + seen_collision_markers.add(marker) + collision_records.append( + { + "token": token, + "preferred_id": preferred_id, + "alternate_id": alternate_id, + }, + ) + + return lookup, collision_records + + +def _build_github_org_account_unit( + *, + github_handle: str, + parent_org_id: str, +) -> dict[str, Any]: + github_org_id = _github_org_account_id(github_handle) + return { + "id": github_org_id, + "type": "org:Organization", + "shacl": "pulse:OrganizationShape", + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:githubOrganizationHandle": github_handle, + "uuid": str(uuid4()), + }, + "idSource": "pulse:githubOrganizationHandle", + "schema:name": github_handle, + "schema:identifier": None, + "pulse:githubOrganizationHandle": github_handle, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:OrganizationType": "pulse:OtherOrganizationType", + "pulse:githubOrgFollowers": None, + "org:hasUnit": [], + "org:unitOf": [parent_org_id] if isinstance(parent_org_id, str) and parent_org_id else [], + "pulse:owns": [], + } + + +def _ensure_github_org_units_for_repository_owners( + *, + organizations: list[dict[str, Any]], + repositories: list[dict[str, Any]], +) -> None: + repository_owner_handles = { + owner + for repository in repositories + for owner in [repository.get("pulse:ownedBy")] + if isinstance(owner, str) and owner + } + if not repository_owner_handles: + return + + organizations_by_id: dict[str, dict[str, Any]] = { + organization["id"]: organization + for organization in organizations + if isinstance(organization.get("id"), str) + } + + for organization in list(organizations): + canonical_org_id = organization.get("id") + if not isinstance(canonical_org_id, str) or not canonical_org_id: + continue + + github_handle = _organization_github_handle(organization) + if ( + not isinstance(github_handle, str) + or not github_handle + or github_handle == canonical_org_id + or github_handle not in repository_owner_handles + ): + continue + + github_org_id = _github_org_account_id(github_handle) + org_units = organization.get("org:hasUnit") + if not isinstance(org_units, list): + org_units = [] + organization["org:hasUnit"] = _dedupe_preserve_order( + [ + *[ + github_org_id if value == github_handle else value + for value in org_units + if isinstance(value, str) and value + ], + github_org_id, + ], + ) + + github_unit = organizations_by_id.get(github_org_id) + legacy_github_unit = organizations_by_id.get(github_handle) + if github_unit is None and isinstance(legacy_github_unit, dict): + legacy_github_unit["id"] = github_org_id + organizations_by_id.pop(github_handle, None) + organizations_by_id[github_org_id] = legacy_github_unit + github_unit = legacy_github_unit + if github_unit is None: + github_unit = _build_github_org_account_unit( + github_handle=github_handle, + parent_org_id=canonical_org_id, + ) + organizations.append(github_unit) + organizations_by_id[github_org_id] = github_unit + + github_unit["org:unitOf"] = [canonical_org_id] + github_unit["type"] = "org:Organization" + github_unit["shacl"] = "pulse:OrganizationShape" + github_unit["pulse:githubOrganizationHandle"] = github_handle + + github_identifiers = github_unit.get("identifiers") + if not isinstance(github_identifiers, dict): + github_identifiers = {} + github_identifiers["pulse:githubOrganizationHandle"] = github_handle + if not isinstance(github_identifiers.get("uuid"), str) or not github_identifiers.get("uuid"): + github_identifiers["uuid"] = str(uuid4()) + github_unit["identifiers"] = github_identifiers + + +def _prune_unresolved_organization_hierarchy_links( + *, + organizations: list[dict[str, Any]], + organization_lookup: dict[str, str], +) -> list[str]: + organization_ids = { + organization["id"] + for organization in organizations + if isinstance(organization.get("id"), str) + } + + dropped_has_unit = 0 + dropped_unit_of = 0 + + for organization in organizations: + organization_id = organization.get("id") + if not isinstance(organization_id, str): + continue + + has_unit_refs = organization.get("org:hasUnit") + canonical_has_units: list[str] = [] + if isinstance(has_unit_refs, list): + for has_unit_ref in has_unit_refs: + if not isinstance(has_unit_ref, str) or not has_unit_ref: + continue + canonical_has_unit = _resolve_lookup_token(organization_lookup, has_unit_ref) + if ( + canonical_has_unit is None + or canonical_has_unit not in organization_ids + ): + dropped_has_unit += 1 + continue + canonical_has_units.append(canonical_has_unit) + organization["org:hasUnit"] = _dedupe_preserve_order(canonical_has_units) + + unit_of_refs = organization.get("org:unitOf") + if isinstance(unit_of_refs, str): + unit_of_refs = [unit_of_refs] + if not isinstance(unit_of_refs, list) or not unit_of_refs: + organization["org:unitOf"] = [] + continue + + canonical_unit_of: list[str] = [] + for ref in unit_of_refs: + if not isinstance(ref, str) or not ref: + continue + resolved = _resolve_lookup_token(organization_lookup, ref) + if resolved is None or resolved not in organization_ids: + dropped_unit_of += 1 + continue + canonical_unit_of.append(resolved) + organization["org:unitOf"] = _dedupe_preserve_order(canonical_unit_of) + + warnings: list[str] = [] + if dropped_has_unit > 0 or dropped_unit_of > 0: + warnings.append( + ( + "Dropped unresolved organization hierarchy references during reconciliation: " + f"org:hasUnit={dropped_has_unit}, org:unitOf={dropped_unit_of}" + ), + ) + return warnings + + +def _register_repository_lookup_tokens( + lookup: dict[str, str], + repository: dict[str, Any], +) -> None: + canonical_id = repository["id"] + _register_lookup_token(lookup, canonical_id, canonical_id) + _register_lookup_token(lookup, repository.get("pulse:githubRepositoryHandle"), canonical_id) + _register_lookup_token(lookup, repository.get("schema:citation"), canonical_id) + + identifiers = repository.get("identifiers") + if isinstance(identifiers, dict): + _register_lookup_token(lookup, identifiers.get("pulse:githubRepositoryHandle"), canonical_id) + _register_lookup_token(lookup, identifiers.get("schema:citation"), canonical_id) + + +def _extract_affiliation_reference(affiliation: Any) -> str | None: + if isinstance(affiliation, str): + return affiliation + if isinstance(affiliation, dict): + for key in ("organizationId", "name", "schema:name"): + value = affiliation.get(key) + if isinstance(value, str) and value: + return value + return None + + +def _detect_repository_fork_cycles(repositories: list[dict[str, Any]]) -> list[str]: + repository_ids = { + repository["id"] + for repository in repositories + if isinstance(repository.get("id"), str) + } + graph = { + repository["id"]: repository.get("pulse:isForkOf") + for repository in repositories + if isinstance(repository.get("id"), str) + and isinstance(repository.get("pulse:isForkOf"), str) + and repository.get("pulse:isForkOf") in repository_ids + } + + warnings: list[str] = [] + visit_state: dict[str, int] = {} + stack: list[str] = [] + + def _visit(node_id: str) -> None: + visit_state[node_id] = 1 + stack.append(node_id) + + target = graph.get(node_id) + if isinstance(target, str): + target_state = visit_state.get(target, 0) + if target_state == 0: + _visit(target) + elif target_state == 1: + cycle_start_index = stack.index(target) + cycle_path = " -> ".join([*stack[cycle_start_index:], target]) + warnings.append(f"Circular repository fork reference detected: {cycle_path}") + + stack.pop() + visit_state[node_id] = 2 + + for node in graph: + if visit_state.get(node, 0) == 0: + _visit(node) + + return _dedupe_preserve_order(warnings) + + +def _extract_composite_pair(composite_id: Any) -> tuple[str | None, str | None]: + if not isinstance(composite_id, str) or "_" not in composite_id: + return None, None + left, right = composite_id.split("_", maxsplit=1) + if not left or not right: + return None, None + return left, right + + +def _normalize_membership_entities( + memberships: list[dict[str, Any]], + *, + person_lookup: dict[str, str], + organization_lookup: dict[str, str], +) -> tuple[list[dict[str, Any]], set[tuple[str, str]], list[str]]: + normalized_memberships: list[dict[str, Any]] = [] + covered_pairs: set[tuple[str, str]] = set() + warnings: list[str] = [] + seen_membership_ids: set[str] = set() + + for membership in memberships: + composite_id_ref: Any = membership.get("id") + identifiers = membership.get("identifiers") + if isinstance(identifiers, dict): + composite_id_ref = identifiers.get("pulse:composite") or composite_id_ref + + person_ref, org_ref = _extract_composite_pair(composite_id_ref) + organization_ref = membership.get("org:organization") + if isinstance(organization_ref, str): + org_ref = organization_ref + + canonical_person_id = _resolve_lookup_token(person_lookup, person_ref) + canonical_org_id = _resolve_lookup_token(organization_lookup, org_ref) + if canonical_person_id is None or canonical_org_id is None: + warnings.append( + ( + "Unresolved class membership reference during reconciliation: " + f"id={membership.get('id')}, person={person_ref}, organization={org_ref}" + ), + ) + continue + + canonical_membership_id = f"{canonical_person_id}_{canonical_org_id}" + if canonical_membership_id in seen_membership_ids: + continue + seen_membership_ids.add(canonical_membership_id) + covered_pairs.add((canonical_person_id, canonical_org_id)) + + normalized_membership = deepcopy(membership) + normalized_identifiers = ( + deepcopy(identifiers) + if isinstance(identifiers, dict) + else {} + ) + normalized_identifiers["pulse:composite"] = canonical_membership_id + uuid_value = normalized_identifiers.get("uuid") + if not isinstance(uuid_value, str) or not uuid_value: + normalized_identifiers["uuid"] = str(uuid4()) + + normalized_membership["id"] = canonical_membership_id + normalized_membership["type"] = "org:Membership" + normalized_membership["shacl"] = "pulse:MembershipShape" + normalized_membership["identifiers"] = normalized_identifiers + normalized_membership["idSource"] = "pulse:composite" + normalized_membership["org:organization"] = canonical_org_id + normalized_membership["_person_ref"] = canonical_person_id + if not isinstance(normalized_membership.get("org:role"), str): + normalized_membership["org:role"] = None + if not isinstance(normalized_membership.get("time:hasBeginning"), str): + normalized_membership["time:hasBeginning"] = None + if not isinstance(normalized_membership.get("time:hasEnd"), str): + normalized_membership["time:hasEnd"] = None + + normalized_memberships.append(normalized_membership) + + return normalized_memberships, covered_pairs, warnings + + +def _normalize_contribution_entities( # noqa: C901 + contributions: list[dict[str, Any]], + *, + person_lookup: dict[str, str], + repository_lookup: dict[str, str], +) -> tuple[list[dict[str, Any]], set[tuple[str, str]], list[str]]: + normalized_contributions: list[dict[str, Any]] = [] + covered_pairs: set[tuple[str, str]] = set() + warnings: list[str] = [] + seen_contribution_ids: set[str] = set() + + for contribution in contributions: + composite_id_ref: Any = contribution.get("id") + identifiers = contribution.get("identifiers") + if isinstance(identifiers, dict): + composite_id_ref = identifiers.get("pulse:composite") or composite_id_ref + + person_ref, repository_ref = _extract_composite_pair(composite_id_ref) + schema_author = contribution.get("schema:author") + if isinstance(schema_author, str): + person_ref = schema_author + contribution_to = contribution.get("pulse:contributionTo") + if isinstance(contribution_to, str): + repository_ref = contribution_to + + canonical_person_id = _resolve_lookup_token(person_lookup, person_ref) + canonical_repository_id = _resolve_lookup_token(repository_lookup, repository_ref) + if canonical_person_id is None or canonical_repository_id is None: + warnings.append( + ( + "Unresolved class contribution reference during reconciliation: " + f"id={contribution.get('id')}, person={person_ref}, " + f"repository={repository_ref}" + ), + ) + continue + + canonical_contribution_id = f"{canonical_person_id}_{canonical_repository_id}" + if canonical_contribution_id in seen_contribution_ids: + continue + seen_contribution_ids.add(canonical_contribution_id) + covered_pairs.add((canonical_person_id, canonical_repository_id)) + + normalized_contribution = deepcopy(contribution) + normalized_identifiers = ( + deepcopy(identifiers) + if isinstance(identifiers, dict) + else {} + ) + normalized_identifiers["pulse:composite"] = canonical_contribution_id + uuid_value = normalized_identifiers.get("uuid") + if not isinstance(uuid_value, str) or not uuid_value: + normalized_identifiers["uuid"] = str(uuid4()) + + contribution_count = normalized_contribution.get("pulse:contributionCount") + if not isinstance(contribution_count, int) or contribution_count < 0: + contribution_count = 0 + + normalized_contribution["id"] = canonical_contribution_id + normalized_contribution["type"] = "pulse:Contribution" + normalized_contribution["shacl"] = "pulse:ContributionShape" + normalized_contribution["identifiers"] = normalized_identifiers + normalized_contribution["idSource"] = "pulse:composite" + normalized_contribution["schema:author"] = canonical_person_id + normalized_contribution["pulse:contributionTo"] = canonical_repository_id + normalized_contribution["pulse:contributionCount"] = contribution_count + if not isinstance(normalized_contribution.get("pulse:firstContributionDate"), str): + normalized_contribution["pulse:firstContributionDate"] = None + if not isinstance(normalized_contribution.get("pulse:lastContributionDate"), str): + normalized_contribution["pulse:lastContributionDate"] = None + + normalized_contributions.append(normalized_contribution) + + return normalized_contributions, covered_pairs, warnings + + +def reconcile_entities( # noqa: C901, PLR0912, PLR0915 + entities_by_type: dict[str, Any], +) -> ReconciledEntities: + reconciled_entities: dict[str, list[dict[str, Any]]] = { + "persons": deepcopy(_as_entity_list(entities_by_type.get("persons"))), + "organizations": deepcopy(_as_entity_list(entities_by_type.get("organizations"))), + "repositories": deepcopy(_as_entity_list(entities_by_type.get("repositories"))), + "articles": deepcopy(_as_entity_list(entities_by_type.get("articles"))), + } + class_memberships = deepcopy(_as_entity_list(entities_by_type.get("memberships"))) + class_contributions = deepcopy(_as_entity_list(entities_by_type.get("contributions"))) + + persons = reconciled_entities["persons"] + organizations = reconciled_entities["organizations"] + repositories = reconciled_entities["repositories"] + articles = reconciled_entities["articles"] + + person_lookup: dict[str, str] = {} + repository_lookup: dict[str, str] = {} + link_warnings: list[str] = [] + + for person in persons: + canonical_id, id_source = resolve_person_id(person) + person["id"] = canonical_id + person["idSource"] = id_source + _normalize_person_identifiers(person) + _register_person_lookup_tokens(person_lookup, person) + email = person.get("schema:email") + if isinstance(email, str): + person["schema:email"] = anonymize_email(email) + + for organization in organizations: + existing_id_source = organization.get("idSource") + _normalize_organization_identifiers(organization) + canonical_id, id_source = resolve_organization_id(organization) + organization["id"] = canonical_id + organization["idSource"] = id_source + if isinstance(existing_id_source, str) and existing_id_source != id_source: + link_warnings.append( + ( + "Organization ID hierarchy override during reconciliation: " + f"name={organization.get('schema:name')}, " + f"from={existing_id_source}, to={id_source}, id={canonical_id}" + ), + ) + + for repository in repositories: + canonical_id, id_source = resolve_repository_id(repository) + repository["id"] = canonical_id + repository["idSource"] = id_source + _register_repository_lookup_tokens(repository_lookup, repository) + + for article in articles: + canonical_id, id_source = resolve_article_id(article) + article["id"] = canonical_id + article["idSource"] = id_source + + organizations, organization_id_remap, merged_group_count, merged_entity_count = ( + _merge_equivalent_organizations(organizations) + ) + reconciled_entities["organizations"] = organizations + _apply_org_remap_to_entities( + organizations=organizations, + repositories=repositories, + articles=articles, + persons=persons, + memberships=class_memberships, + org_id_remap=organization_id_remap, + ) + + _ensure_github_org_units_for_repository_owners( + organizations=organizations, + repositories=repositories, + ) + organization_lookup, token_collision_records = _build_organization_lookup_with_collisions( + organizations, + ) + if token_collision_records: + token_collision_samples = token_collision_records[:DEBUG_SAMPLE_LIMIT] + sample_text = "; ".join( + ( + f"token='{sample['token']}' preferred={sample['preferred_id']} " + f"alternate={sample['alternate_id']}" + ) + for sample in token_collision_samples + ) + link_warnings.append( + ( + "Ambiguous organization lookup tokens detected during reconciliation: " + f"count={len(token_collision_records)}. " + "Preferred ROR-backed canonical organizations when available. " + f"Samples: {sample_text}" + ), + ) + + link_warnings.extend( + _prune_unresolved_organization_hierarchy_links( + organizations=organizations, + organization_lookup=organization_lookup, + ), + ) + + for repository in repositories: + repository_id = repository["id"] + author_refs_value = repository.get("schema:author") + author_refs: list[Any] + if isinstance(author_refs_value, list): + author_refs = author_refs_value + else: + fallback_authors = repository.get("authors") + author_refs = fallback_authors if isinstance(fallback_authors, list) else [] + + canonical_authors: list[str] = [] + unresolved_author_refs: list[str] = [] + for author_ref in author_refs: + canonical_author = _resolve_lookup_token(person_lookup, author_ref) + if canonical_author is None: + if _resolve_lookup_token(organization_lookup, author_ref) is not None: + continue + link_warnings.append( + ( + "Orphan person reference from repository author list: " + f"repo={repository_id}, author={author_ref}" + ), + ) + if isinstance(author_ref, str) and author_ref: + unresolved_author_refs.append(author_ref) + continue + canonical_authors.append(canonical_author) + + canonical_authors = _dedupe_preserve_order(canonical_authors) + repository["schema:author"] = canonical_authors + if "authors" in repository: + repository["authors"] = list(canonical_authors) + + owned_by = repository.get("pulse:ownedBy") + if isinstance(owned_by, str): + canonical_owner = _resolve_lookup_token(person_lookup, owned_by) or _resolve_lookup_token( + organization_lookup, + owned_by, + ) + if canonical_owner is None: + link_warnings.append( + ( + "Orphan owner reference from repository: " + f"repo={repository_id}, owner={owned_by}" + ), + ) + else: + repository["pulse:ownedBy"] = canonical_owner + + fork_of = repository.get("pulse:isForkOf") + if isinstance(fork_of, str): + canonical_fork_of = _resolve_lookup_token(repository_lookup, fork_of) + if canonical_fork_of is None: + link_warnings.append( + ( + "Orphan repository fork reference: " + f"repo={repository_id}, fork={fork_of}" + ), + ) + else: + repository["pulse:isForkOf"] = canonical_fork_of + + for article in articles: + article_id = article["id"] + author_refs_value = article.get("schema:author") + if isinstance(author_refs_value, list): + canonical_article_authors: list[str] = [] + for author_ref in author_refs_value: + canonical_author = _resolve_lookup_token(person_lookup, author_ref) + if canonical_author is None: + link_warnings.append( + ( + "Orphan person reference from article author list: " + f"article={article_id}, author={author_ref}" + ), + ) + continue + canonical_article_authors.append(canonical_author) + article["schema:author"] = _dedupe_preserve_order(canonical_article_authors) + + source_org_ref = article.get("schema:sourceOrganization") + if isinstance(source_org_ref, str): + canonical_source_org = _resolve_lookup_token(organization_lookup, source_org_ref) + if canonical_source_org is None: + link_warnings.append( + ( + "Orphan organization reference from article source organization: " + f"article={article_id}, organization={source_org_ref}" + ), + ) + else: + article["schema:sourceOrganization"] = canonical_source_org + + for person in persons: + person_id = person["id"] + affiliations = person.get("affiliations") + canonical_affiliations: list[str] = [] + if isinstance(affiliations, list): + for affiliation in affiliations: + affiliation_ref = _extract_affiliation_reference(affiliation) + canonical_affiliation = _resolve_lookup_token(organization_lookup, affiliation_ref) + if canonical_affiliation is None: + if isinstance(affiliation_ref, str): + link_warnings.append( + ( + "Orphan organization reference from person affiliation: " + f"person={person_id}, affiliation={affiliation_ref}" + ), + ) + continue + canonical_affiliations.append(canonical_affiliation) + person["affiliations"] = _dedupe_preserve_order(canonical_affiliations) + + owns_refs = person.get("pulse:owns") + if isinstance(owns_refs, list): + canonical_owns: list[str] = [] + for owned_repo in owns_refs: + canonical_repo_id = _resolve_lookup_token(repository_lookup, owned_repo) + if canonical_repo_id is None: + if isinstance(owned_repo, str): + link_warnings.append( + ( + "Orphan repository ownership reference from person: " + f"person={person_id}, repository={owned_repo}" + ), + ) + continue + canonical_owns.append(canonical_repo_id) + person["pulse:owns"] = _dedupe_preserve_order(canonical_owns) + + organization_ids = { + organization["id"] + for organization in organizations + if isinstance(organization.get("id"), str) + } + owned_repository_ids_by_org: dict[str, list[str]] = defaultdict(list) + for repository in repositories: + repository_id = repository.get("id") + owner_ref = repository.get("pulse:ownedBy") + if not isinstance(repository_id, str) or not repository_id: + continue + if not isinstance(owner_ref, str) or not owner_ref: + continue + canonical_owner = _resolve_lookup_token(organization_lookup, owner_ref) + if ( + isinstance(canonical_owner, str) + and canonical_owner in organization_ids + ): + owned_repository_ids_by_org[canonical_owner].append(repository_id) + + # NOTE: Ownership propagation from GitHub org-account units to canonical + # parent organizations is intentionally disabled. `pulse:owns` should reflect + # only direct repository owners resolved from `repository.pulse:ownedBy`. + # + # If needed in the future, re-enable propagation only for strict same-handle + # parent/child pairs and with explicit semantic approval. + + for organization in organizations: + organization_id = organization["id"] + github_handle = _organization_github_handle(organization) + if not isinstance(github_handle, str) or not github_handle: + organization["pulse:owns"] = [] + continue + organization["pulse:owns"] = _dedupe_preserve_order( + owned_repository_ids_by_org.get(organization_id, []), + ) + + memberships, _covered_membership_pairs, class_membership_warnings = _normalize_membership_entities( + class_memberships, + person_lookup=person_lookup, + organization_lookup=organization_lookup, + ) + link_warnings.extend(class_membership_warnings) + + contributions, _covered_contribution_pairs, class_contribution_warnings = _normalize_contribution_entities( + class_contributions, + person_lookup=person_lookup, + repository_lookup=repository_lookup, + ) + link_warnings.extend(class_contribution_warnings) + + membership_ids_by_person: dict[str, list[str]] = {} + for membership in memberships: + membership_id = membership["id"] + person_id = membership.get("_person_ref") + if person_id is None: + continue + membership_ids_by_person.setdefault(person_id, []).append(membership_id) + + contribution_ids_by_person: dict[str, list[str]] = {} + for contribution in contributions: + contribution_id = contribution["id"] + person_id = contribution.get("schema:author") + if person_id is None: + continue + contribution_ids_by_person.setdefault(person_id, []).append(contribution_id) + + for person in persons: + person_id = person["id"] + person["org:hasMembership"] = _dedupe_preserve_order( + membership_ids_by_person.get(person_id, []), + ) + person["pulse:hasContribution"] = _dedupe_preserve_order( + contribution_ids_by_person.get(person_id, []), + ) + + _strip_organization_lookup_fields(organizations) + + link_warnings.extend(_detect_repository_fork_cycles(repositories)) + org_remap_entries = [ + {"from": source_id, "to": target_id} + for source_id, target_id in sorted(organization_id_remap.items()) + if source_id != target_id + ] + reconciliation_debug = { + "merged_group_count": merged_group_count, + "merged_entity_count": merged_entity_count, + "org_remap_count": len(org_remap_entries), + "org_remap_sample": org_remap_entries[:DEBUG_SAMPLE_LIMIT], + "token_collision_count": len(token_collision_records), + "token_collision_sample": token_collision_records[:DEBUG_SAMPLE_LIMIT], + } + return ReconciledEntities( + entities=reconciled_entities, + memberships=memberships, + contributions=contributions, + link_warnings=_dedupe_preserve_order(link_warnings), + reconciliation_debug=reconciliation_debug, + ) diff --git a/src/v2/pipeline/stages/refine_with_llm.py b/src/v2/pipeline/stages/refine_with_llm.py new file mode 100644 index 0000000..deddb67 --- /dev/null +++ b/src/v2/pipeline/stages/refine_with_llm.py @@ -0,0 +1,504 @@ +"""Hybrid-runtime LLM refinement stage. + +Runs after `reconcile_entities`. For each Organization, Repository, and Person +in the reconciled graph, calls a typed LLM "refiner" that may propose a small +patch (whitelisted fields only). The whitelist is enforced here — fields +outside it are dropped and a warning is logged. + +Determinístic fields (counts, stars/forks, identifiers, dates) are never +exposed to the refiner contract via the whitelist, so the LLM cannot +accidentally clobber them even when its model emits them. + +Opt-in via ``agent_runtime=hybrid``. Each entity runs through its refiner +independently with bounded concurrency. +""" + +from __future__ import annotations + +import asyncio +import logging +import os +from copy import deepcopy +from dataclasses import dataclass, field +from time import perf_counter +from typing import Any + +from src.v2.agents.llm.agent_tools.graph_neighbors import make_get_entity_neighbors_tool +from src.v2.agents.llm.refiners import ( + MembershipRefinerAgent, + MembershipRefinerInput, + OrganizationRefinerAgent, + OrganizationRefinerInput, + PersonRefinerAgent, + PersonRefinerInput, + RepositoryRefinerAgent, + RepositoryRefinerInput, +) +from src.v2.agents.llm.runtime import LLMRuntimeError +from src.v2.ingest.providers.epfl_graph_rag import EpflGraphRagProvider +from src.v2.pipeline.stages.models import ReconciledEntities + +logger = logging.getLogger(__name__) + +ORG_PATCHABLE_FIELDS: frozenset[str] = frozenset({"pulse:OrganizationType"}) +REPO_PATCHABLE_FIELDS: frozenset[str] = frozenset( + {"pulse:discipline", "pulse:repositoryType"}, +) +PERSON_PATCHABLE_FIELDS: frozenset[str] = frozenset({"schema:name"}) +MEMBERSHIP_PATCHABLE_FIELDS: frozenset[str] = frozenset({"org:role"}) + +README_CONTEXT_MAX_CHARS = 1500 +DEFAULT_MAX_CONCURRENCY = 4 + +EPFL_GRAPH_HITS_TOP_K = 8 +EPFL_GRAPH_HIT_FIELDS: frozenset[str] = frozenset( + {"category_id", "name", "depth", "parent_id", "wikipedia_url", "score"}, +) + + +@dataclass(slots=True) +class RefineWithLLMResult: + """Outcome of the refinement stage across all entity types.""" + + reconciled: ReconciledEntities + warnings: list[str] = field(default_factory=list) + refined_count: int = 0 + skipped_count: int = 0 + failed_count: int = 0 + by_type: dict[str, dict[str, int]] = field(default_factory=dict) + + +def is_enabled() -> bool: + """Return True when the hybrid refiner stage is enabled (default true).""" + + raw = os.getenv("V2_HYBRID_REFINER_ENABLED") + if raw is None: + return True + return raw.strip().lower() not in {"0", "false", "f", "no", "n", "off"} + + +def _build_repo_context_summary( + *, + gathered_context: dict[str, Any] | None, +) -> dict[str, Any]: + if not isinstance(gathered_context, dict): + return {} + repository_context = gathered_context.get("repository") + if not isinstance(repository_context, dict): + return {} + + summary: dict[str, Any] = {} + metadata = repository_context.get("metadata") + if isinstance(metadata, dict): + meta_summary = { + key: metadata.get(key) + for key in ("name", "full_name", "description", "owner") + if metadata.get(key) is not None + } + if meta_summary: + summary["metadata"] = meta_summary + + readme = repository_context.get("readme_content") + if isinstance(readme, str) and readme: + summary["readme_excerpt"] = readme[:README_CONTEXT_MAX_CHARS] + + return summary + + +async def _fetch_epfl_graph_hits( + *, + provider: EpflGraphRagProvider | None, + repo_context_summary: dict[str, Any], +) -> list[dict[str, Any]]: + """Run one EPFL Graph disciplines RAG search using README + description. + + Returns a thin list of `{category_id, name, depth, parent_id, wikipedia_url, score}` + dicts (top-K) that the repository refiner uses to ground its discipline choice. + Returns `[]` on any error / missing input — never raises. + """ + + if provider is None: + return [] + + metadata = repo_context_summary.get("metadata") or {} + parts: list[str] = [] + name = metadata.get("name") + description = metadata.get("description") + readme = repo_context_summary.get("readme_excerpt") + for value in (name, description, readme): + if isinstance(value, str) and value.strip(): + parts.append(value.strip()) + query = "\n\n".join(parts).strip() + if not query: + return [] + + try: + hits = await provider.search(query, top_k=EPFL_GRAPH_HITS_TOP_K, rerank=True) + except Exception as exc: # noqa: BLE001 — RAG availability is optional + logger.warning("refine_with_llm: EPFL Graph RAG search failed — %s", exc) + return [] + + return [ + {key: hit.get(key) for key in EPFL_GRAPH_HIT_FIELDS if key in hit} + for hit in hits + ] + + +def _apply_patch( + *, + entity: dict[str, Any], + patch: dict[str, Any], + whitelist: frozenset[str], + type_label: str, +) -> tuple[bool, list[str]]: + """Merge `patch` into `entity` honouring the whitelist.""" + + warnings: list[str] = [] + changed = False + for key, value in patch.items(): + if key not in whitelist: + warnings.append( + f"refine_with_llm: dropped non-whitelisted field '{key}' " + f"({type_label}={entity.get('schema:name')!r})", + ) + continue + current = entity.get(key) + if current == value: + continue + entity[key] = value + changed = True + return changed, warnings + + +async def _run_refiner_safely( + *, + coro: Any, + entity: dict[str, Any], + type_label: str, +) -> tuple[dict[str, Any], dict[str, Any], list[str]]: + """Wrap a refiner call so a single failure cannot kill the stage.""" + + name = entity.get("schema:name") or entity.get("id", "?") + try: + patch = await coro + return entity, patch, [] + except LLMRuntimeError as exc: + warning = f"refine_with_llm: {type_label} refiner failed for {name!r}: {exc}" + logger.warning(warning) + return entity, {}, [warning] + except Exception as exc: # noqa: BLE001 — boundary for stage isolation + warning = ( + f"refine_with_llm: unexpected error in {type_label} refiner for " + f"{name!r}: {exc}" + ) + logger.exception(warning) + return entity, {}, [warning] + + +async def _refine_org( + *, + organization: dict[str, Any], + refiner: OrganizationRefinerAgent, + repo_context_summary: dict[str, Any], + neighbors_tool: Any, + semaphore: asyncio.Semaphore, +) -> tuple[dict[str, Any], dict[str, Any], list[str]]: + async with semaphore: + return await _run_refiner_safely( + coro=refiner.run( + refiner_input=OrganizationRefinerInput( + entity=deepcopy(organization), + repo_context_summary=repo_context_summary, + ), + tools=[neighbors_tool], + ), + entity=organization, + type_label="organization", + ) + + +async def _refine_repo( + *, + repository: dict[str, Any], + refiner: RepositoryRefinerAgent, + repo_context_summary: dict[str, Any], + neighbors_tool: Any, + semaphore: asyncio.Semaphore, +) -> tuple[dict[str, Any], dict[str, Any], list[str]]: + async with semaphore: + return await _run_refiner_safely( + coro=refiner.run( + refiner_input=RepositoryRefinerInput( + entity=deepcopy(repository), + repo_context_summary=repo_context_summary, + ), + tools=[neighbors_tool], + ), + entity=repository, + type_label="repository", + ) + + +async def _refine_person( + *, + person: dict[str, Any], + refiner: PersonRefinerAgent, + repo_context_summary: dict[str, Any], + neighbors_tool: Any, + semaphore: asyncio.Semaphore, +) -> tuple[dict[str, Any], dict[str, Any], list[str]]: + async with semaphore: + return await _run_refiner_safely( + coro=refiner.run( + refiner_input=PersonRefinerInput( + entity=deepcopy(person), + repo_context_summary=repo_context_summary, + ), + tools=[neighbors_tool], + ), + entity=person, + type_label="person", + ) + + +async def _refine_membership( + *, + membership: dict[str, Any], + refiner: MembershipRefinerAgent, + organization_name: str | None, + semaphore: asyncio.Semaphore, +) -> tuple[dict[str, Any], dict[str, Any], list[str]]: + async with semaphore: + return await _run_refiner_safely( + coro=refiner.run( + refiner_input=MembershipRefinerInput( + entity=deepcopy(membership), + organization_name=organization_name, + ), + ), + entity=membership, + type_label="membership", + ) + + +def _resolve_org_name_for_membership( + *, + membership: dict[str, Any], + org_index: dict[str, str], +) -> str | None: + org_ref = membership.get("org:organization") + if isinstance(org_ref, dict): + org_ref = org_ref.get("@id") or org_ref.get("id") + if isinstance(org_ref, str): + return org_index.get(org_ref) + return None + + +def _gate_repo_type_patch( + *, + repository: dict[str, Any], + patch: dict[str, Any], +) -> tuple[dict[str, Any], list[str]]: + """Drop `pulse:repositoryType` from the patch when the current value is not `pulse:Other`. + + The prompt already tells the model to skip; this is a server-side safety net. + """ + + if "pulse:repositoryType" not in patch: + return patch, [] + current = repository.get("pulse:repositoryType") + if current == "pulse:Other": + return patch, [] + pruned = {k: v for k, v in patch.items() if k != "pulse:repositoryType"} + warning = ( + "refine_with_llm: dropped repository type rewrite " + f"({repository.get('schema:name')!r}: current={current} → proposed={patch['pulse:repositoryType']}) — " + "only `pulse:Other` may be reclassified" + ) + return pruned, [warning] + + +async def run_refine_with_llm_stage( # noqa: PLR0913, PLR0915 + *, + reconciled: ReconciledEntities, + gathered_context: dict[str, Any] | None, + org_refiner: OrganizationRefinerAgent | None = None, + repo_refiner: RepositoryRefinerAgent | None = None, + person_refiner: PersonRefinerAgent | None = None, + membership_refiner: MembershipRefinerAgent | None = None, + epfl_graph_provider: EpflGraphRagProvider | None = None, + max_concurrency: int = DEFAULT_MAX_CONCURRENCY, +) -> RefineWithLLMResult: + """Run the hybrid LLM refinement stage over reconciled entities. + + Mutates ``reconciled`` in place — the caller receives the same instance back. + """ + + organizations = reconciled.entities.get("organizations") or [] + repositories = reconciled.entities.get("repositories") or [] + persons = reconciled.entities.get("persons") or [] + memberships_with_role = [m for m in reconciled.memberships if m.get("org:role")] + + if not (organizations or repositories or persons or memberships_with_role): + return RefineWithLLMResult(reconciled=reconciled) + + org_refiner = org_refiner or OrganizationRefinerAgent() + repo_refiner = repo_refiner or RepositoryRefinerAgent() + person_refiner = person_refiner or PersonRefinerAgent() + membership_refiner = membership_refiner or MembershipRefinerAgent() + + repo_context_summary = _build_repo_context_summary(gathered_context=gathered_context) + epfl_graph_hits = await _fetch_epfl_graph_hits( + provider=epfl_graph_provider, + repo_context_summary=repo_context_summary, + ) + if epfl_graph_hits: + logger.info( + "refine_with_llm: EPFL Graph hits prefetched count=%d top_score=%.3f", + len(epfl_graph_hits), + epfl_graph_hits[0].get("score") or 0.0, + ) + repo_context_summary_for_repo = ( + {**repo_context_summary, "epfl_graph_hits": epfl_graph_hits} + if epfl_graph_hits + else repo_context_summary + ) + neighbors_tool = make_get_entity_neighbors_tool( + entities=reconciled.entities, + memberships=reconciled.memberships, + contributions=reconciled.contributions, + ) + semaphore = asyncio.Semaphore(max(1, int(max_concurrency))) + + org_name_index = { + org["id"]: org.get("schema:name", "") + for org in organizations + if isinstance(org.get("id"), str) + } + + started_at = perf_counter() + tasks: list[Any] = [] + routing: list[tuple[str, frozenset[str]]] = [] + + for organization in organizations: + tasks.append( + _refine_org( + organization=organization, + refiner=org_refiner, + repo_context_summary=repo_context_summary, + neighbors_tool=neighbors_tool, + semaphore=semaphore, + ), + ) + routing.append(("organization", ORG_PATCHABLE_FIELDS)) + + for repository in repositories: + tasks.append( + _refine_repo( + repository=repository, + refiner=repo_refiner, + repo_context_summary=repo_context_summary_for_repo, + neighbors_tool=neighbors_tool, + semaphore=semaphore, + ), + ) + routing.append(("repository", REPO_PATCHABLE_FIELDS)) + + for person in persons: + tasks.append( + _refine_person( + person=person, + refiner=person_refiner, + repo_context_summary=repo_context_summary, + neighbors_tool=neighbors_tool, + semaphore=semaphore, + ), + ) + routing.append(("person", PERSON_PATCHABLE_FIELDS)) + + for membership in memberships_with_role: + tasks.append( + _refine_membership( + membership=membership, + refiner=membership_refiner, + organization_name=_resolve_org_name_for_membership( + membership=membership, + org_index=org_name_index, + ), + semaphore=semaphore, + ), + ) + routing.append(("membership", MEMBERSHIP_PATCHABLE_FIELDS)) + + results = await asyncio.gather(*tasks, return_exceptions=False) + + warnings: list[str] = [] + by_type: dict[str, dict[str, int]] = { + "organization": {"refined": 0, "skipped": 0, "failed": 0}, + "repository": {"refined": 0, "skipped": 0, "failed": 0}, + "person": {"refined": 0, "skipped": 0, "failed": 0}, + "membership": {"refined": 0, "skipped": 0, "failed": 0}, + } + for (type_label, whitelist), (entity, patch, run_warnings) in zip( + routing, + results, + strict=True, + ): + warnings.extend(run_warnings) + if run_warnings and not patch: + by_type[type_label]["failed"] += 1 + continue + if type_label == "repository": + patch, gate_warnings = _gate_repo_type_patch(repository=entity, patch=patch) + warnings.extend(gate_warnings) + if not patch: + by_type[type_label]["skipped"] += 1 + continue + changed, merge_warnings = _apply_patch( + entity=entity, + patch=patch, + whitelist=whitelist, + type_label=type_label, + ) + warnings.extend(merge_warnings) + if changed: + by_type[type_label]["refined"] += 1 + else: + by_type[type_label]["skipped"] += 1 + + refined_count = sum(stats["refined"] for stats in by_type.values()) + skipped_count = sum(stats["skipped"] for stats in by_type.values()) + failed_count = sum(stats["failed"] for stats in by_type.values()) + + logger.info( + "refine_with_llm: refined=%d skipped=%d failed=%d " + "(orgs=%s repos=%s persons=%s memberships=%s) in %.2fs", + refined_count, + skipped_count, + failed_count, + by_type["organization"], + by_type["repository"], + by_type["person"], + by_type["membership"], + perf_counter() - started_at, + ) + + return RefineWithLLMResult( + reconciled=reconciled, + warnings=warnings, + refined_count=refined_count, + skipped_count=skipped_count, + failed_count=failed_count, + by_type=by_type, + ) + + +__all__ = [ + "MEMBERSHIP_PATCHABLE_FIELDS", + "ORG_PATCHABLE_FIELDS", + "PERSON_PATCHABLE_FIELDS", + "REPO_PATCHABLE_FIELDS", + "RefineWithLLMResult", + "is_enabled", + "run_refine_with_llm_stage", +] diff --git a/src/v2/pipeline/stages/stats.py b/src/v2/pipeline/stages/stats.py new file mode 100644 index 0000000..a93e23c --- /dev/null +++ b/src/v2/pipeline/stages/stats.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +from rdflib.namespace import RDF +from rdflib.term import URIRef + +from src.v2.api_models import V2Stats + +if TYPE_CHECKING: + from rdflib import Graph + +DEFAULT_STATS_RUN_ID = "in-memory" + + +def _count_entities_from_graph(graph: Graph) -> int: + subjects = { + subject + for subject in graph.subjects(RDF.type, None) + if isinstance(subject, URIRef) + } + return len(subjects) + + +def compute_stats( + *, + graph: Graph | None = None, + run_id: str | None = None, + duration_ms: int = 0, + stages_completed: list[str] | None = None, + entities_count: int | None = None, +) -> V2Stats: + triples_count = len(graph) if graph is not None else 0 + if entities_count is None: + entities_count = _count_entities_from_graph(graph) if graph is not None else 0 + + return V2Stats( + entities_count=entities_count, + triples_count=triples_count, + run_id=run_id or DEFAULT_STATS_RUN_ID, + duration_ms=duration_ms, + stages_completed=stages_completed or [], + ) diff --git a/src/v2/pipeline/stages/validate_org_github_handles.py b/src/v2/pipeline/stages/validate_org_github_handles.py new file mode 100644 index 0000000..5d44045 --- /dev/null +++ b/src/v2/pipeline/stages/validate_org_github_handles.py @@ -0,0 +1,154 @@ +"""Validate `@handle`-style organization names against the GitHub API. + +Some agents emit Organization entities whose `schema:name` is a bare +GitHub mention (e.g. `@10xGenomics`, `@stripe`) extracted from README or +prompt text but never resolved to a real handle. These entities have no +identifiers populated and fail strict validation (which requires at +least one of `schema:identifier`, `pulse:githubOrganizationHandle`, or +`pulse:infoscienceOrganizationIdentifier`). + +This stage: +1. Iterates `org:Organization` entities whose `pulse:githubOrganizationHandle` + is unset and whose `schema:name` matches `^@[A-Za-z0-9](-?[A-Za-z0-9]){0,38}$`. +2. Probes `providers.github.get_user(handle)` (cached). +3. If the handle resolves to a real GitHub `Organization` → + stamps `pulse:githubOrganizationHandle`, the matching identifiers + entry, and rewrites `schema:name` to drop the leading `@`. +4. If the handle is a `User` or 404 → drops the entity (it's a + hallucinated org from a markdown @-mention). The drop is recorded + as a warning and the entity is removed from the reconciled bucket. + +Runs between `guarantee_repo_author` and `strict_validation`. +""" + +from __future__ import annotations + +import logging +import re +from typing import Any + +from src.v2.agents.models import ProviderSet +from src.v2.pipeline.stages.models import ReconciledEntities + +logger = logging.getLogger(__name__) + +ORG_TYPE = "org:Organization" +HANDLE_KEY = "pulse:githubOrganizationHandle" +ORG_BUCKET = "organizations" + +# GitHub login pattern (mirrors the orchestrator's GITHUB_LOGIN_PATTERN). +_AT_HANDLE_PATTERN = re.compile(r"^@([A-Za-z\d](?:[A-Za-z\d]|-(?=[A-Za-z\d])){0,38})$") + + +def _extract_at_handle(name: Any) -> str | None: + if not isinstance(name, str): + return None + match = _AT_HANDLE_PATTERN.match(name.strip()) + return match.group(1) if match else None + + +def _existing_handle(entity: dict[str, Any]) -> str | None: + direct = entity.get(HANDLE_KEY) + if isinstance(direct, str) and direct.strip(): + return direct.strip() + identifiers = entity.get("identifiers") + if isinstance(identifiers, dict): + nested = identifiers.get(HANDLE_KEY) + if isinstance(nested, str) and nested.strip(): + return nested.strip() + return None + + +def _stamp_handle(entity: dict[str, Any], handle: str) -> None: + entity[HANDLE_KEY] = handle + identifiers = entity.get("identifiers") + if not isinstance(identifiers, dict): + identifiers = {} + entity["identifiers"] = identifiers + identifiers[HANDLE_KEY] = handle + name = entity.get("schema:name") + if isinstance(name, str) and name.strip().startswith("@"): + entity["schema:name"] = name.strip().lstrip("@") + + +def _label(entity: dict[str, Any]) -> str: + return ( + entity.get("schema:name") + or entity.get("id") + or entity.get("@id") + or "" + ) + + +def validate_org_github_handles( + reconciled: ReconciledEntities, + providers: ProviderSet, +) -> tuple[ReconciledEntities, list[str]]: + """Stamp or drop `@handle`-named organizations using GitHub. + + Returns the same ``reconciled`` (mutated in place — the dataclass is + not frozen) plus the list of warnings emitted by this stage. + """ + warnings: list[str] = [] + organizations = reconciled.entities.get(ORG_BUCKET) + if not isinstance(organizations, list) or not organizations: + return reconciled, warnings + + github_provider = getattr(providers, "github", None) + if github_provider is None: + return reconciled, warnings + + kept: list[dict[str, Any]] = [] + for entity in organizations: + if not isinstance(entity, dict): + kept.append(entity) + continue + if entity.get("type") != ORG_TYPE: + kept.append(entity) + continue + if _existing_handle(entity) is not None: + kept.append(entity) + continue + candidate = _extract_at_handle(entity.get("schema:name")) + if candidate is None: + kept.append(entity) + continue + + try: + github_user = github_provider.get_user(candidate) + except Exception as exc: # noqa: BLE001 — provider failures are non-fatal + logger.warning( + "validate_org_github_handles: github lookup for '@%s' failed: %s", + candidate, + exc, + ) + kept.append(entity) + continue + + raw_type = ( + github_user.get("type") or github_user.get("account_type") + if isinstance(github_user, dict) + else None + ) + normalized_type = ( + raw_type.lower() if isinstance(raw_type, str) and raw_type else None + ) + if normalized_type == "organization": + _stamp_handle(entity, candidate) + warnings.append( + f"Stamped pulse:githubOrganizationHandle='{candidate}' on " + f"{_label(entity)} (validated against GitHub).", + ) + kept.append(entity) + elif normalized_type == "user" or not github_user: + warnings.append( + f"Dropped hallucinated organization '@{candidate}' " + f"({_label(entity)}): GitHub login resolves to " + f"{normalized_type or 'no account'}, not an organization.", + ) + else: + # Unknown shape — keep the entity rather than guess. + kept.append(entity) + + reconciled.entities[ORG_BUCKET] = kept + return reconciled, warnings diff --git a/src/v2/schema/__init__.py b/src/v2/schema/__init__.py new file mode 100644 index 0000000..9cf852c --- /dev/null +++ b/src/v2/schema/__init__.py @@ -0,0 +1,27 @@ +"""Schema assets and generated model bindings for v2 extraction.""" + +from __future__ import annotations + +import json +from functools import lru_cache +from pathlib import Path +from typing import Any + +JSONLD_CONTEXT_PATH = Path(__file__).resolve().parent / "json" / "context" / "v2.0.jsonld" + + +@lru_cache(maxsize=1) +def load_jsonld_context() -> dict[str, Any]: + """Return the v2 JSON-LD `@context` mapping.""" + payload = json.loads(JSONLD_CONTEXT_PATH.read_text(encoding="utf-8")) + if not isinstance(payload, dict): + message = f"Context file {JSONLD_CONTEXT_PATH} must contain a JSON object" + raise TypeError(message) + raw_context = payload.get("@context") + if not isinstance(raw_context, dict): + message = f"Context file {JSONLD_CONTEXT_PATH} must define a '@context' object" + raise ValueError(message) + return raw_context + + +__all__ = ["JSONLD_CONTEXT_PATH", "load_jsonld_context"] diff --git a/src/v2/schema/json/__init__.py b/src/v2/schema/json/__init__.py new file mode 100644 index 0000000..a01592c --- /dev/null +++ b/src/v2/schema/json/__init__.py @@ -0,0 +1,2 @@ +"""Canonical JSON Schema documents for v2 extraction contracts.""" + diff --git a/src/v2/schema/json/agent/article.schema.json b/src/v2/schema/json/agent/article.schema.json new file mode 100644 index 0000000..4b596a6 --- /dev/null +++ b/src/v2/schema/json/agent/article.schema.json @@ -0,0 +1,74 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:ArticleShape.schema.json", + "title": "ArticleShape", + "description": "Schema for Article entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: doi → infoscienceArticleIdentifier → uuid. SHACL requires: schema:name, schema:identifier (DOI), schema:datePublished, schema:author (min 1).", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name", "schema:identifier", "schema:datePublished", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Set to the first non-null value from: schema:identifier (DOI), pulse:infoscienceArticleIdentifier, or uuid (fallback). Example: '10.1038/s41586-024-07856-z'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'schema:ScholarlyArticle'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:ArticleShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this article. Contains schema:identifier (DOI), pulse:infoscienceArticleIdentifier, and uuid", + "properties": { + "schema:identifier": { + "type": ["string", "null"], + "description": "DOI identifier. Format: 10.XXXX/suffix. Example: '10.1038/s41586-024-07856-z'" + }, + "pulse:infoscienceArticleIdentifier": { + "type": ["string", "null"], + "description": "Infoscience article UUID4 identifier. Format: 8-4-4-4-12 hexadecimal characters. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Example: 'f8a3b2c1-4d5e-4f6a-8b9c-1d2e3f4a5b6c'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'schema:identifier', 'pulse:infoscienceArticleIdentifier', or 'uuid'" + }, + "schema:name": { + "type": "string", + "description": "Article title. REQUIRED per SHACL. Example: 'Open Pulse: A Framework for Tracking Scientific Software Contributions'" + }, + "schema:identifier": { + "type": "string", + "description": "DOI identifier. REQUIRED per SHACL. Format: 10.XXXX/suffix. Example: '10.1038/s41586-024-07856-z'" + }, + "schema:datePublished": { + "type": "string", + "description": "Publication date. REQUIRED per SHACL. Format: ISO 8601 date (YYYY-MM-DD). Example: '2025-06-15'" + }, + "schema:author": { + "type": "array", + "minItems": 1, + "description": "Array of PersonShape hierarchical IDs. REQUIRED per SHACL (min 1 author). Uses target's resolved id (could be ORCID, GitHub username, or UUID). Example: ['0000-0001-2345-6789', 'mweber']", + "items": { + "type": "string" + } + }, + "pulse:infoscienceArticleIdentifier": { + "type": ["string", "null"], + "description": "Infoscience article UUID4 identifier. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'" + }, + "schema:sourceOrganization": { + "type": ["string", "null"], + "description": "Reference to OrganizationShape hierarchical ID. Uses target's resolved id (could be ROR, GitHub org handle, or UUID). Example: 'https://ror.org/02s376052'" + } + } +} diff --git a/src/v2/schema/json/agent/contribution.schema.json b/src/v2/schema/json/agent/contribution.schema.json new file mode 100644 index 0000000..a5d30ba --- /dev/null +++ b/src/v2/schema/json/agent/contribution.schema.json @@ -0,0 +1,62 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:ContributionShape.schema.json", + "title": "ContributionShape", + "description": "Schema for Contribution entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: composite (personId_repoId) → uuid. SHACL requires: pulse:contributionTo, pulse:contributionCount, schema:author. Dates are optional.", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "pulse:contributionTo", "pulse:contributionCount", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Composite format: personId_repoId, or uuid (fallback). Example: '0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'pulse:Contribution'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:ContributionShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this contribution. Contains pulse:composite and uuid", + "properties": { + "pulse:composite": { + "type": "string", + "description": "Composite identifier. Format: personId_repoId (uses underscore separator). Example: '0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Example: 'd1a2b3c4-5e6f-4a7b-8c9d-0e1f2a3b4c5d'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'pulse:composite' or 'uuid'" + }, + "pulse:contributionTo": { + "type": "string", + "description": "Reference to RepositoryShape ID (GitHub handle). REQUIRED per SHACL. Example: 'EPFL-ENAC/geodata-toolkit'" + }, + "pulse:contributionCount": { + "type": "integer", + "description": "Number of commits/contributions. REQUIRED per SHACL. Must be zero or positive. Example: 156" + }, + "pulse:firstContributionDate": { + "type": ["string", "null"], + "description": "First contribution datetime. OPTIONAL per SHACL. Format: ISO 8601 datetime (YYYY-MM-DDTHH:MM:SSZ). Example: '2023-03-15T10:30:00Z'" + }, + "pulse:lastContributionDate": { + "type": ["string", "null"], + "description": "Last contribution datetime. OPTIONAL per SHACL. Format: ISO 8601 datetime (YYYY-MM-DDTHH:MM:SSZ). Example: '2025-01-20T14:45:00Z'" + }, + "schema:author": { + "type": "string", + "description": "Reference to PersonShape ID (hierarchical). REQUIRED per SHACL. Example: '0000-0001-2345-6789'" + } + } +} diff --git a/src/v2/schema/json/agent/membership.schema.json b/src/v2/schema/json/agent/membership.schema.json new file mode 100644 index 0000000..2c8c241 --- /dev/null +++ b/src/v2/schema/json/agent/membership.schema.json @@ -0,0 +1,58 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:MembershipShape.schema.json", + "title": "MembershipShape", + "description": "Schema for Membership entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: composite (personId_orgId) → uuid. SHACL requires: org:organization only. Role and dates are optional.", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "org:organization"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Composite format: personId_orgId, or uuid (fallback). Example: '0000-0001-2345-6789_https://ror.org/02s376052'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'org:Membership'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:MembershipShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this membership. Contains pulse:composite and uuid", + "properties": { + "pulse:composite": { + "type": "string", + "description": "Composite identifier. Format: personId_orgId (uses underscore separator). Example: '0000-0001-2345-6789_https://ror.org/02s376052'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Example: 'c1a2b3c4-5d6e-4f7a-8b9c-0d1e2f3a4b5c'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'pulse:composite' or 'uuid'" + }, + "org:organization": { + "type": "string", + "description": "Reference to OrganizationShape ID (hierarchical). REQUIRED per SHACL. Example: 'https://ror.org/02s376052'" + }, + "org:role": { + "type": ["string", "null"], + "description": "Role or position title within the organization. OPTIONAL per SHACL. Example: 'Research Engineer', 'PhD Student'" + }, + "time:hasBeginning": { + "type": ["string", "null"], + "description": "Start date of membership. Format: ISO 8601 date (YYYY-MM-DD). Example: '2020-09-01'" + }, + "time:hasEnd": { + "type": ["string", "null"], + "description": "End date of membership. Format: ISO 8601 date (YYYY-MM-DD). Null if current/ongoing. Example: '2024-02-28'" + } + } +} diff --git a/src/v2/schema/json/agent/organization.schema.json b/src/v2/schema/json/agent/organization.schema.json new file mode 100644 index 0000000..4b12262 --- /dev/null +++ b/src/v2/schema/json/agent/organization.schema.json @@ -0,0 +1,96 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:OrganizationShape.schema.json", + "title": "OrganizationShape", + "description": "Schema for Organization entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: ror → infoscienceOrganizationIdentifier → githubOrganizationHandle → uuid. SHACL requires: schema:name AND at least ONE of (schema:identifier, pulse:githubOrganizationHandle, pulse:infoscienceOrganizationIdentifier).", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Set to the first non-null value from: pulse:ror, pulse:infoscienceOrganizationIdentifier, pulse:githubOrganizationHandle, or uuid (fallback). Example: 'https://ror.org/02s376052' or 'numpy'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'org:Organization'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:OrganizationShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this organization. Contains pulse:ror, pulse:infoscienceOrganizationIdentifier, pulse:githubOrganizationHandle, and uuid", + "properties": { + "pulse:ror": { + "type": ["string", "null"], + "description": "ROR (Research Organization Registry) identifier. Format: https://ror.org/XXXXXXXXX (9 alphanumeric chars). Example: 'https://ror.org/02s376052'" + }, + "pulse:infoscienceOrganizationIdentifier": { + "type": ["string", "null"], + "description": "Infoscience organization UUID4 identifier. Format: 8-4-4-4-12 hexadecimal characters. Example: '95372c6b-7d45-432e-a84e-660c9fa54e05'" + }, + "pulse:githubOrganizationHandle": { + "type": ["string", "null"], + "description": "GitHub organization handle. Example: 'EPFL-ENAC'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Example: 'e2c4b6a8-1d3f-4e5a-9b7c-8d6e4f2a1b3c'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'pulse:ror', 'pulse:infoscienceOrganizationIdentifier', 'pulse:githubOrganizationHandle', or 'uuid'" + }, + "schema:name": { + "type": "string", + "description": "Organization name. Example: 'École Polytechnique Fédérale de Lausanne'" + }, + "schema:identifier": { + "type": ["string", "null"], + "description": "Canonical identifier (typically ROR URL). Example: 'https://ror.org/02s376052'" + }, + "pulse:githubOrganizationHandle": { + "type": ["string", "null"], + "description": "GitHub organization handle. Example: 'EPFL-ENAC'" + }, + "pulse:infoscienceOrganizationIdentifier": { + "type": ["string", "null"], + "description": "Infoscience organization identifier (UUID format). Example: '41674f42-ba15-4612-9817-2a6f60985c01'" + }, + "pulse:OrganizationType": { + "type": "string", + "enum": ["pulse:University", "pulse:ResearchInstitution", "pulse:GovernmentAgency", "pulse:SoftwareProject", "pulse:PrivateCompany", "pulse:NonProfitOrganization", "pulse:CommunitySpace", "pulse:OtherOrganizationType"], + "description": "Type of organization. One of: 'pulse:University', 'pulse:ResearchInstitution', 'pulse:GovernmentAgency', 'pulse:SoftwareProject', 'pulse:PrivateCompany', 'pulse:NonProfitOrganization', 'pulse:CommunitySpace', 'pulse:OtherOrganizationType'" + }, + "pulse:githubOrgFollowers": { + "type": ["integer", "null"], + "description": "GitHub organization follower count. Example: 342" + }, + "org:hasUnit": { + "type": "array", + "description": "Array of child OrganizationShape IDs (hierarchical). Example: ['95372c6b-7d45-432e-a84e-660c9fa54e05']", + "items": { + "type": "string" + } + }, + "org:unitOf": { + "type": "array", + "description": "Parent OrganizationShape IDs (hierarchical). Most orgs have 0 or 1 parent; arrays support joint affiliations (e.g. a research center jointly run by two universities). Example: ['https://ror.org/02s376052']", + "items": { + "type": "string" + } + }, + "pulse:owns": { + "type": "array", + "description": "Array of RepositoryShape IDs (GitHub handles) owned by this organization. Example: ['EPFL-ENAC/geodata-toolkit']", + "items": { + "type": "string" + } + } + } +} diff --git a/src/v2/schema/json/agent/person.schema.json b/src/v2/schema/json/agent/person.schema.json new file mode 100644 index 0000000..f81ba7d --- /dev/null +++ b/src/v2/schema/json/agent/person.schema.json @@ -0,0 +1,95 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:PersonShape.schema.json", + "title": "PersonShape", + "description": "Schema for Person entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: orcid → infosciencePersonIdentifier → githubUsername → uuid. SHACL requires: schema:name AND at least ONE of (pulse:githubUsername, schema:email, pulse:infosciencePersonIdentifier, pulse:orcidIdentifier).", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Set to the first non-null value from: pulse:orcid, pulse:infosciencePersonIdentifier, pulse:githubUsername, or uuid (fallback). Example: '0000-0001-2345-6789' or 'mweber'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'schema:Person'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:PersonShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this person. Contains pulse:orcid, pulse:infosciencePersonIdentifier, pulse:githubUsername, and uuid (always present)", + "properties": { + "pulse:orcid": { + "type": ["string", "null"], + "description": "ORCID identifier. Format: four groups of 4 digits separated by hyphens, last character can be 0-9 or X. Example: '0000-0001-2345-6789'" + }, + "pulse:infosciencePersonIdentifier": { + "type": ["string", "null"], + "description": "Infoscience person UUID4 identifier. Format: 8-4-4-4-12 hexadecimal characters. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'" + }, + "pulse:githubUsername": { + "type": ["string", "null"], + "description": "GitHub username. Example: 'caviri'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Format: 8-4-4-4-12 hexadecimal characters. Example: 'a7d4e2b1-3c8f-4a5b-9d6e-2f1a3b4c5d6e'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'pulse:orcid', 'pulse:infosciencePersonIdentifier', 'pulse:githubUsername', or 'uuid'" + }, + "schema:name": { + "type": "string", + "description": "Full name of the person. REQUIRED per SHACL. Example: 'Carlos Vivar Rios'" + }, + "schema:email": { + "type": "string", + "description": "Email address. Format: standard email pattern. Example: 'carlos.vivar@epfl.ch'" + }, + "schema:url": { + "type": ["string", "null"], + "description": "Profile URL for personal webpage. Example: 'https://caviri.github.io'" + }, + "pulse:githubUsername": { + "type": ["string", "null"], + "description": "GitHub username. Example: 'caviri'" + }, + "pulse:orcidIdentifier": { + "type": ["string", "null"], + "description": "ORCID identifier. Format: 0000-0001-2345-6789 (four groups of 4 digits, last can be X). Example: '0000-0001-2345-6789'" + }, + "pulse:infosciencePersonIdentifier": { + "type": ["string", "null"], + "description": "Infoscience person UUID4 identifier. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'" + }, + "org:hasMembership": { + "type": "array", + "description": "Array of MembershipShape IDs. Format: composite (personId_orgId). Example: ['0000-0001-2345-6789_https://ror.org/02s376052']", + "items": { + "type": "string" + } + }, + "pulse:hasContribution": { + "type": "array", + "description": "Array of ContributionShape IDs. Format: composite (personId_repoId). Example: ['0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit']", + "items": { + "type": "string" + } + }, + "pulse:owns": { + "type": "array", + "description": "Array of RepositoryShape IDs owned by this person. Format: GitHub handle (owner/repo). Example: ['EPFL-ENAC/geodata-toolkit']", + "items": { + "type": "string" + } + } + } +} diff --git a/src/v2/schema/json/agent/repository.schema.json b/src/v2/schema/json/agent/repository.schema.json new file mode 100644 index 0000000..e345af1 --- /dev/null +++ b/src/v2/schema/json/agent/repository.schema.json @@ -0,0 +1,110 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:RepositoryShape.schema.json", + "title": "RepositoryShape", + "description": "Schema for Repository entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: githubRepositoryHandle → doi → uuid. SHACL requires: schema:name, pulse:githubRepositoryHandle, schema:author (min 1).", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name", "pulse:githubRepositoryHandle", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Set to the first non-null value from: pulse:githubRepositoryHandle, schema:citation (DOI), or uuid (fallback). Example: 'EPFL-ENAC/geodata-toolkit'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'schema:SoftwareSourceCode'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:RepositoryShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this repository. Contains pulse:githubRepositoryHandle, schema:citation (DOI), and uuid", + "properties": { + "pulse:githubRepositoryHandle": { + "type": "string", + "description": "GitHub repository handle. Format: owner/repo. Example: 'EPFL-ENAC/geodata-toolkit'" + }, + "schema:citation": { + "type": ["string", "null"], + "description": "DOI identifier. Example: 'https://doi.org/10.5281/zenodo.1234567845678'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Example: 'b1c2d3e4-5f6a-4b7c-8d9e-0f1a2b3c4d5e'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'pulse:githubRepositoryHandle', 'schema:citation', or 'uuid'" + }, + "schema:name": { + "type": "string", + "description": "Repository display name. REQUIRED per SHACL. Example: 'Open Pulse Platform'" + }, + "pulse:githubRepositoryHandle": { + "type": "string", + "description": "GitHub repository handle. REQUIRED per SHACL. Format: owner/repo. Example: 'EPFL-ENAC/geodata-toolkit'" + }, + "pulse:repositoryType": { + "type": "string", + "enum": ["pulse:Software", "pulse:Data", "pulse:Documentation", "pulse:EducationalResource", "pulse:Other"], + "description": "Type of repository. One of: 'pulse:Software', 'pulse:Data', 'pulse:Documentation', 'pulse:EducationalResource', 'pulse:Other'" + }, + "pulse:discipline": { + "type": "array", + "description": "Array of Wikidata IRIs for disciplines. Format: wd:QXXXXX. Example: ['wd:Q21201', 'wd:Q8434']", + "items": { + "type": "string", + "enum": ["wd:Q1254373", "wd:Q101333", "wd:Q1071", "wd:Q11680831", "wd:Q12271", "wd:Q12483", "wd:Q18351432", "wd:Q188847", "wd:Q192386", "wd:Q21201", "wd:Q2167061", "wd:Q2329", "wd:Q23404", "wd:Q2878974", "wd:Q309", "wd:Q333", "wd:Q3353193", "wd:Q34749", "wd:Q3606845", "wd:Q395", "wd:Q413", "wd:Q420", "wd:Q42240", "wd:Q428691", "wd:Q43035", "wd:Q4830453", "wd:Q580689", "wd:Q5891", "wd:Q7112556", "wd:Q7163", "wd:Q735", "wd:Q7748", "wd:Q77590", "wd:Q7991", "wd:Q8008", "wd:Q80083", "wd:Q8078", "wd:Q8134", "wd:Q8162", "wd:Q816264", "wd:Q8242", "wd:Q83588", "wd:Q8434", "wd:Q843601", "wd:Q9174", "wd:Q9418"] + } + }, + "schema:author": { + "type": "array", + "minItems": 1, + "description": "Array of PersonShape IDs (hierarchical). REQUIRED per SHACL (min 1 author). Example: ['0000-0001-2345-6789', 'mweber']", + "items": { + "type": "string" + } + }, + "pulse:githubRepoStars": { + "type": ["integer", "null"], + "description": "GitHub star count. Example: 45" + }, + "pulse:githubRepoForks": { + "type": ["integer", "null"], + "description": "GitHub fork count. Example: 12" + }, + "schema:dateCreated": { + "type": ["string", "null"], + "description": "Creation datetime in ISO 8601 format. Format: YYYY-MM-DDTHH:MM:SSZ. Example: '2023-03-15T10:30:00Z'" + }, + "schema:license": { + "type": ["string", "null"], + "description": "SPDX license IRI. Example: 'https://spdx.org/licenses/MIT.html'" + }, + "schema:citation": { + "type": ["string", "null"], + "description": "DOI URL for citation. Example: 'https://doi.org/10.5281/zenodo.12345678'" + }, + "schema:programmingLanguage": { + "type": "array", + "description": "Array of programming languages. Example: ['Python', 'TypeScript']", + "items": { + "type": "string" + } + }, + "pulse:ownedBy": { + "type": ["string", "null"], + "description": "PersonShape or OrganizationShape ID (hierarchical) that owns this repository. Example: '95372c6b-7d45-432e-a84e-660c9fa54e05'" + }, + "pulse:isForkOf": { + "type": ["string", "null"], + "description": "Parent RepositoryShape ID (GitHub handle) if this is a fork. Example: 'numpy/numpy'" + } + } +} diff --git a/src/v2/schema/json/context/v2.0.jsonld b/src/v2/schema/json/context/v2.0.jsonld new file mode 100644 index 0000000..da406db --- /dev/null +++ b/src/v2/schema/json/context/v2.0.jsonld @@ -0,0 +1,110 @@ +{ + "context_version": "2.1.2", + "ontology_version": "open-pulse-ontology-v2.1.2", + "@context": { + "sh": "http://www.w3.org/ns/shacl#", + "xsd": "http://www.w3.org/2001/XMLSchema#", + "schema": "http://schema.org/", + "rdfs": "http://www.w3.org/2000/01/rdf-schema#", + "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#", + "org": "http://www.w3.org/ns/org#", + "time": "http://www.w3.org/2006/time#", + "pulse": "https://open-pulse.epfl.ch/ontology#", + "owl": "http://www.w3.org/2002/07/owl#", + "dct": "http://purl.org/dc/terms/", + "wd": "http://www.wikidata.org/entity/", + "skos": "http://www.w3.org/2004/02/skos/core#", + "schema:author": { + "@type": "@id", + "@container": "@set" + }, + "schema:sourceOrganization": { + "@type": "@id" + }, + "schema:url": { + "@type": "@id" + }, + "schema:license": { + "@type": "@id" + }, + "schema:citation": { + "@type": "@id" + }, + "org:organization": { + "@type": "@id" + }, + "org:hasMembership": { + "@type": "@id", + "@container": "@set" + }, + "org:hasUnit": { + "@type": "@id", + "@container": "@set" + }, + "org:unitOf": { + "@type": "@id" + }, + "pulse:hasContribution": { + "@type": "@id", + "@container": "@set" + }, + "pulse:contributionTo": { + "@type": "@id" + }, + "pulse:owns": { + "@type": "@id", + "@container": "@set" + }, + "pulse:ownedBy": { + "@type": "@id" + }, + "pulse:isForkOf": { + "@type": "@id" + }, + "affiliations": { + "@id": "pulse:affiliations", + "@type": "@id", + "@container": "@set" + }, + "pulse:discipline": { + "@type": "@id", + "@container": "@set" + }, + "pulse:repositoryType": { + "@type": "@id" + }, + "pulse:OrganizationType": { + "@type": "@id" + }, + "schema:dateCreated": { + "@type": "xsd:dateTime" + }, + "schema:datePublished": { + "@type": "xsd:date" + }, + "time:hasBeginning": { + "@type": "xsd:date" + }, + "time:hasEnd": { + "@type": "xsd:date" + }, + "pulse:firstContributionDate": { + "@type": "xsd:dateTime" + }, + "pulse:lastContributionDate": { + "@type": "xsd:dateTime" + }, + "pulse:contributionCount": { + "@type": "xsd:integer" + }, + "pulse:githubRepoStars": { + "@type": "xsd:integer" + }, + "pulse:githubRepoForks": { + "@type": "xsd:integer" + }, + "pulse:githubOrgFollowers": { + "@type": "xsd:integer" + } + } +} diff --git a/src/v2/schema/json/strict/article.schema.json b/src/v2/schema/json/strict/article.schema.json new file mode 100644 index 0000000..e50c82a --- /dev/null +++ b/src/v2/schema/json/strict/article.schema.json @@ -0,0 +1,78 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:ArticleShape.schema.json", + "title": "ArticleShape", + "description": "Schema for validating Article entities following Open Pulse Ontology v2.1.2. ID hierarchy: doi → infoscienceArticleIdentifier → uuid. Cross-references use hierarchical IDs when available.", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name", "schema:identifier", "schema:datePublished", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (first non-null in priority: doi → infoscienceArticleIdentifier → uuid)" + }, + "type": { + "type": "string", + "const": "schema:ScholarlyArticle" + }, + "shacl": { + "type": "string", + "const": "pulse:ArticleShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["schema:identifier", "uuid"], + "properties": { + "schema:identifier": { + "type": "string", + "pattern": "^10\\.\\d{4,9}/[-._;()/:a-zA-Z0-9]+$", + "description": "DOI identifier (required per SHACL minCount 1)" + }, + "pulse:infoscienceArticleIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["schema:identifier", "pulse:infoscienceArticleIdentifier", "uuid"] + }, + "schema:name": { + "type": "string", + "minLength": 1, + "description": "Article title" + }, + "schema:identifier": { + "type": "string", + "pattern": "^10\\.\\d{4,9}/[-._;()/:a-zA-Z0-9]+$", + "description": "DOI identifier (required per SHACL minCount 1)" + }, + "schema:datePublished": { + "type": "string", + "pattern": "^\\d{4}-\\d{2}-\\d{2}$", + "description": "Publication date in ISO 8601 date format (YYYY-MM-DD)" + }, + "schema:author": { + "type": "array", + "minItems": 1, + "items": { + "type": "string", + "description": "Reference to PersonShape hierarchical ID (orcid, infosciencePersonIdentifier, githubUsername, or uuid)" + } + }, + "pulse:infoscienceArticleIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + }, + "schema:sourceOrganization": { + "type": ["string", "null"], + "description": "Reference to OrganizationShape hierarchical ID (ror, infoscienceOrganizationIdentifier, githubOrganizationHandle, or uuid)" + } + } +} diff --git a/src/v2/schema/json/strict/contribution.schema.json b/src/v2/schema/json/strict/contribution.schema.json new file mode 100644 index 0000000..8fd92d6 --- /dev/null +++ b/src/v2/schema/json/strict/contribution.schema.json @@ -0,0 +1,65 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:ContributionShape.schema.json", + "title": "ContributionShape", + "description": "Schema for validating Contribution entities following Open Pulse Ontology v2.1.2. ID hierarchy: composite (personId_repoId) → uuid", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "pulse:contributionTo", "pulse:contributionCount", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (composite format: personId_repoId, or uuid fallback)" + }, + "type": { + "type": "string", + "const": "pulse:Contribution" + }, + "shacl": { + "type": "string", + "const": "pulse:ContributionShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["pulse:composite", "uuid"], + "properties": { + "pulse:composite": { + "type": "string", + "description": "Composite identifier in format: personId_repoId" + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["pulse:composite", "uuid"] + }, + "pulse:contributionTo": { + "type": "string", + "description": "Reference to RepositoryShape ID (GitHub handle format: owner/repo)" + }, + "pulse:contributionCount": { + "type": "integer", + "minimum": 0, + "description": "Number of commits/contributions" + }, + "pulse:firstContributionDate": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$", + "description": "First contribution datetime in ISO 8601 format. Optional per SHACL." + }, + "pulse:lastContributionDate": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$", + "description": "Last contribution datetime in ISO 8601 format. Optional per SHACL." + }, + "schema:author": { + "type": "string", + "description": "Reference to PersonShape ID (hierarchical)" + } + } +} diff --git a/src/v2/schema/json/strict/membership.schema.json b/src/v2/schema/json/strict/membership.schema.json new file mode 100644 index 0000000..3af476b --- /dev/null +++ b/src/v2/schema/json/strict/membership.schema.json @@ -0,0 +1,60 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:MembershipShape.schema.json", + "title": "MembershipShape", + "description": "Schema for validating Membership entities following Open Pulse Ontology v2.1.2. ID hierarchy: composite (personId_orgId) → uuid", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "org:organization"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (composite format: personId_orgId, or uuid fallback)" + }, + "type": { + "type": "string", + "const": "org:Membership" + }, + "shacl": { + "type": "string", + "const": "pulse:MembershipShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["pulse:composite", "uuid"], + "properties": { + "pulse:composite": { + "type": "string", + "description": "Composite identifier in format: personId_orgId" + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["pulse:composite", "uuid"] + }, + "org:organization": { + "type": "string", + "description": "Reference to OrganizationShape ID (hierarchical)" + }, + "org:role": { + "type": ["string", "null"], + "description": "Role or position title within the organization. Optional per SHACL (maxCount 1)." + }, + "time:hasBeginning": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{2}-\\d{2}$", + "description": "Start date in ISO 8601 date format (YYYY-MM-DD)" + }, + "time:hasEnd": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{2}-\\d{2}$", + "description": "End date in ISO 8601 date format (YYYY-MM-DD)" + } + } +} diff --git a/src/v2/schema/json/strict/organization.schema.json b/src/v2/schema/json/strict/organization.schema.json new file mode 100644 index 0000000..cf22388 --- /dev/null +++ b/src/v2/schema/json/strict/organization.schema.json @@ -0,0 +1,133 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:OrganizationShape.schema.json", + "title": "OrganizationShape", + "description": "Schema for validating Organization entities following Open Pulse Ontology v2.1.2. ID hierarchy: ror → infoscienceOrganizationIdentifier → githubOrganizationHandle → uuid. SHACL requires: schema:name AND at least ONE of (schema:identifier, pulse:githubOrganizationHandle, pulse:infoscienceOrganizationIdentifier).", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (first non-null in priority: ror → infoscienceOrganizationIdentifier → githubOrganizationHandle → uuid)" + }, + "type": { + "type": "string", + "const": "org:Organization" + }, + "shacl": { + "type": "string", + "const": "pulse:OrganizationShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["uuid"], + "properties": { + "pulse:ror": { + "type": ["string", "null"], + "pattern": "^https://ror\\.org/[0-9a-z]{9}$" + }, + "pulse:infoscienceOrganizationIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + }, + "pulse:githubOrganizationHandle": { + "type": ["string", "null"] + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["pulse:ror", "pulse:infoscienceOrganizationIdentifier", "pulse:githubOrganizationHandle", "uuid"] + }, + "schema:name": { + "type": "string", + "minLength": 1 + }, + "schema:identifier": { + "type": ["string", "null"], + "pattern": "^https://ror\\.org/[0-9a-z]{9}$", + "description": "Canonical identifier (typically ROR URL). Optional per SHACL." + }, + "pulse:githubOrganizationHandle": { + "type": ["string", "null"] + }, + "pulse:infoscienceOrganizationIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + "description": "Infoscience organization identifier (UUID4 format)." + }, + "pulse:OrganizationType": { + "type": "string", + "enum": [ + "pulse:University", + "pulse:ResearchInstitution", + "pulse:GovernmentAgency", + "pulse:SoftwareProject", + "pulse:PrivateCompany", + "pulse:NonProfitOrganization", + "pulse:CommunitySpace", + "pulse:OtherOrganizationType" + ] + }, + "pulse:githubOrgFollowers": { + "type": ["integer", "null"], + "minimum": 0 + }, + "org:hasUnit": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to child OrganizationShape ID (hierarchical)" + } + }, + "org:unitOf": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to parent OrganizationShape ID (hierarchical)" + } + }, + "pulse:owns": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to RepositoryShape ID (GitHub handle)" + } + } + }, + "anyOf": [ + { + "description": "At least schema:identifier required", + "required": ["schema:identifier"], + "properties": { + "schema:identifier": { + "type": "string" + } + } + }, + { + "description": "At least githubOrganizationHandle required", + "required": ["pulse:githubOrganizationHandle"], + "properties": { + "pulse:githubOrganizationHandle": { + "type": "string" + } + } + }, + { + "description": "At least infoscienceOrganizationIdentifier required", + "required": ["pulse:infoscienceOrganizationIdentifier"], + "properties": { + "pulse:infoscienceOrganizationIdentifier": { + "type": "string" + } + } + } + ] +} diff --git a/src/v2/schema/json/strict/person.schema.json b/src/v2/schema/json/strict/person.schema.json new file mode 100644 index 0000000..1f8531b --- /dev/null +++ b/src/v2/schema/json/strict/person.schema.json @@ -0,0 +1,131 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:PersonShape.schema.json", + "title": "PersonShape", + "description": "Schema for validating Person entities following Open Pulse Ontology v2.1.2. ID hierarchy: orcid → infosciencePersonIdentifier → githubUsername → uuid", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (first non-null in priority: orcid → infosciencePersonIdentifier → githubUsername → uuid)" + }, + "type": { + "type": "string", + "const": "schema:Person" + }, + "shacl": { + "type": "string", + "const": "pulse:PersonShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["uuid"], + "properties": { + "pulse:orcid": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{4}-\\d{4}-\\d{3}[0-9X]$" + }, + "pulse:infosciencePersonIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + }, + "pulse:githubUsername": { + "type": ["string", "null"] + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["pulse:orcid", "pulse:infosciencePersonIdentifier", "pulse:githubUsername", "uuid"] + }, + "schema:name": { + "type": "string", + "minLength": 1 + }, + "schema:email": { + "type": "string", + "pattern": "^[\\w\\-\\.]+@([\\w-]+\\.)+[\\w-]{2,4}$" + }, + "schema:url": { + "type": ["string", "null"], + "format": "uri" + }, + "pulse:githubUsername": { + "type": ["string", "null"] + }, + "pulse:orcidIdentifier": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{4}-\\d{4}-\\d{3}[0-9X]$" + }, + "pulse:infosciencePersonIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + }, + "org:hasMembership": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to MembershipShape ID (composite format: personId_orgId)" + } + }, + "pulse:hasContribution": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to ContributionShape ID (composite format: personId_repoId)" + } + }, + "pulse:owns": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to RepositoryShape ID (GitHub handle format: owner/repo)" + } + } + }, + "anyOf": [ + { + "description": "At least githubUsername required", + "required": ["pulse:githubUsername"], + "properties": { + "pulse:githubUsername": { + "type": "string" + } + } + }, + { + "description": "At least email required", + "required": ["schema:email"], + "properties": { + "schema:email": { + "type": "string" + } + } + }, + { + "description": "At least infosciencePersonIdentifier required", + "required": ["pulse:infosciencePersonIdentifier"], + "properties": { + "pulse:infosciencePersonIdentifier": { + "type": "string" + } + } + }, + { + "description": "At least orcidIdentifier required", + "required": ["pulse:orcidIdentifier"], + "properties": { + "pulse:orcidIdentifier": { + "type": "string" + } + } + } + ] +} diff --git a/src/v2/schema/json/strict/repository.schema.json b/src/v2/schema/json/strict/repository.schema.json new file mode 100644 index 0000000..2e46a8e --- /dev/null +++ b/src/v2/schema/json/strict/repository.schema.json @@ -0,0 +1,113 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:RepositoryShape.schema.json", + "title": "RepositoryShape", + "description": "Schema for validating Repository entities following Open Pulse Ontology v2.1.2. ID hierarchy: githubRepositoryHandle → doi → uuid", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name", "pulse:githubRepositoryHandle", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (first non-null in priority: githubRepositoryHandle → doi → uuid)" + }, + "type": { + "type": "string", + "const": "schema:SoftwareSourceCode" + }, + "shacl": { + "type": "string", + "const": "pulse:RepositoryShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["pulse:githubRepositoryHandle", "uuid"], + "properties": { + "pulse:githubRepositoryHandle": { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]+/[a-zA-Z0-9\\-_\\.]+$" + }, + "schema:citation": { + "type": ["string", "null"], + "format": "uri", + "description": "DOI identifier" + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["pulse:githubRepositoryHandle", "schema:citation", "uuid"] + }, + "schema:name": { + "type": "string", + "minLength": 1 + }, + "pulse:githubRepositoryHandle": { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]+/[a-zA-Z0-9\\-_\\.]+$" + }, + "pulse:repositoryType": { + "type": "string", + "enum": ["pulse:Software", "pulse:Data", "pulse:Documentation", "pulse:EducationalResource", "pulse:Other"], + "description": "Repository type. Optional per SHACL." + }, + "pulse:discipline": { + "type": "array", + "items": { + "type": "string", + "enum": ["wd:Q1254373", "wd:Q101333", "wd:Q1071", "wd:Q11680831", "wd:Q12271", "wd:Q12483", "wd:Q18351432", "wd:Q188847", "wd:Q192386", "wd:Q21201", "wd:Q2167061", "wd:Q2329", "wd:Q23404", "wd:Q2878974", "wd:Q309", "wd:Q333", "wd:Q3353193", "wd:Q34749", "wd:Q3606845", "wd:Q395", "wd:Q413", "wd:Q420", "wd:Q42240", "wd:Q428691", "wd:Q43035", "wd:Q4830453", "wd:Q580689", "wd:Q5891", "wd:Q7112556", "wd:Q7163", "wd:Q735", "wd:Q7748", "wd:Q77590", "wd:Q7991", "wd:Q8008", "wd:Q80083", "wd:Q8078", "wd:Q8134", "wd:Q8162", "wd:Q816264", "wd:Q8242", "wd:Q83588", "wd:Q8434", "wd:Q843601", "wd:Q9174", "wd:Q9418"], + "description": "Wikidata IRI for discipline (from Open Pulse Ontology)" + } + }, + "schema:author": { + "type": "array", + "minItems": 1, + "items": { + "type": "string", + "description": "Reference to PersonShape ID (hierarchical)" + } + }, + "pulse:githubRepoStars": { + "type": ["integer", "null"], + "minimum": 0 + }, + "pulse:githubRepoForks": { + "type": ["integer", "null"], + "minimum": 0 + }, + "schema:dateCreated": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$", + "description": "ISO 8601 datetime format" + }, + "schema:license": { + "type": ["string", "null"], + "format": "uri", + "description": "SPDX license IRI" + }, + "schema:citation": { + "type": ["string", "null"], + "format": "uri", + "description": "DOI URL for citation" + }, + "schema:programmingLanguage": { + "type": "array", + "items": { + "type": "string" + } + }, + "pulse:ownedBy": { + "type": ["string", "null"], + "description": "Reference to PersonShape or OrganizationShape ID (hierarchical)" + }, + "pulse:isForkOf": { + "type": ["string", "null"], + "description": "Reference to parent RepositoryShape ID (GitHub handle)" + } + } +} diff --git a/src/v2/schema/models/__init__.py b/src/v2/schema/models/__init__.py new file mode 100644 index 0000000..3a03ca7 --- /dev/null +++ b/src/v2/schema/models/__init__.py @@ -0,0 +1,18 @@ +from src.v2.schema.models.strict import ( + ArticleModel, + ContributionModel, + MembershipModel, + OrganizationModel, + PersonModel, + RepositoryModel, +) + +__all__ = [ + "ArticleModel", + "ContributionModel", + "MembershipModel", + "OrganizationModel", + "PersonModel", + "RepositoryModel", +] + diff --git a/src/v2/schema/models/agent.py b/src/v2/schema/models/agent.py new file mode 100644 index 0000000..3cd3012 --- /dev/null +++ b/src/v2/schema/models/agent.py @@ -0,0 +1,578 @@ +# generated by datamodel-codegen: +# filename: bundle.schema.json +# source-schema-sha256: +# - article.schema.json: 9493d56d6cbc441aa5061c38e4392b70f76b9c41eb4424f5f0f74bc3c5f12028 +# - contribution.schema.json: ac7d1bdb429a359e9e10045faf4874d6575b02e14277e959a5c979af15e456f8 +# - membership.schema.json: 7ec7bd52823ad40aca2e1bafb1747ac54ec6a8a99ac42ccd563c5bbaab004ceb +# - organization.schema.json: 57853768a4ac339989430e661be6df8525b493d6ba149def3e15bf478e440d6c +# - person.schema.json: 2d1201235eac1ecdda4bbb535ceb1e6d552f1fcc5bc72fd3ebc5af9bf6f64c92 +# - repository.schema.json: 446d18e936517fae215c978b6709d9b84e65dd9968b002daf0fe9354cce538d1 +# + +from __future__ import annotations +from pydantic import BaseModel, ConfigDict, Field +from enum import Enum + + +class Identifiers(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + pulse_orcid: str | None = Field( + None, + alias="pulse:orcid", + description="ORCID identifier. Format: four groups of 4 digits separated by hyphens, last character can be 0-9 or X. Example: '0000-0001-2345-6789'", + ) + pulse_infosciencePersonIdentifier: str | None = Field( + None, + alias="pulse:infosciencePersonIdentifier", + description="Infoscience person UUID4 identifier. Format: 8-4-4-4-12 hexadecimal characters. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'", + ) + pulse_githubUsername: str | None = Field( + None, + alias="pulse:githubUsername", + description="GitHub username. Example: 'caviri'", + ) + uuid: str | None = Field( + None, + description="UUID4 fallback identifier. Always present. Format: 8-4-4-4-12 hexadecimal characters. Example: 'a7d4e2b1-3c8f-4a5b-9d6e-2f1a3b4c5d6e'", + ) + + +class AgentPersonShape(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier. Set to the first non-null value from: pulse:orcid, pulse:infosciencePersonIdentifier, pulse:githubUsername, or uuid (fallback). Example: '0000-0001-2345-6789' or 'mweber'", + ) + type: str = Field(..., description="RDF type. Must be 'schema:Person'") + shacl: str = Field( + ..., description="SHACL shape reference. Must be 'pulse:PersonShape'" + ) + identifiers: Identifiers = Field( + ..., + description="All available identifiers for this person. Contains pulse:orcid, pulse:infosciencePersonIdentifier, pulse:githubUsername, and uuid (always present)", + ) + idSource: str = Field( + ..., + description="Indicates which identifier was used as the primary 'id'. One of: 'pulse:orcid', 'pulse:infosciencePersonIdentifier', 'pulse:githubUsername', or 'uuid'", + ) + schema_name: str = Field( + ..., + alias="schema:name", + description="Full name of the person. REQUIRED per SHACL. Example: 'Carlos Vivar Rios'", + ) + schema_email: str | None = Field( + None, + alias="schema:email", + description="Email address. Format: standard email pattern. Example: 'carlos.vivar@epfl.ch'", + ) + schema_url: str | None = Field( + None, + alias="schema:url", + description="Profile URL for personal webpage. Example: 'https://caviri.github.io'", + ) + pulse_githubUsername: str | None = Field( + None, + alias="pulse:githubUsername", + description="GitHub username. Example: 'caviri'", + ) + pulse_orcidIdentifier: str | None = Field( + None, + alias="pulse:orcidIdentifier", + description="ORCID identifier. Format: 0000-0001-2345-6789 (four groups of 4 digits, last can be X). Example: '0000-0001-2345-6789'", + ) + pulse_infosciencePersonIdentifier: str | None = Field( + None, + alias="pulse:infosciencePersonIdentifier", + description="Infoscience person UUID4 identifier. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'", + ) + org_hasMembership: list[str] | None = Field( + None, + alias="org:hasMembership", + description="Array of MembershipShape IDs. Format: composite (personId_orgId). Example: ['0000-0001-2345-6789_https://ror.org/02s376052']", + ) + pulse_hasContribution: list[str] | None = Field( + None, + alias="pulse:hasContribution", + description="Array of ContributionShape IDs. Format: composite (personId_repoId). Example: ['0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit']", + ) + pulse_owns: list[str] | None = Field( + None, + alias="pulse:owns", + description="Array of RepositoryShape IDs owned by this person. Format: GitHub handle (owner/repo). Example: ['EPFL-ENAC/geodata-toolkit']", + ) + + +class Identifiers1(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + pulse_githubRepositoryHandle: str | None = Field( + None, + alias="pulse:githubRepositoryHandle", + description="GitHub repository handle. Format: owner/repo. Example: 'EPFL-ENAC/geodata-toolkit'", + ) + schema_citation: str | None = Field( + None, + alias="schema:citation", + description="DOI identifier. Example: 'https://doi.org/10.5281/zenodo.1234567845678'", + ) + uuid: str | None = Field( + None, + description="UUID4 fallback identifier. Always present. Example: 'b1c2d3e4-5f6a-4b7c-8d9e-0f1a2b3c4d5e'", + ) + + +class PulseRepositoryType(Enum): + pulse_Software = "pulse:Software" + pulse_Data = "pulse:Data" + pulse_Documentation = "pulse:Documentation" + pulse_EducationalResource = "pulse:EducationalResource" + pulse_Other = "pulse:Other" + + +class PulseDisciplineEnum(Enum): + wd_Q1254373 = "wd:Q1254373" + wd_Q101333 = "wd:Q101333" + wd_Q1071 = "wd:Q1071" + wd_Q11680831 = "wd:Q11680831" + wd_Q12271 = "wd:Q12271" + wd_Q12483 = "wd:Q12483" + wd_Q18351432 = "wd:Q18351432" + wd_Q188847 = "wd:Q188847" + wd_Q192386 = "wd:Q192386" + wd_Q21201 = "wd:Q21201" + wd_Q2167061 = "wd:Q2167061" + wd_Q2329 = "wd:Q2329" + wd_Q23404 = "wd:Q23404" + wd_Q2878974 = "wd:Q2878974" + wd_Q309 = "wd:Q309" + wd_Q333 = "wd:Q333" + wd_Q3353193 = "wd:Q3353193" + wd_Q34749 = "wd:Q34749" + wd_Q3606845 = "wd:Q3606845" + wd_Q395 = "wd:Q395" + wd_Q413 = "wd:Q413" + wd_Q420 = "wd:Q420" + wd_Q42240 = "wd:Q42240" + wd_Q428691 = "wd:Q428691" + wd_Q43035 = "wd:Q43035" + wd_Q4830453 = "wd:Q4830453" + wd_Q580689 = "wd:Q580689" + wd_Q5891 = "wd:Q5891" + wd_Q7112556 = "wd:Q7112556" + wd_Q7163 = "wd:Q7163" + wd_Q735 = "wd:Q735" + wd_Q7748 = "wd:Q7748" + wd_Q77590 = "wd:Q77590" + wd_Q7991 = "wd:Q7991" + wd_Q8008 = "wd:Q8008" + wd_Q80083 = "wd:Q80083" + wd_Q8078 = "wd:Q8078" + wd_Q8134 = "wd:Q8134" + wd_Q8162 = "wd:Q8162" + wd_Q816264 = "wd:Q816264" + wd_Q8242 = "wd:Q8242" + wd_Q83588 = "wd:Q83588" + wd_Q8434 = "wd:Q8434" + wd_Q843601 = "wd:Q843601" + wd_Q9174 = "wd:Q9174" + wd_Q9418 = "wd:Q9418" + + +class AgentRepositoryShape(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier. Set to the first non-null value from: pulse:githubRepositoryHandle, schema:citation (DOI), or uuid (fallback). Example: 'EPFL-ENAC/geodata-toolkit'", + ) + type: str = Field(..., description="RDF type. Must be 'schema:SoftwareSourceCode'") + shacl: str = Field( + ..., description="SHACL shape reference. Must be 'pulse:RepositoryShape'" + ) + identifiers: Identifiers1 = Field( + ..., + description="All available identifiers for this repository. Contains pulse:githubRepositoryHandle, schema:citation (DOI), and uuid", + ) + idSource: str = Field( + ..., + description="Indicates which identifier was used as the primary 'id'. One of: 'pulse:githubRepositoryHandle', 'schema:citation', or 'uuid'", + ) + schema_name: str = Field( + ..., + alias="schema:name", + description="Repository display name. REQUIRED per SHACL. Example: 'Open Pulse Platform'", + ) + pulse_githubRepositoryHandle: str = Field( + ..., + alias="pulse:githubRepositoryHandle", + description="GitHub repository handle. REQUIRED per SHACL. Format: owner/repo. Example: 'EPFL-ENAC/geodata-toolkit'", + ) + pulse_repositoryType: PulseRepositoryType | None = Field( + None, + alias="pulse:repositoryType", + description="Type of repository. One of: 'pulse:Software', 'pulse:Data', 'pulse:Documentation', 'pulse:EducationalResource', 'pulse:Other'", + ) + pulse_discipline: list[PulseDisciplineEnum] | None = Field( + None, + alias="pulse:discipline", + description="Array of Wikidata IRIs for disciplines. Format: wd:QXXXXX. Example: ['wd:Q21201', 'wd:Q8434']", + ) + schema_author: list[str] = Field( + ..., + alias="schema:author", + description="Array of PersonShape IDs (hierarchical). REQUIRED per SHACL (min 1 author). Example: ['0000-0001-2345-6789', 'mweber']", + min_length=1, + ) + pulse_githubRepoStars: int | None = Field( + None, + alias="pulse:githubRepoStars", + description="GitHub star count. Example: 45", + ) + pulse_githubRepoForks: int | None = Field( + None, + alias="pulse:githubRepoForks", + description="GitHub fork count. Example: 12", + ) + schema_dateCreated: str | None = Field( + None, + alias="schema:dateCreated", + description="Creation datetime in ISO 8601 format. Format: YYYY-MM-DDTHH:MM:SSZ. Example: '2023-03-15T10:30:00Z'", + ) + schema_license: str | None = Field( + None, + alias="schema:license", + description="SPDX license IRI. Example: 'https://spdx.org/licenses/MIT.html'", + ) + schema_citation: str | None = Field( + None, + alias="schema:citation", + description="DOI URL for citation. Example: 'https://doi.org/10.5281/zenodo.12345678'", + ) + schema_programmingLanguage: list[str] | None = Field( + None, + alias="schema:programmingLanguage", + description="Array of programming languages. Example: ['Python', 'TypeScript']", + ) + pulse_ownedBy: str | None = Field( + None, + alias="pulse:ownedBy", + description="PersonShape or OrganizationShape ID (hierarchical) that owns this repository. Example: '95372c6b-7d45-432e-a84e-660c9fa54e05'", + ) + pulse_isForkOf: str | None = Field( + None, + alias="pulse:isForkOf", + description="Parent RepositoryShape ID (GitHub handle) if this is a fork. Example: 'numpy/numpy'", + ) + + +class Identifiers2(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + pulse_ror: str | None = Field( + None, + alias="pulse:ror", + description="ROR (Research Organization Registry) identifier. Format: https://ror.org/XXXXXXXXX (9 alphanumeric chars). Example: 'https://ror.org/02s376052'", + ) + pulse_infoscienceOrganizationIdentifier: str | None = Field( + None, + alias="pulse:infoscienceOrganizationIdentifier", + description="Infoscience organization UUID4 identifier. Format: 8-4-4-4-12 hexadecimal characters. Example: '95372c6b-7d45-432e-a84e-660c9fa54e05'", + ) + pulse_githubOrganizationHandle: str | None = Field( + None, + alias="pulse:githubOrganizationHandle", + description="GitHub organization handle. Example: 'EPFL-ENAC'", + ) + uuid: str | None = Field( + None, + description="UUID4 fallback identifier. Always present. Example: 'e2c4b6a8-1d3f-4e5a-9b7c-8d6e4f2a1b3c'", + ) + + +class PulseOrganizationType(Enum): + pulse_University = "pulse:University" + pulse_ResearchInstitution = "pulse:ResearchInstitution" + pulse_GovernmentAgency = "pulse:GovernmentAgency" + pulse_SoftwareProject = "pulse:SoftwareProject" + pulse_PrivateCompany = "pulse:PrivateCompany" + pulse_NonProfitOrganization = "pulse:NonProfitOrganization" + pulse_CommunitySpace = "pulse:CommunitySpace" + pulse_OtherOrganizationType = "pulse:OtherOrganizationType" + + +class AgentOrganizationShape(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier. Set to the first non-null value from: pulse:ror, pulse:infoscienceOrganizationIdentifier, pulse:githubOrganizationHandle, or uuid (fallback). Example: 'https://ror.org/02s376052' or 'numpy'", + ) + type: str = Field(..., description="RDF type. Must be 'org:Organization'") + shacl: str = Field( + ..., description="SHACL shape reference. Must be 'pulse:OrganizationShape'" + ) + identifiers: Identifiers2 = Field( + ..., + description="All available identifiers for this organization. Contains pulse:ror, pulse:infoscienceOrganizationIdentifier, pulse:githubOrganizationHandle, and uuid", + ) + idSource: str = Field( + ..., + description="Indicates which identifier was used as the primary 'id'. One of: 'pulse:ror', 'pulse:infoscienceOrganizationIdentifier', 'pulse:githubOrganizationHandle', or 'uuid'", + ) + schema_name: str = Field( + ..., + alias="schema:name", + description="Organization name. Example: 'École Polytechnique Fédérale de Lausanne'", + ) + schema_identifier: str | None = Field( + None, + alias="schema:identifier", + description="Canonical identifier (typically ROR URL). Example: 'https://ror.org/02s376052'", + ) + pulse_githubOrganizationHandle: str | None = Field( + None, + alias="pulse:githubOrganizationHandle", + description="GitHub organization handle. Example: 'EPFL-ENAC'", + ) + pulse_infoscienceOrganizationIdentifier: str | None = Field( + None, + alias="pulse:infoscienceOrganizationIdentifier", + description="Infoscience organization identifier (UUID format). Example: '41674f42-ba15-4612-9817-2a6f60985c01'", + ) + pulse_OrganizationType: PulseOrganizationType | None = Field( + None, + alias="pulse:OrganizationType", + description="Type of organization. One of: 'pulse:University', 'pulse:ResearchInstitution', 'pulse:GovernmentAgency', 'pulse:SoftwareProject', 'pulse:PrivateCompany', 'pulse:NonProfitOrganization', 'pulse:CommunitySpace', 'pulse:OtherOrganizationType'", + ) + pulse_githubOrgFollowers: int | None = Field( + None, + alias="pulse:githubOrgFollowers", + description="GitHub organization follower count. Example: 342", + ) + org_hasUnit: list[str] | None = Field( + None, + alias="org:hasUnit", + description="Array of child OrganizationShape IDs (hierarchical). Example: ['95372c6b-7d45-432e-a84e-660c9fa54e05']", + ) + org_unitOf: list[str] | None = Field( + None, + alias="org:unitOf", + description="Parent OrganizationShape IDs (hierarchical). Most orgs have 0 or 1 parent; arrays support joint affiliations (e.g. a research center jointly run by two universities). Example: ['https://ror.org/02s376052']", + ) + pulse_owns: list[str] | None = Field( + None, + alias="pulse:owns", + description="Array of RepositoryShape IDs (GitHub handles) owned by this organization. Example: ['EPFL-ENAC/geodata-toolkit']", + ) + + +class Identifiers3(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + pulse_composite: str | None = Field( + None, + alias="pulse:composite", + description="Composite identifier. Format: personId_orgId (uses underscore separator). Example: '0000-0001-2345-6789_https://ror.org/02s376052'", + ) + uuid: str | None = Field( + None, + description="UUID4 fallback identifier. Always present. Example: 'c1a2b3c4-5d6e-4f7a-8b9c-0d1e2f3a4b5c'", + ) + + +class AgentMembershipShape(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier. Composite format: personId_orgId, or uuid (fallback). Example: '0000-0001-2345-6789_https://ror.org/02s376052'", + ) + type: str = Field(..., description="RDF type. Must be 'org:Membership'") + shacl: str = Field( + ..., description="SHACL shape reference. Must be 'pulse:MembershipShape'" + ) + identifiers: Identifiers3 = Field( + ..., + description="All available identifiers for this membership. Contains pulse:composite and uuid", + ) + idSource: str = Field( + ..., + description="Indicates which identifier was used as the primary 'id'. One of: 'pulse:composite' or 'uuid'", + ) + org_organization: str = Field( + ..., + alias="org:organization", + description="Reference to OrganizationShape ID (hierarchical). REQUIRED per SHACL. Example: 'https://ror.org/02s376052'", + ) + org_role: str | None = Field( + None, + alias="org:role", + description="Role or position title within the organization. OPTIONAL per SHACL. Example: 'Research Engineer', 'PhD Student'", + ) + time_hasBeginning: str | None = Field( + None, + alias="time:hasBeginning", + description="Start date of membership. Format: ISO 8601 date (YYYY-MM-DD). Example: '2020-09-01'", + ) + time_hasEnd: str | None = Field( + None, + alias="time:hasEnd", + description="End date of membership. Format: ISO 8601 date (YYYY-MM-DD). Null if current/ongoing. Example: '2024-02-28'", + ) + + +class Identifiers4(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + pulse_composite: str | None = Field( + None, + alias="pulse:composite", + description="Composite identifier. Format: personId_repoId (uses underscore separator). Example: '0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit'", + ) + uuid: str | None = Field( + None, + description="UUID4 fallback identifier. Always present. Example: 'd1a2b3c4-5e6f-4a7b-8c9d-0e1f2a3b4c5d'", + ) + + +class AgentContributionShape(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier. Composite format: personId_repoId, or uuid (fallback). Example: '0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit'", + ) + type: str = Field(..., description="RDF type. Must be 'pulse:Contribution'") + shacl: str = Field( + ..., description="SHACL shape reference. Must be 'pulse:ContributionShape'" + ) + identifiers: Identifiers4 = Field( + ..., + description="All available identifiers for this contribution. Contains pulse:composite and uuid", + ) + idSource: str = Field( + ..., + description="Indicates which identifier was used as the primary 'id'. One of: 'pulse:composite' or 'uuid'", + ) + pulse_contributionTo: str = Field( + ..., + alias="pulse:contributionTo", + description="Reference to RepositoryShape ID (GitHub handle). REQUIRED per SHACL. Example: 'EPFL-ENAC/geodata-toolkit'", + ) + pulse_contributionCount: int = Field( + ..., + alias="pulse:contributionCount", + description="Number of commits/contributions. REQUIRED per SHACL. Must be zero or positive. Example: 156", + ) + pulse_firstContributionDate: str | None = Field( + None, + alias="pulse:firstContributionDate", + description="First contribution datetime. OPTIONAL per SHACL. Format: ISO 8601 datetime (YYYY-MM-DDTHH:MM:SSZ). Example: '2023-03-15T10:30:00Z'", + ) + pulse_lastContributionDate: str | None = Field( + None, + alias="pulse:lastContributionDate", + description="Last contribution datetime. OPTIONAL per SHACL. Format: ISO 8601 datetime (YYYY-MM-DDTHH:MM:SSZ). Example: '2025-01-20T14:45:00Z'", + ) + schema_author: str = Field( + ..., + alias="schema:author", + description="Reference to PersonShape ID (hierarchical). REQUIRED per SHACL. Example: '0000-0001-2345-6789'", + ) + + +class Identifiers5(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + schema_identifier: str | None = Field( + None, + alias="schema:identifier", + description="DOI identifier. Format: 10.XXXX/suffix. Example: '10.1038/s41586-024-07856-z'", + ) + pulse_infoscienceArticleIdentifier: str | None = Field( + None, + alias="pulse:infoscienceArticleIdentifier", + description="Infoscience article UUID4 identifier. Format: 8-4-4-4-12 hexadecimal characters. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'", + ) + uuid: str | None = Field( + None, + description="UUID4 fallback identifier. Always present. Example: 'f8a3b2c1-4d5e-4f6a-8b9c-1d2e3f4a5b6c'", + ) + + +class AgentArticleShape(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier. Set to the first non-null value from: schema:identifier (DOI), pulse:infoscienceArticleIdentifier, or uuid (fallback). Example: '10.1038/s41586-024-07856-z'", + ) + type: str = Field(..., description="RDF type. Must be 'schema:ScholarlyArticle'") + shacl: str = Field( + ..., description="SHACL shape reference. Must be 'pulse:ArticleShape'" + ) + identifiers: Identifiers5 = Field( + ..., + description="All available identifiers for this article. Contains schema:identifier (DOI), pulse:infoscienceArticleIdentifier, and uuid", + ) + idSource: str = Field( + ..., + description="Indicates which identifier was used as the primary 'id'. One of: 'schema:identifier', 'pulse:infoscienceArticleIdentifier', or 'uuid'", + ) + schema_name: str = Field( + ..., + alias="schema:name", + description="Article title. REQUIRED per SHACL. Example: 'Open Pulse: A Framework for Tracking Scientific Software Contributions'", + ) + schema_identifier: str = Field( + ..., + alias="schema:identifier", + description="DOI identifier. REQUIRED per SHACL. Format: 10.XXXX/suffix. Example: '10.1038/s41586-024-07856-z'", + ) + schema_datePublished: str = Field( + ..., + alias="schema:datePublished", + description="Publication date. REQUIRED per SHACL. Format: ISO 8601 date (YYYY-MM-DD). Example: '2025-06-15'", + ) + schema_author: list[str] = Field( + ..., + alias="schema:author", + description="Array of PersonShape hierarchical IDs. REQUIRED per SHACL (min 1 author). Uses target's resolved id (could be ORCID, GitHub username, or UUID). Example: ['0000-0001-2345-6789', 'mweber']", + min_length=1, + ) + pulse_infoscienceArticleIdentifier: str | None = Field( + None, + alias="pulse:infoscienceArticleIdentifier", + description="Infoscience article UUID4 identifier. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'", + ) + schema_sourceOrganization: str | None = Field( + None, + alias="schema:sourceOrganization", + description="Reference to OrganizationShape hierarchical ID. Uses target's resolved id (could be ROR, GitHub org handle, or UUID). Example: 'https://ror.org/02s376052'", + ) + + +class AgentEntitiesBundle(BaseModel): + model_config = ConfigDict( + extra="ignore", + ) + agentpersonshape: AgentPersonShape | None = None + agentrepositoryshape: AgentRepositoryShape | None = None + agentorganizationshape: AgentOrganizationShape | None = None + agentmembershipshape: AgentMembershipShape | None = None + agentcontributionshape: AgentContributionShape | None = None + agentarticleshape: AgentArticleShape | None = None diff --git a/src/v2/schema/models/strict.py b/src/v2/schema/models/strict.py new file mode 100644 index 0000000..7cdcfe9 --- /dev/null +++ b/src/v2/schema/models/strict.py @@ -0,0 +1,676 @@ +# generated by datamodel-codegen: +# filename: bundle.schema.json +# source-schema-sha256: +# - article.schema.json: 705c607bd70f982d4b186b7033d41cdc8f74ec86e68f2c6d6ab86c87ce29b4bd +# - contribution.schema.json: 39db27096c58d615240bb69152a84eba68ac503fd9f2000d16ee045305f2ef38 +# - membership.schema.json: c3c9b9e8743bb3e4f199b998cffc9ddf481ce787a7de8f45db6317217581c20d +# - organization.schema.json: 5a2acdb26373aa487b3f859eb997e6ea3dffdaf5448543e61d0214c40cc7014c +# - person.schema.json: 789285c972eaeeece7926ea68b0b0e0088cabeb8c9b234974fe7f9aab437290c +# - repository.schema.json: 0bf4dacdf49adad5be9d77dc233973076de977485d6aa10c6833cf561b725645 +# + +from __future__ import annotations +from pydantic import AnyUrl, BaseModel, ConfigDict, Field, RootModel +from enum import Enum +from typing import Literal + + +class Identifiers(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + pulse_orcid: str | None = Field( + None, alias="pulse:orcid", pattern="^\\d{4}-\\d{4}-\\d{4}-\\d{3}[0-9X]$" + ) + pulse_infosciencePersonIdentifier: str | None = Field( + None, + alias="pulse:infosciencePersonIdentifier", + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + pulse_githubUsername: str | None = Field(None, alias="pulse:githubUsername") + uuid: str = Field( + ..., + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + + +class IdSource(Enum): + pulse_orcid = "pulse:orcid" + pulse_infosciencePersonIdentifier = "pulse:infosciencePersonIdentifier" + pulse_githubUsername = "pulse:githubUsername" + uuid = "uuid" + + +class PersonModel1(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier (first non-null in priority: orcid → infosciencePersonIdentifier → githubUsername → uuid)", + ) + type: Literal["schema:Person"] + shacl: Literal["pulse:PersonShape"] + identifiers: Identifiers + idSource: IdSource + schema_name: str = Field(..., alias="schema:name", min_length=1) + schema_email: str | None = Field( + None, alias="schema:email", pattern="^[\\w\\-\\.]+@([\\w-]+\\.)+[\\w-]{2,4}$" + ) + schema_url: AnyUrl | None = Field(None, alias="schema:url") + pulse_githubUsername: str = Field(..., alias="pulse:githubUsername") + pulse_orcidIdentifier: str | None = Field( + None, + alias="pulse:orcidIdentifier", + pattern="^\\d{4}-\\d{4}-\\d{4}-\\d{3}[0-9X]$", + ) + pulse_infosciencePersonIdentifier: str | None = Field( + None, + alias="pulse:infosciencePersonIdentifier", + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + org_hasMembership: list[str] | None = Field(None, alias="org:hasMembership") + pulse_hasContribution: list[str] | None = Field(None, alias="pulse:hasContribution") + pulse_owns: list[str] | None = Field(None, alias="pulse:owns") + + +class PersonModel2(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier (first non-null in priority: orcid → infosciencePersonIdentifier → githubUsername → uuid)", + ) + type: Literal["schema:Person"] + shacl: Literal["pulse:PersonShape"] + identifiers: Identifiers + idSource: IdSource + schema_name: str = Field(..., alias="schema:name", min_length=1) + schema_email: str = Field( + ..., alias="schema:email", pattern="^[\\w\\-\\.]+@([\\w-]+\\.)+[\\w-]{2,4}$" + ) + schema_url: AnyUrl | None = Field(None, alias="schema:url") + pulse_githubUsername: str | None = Field(None, alias="pulse:githubUsername") + pulse_orcidIdentifier: str | None = Field( + None, + alias="pulse:orcidIdentifier", + pattern="^\\d{4}-\\d{4}-\\d{4}-\\d{3}[0-9X]$", + ) + pulse_infosciencePersonIdentifier: str | None = Field( + None, + alias="pulse:infosciencePersonIdentifier", + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + org_hasMembership: list[str] | None = Field(None, alias="org:hasMembership") + pulse_hasContribution: list[str] | None = Field(None, alias="pulse:hasContribution") + pulse_owns: list[str] | None = Field(None, alias="pulse:owns") + + +class PersonModel3(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier (first non-null in priority: orcid → infosciencePersonIdentifier → githubUsername → uuid)", + ) + type: Literal["schema:Person"] + shacl: Literal["pulse:PersonShape"] + identifiers: Identifiers + idSource: IdSource + schema_name: str = Field(..., alias="schema:name", min_length=1) + schema_email: str | None = Field( + None, alias="schema:email", pattern="^[\\w\\-\\.]+@([\\w-]+\\.)+[\\w-]{2,4}$" + ) + schema_url: AnyUrl | None = Field(None, alias="schema:url") + pulse_githubUsername: str | None = Field(None, alias="pulse:githubUsername") + pulse_orcidIdentifier: str | None = Field( + None, + alias="pulse:orcidIdentifier", + pattern="^\\d{4}-\\d{4}-\\d{4}-\\d{3}[0-9X]$", + ) + pulse_infosciencePersonIdentifier: str = Field( + ..., + alias="pulse:infosciencePersonIdentifier", + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + org_hasMembership: list[str] | None = Field(None, alias="org:hasMembership") + pulse_hasContribution: list[str] | None = Field(None, alias="pulse:hasContribution") + pulse_owns: list[str] | None = Field(None, alias="pulse:owns") + + +class PersonModel4(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier (first non-null in priority: orcid → infosciencePersonIdentifier → githubUsername → uuid)", + ) + type: Literal["schema:Person"] + shacl: Literal["pulse:PersonShape"] + identifiers: Identifiers + idSource: IdSource + schema_name: str = Field(..., alias="schema:name", min_length=1) + schema_email: str | None = Field( + None, alias="schema:email", pattern="^[\\w\\-\\.]+@([\\w-]+\\.)+[\\w-]{2,4}$" + ) + schema_url: AnyUrl | None = Field(None, alias="schema:url") + pulse_githubUsername: str | None = Field(None, alias="pulse:githubUsername") + pulse_orcidIdentifier: str = Field( + ..., + alias="pulse:orcidIdentifier", + pattern="^\\d{4}-\\d{4}-\\d{4}-\\d{3}[0-9X]$", + ) + pulse_infosciencePersonIdentifier: str | None = Field( + None, + alias="pulse:infosciencePersonIdentifier", + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + org_hasMembership: list[str] | None = Field(None, alias="org:hasMembership") + pulse_hasContribution: list[str] | None = Field(None, alias="pulse:hasContribution") + pulse_owns: list[str] | None = Field(None, alias="pulse:owns") + + +class PersonModel(RootModel[PersonModel1 | PersonModel2 | PersonModel3 | PersonModel4]): + root: PersonModel1 | PersonModel2 | PersonModel3 | PersonModel4 = Field( + ..., + description="Schema for validating Person entities following Open Pulse Ontology v2.1.2. ID hierarchy: orcid → infosciencePersonIdentifier → githubUsername → uuid", + title="PersonModel", + ) + + +class Identifiers4(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + pulse_githubRepositoryHandle: str = Field( + ..., + alias="pulse:githubRepositoryHandle", + pattern="^[a-zA-Z0-9\\-_]+/[a-zA-Z0-9\\-_\\.]+$", + ) + schema_citation: AnyUrl | None = Field( + None, alias="schema:citation", description="DOI identifier" + ) + uuid: str = Field( + ..., + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + + +class IdSource4(Enum): + pulse_githubRepositoryHandle = "pulse:githubRepositoryHandle" + schema_citation = "schema:citation" + uuid = "uuid" + + +class PulseRepositoryType(Enum): + pulse_Software = "pulse:Software" + pulse_Data = "pulse:Data" + pulse_Documentation = "pulse:Documentation" + pulse_EducationalResource = "pulse:EducationalResource" + pulse_Other = "pulse:Other" + + +class PulseDisciplineEnum(Enum): + wd_Q1254373 = "wd:Q1254373" + wd_Q101333 = "wd:Q101333" + wd_Q1071 = "wd:Q1071" + wd_Q11680831 = "wd:Q11680831" + wd_Q12271 = "wd:Q12271" + wd_Q12483 = "wd:Q12483" + wd_Q18351432 = "wd:Q18351432" + wd_Q188847 = "wd:Q188847" + wd_Q192386 = "wd:Q192386" + wd_Q21201 = "wd:Q21201" + wd_Q2167061 = "wd:Q2167061" + wd_Q2329 = "wd:Q2329" + wd_Q23404 = "wd:Q23404" + wd_Q2878974 = "wd:Q2878974" + wd_Q309 = "wd:Q309" + wd_Q333 = "wd:Q333" + wd_Q3353193 = "wd:Q3353193" + wd_Q34749 = "wd:Q34749" + wd_Q3606845 = "wd:Q3606845" + wd_Q395 = "wd:Q395" + wd_Q413 = "wd:Q413" + wd_Q420 = "wd:Q420" + wd_Q42240 = "wd:Q42240" + wd_Q428691 = "wd:Q428691" + wd_Q43035 = "wd:Q43035" + wd_Q4830453 = "wd:Q4830453" + wd_Q580689 = "wd:Q580689" + wd_Q5891 = "wd:Q5891" + wd_Q7112556 = "wd:Q7112556" + wd_Q7163 = "wd:Q7163" + wd_Q735 = "wd:Q735" + wd_Q7748 = "wd:Q7748" + wd_Q77590 = "wd:Q77590" + wd_Q7991 = "wd:Q7991" + wd_Q8008 = "wd:Q8008" + wd_Q80083 = "wd:Q80083" + wd_Q8078 = "wd:Q8078" + wd_Q8134 = "wd:Q8134" + wd_Q8162 = "wd:Q8162" + wd_Q816264 = "wd:Q816264" + wd_Q8242 = "wd:Q8242" + wd_Q83588 = "wd:Q83588" + wd_Q8434 = "wd:Q8434" + wd_Q843601 = "wd:Q843601" + wd_Q9174 = "wd:Q9174" + wd_Q9418 = "wd:Q9418" + + +class RepositoryModel(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier (first non-null in priority: githubRepositoryHandle → doi → uuid)", + ) + type: Literal["schema:SoftwareSourceCode"] + shacl: Literal["pulse:RepositoryShape"] + identifiers: Identifiers4 + idSource: IdSource4 + schema_name: str = Field(..., alias="schema:name", min_length=1) + pulse_githubRepositoryHandle: str = Field( + ..., + alias="pulse:githubRepositoryHandle", + pattern="^[a-zA-Z0-9\\-_]+/[a-zA-Z0-9\\-_\\.]+$", + ) + pulse_repositoryType: PulseRepositoryType | None = Field( + None, + alias="pulse:repositoryType", + description="Repository type. Optional per SHACL.", + ) + pulse_discipline: list[PulseDisciplineEnum] | None = Field( + None, alias="pulse:discipline" + ) + schema_author: list[str] = Field(..., alias="schema:author", min_length=1) + pulse_githubRepoStars: int | None = Field(None, alias="pulse:githubRepoStars", ge=0) + pulse_githubRepoForks: int | None = Field(None, alias="pulse:githubRepoForks", ge=0) + schema_dateCreated: str | None = Field( + None, + alias="schema:dateCreated", + description="ISO 8601 datetime format", + pattern="^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$", + ) + schema_license: AnyUrl | None = Field( + None, alias="schema:license", description="SPDX license IRI" + ) + schema_citation: AnyUrl | None = Field( + None, alias="schema:citation", description="DOI URL for citation" + ) + schema_programmingLanguage: list[str] | None = Field( + None, alias="schema:programmingLanguage" + ) + pulse_ownedBy: str | None = Field( + None, + alias="pulse:ownedBy", + description="Reference to PersonShape or OrganizationShape ID (hierarchical)", + ) + pulse_isForkOf: str | None = Field( + None, + alias="pulse:isForkOf", + description="Reference to parent RepositoryShape ID (GitHub handle)", + ) + + +class Identifiers5(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + pulse_ror: str | None = Field( + None, alias="pulse:ror", pattern="^https://ror\\.org/[0-9a-z]{9}$" + ) + pulse_infoscienceOrganizationIdentifier: str | None = Field( + None, + alias="pulse:infoscienceOrganizationIdentifier", + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + pulse_githubOrganizationHandle: str | None = Field( + None, alias="pulse:githubOrganizationHandle" + ) + uuid: str = Field( + ..., + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + + +class IdSource5(Enum): + pulse_ror = "pulse:ror" + pulse_infoscienceOrganizationIdentifier = "pulse:infoscienceOrganizationIdentifier" + pulse_githubOrganizationHandle = "pulse:githubOrganizationHandle" + uuid = "uuid" + + +class PulseOrganizationType(Enum): + pulse_University = "pulse:University" + pulse_ResearchInstitution = "pulse:ResearchInstitution" + pulse_GovernmentAgency = "pulse:GovernmentAgency" + pulse_SoftwareProject = "pulse:SoftwareProject" + pulse_PrivateCompany = "pulse:PrivateCompany" + pulse_NonProfitOrganization = "pulse:NonProfitOrganization" + pulse_CommunitySpace = "pulse:CommunitySpace" + pulse_OtherOrganizationType = "pulse:OtherOrganizationType" + + +class OrganizationModel1(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier (first non-null in priority: ror → infoscienceOrganizationIdentifier → githubOrganizationHandle → uuid)", + ) + type: Literal["org:Organization"] + shacl: Literal["pulse:OrganizationShape"] + identifiers: Identifiers5 + idSource: IdSource5 + schema_name: str = Field(..., alias="schema:name", min_length=1) + schema_identifier: str = Field( + ..., + alias="schema:identifier", + description="Canonical identifier (typically ROR URL). Optional per SHACL.", + pattern="^https://ror\\.org/[0-9a-z]{9}$", + ) + pulse_githubOrganizationHandle: str | None = Field( + None, alias="pulse:githubOrganizationHandle" + ) + pulse_infoscienceOrganizationIdentifier: str | None = Field( + None, + alias="pulse:infoscienceOrganizationIdentifier", + description="Infoscience organization identifier (UUID4 format).", + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + pulse_OrganizationType: PulseOrganizationType | None = Field( + None, alias="pulse:OrganizationType" + ) + pulse_githubOrgFollowers: int | None = Field( + None, alias="pulse:githubOrgFollowers", ge=0 + ) + org_hasUnit: list[str] | None = Field(None, alias="org:hasUnit") + org_unitOf: list[str] | None = Field(None, alias="org:unitOf") + pulse_owns: list[str] | None = Field(None, alias="pulse:owns") + + +class OrganizationModel2(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier (first non-null in priority: ror → infoscienceOrganizationIdentifier → githubOrganizationHandle → uuid)", + ) + type: Literal["org:Organization"] + shacl: Literal["pulse:OrganizationShape"] + identifiers: Identifiers5 + idSource: IdSource5 + schema_name: str = Field(..., alias="schema:name", min_length=1) + schema_identifier: str | None = Field( + None, + alias="schema:identifier", + description="Canonical identifier (typically ROR URL). Optional per SHACL.", + pattern="^https://ror\\.org/[0-9a-z]{9}$", + ) + pulse_githubOrganizationHandle: str = Field( + ..., alias="pulse:githubOrganizationHandle" + ) + pulse_infoscienceOrganizationIdentifier: str | None = Field( + None, + alias="pulse:infoscienceOrganizationIdentifier", + description="Infoscience organization identifier (UUID4 format).", + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + pulse_OrganizationType: PulseOrganizationType | None = Field( + None, alias="pulse:OrganizationType" + ) + pulse_githubOrgFollowers: int | None = Field( + None, alias="pulse:githubOrgFollowers", ge=0 + ) + org_hasUnit: list[str] | None = Field(None, alias="org:hasUnit") + org_unitOf: list[str] | None = Field(None, alias="org:unitOf") + pulse_owns: list[str] | None = Field(None, alias="pulse:owns") + + +class OrganizationModel3(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier (first non-null in priority: ror → infoscienceOrganizationIdentifier → githubOrganizationHandle → uuid)", + ) + type: Literal["org:Organization"] + shacl: Literal["pulse:OrganizationShape"] + identifiers: Identifiers5 + idSource: IdSource5 + schema_name: str = Field(..., alias="schema:name", min_length=1) + schema_identifier: str | None = Field( + None, + alias="schema:identifier", + description="Canonical identifier (typically ROR URL). Optional per SHACL.", + pattern="^https://ror\\.org/[0-9a-z]{9}$", + ) + pulse_githubOrganizationHandle: str | None = Field( + None, alias="pulse:githubOrganizationHandle" + ) + pulse_infoscienceOrganizationIdentifier: str = Field( + ..., + alias="pulse:infoscienceOrganizationIdentifier", + description="Infoscience organization identifier (UUID4 format).", + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + pulse_OrganizationType: PulseOrganizationType | None = Field( + None, alias="pulse:OrganizationType" + ) + pulse_githubOrgFollowers: int | None = Field( + None, alias="pulse:githubOrgFollowers", ge=0 + ) + org_hasUnit: list[str] | None = Field(None, alias="org:hasUnit") + org_unitOf: list[str] | None = Field(None, alias="org:unitOf") + pulse_owns: list[str] | None = Field(None, alias="pulse:owns") + + +class OrganizationModel( + RootModel[OrganizationModel1 | OrganizationModel2 | OrganizationModel3] +): + root: OrganizationModel1 | OrganizationModel2 | OrganizationModel3 = Field( + ..., + description="Schema for validating Organization entities following Open Pulse Ontology v2.1.2. ID hierarchy: ror → infoscienceOrganizationIdentifier → githubOrganizationHandle → uuid. SHACL requires: schema:name AND at least ONE of (schema:identifier, pulse:githubOrganizationHandle, pulse:infoscienceOrganizationIdentifier).", + title="OrganizationModel", + ) + + +class Identifiers8(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + pulse_composite: str = Field( + ..., + alias="pulse:composite", + description="Composite identifier in format: personId_orgId", + ) + uuid: str = Field( + ..., + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + + +class IdSource8(Enum): + pulse_composite = "pulse:composite" + uuid = "uuid" + + +class MembershipModel(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier (composite format: personId_orgId, or uuid fallback)", + ) + type: Literal["org:Membership"] + shacl: Literal["pulse:MembershipShape"] + identifiers: Identifiers8 + idSource: IdSource8 + org_organization: str = Field( + ..., + alias="org:organization", + description="Reference to OrganizationShape ID (hierarchical)", + ) + org_role: str | None = Field( + None, + alias="org:role", + description="Role or position title within the organization. Optional per SHACL (maxCount 1).", + ) + time_hasBeginning: str | None = Field( + None, + alias="time:hasBeginning", + description="Start date in ISO 8601 date format (YYYY-MM-DD)", + pattern="^\\d{4}-\\d{2}-\\d{2}$", + ) + time_hasEnd: str | None = Field( + None, + alias="time:hasEnd", + description="End date in ISO 8601 date format (YYYY-MM-DD)", + pattern="^\\d{4}-\\d{2}-\\d{2}$", + ) + + +class Identifiers9(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + pulse_composite: str = Field( + ..., + alias="pulse:composite", + description="Composite identifier in format: personId_repoId", + ) + uuid: str = Field( + ..., + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + + +class ContributionModel(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier (composite format: personId_repoId, or uuid fallback)", + ) + type: Literal["pulse:Contribution"] + shacl: Literal["pulse:ContributionShape"] + identifiers: Identifiers9 + idSource: IdSource8 + pulse_contributionTo: str = Field( + ..., + alias="pulse:contributionTo", + description="Reference to RepositoryShape ID (GitHub handle format: owner/repo)", + ) + pulse_contributionCount: int = Field( + ..., + alias="pulse:contributionCount", + description="Number of commits/contributions", + ge=0, + ) + pulse_firstContributionDate: str | None = Field( + None, + alias="pulse:firstContributionDate", + description="First contribution datetime in ISO 8601 format. Optional per SHACL.", + pattern="^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$", + ) + pulse_lastContributionDate: str | None = Field( + None, + alias="pulse:lastContributionDate", + description="Last contribution datetime in ISO 8601 format. Optional per SHACL.", + pattern="^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$", + ) + schema_author: str = Field( + ..., + alias="schema:author", + description="Reference to PersonShape ID (hierarchical)", + ) + + +class Identifiers10(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + schema_identifier: str = Field( + ..., + alias="schema:identifier", + description="DOI identifier (required per SHACL minCount 1)", + pattern="^10\\.\\d{4,9}/[-._;()/:a-zA-Z0-9]+$", + ) + pulse_infoscienceArticleIdentifier: str | None = Field( + None, + alias="pulse:infoscienceArticleIdentifier", + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + uuid: str = Field( + ..., + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + + +class IdSource10(Enum): + schema_identifier = "schema:identifier" + pulse_infoscienceArticleIdentifier = "pulse:infoscienceArticleIdentifier" + uuid = "uuid" + + +class ArticleModel(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + id: str = Field( + ..., + description="Resolved hierarchical identifier (first non-null in priority: doi → infoscienceArticleIdentifier → uuid)", + ) + type: Literal["schema:ScholarlyArticle"] + shacl: Literal["pulse:ArticleShape"] + identifiers: Identifiers10 + idSource: IdSource10 + schema_name: str = Field( + ..., alias="schema:name", description="Article title", min_length=1 + ) + schema_identifier: str = Field( + ..., + alias="schema:identifier", + description="DOI identifier (required per SHACL minCount 1)", + pattern="^10\\.\\d{4,9}/[-._;()/:a-zA-Z0-9]+$", + ) + schema_datePublished: str = Field( + ..., + alias="schema:datePublished", + description="Publication date in ISO 8601 date format (YYYY-MM-DD)", + pattern="^\\d{4}-\\d{2}-\\d{2}$", + ) + schema_author: list[str] = Field(..., alias="schema:author", min_length=1) + pulse_infoscienceArticleIdentifier: str | None = Field( + None, + alias="pulse:infoscienceArticleIdentifier", + pattern="^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + ) + schema_sourceOrganization: str | None = Field( + None, + alias="schema:sourceOrganization", + description="Reference to OrganizationShape hierarchical ID (ror, infoscienceOrganizationIdentifier, githubOrganizationHandle, or uuid)", + ) + + +class EntitiesBundle(BaseModel): + model_config = ConfigDict( + extra="forbid", + ) + personmodel: PersonModel | None = None + repositorymodel: RepositoryModel | None = None + organizationmodel: OrganizationModel | None = None + membershipmodel: MembershipModel | None = None + contributionmodel: ContributionModel | None = None + articlemodel: ArticleModel | None = None diff --git a/src/v2/skills/__init__.py b/src/v2/skills/__init__.py new file mode 100644 index 0000000..245b594 --- /dev/null +++ b/src/v2/skills/__init__.py @@ -0,0 +1,7 @@ +"""V2 terminal-agent skills. + +Each subpackage here is a pi-loadable skill: a CLI tool with a README that +the executor LLM consumes. Skills are intentionally subprocess-portable — +any harness that can spawn a command can use them, not just pi. See +`config/v2/terminal_agent.yml` for the active skill list. +""" diff --git a/src/v2/skills/_runtime.py b/src/v2/skills/_runtime.py new file mode 100644 index 0000000..d68eb56 --- /dev/null +++ b/src/v2/skills/_runtime.py @@ -0,0 +1,92 @@ +"""Shared runtime helpers for v2 terminal-agent skills. + +Skills are CLI tools invoked by an external harness (pi.dev by default). +Output contract: +- Success: a single JSON document on stdout, exit 0. +- Failure: a JSON `{"error": "...", "kind": "..."}` on stderr, non-zero exit. + +The harness reads stdout for tool results; stderr surfaces in the +executor's tool-call transcript so the LLM can react to failures. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import os +import sys +from dataclasses import dataclass +from typing import Any, Awaitable, Callable + +logger = logging.getLogger(__name__) + + +@dataclass(slots=True) +class SkillError(Exception): + """Structured error surfaced to the harness on stderr.""" + + message: str + kind: str = "skill_error" + + def __str__(self) -> str: + return self.message + + +def emit_success(payload: Any) -> None: + """Write a single JSON document to stdout and exit 0.""" + json.dump(payload, sys.stdout, ensure_ascii=False, default=str) + sys.stdout.write("\n") + sys.stdout.flush() + + +def emit_error(err: BaseException, *, kind: str | None = None) -> None: + """Write a structured error to stderr and exit non-zero.""" + if isinstance(err, SkillError): + payload = {"error": err.message, "kind": err.kind} + else: + payload = { + "error": str(err) or err.__class__.__name__, + "kind": kind or err.__class__.__name__, + } + json.dump(payload, sys.stderr, ensure_ascii=False, default=str) + sys.stderr.write("\n") + sys.stderr.flush() + + +def run_async_skill(coro_fn: Callable[[], Awaitable[Any]]) -> int: + """Run an async skill body and translate its outcome into a process exit code. + + Wraps the boilerplate every CLI skill needs: configure logging, run the + coroutine on a fresh event loop, route success → stdout JSON, errors → + stderr JSON with a non-zero exit code. + """ + _configure_logging() + try: + result = asyncio.run(coro_fn()) + except SkillError as err: + emit_error(err) + return 1 + except KeyboardInterrupt: + emit_error(SkillError("interrupted", kind="interrupted")) + return 130 + except Exception as err: # noqa: BLE001 — final boundary, surface anything + logger.exception("skill failed") + emit_error(err) + return 2 + emit_success(result) + return 0 + + +def _configure_logging() -> None: + level_name = os.environ.get("V2_SKILL_LOG_LEVEL", "WARNING").upper() + level = getattr(logging, level_name, logging.WARNING) + # Force stderr — stdout is reserved for the JSON result. + logging.basicConfig( + level=level, + format="%(asctime)s %(levelname)s %(name)s: %(message)s", + stream=sys.stderr, + ) + + +__all__ = ["SkillError", "emit_error", "emit_success", "run_async_skill"] diff --git a/src/v2/skills/search_infoscience/SKILL.md b/src/v2/skills/search_infoscience/SKILL.md new file mode 100644 index 0000000..6ab6569 --- /dev/null +++ b/src/v2/skills/search_infoscience/SKILL.md @@ -0,0 +1,56 @@ +--- +name: search-infoscience +description: Semantic search over EPFL's Infoscience repository (papers, authors, EPFL labs/units). Use when README/CITATION mentions a paper or EPFL lab without a clean DOI/ROR id. Returns thin hits; the agent decides whether to ground a claim from a hit. +--- + +# search-infoscience + +Semantic search over the EPFL Infoscience RAG index — covers papers +(chunks + articles), researchers, and EPFL organisational units. + +## When to use this + +Use whenever README, CITATION, or commit history mentions a paper or +EPFL lab/unit without a clean DOI or ROR id and you need the canonical +record. Stronger signal than `search-openalex` for EPFL-affiliated work. + +Do NOT use for: +- Worldwide papers without an EPFL author → `search-openalex` +- Organisations outside EPFL → `search-ror` +- Persons without paper context → `search-orcid` + +## Command + +``` +gme-search-infoscience "" [--collection chunks|articles|persons|organizations] [--top-k N] [--filter K=V] [--rerank] +python -m src.v2.skills.search_infoscience "" [...same flags] +``` + +Collections: +- `chunks` (default): paper body fragments — best for fuzzy text matches. +- `articles`: one row per paper — for precise paper-level lookups. +- `persons`: EPFL researchers. +- `organizations`: EPFL labs/units. + +## Output + +JSON array. Hit shape varies by collection but always includes `id`, +`score`, a `title` or `name`, and a short `snippet`. Identifiers +(`doi`, `orcid`, `ror_id`, `sciper_id`) are present when known. + +## Examples + +``` +gme-search-infoscience "gimie metadata extraction" +gme-search-infoscience "SDSC ORD" --collection organizations --rerank +gme-search-infoscience "Christian Müller" --collection persons --top-k 5 +gme-search-infoscience "imaging plaza" --filter year=2024 --rerank +``` + +## Notes for the executor + +- The first hit's `score` ≥ ~0.6 with reranker is a strong match; + below that, the lookup likely failed — don't over-trust it. +- `chunks` collection has the highest recall (you're matching against + actual paper text); `articles` has the highest precision when you know + the title. diff --git a/src/v2/skills/search_infoscience/__init__.py b/src/v2/skills/search_infoscience/__init__.py new file mode 100644 index 0000000..6c0b1cd --- /dev/null +++ b/src/v2/skills/search_infoscience/__init__.py @@ -0,0 +1 @@ +"""Infoscience semantic-search skill for the v2 terminal agent.""" diff --git a/src/v2/skills/search_infoscience/__main__.py b/src/v2/skills/search_infoscience/__main__.py new file mode 100644 index 0000000..85ef57b --- /dev/null +++ b/src/v2/skills/search_infoscience/__main__.py @@ -0,0 +1,84 @@ +"""CLI entry point for the `search_infoscience` skill. + +Wraps :class:`src.v2.ingest.providers.infoscience_rag.InfoscienceRagProvider`. +PoC scope: search only. The full agent_tool also exposes +fetch_chunks/fetch_records — those become separate skills if/when needed. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from typing import Any + +from src.v2.ingest.providers.infoscience_rag import build_default_provider +from src.v2.skills._runtime import SkillError, run_async_skill + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="gme-search-infoscience", + description="Semantic search over the EPFL Infoscience RAG index.", + ) + parser.add_argument("query", help="Free-text query (paper title, author, lab, keywords).") + parser.add_argument( + "--collection", + choices=("chunks", "articles", "persons", "organizations"), + default="chunks", + help="Infoscience collection (default: chunks — paper body fragments).", + ) + parser.add_argument("--top-k", type=int, default=10, help="Maximum hits (default: 10).") + parser.add_argument( + "--filter", + action="append", + default=[], + metavar="KEY=VALUE", + help=( + "Repeatable filter. Allowlisted keys: has_github_match, has_hf_match, " + "year, publication_type, language, lab_uuid, org_uuids, doi, orcid, " + "ror_id, sciper_id, sciper_unit_id, author_uuids, subjects, keywords." + ), + ) + parser.add_argument("--rerank", action="store_true", help="Cross-encoder rerank.") + return parser + + +def _parse_filters(items: list[str]) -> dict[str, Any] | None: + if not items: + return None + out: dict[str, Any] = {} + for raw in items: + if "=" not in raw: + msg = f"--filter expected KEY=VALUE, got {raw!r}" + raise SkillError(msg, kind="bad_argument") + k, v = raw.split("=", 1) + out[k.strip()] = v.strip() + return out + + +async def _run(args: argparse.Namespace) -> list[dict[str, Any]]: + provider = build_default_provider() + if provider is None: + raise SkillError( + "Infoscience RAG provider unavailable (V2_INFOSCIENCE_RAG_ENABLED disabled " + "or config load failed). Check INDEX_QDRANT_URL and RCP_TOKEN.", + kind="provider_unavailable", + ) + return await provider.search( + args.query, + collection=args.collection, + top_k=args.top_k, + filters=_parse_filters(args.filter), + rerank=args.rerank, + ) + + +def main(argv: list[str] | None = None) -> int: + parser = _build_parser() + args = parser.parse_args(argv) + return run_async_skill(lambda: _run(args)) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/v2/skills/search_openalex/SKILL.md b/src/v2/skills/search_openalex/SKILL.md new file mode 100644 index 0000000..547cc80 --- /dev/null +++ b/src/v2/skills/search_openalex/SKILL.md @@ -0,0 +1,57 @@ +--- +name: search-openalex +description: Semantic search over the OpenAlex worldwide scholarly graph (works, authors, institutions, sources, topics, concepts). Use when an entity is NOT in EPFL Infoscience and you need worldwide ground truth (DOIs, ORCIDs, ROR ids, etc.). +--- + +# search-openalex + +Semantic search over OpenAlex — a free, worldwide scholarly knowledge +graph. Covers papers, authors, institutions, journals, topics. + +## When to use this + +Use whenever you need to ground a paper, author, or institution that +isn't in EPFL Infoscience (i.e. non-EPFL work). Strongest fallback for +worldwide DOIs, ORCIDs, and ROR ids. + +Do NOT use for: +- EPFL papers / EPFL labs → prefer `search-infoscience` (richer local data) +- Pure organisational lookup without paper context → `search-ror` +- Person without paper context → `search-orcid` + +## Command + +``` +gme-search-openalex "" [--collection works|authors|institutions|sources|topics|concepts] [--top-k N] [--filter K=V] [--rerank] +python -m src.v2.skills.search_openalex "" [...same flags] +``` + +Collections: +- `works` (default): papers/preprints/datasets — abstract is indexed. +- `authors`: researchers (worldwide). +- `institutions`: research orgs with ROR ids. +- `sources`: journals/conferences. +- `topics`, `concepts`: subject classifications. + +## Output + +JSON array. Hit shape varies by collection. Common fields: `openalex_id`, +`title` or `display_name`, identifiers (`doi`, `orcid`, `ror`), `score`, +short `snippet` (~320 chars of abstract for works). + +## Examples + +``` +gme-search-openalex "AlphaFold 2 protein structure prediction" --rerank +gme-search-openalex "INRIA" --collection institutions --top-k 3 +gme-search-openalex "Yann LeCun" --collection authors --rerank +gme-search-openalex "imaging biomarkers" --filter year=2024 --top-k 10 +``` + +## Notes for the executor + +- Range filters: pass `year=2023` for exact year, or `--filter year=$gte:2020` is NOT supported here — use scalar matches. +- `country_code` filter on institutions narrows by country (`CH`, `FR`, etc.). +- For ambiguous worldwide searches, ALWAYS turn on `--rerank` — the + worldwide index has many near-duplicates and the cross-encoder + meaningfully tightens precision. diff --git a/src/v2/skills/search_openalex/__init__.py b/src/v2/skills/search_openalex/__init__.py new file mode 100644 index 0000000..c8d6309 --- /dev/null +++ b/src/v2/skills/search_openalex/__init__.py @@ -0,0 +1 @@ +"""OpenAlex semantic-search skill for the v2 terminal agent.""" diff --git a/src/v2/skills/search_openalex/__main__.py b/src/v2/skills/search_openalex/__main__.py new file mode 100644 index 0000000..aed058a --- /dev/null +++ b/src/v2/skills/search_openalex/__main__.py @@ -0,0 +1,86 @@ +"""CLI entry point for the `search_openalex` skill. + +Wraps :class:`src.v2.ingest.providers.openalex_rag.OpenAlexRagProvider`. +OpenAlex is a worldwide scholarly graph (works, authors, institutions, +sources, topics, concepts) — use this when an entity is not in +EPFL-scoped Infoscience. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from typing import Any + +from src.v2.ingest.providers.openalex_rag import build_default_provider +from src.v2.skills._runtime import SkillError, run_async_skill + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="gme-search-openalex", + description="Semantic search over the OpenAlex worldwide scholarly graph.", + ) + parser.add_argument("query", help="Free-text query (paper title, author, institution, topic).") + parser.add_argument( + "--collection", + choices=("works", "authors", "institutions", "sources", "topics", "concepts"), + default="works", + help="OpenAlex collection (default: works — papers).", + ) + parser.add_argument("--top-k", type=int, default=10, help="Maximum hits (default: 10).") + parser.add_argument( + "--filter", + action="append", + default=[], + metavar="KEY=VALUE", + help=( + "Repeatable filter. Allowlisted keys: year, publication_year, doi, " + "entity_type, openalex_id, country_code, type, field_id, domain_id, " + "level, primary_topic_id, primary_source_id, last_known_institution_id, " + "orcid, ror." + ), + ) + parser.add_argument("--rerank", action="store_true", help="Cross-encoder rerank.") + return parser + + +def _parse_filters(items: list[str]) -> dict[str, Any] | None: + if not items: + return None + out: dict[str, Any] = {} + for raw in items: + if "=" not in raw: + msg = f"--filter expected KEY=VALUE, got {raw!r}" + raise SkillError(msg, kind="bad_argument") + k, v = raw.split("=", 1) + out[k.strip()] = v.strip() + return out + + +async def _run(args: argparse.Namespace) -> list[dict[str, Any]]: + provider = build_default_provider() + if provider is None: + raise SkillError( + "OpenAlex RAG provider unavailable (V2_OPENALEX_RAG_ENABLED disabled " + "or config load failed). Check INDEX_QDRANT_URL and RCP_TOKEN.", + kind="provider_unavailable", + ) + return await provider.search( + args.query, + collection=args.collection, + top_k=args.top_k, + filters=_parse_filters(args.filter), + rerank=args.rerank, + ) + + +def main(argv: list[str] | None = None) -> int: + parser = _build_parser() + args = parser.parse_args(argv) + return run_async_skill(lambda: _run(args)) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/v2/skills/search_orcid/SKILL.md b/src/v2/skills/search_orcid/SKILL.md new file mode 100644 index 0000000..52359c6 --- /dev/null +++ b/src/v2/skills/search_orcid/SKILL.md @@ -0,0 +1,68 @@ +--- +name: search-orcid +description: Semantic search over the ORCID registry (persons, employments, educations). Use to ground a contributor by name to an ORCID iD, or to look up their employment/education history. Do NOT use for orgs (use search-ror) or for articles (use search-infoscience or search-openalex). +--- + +# search-orcid + +Semantic search over the ORCID RAG index, scoped to the configured +region (typically EPFL and adjacent Swiss institutions). + +## When to use this + +Use whenever README, CITATION, or AUTHORS surfaces a person's name and +you need their ORCID iD — or when you need to discover their +employment / education history to disambiguate identity. + +Do NOT use for: +- Organisations → use `search-ror` +- Articles → use `search-infoscience` (EPFL) or `search-openalex` (worldwide) + +## Command + +``` +gme-search-orcid "" [--entity-type ] [--top-k ] [--filter K=V] [--rerank] +python -m src.v2.skills.search_orcid "" [...same flags] +``` + +| Arg | Default | Notes | +|---|---|---| +| `query` (positional) | required | Free-text. Person name, biography fragment, lab + role. | +| `--entity-type` | `persons` | One of `persons`, `employments`, `educations`. | +| `--top-k` | `10` | Hits to return. 5–20 is the useful range. | +| `--filter` | unset | Repeatable `KEY=VALUE`. Allowlisted keys only: `orcid_id`, `in_scope`, `discovered_via`, `org_ror`, `organization`, `department`, `role`. | +| `--rerank` | off | Cross-encoder rerank. Slower (~1s) but improves precision when top-1 score is borderline. | + +## Output + +JSON array on stdout. For `persons` entity type, hits look like: + +```json +{ + "orcid_id": "https://orcid.org/0000-0002-1825-0097", + "names": ["Carberry, Josiah"], + "biography_snippet": "...", + "score": 0.81 +} +``` + +For `employments` / `educations` hits include `organization`, `role`, +`department`, year ranges. + +On failure: `{"error": "...", "kind": "..."}` on stderr, non-zero exit. + +## Examples + +``` +gme-search-orcid "Anna Smith EPFL data science" +gme-search-orcid "Marc Levrat" --rerank +gme-search-orcid "PhD machine learning" --entity-type educations --top-k 5 +``` + +## Notes for the executor + +- The first hit's `score` ≥ ~0.7 with reranker on is a strong match; + below ~0.5 the answer is probably wrong — fall back to + `https://github.com/` rather than guessing an ORCID. +- `in_scope=true` filter narrows to the configured EPFL-adjacent set + if you know the person is local. diff --git a/src/v2/skills/search_orcid/__init__.py b/src/v2/skills/search_orcid/__init__.py new file mode 100644 index 0000000..4a200dd --- /dev/null +++ b/src/v2/skills/search_orcid/__init__.py @@ -0,0 +1 @@ +"""ORCID semantic-search skill for the v2 terminal agent.""" diff --git a/src/v2/skills/search_orcid/__main__.py b/src/v2/skills/search_orcid/__main__.py new file mode 100644 index 0000000..df2a294 --- /dev/null +++ b/src/v2/skills/search_orcid/__main__.py @@ -0,0 +1,93 @@ +"""CLI entry point for the `search_orcid` skill. + +Wraps :class:`src.v2.ingest.providers.orcid_rag.OrcidRagProvider`. The +same provider powers the in-process LLM agent tool in +`src/v2/agents/llm/agent_tools/orcid_rag.py`. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from typing import Any + +from src.v2.ingest.providers.orcid_rag import build_default_provider +from src.v2.skills._runtime import SkillError, run_async_skill + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="gme-search-orcid", + description="Semantic search over the ORCID RAG index (persons, employments, educations).", + ) + parser.add_argument("query", help="Free-text query (person name, lab, institution, biography snippet).") + parser.add_argument( + "--entity-type", + choices=("persons", "employments", "educations"), + default="persons", + help="ORCID entity type to search (default: persons).", + ) + parser.add_argument( + "--top-k", + type=int, + default=10, + help="Maximum hits to return (default: 10).", + ) + parser.add_argument( + "--filter", + action="append", + default=[], + metavar="KEY=VALUE", + help=( + "Repeatable filter; only allowlisted keys are honoured " + "(orcid_id, in_scope, discovered_via, org_ror, organization, " + "department, role)." + ), + ) + parser.add_argument( + "--rerank", + action="store_true", + help="Apply the cross-encoder reranker on top of vector recall.", + ) + return parser + + +def _parse_filters(items: list[str]) -> dict[str, Any] | None: + if not items: + return None + out: dict[str, Any] = {} + for raw in items: + if "=" not in raw: + msg = f"--filter expected KEY=VALUE, got {raw!r}" + raise SkillError(msg, kind="bad_argument") + k, v = raw.split("=", 1) + out[k.strip()] = v.strip() + return out + + +async def _run(args: argparse.Namespace) -> list[dict[str, Any]]: + provider = build_default_provider() + if provider is None: + raise SkillError( + "ORCID RAG provider unavailable (V2_ORCID_RAG_ENABLED disabled or " + "config load failed). Check INDEX_QDRANT_URL and RCP_TOKEN.", + kind="provider_unavailable", + ) + return await provider.search( + args.query, + entity_type=args.entity_type, + top_k=args.top_k, + filters=_parse_filters(args.filter), + rerank=args.rerank, + ) + + +def main(argv: list[str] | None = None) -> int: + parser = _build_parser() + args = parser.parse_args(argv) + return run_async_skill(lambda: _run(args)) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/v2/skills/search_ror/SKILL.md b/src/v2/skills/search_ror/SKILL.md new file mode 100644 index 0000000..8b2690d --- /dev/null +++ b/src/v2/skills/search_ror/SKILL.md @@ -0,0 +1,96 @@ +--- +name: search-ror +description: Semantic search over the ROR (Research Organization Registry) for canonical institution identifiers. Use whenever README/CITATION/AUTHORS mentions a research lab, university, or institute by name and you need its ROR id and parent-org chain. Do NOT use for individual people (use search-orcid) or for articles (use search-infoscience). +--- + +# search-ror + +Semantic search over the ROR (Research Organization Registry) RAG index. + +> Skill name uses kebab-case per the Agent Skills spec; the underlying +> Python package directory uses `search_ror` (Python identifier rules). +> Pi will emit a warning about the directory-name mismatch — that is +> expected and harmless. + +## When to use this + +Use this skill whenever the README, CITATION file, or repo metadata +mentions a research lab / university / institute by name and you need its +canonical ROR id and parent-organisation chain. Examples of triggering +phrases: "EPFL", "ETH Zürich", "Max Planck Institute for...", "INRIA", +"Idiap Research Institute". + +Do **not** use this for individual people (use `search_orcid`), for +articles (use `search_infoscience` or `search_openalex`), or for code +hosting platforms. + +## Command + +Two interchangeable invocations — pick whichever your environment +supports: + +``` +# Console script (after `pip install -e .` or in the production image): +gme-search-ror "" [--scope ] [--top-k ] [--country ] [--rerank] + +# Module path (always works as long as the project root is on PYTHONPATH): +python -m src.v2.skills.search_ror "" [--scope ] [--top-k ] [--country ] [--rerank] +``` + +| Arg | Default | Notes | +|---|---|---| +| `query` (positional) | required | Free-text. Institution / lab / university name. | +| `--scope` | `worldwide` | One of `worldwide`, `europe`, `switzerland`, `epfl_ethz`. Narrower scopes are faster and reduce false positives. Pick `epfl_ethz` only when context strongly implies EPFL/ETHZ neighbourhood. | +| `--top-k` | `10` | Hits to return. 5–20 is the useful range. | +| `--country` | unset | ISO 3166-1 alpha-2 (e.g. `CH`, `FR`). Filters at the store layer; only `country_code` is supported. | +| `--rerank` | off | Cross-encoder rerank over `name + types + parent + website`. Slower (~1s) but improves precision; turn on when the top-1 of a non-reranked hit list is wrong. | + +## Output + +A JSON array of hit objects on stdout. Each hit shape: + +```json +{ + "ror_id": "https://ror.org/02s376052", + "name": "École Polytechnique Fédérale de Lausanne", + "country_code": "CH", + "types": ["Education", "Funder"], + "score": 0.91, + "snippet": "EPFL — name + types + parent + website preview" +} +``` + +On failure: a JSON `{"error": "...", "kind": "..."}` on stderr and a +non-zero exit. Common `kind` values: `provider_unavailable` (RAG index +not reachable), `skill_error` (other internal error). + +## Examples + +Find EPFL, narrowest scope: + +``` +gme-search-ror "EPFL" --scope epfl_ethz --top-k 5 +``` + +Find INRIA worldwide with reranking: + +``` +gme-search-ror "INRIA" --top-k 5 --rerank +``` + +Find Swiss institutions matching a fuzzy query: + +``` +gme-search-ror "computational biology Lausanne" --country CH --top-k 10 --rerank +``` + +## Notes for the executor + +- Pick the **narrowest scope that still contains the target**. `worldwide` + has higher recall but more false positives. +- If the top hit's `score` is below ~0.6 and you have no reranking, retry + with `--rerank`. If still low, the institution may not be in ROR — emit + a `urn:pulse:` placeholder rather than guessing. +- `country_code` filtering is a hard constraint at the store, not a soft + bias — if you pass `CH` and the target is actually French, you will get + zero hits. diff --git a/src/v2/skills/search_ror/__init__.py b/src/v2/skills/search_ror/__init__.py new file mode 100644 index 0000000..cca1fd4 --- /dev/null +++ b/src/v2/skills/search_ror/__init__.py @@ -0,0 +1 @@ +"""ROR semantic-search skill for the v2 terminal agent.""" diff --git a/src/v2/skills/search_ror/__main__.py b/src/v2/skills/search_ror/__main__.py new file mode 100644 index 0000000..27f0973 --- /dev/null +++ b/src/v2/skills/search_ror/__main__.py @@ -0,0 +1,76 @@ +"""CLI entry point for the `search_ror` skill. + +Wraps :class:`src.v2.ingest.providers.ror_rag.RorRagProvider`. The same +provider powers the in-process LLM agent tool in +`src/v2/agents/llm/agent_tools/ror_rag.py` — this skill only changes the +transport (stdio + JSON), not the underlying logic. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from typing import Any + +from src.v2.ingest.providers.ror_rag import build_default_provider +from src.v2.skills._runtime import SkillError, run_async_skill + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="gme-search-ror", + description="Semantic search over the ROR (Research Organization Registry) RAG index.", + ) + parser.add_argument("query", help="Free-text query (institution / lab / university name).") + parser.add_argument( + "--scope", + choices=("worldwide", "europe", "switzerland", "epfl_ethz"), + default="worldwide", + help="ROR scope to search (default: worldwide).", + ) + parser.add_argument( + "--top-k", + type=int, + default=10, + help="Maximum hits to return (default: 10).", + ) + parser.add_argument( + "--country", + default=None, + help="Optional ISO 3166-1 alpha-2 country filter (e.g. CH, FR).", + ) + parser.add_argument( + "--rerank", + action="store_true", + help="Apply the cross-encoder reranker on top of vector recall.", + ) + return parser + + +async def _run(args: argparse.Namespace) -> list[dict[str, Any]]: + provider = build_default_provider() + if provider is None: + raise SkillError( + "ROR RAG provider unavailable (V2_ROR_RAG_ENABLED disabled or " + "config load failed). Check INDEX_QDRANT_URL and RCP_TOKEN.", + kind="provider_unavailable", + ) + filters = {"country_code": args.country} if args.country else None + return await provider.search( + args.query, + scope_mode=args.scope, + top_k=args.top_k, + filters=filters, + rerank=args.rerank, + ) + + +def main(argv: list[str] | None = None) -> int: + parser = _build_parser() + args = parser.parse_args(argv) + return run_async_skill(lambda: _run(args)) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/v2/skills/selenium_fetch/SKILL.md b/src/v2/skills/selenium_fetch/SKILL.md new file mode 100644 index 0000000..2ac484f --- /dev/null +++ b/src/v2/skills/selenium_fetch/SKILL.md @@ -0,0 +1,77 @@ +--- +name: selenium-fetch +description: Render an http(s) URL in headless Firefox via Selenium Grid and return its extracted text excerpt + final URL + page title. Use to verify a link the agent emitted, fetch a project's homepage, or pull text from a JS-heavy page that plain HTTP can't render. +--- + +# selenium-fetch + +Render an http(s) URL in headless Firefox via Selenium Grid and return +the extracted text excerpt and metadata. + +## When to use this + +Use when: +- You need to verify a link the agent is about to put in the JSON-LD + output (does it 200? does it really describe the repo?). +- You need text from a JS-heavy page (project homepage, lab landing + page, ROR detail page) that plain `curl`/`wget` can't render. + +Do NOT use for: +- Searching identifier registries (use the `search-*` skills instead). +- Bulk crawling — single-shot only, no rate limiting. + +## Requirements + +- `SELENIUM_REMOTE_URL` env var must point at a running Selenium Grid + hub. If unset the skill fails with `provider_unavailable`. + +## Command + +``` +gme-selenium-fetch "" [--max-chars N] +python -m src.v2.skills.selenium_fetch "" [--max-chars N] +``` + +| Arg | Default | Notes | +|---|---|---| +| `url` (positional) | required | http(s) URL. Other schemes rejected. | +| `--max-chars` | `4000` | Truncate body text. Hard cap: `20000`. | + +## Output + +JSON object on stdout: + +```json +{ + "url": "", + "fetched": true, + "final_url": "", + "title": "", + "text_excerpt": "", + "content_length": 12345, + "error": null +} +``` + +When the page is unreachable or invalid: `fetched: false`, `error` +populated, exit code 3 (still a JSON success on stdout — the failure +is described in the payload, not in stderr). + +When `SELENIUM_REMOTE_URL` is unset: stderr error JSON, exit 1. + +## Examples + +``` +gme-selenium-fetch "https://imaging-plaza.epfl.ch/" +gme-selenium-fetch "https://ror.org/02s376052" --max-chars 8000 +gme-selenium-fetch "https://github.com/SDSC-ORD/gimie" +``` + +## Notes for the executor + +- Use sparingly. Each fetch costs ~5–15 seconds. +- The `text_excerpt` is plain text after BeautifulSoup; JS-rendered + content will be present (Selenium runs the page) but tag structure is + lost. Good for "is this URL really about X?" checks. +- `final_url` differs from `url` when there's a redirect chain — useful + to confirm canonical URLs. diff --git a/src/v2/skills/selenium_fetch/__init__.py b/src/v2/skills/selenium_fetch/__init__.py new file mode 100644 index 0000000..e871579 --- /dev/null +++ b/src/v2/skills/selenium_fetch/__init__.py @@ -0,0 +1 @@ +"""Selenium-backed page-fetch skill for the v2 terminal agent.""" diff --git a/src/v2/skills/selenium_fetch/__main__.py b/src/v2/skills/selenium_fetch/__main__.py new file mode 100644 index 0000000..1e108bc --- /dev/null +++ b/src/v2/skills/selenium_fetch/__main__.py @@ -0,0 +1,78 @@ +"""CLI entry point for the `selenium_fetch` skill. + +Wraps :func:`src.v2.agents.llm.agent_tools.selenium_fetch.fetch_link_content_via_selenium`. +Renders a page in headless Firefox via Selenium Grid, returns the +extracted text excerpt + metadata. The underlying helper is synchronous +so we run it directly without `asyncio.run`. +""" + +from __future__ import annotations + +import argparse +import json +import logging +import os +import sys + +from src.v2.agents.llm.agent_tools.selenium_fetch import ( + DEFAULT_MAX_CHARS, + MAX_ALLOWED_CHARS, + fetch_link_content_via_selenium, +) +from src.v2.skills._runtime import SkillError, emit_error, emit_success + +logger = logging.getLogger(__name__) + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="gme-selenium-fetch", + description="Render an http(s) URL in headless Firefox and return its text excerpt.", + ) + parser.add_argument("url", help="The http(s) URL to fetch.") + parser.add_argument( + "--max-chars", + type=int, + default=DEFAULT_MAX_CHARS, + help=( + f"Maximum characters of body text to return " + f"(default: {DEFAULT_MAX_CHARS}, hard cap: {MAX_ALLOWED_CHARS})." + ), + ) + return parser + + +def main(argv: list[str] | None = None) -> int: + """Synchronous entrypoint — Selenium driver is sync.""" + level_name = os.environ.get("V2_SKILL_LOG_LEVEL", "WARNING").upper() + logging.basicConfig( + level=getattr(logging, level_name, logging.WARNING), + format="%(asctime)s %(levelname)s %(name)s: %(message)s", + stream=sys.stderr, + ) + parser = _build_parser() + args = parser.parse_args(argv) + + if not os.environ.get("SELENIUM_REMOTE_URL", "").strip(): + emit_error( + SkillError( + "Missing SELENIUM_REMOTE_URL — Selenium Grid endpoint is required.", + kind="provider_unavailable", + ) + ) + return 1 + try: + result = fetch_link_content_via_selenium(args.url, max_chars=args.max_chars) + except KeyboardInterrupt: + emit_error(SkillError("interrupted", kind="interrupted")) + return 130 + except Exception as err: # noqa: BLE001 + logger.exception("selenium fetch failed") + emit_error(err) + return 2 + emit_success(result) + return 0 if result.get("fetched") else 3 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/v2/skills/validate_output/SKILL.md b/src/v2/skills/validate_output/SKILL.md new file mode 100644 index 0000000..2931185 --- /dev/null +++ b/src/v2/skills/validate_output/SKILL.md @@ -0,0 +1,91 @@ +--- +name: validate-output +description: Validate `output.jsonld` against the v2 SHACL shapes graph. Returns whether the graph conforms, plus the full list of violations and warnings (focus node, property path, value, message). Call this BEFORE declaring done; iterate on your own output until conforms=true. Structural issues are YOUR responsibility, not the judge's. +--- + +# validate-output + +Run the v2 SHACL shapes graph against your `output.jsonld` and get back +a structured report of every violation, with enough detail to fix them +yourself. + +## When to use this + +**Always**, near the end of every run, before you stop: + +1. Finish your usual loop (read gimie, call skills, write entities). +2. Call `validate-output`. +3. If `conforms=false`, read each violation, edit `output.jsonld` to + remove the offending property OR fix its value, and write it back. +4. Call `validate-output` again. Repeat until `conforms=true`. +5. Only then declare done. + +A SHACL-violating output costs you the run. Catching it yourself is +free; catching it from the judge's feedback in a repair iteration is +expensive (full executor re-run). + +## Command + +``` +gme-validate-output [--path output.jsonld] [--max-violations N] +python -m src.v2.skills.validate_output [--path output.jsonld] [--max-violations N] +``` + +| Arg | Default | Notes | +|---|---|---| +| `--path` | `output.jsonld` | Relative to current working directory (the run's tempdir, where you already are). | +| `--max-violations` | `50` | Truncate the list. Defaults are usually enough. | + +## Output + +JSON object on stdout: + +```json +{ + "conforms": true, + "violation_count": 0, + "warning_count": 0, + "violations": [], + "warnings": [], + "path": "output.jsonld" +} +``` + +Failure mode (file invalid / SHACL deps missing): structured `{error, +kind}` on stderr, non-zero exit. + +When violations exist, each entry has: + +```json +{ + "focus": "https://github.com/foo/bar", + "path": "http://schema.org/contributor", + "value": null, + "severity": "http://www.w3.org/ns/shacl#Violation", + "message": "Node ... is closed. It cannot have value: " +} +``` + +## How to read a violation and fix it + +| Violation message | What to do | +|---|---| +| `Node X is closed. It cannot have value Y on path P` | Remove property `P` from entity `X`. It is not in the closed shape — see `schema_cheatsheet.md` for the allowed list. | +| `Less than 1 values on X->P` | Required property `P` is missing on entity `X`. Add it (cheatsheet shows `required=yes`). | +| `Value is not Literal with datatype xsd:dateTime` | The value is a date-only string like `"2022-12-07"`. Append `T00:00:00Z` → `"2022-12-07T00:00:00Z"`. | +| `Value does not match pattern '^https://ror\.org/...'` | The value is the raw id (`02hdt9m26`) where the shape requires the full URL (`https://ror.org/02hdt9m26`). | + +## Examples + +``` +gme-validate-output +gme-validate-output --path output.jsonld --max-violations 10 +``` + +## Notes for the executor + +- This skill is fast (~5s). Calling it 2-4 times in a run is fine. +- You can also run the same SHACL check on a hand-edited subset by + pointing `--path` at any JSON-LD file inside your tempdir. +- After every successful `conforms=true` you can stop calling tools and + end the run. diff --git a/src/v2/skills/validate_output/__init__.py b/src/v2/skills/validate_output/__init__.py new file mode 100644 index 0000000..7fe8486 --- /dev/null +++ b/src/v2/skills/validate_output/__init__.py @@ -0,0 +1 @@ +"""SHACL self-validation skill for the v2 terminal agent.""" diff --git a/src/v2/skills/validate_output/__main__.py b/src/v2/skills/validate_output/__main__.py new file mode 100644 index 0000000..5e1c17c --- /dev/null +++ b/src/v2/skills/validate_output/__main__.py @@ -0,0 +1,140 @@ +"""CLI entry point for the `validate_output` skill. + +Lets the executor agent self-check its `output.jsonld` against the v2 +SHACL shapes graph BEFORE declaring done. Returns the same conforms / +violations / warnings shape that the runner's `_validate` produces, so +the agent can iterate on its own output until SHACL conforms. + +This skill is the agent's structural-correctness loop. The judge keeps +its focus on coverage + groundedness; structural issues never need to +reach it. +""" + +from __future__ import annotations + +import argparse +import json +import logging +import os +import sys +from pathlib import Path +from typing import Any + +from src.v2.skills._runtime import SkillError, emit_error, emit_success + +logger = logging.getLogger(__name__) + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="gme-validate-output", + description=( + "Validate output.jsonld against the v2 SHACL shapes graph. " + "Returns {conforms, violations, warnings}." + ), + ) + parser.add_argument( + "--path", + default="output.jsonld", + help="Path to the JSON-LD file to validate (default: ./output.jsonld).", + ) + parser.add_argument( + "--max-violations", + type=int, + default=50, + help="Truncate the violations list at this many entries (default: 50).", + ) + return parser + + +def _strip_internal_metadata(payload: Any) -> Any: + if isinstance(payload, dict): + return { + k: _strip_internal_metadata(v) + for k, v in payload.items() + if not (isinstance(k, str) and k.startswith("_")) + } + if isinstance(payload, list): + return [_strip_internal_metadata(v) for v in payload] + return payload + + +def _run(args: argparse.Namespace) -> dict[str, Any]: + target = Path(args.path) + if not target.exists(): + raise SkillError(f"file not found: {target}", kind="not_found") + try: + payload = json.loads(target.read_text(encoding="utf-8")) + except json.JSONDecodeError as err: + raise SkillError(f"jsonld parse failed: {err}", kind="invalid_json") from err + + try: + from rdflib import Graph as RDFGraph # noqa: PLC0415 + + from src.v2.validation import ( # noqa: PLC0415 + SHACLRuntimeUnavailableError, + SHACLValidator, + load_ontology_shapes_graph, + ) + except ImportError as err: + raise SkillError(f"shacl deps missing: {err}", kind="provider_unavailable") from err + + try: + data_graph = RDFGraph() + data_graph.parse(data=json.dumps(_strip_internal_metadata(payload)), format="json-ld") + except Exception as err: # noqa: BLE001 + raise SkillError(f"jsonld -> rdf parse failed: {err}", kind="invalid_jsonld") from err + + try: + result = SHACLValidator().validate_graph(data_graph, load_ontology_shapes_graph()) + except SHACLRuntimeUnavailableError as err: + raise SkillError(str(err), kind="provider_unavailable") from err + except Exception as err: # noqa: BLE001 + raise SkillError(f"shacl validate failed: {err}", kind="shacl_error") from err + + violations = [_violation(v) for v in result.violations[: args.max_violations]] + warnings = [_violation(w) for w in result.warnings[: args.max_violations]] + return { + "conforms": result.conforms, + "violation_count": len(result.violations), + "warning_count": len(result.warnings), + "violations": violations, + "warnings": warnings, + "path": str(target), + } + + +def _violation(v: dict[str, Any]) -> dict[str, Any]: + return { + "focus": v.get("focusNode"), + "path": v.get("path") or v.get("resultPath"), + "value": v.get("value"), + "severity": v.get("severity"), + "message": v.get("message"), + } + + +def main(argv: list[str] | None = None) -> int: + level_name = os.environ.get("V2_SKILL_LOG_LEVEL", "WARNING").upper() + logging.basicConfig( + level=getattr(logging, level_name, logging.WARNING), + format="%(asctime)s %(levelname)s %(name)s: %(message)s", + stream=sys.stderr, + ) + parser = _build_parser() + args = parser.parse_args(argv) + try: + result = _run(args) + except SkillError as err: + emit_error(err) + return 1 + except Exception as err: # noqa: BLE001 — final boundary + logger.exception("validate_output failed") + emit_error(err) + return 2 + emit_success(result) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/v2/validation/__init__.py b/src/v2/validation/__init__.py new file mode 100644 index 0000000..36e2872 --- /dev/null +++ b/src/v2/validation/__init__.py @@ -0,0 +1,27 @@ +"""Validation helpers for the v2 extraction pipeline.""" + +from src.v2.validation.crossref import CrossRefReport, validate_cross_references +from src.v2.validation.ontology import load_ontology_shapes_graph, ontology_ttl_path +from src.v2.validation.schema_validation import ( + BatchValidationResult, + StrictSchemaValidator, + ValidationResult, +) +from src.v2.validation.shacl_validation import ( + SHACLRuntimeUnavailableError, + SHACLValidationResult, + SHACLValidator, +) + +__all__ = [ + "BatchValidationResult", + "CrossRefReport", + "SHACLRuntimeUnavailableError", + "SHACLValidationResult", + "SHACLValidator", + "StrictSchemaValidator", + "ValidationResult", + "load_ontology_shapes_graph", + "ontology_ttl_path", + "validate_cross_references", +] diff --git a/src/v2/validation/crossref.py b/src/v2/validation/crossref.py new file mode 100644 index 0000000..e73f674 --- /dev/null +++ b/src/v2/validation/crossref.py @@ -0,0 +1,310 @@ +from __future__ import annotations + +from collections import defaultdict +from dataclasses import dataclass, field +from typing import Any + + +@dataclass +class CrossRefReport: + """Cross-reference validation summary for generated v2 entities.""" + + valid_refs_count: int = 0 + invalid_refs: list[dict[str, Any]] = field(default_factory=list) + warnings: list[dict[str, Any]] = field(default_factory=list) + + +def _entity_id(entity: dict[str, Any]) -> str | None: + entity_id = entity.get("id") + if isinstance(entity_id, str): + return entity_id + return None + + +def _as_ref_list(value: Any) -> list[str]: + if value is None: + return [] + if isinstance(value, str): + return [value] + if isinstance(value, list): + return [item for item in value if isinstance(item, str)] + return [] + + +def _add_invalid_ref(report: CrossRefReport, issue: dict[str, Any]) -> None: + report.invalid_refs.append(issue) + + +def validate_cross_references( # noqa: C901, PLR0912, PLR0915 + entities_by_type: dict[str, Any], +) -> CrossRefReport: + """Validate cross-entity references in a generated v2 dataset.""" + report = CrossRefReport() + + persons = entities_by_type.get("persons", []) + repositories = entities_by_type.get("repositories", []) + organizations = entities_by_type.get("organizations", []) + memberships = entities_by_type.get("memberships", []) + contributions = entities_by_type.get("contributions", []) + + person_ids = {_entity_id(person) for person in persons} + person_ids.discard(None) + repository_ids = {_entity_id(repo) for repo in repositories} + repository_ids.discard(None) + organization_ids = {_entity_id(org) for org in organizations} + organization_ids.discard(None) + membership_ids = {_entity_id(membership) for membership in memberships} + membership_ids.discard(None) + + owner_claims: dict[str, set[str]] = defaultdict(set) + + # Validate direct contribution references. + for contribution in contributions: + contribution_id = _entity_id(contribution) + + for author_ref in _as_ref_list(contribution.get("schema:author")): + if author_ref in person_ids: + report.valid_refs_count += 1 + else: + _add_invalid_ref( + report, + { + "type": "missing_person_reference", + "source_type": "contribution", + "source_id": contribution_id, + "field": "schema:author", + "reference": author_ref, + "message": ( + "Contribution references a person ID that does not exist " + "in the person set." + ), + }, + ) + + for repo_ref in _as_ref_list(contribution.get("pulse:contributionTo")): + if repo_ref in repository_ids: + report.valid_refs_count += 1 + else: + _add_invalid_ref( + report, + { + "type": "missing_repository_reference", + "source_type": "contribution", + "source_id": contribution_id, + "field": "pulse:contributionTo", + "reference": repo_ref, + "message": ( + "Contribution references a repository ID that does not exist " + "in the repository set." + ), + }, + ) + + # Validate owner -> repository references and build reverse ownership index. + for owner_type, owners in (("person", persons), ("organization", organizations)): + for owner in owners: + owner_id = _entity_id(owner) + for repo_ref in _as_ref_list(owner.get("pulse:owns")): + if repo_ref in repository_ids: + report.valid_refs_count += 1 + if owner_id is not None: + owner_claims[repo_ref].add(owner_id) + else: + _add_invalid_ref( + report, + { + "type": "missing_owned_repository", + "source_type": owner_type, + "source_id": owner_id, + "field": "pulse:owns", + "reference": repo_ref, + "message": ( + "Owner references a repository in pulse:owns that does " + "not exist in the repository set." + ), + }, + ) + + # Validate repository -> owner references and symmetry with owner claims. + owner_ids = person_ids | organization_ids + for repository in repositories: + repository_id = _entity_id(repository) + owned_by = repository.get("pulse:ownedBy") + if not isinstance(owned_by, str): + continue + + if owned_by in owner_ids: + report.valid_refs_count += 1 + else: + _add_invalid_ref( + report, + { + "type": "missing_owner_reference", + "source_type": "repository", + "source_id": repository_id, + "field": "pulse:ownedBy", + "reference": owned_by, + "message": ( + "Repository pulse:ownedBy references an owner that does not " + "exist in person or organization sets." + ), + }, + ) + continue + + claiming_owner_ids = owner_claims.get(repository_id or "", set()) + if owned_by in claiming_owner_ids: + report.valid_refs_count += 1 + else: + _add_invalid_ref( + report, + { + "type": "bidirectional_ownership_mismatch", + "source_type": "repository", + "source_id": repository_id, + "field": "pulse:ownedBy", + "reference": owned_by, + "message": ( + "Repository pulse:ownedBy is not mirrored by owner pulse:owns " + "references." + ), + }, + ) + + if len(claiming_owner_ids) > 1: + report.warnings.append( + { + "type": "multiple_owner_claims", + "source_type": "repository", + "source_id": repository_id, + "field": "pulse:owns", + "reference": repository_id, + "message": ( + "Repository is claimed by multiple owners; pulse:ownedBy chooses " + "a single canonical owner." + ), + }, + ) + + # Validate membership composite IDs and organization references. + for membership in memberships: + membership_id = _entity_id(membership) + if membership_id is None: + _add_invalid_ref( + report, + { + "type": "missing_membership_id", + "source_type": "membership", + "source_id": None, + "field": "id", + "reference": "", + "message": "Membership entry is missing an id.", + }, + ) + continue + + person_ref = membership.get("_person_ref") + org_ref = membership.get("org:organization") + if not isinstance(person_ref, str) or not isinstance(org_ref, str): + _add_invalid_ref( + report, + { + "type": "invalid_membership_refs", + "source_type": "membership", + "source_id": membership_id, + "field": "id", + "reference": membership_id, + "message": ( + "Membership must have _person_ref and org:organization fields." + ), + }, + ) + continue + if person_ref in person_ids: + report.valid_refs_count += 1 + else: + _add_invalid_ref( + report, + { + "type": "membership_unknown_person", + "source_type": "membership", + "source_id": membership_id, + "field": "id", + "reference": person_ref, + "message": ( + "Membership composite id references a person that does not " + "exist in the person set." + ), + }, + ) + + if org_ref in organization_ids: + report.valid_refs_count += 1 + else: + _add_invalid_ref( + report, + { + "type": "membership_unknown_organization", + "source_type": "membership", + "source_id": membership_id, + "field": "id", + "reference": org_ref, + "message": ( + "Membership composite id references an organization that does " + "not exist in the organization set." + ), + }, + ) + + # org:organization is already validated above via org_ref. + + # Build membership → person lookup for ownership checks. + membership_person_map: dict[str, str] = {} + for membership in memberships: + mid = _entity_id(membership) + pref = membership.get("_person_ref") + if mid is not None and isinstance(pref, str): + membership_person_map[mid] = pref + + # Validate person -> membership references. + for person in persons: + person_id = _entity_id(person) + if person_id is None: + continue + for membership_ref in _as_ref_list(person.get("org:hasMembership")): + if membership_ref in membership_ids: + report.valid_refs_count += 1 + if membership_person_map.get(membership_ref) == person_id: + report.valid_refs_count += 1 + else: + _add_invalid_ref( + report, + { + "type": "person_membership_mismatch", + "source_type": "person", + "source_id": person_id, + "field": "org:hasMembership", + "reference": membership_ref, + "message": ( + "Person membership reference points to a membership " + "whose composite id does not include this person id." + ), + }, + ) + else: + _add_invalid_ref( + report, + { + "type": "missing_membership_reference", + "source_type": "person", + "source_id": person_id, + "field": "org:hasMembership", + "reference": membership_ref, + "message": ( + "Person org:hasMembership references a membership that does " + "not exist in the membership set." + ), + }, + ) + + return report diff --git a/src/v2/validation/ontology.py b/src/v2/validation/ontology.py new file mode 100644 index 0000000..5237170 --- /dev/null +++ b/src/v2/validation/ontology.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +from functools import lru_cache +from pathlib import Path + +from rdflib import Graph + +ONTOLOGY_RELATIVE_PATH = Path("dev/ontology-v2-json-response/open-pulse-ontology-v2.1.2.ttl") + + +def ontology_ttl_path() -> Path: + return Path(__file__).resolve().parents[3] / ONTOLOGY_RELATIVE_PATH + + +@lru_cache(maxsize=1) +def load_ontology_shapes_graph() -> Graph: + path = ontology_ttl_path() + if not path.exists(): + raise FileNotFoundError(path) + + graph = Graph() + graph.parse(path, format="turtle") + return graph diff --git a/src/v2/validation/schema_validation.py b/src/v2/validation/schema_validation.py new file mode 100644 index 0000000..cc92308 --- /dev/null +++ b/src/v2/validation/schema_validation.py @@ -0,0 +1,134 @@ +from __future__ import annotations + +import json +from dataclasses import dataclass, field +from functools import lru_cache +from pathlib import Path +from typing import Any + +from jsonschema import Draft7Validator # type: ignore[import-untyped] + +ENTITY_SCHEMA_NAME_MAP = { + "person": "person", + "persons": "person", + "organization": "organization", + "organizations": "organization", + "repository": "repository", + "repositories": "repository", + "membership": "membership", + "memberships": "membership", + "contribution": "contribution", + "contributions": "contribution", + "article": "article", + "articles": "article", +} + + +@dataclass(slots=True) +class ValidationResult: + entity_type: str + is_valid: bool + errors: list[dict[str, str]] = field(default_factory=list) + + +@dataclass(slots=True) +class BatchValidationResult: + valid_entities: list[tuple[str, dict[str, Any]]] = field(default_factory=list) + invalid_entities: list[tuple[str, dict[str, Any], ValidationResult]] = field( + default_factory=list, + ) + warnings: list[str] = field(default_factory=list) + + +def _schema_name_for_entity(entity_type: str) -> str: + schema_name = ENTITY_SCHEMA_NAME_MAP.get(entity_type.strip().lower()) + if schema_name is None: + message = f"Unsupported strict schema entity type: {entity_type}" + raise ValueError(message) + return schema_name + + +def _json_error_path(path_tokens: list[Any]) -> str: + if not path_tokens: + return "" + return ".".join(str(token) for token in path_tokens) + + +@lru_cache(maxsize=8) +def _load_strict_schema(schema_name: str) -> dict[str, Any]: + schema_path = ( + Path(__file__).resolve().parents[1] + / "schema" + / "json" + / "strict" + / f"{schema_name}.schema.json" + ) + with schema_path.open(encoding="utf-8") as handle: + parsed = json.load(handle) + if not isinstance(parsed, dict): + raise TypeError + return parsed + + +class StrictSchemaValidator: + """Strict JSON Schema validator for reconciled v2 entities.""" + + def __init__(self) -> None: + self._validator_cache: dict[str, Draft7Validator] = {} + + def _validator_for_entity(self, entity_type: str) -> Draft7Validator: + schema_name = _schema_name_for_entity(entity_type) + validator = self._validator_cache.get(schema_name) + if validator is None: + validator = Draft7Validator(_load_strict_schema(schema_name)) + self._validator_cache[schema_name] = validator + return validator + + @staticmethod + def _error_payload(error: Any) -> dict[str, str]: + return { + "path": _json_error_path(list(error.path)), + "message": error.message, + "constraint": str(error.validator), + "expected": str(error.validator_value), + } + + def validate(self, entity_type: str, data: dict[str, Any]) -> ValidationResult: + validator = self._validator_for_entity(entity_type) + clean_data = { + k: v for k, v in data.items() if not k.startswith("_") and v is not None + } + errors = sorted( + validator.iter_errors(clean_data), + key=lambda err: ([str(token) for token in err.path], err.message), + ) + formatted_errors = [self._error_payload(error) for error in errors] + return ValidationResult( + entity_type=_schema_name_for_entity(entity_type), + is_valid=not formatted_errors, + errors=formatted_errors, + ) + + def validate_batch( + self, + entities: list[tuple[str, dict[str, Any]]], + ) -> BatchValidationResult: + result = BatchValidationResult() + + for entity_type, payload in entities: + validation = self.validate(entity_type, payload) + if validation.is_valid: + result.valid_entities.append((validation.entity_type, payload)) + continue + + result.invalid_entities.append((validation.entity_type, payload, validation)) + for error in validation.errors: + result.warnings.append( + ( + f"{validation.entity_type} at {error['path']}: " + f"{error['message']} (constraint={error['constraint']}, " + f"expected={error['expected']})" + ), + ) + + return result diff --git a/src/v2/validation/shacl_validation.py b/src/v2/validation/shacl_validation.py new file mode 100644 index 0000000..8c37248 --- /dev/null +++ b/src/v2/validation/shacl_validation.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from rdflib import Graph + +pyshacl_validate: Any | None +try: + from pyshacl import validate as _pyshacl_validate # type: ignore[import-untyped] + pyshacl_validate = _pyshacl_validate +except ModuleNotFoundError: # pragma: no cover - runtime dependency + pyshacl_validate = None + +SHACL_RESULTS_QUERY = """ +PREFIX sh: + +SELECT ?focusNode ?resultPath ?value ?message ?severity ?sourceShape +WHERE { + ?result a sh:ValidationResult ; + sh:focusNode ?focusNode ; + sh:resultSeverity ?severity . + OPTIONAL { ?result sh:resultPath ?resultPath } + OPTIONAL { ?result sh:value ?value } + OPTIONAL { ?result sh:resultMessage ?message } + OPTIONAL { ?result sh:sourceShape ?sourceShape } +} +""" + + +@dataclass(slots=True) +class SHACLValidationResult: + conforms: bool + violations: list[dict[str, str | None]] = field(default_factory=list) + warnings: list[dict[str, str | None]] = field(default_factory=list) + + +class SHACLRuntimeUnavailableError(RuntimeError): + """Raised when SHACL runtime dependency is unavailable.""" + + +def _as_optional_text(value: Any) -> str | None: + if value is None: + return None + return str(value) + + +class SHACLValidator: + """Run SHACL validation against an RDF graph.""" + + def validate_graph( + self, + graph: Graph, + shapes_graph: Graph, + ) -> SHACLValidationResult: + if pyshacl_validate is None: + message = "pyshacl is not installed; skipping SHACL validation gate" + raise SHACLRuntimeUnavailableError(message) + + # Include ontology triples in the data graph so sh:class checks can resolve + # enum instances defined in the ontology itself. + validation_graph = graph + shapes_graph + + conforms, results_graph, _ = pyshacl_validate( + validation_graph, + shacl_graph=shapes_graph, + ont_graph=shapes_graph, + inference="rdfs", + abort_on_first=False, + allow_infos=True, + allow_warnings=True, + meta_shacl=False, + advanced=True, + js=False, + debug=False, + ) + + violations: list[dict[str, str | None]] = [] + warnings: list[dict[str, str | None]] = [] + for row in results_graph.query(SHACL_RESULTS_QUERY): + issue = { + "focusNode": _as_optional_text(row.focusNode), + "path": _as_optional_text(row.resultPath), + "value": _as_optional_text(row.value), + "message": _as_optional_text(row.message), + "sourceShape": _as_optional_text(row.sourceShape), + } + severity = _as_optional_text(row.severity) or "" + if "Violation" in severity: + violations.append(issue) + continue + if "Warning" in severity: + warnings.append(issue) + + return SHACLValidationResult( + conforms=bool(conforms), + violations=violations, + warnings=warnings, + ) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..ad45e5d --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +"""Test package (makes ``tests`` a proper package for Ruff INP001).""" diff --git a/tests/index/__init__.py b/tests/index/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/index/_federated/__init__.py b/tests/index/_federated/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/index/_federated/test_discover_hydrate.py b/tests/index/_federated/test_discover_hydrate.py new file mode 100644 index 0000000..88cd5cc --- /dev/null +++ b/tests/index/_federated/test_discover_hydrate.py @@ -0,0 +1,209 @@ +"""Smoke tests for the discover/hydrate protocols, registry, and dispatcher. + +No network, no DuckDB. Mock discoverers/hydrators register with the +real registry under unique names and exercise the dispatcher. +""" + +from __future__ import annotations + +import json +from typing import Any, Iterable, Iterator + +import pytest + +from src.index._federated import dh_registry +from src.index._federated.dh_registry import ( + dispatch_hydrate, + register_discoverer, + register_hydrator, +) +from src.index._federated.protocols import ( + HydrationSummary, + Seed, +) + + +@pytest.fixture(autouse=True) +def _isolate_registries(monkeypatch): + """Each test runs against a fresh dict so we don't leak state.""" + monkeypatch.setattr(dh_registry, "DISCOVERERS", {}) + monkeypatch.setattr(dh_registry, "HYDRATORS", {}) + yield + + +def test_seed_roundtrips_jsonl(): + seed = Seed( + id="https://openalex.org/W123", + seed_type="openalex_work", + source="from-search", + hint={"query": "ml", "k": 5}, + ) + line = json.dumps(seed.to_jsonl_dict(), ensure_ascii=False) + parsed = Seed.from_jsonl_dict(json.loads(line)) + assert parsed == seed + + +def test_seed_without_hint_omits_field(): + seed = Seed(id="x", seed_type="t", source="s") + d = seed.to_jsonl_dict() + assert "hint" not in d + + +def test_register_rejects_non_protocol_object(): + class BadDiscoverer: + name = "bad" + # Missing accepted_sources + discover() + + with pytest.raises(TypeError): + register_discoverer(BadDiscoverer()) + + +def test_dispatch_routes_by_seed_type(): + captured: dict[str, list[Seed]] = {"a": [], "b": []} + + class HydratorA: + name = "a" + accepted_seed_types = ("type_a",) + + def hydrate(self, seeds: Iterable[Seed], *, only_unfetched: bool = True) -> HydrationSummary: + captured["a"].extend(list(seeds)) + return HydrationSummary(fetched=len(captured["a"])) + + class HydratorB: + name = "b" + accepted_seed_types = ("type_b",) + + def hydrate(self, seeds: Iterable[Seed], *, only_unfetched: bool = True) -> HydrationSummary: + captured["b"].extend(list(seeds)) + return HydrationSummary(fetched=len(captured["b"])) + + register_hydrator(HydratorA()) + register_hydrator(HydratorB()) + + seeds = [ + Seed(id="1", seed_type="type_a", source="t"), + Seed(id="2", seed_type="type_a", source="t"), + Seed(id="3", seed_type="type_b", source="t"), + Seed(id="4", seed_type="type_unknown", source="t"), + ] + summaries = dispatch_hydrate(seeds, only_unfetched=True) + + # A got both type_a seeds; B got only type_b; unknown silently ignored. + assert [s.id for s in captured["a"]] == ["1", "2"] + assert [s.id for s in captured["b"]] == ["3"] + assert summaries["a"].fetched == 2 + assert summaries["b"].fetched == 1 + + +def test_dispatch_fans_out_to_multiple_hydrators_for_same_type(): + """If two hydrators accept the same seed_type, both receive the seeds.""" + counts: dict[str, int] = {"left": 0, "right": 0} + + class Left: + name = "left" + accepted_seed_types = ("doi",) + + def hydrate(self, seeds, *, only_unfetched=True): + counts["left"] = len(list(seeds)) + return HydrationSummary(fetched=counts["left"]) + + class Right: + name = "right" + accepted_seed_types = ("doi",) + + def hydrate(self, seeds, *, only_unfetched=True): + counts["right"] = len(list(seeds)) + return HydrationSummary(fetched=counts["right"]) + + register_hydrator(Left()) + register_hydrator(Right()) + + seeds = [Seed(id="10.1/x", seed_type="doi", source="t")] + dispatch_hydrate(seeds) + assert counts == {"left": 1, "right": 1} + + +def test_dispatcher_catches_hydrator_exceptions(): + class Boom: + name = "boom" + accepted_seed_types = ("doi",) + + def hydrate(self, seeds, *, only_unfetched=True): + raise RuntimeError("kaboom") + + register_hydrator(Boom()) + seeds = [ + Seed(id="a", seed_type="doi", source="t"), + Seed(id="b", seed_type="doi", source="t"), + ] + summaries = dispatch_hydrate(seeds) + # Errors counted, no exception propagated. + assert summaries["boom"].errors == 2 + + +def test_only_unfetched_flag_passes_through(): + seen_flag: dict[str, bool] = {} + + class Recorder: + name = "recorder" + accepted_seed_types = ("x",) + + def hydrate(self, seeds, *, only_unfetched=True): + seen_flag["only_unfetched"] = only_unfetched + list(seeds) + return HydrationSummary() + + register_hydrator(Recorder()) + seeds = [Seed(id="1", seed_type="x", source="s")] + dispatch_hydrate(seeds, only_unfetched=False) + assert seen_flag == {"only_unfetched": False} + + +def test_only_filter_restricts_to_named_hydrators(): + class A: + name = "a" + accepted_seed_types = ("t",) + + def hydrate(self, seeds, *, only_unfetched=True): + return HydrationSummary(fetched=len(list(seeds))) + + class B: + name = "b" + accepted_seed_types = ("t",) + + def hydrate(self, seeds, *, only_unfetched=True): + return HydrationSummary(fetched=len(list(seeds))) + + register_hydrator(A()) + register_hydrator(B()) + seeds = [Seed(id="1", seed_type="t", source="s")] + # Even though both accept "t", only_filter restricts to "a". + summaries = dispatch_hydrate(seeds, only=["a"]) + assert set(summaries) == {"a"} + + +def test_discoverer_yields_seeds(): + class FakeDiscoverer: + name = "fake" + accepted_sources = ("source-x",) + + def discover(self, source: str, **opts: Any) -> Iterator[Seed]: + assert source == "source-x" + for i in range(3): + yield Seed(id=f"id-{i}", seed_type="t", source=source) + + register_discoverer(FakeDiscoverer()) + disc = dh_registry.DISCOVERERS["fake"] + out = list(disc.discover("source-x")) + assert [s.id for s in out] == ["id-0", "id-1", "id-2"] + + +def test_hydration_summary_merge(): + a = HydrationSummary(fetched=1, in_scope=1, errors=0, extras={"k": 1}) + b = HydrationSummary(fetched=2, out_of_scope=1, errors=1, extras={"k": 9, "j": 7}) + merged = a.merge(b) + assert merged.fetched == 3 + assert merged.in_scope == 1 + assert merged.out_of_scope == 1 + assert merged.errors == 1 + assert merged.extras == {"k": 9, "j": 7} # b overrides a diff --git a/tests/index/huggingface/__init__.py b/tests/index/huggingface/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/index/huggingface/conftest.py b/tests/index/huggingface/conftest.py new file mode 100644 index 0000000..805ef85 --- /dev/null +++ b/tests/index/huggingface/conftest.py @@ -0,0 +1,27 @@ +"""Shared fixtures for the HuggingFace index tests.""" + +from __future__ import annotations + +import pytest + +from src.index.huggingface.config import HuggingFaceIndexConfig, load_config +from src.index.huggingface.storage.duckdb_store import DuckDBStore + + +@pytest.fixture() +def tmp_store(tmp_path) -> DuckDBStore: + db_path = tmp_path / "huggingface.duckdb" + store = DuckDBStore(db_path) + store.bootstrap() + yield store + store.close() + + +@pytest.fixture() +def base_config(monkeypatch, tmp_path) -> HuggingFaceIndexConfig: + """Config loaded from the real YAML, with required envs populated and + the data dir redirected at a tmp path so tests don't touch real state.""" + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path / "index_data")) + monkeypatch.setenv("RCP_TOKEN", "test-token") + monkeypatch.setenv("HF_TOKEN", "hf-test-token") + return load_config() diff --git a/tests/index/huggingface/test_chunker.py b/tests/index/huggingface/test_chunker.py new file mode 100644 index 0000000..2daab78 --- /dev/null +++ b/tests/index/huggingface/test_chunker.py @@ -0,0 +1,45 @@ +"""Chunker invariants for the HuggingFace card layout.""" + +from __future__ import annotations + +from src.index.huggingface.embed.chunker import chunk_for_card, chunk_text + + +def test_chunk_text_handles_empty(): + assert chunk_text("", chunk_tokens=128, overlap=16) == [] + + +def test_chunk_text_single_window(): + out = chunk_text("hello world", chunk_tokens=128, overlap=16) + assert len(out) == 1 + assert out[0].text == "hello world" + assert out[0].token_count > 0 + + +def test_chunk_for_card_titles_first(): + chunks = chunk_for_card( + title="epfl-llm/meditron-7b", + tags=["medical", "llama"], + description="Meditron is a clinical LLM.", + readme="# Meditron\n\nA medical LLM trained at EPFL.", + chunk_tokens=128, + overlap=16, + ) + assert chunks + # The repo_id must appear first in the first chunk so the embedder sees it. + assert chunks[0].text.startswith("epfl-llm/meditron-7b") + assert "Tags: medical, llama" in chunks[0].text + + +def test_chunk_for_card_no_content(): + chunks = chunk_for_card( + title="x/y", + tags=None, + description=None, + readme=None, + chunk_tokens=128, + overlap=16, + ) + # Just the title — one chunk. + assert len(chunks) == 1 + assert chunks[0].text == "x/y" diff --git a/tests/index/huggingface/test_config.py b/tests/index/huggingface/test_config.py new file mode 100644 index 0000000..a68f32f --- /dev/null +++ b/tests/index/huggingface/test_config.py @@ -0,0 +1,52 @@ +"""Config loading + scope/seed lookups.""" + +from __future__ import annotations + +import pytest + + +def test_load_config_picks_up_envs(base_config): + assert base_config.rcp.token == "test-token" + assert base_config.huggingface.token == "hf-test-token" + assert base_config.scope.active in {"epfl", "switzerland"} + + +def test_seed_for_known_scope(base_config): + epfl_seed = base_config.seed_for("epfl") + swiss_seed = base_config.seed_for("switzerland") + assert "epfl-llm" in epfl_seed + assert "EPFL-VILAB" in epfl_seed + # Switzerland seed should be a superset of EPFL seed. + assert set(epfl_seed).issubset(set(swiss_seed)) + assert "swiss-ai" in swiss_seed + assert "ZurichNLP" in swiss_seed + + +def test_seed_for_unknown_scope_raises(base_config): + with pytest.raises(ValueError, match="Unknown scope"): + base_config.seed_for("germany") + + +def test_search_terms_for_scope(base_config): + assert "epfl" in base_config.search_terms_for("epfl") + assert "swiss" in base_config.search_terms_for("switzerland") + + +def test_active_scope_env_override(monkeypatch, tmp_path): + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path / "x")) + monkeypatch.setenv("RCP_TOKEN", "t") + monkeypatch.setenv("INDEX_HUGGINGFACE_SCOPE", "switzerland") + from src.index.huggingface.config import load_config + + cfg = load_config() + assert cfg.scope.active == "switzerland" + + +def test_require_rcp(monkeypatch, tmp_path): + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path / "x")) + monkeypatch.delenv("RCP_TOKEN", raising=False) + from src.index.huggingface.config import load_config + + cfg = load_config() + with pytest.raises(ValueError, match="RCP_TOKEN"): + cfg.require_rcp() diff --git a/tests/index/huggingface/test_duckdb_store.py b/tests/index/huggingface/test_duckdb_store.py new file mode 100644 index 0000000..970ea81 --- /dev/null +++ b/tests/index/huggingface/test_duckdb_store.py @@ -0,0 +1,66 @@ +"""DuckDB schema bootstrap + upsert/stream behaviour.""" + +from __future__ import annotations + +import pytest + + +def test_bootstrap_idempotent(tmp_store): + tmp_store.bootstrap() + tmp_store.bootstrap() + assert tmp_store.count("models") == 0 + + +def test_upsert_model_round_trip(tmp_store): + row = { + "repo_id": "epfl-llm/meditron-7b", + "author": "epfl-llm", + "sha": "abc123", + "pipeline_tag": "text-generation", + "library_name": "transformers", + "license": "llama2", + "downloads": 100, + "downloads_all_time": 1000, + "likes": 42, + "gated": False, + "private": False, + "created_at": None, + "last_modified": None, + "tags": ["medical", "llama"], + "card_data": {"license": "llama2", "language": "en"}, + "base_models": ["meta-llama/Llama-2-7b"], + } + tmp_store.upsert_model(row, raw={"id": "epfl-llm/meditron-7b"}) + fetched = tmp_store.fetch_repo("models", "epfl-llm/meditron-7b") + assert fetched is not None + assert fetched["author"] == "epfl-llm" + assert fetched["downloads"] == 100 + assert tmp_store.count("models") == 1 + + +def test_upsert_org_round_trip(tmp_store): + tmp_store.upsert_org(slug="epfl-llm", scope="epfl") + cur = tmp_store.connect().execute("SELECT slug, scope, source FROM orgs") + rows = cur.fetchall() + assert rows == [("epfl-llm", "epfl", "seed")] + + +def test_stream_rows_skips_already_embedded(tmp_store): + tmp_store.upsert_model({"repo_id": "a/b"}, raw={}) + tmp_store.upsert_model({"repo_id": "c/d"}, raw={}) + tmp_store.upsert_chunk( + chunk_id="cid-1", + entity_type="model", + repo_id="a/b", + chunk_index=0, + text="hi", + token_count=1, + vector_id="cid-1", + ) + streamed = list(tmp_store.stream_rows_for_embedding("models")) + assert {r["repo_id"] for r in streamed} == {"c/d"} + + +def test_stream_rows_unknown_table(tmp_store): + with pytest.raises(ValueError, match="Unknown entity table"): + list(tmp_store.stream_rows_for_embedding("widgets")) diff --git a/tests/index/huggingface/test_paths.py b/tests/index/huggingface/test_paths.py new file mode 100644 index 0000000..8acdb1a --- /dev/null +++ b/tests/index/huggingface/test_paths.py @@ -0,0 +1,30 @@ +"""Path resolution: env override + default fallback + cards layout.""" + +from __future__ import annotations + +from src.index.huggingface.paths import get_huggingface_paths + + +def test_default_path_under_repo_root(monkeypatch): + monkeypatch.delenv("INDEX_DATA_DIR", raising=False) + paths = get_huggingface_paths() + assert paths.root.name == "huggingface" + assert paths.duckdb_path.name == "huggingface.duckdb" + assert paths.duckdb_dir.exists() + assert paths.cards_dir.exists() + + +def test_env_override(monkeypatch, tmp_path): + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path / "myindex")) + paths = get_huggingface_paths() + assert paths.root == tmp_path / "myindex" / "huggingface" + assert paths.duckdb_dir.exists() + assert paths.cards_dir.exists() + assert paths.logs_dir.exists() + + +def test_cards_path_for(monkeypatch, tmp_path): + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path / "myindex")) + paths = get_huggingface_paths() + target = paths.cards_path_for("models", "epfl-llm/meditron-7b") + assert target == paths.cards_dir / "models" / "epfl-llm/meditron-7b" diff --git a/tests/index/huggingface/test_pipeline_chunk_id.py b/tests/index/huggingface/test_pipeline_chunk_id.py new file mode 100644 index 0000000..34698f7 --- /dev/null +++ b/tests/index/huggingface/test_pipeline_chunk_id.py @@ -0,0 +1,29 @@ +"""Chunk-ID determinism + namespace alignment.""" + +from __future__ import annotations + +from src.index.huggingface.embed.pipeline import _chunk_id + + +def test_chunk_id_deterministic(): + a = _chunk_id("model", "epfl-llm/meditron-7b", 0) + b = _chunk_id("model", "epfl-llm/meditron-7b", 0) + assert a == b + + +def test_chunk_id_changes_with_index(): + a = _chunk_id("model", "epfl-llm/meditron-7b", 0) + b = _chunk_id("model", "epfl-llm/meditron-7b", 1) + assert a != b + + +def test_chunk_id_changes_with_repo(): + a = _chunk_id("model", "epfl-llm/meditron-7b", 0) + b = _chunk_id("model", "epfl-llm/meditron-70b", 0) + assert a != b + + +def test_chunk_id_changes_with_entity_type(): + a = _chunk_id("model", "x/y", 0) + b = _chunk_id("dataset", "x/y", 0) + assert a != b diff --git a/tests/index/huggingface/test_scope.py b/tests/index/huggingface/test_scope.py new file mode 100644 index 0000000..a5a4f92 --- /dev/null +++ b/tests/index/huggingface/test_scope.py @@ -0,0 +1,20 @@ +"""Scope resolution + seed loading.""" + +from __future__ import annotations + +from src.index.huggingface.ingest.scope import resolve_scope + + +def test_resolve_epfl_scope(base_config): + scope = resolve_scope("epfl", base_config) + assert scope.name == "epfl" + assert "epfl-llm" in scope.seeds + + +def test_resolve_switzerland_scope(base_config): + scope = resolve_scope("switzerland", base_config) + assert scope.name == "switzerland" + assert "swiss-ai" in scope.seeds + # Switzerland seed must be a strict superset of the EPFL seed. + epfl = set(resolve_scope("epfl", base_config).seeds) + assert epfl.issubset(set(scope.seeds)) diff --git a/tests/index/huggingface/test_sql_retrieval.py b/tests/index/huggingface/test_sql_retrieval.py new file mode 100644 index 0000000..d57e301 --- /dev/null +++ b/tests/index/huggingface/test_sql_retrieval.py @@ -0,0 +1,45 @@ +"""Read-only SQL surface: predefined + ad-hoc guards.""" + +from __future__ import annotations + +import pytest + +from src.index.huggingface.retrieval.sql import ( + PREDEFINED_QUERIES, + run_adhoc, + run_predefined, +) + + +def test_predefined_known_names(): + assert "count_by_entity" in PREDEFINED_QUERIES + assert "top_models_by_downloads" in PREDEFINED_QUERIES + + +def test_predefined_unknown_raises(tmp_store): + with pytest.raises(ValueError, match="Unknown predefined query"): + run_predefined("not_a_real_query", store=tmp_store) + + +def test_adhoc_select_allowed(tmp_store): + rows = run_adhoc("SELECT 1 AS n", store=tmp_store) + assert rows == [{"n": 1}] + + +def test_adhoc_rejects_non_select(): + with pytest.raises(ValueError, match="Only SELECT/WITH"): + run_adhoc("DELETE FROM models") + + +def test_adhoc_rejects_forbidden_keyword(): + with pytest.raises(ValueError, match="Forbidden keyword"): + run_adhoc("SELECT * FROM models; DROP TABLE chunks") + + +def test_count_by_entity_predefined(tmp_store): + tmp_store.upsert_model({"repo_id": "a/b"}, raw={}) + rows = run_predefined("count_by_entity", store=tmp_store) + by = {r["entity"]: r["n"] for r in rows} + assert by["models"] == 1 + assert by["datasets"] == 0 + assert by["chunks"] == 0 diff --git a/tests/index/infoscience/__init__.py b/tests/index/infoscience/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/index/infoscience/conftest.py b/tests/index/infoscience/conftest.py new file mode 100644 index 0000000..a91c4c8 --- /dev/null +++ b/tests/index/infoscience/conftest.py @@ -0,0 +1,38 @@ +"""Test fixtures for the Infoscience indexer.""" + +from __future__ import annotations + +import json +import os +from pathlib import Path + +import pytest + +FIXTURES = Path(__file__).parent / "fixtures" + + +@pytest.fixture +def article_json() -> dict: + return json.loads((FIXTURES / "article_with_matches.json").read_text(encoding="utf-8")) + + +@pytest.fixture +def person_json() -> dict: + return json.loads((FIXTURES / "person.json").read_text(encoding="utf-8")) + + +@pytest.fixture +def organization_json() -> dict: + return json.loads((FIXTURES / "organization.json").read_text(encoding="utf-8")) + + +@pytest.fixture +def sample_extracted_text() -> str: + return (FIXTURES / "sample_extracted.txt").read_text(encoding="utf-8") + + +@pytest.fixture(autouse=True) +def isolated_data_dir(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> Path: + """Redirect INDEX_DATA_DIR per test so on-disk state doesn't leak.""" + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path)) + return tmp_path diff --git a/tests/index/infoscience/fixtures/article_with_matches.json b/tests/index/infoscience/fixtures/article_with_matches.json new file mode 100644 index 0000000..54d3dea --- /dev/null +++ b/tests/index/infoscience/fixtures/article_with_matches.json @@ -0,0 +1,738 @@ +{ + "id": 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"confidence": -1, + "place": 5 + } + ], + "dc.date.accessioned": [ + { + "value": "2025-03-05T13:02:35Z", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.date.available": [ + { + "value": "2025-03-05T13:02:35Z", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.date.created": [ + { + "value": "2025-03-04", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.date.issued": [ + { + "value": "2024-04-01", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.date.modified": [ + { + "value": "2025-12-31T09:55:57.088225Z", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.description.abstract": [ + { + "value": "Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use two complementary approaches to ask how the models’ ability to capture human fMRI responses to sentences is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion words against an fMRI benchmark. We consider the 100-million-word model to be developmentally plausible in terms of the amount of training data given that this amount is similar to what children are estimated to be exposed to during the first 10 years of life. Second, we test the performance of a GPT-2 model trained on a 9-billion-token dataset to reach state-of-the-art next-word prediction performance on the human benchmark at different stages during training. Across both approaches, we find that (i) the models trained on a developmentally plausible amount of data already achieve near-maximal performance in capturing fMRI responses to sentences. Further, (ii) lower perplexity—a measure of next-word prediction performance—is associated with stronger alignment with human data, suggesting that models that have received enough training to achieve sufficiently high next-word prediction performance also acquire representations of sentences that are predictive of human fMRI responses. In tandem, these findings establish that although some training is necessary for the models’ predictive ability, a developmentally realistic amount of training (∼100 million words) may suffice.", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.description.sponsorship": [ + { + "value": "UPSCHRIMPF1", + "language": null, + "authority": "a7bc3696-16f4-49a8-a0d1-fabc1d9ad261", + "confidence": 600, + "place": 0, + "securityLevel": 0 + } + ], + "dc.identifier": [ + { + "value": "10.1162/nol_a_00137", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.identifier.doi": [ + { + "value": "10.1162/nol_a_00137", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.identifier.uri": [ + { + "value": "https://infoscience.epfl.ch/handle/20.500.14299/247475", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.language.iso": [ + { + "value": "en", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.publisher": [ + { + "value": "MIT Press One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA~…", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.relation.ispartof": [ + { + "value": "Neurobiology of Language", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.relation.issn": [ + { + "value": "2641-4368", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + } + ], + "dc.relation.journal": [ + { + "value": "Neurobiology of Language", + "language": null, + "authority": "d3ec50d0-2427-4056-a0f7-69c7d82c44fc", + "confidence": 600, + "place": 0 + } + ], + "dc.subject": [ + { + "value": "language network", + "language": null, + "authority": null, + "confidence": -1, + "place": 0 + }, + { + "value": "human behavior", + "language": null, + "authority": null, + "confidence": -1, + "place": 1 + }, + { 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"https://infoscience.epfl.ch/server/api/core/items/87c3d6e1-5ad5-4b4c-9b24-f4cd46bd700e" + } + } +} \ No newline at end of file diff --git a/tests/index/infoscience/fixtures/sample_extracted.txt b/tests/index/infoscience/fixtures/sample_extracted.txt new file mode 100644 index 0000000..02b7974 --- /dev/null +++ b/tests/index/infoscience/fixtures/sample_extracted.txt @@ -0,0 +1,1908 @@ + +SPECIAL ISSUE: +Cognitive Computational Neuroscience of Language + +Artificial Neural Network Language Models Predict +Human Brain Responses to Language Even After a + +Developmentally Realistic Amount of Training + +Eghbal A. Hosseini1,2 , Martin Schrimpf3,4 , Yian Zhang5, Samuel Bowman6,7,8 , +Noga Zaslavsky1,2,9,10 , and Evelina Fedorenko1,2,3,11 + +1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA +2McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA + +3The MIT Quest for Intelligence Initiative, Cambridge, MA, USA +4Swiss Federal Institute of Technology, Lausanne, Switzerland + +5Computer Science Department, Stanford University, Stanford, CA, USA +6Center for Data Science, New York University, New York, NY, USA +7Department of Linguistics, New York University, New York, NY, USA + +8Department of Computer Science, New York University, New York, NY, USA +9K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center, Massachusetts Institute of Technology, + +Cambridge, MA, USA +10Department of Language Science, University of California, Irvine, CA, USA + +11Speech and Hearing Bioscience and Technology Program, Harvard University, Boston, MA, USA + +Keywords: language network, human behavior, artificial neural network, development, ANN- +neural data alignment + +ABSTRACT + +Artificial neural networks have emerged as computationally plausible models of human language +processing. A major criticism of these models is that the amount of training data they receive +far exceeds that of humans during language learning. Here, we use two complementary +approaches to ask how the models’ ability to capture human fMRI responses to sentences is +affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, +10 million, 100 million, or 1 billion words against an fMRI benchmark. We consider the +100-million-word model to be developmentally plausible in terms of the amount of training data +given that this amount is similar to what children are estimated to be exposed to during the first +10 years of life. Second, we test the performance of a GPT-2 model trained on a 9-billion-token +dataset to reach state-of-the-art next-word prediction performance on the human benchmark at +different stages during training. Across both approaches, we find that (i) the models trained on a +developmentally plausible amount of data already achieve near-maximal performance in capturing +fMRI responses to sentences. Further, (ii) lower perplexity—a measure of next-word prediction +performance—is associated with stronger alignment with human data, suggesting that models that +have received enough training to achieve sufficiently high next-word prediction performance also +acquire representations of sentences that are predictive of human fMRI responses. In tandem, these +findings establish that although some training is necessary for the models’ predictive ability, a +developmentally realistic amount of training (~100 million words) may suffice. + +INTRODUCTION + +A central objective in cognitive neuroscience is to develop models that can accurately predict +human brain responses and behavior. In the neuroscience of language, some artificial neural + +an open a c ce s s j o u r na l + +Citation: Hosseini, E. A., Schrimpf, M., +Zhang, Y., Bowman, S., Zaslavsky, N., & +Fedorenko, E. (2024). Artificial neural +network language models predict +human brain responses to language +even after a developmentally realistic +amount of training. Neurobiology of +Language, 5(1), 43–63. https://doi.org +/10.1162/nol_a_00137 + +DOI: +https://doi.org/10.1162/nol_a_00137 + +Supporting Information: +https://doi.org/10.1162/nol_a_00137 + +Received: 29 March 2023 +Accepted: 9 January 2024 + +Competing Interests: The authors have +declared that no competing interests +exist. + +Corresponding Authors: +Eghbal A. Hosseini +ehoseini@mit.edu +Evelina Fedorenko +evelina9@mit.edu + +Handling Editor: +Alessandro Lopopolo + +Copyright: © 2024 +Massachusetts Institute of Technology +Published under a Creative Commons +Attribution 4.0 International +(CC BY 4.0) license + +The MIT Press + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://orcid.org/0000-0002-0088-9765 +https://orcid.org/0000-0001-7766-7223 +https://orcid.org/0000-0001-6737-4603 +https://orcid.org/0000-0003-3941-3518 +https://orcid.org/0000-0003-3823-514X +http://crossmark.crossref.org/dialog/?doi=10.1162/nol_a_00137&domain=pdf&date_stamp=2024-4-6 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +mailto:ehoseini@mit.edu +mailto:evelina9@mit.edu + + +network (ANN) language models were recently shown to be effective at predicting human +brain activity and behavior during language processing (Caucheteux & King, 2022; Gauthier +& Levy, 2019; Goldstein et al., 2022; Jain & Huth, 2018; Schrimpf et al., 2021; Toneva & +Wehbe, 2019; Wilcox et al., 2020). For example, Schrimpf et al. (2021) examined the ability +of over 40 language models to capture human responses to language and found that trans- +former architectures (Radford et al., 2019; Vaswani et al., 2017) fare best in aligning with +human data. However, off-the-shelf models vary along many dimensions, making it difficult +to unambiguously attribute any given model’s success in aligning with human data to partic- +ular model properties (architecture, objective function, amount/kind of training data, etc.). +Gaining insights into human linguistic mechanisms requires controlled experiments’ on the +models, where different properties are systematically manipulated (Hu et al., 2020; Kumar +et al., 2022; Warstadt & Bowman, 2019). This is the approach we adopt here in order to inves- +tigate how the amount of training data affects model-to-human alignment. + +One common criticism of ANN models as models of human language processing is that +their training data size (often, billions of words) far surpasses the amount of language exposure +in humans during their learning phase (Chang & Bergen, 2021; Dupoux, 2018; Frank, 2023; +Linzen & Leonard, 2018; van Schijndel et al., 2019; see Warstadt & Bowman, 2022, for dis- +cussion). For example, Hart and Risley (1992) estimated that children are exposed to 3–11 +million words each year, so by the time they turn 10 and possess adult-like linguistic compe- +tence, they are exposed to 30–110 million words. In contrast to a human child, who can learn +a language from only ~100 million words (or less), many current models get orders of magni- +tude more training data (20,000 human years’ worth for some models; Warstadt & Bowman, +2022). More recently, Gilkerson et al. (2017) estimated that by age of 10, the amount of +language exposure is around 20 million words, and Frank (2023) put this estimate at +between 9 and 110 million words (extrapolating from their estimate of between 200 and +400 million by age 20). Here, we ask whether this extensive amount of training is necessary for +the models to acquire representations that are predictive of human brain responses during +language sentence comprehension. + +Prior studies on the effects of training data on the models’ linguistic ability found that even +with limited amounts of training data, models achieve considerable proficiency (Warstadt & +Bowman, 2022). For example, Hu et al. (2020) and Zhang et al. (2020) report impressive syn- +tactic generalizations in a BERT model (Devlin et al., 2018) trained on only millions of tokens +(see also Huebner & Willits, 2021; Pannitto & Herbelot, 2020, for related evidence from a +RoBERTa model trained on 5 million words of child-directed speech). Pérez-Mayos et al. +(2021) find that a RoBERTa model (Liu et al., 2019) trained on 100 million words performs +similarly to a model trained on 1 billion words on several syntactic benchmarks. These find- +ings suggest that massive amounts of training may not be necessary for models to acquire cer- +tain aspects of linguistic competence. However, it is not known whether models trained on +limited amounts of data can also predict human neural and behavioral responses to language. + +Here, we evaluate how the amount of training data affects model-to-human alignment. In +line with increasing emphasis in the field on robustness and replicability (Button et al., 2013; +Ioannidis et al., 2014; Poldrack et al., 2017; Simmons et al., 2011), we adopt two complemen- +tary approaches (Figure 1). First, we investigate how well GPT-2 models (Radford et al., 2019) +that are trained on different-sized datasets (1 million, 10 million, 100 million, or 1 billion +words) to reach their best training task performance, predict human functional magnetic +resonance imaging (fMRI) and behavioral responses to sentences. Second, we investigate +how a GPT-2 model’s ability to predict human fMRI and behavioral responses to sentences +changes over the course of training on a large dataset to capture the “learning trajectory” of + +Neurobiology of Language 44 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + + + +model-to-brain alignment. In addition, we also examine the role of model perplexity in the +ability of a model to predict human responses. To foreshadow the key results, we find that +models reach high performance in predicting human responses to sentences even with +developmentally realistic amounts of training data. + +MATERIALS AND METHODS + +Human Datasets (Benchmarks) + +Primary benchmark: fMRI dataset + +We used fMRI data from two experiments (Experiments 2 and 3 in Pereira et al., 2018). This +benchmark, hereafter called Pereira2018, is identical to the one reported in Schrimpf et al. +(2021). Experiment 2 (n = 9 native English speakers) consisted of 384 sentences across +96 Wikipedia-style passages, spanning 24 broad topics (professions, clothing, musical +instruments, etc.). There were four passages per topic (e.g., passages about a clarinet, an accor- +dion, a piano, and a violin for the musical instruments topic). Each passage consisted of four +sentences, and the sentences varied in length between seven and 18 words. Experiment 3 (n = +9 native English speakers) consisted of 243 sentences across 72 passages, which were a mix of +Wikipedia-style passages and short narratives. Each passage consisted of three or four sen- +tences, and the sentences varied in length between five and 20 words. The stimuli for both +experiments were constructed so as to span a broad range of topic areas. In both experiments, +each sentence was presented on the screen for 4 s, followed by 4 s of fixation, and each par- +ticipant read the materials three times across three fMRI scanning sessions. The responses were +averaged across the three repetitions to derive a single response per sentence. + +Figure 1. Methodological approach. (A) Unidirectional-attention transformer architecture. Text input is processed sequentially to predict the +next likely token at each step. (B) The setup for Experiments 1 and 2. In Experiment 1, four models were trained using different-sized datasets, +and for each model, the weights with the best validation perplexity were frozen and used in the model-to-brain comparison. In Experiment 2, +the GPT-2 model was trained using a very large dataset, and the weights were frozen at different steps during training and used in the model-to- +brain comparison. (C) Model representations were related to human representations by building a linear regression between unit activations for +each layer of the model and voxel activity (in the language-selective network; Fedorenko et al., 2011) or reading times for the stimuli used in +each of the benchmarks. This regression was then used to make predictions about human neural/behavioral responses for unseen language +stimuli, and a Pearson correlation was computed between these predictions and the observed responses. (D) The general pipeline for predict- +ing human brain and behavioral responses. For each benchmark, each model was exposed to the same language stimuli as humans, and the +model-to-human match was evaluated as shown in C. + +Neurobiology of Language 45 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + + + +Furthermore, in each participant, the analysis was restricted to a set of voxels that were +identified as language-responsive in an independent extensively validated language localizer +task (Fedorenko et al., 2010). In the localizer task, participants read sentences and list +of nonwords (e.g., “blork” or “cre”) in a standard blocked design. Each item consisted of +12 words/nonwords that were presented one at a time at the rate of 450 ms per word/nonword. +Each sentence/nonword list was followed by a simple button-press task (“Press a button when +you see a picture of a finger on a button”). Each trial lasted 6 s. Each block consisted of three +trials and lasted 18 s. Each scanning run consisted of 16 experimental blocks (8 per condition) +and six fixation blocks and lasted 358 s. Each participant completed two runs, and the order +of conditions was counterbalanced across runs. The localizer is available for download at +(https://evlab.mit.edu/funcloc/). The contrast between sentences and nonword lists has been +shown to robustly identify the frontotemporal language-selective network of brain areas +(Fedorenko et al., 2011; Lipkin et al., 2022). These areas support language comprehension +across modalities (listening, reading, etc.) and have been established to be sensitive to both +word meanings and syntactic structure processing (e.g., Fedorenko et al., 2010; Fedorenko +et al., 2020). For each participant, we selected the top 10% of most localizer-responsive vox- +els within a set of 12 broad masks (6 in each hemisphere) that cover inferior frontal and lateral +temporal cortex (the masks were derived from an independent set of 220 participants who +performed the language localizer task and are available at https://evlab.mit.edu/funcloc/). +Thus, the fMRI benchmark consists of—for each of the two experiments—a set of language +responsive voxels in each participant, and for each voxel, we have an estimate of the blood +oxygen level dependent (BOLD) response to each of 384 sentences (Experiment 2 in Pereira +et al., 2018) or 243 sentences (Experiment 3 in Pereira et al., 2018). + +Secondary benchmark: Behavioral (reading-times) dataset + +We used self-paced reading data from (Futrell et al., 2018). Similar to the fMRI benchmark, this +benchmark, hereafter called Futrell2018, is identical to the one reported in Schrimpf et al. +(2021). 179 native adult English-speaking participants (recruited through Mechanical Turk) +read stories that were presented one word at a time; with each button press, the current word +would disappear in place of the new word (e.g., Just et al., 1982). The time it took a participant +to move to the next word, n + 1, was used as a measure of comprehension difficulty at word n. +The stories were based on existing stories but were edited in a way so as to increase the +frequency of rare words and constructions, including constructions that are known to cause +comprehension difficulty (see Futrell et al., 2018, for details). The stories consisted of 33–64 +sentences, and contained between 938 and 1,089 words. Each of 179 participants read +between five and 10 stories and answered comprehension questions at the end of each story; +each story was read by 82–98 participants. Following Futrell et al. (2018), we excluded read- +ing times outside of the [100 ms, 3,000 ms] range. Note that we report the results for this +benchmark in the Supporting Information, available at https://doi.org/10.1162/nol_a_00137 +(see Supplementary Figure 3) because, as will be discussed below, we found that diverse +variants of control (untrained) language models predict these responses well above chance, +which suggests that model predictivity is unlikely to be related to the representation of the +linguistic stimuli. + +Artificial Neural Network Models + +We used two different implementations of a GPT-2-style model. For Experiment 1, where a +model was trained on a dataset with a controlled number of words, we used the GPT-NEOX +library, which is a distributed training framework that uses the DeepSpeed library (Aminabadi + +Neurobiology of Language 46 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://evlab.mit.edu/funcloc/ +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 + + +et al., 2022; Black et al., 2022). We used a unidirectional-attention transformer model (GPT-2; +Radford et al., 2019) with 12 layers and an embedding layer which was learned during train- +ing. Each layer had a size of 768 units and consisted of four main blocks (Figure 1A): (i) first +layer normalization, (ii) self-attention, (iii) second layer normalization, and (iv) the feedforward +layer. The final layer consisted of a linear projection with a sigmoid nonlinearity that mapped +hidden states into probabilities over the dictionary. The context size was 1,024 tokens. To test +whether our results would generalize to bidirectional-attention transformer architectures, we +additionally used publicly available miniBERTa models that were trained on the same datasets +as the GPT-2 models (Zhang et al., 2020; https://huggingface.co/nyu-mll). Note, we did not +include the model trained on the smallest (1 million words) dataset, for which Zhang et al. +(2020) used a smaller-sized model, which therefore would not be directly comparable to +the other models. The miniBERTas use the same design as the RoBERTa base model (Liu +et al., 2019)—a bidirectional-attention model with 12 layers, each 768 units in size, and a +context size of 512 tokens. Importantly, RoBERTa has the same number of parameters as +GPT-2 (125 million), allowing for a relatively controlled comparison of uni- and bidirectional +architectures. + +For Experiment 2, to investigate model training dynamics with a very large dataset, where +during the early stages of the training the model continues to see new input (cf. doing multiple +passes through a smaller-size training corpus as in Experiment 1), we used GPT-2 model +weights from a publicly available model from the Hugging Face Transformers library +(https:// huggingface.co/stanford-crfm). The model has a similar architecture to the +GPT-2 model used in Experiment 1. + +Model Training + +Training datasets + +For Experiment 1, we combined the BookCorpus (Zhu et al., 2015) and English Wikipedia (Liu +et al., 2019; Zhu et al., 2015) with a 1:3 ratio. We then created four different datasets with 1 +million, 10 million, 100 million, and 1 billion words. These were used for training both the +GPT-2 models and the miniBERTa models. For Experiment 2, we used a model that was trained +on the OpenWebText corpus (Gokaslan & Cohen, 2019) with more than 9 billion tokens. + +To characterize the training corpora, we counted the number of unique tokens, token +bi-grams, token tri-grams and token four-grams for different dataset sizes in Experiment 1 and +for different checkpoints in Experiment 2. For this analysis, the data were tokenized as in the +Penn Treebank corpus (Marcus et al., 1993). In particular, contractions were split (e.g., they’re +➔ they + re) and punctuation marks were treated as separate tokens. Afterwards, we counted +unique occurrences of tokens, tokens bi-grams, and so on. As shown in Supplementary +Figure 2B and D, the number of all n-grams increases with corpus size (Experiment 1) and for +later checkpoints (Experiment 2), and the percentage of unique tokens relative to total tokens +is always higher in Experiment 2 compared to Experiment 1. (See also Supplementary +Figure 10 for an illustration of the training dynamics in Experiment 1 vs. Experiment 2; note, +quantifying the variability in syntactic structures is more challenging given the size of the +corpora and the fact that they are not parsed/POS-tagged.) + +In addition, following a reviewer’s request, we tested whether the experimental materials +from the human benchmarks were present in the training corpora. We found that none of the +sentences from the fMRI benchmark were present in any of the training corpora, and only a +very small number of sentences from the behavioral benchmark were present in the training +corpora. In particular, in Experiment 1, three of the sentences from the behavioral benchmark + +Neurobiology of Language 47 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://huggingface.co/nyu-mll +https://huggingface.co/nyu-mll +https://huggingface.co/nyu-mll +https://huggingface.co/nyu-mll +https://huggingface.co/nyu-mll +https://huggingface.co/nyu-mll +https://huggingface.co/nyu-mll +https://huggingface.co/stanford-crfm +https://huggingface.co/stanford-crfm +https://huggingface.co/stanford-crfm +https://huggingface.co/stanford-crfm +https://huggingface.co/stanford-crfm +https://huggingface.co/stanford-crfm +https://huggingface.co/stanford-crfm +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 + + +were found in the 1 million word training dataset, two sentences in the 10 million word +training dataset, four sentences in the 100 million word training dataset, and 17 sentences +in the 1 billion word training dataset; in Experiment 2, four sentences were found in the +training dataset. + +Training procedure + +For Experiment 1, to train the GPT-2 models, we used standard initialization from the GPT- +NEOX library and standard training parameters (Radford et al., 2019; see Supplementary +Figure 1 for details). After training, the model weights with the smallest validation perplexity +were selected for evaluation on the human benchmarks. The smallest validation perplexity +was reached after 1,000 steps for the 1 million word dataset (1,024 tokens * 128 batches * +1,000 steps = 131,072,000 tokens total), after 2,000 steps for the 10 million word dataset +(1,024 tokens * 128 batches * 2,000 steps = 262,144,000 tokens), after 14,250 steps for the +100 million word dataset (1,024 tokens * 128 batches * 14,250 steps = 1,867,776,000 tokens), +and after 310,000 steps for the 1 billion word dataset (1,024 tokens * 128 batches * 310,000 +steps = 60,948,480,000 tokens). To train the miniBERTa models, we used standard initializa- +tion and training parameters from the Hugging Face Transformers library (Liu et al., 2019). + +Predictivity of the trained models was compared to that of untrained models. Here, in addi- +tion to the untrained GPT-2 model available from the Hugging Face library, we created an +alternative untrained GPT-2 model in order to investigate the effects of different weight initial- +izations on the alignment between model representations and human neural responses and +reading behavior, and thus to isolate the effects of model architecture alone (i.e., the units +and the patterns of connections among them) on predictivity. This version implemented the +same unidirectional mask as the trained models and the other untrained model, but all the +weights were set to a Gaussian distribution with a fixed mean and standard deviation (mean: +0, standard deviation: 0.02 for the layer normalization, self-attention, and feedforward layer +weights; see Supplementary Figure 6 for a detailed comparison with the Hugging Face initial- +ization parameters). + +For Experiment 2, the GPT-2 model was trained with standard initialization and training +parameters until it reached state-of-the art perplexity values. We selected several checkpoints +at which we extracted model representations from each layer for evaluation on the human +benchmarks. The model was trained on 16 GPUs, with eight batches per GPU, and updates +were performed after four gradient accumulations. As a result, a training step constituted 1,024 +(tokens) * 8 (batches) * 16 (GPUs) * 4 (gradient accumulations) = 524,288 tokens. Given that +the tokenized OpenWebText corpus contains 9,036,044,288 tokens, it takes close to 20,000 +training steps (specifically, 17.2K steps) to do one complete pass over the corpus. The check- +points were selected in a logarithmic manner: 0, 0.1% (20 training steps, which corresponds to +524,288 tokens * 20 steps = 10,485,760 tokens), 1.0% (200 steps, which corresponds to +524,288 tokens * 200 steps = 104,857,600 tokens), 10% (2K steps, which corresponds +to 524,288 tokens * 2,000 steps = 1,048,576,000 tokens), 100% (20K steps, which corre- +sponds to 524,288 tokens * 20,000 steps = 10,485,760,000 tokens), and 10 × 100% (200K +steps, which corresponds to 524,288 tokens * 200,000 steps = 104,857,600,000 tokens). + +Analyses + +Model comparison to the fMRI benchmark + +We followed the approach in Schrimpf et al. (2021). In particular, we first extracted the rep- +resentation for all the sentences that were used in the human fMRI experiments from each + +Neurobiology of Language 48 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 + + +layer of each model. For each experiment, we split the stimuli into five ~equal-size batches +(Experiment 1: three batches of size 76 sentences and two batches of size 78; Experiment 2: +two batches of size 48 sentences and three batches of size 49 sentences) and used ~80% of the +data to build an ordinary least squares regression model between model unit activations and +voxel-level responses in the language network (defined by an extensively validated language +localizer task, as described in Primary Benchmark: fMRI Dataset, above; Fedorenko et al., +2010; Lipkin et al., 2022). For both experiments, we selected the representation of the last +word in each sentence (for multi-token words, we averaged the representations across the +composite tokens). We then applied the regression to the left-out ~20% of sentences to gen- +erate predictions for BOLD responses in each voxel and compared these predictions against +the observed BOLD responses using Pearson correlation. This procedure was iterated across +the data folds, leaving out a different 20% each time, and the Pearson values were averaged +across these iterations in each voxel of each participant. For each participant averaging across +the two experiments (Experiments 2 and 3 in Pereira et al., 2018), we obtained a single score +by taking the median Pearson value across the language-responsive voxels. A reliable positive +Pearson correlation value indicates that the model is able to predict fMRI responses with a +linear transformation. The resulting Pearson correlation values are divided by the ceiling value +computed by estimating how well a best possible model of an average human would predict +fMRI responses (Schrimpf et al., 2021). This value was estimated to be 0.32 for the Pereira et al. +(2018) dataset. (We acknowledge that the issue of how to compute a noise ceiling remains an +open issue in the field.) + +Statistical testing was performed on the scores from the best model layer (as determined in +Schrimpf et al., 2021) and took the form of independent two-sample t tests. In particular, par- +ticipant scores for each model in Experiment 1 (n = 5 models: untrained, 1M, 10M, 100M, and +1B) or each checkpoint of a model in Experiment 2 (n = 6 checkpoints: untrained, 0.1% of +training steps, 1% of training steps, 10% of training steps, 100% of training steps, and 10 × +100% of training steps) were compared to the scores for the fully trained model (from Schrimpf +et al., 2021). The resulting p values were Bonferroni-corrected for the number of comparisons +(5 and 6 comparisons in Experiments 1 and 2, respectively). + +Model comparison to the behavioral benchmark + +Similar to the fMRI benchmark, we followed the approach in Schrimpf et al. (2021). In partic- +ular, we first extracted the representation for all the stimuli (individual words) that were used in +the behavioral experiment from the last layer of each model. For each participant, we split the +total words (which varied across participants depending on the number of stories that a par- +ticipant read) into five ~equal-sized batches, and used ~80% of the data to build an ordinary +least squares regression model between model unit activations and reading times. In dividing +the data into batches, we ensured that (a) the same word did not appear in the training versus +the test set, and (b) for any given sentence, the words were divided as evenly as possible +between the training and the test set. We then applied the regression to the left-out ~20% +of words to generate predictions for reading times and compared these predictions against +the observed reading times using Pearson correlation. This procedure was iterated across +the data folds, leaving out a different 20% each time, and the Pearson values were averaged +across these iterations to obtain a single score per participant. A reliable positive Pearson cor- +relation value indicates that the model is able to predict reading times with a linear transfor- +mation. The resulting Pearson correlation values are divided by the ceiling value computed by +estimating how well a best possible model of an average human would predict reading times +(Schrimpf et al., 2021). This value was estimated to be 0.76 for the Futrell et al. (2018) dataset. + +Neurobiology of Language 49 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + + + +Statistical testing was performed on the scores from the last model layer and took the form of +independent two-sample t tests, with a Bonferroni correction for the number of tests, similar to +the fMRI benchmark. + +Model perplexity + +Following standard practice (e.g., Jelinek et al., 1977), we used perplexity as a measure of +model performance on the language prediction tasks (next-word prediction for the GPT-2 +models and missing-word prediction for the miniBERTa models). Perplexity (PPL) is defined as: + +PPL ¼ 2H xð Þ + +H xð Þ ¼ − +1 +N + +XN + +i¼1 + +log2P xið Þ + +where H is entropy, and x denotes the tokens. + +For both experiments, we used the test set from the wikitext-103-raw-v1 dataset (Merity +et al., 2016) to compute perplexity. Perplexity was computed using a context size of 1,024 +tokens and a stride of 512 tokens. + +RESULTS + +Models Trained on Small Corpora Predict Human Responses + +We started by examining the performance of a unidirectional transformer model trained on the +standard language modeling task in predicting human fMRI responses during the processing of +sentences. Specifically, we tested a GPT-2 architecture (Figure 1A), which has previously been +shown to best capture human neural and behavioral responses (Schrimpf et al., 2021). We +trained four independent models on 1 million, 10 million, 100 million, and 1 billion words, +respectively (Supplementary Figure 1). A training dataset of size 100 million words is compa- +rable to the amount of language input that children have been estimated to get during the first +decade of life (Frank, 2023; Hart & Risley, 1992). Of course, the nature of the language input +is still quite different between models and children both with respect to the content and the +modality—text only for models vs. multimodal input for children. We return to this point in +the Discussion. After training, we selected the checkpoint with the best perplexity on the +validation set and tested how well the model representations capture human neural (fMRI) +responses to sentences in the language-selective network (Fedorenko et al., 2011) and +human behavioral responses in a self-paced reading task (Figure 1D). + +For the Pereira2018 benchmark (Pereira et al., 2018; Schrimpf et al., 2021), we observed a +consistent increase in performance with an increase in the size of the training set (Figure 2A; +see Supplementary Figure 5 for evidence—for this and the behavioral benchmark—of robust- +ness of this pattern to seed choice during model initialization; cf. Mehrer et al., 2021; see +Frank et al., 2015, and Aurnhammer & Frank, 2019, for similar findings from earlier, pre- +transformer models, including n-gram, RNN, and phrase-structure grammar models). Critically, +however, the model trained on just 100 million words already exhibits fMRI response predic- +tivity that is similar to that of the fully trained GPT-2 model as reported in Schrimpf et al. +(2021), with no significant difference in predictivity values (p = 0.99; the data frames are avail- +able at OSF: see Data and Code Availability Statement). The model trained on 1 billion words +also does not differ from the fully trained model in predictivity (p = 0.82). In contrast, the pre- +dictivity of the untrained model (the version with the Hugging Face initialization parameters) + +Neurobiology of Language 50 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 + + +Figure 2. Model performance on the fMRI (Pereira2018) benchmark as a function of training. (A) Experiment 1 results: performance (normalized +predictivity) of the best-performing GPT-2 layer, as reported in Schrimpf et al. (2021), in predicting language-responsive voxels’ activation in the +Pereira2018 benchmark. The results are shown for (i) two versions of an untrained (Unt.) model (initialized in two different ways: Unt. N(0,0.02) +corresponds to the untrained model initialized with a mean of 0 and a standard deviation of 0.02, and Unt. HF corresponds to the untrained +model initialized with the Hugging Face parameters; black dots); (ii) four models trained on datasets of different sizes (1 million, 10 million, 100 +million, and 1 billion words; blue-to-green dots connected by a line; the model trained on a developmentally plausible amount of data—100 +million—is marked with a red asterisk; see also Supplementary Figure 4 for the results for a 50 million word model); and (iii) a fully trained model, +as reported in Schrimpf et al. (2021; gray dots). Here, in Figure 3A, and in Figure 3, we computed a median score across participants and divided +it by an estimated ceiling value to get a normalized score, and we computed a median absolute deviation over participants for use as error bars. +(B) The number of unique tokens (1 gram), token bi-grams, token tri-grams, and token four-grams in each training dataset. There is at least a +logarithmic increase in the counts with the increase in the dataset size. The rightmost panel shows the percentage of unique tokens relative to all +tokens for each dataset. For Experiment 1, the total number of tokens is computed based on the number of training steps that was needed to reach +best validation loss (see Materials and Methods; see also Supplementary Figure 1 for the illustration of the training dynamics). (C) Experiment 2 +results: performance of the best-performing GPT-2 layer, as reported in Schrimpf et al. (2021), in predicting language-responsive voxels’ activation +in the Pereira2018 benchmark. The results are shown for (i) two versions of an untrained model (initialized in two different ways, as in Figure 2A; +see Materials and Methods; black dots); (ii) a model trained on a large dataset examined at different points during the training (0.1%, 1.0%, 10%, +100%, and 10 × 100% of training steps; purple-to-yellow dots connected by a line); and (iii) a fully trained model, as reported in Schrimpf et al. +(2021; gray dots). (D) Same as in Figure 2B but for the OpenWebText training dataset. (E–F) Exploratory analyses of individual model layers: +performance of the 12 GPT-2 model layers in predicting human neural responses in the Pereira2018 benchmark in (E) Experiment 1 and (F) +Experiment 2. Layer 0 is the token embedding layer, and layer 12 is the last layer. The results are shown for (i) an untrained (Unt.) model (with +the Gaussian initialization; black dots); and (ii) four models trained on datasets of different size (blue-to-green dots connected by a line in A) or a +model trained on a large dataset examined at different points during the training (purple-to-yellow dots connected by a line in B). + +Neurobiology of Language 51 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 + + +and the models trained on 1 million and 10 million words is significantly below the predictiv- +ity of the fully trained model (p values < 0.0001, 0.007, and 0.016, respectively; here and +elsewhere, the values are Bonferroni-corrected, as described in Analyses). + +The untrained model performance differs between the two versions (see Training Proce- +dure, above). The version initialized with the standard Hugging Face parameters performs well +above chance (p < 0.0001), as reported in Schrimpf et al. (2021; see also Caucheteux & King, +2022), but the version initialized with the alternative parameters (all weights set to a normal +distribution with a mean of 0 and a standard deviation of 0.02) performs around 0 (not signif- +icantly different from 0; p = 0.14; Figure 2A). + +The results also generalize, to some degree, to a bidirectional transformer model (mini- +BERTa; Liu et al. 2019; Supplementary Figure 2). In particular, similar to the GPT-2 models, +we observed a consistent increase in model performance with an increase in the training data- +set size, which suggests that this pattern is robust to architecture. However, the 100 million +word model still performs below the fully trained model. This difference between the GPT-2 +and miniBERTa models in the amount of training they require to align with human data is likely +due to the difference in the directionality of the attention mechanisms, with unidirectional- +attention mechanisms being more sample efficient. Generalizing these results to other mini- +mally different variants of uni- vs. bidirectional-attention transformer models will help +strengthen this conclusion. + +In exploratory analyses, in addition to examining the language network as an integrated +system, we examined the effects of the amount of training data on the models’ ability to predict +fMRI responses in individual frontal and temporal language functional regions of interest +(ROIs) for a total of six language fROIs in the left hemisphere (LH), and six homotopic regions +in the right hemisphere (RH; e.g., Lipkin et al., 2022). The results are shown in Supplementary +Figure 9. The overall pattern was similar across all language fROIs, including between the LH +inferior frontal gyrus fROI and the LH post-temporal fROI (which have been argued by some to +differ functionally; e.g., Friederici, 2018; Hagoort, 2019; cf. Fedorenko et al., 2020). The over- +all predictivity was lower in the RH than the LH language fROIs (p << 0.0001 for all models in +Experiment 1 and all checkpoints in Experiment 2), in line with past findings (e.g., Schrimpf +et al., 2021; Tuckute et al., 2024). + +We also investigated the patterns of model-to-brain alignment across model layers. Prior +work in vision (Geiger et al., 2020; Storrs et al., 2021) has suggested that training affects model +performance differently across layers, with early layers already reaching close to maximal per- +formance with a limited amount of training, but later layers continuing to benefit from increas- +ingly more training. In line with these prior observations, for the Pereira2018 benchmark, we +observed that for layers 4–9, performance peaks for the 1 million word model, and for the last +three layers (layers 10–12), a consistent improvement in performance is observed with larger +datasets (Figure 2E). This observation echoes prior work showing that later layers build more +contextualized representation of linguistic stimuli and better capture syntactic and composi- +tional semantic aspects of the linguistic signal (Belinkov et al., 2017; Hewitt & Manning, +2019; Tenney et al., 2019), to which the language brain regions are also deeply sensitive +(e.g., Fedorenko et al., 2010; Fedorenko et al., 2020; Pallier et al., 2011; Shain et al., 2023). + +The general pattern of results was also similar for the secondary, behavioral benchmark +(Futrell2018; Supplementary Figure 3A): The predictivity of the untrained model and +the model trained on 1 million words is significantly below the predictivity of the fully +trained model (p values < 0.0001 and p = 0.00016, respectively); and the predictivity of +the models trained on 10 million words, 100 million words, and 1 billion words does not + +Neurobiology of Language 52 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 + + +significantly differ from that of the fully trained model (p values > 0.05). However, +because both of the untrained models achieve reliably above-zero predictivity on the +Futrell2018 benchmarks (ps < 0.0001), model performance is unlikely to be related to +the representation of linguistic stimuli. As a result, we present these findings in Supporting +Information, for completeness. + +Models Trained on a Small Portion of a Massive Corpus Predict Human Responses + +In the previous section, we investigated how models that are trained on small corpora +(until they reach their best performance on the target language modeling task) perform +in predicting human data. However, humans, including children learning a language, +are continuously exposed to new words and constructions (see Supplementary +Figure 10). To better simulate such scenarios, as well as to evaluate the robustness of +the results to approach, we examined how the ability of a model to predict human fMRI +responses to sentences changes over time as the model is being trained on a very large +corpus, similar to (Caucheteux & King, 2022). To do so, we used a GPT-2 model that +was trained on a corpus consisting of over 9 billion tokens and selected several check- +points during the training process (0.1%, 1.0%, 10.0%, 100%, and 10 × 100% of training +steps, where 100% of training steps approximately equal one complete pass over the full +dataset; see Materials and Methods). At each of these checkpoints, we tested how well the +model representations capture human responses to sentences. + +For the Pereira2018 benchmark, the performance of the fully trained model (i.e., 10 × 100% +of training steps) closely matches the results reported in Schrimpf et al. (2021; where the +Hugging Face version of the model was used; cf. the GPT-NEOX library version here), with +no significant difference in predictivity (p = 0.67). This result shows that model-to-human +alignment is robust to the details of model implementation, as one would hope. Critically, +mirroring the results from Experiment 1, we observed a consistent increase in how well the +model predicts fMRI responses to sentences until the model reaches the 10% checkpoint, at +which point the performance plateaus. Critically, the predictivity of the models trained on +10% or 100% of the training steps does not significantly differ from the predictivity of the +fully trained model (p values > 0.05). In contrast, the predictivity of the untrained model +(the version with the Hugging Face initialization parameters) and models trained on 0.1% or +1.0% of the training steps is significantly below that of a fully trained model (ps < 0.001; +Figure 2C; see Supplementary Figure 4 for evidence of robustness to seed choice during model +initialization). + +The slight decrease in performance with more training (from 100% to 10 × 100%) suggests +that more training does not necessarily lead to better alignment with human brain data, +although it is possible that this result is due to the relatively spatially and temporally coarse +nature of our neural measurements. In particular, a response in a given fMRI voxel reflects an +average activity of a large population (a few hundred thousand) of neurons, and the activity is +averaged over multiple seconds, which necessarily obscures the fast dynamics of language +processing. It is possible that for finer-grained neural data, such as intracranial recordings +(electrocorticography or stereo electroencephalography, or EEG), we might continue to see +improvements with more training. + +In exploratory analyses of the individual model layers, we observed that performance +shows a consistent increase across layers up to the 1.0% checkpoint. After that, the early +and middle layers show a drop in performance from the 10% checkpoint to the 10 × 100% +checkpoint, whereas in the later layers, performance increases from the 10% to the 100% + +Neurobiology of Language 53 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 + + +checkpoint and then reaches a plateau (Figure 2F). Additionally, as in Experiment 1, and in +line with prior work in vision (e.g., Geiger et al., 2020; Storrs et al., 2021), earlier layers reach +close to maximal performance earlier in the training (at the 1% checkpoint), whereas later +layers reach their peak close to the 10% checkpoint (Figure 2F). + +The pattern of results for the secondary, behavioral benchmark (Futrell2018) closely follows +the pattern that we observed with limited-size training datasets in Experiment 1, with predic- +tivity reaching a plateau after the 1% checkpoint (Supplementary Figure 3B). The predictivity +of the untrained model and the models trained on 0.1% or 1% of the training steps is signif- +icantly below the predictivity of the fully trained model (p values < 0.001, < 0.001, and +0.0015, respectively); and the predictivity of the models trained on 10%, 100%, and 10 × +100% of the training steps does not significantly differ from that of the fully trained model +(p values > 0.05). + +Model Perplexity Predicts Model Performance + +For ANN language models, perplexity (a measure of performance on the next-word prediction +task; see Analyses) is a reliable predictor of model performance on diverse NLP benchmarks +(e.g., Brown et al., 2020; Radford et al., 2019). Schrimpf et al. (2021) further found that off-the- +shelf models that perform better on the next-word prediction task are also better able to cap- +ture human neural and behavioral responses (see Antonello & Huth, 2024, for evidence that a +similar relationship obtains for other tasks), in line with prior work showing a similar relation- +ship in pre-transformer models between the amount of training and the ability of a model to +predict ERP responses (Aurnhammer & Frank, 2019; Frank et al., 2015; cf. Pasquiou et al., +2022). Here, we examined the relationship between model perplexity and its ability to predict +human fMRI responses for models that only differ in the size of the training corpus and for a +model at different stages of training, in order to test whether better performance on the next- +word prediction task is associated with representations that are more strongly predictive of +human neural responses to language. + +As expected, perplexity is lower (i.e., the ability to predict upcoming words is better) for +models that are trained on larger datasets (Figure 3A) and for a given model at the later stages +of training (Figure 3B; see Supplementary Figure 3C and D for the results on the behavioral +benchmark). Critically, across both Experiments 1 and 2, we observed a consistent relationship +between perplexity and neural predictivity, such that lower perplexity is associated with higher +predictivity. However, once a model reaches a certain level of perplexity, further improve- +ments in the model’s ability to predict the next word are no longer associated with increases +in predictivity, in line with recent findings (Oh & Schuler, 2022, 2023). + +DISCUSSION + +In this work, we investigated the relationship between the amount of training data and predic- +tivity of fMRI responses to sentences for transformer-based ANN language models. Our study +makes several contributions: (1) Even when trained on a developmentally realistic amount of +data, transformer language models align with human data; (2) alignment between untrained +ANN language models and human fMRI responses is strongly affected by the initial unit weight +configuration; and (3) model perplexity predicts brain scores. + +Performance on Developmentally Realistic Amount of Training Data + +Using an fMRI benchmark (Pereira et al., 2018), we established that even with a develop- +mentally realistic amount of training data (~100 million words, comparable to what humans + +Neurobiology of Language 54 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 + + +get during the first 10 years of life; Frank, 2023; Hart & Risley, 1992), a GPT-2 model +achieves near-maximal predictivity of fMRI responses to sentences. This effect generalizes +to a different model architecture (a bidirectional-attention transformer: RoBERTa), although, +compared to GPT-2, such models appear to be less sample efficient, requiring more training +data to achieve peak predictivity. (The result also generalizes to a behavioral reading-times +benchmark (Futrell et al., 2018); high performance of untrained models on this benchmark +prompted us to move these findings to the Supporting Information.) In a complementary +approach, we showed that when trained on a large dataset, a GPT-2 model already achieves +near-maximal predictivity with only 10% of the training steps, well before a full pass over +the dataset. + +These results align with prior work in vision. For example, Geiger et al. (2020) found that +even a small amount of training can result in model representations that are predictive of neu- +ral responses in macaques. Moreover, the logarithmic nature of the increase in predictivity +between a model trained on 1 million tokens and a model trained on 1 billion tokens aligns +with prior NLP results (e.g., see Kaplan et al., 2020, for evidence of a logarithmic relationship +between training data size and the loss in training, and between model size and loss), as well +as with vision research (e.g., see Geiger et al., 2020, for evidence of a logarithmic relationship +between training data size and predictivity of neural firing rates). + +The key implication of these findings is that although large language models are trained on +vast amounts of data (and performance on some NLP benchmarks continues to improve with +more training), this large amount of training is not necessary for these models to acquire rep- +resentations that are predictive of human brain responses and behavior. The fact that ANN +models trained on a developmentally plausible amount of data can accurately capture fMRI +responses to sentences helps address one of the most common criticisms of these models as +models of human language processing. + +Figure 3. Relationship between model perplexity and model ability to predict human brain responses to sentences. (A) Experiment 1 results: +the relationship between perplexity, i.e., the model’s ability to predict the next token in an independent dataset (wikitext-103-raw-v1), shown +on the x-axis, with lower values corresponding to better performance, and model performance in predicting language-responsive voxels’ acti- +vation in the Pereira2018 fMRI benchmark. The results are shown for (i) two versions of an untrained model (black dots; see Figure 2 caption +for details); and (ii) four models trained on datasets of different sizes (1M, 10M, 100M, and 1B words; blue-to-green dots connected by a line; +the model trained on a developmentally plausible amount of data—100 million words—is marked with a red asterisk). (B) Experiment 2 results: +the relationship between perplexity and model performance in predicting language-responsive voxels’ activation in the Pereira2018 fMRI +benchmark. The results are shown for (i) two versions of an untrained model (black dots; see Figure 2 caption for details); and (ii) a model +trained on a large dataset examined at different points during the training (0.1%, 1.0%, 10%, 100%, and 10 × 100% of training steps; purple- +to-yellow dots connected by a line). + +Neurobiology of Language 55 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://doi.org/10.1162/nol_a_00137 + + +Performance of Untrained Models + +By relating different versions of untrained models to human fMRI responses, this work clarifies +the contributions of architecture to the models’ ability to predict neural responses to linguistic +input. Schrimpf et al. (2021; see also Caucheteux & King, 2022; Pasquiou et al., 2022) have +found that untrained models predict fMRI responses quite well, albeit worse than trained +models. They speculated that good performance of untrained models might be due to the +smoothing of word embeddings across layers in a way that enables the embeddings to capture +some aspects of statistical regularities of language, perhaps something as general as nearby +words being likely to be related to one another. However, what counts as untrained is impor- +tant to clarify. + +Untrained models come with a particular setting of their unit weights. A particular weight +configuration may get “baked into” a model during the process of model development, aimed +at maximizing learning efficiency for the target task. Such potential biases in initial, pre-trained +weights may be akin to innate, evolution-shaped, aspects of brain structure, which may filter +information in specific ways as it travels within or across brain areas, even before any learning +of the input regularities has occurred (e.g., Zador, 2019). We showed that initializing a model +with a normal distribution for all weights leads to the model being unable to predict fMRI +response to sentences (predictivity is at ~0; of course, such a model is also unable to perform +the next-word prediction task). This inability to predict fMRI responses for models initialized +with a normal distribution is not due to the lack of activity propagation across layers, as shown +in Supplementary Figure 8B. We also showed that the standard deviation of weight initializa- +tion only has minimal effect on the predictivity for untrained model (Supplementary +Figure 8D). + +In summary, the ability of untrained models to predict fMRI responses to sentences reported +in previous studies should not be taken as evidence that model architecture alone (i.e., the +units and the patterns of connections among them) can capture human neural responses to +linguistic input, or at least, it should be acknowledged that these effects are due to the partic- +ular pre-trained weight configurations. Furthermore, if a model can (at least partially) match +human data with a few bits of information in the form of the initialization parameters (see +Supplementary Figure 8C for evidence that above-baseline predictivity for some initializations +may result from the representations for different sentences being more similar), then any results +at that alignment level or below for trained models are not meaningful and we should focus on +progress beyond that alignment level. Another implication is that future attempts to align +trained ANN models with human data should generalize their findings across different weight +initializations (Mehrer et al., 2020). + +Model Perplexity + +In line with Schrimpf et al.’s (2021) claim that models that perform better on next-word pre- +diction are better at predicting brain data (see also Caucheteux & King, 2022; cf. Antonello & +Huth, 2024), we found that model perplexity for different amounts of the training data is a +good proxy for model performance in predicting responses to sentences. We observed this +relationship both in Experiment 1, where we varied the size of the training dataset, and in +Experiment 2, where we tested model representations at different points during the training +on a large dataset. These findings provide further evidence that optimizing for predictive +representations—through training the models on the next-word prediction task—may be +critical for ANN models to acquire representations that are predictive of human responses +to linguistic input. + +Neurobiology of Language 56 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 +https://doi.org/10.1162/nol_a_00137 + + +This finding aligns well with earlier work, which showed that surprisal (how predictable a +word is from the preceding context), which is closely related to perplexity, is generally predic- +tive of human behavioral responses (e.g., Smith & Levy, 2013) and neural responses, as esti- +mated with EEG (e.g., Aurnhammer & Frank, 2019; Frank et al., 2015; Rabovsky et al., 2018), +magnetoencephalography (Brodbeck et al., 2022; Heilbron et al., 2022), fMRI (Brennan et al., +2016; Heilbron et al., 2022; Henderson et al., 2016; Lopopolo et al., 2017; Shain et al., 2020; +Willems et al., 2016), or intracranially (Goldstein et al., 2022) during language processing. +However, as recently shown in Tuckute et al. (2024), representations from language models +achieve substantially higher predictivity for fMRI response to sentences than more traditional +surprisal metrics based on n-gram counts or probabilistic context-free grammar parser +probabilities. + +One recent study (Pasquiou et al., 2022) did not observe a relationship between perplexity +and model ability to predict human fMRI responses to linguistic input. We speculate that the +lack of this relationship in Pasquiou et al.’s data may relate to the use of an extended-narrative +stimulus (i.e., the entire The Little Prince book) rather than single sentences or short passages. +The overall low encoding performance for such stimuli imposes a ceiling on the correlations +between model-to-brain alignment and model perplexity (or other variables), making it diffi- +cult to differentiate among models. Alternatively, humans and models may use different infor- +mation for predicting upcoming words, especially in extended linguistic stimuli (Oh & Schuler, +2022). + +Why models struggle with predicting neural responses to long narratives is a separate and +important question. We offer a speculation. In the human brain, division of labor exists +between (i) the language-selective network, which integrates information within +clauses/sentences but does not track longer-range contexts (e.g., Blank & Fedorenko, 2020), +and (ii) the default network(s) (Buckner & DiNicola, 2019), which integrates information over +extended temporal contexts (Lerner et al., 2011). Importantly, the default network does not +operate over word sequences; instead, the information that this system represents is likely +abstract, as evidenced by the fact that it processes long contexts in both linguistic and nonlin- +guistic stimuli (e.g., Baldassano et al., 2017; Simony et al., 2016). As a result, the ANN lan- +guage models (like those used in current work and in Pasquiou et al. (2022) may simply lack +representations that are sufficiently abstract (not directly tied to the stimulus, i.e., to the word +sequences) to match those in the default network, perhaps because language models eventu- +ally have to “go back” to specific words in order to perform the next-word prediction task. +Some of the newer models, like GPT-3, seem to be able to handle a greater degree of abstrac- +tion (Brown et al., 2020) and thus may be promising for future attempts to capture human +neural responses to long and complex linguistic stimuli. + +Limitations and Future Directions + +In general, it is challenging to compare the amount of training data that a model gets to the +amount of linguistic input that a child gets. The consequences of a single token of input for a +computational model depend on many aspects of the model’s architecture, training setup, and +so on; and of course, the fact that, for smaller datasets, the same dataset is repeated multiple +times during the training varies drastically from what humans experience. All the results should +therefore be interpreted in light of these limitations. + +Furthermore, we have here focused on the effects of the amount of training data on the +ANN language models’ ability to capture human responses to language. However, the nature +of the training data is, no doubt, also important. For example, training models on data that are + +Neurobiology of Language 57 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + + + +similar to what children are exposed to could lead to improved neural predictivity (Chang & +Bergen, 2021; Warstadt & Bowman, 2019). Indeed, this approach has been shown to improve +vision models’ ability to capture primate neural responses (Mehrer et al., 2021). It will also be +important to investigate the role of the learning algorithms that the models use and their train- +ing objective, as both likely affect the representations that the models learn (e.g., see Zhuang +et al., 2022, for evidence from vision). Specifically, Zhuang et al. (2022) showed that in an +object categorization task, the negative sampling objective function, which maximizes the +similarity between objects in the same category while minimizing the similarity between +objects in different categories in the internal representation of the model, can alleviate model +failure in capturing human visual behavior, which occurs under the standard objective func- +tion. This failure is due to the presence of categories that are infrequent in the training data, +and this finding can be relevant for language, which also contains infrequent elements (words +and constructions) amid more common ones. + +Another issue that we did not investigate here is the nature of various training parameters +(e.g., learning rate, batch size, randomization of context, length of context, etc.). Such +parameters can affect model performance on NLP tasks and, possibly, their ability to predict +human neural or behavioral responses to language. However, we suspect that the influence +of these parameters would be relatively minimal given the evidence from prior work that +model size, dataset size, and amount of computing power are the main contributors to model +performance after training, as measured in loss for predicting the next token (Kaplan et al., +2020), and that model size is the main contributor to model performance in predicting fMRI +responses to language (Antonello et al., 2023). + +Another aspect of the ANN models that is important for building accurate models of human +language processing is the model architecture. We here generalized our training effects across +uni- and bidirectional-attention transformers, but a systematic investigation of the effects of +diverse architectural parameters (e.g., the number and size of layers, number of attention +heads) on the models’ ability to predict human responses to language would be valuable. +Tightly controlled comparisons between different classes of model architectures are more +challenging but creating numerous model variants all trained on the same dataset (e.g., +Storrs et al., 2021) could enable identification of architectural motifs that are essential +for a good match with human neural and behavioral data. + +Perhaps the biggest limitation of this and related work is the obscurity of both the model +representations and human neural representations. It is not known what aspects of the repre- +sentations change as the models are trained on increasingly more data (aside from knowing +that these changes lead to improved performance on the next-word prediction task), and how +exactly these changes in the models’ representations of linguistic input impact their ability to +predict brain activation or behavioral processing difficulty. Some recent work has begun to +attempt isolating the aspects of model representations that affect model-to-brain alignment. +For example, Kauf et al. (2024) performed a series of experiments where model representations +were obtained for different perturbations of a linguistic stimulus (e.g., scrambling the word +order or dropping/replacing some of the words) and then related to neural representations +of an intact stimulus in order to see which perturbations affect model representations nega- +tively. They found that word-level and compositional semantic information appears to be more +important than information related to the syntactic structure in the model-to-brain alignment. +Still however, much about the details of how models vs. humans represent and process +linguistic stimuli remains to be discovered. Another exciting recent approach (Sexton & +Love, 2022) is to replace a part of a model (the one best aligned with human neural + +Neurobiology of Language 58 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + + + +responses) with fMRI signals to test whether parts of the model representation that align +with neural data affect model behavior. + +In future work, we aim to address these gaps in order to build increasingly more accurate +and interpretable models of language processing in the brain. + +ACKNOWLEDGMENTS + +We are grateful to Josh McDermott and members of the Fedorenko Lab (especially Carina +Kauf, Cory Shain, Greta Tuckute, and Chengxu Zhuang) for helpful discussions and comments +on the drafts of the manuscript; to Stella Biderman (EleutherAI) for help with the setup of Exper- +iment 1; and to Jason Bolton, Laurel Orr, and Siddharth Karamcheti for help with the setup of +Experiment 2. + +FUNDING INFORMATION + +Evelina Fedorenko, National Institute of Neurological Disorders and Stroke (https://dx.doi.org +/10.13039/100000065), Award ID: U01-NS121471. Eghbal A. Hosseini, McGovern Institute +for Brain Research, Massachusetts Institute of Technology (https://dx.doi.org/10.13039 +/100019335), Award ID: Friends of McGovern graduate fellowship. Martin Schrimpf, +McGovern Institute for Brain Research, Massachusetts Institute of Technology (https://dx +.doi.org/10.13039/100019335), Award ID: Friends of McGovern graduate fellowship. Noga +Zaslavsky, McGovern Institute for Brain Research, Massachusetts Institute of Technology +(https://dx.doi.org/10.13039/100019335), Award ID: K. Lisa Young ICoN Center post- +doctoral fellowship. Evelina Fedorenko, National Institute on Deafness and Other Commu- +nication Disorders (https://dx.doi.org/10.13039/100000055), Award ID: DC016607. Evelina +Fedorenko, National Institute on Deafness and Other Communication Disorders (https://dx.doi +.org/10.13039/100000055), Award ID: R01-DC016950. Evelina Fedorenko, McGovern Institute +for Brain Research, Massachusetts Institute of Technology (https://dx.doi.org/10.13039 +/100019335). Evelina Fedorenko, Simons Center for the Social Brain, Massachusetts Institute +of Technology (https://dx.doi.org/10.13039/100018792). Evelina Fedorenko, Massachusetts +Institute of Technology, Award ID: Middleton Professorship. + +AUTHOR CONTRIBUTIONS + +Eghbal A. Hosseini: Conceptualization: Lead; Data curation: Lead; Formal analysis: Lead; +Investigation: Lead; Methodology: Lead; Software: Lead; Validation: Lead; Visualization: Lead; +Writing – original draft: Lead; Writing – review & editing: Lead. Martin Schrimpf: Methodology: +Supporting; Software: Supporting; Writing – review & editing: Supporting. Yian Zhang: Methodology: +Supporting; Resources: Supporting; Software: Supporting. Samuel Bowman: Resources: Supporting; +Writing – review & editing: Supporting. Noga Zaslavsky: Conceptualization: Equal; Supervision: +Supporting; Writing – review & editing: Supporting. Evelina Fedorenko: Conceptualization: +Equal; Investigation: Supporting; Writing – original draft: Equal; Writing – review & editing: +Equal. + +DATA AND CODE AVAILABILITY STATEMENT + +The human benchmarks and the code for relating model representations to the benchmarks +are publicly available at https://github.com/mschrimpf/neural-nlp. The code for reproducing +the main figures in this study is available at https://github.com/eghbalhosseini/ann_brain +_alignment and accompanying data are available at https://osf.io/6bd85/. For Experiment 1, + +Neurobiology of Language 59 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +http://dx.doi.org/10.13039/100000065 +http://dx.doi.org/10.13039/100000065 +http://dx.doi.org/10.13039/100000065 +http://dx.doi.org/10.13039/100000065 +http://dx.doi.org/10.13039/100000065 +http://dx.doi.org/10.13039/100000065 +http://dx.doi.org/10.13039/100000065 +http://dx.doi.org/10.13039/100000065 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100000055 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100019335 +http://dx.doi.org/10.13039/100018792 +http://dx.doi.org/10.13039/100018792 +http://dx.doi.org/10.13039/100018792 +http://dx.doi.org/10.13039/100018792 +http://dx.doi.org/10.13039/100018792 +http://dx.doi.org/10.13039/100018792 +http://dx.doi.org/10.13039/100018792 +http://dx.doi.org/10.13039/100018792 +https://github.com/mschrimpf/neural-nlp +https://github.com/mschrimpf/neural-nlp +https://github.com/mschrimpf/neural-nlp +https://github.com/mschrimpf/neural-nlp +https://github.com/mschrimpf/neural-nlp +https://github.com/mschrimpf/neural-nlp +https://github.com/mschrimpf/neural-nlp +https://github.com/eghbalhosseini/ann_brain_alignment +https://github.com/eghbalhosseini/ann_brain_alignment +https://github.com/eghbalhosseini/ann_brain_alignment +https://github.com/eghbalhosseini/ann_brain_alignment +https://github.com/eghbalhosseini/ann_brain_alignment +https://github.com/eghbalhosseini/ann_brain_alignment +https://github.com/eghbalhosseini/ann_brain_alignment +https://github.com/eghbalhosseini/ann_brain_alignment +https://osf.io/6bd85/ +https://osf.io/6bd85/ +https://osf.io/6bd85/ +https://osf.io/6bd85/ +https://osf.io/6bd85/ +https://osf.io/6bd85/ + + +the GPT-2 models and the representations extracted for all the benchmarks are available at +https://osf.io/6bd85/; the miniBERTa models are available upon request; the checkpoints are +available at https://huggingface.co/nyu-mll. 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ArXiv. https://doi.org/10.48550/arXiv.1609 +.07843 + +Neurobiology of Language 61 + +Data-limited ANN models align with humans + +D +ow + +nloaded from + http://direct.m + +it.edu/nol/article-pdf/5/1/43/2361079/nol_a_00137.pdf by EPFL user on 05 M +arch 2025 + +https://doi.org/10.48550/arXiv.1910.01244 +https://doi.org/10.48550/arXiv.1910.01244 +https://doi.org/10.48550/arXiv.1910.01244 +https://doi.org/10.48550/arXiv.1910.01244 +https://doi.org/10.48550/arXiv.1910.01244 +https://doi.org/10.48550/arXiv.1910.01244 +https://doi.org/10.48550/arXiv.1910.01244 +https://doi.org/10.48550/arXiv.1910.01244 +https://doi.org/10.48550/arXiv.1910.01244 +https://doi.org/10.1101/2020.06.08.140111 +https://doi.org/10.1101/2020.06.08.140111 +https://doi.org/10.1101/2020.06.08.140111 +https://doi.org/10.1101/2020.06.08.140111 +https://doi.org/10.1101/2020.06.08.140111 +https://doi.org/10.1101/2020.06.08.140111 +https://doi.org/10.1101/2020.06.08.140111 +https://doi.org/10.1101/2020.06.08.140111 +https://doi.org/10.1101/2020.06.08.140111 +https://doi.org/10.1101/2020.06.08.140111 +https://doi.org/10.1044/2016_AJSLP-15-0169 +https://doi.org/10.1044/2016_AJSLP-15-0169 +https://doi.org/10.1044/2016_AJSLP-15-0169 +https://doi.org/10.1044/2016_AJSLP-15-0169 +https://doi.org/10.1 \ No newline at end of file diff --git a/tests/index/infoscience/test_chunker.py b/tests/index/infoscience/test_chunker.py new file mode 100644 index 0000000..232ee7c --- /dev/null +++ b/tests/index/infoscience/test_chunker.py @@ -0,0 +1,34 @@ +"""Token-aware chunker.""" + +from __future__ import annotations + +import tiktoken + +from src.index.infoscience.chunker import chunk_text +from src.index.infoscience.config import ChunkingConfig + + +def test_short_text_one_chunk() -> None: + cfg = ChunkingConfig(size_tokens=200, overlap_tokens=20) + out = chunk_text("Hello world. " * 5, cfg) + assert len(out) == 1 + assert out[0].startswith("Hello world.") + + +def test_paragraph_aware_split_respects_size() -> None: + cfg = ChunkingConfig(size_tokens=120, overlap_tokens=10) + enc = tiktoken.get_encoding(cfg.tokenizer) + paras = ["Lorem ipsum dolor sit amet. " * 30, + "Consectetur adipiscing elit. " * 30, + "Sed do eiusmod tempor incididunt. " * 30] + text = "\n\n".join(paras) + out = chunk_text(text, cfg) + assert len(out) >= 3 + for ch in out: + assert len(enc.encode(ch)) <= cfg.size_tokens + cfg.overlap_tokens + 5 + + +def test_empty_input_returns_empty_list() -> None: + cfg = ChunkingConfig(size_tokens=100, overlap_tokens=10) + assert chunk_text("", cfg) == [] + assert chunk_text(" \n\n ", cfg) == [] diff --git a/tests/index/infoscience/test_extract_matches.py b/tests/index/infoscience/test_extract_matches.py new file mode 100644 index 0000000..0c80bf5 --- /dev/null +++ b/tests/index/infoscience/test_extract_matches.py @@ -0,0 +1,109 @@ +"""Regex-based GitHub/HuggingFace URL extraction.""" + +from __future__ import annotations + +from pathlib import Path + +from src.index.infoscience.extract_matches import ( + _canonicalise, + classify_huggingface_url, + extract_matches, +) +from src.index.infoscience.config import ( + ChunkingConfig, + FilterConfig, + InfoscienceConfig, + InfoscienceIndexConfig, + QdrantConfig, + RcpConfig, +) +from src.index.infoscience.paths import ( + matches_path, + raw_items_dir, + text_dir, +) + + +def _stub_config() -> InfoscienceIndexConfig: + return InfoscienceIndexConfig( + rcp=RcpConfig( + base_url="https://stub/v1", + embedding_model="Qwen/Qwen3-Embedding-8B", + embedding_dim=4096, + query_instruction="x", + reranker_model="Qwen/Qwen3-Reranker-8B", + ), + infoscience=InfoscienceConfig(base_url="https://stub/api"), + filter=FilterConfig(terms=["github.com", "huggingface.co", "hf.co"]), + chunking=ChunkingConfig(), + qdrant=QdrantConfig(), + data_dir=Path("/tmp"), + ) + + +def test_classify_hf_model() -> None: + kind, canonical = classify_huggingface_url("https://huggingface.co/Qwen/Qwen3-Embedding-8B") + assert kind == "model" + assert canonical == "https://huggingface.co/Qwen/Qwen3-Embedding-8B" + + +def test_classify_hf_dataset_with_subpath() -> None: + kind, canonical = classify_huggingface_url( + "https://huggingface.co/datasets/squad/viewer/train", + ) + assert kind == "dataset" + # HF dataset slugs can be `owner/name`; keep the two head segments. + assert canonical == "https://huggingface.co/datasets/squad/viewer" + + +def test_classify_hf_dataset_simple() -> None: + kind, canonical = classify_huggingface_url( + "https://huggingface.co/datasets/openai/squad", + ) + assert kind == "dataset" + assert canonical == "https://huggingface.co/datasets/openai/squad" + + +def test_classify_hf_space() -> None: + kind, canonical = classify_huggingface_url("https://huggingface.co/spaces/foo/bar") + assert kind == "space" + assert canonical == "https://huggingface.co/spaces/foo/bar" + + +def test_classify_hf_short_host() -> None: + kind, canonical = classify_huggingface_url("https://hf.co/openai-community/gpt2") + assert kind == "model" + # _hostname check returns hf.co; canonical hard-codes huggingface.co. + assert "openai-community/gpt2" in canonical + + +def test_canonicalise_keeps_unsupported_github_url() -> None: + out = _canonicalise("https://github.com/sdsc-ordes/gimie/issues/42") + assert out == "https://github.com/sdsc-ordes/gimie/issues/42" + + +def test_canonicalise_normalises_repo_url() -> None: + out = _canonicalise("https://github.com/sdsc-ordes/gimie.git") + assert out == "https://github.com/sdsc-ordes/gimie" + + +def test_canonicalise_ignores_unrelated_host() -> None: + assert _canonicalise("https://example.com/x") is None + + +def test_extract_matches_end_to_end( + isolated_data_dir, sample_extracted_text: str, article_json: dict, +) -> None: + # Wire a single text + a corresponding raw item into the data dir. + uuid = article_json["uuid"] + (text_dir() / f"{uuid}.txt").write_text(sample_extracted_text, encoding="utf-8") + (raw_items_dir() / f"{uuid}.json").write_text(__import__("json").dumps(article_json), + encoding="utf-8") + + summary = extract_matches(_stub_config()) + assert summary["with_matches"] == 1 + line = matches_path().read_text(encoding="utf-8").strip() + assert uuid in line + # The fixture mentions both github.com and huggingface.co. + assert "github.com" in line + assert "huggingface.co" in line diff --git a/tests/index/infoscience/test_extract_relations.py b/tests/index/infoscience/test_extract_relations.py new file mode 100644 index 0000000..f48b283 --- /dev/null +++ b/tests/index/infoscience/test_extract_relations.py @@ -0,0 +1,60 @@ +"""Article → Person/Org authority-UUID extraction.""" + +from __future__ import annotations + +import json +from pathlib import Path + +from src.index.infoscience.config import ( + ChunkingConfig, + FilterConfig, + InfoscienceConfig, + InfoscienceIndexConfig, + QdrantConfig, + RcpConfig, +) +from src.index.infoscience.extract_relations import extract_relations +from src.index.infoscience.models import MatchRecord +from src.index.infoscience.paths import ( + matches_path, + organizations_set_path, + persons_set_path, + raw_items_dir, + relations_path, +) + + +def _stub_config() -> InfoscienceIndexConfig: + return InfoscienceIndexConfig( + rcp=RcpConfig(base_url="https://stub/v1", embedding_model="m", + embedding_dim=4096, query_instruction="x", reranker_model="r"), + infoscience=InfoscienceConfig(base_url="https://stub/api"), + filter=FilterConfig(terms=["github.com"]), + chunking=ChunkingConfig(), + qdrant=QdrantConfig(), + data_dir=Path("/tmp"), + ) + + +def test_extract_relations_pulls_authority_uuids(article_json: dict) -> None: + uuid = article_json["uuid"] + (raw_items_dir() / f"{uuid}.json").write_text(json.dumps(article_json), encoding="utf-8") + matches_path().write_text( + MatchRecord(uuid=uuid, matched_urls=["https://github.com/x/y"], + counts_by_host={"github.com": 1}).model_dump_json() + "\n", + encoding="utf-8", + ) + + summary = extract_relations(_stub_config()) + assert summary["articles"] == 1 + assert summary["persons"] >= 1 + assert summary["organizations"] >= 1 + + relation = json.loads(relations_path().read_text(encoding="utf-8").strip()) + assert relation["article_uuid"] == uuid + assert all(len(p) == 36 for p in relation["person_uuids"]) + assert all(len(o) == 36 for o in relation["org_uuids"]) + persons_listing = persons_set_path().read_text(encoding="utf-8").splitlines() + orgs_listing = organizations_set_path().read_text(encoding="utf-8").splitlines() + assert relation["person_uuids"][0] in persons_listing + assert relation["org_uuids"][0] in orgs_listing diff --git a/tests/index/infoscience/test_parsers.py b/tests/index/infoscience/test_parsers.py new file mode 100644 index 0000000..15cce1c --- /dev/null +++ b/tests/index/infoscience/test_parsers.py @@ -0,0 +1,34 @@ +"""DSpace JSON → record parsers exercise.""" + +from __future__ import annotations + +from src.index.infoscience.parsers import ( + parse_article, + parse_organization, + parse_person, +) + + +def test_parse_article(article_json: dict) -> None: + rec = parse_article(article_json, matched_urls=["https://github.com/x/y"]) + assert rec.article_uuid + assert rec.title + assert rec.authors, "expected at least one author name" + # The fixture has at least one author with an authority — UUIDs flow through. + assert any(rec.author_uuids) + assert rec.matched_urls == ["https://github.com/x/y"] + assert rec.infoscience_url.startswith("https://infoscience.epfl.ch/entities/publication/") + + +def test_parse_person(person_json: dict) -> None: + rec = parse_person(person_json) + assert rec.person_uuid + assert rec.name + assert rec.profile_url.startswith("https://infoscience.epfl.ch/entities/person/") + + +def test_parse_organization(organization_json: dict) -> None: + rec = parse_organization(organization_json) + assert rec.org_uuid + assert rec.name + assert rec.infoscience_url.startswith("https://infoscience.epfl.ch/entities/orgunit/") diff --git a/tests/index/infoscience/test_paths.py b/tests/index/infoscience/test_paths.py new file mode 100644 index 0000000..971ac8c --- /dev/null +++ b/tests/index/infoscience/test_paths.py @@ -0,0 +1,27 @@ +"""Path resolution honours INDEX_DATA_DIR.""" + +from __future__ import annotations + +from pathlib import Path + +from src.index.infoscience.paths import ( + infoscience_data_dir, + raw_items_dir, + raw_organizations_dir, + raw_persons_dir, + text_dir, + vector_db_dir, +) + + +def test_data_dir_uses_env_override(isolated_data_dir: Path) -> None: + root = infoscience_data_dir() + assert str(root).startswith(str(isolated_data_dir)) + assert root.name == "infoscience" + + +def test_subdirs_exist_after_call(isolated_data_dir: Path) -> None: + paths = [raw_items_dir(), raw_persons_dir(), raw_organizations_dir(), + text_dir(), vector_db_dir()] + for p in paths: + assert p.exists() and p.is_dir() diff --git a/tests/index/openalex/__init__.py b/tests/index/openalex/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/index/openalex/conftest.py b/tests/index/openalex/conftest.py new file mode 100644 index 0000000..d57141a --- /dev/null +++ b/tests/index/openalex/conftest.py @@ -0,0 +1,26 @@ +"""Shared fixtures for the OpenAlex index tests.""" + +from __future__ import annotations + +import pytest + +from src.index.openalex.config import OpenAlexIndexConfig, load_config +from src.index.openalex.storage.duckdb_store import DuckDBStore + + +@pytest.fixture() +def tmp_store(tmp_path) -> DuckDBStore: + """Fresh DuckDB store rooted in a tmp_path-isolated file.""" + db_path = tmp_path / "openalex.duckdb" + store = DuckDBStore(db_path) + store.bootstrap() + yield store + store.close() + + +@pytest.fixture() +def base_config(monkeypatch) -> OpenAlexIndexConfig: + """Config loaded from the real YAML, with non-empty required envs.""" + monkeypatch.setenv("OPENALEX_MAILTO", "tester@example.com") + monkeypatch.setenv("RCP_TOKEN", "test-token") + return load_config() diff --git a/tests/index/openalex/test_chunker.py b/tests/index/openalex/test_chunker.py new file mode 100644 index 0000000..3fb2e5c --- /dev/null +++ b/tests/index/openalex/test_chunker.py @@ -0,0 +1,80 @@ +"""Sliding-window chunker behavior.""" + +from __future__ import annotations + +import pytest + +from src.index.openalex.embed.chunker import ( + chunk_for_simple_entity, + chunk_for_work, + chunk_text, +) + + +@pytest.mark.openalex() +def test_empty_text_returns_no_chunks(): + assert chunk_text("", chunk_tokens=10, overlap=2) == [] + + +@pytest.mark.openalex() +def test_short_text_single_chunk(): + chunks = chunk_text("hello world", chunk_tokens=100, overlap=10) + assert len(chunks) == 1 + assert chunks[0].index == 0 + assert chunks[0].text == "hello world" + assert chunks[0].token_count > 0 + + +@pytest.mark.openalex() +def test_long_text_produces_multiple_chunks_with_overlap(): + text = " ".join(f"word{i}" for i in range(500)) + chunks = chunk_text(text, chunk_tokens=64, overlap=16) + assert len(chunks) > 1 + indices = [c.index for c in chunks] + assert indices == list(range(len(chunks))) + for chunk in chunks[:-1]: + assert chunk.token_count <= 64 + # Last chunk may be smaller if the encoded length isn't divisible. + assert chunks[-1].token_count <= 64 + + +@pytest.mark.openalex() +def test_invalid_overlap_raises(): + with pytest.raises(ValueError, match="overlap"): + chunk_text("abc", chunk_tokens=10, overlap=10) + + +@pytest.mark.openalex() +def test_invalid_chunk_tokens_raises(): + with pytest.raises(ValueError, match="chunk_tokens"): + chunk_text("abc", chunk_tokens=0, overlap=0) + + +@pytest.mark.openalex() +def test_chunk_for_work_concats_title_and_abstract(): + chunks = chunk_for_work( + "Hello", + "Detailed abstract.", + chunk_tokens=100, + overlap=10, + ) + assert len(chunks) == 1 + assert "Hello" in chunks[0].text + assert "Detailed abstract" in chunks[0].text + + +@pytest.mark.openalex() +def test_chunk_for_work_handles_missing_abstract(): + chunks = chunk_for_work( + "Just a title", + None, + chunk_tokens=100, + overlap=10, + ) + assert len(chunks) == 1 + assert chunks[0].text == "Just a title" + + +@pytest.mark.openalex() +def test_chunk_for_simple_entity_short_circuits_for_empty_input(): + assert chunk_for_simple_entity(None, None, chunk_tokens=10, overlap=2) == [] diff --git a/tests/index/openalex/test_config.py b/tests/index/openalex/test_config.py new file mode 100644 index 0000000..8c71750 --- /dev/null +++ b/tests/index/openalex/test_config.py @@ -0,0 +1,81 @@ +"""Config loader: YAML defaults + env-injected secrets + env overrides.""" + +from __future__ import annotations + +import pytest + +from src.index.openalex.config import ( + MISSING_MAILTO_ERROR, + MISSING_RCP_TOKEN_ERROR, + load_config, +) + + +@pytest.mark.openalex() +def test_defaults_from_yaml(monkeypatch): + monkeypatch.delenv("OPENALEX_MAILTO", raising=False) + monkeypatch.delenv("RCP_TOKEN", raising=False) + cfg = load_config() + assert cfg.rcp.base_url == "https://inference-rcp.epfl.ch/v1" + assert cfg.rcp.embedding_model == "Qwen/Qwen3-Embedding-8B" + assert cfg.rcp.embedding_dim == 4096 + assert cfg.rcp.reranker_model == "Qwen/Qwen3-Reranker-8B" + assert cfg.scope.ror == "https://ror.org/02s376052" + assert cfg.scope.country == "ch" + assert cfg.openalex.base_url == "https://api.openalex.org" + assert cfg.openalex.per_page == 200 + assert cfg.qdrant.url == "http://localhost:6333" + assert cfg.chunking.size_tokens == 256 + assert cfg.chunking.overlap_tokens == 64 + # No env tokens were set. + assert cfg.openalex.mailto is None + assert cfg.rcp.token is None + + +@pytest.mark.openalex() +def test_env_secrets_inject(monkeypatch): + monkeypatch.setenv("OPENALEX_MAILTO", "me@x.com") + monkeypatch.setenv("RCP_TOKEN", "secret") + monkeypatch.setenv("INDEX_QDRANT_API_KEY", "qkey") + cfg = load_config() + assert cfg.openalex.mailto == "me@x.com" + assert cfg.rcp.token == "secret" + assert cfg.qdrant.api_key == "qkey" + + +@pytest.mark.openalex() +def test_env_overrides_apply(monkeypatch): + monkeypatch.setenv("INDEX_QDRANT_URL", "http://qdrant.test:6333") + monkeypatch.setenv("INDEX_QDRANT_PREFER_GRPC", "true") + monkeypatch.setenv("INDEX_OPENALEX_SCOPE_ROR", "https://ror.org/0XXXX") + monkeypatch.setenv("INDEX_OPENALEX_SCOPE_COUNTRY", "de") + cfg = load_config() + assert cfg.qdrant.url == "http://qdrant.test:6333" + assert cfg.qdrant.prefer_grpc is True + assert cfg.scope.ror == "https://ror.org/0XXXX" + assert cfg.scope.country == "de" + + +@pytest.mark.openalex() +def test_require_ingest_blocks_when_mailto_missing(monkeypatch): + monkeypatch.delenv("OPENALEX_MAILTO", raising=False) + cfg = load_config() + with pytest.raises(ValueError) as exc: + cfg.require_ingest() + assert MISSING_MAILTO_ERROR in str(exc.value) + + +@pytest.mark.openalex() +def test_require_rcp_blocks_when_token_missing(monkeypatch): + monkeypatch.delenv("RCP_TOKEN", raising=False) + cfg = load_config() + with pytest.raises(ValueError) as exc: + cfg.require_rcp() + assert MISSING_RCP_TOKEN_ERROR in str(exc.value) + + +@pytest.mark.openalex() +def test_paths_attached_to_config(monkeypatch, tmp_path): + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path)) + cfg = load_config() + assert cfg.paths.root == tmp_path / "openalex" diff --git a/tests/index/openalex/test_duckdb_store.py b/tests/index/openalex/test_duckdb_store.py new file mode 100644 index 0000000..c9f9e0e --- /dev/null +++ b/tests/index/openalex/test_duckdb_store.py @@ -0,0 +1,131 @@ +"""DuckDB store: bootstrap, upserts, idempotency.""" + +from __future__ import annotations + +import pytest + +from src.index.openalex.storage.duckdb_store import DuckDBStore + + +@pytest.mark.openalex() +def test_bootstrap_idempotent(tmp_store: DuckDBStore): + tmp_store.bootstrap() # second call should be a no-op + assert tmp_store.count("works") == 0 + + +@pytest.mark.openalex() +def test_upsert_work_round_trip(tmp_store: DuckDBStore): + work = { + "openalex_id": "https://openalex.org/W1", + "doi": "10.1/x", + "title": "Hello", + "abstract": "World.", + "publication_year": 2024, + "primary_topic_id": None, + "primary_source_id": None, + } + tmp_store.upsert_work(work, raw={"id": work["openalex_id"]}) + assert tmp_store.count("works") == 1 + row = tmp_store.fetch_work("https://openalex.org/W1") + assert row["title"] == "Hello" + assert row["publication_year"] == 2024 + + # Re-upsert with a changed title — same row. + work["title"] = "Hello v2" + tmp_store.upsert_work(work, raw={"id": work["openalex_id"]}) + assert tmp_store.count("works") == 1 + assert tmp_store.fetch_work("https://openalex.org/W1")["title"] == "Hello v2" + + +@pytest.mark.openalex() +def test_upsert_github_url_idempotent(tmp_store: DuckDBStore): + tmp_store.upsert_github_url( + work_id="W1", + url="https://github.com/owner/repo", + normalized_url="https://github.com/owner/repo", + owner="owner", + repo="repo", + source="abstract", + ) + tmp_store.upsert_github_url( + work_id="W1", + url="https://github.com/owner/repo", + normalized_url="https://github.com/owner/repo", + owner="owner", + repo="repo", + source="abstract", + ) + n = tmp_store.connect().execute( + "SELECT count(*) FROM work_github_urls", + ).fetchone()[0] + assert n == 1 + + +@pytest.mark.openalex() +def test_transaction_commits_on_success(tmp_store: DuckDBStore): + with tmp_store.transaction(): + for i in range(3): + tmp_store.upsert_work( + { + "openalex_id": f"W{i}", + "doi": None, + "title": f"T{i}", + "abstract": None, + "publication_year": 2024, + "primary_topic_id": None, + "primary_source_id": None, + }, + raw={"id": f"W{i}"}, + ) + assert tmp_store.count("works") == 3 + + +@pytest.mark.openalex() +def test_transaction_rolls_back_on_error(tmp_store: DuckDBStore): + class BoomError(Exception): + pass + + with pytest.raises(BoomError): + with tmp_store.transaction(): + tmp_store.upsert_work( + { + "openalex_id": "Wgood", + "doi": None, + "title": "ok", + "abstract": None, + "publication_year": 2024, + "primary_topic_id": None, + "primary_source_id": None, + }, + raw={"id": "Wgood"}, + ) + raise BoomError + # The good upsert was rolled back. + assert tmp_store.count("works") == 0 + + +@pytest.mark.openalex() +def test_chunks_unique_constraint_blocks_duplicate_indexes(tmp_store: DuckDBStore): + tmp_store.upsert_chunk( + chunk_id="c1", + entity_type="works", + entity_id="W1", + chunk_index=0, + text="hello", + token_count=1, + vector_id="c1", + ) + # Same chunk_id → upsert overwrites text/token_count/vector_id, no error. + tmp_store.upsert_chunk( + chunk_id="c1", + entity_type="works", + entity_id="W1", + chunk_index=0, + text="updated", + token_count=2, + vector_id="c1", + ) + text = tmp_store.connect().execute( + "SELECT text FROM chunks WHERE chunk_id = 'c1'", + ).fetchone()[0] + assert text == "updated" diff --git a/tests/index/openalex/test_github_extract.py b/tests/index/openalex/test_github_extract.py new file mode 100644 index 0000000..c20b5e5 --- /dev/null +++ b/tests/index/openalex/test_github_extract.py @@ -0,0 +1,71 @@ +"""URL regex extraction + canonicalization via the v2 classifier.""" + +from __future__ import annotations + +import pytest + +from src.index.openalex.ingest.github_extract import ( + extract_and_persist_for_work, + extract_github_urls, +) + + +@pytest.mark.openalex() +def test_extracts_basic_url(): + urls = extract_github_urls( + "Code at https://github.com/sdsc-ordes/gimie is available." + ) + assert urls == ["https://github.com/sdsc-ordes/gimie"] + + +@pytest.mark.openalex() +def test_strips_trailing_punctuation(): + urls = extract_github_urls( + "See https://github.com/owner/repo. And also (https://github.com/owner/two)." + ) + assert "https://github.com/owner/repo" in urls + assert "https://github.com/owner/two" in urls + + +@pytest.mark.openalex() +def test_handles_www_subdomain(): + urls = extract_github_urls("Visit https://www.github.com/owner/repo for code.") + assert urls == ["https://www.github.com/owner/repo"] + + +@pytest.mark.openalex() +def test_persists_canonicalized_url(tmp_store): + text = ( + "Source code is available at https://github.com/sdsc-ordes/gimie.git " + "and the issue tracker at " + "https://github.com/sdsc-ordes/gimie/issues/42." + ) + persisted = extract_and_persist_for_work( + tmp_store, + work_id="https://openalex.org/W1", + text=text, + source="abstract", + ) + # The same canonical repo URL is dedup'd across both .git suffix and + # /issues/ subresource paths. + assert persisted == 1 + rows = tmp_store.connect().execute( + "SELECT normalized_url, owner, repo, source FROM work_github_urls", + ).fetchall() + assert len(rows) == 1 + norm, owner, repo, source = rows[0] + assert norm == "https://github.com/sdsc-ordes/gimie" + assert owner == "sdsc-ordes" + assert repo == "gimie" + assert source == "abstract" + + +@pytest.mark.openalex() +def test_invalid_source_raises(tmp_store): + with pytest.raises(ValueError, match="Invalid source"): + extract_and_persist_for_work( + tmp_store, + work_id="W1", + text="x", + source="bogus", + ) diff --git a/tests/index/openalex/test_paths.py b/tests/index/openalex/test_paths.py new file mode 100644 index 0000000..66d8811 --- /dev/null +++ b/tests/index/openalex/test_paths.py @@ -0,0 +1,34 @@ +"""Path resolution: env override + default fallback.""" + +from __future__ import annotations + +import pytest + +from src.index.openalex.paths import get_openalex_paths + + +@pytest.mark.openalex() +def test_default_path_under_repo_root(monkeypatch): + monkeypatch.delenv("INDEX_DATA_DIR", raising=False) + paths = get_openalex_paths() + assert paths.root.name == "openalex" + assert paths.duckdb_path.name == "openalex.duckdb" + assert paths.duckdb_dir.exists() + + +@pytest.mark.openalex() +def test_env_override(monkeypatch, tmp_path): + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path / "myindex")) + paths = get_openalex_paths() + assert paths.root == tmp_path / "myindex" / "openalex" + assert paths.duckdb_dir.exists() + assert paths.cache_dir.exists() + assert paths.logs_dir.exists() + + +@pytest.mark.openalex() +def test_relative_env_resolves_against_repo(monkeypatch): + monkeypatch.setenv("INDEX_DATA_DIR", "data/index_test") + paths = get_openalex_paths() + assert paths.root.is_absolute() + assert paths.root.name == "openalex" diff --git a/tests/index/openalex/test_rebuild_qdrant.py b/tests/index/openalex/test_rebuild_qdrant.py new file mode 100644 index 0000000..6b6d2eb --- /dev/null +++ b/tests/index/openalex/test_rebuild_qdrant.py @@ -0,0 +1,216 @@ +"""rebuild_qdrant_from_chunks: SQL JOIN, payload shape, chunk_id as point id.""" + +from __future__ import annotations + +from typing import Any +from unittest.mock import patch + +import pytest + +from src.index.openalex.embed import pipeline + + +def _seed_work_with_chunks(store, *, work_id: str, title: str, year: int, n_chunks: int): + store.upsert_work( + { + "openalex_id": work_id, + "doi": None, + "title": title, + "abstract": "abs", + "publication_year": year, + "primary_topic_id": None, + "primary_source_id": None, + }, + raw={"id": work_id}, + ) + for i in range(n_chunks): + store.upsert_chunk( + chunk_id=f"chunk-{work_id}-{i}", + entity_type="works", + entity_id=work_id, + chunk_index=i, + text=f"text-{i}", + token_count=3, + vector_id=f"chunk-{work_id}-{i}", + ) + + +def _seed_author_with_chunks(store, *, author_id: str, name: str, n_chunks: int): + store.upsert_author( + { + "openalex_id": author_id, + "display_name": name, + "orcid": None, + "last_known_institution_id": None, + }, + raw={"id": author_id}, + ) + for i in range(n_chunks): + store.upsert_chunk( + chunk_id=f"chunk-{author_id}-{i}", + entity_type="authors", + entity_id=author_id, + chunk_index=i, + text=f"text-{i}", + token_count=3, + vector_id=f"chunk-{author_id}-{i}", + ) + + +class _FakeRCPClient: + """Records calls so the test can assert on batching behaviour.""" + + def __init__(self, *_args, batch_size: int | None = None, **_kw): + self._batch_size = batch_size or 32 + self.calls: list[list[str]] = [] + + @property + def batch_size(self) -> int: + return self._batch_size + + async def embed_all(self, texts: list[str]) -> list[list[float]]: + self.calls.append(list(texts)) + return [[float(i)] * 4 for i, _ in enumerate(texts)] + + +class _FakeQdrant: + def __init__(self, *_args, **_kw): + self.collections: set[str] = set() + self.upserts: list[dict[str, Any]] = [] + + def ensure_collection(self, name: str) -> None: + self.collections.add(name) + + def upsert_points( + self, + collection: str, + *, + ids: list[str], + vectors: list[list[float]], + payloads: list[dict[str, Any]], + ) -> None: + self.upserts.append( + {"collection": collection, "ids": list(ids), "payloads": list(payloads)}, + ) + + +@pytest.fixture() +def fake_clients(monkeypatch): + fake_rcp = _FakeRCPClient() + fake_qdrant = _FakeQdrant() + monkeypatch.setattr(pipeline, "RCPEmbeddingClient", lambda *a, **k: fake_rcp) + monkeypatch.setattr(pipeline, "QdrantStore", lambda *a, **k: fake_qdrant) + return fake_rcp, fake_qdrant + + +@pytest.mark.openalex() +def test_rebuild_works_payload_includes_title_and_year(tmp_store, base_config, fake_clients): + fake_rcp, fake_qdrant = fake_clients + _seed_work_with_chunks(tmp_store, work_id="W1", title="Some Title", year=2024, n_chunks=2) + + summary = pipeline.rebuild_qdrant_from_chunks( + config=base_config, + store=tmp_store, + entity_types=["works"], + ) + + assert summary == {"works": 2} + assert "works" in fake_qdrant.collections + upsert = fake_qdrant.upserts[0] + assert upsert["collection"] == "works" + assert upsert["ids"] == ["chunk-W1-0", "chunk-W1-1"] + p0 = upsert["payloads"][0] + assert p0["entity_type"] == "works" + assert p0["entity_id"] == "W1" + assert p0["openalex_id"] == "W1" + assert p0["chunk_index"] == 0 + assert p0["title"] == "Some Title" + assert p0["year"] == 2024 + assert "display_name" not in p0 + + +@pytest.mark.openalex() +def test_rebuild_authors_payload_uses_display_name(tmp_store, base_config, fake_clients): + _, fake_qdrant = fake_clients + _seed_author_with_chunks(tmp_store, author_id="A1", name="Jane Doe", n_chunks=1) + + summary = pipeline.rebuild_qdrant_from_chunks( + config=base_config, + store=tmp_store, + entity_types=["authors"], + ) + + assert summary == {"authors": 1} + p = fake_qdrant.upserts[0]["payloads"][0] + assert p["entity_type"] == "authors" + assert p["display_name"] == "Jane Doe" + assert "title" not in p + assert "year" not in p + + +@pytest.mark.openalex() +def test_rebuild_batches_by_rcp_batch_size(tmp_store, base_config, monkeypatch): + # 5 chunks, batch_size 2 → expect 3 RCP calls of sizes [2, 2, 1]. + _seed_work_with_chunks(tmp_store, work_id="W1", title="t", year=2024, n_chunks=5) + + fake_rcp = _FakeRCPClient(batch_size=2) + fake_qdrant = _FakeQdrant() + monkeypatch.setattr(pipeline, "RCPEmbeddingClient", lambda *a, **k: fake_rcp) + monkeypatch.setattr(pipeline, "QdrantStore", lambda *a, **k: fake_qdrant) + + summary = pipeline.rebuild_qdrant_from_chunks( + config=base_config, + store=tmp_store, + entity_types=["works"], + ) + + assert summary == {"works": 5} + assert [len(call) for call in fake_rcp.calls] == [2, 2, 1] + assert [len(u["ids"]) for u in fake_qdrant.upserts] == [2, 2, 1] + + +@pytest.mark.openalex() +def test_rebuild_skips_chunks_with_unknown_entity_id(tmp_store, base_config, fake_clients): + # Chunk references a work that doesn't exist → JOIN drops it silently. + tmp_store.upsert_chunk( + chunk_id="orphan-1", + entity_type="works", + entity_id="W-missing", + chunk_index=0, + text="orphan", + token_count=1, + vector_id="orphan-1", + ) + _seed_work_with_chunks(tmp_store, work_id="W1", title="t", year=2024, n_chunks=1) + + _, fake_qdrant = fake_clients + summary = pipeline.rebuild_qdrant_from_chunks( + config=base_config, + store=tmp_store, + entity_types=["works"], + ) + + assert summary == {"works": 1} + assert fake_qdrant.upserts[0]["ids"] == ["chunk-W1-0"] + + +@pytest.mark.openalex() +def test_rebuild_no_chunks_returns_zero(tmp_store, base_config, fake_clients): + _, fake_qdrant = fake_clients + summary = pipeline.rebuild_qdrant_from_chunks( + config=base_config, + store=tmp_store, + entity_types=["works", "authors"], + ) + assert summary == {"works": 0, "authors": 0} + assert fake_qdrant.upserts == [] + + +@pytest.mark.openalex() +def test_rebuild_rejects_unknown_entity_type(tmp_store, base_config, fake_clients): + with pytest.raises(ValueError, match="Unknown entity_type"): + pipeline.rebuild_qdrant_from_chunks( + config=base_config, + store=tmp_store, + entity_types=["nope"], + ) diff --git a/tests/index/openalex/test_scope_filters.py b/tests/index/openalex/test_scope_filters.py new file mode 100644 index 0000000..71a1c26 --- /dev/null +++ b/tests/index/openalex/test_scope_filters.py @@ -0,0 +1,49 @@ +"""Scope filter shapes.""" + +from __future__ import annotations + +import pytest + +from src.index.openalex.config import OpenAlexIndexConfig +from src.index.openalex.ingest.scope import ( + epfl_scope, + resolve_scope, + switzerland_scope, +) + + +@pytest.mark.openalex() +def test_epfl_works_filter_uses_ror(base_config: OpenAlexIndexConfig): + scope = epfl_scope(base_config) + assert scope.works == { + "authorships": { + "institutions": {"ror": "https://ror.org/02s376052"}, + }, + } + assert scope.institutions == {"ror": "https://ror.org/02s376052"} + # Sources cannot be filtered by ROR — pulled unfiltered. + assert scope.sources == {} + + +@pytest.mark.openalex() +def test_switzerland_works_filter_uses_country_code( + base_config: OpenAlexIndexConfig, +): + scope = switzerland_scope(base_config) + assert scope.works == { + "authorships": {"institutions": {"country_code": "ch"}}, + } + assert scope.institutions == {"country_code": "ch"} + assert scope.sources == {} + + +@pytest.mark.openalex() +def test_resolve_scope_dispatches(base_config: OpenAlexIndexConfig): + assert resolve_scope("epfl", base_config) == epfl_scope(base_config) + assert resolve_scope("switzerland", base_config) == switzerland_scope(base_config) + + +@pytest.mark.openalex() +def test_resolve_scope_unknown(base_config: OpenAlexIndexConfig): + with pytest.raises(ValueError, match="Unknown scope"): + resolve_scope("germany", base_config) # type: ignore[arg-type] diff --git a/tests/index/openalex/test_sql_retrieval.py b/tests/index/openalex/test_sql_retrieval.py new file mode 100644 index 0000000..0e22c19 --- /dev/null +++ b/tests/index/openalex/test_sql_retrieval.py @@ -0,0 +1,90 @@ +"""Read-only SQL surface: predefined queries + ad-hoc guard.""" + +from __future__ import annotations + +import pytest + +from src.index.openalex.retrieval.sql import run_adhoc, run_predefined + + +@pytest.mark.openalex() +def test_adhoc_select_passes(tmp_store): + tmp_store.upsert_work( + { + "openalex_id": "W1", + "doi": None, + "title": "X", + "abstract": None, + "publication_year": 2024, + "primary_topic_id": None, + "primary_source_id": None, + }, + raw={"id": "W1"}, + ) + rows = run_adhoc("SELECT openalex_id FROM works", store=tmp_store) + assert rows == [{"openalex_id": "W1"}] + + +@pytest.mark.openalex() +def test_adhoc_with_cte_passes(tmp_store): + rows = run_adhoc( + "WITH x AS (SELECT 1 AS n) SELECT n FROM x", + store=tmp_store, + ) + assert rows == [{"n": 1}] + + +@pytest.mark.openalex() +@pytest.mark.parametrize( + "bad_sql", + [ + "DROP TABLE works", + "DELETE FROM works", + "ATTACH 'foo.db' AS f", + "PRAGMA database_list", + "INSERT INTO works VALUES (1)", + "UPDATE works SET title = 'x'", + " CREATE TABLE x (a INT)", + ], +) +def test_adhoc_rejects_dangerous_statements(tmp_store, bad_sql: str): + with pytest.raises(ValueError): + run_adhoc(bad_sql, store=tmp_store) + + +@pytest.mark.openalex() +def test_predefined_count_by_entity(tmp_store): + rows = run_predefined("count_by_entity", store=tmp_store) + entities = {r["entity"] for r in rows} + assert "works" in entities + assert "work_github_urls" in entities + + +@pytest.mark.openalex() +def test_predefined_unknown(tmp_store): + with pytest.raises(ValueError, match="Unknown predefined"): + run_predefined("nope", store=tmp_store) + + +@pytest.mark.openalex() +def test_predefined_top_works_by_year(tmp_store): + for i, year in enumerate([2024, 2024, 2023]): + tmp_store.upsert_work( + { + "openalex_id": f"W{i}", + "doi": None, + "title": f"T{i}", + "abstract": None, + "publication_year": year, + "primary_topic_id": None, + "primary_source_id": None, + }, + raw={"id": f"W{i}"}, + ) + rows = run_predefined( + "top_works_by_year", + params={"year": 2024, "limit": 10}, + store=tmp_store, + ) + assert len(rows) == 2 + assert all(r["publication_year"] == 2024 for r in rows) diff --git a/tests/index/openalex/test_works_ingest.py b/tests/index/openalex/test_works_ingest.py new file mode 100644 index 0000000..9b4d001 --- /dev/null +++ b/tests/index/openalex/test_works_ingest.py @@ -0,0 +1,76 @@ +"""Works ingestion: abstract reconstruction + persistence.""" + +from __future__ import annotations + +import pytest + +from src.index.openalex.ingest.works import ( + persist_work, + reconstruct_abstract, +) + + +@pytest.mark.openalex() +def test_reconstruct_abstract_simple(): + inverted = {"hello": [0], "world": [1]} + assert reconstruct_abstract(inverted) == "hello world" + + +@pytest.mark.openalex() +def test_reconstruct_abstract_repeated_token(): + inverted = {"the": [0, 2], "cat": [1], "sat": [3]} + assert reconstruct_abstract(inverted) == "the cat the sat" + + +@pytest.mark.openalex() +def test_reconstruct_abstract_empty(): + assert reconstruct_abstract(None) is None + assert reconstruct_abstract({}) is None + + +@pytest.mark.openalex() +def test_persist_work_writes_join_tables(tmp_store): + item = { + "id": "https://openalex.org/W1", + "doi": "10.1/x", + "title": "Hello", + "abstract_inverted_index": {"abstract": [0], "text": [1]}, + "publication_year": 2024, + "primary_topic": {"id": "https://openalex.org/T1"}, + "primary_location": {"source": {"id": "https://openalex.org/S1"}}, + "authorships": [ + { + "author": {"id": "https://openalex.org/A1"}, + "institutions": [{"id": "https://openalex.org/I1"}], + }, + { + "author": {"id": "https://openalex.org/A2"}, + "institutions": [{"id": "https://openalex.org/I1"}], + }, + ], + } + work_id = persist_work(tmp_store, item) + assert work_id == "https://openalex.org/W1" + + row = tmp_store.fetch_work("https://openalex.org/W1") + assert row["abstract"] == "abstract text" + assert row["primary_topic_id"] == "https://openalex.org/T1" + assert row["primary_source_id"] == "https://openalex.org/S1" + + n_authors = tmp_store.connect().execute( + "SELECT count(*) FROM work_authors WHERE work_id = ?", + [work_id], + ).fetchone()[0] + assert n_authors == 2 + + n_inst = tmp_store.connect().execute( + "SELECT count(*) FROM work_institutions WHERE work_id = ?", + [work_id], + ).fetchone()[0] + assert n_inst == 1 # deduped + + +@pytest.mark.openalex() +def test_persist_work_skips_when_no_id(tmp_store): + assert persist_work(tmp_store, {"title": "no id"}) is None + assert tmp_store.count("works") == 0 diff --git a/tests/index/orcid/__init__.py b/tests/index/orcid/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/index/orcid/conftest.py b/tests/index/orcid/conftest.py new file mode 100644 index 0000000..7e0d8bd --- /dev/null +++ b/tests/index/orcid/conftest.py @@ -0,0 +1,27 @@ +"""Shared fixtures for the ORCID index tests.""" + +from __future__ import annotations + +import pytest + +from src.index.orcid.config import OrcidIndexConfig, load_config +from src.index.orcid.storage.duckdb_store import OrcidDuckDBStore + + +@pytest.fixture() +def tmp_store(tmp_path) -> OrcidDuckDBStore: + """Fresh DuckDB store rooted in a tmp_path-isolated file.""" + db_path = tmp_path / "orcid.duckdb" + store = OrcidDuckDBStore(db_path) + store.bootstrap() + yield store + store.close() + + +@pytest.fixture() +def base_config(monkeypatch, tmp_path) -> OrcidIndexConfig: + """Config loaded from the real YAML, with non-empty required envs and + paths redirected into tmp_path.""" + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path)) + monkeypatch.setenv("RCP_TOKEN", "test-token") + return load_config(scope="epfl") diff --git a/tests/index/orcid/test_chunker.py b/tests/index/orcid/test_chunker.py new file mode 100644 index 0000000..acb8ae1 --- /dev/null +++ b/tests/index/orcid/test_chunker.py @@ -0,0 +1,56 @@ +"""Tests for ORCID-specific text builders + chunking.""" + +from __future__ import annotations + +from src.index.orcid.embed.chunker import ( + chunk_for_affiliation, + chunk_for_person, + person_card_text, +) + + +def test_person_card_text_includes_name_bio_affiliations() -> None: + row = { + "display_name": "Alice Example", + "biography": "Researches quantum graph compilers.", + } + text = person_card_text(row, ["EPFL", "ETHZ"]) + assert text is not None + assert "Alice Example" in text + assert "quantum graph compilers" in text + assert "EPFL" in text and "ETHZ" in text + + +def test_person_card_text_returns_none_when_empty() -> None: + assert person_card_text({}, []) is None + + +def test_chunk_for_person_yields_at_least_one_chunk() -> None: + row = {"display_name": "Alice Example", "biography": "Quantum compilers."} + chunks = chunk_for_person(row, ["EPFL"], chunk_tokens=64, overlap=8) + assert len(chunks) >= 1 + assert chunks[0].text.startswith("Alice Example") + + +def test_chunk_for_affiliation_uses_role_and_dates() -> None: + row = { + "organization": "EPFL", + "department": "School of Engineering", + "role": "Research Engineer", + "start_date": "2021-01-01", + "end_date": None, + } + chunks = chunk_for_affiliation( + "Alice Example", + row, + chunk_tokens=128, + overlap=8, + ) + assert len(chunks) == 1 + body = chunks[0].text + assert "Alice Example" in body + assert "Research Engineer" in body + assert "EPFL" in body + assert "School of Engineering" in body + assert "2021-01-01" in body + assert "present" in body diff --git a/tests/index/orcid/test_duckdb_store.py b/tests/index/orcid/test_duckdb_store.py new file mode 100644 index 0000000..031a93d --- /dev/null +++ b/tests/index/orcid/test_duckdb_store.py @@ -0,0 +1,119 @@ +"""Tests for OrcidDuckDBStore lifecycle + upsert semantics.""" + +from __future__ import annotations + + +def test_bootstrap_creates_tables(tmp_store) -> None: + expected = {"persons", "employments", "educations", "seeds", "chunks"} + cur = tmp_store.connect().execute( + "SELECT table_name FROM information_schema.tables " + "WHERE table_schema = 'main'", + ) + actual = {row[0] for row in cur.fetchall()} + assert expected.issubset(actual) + + +def test_upsert_seed_promotes_to_both(tmp_store) -> None: + tmp_store.upsert_seed(orcid_id="0000-0002-1825-0097", discovered_via="openalex") + tmp_store.upsert_seed(orcid_id="0000-0002-1825-0097", discovered_via="orcid_search") + cur = tmp_store.connect().execute( + "SELECT discovered_via FROM seeds WHERE orcid_id = ?", + ["0000-0002-1825-0097"], + ) + assert cur.fetchone()[0] == "both" + + +def test_upsert_person_then_replace_affiliations(tmp_store) -> None: + person_row = { + "orcid_id": "0000-0002-1825-0097", + "given_name": "Alice", + "family_name": "Example", + "display_name": "Alice Example", + "biography": None, + "in_scope": True, + "scope_reason": "test", + "discovered_via": "manual", + } + tmp_store.upsert_person(person_row, raw={"name": "Alice Example"}) + affiliations = [ + { + "orcid_id": "0000-0002-1825-0097", + "seq": 0, + "organization": "EPFL", + "org_ror": None, + "department": None, + "role": "Researcher", + "start_date": "2020-01-01", + "end_date": None, + }, + { + "orcid_id": "0000-0002-1825-0097", + "seq": 1, + "organization": "ETHZ", + "org_ror": None, + "department": None, + "role": "Postdoc", + "start_date": "2018-01-01", + "end_date": "2019-12-31", + }, + ] + tmp_store.replace_affiliations("employments", "0000-0002-1825-0097", affiliations) + rows = tmp_store.list_employments("0000-0002-1825-0097") + assert len(rows) == 2 + assert {r["organization"] for r in rows} == {"EPFL", "ETHZ"} + + # Replace-all: a fresh write with one row should drop the second. + tmp_store.replace_affiliations( + "employments", + "0000-0002-1825-0097", + affiliations[:1], + ) + rows = tmp_store.list_employments("0000-0002-1825-0097") + assert len(rows) == 1 + assert rows[0]["organization"] == "EPFL" + + +def test_stream_rows_for_embedding_skips_already_chunked(tmp_store) -> None: + person_row = { + "orcid_id": "0000-0002-1825-0097", + "given_name": "Alice", + "family_name": "Example", + "display_name": "Alice Example", + "biography": None, + "in_scope": True, + "scope_reason": "test", + "discovered_via": "manual", + } + tmp_store.upsert_person(person_row, raw={"name": "Alice Example"}) + pending = list(tmp_store.stream_rows_for_embedding("persons")) + assert len(pending) == 1 + + tmp_store.upsert_chunk( + chunk_id="x", + entity_type="persons", + entity_id="0000-0002-1825-0097", + chunk_index=0, + text="Alice Example", + token_count=2, + vector_id="x", + ) + pending_after = list(tmp_store.stream_rows_for_embedding("persons")) + assert pending_after == [] + + +def test_in_scope_filter_excludes_out_of_scope(tmp_store) -> None: + tmp_store.upsert_person( + { + "orcid_id": "0000-0001-3456-7898", + "given_name": None, + "family_name": None, + "display_name": "Out of Scope", + "biography": None, + "in_scope": False, + "scope_reason": None, + "discovered_via": "manual", + }, + raw={}, + ) + pending = list(tmp_store.stream_rows_for_embedding("persons")) + assert pending == [] diff --git a/tests/index/orcid/test_scope.py b/tests/index/orcid/test_scope.py new file mode 100644 index 0000000..a3c7ad8 --- /dev/null +++ b/tests/index/orcid/test_scope.py @@ -0,0 +1,65 @@ +"""Post-filter tests for the ORCID scope module.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import pytest + +from src.index.orcid.ingest.scope import post_filter_record + +FIXTURE_DIR = Path("tests/v2/fixtures/providers/orcid") + + +def _load(name: str) -> dict: + return json.loads((FIXTURE_DIR / name).read_text(encoding="utf-8")) + + +def test_epfl_scope_matches_alias(base_config) -> None: + record = _load("valid_record.json") + in_scope, reason = post_filter_record( + record, + scope="epfl", + config=base_config, + discovered_via="orcid_search", + ) + assert in_scope is True + assert reason is not None + assert "polytechnique" in reason.lower() or "epfl" in reason.lower() + + +def test_epfl_scope_drops_unrelated(base_config) -> None: + record = _load("no_affiliations.json") + in_scope, reason = post_filter_record( + record, + scope="epfl", + config=base_config, + discovered_via="orcid_search", + ) + assert in_scope is False + assert reason is None + + +def test_switzerland_trusts_openalex_seed(base_config) -> None: + record = _load("no_affiliations.json") + in_scope, reason = post_filter_record( + record, + scope="switzerland", + config=base_config, + discovered_via="openalex", + ) + assert in_scope is True + assert reason is not None + assert "openalex" in reason.lower() + + +def test_unknown_scope_raises(base_config) -> None: + record = _load("valid_record.json") + with pytest.raises(ValueError, match="Unknown scope"): + post_filter_record( + record, + scope="mars", # type: ignore[arg-type] + config=base_config, + discovered_via="manual", + ) diff --git a/tests/index/ror/__init__.py b/tests/index/ror/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/index/ror/conftest.py b/tests/index/ror/conftest.py new file mode 100644 index 0000000..672e14f --- /dev/null +++ b/tests/index/ror/conftest.py @@ -0,0 +1,19 @@ +from __future__ import annotations + +import json +from pathlib import Path + +import pytest + +FIXTURES = Path(__file__).resolve().parent / "fixtures" + + +@pytest.fixture +def mini_dump(): + """Hand-crafted 6-record ROR dump used across filter/document/dump_index tests.""" + return json.loads((FIXTURES / "dump_mini.json").read_text(encoding="utf-8")) + + +@pytest.fixture +def mini_dump_path(): + return FIXTURES / "dump_mini.json" diff --git a/tests/index/ror/fixtures/__init__.py b/tests/index/ror/fixtures/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/index/ror/fixtures/dump_mini.json b/tests/index/ror/fixtures/dump_mini.json new file mode 100644 index 0000000..eabfb13 --- /dev/null +++ b/tests/index/ror/fixtures/dump_mini.json @@ -0,0 +1,95 @@ +[ + { + "id": "https://ror.org/02s376052", + "names": [ + {"value": "École polytechnique fédérale de Lausanne", "lang": "fr", "types": ["ror_display", "label"]}, + {"value": "Swiss Federal Institute of Technology Lausanne", "lang": "en", "types": ["label"]}, + {"value": "EPFL", "types": ["acronym"]} + ], + "locations": [ + {"geonames_details": {"country_code": "CH", "name": "Lausanne", "country_name": "Switzerland", "country_subdivision_name": "Vaud"}} + ], + "types": ["education", "facility"], + "status": "active", + "domains": ["epfl.ch"], + "links": [{"type": "website", "value": "https://www.epfl.ch"}], + "external_ids": [{"type": "wikidata", "preferred": "Q262760"}], + "relationships": [ + {"type": "child", "id": "https://ror.org/000epfl-cim", "label": "Center for Imaging EPFL"} + ] + }, + { + "id": "https://ror.org/000epfl-cim", + "names": [ + {"value": "Center for Imaging at EPFL", "lang": "en", "types": ["ror_display", "label"]}, + {"value": "CIM", "types": ["acronym"]} + ], + "locations": [ + {"geonames_details": {"country_code": "CH", "name": "Lausanne", "country_name": "Switzerland"}} + ], + "types": ["facility"], + "status": "active", + "links": [], + "relationships": [ + {"type": "parent", "id": "https://ror.org/02s376052", "label": "École polytechnique fédérale de Lausanne"} + ] + }, + { + "id": "https://ror.org/05a28rw58", + "names": [ + {"value": "ETH Zurich", "lang": "en", "types": ["ror_display", "label"]}, + {"value": "Eidgenössische Technische Hochschule Zürich", "lang": "de", "types": ["label"]}, + {"value": "ETHZ", "types": ["acronym"]} + ], + "locations": [ + {"geonames_details": {"country_code": "CH", "name": "Zürich", "country_name": "Switzerland"}} + ], + "types": ["education", "facility"], + "status": "active", + "domains": ["ethz.ch"], + "links": [{"type": "website", "value": "https://ethz.ch"}], + "external_ids": [{"type": "wikidata", "preferred": "Q11942"}], + "relationships": [] + }, + { + "id": "https://ror.org/02k7v4d05", + "names": [ + {"value": "Universität Bern", "lang": "de", "types": ["ror_display", "label"]}, + {"value": "University of Bern", "lang": "en", "types": ["label"]}, + {"value": "UniBE", "types": ["acronym"]} + ], + "locations": [ + {"geonames_details": {"country_code": "CH", "name": "Bern", "country_name": "Switzerland"}} + ], + "types": ["education"], + "status": "active", + "links": [{"type": "website", "value": "https://www.unibe.ch"}], + "relationships": [] + }, + { + "id": "https://ror.org/0042zzz00", + "names": [ + {"value": "Inactive Swiss Lab", "types": ["ror_display"]} + ], + "locations": [ + {"geonames_details": {"country_code": "CH", "name": "Geneva", "country_name": "Switzerland"}} + ], + "types": ["facility"], + "status": "inactive", + "relationships": [] + }, + { + "id": "https://ror.org/042nb2s44", + "names": [ + {"value": "Massachusetts Institute of Technology", "lang": "en", "types": ["ror_display", "label"]}, + {"value": "MIT", "types": ["acronym"]} + ], + "locations": [ + {"geonames_details": {"country_code": "US", "name": "Cambridge", "country_name": "United States"}} + ], + "types": ["education"], + "status": "active", + "links": [{"type": "website", "value": "https://www.mit.edu"}], + "relationships": [] + } +] diff --git a/tests/index/ror/test_document.py b/tests/index/ror/test_document.py new file mode 100644 index 0000000..8af494c --- /dev/null +++ b/tests/index/ror/test_document.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +from src.index.ror.document import display_name, to_document + + +def _epfl(records): + return next(r for r in records if r["id"] == "https://ror.org/02s376052") + + +def test_display_name_picks_ror_display(mini_dump): + assert display_name(_epfl(mini_dump)) == "École polytechnique fédérale de Lausanne" + + +def test_to_document_includes_core_fields(mini_dump): + doc = to_document(_epfl(mini_dump)) + assert "Name: École polytechnique fédérale de Lausanne" in doc + assert "Acronyms: EPFL" in doc + assert "Other names: Swiss Federal Institute of Technology Lausanne" in doc + assert "Types: education, facility" in doc + assert "Location: Lausanne, Vaud, Switzerland" in doc + assert "Website: https://www.epfl.ch" in doc + assert "Relationships: child of Center for Imaging EPFL" in doc + + +def test_to_document_handles_minimal_record(): + record = { + "id": "https://ror.org/000aaa000", + "names": [{"value": "Solo Lab", "types": ["ror_display"]}], + } + doc = to_document(record) + assert doc.startswith("Name: Solo Lab") + # No acronyms / location / website lines when absent. + assert "Acronyms" not in doc + assert "Location" not in doc + assert "Website" not in doc + + +def test_to_document_falls_back_to_id_when_unnamed(): + record = {"id": "https://ror.org/000xxx000", "names": []} + assert to_document(record) == "Name: https://ror.org/000xxx000" diff --git a/tests/index/ror/test_duckdb_store.py b/tests/index/ror/test_duckdb_store.py new file mode 100644 index 0000000..4733088 --- /dev/null +++ b/tests/index/ror/test_duckdb_store.py @@ -0,0 +1,440 @@ +"""Tests for `src/index/ror/storage/duckdb_store.py` (D16, PR 1). + +Covers bootstrap, full-dump upsert + lookup parity with the in-memory +`DumpIndex`, scope-records bridge to Qdrant, and the manifest single-row +upsert. No live Qdrant or RCP — pure DuckDB on a tmp_path file. +""" + +from __future__ import annotations + +import pytest + +from src.index.ror.storage.duckdb_store import ( + DuckDBStore, + ScopeRecord, + StoreManifest, + build_search_blob, + extract_record_columns, + fold_for_search, + vector_id_for, +) + + +@pytest.fixture +def isolated_data_dir(monkeypatch, tmp_path): + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path)) + return tmp_path + + +@pytest.fixture +def store(isolated_data_dir): + s = DuckDBStore.open() + yield s + s.close() + + +# --------------------------------------------------------------------------- +# Pure helpers +# --------------------------------------------------------------------------- + + +def test_fold_for_search_strips_accents_and_lowercases(): + assert fold_for_search("École") == "ecole" + assert fold_for_search("Universität Bern") == "universitat bern" + assert fold_for_search("EPFL") == "epfl" + + +def test_build_search_blob_concatenates_all_name_variants(): + record = { + "names": [ + {"value": "École polytechnique fédérale de Lausanne", "types": ["ror_display", "label"]}, + {"value": "EPFL", "types": ["acronym"]}, + {"value": "ETH Lausanne", "types": ["alias"]}, + ], + } + blob = build_search_blob(record) + assert "ecole polytechnique federale de lausanne" in blob + assert "epfl" in blob + assert "eth lausanne" in blob + + +def test_vector_id_is_deterministic_uuid5_over_url(): + rid = "https://ror.org/02s376052" + a = vector_id_for(rid) + b = vector_id_for(rid) + assert a == b + # Same shape as the existing Qdrant point id (UUIDv5 hex with dashes). + assert len(a) == 36 and a.count("-") == 4 + + +def test_extract_record_columns_handles_real_record_shape(): + record = { + "id": "https://ror.org/02s376052", + "names": [ + {"value": "École polytechnique fédérale de Lausanne", "types": ["ror_display", "label"]}, + {"value": "EPFL", "types": ["acronym"]}, + ], + "status": "active", + "established": 1853, + "types": ["education", "facility"], + "domains": ["epfl.ch"], + "links": [{"type": "website", "value": "https://www.epfl.ch"}], + "locations": [ + {"geonames_details": { + "country_code": "CH", + "country_name": "Switzerland", + "name": "Lausanne", + "country_subdivision_name": "Vaud", + }}, + ], + "external_ids": [{"type": "wikidata", "preferred": "Q262760"}], + "relationships": [], + } + cols = extract_record_columns(record, ror_release_version="v2.6") + assert cols["ror_id"] == "https://ror.org/02s376052" + assert cols["ror_id_short"] == "02s376052" + assert cols["name"] == "École polytechnique fédérale de Lausanne" + assert cols["country_code"] == "CH" + assert cols["country_name"] == "Switzerland" + assert cols["city"] == "Lausanne" + assert cols["region"] == "Vaud" + assert cols["established"] == 1853 + assert cols["website"] == "https://www.epfl.ch" + assert cols["status"] == "active" + assert cols["types_json"] == ["education", "facility"] + assert cols["acronyms_json"] == ["EPFL"] + assert cols["ror_release_version"] == "v2.6" + assert "ecole polytechnique federale de lausanne" in cols["search_blob"] + assert "epfl" in cols["search_blob"] + + +def test_extract_record_columns_rejects_record_without_id(): + with pytest.raises(ValueError, match="missing a string `id`"): + extract_record_columns({"names": [{"value": "X"}]}) + + +# --------------------------------------------------------------------------- +# Store: bootstrap + records +# --------------------------------------------------------------------------- + + +def test_bootstrap_is_idempotent(isolated_data_dir): + s1 = DuckDBStore.open() + s1.bootstrap() + s1.bootstrap() + s1.close() + # Re-open a second store on the same file. + s2 = DuckDBStore.open() + s2.bootstrap() + assert s2.count_records() == 0 + s2.close() + + +def test_upsert_record_round_trip(store): + record = { + "id": "https://ror.org/02s376052", + "names": [{"value": "EPFL", "types": ["ror_display"]}], + "status": "active", + "locations": [{"geonames_details": {"country_code": "CH"}}], + } + store.upsert_record(extract_record_columns(record)) + assert store.count_records() == 1 + hit = store.fetch_record("02s376052") + assert hit is not None + assert hit["ror_id"] == "https://ror.org/02s376052" + assert hit["country_code"] == "CH" + # JSON columns hydrate back to Python. + assert hit["record"]["id"] == "https://ror.org/02s376052" + + +def test_upsert_record_overrides_existing_row(store): + rid = "https://ror.org/02s376052" + base = {"id": rid, "names": [{"value": "Old", "types": ["ror_display"]}], "status": "active"} + store.upsert_record(extract_record_columns(base)) + updated = {"id": rid, "names": [{"value": "New", "types": ["ror_display"]}], "status": "inactive"} + store.upsert_record(extract_record_columns(updated)) + assert store.count_records() == 1 + hit = store.fetch_record(rid) + assert hit["name"] == "New" + assert hit["status"] == "inactive" + + +def test_bulk_replace_records_via_copy_from_csv(store): + rows = [ + extract_record_columns({ + "id": f"https://ror.org/test{i:04d}", + "names": [{"value": f"Org {i}", "types": ["ror_display"]}], + "status": "active", + "locations": [{"geonames_details": {"country_code": "CH"}}], + }) + for i in range(50) + ] + n = store.bulk_replace_records(rows, csv_chunk_size=20) + assert n == 50 + assert store.count_records() == 50 + hit = store.fetch_record("test0007") + assert hit is not None + assert hit["country_code"] == "CH" + + +def test_bulk_replace_records_truncates_previous_state(store): + first = [extract_record_columns({ + "id": f"https://ror.org/old{i}", "names": [{"value": f"Old {i}", "types": ["ror_display"]}], + }) for i in range(5)] + store.bulk_replace_records(first) + assert store.count_records() == 5 + + second = [extract_record_columns({ + "id": f"https://ror.org/new{i}", "names": [{"value": f"New {i}", "types": ["ror_display"]}], + }) for i in range(3)] + store.bulk_replace_records(second) + assert store.count_records() == 3 + # First-batch records are gone. + assert store.fetch_record("old0") is None + assert store.fetch_record("new0") is not None + + +def test_bulk_replace_records_preserves_json_columns(store): + record = { + "id": "https://ror.org/02s376052", + "names": [ + {"value": "École polytechnique fédérale de Lausanne", "types": ["ror_display", "label"]}, + {"value": "EPFL", "types": ["acronym"]}, + ], + "status": "active", + "types": ["education", "facility"], + "external_ids": [{"type": "wikidata", "preferred": "Q262760"}], + "locations": [{"geonames_details": {"country_code": "CH"}}], + } + store.bulk_replace_records([extract_record_columns(record)]) + hit = store.fetch_record("02s376052") + assert hit is not None + # JSON columns parsed back to Python. + assert hit["types_json"] == ["education", "facility"] + assert hit["acronyms_json"] == ["EPFL"] + assert hit["external_ids_json"][0]["preferred"] == "Q262760" + # `record` round-trip preserves the full original. + assert hit["record"]["id"] == "https://ror.org/02s376052" + assert hit["record"]["names"][0]["value"].startswith("École") + + +def test_bulk_upsert_under_transaction(store): + rows = [ + extract_record_columns({ + "id": f"https://ror.org/test{i:04d}", + "names": [{"value": f"Org {i}", "types": ["ror_display"]}], + "status": "active", + "locations": [{"geonames_details": {"country_code": "CH" if i % 2 == 0 else "FR"}}], + }) + for i in range(20) + ] + with store.transaction(): + n = store.upsert_records(rows) + assert n == 20 + assert store.count_records() == 20 + + +# --------------------------------------------------------------------------- +# Store: lookup parity with DumpIndex +# --------------------------------------------------------------------------- + + +@pytest.fixture +def populated_store(store): + records = [ + { + "id": "https://ror.org/02s376052", + "names": [ + {"value": "École polytechnique fédérale de Lausanne", "types": ["ror_display", "label"]}, + {"value": "EPFL", "types": ["acronym"]}, + ], + "status": "active", + "types": ["education"], + "locations": [{"geonames_details": {"country_code": "CH", "name": "Lausanne"}}], + }, + { + "id": "https://ror.org/05a28rw58", + "names": [ + {"value": "ETH Zurich", "types": ["ror_display", "label"]}, + {"value": "Eidgenössische Technische Hochschule Zürich", "types": ["alias"]}, + ], + "status": "active", + "types": ["education"], + "locations": [{"geonames_details": {"country_code": "CH", "name": "Zürich"}}], + }, + { + "id": "https://ror.org/02nv7yv05", + "names": [{"value": "Universität Bern", "types": ["ror_display", "label"]}], + "status": "active", + "types": ["education"], + "locations": [{"geonames_details": {"country_code": "CH", "name": "Bern"}}], + }, + { + "id": "https://ror.org/abandoned1", + "names": [{"value": "Some Old Lab", "types": ["ror_display"]}], + "status": "withdrawn", + "types": ["facility"], + "locations": [{"geonames_details": {"country_code": "FR", "name": "Paris"}}], + }, + ] + with store.transaction(): + store.upsert_records(extract_record_columns(r) for r in records) + return store + + +def test_lookup_by_exact_ror_id_url_or_bare(populated_store): + by_url = populated_store.lookup(ror_id="https://ror.org/02s376052") + by_bare = populated_store.lookup(ror_id="02s376052") + assert len(by_url) == len(by_bare) == 1 + assert by_url[0]["ror_id"] == "https://ror.org/02s376052" + assert by_bare[0]["ror_id"] == "https://ror.org/02s376052" + + +def test_lookup_by_text_uses_search_blob_with_accent_folding(populated_store): + # `Universitat` (no umlaut) should still hit "Universität Bern". + hits = populated_store.lookup(text="Universitat Bern") + names = [h["name"] for h in hits] + assert "Universität Bern" in names + + # `Ecole` (no acute) should still hit EPFL via its display name. + hits = populated_store.lookup(text="Ecole Lausanne") + names = [h["name"] for h in hits] + assert "École polytechnique fédérale de Lausanne" in names + + +def test_lookup_text_ranks_by_token_match_count(populated_store): + hits = populated_store.lookup(text="Eidgenössische Hochschule Zürich") + # ETH Zurich's alias contains all three tokens (after folding); should rank #1. + assert hits[0]["ror_id"] == "https://ror.org/05a28rw58" + + +def test_lookup_country_filter(populated_store): + fr_only = populated_store.lookup(country="FR") + assert {h["ror_id"] for h in fr_only} == {"https://ror.org/abandoned1"} + + +def test_lookup_status_filter(populated_store): + active_only = populated_store.lookup(status="active", limit=10) + assert all(h["status"] == "active" for h in active_only) + assert len(active_only) == 3 + + +def test_lookup_type_filter(populated_store): + education = populated_store.lookup(type_="education", limit=10) + assert {h["ror_id"] for h in education} == { + "https://ror.org/02s376052", + "https://ror.org/05a28rw58", + "https://ror.org/02nv7yv05", + } + + +def test_lookup_combined_filters(populated_store): + hits = populated_store.lookup(text="Bern", country="CH", status="active", limit=5) + assert [h["name"] for h in hits] == ["Universität Bern"] + + +def test_lookup_text_with_no_matching_tokens_returns_empty(populated_store): + assert populated_store.lookup(text="!!!") == [] + + +def test_lookup_limit_is_respected(populated_store): + hits = populated_store.lookup(country="CH", limit=2) + assert len(hits) == 2 + + +# --------------------------------------------------------------------------- +# Store: scope_records (Qdrant bridge) +# --------------------------------------------------------------------------- + + +def test_set_scope_records_replaces_atomically(store): + rid = "https://ror.org/02s376052" + store.upsert_record(extract_record_columns({ + "id": rid, + "names": [{"value": "EPFL", "types": ["ror_display"]}], + })) + + rows_v1 = [ + ScopeRecord( + scope_mode="epfl_ethz", + ror_id=rid, + text="Name: EPFL", + vector_id=vector_id_for(rid), + ), + ] + n1 = store.set_scope_records("epfl_ethz", rows_v1) + assert n1 == 1 + assert store.count_scope_records("epfl_ethz") == 1 + + # Rebuilding the scope swaps the row out — no leftover from v1. + rows_v2 = [ + ScopeRecord( + scope_mode="epfl_ethz", + ror_id=rid, + text="Name: École polytechnique fédérale de Lausanne", + vector_id=vector_id_for(rid), + ), + ] + n2 = store.set_scope_records("epfl_ethz", rows_v2) + assert n2 == 1 + assert store.count_scope_records("epfl_ethz") == 1 + cur = store.connect().execute( + "SELECT text FROM scope_records WHERE scope_mode = ? AND ror_id = ?", + ["epfl_ethz", rid], + ) + assert cur.fetchone()[0].startswith("Name: École") + + +def test_scope_records_isolate_by_scope_mode(store): + rid = "https://ror.org/02s376052" + store.upsert_record(extract_record_columns({ + "id": rid, "names": [{"value": "EPFL", "types": ["ror_display"]}], + })) + sr = ScopeRecord(scope_mode="x", ror_id=rid, text="t", vector_id=vector_id_for(rid)) + store.set_scope_records("epfl_ethz", [sr.model_copy(update={"scope_mode": "epfl_ethz"})]) + store.set_scope_records("worldwide", [sr.model_copy(update={"scope_mode": "worldwide"})]) + assert store.count_scope_records("epfl_ethz") == 1 + assert store.count_scope_records("worldwide") == 1 + # Replacing one scope leaves the other intact. + store.set_scope_records("epfl_ethz", []) + assert store.count_scope_records("epfl_ethz") == 0 + assert store.count_scope_records("worldwide") == 1 + + +# --------------------------------------------------------------------------- +# Store: manifests +# --------------------------------------------------------------------------- + + +def test_set_manifest_round_trip(store): + m = StoreManifest( + scope_mode="switzerland", + record_count=1854, + embedding_model="Qwen/Qwen3-Embedding-8B", + embedding_dim=4096, + reranker_model="Qwen/Qwen3-Reranker-8B", + ror_release_version="v2.6", + ror_release_doi="10.5281/zenodo.19576723", + ) + store.set_manifest(m) + fetched = store.fetch_manifest("switzerland") + assert fetched is not None + assert fetched["record_count"] == 1854 + assert fetched["embedding_dim"] == 4096 + assert fetched["ror_release_version"] == "v2.6" + + +def test_set_manifest_overrides_on_conflict(store): + base = StoreManifest( + scope_mode="switzerland", + record_count=1000, + embedding_model="Qwen/Qwen3-Embedding-8B", + embedding_dim=4096, + reranker_model="Qwen/Qwen3-Reranker-8B", + ) + store.set_manifest(base) + updated = base.model_copy(update={"record_count": 1854, "ror_release_version": "v2.7"}) + store.set_manifest(updated) + fetched = store.fetch_manifest("switzerland") + assert fetched["record_count"] == 1854 + assert fetched["ror_release_version"] == "v2.7" diff --git a/tests/index/ror/test_filter.py b/tests/index/ror/test_filter.py new file mode 100644 index 0000000..d8b0857 --- /dev/null +++ b/tests/index/ror/test_filter.py @@ -0,0 +1,97 @@ +from __future__ import annotations + +from src.index.ror.filter import ( + EUROPE_COUNTRY_CODES, + filter_countries, + filter_country_code, + filter_subtree, +) + + +def test_subtree_expands_from_epfl_seed_to_child(mini_dump): + kept = filter_subtree( + mini_dump, + seeds=["https://ror.org/02s376052"], + expand_types=("parent", "child", "related"), + max_depth=2, + ) + ids = {r["id"] for r in kept} + assert "https://ror.org/02s376052" in ids + assert "https://ror.org/000epfl-cim" in ids + assert "https://ror.org/05a28rw58" not in ids # ETHZ not reachable from EPFL + + +def test_subtree_with_both_seeds(mini_dump): + kept = filter_subtree( + mini_dump, + seeds=["https://ror.org/02s376052", "https://ror.org/05a28rw58"], + max_depth=2, + ) + ids = {r["id"] for r in kept} + assert ids == { + "https://ror.org/02s376052", + "https://ror.org/000epfl-cim", + "https://ror.org/05a28rw58", + } + + +def test_subtree_handles_bare_id_seed(mini_dump): + kept = filter_subtree(mini_dump, seeds=["02s376052"], max_depth=1) + ids = {r["id"] for r in kept} + assert "https://ror.org/02s376052" in ids + assert "https://ror.org/000epfl-cim" in ids + + +def test_subtree_respects_max_depth(mini_dump): + kept = filter_subtree( + mini_dump, + seeds=["https://ror.org/02s376052"], + max_depth=0, + ) + assert {r["id"] for r in kept} == {"https://ror.org/02s376052"} + + +def test_country_filter_keeps_all_ch_records(mini_dump): + kept = filter_country_code(mini_dump, "CH") + ids = {r["id"] for r in kept} + assert ids == { + "https://ror.org/02s376052", + "https://ror.org/000epfl-cim", + "https://ror.org/05a28rw58", + "https://ror.org/02k7v4d05", + "https://ror.org/0042zzz00", + } + assert "https://ror.org/042nb2s44" not in ids # MIT excluded + + +def test_country_filter_is_case_insensitive(mini_dump): + assert len(filter_country_code(mini_dump, "ch")) == len(filter_country_code(mini_dump, "CH")) + + +def test_country_filter_us(mini_dump): + kept = filter_country_code(mini_dump, "US") + assert {r["id"] for r in kept} == {"https://ror.org/042nb2s44"} + + +def test_filter_countries_accepts_set(mini_dump): + kept = filter_countries(mini_dump, {"CH", "US"}) + ids = {r["id"] for r in kept} + assert "https://ror.org/02s376052" in ids + assert "https://ror.org/042nb2s44" in ids + + +def test_filter_europe_includes_ch_excludes_us(mini_dump): + kept = filter_countries(mini_dump, EUROPE_COUNTRY_CODES) + ids = {r["id"] for r in kept} + assert "https://ror.org/02s376052" in ids # EPFL (CH) + assert "https://ror.org/05a28rw58" in ids # ETHZ (CH) + assert "https://ror.org/042nb2s44" not in ids # MIT (US) + + +def test_europe_country_set_contains_expected_members(): + # Sanity: a handful of well-known European ISO codes must be present. + for code in ["CH", "DE", "FR", "IT", "GB", "ES", "NL", "PL", "SE"]: + assert code in EUROPE_COUNTRY_CODES + # And a non-European code must be absent. + for code in ["US", "JP", "BR", "ZA", "AU", "CN"]: + assert code not in EUROPE_COUNTRY_CODES diff --git a/tests/index/ror/test_migrate_storage.py b/tests/index/ror/test_migrate_storage.py new file mode 100644 index 0000000..5eedd10 --- /dev/null +++ b/tests/index/ror/test_migrate_storage.py @@ -0,0 +1,250 @@ +"""Tests for `src/index/ror/storage/migrate_storage.py` (D16, PR 3). + +Synthetic tmp-path fixtures only — no live Qdrant, no live RCP, no Zenodo. +Validates: dump-discovery, full-dump load, per-scope JSONL→DuckDB porting, +manifest round-trip, and the `--skip-qdrant-check` path. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any +from unittest.mock import MagicMock, patch + +import pytest + +from src.index.ror.storage import migrate_storage +from src.index.ror.storage.duckdb_store import DuckDBStore, vector_id_for + + +# --------------------------------------------------------------------------- +# Tmp-path scaffolding mimicking the on-disk layout the migrator expects. +# --------------------------------------------------------------------------- + + +@pytest.fixture +def isolated_index_dir(monkeypatch, tmp_path): + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path)) + return tmp_path + + +def _seed_dump_json(index_root: Path, version: str = "v2.6") -> Path: + """Write a tiny ROR dump JSON under /ror/dump//.""" + vdir = index_root / "ror" / "dump" / version + vdir.mkdir(parents=True, exist_ok=True) + json_path = vdir / f"{version}-2026-04-14-ror-data.json" + json_path.write_text( + json.dumps([ + { + "id": "https://ror.org/02s376052", + "names": [ + {"value": "École polytechnique fédérale de Lausanne", "types": ["ror_display", "label"]}, + {"value": "EPFL", "types": ["acronym"]}, + ], + "status": "active", + "types": ["education"], + "locations": [{"geonames_details": {"country_code": "CH", "name": "Lausanne"}}], + }, + { + "id": "https://ror.org/05a28rw58", + "names": [{"value": "ETH Zurich", "types": ["ror_display"]}], + "status": "active", + "types": ["education"], + "locations": [{"geonames_details": {"country_code": "CH", "name": "Zürich"}}], + }, + { + "id": "https://ror.org/abandoned1", + "names": [{"value": "Defunct Lab", "types": ["ror_display"]}], + "status": "withdrawn", + "locations": [{"geonames_details": {"country_code": "FR"}}], + }, + ]), + encoding="utf-8", + ) + (vdir / "release.json").write_text( + json.dumps({"version": version, "doi": "10.5281/zenodo.19576723"}), + encoding="utf-8", + ) + return json_path + + +def _seed_scope_sidecar( + index_root: Path, + scope_mode: str, + ror_ids: list[str], + *, + embedding_dim: int = 4096, +) -> None: + sdir = index_root / "ror" / "index" / scope_mode + sdir.mkdir(parents=True, exist_ok=True) + with (sdir / "records.jsonl").open("w", encoding="utf-8") as f: + for i, rid in enumerate(ror_ids): + f.write(json.dumps({ + "row": i, + "ror_id": rid, + "name": rid.rsplit("/", 1)[-1], + "text": f"Name: {rid.rsplit('/', 1)[-1]}", + "record": {"id": rid}, + }) + "\n") + (sdir / "manifest.json").write_text( + json.dumps({ + "scope_mode": scope_mode, + "record_count": len(ror_ids), + "embedding_model": "Qwen/Qwen3-Embedding-8B", + "embedding_dim": embedding_dim, + "reranker_model": "Qwen/Qwen3-Reranker-8B", + "ror_release_version": "v2.6", + "ror_release_doi": "10.5281/zenodo.19576723", + "built_at_iso": "2026-05-01T07:04:45.642340+00:00", + }, indent=2), + encoding="utf-8", + ) + + +# --------------------------------------------------------------------------- +# Discovery +# --------------------------------------------------------------------------- + + +def test_find_cached_dump_json_picks_latest_version(isolated_index_dir): + _seed_dump_json(isolated_index_dir, version="v2.5") + latest = _seed_dump_json(isolated_index_dir, version="v2.6") + found = migrate_storage.find_cached_dump_json() + assert found == latest + + +def test_find_cached_dump_json_returns_none_when_no_dump(isolated_index_dir): + assert migrate_storage.find_cached_dump_json() is None + + +def test_list_scope_dirs_sorted(isolated_index_dir): + for s in ("worldwide", "epfl_ethz", "switzerland", "europe"): + (isolated_index_dir / "ror" / "index" / s).mkdir(parents=True) + assert migrate_storage.list_scope_dirs() == [ + "epfl_ethz", "europe", "switzerland", "worldwide", + ] + + +# --------------------------------------------------------------------------- +# Full-dump and per-scope loaders +# --------------------------------------------------------------------------- + + +def test_populate_full_dump_writes_records_table(isolated_index_dir): + json_path = _seed_dump_json(isolated_index_dir) + store = DuckDBStore.open() + n = migrate_storage.populate_full_dump(store, json_path, release_version="v2.6") + assert n == 3 + assert store.count_records() == 3 + epfl = store.fetch_record("02s376052") + assert epfl is not None + assert epfl["country_code"] == "CH" + assert epfl["ror_release_version"] == "v2.6" + store.close() + + +def test_populate_scope_replaces_rows_and_writes_manifest(isolated_index_dir): + _seed_dump_json(isolated_index_dir) + _seed_scope_sidecar(isolated_index_dir, "epfl_ethz", [ + "https://ror.org/02s376052", + "https://ror.org/05a28rw58", + ]) + store = DuckDBStore.open() + summary = migrate_storage.populate_scope(store, "epfl_ethz") + assert summary["rows"] == 2 + assert store.count_scope_records("epfl_ethz") == 2 + + manifest = store.fetch_manifest("epfl_ethz") + assert manifest is not None + assert manifest["embedding_dim"] == 4096 + assert manifest["ror_release_version"] == "v2.6" + + # vector_id is the deterministic UUIDv5 — matches what Qdrant has for + # the same point id. + cur = store.connect().execute( + "SELECT vector_id FROM scope_records WHERE ror_id = ?", + ["https://ror.org/02s376052"], + ) + assert cur.fetchone()[0] == vector_id_for("https://ror.org/02s376052") + store.close() + + +def test_populate_scope_raises_when_sidecar_missing(isolated_index_dir): + store = DuckDBStore.open() + with pytest.raises(FileNotFoundError): + migrate_storage.populate_scope(store, "ghost_scope") + store.close() + + +# --------------------------------------------------------------------------- +# End-to-end migrate_all +# --------------------------------------------------------------------------- + + +@pytest.fixture +def fake_cfg() -> Any: + """Minimal cfg stub. `migrate_all` only needs it to construct QdrantRorStore, + and we mock that out below.""" + return MagicMock(name="RorIndexConfig") + + +def test_migrate_all_loads_dump_and_scopes(isolated_index_dir, fake_cfg): + _seed_dump_json(isolated_index_dir) + _seed_scope_sidecar(isolated_index_dir, "epfl_ethz", [ + "https://ror.org/02s376052", + ]) + _seed_scope_sidecar(isolated_index_dir, "switzerland", [ + "https://ror.org/02s376052", + "https://ror.org/05a28rw58", + ]) + + fake_qstore = MagicMock() + fake_qstore.count.side_effect = lambda scope: {"epfl_ethz": 1, "switzerland": 2}[scope] + + with patch("src.index.ror.storage.migrate_storage.QdrantRorStore", return_value=fake_qstore): + summary = migrate_storage.migrate_all(fake_cfg) + + assert summary["records_loaded"] == 3 + assert summary["release_version"] == "v2.6" + scope_modes = {s["scope_mode"] for s in summary["scopes"]} + assert scope_modes == {"epfl_ethz", "switzerland"} + checks = {c["scope_mode"]: c for c in summary["qdrant_checks"]} + assert checks["epfl_ethz"]["match"] is True + assert checks["switzerland"]["match"] is True + + +def test_migrate_all_skip_qdrant_check_omits_qdrant_calls(isolated_index_dir, fake_cfg): + _seed_dump_json(isolated_index_dir) + _seed_scope_sidecar(isolated_index_dir, "epfl_ethz", ["https://ror.org/02s376052"]) + + with patch("src.index.ror.storage.migrate_storage.QdrantRorStore") as mock_q: + summary = migrate_storage.migrate_all(fake_cfg, skip_qdrant_check=True) + + mock_q.assert_not_called() + assert summary["qdrant_checks"] == [] + assert len(summary["scopes"]) == 1 + + +def test_migrate_all_raises_when_dump_missing(isolated_index_dir, fake_cfg): + # No dump seeded. + _seed_scope_sidecar(isolated_index_dir, "epfl_ethz", ["https://ror.org/02s376052"]) + with pytest.raises(FileNotFoundError, match="No cached ROR dump"): + migrate_storage.migrate_all(fake_cfg, skip_qdrant_check=True) + + +def test_migrate_all_continues_when_qdrant_unreachable(isolated_index_dir, fake_cfg): + """If Qdrant is down, the DuckDB migration still succeeds and the check + section reports a None count (not an exception).""" + _seed_dump_json(isolated_index_dir) + _seed_scope_sidecar(isolated_index_dir, "epfl_ethz", ["https://ror.org/02s376052"]) + + fake_qstore = MagicMock() + fake_qstore.count.side_effect = ConnectionError("Qdrant down") + + with patch("src.index.ror.storage.migrate_storage.QdrantRorStore", return_value=fake_qstore): + summary = migrate_storage.migrate_all(fake_cfg) + + assert summary["records_loaded"] == 3 + assert summary["qdrant_checks"][0]["qdrant_count"] is None + assert summary["qdrant_checks"][0]["match"] is False diff --git a/tests/index/ror/test_qdrant_store.py b/tests/index/ror/test_qdrant_store.py new file mode 100644 index 0000000..9dc52ae --- /dev/null +++ b/tests/index/ror/test_qdrant_store.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +import uuid + +from src.index.ror.qdrant_store import _stable_point_id + + +def test_stable_point_id_is_deterministic(): + a = _stable_point_id("https://ror.org/02s376052") + b = _stable_point_id("https://ror.org/02s376052") + assert a == b + + +def test_stable_point_id_different_for_different_rors(): + a = _stable_point_id("https://ror.org/02s376052") + b = _stable_point_id("https://ror.org/05a28rw58") + assert a != b + + +def test_stable_point_id_is_valid_uuid(): + raw = _stable_point_id("https://ror.org/02s376052") + parsed = uuid.UUID(raw) + assert parsed.version == 5 diff --git a/tests/index/ror/test_query.py b/tests/index/ror/test_query.py new file mode 100644 index 0000000..6264402 --- /dev/null +++ b/tests/index/ror/test_query.py @@ -0,0 +1,158 @@ +from __future__ import annotations + +import asyncio +import json +from pathlib import Path +from unittest.mock import patch + +import numpy as np +import pytest + +from src.index.ror.config import QdrantConfig, RcpConfig, RorIndexConfig, ScopeConfig +from src.index.ror.models import DumpMatch +from src.index.ror.paths import ror_data_dir +from src.index.ror.query import lookup_dump, query, query_rag +from src.index.ror.rerank import RerankResult +from src.index.ror.storage.duckdb_store import DuckDBStore, extract_record_columns + + +@pytest.fixture +def isolated(monkeypatch, tmp_path): + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path)) + yield tmp_path + + +def _make_cfg(mode="epfl_ethz"): + return RorIndexConfig( + rcp=RcpConfig( + base_url="http://mock", + embedding_model="Qwen/Qwen3-Embedding-8B", + embedding_dim=8, + query_instruction="ignored", + reranker_model="Qwen/Qwen3-Reranker-8B", + ), + scope=ScopeConfig(mode=mode), + qdrant=QdrantConfig(), + data_dir=ror_data_dir().parent, + ) + + +def _seed_duckdb(mini_dump_path: Path) -> None: + """Populate the DuckDB `records` table from the mini dump fixture.""" + records = json.loads(mini_dump_path.read_text(encoding="utf-8")) + store = DuckDBStore.open() + try: + store.bulk_replace_records(extract_record_columns(r) for r in records) + finally: + store.close() + + +class _FakeQdrantStore: + """Stand-in for QdrantRorStore used to bypass network calls in tests.""" + + def __init__(self, candidates): + self.candidates = candidates + + def search(self, scope_mode, *, query_vector, top_k=50, country=None): + return self.candidates[:top_k] + + +def test_query_rag_returns_top_hit_for_epfl(isolated): + cfg = _make_cfg() + fake_candidates = [ + { + "ror_id": "https://ror.org/02s376052", + "name": "École polytechnique fédérale de Lausanne", + "text": "Name: EPFL", + "record": {"id": "https://ror.org/02s376052"}, + }, + { + "ror_id": "https://ror.org/05a28rw58", + "name": "ETH Zurich", + "text": "Name: ETH Zurich", + "record": {"id": "https://ror.org/05a28rw58"}, + }, + ] + + async def fake_embed_query(rcp, text, *, normalize=True): + return np.zeros(8, dtype=np.float32) + + async def fake_rerank(rcp, q, documents, *, top_n=None): + # Reranker prefers EPFL (index 0) over ETHZ (index 1). + return [ + RerankResult(index=0, score=0.91), + RerankResult(index=1, score=0.42), + ] + + with patch("src.index.ror.query.embed_query", side_effect=fake_embed_query), \ + patch("src.index.ror.query.rerank", side_effect=fake_rerank), \ + patch( + "src.index.ror.query.QdrantRorStore", + return_value=_FakeQdrantStore(fake_candidates), + ): + results = asyncio.run(query_rag(cfg, "EPFL", top_k=5, rerank_top_k=2)) + + assert len(results) == 2 + assert results[0].ror_id == "https://ror.org/02s376052" + assert results[0].score == pytest.approx(0.91) + + +def test_lookup_dump_finds_record_outside_embedded_subset(isolated, mini_dump_path): + """Universität Bern is in the dump but not in the embedded EPFL/ETHZ subset. + lookup_dump must still find it — proving the 'query the whole dump' path works.""" + cfg = _make_cfg() + _seed_duckdb(mini_dump_path) + + results = lookup_dump(cfg, text="Universität Bern", country="CH", limit=5) + ids = [r.ror_id for r in results] + assert "https://ror.org/02k7v4d05" in ids + + +def test_lookup_dump_exact_ror_id(isolated, mini_dump_path): + cfg = _make_cfg() + _seed_duckdb(mini_dump_path) + + results = lookup_dump(cfg, ror_id="02s376052") + assert len(results) == 1 + assert results[0].ror_id == "https://ror.org/02s376052" + + +def test_lookup_dump_requires_at_least_one_arg(isolated, mini_dump_path): + cfg = _make_cfg() + _seed_duckdb(mini_dump_path) + with pytest.raises(ValueError): + lookup_dump(cfg) + + +def test_query_auto_falls_back_to_lookup_when_no_rag_hits(isolated, mini_dump_path): + """When semantic returns nothing above floor, auto mode falls back to lexical.""" + _seed_duckdb(mini_dump_path) + cfg = _make_cfg() + + fake_candidates = [ + { + "ror_id": "https://ror.org/02s376052", + "name": "EPFL", + "text": "Name: EPFL", + "record": {}, + }, + ] + + async def fake_embed_query(rcp, text, *, normalize=True): + return np.zeros(8, dtype=np.float32) + + async def fake_rerank(rcp, q, documents, *, top_n=None): + # All candidates score below the floor → fallback should engage. + return [RerankResult(index=0, score=-1.0)] + + with patch("src.index.ror.query.embed_query", side_effect=fake_embed_query), \ + patch("src.index.ror.query.rerank", side_effect=fake_rerank), \ + patch( + "src.index.ror.query.QdrantRorStore", + return_value=_FakeQdrantStore(fake_candidates), + ): + results = asyncio.run(query(cfg, "Universität Bern", mode="auto", score_floor=0.0)) + + assert results + assert all(isinstance(r, DumpMatch) for r in results) + assert any(r.ror_id == "https://ror.org/02k7v4d05" for r in results) diff --git a/tests/index/ror/test_store.py b/tests/index/ror/test_store.py new file mode 100644 index 0000000..75d8c4d --- /dev/null +++ b/tests/index/ror/test_store.py @@ -0,0 +1,79 @@ +"""Legacy sidecar reader tests. + +After D16, `store.py` is read-only legacy support. These tests ensure +`read_records` / `read_manifest` still parse pre-D16 sidecars (so the +`migrate-storage` porter and the D15 FAISS→Qdrant migrator keep working). +""" + +from __future__ import annotations + +import json + +import pytest + +from src.index.ror import paths as ror_paths +from src.index.ror.models import IndexedRecord, IndexManifest +from src.index.ror.store import now_iso, read_manifest, read_records + + +@pytest.fixture +def isolated_index_dir(monkeypatch, tmp_path): + monkeypatch.setenv("INDEX_DATA_DIR", str(tmp_path)) + yield tmp_path + + +def _seed_legacy_sidecar(scope_mode: str, n: int) -> None: + sdir = ror_paths.index_dir(scope_mode) + rp = sdir / "records.jsonl" + mp = sdir / "manifest.json" + rows = [ + IndexedRecord( + row=i, + ror_id=f"https://ror.org/test{i:04d}", + name=f"Test Org {i}", + text=f"Name: Test Org {i}", + record={"id": f"https://ror.org/test{i:04d}"}, + ) + for i in range(n) + ] + with rp.open("w", encoding="utf-8") as f: + for row in rows: + f.write(json.dumps(row.model_dump(), ensure_ascii=False) + "\n") + manifest = IndexManifest( + scope_mode=scope_mode, + record_count=n, + embedding_model="Qwen/Qwen3-Embedding-8B", + embedding_dim=8, + reranker_model="Qwen/Qwen3-Reranker-8B", + ror_release_version="test-1", + ror_release_doi=None, + built_at_iso=now_iso(), + ) + mp.write_text(manifest.model_dump_json(indent=2), encoding="utf-8") + + +def test_read_records_parses_legacy_sidecar(isolated_index_dir): + _seed_legacy_sidecar("epfl_ethz", 5) + read_back = read_records("epfl_ethz") + assert [r.row for r in read_back] == list(range(5)) + assert read_back[0].ror_id == "https://ror.org/test0000" + + +def test_read_manifest_parses_legacy_sidecar(isolated_index_dir): + _seed_legacy_sidecar("switzerland", 3) + manifest = read_manifest("switzerland") + assert manifest.record_count == 3 + assert manifest.embedding_dim == 8 + assert manifest.scope_mode == "switzerland" + + +def test_read_records_raises_when_sidecar_missing(isolated_index_dir): + with pytest.raises(FileNotFoundError): + read_records("ghost_scope") + + +def test_paths_respect_env_var(isolated_index_dir): + expected_root = isolated_index_dir / "ror" + assert ror_paths.ror_data_dir() == expected_root + assert ror_paths.dump_dir() == expected_root / "dump" + assert ror_paths.index_dir("switzerland") == expected_root / "index" / "switzerland" diff --git a/tests/test_organization_enrichment.py b/tests/test_organization_enrichment.py deleted file mode 100644 index 498fcbb..0000000 --- a/tests/test_organization_enrichment.py +++ /dev/null @@ -1,191 +0,0 @@ -""" -Test organization enrichment functionality -""" - -import asyncio - -import pytest - -from src.agents import ( - OrganizationEnrichmentResult, - enrich_organizations_from_dict, -) - -# Sample LLM output for testing -SAMPLE_LLM_OUTPUT = { - "parseTimestamp": "2025-10-04T15:19", - "name": "test-repo", - "description": "A test repository", - "author": [ - { - "name": "Test Author", - "orcidId": "https://orcid.org/0000-0000-0000-0001", - "affiliation": ["EPFL - École Polytechnique Fédérale de Lausanne"], - }, - ], - "gitAuthors": [ - {"name": "testuser", "email": "test.user@epfl.ch", "commits": 10}, - {"name": "anotheruser", "email": "another@ethz.ch", "commits": 5}, - ], - "relatedToOrganizations": ["EPFL"], - "relatedToEPFL": True, - "relatedToEPFLJustification": "Author affiliated with EPFL", -} - - -@pytest.mark.asyncio() -async def test_organization_enrichment_basic(): - """Test basic organization enrichment functionality""" - - result = await enrich_organizations_from_dict( - SAMPLE_LLM_OUTPUT, - "https://github.com/test/repo", - ) - - # Check that result has expected structure - assert "organizations" in result - assert "relatedToEPFL" in result - assert "relatedToEPFLJustification" in result - assert "relatedToEPFLConfidence" in result - - # Should identify at least one organization - assert len(result["organizations"]) > 0 - - # Check organization structure - for org in result["organizations"]: - assert "legalName" in org - # May or may not have ROR ID depending on search results - - -@pytest.mark.asyncio() -async def test_organization_enrichment_with_multiple_emails(): - """Test enrichment with multiple institutional emails""" - - test_data = { - "parseTimestamp": "2025-10-04T15:19", - "name": "multi-org-repo", - "author": [], - "gitAuthors": [ - {"name": "user1", "email": "user1@epfl.ch", "commits": 20}, - {"name": "user2", "email": "user2@ethz.ch", "commits": 15}, - {"name": "user3", "email": "user3@pasteur.fr", "commits": 10}, - ], - } - - result = await enrich_organizations_from_dict( - test_data, - "https://github.com/test/multi-org", - ) - - # Should identify multiple organizations from different email domains - MIN_EXPECTED_ORGS = 2 - assert len(result["organizations"]) >= MIN_EXPECTED_ORGS - - # Check that key institutions are identified (may vary based on ROR results) - # At minimum, should recognize the domains - assert any(org for org in result["organizations"]) - - -@pytest.mark.asyncio() -async def test_organization_enrichment_epfl_detection(): - """Test EPFL relationship detection""" - - test_data = { - "parseTimestamp": "2025-10-04T15:19", - "name": "epfl-repo", - "author": [ - { - "name": "EPFL Researcher", - "affiliation": ["EPFL"], - }, - ], - "gitAuthors": [ - {"name": "researcher", "email": "researcher@epfl.ch", "commits": 50}, - ], - } - - result = await enrich_organizations_from_dict( - test_data, - "https://github.com/test/epfl-repo", - ) - - # Should detect EPFL relationship - assert result["relatedToEPFL"] is True - assert len(result["relatedToEPFLJustification"]) > 0 - # Should have a confidence score between 0.0 and 1.0 - assert "relatedToEPFLConfidence" in result - assert 0.0 <= result["relatedToEPFLConfidence"] <= 1.0 - - -@pytest.mark.asyncio() -async def test_organization_enrichment_no_institutional_emails(): - """Test enrichment with only generic emails""" - - test_data = { - "parseTimestamp": "2025-10-04T15:19", - "name": "generic-repo", - "author": [], - "gitAuthors": [ - {"name": "user1", "email": "user@gmail.com", "commits": 10}, - {"name": "user2", "email": "dev@users.noreply.github.com", "commits": 5}, - ], - } - - result = await enrich_organizations_from_dict( - test_data, - "https://github.com/test/generic", - ) - - # Should still return a result, but may have fewer organizations - assert "organizations" in result - assert "relatedToEPFL" in result - assert "relatedToEPFLConfidence" in result - - -@pytest.mark.asyncio() -async def test_organization_model_fields(): - """Test that enriched organizations have extended fields""" - - result = await enrich_organizations_from_dict( - SAMPLE_LLM_OUTPUT, - "https://github.com/test/repo", - ) - - if result["organizations"]: - org = result["organizations"][0] - - # Check that new fields exist (may be None) - assert "legalName" in org - assert "hasRorId" in org or "hasRorId" not in org # Optional field - assert "organizationType" in org or "organizationType" not in org # Optional - assert "country" in org or "country" not in org # Optional - - -def test_enrichment_result_model(): - """Test that OrganizationEnrichmentResult model works correctly""" - - # Create a test result - result = OrganizationEnrichmentResult( - organizations=[ - { - "legalName": "Test University", - "hasRorId": "https://ror.org/123456", - "organizationType": "Education", - "country": "Switzerland", - }, - ], - relatedToEPFL=True, - relatedToEPFLConfidence=0.85, - relatedToEPFLJustification="Test justification", - analysis_notes="Test notes", - ) - - assert len(result.organizations) == 1 - assert result.relatedToEPFL is True - assert result.analysis_notes == "Test notes" - - -if __name__ == "__main__": - # Run a basic test - asyncio.run(test_organization_enrichment_basic()) - print("✅ Basic test passed!") diff --git a/tests/test_real.sh b/tests/test_real.sh new file mode 100644 index 0000000..c0f6166 --- /dev/null +++ b/tests/test_real.sh @@ -0,0 +1,5 @@ +curl -X 'GET' \ + 'http://localhost:1234/v2/extract/https%3A%2F%2Fwww.github.com%2Fsdsc-ordes%2Fgimie?output_format=jsonld&agent_runtime=llm' \ + -H 'accept: application/json' | jq > .tmp/test.json + +#-C . \ No newline at end of file diff --git a/tests/v1/__init__.py b/tests/v1/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_cache.py b/tests/v1/test_cache.py similarity index 92% rename from tests/test_cache.py rename to tests/v1/test_cache.py index 7c6686f..a89f420 100644 --- a/tests/test_cache.py +++ b/tests/v1/test_cache.py @@ -7,10 +7,24 @@ import time import requests +import pytest # Add src to path for imports -from src.cache.cache import get_cache -from src.cache.cache_manager import get_cache_manager +import src.v1.cache.cache as cache_module +import src.v1.cache.cache_manager as cache_manager_module +from src.v1.cache.cache import get_cache +from src.v1.cache.cache_manager import get_cache_manager + + +@pytest.fixture(autouse=True) +def _isolate_cache_singletons(tmp_path, monkeypatch) -> None: + """Keep cache singleton state isolated between tests.""" + monkeypatch.setenv("CACHE_DB_PATH", str(tmp_path / "cache.db")) + cache_module._cache_instance = None + cache_manager_module._cache_manager = None + yield + cache_module._cache_instance = None + cache_manager_module._cache_manager = None def test_cache_basic_functionality(): diff --git a/tests/v1/test_gimie_cff_date_normalize.py b/tests/v1/test_gimie_cff_date_normalize.py new file mode 100644 index 0000000..06bb55b --- /dev/null +++ b/tests/v1/test_gimie_cff_date_normalize.py @@ -0,0 +1,42 @@ +"""Unit tests for CFF date normalization in ``gimie_methods``.""" +# ruff: noqa: SLF001 + +from __future__ import annotations + +import yaml + +from src.v1.gimie_utils import gimie_methods as gm + + +def test_normalize_cff_swaps_day_month_when_middle_gt_12(): + raw = b"cff-version: 1.2.0\ndate-released: 2025-28-04\n" + out = gm._normalize_cff_calendar_dates(raw).decode() + assert "date-released: 2025-04-28" in out + + +def test_normalize_cff_leaves_valid_iso_unchanged(): + raw = b"date-released: 2025-04-28\n" + out = gm._normalize_cff_calendar_dates(raw).decode() + assert out == raw.decode() + + +def test_normalize_cff_quotes_nonsense_template_dates(): + """Template values like 2025-13-14 are not fixable by swap; must be quoted.""" + raw = b"date-released: 2025-13-14\n" + out = gm._normalize_cff_calendar_dates(raw).decode() + assert 'date-released: "2025-13-14"' in out + loaded = yaml.safe_load(out) + assert loaded["date-released"] == "2025-13-14" + + +def test_rewrite_cff_known_date_value_branches(): + assert gm._rewrite_cff_known_date_value("2025-28-04") == ( + "2025-04-28", + "normalized swapped day/month", + ) + assert gm._rewrite_cff_known_date_value("2025-04-28") is None + assert gm._rewrite_cff_known_date_value("2025-53-05") == ( + '"2025-53-05"', + "quoted invalid calendar (YAML timestamp escape)", + ) + assert gm._rewrite_cff_known_date_value('"2025-13-14"') is None diff --git a/tests/v1/test_gimie_empty_github_repo.py b/tests/v1/test_gimie_empty_github_repo.py new file mode 100644 index 0000000..6a4242c --- /dev/null +++ b/tests/v1/test_gimie_empty_github_repo.py @@ -0,0 +1,22 @@ +"""Regression: GIMIE on GitHub repos with no commits (204 contributors, null HEAD tree).""" + +from __future__ import annotations + +import os + +import pytest + +from src.v1.gimie_utils.gimie_methods import extract_gimie + + +@pytest.mark.skipif( + not os.environ.get("GITHUB_TOKEN"), + reason="GITHUB_TOKEN required for live GitHub API", +) +def test_extract_gimie_empty_github_repository_returns_json_ld_list(): + """raj921/dream-home-ai- is an empty GitHub repo (API regression fixture).""" + out = extract_gimie( + "https://github.com/raj921/dream-home-ai-", + serialization_format="json-ld", + ) + assert isinstance(out, list) diff --git a/tests/v1/test_gimie_endpoint_connectionerror.py b/tests/v1/test_gimie_endpoint_connectionerror.py new file mode 100644 index 0000000..4e4e938 --- /dev/null +++ b/tests/v1/test_gimie_endpoint_connectionerror.py @@ -0,0 +1,67 @@ +"""GIMIE JSON-LD route maps GitHub ConnectionError to 429/503.""" + +from __future__ import annotations + +from datetime import datetime, timezone +from unittest.mock import AsyncMock, patch + +import pytest +from fastapi.testclient import TestClient + +from src.api import app +from src.v1.utils.github_dependency import validate_github_token + + +async def _github_info_override() -> dict: + return { + "valid": True, + "rate_limit_limit": 5000, + "rate_limit_remaining": 4999, + "rate_limit_reset": datetime(2030, 1, 1, tzinfo=timezone.utc), + } + + +@pytest.fixture +def gimie_client() -> TestClient: + app.dependency_overrides[validate_github_token] = _github_info_override + with TestClient(app) as client: + yield client + app.dependency_overrides.clear() + + +@pytest.mark.parametrize( + ("message", "expected_status"), + [ + ( + "API request failed: You have exceeded a secondary rate limit. Wait.", + 429, + ), + ( + "API request failed: rate limit exceeded", + 429, + ), + ( + "API request failed: API rate limit exceeded", + 429, + ), + ( + "API request failed: connection reset by peer", + 503, + ), + ], +) +def test_gimie_connectionerror_status( + gimie_client: TestClient, + message: str, + expected_status: int, +) -> None: + with patch("src.api.Repository") as repo_cls: + inst = repo_cls.return_value + inst.run_analysis = AsyncMock(side_effect=ConnectionError(message)) + + path = "https%3A%2F%2Fgithub.com%2Fowner%2Frepo" + r = gimie_client.get(f"/v1/repository/gimie/json-ld/{path}") + + assert r.status_code == expected_status + body = r.json() + assert body.get("detail") == message diff --git a/tests/test_orcid_validation_pipeline.py b/tests/v1/test_orcid_validation_pipeline.py similarity index 95% rename from tests/test_orcid_validation_pipeline.py rename to tests/v1/test_orcid_validation_pipeline.py index ba9ae36..a3d2ce6 100644 --- a/tests/test_orcid_validation_pipeline.py +++ b/tests/v1/test_orcid_validation_pipeline.py @@ -8,16 +8,16 @@ from fastapi import Response import src.api as api_module -import src.analysis.repositories as repositories_module -import src.cache.cache_manager as cache_manager_module -from src.analysis.repositories import Repository -from src.data_models.conversion import ( +import src.v1.analysis.repositories as repositories_module +import src.v1.cache.cache_manager as cache_manager_module +from src.v1.analysis.repositories import Repository +from src.v1.data_models.conversion import ( convert_pydantic_to_jsonld, create_simplified_model, ) -from src.data_models.models import Person -from src.data_models.repository import SoftwareSourceCode -from src.utils.url_validation import ( +from src.v1.data_models.models import Person +from src.v1.data_models.repository import SoftwareSourceCode +from src.v1.utils.url_validation import ( normalize_orcid_id, normalize_orcid_url, validate_author_urls, diff --git a/tests/v1/test_v1_parity.py b/tests/v1/test_v1_parity.py new file mode 100644 index 0000000..47d7f37 --- /dev/null +++ b/tests/v1/test_v1_parity.py @@ -0,0 +1,78 @@ +# ruff: noqa: INP001, SLF001 +from __future__ import annotations + +import asyncio + +import src.api as api_module +import src.v1.cache.cache_manager as cache_manager_module +import src.v2.api as v2_api_module + +app = api_module.app + +def _configure_cache(tmp_path, monkeypatch) -> None: + monkeypatch.setenv("CACHE_DB_PATH", str(tmp_path / "v1_parity_cache.db")) + cache_manager_module._cache_manager = None + + +def test_v1_routes_remain_registered() -> None: + route_paths = { + path + for route in app.routes + for path in [getattr(route, "path", None)] + if isinstance(path, str) + } + required_v1_routes = { + "/", + "/v1/org/llm/json/{full_path:path}", + "/v1/user/llm/json/{full_path:path}", + "/v1/repository/gimie/json-ld/{full_path:path}", + "/v1/repository/llm/json-ld/{full_path:path}", + "/v1/repository/llm/json/{full_path:path}", + "/v1/cache/stats", + "/v1/cache/entries", + "/v1/cache/cleanup", + "/v1/cache/clear", + "/v1/cache/enable", + "/v1/cache/disable", + "/v1/cache/invalidate/{api_type}", + } + + assert required_v1_routes <= route_paths + + +def test_v1_root_response_shape_is_unchanged() -> None: + payload = api_module.index() + assert isinstance(payload, dict) + assert list(payload) == ["title"] + assert "Git Metadata Extractor v2.0.1" in payload["title"] + + +def test_v1_cache_management_endpoints_respond(tmp_path, monkeypatch) -> None: + _configure_cache(tmp_path, monkeypatch) + + stats_payload = asyncio.run(api_module.get_cache_stats()) + entries_payload = asyncio.run( + api_module.list_cache_entries( + api_type=None, + limit=100, + offset=0, + include_expired=False, + ), + ) + cleanup_payload = asyncio.run(api_module.cleanup_cache()) + clear_payload = asyncio.run(api_module.clear_all_cache()) + + assert "config" in stats_payload + assert "entries" in entries_payload + assert "message" in cleanup_payload + assert "message" in clear_payload + + +def test_v2_routes_do_not_break_v1_cache_access(tmp_path, monkeypatch) -> None: + _configure_cache(tmp_path, monkeypatch) + + v2_health = asyncio.run(v2_api_module.health()) + v1_stats = asyncio.run(api_module.get_cache_stats()) + + assert v2_health.status in {"healthy", "degraded", "unhealthy"} + assert "config" in v1_stats diff --git a/tests/v2/__init__.py b/tests/v2/__init__.py new file mode 100644 index 0000000..4a0929b --- /dev/null +++ b/tests/v2/__init__.py @@ -0,0 +1 @@ +"""Tests for v2 extraction foundation tasks.""" diff --git a/tests/v2/conftest.py b/tests/v2/conftest.py new file mode 100644 index 0000000..edd1f7d --- /dev/null +++ b/tests/v2/conftest.py @@ -0,0 +1,167 @@ +from __future__ import annotations + +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Callable, Iterator + +import pytest + +V2_TESTS_ROOT = Path(__file__).resolve().parent +V2_APP_STATE_FIELDS = ( + "v2_provider_set", + "v2_orchestrator", + "v2_github_provider", + "v2_orcid_provider", + "v2_infoscience_provider", + "v2_ror_provider", +) + + +@dataclass(frozen=True) +class V2TestConfig: + repo_root: Path + tests_root: Path + fixtures_root: Path + schema_fixtures_root: Path + golden_root: Path + + +def _load_json(path: Path) -> Any: + with path.open(encoding="utf-8") as handle: + return json.load(handle) + + +def _normalize_json_name(name: str, *, schema: bool = False) -> str: + if name.endswith(".json"): + return name + if schema: + return f"{name}.schema.json" + return f"{name}.json" + + +def _safe_join(base_dir: Path, subpath: Path, file_name: str) -> Path: + base_resolved = base_dir.resolve() + target_path = (base_resolved / subpath / file_name).resolve() + if not target_path.is_relative_to(base_resolved): + raise ValueError + return target_path + + +def _item_path(item: pytest.Item) -> Path: + if hasattr(item, "path"): + return Path(str(item.path)).resolve() + return Path(str(item.fspath)).resolve() + + +def pytest_collection_modifyitems(items: list[pytest.Item]) -> None: + for item in items: + if _item_path(item).is_relative_to(V2_TESTS_ROOT): + item.add_marker(pytest.mark.v2) + + +@pytest.fixture(autouse=True) +def _isolate_v2_runtime_env(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None: + """Ensure each test uses isolated local storage and mock providers by default.""" + monkeypatch.setenv("V2_GRAPH_DB_PATH", str(tmp_path / "v2_graph.db")) + monkeypatch.setenv("CACHE_DB_PATH", str(tmp_path / "cache.db")) + monkeypatch.setenv("V2_USE_MOCK_PROVIDERS", "true") + # Most tests exercise deterministic rule-based behavior unless they opt into LLM explicitly. + monkeypatch.setenv("V2_AGENT_RUNTIME_DEFAULT", "rule_based") + # Default bearer token for `verify_token`. `tests/v2/test_auth.py` + # overrides or deletes this var inside individual tests to exercise + # the unauthorised / unconfigured paths. + monkeypatch.setenv("API_TOKEN", "test-api-token") + + +@pytest.fixture(autouse=True) +def _isolate_main_app_state() -> Iterator[None]: + """Prevent tests mutating src.api.app.state from leaking across tests.""" + from src.api import app as main_app # noqa: PLC0415 + + sentinel = object() + original_values: dict[str, object] = {} + for field in V2_APP_STATE_FIELDS: + value = getattr(main_app.state, field, sentinel) + original_values[field] = value + if hasattr(main_app.state, field): + delattr(main_app.state, field) + + yield + + for field, value in original_values.items(): + if value is sentinel: + if hasattr(main_app.state, field): + delattr(main_app.state, field) + continue + setattr(main_app.state, field, value) + + +@pytest.fixture(scope="session") +def v2_test_config() -> V2TestConfig: + repo_root = V2_TESTS_ROOT.parents[1] + fixtures_root = V2_TESTS_ROOT / "fixtures" + return V2TestConfig( + repo_root=repo_root, + tests_root=V2_TESTS_ROOT, + fixtures_root=fixtures_root, + schema_fixtures_root=fixtures_root / "schema", + golden_root=V2_TESTS_ROOT / "golden", + ) + + +@pytest.fixture(scope="session") +def load_schema(v2_test_config: V2TestConfig) -> Callable[[str, str], dict[str, Any]]: + def _load(schema_type: str, schema_name: str) -> dict[str, Any]: + if schema_type not in {"strict", "agent"}: + raise ValueError + + schema_file_name = _normalize_json_name(schema_name, schema=True) + schema_path = _safe_join( + v2_test_config.schema_fixtures_root, + Path(schema_type), + schema_file_name, + ) + if not schema_path.exists(): + raise FileNotFoundError(schema_path) + + parsed_schema = _load_json(schema_path) + if not isinstance(parsed_schema, dict): + raise TypeError + return parsed_schema + + return _load + + +@pytest.fixture(scope="session") +def load_fixture(v2_test_config: V2TestConfig) -> Callable[[str, str], Any]: + def _load(fixture_group: str, fixture_name: str) -> Any: + fixture_file_name = _normalize_json_name(fixture_name) + fixture_path = _safe_join( + v2_test_config.fixtures_root, + Path(fixture_group), + fixture_file_name, + ) + if not fixture_path.exists(): + raise FileNotFoundError(fixture_path) + + return _load_json(fixture_path) + + return _load + + +@pytest.fixture(scope="session") +def load_golden(v2_test_config: V2TestConfig) -> Callable[[str, str], Any]: + def _load(golden_group: str, golden_name: str) -> Any: + golden_file_name = _normalize_json_name(golden_name) + golden_path = _safe_join( + v2_test_config.golden_root, + Path(golden_group), + golden_file_name, + ) + if not golden_path.exists(): + raise FileNotFoundError(golden_path) + + return _load_json(golden_path) + + return _load diff --git a/tests/v2/fixtures/__init__.py b/tests/v2/fixtures/__init__.py new file mode 100644 index 0000000..8f2b734 --- /dev/null +++ b/tests/v2/fixtures/__init__.py @@ -0,0 +1 @@ +"""Shared fixture data for v2 tests.""" diff --git a/tests/v2/fixtures/github/dependents/SELECTORS.md b/tests/v2/fixtures/github/dependents/SELECTORS.md new file mode 100644 index 0000000..622098a --- /dev/null +++ b/tests/v2/fixtures/github/dependents/SELECTORS.md @@ -0,0 +1,53 @@ +# GitHub dependents page — HTML selectors + +URL: `https://github.com/{owner}/{repo}/network/dependents?dependent_type=REPOSITORY|PACKAGE` + +Captured 2026-05-01 via Selenium (Firefox headless). All findings verified +against the three fixtures in this directory. + +## Layout overview + +The page has: + +1. A toggle bar with two `` links — one for `REPOSITORY` view, one for + `PACKAGE` view. The currently-shown view carries `class="btn-link selected"`; + the other carries `class="btn-link "` (trailing space significant in the + live HTML). +2. A list of rows (one `
` per + dependent). +3. A pagination footer with `Previous` and `Next` buttons. + +## Selectors + +| What | Selector / pattern | +|---|---| +| Total count + selected view | `a.btn-link.selected` — text reads `" Repositories"` or `" Packages"` | +| Other view's count | `a.btn-link:not(.selected)` — same shape | +| Each dependent row | `[data-test-id="dg-repo-pkg-dependent"]` | +| Owner login | inside row, first `` with `data-hovercard-type` (`organization` or `user`); also available verbatim as the avatar `@<owner>` | +| Repository name | inside row, ``; `href="//"` is the canonical reference | +| Star count | inside row, text node immediately after `svg.octicon-star`. Number is comma-formatted (e.g. `"4,065,575"`). | +| Fork count | inside row, text node immediately after `svg.octicon-repo-forked`. Same format. | +| Next-page link | `Next` | +| End of pagination | `Next` becomes `` | + +## Fixture summary + +| File | Repo | Kind | Rows | Total count | Pagination | +|---|---|---|---:|---:|---| +| `sdsc-ordes_gimie_repository.html` | sdsc-ordes/gimie | REPOSITORY | 4 | 6 | end (disabled) | +| `psf_requests_repository.html` | psf/requests | REPOSITORY | 30 | 4,065,575 | next page available | +| `python-poetry_poetry_package.html` | python-poetry/poetry | PACKAGE | 0 | 0 | n/a (empty) | + +## Edge cases caught by the fixtures + +- **Empty graph** (poetry/PACKAGE): zero rows, `0 Repositories`, `0 Packages`. Pagination buttons still rendered but disabled. +- **End of pagination** (gimie): `Next` is a ` + +
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+
+
+ + + + \ No newline at end of file diff --git a/tests/v2/fixtures/providers/github/.gitkeep b/tests/v2/fixtures/providers/github/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/tests/v2/fixtures/providers/github/contributors_payload.json b/tests/v2/fixtures/providers/github/contributors_payload.json new file mode 100644 index 0000000..e1abd84 --- /dev/null +++ b/tests/v2/fixtures/providers/github/contributors_payload.json @@ -0,0 +1,43 @@ +{ + "rest": [ + { + "login": "octocat", + "id": 1, + "node_id": "MDQ6VXNlcjE=", + "avatar_url": "https://github.com/images/error/octocat_happy.gif", + "html_url": "https://github.com/octocat", + "type": "User", + "site_admin": false, + "contributions": 32 + }, + { + "login": "hubot", + "id": 2, + "node_id": "MDQ6VXNlcjI=", + "avatar_url": "https://github.com/images/error/hubot_happy.gif", + "html_url": "https://github.com/hubot", + "type": "User", + "site_admin": false, + "contributions": 17 + } + ], + "graphql": { + "data": { + 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b/tests/v2/fixtures/providers/github/rate_limited_response.json @@ -0,0 +1,5 @@ +{ + "message": "API rate limit exceeded for 192.30.255.112.", + "documentation_url": "https://docs.github.com/rest/overview/resources-in-the-rest-api#rate-limiting", + "status": 429 +} diff --git a/tests/v2/fixtures/providers/github/repo_payload.json b/tests/v2/fixtures/providers/github/repo_payload.json new file mode 100644 index 0000000..6d7bc14 --- /dev/null +++ b/tests/v2/fixtures/providers/github/repo_payload.json @@ -0,0 +1,95 @@ +{ + "rest": { + "id": 1296269, + "node_id": "MDEwOlJlcG9zaXRvcnkxMjk2MjY5", + "name": "Hello-World", + "full_name": "octocat/Hello-World", + "private": false, + "owner": { + "login": "octocat", + "id": 1, + "node_id": "MDQ6VXNlcjE=", + "avatar_url": "https://github.com/images/error/octocat_happy.gif", + "html_url": "https://github.com/octocat", + "type": "User", + "site_admin": false + }, + "html_url": "https://github.com/octocat/Hello-World", + "description": "This your first repo!", + "fork": false, + "url": "https://api.github.com/repos/octocat/Hello-World", + "forks_url": "https://api.github.com/repos/octocat/Hello-World/forks", + "contributors_url": "https://api.github.com/repos/octocat/Hello-World/contributors", + "languages_url": "https://api.github.com/repos/octocat/Hello-World/languages", + "stargazers_count": 80, + "watchers_count": 80, + "language": "Python", + "topics": [ + "metadata", + "ai" + ], + "forks_count": 9, + "open_issues_count": 0, + "default_branch": "main", + "license": { + "key": "mit", + "name": "MIT License", + "spdx_id": "MIT", + "url": "https://api.github.com/licenses/mit", + "node_id": "MDc6TGljZW5zZW1pdA==" + } + }, + "graphql": { + "data": { + "repository": { + "id": "R_kgDOGM5x", + "name": "Hello-World", + "nameWithOwner": "octocat/Hello-World", + "isPrivate": false, + "repositoryTopics": { + "nodes": [ + { + "topic": { + "name": "metadata" + } + }, + { + "topic": { + "name": "ai" + } + } + ] + }, + "licenseInfo": { + "key": "mit", + "name": "MIT License" + }, + "languages": { + "edges": [ + { + "size": 52310, + "node": { + "name": "Python" + } + }, + { + "size": 1210, + "node": { + "name": "Shell" + } + } + ] + } + } + } + }, + "languages": { + "Python": 52310, + "Shell": 1210 + }, + "private_repo_error": { + "message": "Repository access is forbidden", + "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", + "status": 403 + } +} diff --git a/tests/v2/fixtures/providers/github/user_payload.json b/tests/v2/fixtures/providers/github/user_payload.json new file mode 100644 index 0000000..3272a4f --- /dev/null +++ b/tests/v2/fixtures/providers/github/user_payload.json @@ -0,0 +1,19 @@ +{ + "login": "octocat", + "id": 1, + "node_id": "MDQ6VXNlcjE=", + "avatar_url": "https://github.com/images/error/octocat_happy.gif", + "html_url": "https://github.com/octocat", + "name": "The Octocat", + "company": "GitHub", + "blog": "https://github.blog", + "location": "San Francisco", + 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+ "total_results": 2, + "results": [ + { + "infoscienceOrgUnitIdentifier": "8072fc98-7fc8-44cb-b8f9-805f9b0725f3", + "name": "School of Architecture, Civil and Environmental Engineering", + "acronym": "ENAC", + "parentOrganization": "EPFL", + "url": "https://infoscience.epfl.ch/entities/orgunit/8072fc98-7fc8-44cb-b8f9-805f9b0725f3" + }, + { + "infoscienceOrgUnitIdentifier": "2d49f190-99f2-4c88-b430-4abf4cefa81c", + "name": "Laboratory of Construction Materials", + "acronym": "LMC", + "parentOrganization": "ENAC", + "url": "https://infoscience.epfl.ch/entities/orgunit/2d49f190-99f2-4c88-b430-4abf4cefa81c" + } + ] +} diff --git a/tests/v2/fixtures/providers/infoscience/person_multi_hit.json b/tests/v2/fixtures/providers/infoscience/person_multi_hit.json new file mode 100644 index 0000000..89f4b2e --- /dev/null +++ b/tests/v2/fixtures/providers/infoscience/person_multi_hit.json @@ -0,0 +1,36 @@ +{ + "query": "smith", + "total_results": 3, + "results": [ + { + "infosciencePersonIdentifier": "1f0b2b90-9e33-4f9a-9b14-8468f89f2e4d", + "name": "Alice Smith", + "orcid": "0000-0002-1825-0097", + "affiliations": [ + "EPFL School of Engineering" + ], + "profileUrl": "https://infoscience.epfl.ch/entities/person/1f0b2b90-9e33-4f9a-9b14-8468f89f2e4d", + "score": 98.1 + }, + { + "infosciencePersonIdentifier": "a61d03ec-c38f-43fd-b785-a4555f11f95a", + "name": "Alain Smith", + "orcid": null, + "affiliations": [ + "EPFL ENAC" + ], + "profileUrl": "https://infoscience.epfl.ch/entities/person/a61d03ec-c38f-43fd-b785-a4555f11f95a", + "score": 86.7 + }, + { + "infosciencePersonIdentifier": "fe7c0502-141a-468a-8950-bf2ef389b4ff", + "name": "Anna Maria Smith", + "orcid": "0000-0001-5000-0007", + "affiliations": [ + "EPFL IC" + ], + "profileUrl": "https://infoscience.epfl.ch/entities/person/fe7c0502-141a-468a-8950-bf2ef389b4ff", + "score": 81.4 + } + ] +} diff --git a/tests/v2/fixtures/providers/infoscience/person_single_hit.json 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--git a/tests/v2/fixtures/providers/live_snapshots/github/languages_sdsc-ordes_gimie.meta.json b/tests/v2/fixtures/providers/live_snapshots/github/languages_sdsc-ordes_gimie.meta.json new file mode 100644 index 0000000..3fe6a33 --- /dev/null +++ b/tests/v2/fixtures/providers/live_snapshots/github/languages_sdsc-ordes_gimie.meta.json @@ -0,0 +1,29 @@ +{ + "captured_at": "2026-02-24T14:18:28Z", + "case": "languages_sdsc-ordes_gimie", + "provider": "github", + "request": { + "auth_fallback_used": false, + "headers": {}, + "method": "GET", + "query": {}, + "url": "https://api.github.com/repos/sdsc-ordes/gimie/languages" + }, + "response": { + "elapsed_ms": 237, + "headers": { + "cache-control": "private, max-age=60, s-maxage=60", + "content-type": "application/json; charset=utf-8", + "date": "Tue, 24 Feb 2026 14:18:29 GMT", + "etag": "W/\"c3ac12e58fa31a59cb2bd8eb29b37584b00be66ef8d978eb68722bf01c354240\"", + "last-modified": "Mon, 12 Jan 2026 19:30:11 GMT", + "x-ratelimit-limit": "5000", + 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b/tests/v2/fixtures/providers/live_snapshots/github/org_sdsc-ordes.response.json @@ -0,0 +1,32 @@ +{ + "archived_at": null, + "avatar_url": "https://avatars.githubusercontent.com/u/114115753?v=4", + "blog": "https://www.datascience.ch", + "company": null, + "created_at": "2022-09-21T22:41:03Z", + "description": "Open Research Data (ORD) Engagement & Services team at the Swiss Data Science Center.", + "email": null, + "events_url": "https://api.github.com/orgs/sdsc-ordes/events", + "followers": 24, + "following": 0, + "has_organization_projects": true, + "has_repository_projects": true, + "hooks_url": "https://api.github.com/orgs/sdsc-ordes/hooks", + "html_url": "https://github.com/sdsc-ordes", + "id": 114115753, + "is_verified": false, + "issues_url": "https://api.github.com/orgs/sdsc-ordes/issues", + "location": "Switzerland", + "login": "sdsc-ordes", + "members_url": "https://api.github.com/orgs/sdsc-ordes/members{/member}", + "name": "Swiss Data Science Center - ORD", + "node_id": 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a/tests/v2/fixtures/providers/live_snapshots/infoscience/search_orgunit_epfl.meta.json b/tests/v2/fixtures/providers/live_snapshots/infoscience/search_orgunit_epfl.meta.json new file mode 100644 index 0000000..11771a2 --- /dev/null +++ b/tests/v2/fixtures/providers/live_snapshots/infoscience/search_orgunit_epfl.meta.json @@ -0,0 +1,27 @@ +{ + "captured_at": "2026-02-24T14:18:30Z", + "case": "search_orgunit_epfl", + "provider": "infoscience", + "request": { + "auth_fallback_used": true, + "headers": {}, + "method": "GET", + "query": { + "configuration": "orgunit", + "query": "EPFL", + "size": "10" + }, + "url": "https://infoscience.epfl.ch/server/api/discover/search/objects" + }, + "response": { + "elapsed_ms": 125, + "headers": { + "cache-control": "no-cache, no-store, max-age=0, must-revalidate", + "content-type": "application/json;charset=UTF-8", + "date": "Tue, 24 Feb 2026 14:18:30 GMT" + }, + "initial_status_code": 403, + "ok": true, + "status_code": 200 + } +} diff --git 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The analysis utilizes data corresponding to an integrated luminosity of 103 fb(-1) collected in 2016-2018 with the central CMS detector and the CMS and TOTEM precision proton spectrometer (PPS). Events that have two photons with high transverse momenta (p(T)(gamma) > 100 GeV), back-to-back in azimuth, and with a large diphoton invariant mass (m(gamma gamma) > 350 GeV) are selected. To remove the dominant inclusive diphoton backgrounds, the kinematic properties of the protons detected in PPS are required to match those of the central diphoton system. Only events having opposite-side forward protons detected with a fractional momentum loss between 0.035 and 0.15 (0.18) for the detectors on the negative (positive) side of CMS are considered. One exclusive diphoton candidate is observed for an expected background of 1.1 events. Limits at 95% confidence level are derived for the four-photon anomalous coupling parameters |zeta(1)| < 0.073 TeV-4 and |zeta(2)| < 0.15 TeV-4, using an effective field theory. Additionally, upper limits are placed on the production of axionlike particles with coupling strength to photons f(-1) that varies from 0.03 TeV-1 to 1 TeV-1 over the mass range from 500 to 2000 GeV." + } + ], + "dc.description.sponsorship": [ + { + "authority": "63cff9dd-7d0f-402d-80ff-bc2318c8ead2", + "confidence": 600, + "language": null, + "place": 0, + "securityLevel": 0, + "value": "LPHE-LS" + } + ], + "dc.identifier": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1103/PhysRevD.110.012010" + } + ], + "dc.identifier.doi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1103/PhysRevD.110.012010" + } + ], + "dc.identifier.isi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "WOS:001412689900001" + } + ], + "dc.identifier.uri": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": 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The data correspond to an integrated luminosity of 6 fb(-1) collected at a center-of-mass energy of root s = 13 TeV with the LHCb detector. The CP-violation parameters are measured to be S-psi KS0 = 0.717 +/- 0.013(stat) +/- 0.008(syst) and C-psi KS0 = 0.008 +/- 0.012(stat) +/- 0.003(syst). This measurement of S-psi KS0 represents the most precise single measurement of the CKM angle beta to date and is more precise than the current world average. In addition, measurements of the CP-violation parameters of the individual channels are reported and a combination with the LHCb Run 1 measurements is performed." + } + ], + "dc.description.sponsorship": [ + { + "authority": "0f40ed2e-ee19-4a03-80b3-568df8cee330", + "confidence": 600, + "language": null, + "place": 0, + "securityLevel": 0, + "value": "LPHE-OS" + }, + { + "authority": "7e3c5b78-4d1a-4bbf-89fe-9c496dcde741", + "confidence": 600, + "language": null, + "place": 1, + "securityLevel": 0, + "value": "LPHE-RM" + }, + { + "authority": "63cff9dd-7d0f-402d-80ff-bc2318c8ead2", + "confidence": 600, + "language": null, + "place": 2, + "securityLevel": 0, + "value": "LPHE-LS" + } + ], + "dc.identifier": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1103/PhysRevLett.132.021801" + } + ], + "dc.identifier.doi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1103/PhysRevLett.132.021801" + } 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The ratio of the branching fractions of Lambda(+)(c) -> p pi(0) and Lambda(+)(c) -> p eta is measured as B(Lambda(+)(c) -> p pi(0))/B(Lambda(+)(c) -> p eta) = (0.120 +/- 0.026(stat) +/- 0.007(syst)). This result resolves the longstanding discrepancy between earlier experimental searches, providing both a decisive conclusion and valuable input for QCD-inspired theoretical models. A sophisticated deep learning approach using a Transformer-based architecture is employed to distinguish the signal from the prevalent hadronic backgrounds, complemented by thorough validation and systematic uncertainty quantification." + } + ], + "dc.description.sponsorship": [ + { + "authority": "63cff9dd-7d0f-402d-80ff-bc2318c8ead2", + "confidence": 600, + "language": null, + "place": 0, + "value": "LPHE-LS" + } + ], + "dc.identifier": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1103/PhysRevD.111.L051101" + } + ], + "dc.identifier.doi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1103/PhysRevD.111.L051101" + } + ], + "dc.identifier.isi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "WOS:001639640600001" + } + ], + "dc.identifier.uri": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": 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Their decay branching fractions are determined to be B(chi(c0) -> p (p) over bar eta pi(0)) = (2.42 +/- 0.07 +/- 0.19) x 10(-4), B(chi(c1) -> p (p) over bar eta pi(0)) = (1.95 +/- 0.05 +/- 0.12) x 10(-4), and B(chi(c2) -> p (p) over bar eta pi(0)) = (1.33 +/- 0.05 +/- 0.08) x 10(-4), where the first uncertainties are statistical and the second systematic." + } + ], + "dc.description.sponsorship": [ + { + "authority": "63cff9dd-7d0f-402d-80ff-bc2318c8ead2", + "confidence": 600, + "language": null, + "place": 0, + "value": "LPHE-LS" + } + ], + "dc.identifier": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1103/PhysRevD.111.032008" + } + ], + "dc.identifier.doi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1103/PhysRevD.111.032008" + } + ], + "dc.identifier.isi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "WOS:001639639300001" + } + ], + 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boson to a rho(770)(0), phi(1020), or K-* (892)(0) meson and a photon are searched for using root s = 13 TeV proton-proton collision data collected by the CMS experiment at the LHC. Events are selected assuming the mesons decay into a pair of charged pions, a pair of charged kaons, or a charged kaon and pion, respectively. Depending on the Higgs boson production mode, different triggering and reconstruction techniques are adopted. The analyzed data sets correspond to integrated luminosities up to 138 fb(-1), depending on the reconstructed final state. After combining various data sets and categories, no significant excess above the background expectations is observed. Upper limits at 95% confidence level on the Higgs boson branching fractions into rho(770)(0)gamma, phi(1020)gamma, and K-*(892)(0)gamma are determined to be 3.7 x 10(-4), 3.0 x 10(-4), and 3.0 x 10(-4), respectively. In case of the rho(770)(0)gamma and phi(1020)gamma channels, these are the most stringent experimental limits to date." + } + ], + "dc.description.sponsorship": [ + { + "authority": "63cff9dd-7d0f-402d-80ff-bc2318c8ead2", + "confidence": 600, + "language": null, + "place": 0, + "value": "LPHE-LS" + } + ], + "dc.identifier": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1016/j.physletb.2025.139296" + } + ], + "dc.identifier.doi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1016/j.physletb.2025.139296" + } + ], + "dc.identifier.isi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "WOS:001493694200001" + } + ], + "dc.identifier.uri": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "https://infoscience.epfl.ch/handle/20.500.14299/251404" + } + ], + "dc.language.iso": [ + { + "authority": null, + 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The analysis focuses on the topology of a boosted Higgs boson recoiling against a high-energy photon. The final states of H -> b (b) over bar and H -> 4l are analyzed. This study examines effective HZ gamma and H gamma gamma anomalous couplings within the context of an effective field theory. In this approach, the production cross section is constrained to be sigma(gamma H) < 16.4 fb at 95% confidence level (CL). Simultaneous constraints on four anomalous couplings involving HZ gamma and H gamma gamma are provided. Additionally, the production rate for H -> 4l is examined to assess potential enhancements in the Yukawa couplings between light quarks and the Higgs boson. Assuming the standard model values for the Yukawa couplings of the bottom and top quarks, the following simultaneous constraints are obtained: kappa(u) = (0.0 +/- 1.5) x 10(3),kappa(d) = (0.0(-6.8)(+6.7)) x 10(2), kappa(s) = 0(+30)(-32), and kappa(c) = 0.0(-2.8)(+2.3). This rules out the hypothesis that up- or down-type quarks in the first or second generation have the same Yukawa couplings as those in the third generation, with a CL greater than 95%." + } + ], + "dc.description.sponsorship": [ + { + "authority": "63cff9dd-7d0f-402d-80ff-bc2318c8ead2", + "confidence": 600, + "language": null, + "place": 0, + "value": "LPHE-LS" + } + ], + "dc.identifier": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1103/6s8n-pvmy" + } + ], + "dc.identifier.doi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1103/6s8n-pvmy" + } + ], + "dc.identifier.isi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "WOS:001652154800001" + } + ], + "dc.identifier.uri": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "https://infoscience.epfl.ch/handle/20.500.14299/260616" + } + ], + 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Here, the cross section ratio of excited (psi(2S)) and ground state (J/psi) vector mesons is measured as a function of the charged-particle multiplicity in proton-lead (pPb) collisions at a center-of-mass (CM) energy per nucleon pair of 8.16 TeV. The data corresponding to an integrated luminosity of 175 nb(-1) were collected using the CMS detector. The ratio is measured separately for prompt and nonprompt charmonia in the transverse momentum range 6.5 < p(T) < 30 GeV and in four rapidity ranges spanning -2.865 < y(CM) < 1.935. For the first time, a statistically significant multiplicity dependence of the prompt cross section ratio is observed in proton-nucleus collisions. There is no clear rapidity dependence in the ratio. The prompt measurements are compared with a theoretical model which includes interactions with nearby particles during the evolution of the system. 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The Higgs boson decay into a bottom quark-antiquark pair (H -> b (b) over bar) is targeted, and three different final states of the top quark decays are considered, defined by the number of leptons (electrons or muons) in the event. The analysis utilises proton-proton collision data collected at the CERN LHC with the CMS experiment at root s = 13 TeV in 2016-2018, which correspond to an integrated luminosity of 138 fb(-1). The observed t (t) over barH production rate relative to the standard model expectation is 0.33 +/- 0.26 = 0.33 +/- 0.17(stat) +/- 0.21(syst). Additionally, the t (t) over barH production rate is determined in intervals of Higgs boson transverse momentum. An upper limit at 95% confidence level is set on the tH production rate of 14.6 times the standard model prediction, with an expectation of 19.3(-6.0)(+9.2). Finally, constraints are derived on the strength and structure of the coupling between the Higgs boson and the top quark from simultaneous extraction of the t (t) over barH and tH production rates, and the results are combined with those obtained in other Higgs boson decay channels." + } + ], + "dc.description.sponsorship": [ + { + "authority": "63cff9dd-7d0f-402d-80ff-bc2318c8ead2", + "confidence": 600, + "language": null, + "place": 0, + "value": "LPHE-LS" + } + ], + "dc.identifier": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1007/JHEP02(2025)097" + } + ], + "dc.identifier.doi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1007/JHEP02(2025)097" + } + ], + "dc.identifier.isi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "WOS:001509789000001" + } + ], + "dc.identifier.uri": [ + { + "authority": null, + "confidence": -1, + "language": 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"authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "2025-05-13T06:26:27Z" + } + ], + "dc.date.created": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "2025-05-12" + } + ], + "dc.date.issued": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "2025-02-07" + } + ], + "dc.date.modified": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "2025-09-17T11:54:24.084496Z" + } + ], + "dc.description.abstract": [ + { + "authority": null, + "confidence": -1, + "language": "en", + "place": 0, + "value": "A search is presented for the resonant production of a pair of standard model-like Higgs bosons using data from proton-proton collisions at a centre-of-mass energy of 13 TeV, collected by the CMS experiment at the CERN LHC in 2016-2018, corresponding to an integrated luminosity of 138 fb(-1). The final state consists of two b quark-antiquark pairs. The search is conducted in the region of phase space where at least one of the pairs is highly Lorentz-boosted and is reconstructed as a single large-area jet. The other pair may be either similarly merged or resolved, the latter reconstructed using two b-tagged jets. The data are found to be consistent with standard model processes and are interpreted as 95% confidence level upper limits on the product of the cross sections and the branching fractions of the spin-0 radion and the spin-2 bulk graviton that arise in warped extradimensional models. The limits set are in the range 9.74-0.29 fb and 4.94-0.19 fb for a narrow radion and a graviton, respectively, with masses between 1 and 3 TeV. For a radion and for a bulk graviton with widths 10% of their masses, the limits are in the range 12.5-0.35 fb and 8.23-0.23 fb, respectively, for the same masses. These limits result in the exclusion of a narrow-width graviton with a mass below 1.2 TeV, and of narrow and 10%-width radions with masses below 2.6, and 2.9 TeV, respectively." + } + ], + "dc.description.sponsorship": [ + { + "authority": "63cff9dd-7d0f-402d-80ff-bc2318c8ead2", + "confidence": 600, + "language": null, + "place": 0, + "value": "LPHE-LS" + } + ], + "dc.identifier": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1007/JHEP02(2025)040" + } + ], + "dc.identifier.doi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "10.1007/JHEP02(2025)040" + } + ], + "dc.identifier.isi": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "WOS:001426421100001" + } + ], + "dc.identifier.uri": [ + { + "authority": null, + "confidence": -1, + "language": null, + "place": 0, + "value": "https://infoscience.epfl.ch/handle/20.500.14299/250071" + } + ], + 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--git a/tests/v2/fixtures/providers/live_snapshots/ror/org_02s376052.response.json b/tests/v2/fixtures/providers/live_snapshots/ror/org_02s376052.response.json new file mode 100644 index 0000000..e102e75 --- /dev/null +++ b/tests/v2/fixtures/providers/live_snapshots/ror/org_02s376052.response.json @@ -0,0 +1,133 @@ +{ + "admin": { + "created": { + "date": "2018-11-14", + "schema_version": "1.0" + }, + "last_modified": { + "date": "2024-12-11", + "schema_version": "2.1" + } + }, + "domains": [], + "established": 1853, + "external_ids": [ + { + "all": [ + "501100001703", + "501100001709" + ], + "preferred": "501100001703", + "type": "fundref" + }, + { + "all": [ + "grid.5333.6" + ], + "preferred": "grid.5333.6", + "type": "grid" + }, + { + "all": [ + "0000 0001 2183 9049" + ], + "preferred": null, + "type": "isni" + }, + { + "all": [ + "Q262760" + ], + "preferred": null, + "type": "wikidata" + } + ], + "id": "https://ror.org/02s376052", + "links": [ + { + "type": "website", + "value": "http://www.epfl.ch" + }, + { + "type": "wikipedia", + "value": "http://en.wikipedia.org/wiki/%C3%89cole_Polytechnique_F%C3%A9d%C3%A9rale_de_Lausanne" + } + ], + "locations": [ + { + "geonames_details": { + "continent_code": "EU", + "continent_name": "Europe", + "country_code": "CH", + "country_name": "Switzerland", + "country_subdivision_code": "VD", + "country_subdivision_name": "Vaud", + "lat": 46.516, + "lng": 6.63282, + "name": "Lausanne" + }, + "geonames_id": 2659994 + } + ], + "names": [ + { + "lang": null, + "types": [ + "acronym" + ], + "value": "EPFL" + }, + { + "lang": "en", + "types": [ + "label" + ], + "value": "Swiss Federal Institute of Technology in Lausanne" + }, + { + "lang": "fr", + "types": [ + "ror_display", + "label" + ], + "value": "\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne" + } + ], + "relationships": [ + { + "id": "https://ror.org/033f9yy33", + "label": "NCCR Chemical Biology - Visualisation and Control of Biological Processes Using Chemistry", + "type": "child" + }, + { + "id": "https://ror.org/02hdt9m26", + "label": "Swiss Data Science Center", + "type": "child" + }, + { + "id": "https://ror.org/01rvn4p91", + "label": "Board of the Swiss Federal Institutes of Technology", + "type": "parent" + }, + { + "id": "https://ror.org/05932h694", + "label": "Idiap Research Institute", + "type": "related" + }, + { + "id": "https://ror.org/02z9ca723", + "label": "Swiss Polar Institute", + "type": "related" + }, + { + "id": "https://ror.org/03qf6ek79", + "label": "NCCR Catalysis", + "type": "related" + } + ], + "status": "active", + "types": [ + "education", + "funder" + ] +} diff --git a/tests/v2/fixtures/providers/live_snapshots/ror/search_epfl.meta.json b/tests/v2/fixtures/providers/live_snapshots/ror/search_epfl.meta.json new file mode 100644 index 0000000..0ce5478 --- /dev/null +++ b/tests/v2/fixtures/providers/live_snapshots/ror/search_epfl.meta.json @@ -0,0 +1,23 @@ +{ + "captured_at": "2026-02-24T14:18:29Z", + "case": "search_epfl", + "provider": "ror", + "request": { + "auth_fallback_used": false, + "headers": {}, + "method": "GET", + "query": { + "query": "EPFL" + }, + "url": "https://api.ror.org/v2/organizations" + }, + "response": { + "elapsed_ms": 79, + "headers": { + "content-type": "application/json", + "date": "Tue, 24 Feb 2026 14:18:29 GMT" + }, + "ok": true, + "status_code": 200 + } +} diff --git a/tests/v2/fixtures/providers/live_snapshots/ror/search_epfl.response.json b/tests/v2/fixtures/providers/live_snapshots/ror/search_epfl.response.json new file mode 100644 index 0000000..be2c691 --- /dev/null +++ b/tests/v2/fixtures/providers/live_snapshots/ror/search_epfl.response.json @@ -0,0 +1,235 @@ +{ + "items": [ + { + "admin": { + "created": { + "date": "2018-11-14", + "schema_version": "1.0" + }, + "last_modified": { + "date": "2024-12-11", + "schema_version": "2.1" + } + }, + "domains": [], + "established": 1853, + "external_ids": [ + { + "all": [ + "501100001703", + "501100001709" + ], + "preferred": "501100001703", + "type": "fundref" + }, + { + "all": [ + "grid.5333.6" + ], + "preferred": "grid.5333.6", + "type": "grid" + }, + { + "all": [ + "0000 0001 2183 9049" + ], + "preferred": null, + "type": "isni" + }, + { + "all": [ + "Q262760" + ], + "preferred": null, + "type": "wikidata" + } + ], + "id": "https://ror.org/02s376052", + "links": [ + { + "type": "website", + "value": "http://www.epfl.ch" + }, + { + "type": "wikipedia", + "value": "http://en.wikipedia.org/wiki/%C3%89cole_Polytechnique_F%C3%A9d%C3%A9rale_de_Lausanne" + } + ], + "locations": [ + { + "geonames_details": { + "continent_code": "EU", + "continent_name": "Europe", + "country_code": "CH", + "country_name": "Switzerland", + "country_subdivision_code": "VD", + "country_subdivision_name": "Vaud", + "lat": 46.516, + "lng": 6.63282, + "name": "Lausanne" + }, + "geonames_id": 2659994 + } + ], + "names": [ + { + "lang": null, + "types": [ + "acronym" + ], + "value": "EPFL" + }, + { + "lang": "en", + "types": [ + "label" + ], + "value": "Swiss Federal Institute of Technology in Lausanne" + }, + { + "lang": "fr", + "types": [ + "ror_display", + "label" + ], + "value": "\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne" + } + ], + "relationships": [ + { + "id": "https://ror.org/033f9yy33", + "label": "NCCR Chemical Biology - Visualisation and Control of Biological Processes Using Chemistry", + "type": "child" + }, + { + "id": "https://ror.org/02hdt9m26", + "label": "Swiss Data Science Center", + "type": "child" + }, + { + "id": "https://ror.org/01rvn4p91", + "label": "Board of the Swiss Federal Institutes of Technology", + "type": "parent" + }, + { + "id": "https://ror.org/05932h694", + "label": "Idiap Research Institute", + "type": "related" + }, + { + "id": "https://ror.org/02z9ca723", + "label": "Swiss Polar Institute", + "type": "related" + }, + { + "id": "https://ror.org/03qf6ek79", + "label": "NCCR Catalysis", + "type": "related" + } + ], + "status": "active", + "types": [ + "education", + "funder" + ] + }, + { + "admin": { + "created": { + "date": "2018-11-14", + "schema_version": "1.0" + }, + "last_modified": { + "date": "2024-12-11", + "schema_version": "2.1" + } + }, + "domains": [], + "established": 2009, + "external_ids": [ + { + "all": [ + "grid.483376.d" + ], + "preferred": "grid.483376.d", + "type": "grid" + } + ], + "id": "https://ror.org/016fqsx25", + "links": [ + { + "type": "website", + "value": "http://www.formation-continue-unil-epfl.ch/en/" + } + ], + "locations": [ + { + "geonames_details": { + "continent_code": "EU", + "continent_name": "Europe", + "country_code": "CH", + "country_name": "Switzerland", + "country_subdivision_code": "VD", + "country_subdivision_name": "Vaud", + "lat": 46.52899, + "lng": 6.56261, + "name": "\u00c9cublens" + }, + "geonames_id": 6285791 + } + ], + "names": [ + { + "lang": "en", + "types": [ + "ror_display", + "label" + ], + "value": "Formation Continue UNIL-EPFL" + } + ], + "relationships": [], + "status": "active", + "types": [ + "education" + ] + } + ], + "meta": { + "continents": [ + { + "count": 2, + "id": "eu", + "title": "Europe" + } + ], + "countries": [ + { + "count": 2, + "id": "ch", + "title": "Switzerland" + } + ], + "statuses": [ + { + "count": 2, + "id": "active", + "title": "active" + } + ], + "types": [ + { + "count": 2, + "id": "education", + "title": "education" + }, + { + "count": 1, + "id": "funder", + "title": "funder" + } + ] + }, + "number_of_results": 2, + "time_taken": 2 +} diff --git a/tests/v2/fixtures/providers/orcid/.gitkeep b/tests/v2/fixtures/providers/orcid/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/tests/v2/fixtures/providers/orcid/invalid_checksum.json b/tests/v2/fixtures/providers/orcid/invalid_checksum.json new file mode 100644 index 0000000..8d78053 --- /dev/null +++ b/tests/v2/fixtures/providers/orcid/invalid_checksum.json @@ -0,0 +1,17 @@ +{ + "orcid_id": "0000-0002-1825-0098", + "name": "Invalid Checksum Profile", + "employment": [ + { + "organization": "Invalid Institute", + "department": "Quality Assurance", + "role": "Tester", + "start_date": "2023-01-01", + "end_date": null + } + ], + "education": [], + "affiliations": [ + "Invalid Institute" + ] +} diff --git a/tests/v2/fixtures/providers/orcid/multiple_employment.json b/tests/v2/fixtures/providers/orcid/multiple_employment.json new file mode 100644 index 0000000..c44d6f1 --- /dev/null +++ b/tests/v2/fixtures/providers/orcid/multiple_employment.json @@ -0,0 +1,34 @@ +{ + "orcid_id": "0000-0003-1415-9269", + "name": "Multiple Employment Profile", + "employment": [ + { + "organization": "Swiss Data Science Center", + "department": "Open Science", + "role": "Research Software Engineer", + "start_date": "2022-04-01", + "end_date": null + }, + { + "organization": "CERN", + "department": "Data Analytics", + "role": "Consultant", + "start_date": "2020-01-01", + "end_date": "2022-03-31" + } + ], + "education": [ + { + "organization": "ETH Zurich", + "department": "Computer Science", + "role": "MSc", + "start_date": "2016-09-01", + "end_date": "2018-06-30" + } + ], + "affiliations": [ + "Swiss Data Science Center", + "CERN", + "ETH Zurich" + ] +} diff --git a/tests/v2/fixtures/providers/orcid/no_affiliations.json b/tests/v2/fixtures/providers/orcid/no_affiliations.json new file mode 100644 index 0000000..5762ba1 --- /dev/null +++ b/tests/v2/fixtures/providers/orcid/no_affiliations.json @@ -0,0 +1,7 @@ +{ + "orcid_id": "0000-0001-3456-7898", + "name": "No Affiliation Researcher", + "employment": [], + "education": [], + "affiliations": [] +} diff --git a/tests/v2/fixtures/providers/orcid/valid_record.json b/tests/v2/fixtures/providers/orcid/valid_record.json new file mode 100644 index 0000000..e81e65c --- /dev/null +++ b/tests/v2/fixtures/providers/orcid/valid_record.json @@ -0,0 +1,26 @@ +{ + "orcid_id": "0000-0002-1825-0097", + "name": "Alice Example", + "employment": [ + { + "organization": "Ecole Polytechnique Federale de Lausanne", + "department": "School of Engineering", + "role": "Research Engineer", + "start_date": "2021-01-01", + "end_date": null + } + ], + "education": [ + { + "organization": "University of Lausanne", + "department": "Computer Science", + "role": "PhD", + "start_date": "2017-09-01", + "end_date": "2020-12-31" + } + ], + "affiliations": [ + "Ecole Polytechnique Federale de Lausanne", + "University of Lausanne" + ] +} diff --git a/tests/v2/fixtures/providers/ror/.gitkeep b/tests/v2/fixtures/providers/ror/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/tests/v2/fixtures/providers/ror/not_found.json b/tests/v2/fixtures/providers/ror/not_found.json new file mode 100644 index 0000000..ac51082 --- /dev/null +++ b/tests/v2/fixtures/providers/ror/not_found.json @@ -0,0 +1,4 @@ +{ + "message": "ROR organization not found", + "status": 404 +} diff --git a/tests/v2/fixtures/providers/ror/org_detail.json b/tests/v2/fixtures/providers/ror/org_detail.json new file mode 100644 index 0000000..df323e5 --- /dev/null +++ b/tests/v2/fixtures/providers/ror/org_detail.json @@ -0,0 +1,33 @@ +{ + "organization": { + "id": "https://ror.org/02s376052", + "name": "Ecole Polytechnique Federale de Lausanne", + "aliases": [ + "EPF Lausanne", + "Swiss Federal Institute of Technology Lausanne" + ], + "acronyms": [ + "EPFL" + ], + "types": [ + "Education", + "Facility" + ], + "country": { + "country_name": "Switzerland", + "country_code": "CH" + }, + "links": [ + "https://www.epfl.ch" + ], + "relationships": { + "parent": null, + "children": [ + { + "id": "https://ror.org/04f4a0c74", + "name": "EPFL Valais Wallis" + } + ] + } + } +} diff --git a/tests/v2/fixtures/providers/ror/org_with_aliases.json b/tests/v2/fixtures/providers/ror/org_with_aliases.json new file mode 100644 index 0000000..ddbb0fa --- /dev/null +++ b/tests/v2/fixtures/providers/ror/org_with_aliases.json @@ -0,0 +1,45 @@ +{ + "organization": { + "id": "https://ror.org/03yrm5c26", + "name": "Institute for Multilingual Intelligence", + "aliases": [ + "Institut d'intelligence multilingue", + "Istituto per l'intelligenza multilingue" + ], + "acronyms": [ + "IMI", + "I2M" + ], + "labels": [ + { + "label": "Institute for Multilingual Intelligence", + "iso639": "en" + }, + { + "label": "Institut d'intelligence multilingue", + "iso639": "fr" + }, + { + "label": "Istituto per l'intelligenza multilingue", + "iso639": "it" + } + ], + "types": [ + "Research" + ], + "country": { + "country_name": "Switzerland", + "country_code": "CH" + }, + "links": [ + "https://example.org/imi" + ], + "relationships": { + "parent": { + "id": "https://ror.org/02s376052", + "name": "Ecole Polytechnique Federale de Lausanne" + }, + "children": [] + } + } +} diff --git a/tests/v2/fixtures/providers/ror/parent_org.json b/tests/v2/fixtures/providers/ror/parent_org.json new file mode 100644 index 0000000..e7a6061 --- /dev/null +++ b/tests/v2/fixtures/providers/ror/parent_org.json @@ -0,0 +1,31 @@ +{ + "organization": { + "id": "https://ror.org/05a28rw58", + "name": "ETH Zurich", + "aliases": [ + "Swiss Federal Institute of Technology Zurich" + ], + "acronyms": [ + "ETH" + ], + "types": [ + "Education" + ], + "country": { + "country_name": "Switzerland", + "country_code": "CH" + }, + "links": [ + "https://ethz.ch" + ], + "relationships": { + "parent": null, + "children": [ + { + "id": "https://ror.org/03j4x1x95", + "name": "ETH AI Center" + } + ] + } + } +} diff --git a/tests/v2/fixtures/providers/ror/search_results.json b/tests/v2/fixtures/providers/ror/search_results.json new file mode 100644 index 0000000..e6a31cc --- /dev/null +++ b/tests/v2/fixtures/providers/ror/search_results.json @@ -0,0 +1,46 @@ +{ + "query": "epfl", + "items": [ + { + "id": "https://ror.org/02s376052", + "name": "Ecole Polytechnique Federale de Lausanne", + "score": 0.99, + "aliases": [ + "EPF Lausanne" + ], + "acronyms": [ + "EPFL" + ], + "types": [ + "Education" + ], + "country": { + "country_name": "Switzerland", + "country_code": "CH" + }, + "parent": null + }, + { + "id": "https://ror.org/04f4a0c74", + "name": "EPFL Valais Wallis", + "score": 0.87, + "aliases": [ + "EPFL Valais" + ], + "acronyms": [ + "EPFL-VW" + ], + "types": [ + "Facility" + ], + "country": { + "country_name": "Switzerland", + "country_code": "CH" + }, + "parent": { + "id": "https://ror.org/02s376052", + "name": "Ecole Polytechnique Federale de Lausanne" + } + } + ] +} diff --git a/tests/v2/fixtures/scenarios/.gitkeep b/tests/v2/fixtures/scenarios/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/tests/v2/fixtures/schema/__init__.py b/tests/v2/fixtures/schema/__init__.py new file mode 100644 index 0000000..8b52419 --- /dev/null +++ b/tests/v2/fixtures/schema/__init__.py @@ -0,0 +1 @@ +"""Schema fixtures for v2 tests.""" diff --git a/tests/v2/fixtures/schema/agent/article.schema.json b/tests/v2/fixtures/schema/agent/article.schema.json new file mode 100644 index 0000000..4b596a6 --- /dev/null +++ b/tests/v2/fixtures/schema/agent/article.schema.json @@ -0,0 +1,74 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:ArticleShape.schema.json", + "title": "ArticleShape", + "description": "Schema for Article entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: doi → infoscienceArticleIdentifier → uuid. SHACL requires: schema:name, schema:identifier (DOI), schema:datePublished, schema:author (min 1).", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name", "schema:identifier", "schema:datePublished", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Set to the first non-null value from: schema:identifier (DOI), pulse:infoscienceArticleIdentifier, or uuid (fallback). Example: '10.1038/s41586-024-07856-z'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'schema:ScholarlyArticle'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:ArticleShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this article. Contains schema:identifier (DOI), pulse:infoscienceArticleIdentifier, and uuid", + "properties": { + "schema:identifier": { + "type": ["string", "null"], + "description": "DOI identifier. Format: 10.XXXX/suffix. Example: '10.1038/s41586-024-07856-z'" + }, + "pulse:infoscienceArticleIdentifier": { + "type": ["string", "null"], + "description": "Infoscience article UUID4 identifier. Format: 8-4-4-4-12 hexadecimal characters. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Example: 'f8a3b2c1-4d5e-4f6a-8b9c-1d2e3f4a5b6c'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'schema:identifier', 'pulse:infoscienceArticleIdentifier', or 'uuid'" + }, + "schema:name": { + "type": "string", + "description": "Article title. REQUIRED per SHACL. Example: 'Open Pulse: A Framework for Tracking Scientific Software Contributions'" + }, + "schema:identifier": { + "type": "string", + "description": "DOI identifier. REQUIRED per SHACL. Format: 10.XXXX/suffix. Example: '10.1038/s41586-024-07856-z'" + }, + "schema:datePublished": { + "type": "string", + "description": "Publication date. REQUIRED per SHACL. Format: ISO 8601 date (YYYY-MM-DD). Example: '2025-06-15'" + }, + "schema:author": { + "type": "array", + "minItems": 1, + "description": "Array of PersonShape hierarchical IDs. REQUIRED per SHACL (min 1 author). Uses target's resolved id (could be ORCID, GitHub username, or UUID). Example: ['0000-0001-2345-6789', 'mweber']", + "items": { + "type": "string" + } + }, + "pulse:infoscienceArticleIdentifier": { + "type": ["string", "null"], + "description": "Infoscience article UUID4 identifier. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'" + }, + "schema:sourceOrganization": { + "type": ["string", "null"], + "description": "Reference to OrganizationShape hierarchical ID. Uses target's resolved id (could be ROR, GitHub org handle, or UUID). Example: 'https://ror.org/02s376052'" + } + } +} diff --git a/tests/v2/fixtures/schema/agent/contribution.schema.json b/tests/v2/fixtures/schema/agent/contribution.schema.json new file mode 100644 index 0000000..a5d30ba --- /dev/null +++ b/tests/v2/fixtures/schema/agent/contribution.schema.json @@ -0,0 +1,62 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:ContributionShape.schema.json", + "title": "ContributionShape", + "description": "Schema for Contribution entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: composite (personId_repoId) → uuid. SHACL requires: pulse:contributionTo, pulse:contributionCount, schema:author. Dates are optional.", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "pulse:contributionTo", "pulse:contributionCount", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Composite format: personId_repoId, or uuid (fallback). Example: '0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'pulse:Contribution'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:ContributionShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this contribution. Contains pulse:composite and uuid", + "properties": { + "pulse:composite": { + "type": "string", + "description": "Composite identifier. Format: personId_repoId (uses underscore separator). Example: '0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Example: 'd1a2b3c4-5e6f-4a7b-8c9d-0e1f2a3b4c5d'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'pulse:composite' or 'uuid'" + }, + "pulse:contributionTo": { + "type": "string", + "description": "Reference to RepositoryShape ID (GitHub handle). REQUIRED per SHACL. Example: 'EPFL-ENAC/geodata-toolkit'" + }, + "pulse:contributionCount": { + "type": "integer", + "description": "Number of commits/contributions. REQUIRED per SHACL. Must be zero or positive. Example: 156" + }, + "pulse:firstContributionDate": { + "type": ["string", "null"], + "description": "First contribution datetime. OPTIONAL per SHACL. Format: ISO 8601 datetime (YYYY-MM-DDTHH:MM:SSZ). Example: '2023-03-15T10:30:00Z'" + }, + "pulse:lastContributionDate": { + "type": ["string", "null"], + "description": "Last contribution datetime. OPTIONAL per SHACL. Format: ISO 8601 datetime (YYYY-MM-DDTHH:MM:SSZ). Example: '2025-01-20T14:45:00Z'" + }, + "schema:author": { + "type": "string", + "description": "Reference to PersonShape ID (hierarchical). REQUIRED per SHACL. Example: '0000-0001-2345-6789'" + } + } +} diff --git a/tests/v2/fixtures/schema/agent/membership.schema.json b/tests/v2/fixtures/schema/agent/membership.schema.json new file mode 100644 index 0000000..2c8c241 --- /dev/null +++ b/tests/v2/fixtures/schema/agent/membership.schema.json @@ -0,0 +1,58 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:MembershipShape.schema.json", + "title": "MembershipShape", + "description": "Schema for Membership entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: composite (personId_orgId) → uuid. SHACL requires: org:organization only. Role and dates are optional.", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "org:organization"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Composite format: personId_orgId, or uuid (fallback). Example: '0000-0001-2345-6789_https://ror.org/02s376052'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'org:Membership'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:MembershipShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this membership. Contains pulse:composite and uuid", + "properties": { + "pulse:composite": { + "type": "string", + "description": "Composite identifier. Format: personId_orgId (uses underscore separator). Example: '0000-0001-2345-6789_https://ror.org/02s376052'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Example: 'c1a2b3c4-5d6e-4f7a-8b9c-0d1e2f3a4b5c'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'pulse:composite' or 'uuid'" + }, + "org:organization": { + "type": "string", + "description": "Reference to OrganizationShape ID (hierarchical). REQUIRED per SHACL. Example: 'https://ror.org/02s376052'" + }, + "org:role": { + "type": ["string", "null"], + "description": "Role or position title within the organization. OPTIONAL per SHACL. Example: 'Research Engineer', 'PhD Student'" + }, + "time:hasBeginning": { + "type": ["string", "null"], + "description": "Start date of membership. Format: ISO 8601 date (YYYY-MM-DD). Example: '2020-09-01'" + }, + "time:hasEnd": { + "type": ["string", "null"], + "description": "End date of membership. Format: ISO 8601 date (YYYY-MM-DD). Null if current/ongoing. Example: '2024-02-28'" + } + } +} diff --git a/tests/v2/fixtures/schema/agent/organization.schema.json b/tests/v2/fixtures/schema/agent/organization.schema.json new file mode 100644 index 0000000..4b12262 --- /dev/null +++ b/tests/v2/fixtures/schema/agent/organization.schema.json @@ -0,0 +1,96 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:OrganizationShape.schema.json", + "title": "OrganizationShape", + "description": "Schema for Organization entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: ror → infoscienceOrganizationIdentifier → githubOrganizationHandle → uuid. SHACL requires: schema:name AND at least ONE of (schema:identifier, pulse:githubOrganizationHandle, pulse:infoscienceOrganizationIdentifier).", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Set to the first non-null value from: pulse:ror, pulse:infoscienceOrganizationIdentifier, pulse:githubOrganizationHandle, or uuid (fallback). Example: 'https://ror.org/02s376052' or 'numpy'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'org:Organization'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:OrganizationShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this organization. Contains pulse:ror, pulse:infoscienceOrganizationIdentifier, pulse:githubOrganizationHandle, and uuid", + "properties": { + "pulse:ror": { + "type": ["string", "null"], + "description": "ROR (Research Organization Registry) identifier. Format: https://ror.org/XXXXXXXXX (9 alphanumeric chars). Example: 'https://ror.org/02s376052'" + }, + "pulse:infoscienceOrganizationIdentifier": { + "type": ["string", "null"], + "description": "Infoscience organization UUID4 identifier. Format: 8-4-4-4-12 hexadecimal characters. Example: '95372c6b-7d45-432e-a84e-660c9fa54e05'" + }, + "pulse:githubOrganizationHandle": { + "type": ["string", "null"], + "description": "GitHub organization handle. Example: 'EPFL-ENAC'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Example: 'e2c4b6a8-1d3f-4e5a-9b7c-8d6e4f2a1b3c'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'pulse:ror', 'pulse:infoscienceOrganizationIdentifier', 'pulse:githubOrganizationHandle', or 'uuid'" + }, + "schema:name": { + "type": "string", + "description": "Organization name. Example: 'École Polytechnique Fédérale de Lausanne'" + }, + "schema:identifier": { + "type": ["string", "null"], + "description": "Canonical identifier (typically ROR URL). Example: 'https://ror.org/02s376052'" + }, + "pulse:githubOrganizationHandle": { + "type": ["string", "null"], + "description": "GitHub organization handle. Example: 'EPFL-ENAC'" + }, + "pulse:infoscienceOrganizationIdentifier": { + "type": ["string", "null"], + "description": "Infoscience organization identifier (UUID format). Example: '41674f42-ba15-4612-9817-2a6f60985c01'" + }, + "pulse:OrganizationType": { + "type": "string", + "enum": ["pulse:University", "pulse:ResearchInstitution", "pulse:GovernmentAgency", "pulse:SoftwareProject", "pulse:PrivateCompany", "pulse:NonProfitOrganization", "pulse:CommunitySpace", "pulse:OtherOrganizationType"], + "description": "Type of organization. One of: 'pulse:University', 'pulse:ResearchInstitution', 'pulse:GovernmentAgency', 'pulse:SoftwareProject', 'pulse:PrivateCompany', 'pulse:NonProfitOrganization', 'pulse:CommunitySpace', 'pulse:OtherOrganizationType'" + }, + "pulse:githubOrgFollowers": { + "type": ["integer", "null"], + "description": "GitHub organization follower count. Example: 342" + }, + "org:hasUnit": { + "type": "array", + "description": "Array of child OrganizationShape IDs (hierarchical). Example: ['95372c6b-7d45-432e-a84e-660c9fa54e05']", + "items": { + "type": "string" + } + }, + "org:unitOf": { + "type": "array", + "description": "Parent OrganizationShape IDs (hierarchical). Most orgs have 0 or 1 parent; arrays support joint affiliations (e.g. a research center jointly run by two universities). Example: ['https://ror.org/02s376052']", + "items": { + "type": "string" + } + }, + "pulse:owns": { + "type": "array", + "description": "Array of RepositoryShape IDs (GitHub handles) owned by this organization. Example: ['EPFL-ENAC/geodata-toolkit']", + "items": { + "type": "string" + } + } + } +} diff --git a/tests/v2/fixtures/schema/agent/person.schema.json b/tests/v2/fixtures/schema/agent/person.schema.json new file mode 100644 index 0000000..4cd6a09 --- /dev/null +++ b/tests/v2/fixtures/schema/agent/person.schema.json @@ -0,0 +1,95 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:PersonShape.schema.json", + "title": "PersonShape", + "description": "Schema for Person entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: orcid → infosciencePersonIdentifier → githubUsername → uuid. SHACL requires: schema:name AND at least ONE of (pulse:githubUsername, schema:email, pulse:infosciencePersonIdentifier, pulse:orcidIdentifier).", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Set to the first non-null value from: pulse:orcid, pulse:infosciencePersonIdentifier, pulse:githubUsername, or uuid (fallback). Example: '0000-0001-2345-6789' or 'mweber'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'schema:Person'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:PersonShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this person. Contains pulse:orcid, pulse:infosciencePersonIdentifier, pulse:githubUsername, and uuid (always present)", + "properties": { + "pulse:orcid": { + "type": ["string", "null"], + "description": "ORCID identifier. Format: four groups of 4 digits separated by hyphens, last character can be 0-9 or X. Example: '0000-0001-2345-6789'" + }, + "pulse:infosciencePersonIdentifier": { + "type": ["string", "null"], + "description": "Infoscience person UUID4 identifier. Format: 8-4-4-4-12 hexadecimal characters. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'" + }, + "pulse:githubUsername": { + "type": ["string", "null"], + "description": "GitHub username. Example: 'caviri'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Format: 8-4-4-4-12 hexadecimal characters. Example: 'a7d4e2b1-3c8f-4a5b-9d6e-2f1a3b4c5d6e'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'pulse:orcid', 'pulse:infosciencePersonIdentifier', 'pulse:githubUsername', or 'uuid'" + }, + "schema:name": { + "type": "string", + "description": "Full name of the person. REQUIRED per SHACL. Example: 'Carlos Vivar Rios'" + }, + "schema:email": { + "type": "string", + "description": "Email address. Format: standard email pattern. Example: 'carlos.vivar@epfl.ch'" + }, + "schema:url": { + "type": ["string", "null"], + "description": "Profile URL. Example: 'https://people.epfl.ch/carlos.vivar'" + }, + "pulse:githubUsername": { + "type": ["string", "null"], + "description": "GitHub username. Example: 'caviri'" + }, + "pulse:orcidIdentifier": { + "type": ["string", "null"], + "description": "ORCID identifier. Format: 0000-0001-2345-6789 (four groups of 4 digits, last can be X). Example: '0000-0001-2345-6789'" + }, + "pulse:infosciencePersonIdentifier": { + "type": ["string", "null"], + "description": "Infoscience person UUID4 identifier. Example: 'f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb'" + }, + "org:hasMembership": { + "type": "array", + "description": "Array of MembershipShape IDs. Format: composite (personId_orgId). Example: ['0000-0001-2345-6789_https://ror.org/02s376052']", + "items": { + "type": "string" + } + }, + "pulse:hasContribution": { + "type": "array", + "description": "Array of ContributionShape IDs. Format: composite (personId_repoId). Example: ['0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit']", + "items": { + "type": "string" + } + }, + "pulse:owns": { + "type": "array", + "description": "Array of RepositoryShape IDs owned by this person. Format: GitHub handle (owner/repo). Example: ['EPFL-ENAC/geodata-toolkit']", + "items": { + "type": "string" + } + } + } +} diff --git a/tests/v2/fixtures/schema/agent/repository.schema.json b/tests/v2/fixtures/schema/agent/repository.schema.json new file mode 100644 index 0000000..e345af1 --- /dev/null +++ b/tests/v2/fixtures/schema/agent/repository.schema.json @@ -0,0 +1,110 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:RepositoryShape.schema.json", + "title": "RepositoryShape", + "description": "Schema for Repository entities following Open Pulse Ontology v2.1.2. Optimized for LLM agent parsing with descriptive guidance. ID hierarchy: githubRepositoryHandle → doi → uuid. SHACL requires: schema:name, pulse:githubRepositoryHandle, schema:author (min 1).", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name", "pulse:githubRepositoryHandle", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier. Set to the first non-null value from: pulse:githubRepositoryHandle, schema:citation (DOI), or uuid (fallback). Example: 'EPFL-ENAC/geodata-toolkit'" + }, + "type": { + "type": "string", + "description": "RDF type. Must be 'schema:SoftwareSourceCode'" + }, + "shacl": { + "type": "string", + "description": "SHACL shape reference. Must be 'pulse:RepositoryShape'" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "description": "All available identifiers for this repository. Contains pulse:githubRepositoryHandle, schema:citation (DOI), and uuid", + "properties": { + "pulse:githubRepositoryHandle": { + "type": "string", + "description": "GitHub repository handle. Format: owner/repo. Example: 'EPFL-ENAC/geodata-toolkit'" + }, + "schema:citation": { + "type": ["string", "null"], + "description": "DOI identifier. Example: 'https://doi.org/10.5281/zenodo.1234567845678'" + }, + "uuid": { + "type": "string", + "description": "UUID4 fallback identifier. Always present. Example: 'b1c2d3e4-5f6a-4b7c-8d9e-0f1a2b3c4d5e'" + } + } + }, + "idSource": { + "type": "string", + "description": "Indicates which identifier was used as the primary 'id'. One of: 'pulse:githubRepositoryHandle', 'schema:citation', or 'uuid'" + }, + "schema:name": { + "type": "string", + "description": "Repository display name. REQUIRED per SHACL. Example: 'Open Pulse Platform'" + }, + "pulse:githubRepositoryHandle": { + "type": "string", + "description": "GitHub repository handle. REQUIRED per SHACL. Format: owner/repo. Example: 'EPFL-ENAC/geodata-toolkit'" + }, + "pulse:repositoryType": { + "type": "string", + "enum": ["pulse:Software", "pulse:Data", "pulse:Documentation", "pulse:EducationalResource", "pulse:Other"], + "description": "Type of repository. One of: 'pulse:Software', 'pulse:Data', 'pulse:Documentation', 'pulse:EducationalResource', 'pulse:Other'" + }, + "pulse:discipline": { + "type": "array", + "description": "Array of Wikidata IRIs for disciplines. Format: wd:QXXXXX. Example: ['wd:Q21201', 'wd:Q8434']", + "items": { + "type": "string", + "enum": ["wd:Q1254373", "wd:Q101333", "wd:Q1071", "wd:Q11680831", "wd:Q12271", "wd:Q12483", "wd:Q18351432", "wd:Q188847", "wd:Q192386", "wd:Q21201", "wd:Q2167061", "wd:Q2329", "wd:Q23404", "wd:Q2878974", "wd:Q309", "wd:Q333", "wd:Q3353193", "wd:Q34749", "wd:Q3606845", "wd:Q395", "wd:Q413", "wd:Q420", "wd:Q42240", "wd:Q428691", "wd:Q43035", "wd:Q4830453", "wd:Q580689", "wd:Q5891", "wd:Q7112556", "wd:Q7163", "wd:Q735", "wd:Q7748", "wd:Q77590", "wd:Q7991", "wd:Q8008", "wd:Q80083", "wd:Q8078", "wd:Q8134", "wd:Q8162", "wd:Q816264", "wd:Q8242", "wd:Q83588", "wd:Q8434", "wd:Q843601", "wd:Q9174", "wd:Q9418"] + } + }, + "schema:author": { + "type": "array", + "minItems": 1, + "description": "Array of PersonShape IDs (hierarchical). REQUIRED per SHACL (min 1 author). Example: ['0000-0001-2345-6789', 'mweber']", + "items": { + "type": "string" + } + }, + "pulse:githubRepoStars": { + "type": ["integer", "null"], + "description": "GitHub star count. Example: 45" + }, + "pulse:githubRepoForks": { + "type": ["integer", "null"], + "description": "GitHub fork count. Example: 12" + }, + "schema:dateCreated": { + "type": ["string", "null"], + "description": "Creation datetime in ISO 8601 format. Format: YYYY-MM-DDTHH:MM:SSZ. Example: '2023-03-15T10:30:00Z'" + }, + "schema:license": { + "type": ["string", "null"], + "description": "SPDX license IRI. Example: 'https://spdx.org/licenses/MIT.html'" + }, + "schema:citation": { + "type": ["string", "null"], + "description": "DOI URL for citation. Example: 'https://doi.org/10.5281/zenodo.12345678'" + }, + "schema:programmingLanguage": { + "type": "array", + "description": "Array of programming languages. Example: ['Python', 'TypeScript']", + "items": { + "type": "string" + } + }, + "pulse:ownedBy": { + "type": ["string", "null"], + "description": "PersonShape or OrganizationShape ID (hierarchical) that owns this repository. Example: '95372c6b-7d45-432e-a84e-660c9fa54e05'" + }, + "pulse:isForkOf": { + "type": ["string", "null"], + "description": "Parent RepositoryShape ID (GitHub handle) if this is a fork. Example: 'numpy/numpy'" + } + } +} diff --git a/tests/v2/fixtures/schema/invalid/article_bad_doi.json b/tests/v2/fixtures/schema/invalid/article_bad_doi.json new file mode 100644 index 0000000..18f7629 --- /dev/null +++ b/tests/v2/fixtures/schema/invalid/article_bad_doi.json @@ -0,0 +1,20 @@ +{ + "id": "doi:10.1038/s41586-024-07856-z", + "type": "schema:ScholarlyArticle", + "shacl": "pulse:ArticleShape", + "identifiers": { + "schema:identifier": "doi:10.1038/s41586-024-07856-z", + "pulse:infoscienceArticleIdentifier": "c8e2a1b4-5f3d-47a9-b6c8-9d4e7f2a3b1c", + "uuid": "f8a3b2c1-4d5e-4f6a-8b9c-1d2e3f4a5b6c" + }, + "idSource": "schema:identifier", + "schema:name": "Open Pulse: A Framework for Tracking Scientific Software Contributions", + "schema:identifier": "doi:10.1038/s41586-024-07856-z", + "schema:datePublished": "2025-06-15", + "schema:author": [ + "0000-0001-2345-6789", + "0000-0003-4567-8901" + ], + "pulse:infoscienceArticleIdentifier": "c8e2a1b4-5f3d-47a9-b6c8-9d4e7f2a3b1c", + "schema:sourceOrganization": "95372c6b-7d45-432e-a84e-660c9fa54e05" +} diff --git a/tests/v2/fixtures/schema/invalid/contribution_negative_count.json b/tests/v2/fixtures/schema/invalid/contribution_negative_count.json new file mode 100644 index 0000000..242cf9a --- /dev/null +++ b/tests/v2/fixtures/schema/invalid/contribution_negative_count.json @@ -0,0 +1,15 @@ +{ + "id": "0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit", + "type": "pulse:Contribution", + "shacl": "pulse:ContributionShape", + "identifiers": { + "pulse:composite": "0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit", + "uuid": "d1a2b3c4-5e6f-4a7b-8c9d-0e1f2a3b4c5d" + }, + "idSource": "pulse:composite", + "pulse:contributionTo": "EPFL-ENAC/geodata-toolkit", + "pulse:contributionCount": -1, + "pulse:firstContributionDate": "2023-03-15T10:30:00Z", + "pulse:lastContributionDate": "2025-01-20T14:45:00Z", + "schema:author": "0000-0001-2345-6789" +} diff --git a/tests/v2/fixtures/schema/invalid/membership_extra_properties.json b/tests/v2/fixtures/schema/invalid/membership_extra_properties.json new file mode 100644 index 0000000..33b0cc1 --- /dev/null +++ b/tests/v2/fixtures/schema/invalid/membership_extra_properties.json @@ -0,0 +1,15 @@ +{ + "id": "0000-0001-2345-6789_https://ror.org/02s376052", + "type": "org:Membership", + "shacl": "pulse:MembershipShape", + "identifiers": { + "pulse:composite": "0000-0001-2345-6789_https://ror.org/02s376052", + "uuid": "c1a2b3c4-5d6e-4f7a-8b9c-0d1e2f3a4b5c" + }, + "idSource": "pulse:composite", + "org:organization": "https://ror.org/02s376052", + "org:role": "Research Engineer", + "time:hasBeginning": "2020-09-01", + "time:hasEnd": null, + "pulse:extraProperty": "not-allowed" +} diff --git a/tests/v2/fixtures/schema/invalid/org_unknown_type.json b/tests/v2/fixtures/schema/invalid/org_unknown_type.json new file mode 100644 index 0000000..a0e11db --- /dev/null +++ b/tests/v2/fixtures/schema/invalid/org_unknown_type.json @@ -0,0 +1,23 @@ +{ + "id": "https://ror.org/02s376052", + "type": "org:Organization", + "shacl": "pulse:OrganizationShape", + "identifiers": { + "pulse:ror": "https://ror.org/02s376052", + "pulse:infoscienceOrganizationIdentifier": "41674f42-ba15-4612-9817-2a6f60985c01", + "pulse:githubOrganizationHandle": "epaboratories", + "uuid": "e5a3b1c9-7d8e-4f6a-ab4c-6d8e0f2a4b6c" + }, + "idSource": "pulse:ror", + "schema:name": "Ecole Polytechnique Federale de Lausanne", + "schema:identifier": "https://ror.org/02s376052", + "pulse:infoscienceOrganizationIdentifier": "41674f42-ba15-4612-9817-2a6f60985c01", + "pulse:githubOrganizationHandle": "epaboratories", + "pulse:OrganizationType": "pulse:UnknownType", + "pulse:githubOrgFollowers": 89, + "org:hasUnit": [ + "95372c6b-7d45-432e-a84e-660c9fa54e05" + ], + "org:unitOf": null, + "pulse:owns": [] +} diff --git a/tests/v2/fixtures/schema/invalid/person_bad_orcid.json b/tests/v2/fixtures/schema/invalid/person_bad_orcid.json new file mode 100644 index 0000000..afd36af --- /dev/null +++ b/tests/v2/fixtures/schema/invalid/person_bad_orcid.json @@ -0,0 +1,25 @@ +{ + "id": "0000-0001-2345-6789", + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:orcid": "0000-0001-2345-678", + "pulse:infosciencePersonIdentifier": "f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb", + "pulse:githubUsername": "caviri", + "uuid": "a7d4e2b1-3c8f-4a5b-9d6e-2f1a3b4c5d6e" + }, + "idSource": "pulse:orcid", + "schema:name": "Carlos Vivar Rios", + "schema:email": "carlos.vivar@epfl.ch", + "schema:url": "https://people.epfl.ch/carlos.vivar", + "pulse:githubUsername": "caviri", + "pulse:orcidIdentifier": "0000-0001-2345-678", + "pulse:infosciencePersonIdentifier": "f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb", + "org:hasMembership": [ + "0000-0001-2345-6789_https://ror.org/02s376052" + ], + "pulse:hasContribution": [ + "0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit" + ], + "pulse:owns": [] +} diff --git a/tests/v2/fixtures/schema/invalid/person_missing_name.json b/tests/v2/fixtures/schema/invalid/person_missing_name.json new file mode 100644 index 0000000..ce4d814 --- /dev/null +++ b/tests/v2/fixtures/schema/invalid/person_missing_name.json @@ -0,0 +1,24 @@ +{ + "id": "0000-0001-2345-6789", + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:orcid": "0000-0001-2345-6789", + "pulse:infosciencePersonIdentifier": "f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb", + "pulse:githubUsername": "caviri", + "uuid": "a7d4e2b1-3c8f-4a5b-9d6e-2f1a3b4c5d6e" + }, + "idSource": "pulse:orcid", + "schema:email": "carlos.vivar@epfl.ch", + "schema:url": "https://people.epfl.ch/carlos.vivar", + "pulse:githubUsername": "caviri", + "pulse:orcidIdentifier": "0000-0001-2345-6789", + "pulse:infosciencePersonIdentifier": "f97b60da-bcab-4f2e-ba12-0ee0c4d0d6eb", + "org:hasMembership": [ + "0000-0001-2345-6789_https://ror.org/02s376052" + ], + "pulse:hasContribution": [ + "0000-0001-2345-6789_EPFL-ENAC/geodata-toolkit" + ], + "pulse:owns": [] +} diff --git a/tests/v2/fixtures/schema/invalid/person_no_identifier.json b/tests/v2/fixtures/schema/invalid/person_no_identifier.json new file mode 100644 index 0000000..8fb6526 --- /dev/null +++ b/tests/v2/fixtures/schema/invalid/person_no_identifier.json @@ -0,0 +1,22 @@ +{ + "id": "e1f2a3b4-5c6d-4e7f-8a9b-0c1d2e3f4a5b", + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:orcid": null, + "pulse:infosciencePersonIdentifier": null, + "pulse:githubUsername": null, + "uuid": "e1f2a3b4-5c6d-4e7f-8a9b-0c1d2e3f4a5b" + }, + "idSource": "uuid", + "schema:name": "Luca Rossi", + "schema:url": null, + "pulse:orcidIdentifier": null, + "org:hasMembership": [ + "e1f2a3b4-5c6d-4e7f-8a9b-0c1d2e3f4a5b_https://ror.org/01ggx4157" + ], + "pulse:hasContribution": [ + "e1f2a3b4-5c6d-4e7f-8a9b-0c1d2e3f4a5b_EPFL-ENAC/geodata-toolkit" + ], + "pulse:owns": [] +} diff --git a/tests/v2/fixtures/schema/invalid/repo_bad_github_handle.json b/tests/v2/fixtures/schema/invalid/repo_bad_github_handle.json new file mode 100644 index 0000000..e16c621 --- /dev/null +++ b/tests/v2/fixtures/schema/invalid/repo_bad_github_handle.json @@ -0,0 +1,32 @@ +{ + "id": "EPFL-ENAC geodata-toolkit", + "type": "schema:SoftwareSourceCode", + "shacl": "pulse:RepositoryShape", + "identifiers": { + "pulse:githubRepositoryHandle": "EPFL-ENAC geodata-toolkit", + "schema:citation": "https://doi.org/10.5281/zenodo.12345678", + "uuid": "b1c2d3e4-5f6a-4b7c-8d9e-0f1a2b3c4d5e" + }, + "idSource": "pulse:githubRepositoryHandle", + "schema:name": "Open Pulse Platform", + "pulse:githubRepositoryHandle": "EPFL-ENAC geodata-toolkit", + "pulse:repositoryType": "pulse:Software", + "pulse:discipline": [ + "wd:Q21201", + "wd:Q8434" + ], + "schema:author": [ + "0000-0001-2345-6789" + ], + "pulse:githubRepoStars": 45, + "pulse:githubRepoForks": 12, + "schema:dateCreated": "2023-03-15T10:30:00Z", + "schema:license": "https://spdx.org/licenses/MIT.html", + "schema:citation": "https://doi.org/10.5281/zenodo.12345678", + "schema:programmingLanguage": [ + "Python", + "TypeScript" + ], + "pulse:ownedBy": "95372c6b-7d45-432e-a84e-660c9fa54e05", + "pulse:isForkOf": null +} diff --git a/tests/v2/fixtures/schema/strict/article.schema.json b/tests/v2/fixtures/schema/strict/article.schema.json new file mode 100644 index 0000000..e50c82a --- /dev/null +++ b/tests/v2/fixtures/schema/strict/article.schema.json @@ -0,0 +1,78 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:ArticleShape.schema.json", + "title": "ArticleShape", + "description": "Schema for validating Article entities following Open Pulse Ontology v2.1.2. ID hierarchy: doi → infoscienceArticleIdentifier → uuid. Cross-references use hierarchical IDs when available.", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name", "schema:identifier", "schema:datePublished", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (first non-null in priority: doi → infoscienceArticleIdentifier → uuid)" + }, + "type": { + "type": "string", + "const": "schema:ScholarlyArticle" + }, + "shacl": { + "type": "string", + "const": "pulse:ArticleShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["schema:identifier", "uuid"], + "properties": { + "schema:identifier": { + "type": "string", + "pattern": "^10\\.\\d{4,9}/[-._;()/:a-zA-Z0-9]+$", + "description": "DOI identifier (required per SHACL minCount 1)" + }, + "pulse:infoscienceArticleIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["schema:identifier", "pulse:infoscienceArticleIdentifier", "uuid"] + }, + "schema:name": { + "type": "string", + "minLength": 1, + "description": "Article title" + }, + "schema:identifier": { + "type": "string", + "pattern": "^10\\.\\d{4,9}/[-._;()/:a-zA-Z0-9]+$", + "description": "DOI identifier (required per SHACL minCount 1)" + }, + "schema:datePublished": { + "type": "string", + "pattern": "^\\d{4}-\\d{2}-\\d{2}$", + "description": "Publication date in ISO 8601 date format (YYYY-MM-DD)" + }, + "schema:author": { + "type": "array", + "minItems": 1, + "items": { + "type": "string", + "description": "Reference to PersonShape hierarchical ID (orcid, infosciencePersonIdentifier, githubUsername, or uuid)" + } + }, + "pulse:infoscienceArticleIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + }, + "schema:sourceOrganization": { + "type": ["string", "null"], + "description": "Reference to OrganizationShape hierarchical ID (ror, infoscienceOrganizationIdentifier, githubOrganizationHandle, or uuid)" + } + } +} diff --git a/tests/v2/fixtures/schema/strict/contribution.schema.json b/tests/v2/fixtures/schema/strict/contribution.schema.json new file mode 100644 index 0000000..8fd92d6 --- /dev/null +++ b/tests/v2/fixtures/schema/strict/contribution.schema.json @@ -0,0 +1,65 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:ContributionShape.schema.json", + "title": "ContributionShape", + "description": "Schema for validating Contribution entities following Open Pulse Ontology v2.1.2. ID hierarchy: composite (personId_repoId) → uuid", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "pulse:contributionTo", "pulse:contributionCount", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (composite format: personId_repoId, or uuid fallback)" + }, + "type": { + "type": "string", + "const": "pulse:Contribution" + }, + "shacl": { + "type": "string", + "const": "pulse:ContributionShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["pulse:composite", "uuid"], + "properties": { + "pulse:composite": { + "type": "string", + "description": "Composite identifier in format: personId_repoId" + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["pulse:composite", "uuid"] + }, + "pulse:contributionTo": { + "type": "string", + "description": "Reference to RepositoryShape ID (GitHub handle format: owner/repo)" + }, + "pulse:contributionCount": { + "type": "integer", + "minimum": 0, + "description": "Number of commits/contributions" + }, + "pulse:firstContributionDate": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$", + "description": "First contribution datetime in ISO 8601 format. Optional per SHACL." + }, + "pulse:lastContributionDate": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$", + "description": "Last contribution datetime in ISO 8601 format. Optional per SHACL." + }, + "schema:author": { + "type": "string", + "description": "Reference to PersonShape ID (hierarchical)" + } + } +} diff --git a/tests/v2/fixtures/schema/strict/membership.schema.json b/tests/v2/fixtures/schema/strict/membership.schema.json new file mode 100644 index 0000000..3af476b --- /dev/null +++ b/tests/v2/fixtures/schema/strict/membership.schema.json @@ -0,0 +1,60 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:MembershipShape.schema.json", + "title": "MembershipShape", + "description": "Schema for validating Membership entities following Open Pulse Ontology v2.1.2. ID hierarchy: composite (personId_orgId) → uuid", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "org:organization"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (composite format: personId_orgId, or uuid fallback)" + }, + "type": { + "type": "string", + "const": "org:Membership" + }, + "shacl": { + "type": "string", + "const": "pulse:MembershipShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["pulse:composite", "uuid"], + "properties": { + "pulse:composite": { + "type": "string", + "description": "Composite identifier in format: personId_orgId" + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["pulse:composite", "uuid"] + }, + "org:organization": { + "type": "string", + "description": "Reference to OrganizationShape ID (hierarchical)" + }, + "org:role": { + "type": ["string", "null"], + "description": "Role or position title within the organization. Optional per SHACL (maxCount 1)." + }, + "time:hasBeginning": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{2}-\\d{2}$", + "description": "Start date in ISO 8601 date format (YYYY-MM-DD)" + }, + "time:hasEnd": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{2}-\\d{2}$", + "description": "End date in ISO 8601 date format (YYYY-MM-DD)" + } + } +} diff --git a/tests/v2/fixtures/schema/strict/organization.schema.json b/tests/v2/fixtures/schema/strict/organization.schema.json new file mode 100644 index 0000000..cf22388 --- /dev/null +++ b/tests/v2/fixtures/schema/strict/organization.schema.json @@ -0,0 +1,133 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:OrganizationShape.schema.json", + "title": "OrganizationShape", + "description": "Schema for validating Organization entities following Open Pulse Ontology v2.1.2. ID hierarchy: ror → infoscienceOrganizationIdentifier → githubOrganizationHandle → uuid. SHACL requires: schema:name AND at least ONE of (schema:identifier, pulse:githubOrganizationHandle, pulse:infoscienceOrganizationIdentifier).", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (first non-null in priority: ror → infoscienceOrganizationIdentifier → githubOrganizationHandle → uuid)" + }, + "type": { + "type": "string", + "const": "org:Organization" + }, + "shacl": { + "type": "string", + "const": "pulse:OrganizationShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["uuid"], + "properties": { + "pulse:ror": { + "type": ["string", "null"], + "pattern": "^https://ror\\.org/[0-9a-z]{9}$" + }, + "pulse:infoscienceOrganizationIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + }, + "pulse:githubOrganizationHandle": { + "type": ["string", "null"] + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["pulse:ror", "pulse:infoscienceOrganizationIdentifier", "pulse:githubOrganizationHandle", "uuid"] + }, + "schema:name": { + "type": "string", + "minLength": 1 + }, + "schema:identifier": { + "type": ["string", "null"], + "pattern": "^https://ror\\.org/[0-9a-z]{9}$", + "description": "Canonical identifier (typically ROR URL). Optional per SHACL." + }, + "pulse:githubOrganizationHandle": { + "type": ["string", "null"] + }, + "pulse:infoscienceOrganizationIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", + "description": "Infoscience organization identifier (UUID4 format)." + }, + "pulse:OrganizationType": { + "type": "string", + "enum": [ + "pulse:University", + "pulse:ResearchInstitution", + "pulse:GovernmentAgency", + "pulse:SoftwareProject", + "pulse:PrivateCompany", + "pulse:NonProfitOrganization", + "pulse:CommunitySpace", + "pulse:OtherOrganizationType" + ] + }, + "pulse:githubOrgFollowers": { + "type": ["integer", "null"], + "minimum": 0 + }, + "org:hasUnit": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to child OrganizationShape ID (hierarchical)" + } + }, + "org:unitOf": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to parent OrganizationShape ID (hierarchical)" + } + }, + "pulse:owns": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to RepositoryShape ID (GitHub handle)" + } + } + }, + "anyOf": [ + { + "description": "At least schema:identifier required", + "required": ["schema:identifier"], + "properties": { + "schema:identifier": { + "type": "string" + } + } + }, + { + "description": "At least githubOrganizationHandle required", + "required": ["pulse:githubOrganizationHandle"], + "properties": { + "pulse:githubOrganizationHandle": { + "type": "string" + } + } + }, + { + "description": "At least infoscienceOrganizationIdentifier required", + "required": ["pulse:infoscienceOrganizationIdentifier"], + "properties": { + "pulse:infoscienceOrganizationIdentifier": { + "type": "string" + } + } + } + ] +} diff --git a/tests/v2/fixtures/schema/strict/person.schema.json b/tests/v2/fixtures/schema/strict/person.schema.json new file mode 100644 index 0000000..1f8531b --- /dev/null +++ b/tests/v2/fixtures/schema/strict/person.schema.json @@ -0,0 +1,131 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:PersonShape.schema.json", + "title": "PersonShape", + "description": "Schema for validating Person entities following Open Pulse Ontology v2.1.2. ID hierarchy: orcid → infosciencePersonIdentifier → githubUsername → uuid", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (first non-null in priority: orcid → infosciencePersonIdentifier → githubUsername → uuid)" + }, + "type": { + "type": "string", + "const": "schema:Person" + }, + "shacl": { + "type": "string", + "const": "pulse:PersonShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["uuid"], + "properties": { + "pulse:orcid": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{4}-\\d{4}-\\d{3}[0-9X]$" + }, + "pulse:infosciencePersonIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + }, + "pulse:githubUsername": { + "type": ["string", "null"] + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["pulse:orcid", "pulse:infosciencePersonIdentifier", "pulse:githubUsername", "uuid"] + }, + "schema:name": { + "type": "string", + "minLength": 1 + }, + "schema:email": { + "type": "string", + "pattern": "^[\\w\\-\\.]+@([\\w-]+\\.)+[\\w-]{2,4}$" + }, + "schema:url": { + "type": ["string", "null"], + "format": "uri" + }, + "pulse:githubUsername": { + "type": ["string", "null"] + }, + "pulse:orcidIdentifier": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{4}-\\d{4}-\\d{3}[0-9X]$" + }, + "pulse:infosciencePersonIdentifier": { + "type": ["string", "null"], + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + }, + "org:hasMembership": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to MembershipShape ID (composite format: personId_orgId)" + } + }, + "pulse:hasContribution": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to ContributionShape ID (composite format: personId_repoId)" + } + }, + "pulse:owns": { + "type": "array", + "items": { + "type": "string", + "description": "Reference to RepositoryShape ID (GitHub handle format: owner/repo)" + } + } + }, + "anyOf": [ + { + "description": "At least githubUsername required", + "required": ["pulse:githubUsername"], + "properties": { + "pulse:githubUsername": { + "type": "string" + } + } + }, + { + "description": "At least email required", + "required": ["schema:email"], + "properties": { + "schema:email": { + "type": "string" + } + } + }, + { + "description": "At least infosciencePersonIdentifier required", + "required": ["pulse:infosciencePersonIdentifier"], + "properties": { + "pulse:infosciencePersonIdentifier": { + "type": "string" + } + } + }, + { + "description": "At least orcidIdentifier required", + "required": ["pulse:orcidIdentifier"], + "properties": { + "pulse:orcidIdentifier": { + "type": "string" + } + } + } + ] +} diff --git a/tests/v2/fixtures/schema/strict/pulse_ArticleShape.json b/tests/v2/fixtures/schema/strict/pulse_ArticleShape.json new file mode 100644 index 0000000..f1b4965 --- /dev/null +++ b/tests/v2/fixtures/schema/strict/pulse_ArticleShape.json @@ -0,0 +1,81 @@ +[ + { + "id": "10.1038/s41586-024-07856-z", + "type": "schema:ScholarlyArticle", + "shacl": "pulse:ArticleShape", + "identifiers": { + "schema:identifier": "10.1038/s41586-024-07856-z", + "pulse:infoscienceArticleIdentifier": "c8e2a1b4-5f3d-47a9-b6c8-9d4e7f2a3b1c", + "uuid": "f8a3b2c1-4d5e-4f6a-8b9c-1d2e3f4a5b6c" + }, + "idSource": "schema:identifier", + "schema:name": "Open Pulse: A Framework for Tracking Scientific Software Contributions", + "schema:identifier": "10.1038/s41586-024-07856-z", + "schema:datePublished": "2025-06-15", + "schema:author": [ + "0000-0001-2345-6789", + "0000-0003-4567-8901" + ], + "pulse:infoscienceArticleIdentifier": "c8e2a1b4-5f3d-47a9-b6c8-9d4e7f2a3b1c", + "schema:sourceOrganization": "95372c6b-7d45-432e-a84e-660c9fa54e05" + }, + { + "id": "10.1016/j.softx.2023.101456", + "type": "schema:ScholarlyArticle", + "shacl": "pulse:ArticleShape", + "identifiers": { + "schema:identifier": "10.1016/j.softx.2023.101456", + "pulse:infoscienceArticleIdentifier": "a4c8e2f1-7d3b-49a6-b5c8-2e1f9a7b6c4d", + "uuid": "f1e2d3c4-5b6a-4789-abcd-ef1234567890" + }, + "idSource": "schema:identifier", + "schema:name": "Gimie: Automated Git Metadata Extraction for Research Software", + "schema:identifier": "10.1016/j.softx.2023.101456", + "schema:datePublished": "2023-11-20", + "schema:author": [ + "0000-0001-2345-6789", + "0000-0002-3456-7890" + ], + "pulse:infoscienceArticleIdentifier": "a4c8e2f1-7d3b-49a6-b5c8-2e1f9a7b6c4d", + "schema:sourceOrganization": "https://ror.org/02s376052" + }, + { + "id": "10.1109/TBME.2024.3123456", + "type": "schema:ScholarlyArticle", + "shacl": 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"pulse:orcidIdentifier": null, + "pulse:infosciencePersonIdentifier": null, + "org:hasMembership": ["mweber_https://ror.org/05a28rw58"], + "pulse:hasContribution": [ + "mweber_EPFL-ENAC/structural-analysis", + "mweber_mweber/data-pipeline" + ], + "pulse:owns": ["mweber/data-pipeline"] + }, + { + "id": "e1f2a3b4-5c6d-4e7f-8a9b-0c1d2e3f4a5b", + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:orcid": null, + "pulse:infosciencePersonIdentifier": null, + "pulse:githubUsername": null, + "uuid": "e1f2a3b4-5c6d-4e7f-8a9b-0c1d2e3f4a5b" + }, + "idSource": "uuid", + "schema:name": "Luca Rossi", + "schema:email": "luca.rossi@cern.ch", + "schema:url": null, + "pulse:githubUsername": null, + "pulse:orcidIdentifier": null, + "pulse:infosciencePersonIdentifier": null, + "org:hasMembership": ["e1f2a3b4-5c6d-4e7f-8a9b-0c1d2e3f4a5b_https://ror.org/01ggx4157"], + "pulse:hasContribution": ["e1f2a3b4-5c6d-4e7f-8a9b-0c1d2e3f4a5b_EPFL-ENAC/geodata-toolkit"], + "pulse:owns": [] + } +] diff --git a/tests/v2/fixtures/schema/strict/pulse_RepositoryShape.json b/tests/v2/fixtures/schema/strict/pulse_RepositoryShape.json new file mode 100644 index 0000000..7468713 --- /dev/null +++ b/tests/v2/fixtures/schema/strict/pulse_RepositoryShape.json @@ -0,0 +1,98 @@ +[ + { + "id": "EPFL-ENAC/geodata-toolkit", + "type": "schema:SoftwareSourceCode", + "shacl": "pulse:RepositoryShape", + "identifiers": { + "pulse:githubRepositoryHandle": "EPFL-ENAC/geodata-toolkit", + "schema:citation": "https://doi.org/10.5281/zenodo.12345678", + "uuid": "b1c2d3e4-5f6a-4b7c-8d9e-0f1a2b3c4d5e" + }, + "idSource": "pulse:githubRepositoryHandle", + "schema:name": "Open Pulse Platform", + "pulse:githubRepositoryHandle": "EPFL-ENAC/geodata-toolkit", + "pulse:repositoryType": "pulse:Software", + "pulse:discipline": ["wd:Q21201", "wd:Q8434"], + "schema:author": ["0000-0001-2345-6789"], + "pulse:githubRepoStars": 45, + "pulse:githubRepoForks": 12, + "schema:dateCreated": "2023-03-15T10:30:00Z", + "schema:license": "https://spdx.org/licenses/MIT.html", + "schema:citation": "https://doi.org/10.5281/zenodo.12345678", + "schema:programmingLanguage": ["Python", "TypeScript"], + "pulse:ownedBy": "95372c6b-7d45-432e-a84e-660c9fa54e05", + "pulse:isForkOf": null + }, + { + "id": "EPFL-ENAC/structural-analysis", + "type": "schema:SoftwareSourceCode", + "shacl": "pulse:RepositoryShape", + "identifiers": { + "pulse:githubRepositoryHandle": "EPFL-ENAC/structural-analysis", + "schema:citation": null, + "uuid": "b2d3e4f5-6a7b-4c8d-9e0f-1a2b3c4d5e6f" + }, + "idSource": "pulse:githubRepositoryHandle", + "schema:name": "Gimie", + "pulse:githubRepositoryHandle": "EPFL-ENAC/structural-analysis", + "pulse:repositoryType": "pulse:Software", + "pulse:discipline": ["wd:Q21201"], + "schema:author": ["mweber"], + "pulse:githubRepoStars": 128, + "pulse:githubRepoForks": 34, + "schema:dateCreated": "2022-01-10T08:00:00Z", + "schema:license": "https://spdx.org/licenses/Apache-2.0.html", + "schema:citation": null, + "schema:programmingLanguage": ["Python"], + "pulse:ownedBy": "95372c6b-7d45-432e-a84e-660c9fa54e05", + "pulse:isForkOf": null + }, + { + "id": "mweber/data-pipeline", + "type": "schema:SoftwareSourceCode", + "shacl": "pulse:RepositoryShape", + "identifiers": { + "pulse:githubRepositoryHandle": "mweber/data-pipeline", + "schema:citation": null, + "uuid": "b3e4f5a6-7b8c-4d9e-8f1a-2b3c4d5e6f7a" + }, + "idSource": "pulse:githubRepositoryHandle", + "schema:name": "Data Pipeline", + "pulse:githubRepositoryHandle": "mweber/data-pipeline", + "pulse:repositoryType": "pulse:Data", + "pulse:discipline": ["wd:Q12483"], + "schema:author": ["mweber"], + "pulse:githubRepoStars": 5, + "pulse:githubRepoForks": 1, + "schema:dateCreated": "2024-06-20T14:15:00Z", + "schema:license": "https://spdx.org/licenses/GPL-3.0-only.html", + "schema:citation": null, + "schema:programmingLanguage": ["R", "Python"], + "pulse:ownedBy": "mweber", + "pulse:isForkOf": null + }, + { + "id": "jdupont/geodata-toolkit", + "type": "schema:SoftwareSourceCode", + "shacl": "pulse:RepositoryShape", + "identifiers": { + "pulse:githubRepositoryHandle": "jdupont/geodata-toolkit", + "schema:citation": null, + "uuid": "b4f5a6b7-8c9d-4e0f-a1b2-3c4d5e6f7a8b" + }, + "idSource": "pulse:githubRepositoryHandle", + "schema:name": "Geodata Toolkit (Fork)", + "pulse:githubRepositoryHandle": "jdupont/geodata-toolkit", + "pulse:repositoryType": "pulse:Software", + "pulse:discipline": ["wd:Q21201", "wd:Q8434"], + "schema:author": ["0000-0003-4567-8901"], + "pulse:githubRepoStars": 2, + "pulse:githubRepoForks": 0, + "schema:dateCreated": "2024-02-01T13:00:00Z", + "schema:license": "https://spdx.org/licenses/MIT.html", + "schema:citation": null, + "schema:programmingLanguage": ["Python", "TypeScript"], + "pulse:ownedBy": "0000-0003-4567-8901", + "pulse:isForkOf": "EPFL-ENAC/geodata-toolkit" + } +] diff --git a/tests/v2/fixtures/schema/strict/repository.schema.json b/tests/v2/fixtures/schema/strict/repository.schema.json new file mode 100644 index 0000000..2e46a8e --- /dev/null +++ b/tests/v2/fixtures/schema/strict/repository.schema.json @@ -0,0 +1,113 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "pulse:RepositoryShape.schema.json", + "title": "RepositoryShape", + "description": "Schema for validating Repository entities following Open Pulse Ontology v2.1.2. ID hierarchy: githubRepositoryHandle → doi → uuid", + "type": "object", + "additionalProperties": false, + "required": ["id", "type", "shacl", "identifiers", "idSource", "schema:name", "pulse:githubRepositoryHandle", "schema:author"], + "properties": { + "id": { + "type": "string", + "description": "Resolved hierarchical identifier (first non-null in priority: githubRepositoryHandle → doi → uuid)" + }, + "type": { + "type": "string", + "const": "schema:SoftwareSourceCode" + }, + "shacl": { + "type": "string", + "const": "pulse:RepositoryShape" + }, + "identifiers": { + "type": "object", + "additionalProperties": false, + "required": ["pulse:githubRepositoryHandle", "uuid"], + "properties": { + "pulse:githubRepositoryHandle": { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]+/[a-zA-Z0-9\\-_\\.]+$" + }, + "schema:citation": { + "type": ["string", "null"], + "format": "uri", + "description": "DOI identifier" + }, + "uuid": { + "type": "string", + "pattern": "^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$" + } + } + }, + "idSource": { + "type": "string", + "enum": ["pulse:githubRepositoryHandle", "schema:citation", "uuid"] + }, + "schema:name": { + "type": "string", + "minLength": 1 + }, + "pulse:githubRepositoryHandle": { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]+/[a-zA-Z0-9\\-_\\.]+$" + }, + "pulse:repositoryType": { + "type": "string", + "enum": ["pulse:Software", "pulse:Data", "pulse:Documentation", "pulse:EducationalResource", "pulse:Other"], + "description": "Repository type. Optional per SHACL." + }, + "pulse:discipline": { + "type": "array", + "items": { + "type": "string", + "enum": ["wd:Q1254373", "wd:Q101333", "wd:Q1071", "wd:Q11680831", "wd:Q12271", "wd:Q12483", "wd:Q18351432", "wd:Q188847", "wd:Q192386", "wd:Q21201", "wd:Q2167061", "wd:Q2329", "wd:Q23404", "wd:Q2878974", "wd:Q309", "wd:Q333", "wd:Q3353193", "wd:Q34749", "wd:Q3606845", "wd:Q395", "wd:Q413", "wd:Q420", "wd:Q42240", "wd:Q428691", "wd:Q43035", "wd:Q4830453", "wd:Q580689", "wd:Q5891", "wd:Q7112556", "wd:Q7163", "wd:Q735", "wd:Q7748", "wd:Q77590", "wd:Q7991", "wd:Q8008", "wd:Q80083", "wd:Q8078", "wd:Q8134", "wd:Q8162", "wd:Q816264", "wd:Q8242", "wd:Q83588", "wd:Q8434", "wd:Q843601", "wd:Q9174", "wd:Q9418"], + "description": "Wikidata IRI for discipline (from Open Pulse Ontology)" + } + }, + "schema:author": { + "type": "array", + "minItems": 1, + "items": { + "type": "string", + "description": "Reference to PersonShape ID (hierarchical)" + } + }, + "pulse:githubRepoStars": { + "type": ["integer", "null"], + "minimum": 0 + }, + "pulse:githubRepoForks": { + "type": ["integer", "null"], + "minimum": 0 + }, + "schema:dateCreated": { + "type": ["string", "null"], + "pattern": "^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$", + "description": "ISO 8601 datetime format" + }, + "schema:license": { + "type": ["string", "null"], + "format": "uri", + "description": "SPDX license IRI" + }, + "schema:citation": { + "type": ["string", "null"], + "format": "uri", + "description": "DOI URL for citation" + }, + "schema:programmingLanguage": { + "type": "array", + "items": { + "type": "string" + } + }, + "pulse:ownedBy": { + "type": ["string", "null"], + "description": "Reference to PersonShape or OrganizationShape ID (hierarchical)" + }, + "pulse:isForkOf": { + "type": ["string", "null"], + "description": "Reference to parent RepositoryShape ID (GitHub handle)" + } + } +} diff --git a/tests/v2/golden/extract/.gitkeep b/tests/v2/golden/extract/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/tests/v2/golden/extract/org_github_com_orgname.json b/tests/v2/golden/extract/org_github_com_orgname.json new file mode 100644 index 0000000..d4302c8 --- /dev/null +++ b/tests/v2/golden/extract/org_github_com_orgname.json @@ -0,0 +1,49 @@ +{ + "source_url": "https://github.com/orgs/github", + "detected_type": "organization", + "output_format": "json", + "output": { + "root_entity": { + "id": "https://github.com/github", + "type": "org:Organization", + "pulse:githubOrganizationHandle": "github" + }, + "related_entities": [], + "excluded_entities": [], + "entities_by_type": { + "repositories": [], + "persons": [], + "organizations": [ + { + "id": "https://github.com/github", + "type": "org:Organization", + "pulse:githubOrganizationHandle": "github" + } + ], + "articles": [], + "memberships": [], + "contributions": [] + } + }, + "stats": { + "entities_count": 1, + "triples_count": 0, + "stages_completed": [ + "context_gather", + "org_agent", + "person_agents", + "repo_agents", + "article_agents", + "membership_agents", + "contribution_agents", + "permissive_validation", + "reconciliation", + "strict_validation", + "output_assembly", + "link_veracity", + "jsonld_build", + "shacl_gate", + "graph_write" + ] + } +} diff --git a/tests/v2/golden/extract/repo_github_com_owner_repo.json b/tests/v2/golden/extract/repo_github_com_owner_repo.json new file mode 100644 index 0000000..70de8a5 --- /dev/null +++ b/tests/v2/golden/extract/repo_github_com_owner_repo.json @@ -0,0 +1,54 @@ +{ + "source_url": "https://github.com/octocat/Hello-World", + "detected_type": "repository", + "output_format": "jsonld", + "output": { + "@context": { + "org": "http://www.w3.org/ns/org#", + "schema": "http://schema.org/", + "pulse": "https://open-pulse.epfl.ch/ontology#", + "xsd": "http://www.w3.org/2001/XMLSchema#" + }, + "@graph": [ + { + "@id": "https://github.com/octocat", + "@type": "schema:Person", + "pulse:githubUsername": "octocat" + }, + { + "@id": "https://github.com/octocat/Hello-World", + "@type": "schema:SoftwareSourceCode" + }, + { + "@type": "org:Membership" + }, + { + "@type": "pulse:Contribution" + }, + { + "@type": "org:Organization" + } + ] + }, + "stats": { + "entities_count": 5, + "triples_count": 28, + "stages_completed": [ + "context_gather", + "repo_agent", + "person_agents", + "org_agents", + "article_agents", + "membership_agents", + "contribution_agents", + "permissive_validation", + "reconciliation", + "strict_validation", + "output_assembly", + "link_veracity", + "jsonld_build", + "shacl_gate", + "graph_write" + ] + } +} diff --git a/tests/v2/golden/extract/user_github_com_username.json b/tests/v2/golden/extract/user_github_com_username.json new file mode 100644 index 0000000..36d576e --- /dev/null +++ b/tests/v2/golden/extract/user_github_com_username.json @@ -0,0 +1,49 @@ +{ + "source_url": "https://github.com/octocat", + "detected_type": "user", + "output_format": "json", + "output": { + "root_entity": { + "id": "https://github.com/octocat", + "type": "schema:Person", + "pulse:githubUsername": "octocat" + }, + "related_entities": [], + "excluded_entities": [], + "entities_by_type": { + "repositories": [], + "persons": [ + { + "id": "https://github.com/octocat", + "type": "schema:Person", + "pulse:githubUsername": "octocat" + } + ], + "organizations": [], + "articles": [], + "memberships": [], + "contributions": [] + } + }, + "stats": { + "entities_count": 1, + "triples_count": 0, + "stages_completed": [ + "context_gather", + "person_agent", + "repo_agents", + "org_agents", + "article_agents", + "membership_agents", + "contribution_agents", + "permissive_validation", + "reconciliation", + "strict_validation", + "output_assembly", + "link_veracity", + "jsonld_build", + "shacl_gate", + "graph_write" + ] + } +} diff --git a/tests/v2/golden/graph/.gitkeep b/tests/v2/golden/graph/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/tests/v2/golden/graph/filtered_by_source.json b/tests/v2/golden/graph/filtered_by_source.json new file mode 100644 index 0000000..a25e731 --- /dev/null +++ b/tests/v2/golden/graph/filtered_by_source.json @@ -0,0 +1,138 @@ +{ + "graph_jsonld": { + "@context": { + "sh": "http://www.w3.org/ns/shacl#", + "xsd": "http://www.w3.org/2001/XMLSchema#", + "schema": "http://schema.org/", + "rdfs": "http://www.w3.org/2000/01/rdf-schema#", + "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#", + "org": "http://www.w3.org/ns/org#", + "time": "http://www.w3.org/2006/time#", + "pulse": "https://open-pulse.epfl.ch/ontology#", + "owl": "http://www.w3.org/2002/07/owl#", + "dct": "http://purl.org/dc/terms/", + "wd": "http://www.wikidata.org/entity/", + "skos": "http://www.w3.org/2004/02/skos/core#", + "schema:author": { + "@type": "@id", + "@container": "@set" + }, + "schema:sourceOrganization": { + "@type": "@id" + }, + "schema:url": { + "@type": "@id" + }, + "schema:license": { + "@type": "@id" + }, + "schema:citation": { + "@type": "@id" + }, + "org:organization": { + "@type": "@id" + }, + "org:hasMembership": { + "@type": "@id", + "@container": "@set" + }, + "org:hasUnit": { + "@type": "@id", + "@container": "@set" + }, + "org:unitOf": { + "@type": "@id" + }, + "pulse:hasContribution": { + "@type": "@id", + "@container": "@set" + }, + "pulse:contributionTo": { + "@type": "@id" + }, + "pulse:owns": { + "@type": "@id", + "@container": "@set" + }, + "pulse:ownedBy": { + "@type": "@id" + }, + "pulse:isForkOf": { + "@type": "@id" + }, + "affiliations": { + "@id": "pulse:affiliations", + "@type": "@id", + "@container": "@set" + }, + "pulse:discipline": { + "@type": "@id", + "@container": "@set" + }, + "pulse:repositoryType": { + "@type": "@id" + }, + "pulse:OrganizationType": { + "@type": "@id" + }, + "schema:dateCreated": { + "@type": "xsd:dateTime" + }, + "schema:datePublished": { + "@type": "xsd:date" + }, + "time:hasBeginning": { + "@type": "xsd:date" + }, + "time:hasEnd": { + "@type": "xsd:date" + }, + "pulse:firstContributionDate": { + "@type": "xsd:dateTime" + }, + "pulse:lastContributionDate": { + "@type": "xsd:dateTime" + }, + "pulse:contributionCount": { + "@type": "xsd:integer" + }, + "pulse:githubRepoStars": { + "@type": "xsd:integer" + }, + "pulse:githubRepoForks": { + "@type": "xsd:integer" + }, + "pulse:githubOrgFollowers": { + "@type": "xsd:integer" + } + }, + "@graph": [ + { + "@id": "urn:pulse:0000-0002-1825-0097_owner/repo", + "@type": "pulse:Contribution", + "pulse:identifiers": "{}", + "pulse:source_url": { + "@id": "https://github.com/owner/repo" + } + }, + { + "@id": "urn:pulse:owner/repo", + "@type": "schema:SoftwareSourceCode", + "pulse:identifiers": "{}", + "pulse:source_url": { + "@id": "https://github.com/owner/repo" + }, + "schema:name": "Graph Repository" + } + ] + }, + "stats": { + "entities_count": 2, + "triples_count": 7, + "run_id": "graph-export", + "duration_ms": 0, + "stages_completed": [ + "graph_export" + ] + } +} \ No newline at end of file diff --git a/tests/v2/golden/graph/filtered_by_type.json b/tests/v2/golden/graph/filtered_by_type.json new file mode 100644 index 0000000..6a7b4d4 --- /dev/null +++ b/tests/v2/golden/graph/filtered_by_type.json @@ -0,0 +1,127 @@ +{ + "graph_jsonld": { + "@context": { + "sh": "http://www.w3.org/ns/shacl#", + "xsd": "http://www.w3.org/2001/XMLSchema#", + "schema": "http://schema.org/", + "rdfs": "http://www.w3.org/2000/01/rdf-schema#", + "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#", + "org": "http://www.w3.org/ns/org#", + "time": "http://www.w3.org/2006/time#", + "pulse": "https://open-pulse.epfl.ch/ontology#", + "owl": "http://www.w3.org/2002/07/owl#", + "dct": "http://purl.org/dc/terms/", + "wd": "http://www.wikidata.org/entity/", + "skos": "http://www.w3.org/2004/02/skos/core#", + "schema:author": { + "@type": "@id", + "@container": "@set" + }, + "schema:sourceOrganization": { + "@type": "@id" + }, + "schema:url": { + "@type": "@id" + }, + "schema:license": { + "@type": "@id" + }, + "schema:citation": { + "@type": "@id" + }, + "org:organization": { + "@type": "@id" + }, + "org:hasMembership": { + "@type": "@id", + "@container": "@set" + }, + "org:hasUnit": { + "@type": "@id", + "@container": "@set" + }, + "org:unitOf": { + "@type": "@id" + }, + "pulse:hasContribution": { + "@type": "@id", + "@container": "@set" + }, + "pulse:contributionTo": { + "@type": "@id" + }, + "pulse:owns": { + "@type": "@id", + "@container": "@set" + }, + "pulse:ownedBy": { + "@type": "@id" + }, + "pulse:isForkOf": { + "@type": "@id" + }, + "affiliations": { + "@id": "pulse:affiliations", + "@type": "@id", + "@container": "@set" + }, + "pulse:discipline": { + "@type": "@id", + "@container": "@set" + }, + "pulse:repositoryType": { + "@type": "@id" + }, + "pulse:OrganizationType": { + "@type": "@id" + }, + "schema:dateCreated": { + "@type": "xsd:dateTime" + }, + "schema:datePublished": { + "@type": "xsd:date" + }, + "time:hasBeginning": { + "@type": "xsd:date" + }, + "time:hasEnd": { + "@type": "xsd:date" + }, + "pulse:firstContributionDate": { + "@type": "xsd:dateTime" + }, + "pulse:lastContributionDate": { + "@type": "xsd:dateTime" + }, + "pulse:contributionCount": { + "@type": "xsd:integer" + }, + "pulse:githubRepoStars": { + "@type": "xsd:integer" + }, + "pulse:githubRepoForks": { + "@type": "xsd:integer" + }, + "pulse:githubOrgFollowers": { + "@type": "xsd:integer" + } + }, + "@graph": [ + { + "@id": "urn:pulse:0000-0002-1825-0097", + "@type": "schema:Person", + "pulse:identifiers": "{}", + "schema:name": "Graph Person" + } + ] + }, + "stats": { + "entities_count": 1, + "triples_count": 3, + "run_id": "graph-export", + "duration_ms": 0, + "stages_completed": [ + "graph_export" + ] + } +} \ No newline at end of file diff --git a/tests/v2/golden/graph/full_graph.json b/tests/v2/golden/graph/full_graph.json new file mode 100644 index 0000000..5998714 --- /dev/null +++ b/tests/v2/golden/graph/full_graph.json @@ -0,0 +1,161 @@ +{ + "graph_jsonld": { + "@context": { + "sh": "http://www.w3.org/ns/shacl#", + "xsd": "http://www.w3.org/2001/XMLSchema#", + "schema": "http://schema.org/", + "rdfs": "http://www.w3.org/2000/01/rdf-schema#", + "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#", + "org": "http://www.w3.org/ns/org#", + "time": "http://www.w3.org/2006/time#", + "pulse": "https://open-pulse.epfl.ch/ontology#", + "owl": "http://www.w3.org/2002/07/owl#", + "dct": "http://purl.org/dc/terms/", + "wd": "http://www.wikidata.org/entity/", + "skos": "http://www.w3.org/2004/02/skos/core#", + "schema:author": { + "@type": "@id", + "@container": "@set" + }, + "schema:sourceOrganization": { + "@type": "@id" + }, + "schema:url": { + "@type": "@id" + }, + "schema:license": { + "@type": "@id" + }, + "schema:citation": { + "@type": "@id" + }, + "org:organization": { + "@type": "@id" + }, + "org:hasMembership": { + "@type": "@id", + "@container": "@set" + }, + "org:hasUnit": { + "@type": "@id", + "@container": "@set" + }, + "org:unitOf": { + "@type": "@id" + }, + "pulse:hasContribution": { + "@type": "@id", + "@container": "@set" + }, + "pulse:contributionTo": { + "@type": "@id" + }, + "pulse:owns": { + "@type": "@id", + "@container": "@set" + }, + "pulse:ownedBy": { + "@type": "@id" + }, + "pulse:isForkOf": { + "@type": "@id" + }, + "affiliations": { + "@id": "pulse:affiliations", + "@type": "@id", + "@container": "@set" + }, + "pulse:discipline": { + "@type": "@id", + "@container": "@set" + }, + "pulse:repositoryType": { + "@type": "@id" + }, + "pulse:OrganizationType": { + "@type": "@id" + }, + "schema:dateCreated": { + "@type": "xsd:dateTime" + }, + "schema:datePublished": { + "@type": "xsd:date" + }, + "time:hasBeginning": { + "@type": "xsd:date" + }, + "time:hasEnd": { + "@type": "xsd:date" + }, + "pulse:firstContributionDate": { + "@type": "xsd:dateTime" + }, + "pulse:lastContributionDate": { + "@type": "xsd:dateTime" + }, + "pulse:contributionCount": { + "@type": "xsd:integer" + }, + "pulse:githubRepoStars": { + "@type": "xsd:integer" + }, + "pulse:githubRepoForks": { + "@type": "xsd:integer" + }, + "pulse:githubOrgFollowers": { + "@type": "xsd:integer" + } + }, + "@graph": [ + { + "@id": "https://ror.org/02s376052", + "@type": "org:Organization", + "pulse:identifiers": "{}", + "schema:name": "Graph Organization" + }, + { + "@id": "urn:pulse:0000-0002-1825-0097", + "@type": "schema:Person", + "pulse:identifiers": "{}", + "schema:name": "Graph Person" + }, + { + "@id": "urn:pulse:0000-0002-1825-0097_https://ror.org/02s376052", + "@type": "org:Membership", + "pulse:identifiers": "{}" + }, + { + "@id": "urn:pulse:0000-0002-1825-0097_owner/repo", + "@type": "pulse:Contribution", + "pulse:identifiers": "{}", + "pulse:source_url": { + "@id": "https://github.com/owner/repo" + } + }, + { + "@id": "urn:pulse:10.5281/example0001", + "@type": "schema:ScholarlyArticle", + "pulse:identifiers": "{}", + "schema:name": "Graph Article" + }, + { + "@id": "urn:pulse:owner/repo", + "@type": "schema:SoftwareSourceCode", + "pulse:identifiers": "{}", + "pulse:source_url": { + "@id": "https://github.com/owner/repo" + }, + "schema:name": "Graph Repository" + } + ] + }, + "stats": { + "entities_count": 6, + "triples_count": 18, + "run_id": "graph-export", + "duration_ms": 0, + "stages_completed": [ + "graph_export" + ] + } +} \ No newline at end of file diff --git a/tests/v2/test_agent_retry.py b/tests/v2/test_agent_retry.py new file mode 100644 index 0000000..4fd6a5a --- /dev/null +++ b/tests/v2/test_agent_retry.py @@ -0,0 +1,113 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +from src.v2.agents.models import AgentResult +from src.v2.agents.retry import with_retry + +INITIAL_RETRY_COUNT = 0 +ONE_RETRY = 1 +TWO_RETRIES = 2 +THREE_ATTEMPTS = 3 +TWO_DELAYS = 2 + + +def test_with_retry_passthrough_on_first_success() -> None: + async def _agent(context: dict[str, Any], _providers: Any) -> AgentResult: + return AgentResult(data={"id": context["id"]}, warnings=["ok"]) + + result = asyncio.run(with_retry(_agent, context={"id": "repo-1"})) + + assert result.data == {"id": "repo-1"} + assert result.is_partial is False + assert result.stats["retry_count"] == INITIAL_RETRY_COUNT + + +def test_with_retry_retries_after_exception_and_returns_success() -> None: + attempts = {"count": 0} + + async def _agent(_context: dict[str, Any], _providers: Any) -> AgentResult: + attempts["count"] += 1 + if attempts["count"] == ONE_RETRY: + message = "transient failure" + raise RuntimeError(message) + return AgentResult(data={"id": "recovered"}) + + result = asyncio.run(with_retry(_agent, max_retries=3, backoff_base=0)) + + assert attempts["count"] == TWO_RETRIES + assert result.data == {"id": "recovered"} + assert result.stats["retry_count"] == ONE_RETRY + + +def test_with_retry_returns_partial_after_exhaustion() -> None: + async def _agent(_context: dict[str, Any], _providers: Any) -> AgentResult: + message = "hard failure" + raise ValueError(message) + + result = asyncio.run(with_retry(_agent, max_retries=2, backoff_base=0)) + + assert result.is_partial is True + assert result.failure_reason == "hard failure" + assert result.stats["retry_count"] == TWO_RETRIES + assert result.stats["attempts"] == THREE_ATTEMPTS + + +def test_with_retry_uses_exponential_backoff() -> None: + delays: list[float] = [] + + async def _sleep(delay_seconds: float) -> None: + delays.append(delay_seconds) + + async def _agent(_context: dict[str, Any], _providers: Any) -> AgentResult: + message = "retry me" + raise RuntimeError(message) + + asyncio.run( + with_retry( + _agent, + max_retries=2, + backoff_base=0.2, + sleep_func=_sleep, + ), + ) + + assert len(delays) == TWO_DELAYS + assert delays[1] > delays[0] + + +def test_with_retry_records_retry_stats_on_success() -> None: + attempts = {"count": 0} + + async def _agent(_context: dict[str, Any], _providers: Any) -> AgentResult: + attempts["count"] += 1 + if attempts["count"] < THREE_ATTEMPTS: + message = "still failing" + raise RuntimeError(message) + return AgentResult(data={"id": "stable"}) + + result = asyncio.run(with_retry(_agent, max_retries=3, backoff_base=0)) + + assert result.stats["attempts"] == THREE_ATTEMPTS + assert result.stats["retry_count"] == TWO_RETRIES + assert result.data["id"] == "stable" + + +def test_with_retry_retries_on_permissive_validation_warning() -> None: + attempts = {"count": 0} + + async def _agent(_context: dict[str, Any], _providers: Any) -> AgentResult: + attempts["count"] += 1 + if attempts["count"] == ONE_RETRY: + return AgentResult( + data={"id": "invalid-first-pass"}, + warnings=["Validation warning at : invalid payload"], + ) + return AgentResult(data={"id": "valid-second-pass"}) + + result = asyncio.run(with_retry(_agent, max_retries=2, backoff_base=0)) + + assert attempts["count"] == TWO_RETRIES + assert result.data["id"] == "valid-second-pass" + assert result.stats["retry_count"] == ONE_RETRY diff --git a/tests/v2/test_agent_runtime_scaffolding.py b/tests/v2/test_agent_runtime_scaffolding.py new file mode 100644 index 0000000..3233891 --- /dev/null +++ b/tests/v2/test_agent_runtime_scaffolding.py @@ -0,0 +1,80 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +from src.v2.agents.llm import ( + LLMArticleAgentV2, + LLMContributionAgentV2, + LLMLinkVeracityAgentV2, + LLMMembershipAgentV2, + LLMOrganizationAgentV2, + LLMPersonAgentV2, + LLMRepositoryAgentV2, +) +from src.v2.agents.rule_based import ( + ArticleAgentV2, + ContributionAgentV2, + MembershipAgentV2, + OrganizationAgentV2, + PersonAgentV2, + RepositoryAgentV2, +) + +if TYPE_CHECKING: + from src.v2.agents.contracts import RuntimeAgent + + +def _assert_runtime_agent_shape(agent: RuntimeAgent) -> None: + run_attr = getattr(agent, "run", None) + assert callable(run_attr) + + +def test_rule_based_namespace_re_exports_current_agents() -> None: + _assert_runtime_agent_shape(RepositoryAgentV2()) + _assert_runtime_agent_shape(PersonAgentV2()) + _assert_runtime_agent_shape(OrganizationAgentV2()) + _assert_runtime_agent_shape(ArticleAgentV2()) + _assert_runtime_agent_shape(MembershipAgentV2()) + _assert_runtime_agent_shape(ContributionAgentV2()) + + +def test_llm_namespace_exposes_repository_runtime_agent() -> None: + agent: RuntimeAgent = LLMRepositoryAgentV2() + _assert_runtime_agent_shape(agent) + assert isinstance(agent, LLMRepositoryAgentV2) + + +def test_llm_namespace_exposes_person_runtime_agent() -> None: + agent: RuntimeAgent = LLMPersonAgentV2() + _assert_runtime_agent_shape(agent) + assert isinstance(agent, LLMPersonAgentV2) + + +def test_llm_namespace_exposes_organization_runtime_agent() -> None: + agent: RuntimeAgent = LLMOrganizationAgentV2() + _assert_runtime_agent_shape(agent) + assert isinstance(agent, LLMOrganizationAgentV2) + + +def test_llm_namespace_exposes_article_runtime_agent() -> None: + agent: RuntimeAgent = LLMArticleAgentV2() + _assert_runtime_agent_shape(agent) + assert isinstance(agent, LLMArticleAgentV2) + + +def test_llm_namespace_exposes_membership_runtime_agent() -> None: + agent: RuntimeAgent = LLMMembershipAgentV2() + _assert_runtime_agent_shape(agent) + assert isinstance(agent, LLMMembershipAgentV2) + + +def test_llm_namespace_exposes_contribution_runtime_agent() -> None: + agent: RuntimeAgent = LLMContributionAgentV2() + _assert_runtime_agent_shape(agent) + assert isinstance(agent, LLMContributionAgentV2) + + +def test_llm_namespace_exposes_link_veracity_runtime_agent() -> None: + agent: RuntimeAgent = LLMLinkVeracityAgentV2() + _assert_runtime_agent_shape(agent) + assert isinstance(agent, LLMLinkVeracityAgentV2) diff --git a/tests/v2/test_agent_uuid_generation.py b/tests/v2/test_agent_uuid_generation.py new file mode 100644 index 0000000..d17f34b --- /dev/null +++ b/tests/v2/test_agent_uuid_generation.py @@ -0,0 +1,16 @@ +from __future__ import annotations + +from uuid import UUID + +from src.v2.agents.models import generate_uuid + +UUID_VERSION_4 = 4 + + +def test_generate_uuid_returns_uuid4_strings() -> None: + generated = [generate_uuid() for _ in range(12)] + + assert len(generated) == len(set(generated)) + for value in generated: + parsed = UUID(value) + assert parsed.version == UUID_VERSION_4 diff --git a/tests/v2/test_agent_verdict_cache.py b/tests/v2/test_agent_verdict_cache.py new file mode 100644 index 0000000..d9be23d --- /dev/null +++ b/tests/v2/test_agent_verdict_cache.py @@ -0,0 +1,140 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import pytest + +from src.v2.agents.llm._verdict_cache import ( + get_cached_agent_verdict, + store_agent_verdict, +) +from src.v2.agents.models import AgentResult +from src.v2.ingest.cache import ProviderCache + + +def _result() -> AgentResult: + return AgentResult( + data={"id": "https://github.com/cmdoret", "schema:name": "C M"}, + warnings=["sample warning"], + raw_output={"foo": "bar"}, + is_partial=False, + failure_reason=None, + model="openai/gpt-test", + provider="openai", + tokens_prompt=100, + tokens_completion=50, + stats={"agent_runtime": "llm"}, + ) + + +def test_no_cache_returns_none(tmp_path: Path) -> None: + assert ( + get_cached_agent_verdict( + None, + agent_name="person", + identity={"primary_key": "username", "value": "cmdoret"}, + ) + is None + ) + + +def test_no_identity_returns_none(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "p.db") + assert get_cached_agent_verdict(cache, agent_name="person", identity=None) is None + + +def test_partial_results_are_not_cached(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "p.db") + partial = AgentResult(data={}, warnings=["bad"], is_partial=True) + identity = {"primary_key": "username", "value": "cmdoret"} + store_agent_verdict(cache, agent_name="person", identity=identity, result=partial) + assert get_cached_agent_verdict(cache, agent_name="person", identity=identity) is None + + +def test_round_trip_returns_cached_data_with_cached_flag(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "p.db") + result = _result() + identity = {"primary_key": "username", "value": "cmdoret"} + store_agent_verdict(cache, agent_name="person", identity=identity, result=result) + + hit = get_cached_agent_verdict(cache, agent_name="person", identity=identity) + assert hit is not None + assert hit.data == result.data + assert hit.warnings == ["sample warning"] + assert hit.raw_output == {"foo": "bar"} + # Token/model fields are reset on retrieval. + assert hit.model is None + assert hit.provider is None + assert hit.tokens_prompt == 0 + assert hit.tokens_completion == 0 + # Stats round-trip plus a cached marker. + assert hit.stats["agent_runtime"] == "llm" + assert hit.stats["cached"] is True + + +def test_different_identity_misses(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "p.db") + result = _result() + store_agent_verdict( + cache, + agent_name="person", + identity={"primary_key": "username", "value": "cmdoret"}, + result=result, + ) + miss = get_cached_agent_verdict( + cache, + agent_name="person", + identity={"primary_key": "username", "value": "someone-else"}, + ) + assert miss is None + + +def test_different_agent_name_misses(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "p.db") + result = _result() + identity = {"primary_key": "username", "value": "cmdoret"} + store_agent_verdict(cache, agent_name="person", identity=identity, result=result) + miss = get_cached_agent_verdict(cache, agent_name="organization", identity=identity) + assert miss is None + + +def test_is_root_skips_lookup_even_when_cached(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "p.db") + result = _result() + identity = {"primary_key": "username", "value": "cmdoret"} + # Pre-populate the cache. + store_agent_verdict(cache, agent_name="person", identity=identity, result=result) + # A normal fan-out call would hit. + assert get_cached_agent_verdict(cache, agent_name="person", identity=identity) is not None + # The same lookup with is_root=True must miss. + assert ( + get_cached_agent_verdict( + cache, + agent_name="person", + identity=identity, + is_root=True, + ) + is None + ) + + +def test_root_writes_are_still_persisted(tmp_path: Path) -> None: + """A root extraction skips reads but the result still lands in the cache.""" + cache = ProviderCache(tmp_path / "p.db") + identity = {"primary_key": "username", "value": "cmdoret"} + # Simulate a root run: we never look up, but we still store on success. + assert ( + get_cached_agent_verdict( + cache, + agent_name="person", + identity=identity, + is_root=True, + ) + is None + ) + store_agent_verdict(cache, agent_name="person", identity=identity, result=_result()) + # A subsequent fan-out call should now find it. + hit = get_cached_agent_verdict(cache, agent_name="person", identity=identity) + assert hit is not None + assert hit.stats["cached"] is True diff --git a/tests/v2/test_api_extract_stub.py b/tests/v2/test_api_extract_stub.py new file mode 100644 index 0000000..978727c --- /dev/null +++ b/tests/v2/test_api_extract_stub.py @@ -0,0 +1,646 @@ +from __future__ import annotations + +import asyncio +import time +from typing import Any + +from fastapi import FastAPI +from httpx import ASGITransport, AsyncClient + +from src.v2.agents import ProviderSet +from src.v2.agents.models import AgentResult +from src.v2.api import v2_router +from src.v2.api_models.contracts import V2ExtractJob, V2ExtractResponse +from src.v2.ingest.cache import ProviderCache +from src.v2.pipeline import PipelineOrchestrator +from src.v2.pipeline.stages.models import ContextBundle +from src.v2.ingest.providers.mock_github import MockGitHubProvider +from src.v2.ingest.providers.mock_infoscience import MockInfoscienceProvider +from src.v2.ingest.providers.mock_orcid import MockORCIDProvider +from src.v2.ingest.providers.mock_ror import MockRORProvider + +HTTP_OK = 200 +HTTP_ACCEPTED = 202 +HTTP_NOT_FOUND = 404 +HTTP_UNPROCESSABLE_ENTITY = 422 +HTTP_SERVICE_UNAVAILABLE = 503 + +# Matches the value seeded by the `_isolate_v2_runtime_env` autouse +# fixture in `tests/v2/conftest.py`. Every protected request needs a +# matching bearer header — see `src/v2/auth.py::verify_token`. +TEST_API_TOKEN = "test-api-token" # noqa: S105 — test fixture +_AUTH_HEADERS = {"Authorization": f"Bearer {TEST_API_TOKEN}"} + + +def _build_test_app() -> FastAPI: + app = FastAPI() + app.include_router(v2_router) + app.state.v2_provider_set = ProviderSet( + github=MockGitHubProvider(), + orcid=MockORCIDProvider(), + infoscience=MockInfoscienceProvider(), + ror=MockRORProvider(), + ) + return app + + +def _get_json(path: str, params: dict[str, str] | None = None) -> tuple[int, Any]: + async def _run() -> tuple[int, Any]: + test_app = _build_test_app() + transport = ASGITransport(app=test_app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + headers=_AUTH_HEADERS, + ) as client: + response = await client.get(path, params=params) + return response.status_code, response.json() + + return asyncio.run(_run()) + + +def _get_json_from_app( + app: FastAPI, + path: str, + params: dict[str, str] | None = None, +) -> tuple[int, Any]: + async def _run() -> tuple[int, Any]: + transport = ASGITransport(app=app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + headers=_AUTH_HEADERS, + ) as client: + response = await client.get(path, params=params) + return response.status_code, response.json() + + return asyncio.run(_run()) + + +def _post_json(path: str, payload: dict[str, Any]) -> tuple[int, Any]: + async def _run() -> tuple[int, Any]: + test_app = _build_test_app() + transport = ASGITransport(app=test_app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + headers=_AUTH_HEADERS, + ) as client: + response = await client.post(path, json=payload) + return response.status_code, response.json() + + return asyncio.run(_run()) + + +def _post_json_from_app( + app: FastAPI, + path: str, + payload: dict[str, Any], +) -> tuple[int, Any]: + async def _run() -> tuple[int, Any]: + transport = ASGITransport(app=app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + headers=_AUTH_HEADERS, + ) as client: + response = await client.post(path, json=payload) + return response.status_code, response.json() + + return asyncio.run(_run()) + + +def test_extract_repository_url_returns_detected_repository() -> None: + status_code, payload = _get_json("/v2/extract/github.com/octocat/Hello-World") + + assert status_code == HTTP_OK + assert payload["detected_type"] == "repository" + assert V2ExtractResponse.model_validate(payload) + + +def test_extract_user_url_returns_detected_user() -> None: + status_code, payload = _get_json("/v2/extract/github.com/octocat") + + assert status_code == HTTP_OK + assert payload["detected_type"] == "user" + assert V2ExtractResponse.model_validate(payload) + + +async def _wait_for_job_completion( + client: AsyncClient, + job_id: str, + *, + timeout_seconds: float = 180.0, +) -> dict[str, Any]: + """Poll GET /v2/jobs/{job_id} until status is completed or failed.""" + deadline = time.monotonic() + timeout_seconds + while time.monotonic() < deadline: + response = await client.get(f"/v2/jobs/{job_id}") + body = response.json() + if response.status_code == HTTP_OK and body.get("status") in { + "completed", + "failed", + }: + return body + await asyncio.sleep(0.1) + message = f"job {job_id} did not finish within {timeout_seconds}s" + raise AssertionError(message) + + +def test_extract_post_repository_url_runs_async_job(tmp_path: Any) -> None: + cache_db = tmp_path / "providers.db" + + async def _run() -> tuple[int, dict[str, Any], dict[str, Any]]: + app = _build_test_app() + app.state.v2_provider_cache = ProviderCache(cache_db) + transport = ASGITransport(app=app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + headers=_AUTH_HEADERS, + ) as client: + submit = await client.post( + "/v2/extract", + json={ + "source_url": "github.com/octocat/Hello-World", + "agent_runtime": "rule_based", + }, + ) + submit_payload = submit.json() + job = await _wait_for_job_completion(client, submit_payload["job_id"]) + return submit.status_code, submit_payload, job + + submit_status, submit_payload, job = asyncio.run(_run()) + + assert submit_status == HTTP_ACCEPTED + assert submit_payload["status"] == "pending" + assert submit_payload["status_url"] == f"/v2/jobs/{submit_payload['job_id']}" + + assert job["status"] == "completed" + assert job["result"]["detected_type"] == "repository" + V2ExtractJob.model_validate(job) + V2ExtractResponse.model_validate(job["result"]) + + +def test_extract_unsupported_issue_url_returns_typed_422() -> None: + status_code, payload = _get_json("/v2/extract/github.com/owner/repo/issues/1") + + assert status_code == HTTP_UNPROCESSABLE_ENTITY + assert payload["error_type"] == "unsupported_url" + assert payload["detected_path_kind"] == "issues" + + +def test_extract_accepts_output_format_json() -> None: + status_code, payload = _get_json( + "/v2/extract/github.com/octocat/Hello-World", + params={"output_format": "json"}, + ) + + assert status_code == HTTP_OK + assert payload["output_format"] == "json" + + +def test_extract_rejects_invalid_output_format() -> None: + status_code, payload = _get_json( + "/v2/extract/github.com/octocat/Hello-World", + params={"output_format": "xml"}, + ) + + assert status_code == HTTP_UNPROCESSABLE_ENTITY + assert "detail" in payload + + +def test_extract_accepts_agent_runtime_llm_for_user_routes() -> None: + async def _context_gatherer( + _detected_type: str, + _url_info: Any, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="user", + context={ + "user": { + "username": "octocat", + "profile": {"login": "octocat"}, + "owned_repos": [], + "orcid_data": None, + }, + }, + ) + + class _LLMPersonRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + username = context.get("username", "octocat") + return AgentResult( + data={ + "id": username, + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": username, + "uuid": "11111111-1111-4111-8111-111111111111", + }, + "idSource": "pulse:githubUsername", + "schema:name": username, + "schema:url": f"https://github.com/{username}", + "pulse:githubUsername": username, + "pulse:orcidIdentifier": None, + "pulse:infosciencePersonIdentifier": None, + "org:hasMembership": [], + "pulse:hasContribution": [], + "pulse:owns": [], + }, + ) + + class _LLMNoDataRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult(data={}) + + app = _build_test_app() + app.state.v2_orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_person_agent=_LLMPersonRunner(), + llm_repository_agent=_LLMNoDataRunner(), + llm_organization_agent=_LLMNoDataRunner(), + llm_article_agent=_LLMNoDataRunner(), + llm_membership_agent=_LLMNoDataRunner(), + llm_contribution_agent=_LLMNoDataRunner(), + retry_max_retries=0, + retry_backoff_base=0, + ) + + status_code, payload = _get_json_from_app( + app, + "/v2/extract/github.com/octocat", + params={"agent_runtime": "llm"}, + ) + + assert status_code == HTTP_OK + assert payload["detected_type"] == "user" + assert V2ExtractResponse.model_validate(payload) + + +def test_extract_rejects_invalid_agent_runtime() -> None: + status_code, payload = _get_json( + "/v2/extract/github.com/octocat/Hello-World", + params={"agent_runtime": "not-a-runtime"}, + ) + + assert status_code == HTTP_UNPROCESSABLE_ENTITY + assert "detail" in payload + + +def test_extract_can_include_compiled_context_summary_in_response() -> None: + class _SummaryRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult(data={"summary_markdown": "# Compiled Context\n- Key signal"}) + + class _LLMPersonRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult( + data={ + "id": "octocat", + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": "octocat", + "uuid": "11111111-1111-4111-8111-111111111111", + }, + "idSource": "pulse:githubUsername", + "schema:name": "octocat", + "schema:url": "https://github.com/octocat", + "pulse:githubUsername": "octocat", + "pulse:orcidIdentifier": None, + "pulse:infosciencePersonIdentifier": None, + "org:hasMembership": [], + "pulse:hasContribution": [], + "pulse:owns": [], + }, + ) + + class _LLMNoDataRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult(data={}) + + async def _context_gatherer( + _detected_type: str, + _url_info: Any, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="user", + context={ + "user": { + "username": "octocat", + "profile": {"login": "octocat"}, + "owned_repos": [], + "orcid_data": None, + }, + }, + ) + + app = _build_test_app() + app.state.v2_orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_context_summary_agent=_SummaryRunner(), + llm_person_agent=_LLMPersonRunner(), + llm_repository_agent=_LLMNoDataRunner(), + llm_organization_agent=_LLMNoDataRunner(), + llm_article_agent=_LLMNoDataRunner(), + llm_membership_agent=_LLMNoDataRunner(), + llm_contribution_agent=_LLMNoDataRunner(), + retry_max_retries=0, + retry_backoff_base=0, + ) + + status_code, payload = _get_json_from_app( + app, + "/v2/extract/github.com/octocat", + params={ + "agent_runtime": "llm", + "output_format": "json", + "include_context_summary": "true", + }, + ) + + assert status_code == HTTP_OK + assert payload["context_summary_markdown"].startswith("# Compiled Context") + assert V2ExtractResponse.model_validate(payload) + + +def test_extract_post_can_include_compiled_context_summary_in_response( + tmp_path: Any, +) -> None: + class _SummaryRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult(data={"summary_markdown": "# Compiled Context\n- Key signal"}) + + class _LLMPersonRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult( + data={ + "id": "octocat", + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": "octocat", + "uuid": "11111111-1111-4111-8111-111111111111", + }, + "idSource": "pulse:githubUsername", + "schema:name": "octocat", + "schema:url": "https://github.com/octocat", + "pulse:githubUsername": "octocat", + "pulse:orcidIdentifier": None, + "pulse:infosciencePersonIdentifier": None, + "org:hasMembership": [], + "pulse:hasContribution": [], + "pulse:owns": [], + }, + ) + + class _LLMNoDataRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult(data={}) + + async def _context_gatherer( + _detected_type: str, + _url_info: Any, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="user", + context={ + "user": { + "username": "octocat", + "profile": {"login": "octocat"}, + "owned_repos": [], + "orcid_data": None, + }, + }, + ) + + cache_db = tmp_path / "providers.db" + + async def _run() -> dict[str, Any]: + app = _build_test_app() + app.state.v2_provider_cache = ProviderCache(cache_db) + app.state.v2_orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_context_summary_agent=_SummaryRunner(), + llm_person_agent=_LLMPersonRunner(), + llm_repository_agent=_LLMNoDataRunner(), + llm_organization_agent=_LLMNoDataRunner(), + llm_article_agent=_LLMNoDataRunner(), + llm_membership_agent=_LLMNoDataRunner(), + llm_contribution_agent=_LLMNoDataRunner(), + retry_max_retries=0, + retry_backoff_base=0, + ) + transport = ASGITransport(app=app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + headers=_AUTH_HEADERS, + ) as client: + submit = await client.post( + "/v2/extract", + json={ + "source_url": "github.com/octocat", + "agent_runtime": "llm", + "output_format": "json", + "include_context_summary": True, + }, + ) + assert submit.status_code == HTTP_ACCEPTED + return await _wait_for_job_completion(client, submit.json()["job_id"]) + + job = asyncio.run(_run()) + assert job["status"] == "completed" + assert job["result"]["context_summary_markdown"].startswith("# Compiled Context") + V2ExtractResponse.model_validate(job["result"]) + + +def test_extract_post_returns_422_for_unsupported_url(tmp_path: Any) -> None: + cache_db = tmp_path / "providers.db" + + async def _run() -> tuple[int, dict[str, Any]]: + app = _build_test_app() + app.state.v2_provider_cache = ProviderCache(cache_db) + transport = ASGITransport(app=app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + headers=_AUTH_HEADERS, + ) as client: + response = await client.post( + "/v2/extract", + json={"source_url": "github.com/owner/repo/issues/1"}, + ) + return response.status_code, response.json() + + status_code, payload = asyncio.run(_run()) + assert status_code == HTTP_UNPROCESSABLE_ENTITY + assert payload["error_type"] == "unsupported_url" + assert payload["detected_path_kind"] == "issues" + + +def test_get_job_returns_404_for_unknown_id(tmp_path: Any) -> None: + cache_db = tmp_path / "providers.db" + + async def _run() -> tuple[int, dict[str, Any]]: + app = _build_test_app() + app.state.v2_provider_cache = ProviderCache(cache_db) + transport = ASGITransport(app=app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + headers=_AUTH_HEADERS, + ) as client: + response = await client.get("/v2/jobs/does-not-exist") + return response.status_code, response.json() + + status_code, payload = asyncio.run(_run()) + assert status_code == HTTP_NOT_FOUND + assert payload["error_type"] == "not_found" + + +def test_extract_post_persists_job_in_provider_cache(tmp_path: Any) -> None: + cache_db = tmp_path / "providers.db" + + async def _run() -> tuple[str, dict[str, Any]]: + app = _build_test_app() + app.state.v2_provider_cache = ProviderCache(cache_db) + transport = ASGITransport(app=app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + headers=_AUTH_HEADERS, + ) as client: + submit = await client.post( + "/v2/extract", + json={ + "source_url": "github.com/octocat/Hello-World", + "agent_runtime": "rule_based", + }, + ) + job_id = submit.json()["job_id"] + await _wait_for_job_completion(client, job_id) + cached = app.state.v2_provider_cache.get( + ProviderCache.make_key("v2-extract-job", "record", job_id=job_id), + ) + return job_id, cached + + job_id, cached = asyncio.run(_run()) + assert isinstance(cached, dict) + assert cached["job_id"] == job_id + assert cached["status"] == "completed" + + +def test_pipeline_cache_round_trip_returns_identical_response(tmp_path: Any) -> None: + """Two consecutive extract calls with a pipeline cache return the same payload.""" + cache_db = tmp_path / "providers.db" + + async def _run() -> tuple[int, dict, int, dict, int]: + app = _build_test_app() + app.state.v2_provider_cache = ProviderCache(cache_db) + transport = ASGITransport(app=app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + headers=_AUTH_HEADERS, + ) as client: + r1 = await client.get("/v2/extract/github.com/octocat/Hello-World") + r2 = await client.get("/v2/extract/github.com/octocat/Hello-World") + rows = list( + app.state.v2_provider_cache._connect().execute( # noqa: SLF001 + "SELECT 1 FROM responses", + ), + ) + return r1.status_code, r1.json(), r2.status_code, r2.json(), len(rows) + + sc1, payload1, sc2, payload2, row_count = asyncio.run(_run()) + assert sc1 == HTTP_OK + assert sc2 == HTTP_OK + assert payload1 == payload2 + assert row_count >= 1 + + +def test_pipeline_cache_distinguishes_output_format(tmp_path: Any) -> None: + """Different output_format values produce distinct cache entries.""" + cache_db = tmp_path / "providers.db" + + async def _run() -> tuple[int, str, int, str]: + app = _build_test_app() + app.state.v2_provider_cache = ProviderCache(cache_db) + transport = ASGITransport(app=app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + headers=_AUTH_HEADERS, + ) as client: + r_jsonld = await client.get( + "/v2/extract/github.com/octocat/Hello-World", + params={"output_format": "jsonld"}, + ) + r_json = await client.get( + "/v2/extract/github.com/octocat/Hello-World", + params={"output_format": "json"}, + ) + return ( + r_jsonld.status_code, + r_jsonld.json()["output_format"], + r_json.status_code, + r_json.json()["output_format"], + ) + + sc1, fmt1, sc2, fmt2 = asyncio.run(_run()) + assert sc1 == HTTP_OK + assert fmt1 == "jsonld" + assert sc2 == HTTP_OK + assert fmt2 == "json" diff --git a/tests/v2/test_api_health.py b/tests/v2/test_api_health.py new file mode 100644 index 0000000..83a85ed --- /dev/null +++ b/tests/v2/test_api_health.py @@ -0,0 +1,86 @@ +from __future__ import annotations + +import asyncio +import time +from importlib.metadata import version as package_version +from typing import Any + +from fastapi import FastAPI +from httpx import ASGITransport, AsyncClient + +from src.v2.api import v2_router +from src.v2.api_models.contracts import V2HealthResponse + +HTTP_OK = 200 +PACKAGE_NAME = "git-metadata-extractor" +GITHUB_COMPONENT = "github_token" +HEALTH_ENDPOINT_MAX_MS = 100 + + +def _build_test_app() -> FastAPI: + app = FastAPI() + app.include_router(v2_router) + return app + + +def _get_json(path: str) -> tuple[int, Any, float]: + async def _run() -> tuple[int, Any, float]: + test_app = _build_test_app() + transport = ASGITransport(app=test_app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + ) as client: + started = time.perf_counter() + response = await client.get(path) + elapsed_ms = (time.perf_counter() - started) * 1000 + return response.status_code, response.json(), elapsed_ms + + return asyncio.run(_run()) + + +def test_health_returns_healthy_when_all_checks_pass( + monkeypatch, +) -> None: + monkeypatch.setenv("GITHUB_TOKEN", "test-token") + + status_code, payload, _elapsed_ms = _get_json("/v2/health") + + assert status_code == HTTP_OK + assert payload["status"] == "healthy" + assert payload["components"]["config"] == "healthy" + assert payload["components"]["graph_store"] == "healthy" + assert payload["components"][GITHUB_COMPONENT] == "healthy" + assert V2HealthResponse.model_validate(payload) + + +def test_health_degrades_when_github_token_is_missing( + monkeypatch, +) -> None: + monkeypatch.delenv("GITHUB_TOKEN", raising=False) + + status_code, payload, _elapsed_ms = _get_json("/v2/health") + + assert status_code == HTTP_OK + assert payload["status"] == "degraded" + assert payload["components"][GITHUB_COMPONENT] == "degraded" + + +def test_health_includes_package_version(monkeypatch) -> None: + monkeypatch.setenv("GITHUB_TOKEN", "test-token") + + status_code, payload, _elapsed_ms = _get_json("/v2/health") + + assert status_code == HTTP_OK + assert payload["version"] == package_version(PACKAGE_NAME) + + +def test_health_endpoint_responds_under_100ms( + monkeypatch, +) -> None: + monkeypatch.setenv("GITHUB_TOKEN", "test-token") + + status_code, _payload, elapsed_ms = _get_json("/v2/health") + + assert status_code == HTTP_OK + assert elapsed_ms < HEALTH_ENDPOINT_MAX_MS diff --git a/tests/v2/test_api_mount_v2_router.py b/tests/v2/test_api_mount_v2_router.py new file mode 100644 index 0000000..8e466cf --- /dev/null +++ b/tests/v2/test_api_mount_v2_router.py @@ -0,0 +1,63 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +from httpx import ASGITransport, AsyncClient + +from src.api import app as main_app +from src.api import index +from src.v2.agents import ProviderSet +from src.v2.ingest.providers.mock_github import MockGitHubProvider +from src.v2.ingest.providers.mock_infoscience import MockInfoscienceProvider +from src.v2.ingest.providers.mock_orcid import MockORCIDProvider +from src.v2.ingest.providers.mock_ror import MockRORProvider + +HTTP_OK = 200 + + +def _get_json(path: str) -> tuple[int, Any]: + async def _run() -> tuple[int, Any]: + transport = ASGITransport(app=main_app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + ) as client: + response = await client.get(path) + return response.status_code, response.json() + + return asyncio.run(_run()) + + +def test_main_app_serves_v2_extract_route() -> None: + main_app.state.v2_provider_set = ProviderSet( + github=MockGitHubProvider(), + orcid=MockORCIDProvider(), + infoscience=MockInfoscienceProvider(), + ror=MockRORProvider(), + ) + status_code, payload = _get_json("/v2/extract/github.com/octocat/Hello-World") + + assert status_code == HTTP_OK + assert payload["detected_type"] == "repository" + + +def test_main_app_serves_v2_graph_route() -> None: + status_code, payload = _get_json("/v2/graph") + + assert status_code == HTTP_OK + assert "@graph" in payload["graph_jsonld"] + + +def test_main_app_v1_welcome_still_available() -> None: + route_paths = {route.path for route in main_app.routes} + payload = index() + + assert "/" in route_paths + assert "title" in payload + + +def test_main_app_keeps_v1_repository_jsonld_route_registered() -> None: + route_paths = {route.path for route in main_app.routes} + + assert "/v1/repository/llm/json-ld/{full_path:path}" in route_paths diff --git a/tests/v2/test_article_agent.py b/tests/v2/test_article_agent.py new file mode 100644 index 0000000..17010c0 --- /dev/null +++ b/tests/v2/test_article_agent.py @@ -0,0 +1,453 @@ +from __future__ import annotations + +import asyncio +from copy import deepcopy +from typing import Any, Callable + +from jsonschema import validate + +from src.v2.agents import ArticleAgentV2, ProviderSet +from src.v2.ingest.providers.base import InfoscienceProvider +from src.v2.ingest.providers.mock_github import MockGitHubProvider + +EXPECTED_RANKED_ARTICLE_COUNT = 2 + + +class _RecordingInfoscienceProvider(InfoscienceProvider): + def __init__(self, publications_by_query: dict[str, list[dict[str, Any]]]) -> None: + self._publications_by_query = publications_by_query + self.queries: list[str] = [] + + def search_person(self, query: str) -> list[dict[str, Any]]: + del query + return [] + + def search_orgunit(self, query: str) -> list[dict[str, Any]]: + del query + return [] + + def search_publications(self, query: str) -> list[dict[str, Any]]: + self.queries.append(query) + return deepcopy(self._publications_by_query.get(query, [])) + + +def _build_context(*, detected_type: str = "repository") -> dict[str, Any]: + return { + "detected_type": detected_type, + "full_name": "sdsc-ordes/gimie", + "username": "alice", + "org_name": "Swiss Data Science Center", + "known_persons": [ + { + "id": "https://orcid.org/0000-0002-1825-0097", + "schema:name": "Alice Example", + "pulse:githubUsername": "alice", + }, + { + "id": "https://orcid.org/0000-0002-1825-0097", + "schema:name": "Alice Example", + "pulse:githubUsername": "alice", + }, + ], + "known_organizations": [ + { + "id": "https://ror.org/02s376052", + "schema:name": "Swiss Data Science Center", + "pulse:githubOrganizationHandle": "sdsc-ordes", + }, + ], + } + + +def test_article_agent_query_blend_repository_mode_default_is_repo_only() -> None: + """By default, the article agent only queries Infoscience by repo terms. + + Searching by contributor name or org name returns those people's full + bibliography and over-attributes unrelated publications to the repo + (real DOIs, false attribution). The default repo-only blend keeps + the precision high. + """ + + agent = ArticleAgentV2(max_queries=5) + + queries = agent.build_query_blend(_build_context()) + + assert queries == [ + "sdsc-ordes/gimie", + "gimie", + ] + + +def test_article_agent_query_blend_widens_when_explicitly_enabled() -> None: + agent = ArticleAgentV2( + max_queries=5, + include_person_queries=True, + include_organization_queries=True, + ) + + queries = agent.build_query_blend(_build_context()) + + assert queries == [ + "sdsc-ordes/gimie", + "gimie", + "Alice Example", + "alice", + "Swiss Data Science Center", + ] + + +def test_article_agent_query_blend_includes_root_terms_for_user_and_organization() -> None: + agent = ArticleAgentV2(max_queries=4) + + user_queries = agent.build_query_blend(_build_context(detected_type="user")) + organization_queries = agent.build_query_blend( + _build_context(detected_type="organization"), + ) + + assert user_queries[0] == "alice" + assert organization_queries[0] == "Swiss Data Science Center" + + +def test_article_agent_ranks_dedupes_and_maps_links( + load_schema: Callable[[str, str], dict[str, Any]], +) -> None: + provider = _RecordingInfoscienceProvider( + { + "sdsc-ordes/gimie": [ + { + "infosciencePublicationIdentifier": "pub-1", + "title": "Graph Methods for Metadata", + "doi": "10.1000/graph-1", + "publicationDate": "2025-03-01", + "authors": ["Alice Example", "Unknown Contributor"], + "url": "https://infoscience.epfl.ch/entities/publication/pub-1", + "sourceOrganization": "Swiss Data Science Center", + "score": 14.2, + }, + { + "infosciencePublicationIdentifier": "pub-1-duplicate", + "title": "Graph Methods for Metadata (Duplicate)", + "doi": "10.1000/graph-1", + "publicationDate": "2025-03-01", + "authors": ["Alice Example"], + "url": "https://infoscience.epfl.ch/entities/publication/pub-1-duplicate", + "sourceOrganization": "Swiss Data Science Center", + "score": 10.0, + }, + ], + "Alice Example": [ + { + "infosciencePublicationIdentifier": "pub-2", + "title": "Open Metadata Pipelines", + "doi": "10.1000/graph-2", + "publicationDate": "2024-07-15", + "authors": ["Alice Example"], + "url": "https://infoscience.epfl.ch/entities/publication/pub-2", + "sourceOrganization": "Swiss Data Science Center", + "score": 12.5, + }, + ], + }, + ) + providers = ProviderSet( + github=MockGitHubProvider(), + infoscience=provider, + ) + # This test exercises ranking/dedup over a wide blend (repo + persons + + # orgs); explicitly enable the wide blend since the default is repo-only. + agent = ArticleAgentV2( + max_queries=6, + include_person_queries=True, + include_organization_queries=True, + ) + + result = asyncio.run(agent.run(_build_context(), providers)) + + schema = load_schema("agent", "article") + validate(instance=result.data, schema=schema) + + assert result.data["id"] == "10.1000/graph-1" + assert result.data["idSource"] == "schema:identifier" + assert result.data["schema:sourceOrganization"] == "https://ror.org/02s376052" + assert result.data["schema:author"] == [ + "https://orcid.org/0000-0002-1825-0097", + ] + assert any( + warning.startswith("Dropped unresolved article author references for 1 name(s)") + and "'Unknown Contributor'" in warning + for warning in result.warnings + ) + assert result.stats["ranked_candidate_count"] == EXPECTED_RANKED_ARTICLE_COUNT + assert [article["id"] for article in result.stats["articles"]] == [ + "10.1000/graph-1", + "10.1000/graph-2", + ] + assert provider.queries[:3] == [ + "sdsc-ordes/gimie", + "gimie", + "Alice Example", + ] + + +def test_article_agent_handles_empty_provider_results_without_failure() -> None: + provider = _RecordingInfoscienceProvider({}) + providers = ProviderSet( + github=MockGitHubProvider(), + infoscience=provider, + ) + agent = ArticleAgentV2() + + result = asyncio.run(agent.run(_build_context(), providers)) + + assert result.data == {} + assert "No publications returned for blended article queries" in result.warnings + assert result.stats["queries"] + assert result.stats["articles"] == [] + + +def test_article_agent_drops_unresolved_author_references() -> None: + provider = _RecordingInfoscienceProvider( + { + "sdsc-ordes/gimie": [ + { + "infosciencePublicationIdentifier": "pub-3", + "title": "Mapped Authors Only", + "doi": "10.1000/graph-3", + "publicationDate": "2025-03-01", + "authors": ["Alice Example", "Unknown Contributor"], + "url": "https://infoscience.epfl.ch/entities/publication/pub-3", + "sourceOrganization": "Swiss Data Science Center", + "score": 10.0, + }, + ], + }, + ) + providers = ProviderSet( + github=MockGitHubProvider(), + infoscience=provider, + ) + agent = ArticleAgentV2(max_queries=3) + context = _build_context() + + result = asyncio.run(agent.run(context, providers)) + + assert result.data["id"] == "10.1000/graph-3" + assert result.data["schema:author"] == ["https://orcid.org/0000-0002-1825-0097"] + assert any( + warning.startswith("Dropped unresolved article author references for 1 name(s)") + for warning in result.warnings + ) + + +def test_article_agent_normalizes_year_only_date() -> None: + provider = _RecordingInfoscienceProvider( + { + "sdsc-ordes/gimie": [ + { + "infosciencePublicationIdentifier": "pub-4", + "title": "Year Only Date", + "doi": "10.1000/graph-4", + "publicationDate": "2016", + "authors": ["Alice Example"], + "url": "https://infoscience.epfl.ch/entities/publication/pub-4", + "sourceOrganization": "Swiss Data Science Center", + "score": 10.0, + }, + ], + }, + ) + providers = ProviderSet( + github=MockGitHubProvider(), + infoscience=provider, + ) + agent = ArticleAgentV2(max_queries=3) + context = _build_context() + + result = asyncio.run(agent.run(context, providers)) + + assert result.data["id"] == "10.1000/graph-4" + assert result.data["schema:datePublished"] == "2016-01-01" + assert result.stats["articles"] + assert any( + "Publication date provided as year-only; normalized to '2016-01-01' for schema compatibility" + in warning + for warning in result.warnings + ) + + +def test_article_agent_resolves_accent_and_name_order_variants() -> None: + provider = _RecordingInfoscienceProvider( + { + "sdsc-ordes/gimie": [ + { + "infosciencePublicationIdentifier": "pub-variant-1", + "title": "Accent and Ordering Variants", + "doi": "10.1000/variant-1", + "publicationDate": "2025-04-01", + "authors": ["Alvarez, Jose"], + "url": "https://infoscience.epfl.ch/entities/publication/pub-variant-1", + "sourceOrganization": "Swiss Data Science Center", + "score": 9.0, + }, + ], + }, + ) + providers = ProviderSet( + github=MockGitHubProvider(), + infoscience=provider, + ) + agent = ArticleAgentV2(max_queries=3) + context = _build_context() + context["known_persons"] = [ + { + "id": "https://orcid.org/0000-0003-1234-5678", + "schema:name": "José Alvarez", + "pulse:githubUsername": "josealvarez", + "github_display_name": "Jose Alvarez", + "orcid_record": {"name": "José Alvarez"}, + "infoscience_record": {"name": "Jose Alvarez"}, + }, + ] + + result = asyncio.run(agent.run(context, providers)) + + assert result.data["id"] == "10.1000/variant-1" + assert result.data["schema:author"] == ["https://orcid.org/0000-0003-1234-5678"] + assert not any( + "Skipped article candidate due to missing resolvable authors" + in warning + for warning in result.warnings + ) + + +def test_article_agent_skips_fully_unresolved_authors_with_count_metadata() -> None: + provider = _RecordingInfoscienceProvider( + { + "sdsc-ordes/gimie": [ + { + "infosciencePublicationIdentifier": "pub-variant-2", + "title": "No Resolvable Authors", + "doi": "10.1000/variant-2", + "publicationDate": "2025-05-01", + "authors": ["Unknown One", "Unknown Two"], + "url": "https://infoscience.epfl.ch/entities/publication/pub-variant-2", + "sourceOrganization": "Swiss Data Science Center", + "score": 8.0, + }, + ], + }, + ) + providers = ProviderSet( + github=MockGitHubProvider(), + infoscience=provider, + ) + agent = ArticleAgentV2(max_queries=3) + context = _build_context() + + result = asyncio.run(agent.run(context, providers)) + + assert result.data == {} + assert result.stats["articles"] == [] + assert any( + "Publication has no resolvable author identifiers: doi=10.1000/variant-2" in warning + and "title='No Resolvable Authors'" in warning + and "author_examples='Unknown One', 'Unknown Two'" in warning + for warning in result.warnings + ) + assert any( + "matched_authors=0, unmatched_authors=2" in warning + and "Skipped article candidate due to missing resolvable authors" + in warning + for warning in result.warnings + ) + + +def test_article_agent_skips_candidate_without_doi_required_by_strict_schema() -> None: + provider = _RecordingInfoscienceProvider( + { + "sdsc-ordes/gimie": [ + { + "infosciencePublicationIdentifier": "36f14ad6-3b30-4c6a-9118-2346d8f8a83e", + "title": "No DOI Publication", + "doi": None, + "publicationDate": "2025-01-01", + "authors": ["Alice Example"], + "url": "https://infoscience.epfl.ch/entities/publication/36f14ad6-3b30-4c6a-9118-2346d8f8a83e", + "sourceOrganization": "Swiss Data Science Center", + "score": 7.0, + }, + ], + }, + ) + providers = ProviderSet( + github=MockGitHubProvider(), + infoscience=provider, + ) + agent = ArticleAgentV2(max_queries=3) + context = _build_context() + + result = asyncio.run(agent.run(context, providers)) + + assert result.data == {} + assert result.stats["articles"] == [] + assert any( + "Skipped article candidate due to missing DOI required by strict schema: " + "infoscience=36f14ad6-3b30-4c6a-9118-2346d8f8a83e" + in warning + and "title='No DOI Publication'" in warning + and "url=https://infoscience.epfl.ch/entities/publication/36f14ad6-3b30-4c6a-9118-2346d8f8a83e" + in warning + for warning in result.warnings + ) + + +def test_article_agent_aggregates_missing_resolvable_author_skip_warnings() -> None: + provider = _RecordingInfoscienceProvider( + { + "sdsc-ordes/gimie": [ + { + "infosciencePublicationIdentifier": "pub-a", + "title": "Unmapped Authors A", + "doi": "10.1000/unmapped-a", + "publicationDate": "2025-01-01", + "authors": ["Unknown One", "Unknown Two"], + "url": "https://infoscience.epfl.ch/entities/publication/pub-a", + "sourceOrganization": "Swiss Data Science Center", + "score": 10.0, + }, + { + "infosciencePublicationIdentifier": "pub-b", + "title": "Unmapped Authors B", + "doi": "10.1000/unmapped-b", + "publicationDate": "2025-02-01", + "authors": ["Unknown Three"], + "url": "https://infoscience.epfl.ch/entities/publication/pub-b", + "sourceOrganization": "Swiss Data Science Center", + "score": 9.0, + }, + ], + }, + ) + providers = ProviderSet( + github=MockGitHubProvider(), + infoscience=provider, + ) + agent = ArticleAgentV2(max_queries=3) + context = _build_context() + + result = asyncio.run(agent.run(context, providers)) + + assert result.data == {} + assert result.stats["articles"] == [] + assert any( + "Skipped article candidates due to missing resolvable authors: count=2" + in warning + for warning in result.warnings + ) + assert not any( + warning.startswith( + "Skipped article candidate due to missing resolvable authors:", + ) + and "10.1000/unmapped" in warning + for warning in result.warnings + ) diff --git a/tests/v2/test_auth.py b/tests/v2/test_auth.py new file mode 100644 index 0000000..4cb145a --- /dev/null +++ b/tests/v2/test_auth.py @@ -0,0 +1,125 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +import pytest +from fastapi import FastAPI +from httpx import ASGITransport, AsyncClient + +from src.v2.agents import ProviderSet +from src.v2.api import v2_router +from src.v2.ingest.providers.mock_github import MockGitHubProvider +from src.v2.ingest.providers.mock_infoscience import MockInfoscienceProvider +from src.v2.ingest.providers.mock_orcid import MockORCIDProvider +from src.v2.ingest.providers.mock_ror import MockRORProvider + +HTTP_OK = 200 +HTTP_UNAUTHORIZED = 401 +HTTP_SERVICE_UNAVAILABLE = 503 + +VALID_TOKEN = "s3cret-test-token" # noqa: S105 (test fixture, not a real secret) + + +def _build_test_app() -> FastAPI: + app = FastAPI() + app.include_router(v2_router) + app.state.v2_provider_set = ProviderSet( + github=MockGitHubProvider(), + orcid=MockORCIDProvider(), + infoscience=MockInfoscienceProvider(), + ror=MockRORProvider(), + ) + return app + + +def _request( + method: str, + path: str, + *, + headers: dict[str, str] | None = None, + json_body: dict[str, Any] | None = None, +) -> tuple[int, dict[str, str], Any]: + async def _run() -> tuple[int, dict[str, str], Any]: + app = _build_test_app() + transport = ASGITransport(app=app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + ) as client: + response = await client.request( + method, + path, + headers=headers, + json=json_body, + ) + body: Any + try: + body = response.json() + except ValueError: + body = response.text + return response.status_code, dict(response.headers), body + + return asyncio.run(_run()) + + +@pytest.fixture +def configured_token(monkeypatch: pytest.MonkeyPatch) -> str: + """Pin a known token for the test.""" + monkeypatch.setenv("API_TOKEN", VALID_TOKEN) + return VALID_TOKEN + + +def test_extract_returns_401_when_token_missing(configured_token: str) -> None: + del configured_token + status_code, headers, _body = _request( + "GET", + "/v2/extract/github.com/octocat/Hello-World", + ) + + assert status_code == HTTP_UNAUTHORIZED + assert headers.get("www-authenticate") == "Bearer" + + +def test_extract_returns_401_when_token_wrong(configured_token: str) -> None: + del configured_token + status_code, headers, _body = _request( + "GET", + "/v2/extract/github.com/octocat/Hello-World", + headers={"Authorization": "Bearer wrong-token"}, + ) + + assert status_code == HTTP_UNAUTHORIZED + assert headers.get("www-authenticate") == "Bearer" + + +def test_extract_succeeds_with_correct_token(configured_token: str) -> None: + status_code, _headers, payload = _request( + "GET", + "/v2/extract/github.com/octocat/Hello-World", + headers={"Authorization": f"Bearer {configured_token}"}, + ) + + assert status_code == HTTP_OK + assert payload["detected_type"] == "repository" + + +def test_health_stays_open_without_token(configured_token: str) -> None: + del configured_token + status_code, _headers, payload = _request("GET", "/v2/health") + + assert status_code == HTTP_OK + assert "status" in payload + + +def test_returns_503_when_token_unset(monkeypatch: pytest.MonkeyPatch) -> None: + """Auth fails closed: missing API_TOKEN never silently goes open.""" + monkeypatch.delenv("API_TOKEN", raising=False) + + status_code, _headers, _body = _request( + "GET", + "/v2/extract/github.com/octocat/Hello-World", + headers={"Authorization": "Bearer anything"}, + ) + + assert status_code == HTTP_SERVICE_UNAVAILABLE diff --git a/tests/v2/test_canonical_id_article.py b/tests/v2/test_canonical_id_article.py new file mode 100644 index 0000000..26c22bb --- /dev/null +++ b/tests/v2/test_canonical_id_article.py @@ -0,0 +1,123 @@ +from __future__ import annotations + +import uuid +from typing import Any, Callable + +from src.v2.canonicalization import resolve_article_id +from src.v2.validation.schema_validation import StrictSchemaValidator + +UUID_V5_VERSION = 5 + + +def test_resolve_article_id_prefers_doi() -> None: + article = { + "identifiers": { + "schema:identifier": "10.1038/s41586-024-07856-z", + "pulse:infoscienceArticleIdentifier": "dbce93b0-4ad7-45f2-8a53-b85bf39aeec9", + }, + } + + canonical_id, id_source = resolve_article_id(article) + + assert canonical_id == "https://doi.org/10.1038/s41586-024-07856-z" + assert id_source == "schema:identifier" + + +def test_resolve_article_id_normalizes_infoscience_api_url_with_full_suffix() -> None: + article = { + "identifiers": { + "schema:identifier": None, + "pulse:infoscienceArticleIdentifier": ( + "https://infoscience.epfl.ch/server/api/entities/publication/" + "dbce93b0-4ad7-45f2-8a53-b85bf39aeec9/full" + ), + }, + } + + canonical_id, id_source = resolve_article_id(article) + + assert canonical_id == ( + "https://infoscience.epfl.ch/server/api/core/items/" + "dbce93b0-4ad7-45f2-8a53-b85bf39aeec9" + ) + assert id_source == "pulse:infoscienceArticleIdentifier" + + +def test_resolve_article_id_normalizes_infoscience_core_items_url() -> None: + article = { + "identifiers": { + "schema:identifier": None, + "pulse:infoscienceArticleIdentifier": ( + "https://infoscience.epfl.ch/server/api/core/items/" + "dbce93b0-4ad7-45f2-8a53-b85bf39aeec9" + ), + }, + } + + canonical_id, id_source = resolve_article_id(article) + + assert canonical_id == ( + "https://infoscience.epfl.ch/server/api/core/items/" + "dbce93b0-4ad7-45f2-8a53-b85bf39aeec9" + ) + assert id_source == "pulse:infoscienceArticleIdentifier" + + +def test_resolve_article_id_falls_back_to_uuid_v5() -> None: + article = { + "schema:name": "Graph Article", + "schema:datePublished": "2025-06-15", + "identifiers": { + "schema:identifier": None, + "pulse:infoscienceArticleIdentifier": None, + }, + } + + canonical_id, id_source = resolve_article_id(article) + + parsed = uuid.UUID(canonical_id) + assert parsed.version == UUID_V5_VERSION + assert id_source == "uuid" + + +def test_resolve_article_id_is_idempotent_for_pre_resolved_payload() -> None: + article = { + "id": "https://doi.org/10.1038/s41586-024-07856-z", + "idSource": "schema:identifier", + "identifiers": { + "schema:identifier": "10.1038/s41586-024-07856-z", + }, + } + + canonical_id, id_source = resolve_article_id(article) + + assert canonical_id == "https://doi.org/10.1038/s41586-024-07856-z" + assert id_source == "schema:identifier" + + +def test_resolve_article_id_canonicalizes_pre_resolved_doi_identifier() -> None: + article = { + "id": "10.1038/s41586-024-07856-z", + "idSource": "schema:identifier", + "identifiers": { + "schema:identifier": "10.1038/s41586-024-07856-z", + }, + } + + canonical_id, id_source = resolve_article_id(article) + + assert canonical_id == "https://doi.org/10.1038/s41586-024-07856-z" + assert id_source == "schema:identifier" + + +def test_resolve_article_id_output_is_strict_enum_compatible( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + article = load_fixture("schema/strict", "pulse_ArticleShape")[0] + article["id"] = "https://doi.org/10.1038/s41586-024-07856-z" + article["idSource"] = resolve_article_id(article)[1] + + result = validator.validate("article", article) + + assert result.is_valid is True diff --git a/tests/v2/test_canonical_id_organization.py b/tests/v2/test_canonical_id_organization.py new file mode 100644 index 0000000..81b28fb --- /dev/null +++ b/tests/v2/test_canonical_id_organization.py @@ -0,0 +1,192 @@ +from __future__ import annotations + +import uuid +from typing import Any, Callable + +from src.v2.canonicalization import resolve_organization_id +from src.v2.validation.schema_validation import StrictSchemaValidator + +UUID_V5_VERSION = 5 + + +def test_resolve_organization_id_prefers_ror() -> None: + organization = { + "identifiers": { + "pulse:ror": "05gzmn429", + "pulse:infoscienceOrganizationIdentifier": "org-123", + "pulse:githubOrganizationHandle": "epfl-center-imaging", + }, + } + + canonical_id, id_source = resolve_organization_id(organization) + + assert canonical_id == "https://ror.org/05gzmn429" + assert id_source == "pulse:ror" + + +def test_resolve_organization_id_uses_infoscience_when_ror_missing() -> None: + organization = { + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": "12345", + "pulse:githubOrganizationHandle": "epfl-center-imaging", + }, + } + + canonical_id, id_source = resolve_organization_id(organization) + + assert canonical_id == "https://infoscience.epfl.ch/server/api/core/items/12345" + assert id_source == "pulse:infoscienceOrganizationIdentifier" + + +def test_resolve_organization_id_normalizes_infoscience_api_url_with_full_suffix() -> None: + organization = { + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": ( + "https://infoscience.epfl.ch/server/api/entities/orgunit/" + "6a95499f-7def-427d-ba0a-1ff2a27f58f6/full" + ), + "pulse:githubOrganizationHandle": "epfl-center-imaging", + }, + } + + canonical_id, id_source = resolve_organization_id(organization) + + assert canonical_id == ( + "https://infoscience.epfl.ch/server/api/core/items/" + "6a95499f-7def-427d-ba0a-1ff2a27f58f6" + ) + assert id_source == "pulse:infoscienceOrganizationIdentifier" + + +def test_resolve_organization_id_normalizes_infoscience_core_items_url() -> None: + organization = { + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": ( + "https://infoscience.epfl.ch/server/api/core/items/" + "6a95499f-7def-427d-ba0a-1ff2a27f58f6" + ), + "pulse:githubOrganizationHandle": "epfl-center-imaging", + }, + } + + canonical_id, id_source = resolve_organization_id(organization) + + assert canonical_id == ( + "https://infoscience.epfl.ch/server/api/core/items/" + "6a95499f-7def-427d-ba0a-1ff2a27f58f6" + ) + assert id_source == "pulse:infoscienceOrganizationIdentifier" + + +def test_resolve_organization_id_uses_github_when_higher_priority_ids_are_missing() -> None: + organization = { + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:githubOrganizationHandle": "epfl-center-imaging", + }, + } + + canonical_id, id_source = resolve_organization_id(organization) + + assert canonical_id == "https://github.com/epfl-center-imaging" + assert id_source == "pulse:githubOrganizationHandle" + + +def test_resolve_organization_id_generates_stable_uuid_v5_fallback() -> None: + organization = { + "schema:name": "Imagining Center", + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:githubOrganizationHandle": None, + }, + } + + canonical_id, id_source = resolve_organization_id(organization) + + parsed = uuid.UUID(canonical_id) + assert parsed.version == UUID_V5_VERSION + assert id_source == "uuid" + + +def test_resolve_organization_id_is_deterministic() -> None: + organization = { + "schema:name": "Imagining Center", + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:githubOrganizationHandle": None, + }, + } + + first_id, first_source = resolve_organization_id(organization) + second_id, second_source = resolve_organization_id(organization) + + assert first_id == second_id + assert first_source == second_source == "uuid" + + +def test_resolve_organization_id_canonicalizes_pre_resolved_github_handle() -> None: + organization = { + "id": "epfl-center-imaging", + "idSource": "pulse:githubOrganizationHandle", + "identifiers": { + "pulse:githubOrganizationHandle": "epfl-center-imaging", + }, + } + + canonical_id, id_source = resolve_organization_id(organization) + + assert canonical_id == "https://github.com/epfl-center-imaging" + assert id_source == "pulse:githubOrganizationHandle" + + +def test_resolve_organization_id_overrides_inconsistent_existing_id_source_when_ror_exists() -> None: + organization = { + "id": "https://infoscience.epfl.ch/server/api/core/items/95372c6b-7d45-432e-a84e-660c9fa54e05", + "idSource": "pulse:infoscienceOrganizationIdentifier", + "identifiers": { + "pulse:ror": "https://ror.org/02hdt9m26", + "pulse:infoscienceOrganizationIdentifier": "95372c6b-7d45-432e-a84e-660c9fa54e05", + "pulse:githubOrganizationHandle": "sdsc-ordes", + }, + } + + canonical_id, id_source = resolve_organization_id(organization) + + assert canonical_id == "https://ror.org/02hdt9m26" + assert id_source == "pulse:ror" + + +def test_resolve_organization_id_keeps_existing_resolution_when_consistent() -> None: + organization = { + "id": "https://ror.org/02hdt9m26", + "idSource": "pulse:ror", + "identifiers": { + "pulse:ror": "02hdt9m26", + "pulse:infoscienceOrganizationIdentifier": "95372c6b-7d45-432e-a84e-660c9fa54e05", + "pulse:githubOrganizationHandle": "sdsc-ordes", + }, + } + + canonical_id, id_source = resolve_organization_id(organization) + + assert canonical_id == "https://ror.org/02hdt9m26" + assert id_source == "pulse:ror" + + +def test_resolve_organization_id_output_is_strict_enum_compatible( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + organization = load_fixture("schema/strict", "pulse_OrganizationShape")[0] + organization["id"] = "https://ror.org/05gzmn429" + organization["idSource"] = resolve_organization_id(organization)[1] + + result = validator.validate("organization", organization) + + assert result.is_valid is True diff --git a/tests/v2/test_canonical_id_person.py b/tests/v2/test_canonical_id_person.py new file mode 100644 index 0000000..850af02 --- /dev/null +++ b/tests/v2/test_canonical_id_person.py @@ -0,0 +1,175 @@ +from __future__ import annotations + +import uuid +from typing import Any, Callable + +from src.v2.canonicalization import resolve_person_id +from src.v2.validation.schema_validation import StrictSchemaValidator + +UUID_V5_VERSION = 5 + + +def test_resolve_person_id_prefers_orcid() -> None: + person = { + "identifiers": { + "pulse:orcid": "0000-0002-1825-0097", + "pulse:infosciencePersonIdentifier": "12345", + "pulse:githubUsername": "johndoe", + }, + } + + canonical_id, id_source = resolve_person_id(person) + + assert canonical_id == "https://orcid.org/0000-0002-1825-0097" + assert id_source == "pulse:orcid" + + +def test_resolve_person_id_uses_infoscience_when_orcid_missing() -> None: + person = { + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": "12345", + "pulse:githubUsername": "johndoe", + }, + } + + canonical_id, id_source = resolve_person_id(person) + + assert canonical_id == "https://infoscience.epfl.ch/server/api/core/items/12345" + assert id_source == "pulse:infosciencePersonIdentifier" + + +def test_resolve_person_id_normalizes_infoscience_entity_url_with_full_suffix() -> None: + person = { + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": ( + "https://infoscience.epfl.ch/entities/person/" + "cc69e432-9742-4ebd-a318-02a491f44e69/full" + ), + "pulse:githubUsername": "johndoe", + }, + } + + canonical_id, id_source = resolve_person_id(person) + + assert canonical_id == ( + "https://infoscience.epfl.ch/server/api/core/items/" + "cc69e432-9742-4ebd-a318-02a491f44e69" + ) + assert id_source == "pulse:infosciencePersonIdentifier" + + +def test_resolve_person_id_normalizes_infoscience_core_items_url() -> None: + person = { + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": ( + "https://infoscience.epfl.ch/server/api/core/items/" + "cc69e432-9742-4ebd-a318-02a491f44e69" + ), + "pulse:githubUsername": "johndoe", + }, + } + + canonical_id, id_source = resolve_person_id(person) + + assert canonical_id == ( + "https://infoscience.epfl.ch/server/api/core/items/" + "cc69e432-9742-4ebd-a318-02a491f44e69" + ) + assert id_source == "pulse:infosciencePersonIdentifier" + + +def test_resolve_person_id_uses_github_when_higher_priority_ids_are_missing() -> None: + person = { + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": "johndoe", + }, + } + + canonical_id, id_source = resolve_person_id(person) + + assert canonical_id == "https://github.com/johndoe" + assert id_source == "pulse:githubUsername" + + +def test_resolve_person_id_generates_stable_uuid_v5_fallback() -> None: + person = { + "schema:name": "Jane Doe", + "schema:email": "jane@example.org", + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": None, + }, + } + + canonical_id, id_source = resolve_person_id(person) + + parsed = uuid.UUID(canonical_id) + assert parsed.version == UUID_V5_VERSION + assert id_source == "uuid" + + +def test_resolve_person_id_is_deterministic_for_same_input() -> None: + person = { + "schema:name": "Jane Doe", + "schema:email": "jane@example.org", + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": None, + }, + } + + first_id, first_source = resolve_person_id(person) + second_id, second_source = resolve_person_id(person) + + assert first_id == second_id + assert first_source == second_source == "uuid" + + +def test_resolve_person_id_is_idempotent_for_pre_resolved_payload() -> None: + person = { + "id": "https://orcid.org/0000-0002-1825-0097", + "idSource": "orcid", + "identifiers": { + "pulse:orcid": "0000-0002-1825-0097", + }, + } + + canonical_id, id_source = resolve_person_id(person) + + assert canonical_id == "https://orcid.org/0000-0002-1825-0097" + assert id_source == "pulse:orcid" + + +def test_resolve_person_id_canonicalizes_pre_resolved_github_handle() -> None: + person = { + "id": "johndoe", + "idSource": "pulse:githubUsername", + "identifiers": { + "pulse:githubUsername": "johndoe", + }, + } + + canonical_id, id_source = resolve_person_id(person) + + assert canonical_id == "https://github.com/johndoe" + assert id_source == "pulse:githubUsername" + + +def test_resolve_person_id_output_is_strict_enum_compatible( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + person = load_fixture("schema/strict", "pulse_PersonShape")[0] + person["id"] = "https://orcid.org/0000-0002-1825-0097" + person["idSource"] = resolve_person_id(person)[1] + + result = validator.validate("person", person) + + assert result.is_valid is True diff --git a/tests/v2/test_canonical_id_repository.py b/tests/v2/test_canonical_id_repository.py new file mode 100644 index 0000000..b6874b6 --- /dev/null +++ b/tests/v2/test_canonical_id_repository.py @@ -0,0 +1,95 @@ +from __future__ import annotations + +import uuid +from typing import Any, Callable + +from src.v2.canonicalization import resolve_repository_id +from src.v2.validation.schema_validation import StrictSchemaValidator + +UUID_V5_VERSION = 5 + + +def test_resolve_repository_id_prefers_github_repository_handle() -> None: + repository = { + "identifiers": { + "pulse:githubRepositoryHandle": "owner/repo", + "schema:citation": "10.5281/zenodo.1234", + }, + } + + canonical_id, id_source = resolve_repository_id(repository) + + assert canonical_id == "https://github.com/owner/repo" + assert id_source == "pulse:githubRepositoryHandle" + + +def test_resolve_repository_id_uses_doi_when_github_handle_is_missing() -> None: + repository = { + "identifiers": { + "pulse:githubRepositoryHandle": None, + "schema:citation": "10.5281/zenodo.1234", + }, + } + + canonical_id, id_source = resolve_repository_id(repository) + + assert canonical_id == "https://doi.org/10.5281/zenodo.1234" + assert id_source == "schema:citation" + + +def test_resolve_repository_id_falls_back_to_uuid_v5() -> None: + repository = { + "schema:name": "Some Repository", + "identifiers": { + "pulse:githubRepositoryHandle": None, + "schema:citation": None, + }, + } + + canonical_id, id_source = resolve_repository_id(repository) + + parsed = uuid.UUID(canonical_id) + assert parsed.version == UUID_V5_VERSION + assert id_source == "uuid" + + +def test_resolve_repository_id_validates_github_handle_shape() -> None: + repository = { + "identifiers": { + "pulse:githubRepositoryHandle": "owner/repo/extra", + "schema:citation": "10.5281/zenodo.1234", + }, + } + + canonical_id, id_source = resolve_repository_id(repository) + + assert canonical_id == "https://doi.org/10.5281/zenodo.1234" + assert id_source == "schema:citation" + + +def test_resolve_repository_id_output_is_strict_enum_compatible( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + repository = load_fixture("schema/strict", "pulse_RepositoryShape")[0] + repository["id"] = "https://github.com/owner/repo" + repository["idSource"] = resolve_repository_id(repository)[1] + + result = validator.validate("repository", repository) + + assert result.is_valid is True + + +def test_resolve_repository_id_canonicalizes_pre_resolved_github_handle() -> None: + repository = { + "id": "owner/repo", + "idSource": "pulse:githubRepositoryHandle", + "identifiers": { + "pulse:githubRepositoryHandle": "owner/repo", + }, + } + + canonical_id, id_source = resolve_repository_id(repository) + + assert canonical_id == "https://github.com/owner/repo" + assert id_source == "pulse:githubRepositoryHandle" diff --git a/tests/v2/test_config.py b/tests/v2/test_config.py new file mode 100644 index 0000000..800552d --- /dev/null +++ b/tests/v2/test_config.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +import pytest + +from src.v2.agents.runtime import AgentRuntime +from src.v2.config import V2Config + +V2_CONFIG_ENV_KEYS = { + "GITHUB_TOKEN", + "V2_AGENT_RUNTIME_DEFAULT", +} + + +def _clear_v2_config_env(monkeypatch: pytest.MonkeyPatch) -> None: + for key in V2_CONFIG_ENV_KEYS: + monkeypatch.delenv(key, raising=False) + + +def test_v2_config_with_required_env_is_valid( + monkeypatch: pytest.MonkeyPatch, +) -> None: + _clear_v2_config_env(monkeypatch) + monkeypatch.setenv("GITHUB_TOKEN", "test-value") + + config = V2Config() + config.validate_preflight() + + assert config.GITHUB_TOKEN + + +def test_v2_config_missing_github_token_raises_descriptive_error( + monkeypatch: pytest.MonkeyPatch, +) -> None: + _clear_v2_config_env(monkeypatch) + + config = V2Config() + with pytest.raises(ValueError, match="Missing required environment variable: GITHUB_TOKEN"): + config.validate_preflight() + + +def test_v2_agent_runtime_default_is_llm( + monkeypatch: pytest.MonkeyPatch, +) -> None: + _clear_v2_config_env(monkeypatch) + monkeypatch.setenv("GITHUB_TOKEN", "test-value") + + config = V2Config() + + assert config.V2_AGENT_RUNTIME_DEFAULT == AgentRuntime.LLM + + +def test_v2_agent_runtime_default_can_be_set_to_llm( + monkeypatch: pytest.MonkeyPatch, +) -> None: + _clear_v2_config_env(monkeypatch) + monkeypatch.setenv("GITHUB_TOKEN", "test-value") + monkeypatch.setenv("V2_AGENT_RUNTIME_DEFAULT", "llm") + + config = V2Config() + + assert config.V2_AGENT_RUNTIME_DEFAULT == AgentRuntime.LLM + + +def test_v2_agent_runtime_default_rejects_invalid_values( + monkeypatch: pytest.MonkeyPatch, +) -> None: + _clear_v2_config_env(monkeypatch) + monkeypatch.setenv("GITHUB_TOKEN", "test-value") + monkeypatch.setenv("V2_AGENT_RUNTIME_DEFAULT", "hybrid") + + with pytest.raises(ValueError, match="Invalid runtime value for V2_AGENT_RUNTIME_DEFAULT"): + V2Config() diff --git a/tests/v2/test_context_gather.py b/tests/v2/test_context_gather.py new file mode 100644 index 0000000..eb10dc6 --- /dev/null +++ b/tests/v2/test_context_gather.py @@ -0,0 +1,202 @@ +from __future__ import annotations + +import asyncio +import json +from typing import Any + +import pytest + +from src.v2.agents import ProviderSet +from src.v2.ingest.detection.models import GitHubURLClassification, GitHubURLType +from src.v2.pipeline.stages import gather_context +from src.v2.pipeline.stages.context_gather import RequiredProviderUnavailableError +from src.v2.ingest.providers.base import GitHubProvider, ORCIDProvider, ORCIDRecord + +EXPECTED_CONTRIBUTOR_COUNT = 2 + + +class _DummyGitHubProvider(GitHubProvider): + def get_repository(self, full_name: str) -> dict[str, Any]: + return { + "full_name": full_name, + "readme_content": "# Hello World", + "description": "Hello world repository", + "owner": {"login": "octocat", "type": "User"}, + } + + def get_user(self, username: str) -> dict[str, Any]: + return { + "login": username, + "name": "Alice Example", + "orcid": "0000-0002-1825-0097", + "repositories": [f"{username}/repo-a", "repo-b"], + } + + def get_organization(self, org_name: str) -> dict[str, Any]: + return { + "login": org_name, + "name": "Org Example", + "members": ["alice", "bob"], + "repositories": [f"{org_name}/repo-a", f"{org_name}/repo-b"], + } + + def get_contributors(self, full_name: str) -> list[dict[str, Any]]: + del full_name + return [{"login": "alice"}, {"login": "bob"}] + + def get_languages(self, full_name: str) -> dict[str, int]: + del full_name + return {"Python": 42} + + +class _DummyORCIDProvider(ORCIDProvider): + def get_person_by_orcid(self, orcid_id: str) -> ORCIDRecord: + return ORCIDRecord( + orcid_id=orcid_id, + name="Alice Example", + employment=[], + education=[], + affiliations=["EPFL"], + ) + + +def test_repository_context_contains_expected_sections() -> None: + providers = ProviderSet(github=_DummyGitHubProvider()) + url_info = GitHubURLClassification( + normalized_url="https://github.com/octocat/Hello-World", + detected_type=GitHubURLType.REPOSITORY, + owner="octocat", + repo="Hello-World", + ) + + bundle = asyncio.run(gather_context("repository", url_info, providers)) + + repository_context = bundle.context["repository"] + assert repository_context["metadata"]["full_name"] == "octocat/Hello-World" + assert repository_context["readme_content"] == "# Hello World" + assert len(repository_context["contributors"]) == EXPECTED_CONTRIBUTOR_COUNT + assert "Python" in repository_context["languages"] + + +def test_user_context_contains_profile_repos_and_orcid() -> None: + providers = ProviderSet( + github=_DummyGitHubProvider(), + orcid=_DummyORCIDProvider(), + ) + url_info = GitHubURLClassification( + normalized_url="https://github.com/alice", + detected_type=GitHubURLType.USER, + owner="alice", + repo=None, + ) + + bundle = asyncio.run(gather_context("user", url_info, providers)) + + user_context = bundle.context["user"] + assert user_context["profile"]["login"] == "alice" + assert user_context["owned_repos"] == ["alice/repo-a", "repo-b"] + assert set(user_context["repository_contexts"]) == {"alice/repo-a", "alice/repo-b"} + assert user_context["orcid_data"]["name"] == "Alice Example" + + +def test_org_context_contains_profile_members_and_repositories() -> None: + providers = ProviderSet(github=_DummyGitHubProvider()) + url_info = GitHubURLClassification( + normalized_url="https://github.com/orgs/example", + detected_type=GitHubURLType.ORGANIZATION, + owner="example", + repo=None, + ) + + bundle = asyncio.run(gather_context("organization", url_info, providers)) + + organization_context = bundle.context["organization"] + assert organization_context["profile"]["login"] == "example" + assert organization_context["members"] == ["alice", "bob"] + assert organization_context["owned_repos"] == ["example/repo-a", "example/repo-b"] + assert set(organization_context["repository_contexts"]) == { + "example/repo-a", + "example/repo-b", + } + + +def test_optional_repository_context_failures_are_warnings_for_user_mode() -> None: + class _PartiallyFailingGitHubProvider(_DummyGitHubProvider): + def get_repository(self, full_name: str) -> dict[str, Any]: + if full_name.endswith("repo-b"): + raise RuntimeError("repo unavailable") + return super().get_repository(full_name) + + providers = ProviderSet( + github=_PartiallyFailingGitHubProvider(), + orcid=_DummyORCIDProvider(), + ) + url_info = GitHubURLClassification( + normalized_url="https://github.com/alice", + detected_type=GitHubURLType.USER, + owner="alice", + repo=None, + ) + + bundle = asyncio.run(gather_context("user", url_info, providers)) + + user_context = bundle.context["user"] + assert set(user_context["repository_contexts"]) == {"alice/repo-a"} + assert any( + "Repository metadata lookup failed for alice/repo-b" in warning + for warning in bundle.warnings + ) + + +def test_missing_optional_orcid_adds_warning_instead_of_error() -> None: + class _NoOrcidGitHubProvider(_DummyGitHubProvider): + def get_user(self, username: str) -> dict[str, Any]: + payload = super().get_user(username) + payload.pop("orcid", None) + return payload + + providers = ProviderSet(github=_NoOrcidGitHubProvider()) + url_info = GitHubURLClassification( + normalized_url="https://github.com/alice", + detected_type=GitHubURLType.USER, + owner="alice", + repo=None, + ) + + bundle = asyncio.run(gather_context("user", url_info, providers)) + + assert any("ORCID data unavailable" in warning for warning in bundle.warnings) + + +def test_context_bundle_is_serializable() -> None: + providers = ProviderSet(github=_DummyGitHubProvider()) + url_info = GitHubURLClassification( + normalized_url="https://github.com/octocat/Hello-World", + detected_type=GitHubURLType.REPOSITORY, + owner="octocat", + repo="Hello-World", + ) + + bundle = asyncio.run(gather_context("repository", url_info, providers)) + serialized = bundle.to_dict() + + assert serialized["detected_type"] == "repository" + json.dumps(serialized) + + +def test_repository_context_raises_when_required_github_call_fails() -> None: + class _FailingGitHubProvider(_DummyGitHubProvider): + def get_repository(self, full_name: str) -> dict[str, Any]: + del full_name + raise RuntimeError("github unavailable") + + providers = ProviderSet(github=_FailingGitHubProvider()) + url_info = GitHubURLClassification( + normalized_url="https://github.com/octocat/Hello-World", + detected_type=GitHubURLType.REPOSITORY, + owner="octocat", + repo="Hello-World", + ) + + with pytest.raises(RequiredProviderUnavailableError, match="repository metadata lookup"): + asyncio.run(gather_context("repository", url_info, providers)) diff --git a/tests/v2/test_contribution_agent.py b/tests/v2/test_contribution_agent.py new file mode 100644 index 0000000..8372577 --- /dev/null +++ b/tests/v2/test_contribution_agent.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import asyncio +from copy import deepcopy +from typing import Any, Callable +from uuid import UUID + +from jsonschema import validate + +from src.v2.agents import ContributionAgentV2, ProviderSet +from src.v2.ingest.providers.mock_github import MockGitHubProvider + +EXPECTED_CONTRIBUTION_COUNT = 2 +EXPECTED_ALICE_CONTRIBUTION_COUNT = 7 +UUID_VERSION_4 = 4 + + +def _assert_uuid4(value: str) -> None: + parsed = UUID(value) + assert parsed.version == UUID_VERSION_4 + + +def _strip_contribution_uuids(contributions: list[dict[str, Any]]) -> list[dict[str, Any]]: + sanitized = deepcopy(contributions) + for contribution in sanitized: + identifiers = contribution.get("identifiers") + if isinstance(identifiers, dict) and isinstance(identifiers.get("uuid"), str): + identifiers["uuid"] = "" + return sanitized + + +def _contribution_context() -> dict[str, Any]: + return { + "known_persons": [ + { + "id": "https://orcid.org/0000-0002-1825-0097", + "schema:name": "Alice Example", + "pulse:githubUsername": "alice", + }, + { + "id": "https://github.com/bob", + "schema:name": "Bob Example", + "pulse:githubUsername": "bob", + }, + ], + "known_repositories": [ + { + "id": "sdsc-ordes/gimie", + "contributors": [ + { + "login": "alice", + "contributions": 5, + "firstContributionDate": "2024-01-01T00:00:00Z", + "lastContributionDate": "2024-02-01T00:00:00Z", + }, + { + "login": "alice", + "contributions": 7, + "firstContributionDate": "2023-12-01T00:00:00Z", + "lastContributionDate": "2024-06-01T00:00:00Z", + }, + { + "login": "bob", + }, + { + "login": "unknown-contributor", + "contributions": 1, + }, + ], + }, + ], + } + + +def test_contribution_agent_derives_deduplicated_contributions_with_uuid4_identifiers( + load_schema: Callable[[str, str], dict[str, Any]], +) -> None: + providers = ProviderSet(github=MockGitHubProvider()) + agent = ContributionAgentV2() + + first_result = asyncio.run(agent.run(_contribution_context(), providers)) + second_result = asyncio.run(agent.run(_contribution_context(), providers)) + + contributions = first_result.stats["contributions"] + contribution_schema = load_schema("agent", "contribution") + for contribution in contributions: + validate(instance=contribution, schema=contribution_schema) + + assert len(contributions) == EXPECTED_CONTRIBUTION_COUNT + assert [contribution["id"] for contribution in contributions] == [ + "https://github.com/bob_sdsc-ordes/gimie", + "https://orcid.org/0000-0002-1825-0097_sdsc-ordes/gimie", + ] + for contribution in contributions: + identifiers = contribution.get("identifiers") + assert isinstance(identifiers, dict) + assert isinstance(identifiers.get("uuid"), str) + _assert_uuid4(str(identifiers["uuid"])) + for contribution in second_result.stats["contributions"]: + identifiers = contribution.get("identifiers") + assert isinstance(identifiers, dict) + assert isinstance(identifiers.get("uuid"), str) + _assert_uuid4(str(identifiers["uuid"])) + assert _strip_contribution_uuids(first_result.stats["contributions"]) == _strip_contribution_uuids( + second_result.stats["contributions"], + ) + + +def test_contribution_agent_populates_count_and_nullable_dates() -> None: + providers = ProviderSet(github=MockGitHubProvider()) + agent = ContributionAgentV2() + + result = asyncio.run(agent.run(_contribution_context(), providers)) + contributions = result.stats["contributions"] + alice_contribution = next( + contribution + for contribution in contributions + if contribution["schema:author"] == "https://orcid.org/0000-0002-1825-0097" + ) + bob_contribution = next( + contribution + for contribution in contributions + if contribution["schema:author"] == "https://github.com/bob" + ) + + assert ( + alice_contribution["pulse:contributionCount"] + == EXPECTED_ALICE_CONTRIBUTION_COUNT + ) + assert alice_contribution["pulse:firstContributionDate"] == "2023-12-01T00:00:00Z" + assert alice_contribution["pulse:lastContributionDate"] == "2024-06-01T00:00:00Z" + assert bob_contribution["pulse:contributionCount"] == 0 + assert bob_contribution["pulse:firstContributionDate"] is None + assert bob_contribution["pulse:lastContributionDate"] is None + assert any("Unresolved contribution person mapping" in warning for warning in result.warnings) + + +def test_contribution_agent_handles_unresolvable_contributors_without_failure() -> None: + providers = ProviderSet(github=MockGitHubProvider()) + agent = ContributionAgentV2() + context = deepcopy(_contribution_context()) + context["known_persons"] = [] + + result = asyncio.run(agent.run(context, providers)) + + assert result.data == {} + assert result.stats["contributions"] == [] + assert any("Unresolved contribution person mapping" in warning for warning in result.warnings) + + +def test_contribution_agent_ignores_organization_contributor_entries() -> None: + providers = ProviderSet(github=MockGitHubProvider()) + agent = ContributionAgentV2() + context = deepcopy(_contribution_context()) + context["known_repositories"][0]["contributors"].append( + {"login": "sdsc-ordes", "type": "Organization", "contributions": 42}, + ) + context["known_organizations"] = [ + { + "id": "https://ror.org/02hdt9m26", + "schema:name": "Swiss Data Science Center", + "pulse:githubOrganizationHandle": "sdsc-ordes", + }, + ] + + result = asyncio.run(agent.run(context, providers)) + + assert not any("contributor=sdsc-ordes" in warning for warning in result.warnings) + + +def test_contribution_agent_scopes_to_target_person_when_provided() -> None: + """The orchestrator passes one (person, repo) pair per fanout via + `target_person`. The rule-based agent must scope its iteration to that + person — emitting one Contribution and not warning about other + contributors that belong to a different fanout work item. + """ + providers = ProviderSet(github=MockGitHubProvider()) + agent = ContributionAgentV2() + context = deepcopy(_contribution_context()) + context["known_persons"] = [ + { + "id": "https://github.com/bob", + "schema:name": "Bob Example", + "pulse:githubUsername": "bob", + }, + ] + context["target_person"] = context["known_persons"][0] + + result = asyncio.run(agent.run(context, providers)) + + contributions = result.stats["contributions"] + assert len(contributions) == 1 + assert contributions[0]["schema:author"] == "https://github.com/bob" + # Other contributors (alice, unknown-contributor) belong to other fanout + # items and must not surface as "Unresolved" here. + assert not any( + "Unresolved contribution person mapping" in warning + for warning in result.warnings + ) diff --git a/tests/v2/test_dependencies.py b/tests/v2/test_dependencies.py new file mode 100644 index 0000000..1d6d752 --- /dev/null +++ b/tests/v2/test_dependencies.py @@ -0,0 +1,83 @@ +from __future__ import annotations + +import asyncio + +from fastapi import FastAPI +from starlette.requests import Request + +from src.v2.dependencies import get_provider_set +from src.v2.ingest.providers.github_provider import RealGitHubProvider +from src.v2.ingest.providers.mock_github import MockGitHubProvider + + +def _build_request( + *, + full_path: str = "github.com/octocat/Hello-World", + query_string: str = "", +) -> Request: + extract_path = f"/v2/extract/{full_path}" + app = FastAPI() + scope = { + "type": "http", + "http_version": "1.1", + "method": "GET", + "scheme": "http", + "path": extract_path, + "raw_path": extract_path.encode("utf-8"), + "query_string": query_string.encode("utf-8"), + "headers": [], + "client": ("testclient", 50000), + "server": ("testserver", 80), + "app": app, + "path_params": {"full_path": full_path}, + } + return Request(scope) + + +def test_get_provider_set_uses_mock_provider_by_default(monkeypatch) -> None: + monkeypatch.setenv("V2_USE_MOCK_PROVIDERS", "true") + + + provider_set = asyncio.run(get_provider_set(_build_request())) + + assert isinstance(provider_set.github, MockGitHubProvider) + + +def test_get_provider_set_creates_real_provider_without_cache( + monkeypatch, +) -> None: + monkeypatch.setenv("V2_USE_MOCK_PROVIDERS", "false") + + provider_set = asyncio.run(get_provider_set(_build_request())) + + assert isinstance(provider_set.github, RealGitHubProvider) + + +def test_get_provider_set_disables_github_repo_expansion_for_repository_extract( + monkeypatch, +) -> None: + monkeypatch.setenv("V2_USE_MOCK_PROVIDERS", "false") + + + provider_set = asyncio.run( + get_provider_set(_build_request(full_path="github.com/octocat/Hello-World")), + ) + + assert isinstance(provider_set.github, RealGitHubProvider) + assert provider_set.github.include_user_repositories is False + assert provider_set.github.include_organization_repositories is False + + +def test_get_provider_set_keeps_github_repo_expansion_for_user_extract( + monkeypatch, +) -> None: + monkeypatch.setenv("V2_USE_MOCK_PROVIDERS", "false") + + + provider_set = asyncio.run( + get_provider_set(_build_request(full_path="github.com/octocat")), + ) + + assert isinstance(provider_set.github, RealGitHubProvider) + assert provider_set.github.include_user_repositories is True + assert provider_set.github.include_organization_repositories is True diff --git a/tests/v2/test_dependents_scraper.py b/tests/v2/test_dependents_scraper.py new file mode 100644 index 0000000..a9778a5 --- /dev/null +++ b/tests/v2/test_dependents_scraper.py @@ -0,0 +1,161 @@ +"""Pure-function parser tests against captured fixtures. + +The fixtures under `tests/v2/fixtures/github/dependents/` were captured +via Selenium on 2026-05-01. See `SELECTORS.md` in that directory for the +documented selectors and the expected counts each fixture exercises. +""" +from __future__ import annotations + +from pathlib import Path + +import pytest + +from src.module.dependents.scraper import ( + build_dependents_url, + iterate_dependents, + parse_dependents_page, +) + +FIXTURE_DIR = Path(__file__).resolve().parent / "fixtures" / "github" / "dependents" + + +def _load(name: str) -> str: + return (FIXTURE_DIR / name).read_text() + + +def test_parse_small_repo_with_few_dependents() -> None: + page = parse_dependents_page(_load("sdsc-ordes_gimie_repository.html")) + + assert page.selected_kind == "REPOSITORY" + assert page.repository_count == 6 + assert page.package_count == 0 + # GitHub shows 4 rows even though the count is 6 (forks/duplicates not listed). + assert len(page.items) == 4 + assert page.next_cursor_url is None # single-page result + + first = page.items[0] + assert first.full_name == "Imaging-Plaza/git-metadata-extractor" + assert first.owner == "Imaging-Plaza" + assert first.repo == "git-metadata-extractor" + assert first.stars >= 0 + assert first.forks >= 0 + + +def test_parse_huge_repo_extracts_paginated_cursor() -> None: + page = parse_dependents_page(_load("psf_requests_repository.html")) + + assert page.selected_kind == "REPOSITORY" + assert page.repository_count == 4_065_575 + assert page.package_count == 138_519 + assert len(page.items) == 30 # GitHub paginates by 30 rows per page + assert page.next_cursor_url is not None + assert "dependents_after=" in page.next_cursor_url + + +def test_parse_empty_dependents_page() -> None: + page = parse_dependents_page(_load("python-poetry_poetry_package.html")) + + assert page.selected_kind == "PACKAGE" + assert page.repository_count == 0 + assert page.package_count == 0 + assert page.items == [] + assert page.next_cursor_url is None + + +def test_parse_handles_empty_html_safely() -> None: + page = parse_dependents_page("") + assert page.repository_count == 0 + assert page.package_count == 0 + assert page.selected_kind is None + assert page.items == [] + assert page.next_cursor_url is None + + +def test_iterate_dependents_walks_pages_via_fetcher_until_no_next() -> None: + """Iterator should stop when a page returns no Next link.""" + + gimie_html = _load("sdsc-ordes_gimie_repository.html") + + items = list( + iterate_dependents( + "sdsc-ordes/gimie", + kind="REPOSITORY", + fetcher=[gimie_html], + max_pages=10, + max_items=100, + ), + ) + + assert len(items) == 4 + full_names = [item.full_name for item, _ in items] + assert full_names[0] == "Imaging-Plaza/git-metadata-extractor" + + +def test_iterate_dependents_caps_at_max_items() -> None: + """Iterator must stop yielding after `max_items`, even if pages have more.""" + + requests_html = _load("psf_requests_repository.html") + items = list( + iterate_dependents( + "psf/requests", + kind="REPOSITORY", + fetcher=[requests_html, requests_html, requests_html], + max_pages=10, + max_items=5, + ), + ) + assert len(items) == 5 + + +def test_iterate_dependents_caps_at_max_pages() -> None: + """Iterator must stop processing after `max_pages` regardless of items.""" + + requests_html = _load("psf_requests_repository.html") + items = list( + iterate_dependents( + "psf/requests", + kind="REPOSITORY", + fetcher=[requests_html, requests_html, requests_html], + max_pages=2, + max_items=200, + ), + ) + # Each page has 30 rows → 2 pages = 60 rows, capped before page 3. + assert len(items) == 60 + + +def test_iterate_dependents_stops_on_empty_html() -> None: + """Empty HTML signals fetch failure — iterator should stop, not loop.""" + + items = list( + iterate_dependents( + "any/repo", + fetcher=["", ""], + max_pages=5, + max_items=100, + ), + ) + assert items == [] + + +def test_build_dependents_url_default_kind() -> None: + url = build_dependents_url("sdsc-ordes/gimie") + assert url == ( + "https://github.com/sdsc-ordes/gimie/network/dependents" + "?dependent_type=REPOSITORY" + ) + + +def test_build_dependents_url_with_cursor() -> None: + url = build_dependents_url( + "psf/requests", + kind="REPOSITORY", + cursor="abc123", + ) + assert "dependent_type=REPOSITORY" in url + assert "dependents_after=abc123" in url + + +def test_build_dependents_url_rejects_invalid_full_name() -> None: + with pytest.raises(ValueError, match="owner/repo"): + build_dependents_url("not-a-slug") diff --git a/tests/v2/test_dependents_service.py b/tests/v2/test_dependents_service.py new file mode 100644 index 0000000..bbb7993 --- /dev/null +++ b/tests/v2/test_dependents_service.py @@ -0,0 +1,176 @@ +"""Service-level tests: caching round-trip + degradation paths. + +The service layer is exercised by passing an explicit `fetcher` callable +to `list_dependents`, so we never hit Selenium in tests. +""" +from __future__ import annotations + +from pathlib import Path + +import pytest + +from src.module.dependents.scraper import build_dependents_url +from src.module.dependents.service import list_dependents +from src.v2.ingest.cache import ProviderCache + +FIXTURE_DIR = Path(__file__).resolve().parent / "fixtures" / "github" / "dependents" + + +def _load(name: str) -> str: + return (FIXTURE_DIR / name).read_text() + + +def _build_fixture_fetcher(url_to_html: dict[str, str]): + """Make a `(url) -> html` callable that pretends to fetch our fixtures.""" + + def _fetch(url: str) -> str: + return url_to_html.get(url, "") + + return _fetch + + +def test_list_dependents_returns_structured_result_for_small_repo() -> None: + fetcher = _build_fixture_fetcher( + { + build_dependents_url("sdsc-ordes/gimie", kind="REPOSITORY"): + _load("sdsc-ordes_gimie_repository.html"), + }, + ) + + result = list_dependents( + "sdsc-ordes/gimie", + kind="REPOSITORY", + fetcher=fetcher, + ) + + assert result.full_name == "sdsc-ordes/gimie" + assert result.kind == "REPOSITORY" + assert result.total_count == 6 + assert result.fetched_count == 4 + assert result.truncated is False # we got every available row + assert result.available is True + assert result.warnings == [] + assert result.items[0].full_name == "Imaging-Plaza/git-metadata-extractor" + + +def test_list_dependents_marks_truncated_when_capped() -> None: + fetcher = _build_fixture_fetcher( + { + build_dependents_url("psf/requests", kind="REPOSITORY"): + _load("psf_requests_repository.html"), + }, + ) + + result = list_dependents( + "psf/requests", + kind="REPOSITORY", + max_pages=1, + max_items=10, + fetcher=fetcher, + ) + + assert result.fetched_count == 10 + assert result.total_count == 4_065_575 + assert result.truncated is True + + +def test_list_dependents_returns_empty_result_for_disabled_graph() -> None: + fetcher = _build_fixture_fetcher( + { + build_dependents_url("python-poetry/poetry", kind="PACKAGE"): + _load("python-poetry_poetry_package.html"), + }, + ) + + result = list_dependents( + "python-poetry/poetry", + kind="PACKAGE", + fetcher=fetcher, + ) + + assert result.total_count == 0 + assert result.fetched_count == 0 + assert result.available is True + assert any("zero dependents" in w.lower() for w in result.warnings) + + +def test_list_dependents_rejects_invalid_full_name() -> None: + result = list_dependents("not-a-slug", fetcher=lambda _u: "") + + assert result.available is False + assert result.fetched_count == 0 + assert any("owner/repo" in w for w in result.warnings) + + +def test_list_dependents_caches_aggregated_result(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "providers.db") + + gimie_url = build_dependents_url("sdsc-ordes/gimie", kind="REPOSITORY") + fetcher_calls: list[str] = [] + + def _fetcher(url: str) -> str: + fetcher_calls.append(url) + return _load("sdsc-ordes_gimie_repository.html") if url == gimie_url else "" + + # First call populates cache. + r1 = list_dependents( + "sdsc-ordes/gimie", + kind="REPOSITORY", + cache=cache, + fetcher=_fetcher, + ) + fetcher_call_count_after_first = len(fetcher_calls) + assert r1.fetched_count == 4 + assert fetcher_call_count_after_first >= 1 # at least one page fetched + + # Second call with the same args + a fetcher that always returns empty — + # if cache is honoured, we still get the original result. + def _empty_fetcher(_url: str) -> str: + fetcher_calls.append("UNEXPECTED-FETCH") + return "" + + r2 = list_dependents( + "sdsc-ordes/gimie", + kind="REPOSITORY", + cache=cache, + fetcher=_empty_fetcher, + ) + + assert r2.fetched_count == r1.fetched_count + assert r2.total_count == r1.total_count + assert r2.items[0].full_name == r1.items[0].full_name + # No additional fetch calls after the cache populated. + assert "UNEXPECTED-FETCH" not in fetcher_calls + + +def test_list_dependents_different_max_items_uses_different_cache_key( + tmp_path: Path, +) -> None: + """Different `max_items` must produce a different cache key.""" + + cache = ProviderCache(tmp_path / "providers.db") + gimie_url = build_dependents_url("sdsc-ordes/gimie", kind="REPOSITORY") + fetcher_calls: list[str] = [] + + def _fetcher(url: str) -> str: + fetcher_calls.append(url) + return _load("sdsc-ordes_gimie_repository.html") if url == gimie_url else "" + + list_dependents( + "sdsc-ordes/gimie", + kind="REPOSITORY", + max_items=2, + cache=cache, + fetcher=_fetcher, + ) + calls_after_first = len(fetcher_calls) + + # max_items=3 — different cache key, must re-fetch. + list_dependents( + "sdsc-ordes/gimie", + kind="REPOSITORY", + max_items=3, + cache=cache, + fetcher=_fetcher, + ) + assert len(fetcher_calls) > calls_after_first diff --git a/tests/v2/test_dependents_tool.py b/tests/v2/test_dependents_tool.py new file mode 100644 index 0000000..c8d3f40 --- /dev/null +++ b/tests/v2/test_dependents_tool.py @@ -0,0 +1,66 @@ +"""Tool factory tests — mirror `test_llm_query_dependencies_tool.py`'s shape.""" +from __future__ import annotations + +from pathlib import Path +from typing import Any + +from src.module.dependents.scraper import build_dependents_url +from src.module.dependents.tool import make_query_dependents_tool + +FIXTURE_DIR = Path(__file__).resolve().parent / "fixtures" / "github" / "dependents" + + +def test_make_query_dependents_tool_exposes_expected_metadata() -> None: + tool = make_query_dependents_tool() + + assert tool.name == "query_dependents" + assert tool.description + # Description should mention the key concept ("dependents") and explain + # the fail-soft behaviour so the LLM doesn't panic on degraded results. + assert "dependent" in tool.description.lower() + assert "available" in tool.description.lower() + + +def test_query_dependents_tool_returns_dict_with_expected_keys( + monkeypatch: Any, +) -> None: + """End-to-end: tool function delegates to service, which uses our fetcher.""" + + fixture_html = (FIXTURE_DIR / "sdsc-ordes_gimie_repository.html").read_text() + expected_url = build_dependents_url("sdsc-ordes/gimie", kind="REPOSITORY") + + # Monkey-patch fetch_dependents_html so the tool's call path doesn't hit + # Selenium. The tool uses `list_dependents` internally, which calls + # `iterate_dependents` → `fetch_dependents_html`. + monkeypatch.setattr( + "src.module.dependents.scraper.fetch_dependents_html", + lambda url, **_kwargs: fixture_html if url == expected_url else "", + ) + # Some env paths short-circuit if SELENIUM_REMOTE_URL is missing — keep + # the service from refusing the lookup. + monkeypatch.setenv("SELENIUM_REMOTE_URL", "http://test-selenium:4444") + + tool = make_query_dependents_tool() + # Pull out the underlying callable — pydantic-ai exposes it via `.function`. + func = getattr(tool, "function", None) or tool._function # type: ignore[attr-defined] + payload: dict[str, Any] = func("sdsc-ordes/gimie") + + assert payload["full_name"] == "sdsc-ordes/gimie" + assert payload["kind"] == "REPOSITORY" + assert payload["fetched_count"] == 4 + assert payload["total_count"] == 6 + assert payload["available"] is True + assert isinstance(payload["items"], list) + assert payload["items"][0]["full_name"] == "Imaging-Plaza/git-metadata-extractor" + + +def test_query_dependents_tool_invalid_input_returns_unavailable_result() -> None: + """The tool should never raise for malformed input — it returns a degraded result.""" + + tool = make_query_dependents_tool() + func = getattr(tool, "function", None) or tool._function # type: ignore[attr-defined] + payload: dict[str, Any] = func("not-a-slug") + + assert payload["available"] is False + assert payload["fetched_count"] == 0 + assert payload["warnings"] diff --git a/tests/v2/test_email_anonymization.py b/tests/v2/test_email_anonymization.py new file mode 100644 index 0000000..f15f33e --- /dev/null +++ b/tests/v2/test_email_anonymization.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +from src.v1.data_models.models import Person +from src.v2.pipeline.stages.privacy import anonymize_email +from src.v2.pipeline.stages.reconciliation import reconcile_entities + + +def _v1_anonymized_email(email: str) -> str: + person = Person(name="Example User", emails=[email]) + assert person.emails is not None + return person.emails[0] + + +def _person_with_email(email: str) -> dict[str, object]: + return { + "schema:name": "Example User", + "schema:email": email, + "pulse:githubUsername": "example-user", + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": "example-user", + "uuid": "e3c8e4f8-8c9f-4f74-a857-73feefc57dd2", + }, + } + + +def test_anonymize_email_matches_v1_hashing_logic() -> None: + raw_email = "user@example.com" + assert anonymize_email(raw_email) == _v1_anonymized_email(raw_email) + + +def test_anonymize_email_is_deterministic() -> None: + raw_email = "deterministic@example.com" + assert anonymize_email(raw_email) == anonymize_email(raw_email) + + +def test_anonymize_email_preserves_domain_and_hides_local_part() -> None: + raw_email = "privacy@example.com" + anonymized = anonymize_email(raw_email) + assert anonymized.endswith("@example.com") + assert not anonymized.startswith("privacy@") + + +def test_anonymize_email_does_not_double_hash_pre_anonymized_values() -> None: + first_pass = anonymize_email("already@example.com") + assert anonymize_email(first_pass) == first_pass + + +def test_reconciliation_anonymizes_person_email_in_output() -> None: + reconciled = reconcile_entities( + { + "persons": [_person_with_email("visible@example.com")], + "organizations": [], + "repositories": [], + }, + ) + + person = reconciled.entities["persons"][0] + assert person["schema:email"] == anonymize_email("visible@example.com") + assert person["schema:email"] != "visible@example.com" diff --git a/tests/v2/test_enum_alignment.py b/tests/v2/test_enum_alignment.py new file mode 100644 index 0000000..8d59269 --- /dev/null +++ b/tests/v2/test_enum_alignment.py @@ -0,0 +1,59 @@ +from __future__ import annotations + +from functools import lru_cache + +from scripts.v2.extract_enums_from_ttl import default_ttl_path, extract_enums_from_ttl +from src.v2.api_models.enums import DisciplineV2, OrganizationTypeV2, RepositoryTypeV2 + +EXPECTED_COMPUTER_SCIENCE_WIKIDATA_URI = "http://www.wikidata.org/entity/Q428691" + + +@lru_cache(maxsize=1) +def _ttl_enum_ids() -> dict[str, set[str]]: + payload = extract_enums_from_ttl(default_ttl_path()) + return { + "disciplines": {entry["id"] for entry in payload["disciplines"]}, + "repository_types": {entry["id"] for entry in payload["repository_types"]}, + "organization_types": {entry["id"] for entry in payload["organization_types"]}, + } + + +def test_every_ttl_discipline_has_a_matching_discipline_enum_member() -> None: + ttl_disciplines = _ttl_enum_ids()["disciplines"] + enum_disciplines = {discipline.value for discipline in DisciplineV2} + + assert ttl_disciplines <= enum_disciplines + + +def test_discipline_enum_has_no_members_missing_from_ttl() -> None: + ttl_disciplines = _ttl_enum_ids()["disciplines"] + enum_disciplines = {discipline.value for discipline in DisciplineV2} + + assert enum_disciplines <= ttl_disciplines + + +def test_repository_type_enum_matches_ttl_repository_type_enumeration() -> None: + ttl_repository_types = _ttl_enum_ids()["repository_types"] + enum_repository_types = {repo_type.value for repo_type in RepositoryTypeV2} + + assert enum_repository_types == ttl_repository_types + + +def test_organization_type_enum_matches_ttl_organization_type_enumeration() -> None: + ttl_organization_types = _ttl_enum_ids()["organization_types"] + enum_organization_types = {org_type.value for org_type in OrganizationTypeV2} + + assert enum_organization_types == ttl_organization_types + + +def test_computer_science_alias_exposes_expected_wikidata_uri() -> None: + assert DisciplineV2.COMPUTER_SCIENCE.wikidata_uri == EXPECTED_COMPUTER_SCIENCE_WIKIDATA_URI + + +def test_enum_alignment_detects_ttl_drift_with_bidirectional_set_checks() -> None: + ttl_ids = _ttl_enum_ids() + assert len(ttl_ids["disciplines"]) == len({discipline.value for discipline in DisciplineV2}) + assert len(ttl_ids["repository_types"]) == len({repo_type.value for repo_type in RepositoryTypeV2}) + assert len(ttl_ids["organization_types"]) == len( + {org_type.value for org_type in OrganizationTypeV2}, + ) diff --git a/tests/v2/test_error_models.py b/tests/v2/test_error_models.py new file mode 100644 index 0000000..60f7e3c --- /dev/null +++ b/tests/v2/test_error_models.py @@ -0,0 +1,63 @@ +from __future__ import annotations + +from fastapi import HTTPException + +from src.v2.api_models.errors import V2ErrorResponse, V2ErrorType, V2FieldError + +HTTP_UNPROCESSABLE_ENTITY = 422 + + +def test_error_response_serializes_correctly_for_unsupported_url() -> None: + payload = V2ErrorResponse( + error_type="unsupported_url", + detail="repository subresource URLs not supported", + ).model_dump(mode="json", exclude_none=True) + + assert payload == { + "error_type": "unsupported_url", + "detail": "repository subresource URLs not supported", + } + + +def test_error_response_shape_works_with_fastapi_http_exception() -> None: + response_model = V2ErrorResponse( + error_type=V2ErrorType.VALIDATION_ERROR, + detail="Schema validation failed", + source_url="https://github.com/owner/repo", + ) + exception = HTTPException( + status_code=HTTP_UNPROCESSABLE_ENTITY, + detail=response_model.model_dump(mode="json"), + ) + + assert exception.status_code == HTTP_UNPROCESSABLE_ENTITY + assert exception.detail["error_type"] == "validation_error" + assert exception.detail["source_url"] == "https://github.com/owner/repo" + + +def test_error_type_enum_contains_all_planned_values() -> None: + assert {error_type.value for error_type in V2ErrorType} == { + "unsupported_url", + "validation_error", + "provider_error", + "pipeline_error", + "not_found", + } + + +def test_field_error_captures_field_message_and_value() -> None: + field_error = V2FieldError( + field="output_format", + message="unexpected value", + value="xml", + ) + + assert field_error.field == "output_format" + assert field_error.message == "unexpected value" + assert field_error.value == "xml" + + +def test_error_models_are_importable() -> None: + assert V2ErrorResponse.__name__ == "V2ErrorResponse" + assert V2ErrorType.__name__ == "V2ErrorType" + assert V2FieldError.__name__ == "V2FieldError" diff --git a/tests/v2/test_extract_golden.py b/tests/v2/test_extract_golden.py new file mode 100644 index 0000000..b7c6fbc --- /dev/null +++ b/tests/v2/test_extract_golden.py @@ -0,0 +1,176 @@ +from __future__ import annotations + +import asyncio +from pathlib import Path +from typing import Any, Callable +from uuid import UUID + +import pytest +from httpx import ASGITransport, AsyncClient + +from src.api import app +from src.v2.agents import ProviderSet +from src.v2.ingest.providers.mock_github import MockGitHubProvider +from src.v2.ingest.providers.mock_infoscience import MockInfoscienceProvider +from src.v2.ingest.providers.mock_orcid import MockORCIDProvider +from src.v2.ingest.providers.mock_ror import MockRORProvider + +GITHUB_FIXTURE_ROOT = Path(__file__).resolve().parent / "fixtures" / "providers" / "github" +HTTP_OK = 200 +EXPECTED_JSON_ENTITY_BUCKETS = { + "repositories", + "persons", + "organizations", + "articles", + "memberships", + "contributions", +} +EXPECTED_STAGE_SEQUENCE_BY_DETECTED_TYPE = { + "repository": [ + "context_gather", + "repo_agent", + "person_agents", + "org_agents", + "article_agents", + "membership_agents", + "contribution_agents", + "permissive_validation", + "reconciliation", + "strict_validation", + "output_assembly", + "link_veracity", + "jsonld_build", + "shacl_gate", + "graph_write", + ], + "user": [ + "context_gather", + "person_agent", + "repo_agents", + "org_agents", + "article_agents", + "membership_agents", + "contribution_agents", + "permissive_validation", + "reconciliation", + "strict_validation", + "output_assembly", + "link_veracity", + "jsonld_build", + "shacl_gate", + "graph_write", + ], + "organization": [ + "context_gather", + "org_agent", + "person_agents", + "repo_agents", + "article_agents", + "membership_agents", + "contribution_agents", + "permissive_validation", + "reconciliation", + "strict_validation", + "output_assembly", + "link_veracity", + "jsonld_build", + "shacl_gate", + "graph_write", + ], +} + + +@pytest.fixture +def mock_provider_set() -> ProviderSet: + return ProviderSet( + github=MockGitHubProvider(fixture_root=GITHUB_FIXTURE_ROOT), + orcid=MockORCIDProvider(), + infoscience=MockInfoscienceProvider(), + ror=MockRORProvider(), + ) + + +def _get_json(path: str, params: dict[str, str]) -> tuple[int, Any]: + async def _run() -> tuple[int, Any]: + transport = ASGITransport(app=app) + async with AsyncClient( + transport=transport, + base_url="http://testserver", + ) as client: + response = await client.get(path, params=params) + return response.status_code, response.json() + + return asyncio.run(_run()) + + +def _assert_subset(expected: Any, actual: Any) -> None: + if isinstance(expected, dict): + assert isinstance(actual, dict) + for key, expected_value in expected.items(): + assert key in actual + _assert_subset(expected_value, actual[key]) + return + + if isinstance(expected, list): + assert isinstance(actual, list) + assert len(actual) >= len(expected) + for index, expected_item in enumerate(expected): + _assert_subset(expected_item, actual[index]) + return + + assert actual == expected + + +@pytest.mark.parametrize( + ("source_url", "golden_name"), + [ + ("github.com/octocat/Hello-World", "repo_github_com_owner_repo"), + ("github.com/octocat", "user_github_com_username"), + ("github.com/orgs/github", "org_github_com_orgname"), + ], +) +def test_extract_endpoint_matches_golden_contract( + source_url: str, + golden_name: str, + load_golden: Callable[[str, str], Any], + mock_provider_set: ProviderSet, +) -> None: + app.state.v2_provider_set = mock_provider_set + expected = load_golden("extract", golden_name) + status_code, actual_payload = _get_json( + f"/v2/extract/{source_url}", + params={"output_format": expected["output_format"]}, + ) + + assert status_code == HTTP_OK + assert actual_payload["source_url"] == expected["source_url"] + assert actual_payload["detected_type"] == expected["detected_type"] + assert actual_payload["output_format"] == expected["output_format"] + _assert_subset(expected["output"], actual_payload["output"]) + assert actual_payload["stats"]["entities_count"] == expected["stats"]["entities_count"] + assert actual_payload["stats"]["triples_count"] == expected["stats"]["triples_count"] + assert actual_payload["stats"]["stages_completed"] == expected["stats"]["stages_completed"] + assert actual_payload["stats"]["stages_completed"] == EXPECTED_STAGE_SEQUENCE_BY_DETECTED_TYPE[ + actual_payload["detected_type"] + ] + + if actual_payload["output_format"] == "json": + assert set(actual_payload["output"]) == { + "root_entity", + "related_entities", + "excluded_entities", + "entities_by_type", + } + assert set(actual_payload["output"]["entities_by_type"]) == EXPECTED_JSON_ENTITY_BUCKETS + else: + assert "@context" in actual_payload["output"] + assert "@graph" in actual_payload["output"] + assert isinstance(actual_payload["output"]["@graph"], list) + for node in actual_payload["output"]["@graph"]: + assert isinstance(node, dict) + assert "@id" in node + assert "@type" in node + + assert isinstance(actual_payload["warnings"], list) + assert isinstance(actual_payload["stats"]["duration_ms"], int) + assert str(UUID(actual_payload["stats"]["run_id"])) == actual_payload["stats"]["run_id"] diff --git a/tests/v2/test_generated_models.py b/tests/v2/test_generated_models.py new file mode 100644 index 0000000..75c538e --- /dev/null +++ b/tests/v2/test_generated_models.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +import json +from pathlib import Path + +from src.v2.schema.models.strict import PersonModel, RepositoryModel + +REPO_ROOT = Path(__file__).resolve().parents[2] +STRICT_FIXTURE_DIR = REPO_ROOT / "tests" / "v2" / "fixtures" / "schema" / "strict" +GENERATED_MODELS_PATH = REPO_ROOT / "src" / "v2" / "schema" / "models" / "strict.py" + + +def _load_fixture_list(file_name: str) -> list[dict[str, object]]: + payload = json.loads((STRICT_FIXTURE_DIR / file_name).read_text(encoding="utf-8")) + assert isinstance(payload, list) + return payload + + +def test_generated_models_file_has_codegen_header() -> None: + first_line = GENERATED_MODELS_PATH.read_text(encoding="utf-8").splitlines()[0] + assert first_line.startswith("# generated by datamodel-codegen") + + +def test_person_model_validates_person_fixture_payloads() -> None: + for payload in _load_fixture_list("pulse_PersonShape.json"): + model = PersonModel.model_validate(payload) + assert model.root.id == payload["id"] + assert model.root.model_dump(by_alias=True)["schema:name"] == payload["schema:name"] + + +def test_repository_model_validates_repository_fixture_payloads() -> None: + for payload in _load_fixture_list("pulse_RepositoryShape.json"): + model = RepositoryModel.model_validate(payload) + assert model.id == payload["id"] + assert model.model_dump(by_alias=True)["schema:name"] == payload["schema:name"] diff --git a/tests/v2/test_github_rate_limit.py b/tests/v2/test_github_rate_limit.py new file mode 100644 index 0000000..96bb898 --- /dev/null +++ b/tests/v2/test_github_rate_limit.py @@ -0,0 +1,110 @@ +from __future__ import annotations + +from datetime import datetime, timezone + +from src.v2.observation.github_rate_limit import ( + GitHubTokenStatus, + _summarize, +) + + +def _ts(epoch: int) -> datetime: + return datetime.fromtimestamp(epoch, tz=timezone.utc) + + +def test_summarize_empty_pool_is_unhealthy() -> None: + summary = _summarize([]) + assert summary.status == "unhealthy" + assert summary.total_remaining == 0 + assert summary.earliest_reset is None + + +def test_summarize_all_invalid_is_unhealthy() -> None: + summary = _summarize( + [ + GitHubTokenStatus(index=1, status="invalid"), + GitHubTokenStatus(index=2, status="invalid"), + ], + ) + assert summary.status == "unhealthy" + + +def test_summarize_all_rate_limited_is_degraded() -> None: + summary = _summarize( + [ + GitHubTokenStatus( + index=1, + status="rate_limited", + core_remaining=0, + core_limit=5000, + core_reset=_ts(1_700_000_000), + ), + GitHubTokenStatus( + index=2, + status="rate_limited", + core_remaining=0, + core_limit=5000, + core_reset=_ts(1_700_000_900), + ), + ], + ) + assert summary.status == "degraded" + assert summary.total_remaining == 0 + assert summary.earliest_reset == _ts(1_700_000_000) + + +def test_summarize_low_total_remaining_is_degraded() -> None: + summary = _summarize( + [ + GitHubTokenStatus( + index=1, + status="ok", + core_remaining=100, + core_limit=5000, + core_reset=_ts(1_700_000_000), + ), + ], + ) + assert summary.status == "degraded" + assert summary.total_remaining == 100 + + +def test_summarize_one_ok_one_rate_limited_is_healthy() -> None: + summary = _summarize( + [ + GitHubTokenStatus( + index=1, + status="rate_limited", + core_remaining=0, + core_limit=5000, + core_reset=_ts(1_700_000_000), + ), + GitHubTokenStatus( + index=2, + status="ok", + core_remaining=4500, + core_limit=5000, + core_reset=_ts(1_700_000_900), + ), + ], + ) + assert summary.status == "healthy" + assert summary.total_remaining == 4500 + assert summary.earliest_reset == _ts(1_700_000_000) + + +def test_summarize_unreachable_token_does_not_block_healthy() -> None: + summary = _summarize( + [ + GitHubTokenStatus(index=1, status="unreachable"), + GitHubTokenStatus( + index=2, + status="ok", + core_remaining=4800, + core_limit=5000, + core_reset=_ts(1_700_000_000), + ), + ], + ) + assert summary.status == "healthy" + assert summary.total_remaining == 4800 diff --git a/tests/v2/test_github_sbom.py b/tests/v2/test_github_sbom.py new file mode 100644 index 0000000..1e5d129 --- /dev/null +++ b/tests/v2/test_github_sbom.py @@ -0,0 +1,283 @@ +from __future__ import annotations + +from typing import Any +from unittest.mock import patch + +import pytest + +from src.v2.ingest.providers.github_provider import ( + RealGitHubProvider, + _normalize_sbom, + _parse_purl, +) + + +class _FakeResponse: + def __init__(self, *, status_code: int, payload: Any = None, raise_json: bool = False) -> None: + self.status_code = status_code + self._payload = payload + self._raise_json = raise_json + + def json(self) -> Any: + if self._raise_json: + raise ValueError("not json") + return self._payload + + +def _build_provider() -> RealGitHubProvider: + return RealGitHubProvider( + gimie_extractor=lambda _url, _fmt: {}, + user_lookup=lambda username: {"login": username}, + organization_lookup=lambda org_name: {"login": org_name}, + ) + + +# ---------- _parse_purl ---------- + + +@pytest.mark.parametrize( + ("purl", "expected"), + [ + ( + "pkg:pypi/requests@2.31.0", + {"ecosystem": "pypi", "name": "requests", "version": "2.31.0"}, + ), + ( + "pkg:npm/@scope/name@1.0.0", + {"ecosystem": "npm", "name": "@scope/name", "version": "1.0.0"}, + ), + ( + "pkg:maven/org.apache/commons-lang@2.6", + {"ecosystem": "maven", "name": "org.apache/commons-lang", "version": "2.6"}, + ), + ( + "pkg:cargo/serde", + {"ecosystem": "cargo", "name": "serde", "version": None}, + ), + ( + "pkg:githubactions/actions/checkout@v4?ref=main", + {"ecosystem": "githubactions", "name": "actions/checkout", "version": "v4"}, + ), + ], +) +def test_parse_purl_extracts_ecosystem_name_and_version( + purl: str, + expected: dict[str, str | None], +) -> None: + assert _parse_purl(purl) == expected + + +@pytest.mark.parametrize( + "invalid", + [ + "", + "not-a-purl", + "pkg:", + "pkg:pypi", + 123, + None, + ], +) +def test_parse_purl_returns_none_for_invalid_input(invalid: Any) -> None: + assert _parse_purl(invalid) is None # type: ignore[arg-type] + + +# ---------- _normalize_sbom ---------- + + +def test_normalize_sbom_extracts_packages_with_purl_refs() -> None: + spdx_payload = { + "sbom": { + "spdxVersion": "SPDX-2.3", + "packages": [ + { + "SPDXID": "SPDXRef-Repo-octocat-Hello-World", + "name": "octocat/Hello-World", + "externalRefs": [], + }, + { + "SPDXID": "SPDXRef-pypi-requests", + "name": "requests", + "versionInfo": "2.31.0", + "externalRefs": [ + { + "referenceCategory": "PACKAGE-MANAGER", + "referenceType": "purl", + "referenceLocator": "pkg:pypi/requests@2.31.0", + }, + ], + }, + { + "SPDXID": "SPDXRef-npm-left-pad", + "name": "left-pad", + "externalRefs": [ + { + "referenceType": "purl", + "referenceLocator": "pkg:npm/left-pad@1.3.0", + }, + ], + }, + ], + }, + } + + result = _normalize_sbom(spdx_payload) + + assert result == [ + { + "name": "requests", + "ecosystem": "pypi", + "version": "2.31.0", + "spdxId": "SPDXRef-pypi-requests", + }, + { + "name": "left-pad", + "ecosystem": "npm", + "version": "1.3.0", + "spdxId": "SPDXRef-npm-left-pad", + }, + ] + + +def test_normalize_sbom_falls_back_to_version_info_when_purl_omits_version() -> None: + spdx_payload = { + "sbom": { + "packages": [ + { + "SPDXID": "SPDXRef-pypi-numpy", + "name": "numpy", + "versionInfo": "1.26.0", + "externalRefs": [ + {"referenceType": "purl", "referenceLocator": "pkg:pypi/numpy"}, + ], + }, + ], + }, + } + + result = _normalize_sbom(spdx_payload) + + assert result == [ + { + "name": "numpy", + "ecosystem": "pypi", + "version": "1.26.0", + "spdxId": "SPDXRef-pypi-numpy", + }, + ] + + +def test_normalize_sbom_returns_empty_for_unexpected_shapes() -> None: + assert _normalize_sbom(None) == [] + assert _normalize_sbom({}) == [] + assert _normalize_sbom({"sbom": "not-a-dict"}) == [] + assert _normalize_sbom({"sbom": {"packages": "not-a-list"}}) == [] + + +def test_normalize_sbom_accepts_unwrapped_payload() -> None: + spdx_payload = { + "packages": [ + { + "SPDXID": "SPDXRef-pypi-flask", + "externalRefs": [ + {"referenceType": "purl", "referenceLocator": "pkg:pypi/flask@3.0.0"}, + ], + }, + ], + } + + result = _normalize_sbom(spdx_payload) + + assert result == [ + { + "name": "flask", + "ecosystem": "pypi", + "version": "3.0.0", + "spdxId": "SPDXRef-pypi-flask", + }, + ] + + +# ---------- RealGitHubProvider.get_repository_sbom ---------- + + +def test_get_repository_sbom_returns_normalized_list_on_200() -> None: + provider = _build_provider() + payload = { + "sbom": { + "packages": [ + { + "SPDXID": "SPDXRef-pypi-requests", + "externalRefs": [ + { + "referenceType": "purl", + "referenceLocator": "pkg:pypi/requests@2.31.0", + }, + ], + }, + ], + }, + } + response = _FakeResponse(status_code=200, payload=payload) + + with patch( + "src.v2.ingest.providers.github_provider.requests.get", + return_value=response, + ) as fake_get: + result = provider.get_repository_sbom("octocat/Hello-World") + + assert result == [ + { + "name": "requests", + "ecosystem": "pypi", + "version": "2.31.0", + "spdxId": "SPDXRef-pypi-requests", + }, + ] + assert fake_get.call_count == 1 + args, _kwargs = fake_get.call_args + assert args[0].endswith("/repos/octocat/Hello-World/dependency-graph/sbom") + + +@pytest.mark.parametrize("status_code", [404, 403]) +def test_get_repository_sbom_returns_none_on_missing_or_forbidden(status_code: int) -> None: + provider = _build_provider() + response = _FakeResponse(status_code=status_code, payload={"message": "nope"}) + + with patch( + "src.v2.ingest.providers.github_provider.requests.get", + return_value=response, + ): + assert provider.get_repository_sbom("ghost/repo") is None + + +def test_get_repository_sbom_returns_none_on_other_http_error() -> None: + provider = _build_provider() + response = _FakeResponse(status_code=500, payload={"message": "boom"}) + + with patch( + "src.v2.ingest.providers.github_provider.requests.get", + return_value=response, + ): + assert provider.get_repository_sbom("octocat/Hello-World") is None + + +def test_get_repository_sbom_returns_none_when_response_not_json() -> None: + provider = _build_provider() + response = _FakeResponse(status_code=200, raise_json=True) + + with patch( + "src.v2.ingest.providers.github_provider.requests.get", + return_value=response, + ): + assert provider.get_repository_sbom("octocat/Hello-World") is None + + +def test_get_repository_sbom_returns_none_when_request_raises() -> None: + provider = _build_provider() + + with patch( + "src.v2.ingest.providers.github_provider.requests.get", + side_effect=ConnectionError("boom"), + ): + assert provider.get_repository_sbom("octocat/Hello-World") is None diff --git a/tests/v2/test_jsonld_build.py b/tests/v2/test_jsonld_build.py new file mode 100644 index 0000000..9504b69 --- /dev/null +++ b/tests/v2/test_jsonld_build.py @@ -0,0 +1,143 @@ +from __future__ import annotations + +import json +from typing import Any + +from rdflib import Graph, Literal, URIRef + +from src.v2.pipeline.stages.jsonld_build import ENTITY_URI_PREFIX, build_jsonld_output +from src.v2.pipeline.stages.models import AssembledOutput +from src.v2.schema import load_jsonld_context + + +def _context() -> dict[str, Any]: + return load_jsonld_context() + + +def _node_by_id(payload: dict[str, Any], *, node_id: str) -> dict[str, Any]: + graph = payload.get("@graph") + assert isinstance(graph, list) + for node in graph: + if isinstance(node, dict) and node.get("@id") == node_id: + return node + raise AssertionError + + +def test_build_jsonld_output_preserves_literal_fields_even_when_matching_entity_ids() -> None: + assembled = AssembledOutput( + root_entity={ + "id": "owner/repo", + "type": "schema:SoftwareSourceCode", + "schema:name": "owner/repo", + "schema:author": ["supermaxiste"], + }, + related_entities=[ + { + "id": "supermaxiste", + "type": "schema:Person", + "schema:name": "supermaxiste", + "pulse:githubUsername": "supermaxiste", + }, + ], + ) + + payload = build_jsonld_output( + assembled=assembled, + jsonld_context=_context(), + ) + + repo_node = _node_by_id( + payload, + node_id=f"{ENTITY_URI_PREFIX}owner/repo", + ) + person_node = _node_by_id( + payload, + node_id=f"{ENTITY_URI_PREFIX}supermaxiste", + ) + + assert repo_node["schema:author"] == [{"@id": f"{ENTITY_URI_PREFIX}supermaxiste"}] + assert person_node["schema:name"] == "supermaxiste" + assert person_node["pulse:githubUsername"] == "supermaxiste" + + +def test_build_jsonld_output_emits_schema_url_as_iri_node() -> None: + assembled = AssembledOutput( + root_entity={ + "id": "alice", + "type": "schema:Person", + "schema:name": "Alice", + "schema:url": "https://github.com/alice", + }, + ) + payload = build_jsonld_output( + assembled=assembled, + jsonld_context=_context(), + ) + + graph = Graph() + graph.parse(data=json.dumps(payload), format="json-ld") + + subject = URIRef(f"{ENTITY_URI_PREFIX}alice") + predicate = URIRef("http://schema.org/url") + objects = list(graph.objects(subject, predicate)) + + assert len(objects) == 1 + assert objects[0] == URIRef("https://github.com/alice") + assert not isinstance(objects[0], Literal) + + +def test_build_jsonld_output_emits_license_citation_and_org_hierarchy_as_iris() -> None: + assembled = AssembledOutput( + root_entity={ + "id": "owner/repo", + "type": "schema:SoftwareSourceCode", + "schema:name": "owner/repo", + "schema:author": ["alice"], + "schema:license": "https://spdx.org/licenses/MIT.html", + "schema:citation": "https://doi.org/10.1234/example", + }, + related_entities=[ + { + "id": "alice", + "type": "schema:Person", + "schema:name": "Alice", + "pulse:githubUsername": "alice", + }, + { + "id": "https://ror.org/02s376052", + "type": "org:Organization", + "schema:name": "EPFL", + "org:hasUnit": ["https://github.com/sdsc-ordes"], + }, + { + "id": "https://github.com/sdsc-ordes", + "type": "org:Organization", + "schema:name": "sdsc-ordes", + "org:unitOf": "https://ror.org/02s376052", + }, + ], + ) + payload = build_jsonld_output( + assembled=assembled, + jsonld_context=_context(), + ) + + graph = Graph() + graph.parse(data=json.dumps(payload), format="json-ld") + + repo_subject = URIRef(f"{ENTITY_URI_PREFIX}owner/repo") + org_parent = URIRef("https://ror.org/02s376052") + org_child = URIRef("https://github.com/sdsc-ordes") + + assert list(graph.objects(repo_subject, URIRef("http://schema.org/license"))) == [ + URIRef("https://spdx.org/licenses/MIT.html"), + ] + assert list(graph.objects(repo_subject, URIRef("http://schema.org/citation"))) == [ + URIRef("https://doi.org/10.1234/example"), + ] + assert list(graph.objects(org_parent, URIRef("http://www.w3.org/ns/org#hasUnit"))) == [ + org_child, + ] + assert list(graph.objects(org_child, URIRef("http://www.w3.org/ns/org#unitOf"))) == [ + org_parent, + ] diff --git a/tests/v2/test_link_veracity_stage.py b/tests/v2/test_link_veracity_stage.py new file mode 100644 index 0000000..6b88017 --- /dev/null +++ b/tests/v2/test_link_veracity_stage.py @@ -0,0 +1,252 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +from src.v2.agents import ProviderSet +from src.v2.agents.models import AgentResult +from src.v2.pipeline.stages import ( + AssembledOutput, + apply_link_pruning_to_assembled_output, + collect_unique_http_link_contexts, + run_link_veracity_stage, +) +from src.v2.ingest.providers.mock_github import MockGitHubProvider + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +def test_collect_unique_http_link_contexts_deduplicates_nested_links() -> None: + payload = { + "@context": {}, + "@graph": [ + { + "@id": "urn:node:1", + "schema:url": "https://example.org/a", + "schema:license": {"@id": "https://spdx.org/licenses/MIT.html"}, + "nested": { + "values": [ + "https://example.org/a", + "https://example.org/b", + ], + }, + }, + { + "@id": "urn:node:2", + "schema:url": "https://example.org/b", + "schema:sameAs": [{"@id": "https://example.org/c"}], + }, + ], + } + + contexts = collect_unique_http_link_contexts(payload) + + assert [context["link"] for context in contexts] == [ + "https://example.org/a", + "https://example.org/b", + "https://example.org/c", + "https://spdx.org/licenses/MIT.html", + ] + + +def test_run_link_veracity_stage_counts_and_warnings(monkeypatch) -> None: + import src.v2.pipeline.stages.link_veracity as stage_module + + class _FakeVerifier: + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + cache: Any | None = None, + ) -> None: + del llm_runtime, llm_call_timeout_seconds, cache + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + link = context["link"] + if link.endswith("/fail"): + raise RuntimeError("link timeout") + return AgentResult( + data={ + "link": link, + "relationship_supported": link.endswith("/yes"), + "relationship_summary": "ok", + "fetched_successfully": not link.endswith("/fetch-fail"), + }, + model="openai/gpt-test", + provider="openai", + tokens_prompt=10, + tokens_completion=5, + ) + + monkeypatch.setattr(stage_module, "LLMLinkVeracityAgentV2", _FakeVerifier) + + payload = { + "@context": {}, + "@graph": [ + { + "@id": "urn:node:1", + "schema:url": "https://example.org/yes", + "schema:license": "https://example.org/no", + }, + { + "@id": "urn:node:2", + "schema:url": "https://example.org/fail", + }, + { + "@id": "urn:node:3", + "schema:url": "https://example.org/fetch-fail", + }, + ], + } + + result = asyncio.run( + run_link_veracity_stage( + jsonld_payload=payload, + source_url="https://github.com/owner/repo", + providers=_providers(), + max_concurrency=2, + ), + ) + + assert result.checked_count == 4 + assert result.supported_count == 1 + assert result.unsupported_count == 1 + assert result.failed_count == 2 + assert any("unsupported relationship" in warning for warning in result.warnings) + assert any("check failed" in warning for warning in result.warnings) + assert any("fetch failed" in warning for warning in result.warnings) + assert "https://example.org/fetch-fail" in result.invalid_links + + +def test_run_link_veracity_stage_handles_no_links() -> None: + payload = { + "@context": {}, + "@graph": [ + {"@id": "urn:node:1", "schema:name": "Example"}, + {"@id": "urn:node:2", "schema:author": ["urn:node:1"]}, + ], + } + + result = asyncio.run( + run_link_veracity_stage( + jsonld_payload=payload, + source_url="https://github.com/owner/repo", + providers=_providers(), + ), + ) + + assert result.checked_count == 0 + assert result.records == [] + assert result.warnings == [] + + +def test_run_link_veracity_stage_scans_entities_and_derives_article_doi_link(monkeypatch) -> None: + import src.v2.pipeline.stages.link_veracity as stage_module + + class _FakeVerifier: + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + cache: Any | None = None, + ) -> None: + del llm_runtime, llm_call_timeout_seconds, cache + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + return AgentResult( + data={ + "link": context["link"], + "relationship_supported": True, + "relationship_summary": "ok", + "fetched_successfully": True, + }, + ) + + monkeypatch.setattr(stage_module, "LLMLinkVeracityAgentV2", _FakeVerifier) + + entities = [ + { + "id": "https://doi.org/10.1000/example", + "type": "schema:ScholarlyArticle", + "schema:identifier": "10.1000/example", + "schema:url": "https://example.org/article", + }, + ] + + result = asyncio.run( + run_link_veracity_stage( + entities=entities, + source_url="https://github.com/owner/repo", + providers=_providers(), + ), + ) + + # DOI url == entity id, so it's a self-reference and skipped by veracity. + # Only the distinct schema:url link is checked. + assert result.checked_count == 1 + assert result.article_identifier_link_map["https://doi.org/10.1000/example"] == "https://doi.org/10.1000/example" + assert "https://example.org/article" in result.entity_link_map["https://doi.org/10.1000/example"] + assert "https://doi.org/10.1000/example" in result.entity_link_map["https://doi.org/10.1000/example"] + + +def test_apply_link_pruning_to_assembled_output_prunes_links_and_drops_entity() -> None: + assembled = AssembledOutput( + root_entity={ + "id": "https://github.com/owner/repo", + "type": "schema:SoftwareSourceCode", + "schema:url": "https://github.com/owner/repo", + "schema:license": "https://example.org/bad-license", + }, + related_entities=[ + { + "id": "https://doi.org/10.1000/bad-doi", + "type": "schema:ScholarlyArticle", + "schema:identifier": "10.1000/bad-doi", + "schema:url": "https://example.org/bad-article", + }, + ], + ) + updated, warnings = apply_link_pruning_to_assembled_output( + assembled=assembled, + invalid_links={ + "https://example.org/bad-license", + "https://doi.org/10.1000/bad-doi", + "https://example.org/bad-article", + }, + entity_link_map={ + "https://github.com/owner/repo": [ + "https://github.com/owner/repo", + "https://example.org/bad-license", + ], + "https://doi.org/10.1000/bad-doi": [ + "https://doi.org/10.1000/bad-doi", + "https://example.org/bad-article", + ], + }, + article_identifier_link_map={ + "https://doi.org/10.1000/bad-doi": "https://doi.org/10.1000/bad-doi", + }, + ) + + assert isinstance(updated.root_entity, dict) + assert updated.root_entity["schema:url"] == "https://github.com/owner/repo" + assert updated.root_entity["schema:license"] is None + assert updated.related_entities == [] + assert len(updated.excluded_entities) == 1 + assert updated.excluded_entities[0]["entity_type"] == "article" + assert any("Removed invalid link(s) from entity 'https://github.com/owner/repo'" in warning for warning in warnings) + assert any("Removed article entity 'https://doi.org/10.1000/bad-doi'" in warning for warning in warnings) diff --git a/tests/v2/test_live_provider_connectivity.py b/tests/v2/test_live_provider_connectivity.py new file mode 100644 index 0000000..f219fd7 --- /dev/null +++ b/tests/v2/test_live_provider_connectivity.py @@ -0,0 +1,34 @@ +from __future__ import annotations + +import pytest + +from scripts.v2.check_provider_connectivity import ( + DEFAULT_PROVIDERS, + get_missing_required_env_vars, + run_provider_connectivity_checks, +) + +pytestmark = [pytest.mark.live_provider] + + +@pytest.fixture(autouse=True) +def _require_live_marker_selection( + pytestconfig: pytest.Config, +) -> None: + mark_expression = pytestconfig.getoption("-m") or "" + if "live_provider" not in mark_expression: + pytest.skip( + "live_provider tests run only when explicitly selected with -m live_provider", + ) + + +def test_live_provider_connectivity_smoke() -> None: + missing_vars = get_missing_required_env_vars() + if missing_vars: + missing = ", ".join(sorted(missing_vars)) + pytest.skip( + f"Missing required environment variable(s) for live connectivity: {missing}", + ) + + failures = run_provider_connectivity_checks(DEFAULT_PROVIDERS) + assert failures == [] diff --git a/tests/v2/test_llm_agent_tools_uuid_and_selenium.py b/tests/v2/test_llm_agent_tools_uuid_and_selenium.py new file mode 100644 index 0000000..d8fda63 --- /dev/null +++ b/tests/v2/test_llm_agent_tools_uuid_and_selenium.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +import uuid + +from src.v2.agents.llm.agent_tools.email_hash import hash_user_email +from src.v2.agents.llm.agent_tools.selenium_fetch import fetch_link_content_via_selenium +from src.v2.agents.llm.agent_tools.uuid import generate_uuid_v4, generate_uuid_v4_batch + + +def test_generate_uuid_v4_returns_valid_uuid4() -> None: + value = generate_uuid_v4() + parsed = uuid.UUID(value) + assert parsed.version == 4 + + +def test_generate_uuid_v4_batch_respects_bounds_and_returns_uuid4_values() -> None: + many = generate_uuid_v4_batch(500) + assert len(many) == 100 + assert len(set(many)) == len(many) + for value in many: + parsed = uuid.UUID(value) + assert parsed.version == 4 + + minimum = generate_uuid_v4_batch(0) + assert len(minimum) == 1 + assert uuid.UUID(minimum[0]).version == 4 + + +def test_fetch_link_content_via_selenium_rejects_non_http_urls() -> None: + result = fetch_link_content_via_selenium("urn:pulse:foo") + assert result["fetched"] is False + assert result["error"] == "Invalid http(s) URL" + + +def test_hash_user_email_hashes_local_part_and_keeps_domain() -> None: + hashed = hash_user_email("alice@example.org") + assert hashed == "2bd806c97f0e@example.org" + + +def test_hash_user_email_is_idempotent_for_pre_hashed_local_part() -> None: + already_hashed = "2bd806c97f0e@example.org" + assert hash_user_email(already_hashed) == already_hashed diff --git a/tests/v2/test_llm_article_agent.py b/tests/v2/test_llm_article_agent.py new file mode 100644 index 0000000..97ed9b8 --- /dev/null +++ b/tests/v2/test_llm_article_agent.py @@ -0,0 +1,187 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +import pytest +from jsonschema import validate + +from src.v2.agents.llm.article import LLMArticleAgentV2 +from src.v2.agents.models import ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeError, LLMRuntimeResult +from src.v2.ingest.providers.mock_github import MockGitHubProvider + +EXPECTED_PROMPT_TOKENS = 13 +EXPECTED_COMPLETION_TOKENS = 29 + + +class _FakeLLMRuntime: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del output_type, tools + assert system_prompt + assert user_prompt + return LLMRuntimeResult( + payload=dict(self._payload), + model="openai/gpt-test", + provider="openai", + tokens_prompt=EXPECTED_PROMPT_TOKENS, + tokens_completion=EXPECTED_COMPLETION_TOKENS, + ) + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +def _valid_article_payload() -> dict[str, Any]: + return { + "id": "10.1000/article-1", + "type": "schema:ScholarlyArticle", + "shacl": "pulse:ArticleShape", + "identifiers": { + "schema:identifier": "10.1000/article-1", + "pulse:infoscienceArticleIdentifier": None, + "uuid": "f887181e-9b8c-4c55-8ee6-d34285fdbed4", + }, + "idSource": "schema:identifier", + "schema:name": "A paper", + "schema:identifier": "10.1000/article-1", + "schema:datePublished": "2024-01-01", + "schema:author": ["alice"], + "pulse:infoscienceArticleIdentifier": None, + "schema:sourceOrganization": "org:epfl", + } + + +def test_llm_article_agent_validates_payload_and_exposes_model_metadata( + load_schema, +) -> None: + agent = LLMArticleAgentV2(llm_runtime=_FakeLLMRuntime(_valid_article_payload())) + result = asyncio.run( + agent.run( + { + "article_seed": "octocat/Hello-World", + "known_persons": [{"id": "alice", "type": "schema:Person"}], + }, + _providers(), + ), + ) + + schema = load_schema("agent", "article") + validate(instance=result.data, schema=schema) + + assert result.model == "openai/gpt-test" + assert result.provider == "openai" + assert result.tokens_prompt == EXPECTED_PROMPT_TOKENS + assert result.tokens_completion == EXPECTED_COMPLETION_TOKENS + assert result.stats["agent_runtime"] == "llm" + assert result.stats["article_count"] == 1 + + +def test_llm_article_agent_propagates_llm_runtime_error() -> None: + class _RaisingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + raise LLMRuntimeError("Schema validation failed after retries") + + agent = LLMArticleAgentV2(llm_runtime=_RaisingRuntime()) + + with pytest.raises(LLMRuntimeError, match="Schema validation failed after retries"): + asyncio.run(agent.run({"article_seed": "owner/repo"}, _providers())) + + +def test_llm_article_agent_timeout_includes_identifier_and_timeout_seconds() -> None: + class _SlowRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + await asyncio.sleep(60) + raise AssertionError("unreachable") + + agent = LLMArticleAgentV2( + llm_runtime=_SlowRuntime(), + llm_call_timeout_seconds=0.1, + ) + + with pytest.raises(LLMRuntimeError, match=r"owner/repo.*timed out after 0\.1s"): + asyncio.run(agent.run({"article_seed": "owner/repo"}, _providers())) + + +def test_llm_article_agent_records_strict_schema_warnings() -> None: + payload = _valid_article_payload() + payload["schema:datePublished"] = "not-a-date" + agent = LLMArticleAgentV2(llm_runtime=_FakeLLMRuntime(payload)) + + result = asyncio.run(agent.run({"article_seed": "owner/repo"}, _providers())) + + assert result.warnings, "Expected strict-schema warnings but got none" + assert any("schema:datePublished" in warning for warning in result.warnings) + + +def test_llm_article_agent_appends_runtime_prompt_context_blocks() -> None: + captured_user_prompts: list[str] = [] + captured_tool_names: list[str] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, output_type + captured_user_prompts.append(user_prompt) + captured_tool_names.extend(getattr(tool, "name", "") for tool in (tools or [])) + return LLMRuntimeResult( + payload=_valid_article_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMArticleAgentV2(llm_runtime=_CapturingRuntime()) + upstream_json = '{"repo_agent":{"id":"repo-root"}}' + prompt_appendix = "README_FRAGMENT" + + asyncio.run( + agent.run( + { + "article_seed": "owner/repo", + "upstream_stage_outputs_json": upstream_json, + "user_prompt_appendix": prompt_appendix, + }, + _providers(), + ), + ) + + assert len(captured_user_prompts) == 1 + prompt = captured_user_prompts[0] + assert "## Upstream Stage Outputs (JSON)" in prompt + assert upstream_json in prompt + assert "## Additional Context (verbatim text)" in prompt + assert prompt_appendix in prompt + assert "fetch_link_content_via_selenium" in captured_tool_names diff --git a/tests/v2/test_llm_context_summary_agent.py b/tests/v2/test_llm_context_summary_agent.py new file mode 100644 index 0000000..666dd72 --- /dev/null +++ b/tests/v2/test_llm_context_summary_agent.py @@ -0,0 +1,104 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +from src.v2.agents.llm.context_summary import LLMContextSummaryAgentV2 +from src.v2.agents.models import ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeError, LLMRuntimeResult +from src.v2.ingest.providers.mock_github import MockGitHubProvider + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +def test_llm_context_summary_agent_compiles_markdown_and_exposes_grep_tool() -> None: + captured_tools: list[Any] = [] + captured_user_prompt: list[str] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del output_type + assert "repository context compiler" in system_prompt.lower() + captured_user_prompt.append(user_prompt) + captured_tools.extend(tools or []) + return LLMRuntimeResult( + payload={"summary_markdown": "# Summary\n- signal"}, + model="openai/gpt-test", + provider="openai", + tokens_prompt=21, + tokens_completion=34, + ) + + agent = LLMContextSummaryAgentV2(llm_runtime=_CapturingRuntime()) + result = asyncio.run( + agent.run( + { + "detected_type": "repository", + "source_url": "https://github.com/owner/repo", + "gathered_context": { + "repository": { + "full_name": "owner/repo", + "readme_content": "# Hello\nUse uv", + "gimie_jsonld": {"@id": "https://github.com/owner/repo"}, + "repository_files": [ + {"path": "pyproject.toml", "content": "[tool.uv]\n"}, + ], + }, + }, + }, + _providers(), + ), + ) + + assert result.data["summary_markdown"].startswith("# Summary") + assert result.model == "openai/gpt-test" + assert result.provider == "openai" + assert result.tokens_prompt == 21 + assert result.tokens_completion == 34 + assert result.stats["document_count"] == 3 + assert captured_tools + tool_names = [getattr(tool, "name", None) for tool in captured_tools] + assert "grep_repository_corpus" in tool_names + assert "search_on_the_internet" in tool_names + assert captured_user_prompt + assert "corpus_manifest" in captured_user_prompt[0] + + +def test_llm_context_summary_agent_is_fail_open_on_runtime_error() -> None: + class _FailingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + raise LLMRuntimeError("boom") + + agent = LLMContextSummaryAgentV2(llm_runtime=_FailingRuntime()) + result = asyncio.run( + agent.run( + { + "detected_type": "repository", + "source_url": "https://github.com/owner/repo", + "gathered_context": {"repository": {"full_name": "owner/repo", "readme_content": "README"}}, + }, + _providers(), + ), + ) + + assert result.is_partial is True + assert result.failure_reason == "boom" + assert result.data["summary_markdown"] == "" + assert any("proceeding without compiled summary" in warning for warning in result.warnings) diff --git a/tests/v2/test_llm_contribution_agent.py b/tests/v2/test_llm_contribution_agent.py new file mode 100644 index 0000000..643c29f --- /dev/null +++ b/tests/v2/test_llm_contribution_agent.py @@ -0,0 +1,190 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +import pytest +from jsonschema import validate + +from src.v2.agents.llm.contribution import LLMContributionAgentV2 +from src.v2.agents.models import ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeError, LLMRuntimeResult +from src.v2.ingest.providers.mock_github import MockGitHubProvider + +EXPECTED_PROMPT_TOKENS = 13 +EXPECTED_COMPLETION_TOKENS = 29 + + +class _FakeLLMRuntime: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del output_type, tools + assert system_prompt + assert user_prompt + return LLMRuntimeResult( + payload=dict(self._payload), + model="openai/gpt-test", + provider="openai", + tokens_prompt=EXPECTED_PROMPT_TOKENS, + tokens_completion=EXPECTED_COMPLETION_TOKENS, + ) + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +def _valid_contribution_payload() -> dict[str, Any]: + return { + "id": "alice_owner/repo", + "type": "pulse:Contribution", + "shacl": "pulse:ContributionShape", + "identifiers": { + "pulse:composite": "alice_owner/repo", + "uuid": "f887181e-9b8c-4c55-8ee6-d34285fdbed4", + }, + "idSource": "pulse:composite", + "pulse:contributionTo": "owner/repo", + "pulse:contributionCount": 12, + "pulse:firstContributionDate": "2020-01-01T00:00:00Z", + "pulse:lastContributionDate": "2024-01-01T00:00:00Z", + "schema:author": "alice", + } + + +def test_llm_contribution_agent_validates_payload_and_exposes_model_metadata( + load_schema, +) -> None: + agent = LLMContributionAgentV2( + llm_runtime=_FakeLLMRuntime(_valid_contribution_payload()), + ) + result = asyncio.run( + agent.run( + { + "contribution_seed": "owner/repo", + "known_persons": [{"id": "alice", "type": "schema:Person"}], + "known_repositories": [ + {"id": "owner/repo", "type": "schema:SoftwareSourceCode"}, + ], + }, + _providers(), + ), + ) + + schema = load_schema("agent", "contribution") + validate(instance=result.data, schema=schema) + + assert result.model == "openai/gpt-test" + assert result.provider == "openai" + assert result.tokens_prompt == EXPECTED_PROMPT_TOKENS + assert result.tokens_completion == EXPECTED_COMPLETION_TOKENS + assert result.stats["agent_runtime"] == "llm" + assert result.stats["contribution_count"] == 1 + + +def test_llm_contribution_agent_propagates_llm_runtime_error() -> None: + class _RaisingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + raise LLMRuntimeError("Schema validation failed after retries") + + agent = LLMContributionAgentV2(llm_runtime=_RaisingRuntime()) + + with pytest.raises(LLMRuntimeError, match="Schema validation failed after retries"): + asyncio.run(agent.run({"contribution_seed": "owner/repo"}, _providers())) + + +def test_llm_contribution_agent_timeout_includes_identifier_and_timeout_seconds() -> None: + class _SlowRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + await asyncio.sleep(60) + raise AssertionError("unreachable") + + agent = LLMContributionAgentV2( + llm_runtime=_SlowRuntime(), + llm_call_timeout_seconds=0.1, + ) + + with pytest.raises(LLMRuntimeError, match=r"owner/repo.*timed out after 0\.1s"): + asyncio.run(agent.run({"contribution_seed": "owner/repo"}, _providers())) + + +def test_llm_contribution_agent_records_strict_schema_warnings() -> None: + payload = _valid_contribution_payload() + payload["pulse:firstContributionDate"] = "not-a-date" + agent = LLMContributionAgentV2(llm_runtime=_FakeLLMRuntime(payload)) + + result = asyncio.run(agent.run({"contribution_seed": "owner/repo"}, _providers())) + + assert result.warnings, "Expected strict-schema warnings but got none" + assert any("pulse:firstContributionDate" in warning for warning in result.warnings) + + +def test_llm_contribution_agent_appends_runtime_prompt_context_blocks() -> None: + captured_user_prompts: list[str] = [] + captured_tool_names: list[str] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, output_type + captured_user_prompts.append(user_prompt) + captured_tool_names.extend(getattr(tool, "name", "") for tool in (tools or [])) + return LLMRuntimeResult( + payload=_valid_contribution_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMContributionAgentV2(llm_runtime=_CapturingRuntime()) + upstream_json = '{"repo_agent":{"id":"owner/repo"}}' + prompt_appendix = "CONTRIBUTOR_CONTEXT" + + asyncio.run( + agent.run( + { + "contribution_seed": "owner/repo", + "upstream_stage_outputs_json": upstream_json, + "user_prompt_appendix": prompt_appendix, + }, + _providers(), + ), + ) + + assert len(captured_user_prompts) == 1 + prompt = captured_user_prompts[0] + assert "## Upstream Stage Outputs (JSON)" in prompt + assert upstream_json in prompt + assert "## Additional Context (verbatim text)" in prompt + assert prompt_appendix in prompt + assert "fetch_link_content_via_selenium" in captured_tool_names diff --git a/tests/v2/test_llm_critic_agent.py b/tests/v2/test_llm_critic_agent.py new file mode 100644 index 0000000..afe1cba --- /dev/null +++ b/tests/v2/test_llm_critic_agent.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +from src.v2.agents.llm.critic.agent import LLMCriticAgentV2 +from src.v2.agents.models import ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeResult +from src.v2.ingest.providers.mock_github import MockGitHubProvider + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +def test_llm_critic_agent_exposes_duckduckgo_and_owner_context_tools() -> None: + captured_tools: list[Any] = [] + captured_user_prompt: list[str] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del output_type + assert "relevance critic" in system_prompt.lower() + captured_user_prompt.append(user_prompt) + captured_tools.extend(tools or []) + return LLMRuntimeResult( + payload={ + "organizations": [], + "persons": [], + "repositories": [], + "articles": [], + }, + model="openai/gpt-test", + provider="openai", + tokens_prompt=10, + tokens_completion=12, + ) + + agent = LLMCriticAgentV2(llm_runtime=_CapturingRuntime()) + result = asyncio.run( + agent.run( + { + "source_url": "https://github.com/sdsc-ordes/gimie", + "detected_type": "repository", + "initial_context": { + "repository": { + "full_name": "sdsc-ordes/gimie", + "metadata": { + "html_url": "https://github.com/sdsc-ordes/gimie", + "owner": {"login": "sdsc-ordes", "type": "Organization"}, + }, + }, + }, + "pipeline_outputs": {}, + "reconciled_entities": { + "organizations": [], + "persons": [], + "repositories": [], + "articles": [], + }, + "memberships": [], + "contributions": [], + }, + _providers(), + ), + ) + + assert result.data["organizations"] == [] + tool_names = [getattr(tool, "name", None) for tool in captured_tools] + assert "search_on_the_internet" in tool_names + assert "get_github_organization_metadata" in tool_names + assert captured_user_prompt + assert "owner_provenance" in captured_user_prompt[0] + assert "sdsc-ordes" in captured_user_prompt[0] + diff --git a/tests/v2/test_llm_critic_stage.py b/tests/v2/test_llm_critic_stage.py new file mode 100644 index 0000000..791c795 --- /dev/null +++ b/tests/v2/test_llm_critic_stage.py @@ -0,0 +1,351 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +from src.v2.agents import ProviderSet +from src.v2.agents.models import AgentResult +from src.v2.pipeline.stages.llm_critic import run_llm_critic_stage +from src.v2.pipeline.stages.models import ReconciledEntities +from src.v2.ingest.providers.mock_github import MockGitHubProvider + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +class _StaticCriticAgent: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult(data=self._payload) + + +class _CapturingCriticAgent: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + self.captured_context: dict[str, Any] | None = None + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + self.captured_context = context + return AgentResult(data=self._payload) + + +def test_llm_critic_stage_receives_initial_context_and_pipeline_outputs() -> None: + agent = _CapturingCriticAgent( + { + "organizations": [], + "persons": [], + "repositories": [], + "articles": [], + }, + ) + reconciled = ReconciledEntities( + entities={ + "repositories": [{"id": "repo-1", "type": "schema:SoftwareSourceCode"}], + "persons": [], + "organizations": [], + "articles": [], + }, + memberships=[], + contributions=[], + ) + + asyncio.run( + run_llm_critic_stage( + reconciled=reconciled, + source_url="https://github.com/owner/repo", + detected_type="repository", + providers=_providers(), + initial_context={"repository": {"readme_content": "README"}}, + pipeline_outputs={"repo_agent": {"id": "repo-1"}}, + agent=agent, + ), + ) + + assert isinstance(agent.captured_context, dict) + assert agent.captured_context["source_url"] == "https://github.com/owner/repo" + assert agent.captured_context["detected_type"] == "repository" + assert agent.captured_context["initial_context"]["repository"]["readme_content"] == "README" + assert agent.captured_context["pipeline_outputs"]["repo_agent"]["id"] == "repo-1" + + +def test_llm_critic_root_protection_and_cascade_cleanup() -> None: + repo_id = "https://github.com/owner/repo" + person_keep = "https://orcid.org/0000-0000-0000-0001" + person_drop = "https://orcid.org/0000-0000-0000-0002" + org_keep = "https://ror.org/02hdt9m26" + org_drop = "https://ror.org/01swzsf04" + + reconciled = ReconciledEntities( + entities={ + "repositories": [ + { + "id": repo_id, + "type": "schema:SoftwareSourceCode", + "schema:author": [person_keep, person_drop], + "pulse:ownedBy": org_keep, + }, + ], + "persons": [ + { + "id": person_keep, + "type": "schema:Person", + "org:hasMembership": [], + "pulse:hasContribution": [], + "pulse:owns": [repo_id], + }, + { + "id": person_drop, + "type": "schema:Person", + "org:hasMembership": [f"{person_drop}_{org_drop}"], + "pulse:hasContribution": [f"{person_drop}_{repo_id}"], + "pulse:owns": [repo_id], + }, + ], + "organizations": [ + { + "id": org_keep, + "type": "org:Organization", + "org:hasUnit": [org_drop], + "org:unitOf": None, + "pulse:owns": [repo_id], + }, + { + "id": org_drop, + "type": "org:Organization", + "org:hasUnit": [], + "org:unitOf": org_keep, + "pulse:owns": [], + }, + ], + "articles": [ + { + "id": "https://doi.org/10.1234/drop-me", + "type": "schema:ScholarlyArticle", + "schema:author": [person_drop], + "schema:sourceOrganization": org_drop, + }, + ], + }, + memberships=[ + { + "id": f"{person_drop}_{org_drop}", + "type": "org:Membership", + "_person_ref": person_drop, + "org:organization": org_drop, + }, + ], + contributions=[ + { + "id": f"{person_drop}_{repo_id}", + "type": "pulse:Contribution", + "schema:author": person_drop, + "pulse:contributionTo": repo_id, + }, + ], + ) + + result = asyncio.run( + run_llm_critic_stage( + reconciled=reconciled, + source_url="https://github.com/owner/repo", + detected_type="repository", + providers=_providers(), + agent=_StaticCriticAgent( + { + "organizations": [{"id": org_drop, "reason": "not relevant"}], + "persons": [{"id": person_drop, "reason": "not relevant"}], + "repositories": [{"id": repo_id, "reason": "should not drop root"}], + "articles": [{"id": "https://doi.org/10.1234/drop-me", "reason": "off-topic"}], + }, + ), + ), + ) + + assert repo_id in result.applied["protected_root_ids"] + assert result.applied["applied_drop_count"] == 3 + + repositories = result.reconciled.entities["repositories"] + assert len(repositories) == 1 + assert repositories[0]["id"] == repo_id + assert repositories[0]["schema:author"] == [person_keep] + + persons = result.reconciled.entities["persons"] + assert len(persons) == 1 + assert persons[0]["id"] == person_keep + + organizations = result.reconciled.entities["organizations"] + assert len(organizations) == 1 + assert organizations[0]["id"] == org_keep + assert organizations[0]["org:hasUnit"] == [] + + assert result.reconciled.entities["articles"] == [] + assert result.reconciled.memberships == [] + assert result.reconciled.contributions == [] + + assert len(result.pruned_excluded_entities) == 3 + assert all( + entry["reason"][0]["message"] == "critic_pruned" + for entry in result.pruned_excluded_entities + ) + + +def test_llm_critic_protects_owner_org_ancestors_from_prune() -> None: + repo_id = "https://github.com/sdsc-ordes/gimie" + owner_org_id = "https://ror.org/02hdt9m26" + host_org_id = "https://ror.org/02s376052" + + reconciled = ReconciledEntities( + entities={ + "repositories": [ + { + "id": repo_id, + "type": "schema:SoftwareSourceCode", + "pulse:ownedBy": owner_org_id, + }, + ], + "persons": [], + "organizations": [ + { + "id": owner_org_id, + "type": "org:Organization", + "org:unitOf": host_org_id, + }, + { + "id": host_org_id, + "type": "org:Organization", + "org:unitOf": None, + }, + ], + "articles": [], + }, + memberships=[], + contributions=[], + ) + + result = asyncio.run( + run_llm_critic_stage( + reconciled=reconciled, + source_url=repo_id, + detected_type="repository", + providers=_providers(), + agent=_StaticCriticAgent( + { + "organizations": [ + { + "id": host_org_id, + "reason": ( + "Affiliated institution of a contributor, " + "unrelated to repository ownership" + ), + }, + ], + "persons": [], + "repositories": [], + "articles": [], + }, + ), + ), + ) + + organizations = result.reconciled.entities["organizations"] + assert {organization["id"] for organization in organizations} == {owner_org_id, host_org_id} + assert result.applied["applied_drop_count"] == 0 + assert host_org_id in result.applied["protected_owner_context_org_ids"] + assert any( + "owner-context org protection override" in warning + for warning in result.warnings + ) + + +def test_llm_critic_protects_contributor_affiliation_orgs_for_kept_contributors() -> None: + repo_id = "https://github.com/sdsc-ordes/gimie" + person_id = "https://orcid.org/0000-0002-2961-2655" + org_id = "https://ror.org/02s376052" + + reconciled = ReconciledEntities( + entities={ + "repositories": [ + { + "id": repo_id, + "type": "schema:SoftwareSourceCode", + }, + ], + "persons": [ + { + "id": person_id, + "type": "schema:Person", + }, + ], + "organizations": [ + { + "id": org_id, + "type": "org:Organization", + }, + ], + "articles": [], + }, + memberships=[ + { + "id": f"{person_id}_{org_id}", + "type": "org:Membership", + "_person_ref": person_id, + "org:organization": org_id, + }, + ], + contributions=[ + { + "id": f"{person_id}_{repo_id}", + "type": "pulse:Contribution", + "schema:author": person_id, + "pulse:contributionTo": repo_id, + }, + ], + ) + + result = asyncio.run( + run_llm_critic_stage( + reconciled=reconciled, + source_url=repo_id, + detected_type="repository", + providers=_providers(), + agent=_StaticCriticAgent( + { + "organizations": [ + { + "id": org_id, + "reason": ( + "University affiliation only via member profiles, " + "not directly linked to repository ownership or contribution" + ), + }, + ], + "persons": [], + "repositories": [], + "articles": [], + }, + ), + ), + ) + + organizations = result.reconciled.entities["organizations"] + assert len(organizations) == 1 + assert organizations[0]["id"] == org_id + assert result.applied["applied_drop_count"] == 0 + assert org_id in result.applied["protected_contributor_affiliation_org_ids"] + assert any( + "contributor-affiliation org protection override" in warning + for warning in result.warnings + ) diff --git a/tests/v2/test_llm_dedup_stage.py b/tests/v2/test_llm_dedup_stage.py new file mode 100644 index 0000000..fdb0942 --- /dev/null +++ b/tests/v2/test_llm_dedup_stage.py @@ -0,0 +1,271 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +from src.v2.agents import ProviderSet +from src.v2.agents.models import AgentResult +from src.v2.pipeline.stages.llm_dedup import run_llm_dedup_stage +from src.v2.ingest.providers.mock_github import MockGitHubProvider + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +class _StaticDedupAgent: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult(data=self._payload) + + +class _CapturingDedupAgent: + def __init__(self) -> None: + self.captured_context: dict[str, Any] | None = None + + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + self.captured_context = context + return AgentResult( + data={ + "organizations": [], + "persons": [], + "repositories": [], + "articles": [], + }, + ) + + +def test_llm_dedup_stage_passes_serialized_buckets_to_agent_context() -> None: + agent = _CapturingDedupAgent() + buckets = { + "repositories": [{"id": "repo-1", "type": "schema:SoftwareSourceCode"}], + "persons": [{"id": "person-1", "type": "schema:Person"}], + "organizations": [{"id": "org-1", "type": "org:Organization"}], + "articles": [{"id": "article-1", "type": "schema:ScholarlyArticle"}], + "memberships": [], + "contributions": [], + } + + asyncio.run( + run_llm_dedup_stage( + typed_entity_buckets=buckets, + source_url="https://github.com/owner/repo", + detected_type="repository", + providers=_providers(), + initial_context={"repository": {"readme_content": "README"}}, + pipeline_outputs={"repo_agent": {"id": "repo-1"}}, + agent=agent, + ), + ) + + assert isinstance(agent.captured_context, dict) + assert agent.captured_context["source_url"] == "https://github.com/owner/repo" + assert agent.captured_context["detected_type"] == "repository" + assert set(agent.captured_context["typed_entity_buckets"]) == { + "repositories", + "persons", + "organizations", + "articles", + "memberships", + "contributions", + } + + +def test_llm_dedup_merges_person_cluster_and_propagates_composite_ids() -> None: + canonical_person_id = "https://orcid.org/0000-0000-0000-0001" + duplicate_person_id = "https://github.com/alice" + org_id = "https://ror.org/02hdt9m26" + repo_id = "https://github.com/owner/repo" + + typed_entity_buckets = { + "repositories": [ + { + "id": repo_id, + "type": "schema:SoftwareSourceCode", + "schema:author": [duplicate_person_id], + "pulse:ownedBy": org_id, + "identifiers": { + "pulse:githubRepositoryHandle": "owner/repo", + "schema:citation": None, + "uuid": "3adbb05f-80c1-44c6-8f8b-066eb65407ec", + }, + "idSource": "pulse:githubRepositoryHandle", + }, + ], + "persons": [ + { + "id": canonical_person_id, + "type": "schema:Person", + "identifiers": { + "pulse:orcid": "0000-0000-0000-0001", + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": None, + "uuid": "8cf4fd8d-00da-4f87-a6f8-e0bfe6f0c9d2", + }, + "idSource": "pulse:orcid", + "schema:name": "Alice Canonical", + }, + { + "id": duplicate_person_id, + "type": "schema:Person", + "identifiers": { + "pulse:orcid": "0000-0000-0000-0001", + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": "alice", + "uuid": "dd8cc8f8-8e7f-4fef-9d79-1fc297f52f70", + }, + "idSource": "pulse:githubUsername", + "schema:name": "alice", + "schema:url": duplicate_person_id, + }, + ], + "organizations": [ + { + "id": org_id, + "type": "org:Organization", + "identifiers": { + "pulse:ror": org_id, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:githubOrganizationHandle": "owner", + "uuid": "2ca0c4f8-2a62-4885-b736-c90aeb913f90", + }, + "idSource": "pulse:ror", + "schema:name": "Owner Org", + }, + ], + "articles": [], + "memberships": [ + { + "id": f"{duplicate_person_id}_{org_id}", + "type": "org:Membership", + "identifiers": { + "pulse:composite": f"{duplicate_person_id}_{org_id}", + "uuid": "61a1b652-0527-44b9-87c4-5527f8fc35a3", + }, + "idSource": "pulse:composite", + "_person_ref": duplicate_person_id, + "org:organization": org_id, + }, + ], + "contributions": [ + { + "id": f"{duplicate_person_id}_{repo_id}", + "type": "pulse:Contribution", + "identifiers": { + "pulse:composite": f"{duplicate_person_id}_{repo_id}", + "uuid": "d2e38f7d-2d8f-4fee-8855-073d7528ac6c", + }, + "idSource": "pulse:composite", + "schema:author": duplicate_person_id, + "pulse:contributionTo": repo_id, + }, + ], + } + + result = asyncio.run( + run_llm_dedup_stage( + typed_entity_buckets=typed_entity_buckets, + source_url="https://github.com/owner/repo", + detected_type="repository", + providers=_providers(), + agent=_StaticDedupAgent( + { + "organizations": [], + "persons": [{"ids": [canonical_person_id, duplicate_person_id]}], + "repositories": [], + "articles": [], + }, + ), + ), + ) + + assert result.accepted_cluster_count == 1 + assert result.rejected_cluster_count == 0 + assert result.remap_count >= 1 + + persons = result.typed_entity_buckets["persons"] + assert len(persons) == 1 + assert persons[0]["id"] == canonical_person_id + + repository = result.typed_entity_buckets["repositories"][0] + assert repository["schema:author"] == [canonical_person_id] + + memberships = result.typed_entity_buckets["memberships"] + assert len(memberships) == 1 + assert memberships[0]["id"] == f"{canonical_person_id}_{org_id}" + assert memberships[0]["identifiers"]["pulse:composite"] == f"{canonical_person_id}_{org_id}" + + contributions = result.typed_entity_buckets["contributions"] + assert len(contributions) == 1 + assert contributions[0]["id"] == f"{canonical_person_id}_{repo_id}" + assert contributions[0]["identifiers"]["pulse:composite"] == f"{canonical_person_id}_{repo_id}" + + assert persons[0]["org:hasMembership"] == [f"{canonical_person_id}_{org_id}"] + assert persons[0]["pulse:hasContribution"] == [f"{canonical_person_id}_{repo_id}"] + + +def test_llm_dedup_rejects_conflicting_person_orcid_cluster() -> None: + person_a = { + "id": "https://orcid.org/0000-0000-0000-0001", + "type": "schema:Person", + "identifiers": { + "pulse:orcid": "0000-0000-0000-0001", + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": None, + "uuid": "4254f017-a4d4-4a38-b116-c5103f3932cd", + }, + "idSource": "pulse:orcid", + } + person_b = { + "id": "https://orcid.org/0000-0000-0000-0002", + "type": "schema:Person", + "identifiers": { + "pulse:orcid": "0000-0000-0000-0002", + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": None, + "uuid": "e95c153a-5ba2-4847-b7eb-f9f5d6f23616", + }, + "idSource": "pulse:orcid", + } + + result = asyncio.run( + run_llm_dedup_stage( + typed_entity_buckets={ + "repositories": [], + "persons": [person_a, person_b], + "organizations": [], + "articles": [], + "memberships": [], + "contributions": [], + }, + source_url="https://github.com/owner/repo", + detected_type="repository", + providers=_providers(), + agent=_StaticDedupAgent( + { + "organizations": [], + "persons": [{"ids": [person_a["id"], person_b["id"]]}], + "repositories": [], + "articles": [], + }, + ), + ), + ) + + assert result.accepted_cluster_count == 0 + assert result.rejected_cluster_count == 1 + assert len(result.typed_entity_buckets["persons"]) == 2 + rejected = result.resolution["rejected_clusters"]["persons"][0] + assert rejected["reason_code"] == "conflicting_orcid_identifiers" diff --git a/tests/v2/test_llm_duckduckgo_search_tool.py b/tests/v2/test_llm_duckduckgo_search_tool.py new file mode 100644 index 0000000..e14078e --- /dev/null +++ b/tests/v2/test_llm_duckduckgo_search_tool.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +from typing import Any + +from src.v2.agents.llm.agent_tools.duckduckgo_search import ( + DUCKDUCKGO_INSTANT_ANSWER_URL, + make_duckduckgo_search_tool, +) + + +class _FakeResponse: + def __init__(self, payload: Any, *, status_code: int = 200) -> None: + self._payload = payload + self.status_code = status_code + + def raise_for_status(self) -> None: + if self.status_code >= 400: + raise ValueError(f"http {self.status_code}") + + def json(self) -> Any: + return self._payload + + +def test_search_on_the_internet_returns_compact_context_rows() -> None: + captured_calls: list[dict[str, Any]] = [] + + def _fake_get(url: str, *, params: dict[str, Any], timeout: float) -> _FakeResponse: + captured_calls.append({"url": url, "params": params, "timeout": timeout}) + return _FakeResponse( + { + "Heading": "Swiss Data Science Center", + "AbstractURL": "https://www.datascience.ch/", + "AbstractText": "Swiss Data Science Center official site.", + "RelatedTopics": [ + { + "Text": "Swiss Data Science Center - EPFL", + "FirstURL": "https://www.epfl.ch/labs/sdsc/", + }, + { + "Text": "Duplicate entry", + "FirstURL": "https://www.datascience.ch/", + }, + ], + }, + ) + + tool = make_duckduckgo_search_tool(http_get=_fake_get) + payload = tool.function("sdsc epfl", max_results=3) + + assert captured_calls + assert captured_calls[0]["url"] == DUCKDUCKGO_INSTANT_ANSWER_URL + assert captured_calls[0]["params"]["q"] == "sdsc epfl" + assert payload["query"] == "sdsc epfl" + assert payload["result_count"] == 2 + assert payload["results"][0]["url"] == "https://www.datascience.ch/" + assert payload["results"][0]["source"] == "abstract" + assert payload["results"][1]["url"] == "https://www.epfl.ch/labs/sdsc/" + + +def test_search_on_the_internet_rejects_empty_query() -> None: + tool = make_duckduckgo_search_tool(http_get=lambda *_args, **_kwargs: _FakeResponse({})) + + payload = tool.function(" ") + + assert payload["result_count"] == 0 + assert payload["results"] == [] + assert payload["error"] == "empty_query" + + +def test_search_on_the_internet_returns_error_on_http_failure() -> None: + def _failing_get(url: str, *, params: dict[str, Any], timeout: float) -> _FakeResponse: + del url, params, timeout + raise RuntimeError("network down") + + tool = make_duckduckgo_search_tool(http_get=_failing_get) + payload = tool.function("open pulse") + + assert payload["query"] == "open pulse" + assert payload["result_count"] == 0 + assert payload["results"] == [] + assert "network down" in payload["error"] + diff --git a/tests/v2/test_llm_ethz_research_collection_rag_tool.py b/tests/v2/test_llm_ethz_research_collection_rag_tool.py new file mode 100644 index 0000000..baf3259 --- /dev/null +++ b/tests/v2/test_llm_ethz_research_collection_rag_tool.py @@ -0,0 +1,389 @@ +from __future__ import annotations + +import asyncio +import uuid +from typing import Any + +import pytest + +from src.index.ethz_research_collection.rerank import RerankHit +from src.index.ethz_research_collection.store import ( + ARTICLES_COLLECTION, + CHUNKS_COLLECTION, + ORGANIZATIONS_COLLECTION, + PERSONS_COLLECTION, +) +from src.v2.agents.llm.agent_tools.ethz_research_collection_rag import ( + make_ethz_research_collection_rag_fetch_chunks_tool, + make_ethz_research_collection_rag_fetch_records_tool, + make_ethz_research_collection_rag_search_tool, +) +from src.v2.ingest.providers.ethz_research_collection_rag import EthzResearchCollectionRagProvider + + +class _FakeStore: + """Duck-typed `QdrantStore` that records calls and returns canned data.""" + + def __init__(self) -> None: + self.search_calls: list[dict[str, Any]] = [] + self.scroll_calls: list[dict[str, Any]] = [] + self.lookup_calls: list[dict[str, Any]] = [] + self._search_returns: list[dict[str, Any]] = [] + self._scroll_returns: list[dict[str, Any]] = [] + self._lookup_returns: list[dict[str, Any]] = [] + + def set_search(self, hits: list[dict[str, Any]]) -> None: + self._search_returns = hits + + def set_scroll(self, recs: list[dict[str, Any]]) -> None: + self._scroll_returns = recs + + def set_lookup(self, recs: list[dict[str, Any]]) -> None: + self._lookup_returns = recs + + def search(self, collection: str, *, query_vector, top_k, query_filter): + self.search_calls.append({ + "collection": collection, + "vector": list(query_vector), + "top_k": top_k, + "filter": query_filter, + }) + return list(self._search_returns) + + def scroll(self, collection: str, *, query_filter, limit): + self.scroll_calls.append({ + "collection": collection, + "filter": query_filter, + "limit": limit, + }) + return list(self._scroll_returns) + + def lookup(self, collection: str, *, ids): + self.lookup_calls.append({"collection": collection, "ids": list(ids)}) + return list(self._lookup_returns) + + +class _FakeEmbedder: + """Duck-typed `RCPEmbedder`; pretends to be an async context manager.""" + + def __init__(self, vector: list[float] | None = None, fail: bool = False): + self.vector = vector or [0.1, 0.2, 0.3] + self.fail = fail + self.entered = 0 + self.exited = 0 + self.embed_calls: list[str] = [] + + async def __aenter__(self): + self.entered += 1 + return self + + async def __aexit__(self, exc_type, exc, tb): + self.exited += 1 + + async def embed_query(self, query: str, instruction: str | None = None): + self.embed_calls.append(query) + if self.fail: + from src.index.ethz_research_collection.embed import EmbedError + raise EmbedError("forced failure") + return list(self.vector) + + +class _FakeReranker: + def __init__(self, hits: list[RerankHit] | None = None, fail: bool = False): + self._hits = hits or [] + self.fail = fail + self.entered = 0 + self.calls: list[dict[str, Any]] = [] + + async def __aenter__(self): + self.entered += 1 + return self + + async def __aexit__(self, exc_type, exc, tb): + return None + + async def rerank(self, query: str, documents: list[str], *, top_n=None): + self.calls.append({ + "query": query, + "documents": list(documents), + "top_n": top_n, + }) + if self.fail: + from src.index.ethz_research_collection.rerank import RerankError + raise RerankError("forced failure") + return list(self._hits) + + +def _make_provider( + *, + search_hits: list[dict[str, Any]] | None = None, + scroll_recs: list[dict[str, Any]] | None = None, + lookup_recs: list[dict[str, Any]] | None = None, + embedder: _FakeEmbedder | None = None, + reranker: _FakeReranker | None = None, +) -> tuple[EthzResearchCollectionRagProvider, _FakeStore, _FakeEmbedder, _FakeReranker | None]: + store = _FakeStore() + if search_hits is not None: + store.set_search(search_hits) + if scroll_recs is not None: + store.set_scroll(scroll_recs) + if lookup_recs is not None: + store.set_lookup(lookup_recs) + emb = embedder or _FakeEmbedder() + rer = reranker + provider = EthzResearchCollectionRagProvider( + store=store, # type: ignore[arg-type] + embedder=emb, # type: ignore[arg-type] + reranker=rer, # type: ignore[arg-type] + ) + return provider, store, emb, rer + + +def _run(coro): + return asyncio.run(coro) + + +# --------------------------------------------------------------------------- +# search +# --------------------------------------------------------------------------- + + +def test_search_returns_thin_chunk_hits_with_snippet() -> None: + body_text = "This paper introduces a graph attention network. " * 20 + hits = [ + { + "id": str(uuid.uuid4()), + "score": 0.91, + "payload": { + "article_uuid": "article-1", + "chunk_index": 3, + "title": "GAT for Drug Discovery", + "doi": "10.1234/foo", + "year": 2024, + "research_collection_url": "https://research-collection.ethz.ch/record/1", + "text": body_text, + "matched_urls": ["https://github.com/lab/repo"], + }, + } + ] + provider, store, embedder, _rer = _make_provider(search_hits=hits) + + out = _run(provider.search("graph attention network")) + + assert embedder.entered == 1 + assert embedder.embed_calls == ["graph attention network"] + assert store.search_calls[0]["collection"] == CHUNKS_COLLECTION + assert store.search_calls[0]["top_k"] == 10 + assert len(out) == 1 + row = out[0] + assert row["collection"] == "chunks" + assert row["article_uuid"] == "article-1" + assert row["chunk_index"] == 3 + assert row["title"] == "GAT for Drug Discovery" + # Snippet truncates the long body and never exceeds the configured cap. + assert "snippet" in row + assert len(row["snippet"]) <= 320 + assert "text" not in row # full body NOT returned by search + + +def test_search_drops_non_allowlisted_filter_keys(caplog) -> None: + provider, store, _emb, _rer = _make_provider(search_hits=[]) + caplog.set_level("WARNING") + + _run(provider.search( + "x", + filters={"year": 2024, "secret_key": "boom", "doi": "10.1/x"}, + )) + + qfilter = store.search_calls[0]["filter"] + assert qfilter is not None + # Reach into the Qdrant Filter to confirm only allowlisted keys made it. + keys = sorted(getattr(c, "key", None) for c in (qfilter.must or [])) + assert "year" in keys + assert "doi" in keys + assert "secret_key" not in keys + assert any("dropped non-allowlisted filter keys" in r.message for r in caplog.records) + + +def test_search_resolves_collection_aliases() -> None: + provider, store, _emb, _rer = _make_provider(search_hits=[]) + + _run(provider.search("x", collection="articles")) + _run(provider.search("x", collection="persons")) + _run(provider.search("x", collection="organizations")) + + seen = [c["collection"] for c in store.search_calls] + assert seen == [ + ARTICLES_COLLECTION, + PERSONS_COLLECTION, + ORGANIZATIONS_COLLECTION, + ] + + +def test_search_returns_empty_on_blank_query() -> None: + provider, store, embedder, _rer = _make_provider(search_hits=[{"id": "x"}]) + out = _run(provider.search(" ")) + assert out == [] + assert embedder.embed_calls == [] + assert store.search_calls == [] + + +def test_search_returns_empty_on_embed_failure(caplog) -> None: + provider, store, _emb, _rer = _make_provider( + search_hits=[{"id": "x"}], + embedder=_FakeEmbedder(fail=True), + ) + caplog.set_level("WARNING") + out = _run(provider.search("graph attention")) + assert out == [] + assert store.search_calls == [] # never reached + + +def test_search_with_rerank_reorders_hits() -> None: + hits = [ + {"id": "a", "score": 0.5, "payload": {"text": "alpha"}}, + {"id": "b", "score": 0.4, "payload": {"text": "beta"}}, + {"id": "c", "score": 0.3, "payload": {"text": "gamma"}}, + ] + reranker = _FakeReranker(hits=[ + RerankHit(index=2, score=0.99), + RerankHit(index=0, score=0.7), + ]) + provider, store, _emb, _rer = _make_provider( + search_hits=hits, reranker=reranker, + ) + + out = _run(provider.search("q", top_k=2, rerank=True)) + + # candidate_k expands beyond top_k when rerank is on. + assert store.search_calls[0]["top_k"] >= 30 + assert reranker is not None and reranker.calls + ids = [row["id"] for row in out] + assert ids == ["c", "a"] + assert out[0]["score"] == pytest.approx(0.99) + + +def test_search_without_rerank_skips_reranker() -> None: + reranker = _FakeReranker(hits=[RerankHit(index=0, score=0.99)]) + provider, _store, _emb, rer = _make_provider( + search_hits=[{"id": "a", "score": 0.5, "payload": {}}], + reranker=reranker, + ) + + _run(provider.search("q", rerank=False)) + + assert rer is not None and rer.calls == [] # reranker never invoked + assert rer.entered == 0 + + +def test_search_falls_back_when_rerank_fails(caplog) -> None: + reranker = _FakeReranker(fail=True) + hits = [{"id": "a", "score": 0.5, "payload": {"text": "alpha"}}] + provider, _store, _emb, _rer = _make_provider( + search_hits=hits, reranker=reranker, + ) + caplog.set_level("WARNING") + + out = _run(provider.search("q", top_k=1, rerank=True)) + + assert [r["id"] for r in out] == ["a"] + + +# --------------------------------------------------------------------------- +# fetch_chunks +# --------------------------------------------------------------------------- + + +def test_fetch_chunks_orders_by_chunk_index() -> None: + recs = [ + {"id": "p3", "payload": {"article_uuid": "A", "chunk_index": 2, "text": "t2"}}, + {"id": "p1", "payload": {"article_uuid": "A", "chunk_index": 0, "text": "t0"}}, + {"id": "p2", "payload": {"article_uuid": "A", "chunk_index": 1, "text": "t1"}}, + ] + provider, store, _emb, _rer = _make_provider(scroll_recs=recs) + + out = _run(provider.fetch_chunks("A")) + + assert store.scroll_calls[0]["collection"] == CHUNKS_COLLECTION + assert [c["chunk_index"] for c in out] == [0, 1, 2] + assert [c["text"] for c in out] == ["t0", "t1", "t2"] + + +def test_fetch_chunks_blank_uuid_returns_empty() -> None: + provider, store, _emb, _rer = _make_provider(scroll_recs=[{"id": "x", "payload": {}}]) + assert _run(provider.fetch_chunks("")) == [] + assert store.scroll_calls == [] + + +# --------------------------------------------------------------------------- +# fetch_records +# --------------------------------------------------------------------------- + + +def test_fetch_records_returns_full_payload() -> None: + recs = [ + {"id": "u1", "payload": {"name": "Person 1", "orcid": "0000-...-1"}}, + {"id": "u2", "payload": {"name": "Person 2"}}, + ] + provider, store, _emb, _rer = _make_provider(lookup_recs=recs) + + out = _run(provider.fetch_records("persons", ["u1", "u2"])) + + assert store.lookup_calls[0]["collection"] == PERSONS_COLLECTION + assert store.lookup_calls[0]["ids"] == ["u1", "u2"] + assert [r["payload"]["name"] for r in out] == ["Person 1", "Person 2"] + + +def test_fetch_records_rejects_chunks_collection() -> None: + provider, _store, _emb, _rer = _make_provider(lookup_recs=[]) + with pytest.raises(ValueError): + _run(provider.fetch_records("chunks", ["x"])) + + +# --------------------------------------------------------------------------- +# Tool factories +# --------------------------------------------------------------------------- + + +def test_search_tool_factory_wires_provider() -> None: + provider, _store, embedder, _rer = _make_provider( + search_hits=[{"id": "a", "score": 0.5, "payload": {"text": "alpha"}}], + ) + tool = make_ethz_research_collection_rag_search_tool(provider) + + assert tool.name == "search_ethz_research_collection_rag" + rows = _run(tool.function("query", "chunks", 3, None, False)) + assert len(rows) == 1 + assert embedder.embed_calls == ["query"] + + +def test_fetch_chunks_tool_factory_wires_provider() -> None: + provider, store, _emb, _rer = _make_provider( + scroll_recs=[{"id": "x", "payload": {"article_uuid": "A", "chunk_index": 0, "text": "t"}}], + ) + tool = make_ethz_research_collection_rag_fetch_chunks_tool(provider) + + assert tool.name == "fetch_ethz_research_collection_chunks" + rows = _run(tool.function("A", 5)) + assert rows[0]["text"] == "t" + assert store.scroll_calls[0]["limit"] == 5 + + +def test_fetch_records_tool_factory_returns_empty_on_chunks_collection() -> None: + provider, _store, _emb, _rer = _make_provider(lookup_recs=[]) + tool = make_ethz_research_collection_rag_fetch_records_tool(provider) + + assert tool.name == "fetch_ethz_research_collection_records" + # The tool catches ValueError and returns [] so it's safe for the LLM. + out = _run(tool.function("chunks", ["x"])) + assert out == [] + + +def test_fetch_records_tool_factory_returns_records() -> None: + provider, _store, _emb, _rer = _make_provider( + lookup_recs=[{"id": "u1", "payload": {"name": "P"}}], + ) + tool = make_ethz_research_collection_rag_fetch_records_tool(provider) + + out = _run(tool.function("persons", ["u1"])) + assert out[0]["payload"]["name"] == "P" diff --git a/tests/v2/test_llm_github_organization_tool.py b/tests/v2/test_llm_github_organization_tool.py new file mode 100644 index 0000000..11d7a39 --- /dev/null +++ b/tests/v2/test_llm_github_organization_tool.py @@ -0,0 +1,87 @@ +from __future__ import annotations + +from typing import Any + +from src.v2.agents.llm.agent_tools.github_organization import ( + make_github_organization_metadata_tool, +) + + +class _FakeGitHubProvider: + def __init__(self) -> None: + self.calls: list[str] = [] + + def get_organization(self, org_name: str) -> dict[str, Any]: + self.calls.append(org_name) + if org_name == "missing": + raise ValueError("Organization 'missing' not found") + return { + "login": org_name, + "name": "Swiss Data Science Center" if org_name == "sdsc-ordes" else org_name.title(), + "description": "description", + "html_url": f"https://github.com/{org_name}", + "type": "Organization", + "followers": 24, + "public_repos": 7, + "location": "Lausanne, Switzerland", + "blog": "https://example.org", + "email": None, + "company": "EPFL", + "created_at": "2020-01-01T00:00:00Z", + "updated_at": "2026-01-01T00:00:00Z", + "public_members": ["alice", "bob", 5], + "repositories": ["gimie", "other", None], + "teams": ["platform"], + } + + +def test_get_github_organization_metadata_normalizes_url_and_shapes_payload() -> None: + provider = _FakeGitHubProvider() + tool = make_github_organization_metadata_tool(provider) + + payload = tool.function("https://github.com/sdsc-ordes") + + assert provider.calls == ["sdsc-ordes"] + assert payload["normalized_org_name"] == "sdsc-ordes" + assert payload["organization"]["login"] == "sdsc-ordes" + assert payload["organization"]["name"] == "Swiss Data Science Center" + assert payload["organization"]["html_url"] == "https://github.com/sdsc-ordes" + assert payload["organization"]["public_members"] == ["alice", "bob"] + assert payload["organization"]["repositories"] == ["gimie", "other"] + assert payload["organization"]["teams"] == ["platform"] + + +def test_get_github_organization_metadata_normalizes_owner_repo_and_at_handle() -> None: + provider = _FakeGitHubProvider() + tool = make_github_organization_metadata_tool(provider) + + payload_from_repo = tool.function("sdsc-ordes/gimie") + payload_from_handle = tool.function("@sdsc-ordes") + + assert provider.calls == ["sdsc-ordes", "sdsc-ordes"] + assert payload_from_repo["normalized_org_name"] == "sdsc-ordes" + assert payload_from_handle["normalized_org_name"] == "sdsc-ordes" + + +def test_get_github_organization_metadata_returns_error_for_empty_name() -> None: + provider = _FakeGitHubProvider() + tool = make_github_organization_metadata_tool(provider) + + payload = tool.function(" ") + + assert provider.calls == [] + assert payload["normalized_org_name"] == "" + assert payload["organization"] is None + assert payload["error"] == "empty_org_name" + + +def test_get_github_organization_metadata_returns_error_payload_on_lookup_failure() -> None: + provider = _FakeGitHubProvider() + tool = make_github_organization_metadata_tool(provider) + + payload = tool.function("missing") + + assert provider.calls == ["missing"] + assert payload["normalized_org_name"] == "missing" + assert payload["organization"] is None + assert "not found" in payload["error"] diff --git a/tests/v2/test_llm_huggingface_rag_tool.py b/tests/v2/test_llm_huggingface_rag_tool.py new file mode 100644 index 0000000..9e50e2a --- /dev/null +++ b/tests/v2/test_llm_huggingface_rag_tool.py @@ -0,0 +1,155 @@ +from __future__ import annotations + +import asyncio + +from src.v2.agents.llm.agent_tools.huggingface_rag import ( + make_huggingface_rag_search_tool, +) +from src.v2.ingest.providers.huggingface_rag import HuggingFaceRagProvider + + +class _FakeClient: + def __init__(self, *, exists: bool = True) -> None: + self._exists = exists + + def collection_exists(self, name: str) -> bool: # noqa: ARG002 + return self._exists + + +class _FakeStore: + def __init__(self, hits: list[dict] | None = None, *, exists: bool = True) -> None: + self._hits = hits or [] + self.client = _FakeClient(exists=exists) + self.search_calls: list[dict] = [] + + def search(self, collection, *, query_vector, top_k, filter_payload): + self.search_calls.append({ + "collection": collection, "top_k": top_k, "filter": filter_payload, + }) + return list(self._hits) + + +class _FakeEmbedder: + def __init__(self, vector=(0.1, 0.2, 0.3)) -> None: + self.vector = list(vector) + self.calls: list[list[str]] = [] + + async def embed_all(self, inputs): + self.calls.append(list(inputs)) + return [self.vector for _ in inputs] + + +class _FakeReranker: + def __init__(self, results=None) -> None: + self.results = results or [] + self.calls: list[dict] = [] + + async def rerank(self, query, documents, *, top_n=None): + self.calls.append({ + "query": query, "n": len(documents), "top_n": top_n, + }) + return list(self.results) + + +def _run(coro): + return asyncio.run(coro) + + +def test_search_returns_thin_model_hits() -> None: + hits = [{ + "id": "h1", "score": 0.9, + "payload": { + "entity_type": "model", + "repo_id": "ZurichNLP/swissbert", + "author": "ZurichNLP", + "library_name": "transformers", + "pipeline_tag": "fill-mask", + "downloads": 1000, + "likes": 5, + }, + }] + store = _FakeStore(hits) + emb = _FakeEmbedder() + p = HuggingFaceRagProvider(store=store, embedder=emb, reranker=None) + + out = _run(p.search("swiss german model", collection="models", top_k=5)) + + assert store.search_calls[0]["collection"] == "hf_models" + assert emb.calls == [["swiss german model"]] + assert out[0]["repo_id"] == "ZurichNLP/swissbert" + assert out[0]["library_name"] == "transformers" + assert out[0]["collection"] == "models" + assert "entity_type" not in out[0] # not in thin keys + + +def test_search_returns_empty_when_collection_missing() -> None: + store = _FakeStore([{"id": "x"}], exists=False) + p = HuggingFaceRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + assert _run(p.search("q", collection="orgs")) == [] + # search not invoked when collection missing + assert store.search_calls == [] + + +def test_search_respects_filter_allowlist_and_translation() -> None: + store = _FakeStore([]) + p = HuggingFaceRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + + _run(p.search( + "x", + filters={ + "library_name": "transformers", + "downloads": {"$gte": 100, "$lte": 1000}, + "secret_field": "boom", + }, + )) + fp = store.search_calls[0]["filter"] + assert fp == { + "library_name": "transformers", + "downloads": {"gte": 100, "lte": 1000}, + } + + +def test_search_unknown_collection_returns_empty() -> None: + store = _FakeStore([{"id": "x"}]) + p = HuggingFaceRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + assert _run(p.search("q", collection="not_a_collection")) == [] # type: ignore[arg-type] + + +def test_search_blank_query_returns_empty() -> None: + store = _FakeStore([{"id": "x"}]) + emb = _FakeEmbedder() + p = HuggingFaceRagProvider(store=store, embedder=emb, reranker=None) + assert _run(p.search(" ")) == [] + assert emb.calls == [] + assert store.search_calls == [] + + +def test_rerank_reorders_via_synthetic_doc_strings() -> None: + hits = [ + {"id": "a", "score": 0.5, "payload": {"repo_id": "x/alpha", "library_name": "transformers"}}, + {"id": "b", "score": 0.4, "payload": {"repo_id": "x/beta", "library_name": "diffusers"}}, + {"id": "c", "score": 0.3, "payload": {"repo_id": "x/gamma"}}, + ] + store = _FakeStore(hits) + rer = _FakeReranker([ + {"index": 1, "relevance_score": 0.99}, + {"index": 0, "relevance_score": 0.5}, + ]) + p = HuggingFaceRagProvider(store=store, embedder=_FakeEmbedder(), reranker=rer) + + out = _run(p.search("q", collection="models", top_k=2, rerank=True)) + + # candidate_k expands beyond top_k + assert store.search_calls[0]["top_k"] >= 30 + assert rer.calls and rer.calls[0]["n"] == 3 + ids = [r["id"] for r in out] + assert ids == ["b", "a"] + + +def test_search_tool_factory() -> None: + store = _FakeStore([{"id": "h1", "score": 0.9, "payload": {"repo_id": "x/y"}}]) + p = HuggingFaceRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + tool = make_huggingface_rag_search_tool(p) + assert tool.name == "search_huggingface_rag" + rows = _run(tool.function("q", "models", 3, None, False)) + assert rows[0]["repo_id"] == "x/y" diff --git a/tests/v2/test_llm_infoscience_rag_tool.py b/tests/v2/test_llm_infoscience_rag_tool.py new file mode 100644 index 0000000..0591459 --- /dev/null +++ b/tests/v2/test_llm_infoscience_rag_tool.py @@ -0,0 +1,389 @@ +from __future__ import annotations + +import asyncio +import uuid +from typing import Any + +import pytest + +from src.index.infoscience.rerank import RerankHit +from src.index.infoscience.store import ( + ARTICLES_COLLECTION, + CHUNKS_COLLECTION, + ORGANIZATIONS_COLLECTION, + PERSONS_COLLECTION, +) +from src.v2.agents.llm.agent_tools.infoscience_rag import ( + make_infoscience_rag_fetch_chunks_tool, + make_infoscience_rag_fetch_records_tool, + make_infoscience_rag_search_tool, +) +from src.v2.ingest.providers.infoscience_rag import InfoscienceRagProvider + + +class _FakeStore: + """Duck-typed `QdrantStore` that records calls and returns canned data.""" + + def __init__(self) -> None: + self.search_calls: list[dict[str, Any]] = [] + self.scroll_calls: list[dict[str, Any]] = [] + self.lookup_calls: list[dict[str, Any]] = [] + self._search_returns: list[dict[str, Any]] = [] + self._scroll_returns: list[dict[str, Any]] = [] + self._lookup_returns: list[dict[str, Any]] = [] + + def set_search(self, hits: list[dict[str, Any]]) -> None: + self._search_returns = hits + + def set_scroll(self, recs: list[dict[str, Any]]) -> None: + self._scroll_returns = recs + + def set_lookup(self, recs: list[dict[str, Any]]) -> None: + self._lookup_returns = recs + + def search(self, collection: str, *, query_vector, top_k, query_filter): + self.search_calls.append({ + "collection": collection, + "vector": list(query_vector), + "top_k": top_k, + "filter": query_filter, + }) + return list(self._search_returns) + + def scroll(self, collection: str, *, query_filter, limit): + self.scroll_calls.append({ + "collection": collection, + "filter": query_filter, + "limit": limit, + }) + return list(self._scroll_returns) + + def lookup(self, collection: str, *, ids): + self.lookup_calls.append({"collection": collection, "ids": list(ids)}) + return list(self._lookup_returns) + + +class _FakeEmbedder: + """Duck-typed `RCPEmbedder`; pretends to be an async context manager.""" + + def __init__(self, vector: list[float] | None = None, fail: bool = False): + self.vector = vector or [0.1, 0.2, 0.3] + self.fail = fail + self.entered = 0 + self.exited = 0 + self.embed_calls: list[str] = [] + + async def __aenter__(self): + self.entered += 1 + return self + + async def __aexit__(self, exc_type, exc, tb): + self.exited += 1 + + async def embed_query(self, query: str, instruction: str | None = None): + self.embed_calls.append(query) + if self.fail: + from src.index.infoscience.embed import EmbedError + raise EmbedError("forced failure") + return list(self.vector) + + +class _FakeReranker: + def __init__(self, hits: list[RerankHit] | None = None, fail: bool = False): + self._hits = hits or [] + self.fail = fail + self.entered = 0 + self.calls: list[dict[str, Any]] = [] + + async def __aenter__(self): + self.entered += 1 + return self + + async def __aexit__(self, exc_type, exc, tb): + return None + + async def rerank(self, query: str, documents: list[str], *, top_n=None): + self.calls.append({ + "query": query, + "documents": list(documents), + "top_n": top_n, + }) + if self.fail: + from src.index.infoscience.rerank import RerankError + raise RerankError("forced failure") + return list(self._hits) + + +def _make_provider( + *, + search_hits: list[dict[str, Any]] | None = None, + scroll_recs: list[dict[str, Any]] | None = None, + lookup_recs: list[dict[str, Any]] | None = None, + embedder: _FakeEmbedder | None = None, + reranker: _FakeReranker | None = None, +) -> tuple[InfoscienceRagProvider, _FakeStore, _FakeEmbedder, _FakeReranker | None]: + store = _FakeStore() + if search_hits is not None: + store.set_search(search_hits) + if scroll_recs is not None: + store.set_scroll(scroll_recs) + if lookup_recs is not None: + store.set_lookup(lookup_recs) + emb = embedder or _FakeEmbedder() + rer = reranker + provider = InfoscienceRagProvider( + store=store, # type: ignore[arg-type] + embedder=emb, # type: ignore[arg-type] + reranker=rer, # type: ignore[arg-type] + ) + return provider, store, emb, rer + + +def _run(coro): + return asyncio.run(coro) + + +# --------------------------------------------------------------------------- +# search +# --------------------------------------------------------------------------- + + +def test_search_returns_thin_chunk_hits_with_snippet() -> None: + body_text = "This paper introduces a graph attention network. " * 20 + hits = [ + { + "id": str(uuid.uuid4()), + "score": 0.91, + "payload": { + "article_uuid": "article-1", + "chunk_index": 3, + "title": "GAT for Drug Discovery", + "doi": "10.1234/foo", + "year": 2024, + "infoscience_url": "https://infoscience.epfl.ch/record/1", + "text": body_text, + "matched_urls": ["https://github.com/lab/repo"], + }, + } + ] + provider, store, embedder, _rer = _make_provider(search_hits=hits) + + out = _run(provider.search("graph attention network")) + + assert embedder.entered == 1 + assert embedder.embed_calls == ["graph attention network"] + assert store.search_calls[0]["collection"] == CHUNKS_COLLECTION + assert store.search_calls[0]["top_k"] == 10 + assert len(out) == 1 + row = out[0] + assert row["collection"] == "chunks" + assert row["article_uuid"] == "article-1" + assert row["chunk_index"] == 3 + assert row["title"] == "GAT for Drug Discovery" + # Snippet truncates the long body and never exceeds the configured cap. + assert "snippet" in row + assert len(row["snippet"]) <= 320 + assert "text" not in row # full body NOT returned by search + + +def test_search_drops_non_allowlisted_filter_keys(caplog) -> None: + provider, store, _emb, _rer = _make_provider(search_hits=[]) + caplog.set_level("WARNING") + + _run(provider.search( + "x", + filters={"year": 2024, "secret_key": "boom", "doi": "10.1/x"}, + )) + + qfilter = store.search_calls[0]["filter"] + assert qfilter is not None + # Reach into the Qdrant Filter to confirm only allowlisted keys made it. + keys = sorted(getattr(c, "key", None) for c in (qfilter.must or [])) + assert "year" in keys + assert "doi" in keys + assert "secret_key" not in keys + assert any("dropped non-allowlisted filter keys" in r.message for r in caplog.records) + + +def test_search_resolves_collection_aliases() -> None: + provider, store, _emb, _rer = _make_provider(search_hits=[]) + + _run(provider.search("x", collection="articles")) + _run(provider.search("x", collection="persons")) + _run(provider.search("x", collection="organizations")) + + seen = [c["collection"] for c in store.search_calls] + assert seen == [ + ARTICLES_COLLECTION, + PERSONS_COLLECTION, + ORGANIZATIONS_COLLECTION, + ] + + +def test_search_returns_empty_on_blank_query() -> None: + provider, store, embedder, _rer = _make_provider(search_hits=[{"id": "x"}]) + out = _run(provider.search(" ")) + assert out == [] + assert embedder.embed_calls == [] + assert store.search_calls == [] + + +def test_search_returns_empty_on_embed_failure(caplog) -> None: + provider, store, _emb, _rer = _make_provider( + search_hits=[{"id": "x"}], + embedder=_FakeEmbedder(fail=True), + ) + caplog.set_level("WARNING") + out = _run(provider.search("graph attention")) + assert out == [] + assert store.search_calls == [] # never reached + + +def test_search_with_rerank_reorders_hits() -> None: + hits = [ + {"id": "a", "score": 0.5, "payload": {"text": "alpha"}}, + {"id": "b", "score": 0.4, "payload": {"text": "beta"}}, + {"id": "c", "score": 0.3, "payload": {"text": "gamma"}}, + ] + reranker = _FakeReranker(hits=[ + RerankHit(index=2, score=0.99), + RerankHit(index=0, score=0.7), + ]) + provider, store, _emb, _rer = _make_provider( + search_hits=hits, reranker=reranker, + ) + + out = _run(provider.search("q", top_k=2, rerank=True)) + + # candidate_k expands beyond top_k when rerank is on. + assert store.search_calls[0]["top_k"] >= 30 + assert reranker is not None and reranker.calls + ids = [row["id"] for row in out] + assert ids == ["c", "a"] + assert out[0]["score"] == pytest.approx(0.99) + + +def test_search_without_rerank_skips_reranker() -> None: + reranker = _FakeReranker(hits=[RerankHit(index=0, score=0.99)]) + provider, _store, _emb, rer = _make_provider( + search_hits=[{"id": "a", "score": 0.5, "payload": {}}], + reranker=reranker, + ) + + _run(provider.search("q", rerank=False)) + + assert rer is not None and rer.calls == [] # reranker never invoked + assert rer.entered == 0 + + +def test_search_falls_back_when_rerank_fails(caplog) -> None: + reranker = _FakeReranker(fail=True) + hits = [{"id": "a", "score": 0.5, "payload": {"text": "alpha"}}] + provider, _store, _emb, _rer = _make_provider( + search_hits=hits, reranker=reranker, + ) + caplog.set_level("WARNING") + + out = _run(provider.search("q", top_k=1, rerank=True)) + + assert [r["id"] for r in out] == ["a"] + + +# --------------------------------------------------------------------------- +# fetch_chunks +# --------------------------------------------------------------------------- + + +def test_fetch_chunks_orders_by_chunk_index() -> None: + recs = [ + {"id": "p3", "payload": {"article_uuid": "A", "chunk_index": 2, "text": "t2"}}, + {"id": "p1", "payload": {"article_uuid": "A", "chunk_index": 0, "text": "t0"}}, + {"id": "p2", "payload": {"article_uuid": "A", "chunk_index": 1, "text": "t1"}}, + ] + provider, store, _emb, _rer = _make_provider(scroll_recs=recs) + + out = _run(provider.fetch_chunks("A")) + + assert store.scroll_calls[0]["collection"] == CHUNKS_COLLECTION + assert [c["chunk_index"] for c in out] == [0, 1, 2] + assert [c["text"] for c in out] == ["t0", "t1", "t2"] + + +def test_fetch_chunks_blank_uuid_returns_empty() -> None: + provider, store, _emb, _rer = _make_provider(scroll_recs=[{"id": "x", "payload": {}}]) + assert _run(provider.fetch_chunks("")) == [] + assert store.scroll_calls == [] + + +# --------------------------------------------------------------------------- +# fetch_records +# --------------------------------------------------------------------------- + + +def test_fetch_records_returns_full_payload() -> None: + recs = [ + {"id": "u1", "payload": {"name": "Person 1", "orcid": "0000-...-1"}}, + {"id": "u2", "payload": {"name": "Person 2"}}, + ] + provider, store, _emb, _rer = _make_provider(lookup_recs=recs) + + out = _run(provider.fetch_records("persons", ["u1", "u2"])) + + assert store.lookup_calls[0]["collection"] == PERSONS_COLLECTION + assert store.lookup_calls[0]["ids"] == ["u1", "u2"] + assert [r["payload"]["name"] for r in out] == ["Person 1", "Person 2"] + + +def test_fetch_records_rejects_chunks_collection() -> None: + provider, _store, _emb, _rer = _make_provider(lookup_recs=[]) + with pytest.raises(ValueError): + _run(provider.fetch_records("chunks", ["x"])) + + +# --------------------------------------------------------------------------- +# Tool factories +# --------------------------------------------------------------------------- + + +def test_search_tool_factory_wires_provider() -> None: + provider, _store, embedder, _rer = _make_provider( + search_hits=[{"id": "a", "score": 0.5, "payload": {"text": "alpha"}}], + ) + tool = make_infoscience_rag_search_tool(provider) + + assert tool.name == "search_infoscience_rag" + rows = _run(tool.function("query", "chunks", 3, None, False)) + assert len(rows) == 1 + assert embedder.embed_calls == ["query"] + + +def test_fetch_chunks_tool_factory_wires_provider() -> None: + provider, store, _emb, _rer = _make_provider( + scroll_recs=[{"id": "x", "payload": {"article_uuid": "A", "chunk_index": 0, "text": "t"}}], + ) + tool = make_infoscience_rag_fetch_chunks_tool(provider) + + assert tool.name == "fetch_infoscience_chunks" + rows = _run(tool.function("A", 5)) + assert rows[0]["text"] == "t" + assert store.scroll_calls[0]["limit"] == 5 + + +def test_fetch_records_tool_factory_returns_empty_on_chunks_collection() -> None: + provider, _store, _emb, _rer = _make_provider(lookup_recs=[]) + tool = make_infoscience_rag_fetch_records_tool(provider) + + assert tool.name == "fetch_infoscience_records" + # The tool catches ValueError and returns [] so it's safe for the LLM. + out = _run(tool.function("chunks", ["x"])) + assert out == [] + + +def test_fetch_records_tool_factory_returns_records() -> None: + provider, _store, _emb, _rer = _make_provider( + lookup_recs=[{"id": "u1", "payload": {"name": "P"}}], + ) + tool = make_infoscience_rag_fetch_records_tool(provider) + + out = _run(tool.function("persons", ["u1"])) + assert out[0]["payload"]["name"] == "P" diff --git a/tests/v2/test_llm_link_veracity_agent.py b/tests/v2/test_llm_link_veracity_agent.py new file mode 100644 index 0000000..30fa859 --- /dev/null +++ b/tests/v2/test_llm_link_veracity_agent.py @@ -0,0 +1,162 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +import pytest + +from src.v2.agents.llm.link_veracity import LLMLinkVeracityAgentV2 +from src.v2.agents.models import ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeError, LLMRuntimeResult +from src.v2.ingest.providers.mock_github import MockGitHubProvider + +EXPECTED_PROMPT_TOKENS = 13 +EXPECTED_COMPLETION_TOKENS = 29 + + +class _FakeLLMRuntime: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del output_type + assert system_prompt + assert user_prompt + assert tools + return LLMRuntimeResult( + payload=dict(self._payload), + model="openai/gpt-test", + provider="openai", + tokens_prompt=EXPECTED_PROMPT_TOKENS, + tokens_completion=EXPECTED_COMPLETION_TOKENS, + ) + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +def _valid_payload(link: str) -> dict[str, Any]: + return { + "link": link, + "relationship_supported": True, + "relationship_summary": "The page title and body support this relation.", + "fetched_successfully": True, + } + + +def test_llm_link_veracity_agent_returns_boolean_verdict_with_metadata() -> None: + link = "https://example.org/resource" + agent = LLMLinkVeracityAgentV2(llm_runtime=_FakeLLMRuntime(_valid_payload(link))) + result = asyncio.run( + agent.run( + { + "link": link, + "source_entity_id": "urn:entity:source", + "predicate": "schema:url", + }, + _providers(), + ), + ) + + assert result.data["link"] == link + assert result.data["relationship_supported"] is True + assert result.model == "openai/gpt-test" + assert result.provider == "openai" + assert result.tokens_prompt == EXPECTED_PROMPT_TOKENS + assert result.tokens_completion == EXPECTED_COMPLETION_TOKENS + assert result.stats["agent_runtime"] == "llm" + assert result.stats["relationship_supported"] is True + + +def test_llm_link_veracity_agent_propagates_runtime_error() -> None: + class _RaisingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + raise LLMRuntimeError("Schema validation failed after retries") + + agent = LLMLinkVeracityAgentV2(llm_runtime=_RaisingRuntime()) + with pytest.raises(LLMRuntimeError, match="Schema validation failed after retries"): + asyncio.run(agent.run({"link": "https://example.org"}, _providers())) + + +def test_llm_link_veracity_agent_timeout_includes_link_and_timeout_seconds() -> None: + class _SlowRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + await asyncio.sleep(60) + raise AssertionError("unreachable") + + agent = LLMLinkVeracityAgentV2( + llm_runtime=_SlowRuntime(), + llm_call_timeout_seconds=0.1, + ) + with pytest.raises(LLMRuntimeError, match=r"https://example\.org.*timed out after 0\.1s"): + asyncio.run(agent.run({"link": "https://example.org"}, _providers())) + + +def test_llm_link_veracity_agent_appends_runtime_prompt_context_blocks() -> None: + captured_user_prompts: list[str] = [] + captured_tool_names: list[str] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, output_type + captured_user_prompts.append(user_prompt) + captured_tool_names.extend(getattr(tool, "name", "") for tool in (tools or [])) + return LLMRuntimeResult( + payload=_valid_payload("https://example.org"), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMLinkVeracityAgentV2(llm_runtime=_CapturingRuntime()) + upstream_json = '{"repo_agent":{"id":"repo-root"}}' + prompt_appendix = "RAW_RELATIONSHIPS_BLOCK" + + asyncio.run( + agent.run( + { + "link": "https://example.org", + "upstream_stage_outputs_json": upstream_json, + "user_prompt_appendix": prompt_appendix, + }, + _providers(), + ), + ) + + assert len(captured_user_prompts) == 1 + prompt = captured_user_prompts[0] + assert "## Upstream Stage Outputs (JSON)" in prompt + assert upstream_json in prompt + assert "## Additional Context (verbatim text)" in prompt + assert prompt_appendix in prompt + assert captured_tool_names == ["fetch_link_content_via_selenium"] diff --git a/tests/v2/test_llm_membership_agent.py b/tests/v2/test_llm_membership_agent.py new file mode 100644 index 0000000..14d6aad --- /dev/null +++ b/tests/v2/test_llm_membership_agent.py @@ -0,0 +1,245 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +import pytest +from jsonschema import validate + +from src.v2.agents.llm.membership import LLMMembershipAgentV2 +from src.v2.agents.llm.membership import agent as membership_agent_module +from src.v2.agents.models import ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeError, LLMRuntimeResult +from src.v2.ingest.providers.mock_github import MockGitHubProvider +from src.v2.ingest.providers.mock_orcid import MockORCIDProvider + +EXPECTED_PROMPT_TOKENS = 13 +EXPECTED_COMPLETION_TOKENS = 29 + + +class _FakeLLMRuntime: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del output_type, tools + assert system_prompt + assert user_prompt + return LLMRuntimeResult( + payload=dict(self._payload), + model="openai/gpt-test", + provider="openai", + tokens_prompt=EXPECTED_PROMPT_TOKENS, + tokens_completion=EXPECTED_COMPLETION_TOKENS, + ) + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +def _providers_with_orcid() -> ProviderSet: + return ProviderSet( + github=MockGitHubProvider(), + orcid=MockORCIDProvider(), + ) + + +def _valid_membership_payload() -> dict[str, Any]: + return { + "id": "alice_org:epfl", + "type": "org:Membership", + "shacl": "pulse:MembershipShape", + "identifiers": { + "pulse:composite": "alice_org:epfl", + "uuid": "f887181e-9b8c-4c55-8ee6-d34285fdbed4", + }, + "idSource": "pulse:composite", + "org:organization": "org:epfl", + "org:role": "Research Engineer", + "time:hasBeginning": "2020-01-01", + "time:hasEnd": None, + } + + +def test_llm_membership_agent_validates_payload_and_exposes_model_metadata( + load_schema, +) -> None: + agent = LLMMembershipAgentV2( + llm_runtime=_FakeLLMRuntime(_valid_membership_payload()), + ) + result = asyncio.run( + agent.run( + { + "membership_seed": "alice", + "known_persons": [{"id": "alice", "type": "schema:Person"}], + "known_organizations": [{"id": "org:epfl", "type": "org:Organization"}], + }, + _providers(), + ), + ) + + schema = load_schema("agent", "membership") + validate(instance=result.data, schema=schema) + + assert result.model == "openai/gpt-test" + assert result.provider == "openai" + assert result.tokens_prompt == EXPECTED_PROMPT_TOKENS + assert result.tokens_completion == EXPECTED_COMPLETION_TOKENS + assert result.stats["agent_runtime"] == "llm" + assert result.stats["membership_count"] == 1 + + +def test_llm_membership_agent_propagates_llm_runtime_error() -> None: + class _RaisingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + raise LLMRuntimeError("Schema validation failed after retries") + + agent = LLMMembershipAgentV2(llm_runtime=_RaisingRuntime()) + + with pytest.raises(LLMRuntimeError, match="Schema validation failed after retries"): + asyncio.run(agent.run({"membership_seed": "alice"}, _providers())) + + +def test_llm_membership_agent_timeout_includes_identifier_and_timeout_seconds() -> None: + class _SlowRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + await asyncio.sleep(60) + raise AssertionError("unreachable") + + agent = LLMMembershipAgentV2( + llm_runtime=_SlowRuntime(), + llm_call_timeout_seconds=0.1, + ) + + with pytest.raises(LLMRuntimeError, match=r"alice.*timed out after 0\.1s"): + asyncio.run(agent.run({"membership_seed": "alice"}, _providers())) + + +def test_llm_membership_agent_records_strict_schema_warnings() -> None: + payload = _valid_membership_payload() + payload["time:hasBeginning"] = "not-a-date" + agent = LLMMembershipAgentV2(llm_runtime=_FakeLLMRuntime(payload)) + + result = asyncio.run(agent.run({"membership_seed": "alice"}, _providers())) + + assert result.warnings, "Expected strict-schema warnings but got none" + assert any("time:hasBeginning" in warning for warning in result.warnings) + + +def test_llm_membership_agent_appends_runtime_prompt_context_blocks() -> None: + captured_user_prompts: list[str] = [] + captured_tool_names: list[str] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, output_type + captured_user_prompts.append(user_prompt) + captured_tool_names.extend(getattr(tool, "name", "") for tool in (tools or [])) + return LLMRuntimeResult( + payload=_valid_membership_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMMembershipAgentV2(llm_runtime=_CapturingRuntime()) + upstream_json = '{"person_agent:alice":{"id":"alice"}}' + prompt_appendix = "AFFILIATION_CONTEXT" + + asyncio.run( + agent.run( + { + "membership_seed": "alice", + "upstream_stage_outputs_json": upstream_json, + "user_prompt_appendix": prompt_appendix, + }, + _providers(), + ), + ) + + assert len(captured_user_prompts) == 1 + prompt = captured_user_prompts[0] + assert "## Upstream Stage Outputs (JSON)" in prompt + assert upstream_json in prompt + assert "## Additional Context (verbatim text)" in prompt + assert prompt_appendix in prompt + assert "fetch_link_content_via_selenium" in captured_tool_names + assert "get_orcid_record" not in captured_tool_names + + +def test_llm_membership_agent_adds_orcid_tool_when_provider_available() -> None: + captured_tool_names: list[str] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type + captured_tool_names.extend(getattr(tool, "name", "") for tool in (tools or [])) + return LLMRuntimeResult( + payload=_valid_membership_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMMembershipAgentV2(llm_runtime=_CapturingRuntime()) + asyncio.run( + agent.run( + { + "membership_seed": "alice", + "target_person": { + "id": "alice", + "pulse:orcidIdentifier": "0000-0001-5000-0007", + }, + "target_organizations": [{"id": "org:epfl"}], + }, + _providers_with_orcid(), + ), + ) + + assert "fetch_link_content_via_selenium" in captured_tool_names + assert "get_orcid_record" in captured_tool_names + + +def test_llm_membership_system_prompt_prefers_ror_backed_canonical_org_ids() -> None: + prompt = membership_agent_module._SYSTEM_PROMPT + + assert "Use canonical IDs from `known_persons` and `known_organizations` when available." in prompt + assert "Use `target_person` and `target_organizations` as primary context when provided." in prompt + assert "Prefer ROR-backed canonical organization IDs" in prompt + assert "get_orcid_record(orcid_id)" in prompt diff --git a/tests/v2/test_llm_organization_agent.py b/tests/v2/test_llm_organization_agent.py new file mode 100644 index 0000000..61d970f --- /dev/null +++ b/tests/v2/test_llm_organization_agent.py @@ -0,0 +1,398 @@ +from __future__ import annotations + +import asyncio +import os +from typing import Any + +import pytest +from jsonschema import validate + +from src.v2.agents.llm.organization import LLMOrganizationAgentV2 +from src.v2.agents.llm.organization import agent as organization_agent_module +from src.v2.agents.models import ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeError, LLMRuntimeResult +from src.v2.ingest.providers.mock_github import MockGitHubProvider +from src.v2.ingest.providers.mock_infoscience import MockInfoscienceProvider +from src.v2.ingest.providers.mock_ror import MockRORProvider + +_HAS_LLM_CREDENTIALS = bool( + os.getenv("RCP_TOKEN") or os.getenv("OPENAI_API_KEY") or os.getenv("OPENROUTER_API_KEY") +) +llm_integration = pytest.mark.llm_integration +requires_llm_credentials = pytest.mark.skipif( + not _HAS_LLM_CREDENTIALS, + reason="No LLM provider credentials available (RCP_TOKEN / OPENAI_API_KEY / OPENROUTER_API_KEY)", +) + +EXPECTED_PROMPT_TOKENS = 13 +EXPECTED_COMPLETION_TOKENS = 29 + + +class _FakeLLMRuntime: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del output_type, tools + assert system_prompt + assert user_prompt + return LLMRuntimeResult( + payload=dict(self._payload), + model="openai/gpt-test", + provider="openai", + tokens_prompt=EXPECTED_PROMPT_TOKENS, + tokens_completion=EXPECTED_COMPLETION_TOKENS, + ) + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +def _providers_full() -> ProviderSet: + return ProviderSet( + github=MockGitHubProvider(), + ror=MockRORProvider(), + infoscience=MockInfoscienceProvider(), + ) + + +def _providers_ror_only() -> ProviderSet: + return ProviderSet( + github=MockGitHubProvider(), + ror=MockRORProvider(), + ) + + +def _providers_infoscience_only() -> ProviderSet: + return ProviderSet( + github=MockGitHubProvider(), + infoscience=MockInfoscienceProvider(), + ) + + +def _valid_organization_payload() -> dict[str, Any]: + return { + "id": "github", + "type": "org:Organization", + "shacl": "pulse:OrganizationShape", + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:githubOrganizationHandle": "github", + "uuid": "f887181e-9b8c-4c55-8ee6-d34285fdbed4", + }, + "idSource": "pulse:githubOrganizationHandle", + "schema:name": "GitHub", + "schema:identifier": None, + "pulse:githubOrganizationHandle": "github", + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:OrganizationType": "pulse:PrivateCompany", + "pulse:githubOrgFollowers": 123, + "org:hasUnit": [], + "org:unitOf": None, + "pulse:owns": ["owner/repo"], + } + + +def test_llm_organization_agent_validates_payload_and_exposes_model_metadata( + load_schema, +) -> None: + agent = LLMOrganizationAgentV2( + llm_runtime=_FakeLLMRuntime(_valid_organization_payload()), + ) + + result = asyncio.run( + agent.run({"org_name": "github"}, _providers()), + ) + + schema = load_schema("agent", "organization") + validate(instance=result.data, schema=schema) + + assert result.model == "openai/gpt-test" + assert result.provider == "openai" + assert result.tokens_prompt == EXPECTED_PROMPT_TOKENS + assert result.tokens_completion == EXPECTED_COMPLETION_TOKENS + assert result.stats["agent_runtime"] == "llm" + + +def test_llm_organization_agent_propagates_llm_runtime_error() -> None: + class _RaisingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + raise LLMRuntimeError("Schema validation failed after retries") + + agent = LLMOrganizationAgentV2(llm_runtime=_RaisingRuntime()) + + with pytest.raises(LLMRuntimeError, match="Schema validation failed after retries"): + asyncio.run( + agent.run({"org_name": "github"}, _providers()), + ) + + +def test_llm_organization_agent_timeout_includes_identifier_and_timeout_seconds() -> None: + class _SlowRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + await asyncio.sleep(60) + raise AssertionError("unreachable") + + agent = LLMOrganizationAgentV2( + llm_runtime=_SlowRuntime(), + llm_call_timeout_seconds=0.1, + ) + + with pytest.raises(LLMRuntimeError, match=r"github.*timed out after 0\.1s"): + asyncio.run( + agent.run({"org_name": "github"}, _providers()), + ) + + +def test_llm_organization_agent_records_strict_schema_warnings() -> None: + payload = _valid_organization_payload() + payload["pulse:OrganizationType"] = "pulse:NotAValidEnumValue" + + agent = LLMOrganizationAgentV2(llm_runtime=_FakeLLMRuntime(payload)) + + result = asyncio.run( + agent.run({"org_name": "github"}, _providers()), + ) + + assert result.warnings, "Expected strict-schema warnings but got none" + + +def test_llm_organization_agent_builds_tools_from_providers() -> None: + captured_tools: list[Any] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type + if tools: + captured_tools.extend(tools) + return LLMRuntimeResult( + payload=_valid_organization_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMOrganizationAgentV2(llm_runtime=_CapturingRuntime()) + + asyncio.run( + agent.run({"org_name": "github"}, _providers_full()), + ) + + tool_names = [getattr(tool, "name", None) for tool in captured_tools] + assert "fetch_link_content_via_selenium" in tool_names + assert "get_github_organization_metadata" in tool_names + assert "search_organization_identity" in tool_names + assert "search_ror_organizations" not in tool_names + assert "search_infoscience_orgunit" not in tool_names + + +@pytest.mark.parametrize( + ("provider_set", "expected_tool_name"), + [ + (_providers_ror_only(), "search_ror_organizations"), + (_providers_infoscience_only(), "search_infoscience_orgunit"), + ], +) +def test_llm_organization_agent_falls_back_to_single_provider_org_tools( + provider_set: ProviderSet, + expected_tool_name: str, +) -> None: + captured_tools: list[Any] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type + if tools: + captured_tools.extend(tools) + return LLMRuntimeResult( + payload=_valid_organization_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMOrganizationAgentV2(llm_runtime=_CapturingRuntime()) + + asyncio.run( + agent.run({"org_name": "github"}, provider_set), + ) + + tool_names = [getattr(tool, "name", None) for tool in captured_tools] + assert "fetch_link_content_via_selenium" in tool_names + assert "get_github_organization_metadata" in tool_names + assert expected_tool_name in tool_names + assert "search_organization_identity" not in tool_names + + +def test_llm_organization_agent_appends_runtime_prompt_context_blocks() -> None: + captured_user_prompts: list[str] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, output_type, tools + captured_user_prompts.append(user_prompt) + return LLMRuntimeResult( + payload=_valid_organization_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMOrganizationAgentV2(llm_runtime=_CapturingRuntime()) + upstream_json = '{"repo_agent":{"id":"repo-root"}}' + prompt_appendix = "README_FRAGMENT_1\nREADME_FRAGMENT_2" + + asyncio.run( + agent.run( + { + "org_name": "github", + "upstream_stage_outputs_json": upstream_json, + "user_prompt_appendix": prompt_appendix, + }, + _providers(), + ), + ) + + assert len(captured_user_prompts) == 1 + prompt = captured_user_prompts[0] + assert "## Upstream Stage Outputs (JSON)" in prompt + assert upstream_json in prompt + assert "## Additional Context (verbatim text)" in prompt + assert prompt_appendix in prompt + + +def test_llm_organization_agent_exposes_github_metadata_tool_without_optional_providers() -> None: + captured_tools: list[Any] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type + captured_tools.extend(tools or []) + return LLMRuntimeResult( + payload=_valid_organization_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMOrganizationAgentV2(llm_runtime=_CapturingRuntime()) + + asyncio.run( + agent.run({"org_name": "github"}, _providers()), + ) + + tool_names = [getattr(tool, "name", None) for tool in captured_tools] + assert tool_names == [ + "fetch_link_content_via_selenium", + "get_github_organization_metadata", + ] + + +def test_llm_organization_agent_works_with_minimal_context() -> None: + agent = LLMOrganizationAgentV2( + llm_runtime=_FakeLLMRuntime(_valid_organization_payload()), + ) + + result = asyncio.run( + agent.run({"org_name": "github"}, _providers()), + ) + + assert result.data["id"] == "github" + assert result.data["type"] == "org:Organization" + assert result.stats["agent_runtime"] == "llm" + + +def test_llm_organization_agent_raises_on_empty_context() -> None: + agent = LLMOrganizationAgentV2( + llm_runtime=_FakeLLMRuntime(_valid_organization_payload()), + ) + + with pytest.raises(ValueError, match="missing a GitHub organization handle"): + asyncio.run( + agent.run({}, _providers()), + ) + + +def test_llm_organization_system_prompt_includes_acronym_disambiguation_guidance() -> None: + prompt = organization_agent_module._SYSTEM_PROMPT + + assert "Acronym-only matches are insufficient for organization resolution." in prompt + assert "repository owner handle" in prompt + assert "leave `pulse:ror` as `null` instead of guessing." in prompt + assert "get_github_organization_metadata" in prompt + + +@llm_integration +@requires_llm_credentials +def test_llm_organization_agent_real_provider_call() -> None: + agent = LLMOrganizationAgentV2() + context = { + "org_name": "github", + "source_repositories": ["octocat/Hello-World"], + } + + result = asyncio.run(agent.run(context, _providers_full())) + + assert result.data["type"] == "org:Organization" + assert result.data["shacl"] == "pulse:OrganizationShape" + assert result.model is not None + assert result.provider is not None + assert result.stats["agent_runtime"] == "llm" + + import json + + print("\n--- LLM Organization Agent Result ---") + print(f"Model: {result.model}") + print(f"Provider: {result.provider}") + print(f"Tokens: prompt={result.tokens_prompt}, completion={result.tokens_completion}") + print(f"Warnings: {result.warnings}") + print(json.dumps(result.data, indent=2, ensure_ascii=False)) diff --git a/tests/v2/test_llm_organization_identity_tool.py b/tests/v2/test_llm_organization_identity_tool.py new file mode 100644 index 0000000..5022897 --- /dev/null +++ b/tests/v2/test_llm_organization_identity_tool.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +from typing import Any + +from src.v2.agents.llm.agent_tools.organization_identity import ( + make_organization_identity_search_tool, +) + + +class _FakeRORProvider: + def search_organizations(self, query: str) -> list[dict[str, Any]]: + normalized = query.strip().lower() + if normalized in {"sdsc-ordes", "swiss data science center"}: + return [ + { + "id": "https://ror.org/02hdt9m26", + "name": "Swiss Data Science Center", + "aliases": [], + "acronyms": ["SDSC"], + }, + ] + return [] + + +class _FakeInfoscienceProvider: + def search_orgunit(self, query: str) -> list[dict[str, Any]]: + normalized = query.strip().lower() + if normalized in {"swiss data science center", "swiss data science centre"}: + return [ + { + "infoscienceOrgUnitIdentifier": "0469064e-5977-4569-93a2-522b6d758e50", + "name": "Swiss Data Science Centre", + "acronym": "SDSC", + "parentOrganization": "EPFL", + }, + ] + return [] + + +def test_search_organization_identity_links_center_and_centre_variants() -> None: + tool = make_organization_identity_search_tool( + _FakeRORProvider(), + _FakeInfoscienceProvider(), + ) + payload = tool.function("sdsc-ordes") + + assert payload["query"] == "sdsc-ordes" + assert payload["ror_candidates"] + assert payload["infoscience_candidates"] + assert payload["linked_candidates"] + linked = payload["linked_candidates"][0] + assert linked["ror_id"] == "https://ror.org/02hdt9m26" + assert linked["infoscience_orgunit_identifier"] == "0469064e-5977-4569-93a2-522b6d758e50" + assert linked["normalized_name"] == "swiss data science center" + assert "Swiss Data Science Center" in payload["expanded_queries"]["infoscience"] + + +def test_search_organization_identity_handles_empty_query() -> None: + tool = make_organization_identity_search_tool( + _FakeRORProvider(), + _FakeInfoscienceProvider(), + ) + payload = tool.function(" ") + + assert payload["query"] == "" + assert payload["ror_candidates"] == [] + assert payload["infoscience_candidates"] == [] + assert payload["linked_candidates"] == [] + assert payload["expanded_queries"] == {"ror": [], "infoscience": []} + diff --git a/tests/v2/test_llm_other_rag_tools.py b/tests/v2/test_llm_other_rag_tools.py new file mode 100644 index 0000000..6fee59f --- /dev/null +++ b/tests/v2/test_llm_other_rag_tools.py @@ -0,0 +1,324 @@ +"""Combined fast tests for OpenAlex / Zenodo / ORCID / ROR RAG providers.""" +from __future__ import annotations + +import asyncio +from typing import Any + +import numpy as np + +from src.index.ror.rerank import RerankResult +from src.v2.agents.llm.agent_tools.openalex_rag import make_openalex_rag_search_tool +from src.v2.agents.llm.agent_tools.orcid_rag import make_orcid_rag_search_tool +from src.v2.agents.llm.agent_tools.ror_rag import make_ror_rag_search_tool +from src.v2.agents.llm.agent_tools.zenodo_rag import make_zenodo_rag_search_tool +from src.v2.ingest.providers.openalex_rag import OpenAlexRagProvider +from src.v2.ingest.providers.orcid_rag import OrcidRagProvider +from src.v2.ingest.providers.ror_rag import RorRagProvider +from src.v2.ingest.providers.zenodo_rag import ZenodoRagProvider + + +class _FakeClient: + def __init__(self, *, exists: bool = True) -> None: + self._exists = exists + + def collection_exists(self, name: str) -> bool: # noqa: ARG002 + return self._exists + + +class _FakeOpenAlexStore: + def __init__(self, hits: list[dict] | None = None, *, exists: bool = True) -> None: + self._hits = hits or [] + self.client = _FakeClient(exists=exists) + self.search_calls: list[dict] = [] + + def search(self, collection, *, query_vector, top_k, filter_payload): + self.search_calls.append({"collection": collection, "k": top_k, "filter": filter_payload}) + return list(self._hits) + + +class _FakeOrcidStore: + def __init__(self, hits: list[dict] | None = None, *, exists: bool = True) -> None: + self._hits = hits or [] + self.client = _FakeClient(exists=exists) + self.search_calls: list[dict] = [] + + def collection(self, entity_type: str) -> str: + return f"orcid_epfl_{entity_type}" + + def search(self, entity_type, *, query_vector, top_k, filter_payload): + self.search_calls.append({"entity": entity_type, "k": top_k, "filter": filter_payload}) + return list(self._hits) + + +class _FakeRorStore: + def __init__(self, hits: list[dict] | None = None, *, exists: bool = True) -> None: + self._hits = hits or [] + self.client = _FakeClient(exists=exists) + self.search_calls: list[dict] = [] + + def collection_name(self, scope_mode: str) -> str: + return f"ror_{scope_mode}" + + def search(self, scope_mode, *, query_vector, top_k, country=None): + self.search_calls.append({"scope": scope_mode, "k": top_k, "country": country}) + return list(self._hits) + + +class _FakeEmbedder: + def __init__(self) -> None: + self.calls: list[list[str]] = [] + + async def embed_all(self, inputs): + self.calls.append(list(inputs)) + return [[0.1, 0.2, 0.3] for _ in inputs] + + +class _FakeReranker: + def __init__(self, results=None) -> None: + self.results = results or [] + self.calls: list[dict] = [] + + async def rerank(self, query, documents, *, top_n=None): + self.calls.append({"q": query, "n": len(documents), "top_n": top_n}) + return list(self.results) + + +def _run(coro): + return asyncio.run(coro) + + +# -- OpenAlex --------------------------------------------------------------- + + +def test_openalex_works_returns_thin_hits_with_snippet() -> None: + hits = [{"id": "w1", "score": 0.9, "payload": { + "openalex_id": "W1", "title": "Hallmarks of Cancer", + "abstract": "X" * 500, "year": 2022, "doi": "10.1/x", + }}] + store = _FakeOpenAlexStore(hits) + p = OpenAlexRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + out = _run(p.search("cancer", collection="works", top_k=3)) + assert out[0]["openalex_id"] == "W1" + assert out[0]["title"] == "Hallmarks of Cancer" + assert out[0]["snippet"] is not None + assert len(out[0]["snippet"]) <= 320 + assert "abstract" not in out[0] # not in thin keys + + +def test_openalex_filter_year_range() -> None: + store = _FakeOpenAlexStore([]) + p = OpenAlexRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + _run(p.search("x", collection="works", filters={"year": {"$gte": 2020}})) + assert store.search_calls[0]["filter"] == {"year": {"gte": 2020}} + + +def test_openalex_collection_missing_returns_empty() -> None: + store = _FakeOpenAlexStore([{"id": "x"}], exists=False) + p = OpenAlexRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + assert _run(p.search("q", collection="works")) == [] + assert store.search_calls == [] + + +def test_openalex_unknown_collection_returns_empty() -> None: + store = _FakeOpenAlexStore([{"id": "x"}]) + p = OpenAlexRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + assert _run(p.search("q", collection="bogus")) == [] # type: ignore[arg-type] + + +def test_openalex_search_tool_factory() -> None: + store = _FakeOpenAlexStore([{"id": "w1", "score": 0.9, "payload": {"openalex_id": "W1", "title": "T"}}]) + p = OpenAlexRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + tool = make_openalex_rag_search_tool(p) + assert tool.name == "search_openalex_rag" + rows = _run(tool.function("q", "works", 3, None, False)) + assert rows[0]["openalex_id"] == "W1" + + +# -- Zenodo ----------------------------------------------------------------- + + +def test_zenodo_search_thin_hits() -> None: + hits = [{"id": "z1", "score": 0.85, "payload": { + "zenodo_id": "12345", "title": "Dataset Foo", + "doi": "10.5281/zenodo.12345", "year": 2024, + "resource_type": "dataset", "access_right": "open", + }}] + store = _FakeOpenAlexStore(hits) + p = ZenodoRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + out = _run(p.search("dataset", top_k=3)) + assert out[0]["zenodo_id"] == "12345" + assert out[0]["resource_type"] == "dataset" + assert out[0]["collection"] == "zenodo_records" + + +def test_zenodo_collection_missing() -> None: + store = _FakeOpenAlexStore([{"id": "x"}], exists=False) + p = ZenodoRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + assert _run(p.search("q")) == [] + assert store.search_calls == [] + + +def test_zenodo_search_tool_factory() -> None: + store = _FakeOpenAlexStore([{"id": "z1", "score": 0.9, "payload": {"zenodo_id": "12", "title": "T"}}]) + p = ZenodoRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + tool = make_zenodo_rag_search_tool(p) + assert tool.name == "search_zenodo_rag" + rows = _run(tool.function("q", 3, None, False)) + assert rows[0]["zenodo_id"] == "12" + + +# -- ORCID ----------------------------------------------------------------- + + +def test_orcid_persons_thin_hits_with_snippet() -> None: + hits = [{"id": "p1", "score": 0.9, "payload": { + "orcid_id": "0000-0001", + "display_name": "Alice", + "biography": "B" * 500, + "in_scope": True, + }}] + store = _FakeOrcidStore(hits) + p = OrcidRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + out = _run(p.search("alice")) + assert out[0]["orcid_id"] == "0000-0001" + assert out[0]["display_name"] == "Alice" + assert out[0]["snippet"] is not None + assert "biography" not in out[0] + + +def test_orcid_unknown_entity_type_returns_empty() -> None: + store = _FakeOrcidStore([{"id": "x"}]) + p = OrcidRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + assert _run(p.search("q", entity_type="bogus")) == [] # type: ignore[arg-type] + + +def test_orcid_collection_missing() -> None: + store = _FakeOrcidStore([{"id": "x"}], exists=False) + p = OrcidRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + assert _run(p.search("q", entity_type="persons")) == [] + + +def test_orcid_search_tool_factory() -> None: + hits = [{"id": "p1", "score": 0.9, "payload": {"orcid_id": "0000-0001", "display_name": "Alice"}}] + store = _FakeOrcidStore(hits) + p = OrcidRagProvider(store=store, embedder=_FakeEmbedder(), reranker=None) + tool = make_orcid_rag_search_tool(p) + assert tool.name == "search_orcid_rag" + rows = _run(tool.function("q", "persons", 3, None, False)) + assert rows[0]["orcid_id"] == "0000-0001" + + +# -- ROR -------------------------------------------------------------------- + + +class _FakeRorEmbedder: + """Module-style: ROR uses functional embed_query() returning np.ndarray.""" + def __init__(self) -> None: + self.calls = 0 + + async def embed_query(self, _rcp, _text): # noqa: D401, ARG002 + self.calls += 1 + return np.asarray([0.1, 0.2, 0.3], dtype=np.float32) + + +def _patched_ror(monkeypatch, embed_fn=None, rerank_fn=None) -> None: + if embed_fn is not None: + monkeypatch.setattr( + "src.v2.ingest.providers.ror_rag.embed_query", embed_fn, + ) + if rerank_fn is not None: + monkeypatch.setattr( + "src.v2.ingest.providers.ror_rag.rerank", rerank_fn, + ) + + +def test_ror_search_thin_hits(monkeypatch) -> None: + async def fake_embed(_rcp, _text): + return np.asarray([0.1, 0.2], dtype=np.float32) + _patched_ror(monkeypatch, embed_fn=fake_embed) + + raw_hits = [{ + "score": 0.9, "ror_id": "https://ror.org/02s376052", + "name": "EPFL", "text": "Name: EPFL\nTypes: education", + "record": { + "types": ["education"], + "locations": [{"geonames_details": {"country_code": "CH"}}], + }, + }] + store = _FakeRorStore(raw_hits) + rcp = type("RcpStub", (), {})() + p = RorRagProvider(store=store, rcp=rcp) + + out = _run(p.search("epfl", scope_mode="epfl_ethz", top_k=3)) + assert out[0]["ror_id"] == "https://ror.org/02s376052" + assert out[0]["name"] == "EPFL" + assert out[0]["country_code"] == "CH" + assert out[0]["scope_mode"] == "epfl_ethz" + assert "snippet" in out[0] + + +def test_ror_country_filter_extracted(monkeypatch, caplog) -> None: + async def fake_embed(_rcp, _text): + return np.asarray([0.1], dtype=np.float32) + _patched_ror(monkeypatch, embed_fn=fake_embed) + + store = _FakeRorStore([]) + p = RorRagProvider(store=store, rcp=type("R", (), {})()) + caplog.set_level("WARNING") + _run(p.search("ch lab", filters={"country_code": "CH", "year": 2024})) + assert store.search_calls[0]["country"] == "CH" + assert any("dropped" in r.message for r in caplog.records) + + +def test_ror_country_eq_operator(monkeypatch) -> None: + async def fake_embed(_rcp, _text): + return np.asarray([0.1], dtype=np.float32) + _patched_ror(monkeypatch, embed_fn=fake_embed) + + store = _FakeRorStore([]) + p = RorRagProvider(store=store, rcp=type("R", (), {})()) + _run(p.search("x", filters={"country_code": {"$eq": "FR"}})) + assert store.search_calls[0]["country"] == "FR" + + +def test_ror_collection_missing(monkeypatch) -> None: + store = _FakeRorStore([{"score": 0.9}], exists=False) + p = RorRagProvider(store=store, rcp=type("R", (), {})()) + assert _run(p.search("q", scope_mode="worldwide")) == [] + assert store.search_calls == [] + + +def test_ror_rerank_reorders(monkeypatch) -> None: + async def fake_embed(_rcp, _text): + return np.asarray([0.1], dtype=np.float32) + + async def fake_rerank(_rcp, _q, _docs, *, top_n=None): # noqa: ARG001 + return [ + RerankResult(index=2, score=0.99), + RerankResult(index=0, score=0.7), + ] + _patched_ror(monkeypatch, embed_fn=fake_embed, rerank_fn=fake_rerank) + + raw_hits = [ + {"score": 0.5, "ror_id": "a", "name": "A", "text": "alpha", "record": {}}, + {"score": 0.4, "ror_id": "b", "name": "B", "text": "beta", "record": {}}, + {"score": 0.3, "ror_id": "c", "name": "C", "text": "gamma", "record": {}}, + ] + store = _FakeRorStore(raw_hits) + p = RorRagProvider(store=store, rcp=type("R", (), {})()) + out = _run(p.search("q", top_k=2, rerank=True)) + assert [h["ror_id"] for h in out] == ["c", "a"] + + +def test_ror_search_tool_factory(monkeypatch) -> None: + async def fake_embed(_rcp, _text): + return np.asarray([0.1], dtype=np.float32) + _patched_ror(monkeypatch, embed_fn=fake_embed) + + raw_hits = [{"score": 0.9, "ror_id": "X", "name": "X", "text": "x", "record": {}}] + store = _FakeRorStore(raw_hits) + p = RorRagProvider(store=store, rcp=type("R", (), {})()) + tool = make_ror_rag_search_tool(p) + assert tool.name == "search_ror_rag" + rows = _run(tool.function("q", "worldwide", 3, None, False)) + assert rows[0]["ror_id"] == "X" diff --git a/tests/v2/test_llm_person_agent.py b/tests/v2/test_llm_person_agent.py new file mode 100644 index 0000000..af7d0fd --- /dev/null +++ b/tests/v2/test_llm_person_agent.py @@ -0,0 +1,355 @@ +from __future__ import annotations + +import asyncio +import os +from typing import Any + +import pytest +from jsonschema import validate + +from src.v2.agents.llm.person import LLMPersonAgentV2 +from src.v2.agents.models import ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeError, LLMRuntimeResult +from src.v2.ingest.providers.mock_github import MockGitHubProvider +from src.v2.ingest.providers.mock_infoscience import MockInfoscienceProvider +from src.v2.ingest.providers.mock_orcid import MockORCIDProvider + +_HAS_LLM_CREDENTIALS = bool( + os.getenv("RCP_TOKEN") or os.getenv("OPENAI_API_KEY") or os.getenv("OPENROUTER_API_KEY") +) +llm_integration = pytest.mark.llm_integration +requires_llm_credentials = pytest.mark.skipif( + not _HAS_LLM_CREDENTIALS, + reason="No LLM provider credentials available (RCP_TOKEN / OPENAI_API_KEY / OPENROUTER_API_KEY)", +) + +EXPECTED_PROMPT_TOKENS = 13 +EXPECTED_COMPLETION_TOKENS = 29 + + +class _FakeLLMRuntime: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + assert system_prompt + assert user_prompt + return LLMRuntimeResult( + payload=dict(self._payload), + model="openai/gpt-test", + provider="openai", + tokens_prompt=EXPECTED_PROMPT_TOKENS, + tokens_completion=EXPECTED_COMPLETION_TOKENS, + ) + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +def _providers_full() -> ProviderSet: + return ProviderSet( + github=MockGitHubProvider(), + orcid=MockORCIDProvider(), + infoscience=MockInfoscienceProvider(), + ) + + +def _valid_person_payload() -> dict[str, Any]: + return { + "id": "octocat", + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": "octocat", + "uuid": "f887181e-9b8c-4c55-8ee6-d34285fdbed4", + }, + "idSource": "pulse:githubUsername", + "schema:name": "The Octocat", + "pulse:githubUsername": "octocat", + } + + +def test_llm_person_agent_validates_payload_and_exposes_model_metadata( + load_schema, +) -> None: + agent = LLMPersonAgentV2( + llm_runtime=_FakeLLMRuntime(_valid_person_payload()), + ) + + result = asyncio.run( + agent.run( + {"username": "octocat"}, + _providers(), + ), + ) + + schema = load_schema("agent", "person") + validate(instance=result.data, schema=schema) + + assert result.model == "openai/gpt-test" + assert result.provider == "openai" + assert result.tokens_prompt == EXPECTED_PROMPT_TOKENS + assert result.tokens_completion == EXPECTED_COMPLETION_TOKENS + assert result.stats["agent_runtime"] == "llm" + + +def test_llm_person_agent_propagates_llm_runtime_error() -> None: + """LLMRuntimeError from the runtime bubbles up unchanged.""" + + class _RaisingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + raise LLMRuntimeError("Schema validation failed after retries") + + agent = LLMPersonAgentV2(llm_runtime=_RaisingRuntime()) + + with pytest.raises(LLMRuntimeError, match="Schema validation failed after retries"): + asyncio.run( + agent.run({"username": "octocat"}, _providers()), + ) + + +def test_llm_person_agent_timeout_includes_identifier_and_timeout_seconds() -> None: + class _SlowRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, user_prompt, output_type, tools + await asyncio.sleep(60) + raise AssertionError("unreachable") + + agent = LLMPersonAgentV2( + llm_runtime=_SlowRuntime(), + llm_call_timeout_seconds=0.1, + ) + + with pytest.raises(LLMRuntimeError, match=r"octocat.*timed out after 0\.1s"): + asyncio.run( + agent.run({"username": "octocat"}, _providers()), + ) + + +def test_llm_person_agent_records_strict_schema_warnings() -> None: + """Strict-schema violations produce warnings without raising.""" + payload = _valid_person_payload() + payload["idSource"] = "not-a-valid-id-source" + + agent = LLMPersonAgentV2(llm_runtime=_FakeLLMRuntime(payload)) + + result = asyncio.run( + agent.run({"username": "octocat"}, _providers()), + ) + + assert result.warnings, "Expected strict-schema warnings but got none" + + +def test_llm_person_agent_builds_tools_from_providers() -> None: + """When infoscience and orcid providers are present, both tools are passed to the runtime.""" + + captured_tools: list[Any] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + if tools: + captured_tools.extend(tools) + return LLMRuntimeResult( + payload=_valid_person_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMPersonAgentV2(llm_runtime=_CapturingRuntime()) + + asyncio.run( + agent.run( + {"username": "octocat"}, + _providers_full(), + ), + ) + + tool_names = [getattr(tool, "name", None) for tool in captured_tools] + assert "fetch_link_content_via_selenium" in tool_names + assert "hash_user_email" in tool_names + assert "search_infoscience_person" in tool_names + assert "get_orcid_record" in tool_names + + +def test_llm_person_agent_appends_runtime_prompt_context_blocks() -> None: + captured_user_prompts: list[str] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, output_type, tools + captured_user_prompts.append(user_prompt) + return LLMRuntimeResult( + payload=_valid_person_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMPersonAgentV2(llm_runtime=_CapturingRuntime()) + upstream_json = '{"repo_agent":{"id":"repo-root"}}' + prompt_appendix = "README_FRAGMENT_1\nREADME_FRAGMENT_2" + + asyncio.run( + agent.run( + { + "username": "octocat", + "upstream_stage_outputs_json": upstream_json, + "user_prompt_appendix": prompt_appendix, + }, + _providers(), + ), + ) + + assert len(captured_user_prompts) == 1 + prompt = captured_user_prompts[0] + assert "## Upstream Stage Outputs (JSON)" in prompt + assert upstream_json in prompt + assert "## Additional Context (verbatim text)" in prompt + assert prompt_appendix in prompt + + +def test_llm_person_agent_exposes_selenium_tool_without_optional_providers() -> None: + """When only GitHub provider is present, Selenium fetch tool remains available.""" + + captured_tools: list[Any] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + captured_tools.extend(tools or []) + return LLMRuntimeResult( + payload=_valid_person_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMPersonAgentV2(llm_runtime=_CapturingRuntime()) + + asyncio.run( + agent.run( + {"username": "octocat"}, + _providers(), + ), + ) + + tool_names = [getattr(tool, "name", None) for tool in captured_tools] + assert tool_names == ["fetch_link_content_via_selenium", "hash_user_email"] + + +def test_llm_person_agent_works_with_orcid_only_context() -> None: + """Agent succeeds when context has only an ORCID — no GitHub username required.""" + + agent = LLMPersonAgentV2( + llm_runtime=_FakeLLMRuntime( + { + "id": "0000-0002-1825-0097", + "type": "schema:Person", + "shacl": "pulse:PersonShape", + "identifiers": { + "pulse:orcid": "0000-0002-1825-0097", + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": None, + "uuid": "f887181e-9b8c-4c55-8ee6-d34285fdbed4", + }, + "idSource": "pulse:orcid", + "schema:name": "Alice Smith", + "pulse:orcidIdentifier": "0000-0002-1825-0097", + "pulse:infosciencePersonIdentifier": None, + } + ), + ) + + result = asyncio.run( + agent.run( + {"orcid": "0000-0002-1825-0097"}, + _providers(), + ), + ) + + assert result.data["id"] == "0000-0002-1825-0097" + assert result.data["idSource"] == "pulse:orcid" + assert result.stats["agent_runtime"] == "llm" + + +def test_llm_person_agent_raises_on_empty_context() -> None: + """Agent raises ValueError when context contains no usable identity signal.""" + + agent = LLMPersonAgentV2(llm_runtime=_FakeLLMRuntime(_valid_person_payload())) + + with pytest.raises(ValueError, match="no usable identity signal"): + asyncio.run( + agent.run({}, _providers()), + ) + + +@llm_integration +@requires_llm_credentials +def test_llm_person_agent_real_provider_call() -> None: + """Integration test: sends a real prompt to the configured LLM provider.""" + + agent = LLMPersonAgentV2() + + context = { + "username": "octocat", + "source_repositories": ["octocat/Hello-World"], + } + + result = asyncio.run(agent.run(context, _providers_full())) + + assert result.data["type"] == "schema:Person" + assert result.data["shacl"] == "pulse:PersonShape" + assert result.model is not None + assert result.provider is not None + assert result.stats["agent_runtime"] == "llm" + + import json + + print("\n--- LLM Person Agent Result ---") + print(f"Model: {result.model}") + print(f"Provider: {result.provider}") + print(f"Tokens: prompt={result.tokens_prompt}, completion={result.tokens_completion}") + print(f"Warnings: {result.warnings}") + print(json.dumps(result.data, indent=2, ensure_ascii=False)) diff --git a/tests/v2/test_llm_query_dependencies_tool.py b/tests/v2/test_llm_query_dependencies_tool.py new file mode 100644 index 0000000..44116a9 --- /dev/null +++ b/tests/v2/test_llm_query_dependencies_tool.py @@ -0,0 +1,104 @@ +from __future__ import annotations + +from typing import Any + +from src.v2.agents.llm.agent_tools.query_dependencies import ( + make_query_dependencies_tool, +) + + +class _FakeGitHubProvider: + def __init__(self, sbom: list[dict[str, Any]] | None) -> None: + self.calls: list[str] = [] + self._sbom = sbom + + def get_repository_sbom(self, full_name: str) -> list[dict[str, Any]] | None: + self.calls.append(full_name) + return self._sbom + + +_SAMPLE_SBOM = [ + {"name": "requests", "ecosystem": "pypi", "version": "2.31.0", "spdxId": "a"}, + {"name": "numpy", "ecosystem": "pypi", "version": "1.26.0", "spdxId": "b"}, + {"name": "left-pad", "ecosystem": "npm", "version": "1.3.0", "spdxId": "c"}, + {"name": "react", "ecosystem": "npm", "version": "18.2.0", "spdxId": "d"}, +] + + +def test_query_dependencies_returns_full_list_when_no_filters() -> None: + provider = _FakeGitHubProvider(_SAMPLE_SBOM) + tool = make_query_dependencies_tool(provider) + + result = tool.function("octocat/Hello-World") + + assert provider.calls == ["octocat/Hello-World"] + assert result == _SAMPLE_SBOM + # Returned entries are copies — caller mutating them must not corrupt the + # provider's cached payload. + result[0]["name"] = "tampered" + assert _SAMPLE_SBOM[0]["name"] == "requests" + + +def test_query_dependencies_filters_by_ecosystem_case_insensitively() -> None: + tool = make_query_dependencies_tool(_FakeGitHubProvider(_SAMPLE_SBOM)) + + result = tool.function("octocat/Hello-World", ecosystem="PYPI") + + assert [entry["name"] for entry in result] == ["requests", "numpy"] + + +def test_query_dependencies_filters_by_name_substring_case_insensitively() -> None: + tool = make_query_dependencies_tool(_FakeGitHubProvider(_SAMPLE_SBOM)) + + result = tool.function("octocat/Hello-World", name_contains="REACT") + + assert [entry["name"] for entry in result] == ["react"] + + +def test_query_dependencies_combines_filters() -> None: + tool = make_query_dependencies_tool(_FakeGitHubProvider(_SAMPLE_SBOM)) + + result = tool.function( + "octocat/Hello-World", + ecosystem="npm", + name_contains="pad", + ) + + assert [entry["name"] for entry in result] == ["left-pad"] + + +def test_query_dependencies_respects_limit() -> None: + tool = make_query_dependencies_tool(_FakeGitHubProvider(_SAMPLE_SBOM)) + + result = tool.function("octocat/Hello-World", limit=2) + + assert len(result) == 2 + assert [entry["name"] for entry in result] == ["requests", "numpy"] + + +def test_query_dependencies_returns_empty_list_when_no_sbom() -> None: + tool = make_query_dependencies_tool(_FakeGitHubProvider(None)) + + result = tool.function("private/repo") + + assert result == [] + + +def test_query_dependencies_returns_empty_list_when_sbom_is_empty() -> None: + tool = make_query_dependencies_tool(_FakeGitHubProvider([])) + + result = tool.function("octocat/Hello-World") + + assert result == [] + + +def test_query_dependencies_clamps_limit_to_max() -> None: + big_sbom = [ + {"name": f"pkg{i}", "ecosystem": "pypi", "version": "1.0", "spdxId": f"id{i}"} + for i in range(1000) + ] + tool = make_query_dependencies_tool(_FakeGitHubProvider(big_sbom)) + + result = tool.function("octocat/Hello-World", limit=10000) + + assert len(result) == 500 diff --git a/tests/v2/test_llm_rag_helpers.py b/tests/v2/test_llm_rag_helpers.py new file mode 100644 index 0000000..d91d762 --- /dev/null +++ b/tests/v2/test_llm_rag_helpers.py @@ -0,0 +1,124 @@ +from __future__ import annotations + +import logging + +import pytest + +from src.v2.ingest.providers._rag_helpers import ( + apply_rerank_indices, + expand_candidate_k, + filter_allowlist, + make_snippet, + thin_payload, + to_simple_filter_payload, +) + + +def test_filter_allowlist_drops_disallowed_keys(caplog) -> None: + caplog.set_level(logging.WARNING) + out = filter_allowlist( + {"year": 2024, "secret": "x", "doi": "10.1/x"}, + frozenset({"year", "doi"}), + log_label="test", + ) + assert out == {"year": 2024, "doi": "10.1/x"} + assert any("dropped non-allowlisted filter keys" in r.message for r in caplog.records) + + +def test_filter_allowlist_returns_none_when_empty() -> None: + assert filter_allowlist(None, frozenset({"x"}), log_label="t") is None + assert filter_allowlist({}, frozenset({"x"}), log_label="t") is None + # All keys dropped → None + assert filter_allowlist({"a": 1}, frozenset({"b"}), log_label="t") is None + + +def test_to_simple_filter_payload_translates_operators() -> None: + out = to_simple_filter_payload({ + "year": {"$gte": 2020, "$lte": 2024}, + "doi": "10.1/x", + "tags": {"$in": ["a", "b"]}, + "openalex_id": {"$eq": "W1"}, + "names": ["x", "y"], + }) + assert out == { + "year": {"gte": 2020, "lte": 2024}, + "doi": "10.1/x", + "tags": ["a", "b"], + "openalex_id": "W1", + "names": ["x", "y"], + } + + +def test_to_simple_filter_payload_drops_unsupported_operators(caplog) -> None: + caplog.set_level(logging.WARNING) + out = to_simple_filter_payload({ + "year": 2024, + "tag": {"$ne": "draft"}, + "abstract": {"$contains": "foo"}, + }) + assert out == {"year": 2024} + assert sum(1 for r in caplog.records if "unsupported operator" in r.message) == 2 + + +def test_to_simple_filter_payload_handles_empty() -> None: + assert to_simple_filter_payload(None) is None + assert to_simple_filter_payload({}) is None + + +def test_expand_candidate_k_floor_and_multiplier() -> None: + assert expand_candidate_k(3) == 30 # floor wins + assert expand_candidate_k(10) == 50 # multiplier wins + assert expand_candidate_k(20, multiplier=10, floor=5) == 200 + + +def test_apply_rerank_indices_reorders() -> None: + hits = [ + {"id": "a", "score": 0.5, "payload": {}}, + {"id": "b", "score": 0.4, "payload": {}}, + {"id": "c", "score": 0.3, "payload": {}}, + ] + rerank = [ + {"index": 2, "relevance_score": 0.99}, + {"index": 0, "relevance_score": 0.7}, + ] + out = apply_rerank_indices(hits, rerank, top_k=2) + assert [h["id"] for h in out] == ["c", "a"] + assert out[0]["score"] == pytest.approx(0.99) + + +def test_apply_rerank_indices_empty_falls_back() -> None: + hits = [{"id": "a", "score": 0.5}] + assert apply_rerank_indices(hits, [], top_k=1) == [{"id": "a", "score": 0.5}] + + +def test_apply_rerank_indices_skips_invalid_index() -> None: + hits = [{"id": "a"}] + rerank = [{"index": 99, "relevance_score": 0.9}] + assert apply_rerank_indices(hits, rerank, top_k=5) == [] + + +def test_make_snippet_short() -> None: + assert make_snippet("hi") == "hi" + assert make_snippet("") is None + assert make_snippet(None) is None + assert make_snippet(123) is None # type: ignore[arg-type] + + +def test_make_snippet_truncates() -> None: + long = "x" * 1000 + out = make_snippet(long, max_chars=100) + assert out is not None + assert len(out) == 100 + assert out.endswith("…") + + +def test_thin_payload_keeps_keys_and_extras() -> None: + payload = {"name": "foo", "year": 2024, "skip": "x"} + out = thin_payload(payload, ("name", "year"), extras={"id": "p1", "score": 0.9, "drop": None}) + assert out == {"name": "foo", "year": 2024, "id": "p1", "score": 0.9} + + +def test_thin_payload_drops_none_keys() -> None: + payload = {"name": "foo", "year": None} + out = thin_payload(payload, ("name", "year")) + assert out == {"name": "foo"} diff --git a/tests/v2/test_llm_repository_agent.py b/tests/v2/test_llm_repository_agent.py new file mode 100644 index 0000000..db19b7e --- /dev/null +++ b/tests/v2/test_llm_repository_agent.py @@ -0,0 +1,258 @@ +from __future__ import annotations + +import asyncio +import os +from typing import Any + +import pytest +from jsonschema import validate + +from src.v2.agents.llm.repository import LLMRepositoryAgentV2 +from src.v2.agents.models import ProviderSet +from src.v2.agents.llm.runtime import LLMRuntimeError, LLMRuntimeResult +from src.v2.ingest.providers.mock_github import MockGitHubProvider + +_HAS_LLM_CREDENTIALS = bool( + os.getenv("RCP_TOKEN") or os.getenv("OPENAI_API_KEY") or os.getenv("OPENROUTER_API_KEY") +) +llm_integration = pytest.mark.llm_integration +requires_llm_credentials = pytest.mark.skipif( + not _HAS_LLM_CREDENTIALS, + reason="No LLM provider credentials available (RCP_TOKEN / OPENAI_API_KEY / OPENROUTER_API_KEY)", +) + +EXPECTED_PROMPT_TOKENS = 13 +EXPECTED_COMPLETION_TOKENS = 29 + + +class _FakeLLMRuntime: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + assert system_prompt + assert user_prompt + return LLMRuntimeResult( + payload=dict(self._payload), + model="openai/gpt-test", + provider="openai", + tokens_prompt=EXPECTED_PROMPT_TOKENS, + tokens_completion=EXPECTED_COMPLETION_TOKENS, + ) + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +def _valid_repository_payload() -> dict[str, Any]: + return { + "id": "octocat/Hello-World", + "type": "schema:SoftwareSourceCode", + "shacl": "pulse:RepositoryShape", + "identifiers": { + "pulse:githubRepositoryHandle": "octocat/Hello-World", + "schema:citation": None, + "uuid": "f887181e-9b8c-4c55-8ee6-d34285fdbed4", + }, + "idSource": "pulse:githubRepositoryHandle", + "schema:name": "Hello-World", + "pulse:githubRepositoryHandle": "octocat/Hello-World", + "schema:author": ["octocat"], + "pulse:repositoryType": "pulse:Software", + "pulse:discipline": ["wd:Q8434"], + } + + +def test_llm_repository_agent_validates_payload_and_exposes_model_metadata( + load_schema, +) -> None: + agent = LLMRepositoryAgentV2( + llm_runtime=_FakeLLMRuntime(_valid_repository_payload()), + ) + + result = asyncio.run( + agent.run( + { + "full_name": "octocat/Hello-World", + "repository_context": { + "full_name": "octocat/Hello-World", + "metadata": { + "owner": {"login": "octocat", "type": "User"}, + }, + "contributors": [{"login": "octocat", "type": "User"}], + "languages": {"Python": 1}, + }, + }, + _providers(), + ), + ) + + schema = load_schema("agent", "repository") + validate(instance=result.data, schema=schema) + + assert result.model == "openai/gpt-test" + assert result.provider == "openai" + assert result.tokens_prompt == EXPECTED_PROMPT_TOKENS + assert result.tokens_completion == EXPECTED_COMPLETION_TOKENS + assert result.stats["agent_runtime"] == "llm" + + +@llm_integration +@requires_llm_credentials +def test_llm_repository_agent_real_provider_call() -> None: + """Integration test: sends a real prompt to the configured LLM provider.""" + + agent = LLMRepositoryAgentV2() + + context = { + "full_name": "octocat/Hello-World", + "source_url": "https://github.com/octocat/Hello-World", + "repository_context": { + "full_name": "octocat/Hello-World", + "metadata": { + "name": "Hello-World", + "full_name": "octocat/Hello-World", + "description": "My first repository on GitHub!", + "owner": {"login": "octocat", "type": "User"}, + "stargazers_count": 2871, + "forks_count": 2124, + "created_at": "2011-01-26T19:01:12Z", + "license": {"spdx_id": "MIT"}, + "fork": False, + }, + "contributors": [ + {"login": "octocat", "type": "User"}, + {"login": "hubot", "type": "User"}, + ], + "languages": {"Python": 4200, "Shell": 1100}, + }, + } + + result = asyncio.run(agent.run(context, _providers())) + + # Core assertions on the returned result. + # Note: pydantic-ai already validated the output against AgentRepositoryShape + # (first pass) during the LLM call, so a redundant jsonschema.validate() here + # would fail on optional array fields the LLM returns as null rather than []. + assert result.data["id"] == "octocat/Hello-World" + assert result.data["type"] == "schema:SoftwareSourceCode" + assert result.data["shacl"] == "pulse:RepositoryShape" + assert result.data["pulse:githubRepositoryHandle"] == "octocat/Hello-World" + assert len(result.data["schema:author"]) >= 1 + + # LLM metadata should be populated. + assert result.model is not None + assert result.provider is not None + assert result.stats["agent_runtime"] == "llm" + + # Print for manual inspection. + import json + + print("\n--- LLM Repository Agent Result ---") + print(f"Model: {result.model}") + print(f"Provider: {result.provider}") + print(f"Tokens: prompt={result.tokens_prompt}, completion={result.tokens_completion}") + print(f"Warnings: {result.warnings}") + print(json.dumps(result.data, indent=2, ensure_ascii=False)) + + +def test_llm_repository_agent_propagates_llm_runtime_error() -> None: + """LLMRuntimeError from the runtime (e.g. pydantic-ai schema rejection) bubbles up.""" + + class _RaisingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + raise LLMRuntimeError("Schema validation failed after retries") + + agent = LLMRepositoryAgentV2(llm_runtime=_RaisingRuntime()) + + with pytest.raises(LLMRuntimeError, match="Schema validation failed after retries"): + asyncio.run( + agent.run({"full_name": "octocat/Hello-World"}, _providers()), + ) + + +def test_llm_repository_agent_records_strict_schema_warnings() -> None: + """Strict-schema violations produce warnings in the result without raising.""" + payload = _valid_repository_payload() + payload.pop("schema:author") # required by strict RepositoryModel + + agent = LLMRepositoryAgentV2(llm_runtime=_FakeLLMRuntime(payload)) + + result = asyncio.run( + agent.run( + { + "full_name": "octocat/Hello-World", + "repository_context": { + "metadata": {"owner": {"login": "octocat", "type": "User"}}, + "contributors": [{"login": "octocat", "type": "User"}], + "languages": {"Python": 1}, + }, + }, + _providers(), + ), + ) + + assert result.warnings, "Expected strict-schema warnings but got none" + assert any("schema:author" in w for w in result.warnings) + + +def test_llm_repository_agent_appends_runtime_prompt_context_blocks() -> None: + captured_user_prompts: list[str] = [] + captured_tool_names: list[str] = [] + + class _CapturingRuntime: + async def run_json_prompt( + self, + *, + system_prompt: str, + user_prompt: str, + output_type: Any = None, + tools: Any = None, + ) -> LLMRuntimeResult: + del system_prompt, output_type + captured_user_prompts.append(user_prompt) + captured_tool_names.extend(getattr(tool, "name", "") for tool in (tools or [])) + return LLMRuntimeResult( + payload=_valid_repository_payload(), + model="openai/gpt-test", + provider="openai", + ) + + agent = LLMRepositoryAgentV2(llm_runtime=_CapturingRuntime()) + upstream_json = '{"person_agent:alice":{"id":"alice"}}' + prompt_appendix = "COMBINED_FILE_BLOCK" + + asyncio.run( + agent.run( + { + "full_name": "octocat/Hello-World", + "upstream_stage_outputs_json": upstream_json, + "user_prompt_appendix": prompt_appendix, + }, + _providers(), + ), + ) + + assert len(captured_user_prompts) == 1 + prompt = captured_user_prompts[0] + assert "## Upstream Stage Outputs (JSON)" in prompt + assert upstream_json in prompt + assert "## Additional Context (verbatim text)" in prompt + assert prompt_appendix in prompt + assert "list_disciplines" in captured_tool_names + assert "fetch_link_content_via_selenium" in captured_tool_names diff --git a/tests/v2/test_llm_repository_corpus_grep_tool.py b/tests/v2/test_llm_repository_corpus_grep_tool.py new file mode 100644 index 0000000..1652032 --- /dev/null +++ b/tests/v2/test_llm_repository_corpus_grep_tool.py @@ -0,0 +1,67 @@ +from __future__ import annotations + +from src.v2.agents.llm.agent_tools.repository_corpus_grep import ( + make_repository_corpus_grep_tool, +) + + +def test_grep_repository_corpus_returns_markdown_snippets_with_origin_metadata() -> None: + tool = make_repository_corpus_grep_tool( + [ + { + "label": "Repository README", + "repository": "owner/repo", + "origin": "repository_context.readme_content", + "path": "README.md", + "content": "Line one\nInstall with uv\nRun `just test`\nDone", + }, + ], + ) + + result = tool.function("just test", max_matches=3, context_lines=1) + + assert "# Repository Grep Results" in result + assert "- repository: `owner/repo`" in result + assert "- origin: `repository_context.readme_content`" in result + assert "- path: `README.md`" in result + assert "```text" in result + assert "Run `just test`" in result + + +def test_grep_repository_corpus_supports_regex_queries() -> None: + tool = make_repository_corpus_grep_tool( + [ + { + "label": "Repository File", + "repository": "owner/repo", + "origin": "repository_context.repository_files", + "path": "pyproject.toml", + "content": "[tool.ruff]\nline-length = 100\n", + }, + ], + ) + + result = tool.function(r"/line-length\s*=\s*\d+/", max_matches=1) + + assert "line-length = 100" in result + assert "Match 1" in result + + +def test_grep_repository_corpus_reports_no_matches_with_source_inventory() -> None: + tool = make_repository_corpus_grep_tool( + [ + { + "label": "Raw GIMIE JSON-LD", + "repository": "owner/repo", + "origin": "repository_context.gimie_jsonld", + "path": "gimie.jsonld", + "content": '{"@id":"https://github.com/owner/repo"}', + }, + ], + ) + + result = tool.function("non-existent-token") + + assert "No matches for query `non-existent-token`." in result + assert "Available sources:" in result + assert "Raw GIMIE JSON-LD (owner/repo:gimie.jsonld)" in result diff --git a/tests/v2/test_llm_runtime_adapter.py b/tests/v2/test_llm_runtime_adapter.py new file mode 100644 index 0000000..696ffcf --- /dev/null +++ b/tests/v2/test_llm_runtime_adapter.py @@ -0,0 +1,177 @@ +from __future__ import annotations + +import asyncio +from dataclasses import dataclass +from typing import Any + +import pytest + +import src.v2.agents.llm.runtime as runtime_module +from src.v2.agents.llm.runtime import ( + LLMRuntimeConfigError, + LLMRuntimeResponseError, + V2LLMRuntime, +) + +EXPECTED_PROMPT_TOKENS = 17 +EXPECTED_COMPLETION_TOKENS = 23 + + +@dataclass +class _FakeUsage: + input_tokens: int = 11 + output_tokens: int = 7 + requests: int = 3 + tool_calls: int = 2 + details: dict[str, int] | None = None + + +@dataclass +class _FakeResult: + output: Any + usage: Any = None + + +def test_llm_runtime_selects_first_valid_profile_and_returns_tokens( + monkeypatch: pytest.MonkeyPatch, +) -> None: + captured_run_kwargs: dict[str, Any] = {} + + class _FakeAgent: + def __init__(self, **kwargs: Any) -> None: + self._kwargs = kwargs + + async def run( + self, + prompt: str, + model_settings: dict[str, Any] | None = None, + ) -> _FakeResult: + captured_run_kwargs["prompt"] = prompt + captured_run_kwargs["model_settings"] = model_settings + return _FakeResult( + output={"id": "repo-id"}, + usage=_FakeUsage( + input_tokens=EXPECTED_PROMPT_TOKENS, + output_tokens=EXPECTED_COMPLETION_TOKENS, + ), + ) + + monkeypatch.setattr( + runtime_module, + "load_model_config", + lambda _analysis_type: [ + {"provider": "openai", "model": "broken-model", "valid": False}, + { + "provider": "openai", + "model": "gpt-test", + "valid": True, + "timeout": 42.0, + }, + ], + ) + monkeypatch.setattr( + runtime_module, + "validate_config", + lambda config: bool(config.get("valid")), + ) + monkeypatch.setattr( + runtime_module, + "create_pydantic_ai_model", + lambda _config: object(), + ) + monkeypatch.setattr( + runtime_module, + "get_model_parameters", + lambda _config: {"temperature": 0.1}, + ) + monkeypatch.setattr(runtime_module, "Agent", _FakeAgent) + monkeypatch.setenv("OPENAI_API_KEY", "test-value") + + runtime = V2LLMRuntime() + result = asyncio.run( + runtime.run_json_prompt( + system_prompt="system", + user_prompt="user prompt", + ), + ) + + assert result.payload == {"id": "repo-id"} + assert result.model == "gpt-test" + assert result.provider == "openai" + assert result.tokens_prompt == EXPECTED_PROMPT_TOKENS + assert result.tokens_completion == EXPECTED_COMPLETION_TOKENS + assert result.requests == 3 + assert result.tool_calls == 2 + assert captured_run_kwargs["model_settings"] == {"temperature": 0.1, "timeout": 42.0} + + +def test_llm_runtime_reports_missing_provider_credentials_by_env_var_name( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr( + runtime_module, + "load_model_config", + lambda _analysis_type: [ + { + "provider": "openrouter", + "model": "google/gemini-test", + "max_retries": 2, + }, + ], + ) + monkeypatch.setattr(runtime_module, "validate_config", lambda _config: True) + monkeypatch.delenv("OPENROUTER_API_KEY", raising=False) + + runtime = V2LLMRuntime() + with pytest.raises( + LLMRuntimeConfigError, + match="OPENROUTER_API_KEY", + ): + asyncio.run( + runtime.run_json_prompt( + system_prompt="system", + user_prompt="user prompt", + ), + ) + + +def test_llm_runtime_rejects_non_json_string_outputs( + monkeypatch: pytest.MonkeyPatch, +) -> None: + class _FakeAgent: + def __init__(self, **kwargs: Any) -> None: + self._kwargs = kwargs + + async def run( + self, + prompt: str, + model_settings: dict[str, Any] | None = None, + ) -> _FakeResult: + del prompt, model_settings + return _FakeResult(output="not-json") + + monkeypatch.setattr( + runtime_module, + "load_model_config", + lambda _analysis_type: [ + {"provider": "openai", "model": "gpt-test", "valid": True}, + ], + ) + monkeypatch.setattr(runtime_module, "validate_config", lambda _config: True) + monkeypatch.setattr( + runtime_module, + "create_pydantic_ai_model", + lambda _config: object(), + ) + monkeypatch.setattr(runtime_module, "get_model_parameters", lambda _config: {}) + monkeypatch.setattr(runtime_module, "Agent", _FakeAgent) + monkeypatch.setenv("OPENAI_API_KEY", "test-value") + + runtime = V2LLMRuntime() + with pytest.raises(LLMRuntimeResponseError, match="not valid JSON"): + asyncio.run( + runtime.run_json_prompt( + system_prompt="system", + user_prompt="user prompt", + ), + ) diff --git a/tests/v2/test_membership_agent.py b/tests/v2/test_membership_agent.py new file mode 100644 index 0000000..de6261a --- /dev/null +++ b/tests/v2/test_membership_agent.py @@ -0,0 +1,231 @@ +from __future__ import annotations + +import asyncio +from copy import deepcopy +from typing import Any, Callable +from uuid import UUID + +from jsonschema import validate + +from src.v2.agents import MembershipAgentV2, ProviderSet +from src.v2.ingest.providers.mock_github import MockGitHubProvider + +EXPECTED_MEMBERSHIP_COUNT = 2 +UUID_VERSION_4 = 4 + + +def _assert_uuid4(value: str) -> None: + parsed = UUID(value) + assert parsed.version == UUID_VERSION_4 + + +def _strip_membership_uuids(memberships: list[dict[str, Any]]) -> list[dict[str, Any]]: + sanitized = deepcopy(memberships) + for membership in sanitized: + identifiers = membership.get("identifiers") + if isinstance(identifiers, dict) and isinstance(identifiers.get("uuid"), str): + identifiers["uuid"] = "" + return sanitized + + +def _membership_context() -> dict[str, Any]: + return { + "known_persons": [ + { + "id": "https://orcid.org/0000-0002-1825-0097", + "schema:name": "Alice Example", + "affiliations": [ + "EPFL", + "EPFL", + "University of Lausanne", + ], + "orcid_affiliations": [ + { + "organization": "EPFL", + "role": "Research Engineer", + "start_date": "2021-01-01", + "end_date": None, + }, + ], + }, + ], + "known_organizations": [ + { + "id": "https://ror.org/02s376052", + "schema:name": "EPFL", + }, + { + "id": "https://ror.org/019wvm592", + "schema:name": "University of Lausanne", + }, + ], + } + + +def test_membership_agent_derives_deduplicated_memberships_with_uuid4_identifiers( + load_schema: Callable[[str, str], dict[str, Any]], +) -> None: + providers = ProviderSet(github=MockGitHubProvider()) + agent = MembershipAgentV2() + + first_result = asyncio.run(agent.run(_membership_context(), providers)) + second_result = asyncio.run(agent.run(_membership_context(), providers)) + + memberships = first_result.stats["memberships"] + membership_schema = load_schema("agent", "membership") + for membership in memberships: + validate(instance=membership, schema=membership_schema) + + assert len(memberships) == EXPECTED_MEMBERSHIP_COUNT + assert [membership["id"] for membership in memberships] == [ + "https://orcid.org/0000-0002-1825-0097_https://ror.org/019wvm592", + "https://orcid.org/0000-0002-1825-0097_https://ror.org/02s376052", + ] + for membership in memberships: + identifiers = membership.get("identifiers") + assert isinstance(identifiers, dict) + assert isinstance(identifiers.get("uuid"), str) + _assert_uuid4(str(identifiers["uuid"])) + for membership in second_result.stats["memberships"]: + identifiers = membership.get("identifiers") + assert isinstance(identifiers, dict) + assert isinstance(identifiers.get("uuid"), str) + _assert_uuid4(str(identifiers["uuid"])) + assert _strip_membership_uuids(first_result.stats["memberships"]) == _strip_membership_uuids( + second_result.stats["memberships"], + ) + + +def test_membership_agent_enriches_role_and_dates_from_affiliation_context() -> None: + providers = ProviderSet(github=MockGitHubProvider()) + agent = MembershipAgentV2() + + result = asyncio.run(agent.run(_membership_context(), providers)) + memberships = result.stats["memberships"] + epfl_membership = next( + membership + for membership in memberships + if membership["org:organization"] == "https://ror.org/02s376052" + ) + + assert epfl_membership["org:role"] == "Research Engineer" + assert epfl_membership["time:hasBeginning"] == "2021-01-01" + assert epfl_membership["time:hasEnd"] is None + + +def test_membership_agent_handles_unresolved_organizations_with_warnings() -> None: + providers = ProviderSet(github=MockGitHubProvider()) + agent = MembershipAgentV2() + context = deepcopy(_membership_context()) + context["known_persons"][0]["affiliations"] = ["Missing Organization"] + context["known_persons"][0]["orcid_affiliations"] = [] + + result = asyncio.run(agent.run(context, providers)) + + assert result.data == {} + assert result.stats["memberships"] == [] + assert any("Unresolved membership organization mapping" in warning for warning in result.warnings) + + +def test_membership_agent_resolves_prefixed_affiliation_alias_with_separator() -> None: + providers = ProviderSet(github=MockGitHubProvider()) + agent = MembershipAgentV2() + context = { + "known_persons": [ + { + "id": "https://orcid.org/0000-0002-1825-0097", + "schema:name": "Alice Example", + "affiliations": ["EPFL - École Polytechnique Fédérale de Lausanne"], + }, + ], + "known_organizations": [ + { + "id": "https://ror.org/02s376052", + "schema:name": "École Polytechnique Fédérale de Lausanne", + }, + ], + } + + result = asyncio.run(agent.run(context, providers)) + + assert result.stats["membership_count"] == 1 + assert result.data["org:organization"] == "https://ror.org/02s376052" + assert not any("Unresolved membership organization mapping" in warning for warning in result.warnings) + + +def test_membership_agent_resolves_sdsc_alias_variants_from_org_acronym() -> None: + providers = ProviderSet(github=MockGitHubProvider()) + agent = MembershipAgentV2() + context = { + "known_persons": [ + { + "id": "https://orcid.org/0000-0002-1825-0097", + "schema:name": "Alice Example", + "affiliations": ["SDSC-GE", "SDSC"], + }, + ], + "known_organizations": [ + { + "id": "https://ror.org/02hdt9m26", + "schema:name": "Swiss Data Science Center", + }, + ], + } + + result = asyncio.run(agent.run(context, providers)) + + assert result.stats["membership_count"] == 1 + assert result.data["org:organization"] == "https://ror.org/02hdt9m26" + assert not any("Unresolved membership organization mapping" in warning for warning in result.warnings) + + +def test_membership_agent_resolves_wageningen_short_name_from_ampersand_variant() -> None: + providers = ProviderSet(github=MockGitHubProvider()) + agent = MembershipAgentV2() + context = { + "known_persons": [ + { + "id": "https://orcid.org/0000-0002-1825-0097", + "schema:name": "Alice Example", + "affiliations": ["Wageningen University"], + }, + ], + "known_organizations": [ + { + "id": "https://ror.org/04qw24q55", + "schema:name": "Wageningen University & Research", + }, + ], + } + + result = asyncio.run(agent.run(context, providers)) + + assert result.stats["membership_count"] == 1 + assert result.data["org:organization"] == "https://ror.org/04qw24q55" + assert not any("Unresolved membership organization mapping" in warning for warning in result.warnings) + + +def test_membership_agent_resolves_concordia_international_variant_to_university_token() -> None: + providers = ProviderSet(github=MockGitHubProvider()) + agent = MembershipAgentV2() + context = { + "known_persons": [ + { + "id": "https://orcid.org/0000-0002-1825-0097", + "schema:name": "Alice Example", + "affiliations": ["Concordia International University Estonia"], + }, + ], + "known_organizations": [ + { + "id": "https://ror.org/01qxhf360", + "schema:name": "Concordia University", + }, + ], + } + + result = asyncio.run(agent.run(context, providers)) + + assert result.stats["membership_count"] == 1 + assert result.data["org:organization"] == "https://ror.org/01qxhf360" + assert not any("Unresolved membership organization mapping" in warning for warning in result.warnings) diff --git a/tests/v2/test_mock_github_provider.py b/tests/v2/test_mock_github_provider.py new file mode 100644 index 0000000..b7b4613 --- /dev/null +++ b/tests/v2/test_mock_github_provider.py @@ -0,0 +1,112 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Callable + +import pytest + +from src.v2.ingest.providers.base import ( + GitHubProvider, + ProviderNotFoundError, + ProviderPermissionError, + ProviderRateLimitError, +) +from src.v2.ingest.providers.mock_github import MockGitHubProvider + +FIXTURE_ROOT = Path(__file__).resolve().parent / "fixtures" / "providers" / "github" +MIN_CONTRIBUTOR_COUNT = 2 +MIN_FIXTURE_FILE_COUNT = 6 +EXPECTED_FIXTURE_FILES = { + "contributors_payload.json", + "not_found_response.json", + "org_payload.json", + "rate_limited_response.json", + "repo_payload.json", + "user_payload.json", +} + + +@pytest.fixture(scope="module") +def provider() -> MockGitHubProvider: + return MockGitHubProvider(fixture_root=FIXTURE_ROOT) + + +def test_mock_github_provider_implements_base_interface( + provider: MockGitHubProvider, +) -> None: + assert isinstance(provider, GitHubProvider) + assert not MockGitHubProvider.__abstractmethods__ + + +def test_get_repository_returns_expected_rest_payload_shape( + provider: MockGitHubProvider, +) -> None: + repository = provider.get_repository("octocat/Hello-World") + + assert repository["full_name"] == "octocat/Hello-World" + assert repository["owner"]["login"] == "octocat" + assert repository["private"] is False + assert repository["topics"] == ["metadata", "ai"] + assert repository["license"]["spdx_id"] == "MIT" + assert "languages_url" in repository + assert "contributors_url" in repository + + +def test_mock_provider_supports_user_org_contributor_and_language_payloads( + provider: MockGitHubProvider, +) -> None: + user = provider.get_user("octocat") + organization = provider.get_organization("github") + contributors = provider.get_contributors("octocat/Hello-World") + languages = provider.get_languages("octocat/Hello-World") + + assert user["login"] == "octocat" + assert organization["login"] == "github" + assert len(contributors) >= MIN_CONTRIBUTOR_COUNT + assert contributors[0]["login"] == "octocat" + assert languages["Python"] > 0 + + +def test_repository_error_cases_raise_typed_provider_errors( + provider: MockGitHubProvider, +) -> None: + with pytest.raises(ProviderNotFoundError): + provider.get_repository("missing/repo") + + with pytest.raises(ProviderRateLimitError): + provider.get_repository("rate-limited/repo") + + with pytest.raises(ProviderPermissionError): + provider.get_repository("private/repo") + + +def test_github_fixture_catalog_contains_required_files() -> None: + fixture_files = {path.name for path in FIXTURE_ROOT.glob("*.json")} + + assert EXPECTED_FIXTURE_FILES.issubset(fixture_files) + assert len(fixture_files) >= MIN_FIXTURE_FILE_COUNT + + +def test_github_fixture_files_are_valid_json() -> None: + for fixture_path in sorted(FIXTURE_ROOT.glob("*.json")): + with fixture_path.open(encoding="utf-8") as fixture_file: + payload = json.load(fixture_file) + + assert isinstance(payload, dict), ( + f"Expected JSON object fixture: {fixture_path.name}" + ) + + +def test_repo_and_contributor_fixtures_include_rest_and_graphql_shapes( + load_fixture: Callable[[str, str], Any], +) -> None: + repository_payload = load_fixture("providers/github", "repo_payload") + contributors_payload = load_fixture("providers/github", "contributors_payload") + + assert "rest" in repository_payload + assert "graphql" in repository_payload + assert "languages" in repository_payload + + assert "rest" in contributors_payload + assert "graphql" in contributors_payload diff --git a/tests/v2/test_mock_infoscience_provider.py b/tests/v2/test_mock_infoscience_provider.py new file mode 100644 index 0000000..2d95926 --- /dev/null +++ b/tests/v2/test_mock_infoscience_provider.py @@ -0,0 +1,133 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Callable + +import pytest + +from src.v2.ingest.providers.base import ( + INFOSCIENCE_PUBLICATION_OPTIONAL_FIELDS, + INFOSCIENCE_PUBLICATION_REQUIRED_FIELDS, + InfoscienceProvider, +) +from src.v2.ingest.providers.mock_infoscience import MockInfoscienceProvider + +FIXTURE_ROOT = Path(__file__).resolve().parent / "fixtures" / "providers" / "infoscience" +MIN_FIXTURE_COUNT = 5 +MULTI_HIT_MIN_RESULTS = 2 +EXPECTED_PUBLICATION_COUNT = 2 +EXPECTED_FIXTURE_FILES = { + "empty_result.json", + "orgunit_result.json", + "person_multi_hit.json", + "person_single_hit.json", + "publication_result.json", +} + + +@pytest.fixture(scope="module") +def provider() -> MockInfoscienceProvider: + return MockInfoscienceProvider(fixture_root=FIXTURE_ROOT) + + +def test_mock_infoscience_provider_implements_base_interface( + provider: MockInfoscienceProvider, +) -> None: + assert isinstance(provider, InfoscienceProvider) + assert not MockInfoscienceProvider.__abstractmethods__ + + +def test_search_person_single_hit_returns_exact_single_match( + provider: MockInfoscienceProvider, +) -> None: + result = provider.search_person("alice smith") + + assert len(result) == 1 + assert "infosciencePersonIdentifier" in result[0] + + +def test_search_person_multi_hit_returns_ambiguous_candidates( + provider: MockInfoscienceProvider, +) -> None: + result = provider.search_person("smith") + + assert len(result) >= MULTI_HIT_MIN_RESULTS + + +def test_empty_person_search_returns_empty_list( + provider: MockInfoscienceProvider, +) -> None: + result = provider.search_person("missing profile") + + assert result == [] + + +def test_orgunit_and_publication_queries_return_structured_results( + provider: MockInfoscienceProvider, +) -> None: + orgunits = provider.search_orgunit("epfl") + publications = provider.search_publications("geodata") + + assert orgunits + assert publications + assert "infoscienceOrgUnitIdentifier" in orgunits[0] + assert "infosciencePublicationIdentifier" in publications[0] + assert publications[0]["publicationDate"] == "2024-05-12" + assert publications[0]["authors"] == ["Alice Smith", "Marco Weber"] + + +def test_mock_publication_results_match_infoscience_contract( + provider: MockInfoscienceProvider, +) -> None: + publications = provider.search_publications("geodata") + + assert len(publications) == EXPECTED_PUBLICATION_COUNT + assert [ + publication["infosciencePublicationIdentifier"] + for publication in publications + ] == [ + "ac893a20-d10d-4f8a-91ec-9cb7631f60fc", + "8f946f6f-f9f9-4f0e-ab2c-9f635a36b2bc", + ] + + for publication in publications: + for field_name in INFOSCIENCE_PUBLICATION_REQUIRED_FIELDS: + assert field_name in publication + for field_name in INFOSCIENCE_PUBLICATION_OPTIONAL_FIELDS: + if field_name in publication: + assert publication[field_name] is None or isinstance( + publication[field_name], + str, + ) + + +def test_infoscience_fixture_catalog_contains_required_files() -> None: + fixture_files = {path.name for path in FIXTURE_ROOT.glob("*.json")} + + assert EXPECTED_FIXTURE_FILES.issubset(fixture_files) + assert len(fixture_files) >= MIN_FIXTURE_COUNT + + +def test_infoscience_fixture_files_are_valid_json() -> None: + for fixture_path in sorted(FIXTURE_ROOT.glob("*.json")): + with fixture_path.open(encoding="utf-8") as fixture_file: + payload = json.load(fixture_file) + + assert isinstance(payload, dict) + + +def test_infoscience_fixtures_expose_results_array( + load_fixture: Callable[[str, str], Any], +) -> None: + for fixture_name in [ + "person_single_hit", + "person_multi_hit", + "orgunit_result", + "publication_result", + "empty_result", + ]: + payload = load_fixture("providers/infoscience", fixture_name) + + assert "results" in payload + assert isinstance(payload["results"], list) diff --git a/tests/v2/test_mock_orcid_provider.py b/tests/v2/test_mock_orcid_provider.py new file mode 100644 index 0000000..2ce4914 --- /dev/null +++ b/tests/v2/test_mock_orcid_provider.py @@ -0,0 +1,99 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Callable + +import pytest + +from src.v2.ingest.providers.base import ORCIDProvider, ProviderNotFoundError +from src.v2.ingest.providers.mock_orcid import MockORCIDProvider + +FIXTURE_ROOT = Path(__file__).resolve().parent / "fixtures" / "providers" / "orcid" +MIN_FIXTURE_COUNT = 4 +MIN_EMPLOYMENT_ENTRIES = 2 +EXPECTED_FIXTURE_FILES = { + "valid_record.json", + "no_affiliations.json", + "multiple_employment.json", + "invalid_checksum.json", +} + + +@pytest.fixture(scope="module") +def provider() -> MockORCIDProvider: + return MockORCIDProvider(fixture_root=FIXTURE_ROOT) + + +def test_mock_orcid_provider_implements_base_interface(provider: MockORCIDProvider) -> None: + assert isinstance(provider, ORCIDProvider) + assert not MockORCIDProvider.__abstractmethods__ + + +def test_valid_record_returns_structured_affiliation_payload( + provider: MockORCIDProvider, +) -> None: + record = provider.get_person_by_orcid("0000-0002-1825-0097") + + assert record["name"] == "Alice Example" + assert record["employment"] + assert record["education"] + assert record["affiliations"] + + +def test_record_with_no_affiliations_returns_empty_lists( + provider: MockORCIDProvider, +) -> None: + record = provider.get_person_by_orcid("0000-0001-3456-7898") + + assert record["employment"] == [] + assert record["education"] == [] + assert record["affiliations"] == [] + + +def test_record_with_multiple_employment_entries_is_returned( + provider: MockORCIDProvider, +) -> None: + record = provider.get_person_by_orcid("0000-0003-1415-9269") + + assert len(record["employment"]) >= MIN_EMPLOYMENT_ENTRIES + + +def test_invalid_checksum_fixture_triggers_provider_validation_error( + provider: MockORCIDProvider, + load_fixture: Callable[[str, str], Any], +) -> None: + invalid_fixture = load_fixture("providers/orcid", "invalid_checksum") + + with pytest.raises(ValueError, match="checksum"): + provider.get_person_by_orcid(invalid_fixture["orcid_id"]) + + +def test_orcid_format_validation_is_enforced(provider: MockORCIDProvider) -> None: + with pytest.raises(ValueError, match="format"): + provider.get_person_by_orcid("0000-0002-1825-009") + + with pytest.raises(ValueError, match="format"): + provider.get_person_by_orcid("invalid-orcid") + + +def test_unknown_valid_orcid_raises_provider_not_found( + provider: MockORCIDProvider, +) -> None: + with pytest.raises(ProviderNotFoundError): + provider.get_person_by_orcid("7913-0249-0843-820X") + + +def test_orcid_fixture_catalog_contains_required_files() -> None: + fixture_files = {path.name for path in FIXTURE_ROOT.glob("*.json")} + + assert EXPECTED_FIXTURE_FILES.issubset(fixture_files) + assert len(fixture_files) >= MIN_FIXTURE_COUNT + + +def test_orcid_fixture_files_are_valid_json() -> None: + for fixture_path in sorted(FIXTURE_ROOT.glob("*.json")): + with fixture_path.open(encoding="utf-8") as fixture_file: + payload = json.load(fixture_file) + + assert isinstance(payload, dict) diff --git a/tests/v2/test_mock_ror_provider.py b/tests/v2/test_mock_ror_provider.py new file mode 100644 index 0000000..051fe97 --- /dev/null +++ b/tests/v2/test_mock_ror_provider.py @@ -0,0 +1,96 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Callable + +import pytest + +from src.v2.ingest.providers.base import ProviderNotFoundError, RORProvider +from src.v2.ingest.providers.mock_ror import MockRORProvider + +FIXTURE_ROOT = Path(__file__).resolve().parent / "fixtures" / "providers" / "ror" +MIN_FIXTURE_COUNT = 5 +MIN_LABEL_VARIANTS = 2 +EXPECTED_FIXTURE_FILES = { + "not_found.json", + "org_detail.json", + "org_with_aliases.json", + "parent_org.json", + "search_results.json", +} + + +@pytest.fixture(scope="module") +def provider() -> MockRORProvider: + return MockRORProvider(fixture_root=FIXTURE_ROOT) + + +def test_mock_ror_provider_implements_base_interface(provider: MockRORProvider) -> None: + assert isinstance(provider, RORProvider) + assert not MockRORProvider.__abstractmethods__ + + +def test_get_organization_returns_expected_detail_shape( + provider: MockRORProvider, +) -> None: + organization = provider.get_organization("https://ror.org/02s376052") + + assert organization["name"] == "Ecole Polytechnique Federale de Lausanne" + assert organization["aliases"] + assert organization["types"] + assert organization["country"]["country_name"] == "Switzerland" + assert "relationships" in organization + + +def test_search_organizations_returns_ranked_candidates( + provider: MockRORProvider, +) -> None: + results = provider.search_organizations("epfl") + + assert results + scores = [item["score"] for item in results] + assert scores == sorted(scores, reverse=True) + + +def test_org_with_aliases_contains_labels_acronyms_and_alternate_names( + provider: MockRORProvider, +) -> None: + alias_variant = provider.get_organization("03yrm5c26") + + assert alias_variant["aliases"] + assert alias_variant["acronyms"] + assert len(alias_variant["labels"]) >= MIN_LABEL_VARIANTS + + +def test_get_organization_not_found_raises_provider_not_found( + provider: MockRORProvider, +) -> None: + with pytest.raises(ProviderNotFoundError): + provider.get_organization("https://ror.org/000000000") + + +def test_ror_fixture_catalog_contains_required_files() -> None: + fixture_files = {path.name for path in FIXTURE_ROOT.glob("*.json")} + + assert EXPECTED_FIXTURE_FILES.issubset(fixture_files) + assert len(fixture_files) >= MIN_FIXTURE_COUNT + + +def test_ror_fixture_files_are_valid_json() -> None: + for fixture_path in sorted(FIXTURE_ROOT.glob("*.json")): + with fixture_path.open(encoding="utf-8") as fixture_file: + payload = json.load(fixture_file) + + assert isinstance(payload, dict) + + +def test_ror_fixtures_expose_expected_top_level_fields( + load_fixture: Callable[[str, str], Any], +) -> None: + org_detail = load_fixture("providers/ror", "org_detail") + search_results = load_fixture("providers/ror", "search_results") + + assert "organization" in org_detail + assert "items" in search_results + assert isinstance(search_results["items"], list) diff --git a/tests/v2/test_no_new_legacy_imports.py b/tests/v2/test_no_new_legacy_imports.py new file mode 100644 index 0000000..dca3990 --- /dev/null +++ b/tests/v2/test_no_new_legacy_imports.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +import re +from pathlib import Path + +LEGACY_IMPORT_PATTERN = re.compile( + r"src\.v2\.(providers|detection|canonicalization|validation|models|generated|llm\.runtime)", +) + +REPO_ROOT = Path(__file__).resolve().parents[2] +SCAN_ROOTS = [REPO_ROOT / "src", REPO_ROOT / "tests", REPO_ROOT / "scripts"] +ALLOWLIST_PREFIXES = [ + REPO_ROOT / "src" / "v2" / "providers", + REPO_ROOT / "src" / "v2" / "detection", + REPO_ROOT / "src" / "v2" / "canonicalization", + REPO_ROOT / "src" / "v2" / "validation", + REPO_ROOT / "src" / "v2" / "models", + REPO_ROOT / "src" / "v2" / "generated", +] +ALLOWLIST_FILES = { + REPO_ROOT / "src" / "v2" / "llm" / "__init__.py", + REPO_ROOT / "src" / "v2" / "llm" / "runtime.py", + REPO_ROOT / "src" / "v2" / "agents" / "article_agent.py", + REPO_ROOT / "src" / "v2" / "agents" / "contribution_agent.py", + REPO_ROOT / "src" / "v2" / "agents" / "membership_agent.py", + REPO_ROOT / "src" / "v2" / "agents" / "organization_agent.py", + REPO_ROOT / "src" / "v2" / "agents" / "person_agent.py", + REPO_ROOT / "src" / "v2" / "agents" / "repository_agent.py", + REPO_ROOT / "tests" / "v2" / "test_import_compat_shims.py", +} + + +def _is_allowlisted(path: Path) -> bool: + if path in ALLOWLIST_FILES: + return True + return any(path.is_relative_to(prefix) for prefix in ALLOWLIST_PREFIXES) + + +def test_no_new_legacy_imports_outside_compat_shims() -> None: + violations: list[str] = [] + + for root in SCAN_ROOTS: + for path in root.rglob("*.py"): + if _is_allowlisted(path): + continue + + for line_number, line in enumerate(path.read_text(encoding="utf-8").splitlines(), start=1): + if LEGACY_IMPORT_PATTERN.search(line): + relative = path.relative_to(REPO_ROOT) + violations.append(f"{relative}:{line_number}:{line.strip()}") + + assert not violations, "Found forbidden legacy imports:\n" + "\n".join(sorted(violations)) diff --git a/tests/v2/test_orchestrator_execution.py b/tests/v2/test_orchestrator_execution.py new file mode 100644 index 0000000..51d3b15 --- /dev/null +++ b/tests/v2/test_orchestrator_execution.py @@ -0,0 +1,2426 @@ +from __future__ import annotations + +import asyncio +import json +from time import monotonic +from typing import Any + +import pytest + +from src.v2.agents import ProviderSet +from src.v2.agents.models import AgentResult +from src.v2.ingest.detection.models import GitHubURLClassification, GitHubURLType +from src.v2.pipeline import PipelineOrchestrator +from src.v2.pipeline.stages.models import ContextBundle +from src.v2.ingest.providers.mock_github import MockGitHubProvider + +EXPECTED_PARALLEL_AGENT_COUNT = 2 +MAX_PARALLEL_START_DELTA_SECONDS = 0.04 +EXPECTED_BOB_RETRY_ATTEMPTS = 2 +EXPECTED_REPOSITORY_STAGE_ORDER = [ + "context_gather", + "repo_agent", + "person_agents", + "org_agents", + "article_agents", + "membership_agents", + "contribution_agents", +] +EXPECTED_TYPED_BUCKET_KEYS = { + "repositories", + "persons", + "organizations", + "articles", + "memberships", + "contributions", +} +STAGE_ARTICLE_AGENT = "article_agent" +STAGE_MEMBERSHIP_AGENT = "membership_agent" +STAGE_CONTRIBUTION_AGENT = "contribution_agent" + + +def _classification() -> GitHubURLClassification: + return GitHubURLClassification( + normalized_url="https://github.com/octocat/Hello-World", + detected_type=GitHubURLType.REPOSITORY, + owner="octocat", + repo="Hello-World", + ) + + +def _providers() -> ProviderSet: + return ProviderSet(github=MockGitHubProvider()) + + +def _empty_class_agent_runners() -> dict[str, Any]: + async def _article_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + seed = context.get("article_seed") + return AgentResult( + data={ + "id": f"noop-article:{seed}" if isinstance(seed, str) else "noop-article", + "entity_type": "article", + }, + ) + + async def _membership_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + seed = context.get("membership_seed") + return AgentResult( + data={ + "id": ( + f"noop-membership:{seed}" + if isinstance(seed, str) + else "noop-membership" + ), + "entity_type": "membership", + }, + ) + + async def _contribution_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + seed = context.get("contribution_seed") + return AgentResult( + data={ + "id": ( + f"noop-contribution:{seed}" + if isinstance(seed, str) + else "noop-contribution" + ), + "entity_type": "contribution", + }, + ) + + return { + STAGE_ARTICLE_AGENT: _article_agent, + STAGE_MEMBERSHIP_AGENT: _membership_agent, + STAGE_CONTRIBUTION_AGENT: _contribution_agent, + } + + +def test_execute_pipeline_completes_full_repository_plan() -> None: + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [{"login": "alice"}, {"login": "bob"}], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "repo-root"}) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult( + data={ + "id": context["username"], + "repo_visible": "repo_agent" in context["pipeline_outputs"], + }, + ) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["org_name"]}) + + async def _article_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult( + data={ + "id": f"article:{context['article_seed']}", + "entity_type": "article", + }, + ) + + async def _membership_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult( + data={ + "id": f"membership:{context['membership_seed']}", + "entity_type": "membership", + }, + ) + + async def _contribution_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult( + data={ + "id": f"contribution:{context['contribution_seed']}", + "entity_type": "contribution", + }, + ) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + "org_agent": _org_agent, + STAGE_ARTICLE_AGENT: _article_agent, + STAGE_MEMBERSHIP_AGENT: _membership_agent, + STAGE_CONTRIBUTION_AGENT: _contribution_agent, + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={"url_info": _classification(), "source_url": "https://github.com/octocat/Hello-World"}, + ), + ) + + assert result.errors == [] + assert result.stages_completed == EXPECTED_REPOSITORY_STAGE_ORDER + assert "repo_agent" in result.agent_results + assert "person_agent:alice" in result.agent_results + assert "person_agent:bob" in result.agent_results + assert "org_agent:github" in result.agent_results + assert "article_agent:octocat/Hello-World" in result.agent_results + assert "membership_agent:alice" in result.agent_results + assert "membership_agent:bob" in result.agent_results + assert "contribution_agent:alice_repo-root" in result.agent_results + assert "contribution_agent:bob_repo-root" in result.agent_results + assert result.agent_results["person_agent:alice"].data["repo_visible"] is True + + typed_buckets = result.resolved_typed_entity_buckets().to_dict() + assert set(typed_buckets) == EXPECTED_TYPED_BUCKET_KEYS + assert [entity["id"] for entity in typed_buckets["repositories"]] == ["repo-root"] + assert [entity["id"] for entity in typed_buckets["persons"]] == ["alice", "bob"] + assert [entity["id"] for entity in typed_buckets["organizations"]] == ["github"] + assert [entity["id"] for entity in typed_buckets["articles"]] == [ + "article:octocat/Hello-World", + ] + assert [entity["id"] for entity in typed_buckets["memberships"]] == [ + "membership:alice", + "membership:bob", + ] + assert [entity["id"] for entity in typed_buckets["contributions"]] == [ + "contribution:repo-root", + ] + + serialized_result = result.to_dict() + assert serialized_result["typed_entity_buckets"] == typed_buckets + + +def test_execute_can_append_upstream_json_and_verbatim_prompt_text_to_agent_context() -> None: + captured_person_contexts: list[dict[str, Any]] = [] + captured_org_contexts: list[dict[str, Any]] = [] + prompt_appendix = "FILE_A\nFILE_B\nThis is verbatim." + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [{"login": "alice"}], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "repo-root"}) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + captured_person_contexts.append(dict(context)) + return AgentResult( + data={ + "id": "alice", + "org:hasMembership": ["alice_github"], + }, + ) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + captured_org_contexts.append(dict(context)) + return AgentResult(data={"id": context["org_name"]}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + "org_agent": _org_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + include_upstream_stage_outputs_in_prompt=True, + user_prompt_appendix=prompt_appendix, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={ + "url_info": _classification(), + "source_url": "https://github.com/octocat/Hello-World", + }, + ), + ) + + assert len(captured_person_contexts) == 1 + person_context = captured_person_contexts[0] + person_upstream = person_context.get("upstream_stage_outputs_json") + assert isinstance(person_upstream, str) + assert json.loads(person_upstream) == {"repo_agent": {"id": "repo-root"}} + assert person_context.get("user_prompt_appendix") == prompt_appendix + + assert len(captured_org_contexts) == 1 + org_context = captured_org_contexts[0] + org_upstream = org_context.get("upstream_stage_outputs_json") + assert isinstance(org_upstream, str) + assert json.loads(org_upstream) == { + "repo_agent": {"id": "repo-root"}, + "person_agent:alice": { + "id": "alice", + "org:hasMembership": ["alice_github"], + }, + } + assert org_context.get("user_prompt_appendix") == prompt_appendix + + +def test_execute_includes_upstream_stage_outputs_in_prompt_context_by_default() -> None: + captured_person_contexts: list[dict[str, Any]] = [] + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "octocat", "type": "User"}}, + "contributors": [{"login": "alice"}], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "repo-root"}) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + captured_person_contexts.append(dict(context)) + return AgentResult(data={"id": context["username"]}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={"url_info": _classification(), "source_url": "https://github.com/octocat/Hello-World"}, + ), + ) + + # Two person fanouts: one for the listed contributor `alice`, one + # for the User-type owner `octocat` materialised by the orchestrator. + assert len(captured_person_contexts) == 2 + assert {ctx["username"] for ctx in captured_person_contexts} == {"alice", "octocat"} + for captured in captured_person_contexts: + upstream_json = captured.get("upstream_stage_outputs_json") + assert isinstance(upstream_json, str) + assert json.loads(upstream_json) == {"repo_agent": {"id": "repo-root"}} + + # source_repositories drives `pulse:owns` in the rule-based person agent. + # Only the User-account owner (octocat) should get it — contributors who + # are not the repo owner must not claim ownership of the source repo. + contexts_by_login = {ctx["username"]: ctx for ctx in captured_person_contexts} + assert contexts_by_login["octocat"].get("source_repositories") == ["octocat/Hello-World"] + assert "source_repositories" not in contexts_by_login["alice"] + + +def test_execute_skips_github_organization_accounts_from_person_fanout() -> None: + class _GitHubProviderWithOrgContributor(MockGitHubProvider): + def get_user(self, username: str) -> dict[str, Any]: + if username == "alice": + return {"login": "alice", "type": "User", "name": "Alice"} + if username == "sdsc-ordes": + return { + "login": "sdsc-ordes", + "type": "Organization", + "name": "Swiss Data Science Center - ORD", + } + return super().get_user(username) + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "sdsc-ordes/gimie", + "metadata": {"owner": {"login": "octocat", "type": "User"}}, + "contributors": [{"login": "alice"}, {"login": "sdsc-ordes"}], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "repo-root"}) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["username"]}) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["org_name"]}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + "org_agent": _org_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=ProviderSet(github=_GitHubProviderWithOrgContributor()), + context={"url_info": _classification(), "source_url": "https://github.com/sdsc-ordes/gimie"}, + ), + ) + + assert "person_agent:alice" in result.agent_results + assert "person_agent:sdsc-ordes" not in result.agent_results + assert any( + warning == "Skipping person fanout for GitHub organization account: sdsc-ordes" + for warning in result.warnings + ) + + +def test_execute_skips_repository_owner_org_without_user_lookup() -> None: + called_usernames: list[str] = [] + + class _GitHubProviderWithUserLookupTracking(MockGitHubProvider): + def get_user(self, username: str) -> dict[str, Any]: + called_usernames.append(username) + if username == "alice": + return {"login": "alice", "type": "User", "name": "Alice"} + if username == "sdsc-ordes": + raise AssertionError("repository owner org should be filtered before user lookup") + return super().get_user(username) + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "sdsc-ordes/gimie", + "metadata": {"owner": {"login": "SDSC-ORDES", "type": "Organization"}}, + "contributors": [{"login": "alice"}, {"login": "sdsc-ordes"}], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "repo-root"}) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["username"]}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=ProviderSet(github=_GitHubProviderWithUserLookupTracking()), + context={"url_info": _classification(), "source_url": "https://github.com/sdsc-ordes/gimie"}, + ), + ) + + assert "person_agent:alice" in result.agent_results + assert "person_agent:sdsc-ordes" not in result.agent_results + assert "sdsc-ordes" not in called_usernames + + +def test_execute_accepts_empty_class_agent_payload_and_uses_stats_entities() -> None: + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "octocat", "type": "User"}}, + "contributors": [], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "repo-root"}) + + async def _article_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult( + data={}, + warnings=["No article queries could be derived from runtime context"], + stats={ + "articles": [ + {"id": "article:stats-only", "entity_type": "article"}, + ], + }, + ) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + **_empty_class_agent_runners(), + "repo_agent": _repo_agent, + STAGE_ARTICLE_AGENT: _article_agent, + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={"url_info": _classification(), "source_url": "https://github.com/octocat/Hello-World"}, + ), + ) + + assert "article_agent:octocat/Hello-World" in result.agent_results + assert "article_agent:octocat/Hello-World: Agent returned empty or invalid data payload" not in result.warnings + typed_buckets = result.resolved_typed_entity_buckets().to_dict() + assert [entity["id"] for entity in typed_buckets["articles"]] == ["article:stats-only"] + + +def test_execute_runs_agents_within_stage_concurrently() -> None: + start_times: list[float] = [] + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [{"login": "alice"}, {"login": "bob"}], + "languages": {}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "repo-root"}) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + del context + start_times.append(monotonic()) + await asyncio.sleep(0.05) + return AgentResult(data={"id": "person"}) + + async def _org_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "org"}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + "org_agent": _org_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={"url_info": _classification(), "source_url": "https://github.com/octocat/Hello-World"}, + ), + ) + + assert len(start_times) == EXPECTED_PARALLEL_AGENT_COUNT + assert abs(start_times[0] - start_times[1]) < MAX_PARALLEL_START_DELTA_SECONDS + + +def test_execute_collects_partial_failures_without_blocking_siblings() -> None: + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [{"login": "alice"}, {"login": "bob"}], + "languages": {}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + await asyncio.sleep(0.01) + return AgentResult(data={"id": "repo-root"}) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + if context["username"] == "alice": + message = "alice failed" + raise RuntimeError(message) + return AgentResult(data={"id": context["username"]}) + + async def _org_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "org"}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + "org_agent": _org_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + retry_max_retries=1, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={"url_info": _classification(), "source_url": "https://github.com/octocat/Hello-World"}, + ), + ) + + assert "person_agent:alice" in result.agent_results + assert "person_agent:bob" in result.agent_results + assert result.agent_results["person_agent:alice"].is_partial is True + assert result.agent_results["person_agent:bob"].is_partial is False + assert result.duration_ms > 0 + + +def test_repository_org_fanout_marks_direct_and_membership_contexts() -> None: + captured_org_contexts: list[dict[str, Any]] = [] + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "owner-org/source-repo", + "metadata": {"owner": {"login": "owner-org", "type": "Organization"}}, + "contributors": [{"login": "alice"}], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "repo-root"}) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + del context + return AgentResult( + data={ + "id": "alice", + "org:hasMembership": [ + "alice_EPFL", + "alice_owner-org", + ], + }, + ) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + captured_org_contexts.append(dict(context)) + return AgentResult(data={"id": context["org_name"]}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + "org_agent": _org_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={"url_info": _classification(), "source_url": "https://github.com/octocat/Hello-World"}, + ), + ) + + contexts_by_org = { + context["org_name"]: context + for context in captured_org_contexts + } + owner_context = contexts_by_org["owner-org"] + assert owner_context["github_lookup_enabled"] is True + assert owner_context["source_repositories"] == ["owner-org/source-repo"] + assert "repository_context" in owner_context + + epfl_context = contexts_by_org["EPFL"] + assert epfl_context["github_lookup_enabled"] is False + assert epfl_context["source_repositories"] == ["owner-org/source-repo"] + assert "repository_context" in epfl_context + + +def test_repository_person_fanout_ignores_non_github_hash_logins() -> None: + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [ + { + "login": "0b3f69f4d1e5bdc420e7bea74e3e037ab841e385aefc2c37097f40edd13f8cd4", + }, + {"login": "octocat"}, + ], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "repo-root"}) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["username"]}) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["org_name"]}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + "org_agent": _org_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={"url_info": _classification(), "source_url": "https://github.com/octocat/Hello-World"}, + ), + ) + + assert "person_agent:octocat" in result.agent_results + assert ( + "person_agent:0b3f69f4d1e5bdc420e7bea74e3e037ab841e385aefc2c37097f40edd13f8cd4" + not in result.agent_results + ) + + +def test_class_stage_work_items_include_upstream_references_and_typed_buckets() -> None: + captured_article_contexts: list[dict[str, Any]] = [] + captured_membership_contexts: list[dict[str, Any]] = [] + captured_contribution_contexts: list[dict[str, Any]] = [] + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [{"login": "alice"}, {"login": "bob"}], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult( + data={"id": "repo-root"}, + stats={ + "derivation": { + "repository_full_name": "repo-root", + "contributors": [ + {"login": "alice", "contributions": 3}, + {"login": "bob", "contributions": 1}, + ], + }, + }, + ) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + username = context["username"] + return AgentResult( + data={"id": username, "schema:name": username.title()}, + stats={ + "derivation": { + "person_id": username, + "affiliation_names": ["EPFL"], + "orcid_affiliations": [ + { + "organization": "EPFL", + "role": "Research Engineer", + "start_date": "2021-01-01", + "end_date": None, + }, + ], + }, + }, + ) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["org_name"], "schema:name": context["org_name"]}) + + async def _article_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + captured_article_contexts.append(dict(context)) + return AgentResult(data={"id": f"article:{context['article_seed']}", "entity_type": "article"}) + + async def _membership_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + captured_membership_contexts.append(dict(context)) + return AgentResult(data={"id": f"membership:{context['membership_seed']}", "entity_type": "membership"}) + + async def _contribution_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + captured_contribution_contexts.append(dict(context)) + return AgentResult( + data={"id": f"contribution:{context['contribution_seed']}", "entity_type": "contribution"}, + ) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + "org_agent": _org_agent, + STAGE_ARTICLE_AGENT: _article_agent, + STAGE_MEMBERSHIP_AGENT: _membership_agent, + STAGE_CONTRIBUTION_AGENT: _contribution_agent, + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={"url_info": _classification(), "source_url": "https://github.com/octocat/Hello-World"}, + ), + ) + + assert [context["article_seed"] for context in captured_article_contexts] == [ + "octocat/Hello-World", + ] + assert sorted(context["membership_seed"] for context in captured_membership_contexts) == [ + "alice", + "bob", + ] + # One contribution per (person, repo) pair: alice→repo-root and bob→repo-root. + assert [context["contribution_seed"] for context in captured_contribution_contexts] == [ + "repo-root", + "repo-root", + ] + + membership_context = captured_membership_contexts[0] + assert membership_context["known_persons"][0]["affiliations"] == ["EPFL"] + assert membership_context["target_person"]["id"] == membership_context["membership_seed"] + assert isinstance(membership_context["target_organizations"], list) + assert membership_context["target_organizations"] + assert isinstance(membership_context["typed_entity_buckets"], dict) + assert membership_context["typed_entity_buckets"]["persons"] + + contribution_context = captured_contribution_contexts[0] + assert contribution_context["known_repositories"][0]["contributors"] == [ + {"login": "alice", "contributions": 3}, + {"login": "bob", "contributions": 1}, + ] + + +def test_class_stage_fanout_is_deterministic_for_user_and_organization_roots() -> None: + captured_article_seeds: list[str] = [] + captured_membership_seeds: list[str] = [] + captured_contribution_seeds: list[str] = [] + + async def _context_gatherer( + detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + if detected_type == "user": + return ContextBundle( + detected_type="user", + context={ + "user": { + "username": "octocat", + "profile": {"company": "github"}, + "owned_repos": ["octocat/repo-a", "repo-b"], + }, + }, + ) + return ContextBundle( + detected_type="organization", + context={ + "organization": { + "org_name": "github", + "members": ["alice", "bob"], + "owned_repos": ["github/repo-a"], + }, + }, + ) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + username = context["username"] + return AgentResult( + data={"id": username, "schema:name": username}, + stats={"derivation": {"person_id": username, "affiliation_names": ["github"]}}, + ) + + async def _repo_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + full_name = context["full_name"] + return AgentResult( + data={"id": full_name}, + stats={"derivation": {"repository_full_name": full_name, "contributors": []}}, + ) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["org_name"], "schema:name": context["org_name"]}) + + async def _article_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + captured_article_seeds.append(context["article_seed"]) + return AgentResult( + data={ + "id": f"article:{context['article_seed']}", + "entity_type": "article", + }, + ) + + async def _membership_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + captured_membership_seeds.append(context["membership_seed"]) + return AgentResult( + data={ + "id": f"membership:{context['membership_seed']}", + "entity_type": "membership", + }, + ) + + async def _contribution_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + captured_contribution_seeds.append(context["contribution_seed"]) + return AgentResult( + data={ + "id": f"contribution:{context['contribution_seed']}", + "entity_type": "contribution", + }, + ) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + "org_agent": _org_agent, + STAGE_ARTICLE_AGENT: _article_agent, + STAGE_MEMBERSHIP_AGENT: _membership_agent, + STAGE_CONTRIBUTION_AGENT: _contribution_agent, + }, + retry_backoff_base=0, + ) + + user_plan = orchestrator.get_execution_plan("user") + organization_plan = orchestrator.get_execution_plan("organization") + + asyncio.run( + orchestrator.execute( + plan=user_plan, + providers=_providers(), + context={ + "url_info": GitHubURLClassification( + normalized_url="https://github.com/octocat", + detected_type=GitHubURLType.USER, + owner="octocat", + repo=None, + ), + "source_url": "https://github.com/octocat", + }, + ), + ) + asyncio.run( + orchestrator.execute( + plan=organization_plan, + providers=_providers(), + context={ + "url_info": GitHubURLClassification( + normalized_url="https://github.com/orgs/github", + detected_type=GitHubURLType.ORGANIZATION, + owner="github", + repo=None, + ), + "source_url": "https://github.com/orgs/github", + }, + ), + ) + + assert captured_article_seeds == ["octocat", "github"] + assert captured_membership_seeds == ["octocat", "alice", "bob"] + # In the org run, both `alice` and `bob` produce a contribution + # against `github/repo-a` (one (person, repo) pair each). + assert captured_contribution_seeds == [ + "octocat/repo-a", + "octocat/repo-b", + "github/repo-a", + "github/repo-a", + ] + + +def test_membership_context_prioritizes_target_organizations_from_person_links() -> None: + captured_membership_contexts: list[dict[str, Any]] = [] + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [{"login": "alice"}], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult( + data={"id": "repo-root"}, + stats={"derivation": {"repository_full_name": "repo-root", "contributors": []}}, + ) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + username = context["username"] + return AgentResult( + data={ + "id": username, + "schema:name": "Alice", + "affiliations": ["EPFL"], + "org:hasMembership": [f"{username}_EPFL"], + }, + stats={"derivation": {"person_id": username, "affiliation_names": ["EPFL"]}}, + ) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["org_name"], "schema:name": context["org_name"]}) + + async def _membership_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + captured_membership_contexts.append(dict(context)) + return AgentResult( + data={"id": f"membership:{context['membership_seed']}", "entity_type": "membership"}, + ) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + "org_agent": _org_agent, + STAGE_MEMBERSHIP_AGENT: _membership_agent, + **{ + key: value + for key, value in _empty_class_agent_runners().items() + if key != STAGE_MEMBERSHIP_AGENT + }, + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={"url_info": _classification(), "source_url": "https://github.com/octocat/Hello-World"}, + ), + ) + + assert len(captured_membership_contexts) == 1 + membership_context = captured_membership_contexts[0] + target_organization_ids = [ + organization.get("id") + for organization in membership_context["target_organizations"] + if isinstance(organization, dict) + ] + assert target_organization_ids == ["EPFL"] + + +def test_class_stage_partial_failures_keep_sibling_and_downstream_execution() -> None: + attempt_count: dict[str, int] = {} + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [{"login": "alice"}, {"login": "bob"}], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult( + data={"id": "repo-root"}, + stats={"derivation": {"repository_full_name": "repo-root", "contributors": []}}, + ) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + username = context["username"] + return AgentResult( + data={"id": username}, + stats={"derivation": {"person_id": username, "affiliation_names": ["github"]}}, + ) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["org_name"], "schema:name": context["org_name"]}) + + async def _article_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": f"article:{context['article_seed']}", "entity_type": "article"}) + + async def _membership_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + seed = context["membership_seed"] + attempt_count[seed] = attempt_count.get(seed, 0) + 1 + if seed == "alice": + failure_message = "alice membership failed" + raise RuntimeError(failure_message) + if seed == "bob" and attempt_count[seed] == 1: + retry_message = "bob membership transient failure" + raise RuntimeError(retry_message) + return AgentResult(data={"id": f"membership:{seed}", "entity_type": "membership"}) + + async def _contribution_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult( + data={"id": f"contribution:{context['contribution_seed']}", "entity_type": "contribution"}, + ) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + agent_runners={ + "repo_agent": _repo_agent, + "person_agent": _person_agent, + "org_agent": _org_agent, + STAGE_ARTICLE_AGENT: _article_agent, + STAGE_MEMBERSHIP_AGENT: _membership_agent, + STAGE_CONTRIBUTION_AGENT: _contribution_agent, + }, + retry_backoff_base=0, + retry_max_retries=2, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={"url_info": _classification(), "source_url": "https://github.com/octocat/Hello-World"}, + ), + ) + + assert result.agent_results["membership_agent:alice"].is_partial is True + assert result.agent_results["membership_agent:bob"].is_partial is False + assert result.agent_results["contribution_agent:alice_repo-root"].is_partial is False + assert attempt_count["bob"] == EXPECTED_BOB_RETRY_ATTEMPTS + + +def test_execute_uses_llm_repository_runner_for_repository_runtime() -> None: + llm_repo_calls = 0 + llm_org_calls = 0 + llm_article_calls = 0 + rule_repo_calls = 0 + + class _LLMRepositoryRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + nonlocal llm_repo_calls + llm_repo_calls += 1 + return AgentResult(data={"id": "llm-repo-root"}) + + class _LLMOrganizationRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + nonlocal llm_org_calls + llm_org_calls += 1 + return AgentResult(data={"id": f"llm-org:{context['org_name']}"}) + + class _LLMArticleRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + nonlocal llm_article_calls + llm_article_calls += 1 + return AgentResult( + data={ + "id": f"llm-article:{context.get('article_seed', 'seed')}", + "entity_type": "article", + }, + ) + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _rule_repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + nonlocal rule_repo_calls + rule_repo_calls += 1 + return AgentResult(data={"id": "rule-repo-root"}) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["org_name"]}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_repository_agent=_LLMRepositoryRunner(), + llm_organization_agent=_LLMOrganizationRunner(), + llm_article_agent=_LLMArticleRunner(), + llm_membership_agent=_LLMArticleRunner(), + llm_contribution_agent=_LLMArticleRunner(), + agent_runners={ + "repo_agent": _rule_repo_agent, + "org_agent": _org_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={ + "url_info": _classification(), + "source_url": "https://github.com/octocat/Hello-World", + "agent_runtime": "llm", + }, + ), + ) + + assert llm_repo_calls == 1 + assert llm_org_calls == 1 + assert llm_article_calls == 1 + assert rule_repo_calls == 0 + assert result.agent_results["repo_agent"].data["id"] == "llm-repo-root" + assert result.agent_results["org_agent:github"].data["id"] == "llm-org:github" + + +def test_execute_llm_compiles_context_summary_and_strips_raw_repository_blobs() -> None: + summary_calls = 0 + captured_repo_contexts: list[dict[str, Any]] = [] + + class _SummaryRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + nonlocal summary_calls + summary_calls += 1 + repository_context = context["gathered_context"]["repository"] + assert repository_context["readme_content"] == "README RAW" + assert repository_context["gimie_jsonld"] == {"@id": "https://github.com/octocat/Hello-World"} + return AgentResult(data={"summary_markdown": "# Compiled Context\n- fact"}) + + class _LLMRepositoryRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + captured_repo_contexts.append(dict(context)) + repository_context = context.get("repository_context") + assert isinstance(repository_context, dict) + assert "readme_content" not in repository_context + assert "gimie_jsonld" not in repository_context + assert "repository_files" not in repository_context + return AgentResult(data={"id": "llm-repo-root"}) + + class _LLMNoDataRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult(data={}) + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": { + "full_name": "octocat/Hello-World", + "owner": {"login": "octocat", "type": "User"}, + }, + "contributors": [], + "languages": {"Python": 1}, + "readme_content": "README RAW", + "gimie_jsonld": {"@id": "https://github.com/octocat/Hello-World"}, + "repository_files": [ + {"path": "pyproject.toml", "content": "[tool.uv]\n"}, + ], + }, + }, + ) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_context_summary_agent=_SummaryRunner(), + llm_repository_agent=_LLMRepositoryRunner(), + llm_person_agent=_LLMNoDataRunner(), + llm_organization_agent=_LLMNoDataRunner(), + llm_article_agent=_LLMNoDataRunner(), + llm_membership_agent=_LLMNoDataRunner(), + llm_contribution_agent=_LLMNoDataRunner(), + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={ + "url_info": _classification(), + "source_url": "https://github.com/octocat/Hello-World", + "agent_runtime": "llm", + }, + ), + ) + + assert summary_calls == 1 + assert len(captured_repo_contexts) == 1 + assert result.gathered_context["compiled_context_markdown"].startswith("# Compiled Context") + prompt_appendix = captured_repo_contexts[0].get("user_prompt_appendix") + assert isinstance(prompt_appendix, str) + assert "## Compiled Source Summary" in prompt_appendix + assert "# Compiled Context" in prompt_appendix + + +def test_execute_uses_llm_repository_runner_for_user_and_org_in_llm_mode() -> None: + llm_repo_calls = 0 + llm_org_calls = 0 + llm_class_calls = 0 + rule_repo_calls = 0 + + class _LLMRepositoryRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + nonlocal llm_repo_calls + llm_repo_calls += 1 + return AgentResult(data={"id": "llm-repo-root"}) + + class _LLMOrganizationRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + nonlocal llm_org_calls + llm_org_calls += 1 + return AgentResult(data={"id": f"llm-org:{context['org_name']}"}) + + class _LLMPersonRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + return AgentResult( + data={ + "id": context["username"], + "type": "schema:Person", + "org:hasMembership": [], + }, + ) + + class _LLMClassRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + nonlocal llm_class_calls + llm_class_calls += 1 + if "article_seed" in context: + return AgentResult( + data={ + "id": f"llm-article:{context['article_seed']}", + "entity_type": "article", + }, + ) + if "membership_seed" in context: + return AgentResult( + data={ + "id": f"llm-membership:{context['membership_seed']}", + "entity_type": "membership", + }, + ) + return AgentResult( + data={ + "id": f"llm-contribution:{context.get('contribution_seed', 'seed')}", + "entity_type": "contribution", + }, + ) + + async def _context_gatherer( + detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + if detected_type == "user": + return ContextBundle( + detected_type="user", + context={ + "user": { + "username": "octocat", + "owned_repos": ["octocat/repo-a"], + "profile": {"company": "github"}, + }, + }, + ) + return ContextBundle( + detected_type="organization", + context={ + "organization": { + "org_name": "github", + "owned_repos": ["github/repo-a"], + "members": ["alice"], + }, + }, + ) + + async def _person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult( + data={"id": context["username"]}, + stats={"derivation": {"person_id": context["username"], "affiliation_names": []}}, + ) + + async def _rule_repo_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + nonlocal rule_repo_calls + rule_repo_calls += 1 + return AgentResult(data={"id": context["full_name"]}) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["org_name"]}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_repository_agent=_LLMRepositoryRunner(), + llm_person_agent=_LLMPersonRunner(), + llm_organization_agent=_LLMOrganizationRunner(), + llm_article_agent=_LLMClassRunner(), + llm_membership_agent=_LLMClassRunner(), + llm_contribution_agent=_LLMClassRunner(), + agent_runners={ + "person_agent": _person_agent, + "repo_agent": _rule_repo_agent, + "org_agent": _org_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + ) + + user_plan = orchestrator.get_execution_plan("user") + org_plan = orchestrator.get_execution_plan("organization") + + asyncio.run( + orchestrator.execute( + plan=user_plan, + providers=_providers(), + context={ + "url_info": GitHubURLClassification( + normalized_url="https://github.com/octocat", + detected_type=GitHubURLType.USER, + owner="octocat", + repo=None, + ), + "source_url": "https://github.com/octocat", + "agent_runtime": "llm", + }, + ), + ) + asyncio.run( + orchestrator.execute( + plan=org_plan, + providers=_providers(), + context={ + "url_info": GitHubURLClassification( + normalized_url="https://github.com/orgs/github", + detected_type=GitHubURLType.ORGANIZATION, + owner="github", + repo=None, + ), + "source_url": "https://github.com/orgs/github", + "agent_runtime": "llm", + }, + ), + ) + + assert llm_repo_calls == 2 + assert llm_org_calls == 2 + assert llm_class_calls >= 2 + assert rule_repo_calls == 0 + + +def test_execute_uses_llm_organization_runner_for_repository_runtime() -> None: + llm_org_calls = 0 + llm_article_calls = 0 + rule_org_calls = 0 + + class _LLMRepositoryRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult(data={"id": "llm-repo-root"}) + + class _LLMOrganizationRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + nonlocal llm_org_calls + llm_org_calls += 1 + return AgentResult(data={"id": f"llm-org:{context['org_name']}"}) + + class _LLMArticleRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + nonlocal llm_article_calls + llm_article_calls += 1 + return AgentResult( + data={ + "id": f"llm-article:{context.get('article_seed', 'seed')}", + "entity_type": "article", + }, + ) + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _rule_repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "rule-repo-root"}) + + async def _rule_org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + nonlocal rule_org_calls + rule_org_calls += 1 + return AgentResult(data={"id": context["org_name"]}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_repository_agent=_LLMRepositoryRunner(), + llm_organization_agent=_LLMOrganizationRunner(), + llm_article_agent=_LLMArticleRunner(), + llm_membership_agent=_LLMArticleRunner(), + llm_contribution_agent=_LLMArticleRunner(), + agent_runners={ + "repo_agent": _rule_repo_agent, + "org_agent": _rule_org_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={ + "url_info": _classification(), + "source_url": "https://github.com/octocat/Hello-World", + "agent_runtime": "llm", + }, + ), + ) + + assert llm_org_calls == 1 + assert llm_article_calls == 1 + assert rule_org_calls == 0 + assert result.agent_results["org_agent:github"].data["id"] == "llm-org:github" + + +def test_execute_uses_llm_class_runners_for_repository_runtime() -> None: + llm_article_calls = 0 + llm_membership_calls = 0 + llm_contribution_calls = 0 + rule_class_calls = 0 + + class _LLMRepositoryRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + full_name = context.get("full_name", "octocat/Hello-World") + return AgentResult( + data={ + "id": full_name, + "type": "schema:SoftwareSourceCode", + "schema:author": ["alice"], + }, + stats={ + "derivation": { + "repository_full_name": full_name, + "contributors": [{"login": "alice", "contributions": 3}], + }, + }, + ) + + class _LLMPersonRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + username = context.get("username", "alice") + return AgentResult( + data={ + "id": username, + "type": "schema:Person", + "schema:name": username, + "org:hasMembership": ["alice_github"], + }, + stats={"derivation": {"person_id": username, "affiliation_names": ["github"]}}, + ) + + class _LLMOrganizationRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + org_name = context.get("org_name", "github") + return AgentResult( + data={ + "id": org_name, + "type": "org:Organization", + "schema:name": org_name, + }, + ) + + class _LLMArticleRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + nonlocal llm_article_calls + llm_article_calls += 1 + return AgentResult( + data={ + "id": "10.1000/llm-article", + "type": "schema:ScholarlyArticle", + "schema:author": [context.get("article_seed", "alice")], + }, + stats={"articles": []}, + ) + + class _LLMMembershipRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + nonlocal llm_membership_calls + llm_membership_calls += 1 + seed = context.get("membership_seed", "alice") + return AgentResult( + data={ + "id": f"{seed}_github", + "type": "org:Membership", + "org:organization": "github", + }, + stats={"memberships": []}, + ) + + class _LLMContributionRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + nonlocal llm_contribution_calls + llm_contribution_calls += 1 + seed = context.get("contribution_seed", "octocat/Hello-World") + return AgentResult( + data={ + "id": f"alice_{seed}", + "type": "pulse:Contribution", + "schema:author": "alice", + "pulse:contributionTo": seed, + }, + stats={"contributions": []}, + ) + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [{"login": "alice"}], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _rule_repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": "rule-repo-root"}) + + async def _rule_person_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["username"]}) + + async def _rule_org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["org_name"]}) + + async def _rule_class_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + nonlocal rule_class_calls + rule_class_calls += 1 + return AgentResult(data={"id": "rule-class"}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_repository_agent=_LLMRepositoryRunner(), + llm_person_agent=_LLMPersonRunner(), + llm_organization_agent=_LLMOrganizationRunner(), + llm_article_agent=_LLMArticleRunner(), + llm_membership_agent=_LLMMembershipRunner(), + llm_contribution_agent=_LLMContributionRunner(), + agent_runners={ + "repo_agent": _rule_repo_agent, + "person_agent": _rule_person_agent, + "org_agent": _rule_org_agent, + "article_agent": _rule_class_agent, + "membership_agent": _rule_class_agent, + "contribution_agent": _rule_class_agent, + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("repository") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={ + "url_info": _classification(), + "source_url": "https://github.com/octocat/Hello-World", + "agent_runtime": "llm", + }, + ), + ) + + assert llm_article_calls == 1 + assert llm_membership_calls == 1 + assert llm_contribution_calls == 1 + assert rule_class_calls == 0 + assert "article_agent:octocat/Hello-World" in result.agent_results + assert "membership_agent:alice" in result.agent_results + assert "contribution_agent:alice_octocat/Hello-World" in result.agent_results + + +def test_execute_hard_fails_when_llm_repository_runner_errors() -> None: + rule_repo_calls = 0 + + class _FailingLLMRepositoryRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + raise RuntimeError("llm repository failure") + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="repository", + context={ + "repository": { + "full_name": "octocat/Hello-World", + "metadata": {"owner": {"login": "github", "type": "Organization"}}, + "contributors": [], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + ) + + async def _rule_repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + nonlocal rule_repo_calls + rule_repo_calls += 1 + return AgentResult(data={"id": "rule-repo-root"}) + + async def _org_agent( + context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + return AgentResult(data={"id": context["org_name"]}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_repository_agent=_FailingLLMRepositoryRunner(), + agent_runners={ + "repo_agent": _rule_repo_agent, + "org_agent": _org_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + retry_max_retries=1, + ) + plan = orchestrator.get_execution_plan("repository") + + with pytest.raises(RuntimeError, match="LLM repository runtime failed without fallback"): + asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={ + "url_info": _classification(), + "source_url": "https://github.com/octocat/Hello-World", + "agent_runtime": "llm", + }, + ), + ) + + assert rule_repo_calls == 0 + + +def test_execute_uses_llm_repository_runner_for_user_repo_fanout() -> None: + llm_repo_calls = 0 + rule_repo_calls = 0 + + class _LLMRepositoryRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + nonlocal llm_repo_calls + llm_repo_calls += 1 + return AgentResult( + data={ + "id": context["full_name"], + "type": "schema:SoftwareSourceCode", + }, + ) + + class _LLMPersonRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del providers + return AgentResult( + data={ + "id": context["username"], + "type": "schema:Person", + "org:hasMembership": [], + }, + ) + + class _LLMNoDataRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + return AgentResult(data={}) + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="user", + context={ + "user": { + "username": "alice", + "profile": {"login": "alice"}, + "owned_repos": ["alice/repo-a"], + "repository_contexts": { + "alice/repo-a": { + "full_name": "alice/repo-a", + "metadata": {"full_name": "alice/repo-a"}, + "contributors": [], + "languages": {"Python": 1}, + "readme_content": "README", + "gimie_jsonld": {}, + }, + }, + }, + }, + ) + + async def _rule_repo_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + nonlocal rule_repo_calls + rule_repo_calls += 1 + return AgentResult(data={"id": "rule-repo"}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_repository_agent=_LLMRepositoryRunner(), + llm_person_agent=_LLMPersonRunner(), + llm_organization_agent=_LLMNoDataRunner(), + llm_article_agent=_LLMNoDataRunner(), + llm_membership_agent=_LLMNoDataRunner(), + llm_contribution_agent=_LLMNoDataRunner(), + agent_runners={ + "repo_agent": _rule_repo_agent, + "person_agent": _rule_repo_agent, + "org_agent": _rule_repo_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + ) + plan = orchestrator.get_execution_plan("user") + + result = asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={ + "url_info": GitHubURLClassification( + normalized_url="https://github.com/alice", + detected_type=GitHubURLType.USER, + owner="alice", + repo=None, + ), + "source_url": "https://github.com/alice", + "agent_runtime": "llm", + }, + ), + ) + + assert llm_repo_calls == 1 + assert rule_repo_calls == 0 + assert "repo_agent:alice/repo-a" in result.agent_results + + +def test_execute_hard_fails_when_llm_user_root_runner_errors() -> None: + rule_person_calls = 0 + + class _FailingLLMPersonRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + raise RuntimeError("llm person failure") + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="user", + context={ + "user": { + "username": "alice", + "profile": {"login": "alice"}, + "owned_repos": [], + }, + }, + ) + + async def _rule_person_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + nonlocal rule_person_calls + rule_person_calls += 1 + return AgentResult(data={"id": "rule-person"}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_person_agent=_FailingLLMPersonRunner(), + agent_runners={ + "person_agent": _rule_person_agent, + "repo_agent": _rule_person_agent, + "org_agent": _rule_person_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + retry_max_retries=1, + ) + plan = orchestrator.get_execution_plan("user") + + with pytest.raises(RuntimeError, match="LLM user runtime failed without fallback"): + asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={ + "url_info": GitHubURLClassification( + normalized_url="https://github.com/alice", + detected_type=GitHubURLType.USER, + owner="alice", + repo=None, + ), + "source_url": "https://github.com/alice", + "agent_runtime": "llm", + }, + ), + ) + + assert rule_person_calls == 0 + + +def test_execute_hard_fails_when_llm_organization_root_runner_errors() -> None: + rule_org_calls = 0 + + class _FailingLLMOrganizationRunner: + async def run( + self, + context: dict[str, Any], + providers: ProviderSet, + ) -> AgentResult: + del context, providers + raise RuntimeError("llm organization failure") + + async def _context_gatherer( + _detected_type: str, + _url_info: GitHubURLClassification, + _providers: ProviderSet, + ) -> ContextBundle: + return ContextBundle( + detected_type="organization", + context={ + "organization": { + "org_name": "example", + "profile": {"login": "example"}, + "members": [], + "owned_repos": [], + }, + }, + ) + + async def _rule_org_agent( + _context: dict[str, Any], + _providers: ProviderSet, + ) -> AgentResult: + nonlocal rule_org_calls + rule_org_calls += 1 + return AgentResult(data={"id": "rule-org"}) + + orchestrator = PipelineOrchestrator( + context_gatherer=_context_gatherer, + llm_organization_agent=_FailingLLMOrganizationRunner(), + agent_runners={ + "org_agent": _rule_org_agent, + "repo_agent": _rule_org_agent, + "person_agent": _rule_org_agent, + **_empty_class_agent_runners(), + }, + retry_backoff_base=0, + retry_max_retries=1, + ) + plan = orchestrator.get_execution_plan("organization") + + with pytest.raises(RuntimeError, match="LLM organization runtime failed without fallback"): + asyncio.run( + orchestrator.execute( + plan=plan, + providers=_providers(), + context={ + "url_info": GitHubURLClassification( + normalized_url="https://github.com/orgs/example", + detected_type=GitHubURLType.ORGANIZATION, + owner="example", + repo=None, + ), + "source_url": "https://github.com/orgs/example", + "agent_runtime": "llm", + }, + ), + ) + + assert rule_org_calls == 0 diff --git a/tests/v2/test_orchestrator_graph.py b/tests/v2/test_orchestrator_graph.py new file mode 100644 index 0000000..3676aed --- /dev/null +++ b/tests/v2/test_orchestrator_graph.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +from src.v2.pipeline import PipelineOrchestrator + + +def test_repository_execution_plan_stage_order() -> None: + orchestrator = PipelineOrchestrator() + + plan = orchestrator.get_execution_plan("repository") + + assert [stage.name for stage in plan.stages] == [ + "context_gather", + "repo_agent", + "person_agents", + "org_agents", + "article_agents", + "membership_agents", + "contribution_agents", + ] + + +def test_user_execution_plan_stage_order() -> None: + orchestrator = PipelineOrchestrator() + + plan = orchestrator.get_execution_plan("user") + + assert [stage.name for stage in plan.stages] == [ + "context_gather", + "person_agent", + "repo_agents", + "org_agents", + "article_agents", + "membership_agents", + "contribution_agents", + ] + + +def test_organization_execution_plan_stage_order() -> None: + orchestrator = PipelineOrchestrator() + + plan = orchestrator.get_execution_plan("organization") + + assert [stage.name for stage in plan.stages] == [ + "context_gather", + "org_agent", + "person_agents", + "repo_agents", + "article_agents", + "membership_agents", + "contribution_agents", + ] + + +def test_person_agent_fanout_stage_is_parallelizable() -> None: + orchestrator = PipelineOrchestrator() + plan = orchestrator.get_execution_plan("repository") + + person_stage = next(stage for stage in plan.stages if stage.name == "person_agents") + article_stage = next(stage for stage in plan.stages if stage.name == "article_agents") + membership_stage = next( + stage for stage in plan.stages if stage.name == "membership_agents" + ) + contribution_stage = next( + stage for stage in plan.stages if stage.name == "contribution_agents" + ) + + assert person_stage.groups[0].parallelizable is True + assert article_stage.groups[0].parallelizable is True + assert membership_stage.groups[0].parallelizable is True + assert contribution_stage.groups[0].parallelizable is True + + +def test_execution_plans_have_no_circular_dependencies() -> None: + orchestrator = PipelineOrchestrator() + + for detected_type in ("repository", "user", "organization"): + plan = orchestrator.get_execution_plan(detected_type) + assert plan.has_circular_dependencies() is False + + +def test_execution_plan_is_serializable_to_dict() -> None: + orchestrator = PipelineOrchestrator() + plan = orchestrator.get_execution_plan("repository") + + serialized = plan.to_dict() + + assert serialized["detected_type"] == "repository" + assert serialized["stages"][0]["name"] == "context_gather" + assert isinstance(serialized["stages"][0]["groups"], list) diff --git a/tests/v2/test_organization_agent.py b/tests/v2/test_organization_agent.py new file mode 100644 index 0000000..7532b38 --- /dev/null +++ b/tests/v2/test_organization_agent.py @@ -0,0 +1,192 @@ +from __future__ import annotations + +import asyncio +from typing import Any, Callable + +from jsonschema import validate + +from src.v2.agents import OrganizationAgentV2, ProviderSet +from src.v2.ingest.providers.base import GitHubProvider +from src.v2.ingest.providers.mock_github import MockGitHubProvider +from src.v2.ingest.providers.mock_infoscience import MockInfoscienceProvider +from src.v2.ingest.providers.mock_ror import MockRORProvider + + +class _FailingGitHubProvider(GitHubProvider): + def get_repository(self, full_name: str) -> dict[str, Any]: + raise AssertionError(full_name) + + def get_user(self, username: str) -> dict[str, Any]: + raise AssertionError(username) + + def get_organization(self, org_name: str) -> dict[str, Any]: + raise AssertionError(org_name) + + def get_contributors(self, full_name: str) -> list[dict[str, Any]]: + raise AssertionError(full_name) + + def get_languages(self, full_name: str) -> dict[str, int]: + raise AssertionError(full_name) + + +class _GitHubOrganizationWithRepositoriesProvider(GitHubProvider): + def get_repository(self, full_name: str) -> dict[str, Any]: + del full_name + return {} + + def get_user(self, username: str) -> dict[str, Any]: + del username + return {} + + def get_organization(self, org_name: str) -> dict[str, Any]: + return { + "login": org_name, + "name": "GitHub", + "followers": 200000, + "repositories": ["repo-a", "repo-b"], + } + + def get_contributors(self, full_name: str) -> list[dict[str, Any]]: + del full_name + return [] + + def get_languages(self, full_name: str) -> dict[str, int]: + del full_name + return {} + + +def test_organization_agent_output_validates_against_agent_schema( + load_schema: Callable[[str, str], dict[str, Any]], +) -> None: + agent = OrganizationAgentV2() + providers = ProviderSet( + github=MockGitHubProvider(), + ror=MockRORProvider(), + infoscience=MockInfoscienceProvider(), + ) + + result = asyncio.run( + agent.run( + { + "org_name": "github", + "ror_id": "https://ror.org/02s376052", + }, + providers, + ), + ) + + schema = load_schema("agent", "organization") + validate(instance=result.data, schema=schema) + + assert result.data["schema:name"] == "Ecole Polytechnique Federale de Lausanne" + assert result.data["pulse:OrganizationType"] == "pulse:University" + assert result.data["idSource"] == "pulse:ror" + assert result.data["id"] == "https://ror.org/02s376052" + assert "schema:alternateName" not in result.data + assert "source_repositories" not in result.data + + derivation = result.stats.get("derivation") + assert isinstance(derivation, dict) + assert derivation["organization_id"] == "https://ror.org/02s376052" + assert derivation["organization_name"] == "Ecole Polytechnique Federale de Lausanne" + assert isinstance(derivation["owned_repositories"], list) + + +def test_organization_agent_falls_back_when_ror_is_unavailable() -> None: + agent = OrganizationAgentV2() + providers = ProviderSet( + github=MockGitHubProvider(), + ror=MockRORProvider(), + infoscience=MockInfoscienceProvider(), + ) + + result = asyncio.run( + agent.run({"org_name": "github", "ror_id": "https://ror.org/000000000"}, providers), + ) + + assert result.warnings + assert result.data["idSource"] == "pulse:githubOrganizationHandle" + assert result.data["id"] == "github" + + +def test_organization_agent_populates_unit_relationships_from_ror_hierarchy() -> None: + agent = OrganizationAgentV2() + providers = ProviderSet( + github=MockGitHubProvider(), + ror=MockRORProvider(), + infoscience=MockInfoscienceProvider(), + ) + + result = asyncio.run( + agent.run({"org_name": "github", "ror_id": "https://ror.org/02s376052"}, providers), + ) + + assert result.data["org:hasUnit"] + + +def test_organization_agent_permissive_validation_warns_on_malformed_optional_fields() -> None: + agent = OrganizationAgentV2() + providers = ProviderSet( + github=MockGitHubProvider(), + ror=MockRORProvider(), + infoscience=MockInfoscienceProvider(), + ) + + result = asyncio.run( + agent.run( + { + "org_name": "github", + "agent_overrides": {"pulse:OrganizationType": "invalid-type"}, + }, + providers, + ), + ) + + assert result.warnings + assert "pulse:OrganizationType" not in result.data + assert result.raw_output["pulse:OrganizationType"] == "invalid-type" + + +def test_organization_agent_skips_github_lookup_when_disabled() -> None: + agent = OrganizationAgentV2() + providers = ProviderSet( + github=_FailingGitHubProvider(), + ror=MockRORProvider(), + infoscience=MockInfoscienceProvider(), + ) + + result = asyncio.run( + agent.run( + { + "org_name": "EPFL", + "github_lookup_enabled": False, + "ror_id": "https://ror.org/02s376052", + }, + providers, + ), + ) + + assert result.data["idSource"] == "pulse:ror" + assert result.data["id"] == "https://ror.org/02s376052" + assert result.data["identifiers"].get("pulse:githubOrganizationHandle") is None + + +def test_organization_agent_repository_mode_owns_only_source_repo() -> None: + agent = OrganizationAgentV2() + providers = ProviderSet( + github=_GitHubOrganizationWithRepositoriesProvider(), + ror=MockRORProvider(), + infoscience=MockInfoscienceProvider(), + ) + + result = asyncio.run( + agent.run( + { + "org_name": "github", + "source_repositories": ["owner-org/source-repo"], + }, + providers, + ), + ) + + assert result.data["pulse:owns"] == ["owner-org/source-repo"] diff --git a/tests/v2/test_ownership_check.py b/tests/v2/test_ownership_check.py new file mode 100644 index 0000000..ba60cd0 --- /dev/null +++ b/tests/v2/test_ownership_check.py @@ -0,0 +1,320 @@ +from __future__ import annotations + +from src.v2.pipeline.stages.models import AssembledOutput, ReconciledEntities +from src.v2.pipeline.stages.ownership_check import ( + guarantee_repo_author, + infer_owners, + validate_ownership, +) + + +def _person(*, github_username: str, owns: list) -> dict: + return { + "id": f"https://orcid.org/{github_username}-orcid", + "type": "schema:Person", + "schema:name": github_username.title(), + "pulse:githubUsername": github_username, + "pulse:owns": owns, + } + + +def _org(*, handle: str, owns: list) -> dict: + return { + "id": f"https://ror.org/{handle}", + "type": "org:Organization", + "schema:name": handle.upper(), + "pulse:githubOrganizationHandle": handle, + "pulse:owns": owns, + } + + +def test_owner_match_keeps_entry() -> None: + person = _person( + github_username="cmdoret", + owns=[{"@id": "https://github.com/cmdoret/personal-site"}], + ) + promoted, warnings = validate_ownership( + AssembledOutput(root_entity=None, related_entities=[person], excluded_entities=[]), + ) + assert warnings == [] + assert promoted.related_entities[0]["pulse:owns"] == [ + {"@id": "https://github.com/cmdoret/personal-site"}, + ] + + +def test_owner_mismatch_drops_entry() -> None: + """The user's example: rmfranken should not own sdsc-ordes/repository-template.""" + person = _person( + github_username="rmfranken", + owns=[{"@id": "https://github.com/sdsc-ordes/repository-template"}], + ) + promoted, warnings = validate_ownership( + AssembledOutput(root_entity=None, related_entities=[person], excluded_entities=[]), + ) + assert promoted.related_entities[0]["pulse:owns"] is None + assert any( + "sdsc-ordes/repository-template" in w and "rmfranken" in w for w in warnings + ) + + +def test_organization_owner_match() -> None: + org = _org( + handle="sdsc-ordes", + owns=[ + {"@id": "https://github.com/sdsc-ordes/repository-template"}, + {"@id": "https://github.com/sdsc-ordes/gimie"}, + ], + ) + promoted, warnings = validate_ownership( + AssembledOutput(root_entity=None, related_entities=[org], excluded_entities=[]), + ) + assert warnings == [] + assert len(promoted.related_entities[0]["pulse:owns"]) == 2 + + +def test_partial_mismatch_keeps_only_matching_entries() -> None: + org = _org( + handle="sdsc-ordes", + owns=[ + {"@id": "https://github.com/sdsc-ordes/gimie"}, # match + {"@id": "https://github.com/some-other-org/foo"}, # mismatch + ], + ) + promoted, warnings = validate_ownership( + AssembledOutput(root_entity=None, related_entities=[org], excluded_entities=[]), + ) + kept = promoted.related_entities[0]["pulse:owns"] + assert kept == [{"@id": "https://github.com/sdsc-ordes/gimie"}] + assert any("some-other-org/foo" in w for w in warnings) + + +def test_non_github_url_passes_through() -> None: + """Other forges (gitlab, bitbucket, ...) aren't validated yet.""" + person = _person( + github_username="cmdoret", + owns=[{"@id": "https://gitlab.com/somewhere/else"}], + ) + promoted, warnings = validate_ownership( + AssembledOutput(root_entity=None, related_entities=[person], excluded_entities=[]), + ) + assert warnings == [] + assert promoted.related_entities[0]["pulse:owns"] == [ + {"@id": "https://gitlab.com/somewhere/else"}, + ] + + +def test_missing_handle_passes_through() -> None: + """When the source entity has no GitHub handle, we can't validate.""" + person = { + "id": "https://orcid.org/0000-0000-0000-0000", + "type": "schema:Person", + "schema:name": "Anon", + "pulse:owns": [{"@id": "https://github.com/some-org/some-repo"}], + } + promoted, warnings = validate_ownership( + AssembledOutput(root_entity=None, related_entities=[person], excluded_entities=[]), + ) + assert warnings == [] + assert promoted.related_entities[0]["pulse:owns"] == [ + {"@id": "https://github.com/some-org/some-repo"}, + ] + + +def test_pulse_owns_on_non_person_or_org_is_stripped() -> None: + article = { + "id": "https://doi.org/10.1234/abc", + "type": "schema:ScholarlyArticle", + "schema:name": "An Article", + "pulse:owns": [{"@id": "https://github.com/foo/bar"}], + } + promoted, warnings = validate_ownership( + AssembledOutput(root_entity=None, related_entities=[article], excluded_entities=[]), + ) + assert promoted.related_entities[0]["pulse:owns"] is None + assert any("schema:ScholarlyArticle" in w for w in warnings) + + +def test_string_form_owns_entry_is_handled() -> None: + """`pulse:owns` may be a list of strings as well as a list of dicts.""" + person = _person( + github_username="cmdoret", + owns=["https://github.com/cmdoret/site", "https://github.com/wrong/repo"], + ) + promoted, warnings = validate_ownership( + AssembledOutput(root_entity=None, related_entities=[person], excluded_entities=[]), + ) + assert promoted.related_entities[0]["pulse:owns"] == [ + "https://github.com/cmdoret/site", + ] + assert any("wrong/repo" in w for w in warnings) + + +# -- infer_owners ---------------------------------------------------------- + + +def _repo(*, full_name: str, owned_by=None) -> dict: + repo: dict = { + "id": f"https://github.com/{full_name}", + "type": "schema:SoftwareSourceCode", + "schema:name": full_name.split("/", maxsplit=1)[-1], + "pulse:githubRepositoryHandle": full_name, + } + if owned_by is not None: + repo["pulse:ownedBy"] = owned_by + return repo + + +def test_infer_owners_links_repo_to_org() -> None: + org = _org(handle="sdsc-ordes", owns=[]) + repo = _repo(full_name="sdsc-ordes/gimie") + promoted, warnings = infer_owners( + AssembledOutput(root_entity=repo, related_entities=[org], excluded_entities=[]), + ) + assert promoted.root_entity["pulse:ownedBy"] == {"@id": "https://ror.org/sdsc-ordes"} + assert promoted.related_entities[0]["pulse:owns"] == [ + {"@id": "https://github.com/sdsc-ordes/gimie"}, + ] + assert any("Inferred pulse:ownedBy" in w for w in warnings) + assert any("Inferred pulse:owns" in w for w in warnings) + + +def test_infer_owners_links_repo_to_person() -> None: + person = _person(github_username="cmdoret", owns=[]) + repo = _repo(full_name="cmdoret/personal-site") + promoted, _warnings = infer_owners( + AssembledOutput(root_entity=repo, related_entities=[person], excluded_entities=[]), + ) + assert promoted.root_entity["pulse:ownedBy"] == { + "@id": "https://orcid.org/cmdoret-orcid", + } + assert promoted.related_entities[0]["pulse:owns"] == [ + {"@id": "https://github.com/cmdoret/personal-site"}, + ] + + +def test_infer_owners_no_match_leaves_repo_alone() -> None: + org = _org(handle="some-other-org", owns=[]) + repo = _repo(full_name="sdsc-ordes/gimie") + promoted, warnings = infer_owners( + AssembledOutput(root_entity=repo, related_entities=[org], excluded_entities=[]), + ) + assert promoted.root_entity.get("pulse:ownedBy") in (None, "") + assert promoted.related_entities[0]["pulse:owns"] == [] + assert warnings == [] + + +def test_infer_owners_does_not_overwrite_existing_owned_by() -> None: + org = _org(handle="sdsc-ordes", owns=[]) + other_id = "https://example.org/manually-stamped" + repo = _repo(full_name="sdsc-ordes/gimie", owned_by={"@id": other_id}) + promoted, warnings = infer_owners( + AssembledOutput(root_entity=repo, related_entities=[org], excluded_entities=[]), + ) + # ownedBy stays put + assert promoted.root_entity["pulse:ownedBy"] == {"@id": other_id} + # but the org's pulse:owns is still updated (one direction is independent + # of the other) + assert promoted.related_entities[0]["pulse:owns"] == [ + {"@id": "https://github.com/sdsc-ordes/gimie"}, + ] + assert any("not overwriting" in w for w in warnings) + + +def test_infer_owners_dedupes_when_owns_already_contains_repo() -> None: + repo_id = "https://github.com/sdsc-ordes/gimie" + org = _org(handle="sdsc-ordes", owns=[{"@id": repo_id}]) + repo = _repo(full_name="sdsc-ordes/gimie") + promoted, warnings = infer_owners( + AssembledOutput(root_entity=repo, related_entities=[org], excluded_entities=[]), + ) + assert promoted.related_entities[0]["pulse:owns"] == [{"@id": repo_id}] + # only the ownedBy direction was stamped + inferred_warnings = [w for w in warnings if "Inferred" in w] + assert len(inferred_warnings) == 1 + assert "ownedBy" in inferred_warnings[0] + + +def test_infer_owners_skips_non_repository_entities() -> None: + org = _org(handle="sdsc-ordes", owns=[]) + article = { + "id": "https://doi.org/10.1234/abc", + "type": "schema:ScholarlyArticle", + "schema:name": "Some Article", + } + promoted, warnings = infer_owners( + AssembledOutput( + root_entity=None, + related_entities=[org, article], + excluded_entities=[], + ), + ) + assert warnings == [] + assert article in promoted.related_entities + + +def test_guarantee_repo_author_org_owner_synthesizes_distinct_person_stub() -> None: + """When the github owner is an Organization (not a Person), `schema:author` + can't carry the org id (SHACL requires Person). The stage must synthesize a + Person placeholder with a non-clashing id so it survives `validate_author_classes` + and the org entity stays untouched at its github URL. + """ + repo = { + "id": "https://github.com/BWHCNI/OpenMIMS", + "type": "schema:SoftwareSourceCode", + "schema:name": "OpenMIMS", + "pulse:githubRepositoryHandle": "BWHCNI/OpenMIMS", + "schema:author": [], + } + org = { + "id": "https://github.com/BWHCNI", + "type": "org:Organization", + "schema:name": "BWHCNI", + "pulse:githubOrganizationHandle": "BWHCNI", + } + reconciled = ReconciledEntities( + entities={"repositories": [repo], "persons": [], "organizations": [org]}, + ) + + new_reconciled, warnings = guarantee_repo_author(reconciled) + + repos = new_reconciled.entities["repositories"] + persons = new_reconciled.entities["persons"] + assert len(repos) == 1 + assert len(repos[0]["schema:author"]) == 1 + assert len(persons) == 1 + stub_id = repos[0]["schema:author"][0] + # Stub id must NOT collide with the existing Org id at github.com/BWHCNI + assert stub_id != "https://github.com/BWHCNI" + assert persons[0]["id"] == stub_id + assert persons[0]["type"] == "schema:Person" + # Org entity should still exist and be untouched + assert new_reconciled.entities["organizations"] == [org] + assert any("github owner 'BWHCNI' is an Organization" in w for w in warnings) + + +def test_guarantee_repo_author_person_owner_uses_person_id() -> None: + """Existing behavior: when a Person owner exists in the graph, stamp it + directly without synthesizing a stub.""" + repo = { + "id": "https://github.com/alice/repo", + "type": "schema:SoftwareSourceCode", + "pulse:githubRepositoryHandle": "alice/repo", + "schema:author": [], + } + alice = { + "id": "https://github.com/alice", + "type": "schema:Person", + "schema:name": "Alice", + "pulse:githubUsername": "alice", + } + reconciled = ReconciledEntities( + entities={"repositories": [repo], "persons": [alice], "organizations": []}, + ) + + new_reconciled, warnings = guarantee_repo_author(reconciled) + + repos = new_reconciled.entities["repositories"] + assert repos[0]["schema:author"] == ["https://github.com/alice"] + # No new persons synthesized — Person owner was reused. + assert new_reconciled.entities["persons"] == [alice] + assert any("stamped fallback owner 'https://github.com/alice'" in w for w in warnings) diff --git a/tests/v2/test_partial_failure.py b/tests/v2/test_partial_failure.py new file mode 100644 index 0000000..582d8fe --- /dev/null +++ b/tests/v2/test_partial_failure.py @@ -0,0 +1,194 @@ +from __future__ import annotations + +import pytest + +from src.v2.pipeline.stages.models import ReconciledEntities +from src.v2.pipeline.stages.output_assembly import ( + RootEntityValidationError, + assemble_output, +) +from src.v2.validation.schema_validation import ( + BatchValidationResult, + ValidationResult, +) + +UNPROCESSABLE_ENTITY_STATUS = 422 +EXPECTED_EXCLUDED_ENTITIES = 2 +MIN_EXPECTED_WARNINGS = 3 + + +def _repository_entity() -> dict: + return { + "id": "https://github.com/owner/repo", + "schema:name": "owner/repo", + "schema:author": ["https://github.com/johndoe"], + "pulse:ownedBy": "https://github.com/johndoe", + } + + +def _person_entity() -> dict: + return { + "id": "https://github.com/johndoe", + "schema:name": "John Doe", + } + + +def _validation_error(path: str) -> ValidationResult: + return ValidationResult( + entity_type="person", + is_valid=False, + errors=[ + { + "path": path, + "message": "invalid value", + "constraint": "pattern", + "expected": "^[a-z]+$", + }, + ], + ) + + +def test_assemble_output_includes_all_entities_when_all_are_valid() -> None: + repository = _repository_entity() + person = _person_entity() + reconciled = ReconciledEntities( + entities={ + "repositories": [repository], + "persons": [person], + }, + memberships=[], + contributions=[], + link_warnings=[], + ) + strict = BatchValidationResult( + valid_entities=[("repository", repository), ("person", person)], + invalid_entities=[], + warnings=[], + ) + + assembled = assemble_output(reconciled, strict) + + assert assembled.root_entity["id"] == repository["id"] + assert any(entity["id"] == person["id"] for entity in assembled.related_entities) + assert assembled.excluded_entities == [] + assert assembled.warnings == [] + + +def test_assemble_output_excludes_invalid_non_root_entities_and_keeps_root() -> None: + repository = _repository_entity() + person = _person_entity() + reconciled = ReconciledEntities( + entities={"repositories": [repository], "persons": [person]}, + memberships=[], + contributions=[], + link_warnings=[], + ) + strict = BatchValidationResult( + valid_entities=[("repository", repository)], + invalid_entities=[("person", person, _validation_error("schema:name"))], + warnings=[], + ) + + assembled = assemble_output(reconciled, strict) + + assert assembled.root_entity["id"] == repository["id"] + assert not any(entity["id"] == person["id"] for entity in assembled.related_entities) + assert assembled.excluded_entities + assert assembled.warnings + + +def test_assemble_output_raises_422_when_root_entity_fails_strict_validation() -> None: + repository = _repository_entity() + person = _person_entity() + root_validation = ValidationResult( + entity_type="repository", + is_valid=False, + errors=[ + { + "path": "pulse:githubRepositoryHandle", + "message": "missing property", + "constraint": "required", + "expected": "pulse:githubRepositoryHandle", + }, + ], + ) + + reconciled = ReconciledEntities( + entities={"repositories": [repository], "persons": [person]}, + ) + strict = BatchValidationResult( + valid_entities=[], + invalid_entities=[("repository", repository, root_validation)], + warnings=[], + ) + + with pytest.raises(RootEntityValidationError) as raised: + assemble_output(reconciled, strict) + + assert raised.value.status_code == UNPROCESSABLE_ENTITY_STATUS + assert raised.value.entity_id == repository["id"] + assert raised.value.validation_errors == root_validation.errors + + +def test_assemble_output_collects_all_warnings_for_multiple_non_root_failures() -> None: + repository = _repository_entity() + person = _person_entity() + organization = {"id": "https://ror.org/05gzmn429", "schema:name": "EPFL"} + reconciled = ReconciledEntities( + entities={ + "repositories": [repository], + "persons": [person], + "organizations": [organization], + }, + link_warnings=["pre-existing warning"], + ) + strict = BatchValidationResult( + valid_entities=[("repository", repository)], + invalid_entities=[ + ("person", person, _validation_error("schema:name")), + ("organization", organization, _validation_error("schema:identifier")), + ], + ) + + assembled = assemble_output(reconciled, strict) + + assert len(assembled.excluded_entities) == EXPECTED_EXCLUDED_ENTITIES + assert len(assembled.warnings) >= MIN_EXPECTED_WARNINGS + assert "pre-existing warning" in assembled.warnings + + +def test_assemble_output_cleans_excluded_references_from_root_relationship_fields() -> None: + repository = _repository_entity() + person = _person_entity() + reconciled = ReconciledEntities( + entities={"repositories": [repository], "persons": [person]}, + ) + strict = BatchValidationResult( + valid_entities=[("repository", repository)], + invalid_entities=[("person", person, _validation_error("schema:name"))], + ) + + assembled = assemble_output(reconciled, strict) + + assert assembled.root_entity["schema:author"] == [] + assert assembled.root_entity["pulse:ownedBy"] is None + + +def test_assemble_output_records_reasons_for_excluded_entities() -> None: + repository = _repository_entity() + person = _person_entity() + validation = _validation_error("schema:name") + reconciled = ReconciledEntities( + entities={"repositories": [repository], "persons": [person]}, + ) + strict = BatchValidationResult( + valid_entities=[("repository", repository)], + invalid_entities=[("person", person, validation)], + ) + + assembled = assemble_output(reconciled, strict) + + assert assembled.excluded_entities + excluded = assembled.excluded_entities[0] + assert excluded["entity"]["id"] == person["id"] + assert excluded["reason"] == validation.errors diff --git a/tests/v2/test_person_agent.py b/tests/v2/test_person_agent.py new file mode 100644 index 0000000..f8d04a4 --- /dev/null +++ b/tests/v2/test_person_agent.py @@ -0,0 +1,172 @@ +from __future__ import annotations + +import asyncio +from typing import Any, Callable + +from jsonschema import validate + +from src.v2.agents import PersonAgentV2, ProviderSet +from src.v2.ingest.providers.base import GitHubProvider +from src.v2.ingest.providers.mock_github import MockGitHubProvider +from src.v2.ingest.providers.mock_infoscience import MockInfoscienceProvider +from src.v2.ingest.providers.mock_orcid import MockORCIDProvider + +MIN_EXPECTED_MEMBERSHIPS = 2 +INVALID_ORCID_FIELD_VALUE = 123 + + +class _GitHubUserWithRepositoriesProvider(GitHubProvider): + def get_repository(self, full_name: str) -> dict[str, Any]: + del full_name + return {} + + def get_user(self, username: str) -> dict[str, Any]: + return { + "login": username, + "name": "Alice Smith", + "company": "GitHub", + "html_url": f"https://github.com/{username}", + "orcid": "0000-0002-1825-0097", + "repositories": ["repo-1", "repo-2"], + } + + def get_organization(self, org_name: str) -> dict[str, Any]: + del org_name + return {} + + def get_contributors(self, full_name: str) -> list[dict[str, Any]]: + del full_name + return [] + + def get_languages(self, full_name: str) -> dict[str, int]: + del full_name + return {} + + +def test_person_agent_output_validates_and_merges_affiliations( + load_schema: Callable[[str, str], dict[str, Any]], +) -> None: + agent = PersonAgentV2() + providers = ProviderSet( + github=MockGitHubProvider(), + orcid=MockORCIDProvider(), + infoscience=MockInfoscienceProvider(), + ) + + result = asyncio.run( + agent.run( + { + "username": "octocat", + "email": "alice@example.org", + "orcid": "0000-0002-1825-0097", + "person_query": "alice smith", + }, + providers, + ), + ) + + schema = load_schema("agent", "person") + validate(instance=result.data, schema=schema) + + assert result.data["schema:name"] == "Alice Example" + assert result.data["pulse:githubUsername"] == "octocat" + assert result.data["schema:email"] == "2bd806c97f0e@example.org" + assert len(result.data["org:hasMembership"]) >= MIN_EXPECTED_MEMBERSHIPS + assert "affiliations" not in result.data + + derivation = result.stats.get("derivation") + assert isinstance(derivation, dict) + assert derivation["github_username"] == "octocat" + assert derivation["affiliation_names"] + assert isinstance(derivation["orcid_affiliations"], list) + + +def test_person_agent_warns_and_falls_back_when_orcid_record_is_unavailable() -> None: + agent = PersonAgentV2() + providers = ProviderSet( + github=MockGitHubProvider(), + orcid=MockORCIDProvider(), + infoscience=MockInfoscienceProvider(), + ) + + result = asyncio.run( + agent.run( + { + "username": "octocat", + "orcid": "7913-0249-0843-820X", + }, + providers, + ), + ) + + assert result.warnings + assert result.data["idSource"] == "pulse:githubUsername" + assert result.data["pulse:githubUsername"] == "octocat" + + +def test_person_agent_permissive_validation_warns_without_raising() -> None: + agent = PersonAgentV2() + providers = ProviderSet( + github=MockGitHubProvider(), + orcid=MockORCIDProvider(), + infoscience=MockInfoscienceProvider(), + ) + + result = asyncio.run( + agent.run( + { + "username": "octocat", + "agent_overrides": {"pulse:orcidIdentifier": INVALID_ORCID_FIELD_VALUE}, + }, + providers, + ), + ) + + assert result.warnings + assert "pulse:orcidIdentifier" not in result.data + assert result.raw_output["pulse:orcidIdentifier"] == INVALID_ORCID_FIELD_VALUE + + +def test_person_agent_repository_mode_owns_only_source_repo() -> None: + agent = PersonAgentV2() + providers = ProviderSet( + github=_GitHubUserWithRepositoriesProvider(), + orcid=MockORCIDProvider(), + infoscience=MockInfoscienceProvider(), + ) + + result = asyncio.run( + agent.run( + { + "username": "octocat", + "source_repositories": ["owner-org/source-repo"], + }, + providers, + ), + ) + + assert result.data["pulse:owns"] == ["owner-org/source-repo"] + assert len(result.data["org:hasMembership"]) >= MIN_EXPECTED_MEMBERSHIPS + + +def test_person_agent_omits_schema_email_when_no_email_is_available() -> None: + agent = PersonAgentV2() + providers = ProviderSet( + github=MockGitHubProvider(), + orcid=MockORCIDProvider(), + infoscience=MockInfoscienceProvider(), + ) + + result = asyncio.run( + agent.run( + { + "username": "octocat", + "orcid": "0000-0002-1825-0097", + "person_query": "alice smith", + }, + providers, + ), + ) + + assert "schema:email" not in result.data + assert all("schema:email" not in warning for warning in result.warnings) diff --git a/tests/v2/test_promote_failed_id_entities.py b/tests/v2/test_promote_failed_id_entities.py new file mode 100644 index 0000000..ff4e364 --- /dev/null +++ b/tests/v2/test_promote_failed_id_entities.py @@ -0,0 +1,136 @@ +from __future__ import annotations + +from src.v2.pipeline.stages.link_veracity import promote_failed_id_entities +from src.v2.pipeline.stages.models import AssembledOutput + + +def _person_with_orcid_and_github() -> dict: + return { + "id": "https://orcid.org/0000-0000-0000-0000", + "idSource": "pulse:orcid", + "type": "schema:Person", + "schema:name": "Sample Person", + "pulse:orcid": "0000-0000-0000-0000", + "pulse:githubUsername": "sampleuser", + } + + +def test_no_invalid_links_yields_no_rewrites() -> None: + person = _person_with_orcid_and_github() + assembled = AssembledOutput( + root_entity=person, + related_entities=[], + excluded_entities=[], + warnings=[], + ) + promoted, rewrites, warnings = promote_failed_id_entities( + assembled=assembled, + invalid_links=set(), + ) + assert rewrites == {} + assert warnings == [] + assert promoted is assembled + + +def test_orcid_failed_demotes_to_github() -> None: + person = _person_with_orcid_and_github() + repo = { + "id": "https://github.com/sampleuser/repo", + "idSource": "pulse:githubRepositoryHandle", + "type": "schema:SoftwareSourceCode", + "schema:name": "repo", + "pulse:githubRepositoryHandle": "sampleuser/repo", + "schema:author": ["https://orcid.org/0000-0000-0000-0000"], + } + assembled = AssembledOutput( + root_entity=repo, + related_entities=[person], + excluded_entities=[], + warnings=[], + ) + + promoted, rewrites, warnings = promote_failed_id_entities( + assembled=assembled, + invalid_links={"https://orcid.org/0000-0000-0000-0000"}, + ) + + assert rewrites == { + "https://orcid.org/0000-0000-0000-0000": "https://github.com/sampleuser", + } + assert any("Promoted entity id" in w for w in warnings) + # Person itself rewritten + promoted_person = promoted.related_entities[0] + assert promoted_person["id"] == "https://github.com/sampleuser" + assert promoted_person["idSource"] == "pulse:githubUsername" + # Reference inside the repo also rewritten + assert promoted.root_entity["schema:author"] == ["https://github.com/sampleuser"] + + +def test_no_alternative_falls_back_to_uuid() -> None: + # Person with only orcid → if orcid fails, uuid fallback + person = { + "id": "https://orcid.org/0000-1111-2222-3333", + "idSource": "pulse:orcid", + "type": "schema:Person", + "schema:name": "Solo Orcid", + "pulse:orcid": "0000-1111-2222-3333", + } + assembled = AssembledOutput( + root_entity=None, + related_entities=[person], + excluded_entities=[], + warnings=[], + ) + promoted, rewrites, _warnings = promote_failed_id_entities( + assembled=assembled, + invalid_links={"https://orcid.org/0000-1111-2222-3333"}, + ) + assert "https://orcid.org/0000-1111-2222-3333" in rewrites + new_id = rewrites["https://orcid.org/0000-1111-2222-3333"] + assert new_id != "https://orcid.org/0000-1111-2222-3333" + assert promoted.related_entities[0]["idSource"] == "uuid" + + +def test_membership_composite_id_is_left_alone() -> None: + membership = { + "id": "https://orcid.org/0000-0000-0000-0000_https://ror.org/01a2b3c4d", + "idSource": "pulse:composite", + "type": "org:Membership", + } + assembled = AssembledOutput( + root_entity=None, + related_entities=[membership], + excluded_entities=[], + warnings=[], + ) + promoted, rewrites, _warnings = promote_failed_id_entities( + assembled=assembled, + invalid_links={ + "https://orcid.org/0000-0000-0000-0000_https://ror.org/01a2b3c4d", + }, + ) + assert rewrites == {} + assert promoted is assembled + + +def test_org_ror_failed_demotes_to_github() -> None: + org = { + "id": "https://ror.org/0495fxg12", + "idSource": "pulse:ror", + "type": "org:Organization", + "schema:name": "EPFL", + "pulse:ror": "0495fxg12", + "pulse:githubOrganizationHandle": "epfl", + } + assembled = AssembledOutput( + root_entity=None, + related_entities=[org], + excluded_entities=[], + warnings=[], + ) + promoted, rewrites, _warnings = promote_failed_id_entities( + assembled=assembled, + invalid_links={"https://ror.org/0495fxg12"}, + ) + assert rewrites == {"https://ror.org/0495fxg12": "https://github.com/epfl"} + assert promoted.related_entities[0]["idSource"] == "pulse:githubOrganizationHandle" diff --git a/tests/v2/test_promoted_agent_schemas.py b/tests/v2/test_promoted_agent_schemas.py new file mode 100644 index 0000000..4c60f9a --- /dev/null +++ b/tests/v2/test_promoted_agent_schemas.py @@ -0,0 +1,102 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +import pytest +from jsonschema.validators import validator_for + +REPO_ROOT = Path(__file__).resolve().parents[2] +SOURCE_SCHEMA_DIR = ( + REPO_ROOT / "dev" / "ontology-v2-json-response" / "a-001" / "json-schema" / "agent" +) +PROMOTED_SCHEMA_DIR = REPO_ROOT / "src" / "v2" / "schema" / "json" / "agent" +STRICT_SCHEMA_DIR = REPO_ROOT / "src" / "v2" / "schema" / "json" / "strict" + +SCHEMA_FILE_MAP = { + "person.schema.json": "pulse_PersonShape.schema.json", + "repository.schema.json": "pulse_RepositoryShape.schema.json", + "organization.schema.json": "pulse_OrganizationShape.schema.json", + "membership.schema.json": "pulse_MembershipShape.schema.json", + "contribution.schema.json": "pulse_ContributionShape.schema.json", + "article.schema.json": "pulse_ArticleShape.schema.json", +} + + +def _load_json(path: Path) -> dict[str, Any]: + with path.open(encoding="utf-8") as schema_file: + parsed = json.load(schema_file) + assert isinstance(parsed, dict) + return parsed + + +def _collect_property_names(schema: dict[str, Any]) -> set[str]: + property_names: set[str] = set() + + def _walk(node: Any) -> None: + if isinstance(node, dict): + properties = node.get("properties") + if isinstance(properties, dict): + property_names.update(properties.keys()) + for child in properties.values(): + _walk(child) + + for keyword in ( + "allOf", + "anyOf", + "oneOf", + "not", + "if", + "then", + "else", + "items", + "additionalProperties", + "definitions", + "$defs", + ): + if keyword in node: + _walk(node[keyword]) + elif isinstance(node, list): + for item in node: + _walk(item) + + _walk(schema) + return property_names + + +@pytest.mark.parametrize(("promoted_name", "source_name"), SCHEMA_FILE_MAP.items()) +def test_promoted_agent_schema_is_valid_json(promoted_name: str, source_name: str) -> None: + schema_path = PROMOTED_SCHEMA_DIR / promoted_name + source_path = SOURCE_SCHEMA_DIR / source_name + + assert source_path.exists(), f"Missing source schema: {source_path}" + assert schema_path.exists(), f"Missing promoted schema: {schema_path}" + + parsed_schema = _load_json(schema_path) + assert isinstance(parsed_schema, dict) + + + +@pytest.mark.parametrize("promoted_name", SCHEMA_FILE_MAP) +def test_promoted_agent_schema_is_jsonschema_valid(promoted_name: str) -> None: + schema_path = PROMOTED_SCHEMA_DIR / promoted_name + parsed_schema = _load_json(schema_path) + + validator_cls = validator_for(parsed_schema) + validator_cls.check_schema(parsed_schema) + + +@pytest.mark.parametrize("schema_name", SCHEMA_FILE_MAP) +def test_agent_schema_contains_all_strict_property_names(schema_name: str) -> None: + strict_schema = _load_json(STRICT_SCHEMA_DIR / schema_name) + agent_schema = _load_json(PROMOTED_SCHEMA_DIR / schema_name) + + strict_fields = _collect_property_names(strict_schema) + agent_fields = _collect_property_names(agent_schema) + + missing_fields = sorted(strict_fields - agent_fields) + assert not missing_fields, ( + f"Agent schema '{schema_name}' is missing fields defined in strict schema: " + f"{missing_fields}" + ) diff --git a/tests/v2/test_promoted_strict_schemas.py b/tests/v2/test_promoted_strict_schemas.py new file mode 100644 index 0000000..d40d5f7 --- /dev/null +++ b/tests/v2/test_promoted_strict_schemas.py @@ -0,0 +1,53 @@ +import json +from pathlib import Path + +import pytest +from jsonschema.validators import validator_for + +REPO_ROOT = Path(__file__).resolve().parents[2] +SOURCE_SCHEMA_DIR = ( + REPO_ROOT / "dev" / "ontology-v2-json-response" / "a-001" / "json-schema" / "strict" +) +PROMOTED_SCHEMA_DIR = REPO_ROOT / "src" / "v2" / "schema" / "json" / "strict" + +SCHEMA_FILE_MAP = { + "person.schema.json": "pulse_PersonShape.schema.json", + "repository.schema.json": "pulse_RepositoryShape.schema.json", + "organization.schema.json": "pulse_OrganizationShape.schema.json", + "membership.schema.json": "pulse_MembershipShape.schema.json", + "contribution.schema.json": "pulse_ContributionShape.schema.json", + "article.schema.json": "pulse_ArticleShape.schema.json", +} + + +@pytest.mark.parametrize(("promoted_name", "source_name"), SCHEMA_FILE_MAP.items()) +def test_promoted_schema_is_valid_json(promoted_name: str, source_name: str) -> None: + schema_path = PROMOTED_SCHEMA_DIR / promoted_name + source_path = SOURCE_SCHEMA_DIR / source_name + + assert source_path.exists(), f"Missing source schema: {source_path}" + assert schema_path.exists(), f"Missing promoted schema: {schema_path}" + + with schema_path.open(encoding="utf-8") as schema_file: + parsed_schema = json.load(schema_file) + + assert isinstance(parsed_schema, dict) + + +@pytest.mark.parametrize(("promoted_name", "source_name"), SCHEMA_FILE_MAP.items()) +def test_promoted_schema_is_byte_identical(promoted_name: str, source_name: str) -> None: + schema_path = PROMOTED_SCHEMA_DIR / promoted_name + source_path = SOURCE_SCHEMA_DIR / source_name + + assert schema_path.read_bytes() == source_path.read_bytes() + + +@pytest.mark.parametrize("promoted_name", SCHEMA_FILE_MAP) +def test_promoted_schema_is_jsonschema_valid(promoted_name: str) -> None: + schema_path = PROMOTED_SCHEMA_DIR / promoted_name + + with schema_path.open(encoding="utf-8") as schema_file: + parsed_schema = json.load(schema_file) + + validator_cls = validator_for(parsed_schema) + validator_cls.check_schema(parsed_schema) diff --git a/tests/v2/test_provider_cache.py b/tests/v2/test_provider_cache.py new file mode 100644 index 0000000..72e6a25 --- /dev/null +++ b/tests/v2/test_provider_cache.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +import time +from pathlib import Path + +import pytest + +from src.v2.ingest.cache import ProviderCache + + +def test_cache_miss_returns_none(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "p.db") + assert cache.get("missing") is None + + +def test_cache_get_or_set_calls_factory_once(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "p.db") + calls: list[int] = [] + + def factory() -> dict[str, str]: + calls.append(1) + return {"name": "EPFL"} + + key = ProviderCache.make_key("ror", "search_organizations", query="EPFL") + first = cache.get_or_set(key, factory) + second = cache.get_or_set(key, factory) + assert first == {"name": "EPFL"} + assert second == {"name": "EPFL"} + assert calls == [1] + + +def test_cache_set_with_explicit_ttl_expires(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "p.db", default_ttl_seconds=60) + cache.set("k", {"v": 1}, ttl_seconds=0.05) + assert cache.get("k") == {"v": 1} + time.sleep(0.1) + assert cache.get("k") is None + + +def test_factory_exception_does_not_cache(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "p.db") + boom = RuntimeError("boom") + + def factory() -> dict[str, str]: + raise boom + + key = ProviderCache.make_key("github", "get_user", username="ghost") + with pytest.raises(RuntimeError): + cache.get_or_set(key, factory) + assert cache.get(key) is None + + +def test_none_factory_result_is_not_cached(tmp_path: Path) -> None: + cache = ProviderCache(tmp_path / "p.db") + calls: list[int] = [] + + def factory() -> None: + calls.append(1) + return None + + key = ProviderCache.make_key("orcid", "get_person_by_orcid", orcid="0000-0000-0000-0000") + cache.get_or_set(key, factory) + cache.get_or_set(key, factory) + assert calls == [1, 1] + + +def test_make_key_is_deterministic_and_arg_sensitive() -> None: + a = ProviderCache.make_key("github", "get_user", username="a") + b = ProviderCache.make_key("github", "get_user", username="a") + c = ProviderCache.make_key("github", "get_user", username="b") + assert a == b + assert a != c diff --git a/tests/v2/test_provider_interfaces.py b/tests/v2/test_provider_interfaces.py new file mode 100644 index 0000000..7d81f1a --- /dev/null +++ b/tests/v2/test_provider_interfaces.py @@ -0,0 +1,558 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any + +import pytest + +from src.v1.data_models.infoscience import InfoscienceAuthor, InfoscienceSearchResult +from src.v1.parsers.orgs_parser import GitHubOrganizationsParser +from src.v1.parsers.users_parser import GitHubUsersParser +from src.v2.ingest.providers import ( + BaseProvider, + GitHubProvider, + InfoscienceProvider, + MockGitHubProvider, + MockInfoscienceProvider, + MockORCIDProvider, + MockRORProvider, + ORCIDProvider, + RORProvider, + get_provider, +) +from src.v2.ingest.providers.base import ( + INFOSCIENCE_PUBLICATION_OPTIONAL_FIELDS, + INFOSCIENCE_PUBLICATION_REQUIRED_FIELDS, +) +from src.v2.ingest.providers.github_provider import RealGitHubProvider +from src.v2.ingest.providers.infoscience_provider import RealInfoscienceProvider +from src.v2.ingest.providers.orcid_provider import RealORCIDProvider +from src.v2.ingest.providers.ror_provider import RealRORProvider + +STATUS_ERROR_THRESHOLD = 400 + + +class _SpyUsersParser(GitHubUsersParser): + def __init__(self) -> None: + self.repo_calls = 0 + + def _get_rest_user_data(self, username: str) -> dict[str, Any]: + return { + "login": username, + "name": "Alice Smith", + "bio": None, + "email": None, + "location": None, + "company": None, + "blog": None, + "twitter_username": None, + "public_repos": 0, + "public_gists": 0, + "followers": 0, + "following": 0, + "created_at": "2020-01-01T00:00:00Z", + "updated_at": "2020-01-01T00:00:00Z", + "avatar_url": "https://example.org/avatar.png", + "html_url": f"https://github.com/{username}", + } + + def _get_graphql_user_data(self, username: str) -> dict[str, Any]: + del username + return {"social_accounts": []} + + def _get_user_organizations(self, username: str) -> list[str]: + del username + return [] + + def _get_user_readme(self, username: str) -> dict[str, Any]: + del username + return {"url": None, "content": None} + + def _scrape_orcid_from_profile(self, username: str) -> str | None: + del username + return None + + def _scrape_orcid_activities(self, orcid_id: str) -> Any: + del orcid_id + return None + + def _get_user_repositories(self, username: str, limit: int = 100) -> list[str]: + del username, limit + self.repo_calls += 1 + return ["repo-a"] + + +class _SpyOrganizationsParser(GitHubOrganizationsParser): + def __init__(self) -> None: + self.repo_calls = 0 + + def _get_rest_organization_data(self, org_name: str) -> dict[str, Any]: + return { + "login": org_name, + "name": "Org", + "description": None, + "email": None, + "location": None, + "company": None, + "blog": None, + "twitter_username": None, + "public_repos": 0, + "public_gists": 0, + "followers": 0, + "following": 0, + "created_at": "2020-01-01T00:00:00Z", + "updated_at": "2020-01-01T00:00:00Z", + "avatar_url": "https://example.org/avatar.png", + "html_url": f"https://github.com/{org_name}", + "gravatar_id": "", + "type": "Organization", + "node_id": "ORG_1", + "url": f"https://api.github.com/orgs/{org_name}", + "repos_url": f"https://api.github.com/orgs/{org_name}/repos", + "events_url": f"https://api.github.com/orgs/{org_name}/events", + "hooks_url": f"https://api.github.com/orgs/{org_name}/hooks", + "issues_url": f"https://api.github.com/orgs/{org_name}/issues", + "members_url": f"https://api.github.com/orgs/{org_name}/members", + } + + def _get_graphql_organization_data(self, org_name: str) -> dict[str, Any]: + del org_name + return {"social_accounts": [], "pinned_repositories": []} + + def _get_organization_public_members(self, org_name: str) -> list[str]: + del org_name + return [] + + def _get_organization_repositories( + self, + org_name: str, + limit: int = 100, + ) -> list[str]: + del org_name, limit + self.repo_calls += 1 + return ["repo-a"] + + def _get_organization_teams(self, org_name: str) -> list[str]: + del org_name + return [] + + def _get_organization_readme(self, org_name: str) -> dict[str, Any]: + del org_name + return {"url": None, "content": None} + + + +class _FakeResponse: + def __init__(self, *, status_code: int, payload: dict[str, Any]) -> None: + self.status_code = status_code + self._payload = payload + + def raise_for_status(self) -> None: + if self.status_code >= STATUS_ERROR_THRESHOLD: + message = f"HTTP {self.status_code}" + raise RuntimeError(message) + + def json(self) -> dict[str, Any]: + return self._payload + + +class _FakeSession: + def get( + self, + url: str, + *, + params: dict[str, Any] | None = None, + timeout: int | None = None, + headers: dict[str, str] | None = None, + ) -> _FakeResponse: + del params, timeout, headers + if url.endswith("/0000-0002-1825-0097/person"): + return _FakeResponse( + status_code=200, + payload={ + "name": { + "given-names": {"value": "Alice"}, + "family-name": {"value": "Example"}, + }, + }, + ) + if url.endswith("/0000-0002-1825-0097/employments"): + return _FakeResponse( + status_code=200, + payload={ + "affiliation-group": [ + { + "summaries": [ + { + "employment-summary": { + "organization": {"name": "EPFL"}, + "department-name": "School of Engineering", + "role-title": "Research Engineer", + "start-date": { + "year": {"value": "2021"}, + "month": {"value": "01"}, + "day": {"value": "01"}, + }, + }, + }, + ], + }, + ], + }, + ) + if url.endswith("/0000-0002-1825-0097/educations"): + return _FakeResponse( + status_code=200, + payload={"affiliation-group": []}, + ) + if url.endswith("/organizations/02s376052"): + return _FakeResponse( + status_code=200, + payload={ + "id": "https://ror.org/02s376052", + "names": [{"value": "EPFL", "types": ["ror_display"]}], + "acronyms": ["EPFL"], + "types": [{"label": "Education"}], + "locations": [ + { + "geonames_details": { + "country_name": "Switzerland", + "country_code": "CH", + }, + }, + ], + "links": ["https://www.epfl.ch"], + "relationships": [], + }, + ) + if url.endswith("/organizations"): + return _FakeResponse( + status_code=200, + payload={ + "items": [ + { + "id": "https://ror.org/02s376052", + "names": [{"value": "EPFL", "types": ["ror_display"]}], + "acronyms": ["EPFL"], + "types": [{"label": "Education"}], + "locations": [], + "links": [], + "relationships": [], + }, + ], + }, + ) + return _FakeResponse(status_code=404, payload={"error": "not found"}) + + +def _build_real_github_provider(**provider_kwargs: Any) -> RealGitHubProvider: + return RealGitHubProvider( + gimie_extractor=lambda _url, _format: { + "@graph": [ + { + "@type": "schema:SoftwareSourceCode", + "schema:name": "Hello-World", + "schema:codeRepository": "https://github.com/octocat/Hello-World", + "schema:programmingLanguage": ["Python"], + "schema:contributor": [{"@id": "https://github.com/octocat"}], + }, + { + "@id": "https://github.com/octocat", + "@type": ["http://schema.org/Person"], + "http://schema.org/name": [{"@value": "The Octocat"}], + }, + ], + }, + user_lookup=lambda username: {"login": username, "name": "The Octocat"}, + organization_lookup=lambda org_name: {"login": org_name, "name": "GitHub"}, + **provider_kwargs, + ) + + +def _build_real_infoscience_provider() -> RealInfoscienceProvider: + async def _search_authors(_query: str, _max_results: int) -> dict[str, Any]: + return { + "authors": [ + { + "uuid": "1f0b2b90-9e33-4f9a-9b14-8468f89f2e4d", + "name": "Alice Smith", + "orcid": "0000-0002-1825-0097", + "affiliation": "EPFL", + "profile_url": "https://infoscience.epfl.ch/entities/person/1f0b2b90-9e33-4f9a-9b14-8468f89f2e4d", + }, + ], + } + + async def _search_labs(_query: str, _max_results: int) -> dict[str, Any]: + return { + "labs": [ + { + "uuid": "8072fc98-7fc8-44cb-b8f9-805f9b0725f3", + "name": "ENAC", + "parent_organization": "EPFL", + "url": "https://infoscience.epfl.ch/entities/orgunit/8072fc98-7fc8-44cb-b8f9-805f9b0725f3", + }, + ], + } + + async def _search_publications(_query: str, _max_results: int) -> dict[str, Any]: + return { + "publications": [ + { + "uuid": "123", + "title": "Metadata at Scale", + "doi": "10.1234/example", + "authors": ["Alice Example", "Bob Example"], + "publication_date": "2025-01-15", + "lab": "EPFL ENAC", + "url": "https://infoscience.epfl.ch/entities/publication/123", + }, + { + "uuid": "456", + "title": "Metadata Without Extras", + }, + ], + } + + return RealInfoscienceProvider( + search_authors_func=_search_authors, + search_labs_func=_search_labs, + search_publications_func=_search_publications, + ) + + +def test_real_providers_implement_base_interfaces() -> None: + real_github = _build_real_github_provider() + real_infoscience = _build_real_infoscience_provider() + real_orcid = RealORCIDProvider(session=_FakeSession()) + real_ror = RealRORProvider(session=_FakeSession()) + + assert isinstance(real_github, BaseProvider) + assert isinstance(real_infoscience, BaseProvider) + assert isinstance(real_orcid, BaseProvider) + assert isinstance(real_ror, BaseProvider) + + assert isinstance(real_github, GitHubProvider) + assert isinstance(real_infoscience, InfoscienceProvider) + assert isinstance(real_orcid, ORCIDProvider) + assert isinstance(real_ror, RORProvider) + + assert not RealGitHubProvider.__abstractmethods__ + assert not RealInfoscienceProvider.__abstractmethods__ + assert not RealORCIDProvider.__abstractmethods__ + assert not RealRORProvider.__abstractmethods__ + + +def test_real_providers_execute_all_interface_methods_without_notimplementederror() -> None: + real_github = _build_real_github_provider() + real_infoscience = _build_real_infoscience_provider() + real_orcid = RealORCIDProvider(session=_FakeSession()) + real_ror = RealRORProvider(session=_FakeSession()) + + repository = real_github.get_repository("octocat/Hello-World") + user = real_github.get_user("octocat") + organization = real_github.get_organization("github") + contributors = real_github.get_contributors("octocat/Hello-World") + languages = real_github.get_languages("octocat/Hello-World") + + assert repository["full_name"] == "octocat/Hello-World" + assert user["login"] == "octocat" + assert organization["login"] == "github" + assert contributors[0]["login"] == "octocat" + assert "Python" in languages + + people = real_infoscience.search_person("alice smith") + labs = real_infoscience.search_orgunit("epfl") + publications = real_infoscience.search_publications("metadata") + + assert people[0]["infosciencePersonIdentifier"] + assert labs[0]["infoscienceOrgUnitIdentifier"] + assert publications[0]["infosciencePublicationIdentifier"] == "123" + + orcid_record = real_orcid.get_person_by_orcid("0000-0002-1825-0097") + assert orcid_record["name"] == "Alice Example" + assert orcid_record["employment"] + + ror_organization = real_ror.get_organization("https://ror.org/02s376052") + ror_matches = real_ror.search_organizations("epfl") + assert ror_organization["id"] == "https://ror.org/02s376052" + assert isinstance(ror_organization.get("acronyms"), list) + assert isinstance(ror_organization.get("labels"), list) + assert ror_matches + + +def test_real_infoscience_person_profile_url_is_coerced_to_string() -> None: + profile_url = ( + "https://infoscience.epfl.ch/entities/person/" + "1f0b2b90-9e33-4f9a-9b14-8468f89f2e4d" + ) + + async def _search_authors(_query: str, _max_results: int) -> InfoscienceSearchResult: + return InfoscienceSearchResult( + total_results=1, + authors=[ + InfoscienceAuthor( + uuid="1f0b2b90-9e33-4f9a-9b14-8468f89f2e4d", + name="Alice Smith", + orcid="0000-0002-1825-0097", + affiliation="EPFL", + profile_url=profile_url, + ), + ], + ) + + provider = RealInfoscienceProvider(search_authors_func=_search_authors) + people = provider.search_person("alice smith") + + assert len(people) == 1 + assert people[0]["profileUrl"] == profile_url + assert isinstance(people[0]["profileUrl"], str) + + +def _assert_publication_contract(publication: dict[str, Any]) -> None: + for field_name in INFOSCIENCE_PUBLICATION_REQUIRED_FIELDS: + assert field_name in publication + + for field_name in INFOSCIENCE_PUBLICATION_OPTIONAL_FIELDS: + if field_name in publication: + assert publication[field_name] is None or isinstance(publication[field_name], str) + + assert isinstance(publication["authors"], list) + assert publication["authors"] == [ + name + for name in publication["authors"] + if isinstance(name, str) and name + ] + assert publication["publicationDate"] is None or isinstance( + publication["publicationDate"], + str, + ) + assert publication["doi"] is None or isinstance(publication["doi"], str) + assert publication["url"] is None or isinstance(publication["url"], str) + assert publication["title"] is None or isinstance(publication["title"], str) + assert publication["infosciencePublicationIdentifier"] is None or isinstance( + publication["infosciencePublicationIdentifier"], + str, + ) + + +def test_infoscience_publication_contract_is_enforced_for_real_and_mock_providers() -> None: + real_infoscience = _build_real_infoscience_provider() + mock_infoscience = MockInfoscienceProvider() + + real_publications = real_infoscience.search_publications("metadata") + mock_publications = mock_infoscience.search_publications("geodata") + + assert real_publications + assert mock_publications + + for publication in [*real_publications, *mock_publications]: + _assert_publication_contract(publication) + + assert real_publications[0]["authors"] == ["Alice Example", "Bob Example"] + assert real_publications[0]["publicationDate"] == "2025-01-15" + assert real_publications[0]["sourceOrganization"] == "EPFL ENAC" + + # Missing upstream fields are normalized to contract defaults. + assert real_publications[1]["authors"] == [] + assert real_publications[1]["publicationDate"] is None + assert real_publications[1]["doi"] is None + assert real_publications[1]["url"] is None + + +def test_real_github_provider_reads_contributors_from_gimie_payload() -> None: + provider = RealGitHubProvider( + gimie_extractor=lambda _url, _format: [ + { + "@id": "https://github.com/octocat/Hello-World", + "@type": ["http://schema.org/SoftwareSourceCode"], + "http://schema.org/contributor": [ + {"@id": "https://github.com/octocat"}, + {"@id": "https://github.com/hubot"}, + ], + "http://schema.org/author": [ + {"@id": "https://orcid.org/0000-0002-1825-0097"}, + {"@id": "https://github.com/octocat"}, + ], + }, + ], + user_lookup=lambda username: {"login": username}, + organization_lookup=lambda org_name: {"login": org_name}, + ) + + contributors = provider.get_contributors("octocat/Hello-World") + + assert [contributor["login"] for contributor in contributors] == [ + "octocat", + "hubot", + ] + + +def test_get_provider_factory_returns_expected_mock_and_real_implementations() -> None: + assert isinstance(get_provider("github", use_mock=True), MockGitHubProvider) + assert isinstance(get_provider("orcid", use_mock=True), MockORCIDProvider) + assert isinstance(get_provider("infoscience", use_mock=True), MockInfoscienceProvider) + assert isinstance(get_provider("ror", use_mock=True), MockRORProvider) + + real_github = get_provider( + "github", + use_mock=False, + gimie_extractor=lambda _url, _format: {}, + user_lookup=lambda _username: {}, + organization_lookup=lambda _org_name: {}, + ) + assert isinstance(real_github, RealGitHubProvider) + + with pytest.raises(ValueError, match="Unknown provider name"): + get_provider("unsupported") + + +def test_mock_and_real_github_providers_are_interchangeable_by_interface() -> None: + mock_provider: GitHubProvider = MockGitHubProvider() + real_provider: GitHubProvider = _build_real_github_provider() + + def _extract_name(provider: GitHubProvider) -> str: + repository = provider.get_repository("octocat/Hello-World") + return str(repository.get("name", "")) + + assert _extract_name(mock_provider) + assert _extract_name(real_provider) + + +def test_user_parser_skips_repo_endpoint_when_repositories_are_disabled() -> None: + parser = _SpyUsersParser() + + metadata = parser.get_user_metadata("octocat", include_repositories=False) + + assert metadata.repositories == [] + assert parser.repo_calls == 0 + + +def test_org_parser_skips_repo_endpoint_when_repositories_are_disabled() -> None: + parser = _SpyOrganizationsParser() + + metadata = parser.get_organization_metadata( + "github", + include_repositories=False, + ) + + assert metadata.repositories == [] + assert parser.repo_calls == 0 + + +def test_user_parser_passes_include_repositories_to_metadata_call() -> None: + parser = _SpyUsersParser() + + metadata = parser.get_user_metadata("octocat", include_repositories=True) + + assert metadata.login == "octocat" + assert parser.repo_calls == 1 + + +def test_org_parser_passes_include_repositories_to_metadata_call() -> None: + parser = _SpyOrganizationsParser() + + metadata = parser.get_organization_metadata("github", include_repositories=True) + + assert metadata.login == "github" + assert parser.repo_calls == 1 diff --git a/tests/v2/test_query_log.py b/tests/v2/test_query_log.py new file mode 100644 index 0000000..eb39b24 --- /dev/null +++ b/tests/v2/test_query_log.py @@ -0,0 +1,87 @@ +from __future__ import annotations + +import json +from pathlib import Path + +import pytest + +from src.v2.observation.query_log import ( + QueryLog, + current_agent, + current_agent_context_var, + current_agent_var, + query_log_var, + record_query, + stamp_current_agent, +) + + +@pytest.fixture(autouse=True) +def _reset_query_log_contextvars() -> None: + """ContextVars persist across tests — wipe them between every test.""" + query_log_var.set(None) + current_agent_var.set(None) + current_agent_context_var.set(None) + + +def test_record_query_no_active_log_is_noop() -> None: + """When no QueryLog is set on the ContextVar, record_query is a silent no-op.""" + query_log_var.set(None) + record_query(service="infoscience.search_person", query="anyone") # must not raise + + +def test_record_query_groups_by_agent_and_context() -> None: + log = QueryLog(run_id="r-1", extract_full_path="github.com/foo/bar") + query_log_var.set(log) + + with current_agent(name="person_agent", context={"username": "cmdoret"}): + record_query(service="infoscience.search_person", query="Cyril Matthey-Doret") + record_query(service="orcid.get_person_by_orcid", query="0000-...") + + with current_agent(name="org_agent", context={"org_name": "EPFL"}): + record_query(service="ror.search_organizations", query="EPFL") + + payload = log.to_dict() + assert payload["run_id"] == "r-1" + assert payload["extract_full_path"] == "github.com/foo/bar" + agents = {entry["agent"]: entry for entry in payload["agents"]} + assert set(agents) == {"person_agent", "org_agent"} + assert agents["person_agent"]["context"] == {"username": "cmdoret"} + assert len(agents["person_agent"]["queries"]) == 2 + assert agents["org_agent"]["context"] == {"org_name": "EPFL"} + assert len(agents["org_agent"]["queries"]) == 1 + + +def test_stamp_current_agent_sets_context_for_subsequent_calls() -> None: + log = QueryLog(run_id="r-2", extract_full_path="github.com/foo/bar") + query_log_var.set(log) + stamp_current_agent(name="repo_agent", context={"full_name": "foo/bar"}) + record_query(service="github.get_organization", query="foo") + payload = log.to_dict() + assert payload["agents"][0]["agent"] == "repo_agent" + assert payload["agents"][0]["context"] == {"full_name": "foo/bar"} + assert payload["agents"][0]["queries"][0]["service"] == "github.get_organization" + + +def test_write_serialises_to_disk(tmp_path: Path) -> None: + log = QueryLog(run_id="r-3", extract_full_path="github.com/foo/bar") + query_log_var.set(log) + with current_agent(name="person_agent", context={"username": "cmdoret"}): + record_query(service="duckduckgo.search", query="cmdoret github") + + out = log.write(tmp_path) + assert out is not None + assert out.exists() + payload = json.loads(out.read_text(encoding="utf-8")) + assert payload["run_id"] == "r-3" + assert payload["agents"][0]["queries"][0]["query"] == "cmdoret github" + + +def test_unknown_agent_default_when_no_stamp() -> None: + log = QueryLog(run_id="r-4", extract_full_path="github.com/foo/bar") + query_log_var.set(log) + # Don't stamp — record_query must still work and bucket under "unknown". + record_query(service="ror.search_organizations", query="EPFL") + payload = log.to_dict() + assert payload["agents"][0]["agent"] == "unknown" + assert payload["agents"][0]["queries"][0]["query"] == "EPFL" diff --git a/tests/v2/test_rate_limiter.py b/tests/v2/test_rate_limiter.py new file mode 100644 index 0000000..69762d1 --- /dev/null +++ b/tests/v2/test_rate_limiter.py @@ -0,0 +1,176 @@ +from __future__ import annotations + +import asyncio +import logging + +import pytest + +from src.v2.ingest.providers.base import ProviderRateLimitError +from src.v2.ingest.providers.rate_limiter import RateLimiter + +HTTP_OK = 200 +EXPECTED_RETRY_CALLS = 2 +EXPECTED_REMAINING_HIGH = 42 +EXPECTED_THROTTLE_DELAY = 0.5 +EXPECTED_GITHUB_REMAINING = 3 +EXPECTED_ROR_REMAINING = 30 + + +class _Response: + def __init__(self, status_code: int, headers: dict[str, str] | None = None) -> None: + self.status_code = status_code + self.headers = headers or {} + + +class _SleepRecorder: + def __init__(self) -> None: + self.delays: list[float] = [] + + async def __call__(self, delay: float) -> None: + self.delays.append(delay) + + +def test_rate_limiter_retries_after_http_429() -> None: + recorder = _SleepRecorder() + responses = iter([_Response(429), _Response(200)]) + call_count = 0 + + def _request() -> _Response: + nonlocal call_count + call_count += 1 + return next(responses) + + limiter = RateLimiter( + max_retries=2, + base_delay_seconds=0.1, + sleep_func=recorder, + jitter_func=lambda: 0.0, + ) + response = asyncio.run(limiter.with_rate_limit("github", _request)) + + assert response.status_code == HTTP_OK + assert call_count == EXPECTED_RETRY_CALLS + assert recorder.delays == [0.1] + + +def test_rate_limiter_respects_retry_after_header() -> None: + recorder = _SleepRecorder() + responses = iter([_Response(429, {"Retry-After": "2"}), _Response(200)]) + + limiter = RateLimiter( + max_retries=2, + base_delay_seconds=0.1, + sleep_func=recorder, + jitter_func=lambda: 0.0, + ) + asyncio.run(limiter.with_rate_limit("orcid", lambda: next(responses))) + + assert recorder.delays == [2.0] + + +def test_rate_limiter_tracks_remaining_quota_per_provider() -> None: + limiter = RateLimiter(jitter_func=lambda: 0.0) + asyncio.run( + limiter.with_rate_limit( + "ror", + lambda: _Response(200, {"X-RateLimit-Remaining": "42"}), + ), + ) + + assert limiter.get_remaining("ror") == EXPECTED_REMAINING_HIGH + + +def test_rate_limiter_throttles_when_remaining_quota_is_low() -> None: + recorder = _SleepRecorder() + limiter = RateLimiter( + low_remaining_threshold=10, + near_limit_delay_seconds=0.5, + sleep_func=recorder, + jitter_func=lambda: 0.0, + ) + + asyncio.run( + limiter.with_rate_limit( + "github", + lambda: _Response(200, {"X-RateLimit-Remaining": "5"}), + ), + ) + asyncio.run( + limiter.with_rate_limit( + "github", + lambda: _Response(200), + ), + ) + + assert any(delay == EXPECTED_THROTTLE_DELAY for delay in recorder.delays) + + +def test_rate_limiter_raises_after_retry_budget_is_exhausted() -> None: + recorder = _SleepRecorder() + limiter = RateLimiter( + max_retries=2, + base_delay_seconds=0.1, + sleep_func=recorder, + jitter_func=lambda: 0.0, + ) + + with pytest.raises(ProviderRateLimitError): + asyncio.run( + limiter.with_rate_limit( + "infoscience", + lambda: _Response(429), + ), + ) + + assert recorder.delays == [0.1, 0.2] + + +def test_rate_limit_tracking_is_isolated_by_provider() -> None: + limiter = RateLimiter(jitter_func=lambda: 0.0) + asyncio.run( + limiter.with_rate_limit( + "github", + lambda: _Response(200, {"X-RateLimit-Remaining": "3"}), + ), + ) + asyncio.run( + limiter.with_rate_limit( + "ror", + lambda: _Response(200, {"X-RateLimit-Remaining": "30"}), + ), + ) + + assert limiter.get_remaining("github") == EXPECTED_GITHUB_REMAINING + assert limiter.get_remaining("ror") == EXPECTED_ROR_REMAINING + + +def test_rate_limiter_supports_same_provider_across_multiple_event_loops() -> None: + limiter = RateLimiter(jitter_func=lambda: 0.0) + + async def _call_once() -> _Response: + return await limiter.with_rate_limit( + "github", + lambda: _Response(200), + ) + + first = asyncio.run(_call_once()) + second = asyncio.run(_call_once()) + + assert first.status_code == HTTP_OK + assert second.status_code == HTTP_OK + + +def test_rate_limiter_logs_rate_limit_events(caplog: pytest.LogCaptureFixture) -> None: + recorder = _SleepRecorder() + responses = iter([_Response(429), _Response(200)]) + limiter = RateLimiter( + max_retries=1, + base_delay_seconds=0.1, + sleep_func=recorder, + jitter_func=lambda: 0.0, + ) + + with caplog.at_level(logging.WARNING, logger="src.v2.ingest.providers.rate_limiter"): + asyncio.run(limiter.with_rate_limit("github", lambda: next(responses))) + + assert any("Rate limit retry" in record.message for record in caplog.records) diff --git a/tests/v2/test_real_orcid_search.py b/tests/v2/test_real_orcid_search.py new file mode 100644 index 0000000..3422295 --- /dev/null +++ b/tests/v2/test_real_orcid_search.py @@ -0,0 +1,157 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any + +from src.v2.ingest.cache import ProviderCache +from src.v2.ingest.providers.orcid_provider import ( + EXPANDED_SEARCH_EDISMAX, + RealORCIDProvider, +) + +STATUS_ERROR_THRESHOLD = 400 + + +class _FakeResponse: + def __init__(self, *, status_code: int, payload: dict[str, Any]) -> None: + self.status_code = status_code + self._payload = payload + + def raise_for_status(self) -> None: + if self.status_code >= STATUS_ERROR_THRESHOLD: + message = f"HTTP {self.status_code}" + raise RuntimeError(message) + + def json(self) -> dict[str, Any]: + return self._payload + + +class _RecordingSession: + """Fake `requests.Session` that records every GET and returns a canned payload.""" + + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + self.calls: list[dict[str, Any]] = [] + + def get( + self, + url: str, + *, + params: dict[str, Any] | None = None, + timeout: int | None = None, + headers: dict[str, str] | None = None, + ) -> _FakeResponse: + self.calls.append( + {"url": url, "params": params, "timeout": timeout, "headers": headers}, + ) + return _FakeResponse(status_code=200, payload=self._payload) + + +_EXAMPLE_PAYLOAD: dict[str, Any] = { + "expanded-result": [ + { + "orcid-id": "0000-0001-2345-6789", + "given-names": "Noemie", + "family-names": "Mazare", + "credit-name": "Noemie Mazare", + "other-name": ["N. Mazare"], + "email": ["noemie@example.org"], + "institution-name": ["EPFL", "University of Lausanne"], + }, + { + # Garbage-typed entries should be tolerated. + "orcid-id": None, + "given-names": 42, + "family-names": "", + "credit-name": "Other Person", + "other-name": "not-a-list", + "email": ["valid@example.org", 7, ""], + "institution-name": None, + }, + ], + "num-found": 2, +} + + +def test_search_persons_builds_edismax_query_and_passes_pagination() -> None: + session = _RecordingSession(_EXAMPLE_PAYLOAD) + provider = RealORCIDProvider(session=session) + + hits = provider.search_persons("noemie mazare", rows=25, start=10) + + assert len(session.calls) == 1 + call = session.calls[0] + assert call["url"].endswith("/v3.0/expanded-search/") + assert call["params"] == { + "q": f"{EXPANDED_SEARCH_EDISMAX}noemie mazare", + "start": 10, + "rows": 25, + } + assert call["headers"] == {"Accept": "application/json"} + + assert len(hits) == 2 + first = hits[0] + assert first["orcid_id"] == "0000-0001-2345-6789" + assert first["given_names"] == "Noemie" + assert first["family_names"] == "Mazare" + assert first["credit_name"] == "Noemie Mazare" + assert first["other_names"] == ["N. Mazare"] + assert first["institution_names"] == ["EPFL", "University of Lausanne"] + assert first["emails"] == ["noemie@example.org"] + + second = hits[1] + # None / wrong-typed scalars become None; non-list scalars become []. + assert second["orcid_id"] is None + assert second["given_names"] is None + assert second["family_names"] is None + assert second["credit_name"] == "Other Person" + assert second["other_names"] == [] + assert second["institution_names"] == [] + # Empty / non-string entries are filtered out. + assert second["emails"] == ["valid@example.org"] + + +def test_search_persons_strips_query_and_clamps_rows() -> None: + session = _RecordingSession(_EXAMPLE_PAYLOAD) + provider = RealORCIDProvider(session=session) + + provider.search_persons(" noemie ", rows=10_000, start=-5) + + params = session.calls[0]["params"] + assert params["q"] == f"{EXPANDED_SEARCH_EDISMAX}noemie" + assert params["rows"] == 200 # capped at EXPANDED_SEARCH_MAX_ROWS + assert params["start"] == 0 # negative offsets clamped to zero + + +def test_search_persons_empty_query_short_circuits_without_http() -> None: + session = _RecordingSession(_EXAMPLE_PAYLOAD) + provider = RealORCIDProvider(session=session) + + assert provider.search_persons(" ") == [] + assert session.calls == [] + + +def test_search_persons_returns_empty_when_payload_lacks_expanded_result() -> None: + session = _RecordingSession({"num-found": 0}) + provider = RealORCIDProvider(session=session) + + assert provider.search_persons("noemie") == [] + + +def test_search_persons_uses_provider_cache_to_skip_duplicate_calls( + tmp_path: Path, +) -> None: + cache = ProviderCache(tmp_path / "cache.sqlite") + session = _RecordingSession(_EXAMPLE_PAYLOAD) + provider = RealORCIDProvider(session=session, cache=cache) + + first_hits = provider.search_persons("noemie", rows=5, start=0) + second_hits = provider.search_persons("noemie", rows=5, start=0) + + assert first_hits == second_hits + # Second call must be served from cache — no extra HTTP call. + assert len(session.calls) == 1 + + # Different (rows, start) is a distinct cache key and therefore re-hits HTTP. + provider.search_persons("noemie", rows=5, start=5) + assert len(session.calls) == 2 diff --git a/tests/v2/test_reconciliation.py b/tests/v2/test_reconciliation.py new file mode 100644 index 0000000..5a76f52 --- /dev/null +++ b/tests/v2/test_reconciliation.py @@ -0,0 +1,813 @@ +from __future__ import annotations + +import re + +from src.v2.pipeline.stages.reconciliation import reconcile_entities + + +def _person(github_username: str, *, affiliations: list[str] | None = None) -> dict: + return { + "schema:name": github_username, + "pulse:githubUsername": github_username, + "identifiers": { + "pulse:orcid": None, + "pulse:infosciencePersonIdentifier": None, + "pulse:githubUsername": github_username, + }, + "affiliations": affiliations or [], + } + + +def _organization( + name: str, + ror: str, + *, + github_handle: str | None = None, + aliases: list[str] | None = None, +) -> dict: + return { + "schema:name": name, + "aliases": aliases or [], + "schema:identifier": ror, + "identifiers": { + "pulse:ror": ror, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:githubOrganizationHandle": github_handle, + }, + "pulse:githubOrganizationHandle": github_handle, + "pulse:owns": [], + } + + +def _repository( + handle: str, + authors: list[str], + *, + fork_of: str | None = None, + owned_by: str | None = None, +) -> dict: + return { + "schema:name": handle, + "pulse:githubRepositoryHandle": handle, + "identifiers": { + "pulse:githubRepositoryHandle": handle, + "schema:citation": None, + }, + "schema:author": authors, + "pulse:isForkOf": fork_of, + "pulse:ownedBy": owned_by, + } + + +def _article( + *, + doi: str | None, + infoscience_id: str | None, + authors: list[str], + source_organization: str | None = None, +) -> dict: + return { + "schema:name": "Sample Article", + "schema:datePublished": "2025-01-01", + "identifiers": { + "schema:identifier": doi, + "pulse:infoscienceArticleIdentifier": infoscience_id, + "uuid": "5a2ad6f9-0fcf-4fc4-bfc8-7f8c8924eca5", + }, + "schema:identifier": doi, + "pulse:infoscienceArticleIdentifier": infoscience_id, + "schema:author": authors, + "schema:sourceOrganization": source_organization, + } + + +def _membership(person_ref: str, org_ref: str) -> dict: + membership_id = f"{person_ref}_{org_ref}" + return { + "id": membership_id, + "type": "org:Membership", + "shacl": "pulse:MembershipShape", + "identifiers": {"pulse:composite": membership_id, "uuid": "11111111-1111-4111-8111-111111111111"}, + "idSource": "pulse:composite", + "org:organization": org_ref, + "_person_ref": person_ref, + "org:role": "Researcher", + "time:hasBeginning": "2021-01-01", + "time:hasEnd": None, + } + + +def _contribution(person_ref: str, repository_ref: str) -> dict: + contribution_id = f"{person_ref}_{repository_ref}" + return { + "id": contribution_id, + "type": "pulse:Contribution", + "shacl": "pulse:ContributionShape", + "identifiers": {"pulse:composite": contribution_id, "uuid": "22222222-2222-4222-8222-222222222222"}, + "idSource": "pulse:composite", + "pulse:contributionTo": repository_ref, + "pulse:contributionCount": 7, + "pulse:firstContributionDate": "2020-01-01T00:00:00Z", + "pulse:lastContributionDate": "2025-01-01T00:00:00Z", + "schema:author": person_ref, + } + + +def test_reconcile_updates_repository_author_references_to_canonical_person_ids() -> None: + entities = { + "persons": [_person("johndoe")], + "organizations": [], + "repositories": [_repository("owner/repo", ["johndoe"])], + } + + reconciled = reconcile_entities(entities) + + person_id = reconciled.entities["persons"][0]["id"] + assert reconciled.entities["repositories"][0]["schema:author"] == [person_id] + + +def test_reconcile_drops_repository_author_references_that_match_organizations() -> None: + entities = { + "persons": [_person("johndoe")], + "organizations": [ + _organization( + "Swiss Data Science Center", + "https://ror.org/02hdt9m26", + github_handle="sdsc-ordes", + ), + ], + "repositories": [_repository("owner/repo", ["johndoe", "sdsc-ordes"])], + } + + reconciled = reconcile_entities(entities) + person_id = reconciled.entities["persons"][0]["id"] + + assert reconciled.entities["repositories"][0]["schema:author"] == [person_id] + assert not any( + "author=sdsc-ordes" in warning + for warning in reconciled.link_warnings + ) + + +def test_reconcile_links_person_affiliations_without_generating_memberships() -> None: + entities = { + "persons": [_person("johndoe", affiliations=["EPFL"])], + "organizations": [_organization("EPFL", "05gzmn429")], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + + person = reconciled.entities["persons"][0] + organization_id = reconciled.entities["organizations"][0]["id"] + + assert person["affiliations"] == [organization_id] + assert reconciled.memberships == [] + + +def test_reconcile_does_not_generate_contributions_for_person_repository_links() -> None: + entities = { + "persons": [_person("johndoe")], + "organizations": [], + "repositories": [_repository("owner/repo", ["johndoe"])], + } + + reconciled = reconcile_entities(entities) + + assert reconciled.contributions == [] + + +def test_reconcile_emits_warnings_for_orphan_references() -> None: + entities = { + "persons": [_person("johndoe", affiliations=["unknown-org"])], + "organizations": [], + "repositories": [_repository("owner/repo", ["missing-person"])], + } + + reconciled = reconcile_entities(entities) + + assert reconciled.link_warnings + assert any("Orphan person reference" in warning for warning in reconciled.link_warnings) + assert any("Orphan organization reference" in warning for warning in reconciled.link_warnings) + + +def test_reconcile_detects_circular_repository_references_with_warning() -> None: + entities = { + "persons": [], + "organizations": [], + "repositories": [ + _repository("owner/repo-a", [], fork_of="owner/repo-b"), + _repository("owner/repo-b", [], fork_of="owner/repo-a"), + ], + } + + reconciled = reconcile_entities(entities) + + assert any("Circular repository fork reference detected" in w for w in reconciled.link_warnings) + + +def test_reconcile_normalizes_article_ids_and_article_relationship_references() -> None: + entities = { + "persons": [_person("johndoe")], + "organizations": [_organization("EPFL", "05gzmn429")], + "repositories": [], + "articles": [ + _article( + doi=None, + infoscience_id=( + "https://infoscience.epfl.ch/server/api/entities/publication/" + "dbce93b0-4ad7-45f2-8a53-b85bf39aeec9/full" + ), + authors=["johndoe"], + source_organization="EPFL", + ), + ], + } + + reconciled = reconcile_entities(entities) + + article = reconciled.entities["articles"][0] + person_id = reconciled.entities["persons"][0]["id"] + organization_id = reconciled.entities["organizations"][0]["id"] + + assert article["id"] == ( + "https://infoscience.epfl.ch/server/api/core/items/" + "dbce93b0-4ad7-45f2-8a53-b85bf39aeec9" + ) + assert article["idSource"] == "pulse:infoscienceArticleIdentifier" + assert article["schema:author"] == [person_id] + assert article["schema:sourceOrganization"] == organization_id + + +def test_reconcile_resolves_article_author_when_article_references_person_orcid_token() -> None: + person = _person("johndoe") + person["pulse:orcidIdentifier"] = "0000-0002-1825-0097" + person["identifiers"]["pulse:orcid"] = "0000-0002-1825-0097" + entities = { + "persons": [person], + "organizations": [], + "repositories": [], + "articles": [ + _article( + doi="10.1000/linked-author", + infoscience_id=None, + authors=["0000-0002-1825-0097"], + source_organization=None, + ), + ], + } + + reconciled = reconcile_entities(entities) + + article = reconciled.entities["articles"][0] + person_id = reconciled.entities["persons"][0]["id"] + + assert person_id == "https://orcid.org/0000-0002-1825-0097" + assert article["schema:author"] == [person_id] + + +def test_reconcile_normalizes_infoscience_organization_identifier_url_to_uuid() -> None: + infoscience_uuid = "95372c6b-7d45-432e-a84e-660c9fa54e05" + infoscience_url = ( + "https://infoscience.epfl.ch/server/api/entities/organization/" + f"{infoscience_uuid}/full" + ) + entities = { + "persons": [], + "organizations": [ + { + "schema:name": "EPFL Unit", + "schema:identifier": None, + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": infoscience_url, + "pulse:githubOrganizationHandle": None, + }, + "pulse:infoscienceOrganizationIdentifier": infoscience_url, + "pulse:githubOrganizationHandle": None, + "pulse:owns": [], + }, + ], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + organization = reconciled.entities["organizations"][0] + + assert organization["pulse:infoscienceOrganizationIdentifier"] == infoscience_uuid + assert organization["identifiers"]["pulse:infoscienceOrganizationIdentifier"] == infoscience_uuid + assert organization["id"] == f"https://infoscience.epfl.ch/server/api/core/items/{infoscience_uuid}" + assert organization["idSource"] == "pulse:infoscienceOrganizationIdentifier" + + +def test_reconcile_preserves_uuid_infoscience_organization_identifier() -> None: + infoscience_uuid = "41674f42-ba15-4612-9817-2a6f60985c01" + entities = { + "persons": [], + "organizations": [ + { + "schema:name": "Another EPFL Unit", + "schema:identifier": None, + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": infoscience_uuid, + "pulse:githubOrganizationHandle": None, + }, + "pulse:infoscienceOrganizationIdentifier": infoscience_uuid, + "pulse:githubOrganizationHandle": None, + "pulse:owns": [], + }, + ], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + organization = reconciled.entities["organizations"][0] + + assert organization["pulse:infoscienceOrganizationIdentifier"] == infoscience_uuid + assert organization["identifiers"]["pulse:infoscienceOrganizationIdentifier"] == infoscience_uuid + assert organization["id"] == f"https://infoscience.epfl.ch/server/api/core/items/{infoscience_uuid}" + assert organization["idSource"] == "pulse:infoscienceOrganizationIdentifier" + + +def test_reconcile_drops_unresolved_article_author_references() -> None: + unresolved_authors = ["Gehant, Sebastien", "Gfeller, David"] + entities = { + "persons": [], + "organizations": [], + "repositories": [], + "articles": [ + _article( + doi="10.1093/nar/gkv1310", + infoscience_id=None, + authors=unresolved_authors, + ), + ], + } + + reconciled = reconcile_entities(entities) + + assert reconciled.entities["persons"] == [] + assert reconciled.entities["articles"][0]["schema:author"] == [] + orphan_warnings = [ + warning + for warning in reconciled.link_warnings + if "Orphan person reference from article author list" in warning + ] + assert len(orphan_warnings) == len(unresolved_authors) + + +def test_reconcile_uses_class_memberships_and_contributions_as_primary_sources() -> None: + entities = { + "persons": [_person("johndoe", affiliations=["EPFL"])], + "organizations": [_organization("EPFL", "05gzmn429")], + "repositories": [_repository("owner/repo", ["johndoe"])], + "memberships": [_membership("johndoe", "EPFL")], + "contributions": [_contribution("johndoe", "owner/repo")], + } + + reconciled = reconcile_entities(entities) + + person_id = reconciled.entities["persons"][0]["id"] + organization_id = reconciled.entities["organizations"][0]["id"] + repository_id = reconciled.entities["repositories"][0]["id"] + + assert reconciled.memberships == [ + _membership(person_id, organization_id), + ] + assert reconciled.contributions == [ + _contribution(person_id, repository_id), + ] + + +def test_reconcile_does_not_add_memberships_or_contributions_without_class_entities() -> None: + entities = { + "persons": [_person("johndoe", affiliations=["EPFL"])], + "organizations": [_organization("EPFL", "05gzmn429")], + "repositories": [_repository("owner/repo", ["johndoe"])], + "memberships": [], + "contributions": [], + } + + reconciled = reconcile_entities(entities) + + assert reconciled.memberships == [] + assert reconciled.contributions == [] + + +def test_reconcile_models_github_org_account_as_unit_for_repository_owner() -> None: + entities = { + "persons": [], + "organizations": [ + _organization( + "Swiss Data Science Center", + "https://ror.org/02hdt9m26", + github_handle="sdsc-ordes", + ), + ], + "repositories": [ + _repository( + "sdsc-ordes/gimie", + [], + owned_by="sdsc-ordes", + ), + ], + } + + reconciled = reconcile_entities(entities) + + organizations = reconciled.entities["organizations"] + canonical_org = next( + organization + for organization in organizations + if organization["id"] == "https://ror.org/02hdt9m26" + ) + github_org_account = next( + organization + for organization in organizations + if organization["id"] == "https://github.com/sdsc-ordes" + ) + + assert "https://github.com/sdsc-ordes" in canonical_org["org:hasUnit"] + assert github_org_account["org:unitOf"] == "https://ror.org/02hdt9m26" + assert github_org_account["pulse:githubOrganizationHandle"] == "sdsc-ordes" + assert reconciled.entities["repositories"][0]["pulse:ownedBy"] == "https://github.com/sdsc-ordes" + + +def test_reconcile_organization_ownership_is_rebuilt_from_repository_owner_links() -> None: + owner_org = _organization( + "Swiss Data Science Center", + "https://ror.org/02hdt9m26", + github_handle="sdsc-ordes", + ) + unrelated_org = _organization( + "University of Geneva", + "https://ror.org/01swzsf04", + ) + unrelated_org["pulse:owns"] = ["sdsc-ordes/gimie"] + + entities = { + "persons": [], + "organizations": [owner_org, unrelated_org], + "repositories": [ + _repository( + "sdsc-ordes/gimie", + [], + owned_by="sdsc-ordes", + ), + ], + } + + reconciled = reconcile_entities(entities) + organizations = { + organization["id"]: organization + for organization in reconciled.entities["organizations"] + } + + assert organizations["https://ror.org/01swzsf04"]["pulse:owns"] == [] + assert organizations["https://ror.org/02hdt9m26"]["pulse:owns"] == [] + assert organizations["https://github.com/sdsc-ordes"]["pulse:owns"] == ["https://github.com/sdsc-ordes/gimie"] + + +def test_reconcile_resolves_accented_affiliation_variant_from_org_aliases() -> None: + entities = { + "persons": [ + _person( + "johndoe", + affiliations=["EPFL - École Polytechnique Fédérale de Lausanne"], + ), + ], + "organizations": [ + _organization( + "Ecole Polytechnique Federale de Lausanne", + "https://ror.org/02s376052", + aliases=[ + "EPFL", + "EPFL - Ecole Polytechnique Federale de Lausanne", + ], + ), + ], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + organization = reconciled.entities["organizations"][0] + organization_id = organization["id"] + + assert reconciled.entities["persons"][0]["affiliations"] == [organization_id] + assert not any( + "Orphan organization reference from person affiliation" in warning + for warning in reconciled.link_warnings + ) + + +def test_reconcile_resolves_affiliation_with_prefixed_org_alias_without_explicit_alternate_name() -> None: + entities = { + "persons": [ + _person( + "johndoe", + affiliations=["EPFL - École Polytechnique Fédérale de Lausanne"], + ), + ], + "organizations": [ + _organization( + "École Polytechnique Fédérale de Lausanne", + "https://ror.org/02s376052", + aliases=["EPFL"], + ), + ], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + organization_id = reconciled.entities["organizations"][0]["id"] + + assert reconciled.entities["persons"][0]["affiliations"] == [organization_id] + assert not any( + "Orphan organization reference from person affiliation" in warning + for warning in reconciled.link_warnings + ) + + +def test_reconcile_resolves_membership_org_aliases_and_handle_variants() -> None: + entities = { + "persons": [_person("johndoe", affiliations=["SDSC-GE", "@SwissDataScienceCenter"])], + "organizations": [ + _organization( + "Swiss Data Science Center", + "https://ror.org/02hdt9m26", + github_handle="SwissDataScienceCenter", + aliases=["SDSC-GE"], + ), + ], + "repositories": [], + "memberships": [_membership("johndoe", "@SwissDataScienceCenter")], + } + + reconciled = reconcile_entities(entities) + person_id = reconciled.entities["persons"][0]["id"] + organization_id = reconciled.entities["organizations"][0]["id"] + + assert reconciled.entities["persons"][0]["affiliations"] == [organization_id] + assert reconciled.memberships == [_membership(person_id, organization_id)] + assert not any( + "Unresolved class membership reference during reconciliation" in warning + for warning in reconciled.link_warnings + ) + + +def test_reconcile_strips_organization_lookup_fields_after_resolution() -> None: + organization = _organization( + "Swiss Data Science Center", + "https://ror.org/02hdt9m26", + github_handle="sdsc-ordes", + aliases=["SDSC"], + ) + organization["acronyms"] = ["SDSC"] + organization["labels"] = [{"label": "Swiss Data Science Center", "iso639": "en"}] + entities = { + "persons": [_person("alice", affiliations=["SDSC"])], + "organizations": [organization], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + canonical_org = reconciled.entities["organizations"][0] + + assert reconciled.entities["persons"][0]["affiliations"] == ["https://ror.org/02hdt9m26"] + assert "aliases" not in canonical_org + assert "acronyms" not in canonical_org + assert "labels" not in canonical_org + + +def test_reconcile_prunes_unresolved_organization_hierarchy_links() -> None: + organization = _organization( + "Swiss Data Science Center", + "https://ror.org/02hdt9m26", + ) + organization["org:hasUnit"] = ["https://ror.org/999999999"] + organization["org:unitOf"] = "https://ror.org/888888888" + entities = { + "persons": [], + "organizations": [organization], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + reconciled_org = reconciled.entities["organizations"][0] + + assert reconciled_org["org:hasUnit"] == [] + assert reconciled_org["org:unitOf"] is None + assert any( + "Dropped unresolved organization hierarchy references during reconciliation: " + "org:hasUnit=1, org:unitOf=1" + in warning + for warning in reconciled.link_warnings + ) + + +def test_reconcile_preserves_resolvable_organization_hierarchy_links() -> None: + parent = _organization( + "Parent Organization", + "https://ror.org/05gzmn429", + ) + child = _organization( + "Child Organization", + "https://ror.org/04f4a0c74", + ) + parent["org:hasUnit"] = ["https://ror.org/04f4a0c74"] + child["org:unitOf"] = "https://ror.org/05gzmn429" + entities = { + "persons": [], + "organizations": [parent, child], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + organizations = {organization["id"]: organization for organization in reconciled.entities["organizations"]} + + assert organizations["https://ror.org/05gzmn429"]["org:hasUnit"] == ["https://ror.org/04f4a0c74"] + assert organizations["https://ror.org/04f4a0c74"]["org:unitOf"] == "https://ror.org/05gzmn429" + assert not any( + "Dropped unresolved organization hierarchy references during reconciliation" + in warning + for warning in reconciled.link_warnings + ) + + +def test_reconcile_merges_ror_and_infoscience_variants_and_remaps_memberships() -> None: + infoscience_uuid = "95372c6b-7d45-432e-a84e-660c9fa54e05" + infoscience_org_id = ( + "https://infoscience.epfl.ch/server/api/core/items/" + f"{infoscience_uuid}" + ) + entities = { + "persons": [_person("alice", affiliations=[infoscience_org_id])], + "organizations": [ + _organization( + "Swiss Data Science Center", + "https://ror.org/02hdt9m26", + github_handle="sdsc-ordes", + ), + { + "schema:name": "Swiss Data Science Centre", + "schema:identifier": None, + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": infoscience_uuid, + "pulse:githubOrganizationHandle": None, + }, + "pulse:infoscienceOrganizationIdentifier": infoscience_uuid, + "pulse:githubOrganizationHandle": None, + "pulse:owns": [], + }, + ], + "repositories": [], + "memberships": [_membership("alice", infoscience_org_id)], + } + + reconciled = reconcile_entities(entities) + organizations = reconciled.entities["organizations"] + person_id = reconciled.entities["persons"][0]["id"] + + assert len(organizations) == 1 + canonical_org = organizations[0] + assert canonical_org["id"] == "https://ror.org/02hdt9m26" + assert canonical_org["identifiers"]["pulse:ror"] == "https://ror.org/02hdt9m26" + assert ( + canonical_org["identifiers"]["pulse:infoscienceOrganizationIdentifier"] + == infoscience_uuid + ) + assert canonical_org["pulse:infoscienceOrganizationIdentifier"] == infoscience_uuid + assert canonical_org["schema:identifier"] == "https://ror.org/02hdt9m26" + assert reconciled.memberships == [_membership(person_id, "https://ror.org/02hdt9m26")] + assert reconciled.reconciliation_debug["merged_group_count"] == 1 + assert reconciled.reconciliation_debug["org_remap_count"] >= 1 + + +def test_reconcile_acronym_only_overlap_does_not_merge_and_warns_about_ambiguity() -> None: + first = _organization( + "Swiss Data Science Center", + "https://ror.org/02hdt9m26", + ) + second = _organization( + "San Diego Supercomputer Center", + "https://ror.org/04mg3nk07", + ) + first["acronyms"] = ["SDSC"] + second["acronyms"] = ["SDSC"] + entities = { + "persons": [], + "organizations": [first, second], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + + assert len(reconciled.entities["organizations"]) == 2 + assert any( + "Ambiguous organization lookup tokens detected during reconciliation" + in warning + for warning in reconciled.link_warnings + ) + assert reconciled.reconciliation_debug["merged_group_count"] == 0 + + +def test_reconcile_token_collision_prefers_ror_backed_canonical_organization() -> None: + infoscience_uuid = "41674f42-ba15-4612-9817-2a6f60985c01" + ror_org = _organization( + "Swiss Data Science Center", + "https://ror.org/02hdt9m26", + ) + infoscience_org = { + "schema:name": "Some Data Systems Consortium", + "schema:identifier": None, + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": infoscience_uuid, + "pulse:githubOrganizationHandle": None, + }, + "pulse:infoscienceOrganizationIdentifier": infoscience_uuid, + "pulse:githubOrganizationHandle": None, + "pulse:owns": [], + } + ror_org["acronyms"] = ["SDSC"] + infoscience_org["acronyms"] = ["SDSC"] + + entities = { + "persons": [_person("alice", affiliations=["SDSC"])], + "organizations": [ror_org, infoscience_org], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + person = reconciled.entities["persons"][0] + + assert len(reconciled.entities["organizations"]) == 2 + assert person["affiliations"] == ["https://ror.org/02hdt9m26"] + assert reconciled.reconciliation_debug["token_collision_count"] >= 1 + + +def test_reconcile_generates_missing_organization_identifier_uuid() -> None: + entities = { + "persons": [], + "organizations": [ + { + "schema:name": "Swiss Data Science Center", + "schema:identifier": "https://ror.org/02hdt9m26", + "identifiers": { + "pulse:ror": "https://ror.org/02hdt9m26", + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:githubOrganizationHandle": "sdsc-ordes", + "uuid": None, + }, + "pulse:githubOrganizationHandle": "sdsc-ordes", + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:owns": [], + }, + ], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + organization = reconciled.entities["organizations"][0] + uuid_value = organization["identifiers"]["uuid"] + + assert isinstance(uuid_value, str) + assert re.fullmatch( + r"[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}", + uuid_value, + flags=re.IGNORECASE, + ) + + +def test_reconcile_derives_organization_github_handle_from_github_url_id() -> None: + entities = { + "persons": [], + "organizations": [ + { + "id": "https://github.com/sdsc-ordes", + "idSource": "pulse:githubOrganizationHandle", + "schema:name": "sdsc-ordes", + "schema:identifier": None, + "identifiers": { + "pulse:ror": None, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:githubOrganizationHandle": None, + "uuid": None, + }, + "pulse:githubOrganizationHandle": None, + "pulse:infoscienceOrganizationIdentifier": None, + "pulse:owns": [], + }, + ], + "repositories": [], + } + + reconciled = reconcile_entities(entities) + organization = reconciled.entities["organizations"][0] + + assert organization["id"] == "https://github.com/sdsc-ordes" + assert organization["idSource"] == "pulse:githubOrganizationHandle" + assert organization["pulse:githubOrganizationHandle"] == "sdsc-ordes" + assert organization["identifiers"]["pulse:githubOrganizationHandle"] == "sdsc-ordes" diff --git a/tests/v2/test_repository_agent.py b/tests/v2/test_repository_agent.py new file mode 100644 index 0000000..d9b75a5 --- /dev/null +++ b/tests/v2/test_repository_agent.py @@ -0,0 +1,155 @@ +from __future__ import annotations + +import asyncio +from typing import Any, Callable + +from jsonschema import validate + +from src.v2.agents import ProviderSet, RepositoryAgentV2 +from src.v2.ingest.providers.mock_github import MockGitHubProvider + + +class _NoFetchGitHubProvider(MockGitHubProvider): + def get_repository(self, full_name: str) -> dict[str, Any]: + del full_name + message = "repo_agent should reuse gathered repository metadata" + raise AssertionError(message) + + def get_contributors(self, full_name: str) -> list[dict[str, Any]]: + del full_name + message = "repo_agent should reuse gathered contributor list" + raise AssertionError(message) + + def get_languages(self, full_name: str) -> dict[str, int]: + del full_name + message = "repo_agent should reuse gathered language map" + raise AssertionError(message) + + +def test_repository_agent_output_validates_against_agent_schema( + load_schema: Callable[[str, str], dict[str, Any]], +) -> None: + agent = RepositoryAgentV2() + providers = ProviderSet(github=MockGitHubProvider()) + + result = asyncio.run( + agent.run({"full_name": "octocat/Hello-World"}, providers), + ) + + schema = load_schema("agent", "repository") + validate(instance=result.data, schema=schema) + + assert result.data["schema:name"] == "Hello-World" + assert result.data["pulse:githubRepositoryHandle"] == "octocat/Hello-World" + assert result.data["schema:author"] + assert result.data["pulse:repositoryType"] + assert result.data["pulse:discipline"] + assert "contributors" not in result.data + + derivation = result.stats.get("derivation") + assert isinstance(derivation, dict) + assert derivation["repository_full_name"] == "octocat/Hello-World" + assert derivation["owner_login"] == "octocat" + assert "octocat" in derivation["contributor_logins"] + + +def test_repository_agent_uses_permissive_validation_for_malformed_optional_fields() -> None: + agent = RepositoryAgentV2() + providers = ProviderSet(github=MockGitHubProvider()) + + result = asyncio.run( + agent.run( + { + "full_name": "octocat/Hello-World", + "agent_overrides": {"pulse:repositoryType": "invalid-type"}, + }, + providers, + ), + ) + + assert result.warnings + assert "pulse:repositoryType" not in result.data + assert result.raw_output["pulse:repositoryType"] == "invalid-type" + + +def test_repository_agent_preserves_raw_output_for_intermediate_debugging() -> None: + agent = RepositoryAgentV2() + providers = ProviderSet(github=MockGitHubProvider()) + + result = asyncio.run( + agent.run({"full_name": "octocat/Hello-World"}, providers), + ) + + assert result.raw_output["schema:name"] == result.data["schema:name"] + assert result.raw_output["schema:author"] == result.data["schema:author"] + + +def test_repository_agent_reuses_context_gather_payload_without_provider_refetch( + load_schema: Callable[[str, str], dict[str, Any]], +) -> None: + agent = RepositoryAgentV2() + providers = ProviderSet(github=_NoFetchGitHubProvider()) + + result = asyncio.run( + agent.run( + { + "full_name": "octocat/Hello-World", + "repository_context": { + "full_name": "octocat/Hello-World", + "metadata": { + "name": "Hello-World", + "full_name": "octocat/Hello-World", + "owner": {"login": "octocat", "type": "User"}, + "created_at": "2020-01-01", + "license": {"spdx_id": "MIT"}, + "fork": False, + "source": {"full_name": None}, + }, + "contributors": [{"login": "octocat"}], + "languages": {"Python": 1}, + }, + }, + providers, + ), + ) + + schema = load_schema("agent", "repository") + validate(instance=result.data, schema=schema) + assert result.data["schema:author"] == ["octocat"] + assert result.data["schema:dateCreated"] == "2020-01-01T00:00:00Z" + + +def test_repository_agent_filters_organization_contributors_from_authors() -> None: + agent = RepositoryAgentV2() + providers = ProviderSet(github=MockGitHubProvider()) + + result = asyncio.run( + agent.run( + { + "full_name": "sdsc-ordes/gimie", + "repository_context": { + "full_name": "sdsc-ordes/gimie", + "metadata": { + "name": "gimie", + "full_name": "sdsc-ordes/gimie", + "owner": {"login": "sdsc-ordes", "type": "Organization"}, + "created_at": "2020-01-01T00:00:00Z", + "license": {"spdx_id": "MIT"}, + "fork": False, + "source": {"full_name": None}, + }, + "contributors": [ + {"login": "sdsc-ordes", "type": "Organization"}, + {"login": "alice", "type": "User"}, + ], + "languages": {"Python": 1}, + }, + }, + providers, + ), + ) + + assert result.data["schema:author"] == ["alice"] + derivation = result.stats.get("derivation") + assert isinstance(derivation, dict) + assert derivation["contributor_logins"] == ["alice"] diff --git a/tests/v2/test_response_contracts.py b/tests/v2/test_response_contracts.py new file mode 100644 index 0000000..af7de13 --- /dev/null +++ b/tests/v2/test_response_contracts.py @@ -0,0 +1,153 @@ +from __future__ import annotations + +import pytest +from pydantic import ValidationError + +from src.v2.api_models.contracts import ( + V2ExtractResponse, + V2JSONLDOutput, + V2JSONOutputEnvelope, + V2Stats, +) + + +def _sample_stats() -> V2Stats: + return V2Stats( + entities_count=1, + triples_count=2, + run_id="run-test", + duration_ms=3, + stages_completed=["extract"], + ) + + +def test_extract_response_can_be_instantiated_with_required_fields() -> None: + response = V2ExtractResponse( + source_url="https://github.com/owner/repo", + detected_type="repository", + output_format="jsonld", + output={"@context": {}, "@graph": []}, + stats=_sample_stats(), + ) + + assert response.source_url == "https://github.com/owner/repo" + assert response.detected_type == "repository" + + +def test_extract_response_serializes_to_expected_contract_shape() -> None: + response = V2ExtractResponse( + source_url="https://github.com/owner/repo", + detected_type="repository", + output_format="json", + output={ + "root_entity": {"id": "https://github.com/owner/repo"}, + "related_entities": [], + "excluded_entities": [], + "entities_by_type": { + "repositories": [{"id": "https://github.com/owner/repo"}], + "persons": [], + "organizations": [], + "articles": [], + "memberships": [], + "contributions": [], + }, + }, + stats=_sample_stats(), + ) + payload = response.model_dump(mode="json") + + assert payload["source_url"] == "https://github.com/owner/repo" + assert payload["detected_type"] == "repository" + assert payload["output_format"] == "json" + assert payload["output"]["root_entity"]["id"] == "https://github.com/owner/repo" + assert payload["output"]["related_entities"] == [] + assert payload["output"]["excluded_entities"] == [] + assert payload["output"]["entities_by_type"]["repositories"] + assert payload["warnings"] == [] + assert payload["stats"]["entities_count"] == 1 + + +def test_extract_response_output_format_rejects_unsupported_values() -> None: + with pytest.raises(ValidationError, match="output_format"): + V2ExtractResponse( + source_url="https://github.com/owner/repo", + detected_type="repository", + output_format="xml", + output={}, + stats=_sample_stats(), + ) + + +def test_extract_response_warnings_default_to_empty_list() -> None: + response = V2ExtractResponse( + source_url="https://github.com/owner/repo", + detected_type="repository", + output_format="jsonld", + output={"@context": {}, "@graph": []}, + stats=_sample_stats(), + ) + + assert response.warnings == [] + + +def test_extract_response_rejects_deprecated_stage_keyed_json_entities_shape() -> None: + with pytest.raises(ValidationError, match="output"): + V2ExtractResponse( + source_url="https://github.com/owner/repo", + detected_type="repository", + output_format="json", + output={"entities": {"repo_agent": {"id": "https://github.com/owner/repo"}}}, + stats=_sample_stats(), + ) + + +def test_extract_response_output_enforces_jsonld_and_json_contract_pairing() -> None: + with pytest.raises(ValidationError, match="jsonld"): + V2ExtractResponse( + source_url="https://github.com/owner/repo", + detected_type="repository", + output_format="jsonld", + output={ + "root_entity": {"id": "https://github.com/owner/repo"}, + "related_entities": [], + "excluded_entities": [], + "entities_by_type": {}, + }, + stats=_sample_stats(), + ) + + with pytest.raises(ValidationError, match="json envelope"): + V2ExtractResponse( + source_url="https://github.com/owner/repo", + detected_type="repository", + output_format="json", + output={"@context": {}, "@graph": []}, + stats=_sample_stats(), + ) + + +def test_output_contract_models_support_alias_serialization() -> None: + jsonld = V2JSONLDOutput( + **{ + "@context": {"schema": "http://schema.org/"}, + "@graph": [{"@id": "https://example.org/node", "@type": "schema:Thing"}], + }, + ) + assert jsonld.model_dump(mode="json", by_alias=True)["@graph"] + + envelope = V2JSONOutputEnvelope( + root_entity=None, + related_entities=[], + excluded_entities=[], + entities_by_type={ + "repositories": [], + "persons": [], + "organizations": [], + "articles": [], + "memberships": [], + "contributions": [], + }, + ) + assert envelope.root_entity is None + + diff --git a/tests/v2/test_roundtrip.py b/tests/v2/test_roundtrip.py new file mode 100644 index 0000000..b0f1338 --- /dev/null +++ b/tests/v2/test_roundtrip.py @@ -0,0 +1,345 @@ +# ruff: noqa: C901, PLR0912, PLC0206, PERF401, PLR2004 +from __future__ import annotations + +import json +from collections import defaultdict +from copy import deepcopy +from pathlib import Path +from typing import Any + +from rdflib import Graph, Literal, URIRef +from rdflib.namespace import RDF, XSD + +REPO_ROOT = Path(__file__).resolve().parents[2] +JSON_DIR = REPO_ROOT / "dev" / "ontology-v2-json-response" / "a-001" +JSONLD_FILE = JSON_DIR / "test" / "jsonld_output.json" +EXPECTED_ENTITY_COUNT = 33 +EXPECTED_ENTITY_TYPES = 6 + +ARRAY_PROPERTIES_BY_TYPE = { + "schema:SoftwareSourceCode": { + "schema:author", + "pulse:discipline", + "schema:programmingLanguage", + }, + "schema:ScholarlyArticle": {"schema:author"}, + "org:Organization": {"org:hasUnit", "pulse:owns"}, + "schema:Person": {"org:hasMembership", "pulse:hasContribution"}, +} +ARRAY_PROPERTIES = { + "pulse:discipline", + "schema:programmingLanguage", + "org:hasUnit", + "pulse:owns", + "pulse:hasContribution", + "org:hasMembership", +} + +SHAPE_FILES = { + "schema:Person": "pulse_PersonShape.json", + "schema:SoftwareSourceCode": "pulse_RepositoryShape.json", + "org:Organization": "pulse_OrganizationShape.json", + "org:Membership": "pulse_MembershipShape.json", + "pulse:Contribution": "pulse_ContributionShape.json", + "schema:ScholarlyArticle": "pulse_ArticleShape.json", +} + +ENVELOPE_FIELDS = {"shacl", "identifiers", "idSource"} + +NAMESPACES = { + "http://schema.org/": "schema:", + "http://www.w3.org/ns/org#": "org:", + "http://www.w3.org/2006/time#": "time:", + "https://open-pulse.epfl.ch/ontology#": "pulse:", + "http://www.wikidata.org/entity/": "wd:", +} + + +def _to_prefixed(iri: str, *, use_pulse_prefix: bool = True) -> str: + namespaces = { + "https://open-pulse.epfl.ch/ontology#": "pulse:", + "http://www.wikidata.org/entity/": "wd:", + "http://schema.org/": "schema:", + "http://www.w3.org/ns/org#": "org:", + "http://www.w3.org/2006/time#": "time:", + "https://open-pulse.epfl.ch/data/": "", + } + for namespace, prefix in namespaces.items(): + if not iri.startswith(namespace): + continue + local_name = iri[len(namespace) :] + if not prefix or not use_pulse_prefix: + return local_name + return prefix + local_name + return iri + + +def _shorten_id(iri: str) -> str: + pulse_ns = "https://open-pulse.epfl.ch/ontology#" + data_ns = "https://open-pulse.epfl.ch/data/" + if iri.startswith(data_ns): + return iri[len(data_ns) :] + if iri.startswith(pulse_ns): + return iri[len(pulse_ns) :] + return iri + + +def _rdf_value(obj: Any) -> Any: + if isinstance(obj, Literal): + if obj.datatype == XSD.integer: + return int(obj) + if obj.datatype in (XSD.date, XSD.dateTime): + return str(obj) + return str(obj) + if isinstance(obj, URIRef): + return _to_prefixed(str(obj), use_pulse_prefix=True) + return str(obj) + + +def _reconstruct_from_graph(graph: Graph) -> dict[str, list[dict[str, Any]]]: + entities: dict[str, dict[str, Any]] = {} + entity_types: dict[str, str] = {} + + for subject, _, object_value in graph.triples((None, RDF.type, None)): + subject_str = str(subject) + prefixed_type = _to_prefixed(str(object_value)) + if prefixed_type.startswith("http://www.w3.org/"): + continue + if "Shape" in str(object_value) or "Enumeration" in str(object_value): + continue + if str(object_value) in { + "http://www.w3.org/2002/07/owl#Ontology", + "http://www.w3.org/2000/01/rdf-schema#Class", + "http://www.w3.org/1999/02/22-rdf-syntax-ns#Property", + }: + continue + entity_types[subject_str] = prefixed_type + entities.setdefault(subject_str, {}) + + for subject_str in entities: + subject_uri = URIRef(subject_str) + for predicate, object_value in graph.predicate_objects(subject_uri): + predicate_str = str(predicate) + if predicate_str == str(RDF.type): + continue + prefixed_property = _to_prefixed(predicate_str) + value = _rdf_value(object_value) + if prefixed_property in entities[subject_str]: + existing = entities[subject_str][prefixed_property] + if isinstance(existing, list): + existing.append(value) + else: + entities[subject_str][prefixed_property] = [existing, value] + continue + + entity_type = entity_types.get(subject_str) + type_specific_arrays = ( + ARRAY_PROPERTIES_BY_TYPE.get(entity_type, set()) + if entity_type is not None + else set() + ) + if ( + prefixed_property in type_specific_arrays + or prefixed_property in ARRAY_PROPERTIES + ): + entities[subject_str][prefixed_property] = [value] + else: + entities[subject_str][prefixed_property] = value + + by_type: dict[str, list[dict[str, Any]]] = defaultdict(list) + for subject_str, props in entities.items(): + entity_type = entity_types[subject_str] + node = { + "id": _shorten_id(subject_str), + "type": entity_type, + } + node.update(props) + by_type[entity_type].append(node) + + return dict(by_type) + + +def _normalise_for_comparison(obj: dict[str, Any]) -> dict[str, Any]: + result: dict[str, Any] = {} + for key, value in obj.items(): + if key in ENVELOPE_FIELDS or value is None: + continue + if isinstance(value, list): + if not value: + continue + result[key] = sorted(str(item) for item in value) + continue + if isinstance(value, str) and value.endswith("+00:00"): + result[key] = value[:-6] + "Z" + continue + result[key] = value + return result + + +def _compare_entities( + original: dict[str, Any], + reconstructed: dict[str, Any], + entity_id: str, +) -> list[dict[str, Any]]: + differences: list[dict[str, Any]] = [] + original_normalized = _normalise_for_comparison(original) + reconstructed_normalized = _normalise_for_comparison(reconstructed) + + original_keys = set(original_normalized) + reconstructed_keys = set(reconstructed_normalized) + + for key in sorted(original_keys - reconstructed_keys): + differences.append( + { + "entity": entity_id, + "property": key, + "type": "lost_property", + "original": original_normalized[key], + "reconstructed": None, + }, + ) + + for key in sorted(reconstructed_keys - original_keys): + differences.append( + { + "entity": entity_id, + "property": key, + "type": "extra_property", + "original": None, + "reconstructed": reconstructed_normalized[key], + }, + ) + + for key in sorted(original_keys & reconstructed_keys): + original_value = original_normalized[key] + reconstructed_value = reconstructed_normalized[key] + if isinstance(original_value, list) and isinstance(reconstructed_value, list): + if original_value != reconstructed_value: + differences.append( + { + "entity": entity_id, + "property": key, + "type": "value_mismatch", + "original": original_value, + "reconstructed": reconstructed_value, + }, + ) + continue + if str(original_value) != str(reconstructed_value): + differences.append( + { + "entity": entity_id, + "property": key, + "type": "value_mismatch", + "original": original_value, + "reconstructed": reconstructed_value, + }, + ) + + return differences + + +def _load_original_entities() -> dict[str, list[dict[str, Any]]]: + loaded: dict[str, list[dict[str, Any]]] = {} + for entity_type, file_name in SHAPE_FILES.items(): + payload = json.loads((JSON_DIR / file_name).read_text(encoding="utf-8")) + if isinstance(payload, list): + loaded[entity_type] = [item for item in payload if isinstance(item, dict)] + return loaded + + +def _run_comparison( + original_by_type: dict[str, list[dict[str, Any]]], + reconstructed_by_type: dict[str, list[dict[str, Any]]], +) -> dict[str, Any]: + differences: list[dict[str, Any]] = [] + matched_count = 0 + unmatched_count = 0 + + for entity_type, originals in original_by_type.items(): + reconstructed_lookup = { + entity["id"]: entity + for entity in reconstructed_by_type.get(entity_type, []) + if isinstance(entity.get("id"), str) + } + for original in originals: + entity_id = str(original.get("id", "unknown")) + reconstructed = reconstructed_lookup.get(entity_id) + if reconstructed is None: + differences.append( + { + "entity": entity_id, + "property": None, + "type": "missing_entity", + "original": entity_type, + "reconstructed": None, + }, + ) + unmatched_count += 1 + continue + entity_diffs = _compare_entities(original, reconstructed, entity_id) + if entity_diffs: + differences.extend(entity_diffs) + else: + matched_count += 1 + + return { + "matched": matched_count, + "unmatched": unmatched_count, + "differences": differences, + } + + +def _parse_roundtrip_graph() -> dict[str, list[dict[str, Any]]]: + payload = json.loads(JSONLD_FILE.read_text(encoding="utf-8")) + graph = Graph() + graph.parse(data=json.dumps(payload), format="json-ld") + return _reconstruct_from_graph(graph) + + +def _difference_message(differences: list[dict[str, Any]]) -> str: + if not differences: + return "" + preview = "\n".join(str(entry) for entry in differences[:5]) + return f"Roundtrip mismatches ({len(differences)}):\n{preview}" + + +def test_roundtrip_preserves_all_mock_entities_without_differences() -> None: + reconstructed = _parse_roundtrip_graph() + reconstructed_count = sum(len(entities) for entities in reconstructed.values()) + comparison = _run_comparison(_load_original_entities(), reconstructed) + + assert len(reconstructed) == EXPECTED_ENTITY_TYPES + assert reconstructed_count == EXPECTED_ENTITY_COUNT + assert comparison["matched"] == EXPECTED_ENTITY_COUNT + assert comparison["unmatched"] == 0 + assert comparison["differences"] == [], _difference_message(comparison["differences"]) + + +def test_roundtrip_preserves_arrays_numeric_fields_and_datetimes() -> None: + reconstructed = _parse_roundtrip_graph() + repositories = reconstructed["schema:SoftwareSourceCode"] + repository = next( + entity for entity in repositories if entity["id"] == "EPFL-ENAC/geodata-toolkit" + ) + + assert sorted(repository["pulse:discipline"]) == ["wd:Q21201", "wd:Q8434"] + assert sorted(repository["schema:programmingLanguage"]) == ["Python", "TypeScript"] + assert isinstance(repository["pulse:githubRepoStars"], int) + assert repository["pulse:githubRepoStars"] == 45 + assert repository["schema:dateCreated"].replace("+00:00", "Z") == "2023-03-15T10:30:00Z" + + +def test_roundtrip_comparison_reports_field_level_differences() -> None: + originals = _load_original_entities() + reconstructed = deepcopy(originals) + reconstructed["schema:Person"][0]["schema:name"] = "Changed Name" + + comparison = _run_comparison(originals, reconstructed) + mismatch = next( + difference + for difference in comparison["differences"] + if difference["type"] == "value_mismatch" + ) + assert mismatch["entity"] == originals["schema:Person"][0]["id"] + assert mismatch["property"] == "schema:name" diff --git a/tests/v2/test_run_llm_repo_and_persons.py b/tests/v2/test_run_llm_repo_and_persons.py new file mode 100644 index 0000000..0e2da48 --- /dev/null +++ b/tests/v2/test_run_llm_repo_and_persons.py @@ -0,0 +1,115 @@ +from __future__ import annotations + +import asyncio +import json +from types import SimpleNamespace +from typing import Any + +import scripts.v2.run_llm_repo_and_persons as run_script +from src.v2.agents.models import AgentResult, ProviderSet + + +def test_run_llm_repo_and_persons_handles_partial_failures_and_prints_entities( + monkeypatch, + capsys, +) -> None: + class _FakeRepositoryAgent: + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del context, providers + return AgentResult( + data={"id": "repo-id"}, + model="openai/gpt-test", + provider="openai", + tokens_prompt=10, + tokens_completion=5, + ) + + class _FakePersonAgent: + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + ) -> None: + del llm_runtime + self._timeout = llm_call_timeout_seconds + + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del providers + username = context["username"] + if username == "ok-user": + return AgentResult( + data={"id": "ok-user", "type": "schema:Person"}, + model="openai/gpt-test", + provider="openai", + tokens_prompt=20, + tokens_completion=8, + ) + if username == "timeout-user": + await asyncio.sleep(0.05) + raise RuntimeError( + f"{username} — LLM call timed out after {self._timeout:.1f}s", + ) + raise RuntimeError(f"{username} exploded") + + async def _fake_gather_context( + _detected_type: str, + _url_info: Any, + _providers: ProviderSet, + ) -> SimpleNamespace: + return SimpleNamespace( + context={ + "repository": { + "metadata": {"full_name": "octo/repo"}, + "contributors": [ + {"login": "ok-user"}, + {"login": "timeout-user"}, + {"login": "error-user"}, + ], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + warnings=[], + ) + + monkeypatch.setattr( + run_script, + "classify_github_url", + lambda _repo: SimpleNamespace( + owner="octo", + repo="repo", + normalized_url="https://github.com/octo/repo", + ), + ) + monkeypatch.setattr( + run_script, + "_default_provider_set", + lambda *, use_mock_providers: ProviderSet(github=object()), + ) + monkeypatch.setattr(run_script, "gather_context", _fake_gather_context) + monkeypatch.setattr(run_script, "LLMRepositoryAgentV2", _FakeRepositoryAgent) + monkeypatch.setattr(run_script, "LLMPersonAgentV2", _FakePersonAgent) + + asyncio.run( + run_script._run( + "octo/repo", + person_timeout_seconds=180.0, + max_concurrency=3, + heartbeat_seconds=0.01, + ), + ) + + captured = capsys.readouterr() + stdout = captured.out + assert "heartbeat:" in stdout + assert "Person summary: ok=1 timeout=1 error=1" in stdout + assert "Failed contributors:" in stdout + assert "timeout-user: timeout" in stdout + assert "error-user: error" in stdout + assert "Person entities:" in stdout + + entities_section = stdout.split("Person entities:\n", maxsplit=1)[1] + entities_json = entities_section.split(f"\n{run_script._SEP}", maxsplit=1)[0] + parsed_entities = json.loads(entities_json) + assert parsed_entities == [{"id": "ok-user", "type": "schema:Person"}] diff --git a/tests/v2/test_run_llm_repo_persons_and_orgs.py b/tests/v2/test_run_llm_repo_persons_and_orgs.py new file mode 100644 index 0000000..a83bdc6 --- /dev/null +++ b/tests/v2/test_run_llm_repo_persons_and_orgs.py @@ -0,0 +1,632 @@ +from __future__ import annotations + +import asyncio +import json +from types import SimpleNamespace +from typing import Any + +import scripts.v2.run_llm_repo_persons_and_orgs as run_script +from src.v2.agents.models import AgentResult, ProviderSet + + +def test_run_llm_repo_persons_and_orgs_handles_partial_failures_and_prints_jsonld( + monkeypatch, + capsys, +) -> None: + person_ok_id = "urn:pulse:person-ok-user" + person_ok2_id = "urn:pulse:person-ok-user-2" + + class _FakeRepositoryAgent: + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del context, providers + return AgentResult( + data={ + "id": "octo/repo", + "type": "schema:SoftwareSourceCode", + "shacl": "pulse:RepositoryShape", + "schema:author": ["ok-user", "ghost-author"], + }, + model="openai/gpt-test", + provider="openai", + tokens_prompt=10, + tokens_completion=5, + stats={ + "derivation": { + "repository_full_name": "octo/repo", + "contributors": [ + {"login": "ok-user", "contributions": 5}, + {"login": "ok-user-2", "contributions": 2}, + ], + }, + }, + ) + + class _FakePersonAgent: + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + ) -> None: + del llm_runtime + self._timeout = llm_call_timeout_seconds + + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del providers + username = context["username"] + if username == "ok-user": + return AgentResult( + data={ + "id": person_ok_id, + "type": "schema:Person", + "pulse:githubUsername": "ok-user", + "org:hasMembership": [ + "ok-user_org-owner", + "ok-user_org-timeout", + "ok-user_org-error", + "ok-user_org-success", + ], + }, + model="openai/gpt-test", + provider="openai", + tokens_prompt=20, + tokens_completion=8, + stats={ + "derivation": { + "person_id": person_ok_id, + "affiliation_names": ["org-owner"], + "orcid_affiliations": [ + { + "organization": "org-owner", + "role": "Research Engineer", + "start_date": "2020-01-01", + "end_date": None, + }, + ], + }, + }, + ) + if username == "ok-user-2": + return AgentResult( + data={ + "id": person_ok2_id, + "type": "schema:Person", + "pulse:githubUsername": "ok-user-2", + "org:hasMembership": [ + "ok-user-2_org-success", + ], + }, + model="openai/gpt-test", + provider="openai", + tokens_prompt=19, + tokens_completion=7, + stats={ + "derivation": { + "person_id": person_ok2_id, + "affiliation_names": ["org-success"], + "orcid_affiliations": [], + }, + }, + ) + if username == "timeout-user": + await asyncio.sleep(0.05) + raise RuntimeError( + f"{username} — LLM call timed out after {self._timeout:.1f}s", + ) + raise RuntimeError(f"{username} exploded") + + class _FakeOrganizationAgent: + captured_contexts: list[dict[str, Any]] = [] + + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + ) -> None: + del llm_runtime + self._timeout = llm_call_timeout_seconds + + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del providers + _FakeOrganizationAgent.captured_contexts.append(dict(context)) + org_name = context["org_name"] + if org_name in {"org-owner", "org-success"}: + return AgentResult( + data={ + "id": org_name, + "type": "org:Organization", + "schema:name": org_name, + }, + model="openai/gpt-test", + provider="openai", + tokens_prompt=18, + tokens_completion=7, + ) + if org_name == "org-timeout": + await asyncio.sleep(0.05) + raise RuntimeError( + f"{org_name} — LLM call timed out after {self._timeout:.1f}s", + ) + raise RuntimeError(f"{org_name} exploded") + + class _FakeArticleAgent: + captured_contexts: list[dict[str, Any]] = [] + + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + ) -> None: + del llm_runtime + self._timeout = llm_call_timeout_seconds + + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del providers + _FakeArticleAgent.captured_contexts.append(dict(context)) + return AgentResult( + data={ + "id": "10.1000/repo-article", + "type": "schema:ScholarlyArticle", + "schema:author": [person_ok_id], + }, + stats={ + "articles": [ + { + "id": "10.1000/repo-article-2", + "type": "schema:ScholarlyArticle", + "schema:author": [person_ok2_id], + }, + ], + }, + ) + + class _FakeMembershipAgent: + captured_contexts: list[dict[str, Any]] = [] + + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + ) -> None: + del llm_runtime + self._timeout = llm_call_timeout_seconds + + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del providers + _FakeMembershipAgent.captured_contexts.append(dict(context)) + seed = context["membership_seed"] + if seed == person_ok_id: + await asyncio.sleep(0.05) + raise RuntimeError( + f"{seed} — LLM call timed out after {self._timeout:.1f}s", + ) + return AgentResult( + data={ + "id": f"{seed}_org-success", + "type": "org:Membership", + "org:organization": "org-success", + }, + stats={"memberships": []}, + ) + + class _FakeContributionAgent: + captured_contexts: list[dict[str, Any]] = [] + + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + ) -> None: + del llm_runtime + self._timeout = llm_call_timeout_seconds + + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del providers + _FakeContributionAgent.captured_contexts.append(dict(context)) + return AgentResult( + data={ + "id": f"{person_ok_id}_octo/repo", + "type": "pulse:Contribution", + "schema:author": person_ok_id, + "pulse:contributionTo": "octo/repo", + }, + stats={ + "contributions": [ + { + "id": f"{person_ok2_id}_octo/repo", + "type": "pulse:Contribution", + "schema:author": person_ok2_id, + "pulse:contributionTo": "octo/repo", + }, + ], + }, + ) + + async def _fake_gather_context( + _detected_type: str, + _url_info: Any, + _providers: ProviderSet, + ) -> SimpleNamespace: + return SimpleNamespace( + context={ + "repository": { + "metadata": { + "full_name": "octo/repo", + "owner": {"login": "org-owner", "type": "Organization"}, + }, + "contributors": [ + {"login": "ok-user"}, + {"login": "ok-user-2"}, + {"login": "timeout-user"}, + {"login": "error-user"}, + ], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + warnings=[], + ) + + monkeypatch.setattr( + run_script, + "classify_github_url", + lambda _repo: SimpleNamespace( + owner="octo", + repo="repo", + normalized_url="https://github.com/octo/repo", + ), + ) + monkeypatch.setattr( + run_script, + "_default_provider_set", + lambda *, use_mock_providers: ProviderSet(github=object()), + ) + monkeypatch.setattr(run_script, "gather_context", _fake_gather_context) + monkeypatch.setattr(run_script, "LLMRepositoryAgentV2", _FakeRepositoryAgent) + monkeypatch.setattr(run_script, "LLMPersonAgentV2", _FakePersonAgent) + monkeypatch.setattr(run_script, "LLMOrganizationAgentV2", _FakeOrganizationAgent) + monkeypatch.setattr(run_script, "LLMArticleAgentV2", _FakeArticleAgent) + monkeypatch.setattr(run_script, "LLMMembershipAgentV2", _FakeMembershipAgent) + monkeypatch.setattr(run_script, "LLMContributionAgentV2", _FakeContributionAgent) + + asyncio.run( + run_script._run( + "octo/repo", + person_timeout_seconds=180.0, + organization_timeout_seconds=180.0, + article_timeout_seconds=180.0, + membership_timeout_seconds=180.0, + contribution_timeout_seconds=180.0, + max_concurrency=3, + heartbeat_seconds=0.01, + ), + ) + + captured = capsys.readouterr() + stdout = captured.out + + stage_markers = [ + "[ 1 / 7 ] Gathering GIMIE context", + "[ 2 / 7 ] Running LLM repository agent", + "[ 3 / 7 ] Running LLM person agent", + "[ 4 / 7 ] Running LLM organization agent", + "[ 5 / 7 ] Running LLM article agent", + "[ 6 / 7 ] Running LLM membership agent", + "[ 7 / 7 ] Running LLM contribution agent", + ] + stage_positions = [stdout.index(marker) for marker in stage_markers] + assert stage_positions == sorted(stage_positions) + + assert "heartbeat:" in stdout + assert "Person summary: ok=2 timeout=1 error=1" in stdout + assert "Organization summary: ok=2 timeout=1 error=1" in stdout + assert "Article summary: ok=2 timeout=0 error=0" in stdout + assert "Membership summary: ok=1 timeout=1 error=0" in stdout + assert "Contribution summary: ok=2 timeout=0 error=0" in stdout + assert "Raw combined JSON-LD (pre-reconciliation):" in stdout + + jsonld_section = stdout.split( + "Raw combined JSON-LD (pre-reconciliation):\n", + maxsplit=1, + )[1] + jsonld_text = jsonld_section.split(f"\n{run_script._SEP}", maxsplit=1)[0] + jsonld_payload = json.loads(jsonld_text) + assert "@graph" in jsonld_payload + graph_nodes = jsonld_payload["@graph"] + assert any(node.get("@type") == "schema:SoftwareSourceCode" for node in graph_nodes) + assert any(node.get("@type") == "schema:Person" for node in graph_nodes) + assert any(node.get("@type") == "org:Organization" for node in graph_nodes) + assert any(node.get("@type") == "schema:ScholarlyArticle" for node in graph_nodes) + assert any(node.get("@type") == "org:Membership" for node in graph_nodes) + assert any(node.get("@type") == "pulse:Contribution" for node in graph_nodes) + + repository_node = next( + node + for node in graph_nodes + if node.get("@type") == "schema:SoftwareSourceCode" + ) + assert {"@id": person_ok_id} in repository_node.get("schema:author", []) + + assert _FakeOrganizationAgent.captured_contexts + org_context = _FakeOrganizationAgent.captured_contexts[0] + assert "repository_context" in org_context + assert org_context["source_repositories"] == ["octo/repo"] + + upstream_json = org_context.get("upstream_stage_outputs_json") + assert isinstance(upstream_json, str) + upstream_payload = json.loads(upstream_json) + assert "repo_agent" in upstream_payload + assert "person_agent:ok-user" in upstream_payload + assert "person_agent:ok-user-2" in upstream_payload + + assert _FakeArticleAgent.captured_contexts + article_context = _FakeArticleAgent.captured_contexts[0] + assert isinstance(article_context.get("known_persons"), list) + assert isinstance(article_context.get("known_organizations"), list) + assert isinstance(article_context.get("known_repositories"), list) + assert isinstance(article_context.get("pipeline_outputs"), dict) + assert "repository_context" in article_context + + assert _FakeMembershipAgent.captured_contexts + membership_context = _FakeMembershipAgent.captured_contexts[0] + assert membership_context["known_persons"][0]["affiliations"] == ["org-owner"] + assert isinstance(membership_context.get("known_organizations"), list) + assert isinstance(membership_context.get("pipeline_outputs"), dict) + + assert _FakeContributionAgent.captured_contexts + contribution_context = _FakeContributionAgent.captured_contexts[0] + assert contribution_context["known_repositories"][0]["contributors"] == [ + {"login": "ok-user", "contributions": 5}, + {"login": "ok-user-2", "contributions": 2}, + ] + assert isinstance(contribution_context.get("known_persons"), list) + + +def test_normalize_entities_for_debug_jsonld_resolves_authors_merges_orgs_and_strips_nulls() -> None: + entities = [ + { + "id": "owner/repo", + "type": "schema:SoftwareSourceCode", + "schema:name": "owner/repo", + "schema:author": ["alice", "ghost"], + "pulse:githubRepositoryHandle": "owner/repo", + "pulse:ownedBy": None, + }, + { + "id": "urn:pulse:0000-0001-2345-6789", + "type": "schema:Person", + "schema:name": "Alice", + "pulse:githubUsername": "alice", + "pulse:owns": ["owner/repo"], + }, + { + "id": "https://ror.org/019whta54", + "type": "org:Organization", + "schema:name": "University of Lausanne", + "aliases": ["Université de Lausanne"], + "identifiers": { + "pulse:ror": "https://ror.org/019whta54", + }, + "schema:identifier": None, + "pulse:owns": ["owner/repo"], + }, + { + "id": "https://ror.org/019whta54", + "type": "org:Organization", + "schema:name": "University of Lausanne", + "aliases": ["Universite de Lausanne"], + "org:hasUnit": ["https://ror.org/03kwyfa97"], + }, + ] + + normalized = run_script._normalize_entities_for_debug_jsonld(entities) + + repository = next( + entity + for entity in normalized + if entity.get("type") == "schema:SoftwareSourceCode" + ) + assert repository["schema:author"] == [ + "urn:pulse:0000-0001-2345-6789", + "ghost", + ] + assert "pulse:ownedBy" not in repository + + person = next( + entity + for entity in normalized + if entity.get("type") == "schema:Person" + ) + assert person["pulse:owns"] == ["owner/repo"] + + organizations = [ + entity + for entity in normalized + if entity.get("type") == "org:Organization" + ] + assert len(organizations) == 1 + assert organizations[0]["schema:identifier"] == "https://ror.org/019whta54" + assert organizations[0]["pulse:owns"] == ["owner/repo"] + assert sorted(organizations[0]["aliases"]) == [ + "Universite de Lausanne", + "Université de Lausanne", + ] + + +def test_run_llm_repo_persons_and_orgs_can_verify_links_with_independent_agent( + monkeypatch, + capsys, +) -> None: + class _FakeRepositoryAgent: + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del context, providers + return AgentResult( + data={ + "id": "octo/repo", + "type": "schema:SoftwareSourceCode", + "shacl": "pulse:RepositoryShape", + "schema:name": "octo/repo", + "schema:author": ["ok-user"], + "schema:license": "https://spdx.org/licenses/MIT.html", + "pulse:githubRepositoryHandle": "octo/repo", + }, + model="openai/gpt-test", + provider="openai", + ) + + class _FakePersonAgent: + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + ) -> None: + del llm_runtime, llm_call_timeout_seconds + + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del providers + username = context["username"] + return AgentResult( + data={ + "id": username, + "type": "schema:Person", + "schema:name": username, + "pulse:githubUsername": username, + "schema:url": "https://github.com/ok-user", + }, + model="openai/gpt-test", + provider="openai", + ) + + class _FakeOrganizationAgent: + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + ) -> None: + del llm_runtime, llm_call_timeout_seconds + + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del providers + return AgentResult( + data={ + "id": context["org_name"], + "type": "org:Organization", + "schema:name": context["org_name"], + "schema:identifier": "https://ror.org/02s376052", + }, + model="openai/gpt-test", + provider="openai", + ) + + class _NoopClassAgent: + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + ) -> None: + del llm_runtime, llm_call_timeout_seconds + + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del context, providers + return AgentResult(data={}, model="openai/gpt-test", provider="openai") + + class _FakeLinkVeracityAgent: + captured_contexts: list[dict[str, Any]] = [] + + def __init__( + self, + *, + llm_runtime: Any | None = None, + llm_call_timeout_seconds: float = 180.0, + ) -> None: + del llm_runtime, llm_call_timeout_seconds + + async def run(self, context: dict[str, Any], providers: ProviderSet) -> AgentResult: + del providers + _FakeLinkVeracityAgent.captured_contexts.append(dict(context)) + link = context["link"] + return AgentResult( + data={ + "link": link, + "relationship_supported": link.startswith("https://"), + "relationship_summary": "verified", + "fetched_successfully": True, + }, + model="openai/gpt-test", + provider="openai", + ) + + async def _fake_gather_context( + _detected_type: str, + _url_info: Any, + _providers: ProviderSet, + ) -> SimpleNamespace: + return SimpleNamespace( + context={ + "repository": { + "metadata": { + "full_name": "octo/repo", + "owner": {"login": "org-owner", "type": "Organization"}, + }, + "contributors": [{"login": "ok-user"}], + "languages": {"Python": 1}, + "readme_content": "README", + }, + }, + warnings=[], + ) + + monkeypatch.setattr( + run_script, + "classify_github_url", + lambda _repo: SimpleNamespace( + owner="octo", + repo="repo", + normalized_url="https://github.com/octo/repo", + ), + ) + monkeypatch.setattr( + run_script, + "_default_provider_set", + lambda *, use_mock_providers: ProviderSet(github=object()), + ) + monkeypatch.setattr(run_script, "gather_context", _fake_gather_context) + monkeypatch.setattr(run_script, "LLMRepositoryAgentV2", _FakeRepositoryAgent) + monkeypatch.setattr(run_script, "LLMPersonAgentV2", _FakePersonAgent) + monkeypatch.setattr(run_script, "LLMOrganizationAgentV2", _FakeOrganizationAgent) + monkeypatch.setattr(run_script, "LLMArticleAgentV2", _NoopClassAgent) + monkeypatch.setattr(run_script, "LLMMembershipAgentV2", _NoopClassAgent) + monkeypatch.setattr(run_script, "LLMContributionAgentV2", _NoopClassAgent) + monkeypatch.setattr(run_script, "LLMLinkVeracityAgentV2", _FakeLinkVeracityAgent) + + asyncio.run( + run_script._run( + "octo/repo", + verify_links=True, + link_verification_timeout_seconds=60.0, + link_verification_max_concurrency=2, + heartbeat_seconds=0.01, + ), + ) + + stdout = capsys.readouterr().out + assert "[ links ] Verifying unique http(s) links via LLM + Selenium tool" in stdout + assert "Link verification summary: yes=" in stdout + + links = sorted( + { + context["link"] + for context in _FakeLinkVeracityAgent.captured_contexts + if isinstance(context.get("link"), str) + }, + ) + assert "https://github.com/ok-user" in links + assert "https://ror.org/02s376052" in links + assert "https://spdx.org/licenses/MIT.html" in links diff --git a/tests/v2/test_schema_validation_agent.py b/tests/v2/test_schema_validation_agent.py new file mode 100644 index 0000000..b901d41 --- /dev/null +++ b/tests/v2/test_schema_validation_agent.py @@ -0,0 +1,110 @@ +from __future__ import annotations + +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Callable + +import pytest +from jsonschema import validate + +REPO_ROOT = Path(__file__).resolve().parents[2] +STRICT_FIXTURE_DIR = REPO_ROOT / "tests" / "v2" / "fixtures" / "schema" / "strict" + + +@dataclass(frozen=True) +class AgentEntityCase: + schema_name: str + fixture_file_name: str + + +ENTITY_CASES = { + "person": AgentEntityCase( + schema_name="person", + fixture_file_name="pulse_PersonShape.json", + ), + "repository": AgentEntityCase( + schema_name="repository", + fixture_file_name="pulse_RepositoryShape.json", + ), + "organization": AgentEntityCase( + schema_name="organization", + fixture_file_name="pulse_OrganizationShape.json", + ), + "membership": AgentEntityCase( + schema_name="membership", + fixture_file_name="pulse_MembershipShape.json", + ), + "contribution": AgentEntityCase( + schema_name="contribution", + fixture_file_name="pulse_ContributionShape.json", + ), + "article": AgentEntityCase( + schema_name="article", + fixture_file_name="pulse_ArticleShape.json", + ), +} + + +def _load_fixture_instances(file_name: str) -> list[dict[str, Any]]: + fixture_path = STRICT_FIXTURE_DIR / file_name + with fixture_path.open(encoding="utf-8") as fixture_file: + parsed = json.load(fixture_file) + + assert isinstance(parsed, list), f"Expected fixture list in {fixture_path}" + for index, item in enumerate(parsed): + assert isinstance(item, dict), ( + f"Expected object at index {index} in fixture {fixture_path}" + ) + + return parsed + + +ENTITY_FIXTURES = { + entity: _load_fixture_instances(case.fixture_file_name) + for entity, case in ENTITY_CASES.items() +} + +ENTITY_PARAMS = [ + pytest.param(entity, case, id=entity) for entity, case in ENTITY_CASES.items() +] + +INSTANCE_PARAMS = [] +for entity, case in ENTITY_CASES.items(): + for instance_index, instance in enumerate(ENTITY_FIXTURES[entity]): + instance_id = str(instance.get("id", f"index-{instance_index}")) + INSTANCE_PARAMS.append( + pytest.param( + entity, + case.schema_name, + instance_index, + instance, + id=f"{entity}-{instance_index}-{instance_id}", + ), + ) + + +@pytest.mark.parametrize(("entity_type", "case"), ENTITY_PARAMS) +def test_agent_validation_fixture_group_has_instances( + entity_type: str, + case: AgentEntityCase, +) -> None: + assert case.schema_name == entity_type + assert len(ENTITY_FIXTURES[entity_type]) > 0 + + +@pytest.mark.parametrize( + ("entity_type", "schema_name", "instance_index", "instance"), + INSTANCE_PARAMS, +) +def test_strict_fixture_instance_validates_against_agent_schema( + load_schema: Callable[[str, str], dict[str, Any]], + entity_type: str, + schema_name: str, + instance_index: int, + instance: dict[str, Any], +) -> None: + assert entity_type in ENTITY_CASES + assert instance_index >= 0 + schema = load_schema("agent", schema_name) + validate(instance=instance, schema=schema) diff --git a/tests/v2/test_schema_validation_negative.py b/tests/v2/test_schema_validation_negative.py new file mode 100644 index 0000000..6d1ce8e --- /dev/null +++ b/tests/v2/test_schema_validation_negative.py @@ -0,0 +1,69 @@ +from __future__ import annotations + +from typing import Any, Callable + +import pytest +from jsonschema import ValidationError, validate + +INVALID_CASES = [ + pytest.param( + "person_missing_name", + "person", + id="person-missing-required-schema-name", + ), + pytest.param( + "person_bad_orcid", + "person", + id="person-invalid-orcid-pattern", + ), + pytest.param( + "person_no_identifier", + "person", + id="person-missing-github-email-infoscience-anyof", + ), + pytest.param( + "repo_bad_github_handle", + "repository", + id="repository-invalid-github-handle-pattern", + ), + pytest.param( + "org_unknown_type", + "organization", + id="organization-unknown-type-enum", + ), + pytest.param( + "membership_extra_properties", + "membership", + id="membership-extra-property-additionalProperties-false", + ), + pytest.param( + "contribution_negative_count", + "contribution", + id="contribution-negative-count-minimum-zero", + ), + pytest.param( + "article_bad_doi", + "article", + id="article-invalid-doi-pattern", + ), +] + +MIN_INVALID_FIXTURE_COUNT = 8 + + +@pytest.mark.parametrize(("fixture_name", "schema_name"), INVALID_CASES) +def test_invalid_fixture_is_rejected_by_strict_schema( + load_schema: Callable[[str, str], dict[str, Any]], + load_fixture: Callable[[str, str], Any], + fixture_name: str, + schema_name: str, +) -> None: + schema = load_schema("strict", schema_name) + invalid_instance = load_fixture("schema/invalid", fixture_name) + + with pytest.raises(ValidationError): + validate(instance=invalid_instance, schema=schema) + + +def test_invalid_fixture_catalog_has_minimum_coverage() -> None: + assert len(INVALID_CASES) >= MIN_INVALID_FIXTURE_COUNT diff --git a/tests/v2/test_schema_validation_strict.py b/tests/v2/test_schema_validation_strict.py new file mode 100644 index 0000000..f09d0da --- /dev/null +++ b/tests/v2/test_schema_validation_strict.py @@ -0,0 +1,116 @@ +from __future__ import annotations + +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Callable + +import pytest +from jsonschema import validate + +REPO_ROOT = Path(__file__).resolve().parents[2] +STRICT_FIXTURE_DIR = REPO_ROOT / "tests" / "v2" / "fixtures" / "schema" / "strict" + + +@dataclass(frozen=True) +class StrictEntityCase: + schema_name: str + fixture_file_name: str + minimum_count: int + + +ENTITY_CASES = { + "person": StrictEntityCase( + schema_name="person", + fixture_file_name="pulse_PersonShape.json", + minimum_count=5, + ), + "repository": StrictEntityCase( + schema_name="repository", + fixture_file_name="pulse_RepositoryShape.json", + minimum_count=4, + ), + "organization": StrictEntityCase( + schema_name="organization", + fixture_file_name="pulse_OrganizationShape.json", + minimum_count=5, + ), + "membership": StrictEntityCase( + schema_name="membership", + fixture_file_name="pulse_MembershipShape.json", + minimum_count=6, + ), + "contribution": StrictEntityCase( + schema_name="contribution", + fixture_file_name="pulse_ContributionShape.json", + minimum_count=9, + ), + "article": StrictEntityCase( + schema_name="article", + fixture_file_name="pulse_ArticleShape.json", + minimum_count=4, + ), +} + + +def _load_fixture_instances(file_name: str) -> list[dict[str, Any]]: + fixture_path = STRICT_FIXTURE_DIR / file_name + with fixture_path.open(encoding="utf-8") as fixture_file: + parsed = json.load(fixture_file) + + assert isinstance(parsed, list), f"Expected fixture list in {fixture_path}" + for index, item in enumerate(parsed): + assert isinstance(item, dict), ( + f"Expected object at index {index} in fixture {fixture_path}" + ) + + return parsed + + +ENTITY_FIXTURES = { + entity: _load_fixture_instances(case.fixture_file_name) + for entity, case in ENTITY_CASES.items() +} + +ENTITY_PARAMS = [ + pytest.param(entity, case, id=entity) for entity, case in ENTITY_CASES.items() +] + +INSTANCE_PARAMS = [] +for entity, case in ENTITY_CASES.items(): + for instance_index, instance in enumerate(ENTITY_FIXTURES[entity]): + instance_id = str(instance.get("id", f"index-{instance_index}")) + INSTANCE_PARAMS.append( + pytest.param( + entity, + case.schema_name, + instance_index, + instance, + id=f"{entity}-{instance_index}-{instance_id}", + ), + ) + + +@pytest.mark.parametrize(("entity_type", "case"), ENTITY_PARAMS) +def test_strict_fixtures_have_required_minimum_instances( + entity_type: str, + case: StrictEntityCase, +) -> None: + assert len(ENTITY_FIXTURES[entity_type]) >= case.minimum_count + + +@pytest.mark.parametrize( + ("entity_type", "schema_name", "instance_index", "instance"), + INSTANCE_PARAMS, +) +def test_strict_fixture_instance_validates_against_strict_schema( + load_schema: Callable[[str, str], dict[str, Any]], + entity_type: str, + schema_name: str, + instance_index: int, + instance: dict[str, Any], +) -> None: + assert entity_type in ENTITY_CASES + assert instance_index >= 0 + schema = load_schema("strict", schema_name) + validate(instance=instance, schema=schema) diff --git a/tests/v2/test_shacl_validation.py b/tests/v2/test_shacl_validation.py new file mode 100644 index 0000000..f6c0738 --- /dev/null +++ b/tests/v2/test_shacl_validation.py @@ -0,0 +1,348 @@ +from __future__ import annotations + +from typing import Any, Callable +from urllib.parse import quote + +import pytest +from rdflib import RDF, XSD, Graph, Literal, Namespace, URIRef + +from src.v2.validation import shacl_validation as shacl_validation_module +from src.v2.validation.ontology import load_ontology_shapes_graph +from src.v2.validation.shacl_validation import ( + SHACLRuntimeUnavailableError, + SHACLValidationResult, + SHACLValidator, +) + +SCHEMA = Namespace("http://schema.org/") +PULSE = Namespace("https://open-pulse.epfl.ch/ontology#") +ORG = Namespace("http://www.w3.org/ns/org#") +TIME = Namespace("http://www.w3.org/2006/time#") +WD = Namespace("http://www.wikidata.org/entity/") +ENTITY_BASE = "https://open-pulse.epfl.ch/entity/" +HAS_PYSHACL = shacl_validation_module.pyshacl_validate is not None + + +def _entity_ref(value: str) -> URIRef: + if value.startswith(("https://", "http://")): + return URIRef(value) + return URIRef(f"{ENTITY_BASE}{quote(value, safe='')}") + + +def _prefixed_to_iri(value: str) -> URIRef: + if value.startswith("pulse:"): + return PULSE[value.split(":", maxsplit=1)[1]] + if value.startswith("wd:"): + return WD[value.split(":", maxsplit=1)[1]] + if value.startswith(("https://", "http://")): + return URIRef(value) + return _entity_ref(value) + + +def _as_text(value: Any) -> str | None: + if isinstance(value, str) and value: + return value + return None + + +def _as_int(value: Any) -> int | None: + if isinstance(value, int): + return value + return None + + +def _add_person(graph: Graph, person: dict[str, Any]) -> None: + subject = _entity_ref(person["id"]) + graph.add((subject, RDF.type, SCHEMA.Person)) + + for key, predicate in ( + ("schema:name", SCHEMA.name), + ("schema:email", SCHEMA.email), + ("pulse:githubUsername", PULSE.githubUsername), + ("pulse:orcidIdentifier", PULSE.orcidIdentifier), + ("pulse:infosciencePersonIdentifier", PULSE.infosciencePersonIdentifier), + ): + value = _as_text(person.get(key)) + if value is not None: + graph.add((subject, predicate, Literal(value))) + + url_value = _as_text(person.get("schema:url")) + if url_value is not None: + graph.add((subject, SCHEMA.url, URIRef(url_value))) + + for membership in person.get("org:hasMembership", []): + if isinstance(membership, str): + graph.add((subject, ORG.hasMembership, _entity_ref(membership))) + + for contribution in person.get("pulse:hasContribution", []): + if isinstance(contribution, str): + graph.add((subject, PULSE.hasContribution, _entity_ref(contribution))) + + for owned_repo in person.get("pulse:owns", []): + if isinstance(owned_repo, str): + graph.add((subject, PULSE.owns, _entity_ref(owned_repo))) + + +def _add_organization(graph: Graph, organization: dict[str, Any]) -> None: # noqa: C901 + subject = _entity_ref(organization["id"]) + graph.add((subject, RDF.type, ORG.Organization)) + + name = _as_text(organization.get("schema:name")) + if name is not None: + graph.add((subject, SCHEMA.name, Literal(name))) + + ror = _as_text(organization.get("schema:identifier")) + if ror is not None: + graph.add((subject, SCHEMA.identifier, Literal(ror))) + + github_handle = _as_text(organization.get("pulse:githubOrganizationHandle")) + if github_handle is not None: + graph.add((subject, PULSE.githubOrganizationHandle, Literal(github_handle))) + + infoscience_identifier = _as_text( + organization.get("pulse:infoscienceOrganizationIdentifier"), + ) + if infoscience_identifier is not None: + graph.add( + ( + subject, + PULSE.infoscienceOrganizationIdentifier, + Literal(infoscience_identifier), + ), + ) + + org_type = _as_text(organization.get("pulse:OrganizationType")) + if org_type is not None: + graph.add((subject, PULSE.OrganizationType, _prefixed_to_iri(org_type))) + + followers = _as_int(organization.get("pulse:githubOrgFollowers")) + if followers is not None: + graph.add((subject, PULSE.githubOrgFollowers, Literal(followers))) + + for unit in organization.get("org:hasUnit", []): + if isinstance(unit, str): + graph.add((subject, ORG.hasUnit, _entity_ref(unit))) + + unit_of = _as_text(organization.get("org:unitOf")) + if unit_of is not None: + graph.add((subject, ORG.unitOf, _entity_ref(unit_of))) + + for owned_repo in organization.get("pulse:owns", []): + if isinstance(owned_repo, str): + graph.add((subject, PULSE.owns, _entity_ref(owned_repo))) + + +def _add_repository(graph: Graph, repository: dict[str, Any]) -> None: # noqa: C901, PLR0912 + subject = _entity_ref(repository["id"]) + graph.add((subject, RDF.type, SCHEMA.SoftwareSourceCode)) + + for key, predicate in ( + ("schema:name", SCHEMA.name), + ("pulse:githubRepositoryHandle", PULSE.githubRepositoryHandle), + ): + value = _as_text(repository.get(key)) + if value is not None: + graph.add((subject, predicate, Literal(value))) + + repository_type = _as_text(repository.get("pulse:repositoryType")) + if repository_type is not None: + graph.add((subject, PULSE.repositoryType, _prefixed_to_iri(repository_type))) + + for discipline in repository.get("pulse:discipline", []): + if isinstance(discipline, str): + graph.add((subject, PULSE.discipline, _prefixed_to_iri(discipline))) + + for author in repository.get("schema:author", []): + if isinstance(author, str): + graph.add((subject, SCHEMA.author, _entity_ref(author))) + + stars = _as_int(repository.get("pulse:githubRepoStars")) + if stars is not None: + graph.add((subject, PULSE.githubRepoStars, Literal(stars))) + + forks = _as_int(repository.get("pulse:githubRepoForks")) + if forks is not None: + graph.add((subject, PULSE.githubRepoForks, Literal(forks))) + + created_at = _as_text(repository.get("schema:dateCreated")) + if created_at is not None: + graph.add((subject, SCHEMA.dateCreated, Literal(created_at, datatype=XSD.dateTime))) + + license_iri = _as_text(repository.get("schema:license")) + if license_iri is not None: + graph.add((subject, SCHEMA.license, URIRef(license_iri))) + + citation_iri = _as_text(repository.get("schema:citation")) + if citation_iri is not None: + graph.add((subject, SCHEMA.citation, URIRef(citation_iri))) + + for language in repository.get("schema:programmingLanguage", []): + if isinstance(language, str): + graph.add((subject, SCHEMA.programmingLanguage, Literal(language))) + + owned_by = _as_text(repository.get("pulse:ownedBy")) + if owned_by is not None: + graph.add((subject, PULSE.ownedBy, _entity_ref(owned_by))) + + is_fork_of = _as_text(repository.get("pulse:isForkOf")) + if is_fork_of is not None: + graph.add((subject, PULSE.isForkOf, _entity_ref(is_fork_of))) + + +def _add_membership(graph: Graph, membership: dict[str, Any]) -> None: + subject = _entity_ref(membership["id"]) + graph.add((subject, RDF.type, ORG.Membership)) + + organization = _as_text(membership.get("org:organization")) + if organization is not None: + graph.add((subject, ORG.organization, _entity_ref(organization))) + + role = _as_text(membership.get("org:role")) + if role is not None: + graph.add((subject, ORG.role, Literal(role))) + + started = _as_text(membership.get("time:hasBeginning")) + if started is not None: + graph.add((subject, TIME.hasBeginning, Literal(started, datatype=XSD.date))) + + ended = _as_text(membership.get("time:hasEnd")) + if ended is not None: + graph.add((subject, TIME.hasEnd, Literal(ended, datatype=XSD.date))) + + +def _add_contribution(graph: Graph, contribution: dict[str, Any]) -> None: + subject = _entity_ref(contribution["id"]) + graph.add((subject, RDF.type, PULSE.Contribution)) + + contribution_to = _as_text(contribution.get("pulse:contributionTo")) + if contribution_to is not None: + graph.add((subject, PULSE.contributionTo, _entity_ref(contribution_to))) + + contribution_count = _as_int(contribution.get("pulse:contributionCount")) + if contribution_count is not None: + graph.add((subject, PULSE.contributionCount, Literal(contribution_count))) + + first_contribution = _as_text(contribution.get("pulse:firstContributionDate")) + if first_contribution is not None: + graph.add( + ( + subject, + PULSE.firstContributionDate, + Literal(first_contribution, datatype=XSD.dateTime), + ), + ) + + last_contribution = _as_text(contribution.get("pulse:lastContributionDate")) + if last_contribution is not None: + graph.add( + ( + subject, + PULSE.lastContributionDate, + Literal(last_contribution, datatype=XSD.dateTime), + ), + ) + + author = _as_text(contribution.get("schema:author")) + if author is not None: + graph.add((subject, SCHEMA.author, _entity_ref(author))) + + +def _strict_fixtures_to_graph(load_fixture: Callable[[str, str], Any]) -> Graph: + graph = Graph() + for person in load_fixture("schema/strict", "pulse_PersonShape"): + _add_person(graph, person) + for organization in load_fixture("schema/strict", "pulse_OrganizationShape"): + _add_organization(graph, organization) + for repository in load_fixture("schema/strict", "pulse_RepositoryShape"): + _add_repository(graph, repository) + for membership in load_fixture("schema/strict", "pulse_MembershipShape"): + _add_membership(graph, membership) + for contribution in load_fixture("schema/strict", "pulse_ContributionShape"): + _add_contribution(graph, contribution) + return graph + + +@pytest.mark.skipif(not HAS_PYSHACL, reason="pyshacl is not installed") +def test_shacl_validator_accepts_valid_fixture_graph( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = SHACLValidator() + data_graph = _strict_fixtures_to_graph(load_fixture) + shapes_graph = load_ontology_shapes_graph() + + result = validator.validate_graph(data_graph, shapes_graph) + + assert isinstance(result, SHACLValidationResult) + assert result.conforms is True + assert result.violations == [] + + +@pytest.mark.skipif(not HAS_PYSHACL, reason="pyshacl is not installed") +def test_shacl_validator_rejects_person_without_any_identifier() -> None: + validator = SHACLValidator() + shapes_graph = load_ontology_shapes_graph() + graph = Graph() + + person = _entity_ref("person-without-identifiers") + graph.add((person, RDF.type, SCHEMA.Person)) + graph.add((person, SCHEMA.name, Literal("No Identifier Person"))) + + result = validator.validate_graph(graph, shapes_graph) + + assert result.conforms is False + assert result.violations + assert any( + violation["message"] + and any( + token in violation["message"] + for token in ("githubUsername", "email", "infosciencePersonIdentifier", "sh:or") + ) + for violation in result.violations + ) + + +@pytest.mark.skipif(not HAS_PYSHACL, reason="pyshacl is not installed") +def test_shacl_validator_rejects_repository_with_invalid_repository_type() -> None: + validator = SHACLValidator() + shapes_graph = load_ontology_shapes_graph() + graph = Graph() + + person = _entity_ref("valid-person") + graph.add((person, RDF.type, SCHEMA.Person)) + graph.add((person, SCHEMA.name, Literal("Valid Author"))) + graph.add((person, PULSE.githubUsername, Literal("validauthor"))) + + repository = _entity_ref("owner/invalid-repo") + graph.add((repository, RDF.type, SCHEMA.SoftwareSourceCode)) + graph.add((repository, SCHEMA.name, Literal("Invalid Type Repo"))) + graph.add((repository, PULSE.githubRepositoryHandle, Literal("owner/invalid-repo"))) + graph.add((repository, SCHEMA.author, person)) + graph.add((repository, PULSE.repositoryType, PULSE.InvalidRepositoryType)) + + result = validator.validate_graph(graph, shapes_graph) + + assert result.conforms is False + assert result.violations + assert any( + violation["path"] and "repositoryType" in violation["path"] + for violation in result.violations + ) + assert all(violation["message"] for violation in result.violations) + + +def test_ontology_shapes_graph_is_cached() -> None: + first = load_ontology_shapes_graph() + second = load_ontology_shapes_graph() + + assert first is second + + +def test_shacl_validator_reports_missing_runtime_dependency(monkeypatch) -> None: + validator = SHACLValidator() + monkeypatch.setattr(shacl_validation_module, "pyshacl_validate", None) + + with pytest.raises( + SHACLRuntimeUnavailableError, + match="pyshacl is not installed", + ): + validator.validate_graph(Graph(), Graph()) diff --git a/tests/v2/test_strict_validation_gate.py b/tests/v2/test_strict_validation_gate.py new file mode 100644 index 0000000..e0101b2 --- /dev/null +++ b/tests/v2/test_strict_validation_gate.py @@ -0,0 +1,148 @@ +from __future__ import annotations + +from copy import deepcopy +from typing import Any, Callable + +from src.v2.validation.schema_validation import ( + BatchValidationResult, + StrictSchemaValidator, + ValidationResult, +) + +EXPECTED_INVALID_BATCH_COUNT = 2 + + +def test_strict_validator_accepts_valid_person_fixture( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + person = load_fixture("schema/strict", "pulse_PersonShape")[0] + + result = validator.validate("person", person) + + assert isinstance(result, ValidationResult) + assert result.is_valid is True + assert result.errors == [] + assert result.entity_type == "person" + + +def test_strict_validator_rejects_invalid_orcid_pattern( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + invalid_person = load_fixture("schema/invalid", "person_bad_orcid") + + result = validator.validate("person", invalid_person) + + assert result.is_valid is False + assert any( + error["constraint"] == "pattern" and "orcid" in error["path"].lower() + for error in result.errors + ) + + +def test_strict_validator_rejects_missing_required_repository_field( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + repository = deepcopy(load_fixture("schema/strict", "pulse_RepositoryShape")[0]) + repository.pop("pulse:githubRepositoryHandle") + + result = validator.validate("repository", repository) + + assert result.is_valid is False + assert any( + error["constraint"] == "required" + and "pulse:githubRepositoryHandle" in error["message"] + for error in result.errors + ) + + +def test_strict_validator_rejects_unknown_organization_enum( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + invalid_org = load_fixture("schema/invalid", "org_unknown_type") + + result = validator.validate("organization", invalid_org) + + assert result.is_valid is False + assert any( + error["constraint"] == "enum" and "pulse:OrganizationType" in error["path"] + for error in result.errors + ) + + +def test_strict_validator_rejects_additional_properties( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + invalid_membership = load_fixture("schema/invalid", "membership_extra_properties") + + result = validator.validate("membership", invalid_membership) + + assert result.is_valid is False + assert any(error["constraint"] == "additionalProperties" for error in result.errors) + + +def test_strict_validator_batch_result_splits_valid_and_invalid_entities( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + valid_person = load_fixture("schema/strict", "pulse_PersonShape")[0] + invalid_person = load_fixture("schema/invalid", "person_bad_orcid") + invalid_org = load_fixture("schema/invalid", "org_unknown_type") + + batch_result = validator.validate_batch( + [ + ("person", valid_person), + ("person", invalid_person), + ("organization", invalid_org), + ], + ) + + assert isinstance(batch_result, BatchValidationResult) + assert len(batch_result.valid_entities) == 1 + assert len(batch_result.invalid_entities) == EXPECTED_INVALID_BATCH_COUNT + assert batch_result.warnings + + +def test_strict_validator_errors_include_path_and_expected_constraint( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + invalid_person = load_fixture("schema/invalid", "person_bad_orcid") + + result = validator.validate("person", invalid_person) + + assert result.is_valid is False + assert result.errors + first_error = result.errors[0] + assert first_error["path"] + assert first_error["constraint"] + assert first_error["expected"] + + +def test_strict_validator_accepts_prefixed_idsource_values( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + repository = load_fixture("schema/strict", "pulse_RepositoryShape")[0] + repository["idSource"] = "pulse:githubRepositoryHandle" + + result = validator.validate("repository", repository) + + assert result.is_valid is True + + +def test_strict_validator_rejects_legacy_unprefixed_idsource_values( + load_fixture: Callable[[str, str], Any], +) -> None: + validator = StrictSchemaValidator() + repository = load_fixture("schema/strict", "pulse_RepositoryShape")[0] + repository["idSource"] = "githubRepositoryHandle" + + result = validator.validate("repository", repository) + + assert result.is_valid is False + assert any(error["path"] == "idSource" and error["constraint"] == "enum" for error in result.errors) diff --git a/tests/v2/test_test_infrastructure.py b/tests/v2/test_test_infrastructure.py new file mode 100644 index 0000000..fbee4a4 --- /dev/null +++ b/tests/v2/test_test_infrastructure.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any, Callable + + +def test_load_schema_returns_dict( + load_schema: Callable[[str, str], dict[str, Any]], +) -> None: + schema = load_schema("strict", "person") + assert isinstance(schema, dict) + + +def test_load_fixture_supports_nested_groups( + load_fixture: Callable[[str, str], Any], +) -> None: + fixture = load_fixture("schema/strict", "person.schema") + assert isinstance(fixture, dict) + + +def test_v2_test_config_paths_exist(v2_test_config: Any) -> None: + assert v2_test_config.tests_root == Path(__file__).resolve().parent + assert v2_test_config.fixtures_root.exists() + assert v2_test_config.golden_root.exists() diff --git a/tests/v2/test_ttl_schema_alignment.py b/tests/v2/test_ttl_schema_alignment.py new file mode 100644 index 0000000..764001e --- /dev/null +++ b/tests/v2/test_ttl_schema_alignment.py @@ -0,0 +1,205 @@ +from __future__ import annotations + +import json +from functools import lru_cache +from pathlib import Path + +import pytest +from rdflib import Graph, Namespace +from rdflib.namespace import RDF, XSD + +from src.v2.validation.ontology import ontology_ttl_path + +SH = Namespace("http://www.w3.org/ns/shacl#") +SCHEMA = Namespace("http://schema.org/") +ORG = Namespace("http://www.w3.org/ns/org#") +TIME = Namespace("http://www.w3.org/2006/time#") +PULSE = Namespace("https://open-pulse.epfl.ch/ontology#") +WD = Namespace("http://www.wikidata.org/entity/") + +REPO_ROOT = Path(__file__).resolve().parents[2] +STRICT_SCHEMA_DIR = REPO_ROOT / "src" / "v2" / "schema" / "json" / "strict" + +SHAPE_SCHEMA_MAP = { + "PersonShape": "person.schema.json", + "RepositoryShape": "repository.schema.json", + "OrganizationShape": "organization.schema.json", + "MembershipShape": "membership.schema.json", + "ContributionShape": "contribution.schema.json", + "ArticleShape": "article.schema.json", +} + +ENUM_FIELD_MAP = { + "DisciplineEnumeration": ("RepositoryShape", "pulse:discipline"), + "RepositoryTypeEnumeration": ("RepositoryShape", "pulse:repositoryType"), + "OrganizationTypeEnumeration": ("OrganizationShape", "pulse:OrganizationType"), +} + +ENUM_CLASSES = { + "DisciplineEnumeration": PULSE.DisciplineEnumeration, + "RepositoryTypeEnumeration": PULSE.RepositoryTypeEnumeration, + "OrganizationTypeEnumeration": PULSE.OrganizationTypeEnumeration, +} + +ENVELOPE_FIELDS = {"id", "type", "shacl", "identifiers", "idSource"} + +PREFIX_MAP = { + str(SCHEMA): "schema:", + str(ORG): "org:", + str(TIME): "time:", + str(PULSE): "pulse:", + str(WD): "wd:", + str(XSD): "xsd:", +} + +XSD_TO_JSON = { + str(XSD.string): "string", + str(XSD.integer): "integer", + str(XSD.date): "string", + str(XSD.dateTime): "string", +} + + +def _to_prefixed(iri: str) -> str: + for namespace, prefix in PREFIX_MAP.items(): + if iri.startswith(namespace): + return prefix + iri[len(namespace) :] + return iri + + +@lru_cache(maxsize=1) +def _load_graph() -> Graph: + graph = Graph() + graph.parse(ontology_ttl_path(), format="turtle") + return graph + + +@lru_cache(maxsize=1) +def _load_schemas() -> dict[str, dict]: + return { + shape_name: json.loads((STRICT_SCHEMA_DIR / file_name).read_text(encoding="utf-8")) + for shape_name, file_name in SHAPE_SCHEMA_MAP.items() + } + + +def _shape_properties() -> dict[str, dict[str, dict[str, str | int]]]: + graph = _load_graph() + properties_by_shape: dict[str, dict[str, dict[str, str | int]]] = {} + + for shape_node in graph.subjects(RDF.type, SH.NodeShape): + shape_name = str(shape_node).split("#")[-1] + if shape_name not in SHAPE_SCHEMA_MAP: + continue + + shape_properties: dict[str, dict[str, str | int]] = {} + for property_node in graph.objects(shape_node, SH.property): + path_node = next(iter(graph.objects(property_node, SH.path)), None) + if path_node is None: + continue + + property_key = _to_prefixed(str(path_node)) + constraints: dict[str, str | int] = {} + datatype_node = next(iter(graph.objects(property_node, SH.datatype)), None) + if datatype_node is not None: + constraints["datatype"] = str(datatype_node) + pattern_node = next(iter(graph.objects(property_node, SH.pattern)), None) + if pattern_node is not None: + constraints["pattern"] = str(pattern_node) + min_count_node = next(iter(graph.objects(property_node, SH.minCount)), None) + if min_count_node is not None: + constraints["minCount"] = int(str(min_count_node)) + shape_properties[property_key] = constraints + + properties_by_shape[shape_name] = shape_properties + + return properties_by_shape + + +def _schema_enum_values(schema: dict, field_name: str) -> set[str]: + prop = schema["properties"][field_name] + if "enum" in prop: + return set(prop["enum"]) + return set(prop["items"]["enum"]) + + +@pytest.mark.parametrize("shape_name", SHAPE_SCHEMA_MAP) +def test_ttl_shape_properties_exist_in_json_schema(shape_name: str) -> None: + ttl_properties = set(_shape_properties()[shape_name]) + schema_properties = set(_load_schemas()[shape_name]["properties"]) - ENVELOPE_FIELDS + assert ttl_properties <= schema_properties + + +@pytest.mark.parametrize("shape_name", SHAPE_SCHEMA_MAP) +def test_json_schema_properties_exist_in_ttl_shape(shape_name: str) -> None: + ttl_properties = set(_shape_properties()[shape_name]) + schema_properties = set(_load_schemas()[shape_name]["properties"]) - ENVELOPE_FIELDS + assert schema_properties <= ttl_properties + + +@pytest.mark.parametrize("enum_name", ENUM_CLASSES) +def test_ttl_and_json_schema_enums_are_consistent(enum_name: str) -> None: + graph = _load_graph() + shape_name, field_name = ENUM_FIELD_MAP[enum_name] + ttl_values = { + _to_prefixed(str(instance)) + for instance in graph.subjects(RDF.type, ENUM_CLASSES[enum_name]) + } + json_values = _schema_enum_values(_load_schemas()[shape_name], field_name) + assert ttl_values == json_values + + +@pytest.mark.parametrize("shape_name", SHAPE_SCHEMA_MAP) +def test_ttl_regex_patterns_match_json_schema(shape_name: str) -> None: + schema = _load_schemas()[shape_name] + for property_name, constraints in _shape_properties()[shape_name].items(): + ttl_pattern = constraints.get("pattern") + if ttl_pattern is None: + continue + json_pattern = schema["properties"][property_name].get("pattern") + assert json_pattern == ttl_pattern + + +@pytest.mark.parametrize("shape_name", SHAPE_SCHEMA_MAP) +def test_ttl_required_fields_match_json_required(shape_name: str) -> None: + ttl_required = { + property_name + for property_name, constraints in _shape_properties()[shape_name].items() + if int(constraints.get("minCount", 0)) >= 1 + } + json_required = set(_load_schemas()[shape_name].get("required", [])) - ENVELOPE_FIELDS + assert ttl_required == json_required + + +@pytest.mark.parametrize("shape_name", SHAPE_SCHEMA_MAP) +def test_ttl_datatypes_match_json_schema_types(shape_name: str) -> None: + schema = _load_schemas()[shape_name] + for property_name, constraints in _shape_properties()[shape_name].items(): + ttl_datatype = constraints.get("datatype") + if ttl_datatype is None: + continue + expected_json_type = XSD_TO_JSON.get(str(ttl_datatype)) + if expected_json_type is None: + continue + + json_property = schema["properties"][property_name] + json_type = json_property.get("type") + if json_type is None: + continue + + json_types = set(json_type) if isinstance(json_type, list) else {json_type} + if "array" in json_types: + items_type = json_property.get("items", {}).get("type") + if items_type is None: + continue + assert items_type == expected_json_type + continue + + assert expected_json_type in json_types + + +def test_property_alignment_guard_fails_when_ttl_adds_unmapped_property() -> None: + shape_name = "PersonShape" + ttl_properties = set(_shape_properties()[shape_name]) | {"pulse:newProperty"} + schema_properties = set(_load_schemas()[shape_name]["properties"]) - ENVELOPE_FIELDS + with pytest.raises(AssertionError): + assert ttl_properties <= schema_properties diff --git a/tests/v2/test_url_classifier.py b/tests/v2/test_url_classifier.py new file mode 100644 index 0000000..783282a --- /dev/null +++ b/tests/v2/test_url_classifier.py @@ -0,0 +1,64 @@ +from __future__ import annotations + +import pytest + +from src.v2.ingest.detection import GitHubURLType, classify_github_url + + +def test_repository_url_detected_with_owner_and_repo() -> None: + result = classify_github_url("https://github.com/owner/repo") + + assert result.detected_type == GitHubURLType.REPOSITORY + assert result.owner == "owner" + assert result.repo == "repo" + assert result.normalized_url == "https://github.com/owner/repo" + + +def test_repository_url_git_suffix_is_stripped() -> None: + result = classify_github_url("https://github.com/owner/repo.git") + + assert result.detected_type == GitHubURLType.REPOSITORY + assert result.owner == "owner" + assert result.repo == "repo" + assert result.normalized_url == "https://github.com/owner/repo" + + +def test_user_url_detected_from_single_path_segment() -> None: + result = classify_github_url("https://github.com/username") + + assert result.detected_type == GitHubURLType.USER + assert result.owner == "username" + assert result.repo is None + assert result.normalized_url == "https://github.com/username" + + +def test_orgs_url_detected_as_organization() -> None: + result = classify_github_url("https://github.com/orgs/orgname") + + assert result.detected_type == GitHubURLType.ORGANIZATION + assert result.owner == "orgname" + assert result.repo is None + assert result.normalized_url == "https://github.com/orgs/orgname" + + +def test_ambiguous_org_or_user_defaults_to_user() -> None: + result = classify_github_url("https://github.com/orgname") + + assert result.detected_type == GitHubURLType.USER + assert result.owner == "orgname" + assert result.repo is None + assert result.normalized_url == "https://github.com/orgname" + + +def test_query_fragment_and_trailing_slash_are_normalized() -> None: + result = classify_github_url("https://GitHub.com/Owner/Repo.git/?tab=readme#intro") + + assert result.detected_type == GitHubURLType.REPOSITORY + assert result.owner == "Owner" + assert result.repo == "Repo" + assert result.normalized_url == "https://github.com/Owner/Repo" + + +def test_non_github_url_raises_value_error() -> None: + with pytest.raises(ValueError, match="Non-GitHub URL not supported"): + classify_github_url("https://gitlab.com/owner/repo") diff --git a/tests/v2/test_url_classifier_edge_cases.py b/tests/v2/test_url_classifier_edge_cases.py new file mode 100644 index 0000000..adba5ed --- /dev/null +++ b/tests/v2/test_url_classifier_edge_cases.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +import pytest + +from src.v2.ingest.detection import GitHubURLType, UnsupportedGitHubURL, classify_github_url + +UNSUPPORTED_URL_CASES = [ + ("https://github.com/owner/repo/issues/123", "issue URLs not supported"), + ("https://github.com/owner/repo/pull/456", "pull request URLs not supported"), + ("https://github.com/owner/repo/blob/main/file.py", "file URLs not supported"), + ("https://github.com/owner/repo/tree/main", "tree URLs not supported"), + ("https://github.com/owner/repo/commit/abc123", "commit URLs not supported"), + ("https://github.com/owner/repo/actions", "actions URLs not supported"), + ("https://github.com/owner/repo/releases", "releases URLs not supported"), + ("https://github.com/owner/repo/wiki", "wiki URLs not supported"), + ("https://github.com/owner/repo/settings", "settings URLs not supported"), + ("https://github.com/owner/repo/security", "security URLs not supported"), +] + + +@pytest.mark.parametrize(("url", "expected_reason"), UNSUPPORTED_URL_CASES) +def test_unsupported_repository_subresource_urls_raise_typed_error( + url: str, + expected_reason: str, +) -> None: + with pytest.raises(UnsupportedGitHubURL) as error: + classify_github_url(url) + + assert error.value.reason == expected_reason + + +def test_http_url_is_upgraded_to_https() -> None: + result = classify_github_url("http://github.com/owner/repo") + + assert result.detected_type == GitHubURLType.REPOSITORY + assert result.normalized_url == "https://github.com/owner/repo" + + +def test_url_encoded_owner_and_repo_are_decoded() -> None: + result = classify_github_url("https://github.com/open%2Dpulse/repo%2Dname") + + assert result.detected_type == GitHubURLType.REPOSITORY + assert result.owner == "open-pulse" + assert result.repo == "repo-name" + assert result.normalized_url == "https://github.com/open-pulse/repo-name" diff --git a/tests/v2/test_validate_org_github_handles.py b/tests/v2/test_validate_org_github_handles.py new file mode 100644 index 0000000..1b60ddf --- /dev/null +++ b/tests/v2/test_validate_org_github_handles.py @@ -0,0 +1,132 @@ +from __future__ import annotations + +from typing import Any + +from src.v2.agents.models import ProviderSet +from src.v2.pipeline.stages.models import ReconciledEntities +from src.v2.pipeline.stages.validate_org_github_handles import ( + validate_org_github_handles, +) + + +class _StubGitHubProvider: + def __init__(self, lookups: dict[str, dict[str, Any]]) -> None: + self._lookups = lookups + self.calls: list[str] = [] + + def get_user(self, login: str) -> dict[str, Any]: + self.calls.append(login) + return self._lookups.get(login, {}) + + +def _org_with_at_name(name: str) -> dict[str, Any]: + return { + "id": f"urn:pulse:test-{name.lstrip('@')}", + "type": "org:Organization", + "shacl": "pulse:OrganizationShape", + "schema:name": name, + "identifiers": { + "pulse:ror": None, + "pulse:githubOrganizationHandle": None, + "pulse:infoscienceOrganizationIdentifier": None, + "uuid": "00000000-0000-0000-0000-000000000000", + }, + } + + +def test_stamps_handle_when_github_says_organization() -> None: + provider = _StubGitHubProvider( + lookups={"10xGenomics": {"login": "10xGenomics", "type": "Organization"}}, + ) + org = _org_with_at_name("@10xGenomics") + reconciled = ReconciledEntities(entities={"organizations": [org]}) + + result, warnings = validate_org_github_handles( + reconciled, + ProviderSet(github=provider), + ) + + orgs = result.entities["organizations"] + assert len(orgs) == 1 + assert orgs[0]["pulse:githubOrganizationHandle"] == "10xGenomics" + assert orgs[0]["identifiers"]["pulse:githubOrganizationHandle"] == "10xGenomics" + assert orgs[0]["schema:name"] == "10xGenomics" # leading @ stripped + assert any("Stamped pulse:githubOrganizationHandle='10xGenomics'" in w for w in warnings) + + +def test_drops_when_github_says_user() -> None: + provider = _StubGitHubProvider( + lookups={"someone": {"login": "someone", "type": "User"}}, + ) + org = _org_with_at_name("@someone") + reconciled = ReconciledEntities(entities={"organizations": [org]}) + + result, warnings = validate_org_github_handles( + reconciled, + ProviderSet(github=provider), + ) + + assert result.entities["organizations"] == [] + assert any("Dropped hallucinated organization '@someone'" in w for w in warnings) + + +def test_drops_when_github_returns_404() -> None: + provider = _StubGitHubProvider(lookups={}) # any lookup returns {} + org = _org_with_at_name("@nonexistent") + reconciled = ReconciledEntities(entities={"organizations": [org]}) + + result, _warnings = validate_org_github_handles( + reconciled, + ProviderSet(github=provider), + ) + + assert result.entities["organizations"] == [] + + +def test_skips_orgs_already_carrying_a_handle() -> None: + provider = _StubGitHubProvider(lookups={}) + org = _org_with_at_name("@10xGenomics") + org["pulse:githubOrganizationHandle"] = "10xGenomics" + reconciled = ReconciledEntities(entities={"organizations": [org]}) + + result, warnings = validate_org_github_handles( + reconciled, + ProviderSet(github=provider), + ) + + assert result.entities["organizations"] == [org] + assert provider.calls == [] # never probed + assert warnings == [] + + +def test_skips_when_name_is_not_at_handle_shaped() -> None: + provider = _StubGitHubProvider(lookups={}) + org = _org_with_at_name("Acme Research Group") # not an @-mention + reconciled = ReconciledEntities(entities={"organizations": [org]}) + + result, warnings = validate_org_github_handles( + reconciled, + ProviderSet(github=provider), + ) + + assert result.entities["organizations"] == [org] + assert provider.calls == [] + assert warnings == [] + + +def test_provider_lookup_failure_keeps_entity() -> None: + class _Fails(_StubGitHubProvider): + def get_user(self, login: str) -> dict[str, Any]: + raise RuntimeError("boom") + + provider = _Fails(lookups={}) + org = _org_with_at_name("@10xGenomics") + reconciled = ReconciledEntities(entities={"organizations": [org]}) + + result, warnings = validate_org_github_handles( + reconciled, + ProviderSet(github=provider), + ) + + assert result.entities["organizations"] == [org] + assert warnings == [] # informational only via logger diff --git a/uv.lock b/uv.lock index 2fb2764..a5f256d 100644 --- a/uv.lock +++ b/uv.lock @@ -2,7 +2,10 @@ version = 1 revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.11'", + "python_full_version >= '3.14'", + 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