SiamQuantum Atlas

SiamQuantum Atlas is the current web-based research tool for Thai quantum discourse mapping. It ingests a mixed corpus of Thai-relevant quantum sources, stores them in SQLite, enriches them with NLP-derived entities and triplets, and serves a working analyst UI through FastAPI + Jinja2.

This repository is not a speculative product shell. It is the live baseline used for corpus review, graph inspection, taxonomy-aware analytics, and operator-led source expansion.

For builders who want to reuse this pattern for another topic, see the topic-neutral open-source blueprint in docs/open-source-research-atlas-guide.md. It covers architecture, data modeling, algorithms, statistics, optimization, phase-gated CLI prompts, and UI/UX designer prompts for creating similar atlas platforms beyond quantum.

Current stack

• Python 3.11+
• FastAPI + uvicorn (default port 8765)
• SQLite (data/processed/siamquantum_atlas.db)
• GDELT API v2, YouTube Data API v3, Anthropic Claude API
• Jinja2 templates with CDN JavaScript (Leaflet.js, Chart.js, 3d-force-graph)
• Supabase Auth + PostgREST (optional — local auth mode available)
• Prefect 3 (optional — scheduled pipeline orchestration)
• Typer CLI

What the app does

• Ingests source records from GDELT, YouTube, RSS, and curated seeds.
• Stores source, geo, entity, triplet, cache, and community-submission data in SQLite.
• Runs NLP extraction and classification in a resumable pipeline.
• Builds taxonomy-aware statistics and graph metrics caches.
• Serves the viewer pages:
  • /: landing page
  • /dashboard: geographic source map with mobile bottom-sheet detail panel
  • /network: 3D concept network with click-through node research detail
  • /analytics: engagement and taxonomy analysis
  • /database: filtered source browser with XLSX export
  • /submit-data: authenticated submission and review queue with grounded AI URL analysis
  • /profile: login, signup, and editable user profile
  • /admin/submitted-data: admin review queue

Compatibility redirects: /overview → /dashboard, /community → /submit-data

Local run

python -m pip install -e .[dev]
python -m siamquantum serve

Default viewer URL: http://127.0.0.1:8765/dashboard

Run with auto-reload (development):

python -m siamquantum serve --reload

Run on another port:

python -m siamquantum serve --port 9000

Supabase auth setup

The app supports Supabase Auth for browser login, profile management, categories, and user-owned submitted data. If Supabase env vars are absent, the app falls back to local auth mode (SQLite-backed sessions).

Required env vars for Supabase mode:

SUPABASE_URL=...
SUPABASE_PUBLISHABLE_KEY=...
SUPABASE_SECRET_KEY=...

• SUPABASE_PUBLISHABLE_KEY is exposed to browser code by design.
• SUPABASE_SECRET_KEY is server-only — never put it in templates or client JS.
• .env, .env.local, and .env.*.local are gitignored.

SQL migration: supabase/migrations/20260429_auth_profiles_submitted_data.sql

Manual Supabase dashboard steps:

1. Run the SQL migration in the SQL editor.
2. Go to Authentication → Providers → Google and enable Google OAuth.
3. Add Google OAuth client ID and secret.
4. Add your local and production redirect URLs (must land back on /profile).

Operator workflow

Typical local workflow:

python -m siamquantum ingest seeds
python -m siamquantum ingest rss --feed all
python -m siamquantum ingest gdelt --year 2024
python -m siamquantum ingest youtube --year 2024
python -m siamquantum ingest geo --pending
python -m siamquantum analyze nlp --year 2024
python -m siamquantum analyze stats
python -m siamquantum analyze taxonomy-stats
python -m siamquantum analyze graph-metrics
python -m siamquantum filter relevance
python -m siamquantum serve

Not every run needs the full sequence. The system supports incremental operator-led updates.

Full CLI reference

python -m siamquantum db init
python -m siamquantum db reset --confirm
python -m siamquantum db audit [--fix]

python -m siamquantum ingest today
python -m siamquantum ingest seeds
python -m siamquantum ingest rss --feed all
python -m siamquantum ingest gdelt --year YYYY [--all-years]
python -m siamquantum ingest youtube --year YYYY [--all-years]
python -m siamquantum ingest geo --pending

python -m siamquantum analyze nlp --year YYYY
python -m siamquantum analyze stats
python -m siamquantum analyze taxonomy-stats
python -m siamquantum analyze graph-metrics
python -m siamquantum analyze full

python -m siamquantum filter relevance
python -m siamquantum filter recheck-low-confidence

python -m siamquantum serve [--port 8765] [--reload]

python -m siamquantum orchestration refresh
python -m siamquantum orchestration healthcheck
python -m siamquantum orchestration serve
python -m siamquantum orchestration deploy
python -m siamquantum orchestration worker

Scheduled orchestration (Prefect)

The app includes Prefect 3 flows for scheduled, retryable pipeline runs. See docs/prefect.md for setup and CLI reference.

Quick start (no Prefect server required):

pip install prefect>=3
python -m siamquantum orchestration serve

Current major features

• Operational corpus boundary flags with explicit scope wording in the API/UI.
• Taxonomy-aware engagement summaries using bootstrap and nonparametric methods.
• Concept graph metrics including connected components, hub interpretation, and community summaries.
• Clickable network node detail with neighbor, relation, source, and taxonomy context.
• Geo enrichment with MaxMind GeoLite2 City + ASN; approximate location fallback for sources without precise coordinates.
• Source map mobile UI with a draggable details sheet that stays below the global mobile navigation chrome.
• User-owned submitted data with Supabase-backed auth, profile, review flow, and local-auth fallback.
• Grounded AI URL analysis for submitted data: title, description, category, tags, source evidence, confidence, data quality, and review notes are generated only from fetched page context.
• Low-confidence relevance recheck to improve corpus quality iteratively.
• XLSX export for source/entity review.
• Source Adapter system (src/siamquantum/adapters/) for adding new data sources without touching pipeline code.

Auth and submitted data

• Supabase Auth handles email/password login, signup, Google login, session persistence, and logout.
• The browser only receives SUPABASE_PUBLISHABLE_KEY.
• The FastAPI server is the only place that may use SUPABASE_SECRET_KEY.
• First authenticated load auto-creates a profiles row when needed.
• /submit-data writes authenticated user-owned rows into submitted_data.
• The Analyze with AI flow fetches source context first, then asks Claude for grounded metadata.
• AI-filled submission metadata stores the full analysis payload under metadata.ai_analysis, including evidence_quotes, analysis_notes, data_quality, confidence, and needs_review.
• Invalid or invented model categories are normalized to the approved category list before reaching the UI.
• Public visibility is limited to rows with status = 'approved' and analysis_status = 'completed'.
• /profile shows the user profile plus their own private submitted data queue.
• /admin/submitted-data is for users whose profiles.role = 'admin'.

Known limitations

• Relevance flags are operational corpus defaults unless explicit row-level rechecking has been run; do not read as universal classifier truth.
• NLP extraction is best-effort and depends on available source text plus configured model access.
• Submit-data AI analysis is grounded and normalized, but reviewers should still verify low-confidence or sparse-source submissions.
• SQLite is correct for local/demo operation, not for concurrent multi-writer production use.
• The network view is an interpretation aid, not a causal or ontological truth layer.
• Community automation is intentionally partial — the queue is real, downstream processing remains environment-dependent.

Vercel demo deployment

The repo ships a read-only Vercel configuration suitable for demo use.

Entrypoint: api/index.py — resolves the repo root, sets absolute DB path and demo env vars, then imports the FastAPI app.

Routing: vercel.json routes all requests (/*) to api/index.py.

Python version: Specified as 3.11 in vercel.json build config.

Dependencies: api/requirements.txt lists only packages required at serve time (no pipeline/NLP/stats packages), keeping the Vercel function well under the 250 MB limit.

Required Vercel env vars (set in Project → Settings → Environment Variables):

SIAMQUANTUM_DEPLOYMENT_MODE=vercel_demo
SIAMQUANTUM_DATABASE_READ_ONLY=true

Limitations in demo mode:

• No durable writes. Community submissions, ingestion, and NLP runs require a stateful environment.
• XLSX export works but generates the file in-memory per request (no caching).
• Graph node detail cache is rebuilt per cold-start if not pre-populated in the bundled DB.

For durable writes or scheduled ingestion, run locally or on a stateful server.

Repo intent

This repository tracks the current research-platform baseline. The current version has working analytics and viewer pages; UI is being iterated based on user feedback without changing the underlying stack.