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AI Agent — Copilot Instructions

This is a RAG + VLM imaging tool recommender that helps users find the right imaging software for their images and tasks. Users drop an image, describe their task, and get ranked software recommendations with demo links.

Architecture Overview

The system follows a two-stage pipeline:

  1. Retrieval Stage (retriever/, api/pipeline.py): Fast text search using BGE-M3 embeddings + CrossEncoder reranker. No LLM calls. Returns top-K candidates.

  2. Selection Stage (generator/): Single VLM call (OpenAI GPT-4o/mini) that sees the image + candidates + metadata and returns ranked recommendations with accuracy scores.

Key Components

  • api/pipeline.RAGImagingPipeline: Main orchestrator. Handles file validation, metadata extraction, retrieval, and VLM selection.
  • retriever/text_embedder.py, retriever/vector_index.py, retriever/reranker.py, retriever/software_doc.py: Embedding, FAISS indexing, reranking, and catalog schema for retrieval.
  • agent/agent.py: PydanticAI agent that orchestrates tool search, alternatives, and recommendation assembly.
  • utils/image_meta.py: Robust metadata extraction for DICOM, NIfTI, TIFF stacks with medical imaging focus.
  • utils/tags.py: Control tag parsing/stripping utilities (notably [EXCLUDE:tool1|tool2]).

Data Flow Patterns

Input Processing

  • Files validated via utils/file_validator.py (size limits, format checks)
  • Images converted to PNG previews for VLM via utils/previews.py
  • Metadata extracted preserving original format info (critical for format compatibility matching)
  • Format tokens added to retrieval query (e.g. format:DICOM format:NIfTI)

Retrieval Query Construction

# Clean task text + format tokens from uploaded files
query = f"{clean_task} format:{ext_tokens}"  # e.g. "segment lungs format:DICOM"

VLM Selection Input

The VLM receives:

  • Text: User task + candidate table + original file metadata
  • Image: PNG preview (converted from any format)
  • Metadata: Original extension, dimensions, file info (crucial for IO compatibility)

Critical Patterns

Error Handling

  • Graceful degradation: If image conversion fails, continue text-only
  • Robust metadata: All metadata extraction wrapped in try/catch with sensible defaults
  • File validation: Early validation prevents downstream errors

Control Tags System

Users can control behavior via tags in their queries:

  • [EXCLUDE:toolname1|toolname2] - Exclude specific tools from results

Conversation Flow

  • Complete: Normal success with tool recommendations
  • Needs Clarification: VLM asks followup questions when task is ambiguous
  • Terminal No-Tool: No suitable tools found with explanation

Development Workflows

Running the App

# Install with pip using pyproject.toml
pip install -e ".[dev]"

# Configure .env with OPENAI_API_KEY and SOFTWARE_CATALOG path
ai_agent chat  # Launches Gradio chat UI

Testing

  • Run targeted tests in tests/ (e.g., retrieval, agent tools, repo info)
  • Run with: pytest tests/

Change Documentation

  • CHANGELOG.md: Follow Keep a Changelog format
  • Use semantic versioning with sections: Added, Changed, Deprecated, Removed, Fixed, Security
  • Update CHANGELOG.md for ALL user-facing changes before merging PRs
  • Format: ### Added\n- New feature description under version heading
  • Version entries: ## [x.y.z] - YYYY-MM-DD

Environment Management

  • uv: Fast Python package manager used in tools/image/Dockerfile
  • Creates isolated .venv environments for reproducible builds
  • Dockerfile uses uv venv && uv pip install -e . pattern for container builds

Logging & Debugging

  • Set LOG_PROMPTS=1 to save VLM prompts + images to logs/
  • File logs in logs/app_YYYYMMDD.log with structured JSON events
  • Console/file log levels configurable via .env

Project Conventions

Schema Patterns

  • Pydantic models in generator/schema.py with robust field validation and aliasing for catalog compatibility
  • Enum-based conversation states and tool reasons for type safety
  • Field normalization: Dimensions (2D/3D/4D), modalities (CT/MRI/XR), file formats via validators

Catalog Integration

  • Software catalog in JSONL format following schema.org SoftwareSourceCode structure
  • Runnable examples: Links to HuggingFace Spaces, notebooks, web demos
  • Supporting data: Format compatibility info used for matching

Module Boundaries

  • api/: Pipeline orchestration, no UI dependencies
  • generator/: Pure VLM logic, no retrieval dependencies
  • retriever/: Pure vector search, no generation dependencies
  • utils/: Shared utilities, no business logic
  • ui/: Gradio interface only

Configuration

  • Environment-based config via .env (API keys, model names, catalog paths)
  • Sensible defaults for all settings
  • No hardcoded paths or credentials

Medical Imaging Context

This tool specializes in medical/scientific imaging:

  • Modalities: CT, MRI, X-ray, Ultrasound, PET, SPECT, Microscopy
  • Formats: DICOM, NIfTI, TIFF stacks, standard images
  • Dimensions: 2D images, 3D volumes, 4D timeseries
  • Tasks: Segmentation, registration, analysis, visualization

The VLM selection considers format compatibility as a primary factor - tools supporting the user's input format are strongly preferred.

Security Notes

  • Only makes external calls to OpenAI VLM API (with user image preview)
  • Never uploads user data to third-party tool demos
  • Returns links only; user chooses whether to visit demos
  • Prompt logging is optional and local-only