Medical GraphRAG Assistant

# TODO: Medical GraphRAG Assistant **Last Updated**: November 22, 2025 **Current Version**: v2.12.0 --- ## Current Sprint ✅ COMPLETE ### Documentation Review & Cleanup (November 22, 2025) - [x] Clean up root directory (moved to archive/) - [x] Review and update README.md with v2.12.0 features - [x] Create STATUS.md with current system state - [x] Update TODO.md to reflect actual priorities - [x] Organize historical documentation --- ## High Priority 🔴 ### Production Operations - [ ] Set up automated health monitoring for AWS deployment - Cron job for database health checks - Alert on embedding failures - Monitor GPU utilization - Track query performance - [ ] Expand Agent Memory Dataset - Let agent accumulate memories through conversations - Test semantic recall with larger dataset (50+ memories) - Evaluate memory search quality - [ ] Medical Image Dataset Expansion - Download and ingest additional MIMIC-CXR images - Current: 50 images → Target: 1000+ images - Test search quality at scale ### GraphRAG Improvements - [ ] Enhanced entity extraction with NIM LLM - Replace regex-based extraction with LLM-powered extraction - Deploy NVIDIA NIM LLM container on AWS - Improve entity relationship detection - [ ] Multi-hop reasoning - Implement graph traversal for complex queries - Support queries like "medications that treat conditions caused by X" --- ## Medium Priority 🟡 ### Testing & Quality - [ ] Add unit tests for new v2.12.0 features - Memory system tests - Medical image search tests - Embeddings quality tests - [ ] Performance benchmarking at scale - Test with 1000+ images - Test with 100+ memories - Measure query latency under load ### Documentation - [ ] Create end-user documentation - "Getting Started" guide for medical professionals - Example queries with expected outputs - Troubleshooting common user issues - [ ] API documentation - MCP tool specifications - Configuration options - Deployment guide for other AWS regions --- ## Low Priority ⚪ ### Code Quality - [ ] Add type hints to all functions - [ ] Comprehensive docstrings for all modules - [ ] Code coverage analysis ### Features (Nice to Have) - [ ] Export conversation history - [ ] Batch image upload via UI - [ ] Custom memory tags/categories - [ ] GraphRAG visualization in UI --- ## Completed (Recent) ✅ ### v2.12.0: Agent Memory & Medical Image Search (November 22, 2025) - [x] Pure IRIS vector memory system (no SQLite) - [x] Medical image search with NV-CLIP embeddings - [x] Memory editor UI in Streamlit sidebar - [x] Fixed embeddings (real NV-CLIP vectors, not mocks) - [x] Memory search UI session state persistence - [x] Empty search string support (browse all memories) - [x] Type conversion for similarity scores ### Infrastructure & Deployment - [x] AWS EC2 g5.xlarge deployment - [x] NVIDIA NIM NV-CLIP integration (port 8002) - [x] IRIS database with vector tables - [x] GraphRAG knowledge graph (83 entities, 540 relationships) - [x] SSH tunnel setup for local development ### GraphRAG Implementation - [x] Direct FHIR table integration (no SQL Builder) - [x] Companion vector table pattern - [x] Medical entity extraction (6 types) - [x] Relationship mapping - [x] Multi-modal search with RRF fusion - [x] Integration tests (13/13 passing) --- ## Deferred / Not Planned ⏸️ ### Large-Scale Dataset (Blocked: PhysioNet Access) - ⏸️ MIMIC-CXR full dataset (377K images) - Requires PhysioNet credentialed access - May take days/weeks to obtain - Can proceed with current 50 images for development ### Performance Optimization - ⏸️ Batch processing for entity extraction - ⏸️ Parallel extraction with workers - ⏸️ Additional query performance tuning - Current performance acceptable (0.006s - 0.242s queries) - Optimize only when scale demands it ### Licensed IRIS Upgrade - ⏸️ Upgrade from Community to Licensed IRIS - ACORN=1 HNSW optimization (10-50x faster vector search) - Deferred to production deployment phase - Current performance sufficient for development --- ## Feedback Items (For Upstream Projects) ### FHIR-AI-Hackathon-Kit Tutorial Feedback **Status**: Documented in archive/docs/FEEDBACK_SUMMARY.md **Tutorial 2 Issues**: - Remove unused `import base64` - Add explanation about Utils module location - Add `DROP TABLE IF EXISTS` pattern - Fix naming inconsistency: "Notes_Vector" vs "NotesVector" **Tutorial 3 Issues**: - Fix SQL injection vulnerability in vector_search function - Add error handling for when Ollama isn't running - Add clear instructions to pull gemma3 model - Clarify which model to use (gemma3:1b vs gemma3:4b) ### iris-vector-rag Improvements **Status**: Tested v0.5.2-v0.5.4, feedback documented - ✅ ConfigurationManager works excellently - ✅ Environment variable support functional - ⚠️ ConnectionManager ignores config (uses legacy IRIS_* env vars) - ⚠️ SchemaManager dot/colon notation mismatch - ✅ v0.5.4 connection bug fixed --- ## Notes & Context ### Current System State - **Version**: v2.12.0 - **AWS Deployment**: ✅ Operational - **Local Development**: ✅ Active (via SSH tunnel) - **Integration Tests**: 13/13 passing - **Data Scale**: 51 documents, 50 images, 83 entities, ~5 memories ### Performance Benchmarks - Vector search: 1.038s (30 results) - Text search: 0.018s (23 results) - Graph search: 0.014s (9 results) - Full multi-modal: 0.242s - Fast query: 0.006s ### Technical Debt - Minimal - codebase is clean after recent refactoring - Archive directory contains historical implementations - Configuration could be more unified (YAML + env vars) --- ## References - **STATUS.md**: Current system health and metrics - **PROGRESS.md**: Development history (1400+ lines, consider archiving old content) - **README.md**: Main project documentation (updated v2.12.0) - **docs/**: Architecture, deployment, troubleshooting guides - **archive/**: Historical implementations and session docs