Medical GraphRAG Assistant

# TODO: FHIR AI Hackathon Kit ## Completed ✅ ### Tutorial Series - [x] Complete Tutorial 1: FHIR SQL Builder - [x] Complete Tutorial 2: Creating Vector Database - [x] Complete Tutorial 3: Vector Search & LLM Prompting - [x] Document tutorial feedback in FEEDBACK_SUMMARY.md ### Direct FHIR Integration - [x] Proof of concept: Direct access to FHIR native tables - [x] Companion vector table pattern - [x] Document in DIRECT_FHIR_VECTOR_SUCCESS.md ### GraphRAG Knowledge Graph (MVP) - [x] Phase 1: Setup (T001-T006) - [x] Project structure (src/adapters, src/extractors, src/setup, config, tests) - [x] Configuration files and fixtures - [x] Verify rag-templates accessibility - [x] Phase 2: Foundational (T007-T011) - [x] RAG.Entities table with VECTOR(DOUBLE, 384) - [x] RAG.EntityRelationships table - [x] Document IRIS vector type in constitution.md - [x] Phase 3: User Story 1 - Entity Extraction (T012-T030) - [x] FHIR document adapter (hex decoding) - [x] Medical entity extractor (6 entity types) - [x] GraphRAG setup script (init/build/stats modes) - [x] Extract 171 entities from 51 documents - [x] Identify 10 relationships ### Auto-Sync Feature - [x] Implement incremental sync mode (--mode=sync) - [x] Create trigger setup script with 3 options - [x] Document cron/systemd/launchd setup - [x] Test incremental sync (0.10 sec when no changes) - [x] Create TRIGGER_SYNC_SUMMARY.md ### Phase 4: Multi-Modal Search ✅ COMPLETE - [x] T031-T042: Implement src/query/fhir_graphrag_query.py - [x] RRF fusion: Vector + Text + Graph search - [x] Natural language queries - [x] Demo queries and examples - [x] Fix PyTorch environment (downgrade PyTorch) - [x] Fix text search (decode hex-encoded clinical notes) - [x] Test full multi-modal query: Vector (30) + Text (23) + Graph (9) in 0.242s ### Phase 6: Integration Testing ✅ COMPLETE - [x] T055-T064: Create comprehensive test suite - [x] Database schema validation - [x] FHIR data integrity tests - [x] Vector search functionality tests - [x] Text search with hex decoding tests - [x] Graph entity search tests - [x] RRF fusion tests - [x] Patient filtering tests - [x] Edge case validation - [x] Performance benchmarks - [x] Entity extraction quality tests - [x] **Result: 13/13 tests passing (100%)** ## In Progress 🚧 ### NVIDIA NIM Multimodal Integration (Priority P0) - PLANNING COMPLETE ✅ **Phase 1: Large-Scale Test Dataset** - [ ] Install Synthea or download pre-generated 1M patient dataset - [ ] Generate/subset 10,000 synthetic patients in FHIR R4 format - [ ] Access MIMIC-CXR dataset (PhysioNet credentialed access) - [ ] Download 500-2,000 chest X-ray DICOM files + radiology reports - [ ] Create ImagingStudy FHIR resources linking images to synthetic patients - [ ] Load dataset into IRIS FHIR repository - [ ] Verify: 10K patients, 500+ ImagingStudy, 20K+ total resources **Phase 2: Multimodal Architecture** - [ ] Create VectorSearch.FHIRTextVectors table (1024-dim for NIM text embeddings) - [ ] Create VectorSearch.FHIRImageVectors table (TBD-dim for NIM vision embeddings) - [ ] Implement FHIRModalityDetector class (text vs. image detection) - [ ] Design cross-modal RRF fusion algorithm (text + image + graph) - [ ] Update integration tests for multimodal support **Phase 3: NIM Text Embeddings** - [ ] Get NVIDIA API key from build.nvidia.com - [ ] Install langchain-nvidia-ai-endpoints - [ ] Implement NIMTextEmbeddings class (NV-EmbedQA-E5-v5) - [ ] Create nim_text_vectorize.py script - [ ] Re-vectorize existing 51 DocumentReferences with NIM embeddings - [ ] Test query performance vs. SentenceTransformer baseline - [ ] Update fhir_graphrag_query.py to use NIM embeddings **Phase 4: NIM Vision Embeddings** - [ ] Research NIM vision API documentation (Nemotron Nano VL) - [ ] Install pydicom for DICOM processing - [ ] Implement DICOMExtractor class - [ ] Implement NIMVisionEmbeddings class - [ ] Create nim_image_vectorize.py script - [ ] Vectorize all ImagingStudy images with NIM vision embeddings - [ ] Test image search functionality **Phase 5: Cross-Modal Query** - [ ] Implement FHIRMultimodalQuery class - [ ] Add text_search() method (NIM text embeddings) - [ ] Add image_search() method (NIM vision embeddings) - [ ] Add rrf_fusion() method (text + image + graph) - [ ] Create multimodal query CLI interface - [ ] Performance benchmarking with 10K+ dataset - [ ] Integration tests for multimodal search (target: 20/20 passing) **Documentation Created**: - ✅ STATUS.md updated with NVIDIA NIM research findings - ✅ NVIDIA_NIM_MULTIMODAL_PLAN.md (comprehensive implementation plan) ### Optional Enhancements (Not Started) **Phase 5: Performance Optimization (Priority P3)** - Optional - [ ] T043-T054: Batch processing - [ ] Parallel extraction with workers - [ ] Query performance tuning - [ ] Additional performance benchmarks **Phase 7: Production Polish** - Optional - [ ] T065-T074: Documentation and docstrings - [ ] Type hints for all functions - [ ] Monitoring metrics - [ ] Production deployment checklist ## Pending Feedback Items ### Tutorial Feedback (for PR to gabriel-ing/FHIR-AI-Hackathon-Kit) **Tutorial 1 Issues**: - [ ] (Note issues as we find them) **Tutorial 2 Issues**: - [ ] Remove unused `import base64` - only `bytes.fromhex()` is actually used - [ ] Add explanation about Utils module location - [ ] Add note about harmless IRIS warnings - [ ] Add `DROP TABLE IF EXISTS` pattern - [ ] Consider showing only faster insertion method (executemany) - [ ] 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) - [ ] Make LangChain/conversation memory its own section - [ ] Add note about model size requirements ## Production Deployment Tasks ### Auto-Sync Setup (Recommended) - [ ] Set up cron job for incremental sync every 5 minutes - [ ] Configure log rotation for kg_sync.log - [ ] Set up monitoring alerts for sync failures - [ ] Test sync with simulated FHIR data changes ### Documentation - [ ] Add README.md with quick start guide - [ ] Create troubleshooting guide - [ ] Document example queries - [ ] Add performance tuning guide ## Notes **Current Status**: Phase 6 Integration Testing COMPLETE ✅ **Implementation Complete**: - ✅ Direct FHIR integration (Phase 0) - ✅ Knowledge graph extraction (Phases 1-3) - ✅ Auto-sync feature - ✅ Multi-modal search (Phase 4) - ✅ Integration testing (Phase 6) **Performance Metrics**: - **Vector search**: 30 semantic matches in 1.038s - **Text search**: 23 keyword matches in 0.018s (hex decoding) - **Graph search**: 9 entity matches in 0.014s - **Full multi-modal**: 0.242s query time - **Fast query**: 0.006s query time (4x faster) - **Knowledge graph**: 171 entities, 10 relationships **Quality Metrics**: - **Integration tests**: 13/13 passing (100%) - **Entity extraction**: 100% high confidence - **Data integrity**: All tables validated - **Error handling**: All edge cases passed **Architecture**: - Zero modifications to FHIR schema - Backward compatible with direct_fhir_vector_approach.py - Production-ready with comprehensive test coverage **Next Decision Point**: - Continue with Phase 5 (Performance Optimization)? - Set up production auto-sync? - Create PR with tutorial feedback? - Deploy multi-modal search for production use? - Move to Phase 7 (Production Polish)?