Medical GraphRAG Assistant

# Special Task: Direct FHIR Table Vector Integration ## Goal Bypass FHIR SQL Builder and add vector columns directly to FHIR repository's native tables ## Current Approach (Tutorial) 1. FHIR Server stores resources 2. SQL Builder creates projection → VectorSearchApp.DocumentReference 3. Python extracts data, creates vectors 4. New table created → VectorSearch.DocRefVectors ## Target Approach (Direct) 1. FHIR Server stores resources 2. **Add vector column directly to FHIR native table** 3. **Compute vectors on insert/update (ObjectScript trigger?)** 4. Query directly from FHIR tables ## Advantages - No SQL Builder configuration needed - Vectors stay with source data - Automatically updates with new FHIR resources - More elegant architecture ## Status ✅ Found the FHIR resource storage! ## Discovery **Master Resource Table**: `HSFHIR_X0001_R.Rsrc` - Contains 2,739 FHIR resources - `ResourceString` column has full FHIR JSON - Clinical notes at: `JSON.content[0].attachment.data` (hex-encoded) **Decoding Process**: ```python import json data = json.loads(resource_string) encoded = data['content'][0]['attachment']['data'] decoded = bytes.fromhex(encoded).decode('utf-8') ``` ## Proposed Approach Create companion vector table: `HSFHIR_X0001_R.RsrcVector` **Advantages**: - No modification to core FHIR schema - Clean separation of concerns - Easy JOIN: `Rsrc INNER JOIN RsrcVector ON Rsrc.ID = RsrcVector.ResourceID` - Can add vectors for any resource type **Schema**: ```sql CREATE TABLE HSFHIR_X0001_R.RsrcVector ( ResourceID BIGINT PRIMARY KEY, ResourceType VARCHAR(50), Vector VECTOR(DOUBLE, 384), VectorModel VARCHAR(100), LastUpdated TIMESTAMP ) ``` **Next**: Implement and test! ## ✅ PROOF OF CONCEPT COMPLETE! Successfully demonstrated: - Direct access to FHIR native storage - Companion vector table without schema modification - Vector search with JOIN to native FHIR resources - No SQL Builder configuration required! See: **DIRECT_FHIR_VECTOR_SUCCESS.md** for full details. --- # NEW TASK: GraphRAG Implementation ## Goal Implement GraphRAG using rag-templates library with BYOT (Bring Your Own Table) overlay mode ## Research Complete ✅ ### Key Findings 1. **rag-templates location**: `/Users/tdyar/ws/rag-templates` 2. **GraphRAG pipeline**: `iris_rag/pipelines/graphrag.py` (production-hardened) 3. **BYOT support**: Configure custom `table_name` in `storage:iris` config 4. **Entity extraction**: Built-in medical entity extraction with DSPy 5. **Multi-modal search**: Vector + Text + Graph with RRF (Reciprocal Rank Fusion) ### Architecture ``` rag-templates GraphRAG: - Unified API: create_pipeline('graphrag') - Zero-copy BYOT: storage:iris:table_name configuration - Knowledge graph tables: RAG.Entities, RAG.EntityRelationships - Medical entities: SYMPTOM, CONDITION, MEDICATION, PROCEDURE, etc. - Entity relationships: TREATS, CAUSES, LOCATED_IN, CO_OCCURS_WITH ``` ### Integration Plan Leverage existing direct FHIR approach with GraphRAG overlay: ``` HSFHIR_X0001_R.Rsrc (FHIR native) ├─→ VectorSearch.FHIRResourceVectors (existing vectors) └─→ RAG.Entities + RAG.EntityRelationships (NEW: knowledge graph) ``` ## Implementation Plan Created ✅ See: **GRAPHRAG_IMPLEMENTATION_PLAN.md** for complete details ### Planned Components 1. `config/fhir_graphrag_config.yaml` - BYOT configuration 2. `fhir_document_adapter.py` - FHIR → Document adapter 3. `fhir_graphrag_setup.py` - Pipeline initialization 4. `fhir_graphrag_query.py` - Multi-modal query interface 5. `medical_entity_extractor.py` - Medical entity extraction ### Next Steps 1. Create directory structure 2. Implement Phase 1: BYOT configuration 3. Test entity extraction on existing 51 DocumentReferences 4. Validate GraphRAG queries 5. Benchmark performance vs. vector-only approach ## Status: MVP COMPLETE ✅ ### Implementation Complete (Phases 1-3) **Completed**: GraphRAG knowledge graph extraction with automatic synchronization ### What Works Now ✅ 1. **Knowledge Graph Tables** - `RAG.Entities` - Medical entities with native VECTOR(DOUBLE, 384) embeddings - `RAG.EntityRelationships` - Entity relationships with confidence scores 2. **Entity Extraction** - 6 entity types: SYMPTOM, CONDITION, MEDICATION, PROCEDURE, BODY_PART, TEMPORAL - Regex-based extraction with confidence scoring - Relationship mapping: TREATS, CAUSES, LOCATED_IN, CO_OCCURS_WITH, PRECEDES 3. **Setup and Build** - `python3 src/setup/fhir_graphrag_setup.py --mode=init` - Create tables - `python3 src/setup/fhir_graphrag_setup.py --mode=build` - Extract entities - `python3 src/setup/fhir_graphrag_setup.py --mode=stats` - View statistics 4. **Automatic Synchronization** - `python3 src/setup/fhir_graphrag_setup.py --mode=sync` - Incremental updates - Only processes resources WHERE `LastModified > MAX(ExtractedAt)` - Can be scheduled via cron/systemd/launchd ### Results Achieved **Knowledge Graph Build (51 DocumentReference resources)**: - 171 entities extracted (56 symptoms, 51 temporal, 27 body parts, 23 conditions, 9 medications, 5 procedures) - 10 relationships identified (CO_OCCURS_WITH) - Processing time: 0.22 seconds total - Average: 0.004 seconds per document **Incremental Sync Performance**: - No changes: 0.10 seconds - 1 resource changed: ~0.5 seconds - Suitable for scheduled execution every 1-5 minutes ### Architecture Achieved ``` HSFHIR_X0001_R.Rsrc (FHIR native - UNCHANGED, read-only overlay) ├─→ VectorSearch.FHIRResourceVectors (existing vectors - PRESERVED) └─→ RAG.Entities + RAG.EntityRelationships (NEW: knowledge graph) ``` **Zero modifications to FHIR schema** ✅ **Backward compatible with direct_fhir_vector_approach.py** ✅ ### Files Created **Core Implementation**: - `src/adapters/fhir_document_adapter.py` - FHIR JSON to Document conversion - `src/extractors/medical_entity_extractor.py` - Medical entity extraction - `src/setup/create_knowledge_graph_tables.py` - DDL for KG tables - `src/setup/fhir_graphrag_setup.py` - Main orchestration (init/build/sync/stats) - `config/fhir_graphrag_config.yaml` - BYOT configuration **Auto-Sync Components**: - `src/setup/fhir_kg_trigger.py` - Trigger setup script (3 implementation options) - `src/setup/fhir_kg_trigger_helper.py` - Embedded Python helper for triggers - `docs/kg-auto-sync-setup.md` - Complete setup guide - `TRIGGER_SYNC_SUMMARY.md` - Quick reference and testing guide **Memory and Specifications**: - `.specify/memory/constitution.md` - IRIS vector type knowledge (CRITICAL) - `specs/001-fhir-graphrag/` - Complete specification, plan, tasks, contracts ### Search Capabilities Implemented ✅ **Graph-Based Semantic Search** (WORKING): ```bash python3 src/query/fhir_simple_query.py "chest pain" --top-k 3 ``` Results in 0.003 seconds: - Searches 171 medical entities across 51 documents - Entity-based semantic matching (SYMPTOM, CONDITION, MEDICATION, etc.) - RRF fusion combines text + graph ranking - Shows extracted entities with confidence scores **Example Query Results**: - Query: "chest pain" - Found: 9 documents with matching entities - Top results show: chest pain (SYMPTOM), abdominal pain (SYMPTOM), dyspnea (SYMPTOM), hypertension (CONDITION) - Response time: < 0.01 seconds ### Phase 4: Multi-Modal Search ✅ COMPLETE **Full GraphRAG multi-modal search now working!** Query: `python3 src/query/fhir_graphrag_query.py "chest pain" --top-k 5` **All Three Search Methods Functional**: - ✅ **Vector Search** (30 results): Semantic similarity using SentenceTransformer embeddings - ✅ **Text Search** (23 results): Keyword matching in decoded clinical notes - ✅ **Graph Search** (9 results): Entity-based semantic matching via knowledge graph - ✅ **RRF Fusion**: Combines all three sources with Reciprocal Rank Fusion **Performance**: - Query latency: 0.242 seconds (full multi-modal with 51 documents) - Simple query (text + graph only): 0.063 seconds **Issue Resolved**: PyTorch/SentenceTransformer segfault fixed by downgrading PyTorch **Text Search Fixed**: Now decodes hex-encoded clinical notes before keyword matching - Previous: Searched raw JSON with hex data (0 results) - Current: Decodes clinical notes first (23 results for "chest pain") **Query Interfaces**: 1. `src/query/fhir_graphrag_query.py` - Full multi-modal (vector + text + graph) 2. `src/query/fhir_simple_query.py` - Fast query (text + graph, no vector encoding) ### Phase 6: Integration Testing ✅ COMPLETE **Full integration test suite passing!** Test suite: `tests/test_integration.py` Results: **13/13 tests passed (100% pass rate)** **Test Coverage**: 1. ✅ Database Schema - All tables populated 2. ✅ FHIR Data Integrity - 51 DocumentReferences parseable 3. ✅ Vector Table - 51 vectors created 4. ✅ Knowledge Graph - 171 entities, 10 relationships 5. ✅ Vector Search - Semantic similarity working 6. ✅ Text Search - Hex decoding functional (23 results) 7. ✅ Graph Search - Entity matching working (9 results) 8. ✅ RRF Fusion - Multi-modal combining correctly 9. ✅ Patient Filtering - Compartment filtering working 10. ✅ Full Multi-Modal Query - End-to-end in 0.242s 11. ✅ Fast Query - Text + Graph in 0.006s 12. ✅ Edge Cases - Graceful error handling 13. ✅ Entity Quality - 100% high confidence **Key Findings**: - All components working seamlessly together - Performance excellent: Fast query in 6ms, full multi-modal in 242ms - Entity extraction quality: 100% high confidence - No exceptions or failures in any test See `INTEGRATION_TEST_RESULTS.md` for detailed results. ### Next Steps (Optional) **Phase 5: Performance Optimization (Priority P3)** - Batch processing for entity extraction - Parallel extraction with multiple workers - Query performance optimizations **Phase 6: Integration Testing** - End-to-end workflow tests - Edge case validation - Performance benchmarks **Phase 7: Production Polish** - Comprehensive documentation - Type hints and docstrings - Monitoring metrics ### Production Deployment **Recommended Auto-Sync Setup** (macOS with cron): ```bash # Create logs directory mkdir -p logs # Add to crontab (run every 5 minutes) crontab -e # Paste: */5 * * * * cd /Users/tdyar/ws/FHIR-AI-Hackathon-Kit && /usr/bin/python3 src/setup/fhir_graphrag_setup.py --mode=sync >> logs/kg_sync.log 2>&1 ``` See `docs/kg-auto-sync-setup.md` for systemd (Linux) and launchd (macOS) alternatives. --- # NEW TASK: NVIDIA NIM Multimodal Integration ## Goal Integrate NVIDIA NIM for multimodal FHIR data processing with separate embeddings for text and medical images ## Research Complete ✅ ### Key Findings: FHIR Image Storage **Current Database Status**: - 2,739 total FHIR resources - 51 DocumentReference resources (clinical notes, text only) - 107 DiagnosticReport resources - **0 ImagingStudy resources** (no DICOM imaging data) - **0 Media resources** (no medical images) **FHIR Image Storage Patterns**: 1. **ImagingStudy**: DICOM study metadata with DICOMweb endpoints (WADO-RS, QIDO-RS, STOW-RS) 2. **Media/DocumentReference**: Medical images as Binary resources or external URLs 3. **Binary**: Raw image data (DICOM, JPEG, PNG) with base64 encoding or external storage 4. **DICOMweb Integration**: RESTful access to PACS imaging archives ### Test Dataset Options **Current Dataset**: Too small for scalability testing (51 documents, 2,739 resources) **Large-Scale Options**: 1. **Synthea** (Synthetic Patient Generator) - Can generate **millions of synthetic patients** - FHIR R4 export, multiple formats - Pre-generated: 1 million patient dataset (21 GB) - Custom generation: 10K-50K patients recommended - **Limitation**: No native imaging support 2. **MIMIC-IV on FHIR** (Real Clinical Data) - **315,000 patients**, 5.84 million FHIR resources - Demo: 100 patients, 915,000 resources - De-identified ICU data from Beth Israel Deaconess - NDJSON format, PhysioNet repository 3. **MIMIC-CXR** (Medical Imaging) - **377,110 chest X-rays** from 227,835 studies - DICOM format with radiology reports - Real clinical images from 2011-2016 - Pairs with MIMIC-IV for complete clinical context 4. **Hybrid Approach** (Recommended) - **Synthea**: 10,000-50,000 synthetic patients (FHIR structured data) - **MIMIC-CXR**: 500-2,000 radiographic studies (authentic medical images) - **Integration**: Link imaging to synthetic patients via ImagingStudy resources - **Scale**: ~20-100 GB FHIR data + 50-500 GB images ### NVIDIA NIM Architecture for Multimodal **Text Embeddings**: - **NV-EmbedQA-E5-v5**: 1024-dimensional, optimized for Q&A retrieval - **NV-EmbedQA-Mistral7B-v2**: 4096-dimensional, 7B params for complex medical domains - Replace current SentenceTransformer (384-dim) **Vision Embeddings**: - **Nemotron Nano 12B v2 VL**: Multi-image understanding, OCR, medical document processing - **Llama 3.2 Vision**: 11B/90B variants for image reasoning and radiology report generation - Process DICOM, JPEG, PNG from FHIR Binary/Media resources **Integration Pattern**: ``` FHIR Resources ├─→ DocumentReference (clinical notes) │ └─→ NIM Text Embeddings → VectorSearch.FHIRTextVectors (1024-dim) │ ├─→ ImagingStudy/Media (medical images) │ └─→ NIM Vision Embeddings → VectorSearch.FHIRImageVectors (??-dim) │ └─→ Cross-Modal Query Fusion └─→ RRF(text_results, image_results, graph_results) ``` ## Next Steps ### Phase 1: Test Dataset Generation (Priority P0) - [ ] Download Synthea 1M patient dataset OR generate 10K custom patients - [ ] Download MIMIC-CXR sample (100-500 chest X-rays) - [ ] Create ImagingStudy FHIR resources linking images to patients - [ ] Load into IRIS FHIR repository - [ ] Verify: 10K+ patients, 500+ imaging studies ### Phase 2: Multimodal Architecture (Priority P0) - [ ] Design vector table schema for text vs. image embeddings - [ ] Create modality detection layer (resource type → embedding model routing) - [ ] Define cross-modal fusion strategy (extend RRF to handle image results) ### Phase 3: NIM Text Integration (Priority P1) - [ ] Set up NVIDIA NIM API credentials - [ ] Replace SentenceTransformer with NV-EmbedQA-E5-v5 - [ ] Re-vectorize existing 51 DocumentReferences - [ ] Test query performance (1024-dim vs 384-dim) ### Phase 4: NIM Vision Integration (Priority P1) - [ ] Implement DICOM/image extraction from FHIR Binary resources - [ ] Integrate Nemotron Nano VL for image embeddings - [ ] Create VectorSearch.FHIRImageVectors table - [ ] Vectorize medical images ### Phase 5: Cross-Modal Query (Priority P1) - [ ] Implement multimodal query interface (text query → text + image results) - [ ] Extend RRF fusion for text + image + graph modalities - [ ] Performance benchmarking with 10K+ dataset ## Status: Research Complete, Ready for Implementation ✅ --- # AWS EC2 Deployment Status ## Instance Information - **Instance ID**: i-0432eba10b98c4949 - **Public IP**: 3.84.250.46 - **Instance Type**: g5.xlarge (NVIDIA A10G GPU) - **Region**: us-east-1 - **OS**: Ubuntu 24.04 LTS ## Deployment Progress ✅ ### Phase 1: Infrastructure Setup ✅ COMPLETE - ✅ EC2 instance provisioned and running - ✅ GPU drivers installed (NVIDIA 535, CUDA 12.2) - ✅ Docker configured with GPU support - ✅ SSH access configured with key-based authentication ### Phase 2: IRIS Vector Database ✅ COMPLETE - ✅ InterSystems IRIS Community Edition deployed (latest tag) - ✅ Container: `iris-vector-db` running on ports 1972 (SQL) and 52773 (Web) - ✅ Python package: `intersystems-irispython` installed (correct package name in constitution) - ✅ DEMO namespace created via `CREATE SCHEMA` command - ✅ Vector tables created in SQLUser schema: - `SQLUser.ClinicalNoteVectors` - Text embeddings with VECTOR(DOUBLE, 1024) - `SQLUser.MedicalImageVectors` - Image embeddings with VECTOR(DOUBLE, 1024) - ✅ Indexes created on PatientID, DocumentType, StudyType fields - ✅ Python connectivity working from %SYS namespace **Key Learning**: IRIS SQL tables are created in SQLUser schema by default, not in custom namespaces. The `CREATE SCHEMA` command creates the namespace, then `USE` switches to it, but tables end up in SQLUser. This is the correct IRIS behavior. **Setup Script**: `scripts/aws/setup-iris-schema.py` (Python-based schema creation) ### Phase 3: NVIDIA NIM Deployment ✅ COMPLETE - ✅ NVIDIA NIM API configured and tested - ✅ NV-EmbedQA-E5-v5 verified (1024-dim embeddings) - ✅ API endpoint: `https://integrate.api.nvidia.com/v1/embeddings` - ✅ Using NVIDIA API Cloud (no GPU deployment needed) - [ ] Deploy Nemotron Nano VL for image embeddings (future) **Key Decision**: Using NVIDIA API Cloud instead of self-hosted NIM containers - ✅ No GPU required on AWS for embeddings (hosted by NVIDIA) - ✅ Simpler architecture (just API calls) - ✅ Pay-per-use pricing (cost-effective for development) - ✅ Auto-scaling by NVIDIA - ✅ Can switch to self-hosted later if needed **Test Results**: ``` Test 1: Patient presents with chest pain... → 1024-dim ✓ Test 2: Cardiac catheterization... → 1024-dim ✓ Test 3: Atrial fibrillation management... → 1024-dim ✓ ``` ### Phase 4: Integration Testing ✅ COMPLETE - ✅ End-to-end integration validated - ✅ NVIDIA NIM → IRIS vector storage pipeline working - ✅ Similarity search with semantic ranking verified - ✅ Query performance: ~2-3 seconds for full pipeline - ✅ **IRISVectorDBClient validated with AWS** (using proper abstractions) **Integration Test Results**: ``` Query: "chest pain and breathing difficulty" Results (ranked by semantic similarity): 1. Chest pain + SOB note → 0.62 similarity ✓ (best match) 2. Cardiac catheterization → 0.47 similarity ✓ (related) 3. Atrial fibrillation → 0.44 similarity ✓ (less related) ``` **IRISVectorDBClient Test Results** (December 12, 2025): ``` ✅ Connected to AWS IRIS via %SYS namespace ✅ Inserted vectors using clean Python API (no manual TO_VECTOR SQL) ✅ Similarity search with query "chest pain": 1. CLIENT_TEST_001: 0.662 similarity (chest pain description) 2. CLIENT_TEST_002: 0.483 similarity (cardiac catheterization) ✅ Cleanup successful ``` **Key Learning - Namespace Access**: - Connect to `%SYS` namespace (DEMO has access restrictions) - Use fully qualified table names: `SQLUser.ClinicalNoteVectors` - IRISVectorDBClient handles all TO_VECTOR and VECTOR_COSINE syntax internally **Architecture Validated**: - NVIDIA NIM API generates semantically meaningful embeddings - IRIS VECTOR_DOT_PRODUCT ranks results correctly - Remote AWS connectivity working (MacBook → AWS EC2) - Full pipeline: Text → Embedding (1024-dim) → Storage → Search - **IRISVectorDBClient abstraction works correctly with AWS IRIS** ## Next Steps ### Option A: Migrate Existing GraphRAG to AWS (Recommended) Since we already have working GraphRAG locally, simply: 1. ✅ Use `config/fhir_graphrag_config.aws.yaml` (already created) 2. Run existing scripts pointing to AWS IRIS 3. Create knowledge graph tables on AWS 4. Migrate or re-extract entities ### Option B: Continue AWS Infrastructure Development 1. Deploy NVIDIA NIM LLM for enhanced entity extraction (optional) 2. Set up MIMIC-CXR image dataset 3. Implement medical image vectorization 4. Test multi-modal search (text + image) ### Option C: Production Readiness 1. Implement connection pooling for AWS IRIS 2. Add error handling and retry logic 3. Set up monitoring and logging 4. Create backup and disaster recovery plan --- ## AWS GraphRAG Migration Status (December 13, 2025) ### Goal Migrate existing local GraphRAG implementation to AWS IRIS ### Status: ✅ Phase 1 Complete - FHIR Data Migrated and Vectorized **Migration Complete**: - ✅ 51 DocumentReference resources migrated to AWS IRIS - ✅ Stored in `SQLUser.FHIRDocuments` table - ✅ 51 x 1024-dim embeddings generated with NVIDIA Hosted NIM API - ✅ Vectors stored in `SQLUser.ClinicalNoteVectors` - ✅ Model: `nvidia/nv-embedqa-e5-v5` (1024-dimensional) - ✅ Using NVIDIA Cloud API (https://integrate.api.nvidia.com) **Tables Created**: - ✅ `SQLUser.Entities` (VECTOR DOUBLE 1024) - Ready for knowledge graph - ✅ `SQLUser.EntityRelationships` - Ready for knowledge graph - ✅ `SQLUser.ClinicalNoteVectors` - ✅ POPULATED with 51 vectors - ✅ `SQLUser.FHIRDocuments` - ✅ POPULATED with 51 FHIR resources **Configuration Updated**: - ✅ AWS config file with %SYS namespace - ✅ SQLUser schema for all tables - ✅ 1024-dimensional vectors (NVIDIA NIM compatible) - ✅ CloudConfiguration API compliance verified **Vectorization Results**: ``` ✅ Total vectors: 51 ✅ Model: nvidia/nv-embedqa-e5-v5 (1024-dim) ✅ Sample document types: History and physical note ✅ NVIDIA Cloud API: https://integrate.api.nvidia.com/v1/embeddings ``` **Scripts Created**: 1. `scripts/aws/migrate-fhir-to-aws.py` - Full migration pipeline 2. `scripts/aws/vectorize-migrated-fhir.py` - Standalone vectorization 3. `scripts/aws/test-nvidia-hosted-nim.py` - API validation 4. `scripts/aws/deploy-nim-embedding.sh` - Deployment helper **Next Steps**: - [ ] Deploy knowledge graph tables to AWS using iris-vector-rag v0.5.4 - [ ] Extract entities from 51 migrated documents - [ ] Test multi-modal search on AWS deployment - [ ] Benchmark AWS vs. local performance --- ## 🎉 iris-vector-rag Update (December 12, 2025) ### Major Improvements Discovered in iris-vector-rag 0.5.2 After reinstalling iris-vector-rag, we discovered that **the iris-vector-rag team has addressed our critical pain points!** **✅ RESOLVED Issues** (3 of 6 critical/high priority): 1. **Environment Variable Support** 🎉 - Full support for `RAG_` prefixed environment variables - Example: `RAG_DATABASE__IRIS__HOST="3.84.250.46"` - No more hardcoded connection settings! 2. **Configurable Vector Dimensions** 🎉 - `vector_dimension: 1024` now configurable in YAML - Supports 384, 768, 1024, 1536, 3072, 4096+ - Can use NVIDIA NIM, OpenAI, any modern embedding model 3. **Configuration Manager** 🎉 - Production-ready ConfigurationManager - YAML config + env var overrides - Type casting and validation - Nested key support **🟡 Partially Resolved** (2 issues): - Namespace configuration (works, needs docs) - Table name qualification (works, needs testing) **❌ Still Pending** (1 issue): - Data migration utilities (future work) **🎊 New Features Found**: - IRIS EMBEDDING support (auto-vectorization) - HNSW index configuration - Schema Manager (automatic table creation) - Entity extraction pipeline **Impact**: iris-vector-rag is now **production-ready for cloud deployments**! **Next Steps**: 1. Test iris-vector-rag with AWS configuration 2. Consider migrating from IRISVectorDBClient to iris-vector-rag 3. Contribute AWS deployment docs to project **Documentation Created**: - `IRIS_VECTOR_RAG_IMPROVEMENTS_VERIFIED.md` (comprehensive analysis) - Updated `IRIS_VECTOR_RAG_PAIN_POINTS.md` with resolved status --- **Overall Project Status**: ✅ Ready for iris-vector-rag adoption on AWS! --- # AWS DEPLOYMENT: GraphRAG Knowledge Graph ✅ ## Goal Deploy GraphRAG knowledge graph infrastructure on AWS EC2 with NVIDIA NIM embeddings ## Status: **COMPLETE** ✅ ### AWS Environment - **Instance**: g5.xlarge (NVIDIA A10G GPU) - **Region**: us-east-1 - **IP**: 3.84.250.46 - **Database**: InterSystems IRIS Community Edition - **Namespace**: %SYS / SQLUser ### Data Migrated - ✅ 51 FHIR DocumentReference resources - ✅ 51 x 1024-dimensional embeddings (NVIDIA NIM NV-EmbedQA-E5-v5) - ✅ 83 medical entities extracted - ✅ 540 entity relationships mapped ### Knowledge Graph Tables (AWS) #### SQLUser.Entities - **Count**: 83 entities - **Types**: TEMPORAL (43), SYMPTOM (21), BODY_PART (10), CONDITION (6), MEDICATION (2), PROCEDURE (1) - **Embeddings**: 1024-dimensional VECTOR(DOUBLE, 1024) - **Storage**: ~160 KB #### SQLUser.EntityRelationships - **Count**: 540 relationships - **Type**: CO_OCCURS_WITH (entity co-occurrence in documents) - **Schema**: SourceEntityID, TargetEntityID (bigint references) - **Storage**: ~40 KB ### Issues Resolved 1. **iris-vector-rag GraphRAG Bug** ✅ FIXED - Problem: `UnboundLocalError: cannot access local variable 'time'` - Root Cause: Duplicate `import time` statement causing Python scoping issue - Solution: Removed duplicate import from `graphrag.py` line 144 - Status: Fixed in `/Users/tdyar/ws/iris-vector-rag-private` - Documentation: `IRIS_VECTOR_RAG_GRAPHRAG_BUG_RESOLUTION.md` 2. **Entity Relationship Schema Mismatch** ✅ FIXED - Problem: Table expected entity IDs (bigint) but script inserted entity text (varchar) - Solution: Build entity ID mapping, use IDs for relationship insertion - File: `scripts/aws/extract-entities-aws.py` (lines 334-380) 3. **Missing ResourceID Field** ✅ FIXED - Problem: EntityRelationships table requires ResourceID - Solution: Track source document ID with each relationship - Status: All 540 relationships include ResourceID ### Scripts #### Entity Extraction (Direct SQL) **File**: `scripts/aws/extract-entities-aws.py` - Regex-based entity extraction - NVIDIA Hosted NIM embeddings - Batch insertion with ID mapping - **Status**: ✅ Successfully populated knowledge graph #### Pipeline Approach (iris-vector-rag) **File**: `scripts/aws/build-knowledge-graph-aws.py` - ConfigurationManager abstraction - IRISVectorStore integration - GraphRAGPipeline entity extraction - **Status**: ⚠️ Needs NVIDIA NIM API key configuration ### Next Steps 1. **Configure NVIDIA NIM API** - Add API key to `config/fhir_graphrag_config.aws.yaml` - Test iris-vector-rag GraphRAG pipeline - Verify multi-modal search (vector + text + graph) 2. **Deploy NIM LLM Service** (Optional) - Install NVIDIA NIM LLM container - Configure llama-3.1-nemotron-70b-instruct - Enable answer generation in GraphRAG queries 3. **Performance Testing** - Benchmark GraphRAG vs vector-only search - Test graph traversal depth impact - Measure query latency on AWS ### Documentation - ✅ `AWS_GRAPHRAG_KNOWLEDGE_GRAPH_COMPLETE.md` - Full deployment details - ✅ `IRIS_VECTOR_RAG_GRAPHRAG_BUG_REPORT.md` - Bug documentation - ✅ `IRIS_VECTOR_RAG_GRAPHRAG_BUG_RESOLUTION.md` - Fix documentation - ✅ `config/fhir_graphrag_config.aws.yaml` - AWS configuration ### Verification ```sql -- Entity count by type SELECT EntityType, COUNT(*) FROM SQLUser.Entities GROUP BY EntityType ORDER BY COUNT(*) DESC; -- Relationship count SELECT COUNT(*) FROM SQLUser.EntityRelationships; -- Sample entities SELECT TOP 5 EntityText, EntityType, Confidence FROM SQLUser.Entities ORDER BY Confidence DESC; ``` **Status**: ✅ **AWS GRAPHRAG KNOWLEDGE GRAPH OPERATIONAL**