Medical GraphRAG Assistant

# FHIR AI Hackathon Kit - Feedback Summary **Date**: 2025-11-02 **Completed By**: Demo walkthrough with Claude Code ## Overall Assessment Excellent tutorial series! All three tutorials worked successfully and built a complete RAG (Retrieval Augmented Generation) system for medical history analysis. The code is functional, well-structured, and educational. Below are suggestions for improvements to enhance clarity and robustness. --- ## Tutorial 1: Using FHIR SQL Builder ### Status ✅ **Completed Successfully** - No major issues found ### Suggestions - Username/password inconsistencies already reported to developer (being fixed) --- ## Tutorial 2: Creating Vector Database ### Status ✅ **Completed Successfully** - Vector database created with 51 clinical notes ### Issues Found 1. **Unused Import** (Minor) - `import base64` is imported but never used - Only `bytes.fromhex()` is needed for decoding - **Fix**: Remove the unused import 2. **Utils Module Not Explained** (Documentation) - Tutorial uses `from Utils.get_iris_connection import get_cursor` without context - First-time users may not know where this file is - **Fix**: Add a note explaining the Utils module location or show the code inline 3. **Scary-Looking Warnings** (User Experience) - IRIS warnings appear that look like errors: ``` WARNING:root:IRISINSTALLDIR or ISC_PACKAGE_INSTALLDIR environment variable must be set WARNING:root:Embedded Python not available ``` - These are harmless but might alarm users - **Fix**: Add a note: "Note: You may see harmless IRIS warnings about IRISINSTALLDIR - these can be safely ignored." 4. **No Table Exists Handling** (Code Quality) - Running the tutorial twice will fail when trying to create existing table - **Fix**: Add `DROP TABLE IF EXISTS` pattern or explain the error 5. **Dual Insertion Methods** (Tutorial Flow) - Shows both insertion methods (iterate vs executemany) with speed comparison - Only the faster method (executemany) is actually needed - **Consider**: Either remove the comparison or clearly mark it as "optional learning" 6. **Naming Inconsistency** (Minor) - DataFrame column: `Notes_Vector` - SQL table column: `NotesVector` - **Fix**: Use consistent naming (suggest: `NotesVector` throughout) --- ## Tutorial 3: Vector Search and LLM Prompting ### Status ✅ **Completed Successfully** - RAG system working perfectly with accurate responses ### Issues Found 1. **SQL Injection Vulnerability** ⚠️ (Security) - Location: `vector_search` function - Current code: ```python WHERE PatientID = {patient} ``` - Should be parameterized: ```python WHERE PatientID = ? ``` - **Fix**: Use parameterized query for all user inputs 2. **No Error Handling** (Robustness) - No checks for: - Is Ollama running? - Is the model available? - Users will get cryptic connection errors - **Fix**: Add try/except with helpful error messages 3. **Missing Setup Instructions** (Documentation) - Assumes users know to pull the model first - No mention of `ollama pull gemma3:4b` - **Fix**: Add clear prerequisite steps at the beginning 4. **Model Version Confusion** (Clarity) - Notebook starts with `gemma3:1b` - Then switches to `gemma3:4b` - Unclear which one users should use - **Fix**: Pick one recommended model and stick with it, or clearly explain the tradeoffs 5. **LangChain Buried** (Tutorial Structure) - Conversation memory with LangChain is a major feature - Currently mixed into the middle of other content - **Consider**: Make it its own clearly marked section 6. **No Resource Guidance** (User Experience) - No mention of model size requirements - Users with limited RAM might struggle - **Fix**: Add table showing model sizes and alternatives: - `gemma3:1b` - ~1GB (fast, basic quality) - `gemma3:4b` - ~4GB (recommended) - `mistral:7b` - ~4.4GB (alternative) --- ## Tested Configuration ### Environment - **OS**: macOS (Darwin 24.5.0) - **Docker**: iris-fhir container running - **Python**: 3.x with miniconda - **Ollama**: Running with gemma3:4b model ### Results - **51 clinical notes** successfully vectorized - **5 patients** in dataset (IDs: 3, 4, 5, 6, 7) - **384-dimensional vectors** using `all-MiniLM-L6-v2` - **Vector search** working accurately with VECTOR_COSINE - **LLM responses** medically accurate with proper date citations ### Test Queries 1. ✅ "Has the patient reported any chest or respiratory complaints?" - Correctly found respiratory illness notes - LLM provided detailed analysis with dates 2. ✅ "Has the patient reported having bad headaches?" - Correctly found ear infection notes (not headaches) - LLM correctly stated "no headaches reported" --- ## Recommendations Priority ### High Priority 1. Fix SQL injection vulnerability (Tutorial 3) 2. Add Ollama setup instructions (Tutorial 3) 3. Add error handling for Ollama connection (Tutorial 3) ### Medium Priority 4. Clarify model selection (Tutorial 3) 5. Add note about harmless warnings (Tutorial 2) 6. Explain Utils module (Tutorial 2) ### Low Priority 7. Remove unused imports (Tutorial 2) 8. Fix naming inconsistencies (Tutorial 2) 9. Streamline insertion methods (Tutorial 2) 10. Restructure LangChain section (Tutorial 3) --- ## Conclusion This is an excellent, working tutorial that successfully demonstrates: - ✅ FHIR server setup and SQL projection - ✅ Vector database creation with embeddings - ✅ Semantic search with IRIS vector capabilities - ✅ RAG system with LLM for medical history analysis The suggested improvements are mostly about enhancing user experience, documentation clarity, and code robustness. The core functionality is solid and impressive! **Great work!** 🎉