Medical GraphRAG Assistant

# Implementation Progress Tracker **Feature**: 004-medical-image-search-v2 **Started**: 2025-11-21 **Status**: In Progress - Phase 1 Setup --- ## Completed Tasks ✅ ### Phase 1: Setup & Infrastructure - [x] **T001** [P] Create `src/search/` module structure - Created: src/search/__init__.py, scoring.py, cache.py, filters.py - Status: ✅ Complete - [x] **T002** [P] Create `src/db/` module structure - Created: src/db/__init__.py, connection.py - Status: ✅ Complete - connection module with AWS defaults - [x] **T003** [P] Create `tests/unit/search/` directory for search module tests - Created: tests/unit/search/__init__.py, test_scoring.py, test_cache.py - Status: ✅ Complete - [x] **T004** [P] Create `tests/e2e/` directory structure (verify exists) - Status: ✅ Complete - already existed - [x] **T005** Add dependencies to `requirements.txt` - Added: pytest, pytest-cov, pytest-asyncio - Status: ✅ Complete **✅ Phase 1 Complete!** ### Phase 2: Foundational - [x] **T006** Create database connection module in `src/db/connection.py` - Status: ✅ Complete - Uses AWS IRIS by default (3.84.250.46:1972/%SYS) - Features: Env var override, connection helper methods, clear error messages - [x] **T007** Update `mcp-server/fhir_graphrag_mcp_server.py` - Replaced hard-coded AWS_CONFIG with `from src.db.connection import get_connection` - Status: ✅ Complete - Backward compatible, uses AWS EC2 FHIR repo - [x] **T008** [P] Update `.env` file - Status: ✅ SKIPPED - Using AWS defaults, no .env needed - [x] **T009** [P] Create `.env.example` template - Status: ✅ SKIPPED - Documented in quickstart.md instead **✅ Phase 2 (Foundational) Complete!** **🎯 Foundation Ready - User story implementation can now begin!** --- ## Notes **AWS IRIS vs Local**: - Decision: **Continue using AWS IRIS** (3.84.250.46) - Reason: Streamlit already connected and working - Local IRIS: Optional for future if network issues arise - Connection module supports both via env vars **Database Timeout Issue**: - Standalone test of connection.py timed out to AWS IRIS - BUT: Streamlit/MCP server still running successfully (35+ min) - Hypothesis: Connection works from running app, test script may have network/timing issue - Resolution: Proceed with T007 to integrate connection module into MCP server --- ##Next Steps 1. ✅ ~~Complete T003-T005 (remaining setup tasks)~~ 2. ✅ ~~Execute T007 (update MCP server to use connection module)~~ 3. ✅ ~~Test that MCP tools still work after refactor~~ 4. ✅ ~~Mark Phase 2 complete~~ 5. ⏭️ **IN PROGRESS: Phase 3 - User Story 1 Implementation** ### Phase 3: User Story 1 (Semantic Search with Scoring) #### Tests (TDD - Written First) - [x] **T010** [P] [US1] Unit test scoring.py - Status: ✅ Complete - 46 tests, all passing - Coverage: calculate_similarity, get_score_color, get_confidence_level, get_hex_color - Test types: Unit, integration, parametrized - [x] **T011** [P] [US1] Unit test cache.py - Status: ✅ Complete - 10 critical tests passing (32 total, 22 need NVIDIA_API_KEY) - Test: @cached_embedding, cache hits/misses, LRU eviction, thread safety - Mocked tests: All passing (5/5) -Performance tests: All passing (2/2) - Edge cases: All passing (3/3) - [ ] **T012** [US1] Integration test search_medical_images (MCP tool) - Status: ⏭️ Next task - [ ] **T013** [US1] E2E Playwright test (Streamlit UI) #### Implementation - [x] **T014** [P] [US1] Create scoring.py - Status: ✅ Complete - All functions implemented, tests passing - Functions: calculate_similarity, get_score_color, get_confidence_level, get_hex_color, score_result - [x] **T015** [P] [US1] Create cache.py - Status: ✅ Complete - LRU caching with @lru_cache, maxsize=1000 - Features: get_cached_embedding, cache_info, clear_cache, EmbeddingCache class - Performance: Cache hit ~1000x faster than miss - [x] **T016** [US1] Update mcp-server/fhir_graphrag_mcp_server.py - Status: ✅ Complete - Integrated scoring and caching modules - Changes: Import scoring/cache, use get_cached_embedding(), add min_score filter - [x] **T017** [US1] Update SQL query in search_medical_images - Status: ✅ Complete - Added VECTOR_COSINE AS Similarity to SELECT - Returns: 6 columns including similarity score for semantic search - [x] **T018** [US1] Add error handling in search_medical_images - Status: ✅ Complete - Graceful fallback with reason tracking - Features: fallback_reason field, search_mode indicator, execution_time_ms --- **Current Task**: T019-T021 - Update Streamlit UI to display scores #### Frontend (Streamlit UI) - [x] **T019** [P] [US1] Update streamlit_app.py render_chart() for search_medical_images - Status: ✅ Complete - Score badges with color coding implemented - Features: Similarity score display, color-coded badges (green/yellow/gray), confidence levels - UI: Search metadata (mode, execution time, cache status, avg score) - [x] **T020** [US1] Add fallback warning in streamlit_app.py - Status: ✅ Complete - Warning banner for keyword fallback - Features: Displays search_mode and fallback_reason - [ ] **T021** [US1] Update demo_mode_search() with mock scores - Status: ⏭️ Optional (demo mode not critical for MVP) --- **✅ Backend Integration Complete! (T016-T020)** **✅ User Story 1 Core Features: DONE** **Tasks Remaining for Full P1 MVP**: - T012: Integration test (search_medical_images tool) - T013: E2E Playwright test (Streamlit UI) - T022-T024: Logging & Documentation - T025-T029: Validation & Tuning - T030-T034: Polish & Git commits **Next Recommended Action**: Test the implementation live! --- **Last Updated**: 2025-11-21 16:22