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TASK_4_SUMMARY.md
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TASK_4_SUMMARY.md
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# Task 4 Summary: Référentiels Manager Implementation
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## Completed: Subtask 4.1 ✅
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### What was implemented:
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1. **ReferentielsManager Class** (`src/pipeline_mco_pmsi/rag/referentiels_manager.py`)
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- ✅ `__init__()`: Initializes manager with data directory and embedding model configuration
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- ✅ `import_referentiel()`: Imports PDF files, generates SHA-256 hash, extracts text
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- ✅ `get_version_info()`: Retrieves version information for a referentiel type
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- ✅ `_extract_text_from_pdf()`: Extracts text from PDF files using pypdf
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- ✅ `chunk_referentiel()`: Delegates to specific chunking methods
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- ✅ `chunk_guide_mco()`: Basic chunking for Guide Méthodologique MCO
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- ✅ `chunk_cim10()`: Basic chunking for CIM-10 FR
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- ✅ `chunk_ccam()`: Basic chunking for CCAM descriptive
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- ⏳ `build_index()`: Placeholder (to be implemented in subtask 4.3)
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2. **Data Models**
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- ✅ `Chunk`: Represents a chunk of referentiel with metadata
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- ✅ `VectorIndex`: Represents a vector index with metadata
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- ✅ Uses existing `ReferentielVersion` from models.metadata
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3. **Unit Tests** (`tests/test_referentiels_manager.py`)
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- ✅ 15 tests covering all implemented functionality
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- ✅ All tests passing
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- ✅ Test coverage: 79% for referentiels_manager.py
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### Key Features:
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- **SHA-256 Hashing**: Every imported referentiel gets a unique hash for versioning
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- **Text Extraction**: Robust PDF text extraction with error handling
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- **Version Caching**: Imported versions are cached for quick retrieval
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- **Flexible Chunking**: Different chunking strategies for each referentiel type
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- **Error Handling**: Comprehensive error handling with logging
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### Requirements Satisfied:
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- ✅ **Exigence 3.1**: Maintenir des copies versionnées du référentiel CIM-10 PMSI avec hash et date d'import
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- ✅ **Exigence 3.2**: Maintenir des copies versionnées du référentiel CCAM PMSI avec hash et date d'import
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- ✅ **Exigence 3.3**: Maintenir des copies versionnées du guide MCO avec hash et date d'import
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- ✅ **Exigence 13.1**: Générer un hash lors de l'ingestion de nouveaux fichiers de référentiel
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## Remaining: Subtask 4.2 ⏳
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### To be implemented:
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1. **Intelligent Chunking for Guide MCO**
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- Parse chapter/section structure
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- Preserve complete rules (règles d'exclusion, hiérarchisation)
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- Extract eligibility criteria for DP/DAS
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- Target: 500-1000 tokens per chunk with 100 token overlap
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2. **Intelligent Chunking for CIM-10**
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- Parse code blocks with inclusion/exclusion notes
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- Separate vectorization for alphabetical indexes vs analytical codes
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- Maintain natural language ↔ code links (e.g., "Gastrite" → "K29.7")
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- Target: 300-600 tokens per chunk
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3. **Intelligent Chunking for CCAM**
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- Parse acts with ATIH extensions (7+3 character codes)
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- Preserve technical notes and application conditions
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- Vectorize alphabetical indexes for natural language search
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- Target: 400-800 tokens per chunk
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### Requirements to satisfy:
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- ⏳ **Exigence 23.2**: Chunker le Guide Méthodologique MCO en sections logiques préservant le contexte des règles
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- ⏳ **Exigence 23.3**: Chunker la CIM-10 FR en préservant les notes d'inclusion/exclusion et blocs
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- ⏳ **Exigence 23.4**: Chunker la CCAM descriptive en préservant les extensions ATIH et notes techniques
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## Remaining: Subtask 4.3 ⏳
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### To be implemented:
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1. **Embedding Model Integration**
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- Load French medical embedding model (CamemBERT-bio or DrBERT)
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- Configure sentence-transformers
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- Generate embeddings for chunks (768 dimensions)
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- L2 normalization for cosine similarity
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2. **FAISS Index Creation**
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- Build HNSW (Hierarchical Navigable Small World) index
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- Configure index parameters (M, efConstruction)
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- Store index to disk
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- Generate index hash for versioning
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3. **Alphabetical Index Vectorization**
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- Separate vectorization for alphabetical indexes
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- Maintain bidirectional links (terms ↔ codes)
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- Enable natural language search
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### Requirements to satisfy:
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- ⏳ **Exigence 23.1**: Implémenter une architecture RAG pour la recherche dans les référentiels
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- ⏳ **Exigence 23.5**: Vectoriser les index alphabétiques en plus des codes analytiques
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- ⏳ **Exigence 27.1**: Vectoriser les index alphabétiques CIM-10 et CCAM
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## Optional Subtasks
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### Subtask 4.4 (Optional): Property Tests ⏳
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Property tests to implement:
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- **Propriété 8**: Pour tout référentiel, il doit avoir version, hash, et date d'import
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- **Propriété 36**: Pour tout import, un hash SHA-256 doit être généré
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- **Propriété 46**: Pour tout chunk, le contexte doit être préservé
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### Subtask 4.5 (Optional): Unit Tests for Chunking ⏳
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Additional unit tests:
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- Test preservation of CIM-10 inclusion/exclusion notes
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- Test preservation of CCAM ATIH extensions
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- Test chunk size constraints
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- Test overlap behavior
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## Files Created/Modified
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### Created:
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- `src/pipeline_mco_pmsi/rag/referentiels_manager.py` (477 lines)
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- `src/pipeline_mco_pmsi/rag/__init__.py`
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- `tests/test_referentiels_manager.py` (260 lines)
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### Modified:
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- None (all new files)
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## Next Steps
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1. **Implement Subtask 4.2**: Intelligent chunking with structure preservation
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- Parse PDF structure more intelligently
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- Implement rule/note detection
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- Preserve semantic context
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2. **Implement Subtask 4.3**: Vectorization and indexation
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- Integrate sentence-transformers
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- Build FAISS HNSW index
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- Implement alphabetical index vectorization
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3. **Test with Real PDFs**: Verify chunking quality with actual ATIH documents
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- guide_methodo_mco_2026_version_provisoire.pdf
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- cim-10-fr_2026_a_usage_pmsi_version_provisoire_111225.pdf
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- actualisation_ccam_descriptive_a_usage_pmsi_v4_2025.pdf
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4. **Optional**: Implement property-based tests for robustness
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## Notes
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- The current chunking implementation is basic (paragraph-based) and will need to be enhanced in subtask 4.2
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- The placeholder hash ("0" * 64) for index_hash is used until the index is actually built in subtask 4.3
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- All PDF files are available in the workspace root for testing
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- The implementation follows the design document specifications closely
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