aivanov_CIM/TASK_4_SUMMARY.md

Task 4 Summary: Référentiels Manager Implementation

Completed: Subtask 4.1 ✅

What was implemented:

1. ReferentielsManager Class (src/pipeline_mco_pmsi/rag/referentiels_manager.py)

   • ✅ __init__(): Initializes manager with data directory and embedding model configuration
   • ✅ import_referentiel(): Imports PDF files, generates SHA-256 hash, extracts text
   • ✅ get_version_info(): Retrieves version information for a referentiel type
   • ✅ _extract_text_from_pdf(): Extracts text from PDF files using pypdf
   • ✅ chunk_referentiel(): Delegates to specific chunking methods
   • ✅ chunk_guide_mco(): Basic chunking for Guide Méthodologique MCO
   • ✅ chunk_cim10(): Basic chunking for CIM-10 FR
   • ✅ chunk_ccam(): Basic chunking for CCAM descriptive
   • ⏳ build_index(): Placeholder (to be implemented in subtask 4.3)

2. Data Models

   • ✅ Chunk: Represents a chunk of referentiel with metadata
   • ✅ VectorIndex: Represents a vector index with metadata
   • ✅ Uses existing ReferentielVersion from models.metadata

3. Unit Tests (tests/test_referentiels_manager.py)

   • ✅ 15 tests covering all implemented functionality
   • ✅ All tests passing
   • ✅ Test coverage: 79% for referentiels_manager.py

Key Features:

• SHA-256 Hashing: Every imported referentiel gets a unique hash for versioning
• Text Extraction: Robust PDF text extraction with error handling
• Version Caching: Imported versions are cached for quick retrieval
• Flexible Chunking: Different chunking strategies for each referentiel type
• Error Handling: Comprehensive error handling with logging

Requirements Satisfied:

• ✅ Exigence 3.1: Maintenir des copies versionnées du référentiel CIM-10 PMSI avec hash et date d'import
• ✅ Exigence 3.2: Maintenir des copies versionnées du référentiel CCAM PMSI avec hash et date d'import
• ✅ Exigence 3.3: Maintenir des copies versionnées du guide MCO avec hash et date d'import
• ✅ Exigence 13.1: Générer un hash lors de l'ingestion de nouveaux fichiers de référentiel

Remaining: Subtask 4.2 ⏳

To be implemented:

1. Intelligent Chunking for Guide MCO

   • Parse chapter/section structure
   • Preserve complete rules (règles d'exclusion, hiérarchisation)
   • Extract eligibility criteria for DP/DAS
   • Target: 500-1000 tokens per chunk with 100 token overlap

2. Intelligent Chunking for CIM-10

   • Parse code blocks with inclusion/exclusion notes
   • Separate vectorization for alphabetical indexes vs analytical codes
   • Maintain natural language ↔ code links (e.g., "Gastrite" → "K29.7")
   • Target: 300-600 tokens per chunk

3. Intelligent Chunking for CCAM

   • Parse acts with ATIH extensions (7+3 character codes)
   • Preserve technical notes and application conditions
   • Vectorize alphabetical indexes for natural language search
   • Target: 400-800 tokens per chunk

Requirements to satisfy:

• ⏳ Exigence 23.2: Chunker le Guide Méthodologique MCO en sections logiques préservant le contexte des règles
• ⏳ Exigence 23.3: Chunker la CIM-10 FR en préservant les notes d'inclusion/exclusion et blocs
• ⏳ Exigence 23.4: Chunker la CCAM descriptive en préservant les extensions ATIH et notes techniques

Remaining: Subtask 4.3 ⏳

To be implemented:

1. Embedding Model Integration

   • Load French medical embedding model (CamemBERT-bio or DrBERT)
   • Configure sentence-transformers
   • Generate embeddings for chunks (768 dimensions)
   • L2 normalization for cosine similarity

2. FAISS Index Creation

   • Build HNSW (Hierarchical Navigable Small World) index
   • Configure index parameters (M, efConstruction)
   • Store index to disk
   • Generate index hash for versioning

3. Alphabetical Index Vectorization

   • Separate vectorization for alphabetical indexes
   • Maintain bidirectional links (terms ↔ codes)
   • Enable natural language search

Requirements to satisfy:

• ⏳ Exigence 23.1: Implémenter une architecture RAG pour la recherche dans les référentiels
• ⏳ Exigence 23.5: Vectoriser les index alphabétiques en plus des codes analytiques
• ⏳ Exigence 27.1: Vectoriser les index alphabétiques CIM-10 et CCAM

Optional Subtasks

Subtask 4.4 (Optional): Property Tests ⏳

Property tests to implement:

• Propriété 8: Pour tout référentiel, il doit avoir version, hash, et date d'import
• Propriété 36: Pour tout import, un hash SHA-256 doit être généré
• Propriété 46: Pour tout chunk, le contexte doit être préservé

Subtask 4.5 (Optional): Unit Tests for Chunking ⏳

Additional unit tests:

• Test preservation of CIM-10 inclusion/exclusion notes
• Test preservation of CCAM ATIH extensions
• Test chunk size constraints
• Test overlap behavior

Files Created/Modified

Created:

• src/pipeline_mco_pmsi/rag/referentiels_manager.py (477 lines)
• src/pipeline_mco_pmsi/rag/__init__.py
• tests/test_referentiels_manager.py (260 lines)

Modified:

• None (all new files)

Next Steps

1. Implement Subtask 4.2: Intelligent chunking with structure preservation

   • Parse PDF structure more intelligently
   • Implement rule/note detection
   • Preserve semantic context

2. Implement Subtask 4.3: Vectorization and indexation

   • Integrate sentence-transformers
   • Build FAISS HNSW index
   • Implement alphabetical index vectorization

3. Test with Real PDFs: Verify chunking quality with actual ATIH documents

   • guide_methodo_mco_2026_version_provisoire.pdf
   • cim-10-fr_2026_a_usage_pmsi_version_provisoire_111225.pdf
   • actualisation_ccam_descriptive_a_usage_pmsi_v4_2025.pdf

4. Optional: Implement property-based tests for robustness

Notes

• The current chunking implementation is basic (paragraph-based) and will need to be enhanced in subtask 4.2
• The placeholder hash ("0" * 64) for index_hash is used until the index is actually built in subtask 4.3
• All PDF files are available in the workspace root for testing
• The implementation follows the design document specifications closely