# 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
