d104f59e29
This PR implements the complete three-tier hierarchical retrieval architecture as specified in issue #11610, enabling production-grade RAG capabilities. ## Tier 1: Knowledge Base Routing - Auto-route queries to relevant knowledge bases - Per-KB retrieval parameters (KBRetrievalParams dataclass) - Rule-based routing with keyword overlap scoring - LLM-based routing with fallback to rule-based - Configurable routing methods: auto, rule_based, llm_based, all ## Tier 2: Document Filtering - Document-level metadata filtering within selected KBs - Configurable metadata fields for filtering - LLM-generated filter conditions - Metadata similarity matching (fuzzy matching) - Enhanced metadata generation for documents ## Tier 3: Chunk Refinement - Parent-child chunking with summary mapping - Custom prompts for keyword extraction - LLM-based question generation for chunks - Integration with existing retrieval pipeline ## Metadata Management (Batch CRUD) - MetadataService with batch operations: - batch_get_metadata - batch_update_metadata - batch_delete_metadata_fields - batch_set_metadata_field - get_metadata_schema - search_by_metadata - get_metadata_statistics - copy_metadata - REST API endpoints in metadata_app.py ## Integration - HierarchicalConfig dataclass for configuration - Integrated into Dealer class (search.py) - Wired into agent retrieval tool - Non-breaking: disabled by default ## Tests - 48 unit tests covering all components - Tests for config, routing, filtering, and metadata operations |
||
|---|---|---|
| .. | ||
| __init__.py | ||
| query.py | ||
| rag_tokenizer.py | ||
| search.py | ||
| surname.py | ||
| synonym.py | ||
| term_weight.py | ||