272534df64
Implements full three-tier retrieval system with RAGFlow integration. Changes: - Complete Tier 1: KB routing with rule-based, LLM-based, and auto modes - Complete Tier 2: Document filtering with metadata support - Complete Tier 3: Chunk refinement with vector search integration - Integration with RAGFlow's Dealer and search infrastructure - Add hierarchical_retrieval_config field to Dialog model - Database migration for configuration storage - 29 passing unit tests (6 skipped due to NLTK environment dependency) Implementation Details: - HierarchicalRetrieval: Main orchestrator with RAGFlow integration - KBRouter: Standalone router using keyword matching - DocumentFilter: Metadata-based filtering - ChunkRefiner: Vector search integration via rag.nlp.search.Dealer - Rule-based routing uses token overlap scoring - Auto routing analyzes query characteristics - Tier 3 integrates with existing DocStoreConnection and embedding models Test Results: ✅ 29/29 tests passing - All tier tests working - Integration scenarios validated - Config and result dataclasses tested - Edge cases handled Addresses owner feedback: Complete implementation rather than skeleton. Related to #11610 |
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| .. | ||
| app | ||
| flow | ||
| llm | ||
| nlp | ||
| prompts | ||
| res | ||
| retrieval | ||
| svr | ||
| utils | ||
| __init__.py | ||
| benchmark.py | ||
| raptor.py | ||
| settings.py | ||