2 Commits

Author	SHA1	Message	Date
Chris Coutinho	3464b21845	fix: Relax SearchResult validation to support DBSF fusion scores > 1.0 ... Fix false-positive validation error where DBSF (Distribution-Based Score Fusion) correctly produces scores > 1.0 but SearchResult validation incorrectly rejected them. **Root Cause**: SearchResult.__post_init__() enforced scores in [0.0, 1.0] range, but DBSF sums normalized scores from multiple retrieval systems (dense semantic + sparse BM25), resulting in scores like 1.55 when both systems strongly agree a document is relevant. **Changes**: - Relaxed validation to allow any score ≥ 0.0 (algorithms.py:147-157) - Updated SearchResult and SemanticSearchResult documentation to explain score ranges for RRF ([0.0, 1.0]) vs DBSF (unbounded) - Added comprehensive test coverage for both fusion methods - Added DBSF fusion option to vector visualization UI - Updated viz routes and vizApp() to support fusion parameter selection **Testing**: All 157 unit tests pass, type checking passes, ruff passes Fixes error: "Configuration error: Score must be between 0.0 and 1.0, got 1.1528953" 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-11-17 06:32:30 +01:00
Chris Coutinho	6fe5596c13	feat: Implement BM25 hybrid search with native Qdrant RRF fusion ... Replace custom keyword/fuzzy search algorithms with industry-standard BM25 sparse vectors, combined with dense semantic vectors using Qdrant's native Reciprocal Rank Fusion (RRF). This consolidates search architecture and improves relevance for both semantic and keyword queries. Key changes: - Add fastembed dependency for BM25 sparse vector generation - Update Qdrant collection schema to support named vectors (dense + sparse) - Create BM25SparseEmbeddingProvider using FastEmbed's Qdrant/bm25 model - Implement BM25HybridSearchAlgorithm with native Qdrant RRF prefetch - Update document processor to generate both dense and sparse embeddings - Simplify nc_semantic_search() tool to use BM25 hybrid only - Remove legacy keyword.py, fuzzy.py, and custom hybrid.py (736 lines) - Update ADR-014 with implementation notes and test results Benefits: - Consolidated architecture (single Qdrant database) - Native database-level RRF fusion (more efficient) - Industry-standard BM25 (replaces brittle custom keyword search) - Better relevance across semantic and keyword queries - Simplified codebase (-285 net lines) Tests: All 125 tests passing (118 unit, 7 integration) Implements ADR-014: Replace Custom Keyword Search with BM25 Hybrid Search 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-11-16 06:59:44 +01:00