Implement search and reranking functionality with FTS and embedding support

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- Added BaseReranker abstract class for defining reranking interfaces.
- Implemented FastEmbedReranker using fastembed's TextCrossEncoder for scoring document-query pairs.
- Introduced FTSEngine for full-text search capabilities using SQLite FTS5.
- Developed SearchPipeline to integrate embedding, binary search, ANN indexing, FTS, and reranking.
- Added fusion methods for combining results from different search strategies using Reciprocal Rank Fusion.
- Created unit and integration tests for the new search and reranking components.
- Established configuration management for search parameters and models.

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catlog22

2026-03-16 23:03:17 +08:00

parent 5a4b18d9b1

commit de4158597b

41 changed files with 2655 additions and 1848 deletions

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codex-lens-v2/tests/integration/__init__.py Normal file