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catlog22 79a2953862 Add comprehensive tests for vector/semantic search functionality

- Implement full coverage tests for Embedder model loading and embedding generation
- Add CRUD operations and caching tests for VectorStore
- Include cosine similarity computation tests
- Validate semantic search accuracy and relevance through various queries
- Establish performance benchmarks for embedding and search operations
- Ensure edge cases and error handling are covered
- Test thread safety and concurrent access scenarios
- Verify availability of semantic search dependencies

2025-12-14 17:17:09 +08:00

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Semantic Search Integration

Overview

The ChainSearchEngine now supports semantic keyword search in addition to FTS5 full-text search.

Usage

Enable Semantic Search

from pathlib import Path
from codexlens.search.chain_search import ChainSearchEngine, SearchOptions
from codexlens.storage.registry import RegistryStore
from codexlens.storage.path_mapper import PathMapper

# Initialize
registry = RegistryStore()
registry.initialize()
mapper = PathMapper()
engine = ChainSearchEngine(registry, mapper)

# Create options with semantic search enabled
options = SearchOptions(
    include_semantic=True,  # Enable semantic keyword search
    total_limit=50
)

# Execute search
result = engine.search("authentication", Path("./src"), options)

# Results include both FTS and semantic matches
for r in result.results:
    print(f"{r.path}: {r.score:.2f} - {r.excerpt}")

How It Works

FTS Search: Traditional full-text search using SQLite FTS5
Semantic Search: Searches the semantic_metadata.keywords field
Result Merging: Semantic results are added with 0.8x weight
- FTS results: BM25 score from SQLite
- Semantic results: Base score of 10.0 * 0.8 = 8.0
Deduplication: _merge_and_rank() deduplicates by path, keeping highest score

Result Format

FTS results: Regular excerpt from matched content
Semantic results: Keywords: keyword1, keyword2, keyword3, ...

Prerequisites

Files must have semantic metadata generated via:

codex-lens enhance . --tool gemini

This uses CCW CLI to generate summaries, keywords, and purpose descriptions.

Implementation Details

Changes Made

SearchOptions: Added include_semantic: bool = False parameter
_search_parallel(): Passes include_semantic to worker threads
_search_single_index():
- Accepts include_semantic parameter
- Calls DirIndexStore.search_semantic_keywords() when enabled
- Converts semantic matches to SearchResult objects
- Applies 0.8x weight to semantic scores

Score Weighting

# FTS result (from BM25)
SearchResult(path="...", score=12.5, excerpt="...")

# Semantic result (fixed weighted score)
SearchResult(path="...", score=8.0, excerpt="Keywords: ...")

The 0.8x weight ensures semantic matches rank slightly lower than direct FTS matches but still appear in relevant results.

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