- Added ANNIndex class for approximate nearest neighbor search using HNSW.
- Integrated ANN index with VectorStore for enhanced search capabilities.
- Updated test suite for ANN index, including tests for adding, searching, saving, and loading vectors.
- Modified existing tests to accommodate changes in search performance expectations.
- Improved error handling for file operations in tests to ensure compatibility with Windows file locks.
- Adjusted hybrid search performance assertions for increased stability in CI environments.
- Implemented tests for the QueryParser class, covering various identifier splitting methods (CamelCase, snake_case, kebab-case), OR expansion, and FTS5 operator preservation.
- Added parameterized tests to validate expected token outputs for different query formats.
- Created edge case tests to ensure robustness against unusual input scenarios.
- Developed tests for the Reciprocal Rank Fusion (RRF) algorithm, including score computation, weight handling, and result ranking across multiple sources.
- Included tests for normalization of BM25 scores and tagging search results with source metadata.
- Implemented unit tests for the Tokenizer class, covering various text inputs, edge cases, and fallback mechanisms.
- Created performance benchmarks comparing tiktoken and pure Python implementations for token counting.
- Developed extensive tests for TreeSitterSymbolParser across Python, JavaScript, and TypeScript, ensuring accurate symbol extraction and parsing.
- Added configuration documentation for MCP integration and custom prompts, enhancing usability and flexibility.
- Introduced a refactor script for GraphAnalyzer to streamline future improvements.
- Implemented tests for the ChunkConfig and Chunker classes, covering default and custom configurations.
- Added tests for symbol-based chunking, including single and multiple symbols, handling of empty symbols, and preservation of line numbers.
- Developed tests for sliding window chunking, ensuring correct chunking behavior with various content sizes and configurations.
- Created integration tests for semantic search, validating embedding generation, vector storage, and search accuracy across a complex codebase.
- Included performance tests for embedding generation and search operations.
- Established tests for chunking strategies, comparing symbol-based and sliding window approaches.
- Enhanced test coverage for edge cases, including handling of unicode characters and out-of-bounds symbol ranges.
