- Implement comprehensive unit tests for the LspGraphBuilder class to validate its functionality in building code association graphs.
- Tests cover various scenarios including single level graph expansion, max nodes and depth boundaries, concurrent expansion limits, document symbol caching, error handling during node expansion, and edge cases such as empty seed lists and self-referencing nodes.
- Utilize pytest and asyncio for asynchronous testing and mocking of LspBridge methods.
- Added category support for programming and documentation languages in Config.
- Implemented category-based filtering in HybridSearchEngine to improve search relevance based on query intent.
- Introduced functions for filtering results by category and determining file categories based on extensions.
- Updated VectorStore to include a category column in the database schema and modified chunk addition methods to support category tagging.
- Enhanced the WatcherConfig to ignore additional common directories and files.
- Created a benchmark script to compare performance between Binary Cascade, SPLADE, and Vector semantic search methods, including detailed result analysis and overlap comparison.
- Implemented BinaryEmbeddingBackend for fast coarse filtering using 256-dimensional binary vectors.
- Developed DenseEmbeddingBackend for high-precision dense vectors (2048 dimensions) for reranking.
- Created CascadeEmbeddingBackend to combine binary and dense embeddings for two-stage retrieval.
- Introduced utility functions for embedding conversion and distance computation.
chore: Migration 010 - Add multi-vector storage support
- Added 'chunks' table to support multi-vector embeddings for cascade retrieval.
- Included new columns: embedding_binary (256-dim) and embedding_dense (2048-dim) for efficient storage.
- Implemented upgrade and downgrade functions to manage schema changes and data migration.
- Added `splade_encoder.py` for ONNX-optimized SPLADE encoding, including methods for encoding text and batch processing.
- Created `SPLADE_IMPLEMENTATION.md` to document the SPLADE encoder's functionality, design patterns, and integration points.
- Introduced migration script `migration_009_add_splade.py` to add SPLADE metadata and posting list tables to the database.
- Developed `splade_index.py` for managing the SPLADE inverted index, supporting efficient sparse vector retrieval.
- Added verification script `verify_watcher.py` to test FileWatcher event filtering and debouncing functionality.
- Implemented GraphExpander to enhance search results with related symbols using precomputed neighbors.
- Added CrossEncoderReranker for second-stage search ranking, allowing for improved result scoring.
- Created migrations to establish necessary database tables for relationships and graph neighbors.
- Developed tests for graph expansion functionality, ensuring related results are populated correctly.
- Enhanced performance benchmarks for cross-encoder reranking latency and graph expansion overhead.
- Updated schema cleanup tests to reflect changes in versioning and deprecated fields.
- Added new test cases for Treesitter parser to validate relationship extraction with alias resolution.
Add math.isnan() check before math.isfinite() to properly catch
NaN values in weight totals. Prevents division by NaN which could
produce unexpected results in RRF fusion calculations.
Solution-ID: SOL-20251228113631
Issue-ID: ISS-1766921318981-0
Task-ID: T1
- Added integration tests for adaptive RRF weights in hybrid search.
- Enhanced query intent detection with new classifications: keyword, semantic, and mixed.
- Introduced symbol boosting in search results based on explicit symbol matches.
- Implemented embedding-based reranking with configurable options.
- Added global symbol index for efficient symbol lookups across projects.
- Improved file deletion handling on Windows to avoid permission errors.
- Updated chunk configuration to increase overlap for better context.
- Modified package.json test script to target specific test files.
- Created comprehensive writing style guidelines for documentation.
- Added TypeScript tests for query intent detection and adaptive weights.
- Established performance benchmarks for global symbol indexing.
- Added functions to get and update CodexLens embedding rotation configuration.
- Introduced functionality to retrieve enabled embedding providers for rotation.
- Created endpoints for managing rotation configuration via API.
- Enhanced dashboard UI to support multi-provider rotation configuration.
- Updated internationalization strings for new rotation features.
- Adjusted CLI commands and embedding manager to support increased concurrency limits.
- Modified hybrid search weights for improved ranking behavior.
Add functionality to group search results with similar content and scores
into a single representative result with additional locations.
Changes:
- Add AdditionalLocation entity model for storing grouped result locations
- Add additional_locations field to SearchResult for backward compatibility
- Implement group_similar_results() function in ranking.py with:
- Content-based grouping (by excerpt or content field)
- Score-based sub-grouping with configurable threshold
- Metadata preservation with grouped_count tracking
- Add group_results and grouping_threshold options to SearchOptions
- Integrate grouping into ChainSearchEngine.search() after RRF fusion
Test coverage:
- 36 multi-level tests covering unit, boundary, integration, and performance
- Real-world scenario tests for RRF scores and duplicate code detection
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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.
