- Added pre-calculation of estimated chunk count for HNSW capacity in `generate_dense_embeddings_centralized` to optimize indexing performance.
- Implemented binary vector generation with memory-mapped storage for efficient cascade search, including metadata saving.
- Introduced SPLADE sparse index generation with improved handling and metadata storage.
- Updated `ChainSearchEngine` to prefer centralized binary searcher for improved performance and added fallback to legacy binary index.
- Deprecated `BinaryANNIndex` in favor of `BinarySearcher` for better memory management and performance.
- Enhanced `SpladeEncoder` with warmup functionality to reduce latency spikes during first-time inference.
- Improved `SpladeIndex` with cache size adjustments for better query performance.
- Added methods for managing binary vectors in `VectorMetadataStore`, including batch insertion and retrieval.
- Created a new `BinarySearcher` class for efficient binary vector search using Hamming distance, supporting both memory-mapped and database loading modes.
- 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.
- 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.
Adds robust exception handling for os.path.commonpath() in search_symbols()
to prevent crashes on malformed paths and Windows cross-drive scenarios.
Invalid symbols are skipped with debug logging, search continues.
Solution-ID: SOL-1735385400004
Issue-ID: ISS-1766921318981-4
Task-ID: T1
- Introduced a `stream` parameter to control output streaming vs. caching.
- Enhanced status determination logic to prioritize valid output over exit codes.
- Updated output structure to include full stdout and stderr when not streaming.
feat(cli-history-store): extend conversation turn schema and migration
- Added `cached`, `stdout_full`, and `stderr_full` fields to the conversation turn schema.
- Implemented database migration to add new columns if they do not exist.
- Updated upsert logic to handle new fields.
feat(codex-lens): implement global symbol index for fast lookups
- Created `GlobalSymbolIndex` class to manage project-wide symbol indexing.
- Added methods for adding, updating, and deleting symbols in the global index.
- Integrated global index updates into directory indexing processes.
feat(codex-lens): optimize search functionality with global index
- Enhanced `ChainSearchEngine` to utilize the global symbol index for faster searches.
- Added configuration option to enable/disable global symbol indexing.
- Updated tests to validate global index functionality and performance.
- Added search limit, content length, and extra files input fields in the CodexLens manager UI.
- Updated API request parameters to include new fields: max_content_length and extra_files_count.
- Refactored smart-search.ts to support new parameters with default values.
- Implemented result splitting logic to return both full content and additional file paths.
- Updated CLI commands to remove worker limits and allow dynamic scaling based on endpoint count.
- Introduced EmbeddingPoolConfig for improved embedding management and auto-discovery of providers.
- Enhanced search engines to utilize new parameters for fuzzy and exact searches.
- Added support for embedding single texts in the LiteLLM embedder.
- Fix model installation detection using fastembed ONNX cache names
- Add embeddings_config table for model metadata tracking
- Fix hybrid search segfault by using single-threaded GPU mode
- Suppress INFO logs in JSON mode to prevent error display
- Add model dropdown filtering to show only installed models
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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>
- Implement tests for migration 005 to verify removal of deprecated fields in the database schema.
- Ensure that new databases are created with a clean schema.
- Validate that keywords are correctly extracted from the normalized file_keywords table.
- Test symbol insertion without deprecated fields and subdir operations without direct_files.
- Create a detailed search comparison test to evaluate vector search vs hybrid search performance.
- Add a script for reindexing projects to extract code relationships and verify GraphAnalyzer functionality.
- Include a test script to check TreeSitter parser availability and relationship extraction from sample files.
- 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.
- Added a new Storage Manager component to handle storage statistics, project cleanup, and configuration for CCW centralized storage.
- Introduced functions to calculate directory sizes, get project storage stats, and clean specific or all storage.
- Enhanced SQLiteStore with a public API for executing queries securely.
- Updated tests to utilize the new execute_query method and validate storage management functionalities.
- Improved performance by implementing connection pooling with idle timeout management in SQLiteStore.
- Added new fields (token_count, symbol_type) to the symbols table and adjusted related insertions.
- Enhanced error handling and logging for storage operations.
- 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.
- 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
