- 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>
- Added accelerator and providers fields to SemanticStatus interface.
- Updated checkSemanticStatus function to retrieve ONNX providers and accelerator type.
- Introduced detectGpuSupport function to identify available GPU modes (CUDA, DirectML).
- Modified installSemantic function to support GPU acceleration modes and clean up ONNX Runtime installations.
- Updated package requirements in PKG-INFO for semantic-gpu and semantic-directml extras.
- Added new source files for GPU support and enrichment functionalities.
- Updated tests to cover new features and ensure comprehensive testing.
- Updated the Chunker class to adjust the window movement logic, ensuring proper handling of overlap lines.
- Introduced a new smart search tool with features including intent classification, CodexLens integration, multi-backend search routing, and index status checking.
- Implemented various search modes (auto, hybrid, exact, ripgrep, priority) with detailed metadata and error handling.
- Added support for progress tracking during index initialization and enhanced output transformation based on user-defined modes.
- Included comprehensive documentation for usage and parameters in the smart search tool.
- Add SQLite table and CRUD methods for tracking update history
- Create freshness calculation service based on git file changes
- Add API endpoints for freshness data, marking updates, and history
- Display freshness badges in file tree (green/yellow/red indicators)
- Show freshness gauge and details in metadata panel
- Auto-mark files as updated after CLI sync
- Add English and Chinese i18n translations
Freshness algorithm: 100 - min((changedFilesCount / 20) * 100, 100)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Updated the dashboard template to hide the Code Graph Explorer feature.
- Enhanced the `executeCodexLens` function to use `exec` for better cross-platform compatibility and improved command execution.
- Changed the default `maxResults` and `limit` parameters in the smart search tool to 10 for better performance.
- Introduced a new `priority` search mode in the smart search tool, replacing the previous `parallel` mode, which now follows a fallback strategy: hybrid -> exact -> ripgrep.
- Optimized the embedding generation process in the embedding manager by batching operations and using a cached embedder instance to reduce model loading overhead.
- Implemented a thread-safe singleton pattern for the embedder to improve performance across multiple searches.
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>
- Add helper methods to locate match lines and find containing symbols
- Modify search_fts, search_fts_exact, search_fts_fuzzy to return complete
code blocks (functions/methods/classes) instead of short snippets
- Join with files table to get full content and file_id
- Query symbols table to find the smallest symbol containing the match
- Fall back to context lines when no symbol contains the match
- Add return_full_content and context_lines parameters for flexibility
- Include start_line, end_line, symbol_name, symbol_kind in SearchResult
This improves search result quality by returning semantically meaningful
code blocks rather than arbitrary 20-byte snippets.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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.
- Introduced styles for the help view including tab transitions, accordion animations, search highlighting, and responsive design.
- Implemented core memory styles with modal base styles, memory card designs, and knowledge graph visualization.
- Enhanced dark mode support across various components.
- Added loading states and empty state designs for better user experience.
- Updated command patterns across documentation and templates to reflect the new CLI syntax.
- Enhanced CLI tool implementation to support reading prompts from files and multi-line inputs.
- Modified core components and views to ensure compatibility with the new command structure.
- Adjusted help messages and internationalization strings to align with the updated command format.
- Improved error handling and user notifications in the CLI execution flow.
Previously, embeddings were only generated for root directory files (1.6% coverage, 5/303 files).
This fix implements recursive processing across all subdirectory indexes, achieving 100% coverage
with 2,042 semantic chunks across all 303 files in 26 index databases.
Key improvements:
1. **Recursive embeddings generation** (embedding_manager.py):
- Add generate_embeddings_recursive() to process all _index.db files in directory tree
- Add get_embeddings_status() for comprehensive coverage statistics
- Add discover_all_index_dbs() helper for recursive file discovery
2. **Enhanced CLI commands** (commands.py):
- embeddings-generate: Add --recursive flag for full project coverage
- init: Use recursive generation by default for complete indexing
- status: Display embeddings coverage statistics with 50% threshold
3. **Smart search routing improvements** (smart-search.ts):
- Add 50% embeddings coverage threshold for hybrid mode routing
- Auto-fallback to exact mode when coverage insufficient
- Strip ANSI color codes from JSON output for correct parsing
- Add embeddings_coverage_percent to IndexStatus and SearchMetadata
- Provide clear warnings with actionable suggestions
4. **Documentation and analysis**:
- Add SMART_SEARCH_ANALYSIS.md with initial investigation
- Add SMART_SEARCH_CORRECTED_ANALYSIS.md revealing true extent of issue
- Add EMBEDDINGS_FIX_SUMMARY.md with complete fix summary
- Add check_embeddings.py script for coverage verification
Results:
- Coverage improved from 1.6% (5/303 files) to 100% (303/303 files) - 62.5x increase
- Semantic chunks increased from 10 to 2,042 - 204x increase
- All 26 subdirectory indexes now have embeddings vs just 1
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Introduced a detailed guide for intelligent tools selection strategy, including quick reference, tool specifications, prompt templates, and best practices for CLI execution.
- Established a coding philosophy document outlining core beliefs, simplicity principles, and guidelines for effective coding practices.
- Created context requirements documentation emphasizing the importance of understanding existing patterns and dependencies before implementation.
- Developed a file modification workflow detailing the use of edit_file and write_file MCP tools, along with priority logic for file reading and editing.
- Implemented CodexLens auto hybrid mode, enhancing the CLI with automatic vector embedding generation and default hybrid search mode based on embedding availability.
- Implement `inspect_llm_summaries.py` to display LLM-generated summaries from the semantic_chunks table in the database.
- Create `show_llm_analysis.py` to demonstrate LLM analysis of misleading code examples, highlighting discrepancies between comments and actual functionality.
- Develop `test_misleading_comments.py` to compare pure vector search with LLM-enhanced search, focusing on the impact of misleading or missing comments on search results.
- Introduce `test_llm_enhanced_search.py` to provide a test suite for evaluating the effectiveness of LLM-enhanced vector search against pure vector search.
- Ensure all new scripts are integrated with the existing codebase and follow the established coding standards.
- 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 cleanup function to reset the state when navigating away from the graph explorer.
- Updated navigation logic to call the cleanup function before switching views.
- Improved internationalization by adding new translations for graph-related terms.
- Adjusted icon sizes for better UI consistency in the graph explorer.
- Implemented impact analysis button functionality in the graph explorer.
- Refactored CLI tool configuration to use updated model names.
- Enhanced CLI executor to handle prompts correctly for codex commands.
- Introduced code relationship storage for better visualization in the index tree.
- Added support for parsing Markdown and plain text files in the symbol parser.
- Updated tests to reflect changes in language detection logic.
- 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.
- Introduced a comprehensive design document for a Code Semantic Graph aimed at enhancing static analysis capabilities.
- Defined the architecture, core components, and implementation steps for analyzing function calls, data flow, and dependencies.
- Included detailed specifications for nodes and edges in the graph, along with database schema for storage.
- Outlined phases for implementation, technical challenges, success metrics, and application scenarios.
- Added main JavaScript functionality for CLAUDE.md management including file loading, rendering, and editing capabilities.
- Created a test HTML file to validate the functionality of the CLAUDE.md manager.
- Introduced CLI generation examples and documentation for rules creation via CLI.
- Enhanced error handling and notifications for file operations.
- Added active memory configuration for manual interval and Gemini tool.
- Created file modification rules for handling edits and writes.
- Implemented migration manager for managing database schema migrations.
- Added migration 001 to normalize keywords into separate tables.
- Developed tests for validating performance optimizations including keyword normalization, path lookup, and symbol search.
- Created validation script to manually verify optimization implementations.
- 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
Replace overly broad `except Exception` blocks with specific exception
handlers (StorageError, ConfigError, ParseError, SearchError, PermissionError)
across all CLI commands. This provides more precise error messages and
improves debugging experience for end users.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Added `skills-manager.js` for managing Claude Code skills with functionalities for loading, displaying, and editing skills.
- Introduced a Notifier module in `notifier.ts` for CLI to server communication, enabling notifications for UI updates on data changes.
- Created comprehensive documentation for the Chain Search implementation, including usage examples and performance tips.
- Developed a test suite for the Chain Search engine, covering basic search, quick search, symbol search, and files-only search functionalities.
- Introduced `ccw-mcp` command for running CCW tools as an MCP server.
- Updated `package.json` to include new MCP dependencies and scripts.
- Enhanced CLI with new options for `codex_lens` tool.
- Implemented MCP server logic to expose CCW tools via Model Context Protocol.
- Added new tools and updated existing ones for better functionality and documentation.
- Created quick start and full documentation for MCP server usage.
- Added tests for MCP server functionality to ensure reliability.
- 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.
