- Implemented `model-download-custom` command to download HuggingFace models.
- Added support for discovering manually placed models in the cache.
- Enhanced the model list view to display recommended and discovered models separately.
- Introduced JSON editor for direct configuration mode in API settings.
- Added validation and formatting features for JSON input.
- Updated translations for new API settings and common actions.
- Improved user interface for model management, including action buttons and tooltips.
- Introduced best practices requirements specification covering code quality, performance, maintainability, error handling, and documentation standards.
- Established quality standards with overall quality metrics and mandatory checks for security, code quality, performance, and maintainability.
- Created security requirements specification aligned with OWASP Top 10 and CWE Top 25, detailing checks and patterns for common vulnerabilities.
- Developed templates for documenting best practice findings, security findings, and generating reports, including structured markdown and JSON formats.
- Updated dependencies in the project, ensuring compatibility and stability.
- Added test files and README documentation for vector indexing tests.
- Implemented CLI commands for listing, downloading, deleting, and retrieving information about reranker models.
- Enhanced the dashboard UI to support embedding and reranker configurations with internationalization.
- Updated environment variable management for embedding and reranker settings.
- Added functionality to dynamically update model options based on selected backend.
- Improved user experience with status indicators and action buttons for model management.
- Integrated new reranker models with detailed metadata and recommendations.
- Introduced a comprehensive code analysis action template for integrating code exploration and analysis capabilities.
- Added LLM action template for seamless integration of LLM calls with customizable prompts and tools.
- Implemented a benchmark search script to compare multiple search methods across various dimensions including speed, result quality, ranking stability, and coverage.
- Provided preset configurations for common analysis tasks and LLM actions, enhancing usability and flexibility.
- Introduced a comprehensive template for autonomous actions, detailing structure, execution, and error handling.
- Added an orchestrator template to manage state and decision logic for autonomous actions.
- Created a sequential phase template to outline execution steps and objectives for structured workflows.
- Developed a skill documentation template to standardize the generation of skill entry files.
- Implemented a Python script to compare search results between hybrid and cascade methods, analyzing ranking changes.
- 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.
- Added centralized SPLADE database and vector storage configuration in config.py.
- Updated embedding_manager.py to support centralized SPLADE database path.
- Enhanced generate_embeddings and generate_embeddings_recursive functions for centralized storage.
- Introduced centralized ANN index creation in ann_index.py.
- Modified hybrid_search.py to utilize centralized vector index for searches.
- Implemented methods to discover and manage centralized SPLADE and HNSW files.
- 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.
Architecture refactoring for multi-provider rotation:
Backend:
- Add EmbeddingPoolConfig type with autoDiscover support
- Implement discoverProvidersForModel() for auto-aggregation
- Add GET/PUT /api/litellm-api/embedding-pool endpoints
- Add GET /api/litellm-api/embedding-pool/discover/:model preview
- Convert ccw-litellm status check to async with 5-min cache
- Maintain backward compatibility with legacy rotation config
Frontend:
- Add "Embedding Pool" tab in API Settings
- Auto-discover providers when target model selected
- Show provider/key count with include/exclude controls
- Increase sidebar width (280px → 320px)
- Add sync result feedback on save
Other:
- Remove worker count limits (was max=32)
- Add i18n translations (EN/CN)
- Update .gitignore for .mcp.json
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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.
- Added token estimation and batching functionality in LiteLLMEmbedder to handle large text inputs efficiently.
- Updated embed method to support max_tokens_per_batch parameter for better API call management.
- Introduced new API routes for managing custom CLI endpoints, including GET, POST, PUT, and DELETE methods.
- Enhanced CLI history component to support source directory context for native session content.
- Improved error handling and logging in various components for better debugging and user feedback.
- Added internationalization support for new API endpoint features in the i18n module.
- Updated CodexLens CLI commands to allow for concurrent API calls with a max_workers option.
- Enhanced embedding manager to track model information and handle embeddings generation more robustly.
- Added entry points for CLI commands in the package configuration.
- Create ccw-litellm Python package with AbstractEmbedder and AbstractLLMClient interfaces
- Add BaseEmbedder abstraction and factory pattern to codex-lens for pluggable backends
- Implement API Settings dashboard page for provider credentials and custom endpoints
- Add REST API routes for CRUD operations on providers and endpoints
- Extend CLI with --model parameter for custom endpoint routing
- Integrate existing context-cache for @pattern file resolution
- Add provider model registry with predefined models per provider type
- Include i18n translations (en/zh) for all new UI elements
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Introduced a new CodexLens Manager item in the dashboard for easier access.
- Implemented GPU management commands in the CLI, including listing available GPUs, selecting a specific GPU, and resetting to automatic detection.
- Enhanced the embedding generation process to utilize GPU resources more effectively, including batch size optimization for better performance.
- Updated the embedder to support device ID options for GPU selection, ensuring compatibility with DirectML and CUDA.
- Added detailed logging and error handling for GPU detection and selection processes.
- Updated package version to 6.2.9 and added comprehensive documentation for Codex Agent Execution Protocol.
- Fix cli.ts loadPackageInfo() to try root package.json first (../../package.json)
- Add build script and devDependencies to root package.json
- Remove ccw/package.json and ccw/package-lock.json (no longer needed)
- CodexLens: add config.json support for index_dir configuration
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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 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>
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 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.
- 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.
- 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.
