- Introduced CLI options for using AST grep parsers and enabling static graph relationships during indexing.
- Updated configuration management to load new settings for AST parsing and static graph types.
- Enhanced AST grep processor to handle imports with aliases and improve relationship tracking.
- Modified TreeSitter parsers to support synthetic module scopes for better static graph persistence.
- Implemented global relationship updates in the incremental indexer for static graph expansion.
- Added new ArtifactTag and FloatingFileBrowser components to the frontend for improved terminal dashboard functionality.
- Created utility functions for detecting CCW artifacts in terminal output with associated tests.
- Introduced test suite for AstGrepPythonProcessor covering pattern definitions, parsing, and relationship extraction.
- Added comparison tests between tree-sitter and ast-grep for consistency in relationship extraction.
- Implemented tests for ast-grep binding module to verify functionality and availability.
- Ensured tests cover various scenarios including inheritance, function calls, and imports.
- Implement Phase 4: Integration Verification to ensure skill package consistency.
- Implement Phase 5: Validation to verify quality and deliver the final skill package.
- Create role-template.md for generating per-role execution detail files.
- Create skill-router-template.md for generating SKILL.md with role-based routing.
- Add tests for static graph relationship writing during index build in test_static_graph_integration.py.
- Implemented CLI fallback using `ccw team` for various team command operations in `execute.md`, `plan.md`, `review.md`, `spec-analyst.md`, `spec-coordinate.md`, `spec-discuss.md`, `spec-reviewer.md`, `spec-writer.md`, and `test.md`.
- Updated command generation templates to include CLI fallback examples.
- Enhanced validation checks to ensure CLI fallback sections are present.
- Added quality standards for CLI fallback in team command design.
- Introduced a new `GlobalGraphExpander` class for expanding search results with cross-directory relationships.
- Added tests for `GlobalGraphExpander` to verify functionality and score decay factors.
- Added new benchmark result files: compare_2026-02-09_score_fast3.json and compare_2026-02-09_score_fast4.json.
- Implemented KeepAliveLspBridge to maintain a persistent LSP connection across multiple queries, improving performance.
- Created unit tests for staged clustering strategies in test_staged_stage3_fast_strategies.py, ensuring correct behavior of score and dir_rr strategies.
- Added CrossCliSyncPanel component for synchronizing MCP servers between Claude and Codex.
- Implemented server selection, copy operations, and result handling.
- Added tests for path mapping on Windows drives.
- Created E2E tests for ask_question Answer Broker functionality.
- Introduced MCP Tools Test Script for validating modified read_file and edit_file tools.
- Updated path_mapper to ensure correct drive formatting on Windows.
- Added .gitignore for ace-tool directory.
- Introduced a new JSON file for verbose output of the Codex Lens search results.
- Added unit tests for binary search functionality in `test_stage1_binary_search_uses_chunk_lines.py`.
- Implemented regression tests for staged cascade Stage 2 expansion depth in `test_staged_cascade_lsp_depth.py`.
- Created unit tests for staged cascade Stage 2 realtime LSP graph expansion in `test_staged_cascade_realtime_lsp.py`.
- Enhanced the ChainSearchEngine to respect configuration settings for staged LSP depth and improve search accuracy.
- 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.
- 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 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.
Replaces generic Exception handling with specific PermissionError and OSError
handling in __post_init__ and ensure_runtime_dirs(). Provides clear diagnostic
messages to distinguish permission issues from other filesystem errors.
Solution-ID: SOL-1735385400008
Issue-ID: ISS-1766921318981-8
Task-ID: T1
Replaces bare exception handler in load_settings() with logging.warning()
to help users debug configuration file issues (syntax errors, permissions).
Maintains backward compatibility - errors do not break initialization.
Solution-ID: SOL-1735385400001
Issue-ID: ISS-1766921318981-1
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.
- 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 JSON-based settings management in Config class for embedding and LLM configurations.
- Introduced methods to save and load settings from a JSON file.
- Updated BaseEmbedder and its subclasses to include max_tokens property for better token management.
- Enhanced chunking strategy to support recursive splitting of large symbols with improved overlap handling.
- Implemented comprehensive tests for recursive splitting and chunking behavior.
- Added CLI tools configuration management for better integration with external tools.
- Introduced a new command for compacting session memory into structured text for recovery.
- 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.
- 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
