- Add `association_tree` module with components for building and processing call association trees using LSP call hierarchy capabilities.
- Introduce `AssociationTreeBuilder` for constructing call trees from seed locations with depth-first expansion.
- Create data structures: `TreeNode`, `CallTree`, and `UniqueNode` for representing nodes and relationships in the call tree.
- Implement `ResultDeduplicator` to extract unique nodes from call trees and assign relevance scores based on depth, frequency, and kind.
- Add unit tests for `AssociationTreeBuilder` and `ResultDeduplicator` to ensure functionality and correctness.
- Created a comprehensive guide for setting up TypeScript LSP in Claude Code, detailing installation methods, configuration, and troubleshooting.
- Added multiple debugging test scripts to validate LSP communication with pyright, including direct communication tests, configuration checks, and document symbol retrieval.
- Implemented error handling and logging for better visibility during LSP interactions.
- 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.
- Introduced a new test script for the CodexLens LSP tool to validate core functionalities including symbol search, find definition, find references, and get hover.
- Created comprehensive documentation for the MCP endpoint design, detailing the architecture, features, and integration with the CCW MCP Manager.
- Developed a detailed implementation plan for transitioning to a real LSP server, outlining phases, architecture, and acceptance criteria.
- 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.
- Added a function to parse JSON streaming content in core-memory.js, extracting readable text from messages.
- Updated memory detail view to utilize the new parsing function for content and summary.
- Introduced an enableReview option in rules-manager.js, allowing users to toggle review functionality in rule creation.
- Simplified skill creation modal in skills-manager.js by removing generation type selection UI.
- Improved CLI executor to handle tool calls for file writing, ensuring proper output parsing.
- Adjusted CLI command tests to set timeout to 0 for immediate execution.
- Updated file watcher to implement a true debounce mechanism and added a pending queue status for UI updates.
- Enhanced watcher manager to handle queue changes and provide JSON output for better integration with TypeScript backend.
- Established TypeScript naming conventions documentation to standardize code style across the project.
- 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.
- Added `rerankerModels` property to the `ProviderCredential` interface in `litellm-api-config.ts` to support additional reranker configurations.
- Introduced a numerically stable sigmoid function in `FastEmbedReranker` for score normalization.
- Updated the scoring logic in `FastEmbedReranker` to use raw float scores from the encoder and normalize them using the new sigmoid function.
- Adjusted the result mapping to maintain original document order while applying normalization.
- 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.
1. GlobalSymbolIndex constructor: Add project_id parameter lookup
- Get project_id from registry using source_root
- Pass project_id to GlobalSymbolIndex constructor
2. Binary cascade search path handling:
- Add VectorMetadataStore import for centralized search
- Fix _build_results_from_candidates to handle centralized mode
- Use VectorMetadataStore for metadata, source_index_db for embeddings
- Properly distinguish between index_root and index_path
3. Dense reranking for centralized search:
- Get chunk metadata from _vectors_meta.db
- Group chunks by source_index_db
- Retrieve dense embeddings from respective _index.db files
- 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 `get_all_chunks` method in `VectorStore` class to fetch all semantic chunks from the database.
- Added a new benchmark script `analyze_methods.py` for analyzing hybrid search methods and storage architecture.
- Included detailed analysis of method contributions, storage conflicts, and FTS + Rerank fusion experiments.
- Updated results JSON structure to reflect new analysis outputs and method performance metrics.
