brainwires/project-rag

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33 stars 8 forks Качество 71 Тренд 12MIT

A Rust-based Model Context Protocol (MCP) server that provides AI assistants with powerful RAG (Retrieval-Augmented Generation) capabilities for understanding massive codebases.

Обзор

A Rust-based Model Context Protocol (MCP) server that provides AI assistants with powerful RAG (Retrieval-Augmented Generation) capabilities for understanding massive codebases. This MCP server enables AI assistants to efficiently search and understand large projects by: - Creating semantic embeddings of code files - Storing them in a local vector database - Providing fast semantic search capabilities - Supporting incremental updates for efficiency - : All processing happens locally using fastembed-rs (no API keys required) - : Combines vector similarity with BM25 keyword matching using Reciprocal Rank Fusion (RRF) for optimal results - : Uses Tree-sitter to extract semantic units (functions, classes, methods) for 12 languages - : Indexes 40+ file types including code, documentation (with PDF→Markdown conversion), and configuration files - : Search commit history with smart on-demand indexing (default: 10 commits, only indexes deeper as needed) - : Index and query multiple codebases...

README

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Project RAG - MCP Server for Code Understanding

A Rust-based Model Context Protocol (MCP) server that provides AI assistants with powerful RAG (Retrieval-Augmented Generation) capabilities for understanding massive codebases.

Overview

This MCP server enables AI assistants to efficiently search and understand large projects by:

Creating semantic embeddings of code files
Storing them in a local vector database
Providing fast semantic search capabilities
Supporting incremental updates for efficiency

Features

Local-First: All processing happens locally using fastembed-rs (no API keys required)
Hybrid Search: Combines vector similarity with BM25 keyword matching using Reciprocal Rank Fusion (RRF) for optimal results
AST-Based Chunking: Uses Tree-sitter to extract semantic units (functions, classes, methods) for 12 languages
Comprehensive File Support: Indexes 40+ file types including code, documentation (with PDF→Markdown conversion), and configuration files
Git History Search: Search commit history with smart on-demand indexing (default: 10 commits, only indexes deeper as needed)
Multi-Project Support: Index and query multiple codebases simultaneously with project filtering
Smart Indexing: Automatically performs full indexing for new codebases or incremental updates for previously indexed ones
Cross-Process Locking: Filesystem-based locks prevent multiple processes (e.g., multiple Claude Code sessions) from indexing the same codebase simultaneously
Concurrent Access Protection: Safe lock management prevents index corruption when multiple agents try to index simultaneously
Stable Embedded Database: LanceDB vector database (default, no external dependencies) with optional Qdrant support
Language Detection: Automatic detection of 40+ file types (programming languages, documentation formats, and config files)
Advanced Filtering: Search by file type, language, or path patterns
Respects .gitignore: Automatically excludes ignored files during indexing
Code Navigation: Find definitions, references, and call graphs (lightweight LSP-like features)
Adaptive Search Thresholds: Automatically lowers similarity threshold when no results found (0.7 → 0.6 → 0.5 → 0.4 → 0.3)
Slash Commands: 9 convenient slash commands via MCP Prompts

MCP Slash Commands

The server provides 9 slash commands for quick access in Claude Code:

Открыть полный README на GitHub

Установка

docker run -p 6333:6333 -p 6334:6334 \

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Конфигурация

{
  "mcpServers": {
    "project-rag": {
      "command": "/absolute/path/to/project-rag/target/release/project-rag",
      "env": {
        "RUST_LOG": "info"
      }
    }
  }
}