A Retrieval-Augmented Generation (RAG) Model-Controller-Provider (MCP) server designed to assist AI agents and developers in understanding and navigating codebases.

• Getting Started: Installation and Basic Usage.
• User Manual: Tutorials, Features, and Advanced Usage.
• Configuration: Environment variables and performance tuning.
• Architecture: System design and high-level overview.
• Contributing: Developer setup and guidelines.

This tool allows you to "chat" with your code. It indexes your local directories or GitHub repositories into a vector database (Qdrant), allowing Large Language Models to perform semantic searches and retrieve accurate code snippets.

• 🔍 Semantic Search: Find code by meaning, not just keywords.
• 🎯 Two-Stage RAG: 22-31% accuracy improvement with cross-encoder reranking (Qwen3-Reranker).
• 🧠 Intelligent Chunking: Parses code into functions and classes using Tree-sitter for better context.
• ⚡ High Performance: Supports incremental indexing and MPS acceleration on macOS.
• 🌐 Multi-Language: Supports Python, JavaScript, TypeScript, Go, Rust, Java, C++, and more.
• 📝 File Logging: Optional centralized logging with automatic rotation for debugging.

1. Setup:

   ./setup.sh

2. Index Code:

   uv run python manual_indexing.py -d "." -m clear_existing

3. Connect: Follow the instructions from setup.sh to add the server to your MCP client (e.g., Claude Desktop).

For detailed instructions, please visit Getting Started.

Detailed documentation is available in the docs/ directory:

• MCP Tools Reference
• Architecture Deep Dive

This project is licensed under the MIT License - see the LICENSE file for details.