RAG-MCP Server
A general-purpose Retrieval-Augmented Generation (RAG) server using the Model Control Protocol (MCP), designed to be tested with RISC Zero's Bonsai documentation.
Overview
This project implements a RAG server that:
- Uses MCP (Model Control Protocol) for standardized communication
- Implements RAG (Retrieval-Augmented Generation) workflow for document querying
- Can be tested with RISC Zero's Bonsai documentation
- Supports local LLM integration through Ollama
Features
- Document ingestion and indexing
- Semantic search capabilities
- Local LLM integration
- MCP protocol compliance
- RISC Zero Bonsai documentation support
Prerequisites
- Python 3.12+
- Ollama (for local LLM support)
- Poetry (for dependency management)
Installation
- Install Python dependencies:
- Install and start Ollama:
- Pull the required model:
Usage
- Start the MCP server:
- The server will:
- Initialize the LLM and embedding model
- Ingest documents from the data directory
- Process queries using the RAG workflow
- Test with RISC Zero Bonsai docs:
- Place RISC Zero Bonsai documentation in the
data/
directory - Query the server about Bonsai features and implementation
- Place RISC Zero Bonsai documentation in the
Project Structure
mcp_server.py
: Main server implementationrag.py
: RAG workflow implementationdata/
: Directory for document ingestionstorage/
: Vector store and document storagestart_ollama.sh
: Script to start Ollama service
Testing with RISC Zero Bonsai
The server is configured to work with RISC Zero's Bonsai documentation. You can:
- Add Bonsai documentation to the
data/
directory - Query about Bonsai features, implementation details, and usage
- Test the RAG workflow with Bonsai-specific questions
Made with ❤️ by proofofsid
This server cannot be installed
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
Implements a RAG workflow that integrates with any custom knowledge base and can be triggered directly from the Cursor IDE.
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