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

Related MCP server: Shared Knowledge MCP Server

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

1. Install Python dependencies:

2. Install and start Ollama:

3. Pull the required model:

Usage

1. Start the MCP server:

2. The server will:

   • Initialize the LLM and embedding model

   • Ingest documents from the data directory

   • Process queries using the RAG workflow

3. Test with RISC Zero Bonsai docs:

   • Place RISC Zero Bonsai documentation in the data/ directory

   • Query the server about Bonsai features and implementation

Project Structure

• mcp_server.py: Main server implementation

• rag.py: RAG workflow implementation

• data/: Directory for document ingestion

• storage/: Vector store and document storage

• start_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:

1. Add Bonsai documentation to the data/ directory

2. Query about Bonsai features, implementation details, and usage

3. Test the RAG workflow with Bonsai-specific questions

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