MCP-RAG: Model Context Protocol with RAG 🚀

A powerful and efficient RAG (Retrieval-Augmented Generation) implementation using GroundX and OpenAI, built with Modern Context Processing (MCP).

🌟 Features

Advanced RAG Implementation: Utilizes GroundX for high-accuracy document retrieval
Model Context Protocol: Seamless integration with MCP for enhanced context handling
Type-Safe: Built with Pydantic for robust type checking and validation
Flexible Configuration: Easy-to-customize settings through environment variables
Document Ingestion: Support for PDF document ingestion and processing
Intelligent Search: Semantic search capabilities with scoring

Related MCP server: RAG_MCP

🛠️ Prerequisites

Python 3.12 or higher
OpenAI API key
GroundX API key
MCP CLI tools

📦 Installation

Clone the repository:

git clone <repository-url> cd mcp-rag

Create and activate a virtual environment:

uv sync source .venv/bin/activate # On Windows, use `.venv\Scripts\activate`

⚙️ Configuration

Copy the example environment file:

cp .env.example .env

Configure your environment variables in .env:

GROUNDX_API_KEY="your-groundx-api-key" OPENAI_API_KEY="your-openai-api-key" BUCKET_ID="your-bucket-id"

🚀 Usage

Starting the Server

Run the inspect server using:

mcp dev server.py

Document Ingestion

To ingest new documents:

from server import ingest_documents result = ingest_documents("path/to/your/document.pdf") print(result)

Performing Searches

Basic search query:

from server import process_search_query response = process_search_query("your search query here") print(f"Query: {response.query}") print(f"Score: {response.score}") print(f"Result: {response.result}")

With custom configuration:

from server import process_search_query, SearchConfig config = SearchConfig( completion_model="gpt-4", bucket_id="custom-bucket-id" ) response = process_search_query("your query", config)