MCP-RAG Server

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

🛠️ 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)

📚 Dependencies

groundx (≥2.3.0): Core RAG functionality
openai (≥1.75.0): OpenAI API integration
mcp[cli] (≥1.6.0): Modern Context Processing tools
ipykernel (≥6.29.5): Jupyter notebook support

🔒 Security

Never commit your .env file containing API keys
Use environment variables for all sensitive information
Regularly rotate your API keys
Monitor API usage for any unauthorized access

🤝 Contributing

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

This server cannot be installed

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remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Implements Retrieval-Augmented Generation (RAG) using GroundX and OpenAI, allowing users to ingest documents and perform semantic searches with advanced context handling through Modern Context Processing (MCP).

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