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: PDF Knowledgebase MCP Server

🛠️ Prerequisites

• Python 3.12 or higher

• OpenAI API key

• GroundX API key

• MCP CLI tools

📦 Installation

1. Clone the repository:

git clone <repository-url>
cd mcp-rag

2. Create and activate a virtual environment:

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

⚙️ Configuration

1. Copy the example environment file:

cp .env.example .env

2. 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

1. Fork the repository

2. Create your feature branch (git checkout -b feature/amazing-feature)

3. Commit your changes (git commit -m 'Add some amazing feature')

4. Push to the branch (git push origin feature/amazing-feature)

5. Open a Pull Request