mcp 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

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 `.venvScriptsactivate`

⚙️ 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