How do I use MCP-RAG Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP-RAG Server search for information about RAG implementation best practices" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Implements environment variable configuration through .env files, enabling secure storage of API keys and other sensitive information

Provides Git-based workflows for contribution and version control, supporting feature branch development and pull request processes

Supports Jupyter notebook functionality through ipykernel, allowing interactive development and visualization of RAG operations

Integrates with OpenAI's API for model generation capabilities, enabling use of models like GPT-4 for retrieval-augmented generation tasks

Utilizes Pydantic for type-safe operations, providing robust type checking and validation throughout the RAG implementation

Built on Python with specific version requirements (3.12+), leveraging the language's capabilities for document processing and retrieval tasks

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: Agentset

🛠️ 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

MCP-RAG Server