Skip to main content
Glama
enesjakozh

MCP-RAG Server

by apatoliya
GitHub
Python
  • Linux
  • Apple
OverviewInspectNewEndpointsSchemaRelated ServersReviewsScore
Need Help?View Source CodeReport Issue

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

  1. Clone the repository:
git clone <repository-url> cd mcp-rag
  1. 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
  1. 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

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.

  1. 🌟 Features
    1. 🛠️ Prerequisites
      1. 📦 Installation
        1. ⚙️ Configuration
          1. 🚀 Usage
            1. Starting the Server
            2. Document Ingestion
            3. Performing Searches
          2. 📚 Dependencies
            1. 🔒 Security
              1. 🤝 Contributing

                Related MCP Servers

                • MCP Server for Vertex AI Search

                  ubie-oss
                  -
                  security
                  A
                  license
                  -
                  quality
                  A server that enables document searching using Vertex AI with Gemini grounding, improving search results by grounding responses in private data stored in Vertex AI Datastore.
                  Last updated -
                  25
                  Python
                  Apache 2.0

                • RAG_MCP

                  mytechnotalent
                  -
                  security
                  -
                  license
                  -
                  quality
                  A Retrieval-Augmented Generation server that enables semantic PDF search with OCR capabilities, allowing users to query document content through any MCP client and receive intelligent answers.
                  Last updated -
                  1
                  Python
                  Apache 2.0

                • MCP-RAG Server

                  sourangshupal
                  -
                  security
                  F
                  license
                  -
                  quality
                  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).
                  Last updated -
                  4
                  Python
                  • Linux
                  • Apple

                • RAG-MCP Server

                  plaban1981
                  -
                  security
                  A
                  license
                  -
                  quality
                  A server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.
                  Last updated -
                  3
                  Python
                  Apache 2.0

                View all related MCP servers

                Appeared in Searches

                MCP directory API

                We provide all the information about MCP servers via our MCP API.

                curl -X GET 'https://glama.ai/api/mcp/v1/servers/apatoliya/mcp-rag'

                If you have feedback or need assistance with the MCP directory API, please join our Discord server