Skip to main content
Glama
Sign In
enesjakozh

MCP-RAG Server

by sourangshupal
GitHub
Python
1
  • Linux
  • Apple
OverviewInspectNewSchemaRelated ServersReviewsScore
Need Help?View Source CodeReport Issue

Integrations

  • 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

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

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).

  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-apple-notes

                  RafalWilinski
                  -
                  security
                  F
                  license
                  -
                  quality
                  Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.
                  Last updated -
                  158
                  TypeScript
                  • Apple

                • Chroma MCP Server

                  privetin
                  -
                  security
                  F
                  license
                  -
                  quality
                  Enables LLMs to perform semantic search and document management using ChromaDB, supporting natural language queries with intuitive similarity metrics for retrieval augmented generation applications.
                  Last updated -
                  Python
                  • Apple

                • RagDocs MCP Server

                  heltonteixeira
                  -
                  security
                  A
                  license
                  -
                  quality
                  Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.
                  Last updated -
                  5
                  4
                  TypeScript
                  Apache 2.0

                • TxtAi Memory Vector Server

                  rmtech1
                  -
                  security
                  F
                  license
                  -
                  quality
                  Model Context Protocol (MCP) server implementation for semantic search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities. You can use Claude and Cline AI Also
                  Last updated -
                  4
                  Python
                  • Apple

                View all related MCP servers

                ID: do5d4tnskv