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MCP-RAG Server

by apatoliya

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

hybrid server

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

GroundX와 OpenAI를 사용하여 검색 증강 생성을 구현하는 서버로, 향상된 컨텍스트 처리를 위해 Modern Context Processing을 사용하여 의미 검색 및 문서 검색을 지원합니다.

  1. 🌟 특징
    1. 🛠️ 필수 조건
      1. 📦 설치
        1. ⚙️ 구성
          1. 🚀 사용법
            1. 서버 시작
            2. 문서 수집
            3. 검색 수행
          2. 📚 종속성
            1. 🔒 보안
              1. 🤝 기여하기

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