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RAG Information Retriever

by ChandrahaasJ

RAG Information Retriever

A powerful MCP server that implements Retrieval-Augmented Generation (RAG) to efficiently retrieve and process important information from various sources. This server combines the strengths of retrieval-based and generation-based approaches to provide accurate and contextually relevant information.

Features

  1. Intelligent Information Retrieval
    • Semantic search capabilities
    • Context-aware information extraction
    • Relevance scoring and ranking
    • Multi-source data integration
  2. RAG Implementation
    • Document embedding and indexing
    • Query understanding and processing
    • Context-aware response generation
    • Knowledge base integration
  3. Advanced Processing
    • Text chunking and processing
    • Semantic similarity matching
    • Context window management
    • Response synthesis

Setup

  1. Environment Configuration Create a .env file with the following variables:
    OPENAI_API_KEY=your_openai_api_key VECTOR_DB_PATH=path_to_vector_database
  2. Dependencies
    pip install langchain openai chromadb sentence-transformers

Usage

Basic Information Retrieval

# Example: Simple query query = "What are the key features of the system?" # Example: Context-specific query query = "How does the authentication system work?"

Advanced Retrieval

# Example: Multi-context query query = { "question": "What are the system requirements?", "context": ["installation", "deployment", "configuration"] } # Example: Filtered retrieval query = { "question": "Show me the API documentation", "filters": { "category": "api", "version": "2.0" } }

Architecture

retriever/ ├── retrieverServer.py # Main MCP server with RAG implementation ├── embeddings/ # Embedding models and processing ├── database/ # Vector database and storage └── README.md

How It Works

  1. Query Processing
    • Input query is received and preprocessed
    • Query intent is analyzed
    • Relevant context is identified
  2. Information Retrieval
    • Vector similarity search is performed
    • Relevant documents are retrieved
    • Context is assembled and ranked
  3. Response Generation
    • Retrieved information is processed
    • Response is generated with context
    • Results are formatted and returned

Performance Features

  • Efficient vector search
  • Caching of frequent queries
  • Batch processing capabilities
  • Asynchronous operations

Security

  • Input sanitization
  • Rate limiting
  • Access control
  • Data encryption

Running the Server

To start the MCP server in development mode:

mcp dev retrieverServer.py

Error Handling

The system provides comprehensive error handling for:

  • Invalid queries
  • Missing context
  • Database connection issues
  • API rate limits
  • Processing errors

Best Practices

  1. Query Formulation
    • Be specific in your queries
    • Provide relevant context
    • Use appropriate filters
  2. Context Management
    • Keep context windows focused
    • Update knowledge base regularly
    • Monitor relevance scores

Contributing

Feel free to submit issues and enhancement requests!

Security Notes

  • API keys should be kept secure
  • Regular security audits
  • Data privacy compliance
  • Access control implementation
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security - not tested
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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.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

  1. Features
    1. Setup
      1. Usage
        1. Basic Information Retrieval
        2. Advanced Retrieval
      2. Architecture
        1. How It Works
          1. Performance Features
            1. Security
              1. Running the Server
                1. Error Handling
                  1. Best Practices
                    1. Contributing
                      1. Security Notes

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