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