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

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

Setup

Environment Configuration Create a .env file with the following variables:
OPENAI_API_KEY=your_openai_api_key VECTOR_DB_PATH=path_to_vector_database
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

Query Processing
- Input query is received and preprocessed
- Query intent is analyzed
- Relevant context is identified
Information Retrieval
- Vector similarity search is performed
- Relevant documents are retrieved
- Context is assembled and ranked
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

Query Formulation
- Be specific in your queries
- Provide relevant context
- Use appropriate filters
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

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

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

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