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

Advanced Retrieval

Architecture

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:

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