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
.envfile 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