README.md•3.42 kB
# 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**
```bash
pip install langchain openai chromadb sentence-transformers
```
## Usage
### Basic Information Retrieval
```python
# 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
```python
# 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:
```bash
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