MCP RAG Server

# Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) is an AI architecture that combines retrieval systems with generative models to produce more accurate, contextually relevant, and factual outputs. ## How RAG Works 1. **Query Processing**: The user query is processed and analyzed 2. **Retrieval**: Relevant documents or information is retrieved from a knowledge base 3. **Augmentation**: The retrieved information is provided to the generative model as context 4. **Generation**: The model produces a response based on both its training and the retrieved context ## Components of RAG Systems ### Vector Database The vector database is a critical component that: - Stores document embeddings - Enables semantic search based on vector similarity - Supports efficient retrieval of relevant documents - Scales to handle large document collections ### Document Processing Effective document processing involves: - Text extraction and cleaning - Chunking documents into appropriate sizes - Creating high-quality embeddings - Maintaining metadata for context ### Retrieval Mechanism The retrieval system: - Converts queries into the same embedding space as documents - Performs similarity search to find relevant context - Ranks results based on relevance scores - May incorporate re-ranking or filtering ### Generative Model The generative model: - Receives the query and retrieved context - Synthesizes information to create a response - Balances context utilization with its own knowledge - Should cite or reference sources when appropriate ## Benefits of RAG - **Improved Accuracy**: Access to specific information reduces hallucinations - **Up-to-date Information**: Can reference information beyond training cutoff - **Transparency**: Sources can be cited and verified - **Customization**: Knowledge base can be tailored to specific domains ## Challenges and Considerations - **Retrieval Quality**: The system is only as good as its retrieval mechanism - **Context Window Limitations**: Limited space for retrieved documents - **Processing Overhead**: Additional computation for retrieval step - **Knowledge Base Maintenance**: Keeping information current and relevant