Provides a Retrieval-Augmented Generation (RAG) server for efficient document ingestion, vector storage, and AI-powered question answering.
RAG is a robust server leveraging the Model Context Protocol (MCP) to enable advanced Retrieval-Augmented Generation capabilities. It streamlines the process of ingesting diverse document types (PDF, DOCX, TXT), converting them into searchable embeddings, and storing them in a Qdrant vector database. Integrated with Google Gemini for both embeddings and text generation, RAG allows AI assistants to retrieve relevant information from your custom knowledge base and generate accurate, context-aware answers to user queries, all built on an efficient async architecture.