A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.
A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.
RAG-enabled MCP server that uses Google Gemini for embeddings and Supabase for vector storage, enabling semantic search and document similarity matching through natural language queries.
