An agentic Retrieval-Augmented Generation (RAG) system that combines a small curated machine learning knowledge base with real-time web search capabilities, powered by the Model Context Protocol (MCP).
A Model Context Protocol server that exposes a hybrid RAG pipeline (dense+sparse retrieval with reranking) for querying an enterprise knowledge base, enabling autonomous agents to search and retrieve relevant information.
A local RAG server using the Model Context Protocol (MCP) to allow AI assistants to query private documents with persistent memory and support for many file formats.
A citation-grounded RAG server for internal documentation that exposes retrieval tools and resources via the Model Context Protocol, enabling any MCP client to search and access organizational knowledge with structured citations.
A local-first AI backend that combines Model Context Protocol, Retrieval-Augmented Generation, and an agent runtime to answer questions from a private knowledge base.
An MCP server for document parsing, ingestion, query (including multimodal), and lightweight knowledge graph inspection, enabling RAG workflows via the Model Context Protocol.