Enables users to build and query a private knowledge base by uploading documents, which are embedded and stored locally, then accessible via MCP for semantic search and retrieval.
Enables local document question-answering and retrieval via MCP, supporting multi-turn conversation, intent recognition, and tools for document search, Q&A, and summarization.
A headless local knowledge library and RAG substrate that enables LLM clients to search, retrieve chunks, and list documentation packs through read-only MCP tools.
MCP server for a self-hosted RAG system that enables AI tools to search and retrieve grounded answers from locally ingested documents via MCP tools, with local embeddings and no API key required.
Provides AI assistants with a local knowledge base and research library, enabling semantic and full-text retrieval, memory persistence, and multi-agent collaboration via 58 MCP tools.