Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.
Enables grounding AI responses in a local document corpus by exposing MCP tools to list, search, and summarize documents, and generating answers using OpenAI.
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.
Provides tools for ingesting documents into a local vector database and retrieving relevant information via semantic search, enabling retrieval-augmented generation for MCP clients.