Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
Enables MCP-capable AI to perform read-only semantic search over a local document corpus stored in Postgres with pgvector, using mxbai-embed-large embeddings and optional reranking for confidence-gated results.
Connects to a PostgreSQL database (pgvector) to perform semantic retrieval via the search_documents tool, returning raw document snippets for LLM synthesis.
Enables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.
Enables semantic search across text documents using vector embeddings stored in PostgreSQL. Provides multiple search modalities including semantic similarity, question/answer, and style-based search through a retrieval-augmented generation system.