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 AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.
Scrapes documentation sites, chunks and embeds content locally, then retrieves relevant passages for queries. Enables agents to ask questions grounded in documentation.
Crawls documentation websites and provides semantic search capabilities over the content through vector embeddings, enabling natural language queries of technical documentation.