Enables AI assistants to semantically search through indexed documentation websites and local code repositories using OpenAI embeddings and ChromaDB vector storage.
Crawls documentation websites and provides semantic search capabilities over the content through vector embeddings, enabling natural language queries of technical documentation.
Scrapes documentation sites, chunks and embeds content locally, then retrieves relevant passages for queries. Enables agents to ask questions grounded in documentation.
Enables semantic search across documents and code repositories using RAG (Retrieval-Augmented Generation) with vector embeddings. Automatically indexes PDF documents and performs relevance-scored lookups through ChromaDB and sentence transformers.