RAG Documentation Expert Bot with Gemini & Supabase

This advanced n8n workflow creates a Retrieval-Augmented Generation (RAG) pipeline to transform an AI into a domain-specific expert, exemplified by indexing the n8n documentation. It scrapes web pages, chunks content, generates embeddings, and stores them in a Supabase vector database for precise retrieval. The chatbot then queries this knowledge base to provide factual, grounded responses using only relevant document chunks fed to Gemini, eliminating hallucinations and ensuring accuracy.

Part 1 focuses on one-time indexing: automatically fetch all documentation pages, process them into manageable chunks, embed with AI, and build a private vector knowledge base. This 'librarian indexing' step prepares the AI for expertise on any document set, saving hours of manual data preparation.

Part 2 delivers the interactive AI agent: user questions trigger similarity search on embeddings, retrieve top matches, and prompt Gemini to answer strictly from that context. Ideal for support bots, internal knowledge assistants, or specialized Q&A on manuals, policies, or guides.

Benefits include high accuracy, scalability to large docs, easy adaptation to other topics (e.g., travel accommodations, product catalogs), and time savings via automation. Setup takes 15-20 minutes with a free Supabase account, making it accessible yet powerful for businesses needing reliable AI expertise.