Build a RAG knowledge chatbot with OpenAI, Google Drive, and Supabase

🚀 Build Your Own Knowledge Chatbot Using Google Drive Create a smart chatbot that answers questions using your Google Drive PDFs—perfect for support, internal docs, education, or research. 🛠️ Quick Setup Guide Step 1: Prerequisites - n8n instance (cloud or self-hosted) - Google Drive account (with PDFs) - Supabase account (vector database) - OpenAI API key - PostgreSQL database (for chat memory) else remove the node Step 2: Supabase Setup - Create supabase account (its free) - Create a project - Copy the sql and paste it in supabase sql editor SQL -- Enable the pgvector extension to work with embedding vectors create extension vector; -- Create a table to store your documents create table documents ( id bigserial primary key, content text, -- corresponds to Document.pageContent metadata jsonb, -- corresponds to Document.metadata embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed ); -- Create a function to search for documents create function matchdocuments ( queryembedding vector(1536), matchcount int default null, filter jsonb DEFAULT '{}' ) returns table ( id bigint, content text, metadata jsonb, similarity float ) language plpgsql as $$ variableconflict usecolumn begin return query select id, content, metadata, 1 - (documents.embedding <=> queryembedding) as similarity from documents where metadata @> filter order by documents.embedding <=> queryembedding limit matchcount; end; $$; Step 3: Import & Configure n8n Workflow - Import this template into n8n - Add credentials: - OpenAI API key - Google Drive OAuth2 - Supabase URL & service key - PostgreSQL connection - Set your Google Drive folder ID in triggers Step 4: Test & Use - Add a PDF to your Drive folder → check Supabase for new entries - Start the workflow and chat → ask questions about your documents. "What can you help me with?" - Multi-turn chat → context is maintained per user ⚡ Features - Auto-syncs new/updated PDFs from Google Drive - Extracts, chunks, and vectorizes text - Finds relevant info and answers questions - Maintains chat history per user 📝 Troubleshooting - Check folder permissions & IDs if no docs found - Verify API keys & Supabase setup for errors - Ensure PostgreSQL is connected for chat memory Tags: RAG, Chatbot, Google Drive, Supabase, OpenAI, n8n Setup Time: ~20 minutes