Automate Document-Based AI Queries with Google Drive and Supabase

This workflow leverages the power of Retrieval-Augmented Generation (RAG) to convert documents stored in Google Drive into a robust, searchable knowledge base. By automatically extracting text from new files, generating vector embeddings, and storing them in Supabase, it enables fast semantic searches. Users can query the database using natural language, receiving precise answers from OpenAI's advanced model. The workflow also maintains chat history for coherent conversations, making it ideal for businesses and teams seeking quick access to information.