πŸ“– Chat with documents via RAG: Google Drive to GPT-5 with Supabase vector database

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Description

πŸ“„ n8n RAG Ingestion & Query Workflow

Overview

This workflow is your all-in-one pipeline to turn any document into a powerful searchable knowledge base using RAG (Retrieval-Augmented Generation).
From the moment a file lands in your Google Drive, it’s automatically processed, understood, and made ready for instant AI-powered answers.

If you’re looking to unlock hidden value in your files and get answers in seconds instead of hours, this workflow is the foundation you need.


What It Does for You


How It Works

  1. Detect β†’ Watches your Google Drive folder for new files.
  2. Extract β†’ Uses Mistral AI to read all text, including tables.
  3. Chunk β†’ Splits content so one page = one chunk for better context.
  4. Embed β†’ Generates vector embeddings with OpenAI for semantic search.
  5. Store β†’ Inserts processed content into Supabase.
  6. Retrieve & Answer β†’ When you ask, the system searches the database and passes the results to GPT-5.
  7. Remember β†’ Stores conversation history in Postgres for continuity.

Why You Want This


Key Highlights


πŸš€ Imagine having your own private ChatGPT trained on your files.
This workflow makes it happen. Upload, search, and get answers β€” all automatically.

πŸ”— Nodes Used

Slack, Telegram, Telegram Trigger, Google Drive, Gmail, Google Drive Trigger

πŸ“₯ Import

Download workflow.json and import into n8n: Workflow menu β†’ Import from File

πŸ“– Importing guide Β· πŸ”‘ Credential setup