Get Started
Back to Templates

Chat with Google Drive documents using Pinecone and OpenAI RAG

Created by

Created by: Alberto || albidrio

Alberto

Last update

Last update 3 hours ago

Categories

Share

Google Drive → Pinecone RAG Chatbot (Auto-Sync & Query)

This n8n workflow implements a fully automated Retrieval-Augmented Generation (RAG) pipeline powered by Google Drive, OpenAI embeddings, and Pinecone.

It continuously keeps a vector database in sync with your company documents and exposes them through an AI chat interface.

What this workflow does

The workflow monitors a Google Drive folder and automatically reacts to document lifecycle events:

  • File created

  • File updated

  • File deleted

When a document is added or updated:

  • The file is downloaded from Google Drive

  • Its content is chunked using a recursive text splitter

  • Embeddings are generated with OpenAI

  • Vectors are stored or updated in Pinecone

When a document is deleted:

The corresponding vectors are removed from Pinecone, keeping the index clean and consistent

On the chat side:

  • A conversational AI agent retrieves relevant vectors from Pinecone

  • Context is injected into the prompt

  • The assistant answers questions grounded only on your documents

Key features

  • End-to-end RAG pipeline (ingestion + retrieval + chat)

  • Automatic vector updates on file changes

  • Idempotent design (safe re-runs, no duplicated vectors)

  • Google Drive as a live knowledge source

  • Pinecone as scalable vector storage

  • OpenAI embeddings and chat models

  • Ready-to-use AI chat interface inside n8n

Typical use cases

  • Internal company knowledge base

  • AI assistant for policies, manuals, and documentation

  • Team chat over shared Google Drive files

  • Lightweight alternative to full-blown document search platforms

  • Prototyping and production RAG systems

Who this template is for

  • n8n users building AI-powered workflows

  • Teams working with Google Drive documents

  • Developers implementing RAG architectures

  • Anyone who wants a self-hosted, controllable, and transparent AI document chatbot

This template is designed to be robust, maintainable, and production-ready, while remaining easy to extend with additional data sources, metadata filtering, or alternative LLM providers.