Chat with Google Drive documents using Pinecone and OpenAI RAG

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.