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.