Chat with Google Drive documents using OpenAI and Pinecone RAG search

Try it out This n8n workflow template lets you chat with your Google Drive documents (.docx, .json, .md, .txt, .pdf) using OpenAI and Pinecone vector database. It retrieves relevant context from your files in real time so you can get accurate, context-aware answers about your proprietary data—without the need to train your own LLM. Not interested in chunking and embedding your own data or figuring out which search method to use? Try our n8n quickstart for Pinecone Assistant here or check out the full workflow to chat with your Google Drive documents here. Prerequisites A Pinecone account A GCP project with Google Drive API enabled and configured An Open AI account and API key A Cohere account and API key Setup 1. Create a Pinecone index in the Pinecone Console here 1. Name your index n8n-dense-index 2. Select OpenAI's text-embedding-3-small 3. Set the Dimension to 1536 4. Leave everything else as default 5. If you use a different index name, update the related nodes to reflect this change 2. Use the Connect to Pinecone button to authenticate to Pinecone or if you self-host n8n, create a Pinecone credential and add your Pinecone API key directly 3. Setup your Google Drive OAuth2 API, Open AI, and Cohere credentials in n8n 4. Download these files and add them to a Drive folder named n8n-pinecone-demo in the root of your My Drive 1. 2. 3. 4. 5. 5. Activate the workflow or test it with a manual execution to ingest the documents 6. Enter the chat prompts to chat with the Pinecone release notes 1. What support does Pinecone have for MCP? 2. When was fetch by metadata released? Ideas for customizing this workflow - Use your own data and adjust the chunking strategy - Update the AI Agent System Message to reflect how the Pinecone Vector Store Tool will be used. Be sure to include info on what data can be retrieved using that tool. - Update the Pinecone Vector Store Tool Description to reflect what data you are storing in the Pinecone index Need help? You can find help by asking in the Pinecone Discord community or filing an issue on this repo.