📖 Build a RAG knowledge assistant with Google Drive, OpenAI, and Pinecone
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Description
Quick Overview
This workflow syncs documents from a Google Drive folder into a Pinecone vector index using OpenAI embeddings, then exposes a webhook chat endpoint where an OpenAI-powered agent answers questions by retrieving relevant context from Pinecone.
How it works
- Triggers every minute when a file in a specified Google Drive folder is updated.
- Extracts the file’s metadata, downloads the updated file from Google Drive, and parses it into text.
- Splits the text into chunks, generates OpenAI embeddings for each chunk, and inserts them into a Pinecone index under the configured namespace.
- Receives user questions via a POST webhook endpoint.
- Uses an OpenAI chat model with conversation memory and a Pinecone retrieval tool to search for relevant document chunks and answer strictly from the retrieved context.
- Returns the agent’s response to the webhook caller.
Setup
- Connect Google Drive credentials and set the folder to watch in the Google Drive trigger.
- Add an OpenAI API credential for both embedding generation and the chat models.
- Configure a Pinecone index and namespace (and provide Pinecone credentials) to match the workflow’s index name and namespace settings.
- Activate the workflow, copy the webhook URL for the POST endpoint, and configure your client app to send questions (and a session ID if you want memory) to that URL.
🔗 Nodes Used
Webhook, Google Drive, Google Drive Trigger, AI Agent, Embeddings OpenAI, OpenAI Chat Model
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File