Build a RAG knowledge assistant with Google Drive, OpenAI, and Pinecone
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 20 nodes and runs on Google Drive events (file updated) (plus 1 other trigger). Along the way it connects Google Drive, Embeddings OpenAI, Pinecone Vector Store and OpenAI Chat Model and 1 more.
How it runs
Trigger: On Google Drive events (file updated) (plus 1 other trigger).
- Extract File Metadata (Edit Fields (Set)): sets 7 fields: fileId, fileName, mimeType…
- Validate & Normalize Query (Code): runs custom JavaScript code
- Validate File (If): checks a condition and branches into true / false
- Query Valid? (If): checks a condition and branches into true / false
- Download File from Drive (Google Drive): download
- AI Agent: runs an AI agent, with model: Primary LLM (Agent Brain); 1 tool (Vector Search QA Tool); memory: Conversation Memory Buffer
- Respond to Webhook: sends the response back to the webhook caller
- Ingest into Pinecone Vector DB (Pinecone Vector Store): stores documents in the Pinecone vector store, with embeddings: Generate Embeddings (Ingest); document loader: Parse Document Content
- Format RAG Response (Edit Fields (Set)): sets 5 fields: success, question, answer…
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (15)
- AI Agent
@n8n/n8n-nodes-langchain.agent - Respond to Webhook
n8n-nodes-base.respondToWebhook - Google Drive Trigger
n8n-nodes-base.googleDriveTrigger - Edit Fields (Set)
n8n-nodes-base.set - Google Drive
n8n-nodes-base.googleDrive - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Pinecone Vector Store
@n8n/n8n-nodes-langchain.vectorStorePinecone - Webhook
n8n-nodes-base.webhook - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Vector Store Question Answer Tool
@n8n/n8n-nodes-langchain.toolVectorStore - If
n8n-nodes-base.if - Code
n8n-nodes-base.code
From the author
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.…