Create RAG vector database from Google Drive documents using Gemini & Supabase
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 9 nodes and runs when another workflow calls it. Along the way it connects Embeddings Google Gemini, Postgres, Google Drive and Supabase Vector Store.
How it runs
Trigger: When another workflow calls it.
- Code: runs custom JavaScript code
- Execute a SQL query (Postgres): run a query
- Search files and folders (Google Drive): work with file folder
- Loop Over Items (Loop Over Items (Split in Batches)): loops over the items in batches
- Download File (Google Drive): download
- Insert into Supabase Vectorstore (Supabase Vector Store): stores documents in the Supabase vector store, with embeddings: Embeddings Google Gemini; document loader: Default Data Loader
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 (8)
- Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Postgres
n8n-nodes-base.postgres - Code
n8n-nodes-base.code - Execute Workflow Trigger
n8n-nodes-base.executeWorkflowTrigger - Loop Over Items (Split in Batches)
n8n-nodes-base.splitInBatches - Google Drive
n8n-nodes-base.googleDrive - Supabase Vector Store
@n8n/n8n-nodes-langchain.vectorStoreSupabase
From the author
How it works This workflow automates the process of converting Google Drive documents into searchable vector embeddings for AI-powered applications: • Takes a Google Drive folder URL as input • Initializes a Supabase vector database with pgvector extension •…