Search hardware inventory with Supabase vector RAG and Google Gemini
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 11 nodes and runs manually, when you click “Execute workflow” (plus 1 other trigger). Along the way it connects Google Gemini Chat Model, Google Sheets, Supabase Vector Store and Embeddings Google Gemini.
How it runs
Trigger: Manually, when you click “Execute workflow” (plus 1 other trigger).
- Get row(s) in sheet (Google Sheets): uses Google Sheets
- AI Agent: runs an AI agent, with model: Google Gemini; 1 tool (Supabase Vector Store); memory: Simple Memory
- 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 (9)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - AI Agent
@n8n/n8n-nodes-langchain.agent - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Manual Trigger
n8n-nodes-base.manualTrigger - Google Sheets
n8n-nodes-base.googleSheets - Supabase Vector Store
@n8n/n8n-nodes-langchain.vectorStoreSupabase - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini
From the author
Advanced AI Inventory Agent: Supabase Vector RAG & Gemini This workflow upgrades your AI agent from simple sheet reading to high-performance Vector RAG. It allows your assistant to search through thousands of items with lightning speed and high accuracy.…