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.
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
What it does
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. Purpose: To provide a scalable, professional-grade retrieval system for hardware inventory. It uses "semantic search" to find products even when the user makes typos or uses different terminology. Setup Instructions: 1. Supabase: Run the provided SQL to create the documents table and the matchdocuments function. 2. Credentials: Connect your Supabase (Service Role Key) and Google Gemini API credentials. 3. Sync Workflow: Run the "Path A" workflow to index your Google Sheets data into the vector database. 4. Chat Workflow: Use the "Path B" workflow as your production chat interface. 5. Prompt: Customize the System Prompt to define your brand's specific tone and sales rules. Ideal for: Large product catalogs (100+ items), technical hardware inventories, and high-traffic customer support bots. To learn more about how to build and optimize this workflow, read the full blog post here.