Search hardware inventory with Supabase vector RAG and Google Gemini

Advanced AI Inventory Agent: Supabase Vector RAG & Gemini

This workflow upgrades your AI agent from [simple sheet reading](https://n8nplaybook.com/post/2026/02/simple-n8n-inventory-ai-agent/) 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](https://n8nplaybook.com/post/2026/02/scaling-n8n-inventory-ai-agent-supabase-vector-rag/).