Search hardware inventory with Supabase vector RAG and Google Gemini
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Viktor Klepikovskyi
Description
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:
Supabase: Run the provided SQL to create the documents table and the match_documents function.
Credentials: Connect your Supabase (Service Role Key) and Google Gemini API credentials.
Sync Workflow: Run the "Path A" workflow to index your Google Sheets data into the vector database.
Chat Workflow: Use the "Path B" workflow as your production chat interface.
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.
This workflow was created by Viktor Klepikovskyi and shared publicly on the n8n community template library. n8nFind indexes it for discovery — the workflow remains the property of its original creator.