Search hardware inventory with Supabase vector RAG and Google Gemini

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1. Workflow Overview

Advanced AI Inventory Agent: Supabase Vector RAG & Gemini This workflow upgrades your AI agent from simple sheet reading to hi...

Best for

Support Chatbot automation workflows
AI RAG automation workflows
intermediate n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.memorybufferwindow, @n8n/n8n-nodes-langchain.lmchatgooglegemini, n8n-nodes-base.stickynote, n8n-nodes-base.manualtrigger, n8n-nodes-base.googlesheets, @n8n/n8n-nodes-langchain.vectorstoresupabase

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Viktor Klepikovskyi.

Original n8n.io source

1.1 Workflow description

Title: Search hardware inventory with Supabase vector RAG and Google Gemini
Workflow name: 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 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.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - When chat message received

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.4

Block 2 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3.1

Block 3 - Simple Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 4 - Google Gemini

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 5 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 6 - When clicking ‘Execute workflow’

Type / Role: n8n-nodes-base.manualTrigger - manualTrigger
Config choices: Version 1

Block 7 - Get row(s) in sheet

Type / Role: n8n-nodes-base.googleSheets - googleSheets
Config choices: Version 4.7

Block 8 - Supabase Vector Store

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreSupabase - vectorStoreSupabase
Config choices: Version 1.3

Block 9 - Default Data Loader

Type / Role: @n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices: Version 1.1

Block 10 - Embeddings Google Gemini

Type / Role: @n8n/n8n-nodes-langchain.embeddingsGoogleGemini - embeddingsGoogleGemini
Config choices: Version 1

Block 11 - Supabase Vector Store1

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreSupabase - vectorStoreSupabase
Config choices: Version 1.3

Block 12 - Embeddings Google Gemini1

Type / Role: @n8n/n8n-nodes-langchain.embeddingsGoogleGemini - embeddingsGoogleGemini
Config choices: Version 1

3. Summary Table

Workflow	Search hardware inventory with Supabase vector RAG and Google Gemini
Complexity	intermediate
Nodes	12
Categories	Support Chatbot, AI RAG
Author	Viktor Klepikovskyi
Published	15 Feb 2026

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/13410/13410.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Support Chatbot workflows AI RAG workflows

Frequently asked questions

What does Search hardware inventory with Supabase vector RAG and Google Gemini do?

Advanced AI Inventory Agent: Supabase Vector RAG & Gemini This workflow upgrades your AI agent from simple sheet reading to hi...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Support Chatbot, AI RAG use case.

Viktor Klepikovskyi

15 workflows

Nodes

@n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.memorybufferwindow @n8n/n8n-nodes-langchain.lmchatgooglegemini n8n-nodes-base.stickynote n8n-nodes-base.manualtrigger n8n-nodes-base.googlesheets @n8n/n8n-nodes-langchain.vectorstoresupabase

Complexity

intermediate

Published 15 Feb 2026

Likes 0

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Install path: /data/workflows/13410/13410.json

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Search hardware inventory with Supabase vector RAG and Google Gemini

Workflow preview

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Advanced AI Inventory Agent: Supabase Vector RAG & Gemini

Purpose:

Setup Instructions:

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When chat message received

Block 2 - AI Agent

Block 3 - Simple Memory

Block 4 - Google Gemini

Block 5 - Sticky Note

Block 6 - When clicking ‘Execute workflow’

Block 7 - Get row(s) in sheet

Block 8 - Supabase Vector Store

Block 9 - Default Data Loader

Block 10 - Embeddings Google Gemini

Block 11 - Supabase Vector Store1

Block 12 - Embeddings Google Gemini1

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions