Create personal data vector store from Google Sheets with OpenAI & Gemini AI

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

This workflow integrates Google Sheets with Supabase Vector Store for storing personal data as vectors. It utilizes OpenAI and Google Gemini AI models for enhanced data processing and querying. The...

Best for

Engineering automation workflows
Multimodal AI 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.lmchatgooglegemini, n8n-nodes-base.googlesheets, @n8n/n8n-nodes-langchain.vectorstoresupabase, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.memorypostgreschat, @n8n/n8n-nodes-langchain.embeddingsopenai

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Create personal data vector store from Google Sheets with OpenAI & Gemini AI
Workflow name: Create personal data vector store from Google Sheets with OpenAI & Gemini AI

This workflow integrates Google Sheets with Supabase Vector Store for storing personal data as vectors. It utilizes OpenAI and Google Gemini AI models for enhanced data processing and querying.

The workflow performs the following tasks:

Extracts personal data from Google Sheets.
Processes the data using AI tools like OpenAI and Google Gemini for intelligent insights.
Inserts the data into Supabase as vectors, enabling efficient storage and fast querying.
Includes seamless integration with Postgres for memory management.
Supports data loading, embedding, and management.

This template is ideal for:

Personal data storage with AI-driven querying and analysis.
Building intelligent agents that interact with your data.
Efficient vector-based storage for personal information.

Perfect for those looking to integrate AI into their personal data workflows.

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 1.1

Block 2 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2.1

Block 3 - Google Gemini Chat Model

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

Block 4 - Get row(s) in sheet1

Type / Role: n8n-nodes-base.googleSheets - googleSheets
Config choices: Version 4.6

Block 5 - Supabase Vector Store

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

Block 6 - Default Data Loader2

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

Block 7 - Postgres Chat Memory

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

Block 8 - Embeddings OpenAI

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices: Version 1.2

Block 9 - Embeddings OpenAI1

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices: Version 1.2

Block 10 - Convert to File

Type / Role: n8n-nodes-base.convertToFile - convertToFile
Config choices: Version 1.1

Block 11 - Sticky Note

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

Block 12 - Sticky Note1

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

Block 13 - Vector Store

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

3. Summary Table

Workflow	Create personal data vector store from Google Sheets with OpenAI & Gemini AI
Complexity	intermediate
Nodes	13
Categories	Engineering, Multimodal AI
Author	Mustafa Polat
Published	12 Aug 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/7299/7299.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 Engineering workflows Multimodal AI workflows

Frequently asked questions

What does Create personal data vector store from Google Sheets with OpenAI & Gemini AI do?

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 Engineering, Multimodal AI use case.

Mustafa Polat

1 workflow

Nodes

@n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatgooglegemini n8n-nodes-base.googlesheets @n8n/n8n-nodes-langchain.vectorstoresupabase @n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.memorypostgreschat @n8n/n8n-nodes-langchain.embeddingsopenai

Complexity

intermediate

Published 12 Aug 2025

Likes 0

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Install path: /data/workflows/7299/7299.json

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Create personal data vector store from Google Sheets with OpenAI & Gemini AI

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When chat message received

Block 2 - AI Agent

Block 3 - Google Gemini Chat Model

Block 4 - Get row(s) in sheet1

Block 5 - Supabase Vector Store

Block 6 - Default Data Loader2

Block 7 - Postgres Chat Memory

Block 8 - Embeddings OpenAI

Block 9 - Embeddings OpenAI1

Block 10 - Convert to File

Block 11 - Sticky Note

Block 12 - Sticky Note1

Block 13 - Vector Store

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