Create personal data vector store from Google Sheets with OpenAI & Gemini AI
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.
What this workflow does
This AI workflow has 11 nodes and runs when a chat message arrives. Along the way it connects Google Gemini Chat Model, Google Sheets, Supabase Vector Store and Postgres Chat Memory and 1 more.
How it runs
Trigger: When a chat message arrives.
- AI Agent: runs an AI agent, with model: Google Gemini Chat Model; 1 tool (Supabase Vector Store); memory: Postgres Chat Memory
- Get row(s) in sheet (Google Sheets): uses Google Sheets
- Convert to File: converts data to a file
- Vector Store (Supabase Vector Store): stores documents in the Supabase vector store, with embeddings: Embeddings OpenAI; document loader: Default Data Loader
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (9)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - AI Agent
@n8n/n8n-nodes-langchain.agent - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Google Sheets
n8n-nodes-base.googleSheets - Supabase Vector Store
@n8n/n8n-nodes-langchain.vectorStoreSupabase - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Postgres Chat Memory
@n8n/n8n-nodes-langchain.memoryPostgresChat - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Convert to File
n8n-nodes-base.convertToFile
From the author
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…