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.
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
What it does
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.