Automating Personal Data Management with Vector Store

This n8n workflow automates the process of managing and utilizing personal data through vector storage and AI-driven interactions. It begins with a webhook trigger that receives chat messages, which are then processed by a language model (Google Gemini or OpenAI) to generate responses. The workflow integrates Google Sheets to retrieve specific personal details, which are then converted into a format suitable for storage.

The core functionality revolves around storing and retrieving personal data in a vector database hosted on Supabase, enabling quick similarity searches and data retrieval. The AI agent interacts with this stored data, providing personalized responses based on data stored in the vector database. The process also includes managing conversation memory in Postgres, aiding in context-aware interactions.

Practical uses of this workflow include building AI chatbots capable of providing personalized assistance, automating customer support for personal data, or managing user information efficiently within a secure vector storage system. This setup leverages multiple services—Google Sheets, Supabase, OpenAI, and Google Gemini—to create a robust, intelligent data handling pipeline.