Upsert huge documents in a vector store with Supabase and Notion

Purpose This workflow adds the capability to build a RAG on living data. In this case Notion is used as a Knowledge Base. Whenever a page is updated, the embeddings get upserted in a Supabase Vector Store. It can also be fairly easily adapted to PGVector, Pinecone, or Qdrant by using a custom HTTP request for the latter two. Demo []( How it works - A trigger checks every minute for changes in the Notion Database. The manual polling approach improves accuracy and prevents changes from being lost between cached polling intervals. - Afterwards every updated page is processed sequentially - The Vector Database is searched using the Notion Page ID stored in the metadata of each embedding. If old entries exist, they are deleted. - All blocks of the Notion Database Page are retrieved and combined into a single string - The content is embedded and split into chunks if necessary. Metadata, including the Notion Page ID, is added during storage for future reference. - A simple Question and Answer Chain enables users to ask questions about the embedded content through the integrated chat function Prerequisites - To setup a new Vector Store in Supabase, follow this guide - Prepare a simple Database in Notion with each Database Page containing at least a title and some content in the blocks section. You can of course also connect this to an existing Database of your choice. Setup - Select your credentials in the nodes which require those - If you are on an n8n cloud plan, switch to the native Notion Trigger by activating it and deactivating the Schedule Trigger along with its subsequent Notion Node - Choose your Notion Database in the first Node related to Notion - Adjust the chunk size and overlap in the Token Splitter to your preference - Activate the workflow How to use Populate your Notion Database with useful information and use the chat mode of this workflow to ask questions about it. Updates to a Notion Page should quickly reflect in future conversations.