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 ex

Help me set up the n8n workflow "Upsert huge documents in a vector store with Supabase and Notion" (https://n8n.io/workflows/2568). It uses: Notion, Supabase, Question and Answer Chain, Embeddings OpenAI, OpenAI Chat Model, Vector Store Retriever, Token Splitter, Supabase Vector Store, Default Data Loader. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.