Index Google Drive files into a Supabase vector store with OpenAI embeddings

📺 Full walkthrough video: https://youtu.be/r5kNla0O7I Author: Cole Medin Who it's for This workflow is for developers, data engineers, and knowledge management teams who need to automatically ingest documents stored in Google Drive into a searchable vector database — supporting RAG (retrieval-augmented generation) pipelines or semantic search applications. How it works One-time setup: A chat trigger runs SQL queries to create the required Postgres tables (documents, documentmetadata, documentrows) and the vector similarity match function in Supabase/Postgres. Trigger: Two Google Drive triggers detect newly created or updated files in a watched folder and pass them into a batch loop. Clean o

Help me set up the n8n workflow "Index Google Drive files into a Supabase vector store with OpenAI embeddings" (https://n8n.io/workflows/8916). It uses: Postgres, Google Drive, Supabase, Embeddings OpenAI, Character Text 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.