RAG on Living Notion Data with OpenAI

Overview

This workflow automates the process of creating a searchable knowledge base from your Notion pages. It leverages Retrieval Augmented Generation (RAG) to allow users to ask questions and receive answers based on the content within your Notion documents. The process involves fetching Notion page blocks, splitting them into manageable chunks, generating embeddings for each chunk using OpenAI, and storing these embeddings in a vector database (Supabase is used in this example). When a question is received, the workflow retrieves relevant chunks from the vector store, passes them along with the question to an OpenAI language model, and generates a concise answer.

This workflow is particularly useful for teams that rely heavily on Notion for documentation, internal knowledge bases, or project management, as it makes the information more accessible and actionable through natural language queries.