Store Notion's Pages as Vector Documents into Supabase with OpenAI

Workflow updated on 17/06/2024: Added 'Summarize' node to avoid creating a row for each Notion content block in the Supabase table.

Store Notion's Pages as Vector Documents into Supabase

This workflow assumes you have a Supabase project with a table that has a vector column. If you don't have it, follow the instructions here: Supabase Langchain Guide

Workflow Description

This workflow automates the process of storing Notion pages as vector documents in a Supabase database with a vector column. The steps are as follows:

1. Notion Page Added Trigger:

   • Monitors a specified Notion database for newly added pages. You can create a specific Notion database where you copy the pages you want to store in Supabase.
   • Node: Page Added in Notion Database

2. Retrieve Page Content:

   • Fetches all block content from the newly added Notion page.
   • Node: Get Blocks Content

3. Filter Non-Text Content:

   • Excludes blocks of type "image" and "video" to focus on textual content.
   • Node: Filter - Exclude Media Content

4. Summarize Content:

   • Concatenates the Notion blocks content to create a single text for embedding.
   • Node: Summarize - Concatenate Notion's blocks content

5. Store in Supabase:

   • Stores the processed documents and their embeddings into a Supabase table with a vector column.
   • Node: Store Documents in Supabase

6. Generate Embeddings:

   • Utilizes OpenAI's API to generate embeddings for the textual content.
   • Node: Generate Text Embeddings

7. Create Metadata and Load Content:

   • Loads the block content and creates associated metadata, such as page ID and block ID.
   • Node: Load Block Content & Create Metadata

8. Split Content into Chunks:

   • Divides the text into smaller chunks for easier processing and embedding generation.
   • Node: Token Splitter