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