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

This workflow automatically vectorizes the content of pages in Notion and stores it in the Supabase database. By utilizing OpenAI to generate text embeddings, it intelligently processes page content to ensure efficient text indexing and semantic search. This system is suitable for content managers, developers, and enterprise teams looking to enhance document retrieval efficiency, enabling intelligent and convenient knowledge management.

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

Workflow Name

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

Key Features and Highlights

This workflow automates the conversion of Notion page content into vector documents and stores them in the vector column of a Supabase database. It leverages OpenAI to generate text embeddings and performs intelligent chunking and summarization of Notion page text content, ensuring efficient storage and subsequent retrieval of vectorized data.

Core Problems Addressed

Traditional document management systems struggle with intelligent retrieval and analysis of unstructured text. By vectorizing Notion page content, this workflow solves the challenges of efficient indexing and semantic search of textual data while excluding interference from non-text content such as images and videos, making knowledge management smarter and more convenient.

Use Cases

  • Enterprises or individuals who want to convert Notion documents in their knowledge base into searchable and analyzable vector data.
  • Building intelligent Q&A systems, recommendation engines, or similar content retrieval based on text content.
  • Unified management and fast access of document content by integrating Supabase as the backend database.

Main Process Steps

  1. Notion Page Creation Trigger: Real-time monitoring of newly added pages in a specified Notion database.
  2. Retrieve Page Content: Fetch all block contents of the page.
  3. Filter Non-Text Content: Remove multimedia blocks such as images and videos, retaining only text content.
  4. Content Aggregation: Merge all text blocks into a single continuous text.
  5. Content Chunking: Split long text into multiple smaller chunks suitable for vector generation.
  6. Generate Text Vectors: Call OpenAI API to generate vector embeddings for the text.
  7. Create Metadata: Attach metadata such as page ID, creation time, and title to each text chunk.
  8. Store in Supabase: Insert the vectorized documents and metadata into Supabase’s vector column.

Involved Systems or Services

  • Notion: Data source providing document page content.
  • OpenAI: Generates text vector embeddings supporting semantic understanding.
  • Supabase: Serves as the vector database for storing and managing vector documents.
  • n8n Automation Platform: Orchestrates the entire workflow for seamless automation.

Target Users and Value

  • Content managers and knowledge management professionals aiming to improve document retrieval efficiency.
  • Developers and data scientists building semantic search or recommendation systems.
  • Internal enterprise teams implementing intelligent archiving and fast access to document content.
  • Any users needing to vectorize structured document content to enable intelligent applications.

By automating the integration of Notion, OpenAI, and Supabase, this workflow significantly simplifies the process of vectorized text content storage and is an ideal solution for building intelligent document management and semantic search systems.