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

Workflow Name

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

Key Features and Highlights

This workflow automates the entire process of extracting and processing newly added pages in Notion and storing them as vectorized documents in a Supabase database. By leveraging OpenAI to generate semantic vector embeddings of the text, it enables smarter content retrieval and analysis. Non-textual content such as images and videos is filtered out during the process, focusing solely on textual information to enhance data quality and search efficiency.

Core Problems Addressed

• Automatically synchronize newly added pages in Notion, eliminating the need for manual export and organization.
• Use OpenAI-generated vector embeddings to overcome the limitations of traditional text storage, enabling semantic search and intelligent analysis.
• Filter out non-text content to ensure data purity and consistency.
• Segment content into appropriately sized chunks to optimize vectorization effectiveness.

Use Cases

• Enterprises or teams using Notion for knowledge management who need to quickly convert content into a database format suitable for semantic search.
• Building intelligent Q&A systems, recommendation engines, or content aggregation platforms based on vector databases.
• Efficiently storing unstructured document content for subsequent AI-driven analysis and retrieval.

Main Workflow Steps

1. Trigger on New Notion Page: Monitor a specified Notion database for newly added pages.
2. Content Retrieval: Fetch all content blocks from the page.
3. Filter Non-Text Content: Exclude media blocks such as images and videos, retaining only text blocks.
4. Content Aggregation: Merge text blocks into a single continuous text segment.
5. Text Chunking: Split the merged long text into smaller fragments for easier processing.
6. Generate Text Vector Embeddings: Use the OpenAI API to create semantic vector embeddings of the text.
7. Generate Metadata: Extract page ID, creation time, title, and other relevant metadata for the document.
8. Store in Supabase Vector Database: Save the text content, its vector embeddings, and metadata into the corresponding Supabase table.

Involved Systems and Services

• Notion: Content source for monitoring and retrieving newly added pages and their contents.
• OpenAI: Responsible for generating semantic vector embeddings of the text.
• Supabase: Vector storage database for persistent storage of vectors and associated document data.
• n8n Automation Platform: Orchestrates and automates the above workflow steps through node-based execution.

Target Users and Value

• Content managers, knowledge base administrators, and data analysts who want to automate synchronization and structured management of Notion documents.
• AI developers and product managers looking to rapidly build intelligent retrieval or recommendation systems based on text vectors.
• Team collaboration users aiming to enhance information utilization efficiency and enable smarter content queries and knowledge discovery.

This workflow significantly simplifies the conversion of Notion content into an intelligent vector database, empowering organizations and individuals to efficiently manage and leverage their knowledge assets.