AI Agents
Convert Notion pages to vector embeddings in Supabase
9 nodes
304
147
Automatic trigger
Workflow Description
Automation that extracts Notion pages, splits content into manageable text chunks, generates vector embeddings using OpenAI, and stores them in Supabase for semantic search and rapid retrieval of information.
How it works
- 1.Trigger automation when a Notion page is created or updated
- 2.Split page content into text chunks for processing
- 3.Generate vector embeddings for chunks using OpenAI models
- 4.Store embeddings and metadata in Supabase vector database
Use cases
- Build semantic search engine for Notion documentation and knowledge bases
- Enable AI-powered recommendations based on content similarity
- Archive organizational knowledge with instant retrieval capabilities
Requirements
- Valid OpenAI API key with embedding model access
- Notion workspace with permissions to read target pages
- Supabase project with pgvector extension enabled
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
OpenAI
Text Splitter
Notion
Document Default Data Loader
Vector Store
Details
Trigger
Automatic trigger
Nodes
9
Apps
5
Views
304
Downloads
147
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (9)
Sticky Note
Sticky Note
Embeddings OpenAI
OpenAI
Token Splitter
Text Splitter Token Splitter
Notion - Page Added Trigger
Notion
Notion - Retrieve Page Content
Notion
Filter Non-Text Content
Filter
Summarize - Concatenate Notion's blocks content
Summarize
Create metadata and load content
Document Default Data Loader
Supabase Vector Store
Vector Store Supabase