AI Agents
Store Notion pages as vector documents in Supabase using OpenAI
9 nodes
216
127
Automatic trigger
Workflow Description
Automatically convert Notion pages into vector embeddings using OpenAI and store them in Supabase, enabling semantic search and intelligent document retrieval for enterprise knowledge management.
How it works
- 1.Trigger workflow when a new page is added to Notion
- 2.Extract and split page content into manageable text chunks
- 3.Generate embeddings using OpenAI's embedding models
- 4.Filter and summarize relevant documents automatically
- 5.Store vectors and metadata in Supabase for retrieval
Use cases
- Build intelligent search systems for internal Notion documentation
- Create searchable knowledge bases with semantic understanding
- Enable AI agents to access and reference company documents dynamically
Requirements
- Active Notion workspace with API access configured
- OpenAI API key with embedding model permissions
- Supabase instance with vector storage table created
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
216
Downloads
127
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