AI Agents
Store Notion pages as vector documents in Supabase using OpenAI
9 nodes
216
110
Automatic trigger
Workflow Description
Automation extracts Notion pages, splits them into text segments, generates vector embeddings using OpenAI, and stores them in Supabase's vector database to enable semantic search and intelligent document retrieval.
How it works
- 1.Trigger automation when a page is added or updated in Notion
- 2.Load page content and apply default data processing
- 3.Split text into manageable chunks based on token count
- 4.Generate embeddings using OpenAI's embedding model
- 5.Store embeddings with original text in Supabase vector store
Use cases
- Build an intelligent internal search engine for organizational documents and notes
- Create a Q&A system powered by your Notion knowledge base
Requirements
- Active Notion account with page access permissions
- OpenAI API key with embedding model enabled
- Supabase database with vector table configured
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
110
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