AI Agents
Convert Notion pages to vector embeddings and store in Supabase
9 nodes
316
128
Automatic trigger
Workflow Description
Automates extraction of Notion pages, splits content into manageable chunks, generates vector embeddings using OpenAI, and stores them in Supabase for semantic search and AI-powered retrieval without manual data pipelines.
How it works
- 1.Trigger on Notion page creation or update
- 2.Load and retrieve full page content from Notion
- 3.Split text into chunks using token-based segmentation
- 4.Generate vector embeddings via OpenAI API
- 5.Store embeddings and original text in Supabase vector store
Use cases
- Build semantic search over company documentation and internal wikis
- Create retrieval-augmented generation (RAG) systems powered by your knowledge base
- Automate indexing of constantly updated Notion workspaces for AI assistants
Requirements
- Notion workspace with database access via API key
- OpenAI API key with sufficient credits
- Supabase project configured 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
316
Downloads
128
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