Convert Notion pages to vector embeddings and store in Supabase
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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
Details
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 (8)
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
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