Seamlessly Transform Notion Pages into Searchable Vector Data in Supabase
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Automate the process of capturing Notion pages and storing them as searchable vector documents within your Supabase database. Leverage OpenAI embeddings to unlock powerful AI-driven insights and retrieval capabilities for your knowledge base.
About This Workflow
This n8n workflow revolutionizes how you manage and leverage information from Notion. It automatically detects new pages added to a specified Notion database. The content of these pages is then extracted, cleaned to exclude media, and summarized. Using OpenAI's powerful embedding models, the textual content is transformed into vector representations. Finally, these vectors, along with relevant metadata like page and block IDs, are efficiently stored in your Supabase database, specifically within a table equipped with a vector column. This setup creates a robust and searchable knowledge base powered by AI, ready for advanced querying and analysis.
Key Features
- Automated Notion Monitoring: Automatically triggers on new page additions in your Notion database.
- Intelligent Content Extraction: Fetches and processes all relevant textual content from Notion pages.
- AI-Powered Embeddings: Leverages OpenAI to generate high-quality vector embeddings for semantic understanding.
- Supabase Vector Storage: Seamlessly integrates with Supabase's vector column capabilities for efficient storage and retrieval.
- Customizable Data Processing: Filters out non-textual content and chunks text for optimal embedding generation.
How To Use
- Prerequisites: Ensure you have a Supabase project with a table featuring a vector column. Refer to the Supabase Vector Columns Guide for setup instructions.
- Configure Notion Trigger: Set up the
Notion - Page Added Triggernode to monitor your desired Notion database. - Retrieve Page Content: Connect the trigger to the
Notion - Retrieve Page Contentnode to fetch all block content. - Filter Content: Utilize the
Filter Non-Text Contentnode to exclude image and video blocks. - Concatenate Text: Employ the
Summarize - Concatenate Notion's blocks contentnode to combine all text blocks into a single string. - Generate Embeddings: Connect the concatenated text to the
Embeddings OpenAInode to create vector representations. - Chunk and Load: Use the
Token Splitterto segment the text and theCreate metadata and load contentnode to add metadata (page ID, title, etc.). - Store in Supabase: Finally, connect the processed data to the
Supabase Vector Storenode to insert the documents and their embeddings into your Supabase table.
Apps Used
Workflow JSON
{
"id": "df8e9e5a-b7c7-476a-a904-84ce9b231c02",
"name": "Seamlessly Transform Notion Pages into Searchable Vector Data in Supabase",
"nodes": 6,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: df8e9e5a-b7c7...
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