Who is this workflow for? Automate the process of converting Notion pages into vector documents and store them seamlessly in a Supabase database using OpenAI. This workflow enhances data management by leveraging vector embeddings for efficient search and retrieval..

What does this workflow do?

This workflow automates the conversion of Notion pages into vector documents stored in Supabase. Below are the detailed steps:

  • Notion Page Added Trigger
  • Description: Monitors a specified Notion database for newly added pages.
  • Node: Page Added in Notion Database
  • Action: Alerts the workflow when a new page is introduced in the designated Notion database.
  • Retrieve Page Content
  • Description: Fetches all block content from the newly added Notion page.
  • Node: Get Blocks Content
  • Action: Gathers the textual and media blocks contained within the page.
  • Filter Non-Text Content
  • Description: Excludes blocks of type “image” and “video” to focus solely on textual data.
  • Node: Filter - Exclude Media Content
  • Action: Filters out media blocks, ensuring only text is processed further.
  • Summarize Content
  • Description: Concatenates the textual blocks into a single text for embedding.
  • Node: Summarize - Concatenate Notion's Blocks Content
  • Action: Combines all text blocks to form a comprehensive summary suitable for embedding.
  • Generate Embeddings
  • Description: Utilizes OpenAI’s API to create vector embeddings of the summarized text.
  • Node: Generate Text Embeddings
  • Action: Transforms the summarized content into vector format for efficient storage and retrieval.
  • Store in Supabase
  • Description: Saves the processed documents and their embeddings into a Supabase table with a vector column.
  • Node: Store Documents in Supabase
  • Action: Inserts the vector embeddings along with relevant metadata into the Supabase database.
  • Create Metadata and Load Content
  • Description: Generates metadata such as page ID and block ID, and loads the block content.
  • Node: Load Block Content & Create Metadata
  • Action: Associates each vector embedding with its corresponding metadata for easy reference.
  • Split Content into Chunks
  • Description: Divides the text into smaller segments to facilitate easier processing and embedding generation.
  • Node: Token Splitter
  • Action: Ensures that large texts are manageable and optimized for embedding.

🤖 Why Use This Automation Workflow?

  • Streamlined Data Storage: Automatically transform and store Notion content without manual intervention.
  • Enhanced Searchability: Utilize vector embeddings to enable sophisticated search capabilities within your Supabase database.
  • Scalability: Handle large volumes of Notion pages efficiently by summarizing content and avoiding redundant data entries.

👨‍💻 Who is This Workflow For?

This workflow is ideal for developers, data analysts, and teams who use Notion for content management and require advanced search functionalities. It is also suitable for organizations leveraging Supabase for their database needs and seeking to integrate AI-driven data processing.

🎯 Use Cases

  1. Knowledge Bases: Create searchable repositories of internal documentation stored in Notion.
  2. Content Management Systems: Enhance CMS platforms with vector search capabilities for better content discovery.
  3. Research Databases: Organize and search academic papers or research notes efficiently.

TL;DR

This workflow seamlessly integrates Notion, Supabase, and OpenAI to transform and store Notion pages as vector documents. By automating content retrieval, summarization, embedding generation, and storage, it provides a robust solution for enhancing data accessibility and searchability within your applications.

Integrations: GitHub, HTTP Request, Merge, AI Models (OpenAI, Anthropic, Gemini, OpenRouter), SerpAPI, IMDB, AI Agent, Markdown, WhatsApp, Telegram, Google Drive

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