Seamlessly Convert Notion Pages into Powerful Vector Embeddings
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Automate the process of transforming your Notion content into high-dimensional vector embeddings. This workflow intelligently extracts text, filters out media, and loads it into Pinecone using Google Gemini's embedding capabilities.
About This Workflow
This n8n workflow automates the crucial step of preparing your knowledge base for AI applications. It triggers whenever a new page is added to a specified Notion database, then retrieves and cleans the content. Textual information is then segmented into manageable chunks, and enriched with relevant metadata like page ID, creation time, and title. Finally, Google Gemini generates vector embeddings for this processed content, which are then efficiently stored in a Pinecone vector database, making your Notion data readily searchable and usable for advanced AI tasks.
Key Features
- Real-time Notion Integration: Automatically detects and processes new Notion pages.
- Intelligent Content Extraction: Retrieves and consolidates text from Notion blocks.
- Smart Filtering: Excludes non-textual content like images and videos.
- Metadata Enrichment: Augments data with essential page identifiers and timestamps.
- Scalable Vector Storage: Leverages Pinecone for efficient and fast vector data management.
- Powerful Embeddings: Utilizes Google Gemini for high-quality text embeddings.
How To Use
- Configure Notion Trigger: Connect your Notion account and specify the database ID to monitor for new pages.
- Retrieve Page Content: Ensure the 'Notion - Retrieve Page Content' node is correctly set to fetch all blocks from the triggered page.
- Filter Content: Customize the 'Filter Non-Text Content' node to exclude any unwanted media types.
- Summarize Content: Configure the 'Summarize - Concatenate Notion's blocks content' node to combine extracted text into a single field.
- Generate Embeddings: Set up the 'Embeddings Google Gemini' node with your Google Cloud credentials and select the desired model.
- Load to Vector Store: Configure the 'Create metadata and load content' node to include desired metadata fields and select the appropriate JSON mode. Then, connect this to the 'Pinecone Vector Store' node, specifying your Pinecone index name and API credentials.
Apps Used
Workflow JSON
{
"id": "c5978688-733b-49d8-82ad-021aff4765b6",
"name": "Seamlessly Convert Notion Pages into Powerful Vector Embeddings",
"nodes": 8,
"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: c5978688-733b...
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