This workflow automates the process of extracting, summarizing, and embedding Notion page content into a Pinecone vector database for efficient retrieval and search. Triggered when a new Notion page is added, it retrieves the page content, filters out non-text content, summarizes the text, generates embeddings using Google Gemini, and stores the embedded vectors in Pinecone. This setup is ideal for creating a searchable knowledge base or enhancing AI-powered content retrieval systems.

Node Count	6 – 10 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.documentDefaultDataLoader, @n8n/n8n-nodes-langchain.embeddingsGoogleGemini, @n8n/n8n-nodes-langchain.textSplitterTokenSplitter, @n8n/n8n-nodes-langchain.vectorStorePinecone, filter, notion, notionTrigger, summarize