Automate Notion Page Embeddings to Pinecone Vector Store

The workflow begins by monitoring a specified Notion database for newly added pages. Upon detection, it retrieves the full content of these pages, filters out non-text elements, and concatenates the remaining text. The text is then split into manageable tokens and processed to create embeddings using the Google Gemini model. Finally, these embeddings, along with metadata, are stored in a Pinecone vector store, making the data searchable and ready for advanced applications like document retrieval and natural language processing.