Notion to Pinecone Indexing via Gemini

# Automatically index new Notion pages into Pinecone using Google Gemini embeddings

This workflow turns Notion into a live knowledge base for AI applications by automatically converting content from new pages into vector embeddings and storing them in Pinecone. Designed for semantic search, support chatbots, and RAG systems.

## Who it´s for
- AI application developers using Notion as a knowledge source.
- Product managers automating documentation workflows in RAG systems.
- Teams implementing semantic search across internal documents.

## What the automation does
- Monitors a Notion database for newly added pages.
- Extracts text content from blocks, filtering out media (images, videos).
- Concatenates text and sends it to Google Gemini for vector embedding generation.
- Stores embeddings in Pinecone with metadata: page ID, title, creation time.
- Supports trigger-based or scheduled (cron) execution.

## What´s included
- Ready-to-use n8n workflow with LangChain pipeline.
- Trigger and content processing logic.
- Integrations with Notion, Google Gemini, and Pinecone.
- Basic setup and adaptation guide.

## Requirements for setup
- Notion account with API access.
- Google Cloud project with Gemini API enabled and API key.
- Pinecone account and created index.
- Running n8n instance.

## Benefits and outcomes
- Turn Notion into a dynamic AI knowledge base.
- Keep embeddings up to date with every new page.
- Simplify integration with RAG apps and assistants.
- Reduce manual document indexing efforts.
- Enable scalable semantic search across company docs.

## Important: template only
Important: you are purchasing a ready-made automation workflow template only. Rollout into your infrastructure, connecting specific accounts and services, 1:1 setup help, custom adjustments for non-standard stacks and any consulting support are provided as a separate paid service at an individual rate. To discuss custom work or 1:1 help, contact via chat