How to send data from Google Sheets to Pinecone Vector Store | n8n AI automation
Thank you to Obinna-ai for sharing this content request.
Some time ago, I explained how to automatically send data from Google Sheets to Supabase using Make. This setup enabled building a RAG (Retrieval Augmented Generation) chatbot that could pull data from a Supabase vector store - allowing the chatbot to access your own content rather than relying solely on general AI model knowledge.
While this approach works well, there are more efficient alternatives worth considering. In this post, I'll demonstrate a streamlined method using n8n to send data from Google Sheets directly to a Pinecone Index. While the initial setup is straightforward, optimizing the system for effective retrieval requires careful consideration and testing.
Tools involved
For setting up this automation, we are using the following tools:
Google Sheets — stores your content. Here is a video explaining how to send your Youtube videos to a Google Sheets automatically.
OpenAI— provides two key functions:
Generates Embeddings to convert text into AI-readable formats
Provides AI knowledge for answering questions
Note: While OpenAI is our example, you can use other providers through Flowise AI or similar tools. You'll need to create a developer account, get an API key, and ensure you have sufficient credit balance.
Pinecone — stores, organizes, and retrieves vector embeddings of your content.
n8n — automates sending data from Google Sheets to Pinecone.
The process
To get started, you'll need to create a new workflow in n8n. First, set up your n8n account if you don't have one already. You have two options for hosting: use n8n's cloud service, or self-host n8n. While self-hosting requires technical knowledge, it can significantly reduce costs compared to the cloud version.
After setting up your n8n account, you'll create a new workflow consisting of two main components and three AI-related supporting nodes. The workflow structure includes:
Two main nodes for core functionality
Three specialized AI nodes for processing
I'll explain each node in detail below, but you may also want to watch the walkthrough video for a step-by-step visual guide.
Google Sheets Trigger: This node initiates the workflow whenever a new row appears in your content spreadsheet. To set it up, you'll need to connect your Google account through a Google Cloud Console project. When adding a new Google Sheets connection in n8n, you'll see clear instructions for activating the connection in your Cloud Console project.
Pinecone Vector Store: This node creates a new vector in Pinecone whenever a new row appears in your Sheet. You'll need to specify two key parameters:
Target Index: Where your vectors will be stored
Namespace (optional): A way to organize your vectors into separate collections
Think of namespaces as folders within your Pinecone index. They're useful when you want your AI to search only specific parts of your data. If you don't create any namespaces, all vectors will be stored in a single default namespace. You can still organize and filter your data using metadata tags.
The Pinecone Vector Store node works alongside three essential supporting nodes in n8n:
2a. Embedding Node Uses OpenAI Embeddings with the small embeddings model to convert your Google Sheets content into AI-readable vector formats.
2b. Document Node Utilizes the Default Data Loader to prepare content for Pinecone, including important metadata (like title, URL, and publication date) to make future content retrieval easier.
2c. Text Splitter Node Handles long content by breaking it into smaller, manageable chunks using a Recursive Character Text Splitter. This prevents token limit errors and ensures smooth processing. You'll need to adjust the chunk size and overlap based on your specific content needs - learn more about optimal chunk sizing here.
The end product
As shown in the screenshot above, this completes our workflow setup. Before deploying, you can use the test button to verify everything works correctly. Once your content is successfully stored in the Pinecone vector store, you're ready for the next step: creating your AI agent. You can build a chatflow to retrieve these embeddings using tools like Flowise AI - check out my detailed guide on this topic.
Want to enhance your workflow and life with a custom AI agent? Contact me for a chat or personalized consultation about your specific needs.
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