GSP1280

Overview

This lab delves into the Vertex AI Embeddings API, exploring its capabilities for both text and multimodal data (images and video). You'll gain a foundational understanding of embeddings, learning how they transform various content types into numerical representations that capture meaning and relationships. The lab then guides you through hands-on exercises with the Vertex AI Text and Multimodal Embeddings APIs, demonstrating their practical applications in building a simple search system for e-commerce data. You'll learn how to find products based on text queries, images, and even videos, showcasing the power of embeddings in enhancing search and recommendation systems.

Prerequisites

Before starting this lab, you should be familiar with:

• Basic Python programming.
• General API concepts.
• Running Python code in a Jupyter notebook on Vertex AI Workbench.

Objectives

In this lab, you explore:

• Vertex AI Text Embeddings API.
• Vertex AI Multimodal Embeddings API (Images & Video).
• Building simple search with e-commerce data
  • Find product based on text query.
  • Find product based on image.
  • Find Video based on video.

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab—remember, once you start, you cannot pause a lab.

Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details pane.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details pane.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Task 1. Open the notebook in Vertex AI Workbench

1. In the Google Cloud console, on the Navigation menu (), click Vertex AI > Workbench.

2. Find the instance and click on the Open JupyterLab button.

The JupyterLab interface for your Workbench instance opens in a new browser tab.

Task 2. Set up the notebook

1. Open the file.

2. In the Select Kernel dialog, choose Python 3 from the list of available kernels.

3. Run through the Getting Started and the Import libraries sections of the notebook.

   • For Project ID, use , and for Location, use .

Note: You can skip any notebook cells that are noted Colab only. If you experience a 429 response from any of the notebook cell executions, wait 1 minute before running the cell again to proceed.

Task 3. Generate Text Embeddings

In this section, you explore the Text Embeddings API of Gemini.

1. Run through the Generate Text Embeddings section of the notebook.

Click Check my progress to verify the objective. Get the length and first five elements of the text embedding.

Click Check my progress to verify the objective. Compare similarity of text examples using cosine similarity.

Task 4. Generate Image Embeddings

In this section, you explore the Multimodal Embedding API of Gemini.

1. Run through the Generate Image Embeddings section of the notebook.

Click Check my progress to verify the objective. Generate Image Embeddings.

Task 5. Find product based on text query

1. Run through the Find product based on text query section of the notebook.

Click Check my progress to verify the objective. Find product based on text query.

Task 6. Generate Video Embeddings

1. Run through the Generate Video Embeddings section of the notebook.

Click Check my progress to verify the objective. Generate Video Embeddings.

Task 7. Find videos based on text search query

1. Run through the Find videos based on text search query section of the notebook.

Click Check my progress to verify the objective. Find videos based on text search query.

Task 8. Find Similar videos

1. Run through the Find Similar videos section of the notebook.

Click Check my progress to verify the objective. Find Similar videos.

Congratulations!

In this lab, you learned how to use the Vertex AI Text and Multimodal Embeddings APIs to embed content.

Next steps / learn more

Check out the following resources to learn more about Gemini:

• Gemini Overview
• Generative AI on Vertex AI Documentation
• Generative AI on YouTube
• Explore the Vertex AI Cookbook for a curated, searchable gallery of notebooks for Generative AI.
• Explore other notebooks and samples in the Google Cloud Generative AI repository.

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Manual Last Updated May 27, 2025

Lab Last Tested May 27, 2025

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