GSP1277

Overview

This lab will guide you through the process of directly analyzing publicly available YouTube videos with Gemini. You'll begin by using Gemini to generate concise summaries of individual videos. Subsequently, you'll delve into extracting specific, structured outputs from longer videos using Gemini and controlled generation techniques. Finally, you'll learn how to leverage asynchronous generation with Gemini to synthesize insights by analyzing multiple YouTube videos concurrently.

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 will learn how to:

• Summarize a single YouTube video using Gemini
• Extract a specific set of structured outputs from a longer YouTube video using Gemini and controlled generation
• Create insights from analyzing multiple YouTube videos together using asynchronous generation with Gemini

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.

Note: If you do not see notebooks in JupyterLab, please follow these additional steps to reset the instance:

1. Close the browser tab for JupyterLab, and return to the Workbench home page.

2. Select the checkbox next to the instance name, and click Reset.

3. After the Open JupyterLab button is enabled again, wait one minute, and then click Open JupyterLab.

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.

Click Check my progress to verify the objective. Install packages and import libraries

Task 3. Summarize a YouTube video

In this section, you will link to a public YouTube video that you'd like to summarize. Ensure that the video is less than an hour long (if using Gemini 2.0 Flash, as is shown below; can try up to a 2-hour video with Gemini 2.5 Pro) to make sure it fits in the context window.

The default content to be summarized is this 6.5-minute video showing how Major League Baseball (MLB) analyzes data using Google Cloud.

1. Run through the Summarize a YouTube video section of the notebook.

Click Check my progress to verify the objective. Summarize a YouTube video

Task 4. Extract structured output from a YouTube video

Next, you'll learn how to extract structured outputs using controlled generation, in this case from a video that covers multiple topics. You'll see how Gemini 2.0 Flash's industry-leading 1 million token context window can help analyze the full opening keynote from our Next conference back in April - all 1 hour and 41 minutes of it!

1. Run through the Extract structured output from a YouTube video section of the notebook.

Click Check my progress to verify the objective. Extract structured output from a YouTube video

Task 5. Creating insights from analyzing multiple YouTube videos together

Now, consider expanding the problem to a more common enterprise use case: extracting information from multiple YouTube videos at once.

This time, you'll use Google's "Year in Search" videos, which summarize the questions, people, and moments that captured the world's attention in each year. As of fall 2024, there are 14 of these videos, each 2-4 minutes in length, from 2010 through 2023.

1. Run through the Creating insights from analyzing multiple YouTube videos together section of the notebook.

Click Check my progress to verify the objective. Creating insights from analyzing multiple YouTube videos together

Congratulations!

Congratulations! In this lab, you've learned how to directly analyze YouTube videos using Gemini. You've successfully summarized videos, extracted structured data from longer videos, and gained experience in analyzing multiple videos concurrently with asynchronous generation. This demonstrates Gemini's capability to process and understand video content, enabling powerful analysis and insight extraction.

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 23, 2025

Lab Last Tested May 22, 2025

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