GSP1210

Overview

This lab introduces you to Gemini, a family of multimodal generative AI models developed by Google. You'll use the Gemini API to explore how Gemini Flash can understand and generate responses based on text, images, and video.

Gemini's multimodal capabilities enable it to:

• Analyze images: Detect objects, understand user interfaces, interpret diagrams, and compare visual similarities and differences.
• Process videos: Generate descriptions, extract tags and highlights, and answer questions about video content.

You'll experiment with these features through hands-on tasks using the Gemini API in Vertex AI.

Gemini

Gemini is a family of powerful generative AI models developed by Google DeepMind, capable of understanding and generating various forms of content, including text, code, images, audio, and video.

Gemini API in Vertex AI

The Gemini API in Vertex AI provides a unified interface for interacting with Gemini models. This allows developers to easily integrate these powerful AI capabilities into their applications. For the most up-to-date details and specific features of the latest versions, please refer to the official Gemini documentation.

Gemini Models

• Gemini Pro: Designed for complex reasoning, including:
  • Analyzing and summarizing large amounts of information.
  • Sophisticated cross-modal reasoning (across text, code, images, etc.).
  • Effective problem-solving with complex codebases.
• Gemini Flash: Optimized for speed and efficiency, offering:
  • Sub-second response times and high throughput.
  • High quality at a lower cost for a wide range of tasks.
  • Enhanced multimodal capabilities, including improved spatial understanding, new output modalities (text, audio, images), and native tool use (Google Search, code execution, and third-party functions).

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:

• Interact with the Gemini API in Vertex AI.
• Use the Gemini Flash model to analyze images and videos.
• Provide Gemini with text, image, and video prompts to generate informative responses.
• Explore practical applications of Gemini's multimodal capabilities.

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).

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

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 .

Task 3. Use the Gemini Flash model

Gemini Flash (gemini-1.5-flash) is a multimodal model that supports multimodal prompts. You can include text, image(s), and video in your prompt requests and get text or code responses.

In this task, run through the notebook cells to see how to use the Gemini Flash model. Return here to check your progress as you complete the objectives.

Image understanding across multiple images

One of the capabilities of Gemini is being able to reason across multiple images. In this example, you will use Gemini to calculate the total cost of groceries using an image of fruits and a price list.

Run through the Image understanding across multiple images section of the notebook.

Click Check my progress to verify the objective. Image understanding across multiple images

Understanding Screens and Interfaces

Gemini can also extract information from appliance screens, UIs, screenshots, icons, and layouts. In this example, you will use Gemini to extract information from a stove to help a user navigate the UI and respond in different languages:

Run through the Understanding Screens and Interfaces section of the notebook.

Click Check my progress to verify the objective. Understanding Screens and Interfaces

Understanding entity relationships in technical diagrams

Gemini has multimodal capabilities that enable it to understand diagrams and take actionable steps, such as optimization or code generation. In this example, you will see how Gemini can decipher an entity relationship (ER) diagram, understand the relationships between tables, identify requirements for optimization in a specific environment like BigQuery, and even generate corresponding code.

Run through the Understanding entity relationships in technical diagrams section of the notebook.

Click Check my progress to verify the objective. Understanding entity relationships in technical diagrams

Recommendations based on multiple images

Gemini is capable of image comparison and providing recommendations. This may be useful in industries like e-commerce and retail. In this example, you will use Gemini to recommend which pair of glasses would be better suited to an oval-shaped face.

Run through the Recommendations based on multiple images section of the notebook.

Click Check my progress to verify the objective. Recommendations based on multiple images

Similarity/Differences

Gemini can compare images and identify similarities or differences between objects. In this example, you will use Gemini to compare two images of the same location and identify the differences between them.

Run through the Similarity/Differences section of the notebook.

Click Check my progress to verify the objective. Similarity/Differences

Generating a video description

Gemini can generate a description of a video. In this example, you will use Gemini to generate a description of a video of a video of a Mediterranean sea coast.

Run through the Generating a video description section of the notebook.

Click Check my progress to verify the objective. Generating a video description

Extracting tags of objects throughout the video

Gemini can also extract tags throughout a video. In this example, you will use Gemini to extract tags of objects throughout a video of a photo shoot and generate hashtags.

Run through the Extracting tags of objects throughout the video section of the notebook.

Click Check my progress to verify the objective. Extracting tags of objects throughout the video

Asking more questions about a video

Gemini can answer questions about a video. In this example, you will use Gemini to answer questions about the video and return a JSON response.

Run through the Asking more questions about a video section of the notebook.

Click Check my progress to verify the objective. Asking more questions about a video

Retrieving extra information beyond the video

Gemini can also retrieve extra information beyond the video. In this example, you will use Gemini to retrieve extra information about the video, such as asking specific questions about a train route.

Run through the Retrieving extra information beyond the video section of the notebook.

Click Check my progress to verify the objective. Retrieving extra information beyond the video

Congratulations!

You have now completed the lab! In this lab, you learned how to use the Gemini API in Vertex AI to generate text from text and image(s) prompts.

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 December 13, 2024

Lab Last Tested December 13, 2024

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