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Getting Started with the Gemini API in Vertex AI

Lab 1 година universal_currency_alt No cost show_chart Початковий

info This lab may incorporate AI tools to support your learning.

GSP1209
Overview
Objectives
Setup and requirements
Task 1. Open the notebook in Vertex AI Workbench
Task 2. Set up the notebook
Task 3. Use the Gemini 1.5 Pro model
Task 4. Generate text from a multimodal prompt
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GSP1209

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Overview

This lab provides a hands-on introduction to using the Gemini API within Vertex AI. You'll leverage the Vertex AI SDK for Python to interact with the powerful Gemini 1.5 Pro model, exploring its capabilities through a variety of tasks. These tasks include generating text from different input types (text prompts, images, and videos), as well as experimenting with various features and configuration options to fine-tune your results. This experience will equip you with the essential skills to effectively utilize the Gemini API for diverse generative AI applications.

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 learn how to:

Use the Gemini API in Vertex AI with the Vertex AI SDK for Python.
Interact with the Gemini 1.5 Pro (gemini-1.5-pro) model.
Generate text from a text prompt.
Explore various features and configuration options.
Generate text from an image and a text prompt.
Generate text from a video and a text prompt.

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 will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that 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 or private browser window to run this lab. This prevents any 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: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

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

Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel 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
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.
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 panel.
Click Next.
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 panel.
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.
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 view a menu with a list of Google Cloud products and services, click the Navigation menu at the top-left. Navigation menu icon

Task 1. Open the notebook in Vertex AI Workbench

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

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

In the following sections, you will run through the notebook cells to see how to use the Gemini API in Vertex AI.

Task 3. Use the Gemini 1.5 Pro model

The Gemini 1.5 Pro (gemini-1.5-pro) model is designed to handle natural language tasks, multi-turn text and code chat, and code generation. In this task, run through the notebook cells to see how to use the Gemini 1.5 Pro model to generate text from text prompts.

Generate text from text prompts

Send a text prompt to the model using the generate_content method. The generate_content method can handle a wide variety of use cases, including multi-turn chat and multimodal input, depending on what the underlying model supports.

Run through the Generate text from text prompts section of the notebook.

Click Check my progress to verify the objectives.

Generate text from text prompts.

Streaming

By default, the model returns a response after completing the entire generation process. You can also stream the response as it is being generated, and the model will return chunks of the response as soon as they are generated.

Run through the Streaming section of the notebook.

Click Check my progress to verify the objectives.

Streaming.

Try your own prompts

Run through the Try your own prompts section of the notebook.

Click Check my progress to verify the objectives.

Try your own prompts.

Safety filters

The Gemini API provides safety filters that you can adjust across multiple filter categories to restrict or allow certain types of content. You can use these filters to adjust what's appropriate for your use case. See the Configure safety filters page for details.

When you make a request to Gemini, the content is analyzed and assigned a safety rating. You can inspect the safety ratings of the generated content by printing out the model responses, as in this example:

Run through the Safety filters section of the notebook.

Click Check my progress to verify the objectives.

Safety filters.

Test chat prompts

The Gemini API supports natural multi-turn conversations and is ideal for text tasks that require back-and-forth interactions. The following examples show how the model responds during a multi-turn conversation.

Run through the Test chat prompts section of the notebook.

Click Check my progress to verify the objectives.

Test chat prompts.

Task 4. Generate text from a multimodal prompt

Gemini 1.5 Pro (gemini-1.5-pro) 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.