Overview

This lab explores using the Gemini API in Vertex AI to generate synthetic data using Snowfakery. Snowfakery is a powerful framework for creating complex fake data, and Gemini enhances this process by leveraging its natural language capabilities. You'll learn how to define data generation strategies, utilize predefined schemas, and generate different data formats (like blogs and comments) using a Wikipedia page as a seed. This approach is valuable for testing, prompt experimentation, and building few-shot examples.

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 using the Google Gen AI SDK for Python.

• Utilize Snowfakery to define schemas and data generation strategies.

• Leverage Gemini to generate synthetic data in different formats.

• Explore practical applications of synthetic data generation, such as testing and prompt experimentation.

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.

    student-02-55254d8f1594@qwiklabs.net
   

   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.

    0KwOSnPlWy8y
   

   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 vertex-ai-jupyterlab 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 synthetic_data_generation_using_gemini 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 qwiklabs-gcp-02-8a761028b5d9, and for Location, use us-east4.

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.

Check my progress

Task 3. Creating Plugins and Prompts

In this task, you create the two custom plugins needed for this use case along with the necessary prompts. The first plugin gives you the ability to interact with Wikipedia and fetch the contents for a given page. The second plugin allows you to interact with the Gemini API.

1. Run through the Creating Plugins and Prompts section of the notebook.

Click Check my progress to verify the objective.

Creating Plugins and Prompts.

Check my progress

Task 4. Creating the Recipe

In order to generate synthetic data, the schema of the synthetic data must be defined first. This is done by creating a recipe in a YAML format as demonstrated in the notebook, more details on writing recipes can be found here. In this task, you define the schema for the synthetically generated data.

1. Run through the Creating the Recipe section of the notebook.

Click Check my progress to verify the objective.

Creating the Recipe.

Check my progress

Task 5. Generating Data

In this task, you generate the data and view its results in the /outputs folder.

1. Run through the Generating Data section of the notebook.

Note: This step may take several minutes to complete.

Click Check my progress to verify the objective.

Generating Data.

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Solution of Lab