GSP1308

Overview

Imagine you are a data engineer at Cymbal Shops. You want to explore how to use the Gemini API to build intelligent chatbots and data analysis tools. In this lab, you get started with the Gemini API by initializing the client and making your first API calls for text generation and chat.

The Gemini API empowers developers and creators across various fields by providing advanced natural language processing and code generation capabilities. It enables the creation of intelligent chatbots, automated content generation tools, insightful data analysis platforms, and personalized learning experiences. Its versatility stems from its ability to understand context, generate creative text, analyze complex data, and adapt to diverse applications.

The Gemini family offers various Gemini models optimized for different tasks. In this lab, you use the model.

Prerequisites

Before starting this lab, you should be familiar with:

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

Objectives

In this lab, you learn how to perform the following tasks:

• Initialize and authenticate the Gemini API client.
• Generate text using the Gemini API.
• Create multi-turn conversations with Gemini models.

Task 1. Initialize the Gemini client and set the Project ID and Model ID

Initialize the Gemini client and configure the project and model variables to start using the API.

1. In the Jupyter Notebook, navigate to the first code cell (under Import the Library and Initialize Client).
2. Replace the __PROJECT_ID__ placeholder with your Project ID:

{{{project_0.project_id | project_id}}}

3. Replace the __GEMINI_FLASH_LITE_MODEL_ID__ placeholder with the following model ID:

{{{project_0.startup_script.gemini_flash_lite_model_id | model_name}}}

4. Run the cell to import the libraries, initialize the client, and set the model ID.

Task 2. Run the notebook cells

Execute the cells in the Jupyter Notebook to interact with the Gemini API.

• Follow the instructions directly in the Jupyter Notebook to run the remaining cells.

Note: API calls to Gemini require processing time. Wait for the response to appear after executing a notebook cell.

Click Check my progress to verify the objective. Generate Text from the Gemini API.

Click Check my progress to verify the objective. Multi-Turn Conversations.

Congratulations!

Congratulations! In this lab, you learned how to use the Gemini API and make basic API calls to interact with Gemini models.

Next steps / Learn more

Check out the following resources to learn more about Gen AI and the Gemini Enterprise Agent Platform:

• Read the Google DeepMind Gemini Models Overview to learn about core model capabilities.
• Explore the official Agent Platform Documentation for design, scaling, and deployment guidelines.
• Review the Agents on Agent Platform Overview to understand how to build and govern custom agents.
• Watch the Generative AI on YouTube playlist for video tutorials and product demos.
• Explore other notebooks and samples in the Google Cloud Generative AI repository.
• Explore the Google Agent Development Kit (ADK) Samples repository for custom agent templates.

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Manual Last Updated July 27, 2026

Lab Last Tested July 27, 2026

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