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Analyze a Codebase with Gemini in Vertex AI

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Analyze a Codebase with Gemini in Vertex AI

Lab 1 hour 30 minutes universal_currency_alt 1 Credit show_chart Introductory
info This lab may incorporate AI tools to support your learning.
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GSP1273

Overview

Gemini introduces a breakthrough long context window of up to 1 million tokens that can help seamlessly analyze, classify and summarize large amounts of content within a given prompt. With its long-context reasoning, Gemini can analyze an entire codebase for deeper insights.

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 learn how to analyze an entire codebase with Gemini 2.0 and prompt the model to:

  • Analyze: Summarize codebases effortlessly.
  • Guide: Generate clear developer getting-started documentation.
  • Debug: Uncover critical bugs and provide fixes.
  • Enhance: Implement new features and improve reliability and security.

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.

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. Import libraries and set up the notebook

Task 3. Cloning a codebase

In this section, you clone a microservices application to analyze its codebase.

  1. Run through the Cloning a codebase section of the notebook.

Click Check my progress to verify the objective. Clone a codebase

Task 4. Analyzing the codebase with Gemini

In this section, you submit various prompts used to analyze various aspects of the codebase to Gemini to review recommendations and potential issues in the code.

  1. Run through the Analyzing the codebase with Gemini section of the notebook.
Note: Should you encounter a ClientError 499 response during the execution of any notebook cell, indicating that the task was cancelled prior to its completion, please attempt to re-execute the code cell.

Click Check my progress to verify the objective. Load the Gemini 2.0 model and create a context cache

Click Check my progress to verify the objective. Summarize the codebase and create a developer getting started guide

Click Check my progress to verify the objective. Find, fix the bugs and implement a feature request using function calling

Click Check my progress to verify the objective. Create a troubleshooting guide and make the app more reliable and secure

Click Check my progress to verify the objective. Learn the codebase, create a quickstart tutorial and git changelog generator

Congratulations!

You have now completed the lab! In this lab, you learned how to use Gemini to analyze a codebase using its long context window.

Next steps / learn more

Check out the following resources to learn more about Gemini:

Google Cloud training and certification

...helps you make the most of Google Cloud technologies. Our classes include technical skills and best practices to help you get up to speed quickly and continue your learning journey. We offer fundamental to advanced level training, with on-demand, live, and virtual options to suit your busy schedule. Certifications help you validate and prove your skill and expertise in Google Cloud technologies.

Manual Last Updated April 24, 2025

Lab Last Tested April 24, 2025

Copyright 2025 Google LLC All rights reserved. Google and the Google logo are trademarks of Google LLC. All other company and product names may be trademarks of the respective companies with which they are associated.

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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.