# Introduction to Long Context Window with Gemini on Vertex AI - GSP1276 ## **Overview** Gemini 2.0 Flash and Pro offer expansive context windows, holding up to 1 million and 2 million tokens, respectively. This advancement allows Gemini to process vast amounts of information—equivalent to novels, codebases, or hours of multimedia—within a single prompt. This lab delves into the capabilities of Gemini 2.0's long context window, exploring its potential for handling extensive text, video, and audio data. You'll learn how tokenization works for different modalities and discover the advantages of this expanded capacity for in-context learning and complex multimodal tasks. ### 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](https://cloud.google.com/vertex-ai/docs/workbench/introduction). ## **Objectives** In this lab, you will: * Understand the concept of context windows and tokenization in large language models. * Learn how to utilize the long context window of `gemini-2.0-flash` for multimodal prompts. * Explore practical applications of long context windows in handling extensive text, video, and audio data. ## **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. ```apache student-01-82ac482dc037@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. ```apache z1pZNxrLI2J1 ``` 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. ![Navigation menu icon and Search field](https://cdn.qwiklabs.com/9Fk8NYFp3quE9mF%2FilWF6%2FlXY9OUBi3UWtb2Ne4uXNU%3D align="left") ## **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 `intro_long_context` 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-01-db7c0371fc1f`, and for **Location**, use `us-central1`. **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. ## **Task 3. Long-form text** 1. Run through the **Long-form text** sections of the notebook. Click **Check my progress** to verify the objective. Long-form text. ## **Task 4. Long-form video** 1. Run through the **Long-form video** sections of the notebook. Click **Check my progress** to verify the objective. Long-form video. ## **Task 5. Long-form audio** 1. Run through the **Long-form audio** sections of the notebook. Click **Check my progress** to verify the objective. Long-form audio. --- ## Solution of Lab %[https://youtu.be/vqrt4MDMjLU] ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1743150613596/3ba8f647-d1c3-4077-ad04-ae08daa50375.png align="center") ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1743150713477/a7b7f2dc-0e51-4a27-8e29-d9f6ab3ab89d.png align="center")