This project demonstrates how to use Google Cloud's Vertex AI with the Gemini 1.5 Pro model to generate content. Summary

Create and download a service account JSON key Configure environment variables Install dependencies Run the Vertex AI test script

Python 3.8 or higher Google Cloud account Basic command-line knowledge

Create a Google Cloud Project

Go to the Google Cloud Console Click "Select a project" > "New Project" Enter a project name and create the project Note the Project ID (e.g., gen-lang-client-0367976351)

In the Google Cloud Console, navigate to "APIs & Services" > "Library" Search for "Vertex AI API" Click "Enable"

In the Google Cloud Console, go to "IAM & Admin" > "Service Accounts" Click "Create Service Account" Enter a name (e.g., vertex-ai-service-account) Grant the "Vertex AI User" role Click "Done"

In the Service Accounts list, click on your new service account Go to the "Keys" tab Click "Add Key" > "Create new key" Select "JSON" as the key type Click "Create" to download the JSON key file Save the file securely (e.g., as gen-lang-client-03453476351-9c3455342e0.json)

Create a .env file in the project root Add the following to .env:PROJECT_ID=your-project-id SERVICE_ACCOUNT_KEY_PATH=path/to/your-service-account-key.json LOCATION=us-central1

Replace path/to/your-service-account-key.json with the path to your downloaded JSON key file

python -m venv vertex_env

On Windows: vertex_env\Scripts\activate

On macOS/Linux:source vertex_env/bin/activate

pip install -r requirements.txt

python vertex_ai_test.py

project_root/ │── .env │── vertex_ai_test.py │── requirements.txt │── vertex_env/ │── gen-lang-client-0367976351-01bd59c8f2e0.json

Keep the service account JSON key file secure and never commit it to version control The .env file should be added to .gitignore Ensure the LOCATION variable matches your intended Google Cloud region