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Showing content from http://cloud.google.com/vertex-ai/docs/generative-ai/start/quickstarts/api-quickstart below:

Gemini API in Vertex AI quickstart | Generative AI on Vertex AI

This quickstart shows you how to install the Google Gen AI SDK for your language of choice and then make your first API request. It covers the following topics:

• Install the SDK and set up your environment: Set up your development environment for Python, Go, Node.js, Java, or REST.
• Make your first request: Use the generateContent method to send a text prompt and receive a response.
• Generate images: Create an image from a descriptive text prompt.
• Image understanding: Provide an image as input to the model and ask questions about it.
• Code execution: Enable the model to generate and run Python code to solve problems.

Choose your authentication method:

• API key: A simple encrypted string that identifies your project when calling the API, suitable for quick prototyping.
• Application Default Credentials (ADC): A method that automatically finds and uses service account credentials in a Google Cloud environment, recommended for production applications.

Configure application default credentials if you haven't yet.

The following diagram summarizes the overall workflow:

On your local machine, click one of the following tabs to install the SDK for your programming language.

Install and update the Gen AI SDK for Python by running this command.

pip install --upgrade google-genai

Set environment variables:

  # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
  # with appropriate values for your project.
  export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
  export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True
  

Install and update the Gen AI SDK for Go by running this command.

go get google.golang.org/genai

Set environment variables:

  # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
  # with appropriate values for your project.
  export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
  export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True
  

Install and update the Gen AI SDK for Node.js by running this command.

npm install @google/genai

Set environment variables:

  # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
  # with appropriate values for your project.
  export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
  export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True
  

To install the Gen AI SDK for Java, add the following dependency to your Maven pom.xml file:

<dependencies>
  <dependency>
    <groupId>com.google.genai</groupId>
    <artifactId>google-genai</artifactId>
    <version>0.7.0</version>
  </dependency>
</dependencies>
  

Set environment variables:

  # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
  # with appropriate values for your project.
  export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
  export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True
  

Set environment variables:

  GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
  GOOGLE_CLOUD_LOCATION=global
  API_ENDPOINT=YOUR_API_ENDPOINT
  MODEL_ID="gemini-2.5-flash"
  GENERATE_CONTENT_API="generateContent"
  

Use the generateContent method to send a request to the Gemini API in Vertex AI:

To send this prompt request, run the curl command from the command line or include the REST call in your application.

curl \
-X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https://${API_ENDPOINT}/v1/projects/${GOOGLE_CLOUD_PROJECT}/locations/${GOOGLE_CLOUD_LOCATION}/publishers/google/models/${MODEL_ID}:${GENERATE_CONTENT_API}" -d \
$'{
  "contents": {
    "role": "user",
    "parts": {
      "text": "Explain how AI works in a few words"
    }
  }
}'

The model returns a response. Note that the response is generated in sections with each section separately evaluated for safety.

Gemini can generate and process images conversationally. You can prompt Gemini with text, images, or a combination of both to achieve various image-related tasks, such as image generation and editing. The following code demonstrates how to generate an image based on a descriptive prompt:

You must include responseModalities: ["TEXT", "IMAGE"] in your configuration. Image-only output is not supported with these models.

Gemini can understand images as well. The following code uses the image generated in the previous section and uses a different model to infer information about the image:

The Gemini API in Vertex AI code execution feature enables the model to generate and run Python code and learn iteratively from the results until it arrives at a final output. Vertex AI provides code execution as a tool, similar to function calling. You can use this code execution capability to build applications that benefit from code-based reasoning and that produce text output. For example:

For more examples of code execution, check out the code execution documentation.

Now that you made your first API request, you might want to explore the following guides that show how to set up more advanced Vertex AI features for production code:

• Google Gen AI libraries
• OpenAI compatibility

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