Vertex AI client libraries

This page shows how to get started with the Cloud Client Libraries for the Vertex AI API. Client libraries make it easier to access Google Cloud APIs from a supported language. Although you can use Google Cloud APIs directly by making raw requests to the server, client libraries provide simplifications that significantly reduce the amount of code you need to write.

Read more about the Cloud Client Libraries and the older Google API Client Libraries in Client libraries explained.

Install the client library


Install-Package Google.Cloud.AIPlatform.V1 -Pre

For more information, see Setting Up a C# Development Environment.


go get

For more information, see Setting Up a Go Development Environment.


If you are using Maven with BOM, add the following in your pom.xml:


If you are using Maven without BOM, add the following to your pom.xml:


If you are using Gradle without BOM, add the following to your build.gradle:

implementation ''

For more information, see Setting Up a Java Development Environment.


npm install --save @google-cloud/vertexai

For more information, see Setting Up a Node.js Development Environment.


pip install --upgrade google-cloud-aiplatform

For more information, see Setting Up a Python Development Environment.

Set up authentication

To authenticate calls to Google Cloud APIs, client libraries support Application Default Credentials (ADC); the libraries look for credentials in a set of defined locations and use those credentials to authenticate requests to the API. With ADC, you can make credentials available to your application in a variety of environments, such as local development or production, without needing to modify your application code.

For production environments, the way you set up ADC depends on the service and context. For more information, see Set up Application Default Credentials.

For a local development environment, you can set up ADC with the credentials that are associated with your Google Account:

  1. Install and initialize the gcloud CLI.

    When you initialize the gcloud CLI, be sure to specify a Google Cloud project in which you have permission to access the resources your application needs.

  2. Configure ADC:

    gcloud auth application-default login

    A sign-in screen appears. After you sign in, your credentials are stored in the local credential file used by ADC.

Use the client library

The following example shows how to use the client library.


using Google.Api.Gax.Grpc;
using Google.Cloud.AIPlatform.V1;
using System.Text;
using System.Threading.Tasks;

public class GeminiQuickstart
    public async Task<string> GenerateContent(
        string projectId = "your-project-id",
        string location = "us-central1",
        string publisher = "google",
        string model = "gemini-1.5-flash-001"
        // Create client
        var predictionServiceClient = new PredictionServiceClientBuilder
            Endpoint = $"{location}"

        // Initialize content request
        var generateContentRequest = new GenerateContentRequest
            Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}",
            GenerationConfig = new GenerationConfig
                Temperature = 0.4f,
                TopP = 1,
                TopK = 32,
                MaxOutputTokens = 2048
            Contents =
                new Content
                    Role = "USER",
                    Parts =
                        new Part { Text = "What's in this photo?" },
                        new Part { FileData = new() { MimeType = "image/png", FileUri = "gs://generativeai-downloads/images/scones.jpg" } }

        // Make the request, returning a streaming response
        using PredictionServiceClient.StreamGenerateContentStream response = predictionServiceClient.StreamGenerateContent(generateContentRequest);

        StringBuilder fullText = new();

        // Read streaming responses from server until complete
        AsyncResponseStream<GenerateContentResponse> responseStream = response.GetResponseStream();
        await foreach (GenerateContentResponse responseItem in responseStream)

        return fullText.ToString();


import (


func tryGemini(w io.Writer, projectID string, location string, modelName string) error {
	// location := "us-central1"
	// modelName := "gemini-1.5-flash-001"

	ctx := context.Background()
	client, err := genai.NewClient(ctx, projectID, location)
	if err != nil {
		return fmt.Errorf("error creating client: %w", err)
	gemini := client.GenerativeModel(modelName)

	img := genai.FileData{
		MIMEType: "image/jpeg",
		FileURI:  "gs://generativeai-downloads/images/scones.jpg",
	prompt := genai.Text("What is in this image?")

	resp, err := gemini.GenerateContent(ctx, img, prompt)
	if err != nil {
		return fmt.Errorf("error generating content: %w", err)
	rb, err := json.MarshalIndent(resp, "", "  ")
	if err != nil {
		return fmt.Errorf("json.MarshalIndent: %w", err)
	fmt.Fprintln(w, string(rb))
	return nil



public class Quickstart {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-google-cloud-project-id";
    String location = "us-central1";
    String modelName = "gemini-1.5-flash-001";

    String output = quickstart(projectId, location, modelName);

  // Analyzes the provided Multimodal input.
  public static String quickstart(String projectId, String location, String modelName)
      throws IOException {
    // Initialize client that will be used to send requests. This client only needs
    // to be created once, and can be reused for multiple requests.
    try (VertexAI vertexAI = new VertexAI(projectId, location)) {
      String imageUri = "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png";

      GenerativeModel model = new GenerativeModel(modelName, vertexAI);
      GenerateContentResponse response = model.generateContent(ContentMaker.fromMultiModalData(
          PartMaker.fromMimeTypeAndData("image/png", imageUri),
          "What's in this photo"

      return response.toString();


const {VertexAI} = require('@google-cloud/vertexai');

 * TODO(developer): Update these variables before running the sample.
async function createNonStreamingMultipartContent(
  projectId = 'PROJECT_ID',
  location = 'us-central1',
  model = 'gemini-1.5-flash-001',
  image = 'gs://generativeai-downloads/images/scones.jpg',
  mimeType = 'image/jpeg'
) {
  // Initialize Vertex with your Cloud project and location
  const vertexAI = new VertexAI({project: projectId, location: location});

  // Instantiate the model
  const generativeVisionModel = vertexAI.getGenerativeModel({
    model: model,

  // For images, the SDK supports both Google Cloud Storage URI and base64 strings
  const filePart = {
    fileData: {
      fileUri: image,
      mimeType: mimeType,

  const textPart = {
    text: 'what is shown in this image?',

  const request = {
    contents: [{role: 'user', parts: [filePart, textPart]}],

  console.log('Prompt Text:');

  console.log('Non-Streaming Response Text:');
  // Create the response stream
  const responseStream =
    await generativeVisionModel.generateContentStream(request);

  // Wait for the response stream to complete
  const aggregatedResponse = await responseStream.response;

  // Select the text from the response
  const fullTextResponse =



import vertexai

from vertexai.generative_models import GenerativeModel, Part

# TODO(developer): Update and un-comment below line
# project_id = "PROJECT_ID"

vertexai.init(project=project_id, location="us-central1")

model = GenerativeModel(model_name="gemini-1.5-flash-001")

response = model.generate_content(
        "What is shown in this image?",


Additional resources


The following list contains links to more resources related to the client library for C#:


The following list contains links to more resources related to the client library for Go:


The following list contains links to more resources related to the client library for Java:


The following list contains links to more resources related to the client library for Node.js:


The following list contains links to more resources related to the client library for Python: