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    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

    C# 

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

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

    Go 

    go get cloud.google.com/go/vertexai/genai

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

    Java

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

    <dependencyManagement>  
<dependencies>  
<dependency>  
<groupId>com.google.cloud</groupId>  
<artifactId>libraries-bom</artifactId>  
<version>26.34.0</version>  
<type>pom</type>  
<scope>import</scope>  
</dependency>  
</dependencies>
</dependencyManagement>
<dependencies>  
<dependency>  
<groupId>com.google.cloud</groupId>  
<artifactId>google-cloud-vertexai</artifactId>  
</dependency>
</dependencies>

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

    <dependency>  
<groupId>com.google.cloud</groupId>  
<artifactId>google-cloud-vertexai</artifactId>  
<version>1.1.0</version>
</dependency>

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

    implementation 'com.google.cloud:google-cloud-vertexai:1.1.0'

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

    Node.js 

    npm install @google-cloud/vertexai

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

    Python 

    pip install 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 the Google Cloud CLI. After installation, initialize the Google Cloud CLI by running the following command:

      gcloud  
init

      If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

    2. If you're using a local shell, then create local authentication credentials for your user account:

      gcloud  
auth  
application-default  
login

      You don't need to do this if you're using Cloud Shell.

      If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity .

      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.

    C# 

      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-2.0-flash-001" 
  
 ) 
  
 { 
  
 // Create client 
  
 var 
  
 predictionServiceClient 
  
 = 
  
 new 
  
  PredictionServiceClientBuilder 
 
  
 { 
  
 Endpoint 
  
 = 
  
 $"{location}-aiplatform.googleapis.com" 
  
 }. 
 Build 
 (); 
  
 // 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 
 ) 
  
 { 
  
 fullText 
 . 
 Append 
 ( 
 responseItem 
 . 
 Candidates 
 [ 
 0 
 ]. 
  Content 
 
 . 
 Parts 
 [ 
 0 
 ]. 
 Text 
 ); 
  
 } 
  
 return 
  
 fullText 
 . 
 ToString 
 (); 
  
 } 
 }

    Go 

      import 
  
 ( 
  
 "context" 
  
 "encoding/json" 
  
 "fmt" 
  
 "io" 
  
 "cloud.google.com/go/vertexai/genai" 
 ) 
 func 
  
 tryGemini 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 projectID 
  
 string 
 , 
  
 location 
  
 string 
 , 
  
 modelName 
  
 string 
 ) 
  
 error 
  
 { 
  
 // location := "us-central1" 
  
 // modelName := "gemini-2.0-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 
 }

    Java 

      import 
  
 com.google.cloud.vertexai. VertexAI 
 
 ; 
 import 
  
 com.google.cloud.vertexai.api. GenerateContentResponse 
 
 ; 
 import 
  
 com.google.cloud.vertexai.generativeai. ContentMaker 
 
 ; 
 import 
  
 com.google.cloud.vertexai.generativeai. GenerativeModel 
 
 ; 
 import 
  
 com.google.cloud.vertexai.generativeai. PartMaker 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 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-2.0-flash-001" 
 ; 
  
 String 
  
 output 
  
 = 
  
 quickstart 
 ( 
 projectId 
 , 
  
 location 
 , 
  
 modelName 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 output 
 ); 
  
 } 
  
 // 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://generativeai-downloads/images/scones.jpg" 
 ; 
  
  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 
 (); 
  
 } 
  
 } 
 }

    Node.js 

      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-2.0-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 
 ( 
 request 
 . 
 contents 
 [ 
 0 
 ]. 
  parts 
 
 [ 
 1 
 ]. 
 text 
 ); 
  
 console 
 . 
 log 
 ( 
 'Non-Streaming Response Text:' 
 ); 
  
 // Generate a response 
  
 const 
  
 response 
  
 = 
  
 await 
  
 generativeVisionModel 
 . 
 generateContent 
 ( 
 request 
 ); 
  
 // Select the text from the response 
  
 const 
  
 fullTextResponse 
  
 = 
  
 response 
 . 
 response 
 . 
  candidates 
 
 [ 
 0 
 ]. 
  content 
 
 . 
  parts 
 
 [ 
 0 
 ]. 
 text 
 ; 
  
 console 
 . 
 log 
 ( 
 fullTextResponse 
 ); 
 }

    Python 

      import 
  
  vertexai 
 
 from 
  
 vertexai.preview.prompts 
  
 import 
 Prompt 
 # Initialize vertexai 
  vertexai 
 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 "us-central1" 
 ) 
 variables 
 = 
 [ 
 { 
 "animal" 
 : 
 "Eagles" 
 , 
 "activity" 
 : 
 "eat berries" 
 }, 
 { 
 "animal" 
 : 
 "Coyotes" 
 , 
 "activity" 
 : 
 "jump" 
 }, 
 { 
 "animal" 
 : 
 "Squirrels" 
 , 
 "activity" 
 : 
 "fly" 
 } 
 ] 
 # define prompt template 
 prompt 
 = 
 Prompt 
 ( 
 prompt_data 
 = 
 "Do 
 {animal} 
  
 {activity} 
 ?" 
 , 
 model_name 
 = 
 "gemini-2.0-flash-001" 
 , 
 variables 
 = 
 variables 
 , 
 system_instruction 
 = 
 "You are a helpful zoologist" 
 # generation_config=generation_config, # Optional 
 # safety_settings=safety_settings, # Optional 
 ) 
 # Generates content using the assembled prompt. 
 responses 
 = 
 [] 
 for 
 variable_set 
 in 
 prompt 
 . 
 variables 
 : 
 response 
 = 
 prompt 
 . 
  generate_content 
 
 ( 
 contents 
 = 
 prompt 
 . 
  assemble_contents 
 
 ( 
 ** 
 variable_set 
 ) 
 ) 
 responses 
 . 
 append 
 ( 
 response 
 ) 
 for 
 response 
 in 
 responses 
 : 
 print 
 ( 
 response 
 . 
 text 
 , 
 end 
 = 
 "" 
 ) 
 # Example response 
 # Assembled prompt replacing: 1 instances of variable animal, 1 instances of variable activity 
 # Eagles are primarily carnivorous.  While they might *accidentally* ingest a berry......

    Additional resources

    C#

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

    Go

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

    Java

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

    Node.js

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

    Python

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