Generate streaming text content with Generative Model

This sample demonstrates how to use Generative Models to generate text in a streaming format.

Explore further

For detailed documentation that includes this code sample, see the following:

Generate content with the Gemini API in Vertex AI

Code sample

Go

Before trying this sample, follow the Go setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Go API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 genai 
  
 "google.golang.org/genai" 
 ) 
 // generateWithTextStream shows how to generate text stream using a text prompt. 
 func 
  
 generateWithTextStream 
 ( 
 w 
  
 io 
 . 
 Writer 
 ) 
  
 error 
  
 { 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 genai 
 . 
 NewClient 
 ( 
 ctx 
 , 
  
& genai 
 . 
 ClientConfig 
 { 
  
 HTTPOptions 
 : 
  
 genai 
 . 
 HTTPOptions 
 { 
 APIVersion 
 : 
  
 "v1" 
 }, 
  
 }) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "failed to create genai client: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 modelName 
  
 := 
  
 "gemini-2.5-flash" 
  
 contents 
  
 := 
  
 genai 
 . 
 Text 
 ( 
 "Why is the sky blue?" 
 ) 
  
 for 
  
 resp 
 , 
  
 err 
  
 := 
  
 range 
  
 client 
 . 
 Models 
 . 
 GenerateContentStream 
 ( 
 ctx 
 , 
  
 modelName 
 , 
  
 contents 
 , 
  
 nil 
 ) 
  
 { 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "failed to generate content: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 chunk 
  
 := 
  
 resp 
 . 
 Text 
 () 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 chunk 
 ) 
  
 } 
  
 // Example response: 
  
 // The 
  
 //  sky is blue 
  
 //  because of a phenomenon called **Rayleigh scattering**. Here's the breakdown: 
  
 // ... 
  
 return 
  
 nil 
 }

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  import 
  
 com.google.genai.Client 
 ; 
 import 
  
 com.google.genai.ResponseStream 
 ; 
 import 
  
 com.google.genai.types.GenerateContentResponse 
 ; 
 import 
  
 com.google.genai.types.HttpOptions 
 ; 
 public 
  
 class 
 TextGenerationWithTextStream 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 contents 
  
 = 
  
 "Why is the sky blue?" 
 ; 
  
 String 
  
 modelId 
  
 = 
  
 "gemini-2.5-flash" 
 ; 
  
 generateContent 
 ( 
 modelId 
 , 
  
 contents 
 ); 
  
 } 
  
 // Generates text stream with text input 
  
 public 
  
 static 
  
 String 
  
 generateContent 
 ( 
 String 
  
 modelId 
 , 
  
 String 
  
 contents 
 ) 
  
 { 
  
 // 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 
  
 ( 
 Client 
  
 client 
  
 = 
  
 Client 
 . 
 builder 
 () 
  
 . 
 location 
 ( 
 "global" 
 ) 
  
 . 
 vertexAI 
 ( 
 true 
 ) 
  
 . 
 httpOptions 
 ( 
 HttpOptions 
 . 
 builder 
 (). 
 apiVersion 
 ( 
 "v1" 
 ). 
 build 
 ()) 
  
 . 
 build 
 ()) 
  
 { 
  
 StringBuilder 
  
 responseTextBuilder 
  
 = 
  
 new 
  
 StringBuilder 
 (); 
  
 try 
  
 ( 
 ResponseStream<GenerateContentResponse> 
  
 responseStream 
  
 = 
  
 client 
 . 
 models 
 . 
 generateContentStream 
 ( 
 modelId 
 , 
  
 contents 
 , 
  
 null 
 )) 
  
 { 
  
 for 
  
 ( 
 GenerateContentResponse 
  
 chunk 
  
 : 
  
 responseStream 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 print 
 ( 
 chunk 
 . 
 text 
 ()); 
  
 responseTextBuilder 
 . 
 append 
 ( 
 chunk 
 . 
 text 
 ()); 
  
 } 
  
 } 
  
 // Example response: 
  
 // The sky appears blue due to a phenomenon called **Rayleigh scattering**. Here's 
  
 // a breakdown of why: 
  
 // ... 
  
 return 
  
 responseTextBuilder 
 . 
 toString 
 (); 
  
 } 
  
 } 
 }

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  const 
  
 { 
 GoogleGenAI 
 } 
  
 = 
  
 require 
 ( 
 '@google/genai' 
 ); 
 const 
  
 GOOGLE_CLOUD_PROJECT 
  
 = 
  
 process 
 . 
 env 
 . 
 GOOGLE_CLOUD_PROJECT 
 ; 
 const 
  
 GOOGLE_CLOUD_LOCATION 
  
 = 
  
 process 
 . 
 env 
 . 
 GOOGLE_CLOUD_LOCATION 
  
 || 
  
 'global' 
 ; 
 async 
  
 function 
  
 generateContent 
 ( 
  
 projectId 
  
 = 
  
 GOOGLE_CLOUD_PROJECT 
 , 
  
 location 
  
 = 
  
 GOOGLE_CLOUD_LOCATION 
 ) 
  
 { 
  
 const 
  
 client 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 }); 
  
 const 
  
 response 
  
 = 
  
 await 
  
 client 
 . 
 models 
 . 
 generateContentStream 
 ({ 
  
 model 
 : 
  
 'gemini-2.5-flash' 
 , 
  
 contents 
 : 
  
 'Why is the sky blue?' 
 , 
  
 }); 
  
 let 
  
 response_text 
  
 = 
  
 '' 
 ; 
  
 for 
  
 await 
  
 ( 
 const 
  
 chunk 
  
 of 
  
 response 
 ) 
  
 { 
  
 response_text 
  
 += 
  
 chunk 
 . 
 text 
 ; 
  
 console 
 . 
 log 
 ( 
 chunk 
 . 
 text 
 ); 
  
 } 
  
 return 
  
 response_text 
 ; 
 }

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Python API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  from 
  
 google 
  
 import 
 genai 
 from 
  
 google.genai.types 
  
 import 
 HttpOptions 
 client 
 = 
 genai 
 . 
 Client 
 ( 
 http_options 
 = 
 HttpOptions 
 ( 
 api_version 
 = 
 "v1" 
 )) 
 for 
 chunk 
 in 
 client 
 . 
 models 
 . 
 generate_content_stream 
 ( 
 model 
 = 
 "gemini-2.5-flash" 
 , 
 contents 
 = 
 "Why is the sky blue?" 
 , 
 ): 
 print 
 ( 
 chunk 
 . 
 text 
 , 
 end 
 = 
 "" 
 ) 
 # Example response: 
 # The 
 #  sky appears blue due to a phenomenon called **Rayleigh scattering**. Here's 
 #  a breakdown of why: 
 # ...

What's next

To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser .

Generate streaming text content with Generative Model Stay organized with collections Save and categorize content based on your preferences.

Explore further

Code sample

Go

Java

Node.js

Python

What's next

Generate streaming text content with Generative Model