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    Text generation

    This page shows you how to send chat prompts to a Gemini model by using the Google Cloud console, REST API, and supported SDKs.

    To learn how to add images and other media to your request, see Image understanding .

    For a list of languages supported by Gemini, see Language support .

    To explore the generative AI models and APIs that are available on Vertex AI, go to Model Garden in the Google Cloud console.

    Go to Model Garden

    If you're looking for a way to use Gemini directly from your mobile and web apps, see the Firebase AI Logic client SDKs for Swift, Android, Web, Flutter, and Unity apps.

    Generate text

    For testing and iterating on chat prompts, we recommend using the Google Cloud console. To send prompts programmatically to the model, you can use the REST API, Google Gen AI SDK, Vertex AI SDK for Python, or one of the other supported libraries and SDKs.

    You can use system instructions to steer the behavior of the model based on a specific need or use case. For example, you can define a persona or role for a chatbot that responds to customer service requests. For more information, see the system instructions code samples .

    You can use the Google Gen AI SDK to send requests if you're using Gemini 2.0 Flash .

    Here is a simple text generation example.

    Python

    Install

    pip install --upgrade google-genai

    To learn more, see the SDK reference documentation .

    Set environment variables to use the Gen AI SDK with Vertex AI:

     # 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
     
      from 
  
 google 
  
 import 
 genai 
 from 
  
 google.genai.types 
  
 import 
 HttpOptions 
 client 
 = 
 genai 
 . 
 Client 
 ( 
 http_options 
 = 
 HttpOptions 
 ( 
 api_version 
 = 
 "v1" 
 )) 
 response 
 = 
 client 
 . 
 models 
 . 
 generate_content 
 ( 
 model 
 = 
 "gemini-2.5-flash" 
 , 
 contents 
 = 
 "How does AI work?" 
 , 
 ) 
 print 
 ( 
 response 
 . 
 text 
 ) 
 # Example response: 
 # Okay, let's break down how AI works. It's a broad field, so I'll focus on the ... 
 # 
 # Here's a simplified overview: 
 # ...

    Go

    Learn how to install or update the Go .

    To learn more, see the SDK reference documentation .

    Set environment variables to use the Gen AI SDK with Vertex AI:

     # 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
     
      import 
  
 ( 
 "context" 
 "fmt" 
 "io" 
 "google.golang.org/genai" 
 ) 
 // 
 generateWithText 
 shows 
 how 
 to 
 generate 
 text 
 using 
 a 
 text 
 prompt 
 . 
 func 
 generateWithText 
 ( 
 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 
 ) 
 } 
 resp 
 , 
 err 
 := 
 client 
 . 
 Models 
 . 
 GenerateContent 
 ( 
 ctx 
 , 
 "gemini-2.5-flash" 
 , 
 genai 
 . 
 Text 
 ( 
 "How does AI work?" 
 ), 
 nil 
 , 
 ) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to generate content: %w" 
 , 
 err 
 ) 
 } 
 respText 
 := 
 resp 
 . 
 Text 
 () 
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
 respText 
 ) 
 // 
 Example 
 response 
 : 
 // 
 That 
 's a great question! Understanding how AI works can feel like ... 
 // 
 ... 
 // 
 ** 
 1. 
 The 
 Foundation 
 : 
 Data 
 and 
 Algorithms 
 ** 
 // 
 ... 
 return 
 nil 
 }

    Node.js

    Install

    npm install @google/genai

    To learn more, see the SDK reference documentation .

    Set environment variables to use the Gen AI SDK with Vertex AI:

     # 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
     
      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 
  
 ai 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 }); 
  
 const 
  
 response 
  
 = 
  
 await 
  
 ai 
 . 
 models 
 . 
 generateContent 
 ({ 
  
 model 
 : 
  
 'gemini-2.5-flash' 
 , 
  
 contents 
 : 
  
 'How does AI work?' 
 , 
  
 }); 
  
 console 
 . 
 log 
 ( 
 response 
 . 
 text 
 ); 
  
 return 
  
 response 
 . 
 text 
 ; 
 }

    Java

    Learn how to install or update the Java .

    To learn more, see the SDK reference documentation .

    Set environment variables to use the Gen AI SDK with Vertex AI:

     # 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
     
      import 
  
 com.google.genai.Client 
 ; 
 import 
  
 com.google.genai.types.GenerateContentResponse 
 ; 
 import 
  
 com.google.genai.types.HttpOptions 
 ; 
 public 
 class 
  
 TextGenerationWithText 
 { 
 public 
 static 
 void 
 main 
 ( 
 String 
 [] 
 args 
 ) 
 { 
 // 
 TODO 
 ( 
 developer 
 ): 
 Replace 
 these 
 variables 
 before 
 running 
 the 
 sample 
 . 
 String 
 modelId 
 = 
 "gemini-2.5-flash" 
 ; 
 generateContent 
 ( 
 modelId 
 ); 
 } 
 // 
 Generates 
 text 
 with 
 text 
 input 
 public 
 static 
 String 
 generateContent 
 ( 
 String 
 modelId 
 ) 
 { 
 // 
 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 
 ()) 
 { 
 GenerateContentResponse 
 response 
 = 
 client 
 . 
 models 
 . 
 generateContent 
 ( 
 modelId 
 , 
 "How does AI work?" 
 , 
 null 
 ); 
 System 
 . 
 out 
 . 
 print 
 ( 
 response 
 . 
 text 
 ()); 
 // 
 Example 
 response 
 : 
 // 
 Okay 
 , 
 let 
 's break down how AI works. It' 
 s 
 a 
 broad 
 field 
 , 
 so 
 I 
 'll focus on the ... 
 // 
 // 
 Here 
 's a simplified overview: 
 // 
 ... 
 return 
 response 
 . 
 text 
 (); 
 } 
 } 
 }

    Streaming and non-streaming responses

    You can choose whether the model generates streaming responses or non-streaming responses. For streaming responses, you receive each response as soon as its output token is generated. For non-streaming responses, you receive all responses after all of the output tokens are generated.

    Here is a streaming text generation example.

    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" 
 )) 
 chat_session 
 = 
 client 
 . 
 chats 
 . 
 create 
 ( 
 model 
 = 
 "gemini-2.5-flash" 
 ) 
 for 
 chunk 
 in 
 chat_session 
 . 
 send_message_stream 
 ( 
 "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: 
 # ...

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