Configurations to generate content with Multimodal AI Model

This sample demonstrates how to provide user configurations to a Multimodal AI Model

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

Content generation parameters

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" 
 ) 
 // generateWithConfig shows how to generate text using a text prompt and custom configuration. 
 func 
  
 generateWithConfig 
 ( 
 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?" 
 ) 
  
 // See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.types.GenerateContentConfig 
  
 config 
  
 := 
  
& genai 
 . 
 GenerateContentConfig 
 { 
  
 Temperature 
 : 
  
 genai 
 . 
 Ptr 
 ( 
 float32 
 ( 
 0.0 
 )), 
  
 CandidateCount 
 : 
  
 int32 
 ( 
 1 
 ), 
  
 ResponseMIMEType 
 : 
  
 "application/json" 
 , 
  
 } 
  
 resp 
 , 
  
 err 
  
 := 
  
 client 
 . 
 Models 
 . 
 GenerateContent 
 ( 
 ctx 
 , 
  
 modelName 
 , 
  
 contents 
 , 
  
 config 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "failed to generate content: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 respText 
  
 := 
  
 resp 
 . 
 Text 
 () 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 respText 
 ) 
  
 // Example response: 
  
 // { 
  
 //   "explanation": "The sky is blue due to a phenomenon called Rayleigh scattering ... 
  
 // } 
  
 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.types.GenerateContentConfig 
 ; 
 import 
  
 com.google.genai.types.GenerateContentResponse 
 ; 
 import 
  
 com.google.genai.types.HttpOptions 
 ; 
 public 
  
 class 
 TextGenerationConfigWithText 
  
 { 
  
 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 and optional configurations 
  
 public 
  
 static 
  
 String 
  
 generateContent 
 ( 
 String 
  
 modelId 
 ) 
  
 { 
  
 // Client Initialization. Once created, it can be reused for multiple requests. 
  
 try 
  
 ( 
 Client 
  
 client 
  
 = 
  
 Client 
 . 
 builder 
 () 
  
 . 
 location 
 ( 
 "global" 
 ) 
  
 . 
 vertexAI 
 ( 
 true 
 ) 
  
 . 
 httpOptions 
 ( 
 HttpOptions 
 . 
 builder 
 (). 
 apiVersion 
 ( 
 "v1" 
 ). 
 build 
 ()) 
  
 . 
 build 
 ()) 
  
 { 
  
 // Set optional configuration parameters 
  
 GenerateContentConfig 
  
 contentConfig 
  
 = 
  
 GenerateContentConfig 
 . 
 builder 
 () 
  
 . 
 temperature 
 ( 
 0.0F 
 ) 
  
 . 
 candidateCount 
 ( 
 1 
 ) 
  
 . 
 responseMimeType 
 ( 
 "application/json" 
 ) 
  
 . 
 topP 
 ( 
 0.95F 
 ) 
  
 . 
 topK 
 ( 
 20 
 F 
 ) 
  
 . 
 seed 
 ( 
 5 
 ) 
  
 . 
 maxOutputTokens 
 ( 
 500 
 ) 
  
 . 
 stopSequences 
 ( 
 "STOP!" 
 ) 
  
 . 
 presencePenalty 
 ( 
 0.0F 
 ) 
  
 . 
 frequencyPenalty 
 ( 
 0.0F 
 ) 
  
 . 
 build 
 (); 
  
 // Generate content using optional configuration 
  
 GenerateContentResponse 
  
 response 
  
 = 
  
 client 
 . 
 models 
 . 
 generateContent 
 ( 
 modelId 
 , 
  
 "Why is the sky blue?" 
 , 
  
 contentConfig 
 ); 
  
 System 
 . 
 out 
 . 
 print 
 ( 
 response 
 . 
 text 
 ()); 
  
 // Example response: 
  
 // { 
  
 //  "explanation": "The sky appears blue due to a phenomenon called Rayleigh scattering. 
  
 // Sunlight, which appears white, is actually composed of all the colors of the rainbow... 
  
 // } 
  
 return 
  
 response 
 . 
 text 
 (); 
  
 } 
  
 } 
 }

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 
  
 config 
  
 = 
  
 { 
  
 temperature 
 : 
  
 0 
 , 
  
 candidateCount 
 : 
  
 1 
 , 
  
 responseMimeType 
 : 
  
 'application/json' 
 , 
  
 topP 
 : 
  
 0.95 
 , 
  
 topK 
 : 
  
 20 
 , 
  
 seed 
 : 
  
 5 
 , 
  
 maxOutputTokens 
 : 
  
 500 
 , 
  
 stopSequences 
 : 
  
 [ 
 'STOP!' 
 ], 
  
 presencePenalty 
 : 
  
 0.0 
 , 
  
 frequencyPenalty 
 : 
  
 0.0 
 , 
  
 }; 
  
 const 
  
 response 
  
 = 
  
 await 
  
 client 
 . 
 models 
 . 
 generateContent 
 ({ 
  
 model 
 : 
  
 'gemini-2.5-flash' 
 , 
  
 contents 
 : 
  
 'Why is the sky blue?' 
 , 
  
 config 
 : 
  
 config 
 , 
  
 }); 
  
 console 
 . 
 log 
 ( 
 response 
 . 
 text 
 ); 
  
 // Example response: 
  
 // { 
  
 //   "explanation": "The sky appears blue due to a phenomenon called Rayleigh scattering. When ... 
  
 // } 
  
 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 
 GenerateContentConfig 
 , 
 HttpOptions 
 client 
 = 
 genai 
 . 
 Client 
 ( 
 http_options 
 = 
 HttpOptions 
 ( 
 api_version 
 = 
 "v1" 
 )) 
 response 
 = 
 client 
 . 
 models 
 . 
 generate_content 
 ( 
 model 
 = 
 "gemini-2.5-flash" 
 , 
 contents 
 = 
 "Why is the sky blue?" 
 , 
 # See the SDK documentation at 
 # https://googleapis.github.io/python-genai/genai.html#genai.types.GenerateContentConfig 
 config 
 = 
 GenerateContentConfig 
 ( 
 temperature 
 = 
 0 
 , 
 candidate_count 
 = 
 1 
 , 
 response_mime_type 
 = 
 "application/json" 
 , 
 top_p 
 = 
 0.95 
 , 
 top_k 
 = 
 20 
 , 
 seed 
 = 
 5 
 , 
 max_output_tokens 
 = 
 500 
 , 
 stop_sequences 
 = 
 [ 
 "STOP!" 
 ], 
 presence_penalty 
 = 
 0.0 
 , 
 frequency_penalty 
 = 
 0.0 
 , 
 ), 
 ) 
 print 
 ( 
 response 
 . 
 text 
 ) 
 # Example response: 
 # { 
 #   "explanation": "The sky appears blue due to a phenomenon called Rayleigh scattering. When ... 
 # }

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Code sample

Go

Java

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Python

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Configurations to generate content with Multimodal AI Model