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 Gemini Enterprise Agent Platform, 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, Agent Platform SDK, 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 2.5 Flash .

Here is a text generation example.

Console

To use the Agent Studio to send a chat prompt in the Google Cloud console, do the following:

In the Agent Platform section of the Google Cloud console, go to the Agent Studio page.
Go to Agent Studio
In Start a conversation , click Text chat .
Optional: Configure the model and parameters:
- Model : Select Gemini Pro .
- Region : Select the region that you want to use.
- Temperature : Use the slider or textbox to enter a value for temperature.
  The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 means that the highest probability tokens are always selected. In this case, responses for a given prompt are mostly deterministic, but a small amount of variation is still possible.
  If the model returns a response that's too generic, too short, or the model gives a fallback response, try increasing the temperature. If the model enters infinite generation, increasing the temperature to at least 0.1 may lead to improved results.
  1.0 is the recommended starting value for temperature.
- Output token limit : Use the slider or textbox to enter a value for the max output limit.
  Maximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words.
  Specify a lower value for shorter responses and a higher value for potentially longer responses.
- Add stop sequence : Optional. Enter a stop sequence, which is a series of characters that includes spaces. If the model encounters a stop sequence, the response generation stops. The stop sequence isn't included in the response, and you can add up to five stop sequences.
Optional: To configure advanced parameters, click Advanced and configure as follows:
Click to expand advanced configurations
- Top-K : Use the slider or textbox to enter a value for top-K.
  Top-K changes how the model selects tokens for output. A top-K of 1 means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-K of 3 means that the next token is selected from among the three most probable tokens by using temperature.
  For each token selection step, the top-K tokens with the highest probabilities are sampled. Then tokens are further filtered based on top-P with the final token selected using temperature sampling.
  
  Specify a lower value for less random responses and a higher value for more random responses.
- Top-P : Use the slider or textbox to enter a value for top-P. Tokens are selected from most probable to the least until the sum of their probabilities equals the value of top-P. For the least variable results, set top-P to `0`.
- Enable Grounding : Add a grounding source and path to customize this feature.
Enter your text prompt in the Prompt pane. The model uses previous messages as context for new responses.
Optional: To display the number of text tokens, click View tokens . You can view the tokens or token IDs of your text prompt.
- To view the tokens in the text prompt that are highlighted with different colors marking the boundary of each token ID, click Token ID to text . Media tokens aren't supported.
- To view the token IDs, click Token ID .
  To close the tokenizer tool pane, click X , or click outside of the pane.
Click Submit .
Optional: To save your prompt to My prompts , click Save .
Optional: To get the Python code or a curl command for your prompt, click Get code .
Optional: To clear all previous messages, click Clear conversation

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_ENTERPRISE 
 = 
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_ENTERPRISE 
 = 
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_ENTERPRISE 
 = 
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 
  
 client 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 }); 
  
 const 
  
 response 
  
 = 
  
 await 
  
 client 
 . 
 models 
 . 
 generateContent 
 ({ 
  
 model 
 : 
  
 'gemini-3-flash-preview' 
 , 
  
 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_ENTERPRISE 
 = 
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 
 (); 
 } 
 } 
 }

C#

Learn how to install or update the C# .

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_ENTERPRISE 
 = 
True

  using 
  
 Google 
 . 
 GenAI 
 ; 
 using 
  
 Google 
 . 
 GenAI 
 . 
 Types 
 ; 
 using 
  
 System 
 ; 
 using 
  
 System 
 . 
 Threading 
 . 
 Tasks 
 ; 
 public 
  
 class 
  
 TextGenWithTxt 
 { 
  
 public 
  
 async 
  
 Task<string> 
  
 GenerateContent 
 ( 
  
 string 
  
 projectId 
  
 = 
  
 "your-project-id" 
 , 
  
 string 
  
 location 
  
 = 
  
 "global" 
 , 
  
 string 
  
 model 
  
 = 
  
 "gemini-2.5-flash" 
 ) 
  
 { 
  
 await 
  
 using 
  
 var 
  
 client 
  
 = 
  
 new 
  
 Client 
 ( 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 vertexAI 
 : 
  
 true 
 , 
  
 httpOptions 
 : 
  
 new 
  
 HttpOptions 
  
 { 
  
 ApiVersion 
  
 = 
  
 "v1" 
  
 }); 
  
 GenerateContentResponse 
  
 response 
  
 = 
  
 await 
  
 client 
 . 
 Models 
 . 
 GenerateContentAsync 
 ( 
 model 
 : 
  
 model 
 , 
  
 contents 
 : 
  
 "How does AI work?" 
 ); 
  
 string 
  
 responseText 
  
 = 
  
 response 
 . 
 Candidates 
 [ 
 0 
 ] 
 . 
 Content 
 . 
 Parts 
 [ 
 0 
 ] 
 . 
 Text 
 ; 
  
 Console 
 . 
 WriteLine 
 ( 
 responseText 
 ); 
  
 // 
  
 Example 
  
 response 
 : 
  
 // 
  
 AI 
 , 
  
 or 
  
 Artificial 
  
 Intelligence 
 , 
  
 at 
  
 its 
  
 core 
 , 
  
 is 
  
 about 
  
 creating 
  
 machines 
  
 that 
  
 can 
  
 perform 
 ... 
  
 // 
  
 Here 
 's a breakdown of how it generally works... 
  
 return 
  
 responseText 
 ; 
  
 } 
 }

REST

Before using any of the request data, make the following replacements:

GENERATE_RESPONSE_METHOD : The type of response that you want the model to generate. Choose a method that generates how you want the model's response to be returned:
- streamGenerateContent : The response is streamed as it's being generated to reduce the perception of latency to a human audience.
- generateContent : The response is returned after it's fully generated.
LOCATION : The region to process the request. Available options include the following:
Click to expand a partial list of available regions
- us-central1
- us-west4
- northamerica-northeast1
- us-east4
- us-west1
- asia-northeast3
- asia-southeast1
- asia-northeast1
PROJECT_ID : Your [project ID](/resource-manager/docs/creating-managing-projects#identifiers). .
MODEL_ID : The model ID of the multimodal model that you want to use.
```
 TEXT1 
```
The text instructions to include in the first prompt of the multi-turn conversation. For example, What are all the colors in a rainbow?
```
 TEXT2 
```
The text instructions to include in the second prompt. For example, Why does it appear when it rains?
TEMPERATURE : The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 means that the highest probability tokens are always selected. In this case, responses for a given prompt are mostly deterministic, but a small amount of variation is still possible.
If the model returns a response that's too generic, too short, or the model gives a fallback response, try increasing the temperature. If the model enters infinite generation, increasing the temperature to at least 0.1 may lead to improved results.
1.0 is the recommended starting value for temperature.

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell , which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json . Run the following command in the terminal to create or overwrite this file in the current directory:

cat > request.json << 'EOF'
{
  "contents": [
    {
      "role": "user",
      "parts": { "text": " TEXT1 
" }
    },
    {
      "role": "model",
      "parts": { "text": "What a great question!" }
    },
    {
      "role": "user",
      "parts": { "text": " TEXT2 
" }
    }
  ],
  "generation_config": {
    "temperature": TEMPERATURE 
}
}
EOF

Then execute the following command to send your REST request:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
: GENERATE_RESPONSE_METHOD 
"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json . Run the following command in the terminal to create or overwrite this file in the current directory:

@'
{
  "contents": [
    {
      "role": "user",
      "parts": { "text": " TEXT1 
" }
    },
    {
      "role": "model",
      "parts": { "text": "What a great question!" }
    },
    {
      "role": "user",
      "parts": { "text": " TEXT2 
" }
    }
  ],
  "generation_config": {
    "temperature": TEMPERATURE 
}
}
'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
: GENERATE_RESPONSE_METHOD 
" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [
          {
            "text": "You're right to ask that! Rainbows are a beautiful and fascinating phenomenon. Here's the breakdown:\n\n**1. Sunlight and Water Droplets:**\n\n* Rainbows are created when sunlight interacts with water droplets suspended in the air, typically after rain.\n\n**2. Refraction and Reflection:**\n\n* **Refraction:** When sunlight enters a water droplet, it bends or refracts. This bending is different for each color of light (red bends the least, violet the most).\n* **Reflection:** Inside the droplet, the light bounces off the back surface and then refracts again as it exits the droplet.\n\n**3. Dispersion:**\n\n* The refraction and reflection process separates the white sunlight into its component colors, just like a prism. This separation of colors is called dispersion.\n\n**4. Our Perspective:**\n\n* You only see a rainbow when you're standing with the sun behind you and the rain in front of you. The colors appear in an arc because the angle at which the light refracts and reflects is specific for each color.\n\n**In short:**\n\nRainbows are created when sunlight hits water droplets in the air, causing the light to be refracted, reflected, and dispersed into its individual colors.  \n"
          }
        ]
      },
      "finishReason": "STOP",
      "safetyRatings": [
        {
          "category": "HARM_CATEGORY_HATE_SPEECH",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.06255973,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.039937314
        },
        {
          "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.096705794,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.08404062
        },
        {
          "category": "HARM_CATEGORY_HARASSMENT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.10818896,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.036631368
        },
        {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.116764,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.05023736
        }
      ]
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 22,
    "candidatesTokenCount": 256,
    "totalTokenCount": 278
  }
}

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 Agent Platform quickstart using client libraries .

To authenticate to Agent Platform, 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: 
 # ...

Gemini multiturn chat behavior

When you use multiturn chat, Gemini Enterprise Agent Platform locally stores the initial content and prompts that you sent to the model. Gemini Enterprise Agent Platform sends all of this data with each subsequent request to the model. Consequently, the input costs for each message that you send is a running total of all the data that was already sent to the model. If your initial content is sufficiently large, consider using context caching when you create the initial model object to better control input costs.

What's next

Learn how to send multimodal prompt requests:
Learn about responsible AI best practices and Agent Platform's safety filters .

Text generation Stay organized with collections Save and categorize content based on your preferences.

Generate text

Console

Python

Install

Go

Node.js

Install

Java

C#

REST

curl

PowerShell

Response

Streaming and non-streaming responses

Python

Gemini multiturn chat behavior

What's next

Text generation