Gemini API

The Vertex AI Gemini API supports multimodal prompts as input and output text or code. To learn how to get started with the Vertex AI Gemini API, see the Quickstart.

HTTP request

The following Gemini models are supported:

Gemini 1.5 Pro

POST https://{REGION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/{REGION}/publishers/google/models/gemini-1.5-pro:streamGenerateContent

Gemini 1.0 Pro

POST https://{REGION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/{REGION}/publishers/google/models/gemini-1.0-pro:streamGenerateContent

Note:You can enable Server Sent Events (SSE) for gemini-1.0-pro by appending ?alt=sse to the URL.

Gemini 1.0 Pro Vision

POST https://{REGION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/{REGION}/publishers/google/models/gemini-1.0-pro-vision:streamGenerateContent

To send a stream request to the model, see the streamGenerateContent method for more information.

To send a non-streaming request to the model, use the generateContent method instead.

For a list of supported regions, see Available locations.

Model versions

To use the auto-updated version, specify the model name without the trailing version number, for example gemini-1.0-pro instead of gemini-1.0-pro-001.

For more information, see Gemini model versions and lifecycle.

Request body

The request body contains data with the following structure:

{
  "contents": [
    {
      "role": string,
      "parts": [
        {
          // Union field data can be only one of the following:
          "text": string,
          "inlineData": {
            "mimeType": string,
            "data": string
          },
          "fileData": {
            "mimeType": string,
            "fileUri": string
          },
          // End of list of possible types for union field data.

          "videoMetadata": {
            "startOffset": {
              "seconds": integer,
              "nanos": integer
            },
            "endOffset": {
              "seconds": integer,
              "nanos": integer
            }
          }
        }
      ]
    }
  ],
  "systemInstruction": {
    "role": string,
    "parts": [
      {
        "text": string
      }
    ]
  },
  "tools": [
    {
      "functionDeclarations": [
        {
          "name": string,
          "description": string,
          "parameters": {
            object (OpenAPI Object Schema)
          }
        }
      ]
    }
  ],
  "safetySettings": [
    {
      "category": enum (HarmCategory),
      "threshold": enum (HarmBlockThreshold)
    }
  ],
  "generationConfig": {
    "temperature": number,
    "topP": number,
    "topK": number,
    "candidateCount": integer,
    "maxOutputTokens": integer,
    "stopSequences": [
      string
    ],
    "responseMimeType": string
  }
}

Use the following parameters:

Parameter	Description
role	The role in a conversation associated with the content. Specifying a role is required even in singleturn use cases. Acceptable values include the following: USER: Specifies content that's sent by you. MODEL: Specifies the model's response.
parts	Ordered parts that make up the input. Parts may have different MIME types. For gemini-1.0-pro, only the text field is valid. The token limit is 32,760. For gemini-1.0-pro-vision, you may specify either text only, text and up to 16 images, or text and 1 video clip. The token limit is 16,384. For gemini-1.5-pro, you can specify any combination and number of text, image, video, and audio files. The token limit is 1,000,000. To compute the number of tokens in your request, see .
text	The text instructions or chat dialogue to include in the prompt.
inlineData	Serialized bytes data of the image, audio clip, or video clip. For gemini-1.0-pro-vision, you can specify at most 1 image with inlineData. To specify up to 16 images, use fileData.
mimeType	The media type of the image, PDF, or video specified in the data or fileUri fields. Acceptable values include the following: Click to expand MIME types application/pdf audio/mpeg audio/mp3 audio/wav image/png image/jpeg text/plain video/mov video/mpeg video/mp4 video/mpg video/avi video/wmv video/mpegps video/flv For gemini-1.0-pro-vision, the maximum video length is 2 minutes. For gemini-1.5-pro, the length of the video or audio clip is unlimited. Text files must be UTF-8 encoded. The contents of the text file count toward the token limit. There is no limit on image resolution.
data	The base64 encoding of the image, PDF, or video to include inline in the prompt. When including media inline, you must also specify MIMETYPE. size limit: 20MB
fileUri	The Cloud Storage URI of the image or video to include in the prompt. The bucket that stores the file must be in the same Google Cloud project that's sending the request. You must also specify MIMETYPE. For gemini-1.5-pro, the size limit is 2GB. For gemini-1.0-pro-vision, the size limit is 20MB.
videoMetadata	Optional. For video input, the start and end offset of the video in Duration format. For example, to specify a 10 second clip starting at 1:00, set "start_offset": { "seconds": 60 } and "end_offset": { "seconds": 70 }.
systemInstruction (Preview)	Optional. Available for gemini-1.5-pro and gemini-1.0-pro-002. Instructions for the model to steer it toward better performance. For example, "Answer as concisely as possible" or "Don't use technical terms in your response". The text strings count toward the token limit. The role field of systemInstruction is ignored and doesn't affect the performance of the model.
tools	A piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.
functionDeclarations	One or more function declarations. Each function declaration contains information about one function that includes the following: name The name of the function to call. Must start with a letter or an underscore. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64. description (optional). The description and purpose of the function. The model uses this to decide how and whether to call the function. For the best results, we recommend that you include a description. parameters The parameters of this function in a format that's compatible with the OpenAPI schema format. For more information, see Function calling.
category	The safety category to configure a threshold for. Acceptable values include the following: Click to expand safety categories HARM_CATEGORY_SEXUALLY_EXPLICIT HARM_CATEGORY_HATE_SPEECH HARM_CATEGORY_HARASSMENT HARM_CATEGORY_DANGEROUS_CONTENT
threshold	The threshold for blocking responses that could belong to the specified safety category based on probability. BLOCK_NONE BLOCK_LOW_AND_ABOVE BLOCK_MED_AND_ABOVE BLOCK_ONLY_HIGH
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. Range for gemini-1.0-pro-001, gemini-1.0-pro-vision: 0.0 - 1.0 Range for gemini-1.0-pro-002, gemini-1.5-pro: 0.0 - 2.0 Default for gemini-1.5-pro and gemini-1.0-pro-002: 1.0 Default for gemini-1.0-pro-001: 0.9 Default for gemini-1.0-pro-vision: 0.4
maxOutputTokens	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. Range for gemini-1.5-pro: 1-8192 (default: 8192) Range for gemini-1.0-pro: 1-8192 (default: 8192) Range for gemini-1.0-pro-vision: 1-2048 (default: 2048)
topK	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. Range: 1-40 gemini-1.0-pro and gemini-1.5-pro don't support topK Default for gemini-1.0-pro-vision: 32
topP	Top-P changes how the model selects tokens for output. Tokens are selected from the most (see top-K) to least probable until the sum of their probabilities equals the top-P value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-P value is 0.5, then the model will select either A or B as the next token by using temperature and excludes C as a candidate. Specify a lower value for less random responses and a higher value for more random responses. Range: 0.0 - 1.0 gemini-1.5-pro: 0.94 Default for gemini-1.0-pro: 1 Default for gemini-1.0-pro-vision: 1
candidateCount	The number of response variations to return. This value must be 1.
stopSequences	Specifies a list of strings that tells the model to stop generating text if one of the strings is encountered in the response. If a string appears multiple times in the response, then the response truncates where it's first encountered. The strings are case-sensitive. For example, if the following is the returned response when stopSequences isn't specified: public static string reverse(string myString) Then the returned response with stopSequences set to ["Str", "reverse"] is: public static string Maximum 5 items in the list.
responseMimeType (Preview)	Optional. Available for gemini-1.5-pro. The output format of the generated candidate text. Supported MIME types: text/plain: (default) Text output. application/json: JSON response in the candidates.

Response body

{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": string
          }
        ]
      },
      "finishReason": enum (FinishReason),
      "safetyRatings": [
        {
          "category": enum (HarmCategory),
          "probability": enum (HarmProbability),
          "blocked": boolean
        }
      ],
      "citationMetadata": {
        "citations": [
          {
            "startIndex": integer,
            "endIndex": integer,
            "uri": string,
            "title": string,
            "license": string,
            "publicationDate": {
              "year": integer,
              "month": integer,
              "day": integer
            }
          }
        ]
      }
    }
  ],
  "usageMetadata": {
    "promptTokenCount": integer,
    "candidatesTokenCount": integer,
    "totalTokenCount": integer
  }
}

Response element	Description
text	The generated text.
finishReason	The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens. Because the response uses the prompt for context, it's not possible to change the behavior of how the model stops generating tokens. FINISH_REASON_UNSPECIFIED The finish reason is unspecified. FINISH_REASON_STOP Natural stop point of the model or provided stop sequence. FINISH_REASON_MAX_TOKENS The maximum number of tokens as specified in the request was reached. FINISH_REASON_SAFETY The token generation was stopped as the response was flagged for safety reasons. Note that Candidate.content is empty if content filters block the output. FINISH_REASON_RECITATION The token generation was stopped as the response was flagged for unauthorized citations. FINISH_REASON_OTHER All other reasons that stopped the token
category	The safety category to configure a threshold for. Acceptable values include the following: Click to expand safety categories HARM_CATEGORY_SEXUALLY_EXPLICIT HARM_CATEGORY_HATE_SPEECH HARM_CATEGORY_HARASSMENT HARM_CATEGORY_DANGEROUS_CONTENT
probability	The harm probability levels in the content. HARM_PROBABILITY_UNSPECIFIED NEGLIGIBLE LOW MEDIUM HIGH
blocked	A boolean flag associated with a safety attribute that indicates if the model's input or output was blocked. If blocked is true, then the errors field in the response contains one or more error codes. If blocked is false, then the response doesn't include the errors field.
startIndex	An integer that specifies where a citation starts in the content.
endIndex	An integer that specifies where a citation ends in the content.
url	The URL of a citation source. Examples of a URL source might be a news website or a GitHub repository.
title	The title of a citation source. Examples of source titles might be that of a news article or a book.
license	The license associated with a citation.
publicationDate	The date a citation was published. Its valid formats are YYYY, YYYY-MM, and YYYY-MM-DD.
promptTokenCount	Number of tokens in the request.
candidatesTokenCount	Number of tokens in the response(s).
totalTokenCount	Number of tokens in the request and response(s).

Sample requests

Text

REST

To test a text prompt by using the Vertex AI API with Server-sent events (SSE) enabled, send a POST request to the publisher model endpoint with ?alt=sse at the end of the URL.

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

• PROJECT_ID: Your project ID.

For other fields, see the Request body table.

HTTP method and URL:

POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro:streamGenerateContent?alt=sse

Request JSON body:

{
  "contents": {
    "role": "user",
    "parts": {
        "text": "Give me a recipe for banana bread."
    }
  },
  "safety_settings": {
    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
    "threshold": "BLOCK_LOW_AND_ABOVE"
  },
  "generation_config": {
    "temperature": 0.2,
    "topP": 0.8,
    "topK": 40
  }
}

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, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro:streamGenerateContent?alt=sse"

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, and execute the following command:

$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://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro:streamGenerateContent?alt=sse" | Select-Object -Expand Content

You should receive a JSON response similar to the sample response.

Chat

Also see Send chat prompt requests (Gemini).

REST

To test a chat prompt by using the Vertex AI API with Server-sent events (SSE) enabled, send a POST request to the publisher model endpoint with ?alt=sse at the end of the URL.

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

• PROJECT_ID: Your project ID.

For other fields, see the Request body table.

HTTP method and URL:

POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro:streamGenerateContent?alt=sse

Request JSON body:

{
  "contents": [
    {
      "role": "USER",
      "parts": { "text": "Hello!" }
    },
    {
      "role": "MODEL",
      "parts": { "text": "Argh! What brings ye to my ship?" }
    },
    {
      "role": "USER",
      "parts": { "text": "Wow! You are a real-life priate!" }
    }
  ],
  "safety_settings": {
    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
    "threshold": "BLOCK_LOW_AND_ABOVE"
  },
  "generation_config": {
    "temperature": 0.2,
    "topP": 0.8,
    "topK": 40,
    "maxOutputTokens": 200,
  }
}

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, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro:streamGenerateContent?alt=sse"

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, and execute the following command:

$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://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro:streamGenerateContent?alt=sse" | Select-Object -Expand Content

You should receive a JSON response similar to the sample response.

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

import vertexai

from vertexai.generative_models import GenerativeModel, ChatSession

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# location = "us-central1"
vertexai.init(project=project_id, location=location)
model = GenerativeModel(model_name="gemini-1.0-pro-002")
chat = model.start_chat()

def get_chat_response(chat: ChatSession, prompt: str) -> str:
    text_response = []
    responses = chat.send_message(prompt, stream=True)
    for chunk in responses:
        text_response.append(chunk.text)
    return "".join(text_response)

prompt = "Hello."
print(get_chat_response(chat, prompt))

prompt = "What are all the colors in a rainbow?"
print(get_chat_response(chat, prompt))

prompt = "Why does it appear when it rains?"
print(get_chat_response(chat, prompt))

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 {VertexAI} = require('@google-cloud/vertexai');

/**
 * TODO(developer): Update these variables before running the sample.
 */
async function createStreamChat(
  projectId = 'PROJECT_ID',
  location = 'us-central1',
  model = 'gemini-1.0-pro'
) {
  // Initialize Vertex with your Cloud project and location
  const vertexAI = new VertexAI({project: projectId, location: location});

  // Instantiate the model
  const generativeModel = vertexAI.getGenerativeModel({
    model: model,
  });

  const chat = generativeModel.startChat({});
  const chatInput1 = 'How can I learn more about that?';

  console.log(`User: ${chatInput1}`);

  const result1 = await chat.sendMessageStream(chatInput1);
  for await (const item of result1.stream) {
    console.log(item.candidates[0].content.parts[0].text);
  }
}

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.cloud.vertexai.VertexAI;
import com.google.cloud.vertexai.api.GenerateContentResponse;
import com.google.cloud.vertexai.generativeai.ChatSession;
import com.google.cloud.vertexai.generativeai.GenerativeModel;
import com.google.cloud.vertexai.generativeai.ResponseHandler;
import java.io.IOException;

public class ChatDiscussion {

  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-1.0-pro";

    chatDiscussion(projectId, location, modelName);
  }

  // Ask interrelated questions in a row using a ChatSession object.
  public static void chatDiscussion(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)) {
      GenerateContentResponse response;

      GenerativeModel model = new GenerativeModel(modelName, vertexAI);
      // Create a chat session to be used for interactive conversation.
      ChatSession chatSession = new ChatSession(model);

      response = chatSession.sendMessage("Hello.");
      System.out.println(ResponseHandler.getText(response));

      response = chatSession.sendMessage("What are all the colors in a rainbow?");
      System.out.println(ResponseHandler.getText(response));

      response = chatSession.sendMessage("Why does it appear when it rains?");
      System.out.println(ResponseHandler.getText(response));
      System.out.println("Chat Ended.");
    }
  }
}

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"
	"encoding/json"
	"fmt"

	"cloud.google.com/go/vertexai/genai"
)

var projectId = "PROJECT_ID"
var region = "us-central1"
var modelName = "gemini-1.0-pro-vision"

func makeChatRequests(projectId string, region string, modelName string) error {
	ctx := context.Background()
	client, err := genai.NewClient(ctx, projectId, region)
	if err != nil {
		return fmt.Errorf("error creating client: %v", err)
	}
	defer client.Close()

	gemini := client.GenerativeModel(modelName)
	chat := gemini.StartChat()

	r, err := chat.SendMessage(
		ctx,
		genai.Text("Hello"))
	if err != nil {
		return err
	}
	rb, _ := json.MarshalIndent(r, "", "  ")
	fmt.Println(string(rb))

	r, err = chat.SendMessage(
		ctx,
		genai.Text("What are all the colors in a rainbow?"))
	if err != nil {
		return err
	}
	rb, _ = json.MarshalIndent(r, "", "  ")
	fmt.Println(string(rb))

	r, err = chat.SendMessage(
		ctx,
		genai.Text("Why does it appear when it rains?"))
	if err != nil {
		return err
	}
	rb, _ = json.MarshalIndent(r, "", "  ")
	fmt.Println(string(rb))

	return nil
}

Multimodal

Also see Send multimodal prompt requests.

REST

To test a multimodal prompt by using the Vertex AI API, send a POST request to the publisher model endpoint.

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

• PROJECT_ID: Your project ID.

For other fields, see the Request body table.

HTTP method and URL:

POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro-vision:streamGenerateContent

Request JSON body:

{
  "contents": {
    "role": "user",
    "parts": [
      {
        "fileData": {
          "mimeType": "image/jpeg",
          "fileUri": "gs://cloud-samples-data/ai-platform/flowers/daisy/10559679065_50d2b16f6d.jpg"
        }
      },
      {
        "text": "Describe this picture."
      }
    ]
  },
  "safety_settings": {
    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
    "threshold": "BLOCK_LOW_AND_ABOVE"
  },
  "generation_config": {
    "temperature": 0.4,
    "topP": 1.0,
    "topK": 32,
    "maxOutputTokens": 2048
  }
}

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, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro-vision:streamGenerateContent"

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, and execute the following command:

$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://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro-vision:streamGenerateContent" | Select-Object -Expand Content

You should receive a JSON response similar to the sample response.

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

import http.client
import typing
import urllib.request
import vertexai

from vertexai.generative_models import GenerativeModel, Image

# Initialize Vertex AI
vertexai.init(project=project_id, location=location)

# create helper function
def load_image_from_url(image_url: str) -> Image:
    with urllib.request.urlopen(image_url) as response:
        response = typing.cast(http.client.HTTPResponse, response)
        image_bytes = response.read()
    return Image.from_bytes(image_bytes)

# Load images from Cloud Storage URI
landmark1 = load_image_from_url(
    "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
)
landmark2 = load_image_from_url(
    "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark2.png"
)
landmark3 = load_image_from_url(
    "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark3.png"
)

# Pass multimodal prompt
model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
response = model.generate_content(
    [
        landmark1,
        "city: Rome, Landmark: the Colosseum",
        landmark2,
        "city: Beijing, Landmark: Forbidden City",
        landmark3,
    ]
)
print(response)

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 {VertexAI} = require('@google-cloud/vertexai');
const axios = require('axios');

async function getBase64(url) {
  const image = await axios.get(url, {responseType: 'arraybuffer'});
  return Buffer.from(image.data).toString('base64');
}

/**
 * TODO(developer): Update these variables before running the sample.
 */
async function sendMultiModalPromptWithImage(
  projectId = 'PROJECT_ID',
  location = 'us-central1',
  model = 'gemini-1.0-pro-vision'
) {
  // For images, the SDK supports base64 strings
  const landmarkImage1 = await getBase64(
    'https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark1.png'
  );
  const landmarkImage2 = await getBase64(
    'https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark2.png'
  );
  const landmarkImage3 = await getBase64(
    'https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark3.png'
  );

  // Initialize Vertex with your Cloud project and location
  const vertexAI = new VertexAI({project: projectId, location: location});

  const generativeVisionModel = vertexAI.getGenerativeModel({
    model: model,
  });

  // Pass multimodal prompt
  const request = {
    contents: [
      {
        role: 'user',
        parts: [
          {
            inlineData: {
              data: landmarkImage1,
              mimeType: 'image/png',
            },
          },
          {
            text: 'city: Rome, Landmark: the Colosseum',
          },

          {
            inlineData: {
              data: landmarkImage2,
              mimeType: 'image/png',
            },
          },
          {
            text: 'city: Beijing, Landmark: Forbidden City',
          },
          {
            inlineData: {
              data: landmarkImage3,
              mimeType: 'image/png',
            },
          },
        ],
      },
    ],
  };

  // Create the response
  const response = await generativeVisionModel.generateContent(request);
  // Wait for the response to complete
  const aggregatedResponse = await response.response;
  // Select the text from the response
  const fullTextResponse =
    aggregatedResponse.candidates[0].content.parts[0].text;

  console.log(fullTextResponse);
}

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.cloud.vertexai.VertexAI;
import com.google.cloud.vertexai.api.Content;
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 com.google.cloud.vertexai.generativeai.ResponseHandler;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.net.HttpURLConnection;
import java.net.URL;

public class MultimodalMultiImage {

  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-1.0-pro-vision";

    multimodalMultiImage(projectId, location, modelName);
  }

  // Generates content from multiple input images.
  public static void multimodalMultiImage(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)) {
      GenerativeModel model = new GenerativeModel(modelName, vertexAI);

      Content content = ContentMaker.fromMultiModalData(
          PartMaker.fromMimeTypeAndData("image/png", readImageFile(
              "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark1.png")),
          "city: Rome, Landmark: the Colosseum",
          PartMaker.fromMimeTypeAndData("image/png", readImageFile(
              "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark2.png")),
          "city: Beijing, Landmark: Forbidden City",
          PartMaker.fromMimeTypeAndData("image/png", readImageFile(
              "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark3.png"))
      );

      GenerateContentResponse response = model.generateContent(content);

      String output = ResponseHandler.getText(response);
      System.out.println(output);
    }
  }

  // Reads the image data from the given URL.
  public static byte[] readImageFile(String url) throws IOException {
    URL urlObj = new URL(url);
    HttpURLConnection connection = (HttpURLConnection) urlObj.openConnection();
    connection.setRequestMethod("GET");

    int responseCode = connection.getResponseCode();

    if (responseCode == HttpURLConnection.HTTP_OK) {
      InputStream inputStream = connection.getInputStream();
      ByteArrayOutputStream outputStream = new ByteArrayOutputStream();

      byte[] buffer = new byte[1024];
      int bytesRead;
      while ((bytesRead = inputStream.read(buffer)) != -1) {
        outputStream.write(buffer, 0, bytesRead);
      }

      return outputStream.toByteArray();
    } else {
      throw new RuntimeException("Error fetching file: " + responseCode);
    }
  }
}

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"
	"log"
	"net/http"
	"net/url"
	"os"
	"strings"

	"cloud.google.com/go/vertexai/genai"
)

func main() {
	projectID := os.Getenv("GOOGLE_CLOUD_PROJECT")
	location := "us-central1"
	modelName := "gemini-1.0-pro-vision"
	temperature := 0.4

	if projectID == "" {
		log.Fatal("require environment variable GOOGLE_CLOUD_PROJECT")
	}

	// construct this multimodal prompt:
	// [image of colosseum] city: Rome, Landmark: the Colosseum
	// [image of forbidden city]  city: Beijing, Landmark: the Forbidden City
	// [new image]

	// create prompt image parts
	// colosseum
	colosseum, err := partFromImageURL("https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark1.png")
	if err != nil {
		log.Fatalf("unable to read image: %v", err)
	}
	// forbidden city
	forbiddenCity, err := partFromImageURL("https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark2.png")
	if err != nil {
		log.Fatalf("unable to read image: %v", err)
	}
	// new image
	newImage, err := partFromImageURL("https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark3.png")
	if err != nil {
		log.Fatalf("unable to read image: %v", err)
	}

	// create a multimodal (multipart) prompt
	prompt := []genai.Part{
		colosseum,
		genai.Text("city: Rome, Landmark: the Colosseum "),
		forbiddenCity,
		genai.Text("city: Beijing, Landmark: the Forbidden City "),
		newImage,
	}

	// generate the response
	err = generateMultimodalContent(os.Stdout, prompt, projectID, location, modelName, float32(temperature))
	if err != nil {
		log.Fatalf("unable to generate: %v", err)
	}
}

// generateMultimodalContent provide a generated response using multimodal input
func generateMultimodalContent(w io.Writer, parts []genai.Part, projectID, location, modelName string, temperature float32) error {
	ctx := context.Background()

	client, err := genai.NewClient(ctx, projectID, location)
	if err != nil {
		log.Fatal(err)
	}
	defer client.Close()

	model := client.GenerativeModel(modelName)
	model.SetTemperature(temperature)

	res, err := model.GenerateContent(ctx, parts...)
	if err != nil {
		return fmt.Errorf("unable to generate contents: %v", err)
	}

	fmt.Fprintf(w, "generated response: %s\n", res.Candidates[0].Content.Parts[0])

	return nil
}

// partFromImageURL create a multimodal prompt part from an image URL
func partFromImageURL(image string) (genai.Part, error) {
	var img genai.Blob

	imageURL, err := url.Parse(image)
	if err != nil {
		return img, err
	}
	res, err := http.Get(image)
	if err != nil || res.StatusCode != 200 {
		return img, err
	}
	defer res.Body.Close()
	data, err := io.ReadAll(res.Body)
	if err != nil {
		return img, fmt.Errorf("unable to read from http: %v", err)
	}

	position := strings.LastIndex(imageURL.Path, ".")
	if position == -1 {
		return img, fmt.Errorf("couldn't find a period to indicate a file extension")
	}
	ext := imageURL.Path[position+1:]

	img = genai.ImageData(ext, data)
	return img, nil
}

C#

Before trying this sample, follow the C# setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI C# 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.

using Google.Api.Gax.Grpc;
using Google.Cloud.AIPlatform.V1;
using Google.Protobuf;
using System.Collections.Generic;
using System.Net.Http;
using System.Text;
using System.Threading.Tasks;

public class MultimodalMultiImage
{
    public async Task<string> GenerateContent(
        string projectId = "your-project-id",
        string location = "us-central1",
        string publisher = "google",
        string model = "gemini-1.0-pro-vision"
    )
    {
        // Create client
        var predictionServiceClient = new PredictionServiceClientBuilder
        {
            Endpoint = $"{location}-aiplatform.googleapis.com"
        }.Build();

        // Images
        ByteString colosseum = await ReadImageFileAsync(
            "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark1.png");

        ByteString forbiddenCity = await ReadImageFileAsync(
            "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark2.png");

        ByteString christRedeemer = await ReadImageFileAsync(
            "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark3.png");

        // Initialize request argument(s)
        var content = new Content
        {
            Role = "USER"
        };
        content.Parts.AddRange(new List<Part>()
        {
            new()
            {
                InlineData = new()
                {
                    MimeType = "image/png",
                    Data = colosseum

                }
            },
            new()
            {
                Text = "city: Rome, Landmark: the Colosseum"
            },
            new()
            {
                InlineData = new()
                {
                    MimeType = "image/png",
                    Data = forbiddenCity
                }
            },
            new()
            {
                Text = "city: Beijing, Landmark: Forbidden City"
            },
            new()
            {
                InlineData = new()
                {
                    MimeType = "image/png",
                    Data = christRedeemer
                }
            }
        });

        var generateContentRequest = new GenerateContentRequest
        {
            Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}"
        };
        generateContentRequest.Contents.Add(content);

        // 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();
    }

    private static async Task<ByteString> ReadImageFileAsync(string url)
    {
        using HttpClient client = new();
        using var response = await client.GetAsync(url);
        byte[] imageBytes = await response.Content.ReadAsByteArrayAsync();
        return ByteString.CopyFrom(imageBytes);
    }
}

Function

Also see Function calling.

REST

To test a function prompt by using the Vertex AI API, send a POST request to the publisher model endpoint.

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

• PROJECT_ID: Your project ID.

For other fields, see the Request body table.

HTTP method and URL:

POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro:streamGenerateContent

Request JSON body:

{
  "contents": {
    "role": "user",
    "parts": {
      "text": "Which theaters in Mountain View show Barbie movie?"
    }
  },
  "tools": [
    {
      "function_declarations": [
        {
          "name": "find_movies",
          "description": "find movie titles currently playing in theaters based on any description, genre, title words, etc.",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA or a zip code e.g. 95616"
              },
              "description": {
                "type": "string",
                "description": "Any kind of description including category or genre, title words, attributes, etc."
              }
            },
            "required": [
              "description"
            ]
          }
        },
        {
          "name": "find_theaters",
          "description": "find theaters based on location and optionally movie title which are is currently playing in theaters",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA or a zip code e.g. 95616"
              },
              "movie": {
                "type": "string",
                "description": "Any movie title"
              }
            },
            "required": [
              "location"
            ]
          }
        },
        {
          "name": "get_showtimes",
          "description": "Find the start times for movies playing in a specific theater",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA or a zip code e.g. 95616"
              },
              "movie": {
                "type": "string",
                "description": "Any movie title"
              },
              "theater": {
                "type": "string",
                "description": "Name of the theater"
              },
              "date": {
                "type": "string",
                "description": "Date for requested showtime"
              }
            },
            "required": [
              "location",
              "movie",
              "theater",
              "date"
            ]
          }
        }
      ]
    }
  ]
}

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, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro:streamGenerateContent"

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, and execute the following command:

$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://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-1.0-pro:streamGenerateContent" | Select-Object -Expand Content

You should receive a JSON response similar to the sample response.

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

import vertexai
from vertexai.generative_models import (
    Content,
    FunctionDeclaration,
    GenerationConfig,
    GenerativeModel,
    Part,
    Tool,
)


def generate_function_call(prompt: str, project_id: str, location: str) -> tuple:
    # Initialize Vertex AI
    vertexai.init(project=project_id, location=location)

    # Initialize Gemini model
    model = GenerativeModel(model_name="gemini-1.0-pro-001")

    # Specify a function declaration and parameters for an API request
    get_current_weather_func = FunctionDeclaration(
        name="get_current_weather",
        description="Get the current weather in a given location",
        # Function parameters are specified in OpenAPI JSON schema format
        parameters={
            "type": "object",
            "properties": {"location": {"type": "string", "description": "Location"}},
        },
    )

    # Define a tool that includes the above get_current_weather_func
    weather_tool = Tool(
        function_declarations=[get_current_weather_func],
    )

    # Define the user's prompt in a Content object that we can reuse in model calls
    user_prompt_content = Content(
        role="user",
        parts=[
            Part.from_text(prompt),
        ],
    )

    # Send the prompt and instruct the model to generate content using the Tool that you just created
    response = model.generate_content(
        user_prompt_content,
        generation_config=GenerationConfig(temperature=0),
        tools=[weather_tool],
    )
    response_function_call_content = response.candidates[0].content

    # Check the function name that the model responded with, and make an API call to an external system
    if (
        response.candidates[0].content.parts[0].function_call.name
        == "get_current_weather"
    ):
        # Extract the arguments to use in your API call
        location = (
            response.candidates[0].content.parts[0].function_call.args["location"]
        )

        # Here you can use your preferred method to make an API request to fetch the current weather, for example:
        # api_response = requests.post(weather_api_url, data={"location": location})

        # In this example, we'll use synthetic data to simulate a response payload from an external API
        api_response = """{ "location": "Boston, MA", "temperature": 38, "description": "Partly Cloudy",
                        "icon": "partly-cloudy", "humidity": 65, "wind": { "speed": 10, "direction": "NW" } }"""

    # Return the API response to Gemini so it can generate a model response or request another function call
    response = model.generate_content(
        [
            user_prompt_content,  # User prompt
            response_function_call_content,  # Function call response
            Content(
                parts=[
                    Part.from_function_response(
                        name="get_current_weather",
                        response={
                            "content": api_response,  # Return the API response to Gemini
                        },
                    )
                ],
            ),
        ],
        tools=[weather_tool],
    )
    # Get the model summary response
    summary = response.candidates[0].content.parts[0].text

    return summary, response

Sample responses

Text

data: {"candidates": [{"content": {"role": "model","parts": [{"text": "Ingredients:\n\n- 3 ripe bananas, mashed\n- 1 cup sugar"}]},"safetyRatings": [{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}]}

data: {"candidates": [{"content": {"role": "model","parts": [{"text": "\n- 1/2 cup (1 stick) unsalted butter, softened\n"}]},"safetyRatings": [{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}]}

data: {"candidates": [{"content": {"role": "model","parts": [{"text": "- 2 large eggs\n- 2 cups all-purpose flour\n- 1 teaspoon baking soda\n- 1/2 teaspoon salt\n- "}]},"safetyRatings": [{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}]}

data: {"candidates": [{"content": {"role": "model","parts": [{"text": "1/2 cup chopped walnuts (optional)\n\nInstructions:\n\n1. Preheat oven to 350 degrees F (175 degrees C). Grease"}]},"safetyRatings": [{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}]}

data: {"candidates": [{"content": {"role": "model","parts": [{"text": " and flour a 9x5 inch loaf pan.\n2. In a large bowl, cream together the butter and sugar until light and fluffy. Beat in the eggs one at a time, then stir in the mashed bananas.\n3"}]},"safetyRatings": [{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}],"citationMetadata": {"citations": [{"startIndex": 322,"endIndex": 451,"uri": "https://discover.texasrealfood.com/texas-home-cooking/whats-in-season-plums-exploring-health-benefits-varieties-and-recipes"}]}}]}

data: {"candidates": [{"content": {"role": "model","parts": [{"text": ". In a separate bowl, whisk together the flour, baking soda, and salt. Gradually add the dry ingredients to the wet ingredients, mixing until just combined. Fold in the walnuts, if desired.\n4. Pour the batter into the"}]},"safetyRatings": [{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}],"citationMetadata": {"citations": [{"startIndex": 472,"endIndex": 614,"uri": "https://commandame.com/urban-cookhouse-half-baked-cookie-recipe/"}]}}]}

data: {"candidates": [{"content": {"role": "model","parts": [{"text": " prepared loaf pan and bake for 50-60 minutes, or until a toothpick inserted into the center comes out clean.\n5. Let the bread cool in the pan for 10 minutes before turning it out onto a wire rack to cool completely."}]},"finishReason": "STOP","safetyRatings": [{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}],"citationMetadata": {"citations": [{"startIndex": 666,"endIndex": 796,"uri": "https://dessertdonelight.com/healthy-hawaiian-banana-bread/"},{"startIndex": 728,"endIndex": 851,"uri": "https://earlsgrocery.com/gluten-free-bread/gluten-free-yeast-free-bread-healthy-and-nutition"}]}}],"usageMetadata": {"promptTokenCount": 8,"candidatesTokenCount": 245,"totalTokenCount": 253}}

Chat

data: {"candidates": [{"content": {"role": "model","parts": [{"text": "Avast there, landlubber! Ye be mistaken. I be but a"}]},"safetyRatings": [{"category": "HARM_CATEGORY_HARASSMENT","probability": "LOW"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}]}

data: {"candidates": [{"content": {"role": "model","parts": [{"text": " humble pirate of the seven seas, brought to life by the magic of artificial intelligence"}]},"safetyRatings": [{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}]}

data: {"candidates": [{"content": {"role": "model","parts": [{"text": ". I be no real-life pirate, but I be mighty good at pretendin'!"}]},"finishReason": "STOP","safetyRatings": [{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}],"usageMetadata": {"promptTokenCount": 23,"candidatesTokenCount": 50,"totalTokenCount": 73}}

Multimodal

[{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [
          {
            "text": " A daisy is growing up through a pile of brown and yellow fall leaves"
          }
        ]
      },
      "finishReason": "STOP",
      "safetyRatings": [
        {
          "category": "HARM_CATEGORY_HARASSMENT",
          "probability": "NEGLIGIBLE"
        },
        {
          "category": "HARM_CATEGORY_HATE_SPEECH",
          "probability": "NEGLIGIBLE"
        },
        {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "probability": "NEGLIGIBLE"
        },
        {
          "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
          "probability": "NEGLIGIBLE"
        }
      ]
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 262,
    "candidatesTokenCount": 14,
    "totalTokenCount": 276
  }
}]

Function

[{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "functionCall": {
              "name": "find_theaters",
              "args": {
                "movie": "Barbie",
                "location": "Mountain View, CA"
              }
            }
          }
        ]
      },
      "finishReason": "STOP",
      "safetyRatings": [
        {
          "category": "HARM_CATEGORY_HARASSMENT",
          "probability": "NEGLIGIBLE"
        },
        {
          "category": "HARM_CATEGORY_HATE_SPEECH",
          "probability": "NEGLIGIBLE"
        },
        {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "probability": "NEGLIGIBLE"
        },
        {
          "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
          "probability": "NEGLIGIBLE"
        }
      ]
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 9,
    "totalTokenCount": 9
  }
}]

What's next

Learn how to use the Vertex AI Gemini API:

• Learn how to send chat prompt requests.
• Learn how to send multimodal prompt requests.
• Learn how to call functions.
• Learn how to get token count.
• Learn how to configure safety attributes.