Try Gemini 1.5 Pro, our most advanced multimodal model in Vertex AI, and see what you can build with a 1M token context window. Try Gemini 1.5 Pro, our most advanced multimodal model in Vertex AI, and see what you can build with a 1M token context window.

Send multimodal prompt requests

The Gemini model family includes models that work with multimodal prompt requests. The term multimodal indicates that you can use more than one modality, or type of input, in a prompt. Models that aren't multimodal accept prompts only with text. Modalities can include text, audio, video, and more.

There are several ways to implement multimodal solutions using the Gemini API, including the Python, Node.js, Java, and Go SDKs, the Google Cloud console, and the REST API. Code samples later in this document demonstrate how to create multimodal solutions using these options.

The Gemini multimodal models are:

Gemini 1.0 Pro Vision
Gemini 1.5 Pro (Preview)

The following table indicates which modalities each multimodal Gemini API works with in a prompt request.

Model	Text	Code	Images	Audio	Video	Video/audio	PDF
Gemini 1.0 Pro
Gemini 1.0 Pro Vision
Gemini 1.5 Pro (Preview)

To explore multimodal models in the Google Cloud console, select the gemini-1.0-pro-vision or the gemini-1.5-pro model card in the Model Garden.

Go to the Model Garden

For a list of languages supported by Gemini models, see model information Language support. To learn more about how to design multimodal prompts, see Design multimodal prompts. If you're looking for a way to use Gemini directly from your mobile and web apps, see the Google AI SDKs for Android, Swift, and web.

Multimodal model differences

The differences between Gemini multimodal models are specified in the following tables. You can use this information to help you decide which model is best for you.

Text

The following are some of the differences in the text modality between the Gemini multimodal models:

Model	Text modality details
Gemini 1.0 Pro Vision	The maximum number of tokens is 16,384, or about a 128 page book assuming 250 words per page. This maximum includes both input and output tokens. The maximum number of output tokens is 2,048.
Gemini 1.5 Pro (Preview)	The context length is 1 million tokens which is equivalent to approximately a 4,000 page book. This lets the model output long form text, such as books, multiple PDFs, or user manuals.

Code

The following are some of the differences between Gemini multimodal models when working with code:

Model	Code modality details
Gemini 1.0 Pro Vision	The maximum number of tokens is 16,384, or about a 128 page book assuming 250 words per page. This maximum includes both input and output tokens. The maximum number of output tokens is 2,048.
Gemini 1.5 Pro (Preview)	The context length is 1 million tokens which lets the model work with an entire codebase or a whole application codebase.

Image

The following are some of the differences in the image modality between the Gemini multimodal models:

Model	Image modality details
Gemini 1.0 Pro Vision	The maximum number of images per prompt is 16.
Gemini 1.5 Pro (Preview)	The maximum number of images per prompt is 3,000.

Audio (speech only)

The following are some of the differences in the audio modality between the Gemini multimodal models:

Model	Audio modality details
Gemini 1.0 Pro Vision	Audio isn't supported.
Gemini 1.5 Pro (Preview)	The maximum number of hours of audio per prompt is approximately 8.4 hours, or up to 1 million tokens. Speech can be understood for audio summarization, transcription, and translation.

Video

The following are some of the differences in the video modality between the Gemini multimodal models:

Model	Video modality details
Gemini 1.0 Pro Vision	Maximum video length is 2 minutes. The maximum number of videos per prompt is 1. Audio in the video is ignored.
Gemini 1.5 Pro (Preview)	Maximum video length when it includes audio is approximately 50 minutes. The maximum video length for video without audio is 1 hour. Maximum videos per prompt is 10. The model is able to use both video and audio data to answer the prompt. For example, summarizes the video using both the visual content and speech in the video.

PDF

The following are some of the differences in the PDF modality between the Gemini multimodal models:

Model	PDF modality details
Gemini 1.0 Pro Vision	The maximum number of pages per prompt is 16. The maximum file size for a PDF is 50MB.
Gemini 1.5 Pro (Preview)	The maximum number of pages per prompt is 300. The maximum file size for a PDF is 50MB.

Quickstart

Use the following code samples to start using the Gemini API. Each code sample demonstrates working with a different modality. Some code samples in this document work with all Gemini multimodal models, and some work with only Gemini 1.5 Pro (Preview). Each code sample specifies which models it works with.

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

Single image

The sample code on each of the following tabs shows a different way to identify what's in an image. This sample works with all Gemini multimodal models.

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 Vertex AI SDK for Python API reference documentation.

Streaming and non-streaming responses

You can choose whether the model generates a streaming response or a non-streaming response. Streaming involves receiving responses to prompts as they are generated. That is, as soon as the model generates output tokens, the output tokens are sent. A non-streaming response to prompts is sent only after all of the output tokens are generated.

For a streaming response, use the stream parameter in generate_content.

  response = model.generate_content(contents=[...], stream = True)

For a non-streaming response, remove the parameter, or set the parameter to False.

Sample code

import vertexai

from vertexai.generative_models import GenerativeModel, Part

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

# Load the model
model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")

# Load example image
image_url = "gs://generativeai-downloads/images/scones.jpg"
image_content = Part.from_uri(image_url, "image/jpeg")

# Query the model
response = model.generate_content([image_content, "what is this image?"])
print(response)

Node.js

Before trying this sample, follow the Node.js setup instructions in the Generative AI quickstart using the Node.js SDK. For more information, see the Node.js SDK for Gemini reference documentation.

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

Streaming and non-streaming responses

For a streaming response, use the generateContentStream method.

  const streamingResp = await generativeModel.generateContentStream(request);

For a non-streaming response, use the generateContent method.

  const streamingResp = await generativeModel.generateContent(request);

Sample code

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

/**
 * TODO(developer): Update these variables before running the sample.
 */
async function createNonStreamingMultipartContent(
  projectId = 'PROJECT_ID',
  location = 'us-central1',
  model = 'gemini-1.0-pro-vision',
  image = 'gs://generativeai-downloads/images/scones.jpg',
  mimeType = 'image/jpeg'
) {
  // Initialize Vertex with your Cloud project and location
  const vertexAI = new VertexAI({project: projectId, location: location});

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

  // For images, the SDK supports both Google Cloud Storage URI and base64 strings
  const filePart = {
    fileData: {
      fileUri: image,
      mimeType: mimeType,
    },
  };

  const textPart = {
    text: 'what is shown in this image?',
  };

  const request = {
    contents: [{role: 'user', parts: [filePart, textPart]}],
  };

  console.log('Prompt Text:');
  console.log(request.contents[0].parts[1].text);

  console.log('Non-Streaming Response Text:');
  // Create the response stream
  const responseStream =
    await generativeVisionModel.generateContentStream(request);

  // Wait for the response stream to complete
  const aggregatedResponse = await responseStream.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. For more information, see the Vertex AI Java SDK for Gemini reference documentation.

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

Streaming and non-streaming responses

For a streaming response, use the generateContentStream method.

  public ResponseStream generateContentStream(Content content)

For a non-streaming response, use the generateContent method.

  public GenerateContentResponse generateContent(Content content)

Sample code

import com.google.cloud.vertexai.VertexAI;
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.util.Base64;

public class MultimodalQuery {

  public static void main(String[] args) throws Exception {
    // 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";
    String dataImageBase64 = "your-base64-encoded-image";

    String output = multimodalQuery(projectId, location, modelName, dataImageBase64);
    System.out.println(output);
  }


  // Ask the model to recognise the brand associated with the logo image.
  public static String multimodalQuery(String projectId, String location, String modelName,
      String dataImageBase64) throws Exception {
    // 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)) {
      String output;
      byte[] imageBytes = Base64.getDecoder().decode(dataImageBase64);

      GenerativeModel model = new GenerativeModel(modelName, vertexAI);
      GenerateContentResponse response = model.generateContent(
          ContentMaker.fromMultiModalData(
              "What is this image?",
              PartMaker.fromMimeTypeAndData("image/jpg", imageBytes)
          ));

      output = ResponseHandler.getText(response);
      return output;
    }
  }
}

REST

You can use REST to test a text prompt by using the Vertex AI API to send a POST request to the publisher model endpoint.

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 available regions
- us-central1
- us-west4
- northamerica-northeast1
- us-east4
- us-west1
- asia-northeast3
- asia-southeast1
- asia-northeast1
PROJECT_ID: Your project ID.
MODEL_ID: The model ID of the multimodal model that you want to use. The options are:
- gemini-1.0-pro-vision
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.
TEXT: The text instructions to include in the prompt.
B64_BASE: The base64 encoding of the image, PDF, or video to include inline in the prompt. When including media inline, you must also specify MIMETYPE.
FILE_URI: 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.
MIME_TYPE: 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
SAFETY_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. Acceptable values include the following:
Click to expand blocking thresholds
- BLOCK_NONE
- BLOCK_ONLY_HIGH
- BLOCK_MEDIUM_AND_ABOVE (default)
- BLOCK_LOW_AND_ABOVE
BLOCK_LOW_AND_ABOVE blocks the most while BLOCK_ONLY_HIGH blocks the least.
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.
TOP_P: 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.
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.
MAX_OUTPUT_TOKENS: 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.
STOP_SEQUENCES: 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

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:GENERATE_RESPONSE_METHOD

Request JSON body:

{
  "contents": {
    "role": "ROLE",
    "parts": [
      {
        "inlineDATA": {
          "mimeType": "MIME_TYPE",
          "data": "B64_BASE_IMAGE"
        }
      },
      {
        "fileData": {
          "mimeType": "MIME_TYPE",
          "fileUri": "FILE_URI"
        }
      },
      {
        "text": "TEXT"
      }
    ]
  },
  "safety_settings": {
    "category": "SAFETY_CATEGORY",
    "threshold": "THRESHOLD"
  },
  "generation_config": {
    "temperature": TEMPERATURE,
    "topP": TOP_P,
    "topK": TOP_K,
    "candidateCount": 1,
    "maxOutputTokens": MAX_OUTPUT_TOKENS,
    "stopSequences": STOP_SEQUENCES,
  }
}

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://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, 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://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": {
        "parts": [
          {
            "text": " Yes the animal in the picture is a cat."
          }
        ]
      }
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 263,
    "candidatesTokenCount": 10,
    "totalTokenCount": 273
  }
}

Example curl command

LOCATION="us-central1"
MODEL_ID="gemini-1.0-pro-vision"
PROJECT_ID="test-project"

curl \
-X POST \
-H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
-H "Content-Type: application/json"
https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/publishers/google/models/${MODEL_ID}:${GENERATE_RESPONSE_METHOD} -d \
$'{
  "contents": {
    "role": "user",
    "parts": [
      {
        "fileData": {
          "mimeType": "image/png",
          "fileUri": "gs://my-bucket/images/cat.png"
        }
      },
      {
        "text": "Describe this picture."
      },
    ]
  },
  "safety_settings": {
    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
    "threshold": "BLOCK_LOW_AND_ABOVE"
  },
  "generation_config": {
    "temperature": 0.4,
    "topP": 1,
    "topK": 32,
    "maxOutputTokens": 2048,
  }
}'

Single PDF

The following tab shows you how to include a PDF in a prompt request using the Python SDK. This PDF sample works with all Gemini multimodal models.

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 Vertex AI SDK for Python API reference documentation.

Streaming and non-streaming responses

For a streaming response, use the stream parameter in generate_content.

  response = model.generate_content(contents=[...], stream = True)

For a non-streaming response, remove the parameter, or set the parameter to False.

Sample code

import vertexai

from vertexai.generative_models import GenerativeModel, Part

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"

vertexai.init(project=project_id, location="us-central1")

model = GenerativeModel(model_name="gemini-1.5-pro-preview-0409")

prompt = """
You are a very professional document summarization specialist.
Please summarize the given document.
"""

pdf_file_uri = "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf"
pdf_file = Part.from_uri(pdf_file_uri, mime_type="application/pdf")
contents = [pdf_file, prompt]

response = model.generate_content(contents)
print(response.text)

Single video

Each of the following tabs show you a different way to include a video in a prompt request. These PDF samples work with all Gemini multimodal models.

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 Vertex AI SDK for Python API reference documentation.

Streaming and non-streaming responses

For a streaming response, use the stream parameter in generate_content.

  response = model.generate_content(contents=[...], stream = True)

For a non-streaming response, remove the parameter, or set the parameter to False.

Sample code

import vertexai

from vertexai.generative_models import GenerativeModel, Part

# Initialize Vertex AI
vertexai.init(project=project_id, location=location)
# Load the model
vision_model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
# Generate text
response = vision_model.generate_content(
    [
        Part.from_uri(
            "gs://cloud-samples-data/video/animals.mp4", mime_type="video/mp4"
        ),
        "What is in the video?",
    ]
)
print(response)

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 System.Collections.Generic;
using System.Text;
using System.Threading.Tasks;

public class MultimodalVideoInput
{
    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();

        // Prompt
        string prompt = "What's in the video?";
        string videoUri = "gs://cloud-samples-data/video/animals.mp4";

        // Initialize request argument(s)
        var content = new Content
        {
            Role = "USER"
        };
        content.Parts.AddRange(new List<Part>()
        {
            new() {
                Text = prompt
            },
            new() {
                FileData = new() {
                    MimeType = "video/mp4",
                    FileUri = videoUri
                }
            }
        });

        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();
    }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Generative AI quickstart using the Node.js SDK. For more information, see the Node.js SDK for Gemini reference documentation.

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

Streaming and non-streaming responses

For a streaming response, use the generateContentStream method.

  const streamingResp = await generativeModel.generateContentStream(request);

For a non-streaming response, use the generateContent method.

  const streamingResp = await generativeModel.generateContent(request);

Sample code

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

/**
 * TODO(developer): Update these variables before running the sample.
 */
async function sendMultiModalPromptWithVideo(
  projectId = 'PROJECT_ID',
  location = 'us-central1',
  model = 'gemini-1.0-pro-vision'
) {
  // 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: [
          {
            fileData: {
              fileUri: 'gs://cloud-samples-data/video/animals.mp4',
              mimeType: 'video/mp4',
            },
          },
          {
            text: 'What is in the video?',
          },
        ],
      },
    ],
  };

  // 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. For more information, see the Vertex AI Java SDK for Gemini reference documentation.

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

Streaming and non-streaming responses

For a streaming response, use the generateContentStream method.

  public ResponseStream generateContentStream(Content content)

For a non-streaming response, use the generateContent method.

  public GenerateContentResponse generateContent(Content content)

Sample code

import com.google.cloud.vertexai.VertexAI;
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.IOException;

public class MultimodalVideoInput {

  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";

    multimodalVideoInput(projectId, location, modelName);
  }

  // Analyzes the given video input.
  public static void multimodalVideoInput(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)) {
      String videoUri = "gs://cloud-samples-data/video/animals.mp4";

      GenerativeModel model = new GenerativeModel(modelName, vertexAI);
      GenerateContentResponse response = model.generateContent(
          ContentMaker.fromMultiModalData(
              "What is in the video?",
              PartMaker.fromMimeTypeAndData("video/mp4", videoUri)
          ));

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

Go

Before trying this sample, follow the Go setup instructions in the Vertex AI quickstart. For more information, see the Vertex AI Go SDK for Gemini reference documentation.

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

Streaming and non-streaming responses

For a streaming response, use the GenerateContentStream method.

  iter := model.GenerateContentStream(ctx, genai.Text("Tell me a story about a lumberjack and his giant ox. Keep it very short."))

For a non-streaming response, use the GenerateContent method.

  resp, err := model.GenerateContent(ctx, genai.Text("What is the average size of a swallow?"))

Sample code

import (
	"context"
	"errors"
	"fmt"
	"io"
	"mime"
	"path/filepath"

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

// generateMultimodalContent generates a response into w, based upon the prompt
// and video provided.
// video is a Google Cloud Storage path starting with "gs://"
func generateMultimodalContent(w io.Writer, prompt, video, projectID, location, modelName string) error {
	// prompt := "What is in this video?"
	// video := "gs://cloud-samples-data/video/animals.mp4"
	// location := "us-central1"
	// modelName := "gemini-1.0-pro-vision"
	ctx := context.Background()

	client, err := genai.NewClient(ctx, projectID, location)
	if err != nil {
		return fmt.Errorf("unable to create client: %v", err)
	}
	defer client.Close()

	model := client.GenerativeModel(modelName)
	model.SetTemperature(0.4)

	// Given a video file URL, prepare video file as genai.Part
	part := genai.FileData{
		MIMEType: mime.TypeByExtension(filepath.Ext(video)),
		FileURI:  video,
	}

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

	if len(res.Candidates) == 0 ||
		len(res.Candidates[0].Content.Parts) == 0 {
		return errors.New("empty response from model")
	}

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

REST

You can use REST to test a text prompt by using the Vertex AI API to send a POST request to the publisher model endpoint.

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 available regions
- us-central1
- us-west4
- northamerica-northeast1
- us-east4
- us-west1
- asia-northeast3
- asia-southeast1
- asia-northeast1
PROJECT_ID: Your project ID.
MODEL_ID: The model ID of the multimodal model that you want to use. The options are:
- gemini-1.0-pro-vision
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.
TEXT: The text instructions to include in the prompt.
B64_BASE: The base64 encoding of the image, PDF, or video to include inline in the prompt. When including media inline, you must also specify MIMETYPE.
FILE_URI: 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.
MIME_TYPE: 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
SAFETY_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. Acceptable values include the following:
Click to expand blocking thresholds
- BLOCK_NONE
- BLOCK_ONLY_HIGH
- BLOCK_MEDIUM_AND_ABOVE (default)
- BLOCK_LOW_AND_ABOVE
BLOCK_LOW_AND_ABOVE blocks the most while BLOCK_ONLY_HIGH blocks the least.
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.
TOP_P: 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.
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.
MAX_OUTPUT_TOKENS: 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.
STOP_SEQUENCES: 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

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:GENERATE_RESPONSE_METHOD

Request JSON body:

{
  "contents": {
    "role": "ROLE",
    "parts": [
      {
        "inlineDATA": {
          "mimeType": "MIME_TYPE",
          "data": "B64_BASE_IMAGE"
        }
      },
      {
        "fileData": {
          "mimeType": "MIME_TYPE",
          "fileUri": "FILE_URI"
        }
      },
      {
        "text": "TEXT"
      }
    ]
  },
  "safety_settings": {
    "category": "SAFETY_CATEGORY",
    "threshold": "THRESHOLD"
  },
  "generation_config": {
    "temperature": TEMPERATURE,
    "topP": TOP_P,
    "topK": TOP_K,
    "candidateCount": 1,
    "maxOutputTokens": MAX_OUTPUT_TOKENS,
    "stopSequences": STOP_SEQUENCES,
  }
}

To send your request, choose one of these options:

curl

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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:GENERATE_RESPONSE_METHOD"

PowerShell

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://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": {
        "parts": [
          {
            "text": " Yes the animal in the picture is a cat."
          }
        ]
      }
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 263,
    "candidatesTokenCount": 10,
    "totalTokenCount": 273
  }
}

Example curl command

LOCATION="us-central1"
MODEL_ID="gemini-1.0-pro-vision"
PROJECT_ID="test-project"

curl \
-X POST \
-H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
-H "Content-Type: application/json"
https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/publishers/google/models/${MODEL_ID}:${GENERATE_RESPONSE_METHOD} -d \
$'{
  "contents": {
    "role": "user",
    "parts": [
      {
        "fileData": {
          "mimeType": "image/png",
          "fileUri": "gs://my-bucket/images/cat.png"
        }
      },
      {
        "text": "Describe this picture."
      },
    ]
  },
  "safety_settings": {
    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
    "threshold": "BLOCK_LOW_AND_ABOVE"
  },
  "generation_config": {
    "temperature": 0.4,
    "topP": 1,
    "topK": 32,
    "maxOutputTokens": 2048,
  }
}'

Console

To send a multimodal prompt by using the Google Cloud console, do the following:

In the Vertex AI section of the Google Cloud console, go to the Vertex AI Studio page.
Go to Vertex AI Studio
Under Prompt design (single turn), click Open.
Configure the model and parameters:
- Region: Select the region that you want to use.
- Model: Select Gemini Pro Vision.
- 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.
- 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: Enter a stop sequence, which is a series of characters (including spaces) that stops response generation if the model encounters it. The sequence is not included as part of the response. 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.

The Google Cloud console only supports streaming, which involves receiving responses to prompts as they are generated. You are ready to enter a message in the message box to start a conversation with the model.

The model uses the previous messages as context for new responses. To include an image, PDF, or video in the prompt, click the icon.

To learn about multimodal prompts, see Design multimodal prompts.
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

Single audio

The following shows you how to use an audio file to summarize a podcast. This sample works with Gemini 1.5 Pro (Preview) only.

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 Vertex AI SDK for Python API reference documentation.

Streaming and non-streaming responses

For a streaming response, use the stream parameter in generate_content.

  response = model.generate_content(contents=[...], stream = True)

For a non-streaming response, remove the parameter, or set the parameter to False.

Sample code


  import vertexai
  from vertexai.generative_models import GenerativeModel, Part

  # TODO(developer): Update and un-comment below lines
  # project_id = "PROJECT_ID"

  vertexai.init(project=project_id, location="us-central1")

  model = GenerativeModel(model_name="gemini-1.5-pro-preview-0409")

  prompt = """
  Please provide a summary for the audio.
  Provide chapter titles with timestamps, be concise and short, no need to provide chapter summaries.
  Do not make up any information that is not part of the audio and do not be verbose.
"""

  audio_file_uri = "gs://cloud-samples-data/generative-ai/audio/pixel.mp3"
  audio_file = Part.from_uri(audio_file_uri, mime_type="audio/mpeg")

  contents = [audio_file, prompt]

  response = model.generate_content(contents)
  print(response.text)

Advanced samples

The following samples are more complex than the previous samples.

Multiple images

Each of the following tabs show you a different way to include multiple images in a prompt request. These image samples work with all Gemini multimodal models.

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 Vertex AI SDK for Python API reference documentation.

Streaming and non-streaming responses

For a streaming response, use the stream parameter in generate_content.

  response = model.generate_content(contents=[...], stream = True)

For a non-streaming response, remove the parameter, or set the parameter to False.

Sample code

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)

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);
    }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Generative AI quickstart using the Node.js SDK. For more information, see the Node.js SDK for Gemini reference documentation.

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

Streaming and non-streaming responses

For a streaming response, use the generateContentStream method.

  const streamingResp = await generativeModel.generateContentStream(request);

For a non-streaming response, use the generateContent method.

  const streamingResp = await generativeModel.generateContent(request);

Sample code

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. For more information, see the Vertex AI Java SDK for Gemini reference documentation.

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

Streaming and non-streaming responses

For a streaming response, use the generateContentStream method.

  public ResponseStream generateContentStream(Content content)

For a non-streaming response, use the generateContent method.

  public GenerateContentResponse generateContent(Content content)

Sample code

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. For more information, see the Vertex AI Go SDK for Gemini reference documentation.

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

Streaming and non-streaming responses

For a streaming response, use the GenerateContentStream method.

  iter := model.GenerateContentStream(ctx, genai.Text("Tell me a story about a lumberjack and his giant ox. Keep it very short."))

For a non-streaming response, use the GenerateContent method.

  resp, err := model.GenerateContent(ctx, genai.Text("What is the average size of a swallow?"))

Sample code

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
}

REST

You can use REST to test a text prompt by using the Vertex AI API to send a POST request to the publisher model endpoint.

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 available regions
- us-central1
- us-west4
- northamerica-northeast1
- us-east4
- us-west1
- asia-northeast3
- asia-southeast1
- asia-northeast1
PROJECT_ID: Your project ID.
MODEL_ID: The model ID of the multimodal model that you want to use. The options are:
- gemini-1.0-pro-vision
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.
TEXT: The text instructions to include in the prompt.
B64_BASE: The base64 encoding of the image, PDF, or video to include inline in the prompt. When including media inline, you must also specify MIMETYPE.
FILE_URI: 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.
MIME_TYPE: 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
SAFETY_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. Acceptable values include the following:
Click to expand blocking thresholds
- BLOCK_NONE
- BLOCK_ONLY_HIGH
- BLOCK_MEDIUM_AND_ABOVE (default)
- BLOCK_LOW_AND_ABOVE
BLOCK_LOW_AND_ABOVE blocks the most while BLOCK_ONLY_HIGH blocks the least.
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.
TOP_P: 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.
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.
MAX_OUTPUT_TOKENS: 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.
STOP_SEQUENCES: 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

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:GENERATE_RESPONSE_METHOD

Request JSON body:

{
  "contents": {
    "role": "ROLE",
    "parts": [
      {
        "inlineDATA": {
          "mimeType": "MIME_TYPE",
          "data": "B64_BASE_IMAGE"
        }
      },
      {
        "fileData": {
          "mimeType": "MIME_TYPE",
          "fileUri": "FILE_URI"
        }
      },
      {
        "text": "TEXT"
      }
    ]
  },
  "safety_settings": {
    "category": "SAFETY_CATEGORY",
    "threshold": "THRESHOLD"
  },
  "generation_config": {
    "temperature": TEMPERATURE,
    "topP": TOP_P,
    "topK": TOP_K,
    "candidateCount": 1,
    "maxOutputTokens": MAX_OUTPUT_TOKENS,
    "stopSequences": STOP_SEQUENCES,
  }
}

To send your request, choose one of these options:

curl

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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:GENERATE_RESPONSE_METHOD"

PowerShell

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://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": {
        "parts": [
          {
            "text": " Yes the animal in the picture is a cat."
          }
        ]
      }
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 263,
    "candidatesTokenCount": 10,
    "totalTokenCount": 273
  }
}

Example curl command

LOCATION="us-central1"
MODEL_ID="gemini-1.0-pro-vision"
PROJECT_ID="test-project"

curl \
-X POST \
-H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
-H "Content-Type: application/json"
https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/publishers/google/models/${MODEL_ID}:${GENERATE_RESPONSE_METHOD} -d \
$'{
  "contents": {
    "role": "user",
    "parts": [
      {
        "fileData": {
          "mimeType": "image/png",
          "fileUri": "gs://my-bucket/images/cat.png"
        }
      },
      {
        "text": "Describe this picture."
      },
    ]
  },
  "safety_settings": {
    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
    "threshold": "BLOCK_LOW_AND_ABOVE"
  },
  "generation_config": {
    "temperature": 0.4,
    "topP": 1,
    "topK": 32,
    "maxOutputTokens": 2048,
  }
}'

Console

To send a multimodal prompt by using the Google Cloud console, do the following:

In the Vertex AI section of the Google Cloud console, go to the Vertex AI Studio page.
Go to Vertex AI Studio
Under Prompt design (single turn), click Open.
Configure the model and parameters:
- Region: Select the region that you want to use.
- Model: Select Gemini Pro Vision.
- 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.
- 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: Enter a stop sequence, which is a series of characters (including spaces) that stops response generation if the model encounters it. The sequence is not included as part of the response. 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.

The Google Cloud console only supports streaming, which involves receiving responses to prompts as they are generated. You are ready to enter a message in the message box to start a conversation with the model.

The model uses the previous messages as context for new responses. To include an image, PDF, or video in the prompt, click the icon.

To learn about multimodal prompts, see Design multimodal prompts.
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

Audio with timestamps

The following shows you how to use an audio file to generate a podcast transcript with timestamps. This sample works with Gemini 1.5 Pro (Preview) only.

import vertexai
from vertexai.generative_models import GenerativeModel, Part

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"

vertexai.init(project=project_id, location="us-central1")

model = GenerativeModel("gemini-1.5-pro-preview-0409")

prompt = """
  Can you transcribe this interview, in the format of timecode, speaker, caption.
  Use speaker A, speaker B, etc. to identify the speakers.
"""

audio_file_uri = "gs://cloud-samples-data/generative-ai/audio/pixel.mp3"
audio_file = Part.from_uri(audio_file_uri, mime_type="audio/mpeg")

contents = [audio_file, prompt]

response = model.generate_content(contents)
print(response.text)

Video with audio

The following shows you how to summarize a video file with audio and return chapters with timestamps. This sample works with Gemini 1.5 Pro (Preview) only.

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 Vertex AI SDK for Python API reference documentation.

Streaming and non-streaming responses

For a streaming response, use the stream parameter in generate_content.

  response = model.generate_content(contents=[...], stream = True)

For a non-streaming response, remove the parameter, or set the parameter to False.

Sample code


  import vertexai
  from vertexai.generative_models import GenerativeModel, Part

  # TODO(developer): Update and un-comment below lines
  # project_id = "PROJECT_ID"

  vertexai.init(project=project_id, location="us-central1")

  model = GenerativeModel(model_name="gemini-1.5-pro-preview-0409")

  prompt = """
  Provide a description of the video.
  The description should also contain anything important which people say in the video.
"""

  video_file_uri = "gs://cloud-samples-data/generative-ai/video/pixel8.mp4"
  video_file = Part.from_uri(video_file_uri, mime_type="video/mp4")

  contents = [video_file, prompt]

  response = model.generate_content(contents)
  print(response.text)

All modalities

The following shows you how to process images, video, audio, and text at the same time. This sample works with Gemini 1.5 Pro (Preview) only.

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 Vertex AI SDK for Python API reference documentation.

Streaming and non-streaming responses

For a streaming response, use the stream parameter in generate_content.

  response = model.generate_content(contents=[...], stream = True)

For a non-streaming response, remove the parameter, or set the parameter to False.

Sample code


  import vertexai
  from vertexai.generative_models import GenerativeModel, Part

  # TODO(developer): Update and un-comment below lines
  # project_id = "PROJECT_ID"

  vertexai.init(project=project_id, location="us-central1")

  model = GenerativeModel(model_name="gemini-1.5-pro-preview-0409")

  video_file_uri = (
      "gs://cloud-samples-data/generative-ai/video/behind_the_scenes_pixel.mp4"
  )
  video_file = Part.from_uri(video_file_uri, mime_type="video/mp4")

  image_file_uri = "gs://cloud-samples-data/generative-ai/image/a-man-and-a-dog.png"
  image_file = Part.from_uri(image_file_uri, mime_type="image/png")

  prompt = """
  Watch each frame in the video carefully and answer the questions.
  Only base your answers strictly on what information is available in the video attached.
  Do not make up any information that is not part of the video and do not be too
  verbose, be to the point.

  Questions:
  - When is the moment in the image happening in the video? Provide a timestamp.
  - What is the context of the moment and what does the narrator say about it?
"""

  contents = [
      video_file,
      image_file,
      prompt,
  ]

  response = model.generate_content(contents)
  print(response.text)

Set model parameters

The following model parameters can be set on multimodal models:

Top-P

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.

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.

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.

Valid parameter values

Parameter	Gemini 1.0 Pro Vision	Gemini 1.5 Pro (Preview)
Top-K	1 - 40 (default 32)	Not supported
Top-P	0 - 1.0 (default 1.0)	0 - 1.0 (default 0.95)
Temperature	0 - 1.0 (default 0.4)	0 - 2.0 (default 1.0)

Media requirements

When you use a media file in your prompt requests, make sure it meets the following requirements:

Image requirements

Gemini multimodal models support the following image MIME types:

Image MIME type	Gemini 1.0 Pro Vision	Gemini 1.5 Pro (Preview)
PNG - `image/png`
JPEG - `image/jpeg`

There isn't a specific limit to the number of pixels in an image. However, larger images are scaled down and padded to fit a maximum resolution of 3072 x 3072 while preserving their original aspect ratio.

For Gemini 1.0 Pro Vision, each image accounts for 258 tokens.

For Gemini 1.5 Pro (Preview):

Images with an aspect ratio smaller than 1.2 or with the longer dimension smaller than 768 each account for 258 tokens.
Images with the longer dimension larger than 768 and an aspect ratio larger than 1.2 are represented by square tiles that are evenly spaced along the longer dimension. The tiles are used with the original image to represent the image in Gemini. Each tile accounts for 258 tokens. For example, for images with a resolution of 1000 x 2000, 258 x 3 = 774 tokens are used (1 for the original image, and 2 for the 1000x1000 square tiles)

The maximum number of images that can be in a prompt request is:

16 for Gemini 1.0 Pro Vision
3,000 for Gemini 1.5 Pro (Preview)

Audio requirements

Gemini 1.5 Pro (Preview) supports the following audio MIME types. Gemini 1.0 Pro Vision doesn't support audio.

Audio MIME type	Gemini 1.0 Pro Vision	Gemini 1.5 Pro (Preview)
AAC - `audio/aac`
FLAC - `audio/flac`
MP3 - `audio/mp3`
MPA - `audio/m4a`
MPEG - `audio/mpeg`
MPGA - `audio/mpga`
MP4 - `audio/mp4`
OPUS - `audio/opus`
PCM - `audio/pcm`
WAV - `audio/wav`
WEBM - `audio/webm`

Video requirements

Videos are sampled at 1fps. Each video frame accounts for 258 tokens.

For Gemini 1.5 Pro (Preview) only, the audio track is encoded with video frames. The audio track is also broken down into 1-second trunks that each accounts for 32 tokens. The video frame and audio tokens are interleaved together with their timestamps. The timestamps are represented as 7 tokens.

Gemini multimodal models support the following video MIME types:

Video MIME type	Gemini 1.0 Pro Vision	Gemini 1.5 Pro (Preview)
FLV - `video/x-flv`
MOV - `video/mov`
MPEG - `video/mpeg`
MPEGPS - `video/mpegps`
MPG - `video/mpg`
MP4 - `video/mp4`
WEBM - `video/webm`
WMV - `video/wmv`
3GPP - `video/3gpp`

PDF requirements

The required MIME type for a PDF is application/pdf.

Best practices

This section includes best practices for different modalities.

Image best practices

When using images, use the following best practices and information for the best results.

Use prompts with a single image to produce better results than prompts with multiple images when you want to detect text in an image.
If your prompt contains a single image, place the image before the text prompt.
If there are multiple images in the prompt, and you want to refer to them later in your prompt or have the model refer to them in the model response, it can help to give each image an index before the image. Use a b c, or image 1 image 2 image 3 for your index. The following is an example of using indexed images in a prompt:
```
image 1 <piano_recital.jpeg>
image 2 <family_dinner.jpeg>
image 3 <coffee_shop.jpeg>

Write a blogpost about my day using image 1 and image 2. Then, give me ideas
for tomorrow based on image 3.
```
Images with higher resolution yield better results.
Include a few examples in the prompt.
Rotate images to their proper orientation before adding them to the prompt.
Avoid blurry images.

Video best practices

When using video, use the following best practices and information for the best results:

Use no more than one video per prompt.
If your prompt contains a single video, place the video before the text prompt.
If you're using Gemini 1.0 Pro Vision, then the model processes videos as non-contiguous image frames from the video. Audio isn't included. If you notice the model missing some content from the video, try making the video shorter so that the model captures a greater portion of the video content.
If you're using Gemini 1.0 Pro Vision, only information in the first two minutes is processed.
If you're using Gemini 1.0 Pro Vision, no audio information or timestamp metadata is analyzed. Because of this, the model might not perform well in use cases that require audio input, such as captioning audio, or time-related information, such as speed or rhythm.
When timestamp localization in a video with audio is needed, ask the model to generate timestamps in the MM:SS format where the first two digits represent minutes and the last two digits represent seconds. Use the same format for questions that ask about a timestamp.

PDF best practices

When using PDFs, use the following best practices and information for the best results:

PDFs are treated as images, so a single page of a PDF is treated as one image.
- The number of pages supported is limited to the number of images a model can support. For Gemini 1.0 Pro Vision, the limit is 16. For Gemini 1.5 Pro the limit is 300. If you have a long document, consider splitting it into multiple PDFs to process it.
- When using PDFs as an input, the cost follows Gemini image pricing. For example, if you include a two page PDF in a Gemini API call, you incur an input fee of processing two images.
If your prompt contains a single PDF, place the PDF before the text prompt.
Use PDFs created with text rendered as text instead of using text in scanned images. This format ensures text is machine-readable so that it's easier for the model to edit, search, and manipulate compared to scanned image PDFs. This practice provides optimal results when working with text-heavy documents like contracts.

For more multimodal prompting tips, see Design multimodal prompts.

Multimodal limitations

While Gemini multimodal models are powerful in many multimodal user cases, it's important to understand the limitations of the models:

Spatial reasoning: The models aren't precise at locating text or objects in images and PDFs. They might only return the approximated counts of objects.
Medical uses: The models aren't suitable for interpreting medical images (for example, x-rays and CT scans) or providing medical advice.
People recognition: The models aren't meant to be used to identify people who aren't celebrities in images.
Content moderation: The models refuse to provide answers on images or videos that violate our safety policies.
Accuracy: The models might hallucinate or make mistakes when interpreting low-quality, rotated, or extremely low-resolution images. The models might also hallucinate when interpreting handwritten text in images or PDF documents.
Non-speech sound recognition: The models that support audio might make mistakes recognizing sound that's not speech.
High-speed motion: Because of the fixed 1 frame per second (fps) sampling rate, the models might make mistakes understanding high-speed motion in video.

What's next

Learn how to send chat prompt requests.
Learn about responsible AI best practices and Vertex AI's safety filters.