Video understanding

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

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 line
# PROJECT_ID = "your-project-id"

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

vision_model = GenerativeModel("gemini-1.5-flash-002")

# 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.text)
# Example response:
# Here's a summary of the video's content.
# The video shows a series of animals at the Los Angeles Zoo interacting
# with waterproof cameras attached to various devices.
# ...

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 ADC for a local development environment.

Streaming and non-streaming responses

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

For a streaming response, use the generateContentStream method.

  public ResponseStream<GenerateContentResponse> 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-1.5-flash-001";

    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);
    }
  }
}

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 ADC for a local development environment.

Streaming and non-streaming responses

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

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-1.5-flash-001'
) {
  // 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);
}

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 ADC for a local development environment.

Streaming and non-streaming responses

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

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.
func generateMultimodalContent(w io.Writer, projectID, location, modelName string) error {
	// location := "us-central1"
	// modelName := "gemini-1.5-flash-001"
	ctx := context.Background()

	client, err := genai.NewClient(ctx, projectID, location)
	if err != nil {
		return fmt.Errorf("unable to create client: %w", 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("animals.mp4")),
		FileURI:  "gs://cloud-samples-data/video/animals.mp4",
	}

	res, err := model.GenerateContent(ctx, part, genai.Text("What is in this video?"))
	if err != nil {
		return fmt.Errorf("unable to generate contents: %w", 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
}

C#

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

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

Streaming and non-streaming responses

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

For a streaming response, use the StreamGenerateContent method.

  public virtual PredictionServiceClient.StreamGenerateContentStream StreamGenerateContent(GenerateContentRequest request)
  

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

  public virtual Task<GenerateContentResponse> GenerateContentAsync(GenerateContentRequest request)
  

For more information on how the server can stream responses, see Streaming RPCs.

Sample code

using Google.Api.Gax.Grpc;
using Google.Cloud.AIPlatform.V1;
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-1.5-flash-001"
    )
    {
        var predictionServiceClient = new PredictionServiceClientBuilder
        {
            Endpoint = $"{location}-aiplatform.googleapis.com"
        }.Build();

        var generateContentRequest = new GenerateContentRequest
        {
            Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}",
            Contents =
            {
                new Content
                {
                    Role = "USER",
                    Parts =
                    {
                        new Part { Text = "What's in the video?" },
                        new Part { FileData = new() { MimeType = "video/mp4", FileUri = "gs://cloud-samples-data/video/animals.mp4" }}
                    }
                }
            }
        };

        using PredictionServiceClient.StreamGenerateContentStream response = predictionServiceClient.StreamGenerateContent(generateContentRequest);

        StringBuilder fullText = new();

        AsyncResponseStream<GenerateContentResponse> responseStream = response.GetResponseStream();
        await foreach (GenerateContentResponse responseItem in responseStream)
        {
            fullText.Append(responseItem.Candidates[0].Content.Parts[0].Text);
        }
        return fullText.ToString();
    }
}

REST

After you set up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

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

• LOCATION: The region to process the request. Enter a supported region. For the full list of supported regions, see Available locations.

  Click to expand a partial list of available regions

  • us-central1
  • us-west4
  • northamerica-northeast1
  • us-east4
  • us-west1
  • asia-northeast3
  • asia-southeast1
  • asia-northeast1
• PROJECT_ID: Your project ID.
• FILE_URI: The URI or URL of the file to include in the prompt. Acceptable values include the following:
  • Cloud Storage bucket URI: The object must either be publicly readable or reside in the same Google Cloud project that's sending the request. For gemini-1.5-pro and gemini-1.5-flash, the size limit is 2 GB. For gemini-1.0-pro-vision, the size limit is 20 MB.
  • HTTP URL: The file URL must be publicly readable. You can specify one video file, one audio file, and up to 10 image files per request. Audio files, video files, and documents can't exceed 15 MB.
  • YouTube video URL:The YouTube video must be either owned by the account that you used to sign in to the Google Cloud console or is public. Only one YouTube video URL is supported per request.

  When specifying a fileURI, you must also specify the media type (mimeType) of the file. If VPC Service Controls is enabled, specifying a media file URL for fileURI is not supported.

  If you don't have a video file in Cloud Storage, then you can use the following publicly available file: gs://cloud-samples-data/video/animals.mp4 with a mime type of video/mp4. To view this video, open the sample MP4 file.

• MIME_TYPE: The media type of the file 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
  • image/webp
  • text/plain
  • video/mov
  • video/mpeg
  • video/mp4
  • video/mpg
  • video/avi
  • video/wmv
  • video/mpegps
  • video/flv
• TEXT: The text instructions to include in the prompt. For example, What is in the video?

To send your request, choose one of these options:

cat > request.json << 'EOF'
{
  "contents": {
    "role": "USER",
    "parts": [
      {
        "fileData": {
          "fileUri": "FILE_URI",
          "mimeType": "MIME_TYPE"
        }
      },
      {
        "text": "TEXT"
      }
    ]
  }
}
EOF

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/gemini-1.5-flash:generateContent"

@'
{
  "contents": {
    "role": "USER",
    "parts": [
      {
        "fileData": {
          "fileUri": "FILE_URI",
          "mimeType": "MIME_TYPE"
        }
      },
      {
        "text": "TEXT"
      }
    ]
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

$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/gemini-1.5-flash:generateContent" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [
          {
            "text": "This video is a commercial for Google Photos, featuring animals taking selfies
              with the Google Photos app. The commercial plays on the popularity of media in which
              animals act like humans, especially their use of technology. The commercial also
              highlights the app's ability to automatically back up photos."
          }
        ]
      },
      "finishReason": "STOP",
      "safetyRatings": [
        {
          "category": "HARM_CATEGORY_HATE_SPEECH",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.053601142,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.053799648
        },
        {
          "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.06278921,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.07850098
        },
        {
          "category": "HARM_CATEGORY_HARASSMENT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.090253234,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.058453236
        },
        {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.1647851,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.09285216
        }
      ]
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 28916,
    "candidatesTokenCount": 61,
    "totalTokenCount": 28977
  }
}

• Use the generateContent method to request that the response is returned after it's fully generated. To reduce the perception of latency to a human audience, stream the response as it's being generated by using the streamGenerateContent method.
• The multimodal model ID is located at the end of the URL before the method (for example, gemini-1.5-flash or gemini-1.0-pro-vision). This sample may support other models as well.

Console

1. In the Vertex AI section of the Google Cloud console, go to the Vertex AI Studio page.

   Go to Vertex AI Studio

2. Click Open freeform.

3. Optional: Configure the model and parameters:

   • Model: Select a model.
   • Region: Select the region that you want to use.

   • Temperature: Use the slider or textbox to enter a value for temperature.

     The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 means that the highest probability tokens are always selected. In this case, responses for a given prompt are mostly deterministic, but a small amount of variation is still possible.

     If the model returns a response that's too generic, too short, or the model gives a fallback response, try increasing the temperature.

   • Output token limit: Use the slider or textbox to enter a value for the max output limit.

     Maximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words.

     Specify a lower value for shorter responses and a higher value for potentially longer responses.

   • Add stop sequence: Optional. Enter a stop sequence, which is a series of characters that includes spaces. If the model encounters a stop sequence, the response generation stops. The stop sequence isn't included in the response, and you can add up to five stop sequences.

4. 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. (not supported for Gemini 1.5).

     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.
   • Max responses: Use the slider or textbox to enter a value for the number of responses to generate.
   • Streaming responses: Enable to print responses as they're generated.
   • Safety filter threshold: Select the threshold of how likely you are to see responses that could be harmful.
   • Enable Grounding: Grounding isn't supported for multimodal prompts.

5. Click Insert Media, and select a source for your file.

   Upload

   Select the file that you want to upload and click Open.

   By URL

   Enter the URL of the file that you want to use and click Insert.

   YouTube

   Enter the URL of the YouTube video that you want to use and click Insert.

   You can use any public video or a video that's owned by the account that you used to sign in to the Google Cloud console.

   Cloud Storage

   Select the bucket and then the file from the bucket that you want to import and click Select.

   Google Drive

   1. Choose an account and give consent to Vertex AI Studio to access your account the first time you select this option. You can upload multiple files that have a total size of up to 10 MB. A single file can't exceed 7 MB.
   2. Click the file that you want to add.

   3. Click Select.

      The file thumbnail displays in the Prompt pane. The total number of tokens also displays. If your prompt data exceeds the token limit, the tokens are truncated and aren't included in processing your data.

6. Enter your text prompt in the Prompt pane.

7. Optional: To view the Token ID to text and Token IDs, click the tokens count in the Prompt pane.

8. Click Submit.

9. Optional: To save your prompt to My prompts, click save_alt Save.

10. Optional: To get the Python code or a curl command for your prompt, click code Get code.

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

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 line
# PROJECT_ID = "your-project-id"

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

model = GenerativeModel("gemini-1.5-flash-002")

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

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

contents = [video_file, prompt]

response = model.generate_content(contents)
print(response.text)
# Example response:
# Here is a description of the video.
# ... Then, the scene changes to a woman named Saeko Shimada..
# She says, "Tokyo has many faces. The city at night is totally different
# from what you see during the day."
# ...

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 ADC for a local development environment.

Streaming and non-streaming responses

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

For a streaming response, use the generateContentStream method.

  public ResponseStream<GenerateContentResponse> 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 VideoInputWithAudio {

  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-1.5-flash-001";

    videoAudioInput(projectId, location, modelName);
  }

  // Analyzes the given video input, including its audio track.
  public static String videoAudioInput(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/generative-ai/video/pixel8.mp4";

      GenerativeModel model = new GenerativeModel(modelName, vertexAI);
      GenerateContentResponse response = model.generateContent(
          ContentMaker.fromMultiModalData(
              "Provide a description of the video.\n The description should also "
                  + "contain anything important which people say in the video.",
              PartMaker.fromMimeTypeAndData("video/mp4", videoUri)
          ));

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

      return output;
    }
  }
}

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 ADC for a local development environment.

Streaming and non-streaming responses

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

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 analyze_video_with_audio(projectId = 'PROJECT_ID') {
  const vertexAI = new VertexAI({project: projectId, location: 'us-central1'});

  const generativeModel = vertexAI.getGenerativeModel({
    model: 'gemini-1.5-flash-001',
  });

  const filePart = {
    file_data: {
      file_uri: 'gs://cloud-samples-data/generative-ai/video/pixel8.mp4',
      mime_type: 'video/mp4',
    },
  };
  const textPart = {
    text: `
    Provide a description of the video.
    The description should also contain anything important which people say in the video.`,
  };

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

  const resp = await generativeModel.generateContent(request);
  const contentResponse = await resp.response;
  console.log(JSON.stringify(contentResponse));
}

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 ADC for a local development environment.

Streaming and non-streaming responses

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

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 shows how to send video and text prompts to a model, writing the response to
// the provided io.Writer.
func generateMultimodalContent(w io.Writer, projectID, location, modelName string) error {
	// location := "us-central1"
	// modelName := "gemini-1.5-flash-001"
	ctx := context.Background()

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

	model := client.GenerativeModel(modelName)

	// Given a video file URL, prepare video file as genai.Part
	part := genai.FileData{
		MIMEType: mime.TypeByExtension(filepath.Ext("pixel8.mp4")),
		FileURI:  "gs://cloud-samples-data/generative-ai/video/pixel8.mp4",
	}

	res, err := model.GenerateContent(ctx, part, genai.Text(`
			Provide a description of the video.
			The description should also contain anything important which people say in the video.
	`))
	if err != nil {
		return fmt.Errorf("unable to generate contents: %w", 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
}

C#

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

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

Streaming and non-streaming responses

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

For a streaming response, use the StreamGenerateContent method.

  public virtual PredictionServiceClient.StreamGenerateContentStream StreamGenerateContent(GenerateContentRequest request)
  

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

  public virtual Task<GenerateContentResponse> GenerateContentAsync(GenerateContentRequest request)
  

For more information on how the server can stream responses, see Streaming RPCs.

Sample code

using Google.Cloud.AIPlatform.V1;
using System;
using System.Threading.Tasks;

public class VideoInputWithAudio
{
    public async Task<string> DescribeVideo(
        string projectId = "your-project-id",
        string location = "us-central1",
        string publisher = "google",
        string model = "gemini-1.5-flash-001")
    {

        var predictionServiceClient = new PredictionServiceClientBuilder
        {
            Endpoint = $"{location}-aiplatform.googleapis.com"
        }.Build();

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

        var generateContentRequest = new GenerateContentRequest
        {
            Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}",
            Contents =
            {
                new Content
                {
                    Role = "USER",
                    Parts =
                    {
                        new Part { Text = prompt },
                        new Part { FileData = new() { MimeType = "video/mp4", FileUri = "gs://cloud-samples-data/generative-ai/video/pixel8.mp4" }}
                    }
                }
            }
        };

        GenerateContentResponse response = await predictionServiceClient.GenerateContentAsync(generateContentRequest);

        string responseText = response.Candidates[0].Content.Parts[0].Text;
        Console.WriteLine(responseText);

        return responseText;
    }
}

REST

After you set up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

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

• LOCATION: The region to process the request. Enter a supported region. For the full list of supported regions, see Available locations.

  Click to expand a partial list of available regions

  • us-central1
  • us-west4
  • northamerica-northeast1
  • us-east4
  • us-west1
  • asia-northeast3
  • asia-southeast1
  • asia-northeast1
• PROJECT_ID: Your project ID.
• FILE_URI: The URI or URL of the file to include in the prompt. Acceptable values include the following:
  • Cloud Storage bucket URI: The object must either be publicly readable or reside in the same Google Cloud project that's sending the request. For gemini-1.5-pro and gemini-1.5-flash, the size limit is 2 GB. For gemini-1.0-pro-vision, the size limit is 20 MB.
  • HTTP URL: The file URL must be publicly readable. You can specify one video file, one audio file, and up to 10 image files per request. Audio files, video files, and documents can't exceed 15 MB.
  • YouTube video URL:The YouTube video must be either owned by the account that you used to sign in to the Google Cloud console or is public. Only one YouTube video URL is supported per request.

  When specifying a fileURI, you must also specify the media type (mimeType) of the file. If VPC Service Controls is enabled, specifying a media file URL for fileURI is not supported.

  If you don't have a video file in Cloud Storage, then you can use the following publicly available file: gs://cloud-samples-data/generative-ai/video/pixel8.mp4 with a mime type of video/mp4. To view this video, open the sample MP4 file.

• MIME_TYPE: The media type of the file 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
  • image/webp
  • text/plain
  • video/mov
  • video/mpeg
  • video/mp4
  • video/mpg
  • video/avi
  • video/wmv
  • video/mpegps
  • video/flv
• TEXT

  The text instructions to include in the prompt. For example, Provide a description of the video. The description should also contain anything important which people say in the video.

To send your request, choose one of these options:

cat > request.json << 'EOF'
{
  "contents": {
    "role": "USER",
    "parts": [
      {
        "fileData": {
          "fileUri": "FILE_URI",
          "mimeType": "MIME_TYPE"
        }
      },
      {
        "text": "TEXT"
      }
    ]
  }
}
EOF

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/gemini-1.5-flash:generateContent"

@'
{
  "contents": {
    "role": "USER",
    "parts": [
      {
        "fileData": {
          "fileUri": "FILE_URI",
          "mimeType": "MIME_TYPE"
        }
      },
      {
        "text": "TEXT"
      }
    ]
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

$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/gemini-1.5-flash:generateContent" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [
          {
            "text": "The video opens with a shot of a train traveling over a bridge in the night. \n
              \nThe scene changes to a woman walking in the streets of Tokyo. She says "My name is
              Saeko. I am a photographer in Tokyo. Tokyo has many faces. The city at night
              is totally different from what you see during the day. The new Pixel has a feature
              called "Video Boost". In low light, it activates "Night Sight" to make the quality
              even better." \n\nShe then uses her phone to take several photos of different parts of
              the city including a street with a lot of shops, a small alleyway, and a small
              restaurant. She says "Sancha is where I used to live when I first moved to Tokyo. I
              have a lot of great memories here. Oh, I like this." \n\nShe smiles and says
              "Beautiful".\n\nThe video ends with the woman standing in a different part of the
              city. She says "Next, I came to Shibuya." The scene shows the famous Shibuya crossing
              in the night. \n\nThe video features a woman showcasing the camera features of the
              Google Pixel phone while walking around the streets of Tokyo. She mentions "Night
              Sight" and "Video Boost" features. \n"
          }
        ]
      },
      "finishReason": "STOP",
      "safetyRatings": [
        {
          "category": "HARM_CATEGORY_HATE_SPEECH",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.053601142,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.053799648
        },
        {
          "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.06278921,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.07850098
        },
        {
          "category": "HARM_CATEGORY_HARASSMENT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.090253234,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.058453236
        },
        {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.1647851,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.09285216
        }
      ]
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 28916,
    "candidatesTokenCount": 61,
    "totalTokenCount": 28977
  }
}

• Use the generateContent method to request that the response is returned after it's fully generated. To reduce the perception of latency to a human audience, stream the response as it's being generated by using the streamGenerateContent method.
• The multimodal model ID is located at the end of the URL before the method (for example, gemini-1.5-flash or gemini-1.0-pro-vision). This sample may support other models as well.

Console

1. In the Vertex AI section of the Google Cloud console, go to the Vertex AI Studio page.

   Go to Vertex AI Studio

2. Click Open freeform.

3. Optional: Configure the model and parameters:

   • Model: Select a model.
   • Region: Select the region that you want to use.

   • Temperature: Use the slider or textbox to enter a value for temperature.

     The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 means that the highest probability tokens are always selected. In this case, responses for a given prompt are mostly deterministic, but a small amount of variation is still possible.

     If the model returns a response that's too generic, too short, or the model gives a fallback response, try increasing the temperature.

   • Output token limit: Use the slider or textbox to enter a value for the max output limit.

     Maximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words.

     Specify a lower value for shorter responses and a higher value for potentially longer responses.

   • Add stop sequence: Optional. Enter a stop sequence, which is a series of characters that includes spaces. If the model encounters a stop sequence, the response generation stops. The stop sequence isn't included in the response, and you can add up to five stop sequences.

4. 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. (not supported for Gemini 1.5).

     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.
   • Max responses: Use the slider or textbox to enter a value for the number of responses to generate.
   • Streaming responses: Enable to print responses as they're generated.
   • Safety filter threshold: Select the threshold of how likely you are to see responses that could be harmful.
   • Enable Grounding: Grounding isn't supported for multimodal prompts.

5. Click Insert Media, and select a source for your file.

   Upload

   Select the file that you want to upload and click Open.

   By URL

   Enter the URL of the file that you want to use and click Insert.

   YouTube

   Enter the URL of the YouTube video that you want to use and click Insert.

   You can use any public video or a video that's owned by the account that you used to sign in to the Google Cloud console.

   Cloud Storage

   Select the bucket and then the file from the bucket that you want to import and click Select.

   Google Drive

   1. Choose an account and give consent to Vertex AI Studio to access your account the first time you select this option. You can upload multiple files that have a total size of up to 10 MB. A single file can't exceed 7 MB.
   2. Click the file that you want to add.

   3. Click Select.

      The file thumbnail displays in the Prompt pane. The total number of tokens also displays. If your prompt data exceeds the token limit, the tokens are truncated and aren't included in processing your data.

6. Enter your text prompt in the Prompt pane.

7. Optional: To view the Token ID to text and Token IDs, click the tokens count in the Prompt pane.

8. Click Submit.

9. Optional: To save your prompt to My prompts, click save_alt Save.

10. Optional: To get the Python code or a curl command for your prompt, click code Get code.