Generate content stream with Multimodal AI Model

The code sample demonstrates how to use Generative AI Models to generate text in a streaming format based on a combination of video, image, and text inputs

Explore further

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

• Text generation

Code sample

Go

Before trying this sample, follow the Go setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Go API reference documentation.

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

import (
	"context"
	"fmt"
	"io"

	"google.golang.org/genai"
)

// generateChatStreamWithText shows how to generate chat stream using a text prompt.
func generateChatStreamWithText(w io.Writer) error {
	ctx := context.Background()

	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		HTTPOptions: genai.HTTPOptions{APIVersion: "v1"},
	})
	if err != nil {
		return fmt.Errorf("failed to create genai client: %w", err)
	}

	modelName := "gemini-2.5-flash"

	chatSession, err := client.Chats.Create(ctx, modelName, nil, nil)
	if err != nil {
		return fmt.Errorf("failed to create genai chat session: %w", err)
	}

	var streamErr error
	contents := genai.Part{Text: "Why is the sky blue?"}

	stream := chatSession.SendMessageStream(ctx, contents)
	stream(func(resp *genai.GenerateContentResponse, err error) bool {
		if err != nil {
			streamErr = err
			return false
		}
		for _, cand := range resp.Candidates {
			for _, part := range cand.Content.Parts {
				fmt.Fprintln(w, part.Text)
			}
		}
		return true
	})

	// Example response:
	// The
	// sky appears blue due to a phenomenon called **Rayleigh scattering**.
	// Here's a breakdown:
	// ...

	return streamErr
}

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

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

import com.google.genai.Chat;
import com.google.genai.Client;
import com.google.genai.ResponseStream;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.HttpOptions;

public class TextGenerationChatStreamWithText {

  public static void main(String[] args) {
    // TODO(developer): Replace these variables before running the sample.
    String modelId = "gemini-2.5-flash";
    generateContent(modelId);
  }

  // Shows how to create a new chat session stream
  public static String generateContent(String modelId) {
    // Client Initialization. Once created, it can be reused for multiple requests.
    try (Client client =
        Client.builder()
            .location("global")
            .vertexAI(true)
            .httpOptions(HttpOptions.builder().apiVersion("v1").build())
            .build()) {

      Chat chatSession = client.chats.create(modelId);
      StringBuilder responseTextBuilder = new StringBuilder();

      try (ResponseStream<GenerateContentResponse> response =
          chatSession.sendMessageStream("Why is the sky blue?")) {

        for (GenerateContentResponse chunk : response) {
          System.out.println(chunk.text());
          responseTextBuilder.append(chunk.text());
        }

      }
      // Example response:
      //
      // The sky is blue primarily due to a phenomenon called **Rayleigh scattering**,
      // named after the British physicist Lord Rayleigh. Here's a breakdown of how...
      return responseTextBuilder.toString();
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js API reference documentation.

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

const {GoogleGenAI} = require('@google/genai');

const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT;
const GOOGLE_CLOUD_LOCATION = process.env.GOOGLE_CLOUD_LOCATION || 'global';

async function generateText(
  projectId = GOOGLE_CLOUD_PROJECT,
  location = GOOGLE_CLOUD_LOCATION
) {
  const client = new GoogleGenAI({
    vertexai: true,
    project: projectId,
    location: location,
  });

  const chatSession = client.chats.create({
    model: 'gemini-2.5-flash',
  });

  for await (const chunk of await chatSession.sendMessageStream({
    message: 'Why is the sky blue?',
  })) {
    console.log(chunk.text);
  }
  // Example response:
  // The
  // sky appears blue due to a phenomenon called **Rayleigh scattering**. Here's
  // a breakdown of why:
  // ...
  return true;
}

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

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

from google import genai
from google.genai.types import HttpOptions

client = genai.Client(http_options=HttpOptions(api_version="v1"))
chat_session = client.chats.create(model="gemini-2.5-flash")

for chunk in chat_session.send_message_stream("Why is the sky blue?"):
    print(chunk.text, end="")
# Example response:
# The
#  sky appears blue due to a phenomenon called **Rayleigh scattering**. Here's
#  a breakdown of why:
# ...

What's next

To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser.