使用 Gemini 為 YouTube 影片生成摘要

本範例說明如何使用 Gemini 為 YouTube 影片生成摘要。

程式碼範例

Go

在試用這個範例之前，請先按照Go使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Go API 參考說明文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

import (
	"context"
	"fmt"
	"io"

	genai "google.golang.org/genai"
)

// generateWithYTVideo shows how to generate text using a YouTube video as input.
func generateWithYTVideo(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"
	contents := []*genai.Content{
		{Parts: []*genai.Part{
			{Text: "Write a short and engaging blog post based on this video."},
			{FileData: &genai.FileData{
				FileURI:  "https://www.youtube.com/watch?v=3KtWfp0UopM",
				MIMEType: "video/mp4",
			}},
		},
			Role: "user"},
	}

	resp, err := client.Models.GenerateContent(ctx, modelName, contents, nil)
	if err != nil {
		return fmt.Errorf("failed to generate content: %w", err)
	}

	respText := resp.Text()

	fmt.Fprintln(w, respText)

	// Example response:
	// Okay, here’s a short and engaging blog post based on the provided video.
	//
	// **Google's 25th: A Look Back at What We've Searched**
	// ...

	return nil
}

Java

在試用這個範例之前，請先按照Java使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Java API 參考說明文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.HttpOptions;
import com.google.genai.types.Part;

public class TextGenerationWithYoutubeVideo {

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

  // Generates text with YouTube video input
  public static String generateContent(String modelId) {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests.
    try (Client client =
        Client.builder()
            .location("global")
            .vertexAI(true)
            .httpOptions(HttpOptions.builder().apiVersion("v1").build())
            .build()) {

      GenerateContentResponse response =
          client.models.generateContent(
              modelId,
              Content.fromParts(
                  Part.fromUri("https://www.youtube.com/watch?v=3KtWfp0UopM", "video/mp4"),
                  Part.fromText("Write a short and engaging blog post based on this video.")),
              null);

      System.out.print(response.text());
      // Example response:
      // 25 Years of Curiosity: A Google Anniversary Dive into What the World Searched For
      //
      // Remember a time before instant answers were just a click away? 25 years ago, Google
      // launched, unleashing a wave of curiosity that has since charted the collective interests,
      // anxieties, and celebrations of humanity...
      return response.text();
    }
  }
}

Node.js

在試用這個範例之前，請先按照Node.js使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Node.js API 參考說明文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

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 prompt = 'Write a short and engaging blog post based on this video.';

  const ytVideo = {
    fileData: {
      fileUri: 'https://www.youtube.com/watch?v=3KtWfp0UopM',
      mimeType: 'video/mp4',
    },
  };

  const response = await client.models.generateContent({
    model: 'gemini-2.5-flash',
    contents: [ytVideo, prompt],
  });

  console.log(response.text);

  // Example response:
  //  Here's a short blog post based on the video provided:
  //  **Google Turns 25: A Quarter Century of Search!**
  //  ...

  return response.text;
}

Python

在試用這個範例之前，請先按照Python使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Python API 參考說明文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

from google import genai
from google.genai.types import HttpOptions, Part

client = genai.Client(http_options=HttpOptions(api_version="v1"))
model_id = "gemini-2.5-flash"

response = client.models.generate_content(
    model=model_id,
    contents=[
        Part.from_uri(
            file_uri="https://www.youtube.com/watch?v=3KtWfp0UopM",
            mime_type="video/mp4",
        ),
        "Write a short and engaging blog post based on this video.",
    ],
)

print(response.text)
# Example response:
# Here's a short blog post based on the video provided:
#
# **Google Turns 25: A Quarter Century of Search!**
# ...

後續步驟

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