azure-ai-translation-ts

作者: microsoft

Build translation applications using Azure Translation SDKs for JavaScript (@azure-rest/ai-translation-text, @azure-rest/ai-translation-document). Use when implementing text translation, transliteration, language detection, or batch document translation.

npx skills add https://github.com/microsoft/skills --skill azure-ai-translation-ts

Azure Translation SDKs for TypeScript

Text and document translation with REST-style clients.

Installation

# Text translation
npm install @azure-rest/ai-translation-text @azure/identity

# Document translation
npm install @azure-rest/ai-translation-document @azure/identity

Environment Variables

TRANSLATOR_ENDPOINT=https://api.cognitive.microsofttranslator.com
TRANSLATOR_SUBSCRIPTION_KEY=<your-api-key>
TRANSLATOR_REGION=<your-region>  # e.g., westus, eastus
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Text Translation Client

Authentication

import TextTranslationClient, { TranslatorCredential } from "@azure-rest/ai-translation-text";

// API Key + Region
const credential: TranslatorCredential = {
  key: process.env.TRANSLATOR_SUBSCRIPTION_KEY!,
  region: process.env.TRANSLATOR_REGION!,
};
const client = TextTranslationClient(process.env.TRANSLATOR_ENDPOINT!, credential);

// Or just credential (uses global endpoint)
const client2 = TextTranslationClient(credential);

Translate Text

import TextTranslationClient, { isUnexpected } from "@azure-rest/ai-translation-text";

const response = await client.path("/translate").post({
  body: {
    inputs: [
      {
        text: "Hello, how are you?",
        language: "en",  // source (optional, auto-detect)
        targets: [
          { language: "es" },
          { language: "fr" },
        ],
      },
    ],
  },
});

if (isUnexpected(response)) {
  throw response.body.error;
}

for (const result of response.body.value) {
  for (const translation of result.translations) {
    console.log(`${translation.language}: ${translation.text}`);
  }
}

Translate with Options

const response = await client.path("/translate").post({
  body: {
    inputs: [
      {
        text: "Hello world",
        language: "en",
        textType: "Plain",  // or "Html"
        targets: [
          {
            language: "de",
            profanityAction: "NoAction",  // "Marked" | "Deleted"
            tone: "formal",  // LLM-specific
          },
        ],
      },
    ],
  },
});

Get Supported Languages

const response = await client.path("/languages").get();

if (isUnexpected(response)) {
  throw response.body.error;
}

// Translation languages
for (const [code, lang] of Object.entries(response.body.translation || {})) {
  console.log(`${code}: ${lang.name} (${lang.nativeName})`);
}

Transliterate

const response = await client.path("/transliterate").post({
  body: { inputs: [{ text: "这是个测试" }] },
  queryParameters: {
    language: "zh-Hans",
    fromScript: "Hans",
    toScript: "Latn",
  },
});

if (!isUnexpected(response)) {
  for (const t of response.body.value) {
    console.log(`${t.script}: ${t.text}`);  // Latn: zhè shì gè cè shì
  }
}

Detect Language

const response = await client.path("/detect").post({
  body: { inputs: [{ text: "Bonjour le monde" }] },
});

if (!isUnexpected(response)) {
  for (const result of response.body.value) {
    console.log(`Language: ${result.language}, Score: ${result.score}`);
  }
}

Document Translation Client

Authentication

import DocumentTranslationClient from "@azure-rest/ai-translation-document";
import { DefaultAzureCredential, ManagedIdentityCredential } from "@azure/identity";

const endpoint = "https://<translator>.cognitiveservices.azure.com";

// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
const credential = new DefaultAzureCredential({requiredEnvVars: ["AZURE_TOKEN_CREDENTIALS"]});
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/javascript/api/overview/azure/identity-readme?view=azure-node-latest#credential-classes
// const credential = new ManagedIdentityCredential();

// TokenCredential
const client = DocumentTranslationClient(endpoint, credential);

// API Key
const client2 = DocumentTranslationClient(endpoint, { key: "<api-key>" });

Single Document Translation

import DocumentTranslationClient from "@azure-rest/ai-translation-document";
import { writeFile } from "node:fs/promises";

const response = await client.path("/document:translate").post({
  queryParameters: {
    targetLanguage: "es",
    sourceLanguage: "en",  // optional
  },
  contentType: "multipart/form-data",
  body: [
    {
      name: "document",
      body: "Hello, this is a test document.",
      filename: "test.txt",
      contentType: "text/plain",
    },
  ],
}).asNodeStream();

if (response.status === "200") {
  await writeFile("translated.txt", response.body);
}

Batch Document Translation

import { ContainerSASPermissions, BlobServiceClient } from "@azure/storage-blob";

// Generate SAS URLs for source and target containers
const sourceSas = await sourceContainer.generateSasUrl({
  permissions: ContainerSASPermissions.parse("rl"),
  expiresOn: new Date(Date.now() + 24 * 60 * 60 * 1000),
});

const targetSas = await targetContainer.generateSasUrl({
  permissions: ContainerSASPermissions.parse("rwl"),
  expiresOn: new Date(Date.now() + 24 * 60 * 60 * 1000),
});

// Start batch translation
const response = await client.path("/document/batches").post({
  body: {
    inputs: [
      {
        source: { sourceUrl: sourceSas },
        targets: [
          { targetUrl: targetSas, language: "fr" },
        ],
      },
    ],
  },
});

// Get operation ID from header
const operationId = new URL(response.headers["operation-location"])
  .pathname.split("/").pop();

Get Translation Status

import { isUnexpected, paginate } from "@azure-rest/ai-translation-document";

const statusResponse = await client.path("/document/batches/{id}", operationId).get();

if (!isUnexpected(statusResponse)) {
  const status = statusResponse.body;
  console.log(`Status: ${status.status}`);
  console.log(`Total: ${status.summary.total}`);
  console.log(`Success: ${status.summary.success}`);
}

// List documents with pagination
const docsResponse = await client.path("/document/batches/{id}/documents", operationId).get();
const documents = paginate(client, docsResponse);

for await (const doc of documents) {
  console.log(`${doc.id}: ${doc.status}`);
}

Get Supported Formats

const response = await client.path("/document/formats").get();

if (!isUnexpected(response)) {
  for (const format of response.body.value) {
    console.log(`${format.format}: ${format.fileExtensions.join(", ")}`);
  }
}

Key Types

// Text Translation
import type {
  TranslatorCredential,
  TranslatorTokenCredential,
} from "@azure-rest/ai-translation-text";

// Document Translation
import type {
  DocumentTranslateParameters,
  StartTranslationDetails,
  TranslationStatus,
} from "@azure-rest/ai-translation-document";

Best Practices

  1. Auto-detect source - Omit language parameter to auto-detect
  2. Batch requests - Translate multiple texts in one call for efficiency
  3. Use SAS tokens - For document translation, use time-limited SAS URLs
  4. Handle errors - Always check isUnexpected(response) before accessing body
  5. Regional endpoints - Use regional endpoints for lower latency

来自 microsoft 的更多技能

oss-growth
microsoft
OSS增长黑客角色
agent-framework-azure-ai-py
microsoft
使用Microsoft Agent Framework Python SDK(agent-framework-azure-ai)构建Azure AI Foundry代理。在创建使用AzureAIAgentsProvider的持久化代理、使用托管工具(代码解释器、文件搜索、网络搜索)、集成MCP服务器、管理对话线程或实现流式响应时使用。涵盖函数工具、结构化输出和多工具代理。
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
使用Azure Application Insights对Web应用进行插桩的指南。提供遥测模式、SDK设置和配置参考。适用场景:如何对应用进行插桩、App Insights SDK、遥测模式、什么是App Insights、Application Insights指南、插桩示例、APM最佳实践。
devops
applicationinsights-web-ts
microsoft
使用Application Insights JavaScript SDK(@microsoft/applicationinsights-web)为浏览器/Web应用添加检测。用于真实用户监控(RUM)——页面视图、点击、AJAX/fetch依赖项、异常、自定义事件,以及与后端OpenTelemetry追踪关联的浏览器端GenAI代理追踪。涵盖SDK加载器脚本和npm设置、框架扩展(React、React Native、Angular)、点击分析、遥测初始化器,以及从浏览器发出的代理/工具/模型跨度所遵循的OTel GenAI语义约定。
devops
azure-ai-anomalydetector-java
microsoft
使用适用于 Java 的 Azure AI 异常检测器 SDK 构建异常检测应用程序。在实现单变量/多变量异常检测、时间序列分析或 AI 驱动的监控时使用。
development
azure-ai-language-conversations-py
microsoft
使用azure-ai-language-conversations Python SDK实现对话语言理解(CLU)。当使用ConversationAnalysisClient分析对话意图和实体、构建NLP功能或将语言理解集成到应用程序中时使用。
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python。用于机器学习工作区、作业、模型、数据集、计算资源和管道。 触发词:“azure-ai-ml”、“MLClient”、“工作区”、“模型注册表”、“训练作业”、“数据集”。
development