azure-ai-translation-ts

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

Lebih banyak skill dari microsoft

oss-growth
microsoft
Persona peretas pertumbuhan OSS
agent-framework-azure-ai-py
microsoft
Bangun agen Azure AI Foundry menggunakan Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Gunakan saat membuat agen persisten dengan AzureAIAgentsProvider, menggunakan alat yang dihosting (code interpreter, file search, web search), mengintegrasikan server MCP, mengelola utas percakapan, atau mengimplementasikan respons streaming. Mencakup alat fungsi, keluaran terstruktur, dan agen multi-alat.
development
airunway-aks-setup
microsoft
Siapkan AI Runway di AKS — dari klaster kosong hingga model berjalan. Mencakup verifikasi klaster, instalasi controller, penilaian GPU, penyiapan penyedia, dan deployment pertama. KAPAN: "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
Panduan untuk instrumentasi aplikasi web dengan Azure Application Insights. Menyediakan pola telemetri, pengaturan SDK, dan referensi konfigurasi. KAPAN: cara menginstrumentasi aplikasi, SDK App Insights, pola telemetri, apa itu App Insights, panduan Application Insights, contoh instrumentasi, praktik terbaik APM.
devops
applicationinsights-web-ts
microsoft
Instrumentasi aplikasi browser/web dengan Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Digunakan untuk Real User Monitoring (RUM) — tampilan halaman, klik, dependensi AJAX/fetch, pengecualian, peristiwa kustom, dan jejak agen GenAI sisi browser yang dikorelasikan dengan jejak OpenTelemetry backend. Mencakup pengaturan SDK Loader Script dan npm, ekstensi kerangka kerja (React, React Native, Angular), Click Analytics, inisialisasi telemetri, dan konvensi semantik OTel GenAI untuk span agen/alat/model yang dipancarkan dari browser.
devops
azure-ai-anomalydetector-java
microsoft
Bangun aplikasi deteksi anomali dengan Azure AI Anomaly Detector SDK untuk Java. Gunakan saat mengimplementasikan deteksi anomali univariat/multivariat, analisis deret waktu, atau pemantauan bertenaga AI.
development
azure-ai-language-conversations-py
microsoft
Implementasikan Pemahaman Bahasa Percakapan (CLU) menggunakan SDK Python azure-ai-language-conversations. Gunakan saat bekerja dengan ConversationAnalysisClient untuk menganalisis maksud dan entitas percakapan, membangun fitur NLP, atau mengintegrasikan pemahaman bahasa ke dalam aplikasi.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 untuk Python. Gunakan untuk ruang kerja ML, pekerjaan, model, kumpulan data, komputasi, dan pipeline. Pemicu: "azure-ai-ml", "MLClient", "ruang kerja", "registri model", "pekerjaan pelatihan", "kumpulan data".
development