azure-ai-translation-text-py

Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.

31,392 stars
Complexity: easy

About this skill

This skill empowers AI agents with seamless access to the Azure AI Translator Text service via its Python SDK. It provides robust capabilities for real-time text translation between a multitude of languages, transliteration (converting text from one script to another, e.g., Romanized Japanese), automatic language detection of source text, and comprehensive dictionary lookups for alternative translations and contextual examples. Designed for deep integration within AI applications, this skill enables agents to dynamically process and translate textual content, making it an indispensable tool for global communication, content localization, multilingual data processing, and enhancing user experiences across language barriers. It leverages Azure's highly accurate and scalable AI infrastructure for all language operations.

Best use case

Translating user inputs or agent responses in a multilingual conversational AI application. Localizing dynamic content, such as product descriptions or news articles, for different regional audiences. Automatically detecting the language of incoming text for content categorization, routing, or further processing. Providing transliteration for non-Latin script languages to aid readability or search functions (e.g., converting Korean Hangul to Revised Romanization). Offering dictionary lookups for specific terms, including alternative translations, back-translations, and example usages in context. Processing and translating large volumes of text data for business intelligence, compliance, or archiving purposes.

Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.

The AI agent will successfully receive accurately translated, transliterated, or language-identified text, or detailed dictionary lookup results, directly from the Azure AI Translator service. This enables the agent to confidently proceed with subsequent multilingual tasks, provide internationalized responses, or perform informed language-based analyses.

Practical example

Example input

{"function_name": "translate_text", "parameters": {"text_to_translate": "Hello, how are you?", "target_language_code": "fr", "source_language_code": "en"}}

Example output

{"translated_text": "Bonjour, comment allez-vous?", "detected_language": "en", "translation_confidence": 1.0, "source_text": "Hello, how are you?"}

When to use this skill

  • When an AI agent needs to understand or generate content in multiple languages.
  • When the language of incoming text is unknown and requires accurate identification.
  • When converting text between different writing systems is necessary (e.g., for global search or display).
  • When highly accurate, real-time machine translation is critical for agent functionality or user experience.

When not to use this skill

  • When strictly offline or on-device translation capabilities are required, as this skill relies on cloud-based Azure services.
  • For tasks that only involve basic text manipulation (e.g., string concatenation, simple regex) without requiring complex linguistic processing.
  • When dealing with highly specialized or extremely niche domain-specific terminology that might require fine-tuned custom translation models not easily accessible or configurable through the generic service.
  • If ultra-low-latency translation is absolutely paramount, where network round-trip times to Azure could introduce unacceptable delays.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/azure-ai-translation-text-py/SKILL.md --create-dirs "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/plugins/antigravity-awesome-skills-claude/skills/azure-ai-translation-text-py/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/azure-ai-translation-text-py/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How azure-ai-translation-text-py Compares

Feature / Agentazure-ai-translation-text-pyStandard Approach
Platform SupportClaudeLimited / Varies
Context Awareness High Baseline
Installation ComplexityeasyN/A

Frequently Asked Questions

What does this skill do?

Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.

Which AI agents support this skill?

This skill is designed for Claude.

How difficult is it to install?

The installation complexity is rated as easy. You can find the installation instructions above.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Azure AI Text Translation SDK for Python

Client library for Azure AI Translator text translation service for real-time text translation, transliteration, and language operations.

## Installation

```bash
pip install azure-ai-translation-text
```

## Environment Variables

```bash
AZURE_TRANSLATOR_KEY=<your-api-key>
AZURE_TRANSLATOR_REGION=<your-region>  # e.g., eastus, westus2
# Or use custom endpoint
AZURE_TRANSLATOR_ENDPOINT=https://<resource>.cognitiveservices.azure.com
```

## Authentication

### API Key with Region

```python
import os
from azure.ai.translation.text import TextTranslationClient
from azure.core.credentials import AzureKeyCredential

key = os.environ["AZURE_TRANSLATOR_KEY"]
region = os.environ["AZURE_TRANSLATOR_REGION"]

# Create credential with region
credential = AzureKeyCredential(key)
client = TextTranslationClient(credential=credential, region=region)
```

### API Key with Custom Endpoint

```python
endpoint = os.environ["AZURE_TRANSLATOR_ENDPOINT"]

client = TextTranslationClient(
    credential=AzureKeyCredential(key),
    endpoint=endpoint
)
```

### Entra ID (Recommended)

```python
from azure.ai.translation.text import TextTranslationClient
from azure.identity import DefaultAzureCredential

client = TextTranslationClient(
    credential=DefaultAzureCredential(),
    endpoint=os.environ["AZURE_TRANSLATOR_ENDPOINT"]
)
```

## Basic Translation

```python
# Translate to a single language
result = client.translate(
    body=["Hello, how are you?", "Welcome to Azure!"],
    to=["es"]  # Spanish
)

for item in result:
    for translation in item.translations:
        print(f"Translated: {translation.text}")
        print(f"Target language: {translation.to}")
```

## Translate to Multiple Languages

```python
result = client.translate(
    body=["Hello, world!"],
    to=["es", "fr", "de", "ja"]  # Spanish, French, German, Japanese
)

for item in result:
    print(f"Source: {item.detected_language.language if item.detected_language else 'unknown'}")
    for translation in item.translations:
        print(f"  {translation.to}: {translation.text}")
```

## Specify Source Language

```python
result = client.translate(
    body=["Bonjour le monde"],
    from_parameter="fr",  # Source is French
    to=["en", "es"]
)
```

## Language Detection

```python
result = client.translate(
    body=["Hola, como estas?"],
    to=["en"]
)

for item in result:
    if item.detected_language:
        print(f"Detected language: {item.detected_language.language}")
        print(f"Confidence: {item.detected_language.score:.2f}")
```

## Transliteration

Convert text from one script to another:

```python
result = client.transliterate(
    body=["konnichiwa"],
    language="ja",
    from_script="Latn",  # From Latin script
    to_script="Jpan"      # To Japanese script
)

for item in result:
    print(f"Transliterated: {item.text}")
    print(f"Script: {item.script}")
```

## Dictionary Lookup

Find alternate translations and definitions:

```python
result = client.lookup_dictionary_entries(
    body=["fly"],
    from_parameter="en",
    to="es"
)

for item in result:
    print(f"Source: {item.normalized_source} ({item.display_source})")
    for translation in item.translations:
        print(f"  Translation: {translation.normalized_target}")
        print(f"  Part of speech: {translation.pos_tag}")
        print(f"  Confidence: {translation.confidence:.2f}")
```

## Dictionary Examples

Get usage examples for translations:

```python
from azure.ai.translation.text.models import DictionaryExampleTextItem

result = client.lookup_dictionary_examples(
    body=[DictionaryExampleTextItem(text="fly", translation="volar")],
    from_parameter="en",
    to="es"
)

for item in result:
    for example in item.examples:
        print(f"Source: {example.source_prefix}{example.source_term}{example.source_suffix}")
        print(f"Target: {example.target_prefix}{example.target_term}{example.target_suffix}")
```

## Get Supported Languages

```python
# Get all supported languages
languages = client.get_supported_languages()

# Translation languages
print("Translation languages:")
for code, lang in languages.translation.items():
    print(f"  {code}: {lang.name} ({lang.native_name})")

# Transliteration languages
print("\nTransliteration languages:")
for code, lang in languages.transliteration.items():
    print(f"  {code}: {lang.name}")
    for script in lang.scripts:
        print(f"    {script.code} -> {[t.code for t in script.to_scripts]}")

# Dictionary languages
print("\nDictionary languages:")
for code, lang in languages.dictionary.items():
    print(f"  {code}: {lang.name}")
```

## Break Sentence

Identify sentence boundaries:

```python
result = client.find_sentence_boundaries(
    body=["Hello! How are you? I hope you are well."],
    language="en"
)

for item in result:
    print(f"Sentence lengths: {item.sent_len}")
```

## Translation Options

```python
result = client.translate(
    body=["Hello, world!"],
    to=["de"],
    text_type="html",           # "plain" or "html"
    profanity_action="Marked",  # "NoAction", "Deleted", "Marked"
    profanity_marker="Asterisk", # "Asterisk", "Tag"
    include_alignment=True,      # Include word alignment
    include_sentence_length=True # Include sentence boundaries
)

for item in result:
    translation = item.translations[0]
    print(f"Translated: {translation.text}")
    if translation.alignment:
        print(f"Alignment: {translation.alignment.proj}")
    if translation.sent_len:
        print(f"Sentence lengths: {translation.sent_len.src_sent_len}")
```

## Async Client

```python
from azure.ai.translation.text.aio import TextTranslationClient
from azure.core.credentials import AzureKeyCredential

async def translate_text():
    async with TextTranslationClient(
        credential=AzureKeyCredential(key),
        region=region
    ) as client:
        result = await client.translate(
            body=["Hello, world!"],
            to=["es"]
        )
        print(result[0].translations[0].text)
```

## Client Methods

| Method | Description |
|--------|-------------|
| `translate` | Translate text to one or more languages |
| `transliterate` | Convert text between scripts |
| `detect` | Detect language of text |
| `find_sentence_boundaries` | Identify sentence boundaries |
| `lookup_dictionary_entries` | Dictionary lookup for translations |
| `lookup_dictionary_examples` | Get usage examples |
| `get_supported_languages` | List supported languages |

## Best Practices

1. **Batch translations** — Send multiple texts in one request (up to 100)
2. **Specify source language** when known to improve accuracy
3. **Use async client** for high-throughput scenarios
4. **Cache language list** — Supported languages don't change frequently
5. **Handle profanity** appropriately for your application
6. **Use html text_type** when translating HTML content
7. **Include alignment** for applications needing word mapping

## When to Use
This skill is applicable to execute the workflow or actions described in the overview.

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