app-store-optimization/skills/azure-ai-translation-document-py/SKILL.md

name, description, package

name	description	package
azure-ai-translation-document-py	Azure AI Document Translation SDK for batch translation of documents with format preservation. Use for translating Word, PDF, Excel, PowerPoint, and other document formats at scale. Triggers: "document translation", "batch translation", "translate documents", "DocumentTranslationClient".	azure-ai-translation-document

Azure AI Document Translation SDK for Python

Client library for Azure AI Translator document translation service for batch document translation with format preservation.

Installation

pip install azure-ai-translation-document

Environment Variables

AZURE_DOCUMENT_TRANSLATION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
AZURE_DOCUMENT_TRANSLATION_KEY=<your-api-key>  # If using API key

# Storage for source and target documents
AZURE_SOURCE_CONTAINER_URL=https://<storage>.blob.core.windows.net/<container>?<sas>
AZURE_TARGET_CONTAINER_URL=https://<storage>.blob.core.windows.net/<container>?<sas>

Authentication

API Key

import os
from azure.ai.translation.document import DocumentTranslationClient
from azure.core.credentials import AzureKeyCredential

endpoint = os.environ["AZURE_DOCUMENT_TRANSLATION_ENDPOINT"]
key = os.environ["AZURE_DOCUMENT_TRANSLATION_KEY"]

client = DocumentTranslationClient(endpoint, AzureKeyCredential(key))

Entra ID (Recommended)

from azure.ai.translation.document import DocumentTranslationClient
from azure.identity import DefaultAzureCredential

client = DocumentTranslationClient(
    endpoint=os.environ["AZURE_DOCUMENT_TRANSLATION_ENDPOINT"],
    credential=DefaultAzureCredential()
)

Basic Document Translation

from azure.ai.translation.document import DocumentTranslationInput, TranslationTarget

source_url = os.environ["AZURE_SOURCE_CONTAINER_URL"]
target_url = os.environ["AZURE_TARGET_CONTAINER_URL"]

# Start translation job
poller = client.begin_translation(
    inputs=[
        DocumentTranslationInput(
            source_url=source_url,
            targets=[
                TranslationTarget(
                    target_url=target_url,
                    language="es"  # Translate to Spanish
                )
            ]
        )
    ]
)

# Wait for completion
result = poller.result()

print(f"Status: {poller.status()}")
print(f"Documents translated: {poller.details.documents_succeeded_count}")
print(f"Documents failed: {poller.details.documents_failed_count}")

Multiple Target Languages

poller = client.begin_translation(
    inputs=[
        DocumentTranslationInput(
            source_url=source_url,
            targets=[
                TranslationTarget(target_url=target_url_es, language="es"),
                TranslationTarget(target_url=target_url_fr, language="fr"),
                TranslationTarget(target_url=target_url_de, language="de")
            ]
        )
    ]
)

Translate Single Document

from azure.ai.translation.document import SingleDocumentTranslationClient

single_client = SingleDocumentTranslationClient(endpoint, AzureKeyCredential(key))

with open("document.docx", "rb") as f:
    document_content = f.read()

result = single_client.translate(
    body=document_content,
    target_language="es",
    content_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
)

# Save translated document
with open("document_es.docx", "wb") as f:
    f.write(result)

Check Translation Status

# Get all translation operations
operations = client.list_translation_statuses()

for op in operations:
    print(f"Operation ID: {op.id}")
    print(f"Status: {op.status}")
    print(f"Created: {op.created_on}")
    print(f"Total documents: {op.documents_total_count}")
    print(f"Succeeded: {op.documents_succeeded_count}")
    print(f"Failed: {op.documents_failed_count}")

List Document Statuses

# Get status of individual documents in a job
operation_id = poller.id
document_statuses = client.list_document_statuses(operation_id)

for doc in document_statuses:
    print(f"Document: {doc.source_document_url}")
    print(f"  Status: {doc.status}")
    print(f"  Translated to: {doc.translated_to}")
    if doc.error:
        print(f"  Error: {doc.error.message}")

Cancel Translation

# Cancel a running translation
client.cancel_translation(operation_id)

Using Glossary

from azure.ai.translation.document import TranslationGlossary

poller = client.begin_translation(
    inputs=[
        DocumentTranslationInput(
            source_url=source_url,
            targets=[
                TranslationTarget(
                    target_url=target_url,
                    language="es",
                    glossaries=[
                        TranslationGlossary(
                            glossary_url="https://<storage>.blob.core.windows.net/glossary/terms.csv?<sas>",
                            file_format="csv"
                        )
                    ]
                )
            ]
        )
    ]
)

Supported Document Formats

# Get supported formats
formats = client.get_supported_document_formats()

for fmt in formats:
    print(f"Format: {fmt.format}")
    print(f"  Extensions: {fmt.file_extensions}")
    print(f"  Content types: {fmt.content_types}")

Supported Languages

# Get supported languages
languages = client.get_supported_languages()

for lang in languages:
    print(f"Language: {lang.name} ({lang.code})")

Async Client

from azure.ai.translation.document.aio import DocumentTranslationClient
from azure.identity.aio import DefaultAzureCredential

async def translate_documents():
    async with DocumentTranslationClient(
        endpoint=endpoint,
        credential=DefaultAzureCredential()
    ) as client:
        poller = await client.begin_translation(inputs=[...])
        result = await poller.result()

Supported Formats

Category	Formats
Documents	DOCX, PDF, PPTX, XLSX, HTML, TXT, RTF
Structured	CSV, TSV, JSON, XML
Localization	XLIFF, XLF, MHTML

Storage Requirements

• Source and target containers must be Azure Blob Storage
• Use SAS tokens with appropriate permissions:
  • Source: Read, List
  • Target: Write, List

Best Practices

1. Use SAS tokens with minimal required permissions
2. Monitor long-running operations with poller.status()
3. Handle document-level errors by iterating document statuses
4. Use glossaries for domain-specific terminology
5. Separate target containers for each language
6. Use async client for multiple concurrent jobs
7. Check supported formats before submitting documents