azure-ai-translation-document-py

# Azure AI Document Translation SDK for Python Client library for Azure AI Translator document translation service for batch document translation with format preservation. ## Installation ```bash pip install azure-ai-translation-document ``` ## Environment Variables ```bash AZURE_DOCUMENT_TRANSLATION_ENDPOINT=https://<resource>.cognitiveservices.azure.com # Required for all auth methods # Storage for source and target documents AZURE_SOURCE_CONTAINER_URL=https://<storage>.blob.core.windows.net/<container>?<sas> # Required for all auth methods AZURE_TARGET_CONTAINER_URL=https://<storage>.blob.core.windows.net/<container>?<sas> # Required for all auth methods AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production AZURE_DOCUMENT_TRANSLATION_KEY=<your-api-key> # Only required for the legacy API-key auth path below ``` ## Authentication & Lifecycle > **🔑 Two rules apply to every code sample below:** > > 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation. > - Local dev: `DefaultAzureCredential` works as-is. > - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=<specific_credential>`) to constrain the credential chain to production-safe credentials. > 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically: > - Sync: `with <Client>(...) as client:` > - Async: `async with <Client>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`) > > Snippets may abbreviate this setup, but production code should always follow both rules. ```python import os from azure.identity import DefaultAzureCredential, ManagedIdentityCredential from azure.ai.translation.document import DocumentTranslationClient # Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential> credential = DefaultAzureCredential(require_envvar=True) # Or use a specific credential directly in production: # See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes # credential = ManagedIdentityCredential() with DocumentTranslationClient( endpoint=os.environ["AZURE_DOCUMENT_TRANSLATION_ENDPOINT"], credential=credential, ) as client: statuses = list(client.list_translation_statuses()) ``` ### Legacy: API Key (existing keyed deployments) New code should use `DefaultAzureCredential` above. Use `AzureKeyCredential` only if you have an existing keyed deployment that hasn't been migrated to Entra ID yet — for example, regulated environments still completing their Entra rollout. ```python import os from azure.core.credentials import AzureKeyCredential from azure.ai.translation.document import DocumentTranslationClient, SingleDocumentTranslationClient with DocumentTranslationClient( endpoint=os.environ["AZURE_DOCUMENT_TRANSLATION_ENDPOINT"], credential=AzureKeyCredential(os.environ["AZURE_DOCUMENT_TRANSLATION_KEY"]), ) as client: statuses = list(client.list_translation_statuses()) # SingleDocumentTranslationClient accepts the same key-based credential. ``` ## Basic Document Translation ```python 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 ```python 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 ```python from azure.ai.translation.document import SingleDocumentTranslationClient from azure.identity import DefaultAzureCredential with open("document.docx", "rb") as f: document_content = f.read() with SingleDocumentTranslationClient(endpoint, DefaultAzureCredential()) as single_client: 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 ```python # 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 ```python # 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 ```python # Cancel a running translation client.cancel_translation(operation_id) ``` ## Using Glossary ```python 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 ```python # 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 ```python # Get supported languages languages = client.get_supported_languages() for lang in languages: print(f"Language: {lang.name} ({lang.code})") ``` ## Async Client ```python from azure.ai.translation.document.aio import DocumentTranslationClient from azure.identity.aio import DefaultAzureCredential async def translate_documents(): async with DefaultAzureCredential() as credential: async with DocumentTranslationClient( endpoint=endpoint, credential=credential, ) 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. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module. 2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up. 3. **Use SAS tokens** with minimal required permissions 4. **Monitor long-running operations** with `poller.status()` 5. **Handle document-level errors** by iterating document statuses 6. **Use glossaries** for domain-specific terminology 7. **Separate target containers** for each language 8. **Use async client** for multiple concurrent jobs 9. **Check supported formats** before submitting documents ## Reference Files | File | Contents | |------|----------| | [references/capabilities.md](references/capabilities.md) | Additional non-hero capabilities, operation-group coverage, and production checklists. | | [references/non-hero-scenarios.md](references/non-hero-scenarios.md) | Dedicated non-hero examples for secondary/advanced scenarios. |