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 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 ```python 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) ```python 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 ```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 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 ```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 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 ## When to Use This skill is applicable to execute the workflow or actions described in the overview. ## Diff History - **v00.33.0**: Ingested from antigravity-awesome-skills community repo --- ## Why This Skill Exists Implement — Azure AI Document Translation SDK for batch translation of documents with format preservation. Use for translating <!-- SR_40: auto-generated from frontmatter `purpose`/`description` (OPP-Phase3). Expand with domain-specific rationale. --> ## What If Fails - condition: Código não disponível para análise <!-- SR_40: auto-generated from frontmatter `what_if_fails` (OPP-Phase3). -->