Azure AI Translation Document Py
MicrosoftOfficialCreated by Microsoft
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- Tool integration
- Automation and integrations
- MIT
- Low risk
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The Azure AI Document Translation SDK for Python provides a robust interface for batch-processing documents across various formats, including Word, PDF, Excel, and PowerPoint. It integrates with Azure Blob Storage to handle large-scale translation tasks while maintaining original document structure and formatting. The SDK supports both synchronous and asynchronous operations, includes features for glossary-based terminology management, and adheres to modern security practices by utilizing Entra ID authentication via DefaultAzureCredential.
What it does
- Performs batch translation of multiple documents stored in Azure Blob Storage.
- Supports a wide range of document formats including DOCX, PDF, PPTX, XLSX, and structured data like JSON or XML.
- Preserves original document formatting during the translation process.
- Allows for domain-specific terminology customization using translation glossaries.
- Provides monitoring capabilities for long-running translation operations.
- Offers both synchronous and asynchronous client implementations for flexible integration.
When to use it
- When you need to translate large volumes of documents at scale.
- When maintaining the original layout and formatting of documents is a requirement.
- When integrating document translation workflows into Azure-based cloud applications.
- When you need to ensure consistent terminology across translations using custom glossaries.
Advantages
- Native integration with Azure Blob Storage for efficient file handling.
- Supports modern, secure authentication via DefaultAzureCredential.
- Maintains document integrity and formatting across diverse file types.
- Scalable architecture suitable for enterprise-level document processing.
Usage examples
- “How do I start a batch translation job for documents in an Azure container?”
- The agent provides a code snippet using DocumentTranslationClient to initiate a translation job with source and target container URLs, specifying the target language.
- “Can I use a custom glossary for my document translation?”
- The agent demonstrates how to include a TranslationGlossary object within the DocumentTranslationInput to apply specific terminology during the translation process.
- “How do I check the status of my document translation job?”
- The agent shows how to use the operation ID to retrieve and iterate through document statuses, including error reporting for individual files.
- “How do I translate a single document using the Python SDK?”
- The agent provides an example using the SingleDocumentTranslationClient to translate raw document content directly.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/azure-ai-translation-document-py/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip azure-ai-translation-document-py.zip -d .agents/skills
Documentation by Microsoft
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-documentEnvironment Variables
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 belowAuthentication & Lifecycle
🔑 Two rules apply to every code sample below:
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:
DefaultAzureCredentialworks as-is.Production: set
AZURE_TOKEN_CREDENTIALS=prod(orAZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.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:andasync with DefaultAzureCredential() as credential:(fromazure.identity.aio)Snippets may abbreviate this setup, but production code should always follow both rules.
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()
# 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.
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
import os
from azure.ai.translation.document import DocumentTranslationClient, DocumentTranslationInput, TranslationTarget
from azure.core.exceptions import HttpResponseError
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential()
with DocumentTranslationClient(
endpoint=os.environ["AZURE_DOCUMENT_TRANSLATION_ENDPOINT"],
credential=credential,
) as client:
source_url = os.environ["AZURE_SOURCE_CONTAINER_URL"]
target_url = os.environ["AZURE_TARGET_CONTAINER_URL"]
try:
# 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}")
except HttpResponseError as e:
print(f"Translation failed: {e.message}")
raiseMultiple 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
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
# 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 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
Pick sync OR async and stay consistent. Do not mix
azure.xxxsync clients withazure.xxx.aioasync clients in the same call path. Choose one mode per module.Always use context managers for clients and async credentials. Wrap every client in
with Client(...) as client:(sync) orasync with Client(...) as client:(async). For asyncDefaultAzureCredentialfromazure.identity.aio, also useasync with credential:so tokens and transports are cleaned up.Use SAS tokens with minimal required permissions
Monitor long-running operations with
poller.status()Handle document-level errors by iterating document statuses
Use glossaries for domain-specific terminology
Separate target containers for each language
Use async client for multiple concurrent jobs
Check supported formats before submitting documents
Reference Files
| File | Contents |
|---|---|
| references/capabilities.md | Additional non-hero capabilities, operation-group coverage, and production checklists. |
| references/non-hero-scenarios.md | Dedicated non-hero examples for secondary/advanced scenarios. |
Files
- ATTRIBUTION.mdAdded by AutoGineer1007 B
- LICENSELicence file added by AutoGineer1.1 KiB
- SKILL.md10.3 KiB
- references/capabilities.md2.3 KiB
- references/non-hero-scenarios.md3.2 KiB
SKILL.md
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-documentEnvironment Variables
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 belowAuthentication & Lifecycle
🔑 Two rules apply to every code sample below:
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:
DefaultAzureCredentialworks as-is.Production: set
AZURE_TOKEN_CREDENTIALS=prod(orAZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.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:andasync with DefaultAzureCredential() as credential:(fromazure.identity.aio)Snippets may abbreviate this setup, but production code should always follow both rules.
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()
# 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.
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
import os
from azure.ai.translation.document import DocumentTranslationClient, DocumentTranslationInput, TranslationTarget
from azure.core.exceptions import HttpResponseError
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential()
with DocumentTranslationClient(
endpoint=os.environ["AZURE_DOCUMENT_TRANSLATION_ENDPOINT"],
credential=credential,
) as client:
source_url = os.environ["AZURE_SOURCE_CONTAINER_URL"]
target_url = os.environ["AZURE_TARGET_CONTAINER_URL"]
try:
# 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}")
except HttpResponseError as e:
print(f"Translation failed: {e.message}")
raiseMultiple 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
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
# 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 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
Pick sync OR async and stay consistent. Do not mix
azure.xxxsync clients withazure.xxx.aioasync clients in the same call path. Choose one mode per module.Always use context managers for clients and async credentials. Wrap every client in
with Client(...) as client:(sync) orasync with Client(...) as client:(async). For asyncDefaultAzureCredentialfromazure.identity.aio, also useasync with credential:so tokens and transports are cleaned up.Use SAS tokens with minimal required permissions
Monitor long-running operations with
poller.status()Handle document-level errors by iterating document statuses
Use glossaries for domain-specific terminology
Separate target containers for each language
Use async client for multiple concurrent jobs
Check supported formats before submitting documents
Reference Files
| File | Contents |
|---|---|
| references/capabilities.md | Additional non-hero capabilities, operation-group coverage, and production checklists. |
| references/non-hero-scenarios.md | Dedicated non-hero examples for secondary/advanced scenarios. |
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an API key, Needs Python
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Attribution and licence
- Created by
- Microsoft
- Publisher
- Microsoft (Official)
- Source
- microsoft/skills · .github/plugins/azure-sdk-python/skills/azure-ai-translation-document-py · commit 0696600
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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