文档批量翻译_azure-ai-translation-document-py

以下为本文档的中文说明

azure-ai-translation-document-py（Azure AI文档翻译Python SDK）是微软Azure AI翻译服务的Python客户端库技能，用于批量文档翻译并保持原始格式。该技能利用Azure的文档翻译服务，可以将Word（DOCX）、PDF、PPTX、XLSX、HTML、TXT、RTF等文档格式进行大规模批量翻译，同时保留文档的布局、样式和格式。核心功能包括：使用DocumentTranslationClient发起批量翻译任务，支持同时翻译到多个目标语言；使用SingleDocumentTranslationClient处理单个文档的即时翻译；支持翻译状态查询、文档级状态追踪和任务取消。该技能支持两种认证方式——API密钥和Microsoft Entra ID（推荐），环境变量配置包括端点、密钥和Azure Blob Storage的源/目标容器URL。翻译任务使用Azure Blob Storage作为中转，源容器存放待翻译文档，目标容器存放翻译结果。该技能还支持术语表（Glossary）功能，可以上传自定义术语CSV文件，确保特定行业术语的准确翻译。支持的文档格式涵盖办公文档（DOCX、PPTX、XLSX）、结构化数据（CSV、TSV、JSON、XML）和本地化格式（XLIFF、XLF、MHTML）。最佳实践包括：使用最小权限的SAS令牌、监控长时间运行的操作、处理文档级错误、按语种分隔目标容器、以及使用异步客户端处理多个并发任务。该技能特别适合企业级多语言文档处理场景，如跨国公司的合规文档翻译、多语言产品文档生成等。

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

fro
m 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

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

When to Use

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

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.