本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。
# 通过 CLI 进行处理
使用 BDA 处理文档之前，您必须先将文档上传到 S3 存储桶：
**语法**
```
aws s3 cp [--options]
```
示例：
```
aws s3 cp /local/path/document.pdf s3://my-bda-bucket/input/document.pdf
```
------
#### [ Async ]
**基本处理命令结构**
使用 `invoke-data-automation-async` 命令处理文件：
```
aws bedrock-data-automation-runtime invoke-data-automation-async \
--input-configuration '{
"s3Uri": "s3://amzn-s3-demo-bucket/sample-images/sample-image.jpg"
}' \
--output-configuration '{
"s3Uri": "s3://amzn-s3-demo-bucket/output/"
}' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--data-automation-profile-arn "Amazon Resource Name (ARN)"
```
**高级处理命令结构**
**使用时间段的视频处理**
对于视频文件，您可以指定要处理的时间段：
```
aws bedrock-data-automation-runtime invoke-data-automation-async \
--input-configuration '{
"s3Uri": "s3://my-bucket/video.mp4",
"assetProcessingConfiguration": {
"video": {
"segmentConfiguration": {
"timestampSegment": {
"startTimeMillis": 0,
"endTimeMillis": 300000
}
}
}
}
}' \
--output-configuration '{
"s3Uri": "s3://my-bucket/output/"
}' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--data-automation-profile-arn "Amazon Resource Name (ARN)"
```
**使用自定义蓝图**
您可以直接在命令中指定自定义蓝图：
```
aws bedrock-data-automation-runtime invoke-data-automation-async \
--input-configuration '{
"s3Uri": "s3://my-bucket/document.pdf"
}' \
--output-configuration '{
"s3Uri": "s3://my-bucket/output/"
}' \
--blueprints '[
{
"blueprintArn": "Amazon Resource Name (ARN)",
"version": "1",
"stage": "LIVE"
}
]' \
--data-automation-profile-arn "Amazon Resource Name (ARN)"
```
**添加加密配置**
为了增强安全性，您可以添加加密配置：
```
aws bedrock-data-automation-runtime invoke-data-automation-async \
--input-configuration '{
"s3Uri": "s3://my-bucket/document.pdf"
}' \
--output-configuration '{
"s3Uri": "s3://my-bucket/output/"
}' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--encryption-configuration '{
"kmsKeyId": "Amazon Resource Name (ARN)",
"kmsEncryptionContext": {
"Department": "Finance",
"Project": "DocumentProcessing"
}
}' \
--data-automation-profile-arn "Amazon Resource Name (ARN)"
```
**事件通知**
启用处理完成 EventBridge 通知：
```
aws bedrock-data-automation-runtime invoke-data-automation-async \
--input-configuration '{
"s3Uri": "s3://my-bucket/document.pdf"
}' \
--output-configuration '{
"s3Uri": "s3://my-bucket/output/"
}' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--notification-configuration '{
"eventBridgeConfiguration": {
"eventBridgeEnabled": true
}
}' \
--data-automation-profile-arn "Amazon Resource Name (ARN)"
```
**检查处理状态**
使用 `get-data-automation-status` 命令检查您的处理作业的状态：
```
aws bedrock-data-automation-runtime get-data-automation-status \
--invocation-arn "Amazon Resource Name (ARN)"
```
响应将包括当前状态：
```
{
"status": "COMPLETED",
"creationTime": "2025-07-24T12:34:56.789Z",
"lastModifiedTime": "2025-07-24T12:45:12.345Z",
"outputLocation": "s3://my-bucket/output/abcd1234/"
}
```
**检索处理结果**
**在 S3 中查找输出文件**
列出 S3 存储桶中的输出文件：
```
aws s3 ls s3://amzn-s3-demo-bucket/output/
```
将结果下载到本地计算机：
```
aws s3 cp s3://amzn-s3-demo-bucket/output/ ~/Downloads/bda-results/ --recursive
```
**了解输出结构**
输出通常包括：
+ `standard-output.json`：包含标准提取结果
+ `custom-output.json`：包含来自自定义蓝图的结果
+ `metadata.json`：包含处理元数据和置信度分数
**常见的响应字段**
标准输出通常包括：
+ `extractedData`：主要提取的信息
+ `confidence`：提取的每个字段的置信度分数
+ `metadata`：处理信息，包括时间戳和模型详细信息
+ `boundingBoxes`：检测到的元素的位置信息（如果启用）
**错误处理和故障排除**
常见的错误场景和解决方案：
+ **S3 URI 无效**：确保您的 S3 存储桶存在并且您拥有适当的权限
+ **缺失 data-automation-profile-arn**：所有处理请求都需要此参数
+ **未找到项目**：确保您的项目 ARN 正确并且项目存在
+ **不支持的文件格式**：检查 BDA 是否支持您的文件格式
**为处理任务添加标签**
您可以添加标签来帮助整理和跟踪处理作业：
```
aws bedrock-data-automation-runtime invoke-data-automation-async \
--input-configuration '{
"s3Uri": "s3://my-bucket/document.pdf"
}' \
--output-configuration '{
"s3Uri": "s3://my-bucket/output/"
}' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--tags '[
{
"key": "Department",
"value": "Finance"
},
{
"key": "Project",
"value": "InvoiceProcessing"
}
]' \
--data-automation-profile-arn "Amazon Resource Name (ARN)"
```
------
#### [ Sync ]
**基本处理命令结构**
使用 `invoke-data-automation` 命令处理文件：
```
aws bedrock-data-automation-runtime invoke-data-automation \
--input-configuration '{
"s3Uri": "s3://amzn-s3-demo-bucket/sample-images/sample-image.jpg"
}' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--data-automation-profile-arn "Amazon Resource Name (ARN)"
--region "aws-region"
```
**高级处理命令结构**
输出到 S3 存储桶
```
aws bedrock-data-automation-runtime invoke-data-automation \
--input-configuration '{
"s3Uri": "s3://amzn-s3-demo-bucket/sample-images/sample-image.jpg"
}' \
--output-configuration '{"s3Uri": "s3://amzn-s3-demo-bucket/output/" }' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--data-automation-profile-arn "Amazon Resource Name (ARN)"
--region "aws-region" //document only
```
使用字节输入
```
aws bedrock-data-automation-runtime invoke-data-automation \
--input-configuration '{
"bytes": #blob input
}' \
--output-configuration '{"s3Uri": "s3://amzn-s3-demo-bucket/output/" }' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--data-automation-profile-arn "Amazon Resource Name (ARN)"
--region "aws-region"
```
**注意**
**字节**
一组 base64 编码的文档字节。以 blob 字节形式提供的文档的最大大小为 50 MB。类型应为 Base64 编码的二进制数据对象。
**使用自定义蓝图（仅适用于图像）**
```
aws bedrock-data-automation-runtime invoke-data-automation \
--input-configuration '{
"s3Uri": "s3://amzn-s3-demo-bucket/sample-images/sample-image.jpg"
}' \
--blueprints '[{"blueprintArn": "Amazon Resource Name (ARN)", "version": "1", "stage": "LIVE" } ]' \
--data-automation-profile-arn "Amazon Resource Name (ARN)"
--region "aws-region"
```
------
# 处理使用案例
利用 Amazon Bedrock 数据自动化功能，您可以通过命令行界面（CLI）处理文档、图像、音频和视频。对于每种模态，工作流都包括创建项目、调用分析和检索结果。
选择与您的首选方法对应的选项卡，然后按照以下步骤操作：
------
#### [ Documents ]
**从 W2 中提取数据**
![\[带有标准字段的样本 W2 表单，展示布局和要提取的数据字段。\]](http://docs.aws.amazon.com/zh_cn/bedrock/latest/userguide/images/bda/W2.png)
处理 W2 表单时，示例架构如下所示：
```
{
"class": "W2TaxForm",
"description": "Simple schema for extracting key information from W2 tax forms",
"properties": {
"employerName": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The employer's company name"
},
"employeeSSN": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The employee's Social Security Number (SSN)"
},
"employeeName": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The employee's full name"
},
"wagesAndTips": {
"type": "number",
"inferenceType": "explicit",
"instruction": "Wages, tips, other compensation (Box 1)"
},
"federalIncomeTaxWithheld": {
"type": "number",
"inferenceType": "explicit",
"instruction": "Federal income tax withheld (Box 2)"
},
"taxYear": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The tax year for this W2 form"
}
}
}
```
为处理 W2 所调用的命令如下所示：
```
aws bedrock-data-automation-runtime invoke-data-automation-async \
--input-configuration '{
"s3Uri": "s3://w2-processing-bucket-301678011486/input/W2.png"
}' \
--output-configuration '{
"s3Uri": "s3://w2-processing-bucket-301678011486/output/"
}' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--data-automation-profile-arn "Amazon Resource Name (ARN):data-automation-profile/default"
```
预期的输出示例是：
```
{
"documentType": "W2TaxForm",
"extractedData": {
"employerName": "The Big Company",
"employeeSSN": "123-45-6789",
"employeeName": "Jane Doe",
"wagesAndTips": 48500.00,
"federalIncomeTaxWithheld": 6835.00,
"taxYear": "2014"
},
"confidence": {
"employerName": 0.99,
"employeeSSN": 0.97,
"employeeName": 0.99,
"wagesAndTips": 0.98,
"federalIncomeTaxWithheld": 0.97,
"taxYear": 0.99
},
"metadata": {
"processingTimestamp": "2025-07-23T23:15:30Z",
"documentId": "w2-12345",
"modelId": "amazon.titan-document-v1",
"pageCount": 1
}
}
```
------
#### [ Images ]
**旅游广告样本**
![\[样本图片，展示用户如何从广告中提取信息。\]](http://docs.aws.amazon.com/zh_cn/bedrock/latest/userguide/images/bda/TravelAdvertisement.jpg)
用于旅游广告的示例架构如下所示：
```
{
"class": "TravelAdvertisement",
"description": "Schema for extracting information from travel advertisement images",
"properties": {
"destination": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The name of the travel destination being advertised"
},
"tagline": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The main promotional text or tagline in the advertisement"
},
"landscapeType": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The type of landscape shown (e.g., mountains, beach, forest, etc.)"
},
"waterFeatures": {
"type": "string",
"inferenceType": "explicit",
"instruction": "Description of any water features visible in the image (ocean, lake, river, etc.)"
},
"dominantColors": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The dominant colors present in the image"
},
"advertisementType": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The type of travel advertisement (e.g., destination promotion, tour package, etc.)"
}
}
}
```
为处理旅游广告所调用的命令如下所示：
```
aws bedrock-data-automation-runtime invoke-data-automation-async \
--input-configuration '{
"s3Uri": "s3://travel-ads-bucket-301678011486/input/TravelAdvertisement.jpg"
}' \
--output-configuration '{
"s3Uri": "s3://travel-ads-bucket-301678011486/output/"
}' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--data-automation-profile-arn "Amazon Resource Name (ARN):data-automation-profile/default"
```
预期的输出示例是：
```
{
"documentType": "TravelAdvertisement",
"extractedData": {
"destination": "Kauai",
"tagline": "Travel to KAUAI",
"landscapeType": "Coastal mountains with steep cliffs and valleys",
"waterFeatures": "Turquoise ocean with white surf along the coastline",
"dominantColors": "Green, blue, turquoise, brown, white",
"advertisementType": "Destination promotion"
},
"confidence": {
"destination": 0.98,
"tagline": 0.99,
"landscapeType": 0.95,
"waterFeatures": 0.97,
"dominantColors": 0.96,
"advertisementType": 0.92
},
"metadata": {
"processingTimestamp": "2025-07-23T23:45:30Z",
"documentId": "travel-ad-12345",
"modelId": "amazon.titan-image-v1",
"imageWidth": 1920,
"imageHeight": 1080
}
}
```
------
#### [ Audio ]
**转录电话通话**
用于电话呼叫的示例架构如下所示：
```
{
"class": "AudioRecording",
"description": "Schema for extracting information from AWS customer call recordings",
"properties": {
"callType": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The type of call (e.g., technical support, account management, consultation)"
},
"participants": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The number and roles of participants in the call"
},
"mainTopics": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The main topics or AWS services discussed during the call"
},
"customerIssues": {
"type": "string",
"inferenceType": "explicit",
"instruction": "Any customer issues or pain points mentioned during the call"
},
"actionItems": {
"type": "string",
"inferenceType": "explicit",
"instruction": "Action items or next steps agreed upon during the call"
},
"callDuration": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The duration of the call"
},
"callSummary": {
"type": "string",
"inferenceType": "explicit",
"instruction": "A brief summary of the entire call"
}
}
}
```
为处理电话通话所调用的命令如下所示：
```
aws bedrock-data-automation-runtime invoke-data-automation-async \
--input-configuration '{
"s3Uri": "s3://audio-analysis-bucket-301678011486/input/AWS_TCA-Call-Recording-2.wav"
}' \
--output-configuration '{
"s3Uri": "s3://audio-analysis-bucket-301678011486/output/"
}' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--data-automation-profile-arn "Amazon Resource Name (ARN):data-automation-profile/default"
```
预期的输出示例是：
```
{
"documentType": "AudioRecording",
"extractedData": {
"callType": "Technical consultation",
"participants": "3 participants: AWS Solutions Architect, AWS Technical Account Manager, and Customer IT Director",
"mainTopics": "AWS Bedrock implementation, data processing pipelines, model fine-tuning, and cost optimization",
"customerIssues": "Integration challenges with existing ML infrastructure, concerns about latency for real-time processing, questions about data security compliance",
"actionItems": [
"AWS team to provide documentation on Bedrock data processing best practices",
"Customer to share their current ML architecture diagrams",
"Schedule follow-up meeting to review implementation plan",
"AWS to provide cost estimation for proposed solution"
],
"callDuration": "45 minutes and 23 seconds",
"callSummary": "Technical consultation call between AWS team and customer regarding implementation of AWS Bedrock for their machine learning workloads. Discussion covered integration approaches, performance optimization, security considerations, and next steps for implementation planning."
},
"confidence": {
"callType": 0.94,
"participants": 0.89,
"mainTopics": 0.92,
"customerIssues": 0.87,
"actionItems": 0.85,
"callDuration": 0.99,
"callSummary": 0.93
},
"metadata": {
"processingTimestamp": "2025-07-24T00:30:45Z",
"documentId": "audio-12345",
"modelId": "amazon.titan-audio-v1",
"audioDuration": "00:45:23",
"audioFormat": "WAV",
"sampleRate": "44.1 kHz"
},
"transcript": {
"segments": [
{
"startTime": "00:00:03",
"endTime": "00:00:10",
"speaker": "Speaker 1",
"text": "Hello everyone, thank you for joining today's call about implementing AWS Bedrock for your machine learning workloads."
},
{
"startTime": "00:00:12",
"endTime": "00:00:20",
"speaker": "Speaker 2",
"text": "Thanks for having us. We're really interested in understanding how Bedrock can help us streamline our document processing pipeline."
},
{
"startTime": "00:00:22",
"endTime": "00:00:35",
"speaker": "Speaker 3",
"text": "Yes, and specifically we'd like to discuss integration with our existing systems and any potential latency concerns for real-time processing requirements."
}
// Additional transcript segments would continue here
]
}
}
```
------
#### [ Video ]
**处理视频**
用于视频的示例架构如下所示：
```
{
"class": "VideoContent",
"description": "Schema for extracting information from video content",
"properties": {
"title": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The title or name of the video content"
},
"contentType": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The type of content (e.g., tutorial, competition, documentary, advertisement)"
},
"mainSubject": {
"type": "string",
"inferenceType": "explicit",
"instruction": "The main subject or focus of the video"
},
"keyPersons": {
"type": "string",
"inferenceType": "explicit",
"instruction": "Key people appearing in the video (hosts, participants, etc.)"
},
"keyScenes": {
"type": "string",
"inferenceType": "explicit",
"instruction": "Description of important scenes or segments in the video"
},
"audioElements": {
"type": "string",
"inferenceType": "explicit",
"instruction": "Description of notable audio elements (music, narration, dialogue)"
},
"summary": {
"type": "string",
"inferenceType": "explicit",
"instruction": "A brief summary of the video content"
}
}
}
```
为处理视频所调用的命令如下所示：
```
aws bedrock-data-automation-runtime invoke-data-automation-async \
--input-configuration '{
"s3Uri": "s3://video-analysis-bucket-301678011486/input/MakingTheCut.mp4",
"assetProcessingConfiguration": {
"video": {
"segmentConfiguration": {
"timestampSegment": {
"startTimeMillis": 0,
"endTimeMillis": 300000
}
}
}
}
}' \
--output-configuration '{
"s3Uri": "s3://video-analysis-bucket-301678011486/output/"
}' \
--data-automation-configuration '{
"dataAutomationProjectArn": "Amazon Resource Name (ARN)",
"stage": "LIVE"
}' \
--data-automation-profile-arn "Amazon Resource Name (ARN):data-automation-profile/default"
```
预期的输出示例是：
```
{
"documentType": "VideoContent",
"extractedData": {
"title": "Making the Cut",
"contentType": "Fashion design competition",
"mainSubject": "Fashion designers competing to create the best clothing designs",
"keyPersons": "Heidi Klum, Tim Gunn, and various fashion designer contestants",
"keyScenes": [
"Introduction of the competition and contestants",
"Design challenge announcement",
"Designers working in their studios",
"Runway presentation of designs",
"Judges' critique and elimination decision"
],
"audioElements": "Background music, host narration, contestant interviews, and design feedback discussions",
"summary": "An episode of 'Making the Cut' fashion competition where designers compete in a challenge to create innovative designs. The episode includes the challenge announcement, design process, runway presentation, and judging."
},
"confidence": {
"title": 0.99,
"contentType": 0.95,
"mainSubject": 0.92,
"keyPersons": 0.88,
"keyScenes": 0.90,
"audioElements": 0.87,
"summary": 0.94
},
"metadata": {
"processingTimestamp": "2025-07-24T00:15:30Z",
"documentId": "video-12345",
"modelId": "amazon.titan-video-v1",
"videoDuration": "00:45:23",
"analyzedSegment": "00:00:00 - 00:05:00",
"resolution": "1920x1080"
},
"transcript": {
"segments": [
{
"startTime": "00:00:05",
"endTime": "00:00:12",
"speaker": "Heidi Klum",
"text": "Welcome to Making the Cut, where we're searching for the next great global fashion brand."
},
{
"startTime": "00:00:15",
"endTime": "00:00:25",
"speaker": "Tim Gunn",
"text": "Designers, for your first challenge, you'll need to create a look that represents your brand and can be sold worldwide."
}
// Additional transcript segments would continue here
]
}
}
```
------