本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
# 透過 CLI 處理
使用 BDA 處理文件之前，您必須先將文件上傳至 S3 儲存貯體：
**語法**
```
aws s3 cp [--options]
```
範例：
```
aws s3 cp /local/path/document.pdf s3://my-bda-bucket/input/document.pdf
```
------
#### [ Async ]
**基本處理命令結構**
使用 `invoke-data-automation-async` 命令來處理檔案：
```
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)"
```
**進階處理命令結構**
**依照時間段處理影片**
對於影片檔案，您可以指定要處理的時間段：
```
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)"
```
**使用自訂藍圖**
您可以直接在命令中指定自訂藍圖：
```
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)"
```
**新增加密組態**
為了增強安全性，您可以新增加密組態：
```
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**
啟用 EventBridge 通知以完成處理：
```
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)"
```
**檢查處理狀態**
使用 `get-data-automation-status` 命令來檢查處理任務的狀態：
```
aws bedrock-data-automation-runtime get-data-automation-status \
--invocation-arn "Amazon Resource Name (ARN)"
```
回應將包含目前狀態：
```
{
"status": "COMPLETED",
"creationTime": "2025-07-24T12:34:56.789Z",
"lastModifiedTime": "2025-07-24T12:45:12.345Z",
"outputLocation": "s3://my-bucket/output/abcd1234/"
}
```
**擷取處理結果**
**在 S3 中尋找輸出檔案**
列出 S3 儲存貯體中的輸出檔案：
```
aws s3 ls s3://amzn-s3-demo-bucket/output/
```
下載結果到您的本機電腦：
```
aws s3 cp s3://amzn-s3-demo-bucket/output/ ~/Downloads/bda-results/ --recursive
```
**了解輸出結構**
輸出通常包括：
+ `standard-output.json`：包含標準擷取結果
+ `custom-output.json`：包含自訂藍圖的結果
+ `metadata.json`：包含處理中繼資料和可信度分數
**常見回應欄位**
標準輸出通常包括：
+ `extractedData`：主要擷取的資訊
+ `confidence`：每個擷取欄位的可信度分數
+ `metadata`：處理資訊，包括時間戳記和模型詳細資訊
+ `boundingBoxes`：偵測到元素的位置資訊 (如果已啟用)
**錯誤處理和疑難排解**
常見的錯誤案例和解決方案：
+ **無效的 S3 URI**：確保您的 S3 儲存貯體存在且您具有適當的許可
+ **缺少 data-automation-profile-arn**：所有處理請求都需要此參數
+ **找不到專案**：確認您的專案 ARN 正確且專案存在
+ **不支援的檔案格式**：檢查 BDA 是否支援您的檔案格式
**將標籤新增至處理任務**
您可以新增標籤，以協助組織和追蹤處理任務：
```
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 ]
**基本處理命令結構**
使用 `invoke-data-automation` 命令來處理檔案：
```
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"
```
**進階處理命令結構**
輸出至 S3 儲存貯體
```
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
```
使用位元組輸入
```
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**
**位元組**
base64 編碼文件位元組的 Blob。位元組 Blob 中提供的文件大小上限為 50 MB。類型應為 Base64-encoded的二進位資料物件。
**使用自訂藍圖 （僅適用於映像）**
```
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"
```
------
# 處理使用案例
Amazon Bedrock Data Automation 可讓您透過命令列介面 (CLI) 處理文件、影像、音訊和影片。對於每個模態，工作流程包含建立專案、調用分析，以及擷取結果。
選擇您偏好方法的索引標籤，然後遵循下列步驟：
------
#### [ Documents ]
**從 W2 擷取資料**
![\[具有標準欄位的範例 W2 表單，示範要擷取的配置和資料欄位。\]](http://docs.aws.amazon.com/zh_tw/bedrock/latest/userguide/images/bda/W2.png)
處理 W2 表單時，範例結構描述如下：
```
{
"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"
}
}
}
```
調用 W2 處理的命令類似如下：
```
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"
```
預期輸出的範例為：
```
{
"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 ]
**旅遊廣告範例**
![\[範例影像，示範使用者如何從廣告擷取資訊。\]](http://docs.aws.amazon.com/zh_tw/bedrock/latest/userguide/images/bda/TravelAdvertisement.jpg)
旅遊廣告的範例結構描述如下：
```
{
"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.)"
}
}
}
```
調用旅遊廣告處理的命令類似如下：
```
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"
```
預期輸出的範例為：
```
{
"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 ]
**轉錄電話語音**
電話的範例結構描述如下：
```
{
"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"
}
}
}
```
調用電話通話處理的命令類似如下：
```
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"
```
預期輸出的範例為：
```
{
"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 ]
**處理影片**
影片的範例結構描述如下：
```
{
"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"
}
}
}
```
調用視訊處理的命令類似如下：
```
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"
```
預期輸出的範例為：
```
{
"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
]
}
}
```
------