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# CLI を介した処理
BDA でドキュメントを処理する前に、まずドキュメントを S3 バケットにアップロードする必要があります。
**[Syntax]** (構文)
```
aws s3 cp [--options]
```
例:
```
aws s3 cp /local/path/document.pdf s3://my-bda-bucket/input/document.pdf
```
------
#### [ Async ]
**基本的な処理コマンド構造**
ファイルを処理するには、`invoke-data-automation-async` コマンドを使用します。
```
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)"
```
**高度な処理コマンド構造**
**タイムセグメントを使用した動画処理**
動画ファイルの場合、処理するタイムセグメントを指定できます。
```
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)"
```
**カスタムブループリントの使用**
カスタムブループリントは、以下のコマンドで直接指定できます。
```
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)"
```
**暗号化の設定の追加**
セキュリティを強化するために、暗号化の設定を追加できます。
```
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)"
```
**イベント通知**
次のとおり、処理完了の EventBridge 通知を有効にします。
```
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)"
```
**処理ステータスの確認**
プロジェクトの作成ステータスを確認するには、以下のとおり、`get-data-automation-status` コマンドを使用します。
```
aws bedrock-data-automation-runtime get-data-automation-status \
--invocation-arn "Amazon Resource Name (ARN)"
```
レスポンスには、次のとおり現在のステータスが含まれます。
```
{
"status": "COMPLETED",
"creationTime": "2025-07-24T12:34:56.789Z",
"lastModifiedTime": "2025-07-24T12:45:12.345Z",
"outputLocation": "s3://my-bucket/output/abcd1234/"
}
```
**処理結果を取得する**
**S3 での出力ファイルの検索**
次のとおり、S3 バケットのファイルを一覧表示します。
```
aws s3 ls s3://amzn-s3-demo-bucket/output/
```
次のとおり、結果をローカルマシンにダウンロードします。
```
aws s3 cp s3://amzn-s3-demo-bucket/output/ ~/Downloads/bda-results/ --recursive
```
**出力構造の理解**
出力には通常、以下が含まれます。
+ `standard-output.json`: 標準の抽出結果が含まれます。
+ `custom-output.json`: カスタムブループリントの結果が含まれます。
+ `metadata.json`: 処理メタデータと信頼スコアが含まれます。
**一般的なレスポンスフィールド**
標準の出力には通常、以下が含まれます。
+ `extractedData`: 抽出された主な情報
+ `confidence`: 抽出された各フィールドの信頼スコア
+ `metadata`: タイムスタンプやモデルの詳細を含む処理情報
+ `boundingBoxes`: 検出された要素の場所情報 (有効にした場合)
**エラー処理とトラブルシューティング**
一般的なエラーシナリオと解決策:
+ **無効な S3 URI**: S3 バケットが存在し、適切なアクセス許可があることを確認します。
+ **data-automation-profile-arn の欠落**: このパラメータはすべての処理リクエストに必要です。
+ **プロジェクトが見つかりません**: プロジェクト ARN が適切で、該当プロジェクトが存在することを検証します。
+ **サポートされていないファイル形式**: ファイル形式が BDA でサポートされていることを確認します。
**処理ジョブへのタグの追加**
以下のとおり、処理ジョブの整理と追跡に役立つタグを追加できます。
```
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 ]
**基本的な処理コマンド構造**
ファイルを処理するには、`invoke-data-automation` コマンドを使用します。
```
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"
```
**高度な処理コマンド構造**
S3 バケットへの出力
```
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
```
バイト入力を使用する
```
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"
```
**注記**
**バイト**
base64 でエンコードされたドキュメントバイトの BLOB。バイトの BLOB で提供されるドキュメントの最大サイズは 50 MB です。タイプは Base64-encodedされたバイナリデータオブジェクトである必要があります。
**カスタムブループリントを使用する (イメージのみ)**
```
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"
```
------
# ユースケースの処理
Amazon Bedrock Data Automation を使用すると、コマンドラインインターフェイス (CLI) を使用して、ドキュメント、イメージ、音声、動画を処理できます。各モダリティのワークフローは、プロジェクトの作成、分析の呼び出し、結果の取得で構成されます。
任意の方法のタブを選択し、その手順に従います。
------
#### [ Documents ]
**W2 からのデータの抽出**
![\[抽出されるレイアウトフィールドとデータフィールドを示す標準フィールドを含むサンプル W2 フォーム\]](http://docs.aws.amazon.com/ja_jp/bedrock/latest/userguide/images/bda/W2.png)
W2 フォームを処理する場合のスキーマの例は次のとおりです。
```
{
"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"
}
}
}
```
W2 の処理を呼び出すコマンドは、次のようになります。
```
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"
```
期待される値の例は、次のとおりです。
```
{
"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 ]
**旅行広告の例**
![\[ユーザーが広告から情報を抽出する方法を示すサンプルイメージ\]](http://docs.aws.amazon.com/ja_jp/bedrock/latest/userguide/images/bda/TravelAdvertisement.jpg)
旅行広告のスキーマの例は、次のとおりです。
```
{
"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.)"
}
}
}
```
旅行広告の処理を呼び出すコマンドは、次のようになります。
```
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"
```
期待される値の例は、次のとおりです。
```
{
"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 ]
**通話の文字起こし**
旅行広告のスキーマの例は、次のとおりです。
```
{
"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"
}
}
}
```
通話の処理を呼び出すコマンドは、次のようになります。
```
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"
```
期待される値の例は、次のとおりです。
```
{
"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 ]
**動画の処理**
動画のスキーマの例は、次のとおりです。
```
{
"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"
}
}
}
```
動画の処理を呼び出すコマンドは、次のようになります。
```
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"
```
期待される値の例は、次のとおりです。
```
{
"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
]
}
}
```
------