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。
教學課程：開始使用 Amazon A2I API
本教學將說明您可以用來開始使用 Amazon A2I 的API操作。
若要使用 Jupyter 記事本來執行這些作業，請從中選取 Jupyter 記事本，使用 Amazon A2I 的使用案例和範例然後使用將 SageMaker 筆記本實例與 Amazon A2I Jupyter 筆記本一起使用來瞭解如何在記事本執行個體中使用它。 SageMaker
若要進一步了解可與 Amazon A2I 搭配使用的API操作，請參閱。在 Amazon 增強版 AI 中使用 API
建立私有工作團隊
您可以建立私有工作團隊，並將自己新增為工作者，以便預覽 Amazon A2I。
如果您不熟悉 Amazon Cognito，建議您使用 SageMaker 主控台建立私人員工，並將自己新增為私人員工。如需說明，請參閱 步驟 1：建立工作團隊。
如果您熟悉 Amazon Cognito，可以使用下列指示建立私人工作團隊，使用 SageMaker API. 建立工作小組後，請注意工作小組 ARN (WorkteamArn)。
要瞭解有關私有人力資源和其他可用組態的詳細資訊，請參閱使用私有人力。
建立私有人力資源
如果您尚未建立私有人力資源，則您可以使用 Amazon Cognito 使用者集區進行建立。確認您已將自己新增至此使用者集區。您可以使用 AWS SDK for Python (Boto3)
create_workforce功能。如需其他特定語言的資訊SDKs，請參閱中的清單。CreateWorkforce
response = client.create_workforce(
CognitoConfig={
"UserPool": "Pool_ID",
"ClientId": "app-client-id"
},
WorkforceName="workforce-name"
)
建立私有工作團隊
在中建立私人工作團隊之後 AWS 要配置和啟動人工循環的區域，您可以使用 AWS SDK for Python (Boto3) create_workteam功能。如需其他特定語言的資訊SDKs，請參閱中的清單。CreateWorkteam
response = client.create_workteam(
WorkteamName="work-team-name",
WorkforceName= "workforce-name",
MemberDefinitions=[
{
"CognitoMemberDefinition": {
"UserPool": "<aws-region>_ID",
"UserGroup": "user-group",
"ClientId": "app-client-id"
},
}
]
)
訪問您的工作團隊ARN，如下所示：
workteamArn = response["WorkteamArn"]
列出您帳戶中的私有工作團隊
如果您已經建立了私人工作團隊，則可以列出特定工作團隊中的所有工作團隊 AWS 您帳戶中使用的區域 AWS SDK for Python (Boto3) list_workteams功能。如需其他特定語言的資訊SDKs，請參閱中的清單。ListWorkteams
response = client.list_workteams()
如果您的帳戶中有許多工作團隊，則您可能想要使用 MaxResults、SortBy、和 NameContains 來篩選結果。
建立人工審核工作流程
您可以使用 Amazon A2I CreateFlowDefinition 作業來建立人工審核工作流程。建立人工審核工作流程之前，您需要建立人工任務使用者介面。您可以使用 CreateHumanTaskUi 作業來執行此動作。
如果您將 Amazon A2I 與 Amazon Textract 或 Amazon Rekognition 合搭配使用，您可以使用. JSON
建立人工任務使用者介面
如果您要建立與 Amazon Textract 或 Amazon Rekognition 整合搭配使用的人工審核工作流程，則需要使用和修改預先製作的工作者任務範本。對於所有自訂整合，您可以使用自己的自訂工作者任務範本。使用下表瞭解如何使用兩個內建整合的工作者任務範本來建立人工任務使用者介面。將範本取代為您自己的範本，以自訂此請求。
- Amazon Textract – Key-value pair extraction
-
若要進一步了解此類範本，請參閱Amazon Textract 的自訂範本範例。
template = r"""
<script src="https://assets.crowd.aws/crowd-html-elements.js"></script>
{% capture s3_uri %}http://s3.amazonaws.com/{{ task.input.aiServiceRequest.document.s3Object.bucket }}/{{ task.input.aiServiceRequest.document.s3Object.name }}{% endcapture %}
<crowd-form>
<crowd-textract-analyze-document
src="{{ s3_uri | grant_read_access }}"
initial-value="{{ task.input.selectedAiServiceResponse.blocks }}"
header="Review the key-value pairs listed on the right and correct them if they don"t match the following document."
no-key-edit=""
no-geometry-edit=""
keys="{{ task.input.humanLoopContext.importantFormKeys }}"
block-types='["KEY_VALUE_SET"]'>
<short-instructions header="Instructions">
<p>Click on a key-value block to highlight the corresponding key-value pair in the document.
</p><p><br></p>
<p>If it is a valid key-value pair, review the content for the value. If the content is incorrect, correct it.
</p><p><br></p>
<p>The text of the value is incorrect, correct it.</p>
<p><img src="https://assets.crowd.aws/images/a2i-console/correct-value-text.png">
</p><p><br></p>
<p>A wrong value is identified, correct it.</p>
<p><img src="https://assets.crowd.aws/images/a2i-console/correct-value.png">
</p><p><br></p>
<p>If it is not a valid key-value relationship, choose No.</p>
<p><img src="https://assets.crowd.aws/images/a2i-console/not-a-key-value-pair.png">
</p><p><br></p>
<p>If you can’t find the key in the document, choose Key not found.</p>
<p><img src="https://assets.crowd.aws/images/a2i-console/key-is-not-found.png">
</p><p><br></p>
<p>If the content of a field is empty, choose Value is blank.</p>
<p><img src="https://assets.crowd.aws/images/a2i-console/value-is-blank.png">
</p><p><br></p>
<p><strong>Examples</strong></p>
<p>Key and value are often displayed next or below to each other.
</p><p><br></p>
<p>Key and value displayed in one line.</p>
<p><img src="https://assets.crowd.aws/images/a2i-console/sample-key-value-pair-1.png">
</p><p><br></p>
<p>Key and value displayed in two lines.</p>
<p><img src="https://assets.crowd.aws/images/a2i-console/sample-key-value-pair-2.png">
</p><p><br></p>
<p>If the content of the value has multiple lines, enter all the text without line break.
Include all value text even if it extends beyond the highlight box.</p>
<p><img src="https://assets.crowd.aws/images/a2i-console/multiple-lines.png"></p>
</short-instructions>
<full-instructions header="Instructions"></full-instructions>
</crowd-textract-analyze-document>
</crowd-form>
"""
- Amazon Rekognition – Image moderation
-
若要進一步了解此類範本，請參閱Amazon Rekognition 的自訂範本範例。
template = r"""
<script src="https://assets.crowd.aws/crowd-html-elements.js"></script>
{% capture s3_uri %}http://s3.amazonaws.com/{{ task.input.aiServiceRequest.image.s3Object.bucket }}/{{ task.input.aiServiceRequest.image.s3Object.name }}{% endcapture %}
<crowd-form>
<crowd-rekognition-detect-moderation-labels
categories='[
{% for label in task.input.selectedAiServiceResponse.moderationLabels %}
{
name: "{{ label.name }}",
parentName: "{{ label.parentName }}",
},
{% endfor %}
]'
src="{{ s3_uri | grant_read_access }}"
header="Review the image and choose all applicable categories."
>
<short-instructions header="Instructions">
<style>
.instructions {
white-space: pre-wrap;
}
</style>
<p class="instructions">Review the image and choose all applicable categories.
If no categories apply, choose None.
<b>Nudity</b>
Visuals depicting nude male or female person or persons
<b>Partial Nudity</b>
Visuals depicting covered up nudity, for example using hands or pose
<b>Revealing Clothes</b>
Visuals depicting revealing clothes and poses
<b>Physical Violence</b>
Visuals depicting violent physical assault, such as kicking or punching
<b>Weapon Violence</b>
Visuals depicting violence using weapons like firearms or blades, such as shooting
<b>Weapons</b>
Visuals depicting weapons like firearms and blades
</short-instructions>
<full-instructions header="Instructions"></full-instructions>
</crowd-rekognition-detect-moderation-labels>
</crowd-form>"""
- Custom Integration
-
以下是可用於自訂整合的範例範本。這個範本會用於此筆記本，展示了與 Amazon Comprehend 的自訂整合。
template = r"""
<script src="https://assets.crowd.aws/crowd-html-elements.js"></script>
<crowd-form>
<crowd-classifier
name="sentiment"
categories='["Positive", "Negative", "Neutral", "Mixed"]'
initial-value="{{ task.input.initialValue }}"
header="What sentiment does this text convey?"
>
<classification-target>
{{ task.input.taskObject }}
</classification-target>
<full-instructions header="Sentiment Analysis Instructions">
<p><strong>Positive</strong> sentiment include: joy, excitement, delight</p>
<p><strong>Negative</strong> sentiment include: anger, sarcasm, anxiety</p>
<p><strong>Neutral</strong>: neither positive or negative, such as stating a fact</p>
<p><strong>Mixed</strong>: when the sentiment is mixed</p>
</full-instructions>
<short-instructions>
Choose the primary sentiment that is expressed by the text.
</short-instructions>
</crowd-classifier>
</crowd-form>
"""
使用上述指定的範本，您可以使用 AWS SDK for Python (Boto3) create_human_task_ui功能。如需其他特定語言的資訊SDKs，請參閱中的清單。CreateHumanTaskUi
response = client.create_human_task_ui(
HumanTaskUiName="human-task-ui-name",
UiTemplate={
"Content": template
}
)
此響應元素包含人工任務 UI ARN。儲存此項目，如下所示：
humanTaskUiArn = response["HumanTaskUiArn"]
建立JSON以指定啟動條件
對於 Amazon Textract 和 Amazon Rekognition 內建整合，您可以將啟用條件儲存在物件中，並在您的請求中使用此條JSON件。CreateFlowDefinition
接下來，選擇一個標籤以查看可用於這些內建整合的範例啟動條件。有關啟動條件選項的其他資訊，請參閱Amazon Augmented AI 中，適用於 JSON 結構描述的人工循環啟動條件。
- Amazon Textract – Key-value pair extraction
-
此範例指定文件中特定鍵值 (例如 Mail address) 的條件。如果 Amazon Textract 的可信度超出此處設定的閾值，則會將文件傳送給人員進行審核，並提示工作者啟動人工循環的特定鍵值。
import json
humanLoopActivationConditions = json.dumps(
{
"Conditions": [
{
"Or": [
{
"ConditionType": "ImportantFormKeyConfidenceCheck",
"ConditionParameters": {
"ImportantFormKey": "Mail address",
"ImportantFormKeyAliases": ["Mail Address:","Mail address:", "Mailing Add:","Mailing Addresses"],
"KeyValueBlockConfidenceLessThan": 100,
"WordBlockConfidenceLessThan": 100
}
},
{
"ConditionType": "MissingImportantFormKey",
"ConditionParameters": {
"ImportantFormKey": "Mail address",
"ImportantFormKeyAliases": ["Mail Address:","Mail address:","Mailing Add:","Mailing Addresses"]
}
},
{
"ConditionType": "ImportantFormKeyConfidenceCheck",
"ConditionParameters": {
"ImportantFormKey": "Phone Number",
"ImportantFormKeyAliases": ["Phone number:", "Phone No.:", "Number:"],
"KeyValueBlockConfidenceLessThan": 100,
"WordBlockConfidenceLessThan": 100
}
},
{
"ConditionType": "ImportantFormKeyConfidenceCheck",
"ConditionParameters": {
"ImportantFormKey": "*",
"KeyValueBlockConfidenceLessThan": 100,
"WordBlockConfidenceLessThan": 100
}
},
{
"ConditionType": "ImportantFormKeyConfidenceCheck",
"ConditionParameters": {
"ImportantFormKey": "*",
"KeyValueBlockConfidenceGreaterThan": 0,
"WordBlockConfidenceGreaterThan": 0
}
}
]
}
]
}
)
- Amazon Rekognition – Image moderation
-
此處使用的人工循環啟動條件是針對 Amazon Rekognition 內容審核而量身打造的；它們是根據適用於 Suggestive 和 Female Swimwear
Or Underwear 審核標籤的可信度閾值。
import json
humanLoopActivationConditions = json.dumps(
{
"Conditions": [
{
"Or": [
{
"ConditionType": "ModerationLabelConfidenceCheck",
"ConditionParameters": {
"ModerationLabelName": "Suggestive",
"ConfidenceLessThan": 98
}
},
{
"ConditionType": "ModerationLabelConfidenceCheck",
"ConditionParameters": {
"ModerationLabelName": "Female Swimwear Or Underwear",
"ConfidenceGreaterThan": 98
}
}
]
}
]
}
)
建立人工審核工作流程
本節給出了一個例子 CreateFlowDefinition AWS SDK for Python (Boto3) 請求使用在前面的章節中創建的資源。如需其他特定語言的資訊SDKs，請參閱中的清單。CreateFlowDefinition使用下表中的索引標籤來查看為 Amazon Textract 和 Amazon Rekognition 內建整合建立人工審核工作流程的請求。
- Amazon Textract – Key-value pair extraction
-
如果您使用與 Amazon Textract 的內建整合，您必須在 HumanLoopRequestSource 中指定 "AWS/Textract/AnalyzeDocument/Forms/V1" 供給 "AwsManagedHumanLoopRequestSource"。
response = client.create_flow_definition(
FlowDefinitionName="human-review-workflow-name",
HumanLoopRequestSource={
"AwsManagedHumanLoopRequestSource": "AWS/Textract/AnalyzeDocument/Forms/V1"
},
HumanLoopActivationConfig={
"HumanLoopActivationConditionsConfig": {
"HumanLoopActivationConditions": humanLoopActivationConditions
}
},
HumanLoopConfig={
"WorkteamArn": workteamArn,
"HumanTaskUiArn": humanTaskUiArn,
"TaskTitle": "Document entry review",
"TaskDescription": "Review the document and instructions. Complete the task",
"TaskCount": 1,
"TaskAvailabilityLifetimeInSeconds": 43200,
"TaskTimeLimitInSeconds": 3600,
"TaskKeywords": [
"document review",
],
},
OutputConfig={
"S3OutputPath": "s3://amzn-s3-demo-bucket/prefix/",
},
RoleArn="arn:aws:iam::<account-number>:role/<role-name>",
Tags=[
{
"Key": "string",
"Value": "string"
},
]
)
- Amazon Rekognition – Image moderation
-
如果您使用與 Amazon Rekognition 的內建整合，您必須在 HumanLoopRequestSource 中指定 "AWS/Rekognition/DetectModerationLabels/Image/V3" 供給 "AwsManagedHumanLoopRequestSource"。
response = client.create_flow_definition(
FlowDefinitionName="human-review-workflow-name",
HumanLoopRequestSource={
"AwsManagedHumanLoopRequestSource": "AWS/Rekognition/DetectModerationLabels/Image/V3"
},
HumanLoopActivationConfig={
"HumanLoopActivationConditionsConfig": {
"HumanLoopActivationConditions": humanLoopActivationConditions
}
},
HumanLoopConfig={
"WorkteamArn": workteamArn,
"HumanTaskUiArn": humanTaskUiArn,
"TaskTitle": "Image content moderation",
"TaskDescription": "Review the image and instructions. Complete the task",
"TaskCount": 1,
"TaskAvailabilityLifetimeInSeconds": 43200,
"TaskTimeLimitInSeconds": 3600,
"TaskKeywords": [
"content moderation",
],
},
OutputConfig={
"S3OutputPath": "s3://amzn-s3-demo-bucket/prefix/",
},
RoleArn="arn:aws:iam::<account-number>:role/<role-name>",
Tags=[
{
"Key": "string",
"Value": "string"
},
]
)
- Custom Integration
-
如果您使用自訂整合，請排除下列參數：HumanLoopRequestSource、HumanLoopActivationConfig。
response = client.create_flow_definition(
FlowDefinitionName="human-review-workflow-name",
HumanLoopConfig={
"WorkteamArn": workteamArn,
"HumanTaskUiArn": humanTaskUiArn,
"TaskTitle": "Image content moderation",
"TaskDescription": "Review the image and instructions. Complete the task",
"TaskCount": 1,
"TaskAvailabilityLifetimeInSeconds": 43200,
"TaskTimeLimitInSeconds": 3600,
"TaskKeywords": [
"content moderation",
],
},
OutputConfig={
"S3OutputPath": "s3://amzn-s3-demo-bucket/prefix/",
},
RoleArn="arn:aws:iam::<account-number>:role/<role-name>",
Tags=[
{
"Key": "string",
"Value": "string"
},
]
)
建立人工複查工作流程之後，您可以ARN從回應中擷取流程定義：
humanReviewWorkflowArn = response["FlowDefinitionArn"]
建立人工循環
您用來啟動人工迴圈的API作業取決於您使用的 Amazon A2I 整合。
在下表中選取您的任務類型，以查看使用 AWS SDK for Python (Boto3).
- Amazon Textract – Key-value pair extraction
-
下列範例使用 AWS SDK for Python (Boto3) analyze_document在 us-west-2 調用。將紅色斜體文字取代為您的資源。如果您使用的是 Amazon Mechanical Turk 人力資源，請包括 DataAttributes 參數。如需詳細資訊，請參閱中的分析文件 AWS SDK for Python (Boto) API參考。
response = client.analyze_document(
Document={"S3Object": {"Bucket": "amzn-s3-demo-bucket", "Name": "document-name.pdf"},
HumanLoopConfig={
"FlowDefinitionArn":"arn:aws:sagemaker:us-west-2:111122223333:flow-definition/flow-definition-name",
"HumanLoopName":"human-loop-name",
"DataAttributes" : {ContentClassifiers:["FreeOfPersonallyIdentifiableInformation"|"FreeOfAdultContent"]}
}
FeatureTypes=["FORMS"]
)
只有在 Amazon Textract 對文件分析任務的可信度符合您在人工審核工作流程中指定的啟動條件時，才會建立人工循環。您可以檢查 response 元素以確定是否已建立人工循環。要查看此響應中包含的所有內容，請參閱HumanLoopActivationOutput。
if "HumanLoopArn" in analyzeDocumentResponse["HumanLoopActivationOutput"]:
# A human loop has been started!
print(f"A human loop has been started with ARN: {analyzeDocumentResponse["HumanLoopActivationOutput"]["HumanLoopArn"]}"
- Amazon Rekognition – Image moderation
-
下列範例使用 AWS SDK for Python (Boto3) detect_moderation_labels在 us-west-2 調用。將紅色斜體文字取代為您的資源。如果您使用的是 Amazon Mechanical Turk 人力資源，請包括 DataAttributes 參數。如需詳細資訊，請參閱 AWS SDK for Python (Boto) API參考。
response = client.detect_moderation_labels(
Image={"S3Object":{"Bucket": "amzn-s3-demo-bucket", "Name": "image-name.png"}},
HumanLoopConfig={
"FlowDefinitionArn":"arn:aws:sagemaker:us-west-2:111122223333:flow-definition/flow-definition-name",
"HumanLoopName":"human-loop-name",
"DataAttributes":{ContentClassifiers:["FreeOfPersonallyIdentifiableInformation"|"FreeOfAdultContent"]}
}
)
只有在 Amazon Rekognition 對影像審核任務的可信度符合您在人工審核工作流程中指定的啟動條件時，才會建立人工循環。您可以檢查 response 元素以確定是否已建立人工循環。要查看此響應中包含的所有內容，請參閱HumanLoopActivationOutput。
if "HumanLoopArn" in response["HumanLoopActivationOutput"]:
# A human loop has been started!
print(f"A human loop has been started with ARN: {response["HumanLoopActivationOutput"]["HumanLoopArn"]}")
- Custom Integration
-
下列範例使用 AWS SDK for Python (Boto3) start_human_loop在 us-west-2 調用。將紅色斜體文字取代為您的資源。如果您使用的是 Amazon Mechanical Turk 人力資源，請包括 DataAttributes 參數。如需詳細資訊，請參閱 AWS SDK for Python (Boto) API參考。
response = client.start_human_loop(
HumanLoopName= "human-loop-name",
FlowDefinitionArn= "arn:aws:sagemaker:us-west-2:111122223333:flow-definition/flow-definition-name",
HumanLoopInput={"InputContent": inputContentJson},
DataAttributes={"ContentClassifiers":["FreeOfPersonallyIdentifiableInformation"|"FreeOfAdultContent"]}
)
此範例會將輸入內容儲存在變數中 inputContentJsonPositiveNegative、或Neutral)，其格式如下：
inputContent = {
"initialValue": sentiment,
"taskObject": blurb
}
關鍵字 initialValue 和 taskObject 必須相對應工作者任務範本之液體元素中使用的關鍵字。請參閱建立人工任務使用者介面中的自訂範本查看範例。
執行下列步驟來建立 inputContentJson
import json
inputContentJson = json.dumps(inputContent)
每次呼叫 start_human_loop 時會開始一個人工循環。要檢查人工審查循環的狀態，請使用描述_人工_循環：
human_loop_info = a2i.describe_human_loop(HumanLoopName="human_loop_name")
print(f"HumanLoop Status: {resp["HumanLoopStatus"]}")
print(f"HumanLoop Output Destination: {resp["HumanLoopOutput"]}")
