Há mais exemplos de AWS SDK disponíveis no repositório [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub .
As traduções são geradas por tradução automática. Em caso de conflito entre o conteúdo da tradução e da versão original em inglês, a versão em inglês prevalecerá.
# Exemplos de código para o Amazon Comprehend usando AWS SDKs
Os exemplos de código a seguir mostram como usar o Amazon Comprehend com AWS um kit de desenvolvimento de software (SDK).
*Ações* são trechos de código de programas maiores e devem ser executadas em contexto. Embora as ações mostrem como chamar perfis de serviço individuais, você pode ver as ações no contexto em seus cenários relacionados.
*Cenários* são exemplos de código que mostram como realizar tarefas específicas chamando várias funções dentro de um serviço ou combinadas com outros Serviços da AWS.
**Mais atributos**
+ **[Guia do desenvolvedor do Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/what-is.html)**: mais informações sobre o Amazon Comprehend.
+ **[Referência da API Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/APIReference/welcome.html)**: detalhes sobre todas as ações disponíveis do Amazon Comprehend.
+ **[AWS Centro do desenvolvedor](https://aws.amazon.com/developer/code-examples/?awsf.sdk-code-examples-product=product%23comprehend)** — exemplos de código que você pode filtrar por categoria ou pesquisa de texto completo.
+ **[AWS Exemplos de SDK](https://github.com/awsdocs/aws-doc-sdk-examples)** — GitHub repositório com código completo nos idiomas preferidos. Inclui instruções para configurar e executar o código.
**Contents**
+ [Conceitos básicos](comprehend_code_examples_basics.md)
+ [Ações](comprehend_code_examples_actions.md)
+ [`CreateDocumentClassifier`](comprehend_example_comprehend_CreateDocumentClassifier_section.md)
+ [`DeleteDocumentClassifier`](comprehend_example_comprehend_DeleteDocumentClassifier_section.md)
+ [`DescribeDocumentClassificationJob`](comprehend_example_comprehend_DescribeDocumentClassificationJob_section.md)
+ [`DescribeDocumentClassifier`](comprehend_example_comprehend_DescribeDocumentClassifier_section.md)
+ [`DescribeTopicsDetectionJob`](comprehend_example_comprehend_DescribeTopicsDetectionJob_section.md)
+ [`DetectDominantLanguage`](comprehend_example_comprehend_DetectDominantLanguage_section.md)
+ [`DetectEntities`](comprehend_example_comprehend_DetectEntities_section.md)
+ [`DetectKeyPhrases`](comprehend_example_comprehend_DetectKeyPhrases_section.md)
+ [`DetectPiiEntities`](comprehend_example_comprehend_DetectPiiEntities_section.md)
+ [`DetectSentiment`](comprehend_example_comprehend_DetectSentiment_section.md)
+ [`DetectSyntax`](comprehend_example_comprehend_DetectSyntax_section.md)
+ [`ListDocumentClassificationJobs`](comprehend_example_comprehend_ListDocumentClassificationJobs_section.md)
+ [`ListDocumentClassifiers`](comprehend_example_comprehend_ListDocumentClassifiers_section.md)
+ [`ListTopicsDetectionJobs`](comprehend_example_comprehend_ListTopicsDetectionJobs_section.md)
+ [`StartDocumentClassificationJob`](comprehend_example_comprehend_StartDocumentClassificationJob_section.md)
+ [`StartTopicsDetectionJob`](comprehend_example_comprehend_StartTopicsDetectionJob_section.md)
+ [Cenários](comprehend_code_examples_scenarios.md)
+ [Criar uma aplicação de transmissão do Amazon Transcribe](comprehend_example_cross_TranscriptionStreamingApp_section.md)
+ [Criar um chatbot Amazon Lex](comprehend_example_cross_LexChatbotLanguages_section.md)
+ [Crie um aplicativo de mensagem](comprehend_example_cross_SQSMessageApp_section.md)
+ [Criar uma aplicação para analisar o feedback dos clientes](comprehend_example_cross_FSA_section.md)
+ [Detectar elementos do documento](comprehend_example_comprehend_Usage_DetectApis_section.md)
+ [Detectar entidades em texto extraído de uma imagem](comprehend_example_cross_TextractComprehendDetectEntities_section.md)
+ [Execute uma tarefa de modelagem de tópicos em dados de amostra](comprehend_example_comprehend_Usage_TopicModeler_section.md)
+ [Treinar um classificador personalizado e classificar documentos](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
# Exemplos básicos para o Amazon Comprehend usando AWS SDKs
Os exemplos de código a seguir mostram como usar os conceitos básicos do Amazon Comprehend com. AWS SDKs
**Contents**
+ [Ações](comprehend_code_examples_actions.md)
+ [`CreateDocumentClassifier`](comprehend_example_comprehend_CreateDocumentClassifier_section.md)
+ [`DeleteDocumentClassifier`](comprehend_example_comprehend_DeleteDocumentClassifier_section.md)
+ [`DescribeDocumentClassificationJob`](comprehend_example_comprehend_DescribeDocumentClassificationJob_section.md)
+ [`DescribeDocumentClassifier`](comprehend_example_comprehend_DescribeDocumentClassifier_section.md)
+ [`DescribeTopicsDetectionJob`](comprehend_example_comprehend_DescribeTopicsDetectionJob_section.md)
+ [`DetectDominantLanguage`](comprehend_example_comprehend_DetectDominantLanguage_section.md)
+ [`DetectEntities`](comprehend_example_comprehend_DetectEntities_section.md)
+ [`DetectKeyPhrases`](comprehend_example_comprehend_DetectKeyPhrases_section.md)
+ [`DetectPiiEntities`](comprehend_example_comprehend_DetectPiiEntities_section.md)
+ [`DetectSentiment`](comprehend_example_comprehend_DetectSentiment_section.md)
+ [`DetectSyntax`](comprehend_example_comprehend_DetectSyntax_section.md)
+ [`ListDocumentClassificationJobs`](comprehend_example_comprehend_ListDocumentClassificationJobs_section.md)
+ [`ListDocumentClassifiers`](comprehend_example_comprehend_ListDocumentClassifiers_section.md)
+ [`ListTopicsDetectionJobs`](comprehend_example_comprehend_ListTopicsDetectionJobs_section.md)
+ [`StartDocumentClassificationJob`](comprehend_example_comprehend_StartDocumentClassificationJob_section.md)
+ [`StartTopicsDetectionJob`](comprehend_example_comprehend_StartTopicsDetectionJob_section.md)
# Ações para o Amazon Comprehend usando AWS SDKs
Os exemplos de código a seguir demonstram como realizar ações individuais do Amazon Comprehend com. AWS SDKs Cada exemplo inclui um link para GitHub, onde você pode encontrar instruções para configurar e executar o código.
Esses trechos chamam a API do Amazon Comprehend e são trechos de código de programas maiores que devem ser executados no contexto. É possível ver as ações em contexto em [Cenários para o Amazon Comprehend usando AWS SDKs](comprehend_code_examples_scenarios.md).
Os exemplos a seguir incluem apenas as ações mais utilizadas. Para obter uma lista completa, consulte a [Referência de API do Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/APIReference/welcome.html).
**Topics**
+ [`CreateDocumentClassifier`](comprehend_example_comprehend_CreateDocumentClassifier_section.md)
+ [`DeleteDocumentClassifier`](comprehend_example_comprehend_DeleteDocumentClassifier_section.md)
+ [`DescribeDocumentClassificationJob`](comprehend_example_comprehend_DescribeDocumentClassificationJob_section.md)
+ [`DescribeDocumentClassifier`](comprehend_example_comprehend_DescribeDocumentClassifier_section.md)
+ [`DescribeTopicsDetectionJob`](comprehend_example_comprehend_DescribeTopicsDetectionJob_section.md)
+ [`DetectDominantLanguage`](comprehend_example_comprehend_DetectDominantLanguage_section.md)
+ [`DetectEntities`](comprehend_example_comprehend_DetectEntities_section.md)
+ [`DetectKeyPhrases`](comprehend_example_comprehend_DetectKeyPhrases_section.md)
+ [`DetectPiiEntities`](comprehend_example_comprehend_DetectPiiEntities_section.md)
+ [`DetectSentiment`](comprehend_example_comprehend_DetectSentiment_section.md)
+ [`DetectSyntax`](comprehend_example_comprehend_DetectSyntax_section.md)
+ [`ListDocumentClassificationJobs`](comprehend_example_comprehend_ListDocumentClassificationJobs_section.md)
+ [`ListDocumentClassifiers`](comprehend_example_comprehend_ListDocumentClassifiers_section.md)
+ [`ListTopicsDetectionJobs`](comprehend_example_comprehend_ListTopicsDetectionJobs_section.md)
+ [`StartDocumentClassificationJob`](comprehend_example_comprehend_StartDocumentClassificationJob_section.md)
+ [`StartTopicsDetectionJob`](comprehend_example_comprehend_StartTopicsDetectionJob_section.md)
# Use `CreateDocumentClassifier` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `CreateDocumentClassifier`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Treinar um classificador personalizado e classificar documentos](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**Para criar um classificador de documentos para categorizar documentos**
O exemplo de `create-document-classifier` a seguir inicia o processo de treinamento para um modelo de classificador de documentos. O arquivo de dados de treinamento, `training.csv`, está localizado na tag `--input-data-config`. `training.csv` é um documento de duas colunas em que os rótulos ou classificações são fornecidos na primeira coluna e os documentos são fornecidos na segunda coluna.
```
aws comprehend create-document-classifier \
--document-classifier-name example-classifier \
--data-access-arn arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE \
--input-data-config "S3Uri=s3://amzn-s3-demo-bucket/" \
--language-code en
```
Saída:
```
{
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier"
}
```
Para obter mais informações, consulte [Classificação personalizada](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [CreateDocumentClassifier](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/create-document-classifier.html)em *Referência de AWS CLI Comandos*.
------
#### [ Java ]
**SDK para Java 2.x**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import software.amazon.awssdk.services.comprehend.model.CreateDocumentClassifierRequest;
import software.amazon.awssdk.services.comprehend.model.CreateDocumentClassifierResponse;
import software.amazon.awssdk.services.comprehend.model.DocumentClassifierInputDataConfig;
/**
* Before running this code example, you can setup the necessary resources, such
* as the CSV file and IAM Roles, by following this document:
* https://aws.amazon.com/blogs/machine-learning/building-a-custom-classifier-using-amazon-comprehend/
*
* Also, set up your development environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DocumentClassifierDemo {
public static void main(String[] args) {
final String usage = """
Usage:
Where:
dataAccessRoleArn - The ARN value of the role used for this operation.
s3Uri - The Amazon S3 bucket that contains the CSV file.
documentClassifierName - The name of the document classifier.
""";
if (args.length != 3) {
System.out.println(usage);
System.exit(1);
}
String dataAccessRoleArn = args[0];
String s3Uri = args[1];
String documentClassifierName = args[2];
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
createDocumentClassifier(comClient, dataAccessRoleArn, s3Uri, documentClassifierName);
comClient.close();
}
public static void createDocumentClassifier(ComprehendClient comClient, String dataAccessRoleArn, String s3Uri,
String documentClassifierName) {
try {
DocumentClassifierInputDataConfig config = DocumentClassifierInputDataConfig.builder()
.s3Uri(s3Uri)
.build();
CreateDocumentClassifierRequest createDocumentClassifierRequest = CreateDocumentClassifierRequest.builder()
.documentClassifierName(documentClassifierName)
.dataAccessRoleArn(dataAccessRoleArn)
.languageCode("en")
.inputDataConfig(config)
.build();
CreateDocumentClassifierResponse createDocumentClassifierResult = comClient
.createDocumentClassifier(createDocumentClassifierRequest);
String documentClassifierArn = createDocumentClassifierResult.documentClassifierArn();
System.out.println("Document Classifier ARN: " + documentClassifierArn);
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Para obter detalhes da API, consulte [CreateDocumentClassifier](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/CreateDocumentClassifier)a *Referência AWS SDK for Java 2.x da API*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def create(
self,
name,
language_code,
training_bucket,
training_key,
data_access_role_arn,
mode,
):
"""
Creates a custom classifier. After the classifier is created, it immediately
starts training on the data found in the specified Amazon S3 bucket. Training
can take 30 minutes or longer. The `describe_document_classifier` function
can be used to get training status and returns a status of TRAINED when the
classifier is ready to use.
:param name: The name of the classifier.
:param language_code: The language the classifier can operate on.
:param training_bucket: The Amazon S3 bucket that contains the training data.
:param training_key: The prefix used to find training data in the training
bucket. If multiple objects have the same prefix, all
of them are used.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
training bucket.
:return: The ARN of the newly created classifier.
"""
try:
response = self.comprehend_client.create_document_classifier(
DocumentClassifierName=name,
LanguageCode=language_code,
InputDataConfig={"S3Uri": f"s3://{training_bucket}/{training_key}"},
DataAccessRoleArn=data_access_role_arn,
Mode=mode.value,
)
self.classifier_arn = response["DocumentClassifierArn"]
logger.info("Started classifier creation. Arn is: %s.", self.classifier_arn)
except ClientError:
logger.exception("Couldn't create classifier %s.", name)
raise
else:
return self.classifier_arn
```
+ Para obter detalhes da API, consulte a [CreateDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/CreateDocumentClassifier)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->createdocumentclassifier(
iv_documentclassifiername = iv_classifier_name
iv_languagecode = iv_language_code
io_inputdataconfig = NEW /aws1/cl_cpddocclifierinpdat00(
iv_s3uri = iv_training_s3_uri
)
iv_dataaccessrolearn = iv_data_access_role_arn
iv_mode = iv_mode
).
MESSAGE 'Document classifier creation started.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdresrclimitexcdex.
MESSAGE 'Resource limit exceeded.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanytagsex.
MESSAGE 'Too many tags.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [CreateDocumentClassifier](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `DeleteDocumentClassifier` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `DeleteDocumentClassifier`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Treinar um classificador personalizado e classificar documentos](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**Para excluir um classificador de documentos personalizado**
O exemplo de `delete-document-classifier` a seguir exclui um modelo de classificador de documentos personalizado.
```
aws comprehend delete-document-classifier \
--document-classifier-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1
```
Este comando não produz saída.
Para obter mais informações, consulte [Gerenciar endpoints do Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [DeleteDocumentClassifier](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/delete-document-classifier.html)em *Referência de AWS CLI Comandos*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def delete(self):
"""
Deletes the classifier.
"""
try:
self.comprehend_client.delete_document_classifier(
DocumentClassifierArn=self.classifier_arn
)
logger.info("Deleted classifier %s.", self.classifier_arn)
self.classifier_arn = None
except ClientError:
logger.exception("Couldn't deleted classifier %s.", self.classifier_arn)
raise
```
+ Para obter detalhes da API, consulte a [DeleteDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DeleteDocumentClassifier)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->deletedocumentclassifier(
iv_documentclassifierarn = iv_classifier_arn
).
MESSAGE 'Document classifier deleted.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdresourcenotfoundex.
MESSAGE 'Resource not found.' TYPE 'E'.
CATCH /aws1/cx_cpdresourceinuseex.
MESSAGE 'Resource in use.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [DeleteDocumentClassifier](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `DescribeDocumentClassificationJob` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `DescribeDocumentClassificationJob`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Treinar um classificador personalizado e classificar documentos](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**Para descrever um trabalho de classificação de documentos**
O exemplo de `describe-document-classification-job` a seguir obtém as propriedades de um trabalho assíncrono de classificação de documentos.
```
aws comprehend describe-document-classification-job \
--job-id 123456abcdeb0e11022f22a11EXAMPLE
```
Saída:
```
{
"DocumentClassificationJobProperties": {
"JobId": "123456abcdeb0e11022f22a11EXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:document-classification-job/123456abcdeb0e11022f22a11EXAMPLE",
"JobName": "exampleclassificationjob",
"JobStatus": "COMPLETED",
"SubmitTime": "2023-06-14T17:09:51.788000+00:00",
"EndTime": "2023-06-14T17:15:58.582000+00:00",
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/mymodel/version/1",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket/jobdata/",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-CLN-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
},
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole"
}
}
```
Para obter mais informações, consulte [Classificação personalizada](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [DescribeDocumentClassificationJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-document-classification-job.html)em *Referência de AWS CLI Comandos*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def describe_job(self, job_id):
"""
Gets metadata about a classification job.
:param job_id: The ID of the job to look up.
:return: Metadata about the job.
"""
try:
response = self.comprehend_client.describe_document_classification_job(
JobId=job_id
)
job = response["DocumentClassificationJobProperties"]
logger.info("Got classification job %s.", job["JobName"])
except ClientError:
logger.exception("Couldn't get classification job %s.", job_id)
raise
else:
return job
```
+ Para obter detalhes da API, consulte a [DescribeDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassificationJob)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->describedocclassificationjob(
iv_jobid = iv_job_id
).
MESSAGE 'Document classification job described.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdjobnotfoundex.
MESSAGE 'Job not found.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [DescribeDocumentClassificationJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `DescribeDocumentClassifier` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `DescribeDocumentClassifier`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Treinar um classificador personalizado e classificar documentos](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**Para descrever um classificador de documentos**
O exemplo de `describe-document-classifier` a seguir obtém as propriedades de um modelo de classificador de documentos personalizado.
```
aws comprehend describe-document-classifier \
--document-classifier-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1
```
Saída:
```
{
"DocumentClassifierProperties": {
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1",
"LanguageCode": "en",
"Status": "TRAINED",
"SubmitTime": "2023-06-13T19:04:15.735000+00:00",
"EndTime": "2023-06-13T19:42:31.752000+00:00",
"TrainingStartTime": "2023-06-13T19:08:20.114000+00:00",
"TrainingEndTime": "2023-06-13T19:41:35.080000+00:00",
"InputDataConfig": {
"DataFormat": "COMPREHEND_CSV",
"S3Uri": "s3://amzn-s3-demo-bucket/trainingdata"
},
"OutputDataConfig": {},
"ClassifierMetadata": {
"NumberOfLabels": 3,
"NumberOfTrainedDocuments": 5016,
"NumberOfTestDocuments": 557,
"EvaluationMetrics": {
"Accuracy": 0.9856,
"Precision": 0.9919,
"Recall": 0.9459,
"F1Score": 0.9673,
"MicroPrecision": 0.9856,
"MicroRecall": 0.9856,
"MicroF1Score": 0.9856,
"HammingLoss": 0.0144
}
},
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role",
"Mode": "MULTI_CLASS"
}
}
```
Para obter mais informações, consulte [Criação e gerenciamento de modelos personalizados](https://docs.aws.amazon.com/comprehend/latest/dg/manage-models.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [DescribeDocumentClassifier](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-document-classifier.html)em *Referência de AWS CLI Comandos*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def describe(self, classifier_arn=None):
"""
Gets metadata about a custom classifier, including its current status.
:param classifier_arn: The ARN of the classifier to look up.
:return: Metadata about the classifier.
"""
if classifier_arn is not None:
self.classifier_arn = classifier_arn
try:
response = self.comprehend_client.describe_document_classifier(
DocumentClassifierArn=self.classifier_arn
)
classifier = response["DocumentClassifierProperties"]
logger.info("Got classifier %s.", self.classifier_arn)
except ClientError:
logger.exception("Couldn't get classifier %s.", self.classifier_arn)
raise
else:
return classifier
```
+ Para obter detalhes da API, consulte a [DescribeDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassifier)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->describedocumentclassifier(
iv_documentclassifierarn = iv_classifier_arn
).
MESSAGE 'Document classifier described.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdresourcenotfoundex.
MESSAGE 'Resource not found.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [DescribeDocumentClassifier](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `DescribeTopicsDetectionJob` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `DescribeTopicsDetectionJob`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Execute uma tarefa de modelagem de tópicos em dados de amostra](comprehend_example_comprehend_Usage_TopicModeler_section.md)
------
#### [ CLI ]
**AWS CLI**
**Para descrever um trabalho de detecção de tópicos**
O exemplo de `describe-topics-detection-job` a seguir obtém as propriedades de um trabalho assíncrono de detecção de tópicos.
```
aws comprehend describe-topics-detection-job \
--job-id 123456abcdeb0e11022f22a11EXAMPLE
```
Saída:
```
{
"TopicsDetectionJobProperties": {
"JobId": "123456abcdeb0e11022f22a11EXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
"JobName": "example_topics_detection",
"JobStatus": "IN_PROGRESS",
"SubmitTime": "2023-06-09T18:44:43.414000+00:00",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-TOPICS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
},
"NumberOfTopics": 10,
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-examplerole"
}
}
```
Para obter mais informações, consulte [Análise assíncrona para insights do Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [DescribeTopicsDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-topics-detection-job.html)em *Referência de AWS CLI Comandos*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendTopicModeler:
"""Encapsulates a Comprehend topic modeler."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def describe_job(self, job_id):
"""
Gets metadata about a topic modeling job.
:param job_id: The ID of the job to look up.
:return: Metadata about the job.
"""
try:
response = self.comprehend_client.describe_topics_detection_job(
JobId=job_id
)
job = response["TopicsDetectionJobProperties"]
logger.info("Got topic detection job %s.", job_id)
except ClientError:
logger.exception("Couldn't get topic detection job %s.", job_id)
raise
else:
return job
```
+ Para obter detalhes da API, consulte a [DescribeTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeTopicsDetectionJob)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->describetopicsdetectionjob(
iv_jobid = iv_job_id
).
MESSAGE 'Topics detection job described.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdjobnotfoundex.
MESSAGE 'Job not found.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [DescribeTopicsDetectionJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `DetectDominantLanguage` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `DetectDominantLanguage`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Detectar elementos do documento](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK para .NET**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example calls the Amazon Comprehend service to determine the
/// dominant language.
///
public static class DetectDominantLanguage
{
///
/// Calls Amazon Comprehend to determine the dominant language used in
/// the sample text.
///
public static async Task Main()
{
string text = "It is raining today in Seattle.";
var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);
Console.WriteLine("Calling DetectDominantLanguage\n");
var detectDominantLanguageRequest = new DetectDominantLanguageRequest()
{
Text = text,
};
var detectDominantLanguageResponse = await comprehendClient.DetectDominantLanguageAsync(detectDominantLanguageRequest);
foreach (var dl in detectDominantLanguageResponse.Languages)
{
Console.WriteLine($"Language Code: {dl.LanguageCode}, Score: {dl.Score}");
}
Console.WriteLine("Done");
}
}
```
+ Para obter detalhes da API, consulte [DetectDominantLanguage](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectDominantLanguage)a *Referência AWS SDK para .NET da API*.
------
#### [ CLI ]
**AWS CLI**
**Para detectar o idioma dominante do texto de entrada**
O `detect-dominant-language` a seguir analisa o texto de entrada e identifica o idioma dominante. A pontuação de confiança do modelo pré-treinado também é gerada.
```
aws comprehend detect-dominant-language \
--text "It is a beautiful day in Seattle."
```
Saída:
```
{
"Languages": [
{
"LanguageCode": "en",
"Score": 0.9877256155014038
}
]
}
```
Para obter mais informações, consulte [Idioma dominante](https://docs.aws.amazon.com/comprehend/latest/dg/how-languages.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [DetectDominantLanguage](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-dominant-language.html)em *Referência de AWS CLI Comandos*.
------
#### [ Java ]
**SDK para Java 2.x**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import software.amazon.awssdk.services.comprehend.model.DetectDominantLanguageRequest;
import software.amazon.awssdk.services.comprehend.model.DetectDominantLanguageResponse;
import software.amazon.awssdk.services.comprehend.model.DominantLanguage;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectLanguage {
public static void main(String[] args) {
// Specify French text - "It is raining today in Seattle".
String text = "Il pleut aujourd'hui à Seattle";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectDominantLanguage");
detectTheDominantLanguage(comClient, text);
comClient.close();
}
public static void detectTheDominantLanguage(ComprehendClient comClient, String text) {
try {
DetectDominantLanguageRequest request = DetectDominantLanguageRequest.builder()
.text(text)
.build();
DetectDominantLanguageResponse resp = comClient.detectDominantLanguage(request);
List allLanList = resp.languages();
for (DominantLanguage lang : allLanList) {
System.out.println("Language is " + lang.languageCode());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Para obter detalhes da API, consulte [DetectDominantLanguage](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectDominantLanguage)a *Referência AWS SDK for Java 2.x da API*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_languages(self, text):
"""
Detects languages used in a document.
:param text: The document to inspect.
:return: The list of languages along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_dominant_language(Text=text)
languages = response["Languages"]
logger.info("Detected %s languages.", len(languages))
except ClientError:
logger.exception("Couldn't detect languages.")
raise
else:
return languages
```
+ Para obter detalhes da API, consulte a [DetectDominantLanguage](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectDominantLanguage)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectdominantlanguage( iv_text = iv_text ).
MESSAGE 'Languages detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [DetectDominantLanguage](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `DetectEntities` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `DetectEntities`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Detectar elementos do documento](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK para .NET**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use the AmazonComprehend service detect any
/// entities in submitted text.
///
public static class DetectEntities
{
///
/// The main method calls the DetectEntitiesAsync method to find any
/// entities in the sample code.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient();
Console.WriteLine("Calling DetectEntities\n");
var detectEntitiesRequest = new DetectEntitiesRequest()
{
Text = text,
LanguageCode = "en",
};
var detectEntitiesResponse = await comprehendClient.DetectEntitiesAsync(detectEntitiesRequest);
foreach (var e in detectEntitiesResponse.Entities)
{
Console.WriteLine($"Text: {e.Text}, Type: {e.Type}, Score: {e.Score}, BeginOffset: {e.BeginOffset}, EndOffset: {e.EndOffset}");
}
Console.WriteLine("Done");
}
}
```
+ Para obter detalhes da API, consulte [DetectEntities](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectEntities)a *Referência AWS SDK para .NET da API*.
------
#### [ CLI ]
**AWS CLI**
**Para detectar entidades nomeadas no texto de entrada**
O exemplo de `detect-entities` a seguir analisa o texto de entrada e retorna as entidades nomeadas. A pontuação de confiança do modelo pré-treinado também é gerada para cada previsão.
```
aws comprehend detect-entities \
--language-code en \
--text "Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card \
account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings, \
we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. \
Customer feedback for Sunshine Spa, 123 Main St, Anywhere. Send comments to Alice at AnySpa@example.com."
```
Saída:
```
{
"Entities": [
{
"Score": 0.9994556307792664,
"Type": "PERSON",
"Text": "Zhang Wei",
"BeginOffset": 6,
"EndOffset": 15
},
{
"Score": 0.9981022477149963,
"Type": "PERSON",
"Text": "John",
"BeginOffset": 22,
"EndOffset": 26
},
{
"Score": 0.9986887574195862,
"Type": "ORGANIZATION",
"Text": "AnyCompany Financial Services, LLC",
"BeginOffset": 33,
"EndOffset": 67
},
{
"Score": 0.9959119558334351,
"Type": "OTHER",
"Text": "1111-XXXX-1111-XXXX",
"BeginOffset": 88,
"EndOffset": 107
},
{
"Score": 0.9708039164543152,
"Type": "QUANTITY",
"Text": ".53",
"BeginOffset": 133,
"EndOffset": 136
},
{
"Score": 0.9987268447875977,
"Type": "DATE",
"Text": "July 31st",
"BeginOffset": 152,
"EndOffset": 161
},
{
"Score": 0.9858865737915039,
"Type": "OTHER",
"Text": "XXXXXX1111",
"BeginOffset": 271,
"EndOffset": 281
},
{
"Score": 0.9700471758842468,
"Type": "OTHER",
"Text": "XXXXX0000",
"BeginOffset": 306,
"EndOffset": 315
},
{
"Score": 0.9591118693351746,
"Type": "ORGANIZATION",
"Text": "Sunshine Spa",
"BeginOffset": 340,
"EndOffset": 352
},
{
"Score": 0.9797496795654297,
"Type": "LOCATION",
"Text": "123 Main St",
"BeginOffset": 354,
"EndOffset": 365
},
{
"Score": 0.994929313659668,
"Type": "PERSON",
"Text": "Alice",
"BeginOffset": 394,
"EndOffset": 399
},
{
"Score": 0.9949769377708435,
"Type": "OTHER",
"Text": "AnySpa@example.com",
"BeginOffset": 403,
"EndOffset": 418
}
]
}
```
Para obter mais informações, consulte [Entidades](https://docs.aws.amazon.com/comprehend/latest/dg/how-entities.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [DetectEntities](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-entities.html)em *Referência de AWS CLI Comandos*.
------
#### [ Java ]
**SDK para Java 2.x**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.DetectEntitiesRequest;
import software.amazon.awssdk.services.comprehend.model.DetectEntitiesResponse;
import software.amazon.awssdk.services.comprehend.model.Entity;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectEntities {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectEntities");
detectAllEntities(comClient, text);
comClient.close();
}
public static void detectAllEntities(ComprehendClient comClient, String text) {
try {
DetectEntitiesRequest detectEntitiesRequest = DetectEntitiesRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectEntitiesResponse detectEntitiesResult = comClient.detectEntities(detectEntitiesRequest);
List entList = detectEntitiesResult.entities();
for (Entity entity : entList) {
System.out.println("Entity text is " + entity.text());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Para obter detalhes da API, consulte [DetectEntities](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectEntities)a *Referência AWS SDK for Java 2.x da API*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_entities(self, text, language_code):
"""
Detects entities in a document. Entities can be things like people and places
or other common terms.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect entities.")
raise
else:
return entities
```
+ Para obter detalhes da API, consulte a [DetectEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectEntities)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectentities(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Entities detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [DetectEntities](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `DetectKeyPhrases` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `DetectKeyPhrases`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Detectar elementos do documento](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK para .NET**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use the Amazon Comprehend service to
/// search text for key phrases.
///
public static class DetectKeyPhrase
{
///
/// This method calls the Amazon Comprehend method DetectKeyPhrasesAsync
/// to detect any key phrases in the sample text.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);
// Call DetectKeyPhrases API
Console.WriteLine("Calling DetectKeyPhrases");
var detectKeyPhrasesRequest = new DetectKeyPhrasesRequest()
{
Text = text,
LanguageCode = "en",
};
var detectKeyPhrasesResponse = await comprehendClient.DetectKeyPhrasesAsync(detectKeyPhrasesRequest);
foreach (var kp in detectKeyPhrasesResponse.KeyPhrases)
{
Console.WriteLine($"Text: {kp.Text}, Score: {kp.Score}, BeginOffset: {kp.BeginOffset}, EndOffset: {kp.EndOffset}");
}
Console.WriteLine("Done");
}
}
```
+ Para obter detalhes da API, consulte [DetectKeyPhrases](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectKeyPhrases)a *Referência AWS SDK para .NET da API*.
------
#### [ CLI ]
**AWS CLI**
**Para detectar frases-chave no texto de entrada**
O exemplo de `detect-key-phrases` a seguir analisa o texto de entrada e identifica as principais frases nominais. A pontuação de confiança do modelo pré-treinado também é gerada para cada previsão.
```
aws comprehend detect-key-phrases \
--language-code en \
--text "Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card \
account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings, \
we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. \
Customer feedback for Sunshine Spa, 123 Main St, Anywhere. Send comments to Alice at AnySpa@example.com."
```
Saída:
```
{
"KeyPhrases": [
{
"Score": 0.8996376395225525,
"Text": "Zhang Wei",
"BeginOffset": 6,
"EndOffset": 15
},
{
"Score": 0.9992469549179077,
"Text": "John",
"BeginOffset": 22,
"EndOffset": 26
},
{
"Score": 0.988385021686554,
"Text": "Your AnyCompany Financial Services",
"BeginOffset": 28,
"EndOffset": 62
},
{
"Score": 0.8740853071212769,
"Text": "LLC credit card account 1111-XXXX-1111-XXXX",
"BeginOffset": 64,
"EndOffset": 107
},
{
"Score": 0.9999437928199768,
"Text": "a minimum payment",
"BeginOffset": 112,
"EndOffset": 129
},
{
"Score": 0.9998900890350342,
"Text": ".53",
"BeginOffset": 133,
"EndOffset": 136
},
{
"Score": 0.9979453086853027,
"Text": "July 31st",
"BeginOffset": 152,
"EndOffset": 161
},
{
"Score": 0.9983011484146118,
"Text": "your autopay settings",
"BeginOffset": 172,
"EndOffset": 193
},
{
"Score": 0.9996572136878967,
"Text": "your payment",
"BeginOffset": 211,
"EndOffset": 223
},
{
"Score": 0.9995037317276001,
"Text": "the due date",
"BeginOffset": 227,
"EndOffset": 239
},
{
"Score": 0.9702621698379517,
"Text": "your bank account number XXXXXX1111",
"BeginOffset": 245,
"EndOffset": 280
},
{
"Score": 0.9179925918579102,
"Text": "the routing number XXXXX0000.Customer feedback",
"BeginOffset": 286,
"EndOffset": 332
},
{
"Score": 0.9978160858154297,
"Text": "Sunshine Spa",
"BeginOffset": 337,
"EndOffset": 349
},
{
"Score": 0.9706913232803345,
"Text": "123 Main St",
"BeginOffset": 351,
"EndOffset": 362
},
{
"Score": 0.9941995143890381,
"Text": "comments",
"BeginOffset": 379,
"EndOffset": 387
},
{
"Score": 0.9759287238121033,
"Text": "Alice",
"BeginOffset": 391,
"EndOffset": 396
},
{
"Score": 0.8376792669296265,
"Text": "AnySpa@example.com",
"BeginOffset": 400,
"EndOffset": 415
}
]
}
```
Para obter mais informações, consulte [Frases-chave](https://docs.aws.amazon.com/comprehend/latest/dg/how-key-phrases.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [DetectKeyPhrases](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-key-phrases.html)em *Referência de AWS CLI Comandos*.
------
#### [ Java ]
**SDK para Java 2.x**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.DetectKeyPhrasesRequest;
import software.amazon.awssdk.services.comprehend.model.DetectKeyPhrasesResponse;
import software.amazon.awssdk.services.comprehend.model.KeyPhrase;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectKeyPhrases {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectKeyPhrases");
detectAllKeyPhrases(comClient, text);
comClient.close();
}
public static void detectAllKeyPhrases(ComprehendClient comClient, String text) {
try {
DetectKeyPhrasesRequest detectKeyPhrasesRequest = DetectKeyPhrasesRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectKeyPhrasesResponse detectKeyPhrasesResult = comClient.detectKeyPhrases(detectKeyPhrasesRequest);
List phraseList = detectKeyPhrasesResult.keyPhrases();
for (KeyPhrase keyPhrase : phraseList) {
System.out.println("Key phrase text is " + keyPhrase.text());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Para obter detalhes da API, consulte [DetectKeyPhrases](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectKeyPhrases)a *Referência AWS SDK for Java 2.x da API*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_key_phrases(self, text, language_code):
"""
Detects key phrases in a document. A key phrase is typically a noun and its
modifiers.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of key phrases along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_key_phrases(
Text=text, LanguageCode=language_code
)
phrases = response["KeyPhrases"]
logger.info("Detected %s phrases.", len(phrases))
except ClientError:
logger.exception("Couldn't detect phrases.")
raise
else:
return phrases
```
+ Para obter detalhes da API, consulte a [DetectKeyPhrases](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectKeyPhrases)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectkeyphrases(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Key phrases detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [DetectKeyPhrases](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `DetectPiiEntities` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `DetectPiiEntities`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Detectar elementos do documento](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK para .NET**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use the Amazon Comprehend service to find
/// personally identifiable information (PII) within text submitted to the
/// DetectPiiEntitiesAsync method.
///
public class DetectingPII
{
///
/// This method calls the DetectPiiEntitiesAsync method to locate any
/// personally dientifiable information within the supplied text.
///
public static async Task Main()
{
var comprehendClient = new AmazonComprehendClient();
var text = @"Hello Paul Santos. The latest statement for your
credit card account 1111-0000-1111-0000 was
mailed to 123 Any Street, Seattle, WA 98109.";
var request = new DetectPiiEntitiesRequest
{
Text = text,
LanguageCode = "EN",
};
var response = await comprehendClient.DetectPiiEntitiesAsync(request);
if (response.Entities.Count > 0)
{
foreach (var entity in response.Entities)
{
var entityValue = text.Substring(entity.BeginOffset, entity.EndOffset - entity.BeginOffset);
Console.WriteLine($"{entity.Type}: {entityValue}");
}
}
}
}
```
+ Para obter detalhes da API, consulte [DetectPiiEntities](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectPiiEntities)a *Referência AWS SDK para .NET da API*.
------
#### [ CLI ]
**AWS CLI**
**Para detectar entidades PII no texto de entrada**
O exemplo de `detect-pii-entities` a seguir analisa o texto de entrada e identifica entidades que contêm informações de identificação pessoal (PII). A pontuação de confiança do modelo pré-treinado também é gerada para cada previsão.
```
aws comprehend detect-pii-entities \
--language-code en \
--text "Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card \
account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings, \
we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. \
Customer feedback for Sunshine Spa, 123 Main St, Anywhere. Send comments to Alice at AnySpa@example.com."
```
Saída:
```
{
"Entities": [
{
"Score": 0.9998322129249573,
"Type": "NAME",
"BeginOffset": 6,
"EndOffset": 15
},
{
"Score": 0.9998878240585327,
"Type": "NAME",
"BeginOffset": 22,
"EndOffset": 26
},
{
"Score": 0.9994089603424072,
"Type": "CREDIT_DEBIT_NUMBER",
"BeginOffset": 88,
"EndOffset": 107
},
{
"Score": 0.9999760985374451,
"Type": "DATE_TIME",
"BeginOffset": 152,
"EndOffset": 161
},
{
"Score": 0.9999449253082275,
"Type": "BANK_ACCOUNT_NUMBER",
"BeginOffset": 271,
"EndOffset": 281
},
{
"Score": 0.9999847412109375,
"Type": "BANK_ROUTING",
"BeginOffset": 306,
"EndOffset": 315
},
{
"Score": 0.999925434589386,
"Type": "ADDRESS",
"BeginOffset": 354,
"EndOffset": 365
},
{
"Score": 0.9989161491394043,
"Type": "NAME",
"BeginOffset": 394,
"EndOffset": 399
},
{
"Score": 0.9994171857833862,
"Type": "EMAIL",
"BeginOffset": 403,
"EndOffset": 418
}
]
}
```
Para obter mais informações, consulte [Informações de identificação pessoal (PII)](https://docs.aws.amazon.com/comprehend/latest/dg/pii.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [DetectPiiEntities](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-pii-entities.html)em *Referência de AWS CLI Comandos*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_pii(self, text, language_code):
"""
Detects personally identifiable information (PII) in a document. PII can be
things like names, account numbers, or addresses.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of PII entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_pii_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s PII entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect PII entities.")
raise
else:
return entities
```
+ Para obter detalhes da API, consulte a [DetectPiiEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectPiiEntities)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectpiientities(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'PII entities detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [DetectPiiEntities](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `DetectSentiment` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `DetectSentiment`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Detectar elementos do documento](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK para .NET**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to detect the overall sentiment of the supplied
/// text using the Amazon Comprehend service.
///
public static class DetectSentiment
{
///
/// This method calls the DetetectSentimentAsync method to analyze the
/// supplied text and determine the overal sentiment.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);
// Call DetectKeyPhrases API
Console.WriteLine("Calling DetectSentiment");
var detectSentimentRequest = new DetectSentimentRequest()
{
Text = text,
LanguageCode = "en",
};
var detectSentimentResponse = await comprehendClient.DetectSentimentAsync(detectSentimentRequest);
Console.WriteLine($"Sentiment: {detectSentimentResponse.Sentiment}");
Console.WriteLine("Done");
}
}
```
+ Para obter detalhes da API, consulte [DetectSentiment](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectSentiment)a *Referência AWS SDK para .NET da API*.
------
#### [ CLI ]
**AWS CLI**
**Para detectar o sentimento de um texto de entrada**
O exemplo de `detect-sentiment` a seguir analisa o texto de entrada e retorna uma inferência do sentimento predominante (`POSITIVE`, `NEUTRAL`, `MIXED` ou `NEGATIVE`).
```
aws comprehend detect-sentiment \
--language-code en \
--text "It is a beautiful day in Seattle"
```
Saída:
```
{
"Sentiment": "POSITIVE",
"SentimentScore": {
"Positive": 0.9976957440376282,
"Negative": 9.653854067437351e-05,
"Neutral": 0.002169104292988777,
"Mixed": 3.857641786453314e-05
}
}
```
Para obter mais informações, consulte [Sentimento](https://docs.aws.amazon.com/comprehend/latest/dg/how-sentiment.html) no *Guia do desenvolvedor do Amazon Comprehend*
+ Para obter detalhes da API, consulte [DetectSentiment](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-sentiment.html)em *Referência de AWS CLI Comandos*.
------
#### [ Java ]
**SDK para Java 2.x**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import software.amazon.awssdk.services.comprehend.model.DetectSentimentRequest;
import software.amazon.awssdk.services.comprehend.model.DetectSentimentResponse;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectSentiment {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectSentiment");
detectSentiments(comClient, text);
comClient.close();
}
public static void detectSentiments(ComprehendClient comClient, String text) {
try {
DetectSentimentRequest detectSentimentRequest = DetectSentimentRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectSentimentResponse detectSentimentResult = comClient.detectSentiment(detectSentimentRequest);
System.out.println("The Neutral value is " + detectSentimentResult.sentimentScore().neutral());
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Para obter detalhes da API, consulte [DetectSentiment](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectSentiment)a *Referência AWS SDK for Java 2.x da API*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_sentiment(self, text, language_code):
"""
Detects the overall sentiment expressed in a document. Sentiment can
be positive, negative, neutral, or a mixture.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The sentiments along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_sentiment(
Text=text, LanguageCode=language_code
)
logger.info("Detected primary sentiment %s.", response["Sentiment"])
except ClientError:
logger.exception("Couldn't detect sentiment.")
raise
else:
return response
```
+ Para obter detalhes da API, consulte a [DetectSentiment](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSentiment)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectsentiment(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Sentiment detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [DetectSentiment](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `DetectSyntax` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `DetectSyntax`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Detectar elementos do documento](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK para .NET**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use Amazon Comprehend to detect syntax
/// elements by calling the DetectSyntaxAsync method.
///
public class DetectingSyntax
{
///
/// This method calls DetectSynaxAsync to identify the syntax elements
/// in the sample text.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient();
// Call DetectSyntax API
Console.WriteLine("Calling DetectSyntaxAsync\n");
var detectSyntaxRequest = new DetectSyntaxRequest()
{
Text = text,
LanguageCode = "en",
};
DetectSyntaxResponse detectSyntaxResponse = await comprehendClient.DetectSyntaxAsync(detectSyntaxRequest);
foreach (SyntaxToken s in detectSyntaxResponse.SyntaxTokens)
{
Console.WriteLine($"Text: {s.Text}, PartOfSpeech: {s.PartOfSpeech.Tag}, BeginOffset: {s.BeginOffset}, EndOffset: {s.EndOffset}");
}
Console.WriteLine("Done");
}
}
```
+ Para obter detalhes da API, consulte [DetectSyntax](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectSyntax)a *Referência AWS SDK para .NET da API*.
------
#### [ CLI ]
**AWS CLI**
**Para detectar as partes da fala em um texto de entrada**
O exemplo de `detect-syntax` a seguir analisa a sintaxe do texto de entrada e retorna as diferentes partes da fala. A pontuação de confiança do modelo pré-treinado também é gerada para cada previsão.
```
aws comprehend detect-syntax \
--language-code en \
--text "It is a beautiful day in Seattle."
```
Saída:
```
{
"SyntaxTokens": [
{
"TokenId": 1,
"Text": "It",
"BeginOffset": 0,
"EndOffset": 2,
"PartOfSpeech": {
"Tag": "PRON",
"Score": 0.9999740719795227
}
},
{
"TokenId": 2,
"Text": "is",
"BeginOffset": 3,
"EndOffset": 5,
"PartOfSpeech": {
"Tag": "VERB",
"Score": 0.999901294708252
}
},
{
"TokenId": 3,
"Text": "a",
"BeginOffset": 6,
"EndOffset": 7,
"PartOfSpeech": {
"Tag": "DET",
"Score": 0.9999938607215881
}
},
{
"TokenId": 4,
"Text": "beautiful",
"BeginOffset": 8,
"EndOffset": 17,
"PartOfSpeech": {
"Tag": "ADJ",
"Score": 0.9987351894378662
}
},
{
"TokenId": 5,
"Text": "day",
"BeginOffset": 18,
"EndOffset": 21,
"PartOfSpeech": {
"Tag": "NOUN",
"Score": 0.9999796748161316
}
},
{
"TokenId": 6,
"Text": "in",
"BeginOffset": 22,
"EndOffset": 24,
"PartOfSpeech": {
"Tag": "ADP",
"Score": 0.9998047947883606
}
},
{
"TokenId": 7,
"Text": "Seattle",
"BeginOffset": 25,
"EndOffset": 32,
"PartOfSpeech": {
"Tag": "PROPN",
"Score": 0.9940530061721802
}
}
]
}
```
Para obter mais informações, consulte [Análise de sintaxe](https://docs.aws.amazon.com/comprehend/latest/dg/how-syntax.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [DetectSyntax](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-syntax.html)em *Referência de AWS CLI Comandos*.
------
#### [ Java ]
**SDK para Java 2.x**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import software.amazon.awssdk.services.comprehend.model.DetectSyntaxRequest;
import software.amazon.awssdk.services.comprehend.model.DetectSyntaxResponse;
import software.amazon.awssdk.services.comprehend.model.SyntaxToken;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectSyntax {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectSyntax");
detectAllSyntax(comClient, text);
comClient.close();
}
public static void detectAllSyntax(ComprehendClient comClient, String text) {
try {
DetectSyntaxRequest detectSyntaxRequest = DetectSyntaxRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectSyntaxResponse detectSyntaxResult = comClient.detectSyntax(detectSyntaxRequest);
List syntaxTokens = detectSyntaxResult.syntaxTokens();
for (SyntaxToken token : syntaxTokens) {
System.out.println("Language is " + token.text());
System.out.println("Part of speech is " + token.partOfSpeech().tagAsString());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Para obter detalhes da API, consulte [DetectSyntax](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectSyntax)a *Referência AWS SDK for Java 2.x da API*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_syntax(self, text, language_code):
"""
Detects syntactical elements of a document. Syntax tokens are portions of
text along with their use as parts of speech, such as nouns, verbs, and
interjections.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of syntax tokens along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_syntax(
Text=text, LanguageCode=language_code
)
tokens = response["SyntaxTokens"]
logger.info("Detected %s syntax tokens.", len(tokens))
except ClientError:
logger.exception("Couldn't detect syntax.")
raise
else:
return tokens
```
+ Para obter detalhes da API, consulte a [DetectSyntax](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSyntax)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectsyntax(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Syntax tokens detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [DetectSyntax](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `ListDocumentClassificationJobs` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `ListDocumentClassificationJobs`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Treinar um classificador personalizado e classificar documentos](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**Para listar trabalhos de classificação de documento**
O exemplo de `list-document-classification-jobs` a seguir lista todos os trabalhos de classificação de documentos.
```
aws comprehend list-document-classification-jobs
```
Saída:
```
{
"DocumentClassificationJobPropertiesList": [
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classification-job/123456abcdeb0e11022f22a11EXAMPLE",
"JobName": "exampleclassificationjob",
"JobStatus": "COMPLETED",
"SubmitTime": "2023-06-14T17:09:51.788000+00:00",
"EndTime": "2023-06-14T17:15:58.582000+00:00",
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classifier/mymodel/version/12",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket/jobdata/",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/1234567890101-CLN-e758dd56b824aa717ceab551f11749fb/output/output.tar.gz"
},
"DataAccessRoleArn": "arn:aws:iam::1234567890101:role/service-role/AmazonComprehendServiceRole-example-role"
},
{
"JobId": "123456abcdeb0e11022f22a1EXAMPLE2",
"JobArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classification-job/123456abcdeb0e11022f22a1EXAMPLE2",
"JobName": "exampleclassificationjob2",
"JobStatus": "COMPLETED",
"SubmitTime": "2023-06-14T17:22:39.829000+00:00",
"EndTime": "2023-06-14T17:28:46.107000+00:00",
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classifier/mymodel/version/12",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket/jobdata/",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/1234567890101-CLN-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz"
},
"DataAccessRoleArn": "arn:aws:iam::1234567890101:role/service-role/AmazonComprehendServiceRole-example-role"
}
]
}
```
Para obter mais informações, consulte [Classificação personalizada](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [ListDocumentClassificationJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-document-classification-jobs.html)em *Referência de AWS CLI Comandos*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def list_jobs(self):
"""
Lists the classification jobs for the current account.
:return: The list of jobs.
"""
try:
response = self.comprehend_client.list_document_classification_jobs()
jobs = response["DocumentClassificationJobPropertiesList"]
logger.info("Got %s document classification jobs.", len(jobs))
except ClientError:
logger.exception(
"Couldn't get document classification jobs.",
)
raise
else:
return jobs
```
+ Para obter detalhes da API, consulte a [ListDocumentClassificationJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassificationJobs)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->listdocclassificationjobs( ).
MESSAGE 'Document classification jobs listed.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidfilterex.
MESSAGE 'Invalid filter.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [ListDocumentClassificationJobs](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `ListDocumentClassifiers` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `ListDocumentClassifiers`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Treinar um classificador personalizado e classificar documentos](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**Para listar todos os classificadores de documentos**
O exemplo de `list-document-classifiers` a seguir lista todos os modelos de classificadores de documentos treinados e em treinamento.
```
aws comprehend list-document-classifiers
```
Saída:
```
{
"DocumentClassifierPropertiesList": [
{
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier1",
"LanguageCode": "en",
"Status": "TRAINED",
"SubmitTime": "2023-06-13T19:04:15.735000+00:00",
"EndTime": "2023-06-13T19:42:31.752000+00:00",
"TrainingStartTime": "2023-06-13T19:08:20.114000+00:00",
"TrainingEndTime": "2023-06-13T19:41:35.080000+00:00",
"InputDataConfig": {
"DataFormat": "COMPREHEND_CSV",
"S3Uri": "s3://amzn-s3-demo-bucket/trainingdata"
},
"OutputDataConfig": {},
"ClassifierMetadata": {
"NumberOfLabels": 3,
"NumberOfTrainedDocuments": 5016,
"NumberOfTestDocuments": 557,
"EvaluationMetrics": {
"Accuracy": 0.9856,
"Precision": 0.9919,
"Recall": 0.9459,
"F1Score": 0.9673,
"MicroPrecision": 0.9856,
"MicroRecall": 0.9856,
"MicroF1Score": 0.9856,
"HammingLoss": 0.0144
}
},
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-testorle",
"Mode": "MULTI_CLASS"
},
{
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier2",
"LanguageCode": "en",
"Status": "TRAINING",
"SubmitTime": "2023-06-13T21:20:28.690000+00:00",
"InputDataConfig": {
"DataFormat": "COMPREHEND_CSV",
"S3Uri": "s3://amzn-s3-demo-bucket/trainingdata"
},
"OutputDataConfig": {},
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-testorle",
"Mode": "MULTI_CLASS"
}
]
}
```
Para obter mais informações, consulte [Criação e gerenciamento de modelos personalizados](https://docs.aws.amazon.com/comprehend/latest/dg/manage-models.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [ListDocumentClassifiers](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-document-classifiers.html)em *Referência de AWS CLI Comandos*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def list(self):
"""
Lists custom classifiers for the current account.
:return: The list of classifiers.
"""
try:
response = self.comprehend_client.list_document_classifiers()
classifiers = response["DocumentClassifierPropertiesList"]
logger.info("Got %s classifiers.", len(classifiers))
except ClientError:
logger.exception(
"Couldn't get classifiers.",
)
raise
else:
return classifiers
```
+ Para obter detalhes da API, consulte a [ListDocumentClassifiers](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassifiers)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->listdocumentclassifiers( ).
MESSAGE 'Document classifiers listed.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidfilterex.
MESSAGE 'Invalid filter.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [ListDocumentClassifiers](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `ListTopicsDetectionJobs` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `ListTopicsDetectionJobs`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Execute uma tarefa de modelagem de tópicos em dados de amostra](comprehend_example_comprehend_Usage_TopicModeler_section.md)
------
#### [ CLI ]
**AWS CLI**
**Para listar todos os trabalhos de detecção de tópicos**
O exemplo de `list-topics-detection-jobs` a seguir lista todos os trabalhos de detecção de tópicos assíncronos em andamento e concluídos.
```
aws comprehend list-topics-detection-jobs
```
Saída:
```
{
"TopicsDetectionJobPropertiesList": [
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
"JobName" "topic-analysis-1"
"JobStatus": "IN_PROGRESS",
"SubmitTime": "2023-06-09T18:40:35.384000+00:00",
"EndTime": "2023-06-09T18:46:41.936000+00:00",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
},
"NumberOfTopics": 10,
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
},
{
"JobId": "123456abcdeb0e11022f22a1EXAMPLE2",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a1EXAMPLE2",
"JobName": "topic-analysis-2",
"JobStatus": "COMPLETED",
"SubmitTime": "2023-06-09T18:44:43.414000+00:00",
"EndTime": "2023-06-09T18:50:50.872000+00:00",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz"
},
"NumberOfTopics": 10,
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
},
{
"JobId": "123456abcdeb0e11022f22a1EXAMPLE3",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a1EXAMPLE3",
"JobName": "topic-analysis-2",
"JobStatus": "IN_PROGRESS",
"SubmitTime": "2023-06-09T18:50:56.737000+00:00",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a1EXAMPLE3/output/output.tar.gz"
},
"NumberOfTopics": 10,
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
}
]
}
```
Para obter mais informações, consulte [Análise assíncrona para insights do Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [ListTopicsDetectionJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-topics-detection-jobs.html)em *Referência de AWS CLI Comandos*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendTopicModeler:
"""Encapsulates a Comprehend topic modeler."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def list_jobs(self):
"""
Lists topic modeling jobs for the current account.
:return: The list of jobs.
"""
try:
response = self.comprehend_client.list_topics_detection_jobs()
jobs = response["TopicsDetectionJobPropertiesList"]
logger.info("Got %s topic detection jobs.", len(jobs))
except ClientError:
logger.exception("Couldn't get topic detection jobs.")
raise
else:
return jobs
```
+ Para obter detalhes da API, consulte a [ListTopicsDetectionJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListTopicsDetectionJobs)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->listtopicsdetectionjobs( ).
MESSAGE 'Topics detection jobs listed.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidfilterex.
MESSAGE 'Invalid filter.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [ListTopicsDetectionJobs](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `StartDocumentClassificationJob` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `StartDocumentClassificationJob`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Treinar um classificador personalizado e classificar documentos](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**Para iniciar um trabalho de classificação de documento**
O exemplo de `start-document-classification-job` a seguir inicia um trabalho de classificação de documentos com um modelo personalizado em todos os arquivos no endereço especificado pela tag `--input-data-config`. Neste exemplo, o bucket do S3 de entrada contém `SampleSMStext1.txt`, `SampleSMStext2.txt` e `SampleSMStext3.txt`. O modelo foi previamente treinado na classificação de documentos de mensagens de spam e não spam, ou “ham”, e mensagens SMS. Quando o trabalho é concluído, `output.tar.gz` é colocado no local especificado pela tag `--output-data-config`. `output.tar.gz` contém `predictions.jsonl`, que lista a classificação de cada documento. A saída Json é impressa em uma linha por arquivo, mas foi formatada aqui para facilitar a leitura.
```
aws comprehend start-document-classification-job \
--job-name exampleclassificationjob \
--input-data-config "S3Uri=s3://amzn-s3-demo-bucket-INPUT/jobdata/" \
--output-data-config "S3Uri=s3://amzn-s3-demo-destination-bucket/testfolder/" \
--data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
--document-classifier-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/mymodel/version/12
```
Conteúdo de `SampleSMStext1.txt`:
```
"CONGRATULATIONS! TXT 2155550100 to win $5000"
```
Conteúdo de `SampleSMStext2.txt`:
```
"Hi, when do you want me to pick you up from practice?"
```
Conteúdo de `SampleSMStext3.txt`:
```
"Plz send bank account # to 2155550100 to claim prize!!"
```
Saída:
```
{
"JobId": "e758dd56b824aa717ceab551fEXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:document-classification-job/e758dd56b824aa717ceab551fEXAMPLE",
"JobStatus": "SUBMITTED"
}
```
Conteúdo de `predictions.jsonl`:
```
{"File": "SampleSMSText1.txt", "Line": "0", "Classes": [{"Name": "spam", "Score": 0.9999}, {"Name": "ham", "Score": 0.0001}]}
{"File": "SampleSMStext2.txt", "Line": "0", "Classes": [{"Name": "ham", "Score": 0.9994}, {"Name": "spam", "Score": 0.0006}]}
{"File": "SampleSMSText3.txt", "Line": "0", "Classes": [{"Name": "spam", "Score": 0.9999}, {"Name": "ham", "Score": 0.0001}]}
```
Para obter mais informações, consulte [Classificação personalizada](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [StartDocumentClassificationJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-document-classification-job.html)em *Referência de AWS CLI Comandos*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def start_job(
self,
job_name,
input_bucket,
input_key,
input_format,
output_bucket,
output_key,
data_access_role_arn,
):
"""
Starts a classification job. The classifier must be trained or the job
will fail. Input is read from the specified Amazon S3 input bucket and
written to the specified output bucket. Output data is stored in a tar
archive compressed in gzip format. The job runs asynchronously, so you can
call `describe_document_classification_job` to get job status until it
returns a status of SUCCEEDED.
:param job_name: The name of the job.
:param input_bucket: The Amazon S3 bucket that contains input data.
:param input_key: The prefix used to find input data in the input
bucket. If multiple objects have the same prefix, all
of them are used.
:param input_format: The format of the input data, either one document per
file or one document per line.
:param output_bucket: The Amazon S3 bucket where output data is written.
:param output_key: The prefix prepended to the output data.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
input bucket and write to the output bucket.
:return: Information about the job, including the job ID.
"""
try:
response = self.comprehend_client.start_document_classification_job(
DocumentClassifierArn=self.classifier_arn,
JobName=job_name,
InputDataConfig={
"S3Uri": f"s3://{input_bucket}/{input_key}",
"InputFormat": input_format.value,
},
OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
DataAccessRoleArn=data_access_role_arn,
)
logger.info(
"Document classification job %s is %s.", job_name, response["JobStatus"]
)
except ClientError:
logger.exception("Couldn't start classification job %s.", job_name)
raise
else:
return response
```
+ Para obter detalhes da API, consulte a [StartDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartDocumentClassificationJob)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->startdocclassificationjob(
iv_jobname = iv_job_name
iv_documentclassifierarn = iv_classifier_arn
io_inputdataconfig = NEW /aws1/cl_cpdinputdataconfig(
iv_s3uri = iv_input_s3_uri
iv_inputformat = iv_input_format
)
io_outputdataconfig = NEW /aws1/cl_cpdoutputdataconfig(
iv_s3uri = iv_output_s3_uri
)
iv_dataaccessrolearn = iv_data_access_role_arn
).
MESSAGE 'Document classification job started.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdresourcenotfoundex.
MESSAGE 'Resource not found.' TYPE 'E'.
CATCH /aws1/cx_cpdresourceunavailex.
MESSAGE 'Resource unavailable.' TYPE 'E'.
CATCH /aws1/cx_cpdkmskeyvalidationex.
MESSAGE 'KMS key validation error.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanytagsex.
MESSAGE 'Too many tags.' TYPE 'E'.
CATCH /aws1/cx_cpdresrclimitexcdex.
MESSAGE 'Resource limit exceeded.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [StartDocumentClassificationJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `StartTopicsDetectionJob` com um AWS SDK ou CLI
Os exemplos de código a seguir mostram como usar o `StartTopicsDetectionJob`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto no seguinte exemplo de código:
+ [Execute uma tarefa de modelagem de tópicos em dados de amostra](comprehend_example_comprehend_Usage_TopicModeler_section.md)
------
#### [ .NET ]
**SDK para .NET**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example scans the documents in an Amazon Simple Storage Service
/// (Amazon S3) bucket and analyzes it for topics. The results are stored
/// in another bucket and then the resulting job properties are displayed
/// on the screen. This example was created using the AWS SDK for .NEt
/// version 3.7 and .NET Core version 5.0.
///
public static class TopicModeling
{
///
/// This methos calls a topic detection job by calling the Amazon
/// Comprehend StartTopicsDetectionJobRequest.
///
public static async Task Main()
{
var comprehendClient = new AmazonComprehendClient();
string inputS3Uri = "s3://input bucket/input path";
InputFormat inputDocFormat = InputFormat.ONE_DOC_PER_FILE;
string outputS3Uri = "s3://output bucket/output path";
string dataAccessRoleArn = "arn:aws:iam::account ID:role/data access role";
int numberOfTopics = 10;
var startTopicsDetectionJobRequest = new StartTopicsDetectionJobRequest()
{
InputDataConfig = new InputDataConfig()
{
S3Uri = inputS3Uri,
InputFormat = inputDocFormat,
},
OutputDataConfig = new OutputDataConfig()
{
S3Uri = outputS3Uri,
},
DataAccessRoleArn = dataAccessRoleArn,
NumberOfTopics = numberOfTopics,
};
var startTopicsDetectionJobResponse = await comprehendClient.StartTopicsDetectionJobAsync(startTopicsDetectionJobRequest);
var jobId = startTopicsDetectionJobResponse.JobId;
Console.WriteLine("JobId: " + jobId);
var describeTopicsDetectionJobRequest = new DescribeTopicsDetectionJobRequest()
{
JobId = jobId,
};
var describeTopicsDetectionJobResponse = await comprehendClient.DescribeTopicsDetectionJobAsync(describeTopicsDetectionJobRequest);
PrintJobProperties(describeTopicsDetectionJobResponse.TopicsDetectionJobProperties);
var listTopicsDetectionJobsResponse = await comprehendClient.ListTopicsDetectionJobsAsync(new ListTopicsDetectionJobsRequest());
foreach (var props in listTopicsDetectionJobsResponse.TopicsDetectionJobPropertiesList)
{
PrintJobProperties(props);
}
}
///
/// This method is a helper method that displays the job properties
/// from the call to StartTopicsDetectionJobRequest.
///
/// A list of properties from the call to
/// StartTopicsDetectionJobRequest.
private static void PrintJobProperties(TopicsDetectionJobProperties props)
{
Console.WriteLine($"JobId: {props.JobId}, JobName: {props.JobName}, JobStatus: {props.JobStatus}");
Console.WriteLine($"NumberOfTopics: {props.NumberOfTopics}\nInputS3Uri: {props.InputDataConfig.S3Uri}");
Console.WriteLine($"InputFormat: {props.InputDataConfig.InputFormat}, OutputS3Uri: {props.OutputDataConfig.S3Uri}");
}
}
```
+ Para obter detalhes da API, consulte [StartTopicsDetectionJob](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/StartTopicsDetectionJob)a *Referência AWS SDK para .NET da API*.
------
#### [ CLI ]
**AWS CLI**
**Para iniciar um trabalho de análise de detecção de tópicos**
O exemplo de `start-topics-detection-job` a seguir inicia um trabalho assíncrono de detecção de tópicos para todos os arquivos localizados no endereço especificado pela tag `--input-data-config`. Quando o trabalho é concluído, a pasta, `output`, é colocada no local especificado pela tag `--ouput-data-config`. `output` contém topic-terms.csv e doc-topics.csv. O primeiro arquivo de saída, topic-terms.csv, é uma lista de tópicos na coleção. Para cada tópico, a lista inclui por padrão os principais termos por tópico de acordo com seu peso. O segundo arquivo, `doc-topics.csv`, lista os documentos associados a um tópico e a proporção do documento relacionada ao tópico.
```
aws comprehend start-topics-detection-job \
--job-name example_topics_detection_job \
--language-code en \
--input-data-config "S3Uri=s3://amzn-s3-demo-bucket/" \
--output-data-config "S3Uri=s3://amzn-s3-demo-destination-bucket/testfolder/" \
--data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
--language-code en
```
Saída:
```
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
"JobStatus": "SUBMITTED"
}
```
Para obter mais informações, consulte [Modelagem de tópicos](https://docs.aws.amazon.com/comprehend/latest/dg/topic-modeling.html) no *Guia do desenvolvedor do Amazon Comprehend*.
+ Para obter detalhes da API, consulte [StartTopicsDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-topics-detection-job.html)em *Referência de AWS CLI Comandos*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendTopicModeler:
"""Encapsulates a Comprehend topic modeler."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def start_job(
self,
job_name,
input_bucket,
input_key,
input_format,
output_bucket,
output_key,
data_access_role_arn,
):
"""
Starts a topic modeling job. Input is read from the specified Amazon S3
input bucket and written to the specified output bucket. Output data is stored
in a tar archive compressed in gzip format. The job runs asynchronously, so you
can call `describe_topics_detection_job` to get job status until it
returns a status of SUCCEEDED.
:param job_name: The name of the job.
:param input_bucket: An Amazon S3 bucket that contains job input.
:param input_key: The prefix used to find input data in the input
bucket. If multiple objects have the same prefix, all
of them are used.
:param input_format: The format of the input data, either one document per
file or one document per line.
:param output_bucket: The Amazon S3 bucket where output data is written.
:param output_key: The prefix prepended to the output data.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
input bucket and write to the output bucket.
:return: Information about the job, including the job ID.
"""
try:
response = self.comprehend_client.start_topics_detection_job(
JobName=job_name,
DataAccessRoleArn=data_access_role_arn,
InputDataConfig={
"S3Uri": f"s3://{input_bucket}/{input_key}",
"InputFormat": input_format.value,
},
OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
)
logger.info("Started topic modeling job %s.", response["JobId"])
except ClientError:
logger.exception("Couldn't start topic modeling job.")
raise
else:
return response
```
+ Para obter detalhes da API, consulte a [StartTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartTopicsDetectionJob)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->starttopicsdetectionjob(
iv_jobname = iv_job_name
io_inputdataconfig = NEW /aws1/cl_cpdinputdataconfig(
iv_s3uri = iv_input_s3_uri
iv_inputformat = iv_input_format
)
io_outputdataconfig = NEW /aws1/cl_cpdoutputdataconfig(
iv_s3uri = iv_output_s3_uri
)
iv_dataaccessrolearn = iv_data_access_role_arn
).
MESSAGE 'Topics detection job started.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdkmskeyvalidationex.
MESSAGE 'KMS key validation error.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanytagsex.
MESSAGE 'Too many tags.' TYPE 'E'.
CATCH /aws1/cx_cpdresrclimitexcdex.
MESSAGE 'Resource limit exceeded.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [StartTopicsDetectionJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Cenários para o Amazon Comprehend usando AWS SDKs
Os exemplos de código a seguir mostram como implementar cenários comuns no Amazon AWS SDKs Comprehend com. Esses casos mostram como realizar tarefas específicas chamando várias funções dentro do Amazon Comprehend ou combinadas com outros Serviços da AWS. Cada cenário inclui um link para o código-fonte completo, onde podem ser encontradas instruções sobre como configurar e executar o código.
Os cenários têm como alvo um nível intermediário de experiência para ajudar você a compreender ações de serviço em contexto.
**Topics**
+ [Criar uma aplicação de transmissão do Amazon Transcribe](comprehend_example_cross_TranscriptionStreamingApp_section.md)
+ [Criar um chatbot Amazon Lex](comprehend_example_cross_LexChatbotLanguages_section.md)
+ [Crie um aplicativo de mensagem](comprehend_example_cross_SQSMessageApp_section.md)
+ [Criar uma aplicação para analisar o feedback dos clientes](comprehend_example_cross_FSA_section.md)
+ [Detectar elementos do documento](comprehend_example_comprehend_Usage_DetectApis_section.md)
+ [Detectar entidades em texto extraído de uma imagem](comprehend_example_cross_TextractComprehendDetectEntities_section.md)
+ [Execute uma tarefa de modelagem de tópicos em dados de amostra](comprehend_example_comprehend_Usage_TopicModeler_section.md)
+ [Treinar um classificador personalizado e classificar documentos](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
# Criar uma aplicação de transmissão do Amazon Transcribe
O exemplo de código a seguir mostra como construir uma aplicação que registra, transcreve e traduz áudio ao vivo em tempo real, e envia os resultados por e-mail.
------
#### [ JavaScript ]
**SDK para JavaScript (v3)**
Mostra como usar o Amazon Transcribe para construir uma aplicação que registra, transcreve e traduz áudio ao vivo em tempo real, e envia os resultados por e-mail usando o Amazon Simple Email Service (Amazon SES).
Para obter o código-fonte completo e instruções sobre como configurar e executar, veja o exemplo completo em [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/transcribe-streaming-app).
**Serviços usados neste exemplo**
+ Amazon Comprehend
+ Amazon SES
+ Amazon Transcribe
+ Amazon Translate
------
# Crie um chatbot do Amazon Lex para engajar os visitantes do site
Os exemplos de código a seguir mostram como criar um chatbot para engajar os visitantes do seu site.
------
#### [ Java ]
**SDK para Java 2.x**
Mostra como usar a API do Amazon Lex para criar um Chatbot em uma aplicação da web para envolver os visitantes do seu site.
Para obter o código-fonte completo e instruções sobre como configurar e executar, veja o exemplo completo em [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_lex_chatbot).
**Serviços usados neste exemplo**
+ Amazon Comprehend
+ Amazon Lex
+ Amazon Translate
------
#### [ JavaScript ]
**SDK para JavaScript (v3)**
Mostra como usar a API do Amazon Lex para criar um Chatbot em uma aplicação da web para envolver os visitantes do seu site.
Para obter o código-fonte completo e instruções sobre como configurar e executar, consulte o exemplo completo [Criando um chatbot Amazon Lex](https://docs.aws.amazon.com/sdk-for-javascript/v3/developer-guide/lex-bot-example.html) no guia do AWS SDK para JavaScript desenvolvedor.
**Serviços usados neste exemplo**
+ Amazon Comprehend
+ Amazon Lex
+ Amazon Translate
------
# Criar um aplicativo web que envie e recupere mensagens usando o Amazon SQS
Os exemplos de código a seguir mostram como criar uma aplicação de mensagens usando o Amazon SQS.
------
#### [ Java ]
**SDK para Java 2.x**
Mostra como usar a API do Amazon SQS para desenvolver uma API REST que envia e recupera mensagens.
Para obter o código-fonte completo e instruções sobre como configurar e executar, veja o exemplo completo em [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_message_application).
**Serviços usados neste exemplo**
+ Amazon Comprehend
+ Amazon SQS
------
#### [ Kotlin ]
**SDK para Kotlin**
Mostra como usar a API do Amazon SQS para desenvolver uma API REST que envia e recupera mensagens.
Para obter o código-fonte completo e instruções sobre como configurar e executar, veja o exemplo completo em [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/creating_message_application).
**Serviços usados neste exemplo**
+ Amazon Comprehend
+ Amazon SQS
------
# Criar uma aplicação que analise o feedback dos clientes e sintetize o áudio
Os exemplos de código a seguir mostram como criar uma aplicação que analisa os cartões de comentários dos clientes, os traduz do idioma original, determina seus sentimentos e gera um arquivo de áudio do texto traduzido.
------
#### [ .NET ]
**SDK para .NET**
Esta aplicação de exemplo analisa e armazena cartões de feedback de clientes. Especificamente, ela atende à necessidade de um hotel fictício na cidade de Nova York. O hotel recebe feedback dos hóspedes em vários idiomas na forma de cartões de comentários físicos. Esse feedback é enviado para a aplicação por meio de um cliente web. Depois de fazer upload da imagem de um cartão de comentário, ocorrem as seguintes etapas:
+ O texto é extraído da imagem usando o Amazon Textract.
+ O Amazon Comprehend determina o sentimento do texto extraído e o idioma.
+ O texto extraído é traduzido para o inglês com o Amazon Translate.
+ O Amazon Polly sintetiza um arquivo de áudio do texto extraído.
A aplicação completa pode ser implantada com o AWS CDK. Para obter o código-fonte e as instruções de implantação, consulte o projeto em [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/FeedbackSentimentAnalyzer).
**Serviços usados neste exemplo**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
#### [ Java ]
**SDK para Java 2.x**
Esta aplicação de exemplo analisa e armazena cartões de feedback de clientes. Especificamente, ela atende à necessidade de um hotel fictício na cidade de Nova York. O hotel recebe feedback dos hóspedes em vários idiomas na forma de cartões de comentários físicos. Esse feedback é enviado para a aplicação por meio de um cliente web. Depois de fazer upload da imagem de um cartão de comentário, ocorrem as seguintes etapas:
+ O texto é extraído da imagem usando o Amazon Textract.
+ O Amazon Comprehend determina o sentimento do texto extraído e o idioma.
+ O texto extraído é traduzido para o inglês com o Amazon Translate.
+ O Amazon Polly sintetiza um arquivo de áudio do texto extraído.
A aplicação completa pode ser implantada com o AWS CDK. Para obter o código-fonte e as instruções de implantação, consulte o projeto em [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_fsa_app).
**Serviços usados neste exemplo**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
#### [ JavaScript ]
**SDK para JavaScript (v3)**
Esta aplicação de exemplo analisa e armazena cartões de feedback de clientes. Especificamente, ela atende à necessidade de um hotel fictício na cidade de Nova York. O hotel recebe feedback dos hóspedes em vários idiomas na forma de cartões de comentários físicos. Esse feedback é enviado para a aplicação por meio de um cliente web. Depois de fazer upload da imagem de um cartão de comentário, ocorrem as seguintes etapas:
+ O texto é extraído da imagem usando o Amazon Textract.
+ O Amazon Comprehend determina o sentimento do texto extraído e o idioma.
+ O texto extraído é traduzido para o inglês com o Amazon Translate.
+ O Amazon Polly sintetiza um arquivo de áudio do texto extraído.
A aplicação completa pode ser implantada com o AWS CDK. Para obter o código-fonte e as instruções de implantação, consulte o projeto em [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/feedback-sentiment-analyzer). Os trechos a seguir mostram como o AWS SDK para JavaScript é usado nas funções do Lambda.
```
import {
ComprehendClient,
DetectDominantLanguageCommand,
DetectSentimentCommand,
} from "@aws-sdk/client-comprehend";
/**
* Determine the language and sentiment of the extracted text.
*
* @param {{ source_text: string}} extractTextOutput
*/
export const handler = async (extractTextOutput) => {
const comprehendClient = new ComprehendClient({});
const detectDominantLanguageCommand = new DetectDominantLanguageCommand({
Text: extractTextOutput.source_text,
});
// The source language is required for sentiment analysis and
// translation in the next step.
const { Languages } = await comprehendClient.send(
detectDominantLanguageCommand,
);
const languageCode = Languages[0].LanguageCode;
const detectSentimentCommand = new DetectSentimentCommand({
Text: extractTextOutput.source_text,
LanguageCode: languageCode,
});
const { Sentiment } = await comprehendClient.send(detectSentimentCommand);
return {
sentiment: Sentiment,
language_code: languageCode,
};
};
```
```
import {
DetectDocumentTextCommand,
TextractClient,
} from "@aws-sdk/client-textract";
/**
* Fetch the S3 object from the event and analyze it using Amazon Textract.
*
* @param {import("@types/aws-lambda").EventBridgeEvent<"Object Created">} eventBridgeS3Event
*/
export const handler = async (eventBridgeS3Event) => {
const textractClient = new TextractClient();
const detectDocumentTextCommand = new DetectDocumentTextCommand({
Document: {
S3Object: {
Bucket: eventBridgeS3Event.bucket,
Name: eventBridgeS3Event.object,
},
},
});
// Textract returns a list of blocks. A block can be a line, a page, word, etc.
// Each block also contains geometry of the detected text.
// For more information on the Block type, see https://docs.aws.amazon.com/textract/latest/dg/API_Block.html.
const { Blocks } = await textractClient.send(detectDocumentTextCommand);
// For the purpose of this example, we are only interested in words.
const extractedWords = Blocks.filter((b) => b.BlockType === "WORD").map(
(b) => b.Text,
);
return extractedWords.join(" ");
};
```
```
import { PollyClient, SynthesizeSpeechCommand } from "@aws-sdk/client-polly";
import { S3Client } from "@aws-sdk/client-s3";
import { Upload } from "@aws-sdk/lib-storage";
/**
* Synthesize an audio file from text.
*
* @param {{ bucket: string, translated_text: string, object: string}} sourceDestinationConfig
*/
export const handler = async (sourceDestinationConfig) => {
const pollyClient = new PollyClient({});
const synthesizeSpeechCommand = new SynthesizeSpeechCommand({
Engine: "neural",
Text: sourceDestinationConfig.translated_text,
VoiceId: "Ruth",
OutputFormat: "mp3",
});
const { AudioStream } = await pollyClient.send(synthesizeSpeechCommand);
const audioKey = `${sourceDestinationConfig.object}.mp3`;
// Store the audio file in S3.
const s3Client = new S3Client();
const upload = new Upload({
client: s3Client,
params: {
Bucket: sourceDestinationConfig.bucket,
Key: audioKey,
Body: AudioStream,
ContentType: "audio/mp3",
},
});
await upload.done();
return audioKey;
};
```
```
import {
TranslateClient,
TranslateTextCommand,
} from "@aws-sdk/client-translate";
/**
* Translate the extracted text to English.
*
* @param {{ extracted_text: string, source_language_code: string}} textAndSourceLanguage
*/
export const handler = async (textAndSourceLanguage) => {
const translateClient = new TranslateClient({});
const translateCommand = new TranslateTextCommand({
SourceLanguageCode: textAndSourceLanguage.source_language_code,
TargetLanguageCode: "en",
Text: textAndSourceLanguage.extracted_text,
});
const { TranslatedText } = await translateClient.send(translateCommand);
return { translated_text: TranslatedText };
};
```
**Serviços usados neste exemplo**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
#### [ Ruby ]
**SDK para Ruby**
Esta aplicação de exemplo analisa e armazena cartões de feedback de clientes. Especificamente, ela atende à necessidade de um hotel fictício na cidade de Nova York. O hotel recebe feedback dos hóspedes em vários idiomas na forma de cartões de comentários físicos. Esse feedback é enviado para a aplicação por meio de um cliente web. Depois de fazer upload da imagem de um cartão de comentário, ocorrem as seguintes etapas:
+ O texto é extraído da imagem usando o Amazon Textract.
+ O Amazon Comprehend determina o sentimento do texto extraído e o idioma.
+ O texto extraído é traduzido para o inglês com o Amazon Translate.
+ O Amazon Polly sintetiza um arquivo de áudio do texto extraído.
A aplicação completa pode ser implantada com o AWS CDK. Para obter o código-fonte e as instruções de implantação, consulte o projeto em [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/ruby/cross_service_examples/feedback_sentiment_analyzer).
**Serviços usados neste exemplo**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
# Detecte elementos do documento com o Amazon Comprehend e um SDK AWS
O exemplo de código a seguir mostra como:
+ Detecte idiomas, entidades e frases-chave em um documento.
+ Detecte informações de identificação pessoal (PII) em um documento.
+ Detecte o sentimento de um documento.
+ Detectar elementos sintáticos em um documento.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Crie uma classe que envolva as ações do Amazon Comprehend.
```
import logging
from pprint import pprint
import boto3
from botocore.exceptions import ClientError
logger = logging.getLogger(__name__)
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_languages(self, text):
"""
Detects languages used in a document.
:param text: The document to inspect.
:return: The list of languages along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_dominant_language(Text=text)
languages = response["Languages"]
logger.info("Detected %s languages.", len(languages))
except ClientError:
logger.exception("Couldn't detect languages.")
raise
else:
return languages
def detect_entities(self, text, language_code):
"""
Detects entities in a document. Entities can be things like people and places
or other common terms.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect entities.")
raise
else:
return entities
def detect_key_phrases(self, text, language_code):
"""
Detects key phrases in a document. A key phrase is typically a noun and its
modifiers.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of key phrases along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_key_phrases(
Text=text, LanguageCode=language_code
)
phrases = response["KeyPhrases"]
logger.info("Detected %s phrases.", len(phrases))
except ClientError:
logger.exception("Couldn't detect phrases.")
raise
else:
return phrases
def detect_pii(self, text, language_code):
"""
Detects personally identifiable information (PII) in a document. PII can be
things like names, account numbers, or addresses.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of PII entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_pii_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s PII entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect PII entities.")
raise
else:
return entities
def detect_sentiment(self, text, language_code):
"""
Detects the overall sentiment expressed in a document. Sentiment can
be positive, negative, neutral, or a mixture.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The sentiments along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_sentiment(
Text=text, LanguageCode=language_code
)
logger.info("Detected primary sentiment %s.", response["Sentiment"])
except ClientError:
logger.exception("Couldn't detect sentiment.")
raise
else:
return response
def detect_syntax(self, text, language_code):
"""
Detects syntactical elements of a document. Syntax tokens are portions of
text along with their use as parts of speech, such as nouns, verbs, and
interjections.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of syntax tokens along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_syntax(
Text=text, LanguageCode=language_code
)
tokens = response["SyntaxTokens"]
logger.info("Detected %s syntax tokens.", len(tokens))
except ClientError:
logger.exception("Couldn't detect syntax.")
raise
else:
return tokens
```
Chame funções na classe wrapper para detectar entidades, frases e muito mais em um documento.
```
def usage_demo():
print("-" * 88)
print("Welcome to the Amazon Comprehend detection demo!")
print("-" * 88)
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
comp_detect = ComprehendDetect(boto3.client("comprehend"))
with open("detect_sample.txt") as sample_file:
sample_text = sample_file.read()
demo_size = 3
print("Sample text used for this demo:")
print("-" * 88)
print(sample_text)
print("-" * 88)
print("Detecting languages.")
languages = comp_detect.detect_languages(sample_text)
pprint(languages)
lang_code = languages[0]["LanguageCode"]
print("Detecting entities.")
entities = comp_detect.detect_entities(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(entities[:demo_size])
print("Detecting key phrases.")
phrases = comp_detect.detect_key_phrases(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(phrases[:demo_size])
print("Detecting personally identifiable information (PII).")
pii_entities = comp_detect.detect_pii(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(pii_entities[:demo_size])
print("Detecting sentiment.")
sentiment = comp_detect.detect_sentiment(sample_text, lang_code)
print(f"Sentiment: {sentiment['Sentiment']}")
print("SentimentScore:")
pprint(sentiment["SentimentScore"])
print("Detecting syntax elements.")
syntax_tokens = comp_detect.detect_syntax(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(syntax_tokens[:demo_size])
print("Thanks for watching!")
print("-" * 88)
```
+ Para obter detalhes da API, consulte os tópicos a seguir na *Referência da API AWS SDK para Python (Boto3)*.
+ [DetectDominantLanguage](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectDominantLanguage)
+ [DetectEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectEntities)
+ [DetectKeyPhrases](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectKeyPhrases)
+ [DetectPiiEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectPiiEntities)
+ [DetectSentiment](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSentiment)
+ [DetectSyntax](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSyntax)
------
# Detecte entidades no texto extraído de uma imagem usando um SDK AWS
O exemplo de código a seguir mostra como usar o Amazon Comprehend para detectar entidades em texto extraído pelo Amazon Textract de uma imagem armazenada no Amazon S3.
------
#### [ Python ]
**SDK para Python (Boto3)**
Mostra como usar o AWS SDK para Python (Boto3) em um notebook Jupyter para detectar entidades no texto extraído de uma imagem. Este exemplo usa o Amazon Textract para extrair texto de uma imagem armazenada no Amazon Simple Storage Service (Amazon S3) e no Amazon Comprehend para detectar entidades no texto extraído.
Este exemplo é um caderno Jupyter e deve ser executado em um ambiente que possa hospedar blocos de anotações. Para obter instruções sobre como executar o exemplo usando o Amazon SageMaker AI, consulte as instruções em [TextractAndComprehendNotebook.ipynb](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook/TextractAndComprehendNotebook.ipynb).
Para obter o código-fonte completo e instruções sobre como configurar e executar, veja o exemplo completo em [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook#readme).
**Serviços usados neste exemplo**
+ Amazon Comprehend
+ Amazon S3
+ Amazon Textract
------
# Execute um trabalho de modelagem de tópicos do Amazon Comprehend em dados de amostra usando um SDK AWS
O exemplo de código a seguir mostra como:
+ Execute uma tarefa de modelagem de tópicos do Amazon Comprehend em dados de amostra.
+ Informações sobre a tarefa.
+ Extrair dados de saída do trabalho no Amazon S3.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Crie uma classe wrapper para chamar as ações de modelagem de tópicos do Amazon Comprehend.
```
class ComprehendTopicModeler:
"""Encapsulates a Comprehend topic modeler."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def start_job(
self,
job_name,
input_bucket,
input_key,
input_format,
output_bucket,
output_key,
data_access_role_arn,
):
"""
Starts a topic modeling job. Input is read from the specified Amazon S3
input bucket and written to the specified output bucket. Output data is stored
in a tar archive compressed in gzip format. The job runs asynchronously, so you
can call `describe_topics_detection_job` to get job status until it
returns a status of SUCCEEDED.
:param job_name: The name of the job.
:param input_bucket: An Amazon S3 bucket that contains job input.
:param input_key: The prefix used to find input data in the input
bucket. If multiple objects have the same prefix, all
of them are used.
:param input_format: The format of the input data, either one document per
file or one document per line.
:param output_bucket: The Amazon S3 bucket where output data is written.
:param output_key: The prefix prepended to the output data.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
input bucket and write to the output bucket.
:return: Information about the job, including the job ID.
"""
try:
response = self.comprehend_client.start_topics_detection_job(
JobName=job_name,
DataAccessRoleArn=data_access_role_arn,
InputDataConfig={
"S3Uri": f"s3://{input_bucket}/{input_key}",
"InputFormat": input_format.value,
},
OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
)
logger.info("Started topic modeling job %s.", response["JobId"])
except ClientError:
logger.exception("Couldn't start topic modeling job.")
raise
else:
return response
def describe_job(self, job_id):
"""
Gets metadata about a topic modeling job.
:param job_id: The ID of the job to look up.
:return: Metadata about the job.
"""
try:
response = self.comprehend_client.describe_topics_detection_job(
JobId=job_id
)
job = response["TopicsDetectionJobProperties"]
logger.info("Got topic detection job %s.", job_id)
except ClientError:
logger.exception("Couldn't get topic detection job %s.", job_id)
raise
else:
return job
def list_jobs(self):
"""
Lists topic modeling jobs for the current account.
:return: The list of jobs.
"""
try:
response = self.comprehend_client.list_topics_detection_jobs()
jobs = response["TopicsDetectionJobPropertiesList"]
logger.info("Got %s topic detection jobs.", len(jobs))
except ClientError:
logger.exception("Couldn't get topic detection jobs.")
raise
else:
return jobs
```
Use a classe wrapper para executar uma tarefa de modelagem de tópicos e obter dados dela.
```
def usage_demo():
print("-" * 88)
print("Welcome to the Amazon Comprehend topic modeling demo!")
print("-" * 88)
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
input_prefix = "input/"
output_prefix = "output/"
demo_resources = ComprehendDemoResources(
boto3.resource("s3"), boto3.resource("iam")
)
topic_modeler = ComprehendTopicModeler(boto3.client("comprehend"))
print("Setting up storage and security resources needed for the demo.")
demo_resources.setup("comprehend-topic-modeler-demo")
print("Copying sample data from public bucket into input bucket.")
demo_resources.bucket.copy(
{"Bucket": "public-sample-us-west-2", "Key": "TopicModeling/Sample.txt"},
f"{input_prefix}sample.txt",
)
print("Starting topic modeling job on sample data.")
job_info = topic_modeler.start_job(
"demo-topic-modeling-job",
demo_resources.bucket.name,
input_prefix,
JobInputFormat.per_line,
demo_resources.bucket.name,
output_prefix,
demo_resources.data_access_role.arn,
)
print(
f"Waiting for job {job_info['JobId']} to complete. This typically takes "
f"20 - 30 minutes."
)
job_waiter = JobCompleteWaiter(topic_modeler.comprehend_client)
job_waiter.wait(job_info["JobId"])
job = topic_modeler.describe_job(job_info["JobId"])
print(f"Job {job['JobId']} complete:")
pprint(job)
print(
f"Getting job output data from the output Amazon S3 bucket: "
f"{job['OutputDataConfig']['S3Uri']}."
)
job_output = demo_resources.extract_job_output(job)
lines = 10
print(f"First {lines} lines of document topics output:")
pprint(job_output["doc-topics.csv"]["data"][:lines])
print(f"First {lines} lines of terms output:")
pprint(job_output["topic-terms.csv"]["data"][:lines])
print("Cleaning up resources created for the demo.")
demo_resources.cleanup()
print("Thanks for watching!")
print("-" * 88)
```
+ Para obter detalhes da API, consulte os tópicos a seguir na *Referência da API AWS SDK para Python (Boto3)*.
+ [DescribeTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeTopicsDetectionJob)
+ [ListTopicsDetectionJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListTopicsDetectionJobs)
+ [StartTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartTopicsDetectionJob)
------
# Treine um classificador Amazon Comprehend personalizado e classifique documentos usando um SDK AWS
O exemplo de código a seguir mostra como:
+ Crie um classificador de vários rótulos do Amazon Comprehend.
+ Treine o classificador em dados de amostra.
+ Execute uma tarefa de classificação em um segundo conjunto de dados.
+ Extrair dados de saída da tarefa do Amazon S3.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Crie uma classe wrapper para chamar as ações do classificador de documentos Amazon Comprehend.
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def create(
self,
name,
language_code,
training_bucket,
training_key,
data_access_role_arn,
mode,
):
"""
Creates a custom classifier. After the classifier is created, it immediately
starts training on the data found in the specified Amazon S3 bucket. Training
can take 30 minutes or longer. The `describe_document_classifier` function
can be used to get training status and returns a status of TRAINED when the
classifier is ready to use.
:param name: The name of the classifier.
:param language_code: The language the classifier can operate on.
:param training_bucket: The Amazon S3 bucket that contains the training data.
:param training_key: The prefix used to find training data in the training
bucket. If multiple objects have the same prefix, all
of them are used.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
training bucket.
:return: The ARN of the newly created classifier.
"""
try:
response = self.comprehend_client.create_document_classifier(
DocumentClassifierName=name,
LanguageCode=language_code,
InputDataConfig={"S3Uri": f"s3://{training_bucket}/{training_key}"},
DataAccessRoleArn=data_access_role_arn,
Mode=mode.value,
)
self.classifier_arn = response["DocumentClassifierArn"]
logger.info("Started classifier creation. Arn is: %s.", self.classifier_arn)
except ClientError:
logger.exception("Couldn't create classifier %s.", name)
raise
else:
return self.classifier_arn
def describe(self, classifier_arn=None):
"""
Gets metadata about a custom classifier, including its current status.
:param classifier_arn: The ARN of the classifier to look up.
:return: Metadata about the classifier.
"""
if classifier_arn is not None:
self.classifier_arn = classifier_arn
try:
response = self.comprehend_client.describe_document_classifier(
DocumentClassifierArn=self.classifier_arn
)
classifier = response["DocumentClassifierProperties"]
logger.info("Got classifier %s.", self.classifier_arn)
except ClientError:
logger.exception("Couldn't get classifier %s.", self.classifier_arn)
raise
else:
return classifier
def list(self):
"""
Lists custom classifiers for the current account.
:return: The list of classifiers.
"""
try:
response = self.comprehend_client.list_document_classifiers()
classifiers = response["DocumentClassifierPropertiesList"]
logger.info("Got %s classifiers.", len(classifiers))
except ClientError:
logger.exception(
"Couldn't get classifiers.",
)
raise
else:
return classifiers
def delete(self):
"""
Deletes the classifier.
"""
try:
self.comprehend_client.delete_document_classifier(
DocumentClassifierArn=self.classifier_arn
)
logger.info("Deleted classifier %s.", self.classifier_arn)
self.classifier_arn = None
except ClientError:
logger.exception("Couldn't deleted classifier %s.", self.classifier_arn)
raise
def start_job(
self,
job_name,
input_bucket,
input_key,
input_format,
output_bucket,
output_key,
data_access_role_arn,
):
"""
Starts a classification job. The classifier must be trained or the job
will fail. Input is read from the specified Amazon S3 input bucket and
written to the specified output bucket. Output data is stored in a tar
archive compressed in gzip format. The job runs asynchronously, so you can
call `describe_document_classification_job` to get job status until it
returns a status of SUCCEEDED.
:param job_name: The name of the job.
:param input_bucket: The Amazon S3 bucket that contains input data.
:param input_key: The prefix used to find input data in the input
bucket. If multiple objects have the same prefix, all
of them are used.
:param input_format: The format of the input data, either one document per
file or one document per line.
:param output_bucket: The Amazon S3 bucket where output data is written.
:param output_key: The prefix prepended to the output data.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
input bucket and write to the output bucket.
:return: Information about the job, including the job ID.
"""
try:
response = self.comprehend_client.start_document_classification_job(
DocumentClassifierArn=self.classifier_arn,
JobName=job_name,
InputDataConfig={
"S3Uri": f"s3://{input_bucket}/{input_key}",
"InputFormat": input_format.value,
},
OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
DataAccessRoleArn=data_access_role_arn,
)
logger.info(
"Document classification job %s is %s.", job_name, response["JobStatus"]
)
except ClientError:
logger.exception("Couldn't start classification job %s.", job_name)
raise
else:
return response
def describe_job(self, job_id):
"""
Gets metadata about a classification job.
:param job_id: The ID of the job to look up.
:return: Metadata about the job.
"""
try:
response = self.comprehend_client.describe_document_classification_job(
JobId=job_id
)
job = response["DocumentClassificationJobProperties"]
logger.info("Got classification job %s.", job["JobName"])
except ClientError:
logger.exception("Couldn't get classification job %s.", job_id)
raise
else:
return job
def list_jobs(self):
"""
Lists the classification jobs for the current account.
:return: The list of jobs.
"""
try:
response = self.comprehend_client.list_document_classification_jobs()
jobs = response["DocumentClassificationJobPropertiesList"]
logger.info("Got %s document classification jobs.", len(jobs))
except ClientError:
logger.exception(
"Couldn't get document classification jobs.",
)
raise
else:
return jobs
```
Crie uma classe para ajudar na execução do cenário.
```
class ClassifierDemo:
"""
Encapsulates functions used to run the demonstration.
"""
def __init__(self, demo_resources):
"""
:param demo_resources: A ComprehendDemoResources class that manages resources
for the demonstration.
"""
self.demo_resources = demo_resources
self.training_prefix = "training/"
self.input_prefix = "input/"
self.input_format = JobInputFormat.per_line
self.output_prefix = "output/"
def setup(self):
"""Creates AWS resources used by the demo."""
self.demo_resources.setup("comprehend-classifier-demo")
def cleanup(self):
"""Deletes AWS resources used by the demo."""
self.demo_resources.cleanup()
@staticmethod
def _sanitize_text(text):
"""Removes characters that cause errors for the document parser."""
return text.replace("\r", " ").replace("\n", " ").replace(",", ";")
@staticmethod
def _get_issues(query, issue_count):
"""
Gets issues from GitHub using the specified query parameters.
:param query: The query string used to request issues from the GitHub API.
:param issue_count: The number of issues to retrieve.
:return: The list of issues retrieved from GitHub.
"""
issues = []
logger.info("Requesting issues from %s?%s.", GITHUB_SEARCH_URL, query)
response = requests.get(f"{GITHUB_SEARCH_URL}?{query}&per_page={issue_count}")
if response.status_code == 200:
issue_page = response.json()["items"]
logger.info("Got %s issues.", len(issue_page))
issues = [
{
"title": ClassifierDemo._sanitize_text(issue["title"]),
"body": ClassifierDemo._sanitize_text(issue["body"]),
"labels": {label["name"] for label in issue["labels"]},
}
for issue in issue_page
]
else:
logger.error(
"GitHub returned error code %s with message %s.",
response.status_code,
response.json(),
)
logger.info("Found %s issues.", len(issues))
return issues
def get_training_issues(self, training_labels):
"""
Gets issues used for training the custom classifier. Training issues are
closed issues from the Boto3 repo that have known labels. Comprehend
requires a minimum of ten training issues per label.
:param training_labels: The issue labels to use for training.
:return: The set of issues used for training.
"""
issues = []
per_label_count = 15
for label in training_labels:
issues += self._get_issues(
f"q=type:issue+repo:boto/boto3+state:closed+label:{label}",
per_label_count,
)
for issue in issues:
issue["labels"] = issue["labels"].intersection(training_labels)
return issues
def get_input_issues(self, training_labels):
"""
Gets input issues from GitHub. For demonstration purposes, input issues
are open issues from the Boto3 repo with known labels, though in practice
any issue could be submitted to the classifier for labeling.
:param training_labels: The set of labels to query for.
:return: The set of issues used for input.
"""
issues = []
per_label_count = 5
for label in training_labels:
issues += self._get_issues(
f"q=type:issue+repo:boto/boto3+state:open+label:{label}",
per_label_count,
)
return issues
def upload_issue_data(self, issues, training=False):
"""
Uploads issue data to an Amazon S3 bucket, either for training or for input.
The data is first put into the format expected by Comprehend. For training,
the set of pipe-delimited labels is prepended to each document. For
input, labels are not sent.
:param issues: The set of issues to upload to Amazon S3.
:param training: Indicates whether the issue data is used for training or
input.
"""
try:
obj_key = (
self.training_prefix if training else self.input_prefix
) + "issues.txt"
if training:
issue_strings = [
f"{'|'.join(issue['labels'])},{issue['title']} {issue['body']}"
for issue in issues
]
else:
issue_strings = [
f"{issue['title']} {issue['body']}" for issue in issues
]
issue_bytes = BytesIO("\n".join(issue_strings).encode("utf-8"))
self.demo_resources.bucket.upload_fileobj(issue_bytes, obj_key)
logger.info(
"Uploaded data as %s to bucket %s.",
obj_key,
self.demo_resources.bucket.name,
)
except ClientError:
logger.exception(
"Couldn't upload data to bucket %s.", self.demo_resources.bucket.name
)
raise
def extract_job_output(self, job):
"""Extracts job output from Amazon S3."""
return self.demo_resources.extract_job_output(job)
@staticmethod
def reconcile_job_output(input_issues, output_dict):
"""
Reconciles job output with the list of input issues. Because the input issues
have known labels, these can be compared with the labels added by the
classifier to judge the accuracy of the output.
:param input_issues: The list of issues used as input.
:param output_dict: The dictionary of data that is output by the classifier.
:return: The list of reconciled input and output data.
"""
reconciled = []
for archive in output_dict.values():
for line in archive["data"]:
in_line = int(line["Line"])
in_labels = input_issues[in_line]["labels"]
out_labels = {
label["Name"]
for label in line["Labels"]
if float(label["Score"]) > 0.3
}
reconciled.append(
f"{line['File']}, line {in_line} has labels {in_labels}.\n"
f"\tClassifier assigned {out_labels}."
)
logger.info("Reconciled input and output labels.")
return reconciled
```
Treine um classificador em um conjunto de GitHub problemas com rótulos conhecidos e, em seguida, envie um segundo conjunto de GitHub problemas ao classificador para que eles possam ser rotulados.
```
def usage_demo():
print("-" * 88)
print("Welcome to the Amazon Comprehend custom document classifier demo!")
print("-" * 88)
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
comp_demo = ClassifierDemo(
ComprehendDemoResources(boto3.resource("s3"), boto3.resource("iam"))
)
comp_classifier = ComprehendClassifier(boto3.client("comprehend"))
classifier_trained_waiter = ClassifierTrainedWaiter(
comp_classifier.comprehend_client
)
training_labels = {"bug", "feature-request", "dynamodb", "s3"}
print("Setting up storage and security resources needed for the demo.")
comp_demo.setup()
print("Getting training data from GitHub and uploading it to Amazon S3.")
training_issues = comp_demo.get_training_issues(training_labels)
comp_demo.upload_issue_data(training_issues, True)
classifier_name = "doc-example-classifier"
print(f"Creating document classifier {classifier_name}.")
comp_classifier.create(
classifier_name,
"en",
comp_demo.demo_resources.bucket.name,
comp_demo.training_prefix,
comp_demo.demo_resources.data_access_role.arn,
ClassifierMode.multi_label,
)
print(
f"Waiting until {classifier_name} is trained. This typically takes "
f"30–40 minutes."
)
classifier_trained_waiter.wait(comp_classifier.classifier_arn)
print(f"Classifier {classifier_name} is trained:")
pprint(comp_classifier.describe())
print("Getting input data from GitHub and uploading it to Amazon S3.")
input_issues = comp_demo.get_input_issues(training_labels)
comp_demo.upload_issue_data(input_issues)
print("Starting classification job on input data.")
job_info = comp_classifier.start_job(
"issue_classification_job",
comp_demo.demo_resources.bucket.name,
comp_demo.input_prefix,
comp_demo.input_format,
comp_demo.demo_resources.bucket.name,
comp_demo.output_prefix,
comp_demo.demo_resources.data_access_role.arn,
)
print(f"Waiting for job {job_info['JobId']} to complete.")
job_waiter = JobCompleteWaiter(comp_classifier.comprehend_client)
job_waiter.wait(job_info["JobId"])
job = comp_classifier.describe_job(job_info["JobId"])
print(f"Job {job['JobId']} complete:")
pprint(job)
print(
f"Getting job output data from Amazon S3: "
f"{job['OutputDataConfig']['S3Uri']}."
)
job_output = comp_demo.extract_job_output(job)
print("Job output:")
pprint(job_output)
print("Reconciling job output with labels from GitHub:")
reconciled_output = comp_demo.reconcile_job_output(input_issues, job_output)
print(*reconciled_output, sep="\n")
answer = input(f"Do you want to delete the classifier {classifier_name} (y/n)? ")
if answer.lower() == "y":
print(f"Deleting {classifier_name}.")
comp_classifier.delete()
print("Cleaning up resources created for the demo.")
comp_demo.cleanup()
print("Thanks for watching!")
print("-" * 88)
```
+ Para ver detalhes da API, consulte os tópicos a seguir na *Referência da API do SDK da AWS para Python (Boto3)*.
+ [CreateDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/CreateDocumentClassifier)
+ [DeleteDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DeleteDocumentClassifier)
+ [DescribeDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassificationJob)
+ [DescribeDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassifier)
+ [ListDocumentClassificationJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassificationJobs)
+ [ListDocumentClassifiers](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassifiers)
+ [StartDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartDocumentClassificationJob)
------