Source code for airflow.providers.amazon.aws.operators.comprehend

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from __future__ import annotations

from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence

from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.comprehend import ComprehendHook
from airflow.providers.amazon.aws.operators.base_aws import AwsBaseOperator
from airflow.providers.amazon.aws.triggers.comprehend import (
    ComprehendCreateDocumentClassifierCompletedTrigger,
    ComprehendPiiEntitiesDetectionJobCompletedTrigger,
)
from airflow.providers.amazon.aws.utils import validate_execute_complete_event
from airflow.providers.amazon.aws.utils.mixins import aws_template_fields
from airflow.utils.timezone import utcnow

if TYPE_CHECKING:
    import boto3

    from airflow.utils.context import Context


[docs]class ComprehendBaseOperator(AwsBaseOperator[ComprehendHook]): """ This is the base operator for Comprehend Service operators (not supposed to be used directly in DAGs). :param input_data_config: The input properties for a PII entities detection job. (templated) :param output_data_config: Provides `configuration` parameters for the output of PII entity detection jobs. (templated) :param data_access_role_arn: The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data. (templated) :param language_code: The language of the input documents. (templated) """
[docs] aws_hook_class = ComprehendHook
[docs] template_fields: Sequence[str] = aws_template_fields( "input_data_config", "output_data_config", "data_access_role_arn", "language_code" )
[docs] template_fields_renderers: dict = {"input_data_config": "json", "output_data_config": "json"}
def __init__( self, input_data_config: dict, output_data_config: dict, data_access_role_arn: str, language_code: str, **kwargs, ): super().__init__(**kwargs) self.input_data_config = input_data_config self.output_data_config = output_data_config self.data_access_role_arn = data_access_role_arn self.language_code = language_code @cached_property
[docs] def client(self) -> boto3.client: """Create and return the Comprehend client.""" return self.hook.conn
[docs] def execute(self, context: Context): """Must overwrite in child classes.""" raise NotImplementedError("Please implement execute() in subclass")
[docs]class ComprehendStartPiiEntitiesDetectionJobOperator(ComprehendBaseOperator): """ Create a comprehend pii entities detection job for a collection of documents. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:ComprehendStartPiiEntitiesDetectionJobOperator` :param input_data_config: The input properties for a PII entities detection job. (templated) :param output_data_config: Provides `configuration` parameters for the output of PII entity detection jobs. (templated) :param mode: Specifies whether the output provides the locations (offsets) of PII entities or a file in which PII entities are redacted. If you set the mode parameter to ONLY_REDACTION. In that case you must provide a RedactionConfig in start_pii_entities_kwargs. :param data_access_role_arn: The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data. (templated) :param language_code: The language of the input documents. (templated) :param start_pii_entities_kwargs: Any optional parameters to pass to the job. If JobName is not provided in start_pii_entities_kwargs, operator will create. :param wait_for_completion: Whether to wait for job to stop. (default: True) :param waiter_delay: Time in seconds to wait between status checks. (default: 60) :param waiter_max_attempts: Maximum number of attempts to check for job completion. (default: 20) :param deferrable: If True, the operator will wait asynchronously for the job to stop. This implies waiting for completion. This mode requires aiobotocore module to be installed. (default: False) :param aws_conn_id: The Airflow connection used for AWS credentials. If this is ``None`` or empty then the default boto3 behaviour is used. If running Airflow in a distributed manner and aws_conn_id is None or empty, then default boto3 configuration would be used (and must be maintained on each worker node). :param region_name: AWS region_name. If not specified then the default boto3 behaviour is used. :param verify: Whether to verify SSL certificates. See: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html :param botocore_config: Configuration dictionary (key-values) for botocore client. See: https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html """ def __init__( self, input_data_config: dict, output_data_config: dict, mode: str, data_access_role_arn: str, language_code: str, start_pii_entities_kwargs: dict[str, Any] | None = None, wait_for_completion: bool = True, waiter_delay: int = 60, waiter_max_attempts: int = 20, deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False), **kwargs, ): super().__init__( input_data_config=input_data_config, output_data_config=output_data_config, data_access_role_arn=data_access_role_arn, language_code=language_code, **kwargs, ) self.mode = mode self.start_pii_entities_kwargs = start_pii_entities_kwargs or {} self.wait_for_completion = wait_for_completion self.waiter_delay = waiter_delay self.waiter_max_attempts = waiter_max_attempts self.deferrable = deferrable
[docs] def execute(self, context: Context) -> str: if self.start_pii_entities_kwargs.get("JobName", None) is None: self.start_pii_entities_kwargs["JobName"] = ( f"start_pii_entities_detection_job-{int(utcnow().timestamp())}" ) self.log.info( "Submitting start pii entities detection job '%s'.", self.start_pii_entities_kwargs["JobName"] ) job_id = self.client.start_pii_entities_detection_job( InputDataConfig=self.input_data_config, OutputDataConfig=self.output_data_config, Mode=self.mode, DataAccessRoleArn=self.data_access_role_arn, LanguageCode=self.language_code, **self.start_pii_entities_kwargs, )["JobId"] message_description = f"start pii entities detection job {job_id} to complete." if self.deferrable: self.log.info("Deferring %s", message_description) self.defer( trigger=ComprehendPiiEntitiesDetectionJobCompletedTrigger( job_id=job_id, waiter_delay=self.waiter_delay, waiter_max_attempts=self.waiter_max_attempts, aws_conn_id=self.aws_conn_id, ), method_name="execute_complete", ) elif self.wait_for_completion: self.log.info("Waiting for %s", message_description) self.hook.get_waiter("pii_entities_detection_job_complete").wait( JobId=job_id, WaiterConfig={"Delay": self.waiter_delay, "MaxAttempts": self.waiter_max_attempts}, ) return job_id
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> str: event = validate_execute_complete_event(event) if event["status"] != "success": raise AirflowException("Error while running job: %s", event) self.log.info("Comprehend pii entities detection job `%s` complete.", event["job_id"]) return event["job_id"]
[docs]class ComprehendCreateDocumentClassifierOperator(AwsBaseOperator[ComprehendHook]): """ Create a comprehend document classifier that can categorize documents. Provide a set of training documents that are labeled with the categories. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:ComprehendCreateDocumentClassifierOperator` :param document_classifier_name: The name of the document classifier. (templated) :param input_data_config: Specifies the format and location of the input data for the job. (templated) :param mode: Indicates the mode in which the classifier will be trained. (templated) :param data_access_role_arn: The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data. (templated) :param language_code: The language of the input documents. You can specify any of the languages supported by Amazon Comprehend. All documents must be in the same language. (templated) :param fail_on_warnings: If set to True, the document classifier training job will throw an error when the status is TRAINED_WITH_WARNING. (default False) :param output_data_config: Specifies the location for the output files from a custom classifier job. This parameter is required for a request that creates a native document model. (templated) :param document_classifier_kwargs: Any optional parameters to pass to the document classifier. (templated) :param wait_for_completion: Whether to wait for job to stop. (default: True) :param waiter_delay: Time in seconds to wait between status checks. (default: 60) :param waiter_max_attempts: Maximum number of attempts to check for job completion. (default: 20) :param deferrable: If True, the operator will wait asynchronously for the job to stop. This implies waiting for completion. This mode requires aiobotocore module to be installed. (default: False) :param aws_conn_id: The Airflow connection used for AWS credentials. If this is ``None`` or empty then the default boto3 behaviour is used. If running Airflow in a distributed manner and aws_conn_id is None or empty, then default boto3 configuration would be used (and must be maintained on each worker node). :param region_name: AWS region_name. If not specified then the default boto3 behaviour is used. :param verify: Whether to verify SSL certificates. See: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html :param botocore_config: Configuration dictionary (key-values) for botocore client. See: https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html """
[docs] aws_hook_class = ComprehendHook
[docs] template_fields: Sequence[str] = aws_template_fields( "document_classifier_name", "input_data_config", "mode", "data_access_role_arn", "language_code", "output_data_config", "document_classifier_kwargs", )
[docs] template_fields_renderers: dict = { "input_data_config": "json", "output_data_config": "json", "document_classifier_kwargs": "json", }
def __init__( self, document_classifier_name: str, input_data_config: dict[str, Any], mode: str, data_access_role_arn: str, language_code: str, fail_on_warnings: bool = False, output_data_config: dict[str, Any] | None = None, document_classifier_kwargs: dict[str, Any] | None = None, wait_for_completion: bool = True, waiter_delay: int = 60, waiter_max_attempts: int = 20, deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False), aws_conn_id: str | None = "aws_default", **kwargs, ): super().__init__(**kwargs) self.document_classifier_name = document_classifier_name self.input_data_config = input_data_config self.mode = mode self.data_access_role_arn = data_access_role_arn self.language_code = language_code self.fail_on_warnings = fail_on_warnings self.output_data_config = output_data_config self.document_classifier_kwargs = document_classifier_kwargs or {} self.wait_for_completion = wait_for_completion self.waiter_delay = waiter_delay self.waiter_max_attempts = waiter_max_attempts self.deferrable = deferrable self.aws_conn_id = aws_conn_id
[docs] def execute(self, context: Context) -> str: if self.output_data_config: self.document_classifier_kwargs["OutputDataConfig"] = self.output_data_config document_classifier_arn = self.hook.conn.create_document_classifier( DocumentClassifierName=self.document_classifier_name, InputDataConfig=self.input_data_config, Mode=self.mode, DataAccessRoleArn=self.data_access_role_arn, LanguageCode=self.language_code, **self.document_classifier_kwargs, )["DocumentClassifierArn"] message_description = f"document classifier {document_classifier_arn} to complete." if self.deferrable: self.log.info("Deferring %s", message_description) self.defer( trigger=ComprehendCreateDocumentClassifierCompletedTrigger( document_classifier_arn=document_classifier_arn, waiter_delay=self.waiter_delay, waiter_max_attempts=self.waiter_max_attempts, aws_conn_id=self.aws_conn_id, ), method_name="execute_complete", ) elif self.wait_for_completion: self.log.info("Waiting for %s", message_description) self.hook.get_waiter("create_document_classifier_complete").wait( DocumentClassifierArn=document_classifier_arn, WaiterConfig={"Delay": self.waiter_delay, "MaxAttempts": self.waiter_max_attempts}, ) self.hook.validate_document_classifier_training_status( document_classifier_arn=document_classifier_arn, fail_on_warnings=self.fail_on_warnings ) return document_classifier_arn
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> str: event = validate_execute_complete_event(event) if event["status"] != "success": raise AirflowException("Error while running comprehend create document classifier: %s", event) self.hook.validate_document_classifier_training_status( document_classifier_arn=event["document_classifier_arn"], fail_on_warnings=self.fail_on_warnings ) self.log.info("Comprehend document classifier `%s` complete.", event["document_classifier_arn"]) return event["document_classifier_arn"]

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