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from __future__ import annotations
from collections.abc import Sequence
from functools import cached_property
from typing import TYPE_CHECKING, Any, ClassVar
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: ClassVar[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: ClassVar[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"]