Source code for airflow.contrib.operators.gcp_natural_language_operator

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from google.protobuf.json_format import MessageToDict

from airflow.contrib.hooks.gcp_natural_language_hook import CloudNaturalLanguageHook
from airflow.models import BaseOperator


[docs]class CloudLanguageAnalyzeEntitiesOperator(BaseOperator): """ Finds named entities in the text along with entity types, salience, mentions for each entity, and other properties. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:CloudLanguageAnalyzeEntitiesOperator` :param document: Input document. If a dict is provided, it must be of the same form as the protobuf message Document :type document: dict or google.cloud.language_v1.types.Document :param encoding_type: The encoding type used by the API to calculate offsets. :type encoding_type: google.cloud.language_v1.types.EncodingType :param retry: A retry object used to retry requests. If None is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt. :type timeout: float :param metadata: Additional metadata that is provided to the method. :type metadata: seq[tuple[str, str]]] :param gcp_conn_id: The connection ID to use connecting to Google Cloud Platform. :type gcp_conn_id: str """ # [START natural_language_analyze_entities_template_fields]
[docs] template_fields = ("document", "gcp_conn_id")
# [END natural_language_analyze_entities_template_fields] def __init__( self, document, encoding_type=None, retry=None, timeout=None, metadata=None, gcp_conn_id="google_cloud_default", *args, **kwargs ): super(CloudLanguageAnalyzeEntitiesOperator, self).__init__(*args, **kwargs) self.document = document self.encoding_type = encoding_type self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id
[docs] def execute(self, context): hook = CloudNaturalLanguageHook(gcp_conn_id=self.gcp_conn_id) self.log.info("Start analyzing entities") response = hook.analyze_entities( document=self.document, retry=self.retry, timeout=self.timeout, metadata=self.metadata ) self.log.info("Finished analyzing entities") return MessageToDict(response)
[docs]class CloudLanguageAnalyzeEntitySentimentOperator(BaseOperator): """ Finds entities, similar to AnalyzeEntities in the text and analyzes sentiment associated with each entity and its mentions. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:CloudLanguageAnalyzeEntitySentimentOperator` :param document: Input document. If a dict is provided, it must be of the same form as the protobuf message Document :type document: dict or google.cloud.language_v1.types.Document :param encoding_type: The encoding type used by the API to calculate offsets. :type encoding_type: google.cloud.language_v1.types.EncodingType :param retry: A retry object used to retry requests. If None is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt. :type timeout: float :param metadata: Additional metadata that is provided to the method. :type metadata: seq[tuple[str, str]]] :rtype: google.cloud.language_v1.types.AnalyzeEntitiesResponse :param gcp_conn_id: The connection ID to use connecting to Google Cloud Platform. :type gcp_conn_id: str """ # [START natural_language_analyze_entity_sentiment_template_fields]
[docs] template_fields = ("document", "gcp_conn_id")
# [END natural_language_analyze_entity_sentiment_template_fields] def __init__( self, document, encoding_type=None, retry=None, timeout=None, metadata=None, gcp_conn_id="google_cloud_default", *args, **kwargs ): super(CloudLanguageAnalyzeEntitySentimentOperator, self).__init__(*args, **kwargs) self.document = document self.encoding_type = encoding_type self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id
[docs] def execute(self, context): hook = CloudNaturalLanguageHook(gcp_conn_id=self.gcp_conn_id) self.log.info("Start entity sentiment analyze") response = hook.analyze_entity_sentiment( document=self.document, encoding_type=self.encoding_type, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) self.log.info("Finished entity sentiment analyze") return MessageToDict(response)
[docs]class CloudLanguageAnalyzeSentimentOperator(BaseOperator): """ Analyzes the sentiment of the provided text. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:CloudLanguageAnalyzeSentimentOperator` :param document: Input document. If a dict is provided, it must be of the same form as the protobuf message Document :type document: dict or google.cloud.language_v1.types.Document :param encoding_type: The encoding type used by the API to calculate offsets. :type encoding_type: google.cloud.language_v1.types.EncodingType :param retry: A retry object used to retry requests. If None is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt. :type timeout: float :param metadata: Additional metadata that is provided to the method. :type metadata: sequence[tuple[str, str]]] :rtype: google.cloud.language_v1.types.AnalyzeEntitiesResponse :param gcp_conn_id: The connection ID to use connecting to Google Cloud Platform. :type gcp_conn_id: str """ # [START natural_language_analyze_sentiment_template_fields]
[docs] template_fields = ("document", "gcp_conn_id")
# [END natural_language_analyze_sentiment_template_fields] def __init__( self, document, encoding_type=None, retry=None, timeout=None, metadata=None, gcp_conn_id="google_cloud_default", *args, **kwargs ): super(CloudLanguageAnalyzeSentimentOperator, self).__init__(*args, **kwargs) self.document = document self.encoding_type = encoding_type self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id
[docs] def execute(self, context): hook = CloudNaturalLanguageHook(gcp_conn_id=self.gcp_conn_id) self.log.info("Start sentiment analyze") response = hook.analyze_sentiment( document=self.document, retry=self.retry, timeout=self.timeout, metadata=self.metadata ) self.log.info("Finished sentiment analyze") return MessageToDict(response)
[docs]class CloudLanguageClassifyTextOperator(BaseOperator): """ Classifies a document into categories. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:CloudLanguageClassifyTextOperator` :param document: Input document. If a dict is provided, it must be of the same form as the protobuf message Document :type document: dict or google.cloud.language_v1.types.Document :param retry: A retry object used to retry requests. If None is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt. :type timeout: float :param metadata: Additional metadata that is provided to the method. :type metadata: sequence[tuple[str, str]]] :param gcp_conn_id: The connection ID to use connecting to Google Cloud Platform. :type gcp_conn_id: str """ # [START natural_language_classify_text_template_fields]
[docs] template_fields = ("document", "gcp_conn_id")
# [END natural_language_classify_text_template_fields] def __init__( self, document, retry=None, timeout=None, metadata=None, gcp_conn_id="google_cloud_default", *args, **kwargs ): super(CloudLanguageClassifyTextOperator, self).__init__(*args, **kwargs) self.document = document self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id
[docs] def execute(self, context): hook = CloudNaturalLanguageHook(gcp_conn_id=self.gcp_conn_id) self.log.info("Start text classify") response = hook.classify_text( document=self.document, retry=self.retry, timeout=self.timeout, metadata=self.metadata ) self.log.info("Finished text classify") return MessageToDict(response)

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