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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)