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chore: use gapic-generator-python 0.63.2 #262

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Feb 11, 2022
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chore: use gapic-generator-python 0.63.2 #262

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chore: use gapic-generator-python 0.63.2
gcf-owl-bot[bot] Feb 10, 2022
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gcf-owl-bot[bot] Feb 10, 2022
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136 changes: 136 additions & 0 deletions google/cloud/language_v1/services/language_service/async_client.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -213,6 +213,28 @@ async def analyze_sentiment(
) -> language_service.AnalyzeSentimentResponse:
r"""Analyzes the sentiment of the provided text.

.. code-block::

from google.cloud import language_v1

def sample_analyze_sentiment():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.AnalyzeSentimentRequest(
document=document,
)

# Make the request
response = client.analyze_sentiment(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.AnalyzeSentimentRequest, dict]):
The request object. The sentiment analysis request
Expand Down Expand Up @@ -299,6 +321,29 @@ async def analyze_entities(
salience, mentions for each entity, and other
properties.


.. code-block::

from google.cloud import language_v1

def sample_analyze_entities():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.AnalyzeEntitiesRequest(
document=document,
)

# Make the request
response = client.analyze_entities(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.AnalyzeEntitiesRequest, dict]):
The request object. The entity analysis request message.
Expand Down Expand Up @@ -382,6 +427,29 @@ async def analyze_entity_sentiment(
in the text and analyzes sentiment associated with each entity
and its mentions.


.. code-block::

from google.cloud import language_v1

def sample_analyze_entity_sentiment():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.AnalyzeEntitySentimentRequest(
document=document,
)

# Make the request
response = client.analyze_entity_sentiment(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.AnalyzeEntitySentimentRequest, dict]):
The request object. The entity-level sentiment analysis
Expand Down Expand Up @@ -467,6 +535,29 @@ async def analyze_syntax(
boundaries and tokenization along with part of speech
tags, dependency trees, and other properties.


.. code-block::

from google.cloud import language_v1

def sample_analyze_syntax():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.AnalyzeSyntaxRequest(
document=document,
)

# Make the request
response = client.analyze_syntax(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.AnalyzeSyntaxRequest, dict]):
The request object. The syntax analysis request message.
Expand Down Expand Up @@ -546,6 +637,28 @@ async def classify_text(
) -> language_service.ClassifyTextResponse:
r"""Classifies a document into categories.

.. code-block::

from google.cloud import language_v1

def sample_classify_text():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.ClassifyTextRequest(
document=document,
)

# Make the request
response = client.classify_text(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.ClassifyTextRequest, dict]):
The request object. The document classification request
Expand Down Expand Up @@ -623,6 +736,29 @@ async def annotate_text(
that analyzeSentiment, analyzeEntities, and
analyzeSyntax provide in one call.


.. code-block::

from google.cloud import language_v1

def sample_annotate_text():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.AnnotateTextRequest(
document=document,
)

# Make the request
response = client.annotate_text(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.AnnotateTextRequest, dict]):
The request object. The request message for the text
Expand Down
142 changes: 142 additions & 0 deletions google/cloud/language_v1/services/language_service/client.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -396,6 +396,29 @@ def analyze_sentiment(
) -> language_service.AnalyzeSentimentResponse:
r"""Analyzes the sentiment of the provided text.


.. code-block::

from google.cloud import language_v1

def sample_analyze_sentiment():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.AnalyzeSentimentRequest(
document=document,
)

# Make the request
response = client.analyze_sentiment(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.AnalyzeSentimentRequest, dict]):
The request object. The sentiment analysis request
Expand Down Expand Up @@ -472,6 +495,30 @@ def analyze_entities(
salience, mentions for each entity, and other
properties.



.. code-block::

from google.cloud import language_v1

def sample_analyze_entities():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.AnalyzeEntitiesRequest(
document=document,
)

# Make the request
response = client.analyze_entities(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.AnalyzeEntitiesRequest, dict]):
The request object. The entity analysis request message.
Expand Down Expand Up @@ -545,6 +592,30 @@ def analyze_entity_sentiment(
in the text and analyzes sentiment associated with each entity
and its mentions.



.. code-block::

from google.cloud import language_v1

def sample_analyze_entity_sentiment():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.AnalyzeEntitySentimentRequest(
document=document,
)

# Make the request
response = client.analyze_entity_sentiment(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.AnalyzeEntitySentimentRequest, dict]):
The request object. The entity-level sentiment analysis
Expand Down Expand Up @@ -620,6 +691,30 @@ def analyze_syntax(
boundaries and tokenization along with part of speech
tags, dependency trees, and other properties.



.. code-block::

from google.cloud import language_v1

def sample_analyze_syntax():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.AnalyzeSyntaxRequest(
document=document,
)

# Make the request
response = client.analyze_syntax(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.AnalyzeSyntaxRequest, dict]):
The request object. The syntax analysis request message.
Expand Down Expand Up @@ -689,6 +784,29 @@ def classify_text(
) -> language_service.ClassifyTextResponse:
r"""Classifies a document into categories.


.. code-block::

from google.cloud import language_v1

def sample_classify_text():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.ClassifyTextRequest(
document=document,
)

# Make the request
response = client.classify_text(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.ClassifyTextRequest, dict]):
The request object. The document classification request
Expand Down Expand Up @@ -756,6 +874,30 @@ def annotate_text(
that analyzeSentiment, analyzeEntities, and
analyzeSyntax provide in one call.



.. code-block::

from google.cloud import language_v1

def sample_annotate_text():
# Create a client
client = language_v1.LanguageServiceClient()

# Initialize request argument(s)
document = language_v1.Document()
document.content = "content_value"

request = language_v1.AnnotateTextRequest(
document=document,
)

# Make the request
response = client.annotate_text(request=request)

# Handle the response
print(response)

Args:
request (Union[google.cloud.language_v1.types.AnnotateTextRequest, dict]):
The request object. The request message for the text
Expand Down
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