GCS_CONTENT_URI = "gs://my-text-bucket/sentiment-me.txt"
document_gcs = Document(gcs_content_uri=GCS_CONTENT_URI, type="PLAIN_TEXT")
# [END howto_operator_gcp_natural_language_document_gcs]
default_args = {"start_date": days_ago(1)}
with models.DAG(
"example_gcp_natural_language",
default_args=default_args,
schedule_interval=None, # Override to match your needs
) as dag:
# [START howto_operator_gcp_natural_language_analyze_entities]
analyze_entities = \
CloudNaturalLanguageAnalyzeEntitiesOperator(document=document, task_id="analyze_entities")
# [END howto_operator_gcp_natural_language_analyze_entities]
# [START howto_operator_gcp_natural_language_analyze_entities_result]
analyze_entities_result = BashOperator(
bash_command="echo \"{{ task_instance.xcom_pull('analyze_entities') }}\"",
task_id="analyze_entities_result",
)
# [END howto_operator_gcp_natural_language_analyze_entities_result]
# [START howto_operator_gcp_natural_language_analyze_entity_sentiment]
analyze_entity_sentiment = CloudNaturalLanguageAnalyzeEntitySentimentOperator(
document=document, task_id="analyze_entity_sentiment"
)
# [END howto_operator_gcp_natural_language_analyze_entity_sentiment]
def test_minimal_green_path(self, hook_mock):
hook_mock.return_value.analyze_entities.return_value = ANALYZE_ENTITIES_RESPONSE
op = CloudNaturalLanguageAnalyzeEntitiesOperator(task_id="task-id", document=DOCUMENT)
resp = op.execute({})
self.assertEqual(resp, {})