Source code for tests.system.providers.google.cloud.natural_language.example_natural_language

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"""
Example Airflow DAG for Google Cloud Natural Language service
"""
from __future__ import annotations

import os
from datetime import datetime

from google.cloud.language_v1.proto.language_service_pb2 import Document

from airflow import models
from airflow.operators.bash import BashOperator
from airflow.providers.google.cloud.operators.natural_language import (
    CloudNaturalLanguageAnalyzeEntitiesOperator,
    CloudNaturalLanguageAnalyzeEntitySentimentOperator,
    CloudNaturalLanguageAnalyzeSentimentOperator,
    CloudNaturalLanguageClassifyTextOperator,
)

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = "example_gcp_natural_language"
# [START howto_operator_gcp_natural_language_document_text]
[docs]TEXT = """Airflow is a platform to programmatically author, schedule and monitor workflows. Use Airflow to author workflows as Directed Acyclic Graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. """
[docs]document = Document(content=TEXT, type="PLAIN_TEXT")
# [END howto_operator_gcp_natural_language_document_text] # [START howto_operator_gcp_natural_language_document_gcs]
[docs]GCS_CONTENT_URI = "gs://INVALID BUCKET NAME/sentiment-me.txt"
[docs]document_gcs = Document(gcs_content_uri=GCS_CONTENT_URI, type="PLAIN_TEXT")
# [END howto_operator_gcp_natural_language_document_gcs] with models.DAG( DAG_ID, schedule="@once", # Override to match your needs start_date=datetime(2021, 1, 1), catchup=False, tags=["example"], ) as dag: # [START howto_operator_gcp_natural_language_analyze_entities]
[docs] 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=f"echo {analyze_entities.output}", 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] # [START howto_operator_gcp_natural_language_analyze_entity_sentiment_result] analyze_entity_sentiment_result = BashOperator( bash_command=f"echo {analyze_entity_sentiment.output}", task_id="analyze_entity_sentiment_result", ) # [END howto_operator_gcp_natural_language_analyze_entity_sentiment_result] # [START howto_operator_gcp_natural_language_analyze_sentiment] analyze_sentiment = CloudNaturalLanguageAnalyzeSentimentOperator( document=document, task_id="analyze_sentiment" ) # [END howto_operator_gcp_natural_language_analyze_sentiment] # [START howto_operator_gcp_natural_language_analyze_sentiment_result] analyze_sentiment_result = BashOperator( bash_command=f"echo {analyze_sentiment.output}", task_id="analyze_sentiment_result", ) # [END howto_operator_gcp_natural_language_analyze_sentiment_result] # [START howto_operator_gcp_natural_language_analyze_classify_text] analyze_classify_text = CloudNaturalLanguageClassifyTextOperator( document=document, task_id="analyze_classify_text" ) # [END howto_operator_gcp_natural_language_analyze_classify_text] # [START howto_operator_gcp_natural_language_analyze_classify_text_result] analyze_classify_text_result = BashOperator( bash_command=f"echo {analyze_classify_text.output}", task_id="analyze_classify_text_result", ) # [END howto_operator_gcp_natural_language_analyze_classify_text_result] analyze_entities >> analyze_entities_result analyze_entity_sentiment >> analyze_entity_sentiment_result analyze_sentiment >> analyze_sentiment_result analyze_classify_text >> analyze_classify_text_result from tests.system.utils import get_test_run # noqa: E402 # Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)

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