Source code for airflow.contrib.example_dags.example_gcp_natural_language

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"""
Example Airflow DAG for Google Cloud Natural Language service
"""


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

import airflow
from airflow import models
from airflow.contrib.operators.gcp_natural_language_operator import (
    CloudLanguageAnalyzeEntitiesOperator,
    CloudLanguageAnalyzeEntitySentimentOperator,
    CloudLanguageAnalyzeSentimentOperator,
    CloudLanguageClassifyTextOperator,
)
from airflow.operators.bash_operator import BashOperator

# [START howto_operator_gcp_natural_language_document_text]
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.
"""
document = Document(content=TEXT, type="PLAIN_TEXT")
# [END howto_operator_gcp_natural_language_document_text]

# [START howto_operator_gcp_natural_language_document_gcs]
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": airflow.utils.dates.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 = CloudLanguageAnalyzeEntitiesOperator(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 = CloudLanguageAnalyzeEntitySentimentOperator(
        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="echo \"{{ task_instance.xcom_pull('analyze_entity_sentiment') }}\"",
        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 = CloudLanguageAnalyzeSentimentOperator(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="echo \"{{ task_instance.xcom_pull('analyze_sentiment') }}\"",
        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 = CloudLanguageClassifyTextOperator(
        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="echo \"{{ task_instance.xcom_pull('analyze_classify_text') }}\"",
        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