Source code for tests.system.providers.google.cloud.vertex_ai.example_vertex_ai_generative_model

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"""
Example Airflow DAG for Google Vertex AI Generative Model prompting.
"""

from __future__ import annotations

import os
from datetime import datetime

from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.vertex_ai.generative_model import (
    GenerateTextEmbeddingsOperator,
    PromptLanguageModelOperator,
    PromptMultimodalModelOperator,
    PromptMultimodalModelWithMediaOperator,
)


[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")


[docs]DAG_ID = "example_vertex_ai_generative_model_dag"


[docs]REGION = "us-central1"


[docs]PROMPT = "In 10 words or less, why is Apache Airflow amazing?"


[docs]LANGUAGE_MODEL = "text-bison"


[docs]TEXT_EMBEDDING_MODEL = "textembedding-gecko"


[docs]MULTIMODAL_MODEL = "gemini-pro"


[docs]MULTIMODAL_VISION_MODEL = "gemini-pro-vision"


[docs]VISION_PROMPT = "In 10 words or less, describe this content."


[docs]MEDIA_GCS_PATH = "gs://download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg"


[docs]MIME_TYPE = "image/jpeg"


with DAG(
    dag_id=DAG_ID,
    description="Sample DAG with generative models.",
    schedule="@once",
    start_date=datetime(2024, 1, 1),
    catchup=False,
    tags=["example", "vertex_ai", "generative_model"],
) as dag:
    # [START how_to_cloud_vertex_ai_prompt_language_model_operator]

[docs]    prompt_language_model_task = PromptLanguageModelOperator(
        task_id="prompt_language_model_task",
        project_id=PROJECT_ID,
        location=REGION,
        prompt=PROMPT,
        pretrained_model=LANGUAGE_MODEL,
    )

    # [END how_to_cloud_vertex_ai_prompt_language_model_operator]

    # [START how_to_cloud_vertex_ai_generate_text_embeddings_operator]
    generate_text_embeddings_task = GenerateTextEmbeddingsOperator(
        task_id="generate_text_embeddings_task",
        project_id=PROJECT_ID,
        location=REGION,
        prompt=PROMPT,
        pretrained_model=TEXT_EMBEDDING_MODEL,
    )
    # [END how_to_cloud_vertex_ai_generate_text_embeddings_operator]

    # [START how_to_cloud_vertex_ai_prompt_multimodal_model_operator]
    prompt_multimodal_model_task = PromptMultimodalModelOperator(
        task_id="prompt_multimodal_model_task",
        project_id=PROJECT_ID,
        location=REGION,
        prompt=PROMPT,
        pretrained_model=MULTIMODAL_MODEL,
    )
    # [END how_to_cloud_vertex_ai_prompt_multimodal_model_operator]

    # [START how_to_cloud_vertex_ai_prompt_multimodal_model_with_media_operator]
    prompt_multimodal_model_with_media_task = PromptMultimodalModelWithMediaOperator(
        task_id="prompt_multimodal_model_with_media_task",
        project_id=PROJECT_ID,
        location=REGION,
        prompt=VISION_PROMPT,
        pretrained_model=MULTIMODAL_VISION_MODEL,
        media_gcs_path=MEDIA_GCS_PATH,
        mime_type=MIME_TYPE,
    )
    # [END how_to_cloud_vertex_ai_prompt_multimodal_model_with_media_operator]

    from tests.system.utils.watcher import watcher

    # This test needs watcher in order to properly mark success/failure
    # when "tearDown" task with trigger rule is part of the DAG
    list(dag.tasks) >> watcher()


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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