Source code for airflow.providers.common.ai.example_dags.example_llm_classification

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"""Example DAG: classify pipeline incidents by severity using @task.llm with Literal output."""

from __future__ import annotations

from typing import Literal

from airflow.providers.common.compat.sdk import dag, task


# [START howto_decorator_llm_classification]
@dag

[docs]
def example_llm_classification():
    @task.llm(
        llm_conn_id="pydanticai_default",
        system_prompt=(
            "Classify the severity of the given pipeline incident. "
            "Use 'critical' for data loss or complete pipeline failure, "
            "'high' for significant delays or partial failures, "
            "'medium' for degraded performance, "
            "'low' for cosmetic issues or minor warnings."
        ),
        output_type=Literal["critical", "high", "medium", "low"],
    )
    def classify_incident(description: str):
        # Pre-process the description before sending to the LLM
        return f"Classify this incident:\n{description.strip()}"

    classify_incident(
        "Scheduler heartbeat lost for 15 minutes. "
        "Multiple DAG runs stuck in queued state. "
        "No new tasks being scheduled across all DAGs."
    )



# [END howto_decorator_llm_classification]

example_llm_classification()