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

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"""Example DAGs demonstrating PydanticAIHook and direct pydantic-ai Agent usage."""

from __future__ import annotations

from pydantic import BaseModel

from airflow.providers.common.ai.hooks.pydantic_ai import PydanticAIHook
from airflow.providers.common.compat.sdk import dag, task


# [START howto_hook_pydantic_ai_basic]
@dag(schedule=None, tags=["example"])

[docs]
def example_pydantic_ai_hook():
    @task
    def generate_summary(text: str) -> str:
        hook = PydanticAIHook(llm_conn_id="pydanticai_default")
        agent = hook.create_agent(output_type=str, instructions="Summarize concisely.")
        result = agent.run_sync(text)
        return result.output

    generate_summary("Apache Airflow is a platform for programmatically authoring...")



# [END howto_hook_pydantic_ai_basic]

example_pydantic_ai_hook()


# [START howto_hook_pydantic_ai_structured_output]
@dag(schedule=None, tags=["example"])

[docs]
def example_pydantic_ai_structured_output():
    @task
    def generate_sql(prompt: str) -> dict:
        class SQLResult(BaseModel):
            query: str
            explanation: str

        hook = PydanticAIHook(llm_conn_id="pydanticai_default")
        agent = hook.create_agent(
            output_type=SQLResult,
            instructions="Generate a SQL query and explain it.",
        )
        result = agent.run_sync(prompt)
        return result.output.model_dump()

    generate_sql("Find the top 10 customers by revenue")



# [END howto_hook_pydantic_ai_structured_output]

example_pydantic_ai_structured_output()


# [START howto_task_with_toolsets]
@dag(schedule=None, tags=["example"])

[docs]
def example_task_with_toolsets():
    """Use toolsets directly in a @task function without AgentOperator."""

    @task
    def analyze_revenue() -> str:
        from airflow.providers.common.ai.toolsets.sql import SQLToolset

        hook = PydanticAIHook(llm_conn_id="pydanticai_default")
        agent = hook.create_agent(
            output_type=str,
            instructions=(
                "You are a sales analytics assistant. "
                "Use the SQL tools to explore the database schema and answer questions."
            ),
            toolsets=[
                SQLToolset(
                    db_conn_id="my_database",
                    allowed_tables=["customers", "orders"],
                    max_rows=20,
                ),
            ],
        )
        result = agent.run_sync("Which customers have spent the most? Show the top 5.")
        return result.output

    analyze_revenue()



# [END howto_task_with_toolsets]

example_task_with_toolsets()