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# to you under the Apache License, Version 2.0 (the
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#
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#
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"""
TaskFlow decorator for cross-system schema drift detection.
The user writes a function that **returns the prompt**. The decorator handles
schema introspection from multiple data sources, LLM-powered comparison, and
structured output of detected mismatches.
"""
from __future__ import annotations
from collections.abc import Callable, Collection, Mapping, Sequence
from typing import TYPE_CHECKING, Any, ClassVar
from airflow.providers.common.ai.operators.llm_schema_compare import LLMSchemaCompareOperator
from airflow.providers.common.compat.sdk import (
DecoratedOperator,
TaskDecorator,
context_merge,
determine_kwargs,
task_decorator_factory,
)
from airflow.sdk.definitions._internal.types import SET_DURING_EXECUTION
if TYPE_CHECKING:
from airflow.sdk import Context
class _LLMSchemaCompareDecoratedOperator(DecoratedOperator, LLMSchemaCompareOperator):
"""
Wraps a callable that returns a prompt for LLM schema comparison.
The user function is called at execution time to produce the prompt string.
All other parameters (``llm_conn_id``, ``db_conn_ids``, ``table_names``,
``datasource_configs``, etc.) are passed through to
:class:`~airflow.providers.common.ai.operators.llm_schema_compare.LLMSchemaCompareOperator`.
:param python_callable: A reference to a callable that returns the prompt string.
:param op_args: Positional arguments for the callable.
:param op_kwargs: Keyword arguments for the callable.
"""
template_fields: Sequence[str] = (
*DecoratedOperator.template_fields,
*LLMSchemaCompareOperator.template_fields,
)
template_fields_renderers: ClassVar[dict[str, str]] = {
**DecoratedOperator.template_fields_renderers,
}
custom_operator_name: str = "@task.llm_schema_compare"
def __init__(
self,
*,
python_callable: Callable,
op_args: Collection[Any] | None = None,
op_kwargs: Mapping[str, Any] | None = None,
**kwargs,
) -> None:
super().__init__(
python_callable=python_callable,
op_args=op_args,
op_kwargs=op_kwargs,
prompt=SET_DURING_EXECUTION,
**kwargs,
)
def execute(self, context: Context) -> Any:
context_merge(context, self.op_kwargs)
kwargs = determine_kwargs(self.python_callable, self.op_args, context)
self.prompt = self.python_callable(*self.op_args, **kwargs)
if not isinstance(self.prompt, str) or not self.prompt.strip():
raise TypeError(
"The returned value from the @task.llm_schema_compare callable must be a non-empty string."
)
self.render_template_fields(context)
return LLMSchemaCompareOperator.execute(self, context)
[docs]
def llm_schema_compare_task(
python_callable: Callable | None = None,
**kwargs,
) -> TaskDecorator:
"""
Wrap a function that returns a prompt into an LLM schema comparison task.
The function body constructs the prompt (can use Airflow context, XCom, etc.).
The decorator handles: schema introspection from multiple data sources,
LLM-powered cross-system type comparison, and structured mismatch output.
Usage::
@task.llm_schema_compare(
llm_conn_id="openai_default",
db_conn_ids=["postgres_source", "snowflake_target"],
table_names=["customers"],
)
def check_migration_readiness():
return "Compare schemas and flag breaking changes"
:param python_callable: Function to decorate.
"""
return task_decorator_factory(
python_callable=python_callable,
decorated_operator_class=_LLMSchemaCompareDecoratedOperator,
**kwargs,
)