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# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
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"""
TaskFlow decorator for general-purpose LLM calls.
The user writes a function that **returns the prompt string**. The decorator
handles hook creation, agent configuration, and the LLM call. When
``output_type`` is a Pydantic ``BaseModel`` subclass, the model instance is
returned to XCom unchanged so downstream tasks can type-hint it directly.
The class must be defined at module scope.
"""
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 import LLMOperator
from airflow.providers.common.ai.utils.validation import (
reject_sequence_with_unsupported_feature,
validate_prompt,
)
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 _LLMDecoratedOperator(DecoratedOperator, LLMOperator):
"""
Wraps a callable that returns a prompt for a general-purpose LLM call.
The user function is called at execution time to produce the prompt string.
All other parameters (``llm_conn_id``, ``model_id``, ``system_prompt``, etc.)
are passed through to :class:`~airflow.providers.common.ai.operators.llm.LLMOperator`.
: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,
*LLMOperator.template_fields,
)
template_fields_renderers: ClassVar[dict[str, str]] = {
**DecoratedOperator.template_fields_renderers,
}
custom_operator_name: str = "@task.llm"
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)
validate_prompt(self.prompt, decorator_name="@task.llm")
reject_sequence_with_unsupported_feature(
self.prompt,
decorator_name="@task.llm",
feature_name="require_approval",
feature_enabled=self.require_approval,
)
self.render_template_fields(context)
return LLMOperator.execute(self, context)
[docs]
def llm_task(
python_callable: Callable | None = None,
**kwargs,
) -> TaskDecorator:
"""
Wrap a function that returns a prompt into a general-purpose LLM task.
The function body constructs the prompt (can use Airflow context, XCom, etc.).
The decorator handles hook creation, agent configuration, LLM call, and output
serialization.
Usage::
@task.llm(
llm_conn_id="openai_default",
system_prompt="Summarize concisely.",
)
def summarize(text: str):
return f"Summarize this article: {text}"
With structured output::
@task.llm(
llm_conn_id="openai_default",
system_prompt="Extract named entities.",
output_type=Entities,
)
def extract(text: str):
return f"Extract entities from: {text}"
:param python_callable: Function to decorate.
"""
return task_decorator_factory(
python_callable=python_callable,
decorated_operator_class=_LLMDecoratedOperator,
**kwargs,
)