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# to you under the Apache License, Version 2.0 (the
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# 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
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from __future__ import annotations
import time
from collections.abc import Sequence
from datetime import timedelta
from functools import cached_property
from typing import TYPE_CHECKING, Any
from airflow.providers.anthropic.exceptions import AnthropicBatchJobError, AnthropicBatchTimeout
from airflow.providers.anthropic.hooks.anthropic import (
AnthropicHook,
BatchStatus,
evaluate_batch_counts,
validate_execute_complete_event,
)
from airflow.providers.anthropic.triggers.batch import AnthropicBatchTrigger
from airflow.providers.common.compat.sdk import BaseSensorOperator, conf
if TYPE_CHECKING:
from airflow.providers.common.compat.sdk import Context
[docs]
class AnthropicBatchSensor(BaseSensorOperator):
"""
Wait for an already-submitted Anthropic Message Batch to reach a terminal status.
Pairs with ``AnthropicBatchOperator(wait_for_completion=False)`` (or any out-of-band
submission) for a fire-and-forget submit + re-entrant await. Because the sensor only
polls an existing ``batch_id``, it is naturally idempotent across retries — unlike a
submit step, retrying it never creates a new batch.
On a terminal batch it applies the same policy as the operator: a fully-cancelled
batch skips the task, and ``fail_on_partial_error`` controls whether errored/expired
requests fail it.
.. seealso::
For more information, take a look at the guide:
:ref:`howto/sensor:AnthropicBatchSensor`
:param batch_id: The ID of the batch to wait for.
:param conn_id: The Anthropic connection ID to use.
:param deferrable: Run the sensor in deferrable mode (polls via a trigger).
:param fail_on_partial_error: If ``True``, fail when any request errored or expired.
Defaults to ``False`` (succeed and log a warning).
"""
[docs]
template_fields: Sequence[str] = ("batch_id",)
def __init__(
self,
*,
batch_id: str,
conn_id: str = AnthropicHook.default_conn_name,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
fail_on_partial_error: bool = False,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
[docs]
self.batch_id = batch_id
[docs]
self.deferrable = deferrable
[docs]
self.fail_on_partial_error = fail_on_partial_error
@cached_property
[docs]
def hook(self) -> AnthropicHook:
"""Return an instance of the AnthropicHook."""
return AnthropicHook(conn_id=self.conn_id)
[docs]
def poke(self, context: Context) -> bool:
batch = self.hook.get_batch(self.batch_id)
if BatchStatus.is_in_progress(batch.processing_status):
return False
counts = batch.request_counts
evaluate_batch_counts(
batch_id=self.batch_id,
canceled=counts.canceled,
errored=counts.errored,
expired=counts.expired,
succeeded=counts.succeeded,
fail_on_partial_error=self.fail_on_partial_error,
)
return True
[docs]
def execute(self, context: Context) -> None:
if self.deferrable:
self.defer(
timeout=timedelta(seconds=self.timeout),
trigger=AnthropicBatchTrigger(
conn_id=self.conn_id,
batch_id=self.batch_id,
poll_interval=self.poke_interval,
end_time=time.time() + self.timeout,
),
method_name="execute_complete",
)
super().execute(context)
[docs]
def execute_complete(self, context: Context, event: Any = None) -> None:
event = validate_execute_complete_event(event)
status = event["status"]
if status == "timeout":
raise AnthropicBatchTimeout(event["message"])
if status == "error":
raise AnthropicBatchJobError(event["message"])
counts = event.get("request_counts") or {}
evaluate_batch_counts(
batch_id=event["batch_id"],
canceled=counts.get("canceled", 0),
errored=counts.get("errored", 0),
expired=counts.get("expired", 0),
succeeded=counts.get("succeeded", 0),
fail_on_partial_error=self.fail_on_partial_error,
)
self.log.info("Batch %s reached a terminal status.", event["batch_id"])