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# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# 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
#
# Unless required by applicable law or agreed to in writing,
# 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
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import asyncio
import time
from collections.abc import AsyncIterator
from typing import Any
from airflow.providers.openai.hooks.openai import BatchStatus, OpenAIHook
from airflow.triggers.base import BaseTrigger, TriggerEvent
[docs]
class OpenAIBatchTrigger(BaseTrigger):
"""
Triggers OpenAI Batch API.
:param conn_id: The OpenAI connection ID to use.
:param batch_id: The ID of the OpenAI batch to wait on.
:param poll_interval: Seconds between batch status polls.
:param timeout: Total seconds to wait for the batch to reach a terminal
state before giving up. This is the preferred way to bound the wait
because the trigger measures elapsed time with :func:`time.monotonic`,
which is not affected by wall-clock jumps (NTP corrections, DST,
container clock skew, VM pause/resume). Either ``timeout`` or
``end_time`` must be provided.
:param end_time: Deprecated. Absolute wall-clock deadline (``time.time()``
based) after which the trigger reports a timeout. Kept for backward
compatibility with triggers that were serialized by the previous
version of the operator and are still in flight during an upgrade.
Prefer ``timeout`` for new code.
"""
def __init__(
self,
conn_id: str,
batch_id: str,
poll_interval: float,
end_time: float | None = None,
timeout: float | None = None,
) -> None:
if timeout is None and end_time is None:
raise ValueError("OpenAIBatchTrigger requires either 'timeout' or 'end_time'.")
if timeout is not None and end_time is not None:
raise ValueError("OpenAIBatchTrigger accepts either 'timeout' or 'end_time', not both.")
super().__init__()
[docs]
self.poll_interval = poll_interval
[docs]
self.batch_id = batch_id
[docs]
self.end_time = end_time
[docs]
def serialize(self) -> tuple[str, dict[str, Any]]:
"""
Serialize OpenAIBatchTrigger arguments and class path.
The trigger stores exactly the argument it was constructed with
(``timeout`` or ``end_time``) so that a rolling upgrade never rewrites
the schema of an in-flight deferred trigger.
"""
kwargs: dict[str, Any] = {
"conn_id": self.conn_id,
"batch_id": self.batch_id,
"poll_interval": self.poll_interval,
}
if self.timeout is not None:
kwargs["timeout"] = self.timeout
else:
kwargs["end_time"] = self.end_time
return ("airflow.providers.openai.triggers.openai.OpenAIBatchTrigger", kwargs)
[docs]
async def run(self) -> AsyncIterator[TriggerEvent]:
"""Make connection to OpenAI Client, and poll the status of batch."""
# Measure elapsed time with time.monotonic() so the timeout is robust
# against wall-clock adjustments (NTP, DST, VM pause/resume, etc.).
# For legacy ``end_time`` callers we derive a best-effort remaining
# duration from the wall clock exactly once, then track the rest with
# the monotonic clock.
if self.timeout is not None:
timeout = self.timeout
else:
timeout = max(0.0, self.end_time - time.time()) # type: ignore[operator]
start_monotonic = time.monotonic()
hook = OpenAIHook(conn_id=self.conn_id)
try:
while (batch := hook.get_batch(self.batch_id)) and BatchStatus.is_in_progress(batch.status):
elapsed = time.monotonic() - start_monotonic
if elapsed >= timeout:
yield TriggerEvent(
{
"status": "error",
"message": (
f"Batch {self.batch_id} has not reached a terminal status after "
f"{elapsed:.0f} seconds."
),
"batch_id": self.batch_id,
}
)
return
await asyncio.sleep(self.poll_interval)
if batch.status == BatchStatus.COMPLETED:
yield TriggerEvent(
{
"status": "success",
"message": f"Batch {self.batch_id} has completed successfully.",
"batch_id": self.batch_id,
}
)
elif batch.status in {BatchStatus.CANCELLED, BatchStatus.CANCELLING}:
yield TriggerEvent(
{
"status": "cancelled",
"message": f"Batch {self.batch_id} has been cancelled.",
"batch_id": self.batch_id,
}
)
elif batch.status == BatchStatus.FAILED:
yield TriggerEvent(
{
"status": "error",
"message": f"Batch failed:\n{self.batch_id}",
"batch_id": self.batch_id,
}
)
elif batch.status == BatchStatus.EXPIRED:
yield TriggerEvent(
{
"status": "error",
"message": f"Batch couldn't be completed within the hour time window :\n{self.batch_id}",
"batch_id": self.batch_id,
}
)
else:
yield TriggerEvent(
{
"status": "error",
"message": f"Batch {self.batch_id} has failed.",
"batch_id": self.batch_id,
}
)
except Exception as e:
yield TriggerEvent({"status": "error", "message": str(e), "batch_id": self.batch_id})