Lee-W commented on code in PR #41554:
URL: https://github.com/apache/airflow/pull/41554#discussion_r1723033501
##########
airflow/providers/openai/hooks/openai.py:
##########
@@ -43,9 +46,26 @@
ChatCompletionUserMessageParam,
)
+from airflow.exceptions import AirflowException
from airflow.hooks.base import BaseHook
+class BatchStatus(str, Enum):
+ """Enum for the status of a batch."""
+
+ VALIDATING = "validating"
+ FAILED = "failed"
+ IN_PROGRESS = "in_progress"
+ FINALIZING = "finalizing"
+ COMPLETED = "completed"
+ EXPIRED = "expired"
+ CANCELLING = "cancelling"
+ CANCELLED = "cancelled"
+
+ def __str__(self) -> str:
+ return str(self.value)
Review Comment:
Do we need this method?
##########
airflow/providers/openai/hooks/openai.py:
##########
@@ -393,3 +413,76 @@ def delete_vector_store_file(self, vector_store_id: str,
file_id: str) -> Vector
"""
response =
self.conn.beta.vector_stores.files.delete(vector_store_id=vector_store_id,
file_id=file_id)
return response
+
+ def create_batch(
+ self,
+ file_id: str,
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings",
"/v1/completions"],
+ metadata: dict[str, str] | None = None,
+ completion_window: Literal["24h"] = "24h",
+ ) -> Batch:
+ """
+ Create a batch for a given model and files.
+
+ :param file_id: The ID of the file to be used for this batch.
+ :param endpoint: The endpoint to use for this batch. Allowed values
include:
+ '/v1/chat/completions', '/v1/embeddings', '/v1/completions'.
+ :param metadata: A set of key-value pairs that can be attached to an
object.
+ :param completion_window: The time window for the batch to complete.
Default is 24 hours.
+ """
+ batch = self.conn.batches.create(
+ input_file_id=file_id, endpoint=endpoint, metadata=metadata,
completion_window=completion_window
+ )
+ return batch
+
+ def get_batch(self, batch_id: str) -> Batch:
+ """
+ Get the status of a batch.
+
+ :param batch_id: The ID of the batch to get the status of.
+ """
+ batch = self.conn.batches.retrieve(batch_id=batch_id)
+ return batch
+
+ def wait_for_batch(self, batch_id: str, wait_seconds: float = 3, timeout:
float = 3600) -> None:
+ """
+ Poll a batch to check if it finishes.
+
+ :param batch_id: Id of the Batch to wait for.
+ :param wait_seconds: Optional. Number of seconds between checks.
+ :param timeout: Optional. How many seconds wait for batch to be ready.
+ Used only if not ran in deferred operator.
+ """
+ start = time.monotonic()
+ while True:
+ if start + timeout < time.monotonic():
+ self.cancel_batch(batch_id=batch_id)
+ raise AirflowException(f"Timeout: OpenAI Batch {batch_id} is
not ready after {timeout}s")
+ time.sleep(wait_seconds)
+ batch = self.get_batch(batch_id=batch_id)
+
+ if batch.status in {BatchStatus.IN_PROGRESS,
BatchStatus.VALIDATING, BatchStatus.FINALIZING}:
+ continue
+ if batch.status == BatchStatus.COMPLETED:
+ break
+ if batch.status == BatchStatus.FAILED:
+ raise AirflowException(f"Batch failed - \n{batch_id}")
Review Comment:
It would be better if we could create custom exceptions for these few and
not use `AirflowException` here. We could inherit it thought
##########
airflow/providers/openai/hooks/openai.py:
##########
@@ -393,3 +413,76 @@ def delete_vector_store_file(self, vector_store_id: str,
file_id: str) -> Vector
"""
response =
self.conn.beta.vector_stores.files.delete(vector_store_id=vector_store_id,
file_id=file_id)
return response
+
+ def create_batch(
+ self,
+ file_id: str,
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings",
"/v1/completions"],
+ metadata: dict[str, str] | None = None,
+ completion_window: Literal["24h"] = "24h",
+ ) -> Batch:
+ """
+ Create a batch for a given model and files.
+
+ :param file_id: The ID of the file to be used for this batch.
+ :param endpoint: The endpoint to use for this batch. Allowed values
include:
+ '/v1/chat/completions', '/v1/embeddings', '/v1/completions'.
+ :param metadata: A set of key-value pairs that can be attached to an
object.
+ :param completion_window: The time window for the batch to complete.
Default is 24 hours.
+ """
+ batch = self.conn.batches.create(
+ input_file_id=file_id, endpoint=endpoint, metadata=metadata,
completion_window=completion_window
+ )
+ return batch
+
+ def get_batch(self, batch_id: str) -> Batch:
+ """
+ Get the status of a batch.
+
+ :param batch_id: The ID of the batch to get the status of.
+ """
+ batch = self.conn.batches.retrieve(batch_id=batch_id)
+ return batch
+
+ def wait_for_batch(self, batch_id: str, wait_seconds: float = 3, timeout:
float = 3600) -> None:
+ """
+ Poll a batch to check if it finishes.
+
+ :param batch_id: Id of the Batch to wait for.
+ :param wait_seconds: Optional. Number of seconds between checks.
+ :param timeout: Optional. How many seconds wait for batch to be ready.
+ Used only if not ran in deferred operator.
+ """
+ start = time.monotonic()
+ while True:
+ if start + timeout < time.monotonic():
+ self.cancel_batch(batch_id=batch_id)
+ raise AirflowException(f"Timeout: OpenAI Batch {batch_id} is
not ready after {timeout}s")
+ time.sleep(wait_seconds)
+ batch = self.get_batch(batch_id=batch_id)
+
+ if batch.status in {BatchStatus.IN_PROGRESS,
BatchStatus.VALIDATING, BatchStatus.FINALIZING}:
+ continue
+ if batch.status == BatchStatus.COMPLETED:
+ break
+ if batch.status == BatchStatus.FAILED:
+ raise AirflowException(f"Batch failed - \n{batch_id}")
+ elif batch.status in {BatchStatus.CANCELLED,
BatchStatus.CANCELLING}:
+ raise AirflowException(f"Batch failed - batch was
cancelled:\n{batch_id}")
+ elif batch.status == BatchStatus.EXPIRED:
+ raise AirflowException(
+ f"Batch failed - batch couldn't be completed within the
hour time window :\n{batch_id}"
+ )
+ else:
+ raise AirflowException(
+ f"Batch failed - encountered unexpected status
`{batch.status}` for batch_id `{batch_id}`"
+ )
Review Comment:
```suggestion
raise AirflowException(
f"Batch failed - encountered unexpected status
`{batch.status}` for batch_id `{batch_id}`"
)
```
##########
airflow/providers/openai/hooks/openai.py:
##########
@@ -393,3 +413,76 @@ def delete_vector_store_file(self, vector_store_id: str,
file_id: str) -> Vector
"""
response =
self.conn.beta.vector_stores.files.delete(vector_store_id=vector_store_id,
file_id=file_id)
return response
+
+ def create_batch(
+ self,
+ file_id: str,
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings",
"/v1/completions"],
+ metadata: dict[str, str] | None = None,
+ completion_window: Literal["24h"] = "24h",
Review Comment:
Should it be a string instead? Looks like this can be any value
##########
airflow/providers/openai/hooks/openai.py:
##########
@@ -393,3 +413,76 @@ def delete_vector_store_file(self, vector_store_id: str,
file_id: str) -> Vector
"""
response =
self.conn.beta.vector_stores.files.delete(vector_store_id=vector_store_id,
file_id=file_id)
return response
+
+ def create_batch(
+ self,
+ file_id: str,
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings",
"/v1/completions"],
+ metadata: dict[str, str] | None = None,
+ completion_window: Literal["24h"] = "24h",
+ ) -> Batch:
+ """
+ Create a batch for a given model and files.
+
+ :param file_id: The ID of the file to be used for this batch.
+ :param endpoint: The endpoint to use for this batch. Allowed values
include:
+ '/v1/chat/completions', '/v1/embeddings', '/v1/completions'.
+ :param metadata: A set of key-value pairs that can be attached to an
object.
+ :param completion_window: The time window for the batch to complete.
Default is 24 hours.
+ """
+ batch = self.conn.batches.create(
+ input_file_id=file_id, endpoint=endpoint, metadata=metadata,
completion_window=completion_window
+ )
+ return batch
+
+ def get_batch(self, batch_id: str) -> Batch:
+ """
+ Get the status of a batch.
+
+ :param batch_id: The ID of the batch to get the status of.
+ """
+ batch = self.conn.batches.retrieve(batch_id=batch_id)
+ return batch
+
+ def wait_for_batch(self, batch_id: str, wait_seconds: float = 3, timeout:
float = 3600) -> None:
+ """
+ Poll a batch to check if it finishes.
+
+ :param batch_id: Id of the Batch to wait for.
+ :param wait_seconds: Optional. Number of seconds between checks.
+ :param timeout: Optional. How many seconds wait for batch to be ready.
+ Used only if not ran in deferred operator.
+ """
+ start = time.monotonic()
+ while True:
+ if start + timeout < time.monotonic():
+ self.cancel_batch(batch_id=batch_id)
+ raise AirflowException(f"Timeout: OpenAI Batch {batch_id} is
not ready after {timeout}s")
+ time.sleep(wait_seconds)
+ batch = self.get_batch(batch_id=batch_id)
+
+ if batch.status in {BatchStatus.IN_PROGRESS,
BatchStatus.VALIDATING, BatchStatus.FINALIZING}:
Review Comment:
```suggestion
if batch.status in (BatchStatus.IN_PROGRESS,
BatchStatus.VALIDATING, BatchStatus.FINALIZING):
```
I guess we don't need a set?
##########
airflow/providers/openai/hooks/openai.py:
##########
@@ -393,3 +413,76 @@ def delete_vector_store_file(self, vector_store_id: str,
file_id: str) -> Vector
"""
response =
self.conn.beta.vector_stores.files.delete(vector_store_id=vector_store_id,
file_id=file_id)
return response
+
+ def create_batch(
+ self,
+ file_id: str,
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings",
"/v1/completions"],
+ metadata: dict[str, str] | None = None,
+ completion_window: Literal["24h"] = "24h",
+ ) -> Batch:
+ """
+ Create a batch for a given model and files.
+
+ :param file_id: The ID of the file to be used for this batch.
+ :param endpoint: The endpoint to use for this batch. Allowed values
include:
+ '/v1/chat/completions', '/v1/embeddings', '/v1/completions'.
+ :param metadata: A set of key-value pairs that can be attached to an
object.
+ :param completion_window: The time window for the batch to complete.
Default is 24 hours.
+ """
+ batch = self.conn.batches.create(
+ input_file_id=file_id, endpoint=endpoint, metadata=metadata,
completion_window=completion_window
+ )
+ return batch
+
+ def get_batch(self, batch_id: str) -> Batch:
+ """
+ Get the status of a batch.
+
+ :param batch_id: The ID of the batch to get the status of.
+ """
+ batch = self.conn.batches.retrieve(batch_id=batch_id)
+ return batch
+
+ def wait_for_batch(self, batch_id: str, wait_seconds: float = 3, timeout:
float = 3600) -> None:
+ """
+ Poll a batch to check if it finishes.
+
+ :param batch_id: Id of the Batch to wait for.
+ :param wait_seconds: Optional. Number of seconds between checks.
+ :param timeout: Optional. How many seconds wait for batch to be ready.
+ Used only if not ran in deferred operator.
+ """
+ start = time.monotonic()
+ while True:
+ if start + timeout < time.monotonic():
+ self.cancel_batch(batch_id=batch_id)
+ raise AirflowException(f"Timeout: OpenAI Batch {batch_id} is
not ready after {timeout}s")
Review Comment:
It would be better if we could create a customer exception (or maybe there's
already a suitable one in airflow.exceptions). `AirflowException` is a bit too
broad for this one
##########
airflow/providers/openai/triggers/openai.py:
##########
@@ -0,0 +1,116 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# 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 typing import Any, AsyncIterator
+
+from airflow.providers.openai.hooks.openai import BatchStatus, OpenAIHook
+from airflow.triggers.base import BaseTrigger, TriggerEvent
+
+
+class OpenAIBatchTrigger(BaseTrigger):
+ """Triggers OpenAI Batch API."""
+
+ def __init__(
+ self,
+ conn_id: str,
+ batch_id: str,
+ poll_interval: float,
+ end_time: float,
+ ):
+ super().__init__()
+ self.conn_id = conn_id
+ self.poll_interval = poll_interval
+ self.batch_id = batch_id
+ self.end_time = end_time
+
+ def serialize(self) -> tuple[str, dict[str, Any]]:
+ """Serialize OpenAIBatchTrigger arguments and class path."""
+ return (
+ "airflow.providers.openai.triggers.openai.OpenAIBatchTrigger",
+ {
+ "conn_id": self.conn_id,
+ "batch_id": self.batch_id,
+ "poll_interval": self.poll_interval,
+ "end_time": self.end_time,
+ },
+ )
+
+ async def run(self) -> AsyncIterator[TriggerEvent]:
+ """Make connection to OpenAI Client, and poll the status of batch."""
+ hook = OpenAIHook(conn_id=self.conn_id)
+ try:
+ while (batch := hook.get_batch(self.batch_id)) and batch.status in
{
+ BatchStatus.VALIDATING,
+ BatchStatus.IN_PROGRESS,
+ BatchStatus.FINALIZING,
+ }:
+ if self.end_time < time.time():
+ yield TriggerEvent(
+ {
+ "status": "error",
+ "message": f"Batch {self.batch_id} has not reached
a terminal status after "
+ f"{time.time() - self.end_time} 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,
+ }
+ )
Review Comment:
```suggestion
yield TriggerEvent(
{
"status": "error",
"message": f"Batch {self.batch_id} has failed.",
"batch_id": self.batch_id,
}
)
```
##########
airflow/providers/openai/triggers/openai.py:
##########
@@ -0,0 +1,116 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# 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 typing import Any, AsyncIterator
+
+from airflow.providers.openai.hooks.openai import BatchStatus, OpenAIHook
+from airflow.triggers.base import BaseTrigger, TriggerEvent
+
+
+class OpenAIBatchTrigger(BaseTrigger):
+ """Triggers OpenAI Batch API."""
+
+ def __init__(
+ self,
+ conn_id: str,
+ batch_id: str,
+ poll_interval: float,
+ end_time: float,
+ ):
+ super().__init__()
+ self.conn_id = conn_id
+ self.poll_interval = poll_interval
+ self.batch_id = batch_id
+ self.end_time = end_time
+
+ def serialize(self) -> tuple[str, dict[str, Any]]:
+ """Serialize OpenAIBatchTrigger arguments and class path."""
+ return (
+ "airflow.providers.openai.triggers.openai.OpenAIBatchTrigger",
+ {
+ "conn_id": self.conn_id,
+ "batch_id": self.batch_id,
+ "poll_interval": self.poll_interval,
+ "end_time": self.end_time,
+ },
+ )
+
+ async def run(self) -> AsyncIterator[TriggerEvent]:
+ """Make connection to OpenAI Client, and poll the status of batch."""
+ hook = OpenAIHook(conn_id=self.conn_id)
+ try:
+ while (batch := hook.get_batch(self.batch_id)) and batch.status in
{
+ BatchStatus.VALIDATING,
+ BatchStatus.IN_PROGRESS,
+ BatchStatus.FINALIZING,
+ }:
Review Comment:
This combination is used for a few times. Might worth making it a variable
##########
airflow/providers/openai/triggers/openai.py:
##########
@@ -0,0 +1,116 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# 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 typing import Any, AsyncIterator
+
+from airflow.providers.openai.hooks.openai import BatchStatus, OpenAIHook
+from airflow.triggers.base import BaseTrigger, TriggerEvent
+
+
+class OpenAIBatchTrigger(BaseTrigger):
+ """Triggers OpenAI Batch API."""
+
+ def __init__(
+ self,
+ conn_id: str,
+ batch_id: str,
+ poll_interval: float,
+ end_time: float,
+ ):
+ super().__init__()
+ self.conn_id = conn_id
+ self.poll_interval = poll_interval
+ self.batch_id = batch_id
+ self.end_time = end_time
+
+ def serialize(self) -> tuple[str, dict[str, Any]]:
+ """Serialize OpenAIBatchTrigger arguments and class path."""
+ return (
+ "airflow.providers.openai.triggers.openai.OpenAIBatchTrigger",
+ {
+ "conn_id": self.conn_id,
+ "batch_id": self.batch_id,
+ "poll_interval": self.poll_interval,
+ "end_time": self.end_time,
+ },
+ )
+
+ async def run(self) -> AsyncIterator[TriggerEvent]:
+ """Make connection to OpenAI Client, and poll the status of batch."""
+ hook = OpenAIHook(conn_id=self.conn_id)
+ try:
+ while (batch := hook.get_batch(self.batch_id)) and batch.status in
{
+ BatchStatus.VALIDATING,
+ BatchStatus.IN_PROGRESS,
+ BatchStatus.FINALIZING,
+ }:
Review Comment:
```suggestion
while (batch := hook.get_batch(self.batch_id)) and batch.status
in (
BatchStatus.VALIDATING,
BatchStatus.IN_PROGRESS,
BatchStatus.FINALIZING,
):
```
##########
airflow/providers/openai/triggers/openai.py:
##########
@@ -0,0 +1,116 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# 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 typing import Any, AsyncIterator
+
+from airflow.providers.openai.hooks.openai import BatchStatus, OpenAIHook
+from airflow.triggers.base import BaseTrigger, TriggerEvent
+
+
+class OpenAIBatchTrigger(BaseTrigger):
+ """Triggers OpenAI Batch API."""
+
+ def __init__(
+ self,
+ conn_id: str,
+ batch_id: str,
+ poll_interval: float,
+ end_time: float,
+ ):
Review Comment:
```suggestion
) -> None:
```
##########
airflow/providers/openai/operators/openai.py:
##########
@@ -74,3 +78,92 @@ def execute(self, context: Context) -> list[float]:
embeddings = self.hook.create_embeddings(self.input_text,
model=self.model, **self.embedding_kwargs)
self.log.info("Generated embeddings for %d items", len(embeddings))
return embeddings
+
+
+class OpenAITriggerBatchOperator(BaseOperator):
+ """
+ Operator that triggers an OpenAI Batch API endpoint and waits for the
batch to complete.
+
+ :param file_id: Required. The ID of the batch file to trigger.
+ :param endpoint: Required. The OpenAI Batch API endpoint to trigger.
+ :param conn_id: Optional. The OpenAI connection ID to use. Defaults to
'openai_default'.
+ :param deferrable: Optional. Run operator in the deferrable mode.
+ :param wait_seconds: Optional. Number of seconds between checks. Only used
when ``deferrable`` is False.
+ Defaults to 3 seconds.
+ :param timeout: Optional. The amount of time, in seconds, to wait for the
request to complete.
+ Only used when ``deferrable`` is False. Defaults to 24 hour, which is
the SLA for OpenAI Batch API.
+ :param wait_for_completion: Optional. Whether to wait for the batch to
complete. If set to False, the operator
+ will return immediately after triggering the batch. Defaults to True.
+
+ .. seealso::
+ For more information on how to use this operator, please take a look
at the guide:
+ :ref:`howto/operator:OpenAITriggerBatchOperator`
+ """
+
+ template_fields: Sequence[str] = ("file_id",)
+
+ def __init__(
+ self,
+ file_id: str,
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings",
"/v1/completions"],
+ conn_id: str = OpenAIHook.default_conn_name,
+ deferrable: bool = conf.getboolean("operators", "default_deferrable",
fallback=False),
+ wait_seconds: float = 3,
+ timeout: float = 24 * 60 * 60,
+ wait_for_completion: bool = True,
+ **kwargs: Any,
+ ):
+ super().__init__(**kwargs)
+ self.conn_id = conn_id
+ self.file_id = file_id
+ self.endpoint = endpoint
+ self.deferrable = deferrable
+ self.wait_seconds = wait_seconds
+ self.timeout = timeout
+ self.wait_for_completion = wait_for_completion
+ self.batch_id: str | None = None
+
+ @cached_property
+ def hook(self) -> OpenAIHook:
+ """Return an instance of the OpenAIHook."""
+ return OpenAIHook(conn_id=self.conn_id)
+
+ def execute(self, context: Context) -> str:
+ batch = self.hook.create_batch(file_id=self.file_id,
endpoint=self.endpoint)
+ self.batch_id = batch.id
+ if not self.wait_for_completion:
+ return self.batch_id
+ if self.deferrable:
+ self.defer(
+ timeout=self.execution_timeout,
+ trigger=OpenAIBatchTrigger(
+ conn_id=self.conn_id,
+ batch_id=self.batch_id,
+ poll_interval=60,
+ end_time=time.time() + self.timeout,
+ ),
+ method_name="execute_complete",
+ )
+ else:
+ self.log.info("Waiting for batch %s to complete", self.batch_id)
+ self.hook.wait_for_batch(self.batch_id,
wait_seconds=self.wait_seconds, timeout=self.timeout)
+ return self.batch_id
+
+ def execute_complete(self, context: Context, event: Any = None) -> str:
+ """
+ Invoke this callback when the trigger fires; return immediately.
+
+ Relies on trigger to throw an exception, otherwise it assumes
execution was
+ successful.
+ """
+ if event["status"] == "error":
+ raise AirflowException(event["message"])
Review Comment:
Custom error would be better
##########
airflow/providers/openai/operators/openai.py:
##########
@@ -74,3 +78,92 @@ def execute(self, context: Context) -> list[float]:
embeddings = self.hook.create_embeddings(self.input_text,
model=self.model, **self.embedding_kwargs)
self.log.info("Generated embeddings for %d items", len(embeddings))
return embeddings
+
+
+class OpenAITriggerBatchOperator(BaseOperator):
+ """
+ Operator that triggers an OpenAI Batch API endpoint and waits for the
batch to complete.
+
+ :param file_id: Required. The ID of the batch file to trigger.
+ :param endpoint: Required. The OpenAI Batch API endpoint to trigger.
+ :param conn_id: Optional. The OpenAI connection ID to use. Defaults to
'openai_default'.
+ :param deferrable: Optional. Run operator in the deferrable mode.
+ :param wait_seconds: Optional. Number of seconds between checks. Only used
when ``deferrable`` is False.
+ Defaults to 3 seconds.
+ :param timeout: Optional. The amount of time, in seconds, to wait for the
request to complete.
+ Only used when ``deferrable`` is False. Defaults to 24 hour, which is
the SLA for OpenAI Batch API.
+ :param wait_for_completion: Optional. Whether to wait for the batch to
complete. If set to False, the operator
+ will return immediately after triggering the batch. Defaults to True.
+
+ .. seealso::
+ For more information on how to use this operator, please take a look
at the guide:
+ :ref:`howto/operator:OpenAITriggerBatchOperator`
+ """
+
+ template_fields: Sequence[str] = ("file_id",)
+
+ def __init__(
+ self,
+ file_id: str,
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings",
"/v1/completions"],
+ conn_id: str = OpenAIHook.default_conn_name,
+ deferrable: bool = conf.getboolean("operators", "default_deferrable",
fallback=False),
+ wait_seconds: float = 3,
+ timeout: float = 24 * 60 * 60,
+ wait_for_completion: bool = True,
+ **kwargs: Any,
+ ):
+ super().__init__(**kwargs)
+ self.conn_id = conn_id
+ self.file_id = file_id
+ self.endpoint = endpoint
+ self.deferrable = deferrable
+ self.wait_seconds = wait_seconds
+ self.timeout = timeout
+ self.wait_for_completion = wait_for_completion
+ self.batch_id: str | None = None
+
+ @cached_property
+ def hook(self) -> OpenAIHook:
+ """Return an instance of the OpenAIHook."""
+ return OpenAIHook(conn_id=self.conn_id)
+
+ def execute(self, context: Context) -> str:
+ batch = self.hook.create_batch(file_id=self.file_id,
endpoint=self.endpoint)
+ self.batch_id = batch.id
+ if not self.wait_for_completion:
+ return self.batch_id
+ if self.deferrable:
+ self.defer(
+ timeout=self.execution_timeout,
+ trigger=OpenAIBatchTrigger(
+ conn_id=self.conn_id,
+ batch_id=self.batch_id,
+ poll_interval=60,
+ end_time=time.time() + self.timeout,
+ ),
+ method_name="execute_complete",
+ )
+ else:
+ self.log.info("Waiting for batch %s to complete", self.batch_id)
+ self.hook.wait_for_batch(self.batch_id,
wait_seconds=self.wait_seconds, timeout=self.timeout)
+ return self.batch_id
+
+ def execute_complete(self, context: Context, event: Any = None) -> str:
+ """
+ Invoke this callback when the trigger fires; return immediately.
+
+ Relies on trigger to throw an exception, otherwise it assumes
execution was
+ successful.
+ """
+ if event["status"] == "error":
+ raise AirflowException(event["message"])
+
+ self.log.info("%s completed successfully.", self.task_id)
+ return event["batch_id"]
+
+ def on_kill(self):
Review Comment:
```suggestion
def on_kill(self) -> None:
```
##########
airflow/providers/openai/hooks/openai.py:
##########
@@ -393,3 +413,76 @@ def delete_vector_store_file(self, vector_store_id: str,
file_id: str) -> Vector
"""
response =
self.conn.beta.vector_stores.files.delete(vector_store_id=vector_store_id,
file_id=file_id)
return response
+
+ def create_batch(
+ self,
+ file_id: str,
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings",
"/v1/completions"],
+ metadata: dict[str, str] | None = None,
+ completion_window: Literal["24h"] = "24h",
+ ) -> Batch:
+ """
+ Create a batch for a given model and files.
+
+ :param file_id: The ID of the file to be used for this batch.
+ :param endpoint: The endpoint to use for this batch. Allowed values
include:
+ '/v1/chat/completions', '/v1/embeddings', '/v1/completions'.
+ :param metadata: A set of key-value pairs that can be attached to an
object.
+ :param completion_window: The time window for the batch to complete.
Default is 24 hours.
+ """
+ batch = self.conn.batches.create(
+ input_file_id=file_id, endpoint=endpoint, metadata=metadata,
completion_window=completion_window
+ )
+ return batch
+
+ def get_batch(self, batch_id: str) -> Batch:
+ """
+ Get the status of a batch.
+
+ :param batch_id: The ID of the batch to get the status of.
+ """
+ batch = self.conn.batches.retrieve(batch_id=batch_id)
+ return batch
+
+ def wait_for_batch(self, batch_id: str, wait_seconds: float = 3, timeout:
float = 3600) -> None:
+ """
+ Poll a batch to check if it finishes.
+
+ :param batch_id: Id of the Batch to wait for.
+ :param wait_seconds: Optional. Number of seconds between checks.
+ :param timeout: Optional. How many seconds wait for batch to be ready.
+ Used only if not ran in deferred operator.
+ """
+ start = time.monotonic()
+ while True:
+ if start + timeout < time.monotonic():
+ self.cancel_batch(batch_id=batch_id)
+ raise AirflowException(f"Timeout: OpenAI Batch {batch_id} is
not ready after {timeout}s")
+ time.sleep(wait_seconds)
+ batch = self.get_batch(batch_id=batch_id)
+
+ if batch.status in {BatchStatus.IN_PROGRESS,
BatchStatus.VALIDATING, BatchStatus.FINALIZING}:
+ continue
+ if batch.status == BatchStatus.COMPLETED:
+ break
+ if batch.status == BatchStatus.FAILED:
+ raise AirflowException(f"Batch failed - \n{batch_id}")
+ elif batch.status in {BatchStatus.CANCELLED,
BatchStatus.CANCELLING}:
Review Comment:
```suggestion
elif batch.status in (BatchStatus.CANCELLED,
BatchStatus.CANCELLING):
```
##########
airflow/providers/openai/hooks/openai.py:
##########
@@ -393,3 +413,76 @@ def delete_vector_store_file(self, vector_store_id: str,
file_id: str) -> Vector
"""
response =
self.conn.beta.vector_stores.files.delete(vector_store_id=vector_store_id,
file_id=file_id)
return response
+
+ def create_batch(
+ self,
+ file_id: str,
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings",
"/v1/completions"],
+ metadata: dict[str, str] | None = None,
+ completion_window: Literal["24h"] = "24h",
+ ) -> Batch:
+ """
+ Create a batch for a given model and files.
+
+ :param file_id: The ID of the file to be used for this batch.
+ :param endpoint: The endpoint to use for this batch. Allowed values
include:
+ '/v1/chat/completions', '/v1/embeddings', '/v1/completions'.
+ :param metadata: A set of key-value pairs that can be attached to an
object.
+ :param completion_window: The time window for the batch to complete.
Default is 24 hours.
+ """
+ batch = self.conn.batches.create(
+ input_file_id=file_id, endpoint=endpoint, metadata=metadata,
completion_window=completion_window
+ )
+ return batch
+
+ def get_batch(self, batch_id: str) -> Batch:
+ """
+ Get the status of a batch.
+
+ :param batch_id: The ID of the batch to get the status of.
+ """
+ batch = self.conn.batches.retrieve(batch_id=batch_id)
+ return batch
+
+ def wait_for_batch(self, batch_id: str, wait_seconds: float = 3, timeout:
float = 3600) -> None:
+ """
+ Poll a batch to check if it finishes.
+
+ :param batch_id: Id of the Batch to wait for.
+ :param wait_seconds: Optional. Number of seconds between checks.
+ :param timeout: Optional. How many seconds wait for batch to be ready.
+ Used only if not ran in deferred operator.
+ """
+ start = time.monotonic()
+ while True:
+ if start + timeout < time.monotonic():
+ self.cancel_batch(batch_id=batch_id)
+ raise AirflowException(f"Timeout: OpenAI Batch {batch_id} is
not ready after {timeout}s")
+ time.sleep(wait_seconds)
+ batch = self.get_batch(batch_id=batch_id)
+
+ if batch.status in {BatchStatus.IN_PROGRESS,
BatchStatus.VALIDATING, BatchStatus.FINALIZING}:
+ continue
+ if batch.status == BatchStatus.COMPLETED:
+ break
Review Comment:
I guess we can just return here?
```suggestion
return
```
##########
airflow/providers/openai/operators/openai.py:
##########
@@ -74,3 +78,92 @@ def execute(self, context: Context) -> list[float]:
embeddings = self.hook.create_embeddings(self.input_text,
model=self.model, **self.embedding_kwargs)
self.log.info("Generated embeddings for %d items", len(embeddings))
return embeddings
+
+
+class OpenAITriggerBatchOperator(BaseOperator):
+ """
+ Operator that triggers an OpenAI Batch API endpoint and waits for the
batch to complete.
+
+ :param file_id: Required. The ID of the batch file to trigger.
+ :param endpoint: Required. The OpenAI Batch API endpoint to trigger.
+ :param conn_id: Optional. The OpenAI connection ID to use. Defaults to
'openai_default'.
+ :param deferrable: Optional. Run operator in the deferrable mode.
+ :param wait_seconds: Optional. Number of seconds between checks. Only used
when ``deferrable`` is False.
+ Defaults to 3 seconds.
+ :param timeout: Optional. The amount of time, in seconds, to wait for the
request to complete.
+ Only used when ``deferrable`` is False. Defaults to 24 hour, which is
the SLA for OpenAI Batch API.
+ :param wait_for_completion: Optional. Whether to wait for the batch to
complete. If set to False, the operator
+ will return immediately after triggering the batch. Defaults to True.
+
+ .. seealso::
+ For more information on how to use this operator, please take a look
at the guide:
+ :ref:`howto/operator:OpenAITriggerBatchOperator`
+ """
+
+ template_fields: Sequence[str] = ("file_id",)
+
+ def __init__(
+ self,
+ file_id: str,
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings",
"/v1/completions"],
+ conn_id: str = OpenAIHook.default_conn_name,
+ deferrable: bool = conf.getboolean("operators", "default_deferrable",
fallback=False),
+ wait_seconds: float = 3,
+ timeout: float = 24 * 60 * 60,
+ wait_for_completion: bool = True,
+ **kwargs: Any,
+ ):
+ super().__init__(**kwargs)
+ self.conn_id = conn_id
+ self.file_id = file_id
+ self.endpoint = endpoint
+ self.deferrable = deferrable
+ self.wait_seconds = wait_seconds
+ self.timeout = timeout
+ self.wait_for_completion = wait_for_completion
+ self.batch_id: str | None = None
+
+ @cached_property
+ def hook(self) -> OpenAIHook:
+ """Return an instance of the OpenAIHook."""
+ return OpenAIHook(conn_id=self.conn_id)
+
+ def execute(self, context: Context) -> str:
+ batch = self.hook.create_batch(file_id=self.file_id,
endpoint=self.endpoint)
+ self.batch_id = batch.id
+ if not self.wait_for_completion:
+ return self.batch_id
Review Comment:
We probably would want to handle the case that `not self.wait_for_completion
and self.deferrable` to avoid confusion
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