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from __future__ import annotations
import time
from functools import cached_property
from typing import TYPE_CHECKING, Any, Literal, Sequence
from airflow.configuration import conf
from airflow.models import BaseOperator
from airflow.providers.openai.exceptions import OpenAIBatchJobException
from airflow.providers.openai.hooks.openai import OpenAIHook
from airflow.providers.openai.triggers.openai import OpenAIBatchTrigger
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class OpenAIEmbeddingOperator(BaseOperator):
"""
Operator that accepts input text to generate OpenAI embeddings using the specified model.
:param conn_id: The OpenAI connection ID to use.
:param input_text: The text to generate OpenAI embeddings for. This can be a string, a list of strings,
a list of integers, or a list of lists of integers.
:param model: The OpenAI model to be used for generating the embeddings.
:param embedding_kwargs: Additional keyword arguments to pass to the OpenAI `create_embeddings` method.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:OpenAIEmbeddingOperator`
For possible options for `embedding_kwargs`, see:
https://platform.openai.com/docs/api-reference/embeddings/create
"""
[docs] template_fields: Sequence[str] = ("input_text",)
def __init__(
self,
conn_id: str,
input_text: str | list[str] | list[int] | list[list[int]],
model: str = "text-embedding-ada-002",
embedding_kwargs: dict | None = None,
**kwargs: Any,
):
super().__init__(**kwargs)
self.conn_id = conn_id
self.input_text = input_text
self.model = model
self.embedding_kwargs = embedding_kwargs or {}
@cached_property
[docs] def hook(self) -> OpenAIHook:
"""Return an instance of the OpenAIHook."""
return OpenAIHook(conn_id=self.conn_id)
[docs] def execute(self, context: Context) -> list[float]:
if not self.input_text or not isinstance(self.input_text, (str, list)):
raise ValueError(
"The 'input_text' must be a non-empty string, list of strings, list of integers, or list of lists of integers."
)
self.log.info("Generating embeddings for the input text of length: %d", len(self.input_text))
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
[docs]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`
"""
[docs] 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
[docs] def hook(self) -> OpenAIHook:
"""Return an instance of the OpenAIHook."""
return OpenAIHook(conn_id=self.conn_id)
[docs] 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 self.wait_for_completion:
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
[docs] 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 OpenAIBatchJobException(event["message"])
self.log.info("%s completed successfully.", self.task_id)
return event["batch_id"]
[docs] def on_kill(self) -> None:
"""Cancel the batch if task is cancelled."""
if self.batch_id:
self.log.info("on_kill: cancel the OpenAI Batch %s", self.batch_id)
self.hook.cancel_batch(self.batch_id)