Source code for airflow.providers.openai.operators.openai
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from __future__ import annotations
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence
from airflow.models import BaseOperator
from airflow.providers.openai.hooks.openai import OpenAIHook
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