class_docstr: 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
code: 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
  
file_path: airflow/airflow/providers/openai/operators/openai.py
imports: 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
label_desc: Use image node_id 3 for calling the OpenAIEmbeddingOperator obj's underlying member method code with example usage: obj.execute(context) and returns: embeddings