ylnsnv commented on code in PR #35547:
URL: https://github.com/apache/airflow/pull/35547#discussion_r1390719284


##########
airflow/providers/openai/operators/openai.py:
##########
@@ -31,41 +32,46 @@ 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`
-
-    :param conn_id: The OpenAI connection.
-    :param input_text: The text to generate OpenAI embeddings on. Either 
input_text or input_callable
-        should be provided.
-    :param model: The OpenAI model to be used for generating the embeddings.
-    :param embedding_kwargs: For possible option check
-        .. seealso:: 
https://platform.openai.com/docs/api-reference/embeddings/create
+        For possible options for `embedding_kwargs`, see:
+        https://platform.openai.com/docs/api-reference/embeddings/create
     """
 
     template_fields: Sequence[str] = ("input_text",)
 
     def __init__(
         self,
         conn_id: str,
-        input_text: str | list[Any],
+        input_text: str | list[str] | list[int] | list[list[int]],
         model: str = "text-embedding-ada-002",
         embedding_kwargs: dict | None = None,
         **kwargs: Any,
     ):
-        self.embedding_kwargs = embedding_kwargs or {}
         super().__init__(**kwargs)
         self.conn_id = conn_id
         self.input_text = input_text
         self.model = model
+        self.embedding_kwargs = embedding_kwargs or {}
 
     @cached_property
     def hook(self) -> OpenAIHook:
         """Return an instance of the OpenAIHook."""
         return OpenAIHook(conn_id=self.conn_id)
 
     def execute(self, context: Context) -> list[float]:
-        self.log.info("Input text: %s", self.input_text)
+        if not self.input_text or not isinstance(self.input_text, (str, list)):

Review Comment:
   Relocated :)



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