diff --git a/langchain/embeddings/openai.py b/langchain/embeddings/openai.py
index 09daf764..82d248bb 100644
--- a/langchain/embeddings/openai.py
+++ b/langchain/embeddings/openai.py
@@ -43,14 +43,14 @@ def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any
 
 
 def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
-    """Use tenacity to retry the completion call."""
+    """Use tenacity to retry the embedding call."""
     retry_decorator = _create_retry_decorator(embeddings)
 
     @retry_decorator
-    def _completion_with_retry(**kwargs: Any) -> Any:
+    def _embed_with_retry(**kwargs: Any) -> Any:
         return embeddings.client.create(**kwargs)
 
-    return _completion_with_retry(**kwargs)
+    return _embed_with_retry(**kwargs)
 
 
 class OpenAIEmbeddings(BaseModel, Embeddings):
@@ -231,10 +231,11 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
 
     def _embedding_func(self, text: str, *, engine: str) -> List[float]:
         """Call out to OpenAI's embedding endpoint."""
-        # replace newlines, which can negatively affect performance.
+        # handle large input text
         if self.embedding_ctx_length > 0:
             return self._get_len_safe_embeddings([text], engine=engine)[0]
         else:
+            # replace newlines, which can negatively affect performance.
             text = text.replace("\n", " ")
             return embed_with_retry(self, input=[text], engine=engine)["data"][0][
                 "embedding"
@@ -253,7 +254,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         Returns:
             List of embeddings, one for each text.
         """
-        # handle large batches of texts
+        # handle batches of large input text
         if self.embedding_ctx_length > 0:
             return self._get_len_safe_embeddings(texts, engine=self.document_model_name)
         else:
@@ -275,7 +276,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
             text: The text to embed.
 
         Returns:
-            Embeddings for the text.
+            Embedding for the text.
         """
         embedding = self._embedding_func(text, engine=self.query_model_name)
         return embedding