diff --git a/langchain/embeddings/openai.py b/langchain/embeddings/openai.py
index e52b695a..7caa304f 100644
--- a/langchain/embeddings/openai.py
+++ b/langchain/embeddings/openai.py
@@ -207,7 +207,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
     def _embedding_func(self, text: str, *, engine: str) -> List[float]:
         """Call out to OpenAI's embedding endpoint."""
         # handle large input text
-        if self.embedding_ctx_length > 0:
+        if len(text) > self.embedding_ctx_length:
             return self._get_len_safe_embeddings([text], engine=engine)[0]
         else:
             # replace newlines, which can negatively affect performance.
@@ -229,20 +229,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         Returns:
             List of embeddings, one for each text.
         """
-        # handle batches of large input text
-        if self.embedding_ctx_length > 0:
-            return self._get_len_safe_embeddings(texts, engine=self.deployment)
-        else:
-            results = []
-            _chunk_size = chunk_size or self.chunk_size
-            for i in range(0, len(texts), _chunk_size):
-                response = embed_with_retry(
-                    self,
-                    input=texts[i : i + _chunk_size],
-                    engine=self.deployment,
-                )
-                results += [r["embedding"] for r in response["data"]]
-            return results
+        # NOTE: to keep things simple, we assume the list may contain texts longer
+        #       than the maximum context and use length-safe embedding function.
+        return self._get_len_safe_embeddings(texts, engine=self.deployment)
 
     def embed_query(self, text: str) -> List[float]:
         """Call out to OpenAI's embedding endpoint for embedding query text.