partners[openai]: embeddings: supported chunk_size when check_embeddi…

…ng_ctx_length is disabled

libs/partners/openai/langchain_openai/embeddings/base.py

@@ -184,14 +184,14 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
     retry_max_seconds: int = 20
     """Max number of seconds to wait between retries"""
     http_client: Union[Any, None] = None
-    """Optional httpx.Client. Only used for sync invocations. Must specify 
+    """Optional httpx.Client. Only used for sync invocations. Must specify
         http_async_client as well if you'd like a custom client for async invocations.
     """
     http_async_client: Union[Any, None] = None
-    """Optional httpx.AsyncClient. Only used for async invocations. Must specify 
+    """Optional httpx.AsyncClient. Only used for async invocations. Must specify
         http_client as well if you'd like a custom client for sync invocations."""
     check_embedding_ctx_length: bool = True
-    """Whether to check the token length of inputs and automatically split inputs 
+    """Whether to check the token length of inputs and automatically split inputs
         longer than embedding_ctx_length."""
 
     class Config:
@@ -525,9 +525,7 @@ def embed_documents(
         engine = cast(str, self.deployment)
         return self._get_len_safe_embeddings(texts, engine=engine)
 
-    async def aembed_documents(
-        self, texts: List[str], chunk_size: Optional[int] = 0
-    ) -> List[List[float]]:
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
         """Call out to OpenAI's embedding endpoint async for embedding search docs.
 
         Args:
@@ -540,9 +538,9 @@ async def aembed_documents(
         """
         if not self.check_embedding_ctx_length:
             embeddings: List[List[float]] = []
-            for text in texts:
+            for i in range(0, len(texts), self.chunk_size):
                 response = await self.async_client.create(
-                    input=text, **self._invocation_params
+                    input=texts[i : i + self.chunk_size], **self._invocation_params
                 )
                 if not isinstance(response, dict):
                     response = response.dict()