diff --git a/libs/community/langchain_community/vectorstores/opensearch_vector_search.py b/libs/community/langchain_community/vectorstores/opensearch_vector_search.py
index f08620eef6383..734153d989a9a 100644
--- a/libs/community/langchain_community/vectorstores/opensearch_vector_search.py
+++ b/libs/community/langchain_community/vectorstores/opensearch_vector_search.py
@@ -402,6 +402,7 @@ def __init__(
         self.client = _get_opensearch_client(opensearch_url, **kwargs)
         self.async_client = _get_async_opensearch_client(opensearch_url, **kwargs)
         self.engine = kwargs.get("engine", "nmslib")
+        self.bulk_size = kwargs.get("bulk_size", 500)
 
     @property
     def embeddings(self) -> Embeddings:
@@ -413,9 +414,10 @@ def __add(
         embeddings: List[List[float]],
         metadatas: Optional[List[dict]] = None,
         ids: Optional[List[str]] = None,
-        bulk_size: int = 500,
+        bulk_size: Optional[int] = None,
         **kwargs: Any,
     ) -> List[str]:
+        bulk_size = bulk_size if bulk_size is not None else self.bulk_size
         _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
         index_name = kwargs.get("index_name", self.index_name)
         text_field = kwargs.get("text_field", "text")
@@ -454,9 +456,10 @@ async def __aadd(
         embeddings: List[List[float]],
         metadatas: Optional[List[dict]] = None,
         ids: Optional[List[str]] = None,
-        bulk_size: int = 500,
+        bulk_size: Optional[int] = None,
         **kwargs: Any,
     ) -> List[str]:
+        bulk_size = bulk_size if bulk_size is not None else self.bulk_size
         _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
         index_name = kwargs.get("index_name", self.index_name)
         text_field = kwargs.get("text_field", "text")
@@ -560,7 +563,7 @@ def add_texts(
         texts: Iterable[str],
         metadatas: Optional[List[dict]] = None,
         ids: Optional[List[str]] = None,
-        bulk_size: int = 500,
+        bulk_size: Optional[int] = None,
         **kwargs: Any,
     ) -> List[str]:
         """Run more texts through the embeddings and add to the vectorstore.
@@ -582,6 +585,7 @@ def add_texts(
             to "text".
         """
         embeddings = self.embedding_function.embed_documents(list(texts))
+        bulk_size = bulk_size if bulk_size is not None else self.bulk_size
         return self.__add(
             texts,
             embeddings,
@@ -596,7 +600,7 @@ async def aadd_texts(
         texts: Iterable[str],
         metadatas: Optional[List[dict]] = None,
         ids: Optional[List[str]] = None,
-        bulk_size: int = 500,
+        bulk_size: Optional[int] = None,
         **kwargs: Any,
     ) -> List[str]:
         """
@@ -604,6 +608,7 @@ async def aadd_texts(
         and add to the vectorstore.
         """
         embeddings = await self.embedding_function.aembed_documents(list(texts))
+        bulk_size = bulk_size if bulk_size is not None else self.bulk_size
         return await self.__aadd(
             texts,
             embeddings,
@@ -618,7 +623,7 @@ def add_embeddings(
         text_embeddings: Iterable[Tuple[str, List[float]]],
         metadatas: Optional[List[dict]] = None,
         ids: Optional[List[str]] = None,
-        bulk_size: int = 500,
+        bulk_size: Optional[int] = None,
         **kwargs: Any,
     ) -> List[str]:
         """Add the given texts and embeddings to the vectorstore.
@@ -641,6 +646,7 @@ def add_embeddings(
             to "text".
         """
         texts, embeddings = zip(*text_embeddings)
+        bulk_size = bulk_size if bulk_size is not None else self.bulk_size
         return self.__add(
             list(texts),
             list(embeddings),
@@ -1085,7 +1091,7 @@ def from_texts(
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
-        bulk_size: int = 500,
+        bulk_size: Optional[int] = None,
         ids: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> OpenSearchVectorSearch:
@@ -1134,6 +1140,7 @@ def from_texts(
 
         """
         embeddings = embedding.embed_documents(texts)
+        bulk_size = bulk_size if bulk_size is not None else cls.bulk_size
         return cls.from_embeddings(
             embeddings,
             texts,
@@ -1150,7 +1157,7 @@ async def afrom_texts(
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
-        bulk_size: int = 500,
+        bulk_size: Optional[int] = None,
         ids: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> OpenSearchVectorSearch:
@@ -1199,6 +1206,7 @@ async def afrom_texts(
 
         """
         embeddings = await embedding.aembed_documents(texts)
+        bulk_size = bulk_size if bulk_size is not None else cls.bulk_size
         return await cls.afrom_embeddings(
             embeddings,
             texts,
@@ -1216,7 +1224,7 @@ def from_embeddings(
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
-        bulk_size: int = 500,
+        bulk_size: Optional[int] = None,
         ids: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> OpenSearchVectorSearch:
@@ -1285,6 +1293,7 @@ def from_embeddings(
             "max_chunk_bytes",
             "is_aoss",
         ]
+        bulk_size = bulk_size if bulk_size is not None else cls.bulk_size
         _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
         dim = len(embeddings[0])
         # Get the index name from either from kwargs or ENV Variable
@@ -1346,7 +1355,7 @@ async def afrom_embeddings(
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
-        bulk_size: int = 500,
+        bulk_size: Optional[int] = None,
         ids: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> OpenSearchVectorSearch:
@@ -1417,6 +1426,7 @@ async def afrom_embeddings(
             "max_chunk_bytes",
             "is_aoss",
         ]
+        bulk_size = bulk_size if bulk_size is not None else cls.bulk_size
         _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
         dim = len(embeddings[0])
         # Get the index name from either from kwargs or ENV Variable