diff --git a/libs/langchain/langchain/embeddings/openai.py b/libs/langchain/langchain/embeddings/openai.py
index 976c879f95e25..88cb7c9332639 100644
--- a/libs/langchain/langchain/embeddings/openai.py
+++ b/libs/langchain/langchain/embeddings/openai.py
@@ -87,8 +87,8 @@ async def wrapped_f(*args: Any, **kwargs: Any) -> Callable:
 
 
 # https://stackoverflow.com/questions/76469415/getting-embeddings-of-length-1-from-langchain-openaiembeddings
-def _check_response(response: dict) -> dict:
-    if any(len(d["embedding"]) == 1 for d in response["data"]):
+def _check_response(response: dict, skip_empty: bool = False) -> dict:
+    if any(len(d["embedding"]) == 1 for d in response["data"]) and not skip_empty:
         import openai
 
         raise openai.error.APIError("OpenAI API returned an empty embedding")
@@ -102,7 +102,7 @@ def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
     @retry_decorator
     def _embed_with_retry(**kwargs: Any) -> Any:
         response = embeddings.client.create(**kwargs)
-        return _check_response(response)
+        return _check_response(response, skip_empty=embeddings.skip_empty)
 
     return _embed_with_retry(**kwargs)
 
@@ -113,7 +113,7 @@ async def async_embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) ->
     @_async_retry_decorator(embeddings)
     async def _async_embed_with_retry(**kwargs: Any) -> Any:
         response = await embeddings.client.acreate(**kwargs)
-        return _check_response(response)
+        return _check_response(response, skip_empty=embeddings.skip_empty)
 
     return await _async_embed_with_retry(**kwargs)
 
@@ -196,6 +196,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
     """Whether to show a progress bar when embedding."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for `create` call not explicitly specified."""
+    skip_empty: bool = False
+    """Whether to skip empty strings when embedding or raise an error.
+    Defaults to not skipping."""
 
     class Config:
         """Configuration for this pydantic object."""
@@ -371,6 +374,8 @@ def _get_len_safe_embeddings(
         results: List[List[List[float]]] = [[] for _ in range(len(texts))]
         num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
         for i in range(len(indices)):
+            if self.skip_empty and len(batched_embeddings[i]) == 1:
+                continue
             results[indices[i]].append(batched_embeddings[i])
             num_tokens_in_batch[indices[i]].append(len(tokens[i]))