diff --git a/libs/langchain/langchain/embeddings/huggingface.py b/libs/langchain/langchain/embeddings/huggingface.py
index b2d4a183333..72243705d2b 100644
--- a/libs/langchain/langchain/embeddings/huggingface.py
+++ b/libs/langchain/langchain/embeddings/huggingface.py
@@ -47,6 +47,8 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
     """Key word arguments to pass to the model."""
     encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Key word arguments to pass when calling the `encode` method of the model."""
+    multi_process: bool = False
+    """Run encode() on multiple GPUs."""
 
     def __init__(self, **kwargs: Any):
         """Initialize the sentence_transformer."""
@@ -78,8 +80,16 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         Returns:
             List of embeddings, one for each text.
         """
+        import sentence_transformers
+
         texts = list(map(lambda x: x.replace("\n", " "), texts))
-        embeddings = self.client.encode(texts, **self.encode_kwargs)
+        if self.multi_process:
+            pool = self.client.start_multi_process_pool()
+            embeddings = self.client.encode_multi_process(texts, pool)
+            sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
+        else:
+            embeddings = self.client.encode(texts, **self.encode_kwargs)
+
         return embeddings.tolist()
 
     def embed_query(self, text: str) -> List[float]:
@@ -91,9 +101,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         Returns:
             Embeddings for the text.
         """
-        text = text.replace("\n", " ")
-        embedding = self.client.encode(text, **self.encode_kwargs)
-        return embedding.tolist()
+        return self.embed_documents([text])[0]
 
 
 class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):