diff --git a/libs/langchain/langchain/embeddings/huggingface.py b/libs/langchain/langchain/embeddings/huggingface.py
index afbd0cd8064..6854ab4ce08 100644
--- a/libs/langchain/langchain/embeddings/huggingface.py
+++ b/libs/langchain/langchain/embeddings/huggingface.py
@@ -11,12 +11,10 @@ DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
 DEFAULT_QUERY_INSTRUCTION = (
     "Represent the question for retrieving supporting documents: "
 )
-DEFAULT_EMBED_BGE_INSTRUCTION = (
-    "Represent this sentence for searching relevant passages: "
-)
-DEFAULT_QUERY_BGE_INSTRUCTION = (
+DEFAULT_QUERY_BGE_INSTRUCTION_EN = (
     "Represent this question for searching relevant passages: "
 )
+DEFAULT_QUERY_BGE_INSTRUCTION_ZH = "为这个句子生成表示以用于检索相关文章："
 
 
 class HuggingFaceEmbeddings(BaseModel, Embeddings):
@@ -190,7 +188,7 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
 
             model_name = "BAAI/bge-large-en"
             model_kwargs = {'device': 'cpu'}
-            encode_kwargs = {'normalize_embeddings': False}
+            encode_kwargs = {'normalize_embeddings': True}
             hf = HuggingFaceBgeEmbeddings(
                 model_name=model_name,
                 model_kwargs=model_kwargs,
@@ -208,9 +206,7 @@ class HuggingFaceBgeEmbeddings(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."""
-    embed_instruction: str = DEFAULT_EMBED_BGE_INSTRUCTION
-    """Instruction to use for embedding documents."""
-    query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION
+    query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN
     """Instruction to use for embedding query."""
 
     def __init__(self, **kwargs: Any):
@@ -228,6 +224,8 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
         self.client = sentence_transformers.SentenceTransformer(
             self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
         )
+        if "-zh" in self.model_name:
+            self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
 
     class Config:
         """Configuration for this pydantic object."""
@@ -243,8 +241,8 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
         Returns:
             List of embeddings, one for each text.
         """
-        instruction_pairs = [[self.embed_instruction, text] for text in texts]
-        embeddings = self.client.encode(instruction_pairs, **self.encode_kwargs)
+texts = [t.replace("\n", " ") for t in texts]
+        embeddings = self.client.encode(texts, **self.encode_kwargs)
         return embeddings.tolist()
 
     def embed_query(self, text: str) -> List[float]:
@@ -256,6 +254,8 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
         Returns:
             Embeddings for the text.
         """
-        instruction_pair = [self.query_instruction, text]
-        embedding = self.client.encode([instruction_pair], **self.encode_kwargs)[0]
+        text = text.replace("\n", " ")
+        embedding = self.client.encode(
+            self.query_instruction + text, **self.encode_kwargs
+        )
         return embedding.tolist()