diff --git a/python/llm/src/bigdl/llm/langchain/embeddings/__init__.py b/python/llm/src/bigdl/llm/langchain/embeddings/__init__.py
index d001919c976..e6ec52acf8d 100644
--- a/python/llm/src/bigdl/llm/langchain/embeddings/__init__.py
+++ b/python/llm/src/bigdl/llm/langchain/embeddings/__init__.py
@@ -20,7 +20,7 @@
 # only search the first bigdl package and end up finding only one sub-package.
 
 from .bigdlllm import *
-from .transformersembeddings import TransformersEmbeddings
+from .transformersembeddings import TransformersEmbeddings, TransformersBgeEmbeddings
 
 __all__ = [
     "BigdlNativeEmbeddings",
@@ -28,5 +28,6 @@
     "BloomEmbeddings",
     "GptneoxEmbeddings",
     "StarcoderEmbeddings",
-    "TransformersEmbeddings"
+    "TransformersEmbeddings",
+    "TransformersBgeEmbeddings"
 ]
diff --git a/python/llm/src/bigdl/llm/langchain/embeddings/transformersembeddings.py b/python/llm/src/bigdl/llm/langchain/embeddings/transformersembeddings.py
index c52a8adf285..9c69f4744c3 100644
--- a/python/llm/src/bigdl/llm/langchain/embeddings/transformersembeddings.py
+++ b/python/llm/src/bigdl/llm/langchain/embeddings/transformersembeddings.py
@@ -45,6 +45,7 @@
 # THE SOFTWARE.
 
 """Wrapper around BigdlLLM embedding models."""
+import torch
 from typing import Any, Dict, List, Optional
 import numpy as np
 
@@ -181,3 +182,14 @@ def embed_query(self, text: str) -> List[float]:
         text = text.replace("\n", " ")
         embedding = self.embed(text, **self.encode_kwargs)
         return embedding.tolist()
+
+# fit specific encode method for langchain.embeddings.HuggingFaceBgeEmbeddings
+# TODO: directly support HuggingFaceBgeEmbeddings
+class TransformersBgeEmbeddings(TransformersEmbeddings):
+
+    def embed(self, text: str, **kwargs):
+        input_ids = self.tokenizer.encode(text, return_tensors="pt", **kwargs)
+        input_ids = input_ids.to(self.model.device)
+        embeddings = self.model(input_ids, return_dict=False)[0].cpu()
+        embeddings = torch.nn.functional.normalize(embeddings[:, 0], p=2, dim=1)
+        return embeddings[0]