diff --git a/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py
index d0487c62b0..b547f59d95 100644
--- a/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py
@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, Tuple
 
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult, EmbeddingUsage
@@ -38,6 +38,50 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
             raise ValueError('Invalid model name')
         if not api_key:
             raise CredentialsValidateFailedError('api_key is required')
+        
+        # split into chunks of batch size 16
+        chunks = []
+        for i in range(0, len(texts), 16):
+            chunks.append(texts[i:i + 16])
+
+        embeddings = []
+        token_usage = 0
+
+        for chunk in chunks:
+            # embeding chunk
+            chunk_embeddings, chunk_usage = self.embedding(
+                model=model,
+                api_key=api_key,
+                texts=chunk,
+                user=user
+            )
+
+            embeddings.extend(chunk_embeddings)
+            token_usage += chunk_usage
+
+        result = TextEmbeddingResult(
+            model=model,
+            embeddings=embeddings,
+            usage=self._calc_response_usage(
+                model=model,
+                credentials=credentials,
+                tokens=token_usage
+            )
+        )
+
+        return result
+    
+    def embedding(self, model: str, api_key, texts: list[str], user: Optional[str] = None) \
+            -> Tuple[list[list[float]], int]:
+        """
+        Embed given texts
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :return: embeddings result
+        """
         url = self.api_base
         headers = {
             'Authorization': 'Bearer ' + api_key,
@@ -85,17 +129,10 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
         except Exception as e:
             raise InternalServerError(f"Failed to convert response to json: {e} with text: {response.text}")
 
-        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage['total_tokens'])
+        return [
+            data['embedding'] for data in embeddings
+        ], usage['total_tokens']
 
-        result = TextEmbeddingResult(
-            model=model,
-            embeddings=[[
-                float(data) for data in x['embedding']
-            ] for x in embeddings],
-            usage=usage
-        )
-
-        return result
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
diff --git a/api/tests/integration_tests/model_runtime/baichuan/test_text_embedding.py b/api/tests/integration_tests/model_runtime/baichuan/test_text_embedding.py
index b6d806df02..b0a6620bb0 100644
--- a/api/tests/integration_tests/model_runtime/baichuan/test_text_embedding.py
+++ b/api/tests/integration_tests/model_runtime/baichuan/test_text_embedding.py
@@ -59,3 +59,40 @@ def test_get_num_tokens():
     )
 
     assert num_tokens == 2
+
+def test_max_chunks():
+    model = BaichuanTextEmbeddingModel()
+
+    result = model.invoke(
+        model='baichuan-text-embedding',
+        credentials={
+            'api_key': os.environ.get('BAICHUAN_API_KEY'),
+        },
+        texts=[
+            "hello",
+            "world",
+            "hello",
+            "world",
+            "hello",
+            "world",
+            "hello",
+            "world",
+            "hello",
+            "world",
+            "hello",
+            "world",
+            "hello",
+            "world",
+            "hello",
+            "world",
+            "hello",
+            "world",
+            "hello",
+            "world",
+            "hello",
+            "world",
+        ]
+    )
+
+    assert isinstance(result, TextEmbeddingResult)
+    assert len(result.embeddings) == 22
\ No newline at end of file