diff --git a/rag/llm/embedding_model.py b/rag/llm/embedding_model.py
index 24b312df2..c7dd655ac 100644
--- a/rag/llm/embedding_model.py
+++ b/rag/llm/embedding_model.py
@@ -379,7 +379,7 @@ class JinaMultiVecEmbed(Base):
             data = {"model": self.model_name, "input": input[i : i + batch_size]}
             if "v4" in self.model_name:
                 data["return_multivector"] = True
-            
+
             if "v3" in self.model_name or "v4" in self.model_name:
                 data['task'] = task
                 data['truncate'] = True
@@ -391,7 +391,7 @@ class JinaMultiVecEmbed(Base):
                     if data.get("return_multivector", False): # v4
                         token_embs = np.asarray(d['embeddings'], dtype=np.float32)
                         chunk_emb = token_embs.mean(axis=0)
-                    
+
                     else:
                         # v2/v3
                         chunk_emb = np.asarray(d['embedding'], dtype=np.float32)
@@ -481,7 +481,7 @@ class BedrockEmbed(Base):
         self.model_name = model_name
         self.is_amazon = self.model_name.split(".")[0] == "amazon"
         self.is_cohere = self.model_name.split(".")[0] == "cohere"
-        
+
         if mode == "access_key_secret":
             self.bedrock_ak = key.get("bedrock_ak")
             self.bedrock_sk = key.get("bedrock_sk")
@@ -885,15 +885,70 @@ class HuggingFaceEmbed(Base):
             raise Exception(f"Error: {response.status_code} - {response.text}")
 
 
-class VolcEngineEmbed(OpenAIEmbed):
+class VolcEngineEmbed(Base):
     _FACTORY_NAME = "VolcEngine"
 
     def __init__(self, key, model_name, base_url="https://ark.cn-beijing.volces.com/api/v3"):
         if not base_url:
             base_url = "https://ark.cn-beijing.volces.com/api/v3"
-        ark_api_key = json.loads(key).get("ark_api_key", "")
-        model_name = json.loads(key).get("ep_id", "") + json.loads(key).get("endpoint_id", "")
-        super().__init__(ark_api_key, model_name, base_url)
+        self.base_url = base_url
+
+        cfg = json.loads(key)
+        self.ark_api_key = cfg.get("ark_api_key", "")
+        self.model_name = model_name
+
+    @staticmethod
+    def _extract_embedding(result: dict) -> list[float]:
+        if not isinstance(result, dict):
+            raise TypeError(f"Unexpected response type: {type(result)}")
+
+        data = result.get("data")
+        if data is None:
+            raise KeyError("Missing 'data' in response")
+
+        if isinstance(data, list):
+            if not data:
+                raise ValueError("Empty 'data' in response")
+            item = data[0]
+        elif isinstance(data, dict):
+            item = data
+        else:
+            raise TypeError(f"Unexpected 'data' type: {type(data)}")
+
+        if not isinstance(item, dict):
+            raise TypeError("Unexpected item shape in 'data'")
+        if "embedding" not in item:
+            raise KeyError("Missing 'embedding' in response item")
+        return item["embedding"]
+
+    def _encode_texts(self, texts: list[str]):
+        from common.http_client import sync_request
+
+        url = f"{self.base_url}/embeddings/multimodal"
+        headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.ark_api_key}"}
+
+        ress: list[list[float]] = []
+        total_tokens = 0
+        for text in texts:
+            request_body = {"model": self.model_name, "input": [{"type": "text", "text": text}]}
+            response = sync_request(method="POST", url=url, headers=headers, json=request_body, timeout=60)
+            if response.status_code != 200:
+                raise Exception(f"Error: {response.status_code} - {response.text}")
+            result = response.json()
+            try:
+                ress.append(self._extract_embedding(result))
+                total_tokens += total_token_count_from_response(result)
+            except Exception as _e:
+                log_exception(_e)
+
+        return np.array(ress), total_tokens
+
+    def encode(self, texts: list):
+        return self._encode_texts(texts)
+
+    def encode_queries(self, text: str):
+        embeddings, tokens = self._encode_texts([text])
+        return embeddings[0], tokens
 
 
 class GPUStackEmbed(OpenAIEmbed):