fix #994 (#1006)

### What problem does this PR solve?

#994 

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

rag/llm/embedding_model.py

 
     def encode(self, texts: list, batch_size=10):
         import dashscope
-        res = []
-        token_count = 0
-        texts = [truncate(t, 2048) for t in texts]
-        for i in range(0, len(texts), batch_size):
+        try:
+            res = []
+            token_count = 0
+            texts = [truncate(t, 2048) for t in texts]
+            for i in range(0, len(texts), batch_size):
+                resp = dashscope.TextEmbedding.call(
+                    model=self.model_name,
+                    input=texts[i:i + batch_size],
+                    text_type="document"
+                )
+                embds = [[] for _ in range(len(resp["output"]["embeddings"]))]
+                for e in resp["output"]["embeddings"]:
+                    embds[e["text_index"]] = e["embedding"]
+                res.extend(embds)
+                token_count += resp["usage"]["total_tokens"]
+            return np.array(res), token_count
+        except Exception as e:
+            raise Exception("Account abnormal. Please ensure it's on good standing.")
+        return np.array([]), 0
+
+    def encode_queries(self, text):
+        try:
             resp = dashscope.TextEmbedding.call(
                 model=self.model_name,
-                input=texts[i:i + batch_size],
-                text_type="document"
+                input=text[:2048],
+                text_type="query"
             )
-            embds = [[] for _ in range(len(resp["output"]["embeddings"]))]
-            for e in resp["output"]["embeddings"]:
-                embds[e["text_index"]] = e["embedding"]
-            res.extend(embds)
-            token_count += resp["usage"]["total_tokens"]
-        return np.array(res), token_count
-
-    def encode_queries(self, text):
-        resp = dashscope.TextEmbedding.call(
-            model=self.model_name,
-            input=text[:2048],
-            text_type="query"
-        )
-        return np.array(resp["output"]["embeddings"][0]
-                        ["embedding"]), resp["usage"]["total_tokens"]
+            return np.array(resp["output"]["embeddings"][0]
+                            ["embedding"]), resp["usage"]["total_tokens"]
+        except Exception as e:
+            raise Exception("Account abnormal. Please ensure it's on good standing.")
+        return np.array([]), 0
 
 
 class ZhipuEmbed(Base):