diff --git a/examples/pytorch/application/qwen_doc_search_QA_based_on_dashscope.ipynb b/examples/pytorch/application/qwen_doc_search_QA_based_on_dashscope.ipynb
index 98276726..475fc48e 100644
--- a/examples/pytorch/application/qwen_doc_search_QA_based_on_dashscope.ipynb
+++ b/examples/pytorch/application/qwen_doc_search_QA_based_on_dashscope.ipynb
@@ -30,32 +30,6 @@
     "!git clone https://github.com/shijiebei2009/CEC-Corpus.git"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0b53db17-7d6d-4192-a145-e470d6d2a6ec",
-   "metadata": {
-    "ExecutionIndicator": {
-     "show": true
-    },
-    "execution": {
-     "iopub.execute_input": "2023-08-10T10:31:46.520109Z",
-     "iopub.status.busy": "2023-08-10T10:31:46.519793Z",
-     "iopub.status.idle": "2023-08-10T10:31:46.894428Z",
-     "shell.execute_reply": "2023-08-10T10:31:46.893761Z",
-     "shell.execute_reply.started": "2023-08-10T10:31:46.520085Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import dashscope\n",
-    "import os\n",
-    "from dotenv import load_dotenv\n",
-    "from dashscope import TextEmbedding\n",
-    "from dashvector import Client, Doc"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -75,7 +49,13 @@
    },
    "outputs": [],
    "source": [
-    "# get env varible from .env, please make sure add DASHSCOPE_KEY in .env\n",
+    "import dashscope\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from dashscope import TextEmbedding\n",
+    "from dashvector import Client, Doc\n",
+    "\n",
+    "# get env variable from .env, please make sure add DASHSCOPE_KEY in .env\n",
     "load_dotenv()\n",
     "api_key = os.getenv('DASHSCOPE_KEY')\n",
     "dashscope.api_key = api_key\n",
@@ -149,15 +129,15 @@
     "def prepare_data_from_file(path, size):\n",
     "    # prepare the data from file in order to upsert to dashvector with a reasonable doc's size.\n",
     "    batch_docs = []\n",
-    "    batch_size = 12\n",
+    "    chunk_size = 12\n",
     "    with open(path, 'r', encoding='utf-8') as f:\n",
     "        doc = ''\n",
     "        count = 0\n",
     "        for line in f:\n",
-    "            if count < batch_size and line.strip() != '':\n",
+    "            if count < chunk_size and line.strip() != '':\n",
     "                doc += line\n",
     "                count += 1\n",
-    "            if count == batch_size:\n",
+    "            if count == chunk_size:\n",
     "                batch_docs.append(doc)\n",
     "                if len(batch_docs) == size:\n",
     "                    yield batch_docs[:]\n",
@@ -188,11 +168,10 @@
    },
    "outputs": [],
    "source": [
-    "def generate_embeddings(news):\n",
+    "def generate_embeddings(docs):\n",
     "    # create embeddings via DashScope's TextEmbedding model API\n",
     "    rsp = TextEmbedding.call(model=TextEmbedding.Models.text_embedding_v1,\n",
-    "                             input=news)\n",
-    "    #print(rsp)\n",
+    "                             input=docs)\n",
     "    embeddings = [record['embedding'] for record in rsp.output['embeddings']]\n",
     "    return embeddings if isinstance(news, list) else embeddings[0]"
    ]
@@ -210,19 +189,20 @@
    "outputs": [],
    "source": [
     "id = 0\n",
-    "# file_name = '天龙八部.txt'\n",
     "dir_name = 'CEC-Corpus/raw corpus/allSourceText'\n",
     "\n",
     "# indexing the raw docs with index to dashvector\n",
     "collection = ds_client.get(collection_name)\n",
-    "batch_size = 4  # embedding api max batch size\n",
+    "\n",
+    "# embedding api max batch size\n",
+    "batch_size = 4  \n",
+    "\n",
     "for news in list(prepare_data_from_dir(dir_name, batch_size)):\n",
     "    ids = [id + i for i, _ in enumerate(news)]\n",
     "    id += len(news)\n",
     "    # generate embedding from raw docs\n",
     "    vectors = generate_embeddings(news)\n",
-    "    print(news)\n",
-    "    # upsert and indexing\n",
+    "    # upsert and index\n",
     "    ret = collection.upsert(\n",
     "        [\n",
     "            Doc(id=str(id), vector=vector, fields={\"raw\": doc})\n",
@@ -302,9 +282,7 @@
      "output_type": "stream",
      "text": [
       "2006-08-26 10:41:45\n",
-      "8月23日上午9时40分，京沪高速公路沧州服务区附近，一辆由北向南行驶的金杯面包车撞到高速公路护栏上，车上5名清华大学博士后研究人员及1名司机受伤，被紧急送往沧州二医院抢救。截至发稿时，仍有一名张姓博士后研究人员尚未脱离危险。\n",
-      "\n",
-      "\n"
+      "8月23日上午9时40分，京沪高速公路沧州服务区附近，一辆由北向南行驶的金杯面包车撞到高速公路护栏上，车上5名清华大学博士后研究人员及1名司机受伤，被紧急送往沧州二医院抢救。截至发稿时，仍有一名张姓博士后研究人员尚未脱离危险。\n"
      ]
     }
    ],