diff --git a/pilot/scene/chat_knowledge/custom/prompt.py b/pilot/scene/chat_knowledge/custom/prompt.py
index c3153c819..110250221 100644
--- a/pilot/scene/chat_knowledge/custom/prompt.py
+++ b/pilot/scene/chat_knowledge/custom/prompt.py
@@ -31,7 +31,7 @@ prompt = PromptTemplate(
     template_scene=ChatScene.ChatNewKnowledge.value,
     input_variables=["context", "question"],
     response_format=None,
-    template_define=None,
+    template_define=PROMPT_SCENE_DEFINE,
     template=_DEFAULT_TEMPLATE,
     stream_out=PROMPT_NEED_NEED_STREAM_OUT,
     output_parser=NormalChatOutputParser(
diff --git a/pilot/scene/chat_knowledge/default/prompt.py b/pilot/scene/chat_knowledge/default/prompt.py
index 51d2419d5..0526be69b 100644
--- a/pilot/scene/chat_knowledge/default/prompt.py
+++ b/pilot/scene/chat_knowledge/default/prompt.py
@@ -11,6 +11,10 @@ from pilot.scene.chat_normal.out_parser import NormalChatOutputParser
 
 CFG = Config()
 
+PROMPT_SCENE_DEFINE = """A chat between a curious user and an artificial intelligence assistant, who very familiar with database related knowledge. 
+    The assistant gives helpful, detailed, professional and polite answers to the user's questions. """
+
+
 _DEFAULT_TEMPLATE = """ 基于以下已知的信息, 专业、简要的回答用户的问题,
             如果无法从提供的内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题" 禁止胡乱编造。 
             已知内容: 
@@ -28,7 +32,7 @@ prompt = PromptTemplate(
     template_scene=ChatScene.ChatKnowledge.value,
     input_variables=["context", "question"],
     response_format=None,
-    template_define=None,
+    template_define=PROMPT_SCENE_DEFINE,
     template=_DEFAULT_TEMPLATE,
     stream_out=PROMPT_NEED_NEED_STREAM_OUT,
     output_parser=NormalChatOutputParser(
diff --git a/pilot/scene/chat_knowledge/url/prompt.py b/pilot/scene/chat_knowledge/url/prompt.py
index 20a69d8b2..96e1ee520 100644
--- a/pilot/scene/chat_knowledge/url/prompt.py
+++ b/pilot/scene/chat_knowledge/url/prompt.py
@@ -11,10 +11,21 @@ from pilot.scene.chat_normal.out_parser import NormalChatOutputParser
 
 CFG = Config()
 
-_DEFAULT_TEMPLATE = """ Based on the known information, provide professional and concise answers to the user's questions. If the answer cannot be obtained from the provided content, please say: 'The information provided in the knowledge base is not sufficient to answer this question.' Fabrication is prohibited.。 
-            known information: 
+PROMPT_SCENE_DEFINE = """A chat between a curious user and an artificial intelligence assistant, who very familiar with database related knowledge. 
+    The assistant gives helpful, detailed, professional and polite answers to the user's questions. """
+
+
+# _DEFAULT_TEMPLATE = """ Based on the known information, provide professional and concise answers to the user's questions. If the answer cannot be obtained from the provided content, please say: 'The information provided in the knowledge base is not sufficient to answer this question.' Fabrication is prohibited.。
+#             known information:
+#             {context}
+#             question:
+#             {question}
+# """
+_DEFAULT_TEMPLATE = """ 基于以下已知的信息, 专业、简要的回答用户的问题,
+            如果无法从提供的内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题" 禁止胡乱编造。 
+            已知内容: 
             {context}
-            question:
+            问题:
             {question}
 """
 
@@ -27,7 +38,7 @@ prompt = PromptTemplate(
     template_scene=ChatScene.ChatUrlKnowledge.value,
     input_variables=["context", "question"],
     response_format=None,
-    template_define=None,
+    template_define=PROMPT_SCENE_DEFINE,
     template=_DEFAULT_TEMPLATE,
     stream_out=PROMPT_NEED_NEED_STREAM_OUT,
     output_parser=NormalChatOutputParser(
diff --git a/pilot/source_embedding/url_embedding.py b/pilot/source_embedding/url_embedding.py
index e74defa80..774f6e852 100644
--- a/pilot/source_embedding/url_embedding.py
+++ b/pilot/source_embedding/url_embedding.py
@@ -23,7 +23,7 @@ class URLEmbedding(SourceEmbedding):
         """Load from url path."""
         loader = WebBaseLoader(web_path=self.file_path)
         text_splitor = CharacterTextSplitter(
-            chunk_size=1000, chunk_overlap=20, length_function=len
+            chunk_size=100, chunk_overlap=20, length_function=len
         )
         return loader.load_and_split(text_splitor)