diff --git a/.gitignore b/.gitignore
index 834faba..4b7a90f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,4 +8,5 @@ app/core/data/
 *.sqlite3
 *.log
 db
-*.sqlite
\ No newline at end of file
+*.sqlite
+*.png
\ No newline at end of file
diff --git a/app/server/ChatbotAgent/agent_server.py b/app/server/ChatbotAgent/agent_server.py
index bc5787c..3e44e9d 100644
--- a/app/server/ChatbotAgent/agent_server.py
+++ b/app/server/ChatbotAgent/agent_server.py
@@ -17,6 +17,7 @@ logger = logging.getLogger(__name__)
 
 class AgentRequestModel(BaseModel):
     user_id: str
+    session_id: str
     num_outfits: int
     stylist_path: str
     callback_url: str
@@ -71,7 +72,7 @@ class LCAgent(ls.LitAPI):
 
     async def background_run(self, request: AgentRequestModel):
         # 1. 根据用户ID查询对话历史，总结对话内容
-        request_summary = await self.get_conversation_summary(request.user_id)
+        request_summary = await self.get_conversation_summary(request.session_id)
         logger.info(f"request_summary: {request_summary}")
 
         # 2.根据对话总结推荐搭配
@@ -89,13 +90,13 @@ class LCAgent(ls.LitAPI):
         for failed in recommendation_results.get("failed_outfits", []):
             logger.error(f"❌ {failed}")
 
-    async def get_conversation_summary(self, user_id: str) -> str:
+    async def get_conversation_summary(self, session_id: str) -> str:
         """
         分析用户的完整会话历史，并打包成一个简洁的需求总结。
 
         这个总结可以直接作为输入 Prompt 传递给 Stylist Agent。`
         """
-        history_messages = self.redis.get_history(user_id)
+        history_messages = self.redis.get_history(session_id)
         input_message = "\n".join([f"{msg.role.value}: {msg.content}" for msg in history_messages])
         # 临时调用 LLM 或使用本地逻辑生成总结
         summary = await self.llm.generate_response(history=[Message(role=Role.USER, content=input_message)],
diff --git a/app/server/ChatbotAgent/chatbot_server.py b/app/server/ChatbotAgent/chatbot_server.py
index 518d0df..6c1d0d9 100644
--- a/app/server/ChatbotAgent/chatbot_server.py
+++ b/app/server/ChatbotAgent/chatbot_server.py
@@ -18,6 +18,7 @@ logger = logging.getLogger(__name__)
 
 class PredictRequest(BaseModel):
     user_id: str  # 用戶ID
+    session_id: str
     user_message: str  # 用戶輸入
     gender: str  # 服装类型
 
@@ -55,8 +56,10 @@ class LCChatBot(ls.LitAPI):
         # 添加用户消息到历史
         user_message = request.user_message
         user_id = request.user_id
+        session_id = request.session_id
+
         user_msg = Message(role=Role.USER, content=user_message)
-        chat_history = self.redis.get_history(user_id)
+        chat_history = self.redis.get_history(session_id)
         chat_history.append(user_msg)
         if request.gender == 'male':
             BASIC_PROMPT = MEN_BASIC_PROMPT
@@ -98,8 +101,8 @@ class LCChatBot(ls.LitAPI):
         else:
             assistant_msg = Message(role=Role.ASSISTANT, content="No response generated. Try again later.")
 
-        self.redis.save_message(user_id, user_msg)
-        self.redis.save_message(user_id, assistant_msg)
+        self.redis.save_message(session_id, user_msg)
+        self.redis.save_message(session_id, assistant_msg)
 
     async def encode_response(self, output):
         # The for-loop must have async keyword here since output is an AsyncGenerator