push gitignore

app/server/ChatbotAgent/agent_server.py

@@ -34,7 +34,7 @@ class LCAgent(ls.LitAPI):
             key_prefix=settings.REDIS_HISTORY_KEY_PREFIX
         )
         self.vector_db = VectorDatabase(
-            vector_db_dir=os.getenv('VECTOR_DB_DIR', '/app/app/db'),
+            vector_db_dir=settings.VECTOR_DB_DIR,
             collection_name=settings.COLLECTION_NAME,
             embedding_model_name=settings.EMBEDDING_MODEL_NAME
         )
@@ -43,6 +43,7 @@ class LCAgent(ls.LitAPI):
             'max_len': 5,
             'gemini_model_name': settings.LLM_MODEL_NAME
         }
+        self.outfit_ids = []
 
     async def decode_request(self, request: AgentRequestModel):
         """
@@ -59,8 +60,11 @@ class LCAgent(ls.LitAPI):
         return request
 
     async def predict(self, request):
+
+        self.outfit_ids = [str(uuid.uuid4()) for _ in range(request.num_outfits)]
+
         asyncio.create_task(self.background_run(request))
-        return {"status": "Task initiated in background."}
+        return {"status": "Task initiated in background.", "outfit_ids": self.outfit_ids}
 
     async def encode_response(self, output):
         return output
@@ -108,7 +112,8 @@ class LCAgent(ls.LitAPI):
         if start_outfit is None:
             start_outfit = []
         tasks = []
-        for _ in range(num_outfits):
+        for i in range(num_outfits):
+            self.stylist_agent_kwages['outfit_id'] = self.outfit_ids[i]
             agent = AsyncStylistAgent(**self.stylist_agent_kwages)
             task = agent.run_styling_process(
                 request_summary=request_summary,

app/server/ChatbotAgent/chatbot_server.py

@@ -12,7 +12,7 @@ from app.core.data_structure import Role, Message
 from app.core.llm_interface import AsyncGeminiLLM
 from app.core.redis_manager import RedisManager
 from app.core.stylist_agent import AsyncStylistAgent
-from app.core.system_prompt import BASIC_PROMPT, SUMMARY_PROMPT
+from app.core.system_prompt import BASIC_PROMPT, SUMMARY_PROMPT, MEN_BASIC_PROMPT, WOMEN_BASIC_PROMPT
 from app.core.vector_database import VectorDatabase
 from google.genai import types
 
@@ -22,6 +22,7 @@ logger = logging.getLogger(__name__)
 class PredictRequest(BaseModel):
     user_id: str  # 用戶ID
     user_message: str  # 用戶輸入
+    gender: str  # 服装类型
 
 
 class LCChatBot(ls.LitAPI):
@@ -60,6 +61,10 @@ class LCChatBot(ls.LitAPI):
         user_msg = Message(role=Role.USER, content=user_message)
         chat_history = self.redis.get_history(user_id)
         chat_history.append(user_msg)
+        if request.gender == 'male':
+            BASIC_PROMPT = MEN_BASIC_PROMPT
+        else:
+            BASIC_PROMPT = WOMEN_BASIC_PROMPT
 
         contents = []
 
@@ -103,86 +108,3 @@ class LCChatBot(ls.LitAPI):
         # The for-loop must have async keyword here since output is an AsyncGenerator
         async for out in output:
             yield {"output": out}
-
-    async def process_query(self, user_id: str, user_message: str) -> str:
-        """
-        处理用户的最新输入，调用 LLM, 并更新历史记录。
-        """
-
-        # 添加用户消息到历史
-        user_msg = Message(role=Role.USER, content=user_message)
-        chat_history = self.redis.get_history(user_id)
-        chat_history.append(user_msg)
-
-        # 生成 LLM 回复
-        try:
-            response_text = await self.llm.generate_response(chat_history, system_prompt=BASIC_PROMPT)
-        except Exception as e:
-            logger("\n--- Final Recommendation Results ---")
-
-            logger.error(f"LLM 调用失败: {e}")
-            response_text = "抱歉，系统暂时无法响应，请稍后再试。"
-
-        # 添加助手消息到历史
-        if response_text:
-            assistant_msg = Message(role=Role.ASSISTANT, content=response_text)
-        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)
-
-        return response_text
-
-    async def get_conversation_summary(self, user_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)
-        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)], system_prompt=SUMMARY_PROMPT)
-
-        return summary
-
-    async def recommend_outfit(self, request_summary: str, stylist_name: str, start_outfit=None, num_outfits: int = 1):
-        """
-        基于用户的对话历史和需求，推荐一套搭配。
-
-        Args:
-            request_summary: 用户的request
-            start_outfit: 可选的初始搭配列表，每个元素包含 'item_id' 和 'category'。
-        """
-        if start_outfit is None:
-            start_outfit = []
-        tasks = []
-        for _ in range(num_outfits):
-            agent = AsyncStylistAgent(**self.stylist_agent_kwages)
-            task = agent.run_styling_process(request_summary, stylist_name, start_outfit)
-            tasks.append(task)
-        logger.info(f"--- Starting {num_outfits} concurrent outfit generation tasks. ---")
-
-        try:
-            results = await asyncio.gather(*tasks, return_exceptions=True)
-
-            successful_outfits = []
-            failed_outfits = []
-            for result in results:
-                if isinstance(result, Exception):
-                    # 任务执行中发生异常
-                    failed_outfits.append(f"Failed: {result}")
-                else:
-                    # 任务成功，result 是 run_styling_process 返回的图片路径
-                    successful_outfits.append(result)
-
-            return {
-                "successful_outfits": successful_outfits,
-                "failed_outfits": failed_outfits
-            }
-
-        except Exception as e:
-            logger.error(f"An unexpected error occurred during concurrent recommendation: {e}")
-            return {"error": str(e)}