agent init

api/agents/conversation_agent.py

@@ -8,7 +8,8 @@ from langchain.memory import ConversationBufferMemory
 from langchain.prompts import PromptTemplate
 
 from services.llm_service import llm_service
-from .prompts import ConversationStage, get_conversation_prompt
+from .prompts import ConversationStage, get_conversation_prompt, get_guided_conversation_prompt
+from .state_schema import MemoirStateSchema
 
 
 class ConversationAgent:
@@ -82,6 +83,60 @@ class ConversationAgent:
         response = chain.predict(input=user_message)
         
         return response
+
+    def generate_response_with_state(
+        self,
+        conversation_id: str,
+        user_message: str,
+        memoir_state: MemoirStateSchema
+    ) -> List[str]:
+        """
+        基于共享状态生成引导式回复
+
+        Args:
+            conversation_id: 对话 ID
+            user_message: 用户消息
+            memoir_state: 共享状态
+
+        Returns:
+            Agent 回应文本列表（支持多条消息）
+        """
+        if not self.llm:
+            return ["抱歉，LLM 服务未配置。请设置 DEEPSEEK_API_KEY 或 LLM_API_KEY 环境变量。"]
+
+        empty_slots = memoir_state.empty_slots_for_current_stage()
+        filled_slots = {
+            key: value.snippet
+            for key, value in memoir_state.slots.get(memoir_state.current_stage, {}).items()
+            if value.snippet
+        }
+
+        system_prompt = get_guided_conversation_prompt(
+            current_stage=memoir_state.current_stage,
+            empty_slots=empty_slots,
+            filled_slots=filled_slots,
+            user_message=user_message,
+        )
+
+        memory = self._get_memory(conversation_id)
+        prompt_template = PromptTemplate(
+            input_variables=["history", "input"],
+            template=f"{system_prompt}\n\n{{history}}\n\nHuman: {{input}}\n\nAssistant:"
+        )
+
+        chain = ConversationChain(
+            llm=self.llm,
+            prompt=prompt_template,
+            memory=memory,
+            verbose=False
+        )
+
+        response = chain.predict(input=user_message)
+        
+        # 支持多条消息，用 [SPLIT] 分隔
+        messages = [msg.strip() for msg in response.split("[SPLIT]") if msg.strip()]
+        # 最多返回 3 条
+        return messages[:3] if messages else [response]
     
     def detect_stage(self, conversation_id: str, user_message: str) -> ConversationStage:
         """