add training configuration APIs

app/agents/orchestrator.py

@@ -20,7 +20,7 @@ class MedicalConsultationOrchestrator:
 
     async def patient_reply(self, session: TrainingSession, case: CaseBase, memory_messages: list[dict], message: str) -> LLMResponse:
         """问诊编排：调用 Patient Agent 生成 AI 病人回复。"""
-        return await self.patient_agent.reply(case, memory_messages, message, session.mode)
+        return await self.patient_agent.reply(case, memory_messages, message, session.mode, self._patient_config(session))
 
     async def patient_stream_reply(
         self,
@@ -30,7 +30,7 @@ class MedicalConsultationOrchestrator:
         message: str,
     ) -> AsyncIterator[LLMStreamChunk]:
         """流式问诊编排：调用 Patient Agent 并返回流式片段。"""
-        async for chunk in self.patient_agent.stream_reply(case, memory_messages, message, session.mode):
+        async for chunk in self.patient_agent.stream_reply(case, memory_messages, message, session.mode, self._patient_config(session)):
             yield chunk
 
     async def evaluate(
@@ -67,3 +67,9 @@ class MedicalConsultationOrchestrator:
     ) -> dict:
         """新手提示编排：基于当前会话上下文生成轻量训练提醒。"""
         return await self.hint_agent.generate(session, case, memory_messages, orders, last_user_message)
+
+    def _patient_config(self, session: TrainingSession) -> dict | None:
+        """病人配置：从会话 metadata 读取训练页初始化配置，传递给 Patient Agent。"""
+        metadata = session.metadata_ or {}
+        patient_config = metadata.get("patient_config") if isinstance(metadata, dict) else None
+        return patient_config if isinstance(patient_config, dict) else None