add training configuration APIs

app/agents/patient_agent.py

@@ -11,9 +11,16 @@ class PatientAgent:
     def __init__(self, llm: DeepSeekClient | None = None) -> None:
         self.llm = llm or DeepSeekClient()
 
-    async def reply(self, case: CaseBase, memory_messages: list[dict], user_message: str, mode: str) -> LLMResponse:
+    async def reply(
+        self,
+        case: CaseBase,
+        memory_messages: list[dict],
+        user_message: str,
+        mode: str,
+        patient_config: dict | None = None,
+    ) -> LLMResponse:
         """问诊回复：拼接病例上下文、短期记忆和用户输入后调用 Patient Agent。"""
-        messages = self._build_messages(case, memory_messages, user_message, mode)
+        messages = self._build_messages(case, memory_messages, user_message, mode, patient_config)
         return await self.llm.chat(
             messages,
             settings.llm_fast_model,
@@ -27,9 +34,10 @@ class PatientAgent:
         memory_messages: list[dict],
         user_message: str,
         mode: str,
+        patient_config: dict | None = None,
     ) -> AsyncIterator[LLMStreamChunk]:
         """流式问诊：以 SSE 方式返回 AI 病人增量回复。"""
-        messages = self._build_messages(case, memory_messages, user_message, mode)
+        messages = self._build_messages(case, memory_messages, user_message, mode, patient_config)
         async for chunk in self.llm.stream_chat(
             messages,
             settings.llm_fast_model,
@@ -38,10 +46,18 @@ class PatientAgent:
         ):
             yield chunk
 
-    def _build_messages(self, case: CaseBase, memory_messages: list[dict], user_message: str, mode: str) -> list[dict]:
+    def _build_messages(
+        self,
+        case: CaseBase,
+        memory_messages: list[dict],
+        user_message: str,
+        mode: str,
+        patient_config: dict | None = None,
+    ) -> list[dict]:
         """提示词拼接：构造 AI 病人的系统提示词和对话历史。"""
         profile = case.ai_patient_profile or {}
         hidden_info = case.hidden_patient_info or {}
+        config_rule = self._build_patient_config_rule(patient_config)
         mode_rule = {
             "novice": "新手模式：回答清楚，必要时可提示医生继续追问症状、既往史或检查。",
             "practice": "练习模式：只回答被问到的信息，不主动给诊断建议。",
@@ -52,6 +68,7 @@ class PatientAgent:
 病例主诉：{case.chief_complaint}
 患者人设：{profile}
 隐藏信息：{hidden_info}
+病人初始化配置：{config_rule}
 回答规则：
 1. 不主动透露未被问到的隐藏信息。
 2. 不替医生做诊断，不提供治疗方案。
@@ -66,6 +83,21 @@ class PatientAgent:
         messages.append({"role": "user", "content": user_message})
         return messages
 
+    def _build_patient_config_rule(self, patient_config: dict | None) -> str:
+        """配置提示：把训练页初始化配置转成 AI 病人表达约束。"""
+        if not patient_config:
+            return "使用默认门诊、青年、高等教育、平和性格的表达方式。"
+        labels = patient_config.get("labels") if isinstance(patient_config, dict) else None
+        values = labels or (patient_config.get("values") if isinstance(patient_config, dict) else {}) or {}
+        visit_environment = values.get("visit_environment", "门诊")
+        age_group = values.get("age_group", "青年")
+        education_level = values.get("education_level", "高等教育")
+        personality = values.get("personality", "平和")
+        return (
+            f"就诊环境={visit_environment}；年龄段={age_group}；文化程度={education_level}；性格={personality}。"
+            "回答时根据性格调整情绪和配合度，根据文化程度调整表达清晰度，但不得改变病例事实。"
+        )
+
     def _to_llm_history(self, memory_messages: list[dict]) -> list[dict]:
         """历史转换：把业务角色 doctor/patient 转换为 LLM role。"""
         role_map = {"doctor": "user", "patient": "assistant", "system": "system", "tool": "assistant"}