重构了一下

function_calls/llm.py

@@ -49,10 +49,12 @@ class FunctionCallLLM:
             self.logger.error("无可用的AI模型")
             return LLMRunResult(handled=False, error="no_model")
 
+        if not hasattr(chat_model, "call_with_functions"):
+            self.logger.error("当前模型不支持函数调用接口，请配置支持 function calling 的模型")
+            return LLMRunResult(handled=False, error="no_function_call_support")
+
         try:
-            if hasattr(chat_model, "call_with_functions"):
-                return self._run_native_loop(ctx, chat_model, functions, executor, formatter)
-            return self._run_prompt_loop(ctx, chat_model, functions, executor)
+            return self._run_native_loop(ctx, chat_model, functions, executor, formatter)
         except Exception as exc:  # pragma: no cover - safeguard
             self.logger.error(f"LLM 调用失败: {exc}")
             return LLMRunResult(handled=False, error=str(exc))
@@ -133,62 +135,6 @@ class FunctionCallLLM:
         self.logger.warning("达到最大函数调用轮数，未得到最终回答")
         return LLMRunResult(handled=False, error="max_rounds")
 
-    # ---------------------------------------------------------------------
-    # Prompt-based fallback workflow
-    # ---------------------------------------------------------------------
-
-    def _run_prompt_loop(
-        self,
-        ctx: MessageContext,
-        chat_model: Any,
-        functions: Dict[str, FunctionSpec],
-        executor: Callable[[FunctionSpec, Dict[str, Any]], FunctionResult],
-    ) -> LLMRunResult:
-        system_prompt = self._build_prompt_system_text(functions)
-        user_input = f"用户输入：{ctx.text}"
-
-        ai_response = chat_model.get_answer(
-            user_input,
-            wxid=ctx.get_receiver(),
-            system_prompt_override=system_prompt,
-        )
-
-        json_match = re.search(r"\{.*\}", ai_response, re.DOTALL)
-        if not json_match:
-            self.logger.warning(f"提示词模式下无法解析JSON: {ai_response}")
-            return LLMRunResult(handled=False)
-
-        try:
-            decision = json.loads(json_match.group(0))
-        except json.JSONDecodeError as exc:
-            self.logger.error(f"提示词模式 JSON 解析失败: {exc}")
-            return LLMRunResult(handled=False)
-
-        action_type = decision.get("action_type")
-        if action_type == "chat":
-            # 提示词模式下无法获得模型最终回答，交给上层兜底
-            return LLMRunResult(handled=False)
-
-        if action_type != "function":
-            self.logger.warning(f"未知的action_type: {action_type}")
-            return LLMRunResult(handled=False)
-
-        function_name = decision.get("function_name")
-        if function_name not in functions:
-            self.logger.warning(f"未知的功能名 - {function_name}")
-            return LLMRunResult(handled=False)
-
-        arguments = decision.get("arguments", {})
-        result = executor(functions[function_name], arguments)
-        if not result.handled:
-            return LLMRunResult(handled=False)
-
-        return LLMRunResult(handled=True, final_response="\n".join(result.messages))
-
-    # ------------------------------------------------------------------
-    # Helpers
-    # ------------------------------------------------------------------
-
     @staticmethod
     def _convert_assistant_message(message: Any) -> Dict[str, Any]:
         entry: Dict[str, Any] = {
@@ -224,16 +170,6 @@ class FunctionCallLLM:
             )
         return openai_functions
 
-    @staticmethod
-    def _build_prompt_system_text(functions: Dict[str, FunctionSpec]) -> str:
-        prompt = """你是一个智能路由助手。根据用户输入判断是否需要调用以下函数之一。"""
-        for spec in functions.values():
-            prompt += f"\n- {spec.name}: {spec.description}"
-        prompt += """
-请严格输出JSON：{"action_type": "chat"} 或 {"action_type": "function", "function_name": "...", "arguments": {...}}
-"""
-        return prompt
-
     def validate_arguments(self, arguments: Dict[str, Any], schema: Dict[str, Any]) -> bool:
         try:
             required_fields = schema.get("required", [])