fix：优化工具解析

src/heart_flow/sub_mind.py

@@ -67,6 +67,9 @@ class SubMind:
         self.past_mind = []
         self.structured_info = {}
         
+        name = chat_manager.get_stream_name(self.subheartflow_id)
+        self.log_prefix = f"[{name}] "
+
     async def do_thinking_before_reply(self, history_cycle: list[CycleInfo] = None):
         """
         在回复前进行思考，生成内心想法并收集工具调用结果
@@ -77,6 +80,8 @@ class SubMind:
         # 更新活跃时间
         self.last_active_time = time.time()
 
+        
+        
         # ---------- 1. 准备基础数据 ----------
         # 获取现有想法和情绪状态
         current_thinking_info = self.current_mind
@@ -85,7 +90,7 @@ class SubMind:
         # 获取观察对象
         observation = self.observations[0]
         if not observation:
-            logger.error(f"[{self.subheartflow_id}] 无法获取观察对象")
+            logger.error(f"{self.log_prefix} 无法获取观察对象")
             self.update_current_mind("(我没看到任何聊天内容...)")
             return self.current_mind, self.past_mind
 
@@ -223,28 +228,17 @@ class SubMind:
 
         try:
             # 调用LLM生成响应
-            response = await self.llm_model.generate_response_tool_async(prompt=prompt, tools=tools)
+            response, _reasoning_content, tool_calls = await self.llm_model.generate_response_tool_async(prompt=prompt, tools=tools)
 
-            # 标准化响应格式
-            success, normalized_response, error_msg = normalize_llm_response(
-                response, log_prefix=f"[{self.subheartflow_id}] "
-            )
+            logger.debug(f"{self.log_prefix} 子心流输出的原始LLM响应: {response}")
             
-            if not success:
-                # 处理标准化失败情况
-                logger.warning(f"[{self.subheartflow_id}] {error_msg}")
-                content = "LLM响应格式无法处理"
-            else:
-                # 从标准化响应中提取内容
-                if len(normalized_response) >= 2:
-                    content = normalized_response[0]
-                    _reasoning_content = normalized_response[1] if len(normalized_response) > 1 else ""
+            # 直接使用LLM返回的文本响应作为 content
+            content = response if response else ""
 
-                # 处理可能的工具调用
-                if len(normalized_response) == 3:
-                    # 提取并验证工具调用
+            if tool_calls:
+                # 直接将 tool_calls 传递给处理函数
                 success, valid_tool_calls, error_msg = process_llm_tool_calls(
-                        normalized_response, log_prefix=f"[{self.subheartflow_id}] "
+                        tool_calls, log_prefix=f"{self.log_prefix} "
                 )
                 
                 if success and valid_tool_calls:
@@ -253,27 +247,29 @@ class SubMind:
                         [call.get("function", {}).get("name", "未知工具") for call in valid_tool_calls]
                     )
                     logger.info(
-                            f"[{self.subheartflow_id}] 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}"
+                        f"{self.log_prefix} 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}"
                     )
 
                     # 收集工具执行结果
                     await self._execute_tool_calls(valid_tool_calls, tool_instance)
                 elif not success:
-                        logger.warning(f"[{self.subheartflow_id}] {error_msg}")
+                    logger.warning(f"{self.log_prefix} 处理工具调用时出错: {error_msg}")
+            else:
+                logger.info(f"{self.log_prefix} 心流未使用工具") # 修改日志信息，明确是未使用工具而不是未处理
+
         except Exception as e:
             # 处理总体异常
-            logger.error(f"[{self.subheartflow_id}] 执行LLM请求或处理响应时出错: {e}")
+            logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
             logger.error(traceback.format_exc())
             content = "思考过程中出现错误"
 
         # 记录最终思考结果
-        name = chat_manager.get_stream_name(self.subheartflow_id)
-        logger.debug(f"[{name}] \nPrompt:\n{prompt}\n\n心流思考结果:\n{content}\n")
+        logger.debug(f"{self.log_prefix} \nPrompt:\n{prompt}\n\n心流思考结果:\n{content}\n")
 
         # 处理空响应情况
         if not content:
             content = "(不知道该想些什么...)"
-            logger.warning(f"[{self.subheartflow_id}] LLM返回空结果，思考失败。")
+            logger.warning(f"{self.log_prefix} LLM返回空结果，思考失败。")
 
         # ---------- 6. 更新思考状态并返回结果 ----------
         # 更新当前思考内容

src/plugins/heartFC_chat/heartFC_chat.py

@@ -17,7 +17,7 @@ from src.plugins.utils.timer_calculator import Timer  # <--- Import Timer
 from src.plugins.heartFC_chat.heartFC_generator import HeartFCGenerator
 from src.do_tool.tool_use import ToolUser
 from src.plugins.emoji_system.emoji_manager import emoji_manager
-from src.plugins.utils.json_utils import process_llm_tool_response  # 导入新的JSON工具
+from src.plugins.utils.json_utils import process_llm_tool_calls, extract_tool_call_arguments
 from src.heart_flow.sub_mind import SubMind
 from src.heart_flow.observation import Observation
 from src.plugins.heartFC_chat.heartflow_prompt_builder import global_prompt_manager
@@ -401,20 +401,24 @@ class HeartFChatting:
             with Timer("决策", cycle_timers):
                 planner_result = await self._planner(current_mind, cycle_timers)
 
-            action = planner_result.get("action", "error")
-            reasoning = planner_result.get("reasoning", "未提供理由")
             
-            self._current_cycle.set_action_info(action, reasoning, False)
+            # 效果不太好，还没处理replan导致观察时间点改变的问题
+            
+            # action = planner_result.get("action", "error")
+            # reasoning = planner_result.get("reasoning", "未提供理由")
+
+            # self._current_cycle.set_action_info(action, reasoning, False)
 
             # 在获取规划结果后检查新消息
-            if await self._check_new_messages(planner_start_db_time):
-                if random.random() < 0.2:
-                    logger.info(f"{self.log_prefix} 看到了新消息，麦麦决定重新观察和规划...")
-                    # 重新规划
-                    with Timer("重新决策", cycle_timers):
-                        self._current_cycle.replanned = True
-                        planner_result = await self._planner(current_mind, cycle_timers, is_re_planned=True)
-                    logger.info(f"{self.log_prefix} 重新规划完成.")
+            
+            # if await self._check_new_messages(planner_start_db_time):
+            #     if random.random() < 0.2:
+            #         logger.info(f"{self.log_prefix} 看到了新消息，麦麦决定重新观察和规划...")
+            #         # 重新规划
+            #         with Timer("重新决策", cycle_timers):
+            #             self._current_cycle.replanned = True
+            #             planner_result = await self._planner(current_mind, cycle_timers, is_re_planned=True)
+            #         logger.info(f"{self.log_prefix} 重新规划完成.")
 
             # 解析规划结果
             action = planner_result.get("action", "error")
@@ -736,94 +740,104 @@ class HeartFChatting:
         observed_messages_str = observation.talking_message_str
 
         # --- 使用 LLM 进行决策 --- #
-        action = "no_reply"  # 默认动作
-        emoji_query = ""  # 默认表情查询
         reasoning = "默认决策或获取决策失败"
         llm_error = False  # LLM错误标志
+        arguments = None # 初始化参数变量
+        emoji_query = "" # <--- 在这里初始化 emoji_query
 
         try:
-            # 构建提示词
-
+            # --- 构建提示词 ---
+            replan_prompt_str = ""
             if is_re_planned:
-                replan_prompt = await self._build_replan_prompt(
+                replan_prompt_str = await self._build_replan_prompt(
                     self._current_cycle.action_type, self._current_cycle.reasoning
                 )
-                prompt = replan_prompt
-            else:
-                replan_prompt = ""
             prompt = await self._build_planner_prompt(
-                observed_messages_str, current_mind, self.sub_mind.structured_info, replan_prompt
+                observed_messages_str, current_mind, self.sub_mind.structured_info, replan_prompt_str
             )
-            payload = {
-                "model": global_config.llm_plan["name"],
-                "messages": [{"role": "user", "content": prompt}],
-                "tools": self.action_manager.get_planner_tool_definition(),
-                "tool_choice": {"type": "function", "function": {"name": "decide_reply_action"}},
-            }
-
-            # 执行LLM请求
 
+            # --- 调用 LLM --- 
             try:
-                print("prompt")
-                print("prompt")
-                print("prompt")
-                print(payload)
-                print(prompt)
-                response = await self.planner_llm._execute_request(
-                    endpoint="/chat/completions", payload=payload, prompt=prompt
+                planner_tools = self.action_manager.get_planner_tool_definition()
+                _response_text, _reasoning_content, tool_calls = await self.planner_llm.generate_response_tool_async(
+                    prompt=prompt,
+                    tools=planner_tools,
                 )
-                print(response)
+                logger.debug(f"{self.log_prefix}[Planner] 原始人 LLM响应: {_response_text}")
             except Exception as req_e:
                 logger.error(f"{self.log_prefix}[Planner] LLM请求执行失败: {req_e}")
+                action = "error"
+                reasoning = f"LLM请求失败: {req_e}"
+                llm_error = True
+                # 直接返回错误结果
                 return {
-                    "action": "error",
-                    "reasoning": f"LLM请求执行失败: {req_e}",
+                    "action": action,
+                    "reasoning": reasoning,
                     "emoji_query": "",
                     "current_mind": current_mind,
                     "observed_messages": observed_messages,
-                    "llm_error": True,
+                    "llm_error": llm_error,
                 }
 
-            # 处理LLM响应
-            with Timer("使用工具", cycle_timers):
-                # 使用辅助函数处理工具调用响应
-                print(1111122222222222)
-                print(response)
+            # 默认错误状态
+            action = "error"
+            reasoning = "处理工具调用时出错"
+            llm_error = True
 
-                success, arguments, error_msg = process_llm_tool_response(
-                    response, expected_tool_name="decide_reply_action", log_prefix=f"{self.log_prefix}[Planner] "
+            # 1. 验证工具调用
+            success, valid_tool_calls, error_msg = process_llm_tool_calls(
+                tool_calls, log_prefix=f"{self.log_prefix}[Planner] "
             )
 
-                if success:
-                    # 提取决策参数
-                    action = arguments.get("action", "no_reply")
-                    # 验证动作是否在可用动作集中
-                    if action not in self.action_manager.get_available_actions():
+            if success and valid_tool_calls:
+                # 2. 提取第一个调用并获取参数
+                first_tool_call = valid_tool_calls[0]
+                tool_name = first_tool_call.get("function", {}).get("name")
+                arguments = extract_tool_call_arguments(first_tool_call, None)
+                
+                # 3. 检查名称和参数
+                expected_tool_name = "decide_reply_action"
+                if tool_name == expected_tool_name and arguments is not None:
+                    # 4. 成功，提取决策
+                    extracted_action = arguments.get("action", "no_reply")
+                    # 验证动作
+                    if extracted_action not in self.action_manager.get_available_actions():
                         logger.warning(
-                            f"{self.log_prefix}[Planner] LLM返回了未授权的动作: {action}，使用默认动作no_reply"
+                            f"{self.log_prefix}[Planner] LLM返回了未授权的动作: {extracted_action}，使用默认动作no_reply"
                         )
                         action = "no_reply"
-                        reasoning = f"LLM返回了未授权的动作: {action}"
+                        reasoning = f"LLM返回了未授权的动作: {extracted_action}"
+                        emoji_query = ""
+                        llm_error = False # 视为非LLM错误，只是逻辑修正
                     else:
+                        # 动作有效，使用提取的值
+                        action = extracted_action
                         reasoning = arguments.get("reasoning", "未提供理由")
                         emoji_query = arguments.get("emoji_query", "")
-
+                        llm_error = False # 成功处理
                         # 记录决策结果
                         logger.debug(
                             f"{self.log_prefix}[要做什么]\nPrompt:\n{prompt}\n\n决策结果: {action}, 理由: {reasoning}, 表情查询: '{emoji_query}'"
                         )
-                else:
-                    # 处理工具调用失败
-                    logger.warning(f"{self.log_prefix}[Planner] {error_msg}")
-                    action = "error"
-                    reasoning = error_msg
-                    llm_error = True
+                elif tool_name != expected_tool_name:
+                    reasoning = f"LLM返回了非预期的工具: {tool_name}"
+                    logger.warning(f"{self.log_prefix}[Planner] {reasoning}")
+                else: # arguments is None
+                    reasoning = f"无法提取工具 {tool_name} 的参数"
+                    logger.warning(f"{self.log_prefix}[Planner] {reasoning}")
+            elif not success:
+                reasoning = f"验证工具调用失败: {error_msg}"
+                logger.warning(f"{self.log_prefix}[Planner] {reasoning}")
+            else: # not valid_tool_calls
+                reasoning = "LLM未返回有效的工具调用"
+                logger.warning(f"{self.log_prefix}[Planner] {reasoning}")
+            # 如果 llm_error 仍然是 True，说明在处理过程中有错误发生
 
         except Exception as llm_e:
-            logger.error(f"{self.log_prefix}[Planner] Planner LLM处理过程中出错: {llm_e}")
-            logger.error(traceback.format_exc())  # 记录完整堆栈以便调试
+            logger.error(f"{self.log_prefix}[Planner] Planner LLM处理过程中发生意外错误: {llm_e}")
+            logger.error(traceback.format_exc())
             action = "error"
-            reasoning = f"LLM处理失败: {llm_e}"
+            reasoning = f"Planner内部处理错误: {llm_e}"
             llm_error = True
         # --- 结束 LLM 决策 --- #
 

src/plugins/models/utils_model.py

@@ -739,7 +739,7 @@ class LLMRequest:
 
         return response
 
-    async def generate_response_tool_async(self, prompt: str, tools: list, **kwargs) -> Union[str, Tuple]:
+    async def generate_response_tool_async(self, prompt: str, tools: list, **kwargs) -> tuple[str, str, list]:
         """异步方式根据输入的提示生成模型的响应"""
         # 构建请求体，不硬编码max_tokens
         data = {
@@ -750,16 +750,18 @@ class LLMRequest:
             "tools": tools,
         }
 
-        logger.debug(f"向模型 {self.model_name} 发送工具调用请求，包含 {len(tools)} 个工具")
+        
         response = await self._execute_request(endpoint="/chat/completions", payload=data, prompt=prompt)
+        logger.debug(f"向模型 {self.model_name} 发送工具调用请求，包含 {len(tools)} 个工具，返回结果: {response}")
         # 检查响应是否包含工具调用
-        if isinstance(response, tuple) and len(response) == 3:
+        if len(response) == 3:
             content, reasoning_content, tool_calls = response
             logger.debug(f"收到工具调用响应，包含 {len(tool_calls) if tool_calls else 0} 个工具调用")
             return content, reasoning_content, tool_calls
         else:
+            content, reasoning_content = response
             logger.debug("收到普通响应，无工具调用")
-            return response
+            return content, reasoning_content, None
 
     async def get_embedding(self, text: str) -> Union[list, None]:
         """异步方法：获取文本的embedding向量

src/plugins/utils/json_utils.py

@@ -70,55 +70,6 @@ def extract_tool_call_arguments(tool_call: Dict[str, Any], default_value: Dict[s
         return default_result
 
 
-def get_json_value(
-    json_obj: Dict[str, Any], key_path: str, default_value: T = None, transform_func: Callable[[Any], T] = None
-) -> Union[Any, T]:
-    """
-    从JSON对象中按照路径提取值，支持点表示法路径，如"data.items.0.name"
-
-    参数:
-        json_obj: JSON对象(已解析的字典)
-        key_path: 键路径，使用点表示法，如"data.items.0.name"
-        default_value: 获取失败时返回的默认值
-        transform_func: 可选的转换函数，用于对获取的值进行转换
-
-    返回:
-        路径指向的值，或在获取失败时返回default_value
-    """
-    if not json_obj or not key_path:
-        return default_value
-
-    try:
-        # 分割路径
-        keys = key_path.split(".")
-        current = json_obj
-
-        # 遍历路径
-        for key in keys:
-            # 处理数组索引
-            if key.isdigit() and isinstance(current, list):
-                index = int(key)
-                if 0 <= index < len(current):
-                    current = current[index]
-                else:
-                    return default_value
-            # 处理字典键
-            elif isinstance(current, dict):
-                if key in current:
-                    current = current[key]
-                else:
-                    return default_value
-            else:
-                return default_value
-
-        # 应用转换函数(如果提供)
-        if transform_func and current is not None:
-            return transform_func(current)
-        return current
-    except Exception as e:
-        logger.error(f"从JSON获取值时出错: {e}, 路径: {key_path}")
-        return default_value
-
 
 def safe_json_dumps(obj: Any, default_value: str = "{}", ensure_ascii: bool = False, pretty: bool = False) -> str:
     """
@@ -144,21 +95,6 @@ def safe_json_dumps(obj: Any, default_value: str = "{}", ensure_ascii: bool = Fa
         return default_value
 
 
-def merge_json_objects(*objects: Dict[str, Any]) -> Dict[str, Any]:
-    """
-    合并多个JSON对象(字典)
-
-    参数:
-        *objects: 要合并的JSON对象(字典)
-
-    返回:
-        合并后的字典，后面的对象会覆盖前面对象的相同键
-    """
-    result = {}
-    for obj in objects:
-        if obj and isinstance(obj, dict):
-            result.update(obj)
-    return result
 
 
 def normalize_llm_response(response: Any, log_prefix: str = "") -> Tuple[bool, List[Any], str]:
@@ -172,6 +108,9 @@ def normalize_llm_response(response: Any, log_prefix: str = "") -> Tuple[bool, L
     返回:
         元组 (成功标志, 标准化后的响应列表, 错误消息)
     """
+    
+    logger.debug(f"{log_prefix}原始人 LLM响应: {response}")
+    
     # 检查是否为None
     if response is None:
         return False, [], "LLM响应为None"
@@ -201,114 +140,60 @@ def normalize_llm_response(response: Any, log_prefix: str = "") -> Tuple[bool, L
     return True, response, ""
 
 
-def process_llm_tool_calls(response: List[Any], log_prefix: str = "") -> Tuple[bool, List[Dict[str, Any]], str]:
+def process_llm_tool_calls(tool_calls: List[Dict[str, Any]], log_prefix: str = "") -> Tuple[bool, List[Dict[str, Any]], str]:
     """
-    处理并提取LLM响应中的工具调用列表
+    处理并验证LLM响应中的工具调用列表
 
     参数:
-        response: 标准化后的LLM响应列表
+        tool_calls: 从LLM响应中直接获取的工具调用列表
         log_prefix: 日志前缀
 
     返回:
-        元组 (成功标志, 工具调用列表, 错误消息)
+        元组 (成功标志, 验证后的工具调用列表, 错误消息)
     """
-    # 确保响应格式正确
-    print(response)
-    print(11111111111111111)
 
-    if len(response) != 3:
-        return False, [], f"LLM响应元素数量不正确: 预期3个元素，实际{len(response)}个"
+    # 如果列表为空，表示没有工具调用，这不是错误
+    if not tool_calls:
+        return True, [], "工具调用列表为空"
 
-    # 提取工具调用部分
-    tool_calls = response[2]
-
-    # 检查工具调用是否有效
-    if tool_calls is None:
-        return False, [], "工具调用部分为None"
-
-    if not isinstance(tool_calls, list):
-        return False, [], f"工具调用部分不是列表: {type(tool_calls).__name__}"
-
-    if len(tool_calls) == 0:
-        return False, [], "工具调用列表为空"
-
-    # 检查工具调用是否格式正确
+    # 验证每个工具调用的格式
     valid_tool_calls = []
     for i, tool_call in enumerate(tool_calls):
         if not isinstance(tool_call, dict):
-            logger.warning(f"{log_prefix}工具调用[{i}]不是字典: {type(tool_call).__name__}")
+            logger.warning(f"{log_prefix}工具调用[{i}]不是字典: {type(tool_call).__name__}, 内容: {tool_call}")
             continue
 
+        # 检查基本结构
         if tool_call.get("type") != "function":
-            logger.warning(f"{log_prefix}工具调用[{i}]不是函数类型: {tool_call.get('type', '未知')}")
+            logger.warning(f"{log_prefix}工具调用[{i}]不是function类型: type={tool_call.get('type', '未定义')}, 内容: {tool_call}")
             continue
 
-        if "function" not in tool_call or not isinstance(tool_call["function"], dict):
-            logger.warning(f"{log_prefix}工具调用[{i}]缺少function字段或格式不正确")
+        if "function" not in tool_call or not isinstance(tool_call.get("function"), dict):
+            logger.warning(f"{log_prefix}工具调用[{i}]缺少'function'字段或其类型不正确: {tool_call}")
+            continue
+
+        func_details = tool_call["function"]
+        if "name" not in func_details or not isinstance(func_details.get("name"), str):
+            logger.warning(f"{log_prefix}工具调用[{i}]的'function'字段缺少'name'或类型不正确: {func_details}")
+            continue
+        if "arguments" not in func_details or not isinstance(func_details.get("arguments"), str): # 参数是字符串形式的JSON
+            logger.warning(f"{log_prefix}工具调用[{i}]的'function'字段缺少'arguments'或类型不正确: {func_details}")
+            continue
+
+        # 可选：尝试解析参数JSON，确保其有效
+        args_str = func_details["arguments"]
+        try:
+            json.loads(args_str) # 尝试解析，但不存储结果
+        except json.JSONDecodeError as e:
+            logger.warning(f"{log_prefix}工具调用[{i}]的'arguments'不是有效的JSON字符串: {e}, 内容: {args_str[:100]}...")
+            continue
+        except Exception as e:
+            logger.warning(f"{log_prefix}解析工具调用[{i}]的'arguments'时发生意外错误: {e}, 内容: {args_str[:100]}...")
             continue
 
         valid_tool_calls.append(tool_call)
 
-    # 检查是否有有效的工具调用
-    if not valid_tool_calls:
-        return False, [], "没有找到有效的工具调用"
+    if not valid_tool_calls and tool_calls: # 如果原始列表不为空，但验证后为空
+        return False, [], "所有工具调用格式均无效"
 
     return True, valid_tool_calls, ""
-
-
-def process_llm_tool_response(
-    response: Any, expected_tool_name: str = None, log_prefix: str = ""
-) -> Tuple[bool, Dict[str, Any], str]:
-    """
-    处理LLM返回的工具调用响应，进行常见错误检查并提取参数
-
-    参数:
-        response: LLM的响应，预期是[content, reasoning, tool_calls]格式的列表或元组
-        expected_tool_name: 预期的工具名称，如不指定则不检查
-        log_prefix: 日志前缀，用于标识日志来源
-
-    返回:
-        三元组(成功标志, 参数字典, 错误描述)
-        - 如果成功解析，返回(True, 参数字典, "")
-        - 如果解析失败，返回(False, {}, 错误描述)
-    """
-    # 使用新的标准化函数
-    success, normalized_response, error_msg = normalize_llm_response(response, log_prefix)
-    if not success:
-        return False, {}, error_msg
-
-    # 新增检查：确保响应包含预期的工具调用部分
-    if len(normalized_response) != 3:
-        # 如果长度不为3，说明LLM响应不包含工具调用部分，这在期望工具调用的上下文中是错误的
-        error_msg = (
-            f"LLM响应未包含预期的工具调用部分: 元素数量{len(normalized_response)}，响应内容：{normalized_response}"
-        )
-        logger.warning(f"{log_prefix}{error_msg}")
-        return False, {}, error_msg
-
-    # 使用新的工具调用处理函数
-    # 此时已知 normalized_response 长度必定为 3
-    success, valid_tool_calls, error_msg = process_llm_tool_calls(normalized_response, log_prefix)
-    if not success:
-        return False, {}, error_msg
-
-    # 检查是否有工具调用
-    if not valid_tool_calls:
-        return False, {}, "没有有效的工具调用"
-
-    # 获取第一个工具调用
-    tool_call = valid_tool_calls[0]
-
-    # 检查工具名称(如果提供了预期名称)
-    if expected_tool_name:
-        actual_name = tool_call.get("function", {}).get("name")
-        if actual_name != expected_tool_name:
-            return False, {}, f"工具名称不匹配: 预期'{expected_tool_name}'，实际'{actual_name}'"
-
-    # 提取并解析参数
-    try:
-        arguments = extract_tool_call_arguments(tool_call, {})
-        return True, arguments, ""
-    except Exception as e:
-        logger.error(f"{log_prefix}解析工具参数时出错: {e}")
-        return False, {}, f"解析参数失败: {str(e)}"