refac：tool去处理器化
This commit is contained in:
SengokuCola
@@ -25,7 +25,6 @@ class CycleDetail:
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self.loop_processor_info: Dict[str, Any] = {} # 前处理器信息
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self.loop_plan_info: Dict[str, Any] = {}
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self.loop_action_info: Dict[str, Any] = {}
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self.loop_post_processor_info: Dict[str, Any] = {} # 后处理器信息
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def to_dict(self) -> Dict[str, Any]:
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"""将循环信息转换为字典格式"""
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@@ -80,7 +79,6 @@ class CycleDetail:
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"loop_processor_info": convert_to_serializable(self.loop_processor_info),
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"loop_plan_info": convert_to_serializable(self.loop_plan_info),
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"loop_action_info": convert_to_serializable(self.loop_action_info),
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"loop_post_processor_info": convert_to_serializable(self.loop_post_processor_info),
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}
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def complete_cycle(self):
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@@ -135,4 +133,3 @@ class CycleDetail:
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self.loop_processor_info = loop_info["loop_processor_info"]
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self.loop_plan_info = loop_info["loop_plan_info"]
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self.loop_action_info = loop_info["loop_action_info"]
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self.loop_post_processor_info = loop_info["loop_post_processor_info"]
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@@ -19,7 +19,7 @@ from src.chat.heart_flow.observation.working_observation import WorkingMemoryObs
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from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
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from src.chat.heart_flow.observation.structure_observation import StructureObservation
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from src.chat.heart_flow.observation.actions_observation import ActionObservation
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from src.chat.focus_chat.info_processors.tool_processor import ToolProcessor
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from src.chat.focus_chat.memory_activator import MemoryActivator
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from src.chat.focus_chat.info_processors.base_processor import BaseProcessor
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from src.chat.focus_chat.planners.planner_factory import PlannerFactory
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@@ -34,8 +34,7 @@ from src.person_info.relationship_builder_manager import relationship_builder_ma
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install(extra_lines=3)
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# 超时常量配置
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ACTION_MODIFICATION_TIMEOUT = 15.0 # 动作修改任务超时时限（秒）
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# 注释：原来的动作修改超时常量已移除，因为改为顺序执行
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# 定义观察器映射：键是观察器名称，值是 (观察器类, 初始化参数)
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OBSERVATION_CLASSES = {
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@@ -51,11 +50,6 @@ PROCESSOR_CLASSES = {
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"WorkingMemoryProcessor": (WorkingMemoryProcessor, "working_memory_processor"),
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}
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# 定义后期处理器映射：在规划后、动作执行前运行的处理器
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POST_PLANNING_PROCESSOR_CLASSES = {
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"ToolProcessor": (ToolProcessor, "tool_use_processor"),
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}
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logger = get_logger("hfc") # Logger Name Changed
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@@ -128,23 +122,11 @@ class HeartFChatting:
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if not config_key or getattr(config_processor_settings, config_key, True):
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self.enabled_processor_names.append(proc_name)
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# 初始化后期处理器（规划后执行的处理器）
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self.enabled_post_planning_processor_names = []
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for proc_name, (_proc_class, config_key) in POST_PLANNING_PROCESSOR_CLASSES.items():
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# 对于关系相关处理器，需要同时检查关系配置项
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if not config_key or getattr(config_processor_settings, config_key, True):
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self.enabled_post_planning_processor_names.append(proc_name)
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# logger.info(f"{self.log_prefix} 将启用的处理器: {self.enabled_processor_names}")
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# logger.info(f"{self.log_prefix} 将启用的后期处理器: {self.enabled_post_planning_processor_names}")
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self.processors: List[BaseProcessor] = []
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self._register_default_processors()
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# 初始化后期处理器
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self.post_planning_processors: List[BaseProcessor] = []
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self._register_post_planning_processors()
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self.action_manager = ActionManager()
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self.action_planner = PlannerFactory.create_planner(
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log_prefix=self.log_prefix, action_manager=self.action_manager
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@@ -186,7 +168,7 @@ class HeartFChatting:
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# 检查是否需要跳过WorkingMemoryObservation
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if name == "WorkingMemoryObservation":
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# 如果工作记忆处理器被禁用，则跳过WorkingMemoryObservation
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if not global_config.focus_chat_processor.working_memory_processor:
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if not global_config.focus_chat.working_memory_processor:
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logger.debug(f"{self.log_prefix} 工作记忆处理器已禁用，跳过注册观察器 {name}")
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continue
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@@ -211,16 +193,13 @@ class HeartFChatting:
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processor_info = PROCESSOR_CLASSES.get(name) # processor_info is (ProcessorClass, config_key)
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if processor_info:
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processor_actual_class = processor_info[0] # 获取实际的类定义
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# 根据处理器类名判断是否需要 subheartflow_id
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if name in [
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"WorkingMemoryProcessor",
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]:
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self.processors.append(processor_actual_class(subheartflow_id=self.stream_id))
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elif name == "ChattingInfoProcessor":
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# 根据处理器类名判断构造参数
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if name == "ChattingInfoProcessor":
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self.processors.append(processor_actual_class())
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elif name == "WorkingMemoryProcessor":
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self.processors.append(processor_actual_class(subheartflow_id=self.stream_id))
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else:
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# 对于PROCESSOR_CLASSES中定义但此处未明确处理构造的处理器
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# (例如, 新增了一个处理器到PROCESSOR_CLASSES, 它不需要id, 也不叫ChattingInfoProcessor)
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try:
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self.processors.append(processor_actual_class()) # 尝试无参构造
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logger.debug(f"{self.log_prefix} 注册处理器 {name} (尝试无参构造).")
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@@ -239,46 +218,7 @@ class HeartFChatting:
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else:
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logger.warning(f"{self.log_prefix} 没有注册任何处理器。这可能是由于配置错误或所有处理器都被禁用了。")
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def _register_post_planning_processors(self):
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"""根据 self.enabled_post_planning_processor_names 注册后期处理器"""
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self.post_planning_processors = [] # 清空已有的
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for name in self.enabled_post_planning_processor_names: # 'name' is "PersonImpressionpProcessor", etc.
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processor_info = POST_PLANNING_PROCESSOR_CLASSES.get(name) # processor_info is (ProcessorClass, config_key)
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if processor_info:
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processor_actual_class = processor_info[0] # 获取实际的类定义
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# 根据处理器类名判断是否需要 subheartflow_id
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if name in [
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"ToolProcessor",
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"RelationshipBuildProcessor",
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"RealTimeInfoProcessor",
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"ExpressionSelectorProcessor",
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]:
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self.post_planning_processors.append(processor_actual_class(subheartflow_id=self.stream_id))
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else:
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# 对于POST_PLANNING_PROCESSOR_CLASSES中定义但此处未明确处理构造的处理器
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# (例如, 新增了一个处理器到POST_PLANNING_PROCESSOR_CLASSES, 它不需要id, 也不叫PersonImpressionpProcessor)
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try:
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self.post_planning_processors.append(processor_actual_class()) # 尝试无参构造
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logger.debug(f"{self.log_prefix} 注册后期处理器 {name} (尝试无参构造).")
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except TypeError:
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logger.error(
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f"{self.log_prefix} 后期处理器 {name} 构造失败。它可能需要参数（如 subheartflow_id）但未在注册逻辑中明确处理。"
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)
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else:
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# 这理论上不应该发生，因为 enabled_post_planning_processor_names 是从 POST_PLANNING_PROCESSOR_CLASSES 的键生成的
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logger.warning(
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f"{self.log_prefix} 在 POST_PLANNING_PROCESSOR_CLASSES 中未找到名为 '{name}' 的处理器定义，将跳过注册。"
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)
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if self.post_planning_processors:
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logger.info(
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f"{self.log_prefix} 已注册后期处理器: {[p.__class__.__name__ for p in self.post_planning_processors]}"
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)
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else:
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logger.warning(
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f"{self.log_prefix} 没有注册任何后期处理器。这可能是由于配置错误或所有后期处理器都被禁用了。"
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)
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async def start(self):
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"""检查是否需要启动主循环，如果未激活则启动。"""
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@@ -460,19 +400,7 @@ class HeartFChatting:
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("\n前处理器耗时: " + "; ".join(processor_time_strings)) if processor_time_strings else ""
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)
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# 新增：输出每个后处理器的耗时
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post_processor_time_costs = self._current_cycle_detail.loop_post_processor_info.get(
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"post_processor_time_costs", {}
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)
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post_processor_time_strings = []
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for pname, ptime in post_processor_time_costs.items():
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formatted_ptime = f"{ptime * 1000:.2f}毫秒" if ptime < 1 else f"{ptime:.2f}秒"
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post_processor_time_strings.append(f"{pname}: {formatted_ptime}")
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post_processor_time_log = (
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("\n后处理器耗时: " + "; ".join(post_processor_time_strings))
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if post_processor_time_strings
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else ""
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)
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logger.info(
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f"{self.log_prefix} 第{self._current_cycle_detail.cycle_id}次思考,"
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@@ -480,7 +408,6 @@ class HeartFChatting:
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f"动作: {self._current_cycle_detail.loop_plan_info.get('action_result', {}).get('action_type', '未知动作')}"
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+ (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "")
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+ processor_time_log
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+ post_processor_time_log
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)
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# 记录性能数据
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@@ -491,8 +418,7 @@ class HeartFChatting:
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"action_type": action_result.get("action_type", "unknown"),
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"total_time": self._current_cycle_detail.end_time - self._current_cycle_detail.start_time,
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"step_times": cycle_timers.copy(),
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"processor_time_costs": processor_time_costs, # 前处理器时间
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"post_processor_time_costs": post_processor_time_costs, # 后处理器时间
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"processor_time_costs": processor_time_costs, # 处理器时间
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"reasoning": action_result.get("reasoning", ""),
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"success": self._current_cycle_detail.loop_action_info.get("action_taken", False),
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}
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@@ -634,122 +560,7 @@ class HeartFChatting:
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return all_plan_info, processor_time_costs
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async def _process_post_planning_processors_with_timing(
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self, observations: List[Observation], action_type: str, action_data: dict
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) -> tuple[dict, dict]:
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"""
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处理后期处理器（规划后执行的处理器）并收集详细时间统计
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包括：关系处理器、表达选择器、记忆激活器
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参数:
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observations: 观察器列表
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action_type: 动作类型
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action_data: 原始动作数据
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返回:
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tuple[dict, dict]: (更新后的动作数据, 后处理器时间统计)
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"""
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logger.info(f"{self.log_prefix} 开始执行后期处理器（带详细统计）")
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# 创建所有后期任务
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task_list = []
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task_to_name_map = {}
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task_start_times = {}
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post_processor_time_costs = {}
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# 添加后期处理器任务
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for processor in self.post_planning_processors:
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processor_name = processor.__class__.__name__
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async def run_processor_with_timeout_and_timing(proc=processor, name=processor_name):
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start_time = time.time()
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try:
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result = await asyncio.wait_for(
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proc.process_info(observations=observations, action_type=action_type, action_data=action_data),
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30,
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)
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end_time = time.time()
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post_processor_time_costs[name] = end_time - start_time
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logger.debug(f"{self.log_prefix} 后期处理器 {name} 耗时: {end_time - start_time:.3f}秒")
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return result
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except Exception as e:
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end_time = time.time()
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post_processor_time_costs[name] = end_time - start_time
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logger.warning(f"{self.log_prefix} 后期处理器 {name} 执行异常，耗时: {end_time - start_time:.3f}秒")
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raise e
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task = asyncio.create_task(run_processor_with_timeout_and_timing())
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task_list.append(task)
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task_to_name_map[task] = ("processor", processor_name)
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task_start_times[task] = time.time()
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logger.info(f"{self.log_prefix} 启动后期处理器任务: {processor_name}")
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# 如果没有任何后期任务，直接返回
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if not task_list:
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logger.info(f"{self.log_prefix} 没有启用的后期处理器或记忆激活器")
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return action_data, {}
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# 等待所有任务完成
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pending_tasks = set(task_list)
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all_post_plan_info = []
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while pending_tasks:
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done, pending_tasks = await asyncio.wait(pending_tasks, return_when=asyncio.FIRST_COMPLETED)
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for task in done:
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task_type, task_name = task_to_name_map[task]
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try:
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result = await task
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if task_type == "processor":
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logger.info(f"{self.log_prefix} 后期处理器 {task_name} 已完成!")
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if result is not None:
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all_post_plan_info.extend(result)
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else:
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logger.warning(f"{self.log_prefix} 后期处理器 {task_name} 返回了 None")
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except asyncio.TimeoutError:
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# 对于超时任务，记录已用时间
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elapsed_time = time.time() - task_start_times[task]
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if task_type == "processor":
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post_processor_time_costs[task_name] = elapsed_time
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logger.warning(
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f"{self.log_prefix} 后期处理器 {task_name} 超时（>30s），已跳过，耗时: {elapsed_time:.3f}秒"
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)
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except Exception as e:
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# 对于异常任务，记录已用时间
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elapsed_time = time.time() - task_start_times[task]
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if task_type == "processor":
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post_processor_time_costs[task_name] = elapsed_time
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logger.error(
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f"{self.log_prefix} 后期处理器 {task_name} 执行失败，耗时: {elapsed_time:.3f}秒. 错误: {e}",
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exc_info=True,
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)
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# 将后期处理器的结果整合到 action_data 中
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updated_action_data = action_data.copy()
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structured_info = ""
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for info in all_post_plan_info:
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if isinstance(info, StructuredInfo):
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structured_info = info.get_processed_info()
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if structured_info:
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updated_action_data["structured_info"] = structured_info
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if all_post_plan_info:
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logger.info(f"{self.log_prefix} 后期处理完成，产生了 {len(all_post_plan_info)} 个信息项")
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# 输出详细统计信息
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if post_processor_time_costs:
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stats_str = ", ".join(
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[f"{name}: {time_cost:.3f}s" for name, time_cost in post_processor_time_costs.items()]
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)
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logger.info(f"{self.log_prefix} 后期处理器详细耗时统计: {stats_str}")
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return updated_action_data, post_processor_time_costs
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async def _observe_process_plan_action_loop(self, cycle_timers: dict, thinking_id: str) -> dict:
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try:
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@@ -765,10 +576,10 @@ class HeartFChatting:
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await self.relationship_builder.build_relation()
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# 并行执行调整动作、回忆和处理器阶段
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with Timer("调整动作、处理", cycle_timers):
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# 创建并行任务
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async def modify_actions_task():
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# 顺序执行调整动作和处理器阶段
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# 第一步：动作修改
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with Timer("动作修改", cycle_timers):
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try:
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# 调用完整的动作修改流程
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await self.action_modifier.modify_actions(
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observations=self.observations,
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@@ -776,44 +587,17 @@ class HeartFChatting:
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await self.action_observation.observe()
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self.observations.append(self.action_observation)
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return True
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# 创建两个并行任务，为LLM调用添加超时保护
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action_modify_task = asyncio.create_task(
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asyncio.wait_for(modify_actions_task(), timeout=ACTION_MODIFICATION_TIMEOUT)
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)
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processor_task = asyncio.create_task(self._process_processors(self.observations))
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# 等待两个任务完成，使用超时保护和详细错误处理
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action_modify_result = None
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all_plan_info = []
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processor_time_costs = {}
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try:
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action_modify_result, (all_plan_info, processor_time_costs) = await asyncio.gather(
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action_modify_task, processor_task, return_exceptions=True
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)
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# 检查各个任务的结果
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if isinstance(action_modify_result, Exception):
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if isinstance(action_modify_result, asyncio.TimeoutError):
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logger.error(f"{self.log_prefix} 动作修改任务超时")
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else:
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logger.error(f"{self.log_prefix} 动作修改任务失败: {action_modify_result}")
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processor_result = (all_plan_info, processor_time_costs)
|
||||
if isinstance(processor_result, Exception):
|
||||
if isinstance(processor_result, asyncio.TimeoutError):
|
||||
logger.error(f"{self.log_prefix} 处理器任务超时")
|
||||
else:
|
||||
logger.error(f"{self.log_prefix} 处理器任务失败: {processor_result}")
|
||||
all_plan_info = []
|
||||
processor_time_costs = {}
|
||||
else:
|
||||
all_plan_info, processor_time_costs = processor_result
|
||||
|
||||
logger.debug(f"{self.log_prefix} 动作修改完成")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 并行任务gather失败: {e}")
|
||||
logger.error(f"{self.log_prefix} 动作修改失败: {e}")
|
||||
# 继续执行，不中断流程
|
||||
|
||||
# 第二步：信息处理器
|
||||
with Timer("信息处理器", cycle_timers):
|
||||
try:
|
||||
all_plan_info, processor_time_costs = await self._process_processors(self.observations)
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 信息处理器失败: {e}")
|
||||
# 设置默认值以继续执行
|
||||
all_plan_info = []
|
||||
processor_time_costs = {}
|
||||
@@ -833,7 +617,6 @@ class HeartFChatting:
|
||||
"observed_messages": plan_result.get("observed_messages", ""),
|
||||
}
|
||||
|
||||
# 修正：将后期处理器从执行动作Timer中分离出来
|
||||
action_type, action_data, reasoning = (
|
||||
plan_result.get("action_result", {}).get("action_type", "error"),
|
||||
plan_result.get("action_result", {}).get("action_data", {}),
|
||||
@@ -849,22 +632,7 @@ class HeartFChatting:
|
||||
|
||||
logger.debug(f"{self.log_prefix} 麦麦想要：'{action_str}'")
|
||||
|
||||
# 添加：单独计时后期处理器，并收集详细统计
|
||||
post_processor_time_costs = {}
|
||||
if action_type != "no_reply":
|
||||
with Timer("后期处理器", cycle_timers):
|
||||
logger.debug(f"{self.log_prefix} 执行后期处理器（动作类型: {action_type}）")
|
||||
# 记录详细的后处理器时间
|
||||
post_start_time = time.time()
|
||||
action_data, post_processor_time_costs = await self._process_post_planning_processors_with_timing(
|
||||
self.observations, action_type, action_data
|
||||
)
|
||||
post_end_time = time.time()
|
||||
logger.info(f"{self.log_prefix} 后期处理器总耗时: {post_end_time - post_start_time:.3f}秒")
|
||||
else:
|
||||
logger.debug(f"{self.log_prefix} 跳过后期处理器（动作类型: {action_type}）")
|
||||
|
||||
# 修正：纯动作执行计时
|
||||
# 动作执行计时
|
||||
with Timer("动作执行", cycle_timers):
|
||||
success, reply_text, command = await self._handle_action(
|
||||
action_type, reasoning, action_data, cycle_timers, thinking_id
|
||||
@@ -877,17 +645,11 @@ class HeartFChatting:
|
||||
"taken_time": time.time(),
|
||||
}
|
||||
|
||||
# 添加后处理器统计到loop_info
|
||||
loop_post_processor_info = {
|
||||
"post_processor_time_costs": post_processor_time_costs,
|
||||
}
|
||||
|
||||
loop_info = {
|
||||
"loop_observation_info": loop_observation_info,
|
||||
"loop_processor_info": loop_processor_info,
|
||||
"loop_plan_info": loop_plan_info,
|
||||
"loop_action_info": loop_action_info,
|
||||
"loop_post_processor_info": loop_post_processor_info, # 新增
|
||||
}
|
||||
|
||||
return loop_info
|
||||
|
||||
@@ -1,71 +0,0 @@
|
||||
from dataclasses import dataclass
|
||||
from typing import List, Dict
|
||||
from .info_base import InfoBase
|
||||
|
||||
|
||||
@dataclass
|
||||
class ExpressionSelectionInfo(InfoBase):
|
||||
"""表达选择信息类
|
||||
|
||||
用于存储和管理选中的表达方式信息。
|
||||
|
||||
Attributes:
|
||||
type (str): 信息类型标识符，默认为 "expression_selection"
|
||||
data (Dict[str, Any]): 包含选中表达方式的数据字典
|
||||
"""
|
||||
|
||||
type: str = "expression_selection"
|
||||
|
||||
def get_selected_expressions(self) -> List[Dict[str, str]]:
|
||||
"""获取选中的表达方式列表
|
||||
|
||||
Returns:
|
||||
List[Dict[str, str]]: 选中的表达方式列表
|
||||
"""
|
||||
return self.get_info("selected_expressions") or []
|
||||
|
||||
def set_selected_expressions(self, expressions: List[Dict[str, str]]) -> None:
|
||||
"""设置选中的表达方式列表
|
||||
|
||||
Args:
|
||||
expressions: 选中的表达方式列表
|
||||
"""
|
||||
self.data["selected_expressions"] = expressions
|
||||
|
||||
def get_expressions_count(self) -> int:
|
||||
"""获取选中表达方式的数量
|
||||
|
||||
Returns:
|
||||
int: 表达方式数量
|
||||
"""
|
||||
return len(self.get_selected_expressions())
|
||||
|
||||
def get_processed_info(self) -> str:
|
||||
"""获取处理后的信息
|
||||
|
||||
Returns:
|
||||
str: 处理后的信息字符串
|
||||
"""
|
||||
expressions = self.get_selected_expressions()
|
||||
if not expressions:
|
||||
return ""
|
||||
|
||||
# 格式化表达方式为可读文本
|
||||
formatted_expressions = []
|
||||
for expr in expressions:
|
||||
situation = expr.get("situation", "")
|
||||
style = expr.get("style", "")
|
||||
expr.get("type", "")
|
||||
|
||||
if situation and style:
|
||||
formatted_expressions.append(f"当{situation}时，使用 {style}")
|
||||
|
||||
return "\n".join(formatted_expressions)
|
||||
|
||||
def get_expressions_for_action_data(self) -> List[Dict[str, str]]:
|
||||
"""获取用于action_data的表达方式数据
|
||||
|
||||
Returns:
|
||||
List[Dict[str, str]]: 格式化后的表达方式数据
|
||||
"""
|
||||
return self.get_selected_expressions()
|
||||
@@ -1,34 +0,0 @@
|
||||
from typing import Dict, Any
|
||||
from dataclasses import dataclass, field
|
||||
from .info_base import InfoBase
|
||||
|
||||
|
||||
@dataclass
|
||||
class MindInfo(InfoBase):
|
||||
"""思维信息类
|
||||
|
||||
用于存储和管理当前思维状态的信息。
|
||||
|
||||
Attributes:
|
||||
type (str): 信息类型标识符，默认为 "mind"
|
||||
data (Dict[str, Any]): 包含 current_mind 的数据字典
|
||||
"""
|
||||
|
||||
type: str = "mind"
|
||||
data: Dict[str, Any] = field(default_factory=lambda: {"current_mind": ""})
|
||||
|
||||
def get_current_mind(self) -> str:
|
||||
"""获取当前思维状态
|
||||
|
||||
Returns:
|
||||
str: 当前思维状态
|
||||
"""
|
||||
return self.get_info("current_mind") or ""
|
||||
|
||||
def set_current_mind(self, mind: str) -> None:
|
||||
"""设置当前思维状态
|
||||
|
||||
Args:
|
||||
mind: 要设置的思维状态
|
||||
"""
|
||||
self.data["current_mind"] = mind
|
||||
@@ -1,40 +0,0 @@
|
||||
from dataclasses import dataclass
|
||||
from .info_base import InfoBase
|
||||
|
||||
|
||||
@dataclass
|
||||
class RelationInfo(InfoBase):
|
||||
"""关系信息类
|
||||
|
||||
用于存储和管理当前关系状态的信息。
|
||||
|
||||
Attributes:
|
||||
type (str): 信息类型标识符，默认为 "relation"
|
||||
data (Dict[str, Any]): 包含 current_relation 的数据字典
|
||||
"""
|
||||
|
||||
type: str = "relation"
|
||||
|
||||
def get_relation_info(self) -> str:
|
||||
"""获取当前关系状态
|
||||
|
||||
Returns:
|
||||
str: 当前关系状态
|
||||
"""
|
||||
return self.get_info("relation_info") or ""
|
||||
|
||||
def set_relation_info(self, relation_info: str) -> None:
|
||||
"""设置当前关系状态
|
||||
|
||||
Args:
|
||||
relation_info: 要设置的关系状态
|
||||
"""
|
||||
self.data["relation_info"] = relation_info
|
||||
|
||||
def get_processed_info(self) -> str:
|
||||
"""获取处理后的信息
|
||||
|
||||
Returns:
|
||||
str: 处理后的信息
|
||||
"""
|
||||
return self.get_relation_info() or ""
|
||||
@@ -1,85 +0,0 @@
|
||||
from typing import Dict, Optional, Any, List
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
|
||||
@dataclass
|
||||
class StructuredInfo:
|
||||
"""信息基类
|
||||
|
||||
这是一个基础信息类，用于存储和管理各种类型的信息数据。
|
||||
所有具体的信息类都应该继承自这个基类。
|
||||
|
||||
Attributes:
|
||||
type (str): 信息类型标识符，默认为 "base"
|
||||
data (Dict[str, Union[str, Dict, list]]): 存储具体信息数据的字典，
|
||||
支持存储字符串、字典、列表等嵌套数据结构
|
||||
"""
|
||||
|
||||
type: str = "structured_info"
|
||||
data: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
def get_type(self) -> str:
|
||||
"""获取信息类型
|
||||
|
||||
Returns:
|
||||
str: 当前信息对象的类型标识符
|
||||
"""
|
||||
return self.type
|
||||
|
||||
def get_data(self) -> Dict[str, Any]:
|
||||
"""获取所有信息数据
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 包含所有信息数据的字典
|
||||
"""
|
||||
return self.data
|
||||
|
||||
def get_info(self, key: str) -> Optional[Any]:
|
||||
"""获取特定属性的信息
|
||||
|
||||
Args:
|
||||
key: 要获取的属性键名
|
||||
|
||||
Returns:
|
||||
Optional[Any]: 属性值，如果键不存在则返回 None
|
||||
"""
|
||||
return self.data.get(key)
|
||||
|
||||
def get_info_list(self, key: str) -> List[Any]:
|
||||
"""获取特定属性的信息列表
|
||||
|
||||
Args:
|
||||
key: 要获取的属性键名
|
||||
|
||||
Returns:
|
||||
List[Any]: 属性值列表，如果键不存在则返回空列表
|
||||
"""
|
||||
value = self.data.get(key)
|
||||
if isinstance(value, list):
|
||||
return value
|
||||
return []
|
||||
|
||||
def set_info(self, key: str, value: Any) -> None:
|
||||
"""设置特定属性的信息值
|
||||
|
||||
Args:
|
||||
key: 要设置的属性键名
|
||||
value: 要设置的属性值
|
||||
"""
|
||||
self.data[key] = value
|
||||
|
||||
def get_processed_info(self) -> str:
|
||||
"""获取处理后的信息
|
||||
|
||||
Returns:
|
||||
str: 处理后的信息字符串
|
||||
"""
|
||||
|
||||
info_str = ""
|
||||
# print(f"self.data: {self.data}")
|
||||
|
||||
for key, value in self.data.items():
|
||||
# print(f"key: {key}, value: {value}")
|
||||
info_str += f"信息类型：{key}，信息内容：{value}\n"
|
||||
|
||||
return info_str
|
||||
@@ -1,186 +0,0 @@
|
||||
from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
import time
|
||||
from src.common.logger import get_logger
|
||||
from src.individuality.individuality import get_individuality
|
||||
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
|
||||
from src.tools.tool_use import ToolUser
|
||||
from src.chat.utils.json_utils import process_llm_tool_calls
|
||||
from .base_processor import BaseProcessor
|
||||
from typing import List
|
||||
from src.chat.heart_flow.observation.observation import Observation
|
||||
from src.chat.focus_chat.info.structured_info import StructuredInfo
|
||||
from src.chat.heart_flow.observation.structure_observation import StructureObservation
|
||||
|
||||
logger = get_logger("processor")
|
||||
|
||||
|
||||
def init_prompt():
|
||||
# ... 原有代码 ...
|
||||
|
||||
# 添加工具执行器提示词
|
||||
tool_executor_prompt = """
|
||||
你是一个专门执行工具的助手。你的名字是{bot_name}。现在是{time_now}。
|
||||
群里正在进行的聊天内容：
|
||||
{chat_observe_info}
|
||||
|
||||
请仔细分析聊天内容，考虑以下几点：
|
||||
1. 内容中是否包含需要查询信息的问题
|
||||
2. 是否有明确的工具使用指令
|
||||
|
||||
If you need to use a tool, please directly call the corresponding tool function. If you do not need to use any tool, simply output "No tool needed".
|
||||
"""
|
||||
Prompt(tool_executor_prompt, "tool_executor_prompt")
|
||||
|
||||
|
||||
class ToolProcessor(BaseProcessor):
|
||||
log_prefix = "工具执行器"
|
||||
|
||||
def __init__(self, subheartflow_id: str):
|
||||
super().__init__()
|
||||
self.subheartflow_id = subheartflow_id
|
||||
self.log_prefix = f"[{subheartflow_id}:ToolExecutor] "
|
||||
self.llm_model = LLMRequest(
|
||||
model=global_config.model.focus_tool_use,
|
||||
request_type="focus.processor.tool",
|
||||
)
|
||||
self.structured_info = []
|
||||
|
||||
async def process_info(
|
||||
self,
|
||||
observations: List[Observation] = None,
|
||||
action_type: str = None,
|
||||
action_data: dict = None,
|
||||
**kwargs,
|
||||
) -> List[StructuredInfo]:
|
||||
"""处理信息对象
|
||||
|
||||
Args:
|
||||
observations: 可选的观察列表，包含ChattingObservation和StructureObservation类型
|
||||
action_type: 动作类型
|
||||
action_data: 动作数据
|
||||
**kwargs: 其他可选参数
|
||||
|
||||
Returns:
|
||||
list: 处理后的结构化信息列表
|
||||
"""
|
||||
|
||||
working_infos = []
|
||||
result = []
|
||||
|
||||
if observations:
|
||||
for observation in observations:
|
||||
if isinstance(observation, ChattingObservation):
|
||||
result, used_tools, prompt = await self.execute_tools(observation)
|
||||
|
||||
logger.info(f"工具调用结果: {result}")
|
||||
# 更新WorkingObservation中的结构化信息
|
||||
for observation in observations:
|
||||
if isinstance(observation, StructureObservation):
|
||||
for structured_info in result:
|
||||
# logger.debug(f"{self.log_prefix} 更新WorkingObservation中的结构化信息: {structured_info}")
|
||||
observation.add_structured_info(structured_info)
|
||||
|
||||
working_infos = observation.get_observe_info()
|
||||
logger.debug(f"{self.log_prefix} 获取更新后WorkingObservation中的结构化信息: {working_infos}")
|
||||
|
||||
structured_info = StructuredInfo()
|
||||
if working_infos:
|
||||
for working_info in working_infos:
|
||||
structured_info.set_info(key=working_info.get("type"), value=working_info.get("content"))
|
||||
|
||||
return [structured_info]
|
||||
|
||||
async def execute_tools(self, observation: ChattingObservation, action_type: str = None, action_data: dict = None):
|
||||
"""
|
||||
并行执行工具，返回结构化信息
|
||||
|
||||
参数:
|
||||
sub_mind: 子思维对象
|
||||
chat_target_name: 聊天目标名称，默认为"对方"
|
||||
is_group_chat: 是否为群聊，默认为False
|
||||
return_details: 是否返回详细信息，默认为False
|
||||
cycle_info: 循环信息对象，可用于记录详细执行信息
|
||||
action_type: 动作类型
|
||||
action_data: 动作数据
|
||||
|
||||
返回:
|
||||
如果return_details为False:
|
||||
List[Dict]: 工具执行结果的结构化信息列表
|
||||
如果return_details为True:
|
||||
Tuple[List[Dict], List[str], str]: (工具执行结果列表, 使用的工具列表, 工具执行提示词)
|
||||
"""
|
||||
tool_instance = ToolUser()
|
||||
tools = tool_instance._define_tools()
|
||||
|
||||
# logger.debug(f"observation: {observation}")
|
||||
# logger.debug(f"observation.chat_target_info: {observation.chat_target_info}")
|
||||
# logger.debug(f"observation.is_group_chat: {observation.is_group_chat}")
|
||||
# logger.debug(f"observation.person_list: {observation.person_list}")
|
||||
|
||||
is_group_chat = observation.is_group_chat
|
||||
|
||||
# chat_observe_info = observation.get_observe_info()
|
||||
chat_observe_info = observation.talking_message_str_truncate_short
|
||||
# person_list = observation.person_list
|
||||
|
||||
# 获取时间信息
|
||||
time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
|
||||
|
||||
# 构建专用于工具调用的提示词
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
"tool_executor_prompt",
|
||||
chat_observe_info=chat_observe_info,
|
||||
is_group_chat=is_group_chat,
|
||||
bot_name=get_individuality().name,
|
||||
time_now=time_now,
|
||||
)
|
||||
|
||||
# 调用LLM，专注于工具使用
|
||||
# logger.info(f"开始执行工具调用{prompt}")
|
||||
response, other_info = await self.llm_model.generate_response_async(prompt=prompt, tools=tools)
|
||||
|
||||
if len(other_info) == 3:
|
||||
reasoning_content, model_name, tool_calls = other_info
|
||||
else:
|
||||
reasoning_content, model_name = other_info
|
||||
tool_calls = None
|
||||
|
||||
# print("tooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltool")
|
||||
if tool_calls:
|
||||
logger.info(f"获取到工具原始输出:\n{tool_calls}")
|
||||
# 处理工具调用和结果收集，类似于SubMind中的逻辑
|
||||
new_structured_items = []
|
||||
used_tools = [] # 记录使用了哪些工具
|
||||
|
||||
if tool_calls:
|
||||
success, valid_tool_calls, error_msg = process_llm_tool_calls(tool_calls)
|
||||
if success and valid_tool_calls:
|
||||
for tool_call in valid_tool_calls:
|
||||
try:
|
||||
# 记录使用的工具名称
|
||||
tool_name = tool_call.get("name", "unknown_tool")
|
||||
used_tools.append(tool_name)
|
||||
|
||||
result = await tool_instance._execute_tool_call(tool_call)
|
||||
|
||||
name = result.get("type", "unknown_type")
|
||||
content = result.get("content", "")
|
||||
|
||||
logger.info(f"工具{name}，获得信息:{content}")
|
||||
if result:
|
||||
new_item = {
|
||||
"type": result.get("type", "unknown_type"),
|
||||
"id": result.get("id", f"tool_exec_{time.time()}"),
|
||||
"content": result.get("content", ""),
|
||||
"ttl": 3,
|
||||
}
|
||||
new_structured_items.append(new_item)
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}工具执行失败: {e}")
|
||||
|
||||
return new_structured_items, used_tools, prompt
|
||||
|
||||
|
||||
init_prompt()
|
||||
@@ -28,6 +28,7 @@ from datetime import datetime
|
||||
import re
|
||||
from src.chat.knowledge.knowledge_lib import qa_manager
|
||||
from src.chat.focus_chat.memory_activator import MemoryActivator
|
||||
from src.tools.tool_executor import ToolExecutor
|
||||
|
||||
logger = get_logger("replyer")
|
||||
|
||||
@@ -42,7 +43,7 @@ def init_prompt():
|
||||
Prompt(
|
||||
"""
|
||||
{expression_habits_block}
|
||||
{structured_info_block}
|
||||
{tool_info_block}
|
||||
{memory_block}
|
||||
{relation_info_block}
|
||||
{extra_info_block}
|
||||
@@ -67,7 +68,7 @@ def init_prompt():
|
||||
Prompt(
|
||||
"""
|
||||
{expression_habits_block}
|
||||
{structured_info_block}
|
||||
{tool_info_block}
|
||||
{memory_block}
|
||||
{relation_info_block}
|
||||
{extra_info_block}
|
||||
@@ -157,12 +158,20 @@ class DefaultReplyer:
|
||||
fallback_config.setdefault("weight", 1.0)
|
||||
self.express_model_configs = [fallback_config]
|
||||
|
||||
self.heart_fc_sender = HeartFCSender()
|
||||
self.memory_activator = MemoryActivator()
|
||||
|
||||
self.chat_stream = chat_stream
|
||||
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.stream_id)
|
||||
|
||||
self.heart_fc_sender = HeartFCSender()
|
||||
self.memory_activator = MemoryActivator()
|
||||
self.tool_executor = ToolExecutor(
|
||||
chat_id=self.chat_stream.stream_id,
|
||||
enable_cache=True,
|
||||
cache_ttl=3
|
||||
)
|
||||
|
||||
|
||||
|
||||
|
||||
def _select_weighted_model_config(self) -> Dict[str, Any]:
|
||||
"""使用加权随机选择来挑选一个模型配置"""
|
||||
configs = self.express_model_configs
|
||||
@@ -394,6 +403,54 @@ class DefaultReplyer:
|
||||
|
||||
return memory_block
|
||||
|
||||
async def build_tool_info(self, reply_data=None, chat_history=None):
|
||||
"""构建工具信息块
|
||||
|
||||
Args:
|
||||
reply_data: 回复数据，包含要回复的消息内容
|
||||
chat_history: 聊天历史
|
||||
|
||||
Returns:
|
||||
str: 工具信息字符串
|
||||
"""
|
||||
if not reply_data:
|
||||
return ""
|
||||
|
||||
reply_to = reply_data.get("reply_to", "")
|
||||
sender, text = self._parse_reply_target(reply_to)
|
||||
|
||||
if not text:
|
||||
return ""
|
||||
|
||||
try:
|
||||
# 使用工具执行器获取信息
|
||||
tool_results = await self.tool_executor.execute_from_chat_message(
|
||||
sender = sender,
|
||||
target_message=text,
|
||||
chat_history=chat_history,
|
||||
return_details=False
|
||||
)
|
||||
|
||||
if tool_results:
|
||||
tool_info_str = "以下是你通过工具获取到的实时信息：\n"
|
||||
for tool_result in tool_results:
|
||||
tool_name = tool_result.get("tool_name", "unknown")
|
||||
content = tool_result.get("content", "")
|
||||
result_type = tool_result.get("type", "info")
|
||||
|
||||
tool_info_str += f"- 【{tool_name}】{result_type}: {content}\n"
|
||||
|
||||
tool_info_str += "以上是你获取到的实时信息，请在回复时参考这些信息。"
|
||||
logger.info(f"{self.log_prefix} 获取到 {len(tool_results)} 个工具结果")
|
||||
return tool_info_str
|
||||
else:
|
||||
logger.debug(f"{self.log_prefix} 未获取到任何工具结果")
|
||||
return ""
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 工具信息获取失败: {e}")
|
||||
return ""
|
||||
|
||||
def _parse_reply_target(self, target_message: str) -> tuple:
|
||||
sender = ""
|
||||
target = ""
|
||||
@@ -502,11 +559,12 @@ class DefaultReplyer:
|
||||
show_actions=True,
|
||||
)
|
||||
|
||||
# 并行执行三个构建任务
|
||||
expression_habits_block, relation_info, memory_block = await asyncio.gather(
|
||||
# 并行执行四个构建任务
|
||||
expression_habits_block, relation_info, memory_block, tool_info = await asyncio.gather(
|
||||
self.build_expression_habits(chat_talking_prompt_half, target),
|
||||
self.build_relation_info(reply_data, chat_talking_prompt_half),
|
||||
self.build_memory_block(chat_talking_prompt_half, target),
|
||||
self.build_tool_info(reply_data, chat_talking_prompt_half),
|
||||
)
|
||||
|
||||
keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
|
||||
@@ -518,6 +576,11 @@ class DefaultReplyer:
|
||||
else:
|
||||
structured_info_block = ""
|
||||
|
||||
if tool_info:
|
||||
tool_info_block = f"{tool_info}"
|
||||
else:
|
||||
tool_info_block = ""
|
||||
|
||||
if extra_info_block:
|
||||
extra_info_block = f"以下是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策\n{extra_info_block}\n以上是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策"
|
||||
else:
|
||||
@@ -590,6 +653,7 @@ class DefaultReplyer:
|
||||
chat_info=chat_talking_prompt,
|
||||
memory_block=memory_block,
|
||||
structured_info_block=structured_info_block,
|
||||
tool_info_block=tool_info_block,
|
||||
extra_info_block=extra_info_block,
|
||||
relation_info_block=relation_info,
|
||||
time_block=time_block,
|
||||
@@ -620,6 +684,7 @@ class DefaultReplyer:
|
||||
chat_info=chat_talking_prompt,
|
||||
memory_block=memory_block,
|
||||
structured_info_block=structured_info_block,
|
||||
tool_info_block=tool_info_block,
|
||||
relation_info_block=relation_info,
|
||||
extra_info_block=extra_info_block,
|
||||
time_block=time_block,
|
||||
|
||||
@@ -314,15 +314,7 @@ class FocusChatConfig(ConfigBase):
|
||||
consecutive_replies: float = 1
|
||||
"""连续回复能力，值越高，麦麦连续回复的概率越高"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class FocusChatProcessorConfig(ConfigBase):
|
||||
"""专注聊天处理器配置类"""
|
||||
|
||||
tool_use_processor: bool = True
|
||||
"""是否启用工具使用处理器"""
|
||||
|
||||
working_memory_processor: bool = True
|
||||
working_memory_processor: bool = False
|
||||
"""是否启用工作记忆处理器"""
|
||||
|
||||
|
||||
|
||||
@@ -8,6 +8,7 @@
|
||||
success, reply_set = await generator_api.generate_reply(chat_stream, action_data, reasoning)
|
||||
"""
|
||||
|
||||
import traceback
|
||||
from typing import Tuple, Any, Dict, List, Optional
|
||||
from src.common.logger import get_logger
|
||||
from src.chat.replyer.default_generator import DefaultReplyer
|
||||
@@ -50,6 +51,7 @@ def get_replyer(
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"[GeneratorAPI] 获取回复器时发生意外错误: {e}", exc_info=True)
|
||||
traceback.print_exc()
|
||||
return None
|
||||
|
||||
|
||||
|
||||
421
src/tools/tool_executor.py
Normal file
421
src/tools/tool_executor.py
Normal file
@@ -0,0 +1,421 @@
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
import time
|
||||
from src.common.logger import get_logger
|
||||
from src.individuality.individuality import get_individuality
|
||||
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
|
||||
from src.tools.tool_use import ToolUser
|
||||
from src.chat.utils.json_utils import process_llm_tool_calls
|
||||
from typing import List, Dict, Tuple, Optional
|
||||
|
||||
logger = get_logger("tool_executor")
|
||||
|
||||
|
||||
def init_tool_executor_prompt():
|
||||
"""初始化工具执行器的提示词"""
|
||||
tool_executor_prompt = """
|
||||
你是一个专门执行工具的助手。你的名字是{bot_name}。现在是{time_now}。
|
||||
群里正在进行的聊天内容：
|
||||
{chat_history}
|
||||
|
||||
现在，{sender}发送了内容:{target_message},你想要回复ta。
|
||||
请仔细分析聊天内容，考虑以下几点：
|
||||
1. 内容中是否包含需要查询信息的问题
|
||||
2. 是否有明确的工具使用指令
|
||||
|
||||
If you need to use a tool, please directly call the corresponding tool function. If you do not need to use any tool, simply output "No tool needed".
|
||||
"""
|
||||
Prompt(tool_executor_prompt, "tool_executor_prompt")
|
||||
|
||||
|
||||
class ToolExecutor:
|
||||
"""独立的工具执行器组件
|
||||
|
||||
可以直接输入聊天消息内容，自动判断并执行相应的工具，返回结构化的工具执行结果。
|
||||
"""
|
||||
|
||||
def __init__(self, chat_id: str = None, enable_cache: bool = True, cache_ttl: int = 3):
|
||||
"""初始化工具执行器
|
||||
|
||||
Args:
|
||||
executor_id: 执行器标识符，用于日志记录
|
||||
enable_cache: 是否启用缓存机制
|
||||
cache_ttl: 缓存生存时间（周期数）
|
||||
"""
|
||||
self.chat_id = chat_id
|
||||
self.log_prefix = f"[ToolExecutor:{self.chat_id}] "
|
||||
self.llm_model = LLMRequest(
|
||||
model=global_config.model.focus_tool_use,
|
||||
request_type="tool_executor",
|
||||
)
|
||||
|
||||
# 初始化工具实例
|
||||
self.tool_instance = ToolUser()
|
||||
|
||||
# 缓存配置
|
||||
self.enable_cache = enable_cache
|
||||
self.cache_ttl = cache_ttl
|
||||
self.tool_cache = {} # 格式: {cache_key: {"result": result, "ttl": ttl, "timestamp": timestamp}}
|
||||
|
||||
logger.info(f"{self.log_prefix}工具执行器初始化完成，缓存{'启用' if enable_cache else '禁用'}，TTL={cache_ttl}")
|
||||
|
||||
async def execute_from_chat_message(
|
||||
self,
|
||||
target_message: str,
|
||||
chat_history: list[str],
|
||||
sender: str,
|
||||
return_details: bool = False
|
||||
) -> List[Dict] | Tuple[List[Dict], List[str], str]:
|
||||
"""从聊天消息执行工具
|
||||
|
||||
Args:
|
||||
target_message: 目标消息内容
|
||||
chat_history: 聊天历史
|
||||
sender: 发送者
|
||||
return_details: 是否返回详细信息(使用的工具列表和提示词)
|
||||
|
||||
Returns:
|
||||
如果return_details为False: List[Dict] - 工具执行结果列表
|
||||
如果return_details为True: Tuple[List[Dict], List[str], str] - (结果列表, 使用的工具, 提示词)
|
||||
"""
|
||||
|
||||
# 首先检查缓存
|
||||
cache_key = self._generate_cache_key(target_message, chat_history, sender)
|
||||
cached_result = self._get_from_cache(cache_key)
|
||||
|
||||
if cached_result:
|
||||
logger.info(f"{self.log_prefix}使用缓存结果，跳过工具执行")
|
||||
if return_details:
|
||||
# 从缓存结果中提取工具名称
|
||||
used_tools = [result.get("tool_name", "unknown") for result in cached_result]
|
||||
return cached_result, used_tools, "使用缓存结果"
|
||||
else:
|
||||
return cached_result
|
||||
|
||||
# 缓存未命中，执行工具调用
|
||||
# 获取可用工具
|
||||
tools = self.tool_instance._define_tools()
|
||||
|
||||
# 获取当前时间
|
||||
time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
|
||||
|
||||
bot_name = global_config.bot.nickname
|
||||
|
||||
# 构建工具调用提示词
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
"tool_executor_prompt",
|
||||
target_message=target_message,
|
||||
chat_history=chat_history,
|
||||
sender=sender,
|
||||
bot_name=bot_name,
|
||||
time_now=time_now,
|
||||
)
|
||||
|
||||
logger.debug(f"{self.log_prefix}开始LLM工具调用分析")
|
||||
|
||||
# 调用LLM进行工具决策
|
||||
response, other_info = await self.llm_model.generate_response_async(
|
||||
prompt=prompt,
|
||||
tools=tools
|
||||
)
|
||||
|
||||
# 解析LLM响应
|
||||
if len(other_info) == 3:
|
||||
reasoning_content, model_name, tool_calls = other_info
|
||||
else:
|
||||
reasoning_content, model_name = other_info
|
||||
tool_calls = None
|
||||
|
||||
# 执行工具调用
|
||||
tool_results, used_tools = await self._execute_tool_calls(tool_calls)
|
||||
|
||||
# 缓存结果
|
||||
if tool_results:
|
||||
self._set_cache(cache_key, tool_results)
|
||||
|
||||
logger.info(f"{self.log_prefix}工具执行完成，共执行{len(used_tools)}个工具: {used_tools}")
|
||||
|
||||
if return_details:
|
||||
return tool_results, used_tools, prompt
|
||||
else:
|
||||
return tool_results
|
||||
|
||||
async def _execute_tool_calls(self, tool_calls) -> Tuple[List[Dict], List[str]]:
|
||||
"""执行工具调用
|
||||
|
||||
Args:
|
||||
tool_calls: LLM返回的工具调用列表
|
||||
|
||||
Returns:
|
||||
Tuple[List[Dict], List[str]]: (工具执行结果列表, 使用的工具名称列表)
|
||||
"""
|
||||
tool_results = []
|
||||
used_tools = []
|
||||
|
||||
if not tool_calls:
|
||||
logger.debug(f"{self.log_prefix}无需执行工具")
|
||||
return tool_results, used_tools
|
||||
|
||||
logger.info(f"{self.log_prefix}开始执行工具调用: {tool_calls}")
|
||||
|
||||
# 处理工具调用
|
||||
success, valid_tool_calls, error_msg = process_llm_tool_calls(tool_calls)
|
||||
|
||||
if not success:
|
||||
logger.error(f"{self.log_prefix}工具调用解析失败: {error_msg}")
|
||||
return tool_results, used_tools
|
||||
|
||||
if not valid_tool_calls:
|
||||
logger.debug(f"{self.log_prefix}无有效工具调用")
|
||||
return tool_results, used_tools
|
||||
|
||||
# 执行每个工具调用
|
||||
for tool_call in valid_tool_calls:
|
||||
try:
|
||||
tool_name = tool_call.get("name", "unknown_tool")
|
||||
used_tools.append(tool_name)
|
||||
|
||||
logger.debug(f"{self.log_prefix}执行工具: {tool_name}")
|
||||
|
||||
# 执行工具
|
||||
result = await self.tool_instance._execute_tool_call(tool_call)
|
||||
|
||||
if result:
|
||||
tool_info = {
|
||||
"type": result.get("type", "unknown_type"),
|
||||
"id": result.get("id", f"tool_exec_{time.time()}"),
|
||||
"content": result.get("content", ""),
|
||||
"tool_name": tool_name,
|
||||
"timestamp": time.time(),
|
||||
}
|
||||
tool_results.append(tool_info)
|
||||
|
||||
logger.info(f"{self.log_prefix}工具{tool_name}执行成功，类型: {tool_info['type']}")
|
||||
logger.debug(f"{self.log_prefix}工具{tool_name}结果内容: {tool_info['content'][:200]}...")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}工具{tool_name}执行失败: {e}")
|
||||
# 添加错误信息到结果中
|
||||
error_info = {
|
||||
"type": "tool_error",
|
||||
"id": f"tool_error_{time.time()}",
|
||||
"content": f"工具{tool_name}执行失败: {str(e)}",
|
||||
"tool_name": tool_name,
|
||||
"timestamp": time.time(),
|
||||
}
|
||||
tool_results.append(error_info)
|
||||
|
||||
return tool_results, used_tools
|
||||
|
||||
def _generate_cache_key(self, target_message: str, chat_history: list[str], sender: str) -> str:
|
||||
"""生成缓存键
|
||||
|
||||
Args:
|
||||
target_message: 目标消息内容
|
||||
chat_history: 聊天历史
|
||||
sender: 发送者
|
||||
|
||||
Returns:
|
||||
str: 缓存键
|
||||
"""
|
||||
import hashlib
|
||||
# 使用消息内容和群聊状态生成唯一缓存键
|
||||
content = f"{target_message}_{chat_history}_{sender}"
|
||||
return hashlib.md5(content.encode()).hexdigest()
|
||||
|
||||
def _get_from_cache(self, cache_key: str) -> Optional[List[Dict]]:
|
||||
"""从缓存获取结果
|
||||
|
||||
Args:
|
||||
cache_key: 缓存键
|
||||
|
||||
Returns:
|
||||
Optional[List[Dict]]: 缓存的结果，如果不存在或过期则返回None
|
||||
"""
|
||||
if not self.enable_cache or cache_key not in self.tool_cache:
|
||||
return None
|
||||
|
||||
cache_item = self.tool_cache[cache_key]
|
||||
if cache_item["ttl"] <= 0:
|
||||
# 缓存过期，删除
|
||||
del self.tool_cache[cache_key]
|
||||
logger.debug(f"{self.log_prefix}缓存过期，删除缓存键: {cache_key}")
|
||||
return None
|
||||
|
||||
# 减少TTL
|
||||
cache_item["ttl"] -= 1
|
||||
logger.debug(f"{self.log_prefix}使用缓存结果，剩余TTL: {cache_item['ttl']}")
|
||||
return cache_item["result"]
|
||||
|
||||
def _set_cache(self, cache_key: str, result: List[Dict]):
|
||||
"""设置缓存
|
||||
|
||||
Args:
|
||||
cache_key: 缓存键
|
||||
result: 要缓存的结果
|
||||
"""
|
||||
if not self.enable_cache:
|
||||
return
|
||||
|
||||
self.tool_cache[cache_key] = {
|
||||
"result": result,
|
||||
"ttl": self.cache_ttl,
|
||||
"timestamp": time.time()
|
||||
}
|
||||
logger.debug(f"{self.log_prefix}设置缓存，TTL: {self.cache_ttl}")
|
||||
|
||||
def _cleanup_expired_cache(self):
|
||||
"""清理过期的缓存"""
|
||||
if not self.enable_cache:
|
||||
return
|
||||
|
||||
expired_keys = []
|
||||
for cache_key, cache_item in self.tool_cache.items():
|
||||
if cache_item["ttl"] <= 0:
|
||||
expired_keys.append(cache_key)
|
||||
|
||||
for key in expired_keys:
|
||||
del self.tool_cache[key]
|
||||
|
||||
if expired_keys:
|
||||
logger.debug(f"{self.log_prefix}清理了{len(expired_keys)}个过期缓存")
|
||||
|
||||
def get_available_tools(self) -> List[str]:
|
||||
"""获取可用工具列表
|
||||
|
||||
Returns:
|
||||
List[str]: 可用工具名称列表
|
||||
"""
|
||||
tools = self.tool_instance._define_tools()
|
||||
return [tool.get("function", {}).get("name", "unknown") for tool in tools]
|
||||
|
||||
async def execute_specific_tool(
|
||||
self,
|
||||
tool_name: str,
|
||||
tool_args: Dict,
|
||||
validate_args: bool = True
|
||||
) -> Optional[Dict]:
|
||||
"""直接执行指定工具
|
||||
|
||||
Args:
|
||||
tool_name: 工具名称
|
||||
tool_args: 工具参数
|
||||
validate_args: 是否验证参数
|
||||
|
||||
Returns:
|
||||
Optional[Dict]: 工具执行结果，失败时返回None
|
||||
"""
|
||||
try:
|
||||
tool_call = {
|
||||
"name": tool_name,
|
||||
"arguments": tool_args
|
||||
}
|
||||
|
||||
logger.info(f"{self.log_prefix}直接执行工具: {tool_name}")
|
||||
|
||||
result = await self.tool_instance._execute_tool_call(tool_call)
|
||||
|
||||
if result:
|
||||
tool_info = {
|
||||
"type": result.get("type", "unknown_type"),
|
||||
"id": result.get("id", f"direct_tool_{time.time()}"),
|
||||
"content": result.get("content", ""),
|
||||
"tool_name": tool_name,
|
||||
"timestamp": time.time(),
|
||||
}
|
||||
logger.info(f"{self.log_prefix}直接工具执行成功: {tool_name}")
|
||||
return tool_info
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}直接工具执行失败 {tool_name}: {e}")
|
||||
|
||||
return None
|
||||
|
||||
def clear_cache(self):
|
||||
"""清空所有缓存"""
|
||||
if self.enable_cache:
|
||||
cache_count = len(self.tool_cache)
|
||||
self.tool_cache.clear()
|
||||
logger.info(f"{self.log_prefix}清空了{cache_count}个缓存项")
|
||||
|
||||
def get_cache_status(self) -> Dict:
|
||||
"""获取缓存状态信息
|
||||
|
||||
Returns:
|
||||
Dict: 包含缓存统计信息的字典
|
||||
"""
|
||||
if not self.enable_cache:
|
||||
return {"enabled": False, "cache_count": 0}
|
||||
|
||||
# 清理过期缓存
|
||||
self._cleanup_expired_cache()
|
||||
|
||||
total_count = len(self.tool_cache)
|
||||
ttl_distribution = {}
|
||||
|
||||
for cache_item in self.tool_cache.values():
|
||||
ttl = cache_item["ttl"]
|
||||
ttl_distribution[ttl] = ttl_distribution.get(ttl, 0) + 1
|
||||
|
||||
return {
|
||||
"enabled": True,
|
||||
"cache_count": total_count,
|
||||
"cache_ttl": self.cache_ttl,
|
||||
"ttl_distribution": ttl_distribution
|
||||
}
|
||||
|
||||
def set_cache_config(self, enable_cache: bool = None, cache_ttl: int = None):
|
||||
"""动态修改缓存配置
|
||||
|
||||
Args:
|
||||
enable_cache: 是否启用缓存
|
||||
cache_ttl: 缓存TTL
|
||||
"""
|
||||
if enable_cache is not None:
|
||||
self.enable_cache = enable_cache
|
||||
logger.info(f"{self.log_prefix}缓存状态修改为: {'启用' if enable_cache else '禁用'}")
|
||||
|
||||
if cache_ttl is not None and cache_ttl > 0:
|
||||
self.cache_ttl = cache_ttl
|
||||
logger.info(f"{self.log_prefix}缓存TTL修改为: {cache_ttl}")
|
||||
|
||||
|
||||
# 初始化提示词
|
||||
init_tool_executor_prompt()
|
||||
|
||||
|
||||
"""
|
||||
使用示例：
|
||||
|
||||
# 1. 基础使用 - 从聊天消息执行工具（启用缓存，默认TTL=3）
|
||||
executor = ToolExecutor(executor_id="my_executor")
|
||||
results = await executor.execute_from_chat_message(
|
||||
talking_message_str="今天天气怎么样？现在几点了？",
|
||||
is_group_chat=False
|
||||
)
|
||||
|
||||
# 2. 禁用缓存的执行器
|
||||
no_cache_executor = ToolExecutor(executor_id="no_cache", enable_cache=False)
|
||||
|
||||
# 3. 自定义缓存TTL
|
||||
long_cache_executor = ToolExecutor(executor_id="long_cache", cache_ttl=10)
|
||||
|
||||
# 4. 获取详细信息
|
||||
results, used_tools, prompt = await executor.execute_from_chat_message(
|
||||
talking_message_str="帮我查询Python相关知识",
|
||||
is_group_chat=False,
|
||||
return_details=True
|
||||
)
|
||||
|
||||
# 5. 直接执行特定工具
|
||||
result = await executor.execute_specific_tool(
|
||||
tool_name="get_knowledge",
|
||||
tool_args={"query": "机器学习"}
|
||||
)
|
||||
|
||||
# 6. 缓存管理
|
||||
available_tools = executor.get_available_tools()
|
||||
cache_status = executor.get_cache_status() # 查看缓存状态
|
||||
executor.clear_cache() # 清空缓存
|
||||
executor.set_cache_config(cache_ttl=5) # 动态修改缓存配置
|
||||
"""
|
||||
@@ -1,5 +1,5 @@
|
||||
[inner]
|
||||
version = "2.29.0"
|
||||
version = "2.30.0"
|
||||
|
||||
#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
|
||||
#如果你想要修改配置文件，请在修改后将version的值进行变更
|
||||
@@ -133,9 +133,6 @@ think_interval = 3 # 思考间隔 单位秒，可以有效减少消耗
|
||||
consecutive_replies = 1 # 连续回复能力，值越高，麦麦连续回复的概率越高
|
||||
compressed_length = 8 # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
|
||||
compress_length_limit = 4 #最多压缩份数，超过该数值的压缩上下文会被删除
|
||||
|
||||
[focus_chat_processor] # 专注聊天处理器，打开可以实现更多功能，但是会增加token消耗
|
||||
tool_use_processor = false # 是否启用工具使用处理器
|
||||
working_memory_processor = false # 是否启用工作记忆处理器，消耗量大
|
||||
|
||||
[emoji]
|
||||
|
||||
Reference in New Issue
Block a user