import orjson import time import traceback import asyncio import math import random import json from typing import Dict, Any, Optional, Tuple, List, TYPE_CHECKING from rich.traceback import install from datetime import datetime from json_repair import repair_json from src.llm_models.utils_model import LLMRequest from src.config.config import global_config, model_config from src.common.logger import get_logger from src.chat.utils.prompt import Prompt, global_prompt_manager from src.chat.utils.chat_message_builder import ( build_readable_actions, get_actions_by_timestamp_with_chat, build_readable_messages_with_id, get_raw_msg_before_timestamp_with_chat, ) from src.chat.utils.utils import get_chat_type_and_target_info from src.chat.planner_actions.action_manager import ActionManager from src.chat.message_receive.chat_stream import get_chat_manager from src.plugin_system.base.component_types import ( ActionInfo, ChatMode, ComponentType, ActionActivationType, PlannerType, ) from src.plugin_system.core.component_registry import component_registry from src.schedule.schedule_manager import schedule_manager from src.mood.mood_manager import mood_manager from src.chat.memory_system.Hippocampus import hippocampus_manager if TYPE_CHECKING: pass logger = get_logger("planner") install(extra_lines=3) def init_prompt(): Prompt( """ {schedule_block} {mood_block} {time_block} {identity_block} {custom_prompt_block} {chat_context_description}，以下是具体的聊天内容。 {chat_content_block} {moderation_prompt} 现在请你根据聊天内容和用户的最新消息选择合适的action和触发action的消息: {actions_before_now_block} {no_action_block} 动作：reply 动作描述：参与聊天回复，发送文本进行表达 - 你想要闲聊或者随便附 - {mentioned_bonus} - 如果你刚刚进行了回复，不要对同一个话题重复回应 - 不要回复自己发送的消息 {{ "action": "reply", "target_message_id":"触发action的消息id", "reason":"回复的原因" }} {action_options_text} 你必须从上面列出的可用action中选择一个，并说明触发action的消息id（不是消息原文）和选择该action的原因。消息id格式:m+数字 请根据动作示例，以严格的 JSON 格式输出，不要输出markdown格式```json等内容，直接输出且仅包含 JSON 内容： """, "planner_prompt", ) Prompt( """ # 主动思考决策 ## 你的内部状态 {time_block} {identity_block} {schedule_block} {mood_block} ## 长期记忆摘要 {long_term_memory_block} ## 最近的聊天内容 {chat_content_block} ## 任务 基于以上所有信息（特别是最近的聊天内容），分析当前情况，决定是否适合主动开启一个**新的、但又与当前氛围相关**的话题。 ## 可用动作 动作：proactive_reply 动作描述：在当前对话的基础上，主动发起一个新的对话，分享一个有趣的想法、见闻或者对未来的计划。 - 当你觉得可以说些什么来活跃气氛，并且内容与当前聊天氛围不冲突时 - 当你有一些新的想法或计划想要分享，并且可以自然地衔接当前话题时 {{ "action": "proactive_reply", "reason": "决定主动发起对话的具体原因", "topic": "你想要发起对话的主题或内容（需要简洁）" }} 动作：do_nothing 动作描述：保持沉默，不主动发起任何动作或对话。 - 当你分析了所有信息后，觉得当前不是一个发起互动的好时机时 - 当最近的聊天内容很连贯，你的插入会打断别人时 {{ "action": "do_nothing", "reason":"决定保持沉默的具体原因" }} 你必须从上面列出的可用action中选择一个。 请以严格的 JSON 格式输出，且仅包含 JSON 内容： """, "proactive_planner_prompt", ) Prompt( """ 动作：{action_name} 动作描述：{action_description} {action_require} {{ "action": "{action_name}",{action_parameters}, "target_message_id":"触发action的消息id", "reason":"触发action的原因" }} """, "action_prompt", ) Prompt( """ {name_block} {chat_context_description}，{time_block}，现在请你根据以下聊天内容，选择一个或多个合适的action。如果没有合适的action，请选择no_action。, {chat_content_block} **要求** 1.action必须符合使用条件，如果符合条件，就选择 2.如果聊天内容不适合使用action，即使符合条件，也不要使用 3.{moderation_prompt} 4.请注意如果相同的内容已经被执行，请不要重复执行 这是你最近执行过的动作: {actions_before_now_block} **可用的action** no_action：不选择任何动作 {{ "action": "no_action", "reason":"不动作的原因" }} {action_options_text} 请选择，并说明触发action的消息id和选择该action的原因。消息id格式:m+数字 请根据动作示例，以严格的 JSON 格式输出，且仅包含 JSON 内容： """, "sub_planner_prompt", ) class ActionPlanner: def __init__(self, chat_id: str, action_manager: ActionManager): self.chat_id = chat_id self.log_prefix = f"[{get_chat_manager().get_stream_name(chat_id) or chat_id}]" self.action_manager = action_manager # LLM规划器配置 # --- 大脑 --- self.planner_llm = LLMRequest( model_set=model_config.model_task_config.planner, request_type="planner" ) # --- 小脑 (新增) --- self.planner_small_llm = LLMRequest( model_set=model_config.model_task_config.planner_small, request_type="planner_small" ) self.last_obs_time_mark = 0.0 async def _get_long_term_memory_context(self) -> str: """ 获取长期记忆上下文 """ try: # 1. 生成时间相关的关键词 now = datetime.now() keywords = ["今天", "日程", "计划"] if 5 <= now.hour < 12: keywords.append("早上") elif 12 <= now.hour < 18: keywords.append("中午") else: keywords.append("晚上") # TODO: 添加与聊天对象相关的关键词 # 2. 调用 hippocampus_manager 检索记忆 retrieved_memories = await hippocampus_manager.get_memory_from_topic( valid_keywords=keywords, max_memory_num=5, max_memory_length=1 ) if not retrieved_memories: return "最近没有什么特别的记忆。" # 3. 格式化记忆 memory_statements = [] for topic, memory_item in retrieved_memories: memory_statements.append(f"关于'{topic}', 你记得'{memory_item}'。") return " ".join(memory_statements) except Exception as e: logger.error(f"获取长期记忆时出错: {e}") return "回忆时出现了一些问题。" async def _build_action_options( self, current_available_actions: Dict[str, ActionInfo], mode: ChatMode, target_prompt: str = "", ) -> str: """ 构建动作选项 """ action_options_block = "" if mode == ChatMode.PROACTIVE: action_options_block += """动作：do_nothing 动作描述：保持沉默，不主动发起任何动作或对话。 - 当你分析了所有信息后，觉得当前不是一个发起互动的好时机时 {{ "action": "do_nothing", "reason":"决定保持沉默的具体原因" }} """ for action_name, action_info in current_available_actions.items(): # TODO: 增加一个字段来判断action是否支持在PROACTIVE模式下使用 param_text = "" if action_info.action_parameters: param_text = "\n" + "\n".join( f' "{p_name}":"{p_desc}"' for p_name, p_desc in action_info.action_parameters.items() ) require_text = "\n".join(f"- {req}" for req in action_info.action_require) using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt") action_options_block += using_action_prompt.format( action_name=action_name, action_description=action_info.description, action_parameters=param_text, action_require=require_text, ) return action_options_block def find_message_by_id(self, message_id: str, message_id_list: list) -> Optional[Dict[str, Any]]: # sourcery skip: use-next """ 根据message_id从message_id_list中查找对应的原始消息 Args: message_id: 要查找的消息ID message_id_list: 消息ID列表，格式为[{'id': str, 'message': dict}, ...] Returns: 找到的原始消息字典，如果未找到则返回None """ # 检测message_id 是否为纯数字 if message_id.isdigit(): message_id = f"m{message_id}" for item in message_id_list: if item.get("id") == message_id: return item.get("message") return None def get_latest_message(self, message_id_list: list) -> Optional[Dict[str, Any]]: """ 获取消息列表中的最新消息 Args: message_id_list: 消息ID列表，格式为[{'id': str, 'message': dict}, ...] Returns: 最新的消息字典，如果列表为空则返回None """ if not message_id_list: return None # 假设消息列表是按时间顺序排列的，最后一个是最新的 return message_id_list[-1].get("message") def _parse_single_action( self, action_json: dict, message_id_list: list, # 使用 planner.py 的 list of dict current_available_actions: list, # 使用 planner.py 的 list of tuple ) -> List[Dict[str, Any]]: """ [注释] 解析单个小脑LLM返回的action JSON，并将其转换为标准化的字典。 """ parsed_actions = [] try: action = action_json.get("action", "no_action") reasoning = action_json.get("reason", "未提供原因") action_data = {k: v for k, v in action_json.items() if k not in ["action", "reason"]} target_message = None if action != "no_action": if target_message_id := action_json.get("target_message_id"): target_message = self.find_message_by_id(target_message_id, message_id_list) if target_message is None: logger.warning(f"{self.log_prefix}无法找到target_message_id '{target_message_id}'") target_message = self.get_latest_message(message_id_list) else: logger.warning(f"{self.log_prefix}动作'{action}'缺少target_message_id") available_action_names = [name for name, _ in current_available_actions] if action not in ["no_action", "reply"] and action not in available_action_names: logger.warning( f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (可用: {available_action_names})，将强制使用 'no_action'" ) reasoning = f"LLM 返回了当前不可用的动作 '{action}' (可用: {available_action_names})。原始理由: {reasoning}" action = "no_action" # 将列表转换为字典格式以供将来使用 available_actions_dict = dict(current_available_actions) parsed_actions.append( { "action_type": action, "reasoning": reasoning, "action_data": action_data, "action_message": target_message, "available_actions": available_actions_dict, } ) except Exception as e: logger.error(f"{self.log_prefix}解析单个action时出错: {e}") parsed_actions.append( { "action_type": "no_action", "reasoning": f"解析action时出错: {e}", "action_data": {}, "action_message": None, "available_actions": dict(current_available_actions), } ) return parsed_actions def _filter_no_actions(self, action_list: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """ [注释] 从一个action字典列表中过滤掉所有的 'no_action'。 如果过滤后列表为空, 则返回一个空的列表, 或者根据需要返回一个默认的no_action字典。 """ non_no_actions = [a for a in action_list if a.get("action_type") not in ["no_action", "no_reply"]] if non_no_actions: return non_no_actions # 如果都是 no_action，则返回一个包含第一个 no_action 的列表，以保留 reason return action_list[:1] if action_list else [] async def sub_plan( self, action_list: list, # 使用 planner.py 的 list of tuple chat_content_block: str, message_id_list: list, # 使用 planner.py 的 list of dict is_group_chat: bool = False, chat_target_info: Optional[dict] = None, ) -> List[Dict[str, Any]]: """ [注释] "小脑"规划器。接收一小组actions，使用轻量级LLM判断其中哪些应该被触发。 这是一个独立的、并行的思考单元。返回一个包含action字典的列表。 """ try: actions_before_now = get_actions_by_timestamp_with_chat( chat_id=self.chat_id, timestamp_start=time.time() - 1200, timestamp_end=time.time(), limit=20, ) action_names_in_list = [name for name, _ in action_list] filtered_actions = [ record for record in actions_before_now if record.get("action_name") in action_names_in_list ] actions_before_now_block = build_readable_actions(actions=filtered_actions) chat_context_description = "你现在正在一个群聊中" if not is_group_chat and chat_target_info: chat_target_name = chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or "对方" chat_context_description = f"你正在和 {chat_target_name} 私聊" action_options_block = "" for using_actions_name, using_actions_info in action_list: param_text = "" if using_actions_info.action_parameters: param_text = "\n" + "\n".join( f' "{p_name}":"{p_desc}"' for p_name, p_desc in using_actions_info.action_parameters.items() ) require_text = "\n".join(f"- {req}" for req in using_actions_info.action_require) using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt") action_options_block += using_action_prompt.format( action_name=using_actions_name, action_description=using_actions_info.description, action_parameters=param_text, action_require=require_text, ) moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。" time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" bot_name = global_config.bot.nickname bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else "" name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。" planner_prompt_template = await global_prompt_manager.get_prompt_async("sub_planner_prompt") prompt = planner_prompt_template.format( time_block=time_block, chat_context_description=chat_context_description, chat_content_block=chat_content_block, actions_before_now_block=actions_before_now_block, action_options_text=action_options_block, moderation_prompt=moderation_prompt_block, name_block=name_block, ) except Exception as e: logger.error(f"构建小脑提示词时出错: {e}\n{traceback.format_exc()}") return [{"action_type": "no_action", "reasoning": f"构建小脑Prompt时出错: {e}"}] action_dicts: List[Dict[str, Any]] = [] try: llm_content, (reasoning_content, _, _) = await self.planner_small_llm.generate_response_async(prompt=prompt) if global_config.debug.show_prompt: logger.info(f"{self.log_prefix}小脑原始提示词: {prompt}") logger.info(f"{self.log_prefix}小脑原始响应: {llm_content}") else: logger.debug(f"{self.log_prefix}小脑原始响应: {llm_content}") if llm_content: parsed_json = orjson.loads(repair_json(llm_content)) if isinstance(parsed_json, list): for item in parsed_json: if isinstance(item, dict): action_dicts.extend(self._parse_single_action(item, message_id_list, action_list)) elif isinstance(parsed_json, dict): action_dicts.extend(self._parse_single_action(parsed_json, message_id_list, action_list)) except Exception as e: logger.warning(f"{self.log_prefix}解析小脑响应JSON失败: {e}. LLM原始输出: '{llm_content}'") action_dicts.append({"action_type": "no_action", "reasoning": f"解析小脑响应失败: {e}"}) if not action_dicts: action_dicts.append({"action_type": "no_action", "reasoning": "小脑未返回有效action"}) return action_dicts async def plan( self, mode: ChatMode = ChatMode.FOCUS, loop_start_time: float = 0.0, available_actions: Optional[Dict[str, ActionInfo]] = None, ) -> Tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]: """ [注释] "大脑"规划器。 1. 启动多个并行的"小脑"(sub_plan)来决定是否执行具体的actions。 2. 自己(大脑)则专注于决定是否进行聊天回复(reply)。 3. 整合大脑和小脑的决策，返回最终要执行的动作列表。 """ # --- 1. 准备上下文信息 --- message_list_before_now = get_raw_msg_before_timestamp_with_chat( chat_id=self.chat_id, timestamp=time.time(), limit=int(global_config.chat.max_context_size * 0.6), ) # 大脑使用较长的上下文 chat_content_block, message_id_list = build_readable_messages_with_id( messages=message_list_before_now, timestamp_mode="normal", read_mark=self.last_obs_time_mark, truncate=True, show_actions=True, ) # 小脑使用较短、较新的上下文 message_list_before_now_short = message_list_before_now[-int(global_config.chat.max_context_size * 0.3) :] chat_content_block_short, message_id_list_short = build_readable_messages_with_id( messages=message_list_before_now_short, timestamp_mode="normal", truncate=False, show_actions=False, ) self.last_obs_time_mark = time.time() is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info() if available_actions is None: available_actions = current_available_actions # --- 2. 启动小脑并行思考 --- all_sub_planner_results: List[Dict[str, Any]] = [] try: sub_planner_actions: Dict[str, ActionInfo] = {} for action_name, action_info in available_actions.items(): if action_info.planner_type not in [PlannerType.SMALL_BRAIN, PlannerType.ALL]: continue if action_info.activation_type in [ActionActivationType.LLM_JUDGE, ActionActivationType.ALWAYS]: sub_planner_actions[action_name] = action_info elif action_info.activation_type == ActionActivationType.RANDOM: if random.random() < action_info.random_activation_probability: sub_planner_actions[action_name] = action_info elif action_info.activation_type == ActionActivationType.KEYWORD: if any(keyword in chat_content_block_short for keyword in action_info.activation_keywords): sub_planner_actions[action_name] = action_info if sub_planner_actions: sub_planner_actions_num = len(sub_planner_actions) planner_size_config = global_config.chat.planner_size sub_planner_size = int(planner_size_config) + ( 1 if random.random() < planner_size_config - int(planner_size_config) else 0 ) sub_planner_num = math.ceil(sub_planner_actions_num / sub_planner_size) logger.info(f"{self.log_prefix}使用{sub_planner_num}个小脑进行思考 (尺寸: {sub_planner_size})") action_items = list(sub_planner_actions.items()) random.shuffle(action_items) sub_planner_lists = [action_items[i::sub_planner_num] for i in range(sub_planner_num)] sub_plan_tasks = [ self.sub_plan( action_list=action_group, chat_content_block=chat_content_block_short, message_id_list=message_id_list_short, is_group_chat=is_group_chat, chat_target_info=chat_target_info, ) for action_group in sub_planner_lists ] sub_plan_results = await asyncio.gather(*sub_plan_tasks) for sub_result in sub_plan_results: all_sub_planner_results.extend(sub_result) sub_actions_str = ", ".join( a["action_type"] for a in all_sub_planner_results if a["action_type"] != "no_action" ) or "no_action" logger.info(f"{self.log_prefix}小脑决策: [{sub_actions_str}]") except Exception as e: logger.error(f"{self.log_prefix}小脑调度过程中出错: {e}\n{traceback.format_exc()}") # --- 3. 大脑独立思考是否回复 --- action, reasoning, action_data, target_message = "no_reply", "大脑初始化默认", {}, None try: big_brain_actions = { name: info for name, info in available_actions.items() if info.planner_type in [PlannerType.BIG_BRAIN, PlannerType.ALL] } prompt, _ = await self.build_planner_prompt( is_group_chat=is_group_chat, chat_target_info=chat_target_info, current_available_actions=big_brain_actions, mode=mode, chat_content_block_override=chat_content_block, message_id_list_override=message_id_list, ) llm_content, _ = await self.planner_llm.generate_response_async(prompt=prompt) if llm_content: parsed_json = orjson.loads(repair_json(llm_content)) parsed_json = parsed_json[-1] if isinstance(parsed_json, list) and parsed_json else parsed_json if isinstance(parsed_json, dict): action = parsed_json.get("action", "no_reply") reasoning = parsed_json.get("reason", "未提供原因") action_data = {k: v for k, v in parsed_json.items() if k not in ["action", "reason"]} if action != "no_reply": if target_id := parsed_json.get("target_message_id"): target_message = self.find_message_by_id(target_id, message_id_list) if not target_message: target_message = self.get_latest_message(message_id_list) logger.info(f"{self.log_prefix}大脑决策: [{action}]") except Exception as e: logger.error(f"{self.log_prefix}大脑处理过程中发生意外错误: {e}\n{traceback.format_exc()}") action, reasoning = "no_reply", f"大脑处理错误: {e}" # --- 4. 整合大脑和小脑的决策 --- is_parallel = True for info in all_sub_planner_results: action_type = info.get("action_type") if action_type and action_type not in ["no_action", "no_reply"]: action_info = available_actions.get(action_type) if action_info and not action_info.parallel_action: is_parallel = False break action_data["loop_start_time"] = loop_start_time final_actions: List[Dict[str, Any]] = [] if is_parallel: logger.info(f"{self.log_prefix}决策模式: 大脑与小脑并行") if action not in ["no_action", "no_reply"]: final_actions.append( { "action_type": action, "reasoning": reasoning, "action_data": action_data, "action_message": target_message, "available_actions": available_actions, } ) final_actions.extend(all_sub_planner_results) else: logger.info(f"{self.log_prefix}决策模式: 小脑优先 (检测到非并行action)") final_actions.extend(all_sub_planner_results) final_actions = self._filter_no_actions(final_actions) if not final_actions: final_actions = [ { "action_type": "no_action", "reasoning": "所有规划器都选择不执行动作", "action_data": {}, "action_message": None, "available_actions": available_actions } ] final_target_message = target_message if not final_target_message and final_actions: final_target_message = next((act.get("action_message") for act in final_actions if act.get("action_message")), None) actions_str = ", ".join([a.get('action_type', 'N/A') for a in final_actions]) logger.info(f"{self.log_prefix}最终执行动作 ({len(final_actions)}): [{actions_str}]") return final_actions, final_target_message async def build_planner_prompt( self, is_group_chat: bool, chat_target_info: Optional[dict], current_available_actions: Dict[str, ActionInfo], mode: ChatMode = ChatMode.FOCUS, chat_content_block_override: Optional[str] = None, message_id_list_override: Optional[List] = None, refresh_time: bool = False, # 添加缺失的参数 ) -> tuple[str, list]: """构建 Planner LLM 的提示词 (获取模板并填充数据)""" try: # --- 通用信息获取 --- time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" bot_name = global_config.bot.nickname bot_nickname = ( f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else "" ) bot_core_personality = global_config.personality.personality_core identity_block = f"你的名字是{bot_name}{bot_nickname}，你{bot_core_personality}：" schedule_block = "" if global_config.planning_system.schedule_enable: if current_activity := schedule_manager.get_current_activity(): schedule_block = f"你当前正在：{current_activity},但注意它与群聊的聊天无关。" mood_block = "" if global_config.mood.enable_mood: chat_mood = mood_manager.get_mood_by_chat_id(self.chat_id) mood_block = f"你现在的心情是：{chat_mood.mood_state}" # --- 根据模式构建不同的Prompt --- if mode == ChatMode.PROACTIVE: long_term_memory_block = await self._get_long_term_memory_context() # 获取最近的聊天记录用于主动思考决策 message_list_short = get_raw_msg_before_timestamp_with_chat( chat_id=self.chat_id, timestamp=time.time(), limit=int(global_config.chat.max_context_size * 0.2), # 主动思考时只看少量最近消息 ) chat_content_block, _ = build_readable_messages_with_id( messages=message_list_short, timestamp_mode="normal", truncate=False, show_actions=False, ) prompt_template = await global_prompt_manager.get_prompt_async("proactive_planner_prompt") prompt = prompt_template.format( time_block=time_block, identity_block=identity_block, schedule_block=schedule_block, mood_block=mood_block, long_term_memory_block=long_term_memory_block, chat_content_block=chat_content_block or "最近没有聊天内容。", ) return prompt, [] # --- FOCUS 和 NORMAL 模式的逻辑 --- message_list_before_now = get_raw_msg_before_timestamp_with_chat( chat_id=self.chat_id, timestamp=time.time(), limit=int(global_config.chat.max_context_size * 0.6), ) chat_content_block, message_id_list = build_readable_messages_with_id( messages=message_list_before_now, timestamp_mode="normal", read_mark=self.last_obs_time_mark, truncate=True, show_actions=True, ) actions_before_now = get_actions_by_timestamp_with_chat( chat_id=self.chat_id, timestamp_start=time.time() - 3600, timestamp_end=time.time(), limit=5, ) actions_before_now_block = build_readable_actions(actions=actions_before_now) actions_before_now_block = f"你刚刚选择并执行过的action是：\n{actions_before_now_block}" if refresh_time: self.last_obs_time_mark = time.time() mentioned_bonus = "" if global_config.chat.mentioned_bot_inevitable_reply: mentioned_bonus = "\n- 有人提到你" if global_config.chat.at_bot_inevitable_reply: mentioned_bonus = "\n- 有人提到你，或者at你" if mode == ChatMode.FOCUS: no_action_block = """ - 'no_reply' 表示不进行回复，等待合适的回复时机 - 当你刚刚发送了消息，没有人回复时，选择no_reply - 当你一次发送了太多消息，为了避免打扰聊天节奏，选择no_reply 动作：no_reply 动作描述：不进行回复，等待合适的回复时机 - 当你刚刚发送了消息，没有人回复时，选择no_reply - 当你一次发送了太多消息，为了避免打扰聊天节奏，选择no_reply {{ "action": "no_reply", "reason":"不回复的原因" }} """ else: # NORMAL Mode no_action_block = """重要说明： - 'reply' 表示只进行普通聊天回复，不执行任何额外动作 - 其他action表示在普通回复的基础上，执行相应的额外动作 {{ "action": "reply", "target_message_id":"触发action的消息id", "reason":"回复的原因" }}""" chat_context_description = "你现在正在一个群聊中" chat_target_name = None if not is_group_chat and chat_target_info: chat_target_name = ( chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or "对方" ) chat_context_description = f"你正在和 {chat_target_name} 私聊" action_options_block = await self._build_action_options(current_available_actions, mode) moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。" custom_prompt_block = "" if global_config.custom_prompt.planner_custom_prompt_content: custom_prompt_block = global_config.custom_prompt.planner_custom_prompt_content planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt") prompt = planner_prompt_template.format( schedule_block=schedule_block, mood_block=mood_block, time_block=time_block, chat_context_description=chat_context_description, chat_content_block=chat_content_block, actions_before_now_block=actions_before_now_block, mentioned_bonus=mentioned_bonus, no_action_block=no_action_block, mentioned_bonus=mentioned_bonus, action_options_text=action_options_block, moderation_prompt=moderation_prompt_block, identity_block=identity_block, custom_prompt_block=custom_prompt_block, bot_name=bot_name, ) return prompt, message_id_list except Exception as e: logger.error(f"构建 Planner 提示词时出错: {e}") logger.error(traceback.format_exc()) return "构建 Planner Prompt 时出错", [] def get_necessary_info(self) -> Tuple[bool, Optional[dict], Dict[str, ActionInfo]]: """ 获取 Planner 需要的必要信息 """ is_group_chat = True is_group_chat, chat_target_info = get_chat_type_and_target_info(self.chat_id) logger.debug(f"{self.log_prefix}获取到聊天信息 - 群聊: {is_group_chat}, 目标信息: {chat_target_info}") current_available_actions_dict = self.action_manager.get_using_actions() # 获取完整的动作信息 all_registered_actions: Dict[str, ActionInfo] = component_registry.get_components_by_type( # type: ignore ComponentType.ACTION ) current_available_actions = {} for action_name in current_available_actions_dict: if action_name in all_registered_actions: current_available_actions[action_name] = all_registered_actions[action_name] else: logger.warning(f"{self.log_prefix}使用中的动作 {action_name} 未在已注册动作中找到") # 将no_reply作为系统级特殊动作添加到可用动作中 # no_reply虽然是系统级决策，但需要让规划器认为它是可用的 no_reply_info = ActionInfo( name="no_reply", component_type=ComponentType.ACTION, description="系统级动作：选择不回复消息的决策", action_parameters={}, activation_keywords=[], plugin_name="SYSTEM", enabled=True, # 始终启用 parallel_action=False, ) current_available_actions["no_reply"] = no_reply_info return is_group_chat, chat_target_info, current_available_actions init_prompt()