import traceback from typing import List, Optional, Dict, Any, Tuple from src.chat.message_receive.message import MessageRecv, MessageThinking, MessageSending from src.chat.message_receive.message import Seg # Local import needed after move from src.chat.message_receive.message import UserInfo from src.chat.message_receive.chat_stream import chat_manager from src.common.logger_manager import get_logger from src.llm_models.utils_model import LLMRequest from src.config.config import global_config from src.chat.utils.utils_image import image_path_to_base64 # Local import needed after move from src.chat.utils.timer_calculator import Timer # <--- Import Timer from src.chat.emoji_system.emoji_manager import emoji_manager from src.chat.focus_chat.heartFC_sender import HeartFCSender from src.chat.utils.utils import process_llm_response from src.chat.utils.info_catcher import info_catcher_manager from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info from src.chat.message_receive.chat_stream import ChatStream from src.chat.focus_chat.hfc_utils import parse_thinking_id_to_timestamp from src.chat.utils.prompt_builder import Prompt, global_prompt_manager from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat import time from src.chat.focus_chat.expressors.exprssion_learner import expression_learner import random logger = get_logger("expressor") def init_prompt(): Prompt( """ 你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中： {style_habbits} 你现在正在群里聊天，以下是群里正在进行的聊天内容： {chat_info} 以上是聊天内容，你需要了解聊天记录中的内容 {chat_target} 你的名字是{bot_name}，{prompt_personality}，在这聊天中，"{target_message}"引起了你的注意，对这句话，你想表达：{in_mind_reply},原因是：{reason}。你现在要思考怎么回复 你需要使用合适的语法和句法，参考聊天内容，组织一条日常且口语化的回复。 请你根据情景使用以下句法： {grammar_habbits} 回复尽量简短一些。可以参考贴吧，知乎和微博的回复风格，你可以完全重组回复，保留最基本的表达含义就好，但注意回复要简短，但重组后保持语意通顺。 回复不要浮夸，不要用夸张修辞，平淡一些。不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )，只输出一条回复就好。 现在，你说： """, "default_expressor_prompt", ) Prompt( """ 你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中： {style_habbits} 你现在正在群里聊天，以下是群里正在进行的聊天内容： {chat_info} 以上是聊天内容，你需要了解聊天记录中的内容 {chat_target} 你的名字是{bot_name}，{prompt_personality}，在这聊天中，"{target_message}"引起了你的注意，对这句话，你想表达：{in_mind_reply},原因是：{reason}。你现在要思考怎么回复 你需要使用合适的语法和句法，参考聊天内容，组织一条日常且口语化的回复。 请你根据情景使用以下句法： {grammar_habbits} 回复尽量简短一些。可以参考贴吧，知乎和微博的回复风格，你可以完全重组回复，保留最基本的表达含义就好，但注意回复要简短，但重组后保持语意通顺。 回复不要浮夸，不要用夸张修辞，平淡一些。不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )，只输出一条回复就好。 现在，你说： """, "default_expressor_private_prompt", # New template for private FOCUSED chat ) class DefaultExpressor: def __init__(self, chat_id: str): self.log_prefix = "expressor" # TODO: API-Adapter修改标记 self.express_model = LLMRequest( model=global_config.model.focus_expressor, # temperature=global_config.model.focus_expressor["temp"], max_tokens=256, request_type="focus_expressor", ) self.heart_fc_sender = HeartFCSender() self.chat_id = chat_id self.chat_stream: Optional[ChatStream] = None self.is_group_chat = True self.chat_target_info = None async def initialize(self): self.is_group_chat, self.chat_target_info = await get_chat_type_and_target_info(self.chat_id) async def _create_thinking_message(self, anchor_message: Optional[MessageRecv], thinking_id: str): """创建思考消息 (尝试锚定到 anchor_message)""" if not anchor_message or not anchor_message.chat_stream: logger.error(f"{self.log_prefix} 无法创建思考消息，缺少有效的锚点消息或聊天流。") return None chat = anchor_message.chat_stream messageinfo = anchor_message.message_info thinking_time_point = parse_thinking_id_to_timestamp(thinking_id) bot_user_info = UserInfo( user_id=global_config.bot.qq_account, user_nickname=global_config.bot.nickname, platform=messageinfo.platform, ) thinking_message = MessageThinking( message_id=thinking_id, chat_stream=chat, bot_user_info=bot_user_info, reply=anchor_message, # 回复的是锚点消息 thinking_start_time=thinking_time_point, ) # logger.debug(f"创建思考消息thinking_message：{thinking_message}") await self.heart_fc_sender.register_thinking(thinking_message) async def deal_reply( self, cycle_timers: dict, action_data: Dict[str, Any], reasoning: str, anchor_message: MessageRecv, thinking_id: str, ) -> tuple[bool, Optional[List[Tuple[str, str]]]]: # 创建思考消息 await self._create_thinking_message(anchor_message, thinking_id) reply = None # 初始化 reply，防止未定义 try: has_sent_something = False # 处理文本部分 text_part = action_data.get("text", []) if text_part: with Timer("生成回复", cycle_timers): # 可以保留原有的文本处理逻辑或进行适当调整 reply = await self.express( in_mind_reply=text_part, anchor_message=anchor_message, thinking_id=thinking_id, reason=reasoning, action_data=action_data, ) with Timer("选择表情", cycle_timers): emoji_keyword = action_data.get("emojis", []) emoji_base64 = await self._choose_emoji(emoji_keyword) if emoji_base64: reply.append(("emoji", emoji_base64)) if reply: with Timer("发送消息", cycle_timers): sent_msg_list = await self.send_response_messages( anchor_message=anchor_message, thinking_id=thinking_id, response_set=reply, ) has_sent_something = True else: logger.warning(f"{self.log_prefix} 文本回复生成失败") if not has_sent_something: logger.warning(f"{self.log_prefix} 回复动作未包含任何有效内容") return has_sent_something, sent_msg_list except Exception as e: logger.error(f"回复失败: {e}") traceback.print_exc() return False, None # --- 回复器 (Replier) 的定义 --- # async def express( self, in_mind_reply: str, reason: str, anchor_message: MessageRecv, thinking_id: str, action_data: Dict[str, Any], ) -> Optional[List[str]]: """ 回复器 (Replier): 核心逻辑，负责生成回复文本。 (已整合原 HeartFCGenerator 的功能) """ try: # 1. 获取情绪影响因子并调整模型温度 # arousal_multiplier = mood_manager.get_arousal_multiplier() # current_temp = float(global_config.model.normal["temp"]) * arousal_multiplier # self.express_model.params["temperature"] = current_temp # 动态调整温度 # 2. 获取信息捕捉器 info_catcher = info_catcher_manager.get_info_catcher(thinking_id) # --- Determine sender_name for private chat --- sender_name_for_prompt = "某人" # Default for group or if info unavailable if not self.is_group_chat and self.chat_target_info: # Prioritize person_name, then nickname sender_name_for_prompt = ( self.chat_target_info.get("person_name") or self.chat_target_info.get("user_nickname") or sender_name_for_prompt ) # --- End determining sender_name --- target_message = action_data.get("target", "") # 3. 构建 Prompt with Timer("构建Prompt", {}): # 内部计时器，可选保留 prompt = await self.build_prompt_focus( chat_stream=self.chat_stream, # Pass the stream object in_mind_reply=in_mind_reply, reason=reason, sender_name=sender_name_for_prompt, # Pass determined name target_message=target_message, ) # 4. 调用 LLM 生成回复 content = None reasoning_content = None model_name = "unknown_model" if not prompt: logger.error(f"{self.log_prefix}[Replier-{thinking_id}] Prompt 构建失败，无法生成回复。") return None try: with Timer("LLM生成", {}): # 内部计时器，可选保留 # TODO: API-Adapter修改标记 # logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n") content, reasoning_content, model_name = await self.express_model.generate_response(prompt) # logger.info(f"{self.log_prefix}\nPrompt:\n{prompt}\n---------------------------\n") logger.info(f"想要表达：{in_mind_reply}||理由：{reason}") logger.info(f"最终回复: {content}\n") info_catcher.catch_after_llm_generated( prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=model_name ) except Exception as llm_e: # 精简报错信息 logger.error(f"{self.log_prefix}LLM 生成失败: {llm_e}") return None # LLM 调用失败则无法生成回复 processed_response = process_llm_response(content) # 5. 处理 LLM 响应 if not content: logger.warning(f"{self.log_prefix}LLM 生成了空内容。") return None if not processed_response: logger.warning(f"{self.log_prefix}处理后的回复为空。") return None reply_set = [] for str in processed_response: reply_seg = ("text", str) reply_set.append(reply_seg) return reply_set except Exception as e: logger.error(f"{self.log_prefix}回复生成意外失败: {e}") traceback.print_exc() return None async def build_prompt_focus( self, reason, chat_stream, sender_name, in_mind_reply, target_message, ) -> str: is_group_chat = bool(chat_stream.group_info) message_list_before_now = get_raw_msg_before_timestamp_with_chat( chat_id=chat_stream.stream_id, timestamp=time.time(), limit=global_config.focus_chat.observation_context_size, ) chat_talking_prompt = await build_readable_messages( message_list_before_now, replace_bot_name=True, merge_messages=True, timestamp_mode="relative", read_mark=0.0, truncate=True, ) ( learnt_style_expressions, learnt_grammar_expressions, personality_expressions, ) = await expression_learner.get_expression_by_chat_id(chat_stream.stream_id) style_habbits = [] grammar_habbits = [] # 1. learnt_expressions加权随机选3条 if learnt_style_expressions: weights = [expr["count"] for expr in learnt_style_expressions] selected_learnt = weighted_sample_no_replacement(learnt_style_expressions, weights, 3) for expr in selected_learnt: if isinstance(expr, dict) and "situation" in expr and "style" in expr: style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}") # 2. learnt_grammar_expressions加权随机选3条 if learnt_grammar_expressions: weights = [expr["count"] for expr in learnt_grammar_expressions] selected_learnt = weighted_sample_no_replacement(learnt_grammar_expressions, weights, 3) for expr in selected_learnt: if isinstance(expr, dict) and "situation" in expr and "style" in expr: grammar_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}") # 3. personality_expressions随机选1条 if personality_expressions: expr = random.choice(personality_expressions) if isinstance(expr, dict) and "situation" in expr and "style" in expr: style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}") style_habbits_str = "\n".join(style_habbits) grammar_habbits_str = "\n".join(grammar_habbits) logger.debug("开始构建 focus prompt") # --- Choose template based on chat type --- if is_group_chat: template_name = "default_expressor_prompt" # Group specific formatting variables (already fetched or default) chat_target_1 = await global_prompt_manager.get_prompt_async("chat_target_group1") # chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2") prompt = await global_prompt_manager.format_prompt( template_name, style_habbits=style_habbits_str, grammar_habbits=grammar_habbits_str, chat_target=chat_target_1, chat_info=chat_talking_prompt, bot_name=global_config.bot.nickname, prompt_personality="", reason=reason, in_mind_reply=in_mind_reply, target_message=target_message, ) else: # Private chat template_name = "default_expressor_private_prompt" chat_target_1 = "你正在和人私聊" prompt = await global_prompt_manager.format_prompt( template_name, style_habbits=style_habbits_str, grammar_habbits=grammar_habbits_str, chat_target=chat_target_1, chat_info=chat_talking_prompt, bot_name=global_config.bot.nickname, prompt_personality="", reason=reason, in_mind_reply=in_mind_reply, target_message=target_message, ) return prompt # --- 发送器 (Sender) --- # async def send_response_messages( self, anchor_message: Optional[MessageRecv], response_set: List[Tuple[str, str]], thinking_id: str = "", display_message: str = "", ) -> Optional[MessageSending]: """发送回复消息 (尝试锚定到 anchor_message)，使用 HeartFCSender""" chat = self.chat_stream chat_id = self.chat_id if chat is None: logger.error(f"{self.log_prefix} 无法发送回复，chat_stream 为空。") return None if not anchor_message: logger.error(f"{self.log_prefix} 无法发送回复，anchor_message 为空。") return None stream_name = chat_manager.get_stream_name(chat_id) or chat_id # 获取流名称用于日志 # 检查思考过程是否仍在进行，并获取开始时间 if thinking_id: thinking_start_time = await self.heart_fc_sender.get_thinking_start_time(chat_id, thinking_id) else: thinking_id = "ds" + str(round(time.time(), 2)) thinking_start_time = time.time() if thinking_start_time is None: logger.error(f"[{stream_name}]思考过程未找到或已结束，无法发送回复。") return None mark_head = False # first_bot_msg: Optional[MessageSending] = None reply_message_ids = [] # 记录实际发送的消息ID sent_msg_list = [] for i, msg_text in enumerate(response_set): # 为每个消息片段生成唯一ID type = msg_text[0] data = msg_text[1] if global_config.experimental.debug_show_chat_mode and type == "text": data += "ᶠ" part_message_id = f"{thinking_id}_{i}" message_segment = Seg(type=type, data=data) if type == "emoji": is_emoji = True else: is_emoji = False reply_to = not mark_head bot_message = await self._build_single_sending_message( anchor_message=anchor_message, message_id=part_message_id, message_segment=message_segment, display_message=display_message, reply_to=reply_to, is_emoji=is_emoji, thinking_id=thinking_id, thinking_start_time=thinking_start_time, ) try: if not mark_head: mark_head = True # first_bot_msg = bot_message # 保存第一个成功发送的消息对象 typing = False else: typing = True if type == "emoji": typing = False if anchor_message.raw_message: set_reply = True else: set_reply = False sent_msg = await self.heart_fc_sender.send_message( bot_message, has_thinking=True, typing=typing, set_reply=set_reply ) reply_message_ids.append(part_message_id) # 记录我们生成的ID sent_msg_list.append((type, sent_msg)) except Exception as e: logger.error(f"{self.log_prefix}发送回复片段 {i} ({part_message_id}) 时失败: {e}") traceback.print_exc() # 这里可以选择是继续发送下一个片段还是中止 # 在尝试发送完所有片段后，完成原始的 thinking_id 状态 try: await self.heart_fc_sender.complete_thinking(chat_id, thinking_id) except Exception as e: logger.error(f"{self.log_prefix}完成思考状态 {thinking_id} 时出错: {e}") return sent_msg_list async def _choose_emoji(self, send_emoji: str): """ 选择表情，根据send_emoji文本选择表情，返回表情base64 """ emoji_base64 = "" emoji_raw = await emoji_manager.get_emoji_for_text(send_emoji) if emoji_raw: emoji_path, _description = emoji_raw emoji_base64 = image_path_to_base64(emoji_path) return emoji_base64 async def _build_single_sending_message( self, anchor_message: MessageRecv, message_id: str, message_segment: Seg, reply_to: bool, is_emoji: bool, thinking_id: str, thinking_start_time: float, display_message: str, ) -> MessageSending: """构建单个发送消息""" bot_user_info = UserInfo( user_id=global_config.bot.qq_account, user_nickname=global_config.bot.nickname, platform=self.chat_stream.platform, ) bot_message = MessageSending( message_id=message_id, # 使用片段的唯一ID chat_stream=self.chat_stream, bot_user_info=bot_user_info, sender_info=anchor_message.message_info.user_info, message_segment=message_segment, reply=anchor_message, # 回复原始锚点 is_head=reply_to, is_emoji=is_emoji, thinking_start_time=thinking_start_time, # 传递原始思考开始时间 display_message=display_message, ) return bot_message def weighted_sample_no_replacement(items, weights, k) -> list: """ 加权且不放回地随机抽取k个元素。 参数： items: 待抽取的元素列表 weights: 每个元素对应的权重（与items等长，且为正数） k: 需要抽取的元素个数 返回： selected: 按权重加权且不重复抽取的k个元素组成的列表 如果 items 中的元素不足 k 个，就只会返回所有可用的元素 实现思路： 每次从当前池中按权重加权随机选出一个元素，选中后将其从池中移除，重复k次。 这样保证了： 1. count越大被选中概率越高 2. 不会重复选中同一个元素 """ selected = [] pool = list(zip(items, weights)) for _ in range(min(k, len(pool))): total = sum(w for _, w in pool) r = random.uniform(0, total) upto = 0 for idx, (item, weight) in enumerate(pool): upto += weight if upto >= r: selected.append(item) pool.pop(idx) break return selected init_prompt()