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 get_chat_manager from src.common.logger import get_logger from src.llm_models.utils_model import LLMRequest from src.config.config import global_config from src.chat.utils.timer_calculator import Timer # <--- Import Timer from src.chat.focus_chat.heartFC_sender import HeartFCSender from src.chat.utils.utils import process_llm_response 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 from src.chat.express.exprssion_learner import get_expression_learner import time import random import ast from src.person_info.person_info import get_person_info_manager from datetime import datetime import re logger = get_logger("replyer") def init_prompt(): Prompt( """ {expression_habits_block} {structured_info_block} {memory_block} {relation_info_block} {extra_info_block} {time_block} {chat_target} {chat_info} {reply_target_block} {identity} 你需要使用合适的语言习惯和句法，参考聊天内容，组织一条日常且口语化的回复。注意不要复读你说过的话。 {config_expression_style}。回复不要浮夸，不要用夸张修辞，平淡一些。 {keywords_reaction_prompt} 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。 不要浮夸，不要夸张修辞，请注意不要输出多余内容(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_generator_prompt", ) Prompt( """ {expression_habits_block} {structured_info_block} {memory_block} {relation_info_block} {extra_info_block} {time_block} {chat_target} {chat_info} 现在"{sender_name}"说:{target_message}。你想要回复对方的这条消息。 {identity}， 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。注意不要复读你说过的话。 {config_expression_style}。回复不要浮夸，不要用夸张修辞，平淡一些。 {keywords_reaction_prompt} 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。 不要浮夸，不要夸张修辞，请注意不要输出多余内容(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_generator_private_prompt", ) Prompt( """ 你可以参考你的以下的语言习惯，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，"{sender_name}"说的"{target_message}"引起了你的注意，对这句话，你想表达：{raw_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。请你修改你想表达的原句，符合你的表达风格和语言习惯 请你根据情景使用以下句法： {grammar_habbits} {config_expression_style}，你可以完全重组回复，保留最基本的表达含义就好，但重组后保持语意通顺。 不要浮夸，不要夸张修辞，平淡且不要输出多余内容(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，"{sender_name}"说的"{target_message}"引起了你的注意，对这句话，你想表达：{raw_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} {config_expression_style}，你可以完全重组回复，保留最基本的表达含义就好，但重组后保持语意通顺。 不要浮夸，不要夸张修辞，平淡且不要输出多余内容(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 DefaultReplyer: def __init__(self, chat_stream: ChatStream): self.log_prefix = "replyer" # TODO: API-Adapter修改标记 self.express_model = LLMRequest( model=global_config.model.replyer_1, request_type="focus.replyer", ) self.heart_fc_sender = HeartFCSender() self.chat_stream = chat_stream self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.stream_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) return None async def generate_reply_with_context( self, reply_data: Dict[str, Any], enable_splitter: bool=True, enable_chinese_typo: bool=True ) -> Tuple[bool, 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: # 3. 构建 Prompt with Timer("构建Prompt", {}): # 内部计时器，可选保留 prompt = await self.build_prompt_reply_context( reply_data=reply_data, # 传递action_data ) # 4. 调用 LLM 生成回复 content = None reasoning_content = None model_name = "unknown_model" try: with Timer("LLM生成", {}): # 内部计时器，可选保留 logger.info(f"{self.log_prefix}Prompt:\n{prompt}\n") content, (reasoning_content, model_name) = await self.express_model.generate_response_async(prompt) logger.info(f"最终回复: {content}") 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 False, None # LLM 调用失败则无法生成回复 processed_response = process_llm_response(content,enable_splitter,enable_chinese_typo) # 5. 处理 LLM 响应 if not content: logger.warning(f"{self.log_prefix}LLM 生成了空内容。") return False, None if not processed_response: logger.warning(f"{self.log_prefix}处理后的回复为空。") return False, None reply_set = [] for str in processed_response: reply_seg = ("text", str) reply_set.append(reply_seg) return True, 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 False, None async def rewrite_reply_with_context( self, reply_data: Dict[str, Any], enable_splitter: bool=True, enable_chinese_typo: bool=True ) -> Tuple[bool, Optional[List[str]]]: """ 表达器 (Expressor): 核心逻辑，负责生成回复文本。 """ try: reply_to = reply_data.get("reply_to", "") raw_reply = reply_data.get("raw_reply", "") reason = reply_data.get("reason", "") with Timer("构建Prompt", {}): # 内部计时器，可选保留 prompt = await self.build_prompt_rewrite_context( raw_reply=raw_reply, reason=reason, reply_to=reply_to, ) content = None reasoning_content = None model_name = "unknown_model" if not prompt: logger.error(f"{self.log_prefix}Prompt 构建失败，无法生成回复。") return False, None try: with Timer("LLM生成", {}): # 内部计时器，可选保留 # TODO: API-Adapter修改标记 content, (reasoning_content, model_name) = await self.express_model.generate_response_async(prompt) logger.info(f"想要表达：{raw_reply}||理由：{reason}") logger.info(f"最终回复: {content}\n") 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 False, None # LLM 调用失败则无法生成回复 processed_response = process_llm_response(content,enable_splitter,enable_chinese_typo) # 5. 处理 LLM 响应 if not content: logger.warning(f"{self.log_prefix}LLM 生成了空内容。") return False, None if not processed_response: logger.warning(f"{self.log_prefix}处理后的回复为空。") return False, None reply_set = [] for str in processed_response: reply_seg = ("text", str) reply_set.append(reply_seg) return True, 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 False, None async def build_prompt_reply_context( self, reply_data=None, ) -> str: chat_stream = self.chat_stream person_info_manager = get_person_info_manager() bot_person_id = person_info_manager.get_person_id("system", "bot_id") is_group_chat = bool(chat_stream.group_info) self_info_block = reply_data.get("self_info_block", "") structured_info = reply_data.get("structured_info", "") relation_info_block = reply_data.get("relation_info_block", "") reply_to = reply_data.get("reply_to", "none") memory_block = reply_data.get("memory_block", "") # 优先使用 extra_info_block，没有则用 extra_info extra_info_block = reply_data.get("extra_info_block", "") or reply_data.get("extra_info", "") sender = "" target = "" if ":" in reply_to or "：" in reply_to: # 使用正则表达式匹配中文或英文冒号 parts = re.split(pattern=r"[:：]", string=reply_to, maxsplit=1) if len(parts) == 2: sender = parts[0].strip() target = parts[1].strip() 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, ) # print(f"message_list_before_now: {message_list_before_now}") chat_talking_prompt = build_readable_messages( message_list_before_now, replace_bot_name=True, merge_messages=False, timestamp_mode="normal_no_YMD", read_mark=0.0, truncate=True, show_actions=True, ) # print(f"chat_talking_prompt: {chat_talking_prompt}") style_habbits = [] grammar_habbits = [] # 使用从处理器传来的选中表达方式 selected_expressions = reply_data.get("selected_expressions", []) if reply_data else [] if selected_expressions: logger.info(f"{self.log_prefix} 使用处理器选中的{len(selected_expressions)}个表达方式") for expr in selected_expressions: if isinstance(expr, dict) and "situation" in expr and "style" in expr: expr_type = expr.get("type", "style") if expr_type == "grammar": grammar_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}") else: style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}") else: logger.debug(f"{self.log_prefix} 没有从处理器获得表达方式，将使用空的表达方式") # 不再在replyer中进行随机选择，全部交给处理器处理 style_habbits_str = "\n".join(style_habbits) grammar_habbits_str = "\n".join(grammar_habbits) # 动态构建expression habits块 expression_habits_block = "" if style_habbits_str.strip(): expression_habits_block += f"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：\n{style_habbits_str}\n\n" if grammar_habbits_str.strip(): expression_habits_block += f"请你根据情景使用以下句法：\n{grammar_habbits_str}\n" if structured_info: structured_info_block = f"以下是一些额外的信息，现在请你阅读以下内容，进行决策\n{structured_info}\n以上是一些额外的信息，现在请你阅读以下内容，进行决策" else: structured_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: extra_info_block = "" # 关键词检测与反应 keywords_reaction_prompt = "" try: # 处理关键词规则 for rule in global_config.keyword_reaction.keyword_rules: if any(keyword in target for keyword in rule.keywords): logger.info(f"检测到关键词规则：{rule.keywords}，触发反应：{rule.reaction}") keywords_reaction_prompt += f"{rule.reaction}，" # 处理正则表达式规则 for rule in global_config.keyword_reaction.regex_rules: for pattern_str in rule.regex: try: pattern = re.compile(pattern_str) if result := pattern.search(target): reaction = rule.reaction for name, content in result.groupdict().items(): reaction = reaction.replace(f"[{name}]", content) logger.info(f"匹配到正则表达式：{pattern_str}，触发反应：{reaction}") keywords_reaction_prompt += reaction + "，" break except re.error as e: logger.error(f"正则表达式编译错误: {pattern_str}, 错误信息: {str(e)}") continue except Exception as e: logger.error(f"关键词检测与反应时发生异常: {str(e)}", exc_info=True) time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" # logger.debug("开始构建 focus prompt") bot_name = global_config.bot.nickname if global_config.bot.alias_names: bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}" else: bot_nickname = "" short_impression = await person_info_manager.get_value(bot_person_id, "short_impression") # 解析字符串形式的Python列表 try: if isinstance(short_impression, str) and short_impression.strip(): short_impression = ast.literal_eval(short_impression) elif not short_impression: logger.warning("short_impression为空，使用默认值") short_impression = ["友好活泼", "人类"] except (ValueError, SyntaxError) as e: logger.error(f"解析short_impression失败: {e}, 原始值: {short_impression}") short_impression = ["友好活泼", "人类"] # 确保short_impression是列表格式且有足够的元素 if not isinstance(short_impression, list) or len(short_impression) < 2: logger.warning(f"short_impression格式不正确: {short_impression}, 使用默认值") short_impression = ["友好活泼", "人类"] personality = short_impression[0] identity = short_impression[1] prompt_personality = personality + "，" + identity indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}：" if sender: reply_target_block = f"现在{sender}说的:{target}。引起了你的注意，你想要在群里发言或者回复这条消息。" elif target: reply_target_block = f"现在{target}引起了你的注意，你想要在群里发言或者回复这条消息。" else: reply_target_block = "现在，你想要在群里发言或者回复消息。" # --- Choose template based on chat type --- if is_group_chat: template_name = "default_generator_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, expression_habits_block=expression_habits_block, chat_target=chat_target_1, chat_info=chat_talking_prompt, memory_block=memory_block, structured_info_block=structured_info_block, extra_info_block=extra_info_block, relation_info_block=relation_info_block, self_info_block=self_info_block, time_block=time_block, reply_target_block=reply_target_block, keywords_reaction_prompt=keywords_reaction_prompt, identity=indentify_block, target_message=target, sender_name=sender, config_expression_style=global_config.expression.expression_style, ) else: # Private chat template_name = "default_generator_private_prompt" # 在私聊时获取对方的昵称信息 chat_target_name = "对方" if self.chat_target_info: chat_target_name = ( self.chat_target_info.get("person_name") or self.chat_target_info.get("user_nickname") or "对方" ) chat_target_1 = f"你正在和 {chat_target_name} 聊天" prompt = await global_prompt_manager.format_prompt( template_name, expression_habits_block=expression_habits_block, chat_target=chat_target_1, chat_info=chat_talking_prompt, memory_block=memory_block, structured_info_block=structured_info_block, relation_info_block=relation_info_block, extra_info_block=extra_info_block, time_block=time_block, keywords_reaction_prompt=keywords_reaction_prompt, identity=indentify_block, target_message=target, sender_name=sender, config_expression_style=global_config.expression.expression_style, ) return prompt async def build_prompt_rewrite_context( self, reason, raw_reply, reply_to, ) -> str: sender = "" target = "" if ":" in reply_to or "：" in reply_to: # 使用正则表达式匹配中文或英文冒号 parts = re.split(pattern=r"[:：]", string=reply_to, maxsplit=1) if len(parts) == 2: sender = parts[0].strip() target = parts[1].strip() chat_stream = self.chat_stream 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 = build_readable_messages( message_list_before_now, replace_bot_name=True, merge_messages=True, timestamp_mode="relative", read_mark=0.0, truncate=True, ) expression_learner = get_expression_learner() ( learnt_style_expressions, learnt_grammar_expressions, personality_expressions, ) = 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, raw_reply=raw_reply, sender_name=sender, target_message=target, config_expression_style=global_config.expression.expression_style, ) else: # Private chat template_name = "default_expressor_private_prompt" # 在私聊时获取对方的昵称信息 chat_target_name = "对方" if self.chat_target_info: chat_target_name = ( self.chat_target_info.get("person_name") or self.chat_target_info.get("user_nickname") or "对方" ) chat_target_1 = f"你正在和 {chat_target_name} 聊天" 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, raw_reply=raw_reply, sender_name=sender, target_message=target, config_expression_style=global_config.expression.expression_style, ) return prompt 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_stream.stream_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 = get_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: # print(f"thinking_id: {thinking_id}") thinking_start_time = await self.heart_fc_sender.get_thinking_start_time(chat_id, thinking_id) else: print("thinking_id is None") # thinking_id = "ds" + str(round(time.time(), 2)) thinking_start_time = time.time() if thinking_start_time is None: logger.error(f"[{stream_name}]replyer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: MessageSending = 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 ( bot_message.is_private_message() or bot_message.reply.processed_plain_text != "[System Trigger Context]" or mark_head ): set_reply = False else: set_reply = True if not mark_head: mark_head = True typing = False else: typing = True sent_msg = await self.heart_fc_sender.send_message(bot_message, 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 _build_single_sending_message( self, message_id: str, message_segment: Seg, reply_to: bool, is_emoji: bool, thinking_start_time: float, display_message: str, anchor_message: MessageRecv = None, ) -> 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, ) # await anchor_message.process() if anchor_message: sender_info = anchor_message.message_info.user_info else: sender_info = None 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=sender_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()