import random import time import re from collections import Counter from typing import Dict, List, Optional import jieba import numpy as np from src.common.logger import get_module_logger from ..models.utils_model import LLMRequest from ..utils.typo_generator import ChineseTypoGenerator from ...config.config import global_config from .message import MessageRecv, Message from maim_message import UserInfo from .chat_stream import ChatStream from ..moods.moods import MoodManager from ...common.database import db logger = get_module_logger("chat_utils") def is_english_letter(char: str) -> bool: """检查字符是否为英文字母（忽略大小写）""" return "a" <= char.lower() <= "z" def db_message_to_str(message_dict: Dict) -> str: logger.debug(f"message_dict: {message_dict}") time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message_dict["time"])) try: name = "[(%s)%s]%s" % ( message_dict["user_id"], message_dict.get("user_nickname", ""), message_dict.get("user_cardname", ""), ) except Exception: name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}" content = message_dict.get("processed_plain_text", "") result = f"[{time_str}] {name}: {content}\n" logger.debug(f"result: {result}") return result def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]: """检查消息是否提到了机器人""" keywords = [global_config.BOT_NICKNAME] nicknames = global_config.BOT_ALIAS_NAMES reply_probability = 0.0 is_at = False is_mentioned = False if ( message.message_info.additional_config is not None and message.message_info.additional_config.get("is_mentioned") is not None ): try: reply_probability = float(message.message_info.additional_config.get("is_mentioned")) is_mentioned = True return is_mentioned, reply_probability except Exception as e: logger.warning(e) logger.warning( f"消息中包含不合理的设置 is_mentioned: {message.message_info.additional_config.get('is_mentioned')}" ) # 判断是否被@ if re.search(f"@[\s\S]*?（id:{global_config.BOT_QQ}）", message.processed_plain_text): is_at = True is_mentioned = True if is_at and global_config.at_bot_inevitable_reply: reply_probability = 1.0 logger.info("被@，回复概率设置为100%") else: if not is_mentioned: # 判断是否被回复 if re.match( f"\[回复 [\s\S]*?\({str(global_config.BOT_QQ)}\)：[\s\S]*?\]，说：", message.processed_plain_text ): is_mentioned = True else: # 判断内容中是否被提及 message_content = re.sub(r"@[\s\S]*?（(\d+)）", "", message.processed_plain_text) message_content = re.sub(r"\[回复 [\s\S]*?\(((\d+)|未知id)\)：[\s\S]*?\]，说：", "", message_content) for keyword in keywords: if keyword in message_content: is_mentioned = True for nickname in nicknames: if nickname in message_content: is_mentioned = True if is_mentioned and global_config.mentioned_bot_inevitable_reply: reply_probability = 1.0 logger.info("被提及，回复概率设置为100%") return is_mentioned, reply_probability async def get_embedding(text, request_type="embedding"): """获取文本的embedding向量""" llm = LLMRequest(model=global_config.embedding, request_type=request_type) # return llm.get_embedding_sync(text) try: embedding = await llm.get_embedding(text) except Exception as e: logger.error(f"获取embedding失败: {str(e)}") embedding = None return embedding async def get_recent_group_messages(chat_id: str, limit: int = 12) -> list: """从数据库获取群组最近的消息记录 Args: chat_id: 群组ID limit: 获取消息数量，默认12条 Returns: list: Message对象列表，按时间正序排列 """ # 从数据库获取最近消息 recent_messages = list( db.messages.find( {"chat_id": chat_id}, ) .sort("time", -1) .limit(limit) ) if not recent_messages: return [] # 转换为 Message对象列表 message_objects = [] for msg_data in recent_messages: try: chat_info = msg_data.get("chat_info", {}) chat_stream = ChatStream.from_dict(chat_info) user_info = msg_data.get("user_info", {}) user_info = UserInfo.from_dict(user_info) msg = Message( message_id=msg_data["message_id"], chat_stream=chat_stream, timestamp=msg_data["time"], user_info=user_info, processed_plain_text=msg_data.get("processed_text", ""), detailed_plain_text=msg_data.get("detailed_plain_text", ""), ) message_objects.append(msg) except KeyError: logger.warning("数据库中存在无效的消息") continue # 按时间正序排列 message_objects.reverse() return message_objects def get_recent_group_detailed_plain_text(chat_stream_id: int, limit: int = 12, combine=False): recent_messages = list( db.messages.find( {"chat_id": chat_stream_id}, { "time": 1, # 返回时间字段 "chat_id": 1, "chat_info": 1, "user_info": 1, "message_id": 1, # 返回消息ID字段 "detailed_plain_text": 1, # 返回处理后的文本字段 }, ) .sort("time", -1) .limit(limit) ) if not recent_messages: return [] message_detailed_plain_text = "" message_detailed_plain_text_list = [] # 反转消息列表，使最新的消息在最后 recent_messages.reverse() if combine: for msg_db_data in recent_messages: message_detailed_plain_text += str(msg_db_data["detailed_plain_text"]) return message_detailed_plain_text else: for msg_db_data in recent_messages: message_detailed_plain_text_list.append(msg_db_data["detailed_plain_text"]) return message_detailed_plain_text_list def get_recent_group_speaker(chat_stream_id: int, sender, limit: int = 12) -> list: # 获取当前群聊记录内发言的人 recent_messages = list( db.messages.find( {"chat_id": chat_stream_id}, { "user_info": 1, }, ) .sort("time", -1) .limit(limit) ) if not recent_messages: return [] who_chat_in_group = [] for msg_db_data in recent_messages: user_info = UserInfo.from_dict(msg_db_data["user_info"]) if ( (user_info.platform, user_info.user_id) != sender and user_info.user_id != global_config.BOT_QQ and (user_info.platform, user_info.user_id, user_info.user_nickname) not in who_chat_in_group and len(who_chat_in_group) < 5 ): # 排除重复，排除消息发送者，排除bot，限制加载的关系数目 who_chat_in_group.append((user_info.platform, user_info.user_id, user_info.user_nickname)) return who_chat_in_group def split_into_sentences_w_remove_punctuation(text: str) -> List[str]: """将文本分割成句子，并根据概率合并 1. 识别分割点（, ， 。 ; 空格），但如果分割点左右都是英文字母则不分割。 2. 将文本分割成 (内容, 分隔符) 的元组。 3. 根据原始文本长度计算合并概率，概率性地合并相邻段落。 注意：此函数假定颜文字已在上层被保护。 Args: text: 要分割的文本字符串 (假定颜文字已被保护) Returns: List[str]: 分割和合并后的句子列表 """ # 预处理：处理多余的换行符 # 1. 将连续的换行符替换为单个换行符 text = re.sub(r"\n\s*\n+", "\n", text) # 2. 处理换行符和其他分隔符的组合 text = re.sub(r"\n\s*([，,。;\s])", r"\1", text) text = re.sub(r"([，,。;\s])\s*\n", r"\1", text) # 处理两个汉字中间的换行符 text = re.sub(r"([\u4e00-\u9fff])\n([\u4e00-\u9fff])", r"\1。\2", text) len_text = len(text) if len_text < 3: if random.random() < 0.01: return list(text) # 如果文本很短且触发随机条件,直接按字符分割 else: return [text] # 定义分隔符 separators = {"，", ",", " ", "。", ";"} segments = [] current_segment = "" # 1. 分割成 (内容, 分隔符) 元组 i = 0 while i < len(text): char = text[i] if char in separators: # 检查分割条件：如果分隔符左右都是英文字母，则不分割 can_split = True if i > 0 and i < len(text) - 1: prev_char = text[i - 1] next_char = text[i + 1] # if is_english_letter(prev_char) and is_english_letter(next_char) and char == ' ': # 原计划只对空格应用此规则，现应用于所有分隔符 if is_english_letter(prev_char) and is_english_letter(next_char): can_split = False if can_split: # 只有当当前段不为空时才添加 if current_segment: segments.append((current_segment, char)) # 如果当前段为空，但分隔符是空格，则也添加一个空段（保留空格） elif char == " ": segments.append(("", char)) current_segment = "" else: # 不分割，将分隔符加入当前段 current_segment += char else: current_segment += char i += 1 # 添加最后一个段（没有后续分隔符） if current_segment: segments.append((current_segment, "")) # 过滤掉完全空的段（内容和分隔符都为空） segments = [(content, sep) for content, sep in segments if content or sep] # 如果分割后为空（例如，输入全是分隔符且不满足保留条件），恢复颜文字并返回 if not segments: # recovered_text = recover_kaomoji([text], mapping) # 恢复原文本中的颜文字 - 已移至上层处理 # return [s for s in recovered_text if s] # 返回非空结果 return [text] if text else [] # 如果原始文本非空，则返回原始文本（可能只包含未被分割的字符或颜文字占位符） # 2. 概率合并 if len_text < 12: split_strength = 0.2 elif len_text < 32: split_strength = 0.6 else: split_strength = 0.7 # 合并概率与分割强度相反 merge_probability = 1.0 - split_strength merged_segments = [] idx = 0 while idx < len(segments): current_content, current_sep = segments[idx] # 检查是否可以与下一段合并 # 条件：不是最后一段，且随机数小于合并概率，且当前段有内容（避免合并空段） if idx + 1 < len(segments) and random.random() < merge_probability and current_content: next_content, next_sep = segments[idx + 1] # 合并: (内容1 + 分隔符1 + 内容2, 分隔符2) # 只有当下一段也有内容时才合并文本，否则只传递分隔符 if next_content: merged_content = current_content + current_sep + next_content merged_segments.append((merged_content, next_sep)) else: # 下一段内容为空，只保留当前内容和下一段的分隔符 merged_segments.append((current_content, next_sep)) idx += 2 # 跳过下一段，因为它已被合并 else: # 不合并，直接添加当前段 merged_segments.append((current_content, current_sep)) idx += 1 # 提取最终的句子内容 final_sentences = [content for content, sep in merged_segments if content] # 只保留有内容的段 # 清理可能引入的空字符串和仅包含空白的字符串 final_sentences = [ s for s in final_sentences if s.strip() ] # 过滤掉空字符串以及仅包含空白（如换行符、空格）的字符串 logger.debug(f"分割并合并后的句子: {final_sentences}") return final_sentences def random_remove_punctuation(text: str) -> str: """随机处理标点符号，模拟人类打字习惯 Args: text: 要处理的文本 Returns: str: 处理后的文本 """ result = "" text_len = len(text) for i, char in enumerate(text): if char == "。" and i == text_len - 1: # 结尾的句号 if random.random() > 0.1: # 90%概率删除结尾句号 continue elif char == "，": rand = random.random() if rand < 0.25: # 5%概率删除逗号 continue elif rand < 0.25: # 20%概率把逗号变成空格 result += " " continue result += char return result def process_llm_response(text: str) -> List[str]: # 先保护颜文字 if global_config.enable_kaomoji_protection: protected_text, kaomoji_mapping = protect_kaomoji(text) logger.trace(f"保护颜文字后的文本: {protected_text}") else: protected_text = text kaomoji_mapping = {} # 提取被 () 或 [] 包裹且包含中文的内容 pattern = re.compile(r"[\(\[\（](?=.*[\u4e00-\u9fff]).*?[\)\]\）]") # _extracted_contents = pattern.findall(text) _extracted_contents = pattern.findall(protected_text) # 在保护后的文本上查找 # 去除 () 和 [] 及其包裹的内容 cleaned_text = pattern.sub("", protected_text) if cleaned_text == "": return ["呃呃"] logger.debug(f"{text}去除括号处理后的文本: {cleaned_text}") # 对清理后的文本进行进一步处理 max_length = global_config.response_max_length * 2 max_sentence_num = global_config.response_max_sentence_num # 如果基本上是中文，则进行长度过滤 if get_western_ratio(cleaned_text) < 0.1: if len(cleaned_text) > max_length: logger.warning(f"回复过长 ({len(cleaned_text)} 字符)，返回默认回复") return ["懒得说"] typo_generator = ChineseTypoGenerator( error_rate=global_config.chinese_typo_error_rate, min_freq=global_config.chinese_typo_min_freq, tone_error_rate=global_config.chinese_typo_tone_error_rate, word_replace_rate=global_config.chinese_typo_word_replace_rate, ) if global_config.enable_response_splitter: split_sentences = split_into_sentences_w_remove_punctuation(cleaned_text) else: split_sentences = [cleaned_text] sentences = [] for sentence in split_sentences: if global_config.chinese_typo_enable: typoed_text, typo_corrections = typo_generator.create_typo_sentence(sentence) sentences.append(typoed_text) if typo_corrections: sentences.append(typo_corrections) else: sentences.append(sentence) if len(sentences) > max_sentence_num: logger.warning(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复") return [f"{global_config.BOT_NICKNAME}不知道哦"] # if extracted_contents: # for content in extracted_contents: # sentences.append(content) # 在所有句子处理完毕后，对包含占位符的列表进行恢复 if global_config.enable_kaomoji_protection: sentences = recover_kaomoji(sentences, kaomoji_mapping) return sentences def calculate_typing_time( input_string: str, thinking_start_time: float, chinese_time: float = 0.2, english_time: float = 0.1, is_emoji: bool = False, ) -> float: """ 计算输入字符串所需的时间，中文和英文字符有不同的输入时间 input_string (str): 输入的字符串 chinese_time (float): 中文字符的输入时间，默认为0.2秒 english_time (float): 英文字符的输入时间，默认为0.1秒 is_emoji (bool): 是否为emoji，默认为False 特殊情况： - 如果只有一个中文字符，将使用3倍的中文输入时间 - 在所有输入结束后，额外加上回车时间0.3秒 - 如果is_emoji为True，将使用固定1秒的输入时间 """ mood_manager = MoodManager.get_instance() # 将0-1的唤醒度映射到-1到1 mood_arousal = mood_manager.current_mood.arousal # 映射到0.5到2倍的速度系数 typing_speed_multiplier = 1.5**mood_arousal # 唤醒度为1时速度翻倍,为-1时速度减半 chinese_time *= 1 / typing_speed_multiplier english_time *= 1 / typing_speed_multiplier # 计算中文字符数 chinese_chars = sum(1 for char in input_string if "\u4e00" <= char <= "\u9fff") # 如果只有一个中文字符，使用3倍时间 if chinese_chars == 1 and len(input_string.strip()) == 1: return chinese_time * 3 + 0.3 # 加上回车时间 # 正常计算所有字符的输入时间 total_time = 0.0 for char in input_string: if "\u4e00" <= char <= "\u9fff": # 判断是否为中文字符 total_time += chinese_time else: # 其他字符（如英文） total_time += english_time if is_emoji: total_time = 1 if time.time() - thinking_start_time > 10: total_time = 1 # print(f"thinking_start_time:{thinking_start_time}") # print(f"nowtime:{time.time()}") # print(f"nowtime - thinking_start_time:{time.time() - thinking_start_time}") # print(f"{total_time}") return total_time # 加上回车时间 def cosine_similarity(v1, v2): """计算余弦相似度""" dot_product = np.dot(v1, v2) norm1 = np.linalg.norm(v1) norm2 = np.linalg.norm(v2) if norm1 == 0 or norm2 == 0: return 0 return dot_product / (norm1 * norm2) def text_to_vector(text): """将文本转换为词频向量""" # 分词 words = jieba.lcut(text) # 统计词频 word_freq = Counter(words) return word_freq def find_similar_topics_simple(text: str, topics: list, top_k: int = 5) -> list: """使用简单的余弦相似度计算文本相似度""" # 将输入文本转换为词频向量 text_vector = text_to_vector(text) # 计算每个主题的相似度 similarities = [] for topic in topics: topic_vector = text_to_vector(topic) # 获取所有唯一词 all_words = set(text_vector.keys()) | set(topic_vector.keys()) # 构建向量 v1 = [text_vector.get(word, 0) for word in all_words] v2 = [topic_vector.get(word, 0) for word in all_words] # 计算相似度 similarity = cosine_similarity(v1, v2) similarities.append((topic, similarity)) # 按相似度降序排序并返回前k个 return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k] def truncate_message(message: str, max_length=20) -> str: """截断消息，使其不超过指定长度""" if len(message) > max_length: return message[:max_length] + "..." return message def protect_kaomoji(sentence): """ " 识别并保护句子中的颜文字（含括号与无括号），将其替换为占位符， 并返回替换后的句子和占位符到颜文字的映射表。 Args: sentence (str): 输入的原始句子 Returns: tuple: (处理后的句子, {占位符: 颜文字}) """ kaomoji_pattern = re.compile( r"(" r"[(\[（【]" # 左括号 r"[^()\[\]（）【】]*?" # 非括号字符（惰性匹配） r"[^一-龥a-zA-Z0-9\s]" # 非中文、非英文、非数字、非空格字符（必须包含至少一个） r"[^()\[\]（）【】]*?" # 非括号字符（惰性匹配） r"[\)\]）】" # 右括号 r"]" r")" r"|" r"([▼▽・ᴥω･﹏^><≧≦￣｀´∀ヮДд︿﹀へ｡ﾟ╥╯╰︶︹•⁄]{2,15})" ) kaomoji_matches = kaomoji_pattern.findall(sentence) placeholder_to_kaomoji = {} for idx, match in enumerate(kaomoji_matches): kaomoji = match[0] if match[0] else match[1] placeholder = f"__KAOMOJI_{idx}__" sentence = sentence.replace(kaomoji, placeholder, 1) placeholder_to_kaomoji[placeholder] = kaomoji return sentence, placeholder_to_kaomoji def recover_kaomoji(sentences, placeholder_to_kaomoji): """ 根据映射表恢复句子中的颜文字。 Args: sentences (list): 含有占位符的句子列表 placeholder_to_kaomoji (dict): 占位符到颜文字的映射表 Returns: list: 恢复颜文字后的句子列表 """ recovered_sentences = [] for sentence in sentences: for placeholder, kaomoji in placeholder_to_kaomoji.items(): sentence = sentence.replace(placeholder, kaomoji) recovered_sentences.append(sentence) return recovered_sentences def get_western_ratio(paragraph): """计算段落中字母数字字符的西文比例 原理：检查段落中字母数字字符的西文比例 通过is_english_letter函数判断每个字符是否为西文 只检查字母数字字符，忽略标点符号和空格等非字母数字字符 Args: paragraph: 要检查的文本段落 Returns: float: 西文字符比例(0.0-1.0)，如果没有字母数字字符则返回0.0 """ alnum_chars = [char for char in paragraph if char.isalnum()] if not alnum_chars: return 0.0 western_count = sum(1 for char in alnum_chars if is_english_letter(char)) return western_count / len(alnum_chars) def count_messages_between(start_time: float, end_time: float, stream_id: str) -> tuple[int, int]: """计算两个时间点之间的消息数量和文本总长度 Args: start_time (float): 起始时间戳 end_time (float): 结束时间戳 stream_id (str): 聊天流ID Returns: tuple[int, int]: (消息数量, 文本总长度) - 消息数量：包含起始时间的消息，不包含结束时间的消息 - 文本总长度：所有消息的processed_plain_text长度之和 """ try: # 获取开始时间之前最新的一条消息 start_message = db.messages.find_one( {"chat_id": stream_id, "time": {"$lte": start_time}}, sort=[("time", -1), ("_id", -1)], # 按时间倒序，_id倒序（最后插入的在前） ) # 获取结束时间最近的一条消息 # 先找到结束时间点的所有消息 end_time_messages = list( db.messages.find( {"chat_id": stream_id, "time": {"$lte": end_time}}, sort=[("time", -1)], # 先按时间倒序 ).limit(10) ) # 限制查询数量，避免性能问题 if not end_time_messages: logger.warning(f"未找到结束时间 {end_time} 之前的消息") return 0, 0 # 找到最大时间 max_time = end_time_messages[0]["time"] # 在最大时间的消息中找最后插入的（_id最大的） end_message = max([msg for msg in end_time_messages if msg["time"] == max_time], key=lambda x: x["_id"]) if not start_message: logger.warning(f"未找到开始时间 {start_time} 之前的消息") return 0, 0 # 调试输出 # print("\n=== 消息范围信息 ===") # print("Start message:", { # "message_id": start_message.get("message_id"), # "time": start_message.get("time"), # "text": start_message.get("processed_plain_text", ""), # "_id": str(start_message.get("_id")) # }) # print("End message:", { # "message_id": end_message.get("message_id"), # "time": end_message.get("time"), # "text": end_message.get("processed_plain_text", ""), # "_id": str(end_message.get("_id")) # }) # print("Stream ID:", stream_id) # 如果结束消息的时间等于开始时间，返回0 if end_message["time"] == start_message["time"]: return 0, 0 # 获取并打印这个时间范围内的所有消息 # print("\n=== 时间范围内的所有消息 ===") all_messages = list( db.messages.find( {"chat_id": stream_id, "time": {"$gte": start_message["time"], "$lte": end_message["time"]}}, sort=[("time", 1), ("_id", 1)], # 按时间正序，_id正序 ) ) count = 0 total_length = 0 for msg in all_messages: count += 1 text_length = len(msg.get("processed_plain_text", "")) total_length += text_length # print(f"\n消息 {count}:") # print({ # "message_id": msg.get("message_id"), # "time": msg.get("time"), # "text": msg.get("processed_plain_text", ""), # "text_length": text_length, # "_id": str(msg.get("_id")) # }) # 如果时间不同，需要把end_message本身也计入 return count - 1, total_length except Exception as e: logger.error(f"计算消息数量时出错: {str(e)}") return 0, 0 def translate_timestamp_to_human_readable(timestamp: float, mode: str = "normal") -> Optional[str]: """将时间戳转换为人类可读的时间格式 Args: timestamp: 时间戳 mode: 转换模式，"normal"为标准格式，"relative"为相对时间格式 Returns: str: 格式化后的时间字符串 """ if mode == "normal": return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timestamp)) elif mode == "relative": now = time.time() diff = now - timestamp if diff < 20: return "刚刚:\n" elif diff < 60: return f"{int(diff)}秒前:\n" elif diff < 3600: return f"{int(diff / 60)}分钟前:\n" elif diff < 86400: return f"{int(diff / 3600)}小时前:\n" elif diff < 86400 * 2: return f"{int(diff / 86400)}天前:\n" else: return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timestamp)) + ":\n" elif mode == "lite": # 只返回时分秒格式，喵~ return time.strftime("%H:%M:%S", time.localtime(timestamp)) return None def parse_text_timestamps(text: str, mode: str = "normal") -> str: """解析文本中的时间戳并转换为可读时间格式 Args: text: 包含时间戳的文本，时间戳应以[]包裹 mode: 转换模式，传递给translate_timestamp_to_human_readable，"normal"或"relative" Returns: str: 替换后的文本 转换规则: - normal模式: 将文本中所有时间戳转换为可读格式 - lite模式: - 第一个和最后一个时间戳必须转换 - 以5秒为间隔划分时间段，每段最多转换一个时间戳 - 不转换的时间戳替换为空字符串 """ # 匹配[数字]或[数字.数字]格式的时间戳 pattern = r"\[(\d+(?:\.\d+)?)\]" # 找出所有匹配的时间戳 matches = list(re.finditer(pattern, text)) if not matches: return text # normal模式: 直接转换所有时间戳 if mode == "normal": result_text = text for match in matches: timestamp = float(match.group(1)) readable_time = translate_timestamp_to_human_readable(timestamp, "normal") # 由于替换会改变文本长度，需要使用正则替换而非直接替换 pattern_instance = re.escape(match.group(0)) result_text = re.sub(pattern_instance, readable_time, result_text, count=1) return result_text else: # lite模式: 按5秒间隔划分并选择性转换 result_text = text # 提取所有时间戳及其位置 timestamps = [(float(m.group(1)), m) for m in matches] timestamps.sort(key=lambda x: x[0]) # 按时间戳升序排序 if not timestamps: return text # 获取第一个和最后一个时间戳 first_timestamp, first_match = timestamps[0] last_timestamp, last_match = timestamps[-1] # 将时间范围划分成5秒间隔的时间段 time_segments = {} # 对所有时间戳按15秒间隔分组 for ts, match in timestamps: segment_key = int(ts // 15) # 将时间戳除以15取整，作为时间段的键 if segment_key not in time_segments: time_segments[segment_key] = [] time_segments[segment_key].append((ts, match)) # 记录需要转换的时间戳 to_convert = [] # 从每个时间段中选择一个时间戳进行转换 for _, segment_timestamps in time_segments.items(): # 选择这个时间段中的第一个时间戳 to_convert.append(segment_timestamps[0]) # 确保第一个和最后一个时间戳在转换列表中 first_in_list = False last_in_list = False for ts, _ in to_convert: if ts == first_timestamp: first_in_list = True if ts == last_timestamp: last_in_list = True if not first_in_list: to_convert.append((first_timestamp, first_match)) if not last_in_list: to_convert.append((last_timestamp, last_match)) # 创建需要转换的时间戳集合，用于快速查找 to_convert_set = {match.group(0) for _, match in to_convert} # 首先替换所有不需要转换的时间戳为空字符串 for _, match in timestamps: if match.group(0) not in to_convert_set: pattern_instance = re.escape(match.group(0)) result_text = re.sub(pattern_instance, "", result_text, count=1) # 按照时间戳原始顺序排序，避免替换时位置错误 to_convert.sort(key=lambda x: x[1].start()) # 执行替换 # 由于替换会改变文本长度，从后向前替换 to_convert.reverse() for ts, match in to_convert: readable_time = translate_timestamp_to_human_readable(ts, "relative") pattern_instance = re.escape(match.group(0)) result_text = re.sub(pattern_instance, readable_time, result_text, count=1) return result_text