import math import random import time import re from collections import Counter from typing import Dict, List import jieba import numpy as np from nonebot import get_driver from src.common.logger import get_module_logger from ..models.utils_model import LLM_request from ..utils.typo_generator import ChineseTypoGenerator from .config import global_config from .message import MessageRecv, Message from .message_base import UserInfo from .chat_stream import ChatStream from ..moods.moods import MoodManager from ...common.database import db driver = get_driver() config = driver.config logger = get_module_logger("chat_utils") 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) -> bool: """检查消息是否提到了机器人""" keywords = [global_config.BOT_NICKNAME] nicknames = global_config.BOT_ALIAS_NAMES for keyword in keywords: if keyword in message.processed_plain_text: return True for nickname in nicknames: if nickname in message.processed_plain_text: return True return False async def get_embedding(text, request_type="embedding"): """获取文本的embedding向量""" llm = LLM_request(model=global_config.embedding, request_type=request_type) # return llm.get_embedding_sync(text) return await llm.get_embedding(text) def calculate_information_content(text): """计算文本的信息量（熵）""" char_count = Counter(text) total_chars = len(text) entropy = 0 for count in char_count.values(): probability = count / total_chars entropy -= probability * math.log2(probability) return entropy def get_closest_chat_from_db(length: int, timestamp: str): # print(f"获取最接近指定时间戳的聊天记录，长度: {length}, 时间戳: {timestamp}") # print(f"当前时间: {timestamp},转换后时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(timestamp))}") chat_records = [] closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[("time", -1)]) # print(f"最接近的记录: {closest_record}") if closest_record: closest_time = closest_record["time"] chat_id = closest_record["chat_id"] # 获取chat_id # 获取该时间戳之后的length条消息，保持相同的chat_id chat_records = list( db.messages.find( { "time": {"$gt": closest_time}, "chat_id": chat_id, # 添加chat_id过滤 } ) .sort("time", 1) .limit(length) ) # print(f"获取到的记录: {chat_records}") length = len(chat_records) # print(f"获取到的记录长度: {length}") # 转换记录格式 formatted_records = [] for record in chat_records: # 兼容行为，前向兼容老数据 formatted_records.append( { "_id": record["_id"], "time": record["time"], "chat_id": record["chat_id"], "detailed_plain_text": record.get("detailed_plain_text", ""), # 添加文本内容 "memorized_times": record.get("memorized_times", 0), # 添加记忆次数 } ) return formatted_records return [] 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: group_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, time=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}, { "chat_info": 1, "user_info": 1, }, ) .sort("time", -1) .limit(limit) ) if not recent_messages: return [] who_chat_in_group = [] # ChatStream列表 duplicate_removal = [] for msg_db_data in recent_messages: user_info = UserInfo.from_dict(msg_db_data["user_info"]) if ( (user_info.user_id, user_info.platform) != sender and (user_info.user_id, user_info.platform) != (global_config.BOT_QQ, "qq") and (user_info.user_id, user_info.platform) not in duplicate_removal and len(duplicate_removal) < 5 ): # 排除重复，排除消息发送者，排除bot(此处bot的平台强制为了qq，可能需要更改)，限制加载的关系数目 duplicate_removal.append((user_info.user_id, user_info.platform)) chat_info = msg_db_data.get("chat_info", {}) who_chat_in_group.append(ChatStream.from_dict(chat_info)) return who_chat_in_group def split_into_sentences_w_remove_punctuation(text: str) -> List[str]: """将文本分割成句子，但保持书名号中的内容完整 Args: text: 要分割的文本字符串 Returns: List[str]: 分割后的句子列表 """ len_text = len(text) if len_text < 5: 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] if len_text < 12: split_strength = 0.3 elif len_text < 32: split_strength = 0.7 else: split_strength = 0.9 # 先移除换行符 # print(f"split_strength: {split_strength}") # print(f"处理前的文本: {text}") # 检查是否为西文字符段落 if not is_western_paragraph(text): # 当语言为中文时，统一将英文逗号转换为中文逗号 text = text.replace(",", "，") text = text.replace("\n", " ") else: # 用"|seg|"作为分割符分开 text = re.sub(r"([.!?]) +", r"\1\|seg\|", text) text = text.replace("\n", "\|seg\|") text, mapping = protect_kaomoji(text) # print(f"处理前的文本: {text}") text_no_1 = "" for letter in text: # print(f"当前字符: {letter}") if letter in ["!", "！", "?", "？"]: # print(f"当前字符: {letter}, 随机数: {random.random()}") if random.random() < split_strength: letter = "" if letter in ["。", "…"]: # print(f"当前字符: {letter}, 随机数: {random.random()}") if random.random() < 1 - split_strength: letter = "" text_no_1 += letter # 对每个逗号单独判断是否分割 sentences = [text_no_1] new_sentences = [] for sentence in sentences: parts = sentence.split("，") current_sentence = parts[0] if not is_western_paragraph(current_sentence): for part in parts[1:]: if random.random() < split_strength: new_sentences.append(current_sentence.strip()) current_sentence = part else: current_sentence += "，" + part # 处理空格分割 space_parts = current_sentence.split(" ") current_sentence = space_parts[0] for part in space_parts[1:]: if random.random() < split_strength: new_sentences.append(current_sentence.strip()) current_sentence = part else: current_sentence += " " + part else: # 处理分割符 space_parts = current_sentence.split("\|seg\|") current_sentence = space_parts[0] for part in space_parts[1:]: new_sentences.append(current_sentence.strip()) current_sentence = part new_sentences.append(current_sentence.strip()) sentences = [s for s in new_sentences if s] # 移除空字符串 sentences = recover_kaomoji(sentences, mapping) # print(f"分割后的句子: {sentences}") sentences_done = [] for sentence in sentences: sentence = sentence.rstrip("，,") # 西文字符句子不进行随机合并 if not is_western_paragraph(current_sentence): if random.random() < split_strength * 0.5: sentence = sentence.replace("，", "").replace(",", "") elif random.random() < split_strength: sentence = sentence.replace("，", " ").replace(",", " ") sentences_done.append(sentence) logger.debug(f"处理后的句子: {sentences_done}") return sentences_done 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() > 0.4: # 80%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]: # processed_response = process_text_with_typos(content) # 对西文字符段落的回复长度设置为汉字字符的两倍 if len(text) > 100 and not is_western_paragraph(text) : logger.warning(f"回复过长 ({len(text)} 字符)，返回默认回复") return ["懒得说"] elif len(text) > 200 : logger.warning(f"回复过长 ({len(text)} 字符)，返回默认回复") return ["懒得说"] # 处理长消息 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, ) split_sentences = split_into_sentences_w_remove_punctuation(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) # 检查分割后的消息数量是否过多（超过3条） if len(sentences) > 3: 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"] return sentences def calculate_typing_time(input_string: str, chinese_time: float = 0.4, english_time: float = 0.2) -> 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 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 """ 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 return total_time + 0.3 # 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"[^\u4e00-\u9fa5a-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}" r")" ) 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 is_western_char(char): """检测是否为西文字符""" return len(char.encode('utf-8')) <= 2 def is_western_paragraph(paragraph): """检测是否为西文字符段落""" return all(is_western_char(char) for char in paragraph if char.isalnum())