623 lines
22 KiB
Python
623 lines
22 KiB
Python
import random
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import time
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import re
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from collections import Counter
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from typing import Dict, List
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import jieba
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import numpy as np
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from src.common.logger import get_module_logger
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from ..models.utils_model import LLM_request
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from ..utils.typo_generator import ChineseTypoGenerator
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from ..config.config import global_config
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from .message import MessageRecv, Message
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from ..message.message_base import UserInfo
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from .chat_stream import ChatStream
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from ..moods.moods import MoodManager
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from ...common.database import db
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logger = get_module_logger("chat_utils")
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def db_message_to_str(message_dict: Dict) -> str:
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logger.debug(f"message_dict: {message_dict}")
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time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message_dict["time"]))
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try:
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name = "[(%s)%s]%s" % (
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message_dict["user_id"],
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message_dict.get("user_nickname", ""),
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message_dict.get("user_cardname", ""),
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)
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except Exception:
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name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}"
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content = message_dict.get("processed_plain_text", "")
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result = f"[{time_str}] {name}: {content}\n"
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logger.debug(f"result: {result}")
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return result
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def is_mentioned_bot_in_message(message: MessageRecv) -> bool:
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"""检查消息是否提到了机器人"""
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keywords = [global_config.BOT_NICKNAME]
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nicknames = global_config.BOT_ALIAS_NAMES
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reply_probability = 0
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is_at = False
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is_mentioned = False
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# 判断是否被@
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if re.search(f"@[\s\S]*?（id:{global_config.BOT_QQ}）", message.processed_plain_text):
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is_at = True
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is_mentioned = True
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if is_at and global_config.at_bot_inevitable_reply:
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reply_probability = 1
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logger.info("被@，回复概率设置为100%")
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else:
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if not is_mentioned:
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# 判断是否被回复
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if re.match(f"回复[\s\S]*?\({global_config.BOT_QQ}\)的消息，说：", message.processed_plain_text):
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is_mentioned = True
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# 判断内容中是否被提及
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message_content = re.sub(r"\@[\s\S]*?（(\d+)）", "", message.processed_plain_text)
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message_content = re.sub(r"回复[\s\S]*?\((\d+)\)的消息，说： ", "", message_content)
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for keyword in keywords:
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if keyword in message_content:
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is_mentioned = True
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for nickname in nicknames:
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if nickname in message_content:
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is_mentioned = True
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if is_mentioned and global_config.mentioned_bot_inevitable_reply:
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reply_probability = 1
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logger.info("被提及，回复概率设置为100%")
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return is_mentioned, reply_probability
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async def get_embedding(text, request_type="embedding"):
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"""获取文本的embedding向量"""
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llm = LLM_request(model=global_config.embedding, request_type=request_type)
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# return llm.get_embedding_sync(text)
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try:
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embedding = await llm.get_embedding(text)
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except Exception as e:
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logger.error(f"获取embedding失败: {str(e)}")
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embedding = None
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return embedding
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async def get_recent_group_messages(chat_id: str, limit: int = 12) -> list:
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"""从数据库获取群组最近的消息记录
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Args:
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group_id: 群组ID
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limit: 获取消息数量，默认12条
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Returns:
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list: Message对象列表，按时间正序排列
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"""
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# 从数据库获取最近消息
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recent_messages = list(
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db.messages.find(
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{"chat_id": chat_id},
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)
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.sort("time", -1)
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.limit(limit)
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)
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if not recent_messages:
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return []
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# 转换为 Message对象列表
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message_objects = []
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for msg_data in recent_messages:
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try:
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chat_info = msg_data.get("chat_info", {})
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chat_stream = ChatStream.from_dict(chat_info)
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user_info = msg_data.get("user_info", {})
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user_info = UserInfo.from_dict(user_info)
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msg = Message(
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message_id=msg_data["message_id"],
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chat_stream=chat_stream,
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time=msg_data["time"],
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user_info=user_info,
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processed_plain_text=msg_data.get("processed_text", ""),
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detailed_plain_text=msg_data.get("detailed_plain_text", ""),
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)
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message_objects.append(msg)
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except KeyError:
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logger.warning("数据库中存在无效的消息")
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continue
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# 按时间正序排列
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message_objects.reverse()
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return message_objects
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def get_recent_group_detailed_plain_text(chat_stream_id: int, limit: int = 12, combine=False):
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recent_messages = list(
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db.messages.find(
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{"chat_id": chat_stream_id},
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{
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"time": 1, # 返回时间字段
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"chat_id": 1,
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"chat_info": 1,
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"user_info": 1,
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"message_id": 1, # 返回消息ID字段
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"detailed_plain_text": 1, # 返回处理后的文本字段
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},
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)
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.sort("time", -1)
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.limit(limit)
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)
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if not recent_messages:
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return []
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message_detailed_plain_text = ""
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message_detailed_plain_text_list = []
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# 反转消息列表，使最新的消息在最后
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recent_messages.reverse()
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if combine:
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for msg_db_data in recent_messages:
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message_detailed_plain_text += str(msg_db_data["detailed_plain_text"])
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return message_detailed_plain_text
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else:
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for msg_db_data in recent_messages:
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message_detailed_plain_text_list.append(msg_db_data["detailed_plain_text"])
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return message_detailed_plain_text_list
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def get_recent_group_speaker(chat_stream_id: int, sender, limit: int = 12) -> list:
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# 获取当前群聊记录内发言的人
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recent_messages = list(
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db.messages.find(
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{"chat_id": chat_stream_id},
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{
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"user_info": 1,
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},
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)
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.sort("time", -1)
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.limit(limit)
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)
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if not recent_messages:
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return []
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who_chat_in_group = []
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for msg_db_data in recent_messages:
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user_info = UserInfo.from_dict(msg_db_data["user_info"])
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if (
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(user_info.platform, user_info.user_id) != sender
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and user_info.user_id != global_config.BOT_QQ
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and (user_info.platform, user_info.user_id, user_info.user_nickname) not in who_chat_in_group
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and len(who_chat_in_group) < 5
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): # 排除重复，排除消息发送者，排除bot，限制加载的关系数目
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who_chat_in_group.append((user_info.platform, user_info.user_id, user_info.user_nickname))
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return who_chat_in_group
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def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
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"""将文本分割成句子，但保持书名号中的内容完整
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Args:
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text: 要分割的文本字符串
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Returns:
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List[str]: 分割后的句子列表
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"""
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len_text = len(text)
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if len_text < 4:
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if random.random() < 0.01:
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return list(text) # 如果文本很短且触发随机条件,直接按字符分割
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else:
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return [text]
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if len_text < 12:
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split_strength = 0.2
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elif len_text < 32:
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split_strength = 0.6
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else:
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split_strength = 0.7
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# 检查是否为西文字符段落
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if not is_western_paragraph(text):
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# 当语言为中文时，统一将英文逗号转换为中文逗号
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text = text.replace(",", "，")
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text = text.replace("\n", " ")
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else:
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# 用"|seg|"作为分割符分开
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text = re.sub(r"([.!?]) +", r"\1\|seg\|", text)
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text = text.replace("\n", "|seg|")
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text, mapping = protect_kaomoji(text)
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# print(f"处理前的文本: {text}")
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text_no_1 = ""
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for letter in text:
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# print(f"当前字符: {letter}")
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if letter in ["!", "！", "?", "？"]:
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# print(f"当前字符: {letter}, 随机数: {random.random()}")
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if random.random() < split_strength:
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letter = ""
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if letter in ["。", "…"]:
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# print(f"当前字符: {letter}, 随机数: {random.random()}")
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if random.random() < 1 - split_strength:
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letter = ""
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text_no_1 += letter
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# 对每个逗号单独判断是否分割
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sentences = [text_no_1]
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new_sentences = []
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for sentence in sentences:
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parts = sentence.split("，")
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current_sentence = parts[0]
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if not is_western_paragraph(current_sentence):
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for part in parts[1:]:
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if random.random() < split_strength:
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new_sentences.append(current_sentence.strip())
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current_sentence = part
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else:
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current_sentence += "，" + part
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# 处理空格分割
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space_parts = current_sentence.split(" ")
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current_sentence = space_parts[0]
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for part in space_parts[1:]:
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if random.random() < split_strength:
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new_sentences.append(current_sentence.strip())
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current_sentence = part
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else:
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current_sentence += " " + part
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else:
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# 处理分割符
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space_parts = current_sentence.split("|seg|")
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current_sentence = space_parts[0]
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for part in space_parts[1:]:
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new_sentences.append(current_sentence.strip())
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current_sentence = part
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new_sentences.append(current_sentence.strip())
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sentences = [s for s in new_sentences if s] # 移除空字符串
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sentences = recover_kaomoji(sentences, mapping)
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# print(f"分割后的句子: {sentences}")
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sentences_done = []
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for sentence in sentences:
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sentence = sentence.rstrip("，,")
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# 西文字符句子不进行随机合并
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if not is_western_paragraph(current_sentence):
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if random.random() < split_strength * 0.5:
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sentence = sentence.replace("，", "").replace(",", "")
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elif random.random() < split_strength:
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sentence = sentence.replace("，", " ").replace(",", " ")
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sentences_done.append(sentence)
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logger.debug(f"处理后的句子: {sentences_done}")
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return sentences_done
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def random_remove_punctuation(text: str) -> str:
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"""随机处理标点符号，模拟人类打字习惯
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Args:
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text: 要处理的文本
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Returns:
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str: 处理后的文本
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"""
|
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result = ""
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text_len = len(text)
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for i, char in enumerate(text):
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if char == "。" and i == text_len - 1: # 结尾的句号
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if random.random() > 0.1: # 90%概率删除结尾句号
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continue
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elif char == "，":
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rand = random.random()
|
||
if rand < 0.25: # 5%概率删除逗号
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continue
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||
elif rand < 0.25: # 20%概率把逗号变成空格
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||
result += " "
|
||
continue
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result += char
|
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return result
|
||
|
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def process_llm_response(text: str) -> List[str]:
|
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# processed_response = process_text_with_typos(content)
|
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# 对西文字符段落的回复长度设置为汉字字符的两倍
|
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max_length = global_config.response_max_length
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max_sentence_num = global_config.response_max_sentence_num
|
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if len(text) > max_length and not is_western_paragraph(text):
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logger.warning(f"回复过长 ({len(text)} 字符)，返回默认回复")
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return ["懒得说"]
|
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elif len(text) > 200:
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logger.warning(f"回复过长 ({len(text)} 字符)，返回默认回复")
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return ["懒得说"]
|
||
# 处理长消息
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||
typo_generator = ChineseTypoGenerator(
|
||
error_rate=global_config.chinese_typo_error_rate,
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||
min_freq=global_config.chinese_typo_min_freq,
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||
tone_error_rate=global_config.chinese_typo_tone_error_rate,
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||
word_replace_rate=global_config.chinese_typo_word_replace_rate,
|
||
)
|
||
if global_config.enable_response_spliter:
|
||
split_sentences = split_into_sentences_w_remove_punctuation(text)
|
||
else:
|
||
split_sentences = [text]
|
||
sentences = []
|
||
for sentence in split_sentences:
|
||
if global_config.chinese_typo_enable:
|
||
typoed_text, typo_corrections = typo_generator.create_typo_sentence(sentence)
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||
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) > 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"]
|
||
|
||
return sentences
|
||
|
||
|
||
def calculate_typing_time(
|
||
input_string: str,
|
||
thinking_start_time: float,
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||
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"[^\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())
|
||
|
||
|
||
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
|