406 lines
13 KiB
Python
406 lines
13 KiB
Python
import time
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import random
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from typing import List
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from .message import Message
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import requests
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import numpy as np
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from .config import global_config
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import re
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from typing import Dict
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from collections import Counter
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import math
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from nonebot import get_driver
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from ..models.utils_model import LLM_request
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import aiohttp
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import jieba
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from ..utils.typo_generator import ChineseTypoGenerator
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driver = get_driver()
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config = driver.config
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def combine_messages(messages: List[Message]) -> str:
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"""将消息列表组合成格式化的字符串
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Args:
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messages: Message对象列表
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Returns:
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str: 格式化后的消息字符串
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"""
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result = ""
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for message in messages:
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time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message.time))
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name = message.user_nickname or f"用户{message.user_id}"
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content = message.processed_plain_text or message.plain_text
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result += f"[{time_str}] {name}: {content}\n"
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return result
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def db_message_to_str(message_dict: Dict) -> str:
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print(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'], message_dict.get("user_nickname", ""), message_dict.get("user_cardname", ""))
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except:
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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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print(f"result: {result}")
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return result
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def is_mentioned_bot_in_message(message: Message) -> 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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for keyword in keywords:
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if keyword in message.processed_plain_text:
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return True
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return False
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def is_mentioned_bot_in_txt(message: str) -> 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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for keyword in keywords:
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if keyword in message:
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return True
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return False
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async def get_embedding(text):
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"""获取文本的embedding向量"""
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llm = LLM_request(model=global_config.embedding)
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# return llm.get_embedding_sync(text)
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return await llm.get_embedding(text)
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def cosine_similarity(v1, v2):
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dot_product = np.dot(v1, v2)
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norm1 = np.linalg.norm(v1)
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norm2 = np.linalg.norm(v2)
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return dot_product / (norm1 * norm2)
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def calculate_information_content(text):
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"""计算文本的信息量（熵）"""
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char_count = Counter(text)
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total_chars = len(text)
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entropy = 0
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for count in char_count.values():
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probability = count / total_chars
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entropy -= probability * math.log2(probability)
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return entropy
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def get_cloest_chat_from_db(db, length: int, timestamp: str):
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"""从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数"""
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chat_text = ''
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closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])
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if closest_record and closest_record.get('memorized', 0) < 4:
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closest_time = closest_record['time']
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group_id = closest_record['group_id'] # 获取groupid
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# 获取该时间戳之后的length条消息，且groupid相同
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chat_records = list(db.db.messages.find(
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{"time": {"$gt": closest_time}, "group_id": group_id}
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).sort('time', 1).limit(length))
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# 更新每条消息的memorized属性
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for record in chat_records:
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# 检查当前记录的memorized值
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current_memorized = record.get('memorized', 0)
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if current_memorized > 3:
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# print(f"消息已读取3次，跳过")
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return ''
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# 更新memorized值
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db.db.messages.update_one(
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{"_id": record["_id"]},
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{"$set": {"memorized": current_memorized + 1}}
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)
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chat_text += record["detailed_plain_text"]
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return chat_text
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# print(f"消息已读取3次，跳过")
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return ''
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async def get_recent_group_messages(db, group_id: int, 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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db: Database实例
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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(db.db.messages.find(
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{"group_id": group_id},
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# {
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# "time": 1,
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# "user_id": 1,
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# "user_nickname": 1,
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# "message_id": 1,
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# "raw_message": 1,
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# "processed_text": 1
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# }
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).sort("time", -1).limit(limit))
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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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from .message import Message
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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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msg = Message(
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time=msg_data["time"],
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user_id=msg_data["user_id"],
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user_nickname=msg_data.get("user_nickname", ""),
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message_id=msg_data["message_id"],
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raw_message=msg_data["raw_message"],
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processed_plain_text=msg_data.get("processed_text", ""),
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group_id=group_id
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)
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await msg.initialize()
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message_objects.append(msg)
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except KeyError:
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print("[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(db, group_id: int, limit: int = 12, combine=False):
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recent_messages = list(db.db.messages.find(
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{"group_id": group_id},
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{
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"time": 1, # 返回时间字段
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"user_id": 1, # 返回用户ID字段
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"user_nickname": 1, # 返回用户昵称字段
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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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).sort("time", -1).limit(limit))
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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 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 < 5:
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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 = 0.3
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elif len_text < 32:
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split_strength = 0.7
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else:
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split_strength = 0.9
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# 先移除换行符
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# print(f"split_strength: {split_strength}")
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# print(f"处理前的文本: {text}")
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# 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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# 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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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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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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# 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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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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print(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() > 0.4: # 80%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()
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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 += ' '
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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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if len(text) > 300:
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print(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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# 处理长消息
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typo_generator = ChineseTypoGenerator(
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error_rate=0.03,
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min_freq=7,
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tone_error_rate=0.2,
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word_replace_rate=0.02
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)
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typoed_text = typo_generator.create_typo_sentence(text)[0]
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sentences = split_into_sentences_w_remove_punctuation(typoed_text)
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# 检查分割后的消息数量是否过多（超过3条）
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if len(sentences) > 4:
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print(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
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return [f'{global_config.BOT_NICKNAME}不知道哦']
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return sentences
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def calculate_typing_time(input_string: str, chinese_time: float = 0.2, english_time: float = 0.1) -> float:
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"""
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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
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input_string (str): 输入的字符串
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chinese_time (float): 中文字符的输入时间，默认为0.3秒
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english_time (float): 英文字符的输入时间，默认为0.15秒
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"""
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total_time = 0.0
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for char in input_string:
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if '\u4e00' <= char <= '\u9fff': # 判断是否为中文字符
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total_time += chinese_time
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else: # 其他字符（如英文）
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total_time += english_time
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return total_time
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def cosine_similarity(v1, v2):
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"""计算余弦相似度"""
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dot_product = np.dot(v1, v2)
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norm1 = np.linalg.norm(v1)
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norm2 = np.linalg.norm(v2)
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if norm1 == 0 or norm2 == 0:
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return 0
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return dot_product / (norm1 * norm2)
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def text_to_vector(text):
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"""将文本转换为词频向量"""
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# 分词
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words = jieba.lcut(text)
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# 统计词频
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word_freq = Counter(words)
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return word_freq
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def find_similar_topics_simple(text: str, topics: list, top_k: int = 5) -> list:
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"""使用简单的余弦相似度计算文本相似度"""
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# 将输入文本转换为词频向量
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text_vector = text_to_vector(text)
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# 计算每个主题的相似度
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similarities = []
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for topic in topics:
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topic_vector = text_to_vector(topic)
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# 获取所有唯一词
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all_words = set(text_vector.keys()) | set(topic_vector.keys())
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# 构建向量
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v1 = [text_vector.get(word, 0) for word in all_words]
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v2 = [topic_vector.get(word, 0) for word in all_words]
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# 计算相似度
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similarity = cosine_similarity(v1, v2)
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similarities.append((topic, similarity))
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# 按相似度降序排序并返回前k个
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return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k]
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