mirror of https://github.com/Mai-with-u/MaiBot.git
885 lines
34 KiB
Python
885 lines
34 KiB
Python
import hashlib
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import asyncio
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import json
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import time
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import random
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import math
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from json_repair import repair_json
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from typing import Union, Optional, Dict
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from datetime import datetime
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from sqlmodel import col, select
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from src.common.logger import get_logger
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from src.common.database.database import get_db_session
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from src.common.database.database_model import PersonInfo
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config, model_config
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from src.chat.message_receive.chat_stream import get_chat_manager
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logger = get_logger("person_info")
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relation_selection_model = LLMRequest(
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model_set=model_config.model_task_config.tool_use, request_type="relation_selection"
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)
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def get_person_id(platform: str, user_id: Union[int, str]) -> str:
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"""获取唯一id"""
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if "-" in platform:
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platform = platform.split("-")[1]
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components = [platform, str(user_id)]
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key = "_".join(components)
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return hashlib.md5(key.encode()).hexdigest()
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def get_person_id_by_person_name(person_name: str) -> str:
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"""根据用户名获取用户ID"""
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try:
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with get_db_session() as session:
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statement = select(PersonInfo).where(col(PersonInfo.person_name) == person_name).limit(1)
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record = session.exec(statement).first()
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return record.person_id if record else ""
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except Exception as e:
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logger.error(f"根据用户名 {person_name} 获取用户ID时出错: {e}")
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return ""
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def is_person_known(
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person_id: Optional[str] = None,
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user_id: Optional[str] = None,
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platform: Optional[str] = None,
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person_name: Optional[str] = None,
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) -> bool:
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if person_id:
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with get_db_session() as session:
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statement = select(PersonInfo).where(col(PersonInfo.person_id) == person_id).limit(1)
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person = session.exec(statement).first()
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return person.is_known if person else False
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elif user_id and platform:
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person_id = get_person_id(platform, user_id)
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with get_db_session() as session:
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statement = select(PersonInfo).where(col(PersonInfo.person_id) == person_id).limit(1)
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person = session.exec(statement).first()
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return person.is_known if person else False
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elif person_name:
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person_id = get_person_id_by_person_name(person_name)
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with get_db_session() as session:
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statement = select(PersonInfo).where(col(PersonInfo.person_id) == person_id).limit(1)
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person = session.exec(statement).first()
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return person.is_known if person else False
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else:
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return False
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def get_category_from_memory(memory_point: str) -> Optional[str]:
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"""从记忆点中获取分类"""
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# 按照最左边的:符号进行分割，返回分割后的第一个部分作为分类
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if not isinstance(memory_point, str):
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return None
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parts = memory_point.split(":", 1)
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return parts[0].strip() if len(parts) > 1 else None
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def get_weight_from_memory(memory_point: str) -> float:
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"""从记忆点中获取权重"""
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# 按照最右边的:符号进行分割，返回分割后的最后一个部分作为权重
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if not isinstance(memory_point, str):
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return -math.inf
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parts = memory_point.rsplit(":", 1)
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if len(parts) <= 1:
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return -math.inf
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try:
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return float(parts[-1].strip())
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except Exception:
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return -math.inf
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def get_memory_content_from_memory(memory_point: str) -> str:
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"""从记忆点中获取记忆内容"""
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# 按:进行分割，去掉第一段和最后一段，返回中间部分作为记忆内容
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if not isinstance(memory_point, str):
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return ""
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parts = memory_point.split(":")
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return ":".join(parts[1:-1]).strip() if len(parts) > 2 else ""
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def extract_categories_from_response(response: str) -> list[str]:
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"""从response中提取所有<>包裹的内容"""
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if not isinstance(response, str):
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return []
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import re
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pattern = r"<([^<>]+)>"
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matches = re.findall(pattern, response)
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return matches
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def calculate_string_similarity(s1: str, s2: str) -> float:
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"""
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计算两个字符串的相似度
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Args:
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s1: 第一个字符串
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s2: 第二个字符串
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Returns:
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float: 相似度，范围0-1，1表示完全相同
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"""
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if s1 == s2:
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return 1.0
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if not s1 or not s2:
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return 0.0
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# 计算Levenshtein距离
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distance = levenshtein_distance(s1, s2)
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max_len = max(len(s1), len(s2))
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# 计算相似度：1 - (编辑距离 / 最大长度)
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similarity = 1 - (distance / max_len if max_len > 0 else 0)
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return similarity
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def levenshtein_distance(s1: str, s2: str) -> int:
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"""
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计算两个字符串的编辑距离
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Args:
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s1: 第一个字符串
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s2: 第二个字符串
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Returns:
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int: 编辑距离
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"""
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if len(s1) < len(s2):
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return levenshtein_distance(s2, s1)
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if len(s2) == 0:
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return len(s1)
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previous_row = range(len(s2) + 1)
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for i, c1 in enumerate(s1):
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current_row = [i + 1]
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for j, c2 in enumerate(s2):
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insertions = previous_row[j + 1] + 1
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deletions = current_row[j] + 1
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substitutions = previous_row[j] + (c1 != c2)
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current_row.append(min(insertions, deletions, substitutions))
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previous_row = current_row
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return previous_row[-1]
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class Person:
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@classmethod
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def register_person(
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cls,
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platform: str,
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user_id: str,
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nickname: str,
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group_id: Optional[str] = None,
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group_nick_name: Optional[str] = None,
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):
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"""
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注册新用户的类方法
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必须输入 platform、user_id 和 nickname 参数
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Args:
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platform: 平台名称
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user_id: 用户ID
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nickname: 用户昵称
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group_id: 群号（可选，仅在群聊时提供）
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group_nick_name: 群昵称（可选，仅在群聊时提供）
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Returns:
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Person: 新注册的Person实例
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"""
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if not platform or not user_id or not nickname:
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logger.error("注册用户失败：platform、user_id 和 nickname 都是必需参数")
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return None
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# 生成唯一的person_id
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person_id = get_person_id(platform, user_id)
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if is_person_known(person_id=person_id):
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logger.debug(f"用户 {nickname} 已存在")
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person = Person(person_id=person_id)
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# 如果是群聊，更新群昵称
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if group_id and group_nick_name:
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person.add_group_nick_name(group_id, group_nick_name)
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return person
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# 创建Person实例
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person = cls.__new__(cls)
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# 设置基本属性
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person.person_id = person_id
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person.platform = platform
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person.user_id = user_id
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person.nickname = nickname
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# 初始化默认值
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person.is_known = True # 注册后立即标记为已认识
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person.person_name = nickname # 使用nickname作为初始person_name
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person.name_reason = "用户注册时设置的昵称"
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person.know_times = 1
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person.know_since = time.time()
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person.last_know = time.time()
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person.memory_points = []
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person.group_nick_name = [] # 初始化群昵称列表
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# 如果是群聊，添加群昵称
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if group_id and group_nick_name:
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person.add_group_nick_name(group_id, group_nick_name)
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# 同步到数据库
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person.sync_to_database()
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logger.info(f"成功注册新用户：{person_id}，平台：{platform}，昵称：{nickname}")
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return person
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def _is_bot_self(self, platform: str, user_id: str) -> bool:
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"""判断给定的平台和用户ID是否是机器人自己
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这个函数统一处理所有平台（包括 QQ、Telegram、WebUI 等）的机器人识别逻辑。
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Args:
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platform: 消息平台（如 "qq", "telegram", "webui" 等）
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user_id: 用户ID
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Returns:
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bool: 如果是机器人自己则返回 True，否则返回 False
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"""
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if not platform or not user_id:
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return False
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# 将 user_id 转为字符串进行比较
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user_id_str = str(user_id)
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# 获取机器人的 QQ 账号（主账号）
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qq_account = str(global_config.bot.qq_account or "")
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# QQ 平台：直接比较 QQ 账号
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if platform == "qq":
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return user_id_str == qq_account
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# WebUI 平台：机器人回复时使用的是 QQ 账号，所以也比较 QQ 账号
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if platform == "webui":
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return user_id_str == qq_account
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# 获取各平台账号映射
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platforms_list = getattr(global_config.bot, "platforms", []) or []
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platform_accounts = {}
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for platform_entry in platforms_list:
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if ":" in platform_entry:
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platform_name, account = platform_entry.split(":", 1)
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platform_accounts[platform_name.strip()] = account.strip()
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# Telegram 平台
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if platform == "telegram":
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tg_account = platform_accounts.get("tg", "") or platform_accounts.get("telegram", "")
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return user_id_str == tg_account if tg_account else False
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# 其他平台：尝试从 platforms 配置中查找
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platform_account = platform_accounts.get(platform, "")
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if platform_account:
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return user_id_str == platform_account
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# 默认情况：与主 QQ 账号比较（兼容性）
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return user_id_str == qq_account
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def __init__(self, platform: str = "", user_id: str = "", person_id: str = "", person_name: str = ""):
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# 使用统一的机器人识别函数（支持多平台，包括 WebUI）
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if self._is_bot_self(platform, user_id):
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self.is_known = True
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self.person_id = get_person_id(platform, user_id)
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self.user_id = user_id
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self.platform = platform
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self.nickname = global_config.bot.nickname
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self.person_name = global_config.bot.nickname
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self.group_nick_name: list[dict[str, str]] = []
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return
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self.user_id = ""
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self.platform = ""
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if person_id:
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self.person_id = person_id
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elif person_name:
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self.person_id = get_person_id_by_person_name(person_name)
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if not self.person_id:
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self.is_known = False
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logger.warning(f"根据用户名 {person_name} 获取用户ID时，不存在用户{person_name}")
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return
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elif platform and user_id:
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self.person_id = get_person_id(platform, user_id)
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self.user_id = user_id
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self.platform = platform
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else:
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logger.error("Person 初始化失败，缺少必要参数")
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raise ValueError("Person 初始化失败，缺少必要参数")
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if not is_person_known(person_id=self.person_id):
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self.is_known = False
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logger.debug(f"用户 {platform}:{user_id}:{person_name}:{person_id} 尚未认识")
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self.person_name = f"未知用户{self.person_id[:4]}"
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return
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# raise ValueError(f"用户 {platform}:{user_id}:{person_name}:{person_id} 尚未认识")
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self.is_known = False
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# 初始化默认值
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self.nickname = ""
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self.person_name: Optional[str] = None
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self.name_reason: Optional[str] = None
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self.know_times = 0
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self.know_since = None
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self.last_know: Optional[float] = None
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self.memory_points = []
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self.group_nick_name: list[dict[str, str]] = [] # 群昵称列表，存储 {"group_id": str, "group_nick_name": str}
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# 从数据库加载数据
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self.load_from_database()
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def del_memory(self, category: str, memory_content: str, similarity_threshold: float = 0.95):
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"""
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删除指定分类和记忆内容的记忆点
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Args:
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category: 记忆分类
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memory_content: 要删除的记忆内容
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similarity_threshold: 相似度阈值，默认0.95（95%）
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Returns:
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int: 删除的记忆点数量
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"""
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if not self.memory_points:
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return 0
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deleted_count = 0
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memory_points_to_keep = []
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for memory_point in self.memory_points:
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# 跳过None值
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if memory_point is None:
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continue
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# 解析记忆点
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parts = memory_point.split(":", 2) # 最多分割2次，保留记忆内容中的冒号
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if len(parts) < 3:
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# 格式不正确，保留原样
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memory_points_to_keep.append(memory_point)
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continue
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memory_category = parts[0].strip()
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memory_text = parts[1].strip()
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_memory_weight = parts[2].strip()
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# 检查分类是否匹配
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if memory_category != category:
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memory_points_to_keep.append(memory_point)
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continue
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# 计算记忆内容的相似度
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similarity = calculate_string_similarity(memory_content, memory_text)
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# 如果相似度达到阈值，则删除（不添加到保留列表）
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if similarity >= similarity_threshold:
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deleted_count += 1
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logger.debug(f"删除记忆点: {memory_point} (相似度: {similarity:.4f})")
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else:
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memory_points_to_keep.append(memory_point)
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# 更新memory_points
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self.memory_points = memory_points_to_keep
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# 同步到数据库
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if deleted_count > 0:
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self.sync_to_database()
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logger.info(f"成功删除 {deleted_count} 个记忆点，分类: {category}")
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return deleted_count
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def get_all_category(self):
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category_list = []
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for memory in self.memory_points:
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if memory is None:
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continue
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category = get_category_from_memory(memory)
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if category and category not in category_list:
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category_list.append(category)
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return category_list
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def get_memory_list_by_category(self, category: str):
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memory_list = []
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for memory in self.memory_points:
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if memory is None:
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continue
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if get_category_from_memory(memory) == category:
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memory_list.append(memory)
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return memory_list
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def get_random_memory_by_category(self, category: str, num: int = 1):
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memory_list = self.get_memory_list_by_category(category)
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if len(memory_list) < num:
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return memory_list
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return random.sample(memory_list, num)
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def add_group_nick_name(self, group_id: str, group_nick_name: str):
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"""
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添加或更新群昵称
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Args:
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group_id: 群号
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group_nick_name: 群昵称
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"""
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if not group_id or not group_nick_name:
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return
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# 检查是否已存在该群号的记录
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for item in self.group_nick_name:
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if item.get("group_id") == group_id:
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# 更新现有记录
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item["group_nick_name"] = group_nick_name
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self.sync_to_database()
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logger.debug(f"更新用户 {self.person_id} 在群 {group_id} 的群昵称为 {group_nick_name}")
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return
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# 添加新记录
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self.group_nick_name.append({"group_id": group_id, "group_nick_name": group_nick_name})
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self.sync_to_database()
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logger.debug(f"添加用户 {self.person_id} 在群 {group_id} 的群昵称 {group_nick_name}")
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def load_from_database(self):
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"""从数据库加载个人信息数据"""
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try:
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with get_db_session() as session:
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statement = select(PersonInfo).where(col(PersonInfo.person_id) == self.person_id).limit(1)
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record = session.exec(statement).first()
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if record:
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self.user_id = record.user_id or ""
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self.platform = record.platform or ""
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self.is_known = record.is_known or False
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self.nickname = record.user_nickname or ""
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self.person_name = record.person_name or self.nickname
|
||
self.name_reason = record.name_reason or None
|
||
self.know_times = record.know_counts or 0
|
||
|
||
# 处理points字段（JSON格式的列表）
|
||
if record.memory_points:
|
||
try:
|
||
loaded_points = json.loads(record.memory_points)
|
||
# 过滤掉None值，确保数据质量
|
||
if isinstance(loaded_points, list):
|
||
self.memory_points = [point for point in loaded_points if point is not None]
|
||
else:
|
||
self.memory_points = []
|
||
except (json.JSONDecodeError, TypeError):
|
||
logger.warning(f"解析用户 {self.person_id} 的points字段失败，使用默认值")
|
||
self.memory_points = []
|
||
else:
|
||
self.memory_points = []
|
||
|
||
# 处理group_nick_name字段（JSON格式的列表）
|
||
if record.group_nickname:
|
||
try:
|
||
loaded_group_nick_names = json.loads(record.group_nickname)
|
||
# 确保是列表格式
|
||
if isinstance(loaded_group_nick_names, list):
|
||
self.group_nick_name = loaded_group_nick_names
|
||
else:
|
||
self.group_nick_name = []
|
||
except (json.JSONDecodeError, TypeError):
|
||
logger.warning(f"解析用户 {self.person_id} 的group_nickname字段失败，使用默认值")
|
||
self.group_nick_name = []
|
||
else:
|
||
self.group_nick_name = []
|
||
|
||
logger.debug(f"已从数据库加载用户 {self.person_id} 的信息")
|
||
else:
|
||
self.sync_to_database()
|
||
logger.info(f"用户 {self.person_id} 在数据库中不存在，使用默认值并创建")
|
||
|
||
except Exception as e:
|
||
logger.error(f"从数据库加载用户 {self.person_id} 信息时出错: {e}")
|
||
# 出错时保持默认值
|
||
|
||
def sync_to_database(self):
|
||
"""将所有属性同步回数据库"""
|
||
if not self.is_known:
|
||
return
|
||
try:
|
||
memory_points_value = (
|
||
json.dumps([point for point in self.memory_points if point is not None], ensure_ascii=False)
|
||
if self.memory_points
|
||
else json.dumps([], ensure_ascii=False)
|
||
)
|
||
group_nickname_value = (
|
||
json.dumps(self.group_nick_name, ensure_ascii=False)
|
||
if self.group_nick_name
|
||
else json.dumps([], ensure_ascii=False)
|
||
)
|
||
first_known_time = datetime.fromtimestamp(self.know_since) if self.know_since else None
|
||
last_known_time = datetime.fromtimestamp(self.last_know) if self.last_know else None
|
||
|
||
with get_db_session() as session:
|
||
statement = select(PersonInfo).where(col(PersonInfo.person_id) == self.person_id).limit(1)
|
||
record = session.exec(statement).first()
|
||
|
||
if record:
|
||
record.person_id = self.person_id
|
||
record.is_known = self.is_known
|
||
record.platform = self.platform
|
||
record.user_id = self.user_id
|
||
record.user_nickname = self.nickname
|
||
record.person_name = self.person_name
|
||
record.name_reason = self.name_reason
|
||
record.know_counts = self.know_times
|
||
record.first_known_time = first_known_time
|
||
record.last_known_time = last_known_time
|
||
record.memory_points = memory_points_value
|
||
record.group_nickname = group_nickname_value
|
||
session.add(record)
|
||
logger.debug(f"已同步用户 {self.person_id} 的信息到数据库")
|
||
else:
|
||
record = PersonInfo(
|
||
person_id=self.person_id,
|
||
is_known=self.is_known,
|
||
platform=self.platform,
|
||
user_id=self.user_id,
|
||
user_nickname=self.nickname,
|
||
person_name=self.person_name,
|
||
name_reason=self.name_reason,
|
||
know_counts=self.know_times,
|
||
first_known_time=first_known_time,
|
||
last_known_time=last_known_time,
|
||
memory_points=memory_points_value,
|
||
group_nickname=group_nickname_value,
|
||
)
|
||
session.add(record)
|
||
logger.debug(f"已创建用户 {self.person_id} 的信息到数据库")
|
||
|
||
except Exception as e:
|
||
logger.error(f"同步用户 {self.person_id} 信息到数据库时出错: {e}")
|
||
|
||
async def build_relationship(self, chat_content: str = "", info_type=""):
|
||
if not self.is_known:
|
||
return ""
|
||
# 构建points文本
|
||
|
||
nickname_str = ""
|
||
if self.person_name != self.nickname:
|
||
nickname_str = f"(ta在{self.platform}上的昵称是{self.nickname})"
|
||
|
||
relation_info = ""
|
||
|
||
points_text = ""
|
||
category_list = self.get_all_category()
|
||
|
||
if chat_content:
|
||
prompt = f"""当前聊天内容：
|
||
{chat_content}
|
||
|
||
分类列表：
|
||
{category_list}
|
||
**要求**：请你根据当前聊天内容，从以下分类中选择一个与聊天内容相关的分类，并用<>包裹输出，不要输出其他内容，不要输出引号或[]，严格用<>包裹：
|
||
例如:
|
||
<分类1><分类2><分类3>......
|
||
如果没有相关的分类，请输出<none>"""
|
||
|
||
response, _ = await relation_selection_model.generate_response_async(prompt)
|
||
# print(prompt)
|
||
# print(response)
|
||
category_list = extract_categories_from_response(response)
|
||
if "none" not in category_list:
|
||
for category in category_list:
|
||
random_memory = self.get_random_memory_by_category(category, 2)
|
||
if random_memory:
|
||
random_memory_str = "\n".join(
|
||
[get_memory_content_from_memory(memory) for memory in random_memory]
|
||
)
|
||
points_text = f"有关 {category} 的内容：{random_memory_str}"
|
||
break
|
||
elif info_type:
|
||
prompt = f"""你需要获取用户{self.person_name}的 **{info_type}** 信息。
|
||
|
||
现有信息类别列表：
|
||
{category_list}
|
||
**要求**：请你根据**{info_type}**，从以下分类中选择一个与**{info_type}**相关的分类，并用<>包裹输出，不要输出其他内容，不要输出引号或[]，严格用<>包裹：
|
||
例如:
|
||
<分类1><分类2><分类3>......
|
||
如果没有相关的分类，请输出<none>"""
|
||
response, _ = await relation_selection_model.generate_response_async(prompt)
|
||
# print(prompt)
|
||
# print(response)
|
||
category_list = extract_categories_from_response(response)
|
||
if "none" not in category_list:
|
||
for category in category_list:
|
||
random_memory = self.get_random_memory_by_category(category, 3)
|
||
if random_memory:
|
||
random_memory_str = "\n".join(
|
||
[get_memory_content_from_memory(memory) for memory in random_memory]
|
||
)
|
||
points_text = f"有关 {category} 的内容：{random_memory_str}"
|
||
break
|
||
else:
|
||
for category in category_list:
|
||
random_memory = self.get_random_memory_by_category(category, 1)[0]
|
||
if random_memory:
|
||
points_text = f"有关 {category} 的内容：{get_memory_content_from_memory(random_memory)}"
|
||
break
|
||
|
||
points_info = ""
|
||
if points_text:
|
||
points_info = f"你还记得有关{self.person_name}的内容：{points_text}"
|
||
|
||
if not (nickname_str or points_info):
|
||
return ""
|
||
relation_info = f"{self.person_name}:{nickname_str}{points_info}"
|
||
|
||
return relation_info
|
||
|
||
|
||
class PersonInfoManager:
|
||
def __init__(self):
|
||
self.person_name_list = {}
|
||
self.qv_name_llm = LLMRequest(model_set=model_config.model_task_config.utils, request_type="relation.qv_name")
|
||
try:
|
||
with get_db_session() as _:
|
||
pass
|
||
except Exception as e:
|
||
logger.error(f"数据库连接或 PersonInfo 表创建失败: {e}")
|
||
|
||
# 初始化时读取所有person_name
|
||
try:
|
||
with get_db_session() as session:
|
||
statement = select(PersonInfo.person_id, PersonInfo.person_name).where(
|
||
col(PersonInfo.person_name).is_not(None)
|
||
)
|
||
for person_id, person_name in session.exec(statement).all():
|
||
if person_name:
|
||
self.person_name_list[person_id] = person_name
|
||
logger.debug(f"已加载 {len(self.person_name_list)} 个用户名称")
|
||
except Exception as e:
|
||
logger.error(f"加载 person_name_list 失败: {e}")
|
||
|
||
@staticmethod
|
||
def _extract_json_from_text(text: str) -> Dict[str, str]:
|
||
"""从文本中提取JSON数据的高容错方法"""
|
||
try:
|
||
fixed_json = repair_json(text)
|
||
if isinstance(fixed_json, str):
|
||
parsed_json = json.loads(fixed_json)
|
||
else:
|
||
parsed_json = fixed_json
|
||
|
||
if isinstance(parsed_json, list) and parsed_json:
|
||
parsed_json = parsed_json[0]
|
||
|
||
if isinstance(parsed_json, dict):
|
||
return parsed_json
|
||
|
||
except Exception as e:
|
||
logger.warning(f"JSON提取失败: {e}")
|
||
|
||
logger.warning(f"无法从文本中提取有效的JSON字典: {text}")
|
||
logger.info(f"文本: {text}")
|
||
return {"nickname": "", "reason": ""}
|
||
|
||
async def _generate_unique_person_name(self, base_name: str) -> str:
|
||
"""生成唯一的 person_name，如果存在重复则添加数字后缀"""
|
||
# 处理空昵称的情况
|
||
if not base_name or base_name.isspace():
|
||
base_name = "空格"
|
||
|
||
# 检查基础名称是否已存在
|
||
if base_name not in self.person_name_list.values():
|
||
return base_name
|
||
|
||
# 如果存在，添加数字后缀
|
||
counter = 1
|
||
while True:
|
||
new_name = f"{base_name}[{counter}]"
|
||
if new_name not in self.person_name_list.values():
|
||
return new_name
|
||
counter += 1
|
||
|
||
async def qv_person_name(
|
||
self, person_id: str, user_nickname: str, user_cardname: str, user_avatar: str, request: str = ""
|
||
):
|
||
"""给某个用户取名"""
|
||
if not person_id:
|
||
logger.debug("取名失败：person_id不能为空")
|
||
return None
|
||
|
||
person = Person(person_id=person_id)
|
||
old_name = person.person_name
|
||
old_reason = person.name_reason
|
||
|
||
max_retries = 8
|
||
current_try = 0
|
||
existing_names_str = ""
|
||
current_name_set = set(self.person_name_list.values())
|
||
|
||
while current_try < max_retries:
|
||
bot_name = global_config.bot.nickname
|
||
|
||
qv_name_prompt = f"你是{bot_name}，一个伪装成人类的AI，你不能让别人发现这一点，"
|
||
qv_name_prompt += f"现在你想给一个用户取一个昵称，用户的qq昵称是{user_nickname}，"
|
||
qv_name_prompt += f"用户的qq群昵称名是{user_cardname}，"
|
||
if user_avatar:
|
||
qv_name_prompt += f"用户的qq头像是{user_avatar}，"
|
||
if old_name:
|
||
qv_name_prompt += f"你之前叫他{old_name}，是因为{old_reason}，"
|
||
|
||
qv_name_prompt += f"\n其他取名的要求是：{request}，不要太浮夸，简短，"
|
||
qv_name_prompt += "\n请根据以上用户信息，想想你叫他什么比较好，不要太浮夸，请最好使用用户的qq昵称或群昵称原文，可以稍作修改，优先使用原文。优先使用用户的qq昵称或者群昵称原文。"
|
||
|
||
if existing_names_str:
|
||
qv_name_prompt += f"\n请注意，以下名称已被你尝试过或已知存在，请避免：{existing_names_str}。\n"
|
||
|
||
if len(current_name_set) < 50 and current_name_set:
|
||
qv_name_prompt += f"已知的其他昵称有: {', '.join(list(current_name_set)[:10])}等。\n"
|
||
|
||
qv_name_prompt += "请用json给出你的想法，并给出理由，示例如下："
|
||
qv_name_prompt += """{
|
||
"nickname": "昵称",
|
||
"reason": "理由"
|
||
}"""
|
||
response, _ = await self.qv_name_llm.generate_response_async(qv_name_prompt)
|
||
# logger.info(f"取名提示词：{qv_name_prompt}\n取名回复：{response}")
|
||
result = self._extract_json_from_text(response)
|
||
|
||
if not result or not result.get("nickname"):
|
||
logger.error("生成的昵称为空或结果格式不正确，重试中...")
|
||
current_try += 1
|
||
continue
|
||
|
||
generated_nickname = result["nickname"]
|
||
|
||
is_duplicate = False
|
||
if generated_nickname in current_name_set:
|
||
is_duplicate = True
|
||
logger.info(f"尝试给用户{user_nickname} {person_id} 取名，但是 {generated_nickname} 已存在，重试中...")
|
||
else:
|
||
|
||
def _db_check_name_exists_sync(name_to_check):
|
||
with get_db_session() as session:
|
||
statement = select(PersonInfo.person_id).where(col(PersonInfo.person_name) == name_to_check)
|
||
return session.exec(statement).first() is not None
|
||
|
||
if await asyncio.to_thread(_db_check_name_exists_sync, generated_nickname):
|
||
is_duplicate = True
|
||
current_name_set.add(generated_nickname)
|
||
|
||
if not is_duplicate:
|
||
person.person_name = generated_nickname
|
||
person.name_reason = result.get("reason", "未提供理由")
|
||
person.sync_to_database()
|
||
|
||
logger.info(
|
||
f"成功给用户{user_nickname} {person_id} 取名 {generated_nickname}，理由：{result.get('reason', '未提供理由')}"
|
||
)
|
||
|
||
self.person_name_list[person_id] = generated_nickname
|
||
return result
|
||
else:
|
||
if existing_names_str:
|
||
existing_names_str += "、"
|
||
existing_names_str += generated_nickname
|
||
logger.debug(f"生成的昵称 {generated_nickname} 已存在，重试中...")
|
||
current_try += 1
|
||
|
||
# 如果多次尝试后仍未成功，使用唯一的 user_nickname 作为默认值
|
||
unique_nickname = await self._generate_unique_person_name(user_nickname)
|
||
logger.warning(f"在{max_retries}次尝试后未能生成唯一昵称，使用默认昵称 {unique_nickname}")
|
||
person.person_name = unique_nickname
|
||
person.name_reason = "使用用户原始昵称作为默认值"
|
||
person.sync_to_database()
|
||
self.person_name_list[person_id] = unique_nickname
|
||
return {"nickname": unique_nickname, "reason": "使用用户原始昵称作为默认值"}
|
||
|
||
|
||
person_info_manager = PersonInfoManager()
|
||
|
||
|
||
async def store_person_memory_from_answer(person_name: str, memory_content: str, chat_id: str) -> None:
|
||
"""将人物信息存入person_info的memory_points
|
||
|
||
Args:
|
||
person_name: 人物名称
|
||
memory_content: 记忆内容
|
||
chat_id: 聊天ID
|
||
"""
|
||
try:
|
||
# 从chat_id获取chat_stream
|
||
chat_stream = get_chat_manager().get_stream(chat_id)
|
||
if not chat_stream:
|
||
logger.warning(f"无法获取chat_stream for chat_id: {chat_id}")
|
||
return
|
||
|
||
platform = chat_stream.platform
|
||
|
||
# 尝试从person_name查找person_id
|
||
# 首先尝试通过person_name查找
|
||
person_id = get_person_id_by_person_name(person_name)
|
||
|
||
if not person_id:
|
||
# 如果通过person_name找不到，尝试从chat_stream获取user_info
|
||
if platform and chat_stream.user_info and chat_stream.user_info.user_id:
|
||
user_id = chat_stream.user_info.user_id
|
||
person_id = get_person_id(platform, user_id)
|
||
else:
|
||
logger.warning(f"无法确定person_id for person_name: {person_name}, chat_id: {chat_id}")
|
||
return
|
||
|
||
# 创建或获取Person对象
|
||
person = Person(person_id=person_id)
|
||
|
||
if not person.is_known:
|
||
logger.warning(f"用户 {person_name} (person_id: {person_id}) 尚未认识，无法存储记忆")
|
||
return
|
||
|
||
# 确定记忆分类（可以根据memory_content判断，这里使用通用分类）
|
||
category = "其他" # 默认分类，可以根据需要调整
|
||
|
||
# 记忆点格式：category:content:weight
|
||
weight = "1.0" # 默认权重
|
||
memory_point = f"{category}:{memory_content}:{weight}"
|
||
|
||
# 添加到memory_points
|
||
if not person.memory_points:
|
||
person.memory_points = []
|
||
|
||
# 检查是否已存在相似的记忆点（避免重复）
|
||
is_duplicate = False
|
||
for existing_point in person.memory_points:
|
||
if existing_point and isinstance(existing_point, str):
|
||
parts = existing_point.split(":", 2)
|
||
if len(parts) >= 2:
|
||
existing_content = parts[1].strip()
|
||
# 简单相似度检查（如果内容相同或非常相似，则跳过）
|
||
if (
|
||
existing_content == memory_content
|
||
or memory_content in existing_content
|
||
or existing_content in memory_content
|
||
):
|
||
is_duplicate = True
|
||
break
|
||
|
||
if not is_duplicate:
|
||
person.memory_points.append(memory_point)
|
||
person.sync_to_database()
|
||
logger.info(f"成功添加记忆点到 {person_name} (person_id: {person_id}): {memory_point}")
|
||
else:
|
||
logger.debug(f"记忆点已存在，跳过: {memory_point}")
|
||
|
||
except Exception as e:
|
||
logger.error(f"存储人物记忆失败: {e}")
|