mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'groupnickname' of https://github.com/Dax233/MaiMBot into G-Test
Bakadax
commit
429eaaae4f
15
bot.py
15
bot.py
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@ -14,6 +14,8 @@ from src.common.logger_manager import get_logger
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from src.common.crash_logger import install_crash_handler
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from src.main import MainSystem
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from rich.traceback import install
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from src.plugins.group_nickname.nickname_manager import nickname_manager
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import atexit
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install(extra_lines=3)
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@ -221,6 +223,19 @@ def raw_main():
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env_config = {key: os.getenv(key) for key in os.environ}
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scan_provider(env_config)
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# 确保 NicknameManager 单例实例存在并已初始化
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# (单例模式下，导入时或第一次调用时会自动初始化)
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_ = nickname_manager # 显式引用一次
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# 启动 NicknameManager 的后台处理器线程
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logger.info("准备启动绰号处理管理器...")
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nickname_manager.start_processor() # 调用实例的方法
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logger.info("已调用启动绰号处理管理器。")
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# 注册 NicknameManager 的停止方法到 atexit，确保程序退出时线程能被清理
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atexit.register(nickname_manager.stop_processor) # 注册实例的方法
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logger.info("已注册绰号处理管理器的退出处理程序。")
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# 返回MainSystem实例
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return MainSystem()
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@ -1,7 +1,7 @@
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import os
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import re
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from dataclasses import dataclass, field
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from typing import Dict, List, Optional
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from typing import Dict, List, Optional, Any
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from dateutil import tz
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import tomli
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@ -274,6 +274,13 @@ class BotConfig:
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talk_allowed_private = set()
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enable_pfc_chatting: bool = False # 是否启用PFC聊天
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# Group Nickname
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ENABLE_NICKNAME_MAPPING: bool = False # 绰号映射功能总开关
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MAX_NICKNAMES_IN_PROMPT: int = 10 # Prompt 中最多注入的绰号数量
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NICKNAME_PROBABILITY_SMOOTHING: int = 1 # 绰号加权随机选择的平滑因子
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NICKNAME_QUEUE_MAX_SIZE: int = 100 # 绰号处理队列最大容量
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NICKNAME_PROCESS_SLEEP_INTERVAL: float = 60 # 绰号处理进程休眠间隔（秒）
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# 模型配置
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llm_reasoning: dict[str, str] = field(default_factory=lambda: {})
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# llm_reasoning_minor: dict[str, str] = field(default_factory=lambda: {})
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@ -289,6 +296,7 @@ class BotConfig:
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llm_heartflow: Dict[str, str] = field(default_factory=lambda: {})
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llm_tool_use: Dict[str, str] = field(default_factory=lambda: {})
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llm_plan: Dict[str, str] = field(default_factory=lambda: {})
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llm_nickname_mapping: Dict[str, Any] = field(default_factory=dict)
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api_urls: Dict[str, str] = field(default_factory=lambda: {})
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@ -402,6 +410,25 @@ class BotConfig:
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config.save_emoji = emoji_config.get("save_emoji", config.save_emoji)
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config.steal_emoji = emoji_config.get("steal_emoji", config.steal_emoji)
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def group_nickname(parent: dict):
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if config.INNER_VERSION in SpecifierSet(">=1.6.2"):
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gn_config = parent.get("group_nickname", {})
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config.ENABLE_NICKNAME_MAPPING = gn_config.get(
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"enable_nickname_mapping", config.ENABLE_NICKNAME_MAPPING
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)
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config.MAX_NICKNAMES_IN_PROMPT = gn_config.get(
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"max_nicknames_in_prompt", config.MAX_NICKNAMES_IN_PROMPT
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)
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config.NICKNAME_PROBABILITY_SMOOTHING = gn_config.get(
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"nickname_probability_smoothing", config.NICKNAME_PROBABILITY_SMOOTHING
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)
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config.NICKNAME_QUEUE_MAX_SIZE = gn_config.get(
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"nickname_queue_max_size", config.NICKNAME_QUEUE_MAX_SIZE
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)
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config.NICKNAME_PROCESS_SLEEP_INTERVAL = gn_config.get(
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"nickname_process_sleep_interval", config.NICKNAME_PROCESS_SLEEP_INTERVAL
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)
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def bot(parent: dict):
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# 机器人基础配置
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bot_config = parent["bot"]
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@ -487,6 +514,7 @@ class BotConfig:
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"llm_PFC_action_planner",
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"llm_PFC_chat",
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"llm_PFC_reply_checker",
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"llm_nickname_mapping",
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]
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for item in config_list:
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@ -692,6 +720,7 @@ class BotConfig:
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"chat": {"func": chat, "support": ">=1.6.0", "necessary": False},
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"normal_chat": {"func": normal_chat, "support": ">=1.6.0", "necessary": False},
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"focus_chat": {"func": focus_chat, "support": ">=1.6.0", "necessary": False},
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"group_nickname": {"func": group_nickname, "support": ">=0.6.3", "necessary": False},
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}
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# 原地修改，将 字符串版本表达式 转换成 版本对象
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@ -0,0 +1,160 @@
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from pymongo.collection import Collection
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from pymongo.errors import OperationFailure, DuplicateKeyError
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from src.common.logger_manager import get_logger
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from typing import Optional
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logger = get_logger("nickname_db")
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class NicknameDB:
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"""
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处理与群组绰号相关的数据库操作 (MongoDB)。
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封装了对 'person_info' 集合的读写操作。
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"""
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def __init__(self, person_info_collection: Optional[Collection]):
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"""
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初始化 NicknameDB 处理器。
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Args:
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person_info_collection: MongoDB 'person_info' 集合对象。
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如果为 None，则数据库操作将被禁用。
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"""
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if person_info_collection is None:
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logger.error("未提供 person_info 集合，NicknameDB 操作将被禁用。")
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self.person_info_collection = None
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else:
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self.person_info_collection = person_info_collection
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logger.info("NicknameDB 初始化成功。")
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def is_available(self) -> bool:
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"""检查数据库集合是否可用。"""
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return self.person_info_collection is not None
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def upsert_person(self, person_id: str, user_id_int: int, platform: str):
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"""
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确保数据库中存在指定 person_id 的文档 (Upsert)。
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如果文档不存在，则使用提供的用户信息创建它。
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Args:
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person_id: 要查找或创建的 person_id。
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user_id_int: 用户的整数 ID。
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platform: 平台名称。
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Returns:
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UpdateResult 或 None: MongoDB 更新操作的结果，如果数据库不可用则返回 None。
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Raises:
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DuplicateKeyError: 如果发生重复键错误 (理论上不应由 upsert 触发)。
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Exception: 其他数据库操作错误。
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"""
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if not self.is_available():
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logger.error("数据库集合不可用，无法执行 upsert_person。")
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return None
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try:
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# 关键步骤：基于 person_id 执行 Upsert
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result = self.person_info_collection.update_one(
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{"person_id": person_id},
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{
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"$setOnInsert": {
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"person_id": person_id,
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"user_id": user_id_int,
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"platform": platform,
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"group_nicknames": [], # 初始化 group_nicknames 数组
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}
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},
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upsert=True,
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)
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if result.upserted_id:
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logger.debug(f"Upsert 创建了新的 person 文档: {person_id}")
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return result
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except DuplicateKeyError as dk_err:
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# 这个错误理论上不应该再由 upsert 触发。
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logger.error(
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f"数据库操作失败 (DuplicateKeyError): person_id {person_id}. 错误: {dk_err}. 这不应该发生，请检查 person_id 生成逻辑和数据库状态。"
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)
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raise # 将异常向上抛出
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except Exception as e:
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logger.exception(f"对 person_id {person_id} 执行 Upsert 时失败: {e}")
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raise # 将异常向上抛出
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def update_group_nickname_count(self, person_id: str, group_id_str: str, nickname: str):
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"""
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尝试更新 person_id 文档中特定群组的绰号计数，或添加新条目。
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按顺序尝试：增加计数 -> 添加绰号 -> 添加群组。
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Args:
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person_id: 目标文档的 person_id。
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group_id_str: 目标群组的 ID (字符串)。
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nickname: 要更新或添加的绰号。
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"""
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if not self.is_available():
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logger.error("数据库集合不可用，无法执行 update_group_nickname_count。")
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return
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try:
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# 3a. 尝试增加现有群组中现有绰号的计数
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result_inc = self.person_info_collection.update_one(
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{
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"person_id": person_id,
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"group_nicknames": {"$elemMatch": {"group_id": group_id_str, "nicknames.name": nickname}},
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},
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{"$inc": {"group_nicknames.$[group].nicknames.$[nick].count": 1}},
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array_filters=[
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{"group.group_id": group_id_str},
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{"nick.name": nickname},
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],
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)
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if result_inc.modified_count > 0:
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# logger.debug(f"成功增加 person_id {person_id} 在群组 {group_id_str} 中绰号 '{nickname}' 的计数。")
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return # 成功增加计数，操作完成
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# 3b. 如果上一步未修改 (绰号不存在于该群组)，尝试将新绰号添加到现有群组
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result_push_nick = self.person_info_collection.update_one(
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{
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"person_id": person_id,
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"group_nicknames.group_id": group_id_str, # 检查群组是否存在
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},
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{"$push": {"group_nicknames.$[group].nicknames": {"name": nickname, "count": 1}}},
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array_filters=[{"group.group_id": group_id_str}],
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)
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if result_push_nick.modified_count > 0:
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logger.debug(f"成功为 person_id {person_id} 在现有群组 {group_id_str} 中添加新绰号 '{nickname}'。")
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return # 成功添加绰号，操作完成
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# 3c. 如果上一步也未修改 (群组条目本身不存在)，则添加新的群组条目和绰号
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# 确保 group_nicknames 数组存在 (作为保险措施)
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self.person_info_collection.update_one(
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{"person_id": person_id, "group_nicknames": {"$exists": False}},
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{"$set": {"group_nicknames": []}},
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)
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# 推送新的群组对象到 group_nicknames 数组
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result_push_group = self.person_info_collection.update_one(
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{
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"person_id": person_id,
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"group_nicknames.group_id": {"$ne": group_id_str}, # 确保该群组 ID 尚未存在
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},
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{
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"$push": {
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"group_nicknames": {
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"group_id": group_id_str,
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"nicknames": [{"name": nickname, "count": 1}],
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}
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}
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},
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)
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if result_push_group.modified_count > 0:
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logger.debug(f"为 person_id {person_id} 添加了新的群组 {group_id_str} 和绰号 '{nickname}'。")
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# else:
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# logger.warning(f"尝试为 person_id {person_id} 添加新群组 {group_id_str} 失败，可能群组已存在但结构不符合预期。")
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except (OperationFailure, DuplicateKeyError) as db_err:
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logger.exception(
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f"数据库操作失败 ({type(db_err).__name__}): person_id {person_id}, 群组 {group_id_str}, 绰号 {nickname}. 错误: {db_err}"
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)
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# 根据需要决定是否向上抛出 raise db_err
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except Exception as e:
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logger.exception(
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f"更新群组绰号计数时发生意外错误: person_id {person_id}, group {group_id_str}, nick {nickname}. Error: {e}"
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)
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# 根据需要决定是否向上抛出 raise e
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|
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@ -0,0 +1,528 @@
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import asyncio
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import threading
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import time
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import json
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import re
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from typing import Dict, Optional, List, Any
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from pymongo.errors import OperationFailure, DuplicateKeyError
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from src.common.logger_manager import get_logger
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from src.common.database import db
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from src.config.config import global_config
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from src.plugins.models.utils_model import LLMRequest
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from .nickname_db import NicknameDB
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from .nickname_mapper import _build_mapping_prompt
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from .nickname_utils import select_nicknames_for_prompt, format_nickname_prompt_injection
|
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from ..person_info.person_info import person_info_manager
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from ..person_info.relationship_manager import relationship_manager
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from src.plugins.chat.chat_stream import ChatStream
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from src.plugins.chat.message import MessageRecv
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from src.plugins.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
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logger = get_logger("NicknameManager")
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logger_helper = get_logger("AsyncLoopHelper") # 为辅助函数创建单独的 logger
|
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|
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|
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def run_async_loop(loop: asyncio.AbstractEventLoop, coro):
|
||||
"""
|
||||
运行给定的协程直到完成，并确保循环最终关闭。
|
||||
|
||||
Args:
|
||||
loop: 要使用的 asyncio 事件循环。
|
||||
coro: 要在循环中运行的主协程。
|
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"""
|
||||
try:
|
||||
logger_helper.debug(f"Running coroutine in loop {id(loop)}...")
|
||||
result = loop.run_until_complete(coro)
|
||||
logger_helper.debug(f"Coroutine completed in loop {id(loop)}.")
|
||||
return result
|
||||
except asyncio.CancelledError:
|
||||
logger_helper.info(f"Coroutine in loop {id(loop)} was cancelled.")
|
||||
# 取消是预期行为，不视为错误
|
||||
except Exception as e:
|
||||
logger_helper.error(f"Error in async loop {id(loop)}: {e}", exc_info=True)
|
||||
finally:
|
||||
try:
|
||||
# 1. 取消所有剩余任务
|
||||
all_tasks = asyncio.all_tasks(loop)
|
||||
current_task = asyncio.current_task(loop)
|
||||
tasks_to_cancel = [
|
||||
task for task in all_tasks if task is not current_task
|
||||
] # 避免取消 run_until_complete 本身
|
||||
if tasks_to_cancel:
|
||||
logger_helper.info(f"Cancelling {len(tasks_to_cancel)} outstanding tasks in loop {id(loop)}...")
|
||||
for task in tasks_to_cancel:
|
||||
task.cancel()
|
||||
# 等待取消完成
|
||||
loop.run_until_complete(asyncio.gather(*tasks_to_cancel, return_exceptions=True))
|
||||
logger_helper.info(f"Outstanding tasks cancelled in loop {id(loop)}.")
|
||||
|
||||
# 2. 停止循环 (如果仍在运行)
|
||||
if loop.is_running():
|
||||
loop.stop()
|
||||
logger_helper.info(f"Asyncio loop {id(loop)} stopped.")
|
||||
|
||||
# 3. 关闭循环 (如果未关闭)
|
||||
if not loop.is_closed():
|
||||
# 在关闭前再运行一次以处理挂起的关闭回调
|
||||
loop.run_until_complete(loop.shutdown_asyncgens()) # 关闭异步生成器
|
||||
loop.close()
|
||||
logger_helper.info(f"Asyncio loop {id(loop)} closed.")
|
||||
except Exception as close_err:
|
||||
logger_helper.error(f"Error during asyncio loop cleanup for loop {id(loop)}: {close_err}", exc_info=True)
|
||||
|
||||
|
||||
class NicknameManager:
|
||||
"""
|
||||
管理群组绰号分析、处理、存储和使用的单例类。
|
||||
封装了 LLM 调用、后台处理线程和数据库交互。
|
||||
"""
|
||||
|
||||
_instance = None
|
||||
_lock = threading.Lock()
|
||||
|
||||
def __new__(cls, *args, **kwargs):
|
||||
if not cls._instance:
|
||||
with cls._lock:
|
||||
if not cls._instance:
|
||||
logger.info("正在创建 NicknameManager 单例实例...")
|
||||
cls._instance = super(NicknameManager, cls).__new__(cls)
|
||||
cls._instance._initialized = False
|
||||
return cls._instance
|
||||
|
||||
def __init__(self):
|
||||
"""
|
||||
初始化 NicknameManager。
|
||||
使用锁和标志确保实际初始化只执行一次。
|
||||
"""
|
||||
if hasattr(self, "_initialized") and self._initialized:
|
||||
return
|
||||
|
||||
with self._lock:
|
||||
if hasattr(self, "_initialized") and self._initialized:
|
||||
return
|
||||
|
||||
logger.info("正在初始化 NicknameManager 组件...")
|
||||
self.config = global_config
|
||||
self.is_enabled = self.config.ENABLE_NICKNAME_MAPPING
|
||||
|
||||
# 数据库处理器
|
||||
person_info_collection = getattr(db, "person_info", None)
|
||||
self.db_handler = NicknameDB(person_info_collection)
|
||||
if not self.db_handler.is_available():
|
||||
logger.error("数据库处理器初始化失败，NicknameManager 功能受限。")
|
||||
self.is_enabled = False
|
||||
|
||||
# LLM 映射器
|
||||
self.llm_mapper: Optional[LLMRequest] = None
|
||||
if self.is_enabled:
|
||||
try:
|
||||
model_config = self.config.llm_nickname_mapping
|
||||
if model_config and model_config.get("name"):
|
||||
self.llm_mapper = LLMRequest(
|
||||
model=model_config,
|
||||
temperature=model_config.get("temp", 0.5),
|
||||
max_tokens=model_config.get("max_tokens", 256),
|
||||
request_type="nickname_mapping",
|
||||
)
|
||||
logger.info("绰号映射 LLM 映射器初始化成功。")
|
||||
else:
|
||||
logger.warning("绰号映射 LLM 配置无效或缺失 'name'，功能禁用。")
|
||||
self.is_enabled = False
|
||||
except KeyError as ke:
|
||||
logger.error(f"初始化绰号映射 LLM 时缺少配置项: {ke}，功能禁用。", exc_info=True)
|
||||
self.llm_mapper = None
|
||||
self.is_enabled = False
|
||||
except Exception as e:
|
||||
logger.error(f"初始化绰号映射 LLM 映射器失败: {e}，功能禁用。", exc_info=True)
|
||||
self.llm_mapper = None
|
||||
self.is_enabled = False
|
||||
|
||||
# 队列和线程
|
||||
self.queue_max_size = getattr(self.config, "NICKNAME_QUEUE_MAX_SIZE", 100)
|
||||
# 使用 asyncio.Queue
|
||||
self.nickname_queue: asyncio.Queue = asyncio.Queue(maxsize=self.queue_max_size)
|
||||
self._stop_event = threading.Event() # stop_event 仍然使用 threading.Event，因为它是由另一个线程设置的
|
||||
self._nickname_thread: Optional[threading.Thread] = None
|
||||
self.sleep_interval = getattr(self.config, "NICKNAME_PROCESS_SLEEP_INTERVAL", 0.5) # 超时时间
|
||||
|
||||
self._initialized = True
|
||||
logger.info("NicknameManager 初始化完成。")
|
||||
|
||||
def start_processor(self):
|
||||
"""启动后台处理线程（如果已启用且未运行）。"""
|
||||
if not self.is_enabled:
|
||||
logger.info("绰号处理功能已禁用，处理器未启动。")
|
||||
return
|
||||
if self._nickname_thread is None or not self._nickname_thread.is_alive():
|
||||
logger.info("正在启动绰号处理器线程...")
|
||||
self._stop_event.clear()
|
||||
self._nickname_thread = threading.Thread(
|
||||
target=self._run_processor_in_thread, # 线程目标函数不变
|
||||
daemon=True,
|
||||
)
|
||||
self._nickname_thread.start()
|
||||
logger.info(f"绰号处理器线程已启动 (ID: {self._nickname_thread.ident})")
|
||||
else:
|
||||
logger.warning("绰号处理器线程已在运行中。")
|
||||
|
||||
def stop_processor(self):
|
||||
"""停止后台处理线程。"""
|
||||
if self._nickname_thread and self._nickname_thread.is_alive():
|
||||
logger.info("正在停止绰号处理器线程...")
|
||||
self._stop_event.set() # 设置停止事件，_processing_loop 会检测到
|
||||
try:
|
||||
# 不需要清空 asyncio.Queue，让循环自然结束或被取消
|
||||
self._nickname_thread.join(timeout=10) # 等待线程结束
|
||||
if self._nickname_thread.is_alive():
|
||||
logger.warning("绰号处理器线程在超时后仍未停止。")
|
||||
except Exception as e:
|
||||
logger.error(f"停止绰号处理器线程时出错: {e}", exc_info=True)
|
||||
finally:
|
||||
if self._nickname_thread and not self._nickname_thread.is_alive():
|
||||
logger.info("绰号处理器线程已成功停止。")
|
||||
self._nickname_thread = None
|
||||
else:
|
||||
logger.info("绰号处理器线程未在运行或已被清理。")
|
||||
|
||||
async def trigger_nickname_analysis(
|
||||
self,
|
||||
anchor_message: MessageRecv,
|
||||
bot_reply: List[str],
|
||||
chat_stream: Optional[ChatStream] = None,
|
||||
):
|
||||
"""
|
||||
准备数据并将其排队等待绰号分析（如果满足条件）。
|
||||
(现在调用异步的 _add_to_queue)
|
||||
"""
|
||||
if not self.is_enabled:
|
||||
return
|
||||
|
||||
current_chat_stream = chat_stream or anchor_message.chat_stream
|
||||
if not current_chat_stream or not current_chat_stream.group_info:
|
||||
logger.debug("跳过绰号分析：非群聊或无效的聊天流。")
|
||||
return
|
||||
|
||||
log_prefix = f"[{current_chat_stream.stream_id}]"
|
||||
try:
|
||||
# 1. 获取历史记录
|
||||
history_limit = getattr(self.config, "NICKNAME_ANALYSIS_HISTORY_LIMIT", 30)
|
||||
history_messages = get_raw_msg_before_timestamp_with_chat(
|
||||
chat_id=current_chat_stream.stream_id,
|
||||
timestamp=time.time(),
|
||||
limit=history_limit,
|
||||
)
|
||||
# 格式化历史记录
|
||||
chat_history_str = await build_readable_messages(
|
||||
messages=history_messages,
|
||||
replace_bot_name=True,
|
||||
merge_messages=False,
|
||||
timestamp_mode="relative",
|
||||
read_mark=0.0,
|
||||
truncate=False,
|
||||
)
|
||||
# 2. 获取 Bot 回复
|
||||
bot_reply_str = " ".join(bot_reply) if bot_reply else ""
|
||||
# 3. 获取群组和平台信息
|
||||
group_id = str(current_chat_stream.group_info.group_id)
|
||||
platform = current_chat_stream.platform
|
||||
# 4. 构建用户 ID 到名称的映射 (user_name_map)
|
||||
user_ids_in_history = {
|
||||
str(msg["user_info"]["user_id"]) for msg in history_messages if msg.get("user_info", {}).get("user_id")
|
||||
}
|
||||
user_name_map = {}
|
||||
if user_ids_in_history:
|
||||
try:
|
||||
names_data = await relationship_manager.get_person_names_batch(platform, list(user_ids_in_history))
|
||||
except Exception as e:
|
||||
logger.error(f"{log_prefix} 批量获取 person_name 时出错: {e}", exc_info=True)
|
||||
names_data = {}
|
||||
for user_id in user_ids_in_history:
|
||||
if user_id in names_data:
|
||||
user_name_map[user_id] = names_data[user_id]
|
||||
else:
|
||||
latest_nickname = next(
|
||||
(
|
||||
m["user_info"].get("user_nickname")
|
||||
for m in reversed(history_messages)
|
||||
if str(m["user_info"].get("user_id")) == user_id and m["user_info"].get("user_nickname")
|
||||
),
|
||||
None,
|
||||
)
|
||||
user_name_map[user_id] = latest_nickname or f"未知({user_id})"
|
||||
|
||||
item = (chat_history_str, bot_reply_str, platform, group_id, user_name_map)
|
||||
await self._add_to_queue(item, platform, group_id)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{log_prefix} 触发绰号分析时出错: {e}", exc_info=True)
|
||||
|
||||
async def get_nickname_prompt_injection(self, chat_stream: ChatStream, message_list_before_now: List[Dict]) -> str:
|
||||
"""
|
||||
获取并格式化用于 Prompt 注入的绰号信息字符串。
|
||||
"""
|
||||
if not self.is_enabled or not chat_stream or not chat_stream.group_info:
|
||||
return ""
|
||||
|
||||
log_prefix = f"[{chat_stream.stream_id}]"
|
||||
try:
|
||||
group_id = str(chat_stream.group_info.group_id)
|
||||
platform = chat_stream.platform
|
||||
user_ids_in_context = {
|
||||
str(msg["user_info"]["user_id"])
|
||||
for msg in message_list_before_now
|
||||
if msg.get("user_info", {}).get("user_id")
|
||||
}
|
||||
|
||||
if not user_ids_in_context:
|
||||
recent_speakers = chat_stream.get_recent_speakers(limit=5)
|
||||
user_ids_in_context.update(str(speaker["user_id"]) for speaker in recent_speakers)
|
||||
|
||||
if not user_ids_in_context:
|
||||
logger.warning(f"{log_prefix} 未找到上下文用户用于绰号注入。")
|
||||
return ""
|
||||
|
||||
all_nicknames_data = await relationship_manager.get_users_group_nicknames(
|
||||
platform, list(user_ids_in_context), group_id
|
||||
)
|
||||
|
||||
if all_nicknames_data:
|
||||
selected_nicknames = select_nicknames_for_prompt(all_nicknames_data)
|
||||
injection_str = format_nickname_prompt_injection(selected_nicknames)
|
||||
if injection_str:
|
||||
logger.debug(f"{log_prefix} 生成的绰号 Prompt 注入:\n{injection_str}")
|
||||
return injection_str
|
||||
else:
|
||||
return ""
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{log_prefix} 获取绰号注入时出错: {e}", exc_info=True)
|
||||
return ""
|
||||
|
||||
# 私有/内部方法
|
||||
|
||||
async def _add_to_queue(self, item: tuple, platform: str, group_id: str):
|
||||
"""将项目异步添加到内部处理队列 (asyncio.Queue)。"""
|
||||
try:
|
||||
# 使用 await put()，如果队列满则异步等待
|
||||
await self.nickname_queue.put(item)
|
||||
logger.debug(
|
||||
f"已将项目添加到平台 '{platform}' 群组 '{group_id}' 的绰号队列。当前大小: {self.nickname_queue.qsize()}"
|
||||
)
|
||||
except asyncio.QueueFull:
|
||||
# 理论上 await put() 不会直接抛 QueueFull，除非 maxsize=0
|
||||
# 但保留以防万一或未来修改
|
||||
logger.warning(
|
||||
f"绰号队列已满 (最大={self.queue_max_size})。平台 '{platform}' 群组 '{group_id}' 的项目被丢弃。"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"将项目添加到绰号队列时出错: {e}", exc_info=True)
|
||||
|
||||
async def _analyze_and_update_nicknames(self, item: tuple):
|
||||
"""处理单个队列项目：调用 LLM 分析并更新数据库。"""
|
||||
if not isinstance(item, tuple) or len(item) != 5:
|
||||
logger.warning(f"从队列接收到无效项目: {type(item)}")
|
||||
return
|
||||
|
||||
chat_history_str, bot_reply, platform, group_id, user_name_map = item
|
||||
# 使用 asyncio.get_running_loop().call_soon(threading.get_ident) 可能不准确，线程ID是同步概念
|
||||
# 可以考虑移除线程ID日志或寻找异步安全的获取标识符的方式
|
||||
log_prefix = f"[{platform}:{group_id}]" # 简化日志前缀
|
||||
logger.debug(f"{log_prefix} 开始处理绰号分析任务...")
|
||||
|
||||
if not self.llm_mapper:
|
||||
logger.error(f"{log_prefix} LLM 映射器不可用，无法执行分析。")
|
||||
return
|
||||
if not self.db_handler.is_available():
|
||||
logger.error(f"{log_prefix} 数据库处理器不可用，无法更新计数。")
|
||||
return
|
||||
|
||||
# 1. 调用 LLM 分析 (内部逻辑不变)
|
||||
analysis_result = await self._call_llm_for_analysis(chat_history_str, bot_reply, user_name_map)
|
||||
|
||||
# 2. 如果分析成功且找到映射，则更新数据库 (内部逻辑不变)
|
||||
if analysis_result.get("is_exist") and analysis_result.get("data"):
|
||||
nickname_map_to_update = analysis_result["data"]
|
||||
logger.info(f"{log_prefix} LLM 找到绰号映射，准备更新数据库: {nickname_map_to_update}")
|
||||
|
||||
for user_id_str, nickname in nickname_map_to_update.items():
|
||||
# ... (验证和数据库更新逻辑保持不变) ...
|
||||
if not user_id_str or not nickname:
|
||||
logger.warning(f"{log_prefix} 跳过无效条目: user_id='{user_id_str}', nickname='{nickname}'")
|
||||
continue
|
||||
if not user_id_str.isdigit():
|
||||
logger.warning(f"{log_prefix} 无效的用户ID格式 (非纯数字): '{user_id_str}'，跳过。")
|
||||
continue
|
||||
user_id_int = int(user_id_str)
|
||||
|
||||
try:
|
||||
person_id = person_info_manager.get_person_id(platform, user_id_str)
|
||||
if not person_id:
|
||||
logger.error(
|
||||
f"{log_prefix} 无法为 platform='{platform}', user_id='{user_id_str}' 生成 person_id，跳过此用户。"
|
||||
)
|
||||
continue
|
||||
self.db_handler.upsert_person(person_id, user_id_int, platform)
|
||||
self.db_handler.update_group_nickname_count(person_id, group_id, nickname)
|
||||
except (OperationFailure, DuplicateKeyError) as db_err:
|
||||
logger.exception(
|
||||
f"{log_prefix} 数据库操作失败 ({type(db_err).__name__}): 用户 {user_id_str}, 绰号 {nickname}. 错误: {db_err}"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.exception(f"{log_prefix} 处理用户 {user_id_str} 的绰号 '{nickname}' 时发生意外错误：{e}")
|
||||
else:
|
||||
logger.debug(f"{log_prefix} LLM 未找到可靠的绰号映射或分析失败。")
|
||||
|
||||
async def _call_llm_for_analysis(
|
||||
self,
|
||||
chat_history_str: str,
|
||||
bot_reply: str,
|
||||
user_name_map: Dict[str, str],
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
内部方法：调用 LLM 分析聊天记录和 Bot 回复，提取可靠的 用户ID-绰号 映射。
|
||||
"""
|
||||
# ... (此方法内部逻辑保持不变) ...
|
||||
if not self.llm_mapper:
|
||||
logger.error("LLM 映射器未初始化，无法执行分析。")
|
||||
return {"is_exist": False}
|
||||
|
||||
prompt = _build_mapping_prompt(chat_history_str, bot_reply, user_name_map)
|
||||
logger.debug(f"构建的绰号映射 Prompt:\n{prompt[:500]}...")
|
||||
|
||||
try:
|
||||
response_content, _, _ = await self.llm_mapper.generate_response(prompt)
|
||||
logger.debug(f"LLM 原始响应 (绰号映射): {response_content}")
|
||||
|
||||
if not response_content:
|
||||
logger.warning("LLM 返回了空的绰号映射内容。")
|
||||
return {"is_exist": False}
|
||||
|
||||
response_content = response_content.strip()
|
||||
markdown_code_regex = re.compile(r"^```(?:\w+)?\s*\n(.*?)\n\s*```$", re.DOTALL | re.IGNORECASE)
|
||||
match = markdown_code_regex.match(response_content)
|
||||
if match:
|
||||
response_content = match.group(1).strip()
|
||||
elif response_content.startswith("{") and response_content.endswith("}"):
|
||||
pass # 可能是纯 JSON
|
||||
else:
|
||||
json_match = re.search(r"\{.*\}", response_content, re.DOTALL)
|
||||
if json_match:
|
||||
response_content = json_match.group(0)
|
||||
else:
|
||||
logger.warning(f"LLM 响应似乎不包含有效的 JSON 对象。响应: {response_content}")
|
||||
return {"is_exist": False}
|
||||
|
||||
result = json.loads(response_content)
|
||||
|
||||
if not isinstance(result, dict):
|
||||
logger.warning(f"LLM 响应不是一个有效的 JSON 对象 (字典类型)。响应内容: {response_content}")
|
||||
return {"is_exist": False}
|
||||
|
||||
is_exist = result.get("is_exist")
|
||||
|
||||
if is_exist is True:
|
||||
original_data = result.get("data")
|
||||
if isinstance(original_data, dict) and original_data:
|
||||
logger.info(f"LLM 找到的原始绰号映射: {original_data}")
|
||||
filtered_data = self._filter_llm_results(original_data, user_name_map)
|
||||
if not filtered_data:
|
||||
logger.info("所有找到的绰号映射都被过滤掉了。")
|
||||
return {"is_exist": False}
|
||||
else:
|
||||
logger.info(f"过滤后的绰号映射: {filtered_data}")
|
||||
return {"is_exist": True, "data": filtered_data}
|
||||
else:
|
||||
logger.warning(f"LLM 响应格式错误: is_exist=True 但 data 无效。原始 data: {original_data}")
|
||||
return {"is_exist": False}
|
||||
elif is_exist is False:
|
||||
logger.info("LLM 明确指示未找到可靠的绰号映射 (is_exist=False)。")
|
||||
return {"is_exist": False}
|
||||
else:
|
||||
logger.warning(f"LLM 响应格式错误: 'is_exist' 的值 '{is_exist}' 无效。")
|
||||
return {"is_exist": False}
|
||||
|
||||
except json.JSONDecodeError as json_err:
|
||||
logger.error(f"解析 LLM 响应 JSON 失败: {json_err}\n原始响应: {response_content}")
|
||||
return {"is_exist": False}
|
||||
except Exception as e:
|
||||
logger.error(f"绰号映射 LLM 调用或处理过程中发生意外错误: {e}", exc_info=True)
|
||||
return {"is_exist": False}
|
||||
|
||||
def _filter_llm_results(self, original_data: Dict[str, str], user_name_map: Dict[str, str]) -> Dict[str, str]:
|
||||
"""过滤 LLM 返回的绰号映射结果。"""
|
||||
filtered_data = {}
|
||||
bot_qq_str = str(self.config.BOT_QQ) if hasattr(self.config, "BOT_QQ") else None
|
||||
|
||||
for user_id, nickname in original_data.items():
|
||||
if not isinstance(user_id, str):
|
||||
logger.warning(f"过滤掉非字符串 user_id: {user_id}")
|
||||
continue
|
||||
if bot_qq_str and user_id == bot_qq_str:
|
||||
logger.debug(f"过滤掉机器人自身的映射: ID {user_id}")
|
||||
continue
|
||||
if not nickname or nickname.isspace():
|
||||
logger.debug(f"过滤掉用户 {user_id} 的空绰号。")
|
||||
continue
|
||||
# person_name = user_name_map.get(user_id)
|
||||
# if person_name and person_name == nickname:
|
||||
# logger.debug(f"过滤掉用户 {user_id} 的映射: 绰号 '{nickname}' 与其名称 '{person_name}' 相同。")
|
||||
# continue
|
||||
filtered_data[user_id] = nickname.strip()
|
||||
|
||||
return filtered_data
|
||||
|
||||
# 线程相关
|
||||
# 修改：使用 run_async_loop 辅助函数
|
||||
def _run_processor_in_thread(self):
|
||||
"""后台线程入口函数，使用辅助函数管理 asyncio 事件循环。"""
|
||||
thread_id = threading.get_ident() # 获取线程ID用于日志
|
||||
logger.info(f"绰号处理器线程启动 (线程 ID: {thread_id})...")
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop) # 为当前线程设置事件循环
|
||||
logger.info(f"(线程 ID: {thread_id}) Asyncio 事件循环已创建并设置。")
|
||||
|
||||
# 调用辅助函数来运行主处理协程并管理循环生命周期
|
||||
run_async_loop(loop, self._processing_loop())
|
||||
|
||||
logger.info(f"绰号处理器线程结束 (线程 ID: {thread_id}).")
|
||||
|
||||
# 结束修改
|
||||
|
||||
# 修改：使用 asyncio.Queue 和 wait_for
|
||||
async def _processing_loop(self):
|
||||
"""后台线程中运行的异步处理循环 (使用 asyncio.Queue)。"""
|
||||
# 移除线程ID日志，因为它在异步上下文中不一定准确
|
||||
logger.info("绰号异步处理循环已启动。")
|
||||
|
||||
while not self._stop_event.is_set(): # 仍然检查同步的停止事件
|
||||
try:
|
||||
# 使用 asyncio.wait_for 从异步队列获取项目，并设置超时
|
||||
item = await asyncio.wait_for(self.nickname_queue.get(), timeout=self.sleep_interval)
|
||||
|
||||
# 处理获取到的项目 (调用异步方法)
|
||||
await self._analyze_and_update_nicknames(item)
|
||||
|
||||
self.nickname_queue.task_done() # 标记任务完成
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
# 等待超时，相当于之前 queue.Empty，继续循环检查停止事件
|
||||
continue
|
||||
except asyncio.CancelledError:
|
||||
# 协程被取消 (通常在 stop_processor 中发生)
|
||||
logger.info("绰号处理循环被取消。")
|
||||
break # 退出循环
|
||||
except Exception as e:
|
||||
# 捕获处理单个项目时可能发生的其他异常
|
||||
logger.error(f"绰号处理循环出错: {e}", exc_info=True)
|
||||
# 短暂异步休眠避免快速连续失败
|
||||
await asyncio.sleep(5)
|
||||
|
||||
logger.info("绰号异步处理循环已结束。")
|
||||
# 可以在这里添加清理逻辑，比如确保队列为空或处理剩余项目
|
||||
# 例如：await self.nickname_queue.join() # 等待所有任务完成 (如果需要)
|
||||
|
||||
# 结束修改
|
||||
|
||||
|
||||
# 在模块级别创建单例实例
|
||||
nickname_manager = NicknameManager()
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
# src/plugins/group_nickname/nickname_mapper.py
|
||||
from typing import Dict
|
||||
from src.common.logger_manager import get_logger
|
||||
|
||||
# 这个文件现在只负责构建 Prompt，LLM 的初始化和调用移至 NicknameManager
|
||||
|
||||
logger = get_logger("nickname_mapper")
|
||||
|
||||
# LLMRequest 实例和 analyze_chat_for_nicknames 函数已被移除
|
||||
|
||||
|
||||
def _build_mapping_prompt(chat_history_str: str, bot_reply: str, user_name_map: Dict[str, str]) -> str:
|
||||
"""
|
||||
构建用于 LLM 进行绰号映射分析的 Prompt。
|
||||
|
||||
Args:
|
||||
chat_history_str: 格式化后的聊天历史记录字符串。
|
||||
bot_reply: Bot 的最新回复字符串。
|
||||
user_name_map: 用户 ID 到已知名称（person_name 或 fallback nickname）的映射。
|
||||
|
||||
Returns:
|
||||
str: 构建好的 Prompt 字符串。
|
||||
"""
|
||||
# 将 user_name_map 格式化为列表字符串
|
||||
user_list_str = "\n".join([f"- {uid}: {name}" for uid, name in user_name_map.items() if uid and name])
|
||||
if not user_list_str:
|
||||
user_list_str = "无" # 如果映射为空，明确告知
|
||||
|
||||
# 核心 Prompt 内容
|
||||
prompt = f"""
|
||||
任务：仔细分析以下聊天记录和“你的最新回复”，判断其中是否明确提到了某个用户的绰号，并且这个绰号可以清晰地与一个特定的用户 ID 对应起来。
|
||||
|
||||
已知用户信息（ID: 名称）：
|
||||
{user_list_str}
|
||||
*注意：名称后面带有"(你)"表示是你自己。*
|
||||
|
||||
聊天记录：
|
||||
---
|
||||
{chat_history_str}
|
||||
---
|
||||
|
||||
你的最新回复：
|
||||
{bot_reply}
|
||||
|
||||
分析要求与输出格式：
|
||||
1. 找出聊天记录和“你的最新回复”中可能是用户绰号的词语。
|
||||
2. 判断这些绰号是否在上下文中**清晰、无歧义**地指向了“已知用户信息”列表中的**某一个特定用户 ID**。必须是强关联，避免猜测。
|
||||
3. **不要**输出你自己（名称后带"(你)"的用户）的绰号映射。
|
||||
**不要**输出与用户已知名称完全相同的词语作为绰号。
|
||||
**不要**将在“你的最新回复”中你对他人使用的称呼或绰号进行映射（只分析聊天记录中他人对用户的称呼）。
|
||||
**不要**输出指代不明或过于通用的词语（如“大佬”、“兄弟”、“那个谁”等，除非上下文能非常明确地指向特定用户）。
|
||||
4. 如果找到了**至少一个**满足上述所有条件的**明确**的用户 ID 到绰号的映射关系，请输出 JSON 对象：
|
||||
```json
|
||||
{{
|
||||
"is_exist": true,
|
||||
"data": {{
|
||||
"用户A数字id": "绰号_A",
|
||||
"用户B数字id": "绰号_B"
|
||||
}}
|
||||
}}
|
||||
```
|
||||
- `"data"` 字段的键必须是用户的**数字 ID (字符串形式)**，值是对应的**绰号 (字符串形式)**。
|
||||
- 只包含你能**百分百确认**映射关系的条目。宁缺毋滥。
|
||||
如果**无法找到任何一个**满足条件的明确映射关系，请输出 JSON 对象：
|
||||
```json
|
||||
{{
|
||||
"is_exist": false
|
||||
}}
|
||||
```
|
||||
5. 请**仅**输出 JSON 对象，不要包含任何额外的解释、注释或代码块标记之外的文本。
|
||||
|
||||
输出：
|
||||
"""
|
||||
# logger.debug(f"构建的绰号映射 Prompt (部分):\n{prompt[:500]}...") # 可以在 NicknameManager 中记录
|
||||
return prompt
|
||||
|
||||
|
||||
# analyze_chat_for_nicknames 函数已被移除，其逻辑移至 NicknameManager._call_llm_for_analysis
|
||||
|
|
@ -0,0 +1,175 @@
|
|||
import random
|
||||
from typing import List, Dict, Tuple
|
||||
from src.common.logger_manager import get_logger
|
||||
from src.config.config import global_config
|
||||
|
||||
# 这个文件现在只包含纯粹的工具函数，与状态和流程无关
|
||||
|
||||
logger = get_logger("nickname_utils")
|
||||
|
||||
|
||||
def select_nicknames_for_prompt(all_nicknames_info: Dict[str, List[Dict[str, int]]]) -> List[Tuple[str, str, int]]:
|
||||
"""
|
||||
从给定的绰号信息中，根据映射次数加权随机选择最多 N 个绰号用于 Prompt。
|
||||
|
||||
Args:
|
||||
all_nicknames_info: 包含用户及其绰号信息的字典，格式为
|
||||
{ "用户名1": [{"绰号A": 次数}, {"绰号B": 次数}], ... }
|
||||
注意：这里的用户名是 person_name。
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str, int]]: 选中的绰号列表，每个元素为 (用户名, 绰号, 次数)。
|
||||
按次数降序排序。
|
||||
"""
|
||||
if not all_nicknames_info:
|
||||
return []
|
||||
|
||||
candidates = [] # 存储 (用户名, 绰号, 次数, 权重)
|
||||
smoothing_factor = getattr(global_config, "NICKNAME_PROBABILITY_SMOOTHING", 1.0) # 平滑因子，避免权重为0
|
||||
|
||||
for user_name, nicknames in all_nicknames_info.items():
|
||||
if nicknames and isinstance(nicknames, list):
|
||||
for nickname_entry in nicknames:
|
||||
# 确保条目是字典且只有一个键值对
|
||||
if isinstance(nickname_entry, dict) and len(nickname_entry) == 1:
|
||||
nickname, count = list(nickname_entry.items())[0]
|
||||
# 确保次数是正整数
|
||||
if isinstance(count, int) and count > 0 and isinstance(nickname, str) and nickname:
|
||||
weight = count + smoothing_factor # 计算权重
|
||||
candidates.append((user_name, nickname, count, weight))
|
||||
else:
|
||||
logger.warning(
|
||||
f"用户 '{user_name}' 的绰号条目无效: {nickname_entry} (次数非正整数或绰号为空)。已跳过。"
|
||||
)
|
||||
else:
|
||||
logger.warning(f"用户 '{user_name}' 的绰号条目格式无效: {nickname_entry}。已跳过。")
|
||||
|
||||
if not candidates:
|
||||
return []
|
||||
|
||||
# 确定需要选择的数量
|
||||
max_nicknames = getattr(global_config, "MAX_NICKNAMES_IN_PROMPT", 5)
|
||||
num_to_select = min(max_nicknames, len(candidates))
|
||||
|
||||
try:
|
||||
# 调用加权随机抽样（不重复）
|
||||
selected_candidates_with_weight = weighted_sample_without_replacement(candidates, num_to_select)
|
||||
|
||||
# 如果抽样结果数量不足（例如权重问题导致提前退出），可以考虑是否需要补充
|
||||
if len(selected_candidates_with_weight) < num_to_select:
|
||||
logger.debug(
|
||||
f"加权随机选择后数量不足 ({len(selected_candidates_with_weight)}/{num_to_select})，尝试补充选择次数最多的。"
|
||||
)
|
||||
# 筛选出未被选中的候选
|
||||
selected_ids = set(
|
||||
(c[0], c[1]) for c in selected_candidates_with_weight
|
||||
) # 使用 (用户名, 绰号) 作为唯一标识
|
||||
remaining_candidates = [c for c in candidates if (c[0], c[1]) not in selected_ids]
|
||||
remaining_candidates.sort(key=lambda x: x[2], reverse=True) # 按原始次数排序
|
||||
needed = num_to_select - len(selected_candidates_with_weight)
|
||||
selected_candidates_with_weight.extend(remaining_candidates[:needed])
|
||||
|
||||
except Exception as e:
|
||||
# 日志：记录加权随机选择时发生的错误，并回退到简单选择
|
||||
logger.error(f"绰号加权随机选择时出错: {e}。将回退到选择次数最多的 Top N。", exc_info=True)
|
||||
# 出错时回退到选择次数最多的 N 个
|
||||
candidates.sort(key=lambda x: x[2], reverse=True) # 按原始次数排序
|
||||
selected_candidates_with_weight = candidates[:num_to_select]
|
||||
|
||||
# 格式化输出结果为 (用户名, 绰号, 次数)，移除权重
|
||||
result = [(user, nick, count) for user, nick, count, _weight in selected_candidates_with_weight]
|
||||
|
||||
# 按次数降序排序最终结果
|
||||
result.sort(key=lambda x: x[2], reverse=True)
|
||||
|
||||
logger.debug(f"为 Prompt 选择的绰号: {result}")
|
||||
return result
|
||||
|
||||
|
||||
def format_nickname_prompt_injection(selected_nicknames: List[Tuple[str, str, int]]) -> str:
|
||||
"""
|
||||
将选中的绰号信息格式化为注入 Prompt 的字符串。
|
||||
|
||||
Args:
|
||||
selected_nicknames: 选中的绰号列表 (用户名, 绰号, 次数)。
|
||||
|
||||
Returns:
|
||||
str: 格式化后的字符串，如果列表为空则返回空字符串。
|
||||
"""
|
||||
if not selected_nicknames:
|
||||
return ""
|
||||
|
||||
# Prompt 注入部分的标题
|
||||
prompt_lines = ["以下是聊天记录中一些成员在本群的绰号信息（按常用度排序），供你参考："]
|
||||
grouped_by_user: Dict[str, List[str]] = {} # 用于按用户分组
|
||||
|
||||
# 按用户分组绰号
|
||||
for user_name, nickname, _count in selected_nicknames:
|
||||
if user_name not in grouped_by_user:
|
||||
grouped_by_user[user_name] = []
|
||||
# 添加中文引号以区分绰号
|
||||
grouped_by_user[user_name].append(f"“{nickname}”")
|
||||
|
||||
# 构建每个用户的绰号字符串
|
||||
for user_name, nicknames in grouped_by_user.items():
|
||||
nicknames_str = "、".join(nicknames) # 使用中文顿号连接
|
||||
# 格式化输出，例如: "- 张三，ta 可能被称为：“三儿”、“张哥”"
|
||||
prompt_lines.append(f"- {user_name}，ta 可能被称为：{nicknames_str}")
|
||||
|
||||
# 如果只有标题行，返回空字符串，避免注入无意义的标题
|
||||
if len(prompt_lines) > 1:
|
||||
# 末尾加换行符，以便在 Prompt 中正确分隔
|
||||
return "\n".join(prompt_lines) + "\n"
|
||||
else:
|
||||
return ""
|
||||
|
||||
|
||||
def weighted_sample_without_replacement(
|
||||
candidates: List[Tuple[str, str, int, float]], k: int
|
||||
) -> List[Tuple[str, str, int, float]]:
|
||||
"""
|
||||
执行不重复的加权随机抽样。使用 A-ExpJ 算法思想的简化实现。
|
||||
|
||||
Args:
|
||||
candidates: 候选列表，每个元素为 (用户名, 绰号, 次数, 权重)。
|
||||
k: 需要选择的数量。
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str, int, float]]: 选中的元素列表（包含权重）。
|
||||
"""
|
||||
if k <= 0:
|
||||
return []
|
||||
n = len(candidates)
|
||||
if k >= n:
|
||||
return candidates[:] # 返回副本
|
||||
|
||||
# 计算每个元素的 key = U^(1/weight)，其中 U 是 (0, 1) 之间的随机数
|
||||
# 为了数值稳定性，计算 log(key) = log(U) / weight
|
||||
# log(U) 可以用 -Exponential(1) 来生成
|
||||
weighted_keys = []
|
||||
for i in range(n):
|
||||
weight = candidates[i][3]
|
||||
if weight <= 0:
|
||||
# 处理权重为0或负数的情况，赋予一个极小的概率（或极大负数的log_key）
|
||||
log_key = float("-inf") # 或者一个非常大的负数
|
||||
logger.warning(f"候选者 {candidates[i][:2]} 的权重为非正数 ({weight})，抽中概率极低。")
|
||||
else:
|
||||
log_u = -random.expovariate(1.0) # 生成 -Exponential(1) 随机数
|
||||
log_key = log_u / weight
|
||||
weighted_keys.append((log_key, i)) # 存储 (log_key, 原始索引)
|
||||
|
||||
# 按 log_key 降序排序 (相当于按 key 升序排序)
|
||||
weighted_keys.sort(key=lambda x: x[0], reverse=True)
|
||||
|
||||
# 选择 log_key 最大的 k 个元素的原始索引
|
||||
selected_indices = [index for _log_key, index in weighted_keys[:k]]
|
||||
|
||||
# 根据选中的索引从原始 candidates 列表中获取元素
|
||||
selected_items = [candidates[i] for i in selected_indices]
|
||||
|
||||
return selected_items
|
||||
|
||||
|
||||
# 移除旧的流程函数
|
||||
# get_nickname_injection_for_prompt 和 trigger_nickname_analysis_if_needed
|
||||
# 的逻辑现在由 NicknameManager 处理
|
||||
|
|
@ -1,20 +1,20 @@
|
|||
import asyncio
|
||||
import time
|
||||
import traceback
|
||||
import random # <--- 添加导入
|
||||
import json # <--- 确保导入 json
|
||||
import random
|
||||
import json
|
||||
from typing import List, Optional, Dict, Any, Deque, Callable, Coroutine
|
||||
from collections import deque
|
||||
from src.plugins.chat.message import MessageRecv, BaseMessageInfo, MessageThinking, MessageSending
|
||||
from src.plugins.chat.message import Seg # Local import needed after move
|
||||
from src.plugins.chat.message import Seg
|
||||
from src.plugins.chat.chat_stream import ChatStream
|
||||
from src.plugins.chat.message import UserInfo
|
||||
from src.plugins.chat.chat_stream import chat_manager
|
||||
from src.common.logger_manager import get_logger
|
||||
from src.plugins.models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
from src.plugins.chat.utils_image import image_path_to_base64 # Local import needed after move
|
||||
from src.plugins.utils.timer_calculator import Timer # <--- Import Timer
|
||||
from src.plugins.chat.utils_image import image_path_to_base64
|
||||
from src.plugins.utils.timer_calculator import Timer
|
||||
from src.plugins.emoji_system.emoji_manager import emoji_manager
|
||||
from src.heart_flow.sub_mind import SubMind
|
||||
from src.heart_flow.observation import Observation
|
||||
|
|
@ -28,6 +28,8 @@ from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
|
|||
from src.plugins.moods.moods import MoodManager
|
||||
from src.heart_flow.utils_chat import get_chat_type_and_target_info
|
||||
from rich.traceback import install
|
||||
from src.plugins.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat
|
||||
from src.plugins.group_nickname.nickname_manager import nickname_manager
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
|
|
@ -528,11 +530,17 @@ class HeartFChatting:
|
|||
|
||||
try:
|
||||
if action == "text_reply":
|
||||
return await handler(reasoning, emoji_query, cycle_timers)
|
||||
# 调用文本回复处理，它会返回 (bool, thinking_id)
|
||||
success, thinking_id = await handler(reasoning, emoji_query, cycle_timers)
|
||||
return success, thinking_id # 直接返回结果
|
||||
elif action == "emoji_reply":
|
||||
return await handler(reasoning, emoji_query), ""
|
||||
# 调用表情回复处理，它只返回 bool
|
||||
success = await handler(reasoning, emoji_query)
|
||||
return success, "" # thinking_id 为空字符串
|
||||
else: # no_reply
|
||||
return await handler(reasoning, planner_start_db_time, cycle_timers), ""
|
||||
# 调用不回复处理，它只返回 bool
|
||||
success = await handler(reasoning, planner_start_db_time, cycle_timers)
|
||||
return success, "" # thinking_id 为空字符串
|
||||
except HeartFCError as e:
|
||||
logger.error(f"{self.log_prefix} 处理{action}时出错: {e}")
|
||||
# 出错时也重置计数器
|
||||
|
|
@ -549,6 +557,7 @@ class HeartFChatting:
|
|||
2. 创建思考消息
|
||||
3. 生成回复
|
||||
4. 发送消息
|
||||
5. [新增] 触发绰号分析
|
||||
|
||||
参数:
|
||||
reasoning: 回复原因
|
||||
|
|
@ -572,6 +581,7 @@ class HeartFChatting:
|
|||
if not thinking_id:
|
||||
raise PlannerError("无法创建思考消息")
|
||||
|
||||
reply = None # 初始化 reply
|
||||
try:
|
||||
# 生成回复
|
||||
with Timer("生成回复", cycle_timers):
|
||||
|
|
@ -585,7 +595,6 @@ class HeartFChatting:
|
|||
raise ReplierError("回复生成失败")
|
||||
|
||||
# 发送消息
|
||||
|
||||
with Timer("发送消息", cycle_timers):
|
||||
await self._sender(
|
||||
thinking_id=thinking_id,
|
||||
|
|
@ -594,6 +603,9 @@ class HeartFChatting:
|
|||
send_emoji=emoji_query,
|
||||
)
|
||||
|
||||
# 调用工具函数触发绰号分析
|
||||
await nickname_manager.trigger_nickname_analysis(anchor_message, reply, self.chat_stream)
|
||||
|
||||
return True, thinking_id
|
||||
|
||||
except (ReplierError, SenderError) as e:
|
||||
|
|
@ -854,6 +866,17 @@ class HeartFChatting:
|
|||
f"{self.log_prefix}[Planner] 临时移除的动作: {actions_to_remove_temporarily}, 当前可用: {list(current_available_actions.keys())}"
|
||||
)
|
||||
|
||||
# 需要获取用于上下文的历史消息
|
||||
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
|
||||
chat_id=self.stream_id,
|
||||
timestamp=time.time(), # 使用当前时间作为参考点
|
||||
limit=global_config.observation_context_size, # 使用与 prompt 构建一致的 limit
|
||||
)
|
||||
# 调用工具函数获取格式化后的绰号字符串
|
||||
nickname_injection_str = await nickname_manager.get_nickname_prompt_injection(
|
||||
self.chat_stream, message_list_before_now
|
||||
)
|
||||
|
||||
# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
|
||||
prompt = await prompt_builder.build_planner_prompt(
|
||||
is_group_chat=self.is_group_chat, # <-- Pass HFC state
|
||||
|
|
@ -863,6 +886,7 @@ class HeartFChatting:
|
|||
current_mind=current_mind, # <-- Pass argument
|
||||
structured_info=self.sub_mind.structured_info_str, # <-- Pass SubMind info
|
||||
current_available_actions=current_available_actions, # <-- Pass determined actions
|
||||
nickname_info=nickname_injection_str,
|
||||
)
|
||||
|
||||
# --- 调用 LLM (普通文本生成) ---
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
import random
|
||||
import time
|
||||
from typing import Union, Optional, Deque, Dict, Any
|
||||
from ...config.config import global_config
|
||||
from src.common.logger_manager import get_logger
|
||||
from ...individuality.individuality import Individuality
|
||||
|
|
@ -6,14 +8,13 @@ from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
|
|||
from src.plugins.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
|
||||
from src.plugins.person_info.relationship_manager import relationship_manager
|
||||
from src.plugins.chat.utils import get_embedding
|
||||
import time
|
||||
from typing import Union, Optional, Deque, Dict, Any
|
||||
from ...common.database import db
|
||||
from ..chat.utils import get_recent_group_speaker
|
||||
from ..moods.moods import MoodManager
|
||||
from ..memory_system.Hippocampus import HippocampusManager
|
||||
from ..schedule.schedule_generator import bot_schedule
|
||||
from ..knowledge.knowledge_lib import qa_manager
|
||||
from src.plugins.group_nickname.nickname_manager import nickname_manager
|
||||
import traceback
|
||||
from .heartFC_Cycleinfo import CycleInfo
|
||||
|
||||
|
|
@ -24,6 +25,7 @@ def init_prompt():
|
|||
Prompt(
|
||||
"""
|
||||
{info_from_tools}
|
||||
{nickname_info}
|
||||
{chat_target}
|
||||
{chat_talking_prompt}
|
||||
现在你想要在群里发言或者回复。\n
|
||||
|
|
@ -53,6 +55,7 @@ def init_prompt():
|
|||
Prompt(
|
||||
"""你的名字是{bot_name},{prompt_personality}，{chat_context_description}。需要基于以下信息决定如何参与对话：
|
||||
{structured_info_block}
|
||||
{nickname_info}
|
||||
{chat_content_block}
|
||||
{current_mind_block}
|
||||
{cycle_info_block}
|
||||
|
|
@ -118,6 +121,7 @@ JSON 结构如下，包含三个字段 "action", "reasoning", "emoji_query":
|
|||
{relation_prompt}
|
||||
{prompt_info}
|
||||
{schedule_prompt}
|
||||
{nickname_info}
|
||||
{chat_target}
|
||||
{chat_talking_prompt}
|
||||
现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言或者回复这条消息。\n
|
||||
|
|
@ -250,9 +254,15 @@ async def _build_prompt_focus(reason, current_mind_info, structured_info, chat_s
|
|||
chat_target_1 = await global_prompt_manager.get_prompt_async("chat_target_group1")
|
||||
chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2")
|
||||
|
||||
# 调用新的工具函数获取绰号信息
|
||||
nickname_injection_str = await nickname_manager.get_nickname_prompt_injection(
|
||||
chat_stream, message_list_before_now
|
||||
)
|
||||
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
template_name,
|
||||
info_from_tools=structured_info_prompt,
|
||||
nickname_info=nickname_injection_str,
|
||||
chat_target=chat_target_1, # Used in group template
|
||||
chat_talking_prompt=chat_talking_prompt,
|
||||
bot_name=global_config.BOT_NICKNAME,
|
||||
|
|
@ -442,6 +452,11 @@ class PromptBuilder:
|
|||
chat_target_1 = await global_prompt_manager.get_prompt_async("chat_target_group1")
|
||||
chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2")
|
||||
|
||||
# 调用新的工具函数获取绰号信息
|
||||
nickname_injection_str = await nickname_manager.get_nickname_prompt_injection(
|
||||
chat_stream, message_list_before_now
|
||||
)
|
||||
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
template_name,
|
||||
relation_prompt=relation_prompt,
|
||||
|
|
@ -449,6 +464,7 @@ class PromptBuilder:
|
|||
memory_prompt=memory_prompt,
|
||||
prompt_info=prompt_info,
|
||||
schedule_prompt=schedule_prompt,
|
||||
nickname_info=nickname_injection_str, # <--- 注入绰号信息
|
||||
chat_target=chat_target_1,
|
||||
chat_target_2=chat_target_2,
|
||||
chat_talking_prompt=chat_talking_prompt,
|
||||
|
|
@ -486,7 +502,7 @@ class PromptBuilder:
|
|||
prompt_ger=prompt_ger,
|
||||
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
|
||||
)
|
||||
# --- End choosing template ---
|
||||
# --- End choosing template ---
|
||||
|
||||
return prompt
|
||||
|
||||
|
|
@ -755,6 +771,7 @@ class PromptBuilder:
|
|||
current_mind: Optional[str],
|
||||
structured_info: Dict[str, Any],
|
||||
current_available_actions: Dict[str, str],
|
||||
nickname_info: str,
|
||||
# replan_prompt: str, # Replan logic still simplified
|
||||
) -> str:
|
||||
"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
|
||||
|
|
@ -836,6 +853,7 @@ class PromptBuilder:
|
|||
|
||||
prompt = planner_prompt_template.format(
|
||||
bot_name=global_config.BOT_NICKNAME,
|
||||
nickname_info=nickname_info,
|
||||
prompt_personality=prompt_personality,
|
||||
chat_context_description=chat_context_description,
|
||||
structured_info_block=structured_info_block,
|
||||
|
|
|
|||
|
|
@ -20,6 +20,7 @@ from src.plugins.person_info.relationship_manager import relationship_manager
|
|||
from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
|
||||
from src.plugins.utils.timer_calculator import Timer
|
||||
from src.heart_flow.utils_chat import get_chat_type_and_target_info
|
||||
from src.plugins.group_nickname.nickname_manager import nickname_manager
|
||||
|
||||
|
||||
logger = get_logger("chat")
|
||||
|
|
@ -316,6 +317,7 @@ class NormalChat:
|
|||
# 检查 first_bot_msg 是否为 None (例如思考消息已被移除的情况)
|
||||
if first_bot_msg:
|
||||
info_catcher.catch_after_response(timing_results["消息发送"], response_set, first_bot_msg)
|
||||
await nickname_manager.trigger_nickname_analysis(message, response_set, self.chat_stream)
|
||||
else:
|
||||
logger.warning(f"[{self.stream_name}] 思考消息 {thinking_id} 在发送前丢失，无法记录 info_catcher")
|
||||
|
||||
|
|
|
|||
|
|
@ -53,6 +53,7 @@ person_info_default = {
|
|||
"msg_interval_list": [],
|
||||
"user_cardname": None, # 添加群名片
|
||||
"user_avatar": None, # 添加头像信息（例如URL或标识符）
|
||||
"group_nicknames": [],
|
||||
} # 个人信息的各项与默认值在此定义，以下处理会自动创建/补全每一项
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -5,6 +5,8 @@ from bson.decimal128 import Decimal128
|
|||
from .person_info import person_info_manager
|
||||
import time
|
||||
import random
|
||||
from typing import List, Dict
|
||||
from ...common.database import db
|
||||
from maim_message import UserInfo
|
||||
# import re
|
||||
# import traceback
|
||||
|
|
@ -81,6 +83,131 @@ class RelationshipManager:
|
|||
is_known = person_info_manager.is_person_known(platform, user_id)
|
||||
return is_known
|
||||
|
||||
# --- [修改] 使用全局 db 对象进行查询 ---
|
||||
@staticmethod
|
||||
async def get_person_names_batch(platform: str, user_ids: List[str]) -> Dict[str, str]:
|
||||
"""
|
||||
批量获取多个用户的 person_name。
|
||||
"""
|
||||
if not user_ids:
|
||||
return {}
|
||||
|
||||
person_ids = [person_info_manager.get_person_id(platform, str(uid)) for uid in user_ids]
|
||||
names_map = {}
|
||||
try:
|
||||
cursor = db.person_info.find(
|
||||
{"person_id": {"$in": person_ids}},
|
||||
{"_id": 0, "person_id": 1, "user_id": 1, "person_name": 1}, # 只查询需要的字段
|
||||
)
|
||||
|
||||
for doc in cursor:
|
||||
user_id_val = doc.get("user_id") # 获取原始值
|
||||
original_user_id = None # 初始化
|
||||
|
||||
if isinstance(user_id_val, (int, float)): # 检查是否是数字类型
|
||||
original_user_id = str(user_id_val) # 直接转换为字符串
|
||||
elif isinstance(user_id_val, str): # 检查是否是字符串
|
||||
if "_" in user_id_val: # 如果包含下划线，则分割
|
||||
original_user_id = user_id_val.split("_", 1)[-1]
|
||||
else: # 如果不包含下划线，则直接使用该字符串
|
||||
original_user_id = user_id_val
|
||||
# else: # 其他类型或 None，original_user_id 保持为 None
|
||||
|
||||
person_name = doc.get("person_name")
|
||||
|
||||
# 确保 original_user_id 和 person_name 都有效
|
||||
if original_user_id and person_name:
|
||||
names_map[original_user_id] = person_name
|
||||
|
||||
logger.debug(f"批量获取 {len(user_ids)} 个用户的 person_name，找到 {len(names_map)} 个。")
|
||||
except AttributeError as e:
|
||||
# 如果 db 对象没有 person_info 属性，或者 find 方法不存在
|
||||
logger.error(f"访问数据库时出错: {e}。请检查 common/database.py 和集合名称。")
|
||||
except Exception as e:
|
||||
logger.error(f"批量获取 person_name 时出错: {e}", exc_info=True)
|
||||
return names_map
|
||||
|
||||
@staticmethod
|
||||
async def get_users_group_nicknames(
|
||||
platform: str, user_ids: List[str], group_id: str
|
||||
) -> Dict[str, List[Dict[str, int]]]:
|
||||
"""
|
||||
批量获取多个用户在指定群组的绰号信息。
|
||||
|
||||
Args:
|
||||
platform (str): 平台名称。
|
||||
user_ids (List[str]): 用户 ID 列表。
|
||||
group_id (str): 群组 ID。
|
||||
|
||||
Returns:
|
||||
Dict[str, List[Dict[str, int]]]: 映射 {person_name: [{"绰号A": 次数}, ...]}
|
||||
"""
|
||||
if not user_ids or not group_id:
|
||||
return {}
|
||||
|
||||
person_ids = [person_info_manager.get_person_id(platform, str(uid)) for uid in user_ids]
|
||||
nicknames_data = {}
|
||||
group_id_str = str(group_id) # 确保 group_id 是字符串
|
||||
|
||||
try:
|
||||
# 查询包含目标 person_id 的文档
|
||||
cursor = db.person_info.find(
|
||||
{"person_id": {"$in": person_ids}},
|
||||
{"_id": 0, "person_id": 1, "person_name": 1, "group_nicknames": 1}, # 查询所需字段
|
||||
)
|
||||
|
||||
# 假设同步迭代可行
|
||||
for doc in cursor:
|
||||
person_name = doc.get("person_name")
|
||||
if not person_name:
|
||||
continue # 跳过没有 person_name 的用户
|
||||
|
||||
group_nicknames_list = doc.get("group_nicknames", []) # 获取 group_nicknames 数组
|
||||
target_group_nicknames = [] # 存储目标群组的绰号列表
|
||||
|
||||
# 遍历 group_nicknames 数组，查找匹配的 group_id
|
||||
for group_entry in group_nicknames_list:
|
||||
# 确保 group_entry 是字典且包含 group_id 键
|
||||
if isinstance(group_entry, dict) and group_entry.get("group_id") == group_id_str:
|
||||
# 提取 nicknames 列表
|
||||
nicknames_raw = group_entry.get("nicknames", [])
|
||||
if isinstance(nicknames_raw, list):
|
||||
target_group_nicknames = nicknames_raw
|
||||
break # 找到匹配的 group_id 后即可退出内层循环
|
||||
|
||||
# 如果找到了目标群组的绰号列表
|
||||
if target_group_nicknames:
|
||||
valid_nicknames_formatted = [] # 存储格式化后的绰号
|
||||
for item in target_group_nicknames:
|
||||
# 校验每个绰号条目的格式 { "name": str, "count": int }
|
||||
if (
|
||||
isinstance(item, dict)
|
||||
and isinstance(item.get("name"), str)
|
||||
and isinstance(item.get("count"), int)
|
||||
and item["count"] > 0
|
||||
): # 确保 count 是正整数
|
||||
# --- 格式转换：从 { "name": "xxx", "count": y } 转为 { "xxx": y } ---
|
||||
valid_nicknames_formatted.append({item["name"]: item["count"]})
|
||||
# --- 结束格式转换 ---
|
||||
else:
|
||||
logger.warning(
|
||||
f"数据库中用户 {person_name} 群组 {group_id_str} 的绰号格式无效或 count <= 0: {item}"
|
||||
)
|
||||
|
||||
if valid_nicknames_formatted: # 如果存在有效的、格式化后的绰号
|
||||
nicknames_data[person_name] = valid_nicknames_formatted # 使用 person_name 作为 key
|
||||
|
||||
logger.debug(
|
||||
f"批量获取群组 {group_id_str} 中 {len(user_ids)} 个用户的绰号，找到 {len(nicknames_data)} 个用户的数据。"
|
||||
)
|
||||
|
||||
except AttributeError as e:
|
||||
logger.error(f"访问数据库时出错: {e}。请检查 common/database.py 和集合名称 'person_info'。")
|
||||
except Exception as e:
|
||||
logger.error(f"批量获取群组绰号时出错: {e}", exc_info=True)
|
||||
|
||||
return nicknames_data
|
||||
|
||||
@staticmethod
|
||||
async def is_qved_name(platform, user_id):
|
||||
"""判断是否认识某人"""
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
[inner]
|
||||
version = "1.6.1"
|
||||
version = "1.6.2"
|
||||
|
||||
#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
|
||||
#如果你想要修改配置文件，请在修改后将version的值进行变更
|
||||
|
|
@ -123,6 +123,13 @@ steal_emoji = true # 是否偷取表情包，让麦麦可以发送她保存的
|
|||
enable_check = false # 是否启用表情包过滤，只有符合该要求的表情包才会被保存
|
||||
check_prompt = "符合公序良俗" # 表情包过滤要求，只有符合该要求的表情包才会被保存
|
||||
|
||||
[group_nickname]
|
||||
enable_nickname_mapping = false # 绰号映射功能总开关（默认关闭，建议关闭）
|
||||
max_nicknames_in_prompt = 10 # Prompt 中最多注入的绰号数量（防止token数量爆炸）
|
||||
nickname_probability_smoothing = 1 # 绰号加权随机选择的平滑因子
|
||||
nickname_queue_max_size = 100 # 绰号处理队列最大容量
|
||||
nickname_process_sleep_interval = 60 # 绰号处理进程休眠间隔（秒）
|
||||
|
||||
[memory]
|
||||
build_memory_interval = 2000 # 记忆构建间隔 单位秒 间隔越低，麦麦学习越多，但是冗余信息也会增多
|
||||
build_memory_distribution = [6.0,3.0,0.6,32.0,12.0,0.4] # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重
|
||||
|
|
@ -291,6 +298,13 @@ provider = "SILICONFLOW"
|
|||
pri_in = 2
|
||||
pri_out = 8
|
||||
|
||||
#绰号映射生成模型
|
||||
[model.llm_nickname_mapping]
|
||||
name = "deepseek-ai/DeepSeek-V3"
|
||||
provider = "SILICONFLOW"
|
||||
temp = 0.3
|
||||
pri_in = 2
|
||||
pri_out = 8
|
||||
|
||||
#以下模型暂时没有使用！！
|
||||
#以下模型暂时没有使用！！
|
||||
|
|
|
|||
Loading…
Reference in New Issue