196 lines
8.9 KiB
Python
196 lines
8.9 KiB
Python
from dataclasses import dataclass, field
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from typing import Dict, Any, Optional, Set
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import os
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from nonebot.log import logger, default_format
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import logging
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import configparser
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import tomli
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import sys
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from loguru import logger
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from nonebot import get_driver
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@dataclass
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class BotConfig:
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"""机器人配置类"""
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BOT_QQ: Optional[int] = 1
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BOT_NICKNAME: Optional[str] = None
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# 消息处理相关配置
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MIN_TEXT_LENGTH: int = 2 # 最小处理文本长度
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MAX_CONTEXT_SIZE: int = 15 # 上下文最大消息数
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emoji_chance: float = 0.2 # 发送表情包的基础概率
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ENABLE_PIC_TRANSLATE: bool = True # 是否启用图片翻译
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talk_allowed_groups = set()
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talk_frequency_down_groups = set()
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ban_user_id = set()
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build_memory_interval: int = 60 # 记忆构建间隔（秒）
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EMOJI_CHECK_INTERVAL: int = 120 # 表情包检查间隔（分钟）
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EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟）
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ban_words = set()
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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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llm_normal: Dict[str, str] = field(default_factory=lambda: {})
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llm_normal_minor: Dict[str, str] = field(default_factory=lambda: {})
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embedding: Dict[str, str] = field(default_factory=lambda: {})
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vlm: Dict[str, str] = field(default_factory=lambda: {})
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# 主题提取配置
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topic_extract: str = 'snownlp' # 只支持jieba,snownlp,llm
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llm_topic_extract: Dict[str, str] = field(default_factory=lambda: {})
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API_USING: str = "siliconflow" # 使用的API
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API_PAID: bool = False # 是否使用付费API
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MODEL_R1_PROBABILITY: float = 0.8 # R1模型概率
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MODEL_V3_PROBABILITY: float = 0.1 # V3模型概率
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MODEL_R1_DISTILL_PROBABILITY: float = 0.1 # R1蒸馏模型概率
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enable_advance_output: bool = False # 是否启用高级输出
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enable_kuuki_read: bool = True # 是否启用读空气功能
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# 默认人设
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PROMPT_PERSONALITY=[
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"曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧",
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"是一个女大学生，你有黑色头发，你会刷小红书"
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]
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PROMPT_SCHEDULE_GEN="一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书"
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@staticmethod
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def get_config_dir() -> str:
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"""获取配置文件目录"""
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current_dir = os.path.dirname(os.path.abspath(__file__))
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root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..'))
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config_dir = os.path.join(root_dir, 'config')
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if not os.path.exists(config_dir):
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os.makedirs(config_dir)
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return config_dir
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@classmethod
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def load_config(cls, config_path: str = None) -> "BotConfig":
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"""从TOML配置文件加载配置"""
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config = cls()
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if os.path.exists(config_path):
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with open(config_path, "rb") as f:
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toml_dict = tomli.load(f)
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if 'personality' in toml_dict:
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personality_config=toml_dict['personality']
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personality=personality_config.get('prompt_personality')
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if len(personality) >= 2:
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print(f"载入自定义人格:{personality}")
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config.PROMPT_PERSONALITY=personality_config.get('prompt_personality',config.PROMPT_PERSONALITY)
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print(f"载入自定义日程prompt:{personality_config.get('prompt_schedule',config.PROMPT_SCHEDULE_GEN)}")
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config.PROMPT_SCHEDULE_GEN=personality_config.get('prompt_schedule',config.PROMPT_SCHEDULE_GEN)
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if "emoji" in toml_dict:
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emoji_config = toml_dict["emoji"]
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config.EMOJI_CHECK_INTERVAL = emoji_config.get("check_interval", config.EMOJI_CHECK_INTERVAL)
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config.EMOJI_REGISTER_INTERVAL = emoji_config.get("register_interval", config.EMOJI_REGISTER_INTERVAL)
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if "cq_code" in toml_dict:
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cq_code_config = toml_dict["cq_code"]
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config.ENABLE_PIC_TRANSLATE = cq_code_config.get("enable_pic_translate", config.ENABLE_PIC_TRANSLATE)
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# 机器人基础配置
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if "bot" in toml_dict:
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bot_config = toml_dict["bot"]
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bot_qq = bot_config.get("qq")
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config.BOT_QQ = int(bot_qq)
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config.BOT_NICKNAME = bot_config.get("nickname", config.BOT_NICKNAME)
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if "response" in toml_dict:
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response_config = toml_dict["response"]
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config.MODEL_R1_PROBABILITY = response_config.get("model_r1_probability", config.MODEL_R1_PROBABILITY)
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config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY)
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config.MODEL_R1_DISTILL_PROBABILITY = response_config.get("model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY)
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config.API_USING = response_config.get("api_using", config.API_USING)
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config.API_PAID = response_config.get("api_paid", config.API_PAID)
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# 加载模型配置
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if "model" in toml_dict:
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model_config = toml_dict["model"]
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if "llm_reasoning" in model_config:
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config.llm_reasoning = model_config["llm_reasoning"]
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if "llm_reasoning_minor" in model_config:
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config.llm_reasoning_minor = model_config["llm_reasoning_minor"]
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if "llm_normal" in model_config:
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config.llm_normal = model_config["llm_normal"]
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config.llm_topic_extract = config.llm_normal
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if "llm_normal_minor" in model_config:
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config.llm_normal_minor = model_config["llm_normal_minor"]
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if "vlm" in model_config:
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config.vlm = model_config["vlm"]
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if "embedding" in model_config:
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config.embedding = model_config["embedding"]
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if 'topic' in toml_dict:
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topic_config=toml_dict['topic']
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if 'topic_extract' in topic_config:
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config.topic_extract=topic_config.get('topic_extract',config.topic_extract)
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print(f"载入自定义主题提取为{config.topic_extract}")
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if config.topic_extract=='llm' and 'llm_topic' in topic_config:
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config.llm_topic_extract=topic_config['llm_topic']
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print(f"载入自定义主题提取模型为{config.llm_topic_extract['name']}")
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# 消息配置
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if "message" in toml_dict:
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msg_config = toml_dict["message"]
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config.MIN_TEXT_LENGTH = msg_config.get("min_text_length", config.MIN_TEXT_LENGTH)
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config.MAX_CONTEXT_SIZE = msg_config.get("max_context_size", config.MAX_CONTEXT_SIZE)
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config.emoji_chance = msg_config.get("emoji_chance", config.emoji_chance)
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config.ban_words=msg_config.get("ban_words",config.ban_words)
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if "memory" in toml_dict:
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memory_config = toml_dict["memory"]
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config.build_memory_interval = memory_config.get("build_memory_interval", config.build_memory_interval)
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# 群组配置
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if "groups" in toml_dict:
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groups_config = toml_dict["groups"]
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config.talk_allowed_groups = set(groups_config.get("talk_allowed", []))
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config.talk_frequency_down_groups = set(groups_config.get("talk_frequency_down", []))
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config.ban_user_id = set(groups_config.get("ban_user_id", []))
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if "others" in toml_dict:
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others_config = toml_dict["others"]
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config.enable_advance_output = others_config.get("enable_advance_output", config.enable_advance_output)
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config.enable_kuuki_read = others_config.get("enable_kuuki_read", config.enable_kuuki_read)
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logger.success(f"成功加载配置文件: {config_path}")
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return config
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# 获取配置文件路径
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bot_config_floder_path = BotConfig.get_config_dir()
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print(f"正在品鉴配置文件目录: {bot_config_floder_path}")
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bot_config_path = os.path.join(bot_config_floder_path, "bot_config_dev.toml")
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if not os.path.exists(bot_config_path):
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# 如果开发环境配置文件不存在，则使用默认配置文件
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bot_config_path = os.path.join(bot_config_floder_path, "bot_config.toml")
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logger.info("使用bot配置文件")
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else:
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logger.info("已找到开发bot配置文件")
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global_config = BotConfig.load_config(config_path=bot_config_path)
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if not global_config.enable_advance_output:
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# logger.remove()
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pass
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