151 lines
5.0 KiB
TOML
151 lines
5.0 KiB
TOML
[inner]
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version = "0.0.2"
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[bot]
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qq = 123
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nickname = "麦麦"
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[personality]
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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", # 贴吧人格
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"是一个女大学生，你有黑色头发，你会刷小红书", # 小红书人格
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"是一个女大学生，你会刷b站，对ACG文化感兴趣" # b站人格
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]
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personality_1_probability = 0.6 # 第一种人格出现概率
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personality_2_probability = 0.3 # 第二种人格出现概率
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personality_3_probability = 0.1 # 第三种人格出现概率，请确保三个概率相加等于1
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prompt_schedule = "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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[message]
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min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息
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max_context_size = 15 # 麦麦获得的上文数量
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emoji_chance = 0.2 # 麦麦使用表情包的概率
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thinking_timeout = 120 # 麦麦思考时间
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response_willing_amplifier = 1 # 麦麦回复意愿放大系数，一般为1
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response_interested_rate_amplifier = 1 # 麦麦回复兴趣度放大系数,听到记忆里的内容时放大系数
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down_frequency_rate = 3.5 # 降低回复频率的群组回复意愿降低系数
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ban_words = [
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# "403","张三"
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]
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[emoji]
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check_interval = 120 # 检查表情包的时间间隔
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register_interval = 10 # 注册表情包的时间间隔
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auto_save = true # 自动偷表情包
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enable_check = false # 是否启用表情包过滤
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check_prompt = "符合公序良俗" # 表情包过滤要求
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[cq_code]
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enable_pic_translate = false
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[response]
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model_r1_probability = 0.8 # 麦麦回答时选择主要回复模型1 模型的概率
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model_v3_probability = 0.1 # 麦麦回答时选择次要回复模型2 模型的概率
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model_r1_distill_probability = 0.1 # 麦麦回答时选择次要回复模型3 模型的概率
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max_response_length = 1024 # 麦麦回答的最大token数
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[memory]
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build_memory_interval = 300 # 记忆构建间隔 单位秒
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forget_memory_interval = 300 # 记忆遗忘间隔 单位秒
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[mood]
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mood_update_interval = 1.0 # 情绪更新间隔 单位秒
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mood_decay_rate = 0.95 # 情绪衰减率
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mood_intensity_factor = 1.0 # 情绪强度因子
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[keywords_reaction] # 针对某个关键词作出反应
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enable = true # 关键词反应功能的总开关
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[[keywords_reaction.rules]] # 如果想要新增多个关键词，直接复制本条，修改keywords和reaction即可
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enable = true # 是否启用此条（为了人类在未来AI战争能更好地识别AI（bushi），默认开启）
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keywords = ["人机", "bot", "机器", "入机", "robot", "机器人"] # 会触发反应的关键词
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reaction = "有人说你是人机或者机器人，否定这一事实，攻击或反问他。注意，只有认为你是机器人才回复，否则不要否认" # 触发之后添加的提示词
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[[keywords_reaction.rules]] # 就像这样复制
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enable = false # 仅作示例，不会触发
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keywords = ["测试关键词回复","test",""]
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reaction = "回答“测试成功”"
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[others]
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enable_advance_output = true # 是否启用高级输出
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enable_kuuki_read = true # 是否启用读空气功能
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[groups]
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talk_allowed = [
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123,
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123,
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] #可以回复消息的群
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talk_frequency_down = [] #降低回复频率的群
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ban_user_id = [] #禁止回复消息的QQ号
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#V3
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#name = "deepseek-chat"
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#base_url = "DEEP_SEEK_BASE_URL"
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#key = "DEEP_SEEK_KEY"
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#R1
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#name = "deepseek-reasoner"
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#base_url = "DEEP_SEEK_BASE_URL"
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#key = "DEEP_SEEK_KEY"
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#下面的模型若使用硅基流动则不需要更改，使用ds官方则改成.env.prod自定义的宏，使用自定义模型则选择定位相似的模型自己填写
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#推理模型：
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[model.llm_reasoning] #回复模型1 主要回复模型
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name = "Pro/deepseek-ai/DeepSeek-R1"
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provider = "SILICONFLOW"
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pri_in = 0 #模型的输入价格（非必填，可以记录消耗）
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pri_out = 0 #模型的输出价格（非必填，可以记录消耗）
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[model.llm_reasoning_minor] #回复模型3 次要回复模型
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name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
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provider = "SILICONFLOW"
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#非推理模型
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[model.llm_normal] #V3 回复模型2 次要回复模型
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name = "Pro/deepseek-ai/DeepSeek-V3"
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provider = "SILICONFLOW"
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[model.llm_normal_minor] #V2.5
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name = "deepseek-ai/DeepSeek-V2.5"
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provider = "SILICONFLOW"
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[model.llm_emotion_judge] #主题判断 0.7/m
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name = "Qwen/Qwen2.5-14B-Instruct"
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provider = "SILICONFLOW"
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[model.llm_topic_judge] #主题判断：建议使用qwen2.5 7b
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name = "Pro/Qwen/Qwen2.5-7B-Instruct"
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provider = "SILICONFLOW"
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[model.llm_summary_by_topic] #建议使用qwen2.5 32b 及以上
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name = "Qwen/Qwen2.5-32B-Instruct"
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provider = "SILICONFLOW"
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pri_in = 0
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pri_out = 0
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[model.moderation] #内容审核 未启用
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name = ""
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provider = "SILICONFLOW"
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pri_in = 0
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pri_out = 0
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# 识图模型
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[model.vlm] #图像识别 0.35/m
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name = "Pro/Qwen/Qwen2-VL-7B-Instruct"
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provider = "SILICONFLOW"
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#嵌入模型
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[model.embedding] #嵌入
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name = "BAAI/bge-m3"
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provider = "SILICONFLOW"
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