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QvQChat

QvQChat

让 AI 像真人一样参与群聊。

PyPI Python Docker License ErisPulse


基于 ErisPulse 的智能对话模块。多模型池 + 行为绑定 + 多智能体人格 + 长期记忆 + 知识库 + MCP 工具,配合全功能 Dashboard 管理面板——适配器、模型、行为配置全部可视化完成。

核心特性

👀 会窥屏

群聊中默默观察,只在被 @、有人叫名字或话题相关时才开口。预测模式(低 token)批量判断是否参与,不抢话、不刷屏

🕐 有生活感

打字延迟随回复长短变化、清晨迷糊深夜放得开、错字后自己纠正、偶尔半句发出——像真人一样不完美

💬 主动搭话

冲动值驱动:聊天越热闹越想开口;睡觉时段不打扰、没人理就冷却;没有真正想说的就保持沉默

🧠 模型池 + 行为绑定

任意 OpenAI 兼容 API 汇入模型池,按能力标记(chat/vision/tools),多模型冗余故障自动切换,行为级独立分配

📝 会记忆

回复后自动提取长期记忆,群聊支持混合/仅发送者两种模式,下次聊天自然提起

🖥️ 全功能 Dashboard

21 个 API 的 Web 管理面板:模型、行为、智能体、知识库、表情包、配置即时生效,无需手动编辑文件


工作原理

一条消息从进入到回复的完整处理链:

graph LR
    subgraph Platforms[平台]
        P["QQ / 云湖 / OneBot / ..."]
    end

    Adapter["ErisPulse 适配器层<br/>OB12 标准事件"]

    subgraph QvQChat
        Gate["回复判定<br/>@感知 → 活跃模式 → 预测/窥屏策略"]
        Pipeline["提示词注入管线<br/>身份 → 规则 → 场景 → 知识库 → 时间 → 情绪"]
        Dialogue["对话行为<br/>模型池故障转移 + MCP 工具循环"]
        Humanize["拟人化后处理<br/>错字纠正 / 半句发出 / 打字延迟"]
        Send["消息发送<br/>表情包标签 / 多条分段 / 语音"]
    end

    Memory["记忆子系统<br/>短期历史 + 长期提取"]
    Session["会话管理<br/>冲动值 + 速率限制"]
    Proactive["主动发起<br/>冲动值门槛检查"]

    P -->|"OB12 事件"| Adapter
    Adapter --> Gate
    Gate -->|"回复"| Pipeline
    Pipeline --> Dialogue
    Dialogue --> Humanize
    Humanize --> Send
    Send -.->|"回复"| P
    Adapter -.->|"累积"| Memory
    Adapter -.->|"冲动值 +"| Session
    Session -.->|"门槛满足"| Proactive
    Proactive -.->|"主动开口"| Dialogue

Dashboard

所有配置一站式完成,修改即时生效。

Dashboard 概览

行为管理 — 自由定制 AI 人格

内置 7 种行为 + 自定义行为,独立配置提示词、模型、温度。

行为管理

基础设置 + 表情包

窥屏/速率/拟人化参数一目了然;自定义表情包由 AI 按场景自主发送。

基础设置

表情包


功能一览

模块 说明
🧠 模型池 多模型管理,能力标记(对话/视觉/工具调用),故障自动切换
🎭 行为系统 自定义行为、独立提示词、模型分配、触发模式(始终/预测)
👥 多智能体 猫娘/傲娇/温柔大姐姐等人格模板,按群/用户绑定
👀 窥屏模式 群聊默认观察,被 @ / 叫名字 / 活跃模式时才回复
🔮 预测模式 低 token:累积 N 条消息批量判断,命中触发词才进入对话
🕐 拟人化 打字延迟、时间感知、情绪感知、错字纠正、半句发出、多条消息
💬 主动搭话 冲动值驱动,睡眠不打扰、没人理就冷却、宁可沉默不尬聊
📝 记忆系统 自动提取长期记忆,群聊支持混合/仅发送者两种模式
📚 知识库 文档注入对话上下文,支持分类、标签、自动搜索
🔧 MCP 工具 函数调用,让 AI 调用外部 API
🎙️ 语音合成 <|voice style="语气"|> 语音标签,CosyVoice2 合成
🖥️ Dashboard 全功能 Web 面板,所有配置即时生效

快速开始

Docker(推荐)

docker run -d --name qvqchat -p 8000:8000 --restart unless-stopped ghcr.io/wsu2059q/erispulse-qvqchat:latest

启动后打开 http://localhost:8000/Dashboard,适配器、AI 模型、行为配置全部在面板中完成。

使用 pip 安装
pip install ErisPulse
pip install ErisPulse-QvQChat
ep run

初始配置

  1. Dashboard 中添加适配器(如 YunhuAdapter、OneBot11Adapter)
  2. 添加 AI 模型(OpenAI / DeepSeek / SiliconFlow 等任意兼容 API),标记能力
  3. 在行为管理中为各行为分配模型——完成,开始聊天

文档


许可证

本项目基于 MIT License 开源。

驱动于 ErisPulse —— 一次编写,部署到多个聊天平台。

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Release files for ErisPulse-QvQChat 3.2.0

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