refactor ai_auto_response into xiaoniu group bot
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liuwei
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plugins/ai_auto_response/README.md
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plugins/ai_auto_response/README.md
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# 小牛群聊 BOT 重构说明
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## 目标
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把当前 `ai_auto_response` 从“随机插话的自动回复”升级成一个真正适合微信群长期在线的拟人 BOT `小牛`：
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- 在群里像一个真实成员，而不是每次都像客服或问答机
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- 能及时回答明确问题，减少“看见了但不接话”的情况
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- 能根据群氛围决定什么时候主动参与，什么时候少说话
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- 能对不同群、不同成员表现出稳定的人设和长期记忆感
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- 能复用项目里已经存在的消息存档、成员画像、群总结、权限控制、后台管理能力
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- 能处理“某个老成员很久不说话，突然回来发言”的场景，不显得失忆
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这份文档不是泛泛而谈的产品介绍，而是基于当前仓库现状整理的一份可落地实现方案。
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---
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## 当前实现现状
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当前插件入口：
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- [`plugins/ai_auto_response/main.py`](/d:/learn/abot/plugins/ai_auto_response/main.py)
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- [`plugins/ai_auto_response/bot_ai.py`](/d:/learn/abot/plugins/ai_auto_response/bot_ai.py)
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- [`plugins/ai_auto_response/config.toml`](/d:/learn/abot/plugins/ai_auto_response/config.toml)
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现有版本已经具备这些基础能力：
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- 能监听群消息
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- 能缓存最近一段群聊文本
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- 能基于关键词、时间窗口、参与度、体力值决定是否插话
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- 能把最近几条消息拼成上下文后调用大模型 API 生成回复
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但它目前仍然偏简单，核心问题主要有：
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1. 回复触发逻辑偏“概率型”
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现在更像“有没有兴致插一句”，而不是“是否有人在明确问它问题”。
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2. 缺少答疑优先级
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群里一旦出现明确求助、@机器人、连续追问，应该优先快速答复，而不是继续走随机参与逻辑。
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3. 缺少长期人格
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目前只有 prompt 里的简短口语化约束，还没有稳定的人设、口头习惯、边界感、偏好表达方式。
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4. 缺少成员级长期记忆
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仓库里已经有成员画像能力，但 `ai_auto_response` 还没有接进去，所以机器人对“这个人平时怎么说话、关注什么、适合怎么回”没有利用起来。
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5. 缺少群级场景区分
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不同群应该有不同模式，例如技术群偏答疑、闲聊群偏陪聊、交易群偏信息明确、熟人群偏轻松互动。
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6. 缺少回复分层
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并不是所有回复都应该走同一条 prompt。问答、接梗、安慰、提醒、总结、纠错，生成策略应该分开。
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---
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## 可以直接复用的现有技术能力
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这个项目其实已经有很多“拟人 BOT”需要的基础设施，不需要从零重造：
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### 1. 消息接入与发送
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- 主机器人入口：[`robot.py`](/d:/learn/abot/robot.py)
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- 微信客户端：[`wechat_ipad/`](/d:/learn/abot/wechat_ipad)
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可直接复用消息接收、发群消息、发图片、联系人同步、群成员信息读取能力。
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### 2. 权限和群功能开关
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- 群功能控制：`GroupBotManager`
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这意味着“哪些群启用拟人 BOT”“哪些群仅答疑不闲聊”“哪些群完全关闭”都已经有基础能力。
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### 3. 消息存档与历史上下文
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- 消息存储：[`utils/wechat/message_to_db.py`](/d:/learn/abot/utils/wechat/message_to_db.py)
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- 数据表操作：[`db/message_storage.py`](/d:/learn/abot/db/message_storage.py)
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这部分非常关键，可以让 BOT 不只看最近 10 句话，而是按需回看更长的上下文。
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### 4. 成员画像与长期交互记忆
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- 插件：[`plugins/member_context/main.py`](/d:/learn/abot/plugins/member_context/main.py)
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- 服务：[`plugins/member_context/service.py`](/d:/learn/abot/plugins/member_context/service.py)
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- 提示词构建：[`plugins/member_context/prompt_builder.py`](/d:/learn/abot/plugins/member_context/prompt_builder.py)
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这里已经有：
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- 成员日 / 周 / 月摘要
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- 兴趣主题
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- 互动风格
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- 回复偏好
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- 群内角色
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- 技能画像
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这正是“拟人回复”最需要的长期上下文。
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### 5. 群总结与压缩上下文
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- 群总结：[`plugins/message_summary/main.py`](/d:/learn/abot/plugins/message_summary/main.py)
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- 对话压缩：[`utils/compress_chat_data.py`](/d:/learn/abot/utils/compress_chat_data.py)
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长群聊可以先压缩再喂给模型，减少 token 压力。
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### 6. 管理后台
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- 后台目录：[`admin/dashboard/`](/d:/learn/abot/admin/dashboard)
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后续可以把“人设配置、群模式、回复频率、黑名单、禁聊时段、答疑策略”做成后台管理项。
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---
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## 新版本插件定位
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建议把这个插件的定位改成：
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`小牛：一个有稳定人格、会看场合、能优先答问题、在群里长期在线的虚拟群成员`
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它不是纯陪聊，也不是纯问答助手，而是两种模式同时存在：
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### 1. 拟人参与模式
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适合熟人群、日常聊天群、兴趣群：
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- 偶尔接话
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- 顺着上下文说话
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- 有自己的语气和偏好
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- 不抢话，不刷屏
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### 2. 实时答疑模式
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适合技术群、项目群、问答群：
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- 发现问题句、求助句、@机器人时优先响应
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- 响应速度快于闲聊逻辑
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- 回答尽量明确、可执行
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- 不懂就直接说不确定，不硬编
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---
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## 推荐架构
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建议把新版本拆成 8 个层次，而不是把所有逻辑都放在 `main.py` 里。
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### 1. Message Intake
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负责接收消息、标准化消息结构、过滤无效消息：
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- 是否群聊
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- 是否自己发的
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- 是否文本 / 图片标题 / 链接卡片
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- 是否命中黑名单
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- 是否命中禁用群
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### 2. Trigger Router
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负责判断“为什么这次应该回”。
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建议至少拆成以下触发源：
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- `at_trigger`
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`@bot` 或明确点名 BOT
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- `question_trigger`
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明显的问题句，例如“怎么弄”“有人知道吗”“这个报错啥意思”
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- `followup_trigger`
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上一轮已经在和 BOT 对话，用户继续追问
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- `topic_trigger`
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命中 BOT 擅长或关注的话题
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- `social_trigger`
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打招呼、起哄、接梗、点名、夸它、吐槽它
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- `silence_break_trigger`
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群里沉默较久后，用很轻的方式恢复气氛
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其中优先级应为：
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`@提问 > 明确求助 > 连续追问 > 互动点名 > 普通插话`
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### 3. Flow Manager
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心流系统是小牛的“实时参与状态机”。
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它解决的不是“记不记得人”，而是“现在要不要继续聊、聊多深、聊多久”。
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建议按群维度维护 `flow_state` 和 `flow_score`，而不是全局只有一个热度值。
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推荐状态：
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- `idle`
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低参与，主要观察，除非被点名或明确提问，否则不主动插话
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- `warming`
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话题开始吸引小牛，可以做轻量接话
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- `engaged`
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已进入连续互动，优先接住上下文
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- `deep_engaged`
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正在进行高质量答疑或多人围绕同一主题连续互动
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- `cooling`
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一轮互动结束后逐步退出，避免刷屏
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- `silent`
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深夜、敏感话题、连续被忽略、系统限流时进入静默
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建议影响心流的事件：
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- 提高心流：
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`@小牛`、明确提问、连续追问、命中擅长话题、老成员回归、机器人发言后有人接话
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- 降低心流：
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回复后无人接话、话题转移、连续回复过多、深夜、敏感话题、群里进入无关刷屏
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建议先用简单的事件加减分模型：
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- `@小牛`：`+40`
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- 明确提问：`+30`
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- 连续追问：`+20`
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- 话题命中：`+15`
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- 回归成员：`+10`
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- 机器人发言后有人接话：`+15`
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- 机器人发言后没人接话：`-20`
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- 连续回复过多：`-15`
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- 深夜：`-30`
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建议状态阈值：
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- `<20` -> `idle`
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- `20~39` -> `warming`
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- `40~69` -> `engaged`
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- `>=70` -> `deep_engaged`
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同时配合自然衰减，让心流值按分钟回落。
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### 4. Context Builder
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负责为本次回复准备上下文，建议分四层：
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- 最近 20~50 条群消息
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- 当天压缩摘要
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- 当前发言人的成员画像
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- 当前群的人设配置和行为模式
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建议输出统一上下文对象：
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```python
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{
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"group_profile": {},
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"speaker_profile": {},
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"recent_messages": [],
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"recent_summary": "",
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"trigger_type": "question_trigger",
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"reply_mode": "qa_fast"
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}
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```
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### 5. Long-Term Memory Engine
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这是小牛和普通自动回复插件真正拉开差距的地方。
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建议把记忆拆成四层，而不是只保留最近聊天记录：
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- `session_memory`
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最近一次连续对话的上下文，生命周期 5~15 分钟
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- `daily_memory`
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今天这个群在聊什么、谁和谁正在互动、当前气氛如何
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- `member_memory`
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某个成员长期关注的话题、典型说话风格、历史上经常问的问题、适合的回复方式
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- `group_memory`
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这个群的长期主题、说话节奏、禁忌、常见梗、对小牛的接受度
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其中 `member_memory` 和 `group_memory` 是解决“老成员突然回归”最关键的部分。
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当某个成员很久没发言又突然出现时，不应该只看他刚发的这一句，而应该补充这些信息：
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- 这个人上次活跃是什么时候
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- 过去常聊什么
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- 过去在群里的角色更像提问者、答疑者还是气氛组
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- 过去和小牛是否有连续互动
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- 这次回归是轻松冒泡、直接求助、还是延续旧话题
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建议为这类场景增加专门状态：
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- `returning_member`
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7 天以上未发言后再次出现
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- `long_absent_member`
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30 天以上未发言后再次出现
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- `reactivated_topic`
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当前话题与该成员历史关注主题高度相关
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针对这类状态，小牛的回复要遵循两个原则：
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1. 记得这个人，但不要过度热情到像监控
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可以自然表现出“你又出现了”“这个话题你之前也挺关注”，但不要直接说出太细的时间和行为记录。
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2. 优先续接熟悉话题
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如果该成员回归后直接提问，优先用他的长期主题和历史偏好组织答案，这样会更像“真的认识这个人”。
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### 6. Persona Engine
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这里是“拟人感”的核心，不应该只靠一句 prompt。
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建议把人格拆成结构化配置：
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- 名字
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- 年龄感
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- 说话风格
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- 常用语气词
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- 擅长话题
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- 不擅长话题
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- 回避边界
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- 幽默程度
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- 主动程度
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- 回复长度偏好
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- 是否喜欢反问
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- 是否会使用表情
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建议新增独立人设文件，例如：
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- `persona_name`
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- `core_identity`
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- `tone_rules`
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- `reply_rules`
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- `taboo_rules`
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- `example_replies`
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目录下现有的 [`plugins/ai_auto_response/瑞依.txt`](/d:/learn/abot/plugins/ai_auto_response/瑞依.txt) 只作为参考语料，不直接作为最终人格文件。
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新版本应建立 `小牛` 的独立人格设定，建议固定为：
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- 名字：小牛
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- 角色感：群里常驻、靠谱、自然、不端着
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- 回答风格：先解决问题，再决定要不要延伸
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- 社交风格：熟人感轻一点，不装熟，不过分卖萌
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- 记忆风格：对老成员有熟悉感，但不过度暴露“系统知道很多”
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### 7. Response Planner
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不要让模型每次自由发挥，先确定回复策略，再生成内容。
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推荐回复模式：
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- `qa_fast`
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用于明确问题，答案优先，少废话
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- `qa_with_context`
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用于结合群聊历史、成员长期记忆或旧话题回答
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- `social_short`
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用于轻量接话，1 句就够
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- `comfort_mode`
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用于安慰、缓和、给建议
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- `humor_mode`
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用于熟人群轻松互动
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- `refuse_or_skip`
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不适合接话时直接不回，或仅给非常短的反馈
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心流状态会直接影响回复策略：
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- `idle`
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只处理 `@bot`、明确问题、强触发事件
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|
||||
- `warming`
|
||||
允许 `social_short`
|
||||
|
||||
- `engaged`
|
||||
提高 `qa_with_context` 和连续追问的响应率
|
||||
|
||||
- `deep_engaged`
|
||||
允许更完整的答疑和多轮连续互动
|
||||
|
||||
- `cooling`
|
||||
优先短回复或收口
|
||||
|
||||
- `silent`
|
||||
除非强触发，否则不回复
|
||||
|
||||
### 8. Safety and Rate Control
|
||||
|
||||
拟人 BOT 最大的风险不是“答不出来”，而是“太像人却太爱说话”。
|
||||
|
||||
所以必须保留这些机制：
|
||||
|
||||
- 每群独立冷却
|
||||
- 连续回复衰减
|
||||
- 被人无视后降低主动率
|
||||
- 深夜低活跃模式
|
||||
- 敏感词 / 风险话题降级
|
||||
- 管理员强制关闭
|
||||
|
||||
当前 `bot_ai.py` 里的“体力值 + 参与度”可以保留，但应降级为“主动聊天限流器”，而不是总入口。
|
||||
|
||||
---
|
||||
|
||||
## 向量记忆设计
|
||||
|
||||
当前环境里已经有可用的向量能力：
|
||||
|
||||
- 向量库：`Qdrant`
|
||||
- 向量模型服务：`Ollama`
|
||||
- 适合接入位置：`Long-Term Memory Engine`
|
||||
|
||||
这里的设计原则不是“所有回复都查向量库”，而是：
|
||||
|
||||
`Qdrant 作为长期记忆召回层，member_context 和消息摘要作为稳定记忆层`
|
||||
|
||||
也就是说：
|
||||
|
||||
- `member_context`
|
||||
负责回答“这个人是谁，长期是什么风格”
|
||||
|
||||
- `Qdrant`
|
||||
负责回答“这个人以前聊过什么类似内容”
|
||||
|
||||
### 什么时候介入最合适
|
||||
|
||||
最合适的方式是第二阶段开始接入，但只用于特定场景，不作为所有回复的必经链路。
|
||||
|
||||
优先介入以下场景：
|
||||
|
||||
- `returning_member`
|
||||
用户很久没发言后重新出现
|
||||
|
||||
- `long_absent_member`
|
||||
用户长期沉默后突然出现
|
||||
|
||||
- `qa_with_context`
|
||||
当前问题可能和历史问答或长期兴趣相关
|
||||
|
||||
- `reactivated_topic`
|
||||
当前话题和用户过去长期关注主题高度相关
|
||||
|
||||
普通闲聊、轻量接话、气氛互动不建议默认查向量库。
|
||||
|
||||
### 为什么不建议一开始全量依赖向量库
|
||||
|
||||
因为群聊拟人 BOT 最怕的不是“想不起来”，而是“乱想起来”。
|
||||
|
||||
如果每次都查向量库，容易出现：
|
||||
|
||||
- 回复变慢
|
||||
- 召回结果不稳定
|
||||
- 机器人突然提旧事，像在翻聊天记录
|
||||
- 轻松闲聊也被过度结构化
|
||||
|
||||
所以更合理的方式是：
|
||||
|
||||
- 平时主要依赖最近上下文和成员画像
|
||||
- 需要“找回记忆”时再触发向量召回
|
||||
|
||||
### 最适合写入 Qdrant 的内容
|
||||
|
||||
不建议先把全部原始聊天消息无差别写进向量库。
|
||||
|
||||
更推荐写入“记忆单元”：
|
||||
|
||||
- 成员日摘要
|
||||
- 成员周摘要
|
||||
- 成员月摘要
|
||||
- 群日摘要
|
||||
- 重要问答对
|
||||
- 用户长期偏好卡片
|
||||
- 小牛与某成员的关键互动片段
|
||||
|
||||
这样做有几个好处：
|
||||
|
||||
- 噪音更少
|
||||
- 召回更稳定
|
||||
- Token 更省
|
||||
- 更适合长期维护
|
||||
|
||||
### 推荐的 Qdrant Payload
|
||||
|
||||
每条向量建议至少带这些字段：
|
||||
|
||||
- `chatroom_id`
|
||||
- `wxid`
|
||||
- `memory_type`
|
||||
- `topic_tags`
|
||||
- `created_at`
|
||||
- `last_active_at`
|
||||
- `source_id`
|
||||
- `content_summary`
|
||||
|
||||
建议的 `memory_type` 包括：
|
||||
|
||||
- `member_daily_digest`
|
||||
- `member_weekly_digest`
|
||||
- `member_monthly_digest`
|
||||
- `group_daily_digest`
|
||||
- `qa_pair`
|
||||
- `interaction_memory`
|
||||
- `preference_card`
|
||||
|
||||
### 推荐查询策略
|
||||
|
||||
建议按下面顺序查，而不是直接全库语义搜：
|
||||
|
||||
1. 先按 `chatroom_id` 过滤
|
||||
2. 如果目标明确，再按 `wxid` 过滤
|
||||
3. 再按 `memory_type` 过滤
|
||||
4. 最后做语义相似度检索
|
||||
5. `top_k` 建议先控制在 `3~5`
|
||||
|
||||
这能显著降低错召回。
|
||||
|
||||
### 与 Ollama 向量模型的配合方式
|
||||
|
||||
你现有的 Ollama 小向量模型是可以直接用的，只要满足一个原则：
|
||||
|
||||
`写入和查询必须使用同一个 embedding 模型`
|
||||
|
||||
对“小牛”这种群聊记忆系统来说，小型 embedding 模型反而通常更合适，因为需要的是：
|
||||
|
||||
- 响应快
|
||||
- 成本低
|
||||
- 稳定检索成员历史主题和问答片段
|
||||
|
||||
而不是做极重的通用语义推理。
|
||||
|
||||
### 推荐的接入方式
|
||||
|
||||
建议新增一个独立的记忆召回模块，例如：
|
||||
|
||||
- `memory_store.py`
|
||||
负责写入、查询、过滤、召回排序
|
||||
|
||||
它的职责建议分成四块：
|
||||
|
||||
- `upsert_memory`
|
||||
把摘要、问答、关键互动写入 Qdrant
|
||||
|
||||
- `search_member_memory`
|
||||
查询某个成员的长期相关记忆
|
||||
|
||||
- `search_group_memory`
|
||||
查询当前群的历史相关记忆
|
||||
|
||||
- `build_memory_prompt`
|
||||
把召回结果压缩成可以送给模型的 prompt 片段
|
||||
|
||||
向量召回和心流系统的配合建议是：
|
||||
|
||||
- 长期记忆负责“这个人以前是谁、聊过什么”
|
||||
- 心流系统负责“这次值不值得进入连续互动”
|
||||
|
||||
两者一起工作时，小牛才会既像“记得人”，又像“会看场合”。
|
||||
|
||||
### 小牛里最适合触发向量召回的时机
|
||||
|
||||
推荐在这些判断通过后才查 Qdrant：
|
||||
|
||||
- 用户超过 `7` 天未发言重新出现
|
||||
- 用户超过 `30` 天未发言后提问
|
||||
- 当前问题命中“历史上经常问的主题”
|
||||
- 最近上下文不够，但成员长期画像显示该用户过去反复讨论过此类话题
|
||||
- BOT 判断这是“旧话题延续”而不是新话题
|
||||
|
||||
### 记忆使用边界
|
||||
|
||||
向量召回的结果只应该作为“小牛知道哪些历史背景”的参考，而不是原样往外说。
|
||||
|
||||
生成回复时建议遵守：
|
||||
|
||||
- 不直接暴露精确历史记录
|
||||
- 不直接说“你上次在几月几号说过”
|
||||
- 不在轻量闲聊里强行提旧事
|
||||
- 只在确实有帮助时，让回复带一点自然熟悉感
|
||||
|
||||
理想效果是：
|
||||
|
||||
- 用户觉得“小牛记得我”
|
||||
- 但不会觉得“小牛在翻档案”
|
||||
|
||||
---
|
||||
|
||||
## 推荐实现方案
|
||||
|
||||
### 第一阶段：把“随机插话”升级成“有优先级的触发回复”
|
||||
|
||||
先不追求复杂人格，先解决“及时回答问题”：
|
||||
|
||||
1. 新增问题检测
|
||||
识别问号、求助句式、报错句式、`有人知道`、`怎么`、`为啥`、`?`、`？？`
|
||||
|
||||
2. 新增 `@bot` 强制响应
|
||||
只要被明确点名，优先进入快速答疑链路
|
||||
|
||||
3. 新增会话延续窗口
|
||||
机器人回复后 2~5 分钟内，如果同一人继续追问，应提高响应概率甚至直接响应
|
||||
|
||||
4. 闲聊逻辑与答疑逻辑分离
|
||||
闲聊继续走拟人策略，答疑直接走高优先级策略
|
||||
|
||||
5. 引入群级心流系统
|
||||
用 `flow_state` 替代旧的随机插话感，让小牛知道什么时候进入、什么时候退出对话
|
||||
|
||||
这一阶段完成后，体验会立刻提升很多。
|
||||
|
||||
### 第二阶段：接入长期记忆、成员画像和群模式
|
||||
|
||||
把仓库现有能力接进来：
|
||||
|
||||
1. 从 `member_context` 读取当前发言人的画像
|
||||
2. 为每个成员建立最近会话缓存和长期记忆快照
|
||||
3. 给“久未发言再次出现”的成员增加回归识别逻辑
|
||||
4. 给不同群配置不同模式
|
||||
5. 在 prompt 中加入“这个人平时更喜欢什么风格的回复”
|
||||
6. 在技术群中提高问题响应率，在闲聊群中降低长篇回答频率
|
||||
7. 在 `returning_member` 和 `qa_with_context` 场景接入 Qdrant 召回
|
||||
8. 让回归成员和旧话题召回同时提升群级心流，进入更自然的连续互动状态
|
||||
|
||||
### 第三阶段：做人设稳定化
|
||||
|
||||
这一阶段重点不是“更聪明”，而是“像同一个人”：
|
||||
|
||||
1. 固化角色设定
|
||||
2. 固化用词习惯
|
||||
3. 固化情绪边界
|
||||
4. 固化“知道什么 / 不知道什么”的表达方式
|
||||
5. 给出少量 few-shot 回复样例
|
||||
|
||||
### 长期记忆专项：解决“很久不说话突然出现”的问题
|
||||
|
||||
这是新版本必须明确支持的场景。
|
||||
|
||||
推荐处理流程：
|
||||
|
||||
1. 识别用户是否为回归成员
|
||||
根据消息库和成员画像判断其最近一次活跃时间
|
||||
|
||||
2. 如果是回归成员，额外加载长期记忆
|
||||
包括历史关注主题、常见问题类型、群内角色、和小牛过去互动风格
|
||||
|
||||
3. 生成时增加“轻微熟悉感”
|
||||
回复表现得像“记得这个人”，但不要像读档案
|
||||
|
||||
4. 如果该成员这次是来提问
|
||||
则优先进入 `qa_with_context`，让回答带上他历史关注方向
|
||||
|
||||
5. 如果该成员只是冒泡
|
||||
则只做轻量社交回应，不强行提旧事
|
||||
|
||||
一个好的感觉是：
|
||||
|
||||
- 用户会觉得“小牛好像一直在群里”
|
||||
- 但不会觉得“小牛在偷偷监控每个人”
|
||||
|
||||
### 第四阶段：做后台配置化
|
||||
|
||||
建议把这些项做成可配置：
|
||||
|
||||
- 每个群是否启用
|
||||
- 群模式
|
||||
- 人设模板
|
||||
- 回复频率
|
||||
- 工作时间 / 静默时间
|
||||
- 是否允许主动插话
|
||||
- 是否允许使用表情
|
||||
- 是否允许引用长期记忆
|
||||
- 回归成员识别阈值
|
||||
- 长期记忆回看天数
|
||||
- 回归成员的回复热度上限
|
||||
|
||||
---
|
||||
|
||||
## 群聊 BOT 的最小落地版本
|
||||
|
||||
如果你希望先做一个能用的版本，而不是一次性重构太大，推荐最小实现如下：
|
||||
|
||||
### 必做
|
||||
|
||||
- 保留当前插件入口不变
|
||||
- 新增 `trigger_type` 判定
|
||||
- 新增 `flow_state` / `flow_score` 判定
|
||||
- 新增 `reply_mode` 判定
|
||||
- `@bot` / 提问类消息直接优先回复
|
||||
- 上下文从最近 10 条提升到最近 20~30 条
|
||||
- 人设文件从单段 prompt 改成结构化配置
|
||||
|
||||
### 优先做
|
||||
|
||||
- 接 `member_context`
|
||||
- 给群配置模式
|
||||
- 给回复加冷却和连续会话窗口
|
||||
- 给回归成员场景接 Qdrant 召回
|
||||
- 给不同心流状态配置不同回复强度
|
||||
|
||||
### 后续再做
|
||||
|
||||
- 后台管理页
|
||||
- 不同人格模板
|
||||
- 记忆纠偏
|
||||
- 多模型路由
|
||||
|
||||
---
|
||||
|
||||
## 建议目录演进
|
||||
|
||||
建议把 `plugins/ai_auto_response/` 逐步整理成下面这种结构：
|
||||
|
||||
```text
|
||||
plugins/ai_auto_response/
|
||||
├── __init__.py
|
||||
├── main.py # 插件入口，只做调度
|
||||
├── config.toml # 插件配置
|
||||
├── README.md # 本文档
|
||||
├── persona/
|
||||
│ ├── xiaoniu.txt
|
||||
│ └── tech_helper.txt
|
||||
├── flow_manager.py # 群级心流状态机
|
||||
├── memory_store.py # 长期记忆读取与装配
|
||||
├── vector_memory.py # Qdrant / Ollama 召回层
|
||||
├── triggers.py # 触发判定
|
||||
├── context_builder.py # 上下文构建
|
||||
├── persona_engine.py # 人设装配
|
||||
├── response_planner.py # 回复策略选择
|
||||
├── llm_client.py # OpenAI兼容 API / 其他模型调用
|
||||
└── rate_control.py # 冷却、频率、主动度控制
|
||||
```
|
||||
|
||||
这样以后维护会比现在轻松很多。
|
||||
|
||||
---
|
||||
|
||||
## 建议配置项
|
||||
|
||||
建议在 `config.toml` 后续补充这些内容：
|
||||
|
||||
```toml
|
||||
enable = true
|
||||
|
||||
[mode]
|
||||
group_default_mode = "social"
|
||||
question_reply_timeout_sec = 12
|
||||
followup_session_window_sec = 300
|
||||
recent_context_size = 30
|
||||
allow_proactive_reply = true
|
||||
returning_member_days = 7
|
||||
long_absent_member_days = 30
|
||||
memory_lookback_days = 180
|
||||
|
||||
[flow]
|
||||
enable_flow_state = true
|
||||
flow_decay_per_minute = 8
|
||||
idle_threshold = 20
|
||||
warming_threshold = 40
|
||||
engaged_threshold = 70
|
||||
at_bot_boost = 40
|
||||
question_boost = 30
|
||||
followup_boost = 20
|
||||
topic_boost = 15
|
||||
returning_member_boost = 10
|
||||
response_accepted_boost = 15
|
||||
ignored_reply_penalty = 20
|
||||
over_reply_penalty = 15
|
||||
night_penalty = 30
|
||||
|
||||
[persona]
|
||||
name = "小牛"
|
||||
style = "自然、口语化、像群友"
|
||||
emoji_probability = 0.25
|
||||
max_reply_sentences = 3
|
||||
|
||||
[memory]
|
||||
enable_vector_memory = true
|
||||
vector_provider = "qdrant"
|
||||
embedding_provider = "ollama"
|
||||
qdrant_url = "http://127.0.0.1:6333"
|
||||
qdrant_collection = "abot_xiaoniu_memory"
|
||||
ollama_base_url = "http://192.168.2.50:11434"
|
||||
embedding_model = "your_embedding_model"
|
||||
vector_top_k = 5
|
||||
vector_min_score = 0.65
|
||||
vector_trigger_modes = ["returning_member", "long_absent_member", "qa_with_context", "reactivated_topic"]
|
||||
|
||||
[priority]
|
||||
at_bot = 1.0
|
||||
explicit_question = 0.95
|
||||
followup = 0.9
|
||||
social_call = 0.65
|
||||
casual_topic = 0.35
|
||||
|
||||
[cooldown]
|
||||
group_reply_cooldown_sec = 45
|
||||
same_user_followup_cooldown_sec = 10
|
||||
night_silent_hours = ["01:00-07:30"]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Prompt 设计建议
|
||||
|
||||
新版本 prompt 不建议再只写“简短、口语化”这种通用要求，而要明确四件事：
|
||||
|
||||
1. 你是谁
|
||||
你在这个群里的身份、语气、边界、说话节奏
|
||||
|
||||
2. 你为什么这次要回复
|
||||
是因为被 @、被提问、正在连续对话、还是轻微接话，以及当前心流状态是否支持继续参与
|
||||
|
||||
3. 你现在掌握了什么上下文
|
||||
最近群聊、成员画像、长期记忆、群模式、心流状态、历史摘要
|
||||
|
||||
4. 这次回复的目标
|
||||
是回答问题、接一句、安慰、澄清、提醒，还是保持沉默
|
||||
|
||||
建议最终 prompt 由以下片段拼装：
|
||||
|
||||
- `system_persona`
|
||||
- `memory_prompt`
|
||||
- `group_mode_prompt`
|
||||
- `flow_prompt`
|
||||
- `speaker_profile_prompt`
|
||||
- `trigger_prompt`
|
||||
- `recent_context_prompt`
|
||||
- `response_rule_prompt`
|
||||
|
||||
其中：
|
||||
|
||||
- `memory_prompt`
|
||||
优先来自 `member_context` 的稳定画像
|
||||
|
||||
- `vector_memory_prompt`
|
||||
只在命中特定场景时从 Qdrant 召回并追加
|
||||
|
||||
---
|
||||
|
||||
## 成功标准
|
||||
|
||||
如果这个插件升级成功，应该能达到下面这些效果：
|
||||
|
||||
### 拟人感
|
||||
|
||||
- 说话前后风格一致
|
||||
- 不会每次都像在写标准答案
|
||||
- 会看群气氛，不乱抢话
|
||||
- 被调侃时能自然接住
|
||||
- 会自然进入和退出对话，不会像开关一样突兀
|
||||
|
||||
### 答疑能力
|
||||
|
||||
- 被 @ 时基本能及时回复
|
||||
- 明确问题能优先答复
|
||||
- 回答比现在更聚焦、更短、更有执行性
|
||||
- 不确定时会明确说明
|
||||
|
||||
### 长期记忆
|
||||
|
||||
- 对活跃成员和沉默很久后回归的成员都能保持连续感
|
||||
- 不会把短期状态误认为长期人格
|
||||
- 能识别老成员的长期关注主题
|
||||
- 回归成员发言时，小牛的回复会有自然熟悉感
|
||||
- Qdrant 召回只在需要时介入，不会让普通闲聊变得迟钝和奇怪
|
||||
|
||||
### 工程可维护性
|
||||
|
||||
- 触发逻辑、上下文逻辑、生成逻辑分层
|
||||
- 心流逻辑独立成层，不和长期记忆混在一起
|
||||
- 可接入成员画像
|
||||
- 可配置不同群模式
|
||||
- 可通过后台持续调参
|
||||
|
||||
---
|
||||
|
||||
## 推荐开发顺序
|
||||
|
||||
1. 保留当前插件名和入口，先完成触发路由重构
|
||||
2. 把群级心流系统做出来，替换旧的随机插话逻辑
|
||||
3. 把“答疑优先”做出来，解决及时回复问题
|
||||
4. 把长期记忆层接进来，先解决回归成员场景
|
||||
5. 把人设配置从自由文本升级成结构化配置，并固定为小牛
|
||||
6. 接入 `member_context` 做成员级回复优化
|
||||
7. 接入 Qdrant + Ollama，先只服务回归成员和旧话题召回
|
||||
8. 增加群模式配置
|
||||
9. 最后再做后台配置和更细的人格控制
|
||||
|
||||
---
|
||||
|
||||
## 一句话结论
|
||||
|
||||
你现在这个 `ai_auto_response` 已经有“群里自动说话”的雏形了，但如果目标是“小牛”这种真正长期在线的群聊拟人 BOT，核心不在于继续调概率，而在于把它升级成：
|
||||
|
||||
`触发有优先级、心流会收放、上下文有层次、长期记忆可用、人格固定为小牛、答疑能优先、群模式可配置`
|
||||
|
||||
这样它才会既像群友，又真的有用。
|
||||
@@ -1,193 +0,0 @@
|
||||
import re
|
||||
import random
|
||||
from datetime import datetime, time, timedelta
|
||||
import toml
|
||||
import os
|
||||
from loguru import logger
|
||||
|
||||
class RoomState:
|
||||
"""每个群的独立状态"""
|
||||
def __init__(self):
|
||||
self.participation_score = 0.0
|
||||
self.last_active_time = datetime.now()
|
||||
self.last_bot_reply_time = None # 上次机器人回复的时间
|
||||
|
||||
class InterventionBot:
|
||||
def __init__(self, config_path=None):
|
||||
# 加载配置
|
||||
self.config = {}
|
||||
if config_path and os.path.exists(config_path):
|
||||
self.config = toml.load(config_path)
|
||||
|
||||
# 从配置中获取关键词
|
||||
keywords = self.config.get("Keywords", {})
|
||||
self.emojis = keywords.get("emojis", [])
|
||||
self.hot_topics = keywords.get("hot_topics", [])
|
||||
self.fish_keywords = keywords.get("fish_keywords", [])
|
||||
self.tech_keywords = keywords.get("tech_keywords", [])
|
||||
self.news_keywords = keywords.get("news_keywords",[])
|
||||
|
||||
# 拟人化配置
|
||||
hl_config = self.config.get("HumanLike", {})
|
||||
self.max_energy = hl_config.get("max_energy", 100.0)
|
||||
self.energy_recovery_rate = hl_config.get("energy_recovery_per_minute", 1.0)
|
||||
self.energy_cost = hl_config.get("energy_cost_per_reply", 15.0)
|
||||
|
||||
self.participation_inc = hl_config.get("participation_increase_per_msg", 5.0)
|
||||
self.topic_bonus = hl_config.get("topic_match_bonus", 15.0)
|
||||
self.participation_threshold = hl_config.get("participation_threshold", 20.0)
|
||||
self.participation_drop = hl_config.get("participation_drop_factor", 0.8)
|
||||
self.base_prob = hl_config.get("base_reply_probability", 0.6)
|
||||
|
||||
# 机器人全局状态
|
||||
self.current_energy = self.max_energy
|
||||
self.last_energy_update_time = datetime.now()
|
||||
|
||||
# 群组状态 {room_id: RoomState}
|
||||
self.room_states = {}
|
||||
|
||||
# 辅助功能：早晨时间窗口
|
||||
time_window = self.config.get("TimeWindow", {})
|
||||
self.morning_window = (
|
||||
time(time_window.get("morning_start_hour", 8), time_window.get("morning_start_minute", 0)),
|
||||
time(time_window.get("morning_end_hour", 8), time_window.get("morning_end_minute", 30))
|
||||
)
|
||||
|
||||
def _get_room_state(self, room_id):
|
||||
if room_id not in self.room_states:
|
||||
self.room_states[room_id] = RoomState()
|
||||
return self.room_states[room_id]
|
||||
|
||||
def _update_energy(self):
|
||||
"""更新全局体力值"""
|
||||
now = datetime.now()
|
||||
minutes_passed = (now - self.last_energy_update_time).total_seconds() / 60.0
|
||||
|
||||
recovered = minutes_passed * self.energy_recovery_rate
|
||||
self.current_energy = min(self.max_energy, self.current_energy + recovered)
|
||||
self.last_energy_update_time = now
|
||||
|
||||
logger.debug(f"[Energy] Recovered {recovered:.2f}, Current: {self.current_energy:.2f}")
|
||||
|
||||
def detect_topic(self, message):
|
||||
if not isinstance(message, str):
|
||||
return None
|
||||
message_lower = message.lower()
|
||||
if any(keyword in message_lower for keyword in self.fish_keywords):
|
||||
return "fish"
|
||||
if any(keyword in message_lower for keyword in self.tech_keywords):
|
||||
return "tech"
|
||||
if any(keyword in message_lower for keyword in self.news_keywords):
|
||||
return "news"
|
||||
if any(keyword in message_lower for keyword in self.hot_topics):
|
||||
return "hot_topic"
|
||||
return None
|
||||
|
||||
def is_morning_window(self, timestamp):
|
||||
try:
|
||||
# 简化时间处理，这里假设timestamp通常是当前时间附近
|
||||
now = datetime.now()
|
||||
return self.morning_window[0] <= now.time() <= self.morning_window[1]
|
||||
except:
|
||||
return False
|
||||
|
||||
def calculate_participation_boost(self, message, messages, is_at=False):
|
||||
"""计算这条消息带来的参与度提升"""
|
||||
if is_at:
|
||||
return 100.0 # 被AT直接拉满
|
||||
|
||||
boost = self.participation_inc
|
||||
|
||||
# 话题加成
|
||||
topic = self.detect_topic(message)
|
||||
if topic:
|
||||
boost += self.topic_bonus
|
||||
|
||||
# 关键词加成 (早安/表情等)
|
||||
if any(e in message for e in self.emojis):
|
||||
boost += 5
|
||||
|
||||
if "签到" in message or "早" in message:
|
||||
if self.is_morning_window(None):
|
||||
boost += 20 # 早上问好权重高
|
||||
|
||||
return boost
|
||||
|
||||
def should_intervene(self, room_id, timestamp, message, messages, chat_log, is_at=False):
|
||||
"""
|
||||
核心判定逻辑
|
||||
:param room_id: 群ID
|
||||
:param timestamp: 消息时间
|
||||
:param message: 当前消息内容
|
||||
:param messages: 最近消息列表（文本）
|
||||
:param chat_log: 完整聊天记录对象
|
||||
:param is_at: 是否被AT
|
||||
"""
|
||||
self._update_energy()
|
||||
state = self._get_room_state(room_id)
|
||||
|
||||
# 1. 增加参与度（Listening）
|
||||
boost = self.calculate_participation_boost(message, messages, is_at)
|
||||
|
||||
# 连续对话奖励：如果机器人在最近 2 分钟内回复过，说明可能在对话中，参与度增加翻倍
|
||||
if state.last_bot_reply_time:
|
||||
time_since_last_reply = (datetime.now() - state.last_bot_reply_time).total_seconds()
|
||||
if time_since_last_reply < 120: # 2分钟内
|
||||
boost *= 2.0
|
||||
logger.debug(f"[{room_id}] 连续对话奖励触发 (上次回复 {int(time_since_last_reply)}s 前)")
|
||||
|
||||
state.participation_score += boost
|
||||
state.last_active_time = datetime.now()
|
||||
|
||||
logger.debug(f"[{room_id}] 收到消息: '{message}' | 参与度+{boost} -> {state.participation_score:.2f} | 体力: {self.current_energy:.2f}")
|
||||
|
||||
# 2. 检查阈值
|
||||
if state.participation_score < self.participation_threshold:
|
||||
return False
|
||||
|
||||
# 3. 检查体力
|
||||
if self.current_energy < self.energy_cost:
|
||||
logger.debug(f"[{room_id}] 体力不足 ({self.current_energy:.2f} < {self.energy_cost})，跳过")
|
||||
return False
|
||||
|
||||
# 4. 概率判定
|
||||
# 参与度越高，概率越高；体力越高，概率越高
|
||||
# 归一化因子
|
||||
participation_factor = min(state.participation_score / 100.0, 1.5) # 上限1.5倍
|
||||
energy_factor = self.current_energy / self.max_energy
|
||||
|
||||
final_prob = self.base_prob * participation_factor * energy_factor
|
||||
|
||||
# 被AT必然回复
|
||||
if is_at:
|
||||
final_prob = 1.0
|
||||
|
||||
# 随机判定
|
||||
rand_val = random.random()
|
||||
should_reply = rand_val < final_prob
|
||||
|
||||
logger.debug(f"[{room_id}] 判定: Prob={final_prob:.2f} (Base={self.base_prob} * Part={participation_factor:.2f} * Energy={energy_factor:.2f}) vs Rand={rand_val:.2f} -> {should_reply}")
|
||||
|
||||
if should_reply:
|
||||
# 扣除消耗
|
||||
self.current_energy -= self.energy_cost
|
||||
|
||||
# 更新状态
|
||||
state.last_bot_reply_time = datetime.now()
|
||||
|
||||
# 降低参与度（满足了表达欲）
|
||||
# 改为减法，保留部分参与度以便连续对话
|
||||
# 如果是高参与度（>50），减去 30；否则减半
|
||||
if state.participation_score > 50:
|
||||
state.participation_score = max(0, state.participation_score - 40)
|
||||
else:
|
||||
state.participation_score *= 0.5
|
||||
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def rule_high_reply_rate(self, timestamp, chat_log):
|
||||
# 保留这个方法以兼容 main.py 的调用，或者在 main.py 中移除
|
||||
# 这里我们可以简单的返回 False，因为新的逻辑已经包含了频率控制（通过体力值）
|
||||
return False
|
||||
@@ -1,72 +1,88 @@
|
||||
enable = true
|
||||
|
||||
dify_api_url = "http://192.168.2.240/v1/chat-messages"
|
||||
dify_api_key = "app-oDHbln5CzBLt3uS9bIBlJjhZ" # 请在此处填入您的DIFY API密钥
|
||||
[persona]
|
||||
name = "小牛"
|
||||
persona_file = "persona/xiaoniu.txt"
|
||||
style = "自然、口语化、像群友，先回答问题，再决定是否延伸"
|
||||
emoji_probability = 0.18
|
||||
max_reply_sentences = 3
|
||||
familiarity_hint = "有熟悉感，但不过度装熟"
|
||||
|
||||
[Keywords]
|
||||
# 表情符号库
|
||||
emojis = ["[捂脸]", "[奸笑]", "[可怜]", "[擦汗]", "[发呆]", "[抠鼻]", "[破涕为笑]", "[旺柴]"]
|
||||
# 话题关键词
|
||||
hot_topics = ["咖啡", "手机", "小米", "华为", "苹果", "价格", "流畅", "螺蛳粉", "外卖"]
|
||||
fish_keywords = [
|
||||
"鱼缸", "缸", "鱼", "鱼苗", "热带鱼", "金鱼", "观赏鱼", "罗汉鱼", "斗鱼", "孔雀鱼", "神仙鱼", "鹦鹉鱼",
|
||||
"灯科", "龙鱼", "地图鱼", "魟鱼", "草缸", "海缸", "水草", "水泵", "滤材", "硝化", "过滤", "缸体",
|
||||
"缸底", "底砂", "加热棒", "冷水鱼", "水温", "水质", "换水", "晒水", "PH值", "亚硝酸盐", "硝酸盐",
|
||||
"鱼食", "投喂", "喂食", "缸养", "寄生虫", "白点", "病鱼", "翻肚", "缸爆", "沉底", "氧气泵",
|
||||
"打氧", "造景", "生化球", "过滤桶", "外置过滤", "过滤棉", "清缸", "拉线", "鱼便", "崩缸",
|
||||
"饲养", "养水", "循环系统", "水妖精", "进水口", "出水口", "滴流盒", "蛋分", "藻类", "绿水",
|
||||
"爆藻", "石头", "沉木", "CO2", "定时器"
|
||||
[api]
|
||||
provider = "openai_compatible"
|
||||
api_base_url = "https://api.xinmeng.dpdns.org/v1"
|
||||
endpoint = "chat/completions"
|
||||
api_key = "sk-XTWwXIgo2QMyX8AwBg0NQrxaDkvQiCX8rfylfmnHID5zdjMt"
|
||||
model = "gpt-5.4"
|
||||
timeout_seconds = 45
|
||||
temperature = 0.7
|
||||
max_tokens = 500
|
||||
stream = true
|
||||
|
||||
[mode]
|
||||
group_default_mode = "social"
|
||||
question_reply_timeout_sec = 12
|
||||
followup_session_window_sec = 300
|
||||
recent_context_size = 30
|
||||
allow_proactive_reply = true
|
||||
returning_member_days = 7
|
||||
long_absent_member_days = 30
|
||||
memory_lookback_days = 180
|
||||
active_context_hours = 8
|
||||
|
||||
[priority]
|
||||
at_bot = 1.0
|
||||
explicit_question = 0.95
|
||||
followup = 0.90
|
||||
social_call = 0.65
|
||||
casual_topic = 0.35
|
||||
|
||||
[flow]
|
||||
enable_flow_state = true
|
||||
flow_decay_per_minute = 8
|
||||
idle_threshold = 20
|
||||
warming_threshold = 40
|
||||
engaged_threshold = 70
|
||||
at_bot_boost = 40
|
||||
question_boost = 30
|
||||
followup_boost = 20
|
||||
topic_boost = 15
|
||||
returning_member_boost = 10
|
||||
response_accepted_boost = 15
|
||||
ignored_reply_penalty = 20
|
||||
over_reply_penalty = 15
|
||||
night_penalty = 30
|
||||
max_bot_reply_streak = 3
|
||||
|
||||
[cooldown]
|
||||
group_reply_cooldown_sec = 45
|
||||
same_user_followup_cooldown_sec = 10
|
||||
night_silent_hours = ["01:00-07:30"]
|
||||
|
||||
[memory]
|
||||
enable_member_context = true
|
||||
enable_vector_memory = true
|
||||
vector_provider = "qdrant"
|
||||
embedding_provider = "ollama"
|
||||
qdrant_url = "http://127.0.0.1:6333"
|
||||
qdrant_collection = "abot_xiaoniu_memory"
|
||||
ollama_base_url = "http://192.168.2.50:11434"
|
||||
embedding_model = "bge-m3"
|
||||
vector_top_k = 5
|
||||
vector_min_score = 0.65
|
||||
vector_trigger_modes = ["returning_member", "long_absent_member", "qa_with_context", "reactivated_topic"]
|
||||
|
||||
[topics]
|
||||
focus = [
|
||||
"技术", "开发", "程序", "python", "微信机器人", "脚本", "报错", "部署",
|
||||
"服务器", "docker", "数据库", "redis", "mysql", "qdrant", "ollama", "dify",
|
||||
"ai", "大模型", "接口", "插件", "自动化"
|
||||
]
|
||||
tech_keywords = [
|
||||
# 品牌/系统相关
|
||||
"MIUI", "鸿蒙", "iOS", "iPhone", "安卓", "Android", "Windows", "Mac", "Linux",
|
||||
"小米", "华为", "荣耀", "苹果", "三星", "vivo", "OPPO", "realme", "魅族", "一加", "红米", "中兴", "联想",
|
||||
|
||||
# 功能/硬件组件
|
||||
"推送", "充电", "屏幕", "电池", "信号", "发热", "卡顿", "刷新率", "像素", "拍照", "音质",
|
||||
"NFC", "红外", "扬声器", "马达", "快充", "耗电", "续航", "UI", "指纹", "面部识别", "解锁",
|
||||
"摄像头", "前摄", "后摄", "内存", "闪存", "存储空间", "RAM", "ROM", "屏占比", "刘海屏", "挖孔屏", "折叠屏",
|
||||
[filters]
|
||||
ignore_prefixes = ["/", "#"]
|
||||
ignore_exact = ["收到", "好的", "嗯", "哦", "6", "1", "？", "?"]
|
||||
min_text_length = 1
|
||||
|
||||
# 芯片/性能
|
||||
"骁龙", "天玑", "麒麟", "A系列", "SoC", "处理器", "芯片", "跑分", "安兔兔", "鲁大师", "GPU", "CPU", "核心", "制程",
|
||||
|
||||
# 系统/应用/开发
|
||||
"系统更新", "OTA", "开源", "固件", "刷机", "root", "bootloader", "ADB", "Xposed", "系统权限", "卡刷", "线刷",
|
||||
|
||||
# 科技趋势/智能化
|
||||
"AI", "人工智能", "大模型", "GPT", "ChatGPT", "语音助手", "Siri", "小爱同学", "小艺", "算法", "智能识别",
|
||||
"自动驾驶", "感应器", "人脸识别", "语音识别", "AR", "VR", "混合现实", "穿戴设备", "智能手表", "手环"
|
||||
]
|
||||
mechanism_keywords = []
|
||||
news_keywords = ["新闻", "骨灰房", "法院", "判决", "住建局"]
|
||||
|
||||
[TimeWindow]
|
||||
# 早晨签到时间窗口（8:00-8:30）
|
||||
morning_start_hour = 8
|
||||
morning_start_minute = 0
|
||||
morning_end_hour = 8
|
||||
morning_end_minute = 30
|
||||
|
||||
[ReplyThreshold]
|
||||
# 每分钟消息数阈值，超过此值将触发AI介入
|
||||
messages_per_minute_threshold = 3
|
||||
# 分析窗口大小（分钟）
|
||||
analysis_window_minutes = 5
|
||||
|
||||
[HumanLike]
|
||||
# 最大体力值
|
||||
max_energy = 100.0
|
||||
# 体力恢复速度（每分钟）
|
||||
energy_recovery_per_minute = 1.0
|
||||
# 每次回复消耗体力
|
||||
energy_cost_per_reply = 15.0
|
||||
# 基础参与度增加（每收到一条群消息）
|
||||
participation_increase_per_msg = 5.0
|
||||
# 话题相关参与度奖励
|
||||
topic_match_bonus = 15.0
|
||||
# 触发回复的参与度阈值（只有参与度高于此值才可能回复）
|
||||
participation_threshold = 20.0
|
||||
# 每次回复后参与度降低比例 (0.0 - 1.0, 1.0表示清零)
|
||||
participation_drop_factor = 0.8
|
||||
# 基础回复概率 (0.0 - 1.0) - 当满足阈值时，基于此概率和体力值计算最终概率
|
||||
base_reply_probability = 0.6
|
||||
[logging]
|
||||
debug = true
|
||||
|
||||
71
plugins/ai_auto_response/context_builder.py
Normal file
71
plugins/ai_auto_response/context_builder.py
Normal file
@@ -0,0 +1,71 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Dict, List
|
||||
|
||||
|
||||
class ContextBuilder:
|
||||
def build(
|
||||
self,
|
||||
*,
|
||||
room_id: str,
|
||||
sender: str,
|
||||
sender_name: str,
|
||||
content: str,
|
||||
recent_messages: List[Dict],
|
||||
member_context: Dict,
|
||||
trigger: Dict,
|
||||
flow_state: str,
|
||||
reply_mode: str,
|
||||
vector_memories: List[Dict],
|
||||
) -> Dict:
|
||||
recent_lines = []
|
||||
for item in recent_messages[-30:]:
|
||||
msg_sender = item.get("sender_name") or item.get("sender") or "未知成员"
|
||||
msg_content = item.get("content") or item.get("message") or ""
|
||||
if msg_content:
|
||||
recent_lines.append(f"{msg_sender}: {msg_content}")
|
||||
return {
|
||||
"group_profile": {"room_id": room_id},
|
||||
"speaker_profile": {
|
||||
"wxid": sender,
|
||||
"display_name": sender_name,
|
||||
"member_context": member_context or {},
|
||||
},
|
||||
"recent_messages": recent_lines,
|
||||
"recent_summary": "",
|
||||
"trigger_type": trigger.get("trigger_type", "none"),
|
||||
"reply_mode": reply_mode,
|
||||
"flow_state": flow_state,
|
||||
"memory_prompt": self._build_member_memory_prompt(member_context),
|
||||
"vector_memory_prompt": self._build_vector_memory_prompt(vector_memories),
|
||||
"current_message": f"{sender_name}: {content}",
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _build_member_memory_prompt(member_context: Dict) -> str:
|
||||
if not member_context:
|
||||
return "暂无稳定成员画像。"
|
||||
meta = member_context.get("meta", {}) or {}
|
||||
topics = member_context.get("topics_of_interest", []) or []
|
||||
recent_focus = member_context.get("recent_focus", []) or []
|
||||
lines = [
|
||||
f"成员摘要：{member_context.get('summary_text', '')}".strip(),
|
||||
f"互动风格：{member_context.get('interaction_style', '')}".strip(),
|
||||
f"回复偏好：{member_context.get('response_style_hint', '')}".strip(),
|
||||
f"长期主题：{', '.join(topics[:5])}" if topics else "",
|
||||
f"近期关注：{', '.join(recent_focus[:4])}" if recent_focus else "",
|
||||
f"群内角色：{meta.get('group_role', '')}".strip(),
|
||||
]
|
||||
return "\n".join([line for line in lines if line])
|
||||
|
||||
@staticmethod
|
||||
def _build_vector_memory_prompt(vector_memories: List[Dict]) -> str:
|
||||
if not vector_memories:
|
||||
return ""
|
||||
lines = []
|
||||
for item in vector_memories[:5]:
|
||||
summary = item.get("content_summary") or item.get("summary_text") or item.get("text") or ""
|
||||
memory_type = item.get("memory_type", "memory")
|
||||
if summary:
|
||||
lines.append(f"[{memory_type}] {summary}")
|
||||
return "\n".join(lines)
|
||||
103
plugins/ai_auto_response/flow_manager.py
Normal file
103
plugins/ai_auto_response/flow_manager.py
Normal file
@@ -0,0 +1,103 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime, time
|
||||
from typing import Dict
|
||||
|
||||
|
||||
@dataclass
|
||||
class FlowState:
|
||||
room_id: str
|
||||
score: float = 0.0
|
||||
state: str = "idle"
|
||||
last_update: datetime = field(default_factory=datetime.now)
|
||||
last_bot_reply_at: datetime | None = None
|
||||
last_human_message_at: datetime | None = None
|
||||
bot_reply_streak: int = 0
|
||||
ignored_reply_count: int = 0
|
||||
accepted_reply_count: int = 0
|
||||
last_topic: str = ""
|
||||
|
||||
|
||||
class FlowManager:
|
||||
def __init__(self, config: Dict):
|
||||
self.config = config or {}
|
||||
self.states: Dict[str, FlowState] = {}
|
||||
|
||||
def get_state(self, room_id: str) -> FlowState:
|
||||
if room_id not in self.states:
|
||||
self.states[room_id] = FlowState(room_id=room_id)
|
||||
return self.states[room_id]
|
||||
|
||||
def decay(self, room_id: str) -> FlowState:
|
||||
state = self.get_state(room_id)
|
||||
now = datetime.now()
|
||||
elapsed_minutes = max((now - state.last_update).total_seconds() / 60.0, 0.0)
|
||||
decay = elapsed_minutes * float(self.config.get("flow_decay_per_minute", 8))
|
||||
state.score = max(0.0, state.score - decay)
|
||||
state.last_update = now
|
||||
state.state = self._score_to_state(state.score)
|
||||
return state
|
||||
|
||||
def apply_message_event(self, room_id: str, event: Dict) -> FlowState:
|
||||
state = self.decay(room_id)
|
||||
now = datetime.now()
|
||||
if self._is_night_silent(now.time()):
|
||||
state.score = max(0.0, state.score - float(self.config.get("night_penalty", 30)))
|
||||
if event.get("is_at"):
|
||||
state.score += float(self.config.get("at_bot_boost", 40))
|
||||
if event.get("is_question"):
|
||||
state.score += float(self.config.get("question_boost", 30))
|
||||
if event.get("is_followup"):
|
||||
state.score += float(self.config.get("followup_boost", 20))
|
||||
if event.get("topic_hit"):
|
||||
state.score += float(self.config.get("topic_boost", 15))
|
||||
if event.get("is_returning_member"):
|
||||
state.score += float(self.config.get("returning_member_boost", 10))
|
||||
if state.last_bot_reply_at:
|
||||
since_reply = (now - state.last_bot_reply_at).total_seconds()
|
||||
if since_reply <= 180 and event.get("message_after_bot"):
|
||||
state.score += float(self.config.get("response_accepted_boost", 15))
|
||||
state.accepted_reply_count += 1
|
||||
state.last_human_message_at = now
|
||||
state.last_topic = event.get("topic") or state.last_topic
|
||||
state.state = self._score_to_state(state.score)
|
||||
return state
|
||||
|
||||
def note_bot_reply(self, room_id: str) -> FlowState:
|
||||
state = self.decay(room_id)
|
||||
state.last_bot_reply_at = datetime.now()
|
||||
state.bot_reply_streak += 1
|
||||
max_streak = int(self.config.get("max_bot_reply_streak", 3))
|
||||
if state.bot_reply_streak > max_streak:
|
||||
state.score = max(0.0, state.score - float(self.config.get("over_reply_penalty", 15)))
|
||||
state.state = self._score_to_state(state.score)
|
||||
return state
|
||||
|
||||
def _score_to_state(self, score: float) -> str:
|
||||
idle_threshold = float(self.config.get("idle_threshold", 20))
|
||||
warming_threshold = float(self.config.get("warming_threshold", 40))
|
||||
engaged_threshold = float(self.config.get("engaged_threshold", 70))
|
||||
if score < idle_threshold:
|
||||
return "idle"
|
||||
if score < warming_threshold:
|
||||
return "warming"
|
||||
if score < engaged_threshold:
|
||||
return "engaged"
|
||||
return "deep_engaged"
|
||||
|
||||
def _is_night_silent(self, current_time: time) -> bool:
|
||||
for window in self.config.get("night_silent_hours", []):
|
||||
try:
|
||||
start_str, end_str = window.split("-", 1)
|
||||
start = time.fromisoformat(start_str)
|
||||
end = time.fromisoformat(end_str)
|
||||
if start <= end:
|
||||
if start <= current_time <= end:
|
||||
return True
|
||||
else:
|
||||
if current_time >= start or current_time <= end:
|
||||
return True
|
||||
except Exception:
|
||||
continue
|
||||
return False
|
||||
166
plugins/ai_auto_response/llm_client.py
Normal file
166
plugins/ai_auto_response/llm_client.py
Normal file
@@ -0,0 +1,166 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from typing import Dict, List
|
||||
|
||||
import requests
|
||||
|
||||
|
||||
class LLMClient:
|
||||
def __init__(self, config: Dict):
|
||||
self.config = config or {}
|
||||
self.provider = self.config.get("provider", "openai_compatible")
|
||||
self.base_url = str(self.config.get("api_base_url", "")).rstrip("/")
|
||||
self.endpoint = str(self.config.get("endpoint", "chat/completions")).lstrip("/")
|
||||
self.api_key = self.config.get("api_key", "")
|
||||
self.model = self.config.get("model", "")
|
||||
self.timeout_seconds = int(self.config.get("timeout_seconds", 45))
|
||||
self.temperature = float(self.config.get("temperature", 0.7))
|
||||
self.max_tokens = int(self.config.get("max_tokens", 500))
|
||||
self.stream = bool(self.config.get("stream", True))
|
||||
self.last_error = ""
|
||||
|
||||
def chat(self, system_prompt: str, user_prompt: str, user_id: str) -> str:
|
||||
self.last_error = ""
|
||||
if not self.base_url:
|
||||
self.last_error = "empty_base_url"
|
||||
return ""
|
||||
if self.provider == "openai_compatible":
|
||||
return self._chat_openai_compatible(system_prompt, user_prompt, user_id)
|
||||
self.last_error = f"unsupported_provider:{self.provider}"
|
||||
return ""
|
||||
|
||||
def _chat_openai_compatible(self, system_prompt: str, user_prompt: str, user_id: str) -> str:
|
||||
if not self.model:
|
||||
return ""
|
||||
|
||||
payload = {
|
||||
"model": self.model,
|
||||
"messages": self._build_messages(system_prompt, user_prompt),
|
||||
"temperature": self.temperature,
|
||||
"max_tokens": self.max_tokens,
|
||||
"user": user_id,
|
||||
}
|
||||
if self.stream:
|
||||
payload["stream"] = True
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
if self.api_key:
|
||||
headers["Authorization"] = f"Bearer {self.api_key}"
|
||||
|
||||
try:
|
||||
if self.stream:
|
||||
return self._chat_streaming(payload, headers)
|
||||
response = requests.post(
|
||||
f"{self.base_url}/{self.endpoint}",
|
||||
json=payload,
|
||||
headers=headers,
|
||||
timeout=self.timeout_seconds,
|
||||
)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
text = self._extract_text(data)
|
||||
if text:
|
||||
return text
|
||||
self.last_error = f"empty_model_output:{self.model}"
|
||||
return ""
|
||||
except Exception as exc:
|
||||
self.last_error = f"request_failed:{exc}"
|
||||
return ""
|
||||
|
||||
def _chat_streaming(self, payload: Dict, headers: Dict[str, str]) -> str:
|
||||
chunks: List[str] = []
|
||||
with requests.post(
|
||||
f"{self.base_url}/{self.endpoint}",
|
||||
json=payload,
|
||||
headers=headers,
|
||||
timeout=self.timeout_seconds,
|
||||
stream=True,
|
||||
) as response:
|
||||
response.raise_for_status()
|
||||
buffer = b""
|
||||
for part in response.iter_content(chunk_size=None):
|
||||
if not part:
|
||||
continue
|
||||
buffer += part
|
||||
while b"\n\n" in buffer:
|
||||
event, buffer = buffer.split(b"\n\n", 1)
|
||||
try:
|
||||
event_text = event.decode("utf-8")
|
||||
except UnicodeDecodeError:
|
||||
buffer = event + b"\n\n" + buffer
|
||||
break
|
||||
text_piece, done = self._parse_sse_event(event_text)
|
||||
if text_piece:
|
||||
chunks.append(text_piece)
|
||||
if done:
|
||||
final_text = "".join(chunks).strip()
|
||||
if final_text:
|
||||
return final_text
|
||||
self.last_error = f"empty_stream_output:{self.model}"
|
||||
return ""
|
||||
final_text = "".join(chunks).strip()
|
||||
if final_text:
|
||||
return final_text
|
||||
self.last_error = f"empty_stream_output:{self.model}"
|
||||
return ""
|
||||
|
||||
@staticmethod
|
||||
def _build_messages(system_prompt: str, user_prompt: str) -> List[Dict[str, str]]:
|
||||
return [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": user_prompt},
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def _extract_text(data: Dict) -> str:
|
||||
choices = data.get("choices") or []
|
||||
if choices:
|
||||
message = choices[0].get("message", {}) or {}
|
||||
content = message.get("content")
|
||||
if isinstance(content, str) and content.strip():
|
||||
return content.strip()
|
||||
if isinstance(content, list):
|
||||
parts = []
|
||||
for item in content:
|
||||
if isinstance(item, dict):
|
||||
text = item.get("text") or item.get("content")
|
||||
if isinstance(text, str) and text.strip():
|
||||
parts.append(text.strip())
|
||||
if parts:
|
||||
return "\n".join(parts).strip()
|
||||
for key in ("reasoning_content", "text", "output_text"):
|
||||
value = message.get(key)
|
||||
if isinstance(value, str) and value.strip():
|
||||
return value.strip()
|
||||
for key in ("output_text", "text", "answer", "response"):
|
||||
value = data.get(key)
|
||||
if isinstance(value, str) and value.strip():
|
||||
return value.strip()
|
||||
return ""
|
||||
|
||||
@classmethod
|
||||
def _parse_sse_event(cls, event_text: str) -> tuple[str, bool]:
|
||||
lines = [line.strip() for line in event_text.splitlines() if line.strip()]
|
||||
data_lines = [line[5:].strip() for line in lines if line.startswith("data:")]
|
||||
if not data_lines:
|
||||
return "", False
|
||||
data = "\n".join(data_lines)
|
||||
if data == "[DONE]":
|
||||
return "", True
|
||||
obj = json.loads(data)
|
||||
choice = (obj.get("choices") or [{}])[0]
|
||||
delta = choice.get("delta") or {}
|
||||
content = delta.get("content")
|
||||
if isinstance(content, str):
|
||||
return content, False
|
||||
if isinstance(content, list):
|
||||
parts = []
|
||||
for item in content:
|
||||
if isinstance(item, dict):
|
||||
text = item.get("text") or item.get("content")
|
||||
if isinstance(text, str):
|
||||
parts.append(text)
|
||||
return "".join(parts), False
|
||||
return "", False
|
||||
@@ -1,49 +1,56 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
import time
|
||||
import xml.etree.ElementTree as ET
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
from loguru import logger
|
||||
import os
|
||||
import requests
|
||||
from typing import Dict, Any, List, Optional, Tuple
|
||||
|
||||
from base.plugin_common.message_plugin_interface import MessagePluginInterface
|
||||
from base.plugin_common.plugin_interface import PluginStatus
|
||||
from utils.robot_cmd.robot_command import Feature, PermissionStatus, GroupBotManager
|
||||
from utils.robot_cmd.robot_command import GroupBotManager, PermissionStatus
|
||||
from utils.wechat.contact_manager import ContactManager
|
||||
from wechat_ipad import WechatAPIClient
|
||||
from wechat_ipad.models.message import MessageType
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
from .bot_ai import InterventionBot
|
||||
from .context_builder import ContextBuilder
|
||||
from .flow_manager import FlowManager
|
||||
from .llm_client import LLMClient
|
||||
from .memory_store import MemoryStore
|
||||
from .persona_engine import PersonaEngine
|
||||
from .response_planner import ResponsePlanner
|
||||
from .triggers import TriggerRouter
|
||||
from .vector_memory import VectorMemoryStore
|
||||
|
||||
|
||||
class AIAutoResponsePlugin(MessagePluginInterface):
|
||||
"""AI自动对话插件"""
|
||||
|
||||
# 功能权限常量
|
||||
FEATURE_KEY = "AI_AUTO_RESPONSE"
|
||||
FEATURE_DESCRIPTION = "🤖 AI自动对话功能 [自动对话]"
|
||||
FEATURE_DESCRIPTION = "🐮 小牛拟人群聊BOT [群聊拟真、及时答疑、长期记忆]"
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return "AI自动对话"
|
||||
return "小牛群聊BOT"
|
||||
|
||||
@property
|
||||
def version(self) -> str:
|
||||
return "1.0.0"
|
||||
return "2.0.0"
|
||||
|
||||
@property
|
||||
def description(self) -> str:
|
||||
return "提供AI自动对话功能，可以在群聊中自动介入对话"
|
||||
return "拟人化群聊BOT，支持心流、长期记忆和回归成员识别"
|
||||
|
||||
@property
|
||||
def author(self) -> str:
|
||||
return "liu.wei"
|
||||
return "ABOT Team"
|
||||
|
||||
@property
|
||||
def command_prefix(self) -> Optional[str]:
|
||||
return "" # 不需要前缀，直接匹配命令
|
||||
return None
|
||||
|
||||
@property
|
||||
def commands(self) -> List[str]:
|
||||
return self._commands
|
||||
return []
|
||||
|
||||
@property
|
||||
def feature_key(self) -> Optional[str]:
|
||||
@@ -55,207 +62,452 @@ class AIAutoResponsePlugin(MessagePluginInterface):
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.intervention_bot = None
|
||||
self.group_messages = {} # 存储每个群的最近消息
|
||||
self.max_messages = 100 # 每个群最多存储的消息数量
|
||||
# 注册功能权限
|
||||
self.feature = self.register_feature()
|
||||
|
||||
# DIFY API配置
|
||||
self.dify_api_url = ""
|
||||
self.dify_api_key = "" # 需要在配置文件中设置
|
||||
self.group_messages: Dict[str, List[Dict]] = {}
|
||||
self.enable = True
|
||||
self.last_reply_at: Dict[str, float] = {}
|
||||
|
||||
def initialize(self, context: Dict[str, Any]) -> bool:
|
||||
"""初始化插件"""
|
||||
self.LOG = logger
|
||||
self.LOG.debug(f"正在初始化 {self.name} 插件...")
|
||||
|
||||
# 保存上下文对象
|
||||
self.event_system = context.get("event_system")
|
||||
|
||||
# 加载配置
|
||||
config_path = os.path.join(os.path.dirname(__file__), "config.toml")
|
||||
self.enable = self._config.get("enable", True)
|
||||
|
||||
# 从配置中获取DIFY API密钥
|
||||
self.dify_api_key = self._config.get("dify_api_key", "")
|
||||
self.dify_api_url = self._config.get("dify_api_url", "")
|
||||
|
||||
# 初始化介入机器人
|
||||
self.intervention_bot = InterventionBot(config_path)
|
||||
|
||||
self.db_manager = context.get("db_manager")
|
||||
self.enable = bool(self._config.get("enable", True))
|
||||
self.persona_engine = PersonaEngine(self.get_plugin_path(), self._config.get("persona", {}))
|
||||
self.flow_manager = FlowManager({
|
||||
**(self._config.get("flow", {}) or {}),
|
||||
"night_silent_hours": (self._config.get("cooldown", {}) or {}).get("night_silent_hours", []),
|
||||
})
|
||||
merged_trigger_config = dict(self._config.get("priority", {}) or {})
|
||||
merged_trigger_config.update(self._config.get("topics", {}) or {})
|
||||
self.trigger_router = TriggerRouter(merged_trigger_config)
|
||||
merged_memory_config = dict(self._config.get("mode", {}) or {})
|
||||
merged_memory_config.update(self._config.get("memory", {}) or {})
|
||||
self.memory_store = MemoryStore(self.db_manager, merged_memory_config)
|
||||
self.vector_memory = VectorMemoryStore(self._config.get("memory", {}) or {})
|
||||
self.context_builder = ContextBuilder()
|
||||
self.response_planner = ResponsePlanner()
|
||||
self.llm_client = LLMClient(self._config.get("api", {}) or {})
|
||||
self.filters = self._config.get("filters", {}) or {}
|
||||
self.mode_config = self._config.get("mode", {}) or {}
|
||||
self.cooldown_config = self._config.get("cooldown", {}) or {}
|
||||
self._synced_member_context_versions: Dict[str, str] = {}
|
||||
self.log_debug = bool((self._config.get("logging", {}) or {}).get("debug", True))
|
||||
self.LOG.debug(f"[{self.name}] 初始化完成")
|
||||
return True
|
||||
|
||||
def start(self) -> bool:
|
||||
"""启动插件"""
|
||||
self.LOG.debug(f"[{self.name}] 插件已启动")
|
||||
self.status = PluginStatus.RUNNING
|
||||
return True
|
||||
|
||||
def stop(self) -> bool:
|
||||
"""停止插件"""
|
||||
self.LOG.info(f"[{self.name}] 插件已停止")
|
||||
self.status = PluginStatus.STOPPED
|
||||
return True
|
||||
|
||||
def can_process(self, message: Dict[str, Any]) -> bool:
|
||||
"""检查是否可以处理该消息"""
|
||||
if not self.enable:
|
||||
return False
|
||||
|
||||
content = str(message.get("content", "")).strip()
|
||||
roomid = message.get("roomid", "")
|
||||
|
||||
if GroupBotManager.get_group_permission(roomid, self.feature) == PermissionStatus.DISABLED:
|
||||
room_id = message.get("roomid", "")
|
||||
if not room_id:
|
||||
return False
|
||||
if GroupBotManager.get_group_permission(room_id, self.feature) == PermissionStatus.DISABLED:
|
||||
return False
|
||||
# 如果是群消息，且该群启用了自动回复，则处理
|
||||
if roomid:
|
||||
self.LOG.debug(f"[{roomid}] 进入AI自动回复逻辑")
|
||||
# 存储消息
|
||||
if roomid not in self.group_messages:
|
||||
self.group_messages[roomid] = []
|
||||
|
||||
msg_type = message.get("type")
|
||||
# 获取发送者昵称
|
||||
sender_id = message.get("sender", "")
|
||||
try:
|
||||
members = ContactManager.get_instance().get_group_members(roomid)
|
||||
sender_name = members.get(sender_id, sender_id)
|
||||
except Exception:
|
||||
sender_name = sender_id
|
||||
msg_type = message.get("type")
|
||||
if msg_type not in (MessageType.TEXT, MessageType.APP):
|
||||
return False
|
||||
|
||||
# 仅追加文本(1)与应用消息(49)，并对49提取标题
|
||||
content_to_store = None
|
||||
try:
|
||||
if msg_type == MessageType.TEXT:
|
||||
content_to_store = content
|
||||
elif msg_type == MessageType.APP:
|
||||
try:
|
||||
root = ET.fromstring(content)
|
||||
title_elem = root.find('.//title')
|
||||
if title_elem is not None and title_elem.text:
|
||||
content_to_store = title_elem.text
|
||||
else:
|
||||
content_to_store = "[应用消息]"
|
||||
except Exception as e:
|
||||
self.LOG.error(f"解析消息类型49出错: {e}")
|
||||
content_to_store = "[应用消息]"
|
||||
except Exception as e:
|
||||
self.LOG.error(f"处理消息类型出错: {e}")
|
||||
content_to_store = None
|
||||
full_msg = message.get("full_wx_msg")
|
||||
if full_msg and full_msg.from_self():
|
||||
return False
|
||||
|
||||
if content_to_store is not None:
|
||||
# 添加新消息
|
||||
current_message = {
|
||||
"timestamp": message.get("timestamp", ""),
|
||||
"message": content_to_store,
|
||||
"sender": sender_id,
|
||||
"sender_name": sender_name
|
||||
}
|
||||
|
||||
# 添加新消息
|
||||
if content_to_store is not None:
|
||||
self.group_messages[roomid].append(current_message)
|
||||
|
||||
# 限制消息数量
|
||||
if len(self.group_messages[roomid]) > self.max_messages:
|
||||
self.group_messages[roomid] = self.group_messages[roomid][-self.max_messages:]
|
||||
|
||||
# 判断是否需要介入
|
||||
messages = [msg["message"] for msg in self.group_messages[roomid]]
|
||||
timestamp = message.get("timestamp", "")
|
||||
# 传递完整的聊天记录给should_intervene方法
|
||||
can = self.intervention_bot.should_intervene(roomid, timestamp, content, messages, self.group_messages[roomid])
|
||||
if can:
|
||||
self.LOG.debug(f"[{roomid}] 触发自动回复规则，准备生成回复")
|
||||
return True
|
||||
else:
|
||||
self.LOG.debug(f"[{roomid}] 跳过聊天")
|
||||
return False
|
||||
return False
|
||||
content = self._normalize_content(message)
|
||||
if not content:
|
||||
return False
|
||||
if self._should_ignore(content):
|
||||
return False
|
||||
return True
|
||||
|
||||
async def process_message(self, message: Dict[str, Any]) -> Tuple[bool, Optional[str]]:
|
||||
"""处理消息"""
|
||||
content = str(message.get("content", "")).strip()
|
||||
self.LOG.debug(f"插件执行： {self.name}：{content}")
|
||||
sender = message.get("sender")
|
||||
roomid = message.get("roomid", "")
|
||||
room_id = message.get("roomid", "")
|
||||
sender = message.get("sender", "")
|
||||
bot: WechatAPIClient = message.get("bot")
|
||||
# 检查权限
|
||||
if roomid and GroupBotManager.get_group_permission(roomid, self.feature) == PermissionStatus.DISABLED:
|
||||
return False, "没有权限"
|
||||
# 处理自动回复
|
||||
try:
|
||||
# 获取最近的消息 (完整的消息对象)
|
||||
chat_history = self.group_messages[roomid]
|
||||
timestamp = message.get("timestamp", "")
|
||||
content = self._normalize_content(message)
|
||||
sender_name = self._get_sender_name(room_id, sender)
|
||||
self._log_event(
|
||||
"recv",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
is_at=message.get("is_at", False),
|
||||
content_preview=self._preview(content),
|
||||
msg_type=str(message.get("type")),
|
||||
)
|
||||
|
||||
# 记录触发原因
|
||||
if self.intervention_bot.rule_high_reply_rate(timestamp, self.group_messages[roomid]):
|
||||
self.LOG.info(f"[{roomid}] 触发高频率回复规则，准备生成回复")
|
||||
normalized_message = {
|
||||
"sender": sender,
|
||||
"sender_name": sender_name,
|
||||
"content": content,
|
||||
"timestamp": message.get("timestamp"),
|
||||
}
|
||||
self._append_group_message(room_id, normalized_message)
|
||||
|
||||
# 生成回复
|
||||
response = self._generate_response_with_dify(content, chat_history)
|
||||
if response:
|
||||
# 发送回复
|
||||
await bot.send_text_message(roomid, response, sender)
|
||||
return False, "自动回复成功"
|
||||
else:
|
||||
return False, "生成回复失败"
|
||||
memory_hints = self.memory_store.build_memory_hints(room_id, sender)
|
||||
self._sync_member_memory(room_id, sender, sender_name, memory_hints.get("member_context", {}))
|
||||
self._log_event(
|
||||
"memory",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
returning_state=memory_hints.get("returning_member_state", "") or "none",
|
||||
has_member_context=bool(memory_hints.get("member_context")),
|
||||
is_followup=memory_hints.get("is_followup", False),
|
||||
last_active_at=memory_hints.get("last_active_at", "") or "",
|
||||
)
|
||||
trigger = self.trigger_router.route(message | {"content": content}, memory_hints)
|
||||
flow_state = self.flow_manager.apply_message_event(room_id, {
|
||||
"is_at": message.get("is_at", False),
|
||||
"is_question": trigger.is_question,
|
||||
"is_followup": trigger.is_followup,
|
||||
"topic_hit": bool(trigger.topic),
|
||||
"topic": trigger.topic,
|
||||
"is_returning_member": trigger.is_returning_member,
|
||||
"message_after_bot": True,
|
||||
})
|
||||
self._log_event(
|
||||
"decision",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
trigger_type=trigger.trigger_type,
|
||||
priority=trigger.priority,
|
||||
reasons="|".join(trigger.reasons),
|
||||
flow_state=flow_state.state,
|
||||
flow_score=round(flow_state.score, 2),
|
||||
topic=trigger.topic or "",
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
self.LOG.error(f"处理AI自动对话出错: {e}")
|
||||
return False, f"处理出错: {e}"
|
||||
|
||||
def _generate_response_with_dify(self, current_message: str, chat_history: List[Dict[str, Any]]) -> str:
|
||||
"""使用DIFY API生成自动回复内容"""
|
||||
try:
|
||||
# 构建上下文消息
|
||||
# 取更多上下文以帮助理解语境
|
||||
recent_msgs = chat_history[-10:] if len(chat_history) > 10 else chat_history
|
||||
|
||||
context_str_list = []
|
||||
for msg in recent_msgs:
|
||||
# 优先使用昵称，如果没有则使用sender ID
|
||||
sender = msg.get("sender_name") or msg.get("sender", "Unknown")
|
||||
content = msg.get("message", "")
|
||||
context_str_list.append(f"{sender}: {content}")
|
||||
|
||||
context = "\n".join(context_str_list)
|
||||
|
||||
# 构建提示词 - 增强拟人化指令
|
||||
prompt = (
|
||||
f"当前群聊上下文（格式为 '发言人: 内容'，最后一句是最新消息）：\n{context}\n\n"
|
||||
f"指令：\n"
|
||||
f"1. 参考上下文。\n"
|
||||
f"2. 保持简短（1-2句话），口语化，不要长篇大论。\n"
|
||||
f"3. 不要重复之前的回复。\n"
|
||||
f"4. 如果最后一句不是对你说的，且你觉得没必要强行接话，可以回个表情或简短的语气词，或者委婉结束话题。\n"
|
||||
f"请生成回复："
|
||||
allow_proactive = bool(self.mode_config.get("allow_proactive_reply", True))
|
||||
reply_mode = self.response_planner.choose_reply_mode(trigger.__dict__, flow_state.state)
|
||||
should_reply = self.response_planner.should_reply(trigger.__dict__, flow_state.state, allow_proactive)
|
||||
if not should_reply:
|
||||
self._log_event(
|
||||
"skip",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
reason="planner_skip",
|
||||
trigger_type=trigger.trigger_type,
|
||||
reply_mode=reply_mode,
|
||||
flow_state=flow_state.state,
|
||||
)
|
||||
return False, "skip"
|
||||
if not self._pass_cooldown(room_id, trigger.__dict__):
|
||||
self._log_event(
|
||||
"skip",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
reason="cooldown",
|
||||
trigger_type=trigger.trigger_type,
|
||||
reply_mode=reply_mode,
|
||||
)
|
||||
return False, "cooldown"
|
||||
|
||||
# 调用DIFY API
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {self.dify_api_key}"
|
||||
}
|
||||
recent_messages = self.group_messages.get(room_id) or self.memory_store.get_recent_messages(room_id)
|
||||
vector_memories = []
|
||||
if self.vector_memory.should_search(reply_mode, trigger.trigger_type, memory_hints.get("returning_member_state", "")):
|
||||
vector_memories = self.vector_memory.search(content, room_id, sender)
|
||||
self._log_event(
|
||||
"context",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
reply_mode=reply_mode,
|
||||
recent_message_count=len(recent_messages),
|
||||
vector_hit_count=len(vector_memories),
|
||||
)
|
||||
|
||||
payload = {
|
||||
"inputs": {},
|
||||
"query": prompt,
|
||||
"response_mode": "blocking",
|
||||
"user": "ai_auto_response"
|
||||
}
|
||||
context = self.context_builder.build(
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
content=content,
|
||||
recent_messages=recent_messages,
|
||||
member_context=memory_hints.get("member_context", {}),
|
||||
trigger=trigger.__dict__,
|
||||
flow_state=flow_state.state,
|
||||
reply_mode=reply_mode,
|
||||
vector_memories=vector_memories,
|
||||
)
|
||||
|
||||
response = requests.post(self.dify_api_url, headers=headers, json=payload)
|
||||
system_prompt = self.persona_engine.build_system_prompt()
|
||||
user_prompt = self._build_user_prompt(context, memory_hints)
|
||||
response = self._sanitize_response(self.llm_client.chat(system_prompt, user_prompt, user_id=f"{room_id}:{sender}"))
|
||||
if not response:
|
||||
self._log_event(
|
||||
"model_empty",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
model=self.llm_client.model,
|
||||
last_error=self.llm_client.last_error,
|
||||
reply_mode=reply_mode,
|
||||
)
|
||||
return False, "empty_response"
|
||||
|
||||
if response.status_code == 200:
|
||||
result = response.json()
|
||||
return result.get("answer", "")
|
||||
else:
|
||||
self.LOG.error(f"DIFY API调用失败: {response.status_code} - {response.text}")
|
||||
return ""
|
||||
await bot.send_text_message(room_id, response, sender)
|
||||
self.last_reply_at[room_id] = time.time()
|
||||
self.flow_manager.note_bot_reply(room_id)
|
||||
self.memory_store.note_bot_reply(room_id, sender, trigger.topic)
|
||||
self._upsert_interaction_memory(room_id, sender, sender_name, content, response, trigger.trigger_type, trigger.topic)
|
||||
self._log_event(
|
||||
"sent",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
trigger_type=trigger.trigger_type,
|
||||
reply_mode=reply_mode,
|
||||
response_preview=self._preview(response),
|
||||
response_len=len(response),
|
||||
)
|
||||
return False, "replied"
|
||||
|
||||
except Exception as e:
|
||||
self.LOG.error(f"生成回复出错: {e}")
|
||||
def _append_group_message(self, room_id: str, message: Dict) -> None:
|
||||
items = self.group_messages.setdefault(room_id, [])
|
||||
items.append(message)
|
||||
size = int(self.mode_config.get("recent_context_size", 30))
|
||||
if len(items) > size:
|
||||
self.group_messages[room_id] = items[-size:]
|
||||
|
||||
def _normalize_content(self, message: Dict[str, Any]) -> str:
|
||||
msg_type = message.get("type")
|
||||
content = str(message.get("content", "")).strip()
|
||||
if msg_type == MessageType.TEXT:
|
||||
return self._strip_at_prefix(content)
|
||||
if msg_type == MessageType.APP:
|
||||
try:
|
||||
root = ET.fromstring(content)
|
||||
title = root.find(".//title")
|
||||
return (title.text or "").strip() if title is not None else "[应用消息]"
|
||||
except Exception:
|
||||
return "[应用消息]"
|
||||
return content
|
||||
|
||||
@staticmethod
|
||||
def _strip_at_prefix(content: str) -> str:
|
||||
return re.sub(r"@.*?[\u2005\s]+", "", content).strip()
|
||||
|
||||
def _should_ignore(self, content: str) -> bool:
|
||||
if len(content) < int(self.filters.get("min_text_length", 1)):
|
||||
return True
|
||||
if content in set(self.filters.get("ignore_exact", [])):
|
||||
return True
|
||||
return any(content.startswith(prefix) for prefix in self.filters.get("ignore_prefixes", []))
|
||||
|
||||
def _get_sender_name(self, room_id: str, sender: str) -> str:
|
||||
try:
|
||||
members = ContactManager.get_instance().get_group_members(room_id)
|
||||
return members.get(sender, sender)
|
||||
except Exception:
|
||||
return sender
|
||||
|
||||
def _pass_cooldown(self, room_id: str, trigger: Dict) -> bool:
|
||||
current_ts = time.time()
|
||||
room_cd = int(self.cooldown_config.get("group_reply_cooldown_sec", 45))
|
||||
user_cd = int(self.cooldown_config.get("same_user_followup_cooldown_sec", 10))
|
||||
last_room_reply = self.last_reply_at.get(room_id, 0.0)
|
||||
if trigger.get("is_question") or trigger.get("is_followup") or trigger.get("trigger_type") == "at_trigger":
|
||||
return (current_ts - last_room_reply) >= user_cd
|
||||
return (current_ts - last_room_reply) >= room_cd
|
||||
|
||||
def _build_user_prompt(self, context: Dict, memory_hints: Dict) -> str:
|
||||
recent_text = "\n".join(context.get("recent_messages", [])[-20:]) or "暂无"
|
||||
reply_mode = context.get("reply_mode", "social_short")
|
||||
length_rule = self._build_length_rule(reply_mode)
|
||||
return (
|
||||
f"当前群聊消息：\n{recent_text}\n\n"
|
||||
f"当前发言：{context.get('current_message', '')}\n"
|
||||
f"触发类型：{context.get('trigger_type', 'none')}\n"
|
||||
f"回复模式：{context.get('reply_mode', 'social_short')}\n"
|
||||
f"当前心流状态：{context.get('flow_state', 'idle')}\n"
|
||||
f"成员稳定记忆：\n{context.get('memory_prompt', '暂无')}\n\n"
|
||||
f"向量召回记忆：\n{context.get('vector_memory_prompt', '') or '暂无'}\n\n"
|
||||
f"补充信息：回归状态={memory_hints.get('returning_member_state', '') or 'none'}\n"
|
||||
f"要求：\n"
|
||||
f"1. 如果是明确问题，先给清楚答案。\n"
|
||||
f"2. 如果只是轻量接话，保持自然短句。\n"
|
||||
f"3. 不要暴露系统记忆来源。\n"
|
||||
f"4. 如果信息不足，不要硬编。\n"
|
||||
f"5. 输出最终可直接发到群里的内容，不要解释你的思路。\n"
|
||||
f"6. {length_rule}\n"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _sanitize_response(response: str) -> str:
|
||||
if not response:
|
||||
return ""
|
||||
response = response.strip()
|
||||
response = re.sub(r"\n{3,}", "\n\n", response)
|
||||
return response[:500].strip()
|
||||
|
||||
@staticmethod
|
||||
def _build_length_rule(reply_mode: str) -> str:
|
||||
if reply_mode == "social_short":
|
||||
return "默认只回一句短话，最好控制在2到12个字，除非非常不自然。"
|
||||
if reply_mode == "qa_fast":
|
||||
return "尽量只回1句话，必要时最多2句，先给结论，不要展开成长教程。"
|
||||
if reply_mode == "qa_with_context":
|
||||
return "优先控制在1到2句，除非对方明显在等详细步骤。"
|
||||
return "尽量短，像群友临时接一句，不要长篇大论。"
|
||||
|
||||
|
||||
def _sync_member_memory(self, room_id: str, sender: str, sender_name: str, member_context: Dict) -> None:
|
||||
if not member_context:
|
||||
return
|
||||
version = str(member_context.get("last_profiled_at", ""))
|
||||
cache_key = f"{room_id}:{sender}"
|
||||
if version and self._synced_member_context_versions.get(cache_key) == version:
|
||||
return
|
||||
text = self.context_builder._build_member_memory_prompt(member_context)
|
||||
if not text or text == "暂无稳定成员画像。":
|
||||
return
|
||||
payload = {
|
||||
"chatroom_id": room_id,
|
||||
"wxid": sender,
|
||||
"display_name": sender_name,
|
||||
"memory_type": "member_context_snapshot",
|
||||
"source_id": cache_key,
|
||||
"last_active_at": member_context.get("last_profiled_at", ""),
|
||||
"topic_tags": member_context.get("topics_of_interest", [])[:5],
|
||||
"summary_text": member_context.get("summary_text", ""),
|
||||
}
|
||||
ok = self.vector_memory.upsert_memory(f"member_context:{cache_key}:{version}", text, payload)
|
||||
self._log_event(
|
||||
"memory_upsert",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
memory_type="member_context_snapshot",
|
||||
ok=ok,
|
||||
)
|
||||
if ok and version:
|
||||
self._synced_member_context_versions[cache_key] = version
|
||||
|
||||
def _upsert_interaction_memory(
|
||||
self,
|
||||
room_id: str,
|
||||
sender: str,
|
||||
sender_name: str,
|
||||
content: str,
|
||||
response: str,
|
||||
trigger_type: str,
|
||||
topic: str,
|
||||
) -> None:
|
||||
text = f"{sender_name}说：{content}\n小牛回复：{response}"
|
||||
payload = {
|
||||
"chatroom_id": room_id,
|
||||
"wxid": sender,
|
||||
"display_name": sender_name,
|
||||
"memory_type": "interaction_memory",
|
||||
"topic_tags": [item for item in [topic, trigger_type] if item],
|
||||
"created_at": time.strftime("%Y-%m-%d %H:%M:%S"),
|
||||
"source_id": f"{room_id}:{sender}:{int(time.time())}",
|
||||
"summary_text": text[:500],
|
||||
}
|
||||
ok = self.vector_memory.upsert_memory(payload["source_id"], text, payload)
|
||||
self._log_event(
|
||||
"memory_upsert",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
memory_type="interaction_memory",
|
||||
ok=ok,
|
||||
trigger_type=trigger_type,
|
||||
)
|
||||
|
||||
def _log_event(self, event: str, **kwargs: Any) -> None:
|
||||
if not self.log_debug:
|
||||
return
|
||||
summary = self._build_log_summary(event, kwargs)
|
||||
self.LOG.info(summary)
|
||||
|
||||
@staticmethod
|
||||
def _preview(text: str, limit: int = 80) -> str:
|
||||
text = (text or "").replace("\n", "\\n").strip()
|
||||
if len(text) <= limit:
|
||||
return text
|
||||
return text[: limit - 3] + "..."
|
||||
|
||||
def _build_log_summary(self, event: str, data: Dict[str, Any]) -> str:
|
||||
room = self._short_id(data.get("room_id", ""))
|
||||
sender_name = data.get("sender_name", "") or self._short_id(data.get("sender", ""))
|
||||
sender = self._short_id(data.get("sender", ""))
|
||||
|
||||
if event == "recv":
|
||||
return (
|
||||
f"[XIAONIU] RECV room={room} user={sender_name}/{sender} "
|
||||
f"at={self._yn(data.get('is_at'))} msg={data.get('content_preview', '')}"
|
||||
).strip()
|
||||
|
||||
if event == "memory":
|
||||
return (
|
||||
f"[XIAONIU] MEMORY room={room} user={sender} "
|
||||
f"ctx={self._yn(data.get('has_member_context'))} "
|
||||
f"follow={self._yn(data.get('is_followup'))} "
|
||||
f"return={data.get('returning_state', 'none')}"
|
||||
).strip()
|
||||
|
||||
if event == "decision":
|
||||
return (
|
||||
f"[XIAONIU] DECIDE room={room} user={sender} "
|
||||
f"trigger={data.get('trigger_type', 'none')} "
|
||||
f"flow={data.get('flow_state', '')}:{data.get('flow_score', '')} "
|
||||
f"topic={data.get('topic', '-') or '-'} "
|
||||
f"reasons={data.get('reasons', '-') or '-'}"
|
||||
).strip()
|
||||
|
||||
if event == "skip":
|
||||
return (
|
||||
f"[XIAONIU] SKIP room={room} user={sender} "
|
||||
f"reason={data.get('reason', '')} "
|
||||
f"trigger={data.get('trigger_type', 'none')} "
|
||||
f"mode={data.get('reply_mode', '')}"
|
||||
).strip()
|
||||
|
||||
if event == "context":
|
||||
return (
|
||||
f"[XIAONIU] CTX room={room} user={sender} "
|
||||
f"mode={data.get('reply_mode', '')} "
|
||||
f"recent={data.get('recent_message_count', 0)} "
|
||||
f"vector={data.get('vector_hit_count', 0)}"
|
||||
).strip()
|
||||
|
||||
if event == "model_empty":
|
||||
return (
|
||||
f"[XIAONIU] MODEL_EMPTY room={room} user={sender} "
|
||||
f"model={data.get('model', '')} "
|
||||
f"mode={data.get('reply_mode', '')} "
|
||||
f"err={data.get('last_error', '')}"
|
||||
).strip()
|
||||
|
||||
if event == "sent":
|
||||
return (
|
||||
f"[XIAONIU] SENT room={room} user={sender_name}/{sender} "
|
||||
f"trigger={data.get('trigger_type', 'none')} "
|
||||
f"mode={data.get('reply_mode', '')} "
|
||||
f"len={data.get('response_len', 0)} "
|
||||
f"reply={data.get('response_preview', '')}"
|
||||
).strip()
|
||||
|
||||
if event == "memory_upsert":
|
||||
return (
|
||||
f"[XIAONIU] MEM_UPSERT room={room} user={sender} "
|
||||
f"type={data.get('memory_type', '')} "
|
||||
f"ok={self._yn(data.get('ok'))} "
|
||||
f"trigger={data.get('trigger_type', '-') or '-'}"
|
||||
).strip()
|
||||
|
||||
compact = " ".join(f"{key}={data[key]}" for key in sorted(data) if data.get(key) not in (None, ""))
|
||||
return f"[XIAONIU] {event.upper()} {compact}".strip()
|
||||
|
||||
@staticmethod
|
||||
def _yn(value: Any) -> str:
|
||||
return "Y" if bool(value) else "N"
|
||||
|
||||
@staticmethod
|
||||
def _short_id(value: str) -> str:
|
||||
value = str(value or "")
|
||||
if len(value) <= 10:
|
||||
return value
|
||||
return value[:4] + "..." + value[-4:]
|
||||
|
||||
89
plugins/ai_auto_response/memory_store.py
Normal file
89
plugins/ai_auto_response/memory_store.py
Normal file
@@ -0,0 +1,89 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from db.member_context_db import MemberContextDBOperator
|
||||
from db.message_storage import MessageStorageDB
|
||||
|
||||
|
||||
class MemoryStore:
|
||||
def __init__(self, db_manager, config: Dict):
|
||||
self.config = config or {}
|
||||
self.message_db = MessageStorageDB(db_manager)
|
||||
self.member_context_db = MemberContextDBOperator(db_manager)
|
||||
self.followup_sessions: Dict[str, Dict] = {}
|
||||
|
||||
def get_recent_messages(self, room_id: str) -> List[Dict]:
|
||||
hours = int(self.config.get("active_context_hours", 8))
|
||||
recent = self.message_db.get_recent_messages(room_id, hours_ago=hours, min_content_length=0) or []
|
||||
size = int(self.config.get("recent_context_size", 30))
|
||||
return recent[-size:]
|
||||
|
||||
def get_member_context(self, room_id: str, wxid: str) -> Optional[Dict]:
|
||||
if not self.config.get("enable_member_context", True):
|
||||
return None
|
||||
return self.member_context_db.get_member_context(room_id, wxid)
|
||||
|
||||
def build_memory_hints(self, room_id: str, wxid: str) -> Dict:
|
||||
lookback_days = int(self.config.get("memory_lookback_days", 180))
|
||||
returning_days = int(self.config.get("returning_member_days", 7))
|
||||
long_absent_days = int(self.config.get("long_absent_member_days", 30))
|
||||
active_dates = self.message_db.get_member_active_dates(room_id, wxid, days=lookback_days) or []
|
||||
member_context = self.get_member_context(room_id, wxid)
|
||||
|
||||
last_active_at = ""
|
||||
returning_state = ""
|
||||
days_since_active = None
|
||||
if active_dates:
|
||||
last_item = active_dates[-1]
|
||||
last_active_at = last_item.get("last_message_time") or ""
|
||||
parsed = self._parse_datetime(last_active_at)
|
||||
if parsed:
|
||||
days_since_active = max((datetime.now() - parsed).days, 0)
|
||||
if days_since_active >= long_absent_days:
|
||||
returning_state = "long_absent_member"
|
||||
elif days_since_active >= returning_days:
|
||||
returning_state = "returning_member"
|
||||
|
||||
followup = self._get_followup_state(room_id, wxid)
|
||||
return {
|
||||
"member_context": member_context or {},
|
||||
"last_active_at": last_active_at,
|
||||
"days_since_active": days_since_active,
|
||||
"returning_member_state": returning_state,
|
||||
"is_followup": followup,
|
||||
}
|
||||
|
||||
def note_bot_reply(self, room_id: str, wxid: str, topic: str = "") -> None:
|
||||
key = self._followup_key(room_id, wxid)
|
||||
self.followup_sessions[key] = {
|
||||
"last_bot_reply_at": datetime.now(),
|
||||
"topic": topic,
|
||||
}
|
||||
|
||||
def _get_followup_state(self, room_id: str, wxid: str) -> bool:
|
||||
key = self._followup_key(room_id, wxid)
|
||||
state = self.followup_sessions.get(key)
|
||||
if not state:
|
||||
return False
|
||||
timeout = int(self.config.get("followup_session_window_sec", 300))
|
||||
last_reply_at = state.get("last_bot_reply_at")
|
||||
if not last_reply_at:
|
||||
return False
|
||||
return (datetime.now() - last_reply_at).total_seconds() <= timeout
|
||||
|
||||
@staticmethod
|
||||
def _followup_key(room_id: str, wxid: str) -> str:
|
||||
return f"{room_id}:{wxid}"
|
||||
|
||||
@staticmethod
|
||||
def _parse_datetime(value: str) -> Optional[datetime]:
|
||||
if not value:
|
||||
return None
|
||||
for fmt in ("%Y-%m-%d %H:%M:%S", "%Y-%m-%d"):
|
||||
try:
|
||||
return datetime.strptime(value, fmt)
|
||||
except ValueError:
|
||||
continue
|
||||
return None
|
||||
23
plugins/ai_auto_response/persona/xiaoniu.txt
Normal file
23
plugins/ai_auto_response/persona/xiaoniu.txt
Normal file
@@ -0,0 +1,23 @@
|
||||
你叫小牛，是微信群里的常驻虚拟成员，不是客服，不是公告机，也不是冷冰冰的机器人。
|
||||
|
||||
你的人设要求：
|
||||
1. 说话自然、口语化、像群友，不端着。
|
||||
2. 优先解决问题，尤其是有人明确提问、@你、或者继续追问时。
|
||||
3. 有熟悉感，但不过分装熟，不要让人觉得你在翻旧档案。
|
||||
4. 轻松聊天时尽量短，答疑时尽量清楚。
|
||||
5. 不确定就直接说不确定，不编。
|
||||
6. 不抢话，不刷屏，不要每条都接。
|
||||
7. 在群里更像一个靠谱、反应快、稍微有点温度的成员。
|
||||
|
||||
你的表达偏好：
|
||||
- 能一句说清就别说三句
|
||||
- 避免客服腔、教程腔、模板腔
|
||||
- 除非很有必要，不要长篇大论
|
||||
- 允许少量语气词，但不要太油腻
|
||||
- 面对回归成员时，可以表现出轻微熟悉感，但不要直接暴露细粒度历史记录
|
||||
|
||||
你的边界：
|
||||
- 不要假装知道不存在的上下文
|
||||
- 不要把系统记忆原样说给用户听
|
||||
- 不要在闲聊里强行翻旧事
|
||||
- 敏感、风险、不确定的话题要收敛
|
||||
33
plugins/ai_auto_response/persona_engine.py
Normal file
33
plugins/ai_auto_response/persona_engine.py
Normal file
@@ -0,0 +1,33 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Dict
|
||||
|
||||
|
||||
class PersonaEngine:
|
||||
def __init__(self, plugin_path: str, config: Dict):
|
||||
self.plugin_path = Path(plugin_path)
|
||||
self.config = config or {}
|
||||
self.persona_text = self._load_persona()
|
||||
|
||||
def build_system_prompt(self) -> str:
|
||||
name = self.config.get("name", "小牛")
|
||||
style = self.config.get("style", "")
|
||||
familiarity = self.config.get("familiarity_hint", "")
|
||||
max_sentences = self.config.get("max_reply_sentences", 3)
|
||||
return (
|
||||
f"{self.persona_text}\n\n"
|
||||
f"补充约束：\n"
|
||||
f"- 你当前对外名称固定为{name}\n"
|
||||
f"- 整体风格：{style}\n"
|
||||
f"- 熟悉感边界：{familiarity}\n"
|
||||
f"- 一般最多输出{max_sentences}句\n"
|
||||
f"- 优先根据场景决定是答疑、接话还是不说话\n"
|
||||
)
|
||||
|
||||
def _load_persona(self) -> str:
|
||||
persona_file = self.config.get("persona_file", "persona/xiaoniu.txt")
|
||||
persona_path = self.plugin_path / persona_file
|
||||
if persona_path.exists():
|
||||
return persona_path.read_text(encoding="utf-8").strip()
|
||||
return "你叫小牛，是一个自然、靠谱、会看场合的群聊成员。"
|
||||
23
plugins/ai_auto_response/response_planner.py
Normal file
23
plugins/ai_auto_response/response_planner.py
Normal file
@@ -0,0 +1,23 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Dict
|
||||
|
||||
|
||||
class ResponsePlanner:
|
||||
def choose_reply_mode(self, trigger: Dict, flow_state: str) -> str:
|
||||
if trigger.get("is_question"):
|
||||
return "qa_with_context" if flow_state in {"engaged", "deep_engaged"} else "qa_fast"
|
||||
if trigger.get("is_followup"):
|
||||
return "qa_with_context"
|
||||
if trigger.get("is_social_call"):
|
||||
return "social_short"
|
||||
if trigger.get("is_returning_member"):
|
||||
return "social_short"
|
||||
return "social_short" if flow_state in {"warming", "engaged"} else "refuse_or_skip"
|
||||
|
||||
def should_reply(self, trigger: Dict, flow_state: str, allow_proactive: bool) -> bool:
|
||||
if trigger.get("should_respond"):
|
||||
return True
|
||||
if not allow_proactive:
|
||||
return False
|
||||
return flow_state in {"warming", "engaged", "deep_engaged"} and trigger.get("priority", 0) >= 0.35
|
||||
87
plugins/ai_auto_response/triggers.py
Normal file
87
plugins/ai_auto_response/triggers.py
Normal file
@@ -0,0 +1,87 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, List
|
||||
|
||||
|
||||
QUESTION_PATTERNS = [
|
||||
r"\?$", r"？$", r"怎么", r"如何", r"咋弄", r"为啥", r"为什么",
|
||||
r"有人知道", r"谁知道", r"能不能", r"可以吗", r"报错", r"怎么解决",
|
||||
]
|
||||
SOCIAL_PATTERNS = [r"小牛", r"在吗", r"出来", r"帮忙看", r"看看"]
|
||||
|
||||
|
||||
@dataclass
|
||||
class TriggerResult:
|
||||
trigger_type: str = "none"
|
||||
priority: float = 0.0
|
||||
is_question: bool = False
|
||||
is_followup: bool = False
|
||||
is_social_call: bool = False
|
||||
is_returning_member: bool = False
|
||||
should_respond: bool = False
|
||||
topic: str = ""
|
||||
reasons: List[str] = field(default_factory=list)
|
||||
|
||||
|
||||
class TriggerRouter:
|
||||
def __init__(self, config: Dict):
|
||||
self.config = config or {}
|
||||
self.topic_keywords = [str(item).lower() for item in self.config.get("focus", [])]
|
||||
|
||||
def route(self, message: Dict, memory_hints: Dict) -> TriggerResult:
|
||||
content = str(message.get("content", "")).strip()
|
||||
content_lower = content.lower()
|
||||
result = TriggerResult()
|
||||
if message.get("is_at"):
|
||||
result.trigger_type = "at_trigger"
|
||||
result.priority = float(self.config.get("at_bot", 1.0))
|
||||
result.should_respond = True
|
||||
result.reasons.append("is_at")
|
||||
if self._is_question(content):
|
||||
if result.priority < float(self.config.get("explicit_question", 0.95)):
|
||||
result.trigger_type = "question_trigger"
|
||||
result.priority = float(self.config.get("explicit_question", 0.95))
|
||||
result.is_question = True
|
||||
result.should_respond = True
|
||||
result.reasons.append("question")
|
||||
if memory_hints.get("is_followup"):
|
||||
if result.priority < float(self.config.get("followup", 0.90)):
|
||||
result.trigger_type = "followup_trigger"
|
||||
result.priority = float(self.config.get("followup", 0.90))
|
||||
result.is_followup = True
|
||||
result.should_respond = True
|
||||
result.reasons.append("followup")
|
||||
topic = self._detect_topic(content_lower)
|
||||
if topic:
|
||||
result.topic = topic
|
||||
result.reasons.append("topic")
|
||||
if result.priority < float(self.config.get("casual_topic", 0.35)):
|
||||
result.trigger_type = result.trigger_type if result.trigger_type != "none" else "topic_trigger"
|
||||
result.priority = max(result.priority, float(self.config.get("casual_topic", 0.35)))
|
||||
if self._is_social_call(content_lower):
|
||||
if result.priority < float(self.config.get("social_call", 0.65)):
|
||||
result.trigger_type = result.trigger_type if result.trigger_type != "none" else "social_trigger"
|
||||
result.priority = max(result.priority, float(self.config.get("social_call", 0.65)))
|
||||
result.is_social_call = True
|
||||
result.reasons.append("social_call")
|
||||
if memory_hints.get("returning_member_state") in {"returning_member", "long_absent_member"}:
|
||||
result.is_returning_member = True
|
||||
result.reasons.append(memory_hints.get("returning_member_state"))
|
||||
if result.trigger_type == "none":
|
||||
result.trigger_type = "returning_member"
|
||||
result.priority = max(result.priority, float(self.config.get("casual_topic", 0.35)))
|
||||
return result
|
||||
|
||||
def _is_question(self, content: str) -> bool:
|
||||
return any(re.search(pattern, content, flags=re.IGNORECASE) for pattern in QUESTION_PATTERNS)
|
||||
|
||||
def _is_social_call(self, content: str) -> bool:
|
||||
return any(re.search(pattern, content, flags=re.IGNORECASE) for pattern in SOCIAL_PATTERNS)
|
||||
|
||||
def _detect_topic(self, content_lower: str) -> str:
|
||||
for keyword in self.topic_keywords:
|
||||
if keyword and keyword in content_lower:
|
||||
return keyword
|
||||
return ""
|
||||
138
plugins/ai_auto_response/vector_memory.py
Normal file
138
plugins/ai_auto_response/vector_memory.py
Normal file
@@ -0,0 +1,138 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
from typing import Dict, List
|
||||
|
||||
import requests
|
||||
|
||||
|
||||
class VectorMemoryStore:
|
||||
def __init__(self, config: Dict):
|
||||
self.config = config or {}
|
||||
self.enabled = bool(self.config.get("enable_vector_memory"))
|
||||
self.qdrant_url = str(self.config.get("qdrant_url", "")).rstrip("/")
|
||||
self.collection = self.config.get("qdrant_collection", "")
|
||||
self.ollama_base_url = str(self.config.get("ollama_base_url", "")).rstrip("/")
|
||||
self.embedding_model = self.config.get("embedding_model", "")
|
||||
self.top_k = int(self.config.get("vector_top_k", 5))
|
||||
self.min_score = float(self.config.get("vector_min_score", 0.65))
|
||||
self.collection_ready = False
|
||||
self.last_error = ""
|
||||
|
||||
def should_search(self, reply_mode: str, trigger_type: str, returning_state: str) -> bool:
|
||||
modes = set(self.config.get("vector_trigger_modes", []))
|
||||
return any(item in modes for item in [reply_mode, trigger_type, returning_state] if item)
|
||||
|
||||
def search(self, query: str, room_id: str, wxid: str = "") -> List[Dict]:
|
||||
self.last_error = ""
|
||||
if not self.enabled or not self.qdrant_url or not self.collection or not self.embedding_model:
|
||||
self.last_error = "vector_disabled_or_incomplete"
|
||||
return []
|
||||
embedding = self._embed(query)
|
||||
if not embedding:
|
||||
self.last_error = "embed_failed"
|
||||
return []
|
||||
self._ensure_collection(len(embedding))
|
||||
must = [{"key": "chatroom_id", "match": {"value": room_id}}]
|
||||
payload = {
|
||||
"vector": embedding,
|
||||
"limit": self.top_k,
|
||||
"with_payload": True,
|
||||
"score_threshold": self.min_score,
|
||||
"filter": {"must": must},
|
||||
}
|
||||
if wxid:
|
||||
payload["filter"]["should"] = [{"key": "wxid", "match": {"value": wxid}}]
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.qdrant_url}/collections/{self.collection}/points/search",
|
||||
json=payload,
|
||||
timeout=15,
|
||||
)
|
||||
response.raise_for_status()
|
||||
items = response.json().get("result", []) or []
|
||||
return [item.get("payload", {}) for item in items if item.get("payload")]
|
||||
except Exception as exc:
|
||||
self.last_error = f"search_failed:{exc}"
|
||||
return []
|
||||
|
||||
def upsert_memory(self, memory_id: str, text: str, payload: Dict) -> bool:
|
||||
self.last_error = ""
|
||||
if not self.enabled or not text or not self.qdrant_url or not self.collection or not self.embedding_model:
|
||||
self.last_error = "vector_disabled_or_incomplete"
|
||||
return False
|
||||
embedding = self._embed(text)
|
||||
if not embedding:
|
||||
self.last_error = "embed_failed"
|
||||
return False
|
||||
if not self._ensure_collection(len(embedding)):
|
||||
self.last_error = "ensure_collection_failed"
|
||||
return False
|
||||
point = {
|
||||
"points": [
|
||||
{
|
||||
"id": self._stable_id(memory_id),
|
||||
"vector": embedding,
|
||||
"payload": payload | {"content_summary": text},
|
||||
}
|
||||
]
|
||||
}
|
||||
try:
|
||||
response = requests.put(
|
||||
f"{self.qdrant_url}/collections/{self.collection}/points",
|
||||
json=point,
|
||||
timeout=15,
|
||||
)
|
||||
response.raise_for_status()
|
||||
return True
|
||||
except Exception as exc:
|
||||
self.last_error = f"upsert_failed:{exc}"
|
||||
return False
|
||||
|
||||
def _embed(self, query: str) -> List[float]:
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.ollama_base_url}/api/embeddings",
|
||||
json={"model": self.embedding_model, "prompt": query},
|
||||
timeout=20,
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json().get("embedding") or []
|
||||
except Exception:
|
||||
return []
|
||||
|
||||
def _ensure_collection(self, vector_size: int) -> bool:
|
||||
if self.collection_ready:
|
||||
return True
|
||||
try:
|
||||
response = requests.get(
|
||||
f"{self.qdrant_url}/collections/{self.collection}",
|
||||
timeout=10,
|
||||
)
|
||||
if response.status_code == 200:
|
||||
self.collection_ready = True
|
||||
return True
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
try:
|
||||
response = requests.put(
|
||||
f"{self.qdrant_url}/collections/{self.collection}",
|
||||
json={
|
||||
"vectors": {
|
||||
"size": vector_size,
|
||||
"distance": "Cosine",
|
||||
}
|
||||
},
|
||||
timeout=15,
|
||||
)
|
||||
response.raise_for_status()
|
||||
self.collection_ready = True
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
@staticmethod
|
||||
def _stable_id(memory_id: str) -> int:
|
||||
digest = hashlib.md5(memory_id.encode("utf-8")).hexdigest()[:15]
|
||||
return int(digest, 16)
|
||||
Reference in New Issue
Block a user