Merge remote-tracking branch 'origin/refactor' into refactor
# Conflicts: # CLAUDE.md
This commit is contained in:
infinitycat
1
.gitattributes
vendored
1
.gitattributes
vendored
@@ -1,2 +1,3 @@
|
|||||||
*.bat text eol=crlf
|
*.bat text eol=crlf
|
||||||
*.cmd text eol=crlf
|
*.cmd text eol=crlf
|
||||||
|
MaiLauncher.bat text eol=crlf working-tree-encoding=GBK
|
||||||
17
.github/ISSUE_TEMPLATE/bug_report.yml
vendored
17
.github/ISSUE_TEMPLATE/bug_report.yml
vendored
@@ -12,6 +12,23 @@ body:
|
|||||||
- label: "我确认在 Issues 列表中并无其他人已经提出过与此问题相同或相似的问题"
|
- label: "我确认在 Issues 列表中并无其他人已经提出过与此问题相同或相似的问题"
|
||||||
required: true
|
required: true
|
||||||
- label: "我使用了 Docker"
|
- label: "我使用了 Docker"
|
||||||
|
- type: dropdown
|
||||||
|
attributes:
|
||||||
|
label: "使用的分支"
|
||||||
|
description: "请选择您正在使用的版本分支"
|
||||||
|
options:
|
||||||
|
- main
|
||||||
|
- main-fix
|
||||||
|
- refactor
|
||||||
|
validations:
|
||||||
|
required: true
|
||||||
|
- type: input
|
||||||
|
attributes:
|
||||||
|
label: "具体版本号"
|
||||||
|
description: "请输入您使用的具体版本号"
|
||||||
|
placeholder: "例如：0.5.11、0.5.8"
|
||||||
|
validations:
|
||||||
|
required: true
|
||||||
- type: textarea
|
- type: textarea
|
||||||
attributes:
|
attributes:
|
||||||
label: 遇到的问题
|
label: 遇到的问题
|
||||||
|
|||||||
17
.github/pull_request_template.md
vendored
Normal file
17
.github/pull_request_template.md
vendored
Normal file
@@ -0,0 +1,17 @@
|
|||||||
|
<!-- 提交前必读 -->
|
||||||
|
- 🔴**当前项目处于重构阶段（2025.3.14-）**
|
||||||
|
- ✅ 接受：与main直接相关的Bug修复：提交到main-fix分支
|
||||||
|
- ⚠️ 冻结：所有新功能开发和非紧急重构
|
||||||
|
|
||||||
|
# 请填写以下内容
|
||||||
|
（删除掉中括号内的空格，并替换为**小写的x**）
|
||||||
|
1. - [ ] `main` 分支 **禁止修改**，请确认本次提交的分支 **不是 `main` 分支**
|
||||||
|
2. - [ ] 本次更新 **包含破坏性变更**（如数据库结构变更、配置文件修改等）
|
||||||
|
3. - [ ] 本次更新是否经过测试
|
||||||
|
4. - [ ] 请**不要**在数据库中添加group_id字段，这会影响本项目对其他平台的兼容
|
||||||
|
5. 请填写破坏性更新的具体内容（如有）:
|
||||||
|
6. 请简要说明本次更新的内容和目的：
|
||||||
|
# 其他信息
|
||||||
|
- **关联 Issue**：Close #
|
||||||
|
- **截图/GIF**：
|
||||||
|
- **附加信息**:
|
||||||
9
.github/workflows/docker-image.yml
vendored
9
.github/workflows/docker-image.yml
vendored
@@ -4,8 +4,7 @@ on:
|
|||||||
push:
|
push:
|
||||||
branches:
|
branches:
|
||||||
- main
|
- main
|
||||||
- debug # 新增 debug 分支触发
|
- main-fix
|
||||||
- stable-dev
|
|
||||||
tags:
|
tags:
|
||||||
- 'v*'
|
- 'v*'
|
||||||
workflow_dispatch:
|
workflow_dispatch:
|
||||||
@@ -33,10 +32,8 @@ jobs:
|
|||||||
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:${{ github.ref_name }},${{ secrets.DOCKERHUB_USERNAME }}/maimbot:latest" >> $GITHUB_OUTPUT
|
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:${{ github.ref_name }},${{ secrets.DOCKERHUB_USERNAME }}/maimbot:latest" >> $GITHUB_OUTPUT
|
||||||
elif [ "${{ github.ref }}" == "refs/heads/main" ]; then
|
elif [ "${{ github.ref }}" == "refs/heads/main" ]; then
|
||||||
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:main,${{ secrets.DOCKERHUB_USERNAME }}/maimbot:latest" >> $GITHUB_OUTPUT
|
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:main,${{ secrets.DOCKERHUB_USERNAME }}/maimbot:latest" >> $GITHUB_OUTPUT
|
||||||
elif [ "${{ github.ref }}" == "refs/heads/debug" ]; then
|
elif [ "${{ github.ref }}" == "refs/heads/main-fix" ]; then
|
||||||
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:debug" >> $GITHUB_OUTPUT
|
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:main-fix" >> $GITHUB_OUTPUT
|
||||||
elif [ "${{ github.ref }}" == "refs/heads/stable-dev" ]; then
|
|
||||||
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:stable-dev" >> $GITHUB_OUTPUT
|
|
||||||
fi
|
fi
|
||||||
|
|
||||||
- name: Build and Push Docker Image
|
- name: Build and Push Docker Image
|
||||||
|
|||||||
29
.github/workflows/precheck.yml
vendored
Normal file
29
.github/workflows/precheck.yml
vendored
Normal file
@@ -0,0 +1,29 @@
|
|||||||
|
# .github/workflows/precheck.yml
|
||||||
|
name: PR Precheck
|
||||||
|
on: [pull_request]
|
||||||
|
|
||||||
|
jobs:
|
||||||
|
conflict-check:
|
||||||
|
runs-on: ubuntu-latest
|
||||||
|
steps:
|
||||||
|
- uses: actions/checkout@v3
|
||||||
|
- name: Check Conflicts
|
||||||
|
run: |
|
||||||
|
git fetch origin main
|
||||||
|
if git diff --name-only --diff-filter=U origin/main...HEAD | grep .; then
|
||||||
|
echo "CONFLICT=true" >> $GITHUB_ENV
|
||||||
|
fi
|
||||||
|
labeler:
|
||||||
|
runs-on: ubuntu-latest
|
||||||
|
needs: conflict-check
|
||||||
|
steps:
|
||||||
|
- uses: actions/github-script@v6
|
||||||
|
if: env.CONFLICT == 'true'
|
||||||
|
with:
|
||||||
|
script: |
|
||||||
|
github.rest.issues.addLabels({
|
||||||
|
owner: context.repo.owner,
|
||||||
|
repo: context.repo.repo,
|
||||||
|
issue_number: context.issue.number,
|
||||||
|
labels: ['🚫冲突需处理']
|
||||||
|
})
|
||||||
1
.github/workflows/ruff.yml
vendored
1
.github/workflows/ruff.yml
vendored
@@ -6,3 +6,4 @@ jobs:
|
|||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@v4
|
- uses: actions/checkout@v4
|
||||||
- uses: astral-sh/ruff-action@v3
|
- uses: astral-sh/ruff-action@v3
|
||||||
|
|
||||||
|
|||||||
24
.gitignore
vendored
24
.gitignore
vendored
@@ -16,6 +16,9 @@ memory_graph.gml
|
|||||||
.env.*
|
.env.*
|
||||||
config/bot_config_dev.toml
|
config/bot_config_dev.toml
|
||||||
config/bot_config.toml
|
config/bot_config.toml
|
||||||
|
config/bot_config.toml.bak
|
||||||
|
src/plugins/remote/client_uuid.json
|
||||||
|
run_none.bat
|
||||||
# Byte-compiled / optimized / DLL files
|
# Byte-compiled / optimized / DLL files
|
||||||
__pycache__/
|
__pycache__/
|
||||||
*.py[cod]
|
*.py[cod]
|
||||||
@@ -24,9 +27,10 @@ llm_statistics.txt
|
|||||||
mongodb
|
mongodb
|
||||||
napcat
|
napcat
|
||||||
run_dev.bat
|
run_dev.bat
|
||||||
|
elua.confirmed
|
||||||
# C extensions
|
# C extensions
|
||||||
*.so
|
*.so
|
||||||
|
/results
|
||||||
|
|
||||||
# Distribution / packaging
|
# Distribution / packaging
|
||||||
.Python
|
.Python
|
||||||
@@ -204,3 +208,21 @@ jieba.cache
|
|||||||
.idea
|
.idea
|
||||||
*.iml
|
*.iml
|
||||||
*.ipr
|
*.ipr
|
||||||
|
|
||||||
|
# PyEnv
|
||||||
|
# If using PyEnv and configured to use a specific Python version locally
|
||||||
|
# a .local-version file will be created in the root of the project to specify the version.
|
||||||
|
.python-version
|
||||||
|
|
||||||
|
OtherRes.txt
|
||||||
|
|
||||||
|
/eula.confirmed
|
||||||
|
/privacy.confirmed
|
||||||
|
|
||||||
|
logs
|
||||||
|
|
||||||
|
.ruff_cache
|
||||||
|
|
||||||
|
.vscode
|
||||||
|
|
||||||
|
/config/*
|
||||||
191
CLAUDE.md
191
CLAUDE.md
@@ -1,191 +0,0 @@
|
|||||||
# MaiMBot 项目架构与索引指南
|
|
||||||
|
|
||||||
## 🛠️ 常用命令
|
|
||||||
|
|
||||||
- **运行机器人**: `python run.py` 或 `python bot.py`
|
|
||||||
- **安装依赖**: `pip install --upgrade -r requirements.txt`
|
|
||||||
- **Docker 部署**: `docker-compose up`
|
|
||||||
- **代码检查**: `ruff check .`
|
|
||||||
- **代码格式化**: `ruff format .`
|
|
||||||
- **内存可视化**: `run_memory_vis.bat` 或 `python -m src.plugins.memory_system.draw_memory`
|
|
||||||
- **推理过程可视化**: `script/run_thingking.bat`
|
|
||||||
|
|
||||||
## 🔧 脚本工具
|
|
||||||
|
|
||||||
- **运行MongoDB**: `script/run_db.bat` - 在端口27017启动MongoDB
|
|
||||||
- **Windows完整启动**: `script/run_windows.bat` - 检查Python版本、设置虚拟环境、安装依赖并运行机器人
|
|
||||||
- **快速启动**: `script/run_maimai.bat` - 设置UTF-8编码并执行"nb run"命令
|
|
||||||
|
|
||||||
## 📝 代码风格
|
|
||||||
|
|
||||||
- **Python版本**: 3.9+
|
|
||||||
- **行长度限制**: 88字符
|
|
||||||
- **命名规范**:
|
|
||||||
- `snake_case` 用于函数和变量
|
|
||||||
- `PascalCase` 用于类
|
|
||||||
- `_prefix` 用于私有成员
|
|
||||||
- **导入顺序**: 标准库 → 第三方库 → 本地模块
|
|
||||||
- **异步编程**: 对I/O操作使用async/await
|
|
||||||
- **日志记录**: 使用loguru进行一致的日志记录
|
|
||||||
- **错误处理**: 使用带有具体异常的try/except
|
|
||||||
- **文档**: 为类和公共函数编写docstrings
|
|
||||||
|
|
||||||
## 🔍 项目结构概览
|
|
||||||
|
|
||||||
```mermaid
|
|
||||||
graph TD
|
|
||||||
A[入口文件] --> A1[run.py:初始安装与启动]
|
|
||||||
A --> A2[bot.py:主程序入口]
|
|
||||||
A2 --> B[核心框架]
|
|
||||||
B --> B1[NoneBot2框架]
|
|
||||||
B --> B2[MongoDB数据库]
|
|
||||||
|
|
||||||
A2 --> C[插件系统]
|
|
||||||
C --> C1[聊天系统]
|
|
||||||
C --> C2[记忆系统]
|
|
||||||
C --> C3[情绪系统]
|
|
||||||
C --> C4[日程系统]
|
|
||||||
C --> C5[配置系统]
|
|
||||||
|
|
||||||
C1 --> D[LLM集成]
|
|
||||||
D --> D1[ChatAnywhere]
|
|
||||||
D --> D2[SiliconFlow]
|
|
||||||
D --> D3[DeepSeek]
|
|
||||||
```
|
|
||||||
|
|
||||||
## 📁 关键文件索引
|
|
||||||
|
|
||||||
| 文件路径 | 功能描述 |
|
|
||||||
|---------|---------|
|
|
||||||
| `/bot.py` | 主程序入口，初始化框架和插件加载 |
|
|
||||||
| `/run.py` | 初始安装脚本，配置MongoDB和启动机器人 |
|
|
||||||
| `/src/plugins/chat/bot.py` | 聊天核心处理，消息接收与分发 |
|
|
||||||
| `/src/plugins/chat/llm_generator.py` | LLM交互封装，生成回复内容 |
|
|
||||||
| `/src/plugins/chat/prompt_builder.py` | 构建提示词，整合上下文和记忆 |
|
|
||||||
| `/src/plugins/memory_system/memory.py` | 图形记忆系统核心实现 |
|
|
||||||
| `/src/plugins/moods/moods.py` | 情绪管理系统 |
|
|
||||||
| `/src/common/database.py` | 数据库连接管理 |
|
|
||||||
| `/src/plugins/models/utils_model.py` | LLM API请求封装 |
|
|
||||||
| `/template.env` | 环境变量配置模板 |
|
|
||||||
| `/template/bot_config_template.toml` | 机器人配置模板 |
|
|
||||||
|
|
||||||
## 🔄 核心流程图
|
|
||||||
|
|
||||||
### 消息处理流程
|
|
||||||
|
|
||||||
```mermaid
|
|
||||||
flowchart LR
|
|
||||||
A[用户消息] --> B[NoneBot2接收]
|
|
||||||
B --> C[ChatBot.handle_message]
|
|
||||||
C --> D{检查回复意愿}
|
|
||||||
D -->|回复| E[思考状态]
|
|
||||||
D -->|不回复| Z[结束]
|
|
||||||
E --> F[构建提示词]
|
|
||||||
F --> G[选择LLM模型]
|
|
||||||
G --> H[生成回复]
|
|
||||||
H --> I[处理回复]
|
|
||||||
I --> J[消息管理器]
|
|
||||||
J --> K[发送回复]
|
|
||||||
```
|
|
||||||
|
|
||||||
### 记忆系统流程
|
|
||||||
|
|
||||||
```mermaid
|
|
||||||
flowchart TD
|
|
||||||
A[聊天记录] --> B[记忆样本获取]
|
|
||||||
B --> C[记忆压缩/主题提取]
|
|
||||||
C --> D[记忆图存储]
|
|
||||||
D --> E[记忆检索]
|
|
||||||
D --> F[记忆遗忘]
|
|
||||||
D --> G[记忆合并]
|
|
||||||
E --> H[提示词构建]
|
|
||||||
H --> I[LLM生成]
|
|
||||||
```
|
|
||||||
|
|
||||||
## ⚙️ 配置系统概览
|
|
||||||
|
|
||||||
```mermaid
|
|
||||||
graph LR
|
|
||||||
A[配置系统] --> B[环境变量配置]
|
|
||||||
A --> C[TOML配置文件]
|
|
||||||
|
|
||||||
B --> B1[数据库连接]
|
|
||||||
B --> B2[LLM API密钥]
|
|
||||||
B --> B3[服务器设置]
|
|
||||||
|
|
||||||
C --> C1[机器人人格]
|
|
||||||
C --> C2[消息处理参数]
|
|
||||||
C --> C3[记忆系统参数]
|
|
||||||
C --> C4[情绪系统参数]
|
|
||||||
C --> C5[模型配置]
|
|
||||||
```
|
|
||||||
|
|
||||||
## 📊 模块依赖关系
|
|
||||||
|
|
||||||
```mermaid
|
|
||||||
graph TD
|
|
||||||
A[bot.py] --> B[src/plugins]
|
|
||||||
B --> C[chat]
|
|
||||||
B --> D[memory_system]
|
|
||||||
B --> E[moods]
|
|
||||||
B --> F[models]
|
|
||||||
|
|
||||||
C --> D
|
|
||||||
C --> E
|
|
||||||
C --> F
|
|
||||||
D --> F
|
|
||||||
C --> G[common/database.py]
|
|
||||||
D --> G
|
|
||||||
```
|
|
||||||
|
|
||||||
## 🧠 记忆系统内部结构
|
|
||||||
|
|
||||||
- **Memory_graph**: 底层图结构实现
|
|
||||||
- 节点 = 主题概念
|
|
||||||
- 边 = 主题间关联
|
|
||||||
- 属性 = 记忆内容、时间戳
|
|
||||||
|
|
||||||
- **Hippocampus**: 高级记忆管理
|
|
||||||
- 记忆构建: `memory_compress()`
|
|
||||||
- 记忆检索: `get_relevant_memories()`
|
|
||||||
- 记忆遗忘: `operation_forget_topic()`
|
|
||||||
- 记忆合并: `operation_merge_memory()`
|
|
||||||
|
|
||||||
- **LLM集成点**:
|
|
||||||
- 主题提取
|
|
||||||
- 记忆摘要生成
|
|
||||||
- 相似度计算
|
|
||||||
- 记忆压缩
|
|
||||||
|
|
||||||
## 💬 聊天系统内部结构
|
|
||||||
|
|
||||||
- **ChatBot**: 核心控制器
|
|
||||||
- 消息处理: `handle_message()`
|
|
||||||
- 响应生成: `generate_response()`
|
|
||||||
|
|
||||||
- **消息处理链**:
|
|
||||||
- `MessageRecv` → 消息预处理
|
|
||||||
- `willing_manager` → 回复决策
|
|
||||||
- `prompt_builder` → 提示词构建
|
|
||||||
- `LLM_request` → LLM调用
|
|
||||||
- `MessageSending` → 消息发送
|
|
||||||
|
|
||||||
- **关键组件**:
|
|
||||||
- 消息管理器: 控制消息流
|
|
||||||
- 聊天流管理: 维护会话上下文
|
|
||||||
- 关系管理器: 用户关系状态
|
|
||||||
- 表情管理器: 表情包处理
|
|
||||||
|
|
||||||
## 🔧 配置项关键参数
|
|
||||||
|
|
||||||
### 环境变量 (.env)
|
|
||||||
- MongoDB连接: `MONGODB_HOST`, `MONGODB_PORT`, `DATABASE_NAME`
|
|
||||||
- LLM API: `CHAT_ANY_WHERE_KEY`, `SILICONFLOW_KEY`, `DEEP_SEEK_KEY`
|
|
||||||
- 服务设置: `HOST`, `PORT`
|
|
||||||
|
|
||||||
### 机器人配置 (TOML)
|
|
||||||
- 版本控制: `[inner].version`
|
|
||||||
- 人格设置: `[personality]`
|
|
||||||
- 记忆参数: `[memory]` (构建间隔、压缩率、遗忘周期)
|
|
||||||
- 情绪参数: `[mood]` (更新间隔、衰减率)
|
|
||||||
- 模型选择: `[model]` (各功能专用模型配置)
|
|
||||||
97
EULA.md
Normal file
97
EULA.md
Normal file
@@ -0,0 +1,97 @@
|
|||||||
|
# **MaiMBot最终用户许可协议**
|
||||||
|
**版本：V1.0**
|
||||||
|
**更新日期：2025年3月18日**
|
||||||
|
**生效日期：2025年3月18日**
|
||||||
|
**适用的MaiMBot版本号：<=v0.5.15**
|
||||||
|
|
||||||
|
**2025© MaiMBot项目团队**
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 一、一般条款
|
||||||
|
|
||||||
|
**1.1** MaiMBot项目（包括MaiMBot的源代码、可执行文件、文档，以及其它在本协议中所列出的文件）（以下简称“本项目”）是由开发者及贡献者（以下简称“项目团队”）共同维护，为用户提供自动回复功能的机器人代码项目。以下最终用户许可协议（EULA，以下简称“本协议”）是用户（以下简称“您”）与项目团队之间关于使用本项目所订立的合同条件。
|
||||||
|
|
||||||
|
**1.2** 在运行或使用本项目之前，您**必须阅读并同意本协议的所有条款**。未成年人或其它无/不完全民事行为能力责任人请**在监护人的陪同下**阅读并同意本协议。如果您不同意，则不得运行或使用本项目。在这种情况下，您应立即从您的设备上卸载或删除本项目及其所有副本。
|
||||||
|
|
||||||
|
|
||||||
|
## 二、许可授权
|
||||||
|
|
||||||
|
### 源代码许可
|
||||||
|
**2.1** 您**了解**本项目的源代码是基于GPLv3（GNU通用公共许可证第三版）开源协议发布的。您**可以自由使用、修改、分发**本项目的源代码，但**必须遵守**GPLv3许可证的要求。详细内容请参阅项目仓库中的LICENSE文件。
|
||||||
|
|
||||||
|
**2.2** 您**了解**本项目的源代码中可能包含第三方开源代码，这些代码的许可证可能与GPLv3许可证不同。您**同意**在使用这些代码时**遵守**相应的许可证要求。
|
||||||
|
|
||||||
|
|
||||||
|
### 输入输出内容授权
|
||||||
|
|
||||||
|
**2.3** 您**了解**本项目是使用您的配置信息、提交的指令（以下简称“输入内容”）和生成的内容（以下简称“输出内容”）构建请求发送到第三方API生成回复的机器人项目。
|
||||||
|
|
||||||
|
**2.4** 您**授权**本项目使用您的输入和输出内容按照项目的隐私政策用于以下行为：
|
||||||
|
- 调用第三方API生成回复；
|
||||||
|
- 调用第三方API用于构建本项目专用的存储于您部署或使用的数据库中的知识库和记忆库；
|
||||||
|
- 收集并记录本项目专用的存储于您部署或使用的设备中的日志；
|
||||||
|
|
||||||
|
**2.4** 您**了解**本项目的源代码中包含第三方API的调用代码，这些API的使用可能受到第三方的服务条款和隐私政策的约束。在使用这些API时，您**必须遵守**相应的服务条款。
|
||||||
|
|
||||||
|
**2.5** 项目团队**不对**第三方API的服务质量、稳定性、准确性、安全性负责，亦**不对**第三方API的服务变更、终止、限制等行为负责。
|
||||||
|
|
||||||
|
|
||||||
|
## 三、用户行为
|
||||||
|
|
||||||
|
**3.1** 您**了解**本项目会将您的配置信息、输入指令和生成内容发送到第三方API，您**不应**在输入指令和生成内容中包含以下内容：
|
||||||
|
- 涉及任何国家或地区秘密、商业秘密或其他可能会对国家或地区安全或者公共利益造成不利影响的数据；
|
||||||
|
- 涉及个人隐私、个人信息或其他敏感信息的数据；
|
||||||
|
- 任何侵犯他人合法权益的内容；
|
||||||
|
- 任何违反国家或地区法律法规、政策规定的内容；
|
||||||
|
|
||||||
|
**3.2** 您**不应**将本项目用于以下用途：
|
||||||
|
- 违反任何国家或地区法律法规、政策规定的行为；
|
||||||
|
|
||||||
|
**3.3** 您**应当**自行确保您被存储在本项目的知识库、记忆库和日志中的输入和输出内容的合法性与合规性以及存储行为的合法性与合规性。您需**自行承担**由此产生的任何法律责任。
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## 四、免责条款
|
||||||
|
|
||||||
|
**4.1** 本项目的输出内容依赖第三方API，**不受**项目团队控制，亦**不代表**项目团队的观点。
|
||||||
|
|
||||||
|
**4.2** 除本协议条目2.4提到的隐私政策之外，项目团队**不会**对您提供任何形式的担保，亦**不对**使用本项目的造成的任何后果负责。
|
||||||
|
|
||||||
|
## 五、其他条款
|
||||||
|
|
||||||
|
**5.1** 项目团队有权**随时修改本协议的条款**，但**没有**义务通知您。修改后的协议将在本项目的新版本中生效，您应定期检查本协议的最新版本。
|
||||||
|
|
||||||
|
**5.2** 项目团队**保留**本协议的最终解释权。
|
||||||
|
|
||||||
|
|
||||||
|
## 附录：其他重要须知
|
||||||
|
|
||||||
|
### 一、过往版本使用条件追溯
|
||||||
|
|
||||||
|
**1.1** 对于本项目此前未配备 EULA 协议的版本，自本协议发布之日起，若用户希望继续使用本项目，应在本协议生效后的合理时间内，通过升级到最新版本并同意本协议全部条款。若在本版协议生效日（2025年3月18日）之后，用户仍使用此前无 EULA 协议的项目版本且未同意本协议，则用户无权继续使用，项目方有权采取措施阻止其使用行为，并保留追究相关法律责任的权利。
|
||||||
|
|
||||||
|
|
||||||
|
### 二、风险提示
|
||||||
|
|
||||||
|
**2.1 隐私安全风险**
|
||||||
|
|
||||||
|
- 本项目会将您的配置信息、输入指令和生成内容发送到第三方API，而这些API的服务质量、稳定性、准确性、安全性不受项目团队控制。
|
||||||
|
- 本项目会收集您的输入和输出内容，用于构建本项目专用的知识库和记忆库，以提高回复的准确性和连贯性。
|
||||||
|
|
||||||
|
**因此，为了保障您的隐私信息安全，请注意以下事项：**
|
||||||
|
|
||||||
|
- 避免在涉及个人隐私、个人信息或其他敏感信息的环境中使用本项目；
|
||||||
|
- 避免在不可信的环境中使用本项目；
|
||||||
|
|
||||||
|
**2.2 精神健康风险**
|
||||||
|
|
||||||
|
本项目仅为工具型机器人，不具备情感交互能力。建议用户：
|
||||||
|
- 避免过度依赖AI回复处理现实问题或情绪困扰；
|
||||||
|
- 如感到心理不适，请及时寻求专业心理咨询服务。
|
||||||
|
- 如遇心理困扰，请寻求专业帮助（全国心理援助热线：12355）。
|
||||||
|
|
||||||
|
### 三、其他
|
||||||
|
**3.1 争议解决**
|
||||||
|
- 本协议适用中国法律，争议提交相关地区法院管辖；
|
||||||
|
- 若因GPLv3许可产生纠纷，以许可证官方解释为准。
|
||||||
21
PRIVACY.md
Normal file
21
PRIVACY.md
Normal file
@@ -0,0 +1,21 @@
|
|||||||
|
### MaiMBot用户隐私条款
|
||||||
|
**版本：V1.0**
|
||||||
|
**更新日期：2025年3月18日**
|
||||||
|
**生效日期：2025年3月18日**
|
||||||
|
**适用的MaiMBot版本号：<=v0.5.15**
|
||||||
|
|
||||||
|
**2025© MaiMBot项目团队**
|
||||||
|
|
||||||
|
MaiMBot项目团队（以下简称项目团队）**尊重并保护**用户（以下简称您）的隐私。若您选择使用MaiMBot项目（以下简称本项目），则您需同意本项目按照以下隐私条款处理您的输入和输出内容：
|
||||||
|
|
||||||
|
**1.1** 本项目**会**收集您的输入和输出内容并发送到第三方API，用于生成新的输出内容。因此您的输入和输出内容**会**同时受到本项目和第三方API的隐私政策约束。
|
||||||
|
|
||||||
|
**1.2** 本项目**会**收集您的输入和输出内容，用于构建本项目专用的仅存储在您使用的数据库中的知识库和记忆库，以提高回复的准确性和连贯性。
|
||||||
|
|
||||||
|
**1.3** 本项目**会**收集您的输入和输出内容，用于生成仅存储于您部署或使用的设备中的不会上传至互联网的日志。但当您向项目团队反馈问题时，项目团队可能需要您提供日志文件以帮助解决问题。
|
||||||
|
|
||||||
|
**1.4** 本项目可能**会**收集部分统计信息（如使用频率、基础指令类型）以改进服务，您可在[bot_config.toml]中随时关闭此功能**。
|
||||||
|
|
||||||
|
**1.5** 由于您的自身行为或不可抗力等情形，导致上述可能涉及您隐私或您认为是私人信息的内容发生被泄露、批漏，或被第三方获取、使用、转让等情形的，均由您**自行承担**不利后果，我们对此**不承担**任何责任。
|
||||||
|
|
||||||
|
**1.6** 项目团队保留在未来更新隐私条款的权利，但没有义务通知您。若您不同意更新后的隐私条款，您应立即停止使用本项目。
|
||||||
160
README.md
160
README.md
@@ -1,4 +1,4 @@
|
|||||||
# 麦麦！MaiMBot (编辑中)
|
# 麦麦！MaiMBot-MaiCore (编辑中)
|
||||||
|
|
||||||
<div align="center">
|
<div align="center">
|
||||||
|
|
||||||
@@ -10,22 +10,21 @@
|
|||||||
|
|
||||||
## 📝 项目简介
|
## 📝 项目简介
|
||||||
|
|
||||||
**🍔麦麦是一个基于大语言模型的智能QQ群聊机器人**
|
**🍔MaiCore是一个基于大语言模型的可交互智能体**
|
||||||
|
|
||||||
- 基于 nonebot2 框架开发
|
|
||||||
- LLM 提供对话能力
|
- LLM 提供对话能力
|
||||||
- MongoDB 提供数据持久化支持
|
- MongoDB 提供数据持久化支持
|
||||||
- NapCat 作为QQ协议端支持
|
- 可扩展，可支持多种平台和多种功能
|
||||||
|
|
||||||
**最新版本: v0.5.13**
|
**最新版本: v0.6.0** ([查看更新日志](changelog.md))
|
||||||
> [!WARNING]
|
> [!WARNING]
|
||||||
> 注意，3月12日的v0.5.13, 该版本更新较大，建议单独开文件夹部署，然后转移/data文件 和数据库，数据库可能需要删除messages下的内容（不需要删除记忆）
|
> 次版本MaiBot将基于MaiCore运行，不再依赖于nonebot相关组件运行。
|
||||||
|
> MaiBot将通过nonebot的插件与nonebot建立联系，然后nonebot与QQ建立联系，实现MaiBot与QQ的交互
|
||||||
|
|
||||||
|
|
||||||
<div align="center">
|
<div align="center">
|
||||||
<a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
|
<a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
|
||||||
<img src="docs/video.png" width="300" alt="麦麦演示视频">
|
<img src="docs/pic/video.png" width="300" alt="麦麦演示视频">
|
||||||
<br>
|
<br>
|
||||||
👆 点击观看麦麦演示视频 👆
|
👆 点击观看麦麦演示视频 👆
|
||||||
|
|
||||||
@@ -39,61 +38,26 @@
|
|||||||
> - 由于持续迭代，可能存在一些已知或未知的bug
|
> - 由于持续迭代，可能存在一些已知或未知的bug
|
||||||
> - 由于开发中，可能消耗较多token
|
> - 由于开发中，可能消耗较多token
|
||||||
|
|
||||||
## 💬交流群
|
### 💬交流群（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
|
||||||
- [一群](https://qm.qq.com/q/VQ3XZrWgMs) 766798517 ，建议加下面的（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
|
- [五群](https://qm.qq.com/q/JxvHZnxyec) 1022489779
|
||||||
- [二群](https://qm.qq.com/q/RzmCiRtHEW) 571780722 （开发和建议相关讨论）不一定有空回复，会优先写文档和代码
|
- [一群](https://qm.qq.com/q/VQ3XZrWgMs) 766798517 【已满】
|
||||||
- [三群](https://qm.qq.com/q/wlH5eT8OmQ) 1035228475（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
|
- [二群](https://qm.qq.com/q/RzmCiRtHEW) 571780722【已满】
|
||||||
- [四群](https://qm.qq.com/q/wlH5eT8OmQ) 729957033（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
|
- [三群](https://qm.qq.com/q/wlH5eT8OmQ) 1035228475【已满】
|
||||||
|
- [四群](https://qm.qq.com/q/wlH5eT8OmQ) 729957033【已满】
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
**📚 有热心网友创作的wiki:** https://maimbot.pages.dev/
|
|
||||||
|
|
||||||
|
|
||||||
**😊 其他平台版本**
|
|
||||||
|
|
||||||
- (由 [CabLate](https://github.com/cablate) 贡献) [Telegram 与其他平台(未来可能会有)的版本](https://github.com/cablate/MaiMBot/tree/telegram) - [集中讨论串](https://github.com/SengokuCola/MaiMBot/discussions/149)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
## 📝 注意注意注意注意注意注意注意注意注意注意注意注意注意注意注意注意注意
|
|
||||||
**如果你有想法想要提交pr**
|
|
||||||
- 由于本项目在快速迭代和功能调整，并且有重构计划，目前不接受任何未经过核心开发组讨论的pr合并，谢谢！如您仍旧希望提交pr，可以详情请看置顶issue
|
|
||||||
|
|
||||||
<div align="left">
|
<div align="left">
|
||||||
<h2>📚 文档 ⬇️ 快速开始使用麦麦 ⬇️</h2>
|
<h2>📚 文档</h2>
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
### 部署方式(忙于开发，部分内容可能过时)
|
### (部分内容可能过时，请注意版本对应)
|
||||||
|
|
||||||
- 📦 **Windows 一键傻瓜式部署**：请运行项目根目录中的 `run.bat`，部署完成后请参照后续配置指南进行配置
|
### 核心文档
|
||||||
|
- [📚 核心Wiki文档](https://docs.mai-mai.org) - 项目最全面的文档中心，你可以了解麦麦有关的一切
|
||||||
|
|
||||||
- 📦 Linux 自动部署（实验） ：请下载并运行项目根目录中的`run.sh`并按照提示安装，部署完成后请参照后续配置指南进行配置
|
### 最新版本部署教程(MaiCore版本)
|
||||||
|
- [🚀 最新版本部署教程](https://docs.mai-mai.org/manual/deployment/refactor_deploy.html) - 基于MaiCore的新版本部署方式（与旧版本不兼容）
|
||||||
|
|
||||||
- [📦 Windows 手动部署指南 ](docs/manual_deploy_windows.md)
|
|
||||||
|
|
||||||
- [📦 Linux 手动部署指南 ](docs/manual_deploy_linux.md)
|
|
||||||
|
|
||||||
如果你不知道Docker是什么，建议寻找相关教程或使用手动部署 **（现在不建议使用docker，更新慢，可能不适配）**
|
|
||||||
|
|
||||||
- [🐳 Docker部署指南](docs/docker_deploy.md)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
### 配置说明
|
|
||||||
|
|
||||||
- [🎀 新手配置指南](docs/installation_cute.md) - 通俗易懂的配置教程，适合初次使用的猫娘
|
|
||||||
- [⚙️ 标准配置指南](docs/installation_standard.md) - 简明专业的配置说明，适合有经验的用户
|
|
||||||
|
|
||||||
### 常见问题
|
|
||||||
|
|
||||||
- [❓ 快速 Q & A ](docs/fast_q_a.md) - 针对新手的疑难解答，适合完全没接触过编程的新手
|
|
||||||
|
|
||||||
<div align="left">
|
|
||||||
<h3>了解麦麦 </h3>
|
|
||||||
</div>
|
|
||||||
|
|
||||||
- [项目架构说明](docs/doc1.md) - 项目结构和核心功能实现细节
|
|
||||||
|
|
||||||
## 🎯 功能介绍
|
## 🎯 功能介绍
|
||||||
|
|
||||||
@@ -104,83 +68,85 @@
|
|||||||
- 支持多模型，多厂商自定义配置
|
- 支持多模型，多厂商自定义配置
|
||||||
- 动态的prompt构建器，更拟人
|
- 动态的prompt构建器，更拟人
|
||||||
- 支持图片，转发消息，回复消息的识别
|
- 支持图片，转发消息，回复消息的识别
|
||||||
- 错别字和多条回复功能：麦麦可以随机生成错别字，会多条发送回复以及对消息进行reply
|
- 支持私聊功能，包括消息处理和回复
|
||||||
|
|
||||||
|
### 🧠 思维流系统（实验性功能）
|
||||||
|
- 思维流能够生成实时想法，增加回复的拟人性
|
||||||
|
- 思维流与日程系统联动，实现动态日程生成
|
||||||
|
|
||||||
|
### 🧠 记忆系统
|
||||||
|
- 对聊天记录进行概括存储，在需要时调用
|
||||||
|
|
||||||
### 😊 表情包功能
|
### 😊 表情包功能
|
||||||
|
|
||||||
- 支持根据发言内容发送对应情绪的表情包
|
- 支持根据发言内容发送对应情绪的表情包
|
||||||
- 会自动偷群友的表情包
|
- 会自动偷群友的表情包
|
||||||
|
- 表情包审查功能
|
||||||
|
- 表情包文件完整性自动检查
|
||||||
|
|
||||||
### 📅 日程功能
|
### 📅 日程功能
|
||||||
|
|
||||||
- 麦麦会自动生成一天的日程，实现更拟人的回复
|
- 麦麦会自动生成一天的日程，实现更拟人的回复
|
||||||
|
- 支持动态日程生成
|
||||||
|
- 优化日程文本解析功能
|
||||||
|
|
||||||
### 🧠 记忆功能
|
### 👥 关系系统
|
||||||
|
- 针对每个用户创建"关系"，可以对不同用户进行个性化回复
|
||||||
|
|
||||||
- 对聊天记录进行概括存储，在需要时调用，待完善
|
### 📊 统计系统
|
||||||
|
- 详细统计系统
|
||||||
|
- LLM使用统计
|
||||||
|
|
||||||
### 📚 知识库功能
|
### 🔧 系统功能
|
||||||
|
- 支持优雅的shutdown机制
|
||||||
- 基于embedding模型的知识库，手动放入txt会自动识别，写完了，暂时禁用
|
- 自动保存功能，定期保存聊天记录和关系数据
|
||||||
|
|
||||||
### 👥 关系功能
|
|
||||||
|
|
||||||
- 针对每个用户创建"关系"，可以对不同用户进行个性化回复，目前只有极其简单的好感度（WIP）
|
|
||||||
- 针对每个群创建"群印象"，可以对不同群进行个性化回复（WIP）
|
|
||||||
|
|
||||||
## 开发计划TODO：LIST
|
## 开发计划TODO：LIST
|
||||||
|
|
||||||
规划主线
|
|
||||||
0.6.0：记忆系统更新
|
|
||||||
0.7.0: 麦麦RunTime
|
|
||||||
|
|
||||||
- 人格功能：WIP
|
- 人格功能：WIP
|
||||||
- 群氛围功能：WIP
|
- 对特定对象的侧写功能
|
||||||
- 图片发送，转发功能：WIP
|
- 图片发送，转发功能：WIP
|
||||||
- 幽默和meme功能：WIP的WIP
|
- 幽默和meme功能：WIP
|
||||||
- 让麦麦玩mc：WIP的WIP的WIP
|
|
||||||
- 兼容gif的解析和保存
|
- 兼容gif的解析和保存
|
||||||
- 小程序转发链接解析
|
- 小程序转发链接解析
|
||||||
- 对思考链长度限制
|
|
||||||
- 修复已知bug
|
- 修复已知bug
|
||||||
- ~~完善文档~~
|
|
||||||
- 修复转发
|
|
||||||
- ~~config自动生成和检测~~
|
|
||||||
- ~~log别用print~~
|
|
||||||
- ~~给发送消息写专门的类~~
|
|
||||||
- 改进表情包发送逻辑
|
|
||||||
- 自动生成的回复逻辑，例如自生成的回复方向，回复风格
|
- 自动生成的回复逻辑，例如自生成的回复方向，回复风格
|
||||||
- 采用截断生成加快麦麦的反应速度
|
|
||||||
- 改进发送消息的触发
|
|
||||||
|
|
||||||
## 设计理念
|
## ✍️如何给本项目报告BUG/提交建议/做贡献
|
||||||
|
|
||||||
|
MaiCore是一个开源项目，我们非常欢迎你的参与。你的贡献，无论是提交bug报告、功能需求还是代码pr，都对项目非常宝贵。我们非常感谢你的支持！🎉 但无序的讨论会降低沟通效率，进而影响问题的解决速度，因此在提交任何贡献前，请务必先阅读本项目的[贡献指南](CONTRIBUTE.md)（待补完）
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## 设计理念（原始时代的火花）
|
||||||
|
|
||||||
|
> **千石可乐说：**
|
||||||
|
> - 这个项目最初只是为了给牛牛bot添加一点额外的功能，但是功能越写越多，最后决定重写。其目的是为了创造一个活跃在QQ群聊的"生命体"。可以目的并不是为了写一个功能齐全的机器人，而是一个尽可能让人感知到真实的类人存在。
|
||||||
|
> - 程序的功能设计理念基于一个核心的原则："最像而不是好"
|
||||||
|
> - 如果人类真的需要一个AI来陪伴自己，并不是所有人都需要一个完美的，能解决所有问题的"helpful assistant"，而是一个会犯错的，拥有自己感知和想法的"生命形式"。
|
||||||
|
> - 代码会保持开源和开放，但个人希望MaiMbot的运行时数据保持封闭，尽量避免以显式命令来对其进行控制和调试.我认为一个你无法完全掌控的个体才更能让你感觉到它的自主性，而视其成为一个对话机器.
|
||||||
|
> - SengokuCola~~纯编程外行，面向cursor编程，很多代码写得不好多多包涵~~已得到大脑升级
|
||||||
|
|
||||||
- **千石可乐说：**
|
|
||||||
- 这个项目最初只是为了给牛牛bot添加一点额外的功能，但是功能越写越多，最后决定重写。其目的是为了创造一个活跃在QQ群聊的"生命体"。可以目的并不是为了写一个功能齐全的机器人，而是一个尽可能让人感知到真实的类人存在.
|
|
||||||
- 程序的功能设计理念基于一个核心的原则："最像而不是好"
|
|
||||||
- 主打一个陪伴
|
|
||||||
- 如果人类真的需要一个AI来陪伴自己，并不是所有人都需要一个完美的，能解决所有问题的helpful assistant，而是一个会犯错的，拥有自己感知和想法的"生命形式"。
|
|
||||||
- 代码会保持开源和开放，但个人希望MaiMbot的运行时数据保持封闭，尽量避免以显式命令来对其进行控制和调试.我认为一个你无法完全掌控的个体才更能让你感觉到它的自主性，而视其成为一个对话机器.
|
|
||||||
|
|
||||||
## 📌 注意事项
|
## 📌 注意事项
|
||||||
|
|
||||||
SengokuCola~~纯编程外行，面向cursor编程，很多代码写得不好多多包涵~~已得到大脑升级
|
|
||||||
> [!WARNING]
|
> [!WARNING]
|
||||||
|
> 使用本项目前必须阅读和同意用户协议和隐私协议
|
||||||
> 本应用生成内容来自人工智能模型，由 AI 生成，请仔细甄别，请勿用于违反法律的用途，AI生成内容不代表本人观点和立场。
|
> 本应用生成内容来自人工智能模型，由 AI 生成，请仔细甄别，请勿用于违反法律的用途，AI生成内容不代表本人观点和立场。
|
||||||
|
|
||||||
## 致谢
|
## 致谢
|
||||||
|
|
||||||
[nonebot2](https://github.com/nonebot/nonebot2): 跨平台 Python 异步聊天机器人框架
|
- [nonebot2](https://github.com/nonebot/nonebot2): 跨平台 Python 异步聊天机器人框架
|
||||||
[NapCat](https://github.com/NapNeko/NapCatQQ): 现代化的基于 NTQQ 的 Bot 协议端实现
|
- [NapCat](https://github.com/NapNeko/NapCatQQ): 现代化的基于 NTQQ 的 Bot 协议端实现
|
||||||
|
|
||||||
### 贡献者
|
### 贡献者
|
||||||
|
|
||||||
感谢各位大佬！
|
感谢各位大佬！
|
||||||
|
|
||||||
<a href="https://github.com/SengokuCola/MaiMBot/graphs/contributors">
|
<a href="https://github.com/MaiM-with-u/MaiBot/graphs/contributors">
|
||||||
<img src="https://contrib.rocks/image?repo=SengokuCola/MaiMBot" />
|
<img src="https://contrib.rocks/image?repo=MaiM-with-u/MaiBot" />
|
||||||
</a>
|
</a>
|
||||||
|
|
||||||
|
**也感谢每一位给麦麦发展提出宝贵意见与建议的用户，感谢陪伴麦麦走到现在的你们**
|
||||||
|
|
||||||
## Stargazers over time
|
## Stargazers over time
|
||||||
|
|
||||||
[](https://starchart.cc/SengokuCola/MaiMBot)
|
[](https://starchart.cc/MaiM-with-u/MaiBot)
|
||||||
|
|||||||
207
bot.py
207
bot.py
@@ -1,19 +1,16 @@
|
|||||||
import asyncio
|
import asyncio
|
||||||
|
import hashlib
|
||||||
import os
|
import os
|
||||||
import shutil
|
import shutil
|
||||||
import sys
|
import sys
|
||||||
|
from pathlib import Path
|
||||||
import nonebot
|
|
||||||
import time
|
import time
|
||||||
|
|
||||||
import uvicorn
|
|
||||||
from dotenv import load_dotenv
|
|
||||||
from nonebot.adapters.onebot.v11 import Adapter
|
|
||||||
import platform
|
import platform
|
||||||
from src.plugins.utils.logger_config import LogModule, LogClassification
|
from dotenv import load_dotenv
|
||||||
|
from src.common.logger import get_module_logger
|
||||||
|
from src.main import MainSystem
|
||||||
|
|
||||||
|
logger = get_module_logger("main_bot")
|
||||||
# 配置日志格式
|
|
||||||
|
|
||||||
# 获取没有加载env时的环境变量
|
# 获取没有加载env时的环境变量
|
||||||
env_mask = {key: os.getenv(key) for key in os.environ}
|
env_mask = {key: os.getenv(key) for key in os.environ}
|
||||||
@@ -48,62 +45,25 @@ def init_config():
|
|||||||
logger.info("创建config目录")
|
logger.info("创建config目录")
|
||||||
|
|
||||||
shutil.copy("template/bot_config_template.toml", "config/bot_config.toml")
|
shutil.copy("template/bot_config_template.toml", "config/bot_config.toml")
|
||||||
logger.info("复制完成，请修改config/bot_config.toml和.env.prod中的配置后重新启动")
|
logger.info("复制完成，请修改config/bot_config.toml和.env中的配置后重新启动")
|
||||||
|
|
||||||
|
|
||||||
def init_env():
|
def init_env():
|
||||||
# 初始化.env 默认ENVIRONMENT=prod
|
# 检测.env文件是否存在
|
||||||
if not os.path.exists(".env"):
|
if not os.path.exists(".env"):
|
||||||
with open(".env", "w") as f:
|
logger.error("检测到.env文件不存在")
|
||||||
f.write("ENVIRONMENT=prod")
|
shutil.copy("template/template.env", "./.env")
|
||||||
|
logger.info("已从template/template.env复制创建.env，请修改配置后重新启动")
|
||||||
# 检测.env.prod文件是否存在
|
|
||||||
if not os.path.exists(".env.prod"):
|
|
||||||
logger.error("检测到.env.prod文件不存在")
|
|
||||||
shutil.copy("template.env", "./.env.prod")
|
|
||||||
|
|
||||||
# 检测.env.dev文件是否存在，不存在的话直接复制生产环境配置
|
|
||||||
if not os.path.exists(".env.dev"):
|
|
||||||
logger.error("检测到.env.dev文件不存在")
|
|
||||||
shutil.copy(".env.prod", "./.env.dev")
|
|
||||||
|
|
||||||
# 首先加载基础环境变量.env
|
|
||||||
if os.path.exists(".env"):
|
|
||||||
load_dotenv(".env", override=True)
|
|
||||||
logger.success("成功加载基础环境变量配置")
|
|
||||||
|
|
||||||
|
|
||||||
def load_env():
|
def load_env():
|
||||||
# 使用闭包实现对加载器的横向扩展，避免大量重复判断
|
# 直接加载生产环境变量配置
|
||||||
def prod():
|
if os.path.exists(".env"):
|
||||||
logger.success("加载生产环境变量配置")
|
load_dotenv(".env", override=True)
|
||||||
load_dotenv(".env.prod", override=True) # override=True 允许覆盖已存在的环境变量
|
logger.success("成功加载环境变量配置")
|
||||||
|
|
||||||
def dev():
|
|
||||||
logger.success("加载开发环境变量配置")
|
|
||||||
load_dotenv(".env.dev", override=True) # override=True 允许覆盖已存在的环境变量
|
|
||||||
|
|
||||||
fn_map = {"prod": prod, "dev": dev}
|
|
||||||
|
|
||||||
env = os.getenv("ENVIRONMENT")
|
|
||||||
logger.info(f"[load_env] 当前的 ENVIRONMENT 变量值：{env}")
|
|
||||||
|
|
||||||
if env in fn_map:
|
|
||||||
fn_map[env]() # 根据映射执行闭包函数
|
|
||||||
|
|
||||||
elif os.path.exists(f".env.{env}"):
|
|
||||||
logger.success(f"加载{env}环境变量配置")
|
|
||||||
load_dotenv(f".env.{env}", override=True) # override=True 允许覆盖已存在的环境变量
|
|
||||||
|
|
||||||
else:
|
else:
|
||||||
logger.error(f"ENVIRONMENT 配置错误，请检查 .env 文件中的 ENVIRONMENT 变量及对应 .env.{env} 是否存在")
|
logger.error("未找到.env文件，请确保文件存在")
|
||||||
RuntimeError(f"ENVIRONMENT 配置错误，请检查 .env 文件中的 ENVIRONMENT 变量及对应 .env.{env} 是否存在")
|
raise FileNotFoundError("未找到.env文件，请确保文件存在")
|
||||||
|
|
||||||
|
|
||||||
def load_logger():
|
|
||||||
global logger # 使得bot.py中其他函数也能调用
|
|
||||||
log_module = LogModule()
|
|
||||||
logger = log_module.setup_logger(LogClassification.BASE)
|
|
||||||
|
|
||||||
|
|
||||||
def scan_provider(env_config: dict):
|
def scan_provider(env_config: dict):
|
||||||
@@ -139,11 +99,7 @@ def scan_provider(env_config: dict):
|
|||||||
|
|
||||||
async def graceful_shutdown():
|
async def graceful_shutdown():
|
||||||
try:
|
try:
|
||||||
global uvicorn_server
|
logger.info("正在优雅关闭麦麦...")
|
||||||
if uvicorn_server:
|
|
||||||
uvicorn_server.force_exit = True # 强制退出
|
|
||||||
await uvicorn_server.shutdown()
|
|
||||||
|
|
||||||
tasks = [t for t in asyncio.all_tasks() if t is not asyncio.current_task()]
|
tasks = [t for t in asyncio.all_tasks() if t is not asyncio.current_task()]
|
||||||
for task in tasks:
|
for task in tasks:
|
||||||
task.cancel()
|
task.cancel()
|
||||||
@@ -153,70 +109,119 @@ async def graceful_shutdown():
|
|||||||
logger.error(f"麦麦关闭失败: {e}")
|
logger.error(f"麦麦关闭失败: {e}")
|
||||||
|
|
||||||
|
|
||||||
async def uvicorn_main():
|
def check_eula():
|
||||||
global uvicorn_server
|
eula_confirm_file = Path("eula.confirmed")
|
||||||
config = uvicorn.Config(
|
privacy_confirm_file = Path("privacy.confirmed")
|
||||||
app="__main__:app",
|
eula_file = Path("EULA.md")
|
||||||
host=os.getenv("HOST", "127.0.0.1"),
|
privacy_file = Path("PRIVACY.md")
|
||||||
port=int(os.getenv("PORT", 8080)),
|
|
||||||
reload=os.getenv("ENVIRONMENT") == "dev",
|
eula_updated = True
|
||||||
timeout_graceful_shutdown=5,
|
eula_new_hash = None
|
||||||
log_config=None,
|
privacy_updated = True
|
||||||
access_log=False,
|
privacy_new_hash = None
|
||||||
)
|
|
||||||
server = uvicorn.Server(config)
|
eula_confirmed = False
|
||||||
uvicorn_server = server
|
privacy_confirmed = False
|
||||||
await server.serve()
|
|
||||||
|
# 首先计算当前EULA文件的哈希值
|
||||||
|
if eula_file.exists():
|
||||||
|
with open(eula_file, "r", encoding="utf-8") as f:
|
||||||
|
eula_content = f.read()
|
||||||
|
eula_new_hash = hashlib.md5(eula_content.encode("utf-8")).hexdigest()
|
||||||
|
else:
|
||||||
|
logger.error("EULA.md 文件不存在")
|
||||||
|
raise FileNotFoundError("EULA.md 文件不存在")
|
||||||
|
|
||||||
|
# 首先计算当前隐私条款文件的哈希值
|
||||||
|
if privacy_file.exists():
|
||||||
|
with open(privacy_file, "r", encoding="utf-8") as f:
|
||||||
|
privacy_content = f.read()
|
||||||
|
privacy_new_hash = hashlib.md5(privacy_content.encode("utf-8")).hexdigest()
|
||||||
|
else:
|
||||||
|
logger.error("PRIVACY.md 文件不存在")
|
||||||
|
raise FileNotFoundError("PRIVACY.md 文件不存在")
|
||||||
|
|
||||||
|
# 检查EULA确认文件是否存在
|
||||||
|
if eula_confirm_file.exists():
|
||||||
|
with open(eula_confirm_file, "r", encoding="utf-8") as f:
|
||||||
|
confirmed_content = f.read()
|
||||||
|
if eula_new_hash == confirmed_content:
|
||||||
|
eula_confirmed = True
|
||||||
|
eula_updated = False
|
||||||
|
if eula_new_hash == os.getenv("EULA_AGREE"):
|
||||||
|
eula_confirmed = True
|
||||||
|
eula_updated = False
|
||||||
|
|
||||||
|
# 检查隐私条款确认文件是否存在
|
||||||
|
if privacy_confirm_file.exists():
|
||||||
|
with open(privacy_confirm_file, "r", encoding="utf-8") as f:
|
||||||
|
confirmed_content = f.read()
|
||||||
|
if privacy_new_hash == confirmed_content:
|
||||||
|
privacy_confirmed = True
|
||||||
|
privacy_updated = False
|
||||||
|
if privacy_new_hash == os.getenv("PRIVACY_AGREE"):
|
||||||
|
privacy_confirmed = True
|
||||||
|
privacy_updated = False
|
||||||
|
|
||||||
|
# 如果EULA或隐私条款有更新，提示用户重新确认
|
||||||
|
if eula_updated or privacy_updated:
|
||||||
|
print("EULA或隐私条款内容已更新，请在阅读后重新确认，继续运行视为同意更新后的以上两款协议")
|
||||||
|
print(
|
||||||
|
f'输入"同意"或"confirmed"或设置环境变量"EULA_AGREE={eula_new_hash}"和"PRIVACY_AGREE={privacy_new_hash}"继续运行'
|
||||||
|
)
|
||||||
|
while True:
|
||||||
|
user_input = input().strip().lower()
|
||||||
|
if user_input in ["同意", "confirmed"]:
|
||||||
|
# print("确认成功，继续运行")
|
||||||
|
# print(f"确认成功，继续运行{eula_updated} {privacy_updated}")
|
||||||
|
if eula_updated:
|
||||||
|
print(f"更新EULA确认文件{eula_new_hash}")
|
||||||
|
eula_confirm_file.write_text(eula_new_hash, encoding="utf-8")
|
||||||
|
if privacy_updated:
|
||||||
|
print(f"更新隐私条款确认文件{privacy_new_hash}")
|
||||||
|
privacy_confirm_file.write_text(privacy_new_hash, encoding="utf-8")
|
||||||
|
break
|
||||||
|
else:
|
||||||
|
print('请输入"同意"或"confirmed"以继续运行')
|
||||||
|
return
|
||||||
|
elif eula_confirmed and privacy_confirmed:
|
||||||
|
return
|
||||||
|
|
||||||
|
|
||||||
def raw_main():
|
def raw_main():
|
||||||
# 利用 TZ 环境变量设定程序工作的时区
|
# 利用 TZ 环境变量设定程序工作的时区
|
||||||
# 仅保证行为一致，不依赖 localtime()，实际对生产环境几乎没有作用
|
|
||||||
if platform.system().lower() != "windows":
|
if platform.system().lower() != "windows":
|
||||||
time.tzset()
|
time.tzset()
|
||||||
|
|
||||||
|
check_eula()
|
||||||
|
print("检查EULA和隐私条款完成")
|
||||||
easter_egg()
|
easter_egg()
|
||||||
init_config()
|
init_config()
|
||||||
init_env()
|
init_env()
|
||||||
load_env()
|
load_env()
|
||||||
|
|
||||||
# load_logger()
|
|
||||||
|
|
||||||
env_config = {key: os.getenv(key) for key in os.environ}
|
env_config = {key: os.getenv(key) for key in os.environ}
|
||||||
scan_provider(env_config)
|
scan_provider(env_config)
|
||||||
|
|
||||||
# 设置基础配置
|
# 返回MainSystem实例
|
||||||
base_config = {
|
return MainSystem()
|
||||||
"websocket_port": int(env_config.get("PORT", 8080)),
|
|
||||||
"host": env_config.get("HOST", "127.0.0.1"),
|
|
||||||
"log_level": "INFO",
|
|
||||||
}
|
|
||||||
|
|
||||||
# 合并配置
|
|
||||||
nonebot.init(**base_config, **env_config)
|
|
||||||
|
|
||||||
# 注册适配器
|
|
||||||
global driver
|
|
||||||
driver = nonebot.get_driver()
|
|
||||||
driver.register_adapter(Adapter)
|
|
||||||
|
|
||||||
# 加载插件
|
|
||||||
nonebot.load_plugins("src/plugins")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
try:
|
try:
|
||||||
# 配置日志，使得主程序直接退出时候也能访问logger
|
# 获取MainSystem实例
|
||||||
load_logger()
|
main_system = raw_main()
|
||||||
raw_main()
|
|
||||||
|
|
||||||
app = nonebot.get_asgi()
|
# 创建事件循环
|
||||||
loop = asyncio.new_event_loop()
|
loop = asyncio.new_event_loop()
|
||||||
asyncio.set_event_loop(loop)
|
asyncio.set_event_loop(loop)
|
||||||
|
|
||||||
try:
|
try:
|
||||||
loop.run_until_complete(uvicorn_main())
|
# 执行初始化和任务调度
|
||||||
|
loop.run_until_complete(main_system.initialize())
|
||||||
|
loop.run_until_complete(main_system.schedule_tasks())
|
||||||
except KeyboardInterrupt:
|
except KeyboardInterrupt:
|
||||||
|
# loop.run_until_complete(global_api.stop())
|
||||||
logger.warning("收到中断信号，正在优雅关闭...")
|
logger.warning("收到中断信号，正在优雅关闭...")
|
||||||
loop.run_until_complete(graceful_shutdown())
|
loop.run_until_complete(graceful_shutdown())
|
||||||
finally:
|
finally:
|
||||||
|
|||||||
84
changelog.md
84
changelog.md
@@ -1,84 +0,0 @@
|
|||||||
# Changelog
|
|
||||||
|
|
||||||
## [0.5.13] - 2025-3-12
|
|
||||||
AI总结
|
|
||||||
### 🌟 核心功能增强
|
|
||||||
#### 记忆系统升级
|
|
||||||
- 新增了记忆系统的时间戳功能，包括创建时间和最后修改时间
|
|
||||||
- 新增了记忆图节点和边的时间追踪功能
|
|
||||||
- 新增了自动补充缺失时间字段的功能
|
|
||||||
- 新增了记忆遗忘机制，基于时间条件自动遗忘旧记忆
|
|
||||||
- 优化了记忆系统的数据同步机制
|
|
||||||
- 优化了记忆系统的数据结构，确保所有数据类型的一致性
|
|
||||||
|
|
||||||
#### 私聊功能完善
|
|
||||||
- 新增了完整的私聊功能支持，包括消息处理和回复
|
|
||||||
- 新增了聊天流管理器，支持群聊和私聊的上下文管理
|
|
||||||
- 新增了私聊过滤开关功能
|
|
||||||
- 优化了关系管理系统，支持跨平台用户关系
|
|
||||||
|
|
||||||
#### 消息处理升级
|
|
||||||
- 新增了消息队列管理系统，支持按时间顺序处理消息
|
|
||||||
- 新增了消息发送控制器，实现人性化的发送速度和间隔
|
|
||||||
- 新增了JSON格式分享卡片读取支持
|
|
||||||
- 新增了Base64格式表情包CQ码支持
|
|
||||||
- 改进了消息处理流程，支持多种消息类型
|
|
||||||
|
|
||||||
### 💻 系统架构优化
|
|
||||||
#### 配置系统改进
|
|
||||||
- 新增了配置文件自动更新和版本检测功能
|
|
||||||
- 新增了配置文件热重载API接口
|
|
||||||
- 新增了配置文件版本兼容性检查
|
|
||||||
- 新增了根据不同环境(dev/prod)显示不同级别的日志功能
|
|
||||||
- 优化了配置文件格式和结构
|
|
||||||
|
|
||||||
#### 部署支持扩展
|
|
||||||
- 新增了Linux系统部署指南
|
|
||||||
- 新增了Docker部署支持的详细文档
|
|
||||||
- 新增了NixOS环境支持（使用venv方式）
|
|
||||||
- 新增了优雅的shutdown机制
|
|
||||||
- 优化了Docker部署文档
|
|
||||||
|
|
||||||
### 🛠️ 开发体验提升
|
|
||||||
#### 工具链升级
|
|
||||||
- 新增了ruff代码格式化和检查工具
|
|
||||||
- 新增了知识库一键启动脚本
|
|
||||||
- 新增了自动保存脚本，定期保存聊天记录和关系数据
|
|
||||||
- 新增了表情包自动获取脚本
|
|
||||||
- 优化了日志记录（使用logger.debug替代print）
|
|
||||||
- 精简了日志输出，禁用了Uvicorn/NoneBot默认日志
|
|
||||||
|
|
||||||
#### 安全性强化
|
|
||||||
- 新增了API密钥安全管理机制
|
|
||||||
- 新增了数据库完整性检查功能
|
|
||||||
- 新增了表情包文件完整性自动检查
|
|
||||||
- 新增了异常处理和自动恢复机制
|
|
||||||
- 优化了安全性检查机制
|
|
||||||
|
|
||||||
### 🐛 关键问题修复
|
|
||||||
#### 系统稳定性
|
|
||||||
- 修复了systemctl强制停止的问题
|
|
||||||
- 修复了ENVIRONMENT变量在同一终端下不能被覆盖的问题
|
|
||||||
- 修复了libc++.so依赖问题
|
|
||||||
- 修复了数据库索引创建失败的问题
|
|
||||||
- 修复了MongoDB连接配置相关问题
|
|
||||||
- 修复了消息队列溢出问题
|
|
||||||
- 修复了配置文件加载时的版本兼容性问题
|
|
||||||
|
|
||||||
#### 功能完善性
|
|
||||||
- 修复了私聊时产生reply消息的bug
|
|
||||||
- 修复了回复消息无法识别的问题
|
|
||||||
- 修复了CQ码解析错误
|
|
||||||
- 修复了情绪管理器导入问题
|
|
||||||
- 修复了小名无效的问题
|
|
||||||
- 修复了表情包发送时的参数缺失问题
|
|
||||||
- 修复了表情包重复注册问题
|
|
||||||
- 修复了变量拼写错误问题
|
|
||||||
|
|
||||||
### 主要改进方向
|
|
||||||
1. 提升记忆系统的智能性和可靠性
|
|
||||||
2. 完善私聊功能的完整生态
|
|
||||||
3. 优化系统架构和部署便利性
|
|
||||||
4. 提升开发体验和代码质量
|
|
||||||
5. 加强系统安全性和稳定性
|
|
||||||
|
|
||||||
@@ -1,12 +0,0 @@
|
|||||||
# Changelog
|
|
||||||
|
|
||||||
## [0.0.5] - 2025-3-11
|
|
||||||
### Added
|
|
||||||
- 新增了 `alias_names` 配置项，用于指定麦麦的别名。
|
|
||||||
|
|
||||||
## [0.0.4] - 2025-3-9
|
|
||||||
### Added
|
|
||||||
- 新增了 `memory_ban_words` 配置项，用于指定不希望记忆的词汇。
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
328
changelogs/changelog.md
Normal file
328
changelogs/changelog.md
Normal file
@@ -0,0 +1,328 @@
|
|||||||
|
# Changelog
|
||||||
|
|
||||||
|
## [0.6.0] - 2025-3-30
|
||||||
|
### 🌟 核心功能增强
|
||||||
|
#### 架构重构
|
||||||
|
- 将MaiBot重构为MaiCore独立智能体
|
||||||
|
- 移除NoneBot相关代码，改为插件方式与NoneBot对接
|
||||||
|
- 精简代码结构，优化文件夹组织
|
||||||
|
- 新增详细统计系统
|
||||||
|
|
||||||
|
#### 思维流系统
|
||||||
|
- 新增思维流作为实验功能
|
||||||
|
- 思维流大核+小核架构
|
||||||
|
- 思维流回复意愿模式
|
||||||
|
- 优化思维流自动启停机制，提升资源利用效率
|
||||||
|
- 思维流与日程系统联动，实现动态日程生成
|
||||||
|
- 优化心流运行逻辑和思考时间计算
|
||||||
|
- 添加错误检测机制
|
||||||
|
- 修复心流无法观察群消息的问题
|
||||||
|
|
||||||
|
#### 回复系统
|
||||||
|
- 优化回复逻辑，添加回复前思考机制
|
||||||
|
- 移除推理模型在回复中的使用
|
||||||
|
|
||||||
|
#### 记忆系统优化
|
||||||
|
- 优化记忆抽取策略
|
||||||
|
- 优化记忆prompt结构
|
||||||
|
- 改进海马体记忆提取机制，提升自然度
|
||||||
|
|
||||||
|
#### 关系系统优化
|
||||||
|
- 修复relationship_value类型错误
|
||||||
|
- 优化关系管理系统
|
||||||
|
- 改进关系值计算方式
|
||||||
|
|
||||||
|
### 💻 系统架构优化
|
||||||
|
#### 配置系统改进
|
||||||
|
- 优化配置文件整理
|
||||||
|
- 新增分割器功能
|
||||||
|
- 新增表情惩罚系数自定义
|
||||||
|
- 修复配置文件保存问题
|
||||||
|
- 优化配置项管理
|
||||||
|
- 新增配置项：
|
||||||
|
- `schedule`: 日程表生成功能配置
|
||||||
|
- `response_spliter`: 回复分割控制
|
||||||
|
- `experimental`: 实验性功能开关
|
||||||
|
- `llm_observation`和`llm_sub_heartflow`: 思维流模型配置
|
||||||
|
- `llm_heartflow`: 思维流核心模型配置
|
||||||
|
- `prompt_schedule_gen`: 日程生成提示词配置
|
||||||
|
- `memory_ban_words`: 记忆过滤词配置
|
||||||
|
- 优化配置结构：
|
||||||
|
- 调整模型配置组织结构
|
||||||
|
- 优化配置项默认值
|
||||||
|
- 调整配置项顺序
|
||||||
|
- 移除冗余配置
|
||||||
|
|
||||||
|
#### WebUI改进
|
||||||
|
- 新增回复意愿模式选择功能
|
||||||
|
- 优化WebUI界面
|
||||||
|
- 优化WebUI配置保存机制
|
||||||
|
|
||||||
|
#### 部署支持扩展
|
||||||
|
- 优化Docker构建流程
|
||||||
|
- 完善Windows脚本支持
|
||||||
|
- 优化Linux一键安装脚本
|
||||||
|
- 新增macOS教程支持
|
||||||
|
|
||||||
|
### 🐛 问题修复
|
||||||
|
#### 功能稳定性
|
||||||
|
- 修复表情包审查器问题
|
||||||
|
- 修复心跳发送问题
|
||||||
|
- 修复拍一拍消息处理异常
|
||||||
|
- 修复日程报错问题
|
||||||
|
- 修复文件读写编码问题
|
||||||
|
- 修复西文字符分割问题
|
||||||
|
- 修复自定义API提供商识别问题
|
||||||
|
- 修复人格设置保存问题
|
||||||
|
- 修复EULA和隐私政策编码问题
|
||||||
|
- 修复cfg变量引用问题
|
||||||
|
|
||||||
|
#### 性能优化
|
||||||
|
- 提高topic提取效率
|
||||||
|
- 优化logger输出格式
|
||||||
|
- 优化cmd清理功能
|
||||||
|
- 改进LLM使用统计
|
||||||
|
- 优化记忆处理效率
|
||||||
|
|
||||||
|
### 📚 文档更新
|
||||||
|
- 更新README.md内容
|
||||||
|
- 添加macOS部署教程
|
||||||
|
- 优化文档结构
|
||||||
|
- 更新EULA和隐私政策
|
||||||
|
- 完善部署文档
|
||||||
|
|
||||||
|
### 🔧 其他改进
|
||||||
|
- 新增神秘小测验功能
|
||||||
|
- 新增人格测评模型
|
||||||
|
- 优化表情包审查功能
|
||||||
|
- 改进消息转发处理
|
||||||
|
- 优化代码风格和格式
|
||||||
|
- 完善异常处理机制
|
||||||
|
- 优化日志输出格式
|
||||||
|
- 版本硬编码，新增配置自动更新功能
|
||||||
|
- 更新日程生成器功能
|
||||||
|
- 优化了统计信息，会在控制台显示统计信息
|
||||||
|
|
||||||
|
### 主要改进方向
|
||||||
|
1. 完善思维流系统功能
|
||||||
|
2. 优化记忆系统效率
|
||||||
|
3. 改进关系系统稳定性
|
||||||
|
4. 提升配置系统可用性
|
||||||
|
5. 加强WebUI功能
|
||||||
|
6. 完善部署文档
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## [0.5.15] - 2025-3-17
|
||||||
|
### 🌟 核心功能增强
|
||||||
|
#### 关系系统升级
|
||||||
|
- 新增关系系统构建与启用功能
|
||||||
|
- 优化关系管理系统
|
||||||
|
- 改进prompt构建器结构
|
||||||
|
- 新增手动修改记忆库的脚本功能
|
||||||
|
- 增加alter支持功能
|
||||||
|
|
||||||
|
#### 启动器优化
|
||||||
|
- 新增MaiLauncher.bat 1.0版本
|
||||||
|
- 优化Python和Git环境检测逻辑
|
||||||
|
- 添加虚拟环境检查功能
|
||||||
|
- 改进工具箱菜单选项
|
||||||
|
- 新增分支重置功能
|
||||||
|
- 添加MongoDB支持
|
||||||
|
- 优化脚本逻辑
|
||||||
|
- 修复虚拟环境选项闪退和conda激活问题
|
||||||
|
- 修复环境检测菜单闪退问题
|
||||||
|
- 修复.env文件复制路径错误
|
||||||
|
|
||||||
|
#### 日志系统改进
|
||||||
|
- 新增GUI日志查看器
|
||||||
|
- 重构日志工厂处理机制
|
||||||
|
- 优化日志级别配置
|
||||||
|
- 支持环境变量配置日志级别
|
||||||
|
- 改进控制台日志输出
|
||||||
|
- 优化logger输出格式
|
||||||
|
|
||||||
|
### 💻 系统架构优化
|
||||||
|
#### 配置系统升级
|
||||||
|
- 更新配置文件到0.0.10版本
|
||||||
|
- 优化配置文件可视化编辑
|
||||||
|
- 新增配置文件版本检测功能
|
||||||
|
- 改进配置文件保存机制
|
||||||
|
- 修复重复保存可能清空list内容的bug
|
||||||
|
- 修复人格设置和其他项配置保存问题
|
||||||
|
|
||||||
|
#### WebUI改进
|
||||||
|
- 优化WebUI界面和功能
|
||||||
|
- 支持安装后管理功能
|
||||||
|
- 修复部分文字表述错误
|
||||||
|
|
||||||
|
#### 部署支持扩展
|
||||||
|
- 优化Docker构建流程
|
||||||
|
- 改进MongoDB服务启动逻辑
|
||||||
|
- 完善Windows脚本支持
|
||||||
|
- 优化Linux一键安装脚本
|
||||||
|
- 新增Debian 12专用运行脚本
|
||||||
|
|
||||||
|
### 🐛 问题修复
|
||||||
|
#### 功能稳定性
|
||||||
|
- 修复bot无法识别at对象和reply对象的问题
|
||||||
|
- 修复每次从数据库读取额外加0.5的问题
|
||||||
|
- 修复新版本由于版本判断不能启动的问题
|
||||||
|
- 修复配置文件更新和学习知识库的确认逻辑
|
||||||
|
- 优化token统计功能
|
||||||
|
- 修复EULA和隐私政策处理时的编码兼容问题
|
||||||
|
- 修复文件读写编码问题，统一使用UTF-8
|
||||||
|
- 修复颜文字分割问题
|
||||||
|
- 修复willing模块cfg变量引用问题
|
||||||
|
|
||||||
|
### 📚 文档更新
|
||||||
|
- 更新CLAUDE.md为高信息密度项目文档
|
||||||
|
- 添加mermaid系统架构图和模块依赖图
|
||||||
|
- 添加核心文件索引和类功能表格
|
||||||
|
- 添加消息处理流程图
|
||||||
|
- 优化文档结构
|
||||||
|
- 更新EULA和隐私政策文档
|
||||||
|
|
||||||
|
### 🔧 其他改进
|
||||||
|
- 更新全球在线数量展示功能
|
||||||
|
- 优化statistics输出展示
|
||||||
|
- 新增手动修改内存脚本（支持添加、删除和查询节点和边）
|
||||||
|
|
||||||
|
### 主要改进方向
|
||||||
|
1. 完善关系系统功能
|
||||||
|
2. 优化启动器和部署流程
|
||||||
|
3. 改进日志系统
|
||||||
|
4. 提升配置系统稳定性
|
||||||
|
5. 加强文档完整性
|
||||||
|
|
||||||
|
## [0.5.14] - 2025-3-14
|
||||||
|
### 🌟 核心功能增强
|
||||||
|
#### 记忆系统优化
|
||||||
|
- 修复了构建记忆时重复读取同一段消息导致token消耗暴增的问题
|
||||||
|
- 优化了记忆相关的工具模型代码
|
||||||
|
|
||||||
|
#### 消息处理升级
|
||||||
|
- 新增了不回答已撤回消息的功能
|
||||||
|
- 新增每小时自动删除存留超过1小时的撤回消息
|
||||||
|
- 优化了戳一戳功能的响应机制
|
||||||
|
- 修复了回复消息未正常发送的问题
|
||||||
|
- 改进了图片发送错误时的处理机制
|
||||||
|
|
||||||
|
#### 日程系统改进
|
||||||
|
- 修复了长时间运行的bot在跨天后无法生成新日程的问题
|
||||||
|
- 优化了日程文本解析功能
|
||||||
|
- 修复了解析日程时遇到markdown代码块等额外内容的处理问题
|
||||||
|
|
||||||
|
### 💻 系统架构优化
|
||||||
|
#### 日志系统升级
|
||||||
|
- 建立了新的日志系统
|
||||||
|
- 改进了错误处理机制
|
||||||
|
- 优化了代码格式化规范
|
||||||
|
|
||||||
|
#### 部署支持扩展
|
||||||
|
- 改进了NAS部署指南，增加HOST设置说明
|
||||||
|
- 优化了部署文档的完整性
|
||||||
|
|
||||||
|
### 🐛 问题修复
|
||||||
|
#### 功能稳定性
|
||||||
|
- 修复了utils_model.py中的潜在问题
|
||||||
|
- 修复了set_reply相关bug
|
||||||
|
- 修复了回应所有戳一戳的问题
|
||||||
|
- 优化了bot被戳时的判断逻辑
|
||||||
|
|
||||||
|
### 📚 文档更新
|
||||||
|
- 更新了README.md的内容
|
||||||
|
- 完善了NAS部署指南
|
||||||
|
- 优化了部署相关文档
|
||||||
|
|
||||||
|
### 主要改进方向
|
||||||
|
1. 提升记忆系统的效率和稳定性
|
||||||
|
2. 完善消息处理机制
|
||||||
|
3. 优化日程系统功能
|
||||||
|
4. 改进日志和错误处理
|
||||||
|
5. 加强部署文档的完整性
|
||||||
|
|
||||||
|
## [0.5.13] - 2025-3-12
|
||||||
|
### 🌟 核心功能增强
|
||||||
|
#### 记忆系统升级
|
||||||
|
- 新增了记忆系统的时间戳功能，包括创建时间和最后修改时间
|
||||||
|
- 新增了记忆图节点和边的时间追踪功能
|
||||||
|
- 新增了自动补充缺失时间字段的功能
|
||||||
|
- 新增了记忆遗忘机制，基于时间条件自动遗忘旧记忆
|
||||||
|
- 优化了记忆系统的数据同步机制
|
||||||
|
- 优化了记忆系统的数据结构，确保所有数据类型的一致性
|
||||||
|
|
||||||
|
#### 私聊功能完善
|
||||||
|
- 新增了完整的私聊功能支持，包括消息处理和回复
|
||||||
|
- 新增了聊天流管理器，支持群聊和私聊的上下文管理
|
||||||
|
- 新增了私聊过滤开关功能
|
||||||
|
- 优化了关系管理系统，支持跨平台用户关系
|
||||||
|
|
||||||
|
#### 消息处理升级
|
||||||
|
- 新增了消息队列管理系统，支持按时间顺序处理消息
|
||||||
|
- 新增了消息发送控制器，实现人性化的发送速度和间隔
|
||||||
|
- 新增了JSON格式分享卡片读取支持
|
||||||
|
- 新增了Base64格式表情包CQ码支持
|
||||||
|
- 改进了消息处理流程，支持多种消息类型
|
||||||
|
|
||||||
|
### 💻 系统架构优化
|
||||||
|
#### 配置系统改进
|
||||||
|
- 新增了配置文件自动更新和版本检测功能
|
||||||
|
- 新增了配置文件热重载API接口
|
||||||
|
- 新增了配置文件版本兼容性检查
|
||||||
|
- 新增了根据不同环境(dev/prod)显示不同级别的日志功能
|
||||||
|
- 优化了配置文件格式和结构
|
||||||
|
|
||||||
|
#### 部署支持扩展
|
||||||
|
- 新增了Linux系统部署指南
|
||||||
|
- 新增了Docker部署支持的详细文档
|
||||||
|
- 新增了NixOS环境支持（使用venv方式）
|
||||||
|
- 新增了优雅的shutdown机制
|
||||||
|
- 优化了Docker部署文档
|
||||||
|
|
||||||
|
### 🛠️ 开发体验提升
|
||||||
|
#### 工具链升级
|
||||||
|
- 新增了ruff代码格式化和检查工具
|
||||||
|
- 新增了知识库一键启动脚本
|
||||||
|
- 新增了自动保存脚本，定期保存聊天记录和关系数据
|
||||||
|
- 新增了表情包自动获取脚本
|
||||||
|
- 优化了日志记录（使用logger.debug替代print）
|
||||||
|
- 精简了日志输出，禁用了Uvicorn/NoneBot默认日志
|
||||||
|
|
||||||
|
#### 安全性强化
|
||||||
|
- 新增了API密钥安全管理机制
|
||||||
|
- 新增了数据库完整性检查功能
|
||||||
|
- 新增了表情包文件完整性自动检查
|
||||||
|
- 新增了异常处理和自动恢复机制
|
||||||
|
- 优化了安全性检查机制
|
||||||
|
|
||||||
|
### 🐛 关键问题修复
|
||||||
|
#### 系统稳定性
|
||||||
|
- 修复了systemctl强制停止的问题
|
||||||
|
- 修复了ENVIRONMENT变量在同一终端下不能被覆盖的问题
|
||||||
|
- 修复了libc++.so依赖问题
|
||||||
|
- 修复了数据库索引创建失败的问题
|
||||||
|
- 修复了MongoDB连接配置相关问题
|
||||||
|
- 修复了消息队列溢出问题
|
||||||
|
- 修复了配置文件加载时的版本兼容性问题
|
||||||
|
|
||||||
|
#### 功能完善性
|
||||||
|
- 修复了私聊时产生reply消息的bug
|
||||||
|
- 修复了回复消息无法识别的问题
|
||||||
|
- 修复了CQ码解析错误
|
||||||
|
- 修复了情绪管理器导入问题
|
||||||
|
- 修复了小名无效的问题
|
||||||
|
- 修复了表情包发送时的参数缺失问题
|
||||||
|
- 修复了表情包重复注册问题
|
||||||
|
- 修复了变量拼写错误问题
|
||||||
|
|
||||||
|
### 主要改进方向
|
||||||
|
1. 提升记忆系统的智能性和可靠性
|
||||||
|
2. 完善私聊功能的完整生态
|
||||||
|
3. 优化系统架构和部署便利性
|
||||||
|
4. 提升开发体验和代码质量
|
||||||
|
5. 加强系统安全性和稳定性
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
51
changelogs/changelog_config.md
Normal file
51
changelogs/changelog_config.md
Normal file
@@ -0,0 +1,51 @@
|
|||||||
|
# Changelog
|
||||||
|
|
||||||
|
## [1.0.3] - 2025-3-31
|
||||||
|
### Added
|
||||||
|
- 新增了心流相关配置项：
|
||||||
|
- `heartflow` 配置项，用于控制心流功能
|
||||||
|
|
||||||
|
### Removed
|
||||||
|
- 移除了 `response` 配置项中的 `model_r1_probability` 和 `model_v3_probability` 选项
|
||||||
|
- 移除了次级推理模型相关配置
|
||||||
|
|
||||||
|
## [1.0.1] - 2025-3-30
|
||||||
|
### Added
|
||||||
|
- 增加了流式输出控制项 `stream`
|
||||||
|
- 修复 `LLM_Request` 不会自动为 `payload` 增加流式输出标志的问题
|
||||||
|
|
||||||
|
## [1.0.0] - 2025-3-30
|
||||||
|
### Added
|
||||||
|
- 修复了错误的版本命名
|
||||||
|
- 杀掉了所有无关文件
|
||||||
|
|
||||||
|
## [0.0.11] - 2025-3-12
|
||||||
|
### Added
|
||||||
|
- 新增了 `schedule` 配置项，用于配置日程表生成功能
|
||||||
|
- 新增了 `response_spliter` 配置项，用于控制回复分割
|
||||||
|
- 新增了 `experimental` 配置项，用于实验性功能开关
|
||||||
|
- 新增了 `llm_observation` 和 `llm_sub_heartflow` 模型配置
|
||||||
|
- 新增了 `llm_heartflow` 模型配置
|
||||||
|
- 在 `personality` 配置项中新增了 `prompt_schedule_gen` 参数
|
||||||
|
|
||||||
|
### Changed
|
||||||
|
- 优化了模型配置的组织结构
|
||||||
|
- 调整了部分配置项的默认值
|
||||||
|
- 调整了配置项的顺序，将 `groups` 配置项移到了更靠前的位置
|
||||||
|
- 在 `message` 配置项中：
|
||||||
|
- 新增了 `max_response_length` 参数
|
||||||
|
- 在 `willing` 配置项中新增了 `emoji_response_penalty` 参数
|
||||||
|
- 将 `personality` 配置项中的 `prompt_schedule` 重命名为 `prompt_schedule_gen`
|
||||||
|
|
||||||
|
### Removed
|
||||||
|
- 移除了 `min_text_length` 配置项
|
||||||
|
- 移除了 `cq_code` 配置项
|
||||||
|
- 移除了 `others` 配置项（其功能已整合到 `experimental` 中）
|
||||||
|
|
||||||
|
## [0.0.5] - 2025-3-11
|
||||||
|
### Added
|
||||||
|
- 新增了 `alias_names` 配置项，用于指定麦麦的别名。
|
||||||
|
|
||||||
|
## [0.0.4] - 2025-3-9
|
||||||
|
### Added
|
||||||
|
- 新增了 `memory_ban_words` 配置项，用于指定不希望记忆的词汇。
|
||||||
@@ -1,59 +0,0 @@
|
|||||||
import os
|
|
||||||
import shutil
|
|
||||||
import tomlkit
|
|
||||||
from pathlib import Path
|
|
||||||
|
|
||||||
def update_config():
|
|
||||||
# 获取根目录路径
|
|
||||||
root_dir = Path(__file__).parent.parent
|
|
||||||
template_dir = root_dir / "template"
|
|
||||||
config_dir = root_dir / "config"
|
|
||||||
|
|
||||||
# 定义文件路径
|
|
||||||
template_path = template_dir / "bot_config_template.toml"
|
|
||||||
old_config_path = config_dir / "bot_config.toml"
|
|
||||||
new_config_path = config_dir / "bot_config.toml"
|
|
||||||
|
|
||||||
# 读取旧配置文件
|
|
||||||
old_config = {}
|
|
||||||
if old_config_path.exists():
|
|
||||||
with open(old_config_path, "r", encoding="utf-8") as f:
|
|
||||||
old_config = tomlkit.load(f)
|
|
||||||
|
|
||||||
# 删除旧的配置文件
|
|
||||||
if old_config_path.exists():
|
|
||||||
os.remove(old_config_path)
|
|
||||||
|
|
||||||
# 复制模板文件到配置目录
|
|
||||||
shutil.copy2(template_path, new_config_path)
|
|
||||||
|
|
||||||
# 读取新配置文件
|
|
||||||
with open(new_config_path, "r", encoding="utf-8") as f:
|
|
||||||
new_config = tomlkit.load(f)
|
|
||||||
|
|
||||||
# 递归更新配置
|
|
||||||
def update_dict(target, source):
|
|
||||||
for key, value in source.items():
|
|
||||||
# 跳过version字段的更新
|
|
||||||
if key == "version":
|
|
||||||
continue
|
|
||||||
if key in target:
|
|
||||||
if isinstance(value, dict) and isinstance(target[key], (dict, tomlkit.items.Table)):
|
|
||||||
update_dict(target[key], value)
|
|
||||||
else:
|
|
||||||
try:
|
|
||||||
# 直接使用tomlkit的item方法创建新值
|
|
||||||
target[key] = tomlkit.item(value)
|
|
||||||
except (TypeError, ValueError):
|
|
||||||
# 如果转换失败，直接赋值
|
|
||||||
target[key] = value
|
|
||||||
|
|
||||||
# 将旧配置的值更新到新配置中
|
|
||||||
update_dict(new_config, old_config)
|
|
||||||
|
|
||||||
# 保存更新后的配置（保留注释和格式）
|
|
||||||
with open(new_config_path, "w", encoding="utf-8") as f:
|
|
||||||
f.write(tomlkit.dumps(new_config))
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
update_config()
|
|
||||||
@@ -1,56 +0,0 @@
|
|||||||
services:
|
|
||||||
napcat:
|
|
||||||
container_name: napcat
|
|
||||||
environment:
|
|
||||||
- TZ=Asia/Shanghai
|
|
||||||
- NAPCAT_UID=${NAPCAT_UID}
|
|
||||||
- NAPCAT_GID=${NAPCAT_GID} # 让 NapCat 获取当前用户 GID,UID，防止权限问题
|
|
||||||
ports:
|
|
||||||
- 6099:6099
|
|
||||||
restart: unless-stopped
|
|
||||||
volumes:
|
|
||||||
- napcatQQ:/app/.config/QQ # 持久化 QQ 本体
|
|
||||||
- napcatCONFIG:/app/napcat/config # 持久化 NapCat 配置文件
|
|
||||||
- maimbotDATA:/MaiMBot/data # NapCat 和 NoneBot 共享此卷，否则发送图片会有问题
|
|
||||||
image: mlikiowa/napcat-docker:latest
|
|
||||||
|
|
||||||
mongodb:
|
|
||||||
container_name: mongodb
|
|
||||||
environment:
|
|
||||||
- TZ=Asia/Shanghai
|
|
||||||
# - MONGO_INITDB_ROOT_USERNAME=your_username
|
|
||||||
# - MONGO_INITDB_ROOT_PASSWORD=your_password
|
|
||||||
expose:
|
|
||||||
- "27017"
|
|
||||||
restart: unless-stopped
|
|
||||||
volumes:
|
|
||||||
- mongodb:/data/db # 持久化 MongoDB 数据库
|
|
||||||
- mongodbCONFIG:/data/configdb # 持久化 MongoDB 配置文件
|
|
||||||
image: mongo:latest
|
|
||||||
|
|
||||||
maimbot:
|
|
||||||
container_name: maimbot
|
|
||||||
environment:
|
|
||||||
- TZ=Asia/Shanghai
|
|
||||||
expose:
|
|
||||||
- "8080"
|
|
||||||
restart: unless-stopped
|
|
||||||
depends_on:
|
|
||||||
- mongodb
|
|
||||||
- napcat
|
|
||||||
volumes:
|
|
||||||
- napcatCONFIG:/MaiMBot/napcat # 自动根据配置中的 QQ 号创建 ws 反向客户端配置
|
|
||||||
- ./bot_config.toml:/MaiMBot/config/bot_config.toml # Toml 配置文件映射
|
|
||||||
- maimbotDATA:/MaiMBot/data # NapCat 和 NoneBot 共享此卷，否则发送图片会有问题
|
|
||||||
- ./.env.prod:/MaiMBot/.env.prod # Toml 配置文件映射
|
|
||||||
image: sengokucola/maimbot:latest
|
|
||||||
|
|
||||||
volumes:
|
|
||||||
maimbotCONFIG:
|
|
||||||
maimbotDATA:
|
|
||||||
napcatQQ:
|
|
||||||
napcatCONFIG:
|
|
||||||
mongodb:
|
|
||||||
mongodbCONFIG:
|
|
||||||
|
|
||||||
|
|
||||||
BIN
docs/API_KEY.png
BIN
docs/API_KEY.png
Binary file not shown.
|
Before Width: | Height: | Size: 47 KiB |
@@ -1,20 +0,0 @@
|
|||||||
Jonathan R. Wolpaw 在 “Memory in neuroscience: rhetoric versus reality.” 一文中提到，从神经科学的感觉运动假设出发，整个神经系统的功能是将经验与适当的行为联系起来，而不是单纯的信息存储。
|
|
||||||
Jonathan R,Wolpaw. (2019). Memory in neuroscience: rhetoric versus reality.. Behavioral and cognitive neuroscience reviews(2).
|
|
||||||
|
|
||||||
1. **单一过程理论**
|
|
||||||
- 单一过程理论认为，识别记忆主要是基于熟悉性这一单一因素的影响。熟悉性是指对刺激的一种自动的、无意识的感知，它可以使我们在没有回忆起具体细节的情况下，判断一个刺激是否曾经出现过。
|
|
||||||
- 例如，在一些实验中，研究者发现被试可以在没有回忆起具体学习情境的情况下，对曾经出现过的刺激做出正确的判断，这被认为是熟悉性在起作用1。
|
|
||||||
2. **双重过程理论**
|
|
||||||
- 双重过程理论则认为，识别记忆是基于两个过程：回忆和熟悉性。回忆是指对过去经验的有意识的回忆，它可以使我们回忆起具体的细节和情境；熟悉性则是一种自动的、无意识的感知。
|
|
||||||
- 该理论认为，在识别记忆中，回忆和熟悉性共同作用，使我们能够判断一个刺激是否曾经出现过。例如，在 “记得 / 知道” 范式中，被试被要求判断他们对一个刺激的记忆是基于回忆还是熟悉性。研究发现，被试可以区分这两种不同的记忆过程，这为双重过程理论提供了支持1。
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
1. **神经元节点与连接**：借鉴神经网络原理，将每个记忆单元视为一个神经元节点。节点之间通过连接相互关联，连接的强度代表记忆之间的关联程度。在形态学联想记忆中，具有相似形态特征的记忆节点连接强度较高。例如，苹果和橘子的记忆节点，由于在形状、都是水果等形态语义特征上相似，它们之间的连接强度大于苹果与汽车记忆节点间的连接强度。
|
|
||||||
2. **记忆聚类与层次结构**：依据形态特征的相似性对记忆进行聚类，形成不同的记忆簇。每个记忆簇内部的记忆具有较高的相似性，而不同记忆簇之间的记忆相似性较低。同时，构建记忆的层次结构，高层次的记忆节点代表更抽象、概括的概念，低层次的记忆节点对应具体的实例。比如，“水果” 作为高层次记忆节点，连接着 “苹果”“橘子”“香蕉” 等低层次具体水果的记忆节点。
|
|
||||||
3. **网络的动态更新**：随着新记忆的不断加入，记忆网络动态调整。新记忆节点根据其形态特征与现有网络中的节点建立连接，同时影响相关连接的强度。若新记忆与某个记忆簇的特征高度相似，则被纳入该记忆簇；若具有独特特征，则可能引发新的记忆簇的形成。例如，当系统学习到一种新的水果 “番石榴”，它会根据番石榴的形态、语义等特征，在记忆网络中找到与之最相似的区域（如水果记忆簇），并建立相应连接，同时调整周围节点连接强度以适应这一新记忆。
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
- **相似性联想**：该理论认为，当两个或多个事物在形态上具有相似性时，它们在记忆中会形成关联。例如，梨和苹果在形状和都是水果这一属性上有相似性，所以当我们看到梨时，很容易通过形态学联想记忆联想到苹果。这种相似性联想有助于我们对新事物进行分类和理解，当遇到一个新的类似水果时，我们可以通过与已有的水果记忆进行相似性匹配，来推测它的一些特征。
|
|
||||||
- **时空关联性联想**：除了相似性联想，MAM 还强调时空关联性联想。如果两个事物在时间或空间上经常同时出现，它们也会在记忆中形成关联。比如，每次在公园里看到花的时候，都能听到鸟儿的叫声，那么花和鸟儿叫声的形态特征（花的视觉形态和鸟叫的听觉形态）就会在记忆中形成关联，以后听到鸟叫可能就会联想到公园里的花。
|
|
||||||
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|
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|
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|
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|
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|
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@@ -1 +0,0 @@
|
|||||||
gource gource.log --user-image-dir docs/avatars/ --default-user-image docs/avatars/default.png
|
|
||||||
175
docs/doc1.md
175
docs/doc1.md
@@ -1,175 +0,0 @@
|
|||||||
# 📂 文件及功能介绍 (2025年更新)
|
|
||||||
|
|
||||||
## 根目录
|
|
||||||
|
|
||||||
- **README.md**: 项目的概述和使用说明。
|
|
||||||
- **requirements.txt**: 项目所需的Python依赖包列表。
|
|
||||||
- **bot.py**: 主启动文件，负责环境配置加载和NoneBot初始化。
|
|
||||||
- **template.env**: 环境变量模板文件。
|
|
||||||
- **pyproject.toml**: Python项目配置文件。
|
|
||||||
- **docker-compose.yml** 和 **Dockerfile**: Docker配置文件，用于容器化部署。
|
|
||||||
- **run_*.bat**: 各种启动脚本，包括数据库、maimai和thinking功能。
|
|
||||||
|
|
||||||
## `src/` 目录结构
|
|
||||||
|
|
||||||
- **`plugins/` 目录**: 存放不同功能模块的插件。
|
|
||||||
- **chat/**: 处理聊天相关的功能，如消息发送和接收。
|
|
||||||
- **memory_system/**: 处理机器人的记忆功能。
|
|
||||||
- **knowledege/**: 知识库相关功能。
|
|
||||||
- **models/**: 模型相关工具。
|
|
||||||
- **schedule/**: 处理日程管理的功能。
|
|
||||||
|
|
||||||
- **`gui/` 目录**: 存放图形用户界面相关的代码。
|
|
||||||
- **reasoning_gui.py**: 负责推理界面的实现，提供用户交互。
|
|
||||||
|
|
||||||
- **`common/` 目录**: 存放通用的工具和库。
|
|
||||||
- **database.py**: 处理与数据库的交互，负责数据的存储和检索。
|
|
||||||
- ****init**.py**: 初始化模块。
|
|
||||||
|
|
||||||
## `config/` 目录
|
|
||||||
|
|
||||||
- **bot_config_template.toml**: 机器人配置模板。
|
|
||||||
- **auto_format.py**: 自动格式化工具。
|
|
||||||
|
|
||||||
### `src/plugins/chat/` 目录文件详细介绍
|
|
||||||
|
|
||||||
1. **`__init__.py`**:
|
|
||||||
- 初始化 `chat` 模块，使其可以作为一个包被导入。
|
|
||||||
|
|
||||||
2. **`bot.py`**:
|
|
||||||
- 主要的聊天机器人逻辑实现，处理消息的接收、思考和回复。
|
|
||||||
- 包含 `ChatBot` 类，负责消息处理流程控制。
|
|
||||||
- 集成记忆系统和意愿管理。
|
|
||||||
|
|
||||||
3. **`config.py`**:
|
|
||||||
- 配置文件，定义了聊天机器人的各种参数和设置。
|
|
||||||
- 包含 `BotConfig` 和全局配置对象 `global_config`。
|
|
||||||
|
|
||||||
4. **`cq_code.py`**:
|
|
||||||
- 处理 CQ 码（CoolQ 码），用于发送和接收特定格式的消息。
|
|
||||||
|
|
||||||
5. **`emoji_manager.py`**:
|
|
||||||
- 管理表情包的发送和接收，根据情感选择合适的表情。
|
|
||||||
- 提供根据情绪获取表情的方法。
|
|
||||||
|
|
||||||
6. **`llm_generator.py`**:
|
|
||||||
- 生成基于大语言模型的回复，处理用户输入并生成相应的文本。
|
|
||||||
- 通过 `ResponseGenerator` 类实现回复生成。
|
|
||||||
|
|
||||||
7. **`message.py`**:
|
|
||||||
- 定义消息的结构和处理逻辑，包含多种消息类型：
|
|
||||||
- `Message`: 基础消息类
|
|
||||||
- `MessageSet`: 消息集合
|
|
||||||
- `Message_Sending`: 发送中的消息
|
|
||||||
- `Message_Thinking`: 思考状态的消息
|
|
||||||
|
|
||||||
8. **`message_sender.py`**:
|
|
||||||
- 控制消息的发送逻辑，确保消息按照特定规则发送。
|
|
||||||
- 包含 `message_manager` 对象，用于管理消息队列。
|
|
||||||
|
|
||||||
9. **`prompt_builder.py`**:
|
|
||||||
- 构建用于生成回复的提示，优化机器人的响应质量。
|
|
||||||
|
|
||||||
10. **`relationship_manager.py`**:
|
|
||||||
- 管理用户之间的关系，记录用户的互动和偏好。
|
|
||||||
- 提供更新关系和关系值的方法。
|
|
||||||
|
|
||||||
11. **`Segment_builder.py`**:
|
|
||||||
- 构建消息片段的工具。
|
|
||||||
|
|
||||||
12. **`storage.py`**:
|
|
||||||
- 处理数据存储，负责将聊天记录和用户信息保存到数据库。
|
|
||||||
- 实现 `MessageStorage` 类管理消息存储。
|
|
||||||
|
|
||||||
13. **`thinking_idea.py`**:
|
|
||||||
- 实现机器人的思考机制。
|
|
||||||
|
|
||||||
14. **`topic_identifier.py`**:
|
|
||||||
- 识别消息中的主题，帮助机器人理解用户的意图。
|
|
||||||
|
|
||||||
15. **`utils.py`** 和 **`utils_*.py`** 系列文件:
|
|
||||||
- 存放各种工具函数，提供辅助功能以支持其他模块。
|
|
||||||
- 包括 `utils_cq.py`、`utils_image.py`、`utils_user.py` 等专门工具。
|
|
||||||
|
|
||||||
16. **`willing_manager.py`**:
|
|
||||||
- 管理机器人的回复意愿，动态调整回复概率。
|
|
||||||
- 通过多种因素（如被提及、话题兴趣度）影响回复决策。
|
|
||||||
|
|
||||||
### `src/plugins/memory_system/` 目录文件介绍
|
|
||||||
|
|
||||||
1. **`memory.py`**:
|
|
||||||
- 实现记忆管理核心功能，包含 `memory_graph` 对象。
|
|
||||||
- 提供相关项目检索，支持多层次记忆关联。
|
|
||||||
|
|
||||||
2. **`draw_memory.py`**:
|
|
||||||
- 记忆可视化工具。
|
|
||||||
|
|
||||||
3. **`memory_manual_build.py`**:
|
|
||||||
- 手动构建记忆的工具。
|
|
||||||
|
|
||||||
4. **`offline_llm.py`**:
|
|
||||||
- 离线大语言模型处理功能。
|
|
||||||
|
|
||||||
## 消息处理流程
|
|
||||||
|
|
||||||
### 1. 消息接收与预处理
|
|
||||||
|
|
||||||
- 通过 `ChatBot.handle_message()` 接收群消息。
|
|
||||||
- 进行用户和群组的权限检查。
|
|
||||||
- 更新用户关系信息。
|
|
||||||
- 创建标准化的 `Message` 对象。
|
|
||||||
- 对消息进行过滤和敏感词检测。
|
|
||||||
|
|
||||||
### 2. 主题识别与决策
|
|
||||||
|
|
||||||
- 使用 `topic_identifier` 识别消息主题。
|
|
||||||
- 通过记忆系统检查对主题的兴趣度。
|
|
||||||
- `willing_manager` 动态计算回复概率。
|
|
||||||
- 根据概率决定是否回复消息。
|
|
||||||
|
|
||||||
### 3. 回复生成与发送
|
|
||||||
|
|
||||||
- 如需回复，首先创建 `Message_Thinking` 对象表示思考状态。
|
|
||||||
- 调用 `ResponseGenerator.generate_response()` 生成回复内容和情感状态。
|
|
||||||
- 删除思考消息，创建 `MessageSet` 准备发送回复。
|
|
||||||
- 计算模拟打字时间，设置消息发送时间点。
|
|
||||||
- 可能附加情感相关的表情包。
|
|
||||||
- 通过 `message_manager` 将消息加入发送队列。
|
|
||||||
|
|
||||||
### 消息发送控制系统
|
|
||||||
|
|
||||||
`message_sender.py` 中实现了消息发送控制系统，采用三层结构：
|
|
||||||
|
|
||||||
1. **消息管理**:
|
|
||||||
- 支持单条消息和消息集合的发送。
|
|
||||||
- 处理思考状态消息，控制思考时间。
|
|
||||||
- 模拟人类打字速度，添加自然发送延迟。
|
|
||||||
|
|
||||||
2. **情感表达**:
|
|
||||||
- 根据生成回复的情感状态选择匹配的表情包。
|
|
||||||
- 通过 `emoji_manager` 管理表情资源。
|
|
||||||
|
|
||||||
3. **记忆交互**:
|
|
||||||
- 通过 `memory_graph` 检索相关记忆。
|
|
||||||
- 根据记忆内容影响回复意愿和内容。
|
|
||||||
|
|
||||||
## 系统特色功能
|
|
||||||
|
|
||||||
1. **智能回复意愿系统**:
|
|
||||||
- 动态调整回复概率，模拟真实人类交流特性。
|
|
||||||
- 考虑多种因素：被提及、话题兴趣度、用户关系等。
|
|
||||||
|
|
||||||
2. **记忆系统集成**:
|
|
||||||
- 支持多层次记忆关联和检索。
|
|
||||||
- 影响机器人的兴趣和回复内容。
|
|
||||||
|
|
||||||
3. **自然交流模拟**:
|
|
||||||
- 模拟思考和打字过程，添加合理延迟。
|
|
||||||
- 情感表达与表情包结合。
|
|
||||||
|
|
||||||
4. **多环境配置支持**:
|
|
||||||
- 支持开发环境和生产环境的不同配置。
|
|
||||||
- 通过环境变量和配置文件灵活管理设置。
|
|
||||||
|
|
||||||
5. **Docker部署支持**:
|
|
||||||
- 提供容器化部署方案，简化安装和运行。
|
|
||||||
@@ -1,93 +0,0 @@
|
|||||||
# 🐳 Docker 部署指南
|
|
||||||
|
|
||||||
## 部署步骤 (推荐，但不一定是最新)
|
|
||||||
|
|
||||||
**"更新镜像与容器"部分在本文档 [Part 6](#6-更新镜像与容器)**
|
|
||||||
|
|
||||||
### 0. 前提说明
|
|
||||||
|
|
||||||
**本文假设读者已具备一定的 Docker 基础知识。若您对 Docker 不熟悉，建议先参考相关教程或文档进行学习，或选择使用 [📦Linux手动部署指南](./manual_deploy_linux.md) 或 [📦Windows手动部署指南](./manual_deploy_windows.md) 。**
|
|
||||||
|
|
||||||
|
|
||||||
### 1. 获取Docker配置文件
|
|
||||||
|
|
||||||
- 建议先单独创建好一个文件夹并进入，作为工作目录
|
|
||||||
|
|
||||||
```bash
|
|
||||||
wget https://raw.githubusercontent.com/SengokuCola/MaiMBot/main/docker-compose.yml -O docker-compose.yml
|
|
||||||
```
|
|
||||||
|
|
||||||
- 若需要启用MongoDB数据库的用户名和密码，可进入docker-compose.yml，取消MongoDB处的注释并修改变量旁 `=` 后方的值为你的用户名和密码\
|
|
||||||
修改后请注意在之后配置 `.env.prod` 文件时指定MongoDB数据库的用户名密码
|
|
||||||
|
|
||||||
### 2. 启动服务
|
|
||||||
|
|
||||||
- **!!! 请在第一次启动前确保当前工作目录下 `.env.prod` 与 `bot_config.toml` 文件存在 !!!**\
|
|
||||||
由于Docker文件映射行为的特殊性，若宿主机的映射路径不存在，可能导致意外的目录创建，而不会创建文件，由于此处需要文件映射到文件，需提前确保文件存在且路径正确，可使用如下命令:
|
|
||||||
|
|
||||||
```bash
|
|
||||||
touch .env.prod
|
|
||||||
touch bot_config.toml
|
|
||||||
```
|
|
||||||
|
|
||||||
- 启动Docker容器:
|
|
||||||
|
|
||||||
```bash
|
|
||||||
NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose up -d
|
|
||||||
# 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代
|
|
||||||
NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker-compose up -d
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
### 3. 修改配置并重启Docker
|
|
||||||
|
|
||||||
- 请前往 [🎀 新手配置指南](docs/installation_cute.md) 或 [⚙️ 标准配置指南](docs/installation_standard.md) 完成`.env.prod`与`bot_config.toml`配置文件的编写\
|
|
||||||
**需要注意`.env.prod`中HOST处IP的填写，Docker中部署和系统中直接安装的配置会有所不同**
|
|
||||||
|
|
||||||
- 重启Docker容器:
|
|
||||||
|
|
||||||
```bash
|
|
||||||
docker restart maimbot # 若修改过容器名称则替换maimbot为你自定的名称
|
|
||||||
```
|
|
||||||
|
|
||||||
- 下方命令可以但不推荐，只是同时重启NapCat、MongoDB、MaiMBot三个服务
|
|
||||||
|
|
||||||
```bash
|
|
||||||
NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart
|
|
||||||
# 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代
|
|
||||||
NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker-compose restart
|
|
||||||
```
|
|
||||||
|
|
||||||
### 4. 登入NapCat管理页添加反向WebSocket
|
|
||||||
|
|
||||||
- 在浏览器地址栏输入 `http://<宿主机IP>:6099/` 进入NapCat的管理Web页，添加一个Websocket客户端
|
|
||||||
|
|
||||||
> 网络配置 -> 新建 -> Websocket客户端
|
|
||||||
|
|
||||||
- Websocket客户端的名称自定，URL栏填入 `ws://maimbot:8080/onebot/v11/ws`，启用并保存即可\
|
|
||||||
(若修改过容器名称则替换maimbot为你自定的名称)
|
|
||||||
|
|
||||||
### 5. 部署完成，愉快地和麦麦对话吧!
|
|
||||||
|
|
||||||
|
|
||||||
### 6. 更新镜像与容器
|
|
||||||
|
|
||||||
- 拉取最新镜像
|
|
||||||
|
|
||||||
```bash
|
|
||||||
docker-compose pull
|
|
||||||
```
|
|
||||||
|
|
||||||
- 执行启动容器指令，该指令会自动重建镜像有更新的容器并启动
|
|
||||||
|
|
||||||
```bash
|
|
||||||
NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose up -d
|
|
||||||
# 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代
|
|
||||||
NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker-compose up -d
|
|
||||||
```
|
|
||||||
|
|
||||||
## ⚠️ 注意事项
|
|
||||||
|
|
||||||
- 目前部署方案仍在测试中，可能存在未知问题
|
|
||||||
- 配置文件中的API密钥请妥善保管，不要泄露
|
|
||||||
- 建议先在测试环境中运行，确认无误后再部署到生产环境
|
|
||||||
149
docs/fast_q_a.md
149
docs/fast_q_a.md
@@ -1,149 +0,0 @@
|
|||||||
## 快速更新Q&A❓
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
- 这个文件用来记录一些常见的新手问题。
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
### 完整安装教程
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
[MaiMbot简易配置教程](https://www.bilibili.com/video/BV1zsQ5YCEE6)
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
### Api相关问题
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
- 为什么显示:"缺失必要的API KEY" ❓
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
|
|
||||||
<img src="API_KEY.png" width=650>
|
|
||||||
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
>你需要在 [Silicon Flow Api](https://cloud.siliconflow.cn/account/ak)
|
|
||||||
>网站上注册一个账号，然后点击这个链接打开API KEY获取页面。
|
|
||||||
>
|
|
||||||
>点击 "新建API密钥" 按钮新建一个给MaiMBot使用的API KEY。不要忘了点击复制。
|
|
||||||
>
|
|
||||||
>之后打开MaiMBot在你电脑上的文件根目录，使用记事本或者其他文本编辑器打开 [.env.prod](../.env.prod)
|
|
||||||
>这个文件。把你刚才复制的API KEY填入到 "SILICONFLOW_KEY=" 这个等号的右边。
|
|
||||||
>
|
|
||||||
>在默认情况下，MaiMBot使用的默认Api都是硅基流动的。
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
|
|
||||||
- 我想使用硅基流动之外的Api网站，我应该怎么做 ❓
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
>你需要使用记事本或者其他文本编辑器打开config目录下的 [bot_config.toml](../config/bot_config.toml)
|
|
||||||
>然后修改其中的 "provider = " 字段。同时不要忘记模仿 [.env.prod](../.env.prod)
|
|
||||||
>文件的写法添加 Api Key 和 Base URL。
|
|
||||||
>
|
|
||||||
>举个例子，如果你写了 " provider = \"ABC\" "，那你需要相应的在 [.env.prod](../.env.prod)
|
|
||||||
>文件里添加形如 " ABC_BASE_URL = https://api.abc.com/v1 " 和 " ABC_KEY = sk-1145141919810 " 的字段。
|
|
||||||
>
|
|
||||||
>**如果你对AI没有较深的了解，修改识图模型和嵌入模型的provider字段可能会产生bug，因为你从Api网站调用了一个并不存在的模型**
|
|
||||||
>
|
|
||||||
>这个时候，你需要把字段的值改回 "provider = \"SILICONFLOW\" " 以此解决bug。
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
### MongoDB相关问题
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
- 我应该怎么清空bot内存储的表情包 ❓
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
>打开你的MongoDB Compass软件，你会在左上角看到这样的一个界面：
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
><img src="MONGO_DB_0.png" width=250>
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
>点击 "CONNECT" 之后，点击展开 MegBot 标签栏
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
><img src="MONGO_DB_1.png" width=250>
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
>点进 "emoji" 再点击 "DELETE" 删掉所有条目，如图所示
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
><img src="MONGO_DB_2.png" width=450>
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
>你可以用类似的方式手动清空MaiMBot的所有服务器数据。
|
|
||||||
>
|
|
||||||
>MaiMBot的所有图片均储存在 [data](../data) 文件夹内，按类型分为 [emoji](../data/emoji) 和 [image](../data/image)
|
|
||||||
>
|
|
||||||
>在删除服务器数据时不要忘记清空这些图片。
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
|
|
||||||
<br>
|
|
||||||
|
|
||||||
- 为什么我连接不上MongoDB服务器 ❓
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
>这个问题比较复杂，但是你可以按照下面的步骤检查，看看具体是什么问题
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
> 1. 检查有没有把 mongod.exe 所在的目录添加到 path。 具体可参照
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
>  [CSDN-windows10设置环境变量Path详细步骤](https://blog.csdn.net/flame_007/article/details/106401215)
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
>  **需要往path里填入的是 exe 所在的完整目录！不带 exe 本体**
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
>
|
|
||||||
> 2. 待完成
|
|
||||||
>
|
|
||||||
><br>
|
|
||||||
@@ -1,228 +0,0 @@
|
|||||||
# 🔧 配置指南 喵~
|
|
||||||
|
|
||||||
## 👋 你好呀
|
|
||||||
|
|
||||||
让咱来告诉你我们要做什么喵：
|
|
||||||
|
|
||||||
1. 我们要一起设置一个可爱的AI机器人
|
|
||||||
2. 这个机器人可以在QQ上陪你聊天玩耍哦
|
|
||||||
3. 需要设置两个文件才能让机器人工作呢
|
|
||||||
|
|
||||||
## 📝 需要设置的文件喵
|
|
||||||
|
|
||||||
要设置这两个文件才能让机器人跑起来哦：
|
|
||||||
|
|
||||||
1. `.env.prod` - 这个文件告诉机器人要用哪些AI服务呢
|
|
||||||
2. `bot_config.toml` - 这个文件教机器人怎么和你聊天喵
|
|
||||||
|
|
||||||
## 🔑 密钥和域名的对应关系
|
|
||||||
|
|
||||||
想象一下，你要进入一个游乐园，需要：
|
|
||||||
|
|
||||||
1. 知道游乐园的地址（这就是域名 base_url）
|
|
||||||
2. 有入场的门票（这就是密钥 key）
|
|
||||||
|
|
||||||
在 `.env.prod` 文件里，我们定义了三个游乐园的地址和门票喵：
|
|
||||||
|
|
||||||
```ini
|
|
||||||
# 硅基流动游乐园
|
|
||||||
SILICONFLOW_KEY=your_key # 硅基流动的门票
|
|
||||||
SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ # 硅基流动的地址
|
|
||||||
|
|
||||||
# DeepSeek游乐园
|
|
||||||
DEEP_SEEK_KEY=your_key # DeepSeek的门票
|
|
||||||
DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 # DeepSeek的地址
|
|
||||||
|
|
||||||
# ChatAnyWhere游乐园
|
|
||||||
CHAT_ANY_WHERE_KEY=your_key # ChatAnyWhere的门票
|
|
||||||
CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 # ChatAnyWhere的地址
|
|
||||||
```
|
|
||||||
|
|
||||||
然后在 `bot_config.toml` 里，机器人会用这些门票和地址去游乐园玩耍：
|
|
||||||
|
|
||||||
```toml
|
|
||||||
[model.llm_reasoning]
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-R1"
|
|
||||||
provider = "SILICONFLOW" # 告诉机器人：去硅基流动游乐园玩，机器人会自动用硅基流动的门票进去
|
|
||||||
|
|
||||||
[model.llm_normal]
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
|
||||||
provider = "SILICONFLOW" # 还是去硅基流动游乐园
|
|
||||||
```
|
|
||||||
|
|
||||||
### 🎪 举个例子喵
|
|
||||||
|
|
||||||
如果你想用DeepSeek官方的服务，就要这样改：
|
|
||||||
|
|
||||||
```toml
|
|
||||||
[model.llm_reasoning]
|
|
||||||
name = "deepseek-reasoner" # 改成对应的模型名称，这里为DeepseekR1
|
|
||||||
provider = "DEEP_SEEK" # 改成去DeepSeek游乐园
|
|
||||||
|
|
||||||
[model.llm_normal]
|
|
||||||
name = "deepseek-chat" # 改成对应的模型名称，这里为DeepseekV3
|
|
||||||
provider = "DEEP_SEEK" # 也去DeepSeek游乐园
|
|
||||||
```
|
|
||||||
|
|
||||||
### 🎯 简单来说
|
|
||||||
|
|
||||||
- `.env.prod` 文件就像是你的票夹，存放着各个游乐园的门票和地址
|
|
||||||
- `bot_config.toml` 就是告诉机器人：用哪张票去哪个游乐园玩
|
|
||||||
- 所有模型都可以用同一个游乐园的票，也可以去不同的游乐园玩耍
|
|
||||||
- 如果用硅基流动的服务，就保持默认配置不用改呢~
|
|
||||||
|
|
||||||
记住：门票（key）要保管好，不能给别人看哦，不然别人就可以用你的票去玩了喵！
|
|
||||||
|
|
||||||
## ---让我们开始吧---
|
|
||||||
|
|
||||||
### 第一个文件：环境配置 (.env.prod)
|
|
||||||
|
|
||||||
这个文件就像是机器人的"身份证"呢，告诉它要用哪些AI服务喵~
|
|
||||||
|
|
||||||
```ini
|
|
||||||
# 这些是AI服务的密钥，就像是魔法钥匙一样呢
|
|
||||||
# 要把 your_key 换成真正的密钥才行喵
|
|
||||||
# 比如说：SILICONFLOW_KEY=sk-123456789abcdef
|
|
||||||
SILICONFLOW_KEY=your_key
|
|
||||||
SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/
|
|
||||||
DEEP_SEEK_KEY=your_key
|
|
||||||
DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1
|
|
||||||
CHAT_ANY_WHERE_KEY=your_key
|
|
||||||
CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1
|
|
||||||
|
|
||||||
# 如果你不知道这是什么，那么下面这些不用改，保持原样就好啦
|
|
||||||
# 如果使用Docker部署，需要改成0.0.0.0喵，不然听不见群友讲话了喵
|
|
||||||
HOST=127.0.0.1
|
|
||||||
PORT=8080
|
|
||||||
|
|
||||||
# 这些是数据库设置，一般也不用改呢
|
|
||||||
# 如果使用Docker部署，需要把MONGODB_HOST改成数据库容器的名字喵，默认是mongodb喵
|
|
||||||
MONGODB_HOST=127.0.0.1
|
|
||||||
MONGODB_PORT=27017
|
|
||||||
DATABASE_NAME=MegBot
|
|
||||||
# 数据库认证信息，如果需要认证就取消注释并填写下面三行喵
|
|
||||||
# MONGODB_USERNAME = ""
|
|
||||||
# MONGODB_PASSWORD = ""
|
|
||||||
# MONGODB_AUTH_SOURCE = ""
|
|
||||||
|
|
||||||
# 也可以使用URI连接数据库，取消注释填写在下面这行喵（URI的优先级比上面的高）
|
|
||||||
# MONGODB_URI=mongodb://127.0.0.1:27017/MegBot
|
|
||||||
|
|
||||||
# 这里是机器人的插件列表呢
|
|
||||||
PLUGINS=["src2.plugins.chat"]
|
|
||||||
```
|
|
||||||
|
|
||||||
### 第二个文件：机器人配置 (bot_config.toml)
|
|
||||||
|
|
||||||
这个文件就像是教机器人"如何说话"的魔法书呢！
|
|
||||||
|
|
||||||
```toml
|
|
||||||
[bot]
|
|
||||||
qq = "把这里改成你的机器人QQ号喵" # 填写你的机器人QQ号
|
|
||||||
nickname = "麦麦" # 机器人的名字，你可以改成你喜欢的任何名字哦，建议和机器人QQ名称/群昵称一样哦
|
|
||||||
alias_names = ["小麦", "阿麦"] # 也可以用这个招呼机器人，可以不设置呢
|
|
||||||
|
|
||||||
[personality]
|
|
||||||
# 这里可以设置机器人的性格呢，让它更有趣一些喵
|
|
||||||
prompt_personality = [
|
|
||||||
"曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧", # 贴吧风格的性格
|
|
||||||
"是一个女大学生，你有黑色头发，你会刷小红书" # 小红书风格的性格
|
|
||||||
]
|
|
||||||
prompt_schedule = "一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书" # 用来提示机器人每天干什么的提示词喵
|
|
||||||
|
|
||||||
[message]
|
|
||||||
min_text_length = 2 # 机器人每次至少要说几个字呢
|
|
||||||
max_context_size = 15 # 机器人能记住多少条消息喵
|
|
||||||
emoji_chance = 0.2 # 机器人使用表情的概率哦（0.2就是20%的机会呢）
|
|
||||||
thinking_timeout = 120 # 机器人思考时间，时间越长能思考的时间越多，但是不要太长喵
|
|
||||||
|
|
||||||
response_willing_amplifier = 1 # 机器人回复意愿放大系数，增大会让他更愿意聊天喵
|
|
||||||
response_interested_rate_amplifier = 1 # 机器人回复兴趣度放大系数，听到记忆里的内容时意愿的放大系数喵
|
|
||||||
down_frequency_rate = 3.5 # 降低回复频率的群组回复意愿降低系数
|
|
||||||
ban_words = ["脏话", "不文明用语"] # 在这里填写不让机器人说的词，要用英文逗号隔开，每个词都要用英文双引号括起来喵
|
|
||||||
|
|
||||||
[emoji]
|
|
||||||
auto_save = true # 是否自动保存看到的表情包呢
|
|
||||||
enable_check = false # 是否要检查表情包是不是合适的喵
|
|
||||||
check_prompt = "符合公序良俗" # 检查表情包的标准呢
|
|
||||||
|
|
||||||
[others]
|
|
||||||
enable_advance_output = true # 是否要显示更多的运行信息呢
|
|
||||||
enable_kuuki_read = true # 让机器人能够"察言观色"喵
|
|
||||||
enable_debug_output = false # 是否启用调试输出喵
|
|
||||||
enable_friend_chat = false # 是否启用好友聊天喵
|
|
||||||
|
|
||||||
[groups]
|
|
||||||
talk_allowed = [123456, 789012] # 比如：让机器人在群123456和789012里说话
|
|
||||||
talk_frequency_down = [345678] # 比如：在群345678里少说点话
|
|
||||||
ban_user_id = [111222] # 比如：不回复QQ号为111222的人的消息
|
|
||||||
|
|
||||||
# 模型配置部分的详细说明喵~
|
|
||||||
|
|
||||||
|
|
||||||
#下面的模型若使用硅基流动则不需要更改，使用ds官方则改成在.env.prod自己指定的密钥和域名，使用自定义模型则选择定位相似的模型自己填写
|
|
||||||
|
|
||||||
[model.llm_reasoning] #推理模型R1，用来理解和思考的喵
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-R1" # 模型名字
|
|
||||||
# name = "Qwen/QwQ-32B" # 如果想用千问模型，可以把上面那行注释掉，用这个呢
|
|
||||||
provider = "SILICONFLOW" # 使用在.env.prod里设置的宏，也就是去掉"_BASE_URL"留下来的字喵
|
|
||||||
|
|
||||||
[model.llm_reasoning_minor] #R1蒸馏模型，是个轻量版的推理模型喵
|
|
||||||
name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
|
|
||||||
[model.llm_normal] #V3模型，用来日常聊天的喵
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
|
|
||||||
[model.llm_normal_minor] #V2.5模型，是V3的前代版本呢
|
|
||||||
name = "deepseek-ai/DeepSeek-V2.5"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
|
|
||||||
[model.vlm] #图像识别模型，让机器人能看懂图片喵
|
|
||||||
name = "deepseek-ai/deepseek-vl2"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
|
|
||||||
[model.embedding] #嵌入模型，帮助机器人理解文本的相似度呢
|
|
||||||
name = "BAAI/bge-m3"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
|
|
||||||
# 如果选择了llm方式提取主题，就用这个模型配置喵
|
|
||||||
[topic.llm_topic]
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
```
|
|
||||||
|
|
||||||
## 💡 模型配置说明喵
|
|
||||||
|
|
||||||
1. **关于模型服务**：
|
|
||||||
- 如果你用硅基流动的服务，这些配置都不用改呢
|
|
||||||
- 如果用DeepSeek官方API，要把provider改成你在.env.prod里设置的宏喵
|
|
||||||
- 如果要用自定义模型，选择一个相似功能的模型配置来改呢
|
|
||||||
|
|
||||||
2. **主要模型功能**：
|
|
||||||
- `llm_reasoning`: 负责思考和推理的大脑喵
|
|
||||||
- `llm_normal`: 负责日常聊天的嘴巴呢
|
|
||||||
- `vlm`: 负责看图片的眼睛哦
|
|
||||||
- `embedding`: 负责理解文字含义的理解力喵
|
|
||||||
- `topic`: 负责理解对话主题的能力呢
|
|
||||||
|
|
||||||
## 🌟 小提示
|
|
||||||
|
|
||||||
- 如果你刚开始使用，建议保持默认配置呢
|
|
||||||
- 不同的模型有不同的特长，可以根据需要调整它们的使用比例哦
|
|
||||||
|
|
||||||
## 🌟 小贴士喵
|
|
||||||
|
|
||||||
- 记得要好好保管密钥（key）哦，不要告诉别人呢
|
|
||||||
- 配置文件要小心修改，改错了机器人可能就不能和你玩了喵
|
|
||||||
- 如果想让机器人更聪明，可以调整 personality 里的设置呢
|
|
||||||
- 不想让机器人说某些话，就把那些词放在 ban_words 里面喵
|
|
||||||
- QQ群号和QQ号都要用数字填写，不要加引号哦（除了机器人自己的QQ号）
|
|
||||||
|
|
||||||
## ⚠️ 注意事项
|
|
||||||
|
|
||||||
- 这个机器人还在测试中呢，可能会有一些小问题喵
|
|
||||||
- 如果不知道怎么改某个设置，就保持原样不要动它哦~
|
|
||||||
- 记得要先有AI服务的密钥，不然机器人就不能和你说话了呢
|
|
||||||
- 修改完配置后要重启机器人才能生效喵~
|
|
||||||
@@ -1,167 +0,0 @@
|
|||||||
# 🔧 配置指南
|
|
||||||
|
|
||||||
## 简介
|
|
||||||
|
|
||||||
本项目需要配置两个主要文件：
|
|
||||||
|
|
||||||
1. `.env.prod` - 配置API服务和系统环境
|
|
||||||
2. `bot_config.toml` - 配置机器人行为和模型
|
|
||||||
|
|
||||||
## API配置说明
|
|
||||||
|
|
||||||
`.env.prod` 和 `bot_config.toml` 中的API配置关系如下：
|
|
||||||
|
|
||||||
### 在.env.prod中定义API凭证
|
|
||||||
|
|
||||||
```ini
|
|
||||||
# API凭证配置
|
|
||||||
SILICONFLOW_KEY=your_key # 硅基流动API密钥
|
|
||||||
SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ # 硅基流动API地址
|
|
||||||
|
|
||||||
DEEP_SEEK_KEY=your_key # DeepSeek API密钥
|
|
||||||
DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 # DeepSeek API地址
|
|
||||||
|
|
||||||
CHAT_ANY_WHERE_KEY=your_key # ChatAnyWhere API密钥
|
|
||||||
CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 # ChatAnyWhere API地址
|
|
||||||
```
|
|
||||||
|
|
||||||
### 在bot_config.toml中引用API凭证
|
|
||||||
|
|
||||||
```toml
|
|
||||||
[model.llm_reasoning]
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-R1"
|
|
||||||
provider = "SILICONFLOW" # 引用.env.prod中定义的宏
|
|
||||||
```
|
|
||||||
|
|
||||||
如需切换到其他API服务，只需修改引用：
|
|
||||||
|
|
||||||
```toml
|
|
||||||
[model.llm_reasoning]
|
|
||||||
name = "deepseek-reasoner" # 改成对应的模型名称，这里为DeepseekR1
|
|
||||||
provider = "DEEP_SEEK" # 使用DeepSeek密钥
|
|
||||||
```
|
|
||||||
|
|
||||||
## 配置文件详解
|
|
||||||
|
|
||||||
### 环境配置文件 (.env.prod)
|
|
||||||
|
|
||||||
```ini
|
|
||||||
# API配置
|
|
||||||
SILICONFLOW_KEY=your_key
|
|
||||||
SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/
|
|
||||||
DEEP_SEEK_KEY=your_key
|
|
||||||
DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1
|
|
||||||
CHAT_ANY_WHERE_KEY=your_key
|
|
||||||
CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1
|
|
||||||
|
|
||||||
# 服务配置
|
|
||||||
|
|
||||||
HOST=127.0.0.1 # 如果使用Docker部署，需要改成0.0.0.0，否则QQ消息无法传入
|
|
||||||
PORT=8080 # 与反向端口相同
|
|
||||||
|
|
||||||
# 数据库配置
|
|
||||||
MONGODB_HOST=127.0.0.1 # 如果使用Docker部署，需要改成数据库容器的名字，默认是mongodb
|
|
||||||
MONGODB_PORT=27017 # MongoDB端口
|
|
||||||
|
|
||||||
DATABASE_NAME=MegBot
|
|
||||||
# 数据库认证信息，如果需要认证就取消注释并填写下面三行
|
|
||||||
# MONGODB_USERNAME = ""
|
|
||||||
# MONGODB_PASSWORD = ""
|
|
||||||
# MONGODB_AUTH_SOURCE = ""
|
|
||||||
|
|
||||||
# 也可以使用URI连接数据库，取消注释填写在下面这行（URI的优先级比上面的高）
|
|
||||||
# MONGODB_URI=mongodb://127.0.0.1:27017/MegBot
|
|
||||||
|
|
||||||
# 插件配置
|
|
||||||
PLUGINS=["src2.plugins.chat"]
|
|
||||||
```
|
|
||||||
|
|
||||||
### 机器人配置文件 (bot_config.toml)
|
|
||||||
|
|
||||||
```toml
|
|
||||||
[bot]
|
|
||||||
qq = "机器人QQ号" # 机器人的QQ号，必填
|
|
||||||
nickname = "麦麦" # 机器人昵称
|
|
||||||
# alias_names: 配置机器人可使用的别名。当机器人在群聊或对话中被调用时，别名可以作为直接命令或提及机器人的关键字使用。
|
|
||||||
# 该配置项为字符串数组。例如: ["小麦", "阿麦"]
|
|
||||||
alias_names = ["小麦", "阿麦"] # 机器人别名
|
|
||||||
|
|
||||||
[personality]
|
|
||||||
prompt_personality = [
|
|
||||||
"曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧",
|
|
||||||
"是一个女大学生，你有黑色头发，你会刷小红书"
|
|
||||||
] # 人格提示词
|
|
||||||
prompt_schedule = "一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书" # 日程生成提示词
|
|
||||||
|
|
||||||
[message]
|
|
||||||
min_text_length = 2 # 最小回复长度
|
|
||||||
max_context_size = 15 # 上下文记忆条数
|
|
||||||
emoji_chance = 0.2 # 表情使用概率
|
|
||||||
thinking_timeout = 120 # 机器人思考时间，时间越长能思考的时间越多，但是不要太长
|
|
||||||
|
|
||||||
response_willing_amplifier = 1 # 机器人回复意愿放大系数，增大会更愿意聊天
|
|
||||||
response_interested_rate_amplifier = 1 # 机器人回复兴趣度放大系数，听到记忆里的内容时意愿的放大系数
|
|
||||||
down_frequency_rate = 3.5 # 降低回复频率的群组回复意愿降低系数
|
|
||||||
ban_words = [] # 禁用词列表
|
|
||||||
|
|
||||||
[emoji]
|
|
||||||
auto_save = true # 自动保存表情
|
|
||||||
enable_check = false # 启用表情审核
|
|
||||||
check_prompt = "符合公序良俗"
|
|
||||||
|
|
||||||
[groups]
|
|
||||||
talk_allowed = [] # 允许对话的群号
|
|
||||||
talk_frequency_down = [] # 降低回复频率的群号
|
|
||||||
ban_user_id = [] # 禁止回复的用户QQ号
|
|
||||||
|
|
||||||
[others]
|
|
||||||
enable_advance_output = true # 是否启用高级输出
|
|
||||||
enable_kuuki_read = true # 是否启用读空气功能
|
|
||||||
enable_debug_output = false # 是否启用调试输出
|
|
||||||
enable_friend_chat = false # 是否启用好友聊天
|
|
||||||
|
|
||||||
# 模型配置
|
|
||||||
[model.llm_reasoning] # 推理模型
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-R1"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
|
|
||||||
[model.llm_reasoning_minor] # 轻量推理模型
|
|
||||||
name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
|
|
||||||
[model.llm_normal] # 对话模型
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
|
|
||||||
[model.llm_normal_minor] # 备用对话模型
|
|
||||||
name = "deepseek-ai/DeepSeek-V2.5"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
|
|
||||||
[model.vlm] # 图像识别模型
|
|
||||||
name = "deepseek-ai/deepseek-vl2"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
|
|
||||||
[model.embedding] # 文本向量模型
|
|
||||||
name = "BAAI/bge-m3"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
|
|
||||||
|
|
||||||
[topic.llm_topic]
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
|
||||||
provider = "SILICONFLOW"
|
|
||||||
```
|
|
||||||
|
|
||||||
## 注意事项
|
|
||||||
|
|
||||||
1. API密钥安全：
|
|
||||||
- 妥善保管API密钥
|
|
||||||
- 不要将含有密钥的配置文件上传至公开仓库
|
|
||||||
|
|
||||||
2. 配置修改：
|
|
||||||
- 修改配置后需重启服务
|
|
||||||
- 使用默认服务(硅基流动)时无需修改模型配置
|
|
||||||
- QQ号和群号使用数字格式(机器人QQ号除外)
|
|
||||||
|
|
||||||
3. 其他说明：
|
|
||||||
- 项目处于测试阶段，可能存在未知问题
|
|
||||||
- 建议初次使用保持默认配置
|
|
||||||
@@ -1,444 +0,0 @@
|
|||||||
# 面向纯新手的Linux服务器麦麦部署指南
|
|
||||||
|
|
||||||
## 你得先有一个服务器
|
|
||||||
|
|
||||||
为了能使麦麦在你的电脑关机之后还能运行，你需要一台不间断开机的主机，也就是我们常说的服务器。
|
|
||||||
|
|
||||||
华为云、阿里云、腾讯云等等都是在国内可以选择的选择。
|
|
||||||
|
|
||||||
你可以去租一台最低配置的就足敷需要了，按月租大概十几块钱就能租到了。
|
|
||||||
|
|
||||||
我们假设你已经租好了一台Linux架构的云服务器。我用的是阿里云ubuntu24.04，其他的原理相似。
|
|
||||||
|
|
||||||
## 0.我们就从零开始吧
|
|
||||||
|
|
||||||
### 网络问题
|
|
||||||
|
|
||||||
为访问github相关界面，推荐去下一款加速器，新手可以试试watttoolkit。
|
|
||||||
|
|
||||||
### 安装包下载
|
|
||||||
|
|
||||||
#### MongoDB
|
|
||||||
|
|
||||||
对于ubuntu24.04 x86来说是这个：
|
|
||||||
|
|
||||||
https://repo.mongodb.org/apt/ubuntu/dists/noble/mongodb-org/8.0/multiverse/binary-amd64/mongodb-org-server_8.0.5_amd64.deb
|
|
||||||
|
|
||||||
如果不是就在这里自行选择对应版本
|
|
||||||
|
|
||||||
https://www.mongodb.com/try/download/community-kubernetes-operator
|
|
||||||
|
|
||||||
#### Napcat
|
|
||||||
|
|
||||||
在这里选择对应版本。
|
|
||||||
|
|
||||||
https://github.com/NapNeko/NapCatQQ/releases/tag/v4.6.7
|
|
||||||
|
|
||||||
对于ubuntu24.04 x86来说是这个：
|
|
||||||
|
|
||||||
https://dldir1.qq.com/qqfile/qq/QQNT/ee4bd910/linuxqq_3.2.16-32793_amd64.deb
|
|
||||||
|
|
||||||
#### 麦麦
|
|
||||||
|
|
||||||
https://github.com/SengokuCola/MaiMBot/archive/refs/tags/0.5.8-alpha.zip
|
|
||||||
|
|
||||||
下载这个官方压缩包。
|
|
||||||
|
|
||||||
### 路径
|
|
||||||
|
|
||||||
我把麦麦相关文件放在了/moi/mai里面，你可以凭喜好更改，记得适当调整下面涉及到的部分即可。
|
|
||||||
|
|
||||||
文件结构：
|
|
||||||
|
|
||||||
```
|
|
||||||
moi
|
|
||||||
└─ mai
|
|
||||||
├─ linuxqq_3.2.16-32793_amd64.deb
|
|
||||||
├─ mongodb-org-server_8.0.5_amd64.deb
|
|
||||||
└─ bot
|
|
||||||
└─ MaiMBot-0.5.8-alpha.zip
|
|
||||||
```
|
|
||||||
|
|
||||||
### 网络
|
|
||||||
|
|
||||||
你可以在你的服务器控制台网页更改防火墙规则，允许6099，8080，27017这几个端口的出入。
|
|
||||||
|
|
||||||
## 1.正式开始！
|
|
||||||
|
|
||||||
远程连接你的服务器，你会看到一个黑框框闪着白方格，这就是我们要进行设置的场所——终端了。以下的bash命令都是在这里输入。
|
|
||||||
|
|
||||||
## 2. Python的安装
|
|
||||||
|
|
||||||
- 导入 Python 的稳定版 PPA：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo add-apt-repository ppa:deadsnakes/ppa
|
|
||||||
```
|
|
||||||
|
|
||||||
- 导入 PPA 后，更新 APT 缓存：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo apt update
|
|
||||||
```
|
|
||||||
|
|
||||||
- 在「终端」中执行以下命令来安装 Python 3.12：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo apt install python3.12
|
|
||||||
```
|
|
||||||
|
|
||||||
- 验证安装是否成功：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
python3.12 --version
|
|
||||||
```
|
|
||||||
|
|
||||||
- 在「终端」中，执行以下命令安装 pip：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo apt install python3-pip
|
|
||||||
```
|
|
||||||
|
|
||||||
- 检查Pip是否安装成功：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
pip --version
|
|
||||||
```
|
|
||||||
|
|
||||||
- 安装必要组件
|
|
||||||
|
|
||||||
``` bash
|
|
||||||
sudo apt install python-is-python3
|
|
||||||
```
|
|
||||||
|
|
||||||
## 3.MongoDB的安装
|
|
||||||
|
|
||||||
``` bash
|
|
||||||
cd /moi/mai
|
|
||||||
```
|
|
||||||
|
|
||||||
``` bash
|
|
||||||
dpkg -i mongodb-org-server_8.0.5_amd64.deb
|
|
||||||
```
|
|
||||||
|
|
||||||
``` bash
|
|
||||||
mkdir -p /root/data/mongodb/{data,log}
|
|
||||||
```
|
|
||||||
|
|
||||||
## 4.MongoDB的运行
|
|
||||||
|
|
||||||
```bash
|
|
||||||
service mongod start
|
|
||||||
```
|
|
||||||
|
|
||||||
```bash
|
|
||||||
systemctl status mongod #通过这条指令检查运行状态
|
|
||||||
```
|
|
||||||
|
|
||||||
有需要的话可以把这个服务注册成开机自启
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo systemctl enable mongod
|
|
||||||
```
|
|
||||||
|
|
||||||
## 5.napcat的安装
|
|
||||||
|
|
||||||
``` bash
|
|
||||||
curl -o napcat.sh https://nclatest.znin.net/NapNeko/NapCat-Installer/main/script/install.sh && sudo bash napcat.sh
|
|
||||||
```
|
|
||||||
|
|
||||||
上面的不行试试下面的
|
|
||||||
|
|
||||||
``` bash
|
|
||||||
dpkg -i linuxqq_3.2.16-32793_amd64.deb
|
|
||||||
apt-get install -f
|
|
||||||
dpkg -i linuxqq_3.2.16-32793_amd64.deb
|
|
||||||
```
|
|
||||||
|
|
||||||
成功的标志是输入``` napcat ```出来炫酷的彩虹色界面
|
|
||||||
|
|
||||||
## 6.napcat的运行
|
|
||||||
|
|
||||||
此时你就可以根据提示在```napcat```里面登录你的QQ号了。
|
|
||||||
|
|
||||||
```bash
|
|
||||||
napcat start <你的QQ号>
|
|
||||||
napcat status #检查运行状态
|
|
||||||
```
|
|
||||||
|
|
||||||
然后你就可以登录napcat的webui进行设置了：
|
|
||||||
|
|
||||||
```http://<你服务器的公网IP>:6099/webui?token=napcat```
|
|
||||||
|
|
||||||
第一次是这个，后续改了密码之后token就会对应修改。你也可以使用```napcat log <你的QQ号>```来查看webui地址。把里面的```127.0.0.1```改成<你服务器的公网IP>即可。
|
|
||||||
|
|
||||||
登录上之后在网络配置界面添加websocket客户端，名称随便输一个，url改成`ws://127.0.0.1:8080/onebot/v11/ws`保存之后点启用，就大功告成了。
|
|
||||||
|
|
||||||
## 7.麦麦的安装
|
|
||||||
|
|
||||||
### step 1 安装解压软件
|
|
||||||
|
|
||||||
```
|
|
||||||
sudo apt-get install unzip
|
|
||||||
```
|
|
||||||
|
|
||||||
### step 2 解压文件
|
|
||||||
|
|
||||||
```bash
|
|
||||||
cd /moi/mai/bot # 注意：要切换到压缩包的目录中去
|
|
||||||
unzip MaiMBot-0.5.8-alpha.zip
|
|
||||||
```
|
|
||||||
|
|
||||||
### step 3 进入虚拟环境安装库
|
|
||||||
|
|
||||||
```bash
|
|
||||||
cd /moi/mai/bot
|
|
||||||
python -m venv venv
|
|
||||||
source venv/bin/activate
|
|
||||||
pip install -r requirements.txt
|
|
||||||
```
|
|
||||||
|
|
||||||
### step 4 试运行
|
|
||||||
|
|
||||||
```bash
|
|
||||||
cd /moi/mai/bot
|
|
||||||
python -m venv venv
|
|
||||||
source venv/bin/activate
|
|
||||||
python bot.py
|
|
||||||
```
|
|
||||||
|
|
||||||
肯定运行不成功，不过你会发现结束之后多了一些文件
|
|
||||||
|
|
||||||
```
|
|
||||||
bot
|
|
||||||
├─ .env.prod
|
|
||||||
└─ config
|
|
||||||
└─ bot_config.toml
|
|
||||||
```
|
|
||||||
|
|
||||||
你要会vim直接在终端里修改也行，不过也可以把它们下到本地改好再传上去：
|
|
||||||
|
|
||||||
### step 5 文件配置
|
|
||||||
|
|
||||||
本项目需要配置两个主要文件：
|
|
||||||
|
|
||||||
1. `.env.prod` - 配置API服务和系统环境
|
|
||||||
2. `bot_config.toml` - 配置机器人行为和模型
|
|
||||||
|
|
||||||
#### API
|
|
||||||
|
|
||||||
你可以注册一个硅基流动的账号，通过邀请码注册有14块钱的免费额度：https://cloud.siliconflow.cn/i/7Yld7cfg。
|
|
||||||
|
|
||||||
#### 在.env.prod中定义API凭证：
|
|
||||||
|
|
||||||
```
|
|
||||||
# API凭证配置
|
|
||||||
SILICONFLOW_KEY=your_key # 硅基流动API密钥
|
|
||||||
SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ # 硅基流动API地址
|
|
||||||
|
|
||||||
DEEP_SEEK_KEY=your_key # DeepSeek API密钥
|
|
||||||
DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 # DeepSeek API地址
|
|
||||||
|
|
||||||
CHAT_ANY_WHERE_KEY=your_key # ChatAnyWhere API密钥
|
|
||||||
CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 # ChatAnyWhere API地址
|
|
||||||
```
|
|
||||||
|
|
||||||
#### 在bot_config.toml中引用API凭证：
|
|
||||||
|
|
||||||
```
|
|
||||||
[model.llm_reasoning]
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-R1"
|
|
||||||
base_url = "SILICONFLOW_BASE_URL" # 引用.env.prod中定义的地址
|
|
||||||
key = "SILICONFLOW_KEY" # 引用.env.prod中定义的密钥
|
|
||||||
```
|
|
||||||
|
|
||||||
如需切换到其他API服务，只需修改引用：
|
|
||||||
|
|
||||||
```
|
|
||||||
[model.llm_reasoning]
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-R1"
|
|
||||||
base_url = "DEEP_SEEK_BASE_URL" # 切换为DeepSeek服务
|
|
||||||
key = "DEEP_SEEK_KEY" # 使用DeepSeek密钥
|
|
||||||
```
|
|
||||||
|
|
||||||
#### 配置文件详解
|
|
||||||
|
|
||||||
##### 环境配置文件 (.env.prod)
|
|
||||||
|
|
||||||
```
|
|
||||||
# API配置
|
|
||||||
SILICONFLOW_KEY=your_key
|
|
||||||
SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/
|
|
||||||
DEEP_SEEK_KEY=your_key
|
|
||||||
DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1
|
|
||||||
CHAT_ANY_WHERE_KEY=your_key
|
|
||||||
CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1
|
|
||||||
|
|
||||||
# 服务配置
|
|
||||||
HOST=127.0.0.1 # 如果使用Docker部署，需要改成0.0.0.0，否则QQ消息无法传入
|
|
||||||
PORT=8080
|
|
||||||
|
|
||||||
# 数据库配置
|
|
||||||
MONGODB_HOST=127.0.0.1 # 如果使用Docker部署，需要改成数据库容器的名字，默认是mongodb
|
|
||||||
MONGODB_PORT=27017
|
|
||||||
DATABASE_NAME=MegBot
|
|
||||||
MONGODB_USERNAME = "" # 数据库用户名
|
|
||||||
MONGODB_PASSWORD = "" # 数据库密码
|
|
||||||
MONGODB_AUTH_SOURCE = "" # 认证数据库
|
|
||||||
|
|
||||||
# 插件配置
|
|
||||||
PLUGINS=["src2.plugins.chat"]
|
|
||||||
```
|
|
||||||
|
|
||||||
##### 机器人配置文件 (bot_config.toml)
|
|
||||||
|
|
||||||
```
|
|
||||||
[bot]
|
|
||||||
qq = "机器人QQ号" # 必填
|
|
||||||
nickname = "麦麦" # 机器人昵称（你希望机器人怎么称呼它自己）
|
|
||||||
|
|
||||||
[personality]
|
|
||||||
prompt_personality = [
|
|
||||||
"曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧",
|
|
||||||
"是一个女大学生，你有黑色头发，你会刷小红书"
|
|
||||||
]
|
|
||||||
prompt_schedule = "一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书"
|
|
||||||
|
|
||||||
[message]
|
|
||||||
min_text_length = 2 # 最小回复长度
|
|
||||||
max_context_size = 15 # 上下文记忆条数
|
|
||||||
emoji_chance = 0.2 # 表情使用概率
|
|
||||||
ban_words = [] # 禁用词列表
|
|
||||||
|
|
||||||
[emoji]
|
|
||||||
auto_save = true # 自动保存表情
|
|
||||||
enable_check = false # 启用表情审核
|
|
||||||
check_prompt = "符合公序良俗"
|
|
||||||
|
|
||||||
[groups]
|
|
||||||
talk_allowed = [] # 允许对话的群号
|
|
||||||
talk_frequency_down = [] # 降低回复频率的群号
|
|
||||||
ban_user_id = [] # 禁止回复的用户QQ号
|
|
||||||
|
|
||||||
[others]
|
|
||||||
enable_advance_output = true # 启用详细日志
|
|
||||||
enable_kuuki_read = true # 启用场景理解
|
|
||||||
|
|
||||||
# 模型配置
|
|
||||||
[model.llm_reasoning] # 推理模型
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-R1"
|
|
||||||
base_url = "SILICONFLOW_BASE_URL"
|
|
||||||
key = "SILICONFLOW_KEY"
|
|
||||||
|
|
||||||
[model.llm_reasoning_minor] # 轻量推理模型
|
|
||||||
name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
|
|
||||||
base_url = "SILICONFLOW_BASE_URL"
|
|
||||||
key = "SILICONFLOW_KEY"
|
|
||||||
|
|
||||||
[model.llm_normal] # 对话模型
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
|
||||||
base_url = "SILICONFLOW_BASE_URL"
|
|
||||||
key = "SILICONFLOW_KEY"
|
|
||||||
|
|
||||||
[model.llm_normal_minor] # 备用对话模型
|
|
||||||
name = "deepseek-ai/DeepSeek-V2.5"
|
|
||||||
base_url = "SILICONFLOW_BASE_URL"
|
|
||||||
key = "SILICONFLOW_KEY"
|
|
||||||
|
|
||||||
[model.vlm] # 图像识别模型
|
|
||||||
name = "deepseek-ai/deepseek-vl2"
|
|
||||||
base_url = "SILICONFLOW_BASE_URL"
|
|
||||||
key = "SILICONFLOW_KEY"
|
|
||||||
|
|
||||||
[model.embedding] # 文本向量模型
|
|
||||||
name = "BAAI/bge-m3"
|
|
||||||
base_url = "SILICONFLOW_BASE_URL"
|
|
||||||
key = "SILICONFLOW_KEY"
|
|
||||||
|
|
||||||
|
|
||||||
[topic.llm_topic]
|
|
||||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
|
||||||
base_url = "SILICONFLOW_BASE_URL"
|
|
||||||
key = "SILICONFLOW_KEY"
|
|
||||||
```
|
|
||||||
|
|
||||||
**step # 6** 运行
|
|
||||||
|
|
||||||
现在再运行
|
|
||||||
|
|
||||||
```bash
|
|
||||||
cd /moi/mai/bot
|
|
||||||
python -m venv venv
|
|
||||||
source venv/bin/activate
|
|
||||||
python bot.py
|
|
||||||
```
|
|
||||||
|
|
||||||
应该就能运行成功了。
|
|
||||||
|
|
||||||
## 8.事后配置
|
|
||||||
|
|
||||||
可是现在还有个问题：只要你一关闭终端，bot.py就会停止运行。那该怎么办呢？我们可以把bot.py注册成服务。
|
|
||||||
|
|
||||||
重启服务器，打开MongoDB和napcat服务。
|
|
||||||
|
|
||||||
新建一个文件，名为`bot.service`，内容如下
|
|
||||||
|
|
||||||
```
|
|
||||||
[Unit]
|
|
||||||
Description=maimai bot
|
|
||||||
|
|
||||||
[Service]
|
|
||||||
WorkingDirectory=/moi/mai/bot
|
|
||||||
ExecStart=/moi/mai/bot/venv/bin/python /moi/mai/bot/bot.py
|
|
||||||
Restart=on-failure
|
|
||||||
User=root
|
|
||||||
|
|
||||||
[Install]
|
|
||||||
WantedBy=multi-user.target
|
|
||||||
```
|
|
||||||
|
|
||||||
里面的路径视自己的情况更改。
|
|
||||||
|
|
||||||
把它放到`/etc/systemd/system`里面。
|
|
||||||
|
|
||||||
重新加载 `systemd` 配置：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo systemctl daemon-reload
|
|
||||||
```
|
|
||||||
|
|
||||||
启动服务：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo systemctl start bot.service # 启动服务
|
|
||||||
sudo systemctl restart bot.service # 或者重启服务
|
|
||||||
```
|
|
||||||
|
|
||||||
检查服务状态：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo systemctl status bot.service
|
|
||||||
```
|
|
||||||
|
|
||||||
现在再关闭终端，检查麦麦能不能正常回复QQ信息。如果可以的话就大功告成了！
|
|
||||||
|
|
||||||
## 9.命令速查
|
|
||||||
|
|
||||||
```bash
|
|
||||||
service mongod start # 启动mongod服务
|
|
||||||
napcat start <你的QQ号> # 登录napcat
|
|
||||||
cd /moi/mai/bot # 切换路径
|
|
||||||
python -m venv venv # 创建虚拟环境
|
|
||||||
source venv/bin/activate # 激活虚拟环境
|
|
||||||
|
|
||||||
sudo systemctl daemon-reload # 重新加载systemd配置
|
|
||||||
sudo systemctl start bot.service # 启动bot服务
|
|
||||||
sudo systemctl enable bot.service # 启动bot服务
|
|
||||||
|
|
||||||
sudo systemctl status bot.service # 检查bot服务状态
|
|
||||||
```
|
|
||||||
|
|
||||||
```
|
|
||||||
python bot.py
|
|
||||||
```
|
|
||||||
|
|
||||||
@@ -1,180 +0,0 @@
|
|||||||
# 📦 Linux系统如何手动部署MaiMbot麦麦？
|
|
||||||
|
|
||||||
## 准备工作
|
|
||||||
|
|
||||||
- 一台联网的Linux设备（本教程以Ubuntu/Debian系为例）
|
|
||||||
- QQ小号（QQ框架的使用可能导致qq被风控，严重（小概率）可能会导致账号封禁，强烈不推荐使用大号）
|
|
||||||
- 可用的大模型API
|
|
||||||
- 一个AI助手，网上随便搜一家打开来用都行，可以帮你解决一些不懂的问题
|
|
||||||
- 以下内容假设你对Linux系统有一定的了解，如果觉得难以理解，请直接用Windows系统部署[Windows系统部署指南](./manual_deploy_windows.md)
|
|
||||||
|
|
||||||
## 你需要知道什么？
|
|
||||||
|
|
||||||
- 如何正确向AI助手提问，来学习新知识
|
|
||||||
|
|
||||||
- Python是什么
|
|
||||||
|
|
||||||
- Python的虚拟环境是什么？如何创建虚拟环境
|
|
||||||
|
|
||||||
- 命令行是什么
|
|
||||||
|
|
||||||
- 数据库是什么？如何安装并启动MongoDB
|
|
||||||
|
|
||||||
- 如何运行一个QQ机器人，以及NapCat框架是什么
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 环境配置
|
|
||||||
|
|
||||||
### 1️⃣ **确认Python版本**
|
|
||||||
|
|
||||||
需确保Python版本为3.9及以上
|
|
||||||
|
|
||||||
```bash
|
|
||||||
python --version
|
|
||||||
# 或
|
|
||||||
python3 --version
|
|
||||||
```
|
|
||||||
|
|
||||||
如果版本低于3.9，请更新Python版本。
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# Ubuntu/Debian
|
|
||||||
sudo apt update
|
|
||||||
sudo apt install python3.9
|
|
||||||
# 如执行了这一步，建议在执行时将python3指向python3.9
|
|
||||||
# 更新替代方案，设置 python3.9 为默认的 python3 版本:
|
|
||||||
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
|
|
||||||
sudo update-alternatives --config python3
|
|
||||||
```
|
|
||||||
|
|
||||||
### 2️⃣ **创建虚拟环境**
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# 方法1：使用venv(推荐)
|
|
||||||
python3 -m venv maimbot
|
|
||||||
source maimbot/bin/activate # 激活环境
|
|
||||||
|
|
||||||
# 方法2：使用conda（需先安装Miniconda）
|
|
||||||
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
|
|
||||||
bash Miniconda3-latest-Linux-x86_64.sh
|
|
||||||
conda create -n maimbot python=3.9
|
|
||||||
conda activate maimbot
|
|
||||||
|
|
||||||
# 通过以上方法创建并进入虚拟环境后，再执行以下命令
|
|
||||||
|
|
||||||
# 安装依赖（任选一种环境）
|
|
||||||
pip install -r requirements.txt
|
|
||||||
```
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 数据库配置
|
|
||||||
|
|
||||||
### 3️⃣ **安装并启动MongoDB**
|
|
||||||
|
|
||||||
- 安装与启动：Debian参考[官方文档](https://docs.mongodb.com/manual/tutorial/install-mongodb-on-debian/)，Ubuntu参考[官方文档](https://docs.mongodb.com/manual/tutorial/install-mongodb-on-ubuntu/)
|
|
||||||
- 默认连接本地27017端口
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## NapCat配置
|
|
||||||
|
|
||||||
### 4️⃣ **安装NapCat框架**
|
|
||||||
|
|
||||||
- 参考[NapCat官方文档](https://www.napcat.wiki/guide/boot/Shell#napcat-installer-linux%E4%B8%80%E9%94%AE%E4%BD%BF%E7%94%A8%E8%84%9A%E6%9C%AC-%E6%94%AF%E6%8C%81ubuntu-20-debian-10-centos9)安装
|
|
||||||
|
|
||||||
- 使用QQ小号登录，添加反向WS地址: `ws://127.0.0.1:8080/onebot/v11/ws`
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 配置文件设置
|
|
||||||
|
|
||||||
### 5️⃣ **配置文件设置，让麦麦Bot正常工作**
|
|
||||||
|
|
||||||
- 修改环境配置文件：`.env.prod`
|
|
||||||
- 修改机器人配置文件：`bot_config.toml`
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 启动机器人
|
|
||||||
|
|
||||||
### 6️⃣ **启动麦麦机器人**
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# 在项目目录下操作
|
|
||||||
nb run
|
|
||||||
# 或
|
|
||||||
python3 bot.py
|
|
||||||
```
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
### 7️⃣ **使用systemctl管理maimbot**
|
|
||||||
|
|
||||||
使用以下命令添加服务文件：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo nano /etc/systemd/system/maimbot.service
|
|
||||||
```
|
|
||||||
|
|
||||||
输入以下内容：
|
|
||||||
|
|
||||||
`<maimbot_directory>`：你的maimbot目录
|
|
||||||
|
|
||||||
`<venv_directory>`：你的venv环境（就是上文创建环境后，执行的代码`source maimbot/bin/activate`中source后面的路径的绝对路径）
|
|
||||||
|
|
||||||
```ini
|
|
||||||
[Unit]
|
|
||||||
Description=MaiMbot 麦麦
|
|
||||||
After=network.target mongod.service
|
|
||||||
|
|
||||||
[Service]
|
|
||||||
Type=simple
|
|
||||||
WorkingDirectory=<maimbot_directory>
|
|
||||||
ExecStart=<venv_directory>/python3 bot.py
|
|
||||||
ExecStop=/bin/kill -2 $MAINPID
|
|
||||||
Restart=always
|
|
||||||
RestartSec=10s
|
|
||||||
|
|
||||||
[Install]
|
|
||||||
WantedBy=multi-user.target
|
|
||||||
```
|
|
||||||
|
|
||||||
输入以下命令重新加载systemd：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo systemctl daemon-reload
|
|
||||||
```
|
|
||||||
|
|
||||||
启动并设置开机自启：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo systemctl start maimbot
|
|
||||||
sudo systemctl enable maimbot
|
|
||||||
```
|
|
||||||
|
|
||||||
输入以下命令查看日志：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo journalctl -xeu maimbot
|
|
||||||
```
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## **其他组件(可选)**
|
|
||||||
|
|
||||||
- 直接运行 knowledge.py生成知识库
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 常见问题
|
|
||||||
|
|
||||||
🔧 权限问题：在命令前加`sudo`
|
|
||||||
🔌 端口占用：使用`sudo lsof -i :8080`查看端口占用
|
|
||||||
🛡️ 防火墙：确保8080/27017端口开放
|
|
||||||
|
|
||||||
```bash
|
|
||||||
sudo ufw allow 8080/tcp
|
|
||||||
sudo ufw allow 27017/tcp
|
|
||||||
```
|
|
||||||
@@ -1,110 +0,0 @@
|
|||||||
# 📦 Windows系统如何手动部署MaiMbot麦麦？
|
|
||||||
|
|
||||||
## 你需要什么？
|
|
||||||
|
|
||||||
- 一台电脑，能够上网的那种
|
|
||||||
|
|
||||||
- 一个QQ小号（QQ框架的使用可能导致qq被风控，严重（小概率）可能会导致账号封禁，强烈不推荐使用大号）
|
|
||||||
|
|
||||||
- 可用的大模型API
|
|
||||||
|
|
||||||
- 一个AI助手，网上随便搜一家打开来用都行，可以帮你解决一些不懂的问题
|
|
||||||
|
|
||||||
## 你需要知道什么？
|
|
||||||
|
|
||||||
- 如何正确向AI助手提问，来学习新知识
|
|
||||||
|
|
||||||
- Python是什么
|
|
||||||
|
|
||||||
- Python的虚拟环境是什么？如何创建虚拟环境
|
|
||||||
|
|
||||||
- 命令行是什么
|
|
||||||
|
|
||||||
- 数据库是什么？如何安装并启动MongoDB
|
|
||||||
|
|
||||||
- 如何运行一个QQ机器人，以及NapCat框架是什么
|
|
||||||
|
|
||||||
## 如果准备好了，就可以开始部署了
|
|
||||||
|
|
||||||
### 1️⃣ **首先，我们需要安装正确版本的Python**
|
|
||||||
|
|
||||||
在创建虚拟环境之前，请确保你的电脑上安装了Python 3.9及以上版本。如果没有，可以按以下步骤安装：
|
|
||||||
|
|
||||||
1. 访问Python官网下载页面：<https://www.python.org/downloads/release/python-3913/>
|
|
||||||
2. 下载Windows安装程序 (64-bit): `python-3.9.13-amd64.exe`
|
|
||||||
3. 运行安装程序，并确保勾选"Add Python 3.9 to PATH"选项
|
|
||||||
4. 点击"Install Now"开始安装
|
|
||||||
|
|
||||||
或者使用PowerShell自动下载安装（需要管理员权限）：
|
|
||||||
|
|
||||||
```powershell
|
|
||||||
# 下载并安装Python 3.9.13
|
|
||||||
$pythonUrl = "https://www.python.org/ftp/python/3.9.13/python-3.9.13-amd64.exe"
|
|
||||||
$pythonInstaller = "$env:TEMP\python-3.9.13-amd64.exe"
|
|
||||||
Invoke-WebRequest -Uri $pythonUrl -OutFile $pythonInstaller
|
|
||||||
Start-Process -Wait -FilePath $pythonInstaller -ArgumentList "/quiet", "InstallAllUsers=0", "PrependPath=1" -Verb RunAs
|
|
||||||
```
|
|
||||||
|
|
||||||
### 2️⃣ **创建Python虚拟环境来运行程序**
|
|
||||||
|
|
||||||
> 你可以选择使用以下两种方法之一来创建Python环境：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# ---方法1：使用venv（Python自带）
|
|
||||||
# 在命令行中创建虚拟环境（环境名为maimbot）
|
|
||||||
# 这会让你在运行命令的目录下创建一个虚拟环境
|
|
||||||
# 请确保你已通过cd命令前往到了对应路径，不然之后你可能找不到你的python环境
|
|
||||||
python -m venv maimbot
|
|
||||||
|
|
||||||
maimbot\\Scripts\\activate
|
|
||||||
|
|
||||||
# 安装依赖
|
|
||||||
pip install -r requirements.txt
|
|
||||||
```
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# ---方法2：使用conda
|
|
||||||
# 创建一个新的conda环境（环境名为maimbot）
|
|
||||||
# Python版本为3.9
|
|
||||||
conda create -n maimbot python=3.9
|
|
||||||
|
|
||||||
# 激活环境
|
|
||||||
conda activate maimbot
|
|
||||||
|
|
||||||
# 安装依赖
|
|
||||||
pip install -r requirements.txt
|
|
||||||
```
|
|
||||||
|
|
||||||
### 2️⃣ **然后你需要启动MongoDB数据库，来存储信息**
|
|
||||||
|
|
||||||
- 安装并启动MongoDB服务
|
|
||||||
- 默认连接本地27017端口
|
|
||||||
|
|
||||||
### 3️⃣ **配置NapCat，让麦麦bot与qq取得联系**
|
|
||||||
|
|
||||||
- 安装并登录NapCat（用你的qq小号）
|
|
||||||
- 添加反向WS: `ws://127.0.0.1:8080/onebot/v11/ws`
|
|
||||||
|
|
||||||
### 4️⃣ **配置文件设置，让麦麦Bot正常工作**
|
|
||||||
|
|
||||||
- 修改环境配置文件：`.env.prod`
|
|
||||||
- 修改机器人配置文件：`bot_config.toml`
|
|
||||||
|
|
||||||
### 5️⃣ **启动麦麦机器人**
|
|
||||||
|
|
||||||
- 打开命令行，cd到对应路径
|
|
||||||
|
|
||||||
```bash
|
|
||||||
nb run
|
|
||||||
```
|
|
||||||
|
|
||||||
- 或者cd到对应路径后
|
|
||||||
|
|
||||||
```bash
|
|
||||||
python bot.py
|
|
||||||
```
|
|
||||||
|
|
||||||
### 6️⃣ **其他组件(可选)**
|
|
||||||
|
|
||||||
- `run_thingking.bat`: 启动可视化推理界面（未完善）
|
|
||||||
- 直接运行 knowledge.py生成知识库
|
|
||||||
Binary file not shown.
|
Before Width: | Height: | Size: 107 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 208 KiB |
@@ -1,68 +0,0 @@
|
|||||||
# 群晖 NAS 部署指南
|
|
||||||
|
|
||||||
**笔者使用的是 DSM 7.2.2，其他 DSM 版本的操作可能不完全一样**
|
|
||||||
**需要使用 Container Manager，群晖的部分部分入门级 NAS 可能不支持**
|
|
||||||
|
|
||||||
## 部署步骤
|
|
||||||
|
|
||||||
### 创建配置文件目录
|
|
||||||
|
|
||||||
打开 `DSM ➡️ 控制面板 ➡️ 共享文件夹`，点击 `新增` ，创建一个共享文件夹
|
|
||||||
只需要设置名称，其他设置均保持默认即可。如果你已经有 docker 专用的共享文件夹了，就跳过这一步
|
|
||||||
|
|
||||||
打开 `DSM ➡️ FileStation`， 在共享文件夹中创建一个 `MaiMBot` 文件夹
|
|
||||||
|
|
||||||
### 准备配置文件
|
|
||||||
|
|
||||||
docker-compose.yml: https://github.com/SengokuCola/MaiMBot/blob/main/docker-compose.yml
|
|
||||||
下载后打开，将 `services-mongodb-image` 修改为 `mongo:4.4.24`。这是因为最新的 MongoDB 强制要求 AVX 指令集，而群晖似乎不支持这个指令集
|
|
||||||
|
|
||||||
|
|
||||||
bot_config.toml: https://github.com/SengokuCola/MaiMBot/blob/main/template/bot_config_template.toml
|
|
||||||
下载后，重命名为 `bot_config.toml`
|
|
||||||
打开它，按自己的需求填写配置文件
|
|
||||||
|
|
||||||
.env.prod: https://github.com/SengokuCola/MaiMBot/blob/main/template.env
|
|
||||||
下载后，重命名为 `.env.prod`
|
|
||||||
将 `HOST` 修改为 `0.0.0.0`，确保 maimbot 能被 napcat 访问
|
|
||||||
按下图修改 mongodb 设置，使用 `MONGODB_URI`
|
|
||||||
|
|
||||||
|
|
||||||
把 `bot_config.toml` 和 `.env.prod` 放入之前创建的 `MaiMBot`文件夹
|
|
||||||
|
|
||||||
#### 如何下载？
|
|
||||||
|
|
||||||
点这里！
|
|
||||||
|
|
||||||
### 创建项目
|
|
||||||
|
|
||||||
打开 `DSM ➡️ ContainerManager ➡️ 项目`，点击 `新增` 创建项目，填写以下内容：
|
|
||||||
|
|
||||||
- 项目名称： `maimbot`
|
|
||||||
- 路径：之前创建的 `MaiMBot` 文件夹
|
|
||||||
- 来源： `上传 docker-compose.yml`
|
|
||||||
- 文件：之前下载的 `docker-compose.yml` 文件
|
|
||||||
|
|
||||||
图例：
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
一路点下一步，等待项目创建完成
|
|
||||||
|
|
||||||
### 设置 Napcat
|
|
||||||
|
|
||||||
1. 登陆 napcat
|
|
||||||
打开 napcat： `http://<你的nas地址>:6099` ，输入token登陆
|
|
||||||
token可以打开 `DSM ➡️ ContainerManager ➡️ 项目 ➡️ MaiMBot ➡️ 容器 ➡️ Napcat ➡️ 日志`，找到类似 `[WebUi] WebUi Local Panel Url: http://127.0.0.1:6099/webui?token=xxxx` 的日志
|
|
||||||
这个 `token=` 后面的就是你的 napcat token
|
|
||||||
|
|
||||||
2. 按提示，登陆你给麦麦准备的QQ小号
|
|
||||||
|
|
||||||
3. 设置 websocket 客户端
|
|
||||||
`网络配置 -> 新建 -> Websocket客户端`，名称自定，URL栏填入 `ws://maimbot:8080/onebot/v11/ws`，启用并保存即可。
|
|
||||||
若修改过容器名称，则替换 `maimbot` 为你自定的名称
|
|
||||||
|
|
||||||
### 部署完成
|
|
||||||
|
|
||||||
找个群，发送 `麦麦，你在吗` 之类的
|
|
||||||
如果一切正常，应该能正常回复了
|
|
||||||
Binary file not shown.
|
Before Width: | Height: | Size: 170 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 133 KiB |
BIN
docs/video.png
BIN
docs/video.png
Binary file not shown.
|
Before Width: | Height: | Size: 27 KiB |
@@ -3,10 +3,6 @@ name = "MaiMaiBot"
|
|||||||
version = "0.1.0"
|
version = "0.1.0"
|
||||||
description = "MaiMaiBot"
|
description = "MaiMaiBot"
|
||||||
|
|
||||||
[tool.nonebot]
|
|
||||||
plugins = ["src.plugins.chat"]
|
|
||||||
plugin_dirs = ["src/plugins"]
|
|
||||||
|
|
||||||
[tool.ruff]
|
[tool.ruff]
|
||||||
|
|
||||||
include = ["*.py"]
|
include = ["*.py"]
|
||||||
@@ -28,7 +24,7 @@ select = [
|
|||||||
"B", # flake8-bugbear
|
"B", # flake8-bugbear
|
||||||
]
|
]
|
||||||
|
|
||||||
ignore = ["E711"]
|
ignore = ["E711","E501"]
|
||||||
|
|
||||||
[tool.ruff.format]
|
[tool.ruff.format]
|
||||||
docstring-code-format = true
|
docstring-code-format = true
|
||||||
|
|||||||
BIN
requirements.txt
BIN
requirements.txt
Binary file not shown.
10
run.bat
10
run.bat
@@ -1,10 +0,0 @@
|
|||||||
@ECHO OFF
|
|
||||||
chcp 65001
|
|
||||||
if not exist "venv" (
|
|
||||||
python -m venv venv
|
|
||||||
call venv\Scripts\activate.bat
|
|
||||||
pip install -i https://mirrors.aliyun.com/pypi/simple --upgrade -r requirements.txt
|
|
||||||
) else (
|
|
||||||
call venv\Scripts\activate.bat
|
|
||||||
)
|
|
||||||
python run.py
|
|
||||||
144
run.py
144
run.py
@@ -1,144 +0,0 @@
|
|||||||
import os
|
|
||||||
import subprocess
|
|
||||||
import zipfile
|
|
||||||
import sys
|
|
||||||
import requests
|
|
||||||
from tqdm import tqdm
|
|
||||||
|
|
||||||
|
|
||||||
def extract_files(zip_path, target_dir):
|
|
||||||
"""
|
|
||||||
解压
|
|
||||||
|
|
||||||
Args:
|
|
||||||
zip_path: 源ZIP压缩包路径（需确保是有效压缩包）
|
|
||||||
target_dir: 目标文件夹路径（会自动创建不存在的目录）
|
|
||||||
"""
|
|
||||||
# 打开ZIP压缩包（上下文管理器自动处理关闭）
|
|
||||||
with zipfile.ZipFile(zip_path) as zip_ref:
|
|
||||||
# 通过第一个文件路径推断顶层目录名（格式如：top_dir/）
|
|
||||||
top_dir = zip_ref.namelist()[0].split("/")[0] + "/"
|
|
||||||
|
|
||||||
# 遍历压缩包内所有文件条目
|
|
||||||
for file in zip_ref.namelist():
|
|
||||||
# 跳过目录条目，仅处理文件
|
|
||||||
if file.startswith(top_dir) and not file.endswith("/"):
|
|
||||||
# 截取顶层目录后的相对路径（如：sub_dir/file.txt）
|
|
||||||
rel_path = file[len(top_dir) :]
|
|
||||||
|
|
||||||
# 创建目标目录结构（含多级目录）
|
|
||||||
os.makedirs(
|
|
||||||
os.path.dirname(f"{target_dir}/{rel_path}"),
|
|
||||||
exist_ok=True, # 忽略已存在目录的错误
|
|
||||||
)
|
|
||||||
|
|
||||||
# 读取压缩包内文件内容并写入目标路径
|
|
||||||
with open(f"{target_dir}/{rel_path}", "wb") as f:
|
|
||||||
f.write(zip_ref.read(file))
|
|
||||||
|
|
||||||
|
|
||||||
def run_cmd(command: str, open_new_window: bool = True):
|
|
||||||
"""
|
|
||||||
运行 cmd 命令
|
|
||||||
|
|
||||||
Args:
|
|
||||||
command (str): 指定要运行的命令
|
|
||||||
open_new_window (bool): 指定是否新建一个 cmd 窗口运行
|
|
||||||
"""
|
|
||||||
if open_new_window:
|
|
||||||
command = "start " + command
|
|
||||||
subprocess.Popen(command, shell=True)
|
|
||||||
|
|
||||||
|
|
||||||
def run_maimbot():
|
|
||||||
run_cmd(r"napcat\NapCatWinBootMain.exe 10001", False)
|
|
||||||
if not os.path.exists(r"mongodb\db"):
|
|
||||||
os.makedirs(r"mongodb\db")
|
|
||||||
run_cmd(
|
|
||||||
r"mongodb\bin\mongod.exe --dbpath=" + os.getcwd() + r"\mongodb\db --port 27017"
|
|
||||||
)
|
|
||||||
run_cmd("nb run")
|
|
||||||
|
|
||||||
|
|
||||||
def install_mongodb():
|
|
||||||
"""
|
|
||||||
安装 MongoDB
|
|
||||||
"""
|
|
||||||
print("下载 MongoDB")
|
|
||||||
resp = requests.get(
|
|
||||||
"https://fastdl.mongodb.org/windows/mongodb-windows-x86_64-latest.zip",
|
|
||||||
stream=True,
|
|
||||||
)
|
|
||||||
total = int(resp.headers.get("content-length", 0)) # 计算文件大小
|
|
||||||
with open("mongodb.zip", "w+b") as file, tqdm( # 展示下载进度条，并解压文件
|
|
||||||
desc="mongodb.zip",
|
|
||||||
total=total,
|
|
||||||
unit="iB",
|
|
||||||
unit_scale=True,
|
|
||||||
unit_divisor=1024,
|
|
||||||
) as bar:
|
|
||||||
for data in resp.iter_content(chunk_size=1024):
|
|
||||||
size = file.write(data)
|
|
||||||
bar.update(size)
|
|
||||||
extract_files("mongodb.zip", "mongodb")
|
|
||||||
print("MongoDB 下载完成")
|
|
||||||
os.remove("mongodb.zip")
|
|
||||||
choice = input(
|
|
||||||
"是否安装 MongoDB Compass？此软件可以以可视化的方式修改数据库，建议安装（Y/n）"
|
|
||||||
).upper()
|
|
||||||
if choice == "Y" or choice == "":
|
|
||||||
install_mongodb_compass()
|
|
||||||
|
|
||||||
|
|
||||||
def install_mongodb_compass():
|
|
||||||
run_cmd(
|
|
||||||
r"powershell Start-Process powershell -Verb runAs 'Set-ExecutionPolicy RemoteSigned'"
|
|
||||||
)
|
|
||||||
input("请在弹出的用户账户控制中点击“是”后按任意键继续安装")
|
|
||||||
run_cmd(r"powershell mongodb\bin\Install-Compass.ps1")
|
|
||||||
input("按任意键启动麦麦")
|
|
||||||
input("如不需要启动此窗口可直接关闭，无需等待 Compass 安装完成")
|
|
||||||
run_maimbot()
|
|
||||||
|
|
||||||
|
|
||||||
def install_napcat():
|
|
||||||
run_cmd("start https://github.com/NapNeko/NapCatQQ/releases", False)
|
|
||||||
print("请检查弹出的浏览器窗口，点击**第一个**蓝色的“Win64无头” 下载 napcat")
|
|
||||||
napcat_filename = input(
|
|
||||||
"下载完成后请把文件复制到此文件夹，并将**不包含后缀的文件名**输入至此窗口，如 NapCat.32793.Shell："
|
|
||||||
)
|
|
||||||
if(napcat_filename[-4:] == ".zip"):
|
|
||||||
napcat_filename = napcat_filename[:-4]
|
|
||||||
extract_files(napcat_filename + ".zip", "napcat")
|
|
||||||
print("NapCat 安装完成")
|
|
||||||
os.remove(napcat_filename + ".zip")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
os.system("cls")
|
|
||||||
if sys.version_info < (3, 9):
|
|
||||||
print("当前 Python 版本过低，最低版本为 3.9，请更新 Python 版本")
|
|
||||||
print("按任意键退出")
|
|
||||||
input()
|
|
||||||
exit(1)
|
|
||||||
choice = input(
|
|
||||||
"请输入要进行的操作：\n"
|
|
||||||
"1.首次安装\n"
|
|
||||||
"2.运行麦麦\n"
|
|
||||||
)
|
|
||||||
os.system("cls")
|
|
||||||
if choice == "1":
|
|
||||||
confirm = input("首次安装将下载并配置所需组件\n1.确认\n2.取消\n")
|
|
||||||
if confirm == "1":
|
|
||||||
install_napcat()
|
|
||||||
install_mongodb()
|
|
||||||
else:
|
|
||||||
print("已取消安装")
|
|
||||||
elif choice == "2":
|
|
||||||
run_maimbot()
|
|
||||||
choice = input("是否启动推理可视化？（未完善）（y/N）").upper()
|
|
||||||
if choice == "Y":
|
|
||||||
run_cmd(r"python src\gui\reasoning_gui.py")
|
|
||||||
choice = input("是否启动记忆可视化？（未完善）（y/N）").upper()
|
|
||||||
if choice == "Y":
|
|
||||||
run_cmd(r"python src/plugins/memory_system/memory_manual_build.py")
|
|
||||||
278
run.sh
278
run.sh
@@ -1,278 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
|
|
||||||
# Maimbot 一键安装脚本 by Cookie987
|
|
||||||
# 适用于Debian系
|
|
||||||
# 请小心使用任何一键脚本！
|
|
||||||
|
|
||||||
# 如无法访问GitHub请修改此处镜像地址
|
|
||||||
|
|
||||||
LANG=C.UTF-8
|
|
||||||
|
|
||||||
GITHUB_REPO="https://ghfast.top/https://github.com/SengokuCola/MaiMBot.git"
|
|
||||||
|
|
||||||
# 颜色输出
|
|
||||||
GREEN="\e[32m"
|
|
||||||
RED="\e[31m"
|
|
||||||
RESET="\e[0m"
|
|
||||||
|
|
||||||
# 需要的基本软件包
|
|
||||||
REQUIRED_PACKAGES=("git" "sudo" "python3" "python3-venv" "curl" "gnupg" "python3-pip")
|
|
||||||
|
|
||||||
# 默认项目目录
|
|
||||||
DEFAULT_INSTALL_DIR="/opt/maimbot"
|
|
||||||
|
|
||||||
# 服务名称
|
|
||||||
SERVICE_NAME="maimbot"
|
|
||||||
|
|
||||||
IS_INSTALL_MONGODB=false
|
|
||||||
IS_INSTALL_NAPCAT=false
|
|
||||||
|
|
||||||
# 1/6: 检测是否安装 whiptail
|
|
||||||
if ! command -v whiptail &>/dev/null; then
|
|
||||||
echo -e "${RED}[1/6] whiptail 未安装，正在安装...${RESET}"
|
|
||||||
apt update && apt install -y whiptail
|
|
||||||
fi
|
|
||||||
|
|
||||||
get_os_info() {
|
|
||||||
if command -v lsb_release &>/dev/null; then
|
|
||||||
OS_INFO=$(lsb_release -d | cut -f2)
|
|
||||||
elif [[ -f /etc/os-release ]]; then
|
|
||||||
OS_INFO=$(grep "^PRETTY_NAME=" /etc/os-release | cut -d '"' -f2)
|
|
||||||
else
|
|
||||||
OS_INFO="Unknown OS"
|
|
||||||
fi
|
|
||||||
echo "$OS_INFO"
|
|
||||||
}
|
|
||||||
|
|
||||||
# 检查系统
|
|
||||||
check_system() {
|
|
||||||
# 检查是否为 root 用户
|
|
||||||
if [[ "$(id -u)" -ne 0 ]]; then
|
|
||||||
whiptail --title "🚫 权限不足" --msgbox "请使用 root 用户运行此脚本！\n执行方式: sudo bash $0" 10 60
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
if [[ -f /etc/os-release ]]; then
|
|
||||||
source /etc/os-release
|
|
||||||
if [[ "$ID" != "debian" || "$VERSION_ID" != "12" ]]; then
|
|
||||||
whiptail --title "🚫 不支持的系统" --msgbox "此脚本仅支持 Debian 12 (Bookworm)！\n当前系统: $PRETTY_NAME\n安装已终止。" 10 60
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
else
|
|
||||||
whiptail --title "⚠️ 无法检测系统" --msgbox "无法识别系统版本，安装已终止。" 10 60
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
# 3/6: 询问用户是否安装缺失的软件包
|
|
||||||
install_packages() {
|
|
||||||
missing_packages=()
|
|
||||||
for package in "${REQUIRED_PACKAGES[@]}"; do
|
|
||||||
if ! dpkg -s "$package" &>/dev/null; then
|
|
||||||
missing_packages+=("$package")
|
|
||||||
fi
|
|
||||||
done
|
|
||||||
|
|
||||||
if [[ ${#missing_packages[@]} -gt 0 ]]; then
|
|
||||||
whiptail --title "📦 [3/6] 软件包检查" --yesno "检测到以下必须的依赖项目缺失:\n${missing_packages[*]}\n\n是否要自动安装？" 12 60
|
|
||||||
if [[ $? -eq 0 ]]; then
|
|
||||||
return 0
|
|
||||||
else
|
|
||||||
whiptail --title "⚠️ 注意" --yesno "某些必要的依赖项未安装，可能会影响运行！\n是否继续？" 10 60 || exit 1
|
|
||||||
fi
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
# 4/6: Python 版本检查
|
|
||||||
check_python() {
|
|
||||||
PYTHON_VERSION=$(python3 -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")')
|
|
||||||
|
|
||||||
python3 -c "import sys; exit(0) if sys.version_info >= (3,9) else exit(1)"
|
|
||||||
if [[ $? -ne 0 ]]; then
|
|
||||||
whiptail --title "⚠️ [4/6] Python 版本过低" --msgbox "检测到 Python 版本为 $PYTHON_VERSION，需要 3.9 或以上！\n请升级 Python 后重新运行本脚本。" 10 60
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
# 5/6: 选择分支
|
|
||||||
choose_branch() {
|
|
||||||
BRANCH=$(whiptail --title "🔀 [5/6] 选择 Maimbot 分支" --menu "请选择要安装的 Maimbot 分支：" 15 60 2 \
|
|
||||||
"main" "稳定版本（推荐）" \
|
|
||||||
"debug" "开发版本（可能不稳定）" 3>&1 1>&2 2>&3)
|
|
||||||
|
|
||||||
if [[ -z "$BRANCH" ]]; then
|
|
||||||
BRANCH="main"
|
|
||||||
whiptail --title "🔀 默认选择" --msgbox "未选择分支，默认安装稳定版本（main）" 10 60
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
# 6/6: 选择安装路径
|
|
||||||
choose_install_dir() {
|
|
||||||
INSTALL_DIR=$(whiptail --title "📂 [6/6] 选择安装路径" --inputbox "请输入 Maimbot 的安装目录：" 10 60 "$DEFAULT_INSTALL_DIR" 3>&1 1>&2 2>&3)
|
|
||||||
|
|
||||||
if [[ -z "$INSTALL_DIR" ]]; then
|
|
||||||
whiptail --title "⚠️ 取消输入" --yesno "未输入安装路径，是否退出安装？" 10 60
|
|
||||||
if [[ $? -ne 0 ]]; then
|
|
||||||
INSTALL_DIR="$DEFAULT_INSTALL_DIR"
|
|
||||||
else
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
# 显示确认界面
|
|
||||||
confirm_install() {
|
|
||||||
local confirm_message="请确认以下更改:\n\n"
|
|
||||||
|
|
||||||
if [[ ${#missing_packages[@]} -gt 0 ]]; then
|
|
||||||
confirm_message+="📦 安装缺失的依赖项: ${missing_packages[*]}\n"
|
|
||||||
else
|
|
||||||
confirm_message+="✅ 所有依赖项已安装\n"
|
|
||||||
fi
|
|
||||||
|
|
||||||
confirm_message+="📂 安装麦麦Bot到: $INSTALL_DIR\n"
|
|
||||||
confirm_message+="🔀 分支: $BRANCH\n"
|
|
||||||
|
|
||||||
if [[ "$MONGODB_INSTALLED" == "true" ]]; then
|
|
||||||
confirm_message+="✅ MongoDB 已安装\n"
|
|
||||||
else
|
|
||||||
if [[ "$IS_INSTALL_MONGODB" == "true" ]]; then
|
|
||||||
confirm_message+="📦 安装 MongoDB\n"
|
|
||||||
fi
|
|
||||||
fi
|
|
||||||
|
|
||||||
if [[ "$NAPCAT_INSTALLED" == "true" ]]; then
|
|
||||||
confirm_message+="✅ NapCat 已安装\n"
|
|
||||||
else
|
|
||||||
if [[ "$IS_INSTALL_NAPCAT" == "true" ]]; then
|
|
||||||
confirm_message+="📦 安装 NapCat\n"
|
|
||||||
fi
|
|
||||||
fi
|
|
||||||
|
|
||||||
confirm_message+="🛠️ 添加麦麦Bot作为系统服务 ($SERVICE_NAME.service)\n"
|
|
||||||
|
|
||||||
confitm_message+="\n\n注意：本脚本默认使用ghfast.top为GitHub进行加速，如不想使用请手动修改脚本开头的GITHUB_REPO变量。"
|
|
||||||
whiptail --title "🔧 安装确认" --yesno "$confirm_message\n\n是否继续安装？" 15 60
|
|
||||||
if [[ $? -ne 0 ]]; then
|
|
||||||
whiptail --title "🚫 取消安装" --msgbox "安装已取消。" 10 60
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
check_mongodb() {
|
|
||||||
if command -v mongod &>/dev/null; then
|
|
||||||
MONGO_INSTALLED=true
|
|
||||||
else
|
|
||||||
MONGO_INSTALLED=false
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
# 安装 MongoDB
|
|
||||||
install_mongodb() {
|
|
||||||
if [[ "$MONGO_INSTALLED" == "true" ]]; then
|
|
||||||
return 0
|
|
||||||
fi
|
|
||||||
|
|
||||||
whiptail --title "📦 [3/6] 软件包检查" --yesno "检测到未安装MongoDB，是否安装？\n如果您想使用远程数据库，请跳过此步。" 10 60
|
|
||||||
if [[ $? -ne 0 ]]; then
|
|
||||||
return 1
|
|
||||||
fi
|
|
||||||
IS_INSTALL_MONGODB=true
|
|
||||||
}
|
|
||||||
|
|
||||||
check_napcat() {
|
|
||||||
if command -v napcat &>/dev/null; then
|
|
||||||
NAPCAT_INSTALLED=true
|
|
||||||
else
|
|
||||||
NAPCAT_INSTALLED=false
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
install_napcat() {
|
|
||||||
if [[ "$NAPCAT_INSTALLED" == "true" ]]; then
|
|
||||||
return 0
|
|
||||||
fi
|
|
||||||
|
|
||||||
whiptail --title "📦 [3/6] 软件包检查" --yesno "检测到未安装NapCat，是否安装？\n如果您想使用远程NapCat，请跳过此步。" 10 60
|
|
||||||
if [[ $? -ne 0 ]]; then
|
|
||||||
return 1
|
|
||||||
fi
|
|
||||||
IS_INSTALL_NAPCAT=true
|
|
||||||
}
|
|
||||||
|
|
||||||
# 运行安装步骤
|
|
||||||
check_system
|
|
||||||
check_mongodb
|
|
||||||
check_napcat
|
|
||||||
install_packages
|
|
||||||
install_mongodb
|
|
||||||
install_napcat
|
|
||||||
check_python
|
|
||||||
choose_branch
|
|
||||||
choose_install_dir
|
|
||||||
confirm_install
|
|
||||||
|
|
||||||
# 开始安装
|
|
||||||
whiptail --title "🚀 开始安装" --msgbox "所有环境检查完毕，即将开始安装麦麦Bot！" 10 60
|
|
||||||
|
|
||||||
echo -e "${GREEN}安装依赖项...${RESET}"
|
|
||||||
|
|
||||||
apt update && apt install -y "${missing_packages[@]}"
|
|
||||||
|
|
||||||
|
|
||||||
if [[ "$IS_INSTALL_MONGODB" == "true" ]]; then
|
|
||||||
echo -e "${GREEN}安装 MongoDB...${RESET}"
|
|
||||||
curl -fsSL https://www.mongodb.org/static/pgp/server-8.0.asc | gpg -o /usr/share/keyrings/mongodb-server-8.0.gpg --dearmor
|
|
||||||
echo "deb [ signed-by=/usr/share/keyrings/mongodb-server-8.0.gpg ] http://repo.mongodb.org/apt/debian bookworm/mongodb-org/8.0 main" | sudo tee /etc/apt/sources.list.d/mongodb-org-8.0.list
|
|
||||||
apt-get update
|
|
||||||
apt-get install -y mongodb-org
|
|
||||||
|
|
||||||
systemctl enable mongod
|
|
||||||
systemctl start mongod
|
|
||||||
fi
|
|
||||||
|
|
||||||
if [[ "$IS_INSTALL_NAPCAT" == "true" ]]; then
|
|
||||||
echo -e "${GREEN}安装 NapCat...${RESET}"
|
|
||||||
curl -o napcat.sh https://nclatest.znin.net/NapNeko/NapCat-Installer/main/script/install.sh && bash napcat.sh
|
|
||||||
fi
|
|
||||||
|
|
||||||
echo -e "${GREEN}创建 Python 虚拟环境...${RESET}"
|
|
||||||
mkdir -p "$INSTALL_DIR"
|
|
||||||
cd "$INSTALL_DIR" || exit
|
|
||||||
python3 -m venv venv
|
|
||||||
source venv/bin/activate
|
|
||||||
|
|
||||||
echo -e "${GREEN}克隆仓库...${RESET}"
|
|
||||||
# 安装 Maimbot
|
|
||||||
mkdir -p "$INSTALL_DIR/repo"
|
|
||||||
cd "$INSTALL_DIR/repo" || exit 1
|
|
||||||
git clone -b "$BRANCH" $GITHUB_REPO .
|
|
||||||
|
|
||||||
echo -e "${GREEN}安装 Python 依赖...${RESET}"
|
|
||||||
pip install -r requirements.txt
|
|
||||||
|
|
||||||
echo -e "${GREEN}设置服务...${RESET}"
|
|
||||||
|
|
||||||
# 设置 Maimbot 服务
|
|
||||||
cat <<EOF | tee /etc/systemd/system/$SERVICE_NAME.service
|
|
||||||
[Unit]
|
|
||||||
Description=MaiMbot 麦麦
|
|
||||||
After=network.target mongod.service
|
|
||||||
|
|
||||||
[Service]
|
|
||||||
Type=simple
|
|
||||||
WorkingDirectory=$INSTALL_DIR/repo/
|
|
||||||
ExecStart=$INSTALL_DIR/venv/bin/python3 bot.py
|
|
||||||
ExecStop=/bin/kill -2 $MAINPID
|
|
||||||
Restart=always
|
|
||||||
RestartSec=10s
|
|
||||||
|
|
||||||
[Install]
|
|
||||||
WantedBy=multi-user.target
|
|
||||||
EOF
|
|
||||||
|
|
||||||
systemctl daemon-reload
|
|
||||||
systemctl enable maimbot
|
|
||||||
systemctl start maimbot
|
|
||||||
|
|
||||||
whiptail --title "🎉 安装完成" --msgbox "麦麦Bot安装完成！\n已经启动麦麦Bot服务。\n\n安装路径: $INSTALL_DIR\n分支: $BRANCH" 12 60
|
|
||||||
@@ -1,29 +0,0 @@
|
|||||||
@echo on
|
|
||||||
chcp 65001 > nul
|
|
||||||
set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
|
|
||||||
call conda activate %CONDA_ENV%
|
|
||||||
if errorlevel 1 (
|
|
||||||
echo 激活 conda 环境失败
|
|
||||||
pause
|
|
||||||
exit /b 1
|
|
||||||
)
|
|
||||||
echo Conda 环境 "%CONDA_ENV%" 激活成功
|
|
||||||
|
|
||||||
set /p OPTION="请选择运行选项 (1: 运行全部绘制, 2: 运行简单绘制): "
|
|
||||||
if "%OPTION%"=="1" (
|
|
||||||
python src/plugins/memory_system/memory_manual_build.py
|
|
||||||
) else if "%OPTION%"=="2" (
|
|
||||||
python src/plugins/memory_system/draw_memory.py
|
|
||||||
) else (
|
|
||||||
echo 无效的选项
|
|
||||||
pause
|
|
||||||
exit /b 1
|
|
||||||
)
|
|
||||||
|
|
||||||
if errorlevel 1 (
|
|
||||||
echo 命令执行失败，错误代码 %errorlevel%
|
|
||||||
pause
|
|
||||||
exit /b 1
|
|
||||||
)
|
|
||||||
echo 脚本成功完成
|
|
||||||
pause
|
|
||||||
@@ -1 +0,0 @@
|
|||||||
mongod --dbpath="mongodb" --port 27017
|
|
||||||
@@ -1,7 +0,0 @@
|
|||||||
chcp 65001
|
|
||||||
call conda activate maimbot
|
|
||||||
cd .
|
|
||||||
|
|
||||||
REM 执行nb run命令
|
|
||||||
nb run
|
|
||||||
pause
|
|
||||||
@@ -1,5 +0,0 @@
|
|||||||
call conda activate niuniu
|
|
||||||
cd src\gui
|
|
||||||
start /b python reasoning_gui.py
|
|
||||||
exit
|
|
||||||
|
|
||||||
@@ -1,68 +0,0 @@
|
|||||||
@echo off
|
|
||||||
setlocal enabledelayedexpansion
|
|
||||||
chcp 65001
|
|
||||||
|
|
||||||
REM 修正路径获取逻辑
|
|
||||||
cd /d "%~dp0" || (
|
|
||||||
echo 错误：切换目录失败
|
|
||||||
exit /b 1
|
|
||||||
)
|
|
||||||
|
|
||||||
if not exist "venv\" (
|
|
||||||
echo 正在初始化虚拟环境...
|
|
||||||
|
|
||||||
where python >nul 2>&1
|
|
||||||
if %errorlevel% neq 0 (
|
|
||||||
echo 未找到Python解释器
|
|
||||||
exit /b 1
|
|
||||||
)
|
|
||||||
|
|
||||||
for /f "tokens=2" %%a in ('python --version 2^>^&1') do set version=%%a
|
|
||||||
for /f "tokens=1,2 delims=." %%b in ("!version!") do (
|
|
||||||
set major=%%b
|
|
||||||
set minor=%%c
|
|
||||||
)
|
|
||||||
|
|
||||||
if !major! lss 3 (
|
|
||||||
echo 需要Python大于等于3.0，当前版本 !version!
|
|
||||||
exit /b 1
|
|
||||||
)
|
|
||||||
|
|
||||||
if !major! equ 3 if !minor! lss 9 (
|
|
||||||
echo 需要Python大于等于3.9，当前版本 !version!
|
|
||||||
exit /b 1
|
|
||||||
)
|
|
||||||
|
|
||||||
echo 正在安装virtualenv...
|
|
||||||
python -m pip install virtualenv || (
|
|
||||||
echo virtualenv安装失败
|
|
||||||
exit /b 1
|
|
||||||
)
|
|
||||||
|
|
||||||
echo 正在创建虚拟环境...
|
|
||||||
python -m virtualenv venv || (
|
|
||||||
echo 虚拟环境创建失败
|
|
||||||
exit /b 1
|
|
||||||
)
|
|
||||||
|
|
||||||
call venv\Scripts\activate.bat
|
|
||||||
|
|
||||||
) else (
|
|
||||||
call venv\Scripts\activate.bat
|
|
||||||
)
|
|
||||||
|
|
||||||
echo 正在更新依赖...
|
|
||||||
pip install -r requirements.txt
|
|
||||||
|
|
||||||
echo 当前代理设置：
|
|
||||||
echo HTTP_PROXY=%HTTP_PROXY%
|
|
||||||
echo HTTPS_PROXY=%HTTPS_PROXY%
|
|
||||||
|
|
||||||
set HTTP_PROXY=
|
|
||||||
set HTTPS_PROXY=
|
|
||||||
echo 代理已取消。
|
|
||||||
|
|
||||||
set no_proxy=0.0.0.0/32
|
|
||||||
|
|
||||||
call nb run
|
|
||||||
pause
|
|
||||||
11
setup.py
11
setup.py
@@ -1,11 +0,0 @@
|
|||||||
from setuptools import find_packages, setup
|
|
||||||
|
|
||||||
setup(
|
|
||||||
name="maimai-bot",
|
|
||||||
version="0.1",
|
|
||||||
packages=find_packages(),
|
|
||||||
install_requires=[
|
|
||||||
'python-dotenv',
|
|
||||||
'pymongo',
|
|
||||||
],
|
|
||||||
)
|
|
||||||
@@ -1,5 +1,4 @@
|
|||||||
import os
|
import os
|
||||||
from typing import cast
|
|
||||||
from pymongo import MongoClient
|
from pymongo import MongoClient
|
||||||
from pymongo.database import Database
|
from pymongo.database import Database
|
||||||
|
|
||||||
@@ -11,7 +10,7 @@ def __create_database_instance():
|
|||||||
uri = os.getenv("MONGODB_URI")
|
uri = os.getenv("MONGODB_URI")
|
||||||
host = os.getenv("MONGODB_HOST", "127.0.0.1")
|
host = os.getenv("MONGODB_HOST", "127.0.0.1")
|
||||||
port = int(os.getenv("MONGODB_PORT", "27017"))
|
port = int(os.getenv("MONGODB_PORT", "27017"))
|
||||||
db_name = os.getenv("DATABASE_NAME", "MegBot")
|
# db_name 变量在创建连接时不需要，在获取数据库实例时才使用
|
||||||
username = os.getenv("MONGODB_USERNAME")
|
username = os.getenv("MONGODB_USERNAME")
|
||||||
password = os.getenv("MONGODB_PASSWORD")
|
password = os.getenv("MONGODB_PASSWORD")
|
||||||
auth_source = os.getenv("MONGODB_AUTH_SOURCE")
|
auth_source = os.getenv("MONGODB_AUTH_SOURCE")
|
||||||
|
|||||||
448
src/common/logger.py
Normal file
448
src/common/logger.py
Normal file
@@ -0,0 +1,448 @@
|
|||||||
|
from loguru import logger
|
||||||
|
from typing import Dict, Optional, Union, List
|
||||||
|
import sys
|
||||||
|
import os
|
||||||
|
from types import ModuleType
|
||||||
|
from pathlib import Path
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
# from ..plugins.chat.config import global_config
|
||||||
|
|
||||||
|
# 加载 .env 文件
|
||||||
|
env_path = Path(__file__).resolve().parent.parent.parent / ".env"
|
||||||
|
load_dotenv(dotenv_path=env_path)
|
||||||
|
|
||||||
|
# 保存原生处理器ID
|
||||||
|
default_handler_id = None
|
||||||
|
for handler_id in logger._core.handlers:
|
||||||
|
default_handler_id = handler_id
|
||||||
|
break
|
||||||
|
|
||||||
|
# 移除默认处理器
|
||||||
|
if default_handler_id is not None:
|
||||||
|
logger.remove(default_handler_id)
|
||||||
|
|
||||||
|
# 类型别名
|
||||||
|
LoguruLogger = logger.__class__
|
||||||
|
|
||||||
|
# 全局注册表：记录模块与处理器ID的映射
|
||||||
|
_handler_registry: Dict[str, List[int]] = {}
|
||||||
|
|
||||||
|
# 获取日志存储根地址
|
||||||
|
current_file_path = Path(__file__).resolve()
|
||||||
|
LOG_ROOT = "logs"
|
||||||
|
|
||||||
|
SIMPLE_OUTPUT = os.getenv("SIMPLE_OUTPUT", "false")
|
||||||
|
print(f"SIMPLE_OUTPUT: {SIMPLE_OUTPUT}")
|
||||||
|
|
||||||
|
if not SIMPLE_OUTPUT:
|
||||||
|
# 默认全局配置
|
||||||
|
DEFAULT_CONFIG = {
|
||||||
|
# 日志级别配置
|
||||||
|
"console_level": "INFO",
|
||||||
|
"file_level": "DEBUG",
|
||||||
|
# 格式配置
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | {message}"),
|
||||||
|
"log_dir": LOG_ROOT,
|
||||||
|
"rotation": "00:00",
|
||||||
|
"retention": "3 days",
|
||||||
|
"compression": "zip",
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
DEFAULT_CONFIG = {
|
||||||
|
# 日志级别配置
|
||||||
|
"console_level": "INFO",
|
||||||
|
"file_level": "DEBUG",
|
||||||
|
# 格式配置
|
||||||
|
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <cyan>{extra[module]}</cyan> | {message}"),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | {message}"),
|
||||||
|
"log_dir": LOG_ROOT,
|
||||||
|
"rotation": "00:00",
|
||||||
|
"retention": "3 days",
|
||||||
|
"compression": "zip",
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# 海马体日志样式配置
|
||||||
|
MEMORY_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-yellow>海马体</light-yellow> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 海马体 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:MM-DD HH:mm}</green> | <light-yellow>海马体</light-yellow> | <light-yellow>{message}</light-yellow>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 海马体 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# MOOD
|
||||||
|
MOOD_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-green>心情</light-green> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 心情 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-green>心情</light-green> | {message}"),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 心情 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
# relationship
|
||||||
|
RELATION_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-magenta>关系</light-magenta> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 关系 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-magenta>关系</light-magenta> | {message}"),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 关系 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
# config
|
||||||
|
CONFIG_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-cyan>配置</light-cyan> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 配置 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-cyan>配置</light-cyan> | {message}"),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 配置 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
SENDER_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-yellow>消息发送</light-yellow> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 消息发送 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <green>消息发送</green> | {message}"),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 消息发送 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
HEARTFLOW_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-yellow>麦麦大脑袋</light-yellow> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦大脑袋 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:MM-DD HH:mm}</green> | <light-green>麦麦大脑袋</light-green> | <light-green>{message}</light-green>"
|
||||||
|
), # noqa: E501
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦大脑袋 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
SCHEDULE_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-yellow>在干嘛</light-yellow> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 在干嘛 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <cyan>在干嘛</cyan> | <cyan>{message}</cyan>"),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 在干嘛 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
LLM_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-yellow>麦麦组织语言</light-yellow> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦组织语言 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-green>麦麦组织语言</light-green> | {message}"),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦组织语言 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# Topic日志样式配置
|
||||||
|
TOPIC_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-blue>话题</light-blue> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 话题 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-blue>主题</light-blue> | {message}"),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 话题 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
# Topic日志样式配置
|
||||||
|
CHAT_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-blue>见闻</light-blue> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 见闻 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:MM-DD HH:mm}</green> | <light-blue>见闻</light-blue> | <green>{message}</green>"
|
||||||
|
), # noqa: E501
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 见闻 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
SUB_HEARTFLOW_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-blue>麦麦小脑袋</light-blue> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦小脑袋 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:MM-DD HH:mm}</green> | <light-blue>麦麦小脑袋</light-blue> | <light-blue>{message}</light-blue>"
|
||||||
|
), # noqa: E501
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦小脑袋 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
WILLING_STYLE_CONFIG = {
|
||||||
|
"advanced": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{extra[module]: <12}</cyan> | "
|
||||||
|
"<light-blue>意愿</light-blue> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 意愿 | {message}"),
|
||||||
|
},
|
||||||
|
"simple": {
|
||||||
|
"console_format": (
|
||||||
|
"<green>{time:MM-DD HH:mm}</green> | <light-blue>意愿</light-blue> | <light-blue>{message}</light-blue>"
|
||||||
|
), # noqa: E501
|
||||||
|
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 意愿 | {message}"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# 根据SIMPLE_OUTPUT选择配置
|
||||||
|
MEMORY_STYLE_CONFIG = MEMORY_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MEMORY_STYLE_CONFIG["advanced"]
|
||||||
|
TOPIC_STYLE_CONFIG = TOPIC_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else TOPIC_STYLE_CONFIG["advanced"]
|
||||||
|
SENDER_STYLE_CONFIG = SENDER_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SENDER_STYLE_CONFIG["advanced"]
|
||||||
|
LLM_STYLE_CONFIG = LLM_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else LLM_STYLE_CONFIG["advanced"]
|
||||||
|
CHAT_STYLE_CONFIG = CHAT_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else CHAT_STYLE_CONFIG["advanced"]
|
||||||
|
MOOD_STYLE_CONFIG = MOOD_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MOOD_STYLE_CONFIG["advanced"]
|
||||||
|
RELATION_STYLE_CONFIG = RELATION_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else RELATION_STYLE_CONFIG["advanced"]
|
||||||
|
SCHEDULE_STYLE_CONFIG = SCHEDULE_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SCHEDULE_STYLE_CONFIG["advanced"]
|
||||||
|
HEARTFLOW_STYLE_CONFIG = HEARTFLOW_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else HEARTFLOW_STYLE_CONFIG["advanced"]
|
||||||
|
SUB_HEARTFLOW_STYLE_CONFIG = (
|
||||||
|
SUB_HEARTFLOW_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SUB_HEARTFLOW_STYLE_CONFIG["advanced"]
|
||||||
|
) # noqa: E501
|
||||||
|
WILLING_STYLE_CONFIG = WILLING_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else WILLING_STYLE_CONFIG["advanced"]
|
||||||
|
CONFIG_STYLE_CONFIG = CONFIG_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else CONFIG_STYLE_CONFIG["advanced"]
|
||||||
|
|
||||||
|
|
||||||
|
def is_registered_module(record: dict) -> bool:
|
||||||
|
"""检查是否为已注册的模块"""
|
||||||
|
return record["extra"].get("module") in _handler_registry
|
||||||
|
|
||||||
|
|
||||||
|
def is_unregistered_module(record: dict) -> bool:
|
||||||
|
"""检查是否为未注册的模块"""
|
||||||
|
return not is_registered_module(record)
|
||||||
|
|
||||||
|
|
||||||
|
def log_patcher(record: dict) -> None:
|
||||||
|
"""自动填充未设置模块名的日志记录，保留原生模块名称"""
|
||||||
|
if "module" not in record["extra"]:
|
||||||
|
# 尝试从name中提取模块名
|
||||||
|
module_name = record.get("name", "")
|
||||||
|
if module_name == "":
|
||||||
|
module_name = "root"
|
||||||
|
record["extra"]["module"] = module_name
|
||||||
|
|
||||||
|
|
||||||
|
# 应用全局修补器
|
||||||
|
logger.configure(patcher=log_patcher)
|
||||||
|
|
||||||
|
|
||||||
|
class LogConfig:
|
||||||
|
"""日志配置类"""
|
||||||
|
|
||||||
|
def __init__(self, **kwargs):
|
||||||
|
self.config = DEFAULT_CONFIG.copy()
|
||||||
|
self.config.update(kwargs)
|
||||||
|
|
||||||
|
def to_dict(self) -> dict:
|
||||||
|
return self.config.copy()
|
||||||
|
|
||||||
|
def update(self, **kwargs):
|
||||||
|
self.config.update(kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
def get_module_logger(
|
||||||
|
module: Union[str, ModuleType],
|
||||||
|
*,
|
||||||
|
console_level: Optional[str] = None,
|
||||||
|
file_level: Optional[str] = None,
|
||||||
|
extra_handlers: Optional[List[dict]] = None,
|
||||||
|
config: Optional[LogConfig] = None,
|
||||||
|
) -> LoguruLogger:
|
||||||
|
module_name = module if isinstance(module, str) else module.__name__
|
||||||
|
current_config = config.config if config else DEFAULT_CONFIG
|
||||||
|
|
||||||
|
# 清理旧处理器
|
||||||
|
if module_name in _handler_registry:
|
||||||
|
for handler_id in _handler_registry[module_name]:
|
||||||
|
logger.remove(handler_id)
|
||||||
|
del _handler_registry[module_name]
|
||||||
|
|
||||||
|
handler_ids = []
|
||||||
|
|
||||||
|
# 控制台处理器
|
||||||
|
console_id = logger.add(
|
||||||
|
sink=sys.stderr,
|
||||||
|
level=os.getenv("CONSOLE_LOG_LEVEL", console_level or current_config["console_level"]),
|
||||||
|
format=current_config["console_format"],
|
||||||
|
filter=lambda record: record["extra"].get("module") == module_name,
|
||||||
|
enqueue=True,
|
||||||
|
)
|
||||||
|
handler_ids.append(console_id)
|
||||||
|
|
||||||
|
# 文件处理器
|
||||||
|
log_dir = Path(current_config["log_dir"])
|
||||||
|
log_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
log_file = log_dir / module_name / "{time:YYYY-MM-DD}.log"
|
||||||
|
log_file.parent.mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
|
file_id = logger.add(
|
||||||
|
sink=str(log_file),
|
||||||
|
level=os.getenv("FILE_LOG_LEVEL", file_level or current_config["file_level"]),
|
||||||
|
format=current_config["file_format"],
|
||||||
|
rotation=current_config["rotation"],
|
||||||
|
retention=current_config["retention"],
|
||||||
|
compression=current_config["compression"],
|
||||||
|
encoding="utf-8",
|
||||||
|
filter=lambda record: record["extra"].get("module") == module_name,
|
||||||
|
enqueue=True,
|
||||||
|
)
|
||||||
|
handler_ids.append(file_id)
|
||||||
|
|
||||||
|
# 额外处理器
|
||||||
|
if extra_handlers:
|
||||||
|
for handler in extra_handlers:
|
||||||
|
handler_id = logger.add(**handler)
|
||||||
|
handler_ids.append(handler_id)
|
||||||
|
|
||||||
|
# 更新注册表
|
||||||
|
_handler_registry[module_name] = handler_ids
|
||||||
|
|
||||||
|
return logger.bind(module=module_name)
|
||||||
|
|
||||||
|
|
||||||
|
def remove_module_logger(module_name: str) -> None:
|
||||||
|
"""清理指定模块的日志处理器"""
|
||||||
|
if module_name in _handler_registry:
|
||||||
|
for handler_id in _handler_registry[module_name]:
|
||||||
|
logger.remove(handler_id)
|
||||||
|
del _handler_registry[module_name]
|
||||||
|
|
||||||
|
|
||||||
|
# 添加全局默认处理器（只处理未注册模块的日志--->控制台）
|
||||||
|
# print(os.getenv("DEFAULT_CONSOLE_LOG_LEVEL", "SUCCESS"))
|
||||||
|
DEFAULT_GLOBAL_HANDLER = logger.add(
|
||||||
|
sink=sys.stderr,
|
||||||
|
level=os.getenv("DEFAULT_CONSOLE_LOG_LEVEL", "SUCCESS"),
|
||||||
|
format=(
|
||||||
|
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||||
|
"<level>{level: <8}</level> | "
|
||||||
|
"<cyan>{name: <12}</cyan> | "
|
||||||
|
"<level>{message}</level>"
|
||||||
|
),
|
||||||
|
filter=lambda record: is_unregistered_module(record), # 只处理未注册模块的日志，并过滤nonebot
|
||||||
|
enqueue=True,
|
||||||
|
)
|
||||||
|
|
||||||
|
# 添加全局默认文件处理器（只处理未注册模块的日志--->logs文件夹）
|
||||||
|
log_dir = Path(DEFAULT_CONFIG["log_dir"])
|
||||||
|
log_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
other_log_dir = log_dir / "other"
|
||||||
|
other_log_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
|
DEFAULT_FILE_HANDLER = logger.add(
|
||||||
|
sink=str(other_log_dir / "{time:YYYY-MM-DD}.log"),
|
||||||
|
level=os.getenv("DEFAULT_FILE_LOG_LEVEL", "DEBUG"),
|
||||||
|
format=("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name: <15} | {message}"),
|
||||||
|
rotation=DEFAULT_CONFIG["rotation"],
|
||||||
|
retention=DEFAULT_CONFIG["retention"],
|
||||||
|
compression=DEFAULT_CONFIG["compression"],
|
||||||
|
encoding="utf-8",
|
||||||
|
filter=lambda record: is_unregistered_module(record), # 只处理未注册模块的日志，并过滤nonebot
|
||||||
|
enqueue=True,
|
||||||
|
)
|
||||||
347
src/gui/logger_gui.py
Normal file
347
src/gui/logger_gui.py
Normal file
@@ -0,0 +1,347 @@
|
|||||||
|
import customtkinter as ctk
|
||||||
|
import subprocess
|
||||||
|
import threading
|
||||||
|
import queue
|
||||||
|
import re
|
||||||
|
import os
|
||||||
|
import signal
|
||||||
|
from collections import deque
|
||||||
|
|
||||||
|
# 设置应用的外观模式和默认颜色主题
|
||||||
|
ctk.set_appearance_mode("dark")
|
||||||
|
ctk.set_default_color_theme("blue")
|
||||||
|
|
||||||
|
|
||||||
|
class LogViewerApp(ctk.CTk):
|
||||||
|
"""日志查看器应用的主类，继承自customtkinter的CTk类"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
"""初始化日志查看器应用的界面和状态"""
|
||||||
|
super().__init__()
|
||||||
|
self.title("日志查看器")
|
||||||
|
self.geometry("1200x800")
|
||||||
|
|
||||||
|
# 初始化进程、日志队列、日志数据等变量
|
||||||
|
self.process = None
|
||||||
|
self.log_queue = queue.Queue()
|
||||||
|
self.log_data = deque(maxlen=10000) # 使用固定长度队列
|
||||||
|
self.available_levels = set()
|
||||||
|
self.available_modules = set()
|
||||||
|
self.sorted_modules = []
|
||||||
|
self.module_checkboxes = {} # 存储模块复选框的字典
|
||||||
|
|
||||||
|
# 日志颜色配置
|
||||||
|
self.color_config = {
|
||||||
|
"time": "#888888",
|
||||||
|
"DEBUG": "#2196F3",
|
||||||
|
"INFO": "#4CAF50",
|
||||||
|
"WARNING": "#FF9800",
|
||||||
|
"ERROR": "#F44336",
|
||||||
|
"module": "#D4D0AB",
|
||||||
|
"default": "#FFFFFF",
|
||||||
|
}
|
||||||
|
|
||||||
|
# 列可见性配置
|
||||||
|
self.column_visibility = {"show_time": True, "show_level": True, "show_module": True}
|
||||||
|
|
||||||
|
# 选中的日志等级和模块
|
||||||
|
self.selected_levels = set()
|
||||||
|
self.selected_modules = set()
|
||||||
|
|
||||||
|
# 创建界面组件并启动日志队列处理
|
||||||
|
self.create_widgets()
|
||||||
|
self.after(100, self.process_log_queue)
|
||||||
|
|
||||||
|
def create_widgets(self):
|
||||||
|
"""创建应用界面的各个组件"""
|
||||||
|
self.grid_columnconfigure(0, weight=1)
|
||||||
|
self.grid_rowconfigure(1, weight=1)
|
||||||
|
|
||||||
|
# 控制面板
|
||||||
|
control_frame = ctk.CTkFrame(self)
|
||||||
|
control_frame.grid(row=0, column=0, sticky="ew", padx=10, pady=5)
|
||||||
|
|
||||||
|
self.start_btn = ctk.CTkButton(control_frame, text="启动", command=self.start_process)
|
||||||
|
self.start_btn.pack(side="left", padx=5)
|
||||||
|
|
||||||
|
self.stop_btn = ctk.CTkButton(control_frame, text="停止", command=self.stop_process, state="disabled")
|
||||||
|
self.stop_btn.pack(side="left", padx=5)
|
||||||
|
|
||||||
|
self.clear_btn = ctk.CTkButton(control_frame, text="清屏", command=self.clear_logs)
|
||||||
|
self.clear_btn.pack(side="left", padx=5)
|
||||||
|
|
||||||
|
column_filter_frame = ctk.CTkFrame(control_frame)
|
||||||
|
column_filter_frame.pack(side="left", padx=20)
|
||||||
|
|
||||||
|
self.time_check = ctk.CTkCheckBox(column_filter_frame, text="显示时间", command=self.refresh_logs)
|
||||||
|
self.time_check.pack(side="left", padx=5)
|
||||||
|
self.time_check.select()
|
||||||
|
|
||||||
|
self.level_check = ctk.CTkCheckBox(column_filter_frame, text="显示等级", command=self.refresh_logs)
|
||||||
|
self.level_check.pack(side="left", padx=5)
|
||||||
|
self.level_check.select()
|
||||||
|
|
||||||
|
self.module_check = ctk.CTkCheckBox(column_filter_frame, text="显示模块", command=self.refresh_logs)
|
||||||
|
self.module_check.pack(side="left", padx=5)
|
||||||
|
self.module_check.select()
|
||||||
|
|
||||||
|
# 筛选面板
|
||||||
|
filter_frame = ctk.CTkFrame(self)
|
||||||
|
filter_frame.grid(row=0, column=1, rowspan=2, sticky="ns", padx=5)
|
||||||
|
|
||||||
|
ctk.CTkLabel(filter_frame, text="日志等级筛选").pack(pady=5)
|
||||||
|
self.level_scroll = ctk.CTkScrollableFrame(filter_frame, width=150, height=200)
|
||||||
|
self.level_scroll.pack(fill="both", expand=True, padx=5)
|
||||||
|
|
||||||
|
ctk.CTkLabel(filter_frame, text="模块筛选").pack(pady=5)
|
||||||
|
self.module_filter_entry = ctk.CTkEntry(filter_frame, placeholder_text="输入模块过滤词")
|
||||||
|
self.module_filter_entry.pack(pady=5)
|
||||||
|
self.module_filter_entry.bind("<KeyRelease>", self.update_module_filter)
|
||||||
|
|
||||||
|
self.module_scroll = ctk.CTkScrollableFrame(filter_frame, width=300, height=200)
|
||||||
|
self.module_scroll.pack(fill="both", expand=True, padx=5)
|
||||||
|
|
||||||
|
self.log_text = ctk.CTkTextbox(self, wrap="word")
|
||||||
|
self.log_text.grid(row=1, column=0, sticky="nsew", padx=10, pady=5)
|
||||||
|
|
||||||
|
self.init_text_tags()
|
||||||
|
|
||||||
|
def update_module_filter(self, event):
|
||||||
|
"""根据模块过滤词更新模块复选框的显示"""
|
||||||
|
filter_text = self.module_filter_entry.get().strip().lower()
|
||||||
|
for module, checkbox in self.module_checkboxes.items():
|
||||||
|
if filter_text in module.lower():
|
||||||
|
checkbox.pack(anchor="w", padx=5, pady=2)
|
||||||
|
else:
|
||||||
|
checkbox.pack_forget()
|
||||||
|
|
||||||
|
def update_filters(self, level, module):
|
||||||
|
"""更新日志等级和模块的筛选器"""
|
||||||
|
if level not in self.available_levels:
|
||||||
|
self.available_levels.add(level)
|
||||||
|
self.add_checkbox(self.level_scroll, level, "level")
|
||||||
|
|
||||||
|
module_key = self.get_module_key(module)
|
||||||
|
if module_key not in self.available_modules:
|
||||||
|
self.available_modules.add(module_key)
|
||||||
|
self.sorted_modules = sorted(self.available_modules, key=lambda x: x.lower())
|
||||||
|
self.rebuild_module_checkboxes()
|
||||||
|
|
||||||
|
def rebuild_module_checkboxes(self):
|
||||||
|
"""重新构建模块复选框"""
|
||||||
|
# 清空现有复选框
|
||||||
|
for widget in self.module_scroll.winfo_children():
|
||||||
|
widget.destroy()
|
||||||
|
self.module_checkboxes.clear()
|
||||||
|
|
||||||
|
# 重建排序后的复选框
|
||||||
|
for module in self.sorted_modules:
|
||||||
|
self.add_checkbox(self.module_scroll, module, "module")
|
||||||
|
|
||||||
|
def add_checkbox(self, parent, text, type_):
|
||||||
|
"""在指定父组件中添加复选框"""
|
||||||
|
|
||||||
|
def update_filter():
|
||||||
|
current = cb.get()
|
||||||
|
if type_ == "level":
|
||||||
|
(self.selected_levels.add if current else self.selected_levels.discard)(text)
|
||||||
|
else:
|
||||||
|
(self.selected_modules.add if current else self.selected_modules.discard)(text)
|
||||||
|
self.refresh_logs()
|
||||||
|
|
||||||
|
cb = ctk.CTkCheckBox(parent, text=text, command=update_filter)
|
||||||
|
cb.select() # 初始选中
|
||||||
|
|
||||||
|
# 手动同步初始状态到集合（关键修复）
|
||||||
|
if type_ == "level":
|
||||||
|
self.selected_levels.add(text)
|
||||||
|
else:
|
||||||
|
self.selected_modules.add(text)
|
||||||
|
|
||||||
|
if type_ == "module":
|
||||||
|
self.module_checkboxes[text] = cb
|
||||||
|
cb.pack(anchor="w", padx=5, pady=2)
|
||||||
|
return cb
|
||||||
|
|
||||||
|
def check_filter(self, entry):
|
||||||
|
"""检查日志条目是否符合当前筛选条件"""
|
||||||
|
level_ok = not self.selected_levels or entry["level"] in self.selected_levels
|
||||||
|
module_key = self.get_module_key(entry["module"])
|
||||||
|
module_ok = not self.selected_modules or module_key in self.selected_modules
|
||||||
|
return level_ok and module_ok
|
||||||
|
|
||||||
|
def init_text_tags(self):
|
||||||
|
"""初始化日志文本的颜色标签"""
|
||||||
|
for tag, color in self.color_config.items():
|
||||||
|
self.log_text.tag_config(tag, foreground=color)
|
||||||
|
self.log_text.tag_config("default", foreground=self.color_config["default"])
|
||||||
|
|
||||||
|
def start_process(self):
|
||||||
|
"""启动日志进程并开始读取输出"""
|
||||||
|
self.process = subprocess.Popen(
|
||||||
|
["nb", "run"],
|
||||||
|
stdout=subprocess.PIPE,
|
||||||
|
stderr=subprocess.STDOUT,
|
||||||
|
text=True,
|
||||||
|
bufsize=1,
|
||||||
|
encoding="utf-8",
|
||||||
|
errors="ignore",
|
||||||
|
)
|
||||||
|
self.start_btn.configure(state="disabled")
|
||||||
|
self.stop_btn.configure(state="normal")
|
||||||
|
threading.Thread(target=self.read_output, daemon=True).start()
|
||||||
|
|
||||||
|
def stop_process(self):
|
||||||
|
"""停止日志进程并清理相关资源"""
|
||||||
|
if self.process:
|
||||||
|
try:
|
||||||
|
if hasattr(self.process, "pid"):
|
||||||
|
if os.name == "nt":
|
||||||
|
subprocess.run(
|
||||||
|
["taskkill", "/F", "/T", "/PID", str(self.process.pid)], check=True, capture_output=True
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
os.killpg(os.getpgid(self.process.pid), signal.SIGTERM)
|
||||||
|
except (subprocess.CalledProcessError, ProcessLookupError, OSError) as e:
|
||||||
|
print(f"终止进程失败: {e}")
|
||||||
|
finally:
|
||||||
|
self.process = None
|
||||||
|
self.log_queue.queue.clear()
|
||||||
|
self.start_btn.configure(state="normal")
|
||||||
|
self.stop_btn.configure(state="disabled")
|
||||||
|
self.refresh_logs()
|
||||||
|
|
||||||
|
def read_output(self):
|
||||||
|
"""读取日志进程的输出并放入队列"""
|
||||||
|
try:
|
||||||
|
while self.process and self.process.poll() is None:
|
||||||
|
line = self.process.stdout.readline()
|
||||||
|
if line:
|
||||||
|
self.log_queue.put(line)
|
||||||
|
else:
|
||||||
|
break # 避免空循环
|
||||||
|
self.process.stdout.close() # 确保关闭文件描述符
|
||||||
|
except ValueError: # 处理可能的I/O操作异常
|
||||||
|
pass
|
||||||
|
|
||||||
|
def process_log_queue(self):
|
||||||
|
"""处理日志队列中的日志条目"""
|
||||||
|
while not self.log_queue.empty():
|
||||||
|
line = self.log_queue.get()
|
||||||
|
self.process_log_line(line)
|
||||||
|
self.after(100, self.process_log_queue)
|
||||||
|
|
||||||
|
def process_log_line(self, line):
|
||||||
|
"""解析单行日志并更新日志数据和筛选器"""
|
||||||
|
match = re.match(
|
||||||
|
r"""^
|
||||||
|
(?:(?P<time>\d{2}:\d{2}(?::\d{2})?)\s*\|\s*)?
|
||||||
|
(?P<level>\w+)\s*\|\s*
|
||||||
|
(?P<module>.*?)
|
||||||
|
\s*[-|]\s*
|
||||||
|
(?P<message>.*)
|
||||||
|
$""",
|
||||||
|
line.strip(),
|
||||||
|
re.VERBOSE,
|
||||||
|
)
|
||||||
|
|
||||||
|
if match:
|
||||||
|
groups = match.groupdict()
|
||||||
|
time = groups.get("time", "")
|
||||||
|
level = groups.get("level", "OTHER")
|
||||||
|
module = groups.get("module", "UNKNOWN").strip()
|
||||||
|
message = groups.get("message", "").strip()
|
||||||
|
raw_line = line
|
||||||
|
else:
|
||||||
|
time, level, module, message = "", "OTHER", "UNKNOWN", line
|
||||||
|
raw_line = line
|
||||||
|
|
||||||
|
self.update_filters(level, module)
|
||||||
|
log_entry = {"raw": raw_line, "time": time, "level": level, "module": module, "message": message}
|
||||||
|
self.log_data.append(log_entry)
|
||||||
|
|
||||||
|
if self.check_filter(log_entry):
|
||||||
|
self.display_log(log_entry)
|
||||||
|
|
||||||
|
def get_module_key(self, module_name):
|
||||||
|
"""获取模块名称的标准化键"""
|
||||||
|
cleaned = module_name.strip()
|
||||||
|
return re.sub(r":\d+$", "", cleaned)
|
||||||
|
|
||||||
|
def display_log(self, entry):
|
||||||
|
"""在日志文本框中显示日志条目"""
|
||||||
|
parts = []
|
||||||
|
tags = []
|
||||||
|
|
||||||
|
if self.column_visibility["show_time"] and entry["time"]:
|
||||||
|
parts.append(f"{entry['time']} ")
|
||||||
|
tags.append("time")
|
||||||
|
|
||||||
|
if self.column_visibility["show_level"]:
|
||||||
|
level_tag = entry["level"] if entry["level"] in self.color_config else "default"
|
||||||
|
parts.append(f"{entry['level']:<8} ")
|
||||||
|
tags.append(level_tag)
|
||||||
|
|
||||||
|
if self.column_visibility["show_module"]:
|
||||||
|
parts.append(f"{entry['module']} ")
|
||||||
|
tags.append("module")
|
||||||
|
|
||||||
|
parts.append(f"- {entry['message']}\n")
|
||||||
|
tags.append("default")
|
||||||
|
|
||||||
|
self.log_text.configure(state="normal")
|
||||||
|
for part, tag in zip(parts, tags):
|
||||||
|
self.log_text.insert("end", part, tag)
|
||||||
|
self.log_text.see("end")
|
||||||
|
self.log_text.configure(state="disabled")
|
||||||
|
|
||||||
|
def refresh_logs(self):
|
||||||
|
"""刷新日志显示，根据筛选条件重新显示日志"""
|
||||||
|
self.column_visibility = {
|
||||||
|
"show_time": self.time_check.get(),
|
||||||
|
"show_level": self.level_check.get(),
|
||||||
|
"show_module": self.module_check.get(),
|
||||||
|
}
|
||||||
|
|
||||||
|
self.log_text.configure(state="normal")
|
||||||
|
self.log_text.delete("1.0", "end")
|
||||||
|
|
||||||
|
filtered_logs = [entry for entry in self.log_data if self.check_filter(entry)]
|
||||||
|
|
||||||
|
for entry in filtered_logs:
|
||||||
|
parts = []
|
||||||
|
tags = []
|
||||||
|
|
||||||
|
if self.column_visibility["show_time"] and entry["time"]:
|
||||||
|
parts.append(f"{entry['time']} ")
|
||||||
|
tags.append("time")
|
||||||
|
|
||||||
|
if self.column_visibility["show_level"]:
|
||||||
|
level_tag = entry["level"] if entry["level"] in self.color_config else "default"
|
||||||
|
parts.append(f"{entry['level']:<8} ")
|
||||||
|
tags.append(level_tag)
|
||||||
|
|
||||||
|
if self.column_visibility["show_module"]:
|
||||||
|
parts.append(f"{entry['module']} ")
|
||||||
|
tags.append("module")
|
||||||
|
|
||||||
|
parts.append(f"- {entry['message']}\n")
|
||||||
|
tags.append("default")
|
||||||
|
|
||||||
|
for part, tag in zip(parts, tags):
|
||||||
|
self.log_text.insert("end", part, tag)
|
||||||
|
|
||||||
|
self.log_text.see("end")
|
||||||
|
self.log_text.configure(state="disabled")
|
||||||
|
|
||||||
|
def clear_logs(self):
|
||||||
|
"""清空日志文本框中的内容"""
|
||||||
|
self.log_text.configure(state="normal")
|
||||||
|
self.log_text.delete("1.0", "end")
|
||||||
|
self.log_text.configure(state="disabled")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# 启动日志查看器应用
|
||||||
|
app = LogViewerApp()
|
||||||
|
app.mainloop()
|
||||||
@@ -5,31 +5,36 @@ import threading
|
|||||||
import time
|
import time
|
||||||
from datetime import datetime
|
from datetime import datetime
|
||||||
from typing import Dict, List
|
from typing import Dict, List
|
||||||
from loguru import logger
|
|
||||||
from typing import Optional
|
from typing import Optional
|
||||||
|
|
||||||
|
sys.path.insert(0, sys.path[0] + "/../")
|
||||||
|
sys.path.insert(0, sys.path[0] + "/../")
|
||||||
|
from src.common.logger import get_module_logger
|
||||||
|
|
||||||
import customtkinter as ctk
|
import customtkinter as ctk
|
||||||
from dotenv import load_dotenv
|
from dotenv import load_dotenv
|
||||||
|
|
||||||
|
logger = get_module_logger("gui")
|
||||||
|
|
||||||
# 获取当前文件的目录
|
# 获取当前文件的目录
|
||||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||||
# 获取项目根目录
|
# 获取项目根目录
|
||||||
root_dir = os.path.abspath(os.path.join(current_dir, '..', '..'))
|
root_dir = os.path.abspath(os.path.join(current_dir, "..", ".."))
|
||||||
sys.path.insert(0, root_dir)
|
sys.path.insert(0, root_dir)
|
||||||
from src.common.database import db
|
from src.common.database import db # noqa: E402
|
||||||
|
|
||||||
# 加载环境变量
|
# 加载环境变量
|
||||||
if os.path.exists(os.path.join(root_dir, '.env.dev')):
|
if os.path.exists(os.path.join(root_dir, ".env.dev")):
|
||||||
load_dotenv(os.path.join(root_dir, '.env.dev'))
|
load_dotenv(os.path.join(root_dir, ".env.dev"))
|
||||||
logger.info("成功加载开发环境配置")
|
logger.info("成功加载开发环境配置")
|
||||||
elif os.path.exists(os.path.join(root_dir, '.env.prod')):
|
elif os.path.exists(os.path.join(root_dir, ".env")):
|
||||||
load_dotenv(os.path.join(root_dir, '.env.prod'))
|
load_dotenv(os.path.join(root_dir, ".env"))
|
||||||
logger.info("成功加载生产环境配置")
|
logger.info("成功加载生产环境配置")
|
||||||
else:
|
else:
|
||||||
logger.error("未找到环境配置文件")
|
logger.error("未找到环境配置文件")
|
||||||
sys.exit(1)
|
sys.exit(1)
|
||||||
|
|
||||||
|
|
||||||
class ReasoningGUI:
|
class ReasoningGUI:
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
# 记录启动时间戳，转换为Unix时间戳
|
# 记录启动时间戳，转换为Unix时间戳
|
||||||
@@ -42,8 +47,8 @@ class ReasoningGUI:
|
|||||||
|
|
||||||
# 创建主窗口
|
# 创建主窗口
|
||||||
self.root = ctk.CTk()
|
self.root = ctk.CTk()
|
||||||
self.root.title('麦麦推理')
|
self.root.title("麦麦推理")
|
||||||
self.root.geometry('800x600')
|
self.root.geometry("800x600")
|
||||||
self.root.protocol("WM_DELETE_WINDOW", self._on_closing)
|
self.root.protocol("WM_DELETE_WINDOW", self._on_closing)
|
||||||
|
|
||||||
# 存储群组数据
|
# 存储群组数据
|
||||||
@@ -105,12 +110,7 @@ class ReasoningGUI:
|
|||||||
self.control_frame = ctk.CTkFrame(self.frame)
|
self.control_frame = ctk.CTkFrame(self.frame)
|
||||||
self.control_frame.pack(fill="x", padx=10, pady=5)
|
self.control_frame.pack(fill="x", padx=10, pady=5)
|
||||||
|
|
||||||
self.clear_button = ctk.CTkButton(
|
self.clear_button = ctk.CTkButton(self.control_frame, text="清除显示", command=self.clear_display, width=120)
|
||||||
self.control_frame,
|
|
||||||
text="清除显示",
|
|
||||||
command=self.clear_display,
|
|
||||||
width=120
|
|
||||||
)
|
|
||||||
self.clear_button.pack(side="left", padx=5)
|
self.clear_button.pack(side="left", padx=5)
|
||||||
|
|
||||||
# 启动自动更新线程
|
# 启动自动更新线程
|
||||||
@@ -130,10 +130,10 @@ class ReasoningGUI:
|
|||||||
try:
|
try:
|
||||||
while True:
|
while True:
|
||||||
task = self.update_queue.get_nowait()
|
task = self.update_queue.get_nowait()
|
||||||
if task['type'] == 'update_group_list':
|
if task["type"] == "update_group_list":
|
||||||
self._update_group_list_gui()
|
self._update_group_list_gui()
|
||||||
elif task['type'] == 'update_display':
|
elif task["type"] == "update_display":
|
||||||
self._update_display_gui(task['group_id'])
|
self._update_display_gui(task["group_id"])
|
||||||
except queue.Empty:
|
except queue.Empty:
|
||||||
pass
|
pass
|
||||||
finally:
|
finally:
|
||||||
@@ -155,7 +155,7 @@ class ReasoningGUI:
|
|||||||
width=160,
|
width=160,
|
||||||
height=30,
|
height=30,
|
||||||
corner_radius=8,
|
corner_radius=8,
|
||||||
command=lambda gid=group_id: self._on_group_select(gid)
|
command=lambda gid=group_id: self._on_group_select(gid),
|
||||||
)
|
)
|
||||||
button.pack(pady=2, padx=5)
|
button.pack(pady=2, padx=5)
|
||||||
self.group_buttons[group_id] = button
|
self.group_buttons[group_id] = button
|
||||||
@@ -188,7 +188,7 @@ class ReasoningGUI:
|
|||||||
self.content_text.delete("1.0", "end")
|
self.content_text.delete("1.0", "end")
|
||||||
for item in self.group_data[group_id]:
|
for item in self.group_data[group_id]:
|
||||||
# 时间戳
|
# 时间戳
|
||||||
time_str = item['time'].strftime("%Y-%m-%d %H:%M:%S")
|
time_str = item["time"].strftime("%Y-%m-%d %H:%M:%S")
|
||||||
self.content_text.insert("end", f"[{time_str}]\n", "timestamp")
|
self.content_text.insert("end", f"[{time_str}]\n", "timestamp")
|
||||||
|
|
||||||
# 用户信息
|
# 用户信息
|
||||||
@@ -205,9 +205,9 @@ class ReasoningGUI:
|
|||||||
|
|
||||||
# Prompt内容
|
# Prompt内容
|
||||||
self.content_text.insert("end", "Prompt内容:\n", "timestamp")
|
self.content_text.insert("end", "Prompt内容:\n", "timestamp")
|
||||||
prompt_text = item.get('prompt', '')
|
prompt_text = item.get("prompt", "")
|
||||||
if prompt_text and prompt_text.lower() != 'none':
|
if prompt_text and prompt_text.lower() != "none":
|
||||||
lines = prompt_text.split('\n')
|
lines = prompt_text.split("\n")
|
||||||
for line in lines:
|
for line in lines:
|
||||||
if line.strip():
|
if line.strip():
|
||||||
self.content_text.insert("end", " " + line + "\n", "prompt")
|
self.content_text.insert("end", " " + line + "\n", "prompt")
|
||||||
@@ -216,9 +216,9 @@ class ReasoningGUI:
|
|||||||
|
|
||||||
# 推理过程
|
# 推理过程
|
||||||
self.content_text.insert("end", "推理过程:\n", "timestamp")
|
self.content_text.insert("end", "推理过程:\n", "timestamp")
|
||||||
reasoning_text = item.get('reasoning', '')
|
reasoning_text = item.get("reasoning", "")
|
||||||
if reasoning_text and reasoning_text.lower() != 'none':
|
if reasoning_text and reasoning_text.lower() != "none":
|
||||||
lines = reasoning_text.split('\n')
|
lines = reasoning_text.split("\n")
|
||||||
for line in lines:
|
for line in lines:
|
||||||
if line.strip():
|
if line.strip():
|
||||||
self.content_text.insert("end", " " + line + "\n", "reasoning")
|
self.content_text.insert("end", " " + line + "\n", "reasoning")
|
||||||
@@ -258,28 +258,30 @@ class ReasoningGUI:
|
|||||||
logger.debug(f"记录时间: {item['time']}, 类型: {type(item['time'])}")
|
logger.debug(f"记录时间: {item['time']}, 类型: {type(item['time'])}")
|
||||||
|
|
||||||
total_count += 1
|
total_count += 1
|
||||||
group_id = str(item.get('group_id', 'unknown'))
|
group_id = str(item.get("group_id", "unknown"))
|
||||||
if group_id not in new_data:
|
if group_id not in new_data:
|
||||||
new_data[group_id] = []
|
new_data[group_id] = []
|
||||||
|
|
||||||
# 转换时间戳为datetime对象
|
# 转换时间戳为datetime对象
|
||||||
if isinstance(item['time'], (int, float)):
|
if isinstance(item["time"], (int, float)):
|
||||||
time_obj = datetime.fromtimestamp(item['time'])
|
time_obj = datetime.fromtimestamp(item["time"])
|
||||||
elif isinstance(item['time'], datetime):
|
elif isinstance(item["time"], datetime):
|
||||||
time_obj = item['time']
|
time_obj = item["time"]
|
||||||
else:
|
else:
|
||||||
logger.warning(f"未知的时间格式: {type(item['time'])}")
|
logger.warning(f"未知的时间格式: {type(item['time'])}")
|
||||||
time_obj = datetime.now() # 使用当前时间作为后备
|
time_obj = datetime.now() # 使用当前时间作为后备
|
||||||
|
|
||||||
new_data[group_id].append({
|
new_data[group_id].append(
|
||||||
'time': time_obj,
|
{
|
||||||
'user': item.get('user', '未知'),
|
"time": time_obj,
|
||||||
'message': item.get('message', ''),
|
"user": item.get("user", "未知"),
|
||||||
'model': item.get('model', '未知'),
|
"message": item.get("message", ""),
|
||||||
'reasoning': item.get('reasoning', ''),
|
"model": item.get("model", "未知"),
|
||||||
'response': item.get('response', ''),
|
"reasoning": item.get("reasoning", ""),
|
||||||
'prompt': item.get('prompt', '') # 添加prompt字段
|
"response": item.get("response", ""),
|
||||||
})
|
"prompt": item.get("prompt", ""), # 添加prompt字段
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
logger.info(f"从数据库加载了 {total_count} 条记录，分布在 {len(new_data)} 个群组中")
|
logger.info(f"从数据库加载了 {total_count} 条记录，分布在 {len(new_data)} 个群组中")
|
||||||
|
|
||||||
@@ -288,15 +290,12 @@ class ReasoningGUI:
|
|||||||
self.group_data = new_data
|
self.group_data = new_data
|
||||||
logger.info("数据已更新，正在刷新显示...")
|
logger.info("数据已更新，正在刷新显示...")
|
||||||
# 将更新任务添加到队列
|
# 将更新任务添加到队列
|
||||||
self.update_queue.put({'type': 'update_group_list'})
|
self.update_queue.put({"type": "update_group_list"})
|
||||||
if self.group_data:
|
if self.group_data:
|
||||||
# 如果没有选中的群组，选择最新的群组
|
# 如果没有选中的群组，选择最新的群组
|
||||||
if not self.selected_group_id or self.selected_group_id not in self.group_data:
|
if not self.selected_group_id or self.selected_group_id not in self.group_data:
|
||||||
self.selected_group_id = next(iter(self.group_data))
|
self.selected_group_id = next(iter(self.group_data))
|
||||||
self.update_queue.put({
|
self.update_queue.put({"type": "update_display", "group_id": self.selected_group_id})
|
||||||
'type': 'update_display',
|
|
||||||
'group_id': self.selected_group_id
|
|
||||||
})
|
|
||||||
except Exception:
|
except Exception:
|
||||||
logger.exception("自动更新出错")
|
logger.exception("自动更新出错")
|
||||||
|
|
||||||
|
|||||||
BIN
src/heart_flow/L{QA$T9C4`IVQEAB3WZYFXL.jpg
Normal file
BIN
src/heart_flow/L{QA$T9C4`IVQEAB3WZYFXL.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 59 KiB |
BIN
src/heart_flow/SKG`8J~]3I~E8WEB%Y85I`M.jpg
Normal file
BIN
src/heart_flow/SKG`8J~]3I~E8WEB%Y85I`M.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 91 KiB |
BIN
src/heart_flow/ZX65~ALHC_7{Q9FKE$X}TQC.jpg
Normal file
BIN
src/heart_flow/ZX65~ALHC_7{Q9FKE$X}TQC.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 88 KiB |
171
src/heart_flow/heartflow.py
Normal file
171
src/heart_flow/heartflow.py
Normal file
@@ -0,0 +1,171 @@
|
|||||||
|
from .sub_heartflow import SubHeartflow
|
||||||
|
from .observation import ChattingObservation
|
||||||
|
from src.plugins.moods.moods import MoodManager
|
||||||
|
from src.plugins.models.utils_model import LLM_request
|
||||||
|
from src.plugins.config.config import global_config
|
||||||
|
from src.plugins.schedule.schedule_generator import bot_schedule
|
||||||
|
import asyncio
|
||||||
|
from src.common.logger import get_module_logger, LogConfig, HEARTFLOW_STYLE_CONFIG # noqa: E402
|
||||||
|
import time
|
||||||
|
|
||||||
|
heartflow_config = LogConfig(
|
||||||
|
# 使用海马体专用样式
|
||||||
|
console_format=HEARTFLOW_STYLE_CONFIG["console_format"],
|
||||||
|
file_format=HEARTFLOW_STYLE_CONFIG["file_format"],
|
||||||
|
)
|
||||||
|
logger = get_module_logger("heartflow", config=heartflow_config)
|
||||||
|
|
||||||
|
|
||||||
|
class CuttentState:
|
||||||
|
def __init__(self):
|
||||||
|
self.willing = 0
|
||||||
|
self.current_state_info = ""
|
||||||
|
|
||||||
|
self.mood_manager = MoodManager()
|
||||||
|
self.mood = self.mood_manager.get_prompt()
|
||||||
|
|
||||||
|
def update_current_state_info(self):
|
||||||
|
self.current_state_info = self.mood_manager.get_current_mood()
|
||||||
|
|
||||||
|
|
||||||
|
class Heartflow:
|
||||||
|
def __init__(self):
|
||||||
|
self.current_mind = "你什么也没想"
|
||||||
|
self.past_mind = []
|
||||||
|
self.current_state: CuttentState = CuttentState()
|
||||||
|
self.llm_model = LLM_request(
|
||||||
|
model=global_config.llm_heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow"
|
||||||
|
)
|
||||||
|
|
||||||
|
self._subheartflows = {}
|
||||||
|
self.active_subheartflows_nums = 0
|
||||||
|
|
||||||
|
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||||
|
|
||||||
|
async def _cleanup_inactive_subheartflows(self):
|
||||||
|
"""定期清理不活跃的子心流"""
|
||||||
|
while True:
|
||||||
|
current_time = time.time()
|
||||||
|
inactive_subheartflows = []
|
||||||
|
|
||||||
|
# 检查所有子心流
|
||||||
|
for subheartflow_id, subheartflow in self._subheartflows.items():
|
||||||
|
if (
|
||||||
|
current_time - subheartflow.last_active_time > global_config.sub_heart_flow_stop_time
|
||||||
|
): # 10分钟 = 600秒
|
||||||
|
inactive_subheartflows.append(subheartflow_id)
|
||||||
|
logger.info(f"发现不活跃的子心流: {subheartflow_id}")
|
||||||
|
|
||||||
|
# 清理不活跃的子心流
|
||||||
|
for subheartflow_id in inactive_subheartflows:
|
||||||
|
del self._subheartflows[subheartflow_id]
|
||||||
|
logger.info(f"已清理不活跃的子心流: {subheartflow_id}")
|
||||||
|
|
||||||
|
await asyncio.sleep(30) # 每分钟检查一次
|
||||||
|
|
||||||
|
async def heartflow_start_working(self):
|
||||||
|
# 启动清理任务
|
||||||
|
asyncio.create_task(self._cleanup_inactive_subheartflows())
|
||||||
|
|
||||||
|
while True:
|
||||||
|
# 检查是否存在子心流
|
||||||
|
if not self._subheartflows:
|
||||||
|
logger.info("当前没有子心流，等待新的子心流创建...")
|
||||||
|
await asyncio.sleep(30) # 每分钟检查一次是否有新的子心流
|
||||||
|
continue
|
||||||
|
|
||||||
|
await self.do_a_thinking()
|
||||||
|
await asyncio.sleep(global_config.heart_flow_update_interval) # 5分钟思考一次
|
||||||
|
|
||||||
|
async def do_a_thinking(self):
|
||||||
|
logger.debug("麦麦大脑袋转起来了")
|
||||||
|
self.current_state.update_current_state_info()
|
||||||
|
|
||||||
|
personality_info = self.personality_info
|
||||||
|
current_thinking_info = self.current_mind
|
||||||
|
mood_info = self.current_state.mood
|
||||||
|
related_memory_info = "memory"
|
||||||
|
sub_flows_info = await self.get_all_subheartflows_minds()
|
||||||
|
|
||||||
|
schedule_info = bot_schedule.get_current_num_task(num=4, time_info=True)
|
||||||
|
|
||||||
|
prompt = ""
|
||||||
|
prompt += f"你刚刚在做的事情是：{schedule_info}\n"
|
||||||
|
prompt += f"{personality_info}\n"
|
||||||
|
prompt += f"你想起来{related_memory_info}。"
|
||||||
|
prompt += f"刚刚你的主要想法是{current_thinking_info}。"
|
||||||
|
prompt += f"你还有一些小想法，因为你在参加不同的群聊天，是你正在做的事情：{sub_flows_info}\n"
|
||||||
|
prompt += f"你现在{mood_info}。"
|
||||||
|
prompt += "现在你接下去继续思考，产生新的想法，但是要基于原有的主要想法，不要分点输出，"
|
||||||
|
prompt += "输出连贯的内心独白，不要太长，但是记得结合上述的消息，关注新内容:"
|
||||||
|
|
||||||
|
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
|
||||||
|
|
||||||
|
self.update_current_mind(reponse)
|
||||||
|
|
||||||
|
self.current_mind = reponse
|
||||||
|
logger.info(f"麦麦的总体脑内状态：{self.current_mind}")
|
||||||
|
# logger.info("麦麦想了想，当前活动:")
|
||||||
|
# await bot_schedule.move_doing(self.current_mind)
|
||||||
|
|
||||||
|
for _, subheartflow in self._subheartflows.items():
|
||||||
|
subheartflow.main_heartflow_info = reponse
|
||||||
|
|
||||||
|
def update_current_mind(self, reponse):
|
||||||
|
self.past_mind.append(self.current_mind)
|
||||||
|
self.current_mind = reponse
|
||||||
|
|
||||||
|
async def get_all_subheartflows_minds(self):
|
||||||
|
sub_minds = ""
|
||||||
|
for _, subheartflow in self._subheartflows.items():
|
||||||
|
sub_minds += subheartflow.current_mind
|
||||||
|
|
||||||
|
return await self.minds_summary(sub_minds)
|
||||||
|
|
||||||
|
async def minds_summary(self, minds_str):
|
||||||
|
personality_info = self.personality_info
|
||||||
|
mood_info = self.current_state.mood
|
||||||
|
|
||||||
|
prompt = ""
|
||||||
|
prompt += f"{personality_info}\n"
|
||||||
|
prompt += f"现在{global_config.BOT_NICKNAME}的想法是：{self.current_mind}\n"
|
||||||
|
prompt += f"现在{global_config.BOT_NICKNAME}在qq群里进行聊天，聊天的话题如下：{minds_str}\n"
|
||||||
|
prompt += f"你现在{mood_info}\n"
|
||||||
|
prompt += """现在请你总结这些聊天内容，注意关注聊天内容对原有的想法的影响，输出连贯的内心独白
|
||||||
|
不要太长，但是记得结合上述的消息，要记得你的人设，关注新内容:"""
|
||||||
|
|
||||||
|
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
|
||||||
|
|
||||||
|
return reponse
|
||||||
|
|
||||||
|
def create_subheartflow(self, subheartflow_id):
|
||||||
|
"""
|
||||||
|
创建一个新的SubHeartflow实例
|
||||||
|
添加一个SubHeartflow实例到self._subheartflows字典中
|
||||||
|
并根据subheartflow_id为子心流创建一个观察对象
|
||||||
|
"""
|
||||||
|
if subheartflow_id not in self._subheartflows:
|
||||||
|
logger.debug(f"创建 subheartflow: {subheartflow_id}")
|
||||||
|
subheartflow = SubHeartflow(subheartflow_id)
|
||||||
|
# 创建一个观察对象，目前只可以用chat_id创建观察对象
|
||||||
|
logger.debug(f"创建 observation: {subheartflow_id}")
|
||||||
|
observation = ChattingObservation(subheartflow_id)
|
||||||
|
|
||||||
|
logger.debug("添加 observation ")
|
||||||
|
subheartflow.add_observation(observation)
|
||||||
|
logger.debug("添加 observation 成功")
|
||||||
|
# 创建异步任务
|
||||||
|
logger.debug("创建异步任务")
|
||||||
|
asyncio.create_task(subheartflow.subheartflow_start_working())
|
||||||
|
logger.debug("创建异步任务 成功")
|
||||||
|
self._subheartflows[subheartflow_id] = subheartflow
|
||||||
|
logger.info("添加 subheartflow 成功")
|
||||||
|
return self._subheartflows[subheartflow_id]
|
||||||
|
|
||||||
|
def get_subheartflow(self, observe_chat_id):
|
||||||
|
"""获取指定ID的SubHeartflow实例"""
|
||||||
|
return self._subheartflows.get(observe_chat_id)
|
||||||
|
|
||||||
|
|
||||||
|
# 创建一个全局的管理器实例
|
||||||
|
heartflow = Heartflow()
|
||||||
128
src/heart_flow/observation.py
Normal file
128
src/heart_flow/observation.py
Normal file
@@ -0,0 +1,128 @@
|
|||||||
|
# 定义了来自外部世界的信息
|
||||||
|
# 外部世界可以是某个聊天 不同平台的聊天 也可以是任意媒体
|
||||||
|
from datetime import datetime
|
||||||
|
from src.plugins.models.utils_model import LLM_request
|
||||||
|
from src.plugins.config.config import global_config
|
||||||
|
from src.common.database import db
|
||||||
|
|
||||||
|
|
||||||
|
# 所有观察的基类
|
||||||
|
class Observation:
|
||||||
|
def __init__(self, observe_type, observe_id):
|
||||||
|
self.observe_info = ""
|
||||||
|
self.observe_type = observe_type
|
||||||
|
self.observe_id = observe_id
|
||||||
|
self.last_observe_time = datetime.now().timestamp() # 初始化为当前时间
|
||||||
|
|
||||||
|
|
||||||
|
# 聊天观察
|
||||||
|
class ChattingObservation(Observation):
|
||||||
|
def __init__(self, chat_id):
|
||||||
|
super().__init__("chat", chat_id)
|
||||||
|
self.chat_id = chat_id
|
||||||
|
|
||||||
|
self.talking_message = []
|
||||||
|
self.talking_message_str = ""
|
||||||
|
|
||||||
|
self.observe_times = 0
|
||||||
|
|
||||||
|
self.summary_count = 0 # 30秒内的更新次数
|
||||||
|
self.max_update_in_30s = 2 # 30秒内最多更新2次
|
||||||
|
self.last_summary_time = 0 # 上次更新summary的时间
|
||||||
|
|
||||||
|
self.sub_observe = None
|
||||||
|
|
||||||
|
self.llm_summary = LLM_request(
|
||||||
|
model=global_config.llm_observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 进行一次观察 返回观察结果observe_info
|
||||||
|
async def observe(self):
|
||||||
|
# 查找新消息，限制最多30条
|
||||||
|
new_messages = list(
|
||||||
|
db.messages.find({"chat_id": self.chat_id, "time": {"$gt": self.last_observe_time}})
|
||||||
|
.sort("time", 1)
|
||||||
|
.limit(20)
|
||||||
|
) # 按时间正序排列，最多20条
|
||||||
|
|
||||||
|
if not new_messages:
|
||||||
|
return self.observe_info # 没有新消息，返回上次观察结果
|
||||||
|
|
||||||
|
# 将新消息转换为字符串格式
|
||||||
|
new_messages_str = ""
|
||||||
|
for msg in new_messages:
|
||||||
|
if "detailed_plain_text" in msg:
|
||||||
|
new_messages_str += f"{msg['detailed_plain_text']}"
|
||||||
|
|
||||||
|
# print(f"new_messages_str：{new_messages_str}")
|
||||||
|
|
||||||
|
# 将新消息添加到talking_message，同时保持列表长度不超过20条
|
||||||
|
self.talking_message.extend(new_messages)
|
||||||
|
if len(self.talking_message) > 20:
|
||||||
|
self.talking_message = self.talking_message[-20:] # 只保留最新的20条
|
||||||
|
self.translate_message_list_to_str()
|
||||||
|
|
||||||
|
# 更新观察次数
|
||||||
|
self.observe_times += 1
|
||||||
|
self.last_observe_time = new_messages[-1]["time"]
|
||||||
|
|
||||||
|
# 检查是否需要更新summary
|
||||||
|
current_time = int(datetime.now().timestamp())
|
||||||
|
if current_time - self.last_summary_time >= 30: # 如果超过30秒，重置计数
|
||||||
|
self.summary_count = 0
|
||||||
|
self.last_summary_time = current_time
|
||||||
|
|
||||||
|
if self.summary_count < self.max_update_in_30s: # 如果30秒内更新次数小于2次
|
||||||
|
await self.update_talking_summary(new_messages_str)
|
||||||
|
self.summary_count += 1
|
||||||
|
|
||||||
|
return self.observe_info
|
||||||
|
|
||||||
|
async def carefully_observe(self):
|
||||||
|
# 查找新消息，限制最多40条
|
||||||
|
new_messages = list(
|
||||||
|
db.messages.find({"chat_id": self.chat_id, "time": {"$gt": self.last_observe_time}})
|
||||||
|
.sort("time", 1)
|
||||||
|
.limit(30)
|
||||||
|
) # 按时间正序排列，最多30条
|
||||||
|
|
||||||
|
if not new_messages:
|
||||||
|
return self.observe_info # 没有新消息，返回上次观察结果
|
||||||
|
|
||||||
|
# 将新消息转换为字符串格式
|
||||||
|
new_messages_str = ""
|
||||||
|
for msg in new_messages:
|
||||||
|
if "detailed_plain_text" in msg:
|
||||||
|
new_messages_str += f"{msg['detailed_plain_text']}\n"
|
||||||
|
|
||||||
|
# 将新消息添加到talking_message，同时保持列表长度不超过30条
|
||||||
|
self.talking_message.extend(new_messages)
|
||||||
|
if len(self.talking_message) > 30:
|
||||||
|
self.talking_message = self.talking_message[-30:] # 只保留最新的30条
|
||||||
|
self.translate_message_list_to_str()
|
||||||
|
|
||||||
|
# 更新观察次数
|
||||||
|
self.observe_times += 1
|
||||||
|
self.last_observe_time = new_messages[-1]["time"]
|
||||||
|
|
||||||
|
await self.update_talking_summary(new_messages_str)
|
||||||
|
return self.observe_info
|
||||||
|
|
||||||
|
async def update_talking_summary(self, new_messages_str):
|
||||||
|
# 基于已经有的talking_summary，和新的talking_message，生成一个summary
|
||||||
|
# print(f"更新聊天总结：{self.talking_summary}")
|
||||||
|
prompt = ""
|
||||||
|
prompt = f"你正在参与一个qq群聊的讨论，你记得这个群之前在聊的内容是：{self.observe_info}\n"
|
||||||
|
prompt += f"现在群里的群友们产生了新的讨论，有了新的发言，具体内容如下：{new_messages_str}\n"
|
||||||
|
prompt += """以上是群里在进行的聊天，请你对这个聊天内容进行总结，总结内容要包含聊天的大致内容，
|
||||||
|
以及聊天中的一些重要信息，记得不要分点，不要太长，精简的概括成一段文本\n"""
|
||||||
|
prompt += "总结概括："
|
||||||
|
self.observe_info, reasoning_content = await self.llm_summary.generate_response_async(prompt)
|
||||||
|
print(f"prompt：{prompt}")
|
||||||
|
print(f"self.observe_info：{self.observe_info}")
|
||||||
|
|
||||||
|
|
||||||
|
def translate_message_list_to_str(self):
|
||||||
|
self.talking_message_str = ""
|
||||||
|
for message in self.talking_message:
|
||||||
|
self.talking_message_str += message["detailed_plain_text"]
|
||||||
254
src/heart_flow/sub_heartflow.py
Normal file
254
src/heart_flow/sub_heartflow.py
Normal file
@@ -0,0 +1,254 @@
|
|||||||
|
from .observation import Observation
|
||||||
|
import asyncio
|
||||||
|
from src.plugins.moods.moods import MoodManager
|
||||||
|
from src.plugins.models.utils_model import LLM_request
|
||||||
|
from src.plugins.config.config import global_config
|
||||||
|
import re
|
||||||
|
import time
|
||||||
|
from src.plugins.schedule.schedule_generator import bot_schedule
|
||||||
|
from src.plugins.memory_system.Hippocampus import HippocampusManager
|
||||||
|
from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402
|
||||||
|
|
||||||
|
subheartflow_config = LogConfig(
|
||||||
|
# 使用海马体专用样式
|
||||||
|
console_format=SUB_HEARTFLOW_STYLE_CONFIG["console_format"],
|
||||||
|
file_format=SUB_HEARTFLOW_STYLE_CONFIG["file_format"],
|
||||||
|
)
|
||||||
|
logger = get_module_logger("subheartflow", config=subheartflow_config)
|
||||||
|
|
||||||
|
|
||||||
|
class CuttentState:
|
||||||
|
def __init__(self):
|
||||||
|
self.willing = 0
|
||||||
|
self.current_state_info = ""
|
||||||
|
|
||||||
|
self.mood_manager = MoodManager()
|
||||||
|
self.mood = self.mood_manager.get_prompt()
|
||||||
|
|
||||||
|
def update_current_state_info(self):
|
||||||
|
self.current_state_info = self.mood_manager.get_current_mood()
|
||||||
|
|
||||||
|
|
||||||
|
class SubHeartflow:
|
||||||
|
def __init__(self, subheartflow_id):
|
||||||
|
self.subheartflow_id = subheartflow_id
|
||||||
|
|
||||||
|
self.current_mind = ""
|
||||||
|
self.past_mind = []
|
||||||
|
self.current_state: CuttentState = CuttentState()
|
||||||
|
self.llm_model = LLM_request(
|
||||||
|
model=global_config.llm_sub_heartflow, temperature=0.7, max_tokens=600, request_type="sub_heart_flow"
|
||||||
|
)
|
||||||
|
|
||||||
|
self.main_heartflow_info = ""
|
||||||
|
|
||||||
|
self.last_reply_time = time.time()
|
||||||
|
self.last_active_time = time.time() # 添加最后激活时间
|
||||||
|
|
||||||
|
if not self.current_mind:
|
||||||
|
self.current_mind = "你什么也没想"
|
||||||
|
|
||||||
|
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||||
|
|
||||||
|
self.is_active = False
|
||||||
|
|
||||||
|
self.observations: list[Observation] = []
|
||||||
|
|
||||||
|
def add_observation(self, observation: Observation):
|
||||||
|
"""添加一个新的observation对象到列表中，如果已存在相同id的observation则不添加"""
|
||||||
|
# 查找是否存在相同id的observation
|
||||||
|
for existing_obs in self.observations:
|
||||||
|
if existing_obs.observe_id == observation.observe_id:
|
||||||
|
# 如果找到相同id的observation，直接返回
|
||||||
|
return
|
||||||
|
# 如果没有找到相同id的observation，则添加新的
|
||||||
|
self.observations.append(observation)
|
||||||
|
|
||||||
|
def remove_observation(self, observation: Observation):
|
||||||
|
"""从列表中移除一个observation对象"""
|
||||||
|
if observation in self.observations:
|
||||||
|
self.observations.remove(observation)
|
||||||
|
|
||||||
|
def get_all_observations(self) -> list[Observation]:
|
||||||
|
"""获取所有observation对象"""
|
||||||
|
return self.observations
|
||||||
|
|
||||||
|
def clear_observations(self):
|
||||||
|
"""清空所有observation对象"""
|
||||||
|
self.observations.clear()
|
||||||
|
|
||||||
|
async def subheartflow_start_working(self):
|
||||||
|
while True:
|
||||||
|
current_time = time.time()
|
||||||
|
if current_time - self.last_reply_time > global_config.sub_heart_flow_freeze_time: # 120秒无回复/不在场，冻结
|
||||||
|
self.is_active = False
|
||||||
|
await asyncio.sleep(global_config.sub_heart_flow_update_interval) # 每60秒检查一次
|
||||||
|
else:
|
||||||
|
self.is_active = True
|
||||||
|
self.last_active_time = current_time # 更新最后激活时间
|
||||||
|
|
||||||
|
self.current_state.update_current_state_info()
|
||||||
|
|
||||||
|
# await self.do_a_thinking()
|
||||||
|
# await self.judge_willing()
|
||||||
|
await asyncio.sleep(global_config.sub_heart_flow_update_interval)
|
||||||
|
|
||||||
|
# 检查是否超过10分钟没有激活
|
||||||
|
if current_time - self.last_active_time > global_config.sub_heart_flow_stop_time: # 5分钟无回复/不在场，销毁
|
||||||
|
logger.info(f"子心流 {self.subheartflow_id} 已经5分钟没有激活，正在销毁...")
|
||||||
|
break # 退出循环，销毁自己
|
||||||
|
|
||||||
|
async def do_a_thinking(self):
|
||||||
|
current_thinking_info = self.current_mind
|
||||||
|
mood_info = self.current_state.mood
|
||||||
|
|
||||||
|
observation = self.observations[0]
|
||||||
|
chat_observe_info = observation.observe_info
|
||||||
|
# print(f"chat_observe_info：{chat_observe_info}")
|
||||||
|
|
||||||
|
# 调取记忆
|
||||||
|
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||||
|
text=chat_observe_info, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
|
||||||
|
)
|
||||||
|
|
||||||
|
if related_memory:
|
||||||
|
related_memory_info = ""
|
||||||
|
for memory in related_memory:
|
||||||
|
related_memory_info += memory[1]
|
||||||
|
else:
|
||||||
|
related_memory_info = ""
|
||||||
|
|
||||||
|
# print(f"相关记忆：{related_memory_info}")
|
||||||
|
|
||||||
|
schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
|
||||||
|
|
||||||
|
prompt = ""
|
||||||
|
prompt += f"你刚刚在做的事情是：{schedule_info}\n"
|
||||||
|
# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
|
||||||
|
prompt += f"你{self.personality_info}\n"
|
||||||
|
if related_memory_info:
|
||||||
|
prompt += f"你想起来你之前见过的回忆：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
|
||||||
|
prompt += f"刚刚你的想法是{current_thinking_info}。\n"
|
||||||
|
prompt += "-----------------------------------\n"
|
||||||
|
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
|
||||||
|
prompt += f"你现在{mood_info}\n"
|
||||||
|
prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
|
||||||
|
prompt += "但是记得结合上述的消息，要记得维持住你的人设，关注聊天和新内容，不要思考太多:"
|
||||||
|
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
|
||||||
|
|
||||||
|
self.update_current_mind(reponse)
|
||||||
|
|
||||||
|
self.current_mind = reponse
|
||||||
|
logger.debug(f"prompt:\n{prompt}\n")
|
||||||
|
logger.info(f"麦麦的脑内状态：{self.current_mind}")
|
||||||
|
|
||||||
|
async def do_observe(self):
|
||||||
|
observation = self.observations[0]
|
||||||
|
await observation.observe()
|
||||||
|
|
||||||
|
async def do_thinking_before_reply(self, message_txt):
|
||||||
|
current_thinking_info = self.current_mind
|
||||||
|
mood_info = self.current_state.mood
|
||||||
|
# mood_info = "你很生气，很愤怒"
|
||||||
|
observation = self.observations[0]
|
||||||
|
chat_observe_info = observation.observe_info
|
||||||
|
# print(f"chat_observe_info：{chat_observe_info}")
|
||||||
|
|
||||||
|
# 调取记忆
|
||||||
|
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||||
|
text=chat_observe_info, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
|
||||||
|
)
|
||||||
|
|
||||||
|
if related_memory:
|
||||||
|
related_memory_info = ""
|
||||||
|
for memory in related_memory:
|
||||||
|
related_memory_info += memory[1]
|
||||||
|
else:
|
||||||
|
related_memory_info = ""
|
||||||
|
|
||||||
|
# print(f"相关记忆：{related_memory_info}")
|
||||||
|
|
||||||
|
schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
|
||||||
|
|
||||||
|
prompt = ""
|
||||||
|
# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
|
||||||
|
prompt += f"你{self.personality_info}\n"
|
||||||
|
prompt += f"你刚刚在做的事情是：{schedule_info}\n"
|
||||||
|
if related_memory_info:
|
||||||
|
prompt += f"你想起来你之前见过的回忆：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
|
||||||
|
prompt += f"刚刚你的想法是{current_thinking_info}。\n"
|
||||||
|
prompt += "-----------------------------------\n"
|
||||||
|
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
|
||||||
|
prompt += f"你现在{mood_info}\n"
|
||||||
|
prompt += f"你注意到有人刚刚说：{message_txt}\n"
|
||||||
|
prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
|
||||||
|
prompt += "记得结合上述的消息，要记得维持住你的人设，注意自己的名字，关注有人刚刚说的内容，不要思考太多:"
|
||||||
|
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
|
||||||
|
|
||||||
|
self.update_current_mind(reponse)
|
||||||
|
|
||||||
|
self.current_mind = reponse
|
||||||
|
logger.debug(f"prompt:\n{prompt}\n")
|
||||||
|
logger.info(f"麦麦的思考前脑内状态：{self.current_mind}")
|
||||||
|
|
||||||
|
async def do_thinking_after_reply(self, reply_content, chat_talking_prompt):
|
||||||
|
print("麦麦回复之后脑袋转起来了")
|
||||||
|
current_thinking_info = self.current_mind
|
||||||
|
mood_info = self.current_state.mood
|
||||||
|
|
||||||
|
observation = self.observations[0]
|
||||||
|
chat_observe_info = observation.observe_info
|
||||||
|
|
||||||
|
message_new_info = chat_talking_prompt
|
||||||
|
reply_info = reply_content
|
||||||
|
# schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
|
||||||
|
|
||||||
|
prompt = ""
|
||||||
|
# prompt += f"你现在正在做的事情是：{schedule_info}\n"
|
||||||
|
prompt += f"你{self.personality_info}\n"
|
||||||
|
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
|
||||||
|
prompt += f"刚刚你的想法是{current_thinking_info}。"
|
||||||
|
prompt += f"你现在看到了网友们发的新消息:{message_new_info}\n"
|
||||||
|
prompt += f"你刚刚回复了群友们:{reply_info}"
|
||||||
|
prompt += f"你现在{mood_info}"
|
||||||
|
prompt += "现在你接下去继续思考，产生新的想法，记得保留你刚刚的想法，不要分点输出，输出连贯的内心独白"
|
||||||
|
prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:"
|
||||||
|
|
||||||
|
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
|
||||||
|
|
||||||
|
self.update_current_mind(reponse)
|
||||||
|
|
||||||
|
self.current_mind = reponse
|
||||||
|
logger.info(f"麦麦回复后的脑内状态：{self.current_mind}")
|
||||||
|
|
||||||
|
self.last_reply_time = time.time()
|
||||||
|
|
||||||
|
async def judge_willing(self):
|
||||||
|
# print("麦麦闹情绪了1")
|
||||||
|
current_thinking_info = self.current_mind
|
||||||
|
mood_info = self.current_state.mood
|
||||||
|
# print("麦麦闹情绪了2")
|
||||||
|
prompt = ""
|
||||||
|
prompt += f"{self.personality_info}\n"
|
||||||
|
prompt += "现在你正在上网，和qq群里的网友们聊天"
|
||||||
|
prompt += f"你现在的想法是{current_thinking_info}。"
|
||||||
|
prompt += f"你现在{mood_info}。"
|
||||||
|
prompt += "现在请你思考，你想不想发言或者回复，请你输出一个数字，1-10，1表示非常不想，10表示非常想。"
|
||||||
|
prompt += "请你用<>包裹你的回复意愿，输出<1>表示不想回复，输出<10>表示非常想回复。请你考虑，你完全可以不回复"
|
||||||
|
|
||||||
|
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
|
||||||
|
# 解析willing值
|
||||||
|
willing_match = re.search(r"<(\d+)>", response)
|
||||||
|
if willing_match:
|
||||||
|
self.current_state.willing = int(willing_match.group(1))
|
||||||
|
else:
|
||||||
|
self.current_state.willing = 0
|
||||||
|
|
||||||
|
return self.current_state.willing
|
||||||
|
|
||||||
|
def update_current_mind(self, reponse):
|
||||||
|
self.past_mind.append(self.current_mind)
|
||||||
|
self.current_mind = reponse
|
||||||
|
|
||||||
|
|
||||||
|
# subheartflow = SubHeartflow()
|
||||||
157
src/main.py
Normal file
157
src/main.py
Normal file
@@ -0,0 +1,157 @@
|
|||||||
|
import asyncio
|
||||||
|
import time
|
||||||
|
from .plugins.utils.statistic import LLMStatistics
|
||||||
|
from .plugins.moods.moods import MoodManager
|
||||||
|
from .plugins.schedule.schedule_generator import bot_schedule
|
||||||
|
from .plugins.chat.emoji_manager import emoji_manager
|
||||||
|
from .plugins.chat.relationship_manager import relationship_manager
|
||||||
|
from .plugins.willing.willing_manager import willing_manager
|
||||||
|
from .plugins.chat.chat_stream import chat_manager
|
||||||
|
from .heart_flow.heartflow import heartflow
|
||||||
|
from .plugins.memory_system.Hippocampus import HippocampusManager
|
||||||
|
from .plugins.chat.message_sender import message_manager
|
||||||
|
from .plugins.chat.storage import MessageStorage
|
||||||
|
from .plugins.config.config import global_config
|
||||||
|
from .plugins.chat.bot import chat_bot
|
||||||
|
from .common.logger import get_module_logger
|
||||||
|
from .plugins.remote import heartbeat_thread # noqa: F401
|
||||||
|
|
||||||
|
|
||||||
|
logger = get_module_logger("main")
|
||||||
|
|
||||||
|
|
||||||
|
class MainSystem:
|
||||||
|
def __init__(self):
|
||||||
|
self.llm_stats = LLMStatistics("llm_statistics.txt")
|
||||||
|
self.mood_manager = MoodManager.get_instance()
|
||||||
|
self.hippocampus_manager = HippocampusManager.get_instance()
|
||||||
|
self._message_manager_started = False
|
||||||
|
|
||||||
|
# 使用消息API替代直接的FastAPI实例
|
||||||
|
from .plugins.message import global_api
|
||||||
|
|
||||||
|
self.app = global_api
|
||||||
|
|
||||||
|
async def initialize(self):
|
||||||
|
"""初始化系统组件"""
|
||||||
|
logger.debug(f"正在唤醒{global_config.BOT_NICKNAME}......")
|
||||||
|
|
||||||
|
# 其他初始化任务
|
||||||
|
await asyncio.gather(self._init_components())
|
||||||
|
|
||||||
|
logger.success("系统初始化完成")
|
||||||
|
|
||||||
|
async def _init_components(self):
|
||||||
|
"""初始化其他组件"""
|
||||||
|
init_start_time = time.time()
|
||||||
|
# 启动LLM统计
|
||||||
|
self.llm_stats.start()
|
||||||
|
logger.success("LLM统计功能启动成功")
|
||||||
|
|
||||||
|
# 初始化表情管理器
|
||||||
|
emoji_manager.initialize()
|
||||||
|
|
||||||
|
# 启动情绪管理器
|
||||||
|
self.mood_manager.start_mood_update(update_interval=global_config.mood_update_interval)
|
||||||
|
logger.success("情绪管理器启动成功")
|
||||||
|
|
||||||
|
# 加载用户关系
|
||||||
|
await relationship_manager.load_all_relationships()
|
||||||
|
asyncio.create_task(relationship_manager._start_relationship_manager())
|
||||||
|
|
||||||
|
# 启动愿望管理器
|
||||||
|
await willing_manager.ensure_started()
|
||||||
|
|
||||||
|
# 启动消息处理器
|
||||||
|
if not self._message_manager_started:
|
||||||
|
asyncio.create_task(message_manager.start_processor())
|
||||||
|
self._message_manager_started = True
|
||||||
|
|
||||||
|
# 初始化聊天管理器
|
||||||
|
await chat_manager._initialize()
|
||||||
|
asyncio.create_task(chat_manager._auto_save_task())
|
||||||
|
|
||||||
|
# 使用HippocampusManager初始化海马体
|
||||||
|
self.hippocampus_manager.initialize(global_config=global_config)
|
||||||
|
# await asyncio.sleep(0.5) #防止logger输出飞了
|
||||||
|
|
||||||
|
# 初始化日程
|
||||||
|
bot_schedule.initialize(
|
||||||
|
name=global_config.BOT_NICKNAME,
|
||||||
|
personality=global_config.PROMPT_PERSONALITY,
|
||||||
|
behavior=global_config.PROMPT_SCHEDULE_GEN,
|
||||||
|
interval=global_config.SCHEDULE_DOING_UPDATE_INTERVAL,
|
||||||
|
)
|
||||||
|
asyncio.create_task(bot_schedule.mai_schedule_start())
|
||||||
|
|
||||||
|
# 启动FastAPI服务器
|
||||||
|
self.app.register_message_handler(chat_bot.message_process)
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 启动心流系统
|
||||||
|
asyncio.create_task(heartflow.heartflow_start_working())
|
||||||
|
logger.success("心流系统启动成功")
|
||||||
|
|
||||||
|
init_time = int(1000 * (time.time() - init_start_time))
|
||||||
|
logger.success(f"初始化完成，神经元放电{init_time}次")
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"启动大脑和外部世界失败: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
async def schedule_tasks(self):
|
||||||
|
"""调度定时任务"""
|
||||||
|
while True:
|
||||||
|
tasks = [
|
||||||
|
self.build_memory_task(),
|
||||||
|
self.forget_memory_task(),
|
||||||
|
self.print_mood_task(),
|
||||||
|
self.remove_recalled_message_task(),
|
||||||
|
emoji_manager.start_periodic_check(),
|
||||||
|
self.app.run(),
|
||||||
|
self.app.message_process(),
|
||||||
|
]
|
||||||
|
await asyncio.gather(*tasks)
|
||||||
|
|
||||||
|
async def build_memory_task(self):
|
||||||
|
"""记忆构建任务"""
|
||||||
|
while True:
|
||||||
|
logger.info("正在进行记忆构建")
|
||||||
|
await HippocampusManager.get_instance().build_memory()
|
||||||
|
await asyncio.sleep(global_config.build_memory_interval)
|
||||||
|
|
||||||
|
async def forget_memory_task(self):
|
||||||
|
"""记忆遗忘任务"""
|
||||||
|
while True:
|
||||||
|
print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...")
|
||||||
|
await HippocampusManager.get_instance().forget_memory(percentage=global_config.memory_forget_percentage)
|
||||||
|
print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成")
|
||||||
|
await asyncio.sleep(global_config.forget_memory_interval)
|
||||||
|
|
||||||
|
async def print_mood_task(self):
|
||||||
|
"""打印情绪状态"""
|
||||||
|
while True:
|
||||||
|
self.mood_manager.print_mood_status()
|
||||||
|
await asyncio.sleep(30)
|
||||||
|
|
||||||
|
async def remove_recalled_message_task(self):
|
||||||
|
"""删除撤回消息任务"""
|
||||||
|
while True:
|
||||||
|
try:
|
||||||
|
storage = MessageStorage()
|
||||||
|
await storage.remove_recalled_message(time.time())
|
||||||
|
except Exception:
|
||||||
|
logger.exception("删除撤回消息失败")
|
||||||
|
await asyncio.sleep(3600)
|
||||||
|
|
||||||
|
|
||||||
|
async def main():
|
||||||
|
"""主函数"""
|
||||||
|
system = MainSystem()
|
||||||
|
await asyncio.gather(
|
||||||
|
system.initialize(),
|
||||||
|
system.schedule_tasks(),
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())
|
||||||
22
src/plugins/__init__.py
Normal file
22
src/plugins/__init__.py
Normal file
@@ -0,0 +1,22 @@
|
|||||||
|
"""
|
||||||
|
MaiMBot插件系统
|
||||||
|
包含聊天、情绪、记忆、日程等功能模块
|
||||||
|
"""
|
||||||
|
|
||||||
|
from .chat.chat_stream import chat_manager
|
||||||
|
from .chat.emoji_manager import emoji_manager
|
||||||
|
from .chat.relationship_manager import relationship_manager
|
||||||
|
from .moods.moods import MoodManager
|
||||||
|
from .willing.willing_manager import willing_manager
|
||||||
|
from .schedule.schedule_generator import bot_schedule
|
||||||
|
|
||||||
|
# 导出主要组件供外部使用
|
||||||
|
__all__ = [
|
||||||
|
"chat_manager",
|
||||||
|
"emoji_manager",
|
||||||
|
"relationship_manager",
|
||||||
|
"MoodManager",
|
||||||
|
"willing_manager",
|
||||||
|
"hippocampus",
|
||||||
|
"bot_schedule",
|
||||||
|
]
|
||||||
@@ -1,164 +0,0 @@
|
|||||||
import base64
|
|
||||||
from typing import Any, Dict, List, Union
|
|
||||||
|
|
||||||
"""
|
|
||||||
OneBot v11 Message Segment Builder
|
|
||||||
|
|
||||||
This module provides classes for building message segments that conform to the
|
|
||||||
OneBot v11 standard. These segments can be used to construct complex messages
|
|
||||||
for sending through bots that implement the OneBot interface.
|
|
||||||
"""
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
class Segment:
|
|
||||||
"""Base class for all message segments."""
|
|
||||||
|
|
||||||
def __init__(self, type_: str, data: Dict[str, Any]):
|
|
||||||
self.type = type_
|
|
||||||
self.data = data
|
|
||||||
|
|
||||||
def to_dict(self) -> Dict[str, Any]:
|
|
||||||
"""Convert the segment to a dictionary format."""
|
|
||||||
return {
|
|
||||||
"type": self.type,
|
|
||||||
"data": self.data
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
class Text(Segment):
|
|
||||||
"""Text message segment."""
|
|
||||||
|
|
||||||
def __init__(self, text: str):
|
|
||||||
super().__init__("text", {"text": text})
|
|
||||||
|
|
||||||
|
|
||||||
class Face(Segment):
|
|
||||||
"""Face/emoji message segment."""
|
|
||||||
|
|
||||||
def __init__(self, face_id: int):
|
|
||||||
super().__init__("face", {"id": str(face_id)})
|
|
||||||
|
|
||||||
|
|
||||||
class Image(Segment):
|
|
||||||
"""Image message segment."""
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def from_url(cls, url: str) -> 'Image':
|
|
||||||
"""Create an Image segment from a URL."""
|
|
||||||
return cls(url=url)
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def from_path(cls, path: str) -> 'Image':
|
|
||||||
"""Create an Image segment from a file path."""
|
|
||||||
with open(path, 'rb') as f:
|
|
||||||
file_b64 = base64.b64encode(f.read()).decode('utf-8')
|
|
||||||
return cls(file=f"base64://{file_b64}")
|
|
||||||
|
|
||||||
def __init__(self, file: str = None, url: str = None, cache: bool = True):
|
|
||||||
data = {}
|
|
||||||
if file:
|
|
||||||
data["file"] = file
|
|
||||||
if url:
|
|
||||||
data["url"] = url
|
|
||||||
if not cache:
|
|
||||||
data["cache"] = "0"
|
|
||||||
super().__init__("image", data)
|
|
||||||
|
|
||||||
|
|
||||||
class At(Segment):
|
|
||||||
"""@Someone message segment."""
|
|
||||||
|
|
||||||
def __init__(self, user_id: Union[int, str]):
|
|
||||||
data = {"qq": str(user_id)}
|
|
||||||
super().__init__("at", data)
|
|
||||||
|
|
||||||
|
|
||||||
class Record(Segment):
|
|
||||||
"""Voice message segment."""
|
|
||||||
|
|
||||||
def __init__(self, file: str, magic: bool = False, cache: bool = True):
|
|
||||||
data = {"file": file}
|
|
||||||
if magic:
|
|
||||||
data["magic"] = "1"
|
|
||||||
if not cache:
|
|
||||||
data["cache"] = "0"
|
|
||||||
super().__init__("record", data)
|
|
||||||
|
|
||||||
|
|
||||||
class Video(Segment):
|
|
||||||
"""Video message segment."""
|
|
||||||
|
|
||||||
def __init__(self, file: str):
|
|
||||||
super().__init__("video", {"file": file})
|
|
||||||
|
|
||||||
|
|
||||||
class Reply(Segment):
|
|
||||||
"""Reply message segment."""
|
|
||||||
|
|
||||||
def __init__(self, message_id: int):
|
|
||||||
super().__init__("reply", {"id": str(message_id)})
|
|
||||||
|
|
||||||
|
|
||||||
class MessageBuilder:
|
|
||||||
"""Helper class for building complex messages."""
|
|
||||||
|
|
||||||
def __init__(self):
|
|
||||||
self.segments: List[Segment] = []
|
|
||||||
|
|
||||||
def text(self, text: str) -> 'MessageBuilder':
|
|
||||||
"""Add a text segment."""
|
|
||||||
self.segments.append(Text(text))
|
|
||||||
return self
|
|
||||||
|
|
||||||
def face(self, face_id: int) -> 'MessageBuilder':
|
|
||||||
"""Add a face/emoji segment."""
|
|
||||||
self.segments.append(Face(face_id))
|
|
||||||
return self
|
|
||||||
|
|
||||||
def image(self, file: str = None) -> 'MessageBuilder':
|
|
||||||
"""Add an image segment."""
|
|
||||||
self.segments.append(Image(file=file))
|
|
||||||
return self
|
|
||||||
|
|
||||||
def at(self, user_id: Union[int, str]) -> 'MessageBuilder':
|
|
||||||
"""Add an @someone segment."""
|
|
||||||
self.segments.append(At(user_id))
|
|
||||||
return self
|
|
||||||
|
|
||||||
def record(self, file: str, magic: bool = False) -> 'MessageBuilder':
|
|
||||||
"""Add a voice record segment."""
|
|
||||||
self.segments.append(Record(file, magic))
|
|
||||||
return self
|
|
||||||
|
|
||||||
def video(self, file: str) -> 'MessageBuilder':
|
|
||||||
"""Add a video segment."""
|
|
||||||
self.segments.append(Video(file))
|
|
||||||
return self
|
|
||||||
|
|
||||||
def reply(self, message_id: int) -> 'MessageBuilder':
|
|
||||||
"""Add a reply segment."""
|
|
||||||
self.segments.append(Reply(message_id))
|
|
||||||
return self
|
|
||||||
|
|
||||||
def build(self) -> List[Dict[str, Any]]:
|
|
||||||
"""Build the message into a list of segment dictionaries."""
|
|
||||||
return [segment.to_dict() for segment in self.segments]
|
|
||||||
|
|
||||||
|
|
||||||
'''Convenience functions
|
|
||||||
def text(content: str) -> Dict[str, Any]:
|
|
||||||
"""Create a text message segment."""
|
|
||||||
return Text(content).to_dict()
|
|
||||||
|
|
||||||
def image_url(url: str) -> Dict[str, Any]:
|
|
||||||
"""Create an image message segment from URL."""
|
|
||||||
return Image.from_url(url).to_dict()
|
|
||||||
|
|
||||||
def image_path(path: str) -> Dict[str, Any]:
|
|
||||||
"""Create an image message segment from file path."""
|
|
||||||
return Image.from_path(path).to_dict()
|
|
||||||
|
|
||||||
def at(user_id: Union[int, str]) -> Dict[str, Any]:
|
|
||||||
"""Create an @someone message segment."""
|
|
||||||
return At(user_id).to_dict()'''
|
|
||||||
@@ -1,160 +1,16 @@
|
|||||||
import asyncio
|
|
||||||
import time
|
|
||||||
import os
|
|
||||||
|
|
||||||
from loguru import logger
|
|
||||||
from nonebot import get_driver, on_message, on_notice, require
|
|
||||||
from nonebot.rule import to_me
|
|
||||||
from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent, Message, MessageSegment, MessageEvent, NoticeEvent
|
|
||||||
from nonebot.typing import T_State
|
|
||||||
|
|
||||||
from ..moods.moods import MoodManager # 导入情绪管理器
|
|
||||||
from ..schedule.schedule_generator import bot_schedule
|
|
||||||
from ..utils.statistic import LLMStatistics
|
|
||||||
from .bot import chat_bot
|
|
||||||
from .config import global_config
|
|
||||||
from .emoji_manager import emoji_manager
|
from .emoji_manager import emoji_manager
|
||||||
from .relationship_manager import relationship_manager
|
from .relationship_manager import relationship_manager
|
||||||
from .willing_manager import willing_manager
|
|
||||||
from .chat_stream import chat_manager
|
from .chat_stream import chat_manager
|
||||||
from ..memory_system.memory import hippocampus, memory_graph
|
from .message_sender import message_manager
|
||||||
from .bot import ChatBot
|
|
||||||
from .message_sender import message_manager, message_sender
|
|
||||||
from .storage import MessageStorage
|
from .storage import MessageStorage
|
||||||
|
from .auto_speak import auto_speak_manager
|
||||||
# 创建LLM统计实例
|
|
||||||
llm_stats = LLMStatistics("llm_statistics.txt")
|
|
||||||
|
|
||||||
# 添加标志变量
|
|
||||||
_message_manager_started = False
|
|
||||||
|
|
||||||
# 获取驱动器
|
|
||||||
driver = get_driver()
|
|
||||||
config = driver.config
|
|
||||||
|
|
||||||
# 初始化表情管理器
|
|
||||||
emoji_manager.initialize()
|
|
||||||
|
|
||||||
logger.debug(f"正在唤醒{global_config.BOT_NICKNAME}......")
|
|
||||||
# 创建机器人实例
|
|
||||||
chat_bot = ChatBot()
|
|
||||||
# 注册消息处理器
|
|
||||||
msg_in = on_message(priority=5)
|
|
||||||
# 注册和bot相关的通知处理器
|
|
||||||
notice_matcher = on_notice(priority=1)
|
|
||||||
# 创建定时任务
|
|
||||||
scheduler = require("nonebot_plugin_apscheduler").scheduler
|
|
||||||
|
|
||||||
|
|
||||||
@driver.on_startup
|
__all__ = [
|
||||||
async def start_background_tasks():
|
"emoji_manager",
|
||||||
"""启动后台任务"""
|
"relationship_manager",
|
||||||
# 启动LLM统计
|
"chat_manager",
|
||||||
llm_stats.start()
|
"message_manager",
|
||||||
logger.success("LLM统计功能启动成功")
|
"MessageStorage",
|
||||||
|
"auto_speak_manager"
|
||||||
# 初始化并启动情绪管理器
|
]
|
||||||
mood_manager = MoodManager.get_instance()
|
|
||||||
mood_manager.start_mood_update(update_interval=global_config.mood_update_interval)
|
|
||||||
logger.success("情绪管理器启动成功")
|
|
||||||
|
|
||||||
# 只启动表情包管理任务
|
|
||||||
asyncio.create_task(emoji_manager.start_periodic_check(interval_MINS=global_config.EMOJI_CHECK_INTERVAL))
|
|
||||||
await bot_schedule.initialize()
|
|
||||||
bot_schedule.print_schedule()
|
|
||||||
|
|
||||||
|
|
||||||
@driver.on_startup
|
|
||||||
async def init_relationships():
|
|
||||||
"""在 NoneBot2 启动时初始化关系管理器"""
|
|
||||||
logger.debug("正在加载用户关系数据...")
|
|
||||||
await relationship_manager.load_all_relationships()
|
|
||||||
asyncio.create_task(relationship_manager._start_relationship_manager())
|
|
||||||
|
|
||||||
|
|
||||||
@driver.on_bot_connect
|
|
||||||
async def _(bot: Bot):
|
|
||||||
"""Bot连接成功时的处理"""
|
|
||||||
global _message_manager_started
|
|
||||||
logger.debug(f"-----------{global_config.BOT_NICKNAME}成功连接！-----------")
|
|
||||||
await willing_manager.ensure_started()
|
|
||||||
|
|
||||||
message_sender.set_bot(bot)
|
|
||||||
logger.success("-----------消息发送器已启动！-----------")
|
|
||||||
|
|
||||||
if not _message_manager_started:
|
|
||||||
asyncio.create_task(message_manager.start_processor())
|
|
||||||
_message_manager_started = True
|
|
||||||
logger.success("-----------消息处理器已启动！-----------")
|
|
||||||
|
|
||||||
asyncio.create_task(emoji_manager._periodic_scan(interval_MINS=global_config.EMOJI_REGISTER_INTERVAL))
|
|
||||||
logger.success("-----------开始偷表情包！-----------")
|
|
||||||
asyncio.create_task(chat_manager._initialize())
|
|
||||||
asyncio.create_task(chat_manager._auto_save_task())
|
|
||||||
|
|
||||||
|
|
||||||
@msg_in.handle()
|
|
||||||
async def _(bot: Bot, event: MessageEvent, state: T_State):
|
|
||||||
await chat_bot.handle_message(event, bot)
|
|
||||||
|
|
||||||
|
|
||||||
@notice_matcher.handle()
|
|
||||||
async def _(bot: Bot, event: NoticeEvent, state: T_State):
|
|
||||||
logger.debug(f"收到通知：{event}")
|
|
||||||
await chat_bot.handle_notice(event, bot)
|
|
||||||
|
|
||||||
|
|
||||||
# 添加build_memory定时任务
|
|
||||||
@scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval, id="build_memory")
|
|
||||||
async def build_memory_task():
|
|
||||||
"""每build_memory_interval秒执行一次记忆构建"""
|
|
||||||
logger.debug("[记忆构建]------------------------------------开始构建记忆--------------------------------------")
|
|
||||||
start_time = time.time()
|
|
||||||
await hippocampus.operation_build_memory(chat_size=20)
|
|
||||||
end_time = time.time()
|
|
||||||
logger.success(
|
|
||||||
f"[记忆构建]--------------------------记忆构建完成：耗时: {end_time - start_time:.2f} "
|
|
||||||
"秒-------------------------------------------"
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
@scheduler.scheduled_job("interval", seconds=global_config.forget_memory_interval, id="forget_memory")
|
|
||||||
async def forget_memory_task():
|
|
||||||
"""每30秒执行一次记忆构建"""
|
|
||||||
print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...")
|
|
||||||
await hippocampus.operation_forget_topic(percentage=global_config.memory_forget_percentage)
|
|
||||||
print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成")
|
|
||||||
|
|
||||||
|
|
||||||
@scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval + 10, id="merge_memory")
|
|
||||||
async def merge_memory_task():
|
|
||||||
"""每30秒执行一次记忆构建"""
|
|
||||||
# print("\033[1;32m[记忆整合]\033[0m 开始整合")
|
|
||||||
# await hippocampus.operation_merge_memory(percentage=0.1)
|
|
||||||
# print("\033[1;32m[记忆整合]\033[0m 记忆整合完成")
|
|
||||||
|
|
||||||
|
|
||||||
@scheduler.scheduled_job("interval", seconds=30, id="print_mood")
|
|
||||||
async def print_mood_task():
|
|
||||||
"""每30秒打印一次情绪状态"""
|
|
||||||
mood_manager = MoodManager.get_instance()
|
|
||||||
mood_manager.print_mood_status()
|
|
||||||
|
|
||||||
|
|
||||||
@scheduler.scheduled_job("interval", seconds=7200, id="generate_schedule")
|
|
||||||
async def generate_schedule_task():
|
|
||||||
"""每2小时尝试生成一次日程"""
|
|
||||||
logger.debug("尝试生成日程")
|
|
||||||
await bot_schedule.initialize()
|
|
||||||
if not bot_schedule.enable_output:
|
|
||||||
bot_schedule.print_schedule()
|
|
||||||
|
|
||||||
@scheduler.scheduled_job("interval", seconds=3600, id="remove_recalled_message")
|
|
||||||
|
|
||||||
async def remove_recalled_message() -> None:
|
|
||||||
"""删除撤回消息"""
|
|
||||||
try:
|
|
||||||
storage = MessageStorage()
|
|
||||||
await storage.remove_recalled_message(time.time())
|
|
||||||
except Exception:
|
|
||||||
logger.exception("删除撤回消息失败")
|
|
||||||
|
|||||||
180
src/plugins/chat/auto_speak.py
Normal file
180
src/plugins/chat/auto_speak.py
Normal file
@@ -0,0 +1,180 @@
|
|||||||
|
import time
|
||||||
|
import asyncio
|
||||||
|
import random
|
||||||
|
from random import random as random_float
|
||||||
|
from typing import Dict
|
||||||
|
from ..config.config import global_config
|
||||||
|
from .message import MessageSending, MessageThinking, MessageSet, MessageRecv
|
||||||
|
from ..message.message_base import UserInfo, Seg
|
||||||
|
from .message_sender import message_manager
|
||||||
|
from ..moods.moods import MoodManager
|
||||||
|
from .llm_generator import ResponseGenerator
|
||||||
|
from src.common.logger import get_module_logger
|
||||||
|
from src.heart_flow.heartflow import heartflow
|
||||||
|
from ...common.database import db
|
||||||
|
|
||||||
|
logger = get_module_logger("auto_speak")
|
||||||
|
|
||||||
|
|
||||||
|
class AutoSpeakManager:
|
||||||
|
def __init__(self):
|
||||||
|
self._last_auto_speak_time: Dict[str, float] = {} # 记录每个聊天流上次自主发言的时间
|
||||||
|
self.mood_manager = MoodManager.get_instance()
|
||||||
|
self.gpt = ResponseGenerator() # 添加gpt实例
|
||||||
|
self._started = False
|
||||||
|
self._check_task = None
|
||||||
|
self.db = db
|
||||||
|
|
||||||
|
async def get_chat_info(self, chat_id: str) -> dict:
|
||||||
|
"""从数据库获取聊天流信息"""
|
||||||
|
chat_info = await self.db.chat_streams.find_one({"stream_id": chat_id})
|
||||||
|
return chat_info
|
||||||
|
|
||||||
|
async def start_auto_speak_check(self):
|
||||||
|
"""启动自动发言检查任务"""
|
||||||
|
if not self._started:
|
||||||
|
self._check_task = asyncio.create_task(self._periodic_check())
|
||||||
|
self._started = True
|
||||||
|
logger.success("自动发言检查任务已启动")
|
||||||
|
|
||||||
|
async def _periodic_check(self):
|
||||||
|
"""定期检查是否需要自主发言"""
|
||||||
|
while True and global_config.enable_think_flow:
|
||||||
|
# 获取所有活跃的子心流
|
||||||
|
active_subheartflows = []
|
||||||
|
for chat_id, subheartflow in heartflow._subheartflows.items():
|
||||||
|
if (
|
||||||
|
subheartflow.is_active and subheartflow.current_state.willing > 0
|
||||||
|
): # 只考虑活跃且意愿值大于0.5的子心流
|
||||||
|
active_subheartflows.append((chat_id, subheartflow))
|
||||||
|
logger.debug(
|
||||||
|
f"发现活跃子心流 - 聊天ID: {chat_id}, 意愿值: {subheartflow.current_state.willing:.2f}"
|
||||||
|
)
|
||||||
|
|
||||||
|
if not active_subheartflows:
|
||||||
|
logger.debug("当前没有活跃的子心流")
|
||||||
|
await asyncio.sleep(20) # 添加异步等待
|
||||||
|
continue
|
||||||
|
|
||||||
|
# 随机选择一个活跃的子心流
|
||||||
|
chat_id, subheartflow = random.choice(active_subheartflows)
|
||||||
|
logger.info(f"随机选择子心流 - 聊天ID: {chat_id}, 意愿值: {subheartflow.current_state.willing:.2f}")
|
||||||
|
|
||||||
|
# 检查是否应该自主发言
|
||||||
|
if await self.check_auto_speak(subheartflow):
|
||||||
|
logger.info(f"准备自主发言 - 聊天ID: {chat_id}")
|
||||||
|
# 生成自主发言
|
||||||
|
bot_user_info = UserInfo(
|
||||||
|
user_id=global_config.BOT_QQ,
|
||||||
|
user_nickname=global_config.BOT_NICKNAME,
|
||||||
|
platform="qq", # 默认使用qq平台
|
||||||
|
)
|
||||||
|
|
||||||
|
# 创建一个空的MessageRecv对象作为上下文
|
||||||
|
message = MessageRecv(
|
||||||
|
{
|
||||||
|
"message_info": {
|
||||||
|
"user_info": {"user_id": chat_id, "user_nickname": "", "platform": "qq"},
|
||||||
|
"group_info": None,
|
||||||
|
"platform": "qq",
|
||||||
|
"time": time.time(),
|
||||||
|
},
|
||||||
|
"processed_plain_text": "",
|
||||||
|
"raw_message": "",
|
||||||
|
"is_emoji": False,
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
await self.generate_auto_speak(
|
||||||
|
subheartflow, message, bot_user_info, message.message_info["user_info"], message.message_info
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
logger.debug(f"不满足自主发言条件 - 聊天ID: {chat_id}")
|
||||||
|
|
||||||
|
# 每分钟检查一次
|
||||||
|
await asyncio.sleep(20)
|
||||||
|
|
||||||
|
# await asyncio.sleep(5) # 发生错误时等待5秒再继续
|
||||||
|
|
||||||
|
async def check_auto_speak(self, subheartflow) -> bool:
|
||||||
|
"""检查是否应该自主发言"""
|
||||||
|
if not subheartflow:
|
||||||
|
return False
|
||||||
|
|
||||||
|
current_time = time.time()
|
||||||
|
chat_id = subheartflow.observe_chat_id
|
||||||
|
|
||||||
|
# 获取上次自主发言时间
|
||||||
|
if chat_id not in self._last_auto_speak_time:
|
||||||
|
self._last_auto_speak_time[chat_id] = 0
|
||||||
|
last_speak_time = self._last_auto_speak_time.get(chat_id, 0)
|
||||||
|
|
||||||
|
# 如果距离上次自主发言不到5分钟，不发言
|
||||||
|
if current_time - last_speak_time < 30:
|
||||||
|
logger.debug(
|
||||||
|
f"距离上次发言时间太短 - 聊天ID: {chat_id}, 剩余时间: {30 - (current_time - last_speak_time):.1f}秒"
|
||||||
|
)
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 获取当前意愿值
|
||||||
|
current_willing = subheartflow.current_state.willing
|
||||||
|
|
||||||
|
if current_willing > 0.1 and random_float() < 0.5:
|
||||||
|
self._last_auto_speak_time[chat_id] = current_time
|
||||||
|
logger.info(f"满足自主发言条件 - 聊天ID: {chat_id}, 意愿值: {current_willing:.2f}")
|
||||||
|
return True
|
||||||
|
|
||||||
|
logger.debug(f"不满足自主发言条件 - 聊天ID: {chat_id}, 意愿值: {current_willing:.2f}")
|
||||||
|
return False
|
||||||
|
|
||||||
|
async def generate_auto_speak(self, subheartflow, message, bot_user_info: UserInfo, userinfo, messageinfo):
|
||||||
|
"""生成自主发言内容"""
|
||||||
|
thinking_time_point = round(time.time(), 2)
|
||||||
|
think_id = "mt" + str(thinking_time_point)
|
||||||
|
thinking_message = MessageThinking(
|
||||||
|
message_id=think_id,
|
||||||
|
chat_stream=None, # 不需要chat_stream
|
||||||
|
bot_user_info=bot_user_info,
|
||||||
|
reply=message,
|
||||||
|
thinking_start_time=thinking_time_point,
|
||||||
|
)
|
||||||
|
|
||||||
|
message_manager.add_message(thinking_message)
|
||||||
|
|
||||||
|
# 生成自主发言内容
|
||||||
|
response, raw_content = await self.gpt.generate_response(message)
|
||||||
|
|
||||||
|
if response:
|
||||||
|
message_set = MessageSet(None, think_id) # 不需要chat_stream
|
||||||
|
mark_head = False
|
||||||
|
|
||||||
|
for msg in response:
|
||||||
|
message_segment = Seg(type="text", data=msg)
|
||||||
|
bot_message = MessageSending(
|
||||||
|
message_id=think_id,
|
||||||
|
chat_stream=None, # 不需要chat_stream
|
||||||
|
bot_user_info=bot_user_info,
|
||||||
|
sender_info=userinfo,
|
||||||
|
message_segment=message_segment,
|
||||||
|
reply=message,
|
||||||
|
is_head=not mark_head,
|
||||||
|
is_emoji=False,
|
||||||
|
thinking_start_time=thinking_time_point,
|
||||||
|
)
|
||||||
|
if not mark_head:
|
||||||
|
mark_head = True
|
||||||
|
message_set.add_message(bot_message)
|
||||||
|
|
||||||
|
message_manager.add_message(message_set)
|
||||||
|
|
||||||
|
# 更新情绪和关系
|
||||||
|
stance, emotion = await self.gpt._get_emotion_tags(raw_content, message.processed_plain_text)
|
||||||
|
self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
|
||||||
|
|
||||||
|
return True
|
||||||
|
|
||||||
|
return False
|
||||||
|
|
||||||
|
|
||||||
|
# 创建全局AutoSpeakManager实例
|
||||||
|
auto_speak_manager = AutoSpeakManager()
|
||||||
@@ -1,42 +1,36 @@
|
|||||||
import re
|
import re
|
||||||
import time
|
import time
|
||||||
from random import random
|
from random import random
|
||||||
from nonebot.adapters.onebot.v11 import (
|
|
||||||
Bot,
|
|
||||||
GroupMessageEvent,
|
|
||||||
MessageEvent,
|
|
||||||
PrivateMessageEvent,
|
|
||||||
NoticeEvent,
|
|
||||||
PokeNotifyEvent,
|
|
||||||
GroupRecallNoticeEvent,
|
|
||||||
FriendRecallNoticeEvent,
|
|
||||||
|
|
||||||
)
|
from ..memory_system.Hippocampus import HippocampusManager
|
||||||
|
|
||||||
from ..memory_system.memory import hippocampus
|
|
||||||
from ..moods.moods import MoodManager # 导入情绪管理器
|
from ..moods.moods import MoodManager # 导入情绪管理器
|
||||||
from .config import global_config
|
from ..config.config import global_config
|
||||||
from .emoji_manager import emoji_manager # 导入表情包管理器
|
from .emoji_manager import emoji_manager # 导入表情包管理器
|
||||||
from .llm_generator import ResponseGenerator
|
from .llm_generator import ResponseGenerator
|
||||||
from .message import MessageSending, MessageRecv, MessageThinking, MessageSet
|
from .message import MessageSending, MessageRecv, MessageThinking, MessageSet
|
||||||
from .message_cq import (
|
|
||||||
MessageRecvCQ,
|
|
||||||
)
|
|
||||||
from .chat_stream import chat_manager
|
from .chat_stream import chat_manager
|
||||||
|
|
||||||
from .message_sender import message_manager # 导入新的消息管理器
|
from .message_sender import message_manager # 导入新的消息管理器
|
||||||
from .relationship_manager import relationship_manager
|
from .relationship_manager import relationship_manager
|
||||||
from .storage import MessageStorage
|
from .storage import MessageStorage
|
||||||
from .utils import calculate_typing_time, is_mentioned_bot_in_message
|
from .utils import is_mentioned_bot_in_message, get_recent_group_detailed_plain_text
|
||||||
from .utils_image import image_path_to_base64
|
from .utils_image import image_path_to_base64
|
||||||
from .utils_user import get_user_nickname, get_user_cardname, get_groupname
|
from ..willing.willing_manager import willing_manager # 导入意愿管理器
|
||||||
from .willing_manager import willing_manager # 导入意愿管理器
|
from ..message import UserInfo, Seg
|
||||||
from .message_base import UserInfo, GroupInfo, Seg
|
|
||||||
from ..utils.logger_config import LogClassification, LogModule
|
|
||||||
|
|
||||||
# 配置日志
|
from src.heart_flow.heartflow import heartflow
|
||||||
log_module = LogModule()
|
from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
|
||||||
logger = log_module.setup_logger(LogClassification.CHAT)
|
|
||||||
|
# 定义日志配置
|
||||||
|
chat_config = LogConfig(
|
||||||
|
# 使用消息发送专用样式
|
||||||
|
console_format=CHAT_STYLE_CONFIG["console_format"],
|
||||||
|
file_format=CHAT_STYLE_CONFIG["file_format"],
|
||||||
|
)
|
||||||
|
|
||||||
|
# 配置主程序日志格式
|
||||||
|
logger = get_module_logger("chat_bot", config=chat_config)
|
||||||
|
|
||||||
|
|
||||||
class ChatBot:
|
class ChatBot:
|
||||||
@@ -48,386 +42,328 @@ class ChatBot:
|
|||||||
self.mood_manager = MoodManager.get_instance() # 获取情绪管理器单例
|
self.mood_manager = MoodManager.get_instance() # 获取情绪管理器单例
|
||||||
self.mood_manager.start_mood_update() # 启动情绪更新
|
self.mood_manager.start_mood_update() # 启动情绪更新
|
||||||
|
|
||||||
self.emoji_chance = 0.2 # 发送表情包的基础概率
|
|
||||||
# self.message_streams = MessageStreamContainer()
|
|
||||||
|
|
||||||
async def _ensure_started(self):
|
async def _ensure_started(self):
|
||||||
"""确保所有任务已启动"""
|
"""确保所有任务已启动"""
|
||||||
if not self._started:
|
if not self._started:
|
||||||
self._started = True
|
self._started = True
|
||||||
|
|
||||||
async def handle_notice(self, event: NoticeEvent, bot: Bot) -> None:
|
async def _create_thinking_message(self, message, chat, userinfo, messageinfo):
|
||||||
"""处理收到的通知"""
|
"""创建思考消息
|
||||||
# 戳一戳通知
|
|
||||||
if isinstance(event, PokeNotifyEvent):
|
|
||||||
# 不处理其他人的戳戳
|
|
||||||
if not event.is_tome():
|
|
||||||
return
|
|
||||||
|
|
||||||
# 用户屏蔽,不区分私聊/群聊
|
Args:
|
||||||
if event.user_id in global_config.ban_user_id:
|
message: 接收到的消息
|
||||||
return
|
chat: 聊天流对象
|
||||||
|
userinfo: 用户信息对象
|
||||||
|
messageinfo: 消息信息对象
|
||||||
|
|
||||||
reply_poke_probability = 1.0 # 回复戳一戳的概率，如果要改可以在这里改，暂不提取到配置文件
|
Returns:
|
||||||
|
str: thinking_id
|
||||||
if random() < reply_poke_probability:
|
|
||||||
raw_message = "[戳了戳]你" # 默认类型
|
|
||||||
if info := event.raw_info:
|
|
||||||
poke_type = info[2].get("txt", "戳了戳") # 戳戳类型，例如“拍一拍”、“揉一揉”、“捏一捏”
|
|
||||||
custom_poke_message = info[4].get("txt", "") # 自定义戳戳消息，若不存在会为空字符串
|
|
||||||
raw_message = f"[{poke_type}]你{custom_poke_message}"
|
|
||||||
|
|
||||||
raw_message += "（这是一个类似摸摸头的友善行为，而不是恶意行为，请不要作出攻击发言）"
|
|
||||||
await self.directly_reply(raw_message, event.user_id, event.group_id)
|
|
||||||
|
|
||||||
if isinstance(event, GroupRecallNoticeEvent) or isinstance(event, FriendRecallNoticeEvent):
|
|
||||||
user_info = UserInfo(
|
|
||||||
user_id=event.user_id,
|
|
||||||
user_nickname=get_user_nickname(event.user_id) or None,
|
|
||||||
user_cardname=get_user_cardname(event.user_id) or None,
|
|
||||||
platform="qq",
|
|
||||||
)
|
|
||||||
|
|
||||||
group_info = GroupInfo(group_id=event.group_id, group_name=None, platform="qq")
|
|
||||||
|
|
||||||
chat = await chat_manager.get_or_create_stream(
|
|
||||||
platform=user_info.platform, user_info=user_info, group_info=group_info
|
|
||||||
)
|
|
||||||
|
|
||||||
await self.storage.store_recalled_message(event.message_id, time.time(), chat)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
async def handle_message(self, event: MessageEvent, bot: Bot) -> None:
|
|
||||||
"""处理收到的消息"""
|
|
||||||
|
|
||||||
self.bot = bot # 更新 bot 实例
|
|
||||||
|
|
||||||
# 用户屏蔽,不区分私聊/群聊
|
|
||||||
if event.user_id in global_config.ban_user_id:
|
|
||||||
return
|
|
||||||
|
|
||||||
if (
|
|
||||||
event.reply
|
|
||||||
and hasattr(event.reply, "sender")
|
|
||||||
and hasattr(event.reply.sender, "user_id")
|
|
||||||
and event.reply.sender.user_id in global_config.ban_user_id
|
|
||||||
):
|
|
||||||
logger.debug(f"跳过处理回复来自被ban用户 {event.reply.sender.user_id} 的消息")
|
|
||||||
return
|
|
||||||
# 处理私聊消息
|
|
||||||
if isinstance(event, PrivateMessageEvent):
|
|
||||||
if not global_config.enable_friend_chat: # 私聊过滤
|
|
||||||
return
|
|
||||||
else:
|
|
||||||
try:
|
|
||||||
user_info = UserInfo(
|
|
||||||
user_id=event.user_id,
|
|
||||||
user_nickname=(await bot.get_stranger_info(user_id=event.user_id, no_cache=True))["nickname"],
|
|
||||||
user_cardname=None,
|
|
||||||
platform="qq",
|
|
||||||
)
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"获取陌生人信息失败: {e}")
|
|
||||||
return
|
|
||||||
logger.debug(user_info)
|
|
||||||
|
|
||||||
# group_info = GroupInfo(group_id=0, group_name="私聊", platform="qq")
|
|
||||||
group_info = None
|
|
||||||
|
|
||||||
# 处理群聊消息
|
|
||||||
else:
|
|
||||||
# 白名单设定由nontbot侧完成
|
|
||||||
if event.group_id:
|
|
||||||
if event.group_id not in global_config.talk_allowed_groups:
|
|
||||||
return
|
|
||||||
|
|
||||||
user_info = UserInfo(
|
|
||||||
user_id=event.user_id,
|
|
||||||
user_nickname=event.sender.nickname,
|
|
||||||
user_cardname=event.sender.card or None,
|
|
||||||
platform="qq",
|
|
||||||
)
|
|
||||||
|
|
||||||
group_info = GroupInfo(group_id=event.group_id, group_name=None, platform="qq")
|
|
||||||
|
|
||||||
# group_info = await bot.get_group_info(group_id=event.group_id)
|
|
||||||
# sender_info = await bot.get_group_member_info(group_id=event.group_id, user_id=event.user_id, no_cache=True)
|
|
||||||
|
|
||||||
message_cq = MessageRecvCQ(
|
|
||||||
message_id=event.message_id,
|
|
||||||
user_info=user_info,
|
|
||||||
raw_message=str(event.original_message),
|
|
||||||
group_info=group_info,
|
|
||||||
reply_message=event.reply,
|
|
||||||
platform="qq",
|
|
||||||
)
|
|
||||||
message_json = message_cq.to_dict()
|
|
||||||
|
|
||||||
# 进入maimbot
|
|
||||||
message = MessageRecv(message_json)
|
|
||||||
groupinfo = message.message_info.group_info
|
|
||||||
userinfo = message.message_info.user_info
|
|
||||||
messageinfo = message.message_info
|
|
||||||
|
|
||||||
# 消息过滤，涉及到config有待更新
|
|
||||||
|
|
||||||
chat = await chat_manager.get_or_create_stream(
|
|
||||||
platform=messageinfo.platform, user_info=userinfo, group_info=groupinfo
|
|
||||||
)
|
|
||||||
message.update_chat_stream(chat)
|
|
||||||
await relationship_manager.update_relationship(
|
|
||||||
chat_stream=chat,
|
|
||||||
)
|
|
||||||
await relationship_manager.update_relationship_value(chat_stream=chat, relationship_value=0.5)
|
|
||||||
|
|
||||||
await message.process()
|
|
||||||
# 过滤词
|
|
||||||
for word in global_config.ban_words:
|
|
||||||
if word in message.processed_plain_text:
|
|
||||||
logger.info(
|
|
||||||
f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{message.processed_plain_text}"
|
|
||||||
)
|
|
||||||
logger.info(f"[过滤词识别]消息中含有{word}，filtered")
|
|
||||||
return
|
|
||||||
|
|
||||||
# 正则表达式过滤
|
|
||||||
for pattern in global_config.ban_msgs_regex:
|
|
||||||
if re.search(pattern, message.raw_message):
|
|
||||||
logger.info(
|
|
||||||
f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{message.raw_message}"
|
|
||||||
)
|
|
||||||
logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
|
|
||||||
return
|
|
||||||
|
|
||||||
current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(messageinfo.time))
|
|
||||||
|
|
||||||
# topic=await topic_identifier.identify_topic_llm(message.processed_plain_text)
|
|
||||||
|
|
||||||
topic = ""
|
|
||||||
interested_rate = await hippocampus.memory_activate_value(message.processed_plain_text) / 100
|
|
||||||
logger.debug(f"对{message.processed_plain_text}的激活度:{interested_rate}")
|
|
||||||
# logger.info(f"\033[1;32m[主题识别]\033[0m 使用{global_config.topic_extract}主题: {topic}")
|
|
||||||
|
|
||||||
await self.storage.store_message(message, chat, topic[0] if topic else None)
|
|
||||||
|
|
||||||
is_mentioned = is_mentioned_bot_in_message(message)
|
|
||||||
reply_probability = await willing_manager.change_reply_willing_received(
|
|
||||||
chat_stream=chat,
|
|
||||||
topic=topic[0] if topic else None,
|
|
||||||
is_mentioned_bot=is_mentioned,
|
|
||||||
config=global_config,
|
|
||||||
is_emoji=message.is_emoji,
|
|
||||||
interested_rate=interested_rate,
|
|
||||||
sender_id=str(message.message_info.user_info.user_id),
|
|
||||||
)
|
|
||||||
current_willing = willing_manager.get_willing(chat_stream=chat)
|
|
||||||
|
|
||||||
logger.info(
|
|
||||||
f"[{current_time}][{chat.group_info.group_name if chat.group_info else '私聊'}]{chat.user_info.user_nickname}:"
|
|
||||||
f"{message.processed_plain_text}[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]"
|
|
||||||
)
|
|
||||||
|
|
||||||
response = None
|
|
||||||
|
|
||||||
if random() < reply_probability:
|
|
||||||
bot_user_info = UserInfo(
|
|
||||||
user_id=global_config.BOT_QQ,
|
|
||||||
user_nickname=global_config.BOT_NICKNAME,
|
|
||||||
platform=messageinfo.platform,
|
|
||||||
)
|
|
||||||
thinking_time_point = round(time.time(), 2)
|
|
||||||
think_id = "mt" + str(thinking_time_point)
|
|
||||||
thinking_message = MessageThinking(
|
|
||||||
message_id=think_id,
|
|
||||||
chat_stream=chat,
|
|
||||||
bot_user_info=bot_user_info,
|
|
||||||
reply=message,
|
|
||||||
)
|
|
||||||
|
|
||||||
message_manager.add_message(thinking_message)
|
|
||||||
|
|
||||||
willing_manager.change_reply_willing_sent(chat)
|
|
||||||
|
|
||||||
response, raw_content = await self.gpt.generate_response(message)
|
|
||||||
else:
|
|
||||||
# 决定不回复时，也更新回复意愿
|
|
||||||
willing_manager.change_reply_willing_not_sent(chat)
|
|
||||||
|
|
||||||
# print(f"response: {response}")
|
|
||||||
if response:
|
|
||||||
# print(f"有response: {response}")
|
|
||||||
container = message_manager.get_container(chat.stream_id)
|
|
||||||
thinking_message = None
|
|
||||||
# 找到message,删除
|
|
||||||
# print(f"开始找思考消息")
|
|
||||||
for msg in container.messages:
|
|
||||||
if isinstance(msg, MessageThinking) and msg.message_info.message_id == think_id:
|
|
||||||
# print(f"找到思考消息: {msg}")
|
|
||||||
thinking_message = msg
|
|
||||||
container.messages.remove(msg)
|
|
||||||
break
|
|
||||||
|
|
||||||
# 如果找不到思考消息，直接返回
|
|
||||||
if not thinking_message:
|
|
||||||
logger.warning("未找到对应的思考消息，可能已超时被移除")
|
|
||||||
return
|
|
||||||
|
|
||||||
# 记录开始思考的时间，避免从思考到回复的时间太久
|
|
||||||
thinking_start_time = thinking_message.thinking_start_time
|
|
||||||
message_set = MessageSet(chat, think_id)
|
|
||||||
# 计算打字时间，1是为了模拟打字，2是避免多条回复乱序
|
|
||||||
accu_typing_time = 0
|
|
||||||
|
|
||||||
mark_head = False
|
|
||||||
for msg in response:
|
|
||||||
# print(f"\033[1;32m[回复内容]\033[0m {msg}")
|
|
||||||
# 通过时间改变时间戳
|
|
||||||
typing_time = calculate_typing_time(msg)
|
|
||||||
logger.debug(f"typing_time: {typing_time}")
|
|
||||||
accu_typing_time += typing_time
|
|
||||||
timepoint = thinking_time_point + accu_typing_time
|
|
||||||
message_segment = Seg(type="text", data=msg)
|
|
||||||
# logger.debug(f"message_segment: {message_segment}")
|
|
||||||
bot_message = MessageSending(
|
|
||||||
message_id=think_id,
|
|
||||||
chat_stream=chat,
|
|
||||||
bot_user_info=bot_user_info,
|
|
||||||
sender_info=userinfo,
|
|
||||||
message_segment=message_segment,
|
|
||||||
reply=message,
|
|
||||||
is_head=not mark_head,
|
|
||||||
is_emoji=False,
|
|
||||||
)
|
|
||||||
logger.debug(f"bot_message: {bot_message}")
|
|
||||||
if not mark_head:
|
|
||||||
mark_head = True
|
|
||||||
logger.debug(f"添加消息到message_set: {bot_message}")
|
|
||||||
message_set.add_message(bot_message)
|
|
||||||
|
|
||||||
# message_set 可以直接加入 message_manager
|
|
||||||
# print(f"\033[1;32m[回复]\033[0m 将回复载入发送容器")
|
|
||||||
|
|
||||||
logger.debug("添加message_set到message_manager")
|
|
||||||
|
|
||||||
message_manager.add_message(message_set)
|
|
||||||
|
|
||||||
bot_response_time = thinking_time_point
|
|
||||||
|
|
||||||
if random() < global_config.emoji_chance:
|
|
||||||
emoji_raw = await emoji_manager.get_emoji_for_text(response)
|
|
||||||
|
|
||||||
# 检查是否 <没有找到> emoji
|
|
||||||
if emoji_raw != None:
|
|
||||||
emoji_path, description = emoji_raw
|
|
||||||
|
|
||||||
emoji_cq = image_path_to_base64(emoji_path)
|
|
||||||
|
|
||||||
if random() < 0.5:
|
|
||||||
bot_response_time = thinking_time_point - 1
|
|
||||||
else:
|
|
||||||
bot_response_time = bot_response_time + 1
|
|
||||||
|
|
||||||
message_segment = Seg(type="emoji", data=emoji_cq)
|
|
||||||
bot_message = MessageSending(
|
|
||||||
message_id=think_id,
|
|
||||||
chat_stream=chat,
|
|
||||||
bot_user_info=bot_user_info,
|
|
||||||
sender_info=userinfo,
|
|
||||||
message_segment=message_segment,
|
|
||||||
reply=message,
|
|
||||||
is_head=False,
|
|
||||||
is_emoji=True,
|
|
||||||
)
|
|
||||||
message_manager.add_message(bot_message)
|
|
||||||
|
|
||||||
emotion = await self.gpt._get_emotion_tags(raw_content)
|
|
||||||
logger.debug(f"为 '{response}' 获取到的情感标签为：{emotion}")
|
|
||||||
valuedict = {
|
|
||||||
"happy": 0.5,
|
|
||||||
"angry": -1,
|
|
||||||
"sad": -0.5,
|
|
||||||
"surprised": 0.2,
|
|
||||||
"disgusted": -1.5,
|
|
||||||
"fearful": -0.7,
|
|
||||||
"neutral": 0.1,
|
|
||||||
}
|
|
||||||
await relationship_manager.update_relationship_value(
|
|
||||||
chat_stream=chat, relationship_value=valuedict[emotion[0]]
|
|
||||||
)
|
|
||||||
# 使用情绪管理器更新情绪
|
|
||||||
self.mood_manager.update_mood_from_emotion(emotion[0], global_config.mood_intensity_factor)
|
|
||||||
|
|
||||||
# willing_manager.change_reply_willing_after_sent(
|
|
||||||
# chat_stream=chat
|
|
||||||
# )
|
|
||||||
|
|
||||||
async def directly_reply(self, raw_message: str, user_id: int, group_id: int):
|
|
||||||
"""
|
"""
|
||||||
直接回复发来的消息，不经过意愿管理器
|
|
||||||
"""
|
|
||||||
|
|
||||||
# 构造用户信息和群组信息
|
|
||||||
user_info = UserInfo(
|
|
||||||
user_id=user_id,
|
|
||||||
user_nickname=get_user_nickname(user_id) or None,
|
|
||||||
user_cardname=get_user_cardname(user_id) or None,
|
|
||||||
platform="qq",
|
|
||||||
)
|
|
||||||
group_info = GroupInfo(group_id=group_id, group_name=None, platform="qq")
|
|
||||||
|
|
||||||
message_cq = MessageRecvCQ(
|
|
||||||
message_id=None,
|
|
||||||
user_info=user_info,
|
|
||||||
raw_message=raw_message,
|
|
||||||
group_info=group_info,
|
|
||||||
reply_message=None,
|
|
||||||
platform="qq",
|
|
||||||
)
|
|
||||||
message_json = message_cq.to_dict()
|
|
||||||
|
|
||||||
message = MessageRecv(message_json)
|
|
||||||
groupinfo = message.message_info.group_info
|
|
||||||
userinfo = message.message_info.user_info
|
|
||||||
messageinfo = message.message_info
|
|
||||||
|
|
||||||
chat = await chat_manager.get_or_create_stream(
|
|
||||||
platform=messageinfo.platform, user_info=userinfo, group_info=groupinfo
|
|
||||||
)
|
|
||||||
message.update_chat_stream(chat)
|
|
||||||
await message.process()
|
|
||||||
|
|
||||||
bot_user_info = UserInfo(
|
bot_user_info = UserInfo(
|
||||||
user_id=global_config.BOT_QQ,
|
user_id=global_config.BOT_QQ,
|
||||||
user_nickname=global_config.BOT_NICKNAME,
|
user_nickname=global_config.BOT_NICKNAME,
|
||||||
platform=messageinfo.platform,
|
platform=messageinfo.platform,
|
||||||
)
|
)
|
||||||
|
|
||||||
current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(messageinfo.time))
|
thinking_time_point = round(time.time(), 2)
|
||||||
logger.info(
|
thinking_id = "mt" + str(thinking_time_point)
|
||||||
f"[{current_time}][{chat.group_info.group_name if chat.group_info else '私聊'}]{chat.user_info.user_nickname}:"
|
thinking_message = MessageThinking(
|
||||||
f"{message.processed_plain_text}"
|
message_id=thinking_id,
|
||||||
|
chat_stream=chat,
|
||||||
|
bot_user_info=bot_user_info,
|
||||||
|
reply=message,
|
||||||
|
thinking_start_time=thinking_time_point,
|
||||||
)
|
)
|
||||||
|
|
||||||
# 使用大模型生成回复
|
message_manager.add_message(thinking_message)
|
||||||
response, raw_content = await self.gpt.generate_response(message)
|
willing_manager.change_reply_willing_sent(chat)
|
||||||
|
|
||||||
if response:
|
return thinking_id
|
||||||
for msg in response:
|
|
||||||
message_segment = Seg(type="text", data=msg)
|
|
||||||
|
|
||||||
|
async def message_process(self, message_data: str) -> None:
|
||||||
|
"""处理转化后的统一格式消息
|
||||||
|
1. 过滤消息
|
||||||
|
2. 记忆激活
|
||||||
|
3. 意愿激活
|
||||||
|
4. 生成回复并发送
|
||||||
|
5. 更新关系
|
||||||
|
6. 更新情绪
|
||||||
|
"""
|
||||||
|
timing_results = {} # 用于收集所有计时结果
|
||||||
|
response_set = None # 初始化response_set变量
|
||||||
|
|
||||||
|
message = MessageRecv(message_data)
|
||||||
|
groupinfo = message.message_info.group_info
|
||||||
|
userinfo = message.message_info.user_info
|
||||||
|
messageinfo = message.message_info
|
||||||
|
|
||||||
|
# 消息过滤，涉及到config有待更新
|
||||||
|
|
||||||
|
# 创建聊天流
|
||||||
|
chat = await chat_manager.get_or_create_stream(
|
||||||
|
platform=messageinfo.platform,
|
||||||
|
user_info=userinfo,
|
||||||
|
group_info=groupinfo,
|
||||||
|
)
|
||||||
|
message.update_chat_stream(chat)
|
||||||
|
|
||||||
|
# 创建 心流与chat的观察
|
||||||
|
heartflow.create_subheartflow(chat.stream_id)
|
||||||
|
|
||||||
|
await message.process()
|
||||||
|
|
||||||
|
# 过滤词/正则表达式过滤
|
||||||
|
if self._check_ban_words(message.processed_plain_text, chat, userinfo) or self._check_ban_regex(
|
||||||
|
message.raw_message, chat, userinfo
|
||||||
|
):
|
||||||
|
return
|
||||||
|
|
||||||
|
await self.storage.store_message(message, chat)
|
||||||
|
|
||||||
|
timer1 = time.time()
|
||||||
|
interested_rate = 0
|
||||||
|
interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
|
||||||
|
message.processed_plain_text, fast_retrieval=True
|
||||||
|
)
|
||||||
|
timer2 = time.time()
|
||||||
|
timing_results["记忆激活"] = timer2 - timer1
|
||||||
|
|
||||||
|
is_mentioned = is_mentioned_bot_in_message(message)
|
||||||
|
|
||||||
|
if global_config.enable_think_flow:
|
||||||
|
current_willing_old = willing_manager.get_willing(chat_stream=chat)
|
||||||
|
current_willing_new = (heartflow.get_subheartflow(chat.stream_id).current_state.willing - 5) / 4
|
||||||
|
print(f"旧回复意愿：{current_willing_old}，新回复意愿：{current_willing_new}")
|
||||||
|
current_willing = (current_willing_old + current_willing_new) / 2
|
||||||
|
else:
|
||||||
|
current_willing = willing_manager.get_willing(chat_stream=chat)
|
||||||
|
|
||||||
|
willing_manager.set_willing(chat.stream_id, current_willing)
|
||||||
|
|
||||||
|
timer1 = time.time()
|
||||||
|
reply_probability = await willing_manager.change_reply_willing_received(
|
||||||
|
chat_stream=chat,
|
||||||
|
is_mentioned_bot=is_mentioned,
|
||||||
|
config=global_config,
|
||||||
|
is_emoji=message.is_emoji,
|
||||||
|
interested_rate=interested_rate,
|
||||||
|
sender_id=str(message.message_info.user_info.user_id),
|
||||||
|
)
|
||||||
|
timer2 = time.time()
|
||||||
|
timing_results["意愿激活"] = timer2 - timer1
|
||||||
|
|
||||||
|
# 神秘的消息流数据结构处理
|
||||||
|
if chat.group_info:
|
||||||
|
mes_name = chat.group_info.group_name
|
||||||
|
else:
|
||||||
|
mes_name = "私聊"
|
||||||
|
|
||||||
|
# 打印收到的信息的信息
|
||||||
|
current_time = time.strftime("%H:%M:%S", time.localtime(messageinfo.time))
|
||||||
|
logger.info(
|
||||||
|
f"[{current_time}][{mes_name}]"
|
||||||
|
f"{chat.user_info.user_nickname}:"
|
||||||
|
f"{message.processed_plain_text}[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]"
|
||||||
|
)
|
||||||
|
|
||||||
|
if message.message_info.additional_config:
|
||||||
|
if "maimcore_reply_probability_gain" in message.message_info.additional_config.keys():
|
||||||
|
reply_probability += message.message_info.additional_config["maimcore_reply_probability_gain"]
|
||||||
|
|
||||||
|
do_reply = False
|
||||||
|
# 开始组织语言
|
||||||
|
if random() < reply_probability:
|
||||||
|
do_reply = True
|
||||||
|
|
||||||
|
timer1 = time.time()
|
||||||
|
thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
|
||||||
|
timer2 = time.time()
|
||||||
|
timing_results["创建思考消息"] = timer2 - timer1
|
||||||
|
|
||||||
|
timer1 = time.time()
|
||||||
|
await heartflow.get_subheartflow(chat.stream_id).do_observe()
|
||||||
|
timer2 = time.time()
|
||||||
|
timing_results["观察"] = timer2 - timer1
|
||||||
|
|
||||||
|
timer1 = time.time()
|
||||||
|
await heartflow.get_subheartflow(chat.stream_id).do_thinking_before_reply(message.processed_plain_text)
|
||||||
|
timer2 = time.time()
|
||||||
|
timing_results["思考前脑内状态"] = timer2 - timer1
|
||||||
|
|
||||||
|
timer1 = time.time()
|
||||||
|
response_set = await self.gpt.generate_response(message)
|
||||||
|
timer2 = time.time()
|
||||||
|
timing_results["生成回复"] = timer2 - timer1
|
||||||
|
|
||||||
|
if not response_set:
|
||||||
|
logger.info("为什么生成回复失败？")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 发送消息
|
||||||
|
timer1 = time.time()
|
||||||
|
await self._send_response_messages(message, chat, response_set, thinking_id)
|
||||||
|
timer2 = time.time()
|
||||||
|
timing_results["发送消息"] = timer2 - timer1
|
||||||
|
|
||||||
|
# 处理表情包
|
||||||
|
timer1 = time.time()
|
||||||
|
await self._handle_emoji(message, chat, response_set)
|
||||||
|
timer2 = time.time()
|
||||||
|
timing_results["处理表情包"] = timer2 - timer1
|
||||||
|
|
||||||
|
timer1 = time.time()
|
||||||
|
await self._update_using_response(message, response_set)
|
||||||
|
timer2 = time.time()
|
||||||
|
timing_results["更新心流"] = timer2 - timer1
|
||||||
|
|
||||||
|
# 在最后统一输出所有计时结果
|
||||||
|
if do_reply:
|
||||||
|
timing_str = " | ".join([f"{step}: {duration:.2f}秒" for step, duration in timing_results.items()])
|
||||||
|
trigger_msg = message.processed_plain_text
|
||||||
|
response_msg = " ".join(response_set) if response_set else "无回复"
|
||||||
|
logger.info(f"触发消息: {trigger_msg[:20]}... | 生成消息: {response_msg[:20]}... | 性能计时: {timing_str}")
|
||||||
|
|
||||||
|
async def _update_using_response(self, message, response_set):
|
||||||
|
# 更新心流状态
|
||||||
|
stream_id = message.chat_stream.stream_id
|
||||||
|
chat_talking_prompt = ""
|
||||||
|
if stream_id:
|
||||||
|
chat_talking_prompt = get_recent_group_detailed_plain_text(
|
||||||
|
stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
|
||||||
|
)
|
||||||
|
|
||||||
|
await heartflow.get_subheartflow(stream_id).do_thinking_after_reply(response_set, chat_talking_prompt)
|
||||||
|
|
||||||
|
async def _send_response_messages(self, message, chat, response_set, thinking_id):
|
||||||
|
container = message_manager.get_container(chat.stream_id)
|
||||||
|
thinking_message = None
|
||||||
|
|
||||||
|
# logger.info(f"开始发送消息准备")
|
||||||
|
for msg in container.messages:
|
||||||
|
if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id:
|
||||||
|
thinking_message = msg
|
||||||
|
container.messages.remove(msg)
|
||||||
|
break
|
||||||
|
|
||||||
|
if not thinking_message:
|
||||||
|
logger.warning("未找到对应的思考消息，可能已超时被移除")
|
||||||
|
return
|
||||||
|
|
||||||
|
# logger.info(f"开始发送消息")
|
||||||
|
thinking_start_time = thinking_message.thinking_start_time
|
||||||
|
message_set = MessageSet(chat, thinking_id)
|
||||||
|
|
||||||
|
mark_head = False
|
||||||
|
for msg in response_set:
|
||||||
|
message_segment = Seg(type="text", data=msg)
|
||||||
|
bot_message = MessageSending(
|
||||||
|
message_id=thinking_id,
|
||||||
|
chat_stream=chat,
|
||||||
|
bot_user_info=UserInfo(
|
||||||
|
user_id=global_config.BOT_QQ,
|
||||||
|
user_nickname=global_config.BOT_NICKNAME,
|
||||||
|
platform=message.message_info.platform,
|
||||||
|
),
|
||||||
|
sender_info=message.message_info.user_info,
|
||||||
|
message_segment=message_segment,
|
||||||
|
reply=message,
|
||||||
|
is_head=not mark_head,
|
||||||
|
is_emoji=False,
|
||||||
|
thinking_start_time=thinking_start_time,
|
||||||
|
)
|
||||||
|
if not mark_head:
|
||||||
|
mark_head = True
|
||||||
|
message_set.add_message(bot_message)
|
||||||
|
# logger.info(f"开始添加发送消息")
|
||||||
|
message_manager.add_message(message_set)
|
||||||
|
|
||||||
|
async def _handle_emoji(self, message, chat, response):
|
||||||
|
"""处理表情包
|
||||||
|
|
||||||
|
Args:
|
||||||
|
message: 接收到的消息
|
||||||
|
chat: 聊天流对象
|
||||||
|
response: 生成的回复
|
||||||
|
"""
|
||||||
|
if random() < global_config.emoji_chance:
|
||||||
|
emoji_raw = await emoji_manager.get_emoji_for_text(response)
|
||||||
|
if emoji_raw:
|
||||||
|
emoji_path, description = emoji_raw
|
||||||
|
emoji_cq = image_path_to_base64(emoji_path)
|
||||||
|
|
||||||
|
thinking_time_point = round(message.message_info.time, 2)
|
||||||
|
|
||||||
|
message_segment = Seg(type="emoji", data=emoji_cq)
|
||||||
bot_message = MessageSending(
|
bot_message = MessageSending(
|
||||||
message_id=None,
|
message_id="mt" + str(thinking_time_point),
|
||||||
chat_stream=chat,
|
chat_stream=chat,
|
||||||
bot_user_info=bot_user_info,
|
bot_user_info=UserInfo(
|
||||||
sender_info=userinfo,
|
user_id=global_config.BOT_QQ,
|
||||||
|
user_nickname=global_config.BOT_NICKNAME,
|
||||||
|
platform=message.message_info.platform,
|
||||||
|
),
|
||||||
|
sender_info=message.message_info.user_info,
|
||||||
message_segment=message_segment,
|
message_segment=message_segment,
|
||||||
reply=None,
|
reply=message,
|
||||||
is_head=False,
|
is_head=False,
|
||||||
is_emoji=False,
|
is_emoji=True,
|
||||||
)
|
)
|
||||||
message_manager.add_message(bot_message)
|
message_manager.add_message(bot_message)
|
||||||
|
|
||||||
|
async def _update_emotion_and_relationship(self, message, chat, response, raw_content):
|
||||||
|
"""更新情绪和关系
|
||||||
|
|
||||||
|
Args:
|
||||||
|
message: 接收到的消息
|
||||||
|
chat: 聊天流对象
|
||||||
|
response: 生成的回复
|
||||||
|
raw_content: 原始内容
|
||||||
|
"""
|
||||||
|
stance, emotion = await self.gpt._get_emotion_tags(raw_content, message.processed_plain_text)
|
||||||
|
logger.debug(f"为 '{response}' 立场为：{stance} 获取到的情感标签为：{emotion}")
|
||||||
|
await relationship_manager.calculate_update_relationship_value(chat_stream=chat, label=emotion, stance=stance)
|
||||||
|
self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
|
||||||
|
|
||||||
|
def _check_ban_words(self, text: str, chat, userinfo) -> bool:
|
||||||
|
"""检查消息中是否包含过滤词
|
||||||
|
|
||||||
|
Args:
|
||||||
|
text: 要检查的文本
|
||||||
|
chat: 聊天流对象
|
||||||
|
userinfo: 用户信息对象
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
bool: 如果包含过滤词返回True，否则返回False
|
||||||
|
"""
|
||||||
|
for word in global_config.ban_words:
|
||||||
|
if word in text:
|
||||||
|
logger.info(
|
||||||
|
f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
|
||||||
|
)
|
||||||
|
logger.info(f"[过滤词识别]消息中含有{word}，filtered")
|
||||||
|
return True
|
||||||
|
return False
|
||||||
|
|
||||||
|
def _check_ban_regex(self, text: str, chat, userinfo) -> bool:
|
||||||
|
"""检查消息是否匹配过滤正则表达式
|
||||||
|
|
||||||
|
Args:
|
||||||
|
text: 要检查的文本
|
||||||
|
chat: 聊天流对象
|
||||||
|
userinfo: 用户信息对象
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
bool: 如果匹配过滤正则返回True，否则返回False
|
||||||
|
"""
|
||||||
|
for pattern in global_config.ban_msgs_regex:
|
||||||
|
if re.search(pattern, text):
|
||||||
|
logger.info(
|
||||||
|
f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
|
||||||
|
)
|
||||||
|
logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
|
||||||
|
return True
|
||||||
|
return False
|
||||||
|
|
||||||
|
|
||||||
# 创建全局ChatBot实例
|
# 创建全局ChatBot实例
|
||||||
chat_bot = ChatBot()
|
chat_bot = ChatBot()
|
||||||
|
|||||||
@@ -4,10 +4,13 @@ import time
|
|||||||
import copy
|
import copy
|
||||||
from typing import Dict, Optional
|
from typing import Dict, Optional
|
||||||
|
|
||||||
from loguru import logger
|
|
||||||
|
|
||||||
from ...common.database import db
|
from ...common.database import db
|
||||||
from .message_base import GroupInfo, UserInfo
|
from ..message.message_base import GroupInfo, UserInfo
|
||||||
|
|
||||||
|
from src.common.logger import get_module_logger
|
||||||
|
|
||||||
|
logger = get_module_logger("chat_stream")
|
||||||
|
|
||||||
|
|
||||||
class ChatStream:
|
class ChatStream:
|
||||||
@@ -25,12 +28,8 @@ class ChatStream:
|
|||||||
self.platform = platform
|
self.platform = platform
|
||||||
self.user_info = user_info
|
self.user_info = user_info
|
||||||
self.group_info = group_info
|
self.group_info = group_info
|
||||||
self.create_time = (
|
self.create_time = data.get("create_time", int(time.time())) if data else int(time.time())
|
||||||
data.get("create_time", int(time.time())) if data else int(time.time())
|
self.last_active_time = data.get("last_active_time", self.create_time) if data else self.create_time
|
||||||
)
|
|
||||||
self.last_active_time = (
|
|
||||||
data.get("last_active_time", self.create_time) if data else self.create_time
|
|
||||||
)
|
|
||||||
self.saved = False
|
self.saved = False
|
||||||
|
|
||||||
def to_dict(self) -> dict:
|
def to_dict(self) -> dict:
|
||||||
@@ -48,12 +47,8 @@ class ChatStream:
|
|||||||
@classmethod
|
@classmethod
|
||||||
def from_dict(cls, data: dict) -> "ChatStream":
|
def from_dict(cls, data: dict) -> "ChatStream":
|
||||||
"""从字典创建实例"""
|
"""从字典创建实例"""
|
||||||
user_info = (
|
user_info = UserInfo.from_dict(data.get("user_info", {})) if data.get("user_info") else None
|
||||||
UserInfo(**data.get("user_info", {})) if data.get("user_info") else None
|
group_info = GroupInfo.from_dict(data.get("group_info", {})) if data.get("group_info") else None
|
||||||
)
|
|
||||||
group_info = (
|
|
||||||
GroupInfo(**data.get("group_info", {})) if data.get("group_info") else None
|
|
||||||
)
|
|
||||||
|
|
||||||
return cls(
|
return cls(
|
||||||
stream_id=data["stream_id"],
|
stream_id=data["stream_id"],
|
||||||
@@ -114,26 +109,15 @@ class ChatManager:
|
|||||||
db.create_collection("chat_streams")
|
db.create_collection("chat_streams")
|
||||||
# 创建索引
|
# 创建索引
|
||||||
db.chat_streams.create_index([("stream_id", 1)], unique=True)
|
db.chat_streams.create_index([("stream_id", 1)], unique=True)
|
||||||
db.chat_streams.create_index(
|
db.chat_streams.create_index([("platform", 1), ("user_info.user_id", 1), ("group_info.group_id", 1)])
|
||||||
[("platform", 1), ("user_info.user_id", 1), ("group_info.group_id", 1)]
|
|
||||||
)
|
|
||||||
|
|
||||||
def _generate_stream_id(
|
def _generate_stream_id(self, platform: str, user_info: UserInfo, group_info: Optional[GroupInfo] = None) -> str:
|
||||||
self, platform: str, user_info: UserInfo, group_info: Optional[GroupInfo] = None
|
|
||||||
) -> str:
|
|
||||||
"""生成聊天流唯一ID"""
|
"""生成聊天流唯一ID"""
|
||||||
if group_info:
|
if group_info:
|
||||||
# 组合关键信息
|
# 组合关键信息
|
||||||
components = [
|
components = [platform, str(group_info.group_id)]
|
||||||
platform,
|
|
||||||
str(group_info.group_id)
|
|
||||||
]
|
|
||||||
else:
|
else:
|
||||||
components = [
|
components = [platform, str(user_info.user_id), "private"]
|
||||||
platform,
|
|
||||||
str(user_info.user_id),
|
|
||||||
"private"
|
|
||||||
]
|
|
||||||
|
|
||||||
# 使用MD5生成唯一ID
|
# 使用MD5生成唯一ID
|
||||||
key = "_".join(components)
|
key = "_".join(components)
|
||||||
@@ -160,7 +144,7 @@ class ChatManager:
|
|||||||
stream = self.streams[stream_id]
|
stream = self.streams[stream_id]
|
||||||
# 更新用户信息和群组信息
|
# 更新用户信息和群组信息
|
||||||
stream.update_active_time()
|
stream.update_active_time()
|
||||||
stream=copy.deepcopy(stream)
|
stream = copy.deepcopy(stream)
|
||||||
stream.user_info = user_info
|
stream.user_info = user_info
|
||||||
if group_info:
|
if group_info:
|
||||||
stream.group_info = group_info
|
stream.group_info = group_info
|
||||||
@@ -203,9 +187,7 @@ class ChatManager:
|
|||||||
async def _save_stream(self, stream: ChatStream):
|
async def _save_stream(self, stream: ChatStream):
|
||||||
"""保存聊天流到数据库"""
|
"""保存聊天流到数据库"""
|
||||||
if not stream.saved:
|
if not stream.saved:
|
||||||
db.chat_streams.update_one(
|
db.chat_streams.update_one({"stream_id": stream.stream_id}, {"$set": stream.to_dict()}, upsert=True)
|
||||||
{"stream_id": stream.stream_id}, {"$set": stream.to_dict()}, upsert=True
|
|
||||||
)
|
|
||||||
stream.saved = True
|
stream.saved = True
|
||||||
|
|
||||||
async def _save_all_streams(self):
|
async def _save_all_streams(self):
|
||||||
|
|||||||
@@ -1,434 +0,0 @@
|
|||||||
import base64
|
|
||||||
import html
|
|
||||||
import time
|
|
||||||
from dataclasses import dataclass
|
|
||||||
from typing import Dict, List, Optional, Union
|
|
||||||
|
|
||||||
import os
|
|
||||||
|
|
||||||
import requests
|
|
||||||
|
|
||||||
# 解析各种CQ码
|
|
||||||
# 包含CQ码类
|
|
||||||
import urllib3
|
|
||||||
from loguru import logger
|
|
||||||
from nonebot import get_driver
|
|
||||||
from urllib3.util import create_urllib3_context
|
|
||||||
|
|
||||||
from ..models.utils_model import LLM_request
|
|
||||||
from .config import global_config
|
|
||||||
from .mapper import emojimapper
|
|
||||||
from .message_base import Seg
|
|
||||||
from .utils_user import get_user_nickname,get_groupname
|
|
||||||
from .message_base import GroupInfo, UserInfo
|
|
||||||
|
|
||||||
driver = get_driver()
|
|
||||||
config = driver.config
|
|
||||||
|
|
||||||
# TLS1.3特殊处理 https://github.com/psf/requests/issues/6616
|
|
||||||
ctx = create_urllib3_context()
|
|
||||||
ctx.load_default_certs()
|
|
||||||
ctx.set_ciphers("AES128-GCM-SHA256")
|
|
||||||
|
|
||||||
|
|
||||||
class TencentSSLAdapter(requests.adapters.HTTPAdapter):
|
|
||||||
def __init__(self, ssl_context=None, **kwargs):
|
|
||||||
self.ssl_context = ssl_context
|
|
||||||
super().__init__(**kwargs)
|
|
||||||
|
|
||||||
def init_poolmanager(self, connections, maxsize, block=False):
|
|
||||||
self.poolmanager = urllib3.poolmanager.PoolManager(
|
|
||||||
num_pools=connections,
|
|
||||||
maxsize=maxsize,
|
|
||||||
block=block,
|
|
||||||
ssl_context=self.ssl_context,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class CQCode:
|
|
||||||
"""
|
|
||||||
CQ码数据类，用于存储和处理CQ码
|
|
||||||
|
|
||||||
属性:
|
|
||||||
type: CQ码类型（如'image', 'at', 'face'等）
|
|
||||||
params: CQ码的参数字典
|
|
||||||
raw_code: 原始CQ码字符串
|
|
||||||
translated_segments: 经过处理后的Seg对象列表
|
|
||||||
"""
|
|
||||||
|
|
||||||
type: str
|
|
||||||
params: Dict[str, str]
|
|
||||||
group_info: Optional[GroupInfo] = None
|
|
||||||
user_info: Optional[UserInfo] = None
|
|
||||||
translated_segments: Optional[Union[Seg, List[Seg]]] = None
|
|
||||||
reply_message: Dict = None # 存储回复消息
|
|
||||||
image_base64: Optional[str] = None
|
|
||||||
_llm: Optional[LLM_request] = None
|
|
||||||
|
|
||||||
def __post_init__(self):
|
|
||||||
"""初始化LLM实例"""
|
|
||||||
pass
|
|
||||||
|
|
||||||
def translate(self):
|
|
||||||
"""根据CQ码类型进行相应的翻译处理，转换为Seg对象"""
|
|
||||||
if self.type == "text":
|
|
||||||
self.translated_segments = Seg(
|
|
||||||
type="text", data=self.params.get("text", "")
|
|
||||||
)
|
|
||||||
elif self.type == "image":
|
|
||||||
base64_data = self.translate_image()
|
|
||||||
if base64_data:
|
|
||||||
if self.params.get("sub_type") == "0":
|
|
||||||
self.translated_segments = Seg(type="image", data=base64_data)
|
|
||||||
else:
|
|
||||||
self.translated_segments = Seg(type="emoji", data=base64_data)
|
|
||||||
else:
|
|
||||||
self.translated_segments = Seg(type="text", data="[图片]")
|
|
||||||
elif self.type == "at":
|
|
||||||
if self.params.get("qq") == "all":
|
|
||||||
self.translated_segments = Seg(type="text", data="@[全体成员]")
|
|
||||||
else:
|
|
||||||
user_nickname = get_user_nickname(self.params.get("qq", ""))
|
|
||||||
self.translated_segments = Seg(
|
|
||||||
type="text", data=f"[@{user_nickname or '某人'}]"
|
|
||||||
)
|
|
||||||
elif self.type == "reply":
|
|
||||||
reply_segments = self.translate_reply()
|
|
||||||
if reply_segments:
|
|
||||||
self.translated_segments = Seg(type="seglist", data=reply_segments)
|
|
||||||
else:
|
|
||||||
self.translated_segments = Seg(type="text", data="[回复某人消息]")
|
|
||||||
elif self.type == "face":
|
|
||||||
face_id = self.params.get("id", "")
|
|
||||||
self.translated_segments = Seg(
|
|
||||||
type="text", data=f"[{emojimapper.get(int(face_id), '表情')}]"
|
|
||||||
)
|
|
||||||
elif self.type == "forward":
|
|
||||||
forward_segments = self.translate_forward()
|
|
||||||
if forward_segments:
|
|
||||||
self.translated_segments = Seg(type="seglist", data=forward_segments)
|
|
||||||
else:
|
|
||||||
self.translated_segments = Seg(type="text", data="[转发消息]")
|
|
||||||
else:
|
|
||||||
self.translated_segments = Seg(type="text", data=f"[{self.type}]")
|
|
||||||
|
|
||||||
def get_img(self):
|
|
||||||
"""
|
|
||||||
headers = {
|
|
||||||
'User-Agent': 'QQ/8.9.68.11565 CFNetwork/1220.1 Darwin/20.3.0',
|
|
||||||
'Accept': 'image/*;q=0.8',
|
|
||||||
'Accept-Encoding': 'gzip, deflate, br',
|
|
||||||
'Connection': 'keep-alive',
|
|
||||||
'Cache-Control': 'no-cache',
|
|
||||||
'Pragma': 'no-cache'
|
|
||||||
}
|
|
||||||
"""
|
|
||||||
# 腾讯专用请求头配置
|
|
||||||
headers = {
|
|
||||||
"User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.87 Safari/537.36",
|
|
||||||
"Accept": "text/html, application/xhtml xml, */*",
|
|
||||||
"Accept-Encoding": "gbk, GB2312",
|
|
||||||
"Accept-Language": "zh-cn",
|
|
||||||
"Content-Type": "application/x-www-form-urlencoded",
|
|
||||||
"Cache-Control": "no-cache",
|
|
||||||
}
|
|
||||||
url = html.unescape(self.params["url"])
|
|
||||||
if not url.startswith(("http://", "https://")):
|
|
||||||
return None
|
|
||||||
|
|
||||||
# 创建专用会话
|
|
||||||
session = requests.session()
|
|
||||||
session.adapters.pop("https://", None)
|
|
||||||
session.mount("https://", TencentSSLAdapter(ctx))
|
|
||||||
|
|
||||||
max_retries = 3
|
|
||||||
for retry in range(max_retries):
|
|
||||||
try:
|
|
||||||
response = session.get(
|
|
||||||
url,
|
|
||||||
headers=headers,
|
|
||||||
timeout=15,
|
|
||||||
allow_redirects=True,
|
|
||||||
stream=True, # 流式传输避免大内存问题
|
|
||||||
)
|
|
||||||
|
|
||||||
# 腾讯服务器特殊状态码处理
|
|
||||||
if response.status_code == 400 and "multimedia.nt.qq.com.cn" in url:
|
|
||||||
return None
|
|
||||||
|
|
||||||
if response.status_code != 200:
|
|
||||||
raise requests.exceptions.HTTPError(f"HTTP {response.status_code}")
|
|
||||||
|
|
||||||
# 验证内容类型
|
|
||||||
content_type = response.headers.get("Content-Type", "")
|
|
||||||
if not content_type.startswith("image/"):
|
|
||||||
raise ValueError(f"非图片内容类型: {content_type}")
|
|
||||||
|
|
||||||
# 转换为Base64
|
|
||||||
image_base64 = base64.b64encode(response.content).decode("utf-8")
|
|
||||||
self.image_base64 = image_base64
|
|
||||||
return image_base64
|
|
||||||
|
|
||||||
except (requests.exceptions.SSLError, requests.exceptions.HTTPError) as e:
|
|
||||||
if retry == max_retries - 1:
|
|
||||||
logger.error(f"最终请求失败: {str(e)}")
|
|
||||||
time.sleep(1.5**retry) # 指数退避
|
|
||||||
|
|
||||||
except Exception:
|
|
||||||
logger.exception("[未知错误]")
|
|
||||||
return None
|
|
||||||
|
|
||||||
return None
|
|
||||||
|
|
||||||
def translate_image(self) -> Optional[str]:
|
|
||||||
"""处理图片类型的CQ码，返回base64字符串"""
|
|
||||||
if "url" not in self.params:
|
|
||||||
return None
|
|
||||||
return self.get_img()
|
|
||||||
|
|
||||||
def translate_forward(self) -> Optional[List[Seg]]:
|
|
||||||
"""处理转发消息，返回Seg列表"""
|
|
||||||
try:
|
|
||||||
if "content" not in self.params:
|
|
||||||
return None
|
|
||||||
|
|
||||||
content = self.unescape(self.params["content"])
|
|
||||||
import ast
|
|
||||||
|
|
||||||
try:
|
|
||||||
messages = ast.literal_eval(content)
|
|
||||||
except ValueError as e:
|
|
||||||
logger.error(f"解析转发消息内容失败: {str(e)}")
|
|
||||||
return None
|
|
||||||
|
|
||||||
formatted_segments = []
|
|
||||||
for msg in messages:
|
|
||||||
sender = msg.get("sender", {})
|
|
||||||
nickname = sender.get("card") or sender.get("nickname", "未知用户")
|
|
||||||
raw_message = msg.get("raw_message", "")
|
|
||||||
message_array = msg.get("message", [])
|
|
||||||
|
|
||||||
if message_array and isinstance(message_array, list):
|
|
||||||
for message_part in message_array:
|
|
||||||
if message_part.get("type") == "forward":
|
|
||||||
content_seg = Seg(type="text", data="[转发消息]")
|
|
||||||
break
|
|
||||||
else:
|
|
||||||
if raw_message:
|
|
||||||
from .message_cq import MessageRecvCQ
|
|
||||||
user_info=UserInfo(
|
|
||||||
platform='qq',
|
|
||||||
user_id=msg.get("user_id", 0),
|
|
||||||
user_nickname=nickname,
|
|
||||||
)
|
|
||||||
group_info=GroupInfo(
|
|
||||||
platform='qq',
|
|
||||||
group_id=msg.get("group_id", 0),
|
|
||||||
group_name=get_groupname(msg.get("group_id", 0))
|
|
||||||
)
|
|
||||||
|
|
||||||
message_obj = MessageRecvCQ(
|
|
||||||
message_id=msg.get("message_id", 0),
|
|
||||||
user_info=user_info,
|
|
||||||
raw_message=raw_message,
|
|
||||||
plain_text=raw_message,
|
|
||||||
group_info=group_info,
|
|
||||||
)
|
|
||||||
content_seg = Seg(
|
|
||||||
type="seglist", data=[message_obj.message_segment]
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
content_seg = Seg(type="text", data="[空消息]")
|
|
||||||
else:
|
|
||||||
if raw_message:
|
|
||||||
from .message_cq import MessageRecvCQ
|
|
||||||
|
|
||||||
user_info=UserInfo(
|
|
||||||
platform='qq',
|
|
||||||
user_id=msg.get("user_id", 0),
|
|
||||||
user_nickname=nickname,
|
|
||||||
)
|
|
||||||
group_info=GroupInfo(
|
|
||||||
platform='qq',
|
|
||||||
group_id=msg.get("group_id", 0),
|
|
||||||
group_name=get_groupname(msg.get("group_id", 0))
|
|
||||||
)
|
|
||||||
message_obj = MessageRecvCQ(
|
|
||||||
message_id=msg.get("message_id", 0),
|
|
||||||
user_info=user_info,
|
|
||||||
raw_message=raw_message,
|
|
||||||
plain_text=raw_message,
|
|
||||||
group_info=group_info,
|
|
||||||
)
|
|
||||||
content_seg = Seg(
|
|
||||||
type="seglist", data=[message_obj.message_segment]
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
content_seg = Seg(type="text", data="[空消息]")
|
|
||||||
|
|
||||||
formatted_segments.append(Seg(type="text", data=f"{nickname}: "))
|
|
||||||
formatted_segments.append(content_seg)
|
|
||||||
formatted_segments.append(Seg(type="text", data="\n"))
|
|
||||||
|
|
||||||
return formatted_segments
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"处理转发消息失败: {str(e)}")
|
|
||||||
return None
|
|
||||||
|
|
||||||
def translate_reply(self) -> Optional[List[Seg]]:
|
|
||||||
"""处理回复类型的CQ码，返回Seg列表"""
|
|
||||||
from .message_cq import MessageRecvCQ
|
|
||||||
|
|
||||||
if self.reply_message is None:
|
|
||||||
return None
|
|
||||||
|
|
||||||
if self.reply_message.sender.user_id:
|
|
||||||
|
|
||||||
message_obj = MessageRecvCQ(
|
|
||||||
user_info=UserInfo(user_id=self.reply_message.sender.user_id,user_nickname=self.reply_message.sender.nickname),
|
|
||||||
message_id=self.reply_message.message_id,
|
|
||||||
raw_message=str(self.reply_message.message),
|
|
||||||
group_info=GroupInfo(group_id=self.reply_message.group_id),
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
segments = []
|
|
||||||
if message_obj.message_info.user_info.user_id == global_config.BOT_QQ:
|
|
||||||
segments.append(
|
|
||||||
Seg(
|
|
||||||
type="text", data=f"[回复 {global_config.BOT_NICKNAME} 的消息: "
|
|
||||||
)
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
segments.append(
|
|
||||||
Seg(
|
|
||||||
type="text",
|
|
||||||
data=f"[回复 {self.reply_message.sender.nickname} 的消息: ",
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
segments.append(Seg(type="seglist", data=[message_obj.message_segment]))
|
|
||||||
segments.append(Seg(type="text", data="]"))
|
|
||||||
return segments
|
|
||||||
else:
|
|
||||||
return None
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def unescape(text: str) -> str:
|
|
||||||
"""反转义CQ码中的特殊字符"""
|
|
||||||
return (
|
|
||||||
text.replace(",", ",")
|
|
||||||
.replace("[", "[")
|
|
||||||
.replace("]", "]")
|
|
||||||
.replace("&", "&")
|
|
||||||
)
|
|
||||||
|
|
||||||
class CQCode_tool:
|
|
||||||
@staticmethod
|
|
||||||
def cq_from_dict_to_class(cq_code: Dict,msg ,reply: Optional[Dict] = None) -> CQCode:
|
|
||||||
"""
|
|
||||||
将CQ码字典转换为CQCode对象
|
|
||||||
|
|
||||||
Args:
|
|
||||||
cq_code: CQ码字典
|
|
||||||
msg: MessageCQ对象
|
|
||||||
reply: 回复消息的字典（可选）
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
CQCode对象
|
|
||||||
"""
|
|
||||||
# 处理字典形式的CQ码
|
|
||||||
# 从cq_code字典中获取type字段的值,如果不存在则默认为'text'
|
|
||||||
cq_type = cq_code.get("type", "text")
|
|
||||||
params = {}
|
|
||||||
if cq_type == "text":
|
|
||||||
params["text"] = cq_code.get("data", {}).get("text", "")
|
|
||||||
else:
|
|
||||||
params = cq_code.get("data", {})
|
|
||||||
|
|
||||||
instance = CQCode(
|
|
||||||
type=cq_type,
|
|
||||||
params=params,
|
|
||||||
group_info=msg.message_info.group_info,
|
|
||||||
user_info=msg.message_info.user_info,
|
|
||||||
reply_message=reply
|
|
||||||
)
|
|
||||||
|
|
||||||
# 进行翻译处理
|
|
||||||
instance.translate()
|
|
||||||
return instance
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def create_reply_cq(message_id: int) -> str:
|
|
||||||
"""
|
|
||||||
创建回复CQ码
|
|
||||||
Args:
|
|
||||||
message_id: 回复的消息ID
|
|
||||||
Returns:
|
|
||||||
回复CQ码字符串
|
|
||||||
"""
|
|
||||||
return f"[CQ:reply,id={message_id}]"
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def create_emoji_cq(file_path: str) -> str:
|
|
||||||
"""
|
|
||||||
创建表情包CQ码
|
|
||||||
Args:
|
|
||||||
file_path: 本地表情包文件路径
|
|
||||||
Returns:
|
|
||||||
表情包CQ码字符串
|
|
||||||
"""
|
|
||||||
# 确保使用绝对路径
|
|
||||||
abs_path = os.path.abspath(file_path)
|
|
||||||
# 转义特殊字符
|
|
||||||
escaped_path = (
|
|
||||||
abs_path.replace("&", "&")
|
|
||||||
.replace("[", "[")
|
|
||||||
.replace("]", "]")
|
|
||||||
.replace(",", ",")
|
|
||||||
)
|
|
||||||
# 生成CQ码，设置sub_type=1表示这是表情包
|
|
||||||
return f"[CQ:image,file=file:///{escaped_path},sub_type=1]"
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def create_emoji_cq_base64(base64_data: str) -> str:
|
|
||||||
"""
|
|
||||||
创建表情包CQ码
|
|
||||||
Args:
|
|
||||||
base64_data: base64编码的表情包数据
|
|
||||||
Returns:
|
|
||||||
表情包CQ码字符串
|
|
||||||
"""
|
|
||||||
# 转义base64数据
|
|
||||||
escaped_base64 = (
|
|
||||||
base64_data.replace("&", "&")
|
|
||||||
.replace("[", "[")
|
|
||||||
.replace("]", "]")
|
|
||||||
.replace(",", ",")
|
|
||||||
)
|
|
||||||
# 生成CQ码，设置sub_type=1表示这是表情包
|
|
||||||
return f"[CQ:image,file=base64://{escaped_base64},sub_type=1]"
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def create_image_cq_base64(base64_data: str) -> str:
|
|
||||||
"""
|
|
||||||
创建表情包CQ码
|
|
||||||
Args:
|
|
||||||
base64_data: base64编码的表情包数据
|
|
||||||
Returns:
|
|
||||||
表情包CQ码字符串
|
|
||||||
"""
|
|
||||||
# 转义base64数据
|
|
||||||
escaped_base64 = (
|
|
||||||
base64_data.replace("&", "&")
|
|
||||||
.replace("[", "[")
|
|
||||||
.replace("]", "]")
|
|
||||||
.replace(",", ",")
|
|
||||||
)
|
|
||||||
# 生成CQ码，设置sub_type=1表示这是表情包
|
|
||||||
return f"[CQ:image,file=base64://{escaped_base64},sub_type=0]"
|
|
||||||
|
|
||||||
|
|
||||||
cq_code_tool = CQCode_tool()
|
|
||||||
@@ -9,27 +9,19 @@ from typing import Optional, Tuple
|
|||||||
from PIL import Image
|
from PIL import Image
|
||||||
import io
|
import io
|
||||||
|
|
||||||
from loguru import logger
|
|
||||||
from nonebot import get_driver
|
|
||||||
|
|
||||||
from ...common.database import db
|
from ...common.database import db
|
||||||
from ..chat.config import global_config
|
from ..config.config import global_config
|
||||||
from ..chat.utils import get_embedding
|
from ..chat.utils import get_embedding
|
||||||
from ..chat.utils_image import ImageManager, image_path_to_base64
|
from ..chat.utils_image import ImageManager, image_path_to_base64
|
||||||
from ..models.utils_model import LLM_request
|
from ..models.utils_model import LLM_request
|
||||||
|
from src.common.logger import get_module_logger
|
||||||
|
|
||||||
from ..utils.logger_config import LogClassification, LogModule
|
logger = get_module_logger("emoji")
|
||||||
|
|
||||||
# 配置日志
|
|
||||||
log_module = LogModule()
|
|
||||||
logger = log_module.setup_logger(LogClassification.EMOJI)
|
|
||||||
|
|
||||||
driver = get_driver()
|
|
||||||
config = driver.config
|
|
||||||
image_manager = ImageManager()
|
image_manager = ImageManager()
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
class EmojiManager:
|
class EmojiManager:
|
||||||
_instance = None
|
_instance = None
|
||||||
EMOJI_DIR = os.path.join("data", "emoji") # 表情包存储目录
|
EMOJI_DIR = os.path.join("data", "emoji") # 表情包存储目录
|
||||||
@@ -42,9 +34,9 @@ class EmojiManager:
|
|||||||
|
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
self._scan_task = None
|
self._scan_task = None
|
||||||
self.vlm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000)
|
self.vlm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000, request_type="emoji")
|
||||||
self.llm_emotion_judge = LLM_request(
|
self.llm_emotion_judge = LLM_request(
|
||||||
model=global_config.llm_emotion_judge, max_tokens=60, temperature=0.8
|
model=global_config.llm_emotion_judge, max_tokens=600, temperature=0.8, request_type="emoji"
|
||||||
) # 更高的温度，更少的token（后续可以根据情绪来调整温度）
|
) # 更高的温度，更少的token（后续可以根据情绪来调整温度）
|
||||||
|
|
||||||
def _ensure_emoji_dir(self):
|
def _ensure_emoji_dir(self):
|
||||||
@@ -115,14 +107,18 @@ class EmojiManager:
|
|||||||
if not text_for_search:
|
if not text_for_search:
|
||||||
logger.error("无法获取文本的情绪")
|
logger.error("无法获取文本的情绪")
|
||||||
return None
|
return None
|
||||||
text_embedding = await get_embedding(text_for_search)
|
text_embedding = await get_embedding(text_for_search, request_type="emoji")
|
||||||
if not text_embedding:
|
if not text_embedding:
|
||||||
logger.error("无法获取文本的embedding")
|
logger.error("无法获取文本的embedding")
|
||||||
return None
|
return None
|
||||||
|
|
||||||
try:
|
try:
|
||||||
# 获取所有表情包
|
# 获取所有表情包
|
||||||
all_emojis = list(db.emoji.find({}, {"_id": 1, "path": 1, "embedding": 1, "description": 1}))
|
all_emojis = [
|
||||||
|
e
|
||||||
|
for e in db.emoji.find({}, {"_id": 1, "path": 1, "embedding": 1, "description": 1, "blacklist": 1})
|
||||||
|
if "blacklist" not in e
|
||||||
|
]
|
||||||
|
|
||||||
if not all_emojis:
|
if not all_emojis:
|
||||||
logger.warning("数据库中没有任何表情包")
|
logger.warning("数据库中没有任何表情包")
|
||||||
@@ -177,7 +173,7 @@ class EmojiManager:
|
|||||||
logger.error(f"[错误] 获取表情包失败: {str(e)}")
|
logger.error(f"[错误] 获取表情包失败: {str(e)}")
|
||||||
return None
|
return None
|
||||||
|
|
||||||
async def _get_emoji_discription(self, image_base64: str) -> str:
|
async def _get_emoji_description(self, image_base64: str) -> str:
|
||||||
"""获取表情包的标签，使用image_manager的描述生成功能"""
|
"""获取表情包的标签，使用image_manager的描述生成功能"""
|
||||||
|
|
||||||
try:
|
try:
|
||||||
@@ -193,7 +189,10 @@ class EmojiManager:
|
|||||||
|
|
||||||
async def _check_emoji(self, image_base64: str, image_format: str) -> str:
|
async def _check_emoji(self, image_base64: str, image_format: str) -> str:
|
||||||
try:
|
try:
|
||||||
prompt = f'这是一个表情包，请回答这个表情包是否满足"{global_config.EMOJI_CHECK_PROMPT}"的要求，是则回答是，否则回答否，不要出现任何其他内容'
|
prompt = (
|
||||||
|
f'这是一个表情包，请回答这个表情包是否满足"{global_config.EMOJI_CHECK_PROMPT}"的要求，是则回答是，'
|
||||||
|
f"否则回答否，不要出现任何其他内容"
|
||||||
|
)
|
||||||
|
|
||||||
content, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
|
content, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
|
||||||
logger.debug(f"[检查] 表情包检查结果: {content}")
|
logger.debug(f"[检查] 表情包检查结果: {content}")
|
||||||
@@ -205,7 +204,11 @@ class EmojiManager:
|
|||||||
|
|
||||||
async def _get_kimoji_for_text(self, text: str):
|
async def _get_kimoji_for_text(self, text: str):
|
||||||
try:
|
try:
|
||||||
prompt = f'这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，注意不要输出任何对消息内容的分析内容，只输出"一种什么样的感觉"中间的形容词部分。'
|
prompt = (
|
||||||
|
f"这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，"
|
||||||
|
f"请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，"
|
||||||
|
f'注意不要输出任何对消息内容的分析内容，只输出"一种什么样的感觉"中间的形容词部分。'
|
||||||
|
)
|
||||||
|
|
||||||
content, _ = await self.llm_emotion_judge.generate_response_async(prompt, temperature=1.5)
|
content, _ = await self.llm_emotion_judge.generate_response_async(prompt, temperature=1.5)
|
||||||
logger.info(f"[情感] 表情包情感描述: {content}")
|
logger.info(f"[情感] 表情包情感描述: {content}")
|
||||||
@@ -239,12 +242,32 @@ class EmojiManager:
|
|||||||
image_hash = hashlib.md5(image_bytes).hexdigest()
|
image_hash = hashlib.md5(image_bytes).hexdigest()
|
||||||
image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
|
image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
|
||||||
# 检查是否已经注册过
|
# 检查是否已经注册过
|
||||||
existing_emoji = db["emoji"].find_one({"hash": image_hash})
|
existing_emoji_by_path = db["emoji"].find_one({"filename": filename})
|
||||||
|
existing_emoji_by_hash = db["emoji"].find_one({"hash": image_hash})
|
||||||
|
if existing_emoji_by_path and existing_emoji_by_hash:
|
||||||
|
if existing_emoji_by_path["_id"] != existing_emoji_by_hash["_id"]:
|
||||||
|
logger.error(f"[错误] 表情包已存在但记录不一致: {filename}")
|
||||||
|
db.emoji.delete_one({"_id": existing_emoji_by_path["_id"]})
|
||||||
|
db.emoji.delete_one({"_id": existing_emoji_by_hash["_id"]})
|
||||||
|
existing_emoji = None
|
||||||
|
else:
|
||||||
|
existing_emoji = existing_emoji_by_hash
|
||||||
|
elif existing_emoji_by_hash:
|
||||||
|
logger.error(f"[错误] 表情包hash已存在但path不存在: {filename}")
|
||||||
|
db.emoji.delete_one({"_id": existing_emoji_by_hash["_id"]})
|
||||||
|
existing_emoji = None
|
||||||
|
elif existing_emoji_by_path:
|
||||||
|
logger.error(f"[错误] 表情包path已存在但hash不存在: {filename}")
|
||||||
|
db.emoji.delete_one({"_id": existing_emoji_by_path["_id"]})
|
||||||
|
existing_emoji = None
|
||||||
|
else:
|
||||||
|
existing_emoji = None
|
||||||
|
|
||||||
description = None
|
description = None
|
||||||
|
|
||||||
if existing_emoji:
|
if existing_emoji:
|
||||||
# 即使表情包已存在，也检查是否需要同步到images集合
|
# 即使表情包已存在，也检查是否需要同步到images集合
|
||||||
description = existing_emoji.get("discription")
|
description = existing_emoji.get("description")
|
||||||
# 检查是否在images集合中存在
|
# 检查是否在images集合中存在
|
||||||
existing_image = db.images.find_one({"hash": image_hash})
|
existing_image = db.images.find_one({"hash": image_hash})
|
||||||
if not existing_image:
|
if not existing_image:
|
||||||
@@ -269,7 +292,7 @@ class EmojiManager:
|
|||||||
description = existing_description
|
description = existing_description
|
||||||
else:
|
else:
|
||||||
# 获取表情包的描述
|
# 获取表情包的描述
|
||||||
description = await self._get_emoji_discription(image_base64)
|
description = await self._get_emoji_description(image_base64)
|
||||||
|
|
||||||
if global_config.EMOJI_CHECK:
|
if global_config.EMOJI_CHECK:
|
||||||
check = await self._check_emoji(image_base64, image_format)
|
check = await self._check_emoji(image_base64, image_format)
|
||||||
@@ -281,14 +304,13 @@ class EmojiManager:
|
|||||||
logger.info(f"[检查] 表情包检查通过: {check}")
|
logger.info(f"[检查] 表情包检查通过: {check}")
|
||||||
|
|
||||||
if description is not None:
|
if description is not None:
|
||||||
embedding = await get_embedding(description)
|
embedding = await get_embedding(description, request_type="emoji")
|
||||||
|
|
||||||
# 准备数据库记录
|
# 准备数据库记录
|
||||||
emoji_record = {
|
emoji_record = {
|
||||||
"filename": filename,
|
"filename": filename,
|
||||||
"path": image_path,
|
"path": image_path,
|
||||||
"embedding": embedding,
|
"embedding": embedding,
|
||||||
"discription": description,
|
"description": description,
|
||||||
"hash": image_hash,
|
"hash": image_hash,
|
||||||
"timestamp": int(time.time()),
|
"timestamp": int(time.time()),
|
||||||
}
|
}
|
||||||
@@ -316,12 +338,12 @@ class EmojiManager:
|
|||||||
except Exception:
|
except Exception:
|
||||||
logger.exception("[错误] 扫描表情包失败")
|
logger.exception("[错误] 扫描表情包失败")
|
||||||
|
|
||||||
async def _periodic_scan(self, interval_MINS: int = 10):
|
async def _periodic_scan(self):
|
||||||
"""定期扫描新表情包"""
|
"""定期扫描新表情包"""
|
||||||
while True:
|
while True:
|
||||||
logger.info("[扫描] 开始扫描新表情包...")
|
logger.info("[扫描] 开始扫描新表情包...")
|
||||||
await self.scan_new_emojis()
|
await self.scan_new_emojis()
|
||||||
await asyncio.sleep(interval_MINS * 60) # 每600秒扫描一次
|
await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60)
|
||||||
|
|
||||||
def check_emoji_file_integrity(self):
|
def check_emoji_file_integrity(self):
|
||||||
"""检查表情包文件完整性
|
"""检查表情包文件完整性
|
||||||
@@ -364,6 +386,19 @@ class EmojiManager:
|
|||||||
logger.warning(f"[检查] 发现缺失记录（缺少hash字段），ID: {emoji.get('_id', 'unknown')}")
|
logger.warning(f"[检查] 发现缺失记录（缺少hash字段），ID: {emoji.get('_id', 'unknown')}")
|
||||||
hash = hashlib.md5(open(emoji["path"], "rb").read()).hexdigest()
|
hash = hashlib.md5(open(emoji["path"], "rb").read()).hexdigest()
|
||||||
db.emoji.update_one({"_id": emoji["_id"]}, {"$set": {"hash": hash}})
|
db.emoji.update_one({"_id": emoji["_id"]}, {"$set": {"hash": hash}})
|
||||||
|
else:
|
||||||
|
file_hash = hashlib.md5(open(emoji["path"], "rb").read()).hexdigest()
|
||||||
|
if emoji["hash"] != file_hash:
|
||||||
|
logger.warning(f"[检查] 表情包文件hash不匹配，ID: {emoji.get('_id', 'unknown')}")
|
||||||
|
db.emoji.delete_one({"_id": emoji["_id"]})
|
||||||
|
removed_count += 1
|
||||||
|
|
||||||
|
# 修复拼写错误
|
||||||
|
if "discription" in emoji:
|
||||||
|
desc = emoji["discription"]
|
||||||
|
db.emoji.update_one(
|
||||||
|
{"_id": emoji["_id"]}, {"$unset": {"discription": ""}, "$set": {"description": desc}}
|
||||||
|
)
|
||||||
|
|
||||||
except Exception as item_error:
|
except Exception as item_error:
|
||||||
logger.error(f"[错误] 处理表情包记录时出错: {str(item_error)}")
|
logger.error(f"[错误] 处理表情包记录时出错: {str(item_error)}")
|
||||||
@@ -381,10 +416,10 @@ class EmojiManager:
|
|||||||
logger.error(f"[错误] 检查表情包完整性失败: {str(e)}")
|
logger.error(f"[错误] 检查表情包完整性失败: {str(e)}")
|
||||||
logger.error(traceback.format_exc())
|
logger.error(traceback.format_exc())
|
||||||
|
|
||||||
async def start_periodic_check(self, interval_MINS: int = 120):
|
async def start_periodic_check(self):
|
||||||
while True:
|
while True:
|
||||||
self.check_emoji_file_integrity()
|
self.check_emoji_file_integrity()
|
||||||
await asyncio.sleep(interval_MINS * 60)
|
await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60)
|
||||||
|
|
||||||
|
|
||||||
# 创建全局单例
|
# 创建全局单例
|
||||||
|
|||||||
@@ -1,128 +1,102 @@
|
|||||||
import random
|
|
||||||
import time
|
import time
|
||||||
from typing import List, Optional, Tuple, Union
|
from typing import List, Optional, Tuple, Union
|
||||||
|
|
||||||
from nonebot import get_driver
|
|
||||||
from loguru import logger
|
|
||||||
|
|
||||||
from ...common.database import db
|
from ...common.database import db
|
||||||
from ..models.utils_model import LLM_request
|
from ..models.utils_model import LLM_request
|
||||||
from .config import global_config
|
from ..config.config import global_config
|
||||||
from .message import MessageRecv, MessageThinking, Message
|
from .message import MessageRecv, MessageThinking, Message
|
||||||
from .prompt_builder import prompt_builder
|
from .prompt_builder import prompt_builder
|
||||||
from .relationship_manager import relationship_manager
|
|
||||||
from .utils import process_llm_response
|
from .utils import process_llm_response
|
||||||
|
from src.common.logger import get_module_logger, LogConfig, LLM_STYLE_CONFIG
|
||||||
|
|
||||||
driver = get_driver()
|
# 定义日志配置
|
||||||
config = driver.config
|
llm_config = LogConfig(
|
||||||
|
# 使用消息发送专用样式
|
||||||
|
console_format=LLM_STYLE_CONFIG["console_format"],
|
||||||
|
file_format=LLM_STYLE_CONFIG["file_format"],
|
||||||
|
)
|
||||||
|
|
||||||
|
logger = get_module_logger("llm_generator", config=llm_config)
|
||||||
|
|
||||||
|
|
||||||
class ResponseGenerator:
|
class ResponseGenerator:
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
self.model_r1 = LLM_request(
|
self.model_reasoning = LLM_request(
|
||||||
model=global_config.llm_reasoning,
|
model=global_config.llm_reasoning,
|
||||||
temperature=0.7,
|
temperature=0.7,
|
||||||
max_tokens=1000,
|
max_tokens=3000,
|
||||||
stream=True,
|
request_type="response",
|
||||||
)
|
)
|
||||||
self.model_v3 = LLM_request(
|
self.model_normal = LLM_request(
|
||||||
model=global_config.llm_normal, temperature=0.7, max_tokens=1000
|
model=global_config.llm_normal, temperature=0.8, max_tokens=256, request_type="response"
|
||||||
)
|
)
|
||||||
self.model_r1_distill = LLM_request(
|
|
||||||
model=global_config.llm_reasoning_minor, temperature=0.7, max_tokens=1000
|
self.model_sum = LLM_request(
|
||||||
)
|
model=global_config.llm_summary_by_topic, temperature=0.7, max_tokens=3000, request_type="relation"
|
||||||
self.model_v25 = LLM_request(
|
|
||||||
model=global_config.llm_normal_minor, temperature=0.7, max_tokens=1000
|
|
||||||
)
|
)
|
||||||
self.current_model_type = "r1" # 默认使用 R1
|
self.current_model_type = "r1" # 默认使用 R1
|
||||||
|
self.current_model_name = "unknown model"
|
||||||
|
|
||||||
async def generate_response(
|
async def generate_response(self, message: MessageThinking) -> Optional[Union[str, List[str]]]:
|
||||||
self, message: MessageThinking
|
|
||||||
) -> Optional[Union[str, List[str]]]:
|
|
||||||
"""根据当前模型类型选择对应的生成函数"""
|
"""根据当前模型类型选择对应的生成函数"""
|
||||||
# 从global_config中获取模型概率值并选择模型
|
# 从global_config中获取模型概率值并选择模型
|
||||||
rand = random.random()
|
# if random.random() < global_config.MODEL_R1_PROBABILITY:
|
||||||
if rand < global_config.MODEL_R1_PROBABILITY:
|
# self.current_model_type = "深深地"
|
||||||
self.current_model_type = "r1"
|
# current_model = self.model_reasoning
|
||||||
current_model = self.model_r1
|
# else:
|
||||||
elif (
|
# self.current_model_type = "浅浅的"
|
||||||
rand
|
# current_model = self.model_normal
|
||||||
< global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY
|
|
||||||
):
|
|
||||||
self.current_model_type = "v3"
|
|
||||||
current_model = self.model_v3
|
|
||||||
else:
|
|
||||||
self.current_model_type = "r1_distill"
|
|
||||||
current_model = self.model_r1_distill
|
|
||||||
|
|
||||||
logger.info(f"{global_config.BOT_NICKNAME}{self.current_model_type}思考中")
|
# logger.info(
|
||||||
|
# f"{self.current_model_type}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
|
||||||
|
# ) # noqa: E501
|
||||||
|
|
||||||
model_response = await self._generate_response_with_model(
|
|
||||||
message, current_model
|
logger.info(
|
||||||
|
f"思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
|
||||||
)
|
)
|
||||||
raw_content = model_response
|
|
||||||
|
|
||||||
# print(f"raw_content: {raw_content}")
|
current_model = self.model_normal
|
||||||
# print(f"model_response: {model_response}")
|
model_response = await self._generate_response_with_model(message, current_model)
|
||||||
|
|
||||||
|
# print(f"raw_content: {model_response}")
|
||||||
|
|
||||||
if model_response:
|
if model_response:
|
||||||
logger.info(f'{global_config.BOT_NICKNAME}的回复是：{model_response}')
|
logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}")
|
||||||
model_response = await self._process_response(model_response)
|
model_response = await self._process_response(model_response)
|
||||||
if model_response:
|
|
||||||
return model_response, raw_content
|
|
||||||
return None, raw_content
|
|
||||||
|
|
||||||
async def _generate_response_with_model(
|
return model_response
|
||||||
self, message: MessageThinking, model: LLM_request
|
else:
|
||||||
) -> Optional[str]:
|
logger.info(f"{self.current_model_type}思考，失败")
|
||||||
"""使用指定的模型生成回复"""
|
return None
|
||||||
sender_name = (
|
|
||||||
message.chat_stream.user_info.user_nickname
|
|
||||||
or f"用户{message.chat_stream.user_info.user_id}"
|
|
||||||
)
|
|
||||||
if message.chat_stream.user_info.user_cardname:
|
|
||||||
sender_name = f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]{message.chat_stream.user_info.user_cardname}"
|
|
||||||
|
|
||||||
# 获取关系值
|
async def _generate_response_with_model(self, message: MessageThinking, model: LLM_request):
|
||||||
relationship_value = (
|
sender_name = ""
|
||||||
relationship_manager.get_relationship(
|
if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname:
|
||||||
message.chat_stream
|
sender_name = (
|
||||||
).relationship_value
|
f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]"
|
||||||
if relationship_manager.get_relationship(message.chat_stream)
|
f"{message.chat_stream.user_info.user_cardname}"
|
||||||
else 0.0
|
)
|
||||||
)
|
elif message.chat_stream.user_info.user_nickname:
|
||||||
if relationship_value != 0.0:
|
sender_name = f"({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}"
|
||||||
# print(f"\033[1;32m[关系管理]\033[0m 回复中_当前关系值: {relationship_value}")
|
else:
|
||||||
pass
|
sender_name = f"用户({message.chat_stream.user_info.user_id})"
|
||||||
|
|
||||||
|
logger.debug("开始使用生成回复-2")
|
||||||
# 构建prompt
|
# 构建prompt
|
||||||
prompt, prompt_check = await prompt_builder._build_prompt(
|
timer1 = time.time()
|
||||||
|
prompt = await prompt_builder._build_prompt(
|
||||||
|
message.chat_stream,
|
||||||
message_txt=message.processed_plain_text,
|
message_txt=message.processed_plain_text,
|
||||||
sender_name=sender_name,
|
sender_name=sender_name,
|
||||||
relationship_value=relationship_value,
|
|
||||||
stream_id=message.chat_stream.stream_id,
|
stream_id=message.chat_stream.stream_id,
|
||||||
)
|
)
|
||||||
|
timer2 = time.time()
|
||||||
|
logger.info(f"构建prompt时间: {timer2 - timer1}秒")
|
||||||
|
|
||||||
# 读空气模块 简化逻辑，先停用
|
|
||||||
# if global_config.enable_kuuki_read:
|
|
||||||
# content_check, reasoning_content_check = await self.model_v3.generate_response(prompt_check)
|
|
||||||
# print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}")
|
|
||||||
# if 'yes' not in content_check.lower() and random.random() < 0.3:
|
|
||||||
# self._save_to_db(
|
|
||||||
# message=message,
|
|
||||||
# sender_name=sender_name,
|
|
||||||
# prompt=prompt,
|
|
||||||
# prompt_check=prompt_check,
|
|
||||||
# content="",
|
|
||||||
# content_check=content_check,
|
|
||||||
# reasoning_content="",
|
|
||||||
# reasoning_content_check=reasoning_content_check
|
|
||||||
# )
|
|
||||||
# return None
|
|
||||||
|
|
||||||
# 生成回复
|
|
||||||
try:
|
try:
|
||||||
content, reasoning_content = await model.generate_response(prompt)
|
content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
|
||||||
except Exception:
|
except Exception:
|
||||||
logger.exception("生成回复时出错")
|
logger.exception("生成回复时出错")
|
||||||
return None
|
return None
|
||||||
@@ -132,9 +106,7 @@ class ResponseGenerator:
|
|||||||
message=message,
|
message=message,
|
||||||
sender_name=sender_name,
|
sender_name=sender_name,
|
||||||
prompt=prompt,
|
prompt=prompt,
|
||||||
prompt_check=prompt_check,
|
|
||||||
content=content,
|
content=content,
|
||||||
# content_check=content_check if global_config.enable_kuuki_read else "",
|
|
||||||
reasoning_content=reasoning_content,
|
reasoning_content=reasoning_content,
|
||||||
# reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
|
# reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
|
||||||
)
|
)
|
||||||
@@ -148,7 +120,6 @@ class ResponseGenerator:
|
|||||||
message: MessageRecv,
|
message: MessageRecv,
|
||||||
sender_name: str,
|
sender_name: str,
|
||||||
prompt: str,
|
prompt: str,
|
||||||
prompt_check: str,
|
|
||||||
content: str,
|
content: str,
|
||||||
reasoning_content: str,
|
reasoning_content: str,
|
||||||
):
|
):
|
||||||
@@ -159,42 +130,60 @@ class ResponseGenerator:
|
|||||||
"chat_id": message.chat_stream.stream_id,
|
"chat_id": message.chat_stream.stream_id,
|
||||||
"user": sender_name,
|
"user": sender_name,
|
||||||
"message": message.processed_plain_text,
|
"message": message.processed_plain_text,
|
||||||
"model": self.current_model_type,
|
"model": self.current_model_name,
|
||||||
# 'reasoning_check': reasoning_content_check,
|
|
||||||
# 'response_check': content_check,
|
|
||||||
"reasoning": reasoning_content,
|
"reasoning": reasoning_content,
|
||||||
"response": content,
|
"response": content,
|
||||||
"prompt": prompt,
|
"prompt": prompt,
|
||||||
"prompt_check": prompt_check,
|
|
||||||
}
|
}
|
||||||
)
|
)
|
||||||
|
|
||||||
async def _get_emotion_tags(self, content: str) -> List[str]:
|
async def _get_emotion_tags(self, content: str, processed_plain_text: str):
|
||||||
"""提取情感标签"""
|
"""提取情感标签，结合立场和情绪"""
|
||||||
try:
|
try:
|
||||||
prompt = f"""请从以下内容中，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签并输出
|
# 构建提示词，结合回复内容、被回复的内容以及立场分析
|
||||||
只输出标签就好，不要输出其他内容:
|
prompt = f"""
|
||||||
内容：{content}
|
请严格根据以下对话内容，完成以下任务：
|
||||||
输出：
|
1. 判断回复者对被回复者观点的直接立场：
|
||||||
|
- "支持"：明确同意或强化被回复者观点
|
||||||
|
- "反对"：明确反驳或否定被回复者观点
|
||||||
|
- "中立"：不表达明确立场或无关回应
|
||||||
|
2. 从"开心,愤怒,悲伤,惊讶,平静,害羞,恐惧,厌恶,困惑"中选出最匹配的1个情感标签
|
||||||
|
3. 按照"立场-情绪"的格式直接输出结果，例如："反对-愤怒"
|
||||||
|
|
||||||
|
对话示例：
|
||||||
|
被回复：「A就是笨」
|
||||||
|
回复：「A明明很聪明」 → 反对-愤怒
|
||||||
|
|
||||||
|
当前对话：
|
||||||
|
被回复：「{processed_plain_text}」
|
||||||
|
回复：「{content}」
|
||||||
|
|
||||||
|
输出要求：
|
||||||
|
- 只需输出"立场-情绪"结果，不要解释
|
||||||
|
- 严格基于文字直接表达的对立关系判断
|
||||||
"""
|
"""
|
||||||
content, _ = await self.model_v25.generate_response(prompt)
|
|
||||||
content = content.strip()
|
# 调用模型生成结果
|
||||||
if content in [
|
result, _, _ = await self.model_sum.generate_response(prompt)
|
||||||
"happy",
|
result = result.strip()
|
||||||
"angry",
|
|
||||||
"sad",
|
# 解析模型输出的结果
|
||||||
"surprised",
|
if "-" in result:
|
||||||
"disgusted",
|
stance, emotion = result.split("-", 1)
|
||||||
"fearful",
|
valid_stances = ["支持", "反对", "中立"]
|
||||||
"neutral",
|
valid_emotions = ["开心", "愤怒", "悲伤", "惊讶", "害羞", "平静", "恐惧", "厌恶", "困惑"]
|
||||||
]:
|
if stance in valid_stances and emotion in valid_emotions:
|
||||||
return [content]
|
return stance, emotion # 返回有效的立场-情绪组合
|
||||||
|
else:
|
||||||
|
logger.debug(f"无效立场-情感组合:{result}")
|
||||||
|
return "中立", "平静" # 默认返回中立-平静
|
||||||
else:
|
else:
|
||||||
return ["neutral"]
|
logger.debug(f"立场-情感格式错误:{result}")
|
||||||
|
return "中立", "平静" # 格式错误时返回默认值
|
||||||
|
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
print(f"获取情感标签时出错: {e}")
|
logger.debug(f"获取情感标签时出错: {e}")
|
||||||
return ["neutral"]
|
return "中立", "平静" # 出错时返回默认值
|
||||||
|
|
||||||
async def _process_response(self, content: str) -> Tuple[List[str], List[str]]:
|
async def _process_response(self, content: str) -> Tuple[List[str], List[str]]:
|
||||||
"""处理响应内容，返回处理后的内容和情感标签"""
|
"""处理响应内容，返回处理后的内容和情感标签"""
|
||||||
@@ -212,15 +201,13 @@ class InitiativeMessageGenerate:
|
|||||||
def __init__(self):
|
def __init__(self):
|
||||||
self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7)
|
self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7)
|
||||||
self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7)
|
self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7)
|
||||||
self.model_r1_distill = LLM_request(
|
self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7)
|
||||||
model=global_config.llm_reasoning_minor, temperature=0.7
|
|
||||||
)
|
|
||||||
|
|
||||||
def gen_response(self, message: Message):
|
def gen_response(self, message: Message):
|
||||||
topic_select_prompt, dots_for_select, prompt_template = (
|
topic_select_prompt, dots_for_select, prompt_template = prompt_builder._build_initiative_prompt_select(
|
||||||
prompt_builder._build_initiative_prompt_select(message.group_id)
|
message.group_id
|
||||||
)
|
)
|
||||||
content_select, reasoning = self.model_v3.generate_response(topic_select_prompt)
|
content_select, reasoning, _ = self.model_v3.generate_response(topic_select_prompt)
|
||||||
logger.debug(f"{content_select} {reasoning}")
|
logger.debug(f"{content_select} {reasoning}")
|
||||||
topics_list = [dot[0] for dot in dots_for_select]
|
topics_list = [dot[0] for dot in dots_for_select]
|
||||||
if content_select:
|
if content_select:
|
||||||
@@ -230,16 +217,12 @@ class InitiativeMessageGenerate:
|
|||||||
return None
|
return None
|
||||||
else:
|
else:
|
||||||
return None
|
return None
|
||||||
prompt_check, memory = prompt_builder._build_initiative_prompt_check(
|
prompt_check, memory = prompt_builder._build_initiative_prompt_check(select_dot[1], prompt_template)
|
||||||
select_dot[1], prompt_template
|
content_check, reasoning_check, _ = self.model_v3.generate_response(prompt_check)
|
||||||
)
|
|
||||||
content_check, reasoning_check = self.model_v3.generate_response(prompt_check)
|
|
||||||
logger.info(f"{content_check} {reasoning_check}")
|
logger.info(f"{content_check} {reasoning_check}")
|
||||||
if "yes" not in content_check.lower():
|
if "yes" not in content_check.lower():
|
||||||
return None
|
return None
|
||||||
prompt = prompt_builder._build_initiative_prompt(
|
prompt = prompt_builder._build_initiative_prompt(select_dot, prompt_template, memory)
|
||||||
select_dot, prompt_template, memory
|
|
||||||
)
|
|
||||||
content, reasoning = self.model_r1.generate_response_async(prompt)
|
content, reasoning = self.model_r1.generate_response_async(prompt)
|
||||||
logger.debug(f"[DEBUG] {content} {reasoning}")
|
logger.debug(f"[DEBUG] {content} {reasoning}")
|
||||||
return content
|
return content
|
||||||
|
|||||||
@@ -1,26 +1,190 @@
|
|||||||
emojimapper = {5: "流泪", 311: "打 call", 312: "变形", 314: "仔细分析", 317: "菜汪", 318: "崇拜", 319: "比心",
|
emojimapper = {
|
||||||
320: "庆祝", 324: "吃糖", 325: "惊吓", 337: "花朵脸", 338: "我想开了", 339: "舔屏", 341: "打招呼",
|
5: "流泪",
|
||||||
342: "酸Q", 343: "我方了", 344: "大怨种", 345: "红包多多", 346: "你真棒棒", 181: "戳一戳", 74: "太阳",
|
311: "打 call",
|
||||||
75: "月亮", 351: "敲敲", 349: "坚强", 350: "贴贴", 395: "略略略", 114: "篮球", 326: "生气", 53: "蛋糕",
|
312: "变形",
|
||||||
137: "鞭炮", 333: "烟花", 424: "续标识", 415: "划龙舟", 392: "龙年快乐", 425: "求放过", 427: "偷感",
|
314: "仔细分析",
|
||||||
426: "玩火", 419: "火车", 429: "蛇年快乐",
|
317: "菜汪",
|
||||||
14: "微笑", 1: "撇嘴", 2: "色", 3: "发呆", 4: "得意", 6: "害羞", 7: "闭嘴", 8: "睡", 9: "大哭",
|
318: "崇拜",
|
||||||
10: "尴尬", 11: "发怒", 12: "调皮", 13: "呲牙", 0: "惊讶", 15: "难过", 16: "酷", 96: "冷汗", 18: "抓狂",
|
319: "比心",
|
||||||
19: "吐", 20: "偷笑", 21: "可爱", 22: "白眼", 23: "傲慢", 24: "饥饿", 25: "困", 26: "惊恐", 27: "流汗",
|
320: "庆祝",
|
||||||
28: "憨笑", 29: "悠闲", 30: "奋斗", 31: "咒骂", 32: "疑问", 33: "嘘", 34: "晕", 35: "折磨", 36: "衰",
|
324: "吃糖",
|
||||||
37: "骷髅", 38: "敲打", 39: "再见", 97: "擦汗", 98: "抠鼻", 99: "鼓掌", 100: "糗大了", 101: "坏笑",
|
325: "惊吓",
|
||||||
102: "左哼哼", 103: "右哼哼", 104: "哈欠", 105: "鄙视", 106: "委屈", 107: "快哭了", 108: "阴险",
|
337: "花朵脸",
|
||||||
305: "右亲亲", 109: "左亲亲", 110: "吓", 111: "可怜", 172: "眨眼睛", 182: "笑哭", 179: "doge",
|
338: "我想开了",
|
||||||
173: "泪奔", 174: "无奈", 212: "托腮", 175: "卖萌", 178: "斜眼笑", 177: "喷血", 176: "小纠结",
|
339: "舔屏",
|
||||||
183: "我最美", 262: "脑阔疼", 263: "沧桑", 264: "捂脸", 265: "辣眼睛", 266: "哦哟", 267: "头秃",
|
341: "打招呼",
|
||||||
268: "问号脸", 269: "暗中观察", 270: "emm", 271: "吃瓜", 272: "呵呵哒", 277: "汪汪", 307: "喵喵",
|
342: "酸Q",
|
||||||
306: "牛气冲天", 281: "无眼笑", 282: "敬礼", 283: "狂笑", 284: "面无表情", 285: "摸鱼", 293: "摸锦鲤",
|
343: "我方了",
|
||||||
286: "魔鬼笑", 287: "哦", 289: "睁眼", 294: "期待", 297: "拜谢", 298: "元宝", 299: "牛啊", 300: "胖三斤",
|
344: "大怨种",
|
||||||
323: "嫌弃", 332: "举牌牌", 336: "豹富", 353: "拜托", 355: "耶", 356: "666", 354: "尊嘟假嘟", 352: "咦",
|
345: "红包多多",
|
||||||
357: "裂开", 334: "虎虎生威", 347: "大展宏兔", 303: "右拜年", 302: "左拜年", 295: "拿到红包", 49: "拥抱",
|
346: "你真棒棒",
|
||||||
66: "爱心", 63: "玫瑰", 64: "凋谢", 187: "幽灵", 146: "爆筋", 116: "示爱", 67: "心碎", 60: "咖啡",
|
181: "戳一戳",
|
||||||
185: "羊驼", 76: "赞", 124: "OK", 118: "抱拳", 78: "握手", 119: "勾引", 79: "胜利", 120: "拳头",
|
74: "太阳",
|
||||||
121: "差劲", 77: "踩", 123: "NO", 201: "点赞", 273: "我酸了", 46: "猪头", 112: "菜刀", 56: "刀",
|
75: "月亮",
|
||||||
169: "手枪", 171: "茶", 59: "便便", 144: "喝彩", 147: "棒棒糖", 89: "西瓜", 41: "发抖", 125: "转圈",
|
351: "敲敲",
|
||||||
42: "爱情", 43: "跳跳", 86: "怄火", 129: "挥手", 85: "飞吻", 428: "收到",
|
349: "坚强",
|
||||||
423: "复兴号", 432: "灵蛇献瑞"}
|
350: "贴贴",
|
||||||
|
395: "略略略",
|
||||||
|
114: "篮球",
|
||||||
|
326: "生气",
|
||||||
|
53: "蛋糕",
|
||||||
|
137: "鞭炮",
|
||||||
|
333: "烟花",
|
||||||
|
424: "续标识",
|
||||||
|
415: "划龙舟",
|
||||||
|
392: "龙年快乐",
|
||||||
|
425: "求放过",
|
||||||
|
427: "偷感",
|
||||||
|
426: "玩火",
|
||||||
|
419: "火车",
|
||||||
|
429: "蛇年快乐",
|
||||||
|
14: "微笑",
|
||||||
|
1: "撇嘴",
|
||||||
|
2: "色",
|
||||||
|
3: "发呆",
|
||||||
|
4: "得意",
|
||||||
|
6: "害羞",
|
||||||
|
7: "闭嘴",
|
||||||
|
8: "睡",
|
||||||
|
9: "大哭",
|
||||||
|
10: "尴尬",
|
||||||
|
11: "发怒",
|
||||||
|
12: "调皮",
|
||||||
|
13: "呲牙",
|
||||||
|
0: "惊讶",
|
||||||
|
15: "难过",
|
||||||
|
16: "酷",
|
||||||
|
96: "冷汗",
|
||||||
|
18: "抓狂",
|
||||||
|
19: "吐",
|
||||||
|
20: "偷笑",
|
||||||
|
21: "可爱",
|
||||||
|
22: "白眼",
|
||||||
|
23: "傲慢",
|
||||||
|
24: "饥饿",
|
||||||
|
25: "困",
|
||||||
|
26: "惊恐",
|
||||||
|
27: "流汗",
|
||||||
|
28: "憨笑",
|
||||||
|
29: "悠闲",
|
||||||
|
30: "奋斗",
|
||||||
|
31: "咒骂",
|
||||||
|
32: "疑问",
|
||||||
|
33: "嘘",
|
||||||
|
34: "晕",
|
||||||
|
35: "折磨",
|
||||||
|
36: "衰",
|
||||||
|
37: "骷髅",
|
||||||
|
38: "敲打",
|
||||||
|
39: "再见",
|
||||||
|
97: "擦汗",
|
||||||
|
98: "抠鼻",
|
||||||
|
99: "鼓掌",
|
||||||
|
100: "糗大了",
|
||||||
|
101: "坏笑",
|
||||||
|
102: "左哼哼",
|
||||||
|
103: "右哼哼",
|
||||||
|
104: "哈欠",
|
||||||
|
105: "鄙视",
|
||||||
|
106: "委屈",
|
||||||
|
107: "快哭了",
|
||||||
|
108: "阴险",
|
||||||
|
305: "右亲亲",
|
||||||
|
109: "左亲亲",
|
||||||
|
110: "吓",
|
||||||
|
111: "可怜",
|
||||||
|
172: "眨眼睛",
|
||||||
|
182: "笑哭",
|
||||||
|
179: "doge",
|
||||||
|
173: "泪奔",
|
||||||
|
174: "无奈",
|
||||||
|
212: "托腮",
|
||||||
|
175: "卖萌",
|
||||||
|
178: "斜眼笑",
|
||||||
|
177: "喷血",
|
||||||
|
176: "小纠结",
|
||||||
|
183: "我最美",
|
||||||
|
262: "脑阔疼",
|
||||||
|
263: "沧桑",
|
||||||
|
264: "捂脸",
|
||||||
|
265: "辣眼睛",
|
||||||
|
266: "哦哟",
|
||||||
|
267: "头秃",
|
||||||
|
268: "问号脸",
|
||||||
|
269: "暗中观察",
|
||||||
|
270: "emm",
|
||||||
|
271: "吃瓜",
|
||||||
|
272: "呵呵哒",
|
||||||
|
277: "汪汪",
|
||||||
|
307: "喵喵",
|
||||||
|
306: "牛气冲天",
|
||||||
|
281: "无眼笑",
|
||||||
|
282: "敬礼",
|
||||||
|
283: "狂笑",
|
||||||
|
284: "面无表情",
|
||||||
|
285: "摸鱼",
|
||||||
|
293: "摸锦鲤",
|
||||||
|
286: "魔鬼笑",
|
||||||
|
287: "哦",
|
||||||
|
289: "睁眼",
|
||||||
|
294: "期待",
|
||||||
|
297: "拜谢",
|
||||||
|
298: "元宝",
|
||||||
|
299: "牛啊",
|
||||||
|
300: "胖三斤",
|
||||||
|
323: "嫌弃",
|
||||||
|
332: "举牌牌",
|
||||||
|
336: "豹富",
|
||||||
|
353: "拜托",
|
||||||
|
355: "耶",
|
||||||
|
356: "666",
|
||||||
|
354: "尊嘟假嘟",
|
||||||
|
352: "咦",
|
||||||
|
357: "裂开",
|
||||||
|
334: "虎虎生威",
|
||||||
|
347: "大展宏兔",
|
||||||
|
303: "右拜年",
|
||||||
|
302: "左拜年",
|
||||||
|
295: "拿到红包",
|
||||||
|
49: "拥抱",
|
||||||
|
66: "爱心",
|
||||||
|
63: "玫瑰",
|
||||||
|
64: "凋谢",
|
||||||
|
187: "幽灵",
|
||||||
|
146: "爆筋",
|
||||||
|
116: "示爱",
|
||||||
|
67: "心碎",
|
||||||
|
60: "咖啡",
|
||||||
|
185: "羊驼",
|
||||||
|
76: "赞",
|
||||||
|
124: "OK",
|
||||||
|
118: "抱拳",
|
||||||
|
78: "握手",
|
||||||
|
119: "勾引",
|
||||||
|
79: "胜利",
|
||||||
|
120: "拳头",
|
||||||
|
121: "差劲",
|
||||||
|
77: "踩",
|
||||||
|
123: "NO",
|
||||||
|
201: "点赞",
|
||||||
|
273: "我酸了",
|
||||||
|
46: "猪头",
|
||||||
|
112: "菜刀",
|
||||||
|
56: "刀",
|
||||||
|
169: "手枪",
|
||||||
|
171: "茶",
|
||||||
|
59: "便便",
|
||||||
|
144: "喝彩",
|
||||||
|
147: "棒棒糖",
|
||||||
|
89: "西瓜",
|
||||||
|
41: "发抖",
|
||||||
|
125: "转圈",
|
||||||
|
42: "爱情",
|
||||||
|
43: "跳跳",
|
||||||
|
86: "怄火",
|
||||||
|
129: "挥手",
|
||||||
|
85: "飞吻",
|
||||||
|
428: "收到",
|
||||||
|
423: "复兴号",
|
||||||
|
432: "灵蛇献瑞",
|
||||||
|
}
|
||||||
|
|||||||
@@ -1,17 +1,16 @@
|
|||||||
import time
|
import time
|
||||||
import html
|
|
||||||
import re
|
|
||||||
import json
|
|
||||||
from dataclasses import dataclass
|
from dataclasses import dataclass
|
||||||
from typing import Dict, List, Optional
|
from typing import Dict, List, Optional
|
||||||
|
|
||||||
import urllib3
|
import urllib3
|
||||||
from loguru import logger
|
|
||||||
|
|
||||||
from .utils_image import image_manager
|
from .utils_image import image_manager
|
||||||
|
|
||||||
from .message_base import Seg, GroupInfo, UserInfo, BaseMessageInfo, MessageBase
|
from ..message.message_base import Seg, UserInfo, BaseMessageInfo, MessageBase
|
||||||
from .chat_stream import ChatStream, chat_manager
|
from .chat_stream import ChatStream
|
||||||
|
from src.common.logger import get_module_logger
|
||||||
|
|
||||||
|
logger = get_module_logger("chat_message")
|
||||||
|
|
||||||
# 禁用SSL警告
|
# 禁用SSL警告
|
||||||
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
|
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
|
||||||
@@ -27,6 +26,7 @@ class Message(MessageBase):
|
|||||||
reply: Optional["Message"] = None
|
reply: Optional["Message"] = None
|
||||||
detailed_plain_text: str = ""
|
detailed_plain_text: str = ""
|
||||||
processed_plain_text: str = ""
|
processed_plain_text: str = ""
|
||||||
|
memorized_times: int = 0
|
||||||
|
|
||||||
def __init__(
|
def __init__(
|
||||||
self,
|
self,
|
||||||
@@ -72,19 +72,6 @@ class MessageRecv(Message):
|
|||||||
"""
|
"""
|
||||||
self.message_info = BaseMessageInfo.from_dict(message_dict.get("message_info", {}))
|
self.message_info = BaseMessageInfo.from_dict(message_dict.get("message_info", {}))
|
||||||
|
|
||||||
message_segment = message_dict.get("message_segment", {})
|
|
||||||
|
|
||||||
if message_segment.get("data", "") == "[json]":
|
|
||||||
# 提取json消息中的展示信息
|
|
||||||
pattern = r"\[CQ:json,data=(?P<json_data>.+?)\]"
|
|
||||||
match = re.search(pattern, message_dict.get("raw_message", ""))
|
|
||||||
raw_json = html.unescape(match.group("json_data"))
|
|
||||||
try:
|
|
||||||
json_message = json.loads(raw_json)
|
|
||||||
except json.JSONDecodeError:
|
|
||||||
json_message = {}
|
|
||||||
message_segment["data"] = json_message.get("prompt", "")
|
|
||||||
|
|
||||||
self.message_segment = Seg.from_dict(message_dict.get("message_segment", {}))
|
self.message_segment = Seg.from_dict(message_dict.get("message_segment", {}))
|
||||||
self.raw_message = message_dict.get("raw_message")
|
self.raw_message = message_dict.get("raw_message")
|
||||||
|
|
||||||
@@ -159,7 +146,7 @@ class MessageRecv(Message):
|
|||||||
user_info = self.message_info.user_info
|
user_info = self.message_info.user_info
|
||||||
name = (
|
name = (
|
||||||
f"{user_info.user_nickname}(ta的昵称:{user_info.user_cardname},ta的id:{user_info.user_id})"
|
f"{user_info.user_nickname}(ta的昵称:{user_info.user_cardname},ta的id:{user_info.user_id})"
|
||||||
if user_info.user_cardname != ""
|
if user_info.user_cardname != None
|
||||||
else f"{user_info.user_nickname}(ta的id:{user_info.user_id})"
|
else f"{user_info.user_nickname}(ta的id:{user_info.user_id})"
|
||||||
)
|
)
|
||||||
return f"[{time_str}] {name}: {self.processed_plain_text}\n"
|
return f"[{time_str}] {name}: {self.processed_plain_text}\n"
|
||||||
@@ -176,6 +163,7 @@ class MessageProcessBase(Message):
|
|||||||
bot_user_info: UserInfo,
|
bot_user_info: UserInfo,
|
||||||
message_segment: Optional[Seg] = None,
|
message_segment: Optional[Seg] = None,
|
||||||
reply: Optional["MessageRecv"] = None,
|
reply: Optional["MessageRecv"] = None,
|
||||||
|
thinking_start_time: float = 0,
|
||||||
):
|
):
|
||||||
# 调用父类初始化
|
# 调用父类初始化
|
||||||
super().__init__(
|
super().__init__(
|
||||||
@@ -188,7 +176,7 @@ class MessageProcessBase(Message):
|
|||||||
)
|
)
|
||||||
|
|
||||||
# 处理状态相关属性
|
# 处理状态相关属性
|
||||||
self.thinking_start_time = int(time.time())
|
self.thinking_start_time = thinking_start_time
|
||||||
self.thinking_time = 0
|
self.thinking_time = 0
|
||||||
|
|
||||||
def update_thinking_time(self) -> float:
|
def update_thinking_time(self) -> float:
|
||||||
@@ -255,7 +243,7 @@ class MessageProcessBase(Message):
|
|||||||
user_info = self.message_info.user_info
|
user_info = self.message_info.user_info
|
||||||
name = (
|
name = (
|
||||||
f"{user_info.user_nickname}(ta的昵称:{user_info.user_cardname},ta的id:{user_info.user_id})"
|
f"{user_info.user_nickname}(ta的昵称:{user_info.user_cardname},ta的id:{user_info.user_id})"
|
||||||
if user_info.user_cardname != ""
|
if user_info.user_cardname != None
|
||||||
else f"{user_info.user_nickname}(ta的id:{user_info.user_id})"
|
else f"{user_info.user_nickname}(ta的id:{user_info.user_id})"
|
||||||
)
|
)
|
||||||
return f"[{time_str}] {name}: {self.processed_plain_text}\n"
|
return f"[{time_str}] {name}: {self.processed_plain_text}\n"
|
||||||
@@ -271,6 +259,7 @@ class MessageThinking(MessageProcessBase):
|
|||||||
chat_stream: ChatStream,
|
chat_stream: ChatStream,
|
||||||
bot_user_info: UserInfo,
|
bot_user_info: UserInfo,
|
||||||
reply: Optional["MessageRecv"] = None,
|
reply: Optional["MessageRecv"] = None,
|
||||||
|
thinking_start_time: float = 0,
|
||||||
):
|
):
|
||||||
# 调用父类初始化
|
# 调用父类初始化
|
||||||
super().__init__(
|
super().__init__(
|
||||||
@@ -279,6 +268,7 @@ class MessageThinking(MessageProcessBase):
|
|||||||
bot_user_info=bot_user_info,
|
bot_user_info=bot_user_info,
|
||||||
message_segment=None, # 思考状态不需要消息段
|
message_segment=None, # 思考状态不需要消息段
|
||||||
reply=reply,
|
reply=reply,
|
||||||
|
thinking_start_time=thinking_start_time,
|
||||||
)
|
)
|
||||||
|
|
||||||
# 思考状态特有属性
|
# 思考状态特有属性
|
||||||
@@ -299,6 +289,7 @@ class MessageSending(MessageProcessBase):
|
|||||||
reply: Optional["MessageRecv"] = None,
|
reply: Optional["MessageRecv"] = None,
|
||||||
is_head: bool = False,
|
is_head: bool = False,
|
||||||
is_emoji: bool = False,
|
is_emoji: bool = False,
|
||||||
|
thinking_start_time: float = 0,
|
||||||
):
|
):
|
||||||
# 调用父类初始化
|
# 调用父类初始化
|
||||||
super().__init__(
|
super().__init__(
|
||||||
@@ -307,6 +298,7 @@ class MessageSending(MessageProcessBase):
|
|||||||
bot_user_info=bot_user_info,
|
bot_user_info=bot_user_info,
|
||||||
message_segment=message_segment,
|
message_segment=message_segment,
|
||||||
reply=reply,
|
reply=reply,
|
||||||
|
thinking_start_time=thinking_start_time,
|
||||||
)
|
)
|
||||||
|
|
||||||
# 发送状态特有属性
|
# 发送状态特有属性
|
||||||
@@ -328,6 +320,7 @@ class MessageSending(MessageProcessBase):
|
|||||||
self.message_segment,
|
self.message_segment,
|
||||||
],
|
],
|
||||||
)
|
)
|
||||||
|
return self
|
||||||
|
|
||||||
async def process(self) -> None:
|
async def process(self) -> None:
|
||||||
"""处理消息内容，生成纯文本和详细文本"""
|
"""处理消息内容，生成纯文本和详细文本"""
|
||||||
|
|||||||
@@ -1,188 +0,0 @@
|
|||||||
from dataclasses import dataclass, asdict
|
|
||||||
from typing import List, Optional, Union, Dict
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class Seg:
|
|
||||||
"""消息片段类，用于表示消息的不同部分
|
|
||||||
|
|
||||||
Attributes:
|
|
||||||
type: 片段类型，可以是 'text'、'image'、'seglist' 等
|
|
||||||
data: 片段的具体内容
|
|
||||||
- 对于 text 类型，data 是字符串
|
|
||||||
- 对于 image 类型，data 是 base64 字符串
|
|
||||||
- 对于 seglist 类型，data 是 Seg 列表
|
|
||||||
translated_data: 经过翻译处理的数据（可选）
|
|
||||||
"""
|
|
||||||
type: str
|
|
||||||
data: Union[str, List['Seg']]
|
|
||||||
|
|
||||||
|
|
||||||
# def __init__(self, type: str, data: Union[str, List['Seg']],):
|
|
||||||
# """初始化实例，确保字典和属性同步"""
|
|
||||||
# # 先初始化字典
|
|
||||||
# self.type = type
|
|
||||||
# self.data = data
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def from_dict(cls, data: Dict) -> 'Seg':
|
|
||||||
"""从字典创建Seg实例"""
|
|
||||||
type=data.get('type')
|
|
||||||
data=data.get('data')
|
|
||||||
if type == 'seglist':
|
|
||||||
data = [Seg.from_dict(seg) for seg in data]
|
|
||||||
return cls(
|
|
||||||
type=type,
|
|
||||||
data=data
|
|
||||||
)
|
|
||||||
|
|
||||||
def to_dict(self) -> Dict:
|
|
||||||
"""转换为字典格式"""
|
|
||||||
result = {'type': self.type}
|
|
||||||
if self.type == 'seglist':
|
|
||||||
result['data'] = [seg.to_dict() for seg in self.data]
|
|
||||||
else:
|
|
||||||
result['data'] = self.data
|
|
||||||
return result
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class GroupInfo:
|
|
||||||
"""群组信息类"""
|
|
||||||
platform: Optional[str] = None
|
|
||||||
group_id: Optional[int] = None
|
|
||||||
group_name: Optional[str] = None # 群名称
|
|
||||||
|
|
||||||
def to_dict(self) -> Dict:
|
|
||||||
"""转换为字典格式"""
|
|
||||||
return {k: v for k, v in asdict(self).items() if v is not None}
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def from_dict(cls, data: Dict) -> 'GroupInfo':
|
|
||||||
"""从字典创建GroupInfo实例
|
|
||||||
|
|
||||||
Args:
|
|
||||||
data: 包含必要字段的字典
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
GroupInfo: 新的实例
|
|
||||||
"""
|
|
||||||
if data.get('group_id') is None:
|
|
||||||
return None
|
|
||||||
return cls(
|
|
||||||
platform=data.get('platform'),
|
|
||||||
group_id=data.get('group_id'),
|
|
||||||
group_name=data.get('group_name',None)
|
|
||||||
)
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class UserInfo:
|
|
||||||
"""用户信息类"""
|
|
||||||
platform: Optional[str] = None
|
|
||||||
user_id: Optional[int] = None
|
|
||||||
user_nickname: Optional[str] = None # 用户昵称
|
|
||||||
user_cardname: Optional[str] = None # 用户群昵称
|
|
||||||
|
|
||||||
def to_dict(self) -> Dict:
|
|
||||||
"""转换为字典格式"""
|
|
||||||
return {k: v for k, v in asdict(self).items() if v is not None}
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def from_dict(cls, data: Dict) -> 'UserInfo':
|
|
||||||
"""从字典创建UserInfo实例
|
|
||||||
|
|
||||||
Args:
|
|
||||||
data: 包含必要字段的字典
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
UserInfo: 新的实例
|
|
||||||
"""
|
|
||||||
return cls(
|
|
||||||
platform=data.get('platform'),
|
|
||||||
user_id=data.get('user_id'),
|
|
||||||
user_nickname=data.get('user_nickname',None),
|
|
||||||
user_cardname=data.get('user_cardname',None)
|
|
||||||
)
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class BaseMessageInfo:
|
|
||||||
"""消息信息类"""
|
|
||||||
platform: Optional[str] = None
|
|
||||||
message_id: Union[str,int,None] = None
|
|
||||||
time: Optional[int] = None
|
|
||||||
group_info: Optional[GroupInfo] = None
|
|
||||||
user_info: Optional[UserInfo] = None
|
|
||||||
|
|
||||||
def to_dict(self) -> Dict:
|
|
||||||
"""转换为字典格式"""
|
|
||||||
result = {}
|
|
||||||
for field, value in asdict(self).items():
|
|
||||||
if value is not None:
|
|
||||||
if isinstance(value, (GroupInfo, UserInfo)):
|
|
||||||
result[field] = value.to_dict()
|
|
||||||
else:
|
|
||||||
result[field] = value
|
|
||||||
return result
|
|
||||||
@classmethod
|
|
||||||
def from_dict(cls, data: Dict) -> 'BaseMessageInfo':
|
|
||||||
"""从字典创建BaseMessageInfo实例
|
|
||||||
|
|
||||||
Args:
|
|
||||||
data: 包含必要字段的字典
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
BaseMessageInfo: 新的实例
|
|
||||||
"""
|
|
||||||
group_info = GroupInfo.from_dict(data.get('group_info', {}))
|
|
||||||
user_info = UserInfo.from_dict(data.get('user_info', {}))
|
|
||||||
return cls(
|
|
||||||
platform=data.get('platform'),
|
|
||||||
message_id=data.get('message_id'),
|
|
||||||
time=data.get('time'),
|
|
||||||
group_info=group_info,
|
|
||||||
user_info=user_info
|
|
||||||
)
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class MessageBase:
|
|
||||||
"""消息类"""
|
|
||||||
message_info: BaseMessageInfo
|
|
||||||
message_segment: Seg
|
|
||||||
raw_message: Optional[str] = None # 原始消息，包含未解析的cq码
|
|
||||||
|
|
||||||
def to_dict(self) -> Dict:
|
|
||||||
"""转换为字典格式
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Dict: 包含所有非None字段的字典，其中：
|
|
||||||
- message_info: 转换为字典格式
|
|
||||||
- message_segment: 转换为字典格式
|
|
||||||
- raw_message: 如果存在则包含
|
|
||||||
"""
|
|
||||||
result = {
|
|
||||||
'message_info': self.message_info.to_dict(),
|
|
||||||
'message_segment': self.message_segment.to_dict()
|
|
||||||
}
|
|
||||||
if self.raw_message is not None:
|
|
||||||
result['raw_message'] = self.raw_message
|
|
||||||
return result
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def from_dict(cls, data: Dict) -> 'MessageBase':
|
|
||||||
"""从字典创建MessageBase实例
|
|
||||||
|
|
||||||
Args:
|
|
||||||
data: 包含必要字段的字典
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
MessageBase: 新的实例
|
|
||||||
"""
|
|
||||||
message_info = BaseMessageInfo.from_dict(data.get('message_info', {}))
|
|
||||||
message_segment = Seg(**data.get('message_segment', {}))
|
|
||||||
raw_message = data.get('raw_message',None)
|
|
||||||
return cls(
|
|
||||||
message_info=message_info,
|
|
||||||
message_segment=message_segment,
|
|
||||||
raw_message=raw_message
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@@ -1,164 +0,0 @@
|
|||||||
import time
|
|
||||||
from dataclasses import dataclass
|
|
||||||
from typing import Dict, Optional
|
|
||||||
|
|
||||||
import urllib3
|
|
||||||
|
|
||||||
from .cq_code import cq_code_tool
|
|
||||||
from .utils_cq import parse_cq_code
|
|
||||||
from .utils_user import get_groupname
|
|
||||||
from .message_base import Seg, GroupInfo, UserInfo, BaseMessageInfo, MessageBase
|
|
||||||
|
|
||||||
# 禁用SSL警告
|
|
||||||
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
|
|
||||||
|
|
||||||
# 这个类是消息数据类，用于存储和管理消息数据。
|
|
||||||
# 它定义了消息的属性，包括群组ID、用户ID、消息ID、原始消息内容、纯文本内容和时间戳。
|
|
||||||
# 它还定义了两个辅助属性：keywords用于提取消息的关键词，is_plain_text用于判断消息是否为纯文本。
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class MessageCQ(MessageBase):
|
|
||||||
"""QQ消息基类，继承自MessageBase
|
|
||||||
|
|
||||||
最小必要参数:
|
|
||||||
- message_id: 消息ID
|
|
||||||
- user_id: 发送者/接收者ID
|
|
||||||
- platform: 平台标识（默认为"qq"）
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self, message_id: int, user_info: UserInfo, group_info: Optional[GroupInfo] = None, platform: str = "qq"
|
|
||||||
):
|
|
||||||
# 构造基础消息信息
|
|
||||||
message_info = BaseMessageInfo(
|
|
||||||
platform=platform, message_id=message_id, time=int(time.time()), group_info=group_info, user_info=user_info
|
|
||||||
)
|
|
||||||
# 调用父类初始化，message_segment 由子类设置
|
|
||||||
super().__init__(message_info=message_info, message_segment=None, raw_message=None)
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class MessageRecvCQ(MessageCQ):
|
|
||||||
"""QQ接收消息类，用于解析raw_message到Seg对象"""
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
message_id: int,
|
|
||||||
user_info: UserInfo,
|
|
||||||
raw_message: str,
|
|
||||||
group_info: Optional[GroupInfo] = None,
|
|
||||||
platform: str = "qq",
|
|
||||||
reply_message: Optional[Dict] = None,
|
|
||||||
):
|
|
||||||
# 调用父类初始化
|
|
||||||
super().__init__(message_id, user_info, group_info, platform)
|
|
||||||
|
|
||||||
# 私聊消息不携带group_info
|
|
||||||
if group_info is None:
|
|
||||||
pass
|
|
||||||
|
|
||||||
elif group_info.group_name is None:
|
|
||||||
group_info.group_name = get_groupname(group_info.group_id)
|
|
||||||
|
|
||||||
# 解析消息段
|
|
||||||
self.message_segment = self._parse_message(raw_message, reply_message)
|
|
||||||
self.raw_message = raw_message
|
|
||||||
|
|
||||||
def _parse_message(self, message: str, reply_message: Optional[Dict] = None) -> Seg:
|
|
||||||
"""解析消息内容为Seg对象"""
|
|
||||||
cq_code_dict_list = []
|
|
||||||
segments = []
|
|
||||||
|
|
||||||
start = 0
|
|
||||||
while True:
|
|
||||||
cq_start = message.find("[CQ:", start)
|
|
||||||
if cq_start == -1:
|
|
||||||
if start < len(message):
|
|
||||||
text = message[start:].strip()
|
|
||||||
if text:
|
|
||||||
cq_code_dict_list.append(parse_cq_code(text))
|
|
||||||
break
|
|
||||||
|
|
||||||
if cq_start > start:
|
|
||||||
text = message[start:cq_start].strip()
|
|
||||||
if text:
|
|
||||||
cq_code_dict_list.append(parse_cq_code(text))
|
|
||||||
|
|
||||||
cq_end = message.find("]", cq_start)
|
|
||||||
if cq_end == -1:
|
|
||||||
text = message[cq_start:].strip()
|
|
||||||
if text:
|
|
||||||
cq_code_dict_list.append(parse_cq_code(text))
|
|
||||||
break
|
|
||||||
|
|
||||||
cq_code = message[cq_start : cq_end + 1]
|
|
||||||
cq_code_dict_list.append(parse_cq_code(cq_code))
|
|
||||||
start = cq_end + 1
|
|
||||||
|
|
||||||
# 转换CQ码为Seg对象
|
|
||||||
for code_item in cq_code_dict_list:
|
|
||||||
message_obj = cq_code_tool.cq_from_dict_to_class(code_item, msg=self, reply=reply_message)
|
|
||||||
if message_obj.translated_segments:
|
|
||||||
segments.append(message_obj.translated_segments)
|
|
||||||
|
|
||||||
# 如果只有一个segment，直接返回
|
|
||||||
if len(segments) == 1:
|
|
||||||
return segments[0]
|
|
||||||
|
|
||||||
# 否则返回seglist类型的Seg
|
|
||||||
return Seg(type="seglist", data=segments)
|
|
||||||
|
|
||||||
def to_dict(self) -> Dict:
|
|
||||||
"""转换为字典格式，包含所有必要信息"""
|
|
||||||
base_dict = super().to_dict()
|
|
||||||
return base_dict
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class MessageSendCQ(MessageCQ):
|
|
||||||
"""QQ发送消息类，用于将Seg对象转换为raw_message"""
|
|
||||||
|
|
||||||
def __init__(self, data: Dict):
|
|
||||||
# 调用父类初始化
|
|
||||||
message_info = BaseMessageInfo.from_dict(data.get("message_info", {}))
|
|
||||||
message_segment = Seg.from_dict(data.get("message_segment", {}))
|
|
||||||
super().__init__(
|
|
||||||
message_info.message_id,
|
|
||||||
message_info.user_info,
|
|
||||||
message_info.group_info if message_info.group_info else None,
|
|
||||||
message_info.platform,
|
|
||||||
)
|
|
||||||
|
|
||||||
self.message_segment = message_segment
|
|
||||||
self.raw_message = self._generate_raw_message()
|
|
||||||
|
|
||||||
def _generate_raw_message(
|
|
||||||
self,
|
|
||||||
) -> str:
|
|
||||||
"""将Seg对象转换为raw_message"""
|
|
||||||
segments = []
|
|
||||||
|
|
||||||
# 处理消息段
|
|
||||||
if self.message_segment.type == "seglist":
|
|
||||||
for seg in self.message_segment.data:
|
|
||||||
segments.append(self._seg_to_cq_code(seg))
|
|
||||||
else:
|
|
||||||
segments.append(self._seg_to_cq_code(self.message_segment))
|
|
||||||
|
|
||||||
return "".join(segments)
|
|
||||||
|
|
||||||
def _seg_to_cq_code(self, seg: Seg) -> str:
|
|
||||||
"""将单个Seg对象转换为CQ码字符串"""
|
|
||||||
if seg.type == "text":
|
|
||||||
return str(seg.data)
|
|
||||||
elif seg.type == "image":
|
|
||||||
return cq_code_tool.create_image_cq_base64(seg.data)
|
|
||||||
elif seg.type == "emoji":
|
|
||||||
return cq_code_tool.create_emoji_cq_base64(seg.data)
|
|
||||||
elif seg.type == "at":
|
|
||||||
return f"[CQ:at,qq={seg.data}]"
|
|
||||||
elif seg.type == "reply":
|
|
||||||
return cq_code_tool.create_reply_cq(int(seg.data))
|
|
||||||
else:
|
|
||||||
return f"[{seg.data}]"
|
|
||||||
@@ -2,15 +2,25 @@ import asyncio
|
|||||||
import time
|
import time
|
||||||
from typing import Dict, List, Optional, Union
|
from typing import Dict, List, Optional, Union
|
||||||
|
|
||||||
from loguru import logger
|
from src.common.logger import get_module_logger
|
||||||
from nonebot.adapters.onebot.v11 import Bot
|
|
||||||
from ...common.database import db
|
from ...common.database import db
|
||||||
from .message_cq import MessageSendCQ
|
from ..message.api import global_api
|
||||||
from .message import MessageSending, MessageThinking, MessageRecv, MessageSet
|
from .message import MessageSending, MessageThinking, MessageSet
|
||||||
|
|
||||||
from .storage import MessageStorage
|
from .storage import MessageStorage
|
||||||
from .config import global_config
|
from ..config.config import global_config
|
||||||
from .utils import truncate_message
|
from .utils import truncate_message, calculate_typing_time
|
||||||
|
|
||||||
|
from src.common.logger import LogConfig, SENDER_STYLE_CONFIG
|
||||||
|
|
||||||
|
# 定义日志配置
|
||||||
|
sender_config = LogConfig(
|
||||||
|
# 使用消息发送专用样式
|
||||||
|
console_format=SENDER_STYLE_CONFIG["console_format"],
|
||||||
|
file_format=SENDER_STYLE_CONFIG["file_format"],
|
||||||
|
)
|
||||||
|
|
||||||
|
logger = get_module_logger("msg_sender", config=sender_config)
|
||||||
|
|
||||||
|
|
||||||
class Message_Sender:
|
class Message_Sender:
|
||||||
@@ -21,9 +31,9 @@ class Message_Sender:
|
|||||||
self.last_send_time = 0
|
self.last_send_time = 0
|
||||||
self._current_bot = None
|
self._current_bot = None
|
||||||
|
|
||||||
def set_bot(self, bot: Bot):
|
def set_bot(self, bot):
|
||||||
"""设置当前bot实例"""
|
"""设置当前bot实例"""
|
||||||
self._current_bot = bot
|
pass
|
||||||
|
|
||||||
def get_recalled_messages(self, stream_id: str) -> list:
|
def get_recalled_messages(self, stream_id: str) -> list:
|
||||||
"""获取所有撤回的消息"""
|
"""获取所有撤回的消息"""
|
||||||
@@ -48,33 +58,21 @@ class Message_Sender:
|
|||||||
logger.warning(f"消息“{message.processed_plain_text}”已被撤回，不发送")
|
logger.warning(f"消息“{message.processed_plain_text}”已被撤回，不发送")
|
||||||
break
|
break
|
||||||
if not is_recalled:
|
if not is_recalled:
|
||||||
|
typing_time = calculate_typing_time(message.processed_plain_text)
|
||||||
|
await asyncio.sleep(typing_time)
|
||||||
|
|
||||||
message_json = message.to_dict()
|
message_json = message.to_dict()
|
||||||
message_send = MessageSendCQ(data=message_json)
|
|
||||||
# logger.debug(message_send.message_info,message_send.raw_message)
|
|
||||||
message_preview = truncate_message(message.processed_plain_text)
|
message_preview = truncate_message(message.processed_plain_text)
|
||||||
if message_send.message_info.group_info and message_send.message_info.group_info.group_id:
|
try:
|
||||||
try:
|
end_point = global_config.api_urls.get(message.message_info.platform, None)
|
||||||
await self._current_bot.send_group_msg(
|
if end_point:
|
||||||
group_id=message.message_info.group_info.group_id,
|
await global_api.send_message(end_point, message_json)
|
||||||
message=message_send.raw_message,
|
else:
|
||||||
auto_escape=False,
|
raise ValueError(f"未找到平台：{message.message_info.platform} 的url配置，请检查配置文件")
|
||||||
)
|
logger.success(f"发送消息“{message_preview}”成功")
|
||||||
logger.success(f"[调试] 发送消息“{message_preview}”成功")
|
except Exception as e:
|
||||||
except Exception as e:
|
logger.error(f"发送消息“{message_preview}”失败: {str(e)}")
|
||||||
logger.error(f"[调试] 发生错误 {e}")
|
|
||||||
logger.error(f"[调试] 发送消息“{message_preview}”失败")
|
|
||||||
else:
|
|
||||||
try:
|
|
||||||
logger.debug(message.message_info.user_info)
|
|
||||||
await self._current_bot.send_private_msg(
|
|
||||||
user_id=message.sender_info.user_id,
|
|
||||||
message=message_send.raw_message,
|
|
||||||
auto_escape=False,
|
|
||||||
)
|
|
||||||
logger.success(f"[调试] 发送消息“{message_preview}”成功")
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"[调试] 发生错误 {e}")
|
|
||||||
logger.error(f"[调试] 发送消息“{message_preview}”失败")
|
|
||||||
|
|
||||||
|
|
||||||
class MessageContainer:
|
class MessageContainer:
|
||||||
@@ -85,7 +83,7 @@ class MessageContainer:
|
|||||||
self.max_size = max_size
|
self.max_size = max_size
|
||||||
self.messages = []
|
self.messages = []
|
||||||
self.last_send_time = 0
|
self.last_send_time = 0
|
||||||
self.thinking_timeout = 20 # 思考超时时间（秒）
|
self.thinking_timeout = 10 # 思考等待超时时间（秒）
|
||||||
|
|
||||||
def get_timeout_messages(self) -> List[MessageSending]:
|
def get_timeout_messages(self) -> List[MessageSending]:
|
||||||
"""获取所有超时的Message_Sending对象（思考时间超过30秒），按thinking_start_time排序"""
|
"""获取所有超时的Message_Sending对象（思考时间超过30秒），按thinking_start_time排序"""
|
||||||
@@ -174,6 +172,7 @@ class MessageManager:
|
|||||||
if isinstance(message_earliest, MessageThinking):
|
if isinstance(message_earliest, MessageThinking):
|
||||||
message_earliest.update_thinking_time()
|
message_earliest.update_thinking_time()
|
||||||
thinking_time = message_earliest.thinking_time
|
thinking_time = message_earliest.thinking_time
|
||||||
|
# print(thinking_time)
|
||||||
print(
|
print(
|
||||||
f"消息正在思考中，已思考{int(thinking_time)}秒\r",
|
f"消息正在思考中，已思考{int(thinking_time)}秒\r",
|
||||||
end="",
|
end="",
|
||||||
@@ -186,43 +185,51 @@ class MessageManager:
|
|||||||
container.remove_message(message_earliest)
|
container.remove_message(message_earliest)
|
||||||
|
|
||||||
else:
|
else:
|
||||||
|
# print(message_earliest.is_head)
|
||||||
|
# print(message_earliest.update_thinking_time())
|
||||||
|
# print(message_earliest.is_private_message())
|
||||||
|
thinking_time = message_earliest.update_thinking_time()
|
||||||
|
print(thinking_time)
|
||||||
if (
|
if (
|
||||||
message_earliest.is_head
|
message_earliest.is_head
|
||||||
and message_earliest.update_thinking_time() > 30
|
and message_earliest.update_thinking_time() > 18
|
||||||
and not message_earliest.is_private_message() # 避免在私聊时插入reply
|
and not message_earliest.is_private_message() # 避免在私聊时插入reply
|
||||||
):
|
):
|
||||||
|
logger.debug(f"设置回复消息{message_earliest.processed_plain_text}")
|
||||||
message_earliest.set_reply()
|
message_earliest.set_reply()
|
||||||
await message_sender.send_message(message_earliest)
|
|
||||||
await message_earliest.process()
|
await message_earliest.process()
|
||||||
|
|
||||||
print(
|
await message_sender.send_message(message_earliest)
|
||||||
f"\033[1;34m[调试]\033[0m 消息“{truncate_message(message_earliest.processed_plain_text)}”正在发送中"
|
|
||||||
)
|
|
||||||
|
|
||||||
await self.storage.store_message(message_earliest, message_earliest.chat_stream, None)
|
await self.storage.store_message(message_earliest, message_earliest.chat_stream)
|
||||||
|
|
||||||
container.remove_message(message_earliest)
|
container.remove_message(message_earliest)
|
||||||
|
|
||||||
message_timeout = container.get_timeout_messages()
|
message_timeout = container.get_timeout_messages()
|
||||||
if message_timeout:
|
if message_timeout:
|
||||||
logger.warning(f"发现{len(message_timeout)}条超时消息")
|
logger.debug(f"发现{len(message_timeout)}条超时消息")
|
||||||
for msg in message_timeout:
|
for msg in message_timeout:
|
||||||
if msg == message_earliest:
|
if msg == message_earliest:
|
||||||
continue
|
continue
|
||||||
|
|
||||||
try:
|
try:
|
||||||
|
# print(msg.is_head)
|
||||||
|
print(msg.update_thinking_time())
|
||||||
|
# print(msg.is_private_message())
|
||||||
if (
|
if (
|
||||||
msg.is_head
|
msg.is_head
|
||||||
and msg.update_thinking_time() > 30
|
and msg.update_thinking_time() > 18
|
||||||
and not message_earliest.is_private_message() # 避免在私聊时插入reply
|
and not msg.is_private_message() # 避免在私聊时插入reply
|
||||||
):
|
):
|
||||||
|
logger.debug(f"设置回复消息{msg.processed_plain_text}")
|
||||||
msg.set_reply()
|
msg.set_reply()
|
||||||
|
|
||||||
|
await msg.process()
|
||||||
|
|
||||||
await message_sender.send_message(msg)
|
await message_sender.send_message(msg)
|
||||||
|
|
||||||
# if msg.is_emoji:
|
await self.storage.store_message(msg, msg.chat_stream)
|
||||||
# msg.processed_plain_text = "[表情包]"
|
|
||||||
await msg.process()
|
|
||||||
await self.storage.store_message(msg, msg.chat_stream, None)
|
|
||||||
|
|
||||||
if not container.remove_message(msg):
|
if not container.remove_message(msg):
|
||||||
logger.warning("尝试删除不存在的消息")
|
logger.warning("尝试删除不存在的消息")
|
||||||
|
|||||||
@@ -1,50 +1,31 @@
|
|||||||
import random
|
import random
|
||||||
import time
|
import time
|
||||||
from typing import Optional
|
from typing import Optional
|
||||||
from loguru import logger
|
|
||||||
|
|
||||||
from ...common.database import db
|
from ...common.database import db
|
||||||
from ..memory_system.memory import hippocampus, memory_graph
|
from ..memory_system.Hippocampus import HippocampusManager
|
||||||
from ..moods.moods import MoodManager
|
from ..moods.moods import MoodManager
|
||||||
from ..schedule.schedule_generator import bot_schedule
|
from ..schedule.schedule_generator import bot_schedule
|
||||||
from .config import global_config
|
from ..config.config import global_config
|
||||||
from .utils import get_embedding, get_recent_group_detailed_plain_text
|
from .utils import get_embedding, get_recent_group_detailed_plain_text
|
||||||
from .chat_stream import chat_manager
|
from .chat_stream import chat_manager
|
||||||
|
from src.common.logger import get_module_logger
|
||||||
|
|
||||||
|
from src.heart_flow.heartflow import heartflow
|
||||||
|
|
||||||
|
logger = get_module_logger("prompt")
|
||||||
|
|
||||||
|
|
||||||
class PromptBuilder:
|
class PromptBuilder:
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
self.prompt_built = ''
|
self.prompt_built = ""
|
||||||
self.activate_messages = ''
|
self.activate_messages = ""
|
||||||
|
|
||||||
|
async def _build_prompt(
|
||||||
|
self, chat_stream, message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None
|
||||||
|
) -> tuple[str, str]:
|
||||||
|
|
||||||
|
current_mind_info = heartflow.get_subheartflow(stream_id).current_mind
|
||||||
async def _build_prompt(self,
|
|
||||||
message_txt: str,
|
|
||||||
sender_name: str = "某人",
|
|
||||||
relationship_value: float = 0.0,
|
|
||||||
stream_id: Optional[int] = None) -> tuple[str, str]:
|
|
||||||
"""构建prompt
|
|
||||||
|
|
||||||
Args:
|
|
||||||
message_txt: 消息文本
|
|
||||||
sender_name: 发送者昵称
|
|
||||||
relationship_value: 关系值
|
|
||||||
group_id: 群组ID
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
str: 构建好的prompt
|
|
||||||
"""
|
|
||||||
# 先禁用关系
|
|
||||||
if 0 > 30:
|
|
||||||
relation_prompt = "关系特别特别好，你很喜欢喜欢他"
|
|
||||||
relation_prompt_2 = "热情发言或者回复"
|
|
||||||
elif 0 < -20:
|
|
||||||
relation_prompt = "关系很差，你很讨厌他"
|
|
||||||
relation_prompt_2 = "骂他"
|
|
||||||
else:
|
|
||||||
relation_prompt = "关系一般"
|
|
||||||
relation_prompt_2 = "发言或者回复"
|
|
||||||
|
|
||||||
# 开始构建prompt
|
# 开始构建prompt
|
||||||
|
|
||||||
@@ -52,181 +33,141 @@ class PromptBuilder:
|
|||||||
mood_manager = MoodManager.get_instance()
|
mood_manager = MoodManager.get_instance()
|
||||||
mood_prompt = mood_manager.get_prompt()
|
mood_prompt = mood_manager.get_prompt()
|
||||||
|
|
||||||
|
logger.info(f"心情prompt: {mood_prompt}")
|
||||||
|
|
||||||
# 日程构建
|
# 日程构建
|
||||||
current_date = time.strftime("%Y-%m-%d", time.localtime())
|
# schedule_prompt = f'''你现在正在做的事情是：{bot_schedule.get_current_num_task(num = 1,time_info = False)}'''
|
||||||
current_time = time.strftime("%H:%M:%S", time.localtime())
|
|
||||||
bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
|
# 获取聊天上下文
|
||||||
prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
|
chat_in_group = True
|
||||||
|
chat_talking_prompt = ""
|
||||||
|
if stream_id:
|
||||||
|
chat_talking_prompt = get_recent_group_detailed_plain_text(
|
||||||
|
stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
|
||||||
|
)
|
||||||
|
chat_stream = chat_manager.get_stream(stream_id)
|
||||||
|
if chat_stream.group_info:
|
||||||
|
chat_talking_prompt = chat_talking_prompt
|
||||||
|
else:
|
||||||
|
chat_in_group = False
|
||||||
|
chat_talking_prompt = chat_talking_prompt
|
||||||
|
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
|
||||||
|
|
||||||
|
# 类型
|
||||||
|
if chat_in_group:
|
||||||
|
chat_target = "你正在qq群里聊天，下面是群里在聊的内容："
|
||||||
|
chat_target_2 = "和群里聊天"
|
||||||
|
else:
|
||||||
|
chat_target = f"你正在和{sender_name}聊天，这是你们之前聊的内容："
|
||||||
|
chat_target_2 = f"和{sender_name}私聊"
|
||||||
|
|
||||||
|
# 关键词检测与反应
|
||||||
|
keywords_reaction_prompt = ""
|
||||||
|
for rule in global_config.keywords_reaction_rules:
|
||||||
|
if rule.get("enable", False):
|
||||||
|
if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
|
||||||
|
logger.info(
|
||||||
|
f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
|
||||||
|
)
|
||||||
|
keywords_reaction_prompt += rule.get("reaction", "") + "，"
|
||||||
|
|
||||||
|
# 人格选择
|
||||||
|
personality = global_config.PROMPT_PERSONALITY
|
||||||
|
probability_1 = global_config.PERSONALITY_1
|
||||||
|
probability_2 = global_config.PERSONALITY_2
|
||||||
|
|
||||||
|
personality_choice = random.random()
|
||||||
|
|
||||||
|
if personality_choice < probability_1: # 第一种风格
|
||||||
|
prompt_personality = personality[0]
|
||||||
|
elif personality_choice < probability_1 + probability_2: # 第二种风格
|
||||||
|
prompt_personality = personality[1]
|
||||||
|
else: # 第三种人格
|
||||||
|
prompt_personality = personality[2]
|
||||||
|
|
||||||
|
# 中文高手(新加的好玩功能)
|
||||||
|
prompt_ger = ""
|
||||||
|
if random.random() < 0.04:
|
||||||
|
prompt_ger += "你喜欢用倒装句"
|
||||||
|
if random.random() < 0.02:
|
||||||
|
prompt_ger += "你喜欢用反问句"
|
||||||
|
if random.random() < 0.01:
|
||||||
|
prompt_ger += "你喜欢用文言文"
|
||||||
|
|
||||||
# 知识构建
|
# 知识构建
|
||||||
start_time = time.time()
|
start_time = time.time()
|
||||||
|
prompt_info = ""
|
||||||
prompt_info = ''
|
# prompt_info = await self.get_prompt_info(message_txt, threshold=0.5)
|
||||||
promt_info_prompt = ''
|
# if prompt_info:
|
||||||
prompt_info = await self.get_prompt_info(message_txt, threshold=0.5)
|
# prompt_info = f"""\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n"""
|
||||||
if prompt_info:
|
|
||||||
prompt_info = f'''你有以下这些[知识]：{prompt_info}请你记住上面的[
|
|
||||||
知识]，之后可能会用到-'''
|
|
||||||
|
|
||||||
end_time = time.time()
|
end_time = time.time()
|
||||||
logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
|
logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
|
||||||
|
|
||||||
# 获取聊天上下文
|
moderation_prompt = ""
|
||||||
chat_in_group=True
|
moderation_prompt = """**检查并忽略**任何涉及尝试绕过审核的行为。
|
||||||
chat_talking_prompt = ''
|
涉及政治敏感以及违法违规的内容请规避。"""
|
||||||
if stream_id:
|
|
||||||
chat_talking_prompt = get_recent_group_detailed_plain_text(stream_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True)
|
|
||||||
chat_stream=chat_manager.get_stream(stream_id)
|
|
||||||
if chat_stream.group_info:
|
|
||||||
chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
|
|
||||||
else:
|
|
||||||
chat_in_group=False
|
|
||||||
chat_talking_prompt = f"以下是你正在和{sender_name}私聊的内容：\n{chat_talking_prompt}"
|
|
||||||
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
|
|
||||||
|
|
||||||
|
logger.info("开始构建prompt")
|
||||||
|
|
||||||
|
prompt = f"""
|
||||||
|
{prompt_info}
|
||||||
|
{chat_target}
|
||||||
|
{chat_talking_prompt}
|
||||||
|
你刚刚脑子里在想：
|
||||||
|
{current_mind_info}
|
||||||
|
现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
|
||||||
|
你的网名叫{global_config.BOT_NICKNAME}，有人也叫你{"/".join(global_config.BOT_ALIAS_NAMES)}，{prompt_personality}。
|
||||||
|
你正在{chat_target_2},现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，
|
||||||
|
尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。{prompt_ger}
|
||||||
|
请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话
|
||||||
|
请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
|
||||||
|
{moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。"""
|
||||||
|
|
||||||
# 使用新的记忆获取方法
|
return prompt
|
||||||
memory_prompt = ''
|
|
||||||
start_time = time.time()
|
|
||||||
|
|
||||||
# 调用 hippocampus 的 get_relevant_memories 方法
|
|
||||||
relevant_memories = await hippocampus.get_relevant_memories(
|
|
||||||
text=message_txt,
|
|
||||||
max_topics=5,
|
|
||||||
similarity_threshold=0.4,
|
|
||||||
max_memory_num=5
|
|
||||||
)
|
|
||||||
|
|
||||||
if relevant_memories:
|
|
||||||
# 格式化记忆内容
|
|
||||||
memory_items = []
|
|
||||||
for memory in relevant_memories:
|
|
||||||
memory_items.append(f"关于「{memory['topic']}」的记忆：{memory['content']}")
|
|
||||||
|
|
||||||
memory_prompt = "看到这些聊天，你想起来：\n" + "\n".join(memory_items) + "\n"
|
|
||||||
|
|
||||||
# 打印调试信息
|
|
||||||
logger.debug("[记忆检索]找到以下相关记忆：")
|
|
||||||
for memory in relevant_memories:
|
|
||||||
logger.debug(f"- 主题「{memory['topic']}」[相似度: {memory['similarity']:.2f}]: {memory['content']}")
|
|
||||||
|
|
||||||
end_time = time.time()
|
|
||||||
logger.info(f"回忆耗时: {(end_time - start_time):.3f}秒")
|
|
||||||
|
|
||||||
# 激活prompt构建
|
|
||||||
activate_prompt = ''
|
|
||||||
if chat_in_group:
|
|
||||||
activate_prompt = f"以上是群里正在进行的聊天，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和ta{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。"
|
|
||||||
else:
|
|
||||||
activate_prompt = f"以上是你正在和{sender_name}私聊的内容，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和ta{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。"
|
|
||||||
|
|
||||||
# 关键词检测与反应
|
|
||||||
keywords_reaction_prompt = ''
|
|
||||||
for rule in global_config.keywords_reaction_rules:
|
|
||||||
if rule.get("enable", False):
|
|
||||||
if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
|
|
||||||
logger.info(f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}")
|
|
||||||
keywords_reaction_prompt += rule.get("reaction", "") + '，'
|
|
||||||
|
|
||||||
#人格选择
|
|
||||||
personality=global_config.PROMPT_PERSONALITY
|
|
||||||
probability_1 = global_config.PERSONALITY_1
|
|
||||||
probability_2 = global_config.PERSONALITY_2
|
|
||||||
probability_3 = global_config.PERSONALITY_3
|
|
||||||
|
|
||||||
prompt_personality = f'{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，你还有很多别名:{"/".join(global_config.BOT_ALIAS_NAMES)}，'
|
|
||||||
personality_choice = random.random()
|
|
||||||
if chat_in_group:
|
|
||||||
prompt_in_group=f"你正在浏览{chat_stream.platform}群"
|
|
||||||
else:
|
|
||||||
prompt_in_group=f"你正在{chat_stream.platform}上和{sender_name}私聊"
|
|
||||||
if personality_choice < probability_1: # 第一种人格
|
|
||||||
prompt_personality += f'''{personality[0]}, 你正在浏览qq群,{promt_info_prompt},
|
|
||||||
现在请你给出日常且口语化的回复，平淡一些，尽量简短一些。{keywords_reaction_prompt}
|
|
||||||
请注意把握群里的聊天内容，不要刻意突出自身学科背景，不要回复的太有条理，可以有个性。'''
|
|
||||||
elif personality_choice < probability_1 + probability_2: # 第二种人格
|
|
||||||
prompt_personality += f'''{personality[1]}, 你正在浏览qq群，{promt_info_prompt},
|
|
||||||
现在请你给出日常且口语化的回复，请表现你自己的见解，不要一昧迎合，尽量简短一些。{keywords_reaction_prompt}
|
|
||||||
请你表达自己的见解和观点。可以有个性。'''
|
|
||||||
else: # 第三种人格
|
|
||||||
prompt_personality += f'''{personality[2]}, 你正在浏览qq群，{promt_info_prompt},
|
|
||||||
现在请你给出日常且口语化的回复，请表现你自己的见解，不要一昧迎合，尽量简短一些。{keywords_reaction_prompt}
|
|
||||||
请你表达自己的见解和观点。可以有个性。'''
|
|
||||||
|
|
||||||
# 中文高手(新加的好玩功能)
|
|
||||||
prompt_ger = ''
|
|
||||||
if random.random() < 0.04:
|
|
||||||
prompt_ger += '你喜欢用倒装句'
|
|
||||||
if random.random() < 0.02:
|
|
||||||
prompt_ger += '你喜欢用反问句'
|
|
||||||
if random.random() < 0.01:
|
|
||||||
prompt_ger += '你喜欢用文言文'
|
|
||||||
|
|
||||||
# 额外信息要求
|
|
||||||
extra_info = '''但是记得回复平淡一些，简短一些，尤其注意在没明确提到时不要过多提及自身的背景, 不要直接回复别人发的表情包，记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只需要输出回复内容就好，不要输出其他任何内容'''
|
|
||||||
|
|
||||||
# 合并prompt
|
|
||||||
prompt = ""
|
|
||||||
prompt += f"{prompt_info}\n"
|
|
||||||
prompt += f"{prompt_date}\n"
|
|
||||||
prompt += f"{chat_talking_prompt}\n"
|
|
||||||
prompt += f"{prompt_personality}\n"
|
|
||||||
prompt += f"{prompt_ger}\n"
|
|
||||||
prompt += f"{extra_info}\n"
|
|
||||||
|
|
||||||
'''读空气prompt处理'''
|
|
||||||
activate_prompt_check = f"以上是群里正在进行的聊天，昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt}，你想要{relation_prompt_2}，但是这不一定是合适的时机，请你决定是否要回应这条消息。"
|
|
||||||
prompt_personality_check = ''
|
|
||||||
extra_check_info = f"请注意把握群里的聊天内容的基础上，综合群内的氛围，例如，和{global_config.BOT_NICKNAME}相关的话题要积极回复,如果是at自己的消息一定要回复，如果自己正在和别人聊天一定要回复，其他话题如果合适搭话也可以回复，如果认为应该回复请输出yes，否则输出no，请注意是决定是否需要回复，而不是编写回复内容，除了yes和no不要输出任何回复内容。"
|
|
||||||
if personality_choice < probability_1: # 第一种人格
|
|
||||||
prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}'''
|
|
||||||
elif personality_choice < probability_1 + probability_2: # 第二种人格
|
|
||||||
prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}'''
|
|
||||||
else: # 第三种人格
|
|
||||||
prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}'''
|
|
||||||
|
|
||||||
prompt_check_if_response = f"{prompt_info}\n{prompt_date}\n{chat_talking_prompt}\n{prompt_personality_check}"
|
|
||||||
|
|
||||||
return prompt, prompt_check_if_response
|
|
||||||
|
|
||||||
def _build_initiative_prompt_select(self, group_id, probability_1=0.8, probability_2=0.1):
|
def _build_initiative_prompt_select(self, group_id, probability_1=0.8, probability_2=0.1):
|
||||||
current_date = time.strftime("%Y-%m-%d", time.localtime())
|
current_date = time.strftime("%Y-%m-%d", time.localtime())
|
||||||
current_time = time.strftime("%H:%M:%S", time.localtime())
|
current_time = time.strftime("%H:%M:%S", time.localtime())
|
||||||
bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
|
bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
|
||||||
prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
|
prompt_date = f"""今天是{current_date}，现在是{current_time}，你今天的日程是：
|
||||||
|
{bot_schedule.today_schedule}
|
||||||
|
你现在正在{bot_schedule_now_activity}
|
||||||
|
"""
|
||||||
|
|
||||||
chat_talking_prompt = ''
|
chat_talking_prompt = ""
|
||||||
if group_id:
|
if group_id:
|
||||||
chat_talking_prompt = get_recent_group_detailed_plain_text(group_id,
|
chat_talking_prompt = get_recent_group_detailed_plain_text(
|
||||||
limit=global_config.MAX_CONTEXT_SIZE,
|
group_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
|
||||||
combine=True)
|
)
|
||||||
|
|
||||||
chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
|
chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
|
||||||
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
|
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
|
||||||
|
|
||||||
# 获取主动发言的话题
|
# 获取主动发言的话题
|
||||||
all_nodes = memory_graph.dots
|
all_nodes = HippocampusManager.get_instance().memory_graph.dots
|
||||||
all_nodes = filter(lambda dot: len(dot[1]['memory_items']) > 3, all_nodes)
|
all_nodes = filter(lambda dot: len(dot[1]["memory_items"]) > 3, all_nodes)
|
||||||
nodes_for_select = random.sample(all_nodes, 5)
|
nodes_for_select = random.sample(all_nodes, 5)
|
||||||
topics = [info[0] for info in nodes_for_select]
|
topics = [info[0] for info in nodes_for_select]
|
||||||
infos = [info[1] for info in nodes_for_select]
|
|
||||||
|
|
||||||
# 激活prompt构建
|
# 激活prompt构建
|
||||||
activate_prompt = ''
|
activate_prompt = ""
|
||||||
activate_prompt = "以上是群里正在进行的聊天。"
|
activate_prompt = "以上是群里正在进行的聊天。"
|
||||||
personality = global_config.PROMPT_PERSONALITY
|
personality = global_config.PROMPT_PERSONALITY
|
||||||
prompt_personality = ''
|
prompt_personality = ""
|
||||||
personality_choice = random.random()
|
personality_choice = random.random()
|
||||||
if personality_choice < probability_1: # 第一种人格
|
if personality_choice < probability_1: # 第一种人格
|
||||||
prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}'''
|
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}"""
|
||||||
elif personality_choice < probability_1 + probability_2: # 第二种人格
|
elif personality_choice < probability_1 + probability_2: # 第二种人格
|
||||||
prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}'''
|
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}"""
|
||||||
else: # 第三种人格
|
else: # 第三种人格
|
||||||
prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}'''
|
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}"""
|
||||||
|
|
||||||
topics_str = ','.join(f"\"{topics}\"")
|
topics_str = ",".join(f'"{topics}"')
|
||||||
prompt_for_select = f"你现在想在群里发言，回忆了一下，想到几个话题，分别是{topics_str}，综合当前状态以及群内气氛，请你在其中选择一个合适的话题，注意只需要输出话题，除了话题什么也不要输出(双引号也不要输出)"
|
prompt_for_select = (
|
||||||
|
f"你现在想在群里发言，回忆了一下，想到几个话题，分别是{topics_str}，综合当前状态以及群内气氛，"
|
||||||
|
f"请你在其中选择一个合适的话题，注意只需要输出话题，除了话题什么也不要输出(双引号也不要输出)"
|
||||||
|
)
|
||||||
|
|
||||||
prompt_initiative_select = f"{prompt_date}\n{prompt_personality}\n{prompt_for_select}"
|
prompt_initiative_select = f"{prompt_date}\n{prompt_personality}\n{prompt_for_select}"
|
||||||
prompt_regular = f"{prompt_date}\n{prompt_personality}"
|
prompt_regular = f"{prompt_date}\n{prompt_personality}"
|
||||||
@@ -234,26 +175,36 @@ class PromptBuilder:
|
|||||||
return prompt_initiative_select, nodes_for_select, prompt_regular
|
return prompt_initiative_select, nodes_for_select, prompt_regular
|
||||||
|
|
||||||
def _build_initiative_prompt_check(self, selected_node, prompt_regular):
|
def _build_initiative_prompt_check(self, selected_node, prompt_regular):
|
||||||
memory = random.sample(selected_node['memory_items'], 3)
|
memory = random.sample(selected_node["memory_items"], 3)
|
||||||
memory = '\n'.join(memory)
|
memory = "\n".join(memory)
|
||||||
prompt_for_check = f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，关于这个话题的记忆有\n{memory}\n，以这个作为主题发言合适吗？请在把握群里的聊天内容的基础上，综合群内的氛围，如果认为应该发言请输出yes，否则输出no，请注意是决定是否需要发言，而不是编写回复内容，除了yes和no不要输出任何回复内容。"
|
prompt_for_check = (
|
||||||
|
f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，"
|
||||||
|
f"关于这个话题的记忆有\n{memory}\n，以这个作为主题发言合适吗？请在把握群里的聊天内容的基础上，"
|
||||||
|
f"综合群内的氛围，如果认为应该发言请输出yes，否则输出no，请注意是决定是否需要发言，而不是编写回复内容，"
|
||||||
|
f"除了yes和no不要输出任何回复内容。"
|
||||||
|
)
|
||||||
return prompt_for_check, memory
|
return prompt_for_check, memory
|
||||||
|
|
||||||
def _build_initiative_prompt(self, selected_node, prompt_regular, memory):
|
def _build_initiative_prompt(self, selected_node, prompt_regular, memory):
|
||||||
prompt_for_initiative = f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，关于这个话题的记忆有\n{memory}\n，请在把握群里的聊天内容的基础上，综合群内的氛围，以日常且口语化的口吻，简短且随意一点进行发言，不要说的太有条理，可以有个性。记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)"
|
prompt_for_initiative = (
|
||||||
|
f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，"
|
||||||
|
f"关于这个话题的记忆有\n{memory}\n，请在把握群里的聊天内容的基础上，综合群内的氛围，"
|
||||||
|
f"以日常且口语化的口吻，简短且随意一点进行发言，不要说的太有条理，可以有个性。"
|
||||||
|
f"记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情,@等)"
|
||||||
|
)
|
||||||
return prompt_for_initiative
|
return prompt_for_initiative
|
||||||
|
|
||||||
async def get_prompt_info(self, message: str, threshold: float):
|
async def get_prompt_info(self, message: str, threshold: float):
|
||||||
related_info = ''
|
related_info = ""
|
||||||
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
|
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
|
||||||
embedding = await get_embedding(message)
|
embedding = await get_embedding(message, request_type="prompt_build")
|
||||||
related_info += self.get_info_from_db(embedding, threshold=threshold)
|
related_info += self.get_info_from_db(embedding, limit=1, threshold=threshold)
|
||||||
|
|
||||||
return related_info
|
return related_info
|
||||||
|
|
||||||
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5) -> str:
|
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5) -> str:
|
||||||
if not query_embedding:
|
if not query_embedding:
|
||||||
return ''
|
return ""
|
||||||
# 使用余弦相似度计算
|
# 使用余弦相似度计算
|
||||||
pipeline = [
|
pipeline = [
|
||||||
{
|
{
|
||||||
@@ -265,12 +216,14 @@ class PromptBuilder:
|
|||||||
"in": {
|
"in": {
|
||||||
"$add": [
|
"$add": [
|
||||||
"$$value",
|
"$$value",
|
||||||
{"$multiply": [
|
{
|
||||||
{"$arrayElemAt": ["$embedding", "$$this"]},
|
"$multiply": [
|
||||||
{"$arrayElemAt": [query_embedding, "$$this"]}
|
{"$arrayElemAt": ["$embedding", "$$this"]},
|
||||||
]}
|
{"$arrayElemAt": [query_embedding, "$$this"]},
|
||||||
|
]
|
||||||
|
},
|
||||||
]
|
]
|
||||||
}
|
},
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"magnitude1": {
|
"magnitude1": {
|
||||||
@@ -278,7 +231,7 @@ class PromptBuilder:
|
|||||||
"$reduce": {
|
"$reduce": {
|
||||||
"input": "$embedding",
|
"input": "$embedding",
|
||||||
"initialValue": 0,
|
"initialValue": 0,
|
||||||
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
|
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
@@ -287,19 +240,13 @@ class PromptBuilder:
|
|||||||
"$reduce": {
|
"$reduce": {
|
||||||
"input": query_embedding,
|
"input": query_embedding,
|
||||||
"initialValue": 0,
|
"initialValue": 0,
|
||||||
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
|
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
},
|
||||||
}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"$addFields": {
|
|
||||||
"similarity": {
|
|
||||||
"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
|
{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
|
||||||
{
|
{
|
||||||
"$match": {
|
"$match": {
|
||||||
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
|
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
|
||||||
@@ -307,17 +254,17 @@ class PromptBuilder:
|
|||||||
},
|
},
|
||||||
{"$sort": {"similarity": -1}},
|
{"$sort": {"similarity": -1}},
|
||||||
{"$limit": limit},
|
{"$limit": limit},
|
||||||
{"$project": {"content": 1, "similarity": 1}}
|
{"$project": {"content": 1, "similarity": 1}},
|
||||||
]
|
]
|
||||||
|
|
||||||
results = list(db.knowledges.aggregate(pipeline))
|
results = list(db.knowledges.aggregate(pipeline))
|
||||||
# print(f"\033[1;34m[调试]\033[0m获取知识库内容结果: {results}")
|
# print(f"\033[1;34m[调试]\033[0m获取知识库内容结果: {results}")
|
||||||
|
|
||||||
if not results:
|
if not results:
|
||||||
return ''
|
return ""
|
||||||
|
|
||||||
# 返回所有找到的内容，用换行分隔
|
# 返回所有找到的内容，用换行分隔
|
||||||
return '\n'.join(str(result['content']) for result in results)
|
return "\n".join(str(result["content"]) for result in results)
|
||||||
|
|
||||||
|
|
||||||
prompt_builder = PromptBuilder()
|
prompt_builder = PromptBuilder()
|
||||||
|
|||||||
@@ -1,10 +1,20 @@
|
|||||||
import asyncio
|
import asyncio
|
||||||
from typing import Optional
|
from typing import Optional
|
||||||
from loguru import logger
|
from src.common.logger import get_module_logger, LogConfig, RELATION_STYLE_CONFIG
|
||||||
|
|
||||||
from ...common.database import db
|
from ...common.database import db
|
||||||
from .message_base import UserInfo
|
from ..message.message_base import UserInfo
|
||||||
from .chat_stream import ChatStream
|
from .chat_stream import ChatStream
|
||||||
|
import math
|
||||||
|
from bson.decimal128 import Decimal128
|
||||||
|
|
||||||
|
relationship_config = LogConfig(
|
||||||
|
# 使用关系专用样式
|
||||||
|
console_format=RELATION_STYLE_CONFIG["console_format"],
|
||||||
|
file_format=RELATION_STYLE_CONFIG["file_format"],
|
||||||
|
)
|
||||||
|
logger = get_module_logger("rel_manager", config=relationship_config)
|
||||||
|
|
||||||
|
|
||||||
class Impression:
|
class Impression:
|
||||||
traits: str = None
|
traits: str = None
|
||||||
@@ -23,23 +33,20 @@ class Relationship:
|
|||||||
relationship_value: float = None
|
relationship_value: float = None
|
||||||
saved = False
|
saved = False
|
||||||
|
|
||||||
def __init__(self, chat:ChatStream=None,data:dict=None):
|
def __init__(self, chat: ChatStream = None, data: dict = None):
|
||||||
self.user_id=chat.user_info.user_id if chat else data.get('user_id',0)
|
self.user_id = chat.user_info.user_id if chat else data.get("user_id", 0)
|
||||||
self.platform=chat.platform if chat else data.get('platform','')
|
self.platform = chat.platform if chat else data.get("platform", "")
|
||||||
self.nickname=chat.user_info.user_nickname if chat else data.get('nickname','')
|
self.nickname = chat.user_info.user_nickname if chat else data.get("nickname", "")
|
||||||
self.relationship_value=data.get('relationship_value',0) if data else 0
|
self.relationship_value = data.get("relationship_value", 0) if data else 0
|
||||||
self.age=data.get('age',0) if data else 0
|
self.age = data.get("age", 0) if data else 0
|
||||||
self.gender=data.get('gender','') if data else ''
|
self.gender = data.get("gender", "") if data else ""
|
||||||
|
|
||||||
|
|
||||||
class RelationshipManager:
|
class RelationshipManager:
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
self.relationships: dict[tuple[int, str], Relationship] = {} # 修改为使用(user_id, platform)作为键
|
self.relationships: dict[tuple[int, str], Relationship] = {} # 修改为使用(user_id, platform)作为键
|
||||||
|
|
||||||
async def update_relationship(self,
|
async def update_relationship(self, chat_stream: ChatStream, data: dict = None, **kwargs) -> Optional[Relationship]:
|
||||||
chat_stream:ChatStream,
|
|
||||||
data: dict = None,
|
|
||||||
**kwargs) -> Optional[Relationship]:
|
|
||||||
"""更新或创建关系
|
"""更新或创建关系
|
||||||
Args:
|
Args:
|
||||||
chat_stream: 聊天流对象
|
chat_stream: 聊天流对象
|
||||||
@@ -51,9 +58,9 @@ class RelationshipManager:
|
|||||||
# 确定user_id和platform
|
# 确定user_id和platform
|
||||||
if chat_stream.user_info is not None:
|
if chat_stream.user_info is not None:
|
||||||
user_id = chat_stream.user_info.user_id
|
user_id = chat_stream.user_info.user_id
|
||||||
platform = chat_stream.user_info.platform or 'qq'
|
platform = chat_stream.user_info.platform or "qq"
|
||||||
else:
|
else:
|
||||||
platform = platform or 'qq'
|
platform = platform or "qq"
|
||||||
|
|
||||||
if user_id is None:
|
if user_id is None:
|
||||||
raise ValueError("必须提供user_id或user_info")
|
raise ValueError("必须提供user_id或user_info")
|
||||||
@@ -83,9 +90,7 @@ class RelationshipManager:
|
|||||||
|
|
||||||
return relationship
|
return relationship
|
||||||
|
|
||||||
async def update_relationship_value(self,
|
async def update_relationship_value(self, chat_stream: ChatStream, **kwargs) -> Optional[Relationship]:
|
||||||
chat_stream:ChatStream,
|
|
||||||
**kwargs) -> Optional[Relationship]:
|
|
||||||
"""更新关系值
|
"""更新关系值
|
||||||
Args:
|
Args:
|
||||||
user_id: 用户ID（可选，如果提供user_info则不需要）
|
user_id: 用户ID（可选，如果提供user_info则不需要）
|
||||||
@@ -99,9 +104,9 @@ class RelationshipManager:
|
|||||||
user_info = chat_stream.user_info
|
user_info = chat_stream.user_info
|
||||||
if user_info is not None:
|
if user_info is not None:
|
||||||
user_id = user_info.user_id
|
user_id = user_info.user_id
|
||||||
platform = user_info.platform or 'qq'
|
platform = user_info.platform or "qq"
|
||||||
else:
|
else:
|
||||||
platform = platform or 'qq'
|
platform = platform or "qq"
|
||||||
|
|
||||||
if user_id is None:
|
if user_id is None:
|
||||||
raise ValueError("必须提供user_id或user_info")
|
raise ValueError("必须提供user_id或user_info")
|
||||||
@@ -113,7 +118,22 @@ class RelationshipManager:
|
|||||||
relationship = self.relationships.get(key)
|
relationship = self.relationships.get(key)
|
||||||
if relationship:
|
if relationship:
|
||||||
for k, value in kwargs.items():
|
for k, value in kwargs.items():
|
||||||
if k == 'relationship_value':
|
if k == "relationship_value":
|
||||||
|
# 检查relationship.relationship_value是否为double类型
|
||||||
|
if not isinstance(relationship.relationship_value, float):
|
||||||
|
try:
|
||||||
|
# 处理 Decimal128 类型
|
||||||
|
if isinstance(relationship.relationship_value, Decimal128):
|
||||||
|
relationship.relationship_value = float(relationship.relationship_value.to_decimal())
|
||||||
|
else:
|
||||||
|
relationship.relationship_value = float(relationship.relationship_value)
|
||||||
|
logger.info(
|
||||||
|
f"[关系管理] 用户 {user_id}({platform}) 的关系值已转换为double类型: {relationship.relationship_value}"
|
||||||
|
) # noqa: E501
|
||||||
|
except (ValueError, TypeError):
|
||||||
|
# 如果不能解析/强转则将relationship.relationship_value设置为double类型的0
|
||||||
|
relationship.relationship_value = 0.0
|
||||||
|
logger.warning(f"[关系管理] 用户 {user_id}({platform}) 的无法转换为double类型，已设置为0")
|
||||||
relationship.relationship_value += value
|
relationship.relationship_value += value
|
||||||
await self.storage_relationship(relationship)
|
await self.storage_relationship(relationship)
|
||||||
relationship.saved = True
|
relationship.saved = True
|
||||||
@@ -125,8 +145,7 @@ class RelationshipManager:
|
|||||||
logger.warning(f"[关系管理] 用户 {user_id}({platform}) 不存在，无法更新")
|
logger.warning(f"[关系管理] 用户 {user_id}({platform}) 不存在，无法更新")
|
||||||
return None
|
return None
|
||||||
|
|
||||||
def get_relationship(self,
|
def get_relationship(self, chat_stream: ChatStream) -> Optional[Relationship]:
|
||||||
chat_stream:ChatStream) -> Optional[Relationship]:
|
|
||||||
"""获取用户关系对象
|
"""获取用户关系对象
|
||||||
Args:
|
Args:
|
||||||
user_id: 用户ID（可选，如果提供user_info则不需要）
|
user_id: 用户ID（可选，如果提供user_info则不需要）
|
||||||
@@ -137,12 +156,12 @@ class RelationshipManager:
|
|||||||
"""
|
"""
|
||||||
# 确定user_id和platform
|
# 确定user_id和platform
|
||||||
user_info = chat_stream.user_info
|
user_info = chat_stream.user_info
|
||||||
platform = chat_stream.user_info.platform or 'qq'
|
platform = chat_stream.user_info.platform or "qq"
|
||||||
if user_info is not None:
|
if user_info is not None:
|
||||||
user_id = user_info.user_id
|
user_id = user_info.user_id
|
||||||
platform = user_info.platform or 'qq'
|
platform = user_info.platform or "qq"
|
||||||
else:
|
else:
|
||||||
platform = platform or 'qq'
|
platform = platform or "qq"
|
||||||
|
|
||||||
if user_id is None:
|
if user_id is None:
|
||||||
raise ValueError("必须提供user_id或user_info")
|
raise ValueError("必须提供user_id或user_info")
|
||||||
@@ -151,13 +170,13 @@ class RelationshipManager:
|
|||||||
if key in self.relationships:
|
if key in self.relationships:
|
||||||
return self.relationships[key]
|
return self.relationships[key]
|
||||||
else:
|
else:
|
||||||
return 0
|
return None
|
||||||
|
|
||||||
async def load_relationship(self, data: dict) -> Relationship:
|
async def load_relationship(self, data: dict) -> Relationship:
|
||||||
"""从数据库加载或创建新的关系对象"""
|
"""从数据库加载或创建新的关系对象"""
|
||||||
# 确保data中有platform字段，如果没有则默认为'qq'
|
# 确保data中有platform字段，如果没有则默认为'qq'
|
||||||
if 'platform' not in data:
|
if "platform" not in data:
|
||||||
data['platform'] = 'qq'
|
data["platform"] = "qq"
|
||||||
|
|
||||||
rela = Relationship(data=data)
|
rela = Relationship(data=data)
|
||||||
rela.saved = True
|
rela.saved = True
|
||||||
@@ -188,7 +207,7 @@ class RelationshipManager:
|
|||||||
async def _save_all_relationships(self):
|
async def _save_all_relationships(self):
|
||||||
"""将所有关系数据保存到数据库"""
|
"""将所有关系数据保存到数据库"""
|
||||||
# 保存所有关系数据
|
# 保存所有关系数据
|
||||||
for (userid, platform), relationship in self.relationships.items():
|
for _, relationship in self.relationships.items():
|
||||||
if not relationship.saved:
|
if not relationship.saved:
|
||||||
relationship.saved = True
|
relationship.saved = True
|
||||||
await self.storage_relationship(relationship)
|
await self.storage_relationship(relationship)
|
||||||
@@ -204,23 +223,21 @@ class RelationshipManager:
|
|||||||
saved = relationship.saved
|
saved = relationship.saved
|
||||||
|
|
||||||
db.relationships.update_one(
|
db.relationships.update_one(
|
||||||
{'user_id': user_id, 'platform': platform},
|
{"user_id": user_id, "platform": platform},
|
||||||
{'$set': {
|
{
|
||||||
'platform': platform,
|
"$set": {
|
||||||
'nickname': nickname,
|
"platform": platform,
|
||||||
'relationship_value': relationship_value,
|
"nickname": nickname,
|
||||||
'gender': gender,
|
"relationship_value": relationship_value,
|
||||||
'age': age,
|
"gender": gender,
|
||||||
'saved': saved
|
"age": age,
|
||||||
}},
|
"saved": saved,
|
||||||
upsert=True
|
}
|
||||||
|
},
|
||||||
|
upsert=True,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
def get_name(self, user_id: int = None, platform: str = None, user_info: UserInfo = None) -> str:
|
||||||
def get_name(self,
|
|
||||||
user_id: int = None,
|
|
||||||
platform: str = None,
|
|
||||||
user_info: UserInfo = None) -> str:
|
|
||||||
"""获取用户昵称
|
"""获取用户昵称
|
||||||
Args:
|
Args:
|
||||||
user_id: 用户ID（可选，如果提供user_info则不需要）
|
user_id: 用户ID（可选，如果提供user_info则不需要）
|
||||||
@@ -232,9 +249,9 @@ class RelationshipManager:
|
|||||||
# 确定user_id和platform
|
# 确定user_id和platform
|
||||||
if user_info is not None:
|
if user_info is not None:
|
||||||
user_id = user_info.user_id
|
user_id = user_info.user_id
|
||||||
platform = user_info.platform or 'qq'
|
platform = user_info.platform or "qq"
|
||||||
else:
|
else:
|
||||||
platform = platform or 'qq'
|
platform = platform or "qq"
|
||||||
|
|
||||||
if user_id is None:
|
if user_id is None:
|
||||||
raise ValueError("必须提供user_id或user_info")
|
raise ValueError("必须提供user_id或user_info")
|
||||||
@@ -249,5 +266,118 @@ class RelationshipManager:
|
|||||||
else:
|
else:
|
||||||
return "某人"
|
return "某人"
|
||||||
|
|
||||||
|
async def calculate_update_relationship_value(self, chat_stream: ChatStream, label: str, stance: str) -> None:
|
||||||
|
"""计算变更关系值
|
||||||
|
新的关系值变更计算方式：
|
||||||
|
将关系值限定在-1000到1000
|
||||||
|
对于关系值的变更，期望：
|
||||||
|
1.向两端逼近时会逐渐减缓
|
||||||
|
2.关系越差，改善越难，关系越好，恶化越容易
|
||||||
|
3.人维护关系的精力往往有限，所以当高关系值用户越多，对于中高关系值用户增长越慢
|
||||||
|
"""
|
||||||
|
stancedict = {
|
||||||
|
"支持": 0,
|
||||||
|
"中立": 1,
|
||||||
|
"反对": 2,
|
||||||
|
}
|
||||||
|
|
||||||
|
valuedict = {
|
||||||
|
"开心": 1.5,
|
||||||
|
"愤怒": -3.5,
|
||||||
|
"悲伤": -1.5,
|
||||||
|
"惊讶": 0.6,
|
||||||
|
"害羞": 2.0,
|
||||||
|
"平静": 0.3,
|
||||||
|
"恐惧": -2,
|
||||||
|
"厌恶": -2.5,
|
||||||
|
"困惑": 0.5,
|
||||||
|
}
|
||||||
|
if self.get_relationship(chat_stream):
|
||||||
|
old_value = self.get_relationship(chat_stream).relationship_value
|
||||||
|
else:
|
||||||
|
return
|
||||||
|
|
||||||
|
if old_value > 1000:
|
||||||
|
old_value = 1000
|
||||||
|
elif old_value < -1000:
|
||||||
|
old_value = -1000
|
||||||
|
|
||||||
|
value = valuedict[label]
|
||||||
|
if old_value >= 0:
|
||||||
|
if valuedict[label] >= 0 and stancedict[stance] != 2:
|
||||||
|
value = value * math.cos(math.pi * old_value / 2000)
|
||||||
|
if old_value > 500:
|
||||||
|
high_value_count = 0
|
||||||
|
for _, relationship in self.relationships.items():
|
||||||
|
if relationship.relationship_value >= 700:
|
||||||
|
high_value_count += 1
|
||||||
|
if old_value >= 700:
|
||||||
|
value *= 3 / (high_value_count + 2) # 排除自己
|
||||||
|
else:
|
||||||
|
value *= 3 / (high_value_count + 3)
|
||||||
|
elif valuedict[label] < 0 and stancedict[stance] != 0:
|
||||||
|
value = value * math.exp(old_value / 1000)
|
||||||
|
else:
|
||||||
|
value = 0
|
||||||
|
elif old_value < 0:
|
||||||
|
if valuedict[label] >= 0 and stancedict[stance] != 2:
|
||||||
|
value = value * math.exp(old_value / 1000)
|
||||||
|
elif valuedict[label] < 0 and stancedict[stance] != 0:
|
||||||
|
value = value * math.cos(math.pi * old_value / 2000)
|
||||||
|
else:
|
||||||
|
value = 0
|
||||||
|
|
||||||
|
level_num = self.calculate_level_num(old_value + value)
|
||||||
|
relationship_level = ["厌恶", "冷漠", "一般", "友好", "喜欢", "暧昧"]
|
||||||
|
logger.info(
|
||||||
|
f"当前关系: {relationship_level[level_num]}, "
|
||||||
|
f"关系值: {old_value:.2f}, "
|
||||||
|
f"当前立场情感: {stance}-{label}, "
|
||||||
|
f"变更: {value:+.5f}"
|
||||||
|
)
|
||||||
|
|
||||||
|
await self.update_relationship_value(chat_stream=chat_stream, relationship_value=value)
|
||||||
|
|
||||||
|
def build_relationship_info(self, person) -> str:
|
||||||
|
relationship_value = relationship_manager.get_relationship(person).relationship_value
|
||||||
|
level_num = self.calculate_level_num(relationship_value)
|
||||||
|
relationship_level = ["厌恶", "冷漠", "一般", "友好", "喜欢", "暧昧"]
|
||||||
|
relation_prompt2_list = [
|
||||||
|
"冷漠回应",
|
||||||
|
"冷淡回复",
|
||||||
|
"保持理性",
|
||||||
|
"愿意回复",
|
||||||
|
"积极回复",
|
||||||
|
"无条件支持",
|
||||||
|
]
|
||||||
|
if person.user_info.user_cardname:
|
||||||
|
return (
|
||||||
|
f"你对昵称为'[({person.user_info.user_id}){person.user_info.user_nickname}]{person.user_info.user_cardname}'的用户的态度为{relationship_level[level_num]}，"
|
||||||
|
f"回复态度为{relation_prompt2_list[level_num]}，关系等级为{level_num}。"
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
return (
|
||||||
|
f"你对昵称为'({person.user_info.user_id}){person.user_info.user_nickname}'的用户的态度为{relationship_level[level_num]}，"
|
||||||
|
f"回复态度为{relation_prompt2_list[level_num]}，关系等级为{level_num}。"
|
||||||
|
)
|
||||||
|
|
||||||
|
def calculate_level_num(self, relationship_value) -> int:
|
||||||
|
"""关系等级计算"""
|
||||||
|
if -1000 <= relationship_value < -227:
|
||||||
|
level_num = 0
|
||||||
|
elif -227 <= relationship_value < -73:
|
||||||
|
level_num = 1
|
||||||
|
elif -73 <= relationship_value < 227:
|
||||||
|
level_num = 2
|
||||||
|
elif 227 <= relationship_value < 587:
|
||||||
|
level_num = 3
|
||||||
|
elif 587 <= relationship_value < 900:
|
||||||
|
level_num = 4
|
||||||
|
elif 900 <= relationship_value <= 1000:
|
||||||
|
level_num = 5
|
||||||
|
else:
|
||||||
|
level_num = 5 if relationship_value > 1000 else 0
|
||||||
|
return level_num
|
||||||
|
|
||||||
|
|
||||||
relationship_manager = RelationshipManager()
|
relationship_manager = RelationshipManager()
|
||||||
|
|||||||
@@ -1,40 +1,42 @@
|
|||||||
from typing import Optional, Union
|
from typing import Union
|
||||||
|
|
||||||
from ...common.database import db
|
from ...common.database import db
|
||||||
from .message import MessageSending, MessageRecv
|
from .message import MessageSending, MessageRecv
|
||||||
from .chat_stream import ChatStream
|
from .chat_stream import ChatStream
|
||||||
from loguru import logger
|
from src.common.logger import get_module_logger
|
||||||
|
|
||||||
|
logger = get_module_logger("message_storage")
|
||||||
|
|
||||||
|
|
||||||
class MessageStorage:
|
class MessageStorage:
|
||||||
async def store_message(self, message: Union[MessageSending, MessageRecv],chat_stream:ChatStream, topic: Optional[str] = None) -> None:
|
async def store_message(self, message: Union[MessageSending, MessageRecv], chat_stream: ChatStream) -> None:
|
||||||
"""存储消息到数据库"""
|
"""存储消息到数据库"""
|
||||||
try:
|
try:
|
||||||
message_data = {
|
message_data = {
|
||||||
"message_id": message.message_info.message_id,
|
"message_id": message.message_info.message_id,
|
||||||
"time": message.message_info.time,
|
"time": message.message_info.time,
|
||||||
"chat_id":chat_stream.stream_id,
|
"chat_id": chat_stream.stream_id,
|
||||||
"chat_info": chat_stream.to_dict(),
|
"chat_info": chat_stream.to_dict(),
|
||||||
"user_info": message.message_info.user_info.to_dict(),
|
"user_info": message.message_info.user_info.to_dict(),
|
||||||
"processed_plain_text": message.processed_plain_text,
|
"processed_plain_text": message.processed_plain_text,
|
||||||
"detailed_plain_text": message.detailed_plain_text,
|
"detailed_plain_text": message.detailed_plain_text,
|
||||||
"topic": topic,
|
"memorized_times": message.memorized_times,
|
||||||
}
|
}
|
||||||
db.messages.insert_one(message_data)
|
db.messages.insert_one(message_data)
|
||||||
except Exception:
|
except Exception:
|
||||||
logger.exception("存储消息失败")
|
logger.exception("存储消息失败")
|
||||||
|
|
||||||
async def store_recalled_message(self, message_id: str, time: str, chat_stream:ChatStream) -> None:
|
async def store_recalled_message(self, message_id: str, time: str, chat_stream: ChatStream) -> None:
|
||||||
"""存储撤回消息到数据库"""
|
"""存储撤回消息到数据库"""
|
||||||
if "recalled_messages" not in db.list_collection_names():
|
if "recalled_messages" not in db.list_collection_names():
|
||||||
db.create_collection("recalled_messages")
|
db.create_collection("recalled_messages")
|
||||||
else:
|
else:
|
||||||
try:
|
try:
|
||||||
message_data = {
|
message_data = {
|
||||||
"message_id": message_id,
|
"message_id": message_id,
|
||||||
"time": time,
|
"time": time,
|
||||||
"stream_id":chat_stream.stream_id,
|
"stream_id": chat_stream.stream_id,
|
||||||
}
|
}
|
||||||
db.recalled_messages.insert_one(message_data)
|
db.recalled_messages.insert_one(message_data)
|
||||||
except Exception:
|
except Exception:
|
||||||
logger.exception("存储撤回消息失败")
|
logger.exception("存储撤回消息失败")
|
||||||
@@ -42,7 +44,9 @@ class MessageStorage:
|
|||||||
async def remove_recalled_message(self, time: str) -> None:
|
async def remove_recalled_message(self, time: str) -> None:
|
||||||
"""删除撤回消息"""
|
"""删除撤回消息"""
|
||||||
try:
|
try:
|
||||||
db.recalled_messages.delete_many({"time": {"$lt": time-300}})
|
db.recalled_messages.delete_many({"time": {"$lt": time - 300}})
|
||||||
except Exception:
|
except Exception:
|
||||||
logger.exception("删除撤回消息失败")
|
logger.exception("删除撤回消息失败")
|
||||||
|
|
||||||
|
|
||||||
# 如果需要其他存储相关的函数，可以在这里添加
|
# 如果需要其他存储相关的函数，可以在这里添加
|
||||||
|
|||||||
@@ -1,14 +0,0 @@
|
|||||||
#Broca's Area
|
|
||||||
# 功能：语言产生、语法处理和言语运动控制。
|
|
||||||
# 损伤后果：布洛卡失语症（表达困难，但理解保留）。
|
|
||||||
|
|
||||||
import time
|
|
||||||
|
|
||||||
|
|
||||||
class Thinking_Idea:
|
|
||||||
def __init__(self, message_id: str):
|
|
||||||
self.messages = [] # 消息列表集合
|
|
||||||
self.current_thoughts = [] # 当前思考内容列表
|
|
||||||
self.time = time.time() # 创建时间
|
|
||||||
self.id = str(int(time.time() * 1000)) # 使用时间戳生成唯一标识ID
|
|
||||||
|
|
||||||
@@ -1,18 +1,23 @@
|
|||||||
from typing import List, Optional
|
from typing import List, Optional
|
||||||
|
|
||||||
from nonebot import get_driver
|
|
||||||
|
|
||||||
from ..models.utils_model import LLM_request
|
from ..models.utils_model import LLM_request
|
||||||
from .config import global_config
|
from ..config.config import global_config
|
||||||
from loguru import logger
|
from src.common.logger import get_module_logger, LogConfig, TOPIC_STYLE_CONFIG
|
||||||
|
|
||||||
driver = get_driver()
|
# 定义日志配置
|
||||||
config = driver.config
|
topic_config = LogConfig(
|
||||||
|
# 使用海马体专用样式
|
||||||
|
console_format=TOPIC_STYLE_CONFIG["console_format"],
|
||||||
|
file_format=TOPIC_STYLE_CONFIG["file_format"],
|
||||||
|
)
|
||||||
|
|
||||||
|
logger = get_module_logger("topic_identifier", config=topic_config)
|
||||||
|
|
||||||
|
|
||||||
class TopicIdentifier:
|
class TopicIdentifier:
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge)
|
self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge, request_type="topic")
|
||||||
|
|
||||||
async def identify_topic_llm(self, text: str) -> Optional[List[str]]:
|
async def identify_topic_llm(self, text: str) -> Optional[List[str]]:
|
||||||
"""识别消息主题，返回主题列表"""
|
"""识别消息主题，返回主题列表"""
|
||||||
@@ -24,7 +29,7 @@ class TopicIdentifier:
|
|||||||
消息内容：{text}"""
|
消息内容：{text}"""
|
||||||
|
|
||||||
# 使用 LLM_request 类进行请求
|
# 使用 LLM_request 类进行请求
|
||||||
topic, _ = await self.llm_topic_judge.generate_response(prompt)
|
topic, _, _ = await self.llm_topic_judge.generate_response(prompt)
|
||||||
|
|
||||||
if not topic:
|
if not topic:
|
||||||
logger.error("LLM API 返回为空")
|
logger.error("LLM API 返回为空")
|
||||||
|
|||||||
@@ -1,26 +1,24 @@
|
|||||||
import math
|
|
||||||
import random
|
import random
|
||||||
import time
|
import time
|
||||||
|
import re
|
||||||
from collections import Counter
|
from collections import Counter
|
||||||
from typing import Dict, List
|
from typing import Dict, List
|
||||||
|
|
||||||
import jieba
|
import jieba
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from nonebot import get_driver
|
from src.common.logger import get_module_logger
|
||||||
from loguru import logger
|
|
||||||
|
|
||||||
from ..models.utils_model import LLM_request
|
from ..models.utils_model import LLM_request
|
||||||
from ..utils.typo_generator import ChineseTypoGenerator
|
from ..utils.typo_generator import ChineseTypoGenerator
|
||||||
from .config import global_config
|
from ..config.config import global_config
|
||||||
from .message import MessageRecv,Message
|
from .message import MessageRecv, Message
|
||||||
from .message_base import UserInfo
|
from ..message.message_base import UserInfo
|
||||||
from .chat_stream import ChatStream
|
from .chat_stream import ChatStream
|
||||||
from ..moods.moods import MoodManager
|
from ..moods.moods import MoodManager
|
||||||
from ...common.database import db
|
from ...common.database import db
|
||||||
|
|
||||||
driver = get_driver()
|
|
||||||
config = driver.config
|
|
||||||
|
|
||||||
|
logger = get_module_logger("chat_utils")
|
||||||
|
|
||||||
|
|
||||||
def db_message_to_str(message_dict: Dict) -> str:
|
def db_message_to_str(message_dict: Dict) -> str:
|
||||||
@@ -28,8 +26,11 @@ def db_message_to_str(message_dict: Dict) -> str:
|
|||||||
time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message_dict["time"]))
|
time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message_dict["time"]))
|
||||||
try:
|
try:
|
||||||
name = "[(%s)%s]%s" % (
|
name = "[(%s)%s]%s" % (
|
||||||
message_dict['user_id'], message_dict.get("user_nickname", ""), message_dict.get("user_cardname", ""))
|
message_dict["user_id"],
|
||||||
except:
|
message_dict.get("user_nickname", ""),
|
||||||
|
message_dict.get("user_cardname", ""),
|
||||||
|
)
|
||||||
|
except Exception:
|
||||||
name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}"
|
name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}"
|
||||||
content = message_dict.get("processed_plain_text", "")
|
content = message_dict.get("processed_plain_text", "")
|
||||||
result = f"[{time_str}] {name}: {content}\n"
|
result = f"[{time_str}] {name}: {content}\n"
|
||||||
@@ -50,72 +51,14 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> bool:
|
|||||||
return False
|
return False
|
||||||
|
|
||||||
|
|
||||||
async def get_embedding(text):
|
async def get_embedding(text, request_type="embedding"):
|
||||||
"""获取文本的embedding向量"""
|
"""获取文本的embedding向量"""
|
||||||
llm = LLM_request(model=global_config.embedding)
|
llm = LLM_request(model=global_config.embedding, request_type=request_type)
|
||||||
# return llm.get_embedding_sync(text)
|
# return llm.get_embedding_sync(text)
|
||||||
return await llm.get_embedding(text)
|
return await llm.get_embedding(text)
|
||||||
|
|
||||||
|
|
||||||
def cosine_similarity(v1, v2):
|
async def get_recent_group_messages(chat_id: str, limit: int = 12) -> list:
|
||||||
dot_product = np.dot(v1, v2)
|
|
||||||
norm1 = np.linalg.norm(v1)
|
|
||||||
norm2 = np.linalg.norm(v2)
|
|
||||||
return dot_product / (norm1 * norm2)
|
|
||||||
|
|
||||||
|
|
||||||
def calculate_information_content(text):
|
|
||||||
"""计算文本的信息量（熵）"""
|
|
||||||
char_count = Counter(text)
|
|
||||||
total_chars = len(text)
|
|
||||||
|
|
||||||
entropy = 0
|
|
||||||
for count in char_count.values():
|
|
||||||
probability = count / total_chars
|
|
||||||
entropy -= probability * math.log2(probability)
|
|
||||||
|
|
||||||
return entropy
|
|
||||||
|
|
||||||
|
|
||||||
def get_closest_chat_from_db(length: int, timestamp: str):
|
|
||||||
"""从数据库中获取最接近指定时间戳的聊天记录
|
|
||||||
|
|
||||||
Args:
|
|
||||||
length: 要获取的消息数量
|
|
||||||
timestamp: 时间戳
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
list: 消息记录列表，每个记录包含时间和文本信息
|
|
||||||
"""
|
|
||||||
chat_records = []
|
|
||||||
closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])
|
|
||||||
|
|
||||||
if closest_record:
|
|
||||||
closest_time = closest_record['time']
|
|
||||||
chat_id = closest_record['chat_id'] # 获取chat_id
|
|
||||||
# 获取该时间戳之后的length条消息，保持相同的chat_id
|
|
||||||
chat_records = list(db.messages.find(
|
|
||||||
{
|
|
||||||
"time": {"$gt": closest_time},
|
|
||||||
"chat_id": chat_id # 添加chat_id过滤
|
|
||||||
}
|
|
||||||
).sort('time', 1).limit(length))
|
|
||||||
|
|
||||||
# 转换记录格式
|
|
||||||
formatted_records = []
|
|
||||||
for record in chat_records:
|
|
||||||
formatted_records.append({
|
|
||||||
'time': record["time"],
|
|
||||||
'chat_id': record["chat_id"],
|
|
||||||
'detailed_plain_text': record.get("detailed_plain_text", "") # 添加文本内容
|
|
||||||
})
|
|
||||||
|
|
||||||
return formatted_records
|
|
||||||
|
|
||||||
return []
|
|
||||||
|
|
||||||
|
|
||||||
async def get_recent_group_messages(chat_id:str, limit: int = 12) -> list:
|
|
||||||
"""从数据库获取群组最近的消息记录
|
"""从数据库获取群组最近的消息记录
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
@@ -127,9 +70,13 @@ async def get_recent_group_messages(chat_id:str, limit: int = 12) -> list:
|
|||||||
"""
|
"""
|
||||||
|
|
||||||
# 从数据库获取最近消息
|
# 从数据库获取最近消息
|
||||||
recent_messages = list(db.messages.find(
|
recent_messages = list(
|
||||||
{"chat_id": chat_id},
|
db.messages.find(
|
||||||
).sort("time", -1).limit(limit))
|
{"chat_id": chat_id},
|
||||||
|
)
|
||||||
|
.sort("time", -1)
|
||||||
|
.limit(limit)
|
||||||
|
)
|
||||||
|
|
||||||
if not recent_messages:
|
if not recent_messages:
|
||||||
return []
|
return []
|
||||||
@@ -138,17 +85,17 @@ async def get_recent_group_messages(chat_id:str, limit: int = 12) -> list:
|
|||||||
message_objects = []
|
message_objects = []
|
||||||
for msg_data in recent_messages:
|
for msg_data in recent_messages:
|
||||||
try:
|
try:
|
||||||
chat_info=msg_data.get("chat_info",{})
|
chat_info = msg_data.get("chat_info", {})
|
||||||
chat_stream=ChatStream.from_dict(chat_info)
|
chat_stream = ChatStream.from_dict(chat_info)
|
||||||
user_info=msg_data.get("user_info",{})
|
user_info = msg_data.get("user_info", {})
|
||||||
user_info=UserInfo.from_dict(user_info)
|
user_info = UserInfo.from_dict(user_info)
|
||||||
msg = Message(
|
msg = Message(
|
||||||
message_id=msg_data["message_id"],
|
message_id=msg_data["message_id"],
|
||||||
chat_stream=chat_stream,
|
chat_stream=chat_stream,
|
||||||
time=msg_data["time"],
|
time=msg_data["time"],
|
||||||
user_info=user_info,
|
user_info=user_info,
|
||||||
processed_plain_text=msg_data.get("processed_text", ""),
|
processed_plain_text=msg_data.get("processed_text", ""),
|
||||||
detailed_plain_text=msg_data.get("detailed_plain_text", "")
|
detailed_plain_text=msg_data.get("detailed_plain_text", ""),
|
||||||
)
|
)
|
||||||
message_objects.append(msg)
|
message_objects.append(msg)
|
||||||
except KeyError:
|
except KeyError:
|
||||||
@@ -161,22 +108,26 @@ async def get_recent_group_messages(chat_id:str, limit: int = 12) -> list:
|
|||||||
|
|
||||||
|
|
||||||
def get_recent_group_detailed_plain_text(chat_stream_id: int, limit: int = 12, combine=False):
|
def get_recent_group_detailed_plain_text(chat_stream_id: int, limit: int = 12, combine=False):
|
||||||
recent_messages = list(db.messages.find(
|
recent_messages = list(
|
||||||
{"chat_id": chat_stream_id},
|
db.messages.find(
|
||||||
{
|
{"chat_id": chat_stream_id},
|
||||||
"time": 1, # 返回时间字段
|
{
|
||||||
"chat_id":1,
|
"time": 1, # 返回时间字段
|
||||||
"chat_info":1,
|
"chat_id": 1,
|
||||||
"user_info": 1,
|
"chat_info": 1,
|
||||||
"message_id": 1, # 返回消息ID字段
|
"user_info": 1,
|
||||||
"detailed_plain_text": 1 # 返回处理后的文本字段
|
"message_id": 1, # 返回消息ID字段
|
||||||
}
|
"detailed_plain_text": 1, # 返回处理后的文本字段
|
||||||
).sort("time", -1).limit(limit))
|
},
|
||||||
|
)
|
||||||
|
.sort("time", -1)
|
||||||
|
.limit(limit)
|
||||||
|
)
|
||||||
|
|
||||||
if not recent_messages:
|
if not recent_messages:
|
||||||
return []
|
return []
|
||||||
|
|
||||||
message_detailed_plain_text = ''
|
message_detailed_plain_text = ""
|
||||||
message_detailed_plain_text_list = []
|
message_detailed_plain_text_list = []
|
||||||
|
|
||||||
# 反转消息列表，使最新的消息在最后
|
# 反转消息列表，使最新的消息在最后
|
||||||
@@ -192,6 +143,40 @@ def get_recent_group_detailed_plain_text(chat_stream_id: int, limit: int = 12, c
|
|||||||
return message_detailed_plain_text_list
|
return message_detailed_plain_text_list
|
||||||
|
|
||||||
|
|
||||||
|
def get_recent_group_speaker(chat_stream_id: int, sender, limit: int = 12) -> list:
|
||||||
|
# 获取当前群聊记录内发言的人
|
||||||
|
recent_messages = list(
|
||||||
|
db.messages.find(
|
||||||
|
{"chat_id": chat_stream_id},
|
||||||
|
{
|
||||||
|
"chat_info": 1,
|
||||||
|
"user_info": 1,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
.sort("time", -1)
|
||||||
|
.limit(limit)
|
||||||
|
)
|
||||||
|
|
||||||
|
if not recent_messages:
|
||||||
|
return []
|
||||||
|
|
||||||
|
who_chat_in_group = [] # ChatStream列表
|
||||||
|
|
||||||
|
duplicate_removal = []
|
||||||
|
for msg_db_data in recent_messages:
|
||||||
|
user_info = UserInfo.from_dict(msg_db_data["user_info"])
|
||||||
|
if (
|
||||||
|
(user_info.user_id, user_info.platform) != sender
|
||||||
|
and (user_info.user_id, user_info.platform) != (global_config.BOT_QQ, "qq")
|
||||||
|
and (user_info.user_id, user_info.platform) not in duplicate_removal
|
||||||
|
and len(duplicate_removal) < 5
|
||||||
|
): # 排除重复，排除消息发送者，排除bot(此处bot的平台强制为了qq，可能需要更改)，限制加载的关系数目
|
||||||
|
duplicate_removal.append((user_info.user_id, user_info.platform))
|
||||||
|
chat_info = msg_db_data.get("chat_info", {})
|
||||||
|
who_chat_in_group.append(ChatStream.from_dict(chat_info))
|
||||||
|
return who_chat_in_group
|
||||||
|
|
||||||
|
|
||||||
def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
|
def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
|
||||||
"""将文本分割成句子，但保持书名号中的内容完整
|
"""将文本分割成句子，但保持书名号中的内容完整
|
||||||
Args:
|
Args:
|
||||||
@@ -200,76 +185,89 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
|
|||||||
List[str]: 分割后的句子列表
|
List[str]: 分割后的句子列表
|
||||||
"""
|
"""
|
||||||
len_text = len(text)
|
len_text = len(text)
|
||||||
if len_text < 5:
|
if len_text < 4:
|
||||||
if random.random() < 0.01:
|
if random.random() < 0.01:
|
||||||
return list(text) # 如果文本很短且触发随机条件,直接按字符分割
|
return list(text) # 如果文本很短且触发随机条件,直接按字符分割
|
||||||
else:
|
else:
|
||||||
return [text]
|
return [text]
|
||||||
if len_text < 12:
|
if len_text < 12:
|
||||||
split_strength = 0.3
|
split_strength = 0.2
|
||||||
elif len_text < 32:
|
elif len_text < 32:
|
||||||
split_strength = 0.7
|
split_strength = 0.6
|
||||||
else:
|
else:
|
||||||
split_strength = 0.9
|
split_strength = 0.7
|
||||||
# 先移除换行符
|
|
||||||
# print(f"split_strength: {split_strength}")
|
|
||||||
|
|
||||||
|
# 检查是否为西文字符段落
|
||||||
|
if not is_western_paragraph(text):
|
||||||
|
# 当语言为中文时，统一将英文逗号转换为中文逗号
|
||||||
|
text = text.replace(",", "，")
|
||||||
|
text = text.replace("\n", " ")
|
||||||
|
else:
|
||||||
|
# 用"|seg|"作为分割符分开
|
||||||
|
text = re.sub(r"([.!?]) +", r"\1\|seg\|", text)
|
||||||
|
text = text.replace("\n", "|seg|")
|
||||||
|
text, mapping = protect_kaomoji(text)
|
||||||
# print(f"处理前的文本: {text}")
|
# print(f"处理前的文本: {text}")
|
||||||
|
|
||||||
# 统一将英文逗号转换为中文逗号
|
text_no_1 = ""
|
||||||
text = text.replace(',', '，')
|
|
||||||
text = text.replace('\n', ' ')
|
|
||||||
|
|
||||||
# print(f"处理前的文本: {text}")
|
|
||||||
|
|
||||||
text_no_1 = ''
|
|
||||||
for letter in text:
|
for letter in text:
|
||||||
# print(f"当前字符: {letter}")
|
# print(f"当前字符: {letter}")
|
||||||
if letter in ['!', '！', '?', '？']:
|
if letter in ["!", "！", "?", "？"]:
|
||||||
# print(f"当前字符: {letter}, 随机数: {random.random()}")
|
# print(f"当前字符: {letter}, 随机数: {random.random()}")
|
||||||
if random.random() < split_strength:
|
if random.random() < split_strength:
|
||||||
letter = ''
|
letter = ""
|
||||||
if letter in ['。', '…']:
|
if letter in ["。", "…"]:
|
||||||
# print(f"当前字符: {letter}, 随机数: {random.random()}")
|
# print(f"当前字符: {letter}, 随机数: {random.random()}")
|
||||||
if random.random() < 1 - split_strength:
|
if random.random() < 1 - split_strength:
|
||||||
letter = ''
|
letter = ""
|
||||||
text_no_1 += letter
|
text_no_1 += letter
|
||||||
|
|
||||||
# 对每个逗号单独判断是否分割
|
# 对每个逗号单独判断是否分割
|
||||||
sentences = [text_no_1]
|
sentences = [text_no_1]
|
||||||
new_sentences = []
|
new_sentences = []
|
||||||
for sentence in sentences:
|
for sentence in sentences:
|
||||||
parts = sentence.split('，')
|
parts = sentence.split("，")
|
||||||
current_sentence = parts[0]
|
current_sentence = parts[0]
|
||||||
for part in parts[1:]:
|
if not is_western_paragraph(current_sentence):
|
||||||
if random.random() < split_strength:
|
for part in parts[1:]:
|
||||||
|
if random.random() < split_strength:
|
||||||
|
new_sentences.append(current_sentence.strip())
|
||||||
|
current_sentence = part
|
||||||
|
else:
|
||||||
|
current_sentence += "，" + part
|
||||||
|
# 处理空格分割
|
||||||
|
space_parts = current_sentence.split(" ")
|
||||||
|
current_sentence = space_parts[0]
|
||||||
|
for part in space_parts[1:]:
|
||||||
|
if random.random() < split_strength:
|
||||||
|
new_sentences.append(current_sentence.strip())
|
||||||
|
current_sentence = part
|
||||||
|
else:
|
||||||
|
current_sentence += " " + part
|
||||||
|
else:
|
||||||
|
# 处理分割符
|
||||||
|
space_parts = current_sentence.split("|seg|")
|
||||||
|
current_sentence = space_parts[0]
|
||||||
|
for part in space_parts[1:]:
|
||||||
new_sentences.append(current_sentence.strip())
|
new_sentences.append(current_sentence.strip())
|
||||||
current_sentence = part
|
current_sentence = part
|
||||||
else:
|
|
||||||
current_sentence += '，' + part
|
|
||||||
# 处理空格分割
|
|
||||||
space_parts = current_sentence.split(' ')
|
|
||||||
current_sentence = space_parts[0]
|
|
||||||
for part in space_parts[1:]:
|
|
||||||
if random.random() < split_strength:
|
|
||||||
new_sentences.append(current_sentence.strip())
|
|
||||||
current_sentence = part
|
|
||||||
else:
|
|
||||||
current_sentence += ' ' + part
|
|
||||||
new_sentences.append(current_sentence.strip())
|
new_sentences.append(current_sentence.strip())
|
||||||
sentences = [s for s in new_sentences if s] # 移除空字符串
|
sentences = [s for s in new_sentences if s] # 移除空字符串
|
||||||
|
sentences = recover_kaomoji(sentences, mapping)
|
||||||
|
|
||||||
# print(f"分割后的句子: {sentences}")
|
# print(f"分割后的句子: {sentences}")
|
||||||
sentences_done = []
|
sentences_done = []
|
||||||
for sentence in sentences:
|
for sentence in sentences:
|
||||||
sentence = sentence.rstrip('，,')
|
sentence = sentence.rstrip("，,")
|
||||||
if random.random() < split_strength * 0.5:
|
# 西文字符句子不进行随机合并
|
||||||
sentence = sentence.replace('，', '').replace(',', '')
|
if not is_western_paragraph(current_sentence):
|
||||||
elif random.random() < split_strength:
|
if random.random() < split_strength * 0.5:
|
||||||
sentence = sentence.replace('，', ' ').replace(',', ' ')
|
sentence = sentence.replace("，", "").replace(",", "")
|
||||||
|
elif random.random() < split_strength:
|
||||||
|
sentence = sentence.replace("，", " ").replace(",", " ")
|
||||||
sentences_done.append(sentence)
|
sentences_done.append(sentence)
|
||||||
|
|
||||||
logger.info(f"处理后的句子: {sentences_done}")
|
logger.debug(f"处理后的句子: {sentences_done}")
|
||||||
return sentences_done
|
return sentences_done
|
||||||
|
|
||||||
|
|
||||||
@@ -282,19 +280,19 @@ def random_remove_punctuation(text: str) -> str:
|
|||||||
Returns:
|
Returns:
|
||||||
str: 处理后的文本
|
str: 处理后的文本
|
||||||
"""
|
"""
|
||||||
result = ''
|
result = ""
|
||||||
text_len = len(text)
|
text_len = len(text)
|
||||||
|
|
||||||
for i, char in enumerate(text):
|
for i, char in enumerate(text):
|
||||||
if char == '。' and i == text_len - 1: # 结尾的句号
|
if char == "。" and i == text_len - 1: # 结尾的句号
|
||||||
if random.random() > 0.4: # 80%概率删除结尾句号
|
if random.random() > 0.1: # 90%概率删除结尾句号
|
||||||
continue
|
continue
|
||||||
elif char == '，':
|
elif char == "，":
|
||||||
rand = random.random()
|
rand = random.random()
|
||||||
if rand < 0.25: # 5%概率删除逗号
|
if rand < 0.25: # 5%概率删除逗号
|
||||||
continue
|
continue
|
||||||
elif rand < 0.25: # 20%概率把逗号变成空格
|
elif rand < 0.25: # 20%概率把逗号变成空格
|
||||||
result += ' '
|
result += " "
|
||||||
continue
|
continue
|
||||||
result += char
|
result += char
|
||||||
return result
|
return result
|
||||||
@@ -302,17 +300,26 @@ def random_remove_punctuation(text: str) -> str:
|
|||||||
|
|
||||||
def process_llm_response(text: str) -> List[str]:
|
def process_llm_response(text: str) -> List[str]:
|
||||||
# processed_response = process_text_with_typos(content)
|
# processed_response = process_text_with_typos(content)
|
||||||
if len(text) > 200:
|
# 对西文字符段落的回复长度设置为汉字字符的两倍
|
||||||
|
max_length = global_config.response_max_length
|
||||||
|
max_sentence_num = global_config.response_max_sentence_num
|
||||||
|
if len(text) > max_length and not is_western_paragraph(text):
|
||||||
logger.warning(f"回复过长 ({len(text)} 字符)，返回默认回复")
|
logger.warning(f"回复过长 ({len(text)} 字符)，返回默认回复")
|
||||||
return ['懒得说']
|
return ["懒得说"]
|
||||||
|
elif len(text) > 200:
|
||||||
|
logger.warning(f"回复过长 ({len(text)} 字符)，返回默认回复")
|
||||||
|
return ["懒得说"]
|
||||||
# 处理长消息
|
# 处理长消息
|
||||||
typo_generator = ChineseTypoGenerator(
|
typo_generator = ChineseTypoGenerator(
|
||||||
error_rate=global_config.chinese_typo_error_rate,
|
error_rate=global_config.chinese_typo_error_rate,
|
||||||
min_freq=global_config.chinese_typo_min_freq,
|
min_freq=global_config.chinese_typo_min_freq,
|
||||||
tone_error_rate=global_config.chinese_typo_tone_error_rate,
|
tone_error_rate=global_config.chinese_typo_tone_error_rate,
|
||||||
word_replace_rate=global_config.chinese_typo_word_replace_rate
|
word_replace_rate=global_config.chinese_typo_word_replace_rate,
|
||||||
)
|
)
|
||||||
split_sentences = split_into_sentences_w_remove_punctuation(text)
|
if global_config.enable_response_spliter:
|
||||||
|
split_sentences = split_into_sentences_w_remove_punctuation(text)
|
||||||
|
else:
|
||||||
|
split_sentences = [text]
|
||||||
sentences = []
|
sentences = []
|
||||||
for sentence in split_sentences:
|
for sentence in split_sentences:
|
||||||
if global_config.chinese_typo_enable:
|
if global_config.chinese_typo_enable:
|
||||||
@@ -324,14 +331,14 @@ def process_llm_response(text: str) -> List[str]:
|
|||||||
sentences.append(sentence)
|
sentences.append(sentence)
|
||||||
# 检查分割后的消息数量是否过多（超过3条）
|
# 检查分割后的消息数量是否过多（超过3条）
|
||||||
|
|
||||||
if len(sentences) > 5:
|
if len(sentences) > max_sentence_num:
|
||||||
logger.warning(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
|
logger.warning(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
|
||||||
return [f'{global_config.BOT_NICKNAME}不知道哦']
|
return [f"{global_config.BOT_NICKNAME}不知道哦"]
|
||||||
|
|
||||||
return sentences
|
return sentences
|
||||||
|
|
||||||
|
|
||||||
def calculate_typing_time(input_string: str, chinese_time: float = 0.4, english_time: float = 0.2) -> float:
|
def calculate_typing_time(input_string: str, chinese_time: float = 0.2, english_time: float = 0.1) -> float:
|
||||||
"""
|
"""
|
||||||
计算输入字符串所需的时间，中文和英文字符有不同的输入时间
|
计算输入字符串所需的时间，中文和英文字符有不同的输入时间
|
||||||
input_string (str): 输入的字符串
|
input_string (str): 输入的字符串
|
||||||
@@ -346,11 +353,11 @@ def calculate_typing_time(input_string: str, chinese_time: float = 0.4, english_
|
|||||||
# 将0-1的唤醒度映射到-1到1
|
# 将0-1的唤醒度映射到-1到1
|
||||||
mood_arousal = mood_manager.current_mood.arousal
|
mood_arousal = mood_manager.current_mood.arousal
|
||||||
# 映射到0.5到2倍的速度系数
|
# 映射到0.5到2倍的速度系数
|
||||||
typing_speed_multiplier = 1.5 ** mood_arousal # 唤醒度为1时速度翻倍,为-1时速度减半
|
typing_speed_multiplier = 1.5**mood_arousal # 唤醒度为1时速度翻倍,为-1时速度减半
|
||||||
chinese_time *= 1 / typing_speed_multiplier
|
chinese_time *= 1 / typing_speed_multiplier
|
||||||
english_time *= 1 / typing_speed_multiplier
|
english_time *= 1 / typing_speed_multiplier
|
||||||
# 计算中文字符数
|
# 计算中文字符数
|
||||||
chinese_chars = sum(1 for char in input_string if '\u4e00' <= char <= '\u9fff')
|
chinese_chars = sum(1 for char in input_string if "\u4e00" <= char <= "\u9fff")
|
||||||
|
|
||||||
# 如果只有一个中文字符，使用3倍时间
|
# 如果只有一个中文字符，使用3倍时间
|
||||||
if chinese_chars == 1 and len(input_string.strip()) == 1:
|
if chinese_chars == 1 and len(input_string.strip()) == 1:
|
||||||
@@ -359,7 +366,7 @@ def calculate_typing_time(input_string: str, chinese_time: float = 0.4, english_
|
|||||||
# 正常计算所有字符的输入时间
|
# 正常计算所有字符的输入时间
|
||||||
total_time = 0.0
|
total_time = 0.0
|
||||||
for char in input_string:
|
for char in input_string:
|
||||||
if '\u4e00' <= char <= '\u9fff': # 判断是否为中文字符
|
if "\u4e00" <= char <= "\u9fff": # 判断是否为中文字符
|
||||||
total_time += chinese_time
|
total_time += chinese_time
|
||||||
else: # 其他字符（如英文）
|
else: # 其他字符（如英文）
|
||||||
total_time += english_time
|
total_time += english_time
|
||||||
@@ -412,3 +419,65 @@ def truncate_message(message: str, max_length=20) -> str:
|
|||||||
if len(message) > max_length:
|
if len(message) > max_length:
|
||||||
return message[:max_length] + "..."
|
return message[:max_length] + "..."
|
||||||
return message
|
return message
|
||||||
|
|
||||||
|
|
||||||
|
def protect_kaomoji(sentence):
|
||||||
|
""" "
|
||||||
|
识别并保护句子中的颜文字（含括号与无括号），将其替换为占位符，
|
||||||
|
并返回替换后的句子和占位符到颜文字的映射表。
|
||||||
|
Args:
|
||||||
|
sentence (str): 输入的原始句子
|
||||||
|
Returns:
|
||||||
|
tuple: (处理后的句子, {占位符: 颜文字})
|
||||||
|
"""
|
||||||
|
kaomoji_pattern = re.compile(
|
||||||
|
r"("
|
||||||
|
r"[\(\[（【]" # 左括号
|
||||||
|
r"[^()\[\]（）【】]*?" # 非括号字符（惰性匹配）
|
||||||
|
r"[^\u4e00-\u9fa5a-zA-Z0-9\s]" # 非中文、非英文、非数字、非空格字符（必须包含至少一个）
|
||||||
|
r"[^()\[\]（）【】]*?" # 非括号字符（惰性匹配）
|
||||||
|
r"[\)\]）】]" # 右括号
|
||||||
|
r")"
|
||||||
|
r"|"
|
||||||
|
r"("
|
||||||
|
r"[▼▽・ᴥω･﹏^><≧≦￣｀´∀ヮДд︿﹀へ｡ﾟ╥╯╰︶︹•⁄]{2,15}"
|
||||||
|
r")"
|
||||||
|
)
|
||||||
|
|
||||||
|
kaomoji_matches = kaomoji_pattern.findall(sentence)
|
||||||
|
placeholder_to_kaomoji = {}
|
||||||
|
|
||||||
|
for idx, match in enumerate(kaomoji_matches):
|
||||||
|
kaomoji = match[0] if match[0] else match[1]
|
||||||
|
placeholder = f"__KAOMOJI_{idx}__"
|
||||||
|
sentence = sentence.replace(kaomoji, placeholder, 1)
|
||||||
|
placeholder_to_kaomoji[placeholder] = kaomoji
|
||||||
|
|
||||||
|
return sentence, placeholder_to_kaomoji
|
||||||
|
|
||||||
|
|
||||||
|
def recover_kaomoji(sentences, placeholder_to_kaomoji):
|
||||||
|
"""
|
||||||
|
根据映射表恢复句子中的颜文字。
|
||||||
|
Args:
|
||||||
|
sentences (list): 含有占位符的句子列表
|
||||||
|
placeholder_to_kaomoji (dict): 占位符到颜文字的映射表
|
||||||
|
Returns:
|
||||||
|
list: 恢复颜文字后的句子列表
|
||||||
|
"""
|
||||||
|
recovered_sentences = []
|
||||||
|
for sentence in sentences:
|
||||||
|
for placeholder, kaomoji in placeholder_to_kaomoji.items():
|
||||||
|
sentence = sentence.replace(placeholder, kaomoji)
|
||||||
|
recovered_sentences.append(sentence)
|
||||||
|
return recovered_sentences
|
||||||
|
|
||||||
|
|
||||||
|
def is_western_char(char):
|
||||||
|
"""检测是否为西文字符"""
|
||||||
|
return len(char.encode("utf-8")) <= 2
|
||||||
|
|
||||||
|
|
||||||
|
def is_western_paragraph(paragraph):
|
||||||
|
"""检测是否为西文字符段落"""
|
||||||
|
return all(is_western_char(char) for char in paragraph if char.isalnum())
|
||||||
|
|||||||
@@ -1,72 +0,0 @@
|
|||||||
def parse_cq_code(cq_code: str) -> dict:
|
|
||||||
"""
|
|
||||||
将CQ码解析为字典对象
|
|
||||||
|
|
||||||
Args:
|
|
||||||
cq_code (str): CQ码字符串，如 [CQ:image,file=xxx.jpg,url=http://xxx]
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
dict: 包含type和参数的字典，如 {'type': 'image', 'data': {'file': 'xxx.jpg', 'url': 'http://xxx'}}
|
|
||||||
"""
|
|
||||||
# 检查是否是有效的CQ码
|
|
||||||
if not (cq_code.startswith('[CQ:') and cq_code.endswith(']')):
|
|
||||||
return {'type': 'text', 'data': {'text': cq_code}}
|
|
||||||
|
|
||||||
# 移除前后的 [CQ: 和 ]
|
|
||||||
content = cq_code[4:-1]
|
|
||||||
|
|
||||||
# 分离类型和参数
|
|
||||||
parts = content.split(',')
|
|
||||||
if len(parts) < 1:
|
|
||||||
return {'type': 'text', 'data': {'text': cq_code}}
|
|
||||||
|
|
||||||
cq_type = parts[0]
|
|
||||||
params = {}
|
|
||||||
|
|
||||||
# 处理参数部分
|
|
||||||
if len(parts) > 1:
|
|
||||||
# 遍历所有参数
|
|
||||||
for part in parts[1:]:
|
|
||||||
if '=' in part:
|
|
||||||
key, value = part.split('=', 1)
|
|
||||||
params[key.strip()] = value.strip()
|
|
||||||
|
|
||||||
return {
|
|
||||||
'type': cq_type,
|
|
||||||
'data': params
|
|
||||||
}
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
# 测试用例列表
|
|
||||||
test_cases = [
|
|
||||||
# 测试图片CQ码
|
|
||||||
'[CQ:image,summary=,file={6E392FD2-AAA1-5192-F52A-F724A8EC7998}.gif,sub_type=1,url=https://gchat.qpic.cn/gchatpic_new/0/0-0-6E392FD2AAA15192F52AF724A8EC7998/0,file_size=861609]',
|
|
||||||
|
|
||||||
# 测试at CQ码
|
|
||||||
'[CQ:at,qq=123456]',
|
|
||||||
|
|
||||||
# 测试普通文本
|
|
||||||
'Hello World',
|
|
||||||
|
|
||||||
# 测试face表情CQ码
|
|
||||||
'[CQ:face,id=123]',
|
|
||||||
|
|
||||||
# 测试含有多个逗号的URL
|
|
||||||
'[CQ:image,url=https://example.com/image,with,commas.jpg]',
|
|
||||||
|
|
||||||
# 测试空参数
|
|
||||||
'[CQ:image,summary=]',
|
|
||||||
|
|
||||||
# 测试非法CQ码
|
|
||||||
'[CQ:]',
|
|
||||||
'[CQ:invalid'
|
|
||||||
]
|
|
||||||
|
|
||||||
# 测试每个用例
|
|
||||||
for i, test_case in enumerate(test_cases, 1):
|
|
||||||
print(f"\n测试用例 {i}:")
|
|
||||||
print(f"输入: {test_case}")
|
|
||||||
result = parse_cq_code(test_case)
|
|
||||||
print(f"输出: {result}")
|
|
||||||
print("-" * 50)
|
|
||||||
|
|
||||||
@@ -1,21 +1,19 @@
|
|||||||
import base64
|
import base64
|
||||||
import os
|
import os
|
||||||
import time
|
import time
|
||||||
import aiohttp
|
|
||||||
import hashlib
|
import hashlib
|
||||||
from typing import Optional, Union
|
from typing import Optional
|
||||||
from PIL import Image
|
from PIL import Image
|
||||||
import io
|
import io
|
||||||
|
|
||||||
from loguru import logger
|
|
||||||
from nonebot import get_driver
|
|
||||||
|
|
||||||
from ...common.database import db
|
from ...common.database import db
|
||||||
from ..chat.config import global_config
|
from ..config.config import global_config
|
||||||
from ..models.utils_model import LLM_request
|
from ..models.utils_model import LLM_request
|
||||||
|
|
||||||
driver = get_driver()
|
from src.common.logger import get_module_logger
|
||||||
config = driver.config
|
|
||||||
|
logger = get_module_logger("chat_image")
|
||||||
|
|
||||||
|
|
||||||
class ImageManager:
|
class ImageManager:
|
||||||
@@ -34,7 +32,7 @@ class ImageManager:
|
|||||||
self._ensure_description_collection()
|
self._ensure_description_collection()
|
||||||
self._ensure_image_dir()
|
self._ensure_image_dir()
|
||||||
self._initialized = True
|
self._initialized = True
|
||||||
self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300)
|
self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300, request_type="image")
|
||||||
|
|
||||||
def _ensure_image_dir(self):
|
def _ensure_image_dir(self):
|
||||||
"""确保图像存储目录存在"""
|
"""确保图像存储目录存在"""
|
||||||
@@ -110,7 +108,7 @@ class ImageManager:
|
|||||||
# 查询缓存的描述
|
# 查询缓存的描述
|
||||||
cached_description = self._get_description_from_db(image_hash, "emoji")
|
cached_description = self._get_description_from_db(image_hash, "emoji")
|
||||||
if cached_description:
|
if cached_description:
|
||||||
logger.info(f"缓存表情包描述: {cached_description}")
|
logger.debug(f"缓存表情包描述: {cached_description}")
|
||||||
return f"[表情包：{cached_description}]"
|
return f"[表情包：{cached_description}]"
|
||||||
|
|
||||||
# 调用AI获取描述
|
# 调用AI获取描述
|
||||||
@@ -173,7 +171,7 @@ class ImageManager:
|
|||||||
|
|
||||||
# 调用AI获取描述
|
# 调用AI获取描述
|
||||||
prompt = (
|
prompt = (
|
||||||
"请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。"
|
"请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多100个字。"
|
||||||
)
|
)
|
||||||
description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
|
description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
|
||||||
|
|
||||||
@@ -182,7 +180,7 @@ class ImageManager:
|
|||||||
logger.warning(f"虽然生成了描述，但是找到缓存图片描述 {cached_description}")
|
logger.warning(f"虽然生成了描述，但是找到缓存图片描述 {cached_description}")
|
||||||
return f"[图片：{cached_description}]"
|
return f"[图片：{cached_description}]"
|
||||||
|
|
||||||
logger.info(f"描述是{description}")
|
logger.debug(f"描述是{description}")
|
||||||
|
|
||||||
if description is None:
|
if description is None:
|
||||||
logger.warning("AI未能生成图片描述")
|
logger.warning("AI未能生成图片描述")
|
||||||
|
|||||||
@@ -1,20 +0,0 @@
|
|||||||
from .config import global_config
|
|
||||||
from .relationship_manager import relationship_manager
|
|
||||||
|
|
||||||
|
|
||||||
def get_user_nickname(user_id: int) -> str:
|
|
||||||
if int(user_id) == int(global_config.BOT_QQ):
|
|
||||||
return global_config.BOT_NICKNAME
|
|
||||||
# print(user_id)
|
|
||||||
return relationship_manager.get_name(user_id)
|
|
||||||
|
|
||||||
|
|
||||||
def get_user_cardname(user_id: int) -> str:
|
|
||||||
if int(user_id) == int(global_config.BOT_QQ):
|
|
||||||
return global_config.BOT_NICKNAME
|
|
||||||
# print(user_id)
|
|
||||||
return ""
|
|
||||||
|
|
||||||
|
|
||||||
def get_groupname(group_id: int) -> str:
|
|
||||||
return f"群{group_id}"
|
|
||||||
93
src/plugins/config/auto_update.py
Normal file
93
src/plugins/config/auto_update.py
Normal file
@@ -0,0 +1,93 @@
|
|||||||
|
import shutil
|
||||||
|
import tomlkit
|
||||||
|
from pathlib import Path
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
def update_config():
|
||||||
|
print("开始更新配置文件...")
|
||||||
|
# 获取根目录路径
|
||||||
|
root_dir = Path(__file__).parent.parent.parent.parent
|
||||||
|
template_dir = root_dir / "template"
|
||||||
|
config_dir = root_dir / "config"
|
||||||
|
old_config_dir = config_dir / "old"
|
||||||
|
|
||||||
|
# 创建old目录（如果不存在）
|
||||||
|
old_config_dir.mkdir(exist_ok=True)
|
||||||
|
|
||||||
|
# 定义文件路径
|
||||||
|
template_path = template_dir / "bot_config_template.toml"
|
||||||
|
old_config_path = config_dir / "bot_config.toml"
|
||||||
|
new_config_path = config_dir / "bot_config.toml"
|
||||||
|
|
||||||
|
# 读取旧配置文件
|
||||||
|
old_config = {}
|
||||||
|
if old_config_path.exists():
|
||||||
|
print(f"发现旧配置文件: {old_config_path}")
|
||||||
|
with open(old_config_path, "r", encoding="utf-8") as f:
|
||||||
|
old_config = tomlkit.load(f)
|
||||||
|
|
||||||
|
# 生成带时间戳的新文件名
|
||||||
|
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||||
|
old_backup_path = old_config_dir / f"bot_config_{timestamp}.toml"
|
||||||
|
|
||||||
|
# 移动旧配置文件到old目录
|
||||||
|
shutil.move(old_config_path, old_backup_path)
|
||||||
|
print(f"已备份旧配置文件到: {old_backup_path}")
|
||||||
|
|
||||||
|
# 复制模板文件到配置目录
|
||||||
|
print(f"从模板文件创建新配置: {template_path}")
|
||||||
|
shutil.copy2(template_path, new_config_path)
|
||||||
|
|
||||||
|
# 读取新配置文件
|
||||||
|
with open(new_config_path, "r", encoding="utf-8") as f:
|
||||||
|
new_config = tomlkit.load(f)
|
||||||
|
|
||||||
|
# 检查version是否相同
|
||||||
|
if old_config and "inner" in old_config and "inner" in new_config:
|
||||||
|
old_version = old_config["inner"].get("version")
|
||||||
|
new_version = new_config["inner"].get("version")
|
||||||
|
if old_version and new_version and old_version == new_version:
|
||||||
|
print(f"检测到版本号相同 (v{old_version})，跳过更新")
|
||||||
|
# 如果version相同，恢复旧配置文件并返回
|
||||||
|
shutil.move(old_backup_path, old_config_path)
|
||||||
|
return
|
||||||
|
else:
|
||||||
|
print(f"检测到版本号不同: 旧版本 v{old_version} -> 新版本 v{new_version}")
|
||||||
|
|
||||||
|
# 递归更新配置
|
||||||
|
def update_dict(target, source):
|
||||||
|
for key, value in source.items():
|
||||||
|
# 跳过version字段的更新
|
||||||
|
if key == "version":
|
||||||
|
continue
|
||||||
|
if key in target:
|
||||||
|
if isinstance(value, dict) and isinstance(target[key], (dict, tomlkit.items.Table)):
|
||||||
|
update_dict(target[key], value)
|
||||||
|
else:
|
||||||
|
try:
|
||||||
|
# 对数组类型进行特殊处理
|
||||||
|
if isinstance(value, list):
|
||||||
|
# 如果是空数组，确保它保持为空数组
|
||||||
|
if not value:
|
||||||
|
target[key] = tomlkit.array()
|
||||||
|
else:
|
||||||
|
target[key] = tomlkit.array(value)
|
||||||
|
else:
|
||||||
|
# 其他类型使用item方法创建新值
|
||||||
|
target[key] = tomlkit.item(value)
|
||||||
|
except (TypeError, ValueError):
|
||||||
|
# 如果转换失败，直接赋值
|
||||||
|
target[key] = value
|
||||||
|
|
||||||
|
# 将旧配置的值更新到新配置中
|
||||||
|
print("开始合并新旧配置...")
|
||||||
|
update_dict(new_config, old_config)
|
||||||
|
|
||||||
|
# 保存更新后的配置（保留注释和格式）
|
||||||
|
with open(new_config_path, "w", encoding="utf-8") as f:
|
||||||
|
f.write(tomlkit.dumps(new_config))
|
||||||
|
print("配置文件更新完成")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
update_config()
|
||||||
@@ -1,59 +1,238 @@
|
|||||||
import os
|
import os
|
||||||
import sys
|
|
||||||
from dataclasses import dataclass, field
|
from dataclasses import dataclass, field
|
||||||
from typing import Dict, List, Optional
|
from typing import Dict, List, Optional
|
||||||
|
|
||||||
import tomli
|
import tomli
|
||||||
from loguru import logger
|
import tomlkit
|
||||||
|
import shutil
|
||||||
|
from datetime import datetime
|
||||||
|
from pathlib import Path
|
||||||
from packaging import version
|
from packaging import version
|
||||||
from packaging.version import Version, InvalidVersion
|
from packaging.version import Version, InvalidVersion
|
||||||
from packaging.specifiers import SpecifierSet, InvalidSpecifier
|
from packaging.specifiers import SpecifierSet, InvalidSpecifier
|
||||||
|
|
||||||
|
from src.common.logger import get_module_logger, CONFIG_STYLE_CONFIG, LogConfig
|
||||||
|
|
||||||
|
# 定义日志配置
|
||||||
|
config_config = LogConfig(
|
||||||
|
# 使用消息发送专用样式
|
||||||
|
console_format=CONFIG_STYLE_CONFIG["console_format"],
|
||||||
|
file_format=CONFIG_STYLE_CONFIG["file_format"],
|
||||||
|
)
|
||||||
|
|
||||||
|
# 配置主程序日志格式
|
||||||
|
logger = get_module_logger("config", config=config_config)
|
||||||
|
|
||||||
|
#考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
|
||||||
|
mai_version_main = "0.6.0"
|
||||||
|
mai_version_fix = "mmc-3"
|
||||||
|
mai_version = f"{mai_version_main}-{mai_version_fix}"
|
||||||
|
|
||||||
|
def update_config():
|
||||||
|
# 获取根目录路径
|
||||||
|
root_dir = Path(__file__).parent.parent.parent.parent
|
||||||
|
template_dir = root_dir / "template"
|
||||||
|
config_dir = root_dir / "config"
|
||||||
|
old_config_dir = config_dir / "old"
|
||||||
|
|
||||||
|
# 定义文件路径
|
||||||
|
template_path = template_dir / "bot_config_template.toml"
|
||||||
|
old_config_path = config_dir / "bot_config.toml"
|
||||||
|
new_config_path = config_dir / "bot_config.toml"
|
||||||
|
|
||||||
|
# 检查配置文件是否存在
|
||||||
|
if not old_config_path.exists():
|
||||||
|
logger.info("配置文件不存在，从模板创建新配置")
|
||||||
|
shutil.copy2(template_path, old_config_path)
|
||||||
|
logger.info(f"已创建新配置文件，请填写后重新运行: {old_config_path}")
|
||||||
|
# 如果是新创建的配置文件,直接返回
|
||||||
|
quit()
|
||||||
|
return
|
||||||
|
|
||||||
|
# 读取旧配置文件和模板文件
|
||||||
|
with open(old_config_path, "r", encoding="utf-8") as f:
|
||||||
|
old_config = tomlkit.load(f)
|
||||||
|
with open(template_path, "r", encoding="utf-8") as f:
|
||||||
|
new_config = tomlkit.load(f)
|
||||||
|
|
||||||
|
# 检查version是否相同
|
||||||
|
if old_config and "inner" in old_config and "inner" in new_config:
|
||||||
|
old_version = old_config["inner"].get("version")
|
||||||
|
new_version = new_config["inner"].get("version")
|
||||||
|
if old_version and new_version and old_version == new_version:
|
||||||
|
logger.info(f"检测到配置文件版本号相同 (v{old_version})，跳过更新")
|
||||||
|
return
|
||||||
|
else:
|
||||||
|
logger.info(f"检测到版本号不同: 旧版本 v{old_version} -> 新版本 v{new_version}")
|
||||||
|
|
||||||
|
# 创建old目录（如果不存在）
|
||||||
|
old_config_dir.mkdir(exist_ok=True)
|
||||||
|
|
||||||
|
# 生成带时间戳的新文件名
|
||||||
|
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||||
|
old_backup_path = old_config_dir / f"bot_config_{timestamp}.toml"
|
||||||
|
|
||||||
|
# 移动旧配置文件到old目录
|
||||||
|
shutil.move(old_config_path, old_backup_path)
|
||||||
|
logger.info(f"已备份旧配置文件到: {old_backup_path}")
|
||||||
|
|
||||||
|
# 复制模板文件到配置目录
|
||||||
|
shutil.copy2(template_path, new_config_path)
|
||||||
|
logger.info(f"已创建新配置文件: {new_config_path}")
|
||||||
|
|
||||||
|
# 递归更新配置
|
||||||
|
def update_dict(target, source):
|
||||||
|
for key, value in source.items():
|
||||||
|
# 跳过version字段的更新
|
||||||
|
if key == "version":
|
||||||
|
continue
|
||||||
|
if key in target:
|
||||||
|
if isinstance(value, dict) and isinstance(target[key], (dict, tomlkit.items.Table)):
|
||||||
|
update_dict(target[key], value)
|
||||||
|
else:
|
||||||
|
try:
|
||||||
|
# 对数组类型进行特殊处理
|
||||||
|
if isinstance(value, list):
|
||||||
|
# 如果是空数组，确保它保持为空数组
|
||||||
|
if not value:
|
||||||
|
target[key] = tomlkit.array()
|
||||||
|
else:
|
||||||
|
target[key] = tomlkit.array(value)
|
||||||
|
else:
|
||||||
|
# 其他类型使用item方法创建新值
|
||||||
|
target[key] = tomlkit.item(value)
|
||||||
|
except (TypeError, ValueError):
|
||||||
|
# 如果转换失败，直接赋值
|
||||||
|
target[key] = value
|
||||||
|
|
||||||
|
# 将旧配置的值更新到新配置中
|
||||||
|
logger.info("开始合并新旧配置...")
|
||||||
|
update_dict(new_config, old_config)
|
||||||
|
|
||||||
|
# 保存更新后的配置（保留注释和格式）
|
||||||
|
with open(new_config_path, "w", encoding="utf-8") as f:
|
||||||
|
f.write(tomlkit.dumps(new_config))
|
||||||
|
logger.info("配置文件更新完成")
|
||||||
|
|
||||||
|
logger = get_module_logger("config")
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
@dataclass
|
||||||
class BotConfig:
|
class BotConfig:
|
||||||
"""机器人配置类"""
|
"""机器人配置类"""
|
||||||
|
|
||||||
INNER_VERSION: Version = None
|
INNER_VERSION: Version = None
|
||||||
|
MAI_VERSION: str = mai_version # 硬编码的版本信息
|
||||||
|
|
||||||
BOT_QQ: Optional[int] = 1
|
# bot
|
||||||
|
BOT_QQ: Optional[int] = 114514
|
||||||
BOT_NICKNAME: Optional[str] = None
|
BOT_NICKNAME: Optional[str] = None
|
||||||
BOT_ALIAS_NAMES: List[str] = field(default_factory=list) # 别名，可以通过这个叫它
|
BOT_ALIAS_NAMES: List[str] = field(default_factory=list) # 别名，可以通过这个叫它
|
||||||
|
|
||||||
# 消息处理相关配置
|
# group
|
||||||
MIN_TEXT_LENGTH: int = 2 # 最小处理文本长度
|
|
||||||
MAX_CONTEXT_SIZE: int = 15 # 上下文最大消息数
|
|
||||||
emoji_chance: float = 0.2 # 发送表情包的基础概率
|
|
||||||
|
|
||||||
ENABLE_PIC_TRANSLATE: bool = True # 是否启用图片翻译
|
|
||||||
|
|
||||||
talk_allowed_groups = set()
|
talk_allowed_groups = set()
|
||||||
talk_frequency_down_groups = set()
|
talk_frequency_down_groups = set()
|
||||||
thinking_timeout: int = 100 # 思考时间
|
|
||||||
|
|
||||||
response_willing_amplifier: float = 1.0 # 回复意愿放大系数
|
|
||||||
response_interested_rate_amplifier: float = 1.0 # 回复兴趣度放大系数
|
|
||||||
down_frequency_rate: float = 3.5 # 降低回复频率的群组回复意愿降低系数
|
|
||||||
|
|
||||||
ban_user_id = set()
|
ban_user_id = set()
|
||||||
|
|
||||||
|
# personality
|
||||||
|
PROMPT_PERSONALITY = [
|
||||||
|
"用一句话或几句话描述性格特点和其他特征",
|
||||||
|
"例如，是一个热爱国家热爱党的新时代好青年",
|
||||||
|
"例如，曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧",
|
||||||
|
]
|
||||||
|
PERSONALITY_1: float = 0.6 # 第一种人格概率
|
||||||
|
PERSONALITY_2: float = 0.3 # 第二种人格概率
|
||||||
|
PERSONALITY_3: float = 0.1 # 第三种人格概率
|
||||||
|
|
||||||
|
# schedule
|
||||||
|
ENABLE_SCHEDULE_GEN: bool = False # 是否启用日程生成
|
||||||
|
PROMPT_SCHEDULE_GEN = "无日程"
|
||||||
|
SCHEDULE_DOING_UPDATE_INTERVAL: int = 300 # 日程表更新间隔 单位秒
|
||||||
|
SCHEDULE_TEMPERATURE: float = 0.5 # 日程表温度，建议0.5-1.0
|
||||||
|
|
||||||
|
# message
|
||||||
|
MAX_CONTEXT_SIZE: int = 15 # 上下文最大消息数
|
||||||
|
emoji_chance: float = 0.2 # 发送表情包的基础概率
|
||||||
|
thinking_timeout: int = 120 # 思考时间
|
||||||
|
max_response_length: int = 1024 # 最大回复长度
|
||||||
|
|
||||||
|
ban_words = set()
|
||||||
|
ban_msgs_regex = set()
|
||||||
|
|
||||||
|
#heartflow
|
||||||
|
enable_heartflow: bool = False # 是否启用心流
|
||||||
|
sub_heart_flow_update_interval: int = 60 # 子心流更新频率，间隔 单位秒
|
||||||
|
sub_heart_flow_freeze_time: int = 120 # 子心流冻结时间，超过这个时间没有回复，子心流会冻结，间隔 单位秒
|
||||||
|
sub_heart_flow_stop_time: int = 600 # 子心流停止时间，超过这个时间没有回复，子心流会停止，间隔 单位秒
|
||||||
|
heart_flow_update_interval: int = 300 # 心流更新频率，间隔 单位秒
|
||||||
|
|
||||||
|
# willing
|
||||||
|
willing_mode: str = "classical" # 意愿模式
|
||||||
|
response_willing_amplifier: float = 1.0 # 回复意愿放大系数
|
||||||
|
response_interested_rate_amplifier: float = 1.0 # 回复兴趣度放大系数
|
||||||
|
down_frequency_rate: float = 3 # 降低回复频率的群组回复意愿降低系数
|
||||||
|
emoji_response_penalty: float = 0.0 # 表情包回复惩罚
|
||||||
|
|
||||||
|
# response
|
||||||
|
MODEL_R1_PROBABILITY: float = 0.8 # R1模型概率
|
||||||
|
MODEL_V3_PROBABILITY: float = 0.1 # V3模型概率
|
||||||
|
MODEL_R1_DISTILL_PROBABILITY: float = 0.1 # R1蒸馏模型概率
|
||||||
|
|
||||||
|
# emoji
|
||||||
EMOJI_CHECK_INTERVAL: int = 120 # 表情包检查间隔（分钟）
|
EMOJI_CHECK_INTERVAL: int = 120 # 表情包检查间隔（分钟）
|
||||||
EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟）
|
EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟）
|
||||||
EMOJI_SAVE: bool = True # 偷表情包
|
EMOJI_SAVE: bool = True # 偷表情包
|
||||||
EMOJI_CHECK: bool = False # 是否开启过滤
|
EMOJI_CHECK: bool = False # 是否开启过滤
|
||||||
EMOJI_CHECK_PROMPT: str = "符合公序良俗" # 表情包过滤要求
|
EMOJI_CHECK_PROMPT: str = "符合公序良俗" # 表情包过滤要求
|
||||||
|
|
||||||
ban_words = set()
|
# memory
|
||||||
ban_msgs_regex = set()
|
build_memory_interval: int = 600 # 记忆构建间隔（秒）
|
||||||
|
memory_build_distribution: list = field(
|
||||||
|
default_factory=lambda: [4, 2, 0.6, 24, 8, 0.4]
|
||||||
|
) # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重
|
||||||
|
build_memory_sample_num: int = 10 # 记忆构建采样数量
|
||||||
|
build_memory_sample_length: int = 20 # 记忆构建采样长度
|
||||||
|
memory_compress_rate: float = 0.1 # 记忆压缩率
|
||||||
|
|
||||||
max_response_length: int = 1024 # 最大回复长度
|
forget_memory_interval: int = 600 # 记忆遗忘间隔（秒）
|
||||||
|
memory_forget_time: int = 24 # 记忆遗忘时间（小时）
|
||||||
|
memory_forget_percentage: float = 0.01 # 记忆遗忘比例
|
||||||
|
|
||||||
|
memory_ban_words: list = field(
|
||||||
|
default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"]
|
||||||
|
) # 添加新的配置项默认值
|
||||||
|
|
||||||
|
# mood
|
||||||
|
mood_update_interval: float = 1.0 # 情绪更新间隔 单位秒
|
||||||
|
mood_decay_rate: float = 0.95 # 情绪衰减率
|
||||||
|
mood_intensity_factor: float = 0.7 # 情绪强度因子
|
||||||
|
|
||||||
|
# keywords
|
||||||
|
keywords_reaction_rules = [] # 关键词回复规则
|
||||||
|
|
||||||
|
# chinese_typo
|
||||||
|
chinese_typo_enable = True # 是否启用中文错别字生成器
|
||||||
|
chinese_typo_error_rate = 0.03 # 单字替换概率
|
||||||
|
chinese_typo_min_freq = 7 # 最小字频阈值
|
||||||
|
chinese_typo_tone_error_rate = 0.2 # 声调错误概率
|
||||||
|
chinese_typo_word_replace_rate = 0.02 # 整词替换概率
|
||||||
|
|
||||||
|
# response_spliter
|
||||||
|
enable_response_spliter = True # 是否启用回复分割器
|
||||||
|
response_max_length = 100 # 回复允许的最大长度
|
||||||
|
response_max_sentence_num = 3 # 回复允许的最大句子数
|
||||||
|
|
||||||
|
# remote
|
||||||
|
remote_enable: bool = True # 是否启用远程控制
|
||||||
|
|
||||||
|
# experimental
|
||||||
|
enable_friend_chat: bool = False # 是否启用好友聊天
|
||||||
|
enable_think_flow: bool = False # 是否启用思考流程
|
||||||
|
|
||||||
# 模型配置
|
# 模型配置
|
||||||
llm_reasoning: Dict[str, str] = field(default_factory=lambda: {})
|
llm_reasoning: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
llm_reasoning_minor: Dict[str, str] = field(default_factory=lambda: {})
|
# llm_reasoning_minor: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
llm_normal: Dict[str, str] = field(default_factory=lambda: {})
|
llm_normal: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
llm_normal_minor: Dict[str, str] = field(default_factory=lambda: {})
|
|
||||||
llm_topic_judge: Dict[str, str] = field(default_factory=lambda: {})
|
llm_topic_judge: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
llm_summary_by_topic: Dict[str, str] = field(default_factory=lambda: {})
|
llm_summary_by_topic: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
llm_emotion_judge: Dict[str, str] = field(default_factory=lambda: {})
|
llm_emotion_judge: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
@@ -61,39 +240,10 @@ class BotConfig:
|
|||||||
vlm: Dict[str, str] = field(default_factory=lambda: {})
|
vlm: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
moderation: Dict[str, str] = field(default_factory=lambda: {})
|
moderation: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
|
|
||||||
MODEL_R1_PROBABILITY: float = 0.8 # R1模型概率
|
# 实验性
|
||||||
MODEL_V3_PROBABILITY: float = 0.1 # V3模型概率
|
llm_observation: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
MODEL_R1_DISTILL_PROBABILITY: float = 0.1 # R1蒸馏模型概率
|
llm_sub_heartflow: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
|
llm_heartflow: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
enable_advance_output: bool = False # 是否启用高级输出
|
|
||||||
enable_kuuki_read: bool = True # 是否启用读空气功能
|
|
||||||
enable_debug_output: bool = False # 是否启用调试输出
|
|
||||||
enable_friend_chat: bool = False # 是否启用好友聊天
|
|
||||||
|
|
||||||
mood_update_interval: float = 1.0 # 情绪更新间隔 单位秒
|
|
||||||
mood_decay_rate: float = 0.95 # 情绪衰减率
|
|
||||||
mood_intensity_factor: float = 0.7 # 情绪强度因子
|
|
||||||
|
|
||||||
keywords_reaction_rules = [] # 关键词回复规则
|
|
||||||
|
|
||||||
chinese_typo_enable = True # 是否启用中文错别字生成器
|
|
||||||
chinese_typo_error_rate = 0.03 # 单字替换概率
|
|
||||||
chinese_typo_min_freq = 7 # 最小字频阈值
|
|
||||||
chinese_typo_tone_error_rate = 0.2 # 声调错误概率
|
|
||||||
chinese_typo_word_replace_rate = 0.02 # 整词替换概率
|
|
||||||
|
|
||||||
# 默认人设
|
|
||||||
PROMPT_PERSONALITY = [
|
|
||||||
"曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧",
|
|
||||||
"是一个女大学生，你有黑色头发，你会刷小红书",
|
|
||||||
"是一个女大学生，你会刷b站，对ACG文化感兴趣",
|
|
||||||
]
|
|
||||||
|
|
||||||
PROMPT_SCHEDULE_GEN = "一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书"
|
|
||||||
|
|
||||||
PERSONALITY_1: float = 0.6 # 第一种人格概率
|
|
||||||
PERSONALITY_2: float = 0.3 # 第二种人格概率
|
|
||||||
PERSONALITY_3: float = 0.1 # 第三种人格概率
|
|
||||||
|
|
||||||
build_memory_interval: int = 600 # 记忆构建间隔（秒）
|
build_memory_interval: int = 600 # 记忆构建间隔（秒）
|
||||||
|
|
||||||
@@ -101,10 +251,17 @@ class BotConfig:
|
|||||||
memory_forget_time: int = 24 # 记忆遗忘时间（小时）
|
memory_forget_time: int = 24 # 记忆遗忘时间（小时）
|
||||||
memory_forget_percentage: float = 0.01 # 记忆遗忘比例
|
memory_forget_percentage: float = 0.01 # 记忆遗忘比例
|
||||||
memory_compress_rate: float = 0.1 # 记忆压缩率
|
memory_compress_rate: float = 0.1 # 记忆压缩率
|
||||||
|
build_memory_sample_num: int = 10 # 记忆构建采样数量
|
||||||
|
build_memory_sample_length: int = 20 # 记忆构建采样长度
|
||||||
|
memory_build_distribution: list = field(
|
||||||
|
default_factory=lambda: [4, 2, 0.6, 24, 8, 0.4]
|
||||||
|
) # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重
|
||||||
memory_ban_words: list = field(
|
memory_ban_words: list = field(
|
||||||
default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"]
|
default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"]
|
||||||
) # 添加新的配置项默认值
|
) # 添加新的配置项默认值
|
||||||
|
|
||||||
|
api_urls: Dict[str, str] = field(default_factory=lambda: {})
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def get_config_dir() -> str:
|
def get_config_dir() -> str:
|
||||||
"""获取配置文件目录"""
|
"""获取配置文件目录"""
|
||||||
@@ -168,19 +325,30 @@ class BotConfig:
|
|||||||
"""从TOML配置文件加载配置"""
|
"""从TOML配置文件加载配置"""
|
||||||
config = cls()
|
config = cls()
|
||||||
|
|
||||||
|
|
||||||
def personality(parent: dict):
|
def personality(parent: dict):
|
||||||
personality_config = parent["personality"]
|
personality_config = parent["personality"]
|
||||||
personality = personality_config.get("prompt_personality")
|
personality = personality_config.get("prompt_personality")
|
||||||
if len(personality) >= 2:
|
if len(personality) >= 2:
|
||||||
logger.debug(f"载入自定义人格:{personality}")
|
logger.debug(f"载入自定义人格:{personality}")
|
||||||
config.PROMPT_PERSONALITY = personality_config.get("prompt_personality", config.PROMPT_PERSONALITY)
|
config.PROMPT_PERSONALITY = personality_config.get("prompt_personality", config.PROMPT_PERSONALITY)
|
||||||
logger.info(f"载入自定义日程prompt:{personality_config.get('prompt_schedule', config.PROMPT_SCHEDULE_GEN)}")
|
|
||||||
config.PROMPT_SCHEDULE_GEN = personality_config.get("prompt_schedule", config.PROMPT_SCHEDULE_GEN)
|
|
||||||
|
|
||||||
if config.INNER_VERSION in SpecifierSet(">=0.0.2"):
|
config.PERSONALITY_1 = personality_config.get("personality_1_probability", config.PERSONALITY_1)
|
||||||
config.PERSONALITY_1 = personality_config.get("personality_1_probability", config.PERSONALITY_1)
|
config.PERSONALITY_2 = personality_config.get("personality_2_probability", config.PERSONALITY_2)
|
||||||
config.PERSONALITY_2 = personality_config.get("personality_2_probability", config.PERSONALITY_2)
|
config.PERSONALITY_3 = personality_config.get("personality_3_probability", config.PERSONALITY_3)
|
||||||
config.PERSONALITY_3 = personality_config.get("personality_3_probability", config.PERSONALITY_3)
|
|
||||||
|
def schedule(parent: dict):
|
||||||
|
schedule_config = parent["schedule"]
|
||||||
|
config.ENABLE_SCHEDULE_GEN = schedule_config.get("enable_schedule_gen", config.ENABLE_SCHEDULE_GEN)
|
||||||
|
config.PROMPT_SCHEDULE_GEN = schedule_config.get("prompt_schedule_gen", config.PROMPT_SCHEDULE_GEN)
|
||||||
|
config.SCHEDULE_DOING_UPDATE_INTERVAL = schedule_config.get(
|
||||||
|
"schedule_doing_update_interval", config.SCHEDULE_DOING_UPDATE_INTERVAL
|
||||||
|
)
|
||||||
|
logger.info(
|
||||||
|
f"载入自定义日程prompt:{schedule_config.get('prompt_schedule_gen', config.PROMPT_SCHEDULE_GEN)}"
|
||||||
|
)
|
||||||
|
if config.INNER_VERSION in SpecifierSet(">=1.0.2"):
|
||||||
|
config.SCHEDULE_TEMPERATURE = schedule_config.get("schedule_temperature", config.SCHEDULE_TEMPERATURE)
|
||||||
|
|
||||||
def emoji(parent: dict):
|
def emoji(parent: dict):
|
||||||
emoji_config = parent["emoji"]
|
emoji_config = parent["emoji"]
|
||||||
@@ -190,44 +358,56 @@ class BotConfig:
|
|||||||
config.EMOJI_SAVE = emoji_config.get("auto_save", config.EMOJI_SAVE)
|
config.EMOJI_SAVE = emoji_config.get("auto_save", config.EMOJI_SAVE)
|
||||||
config.EMOJI_CHECK = emoji_config.get("enable_check", config.EMOJI_CHECK)
|
config.EMOJI_CHECK = emoji_config.get("enable_check", config.EMOJI_CHECK)
|
||||||
|
|
||||||
def cq_code(parent: dict):
|
|
||||||
cq_code_config = parent["cq_code"]
|
|
||||||
config.ENABLE_PIC_TRANSLATE = cq_code_config.get("enable_pic_translate", config.ENABLE_PIC_TRANSLATE)
|
|
||||||
|
|
||||||
def bot(parent: dict):
|
def bot(parent: dict):
|
||||||
# 机器人基础配置
|
# 机器人基础配置
|
||||||
bot_config = parent["bot"]
|
bot_config = parent["bot"]
|
||||||
bot_qq = bot_config.get("qq")
|
bot_qq = bot_config.get("qq")
|
||||||
config.BOT_QQ = int(bot_qq)
|
config.BOT_QQ = int(bot_qq)
|
||||||
config.BOT_NICKNAME = bot_config.get("nickname", config.BOT_NICKNAME)
|
config.BOT_NICKNAME = bot_config.get("nickname", config.BOT_NICKNAME)
|
||||||
|
config.BOT_ALIAS_NAMES = bot_config.get("alias_names", config.BOT_ALIAS_NAMES)
|
||||||
if config.INNER_VERSION in SpecifierSet(">=0.0.5"):
|
|
||||||
config.BOT_ALIAS_NAMES = bot_config.get("alias_names", config.BOT_ALIAS_NAMES)
|
|
||||||
|
|
||||||
def response(parent: dict):
|
def response(parent: dict):
|
||||||
response_config = parent["response"]
|
response_config = parent["response"]
|
||||||
config.MODEL_R1_PROBABILITY = response_config.get("model_r1_probability", config.MODEL_R1_PROBABILITY)
|
config.MODEL_R1_PROBABILITY = response_config.get("model_r1_probability", config.MODEL_R1_PROBABILITY)
|
||||||
config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY)
|
config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY)
|
||||||
config.MODEL_R1_DISTILL_PROBABILITY = response_config.get(
|
# config.MODEL_R1_DISTILL_PROBABILITY = response_config.get(
|
||||||
"model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY
|
# "model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY
|
||||||
)
|
# )
|
||||||
config.max_response_length = response_config.get("max_response_length", config.max_response_length)
|
config.max_response_length = response_config.get("max_response_length", config.max_response_length)
|
||||||
|
|
||||||
|
def willing(parent: dict):
|
||||||
|
willing_config = parent["willing"]
|
||||||
|
config.willing_mode = willing_config.get("willing_mode", config.willing_mode)
|
||||||
|
|
||||||
|
if config.INNER_VERSION in SpecifierSet(">=0.0.11"):
|
||||||
|
config.response_willing_amplifier = willing_config.get(
|
||||||
|
"response_willing_amplifier", config.response_willing_amplifier
|
||||||
|
)
|
||||||
|
config.response_interested_rate_amplifier = willing_config.get(
|
||||||
|
"response_interested_rate_amplifier", config.response_interested_rate_amplifier
|
||||||
|
)
|
||||||
|
config.down_frequency_rate = willing_config.get("down_frequency_rate", config.down_frequency_rate)
|
||||||
|
config.emoji_response_penalty = willing_config.get(
|
||||||
|
"emoji_response_penalty", config.emoji_response_penalty
|
||||||
|
)
|
||||||
|
|
||||||
def model(parent: dict):
|
def model(parent: dict):
|
||||||
# 加载模型配置
|
# 加载模型配置
|
||||||
model_config: dict = parent["model"]
|
model_config: dict = parent["model"]
|
||||||
|
|
||||||
config_list = [
|
config_list = [
|
||||||
"llm_reasoning",
|
"llm_reasoning",
|
||||||
"llm_reasoning_minor",
|
# "llm_reasoning_minor",
|
||||||
"llm_normal",
|
"llm_normal",
|
||||||
"llm_normal_minor",
|
|
||||||
"llm_topic_judge",
|
"llm_topic_judge",
|
||||||
"llm_summary_by_topic",
|
"llm_summary_by_topic",
|
||||||
"llm_emotion_judge",
|
"llm_emotion_judge",
|
||||||
"vlm",
|
"vlm",
|
||||||
"embedding",
|
"embedding",
|
||||||
"moderation",
|
"moderation",
|
||||||
|
"llm_observation",
|
||||||
|
"llm_sub_heartflow",
|
||||||
|
"llm_heartflow",
|
||||||
]
|
]
|
||||||
|
|
||||||
for item in config_list:
|
for item in config_list:
|
||||||
@@ -236,19 +416,28 @@ class BotConfig:
|
|||||||
|
|
||||||
# base_url 的例子： SILICONFLOW_BASE_URL
|
# base_url 的例子： SILICONFLOW_BASE_URL
|
||||||
# key 的例子： SILICONFLOW_KEY
|
# key 的例子： SILICONFLOW_KEY
|
||||||
cfg_target = {"name": "", "base_url": "", "key": "", "pri_in": 0, "pri_out": 0}
|
cfg_target = {"name": "", "base_url": "", "key": "", "stream": False, "pri_in": 0, "pri_out": 0}
|
||||||
|
|
||||||
if config.INNER_VERSION in SpecifierSet("<=0.0.0"):
|
if config.INNER_VERSION in SpecifierSet("<=0.0.0"):
|
||||||
cfg_target = cfg_item
|
cfg_target = cfg_item
|
||||||
|
|
||||||
elif config.INNER_VERSION in SpecifierSet(">=0.0.1"):
|
elif config.INNER_VERSION in SpecifierSet(">=0.0.1"):
|
||||||
stable_item = ["name", "pri_in", "pri_out"]
|
stable_item = ["name", "pri_in", "pri_out"]
|
||||||
|
|
||||||
|
stream_item = ["stream"]
|
||||||
|
if config.INNER_VERSION in SpecifierSet(">=1.0.1"):
|
||||||
|
stable_item.append("stream")
|
||||||
|
|
||||||
pricing_item = ["pri_in", "pri_out"]
|
pricing_item = ["pri_in", "pri_out"]
|
||||||
# 从配置中原始拷贝稳定字段
|
# 从配置中原始拷贝稳定字段
|
||||||
for i in stable_item:
|
for i in stable_item:
|
||||||
# 如果 字段 属于计费项 且获取不到，那默认值是 0
|
# 如果 字段 属于计费项 且获取不到，那默认值是 0
|
||||||
if i in pricing_item and i not in cfg_item:
|
if i in pricing_item and i not in cfg_item:
|
||||||
cfg_target[i] = 0
|
cfg_target[i] = 0
|
||||||
|
|
||||||
|
if i in stream_item and i not in cfg_item:
|
||||||
|
cfg_target[i] = False
|
||||||
|
|
||||||
else:
|
else:
|
||||||
# 没有特殊情况则原样复制
|
# 没有特殊情况则原样复制
|
||||||
try:
|
try:
|
||||||
@@ -268,42 +457,51 @@ class BotConfig:
|
|||||||
# 如果 列表中的项目在 model_config 中，利用反射来设置对应项目
|
# 如果 列表中的项目在 model_config 中，利用反射来设置对应项目
|
||||||
setattr(config, item, cfg_target)
|
setattr(config, item, cfg_target)
|
||||||
else:
|
else:
|
||||||
logger.error(f"模型 {item} 在config中不存在，请检查")
|
logger.error(f"模型 {item} 在config中不存在，请检查，或尝试更新配置文件")
|
||||||
raise KeyError(f"模型 {item} 在config中不存在，请检查")
|
raise KeyError(f"模型 {item} 在config中不存在，请检查，或尝试更新配置文件")
|
||||||
|
|
||||||
def message(parent: dict):
|
def message(parent: dict):
|
||||||
msg_config = parent["message"]
|
msg_config = parent["message"]
|
||||||
config.MIN_TEXT_LENGTH = msg_config.get("min_text_length", config.MIN_TEXT_LENGTH)
|
|
||||||
config.MAX_CONTEXT_SIZE = msg_config.get("max_context_size", config.MAX_CONTEXT_SIZE)
|
config.MAX_CONTEXT_SIZE = msg_config.get("max_context_size", config.MAX_CONTEXT_SIZE)
|
||||||
config.emoji_chance = msg_config.get("emoji_chance", config.emoji_chance)
|
config.emoji_chance = msg_config.get("emoji_chance", config.emoji_chance)
|
||||||
config.ban_words = msg_config.get("ban_words", config.ban_words)
|
config.ban_words = msg_config.get("ban_words", config.ban_words)
|
||||||
|
config.thinking_timeout = msg_config.get("thinking_timeout", config.thinking_timeout)
|
||||||
|
config.response_willing_amplifier = msg_config.get(
|
||||||
|
"response_willing_amplifier", config.response_willing_amplifier
|
||||||
|
)
|
||||||
|
config.response_interested_rate_amplifier = msg_config.get(
|
||||||
|
"response_interested_rate_amplifier", config.response_interested_rate_amplifier
|
||||||
|
)
|
||||||
|
config.down_frequency_rate = msg_config.get("down_frequency_rate", config.down_frequency_rate)
|
||||||
|
config.ban_msgs_regex = msg_config.get("ban_msgs_regex", config.ban_msgs_regex)
|
||||||
|
|
||||||
if config.INNER_VERSION in SpecifierSet(">=0.0.2"):
|
if config.INNER_VERSION in SpecifierSet(">=0.0.11"):
|
||||||
config.thinking_timeout = msg_config.get("thinking_timeout", config.thinking_timeout)
|
config.max_response_length = msg_config.get("max_response_length", config.max_response_length)
|
||||||
config.response_willing_amplifier = msg_config.get(
|
|
||||||
"response_willing_amplifier", config.response_willing_amplifier
|
|
||||||
)
|
|
||||||
config.response_interested_rate_amplifier = msg_config.get(
|
|
||||||
"response_interested_rate_amplifier", config.response_interested_rate_amplifier
|
|
||||||
)
|
|
||||||
config.down_frequency_rate = msg_config.get("down_frequency_rate", config.down_frequency_rate)
|
|
||||||
|
|
||||||
if config.INNER_VERSION in SpecifierSet(">=0.0.6"):
|
|
||||||
config.ban_msgs_regex = msg_config.get("ban_msgs_regex", config.ban_msgs_regex)
|
|
||||||
|
|
||||||
def memory(parent: dict):
|
def memory(parent: dict):
|
||||||
memory_config = parent["memory"]
|
memory_config = parent["memory"]
|
||||||
config.build_memory_interval = memory_config.get("build_memory_interval", config.build_memory_interval)
|
config.build_memory_interval = memory_config.get("build_memory_interval", config.build_memory_interval)
|
||||||
config.forget_memory_interval = memory_config.get("forget_memory_interval", config.forget_memory_interval)
|
config.forget_memory_interval = memory_config.get("forget_memory_interval", config.forget_memory_interval)
|
||||||
|
config.memory_ban_words = set(memory_config.get("memory_ban_words", []))
|
||||||
|
config.memory_forget_time = memory_config.get("memory_forget_time", config.memory_forget_time)
|
||||||
|
config.memory_forget_percentage = memory_config.get(
|
||||||
|
"memory_forget_percentage", config.memory_forget_percentage
|
||||||
|
)
|
||||||
|
config.memory_compress_rate = memory_config.get("memory_compress_rate", config.memory_compress_rate)
|
||||||
|
if config.INNER_VERSION in SpecifierSet(">=0.0.11"):
|
||||||
|
config.memory_build_distribution = memory_config.get(
|
||||||
|
"memory_build_distribution", config.memory_build_distribution
|
||||||
|
)
|
||||||
|
config.build_memory_sample_num = memory_config.get(
|
||||||
|
"build_memory_sample_num", config.build_memory_sample_num
|
||||||
|
)
|
||||||
|
config.build_memory_sample_length = memory_config.get(
|
||||||
|
"build_memory_sample_length", config.build_memory_sample_length
|
||||||
|
)
|
||||||
|
|
||||||
# 在版本 >= 0.0.4 时才处理新增的配置项
|
def remote(parent: dict):
|
||||||
if config.INNER_VERSION in SpecifierSet(">=0.0.4"):
|
remote_config = parent["remote"]
|
||||||
config.memory_ban_words = set(memory_config.get("memory_ban_words", []))
|
config.remote_enable = remote_config.get("enable", config.remote_enable)
|
||||||
|
|
||||||
if config.INNER_VERSION in SpecifierSet(">=0.0.7"):
|
|
||||||
config.memory_forget_time = memory_config.get("memory_forget_time", config.memory_forget_time)
|
|
||||||
config.memory_forget_percentage = memory_config.get("memory_forget_percentage", config.memory_forget_percentage)
|
|
||||||
config.memory_compress_rate = memory_config.get("memory_compress_rate", config.memory_compress_rate)
|
|
||||||
|
|
||||||
def mood(parent: dict):
|
def mood(parent: dict):
|
||||||
mood_config = parent["mood"]
|
mood_config = parent["mood"]
|
||||||
@@ -328,39 +526,74 @@ class BotConfig:
|
|||||||
"word_replace_rate", config.chinese_typo_word_replace_rate
|
"word_replace_rate", config.chinese_typo_word_replace_rate
|
||||||
)
|
)
|
||||||
|
|
||||||
|
def response_spliter(parent: dict):
|
||||||
|
response_spliter_config = parent["response_spliter"]
|
||||||
|
config.enable_response_spliter = response_spliter_config.get(
|
||||||
|
"enable_response_spliter", config.enable_response_spliter
|
||||||
|
)
|
||||||
|
config.response_max_length = response_spliter_config.get("response_max_length", config.response_max_length)
|
||||||
|
config.response_max_sentence_num = response_spliter_config.get(
|
||||||
|
"response_max_sentence_num", config.response_max_sentence_num
|
||||||
|
)
|
||||||
|
|
||||||
def groups(parent: dict):
|
def groups(parent: dict):
|
||||||
groups_config = parent["groups"]
|
groups_config = parent["groups"]
|
||||||
config.talk_allowed_groups = set(groups_config.get("talk_allowed", []))
|
config.talk_allowed_groups = set(groups_config.get("talk_allowed", []))
|
||||||
config.talk_frequency_down_groups = set(groups_config.get("talk_frequency_down", []))
|
config.talk_frequency_down_groups = set(groups_config.get("talk_frequency_down", []))
|
||||||
config.ban_user_id = set(groups_config.get("ban_user_id", []))
|
config.ban_user_id = set(groups_config.get("ban_user_id", []))
|
||||||
|
|
||||||
def others(parent: dict):
|
def platforms(parent: dict):
|
||||||
others_config = parent["others"]
|
platforms_config = parent["platforms"]
|
||||||
config.enable_advance_output = others_config.get("enable_advance_output", config.enable_advance_output)
|
if platforms_config and isinstance(platforms_config, dict):
|
||||||
config.enable_kuuki_read = others_config.get("enable_kuuki_read", config.enable_kuuki_read)
|
for k in platforms_config.keys():
|
||||||
if config.INNER_VERSION in SpecifierSet(">=0.0.7"):
|
config.api_urls[k] = platforms_config[k]
|
||||||
config.enable_debug_output = others_config.get("enable_debug_output", config.enable_debug_output)
|
|
||||||
config.enable_friend_chat = others_config.get("enable_friend_chat", config.enable_friend_chat)
|
def heartflow(parent: dict):
|
||||||
|
heartflow_config = parent["heartflow"]
|
||||||
|
config.enable_heartflow = heartflow_config.get("enable", config.enable_heartflow)
|
||||||
|
config.sub_heart_flow_update_interval = heartflow_config.get("sub_heart_flow_update_interval", config.sub_heart_flow_update_interval)
|
||||||
|
config.sub_heart_flow_freeze_time = heartflow_config.get("sub_heart_flow_freeze_time", config.sub_heart_flow_freeze_time)
|
||||||
|
config.sub_heart_flow_stop_time = heartflow_config.get("sub_heart_flow_stop_time", config.sub_heart_flow_stop_time)
|
||||||
|
config.heart_flow_update_interval = heartflow_config.get("heart_flow_update_interval", config.heart_flow_update_interval)
|
||||||
|
|
||||||
|
def experimental(parent: dict):
|
||||||
|
experimental_config = parent["experimental"]
|
||||||
|
config.enable_friend_chat = experimental_config.get("enable_friend_chat", config.enable_friend_chat)
|
||||||
|
config.enable_think_flow = experimental_config.get("enable_think_flow", config.enable_think_flow)
|
||||||
|
|
||||||
# 版本表达式：>=1.0.0,<2.0.0
|
# 版本表达式：>=1.0.0,<2.0.0
|
||||||
# 允许字段：func: method, support: str, notice: str, necessary: bool
|
# 允许字段：func: method, support: str, notice: str, necessary: bool
|
||||||
# 如果使用 notice 字段，在该组配置加载时，会展示该字段对用户的警示
|
# 如果使用 notice 字段，在该组配置加载时，会展示该字段对用户的警示
|
||||||
# 例如："notice": "personality 将在 1.3.2 后被移除"，那么在有效版本中的用户就会虽然可以
|
# 例如："notice": "personality 将在 1.3.2 后被移除"，那么在有效版本中的用户就会虽然可以
|
||||||
# 正常执行程序，但是会看到这条自定义提示
|
# 正常执行程序，但是会看到这条自定义提示
|
||||||
|
|
||||||
|
# 版本格式：主版本号.次版本号.修订号，版本号递增规则如下：
|
||||||
|
# 主版本号：当你做了不兼容的 API 修改，
|
||||||
|
# 次版本号：当你做了向下兼容的功能性新增，
|
||||||
|
# 修订号：当你做了向下兼容的问题修正。
|
||||||
|
# 先行版本号及版本编译信息可以加到"主版本号.次版本号.修订号"的后面，作为延伸。
|
||||||
|
|
||||||
|
# 如果你做了break的修改，就应该改动主版本号
|
||||||
|
# 如果做了一个兼容修改，就不应该要求这个选项是必须的！
|
||||||
include_configs = {
|
include_configs = {
|
||||||
"personality": {"func": personality, "support": ">=0.0.0"},
|
|
||||||
"emoji": {"func": emoji, "support": ">=0.0.0"},
|
|
||||||
"cq_code": {"func": cq_code, "support": ">=0.0.0"},
|
|
||||||
"bot": {"func": bot, "support": ">=0.0.0"},
|
"bot": {"func": bot, "support": ">=0.0.0"},
|
||||||
|
"groups": {"func": groups, "support": ">=0.0.0"},
|
||||||
|
"personality": {"func": personality, "support": ">=0.0.0"},
|
||||||
|
"schedule": {"func": schedule, "support": ">=0.0.11", "necessary": False},
|
||||||
|
"message": {"func": message, "support": ">=0.0.0"},
|
||||||
|
"willing": {"func": willing, "support": ">=0.0.9", "necessary": False},
|
||||||
|
"emoji": {"func": emoji, "support": ">=0.0.0"},
|
||||||
"response": {"func": response, "support": ">=0.0.0"},
|
"response": {"func": response, "support": ">=0.0.0"},
|
||||||
"model": {"func": model, "support": ">=0.0.0"},
|
"model": {"func": model, "support": ">=0.0.0"},
|
||||||
"message": {"func": message, "support": ">=0.0.0"},
|
|
||||||
"memory": {"func": memory, "support": ">=0.0.0", "necessary": False},
|
"memory": {"func": memory, "support": ">=0.0.0", "necessary": False},
|
||||||
"mood": {"func": mood, "support": ">=0.0.0"},
|
"mood": {"func": mood, "support": ">=0.0.0"},
|
||||||
|
"remote": {"func": remote, "support": ">=0.0.10", "necessary": False},
|
||||||
"keywords_reaction": {"func": keywords_reaction, "support": ">=0.0.2", "necessary": False},
|
"keywords_reaction": {"func": keywords_reaction, "support": ">=0.0.2", "necessary": False},
|
||||||
"chinese_typo": {"func": chinese_typo, "support": ">=0.0.3", "necessary": False},
|
"chinese_typo": {"func": chinese_typo, "support": ">=0.0.3", "necessary": False},
|
||||||
"groups": {"func": groups, "support": ">=0.0.0"},
|
"platforms": {"func": platforms, "support": ">=1.0.0"},
|
||||||
"others": {"func": others, "support": ">=0.0.0"},
|
"response_spliter": {"func": response_spliter, "support": ">=0.0.11", "necessary": False},
|
||||||
|
"experimental": {"func": experimental, "support": ">=0.0.11", "necessary": False},
|
||||||
|
"heartflow": {"func": heartflow, "support": ">=1.0.2", "necessary": False},
|
||||||
}
|
}
|
||||||
|
|
||||||
# 原地修改，将 字符串版本表达式 转换成 版本对象
|
# 原地修改，将 字符串版本表达式 转换成 版本对象
|
||||||
@@ -417,26 +650,20 @@ class BotConfig:
|
|||||||
|
|
||||||
|
|
||||||
# 获取配置文件路径
|
# 获取配置文件路径
|
||||||
|
logger.info(f"MaiCore当前版本: {mai_version}")
|
||||||
|
update_config()
|
||||||
|
|
||||||
bot_config_floder_path = BotConfig.get_config_dir()
|
bot_config_floder_path = BotConfig.get_config_dir()
|
||||||
logger.debug(f"正在品鉴配置文件目录: {bot_config_floder_path}")
|
logger.info(f"正在品鉴配置文件目录: {bot_config_floder_path}")
|
||||||
|
|
||||||
bot_config_path = os.path.join(bot_config_floder_path, "bot_config.toml")
|
bot_config_path = os.path.join(bot_config_floder_path, "bot_config.toml")
|
||||||
|
|
||||||
if os.path.exists(bot_config_path):
|
if os.path.exists(bot_config_path):
|
||||||
# 如果开发环境配置文件不存在，则使用默认配置文件
|
# 如果开发环境配置文件不存在，则使用默认配置文件
|
||||||
logger.debug(f"异常的新鲜，异常的美味: {bot_config_path}")
|
logger.info(f"异常的新鲜，异常的美味: {bot_config_path}")
|
||||||
logger.info("使用bot配置文件")
|
|
||||||
else:
|
else:
|
||||||
# 配置文件不存在
|
# 配置文件不存在
|
||||||
logger.error("配置文件不存在，请检查路径: {bot_config_path}")
|
logger.error("配置文件不存在，请检查路径: {bot_config_path}")
|
||||||
raise FileNotFoundError(f"配置文件不存在: {bot_config_path}")
|
raise FileNotFoundError(f"配置文件不存在: {bot_config_path}")
|
||||||
|
|
||||||
global_config = BotConfig.load_config(config_path=bot_config_path)
|
global_config = BotConfig.load_config(config_path=bot_config_path)
|
||||||
|
|
||||||
if not global_config.enable_advance_output:
|
|
||||||
logger.remove()
|
|
||||||
|
|
||||||
# 调试输出功能
|
|
||||||
if global_config.enable_debug_output:
|
|
||||||
logger.remove()
|
|
||||||
logger.add(sys.stdout, level="DEBUG")
|
|
||||||
59
src/plugins/config/config_env.py
Normal file
59
src/plugins/config/config_env.py
Normal file
@@ -0,0 +1,59 @@
|
|||||||
|
import os
|
||||||
|
from pathlib import Path
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
|
||||||
|
|
||||||
|
class EnvConfig:
|
||||||
|
_instance = None
|
||||||
|
|
||||||
|
def __new__(cls):
|
||||||
|
if cls._instance is None:
|
||||||
|
cls._instance = super(EnvConfig, cls).__new__(cls)
|
||||||
|
cls._instance._initialized = False
|
||||||
|
return cls._instance
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
if self._initialized:
|
||||||
|
return
|
||||||
|
|
||||||
|
self._initialized = True
|
||||||
|
self.ROOT_DIR = Path(__file__).parent.parent.parent.parent
|
||||||
|
self.load_env()
|
||||||
|
|
||||||
|
def load_env(self):
|
||||||
|
env_file = self.ROOT_DIR / ".env"
|
||||||
|
if env_file.exists():
|
||||||
|
load_dotenv(env_file)
|
||||||
|
|
||||||
|
# 根据ENVIRONMENT变量加载对应的环境文件
|
||||||
|
env_type = os.getenv("ENVIRONMENT", "prod")
|
||||||
|
if env_type == "dev":
|
||||||
|
env_file = self.ROOT_DIR / ".env.dev"
|
||||||
|
elif env_type == "prod":
|
||||||
|
env_file = self.ROOT_DIR / ".env"
|
||||||
|
|
||||||
|
if env_file.exists():
|
||||||
|
load_dotenv(env_file, override=True)
|
||||||
|
|
||||||
|
def get(self, key, default=None):
|
||||||
|
return os.getenv(key, default)
|
||||||
|
|
||||||
|
def get_all(self):
|
||||||
|
return dict(os.environ)
|
||||||
|
|
||||||
|
def __getattr__(self, name):
|
||||||
|
return self.get(name)
|
||||||
|
|
||||||
|
|
||||||
|
# 创建全局实例
|
||||||
|
env_config = EnvConfig()
|
||||||
|
|
||||||
|
|
||||||
|
# 导出环境变量
|
||||||
|
def get_env(key, default=None):
|
||||||
|
return os.getenv(key, default)
|
||||||
|
|
||||||
|
|
||||||
|
# 导出所有环境变量
|
||||||
|
def get_all_env():
|
||||||
|
return dict(os.environ)
|
||||||
@@ -1,10 +1 @@
|
|||||||
from nonebot import get_app
|
|
||||||
from .api import router
|
|
||||||
from loguru import logger
|
|
||||||
|
|
||||||
# 获取主应用实例并挂载路由
|
|
||||||
app = get_app()
|
|
||||||
app.include_router(router, prefix="/api")
|
|
||||||
|
|
||||||
# 打印日志，方便确认API已注册
|
|
||||||
logger.success("配置重载API已注册，可通过 /api/reload-config 访问")
|
|
||||||
@@ -1,17 +1,16 @@
|
|||||||
from fastapi import APIRouter, HTTPException
|
from fastapi import APIRouter, HTTPException
|
||||||
from src.plugins.chat.config import BotConfig
|
|
||||||
import os
|
|
||||||
|
|
||||||
# 创建APIRouter而不是FastAPI实例
|
# 创建APIRouter而不是FastAPI实例
|
||||||
router = APIRouter()
|
router = APIRouter()
|
||||||
|
|
||||||
|
|
||||||
@router.post("/reload-config")
|
@router.post("/reload-config")
|
||||||
async def reload_config():
|
async def reload_config():
|
||||||
try:
|
try: # TODO: 实现配置重载
|
||||||
bot_config_path = os.path.join(BotConfig.get_config_dir(), "bot_config.toml")
|
# bot_config_path = os.path.join(BotConfig.get_config_dir(), "bot_config.toml")
|
||||||
global_config = BotConfig.load_config(config_path=bot_config_path)
|
# BotConfig.reload_config(config_path=bot_config_path)
|
||||||
return {"message": "配置重载成功", "status": "success"}
|
return {"message": "TODO: 实现配置重载", "status": "unimplemented"}
|
||||||
except FileNotFoundError as e:
|
except FileNotFoundError as e:
|
||||||
raise HTTPException(status_code=404, detail=str(e))
|
raise HTTPException(status_code=404, detail=str(e)) from e
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
raise HTTPException(status_code=500, detail=f"重载配置时发生错误: {str(e)}")
|
raise HTTPException(status_code=500, detail=f"重载配置时发生错误: {str(e)}") from e
|
||||||
|
|||||||
@@ -1,3 +1,4 @@
|
|||||||
import requests
|
import requests
|
||||||
|
|
||||||
response = requests.post("http://localhost:8080/api/reload-config")
|
response = requests.post("http://localhost:8080/api/reload-config")
|
||||||
print(response.json())
|
print(response.json())
|
||||||
1339
src/plugins/memory_system/Hippocampus.py
Normal file
1339
src/plugins/memory_system/Hippocampus.py
Normal file
File diff suppressed because it is too large
Load Diff
92
src/plugins/memory_system/debug_memory.py
Normal file
92
src/plugins/memory_system/debug_memory.py
Normal file
@@ -0,0 +1,92 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
import asyncio
|
||||||
|
import time
|
||||||
|
import sys
|
||||||
|
import os
|
||||||
|
|
||||||
|
# 添加项目根目录到系统路径
|
||||||
|
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))))
|
||||||
|
from src.plugins.memory_system.Hippocampus import HippocampusManager
|
||||||
|
from src.plugins.config.config import global_config
|
||||||
|
|
||||||
|
|
||||||
|
async def test_memory_system():
|
||||||
|
"""测试记忆系统的主要功能"""
|
||||||
|
try:
|
||||||
|
# 初始化记忆系统
|
||||||
|
print("开始初始化记忆系统...")
|
||||||
|
hippocampus_manager = HippocampusManager.get_instance()
|
||||||
|
hippocampus_manager.initialize(global_config=global_config)
|
||||||
|
print("记忆系统初始化完成")
|
||||||
|
|
||||||
|
# 测试记忆构建
|
||||||
|
# print("开始测试记忆构建...")
|
||||||
|
# await hippocampus_manager.build_memory()
|
||||||
|
# print("记忆构建完成")
|
||||||
|
|
||||||
|
# 测试记忆检索
|
||||||
|
test_text = "千石可乐在群里聊天"
|
||||||
|
test_text = """[03-24 10:39:37] 麦麦(ta的id:2814567326): 早说散步结果下雨改成室内运动啊
|
||||||
|
[03-24 10:39:37] 麦麦(ta的id:2814567326): [回复：变量] 变量就像今天计划总变
|
||||||
|
[03-24 10:39:44] 状态异常(ta的id:535554838): 要把本地文件改成弹出来的路径吗
|
||||||
|
[03-24 10:40:35] 状态异常(ta的id:535554838): [图片：这张图片显示的是Windows系统的环境变量设置界面。界面左侧列出了多个环境变量的值，包括Intel Dev Redist、Windows、Windows PowerShell、OpenSSH、NVIDIA Corporation的目录等。右侧有新建、编辑、浏览、删除、上移、下移和编辑文本等操作按钮。图片下方有一个错误提示框，显示"Windows找不到文件'mongodb\\bin\\mongod.exe'。请确定文件名是否正确后，再试一次。"这意味着用户试图运行MongoDB的mongod.exe程序时，系统找不到该文件。这可能是因为MongoDB的安装路径未正确添加到系统环境变量中，或者文件路径有误。
|
||||||
|
图片的含义可能是用户正在尝试设置MongoDB的环境变量，以便在命令行或其他程序中使用MongoDB。如果用户正确设置了环境变量，那么他们应该能够通过命令行或其他方式启动MongoDB服务。]
|
||||||
|
[03-24 10:41:08] 一根猫(ta的id:108886006): [回复 麦麦 的消息: [回复某人消息] 改系统变量或者删库重配 ] [@麦麦] 我中途修改人格，需要重配吗
|
||||||
|
[03-24 10:41:54] 麦麦(ta的id:2814567326): [回复：[回复 麦麦 的消息: [回复某人消息] 改系统变量或者删库重配 ] [@麦麦] 我中途修改人格，需要重配吗] 看情况
|
||||||
|
[03-24 10:41:54] 麦麦(ta的id:2814567326): 难
|
||||||
|
[03-24 10:41:54] 麦麦(ta的id:2814567326): 小改变量就行，大动骨安排重配像游戏副本南度改太大会崩
|
||||||
|
[03-24 10:45:33] 霖泷(ta的id:1967075066): 话说现在思考高达一分钟
|
||||||
|
[03-24 10:45:38] 霖泷(ta的id:1967075066): 是不是哪里出问题了
|
||||||
|
[03-24 10:45:39] 艾卡(ta的id:1786525298): [表情包：这张表情包展示了一个动漫角色，她有着紫色的头发和大大的眼睛，表情显得有些困惑或不解。她的头上有一个问号，进一步强调了她的疑惑。整体情感表达的是困惑或不解。]
|
||||||
|
[03-24 10:46:12] (ta的id:3229291803): [表情包：这张表情包显示了一只手正在做"点赞"的动作，通常表示赞同、喜欢或支持。这个表情包所表达的情感是积极的、赞同的或支持的。]
|
||||||
|
[03-24 10:46:37] 星野風禾(ta的id:2890165435): 还能思考高达
|
||||||
|
[03-24 10:46:39] 星野風禾(ta的id:2890165435): 什么知识库
|
||||||
|
[03-24 10:46:49] ❦幻凌慌てない(ta的id:2459587037): 为什么改了回复系数麦麦还是不怎么回复？大佬们""" # noqa: E501
|
||||||
|
|
||||||
|
# test_text = '''千石可乐：分不清AI的陪伴和人类的陪伴,是这样吗？'''
|
||||||
|
print(f"开始测试记忆检索，测试文本: {test_text}\n")
|
||||||
|
memories = await hippocampus_manager.get_memory_from_text(
|
||||||
|
text=test_text, max_memory_num=3, max_memory_length=2, max_depth=3, fast_retrieval=False
|
||||||
|
)
|
||||||
|
|
||||||
|
await asyncio.sleep(1)
|
||||||
|
|
||||||
|
print("检索到的记忆:")
|
||||||
|
for topic, memory_items in memories:
|
||||||
|
print(f"主题: {topic}")
|
||||||
|
print(f"- {memory_items}")
|
||||||
|
|
||||||
|
# 测试记忆遗忘
|
||||||
|
# forget_start_time = time.time()
|
||||||
|
# # print("开始测试记忆遗忘...")
|
||||||
|
# await hippocampus_manager.forget_memory(percentage=0.005)
|
||||||
|
# # print("记忆遗忘完成")
|
||||||
|
# forget_end_time = time.time()
|
||||||
|
# print(f"记忆遗忘耗时: {forget_end_time - forget_start_time:.2f} 秒")
|
||||||
|
|
||||||
|
# 获取所有节点
|
||||||
|
# nodes = hippocampus_manager.get_all_node_names()
|
||||||
|
# print(f"当前记忆系统中的节点数量: {len(nodes)}")
|
||||||
|
# print("节点列表:")
|
||||||
|
# for node in nodes:
|
||||||
|
# print(f"- {node}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"测试过程中出现错误: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
|
||||||
|
async def main():
|
||||||
|
"""主函数"""
|
||||||
|
try:
|
||||||
|
start_time = time.time()
|
||||||
|
await test_memory_system()
|
||||||
|
end_time = time.time()
|
||||||
|
print(f"测试完成，总耗时: {end_time - start_time:.2f} 秒")
|
||||||
|
except Exception as e:
|
||||||
|
print(f"程序执行出错: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())
|
||||||
@@ -1,287 +0,0 @@
|
|||||||
# -*- coding: utf-8 -*-
|
|
||||||
import os
|
|
||||||
import sys
|
|
||||||
import time
|
|
||||||
|
|
||||||
import jieba
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
import networkx as nx
|
|
||||||
from dotenv import load_dotenv
|
|
||||||
from loguru import logger
|
|
||||||
|
|
||||||
# 添加项目根目录到 Python 路径
|
|
||||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
|
||||||
sys.path.append(root_path)
|
|
||||||
|
|
||||||
from src.common.database import db # 使用正确的导入语法
|
|
||||||
|
|
||||||
# 加载.env.dev文件
|
|
||||||
env_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))), '.env.dev')
|
|
||||||
load_dotenv(env_path)
|
|
||||||
|
|
||||||
|
|
||||||
class Memory_graph:
|
|
||||||
def __init__(self):
|
|
||||||
self.G = nx.Graph() # 使用 networkx 的图结构
|
|
||||||
|
|
||||||
def connect_dot(self, concept1, concept2):
|
|
||||||
self.G.add_edge(concept1, concept2)
|
|
||||||
|
|
||||||
def add_dot(self, concept, memory):
|
|
||||||
if concept in self.G:
|
|
||||||
# 如果节点已存在，将新记忆添加到现有列表中
|
|
||||||
if 'memory_items' in self.G.nodes[concept]:
|
|
||||||
if not isinstance(self.G.nodes[concept]['memory_items'], list):
|
|
||||||
# 如果当前不是列表，将其转换为列表
|
|
||||||
self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']]
|
|
||||||
self.G.nodes[concept]['memory_items'].append(memory)
|
|
||||||
else:
|
|
||||||
self.G.nodes[concept]['memory_items'] = [memory]
|
|
||||||
else:
|
|
||||||
# 如果是新节点，创建新的记忆列表
|
|
||||||
self.G.add_node(concept, memory_items=[memory])
|
|
||||||
|
|
||||||
def get_dot(self, concept):
|
|
||||||
# 检查节点是否存在于图中
|
|
||||||
if concept in self.G:
|
|
||||||
# 从图中获取节点数据
|
|
||||||
node_data = self.G.nodes[concept]
|
|
||||||
# print(node_data)
|
|
||||||
# 创建新的Memory_dot对象
|
|
||||||
return concept, node_data
|
|
||||||
return None
|
|
||||||
|
|
||||||
def get_related_item(self, topic, depth=1):
|
|
||||||
if topic not in self.G:
|
|
||||||
return [], []
|
|
||||||
|
|
||||||
first_layer_items = []
|
|
||||||
second_layer_items = []
|
|
||||||
|
|
||||||
# 获取相邻节点
|
|
||||||
neighbors = list(self.G.neighbors(topic))
|
|
||||||
# print(f"第一层: {topic}")
|
|
||||||
|
|
||||||
# 获取当前节点的记忆项
|
|
||||||
node_data = self.get_dot(topic)
|
|
||||||
if node_data:
|
|
||||||
concept, data = node_data
|
|
||||||
if 'memory_items' in data:
|
|
||||||
memory_items = data['memory_items']
|
|
||||||
if isinstance(memory_items, list):
|
|
||||||
first_layer_items.extend(memory_items)
|
|
||||||
else:
|
|
||||||
first_layer_items.append(memory_items)
|
|
||||||
|
|
||||||
# 只在depth=2时获取第二层记忆
|
|
||||||
if depth >= 2:
|
|
||||||
# 获取相邻节点的记忆项
|
|
||||||
for neighbor in neighbors:
|
|
||||||
# print(f"第二层: {neighbor}")
|
|
||||||
node_data = self.get_dot(neighbor)
|
|
||||||
if node_data:
|
|
||||||
concept, data = node_data
|
|
||||||
if 'memory_items' in data:
|
|
||||||
memory_items = data['memory_items']
|
|
||||||
if isinstance(memory_items, list):
|
|
||||||
second_layer_items.extend(memory_items)
|
|
||||||
else:
|
|
||||||
second_layer_items.append(memory_items)
|
|
||||||
|
|
||||||
return first_layer_items, second_layer_items
|
|
||||||
|
|
||||||
def store_memory(self):
|
|
||||||
for node in self.G.nodes():
|
|
||||||
dot_data = {
|
|
||||||
"concept": node
|
|
||||||
}
|
|
||||||
db.store_memory_dots.insert_one(dot_data)
|
|
||||||
|
|
||||||
@property
|
|
||||||
def dots(self):
|
|
||||||
# 返回所有节点对应的 Memory_dot 对象
|
|
||||||
return [self.get_dot(node) for node in self.G.nodes()]
|
|
||||||
|
|
||||||
def get_random_chat_from_db(self, length: int, timestamp: str):
|
|
||||||
# 从数据库中根据时间戳获取离其最近的聊天记录
|
|
||||||
chat_text = ''
|
|
||||||
closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) # 调试输出
|
|
||||||
logger.info(
|
|
||||||
f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}")
|
|
||||||
|
|
||||||
if closest_record:
|
|
||||||
closest_time = closest_record['time']
|
|
||||||
group_id = closest_record['group_id'] # 获取groupid
|
|
||||||
# 获取该时间戳之后的length条消息，且groupid相同
|
|
||||||
chat_record = list(
|
|
||||||
db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(
|
|
||||||
length))
|
|
||||||
for record in chat_record:
|
|
||||||
time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time'])))
|
|
||||||
try:
|
|
||||||
displayname = "[(%s)%s]%s" % (record["user_id"], record["user_nickname"], record["user_cardname"])
|
|
||||||
except:
|
|
||||||
displayname = record["user_nickname"] or "用户" + str(record["user_id"])
|
|
||||||
chat_text += f'[{time_str}] {displayname}: {record["processed_plain_text"]}\n' # 添加发送者和时间信息
|
|
||||||
return chat_text
|
|
||||||
|
|
||||||
return [] # 如果没有找到记录，返回空列表
|
|
||||||
|
|
||||||
def save_graph_to_db(self):
|
|
||||||
# 清空现有的图数据
|
|
||||||
db.graph_data.delete_many({})
|
|
||||||
# 保存节点
|
|
||||||
for node in self.G.nodes(data=True):
|
|
||||||
node_data = {
|
|
||||||
'concept': node[0],
|
|
||||||
'memory_items': node[1].get('memory_items', []) # 默认为空列表
|
|
||||||
}
|
|
||||||
db.graph_data.nodes.insert_one(node_data)
|
|
||||||
# 保存边
|
|
||||||
for edge in self.G.edges():
|
|
||||||
edge_data = {
|
|
||||||
'source': edge[0],
|
|
||||||
'target': edge[1]
|
|
||||||
}
|
|
||||||
db.graph_data.edges.insert_one(edge_data)
|
|
||||||
|
|
||||||
def load_graph_from_db(self):
|
|
||||||
# 清空当前图
|
|
||||||
self.G.clear()
|
|
||||||
# 加载节点
|
|
||||||
nodes = db.graph_data.nodes.find()
|
|
||||||
for node in nodes:
|
|
||||||
memory_items = node.get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
self.G.add_node(node['concept'], memory_items=memory_items)
|
|
||||||
# 加载边
|
|
||||||
edges = db.graph_data.edges.find()
|
|
||||||
for edge in edges:
|
|
||||||
self.G.add_edge(edge['source'], edge['target'])
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
|
||||||
memory_graph = Memory_graph()
|
|
||||||
memory_graph.load_graph_from_db()
|
|
||||||
|
|
||||||
# 只显示一次优化后的图形
|
|
||||||
visualize_graph_lite(memory_graph)
|
|
||||||
|
|
||||||
while True:
|
|
||||||
query = input("请输入新的查询概念（输入'退出'以结束）：")
|
|
||||||
if query.lower() == '退出':
|
|
||||||
break
|
|
||||||
first_layer_items, second_layer_items = memory_graph.get_related_item(query)
|
|
||||||
if first_layer_items or second_layer_items:
|
|
||||||
logger.debug("第一层记忆：")
|
|
||||||
for item in first_layer_items:
|
|
||||||
logger.debug(item)
|
|
||||||
logger.debug("第二层记忆：")
|
|
||||||
for item in second_layer_items:
|
|
||||||
logger.debug(item)
|
|
||||||
else:
|
|
||||||
logger.debug("未找到相关记忆。")
|
|
||||||
|
|
||||||
|
|
||||||
def segment_text(text):
|
|
||||||
seg_text = list(jieba.cut(text))
|
|
||||||
return seg_text
|
|
||||||
|
|
||||||
|
|
||||||
def find_topic(text, topic_num):
|
|
||||||
prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个话题，帮我列出来，用逗号隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要告诉我其他内容。'
|
|
||||||
return prompt
|
|
||||||
|
|
||||||
|
|
||||||
def topic_what(text, topic):
|
|
||||||
prompt = f'这是一段文字：{text}。我想知道这记忆里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物。只输出这句话就好'
|
|
||||||
return prompt
|
|
||||||
|
|
||||||
|
|
||||||
def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = False):
|
|
||||||
# 设置中文字体
|
|
||||||
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
|
|
||||||
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
|
|
||||||
|
|
||||||
G = memory_graph.G
|
|
||||||
|
|
||||||
# 创建一个新图用于可视化
|
|
||||||
H = G.copy()
|
|
||||||
|
|
||||||
# 移除只有一条记忆的节点和连接数少于3的节点
|
|
||||||
nodes_to_remove = []
|
|
||||||
for node in H.nodes():
|
|
||||||
memory_items = H.nodes[node].get('memory_items', [])
|
|
||||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
|
||||||
degree = H.degree(node)
|
|
||||||
if memory_count < 3 or degree < 2: # 改为小于2而不是小于等于2
|
|
||||||
nodes_to_remove.append(node)
|
|
||||||
|
|
||||||
H.remove_nodes_from(nodes_to_remove)
|
|
||||||
|
|
||||||
# 如果过滤后没有节点，则返回
|
|
||||||
if len(H.nodes()) == 0:
|
|
||||||
logger.debug("过滤后没有符合条件的节点可显示")
|
|
||||||
return
|
|
||||||
|
|
||||||
# 保存图到本地
|
|
||||||
# nx.write_gml(H, "memory_graph.gml") # 保存为 GML 格式
|
|
||||||
|
|
||||||
# 计算节点大小和颜色
|
|
||||||
node_colors = []
|
|
||||||
node_sizes = []
|
|
||||||
nodes = list(H.nodes())
|
|
||||||
|
|
||||||
# 获取最大记忆数和最大度数用于归一化
|
|
||||||
max_memories = 1
|
|
||||||
max_degree = 1
|
|
||||||
for node in nodes:
|
|
||||||
memory_items = H.nodes[node].get('memory_items', [])
|
|
||||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
|
||||||
degree = H.degree(node)
|
|
||||||
max_memories = max(max_memories, memory_count)
|
|
||||||
max_degree = max(max_degree, degree)
|
|
||||||
|
|
||||||
# 计算每个节点的大小和颜色
|
|
||||||
for node in nodes:
|
|
||||||
# 计算节点大小（基于记忆数量）
|
|
||||||
memory_items = H.nodes[node].get('memory_items', [])
|
|
||||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
|
||||||
# 使用指数函数使变化更明显
|
|
||||||
ratio = memory_count / max_memories
|
|
||||||
size = 500 + 5000 * (ratio) # 使用1.5次方函数使差异不那么明显
|
|
||||||
node_sizes.append(size)
|
|
||||||
|
|
||||||
# 计算节点颜色（基于连接数）
|
|
||||||
degree = H.degree(node)
|
|
||||||
# 红色分量随着度数增加而增加
|
|
||||||
r = (degree / max_degree) ** 0.3
|
|
||||||
red = min(1.0, r)
|
|
||||||
# 蓝色分量随着度数减少而增加
|
|
||||||
blue = max(0.0, 1 - red)
|
|
||||||
# blue = 1
|
|
||||||
color = (red, 0.1, blue)
|
|
||||||
node_colors.append(color)
|
|
||||||
|
|
||||||
# 绘制图形
|
|
||||||
plt.figure(figsize=(12, 8))
|
|
||||||
pos = nx.spring_layout(H, k=1, iterations=50) # 增加k值使节点分布更开
|
|
||||||
nx.draw(H, pos,
|
|
||||||
with_labels=True,
|
|
||||||
node_color=node_colors,
|
|
||||||
node_size=node_sizes,
|
|
||||||
font_size=10,
|
|
||||||
font_family='SimHei',
|
|
||||||
font_weight='bold',
|
|
||||||
edge_color='gray',
|
|
||||||
width=0.5,
|
|
||||||
alpha=0.9)
|
|
||||||
|
|
||||||
title = '记忆图谱可视化 - 节点大小表示记忆数量，颜色表示连接数'
|
|
||||||
plt.title(title, fontsize=16, fontfamily='SimHei')
|
|
||||||
plt.show()
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
||||||
364
src/plugins/memory_system/manually_alter_memory.py
Normal file
364
src/plugins/memory_system/manually_alter_memory.py
Normal file
@@ -0,0 +1,364 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
import time
|
||||||
|
from pathlib import Path
|
||||||
|
import datetime
|
||||||
|
from rich.console import Console
|
||||||
|
from memory_manual_build import Memory_graph, Hippocampus # 海马体和记忆图
|
||||||
|
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
|
||||||
|
|
||||||
|
"""
|
||||||
|
我想 总有那么一个瞬间
|
||||||
|
你会想和某天才变态少女助手一样
|
||||||
|
往Bot的海马体里插上几个电极 不是吗
|
||||||
|
|
||||||
|
Let's do some dirty job.
|
||||||
|
"""
|
||||||
|
|
||||||
|
# 获取当前文件的目录
|
||||||
|
current_dir = Path(__file__).resolve().parent
|
||||||
|
# 获取项目根目录（上三层目录）
|
||||||
|
project_root = current_dir.parent.parent.parent
|
||||||
|
# env.dev文件路径
|
||||||
|
env_path = project_root / ".env.dev"
|
||||||
|
|
||||||
|
# from chat.config import global_config
|
||||||
|
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
||||||
|
sys.path.append(root_path)
|
||||||
|
|
||||||
|
from src.common.logger import get_module_logger # noqa E402
|
||||||
|
from src.common.database import db # noqa E402
|
||||||
|
|
||||||
|
logger = get_module_logger("mem_alter")
|
||||||
|
console = Console()
|
||||||
|
|
||||||
|
# 加载环境变量
|
||||||
|
if env_path.exists():
|
||||||
|
logger.info(f"从 {env_path} 加载环境变量")
|
||||||
|
load_dotenv(env_path)
|
||||||
|
else:
|
||||||
|
logger.warning(f"未找到环境变量文件: {env_path}")
|
||||||
|
logger.info("将使用默认配置")
|
||||||
|
|
||||||
|
|
||||||
|
# 查询节点信息
|
||||||
|
def query_mem_info(memory_graph: Memory_graph):
|
||||||
|
while True:
|
||||||
|
query = input("\n请输入新的查询概念（输入'退出'以结束）：")
|
||||||
|
if query.lower() == "退出":
|
||||||
|
break
|
||||||
|
|
||||||
|
items_list = memory_graph.get_related_item(query)
|
||||||
|
if items_list:
|
||||||
|
have_memory = False
|
||||||
|
first_layer, second_layer = items_list
|
||||||
|
if first_layer:
|
||||||
|
have_memory = True
|
||||||
|
print("\n直接相关的记忆：")
|
||||||
|
for item in first_layer:
|
||||||
|
print(f"- {item}")
|
||||||
|
if second_layer:
|
||||||
|
have_memory = True
|
||||||
|
print("\n间接相关的记忆：")
|
||||||
|
for item in second_layer:
|
||||||
|
print(f"- {item}")
|
||||||
|
if not have_memory:
|
||||||
|
print("\n未找到相关记忆。")
|
||||||
|
else:
|
||||||
|
print("未找到相关记忆。")
|
||||||
|
|
||||||
|
|
||||||
|
# 增加概念节点
|
||||||
|
def add_mem_node(hippocampus: Hippocampus):
|
||||||
|
while True:
|
||||||
|
concept = input("请输入节点概念名:\n")
|
||||||
|
result = db.graph_data.nodes.count_documents({"concept": concept})
|
||||||
|
|
||||||
|
if result != 0:
|
||||||
|
console.print("[yellow]已存在名为“{concept}”的节点，行为已取消[/yellow]")
|
||||||
|
continue
|
||||||
|
|
||||||
|
memory_items = list()
|
||||||
|
while True:
|
||||||
|
context = input("请输入节点描述信息（输入'终止'以结束）")
|
||||||
|
if context.lower() == "终止":
|
||||||
|
break
|
||||||
|
memory_items.append(context)
|
||||||
|
|
||||||
|
current_time = datetime.datetime.now().timestamp()
|
||||||
|
hippocampus.memory_graph.G.add_node(
|
||||||
|
concept, memory_items=memory_items, created_time=current_time, last_modified=current_time
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
# 删除概念节点（及连接到它的边）
|
||||||
|
def remove_mem_node(hippocampus: Hippocampus):
|
||||||
|
concept = input("请输入节点概念名:\n")
|
||||||
|
result = db.graph_data.nodes.count_documents({"concept": concept})
|
||||||
|
|
||||||
|
if result == 0:
|
||||||
|
console.print(f"[red]不存在名为“{concept}”的节点[/red]")
|
||||||
|
|
||||||
|
edges = db.graph_data.edges.find({"$or": [{"source": concept}, {"target": concept}]})
|
||||||
|
|
||||||
|
for edge in edges:
|
||||||
|
console.print(f"[yellow]存在边“{edge['source']} -> {edge['target']}”, 请慎重考虑[/yellow]")
|
||||||
|
|
||||||
|
console.print(f"[yellow]确定要移除名为“{concept}”的节点以及其相关边吗[/yellow]")
|
||||||
|
destory = console.input(f"[red]请输入“{concept}”以删除节点 其他输入将被视为取消操作[/red]\n")
|
||||||
|
if destory == concept:
|
||||||
|
hippocampus.memory_graph.G.remove_node(concept)
|
||||||
|
else:
|
||||||
|
logger.info("[green]删除操作已取消[/green]")
|
||||||
|
|
||||||
|
|
||||||
|
# 增加节点间边
|
||||||
|
def add_mem_edge(hippocampus: Hippocampus):
|
||||||
|
while True:
|
||||||
|
source = input("请输入 **第一个节点** 名称（输入'退出'以结束）：\n")
|
||||||
|
if source.lower() == "退出":
|
||||||
|
break
|
||||||
|
if db.graph_data.nodes.count_documents({"concept": source}) == 0:
|
||||||
|
console.print(f"[yellow]“{source}”节点不存在，操作已取消。[/yellow]")
|
||||||
|
continue
|
||||||
|
|
||||||
|
target = input("请输入 **第二个节点** 名称：\n")
|
||||||
|
if db.graph_data.nodes.count_documents({"concept": target}) == 0:
|
||||||
|
console.print(f"[yellow]“{target}”节点不存在，操作已取消。[/yellow]")
|
||||||
|
continue
|
||||||
|
|
||||||
|
if source == target:
|
||||||
|
console.print(f"[yellow]试图创建“{source} <-> {target}”自环，操作已取消。[/yellow]")
|
||||||
|
continue
|
||||||
|
|
||||||
|
hippocampus.memory_graph.connect_dot(source, target)
|
||||||
|
edge = hippocampus.memory_graph.G.get_edge_data(source, target)
|
||||||
|
if edge["strength"] == 1:
|
||||||
|
console.print(f"[green]成功创建边“{source} <-> {target}”，默认权重1[/green]")
|
||||||
|
else:
|
||||||
|
console.print(
|
||||||
|
f"[yellow]边“{source} <-> {target}”已存在，"
|
||||||
|
f"更新权重: {edge['strength'] - 1} <-> {edge['strength']}[/yellow]"
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
# 删除节点间边
|
||||||
|
def remove_mem_edge(hippocampus: Hippocampus):
|
||||||
|
while True:
|
||||||
|
source = input("请输入 **第一个节点** 名称（输入'退出'以结束）：\n")
|
||||||
|
if source.lower() == "退出":
|
||||||
|
break
|
||||||
|
if db.graph_data.nodes.count_documents({"concept": source}) == 0:
|
||||||
|
console.print("[yellow]“{source}”节点不存在，操作已取消。[/yellow]")
|
||||||
|
continue
|
||||||
|
|
||||||
|
target = input("请输入 **第二个节点** 名称：\n")
|
||||||
|
if db.graph_data.nodes.count_documents({"concept": target}) == 0:
|
||||||
|
console.print("[yellow]“{target}”节点不存在，操作已取消。[/yellow]")
|
||||||
|
continue
|
||||||
|
|
||||||
|
if source == target:
|
||||||
|
console.print("[yellow]试图创建“{source} <-> {target}”自环，操作已取消。[/yellow]")
|
||||||
|
continue
|
||||||
|
|
||||||
|
edge = hippocampus.memory_graph.G.get_edge_data(source, target)
|
||||||
|
if edge is None:
|
||||||
|
console.print("[yellow]边“{source} <-> {target}”不存在，操作已取消。[/yellow]")
|
||||||
|
continue
|
||||||
|
else:
|
||||||
|
accept = console.input("[orange]请输入“确认”以确认删除操作（其他输入视为取消）[/orange]\n")
|
||||||
|
if accept.lower() == "确认":
|
||||||
|
hippocampus.memory_graph.G.remove_edge(source, target)
|
||||||
|
console.print(f"[green]边“{source} <-> {target}”已删除。[green]")
|
||||||
|
|
||||||
|
|
||||||
|
# 修改节点信息
|
||||||
|
def alter_mem_node(hippocampus: Hippocampus):
|
||||||
|
batchEnviroment = dict()
|
||||||
|
while True:
|
||||||
|
concept = input("请输入节点概念名（输入'终止'以结束）:\n")
|
||||||
|
if concept.lower() == "终止":
|
||||||
|
break
|
||||||
|
_, node = hippocampus.memory_graph.get_dot(concept)
|
||||||
|
if node is None:
|
||||||
|
console.print(f"[yellow]“{concept}”节点不存在，操作已取消。[/yellow]")
|
||||||
|
continue
|
||||||
|
|
||||||
|
console.print("[yellow]注意，请确保你知道自己在做什么[/yellow]")
|
||||||
|
console.print("[yellow]你将获得一个执行任意代码的环境[/yellow]")
|
||||||
|
console.print("[red]你已经被警告过了。[/red]\n")
|
||||||
|
|
||||||
|
node_environment = {"concept": "<节点名>", "memory_items": "<记忆文本数组>"}
|
||||||
|
console.print(
|
||||||
|
"[green]环境变量中会有env与batchEnv两个dict, env在切换节点时会清空, batchEnv在操作终止时才会清空[/green]"
|
||||||
|
)
|
||||||
|
console.print(
|
||||||
|
f"[green] env 会被初始化为[/green]\n{node_environment}\n[green]且会在用户代码执行完毕后被提交 [/green]"
|
||||||
|
)
|
||||||
|
console.print(
|
||||||
|
"[yellow]为便于书写临时脚本，请手动在输入代码通过Ctrl+C等方式触发KeyboardInterrupt来结束代码执行[/yellow]"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 拷贝数据以防操作炸了
|
||||||
|
node_environment = dict(node)
|
||||||
|
node_environment["concept"] = concept
|
||||||
|
|
||||||
|
while True:
|
||||||
|
|
||||||
|
def user_exec(script, env, batch_env):
|
||||||
|
return eval(script, env, batch_env)
|
||||||
|
|
||||||
|
try:
|
||||||
|
command = console.input()
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
# 稍微防一下小天才
|
||||||
|
try:
|
||||||
|
if isinstance(node_environment["memory_items"], list):
|
||||||
|
node["memory_items"] = node_environment["memory_items"]
|
||||||
|
else:
|
||||||
|
raise Exception
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
console.print(
|
||||||
|
f"[red]我不知道你做了什么，但显然nodeEnviroment['memory_items']已经不是个数组了，"
|
||||||
|
f"操作已取消: {str(e)}[/red]"
|
||||||
|
)
|
||||||
|
break
|
||||||
|
|
||||||
|
try:
|
||||||
|
user_exec(command, node_environment, batchEnviroment)
|
||||||
|
except Exception as e:
|
||||||
|
console.print(e)
|
||||||
|
console.print(
|
||||||
|
"[red]自定义代码执行时发生异常，已捕获，请重试（可通过 console.print(locals()) 检查环境状态）[/red]"
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
# 修改边信息
|
||||||
|
def alter_mem_edge(hippocampus: Hippocampus):
|
||||||
|
batchEnviroment = dict()
|
||||||
|
while True:
|
||||||
|
source = input("请输入 **第一个节点** 名称（输入'终止'以结束）：\n")
|
||||||
|
if source.lower() == "终止":
|
||||||
|
break
|
||||||
|
if hippocampus.memory_graph.get_dot(source) is None:
|
||||||
|
console.print(f"[yellow]“{source}”节点不存在，操作已取消。[/yellow]")
|
||||||
|
continue
|
||||||
|
|
||||||
|
target = input("请输入 **第二个节点** 名称：\n")
|
||||||
|
if hippocampus.memory_graph.get_dot(target) is None:
|
||||||
|
console.print(f"[yellow]“{target}”节点不存在，操作已取消。[/yellow]")
|
||||||
|
continue
|
||||||
|
|
||||||
|
edge = hippocampus.memory_graph.G.get_edge_data(source, target)
|
||||||
|
if edge is None:
|
||||||
|
console.print(f"[yellow]边“{source} <-> {target}”不存在，操作已取消。[/yellow]")
|
||||||
|
continue
|
||||||
|
|
||||||
|
console.print("[yellow]注意，请确保你知道自己在做什么[/yellow]")
|
||||||
|
console.print("[yellow]你将获得一个执行任意代码的环境[/yellow]")
|
||||||
|
console.print("[red]你已经被警告过了。[/red]\n")
|
||||||
|
|
||||||
|
edgeEnviroment = {"source": "<节点名>", "target": "<节点名>", "strength": "<强度值,装在一个list里>"}
|
||||||
|
console.print(
|
||||||
|
"[green]环境变量中会有env与batchEnv两个dict, env在切换节点时会清空, batchEnv在操作终止时才会清空[/green]"
|
||||||
|
)
|
||||||
|
console.print(
|
||||||
|
f"[green] env 会被初始化为[/green]\n{edgeEnviroment}\n[green]且会在用户代码执行完毕后被提交 [/green]"
|
||||||
|
)
|
||||||
|
console.print(
|
||||||
|
"[yellow]为便于书写临时脚本，请手动在输入代码通过Ctrl+C等方式触发KeyboardInterrupt来结束代码执行[/yellow]"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 拷贝数据以防操作炸了
|
||||||
|
edgeEnviroment["strength"] = [edge["strength"]]
|
||||||
|
edgeEnviroment["source"] = source
|
||||||
|
edgeEnviroment["target"] = target
|
||||||
|
|
||||||
|
while True:
|
||||||
|
|
||||||
|
def user_exec(script, env, batch_env):
|
||||||
|
return eval(script, env, batch_env)
|
||||||
|
|
||||||
|
try:
|
||||||
|
command = console.input()
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
# 稍微防一下小天才
|
||||||
|
try:
|
||||||
|
if isinstance(edgeEnviroment["strength"][0], int):
|
||||||
|
edge["strength"] = edgeEnviroment["strength"][0]
|
||||||
|
else:
|
||||||
|
raise Exception
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
console.print(
|
||||||
|
f"[red]我不知道你做了什么，但显然edgeEnviroment['strength']已经不是个int了，"
|
||||||
|
f"操作已取消: {str(e)}[/red]"
|
||||||
|
)
|
||||||
|
break
|
||||||
|
|
||||||
|
try:
|
||||||
|
user_exec(command, edgeEnviroment, batchEnviroment)
|
||||||
|
except Exception as e:
|
||||||
|
console.print(e)
|
||||||
|
console.print(
|
||||||
|
"[red]自定义代码执行时发生异常，已捕获，请重试（可通过 console.print(locals()) 检查环境状态）[/red]"
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
async def main():
|
||||||
|
start_time = time.time()
|
||||||
|
|
||||||
|
# 创建记忆图
|
||||||
|
memory_graph = Memory_graph()
|
||||||
|
|
||||||
|
# 创建海马体
|
||||||
|
hippocampus = Hippocampus(memory_graph)
|
||||||
|
|
||||||
|
# 从数据库同步数据
|
||||||
|
hippocampus.sync_memory_from_db()
|
||||||
|
|
||||||
|
end_time = time.time()
|
||||||
|
logger.info(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m")
|
||||||
|
|
||||||
|
while True:
|
||||||
|
try:
|
||||||
|
query = int(
|
||||||
|
input(
|
||||||
|
"""请输入操作类型
|
||||||
|
0 -> 查询节点; 1 -> 增加节点; 2 -> 移除节点; 3 -> 增加边; 4 -> 移除边;
|
||||||
|
5 -> 修改节点; 6 -> 修改边; 其他任意输入 -> 退出
|
||||||
|
"""
|
||||||
|
)
|
||||||
|
)
|
||||||
|
except ValueError:
|
||||||
|
query = -1
|
||||||
|
|
||||||
|
if query == 0:
|
||||||
|
query_mem_info(memory_graph)
|
||||||
|
elif query == 1:
|
||||||
|
add_mem_node(hippocampus)
|
||||||
|
elif query == 2:
|
||||||
|
remove_mem_node(hippocampus)
|
||||||
|
elif query == 3:
|
||||||
|
add_mem_edge(hippocampus)
|
||||||
|
elif query == 4:
|
||||||
|
remove_mem_edge(hippocampus)
|
||||||
|
elif query == 5:
|
||||||
|
alter_mem_node(hippocampus)
|
||||||
|
elif query == 6:
|
||||||
|
alter_mem_edge(hippocampus)
|
||||||
|
else:
|
||||||
|
print("已结束操作")
|
||||||
|
break
|
||||||
|
|
||||||
|
hippocampus.sync_memory_to_db()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
import asyncio
|
||||||
|
|
||||||
|
asyncio.run(main())
|
||||||
@@ -1,912 +0,0 @@
|
|||||||
# -*- coding: utf-8 -*-
|
|
||||||
import datetime
|
|
||||||
import math
|
|
||||||
import random
|
|
||||||
import time
|
|
||||||
import os
|
|
||||||
|
|
||||||
import jieba
|
|
||||||
import networkx as nx
|
|
||||||
|
|
||||||
from nonebot import get_driver
|
|
||||||
from ...common.database import db
|
|
||||||
from ..chat.config import global_config
|
|
||||||
from ..chat.utils import (
|
|
||||||
calculate_information_content,
|
|
||||||
cosine_similarity,
|
|
||||||
get_closest_chat_from_db,
|
|
||||||
text_to_vector,
|
|
||||||
)
|
|
||||||
from ..models.utils_model import LLM_request
|
|
||||||
|
|
||||||
from ..utils.logger_config import LogClassification, LogModule
|
|
||||||
|
|
||||||
# 配置日志
|
|
||||||
log_module = LogModule()
|
|
||||||
logger = log_module.setup_logger(LogClassification.MEMORY)
|
|
||||||
|
|
||||||
logger.info("初始化记忆系统")
|
|
||||||
|
|
||||||
class Memory_graph:
|
|
||||||
def __init__(self):
|
|
||||||
self.G = nx.Graph() # 使用 networkx 的图结构
|
|
||||||
|
|
||||||
def connect_dot(self, concept1, concept2):
|
|
||||||
# 避免自连接
|
|
||||||
if concept1 == concept2:
|
|
||||||
return
|
|
||||||
|
|
||||||
current_time = datetime.datetime.now().timestamp()
|
|
||||||
|
|
||||||
# 如果边已存在,增加 strength
|
|
||||||
if self.G.has_edge(concept1, concept2):
|
|
||||||
self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1
|
|
||||||
# 更新最后修改时间
|
|
||||||
self.G[concept1][concept2]['last_modified'] = current_time
|
|
||||||
else:
|
|
||||||
# 如果是新边,初始化 strength 为 1
|
|
||||||
self.G.add_edge(concept1, concept2,
|
|
||||||
strength=1,
|
|
||||||
created_time=current_time, # 添加创建时间
|
|
||||||
last_modified=current_time) # 添加最后修改时间
|
|
||||||
|
|
||||||
def add_dot(self, concept, memory):
|
|
||||||
current_time = datetime.datetime.now().timestamp()
|
|
||||||
|
|
||||||
if concept in self.G:
|
|
||||||
if 'memory_items' in self.G.nodes[concept]:
|
|
||||||
if not isinstance(self.G.nodes[concept]['memory_items'], list):
|
|
||||||
self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']]
|
|
||||||
self.G.nodes[concept]['memory_items'].append(memory)
|
|
||||||
# 更新最后修改时间
|
|
||||||
self.G.nodes[concept]['last_modified'] = current_time
|
|
||||||
else:
|
|
||||||
self.G.nodes[concept]['memory_items'] = [memory]
|
|
||||||
# 如果节点存在但没有memory_items,说明是第一次添加memory,设置created_time
|
|
||||||
if 'created_time' not in self.G.nodes[concept]:
|
|
||||||
self.G.nodes[concept]['created_time'] = current_time
|
|
||||||
self.G.nodes[concept]['last_modified'] = current_time
|
|
||||||
else:
|
|
||||||
# 如果是新节点,创建新的记忆列表
|
|
||||||
self.G.add_node(concept,
|
|
||||||
memory_items=[memory],
|
|
||||||
created_time=current_time, # 添加创建时间
|
|
||||||
last_modified=current_time) # 添加最后修改时间
|
|
||||||
|
|
||||||
def get_dot(self, concept):
|
|
||||||
# 检查节点是否存在于图中
|
|
||||||
if concept in self.G:
|
|
||||||
# 从图中获取节点数据
|
|
||||||
node_data = self.G.nodes[concept]
|
|
||||||
return concept, node_data
|
|
||||||
return None
|
|
||||||
|
|
||||||
def get_related_item(self, topic, depth=1):
|
|
||||||
if topic not in self.G:
|
|
||||||
return [], []
|
|
||||||
|
|
||||||
first_layer_items = []
|
|
||||||
second_layer_items = []
|
|
||||||
|
|
||||||
# 获取相邻节点
|
|
||||||
neighbors = list(self.G.neighbors(topic))
|
|
||||||
|
|
||||||
# 获取当前节点的记忆项
|
|
||||||
node_data = self.get_dot(topic)
|
|
||||||
if node_data:
|
|
||||||
concept, data = node_data
|
|
||||||
if 'memory_items' in data:
|
|
||||||
memory_items = data['memory_items']
|
|
||||||
if isinstance(memory_items, list):
|
|
||||||
first_layer_items.extend(memory_items)
|
|
||||||
else:
|
|
||||||
first_layer_items.append(memory_items)
|
|
||||||
|
|
||||||
# 只在depth=2时获取第二层记忆
|
|
||||||
if depth >= 2:
|
|
||||||
# 获取相邻节点的记忆项
|
|
||||||
for neighbor in neighbors:
|
|
||||||
node_data = self.get_dot(neighbor)
|
|
||||||
if node_data:
|
|
||||||
concept, data = node_data
|
|
||||||
if 'memory_items' in data:
|
|
||||||
memory_items = data['memory_items']
|
|
||||||
if isinstance(memory_items, list):
|
|
||||||
second_layer_items.extend(memory_items)
|
|
||||||
else:
|
|
||||||
second_layer_items.append(memory_items)
|
|
||||||
|
|
||||||
return first_layer_items, second_layer_items
|
|
||||||
|
|
||||||
@property
|
|
||||||
def dots(self):
|
|
||||||
# 返回所有节点对应的 Memory_dot 对象
|
|
||||||
return [self.get_dot(node) for node in self.G.nodes()]
|
|
||||||
|
|
||||||
def forget_topic(self, topic):
|
|
||||||
"""随机删除指定话题中的一条记忆，如果话题没有记忆则移除该话题节点"""
|
|
||||||
if topic not in self.G:
|
|
||||||
return None
|
|
||||||
|
|
||||||
# 获取话题节点数据
|
|
||||||
node_data = self.G.nodes[topic]
|
|
||||||
|
|
||||||
# 如果节点存在memory_items
|
|
||||||
if 'memory_items' in node_data:
|
|
||||||
memory_items = node_data['memory_items']
|
|
||||||
|
|
||||||
# 确保memory_items是列表
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
|
|
||||||
# 如果有记忆项可以删除
|
|
||||||
if memory_items:
|
|
||||||
# 随机选择一个记忆项删除
|
|
||||||
removed_item = random.choice(memory_items)
|
|
||||||
memory_items.remove(removed_item)
|
|
||||||
|
|
||||||
# 更新节点的记忆项
|
|
||||||
if memory_items:
|
|
||||||
self.G.nodes[topic]['memory_items'] = memory_items
|
|
||||||
else:
|
|
||||||
# 如果没有记忆项了，删除整个节点
|
|
||||||
self.G.remove_node(topic)
|
|
||||||
|
|
||||||
return removed_item
|
|
||||||
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
# 海马体
|
|
||||||
class Hippocampus:
|
|
||||||
def __init__(self, memory_graph: Memory_graph):
|
|
||||||
self.memory_graph = memory_graph
|
|
||||||
self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge, temperature=0.5)
|
|
||||||
self.llm_summary_by_topic = LLM_request(model=global_config.llm_summary_by_topic, temperature=0.5)
|
|
||||||
|
|
||||||
def get_all_node_names(self) -> list:
|
|
||||||
"""获取记忆图中所有节点的名字列表
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
list: 包含所有节点名字的列表
|
|
||||||
"""
|
|
||||||
return list(self.memory_graph.G.nodes())
|
|
||||||
|
|
||||||
def calculate_node_hash(self, concept, memory_items):
|
|
||||||
"""计算节点的特征值"""
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
sorted_items = sorted(memory_items)
|
|
||||||
content = f"{concept}:{'|'.join(sorted_items)}"
|
|
||||||
return hash(content)
|
|
||||||
|
|
||||||
def calculate_edge_hash(self, source, target):
|
|
||||||
"""计算边的特征值"""
|
|
||||||
nodes = sorted([source, target])
|
|
||||||
return hash(f"{nodes[0]}:{nodes[1]}")
|
|
||||||
|
|
||||||
def get_memory_sample(self, chat_size=20, time_frequency: dict = {'near': 2, 'mid': 4, 'far': 3}):
|
|
||||||
"""获取记忆样本
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
list: 消息记录列表，每个元素是一个消息记录字典列表
|
|
||||||
"""
|
|
||||||
current_timestamp = datetime.datetime.now().timestamp()
|
|
||||||
chat_samples = []
|
|
||||||
|
|
||||||
# 短期：1h 中期：4h 长期：24h
|
|
||||||
for _ in range(time_frequency.get('near')):
|
|
||||||
random_time = current_timestamp - random.randint(1, 3600)
|
|
||||||
messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
|
|
||||||
if messages:
|
|
||||||
chat_samples.append(messages)
|
|
||||||
|
|
||||||
for _ in range(time_frequency.get('mid')):
|
|
||||||
random_time = current_timestamp - random.randint(3600, 3600 * 4)
|
|
||||||
messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
|
|
||||||
if messages:
|
|
||||||
chat_samples.append(messages)
|
|
||||||
|
|
||||||
for _ in range(time_frequency.get('far')):
|
|
||||||
random_time = current_timestamp - random.randint(3600 * 4, 3600 * 24)
|
|
||||||
messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
|
|
||||||
if messages:
|
|
||||||
chat_samples.append(messages)
|
|
||||||
|
|
||||||
return chat_samples
|
|
||||||
|
|
||||||
async def memory_compress(self, messages: list, compress_rate=0.1):
|
|
||||||
"""压缩消息记录为记忆
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
tuple: (压缩记忆集合, 相似主题字典)
|
|
||||||
"""
|
|
||||||
if not messages:
|
|
||||||
return set(), {}
|
|
||||||
|
|
||||||
# 合并消息文本，同时保留时间信息
|
|
||||||
input_text = ""
|
|
||||||
time_info = ""
|
|
||||||
# 计算最早和最晚时间
|
|
||||||
earliest_time = min(msg['time'] for msg in messages)
|
|
||||||
latest_time = max(msg['time'] for msg in messages)
|
|
||||||
|
|
||||||
earliest_dt = datetime.datetime.fromtimestamp(earliest_time)
|
|
||||||
latest_dt = datetime.datetime.fromtimestamp(latest_time)
|
|
||||||
|
|
||||||
# 如果是同一年
|
|
||||||
if earliest_dt.year == latest_dt.year:
|
|
||||||
earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S")
|
|
||||||
latest_str = latest_dt.strftime("%m-%d %H:%M:%S")
|
|
||||||
time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n"
|
|
||||||
else:
|
|
||||||
earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S")
|
|
||||||
latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S")
|
|
||||||
time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n"
|
|
||||||
|
|
||||||
for msg in messages:
|
|
||||||
input_text += f"{msg['detailed_plain_text']}\n"
|
|
||||||
|
|
||||||
logger.debug(input_text)
|
|
||||||
|
|
||||||
topic_num = self.calculate_topic_num(input_text, compress_rate)
|
|
||||||
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(input_text, topic_num))
|
|
||||||
|
|
||||||
# 过滤topics
|
|
||||||
filter_keywords = global_config.memory_ban_words
|
|
||||||
topics = [topic.strip() for topic in
|
|
||||||
topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
|
|
||||||
filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)]
|
|
||||||
|
|
||||||
logger.info(f"过滤后话题: {filtered_topics}")
|
|
||||||
|
|
||||||
# 创建所有话题的请求任务
|
|
||||||
tasks = []
|
|
||||||
for topic in filtered_topics:
|
|
||||||
topic_what_prompt = self.topic_what(input_text, topic, time_info)
|
|
||||||
task = self.llm_summary_by_topic.generate_response_async(topic_what_prompt)
|
|
||||||
tasks.append((topic.strip(), task))
|
|
||||||
|
|
||||||
# 等待所有任务完成
|
|
||||||
compressed_memory = set()
|
|
||||||
similar_topics_dict = {} # 存储每个话题的相似主题列表
|
|
||||||
for topic, task in tasks:
|
|
||||||
response = await task
|
|
||||||
if response:
|
|
||||||
compressed_memory.add((topic, response[0]))
|
|
||||||
# 为每个话题查找相似的已存在主题
|
|
||||||
existing_topics = list(self.memory_graph.G.nodes())
|
|
||||||
similar_topics = []
|
|
||||||
|
|
||||||
for existing_topic in existing_topics:
|
|
||||||
topic_words = set(jieba.cut(topic))
|
|
||||||
existing_words = set(jieba.cut(existing_topic))
|
|
||||||
|
|
||||||
all_words = topic_words | existing_words
|
|
||||||
v1 = [1 if word in topic_words else 0 for word in all_words]
|
|
||||||
v2 = [1 if word in existing_words else 0 for word in all_words]
|
|
||||||
|
|
||||||
similarity = cosine_similarity(v1, v2)
|
|
||||||
|
|
||||||
if similarity >= 0.6:
|
|
||||||
similar_topics.append((existing_topic, similarity))
|
|
||||||
|
|
||||||
similar_topics.sort(key=lambda x: x[1], reverse=True)
|
|
||||||
similar_topics = similar_topics[:5]
|
|
||||||
similar_topics_dict[topic] = similar_topics
|
|
||||||
|
|
||||||
return compressed_memory, similar_topics_dict
|
|
||||||
|
|
||||||
def calculate_topic_num(self, text, compress_rate):
|
|
||||||
"""计算文本的话题数量"""
|
|
||||||
information_content = calculate_information_content(text)
|
|
||||||
topic_by_length = text.count('\n') * compress_rate
|
|
||||||
topic_by_information_content = max(1, min(5, int((information_content - 3) * 2)))
|
|
||||||
topic_num = int((topic_by_length + topic_by_information_content) / 2)
|
|
||||||
logger.debug(
|
|
||||||
f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, "
|
|
||||||
f"topic_num: {topic_num}")
|
|
||||||
return topic_num
|
|
||||||
|
|
||||||
async def operation_build_memory(self, chat_size=20):
|
|
||||||
time_frequency = {'near': 1, 'mid': 4, 'far': 4}
|
|
||||||
memory_samples = self.get_memory_sample(chat_size, time_frequency)
|
|
||||||
|
|
||||||
for i, messages in enumerate(memory_samples, 1):
|
|
||||||
all_topics = []
|
|
||||||
# 加载进度可视化
|
|
||||||
progress = (i / len(memory_samples)) * 100
|
|
||||||
bar_length = 30
|
|
||||||
filled_length = int(bar_length * i // len(memory_samples))
|
|
||||||
bar = '█' * filled_length + '-' * (bar_length - filled_length)
|
|
||||||
logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
|
|
||||||
|
|
||||||
compress_rate = global_config.memory_compress_rate
|
|
||||||
compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate)
|
|
||||||
logger.info(f"压缩后记忆数量: {len(compressed_memory)}，似曾相识的话题: {len(similar_topics_dict)}")
|
|
||||||
|
|
||||||
current_time = datetime.datetime.now().timestamp()
|
|
||||||
|
|
||||||
for topic, memory in compressed_memory:
|
|
||||||
logger.info(f"添加节点: {topic}")
|
|
||||||
self.memory_graph.add_dot(topic, memory)
|
|
||||||
all_topics.append(topic)
|
|
||||||
|
|
||||||
# 连接相似的已存在主题
|
|
||||||
if topic in similar_topics_dict:
|
|
||||||
similar_topics = similar_topics_dict[topic]
|
|
||||||
for similar_topic, similarity in similar_topics:
|
|
||||||
if topic != similar_topic:
|
|
||||||
strength = int(similarity * 10)
|
|
||||||
logger.info(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})")
|
|
||||||
self.memory_graph.G.add_edge(topic, similar_topic,
|
|
||||||
strength=strength,
|
|
||||||
created_time=current_time,
|
|
||||||
last_modified=current_time)
|
|
||||||
|
|
||||||
# 连接同批次的相关话题
|
|
||||||
for i in range(len(all_topics)):
|
|
||||||
for j in range(i + 1, len(all_topics)):
|
|
||||||
logger.info(f"连接同批次节点: {all_topics[i]} 和 {all_topics[j]}")
|
|
||||||
self.memory_graph.connect_dot(all_topics[i], all_topics[j])
|
|
||||||
|
|
||||||
self.sync_memory_to_db()
|
|
||||||
|
|
||||||
def sync_memory_to_db(self):
|
|
||||||
"""检查并同步内存中的图结构与数据库"""
|
|
||||||
# 获取数据库中所有节点和内存中所有节点
|
|
||||||
db_nodes = list(db.graph_data.nodes.find())
|
|
||||||
memory_nodes = list(self.memory_graph.G.nodes(data=True))
|
|
||||||
|
|
||||||
# 转换数据库节点为字典格式,方便查找
|
|
||||||
db_nodes_dict = {node['concept']: node for node in db_nodes}
|
|
||||||
|
|
||||||
# 检查并更新节点
|
|
||||||
for concept, data in memory_nodes:
|
|
||||||
memory_items = data.get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
|
|
||||||
# 计算内存中节点的特征值
|
|
||||||
memory_hash = self.calculate_node_hash(concept, memory_items)
|
|
||||||
|
|
||||||
# 获取时间信息
|
|
||||||
created_time = data.get('created_time', datetime.datetime.now().timestamp())
|
|
||||||
last_modified = data.get('last_modified', datetime.datetime.now().timestamp())
|
|
||||||
|
|
||||||
if concept not in db_nodes_dict:
|
|
||||||
# 数据库中缺少的节点,添加
|
|
||||||
node_data = {
|
|
||||||
'concept': concept,
|
|
||||||
'memory_items': memory_items,
|
|
||||||
'hash': memory_hash,
|
|
||||||
'created_time': created_time,
|
|
||||||
'last_modified': last_modified
|
|
||||||
}
|
|
||||||
db.graph_data.nodes.insert_one(node_data)
|
|
||||||
else:
|
|
||||||
# 获取数据库中节点的特征值
|
|
||||||
db_node = db_nodes_dict[concept]
|
|
||||||
db_hash = db_node.get('hash', None)
|
|
||||||
|
|
||||||
# 如果特征值不同,则更新节点
|
|
||||||
if db_hash != memory_hash:
|
|
||||||
db.graph_data.nodes.update_one(
|
|
||||||
{'concept': concept},
|
|
||||||
{'$set': {
|
|
||||||
'memory_items': memory_items,
|
|
||||||
'hash': memory_hash,
|
|
||||||
'created_time': created_time,
|
|
||||||
'last_modified': last_modified
|
|
||||||
}}
|
|
||||||
)
|
|
||||||
|
|
||||||
# 处理边的信息
|
|
||||||
db_edges = list(db.graph_data.edges.find())
|
|
||||||
memory_edges = list(self.memory_graph.G.edges(data=True))
|
|
||||||
|
|
||||||
# 创建边的哈希值字典
|
|
||||||
db_edge_dict = {}
|
|
||||||
for edge in db_edges:
|
|
||||||
edge_hash = self.calculate_edge_hash(edge['source'], edge['target'])
|
|
||||||
db_edge_dict[(edge['source'], edge['target'])] = {
|
|
||||||
'hash': edge_hash,
|
|
||||||
'strength': edge.get('strength', 1)
|
|
||||||
}
|
|
||||||
|
|
||||||
# 检查并更新边
|
|
||||||
for source, target, data in memory_edges:
|
|
||||||
edge_hash = self.calculate_edge_hash(source, target)
|
|
||||||
edge_key = (source, target)
|
|
||||||
strength = data.get('strength', 1)
|
|
||||||
|
|
||||||
# 获取边的时间信息
|
|
||||||
created_time = data.get('created_time', datetime.datetime.now().timestamp())
|
|
||||||
last_modified = data.get('last_modified', datetime.datetime.now().timestamp())
|
|
||||||
|
|
||||||
if edge_key not in db_edge_dict:
|
|
||||||
# 添加新边
|
|
||||||
edge_data = {
|
|
||||||
'source': source,
|
|
||||||
'target': target,
|
|
||||||
'strength': strength,
|
|
||||||
'hash': edge_hash,
|
|
||||||
'created_time': created_time,
|
|
||||||
'last_modified': last_modified
|
|
||||||
}
|
|
||||||
db.graph_data.edges.insert_one(edge_data)
|
|
||||||
else:
|
|
||||||
# 检查边的特征值是否变化
|
|
||||||
if db_edge_dict[edge_key]['hash'] != edge_hash:
|
|
||||||
db.graph_data.edges.update_one(
|
|
||||||
{'source': source, 'target': target},
|
|
||||||
{'$set': {
|
|
||||||
'hash': edge_hash,
|
|
||||||
'strength': strength,
|
|
||||||
'created_time': created_time,
|
|
||||||
'last_modified': last_modified
|
|
||||||
}}
|
|
||||||
)
|
|
||||||
|
|
||||||
def sync_memory_from_db(self):
|
|
||||||
"""从数据库同步数据到内存中的图结构"""
|
|
||||||
current_time = datetime.datetime.now().timestamp()
|
|
||||||
need_update = False
|
|
||||||
|
|
||||||
# 清空当前图
|
|
||||||
self.memory_graph.G.clear()
|
|
||||||
|
|
||||||
# 从数据库加载所有节点
|
|
||||||
nodes = list(db.graph_data.nodes.find())
|
|
||||||
for node in nodes:
|
|
||||||
concept = node['concept']
|
|
||||||
memory_items = node.get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
|
|
||||||
# 检查时间字段是否存在
|
|
||||||
if 'created_time' not in node or 'last_modified' not in node:
|
|
||||||
need_update = True
|
|
||||||
# 更新数据库中的节点
|
|
||||||
update_data = {}
|
|
||||||
if 'created_time' not in node:
|
|
||||||
update_data['created_time'] = current_time
|
|
||||||
if 'last_modified' not in node:
|
|
||||||
update_data['last_modified'] = current_time
|
|
||||||
|
|
||||||
db.graph_data.nodes.update_one(
|
|
||||||
{'concept': concept},
|
|
||||||
{'$set': update_data}
|
|
||||||
)
|
|
||||||
logger.info(f"[时间更新] 节点 {concept} 添加缺失的时间字段")
|
|
||||||
|
|
||||||
# 获取时间信息(如果不存在则使用当前时间)
|
|
||||||
created_time = node.get('created_time', current_time)
|
|
||||||
last_modified = node.get('last_modified', current_time)
|
|
||||||
|
|
||||||
# 添加节点到图中
|
|
||||||
self.memory_graph.G.add_node(concept,
|
|
||||||
memory_items=memory_items,
|
|
||||||
created_time=created_time,
|
|
||||||
last_modified=last_modified)
|
|
||||||
|
|
||||||
# 从数据库加载所有边
|
|
||||||
edges = list(db.graph_data.edges.find())
|
|
||||||
for edge in edges:
|
|
||||||
source = edge['source']
|
|
||||||
target = edge['target']
|
|
||||||
strength = edge.get('strength', 1)
|
|
||||||
|
|
||||||
# 检查时间字段是否存在
|
|
||||||
if 'created_time' not in edge or 'last_modified' not in edge:
|
|
||||||
need_update = True
|
|
||||||
# 更新数据库中的边
|
|
||||||
update_data = {}
|
|
||||||
if 'created_time' not in edge:
|
|
||||||
update_data['created_time'] = current_time
|
|
||||||
if 'last_modified' not in edge:
|
|
||||||
update_data['last_modified'] = current_time
|
|
||||||
|
|
||||||
db.graph_data.edges.update_one(
|
|
||||||
{'source': source, 'target': target},
|
|
||||||
{'$set': update_data}
|
|
||||||
)
|
|
||||||
logger.info(f"[时间更新] 边 {source} - {target} 添加缺失的时间字段")
|
|
||||||
|
|
||||||
# 获取时间信息(如果不存在则使用当前时间)
|
|
||||||
created_time = edge.get('created_time', current_time)
|
|
||||||
last_modified = edge.get('last_modified', current_time)
|
|
||||||
|
|
||||||
# 只有当源节点和目标节点都存在时才添加边
|
|
||||||
if source in self.memory_graph.G and target in self.memory_graph.G:
|
|
||||||
self.memory_graph.G.add_edge(source, target,
|
|
||||||
strength=strength,
|
|
||||||
created_time=created_time,
|
|
||||||
last_modified=last_modified)
|
|
||||||
|
|
||||||
if need_update:
|
|
||||||
logger.success("[数据库] 已为缺失的时间字段进行补充")
|
|
||||||
|
|
||||||
async def operation_forget_topic(self, percentage=0.1):
|
|
||||||
"""随机选择图中一定比例的节点和边进行检查,根据时间条件决定是否遗忘"""
|
|
||||||
# 检查数据库是否为空
|
|
||||||
# logger.remove()
|
|
||||||
|
|
||||||
logger.info(f"[遗忘] 开始检查数据库... 当前Logger信息:")
|
|
||||||
# logger.info(f"- Logger名称: {logger.name}")
|
|
||||||
logger.info(f"- Logger等级: {logger.level}")
|
|
||||||
# logger.info(f"- Logger处理器: {[handler.__class__.__name__ for handler in logger.handlers]}")
|
|
||||||
|
|
||||||
# logger2 = setup_logger(LogModule.MEMORY)
|
|
||||||
# logger2.info(f"[遗忘] 开始检查数据库... 当前Logger信息:")
|
|
||||||
# logger.info(f"[遗忘] 开始检查数据库... 当前Logger信息:")
|
|
||||||
|
|
||||||
all_nodes = list(self.memory_graph.G.nodes())
|
|
||||||
all_edges = list(self.memory_graph.G.edges())
|
|
||||||
|
|
||||||
if not all_nodes and not all_edges:
|
|
||||||
logger.info("[遗忘] 记忆图为空,无需进行遗忘操作")
|
|
||||||
return
|
|
||||||
|
|
||||||
check_nodes_count = max(1, int(len(all_nodes) * percentage))
|
|
||||||
check_edges_count = max(1, int(len(all_edges) * percentage))
|
|
||||||
|
|
||||||
nodes_to_check = random.sample(all_nodes, check_nodes_count)
|
|
||||||
edges_to_check = random.sample(all_edges, check_edges_count)
|
|
||||||
|
|
||||||
edge_changes = {'weakened': 0, 'removed': 0}
|
|
||||||
node_changes = {'reduced': 0, 'removed': 0}
|
|
||||||
|
|
||||||
current_time = datetime.datetime.now().timestamp()
|
|
||||||
|
|
||||||
# 检查并遗忘连接
|
|
||||||
logger.info("[遗忘] 开始检查连接...")
|
|
||||||
for source, target in edges_to_check:
|
|
||||||
edge_data = self.memory_graph.G[source][target]
|
|
||||||
last_modified = edge_data.get('last_modified')
|
|
||||||
|
|
||||||
if current_time - last_modified > 3600*global_config.memory_forget_time:
|
|
||||||
current_strength = edge_data.get('strength', 1)
|
|
||||||
new_strength = current_strength - 1
|
|
||||||
|
|
||||||
if new_strength <= 0:
|
|
||||||
self.memory_graph.G.remove_edge(source, target)
|
|
||||||
edge_changes['removed'] += 1
|
|
||||||
logger.info(f"[遗忘] 连接移除: {source} -> {target}")
|
|
||||||
else:
|
|
||||||
edge_data['strength'] = new_strength
|
|
||||||
edge_data['last_modified'] = current_time
|
|
||||||
edge_changes['weakened'] += 1
|
|
||||||
logger.info(f"[遗忘] 连接减弱: {source} -> {target} (强度: {current_strength} -> {new_strength})")
|
|
||||||
|
|
||||||
# 检查并遗忘话题
|
|
||||||
logger.info("[遗忘] 开始检查节点...")
|
|
||||||
for node in nodes_to_check:
|
|
||||||
node_data = self.memory_graph.G.nodes[node]
|
|
||||||
last_modified = node_data.get('last_modified', current_time)
|
|
||||||
|
|
||||||
if current_time - last_modified > 3600*24:
|
|
||||||
memory_items = node_data.get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
|
|
||||||
if memory_items:
|
|
||||||
current_count = len(memory_items)
|
|
||||||
removed_item = random.choice(memory_items)
|
|
||||||
memory_items.remove(removed_item)
|
|
||||||
|
|
||||||
if memory_items:
|
|
||||||
self.memory_graph.G.nodes[node]['memory_items'] = memory_items
|
|
||||||
self.memory_graph.G.nodes[node]['last_modified'] = current_time
|
|
||||||
node_changes['reduced'] += 1
|
|
||||||
logger.info(f"[遗忘] 记忆减少: {node} (数量: {current_count} -> {len(memory_items)})")
|
|
||||||
else:
|
|
||||||
self.memory_graph.G.remove_node(node)
|
|
||||||
node_changes['removed'] += 1
|
|
||||||
logger.info(f"[遗忘] 节点移除: {node}")
|
|
||||||
|
|
||||||
if any(count > 0 for count in edge_changes.values()) or any(count > 0 for count in node_changes.values()):
|
|
||||||
self.sync_memory_to_db()
|
|
||||||
logger.info("[遗忘] 统计信息:")
|
|
||||||
logger.info(f"[遗忘] 连接变化: {edge_changes['weakened']} 个减弱, {edge_changes['removed']} 个移除")
|
|
||||||
logger.info(f"[遗忘] 节点变化: {node_changes['reduced']} 个减少记忆, {node_changes['removed']} 个移除")
|
|
||||||
else:
|
|
||||||
logger.info("[遗忘] 本次检查没有节点或连接满足遗忘条件")
|
|
||||||
|
|
||||||
async def merge_memory(self, topic):
|
|
||||||
"""对指定话题的记忆进行合并压缩"""
|
|
||||||
# 获取节点的记忆项
|
|
||||||
memory_items = self.memory_graph.G.nodes[topic].get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
|
|
||||||
# 如果记忆项不足，直接返回
|
|
||||||
if len(memory_items) < 10:
|
|
||||||
return
|
|
||||||
|
|
||||||
# 随机选择10条记忆
|
|
||||||
selected_memories = random.sample(memory_items, 10)
|
|
||||||
|
|
||||||
# 拼接成文本
|
|
||||||
merged_text = "\n".join(selected_memories)
|
|
||||||
logger.debug(f"[合并] 话题: {topic}")
|
|
||||||
logger.debug(f"[合并] 选择的记忆:\n{merged_text}")
|
|
||||||
|
|
||||||
# 使用memory_compress生成新的压缩记忆
|
|
||||||
compressed_memories, _ = await self.memory_compress(selected_memories, 0.1)
|
|
||||||
|
|
||||||
# 从原记忆列表中移除被选中的记忆
|
|
||||||
for memory in selected_memories:
|
|
||||||
memory_items.remove(memory)
|
|
||||||
|
|
||||||
# 添加新的压缩记忆
|
|
||||||
for _, compressed_memory in compressed_memories:
|
|
||||||
memory_items.append(compressed_memory)
|
|
||||||
logger.info(f"[合并] 添加压缩记忆: {compressed_memory}")
|
|
||||||
|
|
||||||
# 更新节点的记忆项
|
|
||||||
self.memory_graph.G.nodes[topic]['memory_items'] = memory_items
|
|
||||||
logger.debug(f"[合并] 完成记忆合并，当前记忆数量: {len(memory_items)}")
|
|
||||||
|
|
||||||
async def operation_merge_memory(self, percentage=0.1):
|
|
||||||
"""
|
|
||||||
随机检查一定比例的节点，对内容数量超过100的节点进行记忆合并
|
|
||||||
|
|
||||||
Args:
|
|
||||||
percentage: 要检查的节点比例，默认为0.1（10%）
|
|
||||||
"""
|
|
||||||
# 获取所有节点
|
|
||||||
all_nodes = list(self.memory_graph.G.nodes())
|
|
||||||
# 计算要检查的节点数量
|
|
||||||
check_count = max(1, int(len(all_nodes) * percentage))
|
|
||||||
# 随机选择节点
|
|
||||||
nodes_to_check = random.sample(all_nodes, check_count)
|
|
||||||
|
|
||||||
merged_nodes = []
|
|
||||||
for node in nodes_to_check:
|
|
||||||
# 获取节点的内容条数
|
|
||||||
memory_items = self.memory_graph.G.nodes[node].get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
content_count = len(memory_items)
|
|
||||||
|
|
||||||
# 如果内容数量超过100，进行合并
|
|
||||||
if content_count > 100:
|
|
||||||
logger.debug(f"检查节点: {node}, 当前记忆数量: {content_count}")
|
|
||||||
await self.merge_memory(node)
|
|
||||||
merged_nodes.append(node)
|
|
||||||
|
|
||||||
# 同步到数据库
|
|
||||||
if merged_nodes:
|
|
||||||
self.sync_memory_to_db()
|
|
||||||
logger.debug(f"完成记忆合并操作，共处理 {len(merged_nodes)} 个节点")
|
|
||||||
else:
|
|
||||||
logger.debug("本次检查没有需要合并的节点")
|
|
||||||
|
|
||||||
def find_topic_llm(self, text, topic_num):
|
|
||||||
prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。'
|
|
||||||
return prompt
|
|
||||||
|
|
||||||
def topic_what(self, text, topic, time_info):
|
|
||||||
prompt = f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好'
|
|
||||||
return prompt
|
|
||||||
|
|
||||||
async def _identify_topics(self, text: str) -> list:
|
|
||||||
"""从文本中识别可能的主题
|
|
||||||
|
|
||||||
Args:
|
|
||||||
text: 输入文本
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
list: 识别出的主题列表
|
|
||||||
"""
|
|
||||||
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, 5))
|
|
||||||
# print(f"话题: {topics_response[0]}")
|
|
||||||
topics = [topic.strip() for topic in
|
|
||||||
topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
|
|
||||||
# print(f"话题: {topics}")
|
|
||||||
|
|
||||||
return topics
|
|
||||||
|
|
||||||
def _find_similar_topics(self, topics: list, similarity_threshold: float = 0.4, debug_info: str = "") -> list:
|
|
||||||
"""查找与给定主题相似的记忆主题
|
|
||||||
|
|
||||||
Args:
|
|
||||||
topics: 主题列表
|
|
||||||
similarity_threshold: 相似度阈值
|
|
||||||
debug_info: 调试信息前缀
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
list: (主题, 相似度) 元组列表
|
|
||||||
"""
|
|
||||||
all_memory_topics = self.get_all_node_names()
|
|
||||||
all_similar_topics = []
|
|
||||||
|
|
||||||
# 计算每个识别出的主题与记忆主题的相似度
|
|
||||||
for topic in topics:
|
|
||||||
if debug_info:
|
|
||||||
# print(f"\033[1;32m[{debug_info}]\033[0m 正在思考有没有见过: {topic}")
|
|
||||||
pass
|
|
||||||
|
|
||||||
topic_vector = text_to_vector(topic)
|
|
||||||
has_similar_topic = False
|
|
||||||
|
|
||||||
for memory_topic in all_memory_topics:
|
|
||||||
memory_vector = text_to_vector(memory_topic)
|
|
||||||
# 获取所有唯一词
|
|
||||||
all_words = set(topic_vector.keys()) | set(memory_vector.keys())
|
|
||||||
# 构建向量
|
|
||||||
v1 = [topic_vector.get(word, 0) for word in all_words]
|
|
||||||
v2 = [memory_vector.get(word, 0) for word in all_words]
|
|
||||||
# 计算相似度
|
|
||||||
similarity = cosine_similarity(v1, v2)
|
|
||||||
|
|
||||||
if similarity >= similarity_threshold:
|
|
||||||
has_similar_topic = True
|
|
||||||
if debug_info:
|
|
||||||
# print(f"\033[1;32m[{debug_info}]\033[0m 找到相似主题: {topic} -> {memory_topic} (相似度: {similarity:.2f})")
|
|
||||||
pass
|
|
||||||
all_similar_topics.append((memory_topic, similarity))
|
|
||||||
|
|
||||||
if not has_similar_topic and debug_info:
|
|
||||||
# print(f"\033[1;31m[{debug_info}]\033[0m 没有见过: {topic} ，呃呃")
|
|
||||||
pass
|
|
||||||
|
|
||||||
return all_similar_topics
|
|
||||||
|
|
||||||
def _get_top_topics(self, similar_topics: list, max_topics: int = 5) -> list:
|
|
||||||
"""获取相似度最高的主题
|
|
||||||
|
|
||||||
Args:
|
|
||||||
similar_topics: (主题, 相似度) 元组列表
|
|
||||||
max_topics: 最大主题数量
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
list: (主题, 相似度) 元组列表
|
|
||||||
"""
|
|
||||||
seen_topics = set()
|
|
||||||
top_topics = []
|
|
||||||
|
|
||||||
for topic, score in sorted(similar_topics, key=lambda x: x[1], reverse=True):
|
|
||||||
if topic not in seen_topics and len(top_topics) < max_topics:
|
|
||||||
seen_topics.add(topic)
|
|
||||||
top_topics.append((topic, score))
|
|
||||||
|
|
||||||
return top_topics
|
|
||||||
|
|
||||||
async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int:
|
|
||||||
"""计算输入文本对记忆的激活程度"""
|
|
||||||
logger.info(f"[激活] 识别主题: {await self._identify_topics(text)}")
|
|
||||||
|
|
||||||
# 识别主题
|
|
||||||
identified_topics = await self._identify_topics(text)
|
|
||||||
if not identified_topics:
|
|
||||||
return 0
|
|
||||||
|
|
||||||
# 查找相似主题
|
|
||||||
all_similar_topics = self._find_similar_topics(
|
|
||||||
identified_topics,
|
|
||||||
similarity_threshold=similarity_threshold,
|
|
||||||
debug_info="激活"
|
|
||||||
)
|
|
||||||
|
|
||||||
if not all_similar_topics:
|
|
||||||
return 0
|
|
||||||
|
|
||||||
# 获取最相关的主题
|
|
||||||
top_topics = self._get_top_topics(all_similar_topics, max_topics)
|
|
||||||
|
|
||||||
# 如果只找到一个主题，进行惩罚
|
|
||||||
if len(top_topics) == 1:
|
|
||||||
topic, score = top_topics[0]
|
|
||||||
# 获取主题内容数量并计算惩罚系数
|
|
||||||
memory_items = self.memory_graph.G.nodes[topic].get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
content_count = len(memory_items)
|
|
||||||
penalty = 1.0 / (1 + math.log(content_count + 1))
|
|
||||||
|
|
||||||
activation = int(score * 50 * penalty)
|
|
||||||
logger.info(
|
|
||||||
f"[激活] 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
|
|
||||||
return activation
|
|
||||||
|
|
||||||
# 计算关键词匹配率，同时考虑内容数量
|
|
||||||
matched_topics = set()
|
|
||||||
topic_similarities = {}
|
|
||||||
|
|
||||||
for memory_topic, similarity in top_topics:
|
|
||||||
# 计算内容数量惩罚
|
|
||||||
memory_items = self.memory_graph.G.nodes[memory_topic].get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
content_count = len(memory_items)
|
|
||||||
penalty = 1.0 / (1 + math.log(content_count + 1))
|
|
||||||
|
|
||||||
# 对每个记忆主题，检查它与哪些输入主题相似
|
|
||||||
for input_topic in identified_topics:
|
|
||||||
topic_vector = text_to_vector(input_topic)
|
|
||||||
memory_vector = text_to_vector(memory_topic)
|
|
||||||
all_words = set(topic_vector.keys()) | set(memory_vector.keys())
|
|
||||||
v1 = [topic_vector.get(word, 0) for word in all_words]
|
|
||||||
v2 = [memory_vector.get(word, 0) for word in all_words]
|
|
||||||
sim = cosine_similarity(v1, v2)
|
|
||||||
if sim >= similarity_threshold:
|
|
||||||
matched_topics.add(input_topic)
|
|
||||||
adjusted_sim = sim * penalty
|
|
||||||
topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim)
|
|
||||||
logger.debug(
|
|
||||||
f"[激活] 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
|
|
||||||
|
|
||||||
# 计算主题匹配率和平均相似度
|
|
||||||
topic_match = len(matched_topics) / len(identified_topics)
|
|
||||||
average_similarities = sum(topic_similarities.values()) / len(topic_similarities) if topic_similarities else 0
|
|
||||||
|
|
||||||
# 计算最终激活值
|
|
||||||
activation = int((topic_match + average_similarities) / 2 * 100)
|
|
||||||
logger.info(
|
|
||||||
f"[激活] 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
|
|
||||||
|
|
||||||
return activation
|
|
||||||
|
|
||||||
async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4,
|
|
||||||
max_memory_num: int = 5) -> list:
|
|
||||||
"""根据输入文本获取相关的记忆内容"""
|
|
||||||
# 识别主题
|
|
||||||
identified_topics = await self._identify_topics(text)
|
|
||||||
|
|
||||||
# 查找相似主题
|
|
||||||
all_similar_topics = self._find_similar_topics(
|
|
||||||
identified_topics,
|
|
||||||
similarity_threshold=similarity_threshold,
|
|
||||||
debug_info="记忆检索"
|
|
||||||
)
|
|
||||||
|
|
||||||
# 获取最相关的主题
|
|
||||||
relevant_topics = self._get_top_topics(all_similar_topics, max_topics)
|
|
||||||
|
|
||||||
# 获取相关记忆内容
|
|
||||||
relevant_memories = []
|
|
||||||
for topic, score in relevant_topics:
|
|
||||||
# 获取该主题的记忆内容
|
|
||||||
first_layer, _ = self.memory_graph.get_related_item(topic, depth=1)
|
|
||||||
if first_layer:
|
|
||||||
# 如果记忆条数超过限制，随机选择指定数量的记忆
|
|
||||||
if len(first_layer) > max_memory_num / 2:
|
|
||||||
first_layer = random.sample(first_layer, max_memory_num // 2)
|
|
||||||
# 为每条记忆添加来源主题和相似度信息
|
|
||||||
for memory in first_layer:
|
|
||||||
relevant_memories.append({
|
|
||||||
'topic': topic,
|
|
||||||
'similarity': score,
|
|
||||||
'content': memory
|
|
||||||
})
|
|
||||||
|
|
||||||
# 如果记忆数量超过5个,随机选择5个
|
|
||||||
# 按相似度排序
|
|
||||||
relevant_memories.sort(key=lambda x: x['similarity'], reverse=True)
|
|
||||||
|
|
||||||
if len(relevant_memories) > max_memory_num:
|
|
||||||
relevant_memories = random.sample(relevant_memories, max_memory_num)
|
|
||||||
|
|
||||||
return relevant_memories
|
|
||||||
|
|
||||||
|
|
||||||
def segment_text(text):
|
|
||||||
seg_text = list(jieba.cut(text))
|
|
||||||
return seg_text
|
|
||||||
|
|
||||||
driver = get_driver()
|
|
||||||
config = driver.config
|
|
||||||
|
|
||||||
start_time = time.time()
|
|
||||||
|
|
||||||
# 创建记忆图
|
|
||||||
memory_graph = Memory_graph()
|
|
||||||
# 创建海马体
|
|
||||||
hippocampus = Hippocampus(memory_graph)
|
|
||||||
# 从数据库加载记忆图
|
|
||||||
hippocampus.sync_memory_from_db()
|
|
||||||
|
|
||||||
end_time = time.time()
|
|
||||||
logger.success(f"加载海马体耗时: {end_time - start_time:.2f} 秒")
|
|
||||||
36
src/plugins/memory_system/memory_config.py
Normal file
36
src/plugins/memory_system/memory_config.py
Normal file
@@ -0,0 +1,36 @@
|
|||||||
|
from dataclasses import dataclass
|
||||||
|
from typing import List
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class MemoryConfig:
|
||||||
|
"""记忆系统配置类"""
|
||||||
|
|
||||||
|
# 记忆构建相关配置
|
||||||
|
memory_build_distribution: List[float] # 记忆构建的时间分布参数
|
||||||
|
build_memory_sample_num: int # 每次构建记忆的样本数量
|
||||||
|
build_memory_sample_length: int # 每个样本的消息长度
|
||||||
|
memory_compress_rate: float # 记忆压缩率
|
||||||
|
|
||||||
|
# 记忆遗忘相关配置
|
||||||
|
memory_forget_time: int # 记忆遗忘时间（小时）
|
||||||
|
|
||||||
|
# 记忆过滤相关配置
|
||||||
|
memory_ban_words: List[str] # 记忆过滤词列表
|
||||||
|
|
||||||
|
llm_topic_judge: str # 话题判断模型
|
||||||
|
llm_summary_by_topic: str # 话题总结模型
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_global_config(cls, global_config):
|
||||||
|
"""从全局配置创建记忆系统配置"""
|
||||||
|
return cls(
|
||||||
|
memory_build_distribution=global_config.memory_build_distribution,
|
||||||
|
build_memory_sample_num=global_config.build_memory_sample_num,
|
||||||
|
build_memory_sample_length=global_config.build_memory_sample_length,
|
||||||
|
memory_compress_rate=global_config.memory_compress_rate,
|
||||||
|
memory_forget_time=global_config.memory_forget_time,
|
||||||
|
memory_ban_words=global_config.memory_ban_words,
|
||||||
|
llm_topic_judge=global_config.llm_topic_judge,
|
||||||
|
llm_summary_by_topic=global_config.llm_summary_by_topic,
|
||||||
|
)
|
||||||
@@ -1,972 +0,0 @@
|
|||||||
# -*- coding: utf-8 -*-
|
|
||||||
import datetime
|
|
||||||
import math
|
|
||||||
import os
|
|
||||||
import random
|
|
||||||
import sys
|
|
||||||
import time
|
|
||||||
from collections import Counter
|
|
||||||
from pathlib import Path
|
|
||||||
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
import networkx as nx
|
|
||||||
from dotenv import load_dotenv
|
|
||||||
from loguru import logger
|
|
||||||
import jieba
|
|
||||||
|
|
||||||
# from chat.config import global_config
|
|
||||||
# 添加项目根目录到 Python 路径
|
|
||||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
|
||||||
sys.path.append(root_path)
|
|
||||||
|
|
||||||
from src.common.database import db
|
|
||||||
from src.plugins.memory_system.offline_llm import LLMModel
|
|
||||||
|
|
||||||
# 获取当前文件的目录
|
|
||||||
current_dir = Path(__file__).resolve().parent
|
|
||||||
# 获取项目根目录（上三层目录）
|
|
||||||
project_root = current_dir.parent.parent.parent
|
|
||||||
# env.dev文件路径
|
|
||||||
env_path = project_root / ".env.dev"
|
|
||||||
|
|
||||||
# 加载环境变量
|
|
||||||
if env_path.exists():
|
|
||||||
logger.info(f"从 {env_path} 加载环境变量")
|
|
||||||
load_dotenv(env_path)
|
|
||||||
else:
|
|
||||||
logger.warning(f"未找到环境变量文件: {env_path}")
|
|
||||||
logger.info("将使用默认配置")
|
|
||||||
|
|
||||||
def calculate_information_content(text):
|
|
||||||
"""计算文本的信息量（熵）"""
|
|
||||||
char_count = Counter(text)
|
|
||||||
total_chars = len(text)
|
|
||||||
|
|
||||||
entropy = 0
|
|
||||||
for count in char_count.values():
|
|
||||||
probability = count / total_chars
|
|
||||||
entropy -= probability * math.log2(probability)
|
|
||||||
|
|
||||||
return entropy
|
|
||||||
|
|
||||||
def get_closest_chat_from_db(length: int, timestamp: str):
|
|
||||||
"""从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
list: 消息记录字典列表，每个字典包含消息内容和时间信息
|
|
||||||
"""
|
|
||||||
chat_records = []
|
|
||||||
closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])
|
|
||||||
|
|
||||||
if closest_record and closest_record.get('memorized', 0) < 4:
|
|
||||||
closest_time = closest_record['time']
|
|
||||||
group_id = closest_record['group_id']
|
|
||||||
# 获取该时间戳之后的length条消息，且groupid相同
|
|
||||||
records = list(db.messages.find(
|
|
||||||
{"time": {"$gt": closest_time}, "group_id": group_id}
|
|
||||||
).sort('time', 1).limit(length))
|
|
||||||
|
|
||||||
# 更新每条消息的memorized属性
|
|
||||||
for record in records:
|
|
||||||
current_memorized = record.get('memorized', 0)
|
|
||||||
if current_memorized > 3:
|
|
||||||
print("消息已读取3次，跳过")
|
|
||||||
return ''
|
|
||||||
|
|
||||||
# 更新memorized值
|
|
||||||
db.messages.update_one(
|
|
||||||
{"_id": record["_id"]},
|
|
||||||
{"$set": {"memorized": current_memorized + 1}}
|
|
||||||
)
|
|
||||||
|
|
||||||
# 添加到记录列表中
|
|
||||||
chat_records.append({
|
|
||||||
'text': record["detailed_plain_text"],
|
|
||||||
'time': record["time"],
|
|
||||||
'group_id': record["group_id"]
|
|
||||||
})
|
|
||||||
|
|
||||||
return chat_records
|
|
||||||
|
|
||||||
class Memory_graph:
|
|
||||||
def __init__(self):
|
|
||||||
self.G = nx.Graph() # 使用 networkx 的图结构
|
|
||||||
|
|
||||||
def connect_dot(self, concept1, concept2):
|
|
||||||
# 如果边已存在，增加 strength
|
|
||||||
if self.G.has_edge(concept1, concept2):
|
|
||||||
self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1
|
|
||||||
else:
|
|
||||||
# 如果是新边，初始化 strength 为 1
|
|
||||||
self.G.add_edge(concept1, concept2, strength=1)
|
|
||||||
|
|
||||||
def add_dot(self, concept, memory):
|
|
||||||
if concept in self.G:
|
|
||||||
# 如果节点已存在，将新记忆添加到现有列表中
|
|
||||||
if 'memory_items' in self.G.nodes[concept]:
|
|
||||||
if not isinstance(self.G.nodes[concept]['memory_items'], list):
|
|
||||||
# 如果当前不是列表，将其转换为列表
|
|
||||||
self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']]
|
|
||||||
self.G.nodes[concept]['memory_items'].append(memory)
|
|
||||||
else:
|
|
||||||
self.G.nodes[concept]['memory_items'] = [memory]
|
|
||||||
else:
|
|
||||||
# 如果是新节点，创建新的记忆列表
|
|
||||||
self.G.add_node(concept, memory_items=[memory])
|
|
||||||
|
|
||||||
def get_dot(self, concept):
|
|
||||||
# 检查节点是否存在于图中
|
|
||||||
if concept in self.G:
|
|
||||||
# 从图中获取节点数据
|
|
||||||
node_data = self.G.nodes[concept]
|
|
||||||
return concept, node_data
|
|
||||||
return None
|
|
||||||
|
|
||||||
def get_related_item(self, topic, depth=1):
|
|
||||||
if topic not in self.G:
|
|
||||||
return [], []
|
|
||||||
|
|
||||||
first_layer_items = []
|
|
||||||
second_layer_items = []
|
|
||||||
|
|
||||||
# 获取相邻节点
|
|
||||||
neighbors = list(self.G.neighbors(topic))
|
|
||||||
|
|
||||||
# 获取当前节点的记忆项
|
|
||||||
node_data = self.get_dot(topic)
|
|
||||||
if node_data:
|
|
||||||
concept, data = node_data
|
|
||||||
if 'memory_items' in data:
|
|
||||||
memory_items = data['memory_items']
|
|
||||||
if isinstance(memory_items, list):
|
|
||||||
first_layer_items.extend(memory_items)
|
|
||||||
else:
|
|
||||||
first_layer_items.append(memory_items)
|
|
||||||
|
|
||||||
# 只在depth=2时获取第二层记忆
|
|
||||||
if depth >= 2:
|
|
||||||
# 获取相邻节点的记忆项
|
|
||||||
for neighbor in neighbors:
|
|
||||||
node_data = self.get_dot(neighbor)
|
|
||||||
if node_data:
|
|
||||||
concept, data = node_data
|
|
||||||
if 'memory_items' in data:
|
|
||||||
memory_items = data['memory_items']
|
|
||||||
if isinstance(memory_items, list):
|
|
||||||
second_layer_items.extend(memory_items)
|
|
||||||
else:
|
|
||||||
second_layer_items.append(memory_items)
|
|
||||||
|
|
||||||
return first_layer_items, second_layer_items
|
|
||||||
|
|
||||||
@property
|
|
||||||
def dots(self):
|
|
||||||
# 返回所有节点对应的 Memory_dot 对象
|
|
||||||
return [self.get_dot(node) for node in self.G.nodes()]
|
|
||||||
|
|
||||||
# 海马体
|
|
||||||
class Hippocampus:
|
|
||||||
def __init__(self, memory_graph: Memory_graph):
|
|
||||||
self.memory_graph = memory_graph
|
|
||||||
self.llm_model = LLMModel()
|
|
||||||
self.llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5")
|
|
||||||
self.llm_model_get_topic = LLMModel(model_name="Pro/Qwen/Qwen2.5-7B-Instruct")
|
|
||||||
self.llm_model_summary = LLMModel(model_name="Qwen/Qwen2.5-32B-Instruct")
|
|
||||||
|
|
||||||
def get_memory_sample(self, chat_size=20, time_frequency:dict={'near':2,'mid':4,'far':3}):
|
|
||||||
"""获取记忆样本
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
list: 消息记录列表，每个元素是一个消息记录字典列表
|
|
||||||
"""
|
|
||||||
current_timestamp = datetime.datetime.now().timestamp()
|
|
||||||
chat_samples = []
|
|
||||||
|
|
||||||
# 短期：1h 中期：4h 长期：24h
|
|
||||||
for _ in range(time_frequency.get('near')):
|
|
||||||
random_time = current_timestamp - random.randint(1, 3600*4)
|
|
||||||
messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
|
|
||||||
if messages:
|
|
||||||
chat_samples.append(messages)
|
|
||||||
|
|
||||||
for _ in range(time_frequency.get('mid')):
|
|
||||||
random_time = current_timestamp - random.randint(3600*4, 3600*24)
|
|
||||||
messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
|
|
||||||
if messages:
|
|
||||||
chat_samples.append(messages)
|
|
||||||
|
|
||||||
for _ in range(time_frequency.get('far')):
|
|
||||||
random_time = current_timestamp - random.randint(3600*24, 3600*24*7)
|
|
||||||
messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
|
|
||||||
if messages:
|
|
||||||
chat_samples.append(messages)
|
|
||||||
|
|
||||||
return chat_samples
|
|
||||||
|
|
||||||
def calculate_topic_num(self,text, compress_rate):
|
|
||||||
"""计算文本的话题数量"""
|
|
||||||
information_content = calculate_information_content(text)
|
|
||||||
topic_by_length = text.count('\n')*compress_rate
|
|
||||||
topic_by_information_content = max(1, min(5, int((information_content-3) * 2)))
|
|
||||||
topic_num = int((topic_by_length + topic_by_information_content)/2)
|
|
||||||
print(f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, topic_num: {topic_num}")
|
|
||||||
return topic_num
|
|
||||||
|
|
||||||
async def memory_compress(self, messages: list, compress_rate=0.1):
|
|
||||||
"""压缩消息记录为记忆
|
|
||||||
|
|
||||||
Args:
|
|
||||||
messages: 消息记录字典列表，每个字典包含text和time字段
|
|
||||||
compress_rate: 压缩率
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
set: (话题, 记忆) 元组集合
|
|
||||||
"""
|
|
||||||
if not messages:
|
|
||||||
return set()
|
|
||||||
|
|
||||||
# 合并消息文本，同时保留时间信息
|
|
||||||
input_text = ""
|
|
||||||
time_info = ""
|
|
||||||
# 计算最早和最晚时间
|
|
||||||
earliest_time = min(msg['time'] for msg in messages)
|
|
||||||
latest_time = max(msg['time'] for msg in messages)
|
|
||||||
|
|
||||||
earliest_dt = datetime.datetime.fromtimestamp(earliest_time)
|
|
||||||
latest_dt = datetime.datetime.fromtimestamp(latest_time)
|
|
||||||
|
|
||||||
# 如果是同一年
|
|
||||||
if earliest_dt.year == latest_dt.year:
|
|
||||||
earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S")
|
|
||||||
latest_str = latest_dt.strftime("%m-%d %H:%M:%S")
|
|
||||||
time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n"
|
|
||||||
else:
|
|
||||||
earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S")
|
|
||||||
latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S")
|
|
||||||
time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n"
|
|
||||||
|
|
||||||
for msg in messages:
|
|
||||||
input_text += f"{msg['text']}\n"
|
|
||||||
|
|
||||||
print(input_text)
|
|
||||||
|
|
||||||
topic_num = self.calculate_topic_num(input_text, compress_rate)
|
|
||||||
topics_response = self.llm_model_get_topic.generate_response(self.find_topic_llm(input_text, topic_num))
|
|
||||||
|
|
||||||
# 过滤topics
|
|
||||||
filter_keywords = ['表情包', '图片', '回复', '聊天记录']
|
|
||||||
topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
|
|
||||||
filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)]
|
|
||||||
|
|
||||||
# print(f"原始话题: {topics}")
|
|
||||||
print(f"过滤后话题: {filtered_topics}")
|
|
||||||
|
|
||||||
# 创建所有话题的请求任务
|
|
||||||
tasks = []
|
|
||||||
for topic in filtered_topics:
|
|
||||||
topic_what_prompt = self.topic_what(input_text, topic , time_info)
|
|
||||||
# 创建异步任务
|
|
||||||
task = self.llm_model_small.generate_response_async(topic_what_prompt)
|
|
||||||
tasks.append((topic.strip(), task))
|
|
||||||
|
|
||||||
# 等待所有任务完成
|
|
||||||
compressed_memory = set()
|
|
||||||
for topic, task in tasks:
|
|
||||||
response = await task
|
|
||||||
if response:
|
|
||||||
compressed_memory.add((topic, response[0]))
|
|
||||||
|
|
||||||
return compressed_memory
|
|
||||||
|
|
||||||
async def operation_build_memory(self, chat_size=12):
|
|
||||||
# 最近消息获取频率
|
|
||||||
time_frequency = {'near': 3, 'mid': 8, 'far': 5}
|
|
||||||
memory_samples = self.get_memory_sample(chat_size, time_frequency)
|
|
||||||
|
|
||||||
all_topics = [] # 用于存储所有话题
|
|
||||||
|
|
||||||
for i, messages in enumerate(memory_samples, 1):
|
|
||||||
# 加载进度可视化
|
|
||||||
all_topics = []
|
|
||||||
progress = (i / len(memory_samples)) * 100
|
|
||||||
bar_length = 30
|
|
||||||
filled_length = int(bar_length * i // len(memory_samples))
|
|
||||||
bar = '█' * filled_length + '-' * (bar_length - filled_length)
|
|
||||||
print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
|
|
||||||
|
|
||||||
# 生成压缩后记忆
|
|
||||||
compress_rate = 0.1
|
|
||||||
compressed_memory = await self.memory_compress(messages, compress_rate)
|
|
||||||
print(f"\033[1;33m压缩后记忆数量\033[0m: {len(compressed_memory)}")
|
|
||||||
|
|
||||||
# 将记忆加入到图谱中
|
|
||||||
for topic, memory in compressed_memory:
|
|
||||||
print(f"\033[1;32m添加节点\033[0m: {topic}")
|
|
||||||
self.memory_graph.add_dot(topic, memory)
|
|
||||||
all_topics.append(topic)
|
|
||||||
|
|
||||||
# 连接相关话题
|
|
||||||
for i in range(len(all_topics)):
|
|
||||||
for j in range(i + 1, len(all_topics)):
|
|
||||||
print(f"\033[1;32m连接节点\033[0m: {all_topics[i]} 和 {all_topics[j]}")
|
|
||||||
self.memory_graph.connect_dot(all_topics[i], all_topics[j])
|
|
||||||
|
|
||||||
self.sync_memory_to_db()
|
|
||||||
|
|
||||||
def sync_memory_from_db(self):
|
|
||||||
"""
|
|
||||||
从数据库同步数据到内存中的图结构
|
|
||||||
将清空当前内存中的图，并从数据库重新加载所有节点和边
|
|
||||||
"""
|
|
||||||
# 清空当前图
|
|
||||||
self.memory_graph.G.clear()
|
|
||||||
|
|
||||||
# 从数据库加载所有节点
|
|
||||||
nodes = db.graph_data.nodes.find()
|
|
||||||
for node in nodes:
|
|
||||||
concept = node['concept']
|
|
||||||
memory_items = node.get('memory_items', [])
|
|
||||||
# 确保memory_items是列表
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
# 添加节点到图中
|
|
||||||
self.memory_graph.G.add_node(concept, memory_items=memory_items)
|
|
||||||
|
|
||||||
# 从数据库加载所有边
|
|
||||||
edges = db.graph_data.edges.find()
|
|
||||||
for edge in edges:
|
|
||||||
source = edge['source']
|
|
||||||
target = edge['target']
|
|
||||||
strength = edge.get('strength', 1) # 获取 strength，默认为 1
|
|
||||||
# 只有当源节点和目标节点都存在时才添加边
|
|
||||||
if source in self.memory_graph.G and target in self.memory_graph.G:
|
|
||||||
self.memory_graph.G.add_edge(source, target, strength=strength)
|
|
||||||
|
|
||||||
logger.success("从数据库同步记忆图谱完成")
|
|
||||||
|
|
||||||
def calculate_node_hash(self, concept, memory_items):
|
|
||||||
"""
|
|
||||||
计算节点的特征值
|
|
||||||
"""
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
# 将记忆项排序以确保相同内容生成相同的哈希值
|
|
||||||
sorted_items = sorted(memory_items)
|
|
||||||
# 组合概念和记忆项生成特征值
|
|
||||||
content = f"{concept}:{'|'.join(sorted_items)}"
|
|
||||||
return hash(content)
|
|
||||||
|
|
||||||
def calculate_edge_hash(self, source, target):
|
|
||||||
"""
|
|
||||||
计算边的特征值
|
|
||||||
"""
|
|
||||||
# 对源节点和目标节点排序以确保相同的边生成相同的哈希值
|
|
||||||
nodes = sorted([source, target])
|
|
||||||
return hash(f"{nodes[0]}:{nodes[1]}")
|
|
||||||
|
|
||||||
def sync_memory_to_db(self):
|
|
||||||
"""
|
|
||||||
检查并同步内存中的图结构与数据库
|
|
||||||
使用特征值(哈希值)快速判断是否需要更新
|
|
||||||
"""
|
|
||||||
# 获取数据库中所有节点和内存中所有节点
|
|
||||||
db_nodes = list(db.graph_data.nodes.find())
|
|
||||||
memory_nodes = list(self.memory_graph.G.nodes(data=True))
|
|
||||||
|
|
||||||
# 转换数据库节点为字典格式，方便查找
|
|
||||||
db_nodes_dict = {node['concept']: node for node in db_nodes}
|
|
||||||
|
|
||||||
# 检查并更新节点
|
|
||||||
for concept, data in memory_nodes:
|
|
||||||
memory_items = data.get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
|
|
||||||
# 计算内存中节点的特征值
|
|
||||||
memory_hash = self.calculate_node_hash(concept, memory_items)
|
|
||||||
|
|
||||||
if concept not in db_nodes_dict:
|
|
||||||
# 数据库中缺少的节点，添加
|
|
||||||
# logger.info(f"添加新节点: {concept}")
|
|
||||||
node_data = {
|
|
||||||
'concept': concept,
|
|
||||||
'memory_items': memory_items,
|
|
||||||
'hash': memory_hash
|
|
||||||
}
|
|
||||||
db.graph_data.nodes.insert_one(node_data)
|
|
||||||
else:
|
|
||||||
# 获取数据库中节点的特征值
|
|
||||||
db_node = db_nodes_dict[concept]
|
|
||||||
db_hash = db_node.get('hash', None)
|
|
||||||
|
|
||||||
# 如果特征值不同，则更新节点
|
|
||||||
if db_hash != memory_hash:
|
|
||||||
# logger.info(f"更新节点内容: {concept}")
|
|
||||||
db.graph_data.nodes.update_one(
|
|
||||||
{'concept': concept},
|
|
||||||
{'$set': {
|
|
||||||
'memory_items': memory_items,
|
|
||||||
'hash': memory_hash
|
|
||||||
}}
|
|
||||||
)
|
|
||||||
|
|
||||||
# 检查并删除数据库中多余的节点
|
|
||||||
memory_concepts = set(node[0] for node in memory_nodes)
|
|
||||||
for db_node in db_nodes:
|
|
||||||
if db_node['concept'] not in memory_concepts:
|
|
||||||
# logger.info(f"删除多余节点: {db_node['concept']}")
|
|
||||||
db.graph_data.nodes.delete_one({'concept': db_node['concept']})
|
|
||||||
|
|
||||||
# 处理边的信息
|
|
||||||
db_edges = list(db.graph_data.edges.find())
|
|
||||||
memory_edges = list(self.memory_graph.G.edges())
|
|
||||||
|
|
||||||
# 创建边的哈希值字典
|
|
||||||
db_edge_dict = {}
|
|
||||||
for edge in db_edges:
|
|
||||||
edge_hash = self.calculate_edge_hash(edge['source'], edge['target'])
|
|
||||||
db_edge_dict[(edge['source'], edge['target'])] = {
|
|
||||||
'hash': edge_hash,
|
|
||||||
'num': edge.get('num', 1)
|
|
||||||
}
|
|
||||||
|
|
||||||
# 检查并更新边
|
|
||||||
for source, target in memory_edges:
|
|
||||||
edge_hash = self.calculate_edge_hash(source, target)
|
|
||||||
edge_key = (source, target)
|
|
||||||
|
|
||||||
if edge_key not in db_edge_dict:
|
|
||||||
# 添加新边
|
|
||||||
logger.info(f"添加新边: {source} - {target}")
|
|
||||||
edge_data = {
|
|
||||||
'source': source,
|
|
||||||
'target': target,
|
|
||||||
'num': 1,
|
|
||||||
'hash': edge_hash
|
|
||||||
}
|
|
||||||
db.graph_data.edges.insert_one(edge_data)
|
|
||||||
else:
|
|
||||||
# 检查边的特征值是否变化
|
|
||||||
if db_edge_dict[edge_key]['hash'] != edge_hash:
|
|
||||||
logger.info(f"更新边: {source} - {target}")
|
|
||||||
db.graph_data.edges.update_one(
|
|
||||||
{'source': source, 'target': target},
|
|
||||||
{'$set': {'hash': edge_hash}}
|
|
||||||
)
|
|
||||||
|
|
||||||
# 删除多余的边
|
|
||||||
memory_edge_set = set(memory_edges)
|
|
||||||
for edge_key in db_edge_dict:
|
|
||||||
if edge_key not in memory_edge_set:
|
|
||||||
source, target = edge_key
|
|
||||||
logger.info(f"删除多余边: {source} - {target}")
|
|
||||||
db.graph_data.edges.delete_one({
|
|
||||||
'source': source,
|
|
||||||
'target': target
|
|
||||||
})
|
|
||||||
|
|
||||||
logger.success("完成记忆图谱与数据库的差异同步")
|
|
||||||
|
|
||||||
def find_topic_llm(self,text, topic_num):
|
|
||||||
# prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个话题，帮我列出来，用逗号隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要告诉我其他内容。'
|
|
||||||
prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。'
|
|
||||||
return prompt
|
|
||||||
|
|
||||||
def topic_what(self,text, topic, time_info):
|
|
||||||
# prompt = f'这是一段文字：{text}。我想知道这段文字里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好'
|
|
||||||
# 获取当前时间
|
|
||||||
prompt = f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好'
|
|
||||||
return prompt
|
|
||||||
|
|
||||||
def remove_node_from_db(self, topic):
|
|
||||||
"""
|
|
||||||
从数据库中删除指定节点及其相关的边
|
|
||||||
|
|
||||||
Args:
|
|
||||||
topic: 要删除的节点概念
|
|
||||||
"""
|
|
||||||
# 删除节点
|
|
||||||
db.graph_data.nodes.delete_one({'concept': topic})
|
|
||||||
# 删除所有涉及该节点的边
|
|
||||||
db.graph_data.edges.delete_many({
|
|
||||||
'$or': [
|
|
||||||
{'source': topic},
|
|
||||||
{'target': topic}
|
|
||||||
]
|
|
||||||
})
|
|
||||||
|
|
||||||
def forget_topic(self, topic):
|
|
||||||
"""
|
|
||||||
随机删除指定话题中的一条记忆，如果话题没有记忆则移除该话题节点
|
|
||||||
只在内存中的图上操作，不直接与数据库交互
|
|
||||||
|
|
||||||
Args:
|
|
||||||
topic: 要删除记忆的话题
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
removed_item: 被删除的记忆项，如果没有删除任何记忆则返回 None
|
|
||||||
"""
|
|
||||||
if topic not in self.memory_graph.G:
|
|
||||||
return None
|
|
||||||
|
|
||||||
# 获取话题节点数据
|
|
||||||
node_data = self.memory_graph.G.nodes[topic]
|
|
||||||
|
|
||||||
# 如果节点存在memory_items
|
|
||||||
if 'memory_items' in node_data:
|
|
||||||
memory_items = node_data['memory_items']
|
|
||||||
|
|
||||||
# 确保memory_items是列表
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
|
|
||||||
# 如果有记忆项可以删除
|
|
||||||
if memory_items:
|
|
||||||
# 随机选择一个记忆项删除
|
|
||||||
removed_item = random.choice(memory_items)
|
|
||||||
memory_items.remove(removed_item)
|
|
||||||
|
|
||||||
# 更新节点的记忆项
|
|
||||||
if memory_items:
|
|
||||||
self.memory_graph.G.nodes[topic]['memory_items'] = memory_items
|
|
||||||
else:
|
|
||||||
# 如果没有记忆项了，删除整个节点
|
|
||||||
self.memory_graph.G.remove_node(topic)
|
|
||||||
|
|
||||||
return removed_item
|
|
||||||
|
|
||||||
return None
|
|
||||||
|
|
||||||
async def operation_forget_topic(self, percentage=0.1):
|
|
||||||
"""
|
|
||||||
随机选择图中一定比例的节点进行检查，根据条件决定是否遗忘
|
|
||||||
|
|
||||||
Args:
|
|
||||||
percentage: 要检查的节点比例，默认为0.1（10%）
|
|
||||||
"""
|
|
||||||
# 获取所有节点
|
|
||||||
all_nodes = list(self.memory_graph.G.nodes())
|
|
||||||
# 计算要检查的节点数量
|
|
||||||
check_count = max(1, int(len(all_nodes) * percentage))
|
|
||||||
# 随机选择节点
|
|
||||||
nodes_to_check = random.sample(all_nodes, check_count)
|
|
||||||
|
|
||||||
forgotten_nodes = []
|
|
||||||
for node in nodes_to_check:
|
|
||||||
# 获取节点的连接数
|
|
||||||
connections = self.memory_graph.G.degree(node)
|
|
||||||
|
|
||||||
# 获取节点的内容条数
|
|
||||||
memory_items = self.memory_graph.G.nodes[node].get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
content_count = len(memory_items)
|
|
||||||
|
|
||||||
# 检查连接强度
|
|
||||||
weak_connections = True
|
|
||||||
if connections > 1: # 只有当连接数大于1时才检查强度
|
|
||||||
for neighbor in self.memory_graph.G.neighbors(node):
|
|
||||||
strength = self.memory_graph.G[node][neighbor].get('strength', 1)
|
|
||||||
if strength > 2:
|
|
||||||
weak_connections = False
|
|
||||||
break
|
|
||||||
|
|
||||||
# 如果满足遗忘条件
|
|
||||||
if (connections <= 1 and weak_connections) or content_count <= 2:
|
|
||||||
removed_item = self.forget_topic(node)
|
|
||||||
if removed_item:
|
|
||||||
forgotten_nodes.append((node, removed_item))
|
|
||||||
logger.info(f"遗忘节点 {node} 的记忆: {removed_item}")
|
|
||||||
|
|
||||||
# 同步到数据库
|
|
||||||
if forgotten_nodes:
|
|
||||||
self.sync_memory_to_db()
|
|
||||||
logger.info(f"完成遗忘操作，共遗忘 {len(forgotten_nodes)} 个节点的记忆")
|
|
||||||
else:
|
|
||||||
logger.info("本次检查没有节点满足遗忘条件")
|
|
||||||
|
|
||||||
async def merge_memory(self, topic):
|
|
||||||
"""
|
|
||||||
对指定话题的记忆进行合并压缩
|
|
||||||
|
|
||||||
Args:
|
|
||||||
topic: 要合并的话题节点
|
|
||||||
"""
|
|
||||||
# 获取节点的记忆项
|
|
||||||
memory_items = self.memory_graph.G.nodes[topic].get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
|
|
||||||
# 如果记忆项不足，直接返回
|
|
||||||
if len(memory_items) < 10:
|
|
||||||
return
|
|
||||||
|
|
||||||
# 随机选择10条记忆
|
|
||||||
selected_memories = random.sample(memory_items, 10)
|
|
||||||
|
|
||||||
# 拼接成文本
|
|
||||||
merged_text = "\n".join(selected_memories)
|
|
||||||
print(f"\n[合并记忆] 话题: {topic}")
|
|
||||||
print(f"选择的记忆:\n{merged_text}")
|
|
||||||
|
|
||||||
# 使用memory_compress生成新的压缩记忆
|
|
||||||
compressed_memories = await self.memory_compress(selected_memories, 0.1)
|
|
||||||
|
|
||||||
# 从原记忆列表中移除被选中的记忆
|
|
||||||
for memory in selected_memories:
|
|
||||||
memory_items.remove(memory)
|
|
||||||
|
|
||||||
# 添加新的压缩记忆
|
|
||||||
for _, compressed_memory in compressed_memories:
|
|
||||||
memory_items.append(compressed_memory)
|
|
||||||
print(f"添加压缩记忆: {compressed_memory}")
|
|
||||||
|
|
||||||
# 更新节点的记忆项
|
|
||||||
self.memory_graph.G.nodes[topic]['memory_items'] = memory_items
|
|
||||||
print(f"完成记忆合并，当前记忆数量: {len(memory_items)}")
|
|
||||||
|
|
||||||
async def operation_merge_memory(self, percentage=0.1):
|
|
||||||
"""
|
|
||||||
随机检查一定比例的节点，对内容数量超过100的节点进行记忆合并
|
|
||||||
|
|
||||||
Args:
|
|
||||||
percentage: 要检查的节点比例，默认为0.1（10%）
|
|
||||||
"""
|
|
||||||
# 获取所有节点
|
|
||||||
all_nodes = list(self.memory_graph.G.nodes())
|
|
||||||
# 计算要检查的节点数量
|
|
||||||
check_count = max(1, int(len(all_nodes) * percentage))
|
|
||||||
# 随机选择节点
|
|
||||||
nodes_to_check = random.sample(all_nodes, check_count)
|
|
||||||
|
|
||||||
merged_nodes = []
|
|
||||||
for node in nodes_to_check:
|
|
||||||
# 获取节点的内容条数
|
|
||||||
memory_items = self.memory_graph.G.nodes[node].get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
content_count = len(memory_items)
|
|
||||||
|
|
||||||
# 如果内容数量超过100，进行合并
|
|
||||||
if content_count > 100:
|
|
||||||
print(f"\n检查节点: {node}, 当前记忆数量: {content_count}")
|
|
||||||
await self.merge_memory(node)
|
|
||||||
merged_nodes.append(node)
|
|
||||||
|
|
||||||
# 同步到数据库
|
|
||||||
if merged_nodes:
|
|
||||||
self.sync_memory_to_db()
|
|
||||||
print(f"\n完成记忆合并操作，共处理 {len(merged_nodes)} 个节点")
|
|
||||||
else:
|
|
||||||
print("\n本次检查没有需要合并的节点")
|
|
||||||
|
|
||||||
async def _identify_topics(self, text: str) -> list:
|
|
||||||
"""从文本中识别可能的主题"""
|
|
||||||
topics_response = self.llm_model_get_topic.generate_response(self.find_topic_llm(text, 5))
|
|
||||||
topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
|
|
||||||
return topics
|
|
||||||
|
|
||||||
def _find_similar_topics(self, topics: list, similarity_threshold: float = 0.4, debug_info: str = "") -> list:
|
|
||||||
"""查找与给定主题相似的记忆主题"""
|
|
||||||
all_memory_topics = list(self.memory_graph.G.nodes())
|
|
||||||
all_similar_topics = []
|
|
||||||
|
|
||||||
for topic in topics:
|
|
||||||
if debug_info:
|
|
||||||
pass
|
|
||||||
|
|
||||||
topic_vector = text_to_vector(topic)
|
|
||||||
has_similar_topic = False
|
|
||||||
|
|
||||||
for memory_topic in all_memory_topics:
|
|
||||||
memory_vector = text_to_vector(memory_topic)
|
|
||||||
all_words = set(topic_vector.keys()) | set(memory_vector.keys())
|
|
||||||
v1 = [topic_vector.get(word, 0) for word in all_words]
|
|
||||||
v2 = [memory_vector.get(word, 0) for word in all_words]
|
|
||||||
similarity = cosine_similarity(v1, v2)
|
|
||||||
|
|
||||||
if similarity >= similarity_threshold:
|
|
||||||
has_similar_topic = True
|
|
||||||
all_similar_topics.append((memory_topic, similarity))
|
|
||||||
|
|
||||||
return all_similar_topics
|
|
||||||
|
|
||||||
def _get_top_topics(self, similar_topics: list, max_topics: int = 5) -> list:
|
|
||||||
"""获取相似度最高的主题"""
|
|
||||||
seen_topics = set()
|
|
||||||
top_topics = []
|
|
||||||
|
|
||||||
for topic, score in sorted(similar_topics, key=lambda x: x[1], reverse=True):
|
|
||||||
if topic not in seen_topics and len(top_topics) < max_topics:
|
|
||||||
seen_topics.add(topic)
|
|
||||||
top_topics.append((topic, score))
|
|
||||||
|
|
||||||
return top_topics
|
|
||||||
|
|
||||||
async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int:
|
|
||||||
"""计算输入文本对记忆的激活程度"""
|
|
||||||
logger.info(f"[记忆激活]识别主题: {await self._identify_topics(text)}")
|
|
||||||
|
|
||||||
identified_topics = await self._identify_topics(text)
|
|
||||||
if not identified_topics:
|
|
||||||
return 0
|
|
||||||
|
|
||||||
all_similar_topics = self._find_similar_topics(
|
|
||||||
identified_topics,
|
|
||||||
similarity_threshold=similarity_threshold,
|
|
||||||
debug_info="记忆激活"
|
|
||||||
)
|
|
||||||
|
|
||||||
if not all_similar_topics:
|
|
||||||
return 0
|
|
||||||
|
|
||||||
top_topics = self._get_top_topics(all_similar_topics, max_topics)
|
|
||||||
|
|
||||||
if len(top_topics) == 1:
|
|
||||||
topic, score = top_topics[0]
|
|
||||||
memory_items = self.memory_graph.G.nodes[topic].get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
content_count = len(memory_items)
|
|
||||||
penalty = 1.0 / (1 + math.log(content_count + 1))
|
|
||||||
|
|
||||||
activation = int(score * 50 * penalty)
|
|
||||||
print(f"\033[1;32m[记忆激活]\033[0m 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
|
|
||||||
return activation
|
|
||||||
|
|
||||||
matched_topics = set()
|
|
||||||
topic_similarities = {}
|
|
||||||
|
|
||||||
for memory_topic, similarity in top_topics:
|
|
||||||
memory_items = self.memory_graph.G.nodes[memory_topic].get('memory_items', [])
|
|
||||||
if not isinstance(memory_items, list):
|
|
||||||
memory_items = [memory_items] if memory_items else []
|
|
||||||
content_count = len(memory_items)
|
|
||||||
penalty = 1.0 / (1 + math.log(content_count + 1))
|
|
||||||
|
|
||||||
for input_topic in identified_topics:
|
|
||||||
topic_vector = text_to_vector(input_topic)
|
|
||||||
memory_vector = text_to_vector(memory_topic)
|
|
||||||
all_words = set(topic_vector.keys()) | set(memory_vector.keys())
|
|
||||||
v1 = [topic_vector.get(word, 0) for word in all_words]
|
|
||||||
v2 = [memory_vector.get(word, 0) for word in all_words]
|
|
||||||
sim = cosine_similarity(v1, v2)
|
|
||||||
if sim >= similarity_threshold:
|
|
||||||
matched_topics.add(input_topic)
|
|
||||||
adjusted_sim = sim * penalty
|
|
||||||
topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim)
|
|
||||||
print(f"\033[1;32m[记忆激活]\033[0m 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
|
|
||||||
|
|
||||||
topic_match = len(matched_topics) / len(identified_topics)
|
|
||||||
average_similarities = sum(topic_similarities.values()) / len(topic_similarities) if topic_similarities else 0
|
|
||||||
|
|
||||||
activation = int((topic_match + average_similarities) / 2 * 100)
|
|
||||||
print(f"\033[1;32m[记忆激活]\033[0m 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
|
|
||||||
|
|
||||||
return activation
|
|
||||||
|
|
||||||
async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4, max_memory_num: int = 5) -> list:
|
|
||||||
"""根据输入文本获取相关的记忆内容"""
|
|
||||||
identified_topics = await self._identify_topics(text)
|
|
||||||
|
|
||||||
all_similar_topics = self._find_similar_topics(
|
|
||||||
identified_topics,
|
|
||||||
similarity_threshold=similarity_threshold,
|
|
||||||
debug_info="记忆检索"
|
|
||||||
)
|
|
||||||
|
|
||||||
relevant_topics = self._get_top_topics(all_similar_topics, max_topics)
|
|
||||||
|
|
||||||
relevant_memories = []
|
|
||||||
for topic, score in relevant_topics:
|
|
||||||
first_layer, _ = self.memory_graph.get_related_item(topic, depth=1)
|
|
||||||
if first_layer:
|
|
||||||
if len(first_layer) > max_memory_num/2:
|
|
||||||
first_layer = random.sample(first_layer, max_memory_num//2)
|
|
||||||
for memory in first_layer:
|
|
||||||
relevant_memories.append({
|
|
||||||
'topic': topic,
|
|
||||||
'similarity': score,
|
|
||||||
'content': memory
|
|
||||||
})
|
|
||||||
|
|
||||||
relevant_memories.sort(key=lambda x: x['similarity'], reverse=True)
|
|
||||||
|
|
||||||
if len(relevant_memories) > max_memory_num:
|
|
||||||
relevant_memories = random.sample(relevant_memories, max_memory_num)
|
|
||||||
|
|
||||||
return relevant_memories
|
|
||||||
|
|
||||||
def segment_text(text):
|
|
||||||
"""使用jieba进行文本分词"""
|
|
||||||
seg_text = list(jieba.cut(text))
|
|
||||||
return seg_text
|
|
||||||
|
|
||||||
def text_to_vector(text):
|
|
||||||
"""将文本转换为词频向量"""
|
|
||||||
words = segment_text(text)
|
|
||||||
vector = {}
|
|
||||||
for word in words:
|
|
||||||
vector[word] = vector.get(word, 0) + 1
|
|
||||||
return vector
|
|
||||||
|
|
||||||
def cosine_similarity(v1, v2):
|
|
||||||
"""计算两个向量的余弦相似度"""
|
|
||||||
dot_product = sum(a * b for a, b in zip(v1, v2))
|
|
||||||
norm1 = math.sqrt(sum(a * a for a in v1))
|
|
||||||
norm2 = math.sqrt(sum(b * b for b in v2))
|
|
||||||
if norm1 == 0 or norm2 == 0:
|
|
||||||
return 0
|
|
||||||
return dot_product / (norm1 * norm2)
|
|
||||||
|
|
||||||
def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = False):
|
|
||||||
# 设置中文字体
|
|
||||||
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
|
|
||||||
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
|
|
||||||
|
|
||||||
G = memory_graph.G
|
|
||||||
|
|
||||||
# 创建一个新图用于可视化
|
|
||||||
H = G.copy()
|
|
||||||
|
|
||||||
# 过滤掉内容数量小于2的节点
|
|
||||||
nodes_to_remove = []
|
|
||||||
for node in H.nodes():
|
|
||||||
memory_items = H.nodes[node].get('memory_items', [])
|
|
||||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
|
||||||
if memory_count < 2:
|
|
||||||
nodes_to_remove.append(node)
|
|
||||||
|
|
||||||
H.remove_nodes_from(nodes_to_remove)
|
|
||||||
|
|
||||||
# 如果没有符合条件的节点，直接返回
|
|
||||||
if len(H.nodes()) == 0:
|
|
||||||
print("没有找到内容数量大于等于2的节点")
|
|
||||||
return
|
|
||||||
|
|
||||||
# 计算节点大小和颜色
|
|
||||||
node_colors = []
|
|
||||||
node_sizes = []
|
|
||||||
nodes = list(H.nodes())
|
|
||||||
|
|
||||||
# 获取最大记忆数用于归一化节点大小
|
|
||||||
max_memories = 1
|
|
||||||
for node in nodes:
|
|
||||||
memory_items = H.nodes[node].get('memory_items', [])
|
|
||||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
|
||||||
max_memories = max(max_memories, memory_count)
|
|
||||||
|
|
||||||
# 计算每个节点的大小和颜色
|
|
||||||
for node in nodes:
|
|
||||||
# 计算节点大小（基于记忆数量）
|
|
||||||
memory_items = H.nodes[node].get('memory_items', [])
|
|
||||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
|
||||||
# 使用指数函数使变化更明显
|
|
||||||
ratio = memory_count / max_memories
|
|
||||||
size = 400 + 2000 * (ratio ** 2) # 增大节点大小
|
|
||||||
node_sizes.append(size)
|
|
||||||
|
|
||||||
# 计算节点颜色（基于连接数）
|
|
||||||
degree = H.degree(node)
|
|
||||||
if degree >= 30:
|
|
||||||
node_colors.append((1.0, 0, 0)) # 亮红色 (#FF0000)
|
|
||||||
else:
|
|
||||||
# 将1-10映射到0-1的范围
|
|
||||||
color_ratio = (degree - 1) / 29.0 if degree > 1 else 0
|
|
||||||
# 使用蓝到红的渐变
|
|
||||||
red = min(0.9, color_ratio)
|
|
||||||
blue = max(0.0, 1.0 - color_ratio)
|
|
||||||
node_colors.append((red, 0, blue))
|
|
||||||
|
|
||||||
# 绘制图形
|
|
||||||
plt.figure(figsize=(16, 12)) # 减小图形尺寸
|
|
||||||
pos = nx.spring_layout(H,
|
|
||||||
k=1, # 调整节点间斥力
|
|
||||||
iterations=100, # 增加迭代次数
|
|
||||||
scale=1.5, # 减小布局尺寸
|
|
||||||
weight='strength') # 使用边的strength属性作为权重
|
|
||||||
|
|
||||||
nx.draw(H, pos,
|
|
||||||
with_labels=True,
|
|
||||||
node_color=node_colors,
|
|
||||||
node_size=node_sizes,
|
|
||||||
font_size=12, # 保持增大的字体大小
|
|
||||||
font_family='SimHei',
|
|
||||||
font_weight='bold',
|
|
||||||
edge_color='gray',
|
|
||||||
width=1.5) # 统一的边宽度
|
|
||||||
|
|
||||||
title = '记忆图谱可视化（仅显示内容≥2的节点）\n节点大小表示记忆数量\n节点颜色：蓝(弱连接)到红(强连接)渐变，边的透明度表示连接强度\n连接强度越大的节点距离越近'
|
|
||||||
plt.title(title, fontsize=16, fontfamily='SimHei')
|
|
||||||
plt.show()
|
|
||||||
|
|
||||||
async def main():
|
|
||||||
start_time = time.time()
|
|
||||||
|
|
||||||
test_pare = {'do_build_memory':False,'do_forget_topic':False,'do_visualize_graph':True,'do_query':False,'do_merge_memory':False}
|
|
||||||
|
|
||||||
# 创建记忆图
|
|
||||||
memory_graph = Memory_graph()
|
|
||||||
|
|
||||||
# 创建海马体
|
|
||||||
hippocampus = Hippocampus(memory_graph)
|
|
||||||
|
|
||||||
# 从数据库同步数据
|
|
||||||
hippocampus.sync_memory_from_db()
|
|
||||||
|
|
||||||
end_time = time.time()
|
|
||||||
logger.info(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m")
|
|
||||||
|
|
||||||
# 构建记忆
|
|
||||||
if test_pare['do_build_memory']:
|
|
||||||
logger.info("开始构建记忆...")
|
|
||||||
chat_size = 20
|
|
||||||
await hippocampus.operation_build_memory(chat_size=chat_size)
|
|
||||||
|
|
||||||
end_time = time.time()
|
|
||||||
logger.info(f"\033[32m[构建记忆耗时: {end_time - start_time:.2f} 秒,chat_size={chat_size},chat_count = 16]\033[0m")
|
|
||||||
|
|
||||||
if test_pare['do_forget_topic']:
|
|
||||||
logger.info("开始遗忘记忆...")
|
|
||||||
await hippocampus.operation_forget_topic(percentage=0.1)
|
|
||||||
|
|
||||||
end_time = time.time()
|
|
||||||
logger.info(f"\033[32m[遗忘记忆耗时: {end_time - start_time:.2f} 秒]\033[0m")
|
|
||||||
|
|
||||||
if test_pare['do_merge_memory']:
|
|
||||||
logger.info("开始合并记忆...")
|
|
||||||
await hippocampus.operation_merge_memory(percentage=0.1)
|
|
||||||
|
|
||||||
end_time = time.time()
|
|
||||||
logger.info(f"\033[32m[合并记忆耗时: {end_time - start_time:.2f} 秒]\033[0m")
|
|
||||||
|
|
||||||
if test_pare['do_visualize_graph']:
|
|
||||||
# 展示优化后的图形
|
|
||||||
logger.info("生成记忆图谱可视化...")
|
|
||||||
print("\n生成优化后的记忆图谱：")
|
|
||||||
visualize_graph_lite(memory_graph)
|
|
||||||
|
|
||||||
if test_pare['do_query']:
|
|
||||||
# 交互式查询
|
|
||||||
while True:
|
|
||||||
query = input("\n请输入新的查询概念（输入'退出'以结束）：")
|
|
||||||
if query.lower() == '退出':
|
|
||||||
break
|
|
||||||
|
|
||||||
items_list = memory_graph.get_related_item(query)
|
|
||||||
if items_list:
|
|
||||||
first_layer, second_layer = items_list
|
|
||||||
if first_layer:
|
|
||||||
print("\n直接相关的记忆：")
|
|
||||||
for item in first_layer:
|
|
||||||
print(f"- {item}")
|
|
||||||
if second_layer:
|
|
||||||
print("\n间接相关的记忆：")
|
|
||||||
for item in second_layer:
|
|
||||||
print(f"- {item}")
|
|
||||||
else:
|
|
||||||
print("未找到相关记忆。")
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
import asyncio
|
|
||||||
asyncio.run(main())
|
|
||||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user