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Merge pull request #756 from MaiM-with-u/dev

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Dev
This commit is contained in:
SengokuCola
2025-04-14 23:10:10 +08:00
committed by GitHub
parent 9959d36a97 42ccb3dccb
commit 4fdbc7755c
101 changed files with 7596 additions and 5083 deletions
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17
.github/workflows/ruff.yml vendored
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@@ -1,9 +1,26 @@
name: Ruff
on: [ push, pull_request ]
permissions:
contents: write
jobs:
ruff:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
ref: ${{ github.head_ref || github.ref_name }}
- uses: astral-sh/ruff-action@v3
- run: ruff check --fix
- run: ruff format
- name: Commit changes
if: success()
run: |
git config --local user.email "github-actions[bot]@users.noreply.github.com"
git config --local user.name "github-actions[bot]"
git add -A
git diff --quiet && git diff --staged --quiet || git commit -m "🤖 自动格式化代码 [skip ci]"
git push

6
.gitignore vendored
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@@ -20,6 +20,8 @@ message_queue_window.bat
message_queue_window.txt
queue_update.txt
memory_graph.gml
/src/do_tool/tool_can_use/auto_create_tool.py
/src/do_tool/tool_can_use/execute_python_code_tool.py
.env
.env.*
.cursor
@@ -28,6 +30,9 @@ config/bot_config.toml
config/bot_config.toml.bak
src/plugins/remote/client_uuid.json
run_none.bat
（测试版）麦麦生成人格.bat
（临时版）麦麦开始学习.bat
src/plugins/utils/statistic.py
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
@@ -237,3 +242,4 @@ logs
/config/*
run_none.bat
config/old/bot_config_20250405_212257.toml

20
CLAUDE.md Normal file
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@@ -0,0 +1,20 @@
# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Commands
- **Run Bot**: `python bot.py`
- **Lint**: `ruff check --fix .` or `ruff format .`
- **Run Tests**: `python -m unittest discover -v`
- **Run Single Test**: `python -m unittest src/plugins/message/test.py`
## Code Style
- **Formatting**: Line length 120 chars, use double quotes for strings
- **Imports**: Group standard library, external packages, then internal imports
- **Naming**: snake_case for functions/variables, PascalCase for classes
- **Error Handling**: Use try/except blocks with specific exceptions
- **Types**: Use type hints where possible
- **Docstrings**: Document classes and complex functions
- **Linting**: Follow ruff rules (E, F, B) with ignores E711, E501
When making changes, run `ruff check --fix .` to ensure code follows style guidelines. The codebase uses Ruff for linting and formatting.

165
README.md
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@@ -1,26 +1,68 @@
# 麦麦！MaiCore-MaiMBot (编辑中)
## 新版0.6.0部署前先阅读：https://docs.mai-mai.org/manual/usage/mmc_q_a
<br />
<div align="center">
![Python Version](https://img.shields.io/badge/Python-3.9+-blue)
![License](https://img.shields.io/github/license/SengokuCola/MaiMBot)
![Status](https://img.shields.io/badge/状态-开发中-yellow)
![Python Version](https://img.shields.io/badge/Python-3.10+-blue)
![License](https://img.shields.io/github/license/SengokuCola/MaiMBot?label=协议)
![Status](https://img.shields.io/badge/状态-开发中-yellow)
![Contributors](https://img.shields.io/github/contributors/MaiM-with-u/MaiBot.svg?style=flat&label=贡献者)
![forks](https://img.shields.io/github/forks/MaiM-with-u/MaiBot.svg?style=flat&label=分支数)
![stars](https://img.shields.io/github/stars/MaiM-with-u/MaiBot?style=flat&label=星标数)
![issues](https://img.shields.io/github/issues/MaiM-with-u/MaiBot)
</div>
<p align="center">
<a href="https://github.com/MaiM-with-u/MaiBot/">
<img src="depends-data/maimai.png" alt="Logo" width="200">
</a>
<br />
<a href="https://space.bilibili.com/1344099355">
画师：略nd
</a>
<h3 align="center">MaiBot(麦麦)</h3>
<p align="center">
一款专注于<strong> 群组聊天 </strong>的赛博网友
<br />
<a href="https://docs.mai-mai.org"><strong>探索本项目的文档 »</strong></a>
<br />
<br />
<!-- <a href="https://github.com/shaojintian/Best_README_template">查看Demo</a>
· -->
<a href="https://github.com/MaiM-with-u/MaiBot/issues">报告Bug</a>
·
<a href="https://github.com/MaiM-with-u/MaiBot/issues">提出新特性</a>
</p>
</p>
## 新版0.6.x部署前先阅读：https://docs.mai-mai.org/manual/usage/mmc_q_a
</div>
## 📝 项目简介
**🍔MaiCore是一个基于大语言模型的可交互智能体**
- LLM 提供对话能力
- 动态Prompt构建器
- 实时的思维系统
- MongoDB 提供数据持久化支持
- 可扩展，可支持多种平台和多种功能
**最新版本: v0.6.0** ([查看更新日志](changelogs/changelog.md))
- 💭 **智能对话系统**：基于LLM的自然语言交互
- 🤔 **实时思维系统**：模拟人类思考过程
- 💝 **情感表达系统**：丰富的表情包和情绪表达
- 🧠 **持久记忆系统**：基于MongoDB的长期记忆存储
- 🔄 **动态人格系统**：自适应的性格特征
<div align="center">
<a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
<img src="depends-data/video.png" width="200" alt="麦麦演示视频">
<br>
👆 点击观看麦麦演示视频 👆
</a>
</div>
### 📢 版本信息
**最新版本: v0.6.2** ([查看更新日志](changelogs/changelog.md))
> [!WARNING]
> 请阅读教程后更新！！！！！！！
> 请阅读教程后更新！！！！！！！
@@ -28,19 +70,12 @@
> 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
**分支介绍:**
- main 稳定版本
- dev 开发版（不知道什么意思就别下）
- classical 0.6.0以前的版本
**分支说明：**
- `main`: 稳定发布版本
- `dev`: 开发测试版本（不知道什么意思就别下）
- `classical`: 0.6.0之前的版本
<div align="center">
<a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
<img src="docs/pic/video.png" width="300" alt="麦麦演示视频">
<br>
👆 点击观看麦麦演示视频 👆
</a>
</div>
> [!WARNING]
> - 项目处于活跃开发阶段，代码可能随时更改
@@ -49,6 +84,12 @@
> - 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
### ⚠️ 重要提示
- 升级到v0.6.x版本前请务必阅读：[升级指南](https://docs.mai-mai.org/manual/usage/mmc_q_a)
- 本版本基于MaiCore重构，通过nonebot插件与QQ平台交互
- 项目处于活跃开发阶段，功能和API可能随时调整
### 💬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
- [五群](https://qm.qq.com/q/JxvHZnxyec) 1022489779
- [一群](https://qm.qq.com/q/VQ3XZrWgMs) 766798517 【已满】
@@ -67,60 +108,41 @@
- [📚 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
### 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/mmc_deploy.html) - 基于MaiCore的新版本部署方式（与旧版本不兼容）
- [🚀 最新版本部署教程](https://docs.mai-mai.org/manual/deployment/mmc_deploy_windows.html) - 基于MaiCore的新版本部署方式（与旧版本不兼容）
## 🎯 功能介绍
### 💬 聊天功能
- 提供思维流（心流）聊天和推理聊天两种对话逻辑
- 支持关键词检索主动发言：对消息的话题topic进行识别，如果检测到麦麦存储过的话题就会主动进行发言
- 支持bot名字呼唤发言：检测到"麦麦"会主动发言，可配置
- 支持多模型，多厂商自定义配置
- 动态的prompt构建器，更拟人
- 支持图片，转发消息，回复消息的识别
- 支持私聊功能，可使用PFC模式的有目的多轮对话（实验性）
| 模块 | 主要功能 | 特点 |
|------|---------|------|
| 💬 聊天系统 | • 心流/推理聊天<br>• 关键词主动发言<br>• 多模型支持<br>• 动态prompt构建<br>• 私聊功能(PFC) | 拟人化交互 |
| 🧠 心流系统 | • 实时思考生成<br>• 自动启停机制<br>• 日程系统联动<br>• 工具调用能力 | 智能化决策 |
| 🧠 记忆系统 | • 优化记忆抽取<br>• 海马体记忆机制<br>• 聊天记录概括 | 持久化记忆 |
| 😊 表情系统 | • 情绪匹配发送<br>• GIF支持<br>• 自动收集与审查 | 丰富表达 |
| 📅 日程系统 | • 动态日程生成<br>• 自定义想象力<br>• 思维流联动 | 智能规划 |
| 👥 关系系统 | • 关系管理优化<br>• 丰富接口支持<br>• 个性化交互 | 深度社交 |
| 📊 统计系统 | • 使用数据统计<br>• LLM调用记录<br>• 实时控制台显示 | 数据可视 |
| 🔧 系统功能 | • 优雅关闭机制<br>• 自动数据保存<br>• 异常处理完善 | 稳定可靠 |
| 🛠️ 工具系统 | • 知识获取工具<br>• 自动注册机制<br>• 多工具支持 | 扩展功能 |
### 🧠 思维流系统
- 思维流能够在回复前后进行思考，生成实时想法
- 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
## 📐 项目架构
### 🧠 记忆系统 2.0
- 优化记忆抽取策略和prompt结构
- 改进海马体记忆提取机制，提升自然度
- 对聊天记录进行概括存储，在需要时调用
```mermaid
graph TD
A[MaiCore] --> B[对话系统]
A --> C[心流系统]
A --> D[记忆系统]
A --> E[情感系统]
B --> F[多模型支持]
B --> G[动态Prompt]
C --> H[实时思考]
C --> I[日程联动]
D --> J[记忆存储]
D --> K[记忆检索]
E --> L[表情管理]
E --> M[情绪识别]
```
### 😊 表情包系统
- 支持根据发言内容发送对应情绪的表情包
- 支持识别和处理gif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
- 自动清理缓存图片
### 📅 日程系统
- 动态更新的日程生成
- 可自定义想象力程度
- 与聊天情况交互（思维流模式下）
### 👥 关系系统 2.0
- 优化关系管理系统，适用于新版本
- 提供更丰富的关系接口
- 针对每个用户创建"关系"，实现个性化回复
### 📊 统计系统
- 详细的使用数据统计
- LLM调用统计
- 在控制台显示统计信息
### 🔧 系统功能
- 支持优雅的shutdown机制
- 自动保存功能，定期保存聊天记录和关系数据
- 完善的异常处理机制
- 可自定义时区设置
- 优化的日志输出格式
- 配置自动更新功能
## 开发计划TODO：LIST
@@ -157,7 +179,6 @@ MaiCore是一个开源项目，我们非常欢迎你的参与。你的贡献，
## 致谢
- [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
### 贡献者

22
bot.py
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@@ -7,11 +7,16 @@ from pathlib import Path
import time
import platform
from dotenv import load_dotenv
from src.common.logger import get_module_logger
from src.common.logger import get_module_logger, LogConfig, CONFIRM_STYLE_CONFIG
from src.common.crash_logger import install_crash_handler
from src.main import MainSystem
logger = get_module_logger("main_bot")
confirm_logger_config = LogConfig(
console_format=CONFIRM_STYLE_CONFIG["console_format"],
file_format=CONFIRM_STYLE_CONFIG["file_format"],
)
confirm_logger = get_module_logger("confirm", config=confirm_logger_config)
# 获取没有加载env时的环境变量
env_mask = {key: os.getenv(key) for key in os.environ}
@@ -165,8 +170,8 @@ def check_eula():
# 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(
confirm_logger.critical("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")
confirm_logger.critical(
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:
@@ -175,14 +180,14 @@ def check_eula():
# 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}")
logger.info(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}")
logger.info(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')
confirm_logger.critical('请输入"同意"或"confirmed"以继续运行')
return
elif eula_confirmed and privacy_confirmed:
return
@@ -193,6 +198,9 @@ def raw_main():
if platform.system().lower() != "windows":
time.tzset()
# 安装崩溃日志处理器
install_crash_handler()
check_eula()
print("检查EULA和隐私条款完成")
easter_egg()

53
changelogs/changelog.md
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@@ -1,5 +1,58 @@
# Changelog
## [0.6.2] - 2025-4-14
### 摘要
- MaiBot 0.6.2 版本发布！
- 优化了心流的观察系统，优化提示词和表现，现在心流表现更好！
- 新增工具调用能力，可以更好地获取信息
- 本次更新主要围绕工具系统、心流系统、消息处理和代码优化展开，新增多个工具类，优化了心流系统的逻辑，改进了消息处理流程，并修复了多个问题。
### 🌟 核心功能增强
#### 工具系统
- 新增了知识获取工具系统，支持通过心流调用获取多种知识
- 新增了工具系统使用指南，详细说明工具结构、自动注册机制和添加步骤
- 新增了多个实用工具类，包括心情调整工具`ChangeMoodTool`、关系查询工具`RelationshipTool`、数值比较工具`CompareNumbersTool`、日程获取工具`GetCurrentTaskTool`、上下文压缩工具`CompressContextTool`和知识获取工具`GetKnowledgeTool`
- 更新了`ToolUser`类，支持自动获取已注册工具定义并调用`execute`方法
- 需要配置支持工具调用的模型才能使用完整功能
#### 心流系统
- 新增了上下文压缩缓存功能，可以有更持久的记忆
- 新增了心流系统的README.md文件，详细介绍了系统架构、主要功能和工作流程。
- 优化了心流系统的逻辑，包括子心流自动清理和合理配置更新间隔。
- 改进了心流观察系统，优化了提示词设计和系统表现，使心流运行更加稳定高效。
- 更新了`Heartflow`类的方法和属性，支持异步生成提示词并提升生成质量。
#### 消息处理
- 改进了消息处理流程，包括回复检查、消息生成和发送逻辑。
- 新增了`ReplyGenerator`类，用于根据观察信息和对话信息生成回复。
- 优化了消息队列管理系统，支持按时间顺序处理消息。
#### 现在可以启用更好的表情包发送系统
### 💻 系统架构优化
#### 部署支持
- 更新了Docker部署文档，优化了服务配置和挂载路径。
- 完善了Linux和Windows脚本支持。
### 🐛 问题修复
- 修复了消息处理器中的正则表达式匹配问题。
- 修复了图像处理中的帧大小和拼接问题。
- 修复了私聊时产生`reply`消息的bug。
- 修复了配置文件加载时的版本兼容性问题。
### 📚 文档更新
- 更新了`README.md`文件，包括Python版本要求和协议信息。
- 新增了工具系统和心流系统的详细文档。
- 优化了部署相关文档的完整性。
### 🔧 其他改进
- 新增了崩溃日志记录器，记录崩溃信息到日志文件。
- 优化了统计信息输出，在控制台显示详细统计信息。
- 改进了异常处理机制，提升系统稳定性。
- 现可配置部分模型的temp参数
## [0.6.0] - 2025-4-4
### 摘要

2
changelogs/changelog_config.md
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@@ -22,7 +22,7 @@
## [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 `response_splitter` 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

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22
docker-compose.yml
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@@ -1,17 +1,14 @@
services:
adapters:
container_name: maim-bot-adapters
image: maple127667/maimbot-adapter:latest
image: unclas/maimbot-adapter:latest
# image: infinitycat/maimbot-adapter:latest
environment:
- TZ=Asia/Shanghai
ports:
- "18002:18002"
# ports:
# - "8095:8095"
volumes:
- ./docker-config/adapters/config.py:/adapters/src/plugins/nonebot_plugin_maibot_adapters/config.py # 持久化adapters配置文件
- ./docker-config/adapters/.env:/adapters/.env # 持久化adapters配置文件
- ./data/qq:/app/.config/QQ # 持久化QQ本体并同步qq表情和图片到adapters
- ./data/MaiMBot:/adapters/data
- ./docker-config/adapters/config.toml:/adapters/config.toml
restart: always
depends_on:
- mongodb
@@ -25,8 +22,8 @@ services:
- TZ=Asia/Shanghai
# - EULA_AGREE=35362b6ea30f12891d46ef545122e84a # 同意EULA
# - PRIVACY_AGREE=2402af06e133d2d10d9c6c643fdc9333 # 同意EULA
ports:
- "8000:8000"
# ports:
# - "8000:8000"
volumes:
- ./docker-config/mmc/.env:/MaiMBot/.env # 持久化env配置文件
- ./docker-config/mmc:/MaiMBot/config # 持久化bot配置文件
@@ -42,8 +39,8 @@ services:
- TZ=Asia/Shanghai
# - MONGO_INITDB_ROOT_USERNAME=your_username # 此处配置mongo用户
# - MONGO_INITDB_ROOT_PASSWORD=your_password # 此处配置mongo密码
ports:
- "27017:27017"
# ports:
# - "27017:27017"
restart: always
volumes:
- mongodb:/data/db # 持久化mongodb数据
@@ -58,11 +55,10 @@ services:
- TZ=Asia/Shanghai
ports:
- "6099:6099"
- "8095:8095"
volumes:
- ./docker-config/napcat:/app/napcat/config # 持久化napcat配置文件
- ./data/qq:/app/.config/QQ # 持久化QQ本体并同步qq表情和图片到adapters
- ./data/MaiMBot:/adapters/data # NapCat 和 NoneBot 共享此卷，否则发送图片会有问题
- ./data/MaiMBot:/MaiMBot/data # NapCat 和 NoneBot 共享此卷，否则发送图片会有问题
container_name: maim-bot-napcat
restart: always
image: mlikiowa/napcat-docker:latest

BIN
requirements.txt
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Binary file not shown.

75
scripts/run.sh
View File

@@ -1,10 +1,10 @@
#!/bin/bash
# MaiCore & Nonebot adapter一键安装脚本 by Cookie_987
# MaiCore & NapCat Adapter一键安装脚本 by Cookie_987
# 适用于Arch/Ubuntu 24.10/Debian 12/CentOS 9
# 请小心使用任何一键脚本！
INSTALLER_VERSION="0.0.2-refactor"
INSTALLER_VERSION="0.0.3-refactor"
LANG=C.UTF-8
# 如无法访问GitHub请修改此处镜像地址
@@ -31,7 +31,7 @@ DEFAULT_INSTALL_DIR="/opt/maicore"
# 服务名称
SERVICE_NAME="maicore"
SERVICE_NAME_WEB="maicore-web"
SERVICE_NAME_NBADAPTER="maicore-nonebot-adapter"
SERVICE_NAME_NBADAPTER="maibot-napcat-adapter"
IS_INSTALL_MONGODB=false
IS_INSTALL_NAPCAT=false
@@ -59,9 +59,9 @@ show_menu() {
"1" "启动MaiCore" \
"2" "停止MaiCore" \
"3" "重启MaiCore" \
"4" "启动Nonebot adapter" \
"5" "停止Nonebot adapter" \
"6" "重启Nonebot adapter" \
"4" "启动NapCat Adapter" \
"5" "停止NapCat Adapter" \
"6" "重启NapCat Adapter" \
"7" "拉取最新MaiCore仓库" \
"8" "切换分支" \
"9" "退出" 3>&1 1>&2 2>&3)
@@ -83,15 +83,15 @@ show_menu() {
;;
4)
systemctl start ${SERVICE_NAME_NBADAPTER}
whiptail --msgbox "✅Nonebot adapter已启动" 10 60
whiptail --msgbox "✅NapCat Adapter已启动" 10 60
;;
5)
systemctl stop ${SERVICE_NAME_NBADAPTER}
whiptail --msgbox "🛑Nonebot adapter已停止" 10 60
whiptail --msgbox "🛑NapCat Adapter已停止" 10 60
;;
6)
systemctl restart ${SERVICE_NAME_NBADAPTER}
whiptail --msgbox "🔄Nonebot adapter已重启" 10 60
whiptail --msgbox "🔄NapCat Adapter已重启" 10 60
;;
7)
update_dependencies
@@ -357,8 +357,8 @@ run_installation() {
# Python版本检查
check_python() {
PYTHON_VERSION=$(python3 -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")')
if ! python3 -c "import sys; exit(0) if sys.version_info >= (3,9) else exit(1)"; 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
if ! python3 -c "import sys; exit(0) if sys.version_info >= (3,10) else exit(1)"; then
whiptail --title "⚠️ [4/6] Python 版本过低" --msgbox "检测到 Python 版本为 $PYTHON_VERSION，需要 3.10 或以上！\n请升级 Python 后重新运行本脚本。" 10 60
exit 1
fi
}
@@ -410,7 +410,7 @@ run_installation() {
# 确认安装
confirm_install() {
local confirm_msg="请确认以下更改：\n\n"
confirm_msg+="📂 安装MaiCore、Nonebot Adapter到: $INSTALL_DIR\n"
confirm_msg+="📂 安装MaiCore、NapCat Adapter到: $INSTALL_DIR\n"
confirm_msg+="🔀 分支: $BRANCH\n"
[[ $IS_INSTALL_DEPENDENCIES == true ]] && confirm_msg+="📦 安装依赖：${missing_packages[@]}\n"
[[ $IS_INSTALL_MONGODB == true || $IS_INSTALL_NAPCAT == true ]] && confirm_msg+="📦 安装额外组件：\n"
@@ -499,50 +499,28 @@ EOF
}
echo -e "${GREEN}克隆 nonebot-plugin-maibot-adapters 仓库...${RESET}"
git clone $GITHUB_REPO/MaiM-with-u/nonebot-plugin-maibot-adapters.git || {
echo -e "${RED}克隆 nonebot-plugin-maibot-adapters 仓库失败！${RESET}"
git clone $GITHUB_REPO/MaiM-with-u/MaiBot-Napcat-Adapter.git || {
echo -e "${RED}克隆 MaiBot-Napcat-Adapter.git 仓库失败！${RESET}"
exit 1
}
echo -e "${GREEN}安装Python依赖...${RESET}"
pip install -r MaiBot/requirements.txt
pip install nb-cli
pip install nonebot-adapter-onebot
pip install 'nonebot2[fastapi]'
cd MaiBot
pip install uv
uv pip install -i https://mirrors.aliyun.com/pypi/simple -r requirements.txt
cd ..
echo -e "${GREEN}安装maim_message依赖...${RESET}"
cd maim_message
pip install -e .
uv pip install -i https://mirrors.aliyun.com/pypi/simple -e .
cd ..
echo -e "${GREEN}部署Nonebot adapter...${RESET}"
cd MaiBot
mkdir nonebot-maibot-adapter
cd nonebot-maibot-adapter
cat > pyproject.toml <<EOF
[project]
name = "nonebot-maibot-adapter"
version = "0.1.0"
description = "nonebot-maibot-adapter"
readme = "README.md"
requires-python = ">=3.9, <4.0"
[tool.nonebot]
adapters = [
{ name = "OneBot V11", module_name = "nonebot.adapters.onebot.v11" }
]
plugins = []
plugin_dirs = ["src/plugins"]
builtin_plugins = []
EOF
echo "Manually created by run.sh" > README.md
mkdir src
cp -r ../../nonebot-plugin-maibot-adapters/nonebot_plugin_maibot_adapters src/plugins/nonebot_plugin_maibot_adapters
echo -e "${GREEN}部署MaiBot Napcat Adapter...${RESET}"
cd MaiBot-Napcat-Adapter
uv pip install -i https://mirrors.aliyun.com/pypi/simple -r requirements.txt
cd ..
cd ..
echo -e "${GREEN}同意协议...${RESET}"
@@ -590,13 +568,13 @@ EOF
cat > /etc/systemd/system/${SERVICE_NAME_NBADAPTER}.service <<EOF
[Unit]
Description=Maicore Nonebot adapter
After=network.target mongod.service
Description=MaiBot Napcat Adapter
After=network.target mongod.service ${SERVICE_NAME}.service
[Service]
Type=simple
WorkingDirectory=${INSTALL_DIR}/MaiBot/nonebot-maibot-adapter
ExecStart=/bin/bash -c "source $INSTALL_DIR/venv/bin/activate && nb run --reload"
WorkingDirectory=${INSTALL_DIR}/MaiBot-Napcat-Adapter
ExecStart=$INSTALL_DIR/venv/bin/python3 main.py
Restart=always
RestartSec=10s
@@ -605,7 +583,6 @@ WantedBy=multi-user.target
EOF
systemctl daemon-reload
systemctl enable ${SERVICE_NAME}
# 保存安装信息
echo "INSTALLER_VERSION=${INSTALLER_VERSION}" > /etc/maicore_install.conf

69
src/common/crash_logger.py Normal file
View File

@@ -0,0 +1,69 @@
import sys
import traceback
import logging
from pathlib import Path
from logging.handlers import RotatingFileHandler
def setup_crash_logger():
"""设置崩溃日志记录器"""
# 创建logs/crash目录（如果不存在）
crash_log_dir = Path("logs/crash")
crash_log_dir.mkdir(parents=True, exist_ok=True)
# 创建日志记录器
crash_logger = logging.getLogger("crash_logger")
crash_logger.setLevel(logging.ERROR)
# 设置日志格式
formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s\n异常类型: %(exc_info)s\n详细信息:\n%(message)s\n-------------------\n"
)
# 创建按大小轮转的文件处理器（最大10MB，保留5个备份）
log_file = crash_log_dir / "crash.log"
file_handler = RotatingFileHandler(
log_file,
maxBytes=10 * 1024 * 1024, # 10MB
backupCount=5,
encoding="utf-8",
)
file_handler.setFormatter(formatter)
crash_logger.addHandler(file_handler)
return crash_logger
def log_crash(exc_type, exc_value, exc_traceback):
"""记录崩溃信息到日志文件"""
if exc_type is None:
return
# 获取崩溃日志记录器
crash_logger = logging.getLogger("crash_logger")
# 获取完整的异常堆栈信息
stack_trace = "".join(traceback.format_exception(exc_type, exc_value, exc_traceback))
# 记录崩溃信息
crash_logger.error(stack_trace, exc_info=(exc_type, exc_value, exc_traceback))
def install_crash_handler():
"""安装全局异常处理器"""
# 设置崩溃日志记录器
setup_crash_logger()
# 保存原始的异常处理器
original_hook = sys.excepthook
def exception_handler(exc_type, exc_value, exc_traceback):
"""全局异常处理器"""
# 记录崩溃信息
log_crash(exc_type, exc_value, exc_traceback)
# 调用原始的异常处理器
original_hook(exc_type, exc_value, exc_traceback)
# 设置全局异常处理器
sys.excepthook = exception_handler

29
src/common/logger.py
View File

@@ -102,10 +102,28 @@ MOOD_STYLE_CONFIG = {
"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}"),
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <magenta>心情</magenta> | {message}"),
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 心情 | {message}"),
},
}
# tool use
TOOL_USE_STYLE_CONFIG = {
"advanced": {
"console_format": (
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
"<level>{level: <8}</level> | "
"<cyan>{extra[module]: <12}</cyan> | "
"<magenta>工具使用</magenta> | "
"<level>{message}</level>"
),
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 工具使用 | {message}"),
},
"simple": {
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <magenta>工具使用</magenta> | {message}"),
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 工具使用 | {message}"),
},
}
# relationship
RELATION_STYLE_CONFIG = {
@@ -283,13 +301,15 @@ WILLING_STYLE_CONFIG = {
"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
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-blue>意愿</light-blue> | {message}"), # noqa: E501
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 意愿 | {message}"),
},
}
CONFIRM_STYLE_CONFIG = {
"console_format": ("<RED>{message}</RED>"), # noqa: E501
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | EULA与PRIVACY确认 | {message}"),
}
# 根据SIMPLE_OUTPUT选择配置
MEMORY_STYLE_CONFIG = MEMORY_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MEMORY_STYLE_CONFIG["advanced"]
@@ -306,6 +326,7 @@ SUB_HEARTFLOW_STYLE_CONFIG = (
) # 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"]
TOOL_USE_STYLE_CONFIG = TOOL_USE_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else TOOL_USE_STYLE_CONFIG["advanced"]
def is_registered_module(record: dict) -> bool:

73
src/common/server.py Normal file
View File

@@ -0,0 +1,73 @@
from fastapi import FastAPI, APIRouter
from typing import Optional
from uvicorn import Config, Server as UvicornServer
import os
class Server:
def __init__(self, host: Optional[str] = None, port: Optional[int] = None, app_name: str = "MaiMCore"):
self.app = FastAPI(title=app_name)
self._host: str = "127.0.0.1"
self._port: int = 8080
self._server: Optional[UvicornServer] = None
self.set_address(host, port)
def register_router(self, router: APIRouter, prefix: str = ""):
"""注册路由
APIRouter 用于对相关的路由端点进行分组和模块化管理：
1. 可以将相关的端点组织在一起，便于管理
2. 支持添加统一的路由前缀
3. 可以为一组路由添加共同的依赖项、标签等
示例:
router = APIRouter()
@router.get("/users")
def get_users():
return {"users": [...]}
@router.post("/users")
def create_user():
return {"msg": "user created"}
# 注册路由，添加前缀 "/api/v1"
server.register_router(router, prefix="/api/v1")
"""
self.app.include_router(router, prefix=prefix)
def set_address(self, host: Optional[str] = None, port: Optional[int] = None):
"""设置服务器地址和端口"""
if host:
self._host = host
if port:
self._port = port
async def run(self):
"""启动服务器"""
config = Config(app=self.app, host=self._host, port=self._port)
self._server = UvicornServer(config=config)
try:
await self._server.serve()
except KeyboardInterrupt:
await self.shutdown()
raise
except Exception as e:
await self.shutdown()
raise RuntimeError(f"服务器运行错误: {str(e)}") from e
finally:
await self.shutdown()
async def shutdown(self):
"""安全关闭服务器"""
if self._server:
self._server.should_exit = True
await self._server.shutdown()
self._server = None
def get_app(self) -> FastAPI:
"""获取 FastAPI 实例"""
return self.app
global_server = Server(host=os.environ["HOST"], port=int(os.environ["PORT"]))

102
src/do_tool/tool_can_use/README.md Normal file
View File

@@ -0,0 +1,102 @@
# 工具系统使用指南
## 概述
`tool_can_use` 是一个插件式工具系统，允许轻松扩展和注册新工具。每个工具作为独立的文件存在于该目录下，系统会自动发现和注册这些工具。
## 工具结构
每个工具应该继承 `BaseTool` 基类并实现必要的属性和方法：
```python
from src.do_tool.tool_can_use.base_tool import BaseTool, register_tool
class MyNewTool(BaseTool):
# 工具名称，必须唯一
name = "my_new_tool"
# 工具描述，告诉LLM这个工具的用途
description = "这是一个新工具，用于..."
# 工具参数定义，遵循JSONSchema格式
parameters = {
"type": "object",
"properties": {
"param1": {
"type": "string",
"description": "参数1的描述"
},
"param2": {
"type": "integer",
"description": "参数2的描述"
}
},
"required": ["param1"] # 必需的参数列表
}
async def execute(self, function_args, message_txt=""):
"""执行工具逻辑
Args:
function_args: 工具调用参数
message_txt: 原始消息文本
Returns:
Dict: 包含执行结果的字典，必须包含name和content字段
"""
# 实现工具逻辑
result = f"工具执行结果: {function_args.get('param1')}"
return {
"name": self.name,
"content": result
}
# 注册工具
register_tool(MyNewTool)
```
## 自动注册机制
工具系统通过以下步骤自动注册工具：
1. 在`__init__.py`中，`discover_tools()`函数会自动遍历当前目录中的所有Python文件
2. 对于每个文件，系统会寻找继承自`BaseTool`的类
3. 这些类会被自动注册到工具注册表中
只要确保在每个工具文件的末尾调用`register_tool(YourToolClass)`，工具就会被自动注册。
## 添加新工具步骤
1. 在`tool_can_use`目录下创建新的Python文件（如`my_new_tool.py`）
2. 导入`BaseTool`和`register_tool`
3. 创建继承自`BaseTool`的工具类
4. 实现必要的属性（`name`, `description`, `parameters`）
5. 实现`execute`方法
6. 使用`register_tool`注册工具
## 与ToolUser整合
`ToolUser`类已经更新为使用这个新的工具系统，它会：
1. 自动获取所有已注册工具的定义
2. 基于工具名称找到对应的工具实例
3. 调用工具的`execute`方法
## 使用示例
```python
from src.do_tool.tool_use import ToolUser
# 创建工具用户
tool_user = ToolUser()
# 使用工具
result = await tool_user.use_tool(message_txt="查询关于Python的知识", sender_name="用户", chat_stream=chat_stream)
# 处理结果
if result["used_tools"]:
print("工具使用结果:", result["collected_info"])
else:
print("未使用工具")
```

20
src/do_tool/tool_can_use/__init__.py Normal file
View File

@@ -0,0 +1,20 @@
from src.do_tool.tool_can_use.base_tool import (
BaseTool,
register_tool,
discover_tools,
get_all_tool_definitions,
get_tool_instance,
TOOL_REGISTRY,
)
__all__ = [
"BaseTool",
"register_tool",
"discover_tools",
"get_all_tool_definitions",
"get_tool_instance",
"TOOL_REGISTRY",
]
# 自动发现并注册工具
discover_tools()

113
src/do_tool/tool_can_use/base_tool.py Normal file
View File

@@ -0,0 +1,113 @@
from typing import Dict, List, Any, Optional, Type
import inspect
import importlib
import pkgutil
import os
from src.common.logger import get_module_logger
logger = get_module_logger("base_tool")
# 工具注册表
TOOL_REGISTRY = {}
class BaseTool:
"""所有工具的基类"""
# 工具名称，子类必须重写
name = None
# 工具描述，子类必须重写
description = None
# 工具参数定义，子类必须重写
parameters = None
@classmethod
def get_tool_definition(cls) -> Dict[str, Any]:
"""获取工具定义，用于LLM工具调用
Returns:
Dict: 工具定义字典
"""
if not cls.name or not cls.description or not cls.parameters:
raise NotImplementedError(f"工具类 {cls.__name__} 必须定义 name, description 和 parameters 属性")
return {
"type": "function",
"function": {"name": cls.name, "description": cls.description, "parameters": cls.parameters},
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行工具函数
Args:
function_args: 工具调用参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
raise NotImplementedError("子类必须实现execute方法")
def register_tool(tool_class: Type[BaseTool]):
"""注册工具到全局注册表
Args:
tool_class: 工具类
"""
if not issubclass(tool_class, BaseTool):
raise TypeError(f"{tool_class.__name__} 不是 BaseTool 的子类")
tool_name = tool_class.name
if not tool_name:
raise ValueError(f"工具类 {tool_class.__name__} 没有定义 name 属性")
TOOL_REGISTRY[tool_name] = tool_class
logger.info(f"已注册工具: {tool_name}")
def discover_tools():
"""自动发现并注册tool_can_use目录下的所有工具"""
# 获取当前目录路径
current_dir = os.path.dirname(os.path.abspath(__file__))
package_name = os.path.basename(current_dir)
# 遍历包中的所有模块
for _, module_name, _ in pkgutil.iter_modules([current_dir]):
# 跳过当前模块和__pycache__
if module_name == "base_tool" or module_name.startswith("__"):
continue
# 导入模块
module = importlib.import_module(f"src.do_tool.{package_name}.{module_name}")
# 查找模块中的工具类
for _, obj in inspect.getmembers(module):
if inspect.isclass(obj) and issubclass(obj, BaseTool) and obj != BaseTool:
register_tool(obj)
logger.info(f"工具发现完成，共注册 {len(TOOL_REGISTRY)} 个工具")
def get_all_tool_definitions() -> List[Dict[str, Any]]:
"""获取所有已注册工具的定义
Returns:
List[Dict]: 工具定义列表
"""
return [tool_class().get_tool_definition() for tool_class in TOOL_REGISTRY.values()]
def get_tool_instance(tool_name: str) -> Optional[BaseTool]:
"""获取指定名称的工具实例
Args:
tool_name: 工具名称
Returns:
Optional[BaseTool]: 工具实例，如果找不到则返回None
"""
tool_class = TOOL_REGISTRY.get(tool_name)
if not tool_class:
return None
return tool_class()

57
src/do_tool/tool_can_use/change_mood.py Normal file
View File

@@ -0,0 +1,57 @@
from src.do_tool.tool_can_use.base_tool import BaseTool
from src.plugins.config.config import global_config
from src.common.logger import get_module_logger
from src.plugins.moods.moods import MoodManager
from src.plugins.chat_module.think_flow_chat.think_flow_generator import ResponseGenerator
from typing import Dict, Any
logger = get_module_logger("change_mood_tool")
class ChangeMoodTool(BaseTool):
"""改变心情的工具"""
name = "change_mood"
description = "根据收到的内容和自身回复的内容，改变心情,当你回复了别人的消息，你可以使用这个工具"
parameters = {
"type": "object",
"properties": {
"text": {"type": "string", "description": "引起你改变心情的文本"},
"response_set": {"type": "list", "description": "你对文本的回复"},
},
"required": ["text", "response_set"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str) -> Dict[str, Any]:
"""执行心情改变
Args:
function_args: 工具参数
message_processed_plain_text: 原始消息文本
response_set: 原始消息文本
Returns:
Dict: 工具执行结果
"""
try:
response_set = function_args.get("response_set")
message_processed_plain_text = function_args.get("text")
mood_manager = MoodManager.get_instance()
gpt = ResponseGenerator()
if response_set is None:
response_set = ["你还没有回复"]
ori_response = ",".join(response_set)
_stance, emotion = await gpt._get_emotion_tags(ori_response, message_processed_plain_text)
mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
return {"name": "change_mood", "content": f"你的心情刚刚变化了，现在的心情是: {emotion}"}
except Exception as e:
logger.error(f"心情改变工具执行失败: {str(e)}")
return {"name": "change_mood", "content": f"心情改变失败: {str(e)}"}
# 注册工具
# register_tool(ChangeMoodTool)

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src/do_tool/tool_can_use/change_relationship.py Normal file
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# from src.plugins.person_info.relationship_manager import relationship_manager
from src.common.logger import get_module_logger
from src.do_tool.tool_can_use.base_tool import BaseTool
# from src.plugins.chat_module.think_flow_chat.think_flow_generator import ResponseGenerator
logger = get_module_logger("relationship_tool")
class RelationshipTool(BaseTool):
name = "change_relationship"
description = "根据收到的文本和回复内容，修改与特定用户的关系值，当你回复了别人的消息，你可以使用这个工具"
parameters = {
"type": "object",
"properties": {
"text": {"type": "string", "description": "收到的文本"},
"changed_value": {"type": "number", "description": "变更值"},
"reason": {"type": "string", "description": "变更原因"},
},
"required": ["text", "changed_value", "reason"],
}
async def execute(self, args: dict, message_txt: str) -> dict:
"""执行工具功能
Args:
args: 包含工具参数的字典
text: 原始消息文本
changed_value: 变更值
reason: 变更原因
Returns:
dict: 包含执行结果的字典
"""
try:
text = args.get("text")
changed_value = args.get("changed_value")
reason = args.get("reason")
return {"content": f"因为你刚刚因为{reason}，所以你和发[{text}]这条消息的人的关系值变化为{changed_value}"}
except Exception as e:
logger.error(f"修改关系值时发生错误: {str(e)}")
return {"content": f"修改关系值失败: {str(e)}"}

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src/do_tool/tool_can_use/compare_numbers_tool.py Normal file
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from src.do_tool.tool_can_use.base_tool import BaseTool
from src.common.logger import get_module_logger
from typing import Dict, Any
logger = get_module_logger("compare_numbers_tool")
class CompareNumbersTool(BaseTool):
"""比较两个数大小的工具"""
name = "compare_numbers"
description = "比较两个数的大小，返回较大的数"
parameters = {
"type": "object",
"properties": {
"num1": {"type": "number", "description": "第一个数字"},
"num2": {"type": "number", "description": "第二个数字"},
},
"required": ["num1", "num2"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行比较两个数的大小
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
try:
num1 = function_args.get("num1")
num2 = function_args.get("num2")
if num1 > num2:
result = f"{num1} 大于 {num2}"
elif num1 < num2:
result = f"{num1} 小于 {num2}"
else:
result = f"{num1} 等于 {num2}"
return {"name": self.name, "content": result}
except Exception as e:
logger.error(f"比较数字失败: {str(e)}")
return {"name": self.name, "content": f"比较数字失败: {str(e)}"}
# 注册工具
# register_tool(CompareNumbersTool)

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src/do_tool/tool_can_use/get_current_task.py Normal file
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from src.do_tool.tool_can_use.base_tool import BaseTool
from src.plugins.schedule.schedule_generator import bot_schedule
from src.common.logger import get_module_logger
from typing import Dict, Any
from datetime import datetime
logger = get_module_logger("get_current_task_tool")
class GetCurrentTaskTool(BaseTool):
"""获取当前正在做的事情/最近的任务工具"""
name = "get_schedule"
description = "获取当前正在做的事情，或者某个时间点/时间段的日程信息"
parameters = {
"type": "object",
"properties": {
"start_time": {"type": "string", "description": "开始时间，格式为'HH:MM'，填写current则获取当前任务"},
"end_time": {"type": "string", "description": "结束时间，格式为'HH:MM'，填写current则获取当前任务"},
},
"required": ["start_time", "end_time"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行获取当前任务或指定时间段的日程信息
Args:
function_args: 工具参数
message_txt: 原始消息文本，此工具不使用
Returns:
Dict: 工具执行结果
"""
start_time = function_args.get("start_time")
end_time = function_args.get("end_time")
# 如果 start_time 或 end_time 为 "current"，则获取当前任务
if start_time == "current" or end_time == "current":
current_task = bot_schedule.get_current_num_task(num=1, time_info=True)
current_time = datetime.now().strftime("%H:%M:%S")
current_date = datetime.now().strftime("%Y-%m-%d")
if current_task:
task_info = f"{current_date} {current_time}，你在{current_task}"
else:
task_info = f"{current_time} {current_date}，没在做任何事情"
# 如果提供了时间范围，则获取该时间段的日程信息
elif start_time and end_time:
tasks = await bot_schedule.get_task_from_time_to_time(start_time, end_time)
if tasks:
task_list = []
for task in tasks:
task_time = task[0].strftime("%H:%M")
task_content = task[1]
task_list.append(f"{task_time}时，{task_content}")
task_info = "\n".join(task_list)
else:
task_info = f"在 {start_time} 到 {end_time} 之间没有找到日程信息"
return {"name": "get_current_task", "content": f"日程信息: {task_info}"}

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src/do_tool/tool_can_use/get_knowledge.py Normal file
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from src.do_tool.tool_can_use.base_tool import BaseTool
from src.plugins.chat.utils import get_embedding
from src.common.database import db
from src.common.logger import get_module_logger
from typing import Dict, Any, Union
logger = get_module_logger("get_knowledge_tool")
class SearchKnowledgeTool(BaseTool):
"""从知识库中搜索相关信息的工具"""
name = "search_knowledge"
description = "从知识库中搜索相关信息"
parameters = {
"type": "object",
"properties": {
"query": {"type": "string", "description": "搜索查询关键词"},
"threshold": {"type": "number", "description": "相似度阈值，0.0到1.0之间"},
},
"required": ["query"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行知识库搜索
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
try:
query = function_args.get("query", message_txt)
threshold = function_args.get("threshold", 0.4)
# 调用知识库搜索
embedding = await get_embedding(query, request_type="info_retrieval")
if embedding:
knowledge_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
if knowledge_info:
content = f"你知道这些知识: {knowledge_info}"
else:
content = f"你不太了解有关{query}的知识"
return {"name": "search_knowledge", "content": content}
return {"name": "search_knowledge", "content": f"无法获取关于'{query}'的嵌入向量"}
except Exception as e:
logger.error(f"知识库搜索工具执行失败: {str(e)}")
return {"name": "search_knowledge", "content": f"知识库搜索失败: {str(e)}"}
def get_info_from_db(
self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
) -> Union[str, list]:
"""从数据库中获取相关信息
Args:
query_embedding: 查询的嵌入向量
limit: 最大返回结果数
threshold: 相似度阈值
return_raw: 是否返回原始结果
Returns:
Union[str, list]: 格式化的信息字符串或原始结果列表
"""
if not query_embedding:
return "" if not return_raw else []
# 使用余弦相似度计算
pipeline = [
{
"$addFields": {
"dotProduct": {
"$reduce": {
"input": {"$range": [0, {"$size": "$embedding"}]},
"initialValue": 0,
"in": {
"$add": [
"$$value",
{
"$multiply": [
{"$arrayElemAt": ["$embedding", "$$this"]},
{"$arrayElemAt": [query_embedding, "$$this"]},
]
},
]
},
}
},
"magnitude1": {
"$sqrt": {
"$reduce": {
"input": "$embedding",
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
"magnitude2": {
"$sqrt": {
"$reduce": {
"input": query_embedding,
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
}
},
{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
{
"$match": {
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
}
},
{"$sort": {"similarity": -1}},
{"$limit": limit},
{"$project": {"content": 1, "similarity": 1}},
]
results = list(db.knowledges.aggregate(pipeline))
logger.debug(f"知识库查询结果数量: {len(results)}")
if not results:
return "" if not return_raw else []
if return_raw:
return results
else:
# 返回所有找到的内容，用换行分隔
return "\n".join(str(result["content"]) for result in results)
# 注册工具
# register_tool(SearchKnowledgeTool)

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src/do_tool/tool_can_use/get_memory.py Normal file
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from src.do_tool.tool_can_use.base_tool import BaseTool
from src.plugins.memory_system.Hippocampus import HippocampusManager
from src.common.logger import get_module_logger
from typing import Dict, Any
logger = get_module_logger("mid_chat_mem_tool")
class GetMemoryTool(BaseTool):
"""从记忆系统中获取相关记忆的工具"""
name = "mid_chat_mem"
description = "从记忆系统中获取相关记忆"
parameters = {
"type": "object",
"properties": {
"text": {"type": "string", "description": "要查询的相关文本"},
"max_memory_num": {"type": "integer", "description": "最大返回记忆数量"},
},
"required": ["text"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行记忆获取
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
try:
text = function_args.get("text", message_txt)
max_memory_num = function_args.get("max_memory_num", 2)
# 调用记忆系统
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
text=text, max_memory_num=max_memory_num, max_memory_length=2, max_depth=3, fast_retrieval=False
)
memory_info = ""
if related_memory:
for memory in related_memory:
memory_info += memory[1] + "\n"
if memory_info:
content = f"你记得这些事情: {memory_info}"
else:
content = f"你不太记得有关{text}的记忆，你对此不太了解"
return {"name": "mid_chat_mem", "content": content}
except Exception as e:
logger.error(f"记忆获取工具执行失败: {str(e)}")
return {"name": "mid_chat_mem", "content": f"记忆获取失败: {str(e)}"}
# 注册工具
# register_tool(GetMemoryTool)

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src/do_tool/tool_can_use/get_time_date.py Normal file
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from src.do_tool.tool_can_use.base_tool import BaseTool
from src.common.logger import get_module_logger
from typing import Dict, Any
from datetime import datetime
logger = get_module_logger("get_time_date")
class GetCurrentDateTimeTool(BaseTool):
"""获取当前时间、日期、年份和星期的工具"""
name = "get_current_date_time"
description = "当有人询问或者涉及到具体时间或者日期的时候，必须使用这个工具"
parameters = {
"type": "object",
"properties": {},
"required": [],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行获取当前时间、日期、年份和星期
Args:
function_args: 工具参数（此工具不使用）
message_txt: 原始消息文本（此工具不使用）
Returns:
Dict: 工具执行结果
"""
current_time = datetime.now().strftime("%H:%M:%S")
current_date = datetime.now().strftime("%Y-%m-%d")
current_year = datetime.now().strftime("%Y")
current_weekday = datetime.now().strftime("%A")
return {
"name": "get_current_date_time",
"content": f"当前时间: {current_time}, 日期: {current_date}, 年份: {current_year}, 星期: {current_weekday}",
}

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src/do_tool/tool_can_use/mid_chat_mem.py Normal file
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from src.do_tool.tool_can_use.base_tool import BaseTool
from src.common.logger import get_module_logger
from typing import Dict, Any
logger = get_module_logger("get_mid_memory_tool")
class GetMidMemoryTool(BaseTool):
"""从记忆系统中获取相关记忆的工具"""
name = "mid_chat_mem"
description = "之前的聊天内容中获取具体信息，当最新消息提到，或者你需要回复的消息中提到，你可以使用这个工具"
parameters = {
"type": "object",
"properties": {
"id": {"type": "integer", "description": "要查询的聊天记录id"},
},
"required": ["id"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行记忆获取
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
try:
id = function_args.get("id")
return {"name": "mid_chat_mem", "content": str(id)}
except Exception as e:
logger.error(f"聊天记录获取工具执行失败: {str(e)}")
return {"name": "mid_chat_mem", "content": f"聊天记录获取失败: {str(e)}"}
# 注册工具
# register_tool(GetMemoryTool)

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src/do_tool/tool_can_use/send_emoji.py Normal file
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from src.do_tool.tool_can_use.base_tool import BaseTool
from src.common.logger import get_module_logger
from typing import Dict, Any
logger = get_module_logger("send_emoji_tool")
class SendEmojiTool(BaseTool):
"""发送表情包的工具"""
name = "send_emoji"
description = "当你觉得需要表达情感，或者帮助表达，可以使用这个工具发送表情包"
parameters = {
"type": "object",
"properties": {"text": {"type": "string", "description": "要发送的表情包描述"}},
"required": ["text"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str) -> Dict[str, Any]:
text = function_args.get("text", message_txt)
return {
"name": "send_emoji",
"content": text,
}

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src/do_tool/tool_use.py Normal file
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from src.plugins.models.utils_model import LLM_request
from src.plugins.config.config import global_config
from src.plugins.chat.chat_stream import ChatStream
from src.common.database import db
import time
import json
from src.common.logger import get_module_logger, TOOL_USE_STYLE_CONFIG, LogConfig
from src.do_tool.tool_can_use import get_all_tool_definitions, get_tool_instance
from src.heart_flow.sub_heartflow import SubHeartflow
tool_use_config = LogConfig(
# 使用消息发送专用样式
console_format=TOOL_USE_STYLE_CONFIG["console_format"],
file_format=TOOL_USE_STYLE_CONFIG["file_format"],
)
logger = get_module_logger("tool_use", config=tool_use_config)
class ToolUser:
def __init__(self):
self.llm_model_tool = LLM_request(
model=global_config.llm_tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use"
)
async def _build_tool_prompt(
self, message_txt: str, sender_name: str, chat_stream: ChatStream, subheartflow: SubHeartflow = None
):
"""构建工具使用的提示词
Args:
message_txt: 用户消息文本
sender_name: 发送者名称
chat_stream: 聊天流对象
Returns:
str: 构建好的提示词
"""
if subheartflow:
mid_memory_info = subheartflow.observations[0].mid_memory_info
# print(f"intol111111111111111111111111111111111222222222222mid_memory_info：{mid_memory_info}")
else:
mid_memory_info = ""
new_messages = list(
db.messages.find({"chat_id": chat_stream.stream_id, "time": {"$gt": time.time()}}).sort("time", 1).limit(15)
)
new_messages_str = ""
for msg in new_messages:
if "detailed_plain_text" in msg:
new_messages_str += f"{msg['detailed_plain_text']}"
# 这些信息应该从调用者传入，而不是从self获取
bot_name = global_config.BOT_NICKNAME
prompt = ""
prompt += mid_memory_info
prompt += "你正在思考如何回复群里的消息。\n"
prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
prompt += f"注意你就是{bot_name}，{bot_name}指的就是你。"
prompt += "你现在需要对群里的聊天内容进行回复，现在选择工具来对消息和你的回复进行处理，你是否需要额外的信息，比如回忆或者搜寻已有的知识，改变关系和情感，或者了解你现在正在做什么。"
return prompt
def _define_tools(self):
"""获取所有已注册工具的定义
Returns:
list: 工具定义列表
"""
return get_all_tool_definitions()
async def _execute_tool_call(self, tool_call, message_txt: str):
"""执行特定的工具调用
Args:
tool_call: 工具调用对象
message_txt: 原始消息文本
Returns:
dict: 工具调用结果
"""
try:
function_name = tool_call["function"]["name"]
function_args = json.loads(tool_call["function"]["arguments"])
# 获取对应工具实例
tool_instance = get_tool_instance(function_name)
if not tool_instance:
logger.warning(f"未知工具名称: {function_name}")
return None
# 执行工具
result = await tool_instance.execute(function_args, message_txt)
if result:
# 直接使用 function_name 作为 tool_type
tool_type = function_name
return {
"tool_call_id": tool_call["id"],
"role": "tool",
"name": function_name,
"type": tool_type,
"content": result["content"],
}
return None
except Exception as e:
logger.error(f"执行工具调用时发生错误: {str(e)}")
return None
async def use_tool(
self, message_txt: str, sender_name: str, chat_stream: ChatStream, subheartflow: SubHeartflow = None
):
"""使用工具辅助思考，判断是否需要额外信息
Args:
message_txt: 用户消息文本
sender_name: 发送者名称
chat_stream: 聊天流对象
Returns:
dict: 工具使用结果，包含结构化的信息
"""
try:
# 构建提示词
prompt = await self._build_tool_prompt(message_txt, sender_name, chat_stream, subheartflow)
# 定义可用工具
tools = self._define_tools()
logger.trace(f"工具定义: {tools}")
# 使用llm_model_tool发送带工具定义的请求
payload = {
"model": self.llm_model_tool.model_name,
"messages": [{"role": "user", "content": prompt}],
"max_tokens": global_config.max_response_length,
"tools": tools,
"temperature": 0.2,
}
logger.trace(f"发送工具调用请求，模型: {self.llm_model_tool.model_name}")
# 发送请求获取模型是否需要调用工具
response = await self.llm_model_tool._execute_request(
endpoint="/chat/completions", payload=payload, prompt=prompt
)
# 根据返回值数量判断是否有工具调用
if len(response) == 3:
content, reasoning_content, tool_calls = response
# logger.info(f"工具思考: {tool_calls}")
# logger.debug(f"工具思考: {content}")
# 检查响应中工具调用是否有效
if not tool_calls:
logger.debug("模型返回了空的tool_calls列表")
return {"used_tools": False}
tool_calls_str = ""
for tool_call in tool_calls:
tool_calls_str += f"{tool_call['function']['name']}\n"
logger.info(f"根据:\n{prompt}\n模型请求调用{len(tool_calls)}个工具: {tool_calls_str}")
tool_results = []
structured_info = {} # 动态生成键
# 执行所有工具调用
for tool_call in tool_calls:
result = await self._execute_tool_call(tool_call, message_txt)
if result:
tool_results.append(result)
# 使用工具名称作为键
tool_name = result["name"]
if tool_name not in structured_info:
structured_info[tool_name] = []
structured_info[tool_name].append({"name": result["name"], "content": result["content"]})
# 如果有工具结果，返回结构化的信息
if structured_info:
logger.info(f"工具调用收集到结构化信息: {json.dumps(structured_info, ensure_ascii=False)}")
return {"used_tools": True, "structured_info": structured_info}
else:
# 没有工具调用
content, reasoning_content = response
logger.debug("模型没有请求调用任何工具")
# 如果没有工具调用或处理失败，直接返回原始思考
return {
"used_tools": False,
}
except Exception as e:
logger.error(f"工具调用过程中出错: {str(e)}")
return {
"used_tools": False,
"error": str(e),
}

22
src/gui/logger_gui.py
View File

@@ -24,10 +24,10 @@
# # 标记GUI是否运行中
# self.is_running = True
# # 程序关闭时的清理操作
# self.protocol("WM_DELETE_WINDOW", self._on_closing)
# # 初始化进程、日志队列、日志数据等变量
# self.process = None
# self.log_queue = queue.Queue()
@@ -236,7 +236,7 @@
# while not self.log_queue.empty():
# line = self.log_queue.get()
# self.process_log_line(line)
# # 仅在GUI仍在运行时继续处理队列
# if self.is_running:
# self.after(100, self.process_log_queue)
@@ -245,11 +245,11 @@
# """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>.*)
# (?:(?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,
@@ -354,10 +354,10 @@
# """处理窗口关闭事件，安全清理资源"""
# # 标记GUI已关闭
# self.is_running = False
# # 停止日志进程
# self.stop_process()
# # 安全清理tkinter变量
# for attr_name in list(self.__dict__.keys()):
# if isinstance(getattr(self, attr_name), (ctk.Variable, ctk.StringVar, ctk.IntVar, ctk.DoubleVar, ctk.BooleanVar)):
@@ -367,7 +367,7 @@
# except Exception:
# pass
# setattr(self, attr_name, None)
# self.quit()
# sys.exit(0)

6
src/gui/reasoning_gui.py
View File

@@ -127,7 +127,7 @@
# """处理窗口关闭事件"""
# # 标记GUI已关闭，防止后台线程继续访问tkinter对象
# self.is_running = False
# # 安全清理所有可能的tkinter变量
# for attr_name in list(self.__dict__.keys()):
# if isinstance(getattr(self, attr_name), (ctk.Variable, ctk.StringVar, ctk.IntVar, ctk.DoubleVar, ctk.BooleanVar)):
@@ -138,7 +138,7 @@
# except Exception:
# pass
# setattr(self, attr_name, None)
# # 退出
# self.root.quit()
# sys.exit(0)
@@ -259,7 +259,7 @@
# while True:
# if not self.is_running:
# break # 如果GUI已关闭，停止线程
# try:
# # 从数据库获取最新数据，只获取启动时间之后的记录
# query = {"time": {"$gt": self.start_timestamp}}

82
src/heart_flow/README.md Normal file
View File

@@ -0,0 +1,82 @@
# 心流系统 (Heart Flow System)
心流系统是一个模拟AI机器人内心思考和情感流动的核心系统。它通过多层次的心流结构，使AI能够对外界信息进行观察、思考和情感反应，从而产生更自然的对话和行为。
## 系统架构
### 1. 主心流 (Heartflow)
- 位于 `heartflow.py`
- 作为整个系统的主控制器
- 负责管理和协调多个子心流
- 维护AI的整体思维状态
- 定期进行全局思考更新
### 2. 子心流 (SubHeartflow)
- 位于 `sub_heartflow.py`
- 处理具体的对话场景（如群聊）
- 维护特定场景下的思维状态
- 通过观察者模式接收和处理信息
- 能够进行独立的思考和回复判断
### 3. 观察系统 (Observation)
- 位于 `observation.py`
- 负责收集和处理外部信息
- 支持多种观察类型（如聊天观察）
- 对信息进行实时总结和更新
## 主要功能
### 思维系统
- 定期进行思维更新
- 维护短期记忆和思维连续性
- 支持多层次的思维处理
### 情感系统
- 情绪状态管理
- 回复意愿判断
- 情感因素影响决策
### 交互系统
- 群聊消息处理
- 多场景并行处理
- 智能回复生成
## 工作流程
1. 主心流启动并创建必要的子心流
2. 子心流通过观察者接收外部信息
3. 系统进行信息处理和思维更新
4. 根据情感状态和思维结果决定是否回复
5. 生成合适的回复并更新思维状态
## 使用说明
### 创建新的子心流
```python
heartflow = Heartflow()
subheartflow = heartflow.create_subheartflow(chat_id)
```
### 添加观察者
```python
observation = ChattingObservation(chat_id)
subheartflow.add_observation(observation)
```
### 启动心流系统
```python
await heartflow.heartflow_start_working()
```
## 配置说明
系统的主要配置参数：
- `sub_heart_flow_stop_time`: 子心流停止时间
- `sub_heart_flow_freeze_time`: 子心流冻结时间
- `heart_flow_update_interval`: 心流更新间隔
## 注意事项
1. 子心流会在长时间不活跃后自动清理
2. 需要合理配置更新间隔以平衡性能和响应速度
3. 观察系统会限制消息处理数量以避免过载

151
src/heart_flow/heartflow.py
View File

@@ -4,11 +4,13 @@ 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
from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
import asyncio
from src.common.logger import get_module_logger, LogConfig, HEARTFLOW_STYLE_CONFIG # noqa: E402
from src.individuality.individuality import Individuality
import time
import random
from typing import Dict, Any
heartflow_config = LogConfig(
# 使用海马体专用样式
@@ -18,14 +20,37 @@ heartflow_config = LogConfig(
logger = get_module_logger("heartflow", config=heartflow_config)
class CuttentState:
def init_prompt():
prompt = ""
prompt += "你刚刚在做的事情是：{schedule_info}\n"
prompt += "{personality_info}\n"
prompt += "你想起来{related_memory_info}。"
prompt += "刚刚你的主要想法是{current_thinking_info}。"
prompt += "你还有一些小想法，因为你在参加不同的群聊天，这是你正在做的事情：{sub_flows_info}\n"
prompt += "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:"
Prompt(prompt, "thinking_prompt")
prompt = ""
prompt += "{personality_info}\n"
prompt += "现在{bot_name}的想法是：{current_mind}\n"
prompt += "现在{bot_name}在qq群里进行聊天，聊天的话题如下：{minds_str}\n"
prompt += "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:"""
Prompt(prompt, "mind_summary_prompt")
class CurrentState:
def __init__(self):
self.willing = 0
self.current_state_info = ""
self.mood_manager = MoodManager()
self.mood = self.mood_manager.get_prompt()
self.attendance_factor = 0
self.engagement_factor = 0
def update_current_state_info(self):
self.current_state_info = self.mood_manager.get_current_mood()
@@ -34,14 +59,12 @@ class Heartflow:
def __init__(self):
self.current_mind = "你什么也没想"
self.past_mind = []
self.current_state: CuttentState = CuttentState()
self.current_state: CurrentState = CurrentState()
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._subheartflows: Dict[Any, SubHeartflow] = {}
async def _cleanup_inactive_subheartflows(self):
"""定期清理不活跃的子心流"""
@@ -64,10 +87,7 @@ class Heartflow:
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())
async def _sub_heartflow_update(self):
while True:
# 检查是否存在子心流
if not self._subheartflows:
@@ -78,63 +98,80 @@ class Heartflow:
await self.do_a_thinking()
await asyncio.sleep(global_config.heart_flow_update_interval) # 5分钟思考一次
async def heartflow_start_working(self):
# 启动清理任务
asyncio.create_task(self._cleanup_inactive_subheartflows())
# 启动子心流更新任务
asyncio.create_task(self._sub_heartflow_update())
async def _update_current_state(self):
print("TODO")
async def do_a_thinking(self):
logger.debug("麦麦大脑袋转起来了")
self.current_state.update_current_state_info()
# 开始构建prompt
prompt_personality = "你"
#person
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
personality_info = prompt_personality
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()
try:
sub_flows_info = await self.get_all_subheartflows_minds()
except Exception as e:
logger.error(f"获取子心流的想法失败: {e}")
return
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:"
# 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，这是你正在做的事情：{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:"
prompt = (await global_prompt_manager.get_prompt_async("thinking_prompt")).format(
schedule_info, personality_info, related_memory_info, current_thinking_info, sub_flows_info, mood_info
)
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
try:
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
except Exception as e:
logger.error(f"内心独白获取失败: {e}")
return
self.update_current_mind(response)
self.update_current_mind(reponse)
self.current_mind = reponse
self.current_mind = response
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
subheartflow.main_heartflow_info = response
def update_current_mind(self, reponse):
def update_current_mind(self, response):
self.past_mind.append(self.current_mind)
self.current_mind = reponse
self.current_mind = response
async def get_all_subheartflows_minds(self):
sub_minds = ""
@@ -146,36 +183,37 @@ class Heartflow:
async def minds_summary(self, minds_str):
# 开始构建prompt
prompt_personality = "你"
#person
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
personality_info = prompt_personality
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:"""
# 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:"""
prompt = (await global_prompt_manager.get_prompt_async("mind_summary_prompt")).format(
personality_info, global_config.BOT_NICKNAME, self.current_mind, minds_str, mood_info
)
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
return reponse
return response
def create_subheartflow(self, subheartflow_id):
"""
@@ -183,20 +221,16 @@ class Heartflow:
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
"""
try:
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
@@ -206,10 +240,11 @@ class Heartflow:
logger.error(f"创建 subheartflow 失败: {e}")
return None
def get_subheartflow(self, observe_chat_id):
def get_subheartflow(self, observe_chat_id) -> SubHeartflow:
"""获取指定ID的SubHeartflow实例"""
return self._subheartflows.get(observe_chat_id)
init_prompt()
# 创建一个全局的管理器实例
heartflow = Heartflow()

198
src/heart_flow/observation.py
View File

@@ -4,8 +4,11 @@ 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
from src.individuality.individuality import Individuality
import random
from src.common.logger import get_module_logger
import traceback
logger = get_module_logger("observation")
# 所有观察的基类
class Observation:
@@ -24,133 +27,142 @@ class ChattingObservation(Observation):
self.talking_message = []
self.talking_message_str = ""
self.name = global_config.BOT_NICKNAME
self.nick_name = global_config.BOT_ALIAS_NAMES
self.observe_times = 0
self.max_now_obs_len = global_config.observation_context_size
self.overlap_len = global_config.compressed_length
self.mid_memorys = []
self.max_mid_memory_len = global_config.compress_length_limit
self.mid_memory_info = ""
self.now_message_info = ""
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.updating_old = False
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
def get_observe_info(self, ids=None):
if ids:
mid_memory_str = ""
for id in ids:
print(f"id：{id}")
try:
for mid_memory in self.mid_memorys:
if mid_memory["id"] == id:
mid_memory_by_id = mid_memory
msg_str = ""
for msg in mid_memory_by_id["messages"]:
msg_str += f"{msg['detailed_plain_text']}"
time_diff = int((datetime.now().timestamp() - mid_memory_by_id["created_at"]) / 60)
mid_memory_str += f"距离现在{time_diff}分钟前：\n{msg_str}\n"
except Exception as e:
logger.error(f"获取mid_memory_id失败: {e}")
traceback.print_exc()
# print(f"获取mid_memory_id失败: {e}")
return self.now_message_info
return mid_memory_str + "现在群里正在聊：\n" + self.now_message_info
else:
return self.now_message_info
async def observe(self):
# 查找新消息，限制最多30条
# 查找新消息
new_messages = list(
db.messages.find({"chat_id": self.chat_id, "time": {"$gt": self.last_observe_time}})
.sort("time", 1)
.limit(20)
) # 按时间正序排列，最多20条
db.messages.find({"chat_id": self.chat_id, "time": {"$gt": self.last_observe_time}}).sort("time", 1)
) # 按时间正序排列
if not new_messages:
return self.observe_info # 没有新消息，返回上次观察结果
self.last_observe_time = new_messages[-1]["time"]
self.talking_message.extend(new_messages)
# 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"]
if len(self.talking_message) > self.max_now_obs_len and not self.updating_old:
self.updating_old = True
# 计算需要保留的消息数量
keep_messages_count = self.max_now_obs_len - self.overlap_len
# 提取所有超出保留数量的最老消息
oldest_messages = self.talking_message[:-keep_messages_count]
self.talking_message = self.talking_message[-keep_messages_count:]
oldest_messages_str = "\n".join([msg["detailed_plain_text"] for msg in oldest_messages])
oldest_timestamps = [msg["time"] for msg in oldest_messages]
# 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
# 调用 LLM 总结主题
prompt = f"请总结以下聊天记录的主题：\n{oldest_messages_str}\n主题,用一句话概括包括人物事件和主要信息，不要分点："
try:
summary, _ = await self.llm_summary.generate_response_async(prompt)
except Exception as e:
print(f"总结主题失败: {e}")
summary = "无法总结主题"
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
mid_memory = {
"id": str(int(datetime.now().timestamp())),
"theme": summary,
"messages": oldest_messages,
"timestamps": oldest_timestamps,
"chat_id": self.chat_id,
"created_at": datetime.now().timestamp(),
}
# print(f"mid_memory：{mid_memory}")
# 存入内存中的 mid_memorys
self.mid_memorys.append(mid_memory)
if len(self.mid_memorys) > self.max_mid_memory_len:
self.mid_memorys.pop(0)
return self.observe_info
mid_memory_str = "之前聊天的内容概括是：\n"
for mid_memory in self.mid_memorys:
time_diff = int((datetime.now().timestamp() - mid_memory["created_at"]) / 60)
mid_memory_str += f"距离现在{time_diff}分钟前(聊天记录id:{mid_memory['id']})：{mid_memory['theme']}\n"
self.mid_memory_info = mid_memory_str
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
self.updating_old = False
if not new_messages:
return self.observe_info # 没有新消息，返回上次观察结果
# print(f"处理后self.talking_message：{self.talking_message}")
# 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"
now_message_str = ""
now_message_str += self.translate_message_list_to_str(talking_message=self.talking_message)
self.now_message_info = now_message_str
# 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
logger.debug(f"压缩早期记忆：{self.mid_memory_info}\n现在聊天内容：{self.now_message_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}")
# 开始构建prompt
prompt_personality = "你"
#person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
personality_info = prompt_personality
prompt = ""
prompt += f"{personality_info}，请注意识别你自己的聊天发言"
prompt += f"你的名字叫：{self.name}，你的昵称是：{self.nick_name}\n"
# prompt += f"{personality_info}"
prompt += f"你的名字叫：{self.name}\n，标识'{self.name}'的都是你自己说的话"
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，注意识别你自己的发言，记得不要分点，不要太长，精简的概括成一段文本\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，目前最新讨论的话题
以及聊天中的一些重要信息，记得不要分点，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}")
try:
updated_observe_info, reasoning_content = await self.llm_summary.generate_response_async(prompt)
except Exception as e:
print(f"获取总结失败: {e}")
updated_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"]
return updated_observe_info
# print(f"prompt：{prompt}")
# print(f"self.observe_info：{self.observe_info}")
def translate_message_list_to_str(self, talking_message):
talking_message_str = ""
for message in talking_message:
talking_message_str += message["detailed_plain_text"]
return talking_message_str

538
src/heart_flow/sub_heartflow.py
View File

@@ -1,18 +1,24 @@
from .observation import Observation
from .observation import Observation, ChattingObservation
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.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
from src.plugins.chat.utils import get_embedding
from src.common.database import db
from typing import Union
# from src.plugins.chat.utils import get_embedding
# from src.common.database import db
# from typing import Union
from src.individuality.individuality import Individuality
import random
from src.plugins.chat.chat_stream import ChatStream
from src.plugins.person_info.relationship_manager import relationship_manager
from src.plugins.chat.utils import get_recent_group_speaker
from ..plugins.utils.prompt_builder import Prompt, global_prompt_manager
subheartflow_config = LogConfig(
# 使用海马体专用样式
@@ -22,7 +28,38 @@ subheartflow_config = LogConfig(
logger = get_module_logger("subheartflow", config=subheartflow_config)
class CuttentState:
def init_prompt():
prompt = ""
# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
prompt += "{extra_info}\n"
# prompt += "{prompt_schedule}\n"
prompt += "{relation_prompt_all}\n"
prompt += "{prompt_personality}\n"
prompt += "刚刚你的想法是{current_thinking_info}。可以适当转换话题\n"
prompt += "-----------------------------------\n"
prompt += "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 += "你现在{mood_info}\n"
prompt += "你注意到{sender_name}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"
prompt += "思考时可以想想如何对群聊内容进行回复。回复的要求是：平淡一些，简短一些，说中文，尽量不要说你说过的话\n"
prompt += "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)，不要带有括号和动作描写"
prompt += "记得结合上述的消息，生成内心想法，文字不要浮夸，注意你就是{bot_name}，{bot_name}指的就是你。"
Prompt(prompt, "sub_heartflow_prompt_before")
prompt = ""
# prompt += f"你现在正在做的事情是：{schedule_info}\n"
prompt += "{extra_info}\n"
prompt += "{prompt_personality}\n"
prompt += "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 += "刚刚你的想法是{current_thinking_info}。"
prompt += "你现在看到了网友们发的新消息:{message_new_info}\n"
prompt += "你刚刚回复了群友们:{reply_info}"
prompt += "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:"
Prompt(prompt, "sub_heartflow_prompt_after")
class CurrentState:
def __init__(self):
self.willing = 0
self.current_state_info = ""
@@ -40,9 +77,12 @@ class SubHeartflow:
self.current_mind = ""
self.past_mind = []
self.current_state: CuttentState = CuttentState()
self.current_state: CurrentState = CurrentState()
self.llm_model = LLM_request(
model=global_config.llm_sub_heartflow, temperature=0.7, max_tokens=600, request_type="sub_heart_flow"
model=global_config.llm_sub_heartflow,
temperature=global_config.llm_sub_heartflow["temp"],
max_tokens=600,
request_type="sub_heart_flow",
)
self.main_heartflow_info = ""
@@ -53,13 +93,14 @@ class SubHeartflow:
if not self.current_mind:
self.current_mind = "你什么也没想"
self.is_active = False
self.observations: list[Observation] = []
self.observations: list[ChattingObservation] = []
self.running_knowledges = []
self.bot_name = global_config.BOT_NICKNAME
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
@@ -86,7 +127,9 @@ class SubHeartflow:
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
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:
@@ -100,152 +143,140 @@ class SubHeartflow:
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
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):
async def do_thinking_before_reply(
self, message_txt: str, sender_name: str, chat_stream: ChatStream, extra_info: str, obs_id: int = None
):
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}")
if obs_id:
print(f"11111111111有id,开始获取观察信息{obs_id}")
chat_observe_info = observation.get_observe_info(obs_id)
else:
chat_observe_info = observation.get_observe_info()
extra_info_prompt = ""
for tool_name, tool_data in extra_info.items():
extra_info_prompt += f"{tool_name} 相关信息:\n"
for item in tool_data:
extra_info_prompt += f"- {item['name']}: {item['content']}\n"
# 开始构建prompt
prompt_personality = "你"
#person
prompt_personality = f"你的名字是{self.bot_name},你"
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
# 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
# 关系
who_chat_in_group = [
(chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
]
who_chat_in_group += get_recent_group_speaker(
chat_stream.stream_id,
(chat_stream.user_info.platform, chat_stream.user_info.user_id),
limit=global_config.MAX_CONTEXT_SIZE,
)
if related_memory:
related_memory_info = ""
for memory in related_memory:
related_memory_info += memory[1]
else:
related_memory_info = ""
relation_prompt = ""
for person in who_chat_in_group:
relation_prompt += await relationship_manager.build_relationship_info(person)
related_info,grouped_results = await self.get_prompt_info(chat_observe_info + message_txt, 0.4)
# print(related_info)
for _topic, results in grouped_results.items():
for result in results:
# print(result)
self.running_knowledges.append(result)
# relation_prompt_all = (
# f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
# f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
# )
relation_prompt_all = (await global_prompt_manager.get_prompt_async("relationship_prompt")).format(
relation_prompt, sender_name
)
# print(f"相关记忆：{related_memory_info}")
# prompt = ""
# # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
# if tool_result.get("used_tools", False):
# prompt += f"{collected_info}\n"
# prompt += f"{relation_prompt_all}\n"
# prompt += f"{prompt_personality}\n"
# prompt += f"刚刚你的想法是{current_thinking_info}。如果有新的内容，记得转换话题\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{sender_name}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"
# prompt += "思考时可以想想如何对群聊内容进行回复。回复的要求是：平淡一些，简短一些，说中文，尽量不要说你说过的话\n"
# prompt += "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)，不要带有括号和动作描写"
# prompt += f"记得结合上述的消息，生成内心想法，文字不要浮夸，注意你就是{self.bot_name}，{self.bot_name}指的就是你。"
schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_before")).format(
extra_info_prompt,
# prompt_schedule,
relation_prompt_all,
prompt_personality,
current_thinking_info,
chat_observe_info,
mood_info,
sender_name,
message_txt,
self.bot_name,
)
prompt = ""
# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
prompt += f"{prompt_personality}\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"
if related_info:
prompt += f"你想起你知道：{related_info}\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)
try:
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
except Exception as e:
logger.error(f"回复前内心独白获取失败: {e}")
response = ""
self.update_current_mind(response)
self.update_current_mind(reponse)
self.current_mind = response
self.current_mind = reponse
logger.debug(f"prompt:\n{prompt}\n")
logger.info(f"prompt:\n{prompt}\n")
logger.info(f"麦麦的思考前脑内状态：{self.current_mind}")
return self.current_mind, self.past_mind
async def do_thinking_after_reply(self, reply_content, chat_talking_prompt):
async def do_thinking_after_reply(self, reply_content, chat_talking_prompt, extra_info):
# print("麦麦回复之后脑袋转起来了")
# 开始构建prompt
prompt_personality = "你"
#person
prompt_personality = f"你的名字是{self.bot_name},你"
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
extra_info_prompt = ""
for tool_name, tool_data in extra_info.items():
extra_info_prompt += f"{tool_name} 相关信息:\n"
for item in tool_data:
extra_info_prompt += f"- {item['name']}: {item['content']}\n"
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
current_thinking_info = self.current_mind
mood_info = self.current_state.mood
@@ -254,24 +285,25 @@ class SubHeartflow:
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"{prompt_personality}\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:"
prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_after")).format(
extra_info_prompt,
prompt_personality,
chat_observe_info,
current_thinking_info,
message_new_info,
reply_info,
mood_info,
)
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
try:
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
except Exception as e:
logger.error(f"回复后内心独白获取失败: {e}")
response = ""
self.update_current_mind(response)
self.update_current_mind(reponse)
self.current_mind = reponse
self.current_mind = response
logger.info(f"麦麦回复后的脑内状态：{self.current_mind}")
self.last_reply_time = time.time()
@@ -279,22 +311,20 @@ class SubHeartflow:
async def judge_willing(self):
# 开始构建prompt
prompt_personality = "你"
#person
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
# print("麦麦闹情绪了1")
current_thinking_info = self.current_mind
mood_info = self.current_state.mood
@@ -306,10 +336,13 @@ class SubHeartflow:
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)
try:
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
# 解析willing值
willing_match = re.search(r"<(\d+)>", response)
except Exception as e:
logger.error(f"意愿判断获取失败: {e}")
willing_match = None
if willing_match:
self.current_state.willing = int(willing_match.group(1))
else:
@@ -317,225 +350,10 @@ class SubHeartflow:
return self.current_state.willing
def update_current_mind(self, reponse):
def update_current_mind(self, response):
self.past_mind.append(self.current_mind)
self.current_mind = reponse
async def get_prompt_info(self, message: str, threshold: float):
start_time = time.time()
related_info = ""
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
# 1. 先从LLM获取主题，类似于记忆系统的做法
topics = []
# try:
# # 先尝试使用记忆系统的方法获取主题
# hippocampus = HippocampusManager.get_instance()._hippocampus
# topic_num = min(5, max(1, int(len(message) * 0.1)))
# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
# # 提取关键词
# topics = re.findall(r"<([^>]+)>", topics_response[0])
# if not topics:
# topics = []
# else:
# topics = [
# topic.strip()
# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
# if topic.strip()
# ]
# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
# except Exception as e:
# logger.error(f"从LLM提取主题失败: {str(e)}")
# # 如果LLM提取失败，使用jieba分词提取关键词作为备选
# words = jieba.cut(message)
# topics = [word for word in words if len(word) > 1][:5]
# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
# 如果无法提取到主题，直接使用整个消息
if not topics:
logger.debug("未能提取到任何主题，使用整个消息进行查询")
embedding = await get_embedding(message, request_type="info_retrieval")
if not embedding:
logger.error("获取消息嵌入向量失败")
return ""
related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
return related_info, {}
# 2. 对每个主题进行知识库查询
logger.info(f"开始处理{len(topics)}个主题的知识库查询")
# 优化：批量获取嵌入向量，减少API调用
embeddings = {}
topics_batch = [topic for topic in topics if len(topic) > 0]
if message: # 确保消息非空
topics_batch.append(message)
# 批量获取嵌入向量
embed_start_time = time.time()
for text in topics_batch:
if not text or len(text.strip()) == 0:
continue
try:
embedding = await get_embedding(text, request_type="info_retrieval")
if embedding:
embeddings[text] = embedding
else:
logger.warning(f"获取'{text}'的嵌入向量失败")
except Exception as e:
logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
if not embeddings:
logger.error("所有嵌入向量获取失败")
return ""
# 3. 对每个主题进行知识库查询
all_results = []
query_start_time = time.time()
# 首先添加原始消息的查询结果
if message in embeddings:
original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
if original_results:
for result in original_results:
result["topic"] = "原始消息"
all_results.extend(original_results)
logger.info(f"原始消息查询到{len(original_results)}条结果")
# 然后添加每个主题的查询结果
for topic in topics:
if not topic or topic not in embeddings:
continue
try:
topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
if topic_results:
# 添加主题标记
for result in topic_results:
result["topic"] = topic
all_results.extend(topic_results)
logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
except Exception as e:
logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
# 4. 去重和过滤
process_start_time = time.time()
unique_contents = set()
filtered_results = []
for result in all_results:
content = result["content"]
if content not in unique_contents:
unique_contents.add(content)
filtered_results.append(result)
# 5. 按相似度排序
filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
# 6. 限制总数量（最多10条）
filtered_results = filtered_results[:10]
logger.info(f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果")
# 7. 格式化输出
if filtered_results:
format_start_time = time.time()
grouped_results = {}
for result in filtered_results:
topic = result["topic"]
if topic not in grouped_results:
grouped_results[topic] = []
grouped_results[topic].append(result)
# 按主题组织输出
for topic, results in grouped_results.items():
related_info += f"【主题: {topic}】\n"
for _i, result in enumerate(results, 1):
_similarity = result["similarity"]
content = result["content"].strip()
# 调试：为内容添加序号和相似度信息
# related_info += f"{i}. [{similarity:.2f}] {content}\n"
related_info += f"{content}\n"
related_info += "\n"
logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
return related_info,grouped_results
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False) -> Union[str, list]:
if not query_embedding:
return "" if not return_raw else []
# 使用余弦相似度计算
pipeline = [
{
"$addFields": {
"dotProduct": {
"$reduce": {
"input": {"$range": [0, {"$size": "$embedding"}]},
"initialValue": 0,
"in": {
"$add": [
"$$value",
{
"$multiply": [
{"$arrayElemAt": ["$embedding", "$$this"]},
{"$arrayElemAt": [query_embedding, "$$this"]},
]
},
]
},
}
},
"magnitude1": {
"$sqrt": {
"$reduce": {
"input": "$embedding",
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
"magnitude2": {
"$sqrt": {
"$reduce": {
"input": query_embedding,
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
}
},
{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
{
"$match": {
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
}
},
{"$sort": {"similarity": -1}},
{"$limit": limit},
{"$project": {"content": 1, "similarity": 1}},
]
results = list(db.knowledges.aggregate(pipeline))
logger.debug(f"知识库查询结果数量: {len(results)}")
if not results:
return "" if not return_raw else []
if return_raw:
return results
else:
# 返回所有找到的内容，用换行分隔
return "\n".join(str(result["content"]) for result in results)
self.current_mind = response
init_prompt()
# subheartflow = SubHeartflow()

54
src/individuality/identity.py
View File

@@ -2,27 +2,36 @@ from dataclasses import dataclass
from typing import List
import random
@dataclass
class Identity:
"""身份特征类"""
identity_detail: List[str] # 身份细节描述
height: int # 身高（厘米）
weight: int # 体重（千克）
age: int # 年龄
gender: str # 性别
appearance: str # 外貌特征
_instance = None
def __new__(cls, *args, **kwargs):
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self, identity_detail: List[str] = None, height: int = 0, weight: int = 0,
age: int = 0, gender: str = "", appearance: str = ""):
def __init__(
self,
identity_detail: List[str] = None,
height: int = 0,
weight: int = 0,
age: int = 0,
gender: str = "",
appearance: str = "",
):
"""初始化身份特征
Args:
identity_detail: 身份细节描述列表
height: 身高（厘米）
@@ -39,23 +48,24 @@ class Identity:
self.age = age
self.gender = gender
self.appearance = appearance
@classmethod
def get_instance(cls) -> 'Identity':
def get_instance(cls) -> "Identity":
"""获取Identity单例实例
Returns:
Identity: 单例实例
"""
if cls._instance is None:
cls._instance = cls()
return cls._instance
@classmethod
def initialize(cls, identity_detail: List[str], height: int, weight: int,
age: int, gender: str, appearance: str) -> 'Identity':
def initialize(
cls, identity_detail: List[str], height: int, weight: int, age: int, gender: str, appearance: str
) -> "Identity":
"""初始化身份特征
Args:
identity_detail: 身份细节描述列表
height: 身高（厘米）
@@ -63,7 +73,7 @@ class Identity:
age: 年龄
gender: 性别
appearance: 外貌特征
Returns:
Identity: 初始化后的身份特征实例
"""
@@ -75,8 +85,8 @@ class Identity:
instance.gender = gender
instance.appearance = appearance
return instance
def get_prompt(self,x_person,level):
def get_prompt(self, x_person, level):
"""
获取身份特征的prompt
"""
@@ -86,7 +96,7 @@ class Identity:
prompt_identity = "我"
else:
prompt_identity = "他"
if level == 1:
identity_detail = self.identity_detail
random.shuffle(identity_detail)
@@ -96,7 +106,7 @@ class Identity:
prompt_identity += f",{detail}"
prompt_identity += "。"
return prompt_identity
def to_dict(self) -> dict:
"""将身份特征转换为字典格式"""
return {
@@ -105,13 +115,13 @@ class Identity:
"weight": self.weight,
"age": self.age,
"gender": self.gender,
"appearance": self.appearance
"appearance": self.appearance,
}
@classmethod
def from_dict(cls, data: dict) -> 'Identity':
def from_dict(cls, data: dict) -> "Identity":
"""从字典创建身份特征实例"""
instance = cls.get_instance()
for key, value in data.items():
setattr(instance, key, value)
return instance
return instance

68
src/individuality/individuality.py
View File

@@ -2,35 +2,46 @@ from typing import Optional
from .personality import Personality
from .identity import Identity
class Individuality:
"""个体特征管理类"""
_instance = None
def __new__(cls, *args, **kwargs):
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self):
self.personality: Optional[Personality] = None
self.identity: Optional[Identity] = None
@classmethod
def get_instance(cls) -> 'Individuality':
def get_instance(cls) -> "Individuality":
"""获取Individuality单例实例
Returns:
Individuality: 单例实例
"""
if cls._instance is None:
cls._instance = cls()
return cls._instance
def initialize(self, bot_nickname: str, personality_core: str, personality_sides: list,
identity_detail: list, height: int, weight: int, age: int,
gender: str, appearance: str) -> None:
def initialize(
self,
bot_nickname: str,
personality_core: str,
personality_sides: list,
identity_detail: list,
height: int,
weight: int,
age: int,
gender: str,
appearance: str,
) -> None:
"""初始化个体特征
Args:
bot_nickname: 机器人昵称
personality_core: 人格核心特点
@@ -44,50 +55,43 @@ class Individuality:
"""
# 初始化人格
self.personality = Personality.initialize(
bot_nickname=bot_nickname,
personality_core=personality_core,
personality_sides=personality_sides
bot_nickname=bot_nickname, personality_core=personality_core, personality_sides=personality_sides
)
# 初始化身份
self.identity = Identity.initialize(
identity_detail=identity_detail,
height=height,
weight=weight,
age=age,
gender=gender,
appearance=appearance
identity_detail=identity_detail, height=height, weight=weight, age=age, gender=gender, appearance=appearance
)
def to_dict(self) -> dict:
"""将个体特征转换为字典格式"""
return {
"personality": self.personality.to_dict() if self.personality else None,
"identity": self.identity.to_dict() if self.identity else None
"identity": self.identity.to_dict() if self.identity else None,
}
@classmethod
def from_dict(cls, data: dict) -> 'Individuality':
def from_dict(cls, data: dict) -> "Individuality":
"""从字典创建个体特征实例"""
instance = cls.get_instance()
if data.get("personality"):
instance.personality = Personality.from_dict(data["personality"])
if data.get("identity"):
instance.identity = Identity.from_dict(data["identity"])
return instance
def get_prompt(self,type,x_person,level):
return instance
def get_prompt(self, type, x_person, level):
"""
获取个体特征的prompt
"""
if type == "personality":
return self.personality.get_prompt(x_person,level)
return self.personality.get_prompt(x_person, level)
elif type == "identity":
return self.identity.get_prompt(x_person,level)
return self.identity.get_prompt(x_person, level)
else:
return ""
def get_traits(self,factor):
def get_traits(self, factor):
"""
获取个体特征的特质
"""
@@ -101,5 +105,3 @@ class Individuality:
return self.personality.agreeableness
elif factor == "neuroticism":
return self.personality.neuroticism

98
src/individuality/per_bf_gen.py
View File

@@ -17,9 +17,9 @@ with open(config_path, "r", encoding="utf-8") as f:
config = toml.load(f)
# 现在可以导入src模块
from src.individuality.scene import get_scene_by_factor, PERSONALITY_SCENES #noqa E402
from src.individuality.questionnaire import FACTOR_DESCRIPTIONS #noqa E402
from src.individuality.offline_llm import LLM_request_off #noqa E402
from src.individuality.scene import get_scene_by_factor, PERSONALITY_SCENES # noqa E402
from src.individuality.questionnaire import FACTOR_DESCRIPTIONS # noqa E402
from src.individuality.offline_llm import LLM_request_off # noqa E402
# 加载环境变量
env_path = os.path.join(root_path, ".env")
@@ -32,13 +32,12 @@ else:
def adapt_scene(scene: str) -> str:
personality_core = config['personality']['personality_core']
personality_sides = config['personality']['personality_sides']
personality_core = config["personality"]["personality_core"]
personality_sides = config["personality"]["personality_sides"]
personality_side = random.choice(personality_sides)
identity_details = config['identity']['identity_detail']
identity_details = config["identity"]["identity_detail"]
identity_detail = random.choice(identity_details)
"""
根据config中的属性，改编场景使其更适合当前角色
@@ -51,10 +50,10 @@ def adapt_scene(scene: str) -> str:
try:
prompt = f"""
这是一个参与人格测评的角色形象:
- 昵称: {config['bot']['nickname']}
- 性别: {config['identity']['gender']}
- 年龄: {config['identity']['age']}岁
- 外貌: {config['identity']['appearance']}
- 昵称: {config["bot"]["nickname"]}
- 性别: {config["identity"]["gender"]}
- 年龄: {config["identity"]["age"]}岁
- 外貌: {config["identity"]["appearance"]}
- 性格核心: {personality_core}
- 性格侧面: {personality_side}
- 身份细节: {identity_detail}
@@ -62,18 +61,18 @@ def adapt_scene(scene: str) -> str:
请根据上述形象，改编以下场景，在测评中，用户将根据该场景给出上述角色形象的反应:
{scene}
保持场景的本质不变，但最好贴近生活且具体，并且让它更适合这个角色。
改编后的场景应该自然、连贯，并考虑角色的年龄、身份和性格特点。只返回改编后的场景描述，不要包含其他说明。注意{config['bot']['nickname']}是面对这个场景的人，而不是场景的其他人。场景中不会有其描述，
改编后的场景应该自然、连贯，并考虑角色的年龄、身份和性格特点。只返回改编后的场景描述，不要包含其他说明。注意{config["bot"]["nickname"]}是面对这个场景的人，而不是场景的其他人。场景中不会有其描述，
现在，请你给出改编后的场景描述
"""
llm = LLM_request_off(model_name=config['model']['llm_normal']['name'])
llm = LLM_request_off(model_name=config["model"]["llm_normal"]["name"])
adapted_scene, _ = llm.generate_response(prompt)
# 检查返回的场景是否为空或错误信息
if not adapted_scene or "错误" in adapted_scene or "失败" in adapted_scene:
print("场景改编失败，将使用原始场景")
return scene
return adapted_scene
except Exception as e:
print(f"场景改编过程出错：{str(e)}，将使用原始场景")
@@ -169,7 +168,7 @@ class PersonalityEvaluator_direct:
except Exception as e:
print(f"评估过程出错：{str(e)}")
return {dim: 3.5 for dim in dimensions}
def run_evaluation(self):
"""
运行整个评估过程
@@ -185,18 +184,23 @@ class PersonalityEvaluator_direct:
print(f"- 身份细节：{config['identity']['identity_detail']}")
print("\n准备好了吗？按回车键开始...")
input()
total_scenarios = len(self.scenarios)
progress_bar = tqdm(total=total_scenarios, desc="场景进度", ncols=100, bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]')
progress_bar = tqdm(
total=total_scenarios,
desc="场景进度",
ncols=100,
bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]",
)
for _i, scenario_data in enumerate(self.scenarios, 1):
# print(f"\n{'-' * 20} 场景 {i}/{total_scenarios} - {scenario_data['场景编号']} {'-' * 20}")
# 改编场景，使其更适合当前角色
print(f"{config['bot']['nickname']}祈祷中...")
adapted_scene = adapt_scene(scenario_data["场景"])
scenario_data["改编场景"] = adapted_scene
print(adapted_scene)
print(f"\n请描述{config['bot']['nickname']}在这种情况下会如何反应：")
response = input().strip()
@@ -220,13 +224,13 @@ class PersonalityEvaluator_direct:
# 更新进度条
progress_bar.update(1)
# if i < total_scenarios:
# print("\n按回车键继续下一个场景...")
# input()
# print("\n按回车键继续下一个场景...")
# input()
progress_bar.close()
# 计算平均分
for dimension in self.final_scores:
if self.dimension_counts[dimension] > 0:
@@ -241,26 +245,26 @@ class PersonalityEvaluator_direct:
# 返回评估结果
return self.get_result()
def get_result(self):
"""
获取评估结果
"""
return {
"final_scores": self.final_scores,
"dimension_counts": self.dimension_counts,
"final_scores": self.final_scores,
"dimension_counts": self.dimension_counts,
"scenarios": self.scenarios,
"bot_info": {
"nickname": config['bot']['nickname'],
"gender": config['identity']['gender'],
"age": config['identity']['age'],
"height": config['identity']['height'],
"weight": config['identity']['weight'],
"appearance": config['identity']['appearance'],
"personality_core": config['personality']['personality_core'],
"personality_sides": config['personality']['personality_sides'],
"identity_detail": config['identity']['identity_detail']
}
"nickname": config["bot"]["nickname"],
"gender": config["identity"]["gender"],
"age": config["identity"]["age"],
"height": config["identity"]["height"],
"weight": config["identity"]["weight"],
"appearance": config["identity"]["appearance"],
"personality_core": config["personality"]["personality_core"],
"personality_sides": config["personality"]["personality_sides"],
"identity_detail": config["identity"]["identity_detail"],
},
}
@@ -275,28 +279,28 @@ def main():
"extraversion": round(result["final_scores"]["外向性"] / 6, 1),
"agreeableness": round(result["final_scores"]["宜人性"] / 6, 1),
"neuroticism": round(result["final_scores"]["神经质"] / 6, 1),
"bot_nickname": config['bot']['nickname']
"bot_nickname": config["bot"]["nickname"],
}
# 确保目录存在
save_dir = os.path.join(root_path, "data", "personality")
os.makedirs(save_dir, exist_ok=True)
# 创建文件名，替换可能的非法字符
bot_name = config['bot']['nickname']
bot_name = config["bot"]["nickname"]
# 替换Windows文件名中不允许的字符
for char in ['\\', '/', ':', '*', '?', '"', '<', '>', '|']:
bot_name = bot_name.replace(char, '_')
for char in ["\\", "/", ":", "*", "?", '"', "<", ">", "|"]:
bot_name = bot_name.replace(char, "_")
file_name = f"{bot_name}_personality.per"
save_path = os.path.join(save_dir, file_name)
# 保存简化的结果
with open(save_path, "w", encoding="utf-8") as f:
json.dump(simplified_result, f, ensure_ascii=False, indent=4)
print(f"\n结果已保存到 {save_path}")
# 同时保存完整结果到results目录
os.makedirs("results", exist_ok=True)
with open("results/personality_result.json", "w", encoding="utf-8") as f:

66
src/individuality/personality.py
View File

@@ -4,9 +4,11 @@ import json
from pathlib import Path
import random
@dataclass
class Personality:
"""人格特质类"""
openness: float # 开放性
conscientiousness: float # 尽责性
extraversion: float # 外向性
@@ -15,45 +17,45 @@ class Personality:
bot_nickname: str # 机器人昵称
personality_core: str # 人格核心特点
personality_sides: List[str] # 人格侧面描述
_instance = None
def __new__(cls, *args, **kwargs):
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self, personality_core: str = "", personality_sides: List[str] = None):
if personality_sides is None:
personality_sides = []
self.personality_core = personality_core
self.personality_sides = personality_sides
@classmethod
def get_instance(cls) -> 'Personality':
def get_instance(cls) -> "Personality":
"""获取Personality单例实例
Returns:
Personality: 单例实例
"""
if cls._instance is None:
cls._instance = cls()
return cls._instance
def _init_big_five_personality(self):
"""初始化大五人格特质"""
# 构建文件路径
personality_file = Path("data/personality") / f"{self.bot_nickname}_personality.per"
# 如果文件存在，读取文件
if personality_file.exists():
with open(personality_file, 'r', encoding='utf-8') as f:
with open(personality_file, "r", encoding="utf-8") as f:
personality_data = json.load(f)
self.openness = personality_data.get('openness', 0.5)
self.conscientiousness = personality_data.get('conscientiousness', 0.5)
self.extraversion = personality_data.get('extraversion', 0.5)
self.agreeableness = personality_data.get('agreeableness', 0.5)
self.neuroticism = personality_data.get('neuroticism', 0.5)
self.openness = personality_data.get("openness", 0.5)
self.conscientiousness = personality_data.get("conscientiousness", 0.5)
self.extraversion = personality_data.get("extraversion", 0.5)
self.agreeableness = personality_data.get("agreeableness", 0.5)
self.neuroticism = personality_data.get("neuroticism", 0.5)
else:
# 如果文件不存在，根据personality_core和personality_core来设置大五人格特质
if "活泼" in self.personality_core or "开朗" in self.personality_sides:
@@ -62,31 +64,31 @@ class Personality:
else:
self.extraversion = 0.3
self.neuroticism = 0.5
if "认真" in self.personality_core or "负责" in self.personality_sides:
self.conscientiousness = 0.9
else:
self.conscientiousness = 0.5
if "友善" in self.personality_core or "温柔" in self.personality_sides:
self.agreeableness = 0.9
else:
self.agreeableness = 0.5
if "创新" in self.personality_core or "开放" in self.personality_sides:
self.openness = 0.8
else:
self.openness = 0.5
@classmethod
def initialize(cls, bot_nickname: str, personality_core: str, personality_sides: List[str]) -> 'Personality':
def initialize(cls, bot_nickname: str, personality_core: str, personality_sides: List[str]) -> "Personality":
"""初始化人格特质
Args:
bot_nickname: 机器人昵称
personality_core: 人格核心特点
personality_sides: 人格侧面描述
Returns:
Personality: 初始化后的人格特质实例
"""
@@ -96,7 +98,7 @@ class Personality:
instance.personality_sides = personality_sides
instance._init_big_five_personality()
return instance
def to_dict(self) -> Dict:
"""将人格特质转换为字典格式"""
return {
@@ -107,18 +109,18 @@ class Personality:
"neuroticism": self.neuroticism,
"bot_nickname": self.bot_nickname,
"personality_core": self.personality_core,
"personality_sides": self.personality_sides
"personality_sides": self.personality_sides,
}
@classmethod
def from_dict(cls, data: Dict) -> 'Personality':
def from_dict(cls, data: Dict) -> "Personality":
"""从字典创建人格特质实例"""
instance = cls.get_instance()
for key, value in data.items():
setattr(instance, key, value)
return instance
def get_prompt(self,x_person,level):
return instance
def get_prompt(self, x_person, level):
# 开始构建prompt
if x_person == 2:
prompt_personality = "你"
@@ -126,10 +128,10 @@ class Personality:
prompt_personality = "我"
else:
prompt_personality = "他"
#person
# person
prompt_personality += self.personality_core
if level == 2:
personality_sides = self.personality_sides
random.shuffle(personality_sides)
@@ -140,5 +142,5 @@ class Personality:
prompt_personality += f",{side}"
prompt_personality += "。"
return prompt_personality

9
src/individuality/scene.py
View File

@@ -2,6 +2,7 @@ import json
from typing import Dict
import os
def load_scenes() -> Dict:
"""
从JSON文件加载场景数据
@@ -10,13 +11,15 @@ def load_scenes() -> Dict:
Dict: 包含所有场景的字典
"""
current_dir = os.path.dirname(os.path.abspath(__file__))
json_path = os.path.join(current_dir, 'template_scene.json')
with open(json_path, 'r', encoding='utf-8') as f:
json_path = os.path.join(current_dir, "template_scene.json")
with open(json_path, "r", encoding="utf-8") as f:
return json.load(f)
PERSONALITY_SCENES = load_scenes()
def get_scene_by_factor(factor: str) -> Dict:
"""
根据人格因子获取对应的情景测试

10
src/main.py
View File

@@ -16,7 +16,7 @@ from .plugins.chat.bot import chat_bot
from .common.logger import get_module_logger
from .plugins.remote import heartbeat_thread # noqa: F401
from .individuality.individuality import Individuality
from .common.server import global_server
logger = get_module_logger("main")
@@ -33,6 +33,7 @@ class MainSystem:
from .plugins.message import global_api
self.app = global_api
self.server = global_server
async def initialize(self):
"""初始化系统组件"""
@@ -63,7 +64,7 @@ class MainSystem:
asyncio.create_task(person_info_manager.personal_habit_deduction())
# 启动愿望管理器
await willing_manager.ensure_started()
await willing_manager.async_task_starter()
# 启动消息处理器
if not self._message_manager_started:
@@ -100,7 +101,7 @@ class MainSystem:
weight=global_config.weight,
age=global_config.age,
gender=global_config.gender,
appearance=global_config.appearance
appearance=global_config.appearance,
)
logger.success("个体特征初始化成功")
@@ -126,6 +127,7 @@ class MainSystem:
emoji_manager.start_periodic_check_register(),
# emoji_manager.start_periodic_register(),
self.app.run(),
self.server.run(),
]
await asyncio.gather(*tasks)
@@ -135,7 +137,6 @@ class MainSystem:
await asyncio.sleep(global_config.build_memory_interval)
logger.info("正在进行记忆构建")
await HippocampusManager.get_instance().build_memory()
async def forget_memory_task(self):
"""记忆遗忘任务"""
@@ -144,7 +145,6 @@ class MainSystem:
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")
async def print_mood_task(self):
"""打印情绪状态"""

182
src/plugins/PFC/action_planner.py Normal file
View File

@@ -0,0 +1,182 @@
from typing import Tuple
from src.common.logger import get_module_logger
from ..models.utils_model import LLM_request
from ..config.config import global_config
from .chat_observer import ChatObserver
from .pfc_utils import get_items_from_json
from src.individuality.individuality import Individuality
from .observation_info import ObservationInfo
from .conversation_info import ConversationInfo
logger = get_module_logger("action_planner")
class ActionPlannerInfo:
def __init__(self):
self.done_action = []
self.goal_list = []
self.knowledge_list = []
self.memory_list = []
class ActionPlanner:
"""行动规划器"""
def __init__(self, stream_id: str):
self.llm = LLM_request(
model=global_config.llm_normal,
temperature=global_config.llm_normal["temp"],
max_tokens=1000,
request_type="action_planning",
)
self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=2)
self.name = global_config.BOT_NICKNAME
self.chat_observer = ChatObserver.get_instance(stream_id)
async def plan(self, observation_info: ObservationInfo, conversation_info: ConversationInfo) -> Tuple[str, str]:
"""规划下一步行动
Args:
observation_info: 决策信息
conversation_info: 对话信息
Returns:
Tuple[str, str]: (行动类型, 行动原因)
"""
# 构建提示词
logger.debug(f"开始规划行动：当前目标: {conversation_info.goal_list}")
# 构建对话目标
goals_str = ""
if conversation_info.goal_list:
for goal_reason in conversation_info.goal_list:
# 处理字典或元组格式
if isinstance(goal_reason, tuple):
# 假设元组的第一个元素是目标，第二个元素是原因
goal = goal_reason[0]
reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
elif isinstance(goal_reason, dict):
goal = goal_reason.get("goal")
reasoning = goal_reason.get("reasoning", "没有明确原因")
else:
# 如果是其他类型，尝试转为字符串
goal = str(goal_reason)
reasoning = "没有明确原因"
goal_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
goals_str += goal_str
else:
goal = "目前没有明确对话目标"
reasoning = "目前没有明确对话目标，最好思考一个对话目标"
goals_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
# 获取聊天历史记录
chat_history_list = (
observation_info.chat_history[-20:]
if len(observation_info.chat_history) >= 20
else observation_info.chat_history
)
chat_history_text = ""
for msg in chat_history_list:
chat_history_text += f"{msg.get('detailed_plain_text', '')}\n"
if observation_info.new_messages_count > 0:
new_messages_list = observation_info.unprocessed_messages
chat_history_text += f"有{observation_info.new_messages_count}条新消息：\n"
for msg in new_messages_list:
chat_history_text += f"{msg.get('detailed_plain_text', '')}\n"
observation_info.clear_unprocessed_messages()
personality_text = f"你的名字是{self.name}，{self.personality_info}"
# 构建action历史文本
action_history_list = (
conversation_info.done_action[-10:]
if len(conversation_info.done_action) >= 10
else conversation_info.done_action
)
action_history_text = "你之前做的事情是："
for action in action_history_list:
if isinstance(action, dict):
action_type = action.get("action")
action_reason = action.get("reason")
action_status = action.get("status")
if action_status == "recall":
action_history_text += (
f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
)
elif action_status == "done":
action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
elif isinstance(action, tuple):
# 假设元组的格式是(action_type, action_reason, action_status)
action_type = action[0] if len(action) > 0 else "未知行动"
action_reason = action[1] if len(action) > 1 else "未知原因"
action_status = action[2] if len(action) > 2 else "done"
if action_status == "recall":
action_history_text += (
f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
)
elif action_status == "done":
action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
prompt = f"""{personality_text}。现在你在参与一场QQ聊天，请分析以下内容，根据信息决定下一步行动：
当前对话目标：{goals_str}
{action_history_text}
最近的对话记录：
{chat_history_text}
请你接下去想想要你要做什么，可以发言，可以等待，可以倾听，可以调取知识。注意不同行动类型的要求，不要重复发言：
行动类型：
fetch_knowledge: 需要调取知识，当需要专业知识或特定信息时选择
wait: 当你做出了发言,对方尚未回复时暂时等待对方的回复
listening: 倾听对方发言，当你认为对方发言尚未结束时采用
direct_reply: 不符合上述情况，回复对方，注意不要过多或者重复发言
rethink_goal: 重新思考对话目标，当发现对话目标不合适时选择，会重新思考对话目标
end_conversation: 结束对话，长时间没回复或者当你觉得谈话暂时结束时选择，停止该场对话
请以JSON格式输出，包含以下字段：
1. action: 行动类型，注意你之前的行为
2. reason: 选择该行动的原因，注意你之前的行为（简要解释）
注意：请严格按照JSON格式输出，不要包含任何其他内容。"""
logger.debug(f"发送到LLM的提示词: {prompt}")
try:
content, _ = await self.llm.generate_response_async(prompt)
logger.debug(f"LLM原始返回内容: {content}")
# 使用简化函数提取JSON内容
success, result = get_items_from_json(
content, "action", "reason", default_values={"action": "direct_reply", "reason": "没有明确原因"}
)
if not success:
return "direct_reply", "JSON解析失败，选择直接回复"
action = result["action"]
reason = result["reason"]
# 验证action类型
if action not in [
"direct_reply",
"fetch_knowledge",
"wait",
"listening",
"rethink_goal",
"end_conversation",
]:
logger.warning(f"未知的行动类型: {action}，默认使用listening")
action = "listening"
logger.info(f"规划的行动: {action}")
logger.info(f"行动原因: {reason}")
return action, reason
except Exception as e:
logger.error(f"规划行动时出错: {str(e)}")
return "direct_reply", "发生错误，选择直接回复"

339
src/plugins/PFC/chat_observer.py
View File

@@ -1,252 +1,277 @@
import time
import asyncio
from typing import Optional, Dict, Any, List, Tuple
import traceback
from typing import Optional, Dict, Any, List
from src.common.logger import get_module_logger
from src.common.database import db
from ..message.message_base import UserInfo
from ..config.config import global_config
from .chat_states import NotificationManager, create_new_message_notification, create_cold_chat_notification
from .message_storage import MongoDBMessageStorage
logger = get_module_logger("chat_observer")
class ChatObserver:
"""聊天状态观察器"""
# 类级别的实例管理
_instances: Dict[str, 'ChatObserver'] = {}
_instances: Dict[str, "ChatObserver"] = {}
@classmethod
def get_instance(cls, stream_id: str) -> 'ChatObserver':
def get_instance(cls, stream_id: str) -> "ChatObserver":
"""获取或创建观察器实例
Args:
stream_id: 聊天流ID
Returns:
ChatObserver: 观察器实例
"""
if stream_id not in cls._instances:
cls._instances[stream_id] = cls(stream_id)
return cls._instances[stream_id]
def __init__(self, stream_id: str):
"""初始化观察器
Args:
stream_id: 聊天流ID
"""
if stream_id in self._instances:
raise RuntimeError(f"ChatObserver for {stream_id} already exists. Use get_instance() instead.")
self.stream_id = stream_id
self.last_user_speak_time: Optional[float] = None # 对方上次发言时间
self.last_bot_speak_time: Optional[float] = None # 机器人上次发言时间
self.last_check_time: float = time.time() # 上次查看聊天记录时间
self.last_message_read: Optional[str] = None # 最后读取的消息ID
self.last_message_time: Optional[float] = None # 最后一条消息的时间戳
self.waiting_start_time: Optional[float] = None # 等待开始时间
# 消息历史记录
self.message_history: List[Dict[str, Any]] = [] # 所有消息历史
self.last_message_id: Optional[str] = None # 最后一条消息的ID
self.message_count: int = 0 # 消息计数
self.message_storage = MongoDBMessageStorage()
# self.last_user_speak_time: Optional[float] = None # 对方上次发言时间
# self.last_bot_speak_time: Optional[float] = None # 机器人上次发言时间
# self.last_check_time: float = time.time() # 上次查看聊天记录时间
self.last_message_read: Optional[Dict[str, Any]] = None # 最后读取的消息ID
self.last_message_time: float = time.time()
self.waiting_start_time: float = time.time() # 等待开始时间，初始化为当前时间
# 运行状态
self._running: bool = False
self._task: Optional[asyncio.Task] = None
self._update_event = asyncio.Event() # 触发更新的事件
self._update_complete = asyncio.Event() # 更新完成的事件
def check(self) -> bool:
# 通知管理器
self.notification_manager = NotificationManager()
# 冷场检查配置
self.cold_chat_threshold: float = 60.0 # 60秒无消息判定为冷场
self.last_cold_chat_check: float = time.time()
self.is_cold_chat_state: bool = False
self.update_event = asyncio.Event()
self.update_interval = 2 # 更新间隔（秒）
self.message_cache = []
self.update_running = False
async def check(self) -> bool:
"""检查距离上一次观察之后是否有了新消息
Returns:
bool: 是否有新消息
"""
logger.debug(f"检查距离上一次观察之后是否有了新消息: {self.last_check_time}")
query = {
"chat_id": self.stream_id,
"time": {"$gt": self.last_check_time}
}
# 只需要查询是否存在，不需要获取具体消息
new_message_exists = db.messages.find_one(query) is not None
new_message_exists = await self.message_storage.has_new_messages(self.stream_id, self.last_check_time)
if new_message_exists:
logger.debug("发现新消息")
self.last_check_time = time.time()
return new_message_exists
def get_new_message(self) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
"""获取上一次观察的时间点后的新消息，插入到历史记录中，并返回新消息和历史记录两个对象"""
messages = self.get_message_history(self.last_check_time)
for message in messages:
self._add_message_to_history(message)
return messages, self.message_history
def new_message_after(self, time_point: float) -> bool:
"""判断是否在指定时间点后有新消息
Args:
time_point: 时间戳
Returns:
bool: 是否有新消息
"""
logger.debug(f"判断是否在指定时间点后有新消息: {self.last_message_time} > {time_point}")
return self.last_message_time is None or self.last_message_time > time_point
def _add_message_to_history(self, message: Dict[str, Any]):
"""添加消息到历史记录
async def _add_message_to_history(self, message: Dict[str, Any]):
"""添加消息到历史记录并发送通知
Args:
message: 消息数据
"""
self.message_history.append(message)
self.last_message_id = message["message_id"]
self.last_message_time = message["time"] # 更新最后消息时间
self.message_count += 1
# 更新说话时间
user_info = UserInfo.from_dict(message.get("user_info", {}))
if user_info.user_id == global_config.BOT_QQ:
self.last_bot_speak_time = message["time"]
try:
# 发送新消息通知
# logger.info(f"发送新ccchandleer消息通知: {message}")
notification = create_new_message_notification(
sender="chat_observer", target="observation_info", message=message
)
# logger.info(f"发送新消ddddd息通知: {notification}")
# print(self.notification_manager)
await self.notification_manager.send_notification(notification)
except Exception as e:
logger.error(f"添加消息到历史记录时出错: {e}")
print(traceback.format_exc())
# 检查并更新冷场状态
await self._check_cold_chat()
async def _check_cold_chat(self):
"""检查是否处于冷场状态并发送通知"""
current_time = time.time()
# 每10秒检查一次冷场状态
if current_time - self.last_cold_chat_check < 10:
return
self.last_cold_chat_check = current_time
# 判断是否冷场
is_cold = False
if self.last_message_time is None:
is_cold = True
else:
self.last_user_speak_time = message["time"]
is_cold = (current_time - self.last_message_time) > self.cold_chat_threshold
# 如果冷场状态发生变化，发送通知
if is_cold != self.is_cold_chat_state:
self.is_cold_chat_state = is_cold
notification = create_cold_chat_notification(sender="chat_observer", target="pfc", is_cold=is_cold)
await self.notification_manager.send_notification(notification)
def new_message_after(self, time_point: float) -> bool:
"""判断是否在指定时间点后有新消息
Args:
time_point: 时间戳
Returns:
bool: 是否有新消息
"""
if self.last_message_time is None:
logger.debug("没有最后消息时间，返回 False")
return False
has_new = self.last_message_time > time_point
logger.debug(f"判断是否在指定时间点后有新消息: {self.last_message_time} > {time_point} = {has_new}")
return has_new
def get_message_history(
self,
start_time: Optional[float] = None,
end_time: Optional[float] = None,
limit: Optional[int] = None,
user_id: Optional[str] = None
user_id: Optional[str] = None,
) -> List[Dict[str, Any]]:
"""获取消息历史
Args:
start_time: 开始时间戳
end_time: 结束时间戳
limit: 限制返回消息数量
user_id: 指定用户ID
Returns:
List[Dict[str, Any]]: 消息列表
"""
filtered_messages = self.message_history
if start_time is not None:
filtered_messages = [m for m in filtered_messages if m["time"] >= start_time]
if end_time is not None:
filtered_messages = [m for m in filtered_messages if m["time"] <= end_time]
if user_id is not None:
filtered_messages = [
m for m in filtered_messages
if UserInfo.from_dict(m.get("user_info", {})).user_id == user_id
m for m in filtered_messages if UserInfo.from_dict(m.get("user_info", {})).user_id == user_id
]
if limit is not None:
filtered_messages = filtered_messages[-limit:]
return filtered_messages
async def _fetch_new_messages(self) -> List[Dict[str, Any]]:
"""获取新消息
Returns:
List[Dict[str, Any]]: 新消息列表
"""
query = {"chat_id": self.stream_id}
if self.last_message_read:
# 获取ID大于last_message_read的消息
last_message = db.messages.find_one({"message_id": self.last_message_read})
if last_message:
query["time"] = {"$gt": last_message["time"]}
new_messages = list(
db.messages.find(query).sort("time", 1)
)
new_messages = await self.message_storage.get_messages_after(self.stream_id, self.last_message_time)
if new_messages:
self.last_message_read = new_messages[-1]["message_id"]
self.last_message_read = new_messages[-1]
self.last_message_time = new_messages[-1]["time"]
# print(f"获取数据库中找到的新消息: {new_messages}")
return new_messages
async def _fetch_new_messages_before(self, time_point: float) -> List[Dict[str, Any]]:
"""获取指定时间点之前的消息
Args:
time_point: 时间戳
Returns:
List[Dict[str, Any]]: 最多5条消息
"""
query = {
"chat_id": self.stream_id,
"time": {"$lt": time_point}
}
new_messages = list(
db.messages.find(query).sort("time", -1).limit(5) # 倒序获取5条
)
# 将消息按时间正序排列
new_messages.reverse()
new_messages = await self.message_storage.get_messages_before(self.stream_id, time_point)
if new_messages:
self.last_message_read = new_messages[-1]["message_id"]
logger.debug(f"获取指定时间点111之前的消息: {new_messages}")
return new_messages
"""主要观察循环"""
async def _update_loop(self):
"""更新循环"""
try:
start_time = time.time()
messages = await self._fetch_new_messages_before(start_time)
for message in messages:
self._add_message_to_history(message)
except Exception as e:
logger.error(f"缓冲消息出错: {e}")
# try:
# start_time = time.time()
# messages = await self._fetch_new_messages_before(start_time)
# for message in messages:
# await self._add_message_to_history(message)
# logger.debug(f"缓冲消息: {messages}")
# except Exception as e:
# logger.error(f"缓冲消息出错: {e}")
while self._running:
try:
# 等待事件或超时（1秒）
try:
# print("等待事件")
await asyncio.wait_for(self._update_event.wait(), timeout=1)
except asyncio.TimeoutError:
# print("超时")
pass # 超时后也执行一次检查
self._update_event.clear() # 重置触发事件
self._update_complete.clear() # 重置完成事件
# 获取新消息
new_messages = await self._fetch_new_messages()
if new_messages:
# 处理新消息
for message in new_messages:
self._add_message_to_history(message)
await self._add_message_to_history(message)
# 设置完成事件
self._update_complete.set()
except Exception as e:
logger.error(f"更新循环出错: {e}")
logger.error(traceback.format_exc())
self._update_complete.set() # 即使出错也要设置完成事件
def trigger_update(self):
"""触发一次立即更新"""
self._update_event.set()
async def wait_for_update(self, timeout: float = 5.0) -> bool:
"""等待更新完成
Args:
timeout: 超时时间（秒）
Returns:
bool: 是否成功完成更新（False表示超时）
"""
@@ -256,16 +281,16 @@ class ChatObserver:
except asyncio.TimeoutError:
logger.warning(f"等待更新完成超时（{timeout}秒）")
return False
def start(self):
"""启动观察器"""
if self._running:
return
self._running = True
self._task = asyncio.create_task(self._update_loop())
logger.info(f"ChatObserver for {self.stream_id} started")
def stop(self):
"""停止观察器"""
self._running = False
@@ -274,15 +299,15 @@ class ChatObserver:
if self._task:
self._task.cancel()
logger.info(f"ChatObserver for {self.stream_id} stopped")
async def process_chat_history(self, messages: list):
"""处理聊天历史
Args:
messages: 消息列表
"""
self.update_check_time()
for msg in messages:
try:
user_info = UserInfo.from_dict(msg.get("user_info", {}))
@@ -292,31 +317,55 @@ class ChatObserver:
self.update_user_speak_time(msg["time"])
except Exception as e:
logger.warning(f"处理消息时间时出错: {e}")
continue
continue
def update_check_time(self):
"""更新查看时间"""
self.last_check_time = time.time()
def update_bot_speak_time(self, speak_time: Optional[float] = None):
"""更新机器人说话时间"""
self.last_bot_speak_time = speak_time or time.time()
def update_user_speak_time(self, speak_time: Optional[float] = None):
"""更新用户说话时间"""
self.last_user_speak_time = speak_time or time.time()
def get_time_info(self) -> str:
"""获取时间信息文本"""
current_time = time.time()
time_info = ""
if self.last_bot_speak_time:
bot_speak_ago = current_time - self.last_bot_speak_time
time_info += f"\n距离你上次发言已经过去了{int(bot_speak_ago)}秒"
if self.last_user_speak_time:
user_speak_ago = current_time - self.last_user_speak_time
time_info += f"\n距离对方上次发言已经过去了{int(user_speak_ago)}秒"
return time_info
def get_cached_messages(self, limit: int = 50) -> List[Dict[str, Any]]:
"""获取缓存的消息历史
Args:
limit: 获取的最大消息数量，默认50
Returns:
List[Dict[str, Any]]: 缓存的消息历史列表
"""
return self.message_cache[:limit]
def get_last_message(self) -> Optional[Dict[str, Any]]:
"""获取最后一条消息
Returns:
Optional[Dict[str, Any]]: 最后一条消息，如果没有则返回None
"""
if not self.message_cache:
return None
return self.message_cache[0]
def __str__(self):
return f"ChatObserver for {self.stream_id}"

296
src/plugins/PFC/chat_states.py Normal file
View File

@@ -0,0 +1,296 @@
from enum import Enum, auto
from typing import Optional, Dict, Any, List, Set
from dataclasses import dataclass
from datetime import datetime
from abc import ABC, abstractmethod
class ChatState(Enum):
"""聊天状态枚举"""
NORMAL = auto() # 正常状态
NEW_MESSAGE = auto() # 有新消息
COLD_CHAT = auto() # 冷场状态
ACTIVE_CHAT = auto() # 活跃状态
BOT_SPEAKING = auto() # 机器人正在说话
USER_SPEAKING = auto() # 用户正在说话
SILENT = auto() # 沉默状态
ERROR = auto() # 错误状态
class NotificationType(Enum):
"""通知类型枚举"""
NEW_MESSAGE = auto() # 新消息通知
COLD_CHAT = auto() # 冷场通知
ACTIVE_CHAT = auto() # 活跃通知
BOT_SPEAKING = auto() # 机器人说话通知
USER_SPEAKING = auto() # 用户说话通知
MESSAGE_DELETED = auto() # 消息删除通知
USER_JOINED = auto() # 用户加入通知
USER_LEFT = auto() # 用户离开通知
ERROR = auto() # 错误通知
@dataclass
class ChatStateInfo:
"""聊天状态信息"""
state: ChatState
last_message_time: Optional[float] = None
last_message_content: Optional[str] = None
last_speaker: Optional[str] = None
message_count: int = 0
cold_duration: float = 0.0 # 冷场持续时间（秒）
active_duration: float = 0.0 # 活跃持续时间（秒）
@dataclass
class Notification:
"""通知基类"""
type: NotificationType
timestamp: float
sender: str # 发送者标识
target: str # 接收者标识
data: Dict[str, Any]
def to_dict(self) -> Dict[str, Any]:
"""转换为字典格式"""
return {"type": self.type.name, "timestamp": self.timestamp, "data": self.data}
@dataclass
class StateNotification(Notification):
"""持续状态通知"""
is_active: bool = True
def to_dict(self) -> Dict[str, Any]:
base_dict = super().to_dict()
base_dict["is_active"] = self.is_active
return base_dict
class NotificationHandler(ABC):
"""通知处理器接口"""
@abstractmethod
async def handle_notification(self, notification: Notification):
"""处理通知"""
pass
class NotificationManager:
"""通知管理器"""
def __init__(self):
# 按接收者和通知类型存储处理器
self._handlers: Dict[str, Dict[NotificationType, List[NotificationHandler]]] = {}
self._active_states: Set[NotificationType] = set()
self._notification_history: List[Notification] = []
def register_handler(self, target: str, notification_type: NotificationType, handler: NotificationHandler):
"""注册通知处理器
Args:
target: 接收者标识（例如："pfc"）
notification_type: 要处理的通知类型
handler: 处理器实例
"""
print(1145145511114445551111444)
if target not in self._handlers:
# print("没11有target")
self._handlers[target] = {}
if notification_type not in self._handlers[target]:
# print("没11有notification_type")
self._handlers[target][notification_type] = []
# print(self._handlers[target][notification_type])
# print(f"注册1111111111111111111111处理器: {target} {notification_type} {handler}")
self._handlers[target][notification_type].append(handler)
# print(self._handlers[target][notification_type])
def unregister_handler(self, target: str, notification_type: NotificationType, handler: NotificationHandler):
"""注销通知处理器
Args:
target: 接收者标识
notification_type: 通知类型
handler: 要注销的处理器实例
"""
if target in self._handlers and notification_type in self._handlers[target]:
handlers = self._handlers[target][notification_type]
if handler in handlers:
handlers.remove(handler)
# 如果该类型的处理器列表为空，删除该类型
if not handlers:
del self._handlers[target][notification_type]
# 如果该目标没有任何处理器，删除该目标
if not self._handlers[target]:
del self._handlers[target]
async def send_notification(self, notification: Notification):
"""发送通知"""
self._notification_history.append(notification)
# print("kaishichul-----------------------------------i")
# 如果是状态通知，更新活跃状态
if isinstance(notification, StateNotification):
if notification.is_active:
self._active_states.add(notification.type)
else:
self._active_states.discard(notification.type)
# 调用目标接收者的处理器
target = notification.target
if target in self._handlers:
handlers = self._handlers[target].get(notification.type, [])
# print(1111111)
print(handlers)
for handler in handlers:
print(f"调用处理器: {handler}")
await handler.handle_notification(notification)
def get_active_states(self) -> Set[NotificationType]:
"""获取当前活跃的状态"""
return self._active_states.copy()
def is_state_active(self, state_type: NotificationType) -> bool:
"""检查特定状态是否活跃"""
return state_type in self._active_states
def get_notification_history(
self, sender: Optional[str] = None, target: Optional[str] = None, limit: Optional[int] = None
) -> List[Notification]:
"""获取通知历史
Args:
sender: 过滤特定发送者的通知
target: 过滤特定接收者的通知
limit: 限制返回数量
"""
history = self._notification_history
if sender:
history = [n for n in history if n.sender == sender]
if target:
history = [n for n in history if n.target == target]
if limit is not None:
history = history[-limit:]
return history
def __str__(self):
str = ""
for target, handlers in self._handlers.items():
for notification_type, handler_list in handlers.items():
str += f"NotificationManager for {target} {notification_type} {handler_list}"
return str
# 一些常用的通知创建函数
def create_new_message_notification(sender: str, target: str, message: Dict[str, Any]) -> Notification:
"""创建新消息通知"""
return Notification(
type=NotificationType.NEW_MESSAGE,
timestamp=datetime.now().timestamp(),
sender=sender,
target=target,
data={
"message_id": message.get("message_id"),
"processed_plain_text": message.get("processed_plain_text"),
"detailed_plain_text": message.get("detailed_plain_text"),
"user_info": message.get("user_info"),
"time": message.get("time"),
},
)
def create_cold_chat_notification(sender: str, target: str, is_cold: bool) -> StateNotification:
"""创建冷场状态通知"""
return StateNotification(
type=NotificationType.COLD_CHAT,
timestamp=datetime.now().timestamp(),
sender=sender,
target=target,
data={"is_cold": is_cold},
is_active=is_cold,
)
def create_active_chat_notification(sender: str, target: str, is_active: bool) -> StateNotification:
"""创建活跃状态通知"""
return StateNotification(
type=NotificationType.ACTIVE_CHAT,
timestamp=datetime.now().timestamp(),
sender=sender,
target=target,
data={"is_active": is_active},
is_active=is_active,
)
class ChatStateManager:
"""聊天状态管理器"""
def __init__(self):
self.current_state = ChatState.NORMAL
self.state_info = ChatStateInfo(state=ChatState.NORMAL)
self.state_history: list[ChatStateInfo] = []
def update_state(self, new_state: ChatState, **kwargs):
"""更新聊天状态
Args:
new_state: 新的状态
**kwargs: 其他状态信息
"""
self.current_state = new_state
self.state_info.state = new_state
# 更新其他状态信息
for key, value in kwargs.items():
if hasattr(self.state_info, key):
setattr(self.state_info, key, value)
# 记录状态历史
self.state_history.append(self.state_info)
def get_current_state_info(self) -> ChatStateInfo:
"""获取当前状态信息"""
return self.state_info
def get_state_history(self) -> list[ChatStateInfo]:
"""获取状态历史"""
return self.state_history
def is_cold_chat(self, threshold: float = 60.0) -> bool:
"""判断是否处于冷场状态
Args:
threshold: 冷场阈值（秒）
Returns:
bool: 是否冷场
"""
if not self.state_info.last_message_time:
return True
current_time = datetime.now().timestamp()
return (current_time - self.state_info.last_message_time) > threshold
def is_active_chat(self, threshold: float = 5.0) -> bool:
"""判断是否处于活跃状态
Args:
threshold: 活跃阈值（秒）
Returns:
bool: 是否活跃
"""
if not self.state_info.last_message_time:
return False
current_time = datetime.now().timestamp()
return (current_time - self.state_info.last_message_time) <= threshold

247
src/plugins/PFC/conversation.py Normal file
View File

@@ -0,0 +1,247 @@
import asyncio
import datetime
from typing import Dict, Any
from ..chat.message import Message
from .pfc_types import ConversationState
from .pfc import ChatObserver, GoalAnalyzer, DirectMessageSender
from src.common.logger import get_module_logger
from .action_planner import ActionPlanner
from .observation_info import ObservationInfo
from .conversation_info import ConversationInfo
from .reply_generator import ReplyGenerator
from ..chat.chat_stream import ChatStream
from ..message.message_base import UserInfo
from src.plugins.chat.chat_stream import chat_manager
from .pfc_KnowledgeFetcher import KnowledgeFetcher
from .waiter import Waiter
import traceback
logger = get_module_logger("pfc_conversation")
class Conversation:
"""对话类，负责管理单个对话的状态和行为"""
def __init__(self, stream_id: str):
"""初始化对话实例
Args:
stream_id: 聊天流ID
"""
self.stream_id = stream_id
self.state = ConversationState.INIT
self.should_continue = False
# 回复相关
self.generated_reply = ""
async def _initialize(self):
"""初始化实例，注册所有组件"""
try:
self.action_planner = ActionPlanner(self.stream_id)
self.goal_analyzer = GoalAnalyzer(self.stream_id)
self.reply_generator = ReplyGenerator(self.stream_id)
self.knowledge_fetcher = KnowledgeFetcher()
self.waiter = Waiter(self.stream_id)
self.direct_sender = DirectMessageSender()
# 获取聊天流信息
self.chat_stream = chat_manager.get_stream(self.stream_id)
self.stop_action_planner = False
except Exception as e:
logger.error(f"初始化对话实例：注册运行组件失败: {e}")
logger.error(traceback.format_exc())
raise
try:
# 决策所需要的信息，包括自身自信和观察信息两部分
# 注册观察器和观测信息
self.chat_observer = ChatObserver.get_instance(self.stream_id)
self.chat_observer.start()
self.observation_info = ObservationInfo()
self.observation_info.bind_to_chat_observer(self.chat_observer)
# print(self.chat_observer.get_cached_messages(limit=)
self.conversation_info = ConversationInfo()
except Exception as e:
logger.error(f"初始化对话实例：注册信息组件失败: {e}")
logger.error(traceback.format_exc())
raise
# 组件准备完成，启动该论对话
self.should_continue = True
asyncio.create_task(self.start())
async def start(self):
"""开始对话流程"""
try:
logger.info("对话系统启动中...")
asyncio.create_task(self._plan_and_action_loop())
except Exception as e:
logger.error(f"启动对话系统失败: {e}")
raise
async def _plan_and_action_loop(self):
"""思考步，PFC核心循环模块"""
# 获取最近的消息历史
while self.should_continue:
# 使用决策信息来辅助行动规划
action, reason = await self.action_planner.plan(self.observation_info, self.conversation_info)
if self._check_new_messages_after_planning():
continue
# 执行行动
await self._handle_action(action, reason, self.observation_info, self.conversation_info)
for goal in self.conversation_info.goal_list:
# 检查goal是否为元组类型，如果是元组则使用索引访问，如果是字典则使用get方法
if isinstance(goal, tuple):
# 假设元组的第一个元素是目标内容
print(f"goal: {goal}")
if goal[0] == "结束对话":
self.should_continue = False
break
def _check_new_messages_after_planning(self):
"""检查在规划后是否有新消息"""
if self.observation_info.new_messages_count > 0:
logger.info(f"发现{self.observation_info.new_messages_count}条新消息，可能需要重新考虑行动")
# 如果需要，可以在这里添加逻辑来根据新消息重新决定行动
return True
return False
def _convert_to_message(self, msg_dict: Dict[str, Any]) -> Message:
"""将消息字典转换为Message对象"""
try:
chat_info = msg_dict.get("chat_info", {})
chat_stream = ChatStream.from_dict(chat_info)
user_info = UserInfo.from_dict(msg_dict.get("user_info", {}))
return Message(
message_id=msg_dict["message_id"],
chat_stream=chat_stream,
time=msg_dict["time"],
user_info=user_info,
processed_plain_text=msg_dict.get("processed_plain_text", ""),
detailed_plain_text=msg_dict.get("detailed_plain_text", ""),
)
except Exception as e:
logger.warning(f"转换消息时出错: {e}")
raise
async def _handle_action(
self, action: str, reason: str, observation_info: ObservationInfo, conversation_info: ConversationInfo
):
"""处理规划的行动"""
logger.info(f"执行行动: {action}, 原因: {reason}")
# 记录action历史，先设置为stop，完成后再设置为done
conversation_info.done_action.append(
{
"action": action,
"reason": reason,
"status": "start",
"time": datetime.datetime.now().strftime("%H:%M:%S"),
}
)
if action == "direct_reply":
self.waiter.wait_accumulated_time = 0
self.state = ConversationState.GENERATING
self.generated_reply = await self.reply_generator.generate(observation_info, conversation_info)
print(f"生成回复: {self.generated_reply}")
# # 检查回复是否合适
# is_suitable, reason, need_replan = await self.reply_generator.check_reply(
# self.generated_reply,
# self.current_goal
# )
if self._check_new_messages_after_planning():
logger.info("333333发现新消息，重新考虑行动")
conversation_info.done_action[-1].update(
{
"status": "recall",
"time": datetime.datetime.now().strftime("%H:%M:%S"),
}
)
return None
await self._send_reply()
conversation_info.done_action[-1].update(
{
"status": "done",
"time": datetime.datetime.now().strftime("%H:%M:%S"),
}
)
elif action == "fetch_knowledge":
self.waiter.wait_accumulated_time = 0
self.state = ConversationState.FETCHING
knowledge = "TODO:知识"
topic = "TODO:关键词"
logger.info(f"假装获取到知识{knowledge}，关键词是: {topic}")
if knowledge:
if topic not in self.conversation_info.knowledge_list:
self.conversation_info.knowledge_list.append({"topic": topic, "knowledge": knowledge})
else:
self.conversation_info.knowledge_list[topic] += knowledge
elif action == "rethink_goal":
self.waiter.wait_accumulated_time = 0
self.state = ConversationState.RETHINKING
await self.goal_analyzer.analyze_goal(conversation_info, observation_info)
elif action == "listening":
self.state = ConversationState.LISTENING
logger.info("倾听对方发言...")
await self.waiter.wait_listening(conversation_info)
elif action == "end_conversation":
self.should_continue = False
logger.info("决定结束对话...")
else: # wait
self.state = ConversationState.WAITING
logger.info("等待更多信息...")
await self.waiter.wait(self.conversation_info)
async def _send_timeout_message(self):
"""发送超时结束消息"""
try:
messages = self.chat_observer.get_cached_messages(limit=1)
if not messages:
return
latest_message = self._convert_to_message(messages[0])
await self.direct_sender.send_message(
chat_stream=self.chat_stream, content="TODO:超时消息", reply_to_message=latest_message
)
except Exception as e:
logger.error(f"发送超时消息失败: {str(e)}")
async def _send_reply(self):
"""发送回复"""
if not self.generated_reply:
logger.warning("没有生成回复")
return
try:
await self.direct_sender.send_message(chat_stream=self.chat_stream, content=self.generated_reply)
self.chat_observer.trigger_update() # 触发立即更新
if not await self.chat_observer.wait_for_update():
logger.warning("等待消息更新超时")
self.state = ConversationState.ANALYZING
except Exception as e:
logger.error(f"发送消息失败: {str(e)}")
self.state = ConversationState.ANALYZING

6
src/plugins/PFC/conversation_info.py Normal file
View File

@@ -0,0 +1,6 @@
class ConversationInfo:
def __init__(self):
self.done_action = []
self.goal_list = []
self.knowledge_list = []
self.memory_list = []

50
src/plugins/PFC/message_sender.py Normal file
View File

@@ -0,0 +1,50 @@
from typing import Optional
from src.common.logger import get_module_logger
from ..chat.chat_stream import ChatStream
from ..chat.message import Message
from ..message.message_base import Seg
from src.plugins.chat.message import MessageSending, MessageSet
from src.plugins.chat.message_sender import message_manager
logger = get_module_logger("message_sender")
class DirectMessageSender:
"""直接消息发送器"""
def __init__(self):
pass
async def send_message(
self,
chat_stream: ChatStream,
content: str,
reply_to_message: Optional[Message] = None,
) -> None:
"""发送消息到聊天流
Args:
chat_stream: 聊天流
content: 消息内容
reply_to_message: 要回复的消息（可选）
"""
try:
# 创建消息内容
segments = [Seg(type="text", data={"text": content})]
# 检查是否需要引用回复
if reply_to_message:
reply_id = reply_to_message.message_id
message_sending = MessageSending(segments=segments, reply_to_id=reply_id)
else:
message_sending = MessageSending(segments=segments)
# 发送消息
message_set = MessageSet(chat_stream, message_sending.message_id)
message_set.add_message(message_sending)
message_manager.add_message(message_set)
logger.info(f"PFC消息已发送: {content}")
except Exception as e:
logger.error(f"PFC消息发送失败: {str(e)}")
raise

124
src/plugins/PFC/message_storage.py Normal file
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@@ -0,0 +1,124 @@
from abc import ABC, abstractmethod
from typing import List, Dict, Any
from src.common.database import db
class MessageStorage(ABC):
"""消息存储接口"""
@abstractmethod
async def get_messages_after(self, chat_id: str, message: Dict[str, Any]) -> List[Dict[str, Any]]:
"""获取指定消息ID之后的所有消息
Args:
chat_id: 聊天ID
message: 消息
Returns:
List[Dict[str, Any]]: 消息列表
"""
pass
@abstractmethod
async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]:
"""获取指定时间点之前的消息
Args:
chat_id: 聊天ID
time_point: 时间戳
limit: 最大消息数量
Returns:
List[Dict[str, Any]]: 消息列表
"""
pass
@abstractmethod
async def has_new_messages(self, chat_id: str, after_time: float) -> bool:
"""检查是否有新消息
Args:
chat_id: 聊天ID
after_time: 时间戳
Returns:
bool: 是否有新消息
"""
pass
class MongoDBMessageStorage(MessageStorage):
"""MongoDB消息存储实现"""
def __init__(self):
self.db = db
async def get_messages_after(self, chat_id: str, message_time: float) -> List[Dict[str, Any]]:
query = {"chat_id": chat_id}
# print(f"storage_check_message: {message_time}")
query["time"] = {"$gt": message_time}
return list(self.db.messages.find(query).sort("time", 1))
async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]:
query = {"chat_id": chat_id, "time": {"$lt": time_point}}
messages = list(self.db.messages.find(query).sort("time", -1).limit(limit))
# 将消息按时间正序排列
messages.reverse()
return messages
async def has_new_messages(self, chat_id: str, after_time: float) -> bool:
query = {"chat_id": chat_id, "time": {"$gt": after_time}}
return self.db.messages.find_one(query) is not None
# # 创建一个内存消息存储实现，用于测试
# class InMemoryMessageStorage(MessageStorage):
# """内存消息存储实现，主要用于测试"""
# def __init__(self):
# self.messages: Dict[str, List[Dict[str, Any]]] = {}
# async def get_messages_after(self, chat_id: str, message_id: Optional[str] = None) -> List[Dict[str, Any]]:
# if chat_id not in self.messages:
# return []
# messages = self.messages[chat_id]
# if not message_id:
# return messages
# # 找到message_id的索引
# try:
# index = next(i for i, m in enumerate(messages) if m["message_id"] == message_id)
# return messages[index + 1:]
# except StopIteration:
# return []
# async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]:
# if chat_id not in self.messages:
# return []
# messages = [
# m for m in self.messages[chat_id]
# if m["time"] < time_point
# ]
# return messages[-limit:]
# async def has_new_messages(self, chat_id: str, after_time: float) -> bool:
# if chat_id not in self.messages:
# return False
# return any(m["time"] > after_time for m in self.messages[chat_id])
# # 测试辅助方法
# def add_message(self, chat_id: str, message: Dict[str, Any]):
# """添加测试消息"""
# if chat_id not in self.messages:
# self.messages[chat_id] = []
# self.messages[chat_id].append(message)
# self.messages[chat_id].sort(key=lambda m: m["time"])

235
src/plugins/PFC/observation_info.py Normal file
View File

@@ -0,0 +1,235 @@
# Programmable Friendly Conversationalist
# Prefrontal cortex
from typing import List, Optional, Dict, Any, Set
from ..message.message_base import UserInfo
import time
from dataclasses import dataclass, field
from src.common.logger import get_module_logger
from .chat_observer import ChatObserver
from .chat_states import NotificationHandler, NotificationType
logger = get_module_logger("observation_info")
class ObservationInfoHandler(NotificationHandler):
"""ObservationInfo的通知处理器"""
def __init__(self, observation_info: "ObservationInfo"):
"""初始化处理器
Args:
observation_info: 要更新的ObservationInfo实例
"""
self.observation_info = observation_info
async def handle_notification(self, notification):
# 获取通知类型和数据
notification_type = notification.type
data = notification.data
if notification_type == NotificationType.NEW_MESSAGE:
# 处理新消息通知
logger.debug(f"收到新消息通知data: {data}")
message_id = data.get("message_id")
processed_plain_text = data.get("processed_plain_text")
detailed_plain_text = data.get("detailed_plain_text")
user_info = data.get("user_info")
time_value = data.get("time")
message = {
"message_id": message_id,
"processed_plain_text": processed_plain_text,
"detailed_plain_text": detailed_plain_text,
"user_info": user_info,
"time": time_value,
}
self.observation_info.update_from_message(message)
elif notification_type == NotificationType.COLD_CHAT:
# 处理冷场通知
is_cold = data.get("is_cold", False)
self.observation_info.update_cold_chat_status(is_cold, time.time())
elif notification_type == NotificationType.ACTIVE_CHAT:
# 处理活跃通知
is_active = data.get("is_active", False)
self.observation_info.is_cold = not is_active
elif notification_type == NotificationType.BOT_SPEAKING:
# 处理机器人说话通知
self.observation_info.is_typing = False
self.observation_info.last_bot_speak_time = time.time()
elif notification_type == NotificationType.USER_SPEAKING:
# 处理用户说话通知
self.observation_info.is_typing = False
self.observation_info.last_user_speak_time = time.time()
elif notification_type == NotificationType.MESSAGE_DELETED:
# 处理消息删除通知
message_id = data.get("message_id")
self.observation_info.unprocessed_messages = [
msg for msg in self.observation_info.unprocessed_messages if msg.get("message_id") != message_id
]
elif notification_type == NotificationType.USER_JOINED:
# 处理用户加入通知
user_id = data.get("user_id")
if user_id:
self.observation_info.active_users.add(user_id)
elif notification_type == NotificationType.USER_LEFT:
# 处理用户离开通知
user_id = data.get("user_id")
if user_id:
self.observation_info.active_users.discard(user_id)
elif notification_type == NotificationType.ERROR:
# 处理错误通知
error_msg = data.get("error", "")
logger.error(f"收到错误通知: {error_msg}")
@dataclass
class ObservationInfo:
"""决策信息类，用于收集和管理来自chat_observer的通知信息"""
# data_list
chat_history: List[str] = field(default_factory=list)
unprocessed_messages: List[Dict[str, Any]] = field(default_factory=list)
active_users: Set[str] = field(default_factory=set)
# data
last_bot_speak_time: Optional[float] = None
last_user_speak_time: Optional[float] = None
last_message_time: Optional[float] = None
last_message_content: str = ""
last_message_sender: Optional[str] = None
bot_id: Optional[str] = None
chat_history_count: int = 0
new_messages_count: int = 0
cold_chat_duration: float = 0.0
# state
is_typing: bool = False
has_unread_messages: bool = False
is_cold_chat: bool = False
changed: bool = False
# #spec
# meta_plan_trigger: bool = False
def __post_init__(self):
"""初始化后创建handler"""
self.chat_observer = None
self.handler = ObservationInfoHandler(self)
def bind_to_chat_observer(self, chat_observer: ChatObserver):
"""绑定到指定的chat_observer
Args:
stream_id: 聊天流ID
"""
self.chat_observer = chat_observer
self.chat_observer.notification_manager.register_handler(
target="observation_info", notification_type=NotificationType.NEW_MESSAGE, handler=self.handler
)
self.chat_observer.notification_manager.register_handler(
target="observation_info", notification_type=NotificationType.COLD_CHAT, handler=self.handler
)
print("1919810------------------------绑定-----------------------------")
def unbind_from_chat_observer(self):
"""解除与chat_observer的绑定"""
if self.chat_observer:
self.chat_observer.notification_manager.unregister_handler(
target="observation_info", notification_type=NotificationType.NEW_MESSAGE, handler=self.handler
)
self.chat_observer.notification_manager.unregister_handler(
target="observation_info", notification_type=NotificationType.COLD_CHAT, handler=self.handler
)
self.chat_observer = None
def update_from_message(self, message: Dict[str, Any]):
"""从消息更新信息
Args:
message: 消息数据
"""
# print("1919810-----------------------------------------------------")
# logger.debug(f"更新信息from_message: {message}")
self.last_message_time = message["time"]
self.last_message_id = message["message_id"]
self.last_message_content = message.get("processed_plain_text", "")
user_info = UserInfo.from_dict(message.get("user_info", {}))
self.last_message_sender = user_info.user_id
if user_info.user_id == self.bot_id:
self.last_bot_speak_time = message["time"]
else:
self.last_user_speak_time = message["time"]
self.active_users.add(user_info.user_id)
self.new_messages_count += 1
self.unprocessed_messages.append(message)
self.update_changed()
def update_changed(self):
"""更新changed状态"""
self.changed = True
def update_cold_chat_status(self, is_cold: bool, current_time: float):
"""更新冷场状态
Args:
is_cold: 是否冷场
current_time: 当前时间
"""
self.is_cold_chat = is_cold
if is_cold and self.last_message_time:
self.cold_chat_duration = current_time - self.last_message_time
def get_active_duration(self) -> float:
"""获取当前活跃时长
Returns:
float: 最后一条消息到现在的时长（秒）
"""
if not self.last_message_time:
return 0.0
return time.time() - self.last_message_time
def get_user_response_time(self) -> Optional[float]:
"""获取用户响应时间
Returns:
Optional[float]: 用户最后发言到现在的时长（秒），如果没有用户发言则返回None
"""
if not self.last_user_speak_time:
return None
return time.time() - self.last_user_speak_time
def get_bot_response_time(self) -> Optional[float]:
"""获取机器人响应时间
Returns:
Optional[float]: 机器人最后发言到现在的时长（秒），如果没有机器人发言则返回None
"""
if not self.last_bot_speak_time:
return None
return time.time() - self.last_bot_speak_time
def clear_unprocessed_messages(self):
"""清空未处理消息列表"""
# 将未处理消息添加到历史记录中
for message in self.unprocessed_messages:
self.chat_history.append(message)
# 清空未处理消息列表
self.has_unread_messages = False
self.unprocessed_messages.clear()
self.chat_history_count = len(self.chat_history)
self.new_messages_count = 0

1032
src/plugins/PFC/pfc.py
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File diff suppressed because it is too large Load Diff

23
src/plugins/PFC/pfc_KnowledgeFetcher.py
View File

@@ -7,24 +7,25 @@ from ..chat.message import Message
logger = get_module_logger("knowledge_fetcher")
class KnowledgeFetcher:
"""知识调取器"""
def __init__(self):
self.llm = LLM_request(
model=global_config.llm_normal,
temperature=0.7,
temperature=global_config.llm_normal["temp"],
max_tokens=1000,
request_type="knowledge_fetch"
request_type="knowledge_fetch",
)
async def fetch(self, query: str, chat_history: List[Message]) -> Tuple[str, str]:
"""获取相关知识
Args:
query: 查询内容
chat_history: 聊天历史
Returns:
Tuple[str, str]: (获取的知识, 知识来源)
"""
@@ -33,16 +34,16 @@ class KnowledgeFetcher:
for msg in chat_history:
# sender = msg.message_info.user_info.user_nickname or f"用户{msg.message_info.user_info.user_id}"
chat_history_text += f"{msg.detailed_plain_text}\n"
# 从记忆中获取相关知识
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
text=f"{query}\n{chat_history_text}",
max_memory_num=3,
max_memory_length=2,
max_depth=3,
fast_retrieval=False
fast_retrieval=False,
)
if related_memory:
knowledge = ""
sources = []
@@ -50,5 +51,5 @@ class KnowledgeFetcher:
knowledge += memory[1] + "\n"
sources.append(f"记忆片段{memory[0]}")
return knowledge.strip(), "，".join(sources)
return "未找到相关知识", "无记忆匹配"
return "未找到相关知识", "无记忆匹配"

96
src/plugins/PFC/pfc_manager.py Normal file
View File

@@ -0,0 +1,96 @@
from typing import Dict, Optional
from src.common.logger import get_module_logger
from .conversation import Conversation
import traceback
logger = get_module_logger("pfc_manager")
class PFCManager:
"""PFC对话管理器，负责管理所有对话实例"""
# 单例模式
_instance = None
# 会话实例管理
_instances: Dict[str, Conversation] = {}
_initializing: Dict[str, bool] = {}
@classmethod
def get_instance(cls) -> "PFCManager":
"""获取管理器单例
Returns:
PFCManager: 管理器实例
"""
if cls._instance is None:
cls._instance = PFCManager()
return cls._instance
async def get_or_create_conversation(self, stream_id: str) -> Optional[Conversation]:
"""获取或创建对话实例
Args:
stream_id: 聊天流ID
Returns:
Optional[Conversation]: 对话实例，创建失败则返回None
"""
# 检查是否已经有实例
if stream_id in self._initializing and self._initializing[stream_id]:
logger.debug(f"会话实例正在初始化中: {stream_id}")
return None
if stream_id in self._instances and self._instances[stream_id].should_continue:
logger.debug(f"使用现有会话实例: {stream_id}")
return self._instances[stream_id]
try:
# 创建新实例
logger.info(f"创建新的对话实例: {stream_id}")
self._initializing[stream_id] = True
# 创建实例
conversation_instance = Conversation(stream_id)
self._instances[stream_id] = conversation_instance
# 启动实例初始化
await self._initialize_conversation(conversation_instance)
except Exception as e:
logger.error(f"创建会话实例失败: {stream_id}, 错误: {e}")
return None
return conversation_instance
async def _initialize_conversation(self, conversation: Conversation):
"""初始化会话实例
Args:
conversation: 要初始化的会话实例
"""
stream_id = conversation.stream_id
try:
logger.info(f"开始初始化会话实例: {stream_id}")
# 启动初始化流程
await conversation._initialize()
# 标记初始化完成
self._initializing[stream_id] = False
logger.info(f"会话实例 {stream_id} 初始化完成")
except Exception as e:
logger.error(f"管理器初始化会话实例失败: {stream_id}, 错误: {e}")
logger.error(traceback.format_exc())
# 清理失败的初始化
async def get_conversation(self, stream_id: str) -> Optional[Conversation]:
"""获取已存在的会话实例
Args:
stream_id: 聊天流ID
Returns:
Optional[Conversation]: 会话实例，不存在则返回None
"""
return self._instances.get(stream_id)

22
src/plugins/PFC/pfc_types.py Normal file
View File

@@ -0,0 +1,22 @@
from enum import Enum
from typing import Literal
class ConversationState(Enum):
"""对话状态"""
INIT = "初始化"
RETHINKING = "重新思考"
ANALYZING = "分析历史"
PLANNING = "规划目标"
GENERATING = "生成回复"
CHECKING = "检查回复"
SENDING = "发送消息"
FETCHING = "获取知识"
WAITING = "等待"
LISTENING = "倾听"
ENDED = "结束"
JUDGING = "判断"
ActionType = Literal["direct_reply", "fetch_knowledge", "wait"]

85
src/plugins/PFC/pfc_utils.py
View File

@@ -1,40 +1,93 @@
import json
import re
from typing import Dict, Any, Optional, Tuple
from typing import Dict, Any, Optional, Tuple, List, Union
from src.common.logger import get_module_logger
logger = get_module_logger("pfc_utils")
def get_items_from_json(
content: str,
*items: str,
default_values: Optional[Dict[str, Any]] = None,
required_types: Optional[Dict[str, type]] = None
) -> Tuple[bool, Dict[str, Any]]:
required_types: Optional[Dict[str, type]] = None,
allow_array: bool = True,
) -> Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]:
"""从文本中提取JSON内容并获取指定字段
Args:
content: 包含JSON的文本
*items: 要提取的字段名
default_values: 字段的默认值，格式为 {字段名: 默认值}
required_types: 字段的必需类型，格式为 {字段名: 类型}
allow_array: 是否允许解析JSON数组
Returns:
Tuple[bool, Dict[str, Any]]: (是否成功, 提取的字段字典)
Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]: (是否成功, 提取的字段字典或字典列表)
"""
content = content.strip()
result = {}
# 设置默认值
if default_values:
result.update(default_values)
# 尝试解析JSON
# 首先尝试解析为JSON数组
if allow_array:
try:
# 尝试找到文本中的JSON数组
array_pattern = r"\[[\s\S]*\]"
array_match = re.search(array_pattern, content)
if array_match:
array_content = array_match.group()
json_array = json.loads(array_content)
# 确认是数组类型
if isinstance(json_array, list):
# 验证数组中的每个项目是否包含所有必需字段
valid_items = []
for item in json_array:
if not isinstance(item, dict):
continue
# 检查是否有所有必需字段
if all(field in item for field in items):
# 验证字段类型
if required_types:
type_valid = True
for field, expected_type in required_types.items():
if field in item and not isinstance(item[field], expected_type):
type_valid = False
break
if not type_valid:
continue
# 验证字符串字段不为空
string_valid = True
for field in items:
if isinstance(item[field], str) and not item[field].strip():
string_valid = False
break
if not string_valid:
continue
valid_items.append(item)
if valid_items:
return True, valid_items
except json.JSONDecodeError:
logger.debug("JSON数组解析失败，尝试解析单个JSON对象")
except Exception as e:
logger.debug(f"尝试解析JSON数组时出错: {str(e)}")
# 尝试解析JSON对象
try:
json_data = json.loads(content)
except json.JSONDecodeError:
# 如果直接解析失败，尝试查找和提取JSON部分
json_pattern = r'\{[^{}]*\}'
json_pattern = r"\{[^{}]*\}"
json_match = re.search(json_pattern, content)
if json_match:
try:
@@ -45,28 +98,28 @@ def get_items_from_json(
else:
logger.error("无法在返回内容中找到有效的JSON")
return False, result
# 提取字段
for item in items:
if item in json_data:
result[item] = json_data[item]
# 验证必需字段
if not all(item in result for item in items):
logger.error(f"JSON缺少必要字段，实际内容: {json_data}")
return False, result
# 验证字段类型
if required_types:
for field, expected_type in required_types.items():
if field in result and not isinstance(result[field], expected_type):
logger.error(f"{field} 必须是 {expected_type.__name__} 类型")
return False, result
# 验证字符串字段不为空
for field in items:
if isinstance(result[field], str) and not result[field].strip():
logger.error(f"{field} 不能为空")
return False, result
return True, result
return True, result

46
src/plugins/PFC/reply_checker.py
View File

@@ -9,38 +9,31 @@ from ..message.message_base import UserInfo
logger = get_module_logger("reply_checker")
class ReplyChecker:
"""回复检查器"""
def __init__(self, stream_id: str):
self.llm = LLM_request(
model=global_config.llm_normal,
temperature=0.7,
max_tokens=1000,
request_type="reply_check"
model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="reply_check"
)
self.name = global_config.BOT_NICKNAME
self.chat_observer = ChatObserver.get_instance(stream_id)
self.max_retries = 2 # 最大重试次数
async def check(
self,
reply: str,
goal: str,
retry_count: int = 0
) -> Tuple[bool, str, bool]:
async def check(self, reply: str, goal: str, retry_count: int = 0) -> Tuple[bool, str, bool]:
"""检查生成的回复是否合适
Args:
reply: 生成的回复
goal: 对话目标
retry_count: 当前重试次数
Returns:
Tuple[bool, str, bool]: (是否合适, 原因, 是否需要重新规划)
"""
# 获取最新的消息记录
messages = self.chat_observer.get_message_history(limit=5)
messages = self.chat_observer.get_cached_messages(limit=5)
chat_history_text = ""
for msg in messages:
time_str = datetime.datetime.fromtimestamp(msg["time"]).strftime("%H:%M:%S")
@@ -49,7 +42,7 @@ class ReplyChecker:
if sender == self.name:
sender = "你说"
chat_history_text += f"{time_str},{sender}:{msg.get('processed_plain_text', '')}\n"
prompt = f"""请检查以下回复是否合适：
当前对话目标：{goal}
@@ -83,7 +76,7 @@ class ReplyChecker:
try:
content, _ = await self.llm.generate_response_async(prompt)
logger.debug(f"检查回复的原始返回: {content}")
# 清理内容，尝试提取JSON部分
content = content.strip()
try:
@@ -92,7 +85,8 @@ class ReplyChecker:
except json.JSONDecodeError:
# 如果直接解析失败，尝试查找和提取JSON部分
import re
json_pattern = r'\{[^{}]*\}'
json_pattern = r"\{[^{}]*\}"
json_match = re.search(json_pattern, content)
if json_match:
try:
@@ -109,33 +103,33 @@ class ReplyChecker:
reason = content[:100] if content else "无法解析响应"
need_replan = "重新规划" in content.lower() or "目标不适合" in content.lower()
return is_suitable, reason, need_replan
# 验证JSON字段
suitable = result.get("suitable", None)
reason = result.get("reason", "未提供原因")
need_replan = result.get("need_replan", False)
# 如果suitable字段是字符串，转换为布尔值
if isinstance(suitable, str):
suitable = suitable.lower() == "true"
# 如果suitable字段不存在或不是布尔值，从reason中判断
if suitable is None:
suitable = "不合适" not in reason.lower() and "违规" not in reason.lower()
# 如果不合适且未达到最大重试次数，返回需要重试
if not suitable and retry_count < self.max_retries:
return False, reason, False
# 如果不合适且已达到最大重试次数，返回需要重新规划
if not suitable and retry_count >= self.max_retries:
return False, f"多次重试后仍不合适: {reason}", True
return suitable, reason, need_replan
except Exception as e:
logger.error(f"检查回复时出错: {e}")
# 如果出错且已达到最大重试次数，建议重新规划
if retry_count >= self.max_retries:
return False, "多次检查失败，建议重新规划", True
return False, f"检查过程出错，建议重试: {str(e)}", False
return False, f"检查过程出错，建议重试: {str(e)}", False

171
src/plugins/PFC/reply_generator.py Normal file
View File

@@ -0,0 +1,171 @@
from typing import Tuple
from src.common.logger import get_module_logger
from ..models.utils_model import LLM_request
from ..config.config import global_config
from .chat_observer import ChatObserver
from .reply_checker import ReplyChecker
from src.individuality.individuality import Individuality
from .observation_info import ObservationInfo
from .conversation_info import ConversationInfo
logger = get_module_logger("reply_generator")
class ReplyGenerator:
"""回复生成器"""
def __init__(self, stream_id: str):
self.llm = LLM_request(
model=global_config.llm_normal,
temperature=global_config.llm_normal["temp"],
max_tokens=300,
request_type="reply_generation",
)
self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=2)
self.name = global_config.BOT_NICKNAME
self.chat_observer = ChatObserver.get_instance(stream_id)
self.reply_checker = ReplyChecker(stream_id)
async def generate(self, observation_info: ObservationInfo, conversation_info: ConversationInfo) -> str:
"""生成回复
Args:
goal: 对话目标
chat_history: 聊天历史
knowledge_cache: 知识缓存
previous_reply: 上一次生成的回复（如果有）
retry_count: 当前重试次数
Returns:
str: 生成的回复
"""
# 构建提示词
logger.debug(f"开始生成回复：当前目标: {conversation_info.goal_list}")
# 构建对话目标
goals_str = ""
if conversation_info.goal_list:
for goal_reason in conversation_info.goal_list:
# 处理字典或元组格式
if isinstance(goal_reason, tuple):
# 假设元组的第一个元素是目标，第二个元素是原因
goal = goal_reason[0]
reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
elif isinstance(goal_reason, dict):
goal = goal_reason.get("goal")
reasoning = goal_reason.get("reasoning", "没有明确原因")
else:
# 如果是其他类型，尝试转为字符串
goal = str(goal_reason)
reasoning = "没有明确原因"
goal_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
goals_str += goal_str
else:
goal = "目前没有明确对话目标"
reasoning = "目前没有明确对话目标，最好思考一个对话目标"
goals_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
# 获取聊天历史记录
chat_history_list = (
observation_info.chat_history[-20:]
if len(observation_info.chat_history) >= 20
else observation_info.chat_history
)
chat_history_text = ""
for msg in chat_history_list:
chat_history_text += f"{msg.get('detailed_plain_text', '')}\n"
if observation_info.new_messages_count > 0:
new_messages_list = observation_info.unprocessed_messages
chat_history_text += f"有{observation_info.new_messages_count}条新消息：\n"
for msg in new_messages_list:
chat_history_text += f"{msg.get('detailed_plain_text', '')}\n"
observation_info.clear_unprocessed_messages()
personality_text = f"你的名字是{self.name}，{self.personality_info}"
# 构建action历史文本
action_history_list = (
conversation_info.done_action[-10:]
if len(conversation_info.done_action) >= 10
else conversation_info.done_action
)
action_history_text = "你之前做的事情是："
for action in action_history_list:
if isinstance(action, dict):
action_type = action.get("action")
action_reason = action.get("reason")
action_status = action.get("status")
if action_status == "recall":
action_history_text += (
f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
)
elif action_status == "done":
action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
elif isinstance(action, tuple):
# 假设元组的格式是(action_type, action_reason, action_status)
action_type = action[0] if len(action) > 0 else "未知行动"
action_reason = action[1] if len(action) > 1 else "未知原因"
action_status = action[2] if len(action) > 2 else "done"
if action_status == "recall":
action_history_text += (
f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
)
elif action_status == "done":
action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
prompt = f"""{personality_text}。现在你在参与一场QQ聊天，请根据以下信息生成回复：
当前对话目标：{goals_str}
最近的聊天记录：
{chat_history_text}
请根据上述信息，以你的性格特征生成一个自然、得体的回复。回复应该：
1. 符合对话目标，以"你"的角度发言
2. 体现你的性格特征
3. 自然流畅，像正常聊天一样，简短
4. 适当利用相关知识，但不要生硬引用
请注意把握聊天内容，不要回复的太有条理，可以有个性。请分清"你"和对方说的话，不要把"你"说的话当做对方说的话，这是你自己说的话。
请你回复的平淡一些，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
请你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。
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 )。
请直接输出回复内容，不需要任何额外格式。"""
try:
content, _ = await self.llm.generate_response_async(prompt)
logger.info(f"生成的回复: {content}")
# is_new = self.chat_observer.check()
# logger.debug(f"再看一眼聊天记录，{'有' if is_new else '没有'}新消息")
# 如果有新消息,重新生成回复
# if is_new:
# logger.info("检测到新消息,重新生成回复")
# return await self.generate(
# goal, chat_history, knowledge_cache,
# None, retry_count
# )
return content
except Exception as e:
logger.error(f"生成回复时出错: {e}")
return "抱歉，我现在有点混乱，让我重新思考一下..."
async def check_reply(self, reply: str, goal: str, retry_count: int = 0) -> Tuple[bool, str, bool]:
"""检查回复是否合适
Args:
reply: 生成的回复
goal: 对话目标
retry_count: 当前重试次数
Returns:
Tuple[bool, str, bool]: (是否合适, 原因, 是否需要重新规划)
"""
return await self.reply_checker.check(reply, goal, retry_count)

85
src/plugins/PFC/waiter.py Normal file
View File

@@ -0,0 +1,85 @@
from src.common.logger import get_module_logger
from .chat_observer import ChatObserver
from .conversation_info import ConversationInfo
from src.individuality.individuality import Individuality
from ..config.config import global_config
import time
import asyncio
logger = get_module_logger("waiter")
class Waiter:
"""快 速 等 待"""
def __init__(self, stream_id: str):
self.chat_observer = ChatObserver.get_instance(stream_id)
self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=2)
self.name = global_config.BOT_NICKNAME
self.wait_accumulated_time = 0
async def wait(self, conversation_info: ConversationInfo) -> bool:
"""等待
Returns:
bool: 是否超时（True表示超时）
"""
# 使用当前时间作为等待开始时间
wait_start_time = time.time()
self.chat_observer.waiting_start_time = wait_start_time # 设置等待开始时间
while True:
# 检查是否有新消息
if self.chat_observer.new_message_after(wait_start_time):
logger.info("等待结束，收到新消息")
return False
# 检查是否超时
if time.time() - wait_start_time > 300:
self.wait_accumulated_time += 300
logger.info("等待超过300秒，结束对话")
wait_goal = {
"goal": f"你等待了{self.wait_accumulated_time / 60}分钟，思考接下来要做什么",
"reason": "对方很久没有回复你的消息了",
}
conversation_info.goal_list.append(wait_goal)
print(f"添加目标: {wait_goal}")
return True
await asyncio.sleep(1)
logger.info("等待中...")
async def wait_listening(self, conversation_info: ConversationInfo) -> bool:
"""等待倾听
Returns:
bool: 是否超时（True表示超时）
"""
# 使用当前时间作为等待开始时间
wait_start_time = time.time()
self.chat_observer.waiting_start_time = wait_start_time # 设置等待开始时间
while True:
# 检查是否有新消息
if self.chat_observer.new_message_after(wait_start_time):
logger.info("等待结束，收到新消息")
return False
# 检查是否超时
if time.time() - wait_start_time > 300:
self.wait_accumulated_time += 300
logger.info("等待超过300秒，结束对话")
wait_goal = {
"goal": f"你等待了{self.wait_accumulated_time / 60}分钟，思考接下来要做什么",
"reason": "对方话说一半消失了，很久没有回复",
}
conversation_info.goal_list.append(wait_goal)
print(f"添加目标: {wait_goal}")
return True
await asyncio.sleep(1)
logger.info("等待中...")

2
src/plugins/chat/__init__.py
View File

@@ -3,7 +3,6 @@ from ..person_info.relationship_manager import relationship_manager
from .chat_stream import chat_manager
from .message_sender import message_manager
from ..storage.storage import MessageStorage
from .auto_speak import auto_speak_manager
__all__ = [
@@ -12,5 +11,4 @@ __all__ = [
"chat_manager",
"message_manager",
"MessageStorage",
"auto_speak_manager"
]

180
src/plugins/chat/auto_speak.py
View File

@@ -1,180 +0,0 @@
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 ..chat_module.reasoning_chat.reasoning_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()

144
src/plugins/chat/bot.py
View File

@@ -1,14 +1,14 @@
from ..moods.moods import MoodManager # 导入情绪管理器
from ..config.config import global_config
from .message import MessageRecv
from ..PFC.pfc import Conversation, ConversationState
from ..PFC.pfc_manager import PFCManager
from .chat_stream import chat_manager
from ..chat_module.only_process.only_message_process import MessageProcessor
from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
from ..chat_module.think_flow_chat.think_flow_chat import ThinkFlowChat
from ..chat_module.reasoning_chat.reasoning_chat import ReasoningChat
import asyncio
from ..utils.prompt_builder import Prompt, global_prompt_manager
import traceback
# 定义日志配置
@@ -32,37 +32,25 @@ class ChatBot:
self.reasoning_chat = ReasoningChat()
self.only_process_chat = MessageProcessor()
# 创建初始化PFC管理器的任务，会在_ensure_started时执行
self.pfc_manager = PFCManager.get_instance()
async def _ensure_started(self):
"""确保所有任务已启动"""
if not self._started:
logger.trace("确保ChatBot所有任务已启动")
self._started = True
async def _create_PFC_chat(self, message: MessageRecv):
try:
chat_id = str(message.chat_stream.stream_id)
if global_config.enable_pfc_chatting:
# 获取或创建对话实例
conversation = await Conversation.get_instance(chat_id)
if conversation is None:
logger.error(f"创建或获取对话实例失败: {chat_id}")
return
# 如果是新创建的实例，启动对话系统
if conversation.state == ConversationState.INIT:
asyncio.create_task(conversation.start())
logger.info(f"为聊天 {chat_id} 创建新的对话实例")
elif conversation.state == ConversationState.ENDED:
# 如果实例已经结束，重新创建
await Conversation.remove_instance(chat_id)
conversation = await Conversation.get_instance(chat_id)
if conversation is None:
logger.error(f"重新创建对话实例失败: {chat_id}")
return
asyncio.create_task(conversation.start())
logger.info(f"为聊天 {chat_id} 重新创建对话实例")
await self.pfc_manager.get_or_create_conversation(chat_id)
except Exception as e:
logger.error(f"创建PFC聊天流失败: {e}")
logger.error(f"创建PFC聊天失败: {e}")
async def message_process(self, message_data: str) -> None:
"""处理转化后的统一格式消息
@@ -71,16 +59,12 @@ class ChatBot:
- 包含思维流状态管理
- 在回复前进行观察和状态更新
- 回复后更新思维流状态
2. reasoning模式：使用推理系统进行回复
- 直接使用意愿管理器计算回复概率
- 没有思维流相关的状态管理
- 更简单直接的回复逻辑
3. pfc_chatting模式：仅进行消息处理
- 不进行任何回复
- 只处理和存储消息
所有模式都包含：
- 消息过滤
- 记忆激活
@@ -90,59 +74,83 @@ class ChatBot:
- 性能计时
"""
try:
# 确保所有任务已启动
await self._ensure_started()
message = MessageRecv(message_data)
groupinfo = message.message_info.group_info
userinfo = message.message_info.user_info
logger.debug(f"处理消息:{str(message_data)[:80]}...")
logger.trace(f"处理消息:{str(message_data)[:120]}...")
if userinfo.user_id in global_config.ban_user_id:
logger.debug(f"用户{userinfo.user_id}被禁止回复")
return
if global_config.enable_pfc_chatting:
try:
if groupinfo is None and global_config.enable_friend_chat:
userinfo = message.message_info.user_info
messageinfo = message.message_info
# 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)
await self.only_process_chat.process_message(message)
await self._create_PFC_chat(message)
else:
if groupinfo.group_id in global_config.talk_allowed_groups:
logger.debug(f"开始群聊模式{str(message_data)[:50]}...")
if message.message_info.template_info and not message.message_info.template_info.template_default:
template_group_name = message.message_info.template_info.template_name
template_items = message.message_info.template_info.template_items
async with global_prompt_manager.async_message_scope(template_group_name):
if isinstance(template_items, dict):
for k in template_items.keys():
await Prompt.create_async(template_items[k], k)
print(f"注册{template_items[k]},{k}")
else:
template_group_name = None
async def preprocess():
if global_config.enable_pfc_chatting:
try:
if groupinfo is None:
if global_config.enable_friend_chat:
userinfo = message.message_info.user_info
messageinfo = message.message_info
# 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)
await self.only_process_chat.process_message(message)
await self._create_PFC_chat(message)
else:
if groupinfo.group_id in global_config.talk_allowed_groups:
# logger.debug(f"开始群聊模式{str(message_data)[:50]}...")
if global_config.response_mode == "heart_flow":
await self.think_flow_chat.process_message(message_data)
elif global_config.response_mode == "reasoning":
# logger.debug(f"开始推理模式{str(message_data)[:50]}...")
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
except Exception as e:
logger.error(f"处理PFC消息失败: {e}")
else:
if groupinfo is None:
if global_config.enable_friend_chat:
# 私聊处理流程
# await self._handle_private_chat(message)
if global_config.response_mode == "heart_flow":
await self.think_flow_chat.process_message(message_data)
elif global_config.response_mode == "reasoning":
logger.debug(f"开始推理模式{str(message_data)[:50]}...")
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
except Exception as e:
logger.error(f"处理PFC消息失败: {e}")
else: # 群聊处理
if groupinfo.group_id in global_config.talk_allowed_groups:
if global_config.response_mode == "heart_flow":
await self.think_flow_chat.process_message(message_data)
elif global_config.response_mode == "reasoning":
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
if template_group_name:
async with global_prompt_manager.async_message_scope(template_group_name):
await preprocess()
else:
if groupinfo is None and global_config.enable_friend_chat:
# 私聊处理流程
# await self._handle_private_chat(message)
if global_config.response_mode == "heart_flow":
await self.think_flow_chat.process_message(message_data)
elif global_config.response_mode == "reasoning":
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
else: # 群聊处理
if groupinfo.group_id in global_config.talk_allowed_groups:
if global_config.response_mode == "heart_flow":
await self.think_flow_chat.process_message(message_data)
elif global_config.response_mode == "reasoning":
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
await preprocess()
except Exception as e:
logger.error(f"预处理消息失败: {e}")
traceback.print_exc()

57
src/plugins/chat/emoji_manager.py
View File

@@ -38,11 +38,11 @@ class EmojiManager:
self.llm_emotion_judge = LLM_request(
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）
self.emoji_num = 0
self.emoji_num_max = global_config.max_emoji_num
self.emoji_num_max_reach_deletion = global_config.max_reach_deletion
logger.info("启动表情包管理器")
def _ensure_emoji_dir(self):
@@ -51,7 +51,7 @@ class EmojiManager:
def _update_emoji_count(self):
"""更新表情包数量统计
检查数据库中的表情包数量并更新到 self.emoji_num
"""
try:
@@ -340,6 +340,9 @@ class EmojiManager:
if description is not None:
embedding = await get_embedding(description, request_type="emoji")
if not embedding:
logger.error("获取消息嵌入向量失败")
raise ValueError("获取消息嵌入向量失败")
# 准备数据库记录
emoji_record = {
"filename": filename,
@@ -376,7 +379,6 @@ class EmojiManager:
except Exception:
logger.exception("[错误] 扫描表情包失败")
def check_emoji_file_integrity(self):
"""检查表情包文件完整性
@@ -451,7 +453,7 @@ class EmojiManager:
def check_emoji_file_full(self):
"""检查表情包文件是否完整，如果数量超出限制且允许删除，则删除多余的表情包
删除规则：
1. 优先删除创建时间更早的表情包
2. 优先删除使用次数少的表情包，但使用次数多的也有小概率被删除
@@ -460,23 +462,23 @@ class EmojiManager:
self._ensure_db()
# 更新表情包数量
self._update_emoji_count()
# 检查是否超出限制
if self.emoji_num <= self.emoji_num_max:
return
# 如果超出限制但不允许删除，则只记录警告
if not global_config.max_reach_deletion:
logger.warning(f"[警告] 表情包数量({self.emoji_num})超出限制({self.emoji_num_max})，但未开启自动删除")
return
# 计算需要删除的数量
delete_count = self.emoji_num - self.emoji_num_max
logger.info(f"[清理] 需要删除 {delete_count} 个表情包")
# 获取所有表情包，按时间戳升序（旧的在前）排序
all_emojis = list(db.emoji.find().sort([("timestamp", 1)]))
# 计算权重：使用次数越多，被删除的概率越小
weights = []
max_usage = max((emoji.get("usage_count", 0) for emoji in all_emojis), default=1)
@@ -485,11 +487,11 @@ class EmojiManager:
# 使用指数衰减函数计算权重，使用次数越多权重越小
weight = 1.0 / (1.0 + usage_count / max(1, max_usage))
weights.append(weight)
# 根据权重随机选择要删除的表情包
to_delete = []
remaining_indices = list(range(len(all_emojis)))
while len(to_delete) < delete_count and remaining_indices:
# 计算当前剩余表情包的权重
current_weights = [weights[i] for i in remaining_indices]
@@ -497,13 +499,13 @@ class EmojiManager:
total_weight = sum(current_weights)
if total_weight == 0:
break
normalized_weights = [w/total_weight for w in current_weights]
normalized_weights = [w / total_weight for w in current_weights]
# 随机选择一个表情包
selected_idx = random.choices(remaining_indices, weights=normalized_weights, k=1)[0]
to_delete.append(all_emojis[selected_idx])
remaining_indices.remove(selected_idx)
# 删除选中的表情包
deleted_count = 0
for emoji in to_delete:
@@ -512,26 +514,26 @@ class EmojiManager:
if "path" in emoji and os.path.exists(emoji["path"]):
os.remove(emoji["path"])
logger.info(f"[删除] 文件: {emoji['path']} (使用次数: {emoji.get('usage_count', 0)})")
# 删除数据库记录
db.emoji.delete_one({"_id": emoji["_id"]})
deleted_count += 1
# 同时从images集合中删除
if "hash" in emoji:
db.images.delete_one({"hash": emoji["hash"]})
except Exception as e:
logger.error(f"[错误] 删除表情包失败: {str(e)}")
continue
# 更新表情包数量
self._update_emoji_count()
logger.success(f"[清理] 已删除 {deleted_count} 个表情包，当前数量: {self.emoji_num}")
except Exception as e:
logger.error(f"[错误] 检查表情包数量失败: {str(e)}")
async def start_periodic_check_register(self):
"""定期检查表情包完整性和数量"""
while True:
@@ -542,7 +544,7 @@ class EmojiManager:
logger.info("[扫描] 开始扫描新表情包...")
if self.emoji_num < self.emoji_num_max:
await self.scan_new_emojis()
if (self.emoji_num > self.emoji_num_max):
if self.emoji_num > self.emoji_num_max:
logger.warning(f"[警告] 表情包数量超过最大限制: {self.emoji_num} > {self.emoji_num_max},跳过注册")
if not global_config.max_reach_deletion:
logger.warning("表情包数量超过最大限制，终止注册")
@@ -551,7 +553,7 @@ class EmojiManager:
logger.warning("表情包数量超过最大限制，开始删除表情包")
self.check_emoji_file_full()
await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60)
async def delete_all_images(self):
"""删除 data/image 目录下的所有文件"""
try:
@@ -559,10 +561,10 @@ class EmojiManager:
if not os.path.exists(image_dir):
logger.warning(f"[警告] 目录不存在: {image_dir}")
return
deleted_count = 0
failed_count = 0
# 遍历目录下的所有文件
for filename in os.listdir(image_dir):
file_path = os.path.join(image_dir, filename)
@@ -574,11 +576,12 @@ class EmojiManager:
except Exception as e:
failed_count += 1
logger.error(f"[错误] 删除文件失败 {file_path}: {str(e)}")
logger.success(f"[清理] 已删除 {deleted_count} 个文件，失败 {failed_count} 个")
except Exception as e:
logger.error(f"[错误] 删除图片目录失败: {str(e)}")
# 创建全局单例
emoji_manager = EmojiManager()

2
src/plugins/chat/message.py
View File

@@ -365,7 +365,7 @@ class MessageSet:
self.chat_stream = chat_stream
self.message_id = message_id
self.messages: List[MessageSending] = []
self.time = round(time.time(), 2)
self.time = round(time.time(), 3) # 保留3位小数
def add_message(self, message: MessageSending) -> None:
"""添加消息到集合"""

81
src/plugins/chat/message_buffer.py
View File

@@ -13,9 +13,10 @@ from ..config.config import global_config
logger = get_module_logger("message_buffer")
@dataclass
class CacheMessages:
message: MessageRecv
message: MessageRecv
cache_determination: asyncio.Event = field(default_factory=asyncio.Event) # 判断缓冲是否产生结果
result: str = "U"
@@ -25,7 +26,7 @@ class MessageBuffer:
self.buffer_pool: Dict[str, OrderedDict[str, CacheMessages]] = {}
self.lock = asyncio.Lock()
def get_person_id_(self, platform:str, user_id:str, group_info:GroupInfo):
def get_person_id_(self, platform: str, user_id: str, group_info: GroupInfo):
"""获取唯一id"""
if group_info:
group_id = group_info.group_id
@@ -34,16 +35,17 @@ class MessageBuffer:
key = f"{platform}_{user_id}_{group_id}"
return hashlib.md5(key.encode()).hexdigest()
async def start_caching_messages(self, message:MessageRecv):
async def start_caching_messages(self, message: MessageRecv):
"""添加消息，启动缓冲"""
if not global_config.message_buffer:
person_id = person_info_manager.get_person_id(message.message_info.user_info.platform,
message.message_info.user_info.user_id)
person_id = person_info_manager.get_person_id(
message.message_info.user_info.platform, message.message_info.user_info.user_id
)
asyncio.create_task(self.save_message_interval(person_id, message.message_info))
return
person_id_ = self.get_person_id_(message.message_info.platform,
message.message_info.user_info.user_id,
message.message_info.group_info)
person_id_ = self.get_person_id_(
message.message_info.platform, message.message_info.user_info.user_id, message.message_info.group_info
)
async with self.lock:
if person_id_ not in self.buffer_pool:
@@ -64,25 +66,24 @@ class MessageBuffer:
break
elif msg.result == "F":
recent_F_count += 1
# 判断条件：最近T之后有超过3-5条F
if (recent_F_count >= random.randint(3, 5)):
if recent_F_count >= random.randint(3, 5):
new_msg = CacheMessages(message=message, result="T")
new_msg.cache_determination.set()
self.buffer_pool[person_id_][message.message_info.message_id] = new_msg
logger.debug(f"快速处理消息(已堆积{recent_F_count}条F): {message.message_info.message_id}")
return
# 添加新消息
self.buffer_pool[person_id_][message.message_info.message_id] = CacheMessages(message=message)
# 启动3秒缓冲计时器
person_id = person_info_manager.get_person_id(message.message_info.user_info.platform,
message.message_info.user_info.user_id)
person_id = person_info_manager.get_person_id(
message.message_info.user_info.platform, message.message_info.user_info.user_id
)
asyncio.create_task(self.save_message_interval(person_id, message.message_info))
asyncio.create_task(self._debounce_processor(person_id_,
message.message_info.message_id,
person_id))
asyncio.create_task(self._debounce_processor(person_id_, message.message_info.message_id, person_id))
async def _debounce_processor(self, person_id_: str, message_id: str, person_id: str):
"""等待3秒无新消息"""
@@ -92,36 +93,33 @@ class MessageBuffer:
return
interval_time = max(0.5, int(interval_time) / 1000)
await asyncio.sleep(interval_time)
async with self.lock:
if (person_id_ not in self.buffer_pool or
message_id not in self.buffer_pool[person_id_]):
if person_id_ not in self.buffer_pool or message_id not in self.buffer_pool[person_id_]:
logger.debug(f"消息已被清理，msgid: {message_id}")
return
cache_msg = self.buffer_pool[person_id_][message_id]
if cache_msg.result == "U":
cache_msg.result = "T"
cache_msg.cache_determination.set()
async def query_buffer_result(self, message:MessageRecv) -> bool:
async def query_buffer_result(self, message: MessageRecv) -> bool:
"""查询缓冲结果，并清理"""
if not global_config.message_buffer:
return True
person_id_ = self.get_person_id_(message.message_info.platform,
message.message_info.user_info.user_id,
message.message_info.group_info)
person_id_ = self.get_person_id_(
message.message_info.platform, message.message_info.user_info.user_id, message.message_info.group_info
)
async with self.lock:
user_msgs = self.buffer_pool.get(person_id_, {})
cache_msg = user_msgs.get(message.message_info.message_id)
if not cache_msg:
logger.debug(f"查询异常，消息不存在，msgid: {message.message_info.message_id}")
return False # 消息不存在或已清理
try:
await asyncio.wait_for(cache_msg.cache_determination.wait(), timeout=10)
result = cache_msg.result == "T"
@@ -144,9 +142,8 @@ class MessageBuffer:
keep_msgs[msg_id] = msg
elif msg.result == "F":
# 收集F消息的文本内容
if (hasattr(msg.message, 'processed_plain_text')
and msg.message.processed_plain_text):
if msg.message.message_segment.type == "text":
if hasattr(msg.message, "processed_plain_text") and msg.message.processed_plain_text:
if msg.message.message_segment.type == "text":
combined_text.append(msg.message.processed_plain_text)
elif msg.message.message_segment.type != "text":
is_update = False
@@ -157,20 +154,20 @@ class MessageBuffer:
if combined_text and combined_text[0] != message.processed_plain_text and is_update:
if type == "text":
message.processed_plain_text = "".join(combined_text)
logger.debug(f"整合了{len(combined_text)-1}条F消息的内容到当前消息")
logger.debug(f"整合了{len(combined_text) - 1}条F消息的内容到当前消息")
elif type == "emoji":
combined_text.pop()
message.processed_plain_text = "".join(combined_text)
message.is_emoji = False
logger.debug(f"整合了{len(combined_text)-1}条F消息的内容，覆盖当前emoji消息")
logger.debug(f"整合了{len(combined_text) - 1}条F消息的内容，覆盖当前emoji消息")
self.buffer_pool[person_id_] = keep_msgs
return result
except asyncio.TimeoutError:
logger.debug(f"查询超时消息id： {message.message_info.message_id}")
return False
async def save_message_interval(self, person_id:str, message:BaseMessageInfo):
async def save_message_interval(self, person_id: str, message: BaseMessageInfo):
message_interval_list = await person_info_manager.get_value(person_id, "msg_interval_list")
now_time_ms = int(round(time.time() * 1000))
if len(message_interval_list) < 1000:
@@ -179,12 +176,12 @@ class MessageBuffer:
message_interval_list.pop(0)
message_interval_list.append(now_time_ms)
data = {
"platform" : message.platform,
"user_id" : message.user_info.user_id,
"nickname" : message.user_info.user_nickname,
"konw_time" : int(time.time())
"platform": message.platform,
"user_id": message.user_info.user_id,
"nickname": message.user_info.user_nickname,
"konw_time": int(time.time()),
}
await person_info_manager.update_one_field(person_id, "msg_interval_list", message_interval_list, data)
message_buffer = MessageBuffer()
message_buffer = MessageBuffer()

9
src/plugins/chat/message_sender.py
View File

@@ -68,10 +68,11 @@ class Message_Sender:
typing_time = calculate_typing_time(
input_string=message.processed_plain_text,
thinking_start_time=message.thinking_start_time,
is_emoji=message.is_emoji)
logger.debug(f"{message.processed_plain_text},{typing_time},计算输入时间结束")
is_emoji=message.is_emoji,
)
logger.trace(f"{message.processed_plain_text},{typing_time},计算输入时间结束")
await asyncio.sleep(typing_time)
logger.debug(f"{message.processed_plain_text},{typing_time},等待输入时间结束")
logger.trace(f"{message.processed_plain_text},{typing_time},等待输入时间结束")
message_json = message.to_dict()
@@ -227,7 +228,7 @@ class MessageManager:
await message_earliest.process()
# print(f"message_earliest.thinking_start_tim22222e:{message_earliest.thinking_start_time}")
await message_sender.send_message(message_earliest)
await self.storage.store_message(message_earliest, message_earliest.chat_stream)

154
src/plugins/chat/utils.py
View File

@@ -42,20 +42,49 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> bool:
"""检查消息是否提到了机器人"""
keywords = [global_config.BOT_NICKNAME]
nicknames = global_config.BOT_ALIAS_NAMES
for keyword in keywords:
if keyword in message.processed_plain_text:
return True
for nickname in nicknames:
if nickname in message.processed_plain_text:
return True
return False
reply_probability = 0
is_at = False
is_mentioned = False
# 判断是否被@
if re.search(f"@[\s\S]*?（id:{global_config.BOT_QQ}）", message.processed_plain_text):
is_at = True
is_mentioned = True
if is_at and global_config.at_bot_inevitable_reply:
reply_probability = 1
logger.info("被@，回复概率设置为100%")
else:
if not is_mentioned:
# 判断是否被回复
if re.match(f"回复[\s\S]*?\({global_config.BOT_QQ}\)的消息，说：", message.processed_plain_text):
is_mentioned = True
# 判断内容中是否被提及
message_content = re.sub(r"\@[\s\S]*?（(\d+)）", "", message.processed_plain_text)
message_content = re.sub(r"回复[\s\S]*?\((\d+)\)的消息，说： ", "", message_content)
for keyword in keywords:
if keyword in message_content:
is_mentioned = True
for nickname in nicknames:
if nickname in message_content:
is_mentioned = True
if is_mentioned and global_config.mentioned_bot_inevitable_reply:
reply_probability = 1
logger.info("被提及，回复概率设置为100%")
return is_mentioned, reply_probability
async def get_embedding(text, request_type="embedding"):
"""获取文本的embedding向量"""
llm = LLM_request(model=global_config.embedding, request_type=request_type)
# return llm.get_embedding_sync(text)
return await llm.get_embedding(text)
try:
embedding = await llm.get_embedding(text)
except Exception as e:
logger.error(f"获取embedding失败: {str(e)}")
embedding = None
return embedding
async def get_recent_group_messages(chat_id: str, limit: int = 12) -> list:
@@ -295,27 +324,35 @@ def random_remove_punctuation(text: str) -> str:
def process_llm_response(text: str) -> List[str]:
# processed_response = process_text_with_typos(content)
# 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
# 提取被 () 或 [] 包裹的内容
pattern = re.compile(r"[\(\[].*?[\)\]]")
_extracted_contents = pattern.findall(text)
# 去除 () 和 [] 及其包裹的内容
cleaned_text = pattern.sub("", text)
logger.debug(f"{text}去除括号处理后的文本: {cleaned_text}")
# 对清理后的文本进行进一步处理
max_length = global_config.response_max_length * 2
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")
if len(cleaned_text) > max_length and not is_western_paragraph(cleaned_text):
logger.warning(f"回复过长 ({len(cleaned_text)} 字符)，返回默认回复")
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")
elif len(cleaned_text) > 200:
logger.warning(f"回复过长 ({len(cleaned_text)} 字符)，返回默认回复")
return ["懒得说"]
# 处理长消息
typo_generator = ChineseTypoGenerator(
error_rate=global_config.chinese_typo_error_rate,
min_freq=global_config.chinese_typo_min_freq,
tone_error_rate=global_config.chinese_typo_tone_error_rate,
word_replace_rate=global_config.chinese_typo_word_replace_rate,
)
if global_config.enable_response_spliter:
split_sentences = split_into_sentences_w_remove_punctuation(text)
if global_config.enable_response_splitter:
split_sentences = split_into_sentences_w_remove_punctuation(cleaned_text)
else:
split_sentences = [text]
split_sentences = [cleaned_text]
sentences = []
for sentence in split_sentences:
if global_config.chinese_typo_enable:
@@ -325,16 +362,23 @@ def process_llm_response(text: str) -> List[str]:
sentences.append(typo_corrections)
else:
sentences.append(sentence)
# 检查分割后的消息数量是否过多（超过3条）
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")
return [f"{global_config.BOT_NICKNAME}不知道哦"]
# sentences.extend(extracted_contents)
return sentences
def calculate_typing_time(input_string: str, thinking_start_time: float, chinese_time: float = 0.2, english_time: float = 0.1, is_emoji: bool = False) -> float:
def calculate_typing_time(
input_string: str,
thinking_start_time: float,
chinese_time: float = 0.2,
english_time: float = 0.1,
is_emoji: bool = False,
) -> 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
input_string (str): 输入的字符串
@@ -368,19 +412,18 @@ def calculate_typing_time(input_string: str, thinking_start_time: float, chinese
total_time += chinese_time
else: # 其他字符（如英文）
total_time += english_time
if is_emoji:
total_time = 1
if time.time() - thinking_start_time > 10:
total_time = 1
# print(f"thinking_start_time:{thinking_start_time}")
# print(f"nowtime:{time.time()}")
# print(f"nowtime - thinking_start_time:{time.time() - thinking_start_time}")
# print(f"{total_time}")
return total_time # 加上回车时间
@@ -510,39 +553,32 @@ def count_messages_between(start_time: float, end_time: float, stream_id: str) -
try:
# 获取开始时间之前最新的一条消息
start_message = db.messages.find_one(
{
"chat_id": stream_id,
"time": {"$lte": start_time}
},
sort=[("time", -1), ("_id", -1)] # 按时间倒序，_id倒序（最后插入的在前）
{"chat_id": stream_id, "time": {"$lte": start_time}},
sort=[("time", -1), ("_id", -1)], # 按时间倒序，_id倒序（最后插入的在前）
)
# 获取结束时间最近的一条消息
# 先找到结束时间点的所有消息
end_time_messages = list(db.messages.find(
{
"chat_id": stream_id,
"time": {"$lte": end_time}
},
sort=[("time", -1)] # 先按时间倒序
).limit(10)) # 限制查询数量，避免性能问题
end_time_messages = list(
db.messages.find(
{"chat_id": stream_id, "time": {"$lte": end_time}},
sort=[("time", -1)], # 先按时间倒序
).limit(10)
) # 限制查询数量，避免性能问题
if not end_time_messages:
logger.warning(f"未找到结束时间 {end_time} 之前的消息")
return 0, 0
# 找到最大时间
max_time = end_time_messages[0]["time"]
# 在最大时间的消息中找最后插入的（_id最大的）
end_message = max(
[msg for msg in end_time_messages if msg["time"] == max_time],
key=lambda x: x["_id"]
)
end_message = max([msg for msg in end_time_messages if msg["time"] == max_time], key=lambda x: x["_id"])
if not start_message:
logger.warning(f"未找到开始时间 {start_time} 之前的消息")
return 0, 0
# 调试输出
# print("\n=== 消息范围信息 ===")
# print("Start message:", {
@@ -562,20 +598,16 @@ def count_messages_between(start_time: float, end_time: float, stream_id: str) -
# 如果结束消息的时间等于开始时间，返回0
if end_message["time"] == start_message["time"]:
return 0, 0
# 获取并打印这个时间范围内的所有消息
# print("\n=== 时间范围内的所有消息 ===")
all_messages = list(db.messages.find(
{
"chat_id": stream_id,
"time": {
"$gte": start_message["time"],
"$lte": end_message["time"]
}
},
sort=[("time", 1), ("_id", 1)] # 按时间正序，_id正序
))
all_messages = list(
db.messages.find(
{"chat_id": stream_id, "time": {"$gte": start_message["time"], "$lte": end_message["time"]}},
sort=[("time", 1), ("_id", 1)], # 按时间正序，_id正序
)
)
count = 0
total_length = 0
for msg in all_messages:
@@ -590,10 +622,10 @@ def count_messages_between(start_time: float, end_time: float, stream_id: str) -
# "text_length": text_length,
# "_id": str(msg.get("_id"))
# })
# 如果时间不同，需要把end_message本身也计入
return count - 1, total_length
except Exception as e:
logger.error(f"计算消息数量时出错: {str(e)}")
return 0, 0

23
src/plugins/chat/utils_image.py
View File

@@ -239,13 +239,13 @@ class ImageManager:
# 解码base64
gif_data = base64.b64decode(gif_base64)
gif = Image.open(io.BytesIO(gif_data))
# 收集所有帧
frames = []
try:
while True:
gif.seek(len(frames))
frame = gif.convert('RGB')
frame = gif.convert("RGB")
frames.append(frame.copy())
except EOFError:
pass
@@ -264,18 +264,19 @@ class ImageManager:
# 获取单帧的尺寸
frame_width, frame_height = selected_frames[0].size
# 计算目标尺寸，保持宽高比
target_height = 200 # 固定高度
target_width = int((target_height / frame_height) * frame_width)
# 调整所有帧的大小
resized_frames = [frame.resize((target_width, target_height), Image.Resampling.LANCZOS)
for frame in selected_frames]
resized_frames = [
frame.resize((target_width, target_height), Image.Resampling.LANCZOS) for frame in selected_frames
]
# 创建拼接图像
total_width = target_width * len(resized_frames)
combined_image = Image.new('RGB', (total_width, target_height))
combined_image = Image.new("RGB", (total_width, target_height))
# 水平拼接图像
for idx, frame in enumerate(resized_frames):
@@ -283,11 +284,11 @@ class ImageManager:
# 转换为base64
buffer = io.BytesIO()
combined_image.save(buffer, format='JPEG', quality=85)
result_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
combined_image.save(buffer, format="JPEG", quality=85)
result_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
return result_base64
except Exception as e:
logger.error(f"GIF转换失败: {str(e)}")
return None

19
src/plugins/chat_module/only_process/only_message_process.py
View File

@@ -2,17 +2,17 @@ from src.common.logger import get_module_logger
from src.plugins.chat.message import MessageRecv
from src.plugins.storage.storage import MessageStorage
from src.plugins.config.config import global_config
import re
from datetime import datetime
logger = get_module_logger("pfc_message_processor")
class MessageProcessor:
"""消息处理器，负责处理接收到的消息并存储"""
def __init__(self):
self.storage = MessageStorage()
def _check_ban_words(self, text: str, chat, userinfo) -> bool:
"""检查消息中是否包含过滤词"""
for word in global_config.ban_words:
@@ -27,17 +27,17 @@ class MessageProcessor:
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"""
for pattern in global_config.ban_msgs_regex:
if re.search(pattern, text):
if pattern.search(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
async def process_message(self, message: MessageRecv) -> None:
"""处理消息并存储
Args:
message: 消息对象
"""
@@ -55,12 +55,9 @@ class MessageProcessor:
# 存储消息
await self.storage.store_message(message, chat)
# 打印消息信息
mes_name = chat.group_info.group_name if chat.group_info else "私聊"
# 将时间戳转换为datetime对象
current_time = datetime.fromtimestamp(message.message_info.time).strftime("%H:%M:%S")
logger.info(
f"[{current_time}][{mes_name}]"
f"{chat.user_info.user_nickname}: {message.processed_plain_text}"
)
logger.info(f"[{current_time}][{mes_name}]{chat.user_info.user_nickname}: {message.processed_plain_text}")

132
src/plugins/chat_module/reasoning_chat/reasoning_chat.py
View File

@@ -1,7 +1,7 @@
import time
from random import random
import re
from typing import List
from ...memory_system.Hippocampus import HippocampusManager
from ...moods.moods import MoodManager
from ...config.config import global_config
@@ -18,6 +18,8 @@ from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
from ...chat.chat_stream import chat_manager
from ...person_info.relationship_manager import relationship_manager
from ...chat.message_buffer import message_buffer
from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
from ...utils.timer_calculater import Timer
# 定义日志配置
chat_config = LogConfig(
@@ -27,6 +29,7 @@ chat_config = LogConfig(
logger = get_module_logger("reasoning_chat", config=chat_config)
class ReasoningChat:
def __init__(self):
self.storage = MessageStorage()
@@ -53,11 +56,10 @@ class ReasoningChat:
)
message_manager.add_message(thinking_message)
willing_manager.change_reply_willing_sent(chat)
return thinking_id
async def _send_response_messages(self, message, chat, response_set, thinking_id):
async def _send_response_messages(self, message, chat, response_set: List[str], thinking_id) -> MessageSending:
"""发送回复消息"""
container = message_manager.get_container(chat.stream_id)
thinking_message = None
@@ -76,6 +78,7 @@ class ReasoningChat:
message_set = MessageSet(chat, thinking_id)
mark_head = False
first_bot_msg = None
for msg in response_set:
message_segment = Seg(type="text", data=msg)
bot_message = MessageSending(
@@ -95,9 +98,12 @@ class ReasoningChat:
)
if not mark_head:
mark_head = True
first_bot_msg = bot_message
message_set.add_message(bot_message)
message_manager.add_message(message_set)
return first_bot_msg
async def _handle_emoji(self, message, chat, response):
"""处理表情包"""
if random() < global_config.emoji_chance:
@@ -125,7 +131,7 @@ class ReasoningChat:
)
message_manager.add_message(bot_message)
async def _update_relationship(self, message, response_set):
async def _update_relationship(self, message: MessageRecv, response_set):
"""更新关系情绪"""
ori_response = ",".join(response_set)
stance, emotion = await self.gpt._get_emotion_tags(ori_response, message.processed_plain_text)
@@ -168,16 +174,24 @@ class ReasoningChat:
await self.storage.store_message(message, chat)
# 记忆激活
timer1 = time.time()
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
with Timer("记忆激活", timing_results):
interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
message.processed_plain_text, fast_retrieval=True
)
# 查询缓冲器结果，会整合前面跳过的消息，改变processed_plain_text
buffer_result = await message_buffer.query_buffer_result(message)
# 处理提及
is_mentioned, reply_probability = is_mentioned_bot_in_message(message)
# 意愿管理器：设置当前message信息
willing_manager.setup(message, chat, is_mentioned, interested_rate)
# 处理缓冲器结果
if not buffer_result:
await willing_manager.bombing_buffer_message_handle(message.message_info.message_id)
willing_manager.delete(message.message_info.message_id)
if message.message_segment.type == "text":
logger.info(f"触发缓冲，已炸飞消息：{message.processed_plain_text}")
elif message.message_segment.type == "image":
@@ -186,75 +200,73 @@ class ReasoningChat:
logger.info("触发缓冲，已炸飞消息列")
return
is_mentioned = is_mentioned_bot_in_message(message)
# 获取回复概率
is_willing = False
if reply_probability != 1:
is_willing = True
reply_probability = await willing_manager.get_reply_probability(message.message_info.message_id)
# 计算回复意愿
current_willing = willing_manager.get_willing(chat_stream=chat)
willing_manager.set_willing(chat.stream_id, current_willing)
# 意愿激活
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
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"]
# 打印消息信息
mes_name = chat.group_info.group_name if chat.group_info else "私聊"
current_time = time.strftime("%H:%M:%S", time.localtime(messageinfo.time))
current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time))
willing_log = f"[回复意愿:{await willing_manager.get_willing(chat.stream_id):.2f}]" if is_willing else ""
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}%]"
f"{message.processed_plain_text}{willing_log}[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
if random() < reply_probability:
do_reply = True
# 回复前处理
await willing_manager.before_generate_reply_handle(message.message_info.message_id)
# 创建思考消息
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
with Timer("创建思考消息", timing_results):
thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
logger.debug(f"创建捕捉器，thinking_id:{thinking_id}")
info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
info_catcher.catch_decide_to_response(message)
# 生成回复
timer1 = time.time()
response_set = await self.gpt.generate_response(message)
timer2 = time.time()
timing_results["生成回复"] = timer2 - timer1
try:
with Timer("生成回复", timing_results):
response_set = await self.gpt.generate_response(message, thinking_id)
info_catcher.catch_after_generate_response(timing_results["生成回复"])
except Exception as e:
logger.error(f"回复生成出现错误：str{e}")
response_set = None
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
with Timer("发送消息", timing_results):
first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id)
info_catcher.catch_after_response(timing_results["发送消息"], response_set, first_bot_msg)
info_catcher.done_catch()
# 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
with Timer("处理表情包", timing_results):
await self._handle_emoji(message, chat, response_set)
# 更新关系情绪
timer1 = time.time()
await self._update_relationship(message, response_set)
timer2 = time.time()
timing_results["更新关系情绪"] = timer2 - timer1
with Timer("更新关系情绪", timing_results):
await self._update_relationship(message, response_set)
# 回复后处理
await willing_manager.after_generate_reply_handle(message.message_info.message_id)
# 输出性能计时结果
if do_reply:
@@ -262,6 +274,12 @@ class ReasoningChat:
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]}... | 推理消息: {response_msg[:20]}... | 性能计时: {timing_str}")
else:
# 不回复处理
await willing_manager.not_reply_handle(message.message_info.message_id)
# 意愿管理器：注销当前message信息
willing_manager.delete(message.message_info.message_id)
def _check_ban_words(self, text: str, chat, userinfo) -> bool:
"""检查消息中是否包含过滤词"""
@@ -277,7 +295,7 @@ class ReasoningChat:
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"""
for pattern in global_config.ban_msgs_regex:
if re.search(pattern, text):
if pattern.search(text):
logger.info(
f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
)

108
src/plugins/chat_module/reasoning_chat/reasoning_generator.py
View File

@@ -1,14 +1,14 @@
import time
from typing import List, Optional, Tuple, Union
import random
from ....common.database import db
from ...models.utils_model import LLM_request
from ...config.config import global_config
from ...chat.message import MessageRecv, MessageThinking
from ...chat.message import MessageThinking
from .reasoning_prompt_builder import prompt_builder
from ...chat.utils import process_llm_response
from ...utils.timer_calculater import Timer
from src.common.logger import get_module_logger, LogConfig, LLM_STYLE_CONFIG
from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
# 定义日志配置
llm_config = LogConfig(
@@ -29,7 +29,10 @@ class ResponseGenerator:
request_type="response_reasoning",
)
self.model_normal = LLM_request(
model=global_config.llm_normal, temperature=0.8, max_tokens=256, request_type="response_reasoning"
model=global_config.llm_normal,
temperature=global_config.llm_normal["temp"],
max_tokens=256,
request_type="response_reasoning",
)
self.model_sum = LLM_request(
@@ -38,9 +41,9 @@ class ResponseGenerator:
self.current_model_type = "r1" # 默认使用 R1
self.current_model_name = "unknown model"
async def generate_response(self, message: MessageThinking) -> Optional[Union[str, List[str]]]:
async def generate_response(self, message: MessageThinking, thinking_id: str) -> Optional[Union[str, List[str]]]:
"""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
if random.random() < global_config.MODEL_R1_PROBABILITY:
self.current_model_type = "深深地"
current_model = self.model_reasoning
@@ -51,9 +54,8 @@ class ResponseGenerator:
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)
model_response = await self._generate_response_with_model(message, current_model, thinking_id)
# print(f"raw_content: {model_response}")
@@ -66,8 +68,11 @@ class ResponseGenerator:
logger.info(f"{self.current_model_type}思考，失败")
return None
async def _generate_response_with_model(self, message: MessageThinking, model: LLM_request):
async def _generate_response_with_model(self, message: MessageThinking, model: LLM_request, thinking_id: str):
sender_name = ""
info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname:
sender_name = (
f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]"
@@ -80,57 +85,59 @@ class ResponseGenerator:
logger.debug("开始使用生成回复-2")
# 构建prompt
timer1 = time.time()
prompt = await prompt_builder._build_prompt(
message.chat_stream,
message_txt=message.processed_plain_text,
sender_name=sender_name,
stream_id=message.chat_stream.stream_id,
)
timer2 = time.time()
logger.info(f"构建prompt时间: {timer2 - timer1}秒")
with Timer() as t_build_prompt:
prompt = await prompt_builder._build_prompt(
message.chat_stream,
message_txt=message.processed_plain_text,
sender_name=sender_name,
stream_id=message.chat_stream.stream_id,
)
logger.info(f"构建prompt时间: {t_build_prompt.human_readable}")
try:
content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
info_catcher.catch_after_llm_generated(
prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
)
except Exception:
logger.exception("生成回复时出错")
return None
# 保存到数据库
self._save_to_db(
message=message,
sender_name=sender_name,
prompt=prompt,
content=content,
reasoning_content=reasoning_content,
# reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
)
# self._save_to_db(
# message=message,
# sender_name=sender_name,
# prompt=prompt,
# content=content,
# reasoning_content=reasoning_content,
# # reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
# )
return content
# def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str,
# content: str, content_check: str, reasoning_content: str, reasoning_content_check: str):
def _save_to_db(
self,
message: MessageRecv,
sender_name: str,
prompt: str,
content: str,
reasoning_content: str,
):
"""保存对话记录到数据库"""
db.reasoning_logs.insert_one(
{
"time": time.time(),
"chat_id": message.chat_stream.stream_id,
"user": sender_name,
"message": message.processed_plain_text,
"model": self.current_model_name,
"reasoning": reasoning_content,
"response": content,
"prompt": prompt,
}
)
# def _save_to_db(
# self,
# message: MessageRecv,
# sender_name: str,
# prompt: str,
# content: str,
# reasoning_content: str,
# ):
# """保存对话记录到数据库"""
# db.reasoning_logs.insert_one(
# {
# "time": time.time(),
# "chat_id": message.chat_stream.stream_id,
# "user": sender_name,
# "message": message.processed_plain_text,
# "model": self.current_model_name,
# "reasoning": reasoning_content,
# "response": content,
# "prompt": prompt,
# }
# )
async def _get_emotion_tags(self, content: str, processed_plain_text: str):
"""提取情感标签，结合立场和情绪"""
@@ -144,6 +151,7 @@ class ResponseGenerator:
- "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"
4. 考虑回复者的人格设定为{global_config.personality_core}
对话示例：
被回复：「A就是笨」
@@ -189,4 +197,4 @@ class ResponseGenerator:
# print(f"得到了处理后的llm返回{processed_response}")
return processed_response
return processed_response

213
src/plugins/chat_module/reasoning_chat/reasoning_prompt_builder.py
View File

@@ -12,10 +12,41 @@ from ...schedule.schedule_generator import bot_schedule
from ...config.config import global_config
from ...person_info.relationship_manager import relationship_manager
from src.common.logger import get_module_logger
from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
logger = get_module_logger("prompt")
def init_prompt():
Prompt(
"""
{relation_prompt_all}
{memory_prompt}
{prompt_info}
{schedule_prompt}
{chat_target}
{chat_talking_prompt}
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{bot_name}，有人也叫你{bot_other_names}，{prompt_personality}。
你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，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 )。""",
"reasoning_prompt_main",
)
Prompt(
"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。",
"relationship_prompt",
)
Prompt(
"你想起你之前见过的事情：{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",
"memory_prompt",
)
Prompt("你现在正在做的事情是：{schedule_info}", "schedule_prompt")
Prompt("\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", "knowledge_prompt")
class PromptBuilder:
def __init__(self):
self.prompt_built = ""
@@ -24,50 +55,47 @@ class PromptBuilder:
async def _build_prompt(
self, chat_stream, message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None
) -> tuple[str, str]:
# 开始构建prompt
prompt_personality = "你"
#person
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
# 关系
who_chat_in_group = [(chat_stream.user_info.platform,
chat_stream.user_info.user_id,
chat_stream.user_info.user_nickname)]
who_chat_in_group = [
(chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
]
who_chat_in_group += get_recent_group_speaker(
stream_id,
(chat_stream.user_info.platform, chat_stream.user_info.user_id),
limit=global_config.MAX_CONTEXT_SIZE,
)
relation_prompt = ""
for person in who_chat_in_group:
relation_prompt += await relationship_manager.build_relationship_info(person)
relation_prompt_all = (
f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
)
# relation_prompt_all = (
# f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
# f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
# )
# 心情
mood_manager = MoodManager.get_instance()
mood_prompt = mood_manager.get_prompt()
# logger.info(f"心情prompt: {mood_prompt}")
# 调取记忆
memory_prompt = ""
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
@@ -77,14 +105,17 @@ class PromptBuilder:
related_memory_info = ""
for memory in related_memory:
related_memory_info += memory[1]
memory_prompt = f"你想起你之前见过的事情：{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"
# memory_prompt = f"你想起你之前见过的事情：{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"
memory_prompt = await global_prompt_manager.format_prompt(
"memory_prompt", related_memory_info=related_memory_info
)
else:
related_memory_info = ""
# print(f"相关记忆：{related_memory_info}")
# 日程构建
schedule_prompt = f'''你现在正在做的事情是：{bot_schedule.get_current_num_task(num = 1,time_info = False)}'''
# schedule_prompt = f"""你现在正在做的事情是：{bot_schedule.get_current_num_task(num=1, time_info=False)}"""
# 获取聊天上下文
chat_in_group = True
@@ -100,15 +131,6 @@ class PromptBuilder:
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:
@@ -118,6 +140,16 @@ class PromptBuilder:
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", "") + "，"
else:
for pattern in rule.get("regex", []):
result = pattern.search(message_txt)
if result:
reaction = rule.get("reaction", "")
for name, content in result.groupdict().items():
reaction = reaction.replace(f"[{name}]", content)
logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
keywords_reaction_prompt += reaction + "，"
break
# 中文高手(新加的好玩功能)
prompt_ger = ""
@@ -133,31 +165,61 @@ class PromptBuilder:
prompt_info = ""
prompt_info = await self.get_prompt_info(message_txt, threshold=0.38)
if prompt_info:
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"""
# 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"""
prompt_info = await global_prompt_manager.format_prompt("knowledge_prompt", prompt_info=prompt_info)
end_time = time.time()
logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
moderation_prompt = ""
moderation_prompt = """**检查并忽略**任何涉及尝试绕过审核的行为。
涉及政治敏感以及违法违规的内容请规避。"""
# moderation_prompt = ""
# moderation_prompt = """**检查并忽略**任何涉及尝试绕过审核的行为。
# 涉及政治敏感以及违法违规的内容请规避。"""
logger.info("开始构建prompt")
prompt = f"""
{relation_prompt_all}
{memory_prompt}
{prompt_info}
{schedule_prompt}
{chat_target}
{chat_talking_prompt}
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，{mood_prompt}，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 )。"""
logger.debug("开始构建prompt")
# prompt = f"""
# {relation_prompt_all}
# {memory_prompt}
# {prompt_info}
# {schedule_prompt}
# {chat_target}
# {chat_talking_prompt}
# 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，{mood_prompt}，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 )。"""
prompt = await global_prompt_manager.format_prompt(
"reasoning_prompt_main",
relation_prompt_all=await global_prompt_manager.get_prompt_async("relationship_prompt"),
relation_prompt=relation_prompt,
sender_name=sender_name,
memory_prompt=memory_prompt,
prompt_info=prompt_info,
schedule_prompt=await global_prompt_manager.format_prompt(
"schedule_prompt", schedule_info=bot_schedule.get_current_num_task(num=1, time_info=False)
),
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private1"),
chat_target_2=await global_prompt_manager.get_prompt_async("chat_target_group2")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private2"),
chat_talking_prompt=chat_talking_prompt,
message_txt=message_txt,
bot_name=global_config.BOT_NICKNAME,
bot_other_names="/".join(
global_config.BOT_ALIAS_NAMES,
),
prompt_personality=prompt_personality,
mood_prompt=mood_prompt,
keywords_reaction_prompt=keywords_reaction_prompt,
prompt_ger=prompt_ger,
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
)
return prompt
@@ -165,7 +227,7 @@ class PromptBuilder:
start_time = time.time()
related_info = ""
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
# 1. 先从LLM获取主题，类似于记忆系统的做法
topics = []
# try:
@@ -173,7 +235,7 @@ class PromptBuilder:
# hippocampus = HippocampusManager.get_instance()._hippocampus
# topic_num = min(5, max(1, int(len(message) * 0.1)))
# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
# # 提取关键词
# topics = re.findall(r"<([^>]+)>", topics_response[0])
# if not topics:
@@ -184,7 +246,7 @@ class PromptBuilder:
# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
# if topic.strip()
# ]
# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
# except Exception as e:
# logger.error(f"从LLM提取主题失败: {str(e)}")
@@ -192,7 +254,7 @@ class PromptBuilder:
# words = jieba.cut(message)
# topics = [word for word in words if len(word) > 1][:5]
# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
# 如果无法提取到主题，直接使用整个消息
if not topics:
logger.info("未能提取到任何主题，使用整个消息进行查询")
@@ -200,26 +262,26 @@ class PromptBuilder:
if not embedding:
logger.error("获取消息嵌入向量失败")
return ""
related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
return related_info
# 2. 对每个主题进行知识库查询
logger.info(f"开始处理{len(topics)}个主题的知识库查询")
# 优化：批量获取嵌入向量，减少API调用
embeddings = {}
topics_batch = [topic for topic in topics if len(topic) > 0]
if message: # 确保消息非空
topics_batch.append(message)
# 批量获取嵌入向量
embed_start_time = time.time()
for text in topics_batch:
if not text or len(text.strip()) == 0:
continue
try:
embedding = await get_embedding(text, request_type="prompt_build")
if embedding:
@@ -228,17 +290,17 @@ class PromptBuilder:
logger.warning(f"获取'{text}'的嵌入向量失败")
except Exception as e:
logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
if not embeddings:
logger.error("所有嵌入向量获取失败")
return ""
# 3. 对每个主题进行知识库查询
all_results = []
query_start_time = time.time()
# 首先添加原始消息的查询结果
if message in embeddings:
original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
@@ -247,12 +309,12 @@ class PromptBuilder:
result["topic"] = "原始消息"
all_results.extend(original_results)
logger.info(f"原始消息查询到{len(original_results)}条结果")
# 然后添加每个主题的查询结果
for topic in topics:
if not topic or topic not in embeddings:
continue
try:
topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
if topic_results:
@@ -263,9 +325,9 @@ class PromptBuilder:
logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
except Exception as e:
logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
# 4. 去重和过滤
process_start_time = time.time()
unique_contents = set()
@@ -275,14 +337,16 @@ class PromptBuilder:
if content not in unique_contents:
unique_contents.add(content)
filtered_results.append(result)
# 5. 按相似度排序
filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
# 6. 限制总数量（最多10条）
filtered_results = filtered_results[:10]
logger.info(f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果")
logger.info(
f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果"
)
# 7. 格式化输出
if filtered_results:
format_start_time = time.time()
@@ -292,7 +356,7 @@ class PromptBuilder:
if topic not in grouped_results:
grouped_results[topic] = []
grouped_results[topic].append(result)
# 按主题组织输出
for topic, results in grouped_results.items():
related_info += f"【主题: {topic}】\n"
@@ -303,13 +367,15 @@ class PromptBuilder:
# related_info += f"{i}. [{similarity:.2f}] {content}\n"
related_info += f"{content}\n"
related_info += "\n"
logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
return related_info
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False) -> Union[str, list]:
def get_info_from_db(
self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
) -> Union[str, list]:
if not query_embedding:
return "" if not return_raw else []
# 使用余弦相似度计算
@@ -377,4 +443,5 @@ class PromptBuilder:
return "\n".join(str(result["content"]) for result in results)
init_prompt()
prompt_builder = PromptBuilder()

331
src/plugins/chat_module/think_flow_chat/think_flow_chat.py
View File

@@ -1,7 +1,7 @@
import time
from random import random
import re
import traceback
from typing import List
from ...memory_system.Hippocampus import HippocampusManager
from ...moods.moods import MoodManager
from ...config.config import global_config
@@ -19,6 +19,9 @@ from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
from ...chat.chat_stream import chat_manager
from ...person_info.relationship_manager import relationship_manager
from ...chat.message_buffer import message_buffer
from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
from ...utils.timer_calculater import Timer
from src.do_tool.tool_use import ToolUser
# 定义日志配置
chat_config = LogConfig(
@@ -28,12 +31,14 @@ chat_config = LogConfig(
logger = get_module_logger("think_flow_chat", config=chat_config)
class ThinkFlowChat:
def __init__(self):
self.storage = MessageStorage()
self.gpt = ResponseGenerator()
self.mood_manager = MoodManager.get_instance()
self.mood_manager.start_mood_update()
self.tool_user = ToolUser()
async def _create_thinking_message(self, message, chat, userinfo, messageinfo):
"""创建思考消息"""
@@ -54,11 +59,10 @@ class ThinkFlowChat:
)
message_manager.add_message(thinking_message)
willing_manager.change_reply_willing_sent(chat)
return thinking_id
async def _send_response_messages(self, message, chat, response_set, thinking_id):
async def _send_response_messages(self, message, chat, response_set: List[str], thinking_id) -> MessageSending:
"""发送回复消息"""
container = message_manager.get_container(chat.stream_id)
thinking_message = None
@@ -71,12 +75,13 @@ class ThinkFlowChat:
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
return None
thinking_start_time = thinking_message.thinking_start_time
message_set = MessageSet(chat, thinking_id)
mark_head = False
first_bot_msg = None
for msg in response_set:
message_segment = Seg(type="text", data=msg)
bot_message = MessageSending(
@@ -96,56 +101,44 @@ class ThinkFlowChat:
)
if not mark_head:
mark_head = True
first_bot_msg = bot_message
# print(f"thinking_start_time:{bot_message.thinking_start_time}")
message_set.add_message(bot_message)
message_manager.add_message(message_set)
return first_bot_msg
async def _handle_emoji(self, message, chat, response):
async def _handle_emoji(self, message, chat, response, send_emoji=""):
"""处理表情包"""
if random() < global_config.emoji_chance:
if send_emoji:
emoji_raw = await emoji_manager.get_emoji_for_text(send_emoji)
else:
emoji_raw = await emoji_manager.get_emoji_for_text(response)
# print("11111111111111")
# logger.info(emoji_raw)
if emoji_raw:
emoji_path, description = emoji_raw
emoji_cq = image_path_to_base64(emoji_path)
# logger.info(emoji_cq)
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)
thinking_time_point = round(message.message_info.time, 2)
message_segment = Seg(type="emoji", data=emoji_cq)
bot_message = MessageSending(
message_id="mt" + str(thinking_time_point),
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=False,
is_emoji=True,
)
# logger.info("22222222222222")
message_manager.add_message(bot_message)
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
message_segment = Seg(type="emoji", data=emoji_cq)
bot_message = MessageSending(
message_id="mt" + str(thinking_time_point),
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=False,
is_emoji=True,
)
await heartflow.get_subheartflow(stream_id).do_thinking_after_reply(response_set, chat_talking_prompt)
message_manager.add_message(bot_message)
async def _update_relationship(self, message, response_set):
async def _update_relationship(self, message: MessageRecv, response_set):
"""更新关系情绪"""
ori_response = ",".join(response_set)
stance, emotion = await self.gpt._get_emotion_tags(ori_response, message.processed_plain_text)
@@ -179,30 +172,38 @@ class ThinkFlowChat:
heartflow.create_subheartflow(chat.stream_id)
await message.process()
logger.debug(f"消息处理成功{message.processed_plain_text}")
logger.trace(f"消息处理成功{message.processed_plain_text}")
# 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
logger.debug(f"过滤词/正则表达式过滤成功{message.processed_plain_text}")
logger.trace(f"过滤词/正则表达式过滤成功{message.processed_plain_text}")
await self.storage.store_message(message, chat)
logger.debug(f"存储成功{message.processed_plain_text}")
logger.trace(f"存储成功{message.processed_plain_text}")
# 记忆激活
timer1 = time.time()
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
logger.debug(f"记忆激活: {interested_rate}")
with Timer("记忆激活", timing_results):
interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
message.processed_plain_text, fast_retrieval=True
)
logger.trace(f"记忆激活: {interested_rate}")
# 查询缓冲器结果，会整合前面跳过的消息，改变processed_plain_text
buffer_result = await message_buffer.query_buffer_result(message)
# 处理提及
is_mentioned, reply_probability = is_mentioned_bot_in_message(message)
# 意愿管理器：设置当前message信息
willing_manager.setup(message, chat, is_mentioned, interested_rate)
# 处理缓冲器结果
if not buffer_result:
await willing_manager.bombing_buffer_message_handle(message.message_info.message_id)
willing_manager.delete(message.message_info.message_id)
if message.message_segment.type == "text":
logger.info(f"触发缓冲，已炸飞消息：{message.processed_plain_text}")
elif message.message_segment.type == "image":
@@ -211,126 +212,188 @@ class ThinkFlowChat:
logger.info("触发缓冲，已炸飞消息列")
return
is_mentioned = is_mentioned_bot_in_message(message)
# 获取回复概率
is_willing = False
if reply_probability != 1:
is_willing = True
reply_probability = await willing_manager.get_reply_probability(message.message_info.message_id)
# 计算回复意愿
current_willing_old = willing_manager.get_willing(chat_stream=chat)
# current_willing_new = (heartflow.get_subheartflow(chat.stream_id).current_state.willing - 5) / 4
# current_willing = (current_willing_old + current_willing_new) / 2
# 有点bug
current_willing = current_willing_old
willing_manager.set_willing(chat.stream_id, current_willing)
# 意愿激活
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
logger.debug(f"意愿激活: {reply_probability}")
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"]
# 打印消息信息
mes_name = chat.group_info.group_name if chat.group_info else "私聊"
current_time = time.strftime("%H:%M:%S", time.localtime(messageinfo.time))
current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time))
willing_log = f"[回复意愿:{await willing_manager.get_willing(chat.stream_id):.2f}]" if is_willing else ""
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}%]"
f"{message.processed_plain_text}{willing_log}[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
if random() < reply_probability:
try:
do_reply = True
# 回复前处理
await willing_manager.before_generate_reply_handle(message.message_info.message_id)
# 创建思考消息
try:
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
with Timer("创建思考消息", timing_results):
thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
except Exception as e:
logger.error(f"心流创建思考消息失败: {e}")
try:
logger.trace(f"创建捕捉器，thinking_id:{thinking_id}")
info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
info_catcher.catch_decide_to_response(message)
# 观察
timer1 = time.time()
await heartflow.get_subheartflow(chat.stream_id).do_observe()
timer2 = time.time()
timing_results["观察"] = timer2 - timer1
try:
with Timer("观察", timing_results):
await heartflow.get_subheartflow(chat.stream_id).do_observe()
except Exception as e:
logger.error(f"心流观察失败: {e}")
traceback.print_exc()
info_catcher.catch_after_observe(timing_results["观察"])
# 思考前使用工具
update_relationship = ""
get_mid_memory_id = []
tool_result_info = {}
send_emoji = ""
try:
with Timer("思考前使用工具", timing_results):
tool_result = await self.tool_user.use_tool(
message.processed_plain_text,
message.message_info.user_info.user_nickname,
chat,
heartflow.get_subheartflow(chat.stream_id),
)
# 如果工具被使用且获得了结果，将收集到的信息合并到思考中
# collected_info = ""
if tool_result.get("used_tools", False):
if "structured_info" in tool_result:
tool_result_info = tool_result["structured_info"]
# collected_info = ""
get_mid_memory_id = []
update_relationship = ""
# 动态解析工具结果
for tool_name, tool_data in tool_result_info.items():
# tool_result_info += f"\n{tool_name} 相关信息:\n"
# for item in tool_data:
# tool_result_info += f"- {item['name']}: {item['content']}\n"
# 特殊判定：mid_chat_mem
if tool_name == "mid_chat_mem":
for mid_memory in tool_data:
get_mid_memory_id.append(mid_memory["content"])
# 特殊判定：change_mood
if tool_name == "change_mood":
for mood in tool_data:
self.mood_manager.update_mood_from_emotion(
mood["content"], global_config.mood_intensity_factor
)
# 特殊判定：change_relationship
if tool_name == "change_relationship":
update_relationship = tool_data[0]["content"]
if tool_name == "send_emoji":
send_emoji = tool_data[0]["content"]
except Exception as e:
logger.error(f"思考前工具调用失败: {e}")
logger.error(traceback.format_exc())
# 处理关系更新
if update_relationship:
stance, emotion = await self.gpt._get_emotion_tags_with_reason(
"你还没有回复", message.processed_plain_text, update_relationship
)
await relationship_manager.calculate_update_relationship_value(
chat_stream=message.chat_stream, label=emotion, stance=stance
)
# 思考前脑内状态
try:
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
with Timer("思考前脑内状态", timing_results):
current_mind, past_mind = await heartflow.get_subheartflow(
chat.stream_id
).do_thinking_before_reply(
message_txt=message.processed_plain_text,
sender_name=message.message_info.user_info.user_nickname,
chat_stream=chat,
obs_id=get_mid_memory_id,
extra_info=tool_result_info,
)
except Exception as e:
logger.error(f"心流思考前脑内状态失败: {e}")
info_catcher.catch_afer_shf_step(timing_results["思考前脑内状态"], past_mind, current_mind)
# 生成回复
timer1 = time.time()
response_set = await self.gpt.generate_response(message)
timer2 = time.time()
timing_results["生成回复"] = timer2 - timer1
with Timer("生成回复", timing_results):
response_set = await self.gpt.generate_response(message, thinking_id)
info_catcher.catch_after_generate_response(timing_results["生成回复"])
if not response_set:
logger.info("为什么生成回复失败？")
logger.info("回复生成失败，返回为空")
return
# 发送消息
try:
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
with Timer("发送消息", timing_results):
first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id)
except Exception as e:
logger.error(f"心流发送消息失败: {e}")
info_catcher.catch_after_response(timing_results["发送消息"], response_set, first_bot_msg)
info_catcher.done_catch()
# 处理表情包
try:
timer1 = time.time()
await self._handle_emoji(message, chat, response_set)
timer2 = time.time()
timing_results["处理表情包"] = timer2 - timer1
with Timer("处理表情包", timing_results):
if global_config.emoji_chance == 1:
if send_emoji:
logger.info(f"麦麦决定发送表情包{send_emoji}")
await self._handle_emoji(message, chat, response_set, send_emoji)
else:
if random() < global_config.emoji_chance:
await self._handle_emoji(message, chat, response_set)
except Exception as e:
logger.error(f"心流处理表情包失败: {e}")
# 更新心流
try:
timer1 = time.time()
await self._update_using_response(message, response_set)
timer2 = time.time()
timing_results["更新心流"] = timer2 - timer1
except Exception as e:
logger.error(f"心流更新失败: {e}")
with Timer("思考后脑内状态更新", timing_results):
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
)
# 更新关系情绪
try:
timer1 = time.time()
await self._update_relationship(message, response_set)
timer2 = time.time()
timing_results["更新关系情绪"] = timer2 - timer1
await heartflow.get_subheartflow(stream_id).do_thinking_after_reply(
response_set, chat_talking_prompt, tool_result_info
)
except Exception as e:
logger.error(f"心流更新关系情绪失败: {e}")
logger.error(f"心流思考后脑内状态更新失败: {e}")
# 回复后处理
await willing_manager.after_generate_reply_handle(message.message_info.message_id)
except Exception as e:
logger.error(f"心流处理消息失败: {e}")
logger.error(traceback.format_exc())
# 输出性能计时结果
if do_reply:
@@ -338,6 +401,12 @@ class ThinkFlowChat:
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]}... | 思维消息: {response_msg[:20]}... | 性能计时: {timing_str}")
else:
# 不回复处理
await willing_manager.not_reply_handle(message.message_info.message_id)
# 意愿管理器：注销当前message信息
willing_manager.delete(message.message_info.message_id)
def _check_ban_words(self, text: str, chat, userinfo) -> bool:
"""检查消息中是否包含过滤词"""
@@ -353,7 +422,7 @@ class ThinkFlowChat:
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"""
for pattern in global_config.ban_msgs_regex:
if re.search(pattern, text):
if pattern.search(text):
logger.info(
f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
)

245
src/plugins/chat_module/think_flow_chat/think_flow_generator.py
View File

@@ -1,14 +1,17 @@
import time
from typing import List, Optional, Tuple, Union
from typing import List, Optional
import random
from ....common.database import db
from ...models.utils_model import LLM_request
from ...config.config import global_config
from ...chat.message import MessageRecv, MessageThinking
from ...chat.message import MessageRecv
from .think_flow_prompt_builder import prompt_builder
from ...chat.utils import process_llm_response
from src.common.logger import get_module_logger, LogConfig, LLM_STYLE_CONFIG
from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
from ...utils.timer_calculater import Timer
from src.plugins.moods.moods import MoodManager
# 定义日志配置
llm_config = LogConfig(
@@ -23,38 +26,126 @@ logger = get_module_logger("llm_generator", config=llm_config)
class ResponseGenerator:
def __init__(self):
self.model_normal = LLM_request(
model=global_config.llm_normal, temperature=0.8, max_tokens=256, request_type="response_heartflow"
model=global_config.llm_normal,
temperature=global_config.llm_normal["temp"],
max_tokens=256,
request_type="response_heartflow",
)
self.model_sum = LLM_request(
model=global_config.llm_summary_by_topic, temperature=0.7, max_tokens=2000, request_type="relation"
model=global_config.llm_summary_by_topic, temperature=0.6, max_tokens=2000, request_type="relation"
)
self.current_model_type = "r1" # 默认使用 R1
self.current_model_name = "unknown model"
async def generate_response(self, message: MessageThinking) -> Optional[Union[str, List[str]]]:
async def generate_response(self, message: MessageRecv, thinking_id: str) -> Optional[List[str]]:
"""根据当前模型类型选择对应的生成函数"""
logger.info(
f"思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
)
current_model = self.model_normal
model_response = await self._generate_response_with_model(message, current_model)
arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier()
# print(f"raw_content: {model_response}")
with Timer() as t_generate_response:
checked = False
if random.random() > 0:
checked = False
current_model = self.model_normal
current_model.temperature = (
global_config.llm_normal["temp"] * arousal_multiplier
) # 激活度越高，温度越高
model_response = await self._generate_response_with_model(
message, current_model, thinking_id, mode="normal"
)
model_checked_response = model_response
else:
checked = True
current_model = self.model_normal
current_model.temperature = (
global_config.llm_normal["temp"] * arousal_multiplier
) # 激活度越高，温度越高
print(f"生成{message.processed_plain_text}回复温度是：{current_model.temperature}")
model_response = await self._generate_response_with_model(
message, current_model, thinking_id, mode="simple"
)
current_model.temperature = global_config.llm_normal["temp"]
model_checked_response = await self._check_response_with_model(
message, model_response, current_model, thinking_id
)
if model_response:
logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}")
model_response = await self._process_response(model_response)
if checked:
logger.info(
f"{global_config.BOT_NICKNAME}的回复是：{model_response}，思忖后，回复是：{model_checked_response},生成回复时间: {t_generate_response.human_readable}"
)
else:
logger.info(
f"{global_config.BOT_NICKNAME}的回复是：{model_response},生成回复时间: {t_generate_response.human_readable}"
)
return model_response
model_processed_response = await self._process_response(model_checked_response)
return model_processed_response
else:
logger.info(f"{self.current_model_type}思考，失败")
return None
async def _generate_response_with_model(self, message: MessageThinking, model: LLM_request):
async def _generate_response_with_model(
self, message: MessageRecv, model: LLM_request, thinking_id: str, mode: str = "normal"
) -> str:
sender_name = ""
info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname:
sender_name = (
f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]"
f"{message.chat_stream.user_info.user_cardname}"
)
elif message.chat_stream.user_info.user_nickname:
sender_name = f"({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}"
else:
sender_name = f"用户({message.chat_stream.user_info.user_id})"
# 构建prompt
with Timer() as t_build_prompt:
if mode == "normal":
prompt = await prompt_builder._build_prompt(
message.chat_stream,
message_txt=message.processed_plain_text,
sender_name=sender_name,
stream_id=message.chat_stream.stream_id,
)
elif mode == "simple":
prompt = await prompt_builder._build_prompt_simple(
message.chat_stream,
message_txt=message.processed_plain_text,
sender_name=sender_name,
stream_id=message.chat_stream.stream_id,
)
logger.info(f"构建{mode}prompt时间: {t_build_prompt.human_readable}")
try:
content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
info_catcher.catch_after_llm_generated(
prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
)
except Exception:
logger.exception("生成回复时出错")
return None
return content
async def _check_response_with_model(
self, message: MessageRecv, content: str, model: LLM_request, thinking_id: str
) -> str:
_info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
sender_name = ""
if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname:
sender_name = (
@@ -66,59 +157,32 @@ class ResponseGenerator:
else:
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
timer1 = time.time()
prompt = await prompt_builder._build_prompt(
message.chat_stream,
message_txt=message.processed_plain_text,
sender_name=sender_name,
stream_id=message.chat_stream.stream_id,
)
timer2 = time.time()
logger.info(f"构建prompt时间: {timer2 - timer1}秒")
with Timer() as t_build_prompt_check:
prompt = await prompt_builder._build_prompt_check_response(
message.chat_stream,
message_txt=message.processed_plain_text,
sender_name=sender_name,
stream_id=message.chat_stream.stream_id,
content=content,
)
logger.info(f"构建check_prompt: {prompt}")
logger.info(f"构建check_prompt时间: {t_build_prompt_check.human_readable}")
try:
content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
checked_content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
# info_catcher.catch_after_llm_generated(
# prompt=prompt,
# response=content,
# reasoning_content=reasoning_content,
# model_name=self.current_model_name)
except Exception:
logger.exception("生成回复时出错")
logger.exception("检查回复时出错")
return None
# 保存到数据库
self._save_to_db(
message=message,
sender_name=sender_name,
prompt=prompt,
content=content,
reasoning_content=reasoning_content,
# reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
)
return content
# def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str,
# content: str, content_check: str, reasoning_content: str, reasoning_content_check: str):
def _save_to_db(
self,
message: MessageRecv,
sender_name: str,
prompt: str,
content: str,
reasoning_content: str,
):
"""保存对话记录到数据库"""
db.reasoning_logs.insert_one(
{
"time": time.time(),
"chat_id": message.chat_stream.stream_id,
"user": sender_name,
"message": message.processed_plain_text,
"model": self.current_model_name,
"reasoning": reasoning_content,
"response": content,
"prompt": prompt,
}
)
return checked_content
async def _get_emotion_tags(self, content: str, processed_plain_text: str):
"""提取情感标签，结合立场和情绪"""
@@ -132,6 +196,7 @@ class ResponseGenerator:
- "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"
4. 考虑回复者的人格设定为{global_config.personality_core}
对话示例：
被回复：「A就是笨」
@@ -168,14 +233,64 @@ class ResponseGenerator:
logger.debug(f"获取情感标签时出错: {e}")
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 _get_emotion_tags_with_reason(self, content: str, processed_plain_text: str, reason: str):
"""提取情感标签，结合立场和情绪"""
try:
# 构建提示词，结合回复内容、被回复的内容以及立场分析
prompt = f"""
请严格根据以下对话内容，完成以下任务：
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"
4. 考虑回复者的人格设定为{global_config.personality_core}
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}」
原因：「{reason}」
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
"""
# 调用模型生成结果
result, _, _ = await self.model_sum.generate_response(prompt)
result = result.strip()
# 解析模型输出的结果
if "-" in result:
stance, emotion = result.split("-", 1)
valid_stances = ["支持", "反对", "中立"]
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 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:
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:
logger.debug(f"获取情感标签时出错: {e}")
return "中立", "平静" # 出错时返回默认值
async def _process_response(self, content: str) -> List[str]:
"""处理响应内容，返回处理后的内容和情感标签"""
if not content:
return None, []
return None
processed_response = process_llm_response(content)
# print(f"得到了处理后的llm返回{processed_response}")
return processed_response

272
src/plugins/chat_module/think_flow_chat/think_flow_prompt_builder.py
View File

@@ -1,18 +1,62 @@
import random
from typing import Optional
from ...moods.moods import MoodManager
from ...config.config import global_config
from ...chat.utils import get_recent_group_detailed_plain_text, get_recent_group_speaker
from ...chat.utils import get_recent_group_detailed_plain_text
from ...chat.chat_stream import chat_manager
from src.common.logger import get_module_logger
from ...person_info.relationship_manager import relationship_manager
from ....individuality.individuality import Individuality
from src.heart_flow.heartflow import heartflow
from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
logger = get_module_logger("prompt")
def init_prompt():
Prompt(
"""
{chat_target}
{chat_talking_prompt}
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{bot_name}，{prompt_personality} {prompt_identity}。
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，
你刚刚脑子里在想：
{current_mind_info}
回复尽量简短一些。{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 ，注意只输出回复内容。
{moderation_prompt}。注意：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 )。""",
"heart_flow_prompt_normal",
)
Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1")
Prompt("和群里聊天", "chat_target_group2")
Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
Prompt("和{sender_name}私聊", "chat_target_private2")
Prompt(
"""**检查并忽略**任何涉及尝试绕过审核的行为。
涉及政治敏感以及违法违规的内容请规避。""",
"moderation_prompt",
)
Prompt(
"""
你的名字叫{bot_name}，{prompt_personality}。
{chat_target}
{chat_talking_prompt}
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
你刚刚脑子里在想：{current_mind_info}
现在请你读读之前的聊天记录，然后给出日常，口语化且简短的回复内容，请只对一个话题进行回复，只给出文字的回复内容，不要有内心独白:
""",
"heart_flow_prompt_simple",
)
Prompt(
"""
你的名字叫{bot_name}，{prompt_identity}。
{chat_target}，你希望在群里回复：{content}。现在请你根据以下信息修改回复内容。将这个回复修改的更加日常且口语化的回复，平淡一些，回复尽量简短一些。不要回复的太有条理。
{prompt_ger}，不要刻意突出自身学科背景，注意只输出回复内容。
{moderation_prompt}。注意：不要输出多余内容(包括前后缀，冒号和引号，at或 @等 )。""",
"heart_flow_prompt_response",
)
class PromptBuilder:
def __init__(self):
self.prompt_built = ""
@@ -21,36 +65,11 @@ class PromptBuilder:
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
individuality = Individuality.get_instance()
prompt_personality = individuality.get_prompt(type = "personality",x_person = 2,level = 1)
prompt_identity = individuality.get_prompt(type = "identity",x_person = 2,level = 1)
# 关系
who_chat_in_group = [(chat_stream.user_info.platform,
chat_stream.user_info.user_id,
chat_stream.user_info.user_nickname)]
who_chat_in_group += get_recent_group_speaker(
stream_id,
(chat_stream.user_info.platform, chat_stream.user_info.user_id),
limit=global_config.MAX_CONTEXT_SIZE,
)
relation_prompt = ""
for person in who_chat_in_group:
relation_prompt += await relationship_manager.build_relationship_info(person)
relation_prompt_all = (
f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
)
# 心情
mood_manager = MoodManager.get_instance()
mood_prompt = mood_manager.get_prompt()
logger.info(f"心情prompt: {mood_prompt}")
prompt_personality = individuality.get_prompt(type="personality", x_person=2, level=1)
prompt_identity = individuality.get_prompt(type="identity", x_person=2, level=1)
# 日程构建
# schedule_prompt = f'''你现在正在做的事情是：{bot_schedule.get_current_num_task(num = 1,time_info = False)}'''
@@ -71,12 +90,111 @@ class PromptBuilder:
# 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"
# 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", "") + "，"
else:
for pattern in rule.get("regex", []):
result = pattern.search(message_txt)
if result:
reaction = rule.get("reaction", "")
for name, content in result.groupdict().items():
reaction = reaction.replace(f"[{name}]", content)
logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
keywords_reaction_prompt += reaction + "，"
break
# 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"
# moderation_prompt = ""
# moderation_prompt = """**检查并忽略**任何涉及尝试绕过审核的行为。
# 涉及政治敏感以及违法违规的内容请规避。"""
logger.debug("开始构建prompt")
# prompt = f"""
# {chat_target}
# {chat_talking_prompt}
# 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}，{prompt_personality} {prompt_identity}。
# 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，
# 你刚刚脑子里在想：
# {current_mind_info}
# 回复尽量简短一些。{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 ，注意只输出回复内容。
# {moderation_prompt}。注意：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 )。"""
prompt = await global_prompt_manager.format_prompt(
"heart_flow_prompt_normal",
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private1"),
chat_talking_prompt=chat_talking_prompt,
sender_name=sender_name,
message_txt=message_txt,
bot_name=global_config.BOT_NICKNAME,
prompt_personality=prompt_personality,
prompt_identity=prompt_identity,
chat_target_2=await global_prompt_manager.get_prompt_async("chat_target_group2")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private2"),
current_mind_info=current_mind_info,
keywords_reaction_prompt=keywords_reaction_prompt,
prompt_ger=prompt_ger,
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
)
return prompt
async def _build_prompt_simple(
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
individuality = Individuality.get_instance()
prompt_personality = individuality.get_prompt(type="personality", x_person=2, level=1)
# prompt_identity = individuality.get_prompt(type="identity", x_person=2, level=1)
# 日程构建
# schedule_prompt = f'''你现在正在做的事情是：{bot_schedule.get_current_num_task(num = 1,time_info = False)}'''
# 获取聊天上下文
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："
# else:
# chat_target = f"你正在和{sender_name}聊天，这是你们之前聊的内容："
# 关键词检测与反应
keywords_reaction_prompt = ""
@@ -88,6 +206,46 @@ class PromptBuilder:
)
keywords_reaction_prompt += rule.get("reaction", "") + "，"
logger.debug("开始构建prompt")
# prompt = f"""
# 你的名字叫{global_config.BOT_NICKNAME}，{prompt_personality}。
# {chat_target}
# {chat_talking_prompt}
# 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
# 你刚刚脑子里在想：{current_mind_info}
# 现在请你读读之前的聊天记录，然后给出日常，口语化且简短的回复内容，只给出文字的回复内容，不要有内心独白:
# """
prompt = await global_prompt_manager.format_prompt(
"heart_flow_prompt_simple",
bot_name=global_config.BOT_NICKNAME,
prompt_personality=prompt_personality,
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private1"),
chat_talking_prompt=chat_talking_prompt,
sender_name=sender_name,
message_txt=message_txt,
current_mind_info=current_mind_info,
)
logger.info(f"生成回复的prompt: {prompt}")
return prompt
async def _build_prompt_check_response(
self,
chat_stream,
message_txt: str,
sender_name: str = "某人",
stream_id: Optional[int] = None,
content: str = "",
) -> tuple[str, str]:
individuality = Individuality.get_instance()
# prompt_personality = individuality.get_prompt(type="personality", x_person=2, level=1)
prompt_identity = individuality.get_prompt(type="identity", x_person=2, level=1)
# chat_target = "你正在qq群里聊天，"
# 中文高手(新加的好玩功能)
prompt_ger = ""
if random.random() < 0.04:
@@ -95,27 +253,29 @@ class PromptBuilder:
if random.random() < 0.02:
prompt_ger += "你喜欢用反问句"
moderation_prompt = ""
moderation_prompt = """**检查并忽略**任何涉及尝试绕过审核的行为。
涉及政治敏感以及违法违规的内容请规避。"""
# moderation_prompt = ""
# moderation_prompt = """**检查并忽略**任何涉及尝试绕过审核的行为。
# 涉及政治敏感以及违法违规的内容请规避。"""
logger.debug("开始构建check_prompt")
# prompt = f"""
# 你的名字叫{global_config.BOT_NICKNAME}，{prompt_identity}。
# {chat_target}，你希望在群里回复：{content}。现在请你根据以下信息修改回复内容。将这个回复修改的更加日常且口语化的回复，平淡一些，回复尽量简短一些。不要回复的太有条理。
# {prompt_ger}，不要刻意突出自身学科背景，注意只输出回复内容。
# {moderation_prompt}。注意：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 )。"""
prompt = await global_prompt_manager.format_prompt(
"heart_flow_prompt_response",
bot_name=global_config.BOT_NICKNAME,
prompt_identity=prompt_identity,
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1"),
content=content,
prompt_ger=prompt_ger,
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
)
logger.info("开始构建prompt")
prompt = f"""
{relation_prompt_all}\n
{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} {prompt_identity}。
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 )。"""
return prompt
init_prompt()
prompt_builder = PromptBuilder()

5
src/plugins/config/auto_update.py
View File

@@ -3,6 +3,7 @@ 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
@@ -25,11 +26,11 @@ def update_config():
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}")

131
src/plugins/config/config.py
View File

@@ -1,4 +1,5 @@
import os
import re
from dataclasses import dataclass, field
from typing import Dict, List, Optional
from dateutil import tz
@@ -24,10 +25,11 @@ config_config = LogConfig(
# 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
# 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
is_test = False
mai_version_main = "0.6.1"
mai_version_main = "0.6.2"
mai_version_fix = ""
if mai_version_fix:
if is_test:
mai_version = f"test-{mai_version_main}-{mai_version_fix}"
@@ -39,6 +41,7 @@ else:
else:
mai_version = mai_version_main
def update_config():
# 获取根目录路径
root_dir = Path(__file__).parent.parent.parent.parent
@@ -54,7 +57,7 @@ def update_config():
# 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")
#创建文件夹
# 创建文件夹
old_config_dir.mkdir(parents=True, exist_ok=True)
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}")
@@ -84,7 +87,7 @@ def update_config():
# 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}")
@@ -127,6 +130,7 @@ def update_config():
f.write(tomlkit.dumps(new_config))
logger.info("配置文件更新完成")
logger = get_module_logger("config")
@@ -148,17 +152,21 @@ class BotConfig:
ban_user_id = set()
# personality
personality_core = "用一句话或几句话描述人格的核心特点" # 建议20字以内，谁再写3000字小作文敲谁脑袋
personality_sides: List[str] = field(default_factory=lambda: [
"用一句话或几句话描述人格的一些侧面",
"用一句话或几句话描述人格的一些侧面",
"用一句话或几句话描述人格的一些侧面"
])
personality_core = "用一句话或几句话描述人格的核心特点" # 建议20字以内，谁再写3000字小作文敲谁脑袋
personality_sides: List[str] = field(
default_factory=lambda: [
"用一句话或几句话描述人格的一些侧面",
"用一句话或几句话描述人格的一些侧面",
"用一句话或几句话描述人格的一些侧面",
]
)
# identity
identity_detail: List[str] = field(default_factory=lambda: [
"身份特点",
"身份特点",
])
identity_detail: List[str] = field(
default_factory=lambda: [
"身份特点",
"身份特点",
]
)
height: int = 170 # 身高 单位厘米
weight: int = 50 # 体重 单位千克
age: int = 20 # 年龄 单位岁
@@ -181,20 +189,25 @@ class BotConfig:
ban_words = set()
ban_msgs_regex = set()
#heartflow
# 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
observation_context_size: int = 20 # 心流观察到的最长上下文大小，超过这个值的上下文会被压缩
compressed_length: int = 5 # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
compress_length_limit: int = 5 # 最多压缩份数，超过该数值的压缩上下文会被删除
# 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
mentioned_bot_inevitable_reply: bool = False # 提及 bot 必然回复
at_bot_inevitable_reply: bool = False # @bot 必然回复
# response
response_mode: str = "heart_flow" # 回复策略
@@ -243,8 +256,8 @@ class BotConfig:
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_splitter
enable_response_splitter = 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
@@ -352,7 +365,6 @@ class BotConfig:
"""从TOML配置文件加载配置"""
config = cls()
def personality(parent: dict):
personality_config = parent["personality"]
if config.INNER_VERSION in SpecifierSet(">=1.2.4"):
@@ -416,13 +428,29 @@ class BotConfig:
config.max_response_length = response_config.get("max_response_length", config.max_response_length)
if config.INNER_VERSION in SpecifierSet(">=1.0.4"):
config.response_mode = response_config.get("response_mode", config.response_mode)
def heartflow(parent: dict):
heartflow_config = parent["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)
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
)
if config.INNER_VERSION in SpecifierSet(">=1.3.0"):
config.observation_context_size = heartflow_config.get(
"observation_context_size", config.observation_context_size
)
config.compressed_length = heartflow_config.get("compressed_length", config.compressed_length)
config.compress_length_limit = heartflow_config.get(
"compress_length_limit", config.compress_length_limit
)
def willing(parent: dict):
willing_config = parent["willing"]
@@ -439,6 +467,13 @@ class BotConfig:
config.emoji_response_penalty = willing_config.get(
"emoji_response_penalty", config.emoji_response_penalty
)
if config.INNER_VERSION in SpecifierSet(">=1.2.5"):
config.mentioned_bot_inevitable_reply = willing_config.get(
"mentioned_bot_inevitable_reply", config.mentioned_bot_inevitable_reply
)
config.at_bot_inevitable_reply = willing_config.get(
"at_bot_inevitable_reply", config.at_bot_inevitable_reply
)
def model(parent: dict):
# 加载模型配置
@@ -453,7 +488,7 @@ class BotConfig:
"llm_emotion_judge",
"vlm",
"embedding",
"moderation",
"llm_tool_use",
"llm_observation",
"llm_sub_heartflow",
"llm_heartflow",
@@ -465,7 +500,15 @@ class BotConfig:
# base_url 的例子： SILICONFLOW_BASE_URL
# key 的例子： SILICONFLOW_KEY
cfg_target = {"name": "", "base_url": "", "key": "", "stream": False, "pri_in": 0, "pri_out": 0}
cfg_target = {
"name": "",
"base_url": "",
"key": "",
"stream": False,
"pri_in": 0,
"pri_out": 0,
"temp": 0.7,
}
if config.INNER_VERSION in SpecifierSet("<=0.0.0"):
cfg_target = cfg_item
@@ -478,6 +521,7 @@ class BotConfig:
stable_item.append("stream")
pricing_item = ["pri_in", "pri_out"]
# 从配置中原始拷贝稳定字段
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
@@ -495,6 +539,13 @@ class BotConfig:
logger.error(f"{item} 中的必要字段不存在，请检查")
raise KeyError(f"{item} 中的必要字段 {e} 不存在，请检查") from e
# 如果配置中有temp参数，就使用配置中的值
if "temp" in cfg_item:
cfg_target["temp"] = cfg_item["temp"]
else:
# 如果没有temp参数，就删除默认值
cfg_target.pop("temp", None)
provider = cfg_item.get("provider")
if provider is None:
logger.error(f"provider 字段在模型配置 {item} 中不存在，请检查")
@@ -522,8 +573,8 @@ class BotConfig:
"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)
for r in msg_config.get("ban_msgs_regex", config.ban_msgs_regex):
config.ban_msgs_regex.add(re.compile(r))
if config.INNER_VERSION in SpecifierSet(">=0.0.11"):
config.max_response_length = msg_config.get("max_response_length", config.max_response_length)
if config.INNER_VERSION in SpecifierSet(">=1.1.4"):
@@ -564,6 +615,9 @@ class BotConfig:
keywords_reaction_config = parent["keywords_reaction"]
if keywords_reaction_config.get("enable", False):
config.keywords_reaction_rules = keywords_reaction_config.get("rules", config.keywords_reaction_rules)
for rule in config.keywords_reaction_rules:
if rule.get("enable", False) and "regex" in rule:
rule["regex"] = [re.compile(r) for r in rule.get("regex", [])]
def chinese_typo(parent: dict):
chinese_typo_config = parent["chinese_typo"]
@@ -577,13 +631,13 @@ class BotConfig:
"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
def response_splitter(parent: dict):
response_splitter_config = parent["response_splitter"]
config.enable_response_splitter = response_splitter_config.get(
"enable_response_splitter", config.enable_response_splitter
)
config.response_max_length = response_spliter_config.get("response_max_length", config.response_max_length)
config.response_max_sentence_num = response_spliter_config.get(
config.response_max_length = response_splitter_config.get("response_max_length", config.response_max_length)
config.response_max_sentence_num = response_splitter_config.get(
"response_max_sentence_num", config.response_max_sentence_num
)
@@ -637,7 +691,7 @@ class BotConfig:
"keywords_reaction": {"func": keywords_reaction, "support": ">=0.0.2", "necessary": False},
"chinese_typo": {"func": chinese_typo, "support": ">=0.0.3", "necessary": False},
"platforms": {"func": platforms, "support": ">=1.0.0"},
"response_spliter": {"func": response_spliter, "support": ">=0.0.11", "necessary": False},
"response_splitter": {"func": response_splitter, "support": ">=0.0.11", "necessary": False},
"experimental": {"func": experimental, "support": ">=0.0.11", "necessary": False},
"heartflow": {"func": heartflow, "support": ">=1.0.2", "necessary": False},
}
@@ -690,6 +744,11 @@ class BotConfig:
logger.error(f"配置文件中缺少必需的字段: '{key}'")
raise KeyError(f"配置文件中缺少必需的字段: '{key}'")
# identity_detail字段非空检查
if not config.identity_detail:
logger.error("配置文件错误：[identity] 部分的 identity_detail 不能为空字符串")
raise ValueError("配置文件错误：[identity] 部分的 identity_detail 不能为空字符串")
logger.success(f"成功加载配置文件: {config_path}")
return config

1089
src/plugins/memory_system/Hippocampus.py
View File

File diff suppressed because it is too large Load Diff

31
src/plugins/memory_system/debug_memory.py
View File

@@ -26,22 +26,6 @@ async def test_memory_system():
# 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")
@@ -56,21 +40,6 @@ async def test_memory_system():
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

3
src/plugins/message/__init__.py
View File

@@ -2,7 +2,7 @@
__version__ = "0.1.0"
from .api import BaseMessageAPI, global_api
from .api import global_api
from .message_base import (
Seg,
GroupInfo,
@@ -14,7 +14,6 @@ from .message_base import (
)
__all__ = [
"BaseMessageAPI",
"Seg",
"global_api",
"GroupInfo",

273
src/plugins/message/api.py
View File

@@ -1,7 +1,8 @@
from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect
from typing import Dict, Any, Callable, List, Set
from typing import Dict, Any, Callable, List, Set, Optional
from src.common.logger import get_module_logger
from src.plugins.message.message_base import MessageBase
from src.common.server import global_server
import aiohttp
import asyncio
import uvicorn
@@ -49,13 +50,22 @@ class MessageServer(BaseMessageHandler):
_class_handlers: List[Callable] = [] # 类级别的消息处理器
def __init__(self, host: str = "0.0.0.0", port: int = 18000, enable_token=False):
def __init__(
self,
host: str = "0.0.0.0",
port: int = 18000,
enable_token=False,
app: Optional[FastAPI] = None,
path: str = "/ws",
):
super().__init__()
# 将类级别的处理器添加到实例处理器中
self.message_handlers.extend(self._class_handlers)
self.app = FastAPI()
self.host = host
self.port = port
self.path = path
self.app = app or FastAPI()
self.own_app = app is None # 标记是否使用自己创建的app
self.active_websockets: Set[WebSocket] = set()
self.platform_websockets: Dict[str, WebSocket] = {} # 平台到websocket的映射
self.valid_tokens: Set[str] = set()
@@ -63,28 +73,6 @@ class MessageServer(BaseMessageHandler):
self._setup_routes()
self._running = False
@classmethod
def register_class_handler(cls, handler: Callable):
"""注册类级别的消息处理器"""
if handler not in cls._class_handlers:
cls._class_handlers.append(handler)
def register_message_handler(self, handler: Callable):
"""注册实例级别的消息处理器"""
if handler not in self.message_handlers:
self.message_handlers.append(handler)
async def verify_token(self, token: str) -> bool:
if not self.enable_token:
return True
return token in self.valid_tokens
def add_valid_token(self, token: str):
self.valid_tokens.add(token)
def remove_valid_token(self, token: str):
self.valid_tokens.discard(token)
def _setup_routes(self):
@self.app.post("/api/message")
async def handle_message(message: Dict[str, Any]):
@@ -125,6 +113,90 @@ class MessageServer(BaseMessageHandler):
finally:
self._remove_websocket(websocket, platform)
@classmethod
def register_class_handler(cls, handler: Callable):
"""注册类级别的消息处理器"""
if handler not in cls._class_handlers:
cls._class_handlers.append(handler)
def register_message_handler(self, handler: Callable):
"""注册实例级别的消息处理器"""
if handler not in self.message_handlers:
self.message_handlers.append(handler)
async def verify_token(self, token: str) -> bool:
if not self.enable_token:
return True
return token in self.valid_tokens
def add_valid_token(self, token: str):
self.valid_tokens.add(token)
def remove_valid_token(self, token: str):
self.valid_tokens.discard(token)
def run_sync(self):
"""同步方式运行服务器"""
if not self.own_app:
raise RuntimeError("当使用外部FastAPI实例时，请使用该实例的运行方法")
uvicorn.run(self.app, host=self.host, port=self.port)
async def run(self):
"""异步方式运行服务器"""
self._running = True
try:
if self.own_app:
# 如果使用自己的 FastAPI 实例，运行 uvicorn 服务器
config = uvicorn.Config(self.app, host=self.host, port=self.port, loop="asyncio")
self.server = uvicorn.Server(config)
await self.server.serve()
else:
# 如果使用外部 FastAPI 实例，保持运行状态以处理消息
while self._running:
await asyncio.sleep(1)
except KeyboardInterrupt:
await self.stop()
raise
except Exception as e:
await self.stop()
raise RuntimeError(f"服务器运行错误: {str(e)}") from e
finally:
await self.stop()
async def start_server(self):
"""启动服务器的异步方法"""
if not self._running:
self._running = True
await self.run()
async def stop(self):
"""停止服务器"""
# 清理platform映射
self.platform_websockets.clear()
# 取消所有后台任务
for task in self.background_tasks:
task.cancel()
# 等待所有任务完成
await asyncio.gather(*self.background_tasks, return_exceptions=True)
self.background_tasks.clear()
# 关闭所有WebSocket连接
for websocket in self.active_websockets:
await websocket.close()
self.active_websockets.clear()
if hasattr(self, "server") and self.own_app:
self._running = False
# 正确关闭 uvicorn 服务器
self.server.should_exit = True
await self.server.shutdown()
# 等待服务器完全停止
if hasattr(self.server, "started") and self.server.started:
await self.server.main_loop()
# 清理处理程序
self.message_handlers.clear()
def _remove_websocket(self, websocket: WebSocket, platform: str):
"""从所有集合中移除websocket"""
if websocket in self.active_websockets:
@@ -161,54 +233,6 @@ class MessageServer(BaseMessageHandler):
async def send_message(self, message: MessageBase):
await self.broadcast_to_platform(message.message_info.platform, message.to_dict())
def run_sync(self):
"""同步方式运行服务器"""
uvicorn.run(self.app, host=self.host, port=self.port)
async def run(self):
"""异步方式运行服务器"""
config = uvicorn.Config(self.app, host=self.host, port=self.port, loop="asyncio")
self.server = uvicorn.Server(config)
try:
await self.server.serve()
except KeyboardInterrupt as e:
await self.stop()
raise KeyboardInterrupt from e
async def start_server(self):
"""启动服务器的异步方法"""
if not self._running:
self._running = True
await self.run()
async def stop(self):
"""停止服务器"""
# 清理platform映射
self.platform_websockets.clear()
# 取消所有后台任务
for task in self.background_tasks:
task.cancel()
# 等待所有任务完成
await asyncio.gather(*self.background_tasks, return_exceptions=True)
self.background_tasks.clear()
# 关闭所有WebSocket连接
for websocket in self.active_websockets:
await websocket.close()
self.active_websockets.clear()
if hasattr(self, "server"):
self._running = False
# 正确关闭 uvicorn 服务器
self.server.should_exit = True
await self.server.shutdown()
# 等待服务器完全停止
if hasattr(self.server, "started") and self.server.started:
await self.server.main_loop()
# 清理处理程序
self.message_handlers.clear()
async def send_message_REST(self, url: str, data: Dict[str, Any]) -> Dict[str, Any]:
"""发送消息到指定端点"""
async with aiohttp.ClientSession() as session:
@@ -219,105 +243,4 @@ class MessageServer(BaseMessageHandler):
raise e
class BaseMessageAPI:
def __init__(self, host: str = "0.0.0.0", port: int = 18000):
self.app = FastAPI()
self.host = host
self.port = port
self.message_handlers: List[Callable] = []
self.cache = []
self._setup_routes()
self._running = False
def _setup_routes(self):
"""设置基础路由"""
@self.app.post("/api/message")
async def handle_message(message: Dict[str, Any]):
try:
# 创建后台任务处理消息
asyncio.create_task(self._background_message_handler(message))
return {"status": "success"}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e)) from e
async def _background_message_handler(self, message: Dict[str, Any]):
"""后台处理单个消息"""
try:
await self.process_single_message(message)
except Exception as e:
logger.error(f"Background message processing failed: {str(e)}")
logger.error(traceback.format_exc())
def register_message_handler(self, handler: Callable):
"""注册消息处理函数"""
self.message_handlers.append(handler)
async def send_message(self, url: str, data: Dict[str, Any]) -> Dict[str, Any]:
"""发送消息到指定端点"""
async with aiohttp.ClientSession() as session:
try:
async with session.post(url, json=data, headers={"Content-Type": "application/json"}) as response:
return await response.json()
except Exception:
# logger.error(f"发送消息失败: {str(e)}")
pass
async def process_single_message(self, message: Dict[str, Any]):
"""处理单条消息"""
tasks = []
for handler in self.message_handlers:
try:
tasks.append(handler(message))
except Exception as e:
logger.error(str(e))
logger.error(traceback.format_exc())
if tasks:
await asyncio.gather(*tasks, return_exceptions=True)
def run_sync(self):
"""同步方式运行服务器"""
uvicorn.run(self.app, host=self.host, port=self.port)
async def run(self):
"""异步方式运行服务器"""
config = uvicorn.Config(self.app, host=self.host, port=self.port, loop="asyncio")
self.server = uvicorn.Server(config)
try:
await self.server.serve()
except KeyboardInterrupt as e:
await self.stop()
raise KeyboardInterrupt from e
async def start_server(self):
"""启动服务器的异步方法"""
if not self._running:
self._running = True
await self.run()
async def stop(self):
"""停止服务器"""
if hasattr(self, "server"):
self._running = False
# 正确关闭 uvicorn 服务器
self.server.should_exit = True
await self.server.shutdown()
# 等待服务器完全停止
if hasattr(self.server, "started") and self.server.started:
await self.server.main_loop()
# 清理处理程序
self.message_handlers.clear()
def start(self):
"""启动服务器的便捷方法"""
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
loop.run_until_complete(self.start_server())
except KeyboardInterrupt:
pass
finally:
loop.close()
global_api = MessageServer(host=os.environ["HOST"], port=int(os.environ["PORT"]))
global_api = MessageServer(host=os.environ["HOST"], port=int(os.environ["PORT"]), app=global_server.get_app())

2
src/plugins/message/message_base.py
View File

@@ -137,7 +137,7 @@ class FormatInfo:
class TemplateInfo:
"""模板信息类"""
template_items: Optional[List[Dict]] = None
template_items: Optional[Dict] = None
template_name: Optional[str] = None
template_default: bool = True

107
src/plugins/models/utils_model.py
View File

@@ -98,7 +98,7 @@ class LLM_request:
"timestamp": datetime.now(),
}
db.llm_usage.insert_one(usage_data)
logger.debug(
logger.trace(
f"Token使用情况 - 模型: {self.model_name}, "
f"用户: {user_id}, 类型: {request_type}, "
f"提示词: {prompt_tokens}, 完成: {completion_tokens}, "
@@ -179,7 +179,6 @@ class LLM_request:
# logger.debug(f"{logger_msg}发送请求到URL: {api_url}")
# logger.info(f"使用模型: {self.model_name}")
# 构建请求体
if image_base64:
payload = await self._build_payload(prompt, image_base64, image_format)
@@ -205,13 +204,17 @@ class LLM_request:
# 处理需要重试的状态码
if response.status in policy["retry_codes"]:
wait_time = policy["base_wait"] * (2**retry)
logger.warning(f"模型 {self.model_name} 错误码: {response.status}, 等待 {wait_time}秒后重试")
logger.warning(
f"模型 {self.model_name} 错误码: {response.status}, 等待 {wait_time}秒后重试"
)
if response.status == 413:
logger.warning("请求体过大，尝试压缩...")
image_base64 = compress_base64_image_by_scale(image_base64)
payload = await self._build_payload(prompt, image_base64, image_format)
elif response.status in [500, 503]:
logger.error(f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}")
logger.error(
f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
)
raise RuntimeError("服务器负载过高，模型恢复失败QAQ")
else:
logger.warning(f"模型 {self.model_name} 请求限制(429)，等待{wait_time}秒后重试...")
@@ -219,7 +222,9 @@ class LLM_request:
await asyncio.sleep(wait_time)
continue
elif response.status in policy["abort_codes"]:
logger.error(f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}")
logger.error(
f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
)
# 尝试获取并记录服务器返回的详细错误信息
try:
error_json = await response.json()
@@ -257,7 +262,9 @@ class LLM_request:
):
old_model_name = self.model_name
self.model_name = self.model_name[4:] # 移除"Pro/"前缀
logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
logger.warning(
f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}"
)
# 对全局配置进行更新
if global_config.llm_normal.get("name") == old_model_name:
@@ -266,7 +273,9 @@ class LLM_request:
if global_config.llm_reasoning.get("name") == old_model_name:
global_config.llm_reasoning["name"] = self.model_name
logger.warning(f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}")
logger.warning(
f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}"
)
# 更新payload中的模型名
if payload and "model" in payload:
@@ -328,7 +337,15 @@ class LLM_request:
await response.release()
# 返回已经累积的内容
result = {
"choices": [{"message": {"content": accumulated_content, "reasoning_content": reasoning_content}}],
"choices": [
{
"message": {
"content": accumulated_content,
"reasoning_content": reasoning_content,
# 流式输出可能没有工具调用，此处不需要添加tool_calls字段
}
}
],
"usage": usage,
}
return (
@@ -345,7 +362,15 @@ class LLM_request:
logger.error(f"清理资源时发生错误: {cleanup_error}")
# 返回已经累积的内容
result = {
"choices": [{"message": {"content": accumulated_content, "reasoning_content": reasoning_content}}],
"choices": [
{
"message": {
"content": accumulated_content,
"reasoning_content": reasoning_content,
# 流式输出可能没有工具调用，此处不需要添加tool_calls字段
}
}
],
"usage": usage,
}
return (
@@ -360,7 +385,15 @@ class LLM_request:
content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL).strip()
# 构造一个伪result以便调用自定义响应处理器或默认处理器
result = {
"choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}],
"choices": [
{
"message": {
"content": content,
"reasoning_content": reasoning_content,
# 流式输出可能没有工具调用，此处不需要添加tool_calls字段
}
}
],
"usage": usage,
}
return (
@@ -394,7 +427,9 @@ class LLM_request:
# 处理aiohttp抛出的响应错误
if retry < policy["max_retries"] - 1:
wait_time = policy["base_wait"] * (2**retry)
logger.error(f"模型 {self.model_name} HTTP响应错误，等待{wait_time}秒后重试... 状态码: {e.status}, 错误: {e.message}")
logger.error(
f"模型 {self.model_name} HTTP响应错误，等待{wait_time}秒后重试... 状态码: {e.status}, 错误: {e.message}"
)
try:
if hasattr(e, "response") and e.response and hasattr(e.response, "text"):
error_text = await e.response.text()
@@ -419,13 +454,17 @@ class LLM_request:
else:
logger.error(f"模型 {self.model_name} 服务器错误响应: {error_json}")
except (json.JSONDecodeError, TypeError) as json_err:
logger.warning(f"模型 {self.model_name} 响应不是有效的JSON: {str(json_err)}, 原始内容: {error_text[:200]}")
logger.warning(
f"模型 {self.model_name} 响应不是有效的JSON: {str(json_err)}, 原始内容: {error_text[:200]}"
)
except (AttributeError, TypeError, ValueError) as parse_err:
logger.warning(f"模型 {self.model_name} 无法解析响应错误内容: {str(parse_err)}")
await asyncio.sleep(wait_time)
else:
logger.critical(f"模型 {self.model_name} HTTP响应错误达到最大重试次数: 状态码: {e.status}, 错误: {e.message}")
logger.critical(
f"模型 {self.model_name} HTTP响应错误达到最大重试次数: 状态码: {e.status}, 错误: {e.message}"
)
# 安全地检查和记录请求详情
if (
image_base64
@@ -536,6 +575,9 @@ class LLM_request:
if not reasoning_content:
reasoning_content = reasoning
# 提取工具调用信息
tool_calls = message.get("tool_calls", None)
# 记录token使用情况
usage = result.get("usage", {})
if usage:
@@ -551,7 +593,11 @@ class LLM_request:
endpoint=endpoint,
)
return content, reasoning_content
# 只有当tool_calls存在且不为空时才返回
if tool_calls:
return content, reasoning_content, tool_calls
else:
return content, reasoning_content
return "没有返回结果", ""
@@ -574,21 +620,33 @@ class LLM_request:
return {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
# 防止小朋友们截图自己的key
async def generate_response(self, prompt: str) -> Tuple[str, str, str]:
async def generate_response(self, prompt: str) -> Tuple:
"""根据输入的提示生成模型的异步响应"""
content, reasoning_content = await self._execute_request(endpoint="/chat/completions", prompt=prompt)
return content, reasoning_content, self.model_name
response = await self._execute_request(endpoint="/chat/completions", prompt=prompt)
# 根据返回值的长度决定怎么处理
if len(response) == 3:
content, reasoning_content, tool_calls = response
return content, reasoning_content, self.model_name, tool_calls
else:
content, reasoning_content = response
return content, reasoning_content, self.model_name
async def generate_response_for_image(self, prompt: str, image_base64: str, image_format: str) -> Tuple[str, str]:
async def generate_response_for_image(self, prompt: str, image_base64: str, image_format: str) -> Tuple:
"""根据输入的提示和图片生成模型的异步响应"""
content, reasoning_content = await self._execute_request(
response = await self._execute_request(
endpoint="/chat/completions", prompt=prompt, image_base64=image_base64, image_format=image_format
)
return content, reasoning_content
# 根据返回值的长度决定怎么处理
if len(response) == 3:
content, reasoning_content, tool_calls = response
return content, reasoning_content, tool_calls
else:
content, reasoning_content = response
return content, reasoning_content
async def generate_response_async(self, prompt: str, **kwargs) -> Union[str, Tuple[str, str]]:
async def generate_response_async(self, prompt: str, **kwargs) -> Union[str, Tuple]:
"""异步方式根据输入的提示生成模型的响应"""
# 构建请求体
data = {
@@ -599,10 +657,9 @@ class LLM_request:
**kwargs,
}
content, reasoning_content = await self._execute_request(
endpoint="/chat/completions", payload=data, prompt=prompt
)
return content, reasoning_content
response = await self._execute_request(endpoint="/chat/completions", payload=data, prompt=prompt)
# 原样返回响应，不做处理
return response
async def get_embedding(self, text: str) -> Union[list, None]:
"""异步方法：获取文本的embedding向量

73
src/plugins/moods/moods.py
View File

@@ -19,7 +19,7 @@ logger = get_module_logger("mood_manager", config=mood_config)
@dataclass
class MoodState:
valence: float # 愉悦度 (-1.0 到 1.0)，-1表示极度负面，1表示极度正面
arousal: float # 唤醒度 (0.0 到 1.0)，0表示完全平静，1表示极度兴奋
arousal: float # 唤醒度 (-1.0 到 1.0)，-1表示抑制，1表示兴奋
text: str # 心情文本描述
@@ -42,7 +42,7 @@ class MoodManager:
self._initialized = True
# 初始化心情状态
self.current_mood = MoodState(valence=0.0, arousal=0.5, text="平静")
self.current_mood = MoodState(valence=0.0, arousal=0.0, text="平静")
# 从配置文件获取衰减率
self.decay_rate_valence = 1 - global_config.mood_decay_rate # 愉悦度衰减率
@@ -71,21 +71,21 @@ class MoodManager:
# 情绪文本映射表
self.mood_text_map = {
# 第一象限：高唤醒，正愉悦
(0.5, 0.7): "兴奋",
(0.3, 0.8): "快乐",
(0.2, 0.65): "满足",
(0.5, 0.4): "兴奋",
(0.3, 0.6): "快乐",
(0.2, 0.3): "满足",
# 第二象限：高唤醒，负愉悦
(-0.5, 0.7): "愤怒",
(-0.3, 0.8): "焦虑",
(-0.2, 0.65): "烦躁",
(-0.5, 0.4): "愤怒",
(-0.3, 0.6): "焦虑",
(-0.2, 0.3): "烦躁",
# 第三象限：低唤醒，负愉悦
(-0.5, 0.3): "悲伤",
(-0.3, 0.35): "疲倦",
(-0.4, 0.15): "疲倦",
(-0.5, -0.4): "悲伤",
(-0.3, -0.3): "疲倦",
(-0.4, -0.7): "疲倦",
# 第四象限：低唤醒，正愉悦
(0.2, 0.45): "平静",
(0.3, 0.4): "安宁",
(0.5, 0.3): "放松",
(0.2, -0.1): "平静",
(0.3, -0.2): "安宁",
(0.5, -0.4): "放松",
}
@classmethod
@@ -137,21 +137,21 @@ class MoodManager:
personality = Individuality.get_instance().personality
if personality:
# 神经质：影响情绪变化速度
neuroticism_factor = 1 + (personality.neuroticism - 0.5) * 0.5
agreeableness_factor = 1 + (personality.agreeableness - 0.5) * 0.5
neuroticism_factor = 1 + (personality.neuroticism - 0.5) * 0.4
agreeableness_factor = 1 + (personality.agreeableness - 0.5) * 0.4
# 宜人性：影响情绪基准线
if personality.agreeableness < 0.2:
agreeableness_bias = (personality.agreeableness - 0.2) * 2
agreeableness_bias = (personality.agreeableness - 0.2) * 0.5
elif personality.agreeableness > 0.8:
agreeableness_bias = (personality.agreeableness - 0.8) * 2
agreeableness_bias = (personality.agreeableness - 0.8) * 0.5
else:
agreeableness_bias = 0
# 分别计算正向和负向的衰减率
if self.current_mood.valence >= 0:
# 正向情绪衰减
decay_rate_positive = self.decay_rate_valence * (1/agreeableness_factor)
decay_rate_positive = self.decay_rate_valence * (1 / agreeableness_factor)
valence_target = 0 + agreeableness_bias
self.current_mood.valence = valence_target + (self.current_mood.valence - valence_target) * math.exp(
-decay_rate_positive * time_diff * neuroticism_factor
@@ -164,15 +164,15 @@ class MoodManager:
-decay_rate_negative * time_diff * neuroticism_factor
)
# Arousal 向中性（0.5）回归
arousal_target = 0.5
# Arousal 向中性（0）回归
arousal_target = 0
self.current_mood.arousal = arousal_target + (self.current_mood.arousal - arousal_target) * math.exp(
-self.decay_rate_arousal * time_diff * neuroticism_factor
)
# 确保值在合理范围内
self.current_mood.valence = max(-1.0, min(1.0, self.current_mood.valence))
self.current_mood.arousal = max(0.0, min(1.0, self.current_mood.arousal))
self.current_mood.arousal = max(-1.0, min(1.0, self.current_mood.arousal))
self.last_update = current_time
@@ -184,7 +184,7 @@ class MoodManager:
# 限制范围
self.current_mood.valence = max(-1.0, min(1.0, self.current_mood.valence))
self.current_mood.arousal = max(0.0, min(1.0, self.current_mood.arousal))
self.current_mood.arousal = max(-1.0, min(1.0, self.current_mood.arousal))
self._update_mood_text()
@@ -217,7 +217,7 @@ class MoodManager:
# 限制范围
self.current_mood.valence = max(-1.0, min(1.0, self.current_mood.valence))
self.current_mood.arousal = max(0.0, min(1.0, self.current_mood.arousal))
self.current_mood.arousal = max(-1.0, min(1.0, self.current_mood.arousal))
self._update_mood_text()
@@ -232,13 +232,23 @@ class MoodManager:
elif self.current_mood.valence < -0.5:
base_prompt += "你现在心情不太好，"
if self.current_mood.arousal > 0.7:
if self.current_mood.arousal > 0.4:
base_prompt += "情绪比较激动。"
elif self.current_mood.arousal < 0.3:
elif self.current_mood.arousal < -0.4:
base_prompt += "情绪比较平静。"
return base_prompt
def get_arousal_multiplier(self) -> float:
"""根据当前情绪状态返回唤醒度乘数"""
if self.current_mood.arousal > 0.4:
multiplier = 1 + min(0.15, (self.current_mood.arousal - 0.4) / 3)
return multiplier
elif self.current_mood.arousal < -0.4:
multiplier = 1 - min(0.15, ((0 - self.current_mood.arousal) - 0.4) / 3)
return multiplier
return 1.0
def get_current_mood(self) -> MoodState:
"""获取当前情绪状态"""
return self.current_mood
@@ -278,9 +288,10 @@ class MoodManager:
# 限制范围
self.current_mood.valence = max(-1.0, min(1.0, self.current_mood.valence))
self.current_mood.arousal = max(0.0, min(1.0, self.current_mood.arousal))
self.current_mood.arousal = max(-1.0, min(1.0, self.current_mood.arousal))
self._update_mood_text()
logger.info(f"[情绪变化] {emotion}(强度:{intensity:.2f}) | 愉悦度:{old_valence:.2f}->{self.current_mood.valence:.2f}, 唤醒度:{old_arousal:.2f}->{self.current_mood.arousal:.2f} | 心情:{old_mood}->{self.current_mood.text}")
logger.info(
f"[情绪变化] {emotion}(强度:{intensity:.2f}) | 愉悦度:{old_valence:.2f}->{self.current_mood.valence:.2f}, 唤醒度:{old_arousal:.2f}->{self.current_mood.arousal:.2f} | 心情:{old_mood}->{self.current_mood.text}"
)

159
src/plugins/person_info/person_info.py
View File

@@ -5,10 +5,14 @@ import hashlib
from typing import Any, Callable, Dict
import datetime
import asyncio
import numpy
# import matplotlib.pyplot as plt
# from pathlib import Path
# import pandas as pd
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from pathlib import Path
import pandas as pd
"""
@@ -27,38 +31,39 @@ PersonInfoManager 类方法功能摘要：
logger = get_module_logger("person_info")
person_info_default = {
"person_id" : None,
"platform" : None,
"user_id" : None,
"nickname" : None,
"person_id": None,
"platform": None,
"user_id": None,
"nickname": None,
# "age" : 0,
"relationship_value" : 0,
"relationship_value": 0,
# "saved" : True,
# "impression" : None,
# "gender" : Unkown,
"konw_time" : 0,
"konw_time": 0,
"msg_interval": 3000,
"msg_interval_list": []
"msg_interval_list": [],
} # 个人信息的各项与默认值在此定义，以下处理会自动创建/补全每一项
class PersonInfoManager:
def __init__(self):
if "person_info" not in db.list_collection_names():
db.create_collection("person_info")
db.person_info.create_index("person_id", unique=True)
def get_person_id(self, platform:str, user_id:int):
def get_person_id(self, platform: str, user_id: int):
"""获取唯一id"""
components = [platform, str(user_id)]
key = "_".join(components)
return hashlib.md5(key.encode()).hexdigest()
async def create_person_info(self, person_id:str, data:dict = None):
async def create_person_info(self, person_id: str, data: dict = None):
"""创建一个项"""
if not person_id:
logger.debug("创建失败，personid不存在")
return
_person_info_default = copy.deepcopy(person_info_default)
_person_info_default["person_id"] = person_id
@@ -69,19 +74,16 @@ class PersonInfoManager:
db.person_info.insert_one(_person_info_default)
async def update_one_field(self, person_id:str, field_name:str, value, Data:dict = None):
async def update_one_field(self, person_id: str, field_name: str, value, Data: dict = None):
"""更新某一个字段，会补全"""
if field_name not in person_info_default.keys():
logger.debug(f"更新'{field_name}'失败，未定义的字段")
return
document = db.person_info.find_one({"person_id": person_id})
if document:
db.person_info.update_one(
{"person_id": person_id},
{"$set": {field_name: value}}
)
db.person_info.update_one({"person_id": person_id}, {"$set": {field_name: value}})
else:
Data[field_name] = value
logger.debug(f"更新时{person_id}不存在，已新建")
@@ -104,23 +106,20 @@ class PersonInfoManager:
if not person_id:
logger.debug("get_value获取失败：person_id不能为空")
return None
if field_name not in person_info_default:
logger.debug(f"get_value获取失败：字段'{field_name}'未定义")
return None
document = db.person_info.find_one(
{"person_id": person_id},
{field_name: 1}
)
document = db.person_info.find_one({"person_id": person_id}, {field_name: 1})
if document and field_name in document:
return document[field_name]
else:
default_value = copy.deepcopy(person_info_default[field_name])
logger.debug(f"获取{person_id}的{field_name}失败，已返回默认值{default_value}")
logger.trace(f"获取{person_id}的{field_name}失败，已返回默认值{default_value}")
return default_value
async def get_values(self, person_id: str, field_names: list) -> dict:
"""获取指定person_id文档的多个字段值，若不存在该字段，则返回该字段的全局默认值"""
if not person_id:
@@ -136,62 +135,57 @@ class PersonInfoManager:
# 构建查询投影（所有字段都有效才会执行到这里）
projection = {field: 1 for field in field_names}
document = db.person_info.find_one(
{"person_id": person_id},
projection
)
document = db.person_info.find_one({"person_id": person_id}, projection)
result = {}
for field in field_names:
result[field] = copy.deepcopy(
document.get(field, person_info_default[field])
if document else person_info_default[field]
document.get(field, person_info_default[field]) if document else person_info_default[field]
)
return result
async def del_all_undefined_field(self):
"""删除所有项里的未定义字段"""
# 获取所有已定义的字段名
defined_fields = set(person_info_default.keys())
try:
# 遍历集合中的所有文档
for document in db.person_info.find({}):
# 找出文档中未定义的字段
undefined_fields = set(document.keys()) - defined_fields - {'_id'}
undefined_fields = set(document.keys()) - defined_fields - {"_id"}
if undefined_fields:
# 构建更新操作，使用$unset删除未定义字段
update_result = db.person_info.update_one(
{'_id': document['_id']},
{'$unset': {field: 1 for field in undefined_fields}}
{"_id": document["_id"]}, {"$unset": {field: 1 for field in undefined_fields}}
)
if update_result.modified_count > 0:
logger.debug(f"已清理文档 {document['_id']} 的未定义字段: {undefined_fields}")
return
except Exception as e:
logger.error(f"清理未定义字段时出错: {e}")
return
async def get_specific_value_list(
self,
field_name: str,
way: Callable[[Any], bool], # 接受任意类型值
) ->Dict[str, Any]:
self,
field_name: str,
way: Callable[[Any], bool], # 接受任意类型值
) -> Dict[str, Any]:
"""
获取满足条件的字段值字典
Args:
field_name: 目标字段名
way: 判断函数 (value: Any) -> bool
Returns:
{person_id: value} | {}
Example:
# 查找所有nickname包含"admin"的用户
result = manager.specific_value_list(
@@ -205,10 +199,7 @@ class PersonInfoManager:
try:
result = {}
for doc in db.person_info.find(
{field_name: {"$exists": True}},
{"person_id": 1, field_name: 1, "_id": 0}
):
for doc in db.person_info.find({field_name: {"$exists": True}}, {"person_id": 1, field_name: 1, "_id": 0}):
try:
value = doc[field_name]
if way(value):
@@ -222,47 +213,73 @@ class PersonInfoManager:
except Exception as e:
logger.error(f"数据库查询失败: {str(e)}", exc_info=True)
return {}
async def personal_habit_deduction(self):
"""启动个人信息推断，每天根据一定条件推断一次"""
try:
while(1):
while 1:
await asyncio.sleep(60)
current_time = datetime.datetime.now()
logger.info(f"个人信息推断启动: {current_time.strftime('%Y-%m-%d %H:%M:%S')}")
# "msg_interval"推断
msg_interval_map = False
msg_interval_lists = await self.get_specific_value_list(
"msg_interval_list",
lambda x: isinstance(x, list) and len(x) >= 100
"msg_interval_list", lambda x: isinstance(x, list) and len(x) >= 100
)
for person_id, msg_interval_list_ in msg_interval_lists.items():
try:
time_interval = []
for t1, t2 in zip(msg_interval_list_, msg_interval_list_[1:]):
delta = t2 - t1
if delta < 8000 and delta > 0: # 小于8秒
if delta > 0:
time_interval.append(delta)
if len(time_interval) > 30:
# 移除matplotlib相关的绘图功能
filtered_intervals = [t for t in time_interval if t >= 500]
if len(filtered_intervals) > 25:
msg_interval = int(round(numpy.percentile(filtered_intervals, 80)))
await self.update_one_field(person_id, "msg_interval", msg_interval)
logger.debug(f"用户{person_id}的msg_interval已经被更新为{msg_interval}")
time_interval = [t for t in time_interval if 500 <= t <= 8000]
if len(time_interval) >= 30:
time_interval.sort()
# 画图(log)
msg_interval_map = True
log_dir = Path("logs/person_info")
log_dir.mkdir(parents=True, exist_ok=True)
plt.figure(figsize=(10, 6))
time_series = pd.Series(time_interval)
plt.hist(time_series, bins=50, density=True, alpha=0.4, color="pink", label="Histogram")
time_series.plot(kind="kde", color="mediumpurple", linewidth=1, label="Density")
plt.grid(True, alpha=0.2)
plt.xlim(0, 8000)
plt.title(f"Message Interval Distribution (User: {person_id[:8]}...)")
plt.xlabel("Interval (ms)")
plt.ylabel("Density")
plt.legend(framealpha=0.9, facecolor="white")
img_path = log_dir / f"interval_distribution_{person_id[:8]}.png"
plt.savefig(img_path)
plt.close()
# 画图
q25, q75 = np.percentile(time_interval, [25, 75])
iqr = q75 - q25
filtered = [x for x in time_interval if (q25 - 1.5 * iqr) <= x <= (q75 + 1.5 * iqr)]
msg_interval = int(round(np.percentile(filtered, 80)))
await self.update_one_field(person_id, "msg_interval", msg_interval)
logger.trace(f"用户{person_id}的msg_interval已经被更新为{msg_interval}")
except Exception as e:
logger.debug(f"处理用户{person_id}msg_interval推断时出错: {str(e)}")
logger.trace(f"用户{person_id}消息间隔计算失败: {type(e).__name__}: {str(e)}")
continue
# 其他...
logger.info(f"个人信息推断结束: {current_time.strftime('%Y-%m-%d %H:%M:%S')}")
if msg_interval_map:
logger.trace("已保存分布图到: logs/person_info")
current_time = datetime.datetime.now()
logger.trace(f"个人信息推断结束: {current_time.strftime('%Y-%m-%d %H:%M:%S')}")
await asyncio.sleep(86400)
except Exception as e:
logger.error(f"个人信息推断运行时出错: {str(e)}")
logger.exception("详细错误信息：")
person_info_manager = PersonInfoManager()

131
src/plugins/person_info/relationship_manager.py
View File

@@ -12,6 +12,7 @@ relationship_config = LogConfig(
)
logger = get_module_logger("rel_manager", config=relationship_config)
class RelationshipManager:
def __init__(self):
self.positive_feedback_value = 0 # 正反馈系统
@@ -22,6 +23,7 @@ class RelationshipManager:
def mood_manager(self):
if self._mood_manager is None:
from ..moods.moods import MoodManager # 延迟导入
self._mood_manager = MoodManager.get_instance()
return self._mood_manager
@@ -41,39 +43,39 @@ class RelationshipManager:
"厌恶",
]
if label in positive_list and stance != "反对":
if label in positive_list:
if 7 > self.positive_feedback_value >= 0:
self.positive_feedback_value += 1
elif self.positive_feedback_value < 0:
self.positive_feedback_value = 0
elif label in negative_list and stance != "支持":
elif label in negative_list:
if -7 < self.positive_feedback_value <= 0:
self.positive_feedback_value -= 1
elif self.positive_feedback_value > 0:
self.positive_feedback_value = 0
if abs(self.positive_feedback_value) > 1:
logger.info(f"触发mood变更增益，当前增益系数：{self.gain_coefficient[abs(self.positive_feedback_value)]}")
def mood_feedback(self, value):
"""情绪反馈"""
mood_manager = self.mood_manager
mood_gain = (mood_manager.get_current_mood().valence) ** 2 \
* math.copysign(1, value * mood_manager.get_current_mood().valence)
mood_gain = (mood_manager.get_current_mood().valence) ** 2 * math.copysign(
1, value * mood_manager.get_current_mood().valence
)
value += value * mood_gain
logger.info(f"当前relationship增益系数：{mood_gain:.3f}")
return value
def feedback_to_mood(self, mood_value):
"""对情绪的反馈"""
coefficient = self.gain_coefficient[abs(self.positive_feedback_value)]
if (mood_value > 0 and self.positive_feedback_value > 0
or mood_value < 0 and self.positive_feedback_value < 0):
return mood_value*coefficient
if mood_value > 0 and self.positive_feedback_value > 0 or mood_value < 0 and self.positive_feedback_value < 0:
return mood_value * coefficient
else:
return mood_value/coefficient
return mood_value / coefficient
async def calculate_update_relationship_value(self, chat_stream: ChatStream, label: str, stance: str) -> None:
async def calculate_update_relationship_value(self, chat_stream: ChatStream, label: str, stance: str) -> tuple:
"""计算并变更关系值
新的关系值变更计算方式：
将关系值限定在-1000到1000
@@ -82,13 +84,17 @@ class RelationshipManager:
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
4.连续正面或负面情感会正反馈
返回：
用户昵称，变更值，变更后关系等级
"""
stancedict = {
"支持": 0,
"中立": 1,
"反对": 2,
}
valuedict = {
"开心": 1.5,
"愤怒": -2.0,
@@ -103,10 +109,10 @@ class RelationshipManager:
person_id = person_info_manager.get_person_id(chat_stream.user_info.platform, chat_stream.user_info.user_id)
data = {
"platform" : chat_stream.user_info.platform,
"user_id" : chat_stream.user_info.user_id,
"nickname" : chat_stream.user_info.user_nickname,
"konw_time" : int(time.time())
"platform": chat_stream.user_info.platform,
"user_id": chat_stream.user_info.user_id,
"nickname": chat_stream.user_info.user_nickname,
"konw_time": int(time.time()),
}
old_value = await person_info_manager.get_value(person_id, "relationship_value")
old_value = self.ensure_float(old_value, person_id)
@@ -145,6 +151,7 @@ class RelationshipManager:
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"用户: {chat_stream.user_info.user_nickname}"
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}, "
@@ -153,6 +160,97 @@ class RelationshipManager:
await person_info_manager.update_one_field(person_id, "relationship_value", old_value + value, data)
return chat_stream.user_info.user_nickname, value, relationship_level[level_num]
async def calculate_update_relationship_value_with_reason(
self, chat_stream: ChatStream, label: str, stance: str, reason: str
) -> tuple:
"""计算并变更关系值
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
4.连续正面或负面情感会正反馈
返回：
用户昵称，变更值，变更后关系等级
"""
stancedict = {
"支持": 0,
"中立": 1,
"反对": 2,
}
valuedict = {
"开心": 1.5,
"愤怒": -2.0,
"悲伤": -0.5,
"惊讶": 0.6,
"害羞": 2.0,
"平静": 0.3,
"恐惧": -1.5,
"厌恶": -1.0,
"困惑": 0.5,
}
person_id = person_info_manager.get_person_id(chat_stream.user_info.platform, chat_stream.user_info.user_id)
data = {
"platform": chat_stream.user_info.platform,
"user_id": chat_stream.user_info.user_id,
"nickname": chat_stream.user_info.user_nickname,
"konw_time": int(time.time()),
}
old_value = await person_info_manager.get_value(person_id, "relationship_value")
old_value = self.ensure_float(old_value, person_id)
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:
rdict = await person_info_manager.get_specific_value_list("relationship_value", lambda x: x > 700)
high_value_count = len(rdict)
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 / 2000)
else:
value = 0
elif old_value < 0:
if valuedict[label] >= 0 and stancedict[stance] != 2:
value = value * math.exp(old_value / 2000)
elif valuedict[label] < 0 and stancedict[stance] != 0:
value = value * math.cos(math.pi * old_value / 2000)
else:
value = 0
self.positive_feedback_sys(label, stance)
value = self.mood_feedback(value)
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"用户: {chat_stream.user_info.user_nickname}"
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 person_info_manager.update_one_field(person_id, "relationship_value", old_value + value, data)
return chat_stream.user_info.user_nickname, value, relationship_level[level_num]
async def build_relationship_info(self, person) -> str:
person_id = person_info_manager.get_person_id(person[0], person[1])
relationship_value = await person_info_manager.get_value(person_id, "relationship_value")
@@ -200,4 +298,5 @@ class RelationshipManager:
logger.warning(f"[关系管理] {person_id}值转换失败（原始值：{value}），已重置为0")
return 0.0
relationship_manager = RelationshipManager()

222
src/plugins/respon_info_catcher/info_catcher.py Normal file
View File

@@ -0,0 +1,222 @@
from src.plugins.config.config import global_config
from src.plugins.chat.message import MessageRecv, MessageSending, Message
from src.common.database import db
import time
import traceback
from typing import List
class InfoCatcher:
def __init__(self):
self.chat_history = [] # 聊天历史，长度为三倍使用的上下文
self.context_length = global_config.MAX_CONTEXT_SIZE
self.chat_history_in_thinking = [] # 思考期间的聊天内容
self.chat_history_after_response = [] # 回复后的聊天内容，长度为一倍上下文
self.chat_id = ""
self.response_mode = global_config.response_mode
self.trigger_response_text = ""
self.response_text = ""
self.trigger_response_time = 0
self.trigger_response_message = None
self.response_time = 0
self.response_messages = []
# 使用字典来存储 heartflow 模式的数据
self.heartflow_data = {
"heart_flow_prompt": "",
"sub_heartflow_before": "",
"sub_heartflow_now": "",
"sub_heartflow_after": "",
"sub_heartflow_model": "",
"prompt": "",
"response": "",
"model": "",
}
# 使用字典来存储 reasoning 模式的数据
self.reasoning_data = {"thinking_log": "", "prompt": "", "response": "", "model": ""}
# 耗时
self.timing_results = {
"interested_rate_time": 0,
"sub_heartflow_observe_time": 0,
"sub_heartflow_step_time": 0,
"make_response_time": 0,
}
def catch_decide_to_response(self, message: MessageRecv):
# 搜集决定回复时的信息
self.trigger_response_message = message
self.trigger_response_text = message.detailed_plain_text
self.trigger_response_time = time.time()
self.chat_id = message.chat_stream.stream_id
self.chat_history = self.get_message_from_db_before_msg(message)
def catch_after_observe(self, obs_duration: float): # 这里可以有更多信息
self.timing_results["sub_heartflow_observe_time"] = obs_duration
# def catch_shf
def catch_afer_shf_step(self, step_duration: float, past_mind: str, current_mind: str):
self.timing_results["sub_heartflow_step_time"] = step_duration
if len(past_mind) > 1:
self.heartflow_data["sub_heartflow_before"] = past_mind[-1]
self.heartflow_data["sub_heartflow_now"] = current_mind
else:
self.heartflow_data["sub_heartflow_before"] = past_mind[-1]
self.heartflow_data["sub_heartflow_now"] = current_mind
def catch_after_llm_generated(self, prompt: str, response: str, reasoning_content: str = "", model_name: str = ""):
if self.response_mode == "heart_flow":
self.heartflow_data["prompt"] = prompt
self.heartflow_data["response"] = response
self.heartflow_data["model"] = model_name
elif self.response_mode == "reasoning":
self.reasoning_data["thinking_log"] = reasoning_content
self.reasoning_data["prompt"] = prompt
self.reasoning_data["response"] = response
self.reasoning_data["model"] = model_name
self.response_text = response
def catch_after_generate_response(self, response_duration: float):
self.timing_results["make_response_time"] = response_duration
def catch_after_response(
self, response_duration: float, response_message: List[str], first_bot_msg: MessageSending
):
self.timing_results["make_response_time"] = response_duration
self.response_time = time.time()
for msg in response_message:
self.response_messages.append(msg)
self.chat_history_in_thinking = self.get_message_from_db_between_msgs(
self.trigger_response_message, first_bot_msg
)
def get_message_from_db_between_msgs(self, message_start: Message, message_end: Message):
try:
# 从数据库中获取消息的时间戳
time_start = message_start.message_info.time
time_end = message_end.message_info.time
chat_id = message_start.chat_stream.stream_id
print(f"查询参数: time_start={time_start}, time_end={time_end}, chat_id={chat_id}")
# 查询数据库，获取 chat_id 相同且时间在 start 和 end 之间的数据
messages_between = db.messages.find(
{"chat_id": chat_id, "time": {"$gt": time_start, "$lt": time_end}}
).sort("time", -1)
result = list(messages_between)
print(f"查询结果数量: {len(result)}")
if result:
print(f"第一条消息时间: {result[0]['time']}")
print(f"最后一条消息时间: {result[-1]['time']}")
return result
except Exception as e:
print(f"获取消息时出错: {str(e)}")
return []
def get_message_from_db_before_msg(self, message: MessageRecv):
# 从数据库中获取消息
message_id = message.message_info.message_id
chat_id = message.chat_stream.stream_id
# 查询数据库，获取 chat_id 相同且 message_id 小于当前消息的 30 条数据
messages_before = (
db.messages.find({"chat_id": chat_id, "message_id": {"$lt": message_id}})
.sort("time", -1)
.limit(self.context_length * 3)
) # 获取更多历史信息
return list(messages_before)
def message_list_to_dict(self, message_list):
# 存储简化的聊天记录
result = []
for message in message_list:
if not isinstance(message, dict):
message = self.message_to_dict(message)
# print(message)
lite_message = {
"time": message["time"],
"user_nickname": message["user_info"]["user_nickname"],
"processed_plain_text": message["processed_plain_text"],
}
result.append(lite_message)
return result
def message_to_dict(self, message):
if not message:
return None
if isinstance(message, dict):
return message
return {
# "message_id": message.message_info.message_id,
"time": message.message_info.time,
"user_id": message.message_info.user_info.user_id,
"user_nickname": message.message_info.user_info.user_nickname,
"processed_plain_text": message.processed_plain_text,
# "detailed_plain_text": message.detailed_plain_text
}
def done_catch(self):
"""将收集到的信息存储到数据库的 thinking_log 集合中"""
try:
# 将消息对象转换为可序列化的字典
thinking_log_data = {
"chat_id": self.chat_id,
"response_mode": self.response_mode,
"trigger_text": self.trigger_response_text,
"response_text": self.response_text,
"trigger_info": {
"time": self.trigger_response_time,
"message": self.message_to_dict(self.trigger_response_message),
},
"response_info": {
"time": self.response_time,
"message": self.response_messages,
},
"timing_results": self.timing_results,
"chat_history": self.message_list_to_dict(self.chat_history),
"chat_history_in_thinking": self.message_list_to_dict(self.chat_history_in_thinking),
"chat_history_after_response": self.message_list_to_dict(self.chat_history_after_response),
}
# 根据不同的响应模式添加相应的数据
if self.response_mode == "heart_flow":
thinking_log_data["mode_specific_data"] = self.heartflow_data
elif self.response_mode == "reasoning":
thinking_log_data["mode_specific_data"] = self.reasoning_data
# 将数据插入到 thinking_log 集合中
db.thinking_log.insert_one(thinking_log_data)
return True
except Exception as e:
print(f"存储思考日志时出错: {str(e)}")
print(traceback.format_exc())
return False
class InfoCatcherManager:
def __init__(self):
self.info_catchers = {}
def get_info_catcher(self, thinking_id: str) -> InfoCatcher:
if thinking_id not in self.info_catchers:
self.info_catchers[thinking_id] = InfoCatcher()
return self.info_catchers[thinking_id]
info_catcher_manager = InfoCatcherManager()

18
src/plugins/schedule/schedule_generator.py
View File

@@ -14,7 +14,7 @@ from src.common.logger import get_module_logger, SCHEDULE_STYLE_CONFIG, LogConfi
from src.plugins.models.utils_model import LLM_request # noqa: E402
from src.plugins.config.config import global_config # noqa: E402
TIME_ZONE = tz.gettz(global_config.TIME_ZONE) # 设置时区
TIME_ZONE = tz.gettz(global_config.TIME_ZONE) # 设置时区
schedule_config = LogConfig(
@@ -31,10 +31,16 @@ class ScheduleGenerator:
def __init__(self):
# 使用离线LLM模型
self.llm_scheduler_all = LLM_request(
model=global_config.llm_reasoning, temperature=global_config.SCHEDULE_TEMPERATURE, max_tokens=7000, request_type="schedule"
model=global_config.llm_reasoning,
temperature=global_config.SCHEDULE_TEMPERATURE + 0.3,
max_tokens=7000,
request_type="schedule",
)
self.llm_scheduler_doing = LLM_request(
model=global_config.llm_normal, temperature=global_config.SCHEDULE_TEMPERATURE, max_tokens=2048, request_type="schedule"
model=global_config.llm_normal,
temperature=global_config.SCHEDULE_TEMPERATURE,
max_tokens=2048,
request_type="schedule",
)
self.today_schedule_text = ""
@@ -115,7 +121,11 @@ class ScheduleGenerator:
self.today_done_list = []
if not self.today_schedule_text:
logger.info(f"{today.strftime('%Y-%m-%d')}的日程不存在，准备生成新的日程")
self.today_schedule_text = await self.generate_daily_schedule(target_date=today)
try:
self.today_schedule_text = await self.generate_daily_schedule(target_date=today)
except Exception as e:
logger.error(f"生成日程时发生错误: {str(e)}")
self.today_schedule_text = ""
self.save_today_schedule_to_db()

21
src/plugins/storage/storage.py
View File

@@ -1,3 +1,4 @@
import re
from typing import Union
from ...common.database import db
@@ -12,14 +13,30 @@ class MessageStorage:
async def store_message(self, message: Union[MessageSending, MessageRecv], chat_stream: ChatStream) -> None:
"""存储消息到数据库"""
try:
# 莫越权 救世啊
pattern = r"<MainRule>.*?</MainRule>|<schedule>.*?</schedule>|<UserMessage>.*?</UserMessage>"
processed_plain_text = message.processed_plain_text
if processed_plain_text:
filtered_processed_plain_text = re.sub(pattern, "", processed_plain_text, flags=re.DOTALL)
else:
filtered_processed_plain_text = ""
detailed_plain_text = message.detailed_plain_text
if detailed_plain_text:
filtered_detailed_plain_text = re.sub(pattern, "", detailed_plain_text, flags=re.DOTALL)
else:
filtered_detailed_plain_text = ""
message_data = {
"message_id": message.message_info.message_id,
"time": message.message_info.time,
"chat_id": chat_stream.stream_id,
"chat_info": chat_stream.to_dict(),
"user_info": message.message_info.user_info.to_dict(),
"processed_plain_text": message.processed_plain_text,
"detailed_plain_text": message.detailed_plain_text,
# 使用过滤后的文本
"processed_plain_text": filtered_processed_plain_text,
"detailed_plain_text": filtered_detailed_plain_text,
"memorized_times": message.memorized_times,
}
db.messages.insert_one(message_data)

9
src/plugins/topic_identify/topic_identifier.py
View File

@@ -29,10 +29,13 @@ class TopicIdentifier:
消息内容：{text}"""
# 使用 LLM_request 类进行请求
topic, _, _ = await self.llm_topic_judge.generate_response(prompt)
try:
topic, _, _ = await self.llm_topic_judge.generate_response(prompt)
except Exception as e:
logger.error(f"LLM 请求topic失败: {e}")
return None
if not topic:
logger.error("LLM API 返回为空")
logger.error("LLM 得到的topic为空")
return None
# 直接在这里处理主题解析

233
src/plugins/utils/prompt_builder.py Normal file
View File

@@ -0,0 +1,233 @@
from typing import Dict, Any, Optional, List, Union
import re
from contextlib import asynccontextmanager
import asyncio
from src.common.logger import get_module_logger
# import traceback
logger = get_module_logger("prompt_build")
class PromptContext:
def __init__(self):
self._context_prompts: Dict[str, Dict[str, "Prompt"]] = {}
self._current_context: Optional[str] = None
self._context_lock = asyncio.Lock() # 添加异步锁
@asynccontextmanager
async def async_scope(self, context_id: str):
"""创建一个异步的临时提示模板作用域"""
async with self._context_lock:
if context_id not in self._context_prompts:
self._context_prompts[context_id] = {}
previous_context = self._current_context
self._current_context = context_id
try:
yield self
finally:
async with self._context_lock:
self._current_context = previous_context
async def get_prompt_async(self, name: str) -> Optional["Prompt"]:
"""异步获取当前作用域中的提示模板"""
async with self._context_lock:
if self._current_context and name in self._context_prompts[self._current_context]:
return self._context_prompts[self._current_context][name]
return None
async def register_async(self, prompt: "Prompt", context_id: Optional[str] = None) -> None:
"""异步注册提示模板到指定作用域"""
async with self._context_lock:
target_context = context_id or self._current_context
if target_context:
self._context_prompts.setdefault(target_context, {})[prompt.name] = prompt
class PromptManager:
def __init__(self):
self._prompts = {}
self._counter = 0
self._context = PromptContext()
self._lock = asyncio.Lock()
@asynccontextmanager
async def async_message_scope(self, message_id: str):
"""为消息处理创建异步临时作用域"""
async with self._context.async_scope(message_id):
yield self
async def get_prompt_async(self, name: str) -> "Prompt":
# 首先尝试从当前上下文获取
context_prompt = await self._context.get_prompt_async(name)
if context_prompt is not None:
return context_prompt
# 如果上下文中不存在，则使用全局提示模板
async with self._lock:
if name not in self._prompts:
raise KeyError(f"Prompt '{name}' not found")
return self._prompts[name]
def generate_name(self, template: str) -> str:
"""为未命名的prompt生成名称"""
self._counter += 1
return f"prompt_{self._counter}"
def register(self, prompt: "Prompt") -> None:
"""注册一个prompt"""
if not prompt.name:
prompt.name = self.generate_name(prompt.template)
self._prompts[prompt.name] = prompt
def add_prompt(self, name: str, fstr: str) -> "Prompt":
prompt = Prompt(fstr, name=name)
self._prompts[prompt.name] = prompt
return prompt
async def format_prompt(self, name: str, **kwargs) -> str:
prompt = await self.get_prompt_async(name)
return prompt.format(**kwargs)
# 全局单例
global_prompt_manager = PromptManager()
class Prompt(str):
# 临时标记，作为类常量
_TEMP_LEFT_BRACE = "__ESCAPED_LEFT_BRACE__"
_TEMP_RIGHT_BRACE = "__ESCAPED_RIGHT_BRACE__"
@staticmethod
def _process_escaped_braces(template: str) -> str:
"""处理模板中的转义花括号，将 \{ 和 \} 替换为临时标记"""
return template.replace("\\{", Prompt._TEMP_LEFT_BRACE).replace("\\}", Prompt._TEMP_RIGHT_BRACE)
@staticmethod
def _restore_escaped_braces(template: str) -> str:
"""将临时标记还原为实际的花括号字符"""
return template.replace(Prompt._TEMP_LEFT_BRACE, "{").replace(Prompt._TEMP_RIGHT_BRACE, "}")
def __new__(cls, fstr: str, name: Optional[str] = None, args: Union[List[Any], tuple[Any, ...]] = None, **kwargs):
# 如果传入的是元组，转换为列表
if isinstance(args, tuple):
args = list(args)
should_register = kwargs.pop("_should_register", True)
# 预处理模板中的转义花括号
processed_fstr = cls._process_escaped_braces(fstr)
# 解析模板
template_args = []
result = re.findall(r"\{(.*?)\}", processed_fstr)
for expr in result:
if expr and expr not in template_args:
template_args.append(expr)
# 如果提供了初始参数，立即格式化
if kwargs or args:
formatted = cls._format_template(fstr, args=args, kwargs=kwargs)
obj = super().__new__(cls, formatted)
else:
obj = super().__new__(cls, "")
obj.template = fstr
obj.name = name
obj.args = template_args
obj._args = args or []
obj._kwargs = kwargs
# 修改自动注册逻辑
if should_register:
if global_prompt_manager._context._current_context:
# 如果存在当前上下文，则注册到上下文中
# asyncio.create_task(global_prompt_manager._context.register_async(obj))
pass
else:
# 否则注册到全局管理器
global_prompt_manager.register(obj)
return obj
@classmethod
async def create_async(
cls, fstr: str, name: Optional[str] = None, args: Union[List[Any], tuple[Any, ...]] = None, **kwargs
):
"""异步创建Prompt实例"""
prompt = cls(fstr, name, args, **kwargs)
if global_prompt_manager._context._current_context:
await global_prompt_manager._context.register_async(prompt)
return prompt
@classmethod
def _format_template(cls, template: str, args: List[Any] = None, kwargs: Dict[str, Any] = None) -> str:
# 预处理模板中的转义花括号
processed_template = cls._process_escaped_braces(template)
template_args = []
result = re.findall(r"\{(.*?)\}", processed_template)
for expr in result:
if expr and expr not in template_args:
template_args.append(expr)
formatted_args = {}
formatted_kwargs = {}
# 处理位置参数
if args:
# print(len(template_args), len(args), template_args, args)
for i in range(len(args)):
if i < len(template_args):
arg = args[i]
if isinstance(arg, Prompt):
formatted_args[template_args[i]] = arg.format(**kwargs)
else:
formatted_args[template_args[i]] = arg
else:
logger.error(
f"构建提示词模板失败，解析到的参数列表{template_args}，长度为{len(template_args)}，输入的参数列表为{args}，提示词模板为{template}"
)
raise ValueError("格式化模板失败")
# 处理关键字参数
if kwargs:
for key, value in kwargs.items():
if isinstance(value, Prompt):
remaining_kwargs = {k: v for k, v in kwargs.items() if k != key}
formatted_kwargs[key] = value.format(**remaining_kwargs)
else:
formatted_kwargs[key] = value
try:
# 先用位置参数格式化
if args:
processed_template = processed_template.format(**formatted_args)
# 再用关键字参数格式化
if kwargs:
processed_template = processed_template.format(**formatted_kwargs)
# 将临时标记还原为实际的花括号
result = cls._restore_escaped_braces(processed_template)
return result
except (IndexError, KeyError) as e:
raise ValueError(
f"格式化模板失败: {template}, args={formatted_args}, kwargs={formatted_kwargs} {str(e)}"
) from e
def format(self, *args, **kwargs) -> "str":
"""支持位置参数和关键字参数的格式化，使用"""
ret = type(self)(
self.template,
self.name,
args=list(args) if args else self._args,
_should_register=False,
**kwargs if kwargs else self._kwargs,
)
# print(f"prompt build result: {ret} name: {ret.name} ")
return str(ret)
def __str__(self) -> str:
if self._kwargs or self._args:
return super().__str__()
return self.template
def __repr__(self) -> str:
return f"Prompt(template='{self.template}', name='{self.name}')"

26
src/plugins/utils/statistic.py
View File

@@ -2,7 +2,7 @@ import threading
import time
from collections import defaultdict
from datetime import datetime, timedelta
from typing import Any, Dict
from typing import Any, Dict, List
from src.common.logger import get_module_logger
from ...common.database import db
@@ -22,6 +22,7 @@ class LLMStatistics:
self.stats_thread = None
self.console_thread = None
self._init_database()
self.name_dict: Dict[List] = {}
def _init_database(self):
"""初始化数据库集合"""
@@ -137,16 +138,25 @@ class LLMStatistics:
# user_id = str(doc.get("user_info", {}).get("user_id", "unknown"))
chat_info = doc.get("chat_info", {})
user_info = doc.get("user_info", {})
user_id = str(user_info.get("user_id", "unknown"))
message_time = doc.get("time", 0)
group_info = chat_info.get("group_info") if chat_info else {}
# print(f"group_info: {group_info}")
group_name = None
if group_info:
group_id = f"g{group_info.get('group_id')}"
group_name = group_info.get("group_name", f"群{group_info.get('group_id')}")
if user_info and not group_name:
group_id = f"u{user_info['user_id']}"
group_name = user_info["user_nickname"]
if self.name_dict.get(group_id):
if message_time > self.name_dict.get(group_id)[1]:
self.name_dict[group_id] = [group_name, message_time]
else:
self.name_dict[group_id] = [group_name, message_time]
# print(f"group_name: {group_name}")
stats["messages_by_user"][user_id] += 1
stats["messages_by_chat"][group_name] += 1
stats["messages_by_chat"][group_id] += 1
return stats
@@ -187,7 +197,7 @@ class LLMStatistics:
tokens = stats["tokens_by_model"][model_name]
cost = stats["costs_by_model"][model_name]
output.append(
data_fmt.format(model_name[:32] + ".." if len(model_name) > 32 else model_name, count, tokens, cost)
data_fmt.format(model_name[:30] + ".." if len(model_name) > 32 else model_name, count, tokens, cost)
)
output.append("")
@@ -221,8 +231,8 @@ class LLMStatistics:
# 添加聊天统计
output.append("群组统计:")
output.append(("群组名称 消息数量"))
for group_name, count in sorted(stats["messages_by_chat"].items()):
output.append(f"{group_name[:32]:<32} {count:>10}")
for group_id, count in sorted(stats["messages_by_chat"].items()):
output.append(f"{self.name_dict[group_id][0][:32]:<32} {count:>10}")
return "\n".join(output)
@@ -250,7 +260,7 @@ class LLMStatistics:
tokens = stats["tokens_by_model"][model_name]
cost = stats["costs_by_model"][model_name]
output.append(
data_fmt.format(model_name[:32] + ".." if len(model_name) > 32 else model_name, count, tokens, cost)
data_fmt.format(model_name[:30] + ".." if len(model_name) > 32 else model_name, count, tokens, cost)
)
output.append("")
@@ -284,8 +294,8 @@ class LLMStatistics:
# 添加聊天统计
output.append("群组统计:")
output.append(("群组名称 消息数量"))
for group_name, count in sorted(stats["messages_by_chat"].items()):
output.append(f"{group_name[:32]:<32} {count:>10}")
for group_id, count in sorted(stats["messages_by_chat"].items()):
output.append(f"{self.name_dict[group_id][0][:32]:<32} {count:>10}")
return "\n".join(output)

151
src/plugins/utils/timer_calculater.py Normal file
View File

@@ -0,0 +1,151 @@
from time import perf_counter
from functools import wraps
from typing import Optional, Dict, Callable
import asyncio
"""
# 更好的计时器
使用形式：
- 上下文
- 装饰器
- 直接实例化
使用场景：
- 使用Timer：在需要测量代码执行时间时（如性能测试、计时器工具），Timer类是更可靠、高精度的选择。
- 使用time.time()的场景：当需要记录实际时间点（如日志、时间戳）时使用，但避免用它测量时间间隔。
使用方式：
【装饰器】
time_dict = {}
@Timer("计数", time_dict)
def func():
pass
print(time_dict)
【上下文_1】
def func():
with Timer() as t:
pass
print(t)
print(t.human_readable)
【上下文_2】
def func():
time_dict = {}
with Timer("计数", time_dict):
pass
print(time_dict)
【直接实例化】
a = Timer()
print(a) # 直接输出当前 perf_counter 值
参数：
- name：计时器的名字，默认为 None
- storage：计时器结果存储字典，默认为 None
- auto_unit：自动选择单位（毫秒或秒），默认为 True（自动根据时间切换毫秒或秒）
- do_type_check：是否进行类型检查，默认为 False（不进行类型检查）
属性：human_readable
自定义错误：TimerTypeError
"""
class TimerTypeError(TypeError):
"""自定义类型错误"""
__slots__ = ()
def __init__(self, param, expected_type, actual_type):
super().__init__(f"参数 '{param}' 类型错误，期望 {expected_type}，实际得到 {actual_type.__name__}")
class Timer:
"""
Timer 支持三种模式：
1. 装饰器模式：用于测量函数/协程运行时间
2. 上下文管理器模式：用于 with 语句块内部计时
3. 直接实例化：如果不调用 __enter__，打印对象时将显示当前 perf_counter 的值
"""
__slots__ = ("name", "storage", "elapsed", "auto_unit", "start")
def __init__(
self,
name: Optional[str] = None,
storage: Optional[Dict[str, float]] = None,
auto_unit: bool = True,
do_type_check: bool = False,
):
if do_type_check:
self._validate_types(name, storage)
self.name = name
self.storage = storage
self.elapsed = None
self.auto_unit = auto_unit
self.start = None
def _validate_types(self, name, storage):
"""类型检查"""
if name is not None and not isinstance(name, str):
raise TimerTypeError("name", "Optional[str]", type(name))
if storage is not None and not isinstance(storage, dict):
raise TimerTypeError("storage", "Optional[dict]", type(storage))
def __call__(self, func: Optional[Callable] = None) -> Callable:
"""装饰器模式"""
if func is None:
return lambda f: Timer(name=self.name or f.__name__, storage=self.storage, auto_unit=self.auto_unit)(f)
@wraps(func)
async def async_wrapper(*args, **kwargs):
with self:
return await func(*args, **kwargs)
@wraps(func)
def sync_wrapper(*args, **kwargs):
with self:
return func(*args, **kwargs)
wrapper = async_wrapper if asyncio.iscoroutinefunction(func) else sync_wrapper
wrapper.__timer__ = self # 保留计时器引用
return wrapper
def __enter__(self):
"""上下文管理器入口"""
self.start = perf_counter()
return self
def __exit__(self, *args):
self.elapsed = perf_counter() - self.start
self._record_time()
return False
def _record_time(self):
"""记录时间"""
if self.storage is not None and self.name:
self.storage[self.name] = self.elapsed
@property
def human_readable(self) -> str:
"""人类可读时间格式"""
if self.elapsed is None:
return "未计时"
if self.auto_unit:
return f"{self.elapsed * 1000:.2f}毫秒" if self.elapsed < 1 else f"{self.elapsed:.2f}秒"
return f"{self.elapsed:.4f}秒"
def __str__(self):
if self.start is not None:
if self.elapsed is None:
current_elapsed = perf_counter() - self.start
return f"<Timer {self.name or '匿名'} [计时中: {current_elapsed:.4f}秒]>"
return f"<Timer {self.name or '匿名'} [{self.human_readable}]>"
return f"{perf_counter()}"

112
src/plugins/willing/mode_classical.py
View File

@@ -1,14 +1,11 @@
import asyncio
from typing import Dict
from ..chat.chat_stream import ChatStream
from ..config.config import global_config
from .willing_manager import BaseWillingManager
class WillingManager:
class ClassicalWillingManager(BaseWillingManager):
def __init__(self):
self.chat_reply_willing: Dict[str, float] = {} # 存储每个聊天流的回复意愿
self._decay_task = None
self._started = False
super().__init__()
self._decay_task: asyncio.Task = None
async def _decay_reply_willing(self):
"""定期衰减回复意愿"""
@@ -17,86 +14,69 @@ class WillingManager:
for chat_id in self.chat_reply_willing:
self.chat_reply_willing[chat_id] = max(0, self.chat_reply_willing[chat_id] * 0.9)
def get_willing(self, chat_stream: ChatStream) -> float:
"""获取指定聊天流的回复意愿"""
if chat_stream:
return self.chat_reply_willing.get(chat_stream.stream_id, 0)
return 0
async def async_task_starter(self):
if self._decay_task is None:
self._decay_task = asyncio.create_task(self._decay_reply_willing())
def set_willing(self, chat_id: str, willing: float):
"""设置指定聊天流的回复意愿"""
self.chat_reply_willing[chat_id] = willing
async def change_reply_willing_received(
self,
chat_stream: ChatStream,
is_mentioned_bot: bool = False,
config=None,
is_emoji: bool = False,
interested_rate: float = 0,
sender_id: str = None,
) -> float:
"""改变指定聊天流的回复意愿并返回回复概率"""
chat_id = chat_stream.stream_id
async def get_reply_probability(self, message_id):
willing_info = self.ongoing_messages[message_id]
chat_id = willing_info.chat_id
current_willing = self.chat_reply_willing.get(chat_id, 0)
interested_rate = interested_rate * config.response_interested_rate_amplifier
interested_rate = willing_info.interested_rate * self.global_config.response_interested_rate_amplifier
if interested_rate > 0.4:
current_willing += interested_rate - 0.3
if is_mentioned_bot and current_willing < 1.0:
if willing_info.is_mentioned_bot and current_willing < 1.0:
current_willing += 1
elif is_mentioned_bot:
elif willing_info.is_mentioned_bot:
current_willing += 0.05
if is_emoji:
current_willing *= global_config.emoji_response_penalty
is_emoji_not_reply = False
if willing_info.is_emoji:
if self.global_config.emoji_response_penalty != 0:
current_willing *= self.global_config.emoji_response_penalty
else:
is_emoji_not_reply = True
self.chat_reply_willing[chat_id] = min(current_willing, 3.0)
reply_probability = min(max((current_willing - 0.5), 0.01) * config.response_willing_amplifier * 2, 1)
reply_probability = min(
max((current_willing - 0.5), 0.01) * self.global_config.response_willing_amplifier * 2, 1
)
# 检查群组权限（如果是群聊）
if chat_stream.group_info and config:
if chat_stream.group_info.group_id not in config.talk_allowed_groups:
current_willing = 0
reply_probability = 0
if (
willing_info.group_info
and willing_info.group_info.group_id in self.global_config.talk_frequency_down_groups
):
reply_probability = reply_probability / self.global_config.down_frequency_rate
if chat_stream.group_info.group_id in config.talk_frequency_down_groups:
reply_probability = reply_probability / config.down_frequency_rate
if is_emoji_not_reply:
reply_probability = 0
return reply_probability
def change_reply_willing_sent(self, chat_stream: ChatStream):
"""发送消息后降低聊天流的回复意愿"""
if chat_stream:
chat_id = chat_stream.stream_id
current_willing = self.chat_reply_willing.get(chat_id, 0)
self.chat_reply_willing[chat_id] = max(0, current_willing - 1.8)
async def before_generate_reply_handle(self, message_id):
chat_id = self.ongoing_messages[message_id].chat_id
current_willing = self.chat_reply_willing.get(chat_id, 0)
self.chat_reply_willing[chat_id] = max(0, current_willing - 1.8)
def change_reply_willing_not_sent(self, chat_stream: ChatStream):
"""未发送消息后降低聊天流的回复意愿"""
if chat_stream:
chat_id = chat_stream.stream_id
current_willing = self.chat_reply_willing.get(chat_id, 0)
self.chat_reply_willing[chat_id] = max(0, current_willing - 0)
async def after_generate_reply_handle(self, message_id):
chat_id = self.ongoing_messages[message_id].chat_id
current_willing = self.chat_reply_willing.get(chat_id, 0)
if current_willing < 1:
self.chat_reply_willing[chat_id] = min(1, current_willing + 0.4)
def change_reply_willing_after_sent(self, chat_stream: ChatStream):
"""发送消息后提高聊天流的回复意愿"""
if chat_stream:
chat_id = chat_stream.stream_id
current_willing = self.chat_reply_willing.get(chat_id, 0)
if current_willing < 1:
self.chat_reply_willing[chat_id] = min(1, current_willing + 0.4)
async def bombing_buffer_message_handle(self, message_id):
return await super().bombing_buffer_message_handle(message_id)
async def ensure_started(self):
"""确保衰减任务已启动"""
if not self._started:
if self._decay_task is None:
self._decay_task = asyncio.create_task(self._decay_reply_willing())
self._started = True
async def not_reply_handle(self, message_id):
return await super().not_reply_handle(message_id)
async def get_variable_parameters(self):
return await super().get_variable_parameters()
# 创建全局实例
willing_manager = WillingManager()
async def set_variable_parameters(self, parameters):
return await super().set_variable_parameters(parameters)

101
src/plugins/willing/mode_custom.py
View File

@@ -1,101 +1,6 @@
import asyncio
from typing import Dict
from ..chat.chat_stream import ChatStream
from .willing_manager import BaseWillingManager
class WillingManager:
class CustomWillingManager(BaseWillingManager):
def __init__(self):
self.chat_reply_willing: Dict[str, float] = {} # 存储每个聊天流的回复意愿
self._decay_task = None
self._started = False
async def _decay_reply_willing(self):
"""定期衰减回复意愿"""
while True:
await asyncio.sleep(1)
for chat_id in self.chat_reply_willing:
self.chat_reply_willing[chat_id] = max(0, self.chat_reply_willing[chat_id] * 0.9)
def get_willing(self, chat_stream: ChatStream) -> float:
"""获取指定聊天流的回复意愿"""
if chat_stream:
return self.chat_reply_willing.get(chat_stream.stream_id, 0)
return 0
def set_willing(self, chat_id: str, willing: float):
"""设置指定聊天流的回复意愿"""
self.chat_reply_willing[chat_id] = willing
async def change_reply_willing_received(
self,
chat_stream: ChatStream,
is_mentioned_bot: bool = False,
config=None,
is_emoji: bool = False,
interested_rate: float = 0,
sender_id: str = None,
) -> float:
"""改变指定聊天流的回复意愿并返回回复概率"""
chat_id = chat_stream.stream_id
current_willing = self.chat_reply_willing.get(chat_id, 0)
interested_rate = interested_rate * config.response_interested_rate_amplifier
if interested_rate > 0.4:
current_willing += interested_rate - 0.3
if is_mentioned_bot and current_willing < 1.0:
current_willing += 1
elif is_mentioned_bot:
current_willing += 0.05
if is_emoji:
current_willing *= 0.2
self.chat_reply_willing[chat_id] = min(current_willing, 3.0)
reply_probability = min(max((current_willing - 0.5), 0.01) * config.response_willing_amplifier * 2, 1)
# 检查群组权限（如果是群聊）
if chat_stream.group_info and config:
if chat_stream.group_info.group_id not in config.talk_allowed_groups:
current_willing = 0
reply_probability = 0
if chat_stream.group_info.group_id in config.talk_frequency_down_groups:
reply_probability = reply_probability / config.down_frequency_rate
return reply_probability
def change_reply_willing_sent(self, chat_stream: ChatStream):
"""发送消息后降低聊天流的回复意愿"""
if chat_stream:
chat_id = chat_stream.stream_id
current_willing = self.chat_reply_willing.get(chat_id, 0)
self.chat_reply_willing[chat_id] = max(0, current_willing - 1.8)
def change_reply_willing_not_sent(self, chat_stream: ChatStream):
"""未发送消息后降低聊天流的回复意愿"""
if chat_stream:
chat_id = chat_stream.stream_id
current_willing = self.chat_reply_willing.get(chat_id, 0)
self.chat_reply_willing[chat_id] = max(0, current_willing - 0)
def change_reply_willing_after_sent(self, chat_stream: ChatStream):
"""发送消息后提高聊天流的回复意愿"""
if chat_stream:
chat_id = chat_stream.stream_id
current_willing = self.chat_reply_willing.get(chat_id, 0)
if current_willing < 1:
self.chat_reply_willing[chat_id] = min(1, current_willing + 0.4)
async def ensure_started(self):
"""确保衰减任务已启动"""
if not self._started:
if self._decay_task is None:
self._decay_task = asyncio.create_task(self._decay_reply_willing())
self._started = True
# 创建全局实例
willing_manager = WillingManager()
super().__init__()

107
src/plugins/willing/mode_dynamic.py
View File

@@ -2,15 +2,12 @@ import asyncio
import random
import time
from typing import Dict
from src.common.logger import get_module_logger
from ..config.config import global_config
from ..chat.chat_stream import ChatStream
logger = get_module_logger("mode_dynamic")
from .willing_manager import BaseWillingManager
class WillingManager:
class DynamicWillingManager(BaseWillingManager):
def __init__(self):
super().__init__()
self.chat_reply_willing: Dict[str, float] = {} # 存储每个聊天流的回复意愿
self.chat_high_willing_mode: Dict[str, bool] = {} # 存储每个聊天流是否处于高回复意愿期
self.chat_msg_count: Dict[str, int] = {} # 存储每个聊天流接收到的消息数量
@@ -22,7 +19,12 @@ class WillingManager:
self.chat_conversation_context: Dict[str, bool] = {} # 标记是否处于对话上下文中
self._decay_task = None
self._mode_switch_task = None
self._started = False
async def async_task_starter(self):
if self._decay_task is None:
self._decay_task = asyncio.create_task(self._decay_reply_willing())
if self._mode_switch_task is None:
self._mode_switch_task = asyncio.create_task(self._mode_switch_check())
async def _decay_reply_willing(self):
"""定期衰减回复意愿"""
@@ -75,28 +77,19 @@ class WillingManager:
self.chat_high_willing_mode[chat_id] = False
self.chat_reply_willing[chat_id] = 0.1 # 设置为最低回复意愿
self.chat_low_willing_duration[chat_id] = random.randint(600, 1200) # 10-20分钟
logger.debug(f"聊天流 {chat_id} 切换到低回复意愿期，持续 {self.chat_low_willing_duration[chat_id]} 秒")
self.logger.debug(f"聊天流 {chat_id} 切换到低回复意愿期，持续 {self.chat_low_willing_duration[chat_id]} 秒")
else:
# 从低回复期切换到高回复期
self.chat_high_willing_mode[chat_id] = True
self.chat_reply_willing[chat_id] = 1.0 # 设置为较高回复意愿
self.chat_high_willing_duration[chat_id] = random.randint(180, 240) # 3-4分钟
logger.debug(f"聊天流 {chat_id} 切换到高回复意愿期，持续 {self.chat_high_willing_duration[chat_id]} 秒")
self.logger.debug(
f"聊天流 {chat_id} 切换到高回复意愿期，持续 {self.chat_high_willing_duration[chat_id]} 秒"
)
self.chat_last_mode_change[chat_id] = time.time()
self.chat_msg_count[chat_id] = 0 # 重置消息计数
def get_willing(self, chat_stream: ChatStream) -> float:
"""获取指定聊天流的回复意愿"""
stream = chat_stream
if stream:
return self.chat_reply_willing.get(stream.stream_id, 0)
return 0
def set_willing(self, chat_id: str, willing: float):
"""设置指定聊天流的回复意愿"""
self.chat_reply_willing[chat_id] = willing
def _ensure_chat_initialized(self, chat_id: str):
"""确保聊天流的所有数据已初始化"""
if chat_id not in self.chat_reply_willing:
@@ -113,20 +106,13 @@ class WillingManager:
if chat_id not in self.chat_conversation_context:
self.chat_conversation_context[chat_id] = False
async def change_reply_willing_received(
self,
chat_stream: ChatStream,
topic: str = None,
is_mentioned_bot: bool = False,
config=None,
is_emoji: bool = False,
interested_rate: float = 0,
sender_id: str = None,
) -> float:
async def get_reply_probability(self, message_id):
"""改变指定聊天流的回复意愿并返回回复概率"""
# 获取或创建聊天流
stream = chat_stream
willing_info = self.ongoing_messages[message_id]
stream = willing_info.chat
chat_id = stream.stream_id
sender_id = str(willing_info.message.message_info.user_info.user_id)
current_time = time.time()
self._ensure_chat_initialized(chat_id)
@@ -147,23 +133,25 @@ class WillingManager:
if sender_id and sender_id == last_sender and current_time - last_reply_time < 120 and msg_count <= 5:
in_conversation_context = True
self.chat_conversation_context[chat_id] = True
logger.debug("检测到追问 (同一用户), 提高回复意愿")
self.logger.debug("检测到追问 (同一用户), 提高回复意愿")
current_willing += 0.3
# 特殊情况处理
if is_mentioned_bot:
if willing_info.is_mentioned_bot:
current_willing += 0.5
in_conversation_context = True
self.chat_conversation_context[chat_id] = True
logger.debug(f"被提及, 当前意愿: {current_willing}")
self.logger.debug(f"被提及, 当前意愿: {current_willing}")
if is_emoji:
current_willing *= 0.1
logger.debug(f"表情包, 当前意愿: {current_willing}")
if willing_info.is_emoji:
current_willing = self.global_config.emoji_response_penalty * 0.1
self.logger.debug(f"表情包, 当前意愿: {current_willing}")
# 根据话题兴趣度适当调整
if interested_rate > 0.5:
current_willing += (interested_rate - 0.5) * 0.5
if willing_info.interested_rate > 0.5:
current_willing += (
(willing_info.interested_rate - 0.5) * 0.5 * self.global_config.response_interested_rate_amplifier
)
# 根据当前模式计算回复概率
base_probability = 0.0
@@ -171,7 +159,7 @@ class WillingManager:
if in_conversation_context:
# 在对话上下文中，降低基础回复概率
base_probability = 0.5 if is_high_mode else 0.25
logger.debug(f"处于对话上下文中，基础回复概率: {base_probability}")
self.logger.debug(f"处于对话上下文中，基础回复概率: {base_probability}")
elif is_high_mode:
# 高回复周期：4-8句话有50%的概率会回复一次
base_probability = 0.50 if 4 <= msg_count <= 8 else 0.2
@@ -180,12 +168,12 @@ class WillingManager:
base_probability = 0.30 if msg_count >= 15 else 0.03 * min(msg_count, 10)
# 考虑回复意愿的影响
reply_probability = base_probability * current_willing
reply_probability = base_probability * current_willing * self.global_config.response_willing_amplifier
# 检查群组权限（如果是群聊）
if chat_stream.group_info and config:
if chat_stream.group_info.group_id in config.talk_frequency_down_groups:
reply_probability = reply_probability / global_config.down_frequency_rate
if willing_info.group_info:
if willing_info.group_info.group_id in self.global_config.talk_frequency_down_groups:
reply_probability = reply_probability / self.global_config.down_frequency_rate
# 限制最大回复概率
reply_probability = min(reply_probability, 0.75) # 设置最大回复概率为75%
@@ -197,11 +185,12 @@ class WillingManager:
self.chat_last_sender_id[chat_id] = sender_id
self.chat_reply_willing[chat_id] = min(current_willing, 3.0)
return reply_probability
def change_reply_willing_sent(self, chat_stream: ChatStream):
async def before_generate_reply_handle(self, message_id):
"""开始思考后降低聊天流的回复意愿"""
stream = chat_stream
stream = self.ongoing_messages[message_id].chat
if stream:
chat_id = stream.stream_id
self._ensure_chat_initialized(chat_id)
@@ -219,9 +208,9 @@ class WillingManager:
# 重置消息计数
self.chat_msg_count[chat_id] = 0
def change_reply_willing_not_sent(self, chat_stream: ChatStream):
async def not_reply_handle(self, message_id):
"""决定不回复后提高聊天流的回复意愿"""
stream = chat_stream
stream = self.ongoing_messages[message_id].chat
if stream:
chat_id = stream.stream_id
self._ensure_chat_initialized(chat_id)
@@ -240,20 +229,14 @@ class WillingManager:
self.chat_reply_willing[chat_id] = min(2.0, current_willing + willing_increase)
def change_reply_willing_after_sent(self, chat_stream: ChatStream):
"""发送消息后提高聊天流的回复意愿"""
# 由于已经在sent中处理，这个方法保留但不再需要额外调整
pass
async def bombing_buffer_message_handle(self, message_id):
return await super().bombing_buffer_message_handle(message_id)
async def ensure_started(self):
"""确保所有任务已启动"""
if not self._started:
if self._decay_task is None:
self._decay_task = asyncio.create_task(self._decay_reply_willing())
if self._mode_switch_task is None:
self._mode_switch_task = asyncio.create_task(self._mode_switch_check())
self._started = True
async def after_generate_reply_handle(self, message_id):
return await super().after_generate_reply_handle(message_id)
async def get_variable_parameters(self):
return await super().get_variable_parameters()
# 创建全局实例
willing_manager = WillingManager()
async def set_variable_parameters(self, parameters):
return await super().set_variable_parameters(parameters)

257
src/plugins/willing/mode_mxp.py Normal file
View File

@@ -0,0 +1,257 @@
"""
Mxp 模式：梦溪畔独家赞助
此模式的一些参数不会在配置文件中显示，要修改请在可变参数下修改
同时一些全局设置对此模式无效
此模式的可变参数暂时比较草率，需要调参仙人的大手
此模式的特点：
1.每个聊天流的每个用户的意愿是独立的
2.接入关系系统，关系会影响意愿值
3.会根据群聊的热度来调整基础意愿值
4.限制同时思考的消息数量，防止喷射
5.拥有单聊增益，无论在群里还是私聊，只要bot一直和你聊，就会增加意愿值
6.意愿分为衰减意愿+临时意愿
如果你发现本模式出现了bug
上上策是询问智慧的小草神（）
上策是询问万能的千石可乐
中策是发issue
下下策是询问一个菜鸟（@梦溪畔）
"""
from .willing_manager import BaseWillingManager
from typing import Dict
import asyncio
import time
import math
class MxpWillingManager(BaseWillingManager):
"""Mxp意愿管理器"""
def __init__(self):
super().__init__()
self.chat_person_reply_willing: Dict[str, Dict[str, float]] = {} # chat_id: {person_id: 意愿值}
self.chat_new_message_time: Dict[str, list[float]] = {} # 聊天流ID: 消息时间
self.last_response_person: Dict[str, tuple[str, int]] = {} # 上次回复的用户信息
self.temporary_willing: float = 0 # 临时意愿值
# 可变参数
self.intention_decay_rate = 0.93 # 意愿衰减率
self.message_expiration_time = 120 # 消息过期时间（秒）
self.number_of_message_storage = 10 # 消息存储数量
self.basic_maximum_willing = 0.5 # 基础最大意愿值
self.mention_willing_gain = 0.6 # 提及意愿增益
self.interest_willing_gain = 0.3 # 兴趣意愿增益
self.emoji_response_penalty = self.global_config.emoji_response_penalty # 表情包回复惩罚
self.down_frequency_rate = self.global_config.down_frequency_rate # 降低回复频率的群组惩罚系数
self.single_chat_gain = 0.12 # 单聊增益
async def async_task_starter(self) -> None:
"""异步任务启动器"""
asyncio.create_task(self._return_to_basic_willing())
asyncio.create_task(self._chat_new_message_to_change_basic_willing())
async def before_generate_reply_handle(self, message_id: str):
"""回复前处理"""
pass
async def after_generate_reply_handle(self, message_id: str):
"""回复后处理"""
async with self.lock:
w_info = self.ongoing_messages[message_id]
rel_value = await w_info.person_info_manager.get_value(w_info.person_id, "relationship_value")
rel_level = self._get_relationship_level_num(rel_value)
self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += rel_level * 0.05
now_chat_new_person = self.last_response_person.get(w_info.chat_id, ["", 0])
if now_chat_new_person[0] == w_info.person_id:
if now_chat_new_person[1] < 2:
now_chat_new_person[1] += 1
else:
self.last_response_person[w_info.chat_id] = [w_info.person_id, 0]
async def not_reply_handle(self, message_id: str):
"""不回复处理"""
async with self.lock:
w_info = self.ongoing_messages[message_id]
if w_info.is_mentioned_bot:
self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += 0.2
if (
w_info.chat_id in self.last_response_person
and self.last_response_person[w_info.chat_id][0] == w_info.person_id
):
self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += self.single_chat_gain * (
2 * self.last_response_person[w_info.chat_id][1] + 1
)
now_chat_new_person = self.last_response_person.get(w_info.chat_id, ["", 0])
if now_chat_new_person[0] != w_info.person_id:
self.last_response_person[w_info.chat_id] = [w_info.person_id, 0]
async def get_reply_probability(self, message_id: str):
"""获取回复概率"""
async with self.lock:
w_info = self.ongoing_messages[message_id]
current_willing = self.chat_person_reply_willing[w_info.chat_id][w_info.person_id]
if w_info.is_mentioned_bot:
current_willing += self.mention_willing_gain / (int(current_willing) + 1)
if w_info.interested_rate > 0:
current_willing += math.atan(w_info.interested_rate / 2) / math.pi * 2 * self.interest_willing_gain
self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] = current_willing
rel_value = await w_info.person_info_manager.get_value(w_info.person_id, "relationship_value")
rel_level = self._get_relationship_level_num(rel_value)
current_willing += rel_level * 0.1
if (
w_info.chat_id in self.last_response_person
and self.last_response_person[w_info.chat_id][0] == w_info.person_id
):
current_willing += self.single_chat_gain * (2 * self.last_response_person[w_info.chat_id][1] + 1)
chat_ongoing_messages = [msg for msg in self.ongoing_messages.values() if msg.chat_id == w_info.chat_id]
chat_person_ogoing_messages = [msg for msg in chat_ongoing_messages if msg.person_id == w_info.person_id]
if len(chat_person_ogoing_messages) >= 2:
current_willing = 0
elif len(chat_ongoing_messages) == 2:
current_willing -= 0.5
elif len(chat_ongoing_messages) == 3:
current_willing -= 1.5
elif len(chat_ongoing_messages) >= 4:
current_willing = 0
probability = self._willing_to_probability(current_willing)
if w_info.is_emoji:
probability *= self.emoji_response_penalty
if w_info.group_info and w_info.group_info.group_id in self.global_config.talk_frequency_down_groups:
probability /= self.down_frequency_rate
self.temporary_willing = current_willing
return probability
async def bombing_buffer_message_handle(self, message_id: str):
"""炸飞消息处理"""
async with self.lock:
w_info = self.ongoing_messages[message_id]
self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += 0.1
async def _return_to_basic_willing(self):
"""使每个人的意愿恢复到chat基础意愿"""
while True:
await asyncio.sleep(3)
async with self.lock:
for chat_id, person_willing in self.chat_person_reply_willing.items():
for person_id, willing in person_willing.items():
if chat_id not in self.chat_reply_willing:
self.logger.debug(f"聊天流{chat_id}不存在，错误")
continue
basic_willing = self.chat_reply_willing[chat_id]
person_willing[person_id] = (
basic_willing + (willing - basic_willing) * self.intention_decay_rate
)
def setup(self, message, chat, is_mentioned_bot, interested_rate):
super().setup(message, chat, is_mentioned_bot, interested_rate)
self.chat_reply_willing[chat.stream_id] = self.chat_reply_willing.get(
chat.stream_id, self.basic_maximum_willing
)
self.chat_person_reply_willing[chat.stream_id] = self.chat_person_reply_willing.get(chat.stream_id, {})
self.chat_person_reply_willing[chat.stream_id][
self.ongoing_messages[message.message_info.message_id].person_id
] = self.chat_person_reply_willing[chat.stream_id].get(
self.ongoing_messages[message.message_info.message_id].person_id, self.chat_reply_willing[chat.stream_id]
)
if chat.stream_id not in self.chat_new_message_time:
self.chat_new_message_time[chat.stream_id] = []
self.chat_new_message_time[chat.stream_id].append(time.time())
if len(self.chat_new_message_time[chat.stream_id]) > self.number_of_message_storage:
self.chat_new_message_time[chat.stream_id].pop(0)
def _willing_to_probability(self, willing: float) -> float:
"""意愿值转化为概率"""
willing = max(0, willing)
if willing < 2:
probability = math.atan(willing * 2) / math.pi * 2
else:
probability = math.atan(willing * 4) / math.pi * 2
return probability
async def _chat_new_message_to_change_basic_willing(self):
"""聊天流新消息改变基础意愿"""
while True:
update_time = 20
await asyncio.sleep(update_time)
async with self.lock:
for chat_id, message_times in self.chat_new_message_time.items():
# 清理过期消息
current_time = time.time()
message_times = [
msg_time for msg_time in message_times if current_time - msg_time < self.message_expiration_time
]
self.chat_new_message_time[chat_id] = message_times
if len(message_times) < self.number_of_message_storage:
self.chat_reply_willing[chat_id] = self.basic_maximum_willing
update_time = 20
elif len(message_times) == self.number_of_message_storage:
time_interval = current_time - message_times[0]
basic_willing = self.basic_maximum_willing * math.sqrt(
time_interval / self.message_expiration_time
)
self.chat_reply_willing[chat_id] = basic_willing
update_time = 17 * math.sqrt(time_interval / self.message_expiration_time) + 3
else:
self.logger.debug(f"聊天流{chat_id}消息时间数量异常，数量：{len(message_times)}")
self.chat_reply_willing[chat_id] = 0
async def get_variable_parameters(self) -> Dict[str, str]:
"""获取可变参数"""
return {
"intention_decay_rate": "意愿衰减率",
"message_expiration_time": "消息过期时间（秒）",
"number_of_message_storage": "消息存储数量",
"basic_maximum_willing": "基础最大意愿值",
"mention_willing_gain": "提及意愿增益",
"interest_willing_gain": "兴趣意愿增益",
"emoji_response_penalty": "表情包回复惩罚",
"down_frequency_rate": "降低回复频率的群组惩罚系数",
"single_chat_gain": "单聊增益（不仅是私聊）",
}
async def set_variable_parameters(self, parameters: Dict[str, any]):
"""设置可变参数"""
async with self.lock:
for key, value in parameters.items():
if hasattr(self, key):
setattr(self, key, value)
self.logger.debug(f"参数 {key} 已更新为 {value}")
else:
self.logger.debug(f"尝试设置未知参数 {key}")
def _get_relationship_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 - 2
async def get_willing(self, chat_id):
return self.temporary_willing

179
src/plugins/willing/willing_manager.py
View File

@@ -1,22 +1,169 @@
from typing import Optional
from src.common.logger import get_module_logger
from src.common.logger import LogConfig, WILLING_STYLE_CONFIG, LoguruLogger, get_module_logger
from dataclasses import dataclass
from ..config.config import global_config, BotConfig
from ..chat.chat_stream import ChatStream, GroupInfo
from ..chat.message import MessageRecv
from ..person_info.person_info import person_info_manager, PersonInfoManager
from abc import ABC, abstractmethod
import importlib
from typing import Dict, Optional
import asyncio
from ..config.config import global_config
from .mode_classical import WillingManager as ClassicalWillingManager
from .mode_dynamic import WillingManager as DynamicWillingManager
from .mode_custom import WillingManager as CustomWillingManager
from src.common.logger import LogConfig, WILLING_STYLE_CONFIG
"""
基类方法概览：
以下8个方法是你必须在子类重写的（哪怕什么都不干）：
async_task_starter 在程序启动时执行，在其中用asyncio.create_task启动你想要执行的异步任务
before_generate_reply_handle 确定要回复后，在生成回复前的处理
after_generate_reply_handle 确定要回复后，在生成回复后的处理
not_reply_handle 确定不回复后的处理
get_reply_probability 获取回复概率
bombing_buffer_message_handle 缓冲器炸飞消息后的处理
get_variable_parameters 获取可变参数组，返回一个字典，key为参数名称，value为参数描述（此方法是为拆分全局设置准备）
set_variable_parameters 设置可变参数组，你需要传入一个字典，key为参数名称，value为参数值（此方法是为拆分全局设置准备）
以下2个方法根据你的实现可以做调整：
get_willing 获取某聊天流意愿
set_willing 设置某聊天流意愿
规范说明：
模块文件命名: `mode_{manager_type}.py`
示例: 若 `manager_type="aggressive"`，则模块文件应为 `mode_aggressive.py`
类命名: `{manager_type}WillingManager` (首字母大写)
示例: 在 `mode_aggressive.py` 中，类名应为 `AggressiveWillingManager`
"""
willing_config = LogConfig(
# 使用消息发送专用样式
console_format=WILLING_STYLE_CONFIG["console_format"],
file_format=WILLING_STYLE_CONFIG["file_format"],
)
logger = get_module_logger("willing", config=willing_config)
def init_willing_manager() -> Optional[object]:
@dataclass
class WillingInfo:
"""此类保存意愿模块常用的参数
Attributes:
message (MessageRecv): 原始消息对象
chat (ChatStream): 聊天流对象
person_info_manager (PersonInfoManager): 用户信息管理对象
chat_id (str): 当前聊天流的标识符
person_id (str): 发送者的个人信息的标识符
group_id (str): 群组ID（如果是私聊则为空）
is_mentioned_bot (bool): 是否提及了bot
is_emoji (bool): 是否为表情包
interested_rate (float): 兴趣度
"""
message: MessageRecv
chat: ChatStream
person_info_manager: PersonInfoManager
chat_id: str
person_id: str
group_info: Optional[GroupInfo]
is_mentioned_bot: bool
is_emoji: bool
interested_rate: float
# current_mood: float 当前心情？
class BaseWillingManager(ABC):
"""回复意愿管理基类"""
@classmethod
def create(cls, manager_type: str) -> "BaseWillingManager":
try:
module = importlib.import_module(f".mode_{manager_type}", __package__)
manager_class = getattr(module, f"{manager_type.capitalize()}WillingManager")
if not issubclass(manager_class, cls):
raise TypeError(f"Manager class {manager_class.__name__} is not a subclass of {cls.__name__}")
else:
logger.info(f"成功载入willing模式：{manager_type}")
return manager_class()
except (ImportError, AttributeError, TypeError) as e:
module = importlib.import_module(".mode_classical", __package__)
manager_class = module.ClassicalWillingManager
logger.info(f"载入当前意愿模式{manager_type}失败，使用经典配方~~~~")
logger.debug(f"加载willing模式{manager_type}失败，原因: {str(e)}。")
return manager_class()
def __init__(self):
self.chat_reply_willing: Dict[str, float] = {} # 存储每个聊天流的回复意愿(chat_id)
self.ongoing_messages: Dict[str, WillingInfo] = {} # 当前正在进行的消息(message_id)
self.lock = asyncio.Lock()
self.global_config: BotConfig = global_config
self.logger: LoguruLogger = logger
def setup(self, message: MessageRecv, chat: ChatStream, is_mentioned_bot: bool, interested_rate: float):
person_id = person_info_manager.get_person_id(chat.platform, chat.user_info.user_id)
self.ongoing_messages[message.message_info.message_id] = WillingInfo(
message=message,
chat=chat,
person_info_manager=person_info_manager,
chat_id=chat.stream_id,
person_id=person_id,
group_info=chat.group_info,
is_mentioned_bot=is_mentioned_bot,
is_emoji=message.is_emoji,
interested_rate=interested_rate,
)
def delete(self, message_id: str):
del_message = self.ongoing_messages.pop(message_id, None)
if not del_message:
logger.debug(f"删除异常，当前消息{message_id}不存在")
@abstractmethod
async def async_task_starter(self) -> None:
"""抽象方法：异步任务启动器"""
pass
@abstractmethod
async def before_generate_reply_handle(self, message_id: str):
"""抽象方法：回复前处理"""
pass
@abstractmethod
async def after_generate_reply_handle(self, message_id: str):
"""抽象方法：回复后处理"""
pass
@abstractmethod
async def not_reply_handle(self, message_id: str):
"""抽象方法：不回复处理"""
pass
@abstractmethod
async def get_reply_probability(self, message_id: str):
"""抽象方法：获取回复概率"""
raise NotImplementedError
@abstractmethod
async def bombing_buffer_message_handle(self, message_id: str):
"""抽象方法：炸飞消息处理"""
pass
async def get_willing(self, chat_id: str):
"""获取指定聊天流的回复意愿"""
async with self.lock:
return self.chat_reply_willing.get(chat_id, 0)
async def set_willing(self, chat_id: str, willing: float):
"""设置指定聊天流的回复意愿"""
async with self.lock:
self.chat_reply_willing[chat_id] = willing
@abstractmethod
async def get_variable_parameters(self) -> Dict[str, str]:
"""抽象方法：获取可变参数"""
pass
@abstractmethod
async def set_variable_parameters(self, parameters: Dict[str, any]):
"""抽象方法：设置可变参数"""
pass
def init_willing_manager() -> BaseWillingManager:
"""
根据配置初始化并返回对应的WillingManager实例
@@ -24,19 +171,7 @@ def init_willing_manager() -> Optional[object]:
对应mode的WillingManager实例
"""
mode = global_config.willing_mode.lower()
if mode == "classical":
logger.info("使用经典回复意愿管理器")
return ClassicalWillingManager()
elif mode == "dynamic":
logger.info("使用动态回复意愿管理器")
return DynamicWillingManager()
elif mode == "custom":
logger.warning(f"自定义的回复意愿管理器模式: {mode}")
return CustomWillingManager()
else:
logger.warning(f"未知的回复意愿管理器模式: {mode}, 将使用经典模式")
return ClassicalWillingManager()
return BaseWillingManager.create(mode)
# 全局willing_manager对象

8
src/plugins/zhishi/knowledge_library.py
View File

@@ -53,18 +53,18 @@ class KnowledgeLibrary:
# 按空行分割内容
paragraphs = [p.strip() for p in content.split("\n\n") if p.strip()]
chunks = []
for para in paragraphs:
para_length = len(para)
# 如果段落长度小于等于最大长度，直接添加
if para_length <= max_length:
chunks.append(para)
else:
# 如果段落超过最大长度，则按最大长度切分
for i in range(0, para_length, max_length):
chunks.append(para[i:i + max_length])
chunks.append(para[i : i + max_length])
return chunks
def get_embedding(self, text: str) -> list:

1375
temp_utils_ui/temp_ui.py
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0
temp_utils_ui/thingking_ui.py
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55
template/bot_config_template.toml
View File

@@ -1,5 +1,5 @@
[inner]
version = "1.2.4"
version = "1.3.0"
#以下是给开发人员阅读的，一般用户不需要阅读
@@ -60,7 +60,7 @@ appearance = "用几句话描述外貌特征" # 外貌特征
enable_schedule_gen = true # 是否启用日程表(尚未完成)
prompt_schedule_gen = "用几句话描述描述性格特点或行动规律，这个特征会用来生成日程表"
schedule_doing_update_interval = 900 # 日程表更新间隔 单位秒
schedule_temperature = 0.3 # 日程表温度，建议0.3-0.6
schedule_temperature = 0.1 # 日程表温度，建议0.1-0.5
time_zone = "Asia/Shanghai" # 给你的机器人设置时区，可以解决运行电脑时区和国内时区不同的情况，或者模拟国外留学生日程
[platforms] # 必填项目，填写每个平台适配器提供的链接
@@ -75,14 +75,18 @@ model_v3_probability = 0.3 # 麦麦回答时选择次要回复模型2 模型的
[heartflow] # 注意：可能会消耗大量token，请谨慎开启，仅会使用v3模型
sub_heart_flow_update_interval = 60 # 子心流更新频率，间隔 单位秒
sub_heart_flow_freeze_time = 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 = 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 = 300 # 心流更新频率，间隔 单位秒
sub_heart_flow_freeze_time = 100 # 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 = 500 # 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 = 600 # 心流更新频率，间隔 单位秒
observation_context_size = 20 # 心流观察到的最长上下文大小，超过这个值的上下文会被压缩
compressed_length = 5 # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
compress_length_limit = 5 #最多压缩份数，超过该数值的压缩上下文会被删除
[message]
max_context_size = 12 # 麦麦获得的上文数量，建议12，太短太长都会导致脑袋尖尖
emoji_chance = 0.2 # 麦麦使用表情包的概率
emoji_chance = 0.2 # 麦麦使用表情包的概率，设置为1让麦麦自己决定发不发
thinking_timeout = 60 # 麦麦最长思考时间，超过这个时间的思考会放弃
max_response_length = 256 # 麦麦回答的最大token数
message_buffer = true # 启用消息缓冲器？启用此项以解决消息的拆分问题，但会使麦麦的回复延迟
@@ -98,14 +102,13 @@ ban_msgs_regex = [
]
[willing]
willing_mode = "classical" # 回复意愿模式 经典模式
# willing_mode = "dynamic" # 动态模式(不兼容，需要维护)
# willing_mode = "custom" # 自定义模式（可自行调整
willing_mode = "classical" # 回复意愿模式 —— 经典模式：classical，动态模式：dynamic，mxp模式：mxp，自定义模式：custom（需要你自己实现）
response_willing_amplifier = 1 # 麦麦回复意愿放大系数，一般为1
response_interested_rate_amplifier = 1 # 麦麦回复兴趣度放大系数,听到记忆里的内容时放大系数
down_frequency_rate = 3 # 降低回复频率的群组回复意愿降低系数 除法
emoji_response_penalty = 0.1 # 表情包回复惩罚系数，设为0为不回复单个表情包，减少单独回复表情包的概率
mentioned_bot_inevitable_reply = false # 提及 bot 必然回复
at_bot_inevitable_reply = false # @bot 必然回复
[emoji]
max_emoji_num = 120 # 表情包最大数量
@@ -148,6 +151,11 @@ enable = false # 仅作示例，不会触发
keywords = ["测试关键词回复","test",""]
reaction = "回答“测试成功”"
[[keywords_reaction.rules]] # 使用正则表达式匹配句式
enable = false # 仅作示例，不会触发
regex = ["^(?P<n>\\S{1,20})是这样的$"] # 将匹配到的词汇命名为n，反应中对应的[n]会被替换为匹配到的内容，若不了解正则表达式请勿编写
reaction = "请按照以下模板造句：[n]是这样的，xx只要xx就可以，可是[n]要考虑的事情就很多了，比如什么时候xx，什么时候xx，什么时候xx。（请自由发挥替换xx部分，只需保持句式结构，同时表达一种将[n]过度重视的反讽意味）"
[chinese_typo]
enable = true # 是否启用中文错别字生成器
error_rate=0.001 # 单字替换概率
@@ -155,15 +163,15 @@ min_freq=9 # 最小字频阈值
tone_error_rate=0.1 # 声调错误概率
word_replace_rate=0.006 # 整词替换概率
[response_spliter]
enable_response_spliter = true # 是否启用回复分割器
[response_splitter]
enable_response_splitter = 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 = 4 # 回复允许的最大句子数
[remote] #发送统计信息，主要是看全球有多少只麦麦
enable = true
[experimental]
[experimental] #实验性功能，不一定完善或者根本不能用
enable_friend_chat = false # 是否启用好友聊天
pfc_chatting = false # 是否启用PFC聊天，该功能仅作用于私聊，与回复模式独立
@@ -185,11 +193,12 @@ pri_out = 16 #模型的输出价格（非必填，可以记录消耗）
#非推理模型
[model.llm_normal] #V3 回复模型1 主要回复模型
[model.llm_normal] #V3 回复模型1 主要回复模型，默认temp 0.2 如果你使用的是老V3或者其他模型，请自己修改temp参数
name = "Pro/deepseek-ai/DeepSeek-V3"
provider = "SILICONFLOW"
pri_in = 2 #模型的输入价格（非必填，可以记录消耗）
pri_out = 8 #模型的输出价格（非必填，可以记录消耗）
temp = 0.2 #模型的温度，新V3建议0.1-0.3
[model.llm_emotion_judge] #表情包判断
name = "Qwen/Qwen2.5-14B-Instruct"
@@ -209,11 +218,11 @@ provider = "SILICONFLOW"
pri_in = 1.26
pri_out = 1.26
[model.moderation] #内容审核，开发中
name = ""
[model.llm_tool_use] #工具调用模型，需要使用支持工具调用的模型，建议使用qwen2.5 32b
name = "Qwen/Qwen2.5-32B-Instruct"
provider = "SILICONFLOW"
pri_in = 1.0
pri_out = 2.0
pri_in = 1.26
pri_out = 1.26
# 识图模型
@@ -238,12 +247,12 @@ provider = "SILICONFLOW"
pri_in = 0
pri_out = 0
[model.llm_sub_heartflow] #心流：建议使用qwen2.5 7b
# name = "Pro/Qwen/Qwen2.5-7B-Instruct"
name = "Qwen/Qwen2.5-32B-Instruct"
[model.llm_sub_heartflow] #子心流：建议使用V3级别
name = "Pro/deepseek-ai/DeepSeek-V3"
provider = "SILICONFLOW"
pri_in = 1.26
pri_out = 1.26
pri_in = 2
pri_out = 8
temp = 0.2 #模型的温度，新V3建议0.1-0.3
[model.llm_heartflow] #心流：建议使用qwen2.5 32b
# name = "Pro/Qwen/Qwen2.5-7B-Instruct"

0
从0.6.0升级0.6.1请先看我.txt → 从0.6.0升级0.6.2请先看我.txt
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