mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'main-fix0' of https://github.com/Dax233/MaiMBot into main-fix2
Bakadax
commit
98720b6e00
|
|
@ -130,7 +130,7 @@ MaiMBot是一个开源项目，我们非常欢迎你的参与。你的贡献，
|
|||
### 💬交流群
|
||||
- [五群](https://qm.qq.com/q/JxvHZnxyec) 1022489779（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
|
||||
- [一群](https://qm.qq.com/q/VQ3XZrWgMs) 766798517 【已满】（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
|
||||
- [二群](https://qm.qq.com/q/RzmCiRtHEW) 571780722 【已满】（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
|
||||
- [二群](https://qm.qq.com/q/RzmCiRtHEW) 571780722（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
|
||||
- [三群](https://qm.qq.com/q/wlH5eT8OmQ) 1035228475【已满】（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
|
||||
- [四群](https://qm.qq.com/q/wlH5eT8OmQ) 729957033【已满】（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
|
||||
|
||||
|
|
@ -143,7 +143,7 @@ MaiMBot是一个开源项目，我们非常欢迎你的参与。你的贡献，
|
|||
|
||||
- 📦 **Windows 一键傻瓜式部署**：请运行项目根目录中的 `run.bat`，部署完成后请参照后续配置指南进行配置
|
||||
|
||||
- 📦 Linux 自动部署（实验） ：请下载并运行项目根目录中的`run.sh`并按照提示安装，部署完成后请参照后续配置指南进行配置
|
||||
- 📦 Linux 自动部署（Arch/CentOS9/Debian12/Ubuntu24.10） ：请下载并运行项目根目录中的`run.sh`并按照提示安装，部署完成后请参照后续配置指南进行配置
|
||||
|
||||
- [📦 Windows 手动部署指南 ](docs/manual_deploy_windows.md)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# 🐳 Docker 部署指南
|
||||
|
||||
## 部署步骤 (推荐，但不一定是最新)
|
||||
## 部署步骤 (不一定是最新)
|
||||
|
||||
**"更新镜像与容器"部分在本文档 [Part 6](#6-更新镜像与容器)**
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,10 @@
|
|||
#!/bin/bash
|
||||
|
||||
# 麦麦Bot一键安装脚本 by Cookie_987
|
||||
# 适用于Debian12
|
||||
# 适用于Arch/Ubuntu 24.10/Debian 12/CentOS 9
|
||||
# 请小心使用任何一键脚本！
|
||||
|
||||
INSTALLER_VERSION="0.0.3"
|
||||
LANG=C.UTF-8
|
||||
|
||||
# 如无法访问GitHub请修改此处镜像地址
|
||||
|
|
@ -15,7 +16,14 @@ RED="\e[31m"
|
|||
RESET="\e[0m"
|
||||
|
||||
# 需要的基本软件包
|
||||
REQUIRED_PACKAGES=("git" "sudo" "python3" "python3-venv" "curl" "gnupg" "python3-pip")
|
||||
|
||||
declare -A REQUIRED_PACKAGES=(
|
||||
["common"]="git sudo python3 curl gnupg"
|
||||
["debian"]="python3-venv python3-pip"
|
||||
["ubuntu"]="python3-venv python3-pip"
|
||||
["centos"]="python3-pip"
|
||||
["arch"]="python-virtualenv python-pip"
|
||||
)
|
||||
|
||||
# 默认项目目录
|
||||
DEFAULT_INSTALL_DIR="/opt/maimbot"
|
||||
|
|
@ -28,8 +36,6 @@ IS_INSTALL_MONGODB=false
|
|||
IS_INSTALL_NAPCAT=false
|
||||
IS_INSTALL_DEPENDENCIES=false
|
||||
|
||||
INSTALLER_VERSION="0.0.1"
|
||||
|
||||
# 检查是否已安装
|
||||
check_installed() {
|
||||
[[ -f /etc/systemd/system/${SERVICE_NAME}.service ]]
|
||||
|
|
@ -193,6 +199,11 @@ check_eula() {
|
|||
# 首先计算当前隐私条款文件的哈希值
|
||||
current_md5_privacy=$(md5sum "${INSTALL_DIR}/repo/PRIVACY.md" | awk '{print $1}')
|
||||
|
||||
# 如果当前的md5值为空，则直接返回
|
||||
if [[ -z $current_md5 || -z $current_md5_privacy ]]; then
|
||||
whiptail --msgbox "🚫 未找到使用协议\n 请检查PRIVACY.md和EULA.md是否存在" 10 60
|
||||
fi
|
||||
|
||||
# 检查eula.confirmed文件是否存在
|
||||
if [[ -f ${INSTALL_DIR}/repo/eula.confirmed ]]; then
|
||||
# 如果存在则检查其中包含的md5与current_md5是否一致
|
||||
|
|
@ -213,8 +224,8 @@ check_eula() {
|
|||
if [[ $current_md5 != $confirmed_md5 || $current_md5_privacy != $confirmed_md5_privacy ]]; then
|
||||
whiptail --title "📜 使用协议更新" --yesno "检测到麦麦Bot EULA或隐私条款已更新。\nhttps://github.com/SengokuCola/MaiMBot/blob/main/EULA.md\nhttps://github.com/SengokuCola/MaiMBot/blob/main/PRIVACY.md\n\n您是否同意上述协议？ \n\n " 12 70
|
||||
if [[ $? -eq 0 ]]; then
|
||||
echo $current_md5 > ${INSTALL_DIR}/repo/eula.confirmed
|
||||
echo $current_md5_privacy > ${INSTALL_DIR}/repo/privacy.confirmed
|
||||
echo -n $current_md5 > ${INSTALL_DIR}/repo/eula.confirmed
|
||||
echo -n $current_md5_privacy > ${INSTALL_DIR}/repo/privacy.confirmed
|
||||
else
|
||||
exit 1
|
||||
fi
|
||||
|
|
@ -227,7 +238,14 @@ run_installation() {
|
|||
# 1/6: 检测是否安装 whiptail
|
||||
if ! command -v whiptail &>/dev/null; then
|
||||
echo -e "${RED}[1/6] whiptail 未安装，正在安装...${RESET}"
|
||||
|
||||
# 这里的多系统适配很神人，但是能用（）
|
||||
|
||||
apt update && apt install -y whiptail
|
||||
|
||||
pacman -S --noconfirm libnewt
|
||||
|
||||
yum install -y newt
|
||||
fi
|
||||
|
||||
# 协议确认
|
||||
|
|
@ -247,8 +265,18 @@ run_installation() {
|
|||
|
||||
if [[ -f /etc/os-release ]]; then
|
||||
source /etc/os-release
|
||||
if [[ "$ID" != "debian" || "$VERSION_ID" != "12" ]]; then
|
||||
whiptail --title "🚫 不支持的系统" --msgbox "此脚本仅支持 Debian 12 (Bookworm)！\n当前系统: $PRETTY_NAME\n安装已终止。" 10 60
|
||||
if [[ "$ID" == "debian" && "$VERSION_ID" == "12" ]]; then
|
||||
return
|
||||
elif [[ "$ID" == "ubuntu" && "$VERSION_ID" == "24.10" ]]; then
|
||||
return
|
||||
elif [[ "$ID" == "centos" && "$VERSION_ID" == "9" ]]; then
|
||||
return
|
||||
elif [[ "$ID" == "arch" ]]; then
|
||||
whiptail --title "⚠️ 兼容性警告" --msgbox "NapCat无可用的 Arch Linux 官方安装方法，将无法自动安装NapCat。\n\n您可尝试在AUR中搜索相关包。" 10 60
|
||||
whiptail --title "⚠️ 兼容性警告" --msgbox "MongoDB无可用的 Arch Linux 官方安装方法，将无法自动安装MongoDB。\n\n您可尝试在AUR中搜索相关包。" 10 60
|
||||
return
|
||||
else
|
||||
whiptail --title "🚫 不支持的系统" --msgbox "此脚本仅支持 Arch/Debian 12 (Bookworm)/Ubuntu 24.10 (Oracular Oriole)/CentOS9！\n当前系统: $PRETTY_NAME\n安装已终止。" 10 60
|
||||
exit 1
|
||||
fi
|
||||
else
|
||||
|
|
@ -258,6 +286,20 @@ run_installation() {
|
|||
}
|
||||
check_system
|
||||
|
||||
# 设置包管理器
|
||||
case "$ID" in
|
||||
debian|ubuntu)
|
||||
PKG_MANAGER="apt"
|
||||
;;
|
||||
centos)
|
||||
PKG_MANAGER="yum"
|
||||
;;
|
||||
arch)
|
||||
# 添加arch包管理器
|
||||
PKG_MANAGER="pacman"
|
||||
;;
|
||||
esac
|
||||
|
||||
# 检查MongoDB
|
||||
check_mongodb() {
|
||||
if command -v mongod &>/dev/null; then
|
||||
|
|
@ -281,18 +323,27 @@ run_installation() {
|
|||
# 安装必要软件包
|
||||
install_packages() {
|
||||
missing_packages=()
|
||||
for package in "${REQUIRED_PACKAGES[@]}"; do
|
||||
if ! dpkg -s "$package" &>/dev/null; then
|
||||
missing_packages+=("$package")
|
||||
fi
|
||||
# 检查 common 及当前系统专属依赖
|
||||
for package in ${REQUIRED_PACKAGES["common"]} ${REQUIRED_PACKAGES["$ID"]}; do
|
||||
case "$PKG_MANAGER" in
|
||||
apt)
|
||||
dpkg -s "$package" &>/dev/null || missing_packages+=("$package")
|
||||
;;
|
||||
yum)
|
||||
rpm -q "$package" &>/dev/null || missing_packages+=("$package")
|
||||
;;
|
||||
pacman)
|
||||
pacman -Qi "$package" &>/dev/null || missing_packages+=("$package")
|
||||
;;
|
||||
esac
|
||||
done
|
||||
|
||||
if [[ ${#missing_packages[@]} -gt 0 ]]; then
|
||||
whiptail --title "📦 [3/6] 软件包检查" --yesno "检测到以下必须的依赖项目缺失:\n${missing_packages[*]}\n\n是否要自动安装？" 12 60
|
||||
whiptail --title "📦 [3/6] 依赖检查" --yesno "以下软件包缺失:\n${missing_packages[*]}\n\n是否自动安装？" 10 60
|
||||
if [[ $? -eq 0 ]]; then
|
||||
IS_INSTALL_DEPENDENCIES=true
|
||||
else
|
||||
whiptail --title "⚠️ 注意" --yesno "某些必要的依赖项未安装，可能会影响运行！\n是否继续？" 10 60 || exit 1
|
||||
whiptail --title "⚠️ 注意" --yesno "未安装某些依赖，可能影响运行！\n是否继续？" 10 60 || exit 1
|
||||
fi
|
||||
fi
|
||||
}
|
||||
|
|
@ -302,27 +353,24 @@ run_installation() {
|
|||
install_mongodb() {
|
||||
[[ $MONGO_INSTALLED == true ]] && return
|
||||
whiptail --title "📦 [3/6] 软件包检查" --yesno "检测到未安装MongoDB，是否安装？\n如果您想使用远程数据库，请跳过此步。" 10 60 && {
|
||||
echo -e "${GREEN}安装 MongoDB...${RESET}"
|
||||
curl -fsSL https://www.mongodb.org/static/pgp/server-8.0.asc | gpg -o /usr/share/keyrings/mongodb-server-8.0.gpg --dearmor
|
||||
echo "deb [ signed-by=/usr/share/keyrings/mongodb-server-8.0.gpg ] http://repo.mongodb.org/apt/debian bookworm/mongodb-org/8.0 main" | tee /etc/apt/sources.list.d/mongodb-org-8.0.list
|
||||
apt update
|
||||
apt install -y mongodb-org
|
||||
systemctl enable --now mongod
|
||||
IS_INSTALL_MONGODB=true
|
||||
}
|
||||
}
|
||||
install_mongodb
|
||||
|
||||
# 仅在非Arch系统上安装MongoDB
|
||||
[[ "$ID" != "arch" ]] && install_mongodb
|
||||
|
||||
|
||||
# 安装NapCat
|
||||
install_napcat() {
|
||||
[[ $NAPCAT_INSTALLED == true ]] && return
|
||||
whiptail --title "📦 [3/6] 软件包检查" --yesno "检测到未安装NapCat，是否安装？\n如果您想使用远程NapCat，请跳过此步。" 10 60 && {
|
||||
echo -e "${GREEN}安装 NapCat...${RESET}"
|
||||
curl -o napcat.sh https://nclatest.znin.net/NapNeko/NapCat-Installer/main/script/install.sh && bash napcat.sh --cli y --docker n
|
||||
IS_INSTALL_NAPCAT=true
|
||||
}
|
||||
}
|
||||
install_napcat
|
||||
|
||||
# 仅在非Arch系统上安装NapCat
|
||||
[[ "$ID" != "arch" ]] && install_napcat
|
||||
|
||||
# Python版本检查
|
||||
check_python() {
|
||||
|
|
@ -332,7 +380,12 @@ run_installation() {
|
|||
exit 1
|
||||
fi
|
||||
}
|
||||
check_python
|
||||
|
||||
# 如果没安装python则不检查python版本
|
||||
if command -v python3 &>/dev/null; then
|
||||
check_python
|
||||
fi
|
||||
|
||||
|
||||
# 选择分支
|
||||
choose_branch() {
|
||||
|
|
@ -358,20 +411,71 @@ run_installation() {
|
|||
local confirm_msg="请确认以下信息：\n\n"
|
||||
confirm_msg+="📂 安装麦麦Bot到: $INSTALL_DIR\n"
|
||||
confirm_msg+="🔀 分支: $BRANCH\n"
|
||||
[[ $IS_INSTALL_DEPENDENCIES == true ]] && confirm_msg+="📦 安装依赖：${missing_packages}\n"
|
||||
[[ $IS_INSTALL_DEPENDENCIES == true ]] && confirm_msg+="📦 安装依赖：${missing_packages[@]}\n"
|
||||
[[ $IS_INSTALL_MONGODB == true || $IS_INSTALL_NAPCAT == true ]] && confirm_msg+="📦 安装额外组件：\n"
|
||||
|
||||
[[ $IS_INSTALL_MONGODB == true ]] && confirm_msg+=" - MongoDB\n"
|
||||
[[ $IS_INSTALL_NAPCAT == true ]] && confirm_msg+=" - NapCat\n"
|
||||
confirm_msg+="\n注意：本脚本默认使用ghfast.top为GitHub进行加速，如不想使用请手动修改脚本开头的GITHUB_REPO变量。"
|
||||
|
||||
whiptail --title "🔧 安装确认" --yesno "$confirm_msg" 16 60 || exit 1
|
||||
whiptail --title "🔧 安装确认" --yesno "$confirm_msg" 20 60 || exit 1
|
||||
}
|
||||
confirm_install
|
||||
|
||||
# 开始安装
|
||||
echo -e "${GREEN}安装依赖...${RESET}"
|
||||
[[ $IS_INSTALL_DEPENDENCIES == true ]] && apt update && apt install -y "${missing_packages[@]}"
|
||||
echo -e "${GREEN}安装${missing_packages[@]}...${RESET}"
|
||||
|
||||
if [[ $IS_INSTALL_DEPENDENCIES == true ]]; then
|
||||
case "$PKG_MANAGER" in
|
||||
apt)
|
||||
apt update && apt install -y "${missing_packages[@]}"
|
||||
;;
|
||||
yum)
|
||||
yum install -y "${missing_packages[@]}" --nobest
|
||||
;;
|
||||
pacman)
|
||||
pacman -S --noconfirm "${missing_packages[@]}"
|
||||
;;
|
||||
esac
|
||||
fi
|
||||
|
||||
if [[ $IS_INSTALL_MONGODB == true ]]; then
|
||||
echo -e "${GREEN}安装 MongoDB...${RESET}"
|
||||
case "$ID" in
|
||||
debian)
|
||||
curl -fsSL https://www.mongodb.org/static/pgp/server-8.0.asc | gpg -o /usr/share/keyrings/mongodb-server-8.0.gpg --dearmor
|
||||
echo "deb [ signed-by=/usr/share/keyrings/mongodb-server-8.0.gpg ] http://repo.mongodb.org/apt/debian bookworm/mongodb-org/8.0 main" | tee /etc/apt/sources.list.d/mongodb-org-8.0.list
|
||||
apt update
|
||||
apt install -y mongodb-org
|
||||
systemctl enable --now mongod
|
||||
;;
|
||||
ubuntu)
|
||||
curl -fsSL https://www.mongodb.org/static/pgp/server-8.0.asc | gpg -o /usr/share/keyrings/mongodb-server-8.0.gpg --dearmor
|
||||
echo "deb [ signed-by=/usr/share/keyrings/mongodb-server-8.0.gpg ] http://repo.mongodb.org/apt/debian bookworm/mongodb-org/8.0 main" | tee /etc/apt/sources.list.d/mongodb-org-8.0.list
|
||||
apt update
|
||||
apt install -y mongodb-org
|
||||
systemctl enable --now mongod
|
||||
;;
|
||||
centos)
|
||||
cat > /etc/yum.repos.d/mongodb-org-8.0.repo <<EOF
|
||||
[mongodb-org-8.0]
|
||||
name=MongoDB Repository
|
||||
baseurl=https://repo.mongodb.org/yum/redhat/9/mongodb-org/8.0/x86_64/
|
||||
gpgcheck=1
|
||||
enabled=1
|
||||
gpgkey=https://pgp.mongodb.com/server-8.0.asc
|
||||
EOF
|
||||
yum install -y mongodb-org
|
||||
systemctl enable --now mongod
|
||||
;;
|
||||
esac
|
||||
|
||||
fi
|
||||
|
||||
if [[ $IS_INSTALL_NAPCAT == true ]]; then
|
||||
echo -e "${GREEN}安装 NapCat...${RESET}"
|
||||
curl -o napcat.sh https://nclatest.znin.net/NapNeko/NapCat-Installer/main/script/install.sh && bash napcat.sh --cli y --docker n
|
||||
fi
|
||||
|
||||
echo -e "${GREEN}创建安装目录...${RESET}"
|
||||
mkdir -p "$INSTALL_DIR"
|
||||
|
|
@ -398,8 +502,8 @@ run_installation() {
|
|||
# 首先计算当前隐私条款文件的哈希值
|
||||
current_md5_privacy=$(md5sum "repo/PRIVACY.md" | awk '{print $1}')
|
||||
|
||||
echo $current_md5 > repo/eula.confirmed
|
||||
echo $current_md5_privacy > repo/privacy.confirmed
|
||||
echo -n $current_md5 > repo/eula.confirmed
|
||||
echo -n $current_md5_privacy > repo/privacy.confirmed
|
||||
|
||||
echo -e "${GREEN}创建系统服务...${RESET}"
|
||||
cat > /etc/systemd/system/${SERVICE_NAME}.service <<EOF
|
||||
|
|
@ -105,6 +105,24 @@ MOOD_STYLE_CONFIG = {
|
|||
},
|
||||
}
|
||||
|
||||
# relationship
|
||||
RELATION_STYLE_CONFIG = {
|
||||
"advanced": {
|
||||
"console_format": (
|
||||
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||
"<level>{level: <8}</level> | "
|
||||
"<cyan>{extra[module]: <12}</cyan> | "
|
||||
"<light-magenta>关系</light-magenta> | "
|
||||
"<level>{message}</level>"
|
||||
),
|
||||
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 关系 | {message}"),
|
||||
},
|
||||
"simple": {
|
||||
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-magenta>关系</light-magenta> | {message}"),
|
||||
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 关系 | {message}"),
|
||||
},
|
||||
}
|
||||
|
||||
SENDER_STYLE_CONFIG = {
|
||||
"advanced": {
|
||||
"console_format": (
|
||||
|
|
@ -122,6 +140,40 @@ SENDER_STYLE_CONFIG = {
|
|||
},
|
||||
}
|
||||
|
||||
HEARTFLOW_STYLE_CONFIG = {
|
||||
"advanced": {
|
||||
"console_format": (
|
||||
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||
"<level>{level: <8}</level> | "
|
||||
"<cyan>{extra[module]: <12}</cyan> | "
|
||||
"<light-yellow>麦麦大脑袋</light-yellow> | "
|
||||
"<level>{message}</level>"
|
||||
),
|
||||
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦大脑袋 | {message}"),
|
||||
},
|
||||
"simple": {
|
||||
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-green>麦麦大脑袋</light-green> | <light-green>{message}</light-green>"), # noqa: E501
|
||||
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦大脑袋 | {message}"),
|
||||
},
|
||||
}
|
||||
|
||||
SCHEDULE_STYLE_CONFIG = {
|
||||
"advanced": {
|
||||
"console_format": (
|
||||
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||
"<level>{level: <8}</level> | "
|
||||
"<cyan>{extra[module]: <12}</cyan> | "
|
||||
"<light-yellow>在干嘛</light-yellow> | "
|
||||
"<level>{message}</level>"
|
||||
),
|
||||
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 在干嘛 | {message}"),
|
||||
},
|
||||
"simple": {
|
||||
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <cyan>在干嘛</cyan> | <cyan>{message}</cyan>"),
|
||||
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 在干嘛 | {message}"),
|
||||
},
|
||||
}
|
||||
|
||||
LLM_STYLE_CONFIG = {
|
||||
"advanced": {
|
||||
"console_format": (
|
||||
|
|
@ -176,6 +228,26 @@ CHAT_STYLE_CONFIG = {
|
|||
},
|
||||
}
|
||||
|
||||
SUB_HEARTFLOW_STYLE_CONFIG = {
|
||||
"advanced": {
|
||||
"console_format": (
|
||||
"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
|
||||
"<level>{level: <8}</level> | "
|
||||
"<cyan>{extra[module]: <12}</cyan> | "
|
||||
"<light-blue>麦麦小脑袋</light-blue> | "
|
||||
"<level>{message}</level>"
|
||||
),
|
||||
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦小脑袋 | {message}"),
|
||||
},
|
||||
"simple": {
|
||||
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-blue>麦麦小脑袋</light-blue> | <green>{message}</green>"), # noqa: E501
|
||||
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦小脑袋 | {message}"),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
# 根据SIMPLE_OUTPUT选择配置
|
||||
MEMORY_STYLE_CONFIG = MEMORY_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MEMORY_STYLE_CONFIG["advanced"]
|
||||
TOPIC_STYLE_CONFIG = TOPIC_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else TOPIC_STYLE_CONFIG["advanced"]
|
||||
|
|
@ -183,6 +255,10 @@ SENDER_STYLE_CONFIG = SENDER_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SENDER
|
|||
LLM_STYLE_CONFIG = LLM_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else LLM_STYLE_CONFIG["advanced"]
|
||||
CHAT_STYLE_CONFIG = CHAT_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else CHAT_STYLE_CONFIG["advanced"]
|
||||
MOOD_STYLE_CONFIG = MOOD_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MOOD_STYLE_CONFIG["advanced"]
|
||||
RELATION_STYLE_CONFIG = RELATION_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else RELATION_STYLE_CONFIG["advanced"]
|
||||
SCHEDULE_STYLE_CONFIG = SCHEDULE_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SCHEDULE_STYLE_CONFIG["advanced"]
|
||||
HEARTFLOW_STYLE_CONFIG = HEARTFLOW_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else HEARTFLOW_STYLE_CONFIG["advanced"]
|
||||
SUB_HEARTFLOW_STYLE_CONFIG = SUB_HEARTFLOW_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SUB_HEARTFLOW_STYLE_CONFIG["advanced"] # noqa: E501
|
||||
|
||||
def is_registered_module(record: dict) -> bool:
|
||||
"""检查是否为已注册的模块"""
|
||||
|
|
|
|||
|
|
@ -9,12 +9,13 @@ from ..moods.moods import MoodManager # 导入情绪管理器
|
|||
from ..schedule.schedule_generator import bot_schedule
|
||||
from ..utils.statistic import LLMStatistics
|
||||
from .bot import chat_bot
|
||||
from .config import global_config
|
||||
from ..config.config import global_config
|
||||
from .emoji_manager import emoji_manager
|
||||
from .relationship_manager import relationship_manager
|
||||
from ..willing.willing_manager import willing_manager
|
||||
from .chat_stream import chat_manager
|
||||
from ..memory_system.memory import hippocampus
|
||||
# from ..memory_system.memory import hippocampus
|
||||
from src.plugins.memory_system.Hippocampus import HippocampusManager
|
||||
from .message_sender import message_manager, message_sender
|
||||
from .storage import MessageStorage
|
||||
from src.common.logger import get_module_logger
|
||||
|
|
@ -59,6 +60,22 @@ async def start_think_flow():
|
|||
logger.error(f"启动大脑和外部世界失败: {e}")
|
||||
raise
|
||||
|
||||
async def start_memory():
|
||||
"""启动记忆系统"""
|
||||
try:
|
||||
start_time = time.time()
|
||||
logger.info("开始初始化记忆系统...")
|
||||
|
||||
# 使用HippocampusManager初始化海马体
|
||||
hippocampus_manager = HippocampusManager.get_instance()
|
||||
hippocampus_manager.initialize(global_config=global_config)
|
||||
|
||||
end_time = time.time()
|
||||
logger.success(f"记忆系统初始化完成，耗时: {end_time - start_time:.2f} 秒")
|
||||
except Exception as e:
|
||||
logger.error(f"记忆系统初始化失败: {e}")
|
||||
raise
|
||||
|
||||
|
||||
@driver.on_startup
|
||||
async def start_background_tasks():
|
||||
|
|
@ -79,10 +96,20 @@ async def start_background_tasks():
|
|||
|
||||
# 只启动表情包管理任务
|
||||
asyncio.create_task(emoji_manager.start_periodic_check())
|
||||
await bot_schedule.initialize()
|
||||
bot_schedule.print_schedule()
|
||||
|
||||
asyncio.create_task(start_memory())
|
||||
|
||||
|
||||
@driver.on_startup
|
||||
async def init_schedule():
|
||||
"""在 NoneBot2 启动时初始化日程系统"""
|
||||
bot_schedule.initialize(
|
||||
name=global_config.BOT_NICKNAME,
|
||||
personality=global_config.PROMPT_PERSONALITY,
|
||||
behavior=global_config.PROMPT_SCHEDULE_GEN,
|
||||
interval=global_config.SCHEDULE_DOING_UPDATE_INTERVAL)
|
||||
asyncio.create_task(bot_schedule.mai_schedule_start())
|
||||
|
||||
@driver.on_startup
|
||||
async def init_relationships():
|
||||
"""在 NoneBot2 启动时初始化关系管理器"""
|
||||
|
|
@ -131,14 +158,14 @@ async def _(bot: Bot, event: NoticeEvent, state: T_State):
|
|||
@scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval, id="build_memory")
|
||||
async def build_memory_task():
|
||||
"""每build_memory_interval秒执行一次记忆构建"""
|
||||
await hippocampus.operation_build_memory()
|
||||
await HippocampusManager.get_instance().build_memory()
|
||||
|
||||
|
||||
@scheduler.scheduled_job("interval", seconds=global_config.forget_memory_interval, id="forget_memory")
|
||||
async def forget_memory_task():
|
||||
"""每30秒执行一次记忆构建"""
|
||||
print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...")
|
||||
await hippocampus.operation_forget_topic(percentage=global_config.memory_forget_percentage)
|
||||
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")
|
||||
|
||||
|
||||
|
|
@ -157,13 +184,13 @@ async def print_mood_task():
|
|||
mood_manager.print_mood_status()
|
||||
|
||||
|
||||
@scheduler.scheduled_job("interval", seconds=7200, id="generate_schedule")
|
||||
async def generate_schedule_task():
|
||||
"""每2小时尝试生成一次日程"""
|
||||
logger.debug("尝试生成日程")
|
||||
await bot_schedule.initialize()
|
||||
if not bot_schedule.enable_output:
|
||||
bot_schedule.print_schedule()
|
||||
# @scheduler.scheduled_job("interval", seconds=7200, id="generate_schedule")
|
||||
# async def generate_schedule_task():
|
||||
# """每2小时尝试生成一次日程"""
|
||||
# logger.debug("尝试生成日程")
|
||||
# await bot_schedule.initialize()
|
||||
# if not bot_schedule.enable_output:
|
||||
# bot_schedule.print_schedule()
|
||||
|
||||
|
||||
@scheduler.scheduled_job("interval", seconds=3600, id="remove_recalled_message")
|
||||
|
|
|
|||
|
|
@ -12,9 +12,9 @@ from nonebot.adapters.onebot.v11 import (
|
|||
FriendRecallNoticeEvent,
|
||||
)
|
||||
|
||||
from ..memory_system.memory import hippocampus
|
||||
from ..memory_system.Hippocampus import HippocampusManager
|
||||
from ..moods.moods import MoodManager # 导入情绪管理器
|
||||
from .config import global_config
|
||||
from ..config.config import global_config
|
||||
from .emoji_manager import emoji_manager # 导入表情包管理器
|
||||
from .llm_generator import ResponseGenerator
|
||||
from .message import MessageSending, MessageRecv, MessageThinking, MessageSet
|
||||
|
|
@ -129,8 +129,10 @@ class ChatBot:
|
|||
|
||||
# 根据话题计算激活度
|
||||
topic = ""
|
||||
interested_rate = await hippocampus.memory_activate_value(message.processed_plain_text) / 100
|
||||
logger.debug(f"对{message.processed_plain_text}的激活度:{interested_rate}")
|
||||
interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
|
||||
message.processed_plain_text,fast_retrieval=True)
|
||||
# interested_rate = 0.1
|
||||
# logger.info(f"对{message.processed_plain_text}的激活度:{interested_rate}")
|
||||
# logger.info(f"\033[1;32m[主题识别]\033[0m 使用{global_config.topic_extract}主题: {topic}")
|
||||
|
||||
await self.storage.store_message(message, chat, topic[0] if topic else None)
|
||||
|
|
@ -311,7 +313,7 @@ class ChatBot:
|
|||
)
|
||||
|
||||
# 使用情绪管理器更新情绪
|
||||
self.mood_manager.update_mood_from_emotion(emotion[0], global_config.mood_intensity_factor)
|
||||
self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
|
||||
|
||||
async def handle_notice(self, event: NoticeEvent, bot: Bot) -> None:
|
||||
"""处理收到的通知"""
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ from src.common.logger import get_module_logger
|
|||
from nonebot import get_driver
|
||||
|
||||
from ..models.utils_model import LLM_request
|
||||
from .config import global_config
|
||||
from ..config.config import global_config
|
||||
from .mapper import emojimapper
|
||||
from .message_base import Seg
|
||||
from .utils_user import get_user_nickname, get_groupname
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ import io
|
|||
from nonebot import get_driver
|
||||
|
||||
from ...common.database import db
|
||||
from ..chat.config import global_config
|
||||
from ..config.config import global_config
|
||||
from ..chat.utils import get_embedding
|
||||
from ..chat.utils_image import ImageManager, image_path_to_base64
|
||||
from ..models.utils_model import LLM_request
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from nonebot import get_driver
|
|||
|
||||
from ...common.database import db
|
||||
from ..models.utils_model import LLM_request
|
||||
from .config import global_config
|
||||
from ..config.config import global_config
|
||||
from .message import MessageRecv, MessageThinking, Message
|
||||
from .prompt_builder import prompt_builder
|
||||
from .utils import process_llm_response
|
||||
|
|
@ -51,13 +51,13 @@ class ResponseGenerator:
|
|||
# 从global_config中获取模型概率值并选择模型
|
||||
rand = random.random()
|
||||
if rand < global_config.MODEL_R1_PROBABILITY:
|
||||
self.current_model_type = "r1"
|
||||
self.current_model_type = "深深地"
|
||||
current_model = self.model_r1
|
||||
elif rand < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY:
|
||||
self.current_model_type = "v3"
|
||||
self.current_model_type = "浅浅的"
|
||||
current_model = self.model_v3
|
||||
else:
|
||||
self.current_model_type = "r1_distill"
|
||||
self.current_model_type = "又浅又浅的"
|
||||
current_model = self.model_r1_distill
|
||||
|
||||
logger.info(f"{global_config.BOT_NICKNAME}{self.current_model_type}思考中")
|
||||
|
|
@ -144,18 +144,25 @@ class ResponseGenerator:
|
|||
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"""
|
||||
请根据以下对话内容，完成以下任务：
|
||||
1. 判断回复者的立场是"supportive"（支持）、"opposed"（反对）还是"neutrality"（中立）。
|
||||
2. 从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签。
|
||||
3. 按照"立场-情绪"的格式输出结果，例如："supportive-happy"。
|
||||
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"
|
||||
|
||||
被回复的内容：
|
||||
{processed_plain_text}
|
||||
对话示例：
|
||||
被回复：「A就是笨」
|
||||
回复：「A明明很聪明」 → 反对-愤怒
|
||||
|
||||
回复内容：
|
||||
{content}
|
||||
当前对话：
|
||||
被回复：「{processed_plain_text}」
|
||||
回复：「{content}」
|
||||
|
||||
请分析回复者的立场和情感倾向，并输出结果：
|
||||
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
|
||||
|
|
@ -165,18 +172,20 @@ class ResponseGenerator:
|
|||
# 解析模型输出的结果
|
||||
if "-" in result:
|
||||
stance, emotion = result.split("-", 1)
|
||||
valid_stances = ["supportive", "opposed", "neutrality"]
|
||||
valid_emotions = ["happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"]
|
||||
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:
|
||||
return "neutrality", "neutral" # 默认返回中立-中性
|
||||
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:
|
||||
return "neutrality", "neutral" # 格式错误时返回默认值
|
||||
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:
|
||||
print(f"获取情感标签时出错: {e}")
|
||||
return "neutrality", "neutral" # 出错时返回默认值
|
||||
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]]:
|
||||
"""处理响应内容，返回处理后的内容和情感标签"""
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from .message_cq import MessageSendCQ
|
|||
from .message import MessageSending, MessageThinking, MessageSet
|
||||
|
||||
from .storage import MessageStorage
|
||||
from .config import global_config
|
||||
from ..config.config import global_config
|
||||
from .utils import truncate_message, calculate_typing_time
|
||||
|
||||
from src.common.logger import LogConfig, SENDER_STYLE_CONFIG
|
||||
|
|
@ -61,6 +61,7 @@ class Message_Sender:
|
|||
if not is_recalled:
|
||||
|
||||
typing_time = calculate_typing_time(message.processed_plain_text)
|
||||
logger.info(f"麦麦正在打字，预计需要{typing_time}秒")
|
||||
await asyncio.sleep(typing_time)
|
||||
|
||||
message_json = message.to_dict()
|
||||
|
|
@ -99,7 +100,7 @@ class MessageContainer:
|
|||
self.max_size = max_size
|
||||
self.messages = []
|
||||
self.last_send_time = 0
|
||||
self.thinking_timeout = 20 # 思考超时时间（秒）
|
||||
self.thinking_timeout = 10 # 思考超时时间（秒）
|
||||
|
||||
def get_timeout_messages(self) -> List[MessageSending]:
|
||||
"""获取所有超时的Message_Sending对象（思考时间超过30秒），按thinking_start_time排序"""
|
||||
|
|
@ -208,7 +209,7 @@ class MessageManager:
|
|||
# print(thinking_time)
|
||||
if (
|
||||
message_earliest.is_head
|
||||
and message_earliest.update_thinking_time() > 15
|
||||
and message_earliest.update_thinking_time() > 20
|
||||
and not message_earliest.is_private_message() # 避免在私聊时插入reply
|
||||
):
|
||||
logger.debug(f"设置回复消息{message_earliest.processed_plain_text}")
|
||||
|
|
@ -235,7 +236,7 @@ class MessageManager:
|
|||
# print(msg.is_private_message())
|
||||
if (
|
||||
msg.is_head
|
||||
and msg.update_thinking_time() > 15
|
||||
and msg.update_thinking_time() > 25
|
||||
and not msg.is_private_message() # 避免在私聊时插入reply
|
||||
):
|
||||
logger.debug(f"设置回复消息{msg.processed_plain_text}")
|
||||
|
|
|
|||
|
|
@ -3,10 +3,10 @@ import time
|
|||
from typing import Optional
|
||||
|
||||
from ...common.database import db
|
||||
from ..memory_system.memory import hippocampus, memory_graph
|
||||
from ..memory_system.Hippocampus import HippocampusManager
|
||||
from ..moods.moods import MoodManager
|
||||
from ..schedule.schedule_generator import bot_schedule
|
||||
from .config import global_config
|
||||
from ..config.config import global_config
|
||||
from .utils import get_embedding, get_recent_group_detailed_plain_text, get_recent_group_speaker
|
||||
from .chat_stream import chat_manager
|
||||
from .relationship_manager import relationship_manager
|
||||
|
|
@ -57,9 +57,7 @@ class PromptBuilder:
|
|||
mood_prompt = mood_manager.get_prompt()
|
||||
|
||||
# 日程构建
|
||||
# current_date = time.strftime("%Y-%m-%d", time.localtime())
|
||||
# current_time = time.strftime("%H:%M:%S", time.localtime())
|
||||
# bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
|
||||
# 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
|
||||
|
|
@ -81,19 +79,26 @@ class PromptBuilder:
|
|||
start_time = time.time()
|
||||
|
||||
# 调用 hippocampus 的 get_relevant_memories 方法
|
||||
relevant_memories = await hippocampus.get_relevant_memories(
|
||||
text=message_txt, max_topics=3, similarity_threshold=0.5, max_memory_num=4
|
||||
relevant_memories = await HippocampusManager.get_instance().get_memory_from_text(
|
||||
text=message_txt,
|
||||
max_memory_num=3,
|
||||
max_memory_length=2,
|
||||
max_depth=3,
|
||||
fast_retrieval=False
|
||||
)
|
||||
memory_str = ""
|
||||
for _topic, memories in relevant_memories:
|
||||
memory_str += f"{memories}\n"
|
||||
# print(f"memory_str: {memory_str}")
|
||||
|
||||
if relevant_memories:
|
||||
# 格式化记忆内容
|
||||
memory_str = "\n".join(m["content"] for m in relevant_memories)
|
||||
memory_prompt = f"你回忆起：\n{memory_str}\n"
|
||||
|
||||
# 打印调试信息
|
||||
logger.debug("[记忆检索]找到以下相关记忆：")
|
||||
for memory in relevant_memories:
|
||||
logger.debug(f"- 主题「{memory['topic']}」[相似度: {memory['similarity']:.2f}]: {memory['content']}")
|
||||
# for topic, memory_items, similarity in relevant_memories:
|
||||
# logger.debug(f"- 主题「{topic}」[相似度: {similarity:.2f}]: {memory_items}")
|
||||
|
||||
end_time = time.time()
|
||||
logger.info(f"回忆耗时: {(end_time - start_time):.3f}秒")
|
||||
|
|
@ -173,8 +178,6 @@ class PromptBuilder:
|
|||
prompt_check_if_response = ""
|
||||
|
||||
|
||||
# print(prompt)
|
||||
|
||||
return prompt, prompt_check_if_response
|
||||
|
||||
def _build_initiative_prompt_select(self, group_id, probability_1=0.8, probability_2=0.1):
|
||||
|
|
@ -196,7 +199,7 @@ 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}")
|
||||
|
||||
# 获取主动发言的话题
|
||||
all_nodes = memory_graph.dots
|
||||
all_nodes = HippocampusManager.get_instance().memory_graph.dots
|
||||
all_nodes = filter(lambda dot: len(dot[1]["memory_items"]) > 3, all_nodes)
|
||||
nodes_for_select = random.sample(all_nodes, 5)
|
||||
topics = [info[0] for info in nodes_for_select]
|
||||
|
|
@ -249,7 +252,7 @@ class PromptBuilder:
|
|||
related_info = ""
|
||||
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
|
||||
embedding = await get_embedding(message, request_type="prompt_build")
|
||||
related_info += self.get_info_from_db(embedding, threshold=threshold)
|
||||
related_info += self.get_info_from_db(embedding, limit=1, threshold=threshold)
|
||||
|
||||
return related_info
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
import asyncio
|
||||
from typing import Optional
|
||||
from src.common.logger import get_module_logger
|
||||
from src.common.logger import get_module_logger, LogConfig, RELATION_STYLE_CONFIG
|
||||
|
||||
from ...common.database import db
|
||||
from .message_base import UserInfo
|
||||
|
|
@ -8,7 +8,12 @@ from .chat_stream import ChatStream
|
|||
import math
|
||||
from bson.decimal128 import Decimal128
|
||||
|
||||
logger = get_module_logger("rel_manager")
|
||||
relationship_config = LogConfig(
|
||||
# 使用关系专用样式
|
||||
console_format=RELATION_STYLE_CONFIG["console_format"],
|
||||
file_format=RELATION_STYLE_CONFIG["file_format"],
|
||||
)
|
||||
logger = get_module_logger("rel_manager", config=relationship_config)
|
||||
|
||||
|
||||
class Impression:
|
||||
|
|
@ -270,19 +275,21 @@ class RelationshipManager:
|
|||
3.人维护关系的精力往往有限，所以当高关系值用户越多，对于中高关系值用户增长越慢
|
||||
"""
|
||||
stancedict = {
|
||||
"supportive": 0,
|
||||
"neutrality": 1,
|
||||
"opposed": 2,
|
||||
"支持": 0,
|
||||
"中立": 1,
|
||||
"反对": 2,
|
||||
}
|
||||
|
||||
valuedict = {
|
||||
"happy": 1.5,
|
||||
"angry": -3.0,
|
||||
"sad": -1.5,
|
||||
"surprised": 0.6,
|
||||
"disgusted": -4.5,
|
||||
"fearful": -2.1,
|
||||
"neutral": 0.3,
|
||||
"开心": 1.5,
|
||||
"愤怒": -3.5,
|
||||
"悲伤": -1.5,
|
||||
"惊讶": 0.6,
|
||||
"害羞": 2.0,
|
||||
"平静": 0.3,
|
||||
"恐惧": -2,
|
||||
"厌恶": -2.5,
|
||||
"困惑": 0.5,
|
||||
}
|
||||
if self.get_relationship(chat_stream):
|
||||
old_value = self.get_relationship(chat_stream).relationship_value
|
||||
|
|
@ -301,9 +308,12 @@ class RelationshipManager:
|
|||
if old_value > 500:
|
||||
high_value_count = 0
|
||||
for _, relationship in self.relationships.items():
|
||||
if relationship.relationship_value >= 850:
|
||||
if relationship.relationship_value >= 700:
|
||||
high_value_count += 1
|
||||
value *= 3 / (high_value_count + 3)
|
||||
if old_value >= 700:
|
||||
value *= 3 / (high_value_count + 2) # 排除自己
|
||||
else:
|
||||
value *= 3 / (high_value_count + 3)
|
||||
elif valuedict[label] < 0 and stancedict[stance] != 0:
|
||||
value = value * math.exp(old_value / 1000)
|
||||
else:
|
||||
|
|
@ -316,27 +326,20 @@ class RelationshipManager:
|
|||
else:
|
||||
value = 0
|
||||
|
||||
logger.info(f"[关系变更] 立场：{stance} 标签：{label} 关系值：{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"当前关系: {relationship_level[level_num]}, "
|
||||
f"关系值: {old_value:.2f}, "
|
||||
f"当前立场情感: {stance}-{label}, "
|
||||
f"变更: {value:+.5f}"
|
||||
)
|
||||
|
||||
await self.update_relationship_value(chat_stream=chat_stream, relationship_value=value)
|
||||
|
||||
def build_relationship_info(self, person) -> str:
|
||||
relationship_value = relationship_manager.get_relationship(person).relationship_value
|
||||
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
|
||||
|
||||
level_num = self.calculate_level_num(relationship_value)
|
||||
relationship_level = ["厌恶", "冷漠", "一般", "友好", "喜欢", "暧昧"]
|
||||
relation_prompt2_list = [
|
||||
"冷漠回应",
|
||||
|
|
@ -356,6 +359,24 @@ class RelationshipManager:
|
|||
f"你对昵称为'({person.user_info.user_id}){person.user_info.user_nickname}'的用户的态度为{relationship_level[level_num]}，"
|
||||
f"回复态度为{relation_prompt2_list[level_num]}，关系等级为{level_num}。"
|
||||
)
|
||||
|
||||
def calculate_level_num(self, relationship_value) -> int:
|
||||
"""关系等级计算"""
|
||||
if -1000 <= relationship_value < -227:
|
||||
level_num = 0
|
||||
elif -227 <= relationship_value < -73:
|
||||
level_num = 1
|
||||
elif -73 <= relationship_value < 227:
|
||||
level_num = 2
|
||||
elif 227 <= relationship_value < 587:
|
||||
level_num = 3
|
||||
elif 587 <= relationship_value < 900:
|
||||
level_num = 4
|
||||
elif 900 <= relationship_value <= 1000:
|
||||
level_num = 5
|
||||
else:
|
||||
level_num = 5 if relationship_value > 1000 else 0
|
||||
return level_num
|
||||
|
||||
|
||||
relationship_manager = RelationshipManager()
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import List, Optional
|
|||
from nonebot import get_driver
|
||||
|
||||
from ..models.utils_model import LLM_request
|
||||
from .config import global_config
|
||||
from ..config.config import global_config
|
||||
from src.common.logger import get_module_logger, LogConfig, TOPIC_STYLE_CONFIG
|
||||
|
||||
# 定义日志配置
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
import math
|
||||
import random
|
||||
import time
|
||||
import re
|
||||
|
|
@ -12,7 +11,7 @@ from src.common.logger import get_module_logger
|
|||
|
||||
from ..models.utils_model import LLM_request
|
||||
from ..utils.typo_generator import ChineseTypoGenerator
|
||||
from .config import global_config
|
||||
from ..config.config import global_config
|
||||
from .message import MessageRecv, Message
|
||||
from .message_base import UserInfo
|
||||
from .chat_stream import ChatStream
|
||||
|
|
@ -66,60 +65,6 @@ async def get_embedding(text, request_type="embedding"):
|
|||
return await llm.get_embedding(text)
|
||||
|
||||
|
||||
def calculate_information_content(text):
|
||||
"""计算文本的信息量（熵）"""
|
||||
char_count = Counter(text)
|
||||
total_chars = len(text)
|
||||
|
||||
entropy = 0
|
||||
for count in char_count.values():
|
||||
probability = count / total_chars
|
||||
entropy -= probability * math.log2(probability)
|
||||
|
||||
return entropy
|
||||
|
||||
|
||||
def get_closest_chat_from_db(length: int, timestamp: str):
|
||||
# print(f"获取最接近指定时间戳的聊天记录，长度: {length}, 时间戳: {timestamp}")
|
||||
# print(f"当前时间: {timestamp},转换后时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(timestamp))}")
|
||||
chat_records = []
|
||||
closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[("time", -1)])
|
||||
# print(f"最接近的记录: {closest_record}")
|
||||
if closest_record:
|
||||
closest_time = closest_record["time"]
|
||||
chat_id = closest_record["chat_id"] # 获取chat_id
|
||||
# 获取该时间戳之后的length条消息，保持相同的chat_id
|
||||
chat_records = list(
|
||||
db.messages.find(
|
||||
{
|
||||
"time": {"$gt": closest_time},
|
||||
"chat_id": chat_id, # 添加chat_id过滤
|
||||
}
|
||||
)
|
||||
.sort("time", 1)
|
||||
.limit(length)
|
||||
)
|
||||
# print(f"获取到的记录: {chat_records}")
|
||||
length = len(chat_records)
|
||||
# print(f"获取到的记录长度: {length}")
|
||||
# 转换记录格式
|
||||
formatted_records = []
|
||||
for record in chat_records:
|
||||
# 兼容行为，前向兼容老数据
|
||||
formatted_records.append(
|
||||
{
|
||||
"_id": record["_id"],
|
||||
"time": record["time"],
|
||||
"chat_id": record["chat_id"],
|
||||
"detailed_plain_text": record.get("detailed_plain_text", ""), # 添加文本内容
|
||||
"memorized_times": record.get("memorized_times", 0), # 添加记忆次数
|
||||
}
|
||||
)
|
||||
|
||||
return formatted_records
|
||||
|
||||
return []
|
||||
|
||||
|
||||
async def get_recent_group_messages(chat_id: str, limit: int = 12) -> list:
|
||||
"""从数据库获取群组最近的消息记录
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import io
|
|||
from nonebot import get_driver
|
||||
|
||||
from ...common.database import db
|
||||
from ..chat.config import global_config
|
||||
from ..config.config import global_config
|
||||
from ..models.utils_model import LLM_request
|
||||
|
||||
from src.common.logger import get_module_logger
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from .config import global_config
|
||||
from ..config.config import global_config
|
||||
from .relationship_manager import relationship_manager
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -42,6 +42,7 @@ class BotConfig:
|
|||
# schedule
|
||||
ENABLE_SCHEDULE_GEN: bool = False # 是否启用日程生成
|
||||
PROMPT_SCHEDULE_GEN = "无日程"
|
||||
SCHEDULE_DOING_UPDATE_INTERVAL: int = 300 # 日程表更新间隔 单位秒
|
||||
|
||||
# message
|
||||
MAX_CONTEXT_SIZE: int = 15 # 上下文最大消息数
|
||||
|
|
@ -221,6 +222,8 @@ class BotConfig:
|
|||
schedule_config = parent["schedule"]
|
||||
config.ENABLE_SCHEDULE_GEN = schedule_config.get("enable_schedule_gen", config.ENABLE_SCHEDULE_GEN)
|
||||
config.PROMPT_SCHEDULE_GEN = schedule_config.get("prompt_schedule_gen", config.PROMPT_SCHEDULE_GEN)
|
||||
config.SCHEDULE_DOING_UPDATE_INTERVAL = schedule_config.get(
|
||||
"schedule_doing_update_interval", config.SCHEDULE_DOING_UPDATE_INTERVAL)
|
||||
logger.info(
|
||||
f"载入自定义日程prompt:{schedule_config.get('prompt_schedule_gen', config.PROMPT_SCHEDULE_GEN)}")
|
||||
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
import os
|
||||
from pathlib import Path
|
||||
from dotenv import load_dotenv
|
||||
|
||||
class EnvConfig:
|
||||
_instance = None
|
||||
|
||||
def __new__(cls):
|
||||
if cls._instance is None:
|
||||
cls._instance = super(EnvConfig, cls).__new__(cls)
|
||||
cls._instance._initialized = False
|
||||
return cls._instance
|
||||
|
||||
def __init__(self):
|
||||
if self._initialized:
|
||||
return
|
||||
|
||||
self._initialized = True
|
||||
self.ROOT_DIR = Path(__file__).parent.parent.parent.parent
|
||||
self.load_env()
|
||||
|
||||
def load_env(self):
|
||||
env_file = self.ROOT_DIR / '.env'
|
||||
if env_file.exists():
|
||||
load_dotenv(env_file)
|
||||
|
||||
# 根据ENVIRONMENT变量加载对应的环境文件
|
||||
env_type = os.getenv('ENVIRONMENT', 'prod')
|
||||
if env_type == 'dev':
|
||||
env_file = self.ROOT_DIR / '.env.dev'
|
||||
elif env_type == 'prod':
|
||||
env_file = self.ROOT_DIR / '.env.prod'
|
||||
|
||||
if env_file.exists():
|
||||
load_dotenv(env_file, override=True)
|
||||
|
||||
def get(self, key, default=None):
|
||||
return os.getenv(key, default)
|
||||
|
||||
def get_all(self):
|
||||
return dict(os.environ)
|
||||
|
||||
def __getattr__(self, name):
|
||||
return self.get(name)
|
||||
|
||||
# 创建全局实例
|
||||
env_config = EnvConfig()
|
||||
|
||||
# 导出环境变量
|
||||
def get_env(key, default=None):
|
||||
return os.getenv(key, default)
|
||||
|
||||
# 导出所有环境变量
|
||||
def get_all_env():
|
||||
return dict(os.environ)
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,95 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
import asyncio
|
||||
import time
|
||||
import sys
|
||||
import os
|
||||
# 添加项目根目录到系统路径
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))))
|
||||
from src.plugins.memory_system.Hippocampus import HippocampusManager
|
||||
from src.plugins.config.config import global_config
|
||||
|
||||
async def test_memory_system():
|
||||
"""测试记忆系统的主要功能"""
|
||||
try:
|
||||
# 初始化记忆系统
|
||||
print("开始初始化记忆系统...")
|
||||
hippocampus_manager = HippocampusManager.get_instance()
|
||||
hippocampus_manager.initialize(global_config=global_config)
|
||||
print("记忆系统初始化完成")
|
||||
|
||||
# 测试记忆构建
|
||||
# print("开始测试记忆构建...")
|
||||
# await hippocampus_manager.build_memory()
|
||||
# print("记忆构建完成")
|
||||
|
||||
# 测试记忆检索
|
||||
test_text = "千石可乐在群里聊天"
|
||||
test_text = '''[03-24 10:39:37] 麦麦(ta的id:2814567326): 早说散步结果下雨改成室内运动啊
|
||||
[03-24 10:39:37] 麦麦(ta的id:2814567326): [回复：变量] 变量就像今天计划总变
|
||||
[03-24 10:39:44] 状态异常(ta的id:535554838): 要把本地文件改成弹出来的路径吗
|
||||
[03-24 10:40:35] 状态异常(ta的id:535554838): [图片：这张图片显示的是Windows系统的环境变量设置界面。界面左侧列出了多个环境变量的值，包括Intel Dev Redist、Windows、Windows PowerShell、OpenSSH、NVIDIA Corporation的目录等。右侧有新建、编辑、浏览、删除、上移、下移和编辑文本等操作按钮。图片下方有一个错误提示框，显示"Windows找不到文件'mongodb\\bin\\mongod.exe'。请确定文件名是否正确后，再试一次。"这意味着用户试图运行MongoDB的mongod.exe程序时，系统找不到该文件。这可能是因为MongoDB的安装路径未正确添加到系统环境变量中，或者文件路径有误。
|
||||
图片的含义可能是用户正在尝试设置MongoDB的环境变量，以便在命令行或其他程序中使用MongoDB。如果用户正确设置了环境变量，那么他们应该能够通过命令行或其他方式启动MongoDB服务。]
|
||||
[03-24 10:41:08] 一根猫(ta的id:108886006): [回复 麦麦 的消息: [回复某人消息] 改系统变量或者删库重配 ] [@麦麦] 我中途修改人格，需要重配吗
|
||||
[03-24 10:41:54] 麦麦(ta的id:2814567326): [回复：[回复 麦麦 的消息: [回复某人消息] 改系统变量或者删库重配 ] [@麦麦] 我中途修改人格，需要重配吗] 看情况
|
||||
[03-24 10:41:54] 麦麦(ta的id:2814567326): 难
|
||||
[03-24 10:41:54] 麦麦(ta的id:2814567326): 小改变量就行，大动骨安排重配像游戏副本南度改太大会崩
|
||||
[03-24 10:45:33] 霖泷(ta的id:1967075066): 话说现在思考高达一分钟
|
||||
[03-24 10:45:38] 霖泷(ta的id:1967075066): 是不是哪里出问题了
|
||||
[03-24 10:45:39] 艾卡(ta的id:1786525298): [表情包：这张表情包展示了一个动漫角色，她有着紫色的头发和大大的眼睛，表情显得有些困惑或不解。她的头上有一个问号，进一步强调了她的疑惑。整体情感表达的是困惑或不解。]
|
||||
[03-24 10:46:12] (ta的id:3229291803): [表情包：这张表情包显示了一只手正在做"点赞"的动作，通常表示赞同、喜欢或支持。这个表情包所表达的情感是积极的、赞同的或支持的。]
|
||||
[03-24 10:46:37] 星野風禾(ta的id:2890165435): 还能思考高达
|
||||
[03-24 10:46:39] 星野風禾(ta的id:2890165435): 什么知识库
|
||||
[03-24 10:46:49] ❦幻凌慌てない(ta的id:2459587037): 为什么改了回复系数麦麦还是不怎么回复？大佬们''' # noqa: E501
|
||||
|
||||
|
||||
# test_text = '''千石可乐：分不清AI的陪伴和人类的陪伴,是这样吗？'''
|
||||
print(f"开始测试记忆检索，测试文本: {test_text}\n")
|
||||
memories = await hippocampus_manager.get_memory_from_text(
|
||||
text=test_text,
|
||||
max_memory_num=3,
|
||||
max_memory_length=2,
|
||||
max_depth=3,
|
||||
fast_retrieval=False
|
||||
)
|
||||
|
||||
await asyncio.sleep(1)
|
||||
|
||||
print("检索到的记忆:")
|
||||
for topic, memory_items in memories:
|
||||
print(f"主题: {topic}")
|
||||
print(f"- {memory_items}")
|
||||
|
||||
|
||||
|
||||
# 测试记忆遗忘
|
||||
# forget_start_time = time.time()
|
||||
# # print("开始测试记忆遗忘...")
|
||||
# await hippocampus_manager.forget_memory(percentage=0.005)
|
||||
# # print("记忆遗忘完成")
|
||||
# forget_end_time = time.time()
|
||||
# print(f"记忆遗忘耗时: {forget_end_time - forget_start_time:.2f} 秒")
|
||||
|
||||
# 获取所有节点
|
||||
# nodes = hippocampus_manager.get_all_node_names()
|
||||
# print(f"当前记忆系统中的节点数量: {len(nodes)}")
|
||||
# print("节点列表:")
|
||||
# for node in nodes:
|
||||
# print(f"- {node}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"测试过程中出现错误: {e}")
|
||||
raise
|
||||
|
||||
async def main():
|
||||
"""主函数"""
|
||||
try:
|
||||
start_time = time.time()
|
||||
await test_memory_system()
|
||||
end_time = time.time()
|
||||
print(f"测试完成，总耗时: {end_time - start_time:.2f} 秒")
|
||||
except Exception as e:
|
||||
print(f"程序执行出错: {e}")
|
||||
raise
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
|
@ -1,298 +0,0 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
|
||||
import jieba
|
||||
import matplotlib.pyplot as plt
|
||||
import networkx as nx
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
# from src.common.logger import get_module_logger
|
||||
|
||||
# logger = get_module_logger("draw_memory")
|
||||
|
||||
# 添加项目根目录到 Python 路径
|
||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
||||
sys.path.append(root_path)
|
||||
|
||||
print(root_path)
|
||||
|
||||
from src.common.database import db # noqa: E402
|
||||
|
||||
# 加载.env.dev文件
|
||||
env_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))), ".env.dev")
|
||||
load_dotenv(env_path)
|
||||
|
||||
|
||||
class Memory_graph:
|
||||
def __init__(self):
|
||||
self.G = nx.Graph() # 使用 networkx 的图结构
|
||||
|
||||
def connect_dot(self, concept1, concept2):
|
||||
self.G.add_edge(concept1, concept2)
|
||||
|
||||
def add_dot(self, concept, memory):
|
||||
if concept in self.G:
|
||||
# 如果节点已存在，将新记忆添加到现有列表中
|
||||
if "memory_items" in self.G.nodes[concept]:
|
||||
if not isinstance(self.G.nodes[concept]["memory_items"], list):
|
||||
# 如果当前不是列表，将其转换为列表
|
||||
self.G.nodes[concept]["memory_items"] = [self.G.nodes[concept]["memory_items"]]
|
||||
self.G.nodes[concept]["memory_items"].append(memory)
|
||||
else:
|
||||
self.G.nodes[concept]["memory_items"] = [memory]
|
||||
else:
|
||||
# 如果是新节点，创建新的记忆列表
|
||||
self.G.add_node(concept, memory_items=[memory])
|
||||
|
||||
def get_dot(self, concept):
|
||||
# 检查节点是否存在于图中
|
||||
if concept in self.G:
|
||||
# 从图中获取节点数据
|
||||
node_data = self.G.nodes[concept]
|
||||
# print(node_data)
|
||||
# 创建新的Memory_dot对象
|
||||
return concept, node_data
|
||||
return None
|
||||
|
||||
def get_related_item(self, topic, depth=1):
|
||||
if topic not in self.G:
|
||||
return [], []
|
||||
|
||||
first_layer_items = []
|
||||
second_layer_items = []
|
||||
|
||||
# 获取相邻节点
|
||||
neighbors = list(self.G.neighbors(topic))
|
||||
# print(f"第一层: {topic}")
|
||||
|
||||
# 获取当前节点的记忆项
|
||||
node_data = self.get_dot(topic)
|
||||
if node_data:
|
||||
concept, data = node_data
|
||||
if "memory_items" in data:
|
||||
memory_items = data["memory_items"]
|
||||
if isinstance(memory_items, list):
|
||||
first_layer_items.extend(memory_items)
|
||||
else:
|
||||
first_layer_items.append(memory_items)
|
||||
|
||||
# 只在depth=2时获取第二层记忆
|
||||
if depth >= 2:
|
||||
# 获取相邻节点的记忆项
|
||||
for neighbor in neighbors:
|
||||
# print(f"第二层: {neighbor}")
|
||||
node_data = self.get_dot(neighbor)
|
||||
if node_data:
|
||||
concept, data = node_data
|
||||
if "memory_items" in data:
|
||||
memory_items = data["memory_items"]
|
||||
if isinstance(memory_items, list):
|
||||
second_layer_items.extend(memory_items)
|
||||
else:
|
||||
second_layer_items.append(memory_items)
|
||||
|
||||
return first_layer_items, second_layer_items
|
||||
|
||||
def store_memory(self):
|
||||
for node in self.G.nodes():
|
||||
dot_data = {"concept": node}
|
||||
db.store_memory_dots.insert_one(dot_data)
|
||||
|
||||
@property
|
||||
def dots(self):
|
||||
# 返回所有节点对应的 Memory_dot 对象
|
||||
return [self.get_dot(node) for node in self.G.nodes()]
|
||||
|
||||
def get_random_chat_from_db(self, length: int, timestamp: str):
|
||||
# 从数据库中根据时间戳获取离其最近的聊天记录
|
||||
chat_text = ""
|
||||
closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[("time", -1)]) # 调试输出
|
||||
logger.info(
|
||||
f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}"
|
||||
)
|
||||
|
||||
if closest_record:
|
||||
closest_time = closest_record["time"]
|
||||
group_id = closest_record["group_id"] # 获取groupid
|
||||
# 获取该时间戳之后的length条消息，且groupid相同
|
||||
chat_record = list(
|
||||
db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort("time", 1).limit(length)
|
||||
)
|
||||
for record in chat_record:
|
||||
time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(int(record["time"])))
|
||||
try:
|
||||
displayname = "[(%s)%s]%s" % (record["user_id"], record["user_nickname"], record["user_cardname"])
|
||||
except (KeyError, TypeError):
|
||||
# 处理缺少键或类型错误的情况
|
||||
displayname = record.get("user_nickname", "") or "用户" + str(record.get("user_id", "未知"))
|
||||
chat_text += f"[{time_str}] {displayname}: {record['processed_plain_text']}\n" # 添加发送者和时间信息
|
||||
return chat_text
|
||||
|
||||
return [] # 如果没有找到记录，返回空列表
|
||||
|
||||
def save_graph_to_db(self):
|
||||
# 清空现有的图数据
|
||||
db.graph_data.delete_many({})
|
||||
# 保存节点
|
||||
for node in self.G.nodes(data=True):
|
||||
node_data = {
|
||||
"concept": node[0],
|
||||
"memory_items": node[1].get("memory_items", []), # 默认为空列表
|
||||
}
|
||||
db.graph_data.nodes.insert_one(node_data)
|
||||
# 保存边
|
||||
for edge in self.G.edges():
|
||||
edge_data = {"source": edge[0], "target": edge[1]}
|
||||
db.graph_data.edges.insert_one(edge_data)
|
||||
|
||||
def load_graph_from_db(self):
|
||||
# 清空当前图
|
||||
self.G.clear()
|
||||
# 加载节点
|
||||
nodes = db.graph_data.nodes.find()
|
||||
for node in nodes:
|
||||
memory_items = node.get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
self.G.add_node(node["concept"], memory_items=memory_items)
|
||||
# 加载边
|
||||
edges = db.graph_data.edges.find()
|
||||
for edge in edges:
|
||||
self.G.add_edge(edge["source"], edge["target"])
|
||||
|
||||
|
||||
def main():
|
||||
memory_graph = Memory_graph()
|
||||
memory_graph.load_graph_from_db()
|
||||
|
||||
# 只显示一次优化后的图形
|
||||
visualize_graph_lite(memory_graph)
|
||||
|
||||
while True:
|
||||
query = input("请输入新的查询概念（输入'退出'以结束）：")
|
||||
if query.lower() == "退出":
|
||||
break
|
||||
first_layer_items, second_layer_items = memory_graph.get_related_item(query)
|
||||
if first_layer_items or second_layer_items:
|
||||
logger.debug("第一层记忆：")
|
||||
for item in first_layer_items:
|
||||
logger.debug(item)
|
||||
logger.debug("第二层记忆：")
|
||||
for item in second_layer_items:
|
||||
logger.debug(item)
|
||||
else:
|
||||
logger.debug("未找到相关记忆。")
|
||||
|
||||
|
||||
def segment_text(text):
|
||||
seg_text = list(jieba.cut(text))
|
||||
return seg_text
|
||||
|
||||
|
||||
def find_topic(text, topic_num):
|
||||
prompt = (
|
||||
f"这是一段文字：{text}。请你从这段话中总结出{topic_num}个话题，帮我列出来，用逗号隔开，尽可能精简。"
|
||||
f"只需要列举{topic_num}个话题就好，不要告诉我其他内容。"
|
||||
)
|
||||
return prompt
|
||||
|
||||
|
||||
def topic_what(text, topic):
|
||||
prompt = (
|
||||
f"这是一段文字：{text}。我想知道这记忆里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物。"
|
||||
f"只输出这句话就好"
|
||||
)
|
||||
return prompt
|
||||
|
||||
|
||||
def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = False):
|
||||
# 设置中文字体
|
||||
plt.rcParams["font.sans-serif"] = ["SimHei"] # 用来正常显示中文标签
|
||||
plt.rcParams["axes.unicode_minus"] = False # 用来正常显示负号
|
||||
|
||||
G = memory_graph.G
|
||||
|
||||
# 创建一个新图用于可视化
|
||||
H = G.copy()
|
||||
|
||||
# 移除只有一条记忆的节点和连接数少于3的节点
|
||||
nodes_to_remove = []
|
||||
for node in H.nodes():
|
||||
memory_items = H.nodes[node].get("memory_items", [])
|
||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
||||
degree = H.degree(node)
|
||||
if memory_count < 3 or degree < 2: # 改为小于2而不是小于等于2
|
||||
nodes_to_remove.append(node)
|
||||
|
||||
H.remove_nodes_from(nodes_to_remove)
|
||||
|
||||
# 如果过滤后没有节点，则返回
|
||||
if len(H.nodes()) == 0:
|
||||
logger.debug("过滤后没有符合条件的节点可显示")
|
||||
return
|
||||
|
||||
# 保存图到本地
|
||||
# nx.write_gml(H, "memory_graph.gml") # 保存为 GML 格式
|
||||
|
||||
# 计算节点大小和颜色
|
||||
node_colors = []
|
||||
node_sizes = []
|
||||
nodes = list(H.nodes())
|
||||
|
||||
# 获取最大记忆数和最大度数用于归一化
|
||||
max_memories = 1
|
||||
max_degree = 1
|
||||
for node in nodes:
|
||||
memory_items = H.nodes[node].get("memory_items", [])
|
||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
||||
degree = H.degree(node)
|
||||
max_memories = max(max_memories, memory_count)
|
||||
max_degree = max(max_degree, degree)
|
||||
|
||||
# 计算每个节点的大小和颜色
|
||||
for node in nodes:
|
||||
# 计算节点大小（基于记忆数量）
|
||||
memory_items = H.nodes[node].get("memory_items", [])
|
||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
||||
# 使用指数函数使变化更明显
|
||||
ratio = memory_count / max_memories
|
||||
size = 500 + 5000 * (ratio) # 使用1.5次方函数使差异不那么明显
|
||||
node_sizes.append(size)
|
||||
|
||||
# 计算节点颜色（基于连接数）
|
||||
degree = H.degree(node)
|
||||
# 红色分量随着度数增加而增加
|
||||
r = (degree / max_degree) ** 0.3
|
||||
red = min(1.0, r)
|
||||
# 蓝色分量随着度数减少而增加
|
||||
blue = max(0.0, 1 - red)
|
||||
# blue = 1
|
||||
color = (red, 0.1, blue)
|
||||
node_colors.append(color)
|
||||
|
||||
# 绘制图形
|
||||
plt.figure(figsize=(12, 8))
|
||||
pos = nx.spring_layout(H, k=1, iterations=50) # 增加k值使节点分布更开
|
||||
nx.draw(
|
||||
H,
|
||||
pos,
|
||||
with_labels=True,
|
||||
node_color=node_colors,
|
||||
node_size=node_sizes,
|
||||
font_size=10,
|
||||
font_family="SimHei",
|
||||
font_weight="bold",
|
||||
edge_color="gray",
|
||||
width=0.5,
|
||||
alpha=0.9,
|
||||
)
|
||||
|
||||
title = "记忆图谱可视化 - 节点大小表示记忆数量，颜色表示连接数"
|
||||
plt.title(title, fontsize=16, fontfamily="SimHei")
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,34 @@
|
|||
from dataclasses import dataclass
|
||||
from typing import List
|
||||
|
||||
@dataclass
|
||||
class MemoryConfig:
|
||||
"""记忆系统配置类"""
|
||||
# 记忆构建相关配置
|
||||
memory_build_distribution: List[float] # 记忆构建的时间分布参数
|
||||
build_memory_sample_num: int # 每次构建记忆的样本数量
|
||||
build_memory_sample_length: int # 每个样本的消息长度
|
||||
memory_compress_rate: float # 记忆压缩率
|
||||
|
||||
# 记忆遗忘相关配置
|
||||
memory_forget_time: int # 记忆遗忘时间（小时）
|
||||
|
||||
# 记忆过滤相关配置
|
||||
memory_ban_words: List[str] # 记忆过滤词列表
|
||||
|
||||
llm_topic_judge: str # 话题判断模型
|
||||
llm_summary_by_topic: str # 话题总结模型
|
||||
|
||||
@classmethod
|
||||
def from_global_config(cls, global_config):
|
||||
"""从全局配置创建记忆系统配置"""
|
||||
return cls(
|
||||
memory_build_distribution=global_config.memory_build_distribution,
|
||||
build_memory_sample_num=global_config.build_memory_sample_num,
|
||||
build_memory_sample_length=global_config.build_memory_sample_length,
|
||||
memory_compress_rate=global_config.memory_compress_rate,
|
||||
memory_forget_time=global_config.memory_forget_time,
|
||||
memory_ban_words=global_config.memory_ban_words,
|
||||
llm_topic_judge=global_config.llm_topic_judge,
|
||||
llm_summary_by_topic=global_config.llm_summary_by_topic
|
||||
)
|
||||
|
|
@ -1,992 +0,0 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
import datetime
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import sys
|
||||
import time
|
||||
from collections import Counter
|
||||
from pathlib import Path
|
||||
import matplotlib.pyplot as plt
|
||||
import networkx as nx
|
||||
from dotenv import load_dotenv
|
||||
sys.path.insert(0, sys.path[0]+"/../")
|
||||
sys.path.insert(0, sys.path[0]+"/../")
|
||||
sys.path.insert(0, sys.path[0]+"/../")
|
||||
sys.path.insert(0, sys.path[0]+"/../")
|
||||
sys.path.insert(0, sys.path[0]+"/../")
|
||||
from src.common.logger import get_module_logger
|
||||
import jieba
|
||||
|
||||
# from chat.config import global_config
|
||||
# 添加项目根目录到 Python 路径
|
||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
||||
sys.path.append(root_path)
|
||||
|
||||
from src.common.database import db # noqa E402
|
||||
from src.plugins.memory_system.offline_llm import LLMModel # noqa E402
|
||||
|
||||
# 获取当前文件的目录
|
||||
current_dir = Path(__file__).resolve().parent
|
||||
# 获取项目根目录（上三层目录）
|
||||
project_root = current_dir.parent.parent.parent
|
||||
# env.dev文件路径
|
||||
env_path = project_root / ".env.dev"
|
||||
|
||||
logger = get_module_logger("mem_manual_bd")
|
||||
|
||||
# 加载环境变量
|
||||
if env_path.exists():
|
||||
logger.info(f"从 {env_path} 加载环境变量")
|
||||
load_dotenv(env_path)
|
||||
else:
|
||||
logger.warning(f"未找到环境变量文件: {env_path}")
|
||||
logger.info("将使用默认配置")
|
||||
|
||||
|
||||
def calculate_information_content(text):
|
||||
"""计算文本的信息量（熵）"""
|
||||
char_count = Counter(text)
|
||||
total_chars = len(text)
|
||||
|
||||
entropy = 0
|
||||
for count in char_count.values():
|
||||
probability = count / total_chars
|
||||
entropy -= probability * math.log2(probability)
|
||||
|
||||
return entropy
|
||||
|
||||
|
||||
def get_closest_chat_from_db(length: int, timestamp: str):
|
||||
"""从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数
|
||||
|
||||
Returns:
|
||||
list: 消息记录字典列表，每个字典包含消息内容和时间信息
|
||||
"""
|
||||
chat_records = []
|
||||
closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[("time", -1)])
|
||||
|
||||
if closest_record and closest_record.get("memorized", 0) < 4:
|
||||
closest_time = closest_record["time"]
|
||||
group_id = closest_record["group_id"]
|
||||
# 获取该时间戳之后的length条消息，且groupid相同
|
||||
records = list(
|
||||
db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort("time", 1).limit(length)
|
||||
)
|
||||
|
||||
# 更新每条消息的memorized属性
|
||||
for record in records:
|
||||
current_memorized = record.get("memorized", 0)
|
||||
if current_memorized > 3:
|
||||
print("消息已读取3次，跳过")
|
||||
return ""
|
||||
|
||||
# 更新memorized值
|
||||
db.messages.update_one({"_id": record["_id"]}, {"$set": {"memorized": current_memorized + 1}})
|
||||
|
||||
# 添加到记录列表中
|
||||
chat_records.append(
|
||||
{"text": record["detailed_plain_text"], "time": record["time"], "group_id": record["group_id"]}
|
||||
)
|
||||
|
||||
return chat_records
|
||||
|
||||
|
||||
class Memory_graph:
|
||||
def __init__(self):
|
||||
self.G = nx.Graph() # 使用 networkx 的图结构
|
||||
|
||||
def connect_dot(self, concept1, concept2):
|
||||
# 如果边已存在，增加 strength
|
||||
if self.G.has_edge(concept1, concept2):
|
||||
self.G[concept1][concept2]["strength"] = self.G[concept1][concept2].get("strength", 1) + 1
|
||||
else:
|
||||
# 如果是新边，初始化 strength 为 1
|
||||
self.G.add_edge(concept1, concept2, strength=1)
|
||||
|
||||
def add_dot(self, concept, memory):
|
||||
if concept in self.G:
|
||||
# 如果节点已存在，将新记忆添加到现有列表中
|
||||
if "memory_items" in self.G.nodes[concept]:
|
||||
if not isinstance(self.G.nodes[concept]["memory_items"], list):
|
||||
# 如果当前不是列表，将其转换为列表
|
||||
self.G.nodes[concept]["memory_items"] = [self.G.nodes[concept]["memory_items"]]
|
||||
self.G.nodes[concept]["memory_items"].append(memory)
|
||||
else:
|
||||
self.G.nodes[concept]["memory_items"] = [memory]
|
||||
else:
|
||||
# 如果是新节点，创建新的记忆列表
|
||||
self.G.add_node(concept, memory_items=[memory])
|
||||
|
||||
def get_dot(self, concept):
|
||||
# 检查节点是否存在于图中
|
||||
if concept in self.G:
|
||||
# 从图中获取节点数据
|
||||
node_data = self.G.nodes[concept]
|
||||
return concept, node_data
|
||||
return None
|
||||
|
||||
def get_related_item(self, topic, depth=1):
|
||||
if topic not in self.G:
|
||||
return [], []
|
||||
|
||||
first_layer_items = []
|
||||
second_layer_items = []
|
||||
|
||||
# 获取相邻节点
|
||||
neighbors = list(self.G.neighbors(topic))
|
||||
|
||||
# 获取当前节点的记忆项
|
||||
node_data = self.get_dot(topic)
|
||||
if node_data:
|
||||
concept, data = node_data
|
||||
if "memory_items" in data:
|
||||
memory_items = data["memory_items"]
|
||||
if isinstance(memory_items, list):
|
||||
first_layer_items.extend(memory_items)
|
||||
else:
|
||||
first_layer_items.append(memory_items)
|
||||
|
||||
# 只在depth=2时获取第二层记忆
|
||||
if depth >= 2:
|
||||
# 获取相邻节点的记忆项
|
||||
for neighbor in neighbors:
|
||||
node_data = self.get_dot(neighbor)
|
||||
if node_data:
|
||||
concept, data = node_data
|
||||
if "memory_items" in data:
|
||||
memory_items = data["memory_items"]
|
||||
if isinstance(memory_items, list):
|
||||
second_layer_items.extend(memory_items)
|
||||
else:
|
||||
second_layer_items.append(memory_items)
|
||||
|
||||
return first_layer_items, second_layer_items
|
||||
|
||||
@property
|
||||
def dots(self):
|
||||
# 返回所有节点对应的 Memory_dot 对象
|
||||
return [self.get_dot(node) for node in self.G.nodes()]
|
||||
|
||||
|
||||
# 海马体
|
||||
class Hippocampus:
|
||||
def __init__(self, memory_graph: Memory_graph):
|
||||
self.memory_graph = memory_graph
|
||||
self.llm_model = LLMModel()
|
||||
self.llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5")
|
||||
self.llm_model_get_topic = LLMModel(model_name="Pro/Qwen/Qwen2.5-7B-Instruct")
|
||||
self.llm_model_summary = LLMModel(model_name="Qwen/Qwen2.5-32B-Instruct")
|
||||
|
||||
def get_memory_sample(self, chat_size=20, time_frequency=None):
|
||||
"""获取记忆样本
|
||||
|
||||
Returns:
|
||||
list: 消息记录列表，每个元素是一个消息记录字典列表
|
||||
"""
|
||||
if time_frequency is None:
|
||||
time_frequency = {"near": 2, "mid": 4, "far": 3}
|
||||
current_timestamp = datetime.datetime.now().timestamp()
|
||||
chat_samples = []
|
||||
|
||||
# 短期：1h 中期：4h 长期：24h
|
||||
for _ in range(time_frequency.get("near")):
|
||||
random_time = current_timestamp - random.randint(1, 3600 * 4)
|
||||
messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
|
||||
if messages:
|
||||
chat_samples.append(messages)
|
||||
|
||||
for _ in range(time_frequency.get("mid")):
|
||||
random_time = current_timestamp - random.randint(3600 * 4, 3600 * 24)
|
||||
messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
|
||||
if messages:
|
||||
chat_samples.append(messages)
|
||||
|
||||
for _ in range(time_frequency.get("far")):
|
||||
random_time = current_timestamp - random.randint(3600 * 24, 3600 * 24 * 7)
|
||||
messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
|
||||
if messages:
|
||||
chat_samples.append(messages)
|
||||
|
||||
return chat_samples
|
||||
|
||||
def calculate_topic_num(self, text, compress_rate):
|
||||
"""计算文本的话题数量"""
|
||||
information_content = calculate_information_content(text)
|
||||
topic_by_length = text.count("\n") * compress_rate
|
||||
topic_by_information_content = max(1, min(5, int((information_content - 3) * 2)))
|
||||
topic_num = int((topic_by_length + topic_by_information_content) / 2)
|
||||
print(
|
||||
f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, "
|
||||
f"topic_num: {topic_num}"
|
||||
)
|
||||
return topic_num
|
||||
|
||||
async def memory_compress(self, messages: list, compress_rate=0.1):
|
||||
"""压缩消息记录为记忆
|
||||
|
||||
Args:
|
||||
messages: 消息记录字典列表，每个字典包含text和time字段
|
||||
compress_rate: 压缩率
|
||||
|
||||
Returns:
|
||||
set: (话题, 记忆) 元组集合
|
||||
"""
|
||||
if not messages:
|
||||
return set()
|
||||
|
||||
# 合并消息文本，同时保留时间信息
|
||||
input_text = ""
|
||||
time_info = ""
|
||||
# 计算最早和最晚时间
|
||||
earliest_time = min(msg["time"] for msg in messages)
|
||||
latest_time = max(msg["time"] for msg in messages)
|
||||
|
||||
earliest_dt = datetime.datetime.fromtimestamp(earliest_time)
|
||||
latest_dt = datetime.datetime.fromtimestamp(latest_time)
|
||||
|
||||
# 如果是同一年
|
||||
if earliest_dt.year == latest_dt.year:
|
||||
earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S")
|
||||
latest_str = latest_dt.strftime("%m-%d %H:%M:%S")
|
||||
time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n"
|
||||
else:
|
||||
earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n"
|
||||
|
||||
for msg in messages:
|
||||
input_text += f"{msg['text']}\n"
|
||||
|
||||
print(input_text)
|
||||
|
||||
topic_num = self.calculate_topic_num(input_text, compress_rate)
|
||||
topics_response = self.llm_model_get_topic.generate_response(self.find_topic_llm(input_text, topic_num))
|
||||
|
||||
# 过滤topics
|
||||
filter_keywords = ["表情包", "图片", "回复", "聊天记录"]
|
||||
topics = [
|
||||
topic.strip()
|
||||
for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||||
if topic.strip()
|
||||
]
|
||||
filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)]
|
||||
|
||||
# print(f"原始话题: {topics}")
|
||||
print(f"过滤后话题: {filtered_topics}")
|
||||
|
||||
# 创建所有话题的请求任务
|
||||
tasks = []
|
||||
for topic in filtered_topics:
|
||||
topic_what_prompt = self.topic_what(input_text, topic, time_info)
|
||||
# 创建异步任务
|
||||
task = self.llm_model_small.generate_response_async(topic_what_prompt)
|
||||
tasks.append((topic.strip(), task))
|
||||
|
||||
# 等待所有任务完成
|
||||
compressed_memory = set()
|
||||
for topic, task in tasks:
|
||||
response = await task
|
||||
if response:
|
||||
compressed_memory.add((topic, response[0]))
|
||||
|
||||
return compressed_memory
|
||||
|
||||
async def operation_build_memory(self, chat_size=12):
|
||||
# 最近消息获取频率
|
||||
time_frequency = {"near": 3, "mid": 8, "far": 5}
|
||||
memory_samples = self.get_memory_sample(chat_size, time_frequency)
|
||||
|
||||
all_topics = [] # 用于存储所有话题
|
||||
|
||||
for i, messages in enumerate(memory_samples, 1):
|
||||
# 加载进度可视化
|
||||
all_topics = []
|
||||
progress = (i / len(memory_samples)) * 100
|
||||
bar_length = 30
|
||||
filled_length = int(bar_length * i // len(memory_samples))
|
||||
bar = "█" * filled_length + "-" * (bar_length - filled_length)
|
||||
print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
|
||||
|
||||
# 生成压缩后记忆
|
||||
compress_rate = 0.1
|
||||
compressed_memory = await self.memory_compress(messages, compress_rate)
|
||||
print(f"\033[1;33m压缩后记忆数量\033[0m: {len(compressed_memory)}")
|
||||
|
||||
# 将记忆加入到图谱中
|
||||
for topic, memory in compressed_memory:
|
||||
print(f"\033[1;32m添加节点\033[0m: {topic}")
|
||||
self.memory_graph.add_dot(topic, memory)
|
||||
all_topics.append(topic)
|
||||
|
||||
# 连接相关话题
|
||||
for i in range(len(all_topics)):
|
||||
for j in range(i + 1, len(all_topics)):
|
||||
print(f"\033[1;32m连接节点\033[0m: {all_topics[i]} 和 {all_topics[j]}")
|
||||
self.memory_graph.connect_dot(all_topics[i], all_topics[j])
|
||||
|
||||
self.sync_memory_to_db()
|
||||
|
||||
def sync_memory_from_db(self):
|
||||
"""
|
||||
从数据库同步数据到内存中的图结构
|
||||
将清空当前内存中的图，并从数据库重新加载所有节点和边
|
||||
"""
|
||||
# 清空当前图
|
||||
self.memory_graph.G.clear()
|
||||
|
||||
# 从数据库加载所有节点
|
||||
nodes = db.graph_data.nodes.find()
|
||||
for node in nodes:
|
||||
concept = node["concept"]
|
||||
memory_items = node.get("memory_items", [])
|
||||
# 确保memory_items是列表
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
# 添加节点到图中
|
||||
self.memory_graph.G.add_node(concept, memory_items=memory_items)
|
||||
|
||||
# 从数据库加载所有边
|
||||
edges = db.graph_data.edges.find()
|
||||
for edge in edges:
|
||||
source = edge["source"]
|
||||
target = edge["target"]
|
||||
strength = edge.get("strength", 1) # 获取 strength，默认为 1
|
||||
# 只有当源节点和目标节点都存在时才添加边
|
||||
if source in self.memory_graph.G and target in self.memory_graph.G:
|
||||
self.memory_graph.G.add_edge(source, target, strength=strength)
|
||||
|
||||
logger.success("从数据库同步记忆图谱完成")
|
||||
|
||||
def calculate_node_hash(self, concept, memory_items):
|
||||
"""
|
||||
计算节点的特征值
|
||||
"""
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
# 将记忆项排序以确保相同内容生成相同的哈希值
|
||||
sorted_items = sorted(memory_items)
|
||||
# 组合概念和记忆项生成特征值
|
||||
content = f"{concept}:{'|'.join(sorted_items)}"
|
||||
return hash(content)
|
||||
|
||||
def calculate_edge_hash(self, source, target):
|
||||
"""
|
||||
计算边的特征值
|
||||
"""
|
||||
# 对源节点和目标节点排序以确保相同的边生成相同的哈希值
|
||||
nodes = sorted([source, target])
|
||||
return hash(f"{nodes[0]}:{nodes[1]}")
|
||||
|
||||
def sync_memory_to_db(self):
|
||||
"""
|
||||
检查并同步内存中的图结构与数据库
|
||||
使用特征值(哈希值)快速判断是否需要更新
|
||||
"""
|
||||
# 获取数据库中所有节点和内存中所有节点
|
||||
db_nodes = list(db.graph_data.nodes.find())
|
||||
memory_nodes = list(self.memory_graph.G.nodes(data=True))
|
||||
|
||||
# 转换数据库节点为字典格式，方便查找
|
||||
db_nodes_dict = {node["concept"]: node for node in db_nodes}
|
||||
|
||||
# 检查并更新节点
|
||||
for concept, data in memory_nodes:
|
||||
memory_items = data.get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
|
||||
# 计算内存中节点的特征值
|
||||
memory_hash = self.calculate_node_hash(concept, memory_items)
|
||||
|
||||
if concept not in db_nodes_dict:
|
||||
# 数据库中缺少的节点，添加
|
||||
# logger.info(f"添加新节点: {concept}")
|
||||
node_data = {"concept": concept, "memory_items": memory_items, "hash": memory_hash}
|
||||
db.graph_data.nodes.insert_one(node_data)
|
||||
else:
|
||||
# 获取数据库中节点的特征值
|
||||
db_node = db_nodes_dict[concept]
|
||||
db_hash = db_node.get("hash", None)
|
||||
|
||||
# 如果特征值不同，则更新节点
|
||||
if db_hash != memory_hash:
|
||||
# logger.info(f"更新节点内容: {concept}")
|
||||
db.graph_data.nodes.update_one(
|
||||
{"concept": concept}, {"$set": {"memory_items": memory_items, "hash": memory_hash}}
|
||||
)
|
||||
|
||||
# 检查并删除数据库中多余的节点
|
||||
memory_concepts = set(node[0] for node in memory_nodes)
|
||||
for db_node in db_nodes:
|
||||
if db_node["concept"] not in memory_concepts:
|
||||
# logger.info(f"删除多余节点: {db_node['concept']}")
|
||||
db.graph_data.nodes.delete_one({"concept": db_node["concept"]})
|
||||
|
||||
# 处理边的信息
|
||||
db_edges = list(db.graph_data.edges.find())
|
||||
memory_edges = list(self.memory_graph.G.edges())
|
||||
|
||||
# 创建边的哈希值字典
|
||||
db_edge_dict = {}
|
||||
for edge in db_edges:
|
||||
edge_hash = self.calculate_edge_hash(edge["source"], edge["target"])
|
||||
db_edge_dict[(edge["source"], edge["target"])] = {"hash": edge_hash, "num": edge.get("num", 1)}
|
||||
|
||||
# 检查并更新边
|
||||
for source, target in memory_edges:
|
||||
edge_hash = self.calculate_edge_hash(source, target)
|
||||
edge_key = (source, target)
|
||||
|
||||
if edge_key not in db_edge_dict:
|
||||
# 添加新边
|
||||
logger.info(f"添加新边: {source} - {target}")
|
||||
edge_data = {"source": source, "target": target, "num": 1, "hash": edge_hash}
|
||||
db.graph_data.edges.insert_one(edge_data)
|
||||
else:
|
||||
# 检查边的特征值是否变化
|
||||
if db_edge_dict[edge_key]["hash"] != edge_hash:
|
||||
logger.info(f"更新边: {source} - {target}")
|
||||
db.graph_data.edges.update_one({"source": source, "target": target}, {"$set": {"hash": edge_hash}})
|
||||
|
||||
# 删除多余的边
|
||||
memory_edge_set = set(memory_edges)
|
||||
for edge_key in db_edge_dict:
|
||||
if edge_key not in memory_edge_set:
|
||||
source, target = edge_key
|
||||
logger.info(f"删除多余边: {source} - {target}")
|
||||
db.graph_data.edges.delete_one({"source": source, "target": target})
|
||||
|
||||
logger.success("完成记忆图谱与数据库的差异同步")
|
||||
|
||||
def find_topic_llm(self, text, topic_num):
|
||||
prompt = (
|
||||
f"这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，"
|
||||
f"用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。"
|
||||
)
|
||||
return prompt
|
||||
|
||||
def topic_what(self, text, topic, time_info):
|
||||
# 获取当前时间
|
||||
prompt = (
|
||||
f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，'
|
||||
f"可以包含时间和人物，以及具体的观点。只输出这句话就好"
|
||||
)
|
||||
return prompt
|
||||
|
||||
def remove_node_from_db(self, topic):
|
||||
"""
|
||||
从数据库中删除指定节点及其相关的边
|
||||
|
||||
Args:
|
||||
topic: 要删除的节点概念
|
||||
"""
|
||||
# 删除节点
|
||||
db.graph_data.nodes.delete_one({"concept": topic})
|
||||
# 删除所有涉及该节点的边
|
||||
db.graph_data.edges.delete_many({"$or": [{"source": topic}, {"target": topic}]})
|
||||
|
||||
def forget_topic(self, topic):
|
||||
"""
|
||||
随机删除指定话题中的一条记忆，如果话题没有记忆则移除该话题节点
|
||||
只在内存中的图上操作，不直接与数据库交互
|
||||
|
||||
Args:
|
||||
topic: 要删除记忆的话题
|
||||
|
||||
Returns:
|
||||
removed_item: 被删除的记忆项，如果没有删除任何记忆则返回 None
|
||||
"""
|
||||
if topic not in self.memory_graph.G:
|
||||
return None
|
||||
|
||||
# 获取话题节点数据
|
||||
node_data = self.memory_graph.G.nodes[topic]
|
||||
|
||||
# 如果节点存在memory_items
|
||||
if "memory_items" in node_data:
|
||||
memory_items = node_data["memory_items"]
|
||||
|
||||
# 确保memory_items是列表
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
|
||||
# 如果有记忆项可以删除
|
||||
if memory_items:
|
||||
# 随机选择一个记忆项删除
|
||||
removed_item = random.choice(memory_items)
|
||||
memory_items.remove(removed_item)
|
||||
|
||||
# 更新节点的记忆项
|
||||
if memory_items:
|
||||
self.memory_graph.G.nodes[topic]["memory_items"] = memory_items
|
||||
else:
|
||||
# 如果没有记忆项了，删除整个节点
|
||||
self.memory_graph.G.remove_node(topic)
|
||||
|
||||
return removed_item
|
||||
|
||||
return None
|
||||
|
||||
async def operation_forget_topic(self, percentage=0.1):
|
||||
"""
|
||||
随机选择图中一定比例的节点进行检查，根据条件决定是否遗忘
|
||||
|
||||
Args:
|
||||
percentage: 要检查的节点比例，默认为0.1（10%）
|
||||
"""
|
||||
# 获取所有节点
|
||||
all_nodes = list(self.memory_graph.G.nodes())
|
||||
# 计算要检查的节点数量
|
||||
check_count = max(1, int(len(all_nodes) * percentage))
|
||||
# 随机选择节点
|
||||
nodes_to_check = random.sample(all_nodes, check_count)
|
||||
|
||||
forgotten_nodes = []
|
||||
for node in nodes_to_check:
|
||||
# 获取节点的连接数
|
||||
connections = self.memory_graph.G.degree(node)
|
||||
|
||||
# 获取节点的内容条数
|
||||
memory_items = self.memory_graph.G.nodes[node].get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
content_count = len(memory_items)
|
||||
|
||||
# 检查连接强度
|
||||
weak_connections = True
|
||||
if connections > 1: # 只有当连接数大于1时才检查强度
|
||||
for neighbor in self.memory_graph.G.neighbors(node):
|
||||
strength = self.memory_graph.G[node][neighbor].get("strength", 1)
|
||||
if strength > 2:
|
||||
weak_connections = False
|
||||
break
|
||||
|
||||
# 如果满足遗忘条件
|
||||
if (connections <= 1 and weak_connections) or content_count <= 2:
|
||||
removed_item = self.forget_topic(node)
|
||||
if removed_item:
|
||||
forgotten_nodes.append((node, removed_item))
|
||||
logger.info(f"遗忘节点 {node} 的记忆: {removed_item}")
|
||||
|
||||
# 同步到数据库
|
||||
if forgotten_nodes:
|
||||
self.sync_memory_to_db()
|
||||
logger.info(f"完成遗忘操作，共遗忘 {len(forgotten_nodes)} 个节点的记忆")
|
||||
else:
|
||||
logger.info("本次检查没有节点满足遗忘条件")
|
||||
|
||||
async def merge_memory(self, topic):
|
||||
"""
|
||||
对指定话题的记忆进行合并压缩
|
||||
|
||||
Args:
|
||||
topic: 要合并的话题节点
|
||||
"""
|
||||
# 获取节点的记忆项
|
||||
memory_items = self.memory_graph.G.nodes[topic].get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
|
||||
# 如果记忆项不足，直接返回
|
||||
if len(memory_items) < 10:
|
||||
return
|
||||
|
||||
# 随机选择10条记忆
|
||||
selected_memories = random.sample(memory_items, 10)
|
||||
|
||||
# 拼接成文本
|
||||
merged_text = "\n".join(selected_memories)
|
||||
print(f"\n[合并记忆] 话题: {topic}")
|
||||
print(f"选择的记忆:\n{merged_text}")
|
||||
|
||||
# 使用memory_compress生成新的压缩记忆
|
||||
compressed_memories = await self.memory_compress(selected_memories, 0.1)
|
||||
|
||||
# 从原记忆列表中移除被选中的记忆
|
||||
for memory in selected_memories:
|
||||
memory_items.remove(memory)
|
||||
|
||||
# 添加新的压缩记忆
|
||||
for _, compressed_memory in compressed_memories:
|
||||
memory_items.append(compressed_memory)
|
||||
print(f"添加压缩记忆: {compressed_memory}")
|
||||
|
||||
# 更新节点的记忆项
|
||||
self.memory_graph.G.nodes[topic]["memory_items"] = memory_items
|
||||
print(f"完成记忆合并，当前记忆数量: {len(memory_items)}")
|
||||
|
||||
async def operation_merge_memory(self, percentage=0.1):
|
||||
"""
|
||||
随机检查一定比例的节点，对内容数量超过100的节点进行记忆合并
|
||||
|
||||
Args:
|
||||
percentage: 要检查的节点比例，默认为0.1（10%）
|
||||
"""
|
||||
# 获取所有节点
|
||||
all_nodes = list(self.memory_graph.G.nodes())
|
||||
# 计算要检查的节点数量
|
||||
check_count = max(1, int(len(all_nodes) * percentage))
|
||||
# 随机选择节点
|
||||
nodes_to_check = random.sample(all_nodes, check_count)
|
||||
|
||||
merged_nodes = []
|
||||
for node in nodes_to_check:
|
||||
# 获取节点的内容条数
|
||||
memory_items = self.memory_graph.G.nodes[node].get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
content_count = len(memory_items)
|
||||
|
||||
# 如果内容数量超过100，进行合并
|
||||
if content_count > 100:
|
||||
print(f"\n检查节点: {node}, 当前记忆数量: {content_count}")
|
||||
await self.merge_memory(node)
|
||||
merged_nodes.append(node)
|
||||
|
||||
# 同步到数据库
|
||||
if merged_nodes:
|
||||
self.sync_memory_to_db()
|
||||
print(f"\n完成记忆合并操作，共处理 {len(merged_nodes)} 个节点")
|
||||
else:
|
||||
print("\n本次检查没有需要合并的节点")
|
||||
|
||||
async def _identify_topics(self, text: str) -> list:
|
||||
"""从文本中识别可能的主题"""
|
||||
topics_response = self.llm_model_get_topic.generate_response(self.find_topic_llm(text, 5))
|
||||
topics = [
|
||||
topic.strip()
|
||||
for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||||
if topic.strip()
|
||||
]
|
||||
return topics
|
||||
|
||||
def _find_similar_topics(self, topics: list, similarity_threshold: float = 0.4, debug_info: str = "") -> list:
|
||||
"""查找与给定主题相似的记忆主题"""
|
||||
all_memory_topics = list(self.memory_graph.G.nodes())
|
||||
all_similar_topics = []
|
||||
|
||||
for topic in topics:
|
||||
if debug_info:
|
||||
pass
|
||||
|
||||
topic_vector = text_to_vector(topic)
|
||||
|
||||
for memory_topic in all_memory_topics:
|
||||
memory_vector = text_to_vector(memory_topic)
|
||||
all_words = set(topic_vector.keys()) | set(memory_vector.keys())
|
||||
v1 = [topic_vector.get(word, 0) for word in all_words]
|
||||
v2 = [memory_vector.get(word, 0) for word in all_words]
|
||||
similarity = cosine_similarity(v1, v2)
|
||||
|
||||
if similarity >= similarity_threshold:
|
||||
all_similar_topics.append((memory_topic, similarity))
|
||||
|
||||
return all_similar_topics
|
||||
|
||||
def _get_top_topics(self, similar_topics: list, max_topics: int = 5) -> list:
|
||||
"""获取相似度最高的主题"""
|
||||
seen_topics = set()
|
||||
top_topics = []
|
||||
|
||||
for topic, score in sorted(similar_topics, key=lambda x: x[1], reverse=True):
|
||||
if topic not in seen_topics and len(top_topics) < max_topics:
|
||||
seen_topics.add(topic)
|
||||
top_topics.append((topic, score))
|
||||
|
||||
return top_topics
|
||||
|
||||
async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int:
|
||||
"""计算输入文本对记忆的激活程度"""
|
||||
logger.info(f"[记忆激活]识别主题: {await self._identify_topics(text)}")
|
||||
|
||||
identified_topics = await self._identify_topics(text)
|
||||
if not identified_topics:
|
||||
return 0
|
||||
|
||||
all_similar_topics = self._find_similar_topics(
|
||||
identified_topics, similarity_threshold=similarity_threshold, debug_info="记忆激活"
|
||||
)
|
||||
|
||||
if not all_similar_topics:
|
||||
return 0
|
||||
|
||||
top_topics = self._get_top_topics(all_similar_topics, max_topics)
|
||||
|
||||
if len(top_topics) == 1:
|
||||
topic, score = top_topics[0]
|
||||
memory_items = self.memory_graph.G.nodes[topic].get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
content_count = len(memory_items)
|
||||
penalty = 1.0 / (1 + math.log(content_count + 1))
|
||||
|
||||
activation = int(score * 50 * penalty)
|
||||
print(
|
||||
f"\033[1;32m[记忆激活]\033[0m 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, "
|
||||
f"激活值: {activation}"
|
||||
)
|
||||
return activation
|
||||
|
||||
matched_topics = set()
|
||||
topic_similarities = {}
|
||||
|
||||
for memory_topic, _similarity in top_topics:
|
||||
memory_items = self.memory_graph.G.nodes[memory_topic].get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
content_count = len(memory_items)
|
||||
penalty = 1.0 / (1 + math.log(content_count + 1))
|
||||
|
||||
for input_topic in identified_topics:
|
||||
topic_vector = text_to_vector(input_topic)
|
||||
memory_vector = text_to_vector(memory_topic)
|
||||
all_words = set(topic_vector.keys()) | set(memory_vector.keys())
|
||||
v1 = [topic_vector.get(word, 0) for word in all_words]
|
||||
v2 = [memory_vector.get(word, 0) for word in all_words]
|
||||
sim = cosine_similarity(v1, v2)
|
||||
if sim >= similarity_threshold:
|
||||
matched_topics.add(input_topic)
|
||||
adjusted_sim = sim * penalty
|
||||
topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim)
|
||||
print(
|
||||
f"\033[1;32m[记忆激活]\033[0m 主题「{input_topic}」-> "
|
||||
f"「{memory_topic}」(内容数: {content_count}, "
|
||||
f"相似度: {adjusted_sim:.3f})"
|
||||
)
|
||||
|
||||
topic_match = len(matched_topics) / len(identified_topics)
|
||||
average_similarities = sum(topic_similarities.values()) / len(topic_similarities) if topic_similarities else 0
|
||||
|
||||
activation = int((topic_match + average_similarities) / 2 * 100)
|
||||
print(
|
||||
f"\033[1;32m[记忆激活]\033[0m 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, "
|
||||
f"激活值: {activation}"
|
||||
)
|
||||
|
||||
return activation
|
||||
|
||||
async def get_relevant_memories(
|
||||
self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4, max_memory_num: int = 5
|
||||
) -> list:
|
||||
"""根据输入文本获取相关的记忆内容"""
|
||||
identified_topics = await self._identify_topics(text)
|
||||
|
||||
all_similar_topics = self._find_similar_topics(
|
||||
identified_topics, similarity_threshold=similarity_threshold, debug_info="记忆检索"
|
||||
)
|
||||
|
||||
relevant_topics = self._get_top_topics(all_similar_topics, max_topics)
|
||||
|
||||
relevant_memories = []
|
||||
for topic, score in relevant_topics:
|
||||
first_layer, _ = self.memory_graph.get_related_item(topic, depth=1)
|
||||
if first_layer:
|
||||
if len(first_layer) > max_memory_num / 2:
|
||||
first_layer = random.sample(first_layer, max_memory_num // 2)
|
||||
for memory in first_layer:
|
||||
relevant_memories.append({"topic": topic, "similarity": score, "content": memory})
|
||||
|
||||
relevant_memories.sort(key=lambda x: x["similarity"], reverse=True)
|
||||
|
||||
if len(relevant_memories) > max_memory_num:
|
||||
relevant_memories = random.sample(relevant_memories, max_memory_num)
|
||||
|
||||
return relevant_memories
|
||||
|
||||
|
||||
def segment_text(text):
|
||||
"""使用jieba进行文本分词"""
|
||||
seg_text = list(jieba.cut(text))
|
||||
return seg_text
|
||||
|
||||
|
||||
def text_to_vector(text):
|
||||
"""将文本转换为词频向量"""
|
||||
words = segment_text(text)
|
||||
vector = {}
|
||||
for word in words:
|
||||
vector[word] = vector.get(word, 0) + 1
|
||||
return vector
|
||||
|
||||
|
||||
def cosine_similarity(v1, v2):
|
||||
"""计算两个向量的余弦相似度"""
|
||||
dot_product = sum(a * b for a, b in zip(v1, v2))
|
||||
norm1 = math.sqrt(sum(a * a for a in v1))
|
||||
norm2 = math.sqrt(sum(b * b for b in v2))
|
||||
if norm1 == 0 or norm2 == 0:
|
||||
return 0
|
||||
return dot_product / (norm1 * norm2)
|
||||
|
||||
|
||||
def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = False):
|
||||
# 设置中文字体
|
||||
plt.rcParams["font.sans-serif"] = ["SimHei"] # 用来正常显示中文标签
|
||||
plt.rcParams["axes.unicode_minus"] = False # 用来正常显示负号
|
||||
|
||||
G = memory_graph.G
|
||||
|
||||
# 创建一个新图用于可视化
|
||||
H = G.copy()
|
||||
|
||||
# 过滤掉内容数量小于2的节点
|
||||
nodes_to_remove = []
|
||||
for node in H.nodes():
|
||||
memory_items = H.nodes[node].get("memory_items", [])
|
||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
||||
if memory_count < 2:
|
||||
nodes_to_remove.append(node)
|
||||
|
||||
H.remove_nodes_from(nodes_to_remove)
|
||||
|
||||
# 如果没有符合条件的节点，直接返回
|
||||
if len(H.nodes()) == 0:
|
||||
print("没有找到内容数量大于等于2的节点")
|
||||
return
|
||||
|
||||
# 计算节点大小和颜色
|
||||
node_colors = []
|
||||
node_sizes = []
|
||||
nodes = list(H.nodes())
|
||||
|
||||
# 获取最大记忆数用于归一化节点大小
|
||||
max_memories = 1
|
||||
for node in nodes:
|
||||
memory_items = H.nodes[node].get("memory_items", [])
|
||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
||||
max_memories = max(max_memories, memory_count)
|
||||
|
||||
# 计算每个节点的大小和颜色
|
||||
for node in nodes:
|
||||
# 计算节点大小（基于记忆数量）
|
||||
memory_items = H.nodes[node].get("memory_items", [])
|
||||
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
|
||||
# 使用指数函数使变化更明显
|
||||
ratio = memory_count / max_memories
|
||||
size = 400 + 2000 * (ratio**2) # 增大节点大小
|
||||
node_sizes.append(size)
|
||||
|
||||
# 计算节点颜色（基于连接数）
|
||||
degree = H.degree(node)
|
||||
if degree >= 30:
|
||||
node_colors.append((1.0, 0, 0)) # 亮红色 (#FF0000)
|
||||
else:
|
||||
# 将1-10映射到0-1的范围
|
||||
color_ratio = (degree - 1) / 29.0 if degree > 1 else 0
|
||||
# 使用蓝到红的渐变
|
||||
red = min(0.9, color_ratio)
|
||||
blue = max(0.0, 1.0 - color_ratio)
|
||||
node_colors.append((red, 0, blue))
|
||||
|
||||
# 绘制图形
|
||||
plt.figure(figsize=(16, 12)) # 减小图形尺寸
|
||||
pos = nx.spring_layout(
|
||||
H,
|
||||
k=1, # 调整节点间斥力
|
||||
iterations=100, # 增加迭代次数
|
||||
scale=1.5, # 减小布局尺寸
|
||||
weight="strength",
|
||||
) # 使用边的strength属性作为权重
|
||||
|
||||
nx.draw(
|
||||
H,
|
||||
pos,
|
||||
with_labels=True,
|
||||
node_color=node_colors,
|
||||
node_size=node_sizes,
|
||||
font_size=12, # 保持增大的字体大小
|
||||
font_family="SimHei",
|
||||
font_weight="bold",
|
||||
edge_color="gray",
|
||||
width=1.5,
|
||||
) # 统一的边宽度
|
||||
|
||||
title = """记忆图谱可视化（仅显示内容≥2的节点）
|
||||
节点大小表示记忆数量
|
||||
节点颜色：蓝(弱连接)到红(强连接)渐变，边的透明度表示连接强度
|
||||
连接强度越大的节点距离越近"""
|
||||
plt.title(title, fontsize=16, fontfamily="SimHei")
|
||||
plt.show()
|
||||
|
||||
|
||||
async def main():
|
||||
start_time = time.time()
|
||||
|
||||
test_pare = {
|
||||
"do_build_memory": False,
|
||||
"do_forget_topic": False,
|
||||
"do_visualize_graph": True,
|
||||
"do_query": False,
|
||||
"do_merge_memory": False,
|
||||
}
|
||||
|
||||
# 创建记忆图
|
||||
memory_graph = Memory_graph()
|
||||
|
||||
# 创建海马体
|
||||
hippocampus = Hippocampus(memory_graph)
|
||||
|
||||
# 从数据库同步数据
|
||||
hippocampus.sync_memory_from_db()
|
||||
|
||||
end_time = time.time()
|
||||
logger.info(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m")
|
||||
|
||||
# 构建记忆
|
||||
if test_pare["do_build_memory"]:
|
||||
logger.info("开始构建记忆...")
|
||||
chat_size = 20
|
||||
await hippocampus.operation_build_memory(chat_size=chat_size)
|
||||
|
||||
end_time = time.time()
|
||||
logger.info(
|
||||
f"\033[32m[构建记忆耗时: {end_time - start_time:.2f} 秒,chat_size={chat_size},chat_count = 16]\033[0m"
|
||||
)
|
||||
|
||||
if test_pare["do_forget_topic"]:
|
||||
logger.info("开始遗忘记忆...")
|
||||
await hippocampus.operation_forget_topic(percentage=0.1)
|
||||
|
||||
end_time = time.time()
|
||||
logger.info(f"\033[32m[遗忘记忆耗时: {end_time - start_time:.2f} 秒]\033[0m")
|
||||
|
||||
if test_pare["do_merge_memory"]:
|
||||
logger.info("开始合并记忆...")
|
||||
await hippocampus.operation_merge_memory(percentage=0.1)
|
||||
|
||||
end_time = time.time()
|
||||
logger.info(f"\033[32m[合并记忆耗时: {end_time - start_time:.2f} 秒]\033[0m")
|
||||
|
||||
if test_pare["do_visualize_graph"]:
|
||||
# 展示优化后的图形
|
||||
logger.info("生成记忆图谱可视化...")
|
||||
print("\n生成优化后的记忆图谱：")
|
||||
visualize_graph_lite(memory_graph)
|
||||
|
||||
if test_pare["do_query"]:
|
||||
# 交互式查询
|
||||
while True:
|
||||
query = input("\n请输入新的查询概念（输入'退出'以结束）：")
|
||||
if query.lower() == "退出":
|
||||
break
|
||||
|
||||
items_list = memory_graph.get_related_item(query)
|
||||
if items_list:
|
||||
first_layer, second_layer = items_list
|
||||
if first_layer:
|
||||
print("\n直接相关的记忆：")
|
||||
for item in first_layer:
|
||||
print(f"- {item}")
|
||||
if second_layer:
|
||||
print("\n间接相关的记忆：")
|
||||
for item in second_layer:
|
||||
print(f"- {item}")
|
||||
else:
|
||||
print("未找到相关记忆。")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
asyncio.run(main())
|
||||
|
|
@ -10,7 +10,7 @@ from src.common.logger import get_module_logger
|
|||
logger = get_module_logger("offline_llm")
|
||||
|
||||
|
||||
class LLMModel:
|
||||
class LLM_request_off:
|
||||
def __init__(self, model_name="deepseek-ai/DeepSeek-V3", **kwargs):
|
||||
self.model_name = model_name
|
||||
self.params = kwargs
|
||||
|
|
|
|||
|
|
@ -6,15 +6,13 @@ from typing import Tuple, Union
|
|||
|
||||
import aiohttp
|
||||
from src.common.logger import get_module_logger
|
||||
from nonebot import get_driver
|
||||
import base64
|
||||
from PIL import Image
|
||||
import io
|
||||
from ...common.database import db
|
||||
from ..chat.config import global_config
|
||||
from ..config.config import global_config
|
||||
from ..config.config_env import env_config
|
||||
|
||||
driver = get_driver()
|
||||
config = driver.config
|
||||
|
||||
logger = get_module_logger("model_utils")
|
||||
|
||||
|
|
@ -34,8 +32,9 @@ class LLM_request:
|
|||
def __init__(self, model, **kwargs):
|
||||
# 将大写的配置键转换为小写并从config中获取实际值
|
||||
try:
|
||||
self.api_key = getattr(config, model["key"])
|
||||
self.base_url = getattr(config, model["base_url"])
|
||||
self.api_key = getattr(env_config, model["key"])
|
||||
self.base_url = getattr(env_config, model["base_url"])
|
||||
# print(self.api_key, self.base_url)
|
||||
except AttributeError as e:
|
||||
logger.error(f"原始 model dict 信息：{model}")
|
||||
logger.error(f"配置错误：找不到对应的配置项 - {str(e)}")
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ import threading
|
|||
import time
|
||||
from dataclasses import dataclass
|
||||
|
||||
from ..chat.config import global_config
|
||||
from ..config.config import global_config
|
||||
from src.common.logger import get_module_logger, LogConfig, MOOD_STYLE_CONFIG
|
||||
|
||||
mood_config = LogConfig(
|
||||
|
|
@ -55,13 +55,15 @@ class MoodManager:
|
|||
|
||||
# 情绪词映射表 (valence, arousal)
|
||||
self.emotion_map = {
|
||||
"happy": (0.8, 0.6), # 高愉悦度，中等唤醒度
|
||||
"angry": (-0.7, 0.7), # 负愉悦度，高唤醒度
|
||||
"sad": (-0.6, 0.3), # 负愉悦度，低唤醒度
|
||||
"surprised": (0.4, 0.8), # 中等愉悦度，高唤醒度
|
||||
"disgusted": (-0.8, 0.5), # 高负愉悦度，中等唤醒度
|
||||
"fearful": (-0.7, 0.6), # 负愉悦度，高唤醒度
|
||||
"neutral": (0.0, 0.5), # 中性愉悦度，中等唤醒度
|
||||
"开心": (0.8, 0.6), # 高愉悦度，中等唤醒度
|
||||
"愤怒": (-0.7, 0.7), # 负愉悦度，高唤醒度
|
||||
"悲伤": (-0.6, 0.3), # 负愉悦度，低唤醒度
|
||||
"惊讶": (0.2, 0.8), # 中等愉悦度，高唤醒度
|
||||
"害羞": (0.5, 0.2), # 中等愉悦度，低唤醒度
|
||||
"平静": (0.0, 0.5), # 中性愉悦度，中等唤醒度
|
||||
"恐惧": (-0.7, 0.6), # 负愉悦度，高唤醒度
|
||||
"厌恶": (-0.4, 0.4), # 负愉悦度，低唤醒度
|
||||
"困惑": (0.0, 0.6), # 中性愉悦度，高唤醒度
|
||||
}
|
||||
|
||||
# 情绪文本映射表
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ import os
|
|||
import json
|
||||
import threading
|
||||
from src.common.logger import get_module_logger
|
||||
from src.plugins.chat.config import global_config
|
||||
from src.plugins.config.config import global_config
|
||||
|
||||
logger = get_module_logger("remote")
|
||||
|
||||
|
|
|
|||
|
|
@ -1,10 +1,7 @@
|
|||
import asyncio
|
||||
import os
|
||||
import time
|
||||
from typing import Tuple, Union
|
||||
|
||||
import aiohttp
|
||||
import requests
|
||||
from src.common.logger import get_module_logger
|
||||
|
||||
logger = get_module_logger("offline_llm")
|
||||
|
|
@ -22,57 +19,7 @@ class LLMModel:
|
|||
|
||||
logger.info(f"API URL: {self.base_url}") # 使用 logger 记录 base_url
|
||||
|
||||
def generate_response(self, prompt: str) -> Union[str, Tuple[str, str]]:
|
||||
"""根据输入的提示生成模型的响应"""
|
||||
headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
|
||||
|
||||
# 构建请求体
|
||||
data = {
|
||||
"model": self.model_name,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": 0.5,
|
||||
**self.params,
|
||||
}
|
||||
|
||||
# 发送请求到完整的 chat/completions 端点
|
||||
api_url = f"{self.base_url.rstrip('/')}/chat/completions"
|
||||
logger.info(f"Request URL: {api_url}") # 记录请求的 URL
|
||||
|
||||
max_retries = 3
|
||||
base_wait_time = 15 # 基础等待时间（秒）
|
||||
|
||||
for retry in range(max_retries):
|
||||
try:
|
||||
response = requests.post(api_url, headers=headers, json=data)
|
||||
|
||||
if response.status_code == 429:
|
||||
wait_time = base_wait_time * (2**retry) # 指数退避
|
||||
logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
|
||||
time.sleep(wait_time)
|
||||
continue
|
||||
|
||||
response.raise_for_status() # 检查其他响应状态
|
||||
|
||||
result = response.json()
|
||||
if "choices" in result and len(result["choices"]) > 0:
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
|
||||
return content, reasoning_content
|
||||
return "没有返回结果", ""
|
||||
|
||||
except Exception as e:
|
||||
if retry < max_retries - 1: # 如果还有重试机会
|
||||
wait_time = base_wait_time * (2**retry)
|
||||
logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
|
||||
time.sleep(wait_time)
|
||||
else:
|
||||
logger.error(f"请求失败: {str(e)}")
|
||||
return f"请求失败: {str(e)}", ""
|
||||
|
||||
logger.error("达到最大重试次数，请求仍然失败")
|
||||
return "达到最大重试次数，请求仍然失败", ""
|
||||
|
||||
async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]:
|
||||
async def generate_response_async(self, prompt: str) -> str:
|
||||
"""异步方式根据输入的提示生成模型的响应"""
|
||||
headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
|
||||
|
||||
|
|
@ -80,7 +27,7 @@ class LLMModel:
|
|||
data = {
|
||||
"model": self.model_name,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": 0.5,
|
||||
"temperature": 0.7,
|
||||
**self.params,
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,159 +1,154 @@
|
|||
import datetime
|
||||
import json
|
||||
import re
|
||||
from typing import Dict, Union
|
||||
|
||||
from nonebot import get_driver
|
||||
|
||||
import os
|
||||
import sys
|
||||
from typing import Dict
|
||||
import asyncio
|
||||
# 添加项目根目录到 Python 路径
|
||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
||||
sys.path.append(root_path)
|
||||
|
||||
from src.plugins.chat.config import global_config
|
||||
from ...common.database import db # 使用正确的导入语法
|
||||
from ..models.utils_model import LLM_request
|
||||
from src.common.logger import get_module_logger
|
||||
from src.common.database import db # noqa: E402
|
||||
from src.common.logger import get_module_logger, SCHEDULE_STYLE_CONFIG, LogConfig # noqa: E402
|
||||
from src.plugins.models.utils_model import LLM_request # noqa: E402
|
||||
from src.plugins.config.config import global_config # noqa: E402
|
||||
|
||||
logger = get_module_logger("scheduler")
|
||||
|
||||
driver = get_driver()
|
||||
config = driver.config
|
||||
schedule_config = LogConfig(
|
||||
# 使用海马体专用样式
|
||||
console_format=SCHEDULE_STYLE_CONFIG["console_format"],
|
||||
file_format=SCHEDULE_STYLE_CONFIG["file_format"],
|
||||
)
|
||||
logger = get_module_logger("scheduler", config=schedule_config)
|
||||
|
||||
|
||||
class ScheduleGenerator:
|
||||
enable_output: bool = True
|
||||
# enable_output: bool = True
|
||||
|
||||
def __init__(self):
|
||||
# 根据global_config.llm_normal这一字典配置指定模型
|
||||
# self.llm_scheduler = LLMModel(model = global_config.llm_normal,temperature=0.9)
|
||||
self.llm_scheduler = LLM_request(model=global_config.llm_normal, temperature=0.9, request_type="scheduler")
|
||||
# 使用离线LLM模型
|
||||
self.llm_scheduler_all = LLM_request(
|
||||
model= global_config.llm_reasoning, temperature=0.9, max_tokens=7000,request_type="schedule")
|
||||
self.llm_scheduler_doing = LLM_request(
|
||||
model= global_config.llm_normal, temperature=0.9, max_tokens=2048,request_type="schedule")
|
||||
|
||||
self.today_schedule_text = ""
|
||||
self.today_schedule = {}
|
||||
self.tomorrow_schedule_text = ""
|
||||
self.tomorrow_schedule = {}
|
||||
self.today_done_list = []
|
||||
|
||||
self.yesterday_schedule_text = ""
|
||||
self.yesterday_schedule = {}
|
||||
self.yesterday_done_list = []
|
||||
|
||||
async def initialize(self):
|
||||
self.name = ""
|
||||
self.personality = ""
|
||||
self.behavior = ""
|
||||
|
||||
self.start_time = datetime.datetime.now()
|
||||
|
||||
self.schedule_doing_update_interval = 300 #最好大于60
|
||||
|
||||
def initialize(
|
||||
self,name: str = "bot_name",
|
||||
personality: str = "你是一个爱国爱党的新时代青年",
|
||||
behavior: str = "你非常外向，喜欢尝试新事物和人交流",
|
||||
interval: int = 60):
|
||||
"""初始化日程系统"""
|
||||
self.name = name
|
||||
self.behavior = behavior
|
||||
self.schedule_doing_update_interval = interval
|
||||
|
||||
for pers in personality:
|
||||
self.personality += pers + "\n"
|
||||
|
||||
|
||||
async def mai_schedule_start(self):
|
||||
"""启动日程系统，每5分钟执行一次move_doing，并在日期变化时重新检查日程"""
|
||||
try:
|
||||
logger.info(f"日程系统启动/刷新时间: {self.start_time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
# 初始化日程
|
||||
await self.check_and_create_today_schedule()
|
||||
self.print_schedule()
|
||||
|
||||
while True:
|
||||
print(self.get_current_num_task(1, True))
|
||||
|
||||
current_time = datetime.datetime.now()
|
||||
|
||||
# 检查是否需要重新生成日程（日期变化）
|
||||
if current_time.date() != self.start_time.date():
|
||||
logger.info("检测到日期变化，重新生成日程")
|
||||
self.start_time = current_time
|
||||
await self.check_and_create_today_schedule()
|
||||
self.print_schedule()
|
||||
|
||||
# 执行当前活动
|
||||
# mind_thinking = subheartflow_manager.current_state.current_mind
|
||||
|
||||
await self.move_doing()
|
||||
|
||||
await asyncio.sleep(self.schedule_doing_update_interval)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"日程系统运行时出错: {str(e)}")
|
||||
logger.exception("详细错误信息：")
|
||||
|
||||
async def check_and_create_today_schedule(self):
|
||||
"""检查昨天的日程，并确保今天有日程安排
|
||||
|
||||
Returns:
|
||||
tuple: (today_schedule_text, today_schedule) 今天的日程文本和解析后的日程字典
|
||||
"""
|
||||
today = datetime.datetime.now()
|
||||
tomorrow = datetime.datetime.now() + datetime.timedelta(days=1)
|
||||
yesterday = datetime.datetime.now() - datetime.timedelta(days=1)
|
||||
yesterday = today - datetime.timedelta(days=1)
|
||||
|
||||
# 先检查昨天的日程
|
||||
self.yesterday_schedule_text, self.yesterday_done_list = self.load_schedule_from_db(yesterday)
|
||||
if self.yesterday_schedule_text:
|
||||
logger.debug(f"已加载{yesterday.strftime('%Y-%m-%d')}的日程")
|
||||
|
||||
# 检查今天的日程
|
||||
self.today_schedule_text, self.today_done_list = self.load_schedule_from_db(today)
|
||||
if not self.today_done_list:
|
||||
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)
|
||||
|
||||
self.today_schedule_text, self.today_schedule = await self.generate_daily_schedule(target_date=today)
|
||||
self.tomorrow_schedule_text, self.tomorrow_schedule = await self.generate_daily_schedule(
|
||||
target_date=tomorrow, read_only=True
|
||||
)
|
||||
self.yesterday_schedule_text, self.yesterday_schedule = await self.generate_daily_schedule(
|
||||
target_date=yesterday, read_only=True
|
||||
)
|
||||
|
||||
async def generate_daily_schedule(
|
||||
self, target_date: datetime.datetime = None, read_only: bool = False
|
||||
) -> Dict[str, str]:
|
||||
self.save_today_schedule_to_db()
|
||||
|
||||
def construct_daytime_prompt(self, target_date: datetime.datetime):
|
||||
date_str = target_date.strftime("%Y-%m-%d")
|
||||
weekday = target_date.strftime("%A")
|
||||
|
||||
schedule_text = str
|
||||
|
||||
existing_schedule = db.schedule.find_one({"date": date_str})
|
||||
if existing_schedule:
|
||||
if self.enable_output:
|
||||
logger.debug(f"{date_str}的日程已存在:")
|
||||
schedule_text = existing_schedule["schedule"]
|
||||
# print(self.schedule_text)
|
||||
|
||||
elif not read_only:
|
||||
logger.debug(f"{date_str}的日程不存在，准备生成新的日程。")
|
||||
prompt = (
|
||||
f"""我是{global_config.BOT_NICKNAME}，{global_config.PROMPT_SCHEDULE_GEN}，请为我生成{date_str}（{weekday}）的日程安排，包括："""
|
||||
+ """
|
||||
1. 早上的学习和工作安排
|
||||
2. 下午的活动和任务
|
||||
3. 晚上的计划和休息时间
|
||||
请按照时间顺序列出具体时间点和对应的活动，用一个时间点而不是时间段来表示时间，用JSON格式返回日程表，
|
||||
仅返回内容，不要返回注释，不要添加任何markdown或代码块样式，时间采用24小时制，
|
||||
格式为{"时间": "活动","时间": "活动",...}。"""
|
||||
)
|
||||
|
||||
try:
|
||||
schedule_text, _, _ = await self.llm_scheduler.generate_response(prompt)
|
||||
db.schedule.insert_one({"date": date_str, "schedule": schedule_text})
|
||||
self.enable_output = True
|
||||
except Exception as e:
|
||||
logger.error(f"生成日程失败: {str(e)}")
|
||||
schedule_text = "生成日程时出错了"
|
||||
# print(self.schedule_text)
|
||||
prompt = f"你是{self.name}，{self.personality}，{self.behavior}"
|
||||
prompt += f"你昨天的日程是：{self.yesterday_schedule_text}\n"
|
||||
prompt += f"请为你生成{date_str}（{weekday}）的日程安排，结合你的个人特点和行为习惯\n"
|
||||
prompt += "推测你的日程安排，包括你一天都在做什么，从起床到睡眠，有什么发现和思考，具体一些，详细一些，需要1500字以上，精确到每半个小时，记得写明时间\n" #noqa: E501
|
||||
prompt += "直接返回你的日程，从起床到睡觉，不要输出其他内容："
|
||||
return prompt
|
||||
|
||||
def construct_doing_prompt(self,time: datetime.datetime,mind_thinking: str = ""):
|
||||
now_time = time.strftime("%H:%M")
|
||||
if self.today_done_list:
|
||||
previous_doings = self.get_current_num_task(5, True)
|
||||
# print(previous_doings)
|
||||
else:
|
||||
if self.enable_output:
|
||||
logger.debug(f"{date_str}的日程不存在。")
|
||||
schedule_text = "忘了"
|
||||
|
||||
return schedule_text, None
|
||||
|
||||
schedule_form = self._parse_schedule(schedule_text)
|
||||
return schedule_text, schedule_form
|
||||
|
||||
def _parse_schedule(self, schedule_text: str) -> Union[bool, Dict[str, str]]:
|
||||
"""解析日程文本，转换为时间和活动的字典"""
|
||||
try:
|
||||
reg = r"\{(.|\r|\n)+\}"
|
||||
matched = re.search(reg, schedule_text)[0]
|
||||
schedule_dict = json.loads(matched)
|
||||
self._check_schedule_validity(schedule_dict)
|
||||
return schedule_dict
|
||||
except json.JSONDecodeError:
|
||||
logger.exception("解析日程失败: {}".format(schedule_text))
|
||||
return False
|
||||
except ValueError as e:
|
||||
logger.exception(f"解析日程失败: {str(e)}")
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.exception(f"解析日程发生错误:{str(e)}")
|
||||
return False
|
||||
|
||||
def _check_schedule_validity(self, schedule_dict: Dict[str, str]):
|
||||
"""检查日程是否合法"""
|
||||
if not schedule_dict:
|
||||
return
|
||||
for time_str in schedule_dict.keys():
|
||||
try:
|
||||
self._parse_time(time_str)
|
||||
except ValueError:
|
||||
raise ValueError("日程时间格式不正确") from None
|
||||
|
||||
def _parse_time(self, time_str: str) -> str:
|
||||
"""解析时间字符串，转换为时间"""
|
||||
return datetime.datetime.strptime(time_str, "%H:%M")
|
||||
|
||||
def get_current_task(self) -> str:
|
||||
"""获取当前时间应该进行的任务"""
|
||||
current_time = datetime.datetime.now().strftime("%H:%M")
|
||||
|
||||
# 找到最接近当前时间的任务
|
||||
closest_time = None
|
||||
min_diff = float("inf")
|
||||
|
||||
# 检查今天的日程
|
||||
if not self.today_schedule:
|
||||
return "摸鱼"
|
||||
for time_str in self.today_schedule.keys():
|
||||
diff = abs(self._time_diff(current_time, time_str))
|
||||
if closest_time is None or diff < min_diff:
|
||||
closest_time = time_str
|
||||
min_diff = diff
|
||||
|
||||
# 检查昨天的日程中的晚间任务
|
||||
if self.yesterday_schedule:
|
||||
for time_str in self.yesterday_schedule.keys():
|
||||
if time_str >= "20:00": # 只考虑晚上8点之后的任务
|
||||
# 计算与昨天这个时间点的差异（需要加24小时）
|
||||
diff = abs(self._time_diff(current_time, time_str))
|
||||
if diff < min_diff:
|
||||
closest_time = time_str
|
||||
min_diff = diff
|
||||
return closest_time, self.yesterday_schedule[closest_time]
|
||||
|
||||
if closest_time:
|
||||
return closest_time, self.today_schedule[closest_time]
|
||||
return "摸鱼"
|
||||
previous_doings = "你没做什么事情"
|
||||
|
||||
|
||||
prompt = f"你是{self.name}，{self.personality}，{self.behavior}"
|
||||
prompt += f"你今天的日程是：{self.today_schedule_text}\n"
|
||||
prompt += f"你之前做了的事情是：{previous_doings}，从之前到现在已经过去了{self.schedule_doing_update_interval/60}分钟了\n" #noqa: E501
|
||||
if mind_thinking:
|
||||
prompt += f"你脑子里在想：{mind_thinking}\n"
|
||||
prompt += f"现在是{now_time}，结合你的个人特点和行为习惯,注意关注你今天的日程安排和想法，这很重要，"
|
||||
prompt += "推测你现在在做什么，具体一些，详细一些\n"
|
||||
prompt += "直接返回你在做的事情，注意是当前时间，不要输出其他内容："
|
||||
return prompt
|
||||
|
||||
async def generate_daily_schedule(
|
||||
self, target_date: datetime.datetime = None,) -> Dict[str, str]:
|
||||
daytime_prompt = self.construct_daytime_prompt(target_date)
|
||||
daytime_response,_ = await self.llm_scheduler_all.generate_response_async(daytime_prompt)
|
||||
return daytime_response
|
||||
|
||||
def _time_diff(self, time1: str, time2: str) -> int:
|
||||
"""计算两个时间字符串之间的分钟差"""
|
||||
|
|
@ -174,14 +169,138 @@ class ScheduleGenerator:
|
|||
|
||||
def print_schedule(self):
|
||||
"""打印完整的日程安排"""
|
||||
if not self._parse_schedule(self.today_schedule_text):
|
||||
logger.warning("今日日程有误，将在两小时后重新生成")
|
||||
if not self.today_schedule_text:
|
||||
logger.warning("今日日程有误，将在下次运行时重新生成")
|
||||
db.schedule.delete_one({"date": datetime.datetime.now().strftime("%Y-%m-%d")})
|
||||
else:
|
||||
logger.info("=== 今日日程安排 ===")
|
||||
for time_str, activity in self.today_schedule.items():
|
||||
logger.info(f"时间[{time_str}]: 活动[{activity}]")
|
||||
logger.info(self.today_schedule_text)
|
||||
logger.info("==================")
|
||||
self.enable_output = False
|
||||
|
||||
async def update_today_done_list(self):
|
||||
# 更新数据库中的 today_done_list
|
||||
today_str = datetime.datetime.now().strftime("%Y-%m-%d")
|
||||
existing_schedule = db.schedule.find_one({"date": today_str})
|
||||
|
||||
if existing_schedule:
|
||||
# 更新数据库中的 today_done_list
|
||||
db.schedule.update_one(
|
||||
{"date": today_str},
|
||||
{"$set": {"today_done_list": self.today_done_list}}
|
||||
)
|
||||
logger.debug(f"已更新{today_str}的已完成活动列表")
|
||||
else:
|
||||
logger.warning(f"未找到{today_str}的日程记录")
|
||||
|
||||
async def move_doing(self,mind_thinking: str = ""):
|
||||
current_time = datetime.datetime.now()
|
||||
if mind_thinking:
|
||||
doing_prompt = self.construct_doing_prompt(current_time,mind_thinking)
|
||||
else:
|
||||
doing_prompt = self.construct_doing_prompt(current_time)
|
||||
|
||||
# print(doing_prompt)
|
||||
doing_response,_ = await self.llm_scheduler_doing.generate_response_async(doing_prompt)
|
||||
self.today_done_list.append((current_time,doing_response))
|
||||
|
||||
await self.update_today_done_list()
|
||||
|
||||
logger.info(f"当前活动: {doing_response}")
|
||||
|
||||
return doing_response
|
||||
|
||||
async def get_task_from_time_to_time(self, start_time: str, end_time: str):
|
||||
"""获取指定时间范围内的任务列表
|
||||
|
||||
Args:
|
||||
start_time (str): 开始时间，格式为"HH:MM"
|
||||
end_time (str): 结束时间，格式为"HH:MM"
|
||||
|
||||
Returns:
|
||||
list: 时间范围内的任务列表
|
||||
"""
|
||||
result = []
|
||||
for task in self.today_done_list:
|
||||
task_time = task[0] # 获取任务的时间戳
|
||||
task_time_str = task_time.strftime("%H:%M")
|
||||
|
||||
# 检查任务时间是否在指定范围内
|
||||
if self._time_diff(start_time, task_time_str) >= 0 and self._time_diff(task_time_str, end_time) >= 0:
|
||||
result.append(task)
|
||||
|
||||
return result
|
||||
|
||||
def get_current_num_task(self, num=1, time_info = False):
|
||||
"""获取最新加入的指定数量的日程
|
||||
|
||||
Args:
|
||||
num (int): 需要获取的日程数量，默认为1
|
||||
|
||||
Returns:
|
||||
list: 最新加入的日程列表
|
||||
"""
|
||||
if not self.today_done_list:
|
||||
return []
|
||||
|
||||
# 确保num不超过列表长度
|
||||
num = min(num, len(self.today_done_list))
|
||||
pre_doings = ""
|
||||
for doing in self.today_done_list[-num:]:
|
||||
|
||||
if time_info:
|
||||
time_str = doing[0].strftime("%H:%M")
|
||||
pre_doings += time_str + "时，" + doing[1] + "\n"
|
||||
else:
|
||||
pre_doings += doing[1] + "\n"
|
||||
|
||||
# 返回最新的num条日程
|
||||
return pre_doings
|
||||
|
||||
def save_today_schedule_to_db(self):
|
||||
"""保存日程到数据库，同时初始化 today_done_list"""
|
||||
date_str = datetime.datetime.now().strftime("%Y-%m-%d")
|
||||
schedule_data = {
|
||||
"date": date_str,
|
||||
"schedule": self.today_schedule_text,
|
||||
"today_done_list": self.today_done_list if hasattr(self, 'today_done_list') else []
|
||||
}
|
||||
# 使用 upsert 操作，如果存在则更新，不存在则插入
|
||||
db.schedule.update_one(
|
||||
{"date": date_str},
|
||||
{"$set": schedule_data},
|
||||
upsert=True
|
||||
)
|
||||
logger.debug(f"已保存{date_str}的日程到数据库")
|
||||
|
||||
def load_schedule_from_db(self, date: datetime.datetime):
|
||||
"""从数据库加载日程，同时加载 today_done_list"""
|
||||
date_str = date.strftime("%Y-%m-%d")
|
||||
existing_schedule = db.schedule.find_one({"date": date_str})
|
||||
|
||||
if existing_schedule:
|
||||
schedule_text = existing_schedule["schedule"]
|
||||
return schedule_text, existing_schedule.get("today_done_list", [])
|
||||
else:
|
||||
logger.debug(f"{date_str}的日程不存在")
|
||||
return None, None
|
||||
|
||||
async def main():
|
||||
# 使用示例
|
||||
scheduler = ScheduleGenerator()
|
||||
scheduler.initialize(
|
||||
name="麦麦",
|
||||
personality="你叫麦麦，你19岁，是一个大二的女大学生，你有一头黑色短发，你会刷贴吧，你现在在学习心理学",
|
||||
behavior="你比较内向，一般熬夜比较晚，然后第二天早上10点起床吃早午饭",
|
||||
interval=60)
|
||||
await scheduler.mai_schedule_start()
|
||||
|
||||
|
||||
|
||||
# 当作为组件导入时使用的实例
|
||||
bot_schedule = ScheduleGenerator()
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
# 当直接运行此文件时执行
|
||||
asyncio.run(main())
|
||||
|
|
|
|||
|
|
@ -1,222 +0,0 @@
|
|||
import datetime
|
||||
import json
|
||||
import re
|
||||
import os
|
||||
import sys
|
||||
from typing import Dict, Union
|
||||
# 添加项目根目录到 Python 路径
|
||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
||||
sys.path.append(root_path)
|
||||
|
||||
from src.common.database import db # noqa: E402
|
||||
from src.common.logger import get_module_logger # noqa: E402
|
||||
from src.plugins.schedule.offline_llm import LLMModel # noqa: E402
|
||||
|
||||
logger = get_module_logger("scheduler")
|
||||
|
||||
|
||||
class ScheduleGenerator:
|
||||
enable_output: bool = True
|
||||
|
||||
def __init__(self, name: str = "bot_name", personality: str = "你是一个爱国爱党的新时代青年", behavior: str = "你非常外向，喜欢尝试新事物和人交流"):
|
||||
# 使用离线LLM模型
|
||||
self.llm_scheduler = LLMModel(model_name="Pro/deepseek-ai/DeepSeek-V3", temperature=0.9)
|
||||
|
||||
self.today_schedule_text = ""
|
||||
self.today_done_list = []
|
||||
|
||||
self.yesterday_schedule_text = ""
|
||||
self.yesterday_done_list = []
|
||||
|
||||
self.name = name
|
||||
self.personality = personality
|
||||
self.behavior = behavior
|
||||
|
||||
self.start_time = datetime.datetime.now()
|
||||
|
||||
async def mai_schedule_start(self):
|
||||
"""启动日程系统，每5分钟执行一次move_doing，并在日期变化时重新检查日程"""
|
||||
try:
|
||||
logger.info(f"日程系统启动/刷新时间: {self.start_time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
# 初始化日程
|
||||
await self.check_and_create_today_schedule()
|
||||
self.print_schedule()
|
||||
|
||||
while True:
|
||||
current_time = datetime.datetime.now()
|
||||
|
||||
# 检查是否需要重新生成日程（日期变化）
|
||||
if current_time.date() != self.start_time.date():
|
||||
logger.info("检测到日期变化，重新生成日程")
|
||||
self.start_time = current_time
|
||||
await self.check_and_create_today_schedule()
|
||||
self.print_schedule()
|
||||
|
||||
# 执行当前活动
|
||||
current_activity = await self.move_doing()
|
||||
logger.info(f"当前活动: {current_activity}")
|
||||
|
||||
# 等待5分钟
|
||||
await asyncio.sleep(300) # 300秒 = 5分钟
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"日程系统运行时出错: {str(e)}")
|
||||
logger.exception("详细错误信息：")
|
||||
|
||||
async def check_and_create_today_schedule(self):
|
||||
"""检查昨天的日程，并确保今天有日程安排
|
||||
|
||||
Returns:
|
||||
tuple: (today_schedule_text, today_schedule) 今天的日程文本和解析后的日程字典
|
||||
"""
|
||||
today = datetime.datetime.now()
|
||||
yesterday = today - datetime.timedelta(days=1)
|
||||
|
||||
# 先检查昨天的日程
|
||||
self.yesterday_schedule_text, self.yesterday_done_list = self.load_schedule_from_db(yesterday)
|
||||
if self.yesterday_schedule_text:
|
||||
logger.debug(f"已加载{yesterday.strftime('%Y-%m-%d')}的日程")
|
||||
|
||||
# 检查今天的日程
|
||||
self.today_schedule_text, self.today_done_list = self.load_schedule_from_db(today)
|
||||
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)
|
||||
|
||||
self.save_today_schedule_to_db()
|
||||
|
||||
def construct_daytime_prompt(self, target_date: datetime.datetime):
|
||||
date_str = target_date.strftime("%Y-%m-%d")
|
||||
weekday = target_date.strftime("%A")
|
||||
|
||||
prompt = f"我是{self.name}，{self.personality}，{self.behavior}"
|
||||
prompt += f"我昨天的日程是：{self.yesterday_schedule_text}\n"
|
||||
prompt += f"请为我生成{date_str}（{weekday}）的日程安排，结合我的个人特点和行为习惯\n"
|
||||
prompt += "推测我的日程安排，包括我一天都在做什么，有什么发现和思考，具体一些，详细一些，记得写明时间\n"
|
||||
prompt += "直接返回我的日程，不要输出其他内容："
|
||||
return prompt
|
||||
|
||||
def construct_doing_prompt(self,time: datetime.datetime):
|
||||
now_time = time.strftime("%H:%M")
|
||||
previous_doing = self.today_done_list[-20:] if len(self.today_done_list) > 20 else self.today_done_list
|
||||
prompt = f"我是{self.name}，{self.personality}，{self.behavior}"
|
||||
prompt += f"我今天的日程是：{self.today_schedule_text}\n"
|
||||
prompt += f"我之前做了的事情是：{previous_doing}\n"
|
||||
prompt += f"现在是{now_time}，结合我的个人特点和行为习惯,"
|
||||
prompt += "推测我现在做什么，具体一些，详细一些\n"
|
||||
prompt += "直接返回我在做的事情，不要输出其他内容："
|
||||
return prompt
|
||||
|
||||
async def generate_daily_schedule(
|
||||
self, target_date: datetime.datetime = None,) -> Dict[str, str]:
|
||||
daytime_prompt = self.construct_daytime_prompt(target_date)
|
||||
daytime_response, _ = await self.llm_scheduler.generate_response(daytime_prompt)
|
||||
return daytime_response
|
||||
|
||||
def _time_diff(self, time1: str, time2: str) -> int:
|
||||
"""计算两个时间字符串之间的分钟差"""
|
||||
if time1 == "24:00":
|
||||
time1 = "23:59"
|
||||
if time2 == "24:00":
|
||||
time2 = "23:59"
|
||||
t1 = datetime.datetime.strptime(time1, "%H:%M")
|
||||
t2 = datetime.datetime.strptime(time2, "%H:%M")
|
||||
diff = int((t2 - t1).total_seconds() / 60)
|
||||
# 考虑时间的循环性
|
||||
if diff < -720:
|
||||
diff += 1440 # 加一天的分钟
|
||||
elif diff > 720:
|
||||
diff -= 1440 # 减一天的分钟
|
||||
# print(f"时间1[{time1}]: 时间2[{time2}]，差值[{diff}]分钟")
|
||||
return diff
|
||||
|
||||
def print_schedule(self):
|
||||
"""打印完整的日程安排"""
|
||||
if not self.today_schedule_text:
|
||||
logger.warning("今日日程有误，将在下次运行时重新生成")
|
||||
db.schedule.delete_one({"date": datetime.datetime.now().strftime("%Y-%m-%d")})
|
||||
else:
|
||||
logger.info("=== 今日日程安排 ===")
|
||||
logger.info(self.today_schedule_text)
|
||||
logger.info("==================")
|
||||
self.enable_output = False
|
||||
|
||||
async def update_today_done_list(self):
|
||||
# 更新数据库中的 today_done_list
|
||||
today_str = datetime.datetime.now().strftime("%Y-%m-%d")
|
||||
existing_schedule = db.schedule.find_one({"date": today_str})
|
||||
|
||||
if existing_schedule:
|
||||
# 更新数据库中的 today_done_list
|
||||
db.schedule.update_one(
|
||||
{"date": today_str},
|
||||
{"$set": {"today_done_list": self.today_done_list}}
|
||||
)
|
||||
logger.debug(f"已更新{today_str}的已完成活动列表")
|
||||
else:
|
||||
logger.warning(f"未找到{today_str}的日程记录")
|
||||
|
||||
async def move_doing(self):
|
||||
current_time = datetime.datetime.now()
|
||||
time_str = current_time.strftime("%H:%M")
|
||||
doing_prompt = self.construct_doing_prompt(current_time)
|
||||
doing_response, _ = await self.llm_scheduler.generate_response(doing_prompt)
|
||||
self.today_done_list.append(current_time,time_str + "在" + doing_response)
|
||||
|
||||
await self.update_today_done_list()
|
||||
|
||||
return doing_response
|
||||
|
||||
|
||||
|
||||
|
||||
def save_today_schedule_to_db(self):
|
||||
"""保存日程到数据库，同时初始化 today_done_list"""
|
||||
date_str = datetime.datetime.now().strftime("%Y-%m-%d")
|
||||
schedule_data = {
|
||||
"date": date_str,
|
||||
"schedule": self.today_schedule_text,
|
||||
"today_done_list": self.today_done_list if hasattr(self, 'today_done_list') else []
|
||||
}
|
||||
# 使用 upsert 操作，如果存在则更新，不存在则插入
|
||||
db.schedule.update_one(
|
||||
{"date": date_str},
|
||||
{"$set": schedule_data},
|
||||
upsert=True
|
||||
)
|
||||
logger.debug(f"已保存{date_str}的日程到数据库")
|
||||
|
||||
def load_schedule_from_db(self, date: datetime.datetime):
|
||||
"""从数据库加载日程，同时加载 today_done_list"""
|
||||
date_str = date.strftime("%Y-%m-%d")
|
||||
existing_schedule = db.schedule.find_one({"date": date_str})
|
||||
|
||||
if existing_schedule:
|
||||
schedule_text = existing_schedule["schedule"]
|
||||
return schedule_text, existing_schedule.get("today_done_list", [])
|
||||
else:
|
||||
logger.debug(f"{date_str}的日程不存在")
|
||||
return None, None
|
||||
|
||||
async def main():
|
||||
# 使用示例
|
||||
scheduler = ScheduleGenerator(name="麦麦", personality="你叫麦麦，你19岁，是一个大二的女大学生，你有一头黑色短发，你会刷贴吧，你现在在学习心理学", behavior="你比较内向")
|
||||
await scheduler.check_and_create_today_schedule()
|
||||
scheduler.print_schedule()
|
||||
print("\n当前任务：")
|
||||
print(await scheduler.get_current_task())
|
||||
|
||||
print("昨天日程：")
|
||||
print(scheduler.yesterday_schedule)
|
||||
print("今天日程：")
|
||||
print(scheduler.today_schedule)
|
||||
print("明天日程：")
|
||||
print(scheduler.tomorrow_schedule)
|
||||
|
||||
# 当作为组件导入时使用的实例
|
||||
bot_schedule = ScheduleGenerator()
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
# 当直接运行此文件时执行
|
||||
asyncio.run(main())
|
||||
|
|
@ -20,6 +20,13 @@ class LLMStatistics:
|
|||
self.output_file = output_file
|
||||
self.running = False
|
||||
self.stats_thread = None
|
||||
self._init_database()
|
||||
|
||||
def _init_database(self):
|
||||
"""初始化数据库集合"""
|
||||
if "online_time" not in db.list_collection_names():
|
||||
db.create_collection("online_time")
|
||||
db.online_time.create_index([("timestamp", 1)])
|
||||
|
||||
def start(self):
|
||||
"""启动统计线程"""
|
||||
|
|
@ -35,6 +42,16 @@ class LLMStatistics:
|
|||
if self.stats_thread:
|
||||
self.stats_thread.join()
|
||||
|
||||
def _record_online_time(self):
|
||||
"""记录在线时间"""
|
||||
try:
|
||||
db.online_time.insert_one({
|
||||
"timestamp": datetime.now(),
|
||||
"duration": 5 # 5分钟
|
||||
})
|
||||
except Exception:
|
||||
logger.exception("记录在线时间失败")
|
||||
|
||||
def _collect_statistics_for_period(self, start_time: datetime) -> Dict[str, Any]:
|
||||
"""收集指定时间段的LLM请求统计数据
|
||||
|
||||
|
|
@ -56,10 +73,11 @@ class LLMStatistics:
|
|||
"tokens_by_type": defaultdict(int),
|
||||
"tokens_by_user": defaultdict(int),
|
||||
"tokens_by_model": defaultdict(int),
|
||||
# 新增在线时间统计
|
||||
"online_time_minutes": 0,
|
||||
}
|
||||
|
||||
cursor = db.llm_usage.find({"timestamp": {"$gte": start_time}})
|
||||
|
||||
total_requests = 0
|
||||
|
||||
for doc in cursor:
|
||||
|
|
@ -74,7 +92,7 @@ class LLMStatistics:
|
|||
|
||||
prompt_tokens = doc.get("prompt_tokens", 0)
|
||||
completion_tokens = doc.get("completion_tokens", 0)
|
||||
total_tokens = prompt_tokens + completion_tokens # 根据数据库字段调整
|
||||
total_tokens = prompt_tokens + completion_tokens
|
||||
stats["tokens_by_type"][request_type] += total_tokens
|
||||
stats["tokens_by_user"][user_id] += total_tokens
|
||||
stats["tokens_by_model"][model_name] += total_tokens
|
||||
|
|
@ -91,6 +109,11 @@ class LLMStatistics:
|
|||
if total_requests > 0:
|
||||
stats["average_tokens"] = stats["total_tokens"] / total_requests
|
||||
|
||||
# 统计在线时间
|
||||
online_time_cursor = db.online_time.find({"timestamp": {"$gte": start_time}})
|
||||
for doc in online_time_cursor:
|
||||
stats["online_time_minutes"] += doc.get("duration", 0)
|
||||
|
||||
return stats
|
||||
|
||||
def _collect_all_statistics(self) -> Dict[str, Dict[str, Any]]:
|
||||
|
|
@ -115,7 +138,8 @@ class LLMStatistics:
|
|||
output.append(f"总请求数: {stats['total_requests']}")
|
||||
if stats["total_requests"] > 0:
|
||||
output.append(f"总Token数: {stats['total_tokens']}")
|
||||
output.append(f"总花费: {stats['total_cost']:.4f}¥\n")
|
||||
output.append(f"总花费: {stats['total_cost']:.4f}¥")
|
||||
output.append(f"在线时间: {stats['online_time_minutes']}分钟\n")
|
||||
|
||||
data_fmt = "{:<32} {:>10} {:>14} {:>13.4f} ¥"
|
||||
|
||||
|
|
@ -184,13 +208,16 @@ class LLMStatistics:
|
|||
"""统计循环，每1分钟运行一次"""
|
||||
while self.running:
|
||||
try:
|
||||
# 记录在线时间
|
||||
self._record_online_time()
|
||||
# 收集并保存统计数据
|
||||
all_stats = self._collect_all_statistics()
|
||||
self._save_statistics(all_stats)
|
||||
except Exception:
|
||||
logger.exception("统计数据处理失败")
|
||||
|
||||
# 等待1分钟
|
||||
for _ in range(60):
|
||||
# 等待5分钟
|
||||
for _ in range(300): # 5分钟 = 300秒
|
||||
if not self.running:
|
||||
break
|
||||
time.sleep(1)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import asyncio
|
||||
from typing import Dict
|
||||
from ..chat.chat_stream import ChatStream
|
||||
from ..chat.config import global_config
|
||||
from ..config.config import global_config
|
||||
|
||||
|
||||
class WillingManager:
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ import random
|
|||
import time
|
||||
from typing import Dict
|
||||
from src.common.logger import get_module_logger
|
||||
from ..chat.config import global_config
|
||||
from ..config.config import global_config
|
||||
from ..chat.chat_stream import ChatStream
|
||||
|
||||
logger = get_module_logger("mode_dynamic")
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from typing import Optional
|
||||
from src.common.logger import get_module_logger
|
||||
|
||||
from ..chat.config import global_config
|
||||
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
|
||||
|
|
|
|||
Binary file not shown.
|
After Width: | Height: | Size: 59 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 91 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 88 KiB |
|
|
@ -1,8 +1,17 @@
|
|||
from .current_mind import SubHeartflow
|
||||
from .sub_heartflow import SubHeartflow
|
||||
from src.plugins.moods.moods import MoodManager
|
||||
from src.plugins.models.utils_model import LLM_request
|
||||
from src.plugins.chat.config import global_config
|
||||
from src.plugins.config.config import global_config, BotConfig
|
||||
from src.plugins.schedule.schedule_generator import bot_schedule
|
||||
import asyncio
|
||||
from src.common.logger import get_module_logger, LogConfig, HEARTFLOW_STYLE_CONFIG # noqa: E402
|
||||
|
||||
heartflow_config = LogConfig(
|
||||
# 使用海马体专用样式
|
||||
console_format=HEARTFLOW_STYLE_CONFIG["console_format"],
|
||||
file_format=HEARTFLOW_STYLE_CONFIG["file_format"],
|
||||
)
|
||||
logger = get_module_logger("heartflow", config=heartflow_config)
|
||||
|
||||
class CuttentState:
|
||||
def __init__(self):
|
||||
|
|
@ -30,22 +39,24 @@ class Heartflow:
|
|||
|
||||
async def heartflow_start_working(self):
|
||||
while True:
|
||||
# await self.do_a_thinking()
|
||||
await asyncio.sleep(60)
|
||||
await self.do_a_thinking()
|
||||
await asyncio.sleep(600)
|
||||
|
||||
async def do_a_thinking(self):
|
||||
print("麦麦大脑袋转起来了")
|
||||
logger.info("麦麦大脑袋转起来了")
|
||||
self.current_state.update_current_state_info()
|
||||
|
||||
personality_info = open("src/think_flow_demo/personality_info.txt", "r", encoding="utf-8").read()
|
||||
personality_info = " ".join(global_config.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()
|
||||
|
||||
schedule_info = bot_schedule.get_current_num_task(num = 5,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"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：{message_stream_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"
|
||||
|
|
@ -58,7 +69,10 @@ class Heartflow:
|
|||
self.update_current_mind(reponse)
|
||||
|
||||
self.current_mind = reponse
|
||||
print(f"麦麦的总体脑内状态：{self.current_mind}")
|
||||
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
|
||||
|
|
@ -77,13 +91,13 @@ class Heartflow:
|
|||
return await self.minds_summary(sub_minds)
|
||||
|
||||
async def minds_summary(self,minds_str):
|
||||
personality_info = open("src/think_flow_demo/personality_info.txt", "r", encoding="utf-8").read()
|
||||
personality_info = " ".join(BotConfig.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"现在麦麦在qq群里进行聊天，聊天的话题如下：{minds_str}\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:'''
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
import asyncio
|
||||
from datetime import datetime
|
||||
from src.plugins.models.utils_model import LLM_request
|
||||
from src.plugins.chat.config import global_config
|
||||
from src.plugins.config.config import global_config
|
||||
from src.common.database import db
|
||||
|
||||
#存储一段聊天的大致内容
|
||||
|
|
@ -16,6 +16,10 @@ class Talking_info:
|
|||
self.observe_times = 0
|
||||
self.activate = 360
|
||||
|
||||
self.last_summary_time = int(datetime.now().timestamp()) # 上次更新summary的时间
|
||||
self.summary_count = 0 # 30秒内的更新次数
|
||||
self.max_update_in_30s = 2
|
||||
|
||||
self.oberve_interval = 3
|
||||
|
||||
self.llm_summary = LLM_request(
|
||||
|
|
@ -60,16 +64,22 @@ class Talking_info:
|
|||
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()
|
||||
# print(self.talking_message_str)
|
||||
self.observe_times += 1
|
||||
self.last_observe_time = new_messages[-1]["time"]
|
||||
|
||||
if self.observe_times > 3:
|
||||
# 检查是否需要更新summary
|
||||
current_time = int(datetime.now().timestamp())
|
||||
if current_time - self.last_summary_time >= 30: # 如果超过30秒，重置计数
|
||||
self.summary_count = 0
|
||||
self.last_summary_time = current_time
|
||||
|
||||
if self.summary_count < self.max_update_in_30s: # 如果30秒内更新次数小于2次
|
||||
await self.update_talking_summary()
|
||||
# print(f"更新了聊天总结：{self.talking_summary}")
|
||||
self.summary_count += 1
|
||||
|
||||
async def update_talking_summary(self):
|
||||
#基于已经有的talking_summary，和新的talking_message，生成一个summary
|
||||
# print(f"更新聊天总结：{self.talking_summary}")
|
||||
prompt = ""
|
||||
prompt = f"你正在参与一个qq群聊的讨论，这个群之前在聊的内容是：{self.talking_summary}\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：{self.talking_message_str}\n"
|
||||
|
|
|
|||
|
|
@ -1 +0,0 @@
|
|||
你叫麦麦，你19岁，是一个大二的女大学生，你有一头黑色短发，你会刷贴吧
|
||||
|
|
@ -2,9 +2,21 @@ from .outer_world import outer_world
|
|||
import asyncio
|
||||
from src.plugins.moods.moods import MoodManager
|
||||
from src.plugins.models.utils_model import LLM_request
|
||||
from src.plugins.chat.config import global_config
|
||||
from src.plugins.config.config import global_config, BotConfig
|
||||
import re
|
||||
import time
|
||||
from src.plugins.schedule.schedule_generator import bot_schedule
|
||||
from src.plugins.memory_system.Hippocampus import HippocampusManager
|
||||
from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402
|
||||
|
||||
subheartflow_config = LogConfig(
|
||||
# 使用海马体专用样式
|
||||
console_format=SUB_HEARTFLOW_STYLE_CONFIG["console_format"],
|
||||
file_format=SUB_HEARTFLOW_STYLE_CONFIG["file_format"],
|
||||
)
|
||||
logger = get_module_logger("subheartflow", config=subheartflow_config)
|
||||
|
||||
|
||||
class CuttentState:
|
||||
def __init__(self):
|
||||
self.willing = 0
|
||||
|
|
@ -34,6 +46,8 @@ class SubHeartflow:
|
|||
|
||||
if not self.current_mind:
|
||||
self.current_mind = "你什么也没想"
|
||||
|
||||
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||
|
||||
def assign_observe(self,stream_id):
|
||||
self.outer_world = outer_world.get_world_by_stream_id(stream_id)
|
||||
|
|
@ -44,29 +58,49 @@ class SubHeartflow:
|
|||
current_time = time.time()
|
||||
if current_time - self.last_reply_time > 180: # 3分钟 = 180秒
|
||||
# print(f"{self.observe_chat_id}麦麦已经3分钟没有回复了，暂时停止思考")
|
||||
await asyncio.sleep(25) # 每30秒检查一次
|
||||
await asyncio.sleep(60) # 每30秒检查一次
|
||||
else:
|
||||
await self.do_a_thinking()
|
||||
await self.judge_willing()
|
||||
await asyncio.sleep(25)
|
||||
await asyncio.sleep(60)
|
||||
|
||||
async def do_a_thinking(self):
|
||||
print("麦麦小脑袋转起来了")
|
||||
self.current_state.update_current_state_info()
|
||||
|
||||
personality_info = open("src/think_flow_demo/personality_info.txt", "r", encoding="utf-8").read()
|
||||
current_thinking_info = self.current_mind
|
||||
mood_info = self.current_state.mood
|
||||
related_memory_info = 'memory'
|
||||
|
||||
message_stream_info = self.outer_world.talking_summary
|
||||
print(f"message_stream_info：{message_stream_info}")
|
||||
|
||||
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||
text=message_stream_info,
|
||||
max_memory_num=3,
|
||||
max_memory_length=2,
|
||||
max_depth=3,
|
||||
fast_retrieval=False
|
||||
)
|
||||
# print(f"相关记忆：{related_memory}")
|
||||
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"{personality_info}\n"
|
||||
prompt += f"你{self.personality_info}\n"
|
||||
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{message_stream_info}\n"
|
||||
prompt += f"你想起来{related_memory_info}。"
|
||||
if related_memory_info:
|
||||
prompt += f"你想起来{related_memory_info}。"
|
||||
prompt += f"刚刚你的想法是{current_thinking_info}。"
|
||||
prompt += f"你现在{mood_info}。"
|
||||
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)
|
||||
|
|
@ -74,48 +108,52 @@ class SubHeartflow:
|
|||
self.update_current_mind(reponse)
|
||||
|
||||
self.current_mind = reponse
|
||||
print(f"麦麦的脑内状态：{self.current_mind}")
|
||||
print(prompt)
|
||||
logger.info(f"麦麦的脑内状态：{self.current_mind}")
|
||||
|
||||
async def do_after_reply(self,reply_content,chat_talking_prompt):
|
||||
# print("麦麦脑袋转起来了")
|
||||
self.current_state.update_current_state_info()
|
||||
|
||||
personality_info = open("src/think_flow_demo/personality_info.txt", "r", encoding="utf-8").read()
|
||||
current_thinking_info = self.current_mind
|
||||
mood_info = self.current_state.mood
|
||||
related_memory_info = 'memory'
|
||||
message_stream_info = self.outer_world.talking_summary
|
||||
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"{personality_info}\n"
|
||||
prompt += f"你刚刚在做的事情是：{schedule_info}\n"
|
||||
prompt += f"你{self.personality_info}\n"
|
||||
|
||||
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{message_stream_info}\n"
|
||||
prompt += f"你想起来{related_memory_info}。"
|
||||
if related_memory_info:
|
||||
prompt += f"你想起来{related_memory_info}。"
|
||||
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，以及你回复的内容，不要思考太多:"
|
||||
prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:"
|
||||
|
||||
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
|
||||
|
||||
self.update_current_mind(reponse)
|
||||
|
||||
self.current_mind = reponse
|
||||
print(f"{self.observe_chat_id}麦麦的脑内状态：{self.current_mind}")
|
||||
logger.info(f"麦麦回复后的脑内状态：{self.current_mind}")
|
||||
|
||||
self.last_reply_time = time.time()
|
||||
|
||||
async def judge_willing(self):
|
||||
# print("麦麦闹情绪了1")
|
||||
personality_info = open("src/think_flow_demo/personality_info.txt", "r", encoding="utf-8").read()
|
||||
current_thinking_info = self.current_mind
|
||||
mood_info = self.current_state.mood
|
||||
# print("麦麦闹情绪了2")
|
||||
prompt = ""
|
||||
prompt += f"{personality_info}\n"
|
||||
prompt += f"{self.personality_info}\n"
|
||||
prompt += "现在你正在上网，和qq群里的网友们聊天"
|
||||
prompt += f"你现在的想法是{current_thinking_info}。"
|
||||
prompt += f"你现在{mood_info}。"
|
||||
|
|
@ -130,7 +168,7 @@ class SubHeartflow:
|
|||
else:
|
||||
self.current_state.willing = 0
|
||||
|
||||
print(f"{self.observe_chat_id}麦麦的回复意愿：{self.current_state.willing}")
|
||||
logger.info(f"{self.observe_chat_id}麦麦的回复意愿：{self.current_state.willing}")
|
||||
|
||||
return self.current_state.willing
|
||||
|
||||
|
|
@ -3,7 +3,7 @@ version = "0.0.11"
|
|||
|
||||
[mai_version]
|
||||
version = "0.6.0"
|
||||
version-fix = "snapshot-1"
|
||||
version-fix = "snapshot-2"
|
||||
|
||||
#以下是给开发人员阅读的，一般用户不需要阅读
|
||||
#如果你想要修改配置文件，请在修改后将version的值进行变更
|
||||
|
|
@ -43,6 +43,7 @@ personality_3_probability = 0.1 # 第三种人格出现概率，请确保三个
|
|||
[schedule]
|
||||
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
|
||||
|
||||
[message]
|
||||
max_context_size = 15 # 麦麦获得的上文数量，建议15，太短太长都会导致脑袋尖尖
|
||||
|
|
@ -85,7 +86,7 @@ check_prompt = "符合公序良俗" # 表情包过滤要求
|
|||
|
||||
[memory]
|
||||
build_memory_interval = 2000 # 记忆构建间隔 单位秒 间隔越低，麦麦学习越多，但是冗余信息也会增多
|
||||
build_memory_distribution = [4,2,0.6,24,8,0.4] # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重
|
||||
build_memory_distribution = [4.0,2.0,0.6,24.0,8.0,0.4] # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重
|
||||
build_memory_sample_num = 10 # 采样数量，数值越高记忆采样次数越多
|
||||
build_memory_sample_length = 20 # 采样长度，数值越高一段记忆内容越丰富
|
||||
memory_compress_rate = 0.1 # 记忆压缩率 控制记忆精简程度 建议保持默认,调高可以获得更多信息，但是冗余信息也会增多
|
||||
|
|
@ -135,6 +136,7 @@ enable = true
|
|||
[experimental]
|
||||
enable_friend_chat = false # 是否启用好友聊天
|
||||
enable_think_flow = false # 是否启用思维流 注意：可能会消耗大量token，请谨慎开启
|
||||
#思维流适合搭配低能耗普通模型使用，例如qwen2.5 32b
|
||||
|
||||
#下面的模型若使用硅基流动则不需要更改，使用ds官方则改成.env.prod自定义的宏，使用自定义模型则选择定位相似的模型自己填写
|
||||
#推理模型
|
||||
|
|
|
|||
659
webui.py
659
webui.py
|
|
@ -5,6 +5,7 @@ import toml
|
|||
import signal
|
||||
import sys
|
||||
import requests
|
||||
import socket
|
||||
try:
|
||||
from src.common.logger import get_module_logger
|
||||
|
||||
|
|
@ -39,50 +40,35 @@ def signal_handler(signum, frame):
|
|||
signal.signal(signal.SIGINT, signal_handler)
|
||||
|
||||
is_share = False
|
||||
debug = True
|
||||
# 检查配置文件是否存在
|
||||
if not os.path.exists("config/bot_config.toml"):
|
||||
logger.error("配置文件 bot_config.toml 不存在，请检查配置文件路径")
|
||||
raise FileNotFoundError("配置文件 bot_config.toml 不存在，请检查配置文件路径")
|
||||
|
||||
if not os.path.exists(".env.prod"):
|
||||
logger.error("环境配置文件 .env.prod 不存在，请检查配置文件路径")
|
||||
raise FileNotFoundError("环境配置文件 .env.prod 不存在，请检查配置文件路径")
|
||||
|
||||
config_data = toml.load("config/bot_config.toml")
|
||||
# 增加对老版本配置文件支持
|
||||
LEGACY_CONFIG_VERSION = version.parse("0.0.1")
|
||||
|
||||
# 增加最低支持版本
|
||||
MIN_SUPPORT_VERSION = version.parse("0.0.8")
|
||||
MIN_SUPPORT_MAIMAI_VERSION = version.parse("0.5.13")
|
||||
|
||||
if "inner" in config_data:
|
||||
CONFIG_VERSION = config_data["inner"]["version"]
|
||||
PARSED_CONFIG_VERSION = version.parse(CONFIG_VERSION)
|
||||
if PARSED_CONFIG_VERSION < MIN_SUPPORT_VERSION:
|
||||
logger.error("您的麦麦版本过低！！已经不再支持，请更新到最新版本！！")
|
||||
logger.error("最低支持的麦麦版本：" + str(MIN_SUPPORT_MAIMAI_VERSION))
|
||||
raise Exception("您的麦麦版本过低！！已经不再支持，请更新到最新版本！！")
|
||||
else:
|
||||
logger.error("您的麦麦版本过低！！已经不再支持，请更新到最新版本！！")
|
||||
logger.error("最低支持的麦麦版本：" + str(MIN_SUPPORT_MAIMAI_VERSION))
|
||||
raise Exception("您的麦麦版本过低！！已经不再支持，请更新到最新版本！！")
|
||||
|
||||
|
||||
HAVE_ONLINE_STATUS_VERSION = version.parse("0.0.9")
|
||||
|
||||
# 定义意愿模式可选项
|
||||
WILLING_MODE_CHOICES = [
|
||||
"classical",
|
||||
"dynamic",
|
||||
"custom",
|
||||
]
|
||||
|
||||
|
||||
# 添加WebUI配置文件版本
|
||||
WEBUI_VERSION = version.parse("0.0.10")
|
||||
debug = False
|
||||
|
||||
def init_model_pricing():
|
||||
"""初始化模型价格配置"""
|
||||
model_list = [
|
||||
"llm_reasoning",
|
||||
"llm_reasoning_minor",
|
||||
"llm_normal",
|
||||
"llm_topic_judge",
|
||||
"llm_summary_by_topic",
|
||||
"llm_emotion_judge",
|
||||
"vlm",
|
||||
"embedding",
|
||||
"moderation"
|
||||
]
|
||||
|
||||
for model in model_list:
|
||||
if model in config_data["model"]:
|
||||
# 检查是否已有pri_in和pri_out配置
|
||||
has_pri_in = "pri_in" in config_data["model"][model]
|
||||
has_pri_out = "pri_out" in config_data["model"][model]
|
||||
|
||||
# 只在缺少配置时添加默认值
|
||||
if not has_pri_in:
|
||||
config_data["model"][model]["pri_in"] = 0
|
||||
logger.info(f"为模型 {model} 添加默认输入价格配置")
|
||||
if not has_pri_out:
|
||||
config_data["model"][model]["pri_out"] = 0
|
||||
logger.info(f"为模型 {model} 添加默认输出价格配置")
|
||||
|
||||
# ==============================================
|
||||
# env环境配置文件读取部分
|
||||
|
|
@ -124,6 +110,68 @@ def parse_env_config(config_file):
|
|||
return env_variables
|
||||
|
||||
|
||||
# 检查配置文件是否存在
|
||||
if not os.path.exists("config/bot_config.toml"):
|
||||
logger.error("配置文件 bot_config.toml 不存在，请检查配置文件路径")
|
||||
raise FileNotFoundError("配置文件 bot_config.toml 不存在，请检查配置文件路径")
|
||||
else:
|
||||
config_data = toml.load("config/bot_config.toml")
|
||||
init_model_pricing()
|
||||
|
||||
if not os.path.exists(".env.prod"):
|
||||
logger.error("环境配置文件 .env.prod 不存在，请检查配置文件路径")
|
||||
raise FileNotFoundError("环境配置文件 .env.prod 不存在，请检查配置文件路径")
|
||||
else:
|
||||
# 载入env文件并解析
|
||||
env_config_file = ".env.prod" # 配置文件路径
|
||||
env_config_data = parse_env_config(env_config_file)
|
||||
|
||||
# 增加最低支持版本
|
||||
MIN_SUPPORT_VERSION = version.parse("0.0.8")
|
||||
MIN_SUPPORT_MAIMAI_VERSION = version.parse("0.5.13")
|
||||
|
||||
if "inner" in config_data:
|
||||
CONFIG_VERSION = config_data["inner"]["version"]
|
||||
PARSED_CONFIG_VERSION = version.parse(CONFIG_VERSION)
|
||||
if PARSED_CONFIG_VERSION < MIN_SUPPORT_VERSION:
|
||||
logger.error("您的麦麦版本过低！！已经不再支持，请更新到最新版本！！")
|
||||
logger.error("最低支持的麦麦版本：" + str(MIN_SUPPORT_MAIMAI_VERSION))
|
||||
raise Exception("您的麦麦版本过低！！已经不再支持，请更新到最新版本！！")
|
||||
else:
|
||||
logger.error("您的麦麦版本过低！！已经不再支持，请更新到最新版本！！")
|
||||
logger.error("最低支持的麦麦版本：" + str(MIN_SUPPORT_MAIMAI_VERSION))
|
||||
raise Exception("您的麦麦版本过低！！已经不再支持，请更新到最新版本！！")
|
||||
|
||||
# 添加麦麦版本
|
||||
|
||||
if "mai_version" in config_data:
|
||||
MAI_VERSION = version.parse(str(config_data["mai_version"]["version"]))
|
||||
logger.info("您的麦麦版本为：" + str(MAI_VERSION))
|
||||
else:
|
||||
logger.info("检测到配置文件中并没有定义麦麦版本，将使用默认版本")
|
||||
MAI_VERSION = version.parse("0.5.15")
|
||||
logger.info("您的麦麦版本为：" + str(MAI_VERSION))
|
||||
|
||||
# 增加在线状态更新版本
|
||||
HAVE_ONLINE_STATUS_VERSION = version.parse("0.0.9")
|
||||
# 增加日程设置重构版本
|
||||
SCHEDULE_CHANGED_VERSION = version.parse("0.0.11")
|
||||
|
||||
# 定义意愿模式可选项
|
||||
WILLING_MODE_CHOICES = [
|
||||
"classical",
|
||||
"dynamic",
|
||||
"custom",
|
||||
]
|
||||
|
||||
|
||||
# 添加WebUI配置文件版本
|
||||
WEBUI_VERSION = version.parse("0.0.11")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# env环境配置文件保存函数
|
||||
def save_to_env_file(env_variables, filename=".env.prod"):
|
||||
"""
|
||||
|
|
@ -482,7 +530,9 @@ def save_personality_config(
|
|||
t_prompt_personality_1,
|
||||
t_prompt_personality_2,
|
||||
t_prompt_personality_3,
|
||||
t_prompt_schedule,
|
||||
t_enable_schedule_gen,
|
||||
t_prompt_schedule_gen,
|
||||
t_schedule_doing_update_interval,
|
||||
t_personality_1_probability,
|
||||
t_personality_2_probability,
|
||||
t_personality_3_probability,
|
||||
|
|
@ -492,8 +542,13 @@ def save_personality_config(
|
|||
config_data["personality"]["prompt_personality"][1] = t_prompt_personality_2
|
||||
config_data["personality"]["prompt_personality"][2] = t_prompt_personality_3
|
||||
|
||||
# 保存日程生成提示词
|
||||
config_data["personality"]["prompt_schedule"] = t_prompt_schedule
|
||||
# 保存日程生成部分
|
||||
if PARSED_CONFIG_VERSION >= SCHEDULE_CHANGED_VERSION:
|
||||
config_data["schedule"]["enable_schedule_gen"] = t_enable_schedule_gen
|
||||
config_data["schedule"]["prompt_schedule_gen"] = t_prompt_schedule_gen
|
||||
config_data["schedule"]["schedule_doing_update_interval"] = t_schedule_doing_update_interval
|
||||
else:
|
||||
config_data["personality"]["prompt_schedule"] = t_prompt_schedule_gen
|
||||
|
||||
# 保存三个人格的概率
|
||||
config_data["personality"]["personality_1_probability"] = t_personality_1_probability
|
||||
|
|
@ -521,13 +576,15 @@ def save_message_and_emoji_config(
|
|||
t_enable_check,
|
||||
t_check_prompt,
|
||||
):
|
||||
config_data["message"]["min_text_length"] = t_min_text_length
|
||||
if PARSED_CONFIG_VERSION < version.parse("0.0.11"):
|
||||
config_data["message"]["min_text_length"] = t_min_text_length
|
||||
config_data["message"]["max_context_size"] = t_max_context_size
|
||||
config_data["message"]["emoji_chance"] = t_emoji_chance
|
||||
config_data["message"]["thinking_timeout"] = t_thinking_timeout
|
||||
config_data["message"]["response_willing_amplifier"] = t_response_willing_amplifier
|
||||
config_data["message"]["response_interested_rate_amplifier"] = t_response_interested_rate_amplifier
|
||||
config_data["message"]["down_frequency_rate"] = t_down_frequency_rate
|
||||
if PARSED_CONFIG_VERSION < version.parse("0.0.11"):
|
||||
config_data["message"]["response_willing_amplifier"] = t_response_willing_amplifier
|
||||
config_data["message"]["response_interested_rate_amplifier"] = t_response_interested_rate_amplifier
|
||||
config_data["message"]["down_frequency_rate"] = t_down_frequency_rate
|
||||
config_data["message"]["ban_words"] = t_ban_words_final_result
|
||||
config_data["message"]["ban_msgs_regex"] = t_ban_msgs_regex_final_result
|
||||
config_data["emoji"]["check_interval"] = t_check_interval
|
||||
|
|
@ -539,6 +596,21 @@ def save_message_and_emoji_config(
|
|||
logger.info("消息和表情配置已保存到 bot_config.toml 文件中")
|
||||
return "消息和表情配置已保存"
|
||||
|
||||
def save_willing_config(
|
||||
t_willing_mode,
|
||||
t_response_willing_amplifier,
|
||||
t_response_interested_rate_amplifier,
|
||||
t_down_frequency_rate,
|
||||
t_emoji_response_penalty,
|
||||
):
|
||||
config_data["willing"]["willing_mode"] = t_willing_mode
|
||||
config_data["willing"]["response_willing_amplifier"] = t_response_willing_amplifier
|
||||
config_data["willing"]["response_interested_rate_amplifier"] = t_response_interested_rate_amplifier
|
||||
config_data["willing"]["down_frequency_rate"] = t_down_frequency_rate
|
||||
config_data["willing"]["emoji_response_penalty"] = t_emoji_response_penalty
|
||||
save_config_to_file(config_data)
|
||||
logger.info("willinng配置已保存到 bot_config.toml 文件中")
|
||||
return "willinng配置已保存"
|
||||
|
||||
def save_response_model_config(
|
||||
t_willing_mode,
|
||||
|
|
@ -552,39 +624,79 @@ def save_response_model_config(
|
|||
t_model1_pri_out,
|
||||
t_model2_name,
|
||||
t_model2_provider,
|
||||
t_model2_pri_in,
|
||||
t_model2_pri_out,
|
||||
t_model3_name,
|
||||
t_model3_provider,
|
||||
t_model3_pri_in,
|
||||
t_model3_pri_out,
|
||||
t_emotion_model_name,
|
||||
t_emotion_model_provider,
|
||||
t_emotion_model_pri_in,
|
||||
t_emotion_model_pri_out,
|
||||
t_topic_judge_model_name,
|
||||
t_topic_judge_model_provider,
|
||||
t_topic_judge_model_pri_in,
|
||||
t_topic_judge_model_pri_out,
|
||||
t_summary_by_topic_model_name,
|
||||
t_summary_by_topic_model_provider,
|
||||
t_summary_by_topic_model_pri_in,
|
||||
t_summary_by_topic_model_pri_out,
|
||||
t_vlm_model_name,
|
||||
t_vlm_model_provider,
|
||||
t_vlm_model_pri_in,
|
||||
t_vlm_model_pri_out,
|
||||
):
|
||||
if PARSED_CONFIG_VERSION >= version.parse("0.0.10"):
|
||||
config_data["willing"]["willing_mode"] = t_willing_mode
|
||||
config_data["response"]["model_r1_probability"] = t_model_r1_probability
|
||||
config_data["response"]["model_v3_probability"] = t_model_r2_probability
|
||||
config_data["response"]["model_r1_distill_probability"] = t_model_r3_probability
|
||||
config_data["response"]["max_response_length"] = t_max_response_length
|
||||
if PARSED_CONFIG_VERSION <= version.parse("0.0.10"):
|
||||
config_data["response"]["max_response_length"] = t_max_response_length
|
||||
|
||||
# 保存模型1配置
|
||||
config_data["model"]["llm_reasoning"]["name"] = t_model1_name
|
||||
config_data["model"]["llm_reasoning"]["provider"] = t_model1_provider
|
||||
config_data["model"]["llm_reasoning"]["pri_in"] = t_model1_pri_in
|
||||
config_data["model"]["llm_reasoning"]["pri_out"] = t_model1_pri_out
|
||||
|
||||
# 保存模型2配置
|
||||
config_data["model"]["llm_normal"]["name"] = t_model2_name
|
||||
config_data["model"]["llm_normal"]["provider"] = t_model2_provider
|
||||
config_data["model"]["llm_normal"]["pri_in"] = t_model2_pri_in
|
||||
config_data["model"]["llm_normal"]["pri_out"] = t_model2_pri_out
|
||||
|
||||
# 保存模型3配置
|
||||
config_data["model"]["llm_reasoning_minor"]["name"] = t_model3_name
|
||||
config_data["model"]["llm_normal"]["provider"] = t_model3_provider
|
||||
config_data["model"]["llm_reasoning_minor"]["provider"] = t_model3_provider
|
||||
config_data["model"]["llm_reasoning_minor"]["pri_in"] = t_model3_pri_in
|
||||
config_data["model"]["llm_reasoning_minor"]["pri_out"] = t_model3_pri_out
|
||||
|
||||
# 保存情感模型配置
|
||||
config_data["model"]["llm_emotion_judge"]["name"] = t_emotion_model_name
|
||||
config_data["model"]["llm_emotion_judge"]["provider"] = t_emotion_model_provider
|
||||
config_data["model"]["llm_emotion_judge"]["pri_in"] = t_emotion_model_pri_in
|
||||
config_data["model"]["llm_emotion_judge"]["pri_out"] = t_emotion_model_pri_out
|
||||
|
||||
# 保存主题判断模型配置
|
||||
config_data["model"]["llm_topic_judge"]["name"] = t_topic_judge_model_name
|
||||
config_data["model"]["llm_topic_judge"]["provider"] = t_topic_judge_model_provider
|
||||
config_data["model"]["llm_topic_judge"]["pri_in"] = t_topic_judge_model_pri_in
|
||||
config_data["model"]["llm_topic_judge"]["pri_out"] = t_topic_judge_model_pri_out
|
||||
|
||||
# 保存主题总结模型配置
|
||||
config_data["model"]["llm_summary_by_topic"]["name"] = t_summary_by_topic_model_name
|
||||
config_data["model"]["llm_summary_by_topic"]["provider"] = t_summary_by_topic_model_provider
|
||||
config_data["model"]["llm_summary_by_topic"]["pri_in"] = t_summary_by_topic_model_pri_in
|
||||
config_data["model"]["llm_summary_by_topic"]["pri_out"] = t_summary_by_topic_model_pri_out
|
||||
|
||||
# 保存识图模型配置
|
||||
config_data["model"]["vlm"]["name"] = t_vlm_model_name
|
||||
config_data["model"]["vlm"]["provider"] = t_vlm_model_provider
|
||||
config_data["model"]["vlm"]["pri_in"] = t_vlm_model_pri_in
|
||||
config_data["model"]["vlm"]["pri_out"] = t_vlm_model_pri_out
|
||||
|
||||
save_config_to_file(config_data)
|
||||
logger.info("回复&模型设置已保存到 bot_config.toml 文件中")
|
||||
return "回复&模型设置已保存"
|
||||
|
|
@ -600,6 +712,12 @@ def save_memory_mood_config(
|
|||
t_mood_update_interval,
|
||||
t_mood_decay_rate,
|
||||
t_mood_intensity_factor,
|
||||
t_build_memory_dist1_mean,
|
||||
t_build_memory_dist1_std,
|
||||
t_build_memory_dist1_weight,
|
||||
t_build_memory_dist2_mean,
|
||||
t_build_memory_dist2_std,
|
||||
t_build_memory_dist2_weight,
|
||||
):
|
||||
config_data["memory"]["build_memory_interval"] = t_build_memory_interval
|
||||
config_data["memory"]["memory_compress_rate"] = t_memory_compress_rate
|
||||
|
|
@ -607,6 +725,15 @@ def save_memory_mood_config(
|
|||
config_data["memory"]["memory_forget_time"] = t_memory_forget_time
|
||||
config_data["memory"]["memory_forget_percentage"] = t_memory_forget_percentage
|
||||
config_data["memory"]["memory_ban_words"] = t_memory_ban_words_final_result
|
||||
if PARSED_CONFIG_VERSION >= version.parse("0.0.11"):
|
||||
config_data["memory"]["build_memory_distribution"] = [
|
||||
t_build_memory_dist1_mean,
|
||||
t_build_memory_dist1_std,
|
||||
t_build_memory_dist1_weight,
|
||||
t_build_memory_dist2_mean,
|
||||
t_build_memory_dist2_std,
|
||||
t_build_memory_dist2_weight,
|
||||
]
|
||||
config_data["mood"]["update_interval"] = t_mood_update_interval
|
||||
config_data["mood"]["decay_rate"] = t_mood_decay_rate
|
||||
config_data["mood"]["intensity_factor"] = t_mood_intensity_factor
|
||||
|
|
@ -627,6 +754,9 @@ def save_other_config(
|
|||
t_tone_error_rate,
|
||||
t_word_replace_rate,
|
||||
t_remote_status,
|
||||
t_enable_response_spliter,
|
||||
t_max_response_length,
|
||||
t_max_sentence_num,
|
||||
):
|
||||
config_data["keywords_reaction"]["enable"] = t_keywords_reaction_enabled
|
||||
config_data["others"]["enable_advance_output"] = t_enable_advance_output
|
||||
|
|
@ -640,6 +770,10 @@ def save_other_config(
|
|||
config_data["chinese_typo"]["word_replace_rate"] = t_word_replace_rate
|
||||
if PARSED_CONFIG_VERSION > HAVE_ONLINE_STATUS_VERSION:
|
||||
config_data["remote"]["enable"] = t_remote_status
|
||||
if PARSED_CONFIG_VERSION >= version.parse("0.0.11"):
|
||||
config_data["response_spliter"]["enable_response_spliter"] = t_enable_response_spliter
|
||||
config_data["response_spliter"]["response_max_length"] = t_max_response_length
|
||||
config_data["response_spliter"]["response_max_sentence_num"] = t_max_sentence_num
|
||||
save_config_to_file(config_data)
|
||||
logger.info("其他设置已保存到 bot_config.toml 文件中")
|
||||
return "其他设置已保存"
|
||||
|
|
@ -657,7 +791,6 @@ def save_group_config(
|
|||
logger.info("群聊设置已保存到 bot_config.toml 文件中")
|
||||
return "群聊设置已保存"
|
||||
|
||||
|
||||
with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
||||
gr.Markdown(
|
||||
value="""
|
||||
|
|
@ -997,11 +1130,33 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
inputs=personality_probability_change_inputs,
|
||||
outputs=[warning_less_text],
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
prompt_schedule = gr.Textbox(
|
||||
label="日程生成提示词", value=config_data["personality"]["prompt_schedule"], interactive=True
|
||||
)
|
||||
gr.Markdown("---")
|
||||
with gr.Row():
|
||||
gr.Markdown("麦麦提示词设置")
|
||||
if PARSED_CONFIG_VERSION >= SCHEDULE_CHANGED_VERSION:
|
||||
with gr.Row():
|
||||
enable_schedule_gen = gr.Checkbox(value=config_data["schedule"]["enable_schedule_gen"],
|
||||
label="是否开启麦麦日程生成(尚未完成)",
|
||||
interactive=True
|
||||
)
|
||||
with gr.Row():
|
||||
prompt_schedule_gen = gr.Textbox(
|
||||
label="日程生成提示词", value=config_data["schedule"]["prompt_schedule_gen"], interactive=True
|
||||
)
|
||||
with gr.Row():
|
||||
schedule_doing_update_interval = gr.Number(
|
||||
value=config_data["schedule"]["schedule_doing_update_interval"],
|
||||
label="日程表更新间隔 单位秒",
|
||||
interactive=True
|
||||
)
|
||||
else:
|
||||
with gr.Row():
|
||||
prompt_schedule_gen = gr.Textbox(
|
||||
label="日程生成提示词", value=config_data["personality"]["prompt_schedule"], interactive=True
|
||||
)
|
||||
enable_schedule_gen = gr.Checkbox(value=False,visible=False,interactive=False)
|
||||
schedule_doing_update_interval = gr.Number(value=0,visible=False,interactive=False)
|
||||
with gr.Row():
|
||||
personal_save_btn = gr.Button(
|
||||
"保存人格配置",
|
||||
|
|
@ -1017,7 +1172,9 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
prompt_personality_1,
|
||||
prompt_personality_2,
|
||||
prompt_personality_3,
|
||||
prompt_schedule,
|
||||
enable_schedule_gen,
|
||||
prompt_schedule_gen,
|
||||
schedule_doing_update_interval,
|
||||
personality_1_probability,
|
||||
personality_2_probability,
|
||||
personality_3_probability,
|
||||
|
|
@ -1027,11 +1184,14 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
with gr.TabItem("3-消息&表情包设置"):
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
with gr.Row():
|
||||
min_text_length = gr.Number(
|
||||
value=config_data["message"]["min_text_length"],
|
||||
label="与麦麦聊天时麦麦只会回答文本大于等于此数的消息",
|
||||
)
|
||||
if PARSED_CONFIG_VERSION < version.parse("0.0.11"):
|
||||
with gr.Row():
|
||||
min_text_length = gr.Number(
|
||||
value=config_data["message"]["min_text_length"],
|
||||
label="与麦麦聊天时麦麦只会回答文本大于等于此数的消息",
|
||||
)
|
||||
else:
|
||||
min_text_length = gr.Number(visible=False,value=0,interactive=False)
|
||||
with gr.Row():
|
||||
max_context_size = gr.Number(
|
||||
value=config_data["message"]["max_context_size"], label="麦麦获得的上文数量"
|
||||
|
|
@ -1049,21 +1209,27 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
value=config_data["message"]["thinking_timeout"],
|
||||
label="麦麦正在思考时，如果超过此秒数，则停止思考",
|
||||
)
|
||||
with gr.Row():
|
||||
response_willing_amplifier = gr.Number(
|
||||
value=config_data["message"]["response_willing_amplifier"],
|
||||
label="麦麦回复意愿放大系数，一般为1",
|
||||
)
|
||||
with gr.Row():
|
||||
response_interested_rate_amplifier = gr.Number(
|
||||
value=config_data["message"]["response_interested_rate_amplifier"],
|
||||
label="麦麦回复兴趣度放大系数,听到记忆里的内容时放大系数",
|
||||
)
|
||||
with gr.Row():
|
||||
down_frequency_rate = gr.Number(
|
||||
value=config_data["message"]["down_frequency_rate"],
|
||||
label="降低回复频率的群组回复意愿降低系数",
|
||||
)
|
||||
if PARSED_CONFIG_VERSION < version.parse("0.0.11"):
|
||||
with gr.Row():
|
||||
response_willing_amplifier = gr.Number(
|
||||
value=config_data["message"]["response_willing_amplifier"],
|
||||
label="麦麦回复意愿放大系数，一般为1",
|
||||
)
|
||||
with gr.Row():
|
||||
response_interested_rate_amplifier = gr.Number(
|
||||
value=config_data["message"]["response_interested_rate_amplifier"],
|
||||
label="麦麦回复兴趣度放大系数,听到记忆里的内容时放大系数",
|
||||
)
|
||||
with gr.Row():
|
||||
down_frequency_rate = gr.Number(
|
||||
value=config_data["message"]["down_frequency_rate"],
|
||||
label="降低回复频率的群组回复意愿降低系数",
|
||||
)
|
||||
else:
|
||||
response_willing_amplifier = gr.Number(visible=False,value=0,interactive=False)
|
||||
response_interested_rate_amplifier = gr.Number(visible=False,value=0,interactive=False)
|
||||
down_frequency_rate = gr.Number(visible=False,value=0,interactive=False)
|
||||
|
||||
with gr.Row():
|
||||
gr.Markdown("### 违禁词列表")
|
||||
with gr.Row():
|
||||
|
|
@ -1207,7 +1373,7 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
],
|
||||
outputs=[emoji_save_message],
|
||||
)
|
||||
with gr.TabItem("4-回复&模型设置"):
|
||||
with gr.TabItem("4-意愿设置"):
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
with gr.Row():
|
||||
|
|
@ -1229,6 +1395,55 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
)
|
||||
else:
|
||||
willing_mode = gr.Textbox(visible=False, value="disabled")
|
||||
if PARSED_CONFIG_VERSION >= version.parse("0.0.11"):
|
||||
with gr.Row():
|
||||
response_willing_amplifier = gr.Number(
|
||||
value=config_data["willing"]["response_willing_amplifier"],
|
||||
label="麦麦回复意愿放大系数，一般为1",
|
||||
)
|
||||
with gr.Row():
|
||||
response_interested_rate_amplifier = gr.Number(
|
||||
value=config_data["willing"]["response_interested_rate_amplifier"],
|
||||
label="麦麦回复兴趣度放大系数,听到记忆里的内容时放大系数",
|
||||
)
|
||||
with gr.Row():
|
||||
down_frequency_rate = gr.Number(
|
||||
value=config_data["willing"]["down_frequency_rate"],
|
||||
label="降低回复频率的群组回复意愿降低系数",
|
||||
)
|
||||
with gr.Row():
|
||||
emoji_response_penalty = gr.Number(
|
||||
value=config_data["willing"]["emoji_response_penalty"],
|
||||
label="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",
|
||||
)
|
||||
else:
|
||||
response_willing_amplifier = gr.Number(visible=False, value=1.0)
|
||||
response_interested_rate_amplifier = gr.Number(visible=False, value=1.0)
|
||||
down_frequency_rate = gr.Number(visible=False, value=1.0)
|
||||
emoji_response_penalty = gr.Number(visible=False, value=1.0)
|
||||
with gr.Row():
|
||||
willing_save_btn = gr.Button(
|
||||
"保存意愿设置设置",
|
||||
variant="primary",
|
||||
elem_id="save_personality_btn",
|
||||
elem_classes="save_personality_btn",
|
||||
)
|
||||
with gr.Row():
|
||||
willing_save_message = gr.Textbox(label="意愿设置保存结果")
|
||||
willing_save_btn.click(
|
||||
save_willing_config,
|
||||
inputs=[
|
||||
willing_mode,
|
||||
response_willing_amplifier,
|
||||
response_interested_rate_amplifier,
|
||||
down_frequency_rate,
|
||||
emoji_response_penalty,
|
||||
],
|
||||
outputs=[emoji_save_message],
|
||||
)
|
||||
with gr.TabItem("4-回复&模型设置"):
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
with gr.Row():
|
||||
model_r1_probability = gr.Slider(
|
||||
minimum=0,
|
||||
|
|
@ -1289,10 +1504,13 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
inputs=[model_r1_probability, model_r2_probability, model_r3_probability],
|
||||
outputs=[model_warning_less_text],
|
||||
)
|
||||
with gr.Row():
|
||||
max_response_length = gr.Number(
|
||||
value=config_data["response"]["max_response_length"], label="麦麦回答的最大token数"
|
||||
)
|
||||
if PARSED_CONFIG_VERSION <= version.parse("0.0.10"):
|
||||
with gr.Row():
|
||||
max_response_length = gr.Number(
|
||||
value=config_data["response"]["max_response_length"], label="麦麦回答的最大token数"
|
||||
)
|
||||
else:
|
||||
max_response_length = gr.Number(visible=False,value=0)
|
||||
with gr.Row():
|
||||
gr.Markdown("""### 模型设置""")
|
||||
with gr.Row():
|
||||
|
|
@ -1336,6 +1554,16 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
value=config_data["model"]["llm_normal"]["provider"],
|
||||
label="模型2提供商",
|
||||
)
|
||||
with gr.Row():
|
||||
model2_pri_in = gr.Number(
|
||||
value=config_data["model"]["llm_normal"]["pri_in"],
|
||||
label="模型2（次要回复模型）的输入价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.Row():
|
||||
model2_pri_out = gr.Number(
|
||||
value=config_data["model"]["llm_normal"]["pri_out"],
|
||||
label="模型2（次要回复模型）的输出价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.TabItem("3-次要模型"):
|
||||
with gr.Row():
|
||||
model3_name = gr.Textbox(
|
||||
|
|
@ -1347,6 +1575,16 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
value=config_data["model"]["llm_reasoning_minor"]["provider"],
|
||||
label="模型3提供商",
|
||||
)
|
||||
with gr.Row():
|
||||
model3_pri_in = gr.Number(
|
||||
value=config_data["model"]["llm_reasoning_minor"]["pri_in"],
|
||||
label="模型3（次要回复模型）的输入价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.Row():
|
||||
model3_pri_out = gr.Number(
|
||||
value=config_data["model"]["llm_reasoning_minor"]["pri_out"],
|
||||
label="模型3（次要回复模型）的输出价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.TabItem("4-情感&主题模型"):
|
||||
with gr.Row():
|
||||
gr.Markdown("""### 情感模型设置""")
|
||||
|
|
@ -1360,6 +1598,16 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
value=config_data["model"]["llm_emotion_judge"]["provider"],
|
||||
label="情感模型提供商",
|
||||
)
|
||||
with gr.Row():
|
||||
emotion_model_pri_in = gr.Number(
|
||||
value=config_data["model"]["llm_emotion_judge"]["pri_in"],
|
||||
label="情感模型的输入价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.Row():
|
||||
emotion_model_pri_out = gr.Number(
|
||||
value=config_data["model"]["llm_emotion_judge"]["pri_out"],
|
||||
label="情感模型的输出价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.Row():
|
||||
gr.Markdown("""### 主题模型设置""")
|
||||
with gr.Row():
|
||||
|
|
@ -1372,6 +1620,18 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
value=config_data["model"]["llm_topic_judge"]["provider"],
|
||||
label="主题判断模型提供商",
|
||||
)
|
||||
with gr.Row():
|
||||
topic_judge_model_pri_in = gr.Number(
|
||||
value=config_data["model"]["llm_topic_judge"]["pri_in"],
|
||||
label="主题判断模型的输入价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.Row():
|
||||
topic_judge_model_pri_out = gr.Number(
|
||||
value=config_data["model"]["llm_topic_judge"]["pri_out"],
|
||||
label="主题判断模型的输出价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.Row():
|
||||
gr.Markdown("""### 主题总结模型设置""")
|
||||
with gr.Row():
|
||||
summary_by_topic_model_name = gr.Textbox(
|
||||
value=config_data["model"]["llm_summary_by_topic"]["name"], label="主题总结模型名称"
|
||||
|
|
@ -1382,6 +1642,16 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
value=config_data["model"]["llm_summary_by_topic"]["provider"],
|
||||
label="主题总结模型提供商",
|
||||
)
|
||||
with gr.Row():
|
||||
summary_by_topic_model_pri_in = gr.Number(
|
||||
value=config_data["model"]["llm_summary_by_topic"]["pri_in"],
|
||||
label="主题总结模型的输入价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.Row():
|
||||
summary_by_topic_model_pri_out = gr.Number(
|
||||
value=config_data["model"]["llm_summary_by_topic"]["pri_out"],
|
||||
label="主题总结模型的输出价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.TabItem("5-识图模型"):
|
||||
with gr.Row():
|
||||
gr.Markdown("""### 识图模型设置""")
|
||||
|
|
@ -1395,6 +1665,16 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
value=config_data["model"]["vlm"]["provider"],
|
||||
label="识图模型提供商",
|
||||
)
|
||||
with gr.Row():
|
||||
vlm_model_pri_in = gr.Number(
|
||||
value=config_data["model"]["vlm"]["pri_in"],
|
||||
label="识图模型的输入价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.Row():
|
||||
vlm_model_pri_out = gr.Number(
|
||||
value=config_data["model"]["vlm"]["pri_out"],
|
||||
label="识图模型的输出价格（非必填，可以记录消耗）",
|
||||
)
|
||||
with gr.Row():
|
||||
save_model_btn = gr.Button("保存回复&模型设置", variant="primary", elem_id="save_model_btn")
|
||||
with gr.Row():
|
||||
|
|
@ -1413,16 +1693,28 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
model1_pri_out,
|
||||
model2_name,
|
||||
model2_provider,
|
||||
model2_pri_in,
|
||||
model2_pri_out,
|
||||
model3_name,
|
||||
model3_provider,
|
||||
model3_pri_in,
|
||||
model3_pri_out,
|
||||
emotion_model_name,
|
||||
emotion_model_provider,
|
||||
emotion_model_pri_in,
|
||||
emotion_model_pri_out,
|
||||
topic_judge_model_name,
|
||||
topic_judge_model_provider,
|
||||
topic_judge_model_pri_in,
|
||||
topic_judge_model_pri_out,
|
||||
summary_by_topic_model_name,
|
||||
summary_by_topic_model_provider,
|
||||
summary_by_topic_model_pri_in,
|
||||
summary_by_topic_model_pri_out,
|
||||
vlm_model_name,
|
||||
vlm_model_provider,
|
||||
vlm_model_pri_in,
|
||||
vlm_model_pri_out,
|
||||
],
|
||||
outputs=[save_btn_message],
|
||||
)
|
||||
|
|
@ -1436,6 +1728,79 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
value=config_data["memory"]["build_memory_interval"],
|
||||
label="记忆构建间隔 单位秒,间隔越低，麦麦学习越多，但是冗余信息也会增多",
|
||||
)
|
||||
if PARSED_CONFIG_VERSION >= version.parse("0.0.11"):
|
||||
with gr.Row():
|
||||
gr.Markdown("---")
|
||||
with gr.Row():
|
||||
gr.Markdown("""### 记忆构建分布设置""")
|
||||
with gr.Row():
|
||||
gr.Markdown("""记忆构建分布参数说明：\n
|
||||
分布1均值：第一个正态分布的均值\n
|
||||
分布1标准差：第一个正态分布的标准差\n
|
||||
分布1权重：第一个正态分布的权重\n
|
||||
分布2均值：第二个正态分布的均值\n
|
||||
分布2标准差：第二个正态分布的标准差\n
|
||||
分布2权重：第二个正态分布的权重
|
||||
""")
|
||||
with gr.Row():
|
||||
with gr.Column(scale=1):
|
||||
build_memory_dist1_mean = gr.Number(
|
||||
value=config_data["memory"].get(
|
||||
"build_memory_distribution",
|
||||
[4.0,2.0,0.6,24.0,8.0,0.4]
|
||||
)[0],
|
||||
label="分布1均值",
|
||||
)
|
||||
with gr.Column(scale=1):
|
||||
build_memory_dist1_std = gr.Number(
|
||||
value=config_data["memory"].get(
|
||||
"build_memory_distribution",
|
||||
[4.0,2.0,0.6,24.0,8.0,0.4]
|
||||
)[1],
|
||||
label="分布1标准差",
|
||||
)
|
||||
with gr.Column(scale=1):
|
||||
build_memory_dist1_weight = gr.Number(
|
||||
value=config_data["memory"].get(
|
||||
"build_memory_distribution",
|
||||
[4.0,2.0,0.6,24.0,8.0,0.4]
|
||||
)[2],
|
||||
label="分布1权重",
|
||||
)
|
||||
with gr.Row():
|
||||
with gr.Column(scale=1):
|
||||
build_memory_dist2_mean = gr.Number(
|
||||
value=config_data["memory"].get(
|
||||
"build_memory_distribution",
|
||||
[4.0,2.0,0.6,24.0,8.0,0.4]
|
||||
)[3],
|
||||
label="分布2均值",
|
||||
)
|
||||
with gr.Column(scale=1):
|
||||
build_memory_dist2_std = gr.Number(
|
||||
value=config_data["memory"].get(
|
||||
"build_memory_distribution",
|
||||
[4.0,2.0,0.6,24.0,8.0,0.4]
|
||||
)[4],
|
||||
label="分布2标准差",
|
||||
)
|
||||
with gr.Column(scale=1):
|
||||
build_memory_dist2_weight = gr.Number(
|
||||
value=config_data["memory"].get(
|
||||
"build_memory_distribution",
|
||||
[4.0,2.0,0.6,24.0,8.0,0.4]
|
||||
)[5],
|
||||
label="分布2权重",
|
||||
)
|
||||
with gr.Row():
|
||||
gr.Markdown("---")
|
||||
else:
|
||||
build_memory_dist1_mean = gr.Number(value=0.0,visible=False,interactive=False)
|
||||
build_memory_dist1_std = gr.Number(value=0.0,visible=False,interactive=False)
|
||||
build_memory_dist1_weight = gr.Number(value=0.0,visible=False,interactive=False)
|
||||
build_memory_dist2_mean = gr.Number(value=0.0,visible=False,interactive=False)
|
||||
build_memory_dist2_std = gr.Number(value=0.0,visible=False,interactive=False)
|
||||
build_memory_dist2_weight = gr.Number(value=0.0,visible=False,interactive=False)
|
||||
with gr.Row():
|
||||
memory_compress_rate = gr.Number(
|
||||
value=config_data["memory"]["memory_compress_rate"],
|
||||
|
|
@ -1538,6 +1903,12 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
mood_update_interval,
|
||||
mood_decay_rate,
|
||||
mood_intensity_factor,
|
||||
build_memory_dist1_mean,
|
||||
build_memory_dist1_std,
|
||||
build_memory_dist1_weight,
|
||||
build_memory_dist2_mean,
|
||||
build_memory_dist2_std,
|
||||
build_memory_dist2_weight,
|
||||
],
|
||||
outputs=[save_memory_mood_message],
|
||||
)
|
||||
|
|
@ -1709,22 +2080,31 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
keywords_reaction_enabled = gr.Checkbox(
|
||||
value=config_data["keywords_reaction"]["enable"], label="是否针对某个关键词作出反应"
|
||||
)
|
||||
with gr.Row():
|
||||
enable_advance_output = gr.Checkbox(
|
||||
value=config_data["others"]["enable_advance_output"], label="是否开启高级输出"
|
||||
)
|
||||
with gr.Row():
|
||||
enable_kuuki_read = gr.Checkbox(
|
||||
value=config_data["others"]["enable_kuuki_read"], label="是否启用读空气功能"
|
||||
)
|
||||
with gr.Row():
|
||||
enable_debug_output = gr.Checkbox(
|
||||
value=config_data["others"]["enable_debug_output"], label="是否开启调试输出"
|
||||
)
|
||||
with gr.Row():
|
||||
enable_friend_chat = gr.Checkbox(
|
||||
value=config_data["others"]["enable_friend_chat"], label="是否开启好友聊天"
|
||||
)
|
||||
if PARSED_CONFIG_VERSION <= version.parse("0.0.10"):
|
||||
with gr.Row():
|
||||
enable_advance_output = gr.Checkbox(
|
||||
value=config_data["others"]["enable_advance_output"], label="是否开启高级输出"
|
||||
)
|
||||
with gr.Row():
|
||||
enable_kuuki_read = gr.Checkbox(
|
||||
value=config_data["others"]["enable_kuuki_read"], label="是否启用读空气功能"
|
||||
)
|
||||
with gr.Row():
|
||||
enable_debug_output = gr.Checkbox(
|
||||
value=config_data["others"]["enable_debug_output"], label="是否开启调试输出"
|
||||
)
|
||||
with gr.Row():
|
||||
enable_friend_chat = gr.Checkbox(
|
||||
value=config_data["others"]["enable_friend_chat"], label="是否开启好友聊天"
|
||||
)
|
||||
elif PARSED_CONFIG_VERSION >= version.parse("0.0.11"):
|
||||
with gr.Row():
|
||||
enable_friend_chat = gr.Checkbox(
|
||||
value=config_data["experimental"]["enable_friend_chat"], label="是否开启好友聊天"
|
||||
)
|
||||
enable_advance_output = gr.Checkbox(value=False,visible=False,interactive=False)
|
||||
enable_kuuki_read = gr.Checkbox(value=False,visible=False,interactive=False)
|
||||
enable_debug_output = gr.Checkbox(value=False,visible=False,interactive=False)
|
||||
if PARSED_CONFIG_VERSION > HAVE_ONLINE_STATUS_VERSION:
|
||||
with gr.Row():
|
||||
gr.Markdown(
|
||||
|
|
@ -1736,7 +2116,28 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
remote_status = gr.Checkbox(
|
||||
value=config_data["remote"]["enable"], label="是否开启麦麦在线全球统计"
|
||||
)
|
||||
|
||||
if PARSED_CONFIG_VERSION >= version.parse("0.0.11"):
|
||||
with gr.Row():
|
||||
gr.Markdown("""### 回复分割器设置""")
|
||||
with gr.Row():
|
||||
enable_response_spliter = gr.Checkbox(
|
||||
value=config_data["response_spliter"]["enable_response_spliter"],
|
||||
label="是否启用回复分割器"
|
||||
)
|
||||
with gr.Row():
|
||||
response_max_length = gr.Number(
|
||||
value=config_data["response_spliter"]["response_max_length"],
|
||||
label="回复允许的最大长度"
|
||||
)
|
||||
with gr.Row():
|
||||
response_max_sentence_num = gr.Number(
|
||||
value=config_data["response_spliter"]["response_max_sentence_num"],
|
||||
label="回复允许的最大句子数"
|
||||
)
|
||||
else:
|
||||
enable_response_spliter = gr.Checkbox(value=False,visible=False,interactive=False)
|
||||
response_max_length = gr.Number(value=0,visible=False,interactive=False)
|
||||
response_max_sentence_num = gr.Number(value=0,visible=False,interactive=False)
|
||||
with gr.Row():
|
||||
gr.Markdown("""### 中文错别字设置""")
|
||||
with gr.Row():
|
||||
|
|
@ -1790,14 +2191,56 @@ with gr.Blocks(title="MaimBot配置文件编辑") as app:
|
|||
tone_error_rate,
|
||||
word_replace_rate,
|
||||
remote_status,
|
||||
enable_response_spliter,
|
||||
response_max_length,
|
||||
response_max_sentence_num
|
||||
],
|
||||
outputs=[save_other_config_message],
|
||||
)
|
||||
app.queue().launch( # concurrency_count=511, max_size=1022
|
||||
server_name="0.0.0.0",
|
||||
inbrowser=True,
|
||||
share=is_share,
|
||||
server_port=7000,
|
||||
debug=debug,
|
||||
quiet=True,
|
||||
)
|
||||
# 检查端口是否可用
|
||||
def is_port_available(port, host='0.0.0.0'):
|
||||
"""检查指定的端口是否可用"""
|
||||
try:
|
||||
# 创建一个socket对象
|
||||
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
||||
# 设置socket重用地址选项
|
||||
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
|
||||
# 尝试绑定端口
|
||||
sock.bind((host, port))
|
||||
# 如果成功绑定，则关闭socket并返回True
|
||||
sock.close()
|
||||
return True
|
||||
except socket.error:
|
||||
# 如果绑定失败，说明端口已被占用
|
||||
return False
|
||||
|
||||
|
||||
# 寻找可用端口
|
||||
def find_available_port(start_port=7000, max_port=8000):
|
||||
"""
|
||||
从start_port开始，寻找可用的端口
|
||||
如果端口被占用，尝试下一个端口，直到找到可用端口或达到max_port
|
||||
"""
|
||||
port = start_port
|
||||
while port <= max_port:
|
||||
if is_port_available(port):
|
||||
logger.info(f"找到可用端口: {port}")
|
||||
return port
|
||||
logger.warning(f"端口 {port} 已被占用，尝试下一个端口")
|
||||
port += 1
|
||||
# 如果所有端口都被占用，返回None
|
||||
logger.error(f"无法找到可用端口 (已尝试 {start_port}-{max_port})")
|
||||
return None
|
||||
|
||||
# 寻找可用端口
|
||||
launch_port = find_available_port(7000, 8000) or 7000
|
||||
|
||||
app.queue().launch( # concurrency_count=511, max_size=1022
|
||||
server_name="0.0.0.0",
|
||||
inbrowser=True,
|
||||
share=is_share,
|
||||
server_port=launch_port,
|
||||
debug=debug,
|
||||
quiet=True,
|
||||
)
|
||||
|
||||
|
|
|
|||
44
配置文件错误排查.py
44
配置文件错误排查.py
|
|
@ -1,8 +1,7 @@
|
|||
import tomli
|
||||
import sys
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Set, Tuple
|
||||
from typing import Dict, Any, List, Tuple
|
||||
|
||||
def load_toml_file(file_path: str) -> Dict[str, Any]:
|
||||
"""加载TOML文件"""
|
||||
|
|
@ -184,10 +183,15 @@ def check_model_configurations(config: Dict[str, Any], env_vars: Dict[str, str])
|
|||
provider = model_config["provider"].upper()
|
||||
|
||||
# 检查拼写错误
|
||||
for known_provider, correct_provider in reverse_mapping.items():
|
||||
for known_provider, _correct_provider in reverse_mapping.items():
|
||||
# 使用模糊匹配检测拼写错误
|
||||
if provider != known_provider and _similar_strings(provider, known_provider) and provider not in reverse_mapping:
|
||||
errors.append(f"[model.{model_name}]的provider '{model_config['provider']}' 可能拼写错误，应为 '{known_provider}'")
|
||||
if (provider != known_provider and
|
||||
_similar_strings(provider, known_provider) and
|
||||
provider not in reverse_mapping):
|
||||
errors.append(
|
||||
f"[model.{model_name}]的provider '{model_config['provider']}' "
|
||||
f"可能拼写错误，应为 '{known_provider}'"
|
||||
)
|
||||
break
|
||||
|
||||
return errors
|
||||
|
|
@ -223,7 +227,7 @@ def check_api_providers(config: Dict[str, Any], env_vars: Dict[str, str]) -> Lis
|
|||
|
||||
# 检查配置文件中使用的所有提供商
|
||||
used_providers = set()
|
||||
for model_category, model_config in config["model"].items():
|
||||
for _model_category, model_config in config["model"].items():
|
||||
if "provider" in model_config:
|
||||
provider = model_config["provider"]
|
||||
used_providers.add(provider)
|
||||
|
|
@ -247,7 +251,7 @@ def check_api_providers(config: Dict[str, Any], env_vars: Dict[str, str]) -> Lis
|
|||
# 特别检查常见的拼写错误
|
||||
for provider in used_providers:
|
||||
if provider.upper() == "SILICONFOLW":
|
||||
errors.append(f"提供商 'SILICONFOLW' 存在拼写错误，应为 'SILICONFLOW'")
|
||||
errors.append("提供商 'SILICONFOLW' 存在拼写错误，应为 'SILICONFLOW'")
|
||||
|
||||
return errors
|
||||
|
||||
|
|
@ -272,7 +276,7 @@ def check_groups_configuration(config: Dict[str, Any]) -> List[str]:
|
|||
"main": "groups.talk_allowed中存在默认示例值'123'，请修改为真实的群号",
|
||||
"details": [
|
||||
f" 当前值: {groups['talk_allowed']}",
|
||||
f" '123'为示例值，需要替换为真实群号"
|
||||
" '123'为示例值，需要替换为真实群号"
|
||||
]
|
||||
})
|
||||
|
||||
|
|
@ -371,7 +375,8 @@ def check_memory_config(config: Dict[str, Any]) -> List[str]:
|
|||
if "memory_compress_rate" in memory and (memory["memory_compress_rate"] <= 0 or memory["memory_compress_rate"] > 1):
|
||||
errors.append(f"memory.memory_compress_rate值无效: {memory['memory_compress_rate']}, 应在0-1之间")
|
||||
|
||||
if "memory_forget_percentage" in memory and (memory["memory_forget_percentage"] <= 0 or memory["memory_forget_percentage"] > 1):
|
||||
if ("memory_forget_percentage" in memory
|
||||
and (memory["memory_forget_percentage"] <= 0 or memory["memory_forget_percentage"] > 1)):
|
||||
errors.append(f"memory.memory_forget_percentage值无效: {memory['memory_forget_percentage']}, 应在0-1之间")
|
||||
|
||||
return errors
|
||||
|
|
@ -393,7 +398,10 @@ def check_personality_config(config: Dict[str, Any]) -> List[str]:
|
|||
else:
|
||||
# 检查数组长度
|
||||
if len(personality["prompt_personality"]) < 1:
|
||||
errors.append(f"personality.prompt_personality数组长度不足，当前长度: {len(personality['prompt_personality'])}, 需要至少1项")
|
||||
errors.append(
|
||||
f"personality.prompt_personality至少需要1项，"
|
||||
f"当前长度: {len(personality['prompt_personality'])}"
|
||||
)
|
||||
else:
|
||||
# 模板默认值
|
||||
template_values = [
|
||||
|
|
@ -452,10 +460,13 @@ def check_bot_config(config: Dict[str, Any]) -> List[str]:
|
|||
|
||||
def format_results(all_errors):
|
||||
"""格式化检查结果"""
|
||||
sections_errors, prob_sum_errors, prob_range_errors, model_errors, api_errors, groups_errors, kr_errors, willing_errors, memory_errors, personality_errors, bot_results = all_errors
|
||||
sections_errors, prob_sum_errors, prob_range_errors, model_errors, api_errors, groups_errors, kr_errors, willing_errors, memory_errors, personality_errors, bot_results = all_errors # noqa: E501, F821
|
||||
bot_errors, bot_infos = bot_results
|
||||
|
||||
if not any([sections_errors, prob_sum_errors, prob_range_errors, model_errors, api_errors, groups_errors, kr_errors, willing_errors, memory_errors, personality_errors, bot_errors]):
|
||||
if not any([
|
||||
sections_errors, prob_sum_errors,
|
||||
prob_range_errors, model_errors, api_errors, groups_errors,
|
||||
kr_errors, willing_errors, memory_errors, personality_errors, bot_errors]):
|
||||
result = "✅ 配置文件检查通过，未发现问题。"
|
||||
|
||||
# 添加机器人信息
|
||||
|
|
@ -574,7 +585,10 @@ def main():
|
|||
bot_results = check_bot_config(config)
|
||||
|
||||
# 格式化并打印结果
|
||||
all_errors = (sections_errors, prob_sum_errors, prob_range_errors, model_errors, api_errors, groups_errors, kr_errors, willing_errors, memory_errors, personality_errors, bot_results)
|
||||
all_errors = (
|
||||
sections_errors, prob_sum_errors,
|
||||
prob_range_errors, model_errors, api_errors, groups_errors,
|
||||
kr_errors, willing_errors, memory_errors, personality_errors, bot_results)
|
||||
result = format_results(all_errors)
|
||||
print("📋 机器人配置检查结果:")
|
||||
print(result)
|
||||
|
|
@ -586,7 +600,9 @@ def main():
|
|||
bot_errors, _ = bot_results
|
||||
|
||||
# 计算普通错误列表的长度
|
||||
for errors in [sections_errors, model_errors, api_errors, groups_errors, kr_errors, willing_errors, memory_errors, bot_errors]:
|
||||
for errors in [
|
||||
sections_errors, model_errors, api_errors,
|
||||
groups_errors, kr_errors, willing_errors, memory_errors, bot_errors]:
|
||||
total_errors += len(errors)
|
||||
|
||||
# 计算元组列表的长度（概率相关错误）
|
||||
|
|
|
|||
Loading…
Reference in New Issue