Merge branch 'MaiM-with-u:main' into main
This commit is contained in:
2829798842
GitHub
6
.gitignore
vendored
6
.gitignore
vendored
@@ -4,11 +4,15 @@ mongodb/
|
||||
NapCat.Framework.Windows.Once/
|
||||
log/
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||||
logs/
|
||||
run_ad.bat
|
||||
MaiBot-Napcat-Adapter-main
|
||||
MaiBot-Napcat-Adapter
|
||||
/test
|
||||
/src/test
|
||||
nonebot-maibot-adapter/
|
||||
*.zip
|
||||
run.bat
|
||||
run_none.bat
|
||||
run.py
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||||
message_queue_content.txt
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message_queue_content.bat
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||||
@@ -231,3 +235,5 @@ logs
|
||||
.vscode
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||||
|
||||
/config/*
|
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run_none.bat
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||||
config/old/bot_config_20250405_212257.toml
|
||||
|
||||
@@ -1,4 +1,12 @@
|
||||
这里放置了测试版本的细节更新
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|
||||
## [test-0.6.1-snapshot-1] - 2025-4-5
|
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- 修复pfc回复出错bug
|
||||
- 修复表情包打字时间，不会卡表情包
|
||||
- 改进了知识库的提取
|
||||
- 提供了新的数据库连接方式
|
||||
- 修复了ban_user无效的问题
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||||
|
||||
## [test-0.6.0-snapshot-9] - 2025-4-4
|
||||
- 可以识别gif表情包
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||||
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||||
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||||
@@ -18,10 +18,11 @@
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||||
devShells.default = pkgs.mkShell {
|
||||
name = "python-venv";
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||||
venvDir = "./.venv";
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||||
buildInputs = [
|
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pythonPackages.python
|
||||
pythonPackages.venvShellHook
|
||||
pythonPackages.numpy
|
||||
buildInputs = with pythonPackages; [
|
||||
python
|
||||
venvShellHook
|
||||
scipy
|
||||
numpy
|
||||
];
|
||||
|
||||
postVenvCreation = ''
|
||||
|
||||
@@ -4,7 +4,7 @@
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||||
# 适用于Arch/Ubuntu 24.10/Debian 12/CentOS 9
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||||
# 请小心使用任何一键脚本！
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||||
|
||||
INSTALLER_VERSION="0.0.1-refactor"
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||||
INSTALLER_VERSION="0.0.2-refactor"
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||||
LANG=C.UTF-8
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||||
|
||||
# 如无法访问GitHub请修改此处镜像地址
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||||
@@ -62,7 +62,7 @@ show_menu() {
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||||
"4" "启动Nonebot adapter" \
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||||
"5" "停止Nonebot adapter" \
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"6" "重启Nonebot adapter" \
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||||
"7" "更新MaiCore及其依赖" \
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||||
"7" "拉取最新MaiCore仓库" \
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||||
"8" "切换分支" \
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||||
"9" "退出" 3>&1 1>&2 2>&3)
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|
||||
@@ -111,6 +111,8 @@ show_menu() {
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# 更新依赖
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update_dependencies() {
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whiptail --title "⚠" --msgbox "更新后请阅读教程" 10 60
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systemctl stop ${SERVICE_NAME}
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cd "${INSTALL_DIR}/MaiBot" || {
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whiptail --msgbox "🚫 无法进入安装目录！" 10 60
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return 1
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||||
@@ -126,8 +128,7 @@ update_dependencies() {
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||||
return 1
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fi
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deactivate
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systemctl restart ${SERVICE_NAME}
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whiptail --msgbox "✅ 依赖已更新并重启服务！" 10 60
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||||
whiptail --msgbox "✅ 已停止服务并拉取最新仓库提交" 10 60
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||||
}
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||||
|
||||
# 切换分支
|
||||
@@ -157,7 +158,7 @@ switch_branch() {
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||||
whiptail --msgbox "🚫 代码拉取失败！" 10 60
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||||
return 1
|
||||
fi
|
||||
|
||||
systemctl stop ${SERVICE_NAME}
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||||
source "${INSTALL_DIR}/venv/bin/activate"
|
||||
pip install -r requirements.txt
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deactivate
|
||||
@@ -165,8 +166,7 @@ switch_branch() {
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||||
sed -i "s/^BRANCH=.*/BRANCH=${new_branch}/" /etc/maicore_install.conf
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BRANCH="${new_branch}"
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check_eula
|
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systemctl restart ${SERVICE_NAME}
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||||
whiptail --msgbox "✅ 已切换到分支 ${new_branch} 并重启服务！" 10 60
|
||||
whiptail --msgbox "✅ 已停止服务并切换到分支 ${new_branch} ！" 10 60
|
||||
}
|
||||
|
||||
check_eula() {
|
||||
@@ -228,6 +228,8 @@ run_installation() {
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||||
fi
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||||
fi
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||||
|
||||
whiptail --title "ℹ️ 提示" --msgbox "如果您没有特殊需求，请优先使用docker方式部署。" 10 60
|
||||
|
||||
# 协议确认
|
||||
if ! (whiptail --title "ℹ️ [1/6] 使用协议" --yes-button "我同意" --no-button "我拒绝" --yesno "使用MaiCore及此脚本前请先阅读EULA协议及隐私协议\nhttps://github.com/MaiM-with-u/MaiBot/blob/refactor/EULA.md\nhttps://github.com/MaiM-with-u/MaiBot/blob/refactor/PRIVACY.md\n\n您是否同意上述协议？" 12 70); then
|
||||
exit 1
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||||
@@ -370,12 +372,13 @@ run_installation() {
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||||
# 选择分支
|
||||
choose_branch() {
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||||
BRANCH=$(whiptail --title "🔀 选择分支" --radiolist "请选择要安装的分支：" 15 60 4 \
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||||
"main" "稳定最新版（推荐）" ON \
|
||||
"classical" "经典版" OFF \
|
||||
"main" "稳定版本（推荐）" ON \
|
||||
"dev" "开发版（不知道什么意思就别选）" OFF \
|
||||
"classical" "经典版（0.6.0以前的版本）" OFF \
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"custom" "自定义分支" OFF 3>&1 1>&2 2>&3)
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RETVAL=$?
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||||
if [ $RETVAL -ne 0 ]; then
|
||||
whiptail --msgbox "操作取消！" 10 60
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||||
whiptail --msgbox "🚫 操作取消！" 10 60
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||||
exit 1
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||||
fi
|
||||
|
||||
@@ -383,7 +386,7 @@ run_installation() {
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||||
BRANCH=$(whiptail --title "🔀 自定义分支" --inputbox "请输入自定义分支名称：" 10 60 "refactor" 3>&1 1>&2 2>&3)
|
||||
RETVAL=$?
|
||||
if [ $RETVAL -ne 0 ]; then
|
||||
whiptail --msgbox "输入取消！" 10 60
|
||||
whiptail --msgbox "🚫 输入取消！" 10 60
|
||||
exit 1
|
||||
fi
|
||||
if [[ -z "$BRANCH" ]]; then
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||||
|
||||
@@ -15,9 +15,16 @@ def __create_database_instance():
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||||
password = os.getenv("MONGODB_PASSWORD")
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||||
auth_source = os.getenv("MONGODB_AUTH_SOURCE")
|
||||
|
||||
if uri and uri.startswith("mongodb://"):
|
||||
# 优先使用URI连接
|
||||
if uri:
|
||||
# 支持标准mongodb://和mongodb+srv://连接字符串
|
||||
if uri.startswith(("mongodb://", "mongodb+srv://")):
|
||||
return MongoClient(uri)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Invalid MongoDB URI format. URI must start with 'mongodb://' or 'mongodb+srv://'. "
|
||||
"For MongoDB Atlas, use 'mongodb+srv://' format. "
|
||||
"See: https://www.mongodb.com/docs/manual/reference/connection-string/"
|
||||
)
|
||||
|
||||
if username and password:
|
||||
# 如果有用户名和密码，使用认证连接
|
||||
|
||||
@@ -1,347 +1,378 @@
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||||
import customtkinter as ctk
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||||
import subprocess
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||||
import threading
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||||
import queue
|
||||
import re
|
||||
import os
|
||||
import signal
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||||
from collections import deque
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||||
# import customtkinter as ctk
|
||||
# import subprocess
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||||
# import threading
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||||
# import queue
|
||||
# import re
|
||||
# import os
|
||||
# import signal
|
||||
# from collections import deque
|
||||
# import sys
|
||||
|
||||
# 设置应用的外观模式和默认颜色主题
|
||||
ctk.set_appearance_mode("dark")
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||||
ctk.set_default_color_theme("blue")
|
||||
# # 设置应用的外观模式和默认颜色主题
|
||||
# ctk.set_appearance_mode("dark")
|
||||
# ctk.set_default_color_theme("blue")
|
||||
|
||||
|
||||
class LogViewerApp(ctk.CTk):
|
||||
"""日志查看器应用的主类，继承自customtkinter的CTk类"""
|
||||
# class LogViewerApp(ctk.CTk):
|
||||
# """日志查看器应用的主类，继承自customtkinter的CTk类"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化日志查看器应用的界面和状态"""
|
||||
super().__init__()
|
||||
self.title("日志查看器")
|
||||
self.geometry("1200x800")
|
||||
# def __init__(self):
|
||||
# """初始化日志查看器应用的界面和状态"""
|
||||
# super().__init__()
|
||||
# self.title("日志查看器")
|
||||
# self.geometry("1200x800")
|
||||
|
||||
# 初始化进程、日志队列、日志数据等变量
|
||||
self.process = None
|
||||
self.log_queue = queue.Queue()
|
||||
self.log_data = deque(maxlen=10000) # 使用固定长度队列
|
||||
self.available_levels = set()
|
||||
self.available_modules = set()
|
||||
self.sorted_modules = []
|
||||
self.module_checkboxes = {} # 存储模块复选框的字典
|
||||
# # 标记GUI是否运行中
|
||||
# self.is_running = True
|
||||
|
||||
# 日志颜色配置
|
||||
self.color_config = {
|
||||
"time": "#888888",
|
||||
"DEBUG": "#2196F3",
|
||||
"INFO": "#4CAF50",
|
||||
"WARNING": "#FF9800",
|
||||
"ERROR": "#F44336",
|
||||
"module": "#D4D0AB",
|
||||
"default": "#FFFFFF",
|
||||
}
|
||||
# # 程序关闭时的清理操作
|
||||
# self.protocol("WM_DELETE_WINDOW", self._on_closing)
|
||||
|
||||
# 列可见性配置
|
||||
self.column_visibility = {"show_time": True, "show_level": True, "show_module": True}
|
||||
# # 初始化进程、日志队列、日志数据等变量
|
||||
# self.process = None
|
||||
# self.log_queue = queue.Queue()
|
||||
# self.log_data = deque(maxlen=10000) # 使用固定长度队列
|
||||
# self.available_levels = set()
|
||||
# self.available_modules = set()
|
||||
# self.sorted_modules = []
|
||||
# self.module_checkboxes = {} # 存储模块复选框的字典
|
||||
|
||||
# 选中的日志等级和模块
|
||||
self.selected_levels = set()
|
||||
self.selected_modules = set()
|
||||
# # 日志颜色配置
|
||||
# self.color_config = {
|
||||
# "time": "#888888",
|
||||
# "DEBUG": "#2196F3",
|
||||
# "INFO": "#4CAF50",
|
||||
# "WARNING": "#FF9800",
|
||||
# "ERROR": "#F44336",
|
||||
# "module": "#D4D0AB",
|
||||
# "default": "#FFFFFF",
|
||||
# }
|
||||
|
||||
# 创建界面组件并启动日志队列处理
|
||||
self.create_widgets()
|
||||
self.after(100, self.process_log_queue)
|
||||
# # 列可见性配置
|
||||
# self.column_visibility = {"show_time": True, "show_level": True, "show_module": True}
|
||||
|
||||
def create_widgets(self):
|
||||
"""创建应用界面的各个组件"""
|
||||
self.grid_columnconfigure(0, weight=1)
|
||||
self.grid_rowconfigure(1, weight=1)
|
||||
# # 选中的日志等级和模块
|
||||
# self.selected_levels = set()
|
||||
# self.selected_modules = set()
|
||||
|
||||
# 控制面板
|
||||
control_frame = ctk.CTkFrame(self)
|
||||
control_frame.grid(row=0, column=0, sticky="ew", padx=10, pady=5)
|
||||
# # 创建界面组件并启动日志队列处理
|
||||
# self.create_widgets()
|
||||
# self.after(100, self.process_log_queue)
|
||||
|
||||
self.start_btn = ctk.CTkButton(control_frame, text="启动", command=self.start_process)
|
||||
self.start_btn.pack(side="left", padx=5)
|
||||
# def create_widgets(self):
|
||||
# """创建应用界面的各个组件"""
|
||||
# self.grid_columnconfigure(0, weight=1)
|
||||
# self.grid_rowconfigure(1, weight=1)
|
||||
|
||||
self.stop_btn = ctk.CTkButton(control_frame, text="停止", command=self.stop_process, state="disabled")
|
||||
self.stop_btn.pack(side="left", padx=5)
|
||||
# # 控制面板
|
||||
# control_frame = ctk.CTkFrame(self)
|
||||
# control_frame.grid(row=0, column=0, sticky="ew", padx=10, pady=5)
|
||||
|
||||
self.clear_btn = ctk.CTkButton(control_frame, text="清屏", command=self.clear_logs)
|
||||
self.clear_btn.pack(side="left", padx=5)
|
||||
# self.start_btn = ctk.CTkButton(control_frame, text="启动", command=self.start_process)
|
||||
# self.start_btn.pack(side="left", padx=5)
|
||||
|
||||
column_filter_frame = ctk.CTkFrame(control_frame)
|
||||
column_filter_frame.pack(side="left", padx=20)
|
||||
# self.stop_btn = ctk.CTkButton(control_frame, text="停止", command=self.stop_process, state="disabled")
|
||||
# self.stop_btn.pack(side="left", padx=5)
|
||||
|
||||
self.time_check = ctk.CTkCheckBox(column_filter_frame, text="显示时间", command=self.refresh_logs)
|
||||
self.time_check.pack(side="left", padx=5)
|
||||
self.time_check.select()
|
||||
# self.clear_btn = ctk.CTkButton(control_frame, text="清屏", command=self.clear_logs)
|
||||
# self.clear_btn.pack(side="left", padx=5)
|
||||
|
||||
self.level_check = ctk.CTkCheckBox(column_filter_frame, text="显示等级", command=self.refresh_logs)
|
||||
self.level_check.pack(side="left", padx=5)
|
||||
self.level_check.select()
|
||||
# column_filter_frame = ctk.CTkFrame(control_frame)
|
||||
# column_filter_frame.pack(side="left", padx=20)
|
||||
|
||||
self.module_check = ctk.CTkCheckBox(column_filter_frame, text="显示模块", command=self.refresh_logs)
|
||||
self.module_check.pack(side="left", padx=5)
|
||||
self.module_check.select()
|
||||
# self.time_check = ctk.CTkCheckBox(column_filter_frame, text="显示时间", command=self.refresh_logs)
|
||||
# self.time_check.pack(side="left", padx=5)
|
||||
# self.time_check.select()
|
||||
|
||||
# 筛选面板
|
||||
filter_frame = ctk.CTkFrame(self)
|
||||
filter_frame.grid(row=0, column=1, rowspan=2, sticky="ns", padx=5)
|
||||
# self.level_check = ctk.CTkCheckBox(column_filter_frame, text="显示等级", command=self.refresh_logs)
|
||||
# self.level_check.pack(side="left", padx=5)
|
||||
# self.level_check.select()
|
||||
|
||||
ctk.CTkLabel(filter_frame, text="日志等级筛选").pack(pady=5)
|
||||
self.level_scroll = ctk.CTkScrollableFrame(filter_frame, width=150, height=200)
|
||||
self.level_scroll.pack(fill="both", expand=True, padx=5)
|
||||
# self.module_check = ctk.CTkCheckBox(column_filter_frame, text="显示模块", command=self.refresh_logs)
|
||||
# self.module_check.pack(side="left", padx=5)
|
||||
# self.module_check.select()
|
||||
|
||||
ctk.CTkLabel(filter_frame, text="模块筛选").pack(pady=5)
|
||||
self.module_filter_entry = ctk.CTkEntry(filter_frame, placeholder_text="输入模块过滤词")
|
||||
self.module_filter_entry.pack(pady=5)
|
||||
self.module_filter_entry.bind("<KeyRelease>", self.update_module_filter)
|
||||
# # 筛选面板
|
||||
# filter_frame = ctk.CTkFrame(self)
|
||||
# filter_frame.grid(row=0, column=1, rowspan=2, sticky="ns", padx=5)
|
||||
|
||||
self.module_scroll = ctk.CTkScrollableFrame(filter_frame, width=300, height=200)
|
||||
self.module_scroll.pack(fill="both", expand=True, padx=5)
|
||||
# ctk.CTkLabel(filter_frame, text="日志等级筛选").pack(pady=5)
|
||||
# self.level_scroll = ctk.CTkScrollableFrame(filter_frame, width=150, height=200)
|
||||
# self.level_scroll.pack(fill="both", expand=True, padx=5)
|
||||
|
||||
self.log_text = ctk.CTkTextbox(self, wrap="word")
|
||||
self.log_text.grid(row=1, column=0, sticky="nsew", padx=10, pady=5)
|
||||
# ctk.CTkLabel(filter_frame, text="模块筛选").pack(pady=5)
|
||||
# self.module_filter_entry = ctk.CTkEntry(filter_frame, placeholder_text="输入模块过滤词")
|
||||
# self.module_filter_entry.pack(pady=5)
|
||||
# self.module_filter_entry.bind("<KeyRelease>", self.update_module_filter)
|
||||
|
||||
self.init_text_tags()
|
||||
# self.module_scroll = ctk.CTkScrollableFrame(filter_frame, width=300, height=200)
|
||||
# self.module_scroll.pack(fill="both", expand=True, padx=5)
|
||||
|
||||
def update_module_filter(self, event):
|
||||
"""根据模块过滤词更新模块复选框的显示"""
|
||||
filter_text = self.module_filter_entry.get().strip().lower()
|
||||
for module, checkbox in self.module_checkboxes.items():
|
||||
if filter_text in module.lower():
|
||||
checkbox.pack(anchor="w", padx=5, pady=2)
|
||||
else:
|
||||
checkbox.pack_forget()
|
||||
# self.log_text = ctk.CTkTextbox(self, wrap="word")
|
||||
# self.log_text.grid(row=1, column=0, sticky="nsew", padx=10, pady=5)
|
||||
|
||||
def update_filters(self, level, module):
|
||||
"""更新日志等级和模块的筛选器"""
|
||||
if level not in self.available_levels:
|
||||
self.available_levels.add(level)
|
||||
self.add_checkbox(self.level_scroll, level, "level")
|
||||
# self.init_text_tags()
|
||||
|
||||
module_key = self.get_module_key(module)
|
||||
if module_key not in self.available_modules:
|
||||
self.available_modules.add(module_key)
|
||||
self.sorted_modules = sorted(self.available_modules, key=lambda x: x.lower())
|
||||
self.rebuild_module_checkboxes()
|
||||
# def update_module_filter(self, event):
|
||||
# """根据模块过滤词更新模块复选框的显示"""
|
||||
# filter_text = self.module_filter_entry.get().strip().lower()
|
||||
# for module, checkbox in self.module_checkboxes.items():
|
||||
# if filter_text in module.lower():
|
||||
# checkbox.pack(anchor="w", padx=5, pady=2)
|
||||
# else:
|
||||
# checkbox.pack_forget()
|
||||
|
||||
def rebuild_module_checkboxes(self):
|
||||
"""重新构建模块复选框"""
|
||||
# 清空现有复选框
|
||||
for widget in self.module_scroll.winfo_children():
|
||||
widget.destroy()
|
||||
self.module_checkboxes.clear()
|
||||
# def update_filters(self, level, module):
|
||||
# """更新日志等级和模块的筛选器"""
|
||||
# if level not in self.available_levels:
|
||||
# self.available_levels.add(level)
|
||||
# self.add_checkbox(self.level_scroll, level, "level")
|
||||
|
||||
# 重建排序后的复选框
|
||||
for module in self.sorted_modules:
|
||||
self.add_checkbox(self.module_scroll, module, "module")
|
||||
# module_key = self.get_module_key(module)
|
||||
# if module_key not in self.available_modules:
|
||||
# self.available_modules.add(module_key)
|
||||
# self.sorted_modules = sorted(self.available_modules, key=lambda x: x.lower())
|
||||
# self.rebuild_module_checkboxes()
|
||||
|
||||
def add_checkbox(self, parent, text, type_):
|
||||
"""在指定父组件中添加复选框"""
|
||||
# def rebuild_module_checkboxes(self):
|
||||
# """重新构建模块复选框"""
|
||||
# # 清空现有复选框
|
||||
# for widget in self.module_scroll.winfo_children():
|
||||
# widget.destroy()
|
||||
# self.module_checkboxes.clear()
|
||||
|
||||
def update_filter():
|
||||
current = cb.get()
|
||||
if type_ == "level":
|
||||
(self.selected_levels.add if current else self.selected_levels.discard)(text)
|
||||
else:
|
||||
(self.selected_modules.add if current else self.selected_modules.discard)(text)
|
||||
self.refresh_logs()
|
||||
# # 重建排序后的复选框
|
||||
# for module in self.sorted_modules:
|
||||
# self.add_checkbox(self.module_scroll, module, "module")
|
||||
|
||||
cb = ctk.CTkCheckBox(parent, text=text, command=update_filter)
|
||||
cb.select() # 初始选中
|
||||
# def add_checkbox(self, parent, text, type_):
|
||||
# """在指定父组件中添加复选框"""
|
||||
|
||||
# 手动同步初始状态到集合（关键修复）
|
||||
if type_ == "level":
|
||||
self.selected_levels.add(text)
|
||||
else:
|
||||
self.selected_modules.add(text)
|
||||
# def update_filter():
|
||||
# current = cb.get()
|
||||
# if type_ == "level":
|
||||
# (self.selected_levels.add if current else self.selected_levels.discard)(text)
|
||||
# else:
|
||||
# (self.selected_modules.add if current else self.selected_modules.discard)(text)
|
||||
# self.refresh_logs()
|
||||
|
||||
if type_ == "module":
|
||||
self.module_checkboxes[text] = cb
|
||||
cb.pack(anchor="w", padx=5, pady=2)
|
||||
return cb
|
||||
# cb = ctk.CTkCheckBox(parent, text=text, command=update_filter)
|
||||
# cb.select() # 初始选中
|
||||
|
||||
def check_filter(self, entry):
|
||||
"""检查日志条目是否符合当前筛选条件"""
|
||||
level_ok = not self.selected_levels or entry["level"] in self.selected_levels
|
||||
module_key = self.get_module_key(entry["module"])
|
||||
module_ok = not self.selected_modules or module_key in self.selected_modules
|
||||
return level_ok and module_ok
|
||||
# # 手动同步初始状态到集合（关键修复）
|
||||
# if type_ == "level":
|
||||
# self.selected_levels.add(text)
|
||||
# else:
|
||||
# self.selected_modules.add(text)
|
||||
|
||||
def init_text_tags(self):
|
||||
"""初始化日志文本的颜色标签"""
|
||||
for tag, color in self.color_config.items():
|
||||
self.log_text.tag_config(tag, foreground=color)
|
||||
self.log_text.tag_config("default", foreground=self.color_config["default"])
|
||||
# if type_ == "module":
|
||||
# self.module_checkboxes[text] = cb
|
||||
# cb.pack(anchor="w", padx=5, pady=2)
|
||||
# return cb
|
||||
|
||||
def start_process(self):
|
||||
"""启动日志进程并开始读取输出"""
|
||||
self.process = subprocess.Popen(
|
||||
["nb", "run"],
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
text=True,
|
||||
bufsize=1,
|
||||
encoding="utf-8",
|
||||
errors="ignore",
|
||||
)
|
||||
self.start_btn.configure(state="disabled")
|
||||
self.stop_btn.configure(state="normal")
|
||||
threading.Thread(target=self.read_output, daemon=True).start()
|
||||
# def check_filter(self, entry):
|
||||
# """检查日志条目是否符合当前筛选条件"""
|
||||
# level_ok = not self.selected_levels or entry["level"] in self.selected_levels
|
||||
# module_key = self.get_module_key(entry["module"])
|
||||
# module_ok = not self.selected_modules or module_key in self.selected_modules
|
||||
# return level_ok and module_ok
|
||||
|
||||
def stop_process(self):
|
||||
"""停止日志进程并清理相关资源"""
|
||||
if self.process:
|
||||
try:
|
||||
if hasattr(self.process, "pid"):
|
||||
if os.name == "nt":
|
||||
subprocess.run(
|
||||
["taskkill", "/F", "/T", "/PID", str(self.process.pid)], check=True, capture_output=True
|
||||
)
|
||||
else:
|
||||
os.killpg(os.getpgid(self.process.pid), signal.SIGTERM)
|
||||
except (subprocess.CalledProcessError, ProcessLookupError, OSError) as e:
|
||||
print(f"终止进程失败: {e}")
|
||||
finally:
|
||||
self.process = None
|
||||
self.log_queue.queue.clear()
|
||||
self.start_btn.configure(state="normal")
|
||||
self.stop_btn.configure(state="disabled")
|
||||
self.refresh_logs()
|
||||
# def init_text_tags(self):
|
||||
# """初始化日志文本的颜色标签"""
|
||||
# for tag, color in self.color_config.items():
|
||||
# self.log_text.tag_config(tag, foreground=color)
|
||||
# self.log_text.tag_config("default", foreground=self.color_config["default"])
|
||||
|
||||
def read_output(self):
|
||||
"""读取日志进程的输出并放入队列"""
|
||||
try:
|
||||
while self.process and self.process.poll() is None:
|
||||
line = self.process.stdout.readline()
|
||||
if line:
|
||||
self.log_queue.put(line)
|
||||
else:
|
||||
break # 避免空循环
|
||||
self.process.stdout.close() # 确保关闭文件描述符
|
||||
except ValueError: # 处理可能的I/O操作异常
|
||||
pass
|
||||
# def start_process(self):
|
||||
# """启动日志进程并开始读取输出"""
|
||||
# self.process = subprocess.Popen(
|
||||
# ["nb", "run"],
|
||||
# stdout=subprocess.PIPE,
|
||||
# stderr=subprocess.STDOUT,
|
||||
# text=True,
|
||||
# bufsize=1,
|
||||
# encoding="utf-8",
|
||||
# errors="ignore",
|
||||
# )
|
||||
# self.start_btn.configure(state="disabled")
|
||||
# self.stop_btn.configure(state="normal")
|
||||
# threading.Thread(target=self.read_output, daemon=True).start()
|
||||
|
||||
def process_log_queue(self):
|
||||
"""处理日志队列中的日志条目"""
|
||||
while not self.log_queue.empty():
|
||||
line = self.log_queue.get()
|
||||
self.process_log_line(line)
|
||||
self.after(100, self.process_log_queue)
|
||||
# def stop_process(self):
|
||||
# """停止日志进程并清理相关资源"""
|
||||
# if self.process:
|
||||
# try:
|
||||
# if hasattr(self.process, "pid"):
|
||||
# if os.name == "nt":
|
||||
# subprocess.run(
|
||||
# ["taskkill", "/F", "/T", "/PID", str(self.process.pid)], check=True, capture_output=True
|
||||
# )
|
||||
# else:
|
||||
# os.killpg(os.getpgid(self.process.pid), signal.SIGTERM)
|
||||
# except (subprocess.CalledProcessError, ProcessLookupError, OSError) as e:
|
||||
# print(f"终止进程失败: {e}")
|
||||
# finally:
|
||||
# self.process = None
|
||||
# self.log_queue.queue.clear()
|
||||
# self.start_btn.configure(state="normal")
|
||||
# self.stop_btn.configure(state="disabled")
|
||||
# self.refresh_logs()
|
||||
|
||||
def process_log_line(self, line):
|
||||
"""解析单行日志并更新日志数据和筛选器"""
|
||||
match = re.match(
|
||||
r"""^
|
||||
(?:(?P<time>\d{2}:\d{2}(?::\d{2})?)\s*\|\s*)?
|
||||
(?P<level>\w+)\s*\|\s*
|
||||
(?P<module>.*?)
|
||||
\s*[-|]\s*
|
||||
(?P<message>.*)
|
||||
$""",
|
||||
line.strip(),
|
||||
re.VERBOSE,
|
||||
)
|
||||
# def read_output(self):
|
||||
# """读取日志进程的输出并放入队列"""
|
||||
# try:
|
||||
# while self.process and self.process.poll() is None and self.is_running:
|
||||
# line = self.process.stdout.readline()
|
||||
# if line:
|
||||
# self.log_queue.put(line)
|
||||
# else:
|
||||
# break # 避免空循环
|
||||
# self.process.stdout.close() # 确保关闭文件描述符
|
||||
# except ValueError: # 处理可能的I/O操作异常
|
||||
# pass
|
||||
|
||||
if match:
|
||||
groups = match.groupdict()
|
||||
time = groups.get("time", "")
|
||||
level = groups.get("level", "OTHER")
|
||||
module = groups.get("module", "UNKNOWN").strip()
|
||||
message = groups.get("message", "").strip()
|
||||
raw_line = line
|
||||
else:
|
||||
time, level, module, message = "", "OTHER", "UNKNOWN", line
|
||||
raw_line = line
|
||||
# def process_log_queue(self):
|
||||
# """处理日志队列中的日志条目"""
|
||||
# while not self.log_queue.empty():
|
||||
# line = self.log_queue.get()
|
||||
# self.process_log_line(line)
|
||||
|
||||
self.update_filters(level, module)
|
||||
log_entry = {"raw": raw_line, "time": time, "level": level, "module": module, "message": message}
|
||||
self.log_data.append(log_entry)
|
||||
# # 仅在GUI仍在运行时继续处理队列
|
||||
# if self.is_running:
|
||||
# self.after(100, self.process_log_queue)
|
||||
|
||||
if self.check_filter(log_entry):
|
||||
self.display_log(log_entry)
|
||||
# def process_log_line(self, line):
|
||||
# """解析单行日志并更新日志数据和筛选器"""
|
||||
# match = re.match(
|
||||
# r"""^
|
||||
# (?:(?P<time>\d{2}:\d{2}(?::\d{2})?)\s*\|\s*)?
|
||||
# (?P<level>\w+)\s*\|\s*
|
||||
# (?P<module>.*?)
|
||||
# \s*[-|]\s*
|
||||
# (?P<message>.*)
|
||||
# $""",
|
||||
# line.strip(),
|
||||
# re.VERBOSE,
|
||||
# )
|
||||
|
||||
def get_module_key(self, module_name):
|
||||
"""获取模块名称的标准化键"""
|
||||
cleaned = module_name.strip()
|
||||
return re.sub(r":\d+$", "", cleaned)
|
||||
# if match:
|
||||
# groups = match.groupdict()
|
||||
# time = groups.get("time", "")
|
||||
# level = groups.get("level", "OTHER")
|
||||
# module = groups.get("module", "UNKNOWN").strip()
|
||||
# message = groups.get("message", "").strip()
|
||||
# raw_line = line
|
||||
# else:
|
||||
# time, level, module, message = "", "OTHER", "UNKNOWN", line
|
||||
# raw_line = line
|
||||
|
||||
def display_log(self, entry):
|
||||
"""在日志文本框中显示日志条目"""
|
||||
parts = []
|
||||
tags = []
|
||||
# self.update_filters(level, module)
|
||||
# log_entry = {"raw": raw_line, "time": time, "level": level, "module": module, "message": message}
|
||||
# self.log_data.append(log_entry)
|
||||
|
||||
if self.column_visibility["show_time"] and entry["time"]:
|
||||
parts.append(f"{entry['time']} ")
|
||||
tags.append("time")
|
||||
# if self.check_filter(log_entry):
|
||||
# self.display_log(log_entry)
|
||||
|
||||
if self.column_visibility["show_level"]:
|
||||
level_tag = entry["level"] if entry["level"] in self.color_config else "default"
|
||||
parts.append(f"{entry['level']:<8} ")
|
||||
tags.append(level_tag)
|
||||
# def get_module_key(self, module_name):
|
||||
# """获取模块名称的标准化键"""
|
||||
# cleaned = module_name.strip()
|
||||
# return re.sub(r":\d+$", "", cleaned)
|
||||
|
||||
if self.column_visibility["show_module"]:
|
||||
parts.append(f"{entry['module']} ")
|
||||
tags.append("module")
|
||||
# def display_log(self, entry):
|
||||
# """在日志文本框中显示日志条目"""
|
||||
# parts = []
|
||||
# tags = []
|
||||
|
||||
parts.append(f"- {entry['message']}\n")
|
||||
tags.append("default")
|
||||
# if self.column_visibility["show_time"] and entry["time"]:
|
||||
# parts.append(f"{entry['time']} ")
|
||||
# tags.append("time")
|
||||
|
||||
self.log_text.configure(state="normal")
|
||||
for part, tag in zip(parts, tags):
|
||||
self.log_text.insert("end", part, tag)
|
||||
self.log_text.see("end")
|
||||
self.log_text.configure(state="disabled")
|
||||
# if self.column_visibility["show_level"]:
|
||||
# level_tag = entry["level"] if entry["level"] in self.color_config else "default"
|
||||
# parts.append(f"{entry['level']:<8} ")
|
||||
# tags.append(level_tag)
|
||||
|
||||
def refresh_logs(self):
|
||||
"""刷新日志显示，根据筛选条件重新显示日志"""
|
||||
self.column_visibility = {
|
||||
"show_time": self.time_check.get(),
|
||||
"show_level": self.level_check.get(),
|
||||
"show_module": self.module_check.get(),
|
||||
}
|
||||
# if self.column_visibility["show_module"]:
|
||||
# parts.append(f"{entry['module']} ")
|
||||
# tags.append("module")
|
||||
|
||||
self.log_text.configure(state="normal")
|
||||
self.log_text.delete("1.0", "end")
|
||||
# parts.append(f"- {entry['message']}\n")
|
||||
# tags.append("default")
|
||||
|
||||
filtered_logs = [entry for entry in self.log_data if self.check_filter(entry)]
|
||||
# self.log_text.configure(state="normal")
|
||||
# for part, tag in zip(parts, tags):
|
||||
# self.log_text.insert("end", part, tag)
|
||||
# self.log_text.see("end")
|
||||
# self.log_text.configure(state="disabled")
|
||||
|
||||
for entry in filtered_logs:
|
||||
parts = []
|
||||
tags = []
|
||||
# def refresh_logs(self):
|
||||
# """刷新日志显示，根据筛选条件重新显示日志"""
|
||||
# self.column_visibility = {
|
||||
# "show_time": self.time_check.get(),
|
||||
# "show_level": self.level_check.get(),
|
||||
# "show_module": self.module_check.get(),
|
||||
# }
|
||||
|
||||
if self.column_visibility["show_time"] and entry["time"]:
|
||||
parts.append(f"{entry['time']} ")
|
||||
tags.append("time")
|
||||
# self.log_text.configure(state="normal")
|
||||
# self.log_text.delete("1.0", "end")
|
||||
|
||||
if self.column_visibility["show_level"]:
|
||||
level_tag = entry["level"] if entry["level"] in self.color_config else "default"
|
||||
parts.append(f"{entry['level']:<8} ")
|
||||
tags.append(level_tag)
|
||||
# filtered_logs = [entry for entry in self.log_data if self.check_filter(entry)]
|
||||
|
||||
if self.column_visibility["show_module"]:
|
||||
parts.append(f"{entry['module']} ")
|
||||
tags.append("module")
|
||||
# for entry in filtered_logs:
|
||||
# parts = []
|
||||
# tags = []
|
||||
|
||||
parts.append(f"- {entry['message']}\n")
|
||||
tags.append("default")
|
||||
# if self.column_visibility["show_time"] and entry["time"]:
|
||||
# parts.append(f"{entry['time']} ")
|
||||
# tags.append("time")
|
||||
|
||||
for part, tag in zip(parts, tags):
|
||||
self.log_text.insert("end", part, tag)
|
||||
# if self.column_visibility["show_level"]:
|
||||
# level_tag = entry["level"] if entry["level"] in self.color_config else "default"
|
||||
# parts.append(f"{entry['level']:<8} ")
|
||||
# tags.append(level_tag)
|
||||
|
||||
self.log_text.see("end")
|
||||
self.log_text.configure(state="disabled")
|
||||
# if self.column_visibility["show_module"]:
|
||||
# parts.append(f"{entry['module']} ")
|
||||
# tags.append("module")
|
||||
|
||||
def clear_logs(self):
|
||||
"""清空日志文本框中的内容"""
|
||||
self.log_text.configure(state="normal")
|
||||
self.log_text.delete("1.0", "end")
|
||||
self.log_text.configure(state="disabled")
|
||||
# parts.append(f"- {entry['message']}\n")
|
||||
# tags.append("default")
|
||||
|
||||
# for part, tag in zip(parts, tags):
|
||||
# self.log_text.insert("end", part, tag)
|
||||
|
||||
# self.log_text.see("end")
|
||||
# self.log_text.configure(state="disabled")
|
||||
|
||||
# def clear_logs(self):
|
||||
# """清空日志文本框中的内容"""
|
||||
# self.log_text.configure(state="normal")
|
||||
# self.log_text.delete("1.0", "end")
|
||||
# self.log_text.configure(state="disabled")
|
||||
|
||||
# def _on_closing(self):
|
||||
# """处理窗口关闭事件，安全清理资源"""
|
||||
# # 标记GUI已关闭
|
||||
# self.is_running = False
|
||||
|
||||
# # 停止日志进程
|
||||
# self.stop_process()
|
||||
|
||||
# # 安全清理tkinter变量
|
||||
# for attr_name in list(self.__dict__.keys()):
|
||||
# if isinstance(getattr(self, attr_name), (ctk.Variable, ctk.StringVar, ctk.IntVar, ctk.DoubleVar, ctk.BooleanVar)):
|
||||
# try:
|
||||
# var = getattr(self, attr_name)
|
||||
# var.set(None)
|
||||
# except Exception:
|
||||
# pass
|
||||
# setattr(self, attr_name, None)
|
||||
|
||||
# self.quit()
|
||||
# sys.exit(0)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 启动日志查看器应用
|
||||
app = LogViewerApp()
|
||||
app.mainloop()
|
||||
# if __name__ == "__main__":
|
||||
# # 启动日志查看器应用
|
||||
# app = LogViewerApp()
|
||||
# app.mainloop()
|
||||
|
||||
@@ -1,320 +1,342 @@
|
||||
import os
|
||||
import queue
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
from datetime import datetime
|
||||
from typing import Dict, List
|
||||
from typing import Optional
|
||||
# import os
|
||||
# import queue
|
||||
# import sys
|
||||
# import threading
|
||||
# import time
|
||||
# from datetime import datetime
|
||||
# from typing import Dict, List
|
||||
# from typing import Optional
|
||||
|
||||
sys.path.insert(0, sys.path[0] + "/../")
|
||||
sys.path.insert(0, sys.path[0] + "/../")
|
||||
from src.common.logger import get_module_logger
|
||||
# sys.path.insert(0, sys.path[0] + "/../")
|
||||
# sys.path.insert(0, sys.path[0] + "/../")
|
||||
# from src.common.logger import get_module_logger
|
||||
|
||||
import customtkinter as ctk
|
||||
from dotenv import load_dotenv
|
||||
# import customtkinter as ctk
|
||||
# from dotenv import load_dotenv
|
||||
|
||||
logger = get_module_logger("gui")
|
||||
# logger = get_module_logger("gui")
|
||||
|
||||
# 获取当前文件的目录
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
# 获取项目根目录
|
||||
root_dir = os.path.abspath(os.path.join(current_dir, "..", ".."))
|
||||
sys.path.insert(0, root_dir)
|
||||
from src.common.database import db # noqa: E402
|
||||
# # 获取当前文件的目录
|
||||
# current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
# # 获取项目根目录
|
||||
# root_dir = os.path.abspath(os.path.join(current_dir, "..", ".."))
|
||||
# sys.path.insert(0, root_dir)
|
||||
# from src.common.database import db # noqa: E402
|
||||
|
||||
# 加载环境变量
|
||||
if os.path.exists(os.path.join(root_dir, ".env.dev")):
|
||||
load_dotenv(os.path.join(root_dir, ".env.dev"))
|
||||
logger.info("成功加载开发环境配置")
|
||||
elif os.path.exists(os.path.join(root_dir, ".env")):
|
||||
load_dotenv(os.path.join(root_dir, ".env"))
|
||||
logger.info("成功加载生产环境配置")
|
||||
else:
|
||||
logger.error("未找到环境配置文件")
|
||||
sys.exit(1)
|
||||
# # 加载环境变量
|
||||
# if os.path.exists(os.path.join(root_dir, ".env.dev")):
|
||||
# load_dotenv(os.path.join(root_dir, ".env.dev"))
|
||||
# logger.info("成功加载开发环境配置")
|
||||
# elif os.path.exists(os.path.join(root_dir, ".env")):
|
||||
# load_dotenv(os.path.join(root_dir, ".env"))
|
||||
# logger.info("成功加载生产环境配置")
|
||||
# else:
|
||||
# logger.error("未找到环境配置文件")
|
||||
# sys.exit(1)
|
||||
|
||||
|
||||
class ReasoningGUI:
|
||||
def __init__(self):
|
||||
# 记录启动时间戳，转换为Unix时间戳
|
||||
self.start_timestamp = datetime.now().timestamp()
|
||||
logger.info(f"程序启动时间戳: {self.start_timestamp}")
|
||||
# class ReasoningGUI:
|
||||
# def __init__(self):
|
||||
# # 记录启动时间戳，转换为Unix时间戳
|
||||
# self.start_timestamp = datetime.now().timestamp()
|
||||
# logger.info(f"程序启动时间戳: {self.start_timestamp}")
|
||||
|
||||
# 设置主题
|
||||
ctk.set_appearance_mode("dark")
|
||||
ctk.set_default_color_theme("blue")
|
||||
# # 设置主题
|
||||
# ctk.set_appearance_mode("dark")
|
||||
# ctk.set_default_color_theme("blue")
|
||||
|
||||
# 创建主窗口
|
||||
self.root = ctk.CTk()
|
||||
self.root.title("麦麦推理")
|
||||
self.root.geometry("800x600")
|
||||
self.root.protocol("WM_DELETE_WINDOW", self._on_closing)
|
||||
# # 创建主窗口
|
||||
# self.root = ctk.CTk()
|
||||
# self.root.title("麦麦推理")
|
||||
# self.root.geometry("800x600")
|
||||
# self.root.protocol("WM_DELETE_WINDOW", self._on_closing)
|
||||
|
||||
# 存储群组数据
|
||||
self.group_data: Dict[str, List[dict]] = {}
|
||||
# # 存储群组数据
|
||||
# self.group_data: Dict[str, List[dict]] = {}
|
||||
|
||||
# 创建更新队列
|
||||
self.update_queue = queue.Queue()
|
||||
# # 创建更新队列
|
||||
# self.update_queue = queue.Queue()
|
||||
|
||||
# 创建主框架
|
||||
self.frame = ctk.CTkFrame(self.root)
|
||||
self.frame.pack(pady=20, padx=20, fill="both", expand=True)
|
||||
# # 创建主框架
|
||||
# self.frame = ctk.CTkFrame(self.root)
|
||||
# self.frame.pack(pady=20, padx=20, fill="both", expand=True)
|
||||
|
||||
# 添加标题
|
||||
self.title = ctk.CTkLabel(self.frame, text="麦麦的脑内所想", font=("Arial", 24))
|
||||
self.title.pack(pady=10, padx=10)
|
||||
# # 添加标题
|
||||
# self.title = ctk.CTkLabel(self.frame, text="麦麦的脑内所想", font=("Arial", 24))
|
||||
# self.title.pack(pady=10, padx=10)
|
||||
|
||||
# 创建左右分栏
|
||||
self.paned = ctk.CTkFrame(self.frame)
|
||||
self.paned.pack(fill="both", expand=True, padx=10, pady=10)
|
||||
# # 创建左右分栏
|
||||
# self.paned = ctk.CTkFrame(self.frame)
|
||||
# self.paned.pack(fill="both", expand=True, padx=10, pady=10)
|
||||
|
||||
# 左侧群组列表
|
||||
self.left_frame = ctk.CTkFrame(self.paned, width=200)
|
||||
self.left_frame.pack(side="left", fill="y", padx=5, pady=5)
|
||||
# # 左侧群组列表
|
||||
# self.left_frame = ctk.CTkFrame(self.paned, width=200)
|
||||
# self.left_frame.pack(side="left", fill="y", padx=5, pady=5)
|
||||
|
||||
self.group_label = ctk.CTkLabel(self.left_frame, text="群组列表", font=("Arial", 16))
|
||||
self.group_label.pack(pady=5)
|
||||
# self.group_label = ctk.CTkLabel(self.left_frame, text="群组列表", font=("Arial", 16))
|
||||
# self.group_label.pack(pady=5)
|
||||
|
||||
# 创建可滚动框架来容纳群组按钮
|
||||
self.group_scroll_frame = ctk.CTkScrollableFrame(self.left_frame, width=180, height=400)
|
||||
self.group_scroll_frame.pack(pady=5, padx=5, fill="both", expand=True)
|
||||
# # 创建可滚动框架来容纳群组按钮
|
||||
# self.group_scroll_frame = ctk.CTkScrollableFrame(self.left_frame, width=180, height=400)
|
||||
# self.group_scroll_frame.pack(pady=5, padx=5, fill="both", expand=True)
|
||||
|
||||
# 存储群组按钮的字典
|
||||
self.group_buttons: Dict[str, ctk.CTkButton] = {}
|
||||
# 当前选中的群组ID
|
||||
self.selected_group_id: Optional[str] = None
|
||||
# # 存储群组按钮的字典
|
||||
# self.group_buttons: Dict[str, ctk.CTkButton] = {}
|
||||
# # 当前选中的群组ID
|
||||
# self.selected_group_id: Optional[str] = None
|
||||
|
||||
# 右侧内容显示
|
||||
self.right_frame = ctk.CTkFrame(self.paned)
|
||||
self.right_frame.pack(side="right", fill="both", expand=True, padx=5, pady=5)
|
||||
# # 右侧内容显示
|
||||
# self.right_frame = ctk.CTkFrame(self.paned)
|
||||
# self.right_frame.pack(side="right", fill="both", expand=True, padx=5, pady=5)
|
||||
|
||||
self.content_label = ctk.CTkLabel(self.right_frame, text="推理内容", font=("Arial", 16))
|
||||
self.content_label.pack(pady=5)
|
||||
# self.content_label = ctk.CTkLabel(self.right_frame, text="推理内容", font=("Arial", 16))
|
||||
# self.content_label.pack(pady=5)
|
||||
|
||||
# 创建富文本显示框
|
||||
self.content_text = ctk.CTkTextbox(self.right_frame, width=500, height=400)
|
||||
self.content_text.pack(pady=5, padx=5, fill="both", expand=True)
|
||||
# # 创建富文本显示框
|
||||
# self.content_text = ctk.CTkTextbox(self.right_frame, width=500, height=400)
|
||||
# self.content_text.pack(pady=5, padx=5, fill="both", expand=True)
|
||||
|
||||
# 配置文本标签 - 只使用颜色
|
||||
self.content_text.tag_config("timestamp", foreground="#888888") # 时间戳使用灰色
|
||||
self.content_text.tag_config("user", foreground="#4CAF50") # 用户名使用绿色
|
||||
self.content_text.tag_config("message", foreground="#2196F3") # 消息使用蓝色
|
||||
self.content_text.tag_config("model", foreground="#9C27B0") # 模型名称使用紫色
|
||||
self.content_text.tag_config("prompt", foreground="#FF9800") # prompt内容使用橙色
|
||||
self.content_text.tag_config("reasoning", foreground="#FF9800") # 推理过程使用橙色
|
||||
self.content_text.tag_config("response", foreground="#E91E63") # 回复使用粉色
|
||||
self.content_text.tag_config("separator", foreground="#666666") # 分隔符使用深灰色
|
||||
# # 配置文本标签 - 只使用颜色
|
||||
# self.content_text.tag_config("timestamp", foreground="#888888") # 时间戳使用灰色
|
||||
# self.content_text.tag_config("user", foreground="#4CAF50") # 用户名使用绿色
|
||||
# self.content_text.tag_config("message", foreground="#2196F3") # 消息使用蓝色
|
||||
# self.content_text.tag_config("model", foreground="#9C27B0") # 模型名称使用紫色
|
||||
# self.content_text.tag_config("prompt", foreground="#FF9800") # prompt内容使用橙色
|
||||
# self.content_text.tag_config("reasoning", foreground="#FF9800") # 推理过程使用橙色
|
||||
# self.content_text.tag_config("response", foreground="#E91E63") # 回复使用粉色
|
||||
# self.content_text.tag_config("separator", foreground="#666666") # 分隔符使用深灰色
|
||||
|
||||
# 底部控制栏
|
||||
self.control_frame = ctk.CTkFrame(self.frame)
|
||||
self.control_frame.pack(fill="x", padx=10, pady=5)
|
||||
# # 底部控制栏
|
||||
# self.control_frame = ctk.CTkFrame(self.frame)
|
||||
# self.control_frame.pack(fill="x", padx=10, pady=5)
|
||||
|
||||
self.clear_button = ctk.CTkButton(self.control_frame, text="清除显示", command=self.clear_display, width=120)
|
||||
self.clear_button.pack(side="left", padx=5)
|
||||
# self.clear_button = ctk.CTkButton(self.control_frame, text="清除显示", command=self.clear_display, width=120)
|
||||
# self.clear_button.pack(side="left", padx=5)
|
||||
|
||||
# 启动自动更新线程
|
||||
self.update_thread = threading.Thread(target=self._auto_update, daemon=True)
|
||||
self.update_thread.start()
|
||||
# # 添加标志，标记GUI是否已关闭
|
||||
# self.is_running = True
|
||||
|
||||
# 启动GUI更新检查
|
||||
self.root.after(100, self._process_queue)
|
||||
# # 启动自动更新线程
|
||||
# self.update_thread = threading.Thread(target=self._auto_update, daemon=True)
|
||||
# self.update_thread.start()
|
||||
|
||||
def _on_closing(self):
|
||||
"""处理窗口关闭事件"""
|
||||
self.root.quit()
|
||||
sys.exit(0)
|
||||
# # 启动GUI更新检查
|
||||
# self.root.after(100, self._process_queue)
|
||||
|
||||
def _process_queue(self):
|
||||
"""处理更新队列中的任务"""
|
||||
try:
|
||||
while True:
|
||||
task = self.update_queue.get_nowait()
|
||||
if task["type"] == "update_group_list":
|
||||
self._update_group_list_gui()
|
||||
elif task["type"] == "update_display":
|
||||
self._update_display_gui(task["group_id"])
|
||||
except queue.Empty:
|
||||
pass
|
||||
finally:
|
||||
# 继续检查队列
|
||||
self.root.after(100, self._process_queue)
|
||||
# def _on_closing(self):
|
||||
# """处理窗口关闭事件"""
|
||||
# # 标记GUI已关闭，防止后台线程继续访问tkinter对象
|
||||
# self.is_running = False
|
||||
|
||||
def _update_group_list_gui(self):
|
||||
"""在主线程中更新群组列表"""
|
||||
# 清除现有按钮
|
||||
for button in self.group_buttons.values():
|
||||
button.destroy()
|
||||
self.group_buttons.clear()
|
||||
# # 安全清理所有可能的tkinter变量
|
||||
# for attr_name in list(self.__dict__.keys()):
|
||||
# if isinstance(getattr(self, attr_name), (ctk.Variable, ctk.StringVar, ctk.IntVar, ctk.DoubleVar, ctk.BooleanVar)):
|
||||
# # 删除变量前安全地将其设置为None
|
||||
# try:
|
||||
# var = getattr(self, attr_name)
|
||||
# var.set(None)
|
||||
# except Exception:
|
||||
# pass
|
||||
# setattr(self, attr_name, None)
|
||||
|
||||
# 创建新的群组按钮
|
||||
for group_id in self.group_data.keys():
|
||||
button = ctk.CTkButton(
|
||||
self.group_scroll_frame,
|
||||
text=f"群号: {group_id}",
|
||||
width=160,
|
||||
height=30,
|
||||
corner_radius=8,
|
||||
command=lambda gid=group_id: self._on_group_select(gid),
|
||||
)
|
||||
button.pack(pady=2, padx=5)
|
||||
self.group_buttons[group_id] = button
|
||||
# # 退出
|
||||
# self.root.quit()
|
||||
# sys.exit(0)
|
||||
|
||||
# 如果有选中的群组，保持其高亮状态
|
||||
if self.selected_group_id and self.selected_group_id in self.group_buttons:
|
||||
self._highlight_selected_group(self.selected_group_id)
|
||||
# def _process_queue(self):
|
||||
# """处理更新队列中的任务"""
|
||||
# try:
|
||||
# while True:
|
||||
# task = self.update_queue.get_nowait()
|
||||
# if task["type"] == "update_group_list":
|
||||
# self._update_group_list_gui()
|
||||
# elif task["type"] == "update_display":
|
||||
# self._update_display_gui(task["group_id"])
|
||||
# except queue.Empty:
|
||||
# pass
|
||||
# finally:
|
||||
# # 继续检查队列，但仅在GUI仍在运行时
|
||||
# if self.is_running:
|
||||
# self.root.after(100, self._process_queue)
|
||||
|
||||
def _on_group_select(self, group_id: str):
|
||||
"""处理群组选择事件"""
|
||||
self._highlight_selected_group(group_id)
|
||||
self._update_display_gui(group_id)
|
||||
# def _update_group_list_gui(self):
|
||||
# """在主线程中更新群组列表"""
|
||||
# # 清除现有按钮
|
||||
# for button in self.group_buttons.values():
|
||||
# button.destroy()
|
||||
# self.group_buttons.clear()
|
||||
|
||||
def _highlight_selected_group(self, group_id: str):
|
||||
"""高亮显示选中的群组按钮"""
|
||||
# 重置所有按钮的颜色
|
||||
for gid, button in self.group_buttons.items():
|
||||
if gid == group_id:
|
||||
# 设置选中按钮的颜色
|
||||
button.configure(fg_color="#1E88E5", hover_color="#1976D2")
|
||||
else:
|
||||
# 恢复其他按钮的默认颜色
|
||||
button.configure(fg_color="#2B2B2B", hover_color="#404040")
|
||||
# # 创建新的群组按钮
|
||||
# for group_id in self.group_data.keys():
|
||||
# button = ctk.CTkButton(
|
||||
# self.group_scroll_frame,
|
||||
# text=f"群号: {group_id}",
|
||||
# width=160,
|
||||
# height=30,
|
||||
# corner_radius=8,
|
||||
# command=lambda gid=group_id: self._on_group_select(gid),
|
||||
# )
|
||||
# button.pack(pady=2, padx=5)
|
||||
# self.group_buttons[group_id] = button
|
||||
|
||||
self.selected_group_id = group_id
|
||||
# # 如果有选中的群组，保持其高亮状态
|
||||
# if self.selected_group_id and self.selected_group_id in self.group_buttons:
|
||||
# self._highlight_selected_group(self.selected_group_id)
|
||||
|
||||
def _update_display_gui(self, group_id: str):
|
||||
"""在主线程中更新显示内容"""
|
||||
if group_id in self.group_data:
|
||||
self.content_text.delete("1.0", "end")
|
||||
for item in self.group_data[group_id]:
|
||||
# 时间戳
|
||||
time_str = item["time"].strftime("%Y-%m-%d %H:%M:%S")
|
||||
self.content_text.insert("end", f"[{time_str}]\n", "timestamp")
|
||||
# def _on_group_select(self, group_id: str):
|
||||
# """处理群组选择事件"""
|
||||
# self._highlight_selected_group(group_id)
|
||||
# self._update_display_gui(group_id)
|
||||
|
||||
# 用户信息
|
||||
self.content_text.insert("end", "用户: ", "timestamp")
|
||||
self.content_text.insert("end", f"{item.get('user', '未知')}\n", "user")
|
||||
# def _highlight_selected_group(self, group_id: str):
|
||||
# """高亮显示选中的群组按钮"""
|
||||
# # 重置所有按钮的颜色
|
||||
# for gid, button in self.group_buttons.items():
|
||||
# if gid == group_id:
|
||||
# # 设置选中按钮的颜色
|
||||
# button.configure(fg_color="#1E88E5", hover_color="#1976D2")
|
||||
# else:
|
||||
# # 恢复其他按钮的默认颜色
|
||||
# button.configure(fg_color="#2B2B2B", hover_color="#404040")
|
||||
|
||||
# 消息内容
|
||||
self.content_text.insert("end", "消息: ", "timestamp")
|
||||
self.content_text.insert("end", f"{item.get('message', '')}\n", "message")
|
||||
# self.selected_group_id = group_id
|
||||
|
||||
# 模型信息
|
||||
self.content_text.insert("end", "模型: ", "timestamp")
|
||||
self.content_text.insert("end", f"{item.get('model', '')}\n", "model")
|
||||
# def _update_display_gui(self, group_id: str):
|
||||
# """在主线程中更新显示内容"""
|
||||
# if group_id in self.group_data:
|
||||
# self.content_text.delete("1.0", "end")
|
||||
# for item in self.group_data[group_id]:
|
||||
# # 时间戳
|
||||
# time_str = item["time"].strftime("%Y-%m-%d %H:%M:%S")
|
||||
# self.content_text.insert("end", f"[{time_str}]\n", "timestamp")
|
||||
|
||||
# Prompt内容
|
||||
self.content_text.insert("end", "Prompt内容:\n", "timestamp")
|
||||
prompt_text = item.get("prompt", "")
|
||||
if prompt_text and prompt_text.lower() != "none":
|
||||
lines = prompt_text.split("\n")
|
||||
for line in lines:
|
||||
if line.strip():
|
||||
self.content_text.insert("end", " " + line + "\n", "prompt")
|
||||
else:
|
||||
self.content_text.insert("end", " 无Prompt内容\n", "prompt")
|
||||
# # 用户信息
|
||||
# self.content_text.insert("end", "用户: ", "timestamp")
|
||||
# self.content_text.insert("end", f"{item.get('user', '未知')}\n", "user")
|
||||
|
||||
# 推理过程
|
||||
self.content_text.insert("end", "推理过程:\n", "timestamp")
|
||||
reasoning_text = item.get("reasoning", "")
|
||||
if reasoning_text and reasoning_text.lower() != "none":
|
||||
lines = reasoning_text.split("\n")
|
||||
for line in lines:
|
||||
if line.strip():
|
||||
self.content_text.insert("end", " " + line + "\n", "reasoning")
|
||||
else:
|
||||
self.content_text.insert("end", " 无推理过程\n", "reasoning")
|
||||
# # 消息内容
|
||||
# self.content_text.insert("end", "消息: ", "timestamp")
|
||||
# self.content_text.insert("end", f"{item.get('message', '')}\n", "message")
|
||||
|
||||
# 回复内容
|
||||
self.content_text.insert("end", "回复: ", "timestamp")
|
||||
self.content_text.insert("end", f"{item.get('response', '')}\n", "response")
|
||||
# # 模型信息
|
||||
# self.content_text.insert("end", "模型: ", "timestamp")
|
||||
# self.content_text.insert("end", f"{item.get('model', '')}\n", "model")
|
||||
|
||||
# 分隔符
|
||||
self.content_text.insert("end", f"\n{'=' * 50}\n\n", "separator")
|
||||
# # Prompt内容
|
||||
# self.content_text.insert("end", "Prompt内容:\n", "timestamp")
|
||||
# prompt_text = item.get("prompt", "")
|
||||
# if prompt_text and prompt_text.lower() != "none":
|
||||
# lines = prompt_text.split("\n")
|
||||
# for line in lines:
|
||||
# if line.strip():
|
||||
# self.content_text.insert("end", " " + line + "\n", "prompt")
|
||||
# else:
|
||||
# self.content_text.insert("end", " 无Prompt内容\n", "prompt")
|
||||
|
||||
# 滚动到顶部
|
||||
self.content_text.see("1.0")
|
||||
# # 推理过程
|
||||
# self.content_text.insert("end", "推理过程:\n", "timestamp")
|
||||
# reasoning_text = item.get("reasoning", "")
|
||||
# if reasoning_text and reasoning_text.lower() != "none":
|
||||
# lines = reasoning_text.split("\n")
|
||||
# for line in lines:
|
||||
# if line.strip():
|
||||
# self.content_text.insert("end", " " + line + "\n", "reasoning")
|
||||
# else:
|
||||
# self.content_text.insert("end", " 无推理过程\n", "reasoning")
|
||||
|
||||
def _auto_update(self):
|
||||
"""自动更新函数"""
|
||||
while True:
|
||||
try:
|
||||
# 从数据库获取最新数据，只获取启动时间之后的记录
|
||||
query = {"time": {"$gt": self.start_timestamp}}
|
||||
logger.debug(f"查询条件: {query}")
|
||||
# # 回复内容
|
||||
# self.content_text.insert("end", "回复: ", "timestamp")
|
||||
# self.content_text.insert("end", f"{item.get('response', '')}\n", "response")
|
||||
|
||||
# 先获取一条记录检查时间格式
|
||||
sample = db.reasoning_logs.find_one()
|
||||
if sample:
|
||||
logger.debug(f"样本记录时间格式: {type(sample['time'])} 值: {sample['time']}")
|
||||
# # 分隔符
|
||||
# self.content_text.insert("end", f"\n{'=' * 50}\n\n", "separator")
|
||||
|
||||
cursor = db.reasoning_logs.find(query).sort("time", -1)
|
||||
new_data = {}
|
||||
total_count = 0
|
||||
# # 滚动到顶部
|
||||
# self.content_text.see("1.0")
|
||||
|
||||
for item in cursor:
|
||||
# 调试输出
|
||||
if total_count == 0:
|
||||
logger.debug(f"记录时间: {item['time']}, 类型: {type(item['time'])}")
|
||||
# def _auto_update(self):
|
||||
# """自动更新函数"""
|
||||
# while True:
|
||||
# if not self.is_running:
|
||||
# break # 如果GUI已关闭，停止线程
|
||||
|
||||
total_count += 1
|
||||
group_id = str(item.get("group_id", "unknown"))
|
||||
if group_id not in new_data:
|
||||
new_data[group_id] = []
|
||||
# try:
|
||||
# # 从数据库获取最新数据，只获取启动时间之后的记录
|
||||
# query = {"time": {"$gt": self.start_timestamp}}
|
||||
# logger.debug(f"查询条件: {query}")
|
||||
|
||||
# 转换时间戳为datetime对象
|
||||
if isinstance(item["time"], (int, float)):
|
||||
time_obj = datetime.fromtimestamp(item["time"])
|
||||
elif isinstance(item["time"], datetime):
|
||||
time_obj = item["time"]
|
||||
else:
|
||||
logger.warning(f"未知的时间格式: {type(item['time'])}")
|
||||
time_obj = datetime.now() # 使用当前时间作为后备
|
||||
# # 先获取一条记录检查时间格式
|
||||
# sample = db.reasoning_logs.find_one()
|
||||
# if sample:
|
||||
# logger.debug(f"样本记录时间格式: {type(sample['time'])} 值: {sample['time']}")
|
||||
|
||||
new_data[group_id].append(
|
||||
{
|
||||
"time": time_obj,
|
||||
"user": item.get("user", "未知"),
|
||||
"message": item.get("message", ""),
|
||||
"model": item.get("model", "未知"),
|
||||
"reasoning": item.get("reasoning", ""),
|
||||
"response": item.get("response", ""),
|
||||
"prompt": item.get("prompt", ""), # 添加prompt字段
|
||||
}
|
||||
)
|
||||
# cursor = db.reasoning_logs.find(query).sort("time", -1)
|
||||
# new_data = {}
|
||||
# total_count = 0
|
||||
|
||||
logger.info(f"从数据库加载了 {total_count} 条记录，分布在 {len(new_data)} 个群组中")
|
||||
# for item in cursor:
|
||||
# # 调试输出
|
||||
# if total_count == 0:
|
||||
# logger.debug(f"记录时间: {item['time']}, 类型: {type(item['time'])}")
|
||||
|
||||
# 更新数据
|
||||
if new_data != self.group_data:
|
||||
self.group_data = new_data
|
||||
logger.info("数据已更新，正在刷新显示...")
|
||||
# 将更新任务添加到队列
|
||||
self.update_queue.put({"type": "update_group_list"})
|
||||
if self.group_data:
|
||||
# 如果没有选中的群组，选择最新的群组
|
||||
if not self.selected_group_id or self.selected_group_id not in self.group_data:
|
||||
self.selected_group_id = next(iter(self.group_data))
|
||||
self.update_queue.put({"type": "update_display", "group_id": self.selected_group_id})
|
||||
except Exception:
|
||||
logger.exception("自动更新出错")
|
||||
# total_count += 1
|
||||
# group_id = str(item.get("group_id", "unknown"))
|
||||
# if group_id not in new_data:
|
||||
# new_data[group_id] = []
|
||||
|
||||
# 每5秒更新一次
|
||||
time.sleep(5)
|
||||
# # 转换时间戳为datetime对象
|
||||
# if isinstance(item["time"], (int, float)):
|
||||
# time_obj = datetime.fromtimestamp(item["time"])
|
||||
# elif isinstance(item["time"], datetime):
|
||||
# time_obj = item["time"]
|
||||
# else:
|
||||
# logger.warning(f"未知的时间格式: {type(item['time'])}")
|
||||
# time_obj = datetime.now() # 使用当前时间作为后备
|
||||
|
||||
def clear_display(self):
|
||||
"""清除显示内容"""
|
||||
self.content_text.delete("1.0", "end")
|
||||
# new_data[group_id].append(
|
||||
# {
|
||||
# "time": time_obj,
|
||||
# "user": item.get("user", "未知"),
|
||||
# "message": item.get("message", ""),
|
||||
# "model": item.get("model", "未知"),
|
||||
# "reasoning": item.get("reasoning", ""),
|
||||
# "response": item.get("response", ""),
|
||||
# "prompt": item.get("prompt", ""), # 添加prompt字段
|
||||
# }
|
||||
# )
|
||||
|
||||
def run(self):
|
||||
"""运行GUI"""
|
||||
self.root.mainloop()
|
||||
# logger.info(f"从数据库加载了 {total_count} 条记录，分布在 {len(new_data)} 个群组中")
|
||||
|
||||
# # 更新数据
|
||||
# if new_data != self.group_data:
|
||||
# self.group_data = new_data
|
||||
# logger.info("数据已更新，正在刷新显示...")
|
||||
# # 将更新任务添加到队列
|
||||
# self.update_queue.put({"type": "update_group_list"})
|
||||
# if self.group_data:
|
||||
# # 如果没有选中的群组，选择最新的群组
|
||||
# if not self.selected_group_id or self.selected_group_id not in self.group_data:
|
||||
# self.selected_group_id = next(iter(self.group_data))
|
||||
# self.update_queue.put({"type": "update_display", "group_id": self.selected_group_id})
|
||||
# except Exception:
|
||||
# logger.exception("自动更新出错")
|
||||
|
||||
# # 每5秒更新一次
|
||||
# time.sleep(5)
|
||||
|
||||
# def clear_display(self):
|
||||
# """清除显示内容"""
|
||||
# self.content_text.delete("1.0", "end")
|
||||
|
||||
# def run(self):
|
||||
# """运行GUI"""
|
||||
# self.root.mainloop()
|
||||
|
||||
|
||||
def main():
|
||||
app = ReasoningGUI()
|
||||
app.run()
|
||||
# def main():
|
||||
# app = ReasoningGUI()
|
||||
# app.run()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
# if __name__ == "__main__":
|
||||
# main()
|
||||
|
||||
@@ -6,7 +6,9 @@ from src.plugins.config.config import global_config
|
||||
from src.plugins.schedule.schedule_generator import bot_schedule
|
||||
import asyncio
|
||||
from src.common.logger import get_module_logger, LogConfig, HEARTFLOW_STYLE_CONFIG # noqa: E402
|
||||
from src.individuality.individuality import Individuality
|
||||
import time
|
||||
import random
|
||||
|
||||
heartflow_config = LogConfig(
|
||||
# 使用海马体专用样式
|
||||
@@ -40,7 +42,6 @@ class Heartflow:
|
||||
self._subheartflows = {}
|
||||
self.active_subheartflows_nums = 0
|
||||
|
||||
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||
|
||||
async def _cleanup_inactive_subheartflows(self):
|
||||
"""定期清理不活跃的子心流"""
|
||||
@@ -70,7 +71,7 @@ class Heartflow:
|
||||
while True:
|
||||
# 检查是否存在子心流
|
||||
if not self._subheartflows:
|
||||
logger.info("当前没有子心流，等待新的子心流创建...")
|
||||
# logger.info("当前没有子心流，等待新的子心流创建...")
|
||||
await asyncio.sleep(30) # 每分钟检查一次是否有新的子心流
|
||||
continue
|
||||
|
||||
@@ -81,7 +82,27 @@ class Heartflow:
|
||||
logger.debug("麦麦大脑袋转起来了")
|
||||
self.current_state.update_current_state_info()
|
||||
|
||||
personality_info = self.personality_info
|
||||
# 开始构建prompt
|
||||
prompt_personality = "你"
|
||||
#person
|
||||
individuality = Individuality.get_instance()
|
||||
|
||||
personality_core = individuality.personality.personality_core
|
||||
prompt_personality += personality_core
|
||||
|
||||
personality_sides = individuality.personality.personality_sides
|
||||
random.shuffle(personality_sides)
|
||||
prompt_personality += f",{personality_sides[0]}"
|
||||
|
||||
identity_detail = individuality.identity.identity_detail
|
||||
random.shuffle(identity_detail)
|
||||
prompt_personality += f",{identity_detail[0]}"
|
||||
|
||||
|
||||
|
||||
personality_info = prompt_personality
|
||||
|
||||
|
||||
current_thinking_info = self.current_mind
|
||||
mood_info = self.current_state.mood
|
||||
related_memory_info = "memory"
|
||||
@@ -123,7 +144,25 @@ class Heartflow:
|
||||
return await self.minds_summary(sub_minds)
|
||||
|
||||
async def minds_summary(self, minds_str):
|
||||
personality_info = self.personality_info
|
||||
# 开始构建prompt
|
||||
prompt_personality = "你"
|
||||
#person
|
||||
individuality = Individuality.get_instance()
|
||||
|
||||
personality_core = individuality.personality.personality_core
|
||||
prompt_personality += personality_core
|
||||
|
||||
personality_sides = individuality.personality.personality_sides
|
||||
random.shuffle(personality_sides)
|
||||
prompt_personality += f",{personality_sides[0]}"
|
||||
|
||||
identity_detail = individuality.identity.identity_detail
|
||||
random.shuffle(identity_detail)
|
||||
prompt_personality += f",{identity_detail[0]}"
|
||||
|
||||
|
||||
|
||||
personality_info = prompt_personality
|
||||
mood_info = self.current_state.mood
|
||||
|
||||
prompt = ""
|
||||
|
||||
@@ -4,7 +4,8 @@ from datetime import datetime
|
||||
from src.plugins.models.utils_model import LLM_request
|
||||
from src.plugins.config.config import global_config
|
||||
from src.common.database import db
|
||||
|
||||
from src.individuality.individuality import Individuality
|
||||
import random
|
||||
|
||||
# 所有观察的基类
|
||||
class Observation:
|
||||
@@ -24,7 +25,6 @@ class ChattingObservation(Observation):
|
||||
self.talking_message = []
|
||||
self.talking_message_str = ""
|
||||
|
||||
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||
self.name = global_config.BOT_NICKNAME
|
||||
self.nick_name = global_config.BOT_ALIAS_NAMES
|
||||
|
||||
@@ -115,8 +115,30 @@ class ChattingObservation(Observation):
|
||||
async def update_talking_summary(self, new_messages_str):
|
||||
# 基于已经有的talking_summary，和新的talking_message，生成一个summary
|
||||
# print(f"更新聊天总结：{self.talking_summary}")
|
||||
# 开始构建prompt
|
||||
prompt_personality = "你"
|
||||
#person
|
||||
individuality = Individuality.get_instance()
|
||||
|
||||
personality_core = individuality.personality.personality_core
|
||||
prompt_personality += personality_core
|
||||
|
||||
personality_sides = individuality.personality.personality_sides
|
||||
random.shuffle(personality_sides)
|
||||
prompt_personality += f",{personality_sides[0]}"
|
||||
|
||||
identity_detail = individuality.identity.identity_detail
|
||||
random.shuffle(identity_detail)
|
||||
prompt_personality += f",{identity_detail[0]}"
|
||||
|
||||
|
||||
|
||||
personality_info = prompt_personality
|
||||
|
||||
|
||||
|
||||
prompt = ""
|
||||
prompt += f"你{self.personality_info}，请注意识别你自己的聊天发言"
|
||||
prompt += f"{personality_info}，请注意识别你自己的聊天发言"
|
||||
prompt += f"你的名字叫：{self.name}，你的昵称是：{self.nick_name}\n"
|
||||
prompt += f"你正在参与一个qq群聊的讨论，你记得这个群之前在聊的内容是：{self.observe_info}\n"
|
||||
prompt += f"现在群里的群友们产生了新的讨论，有了新的发言，具体内容如下：{new_messages_str}\n"
|
||||
|
||||
@@ -8,6 +8,11 @@ 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
|
||||
from src.plugins.chat.utils import get_embedding
|
||||
from src.common.database import db
|
||||
from typing import Union
|
||||
from src.individuality.individuality import Individuality
|
||||
import random
|
||||
|
||||
subheartflow_config = LogConfig(
|
||||
# 使用海马体专用样式
|
||||
@@ -48,12 +53,13 @@ class SubHeartflow:
|
||||
if not self.current_mind:
|
||||
self.current_mind = "你什么也没想"
|
||||
|
||||
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||
|
||||
self.is_active = False
|
||||
|
||||
self.observations: list[Observation] = []
|
||||
|
||||
self.running_knowledges = []
|
||||
|
||||
def add_observation(self, observation: Observation):
|
||||
"""添加一个新的observation对象到列表中，如果已存在相同id的observation则不添加"""
|
||||
# 查找是否存在相同id的observation
|
||||
@@ -98,49 +104,49 @@ class SubHeartflow:
|
||||
logger.info(f"子心流 {self.subheartflow_id} 已经5分钟没有激活，正在销毁...")
|
||||
break # 退出循环，销毁自己
|
||||
|
||||
async def do_a_thinking(self):
|
||||
current_thinking_info = self.current_mind
|
||||
mood_info = self.current_state.mood
|
||||
# async def do_a_thinking(self):
|
||||
# current_thinking_info = self.current_mind
|
||||
# mood_info = self.current_state.mood
|
||||
|
||||
observation = self.observations[0]
|
||||
chat_observe_info = observation.observe_info
|
||||
# print(f"chat_observe_info：{chat_observe_info}")
|
||||
# observation = self.observations[0]
|
||||
# chat_observe_info = observation.observe_info
|
||||
# # print(f"chat_observe_info：{chat_observe_info}")
|
||||
|
||||
# 调取记忆
|
||||
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||
text=chat_observe_info, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
|
||||
)
|
||||
# # 调取记忆
|
||||
# related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||
# text=chat_observe_info, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
|
||||
# )
|
||||
|
||||
if related_memory:
|
||||
related_memory_info = ""
|
||||
for memory in related_memory:
|
||||
related_memory_info += memory[1]
|
||||
else:
|
||||
related_memory_info = ""
|
||||
# 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}")
|
||||
# # print(f"相关记忆：{related_memory_info}")
|
||||
|
||||
schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
|
||||
# schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
|
||||
|
||||
prompt = ""
|
||||
prompt += f"你刚刚在做的事情是：{schedule_info}\n"
|
||||
# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
|
||||
prompt += f"你{self.personality_info}\n"
|
||||
if related_memory_info:
|
||||
prompt += f"你想起来你之前见过的回忆：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
|
||||
prompt += f"刚刚你的想法是{current_thinking_info}。\n"
|
||||
prompt += "-----------------------------------\n"
|
||||
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
|
||||
prompt += f"你现在{mood_info}\n"
|
||||
prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
|
||||
prompt += "但是记得结合上述的消息，要记得维持住你的人设，关注聊天和新内容，不要思考太多:"
|
||||
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
|
||||
# prompt = ""
|
||||
# prompt += f"你刚刚在做的事情是：{schedule_info}\n"
|
||||
# # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
|
||||
# prompt += f"你{self.personality_info}\n"
|
||||
# if related_memory_info:
|
||||
# prompt += f"你想起来你之前见过的回忆：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
|
||||
# prompt += f"刚刚你的想法是{current_thinking_info}。\n"
|
||||
# prompt += "-----------------------------------\n"
|
||||
# prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
|
||||
# prompt += f"你现在{mood_info}\n"
|
||||
# prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
|
||||
# prompt += "但是记得结合上述的消息，要记得维持住你的人设，关注聊天和新内容，不要思考太多:"
|
||||
# reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
|
||||
|
||||
self.update_current_mind(reponse)
|
||||
# self.update_current_mind(reponse)
|
||||
|
||||
self.current_mind = reponse
|
||||
logger.debug(f"prompt:\n{prompt}\n")
|
||||
logger.info(f"麦麦的脑内状态：{self.current_mind}")
|
||||
# self.current_mind = reponse
|
||||
# logger.debug(f"prompt:\n{prompt}\n")
|
||||
# logger.info(f"麦麦的脑内状态：{self.current_mind}")
|
||||
|
||||
async def do_observe(self):
|
||||
observation = self.observations[0]
|
||||
@@ -154,6 +160,25 @@ class SubHeartflow:
|
||||
chat_observe_info = observation.observe_info
|
||||
# print(f"chat_observe_info：{chat_observe_info}")
|
||||
|
||||
# 开始构建prompt
|
||||
prompt_personality = "你"
|
||||
#person
|
||||
individuality = Individuality.get_instance()
|
||||
|
||||
personality_core = individuality.personality.personality_core
|
||||
prompt_personality += personality_core
|
||||
|
||||
personality_sides = individuality.personality.personality_sides
|
||||
random.shuffle(personality_sides)
|
||||
prompt_personality += f",{personality_sides[0]}"
|
||||
|
||||
identity_detail = individuality.identity.identity_detail
|
||||
random.shuffle(identity_detail)
|
||||
prompt_personality += f",{identity_detail[0]}"
|
||||
|
||||
|
||||
|
||||
|
||||
# 调取记忆
|
||||
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||
text=chat_observe_info, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
|
||||
@@ -166,16 +191,25 @@ class SubHeartflow:
|
||||
else:
|
||||
related_memory_info = ""
|
||||
|
||||
related_info,grouped_results = await self.get_prompt_info(chat_observe_info + message_txt, 0.4)
|
||||
# print(related_info)
|
||||
for _topic, results in grouped_results.items():
|
||||
for result in results:
|
||||
# print(result)
|
||||
self.running_knowledges.append(result)
|
||||
|
||||
# print(f"相关记忆：{related_memory_info}")
|
||||
|
||||
schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
|
||||
|
||||
prompt = ""
|
||||
# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
|
||||
prompt += f"你{self.personality_info}\n"
|
||||
prompt += f"{prompt_personality}\n"
|
||||
prompt += f"你刚刚在做的事情是：{schedule_info}\n"
|
||||
if related_memory_info:
|
||||
prompt += f"你想起来你之前见过的回忆：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
|
||||
if related_info:
|
||||
prompt += f"你想起你知道：{related_info}\n"
|
||||
prompt += f"刚刚你的想法是{current_thinking_info}。\n"
|
||||
prompt += "-----------------------------------\n"
|
||||
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
|
||||
@@ -193,6 +227,25 @@ class SubHeartflow:
|
||||
|
||||
async def do_thinking_after_reply(self, reply_content, chat_talking_prompt):
|
||||
# print("麦麦回复之后脑袋转起来了")
|
||||
|
||||
# 开始构建prompt
|
||||
prompt_personality = "你"
|
||||
#person
|
||||
individuality = Individuality.get_instance()
|
||||
|
||||
personality_core = individuality.personality.personality_core
|
||||
prompt_personality += personality_core
|
||||
|
||||
personality_sides = individuality.personality.personality_sides
|
||||
random.shuffle(personality_sides)
|
||||
prompt_personality += f",{personality_sides[0]}"
|
||||
|
||||
identity_detail = individuality.identity.identity_detail
|
||||
random.shuffle(identity_detail)
|
||||
prompt_personality += f",{identity_detail[0]}"
|
||||
|
||||
|
||||
|
||||
current_thinking_info = self.current_mind
|
||||
mood_info = self.current_state.mood
|
||||
|
||||
@@ -205,7 +258,7 @@ class SubHeartflow:
|
||||
|
||||
prompt = ""
|
||||
# prompt += f"你现在正在做的事情是：{schedule_info}\n"
|
||||
prompt += f"你{self.personality_info}\n"
|
||||
prompt += f"{prompt_personality}\n"
|
||||
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
|
||||
prompt += f"刚刚你的想法是{current_thinking_info}。"
|
||||
prompt += f"你现在看到了网友们发的新消息:{message_new_info}\n"
|
||||
@@ -224,12 +277,30 @@ class SubHeartflow:
|
||||
self.last_reply_time = time.time()
|
||||
|
||||
async def judge_willing(self):
|
||||
# 开始构建prompt
|
||||
prompt_personality = "你"
|
||||
#person
|
||||
individuality = Individuality.get_instance()
|
||||
|
||||
personality_core = individuality.personality.personality_core
|
||||
prompt_personality += personality_core
|
||||
|
||||
personality_sides = individuality.personality.personality_sides
|
||||
random.shuffle(personality_sides)
|
||||
prompt_personality += f",{personality_sides[0]}"
|
||||
|
||||
identity_detail = individuality.identity.identity_detail
|
||||
random.shuffle(identity_detail)
|
||||
prompt_personality += f",{identity_detail[0]}"
|
||||
|
||||
|
||||
|
||||
# print("麦麦闹情绪了1")
|
||||
current_thinking_info = self.current_mind
|
||||
mood_info = self.current_state.mood
|
||||
# print("麦麦闹情绪了2")
|
||||
prompt = ""
|
||||
prompt += f"{self.personality_info}\n"
|
||||
prompt += f"{prompt_personality}\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}。"
|
||||
@@ -251,4 +322,220 @@ class SubHeartflow:
|
||||
self.current_mind = reponse
|
||||
|
||||
|
||||
async def get_prompt_info(self, message: str, threshold: float):
|
||||
start_time = time.time()
|
||||
related_info = ""
|
||||
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
|
||||
|
||||
# 1. 先从LLM获取主题，类似于记忆系统的做法
|
||||
topics = []
|
||||
# try:
|
||||
# # 先尝试使用记忆系统的方法获取主题
|
||||
# hippocampus = HippocampusManager.get_instance()._hippocampus
|
||||
# topic_num = min(5, max(1, int(len(message) * 0.1)))
|
||||
# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
|
||||
|
||||
# # 提取关键词
|
||||
# topics = re.findall(r"<([^>]+)>", topics_response[0])
|
||||
# if not topics:
|
||||
# topics = []
|
||||
# else:
|
||||
# topics = [
|
||||
# topic.strip()
|
||||
# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||||
# if topic.strip()
|
||||
# ]
|
||||
|
||||
# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
|
||||
# except Exception as e:
|
||||
# logger.error(f"从LLM提取主题失败: {str(e)}")
|
||||
# # 如果LLM提取失败，使用jieba分词提取关键词作为备选
|
||||
# words = jieba.cut(message)
|
||||
# topics = [word for word in words if len(word) > 1][:5]
|
||||
# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
|
||||
|
||||
# 如果无法提取到主题，直接使用整个消息
|
||||
if not topics:
|
||||
logger.debug("未能提取到任何主题，使用整个消息进行查询")
|
||||
embedding = await get_embedding(message, request_type="info_retrieval")
|
||||
if not embedding:
|
||||
logger.error("获取消息嵌入向量失败")
|
||||
return ""
|
||||
|
||||
related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
|
||||
logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
|
||||
return related_info, {}
|
||||
|
||||
# 2. 对每个主题进行知识库查询
|
||||
logger.info(f"开始处理{len(topics)}个主题的知识库查询")
|
||||
|
||||
# 优化：批量获取嵌入向量，减少API调用
|
||||
embeddings = {}
|
||||
topics_batch = [topic for topic in topics if len(topic) > 0]
|
||||
if message: # 确保消息非空
|
||||
topics_batch.append(message)
|
||||
|
||||
# 批量获取嵌入向量
|
||||
embed_start_time = time.time()
|
||||
for text in topics_batch:
|
||||
if not text or len(text.strip()) == 0:
|
||||
continue
|
||||
|
||||
try:
|
||||
embedding = await get_embedding(text, request_type="info_retrieval")
|
||||
if embedding:
|
||||
embeddings[text] = embedding
|
||||
else:
|
||||
logger.warning(f"获取'{text}'的嵌入向量失败")
|
||||
except Exception as e:
|
||||
logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
|
||||
|
||||
logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
|
||||
|
||||
if not embeddings:
|
||||
logger.error("所有嵌入向量获取失败")
|
||||
return ""
|
||||
|
||||
# 3. 对每个主题进行知识库查询
|
||||
all_results = []
|
||||
query_start_time = time.time()
|
||||
|
||||
# 首先添加原始消息的查询结果
|
||||
if message in embeddings:
|
||||
original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
|
||||
if original_results:
|
||||
for result in original_results:
|
||||
result["topic"] = "原始消息"
|
||||
all_results.extend(original_results)
|
||||
logger.info(f"原始消息查询到{len(original_results)}条结果")
|
||||
|
||||
# 然后添加每个主题的查询结果
|
||||
for topic in topics:
|
||||
if not topic or topic not in embeddings:
|
||||
continue
|
||||
|
||||
try:
|
||||
topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
|
||||
if topic_results:
|
||||
# 添加主题标记
|
||||
for result in topic_results:
|
||||
result["topic"] = topic
|
||||
all_results.extend(topic_results)
|
||||
logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
|
||||
except Exception as e:
|
||||
logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
|
||||
|
||||
logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
|
||||
|
||||
# 4. 去重和过滤
|
||||
process_start_time = time.time()
|
||||
unique_contents = set()
|
||||
filtered_results = []
|
||||
for result in all_results:
|
||||
content = result["content"]
|
||||
if content not in unique_contents:
|
||||
unique_contents.add(content)
|
||||
filtered_results.append(result)
|
||||
|
||||
# 5. 按相似度排序
|
||||
filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
|
||||
|
||||
# 6. 限制总数量（最多10条）
|
||||
filtered_results = filtered_results[:10]
|
||||
logger.info(f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果")
|
||||
|
||||
# 7. 格式化输出
|
||||
if filtered_results:
|
||||
format_start_time = time.time()
|
||||
grouped_results = {}
|
||||
for result in filtered_results:
|
||||
topic = result["topic"]
|
||||
if topic not in grouped_results:
|
||||
grouped_results[topic] = []
|
||||
grouped_results[topic].append(result)
|
||||
|
||||
# 按主题组织输出
|
||||
for topic, results in grouped_results.items():
|
||||
related_info += f"【主题: {topic}】\n"
|
||||
for _i, result in enumerate(results, 1):
|
||||
_similarity = result["similarity"]
|
||||
content = result["content"].strip()
|
||||
# 调试：为内容添加序号和相似度信息
|
||||
# related_info += f"{i}. [{similarity:.2f}] {content}\n"
|
||||
related_info += f"{content}\n"
|
||||
related_info += "\n"
|
||||
|
||||
logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
|
||||
|
||||
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
|
||||
return related_info,grouped_results
|
||||
|
||||
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False) -> Union[str, list]:
|
||||
if not query_embedding:
|
||||
return "" if not return_raw else []
|
||||
# 使用余弦相似度计算
|
||||
pipeline = [
|
||||
{
|
||||
"$addFields": {
|
||||
"dotProduct": {
|
||||
"$reduce": {
|
||||
"input": {"$range": [0, {"$size": "$embedding"}]},
|
||||
"initialValue": 0,
|
||||
"in": {
|
||||
"$add": [
|
||||
"$$value",
|
||||
{
|
||||
"$multiply": [
|
||||
{"$arrayElemAt": ["$embedding", "$$this"]},
|
||||
{"$arrayElemAt": [query_embedding, "$$this"]},
|
||||
]
|
||||
},
|
||||
]
|
||||
},
|
||||
}
|
||||
},
|
||||
"magnitude1": {
|
||||
"$sqrt": {
|
||||
"$reduce": {
|
||||
"input": "$embedding",
|
||||
"initialValue": 0,
|
||||
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
|
||||
}
|
||||
}
|
||||
},
|
||||
"magnitude2": {
|
||||
"$sqrt": {
|
||||
"$reduce": {
|
||||
"input": query_embedding,
|
||||
"initialValue": 0,
|
||||
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
|
||||
}
|
||||
}
|
||||
},
|
||||
}
|
||||
},
|
||||
{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
|
||||
{
|
||||
"$match": {
|
||||
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
|
||||
}
|
||||
},
|
||||
{"$sort": {"similarity": -1}},
|
||||
{"$limit": limit},
|
||||
{"$project": {"content": 1, "similarity": 1}},
|
||||
]
|
||||
|
||||
results = list(db.knowledges.aggregate(pipeline))
|
||||
logger.debug(f"知识库查询结果数量: {len(results)}")
|
||||
|
||||
if not results:
|
||||
return "" if not return_raw else []
|
||||
|
||||
if return_raw:
|
||||
return results
|
||||
else:
|
||||
# 返回所有找到的内容，用换行分隔
|
||||
return "\n".join(str(result["content"]) for result in results)
|
||||
|
||||
|
||||
# subheartflow = SubHeartflow()
|
||||
|
||||
117
src/individuality/identity.py
Normal file
117
src/individuality/identity.py
Normal file
@@ -0,0 +1,117 @@
|
||||
from dataclasses import dataclass
|
||||
from typing import List
|
||||
import random
|
||||
|
||||
@dataclass
|
||||
class Identity:
|
||||
"""身份特征类"""
|
||||
identity_detail: List[str] # 身份细节描述
|
||||
height: int # 身高（厘米）
|
||||
weight: int # 体重（千克）
|
||||
age: int # 年龄
|
||||
gender: str # 性别
|
||||
appearance: str # 外貌特征
|
||||
|
||||
_instance = None
|
||||
|
||||
def __new__(cls, *args, **kwargs):
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
return cls._instance
|
||||
|
||||
def __init__(self, identity_detail: List[str] = None, height: int = 0, weight: int = 0,
|
||||
age: int = 0, gender: str = "", appearance: str = ""):
|
||||
"""初始化身份特征
|
||||
|
||||
Args:
|
||||
identity_detail: 身份细节描述列表
|
||||
height: 身高（厘米）
|
||||
weight: 体重（千克）
|
||||
age: 年龄
|
||||
gender: 性别
|
||||
appearance: 外貌特征
|
||||
"""
|
||||
if identity_detail is None:
|
||||
identity_detail = []
|
||||
self.identity_detail = identity_detail
|
||||
self.height = height
|
||||
self.weight = weight
|
||||
self.age = age
|
||||
self.gender = gender
|
||||
self.appearance = appearance
|
||||
|
||||
@classmethod
|
||||
def get_instance(cls) -> 'Identity':
|
||||
"""获取Identity单例实例
|
||||
|
||||
Returns:
|
||||
Identity: 单例实例
|
||||
"""
|
||||
if cls._instance is None:
|
||||
cls._instance = cls()
|
||||
return cls._instance
|
||||
|
||||
@classmethod
|
||||
def initialize(cls, identity_detail: List[str], height: int, weight: int,
|
||||
age: int, gender: str, appearance: str) -> 'Identity':
|
||||
"""初始化身份特征
|
||||
|
||||
Args:
|
||||
identity_detail: 身份细节描述列表
|
||||
height: 身高（厘米）
|
||||
weight: 体重（千克）
|
||||
age: 年龄
|
||||
gender: 性别
|
||||
appearance: 外貌特征
|
||||
|
||||
Returns:
|
||||
Identity: 初始化后的身份特征实例
|
||||
"""
|
||||
instance = cls.get_instance()
|
||||
instance.identity_detail = identity_detail
|
||||
instance.height = height
|
||||
instance.weight = weight
|
||||
instance.age = age
|
||||
instance.gender = gender
|
||||
instance.appearance = appearance
|
||||
return instance
|
||||
|
||||
def get_prompt(self,x_person,level):
|
||||
"""
|
||||
获取身份特征的prompt
|
||||
"""
|
||||
if x_person == 2:
|
||||
prompt_identity = "你"
|
||||
elif x_person == 1:
|
||||
prompt_identity = "我"
|
||||
else:
|
||||
prompt_identity = "他"
|
||||
|
||||
if level == 1:
|
||||
identity_detail = self.identity_detail
|
||||
random.shuffle(identity_detail)
|
||||
prompt_identity += identity_detail[0]
|
||||
elif level == 2:
|
||||
for detail in identity_detail:
|
||||
prompt_identity += f",{detail}"
|
||||
prompt_identity += "。"
|
||||
return prompt_identity
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""将身份特征转换为字典格式"""
|
||||
return {
|
||||
"identity_detail": self.identity_detail,
|
||||
"height": self.height,
|
||||
"weight": self.weight,
|
||||
"age": self.age,
|
||||
"gender": self.gender,
|
||||
"appearance": self.appearance
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict) -> 'Identity':
|
||||
"""从字典创建身份特征实例"""
|
||||
instance = cls.get_instance()
|
||||
for key, value in data.items():
|
||||
setattr(instance, key, value)
|
||||
return instance
|
||||
105
src/individuality/individuality.py
Normal file
105
src/individuality/individuality.py
Normal file
@@ -0,0 +1,105 @@
|
||||
from typing import Optional
|
||||
from .personality import Personality
|
||||
from .identity import Identity
|
||||
|
||||
class Individuality:
|
||||
"""个体特征管理类"""
|
||||
_instance = None
|
||||
|
||||
def __new__(cls, *args, **kwargs):
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
return cls._instance
|
||||
|
||||
def __init__(self):
|
||||
self.personality: Optional[Personality] = None
|
||||
self.identity: Optional[Identity] = None
|
||||
|
||||
@classmethod
|
||||
def get_instance(cls) -> 'Individuality':
|
||||
"""获取Individuality单例实例
|
||||
|
||||
Returns:
|
||||
Individuality: 单例实例
|
||||
"""
|
||||
if cls._instance is None:
|
||||
cls._instance = cls()
|
||||
return cls._instance
|
||||
|
||||
def initialize(self, bot_nickname: str, personality_core: str, personality_sides: list,
|
||||
identity_detail: list, height: int, weight: int, age: int,
|
||||
gender: str, appearance: str) -> None:
|
||||
"""初始化个体特征
|
||||
|
||||
Args:
|
||||
bot_nickname: 机器人昵称
|
||||
personality_core: 人格核心特点
|
||||
personality_sides: 人格侧面描述
|
||||
identity_detail: 身份细节描述
|
||||
height: 身高（厘米）
|
||||
weight: 体重（千克）
|
||||
age: 年龄
|
||||
gender: 性别
|
||||
appearance: 外貌特征
|
||||
"""
|
||||
# 初始化人格
|
||||
self.personality = Personality.initialize(
|
||||
bot_nickname=bot_nickname,
|
||||
personality_core=personality_core,
|
||||
personality_sides=personality_sides
|
||||
)
|
||||
|
||||
# 初始化身份
|
||||
self.identity = Identity.initialize(
|
||||
identity_detail=identity_detail,
|
||||
height=height,
|
||||
weight=weight,
|
||||
age=age,
|
||||
gender=gender,
|
||||
appearance=appearance
|
||||
)
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""将个体特征转换为字典格式"""
|
||||
return {
|
||||
"personality": self.personality.to_dict() if self.personality else None,
|
||||
"identity": self.identity.to_dict() if self.identity else None
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict) -> 'Individuality':
|
||||
"""从字典创建个体特征实例"""
|
||||
instance = cls.get_instance()
|
||||
if data.get("personality"):
|
||||
instance.personality = Personality.from_dict(data["personality"])
|
||||
if data.get("identity"):
|
||||
instance.identity = Identity.from_dict(data["identity"])
|
||||
return instance
|
||||
|
||||
def get_prompt(self,type,x_person,level):
|
||||
"""
|
||||
获取个体特征的prompt
|
||||
"""
|
||||
if type == "personality":
|
||||
return self.personality.get_prompt(x_person,level)
|
||||
elif type == "identity":
|
||||
return self.identity.get_prompt(x_person,level)
|
||||
else:
|
||||
return ""
|
||||
|
||||
def get_traits(self,factor):
|
||||
"""
|
||||
获取个体特征的特质
|
||||
"""
|
||||
if factor == "openness":
|
||||
return self.personality.openness
|
||||
elif factor == "conscientiousness":
|
||||
return self.personality.conscientiousness
|
||||
elif factor == "extraversion":
|
||||
return self.personality.extraversion
|
||||
elif factor == "agreeableness":
|
||||
return self.personality.agreeableness
|
||||
elif factor == "neuroticism":
|
||||
return self.personality.neuroticism
|
||||
|
||||
|
||||
123
src/individuality/offline_llm.py
Normal file
123
src/individuality/offline_llm.py
Normal file
@@ -0,0 +1,123 @@
|
||||
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")
|
||||
|
||||
|
||||
class LLM_request_off:
|
||||
def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs):
|
||||
self.model_name = model_name
|
||||
self.params = kwargs
|
||||
self.api_key = os.getenv("SILICONFLOW_KEY")
|
||||
self.base_url = os.getenv("SILICONFLOW_BASE_URL")
|
||||
|
||||
if not self.api_key or not self.base_url:
|
||||
raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置")
|
||||
|
||||
# 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": 0.4,
|
||||
**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]]:
|
||||
"""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"}
|
||||
|
||||
# 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
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
for retry in range(max_retries):
|
||||
try:
|
||||
async with session.post(api_url, headers=headers, json=data) as response:
|
||||
if response.status == 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...")
|
||||
await asyncio.sleep(wait_time)
|
||||
continue
|
||||
|
||||
response.raise_for_status() # 检查其他响应状态
|
||||
|
||||
result = await 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)}")
|
||||
await asyncio.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", ""
|
||||
307
src/individuality/per_bf_gen.py
Normal file
307
src/individuality/per_bf_gen.py
Normal file
@@ -0,0 +1,307 @@
|
||||
from typing import Dict, List
|
||||
import json
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
import sys
|
||||
import toml
|
||||
import random
|
||||
from tqdm import tqdm
|
||||
|
||||
# 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)
|
||||
|
||||
# 加载配置文件
|
||||
config_path = os.path.join(root_path, "config", "bot_config.toml")
|
||||
with open(config_path, "r", encoding="utf-8") as f:
|
||||
config = toml.load(f)
|
||||
|
||||
# 现在可以导入src模块
|
||||
from src.individuality.scene import get_scene_by_factor, PERSONALITY_SCENES #noqa E402
|
||||
from src.individuality.questionnaire import FACTOR_DESCRIPTIONS #noqa E402
|
||||
from src.individuality.offline_llm import LLM_request_off #noqa E402
|
||||
|
||||
# 加载环境变量
|
||||
env_path = os.path.join(root_path, ".env")
|
||||
if os.path.exists(env_path):
|
||||
print(f"从 {env_path} 加载环境变量")
|
||||
load_dotenv(env_path)
|
||||
else:
|
||||
print(f"未找到环境变量文件: {env_path}")
|
||||
print("将使用默认配置")
|
||||
|
||||
|
||||
def adapt_scene(scene: str) -> str:
|
||||
|
||||
personality_core = config['personality']['personality_core']
|
||||
personality_sides = config['personality']['personality_sides']
|
||||
personality_side = random.choice(personality_sides)
|
||||
identity_details = config['identity']['identity_detail']
|
||||
identity_detail = random.choice(identity_details)
|
||||
|
||||
"""
|
||||
根据config中的属性，改编场景使其更适合当前角色
|
||||
|
||||
Args:
|
||||
scene: 原始场景描述
|
||||
|
||||
Returns:
|
||||
str: 改编后的场景描述
|
||||
"""
|
||||
try:
|
||||
prompt = f"""
|
||||
这是一个参与人格测评的角色形象:
|
||||
- 昵称: {config['bot']['nickname']}
|
||||
- 性别: {config['identity']['gender']}
|
||||
- 年龄: {config['identity']['age']}岁
|
||||
- 外貌: {config['identity']['appearance']}
|
||||
- 性格核心: {personality_core}
|
||||
- 性格侧面: {personality_side}
|
||||
- 身份细节: {identity_detail}
|
||||
|
||||
请根据上述形象，改编以下场景，在测评中，用户将根据该场景给出上述角色形象的反应:
|
||||
{scene}
|
||||
保持场景的本质不变，但最好贴近生活且具体，并且让它更适合这个角色。
|
||||
改编后的场景应该自然、连贯，并考虑角色的年龄、身份和性格特点。只返回改编后的场景描述，不要包含其他说明。注意{config['bot']['nickname']}是面对这个场景的人，而不是场景的其他人。场景中不会有其描述，
|
||||
现在，请你给出改编后的场景描述
|
||||
"""
|
||||
|
||||
llm = LLM_request_off(model_name=config['model']['llm_normal']['name'])
|
||||
adapted_scene, _ = llm.generate_response(prompt)
|
||||
|
||||
# 检查返回的场景是否为空或错误信息
|
||||
if not adapted_scene or "错误" in adapted_scene or "失败" in adapted_scene:
|
||||
print("场景改编失败，将使用原始场景")
|
||||
return scene
|
||||
|
||||
return adapted_scene
|
||||
except Exception as e:
|
||||
print(f"场景改编过程出错：{str(e)}，将使用原始场景")
|
||||
return scene
|
||||
|
||||
|
||||
class PersonalityEvaluator_direct:
|
||||
def __init__(self):
|
||||
self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
|
||||
self.scenarios = []
|
||||
self.final_scores = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
|
||||
self.dimension_counts = {trait: 0 for trait in self.final_scores.keys()}
|
||||
|
||||
# 为每个人格特质获取对应的场景
|
||||
for trait in PERSONALITY_SCENES:
|
||||
scenes = get_scene_by_factor(trait)
|
||||
if not scenes:
|
||||
continue
|
||||
|
||||
# 从每个维度选择3个场景
|
||||
import random
|
||||
|
||||
scene_keys = list(scenes.keys())
|
||||
selected_scenes = random.sample(scene_keys, min(3, len(scene_keys)))
|
||||
|
||||
for scene_key in selected_scenes:
|
||||
scene = scenes[scene_key]
|
||||
|
||||
# 为每个场景添加评估维度
|
||||
# 主维度是当前特质，次维度随机选择一个其他特质
|
||||
other_traits = [t for t in PERSONALITY_SCENES if t != trait]
|
||||
secondary_trait = random.choice(other_traits)
|
||||
|
||||
self.scenarios.append(
|
||||
{"场景": scene["scenario"], "评估维度": [trait, secondary_trait], "场景编号": scene_key}
|
||||
)
|
||||
|
||||
self.llm = LLM_request_off()
|
||||
|
||||
def evaluate_response(self, scenario: str, response: str, dimensions: List[str]) -> Dict[str, float]:
|
||||
"""
|
||||
使用 DeepSeek AI 评估用户对特定场景的反应
|
||||
"""
|
||||
# 构建维度描述
|
||||
dimension_descriptions = []
|
||||
for dim in dimensions:
|
||||
desc = FACTOR_DESCRIPTIONS.get(dim, "")
|
||||
if desc:
|
||||
dimension_descriptions.append(f"- {dim}：{desc}")
|
||||
|
||||
dimensions_text = "\n".join(dimension_descriptions)
|
||||
|
||||
prompt = f"""请根据以下场景和用户描述，评估用户在大五人格模型中的相关维度得分（1-6分）。
|
||||
|
||||
场景描述：
|
||||
{scenario}
|
||||
|
||||
用户回应：
|
||||
{response}
|
||||
|
||||
需要评估的维度说明：
|
||||
{dimensions_text}
|
||||
|
||||
请按照以下格式输出评估结果（仅输出JSON格式）：
|
||||
{{
|
||||
"{dimensions[0]}": 分数,
|
||||
"{dimensions[1]}": 分数
|
||||
}}
|
||||
|
||||
评分标准：
|
||||
1 = 非常不符合该维度特征
|
||||
2 = 比较不符合该维度特征
|
||||
3 = 有点不符合该维度特征
|
||||
4 = 有点符合该维度特征
|
||||
5 = 比较符合该维度特征
|
||||
6 = 非常符合该维度特征
|
||||
|
||||
请根据用户的回应，结合场景和维度说明进行评分。确保分数在1-6之间，并给出合理的评估。"""
|
||||
|
||||
try:
|
||||
ai_response, _ = self.llm.generate_response(prompt)
|
||||
# 尝试从AI响应中提取JSON部分
|
||||
start_idx = ai_response.find("{")
|
||||
end_idx = ai_response.rfind("}") + 1
|
||||
if start_idx != -1 and end_idx != 0:
|
||||
json_str = ai_response[start_idx:end_idx]
|
||||
scores = json.loads(json_str)
|
||||
# 确保所有分数在1-6之间
|
||||
return {k: max(1, min(6, float(v))) for k, v in scores.items()}
|
||||
else:
|
||||
print("AI响应格式不正确，使用默认评分")
|
||||
return {dim: 3.5 for dim in dimensions}
|
||||
except Exception as e:
|
||||
print(f"评估过程出错：{str(e)}")
|
||||
return {dim: 3.5 for dim in dimensions}
|
||||
|
||||
def run_evaluation(self):
|
||||
"""
|
||||
运行整个评估过程
|
||||
"""
|
||||
print(f"欢迎使用{config['bot']['nickname']}形象创建程序！")
|
||||
print("接下来，将给您呈现一系列有关您bot的场景（共15个）。")
|
||||
print("请想象您的bot在以下场景下会做什么，并描述您的bot的反应。")
|
||||
print("每个场景都会进行不同方面的评估。")
|
||||
print("\n角色基本信息：")
|
||||
print(f"- 昵称：{config['bot']['nickname']}")
|
||||
print(f"- 性格核心：{config['personality']['personality_core']}")
|
||||
print(f"- 性格侧面：{config['personality']['personality_sides']}")
|
||||
print(f"- 身份细节：{config['identity']['identity_detail']}")
|
||||
print("\n准备好了吗？按回车键开始...")
|
||||
input()
|
||||
|
||||
total_scenarios = len(self.scenarios)
|
||||
progress_bar = tqdm(total=total_scenarios, desc="场景进度", ncols=100, bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]')
|
||||
|
||||
for _i, scenario_data in enumerate(self.scenarios, 1):
|
||||
# print(f"\n{'-' * 20} 场景 {i}/{total_scenarios} - {scenario_data['场景编号']} {'-' * 20}")
|
||||
|
||||
# 改编场景，使其更适合当前角色
|
||||
print(f"{config['bot']['nickname']}祈祷中...")
|
||||
adapted_scene = adapt_scene(scenario_data["场景"])
|
||||
scenario_data["改编场景"] = adapted_scene
|
||||
|
||||
print(adapted_scene)
|
||||
print(f"\n请描述{config['bot']['nickname']}在这种情况下会如何反应：")
|
||||
response = input().strip()
|
||||
|
||||
if not response:
|
||||
print("反应描述不能为空！")
|
||||
continue
|
||||
|
||||
print("\n正在评估您的描述...")
|
||||
scores = self.evaluate_response(adapted_scene, response, scenario_data["评估维度"])
|
||||
|
||||
# 更新最终分数
|
||||
for dimension, score in scores.items():
|
||||
self.final_scores[dimension] += score
|
||||
self.dimension_counts[dimension] += 1
|
||||
|
||||
print("\n当前评估结果：")
|
||||
print("-" * 30)
|
||||
for dimension, score in scores.items():
|
||||
print(f"{dimension}: {score}/6")
|
||||
|
||||
# 更新进度条
|
||||
progress_bar.update(1)
|
||||
|
||||
# if i < total_scenarios:
|
||||
# print("\n按回车键继续下一个场景...")
|
||||
# input()
|
||||
|
||||
progress_bar.close()
|
||||
|
||||
# 计算平均分
|
||||
for dimension in self.final_scores:
|
||||
if self.dimension_counts[dimension] > 0:
|
||||
self.final_scores[dimension] = round(self.final_scores[dimension] / self.dimension_counts[dimension], 2)
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print(f" {config['bot']['nickname']}的人格特征评估结果 ".center(50))
|
||||
print("=" * 50)
|
||||
for trait, score in self.final_scores.items():
|
||||
print(f"{trait}: {score}/6".ljust(20) + f"测试场景数：{self.dimension_counts[trait]}".rjust(30))
|
||||
print("=" * 50)
|
||||
|
||||
# 返回评估结果
|
||||
return self.get_result()
|
||||
|
||||
def get_result(self):
|
||||
"""
|
||||
获取评估结果
|
||||
"""
|
||||
return {
|
||||
"final_scores": self.final_scores,
|
||||
"dimension_counts": self.dimension_counts,
|
||||
"scenarios": self.scenarios,
|
||||
"bot_info": {
|
||||
"nickname": config['bot']['nickname'],
|
||||
"gender": config['identity']['gender'],
|
||||
"age": config['identity']['age'],
|
||||
"height": config['identity']['height'],
|
||||
"weight": config['identity']['weight'],
|
||||
"appearance": config['identity']['appearance'],
|
||||
"personality_core": config['personality']['personality_core'],
|
||||
"personality_sides": config['personality']['personality_sides'],
|
||||
"identity_detail": config['identity']['identity_detail']
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
evaluator = PersonalityEvaluator_direct()
|
||||
result = evaluator.run_evaluation()
|
||||
|
||||
# 准备简化的结果数据
|
||||
simplified_result = {
|
||||
"openness": round(result["final_scores"]["开放性"] / 6, 1), # 转换为0-1范围
|
||||
"conscientiousness": round(result["final_scores"]["严谨性"] / 6, 1),
|
||||
"extraversion": round(result["final_scores"]["外向性"] / 6, 1),
|
||||
"agreeableness": round(result["final_scores"]["宜人性"] / 6, 1),
|
||||
"neuroticism": round(result["final_scores"]["神经质"] / 6, 1),
|
||||
"bot_nickname": config['bot']['nickname']
|
||||
}
|
||||
|
||||
# 确保目录存在
|
||||
save_dir = os.path.join(root_path, "data", "personality")
|
||||
os.makedirs(save_dir, exist_ok=True)
|
||||
|
||||
# 创建文件名，替换可能的非法字符
|
||||
bot_name = config['bot']['nickname']
|
||||
# 替换Windows文件名中不允许的字符
|
||||
for char in ['\\', '/', ':', '*', '?', '"', '<', '>', '|']:
|
||||
bot_name = bot_name.replace(char, '_')
|
||||
|
||||
file_name = f"{bot_name}_personality.per"
|
||||
save_path = os.path.join(save_dir, file_name)
|
||||
|
||||
# 保存简化的结果
|
||||
with open(save_path, "w", encoding="utf-8") as f:
|
||||
json.dump(simplified_result, f, ensure_ascii=False, indent=4)
|
||||
|
||||
print(f"\n结果已保存到 {save_path}")
|
||||
|
||||
# 同时保存完整结果到results目录
|
||||
os.makedirs("results", exist_ok=True)
|
||||
with open("results/personality_result.json", "w", encoding="utf-8") as f:
|
||||
json.dump(result, f, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
144
src/individuality/personality.py
Normal file
144
src/individuality/personality.py
Normal file
@@ -0,0 +1,144 @@
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict, List
|
||||
import json
|
||||
from pathlib import Path
|
||||
import random
|
||||
|
||||
@dataclass
|
||||
class Personality:
|
||||
"""人格特质类"""
|
||||
openness: float # 开放性
|
||||
conscientiousness: float # 尽责性
|
||||
extraversion: float # 外向性
|
||||
agreeableness: float # 宜人性
|
||||
neuroticism: float # 神经质
|
||||
bot_nickname: str # 机器人昵称
|
||||
personality_core: str # 人格核心特点
|
||||
personality_sides: List[str] # 人格侧面描述
|
||||
|
||||
_instance = None
|
||||
|
||||
def __new__(cls, *args, **kwargs):
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
return cls._instance
|
||||
|
||||
def __init__(self, personality_core: str = "", personality_sides: List[str] = None):
|
||||
if personality_sides is None:
|
||||
personality_sides = []
|
||||
self.personality_core = personality_core
|
||||
self.personality_sides = personality_sides
|
||||
|
||||
@classmethod
|
||||
def get_instance(cls) -> 'Personality':
|
||||
"""获取Personality单例实例
|
||||
|
||||
Returns:
|
||||
Personality: 单例实例
|
||||
"""
|
||||
if cls._instance is None:
|
||||
cls._instance = cls()
|
||||
return cls._instance
|
||||
|
||||
def _init_big_five_personality(self):
|
||||
"""初始化大五人格特质"""
|
||||
# 构建文件路径
|
||||
personality_file = Path("data/personality") / f"{self.bot_nickname}_personality.per"
|
||||
|
||||
# 如果文件存在，读取文件
|
||||
if personality_file.exists():
|
||||
with open(personality_file, 'r', encoding='utf-8') as f:
|
||||
personality_data = json.load(f)
|
||||
self.openness = personality_data.get('openness', 0.5)
|
||||
self.conscientiousness = personality_data.get('conscientiousness', 0.5)
|
||||
self.extraversion = personality_data.get('extraversion', 0.5)
|
||||
self.agreeableness = personality_data.get('agreeableness', 0.5)
|
||||
self.neuroticism = personality_data.get('neuroticism', 0.5)
|
||||
else:
|
||||
# 如果文件不存在，根据personality_core和personality_core来设置大五人格特质
|
||||
if "活泼" in self.personality_core or "开朗" in self.personality_sides:
|
||||
self.extraversion = 0.8
|
||||
self.neuroticism = 0.2
|
||||
else:
|
||||
self.extraversion = 0.3
|
||||
self.neuroticism = 0.5
|
||||
|
||||
if "认真" in self.personality_core or "负责" in self.personality_sides:
|
||||
self.conscientiousness = 0.9
|
||||
else:
|
||||
self.conscientiousness = 0.5
|
||||
|
||||
if "友善" in self.personality_core or "温柔" in self.personality_sides:
|
||||
self.agreeableness = 0.9
|
||||
else:
|
||||
self.agreeableness = 0.5
|
||||
|
||||
if "创新" in self.personality_core or "开放" in self.personality_sides:
|
||||
self.openness = 0.8
|
||||
else:
|
||||
self.openness = 0.5
|
||||
|
||||
@classmethod
|
||||
def initialize(cls, bot_nickname: str, personality_core: str, personality_sides: List[str]) -> 'Personality':
|
||||
"""初始化人格特质
|
||||
|
||||
Args:
|
||||
bot_nickname: 机器人昵称
|
||||
personality_core: 人格核心特点
|
||||
personality_sides: 人格侧面描述
|
||||
|
||||
Returns:
|
||||
Personality: 初始化后的人格特质实例
|
||||
"""
|
||||
instance = cls.get_instance()
|
||||
instance.bot_nickname = bot_nickname
|
||||
instance.personality_core = personality_core
|
||||
instance.personality_sides = personality_sides
|
||||
instance._init_big_five_personality()
|
||||
return instance
|
||||
|
||||
def to_dict(self) -> Dict:
|
||||
"""将人格特质转换为字典格式"""
|
||||
return {
|
||||
"openness": self.openness,
|
||||
"conscientiousness": self.conscientiousness,
|
||||
"extraversion": self.extraversion,
|
||||
"agreeableness": self.agreeableness,
|
||||
"neuroticism": self.neuroticism,
|
||||
"bot_nickname": self.bot_nickname,
|
||||
"personality_core": self.personality_core,
|
||||
"personality_sides": self.personality_sides
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict) -> 'Personality':
|
||||
"""从字典创建人格特质实例"""
|
||||
instance = cls.get_instance()
|
||||
for key, value in data.items():
|
||||
setattr(instance, key, value)
|
||||
return instance
|
||||
|
||||
def get_prompt(self,x_person,level):
|
||||
# 开始构建prompt
|
||||
if x_person == 2:
|
||||
prompt_personality = "你"
|
||||
elif x_person == 1:
|
||||
prompt_personality = "我"
|
||||
else:
|
||||
prompt_personality = "他"
|
||||
#person
|
||||
|
||||
prompt_personality += self.personality_core
|
||||
|
||||
if level == 2:
|
||||
personality_sides = self.personality_sides
|
||||
random.shuffle(personality_sides)
|
||||
prompt_personality += f",{personality_sides[0]}"
|
||||
elif level == 3:
|
||||
personality_sides = self.personality_sides
|
||||
for side in personality_sides:
|
||||
prompt_personality += f",{side}"
|
||||
|
||||
prompt_personality += "。"
|
||||
|
||||
return prompt_personality
|
||||
40
src/individuality/scene.py
Normal file
40
src/individuality/scene.py
Normal file
@@ -0,0 +1,40 @@
|
||||
import json
|
||||
from typing import Dict
|
||||
import os
|
||||
|
||||
def load_scenes() -> Dict:
|
||||
"""
|
||||
从JSON文件加载场景数据
|
||||
|
||||
Returns:
|
||||
Dict: 包含所有场景的字典
|
||||
"""
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
json_path = os.path.join(current_dir, 'template_scene.json')
|
||||
|
||||
with open(json_path, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
|
||||
PERSONALITY_SCENES = load_scenes()
|
||||
|
||||
def get_scene_by_factor(factor: str) -> Dict:
|
||||
"""
|
||||
根据人格因子获取对应的情景测试
|
||||
|
||||
Args:
|
||||
factor (str): 人格因子名称
|
||||
|
||||
Returns:
|
||||
Dict: 包含情景描述的字典
|
||||
"""
|
||||
return PERSONALITY_SCENES.get(factor, None)
|
||||
|
||||
|
||||
def get_all_scenes() -> Dict:
|
||||
"""
|
||||
获取所有情景测试
|
||||
|
||||
Returns:
|
||||
Dict: 所有情景测试的字典
|
||||
"""
|
||||
return PERSONALITY_SCENES
|
||||
112
src/individuality/template_scene.json
Normal file
112
src/individuality/template_scene.json
Normal file
@@ -0,0 +1,112 @@
|
||||
{
|
||||
"外向性": {
|
||||
"场景1": {
|
||||
"scenario": "你刚刚搬到一个新的城市工作。今天是你入职的第一天，在公司的电梯里，一位同事微笑着和你打招呼：\n\n同事：「嗨！你是新来的同事吧？我是市场部的小林。」\n\n同事看起来很友善，还主动介绍说：「待会午饭时间，我们部门有几个人准备一起去楼下新开的餐厅，你要一起来吗？可以认识一下其他同事。」",
|
||||
"explanation": "这个场景通过职场社交情境，观察个体对于新环境、新社交圈的态度和反应倾向。"
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": "在大学班级群里，班长发起了一个组织班级联谊活动的投票：\n\n班长：「大家好！下周末我们准备举办一次班级联谊活动，地点在学校附近的KTV。想请大家报名参加，也欢迎大家邀请其他班级的同学！」\n\n已经有几个同学在群里积极响应，有人@你问你要不要一起参加。",
|
||||
"explanation": "通过班级活动场景，观察个体对群体社交活动的参与意愿。"
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": "你在社交平台上发布了一条动态，收到了很多陌生网友的评论和私信：\n\n网友A：「你说的这个观点很有意思！想和你多交流一下。」\n\n网友B：「我也对这个话题很感兴趣，要不要建个群一起讨论？」",
|
||||
"explanation": "通过网络社交场景，观察个体对线上社交的态度。"
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": "你暗恋的对象今天主动来找你：\n\n对方：「那个...我最近在准备一个演讲比赛，听说你口才很好。能不能请你帮我看看演讲稿，顺便给我一些建议？如果你有时间的话，可以一起吃个饭聊聊。」",
|
||||
"explanation": "通过恋爱情境，观察个体在面对心仪对象时的社交表现。"
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": "在一次线下读书会上，主持人突然点名让你分享读后感：\n\n主持人：「听说你对这本书很有见解，能不能和大家分享一下你的想法？」\n\n现场有二十多个陌生的读书爱好者，都期待地看着你。",
|
||||
"explanation": "通过即兴发言场景，观察个体的社交表现欲和公众表达能力。"
|
||||
}
|
||||
},
|
||||
"神经质": {
|
||||
"场景1": {
|
||||
"scenario": "你正在准备一个重要的项目演示，这关系到你的晋升机会。就在演示前30分钟，你收到了主管发来的消息：\n\n主管：「临时有个变动，CEO也会来听你的演示。他对这个项目特别感兴趣。」\n\n正当你准备回复时，主管又发来一条：「对了，能不能把演示时间压缩到15分钟？CEO下午还有其他安排。你之前准备的是30分钟的版本对吧？」",
|
||||
"explanation": "这个场景通过突发的压力情境，观察个体在面对计划外变化时的情绪反应和调节能力。"
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": "期末考试前一天晚上，你收到了好朋友发来的消息：\n\n好朋友：「不好意思这么晚打扰你...我看你平时成绩很好，能不能帮我解答几个问题？我真的很担心明天的考试。」\n\n你看了看时间，已经是晚上11点，而你原本计划的复习还没完成。",
|
||||
"explanation": "通过考试压力场景，观察个体在时间紧张时的情绪管理。"
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": "你在社交媒体上发表的一个观点引发了争议，有不少人开始批评你：\n\n网友A：「这种观点也好意思说出来，真是无知。」\n\n网友B：「建议楼主先去补补课再来发言。」\n\n评论区里的负面评论越来越多，还有人开始人身攻击。",
|
||||
"explanation": "通过网络争议场景，观察个体面对批评时的心理承受能力。"
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": "你和恋人约好今天一起看电影，但在约定时间前半小时，对方发来消息：\n\n恋人：「对不起，我临时有点事，可能要迟到一会儿。」\n\n二十分钟后，对方又发来消息：「可能要再等等，抱歉！」\n\n电影快要开始了，但对方还是没有出现。",
|
||||
"explanation": "通过恋爱情境，观察个体对不确定性的忍耐程度。"
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": "在一次重要的小组展示中，你的组员在演示途中突然卡壳了：\n\n组员小声对你说：「我忘词了，接下来的部分是什么来着...」\n\n台下的老师和同学都在等待，气氛有些尴尬。",
|
||||
"explanation": "通过公开场合的突发状况，观察个体的应急反应和压力处理能力。"
|
||||
}
|
||||
},
|
||||
"严谨性": {
|
||||
"场景1": {
|
||||
"scenario": "你是团队的项目负责人，刚刚接手了一个为期两个月的重要项目。在第一次团队会议上：\n\n小王：「老大，我觉得两个月时间很充裕，我们先做着看吧，遇到问题再解决。」\n\n小张：「要不要先列个时间表？不过感觉太详细的计划也没必要，点到为止就行。」\n\n小李：「客户那边说如果能提前完成有奖励，我觉得我们可以先做快一点的部分。」",
|
||||
"explanation": "这个场景通过项目管理情境，体现个体在工作方法、计划性和责任心方面的特征。"
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": "期末小组作业，组长让大家分工完成一份研究报告。在截止日期前三天：\n\n组员A：「我的部分大概写完了，感觉还行。」\n\n组员B：「我这边可能还要一天才能完成，最近太忙了。」\n\n组员C发来一份没有任何引用出处、可能存在抄袭的内容：「我写完了，你们看看怎么样？」",
|
||||
"explanation": "通过学习场景，观察个体对学术规范和质量要求的重视程度。"
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": "你在一个兴趣小组的群聊中，大家正在讨论举办一次线下活动：\n\n成员A：「到时候见面就知道具体怎么玩了！」\n\n成员B：「对啊，随意一点挺好的。」\n\n成员C：「人来了自然就热闹了。」",
|
||||
"explanation": "通过活动组织场景，观察个体对活动计划的态度。"
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": "你的好友小明邀请你一起参加一个重要的演出活动，他说：\n\n小明：「到时候我们就即兴发挥吧！不用排练了，我相信我们的默契。」\n\n距离演出还有三天，但节目内容、配乐和服装都还没有确定。",
|
||||
"explanation": "通过演出准备场景，观察个体的计划性和对不确定性的接受程度。"
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": "在一个重要的团队项目中，你发现一个同事的工作存在明显错误：\n\n同事：「差不多就行了，反正领导也看不出来。」\n\n这个错误可能不会立即造成问题，但长期来看可能会影响项目质量。",
|
||||
"explanation": "通过工作质量场景，观察个体对细节和标准的坚持程度。"
|
||||
}
|
||||
},
|
||||
"开放性": {
|
||||
"场景1": {
|
||||
"scenario": "周末下午，你的好友小美兴致勃勃地给你打电话：\n\n小美：「我刚发现一个特别有意思的沉浸式艺术展！不是传统那种挂画的展览，而是把整个空间都变成了艺术品。观众要穿特制的服装，还要带上VR眼镜，好像还有AI实时互动！」\n\n小美继续说：「虽然票价不便宜，但听说体验很独特。网上评价两极分化，有人说是前所未有的艺术革新，也有人说是哗众取宠。要不要周末一起去体验一下？」",
|
||||
"explanation": "这个场景通过新型艺术体验，反映个体对创新事物的接受程度和尝试意愿。"
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": "在一节创意写作课上，老师提出了一个特别的作业：\n\n老师：「下周的作业是用AI写作工具协助创作一篇小说。你们可以自由探索如何与AI合作，打破传统写作方式。」\n\n班上随即展开了激烈讨论，有人认为这是对创作的亵渎，也有人对这种新形式感到兴奋。",
|
||||
"explanation": "通过新技术应用场景，观察个体对创新学习方式的态度。"
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": "在社交媒体上，你看到一个朋友分享了一种新的学习方式：\n\n「最近我在尝试'沉浸式学习'，就是完全投入到一个全新的领域。比如学习一门陌生的语言，或者尝试完全不同的职业技能。虽然过程会很辛苦，但这种打破舒适圈的感觉真的很棒！」\n\n评论区里争论不断，有人认为这种学习方式效率高，也有人觉得太激进。",
|
||||
"explanation": "通过新型学习方式，观察个体对创新和挑战的态度。"
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": "你的朋友向你推荐了一种新的饮食方式：\n\n朋友：「我最近在尝试'未来食品'，比如人造肉、3D打印食物、昆虫蛋白等。这不仅对环境友好，营养也很均衡。要不要一起来尝试看看？」\n\n这个提议让你感到好奇又犹豫，你之前从未尝试过这些新型食物。",
|
||||
"explanation": "通过饮食创新场景，观察个体对新事物的接受度和尝试精神。"
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": "在一次朋友聚会上，大家正在讨论未来职业规划：\n\n朋友A：「我准备辞职去做自媒体，专门介绍一些小众的文化和艺术。」\n\n朋友B：「我想去学习生物科技，准备转行做人造肉研发。」\n\n朋友C：「我在考虑加入一个区块链创业项目，虽然风险很大。」",
|
||||
"explanation": "通过职业选择场景，观察个体对新兴领域的探索意愿。"
|
||||
}
|
||||
},
|
||||
"宜人性": {
|
||||
"场景1": {
|
||||
"scenario": "在回家的公交车上，你遇到这样一幕：\n\n一位老奶奶颤颤巍巍地上了车，车上座位已经坐满了。她站在你旁边，看起来很疲惫。这时你听到前排两个年轻人的对话：\n\n年轻人A：「那个老太太好像站不稳，看起来挺累的。」\n\n年轻人B：「现在的老年人真是...我看她包里还有菜，肯定是去菜市场买完菜回来的，这么多人都不知道叫子女开车接送。」\n\n就在这时，老奶奶一个趔趄，差点摔倒。她扶住了扶手，但包里的东西洒了一些出来。",
|
||||
"explanation": "这个场景通过公共场合的助人情境，体现个体的同理心和对他人需求的关注程度。"
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": "在班级群里，有同学发起为生病住院的同学捐款：\n\n同学A：「大家好，小林最近得了重病住院，医药费很贵，家里负担很重。我们要不要一起帮帮他？」\n\n同学B：「我觉得这是他家里的事，我们不方便参与吧。」\n\n同学C：「但是都是同学一场，帮帮忙也是应该的。」",
|
||||
"explanation": "通过同学互助场景，观察个体的助人意愿和同理心。"
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": "在一个网络讨论组里，有人发布了求助信息：\n\n求助者：「最近心情很低落，感觉生活很压抑，不知道该怎么办...」\n\n评论区里已经有一些回复：\n「生活本来就是这样，想开点！」\n「你这样子太消极了，要积极面对。」\n「谁还没点烦心事啊，过段时间就好了。」",
|
||||
"explanation": "通过网络互助场景，观察个体的共情能力和安慰方式。"
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": "你的朋友向你倾诉工作压力：\n\n朋友：「最近工作真的好累，感觉快坚持不下去了...」\n\n但今天你也遇到了很多烦心事，心情也不太好。",
|
||||
"explanation": "通过感情关系场景，观察个体在自身状态不佳时的关怀能力。"
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": "在一次团队项目中，新来的同事小王因为经验不足，造成了一个严重的错误。在部门会议上：\n\n主管：「这个错误造成了很大的损失，是谁负责的这部分？」\n\n小王看起来很紧张，欲言又止。你知道是他造成的错误，同时你也是这个项目的共同负责人。",
|
||||
"explanation": "通过职场情境，观察个体在面对他人过错时的态度和处理方式。"
|
||||
}
|
||||
}
|
||||
}
|
||||
27
src/main.py
27
src/main.py
@@ -15,6 +15,7 @@ from .plugins.config.config import global_config
|
||||
from .plugins.chat.bot import chat_bot
|
||||
from .common.logger import get_module_logger
|
||||
from .plugins.remote import heartbeat_thread # noqa: F401
|
||||
from .individuality.individuality import Individuality
|
||||
|
||||
|
||||
logger = get_module_logger("main")
|
||||
@@ -26,6 +27,7 @@ class MainSystem:
|
||||
self.mood_manager = MoodManager.get_instance()
|
||||
self.hippocampus_manager = HippocampusManager.get_instance()
|
||||
self._message_manager_started = False
|
||||
self.individuality = Individuality.get_instance()
|
||||
|
||||
# 使用消息API替代直接的FastAPI实例
|
||||
from .plugins.message import global_api
|
||||
@@ -56,8 +58,9 @@ class MainSystem:
|
||||
self.mood_manager.start_mood_update(update_interval=global_config.mood_update_interval)
|
||||
logger.success("情绪管理器启动成功")
|
||||
|
||||
# 检查并清除person_info冗余字段
|
||||
# 检查并清除person_info冗余字段，启动个人习惯推断
|
||||
await person_info_manager.del_all_undefined_field()
|
||||
asyncio.create_task(person_info_manager.personal_habit_deduction())
|
||||
|
||||
# 启动愿望管理器
|
||||
await willing_manager.ensure_started()
|
||||
@@ -78,7 +81,7 @@ class MainSystem:
|
||||
# 初始化日程
|
||||
bot_schedule.initialize(
|
||||
name=global_config.BOT_NICKNAME,
|
||||
personality=global_config.PROMPT_PERSONALITY,
|
||||
personality=global_config.personality_core,
|
||||
behavior=global_config.PROMPT_SCHEDULE_GEN,
|
||||
interval=global_config.SCHEDULE_DOING_UPDATE_INTERVAL,
|
||||
)
|
||||
@@ -87,6 +90,20 @@ class MainSystem:
|
||||
# 启动FastAPI服务器
|
||||
self.app.register_message_handler(chat_bot.message_process)
|
||||
|
||||
# 初始化个体特征
|
||||
self.individuality.initialize(
|
||||
bot_nickname=global_config.BOT_NICKNAME,
|
||||
personality_core=global_config.personality_core,
|
||||
personality_sides=global_config.personality_sides,
|
||||
identity_detail=global_config.identity_detail,
|
||||
height=global_config.height,
|
||||
weight=global_config.weight,
|
||||
age=global_config.age,
|
||||
gender=global_config.gender,
|
||||
appearance=global_config.appearance
|
||||
)
|
||||
logger.success("个体特征初始化成功")
|
||||
|
||||
try:
|
||||
# 启动心流系统
|
||||
asyncio.create_task(heartflow.heartflow_start_working())
|
||||
@@ -115,17 +132,19 @@ class MainSystem:
|
||||
async def build_memory_task(self):
|
||||
"""记忆构建任务"""
|
||||
while True:
|
||||
await asyncio.sleep(global_config.build_memory_interval)
|
||||
logger.info("正在进行记忆构建")
|
||||
await HippocampusManager.get_instance().build_memory()
|
||||
await asyncio.sleep(global_config.build_memory_interval)
|
||||
|
||||
|
||||
async def forget_memory_task(self):
|
||||
"""记忆遗忘任务"""
|
||||
while True:
|
||||
await asyncio.sleep(global_config.forget_memory_interval)
|
||||
print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...")
|
||||
await HippocampusManager.get_instance().forget_memory(percentage=global_config.memory_forget_percentage)
|
||||
print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成")
|
||||
await asyncio.sleep(global_config.forget_memory_interval)
|
||||
|
||||
|
||||
async def print_mood_task(self):
|
||||
"""打印情绪状态"""
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import time
|
||||
import asyncio
|
||||
from typing import Optional, Dict, Any, List
|
||||
from typing import Optional, Dict, Any, List, Tuple
|
||||
from src.common.logger import get_module_logger
|
||||
from src.common.database import db
|
||||
from ..message.message_base import UserInfo
|
||||
@@ -57,6 +57,35 @@ class ChatObserver:
|
||||
self._update_event = asyncio.Event() # 触发更新的事件
|
||||
self._update_complete = asyncio.Event() # 更新完成的事件
|
||||
|
||||
def check(self) -> bool:
|
||||
"""检查距离上一次观察之后是否有了新消息
|
||||
|
||||
Returns:
|
||||
bool: 是否有新消息
|
||||
"""
|
||||
logger.debug(f"检查距离上一次观察之后是否有了新消息: {self.last_check_time}")
|
||||
|
||||
query = {
|
||||
"chat_id": self.stream_id,
|
||||
"time": {"$gt": self.last_check_time}
|
||||
}
|
||||
|
||||
# 只需要查询是否存在，不需要获取具体消息
|
||||
new_message_exists = db.messages.find_one(query) is not None
|
||||
|
||||
if new_message_exists:
|
||||
logger.debug("发现新消息")
|
||||
self.last_check_time = time.time()
|
||||
|
||||
return new_message_exists
|
||||
|
||||
def get_new_message(self) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
|
||||
"""获取上一次观察的时间点后的新消息，插入到历史记录中，并返回新消息和历史记录两个对象"""
|
||||
messages = self.get_message_history(self.last_check_time)
|
||||
for message in messages:
|
||||
self._add_message_to_history(message)
|
||||
return messages, self.message_history
|
||||
|
||||
def new_message_after(self, time_point: float) -> bool:
|
||||
"""判断是否在指定时间点后有新消息
|
||||
|
||||
@@ -66,6 +95,7 @@ class ChatObserver:
|
||||
Returns:
|
||||
bool: 是否有新消息
|
||||
"""
|
||||
logger.debug(f"判断是否在指定时间点后有新消息: {self.last_message_time} > {time_point}")
|
||||
return self.last_message_time is None or self.last_message_time > time_point
|
||||
|
||||
def _add_message_to_history(self, message: Dict[str, Any]):
|
||||
|
||||
@@ -17,7 +17,8 @@ from ..storage.storage import MessageStorage
|
||||
from .chat_observer import ChatObserver
|
||||
from .pfc_KnowledgeFetcher import KnowledgeFetcher
|
||||
from .reply_checker import ReplyChecker
|
||||
import json
|
||||
from .pfc_utils import get_items_from_json
|
||||
from src.individuality.individuality import Individuality
|
||||
import time
|
||||
|
||||
logger = get_module_logger("pfc")
|
||||
@@ -51,7 +52,7 @@ class ActionPlanner:
|
||||
max_tokens=1000,
|
||||
request_type="action_planning"
|
||||
)
|
||||
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||
self.personality_info = Individuality.get_instance().get_prompt(type = "personality", x_person = 2, level = 2)
|
||||
self.name = global_config.BOT_NICKNAME
|
||||
self.chat_observer = ChatObserver.get_instance(stream_id)
|
||||
|
||||
@@ -67,7 +68,6 @@ class ActionPlanner:
|
||||
|
||||
Args:
|
||||
goal: 对话目标
|
||||
method: 实现方式
|
||||
reasoning: 目标原因
|
||||
action_history: 行动历史记录
|
||||
|
||||
@@ -128,43 +128,18 @@ judge_conversation: 判断对话是否结束，当发现对话目标已经达到
|
||||
content, _ = await self.llm.generate_response_async(prompt)
|
||||
logger.debug(f"LLM原始返回内容: {content}")
|
||||
|
||||
# 清理内容，尝试提取JSON部分
|
||||
content = content.strip()
|
||||
try:
|
||||
# 尝试直接解析
|
||||
result = json.loads(content)
|
||||
except json.JSONDecodeError:
|
||||
# 如果直接解析失败，尝试查找和提取JSON部分
|
||||
import re
|
||||
json_pattern = r'\{[^{}]*\}'
|
||||
json_match = re.search(json_pattern, content)
|
||||
if json_match:
|
||||
try:
|
||||
result = json.loads(json_match.group())
|
||||
except json.JSONDecodeError:
|
||||
logger.error("提取的JSON内容解析失败，返回默认行动")
|
||||
return "direct_reply", "JSON解析失败，选择直接回复"
|
||||
else:
|
||||
# 如果找不到JSON，尝试从文本中提取行动和原因
|
||||
if "direct_reply" in content.lower():
|
||||
return "direct_reply", "从文本中提取的行动"
|
||||
elif "fetch_knowledge" in content.lower():
|
||||
return "fetch_knowledge", "从文本中提取的行动"
|
||||
elif "wait" in content.lower():
|
||||
return "wait", "从文本中提取的行动"
|
||||
elif "listening" in content.lower():
|
||||
return "listening", "从文本中提取的行动"
|
||||
elif "rethink_goal" in content.lower():
|
||||
return "rethink_goal", "从文本中提取的行动"
|
||||
elif "judge_conversation" in content.lower():
|
||||
return "judge_conversation", "从文本中提取的行动"
|
||||
else:
|
||||
logger.error("无法从返回内容中提取行动类型")
|
||||
return "direct_reply", "无法解析响应，选择直接回复"
|
||||
# 使用简化函数提取JSON内容
|
||||
success, result = get_items_from_json(
|
||||
content,
|
||||
"action", "reason",
|
||||
default_values={"action": "direct_reply", "reason": "默认原因"}
|
||||
)
|
||||
|
||||
# 验证JSON字段
|
||||
action = result.get("action", "direct_reply")
|
||||
reason = result.get("reason", "默认原因")
|
||||
if not success:
|
||||
return "direct_reply", "JSON解析失败，选择直接回复"
|
||||
|
||||
action = result["action"]
|
||||
reason = result["reason"]
|
||||
|
||||
# 验证action类型
|
||||
if action not in ["direct_reply", "fetch_knowledge", "wait", "listening", "rethink_goal", "judge_conversation"]:
|
||||
@@ -191,11 +166,16 @@ class GoalAnalyzer:
|
||||
request_type="conversation_goal"
|
||||
)
|
||||
|
||||
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||
self.personality_info = Individuality.get_instance().get_prompt(type = "personality", x_person = 2, level = 2)
|
||||
self.name = global_config.BOT_NICKNAME
|
||||
self.nick_name = global_config.BOT_ALIAS_NAMES
|
||||
self.chat_observer = ChatObserver.get_instance(stream_id)
|
||||
|
||||
# 多目标存储结构
|
||||
self.goals = [] # 存储多个目标
|
||||
self.max_goals = 3 # 同时保持的最大目标数量
|
||||
self.current_goal_and_reason = None
|
||||
|
||||
async def analyze_goal(self) -> Tuple[str, str, str]:
|
||||
"""分析对话历史并设定目标
|
||||
|
||||
@@ -221,11 +201,28 @@ class GoalAnalyzer:
|
||||
|
||||
personality_text = f"你的名字是{self.name}，{self.personality_info}"
|
||||
|
||||
prompt = f"""{personality_text}。现在你在参与一场QQ聊天，请分析以下聊天记录，并根据你的性格特征确定一个明确的对话目标。
|
||||
这个目标应该反映出对话的意图和期望的结果。
|
||||
# 构建当前已有目标的文本
|
||||
existing_goals_text = ""
|
||||
if self.goals:
|
||||
existing_goals_text = "当前已有的对话目标:\n"
|
||||
for i, (goal, _, reason) in enumerate(self.goals):
|
||||
existing_goals_text += f"{i+1}. 目标: {goal}, 原因: {reason}\n"
|
||||
|
||||
prompt = f"""{personality_text}。现在你在参与一场QQ聊天，请分析以下聊天记录，并根据你的性格特征确定多个明确的对话目标。
|
||||
这些目标应该反映出对话的不同方面和意图。
|
||||
|
||||
{existing_goals_text}
|
||||
|
||||
聊天记录：
|
||||
{chat_history_text}
|
||||
请以JSON格式输出，包含以下字段：
|
||||
|
||||
请分析当前对话并确定最适合的对话目标。你可以：
|
||||
1. 保持现有目标不变
|
||||
2. 修改现有目标
|
||||
3. 添加新目标
|
||||
4. 删除不再相关的目标
|
||||
|
||||
请以JSON格式输出一个当前最主要的对话目标，包含以下字段：
|
||||
1. goal: 对话目标（简短的一句话）
|
||||
2. reasoning: 对话原因，为什么设定这个目标（简要解释）
|
||||
|
||||
@@ -239,50 +236,31 @@ class GoalAnalyzer:
|
||||
content, _ = await self.llm.generate_response_async(prompt)
|
||||
logger.debug(f"LLM原始返回内容: {content}")
|
||||
|
||||
# 清理和验证返回内容
|
||||
if not content or not isinstance(content, str):
|
||||
logger.error("LLM返回内容为空或格式不正确")
|
||||
continue
|
||||
# 使用简化函数提取JSON内容
|
||||
success, result = get_items_from_json(
|
||||
content,
|
||||
"goal", "reasoning",
|
||||
required_types={"goal": str, "reasoning": str}
|
||||
)
|
||||
|
||||
# 尝试提取JSON部分
|
||||
content = content.strip()
|
||||
try:
|
||||
# 尝试直接解析
|
||||
result = json.loads(content)
|
||||
except json.JSONDecodeError:
|
||||
# 如果直接解析失败，尝试查找和提取JSON部分
|
||||
import re
|
||||
json_pattern = r'\{[^{}]*\}'
|
||||
json_match = re.search(json_pattern, content)
|
||||
if json_match:
|
||||
try:
|
||||
result = json.loads(json_match.group())
|
||||
except json.JSONDecodeError:
|
||||
logger.error(f"提取的JSON内容解析失败，重试第{retry + 1}次")
|
||||
continue
|
||||
else:
|
||||
logger.error(f"无法在返回内容中找到有效的JSON，重试第{retry + 1}次")
|
||||
continue
|
||||
|
||||
# 验证JSON字段
|
||||
if not all(key in result for key in ["goal", "reasoning"]):
|
||||
logger.error(f"JSON缺少必要字段，实际内容: {result}，重试第{retry + 1}次")
|
||||
if not success:
|
||||
logger.error(f"无法解析JSON，重试第{retry + 1}次")
|
||||
continue
|
||||
|
||||
goal = result["goal"]
|
||||
reasoning = result["reasoning"]
|
||||
|
||||
# 验证字段内容
|
||||
if not isinstance(goal, str) or not isinstance(reasoning, str):
|
||||
logger.error(f"JSON字段类型错误，goal和reasoning必须是字符串，重试第{retry + 1}次")
|
||||
continue
|
||||
|
||||
if not goal.strip() or not reasoning.strip():
|
||||
logger.error(f"JSON字段内容为空，重试第{retry + 1}次")
|
||||
continue
|
||||
|
||||
# 使用默认的方法
|
||||
method = "以友好的态度回应"
|
||||
|
||||
# 更新目标列表
|
||||
await self._update_goals(goal, method, reasoning)
|
||||
|
||||
# 返回当前最主要的目标
|
||||
if self.goals:
|
||||
current_goal, current_method, current_reasoning = self.goals[0]
|
||||
return current_goal, current_method, current_reasoning
|
||||
else:
|
||||
return goal, method, reasoning
|
||||
|
||||
except Exception as e:
|
||||
@@ -294,6 +272,67 @@ class GoalAnalyzer:
|
||||
# 所有重试都失败后的默认返回
|
||||
return "保持友好的对话", "以友好的态度回应", "确保对话顺利进行"
|
||||
|
||||
async def _update_goals(self, new_goal: str, method: str, reasoning: str):
|
||||
"""更新目标列表
|
||||
|
||||
Args:
|
||||
new_goal: 新的目标
|
||||
method: 实现目标的方法
|
||||
reasoning: 目标的原因
|
||||
"""
|
||||
# 检查新目标是否与现有目标相似
|
||||
for i, (existing_goal, _, _) in enumerate(self.goals):
|
||||
if self._calculate_similarity(new_goal, existing_goal) > 0.7: # 相似度阈值
|
||||
# 更新现有目标
|
||||
self.goals[i] = (new_goal, method, reasoning)
|
||||
# 将此目标移到列表前面（最主要的位置）
|
||||
self.goals.insert(0, self.goals.pop(i))
|
||||
return
|
||||
|
||||
# 添加新目标到列表前面
|
||||
self.goals.insert(0, (new_goal, method, reasoning))
|
||||
|
||||
# 限制目标数量
|
||||
if len(self.goals) > self.max_goals:
|
||||
self.goals.pop() # 移除最老的目标
|
||||
|
||||
def _calculate_similarity(self, goal1: str, goal2: str) -> float:
|
||||
"""简单计算两个目标之间的相似度
|
||||
|
||||
这里使用一个简单的实现，实际可以使用更复杂的文本相似度算法
|
||||
|
||||
Args:
|
||||
goal1: 第一个目标
|
||||
goal2: 第二个目标
|
||||
|
||||
Returns:
|
||||
float: 相似度得分 (0-1)
|
||||
"""
|
||||
# 简单实现：检查重叠字数比例
|
||||
words1 = set(goal1)
|
||||
words2 = set(goal2)
|
||||
overlap = len(words1.intersection(words2))
|
||||
total = len(words1.union(words2))
|
||||
return overlap / total if total > 0 else 0
|
||||
|
||||
async def get_all_goals(self) -> List[Tuple[str, str, str]]:
|
||||
"""获取所有当前目标
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str, str]]: 目标列表，每项为(目标, 方法, 原因)
|
||||
"""
|
||||
return self.goals.copy()
|
||||
|
||||
async def get_alternative_goals(self) -> List[Tuple[str, str, str]]:
|
||||
"""获取除了当前主要目标外的其他备选目标
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str, str]]: 备选目标列表
|
||||
"""
|
||||
if len(self.goals) <= 1:
|
||||
return []
|
||||
return self.goals[1:].copy()
|
||||
|
||||
async def analyze_conversation(self, goal, reasoning):
|
||||
messages = self.chat_observer.get_message_history()
|
||||
chat_history_text = ""
|
||||
@@ -330,58 +369,31 @@ class GoalAnalyzer:
|
||||
content, _ = await self.llm.generate_response_async(prompt)
|
||||
logger.debug(f"LLM原始返回内容: {content}")
|
||||
|
||||
# 清理和验证返回内容
|
||||
if not content or not isinstance(content, str):
|
||||
logger.error("LLM返回内容为空或格式不正确")
|
||||
# 使用简化函数提取JSON内容
|
||||
success, result = get_items_from_json(
|
||||
content,
|
||||
"goal_achieved", "stop_conversation", "reason",
|
||||
required_types={
|
||||
"goal_achieved": bool,
|
||||
"stop_conversation": bool,
|
||||
"reason": str
|
||||
}
|
||||
)
|
||||
|
||||
if not success:
|
||||
return False, False, "确保对话顺利进行"
|
||||
|
||||
# 尝试提取JSON部分
|
||||
content = content.strip()
|
||||
try:
|
||||
# 尝试直接解析
|
||||
result = json.loads(content)
|
||||
except json.JSONDecodeError:
|
||||
# 如果直接解析失败，尝试查找和提取JSON部分
|
||||
import re
|
||||
json_pattern = r'\{[^{}]*\}'
|
||||
json_match = re.search(json_pattern, content)
|
||||
if json_match:
|
||||
try:
|
||||
result = json.loads(json_match.group())
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"提取的JSON内容解析失败: {e}")
|
||||
return False, False, "确保对话顺利进行"
|
||||
else:
|
||||
logger.error("无法在返回内容中找到有效的JSON")
|
||||
return False, False, "确保对话顺利进行"
|
||||
# 如果当前目标达成，从目标列表中移除
|
||||
if result["goal_achieved"] and not result["stop_conversation"]:
|
||||
for i, (g, _, _) in enumerate(self.goals):
|
||||
if g == goal:
|
||||
self.goals.pop(i)
|
||||
# 如果还有其他目标，不停止对话
|
||||
if self.goals:
|
||||
result["stop_conversation"] = False
|
||||
break
|
||||
|
||||
# 验证JSON字段
|
||||
if not all(key in result for key in ["goal_achieved", "stop_conversation", "reason"]):
|
||||
logger.error(f"JSON缺少必要字段，实际内容: {result}")
|
||||
return False, False, "确保对话顺利进行"
|
||||
|
||||
goal_achieved = result["goal_achieved"]
|
||||
stop_conversation = result["stop_conversation"]
|
||||
reason = result["reason"]
|
||||
|
||||
# 验证字段类型
|
||||
if not isinstance(goal_achieved, bool):
|
||||
logger.error("goal_achieved 必须是布尔值")
|
||||
return False, False, "确保对话顺利进行"
|
||||
|
||||
if not isinstance(stop_conversation, bool):
|
||||
logger.error("stop_conversation 必须是布尔值")
|
||||
return False, False, "确保对话顺利进行"
|
||||
|
||||
if not isinstance(reason, str):
|
||||
logger.error("reason 必须是字符串")
|
||||
return False, False, "确保对话顺利进行"
|
||||
|
||||
if not reason.strip():
|
||||
logger.error("reason 不能为空")
|
||||
return False, False, "确保对话顺利进行"
|
||||
|
||||
return goal_achieved, stop_conversation, reason
|
||||
return result["goal_achieved"], result["stop_conversation"], result["reason"]
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析对话目标时出错: {str(e)}")
|
||||
@@ -392,7 +404,7 @@ class Waiter:
|
||||
"""快 速 等 待"""
|
||||
def __init__(self, stream_id: str):
|
||||
self.chat_observer = ChatObserver.get_instance(stream_id)
|
||||
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||
self.personality_info = Individuality.get_instance().get_prompt(type = "personality", x_person = 2, level = 2)
|
||||
self.name = global_config.BOT_NICKNAME
|
||||
|
||||
async def wait(self) -> bool:
|
||||
@@ -406,8 +418,8 @@ class Waiter:
|
||||
await asyncio.sleep(1)
|
||||
logger.info("等待中...")
|
||||
# 检查是否超过60秒
|
||||
if time.time() - wait_start_time > 60:
|
||||
logger.info("等待超过60秒，结束对话")
|
||||
if time.time() - wait_start_time > 300:
|
||||
logger.info("等待超过300秒，结束对话")
|
||||
return True
|
||||
logger.info("等待结束")
|
||||
return False
|
||||
@@ -423,7 +435,7 @@ class ReplyGenerator:
|
||||
max_tokens=300,
|
||||
request_type="reply_generation"
|
||||
)
|
||||
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||
self.personality_info = Individuality.get_instance().get_prompt(type = "personality", x_person = 2, level = 2)
|
||||
self.name = global_config.BOT_NICKNAME
|
||||
self.chat_observer = ChatObserver.get_instance(stream_id)
|
||||
self.reply_checker = ReplyChecker(stream_id)
|
||||
@@ -435,19 +447,18 @@ class ReplyGenerator:
|
||||
knowledge_cache: Dict[str, str],
|
||||
previous_reply: Optional[str] = None,
|
||||
retry_count: int = 0
|
||||
) -> Tuple[str, bool]:
|
||||
) -> str:
|
||||
"""生成回复
|
||||
|
||||
Args:
|
||||
goal: 对话目标
|
||||
method: 实现方式
|
||||
chat_history: 聊天历史
|
||||
knowledge_cache: 知识缓存
|
||||
previous_reply: 上一次生成的回复（如果有）
|
||||
retry_count: 当前重试次数
|
||||
|
||||
Returns:
|
||||
Tuple[str, bool]: (生成的回复, 是否需要重新规划)
|
||||
str: 生成的回复
|
||||
"""
|
||||
# 构建提示词
|
||||
logger.debug(f"开始生成回复：当前目标: {goal}")
|
||||
@@ -508,52 +519,104 @@ class ReplyGenerator:
|
||||
try:
|
||||
content, _ = await self.llm.generate_response_async(prompt)
|
||||
logger.info(f"生成的回复: {content}")
|
||||
is_new = self.chat_observer.check()
|
||||
logger.debug(f"再看一眼聊天记录，{'有' if is_new else '没有'}新消息")
|
||||
|
||||
# 检查生成的回复是否合适
|
||||
is_suitable, reason, need_replan = await self.reply_checker.check(
|
||||
content, goal, retry_count
|
||||
)
|
||||
|
||||
if not is_suitable:
|
||||
logger.warning(f"生成的回复不合适，原因: {reason}")
|
||||
if need_replan:
|
||||
logger.info("需要重新规划对话目标")
|
||||
return "让我重新思考一下...", True
|
||||
else:
|
||||
# 递归调用，将当前回复作为previous_reply传入
|
||||
# 如果有新消息,重新生成回复
|
||||
if is_new:
|
||||
logger.info("检测到新消息,重新生成回复")
|
||||
return await self.generate(
|
||||
goal, chat_history, knowledge_cache,
|
||||
content, retry_count + 1
|
||||
None, retry_count
|
||||
)
|
||||
|
||||
return content, False
|
||||
return content
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生成回复时出错: {e}")
|
||||
return "抱歉，我现在有点混乱，让我重新思考一下...", True
|
||||
return "抱歉，我现在有点混乱，让我重新思考一下..."
|
||||
|
||||
async def check_reply(
|
||||
self,
|
||||
reply: str,
|
||||
goal: str,
|
||||
retry_count: int = 0
|
||||
) -> Tuple[bool, str, bool]:
|
||||
"""检查回复是否合适
|
||||
|
||||
Args:
|
||||
reply: 生成的回复
|
||||
goal: 对话目标
|
||||
retry_count: 当前重试次数
|
||||
|
||||
Returns:
|
||||
Tuple[bool, str, bool]: (是否合适, 原因, 是否需要重新规划)
|
||||
"""
|
||||
return await self.reply_checker.check(reply, goal, retry_count)
|
||||
|
||||
|
||||
class Conversation:
|
||||
# 类级别的实例管理
|
||||
_instances: Dict[str, 'Conversation'] = {}
|
||||
_instance_lock = asyncio.Lock() # 类级别的全局锁
|
||||
_init_events: Dict[str, asyncio.Event] = {} # 初始化完成事件
|
||||
_initializing: Dict[str, bool] = {} # 标记是否正在初始化
|
||||
|
||||
@classmethod
|
||||
def get_instance(cls, stream_id: str) -> 'Conversation':
|
||||
"""获取或创建对话实例"""
|
||||
async def get_instance(cls, stream_id: str) -> Optional['Conversation']:
|
||||
"""获取或创建对话实例
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
|
||||
Returns:
|
||||
Optional[Conversation]: 对话实例，如果创建或等待失败则返回None
|
||||
"""
|
||||
try:
|
||||
# 使用全局锁来确保线程安全
|
||||
async with cls._instance_lock:
|
||||
# 如果已经在初始化中，等待初始化完成
|
||||
if stream_id in cls._initializing and cls._initializing[stream_id]:
|
||||
# 释放锁等待初始化
|
||||
cls._instance_lock.release()
|
||||
try:
|
||||
await asyncio.wait_for(cls._init_events[stream_id].wait(), timeout=5.0)
|
||||
except asyncio.TimeoutError:
|
||||
logger.error(f"等待实例 {stream_id} 初始化超时")
|
||||
return None
|
||||
finally:
|
||||
await cls._instance_lock.acquire()
|
||||
|
||||
# 如果实例不存在，创建新实例
|
||||
if stream_id not in cls._instances:
|
||||
cls._instances[stream_id] = cls(stream_id)
|
||||
cls._init_events[stream_id] = asyncio.Event()
|
||||
cls._initializing[stream_id] = True
|
||||
logger.info(f"创建新的对话实例: {stream_id}")
|
||||
|
||||
return cls._instances[stream_id]
|
||||
except Exception as e:
|
||||
logger.error(f"获取对话实例失败: {e}")
|
||||
return None
|
||||
|
||||
@classmethod
|
||||
def remove_instance(cls, stream_id: str):
|
||||
"""删除对话实例"""
|
||||
async def remove_instance(cls, stream_id: str):
|
||||
"""删除对话实例
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
"""
|
||||
async with cls._instance_lock:
|
||||
if stream_id in cls._instances:
|
||||
# 停止相关组件
|
||||
instance = cls._instances[stream_id]
|
||||
instance.chat_observer.stop()
|
||||
# 删除实例
|
||||
del cls._instances[stream_id]
|
||||
if stream_id in cls._init_events:
|
||||
del cls._init_events[stream_id]
|
||||
if stream_id in cls._initializing:
|
||||
del cls._initializing[stream_id]
|
||||
logger.info(f"已删除对话实例 {stream_id}")
|
||||
|
||||
def __init__(self, stream_id: str):
|
||||
@@ -592,12 +655,20 @@ class Conversation:
|
||||
|
||||
async def start(self):
|
||||
"""开始对话流程"""
|
||||
try:
|
||||
logger.info("对话系统启动")
|
||||
self.should_continue = True
|
||||
self.chat_observer.start() # 启动观察器
|
||||
await asyncio.sleep(1)
|
||||
# 启动对话循环
|
||||
await self._conversation_loop()
|
||||
except Exception as e:
|
||||
logger.error(f"启动对话系统失败: {e}")
|
||||
raise
|
||||
finally:
|
||||
# 标记初始化完成
|
||||
self._init_events[self.stream_id].set()
|
||||
self._initializing[self.stream_id] = False
|
||||
|
||||
async def _conversation_loop(self):
|
||||
"""对话循环"""
|
||||
@@ -658,16 +729,100 @@ class Conversation:
|
||||
if action == "direct_reply":
|
||||
self.state = ConversationState.GENERATING
|
||||
messages = self.chat_observer.get_message_history(limit=30)
|
||||
self.generated_reply, need_replan = await self.reply_generator.generate(
|
||||
self.generated_reply = await self.reply_generator.generate(
|
||||
self.current_goal,
|
||||
self.current_method,
|
||||
[self._convert_to_message(msg) for msg in messages],
|
||||
self.knowledge_cache
|
||||
)
|
||||
|
||||
# 检查回复是否合适
|
||||
is_suitable, reason, need_replan = await self.reply_generator.check_reply(
|
||||
self.generated_reply,
|
||||
self.current_goal
|
||||
)
|
||||
|
||||
if not is_suitable:
|
||||
logger.warning(f"生成的回复不合适，原因: {reason}")
|
||||
if need_replan:
|
||||
# 尝试切换到其他备选目标
|
||||
alternative_goals = await self.goal_analyzer.get_alternative_goals()
|
||||
if alternative_goals:
|
||||
# 有备选目标，尝试使用下一个目标
|
||||
self.current_goal, self.current_method, self.goal_reasoning = alternative_goals[0]
|
||||
logger.info(f"切换到备选目标: {self.current_goal}")
|
||||
# 使用新目标生成回复
|
||||
self.generated_reply = await self.reply_generator.generate(
|
||||
self.current_goal,
|
||||
self.current_method,
|
||||
[self._convert_to_message(msg) for msg in messages],
|
||||
self.knowledge_cache
|
||||
)
|
||||
# 检查使用新目标生成的回复是否合适
|
||||
is_suitable, reason, _ = await self.reply_generator.check_reply(
|
||||
self.generated_reply,
|
||||
self.current_goal
|
||||
)
|
||||
if is_suitable:
|
||||
# 如果新目标的回复合适，调整目标优先级
|
||||
await self.goal_analyzer._update_goals(
|
||||
self.current_goal,
|
||||
self.current_method,
|
||||
self.goal_reasoning
|
||||
)
|
||||
else:
|
||||
# 如果新目标还是不合适，重新思考目标
|
||||
self.state = ConversationState.RETHINKING
|
||||
self.current_goal, self.current_method, self.goal_reasoning = await self.goal_analyzer.analyze_goal()
|
||||
return
|
||||
else:
|
||||
# 没有备选目标，重新分析
|
||||
self.state = ConversationState.RETHINKING
|
||||
self.current_goal, self.current_method, self.goal_reasoning = await self.goal_analyzer.analyze_goal()
|
||||
return
|
||||
else:
|
||||
# 重新生成回复
|
||||
self.generated_reply = await self.reply_generator.generate(
|
||||
self.current_goal,
|
||||
self.current_method,
|
||||
[self._convert_to_message(msg) for msg in messages],
|
||||
self.knowledge_cache,
|
||||
self.generated_reply # 将不合适的回复作为previous_reply传入
|
||||
)
|
||||
|
||||
while self.chat_observer.check():
|
||||
if not is_suitable:
|
||||
logger.warning(f"生成的回复不合适，原因: {reason}")
|
||||
if need_replan:
|
||||
# 尝试切换到其他备选目标
|
||||
alternative_goals = await self.goal_analyzer.get_alternative_goals()
|
||||
if alternative_goals:
|
||||
# 有备选目标，尝试使用下一个目标
|
||||
self.current_goal, self.current_method, self.goal_reasoning = alternative_goals[0]
|
||||
logger.info(f"切换到备选目标: {self.current_goal}")
|
||||
# 使用新目标生成回复
|
||||
self.generated_reply = await self.reply_generator.generate(
|
||||
self.current_goal,
|
||||
self.current_method,
|
||||
[self._convert_to_message(msg) for msg in messages],
|
||||
self.knowledge_cache
|
||||
)
|
||||
is_suitable = True # 假设使用新目标后回复是合适的
|
||||
else:
|
||||
# 没有备选目标，重新分析
|
||||
self.state = ConversationState.RETHINKING
|
||||
self.current_goal, self.current_method, self.goal_reasoning = await self.goal_analyzer.analyze_goal()
|
||||
return
|
||||
else:
|
||||
# 重新生成回复
|
||||
self.generated_reply = await self.reply_generator.generate(
|
||||
self.current_goal,
|
||||
self.current_method,
|
||||
[self._convert_to_message(msg) for msg in messages],
|
||||
self.knowledge_cache,
|
||||
self.generated_reply # 将不合适的回复作为previous_reply传入
|
||||
)
|
||||
|
||||
await self._send_reply()
|
||||
|
||||
elif action == "fetch_knowledge":
|
||||
@@ -682,16 +837,57 @@ class Conversation:
|
||||
if knowledge != "未找到相关知识":
|
||||
self.knowledge_cache[sources] = knowledge
|
||||
|
||||
self.generated_reply, need_replan = await self.reply_generator.generate(
|
||||
self.generated_reply = await self.reply_generator.generate(
|
||||
self.current_goal,
|
||||
self.current_method,
|
||||
[self._convert_to_message(msg) for msg in messages],
|
||||
self.knowledge_cache
|
||||
)
|
||||
|
||||
# 检查回复是否合适
|
||||
is_suitable, reason, need_replan = await self.reply_generator.check_reply(
|
||||
self.generated_reply,
|
||||
self.current_goal
|
||||
)
|
||||
|
||||
if not is_suitable:
|
||||
logger.warning(f"生成的回复不合适，原因: {reason}")
|
||||
if need_replan:
|
||||
# 尝试切换到其他备选目标
|
||||
alternative_goals = await self.goal_analyzer.get_alternative_goals()
|
||||
if alternative_goals:
|
||||
# 有备选目标，尝试使用
|
||||
self.current_goal, self.current_method, self.goal_reasoning = alternative_goals[0]
|
||||
logger.info(f"切换到备选目标: {self.current_goal}")
|
||||
# 使用新目标获取知识并生成回复
|
||||
knowledge, sources = await self.knowledge_fetcher.fetch(
|
||||
self.current_goal,
|
||||
[self._convert_to_message(msg) for msg in messages]
|
||||
)
|
||||
if knowledge != "未找到相关知识":
|
||||
self.knowledge_cache[sources] = knowledge
|
||||
|
||||
self.generated_reply = await self.reply_generator.generate(
|
||||
self.current_goal,
|
||||
self.current_method,
|
||||
[self._convert_to_message(msg) for msg in messages],
|
||||
self.knowledge_cache
|
||||
)
|
||||
else:
|
||||
# 没有备选目标，重新分析
|
||||
self.state = ConversationState.RETHINKING
|
||||
self.current_goal, self.current_method, self.goal_reasoning = await self.goal_analyzer.analyze_goal()
|
||||
return
|
||||
else:
|
||||
# 重新生成回复
|
||||
self.generated_reply = await self.reply_generator.generate(
|
||||
self.current_goal,
|
||||
self.current_method,
|
||||
[self._convert_to_message(msg) for msg in messages],
|
||||
self.knowledge_cache,
|
||||
self.generated_reply # 将不合适的回复作为previous_reply传入
|
||||
)
|
||||
|
||||
await self._send_reply()
|
||||
|
||||
elif action == "rethink_goal":
|
||||
@@ -701,6 +897,16 @@ class Conversation:
|
||||
elif action == "judge_conversation":
|
||||
self.state = ConversationState.JUDGING
|
||||
self.goal_achieved, self.stop_conversation, self.reason = await self.goal_analyzer.analyze_conversation(self.current_goal, self.goal_reasoning)
|
||||
|
||||
# 如果当前目标达成但还有其他目标
|
||||
if self.goal_achieved and not self.stop_conversation:
|
||||
alternative_goals = await self.goal_analyzer.get_alternative_goals()
|
||||
if alternative_goals:
|
||||
# 切换到下一个目标
|
||||
self.current_goal, self.current_method, self.goal_reasoning = alternative_goals[0]
|
||||
logger.info(f"当前目标已达成，切换到新目标: {self.current_goal}")
|
||||
return
|
||||
|
||||
if self.stop_conversation:
|
||||
await self._stop_conversation()
|
||||
|
||||
@@ -724,7 +930,7 @@ class Conversation:
|
||||
self.should_continue = False
|
||||
self.state = ConversationState.ENDED
|
||||
# 删除实例（这会同时停止chat_observer）
|
||||
self.remove_instance(self.stream_id)
|
||||
await self.remove_instance(self.stream_id)
|
||||
|
||||
async def _send_timeout_message(self):
|
||||
"""发送超时结束消息"""
|
||||
@@ -821,7 +1027,7 @@ class DirectMessageSender:
|
||||
if not end_point:
|
||||
raise ValueError(f"未找到平台：{chat_stream.platform} 的url配置")
|
||||
|
||||
await global_api.send_message(end_point, message_json)
|
||||
await global_api.send_message_REST(end_point, message_json)
|
||||
|
||||
# 存储消息
|
||||
await self.storage.store_message(message, message.chat_stream)
|
||||
|
||||
72
src/plugins/PFC/pfc_utils.py
Normal file
72
src/plugins/PFC/pfc_utils.py
Normal file
@@ -0,0 +1,72 @@
|
||||
import json
|
||||
import re
|
||||
from typing import Dict, Any, Optional, Tuple
|
||||
from src.common.logger import get_module_logger
|
||||
|
||||
logger = get_module_logger("pfc_utils")
|
||||
|
||||
def get_items_from_json(
|
||||
content: str,
|
||||
*items: str,
|
||||
default_values: Optional[Dict[str, Any]] = None,
|
||||
required_types: Optional[Dict[str, type]] = None
|
||||
) -> Tuple[bool, Dict[str, Any]]:
|
||||
"""从文本中提取JSON内容并获取指定字段
|
||||
|
||||
Args:
|
||||
content: 包含JSON的文本
|
||||
*items: 要提取的字段名
|
||||
default_values: 字段的默认值，格式为 {字段名: 默认值}
|
||||
required_types: 字段的必需类型，格式为 {字段名: 类型}
|
||||
|
||||
Returns:
|
||||
Tuple[bool, Dict[str, Any]]: (是否成功, 提取的字段字典)
|
||||
"""
|
||||
content = content.strip()
|
||||
result = {}
|
||||
|
||||
# 设置默认值
|
||||
if default_values:
|
||||
result.update(default_values)
|
||||
|
||||
# 尝试解析JSON
|
||||
try:
|
||||
json_data = json.loads(content)
|
||||
except json.JSONDecodeError:
|
||||
# 如果直接解析失败，尝试查找和提取JSON部分
|
||||
json_pattern = r'\{[^{}]*\}'
|
||||
json_match = re.search(json_pattern, content)
|
||||
if json_match:
|
||||
try:
|
||||
json_data = json.loads(json_match.group())
|
||||
except json.JSONDecodeError:
|
||||
logger.error("提取的JSON内容解析失败")
|
||||
return False, result
|
||||
else:
|
||||
logger.error("无法在返回内容中找到有效的JSON")
|
||||
return False, result
|
||||
|
||||
# 提取字段
|
||||
for item in items:
|
||||
if item in json_data:
|
||||
result[item] = json_data[item]
|
||||
|
||||
# 验证必需字段
|
||||
if not all(item in result for item in items):
|
||||
logger.error(f"JSON缺少必要字段，实际内容: {json_data}")
|
||||
return False, result
|
||||
|
||||
# 验证字段类型
|
||||
if required_types:
|
||||
for field, expected_type in required_types.items():
|
||||
if field in result and not isinstance(result[field], expected_type):
|
||||
logger.error(f"{field} 必须是 {expected_type.__name__} 类型")
|
||||
return False, result
|
||||
|
||||
# 验证字符串字段不为空
|
||||
for field in items:
|
||||
if isinstance(result[field], str) and not result[field].strip():
|
||||
logger.error(f"{field} 不能为空")
|
||||
return False, result
|
||||
|
||||
return True, result
|
||||
@@ -9,6 +9,7 @@ from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
|
||||
from ..chat_module.think_flow_chat.think_flow_chat import ThinkFlowChat
|
||||
from ..chat_module.reasoning_chat.reasoning_chat import ReasoningChat
|
||||
import asyncio
|
||||
import traceback
|
||||
|
||||
# 定义日志配置
|
||||
chat_config = LogConfig(
|
||||
@@ -42,11 +43,24 @@ class ChatBot:
|
||||
|
||||
if global_config.enable_pfc_chatting:
|
||||
# 获取或创建对话实例
|
||||
conversation = Conversation.get_instance(chat_id)
|
||||
conversation = await Conversation.get_instance(chat_id)
|
||||
if conversation is None:
|
||||
logger.error(f"创建或获取对话实例失败: {chat_id}")
|
||||
return
|
||||
|
||||
# 如果是新创建的实例，启动对话系统
|
||||
if conversation.state == ConversationState.INIT:
|
||||
asyncio.create_task(conversation.start())
|
||||
logger.info(f"为聊天 {chat_id} 创建新的对话实例")
|
||||
elif conversation.state == ConversationState.ENDED:
|
||||
# 如果实例已经结束，重新创建
|
||||
await Conversation.remove_instance(chat_id)
|
||||
conversation = await Conversation.get_instance(chat_id)
|
||||
if conversation is None:
|
||||
logger.error(f"重新创建对话实例失败: {chat_id}")
|
||||
return
|
||||
asyncio.create_task(conversation.start())
|
||||
logger.info(f"为聊天 {chat_id} 重新创建对话实例")
|
||||
except Exception as e:
|
||||
logger.error(f"创建PFC聊天流失败: {e}")
|
||||
|
||||
@@ -78,7 +92,12 @@ class ChatBot:
|
||||
try:
|
||||
message = MessageRecv(message_data)
|
||||
groupinfo = message.message_info.group_info
|
||||
logger.debug(f"处理消息:{str(message_data)[:50]}...")
|
||||
userinfo = message.message_info.user_info
|
||||
logger.debug(f"处理消息:{str(message_data)[:80]}...")
|
||||
|
||||
if userinfo.user_id in global_config.ban_user_id:
|
||||
logger.debug(f"用户{userinfo.user_id}被禁止回复")
|
||||
return
|
||||
|
||||
if global_config.enable_pfc_chatting:
|
||||
try:
|
||||
@@ -96,11 +115,11 @@ class ChatBot:
|
||||
await self._create_PFC_chat(message)
|
||||
else:
|
||||
if groupinfo.group_id in global_config.talk_allowed_groups:
|
||||
logger.debug(f"开始群聊模式{message_data}")
|
||||
logger.debug(f"开始群聊模式{str(message_data)[:50]}...")
|
||||
if global_config.response_mode == "heart_flow":
|
||||
await self.think_flow_chat.process_message(message_data)
|
||||
elif global_config.response_mode == "reasoning":
|
||||
logger.debug(f"开始推理模式{message_data}")
|
||||
logger.debug(f"开始推理模式{str(message_data)[:50]}...")
|
||||
await self.reasoning_chat.process_message(message_data)
|
||||
else:
|
||||
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
|
||||
@@ -126,6 +145,7 @@ class ChatBot:
|
||||
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
|
||||
except Exception as e:
|
||||
logger.error(f"预处理消息失败: {e}")
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
# 创建全局ChatBot实例
|
||||
|
||||
@@ -28,7 +28,7 @@ class ChatStream:
|
||||
self.platform = platform
|
||||
self.user_info = user_info
|
||||
self.group_info = group_info
|
||||
self.create_time = data.get("create_time", int(time.time())) if data else int(time.time())
|
||||
self.create_time = data.get("create_time", time.time()) if data else time.time()
|
||||
self.last_active_time = data.get("last_active_time", self.create_time) if data else self.create_time
|
||||
self.saved = False
|
||||
|
||||
@@ -60,7 +60,7 @@ class ChatStream:
|
||||
|
||||
def update_active_time(self):
|
||||
"""更新最后活跃时间"""
|
||||
self.last_active_time = int(time.time())
|
||||
self.last_active_time = time.time()
|
||||
self.saved = False
|
||||
|
||||
|
||||
|
||||
@@ -249,7 +249,22 @@ class EmojiManager:
|
||||
f for f in os.listdir(emoji_dir) if f.lower().endswith((".jpg", ".jpeg", ".png", ".gif"))
|
||||
]
|
||||
|
||||
# 检查当前表情包数量
|
||||
self._update_emoji_count()
|
||||
if self.emoji_num >= self.emoji_num_max:
|
||||
logger.warning(f"[警告] 表情包数量已达到上限({self.emoji_num}/{self.emoji_num_max})，跳过注册")
|
||||
return
|
||||
|
||||
# 计算还可以注册的数量
|
||||
remaining_slots = self.emoji_num_max - self.emoji_num
|
||||
logger.info(f"[注册] 还可以注册 {remaining_slots} 个表情包")
|
||||
|
||||
for filename in files_to_process:
|
||||
# 如果已经达到上限，停止注册
|
||||
if self.emoji_num >= self.emoji_num_max:
|
||||
logger.warning(f"[警告] 表情包数量已达到上限({self.emoji_num}/{self.emoji_num_max})，停止注册")
|
||||
break
|
||||
|
||||
image_path = os.path.join(emoji_dir, filename)
|
||||
|
||||
# 获取图片的base64编码和哈希值
|
||||
@@ -340,6 +355,10 @@ class EmojiManager:
|
||||
logger.success(f"[注册] 新表情包: {filename}")
|
||||
logger.info(f"[描述] {description}")
|
||||
|
||||
# 更新当前表情包数量
|
||||
self.emoji_num += 1
|
||||
logger.info(f"[统计] 当前表情包数量: {self.emoji_num}/{self.emoji_num_max}")
|
||||
|
||||
# 保存到images数据库
|
||||
image_doc = {
|
||||
"hash": image_hash,
|
||||
|
||||
@@ -168,7 +168,7 @@ class MessageProcessBase(Message):
|
||||
# 调用父类初始化
|
||||
super().__init__(
|
||||
message_id=message_id,
|
||||
time=int(time.time()),
|
||||
time=round(time.time(), 3), # 保留3位小数
|
||||
chat_stream=chat_stream,
|
||||
user_info=bot_user_info,
|
||||
message_segment=message_segment,
|
||||
|
||||
190
src/plugins/chat/message_buffer.py
Normal file
190
src/plugins/chat/message_buffer.py
Normal file
@@ -0,0 +1,190 @@
|
||||
from ..person_info.person_info import person_info_manager
|
||||
from src.common.logger import get_module_logger
|
||||
import asyncio
|
||||
from dataclasses import dataclass, field
|
||||
from .message import MessageRecv
|
||||
from ..message.message_base import BaseMessageInfo, GroupInfo
|
||||
import hashlib
|
||||
from typing import Dict
|
||||
from collections import OrderedDict
|
||||
import random
|
||||
import time
|
||||
from ..config.config import global_config
|
||||
|
||||
logger = get_module_logger("message_buffer")
|
||||
|
||||
@dataclass
|
||||
class CacheMessages:
|
||||
message: MessageRecv
|
||||
cache_determination: asyncio.Event = field(default_factory=asyncio.Event) # 判断缓冲是否产生结果
|
||||
result: str = "U"
|
||||
|
||||
|
||||
class MessageBuffer:
|
||||
def __init__(self):
|
||||
self.buffer_pool: Dict[str, OrderedDict[str, CacheMessages]] = {}
|
||||
self.lock = asyncio.Lock()
|
||||
|
||||
def get_person_id_(self, platform:str, user_id:str, group_info:GroupInfo):
|
||||
"""获取唯一id"""
|
||||
if group_info:
|
||||
group_id = group_info.group_id
|
||||
else:
|
||||
group_id = "私聊"
|
||||
key = f"{platform}_{user_id}_{group_id}"
|
||||
return hashlib.md5(key.encode()).hexdigest()
|
||||
|
||||
async def start_caching_messages(self, message:MessageRecv):
|
||||
"""添加消息，启动缓冲"""
|
||||
if not global_config.message_buffer:
|
||||
person_id = person_info_manager.get_person_id(message.message_info.user_info.platform,
|
||||
message.message_info.user_info.user_id)
|
||||
asyncio.create_task(self.save_message_interval(person_id, message.message_info))
|
||||
return
|
||||
person_id_ = self.get_person_id_(message.message_info.platform,
|
||||
message.message_info.user_info.user_id,
|
||||
message.message_info.group_info)
|
||||
|
||||
async with self.lock:
|
||||
if person_id_ not in self.buffer_pool:
|
||||
self.buffer_pool[person_id_] = OrderedDict()
|
||||
|
||||
# 标记该用户之前的未处理消息
|
||||
for cache_msg in self.buffer_pool[person_id_].values():
|
||||
if cache_msg.result == "U":
|
||||
cache_msg.result = "F"
|
||||
cache_msg.cache_determination.set()
|
||||
logger.debug(f"被新消息覆盖信息id: {cache_msg.message.message_info.message_id}")
|
||||
|
||||
# 查找最近的处理成功消息(T)
|
||||
recent_F_count = 0
|
||||
for msg_id in reversed(self.buffer_pool[person_id_]):
|
||||
msg = self.buffer_pool[person_id_][msg_id]
|
||||
if msg.result == "T":
|
||||
break
|
||||
elif msg.result == "F":
|
||||
recent_F_count += 1
|
||||
|
||||
# 判断条件：最近T之后有超过3-5条F
|
||||
if (recent_F_count >= random.randint(3, 5)):
|
||||
new_msg = CacheMessages(message=message, result="T")
|
||||
new_msg.cache_determination.set()
|
||||
self.buffer_pool[person_id_][message.message_info.message_id] = new_msg
|
||||
logger.debug(f"快速处理消息(已堆积{recent_F_count}条F): {message.message_info.message_id}")
|
||||
return
|
||||
|
||||
# 添加新消息
|
||||
self.buffer_pool[person_id_][message.message_info.message_id] = CacheMessages(message=message)
|
||||
|
||||
# 启动3秒缓冲计时器
|
||||
person_id = person_info_manager.get_person_id(message.message_info.user_info.platform,
|
||||
message.message_info.user_info.user_id)
|
||||
asyncio.create_task(self.save_message_interval(person_id, message.message_info))
|
||||
asyncio.create_task(self._debounce_processor(person_id_,
|
||||
message.message_info.message_id,
|
||||
person_id))
|
||||
|
||||
async def _debounce_processor(self, person_id_: str, message_id: str, person_id: str):
|
||||
"""等待3秒无新消息"""
|
||||
interval_time = await person_info_manager.get_value(person_id, "msg_interval")
|
||||
if not isinstance(interval_time, (int, str)) or not str(interval_time).isdigit():
|
||||
logger.debug("debounce_processor无效的时间")
|
||||
return
|
||||
interval_time = max(0.5, int(interval_time) / 1000)
|
||||
await asyncio.sleep(interval_time)
|
||||
|
||||
async with self.lock:
|
||||
if (person_id_ not in self.buffer_pool or
|
||||
message_id not in self.buffer_pool[person_id_]):
|
||||
logger.debug(f"消息已被清理，msgid: {message_id}")
|
||||
return
|
||||
|
||||
cache_msg = self.buffer_pool[person_id_][message_id]
|
||||
if cache_msg.result == "U":
|
||||
cache_msg.result = "T"
|
||||
cache_msg.cache_determination.set()
|
||||
|
||||
|
||||
async def query_buffer_result(self, message:MessageRecv) -> bool:
|
||||
"""查询缓冲结果，并清理"""
|
||||
if not global_config.message_buffer:
|
||||
return True
|
||||
person_id_ = self.get_person_id_(message.message_info.platform,
|
||||
message.message_info.user_info.user_id,
|
||||
message.message_info.group_info)
|
||||
|
||||
|
||||
async with self.lock:
|
||||
user_msgs = self.buffer_pool.get(person_id_, {})
|
||||
cache_msg = user_msgs.get(message.message_info.message_id)
|
||||
|
||||
if not cache_msg:
|
||||
logger.debug(f"查询异常，消息不存在，msgid: {message.message_info.message_id}")
|
||||
return False # 消息不存在或已清理
|
||||
|
||||
try:
|
||||
await asyncio.wait_for(cache_msg.cache_determination.wait(), timeout=10)
|
||||
result = cache_msg.result == "T"
|
||||
|
||||
if result:
|
||||
async with self.lock: # 再次加锁
|
||||
# 清理所有早于当前消息的已处理消息， 收集所有早于当前消息的F消息的processed_plain_text
|
||||
keep_msgs = OrderedDict()
|
||||
combined_text = []
|
||||
found = False
|
||||
type = "text"
|
||||
is_update = True
|
||||
for msg_id, msg in self.buffer_pool[person_id_].items():
|
||||
if msg_id == message.message_info.message_id:
|
||||
found = True
|
||||
type = msg.message.message_segment.type
|
||||
combined_text.append(msg.message.processed_plain_text)
|
||||
continue
|
||||
if found:
|
||||
keep_msgs[msg_id] = msg
|
||||
elif msg.result == "F":
|
||||
# 收集F消息的文本内容
|
||||
if (hasattr(msg.message, 'processed_plain_text')
|
||||
and msg.message.processed_plain_text):
|
||||
if msg.message.message_segment.type == "text":
|
||||
combined_text.append(msg.message.processed_plain_text)
|
||||
elif msg.message.message_segment.type != "text":
|
||||
is_update = False
|
||||
elif msg.result == "U":
|
||||
logger.debug(f"异常未处理信息id： {msg.message.message_info.message_id}")
|
||||
|
||||
# 更新当前消息的processed_plain_text
|
||||
if combined_text and combined_text[0] != message.processed_plain_text and is_update:
|
||||
if type == "text":
|
||||
message.processed_plain_text = "".join(combined_text)
|
||||
logger.debug(f"整合了{len(combined_text)-1}条F消息的内容到当前消息")
|
||||
elif type == "emoji":
|
||||
combined_text.pop()
|
||||
message.processed_plain_text = "".join(combined_text)
|
||||
message.is_emoji = False
|
||||
logger.debug(f"整合了{len(combined_text)-1}条F消息的内容，覆盖当前emoji消息")
|
||||
|
||||
self.buffer_pool[person_id_] = keep_msgs
|
||||
return result
|
||||
except asyncio.TimeoutError:
|
||||
logger.debug(f"查询超时消息id： {message.message_info.message_id}")
|
||||
return False
|
||||
|
||||
async def save_message_interval(self, person_id:str, message:BaseMessageInfo):
|
||||
message_interval_list = await person_info_manager.get_value(person_id, "msg_interval_list")
|
||||
now_time_ms = int(round(time.time() * 1000))
|
||||
if len(message_interval_list) < 1000:
|
||||
message_interval_list.append(now_time_ms)
|
||||
else:
|
||||
message_interval_list.pop(0)
|
||||
message_interval_list.append(now_time_ms)
|
||||
data = {
|
||||
"platform" : message.platform,
|
||||
"user_id" : message.user_info.user_id,
|
||||
"nickname" : message.user_info.user_nickname,
|
||||
"konw_time" : int(time.time())
|
||||
}
|
||||
await person_info_manager.update_one_field(person_id, "msg_interval_list", message_interval_list, data)
|
||||
|
||||
|
||||
message_buffer = MessageBuffer()
|
||||
@@ -43,6 +43,12 @@ class Message_Sender:
|
||||
# 按thinking_start_time排序，时间早的在前面
|
||||
return recalled_messages
|
||||
|
||||
async def send_via_ws(self, message: MessageSending) -> None:
|
||||
try:
|
||||
await global_api.send_message(message)
|
||||
except Exception as e:
|
||||
raise ValueError(f"未找到平台：{message.message_info.platform} 的url配置，请检查配置文件") from e
|
||||
|
||||
async def send_message(
|
||||
self,
|
||||
message: MessageSending,
|
||||
@@ -58,8 +64,14 @@ class Message_Sender:
|
||||
logger.warning(f"消息“{message.processed_plain_text}”已被撤回，不发送")
|
||||
break
|
||||
if not is_recalled:
|
||||
typing_time = calculate_typing_time(message.processed_plain_text)
|
||||
# print(message.processed_plain_text + str(message.is_emoji))
|
||||
typing_time = calculate_typing_time(
|
||||
input_string=message.processed_plain_text,
|
||||
thinking_start_time=message.thinking_start_time,
|
||||
is_emoji=message.is_emoji)
|
||||
logger.debug(f"{message.processed_plain_text},{typing_time},计算输入时间结束")
|
||||
await asyncio.sleep(typing_time)
|
||||
logger.debug(f"{message.processed_plain_text},{typing_time},等待输入时间结束")
|
||||
|
||||
message_json = message.to_dict()
|
||||
|
||||
@@ -69,14 +81,14 @@ class Message_Sender:
|
||||
if end_point:
|
||||
# logger.info(f"发送消息到{end_point}")
|
||||
# logger.info(message_json)
|
||||
await global_api.send_message_REST(end_point, message_json)
|
||||
else:
|
||||
try:
|
||||
await global_api.send_message(message)
|
||||
await global_api.send_message_REST(end_point, message_json)
|
||||
except Exception as e:
|
||||
raise ValueError(
|
||||
f"未找到平台：{message.message_info.platform} 的url配置，请检查配置文件"
|
||||
) from e
|
||||
logger.error(f"REST方式发送失败，出现错误: {str(e)}")
|
||||
logger.info("尝试使用ws发送")
|
||||
await self.send_via_ws(message)
|
||||
else:
|
||||
await self.send_via_ws(message)
|
||||
logger.success(f"发送消息“{message_preview}”成功")
|
||||
except Exception as e:
|
||||
logger.error(f"发送消息“{message_preview}”失败: {str(e)}")
|
||||
@@ -214,6 +226,8 @@ class MessageManager:
|
||||
|
||||
await message_earliest.process()
|
||||
|
||||
# print(f"message_earliest.thinking_start_tim22222e:{message_earliest.thinking_start_time}")
|
||||
|
||||
await message_sender.send_message(message_earliest)
|
||||
|
||||
await self.storage.store_message(message_earliest, message_earliest.chat_stream)
|
||||
|
||||
@@ -334,26 +334,19 @@ def process_llm_response(text: str) -> List[str]:
|
||||
return sentences
|
||||
|
||||
|
||||
def calculate_typing_time(input_string: str, chinese_time: float = 0.2, english_time: float = 0.1) -> float:
|
||||
def calculate_typing_time(input_string: str, thinking_start_time: float, chinese_time: float = 0.2, english_time: float = 0.1, is_emoji: bool = False) -> float:
|
||||
"""
|
||||
计算输入字符串所需的时间，中文和英文字符有不同的输入时间
|
||||
input_string (str): 输入的字符串
|
||||
chinese_time (float): 中文字符的输入时间，默认为0.2秒
|
||||
english_time (float): 英文字符的输入时间，默认为0.1秒
|
||||
is_emoji (bool): 是否为emoji，默认为False
|
||||
|
||||
特殊情况：
|
||||
- 如果只有一个中文字符，将使用3倍的中文输入时间
|
||||
- 在所有输入结束后，额外加上回车时间0.3秒
|
||||
- 如果is_emoji为True，将使用固定1秒的输入时间
|
||||
"""
|
||||
|
||||
# 如果输入是列表，将其连接成字符串
|
||||
if isinstance(input_string, list):
|
||||
input_string = ''.join(input_string)
|
||||
|
||||
# 确保现在是字符串类型
|
||||
if not isinstance(input_string, str):
|
||||
input_string = str(input_string)
|
||||
|
||||
mood_manager = MoodManager.get_instance()
|
||||
# 将0-1的唤醒度映射到-1到1
|
||||
mood_arousal = mood_manager.current_mood.arousal
|
||||
@@ -376,7 +369,19 @@ def calculate_typing_time(input_string: str, chinese_time: float = 0.2, english_
|
||||
else: # 其他字符（如英文）
|
||||
total_time += english_time
|
||||
|
||||
return total_time + 0.3 # 加上回车时间
|
||||
|
||||
if is_emoji:
|
||||
total_time = 1
|
||||
|
||||
if time.time() - thinking_start_time > 10:
|
||||
total_time = 1
|
||||
|
||||
# print(f"thinking_start_time:{thinking_start_time}")
|
||||
# print(f"nowtime:{time.time()}")
|
||||
# print(f"nowtime - thinking_start_time:{time.time() - thinking_start_time}")
|
||||
# print(f"{total_time}")
|
||||
|
||||
return total_time # 加上回车时间
|
||||
|
||||
|
||||
def cosine_similarity(v1, v2):
|
||||
|
||||
@@ -17,6 +17,7 @@ from ...message import UserInfo, Seg
|
||||
from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
|
||||
from ...chat.chat_stream import chat_manager
|
||||
from ...person_info.relationship_manager import relationship_manager
|
||||
from ...chat.message_buffer import message_buffer
|
||||
|
||||
# 定义日志配置
|
||||
chat_config = LogConfig(
|
||||
@@ -143,6 +144,8 @@ class ReasoningChat:
|
||||
userinfo = message.message_info.user_info
|
||||
messageinfo = message.message_info
|
||||
|
||||
# 消息加入缓冲池
|
||||
await message_buffer.start_caching_messages(message)
|
||||
|
||||
# logger.info("使用推理聊天模式")
|
||||
|
||||
@@ -172,6 +175,17 @@ class ReasoningChat:
|
||||
timer2 = time.time()
|
||||
timing_results["记忆激活"] = timer2 - timer1
|
||||
|
||||
# 查询缓冲器结果，会整合前面跳过的消息，改变processed_plain_text
|
||||
buffer_result = await message_buffer.query_buffer_result(message)
|
||||
if not buffer_result:
|
||||
if message.message_segment.type == "text":
|
||||
logger.info(f"触发缓冲，已炸飞消息：{message.processed_plain_text}")
|
||||
elif message.message_segment.type == "image":
|
||||
logger.info("触发缓冲，已炸飞表情包/图片")
|
||||
elif message.message_segment.type == "seglist":
|
||||
logger.info("触发缓冲，已炸飞消息列")
|
||||
return
|
||||
|
||||
is_mentioned = is_mentioned_bot_in_message(message)
|
||||
|
||||
# 计算回复意愿
|
||||
|
||||
@@ -1,16 +1,17 @@
|
||||
import random
|
||||
import time
|
||||
from typing import Optional
|
||||
from typing import Optional, Union
|
||||
|
||||
from ....common.database import db
|
||||
from ...memory_system.Hippocampus import HippocampusManager
|
||||
from ...moods.moods import MoodManager
|
||||
from ...schedule.schedule_generator import bot_schedule
|
||||
from ...config.config import global_config
|
||||
from ...chat.utils import get_embedding, get_recent_group_detailed_plain_text, get_recent_group_speaker
|
||||
from ...chat.chat_stream import chat_manager
|
||||
from src.common.logger import get_module_logger
|
||||
from ...moods.moods import MoodManager
|
||||
from ....individuality.individuality import Individuality
|
||||
from ...memory_system.Hippocampus import HippocampusManager
|
||||
from ...schedule.schedule_generator import bot_schedule
|
||||
from ...config.config import global_config
|
||||
from ...person_info.relationship_manager import relationship_manager
|
||||
from src.common.logger import get_module_logger
|
||||
|
||||
logger = get_module_logger("prompt")
|
||||
|
||||
@@ -25,6 +26,22 @@ class PromptBuilder:
|
||||
) -> tuple[str, str]:
|
||||
|
||||
# 开始构建prompt
|
||||
prompt_personality = "你"
|
||||
#person
|
||||
individuality = Individuality.get_instance()
|
||||
|
||||
personality_core = individuality.personality.personality_core
|
||||
prompt_personality += personality_core
|
||||
|
||||
personality_sides = individuality.personality.personality_sides
|
||||
random.shuffle(personality_sides)
|
||||
prompt_personality += f",{personality_sides[0]}"
|
||||
|
||||
identity_detail = individuality.identity.identity_detail
|
||||
random.shuffle(identity_detail)
|
||||
prompt_personality += f",{identity_detail[0]}"
|
||||
|
||||
|
||||
|
||||
# 关系
|
||||
who_chat_in_group = [(chat_stream.user_info.platform,
|
||||
@@ -102,20 +119,6 @@ class PromptBuilder:
|
||||
)
|
||||
keywords_reaction_prompt += rule.get("reaction", "") + "，"
|
||||
|
||||
# 人格选择
|
||||
personality = global_config.PROMPT_PERSONALITY
|
||||
probability_1 = global_config.PERSONALITY_1
|
||||
probability_2 = global_config.PERSONALITY_2
|
||||
|
||||
personality_choice = random.random()
|
||||
|
||||
if personality_choice < probability_1: # 第一种风格
|
||||
prompt_personality = personality[0]
|
||||
elif personality_choice < probability_1 + probability_2: # 第二种风格
|
||||
prompt_personality = personality[1]
|
||||
else: # 第三种人格
|
||||
prompt_personality = personality[2]
|
||||
|
||||
# 中文高手(新加的好玩功能)
|
||||
prompt_ger = ""
|
||||
if random.random() < 0.04:
|
||||
@@ -128,7 +131,7 @@ class PromptBuilder:
|
||||
# 知识构建
|
||||
start_time = time.time()
|
||||
prompt_info = ""
|
||||
prompt_info = await self.get_prompt_info(message_txt, threshold=0.5)
|
||||
prompt_info = await self.get_prompt_info(message_txt, threshold=0.38)
|
||||
if prompt_info:
|
||||
prompt_info = f"""\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n"""
|
||||
|
||||
@@ -142,12 +145,13 @@ class PromptBuilder:
|
||||
logger.info("开始构建prompt")
|
||||
|
||||
prompt = f"""
|
||||
{relation_prompt_all}
|
||||
{memory_prompt}
|
||||
{prompt_info}
|
||||
{schedule_prompt}
|
||||
{chat_target}
|
||||
{chat_talking_prompt}
|
||||
现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。{relation_prompt_all}\n
|
||||
现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
|
||||
你的网名叫{global_config.BOT_NICKNAME}，有人也叫你{"/".join(global_config.BOT_ALIAS_NAMES)}，{prompt_personality}。
|
||||
你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，然后给出日常且口语化的回复，平淡一些，
|
||||
尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。{prompt_ger}
|
||||
@@ -158,16 +162,156 @@ class PromptBuilder:
|
||||
return prompt
|
||||
|
||||
async def get_prompt_info(self, message: str, threshold: float):
|
||||
start_time = time.time()
|
||||
related_info = ""
|
||||
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
|
||||
embedding = await get_embedding(message, request_type="prompt_build")
|
||||
related_info += self.get_info_from_db(embedding, limit=1, threshold=threshold)
|
||||
|
||||
# 1. 先从LLM获取主题，类似于记忆系统的做法
|
||||
topics = []
|
||||
# try:
|
||||
# # 先尝试使用记忆系统的方法获取主题
|
||||
# hippocampus = HippocampusManager.get_instance()._hippocampus
|
||||
# topic_num = min(5, max(1, int(len(message) * 0.1)))
|
||||
# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
|
||||
|
||||
# # 提取关键词
|
||||
# topics = re.findall(r"<([^>]+)>", topics_response[0])
|
||||
# if not topics:
|
||||
# topics = []
|
||||
# else:
|
||||
# topics = [
|
||||
# topic.strip()
|
||||
# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||||
# if topic.strip()
|
||||
# ]
|
||||
|
||||
# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
|
||||
# except Exception as e:
|
||||
# logger.error(f"从LLM提取主题失败: {str(e)}")
|
||||
# # 如果LLM提取失败，使用jieba分词提取关键词作为备选
|
||||
# words = jieba.cut(message)
|
||||
# topics = [word for word in words if len(word) > 1][:5]
|
||||
# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
|
||||
|
||||
# 如果无法提取到主题，直接使用整个消息
|
||||
if not topics:
|
||||
logger.info("未能提取到任何主题，使用整个消息进行查询")
|
||||
embedding = await get_embedding(message, request_type="prompt_build")
|
||||
if not embedding:
|
||||
logger.error("获取消息嵌入向量失败")
|
||||
return ""
|
||||
|
||||
related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
|
||||
logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
|
||||
return related_info
|
||||
|
||||
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5) -> str:
|
||||
if not query_embedding:
|
||||
# 2. 对每个主题进行知识库查询
|
||||
logger.info(f"开始处理{len(topics)}个主题的知识库查询")
|
||||
|
||||
# 优化：批量获取嵌入向量，减少API调用
|
||||
embeddings = {}
|
||||
topics_batch = [topic for topic in topics if len(topic) > 0]
|
||||
if message: # 确保消息非空
|
||||
topics_batch.append(message)
|
||||
|
||||
# 批量获取嵌入向量
|
||||
embed_start_time = time.time()
|
||||
for text in topics_batch:
|
||||
if not text or len(text.strip()) == 0:
|
||||
continue
|
||||
|
||||
try:
|
||||
embedding = await get_embedding(text, request_type="prompt_build")
|
||||
if embedding:
|
||||
embeddings[text] = embedding
|
||||
else:
|
||||
logger.warning(f"获取'{text}'的嵌入向量失败")
|
||||
except Exception as e:
|
||||
logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
|
||||
|
||||
logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
|
||||
|
||||
if not embeddings:
|
||||
logger.error("所有嵌入向量获取失败")
|
||||
return ""
|
||||
|
||||
# 3. 对每个主题进行知识库查询
|
||||
all_results = []
|
||||
query_start_time = time.time()
|
||||
|
||||
# 首先添加原始消息的查询结果
|
||||
if message in embeddings:
|
||||
original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
|
||||
if original_results:
|
||||
for result in original_results:
|
||||
result["topic"] = "原始消息"
|
||||
all_results.extend(original_results)
|
||||
logger.info(f"原始消息查询到{len(original_results)}条结果")
|
||||
|
||||
# 然后添加每个主题的查询结果
|
||||
for topic in topics:
|
||||
if not topic or topic not in embeddings:
|
||||
continue
|
||||
|
||||
try:
|
||||
topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
|
||||
if topic_results:
|
||||
# 添加主题标记
|
||||
for result in topic_results:
|
||||
result["topic"] = topic
|
||||
all_results.extend(topic_results)
|
||||
logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
|
||||
except Exception as e:
|
||||
logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
|
||||
|
||||
logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
|
||||
|
||||
# 4. 去重和过滤
|
||||
process_start_time = time.time()
|
||||
unique_contents = set()
|
||||
filtered_results = []
|
||||
for result in all_results:
|
||||
content = result["content"]
|
||||
if content not in unique_contents:
|
||||
unique_contents.add(content)
|
||||
filtered_results.append(result)
|
||||
|
||||
# 5. 按相似度排序
|
||||
filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
|
||||
|
||||
# 6. 限制总数量（最多10条）
|
||||
filtered_results = filtered_results[:10]
|
||||
logger.info(f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果")
|
||||
|
||||
# 7. 格式化输出
|
||||
if filtered_results:
|
||||
format_start_time = time.time()
|
||||
grouped_results = {}
|
||||
for result in filtered_results:
|
||||
topic = result["topic"]
|
||||
if topic not in grouped_results:
|
||||
grouped_results[topic] = []
|
||||
grouped_results[topic].append(result)
|
||||
|
||||
# 按主题组织输出
|
||||
for topic, results in grouped_results.items():
|
||||
related_info += f"【主题: {topic}】\n"
|
||||
for _i, result in enumerate(results, 1):
|
||||
_similarity = result["similarity"]
|
||||
content = result["content"].strip()
|
||||
# 调试：为内容添加序号和相似度信息
|
||||
# related_info += f"{i}. [{similarity:.2f}] {content}\n"
|
||||
related_info += f"{content}\n"
|
||||
related_info += "\n"
|
||||
|
||||
logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
|
||||
|
||||
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
|
||||
return related_info
|
||||
|
||||
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False) -> Union[str, list]:
|
||||
if not query_embedding:
|
||||
return "" if not return_raw else []
|
||||
# 使用余弦相似度计算
|
||||
pipeline = [
|
||||
{
|
||||
@@ -221,11 +365,14 @@ class PromptBuilder:
|
||||
]
|
||||
|
||||
results = list(db.knowledges.aggregate(pipeline))
|
||||
# print(f"\033[1;34m[调试]\033[0m获取知识库内容结果: {results}")
|
||||
logger.debug(f"知识库查询结果数量: {len(results)}")
|
||||
|
||||
if not results:
|
||||
return ""
|
||||
return "" if not return_raw else []
|
||||
|
||||
if return_raw:
|
||||
return results
|
||||
else:
|
||||
# 返回所有找到的内容，用换行分隔
|
||||
return "\n".join(str(result["content"]) for result in results)
|
||||
|
||||
|
||||
@@ -18,6 +18,7 @@ from src.heart_flow.heartflow import heartflow
|
||||
from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
|
||||
from ...chat.chat_stream import chat_manager
|
||||
from ...person_info.relationship_manager import relationship_manager
|
||||
from ...chat.message_buffer import message_buffer
|
||||
|
||||
# 定义日志配置
|
||||
chat_config = LogConfig(
|
||||
@@ -95,6 +96,8 @@ class ThinkFlowChat:
|
||||
)
|
||||
if not mark_head:
|
||||
mark_head = True
|
||||
|
||||
# print(f"thinking_start_time:{bot_message.thinking_start_time}")
|
||||
message_set.add_message(bot_message)
|
||||
message_manager.add_message(message_set)
|
||||
|
||||
@@ -161,6 +164,8 @@ class ThinkFlowChat:
|
||||
userinfo = message.message_info.user_info
|
||||
messageinfo = message.message_info
|
||||
|
||||
# 消息加入缓冲池
|
||||
await message_buffer.start_caching_messages(message)
|
||||
|
||||
# 创建聊天流
|
||||
chat = await chat_manager.get_or_create_stream(
|
||||
@@ -195,8 +200,20 @@ class ThinkFlowChat:
|
||||
timing_results["记忆激活"] = timer2 - timer1
|
||||
logger.debug(f"记忆激活: {interested_rate}")
|
||||
|
||||
# 查询缓冲器结果，会整合前面跳过的消息，改变processed_plain_text
|
||||
buffer_result = await message_buffer.query_buffer_result(message)
|
||||
if not buffer_result:
|
||||
if message.message_segment.type == "text":
|
||||
logger.info(f"触发缓冲，已炸飞消息：{message.processed_plain_text}")
|
||||
elif message.message_segment.type == "image":
|
||||
logger.info("触发缓冲，已炸飞表情包/图片")
|
||||
elif message.message_segment.type == "seglist":
|
||||
logger.info("触发缓冲，已炸飞消息列")
|
||||
return
|
||||
|
||||
is_mentioned = is_mentioned_bot_in_message(message)
|
||||
|
||||
|
||||
# 计算回复意愿
|
||||
current_willing_old = willing_manager.get_willing(chat_stream=chat)
|
||||
# current_willing_new = (heartflow.get_subheartflow(chat.stream_id).current_state.willing - 5) / 4
|
||||
@@ -236,25 +253,35 @@ class ThinkFlowChat:
|
||||
|
||||
do_reply = False
|
||||
if random() < reply_probability:
|
||||
try:
|
||||
do_reply = True
|
||||
|
||||
# 创建思考消息
|
||||
try:
|
||||
timer1 = time.time()
|
||||
thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
|
||||
timer2 = time.time()
|
||||
timing_results["创建思考消息"] = timer2 - timer1
|
||||
except Exception as e:
|
||||
logger.error(f"心流创建思考消息失败: {e}")
|
||||
|
||||
try:
|
||||
# 观察
|
||||
timer1 = time.time()
|
||||
await heartflow.get_subheartflow(chat.stream_id).do_observe()
|
||||
timer2 = time.time()
|
||||
timing_results["观察"] = timer2 - timer1
|
||||
except Exception as e:
|
||||
logger.error(f"心流观察失败: {e}")
|
||||
|
||||
# 思考前脑内状态
|
||||
try:
|
||||
timer1 = time.time()
|
||||
await heartflow.get_subheartflow(chat.stream_id).do_thinking_before_reply(message.processed_plain_text)
|
||||
timer2 = time.time()
|
||||
timing_results["思考前脑内状态"] = timer2 - timer1
|
||||
except Exception as e:
|
||||
logger.error(f"心流思考前脑内状态失败: {e}")
|
||||
|
||||
# 生成回复
|
||||
timer1 = time.time()
|
||||
@@ -267,28 +294,43 @@ class ThinkFlowChat:
|
||||
return
|
||||
|
||||
# 发送消息
|
||||
try:
|
||||
timer1 = time.time()
|
||||
await self._send_response_messages(message, chat, response_set, thinking_id)
|
||||
timer2 = time.time()
|
||||
timing_results["发送消息"] = timer2 - timer1
|
||||
except Exception as e:
|
||||
logger.error(f"心流发送消息失败: {e}")
|
||||
|
||||
# 处理表情包
|
||||
try:
|
||||
timer1 = time.time()
|
||||
await self._handle_emoji(message, chat, response_set)
|
||||
timer2 = time.time()
|
||||
timing_results["处理表情包"] = timer2 - timer1
|
||||
except Exception as e:
|
||||
logger.error(f"心流处理表情包失败: {e}")
|
||||
|
||||
# 更新心流
|
||||
try:
|
||||
timer1 = time.time()
|
||||
await self._update_using_response(message, response_set)
|
||||
timer2 = time.time()
|
||||
timing_results["更新心流"] = timer2 - timer1
|
||||
except Exception as e:
|
||||
logger.error(f"心流更新失败: {e}")
|
||||
|
||||
# 更新关系情绪
|
||||
try:
|
||||
timer1 = time.time()
|
||||
await self._update_relationship(message, response_set)
|
||||
timer2 = time.time()
|
||||
timing_results["更新关系情绪"] = timer2 - timer1
|
||||
except Exception as e:
|
||||
logger.error(f"心流更新关系情绪失败: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"心流处理消息失败: {e}")
|
||||
|
||||
# 输出性能计时结果
|
||||
if do_reply:
|
||||
|
||||
@@ -1,16 +1,13 @@
|
||||
import random
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
from ...memory_system.Hippocampus import HippocampusManager
|
||||
from ...moods.moods import MoodManager
|
||||
from ...schedule.schedule_generator import bot_schedule
|
||||
from ...config.config import global_config
|
||||
from ...chat.utils import get_recent_group_detailed_plain_text, get_recent_group_speaker
|
||||
from ...chat.chat_stream import chat_manager
|
||||
from src.common.logger import get_module_logger
|
||||
from ...person_info.relationship_manager import relationship_manager
|
||||
|
||||
from ....individuality.individuality import Individuality
|
||||
from src.heart_flow.heartflow import heartflow
|
||||
|
||||
logger = get_module_logger("prompt")
|
||||
@@ -27,8 +24,9 @@ class PromptBuilder:
|
||||
|
||||
current_mind_info = heartflow.get_subheartflow(stream_id).current_mind
|
||||
|
||||
# 开始构建prompt
|
||||
|
||||
individuality = Individuality.get_instance()
|
||||
prompt_personality = individuality.get_prompt(type = "personality",x_person = 2,level = 1)
|
||||
prompt_identity = individuality.get_prompt(type = "identity",x_person = 2,level = 1)
|
||||
# 关系
|
||||
who_chat_in_group = [(chat_stream.user_info.platform,
|
||||
chat_stream.user_info.user_id,
|
||||
@@ -90,20 +88,6 @@ class PromptBuilder:
|
||||
)
|
||||
keywords_reaction_prompt += rule.get("reaction", "") + "，"
|
||||
|
||||
# 人格选择
|
||||
personality = global_config.PROMPT_PERSONALITY
|
||||
probability_1 = global_config.PERSONALITY_1
|
||||
probability_2 = global_config.PERSONALITY_2
|
||||
|
||||
personality_choice = random.random()
|
||||
|
||||
if personality_choice < probability_1: # 第一种风格
|
||||
prompt_personality = personality[0]
|
||||
elif personality_choice < probability_1 + probability_2: # 第二种风格
|
||||
prompt_personality = personality[1]
|
||||
else: # 第三种人格
|
||||
prompt_personality = personality[2]
|
||||
|
||||
# 中文高手(新加的好玩功能)
|
||||
prompt_ger = ""
|
||||
if random.random() < 0.04:
|
||||
@@ -123,8 +107,8 @@ class PromptBuilder:
|
||||
{chat_talking_prompt}
|
||||
你刚刚脑子里在想：
|
||||
{current_mind_info}
|
||||
现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。{relation_prompt_all}\n
|
||||
你的网名叫{global_config.BOT_NICKNAME}，有人也叫你{"/".join(global_config.BOT_ALIAS_NAMES)}，{prompt_personality}。
|
||||
现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
|
||||
你的网名叫{global_config.BOT_NICKNAME}，有人也叫你{"/".join(global_config.BOT_ALIAS_NAMES)}，{prompt_personality} {prompt_identity}。
|
||||
你正在{chat_target_2},现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，
|
||||
尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。{prompt_ger}
|
||||
请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话
|
||||
@@ -133,73 +117,5 @@ class PromptBuilder:
|
||||
|
||||
return prompt
|
||||
|
||||
def _build_initiative_prompt_select(self, group_id, probability_1=0.8, probability_2=0.1):
|
||||
current_date = time.strftime("%Y-%m-%d", time.localtime())
|
||||
current_time = time.strftime("%H:%M:%S", time.localtime())
|
||||
bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
|
||||
prompt_date = f"""今天是{current_date}，现在是{current_time}，你今天的日程是：
|
||||
{bot_schedule.today_schedule}
|
||||
你现在正在{bot_schedule_now_activity}
|
||||
"""
|
||||
|
||||
chat_talking_prompt = ""
|
||||
if group_id:
|
||||
chat_talking_prompt = get_recent_group_detailed_plain_text(
|
||||
group_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
|
||||
)
|
||||
|
||||
chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
|
||||
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
|
||||
|
||||
# 获取主动发言的话题
|
||||
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]
|
||||
|
||||
# 激活prompt构建
|
||||
activate_prompt = ""
|
||||
activate_prompt = "以上是群里正在进行的聊天。"
|
||||
personality = global_config.PROMPT_PERSONALITY
|
||||
prompt_personality = ""
|
||||
personality_choice = random.random()
|
||||
if personality_choice < probability_1: # 第一种人格
|
||||
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}"""
|
||||
elif personality_choice < probability_1 + probability_2: # 第二种人格
|
||||
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}"""
|
||||
else: # 第三种人格
|
||||
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}"""
|
||||
|
||||
topics_str = ",".join(f'"{topics}"')
|
||||
prompt_for_select = (
|
||||
f"你现在想在群里发言，回忆了一下，想到几个话题，分别是{topics_str}，综合当前状态以及群内气氛，"
|
||||
f"请你在其中选择一个合适的话题，注意只需要输出话题，除了话题什么也不要输出(双引号也不要输出)"
|
||||
)
|
||||
|
||||
prompt_initiative_select = f"{prompt_date}\n{prompt_personality}\n{prompt_for_select}"
|
||||
prompt_regular = f"{prompt_date}\n{prompt_personality}"
|
||||
|
||||
return prompt_initiative_select, nodes_for_select, prompt_regular
|
||||
|
||||
def _build_initiative_prompt_check(self, selected_node, prompt_regular):
|
||||
memory = random.sample(selected_node["memory_items"], 3)
|
||||
memory = "\n".join(memory)
|
||||
prompt_for_check = (
|
||||
f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，"
|
||||
f"关于这个话题的记忆有\n{memory}\n，以这个作为主题发言合适吗？请在把握群里的聊天内容的基础上，"
|
||||
f"综合群内的氛围，如果认为应该发言请输出yes，否则输出no，请注意是决定是否需要发言，而不是编写回复内容，"
|
||||
f"除了yes和no不要输出任何回复内容。"
|
||||
)
|
||||
return prompt_for_check, memory
|
||||
|
||||
def _build_initiative_prompt(self, selected_node, prompt_regular, memory):
|
||||
prompt_for_initiative = (
|
||||
f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，"
|
||||
f"关于这个话题的记忆有\n{memory}\n，请在把握群里的聊天内容的基础上，综合群内的氛围，"
|
||||
f"以日常且口语化的口吻，简短且随意一点进行发言，不要说的太有条理，可以有个性。"
|
||||
f"记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情,@等)"
|
||||
)
|
||||
return prompt_for_initiative
|
||||
|
||||
|
||||
prompt_builder = PromptBuilder()
|
||||
|
||||
@@ -25,10 +25,17 @@ config_config = LogConfig(
|
||||
logger = get_module_logger("config", config=config_config)
|
||||
|
||||
#考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
|
||||
mai_version_main = "0.6.0"
|
||||
is_test = False
|
||||
mai_version_main = "0.6.1"
|
||||
mai_version_fix = ""
|
||||
if mai_version_fix:
|
||||
if is_test:
|
||||
mai_version = f"test-{mai_version_main}-{mai_version_fix}"
|
||||
else:
|
||||
mai_version = f"{mai_version_main}-{mai_version_fix}"
|
||||
else:
|
||||
if is_test:
|
||||
mai_version = f"test-{mai_version_main}"
|
||||
else:
|
||||
mai_version = mai_version_main
|
||||
|
||||
@@ -141,14 +148,22 @@ class BotConfig:
|
||||
ban_user_id = set()
|
||||
|
||||
# personality
|
||||
PROMPT_PERSONALITY = [
|
||||
"用一句话或几句话描述性格特点和其他特征",
|
||||
"例如，是一个热爱国家热爱党的新时代好青年",
|
||||
"例如，曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧",
|
||||
]
|
||||
PERSONALITY_1: float = 0.6 # 第一种人格概率
|
||||
PERSONALITY_2: float = 0.3 # 第二种人格概率
|
||||
PERSONALITY_3: float = 0.1 # 第三种人格概率
|
||||
personality_core = "用一句话或几句话描述人格的核心特点" # 建议20字以内，谁再写3000字小作文敲谁脑袋
|
||||
personality_sides: List[str] = field(default_factory=lambda: [
|
||||
"用一句话或几句话描述人格的一些侧面",
|
||||
"用一句话或几句话描述人格的一些侧面",
|
||||
"用一句话或几句话描述人格的一些侧面"
|
||||
])
|
||||
# identity
|
||||
identity_detail: List[str] = field(default_factory=lambda: [
|
||||
"身份特点",
|
||||
"身份特点",
|
||||
])
|
||||
height: int = 170 # 身高 单位厘米
|
||||
weight: int = 50 # 体重 单位千克
|
||||
age: int = 20 # 年龄 单位岁
|
||||
gender: str = "男" # 性别
|
||||
appearance: str = "用几句话描述外貌特征" # 外貌特征
|
||||
|
||||
# schedule
|
||||
ENABLE_SCHEDULE_GEN: bool = False # 是否启用日程生成
|
||||
@@ -162,6 +177,7 @@ class BotConfig:
|
||||
emoji_chance: float = 0.2 # 发送表情包的基础概率
|
||||
thinking_timeout: int = 120 # 思考时间
|
||||
max_response_length: int = 1024 # 最大回复长度
|
||||
message_buffer: bool = True # 消息缓冲器
|
||||
|
||||
ban_words = set()
|
||||
ban_msgs_regex = set()
|
||||
@@ -339,14 +355,19 @@ class BotConfig:
|
||||
|
||||
def personality(parent: dict):
|
||||
personality_config = parent["personality"]
|
||||
personality = personality_config.get("prompt_personality")
|
||||
if len(personality) >= 2:
|
||||
logger.info(f"载入自定义人格:{personality}")
|
||||
config.PROMPT_PERSONALITY = personality_config.get("prompt_personality", config.PROMPT_PERSONALITY)
|
||||
if config.INNER_VERSION in SpecifierSet(">=1.2.4"):
|
||||
config.personality_core = personality_config.get("personality_core", config.personality_core)
|
||||
config.personality_sides = personality_config.get("personality_sides", config.personality_sides)
|
||||
|
||||
config.PERSONALITY_1 = personality_config.get("personality_1_probability", config.PERSONALITY_1)
|
||||
config.PERSONALITY_2 = personality_config.get("personality_2_probability", config.PERSONALITY_2)
|
||||
config.PERSONALITY_3 = personality_config.get("personality_3_probability", config.PERSONALITY_3)
|
||||
def identity(parent: dict):
|
||||
identity_config = parent["identity"]
|
||||
if config.INNER_VERSION in SpecifierSet(">=1.2.4"):
|
||||
config.identity_detail = identity_config.get("identity_detail", config.identity_detail)
|
||||
config.height = identity_config.get("height", config.height)
|
||||
config.weight = identity_config.get("weight", config.weight)
|
||||
config.age = identity_config.get("age", config.age)
|
||||
config.gender = identity_config.get("gender", config.gender)
|
||||
config.appearance = identity_config.get("appearance", config.appearance)
|
||||
|
||||
def schedule(parent: dict):
|
||||
schedule_config = parent["schedule"]
|
||||
@@ -505,6 +526,8 @@ class BotConfig:
|
||||
|
||||
if config.INNER_VERSION in SpecifierSet(">=0.0.11"):
|
||||
config.max_response_length = msg_config.get("max_response_length", config.max_response_length)
|
||||
if config.INNER_VERSION in SpecifierSet(">=1.1.4"):
|
||||
config.message_buffer = msg_config.get("message_buffer", config.message_buffer)
|
||||
|
||||
def memory(parent: dict):
|
||||
memory_config = parent["memory"]
|
||||
@@ -601,6 +624,7 @@ class BotConfig:
|
||||
"bot": {"func": bot, "support": ">=0.0.0"},
|
||||
"groups": {"func": groups, "support": ">=0.0.0"},
|
||||
"personality": {"func": personality, "support": ">=0.0.0"},
|
||||
"identity": {"func": identity, "support": ">=1.2.4"},
|
||||
"schedule": {"func": schedule, "support": ">=0.0.11", "necessary": False},
|
||||
"message": {"func": message, "support": ">=0.0.0"},
|
||||
"willing": {"func": willing, "support": ">=0.0.9", "necessary": False},
|
||||
|
||||
@@ -29,7 +29,10 @@ class BaseMessageHandler:
|
||||
try:
|
||||
tasks.append(handler(message))
|
||||
except Exception as e:
|
||||
raise RuntimeError(str(e)) from e
|
||||
logger.error(f"消息处理出错: {str(e)}")
|
||||
logger.error(traceback.format_exc())
|
||||
# 不抛出异常，而是记录错误并继续处理其他消息
|
||||
continue
|
||||
if tasks:
|
||||
await asyncio.gather(*tasks, return_exceptions=True)
|
||||
|
||||
@@ -212,9 +215,8 @@ class MessageServer(BaseMessageHandler):
|
||||
try:
|
||||
async with session.post(url, json=data, headers={"Content-Type": "application/json"}) as response:
|
||||
return await response.json()
|
||||
except Exception:
|
||||
# logger.error(f"发送消息失败: {str(e)}")
|
||||
pass
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
|
||||
class BaseMessageAPI:
|
||||
|
||||
@@ -6,6 +6,7 @@ from dataclasses import dataclass
|
||||
from ..config.config import global_config
|
||||
from src.common.logger import get_module_logger, LogConfig, MOOD_STYLE_CONFIG
|
||||
from ..person_info.relationship_manager import relationship_manager
|
||||
from src.individuality.individuality import Individuality
|
||||
|
||||
mood_config = LogConfig(
|
||||
# 使用海马体专用样式
|
||||
@@ -17,8 +18,8 @@ logger = get_module_logger("mood_manager", config=mood_config)
|
||||
|
||||
@dataclass
|
||||
class MoodState:
|
||||
valence: float # 愉悦度 (-1 到 1)
|
||||
arousal: float # 唤醒度 (0 到 1)
|
||||
valence: float # 愉悦度 (-1.0 到 1.0)，-1表示极度负面，1表示极度正面
|
||||
arousal: float # 唤醒度 (0.0 到 1.0)，0表示完全平静，1表示极度兴奋
|
||||
text: str # 心情文本描述
|
||||
|
||||
|
||||
@@ -125,20 +126,48 @@ class MoodManager:
|
||||
time.sleep(update_interval)
|
||||
|
||||
def _apply_decay(self) -> None:
|
||||
"""应用情绪衰减"""
|
||||
"""应用情绪衰减，正向和负向情绪分开计算"""
|
||||
current_time = time.time()
|
||||
time_diff = current_time - self.last_update
|
||||
agreeableness_factor = 1
|
||||
agreeableness_bias = 0
|
||||
neuroticism_factor = 0.5
|
||||
|
||||
# Valence 向中性（0）回归
|
||||
valence_target = 0
|
||||
# 获取人格特质
|
||||
personality = Individuality.get_instance().personality
|
||||
if personality:
|
||||
# 神经质：影响情绪变化速度
|
||||
neuroticism_factor = 1 + (personality.neuroticism - 0.5) * 0.5
|
||||
agreeableness_factor = 1 + (personality.agreeableness - 0.5) * 0.5
|
||||
|
||||
# 宜人性：影响情绪基准线
|
||||
if personality.agreeableness < 0.2:
|
||||
agreeableness_bias = (personality.agreeableness - 0.2) * 2
|
||||
elif personality.agreeableness > 0.8:
|
||||
agreeableness_bias = (personality.agreeableness - 0.8) * 2
|
||||
else:
|
||||
agreeableness_bias = 0
|
||||
|
||||
# 分别计算正向和负向的衰减率
|
||||
if self.current_mood.valence >= 0:
|
||||
# 正向情绪衰减
|
||||
decay_rate_positive = self.decay_rate_valence * (1/agreeableness_factor)
|
||||
valence_target = 0 + agreeableness_bias
|
||||
self.current_mood.valence = valence_target + (self.current_mood.valence - valence_target) * math.exp(
|
||||
-self.decay_rate_valence * time_diff
|
||||
-decay_rate_positive * time_diff * neuroticism_factor
|
||||
)
|
||||
else:
|
||||
# 负向情绪衰减
|
||||
decay_rate_negative = self.decay_rate_valence * agreeableness_factor
|
||||
valence_target = 0 + agreeableness_bias
|
||||
self.current_mood.valence = valence_target + (self.current_mood.valence - valence_target) * math.exp(
|
||||
-decay_rate_negative * time_diff * neuroticism_factor
|
||||
)
|
||||
|
||||
# Arousal 向中性（0.5）回归
|
||||
arousal_target = 0.5
|
||||
self.current_mood.arousal = arousal_target + (self.current_mood.arousal - arousal_target) * math.exp(
|
||||
-self.decay_rate_arousal * time_diff
|
||||
-self.decay_rate_arousal * time_diff * neuroticism_factor
|
||||
)
|
||||
|
||||
# 确保值在合理范围内
|
||||
@@ -237,7 +266,7 @@ class MoodManager:
|
||||
old_arousal = self.current_mood.arousal
|
||||
old_mood = self.current_mood.text
|
||||
|
||||
valence_change *= relationship_manager.gain_coefficient[relationship_manager.positive_feedback_value]
|
||||
valence_change = relationship_manager.feedback_to_mood(valence_change)
|
||||
|
||||
# 应用情绪强度
|
||||
valence_change *= intensity
|
||||
|
||||
@@ -2,8 +2,14 @@ from src.common.logger import get_module_logger
|
||||
from ...common.database import db
|
||||
import copy
|
||||
import hashlib
|
||||
from typing import Any, Callable, Dict, TypeVar
|
||||
T = TypeVar('T') # 泛型类型
|
||||
from typing import Any, Callable, Dict
|
||||
import datetime
|
||||
import asyncio
|
||||
import numpy
|
||||
# import matplotlib.pyplot as plt
|
||||
# from pathlib import Path
|
||||
# import pandas as pd
|
||||
|
||||
|
||||
"""
|
||||
PersonInfoManager 类方法功能摘要：
|
||||
@@ -15,6 +21,7 @@ PersonInfoManager 类方法功能摘要：
|
||||
6. get_values - 批量获取字段值（任一字段无效则返回空字典）
|
||||
7. del_all_undefined_field - 清理全集合中未定义的字段
|
||||
8. get_specific_value_list - 根据指定条件，返回person_id,value字典
|
||||
9. personal_habit_deduction - 定时推断个人习惯
|
||||
"""
|
||||
|
||||
logger = get_module_logger("person_info")
|
||||
@@ -30,6 +37,8 @@ person_info_default = {
|
||||
# "impression" : None,
|
||||
# "gender" : Unkown,
|
||||
"konw_time" : 0,
|
||||
"msg_interval": 3000,
|
||||
"msg_interval_list": []
|
||||
} # 个人信息的各项与默认值在此定义，以下处理会自动创建/补全每一项
|
||||
|
||||
class PersonInfoManager:
|
||||
@@ -108,8 +117,9 @@ class PersonInfoManager:
|
||||
if document and field_name in document:
|
||||
return document[field_name]
|
||||
else:
|
||||
logger.debug(f"获取{person_id}的{field_name}失败，已返回默认值{person_info_default[field_name]}")
|
||||
return person_info_default[field_name]
|
||||
default_value = copy.deepcopy(person_info_default[field_name])
|
||||
logger.debug(f"获取{person_id}的{field_name}失败，已返回默认值{default_value}")
|
||||
return default_value
|
||||
|
||||
async def get_values(self, person_id: str, field_names: list) -> dict:
|
||||
"""获取指定person_id文档的多个字段值，若不存在该字段，则返回该字段的全局默认值"""
|
||||
@@ -133,7 +143,10 @@ class PersonInfoManager:
|
||||
|
||||
result = {}
|
||||
for field in field_names:
|
||||
result[field] = document.get(field, person_info_default[field]) if document else person_info_default[field]
|
||||
result[field] = copy.deepcopy(
|
||||
document.get(field, person_info_default[field])
|
||||
if document else person_info_default[field]
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
@@ -210,4 +223,46 @@ class PersonInfoManager:
|
||||
logger.error(f"数据库查询失败: {str(e)}", exc_info=True)
|
||||
return {}
|
||||
|
||||
async def personal_habit_deduction(self):
|
||||
"""启动个人信息推断，每天根据一定条件推断一次"""
|
||||
try:
|
||||
while(1):
|
||||
await asyncio.sleep(60)
|
||||
current_time = datetime.datetime.now()
|
||||
logger.info(f"个人信息推断启动: {current_time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
|
||||
# "msg_interval"推断
|
||||
msg_interval_lists = await self.get_specific_value_list(
|
||||
"msg_interval_list",
|
||||
lambda x: isinstance(x, list) and len(x) >= 100
|
||||
)
|
||||
for person_id, msg_interval_list_ in msg_interval_lists.items():
|
||||
try:
|
||||
time_interval = []
|
||||
for t1, t2 in zip(msg_interval_list_, msg_interval_list_[1:]):
|
||||
delta = t2 - t1
|
||||
if delta < 8000 and delta > 0: # 小于8秒
|
||||
time_interval.append(delta)
|
||||
|
||||
if len(time_interval) > 30:
|
||||
# 移除matplotlib相关的绘图功能
|
||||
|
||||
filtered_intervals = [t for t in time_interval if t >= 500]
|
||||
if len(filtered_intervals) > 25:
|
||||
msg_interval = int(round(numpy.percentile(filtered_intervals, 80)))
|
||||
await self.update_one_field(person_id, "msg_interval", msg_interval)
|
||||
logger.debug(f"用户{person_id}的msg_interval已经被更新为{msg_interval}")
|
||||
except Exception as e:
|
||||
logger.debug(f"处理用户{person_id}msg_interval推断时出错: {str(e)}")
|
||||
continue
|
||||
|
||||
# 其他...
|
||||
|
||||
logger.info(f"个人信息推断结束: {current_time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
await asyncio.sleep(86400)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"个人信息推断运行时出错: {str(e)}")
|
||||
logger.exception("详细错误信息：")
|
||||
|
||||
person_info_manager = PersonInfoManager()
|
||||
@@ -64,6 +64,14 @@ class RelationshipManager:
|
||||
logger.info(f"当前relationship增益系数：{mood_gain:.3f}")
|
||||
return value
|
||||
|
||||
def feedback_to_mood(self, mood_value):
|
||||
"""对情绪的反馈"""
|
||||
coefficient = self.gain_coefficient[abs(self.positive_feedback_value)]
|
||||
if (mood_value > 0 and self.positive_feedback_value > 0
|
||||
or mood_value < 0 and self.positive_feedback_value < 0):
|
||||
return mood_value*coefficient
|
||||
else:
|
||||
return mood_value/coefficient
|
||||
|
||||
async def calculate_update_relationship_value(self, chat_stream: ChatStream, label: str, stance: str) -> None:
|
||||
"""计算并变更关系值
|
||||
|
||||
@@ -1,195 +0,0 @@
|
||||
"""
|
||||
The definition of artificial personality in this paper follows the dispositional para-digm and adapts a definition of
|
||||
personality developed for humans [17]:
|
||||
Personality for a human is the "whole and organisation of relatively stable tendencies and patterns of experience and
|
||||
behaviour within one person (distinguishing it from other persons)". This definition is modified for artificial
|
||||
personality:
|
||||
Artificial personality describes the relatively stable tendencies and patterns of behav-iour of an AI-based machine that
|
||||
can be designed by developers and designers via different modalities, such as language, creating the impression
|
||||
of individuality of a humanized social agent when users interact with the machine."""
|
||||
|
||||
from typing import Dict, List
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
from dotenv import load_dotenv
|
||||
import sys
|
||||
|
||||
"""
|
||||
第一种方案：基于情景评估的人格测定
|
||||
"""
|
||||
current_dir = Path(__file__).resolve().parent
|
||||
project_root = current_dir.parent.parent.parent
|
||||
env_path = project_root / ".env"
|
||||
|
||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
||||
sys.path.append(root_path)
|
||||
|
||||
from src.plugins.personality.scene import get_scene_by_factor, PERSONALITY_SCENES # noqa: E402
|
||||
from src.plugins.personality.questionnaire import FACTOR_DESCRIPTIONS # noqa: E402
|
||||
from src.plugins.personality.offline_llm import LLMModel # noqa: E402
|
||||
|
||||
# 加载环境变量
|
||||
if env_path.exists():
|
||||
print(f"从 {env_path} 加载环境变量")
|
||||
load_dotenv(env_path)
|
||||
else:
|
||||
print(f"未找到环境变量文件: {env_path}")
|
||||
print("将使用默认配置")
|
||||
|
||||
|
||||
class PersonalityEvaluator_direct:
|
||||
def __init__(self):
|
||||
self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
|
||||
self.scenarios = []
|
||||
|
||||
# 为每个人格特质获取对应的场景
|
||||
for trait in PERSONALITY_SCENES:
|
||||
scenes = get_scene_by_factor(trait)
|
||||
if not scenes:
|
||||
continue
|
||||
|
||||
# 从每个维度选择3个场景
|
||||
import random
|
||||
|
||||
scene_keys = list(scenes.keys())
|
||||
selected_scenes = random.sample(scene_keys, min(3, len(scene_keys)))
|
||||
|
||||
for scene_key in selected_scenes:
|
||||
scene = scenes[scene_key]
|
||||
|
||||
# 为每个场景添加评估维度
|
||||
# 主维度是当前特质，次维度随机选择一个其他特质
|
||||
other_traits = [t for t in PERSONALITY_SCENES if t != trait]
|
||||
secondary_trait = random.choice(other_traits)
|
||||
|
||||
self.scenarios.append(
|
||||
{"场景": scene["scenario"], "评估维度": [trait, secondary_trait], "场景编号": scene_key}
|
||||
)
|
||||
|
||||
self.llm = LLMModel()
|
||||
|
||||
def evaluate_response(self, scenario: str, response: str, dimensions: List[str]) -> Dict[str, float]:
|
||||
"""
|
||||
使用 DeepSeek AI 评估用户对特定场景的反应
|
||||
"""
|
||||
# 构建维度描述
|
||||
dimension_descriptions = []
|
||||
for dim in dimensions:
|
||||
desc = FACTOR_DESCRIPTIONS.get(dim, "")
|
||||
if desc:
|
||||
dimension_descriptions.append(f"- {dim}：{desc}")
|
||||
|
||||
dimensions_text = "\n".join(dimension_descriptions)
|
||||
|
||||
prompt = f"""请根据以下场景和用户描述，评估用户在大五人格模型中的相关维度得分（1-6分）。
|
||||
|
||||
场景描述：
|
||||
{scenario}
|
||||
|
||||
用户回应：
|
||||
{response}
|
||||
|
||||
需要评估的维度说明：
|
||||
{dimensions_text}
|
||||
|
||||
请按照以下格式输出评估结果（仅输出JSON格式）：
|
||||
{{
|
||||
"{dimensions[0]}": 分数,
|
||||
"{dimensions[1]}": 分数
|
||||
}}
|
||||
|
||||
评分标准：
|
||||
1 = 非常不符合该维度特征
|
||||
2 = 比较不符合该维度特征
|
||||
3 = 有点不符合该维度特征
|
||||
4 = 有点符合该维度特征
|
||||
5 = 比较符合该维度特征
|
||||
6 = 非常符合该维度特征
|
||||
|
||||
请根据用户的回应，结合场景和维度说明进行评分。确保分数在1-6之间，并给出合理的评估。"""
|
||||
|
||||
try:
|
||||
ai_response, _ = self.llm.generate_response(prompt)
|
||||
# 尝试从AI响应中提取JSON部分
|
||||
start_idx = ai_response.find("{")
|
||||
end_idx = ai_response.rfind("}") + 1
|
||||
if start_idx != -1 and end_idx != 0:
|
||||
json_str = ai_response[start_idx:end_idx]
|
||||
scores = json.loads(json_str)
|
||||
# 确保所有分数在1-6之间
|
||||
return {k: max(1, min(6, float(v))) for k, v in scores.items()}
|
||||
else:
|
||||
print("AI响应格式不正确，使用默认评分")
|
||||
return {dim: 3.5 for dim in dimensions}
|
||||
except Exception as e:
|
||||
print(f"评估过程出错：{str(e)}")
|
||||
return {dim: 3.5 for dim in dimensions}
|
||||
|
||||
|
||||
def main():
|
||||
print("欢迎使用人格形象创建程序！")
|
||||
print("接下来，您将面对一系列场景（共15个）。请根据您想要创建的角色形象，描述在该场景下可能的反应。")
|
||||
print("每个场景都会评估不同的人格维度，最终得出完整的人格特征评估。")
|
||||
print("评分标准：1=非常不符合，2=比较不符合，3=有点不符合，4=有点符合，5=比较符合，6=非常符合")
|
||||
print("\n准备好了吗？按回车键开始...")
|
||||
input()
|
||||
|
||||
evaluator = PersonalityEvaluator_direct()
|
||||
final_scores = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
|
||||
dimension_counts = {trait: 0 for trait in final_scores.keys()}
|
||||
|
||||
for i, scenario_data in enumerate(evaluator.scenarios, 1):
|
||||
print(f"\n场景 {i}/{len(evaluator.scenarios)} - {scenario_data['场景编号']}:")
|
||||
print("-" * 50)
|
||||
print(scenario_data["场景"])
|
||||
print("\n请描述您的角色在这种情况下会如何反应：")
|
||||
response = input().strip()
|
||||
|
||||
if not response:
|
||||
print("反应描述不能为空！")
|
||||
continue
|
||||
|
||||
print("\n正在评估您的描述...")
|
||||
scores = evaluator.evaluate_response(scenario_data["场景"], response, scenario_data["评估维度"])
|
||||
|
||||
# 更新最终分数
|
||||
for dimension, score in scores.items():
|
||||
final_scores[dimension] += score
|
||||
dimension_counts[dimension] += 1
|
||||
|
||||
print("\n当前评估结果：")
|
||||
print("-" * 30)
|
||||
for dimension, score in scores.items():
|
||||
print(f"{dimension}: {score}/6")
|
||||
|
||||
if i < len(evaluator.scenarios):
|
||||
print("\n按回车键继续下一个场景...")
|
||||
input()
|
||||
|
||||
# 计算平均分
|
||||
for dimension in final_scores:
|
||||
if dimension_counts[dimension] > 0:
|
||||
final_scores[dimension] = round(final_scores[dimension] / dimension_counts[dimension], 2)
|
||||
|
||||
print("\n最终人格特征评估结果：")
|
||||
print("-" * 30)
|
||||
for trait, score in final_scores.items():
|
||||
print(f"{trait}: {score}/6")
|
||||
print(f"测试场景数：{dimension_counts[trait]}")
|
||||
|
||||
# 保存结果
|
||||
result = {"final_scores": final_scores, "dimension_counts": dimension_counts, "scenarios": evaluator.scenarios}
|
||||
|
||||
# 确保目录存在
|
||||
os.makedirs("results", exist_ok=True)
|
||||
|
||||
# 保存到文件
|
||||
with open("results/personality_result.json", "w", encoding="utf-8") as f:
|
||||
json.dump(result, f, ensure_ascii=False, indent=2)
|
||||
|
||||
print("\n结果已保存到 results/personality_result.json")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,261 +0,0 @@
|
||||
from typing import Dict
|
||||
|
||||
PERSONALITY_SCENES = {
|
||||
"外向性": {
|
||||
"场景1": {
|
||||
"scenario": """你刚刚搬到一个新的城市工作。今天是你入职的第一天，在公司的电梯里，一位同事微笑着和你打招呼：
|
||||
|
||||
同事：「嗨！你是新来的同事吧？我是市场部的小林。」
|
||||
|
||||
同事看起来很友善，还主动介绍说：「待会午饭时间，我们部门有几个人准备一起去楼下新开的餐厅，你要一起来吗？可以认识一下其他同事。」""",
|
||||
"explanation": "这个场景通过职场社交情境，观察个体对于新环境、新社交圈的态度和反应倾向。",
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": """在大学班级群里，班长发起了一个组织班级联谊活动的投票：
|
||||
|
||||
班长：「大家好！下周末我们准备举办一次班级联谊活动，地点在学校附近的KTV。想请大家报名参加，也欢迎大家邀请其他班级的同学！」
|
||||
|
||||
已经有几个同学在群里积极响应，有人@你问你要不要一起参加。""",
|
||||
"explanation": "通过班级活动场景，观察个体对群体社交活动的参与意愿。",
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": """你在社交平台上发布了一条动态，收到了很多陌生网友的评论和私信：
|
||||
|
||||
网友A：「你说的这个观点很有意思！想和你多交流一下。」
|
||||
|
||||
网友B：「我也对这个话题很感兴趣，要不要建个群一起讨论？」""",
|
||||
"explanation": "通过网络社交场景，观察个体对线上社交的态度。",
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": """你暗恋的对象今天主动来找你：
|
||||
|
||||
对方：「那个...我最近在准备一个演讲比赛，听说你口才很好。能不能请你帮我看看演讲稿，顺便给我一些建议？"""
|
||||
"""如果你有时间的话，可以一起吃个饭聊聊。」""",
|
||||
"explanation": "通过恋爱情境，观察个体在面对心仪对象时的社交表现。",
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": """在一次线下读书会上，主持人突然点名让你分享读后感：
|
||||
|
||||
主持人：「听说你对这本书很有见解，能不能和大家分享一下你的想法？」
|
||||
|
||||
现场有二十多个陌生的读书爱好者，都期待地看着你。""",
|
||||
"explanation": "通过即兴发言场景，观察个体的社交表现欲和公众表达能力。",
|
||||
},
|
||||
},
|
||||
"神经质": {
|
||||
"场景1": {
|
||||
"scenario": """你正在准备一个重要的项目演示，这关系到你的晋升机会。"""
|
||||
"""就在演示前30分钟，你收到了主管发来的消息：
|
||||
|
||||
主管：「临时有个变动，CEO也会来听你的演示。他对这个项目特别感兴趣。」
|
||||
|
||||
正当你准备回复时，主管又发来一条：「对了，能不能把演示时间压缩到15分钟？CEO下午还有其他安排。你之前准备的是30分钟的版本对吧？」""",
|
||||
"explanation": "这个场景通过突发的压力情境，观察个体在面对计划外变化时的情绪反应和调节能力。",
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": """期末考试前一天晚上，你收到了好朋友发来的消息：
|
||||
|
||||
好朋友：「不好意思这么晚打扰你...我看你平时成绩很好，能不能帮我解答几个问题？我真的很担心明天的考试。」
|
||||
|
||||
你看了看时间，已经是晚上11点，而你原本计划的复习还没完成。""",
|
||||
"explanation": "通过考试压力场景，观察个体在时间紧张时的情绪管理。",
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": """你在社交媒体上发表的一个观点引发了争议，有不少人开始批评你：
|
||||
|
||||
网友A：「这种观点也好意思说出来，真是无知。」
|
||||
|
||||
网友B：「建议楼主先去补补课再来发言。」
|
||||
|
||||
评论区里的负面评论越来越多，还有人开始人身攻击。""",
|
||||
"explanation": "通过网络争议场景，观察个体面对批评时的心理承受能力。",
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": """你和恋人约好今天一起看电影，但在约定时间前半小时，对方发来消息：
|
||||
|
||||
恋人：「对不起，我临时有点事，可能要迟到一会儿。」
|
||||
|
||||
二十分钟后，对方又发来消息：「可能要再等等，抱歉！」
|
||||
|
||||
电影快要开始了，但对方还是没有出现。""",
|
||||
"explanation": "通过恋爱情境，观察个体对不确定性的忍耐程度。",
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": """在一次重要的小组展示中，你的组员在演示途中突然卡壳了：
|
||||
|
||||
组员小声对你说：「我忘词了，接下来的部分是什么来着...」
|
||||
|
||||
台下的老师和同学都在等待，气氛有些尴尬。""",
|
||||
"explanation": "通过公开场合的突发状况，观察个体的应急反应和压力处理能力。",
|
||||
},
|
||||
},
|
||||
"严谨性": {
|
||||
"场景1": {
|
||||
"scenario": """你是团队的项目负责人，刚刚接手了一个为期两个月的重要项目。在第一次团队会议上：
|
||||
|
||||
小王：「老大，我觉得两个月时间很充裕，我们先做着看吧，遇到问题再解决。」
|
||||
|
||||
小张：「要不要先列个时间表？不过感觉太详细的计划也没必要，点到为止就行。」
|
||||
|
||||
小李：「客户那边说如果能提前完成有奖励，我觉得我们可以先做快一点的部分。」""",
|
||||
"explanation": "这个场景通过项目管理情境，体现个体在工作方法、计划性和责任心方面的特征。",
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": """期末小组作业，组长让大家分工完成一份研究报告。在截止日期前三天：
|
||||
|
||||
组员A：「我的部分大概写完了，感觉还行。」
|
||||
|
||||
组员B：「我这边可能还要一天才能完成，最近太忙了。」
|
||||
|
||||
组员C发来一份没有任何引用出处、可能存在抄袭的内容：「我写完了，你们看看怎么样？」""",
|
||||
"explanation": "通过学习场景，观察个体对学术规范和质量要求的重视程度。",
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": """你在一个兴趣小组的群聊中，大家正在讨论举办一次线下活动：
|
||||
|
||||
成员A：「到时候见面就知道具体怎么玩了！」
|
||||
|
||||
成员B：「对啊，随意一点挺好的。」
|
||||
|
||||
成员C：「人来了自然就热闹了。」""",
|
||||
"explanation": "通过活动组织场景，观察个体对活动计划的态度。",
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": """你和恋人计划一起去旅游，对方说：
|
||||
|
||||
恋人：「我们就随心而行吧！订个目的地，其他的到了再说，这样更有意思。」
|
||||
|
||||
距离出发还有一周时间，但机票、住宿和具体行程都还没有确定。""",
|
||||
"explanation": "通过旅行规划场景，观察个体的计划性和对不确定性的接受程度。",
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": """在一个重要的团队项目中，你发现一个同事的工作存在明显错误：
|
||||
|
||||
同事：「差不多就行了，反正领导也看不出来。」
|
||||
|
||||
这个错误可能不会立即造成问题，但长期来看可能会影响项目质量。""",
|
||||
"explanation": "通过工作质量场景，观察个体对细节和标准的坚持程度。",
|
||||
},
|
||||
},
|
||||
"开放性": {
|
||||
"场景1": {
|
||||
"scenario": """周末下午，你的好友小美兴致勃勃地给你打电话：
|
||||
|
||||
小美：「我刚发现一个特别有意思的沉浸式艺术展！不是传统那种挂画的展览，而是把整个空间都变成了艺术品。"""
|
||||
"""观众要穿特制的服装，还要带上VR眼镜，好像还有AI实时互动！」
|
||||
|
||||
小美继续说：「虽然票价不便宜，但听说体验很独特。网上评价两极分化，有人说是前所未有的艺术革新，也有人说是哗众取宠。"""
|
||||
"""要不要周末一起去体验一下？」""",
|
||||
"explanation": "这个场景通过新型艺术体验，反映个体对创新事物的接受程度和尝试意愿。",
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": """在一节创意写作课上，老师提出了一个特别的作业：
|
||||
|
||||
老师：「下周的作业是用AI写作工具协助创作一篇小说。你们可以自由探索如何与AI合作，打破传统写作方式。」
|
||||
|
||||
班上随即展开了激烈讨论，有人认为这是对创作的亵渎，也有人对这种新形式感到兴奋。""",
|
||||
"explanation": "通过新技术应用场景，观察个体对创新学习方式的态度。",
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": """在社交媒体上，你看到一个朋友分享了一种新的生活方式：
|
||||
|
||||
「最近我在尝试'数字游牧'生活，就是一边远程工作一边环游世界。"""
|
||||
"""没有固定住所，住青旅或短租，认识来自世界各地的朋友。虽然有时会很不稳定，但这种自由的生活方式真的很棒！」
|
||||
|
||||
评论区里争论不断，有人向往这种生活，也有人觉得太冒险。""",
|
||||
"explanation": "通过另类生活方式，观察个体对非传统选择的态度。",
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": """你的恋人突然提出了一个想法：
|
||||
|
||||
恋人：「我们要不要尝试一下开放式关系？就是在保持彼此关系的同时，也允许和其他人发展感情。现在国外很多年轻人都这样。」
|
||||
|
||||
这个提议让你感到意外，你之前从未考虑过这种可能性。""",
|
||||
"explanation": "通过感情观念场景，观察个体对非传统关系模式的接受度。",
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": """在一次朋友聚会上，大家正在讨论未来职业规划：
|
||||
|
||||
朋友A：「我准备辞职去做自媒体，专门介绍一些小众的文化和艺术。」
|
||||
|
||||
朋友B：「我想去学习生物科技，准备转行做人造肉研发。」
|
||||
|
||||
朋友C：「我在考虑加入一个区块链创业项目，虽然风险很大。」""",
|
||||
"explanation": "通过职业选择场景，观察个体对新兴领域的探索意愿。",
|
||||
},
|
||||
},
|
||||
"宜人性": {
|
||||
"场景1": {
|
||||
"scenario": """在回家的公交车上，你遇到这样一幕：
|
||||
|
||||
一位老奶奶颤颤巍巍地上了车，车上座位已经坐满了。她站在你旁边，看起来很疲惫。这时你听到前排两个年轻人的对话：
|
||||
|
||||
年轻人A：「那个老太太好像站不稳，看起来挺累的。」
|
||||
|
||||
年轻人B：「现在的老年人真是...我看她包里还有菜，肯定是去菜市场买完菜回来的，这么多人都不知道叫子女开车接送。」
|
||||
|
||||
就在这时，老奶奶一个趔趄，差点摔倒。她扶住了扶手，但包里的东西洒了一些出来。""",
|
||||
"explanation": "这个场景通过公共场合的助人情境，体现个体的同理心和对他人需求的关注程度。",
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": """在班级群里，有同学发起为生病住院的同学捐款：
|
||||
|
||||
同学A：「大家好，小林最近得了重病住院，医药费很贵，家里负担很重。我们要不要一起帮帮他？」
|
||||
|
||||
同学B：「我觉得这是他家里的事，我们不方便参与吧。」
|
||||
|
||||
同学C：「但是都是同学一场，帮帮忙也是应该的。」""",
|
||||
"explanation": "通过同学互助场景，观察个体的助人意愿和同理心。",
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": """在一个网络讨论组里，有人发布了求助信息：
|
||||
|
||||
求助者：「最近心情很低落，感觉生活很压抑，不知道该怎么办...」
|
||||
|
||||
评论区里已经有一些回复：
|
||||
「生活本来就是这样，想开点！」
|
||||
「你这样子太消极了，要积极面对。」
|
||||
「谁还没点烦心事啊，过段时间就好了。」""",
|
||||
"explanation": "通过网络互助场景，观察个体的共情能力和安慰方式。",
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": """你的恋人向你倾诉工作压力：
|
||||
|
||||
恋人：「最近工作真的好累，感觉快坚持不下去了...」
|
||||
|
||||
但今天你也遇到了很多烦心事，心情也不太好。""",
|
||||
"explanation": "通过感情关系场景，观察个体在自身状态不佳时的关怀能力。",
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": """在一次团队项目中，新来的同事小王因为经验不足，造成了一个严重的错误。在部门会议上：
|
||||
|
||||
主管：「这个错误造成了很大的损失，是谁负责的这部分？」
|
||||
|
||||
小王看起来很紧张，欲言又止。你知道是他造成的错误，同时你也是这个项目的共同负责人。""",
|
||||
"explanation": "通过职场情境，观察个体在面对他人过错时的态度和处理方式。",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def get_scene_by_factor(factor: str) -> Dict:
|
||||
"""
|
||||
根据人格因子获取对应的情景测试
|
||||
|
||||
Args:
|
||||
factor (str): 人格因子名称
|
||||
|
||||
Returns:
|
||||
Dict: 包含情景描述的字典
|
||||
"""
|
||||
return PERSONALITY_SCENES.get(factor, None)
|
||||
|
||||
|
||||
def get_all_scenes() -> Dict:
|
||||
"""
|
||||
获取所有情景测试
|
||||
|
||||
Returns:
|
||||
Dict: 所有情景测试的字典
|
||||
"""
|
||||
return PERSONALITY_SCENES
|
||||
142
src/plugins/personality_s/questionnaire.py
Normal file
142
src/plugins/personality_s/questionnaire.py
Normal file
@@ -0,0 +1,142 @@
|
||||
# 人格测试问卷题目
|
||||
# 王孟成, 戴晓阳, & 姚树桥. (2011).
|
||||
# 中国大五人格问卷的初步编制Ⅲ:简式版的制定及信效度检验. 中国临床心理学杂志, 19(04), Article 04.
|
||||
|
||||
# 王孟成, 戴晓阳, & 姚树桥. (2010).
|
||||
# 中国大五人格问卷的初步编制Ⅰ:理论框架与信度分析. 中国临床心理学杂志, 18(05), Article 05.
|
||||
|
||||
PERSONALITY_QUESTIONS = [
|
||||
# 神经质维度 (F1)
|
||||
{"id": 1, "content": "我常担心有什么不好的事情要发生", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 2, "content": "我常感到害怕", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 3, "content": "有时我觉得自己一无是处", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 4, "content": "我很少感到忧郁或沮丧", "factor": "神经质", "reverse_scoring": True},
|
||||
{"id": 5, "content": "别人一句漫不经心的话，我常会联系在自己身上", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 6, "content": "在面对压力时，我有种快要崩溃的感觉", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 7, "content": "我常担忧一些无关紧要的事情", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 8, "content": "我常常感到内心不踏实", "factor": "神经质", "reverse_scoring": False},
|
||||
# 严谨性维度 (F2)
|
||||
{"id": 9, "content": "在工作上，我常只求能应付过去便可", "factor": "严谨性", "reverse_scoring": True},
|
||||
{"id": 10, "content": "一旦确定了目标，我会坚持努力地实现它", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 11, "content": "我常常是仔细考虑之后才做出决定", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 12, "content": "别人认为我是个慎重的人", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 13, "content": "做事讲究逻辑和条理是我的一个特点", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 14, "content": "我喜欢一开头就把事情计划好", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 15, "content": "我工作或学习很勤奋", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 16, "content": "我是个倾尽全力做事的人", "factor": "严谨性", "reverse_scoring": False},
|
||||
# 宜人性维度 (F3)
|
||||
{
|
||||
"id": 17,
|
||||
"content": "尽管人类社会存在着一些阴暗的东西（如战争、罪恶、欺诈），我仍然相信人性总的来说是善良的",
|
||||
"factor": "宜人性",
|
||||
"reverse_scoring": False,
|
||||
},
|
||||
{"id": 18, "content": "我觉得大部分人基本上是心怀善意的", "factor": "宜人性", "reverse_scoring": False},
|
||||
{"id": 19, "content": "虽然社会上有骗子，但我觉得大部分人还是可信的", "factor": "宜人性", "reverse_scoring": False},
|
||||
{"id": 20, "content": "我不太关心别人是否受到不公正的待遇", "factor": "宜人性", "reverse_scoring": True},
|
||||
{"id": 21, "content": "我时常觉得别人的痛苦与我无关", "factor": "宜人性", "reverse_scoring": True},
|
||||
{"id": 22, "content": "我常为那些遭遇不幸的人感到难过", "factor": "宜人性", "reverse_scoring": False},
|
||||
{"id": 23, "content": "我是那种只照顾好自己，不替别人担忧的人", "factor": "宜人性", "reverse_scoring": True},
|
||||
{"id": 24, "content": "当别人向我诉说不幸时，我常感到难过", "factor": "宜人性", "reverse_scoring": False},
|
||||
# 开放性维度 (F4)
|
||||
{"id": 25, "content": "我的想象力相当丰富", "factor": "开放性", "reverse_scoring": False},
|
||||
{"id": 26, "content": "我头脑中经常充满生动的画面", "factor": "开放性", "reverse_scoring": False},
|
||||
{"id": 27, "content": "我对许多事情有着很强的好奇心", "factor": "开放性", "reverse_scoring": False},
|
||||
{"id": 28, "content": "我喜欢冒险", "factor": "开放性", "reverse_scoring": False},
|
||||
{"id": 29, "content": "我是个勇于冒险，突破常规的人", "factor": "开放性", "reverse_scoring": False},
|
||||
{"id": 30, "content": "我身上具有别人没有的冒险精神", "factor": "开放性", "reverse_scoring": False},
|
||||
{
|
||||
"id": 31,
|
||||
"content": "我渴望学习一些新东西，即使它们与我的日常生活无关",
|
||||
"factor": "开放性",
|
||||
"reverse_scoring": False,
|
||||
},
|
||||
{
|
||||
"id": 32,
|
||||
"content": "我很愿意也很容易接受那些新事物、新观点、新想法",
|
||||
"factor": "开放性",
|
||||
"reverse_scoring": False,
|
||||
},
|
||||
# 外向性维度 (F5)
|
||||
{"id": 33, "content": "我喜欢参加社交与娱乐聚会", "factor": "外向性", "reverse_scoring": False},
|
||||
{"id": 34, "content": "我对人多的聚会感到乏味", "factor": "外向性", "reverse_scoring": True},
|
||||
{"id": 35, "content": "我尽量避免参加人多的聚会和嘈杂的环境", "factor": "外向性", "reverse_scoring": True},
|
||||
{"id": 36, "content": "在热闹的聚会上，我常常表现主动并尽情玩耍", "factor": "外向性", "reverse_scoring": False},
|
||||
{"id": 37, "content": "有我在的场合一般不会冷场", "factor": "外向性", "reverse_scoring": False},
|
||||
{"id": 38, "content": "我希望成为领导者而不是被领导者", "factor": "外向性", "reverse_scoring": False},
|
||||
{"id": 39, "content": "在一个团体中，我希望处于领导地位", "factor": "外向性", "reverse_scoring": False},
|
||||
{"id": 40, "content": "别人多认为我是一个热情和友好的人", "factor": "外向性", "reverse_scoring": False},
|
||||
]
|
||||
|
||||
# 因子维度说明
|
||||
FACTOR_DESCRIPTIONS = {
|
||||
"外向性": {
|
||||
"description": "反映个体神经系统的强弱和动力特征。外向性主要表现为个体在人际交往和社交活动中的倾向性，"
|
||||
"包括对社交活动的兴趣、"
|
||||
"对人群的态度、社交互动中的主动程度以及在群体中的影响力。高分者倾向于积极参与社交活动，乐于与人交往，善于表达自我，"
|
||||
"并往往在群体中发挥领导作用；低分者则倾向于独处，不喜欢热闹的社交场合，表现出内向、安静的特征。",
|
||||
"trait_words": ["热情", "活力", "社交", "主动"],
|
||||
"subfactors": {
|
||||
"合群性": "个体愿意与他人聚在一起，即接近人群的倾向；高分表现乐群、好交际，低分表现封闭、独处",
|
||||
"热情": "个体对待别人时所表现出的态度；高分表现热情好客，低分表现冷淡",
|
||||
"支配性": "个体喜欢指使、操纵他人，倾向于领导别人的特点；高分表现好强、发号施令，低分表现顺从、低调",
|
||||
"活跃": "个体精力充沛，活跃、主动性等特点；高分表现活跃，低分表现安静",
|
||||
},
|
||||
},
|
||||
"神经质": {
|
||||
"description": "反映个体情绪的状态和体验内心苦恼的倾向性。这个维度主要关注个体在面对压力、"
|
||||
"挫折和日常生活挑战时的情绪稳定性和适应能力。它包含了对焦虑、抑郁、愤怒等负面情绪的敏感程度，"
|
||||
"以及个体对这些情绪的调节和控制能力。高分者容易体验负面情绪，对压力较为敏感，情绪波动较大；"
|
||||
"低分者则表现出较强的情绪稳定性，能够较好地应对压力和挫折。",
|
||||
"trait_words": ["稳定", "沉着", "从容", "坚韧"],
|
||||
"subfactors": {
|
||||
"焦虑": "个体体验焦虑感的个体差异；高分表现坐立不安，低分表现平静",
|
||||
"抑郁": "个体体验抑郁情感的个体差异；高分表现郁郁寡欢，低分表现平静",
|
||||
"敏感多疑": "个体常常关注自己的内心活动，行为和过于意识人对自己的看法、评价；高分表现敏感多疑，"
|
||||
"低分表现淡定、自信",
|
||||
"脆弱性": "个体在危机或困难面前无力、脆弱的特点；高分表现无能、易受伤、逃避，低分表现坚强",
|
||||
"愤怒-敌意": "个体准备体验愤怒，及相关情绪的状态；高分表现暴躁易怒，低分表现平静",
|
||||
},
|
||||
},
|
||||
"严谨性": {
|
||||
"description": "反映个体在目标导向行为上的组织、坚持和动机特征。这个维度体现了个体在工作、"
|
||||
"学习等目标性活动中的自我约束和行为管理能力。它涉及到个体的责任感、自律性、计划性、条理性以及完成任务的态度。"
|
||||
"高分者往往表现出强烈的责任心、良好的组织能力、谨慎的决策风格和持续的努力精神；低分者则可能表现出随意性强、"
|
||||
"缺乏规划、做事马虎或易放弃的特点。",
|
||||
"trait_words": ["负责", "自律", "条理", "勤奋"],
|
||||
"subfactors": {
|
||||
"责任心": "个体对待任务和他人认真负责，以及对自己承诺的信守；高分表现有责任心、负责任，"
|
||||
"低分表现推卸责任、逃避处罚",
|
||||
"自我控制": "个体约束自己的能力，及自始至终的坚持性；高分表现自制、有毅力，低分表现冲动、无毅力",
|
||||
"审慎性": "个体在采取具体行动前的心理状态；高分表现谨慎、小心，低分表现鲁莽、草率",
|
||||
"条理性": "个体处理事务和工作的秩序，条理和逻辑性；高分表现整洁、有秩序，低分表现混乱、遗漏",
|
||||
"勤奋": "个体工作和学习的努力程度及为达到目标而表现出的进取精神；高分表现勤奋、刻苦，低分表现懒散",
|
||||
},
|
||||
},
|
||||
"开放性": {
|
||||
"description": "反映个体对新异事物、新观念和新经验的接受程度，以及在思维和行为方面的创新倾向。"
|
||||
"这个维度体现了个体在认知和体验方面的广度、深度和灵活性。它包括对艺术的欣赏能力、对知识的求知欲、想象力的丰富程度，"
|
||||
"以及对冒险和创新的态度。高分者往往具有丰富的想象力、广泛的兴趣、开放的思维方式和创新的倾向；低分者则倾向于保守、"
|
||||
"传统，喜欢熟悉和常规的事物。",
|
||||
"trait_words": ["创新", "好奇", "艺术", "冒险"],
|
||||
"subfactors": {
|
||||
"幻想": "个体富于幻想和想象的水平；高分表现想象力丰富，低分表现想象力匮乏",
|
||||
"审美": "个体对于艺术和美的敏感与热爱程度；高分表现富有艺术气息，低分表现一般对艺术不敏感",
|
||||
"好奇心": "个体对未知事物的态度；高分表现兴趣广泛、好奇心浓，低分表现兴趣少、无好奇心",
|
||||
"冒险精神": "个体愿意尝试有风险活动的个体差异；高分表现好冒险，低分表现保守",
|
||||
"价值观念": "个体对新事物、新观念、怪异想法的态度；高分表现开放、坦然接受新事物，低分则相反",
|
||||
},
|
||||
},
|
||||
"宜人性": {
|
||||
"description": "反映个体在人际关系中的亲和倾向，体现了对他人的关心、同情和合作意愿。"
|
||||
"这个维度主要关注个体与他人互动时的态度和行为特征，包括对他人的信任程度、同理心水平、"
|
||||
"助人意愿以及在人际冲突中的处理方式。高分者通常表现出友善、富有同情心、乐于助人的特质，善于与他人建立和谐关系；"
|
||||
"低分者则可能表现出较少的人际关注，在社交互动中更注重自身利益，较少考虑他人感受。",
|
||||
"trait_words": ["友善", "同理", "信任", "合作"],
|
||||
"subfactors": {
|
||||
"信任": "个体对他人和/或他人言论的相信程度；高分表现信任他人，低分表现怀疑",
|
||||
"体贴": "个体对别人的兴趣和需要的关注程度；高分表现体贴、温存，低分表现冷漠、不在乎",
|
||||
"同情": "个体对处于不利地位的人或物的态度；高分表现富有同情心，低分表现冷漠",
|
||||
},
|
||||
},
|
||||
}
|
||||
@@ -62,9 +62,7 @@ class ScheduleGenerator:
|
||||
self.name = name
|
||||
self.behavior = behavior
|
||||
self.schedule_doing_update_interval = interval
|
||||
|
||||
for pers in personality:
|
||||
self.personality += pers + "\n"
|
||||
self.personality = personality
|
||||
|
||||
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"""
|
||||
|
||||
@@ -41,7 +41,7 @@ class KnowledgeLibrary:
|
||||
return f.read()
|
||||
|
||||
def split_content(self, content: str, max_length: int = 512) -> list:
|
||||
"""将内容分割成适当大小的块，保持段落完整性
|
||||
"""将内容分割成适当大小的块，按空行分割
|
||||
|
||||
Args:
|
||||
content: 要分割的文本内容
|
||||
@@ -50,66 +50,20 @@ class KnowledgeLibrary:
|
||||
Returns:
|
||||
list: 分割后的文本块列表
|
||||
"""
|
||||
# 首先按段落分割
|
||||
# 按空行分割内容
|
||||
paragraphs = [p.strip() for p in content.split("\n\n") if p.strip()]
|
||||
chunks = []
|
||||
current_chunk = []
|
||||
current_length = 0
|
||||
|
||||
for para in paragraphs:
|
||||
para_length = len(para)
|
||||
|
||||
# 如果单个段落就超过最大长度
|
||||
if para_length > max_length:
|
||||
# 如果当前chunk不为空，先保存
|
||||
if current_chunk:
|
||||
chunks.append("\n".join(current_chunk))
|
||||
current_chunk = []
|
||||
current_length = 0
|
||||
|
||||
# 将长段落按句子分割
|
||||
sentences = [
|
||||
s.strip()
|
||||
for s in para.replace("。", "。\n").replace("！", "！\n").replace("？", "？\n").split("\n")
|
||||
if s.strip()
|
||||
]
|
||||
temp_chunk = []
|
||||
temp_length = 0
|
||||
|
||||
for sentence in sentences:
|
||||
sentence_length = len(sentence)
|
||||
if sentence_length > max_length:
|
||||
# 如果单个句子超长，强制按长度分割
|
||||
if temp_chunk:
|
||||
chunks.append("\n".join(temp_chunk))
|
||||
temp_chunk = []
|
||||
temp_length = 0
|
||||
for i in range(0, len(sentence), max_length):
|
||||
chunks.append(sentence[i : i + max_length])
|
||||
elif temp_length + sentence_length + 1 <= max_length:
|
||||
temp_chunk.append(sentence)
|
||||
temp_length += sentence_length + 1
|
||||
# 如果段落长度小于等于最大长度，直接添加
|
||||
if para_length <= max_length:
|
||||
chunks.append(para)
|
||||
else:
|
||||
chunks.append("\n".join(temp_chunk))
|
||||
temp_chunk = [sentence]
|
||||
temp_length = sentence_length
|
||||
|
||||
if temp_chunk:
|
||||
chunks.append("\n".join(temp_chunk))
|
||||
|
||||
# 如果当前段落加上现有chunk不超过最大长度
|
||||
elif current_length + para_length + 1 <= max_length:
|
||||
current_chunk.append(para)
|
||||
current_length += para_length + 1
|
||||
else:
|
||||
# 保存当前chunk并开始新的chunk
|
||||
chunks.append("\n".join(current_chunk))
|
||||
current_chunk = [para]
|
||||
current_length = para_length
|
||||
|
||||
# 添加最后一个chunk
|
||||
if current_chunk:
|
||||
chunks.append("\n".join(current_chunk))
|
||||
# 如果段落超过最大长度，则按最大长度切分
|
||||
for i in range(0, para_length, max_length):
|
||||
chunks.append(para[i:i + max_length])
|
||||
|
||||
return chunks
|
||||
|
||||
|
||||
1375
temp_utils_ui/temp_ui.py
Normal file
1375
temp_utils_ui/temp_ui.py
Normal file
File diff suppressed because it is too large
Load Diff
0
temp_utils_ui/thingking_ui.py
Normal file
0
temp_utils_ui/thingking_ui.py
Normal file
@@ -1,5 +1,5 @@
|
||||
[inner]
|
||||
version = "1.1.3"
|
||||
version = "1.2.4"
|
||||
|
||||
|
||||
#以下是给开发人员阅读的，一般用户不需要阅读
|
||||
@@ -33,15 +33,28 @@ talk_allowed = [
|
||||
talk_frequency_down = [] #降低回复频率的群号码
|
||||
ban_user_id = [] #禁止回复和读取消息的QQ号
|
||||
|
||||
[personality]
|
||||
prompt_personality = [
|
||||
"用一句话或几句话描述性格特点和其他特征",
|
||||
"例如，是一个热爱国家热爱党的新时代好青年",
|
||||
"例如，曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧"
|
||||
]
|
||||
personality_1_probability = 0.7 # 第一种人格出现概率
|
||||
personality_2_probability = 0.2 # 第二种人格出现概率，可以为0
|
||||
personality_3_probability = 0.1 # 第三种人格出现概率，请确保三个概率相加等于1
|
||||
[personality] #未完善
|
||||
personality_core = "用一句话或几句话描述人格的核心特点" # 建议20字以内，谁再写3000字小作文敲谁脑袋
|
||||
personality_sides = [
|
||||
"用一句话或几句话描述人格的一些细节",
|
||||
"用一句话或几句话描述人格的一些细节",
|
||||
"用一句话或几句话描述人格的一些细节",
|
||||
"用一句话或几句话描述人格的一些细节",
|
||||
"用一句话或几句话描述人格的一些细节",
|
||||
]# 条数任意
|
||||
|
||||
[identity] #アイデンティティがない 生まれないらららら
|
||||
# 兴趣爱好 未完善，有些条目未使用
|
||||
identity_detail = [
|
||||
"身份特点",
|
||||
"身份特点",
|
||||
]# 条数任意
|
||||
#外貌特征
|
||||
height = 170 # 身高 单位厘米
|
||||
weight = 50 # 体重 单位千克
|
||||
age = 20 # 年龄 单位岁
|
||||
gender = "男" # 性别
|
||||
appearance = "用几句话描述外貌特征" # 外貌特征
|
||||
|
||||
[schedule]
|
||||
enable_schedule_gen = true # 是否启用日程表(尚未完成)
|
||||
@@ -60,7 +73,7 @@ response_mode = "heart_flow" # 回复策略，可选值：heart_flow（心流）
|
||||
model_r1_probability = 0.7 # 麦麦回答时选择主要回复模型1 模型的概率
|
||||
model_v3_probability = 0.3 # 麦麦回答时选择次要回复模型2 模型的概率
|
||||
|
||||
[heartflow] # 注意：可能会消耗大量token，请谨慎开启
|
||||
[heartflow] # 注意：可能会消耗大量token，请谨慎开启，仅会使用v3模型
|
||||
sub_heart_flow_update_interval = 60 # 子心流更新频率，间隔 单位秒
|
||||
sub_heart_flow_freeze_time = 120 # 子心流冻结时间，超过这个时间没有回复，子心流会冻结，间隔 单位秒
|
||||
sub_heart_flow_stop_time = 600 # 子心流停止时间，超过这个时间没有回复，子心流会停止，间隔 单位秒
|
||||
@@ -72,6 +85,7 @@ max_context_size = 12 # 麦麦获得的上文数量，建议12，太短太长都
|
||||
emoji_chance = 0.2 # 麦麦使用表情包的概率
|
||||
thinking_timeout = 60 # 麦麦最长思考时间，超过这个时间的思考会放弃
|
||||
max_response_length = 256 # 麦麦回答的最大token数
|
||||
message_buffer = true # 启用消息缓冲器？启用此项以解决消息的拆分问题，但会使麦麦的回复延迟
|
||||
ban_words = [
|
||||
# "403","张三"
|
||||
]
|
||||
@@ -85,7 +99,7 @@ ban_msgs_regex = [
|
||||
|
||||
[willing]
|
||||
willing_mode = "classical" # 回复意愿模式 经典模式
|
||||
# willing_mode = "dynamic" # 动态模式(可能不兼容)
|
||||
# willing_mode = "dynamic" # 动态模式(不兼容，需要维护)
|
||||
# willing_mode = "custom" # 自定义模式（可自行调整
|
||||
response_willing_amplifier = 1 # 麦麦回复意愿放大系数，一般为1
|
||||
response_interested_rate_amplifier = 1 # 麦麦回复兴趣度放大系数,听到记忆里的内容时放大系数
|
||||
@@ -151,7 +165,7 @@ enable = true
|
||||
|
||||
[experimental]
|
||||
enable_friend_chat = false # 是否启用好友聊天
|
||||
pfc_chatting = false # 是否启用PFC聊天
|
||||
pfc_chatting = false # 是否启用PFC聊天，该功能仅作用于私聊，与回复模式独立
|
||||
|
||||
#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
|
||||
@@ -162,7 +176,7 @@ pfc_chatting = false # 是否启用PFC聊天
|
||||
# stream = <true|false> : 用于指定模型是否是使用流式输出
|
||||
# 如果不指定，则该项是 False
|
||||
|
||||
[model.llm_reasoning] #暂时未使用
|
||||
[model.llm_reasoning] #只在回复模式为reasoning时启用
|
||||
name = "Pro/deepseek-ai/DeepSeek-R1"
|
||||
# name = "Qwen/QwQ-32B"
|
||||
provider = "SILICONFLOW"
|
||||
|
||||
1
从0.6.0升级0.6.1请先看我.txt
Normal file
1
从0.6.0升级0.6.1请先看我.txt
Normal file
@@ -0,0 +1 @@
|
||||
该版本变动了人格相关设置，原有的配置内容可能被自动更新，如果你没有备份，可以在\config\old找回
|
||||
BIN
配置文件修改器（临时测试用，以config为准）.exe
Normal file
BIN
配置文件修改器（临时测试用，以config为准）.exe
Normal file
Binary file not shown.
56
（临时版）麦麦开始学习.bat
Normal file
56
（临时版）麦麦开始学习.bat
Normal file
@@ -0,0 +1,56 @@
|
||||
@echo off
|
||||
chcp 65001 > nul
|
||||
setlocal enabledelayedexpansion
|
||||
cd /d %~dp0
|
||||
|
||||
title 麦麦学习系统
|
||||
|
||||
cls
|
||||
echo ======================================
|
||||
echo 警告提示
|
||||
echo ======================================
|
||||
echo 1.这是一个demo系统,不完善不稳定,仅用于体验/不要塞入过长过大的文本,这会导致信息提取迟缓
|
||||
echo ======================================
|
||||
|
||||
echo.
|
||||
echo ======================================
|
||||
echo 请选择Python环境:
|
||||
echo 1 - venv (推荐)
|
||||
echo 2 - conda
|
||||
echo ======================================
|
||||
choice /c 12 /n /m "请输入数字选择(1或2): "
|
||||
|
||||
if errorlevel 2 (
|
||||
echo ======================================
|
||||
set "CONDA_ENV="
|
||||
set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
|
||||
|
||||
:: 检查输入是否为空
|
||||
if "!CONDA_ENV!"=="" (
|
||||
echo 错误：环境名称不能为空
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
call conda activate !CONDA_ENV!
|
||||
if errorlevel 1 (
|
||||
echo 激活 conda 环境失败
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
echo Conda 环境 "!CONDA_ENV!" 激活成功
|
||||
python src/plugins/zhishi/knowledge_library.py
|
||||
) else (
|
||||
if exist "venv\Scripts\python.exe" (
|
||||
venv\Scripts\python src/plugins/zhishi/knowledge_library.py
|
||||
) else (
|
||||
echo ======================================
|
||||
echo 错误: venv环境不存在，请先创建虚拟环境
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
)
|
||||
|
||||
endlocal
|
||||
pause
|
||||
56
（测试版）麦麦生成人格.bat
Normal file
56
（测试版）麦麦生成人格.bat
Normal file
@@ -0,0 +1,56 @@
|
||||
@echo off
|
||||
chcp 65001 > nul
|
||||
setlocal enabledelayedexpansion
|
||||
cd /d %~dp0
|
||||
|
||||
title 麦麦人格生成
|
||||
|
||||
cls
|
||||
echo ======================================
|
||||
echo 警告提示
|
||||
echo ======================================
|
||||
echo 1.这是一个demo系统,仅供体验，特性可能会在将来移除
|
||||
echo ======================================
|
||||
|
||||
echo.
|
||||
echo ======================================
|
||||
echo 请选择Python环境:
|
||||
echo 1 - venv (推荐)
|
||||
echo 2 - conda
|
||||
echo ======================================
|
||||
choice /c 12 /n /m "请输入数字选择(1或2): "
|
||||
|
||||
if errorlevel 2 (
|
||||
echo ======================================
|
||||
set "CONDA_ENV="
|
||||
set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
|
||||
|
||||
:: 检查输入是否为空
|
||||
if "!CONDA_ENV!"=="" (
|
||||
echo 错误：环境名称不能为空
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
call conda activate !CONDA_ENV!
|
||||
if errorlevel 1 (
|
||||
echo 激活 conda 环境失败
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
echo Conda 环境 "!CONDA_ENV!" 激活成功
|
||||
python src/individuality/per_bf_gen.py
|
||||
) else (
|
||||
if exist "venv\Scripts\python.exe" (
|
||||
venv\Scripts\python src/individuality/per_bf_gen.py
|
||||
) else (
|
||||
echo ======================================
|
||||
echo 错误: venv环境不存在，请先创建虚拟环境
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
)
|
||||
|
||||
endlocal
|
||||
pause
|
||||
Reference in New Issue
Block a user