Merge branch 'master' into huggingface

This commit is contained in:
binary-husky
2023-04-29 03:53:32 +08:00
19 changed files with 1183 additions and 158 deletions

120
README.md
View File

@@ -18,9 +18,9 @@ pinned: false
> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`
>
# <img src="docs/logo.png" width="40" > ChatGPT 学术优化
# <img src="docs/logo.png" width="40" > GPT 学术优化 (ChatGPT Academic)
**如果喜欢这个项目请给它一个Star如果你发明了更好用的快捷键或函数插件欢迎发issue或者pull requests**
**如果喜欢这个项目请给它一个Star如果你发明了更好用的快捷键或函数插件欢迎发pull requests**
If you like this project, please give it a Star. If you've come up with more useful academic shortcuts or functional plugins, feel free to open an issue or pull request. We also have a README in [English|](docs/README_EN.md)[日本語|](docs/README_JP.md)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md) translated by this project itself.
@@ -38,25 +38,25 @@ If you like this project, please give it a Star. If you've come up with more use
--- | ---
一键润色 | 支持一键润色、一键查找论文语法错误
一键中英互译 | 一键中英互译
一键代码解释 | 可以正确显示代码、解释代码
一键代码解释 | 显示代码、解释代码、生成代码、给代码加注释
[自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键
[配置代理服务器](https://www.bilibili.com/video/BV1rc411W7Dr) | 支持代理连接OpenAI/Google等秒解锁ChatGPT互联网[实时信息聚合](https://www.bilibili.com/video/BV1om4y127ck/)能力
模块化设计 | 支持自定义强大的[函数插件](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions),插件支持[热更新](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
[自我程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] [一键读懂](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)本项目的源代码
[程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] 一键可以剖析其他Python/C/C++/Java/Lua/...项目树
读论文 | [函数插件] 一键解读latex论文全文并生成摘要
读论文、[翻译](https://www.bilibili.com/video/BV1KT411x7Wn)论文 | [函数插件] 一键解读latex/pdf论文全文并生成摘要
Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [函数插件] 一键翻译或润色latex论文
批量注释生成 | [函数插件] 一键批量生成函数注释
chat分析报告生成 | [函数插件] 运行后自动生成总结汇报
Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [函数插件] 看到上面5种语言的[README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)了吗?
[arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [函数插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
chat分析报告生成 | [函数插件] 运行后自动生成总结汇报
[PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [函数插件] PDF论文提取题目&摘要+翻译全文(多线程)
[Arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [函数插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
[谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [函数插件] 给定任意谷歌学术搜索页面URL让gpt帮你[写relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
互联网信息聚合+GPT | [函数插件] 一键[让GPT先从互联网获取信息](https://www.bilibili.com/video/BV1om4y127ck),再回答问题,让信息永不过时
公式/图片/表格显示 | 可以同时显示公式的[tex形式和渲染形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png),支持公式、代码高亮
多线程函数插件支持 | 支持多线调用chatgpt一键处理[海量文本](https://www.bilibili.com/video/BV1FT411H7c5/)或程序
启动暗色gradio[主题](https://github.com/binary-husky/chatgpt_academic/issues/173) | 在浏览器url后面添加```/?__dark-theme=true```可以切换dark主题
[多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持,[API2D](https://api2d.com/)接口支持 | 同时被GPT3.5、GPT4和[清华ChatGLM](https://github.com/THUDM/ChatGLM-6B)伺候的感觉一定会很不错吧?
huggingface免科学上网[在线体验](https://huggingface.co/spaces/qingxu98/gpt-academic) | 登陆huggingface后复制[此空间](https://huggingface.co/spaces/qingxu98/gpt-academic)
更多LLM模型接入支持[huggingface部署](https://huggingface.co/spaces/qingxu98/gpt-academic) | 新加入Newbing测试接口(新必应AI)
…… | ……
</div>
@@ -93,9 +93,6 @@ huggingface免科学上网[在线体验](https://huggingface.co/spaces/qingxu98/
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
多种大语言模型混合调用[huggingface测试版](https://huggingface.co/spaces/qingxu98/academic-chatgpt-beta)huggingface版不支持chatglm
---
## 安装-方法1直接运行 (Windows, Linux or MacOS)
@@ -106,20 +103,16 @@ git clone https://github.com/binary-husky/chatgpt_academic.git
cd chatgpt_academic
```
2. 配置API_KEY和代理设置
2. 配置API_KEY
在`config.py`中配置API KEY等[设置](https://github.com/binary-husky/gpt_academic/issues/1) 。
在`config.py`中,配置 海外Proxy 和 OpenAI API KEY说明如下
```
1. 如果你在国内需要设置海外代理才能够顺利使用OpenAI API设置方法请仔细阅读config.py1.修改其中的USE_PROXY为True; 2.按照说明修改其中的proxies
2. 配置 OpenAI API KEY。支持任意数量的OpenAI的密钥和API2D的密钥共存/负载均衡多个KEY用英文逗号分隔即可例如输入 API_KEY="OpenAI密钥1,API2D密钥2,OpenAI密钥3,OpenAI密钥4"
3. 与代理网络有关的issue网络超时、代理不起作用汇总到 https://github.com/binary-husky/chatgpt_academic/issues/1
```
P.S. 程序运行时会优先检查是否存在名为`config_private.py`的私密配置文件,并用其中的配置覆盖`config.py`的同名配置。因此,如果您能理解我们的配置读取逻辑,我们强烈建议您在`config.py`旁边创建一个名为`config_private.py`的新配置文件,并把`config.py`中的配置转移(复制)到`config_private.py`中。`config_private.py`不受git管控可以让您的隐私信息更加安全。
3. 安装依赖
```sh
# 选择I: 如熟悉python推荐
# 选择I: 如熟悉pythonpython版本3.9以上,越新越好)
python -m pip install -r requirements.txt
# 备注使用官方pip源或者阿里pip源其他pip源如一些大学的pip有可能出问题临时换源方法python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
@@ -141,14 +134,8 @@ python main.py
5. 测试函数插件
```
- 测试Python项目分析
选择1input区域 输入 `./crazy_functions/test_project/python/dqn` 然后点击 "解析整个Python项目"
选择2展开文件上传区将python文件/包含python文件的压缩包拖拽进去在出现反馈提示后 然后点击 "解析整个Python项目"
- 测试自我代码解读(本项目自译解)
点击 "[多线程Demo] 解析此项目本身(源码自译解)"
- 测试函数插件模板函数要求gpt回答历史上的今天发生了什么您可以根据此函数为模板实现更复杂的功能
点击 "[函数插件模板Demo] 历史上的今天"
- 函数插件区下拉菜单中有更多功能可供选择
```
## 安装-方法2使用Docker
@@ -159,7 +146,7 @@ python main.py
# 下载项目
git clone https://github.com/binary-husky/chatgpt_academic.git
cd chatgpt_academic
# 配置 “海外Proxy” “API_KEY” 以及 “WEB_PORT” (例如50923) 等
# 配置 “Proxy” “API_KEY” 以及 “WEB_PORT” (例如50923) 等
用任意文本编辑器编辑 config.py
# 安装
docker build -t gpt-academic .
@@ -182,26 +169,20 @@ docker run --rm -it --net=host --gpus=all gpt-academic
docker run --rm -it --net=host --gpus=all gpt-academic bash
```
## 安装-方法3其他部署姿势
## 安装-方法3其他部署方式需要云服务器知识与经验
1. 如何使用反代URL/AzureAPI
按照`config.py`中的说明配置API_URL_REDIRECT即可。
1. 远程云服务器部署
2. 远程云服务器部署(需要云服务器知识与经验)
请访问[部署wiki-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
2. 使用WSL2Windows Subsystem for Linux 子系统)
3. 使用WSL2Windows Subsystem for Linux 子系统)
请访问[部署wiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
3. 如何在二级网址(如`http://localhost/subpath`)下运行
4. 如何在二级网址(如`http://localhost/subpath`)下运行
请访问[FastAPI运行说明](docs/WithFastapi.md)
## 安装-代理配置
1. 常规方法
[配置代理](https://github.com/binary-husky/chatgpt_academic/issues/1)
2. 纯新手教程
[纯新手教程](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BB%A3%E7%90%86%E8%BD%AF%E4%BB%B6%E9%97%AE%E9%A2%98%E7%9A%84%E6%96%B0%E6%89%8B%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95%EF%BC%88%E6%96%B9%E6%B3%95%E5%8F%AA%E9%80%82%E7%94%A8%E4%BA%8E%E6%96%B0%E6%89%8B%EF%BC%89)
---
## 自定义新的便捷按钮 / 自定义函数插件
@@ -228,74 +209,48 @@ docker run --rm -it --net=host --gpus=all gpt-academic bash
本项目的插件编写、调试难度很低只要您具备一定的python基础知识就可以仿照我们提供的模板实现自己的插件功能。
详情请参考[函数插件指南](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)。
---
## 其他功能说明
## 部分功能展示
1. 图片显示:
1. 对话保存功能。在函数插件区调用 `保存当前的对话` 即可将当前对话保存为可读+可复原的html文件如图
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/228737599-bf0a9d9c-1808-4f43-ae15-dfcc7af0f295.png" width="800" >
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. 本项目的代码自译解(如果一个程序能够读懂并剖析自己):
在函数插件区(下拉菜单)调用 `载入对话历史存档` ,即可还原之前的会话。
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="800" >
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226936618-9b487e4b-ab5b-4b6e-84c6-16942102e917.png" width="800" >
</div>
3. 其他任意Python/Cpp/Java/Go/Rect/...项目剖析:
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="800" >
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="800" >
</div>
4. Latex论文一键阅读理解与摘要生成
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/227504406-86ab97cd-f208-41c3-8e4a-7000e51cf980.png" width="800" >
</div>
5. 自动报告生成
2. 生成报告。大部分插件都会在执行结束后,生成工作报告
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
</div>
6. 模块化功能设计
3. 模块化功能设计,简单的接口却能支持强大的功能
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
7. 源代码转译英文
4. 这是一个能够“自我译解”的开源项目
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229720562-fe6c3508-6142-4635-a83d-21eb3669baee.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
</div>
8. 互联网在线信息综合
5. 译解其他开源项目,不在话下
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/233575247-fb00819e-6d1b-4bb7-bd54-1d7528f03dd9.png" width="800" >
<img src="https://user-images.githubusercontent.com/96192199/233779501-5ce826f0-6cca-4d59-9e5f-b4eacb8cc15f.png" width="800" >
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
</div>
## Todo 与 版本规划:
- version 3.3+ (todo): NewBing支持
## 版本:
- version 3.5(Todo): 使用自然语言调用本项目的所有函数插件(高优先级)
- version 3.4(Todo): 完善chatglm本地大模型的多线支持
- version 3.3: +互联网信息综合功能
- version 3.2: 函数插件支持更多参数接口 (保存对话功能, 解读任意语言代码+同时询问任意的LLM组合)
- version 3.1: 支持同时问询多个gpt模型支持api2d支持多个apikey负载均衡
- version 3.0: 对chatglm和其他小型llm的支持
@@ -308,6 +263,9 @@ docker run --rm -it --net=host --gpus=all gpt-academic bash
- version 2.0: 引入模块化函数插件
- version 1.0: 基础功能
gpt_academic开发者QQ群734063350
## 参考与学习
```

View File

@@ -56,22 +56,24 @@ def patch_and_restart(path):
"""
一键更新协议:覆盖和重启
"""
import distutils
from distutils import dir_util
import shutil
import os
import sys
import time
import glob
from colorful import print亮黄, print亮绿, print亮红
# if not using config_private, move origin config.py as config_private.py
if not os.path.exists('config_private.py'):
print亮黄('由于您没有设置config_private.py私密配置现将您的现有配置移动至config_private.py以防止配置丢失',
'另外您可以随时在history子文件夹下找回旧版的程序。')
shutil.copyfile('config.py', 'config_private.py')
distutils.dir_util.copy_tree(path+'/chatgpt_academic-master', './')
import subprocess
path_new_version = glob.glob(path + '/*-master')[0]
dir_util.copy_tree(path_new_version, './')
print亮绿('代码已经更新即将更新pip包依赖……')
for i in reversed(range(5)): time.sleep(1); print(i)
try:
import subprocess
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
except:
print亮红('pip包依赖安装出现问题需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`,然后在用常规的`python main.py`的方式启动。')

View File

@@ -10,7 +10,7 @@ if USE_PROXY:
# [地址] 懂的都懂不懂就填localhost或者127.0.0.1肯定错不了localhost意思是代理软件安装在本机上
# [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样,但端口号都应该在最显眼的位置上
# 代理网络的地址,打开你的科学上网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
# 代理网络的地址,打开你的*学*网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
proxies = {
# [协议]:// [地址] :[端口]
"http": "socks5h://localhost:11284",
@@ -33,6 +33,7 @@ CODE_HIGHLIGHT = True
# 窗口布局
LAYOUT = "LEFT-RIGHT" # "LEFT-RIGHT"(左右布局) # "TOP-DOWN"(上下布局)
DARK_MODE = True # "LEFT-RIGHT"(左右布局) # "TOP-DOWN"(上下布局)
# 发送请求到OpenAI后等待多久判定为超时
TIMEOUT_SECONDS = 30
@@ -58,8 +59,16 @@ CONCURRENT_COUNT = 100
AUTHENTICATION = []
# 重新URL重新定向实现更换API_URL的作用常规情况下不要修改!!
# 格式 {"https://api.openai.com/v1/chat/completions": "重定向的URL"}
# 高危设置通过修改此设置您将把您的API-KEY和对话隐私完全暴露给您设定的中间人
# 格式 {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
# 例如 API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://ai.open.com/api/conversation"}
API_URL_REDIRECT = {}
# 如果需要在二级路径下运行(常规情况下,不要修改!!需要配合修改main.py才能生效!
CUSTOM_PATH = "/"
# 如果需要使用newbing把newbing的长长的cookie放到这里
NEWBING_STYLE = "creative" # ["creative", "balanced", "precise"]
NEWBING_COOKIES = """
your bing cookies here
"""

View File

@@ -21,15 +21,22 @@ def get_crazy_functions():
from crazy_functions.总结word文档 import 总结word文档
from crazy_functions.解析JupyterNotebook import 解析ipynb文件
from crazy_functions.对话历史存档 import 对话历史存档
function_plugins = {
from crazy_functions.对话历史存档 import 载入对话历史存档
from crazy_functions.对话历史存档 import 删除所有本地对话历史记录
from crazy_functions.批量Markdown翻译 import Markdown英译中
function_plugins = {
"解析整个Python项目": {
"Color": "stop", # 按钮颜色
"Function": HotReload(解析一个Python项目)
},
"保存当前的对话": {
"载入对话历史存档": {
"AsButton":False,
"Function": HotReload(对话历史存档)
"Function": HotReload(载入对话历史存档)
},
"删除所有本地对话历史记录(请谨慎操作)": {
"AsButton":False,
"Function": HotReload(删除所有本地对话历史记录)
},
"[测试功能] 解析Jupyter Notebook文件": {
"Color": "stop",
@@ -81,11 +88,21 @@ def get_crazy_functions():
"Color": "stop", # 按钮颜色
"Function": HotReload(读文章写摘要)
},
"Markdown/Readme英译中": {
# HotReload 的意思是热更新,修改函数插件代码后,不需要重启程序,代码直接生效
"Color": "stop",
"Function": HotReload(Markdown英译中)
},
"批量生成函数注释": {
"Color": "stop", # 按钮颜色
"AsButton": False, # 加入下拉菜单中
"Function": HotReload(批量生成函数注释)
},
"保存当前的对话": {
"Function": HotReload(对话历史存档)
},
"[多线程Demo] 解析此项目本身(源码自译解)": {
"AsButton": False, # 加入下拉菜单中
"Function": HotReload(解析项目本身)
},
"[多线程demo] 把本项目源代码切换成全英文": {
@@ -93,7 +110,7 @@ def get_crazy_functions():
"AsButton": False, # 加入下拉菜单中
"Function": HotReload(全项目切换英文)
},
"[函数插件模板Demo] 历史上的今天": {
"[插件demo] 历史上的今天": {
# HotReload 的意思是热更新,修改函数插件代码后,不需要重启程序,代码直接生效
"Function": HotReload(高阶功能模板函数)
},
@@ -110,7 +127,6 @@ def get_crazy_functions():
from crazy_functions.Latex全文翻译 import Latex中译英
from crazy_functions.Latex全文翻译 import Latex英译中
from crazy_functions.批量Markdown翻译 import Markdown中译英
from crazy_functions.批量Markdown翻译 import Markdown英译中
function_plugins.update({
"批量翻译PDF文档多线程": {
@@ -175,12 +191,7 @@ def get_crazy_functions():
"AsButton": False, # 加入下拉菜单中
"Function": HotReload(Markdown中译英)
},
"[测试功能] 批量Markdown英译中输入路径或上传压缩包": {
# HotReload 的意思是热更新,修改函数插件代码后,不需要重启程序,代码直接生效
"Color": "stop",
"AsButton": False, # 加入下拉菜单中
"Function": HotReload(Markdown英译中)
},
})

View File

@@ -1,5 +1,4 @@
import traceback
from toolbox import update_ui, get_conf
from toolbox import update_ui, get_conf, trimmed_format_exc
def input_clipping(inputs, history, max_token_limit):
import numpy as np
@@ -94,12 +93,12 @@ def request_gpt_model_in_new_thread_with_ui_alive(
continue # 返回重试
else:
# 【选择放弃】
tb_str = '```\n' + traceback.format_exc() + '```'
tb_str = '```\n' + trimmed_format_exc() + '```'
mutable[0] += f"[Local Message] 警告,在执行过程中遭遇问题, Traceback\n\n{tb_str}\n\n"
return mutable[0] # 放弃
except:
# 【第三种情况】:其他错误:重试几次
tb_str = '```\n' + traceback.format_exc() + '```'
tb_str = '```\n' + trimmed_format_exc() + '```'
print(tb_str)
mutable[0] += f"[Local Message] 警告,在执行过程中遭遇问题, Traceback\n\n{tb_str}\n\n"
if retry_op > 0:
@@ -173,7 +172,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
if max_workers == -1: # 读取配置文件
try: max_workers, = get_conf('DEFAULT_WORKER_NUM')
except: max_workers = 8
if max_workers <= 0 or max_workers >= 20: max_workers = 8
if max_workers <= 0: max_workers = 3
# 屏蔽掉 chatglm的多线程可能会导致严重卡顿
if not (llm_kwargs['llm_model'].startswith('gpt-') or llm_kwargs['llm_model'].startswith('api2d-')):
max_workers = 1
@@ -220,14 +219,14 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
continue # 返回重试
else:
# 【选择放弃】
tb_str = '```\n' + traceback.format_exc() + '```'
tb_str = '```\n' + trimmed_format_exc() + '```'
gpt_say += f"[Local Message] 警告,线程{index}在执行过程中遭遇问题, Traceback\n\n{tb_str}\n\n"
if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答:\n\n" + mutable[index][0]
mutable[index][2] = "输入过长已放弃"
return gpt_say # 放弃
except:
# 【第三种情况】:其他错误
tb_str = '```\n' + traceback.format_exc() + '```'
tb_str = '```\n' + trimmed_format_exc() + '```'
print(tb_str)
gpt_say += f"[Local Message] 警告,线程{index}在执行过程中遭遇问题, Traceback\n\n{tb_str}\n\n"
if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答:\n\n" + mutable[index][0]
@@ -564,3 +563,46 @@ def read_and_clean_pdf_text(fp):
# print亮绿('***************************')
return meta_txt, page_one_meta
def get_files_from_everything(txt, type): # type='.md'
"""
这个函数是用来获取指定目录下所有指定类型(如.md的文件并且对于网络上的文件也可以获取它。
下面是对每个参数和返回值的说明:
参数
- txt: 路径或网址,表示要搜索的文件或者文件夹路径或网络上的文件。
- type: 字符串,表示要搜索的文件类型。默认是.md。
返回值
- success: 布尔值,表示函数是否成功执行。
- file_manifest: 文件路径列表,里面包含以指定类型为后缀名的所有文件的绝对路径。
- project_folder: 字符串,表示文件所在的文件夹路径。如果是网络上的文件,就是临时文件夹的路径。
该函数详细注释已添加,请确认是否满足您的需要。
"""
import glob, os
success = True
if txt.startswith('http'):
# 网络的远程文件
import requests
from toolbox import get_conf
proxies, = get_conf('proxies')
r = requests.get(txt, proxies=proxies)
with open('./gpt_log/temp'+type, 'wb+') as f: f.write(r.content)
project_folder = './gpt_log/'
file_manifest = ['./gpt_log/temp'+type]
elif txt.endswith(type):
# 直接给定文件
file_manifest = [txt]
project_folder = os.path.dirname(txt)
elif os.path.exists(txt):
# 本地路径,递归搜索
project_folder = txt
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*'+type, recursive=True)]
if len(file_manifest) == 0:
success = False
else:
project_folder = None
file_manifest = []
success = False
return success, file_manifest, project_folder

View File

@@ -1,7 +1,8 @@
from toolbox import CatchException, update_ui
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
import re
def write_chat_to_file(chatbot, file_name=None):
def write_chat_to_file(chatbot, history=None, file_name=None):
"""
将对话记录history以Markdown格式写入文件中。如果没有指定文件名则使用当前时间生成文件名。
"""
@@ -11,20 +12,62 @@ def write_chat_to_file(chatbot, file_name=None):
file_name = 'chatGPT对话历史' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.html'
os.makedirs('./gpt_log/', exist_ok=True)
with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
from theme import advanced_css
f.write(f'<head><title>对话历史</title><style>{advanced_css}</style></head>')
for i, contents in enumerate(chatbot):
for content in contents:
for j, content in enumerate(contents):
try: # 这个bug没找到触发条件暂时先这样顶一下
if type(content) != str: content = str(content)
except:
continue
f.write(content)
f.write('\n\n')
if j == 0:
f.write('<hr style="border-top: dotted 3px #ccc;">')
f.write('<hr color="red"> \n\n')
f.write('<hr color="blue"> \n\n raw chat context:\n')
f.write('<code>')
for h in history:
f.write("\n>>>" + h)
f.write('</code>')
res = '对话历史写入:' + os.path.abspath(f'./gpt_log/{file_name}')
print(res)
return res
def gen_file_preview(file_name):
try:
with open(file_name, 'r', encoding='utf8') as f:
file_content = f.read()
# pattern to match the text between <head> and </head>
pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
file_content = re.sub(pattern, '', file_content)
html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
history = history.strip('<code>')
history = history.strip('</code>')
history = history.split("\n>>>")
return list(filter(lambda x:x!="", history))[0][:100]
except:
return ""
def read_file_to_chat(chatbot, history, file_name):
with open(file_name, 'r', encoding='utf8') as f:
file_content = f.read()
# pattern to match the text between <head> and </head>
pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
file_content = re.sub(pattern, '', file_content)
html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
history = history.strip('<code>')
history = history.strip('</code>')
history = history.split("\n>>>")
history = list(filter(lambda x:x!="", history))
html = html.split('<hr color="red"> \n\n')
html = list(filter(lambda x:x!="", html))
chatbot.clear()
for i, h in enumerate(html):
i_say, gpt_say = h.split('<hr style="border-top: dotted 3px #ccc;">')
chatbot.append([i_say, gpt_say])
chatbot.append([f"存档文件详情?", f"[Local Message] 载入对话{len(html)}条,上下文{len(history)}条。"])
return chatbot, history
@CatchException
def 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
"""
@@ -37,6 +80,64 @@ def 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
web_port 当前软件运行的端口号
"""
chatbot.append(("保存当前对话", f"[Local Message] {write_chat_to_file(chatbot)}"))
chatbot.append(("保存当前对话",
f"[Local Message] {write_chat_to_file(chatbot, history)},您可以调用“载入对话历史存档”还原当下的对话。\n警告!被保存的对话历史可以被使用该系统的任何人查阅。"))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间我们先及时地做一次界面更新
def hide_cwd(str):
import os
current_path = os.getcwd()
replace_path = "."
return str.replace(current_path, replace_path)
@CatchException
def 载入对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
"""
txt 输入栏用户输入的文本,例如需要翻译的一段话,再例如一个包含了待处理文件的路径
llm_kwargs gpt模型参数如温度和top_p等一般原样传递下去就行
plugin_kwargs 插件模型的参数,暂时没有用武之地
chatbot 聊天显示框的句柄,用于显示给用户
history 聊天历史,前情提要
system_prompt 给gpt的静默提醒
web_port 当前软件运行的端口号
"""
from .crazy_utils import get_files_from_everything
success, file_manifest, _ = get_files_from_everything(txt, type='.html')
if not success:
if txt == "": txt = '空空如也的输入栏'
import glob
local_history = "<br/>".join(["`"+hide_cwd(f)+f" ({gen_file_preview(f)})"+"`" for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True)])
chatbot.append([f"正在查找对话历史文件html格式: {txt}", f"找不到任何html文件: {txt}。但本地存储了以下历史文件,您可以将任意一个文件路径粘贴到输入区,然后重试:<br/>{local_history}"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
try:
chatbot, history = read_file_to_chat(chatbot, history, file_manifest[0])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
except:
chatbot.append([f"载入对话历史文件", f"对话历史文件损坏!"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
@CatchException
def 删除所有本地对话历史记录(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
"""
txt 输入栏用户输入的文本,例如需要翻译的一段话,再例如一个包含了待处理文件的路径
llm_kwargs gpt模型参数如温度和top_p等一般原样传递下去就行
plugin_kwargs 插件模型的参数,暂时没有用武之地
chatbot 聊天显示框的句柄,用于显示给用户
history 聊天历史,前情提要
system_prompt 给gpt的静默提醒
web_port 当前软件运行的端口号
"""
import glob, os
local_history = "<br/>".join(["`"+hide_cwd(f)+"`" for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True)])
for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True):
os.remove(f)
chatbot.append([f"删除所有历史对话文件", f"已删除<br/>{local_history}"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return

View File

@@ -84,7 +84,33 @@ def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
def get_files_from_everything(txt):
import glob, os
success = True
if txt.startswith('http'):
# 网络的远程文件
txt = txt.replace("https://github.com/", "https://raw.githubusercontent.com/")
txt = txt.replace("/blob/", "/")
import requests
from toolbox import get_conf
proxies, = get_conf('proxies')
r = requests.get(txt, proxies=proxies)
with open('./gpt_log/temp.md', 'wb+') as f: f.write(r.content)
project_folder = './gpt_log/'
file_manifest = ['./gpt_log/temp.md']
elif txt.endswith('.md'):
# 直接给定文件
file_manifest = [txt]
project_folder = os.path.dirname(txt)
elif os.path.exists(txt):
# 本地路径,递归搜索
project_folder = txt
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
else:
success = False
return success, file_manifest, project_folder
@CatchException
@@ -98,6 +124,7 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
# 尝试导入依赖,如果缺少依赖,则给出安装建议
try:
import tiktoken
import glob, os
except:
report_execption(chatbot, history,
a=f"解析项目: {txt}",
@@ -105,19 +132,21 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
history = [] # 清空历史,以免输入溢出
import glob, os
if os.path.exists(txt):
project_folder = txt
else:
success, file_manifest, project_folder = get_files_from_everything(txt)
if not success:
# 什么都没有
if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
@@ -135,6 +164,7 @@ def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
# 尝试导入依赖,如果缺少依赖,则给出安装建议
try:
import tiktoken
import glob, os
except:
report_execption(chatbot, history,
a=f"解析项目: {txt}",
@@ -142,18 +172,13 @@ def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
history = [] # 清空历史,以免输入溢出
import glob, os
if os.path.exists(txt):
project_folder = txt
else:
success, file_manifest, project_folder = get_files_from_everything(txt)
if not success:
# 什么都没有
if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
if txt.endswith('.md'):
file_manifest = [txt]
else:
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

View File

@@ -1,5 +1,6 @@
from toolbox import update_ui
from toolbox import CatchException, report_execption, write_results_to_file
from .crazy_utils import input_clipping
def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
import os, copy
@@ -61,13 +62,15 @@ def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
previous_iteration_files.extend([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
previous_iteration_files_string = ', '.join(previous_iteration_files)
current_iteration_focus = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
i_say = f'根据以上分析,对程序的整体功能和构架重新做出概括。然后用一张markdown表格整理每个文件的功能(包括{previous_iteration_files_string}'
i_say = f'用一张Markdown表格简要描述以下文件的功能{previous_iteration_files_string}。根据以上分析,用一句话概括程序的整体功能'
inputs_show_user = f'根据以上分析,对程序的整体功能和构架重新做出概括,由于输入长度限制,可能需要分组处理,本组文件为 {current_iteration_focus} + 已经汇总的文件组。'
this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection)
this_iteration_history.append(last_iteration_result)
# 裁剪input
inputs, this_iteration_history_feed = input_clipping(inputs=i_say, history=this_iteration_history, max_token_limit=2560)
result = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=i_say, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot,
history=this_iteration_history, # 迭代之前的分析
inputs=inputs, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot,
history=this_iteration_history_feed, # 迭代之前的分析
sys_prompt="你是一个程序架构分析师,正在分析一个项目的源代码。")
report_part_2.extend([i_say, result])
last_iteration_result = result

View File

@@ -1,6 +1,6 @@
# How to build | 如何构建: docker build -t gpt-academic --network=host -f Dockerfile+ChatGLM .
# How to run | 如何运行 (1) 直接运行选择0号GPU: docker run --rm -it --net=host --gpus="0" gpt-academic
# How to run | 如何运行 (2) 我想运行之前进容器做一些调整: docker run --rm -it --net=host --gpus="0" gpt-academic bash
# How to run | (1) 我想直接一键运行选择0号GPU: docker run --rm -it --net=host --gpus \"device=0\" gpt-academic
# How to run | (2) 我想运行之前进容器做一些调整选择1号GPU: docker run --rm -it --net=host --gpus \"device=1\" gpt-academic bash
# 从NVIDIA源从而支持显卡运损检查宿主的nvidia-smi中的cuda版本必须>=11.3
FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
@@ -14,6 +14,7 @@ RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
RUN $useProxyNetwork curl cip.cc
RUN sed -i '$ d' /etc/proxychains.conf
RUN sed -i '$ d' /etc/proxychains.conf
# 在这里填写主机的代理协议用于从github拉取代码
RUN echo "socks5 127.0.0.1 10880" >> /etc/proxychains.conf
ARG useProxyNetwork=proxychains
# # comment out above if you do not need proxy network | 如果不需要翻墙 - 从此行向上删除
@@ -21,14 +22,15 @@ ARG useProxyNetwork=proxychains
# use python3 as the system default python
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
# 下载pytorch
RUN $useProxyNetwork python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
# 下载分支
WORKDIR /gpt
RUN $useProxyNetwork git clone https://github.com/binary-husky/chatgpt_academic.git
WORKDIR /gpt/chatgpt_academic
RUN $useProxyNetwork python3 -m pip install -r requirements.txt
RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_chatglm.txt
RUN $useProxyNetwork python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_newbing.txt
# 预热CHATGLM参数非必要 可选步骤)
RUN echo ' \n\
@@ -48,6 +50,7 @@ RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
# 可同时填写多个API-KEY支持openai的key和api2d的key共存用英文逗号分割例如API_KEY = "sk-openaikey1,fkxxxx-api2dkey2,........"
# LLM_MODEL 是选择初始的模型
# LOCAL_MODEL_DEVICE 是选择chatglm等本地模型运行的设备可选 cpu 和 cuda
# [说明: 以下内容与`config.py`一一对应请查阅config.py来完成一下配置的填写]
RUN echo ' \n\
API_KEY = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" \n\
USE_PROXY = True \n\

View File

@@ -0,0 +1,130 @@
sample = """
[1]: https://baike.baidu.com/item/%E8%B4%A8%E8%83%BD%E6%96%B9%E7%A8%8B/1884527 "质能方程质能方程式_百度百科"
[2]: https://www.zhihu.com/question/348249281 "如何理解质能方程 Emc² - 知乎"
[3]: https://zhuanlan.zhihu.com/p/32597385 "质能方程的推导与理解 - 知乎 - 知乎专栏"
你好,这是必应。质能方程是描述质量与能量之间的当量关系的方程[^1^][1]。用tex格式质能方程可以写成$$E=mc^2$$,其中$E$是能量,$m$是质量,$c$是光速[^2^][2] [^3^][3]。
"""
import re
def preprocess_newbing_out(s):
pattern = r'\^(\d+)\^' # 匹配^数字^
pattern2 = r'\[(\d+)\]' # 匹配^数字^
sub = lambda m: '\['+m.group(1)+'\]' # 将匹配到的数字作为替换值
result = re.sub(pattern, sub, s) # 替换操作
if '[1]' in result:
result += '<br/><hr style="border-top: dotted 1px #44ac5c;"><br/><small>' + "<br/>".join([re.sub(pattern2, sub, r) for r in result.split('\n') if r.startswith('[')]) + '</small>'
return result
def close_up_code_segment_during_stream(gpt_reply):
"""
在gpt输出代码的中途输出了前面的```,但还没输出完后面的```),补上后面的```
Args:
gpt_reply (str): GPT模型返回的回复字符串。
Returns:
str: 返回一个新的字符串,将输出代码片段的“后面的```”补上。
"""
if '```' not in gpt_reply:
return gpt_reply
if gpt_reply.endswith('```'):
return gpt_reply
# 排除了以上两个情况,我们
segments = gpt_reply.split('```')
n_mark = len(segments) - 1
if n_mark % 2 == 1:
# print('输出代码片段中!')
return gpt_reply+'\n```'
else:
return gpt_reply
import markdown
from latex2mathml.converter import convert as tex2mathml
from functools import wraps, lru_cache
def markdown_convertion(txt):
"""
将Markdown格式的文本转换为HTML格式。如果包含数学公式则先将公式转换为HTML格式。
"""
pre = '<div class="markdown-body">'
suf = '</div>'
if txt.startswith(pre) and txt.endswith(suf):
# print('警告,输入了已经经过转化的字符串,二次转化可能出问题')
return txt # 已经被转化过,不需要再次转化
markdown_extension_configs = {
'mdx_math': {
'enable_dollar_delimiter': True,
'use_gitlab_delimiters': False,
},
}
find_equation_pattern = r'<script type="math/tex(?:.*?)>(.*?)</script>'
def tex2mathml_catch_exception(content, *args, **kwargs):
try:
content = tex2mathml(content, *args, **kwargs)
except:
content = content
return content
def replace_math_no_render(match):
content = match.group(1)
if 'mode=display' in match.group(0):
content = content.replace('\n', '</br>')
return f"<font color=\"#00FF00\">$$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$$</font>"
else:
return f"<font color=\"#00FF00\">$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$</font>"
def replace_math_render(match):
content = match.group(1)
if 'mode=display' in match.group(0):
if '\\begin{aligned}' in content:
content = content.replace('\\begin{aligned}', '\\begin{array}')
content = content.replace('\\end{aligned}', '\\end{array}')
content = content.replace('&', ' ')
content = tex2mathml_catch_exception(content, display="block")
return content
else:
return tex2mathml_catch_exception(content)
def markdown_bug_hunt(content):
"""
解决一个mdx_math的bug单$包裹begin命令时多余<script>
"""
content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">', '<script type="math/tex; mode=display">')
content = content.replace('</script>\n</script>', '</script>')
return content
if ('$' in txt) and ('```' not in txt): # 有$标识的公式符号,且没有代码段```的标识
# convert everything to html format
split = markdown.markdown(text='---')
convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs)
convert_stage_1 = markdown_bug_hunt(convert_stage_1)
# re.DOTALL: Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s).
# 1. convert to easy-to-copy tex (do not render math)
convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL)
# 2. convert to rendered equation
convert_stage_2_2, n = re.subn(find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL)
# cat them together
return pre + convert_stage_2_1 + f'{split}' + convert_stage_2_2 + suf
else:
return pre + markdown.markdown(txt, extensions=['fenced_code', 'codehilite', 'tables', 'sane_lists']) + suf
sample = preprocess_newbing_out(sample)
sample = close_up_code_segment_during_stream(sample)
sample = markdown_convertion(sample)
with open('tmp.html', 'w', encoding='utf8') as f:
f.write("""
<head>
<title>My Website</title>
<link rel="stylesheet" type="text/css" href="style.css">
</head>
""")
f.write(sample)

View File

@@ -174,9 +174,6 @@ def main():
yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(*args, **kwargs)
click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo)
click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot])
# def expand_file_area(file_upload, area_file_up):
# if len(file_upload)>0: return {area_file_up: gr.update(open=True)}
# click_handle.then(expand_file_area, [file_upload, area_file_up], [area_file_up])
cancel_handles.append(click_handle)
# 终止按钮的回调函数注册
stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
@@ -190,7 +187,9 @@ def main():
print(f"\t(暗色主题): http://localhost:{PORT}/?__dark-theme=true")
def open():
time.sleep(2) # 打开浏览器
webbrowser.open_new_tab(f"http://localhost:{PORT}/?__dark-theme=true")
DARK_MODE, = get_conf('DARK_MODE')
if DARK_MODE: webbrowser.open_new_tab(f"http://localhost:{PORT}/?__dark-theme=true")
else: webbrowser.open_new_tab(f"http://localhost:{PORT}")
threading.Thread(target=open, name="open-browser", daemon=True).start()
threading.Thread(target=auto_update, name="self-upgrade", daemon=True).start()
threading.Thread(target=warm_up_modules, name="warm-up", daemon=True).start()

View File

@@ -11,7 +11,7 @@
import tiktoken
from functools import lru_cache
from concurrent.futures import ThreadPoolExecutor
from toolbox import get_conf
from toolbox import get_conf, trimmed_format_exc
from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui
from .bridge_chatgpt import predict as chatgpt_ui
@@ -19,6 +19,9 @@ from .bridge_chatgpt import predict as chatgpt_ui
from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
from .bridge_chatglm import predict as chatglm_ui
from .bridge_newbing import predict_no_ui_long_connection as newbing_noui
from .bridge_newbing import predict as newbing_ui
# from .bridge_tgui import predict_no_ui_long_connection as tgui_noui
# from .bridge_tgui import predict as tgui_ui
@@ -48,6 +51,7 @@ class LazyloadTiktoken(object):
API_URL_REDIRECT, = get_conf("API_URL_REDIRECT")
openai_endpoint = "https://api.openai.com/v1/chat/completions"
api2d_endpoint = "https://openai.api2d.net/v1/chat/completions"
newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
# 兼容旧版的配置
try:
API_URL, = get_conf("API_URL")
@@ -59,6 +63,7 @@ except:
# 新版配置
if openai_endpoint in API_URL_REDIRECT: openai_endpoint = API_URL_REDIRECT[openai_endpoint]
if api2d_endpoint in API_URL_REDIRECT: api2d_endpoint = API_URL_REDIRECT[api2d_endpoint]
if newbing_endpoint in API_URL_REDIRECT: newbing_endpoint = API_URL_REDIRECT[newbing_endpoint]
# 获取tokenizer
@@ -116,7 +121,15 @@ model_info = {
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
# newbing
"newbing": {
"fn_with_ui": newbing_ui,
"fn_without_ui": newbing_noui,
"endpoint": newbing_endpoint,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
}
@@ -128,10 +141,7 @@ def LLM_CATCH_EXCEPTION(f):
try:
return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
except Exception as e:
from toolbox import get_conf
import traceback
proxies, = get_conf('proxies')
tb_str = '\n```\n' + traceback.format_exc() + '\n```\n'
tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
observe_window[0] = tb_str
return tb_str
return decorated
@@ -182,7 +192,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
def mutex_manager(window_mutex, observe_window):
while True:
time.sleep(0.5)
time.sleep(0.25)
if not window_mutex[-1]: break
# 看门狗watchdog
for i in range(n_model):

View File

@@ -1,6 +1,7 @@
from transformers import AutoModel, AutoTokenizer
import time
import threading
import importlib
from toolbox import update_ui, get_conf
from multiprocessing import Process, Pipe
@@ -18,6 +19,7 @@ class GetGLMHandle(Process):
self.success = True
self.check_dependency()
self.start()
self.threadLock = threading.Lock()
def check_dependency(self):
try:
@@ -72,6 +74,7 @@ class GetGLMHandle(Process):
def stream_chat(self, **kwargs):
# 主进程执行
self.threadLock.acquire()
self.parent.send(kwargs)
while True:
res = self.parent.recv()
@@ -79,7 +82,7 @@ class GetGLMHandle(Process):
yield res
else:
break
return
self.threadLock.release()
global glm_handle
glm_handle = None
@@ -145,10 +148,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
history_feedin.append([history[2*i], history[2*i+1]] )
# 开始接收chatglm的回复
response = "[Local Message]: 等待ChatGLM响应中 ..."
for response in glm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history)
# 总结输出
if response == "[Local Message]: 等待ChatGLM响应中 ...":
response = "[Local Message]: ChatGLM响应异常 ..."
history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history)

View File

@@ -21,7 +21,7 @@ import importlib
# config_private.py放自己的秘密如API和代理网址
# 读取时首先看是否存在私密的config_private配置文件不受git管控如果有则覆盖原config文件
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc
proxies, API_KEY, TIMEOUT_SECONDS, MAX_RETRY = \
get_conf('proxies', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY')
@@ -215,7 +215,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
else:
from toolbox import regular_txt_to_markdown
tb_str = '```\n' + traceback.format_exc() + '```'
tb_str = '```\n' + trimmed_format_exc() + '```'
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded[4:])}")
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
return

View File

@@ -0,0 +1,254 @@
"""
========================================================================
第一部分来自EdgeGPT.py
https://github.com/acheong08/EdgeGPT
========================================================================
"""
from .edge_gpt import NewbingChatbot
load_message = "等待NewBing响应。"
"""
========================================================================
第二部分子进程Worker调用主体
========================================================================
"""
import time
import json
import re
import logging
import asyncio
import importlib
import threading
from toolbox import update_ui, get_conf, trimmed_format_exc
from multiprocessing import Process, Pipe
def preprocess_newbing_out(s):
pattern = r'\^(\d+)\^' # 匹配^数字^
sub = lambda m: '('+m.group(1)+')' # 将匹配到的数字作为替换值
result = re.sub(pattern, sub, s) # 替换操作
if '[1]' in result:
result += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
return result
def preprocess_newbing_out_simple(result):
if '[1]' in result:
result += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
return result
class NewBingHandle(Process):
def __init__(self):
super().__init__(daemon=True)
self.parent, self.child = Pipe()
self.newbing_model = None
self.info = ""
self.success = True
self.local_history = []
self.check_dependency()
self.start()
self.threadLock = threading.Lock()
def check_dependency(self):
try:
self.success = False
import certifi, httpx, rich
self.info = "依赖检测通过等待NewBing响应。注意目前不能多人同时调用NewBing接口有线程锁否则将导致每个人的NewBing问询历史互相渗透。调用NewBing时会自动使用已配置的代理。"
self.success = True
except:
self.info = "缺少的依赖如果要使用Newbing除了基础的pip依赖以外您还需要运行`pip install -r request_llm/requirements_newbing.txt`安装Newbing的依赖。"
self.success = False
def ready(self):
return self.newbing_model is not None
async def async_run(self):
# 读取配置
NEWBING_STYLE, = get_conf('NEWBING_STYLE')
from request_llm.bridge_all import model_info
endpoint = model_info['newbing']['endpoint']
while True:
# 等待
kwargs = self.child.recv()
question=kwargs['query']
history=kwargs['history']
system_prompt=kwargs['system_prompt']
# 是否重置
if len(self.local_history) > 0 and len(history)==0:
await self.newbing_model.reset()
self.local_history = []
# 开始问问题
prompt = ""
if system_prompt not in self.local_history:
self.local_history.append(system_prompt)
prompt += system_prompt + '\n'
# 追加历史
for ab in history:
a, b = ab
if a not in self.local_history:
self.local_history.append(a)
prompt += a + '\n'
# if b not in self.local_history:
# self.local_history.append(b)
# prompt += b + '\n'
# 问题
prompt += question
self.local_history.append(question)
print('question:', prompt)
# 提交
async for final, response in self.newbing_model.ask_stream(
prompt=question,
conversation_style=NEWBING_STYLE, # ["creative", "balanced", "precise"]
wss_link=endpoint, # "wss://sydney.bing.com/sydney/ChatHub"
):
if not final:
print(response)
self.child.send(str(response))
else:
print('-------- receive final ---------')
self.child.send('[Finish]')
# self.local_history.append(response)
def run(self):
"""
这个函数运行在子进程
"""
# 第一次运行,加载参数
self.success = False
self.local_history = []
if (self.newbing_model is None) or (not self.success):
# 代理设置
proxies, = get_conf('proxies')
if proxies is None:
self.proxies_https = None
else:
self.proxies_https = proxies['https']
# cookie
NEWBING_COOKIES, = get_conf('NEWBING_COOKIES')
try:
cookies = json.loads(NEWBING_COOKIES)
except:
self.success = False
tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
self.child.send(f'[Local Message] 不能加载Newbing组件。NEWBING_COOKIES未填写或有格式错误。')
self.child.send('[Fail]')
self.child.send('[Finish]')
raise RuntimeError(f"不能加载Newbing组件。NEWBING_COOKIES未填写或有格式错误。")
try:
self.newbing_model = NewbingChatbot(proxy=self.proxies_https, cookies=cookies)
except:
self.success = False
tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
self.child.send(f'[Local Message] 不能加载Newbing组件。{tb_str}')
self.child.send('[Fail]')
self.child.send('[Finish]')
raise RuntimeError(f"不能加载Newbing组件。")
self.success = True
try:
# 进入任务等待状态
asyncio.run(self.async_run())
except Exception:
tb_str = '```\n' + trimmed_format_exc() + '```'
self.child.send(f'[Local Message] Newbing失败 {tb_str}.')
self.child.send('[Fail]')
self.child.send('[Finish]')
def stream_chat(self, **kwargs):
"""
这个函数运行在主进程
"""
self.threadLock.acquire()
self.parent.send(kwargs) # 发送请求到子进程
while True:
res = self.parent.recv() # 等待newbing回复的片段
if res == '[Finish]':
break # 结束
elif res == '[Fail]':
self.success = False
break
else:
yield res # newbing回复的片段
self.threadLock.release()
"""
========================================================================
第三部分:主进程统一调用函数接口
========================================================================
"""
global newbing_handle
newbing_handle = None
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
"""
多线程方法
函数的说明请见 request_llm/bridge_all.py
"""
global newbing_handle
if (newbing_handle is None) or (not newbing_handle.success):
newbing_handle = NewBingHandle()
observe_window[0] = load_message + "\n\n" + newbing_handle.info
if not newbing_handle.success:
error = newbing_handle.info
newbing_handle = None
raise RuntimeError(error)
# 没有 sys_prompt 接口因此把prompt加入 history
history_feedin = []
for i in range(len(history)//2):
history_feedin.append([history[2*i], history[2*i+1]] )
watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
response = ""
observe_window[0] = "[Local Message]: 等待NewBing响应中 ..."
for response in newbing_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
observe_window[0] = preprocess_newbing_out_simple(response)
if len(observe_window) >= 2:
if (time.time()-observe_window[1]) > watch_dog_patience:
raise RuntimeError("程序终止。")
return preprocess_newbing_out_simple(response)
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
"""
单线程方法
函数的说明请见 request_llm/bridge_all.py
"""
chatbot.append((inputs, "[Local Message]: 等待NewBing响应中 ..."))
global newbing_handle
if (newbing_handle is None) or (not newbing_handle.success):
newbing_handle = NewBingHandle()
chatbot[-1] = (inputs, load_message + "\n\n" + newbing_handle.info)
yield from update_ui(chatbot=chatbot, history=[])
if not newbing_handle.success:
newbing_handle = None
return
if additional_fn is not None:
import core_functional
importlib.reload(core_functional) # 热更新prompt
core_functional = core_functional.get_core_functions()
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数(如果有的话)
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
history_feedin = []
for i in range(len(history)//2):
history_feedin.append([history[2*i], history[2*i+1]] )
chatbot[-1] = (inputs, "[Local Message]: 等待NewBing响应中 ...")
response = "[Local Message]: 等待NewBing响应中 ..."
yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢尚未完成全部响应请耐心完成后再提交新问题。")
for response in newbing_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
chatbot[-1] = (inputs, preprocess_newbing_out(response))
yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢尚未完成全部响应请耐心完成后再提交新问题。")
if response == "[Local Message]: 等待NewBing响应中 ...": response = "[Local Message]: NewBing响应异常请刷新界面重试 ..."
history.extend([inputs, response])
logging.info(f'[raw_input] {inputs}')
logging.info(f'[response] {response}')
yield from update_ui(chatbot=chatbot, history=history, msg="完成全部响应,请提交新问题。")

409
request_llm/edge_gpt.py Normal file
View File

@@ -0,0 +1,409 @@
"""
========================================================================
第一部分来自EdgeGPT.py
https://github.com/acheong08/EdgeGPT
========================================================================
"""
import argparse
import asyncio
import json
import os
import random
import re
import ssl
import sys
import uuid
from enum import Enum
from typing import Generator
from typing import Literal
from typing import Optional
from typing import Union
import websockets.client as websockets
DELIMITER = "\x1e"
# Generate random IP between range 13.104.0.0/14
FORWARDED_IP = (
f"13.{random.randint(104, 107)}.{random.randint(0, 255)}.{random.randint(0, 255)}"
)
HEADERS = {
"accept": "application/json",
"accept-language": "en-US,en;q=0.9",
"content-type": "application/json",
"sec-ch-ua": '"Not_A Brand";v="99", "Microsoft Edge";v="110", "Chromium";v="110"',
"sec-ch-ua-arch": '"x86"',
"sec-ch-ua-bitness": '"64"',
"sec-ch-ua-full-version": '"109.0.1518.78"',
"sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-model": "",
"sec-ch-ua-platform": '"Windows"',
"sec-ch-ua-platform-version": '"15.0.0"',
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-origin",
"x-ms-client-request-id": str(uuid.uuid4()),
"x-ms-useragent": "azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32",
"Referer": "https://www.bing.com/search?q=Bing+AI&showconv=1&FORM=hpcodx",
"Referrer-Policy": "origin-when-cross-origin",
"x-forwarded-for": FORWARDED_IP,
}
HEADERS_INIT_CONVER = {
"authority": "edgeservices.bing.com",
"accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
"accept-language": "en-US,en;q=0.9",
"cache-control": "max-age=0",
"sec-ch-ua": '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"',
"sec-ch-ua-arch": '"x86"',
"sec-ch-ua-bitness": '"64"',
"sec-ch-ua-full-version": '"110.0.1587.69"',
"sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-model": '""',
"sec-ch-ua-platform": '"Windows"',
"sec-ch-ua-platform-version": '"15.0.0"',
"sec-fetch-dest": "document",
"sec-fetch-mode": "navigate",
"sec-fetch-site": "none",
"sec-fetch-user": "?1",
"upgrade-insecure-requests": "1",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.69",
"x-edge-shopping-flag": "1",
"x-forwarded-for": FORWARDED_IP,
}
def get_ssl_context():
import certifi
ssl_context = ssl.create_default_context()
ssl_context.load_verify_locations(certifi.where())
return ssl_context
class NotAllowedToAccess(Exception):
pass
class ConversationStyle(Enum):
creative = "h3imaginative,clgalileo,gencontentv3"
balanced = "galileo"
precise = "h3precise,clgalileo"
CONVERSATION_STYLE_TYPE = Optional[
Union[ConversationStyle, Literal["creative", "balanced", "precise"]]
]
def _append_identifier(msg: dict) -> str:
"""
Appends special character to end of message to identify end of message
"""
# Convert dict to json string
return json.dumps(msg) + DELIMITER
def _get_ran_hex(length: int = 32) -> str:
"""
Returns random hex string
"""
return "".join(random.choice("0123456789abcdef") for _ in range(length))
class _ChatHubRequest:
"""
Request object for ChatHub
"""
def __init__(
self,
conversation_signature: str,
client_id: str,
conversation_id: str,
invocation_id: int = 0,
) -> None:
self.struct: dict = {}
self.client_id: str = client_id
self.conversation_id: str = conversation_id
self.conversation_signature: str = conversation_signature
self.invocation_id: int = invocation_id
def update(
self,
prompt,
conversation_style,
options,
) -> None:
"""
Updates request object
"""
if options is None:
options = [
"deepleo",
"enable_debug_commands",
"disable_emoji_spoken_text",
"enablemm",
]
if conversation_style:
if not isinstance(conversation_style, ConversationStyle):
conversation_style = getattr(ConversationStyle, conversation_style)
options = [
"nlu_direct_response_filter",
"deepleo",
"disable_emoji_spoken_text",
"responsible_ai_policy_235",
"enablemm",
conversation_style.value,
"dtappid",
"cricinfo",
"cricinfov2",
"dv3sugg",
]
self.struct = {
"arguments": [
{
"source": "cib",
"optionsSets": options,
"sliceIds": [
"222dtappid",
"225cricinfo",
"224locals0",
],
"traceId": _get_ran_hex(32),
"isStartOfSession": self.invocation_id == 0,
"message": {
"author": "user",
"inputMethod": "Keyboard",
"text": prompt,
"messageType": "Chat",
},
"conversationSignature": self.conversation_signature,
"participant": {
"id": self.client_id,
},
"conversationId": self.conversation_id,
},
],
"invocationId": str(self.invocation_id),
"target": "chat",
"type": 4,
}
self.invocation_id += 1
class _Conversation:
"""
Conversation API
"""
def __init__(
self,
cookies,
proxy,
) -> None:
self.struct: dict = {
"conversationId": None,
"clientId": None,
"conversationSignature": None,
"result": {"value": "Success", "message": None},
}
import httpx
self.proxy = proxy
proxy = (
proxy
or os.environ.get("all_proxy")
or os.environ.get("ALL_PROXY")
or os.environ.get("https_proxy")
or os.environ.get("HTTPS_PROXY")
or None
)
if proxy is not None and proxy.startswith("socks5h://"):
proxy = "socks5://" + proxy[len("socks5h://") :]
self.session = httpx.Client(
proxies=proxy,
timeout=30,
headers=HEADERS_INIT_CONVER,
)
for cookie in cookies:
self.session.cookies.set(cookie["name"], cookie["value"])
# Send GET request
response = self.session.get(
url=os.environ.get("BING_PROXY_URL")
or "https://edgeservices.bing.com/edgesvc/turing/conversation/create",
)
if response.status_code != 200:
response = self.session.get(
"https://edge.churchless.tech/edgesvc/turing/conversation/create",
)
if response.status_code != 200:
print(f"Status code: {response.status_code}")
print(response.text)
print(response.url)
raise Exception("Authentication failed")
try:
self.struct = response.json()
except (json.decoder.JSONDecodeError, NotAllowedToAccess) as exc:
raise Exception(
"Authentication failed. You have not been accepted into the beta.",
) from exc
if self.struct["result"]["value"] == "UnauthorizedRequest":
raise NotAllowedToAccess(self.struct["result"]["message"])
class _ChatHub:
"""
Chat API
"""
def __init__(self, conversation) -> None:
self.wss = None
self.request: _ChatHubRequest
self.loop: bool
self.task: asyncio.Task
print(conversation.struct)
self.request = _ChatHubRequest(
conversation_signature=conversation.struct["conversationSignature"],
client_id=conversation.struct["clientId"],
conversation_id=conversation.struct["conversationId"],
)
async def ask_stream(
self,
prompt: str,
wss_link: str,
conversation_style: CONVERSATION_STYLE_TYPE = None,
raw: bool = False,
options: dict = None,
) -> Generator[str, None, None]:
"""
Ask a question to the bot
"""
if self.wss and not self.wss.closed:
await self.wss.close()
# Check if websocket is closed
self.wss = await websockets.connect(
wss_link,
extra_headers=HEADERS,
max_size=None,
ssl=get_ssl_context()
)
await self._initial_handshake()
# Construct a ChatHub request
self.request.update(
prompt=prompt,
conversation_style=conversation_style,
options=options,
)
# Send request
await self.wss.send(_append_identifier(self.request.struct))
final = False
while not final:
objects = str(await self.wss.recv()).split(DELIMITER)
for obj in objects:
if obj is None or not obj:
continue
response = json.loads(obj)
if response.get("type") != 2 and raw:
yield False, response
elif response.get("type") == 1 and response["arguments"][0].get(
"messages",
):
resp_txt = response["arguments"][0]["messages"][0]["adaptiveCards"][
0
]["body"][0].get("text")
yield False, resp_txt
elif response.get("type") == 2:
final = True
yield True, response
async def _initial_handshake(self) -> None:
await self.wss.send(_append_identifier({"protocol": "json", "version": 1}))
await self.wss.recv()
async def close(self) -> None:
"""
Close the connection
"""
if self.wss and not self.wss.closed:
await self.wss.close()
class NewbingChatbot:
"""
Combines everything to make it seamless
"""
def __init__(
self,
cookies,
proxy
) -> None:
if cookies is None:
cookies = {}
self.cookies = cookies
self.proxy = proxy
self.chat_hub: _ChatHub = _ChatHub(
_Conversation(self.cookies, self.proxy),
)
async def ask(
self,
prompt: str,
wss_link: str,
conversation_style: CONVERSATION_STYLE_TYPE = None,
options: dict = None,
) -> dict:
"""
Ask a question to the bot
"""
async for final, response in self.chat_hub.ask_stream(
prompt=prompt,
conversation_style=conversation_style,
wss_link=wss_link,
options=options,
):
if final:
return response
await self.chat_hub.wss.close()
return None
async def ask_stream(
self,
prompt: str,
wss_link: str,
conversation_style: CONVERSATION_STYLE_TYPE = None,
raw: bool = False,
options: dict = None,
) -> Generator[str, None, None]:
"""
Ask a question to the bot
"""
async for response in self.chat_hub.ask_stream(
prompt=prompt,
conversation_style=conversation_style,
wss_link=wss_link,
raw=raw,
options=options,
):
yield response
async def close(self) -> None:
"""
Close the connection
"""
await self.chat_hub.close()
async def reset(self) -> None:
"""
Reset the conversation
"""
await self.close()
self.chat_hub = _ChatHub(_Conversation(self.cookies, self.proxy))

View File

@@ -0,0 +1,8 @@
BingImageCreator
certifi
httpx
prompt_toolkit
requests
rich
websockets
httpx[socks]

View File

@@ -5,7 +5,20 @@ import inspect
import re
from latex2mathml.converter import convert as tex2mathml
from functools import wraps, lru_cache
############################### 插件输入输出接驳区 #######################################
"""
========================================================================
第一部分
函数插件输入输出接驳区
- ChatBotWithCookies: 带Cookies的Chatbot类为实现更多强大的功能做基础
- ArgsGeneralWrapper: 装饰器函数,用于重组输入参数,改变输入参数的顺序与结构
- update_ui: 刷新界面用 yield from update_ui(chatbot, history)
- CatchException: 将插件中出的所有问题显示在界面上
- HotReload: 实现插件的热更新
- trimmed_format_exc: 打印traceback为了安全而隐藏绝对地址
========================================================================
"""
class ChatBotWithCookies(list):
def __init__(self, cookie):
self._cookies = cookie
@@ -20,6 +33,7 @@ class ChatBotWithCookies(list):
def get_cookies(self):
return self._cookies
def ArgsGeneralWrapper(f):
"""
装饰器函数,用于重组输入参数,改变输入参数的顺序与结构。
@@ -47,6 +61,7 @@ def ArgsGeneralWrapper(f):
yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
return decorated
def update_ui(chatbot, history, msg='正常', **kwargs): # 刷新界面
"""
刷新用户界面
@@ -54,10 +69,18 @@ def update_ui(chatbot, history, msg='正常', **kwargs): # 刷新界面
assert isinstance(chatbot, ChatBotWithCookies), "在传递chatbot的过程中不要将其丢弃。必要时可用clear将其清空然后用for+append循环重新赋值。"
yield chatbot.get_cookies(), chatbot, history, msg
def trimmed_format_exc():
import os, traceback
str = traceback.format_exc()
current_path = os.getcwd()
replace_path = "."
return str.replace(current_path, replace_path)
def CatchException(f):
"""
装饰器函数捕捉函数f中的异常并封装到一个生成器中返回并显示到聊天当中。
"""
@wraps(f)
def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
try:
@@ -66,9 +89,10 @@ def CatchException(f):
from check_proxy import check_proxy
from toolbox import get_conf
proxies, = get_conf('proxies')
tb_str = '```\n' + traceback.format_exc() + '```'
if chatbot is None or len(chatbot) == 0:
chatbot = [["插件调度异常", "异常原因"]]
tb_str = '```\n' + trimmed_format_exc() + '```'
if len(chatbot) == 0:
chatbot.clear()
chatbot.append(["插件调度异常", "异常原因"])
chatbot[-1] = (chatbot[-1][0],
f"[Local Message] 实验性函数调用出错: \n\n{tb_str} \n\n当前代理可用性: \n\n{check_proxy(proxies)}")
yield from update_ui(chatbot=chatbot, history=history, msg=f'异常 {e}') # 刷新界面
@@ -93,7 +117,23 @@ def HotReload(f):
return decorated
####################################### 其他小工具 #####################################
"""
========================================================================
第二部分
其他小工具:
- write_results_to_file: 将结果写入markdown文件中
- regular_txt_to_markdown: 将普通文本转换为Markdown格式的文本。
- report_execption: 向chatbot中添加简单的意外错误信息
- text_divide_paragraph: 将文本按照段落分隔符分割开生成带有段落标签的HTML代码。
- markdown_convertion: 用多种方式组合将markdown转化为好看的html
- format_io: 接管gradio默认的markdown处理方式
- on_file_uploaded: 处理文件的上传(自动解压)
- on_report_generated: 将生成的报告自动投射到文件上传区
- clip_history: 当历史上下文过长时,自动截断
- get_conf: 获取设置
- select_api_key: 根据当前的模型类别抽取可用的api-key
========================================================================
"""
def get_reduce_token_percent(text):
"""
@@ -113,7 +153,6 @@ def get_reduce_token_percent(text):
return 0.5, '不详'
def write_results_to_file(history, file_name=None):
"""
将对话记录history以Markdown格式写入文件中。如果没有指定文件名则使用当前时间生成文件名。
@@ -178,13 +217,17 @@ def text_divide_paragraph(text):
text = "</br>".join(lines)
return text
@lru_cache(maxsize=128) # 使用 lru缓存 加快转换速度
def markdown_convertion(txt):
"""
将Markdown格式的文本转换为HTML格式。如果包含数学公式则先将公式转换为HTML格式。
"""
pre = '<div class="markdown-body">'
suf = '</div>'
if txt.startswith(pre) and txt.endswith(suf):
# print('警告,输入了已经经过转化的字符串,二次转化可能出问题')
return txt # 已经被转化过,不需要再次转化
markdown_extension_configs = {
'mdx_math': {
'enable_dollar_delimiter': True,
@@ -228,8 +271,14 @@ def markdown_convertion(txt):
content = content.replace('</script>\n</script>', '</script>')
return content
def no_code(txt):
if '```' not in txt:
return True
else:
if '```reference' in txt: return True # newbing
else: return False
if ('$' in txt) and ('```' not in txt): # 有$标识的公式符号,且没有代码段```的标识
if ('$' in txt) and no_code(txt): # 有$标识的公式符号,且没有代码段```的标识
# convert everything to html format
split = markdown.markdown(text='---')
convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs)
@@ -369,6 +418,9 @@ def find_recent_files(directory):
def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
"""
当文件被上传时的回调函数
"""
if len(files) == 0:
return chatbot, txt
import shutil
@@ -388,8 +440,7 @@ def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
err_msg += extract_archive(f'private_upload/{time_tag}/{file_origin_name}',
dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
moved_files = [fp for fp in glob.glob(
'private_upload/**/*', recursive=True)]
moved_files = [fp for fp in glob.glob('private_upload/**/*', recursive=True)]
if "底部输入区" in checkboxes:
txt = ""
txt2 = f'private_upload/{time_tag}'
@@ -414,8 +465,9 @@ def on_report_generated(files, chatbot):
return report_files, chatbot
def is_openai_api_key(key):
API_MATCH = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
return bool(API_MATCH)
API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{32}$", key)
return bool(API_MATCH_ORIGINAL) or bool(API_MATCH_AZURE)
def is_api2d_key(key):
if key.startswith('fk') and len(key) == 41:
@@ -508,7 +560,7 @@ def clear_line_break(txt):
class DummyWith():
"""
这段代码定义了一个名为DummyWith的空上下文管理器
它的作用是……额……用,即在代码结构不变得情况下取代其他的上下文管理器。
它的作用是……额……就是不起作用,即在代码结构不变得情况下取代其他的上下文管理器。
上下文管理器是一种Python对象用于与with语句一起使用
以确保一些资源在代码块执行期间得到正确的初始化和清理。
上下文管理器必须实现两个方法,分别为 __enter__()和 __exit__()。
@@ -522,6 +574,9 @@ class DummyWith():
return
def run_gradio_in_subpath(demo, auth, port, custom_path):
"""
把gradio的运行地址更改到指定的二次路径上
"""
def is_path_legal(path: str)->bool:
'''
check path for sub url

View File

@@ -1,5 +1,5 @@
{
"version": 3.2,
"version": 3.32,
"show_feature": true,
"new_feature": "保存对话功能 <-> 解读任意语言代码+同时询问任意的LLM组合 <-> 添加联网Google回答问题插件 <-> 修复ChatGLM上下文BUG <-> 添加支持清华ChatGLM和GPT-4 <-> 改进架构支持与多个LLM模型同时对话 <-> 添加支持API2D国内可支持gpt4"
"new_feature": "完善对话历史的保存/载入/删除 <-> 我们发现了自动更新模块的BUG此次更新可能需要您手动到Github下载新版程序并覆盖 <-> ChatGLM加线程锁提高并发稳定性 <-> 支持NewBing <-> Markdown翻译功能支持直接输入Readme文件网址 <-> 保存对话功能 <-> 解读任意语言代码+同时询问任意的LLM组合 <-> 添加联网Google回答问题插件 <-> 修复ChatGLM上下文BUG <-> 添加支持清华ChatGLM"
}