Merge branch 'master' into improve_ui_master
This commit is contained in:
binary-husky
commit
f725ab1b31
22
Dockerfile
22
Dockerfile
@ -1,28 +1,34 @@
|
||||
# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
|
||||
# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic .
|
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# 如何运行: docker run --rm -it --net=host gpt-academic
|
||||
# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型或者latex运行依赖，请参考 docker-compose.yml
|
||||
# 如何构建: 先修改 `config.py`， 然后 `docker build -t gpt-academic . `
|
||||
# 如何运行(Linux下): `docker run --rm -it --net=host gpt-academic `
|
||||
# 如何运行(其他操作系统，选择任意一个固定端口50923): `docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic `
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FROM python:3.11
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|
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|
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# 非必要步骤，更换pip源
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RUN echo '[global]' > /etc/pip.conf && \
|
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echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
|
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echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
|
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|
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|
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# 进入工作路径
|
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WORKDIR /gpt
|
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|
||||
|
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|
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|
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# 安装依赖
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# 安装大部分依赖，利用Docker缓存加速以后的构建
|
||||
COPY requirements.txt ./
|
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COPY ./docs/gradio-3.32.2-py3-none-any.whl ./docs/gradio-3.32.2-py3-none-any.whl
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RUN pip3 install -r requirements.txt
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# 装载项目文件
|
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|
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|
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# 装载项目文件，安装剩余依赖
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COPY . .
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RUN pip3 install -r requirements.txt
|
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|
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# 可选步骤，用于预热模块
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|
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# 非必要步骤，用于预热模块
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RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
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|
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|
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# 启动
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CMD ["python3", "-u", "main.py"]
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|
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50
README.md
50
README.md
@ -1,24 +1,26 @@
|
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> **Note**
|
||||
>
|
||||
> 2023.5.27 对Gradio依赖进行了调整，Fork并解决了官方Gradio的若干Bugs。请及时**更新代码**并重新更新pip依赖。安装依赖时，请严格选择`requirements.txt`中**指定的版本**：
|
||||
>
|
||||
> `pip install -r requirements.txt`
|
||||
> 2023.7.5: Gradio依赖调整。请及时**更新代码**
|
||||
>
|
||||
> 2023.7.8: pydantic出现兼容问题，已修改 `requirements.txt`。安装依赖时，请严格选择`requirements.txt`中**指定的版本**
|
||||
>
|
||||
> `pip install -r requirements.txt`
|
||||
|
||||
# <img src="docs/logo.png" width="40" > GPT 学术优化 (GPT 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pull requests**
|
||||
# <div align=center><img src="docs/logo.png" width="40" > GPT 学术优化 (GPT Academic)</div>
|
||||
|
||||
**如果喜欢这个项目，请给它一个Star；如果您发明了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)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md) translated by this project itself.
|
||||
To translate this project to arbitary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).
|
||||
|
||||
> **Note**
|
||||
>
|
||||
> 1.请注意只有**红颜色**标识的函数插件（按钮）才支持读取文件，部分插件位于插件区的**下拉菜单**中。另外我们以**最高优先级**欢迎和处理任何新插件的PR！
|
||||
> 1.请注意只有 **高亮(如红色)** 标识的函数插件（按钮）才支持读取文件，部分插件位于插件区的**下拉菜单**中。另外我们以**最高优先级**欢迎和处理任何新插件的PR。
|
||||
>
|
||||
> 2.本项目中每个文件的功能都在自译解[`self_analysis.md`](https://github.com/binary-husky/gpt_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)详细说明。随着版本的迭代，您也可以随时自行点击相关函数插件，调用GPT重新生成项目的自我解析报告。常见问题汇总在[`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)当中。[安装方法](#installation)。
|
||||
>
|
||||
> 3.本项目兼容并鼓励尝试国产大语言模型chatglm和RWKV, 盘古等等。支持多个api-key共存，可在配置文件中填写如`API_KEY="openai-key1,openai-key2,api2d-key3"`。需要临时更换`API_KEY`时，在输入区输入临时的`API_KEY`然后回车键提交后即可生效。
|
||||
> 3.本项目兼容并鼓励尝试国产大语言模型ChatGLM和Moss等等。支持多个api-key共存，可在配置文件中填写如`API_KEY="openai-key1,openai-key2,api2d-key3"`。需要临时更换`API_KEY`时，在输入区输入临时的`API_KEY`然后回车键提交后即可生效。
|
||||
|
||||
|
||||
|
||||
@ -42,13 +44,13 @@ 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）
|
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[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
|
||||
⭐Arxiv论文精细翻译 | [函数插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，迄今为止最好的论文翻译工具⭐
|
||||
互联网信息聚合+GPT | [函数插件] 一键[让GPT从互联网获取信息](https://www.bilibili.com/video/BV1om4y127ck)回答问题，让信息永不过时
|
||||
⭐Arxiv论文精细翻译 | [函数插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，目前最好的论文翻译工具
|
||||
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/gpt_academic/issues/173) | 在浏览器url后面添加```/?__theme=dark```可以切换dark主题
|
||||
启动暗色[主题](https://github.com/binary-husky/gpt_academic/issues/173) | 在浏览器url后面添加```/?__theme=dark```可以切换dark主题
|
||||
[多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持 | 同时被GPT3.5、GPT4、[清华ChatGLM](https://github.com/THUDM/ChatGLM-6B)、[复旦MOSS](https://github.com/OpenLMLab/MOSS)同时伺候的感觉一定会很不错吧？
|
||||
更多LLM模型接入，支持[huggingface部署](https://huggingface.co/spaces/qingxu98/gpt-academic) | 加入Newbing接口(新必应)，引入清华[Jittorllms](https://github.com/Jittor/JittorLLMs)支持[LLaMA](https://github.com/facebookresearch/llama)，[RWKV](https://github.com/BlinkDL/ChatRWKV)和[盘古α](https://openi.org.cn/pangu/)
|
||||
更多LLM模型接入，支持[huggingface部署](https://huggingface.co/spaces/qingxu98/gpt-academic) | 加入Newbing接口(新必应)，引入清华[Jittorllms](https://github.com/Jittor/JittorLLMs)支持[LLaMA](https://github.com/facebookresearch/llama)和[盘古α](https://openi.org.cn/pangu/)
|
||||
更多新功能展示(图像生成等) …… | 见本文档结尾处 ……
|
||||
|
||||
</div>
|
||||
@ -85,9 +87,8 @@ chat分析报告生成 | [函数插件] 运行后自动生成总结汇报
|
||||
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
|
||||
</div>
|
||||
|
||||
---
|
||||
# Installation
|
||||
## 安装-方法1：直接运行 (Windows, Linux or MacOS)
|
||||
### 安装方法I：直接运行 (Windows, Linux or MacOS)
|
||||
|
||||
1. 下载项目
|
||||
```sh
|
||||
@ -140,7 +141,7 @@ AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-
|
||||
python main.py
|
||||
```
|
||||
|
||||
## 安装-方法2：使用Docker
|
||||
### 安装方法II：使用Docker
|
||||
|
||||
1. 仅ChatGPT（推荐大多数人选择，等价于docker-compose方案1）
|
||||
|
||||
@ -171,7 +172,7 @@ docker-compose up
|
||||
```
|
||||
|
||||
|
||||
## 安装-方法3：其他部署姿势
|
||||
### 安装方法III：其他部署姿势
|
||||
1. 一键运行脚本。
|
||||
完全不熟悉python环境的Windows用户可以下载[Release](https://github.com/binary-husky/gpt_academic/releases)中发布的一键运行脚本安装无本地模型的版本。
|
||||
脚本的贡献来源是[oobabooga](https://github.com/oobabooga/one-click-installers)。
|
||||
@ -194,11 +195,9 @@ docker-compose up
|
||||
7. 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)
|
||||
|
||||
---
|
||||
# Advanced Usage
|
||||
## 自定义新的便捷按钮 / 自定义函数插件
|
||||
|
||||
1. 自定义新的便捷按钮（学术快捷键）
|
||||
# Advanced Usage
|
||||
### I：自定义新的便捷按钮（学术快捷键）
|
||||
任意文本编辑器打开`core_functional.py`，添加条目如下，然后重启程序即可。（如果按钮已经添加成功并可见，那么前缀、后缀都支持热修改，无需重启程序即可生效。）
|
||||
例如
|
||||
```
|
||||
@ -214,15 +213,15 @@ docker-compose up
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
|
||||
</div>
|
||||
|
||||
2. 自定义函数插件
|
||||
### II：自定义函数插件
|
||||
|
||||
编写强大的函数插件来执行任何你想得到的和想不到的任务。
|
||||
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/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)。
|
||||
|
||||
---
|
||||
|
||||
# Latest Update
|
||||
## 新功能动态
|
||||
### I：新功能动态
|
||||
|
||||
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，
|
||||
另外在函数插件区（下拉菜单）调用 `载入对话历史存档` ，即可还原之前的会话。
|
||||
@ -283,7 +282,7 @@ Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史h
|
||||
|
||||
|
||||
|
||||
## 版本:
|
||||
### II：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: +arxiv论文翻译、latex论文批改功能
|
||||
- version 3.3: +互联网信息综合功能
|
||||
@ -305,11 +304,12 @@ gpt_academic开发者QQ群-2：610599535
|
||||
- 某些浏览器翻译插件干扰此软件前端的运行
|
||||
- 官方Gradio目前有很多兼容性Bug，请务必使用`requirement.txt`安装Gradio
|
||||
|
||||
## 主题
|
||||
### III：主题
|
||||
|
||||
1. `Chuanhu-Keldos-Green` [网址](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
|
||||
|
||||
## 参考与学习
|
||||
|
||||
### IV：参考与学习
|
||||
|
||||
```
|
||||
代码中参考了很多其他优秀项目中的设计，顺序不分先后：
|
||||
|
||||
94
config.py
94
config.py
@ -1,17 +1,27 @@
|
||||
# [step 1]>> 例如： API_KEY = "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" （此key无效）
|
||||
API_KEY = "sk-此处填API密钥" # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey1,fkxxxx-api2dkey2"
|
||||
"""
|
||||
以下所有配置也都支持利用环境变量覆写，环境变量配置格式见docker-compose.yml。
|
||||
读取优先级：环境变量 > config_private.py > config.py
|
||||
--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---
|
||||
All the following configurations also support using environment variables to override,
|
||||
and the environment variable configuration format can be seen in docker-compose.yml.
|
||||
Configuration reading priority: environment variable > config_private.py > config.py
|
||||
"""
|
||||
|
||||
# [step 1]>> API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下，还需要填写组织（格式如org-123456789abcdefghijklmno的），请向下翻，找 API_ORG 设置项
|
||||
API_KEY = "此处填API密钥" # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
|
||||
|
||||
|
||||
# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
|
||||
USE_PROXY = False
|
||||
if USE_PROXY:
|
||||
# 填写格式是 [协议]:// [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
|
||||
# 例如 "socks5h://localhost:11284"
|
||||
# [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
|
||||
# [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*学*网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
|
||||
"""
|
||||
填写格式是 [协议]:// [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
|
||||
<配置教程&视频教程> https://github.com/binary-husky/gpt_academic/issues/1>
|
||||
[协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
|
||||
[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*学*网软件查看代理的协议(socks5h / http)、地址(localhost)和端口(11284)
|
||||
proxies = {
|
||||
# [协议]:// [地址] :[端口]
|
||||
"http": "socks5h://localhost:11284", # 再例如 "http": "http://127.0.0.1:7890",
|
||||
@ -20,82 +30,100 @@ if USE_PROXY:
|
||||
else:
|
||||
proxies = None
|
||||
|
||||
# [step 3]>> 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
|
||||
# 一言以蔽之：免费用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
|
||||
# ------------------------------------ 以下配置可以优化体验, 但大部分场合下并不需要修改 ------------------------------------
|
||||
|
||||
# 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!! 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 API_URL_REDIRECT = {"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://reverse-proxy-url/v1/chat/completions"}
|
||||
API_URL_REDIRECT = {}
|
||||
|
||||
|
||||
# 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
|
||||
# 一言以蔽之：免费（5刀）用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
|
||||
DEFAULT_WORKER_NUM = 3
|
||||
|
||||
|
||||
# [step 4]>> 以下配置可以优化体验，但大部分场合下并不需要修改
|
||||
# 对话窗的高度
|
||||
CHATBOT_HEIGHT = 1115
|
||||
|
||||
|
||||
# 代码高亮
|
||||
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）
|
||||
LAYOUT = "LEFT-RIGHT" # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
|
||||
DARK_MODE = True # 暗色模式 / 亮色模式
|
||||
|
||||
|
||||
# 发送请求到OpenAI后，等待多久判定为超时
|
||||
TIMEOUT_SECONDS = 30
|
||||
|
||||
|
||||
# 网页的端口, -1代表随机端口
|
||||
WEB_PORT = -1
|
||||
|
||||
|
||||
# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
|
||||
MAX_RETRY = 2
|
||||
|
||||
# 模型选择是 (注意: LLM_MODEL是默认选中的模型, 同时它必须被包含在AVAIL_LLM_MODELS切换列表中 )
|
||||
|
||||
# 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
|
||||
LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt35", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "newbing-free", "stack-claude"]
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "stack-claude"]
|
||||
# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "newbing-free", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
|
||||
|
||||
# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
|
||||
LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
|
||||
|
||||
|
||||
# 设置gradio的并行线程数（不需要修改）
|
||||
CONCURRENT_COUNT = 100
|
||||
|
||||
|
||||
# 是否在提交时自动清空输入框
|
||||
AUTO_CLEAR_TXT = False
|
||||
|
||||
# 色彩主体，可选 ["Default", "Chuanhu-Keldos-Green"]
|
||||
THEME = "Chuanhu-Keldos-Green"
|
||||
THEME = "Default"
|
||||
|
||||
# 加一个live2d装饰
|
||||
ADD_WAIFU = False
|
||||
|
||||
|
||||
# 设置用户名和密码（不需要修改）（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
|
||||
# [("username", "password"), ("username2", "password2"), ...]
|
||||
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!!）
|
||||
# （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"]
|
||||
# 从现在起，如果您调用"newbing-free"模型，则无需填写NEWBING_COOKIES
|
||||
NEWBING_COOKIES = """
|
||||
your bing cookies here
|
||||
"""
|
||||
|
||||
# 极少数情况下，openai的官方KEY需要伴随组织编码（格式如org-xxxxxxxxxxxxxxxxxxxxxxxx）使用
|
||||
API_ORG = ""
|
||||
|
||||
|
||||
# 如果需要使用Slack Claude，使用教程详情见 request_llm/README.md
|
||||
SLACK_CLAUDE_BOT_ID = ''
|
||||
SLACK_CLAUDE_USER_TOKEN = ''
|
||||
|
||||
|
||||
# 如果需要使用AZURE 详情请见额外文档 docs\use_azure.md
|
||||
AZURE_ENDPOINT = "https://你的api名称.openai.azure.com/"
|
||||
AZURE_API_KEY = "填入azure openai api的密钥"
|
||||
AZURE_API_VERSION = "填入api版本"
|
||||
AZURE_ENGINE = "填入ENGINE"
|
||||
AZURE_ENDPOINT = "https://你亲手写的api名称.openai.azure.com/"
|
||||
AZURE_API_KEY = "填入azure openai api的密钥" # 建议直接在API_KEY处填写，该选项即将被弃用
|
||||
AZURE_ENGINE = "填入你亲手写的部署名" # 读 docs\use_azure.md
|
||||
|
||||
|
||||
# 使用Newbing
|
||||
NEWBING_STYLE = "creative" # ["creative", "balanced", "precise"]
|
||||
NEWBING_COOKIES = """
|
||||
put your new bing cookies here
|
||||
"""
|
||||
|
||||
|
||||
# 阿里云实时语音识别 配置门槛较高 限高级用户使用 参考 https://help.aliyun.com/document_detail/450255.html
|
||||
ENABLE_AUDIO = True
|
||||
ALIYUN_TOKEN="" # 例如 f37f30e0f9934c34a992f6f64f7eba4f
|
||||
ALIYUN_APPKEY="" # 例如 RoPlZrM88DnAFkZK
|
||||
ALIYUN_APPKEY="" # 例如 RoPlZrM88DnAFkZK
|
||||
@ -352,6 +352,32 @@ def get_crazy_functions():
|
||||
})
|
||||
except:
|
||||
print('Load function plugin failed')
|
||||
|
||||
try:
|
||||
from crazy_functions.交互功能函数模板 import 交互功能模板函数
|
||||
function_plugins.update({
|
||||
"交互功能模板函数": {
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Function": HotReload(交互功能模板函数)
|
||||
}
|
||||
})
|
||||
except:
|
||||
print('Load function plugin failed')
|
||||
|
||||
# try:
|
||||
# from crazy_functions.chatglm微调工具 import 微调数据集生成
|
||||
# function_plugins.update({
|
||||
# "黑盒模型学习: 微调数据集生成 (先上传数据集)": {
|
||||
# "Color": "stop",
|
||||
# "AsButton": False,
|
||||
# "AdvancedArgs": True,
|
||||
# "ArgsReminder": "针对数据集输入（如 绿帽子*深蓝色衬衫*黑色运动裤）给出指令，例如您可以将以下命令复制到下方: --llm_to_learn=azure-gpt-3.5 --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、过去经历进行描写。要求：100字以内，用第二人称。' --system_prompt=''",
|
||||
# "Function": HotReload(微调数据集生成)
|
||||
# }
|
||||
# })
|
||||
# except:
|
||||
# print('Load function plugin failed')
|
||||
|
||||
try:
|
||||
from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比
|
||||
|
||||
71
crazy_functions/chatglm微调工具.py
Normal file
71
crazy_functions/chatglm微调工具.py
Normal file
@ -0,0 +1,71 @@
|
||||
from toolbox import CatchException, update_ui, promote_file_to_downloadzone
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
import datetime, json
|
||||
|
||||
def fetch_items(list_of_items, batch_size):
|
||||
for i in range(0, len(list_of_items), batch_size):
|
||||
yield list_of_items[i:i + batch_size]
|
||||
|
||||
def string_to_options(arguments):
|
||||
import argparse
|
||||
import shlex
|
||||
|
||||
# Create an argparse.ArgumentParser instance
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
# Add command-line arguments
|
||||
parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo")
|
||||
parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='')
|
||||
parser.add_argument("--system_prompt", type=str, help="System prompt", default='')
|
||||
parser.add_argument("--batch", type=int, help="System prompt", default=50)
|
||||
|
||||
# Parse the arguments
|
||||
args = parser.parse_args(shlex.split(arguments))
|
||||
|
||||
return args
|
||||
|
||||
@CatchException
|
||||
def 微调数据集生成(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
|
||||
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
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
args = plugin_kwargs.get("advanced_arg", None)
|
||||
if args is None:
|
||||
chatbot.append(("没给定指令", "退出"))
|
||||
yield from update_ui(chatbot=chatbot, history=history); return
|
||||
else:
|
||||
arguments = string_to_options(arguments=args)
|
||||
|
||||
dat = []
|
||||
with open(txt, 'r', encoding='utf8') as f:
|
||||
for line in f.readlines():
|
||||
json_dat = json.loads(line)
|
||||
dat.append(json_dat["content"])
|
||||
|
||||
llm_kwargs['llm_model'] = arguments.llm_to_learn
|
||||
for batch in fetch_items(dat, arguments.batch):
|
||||
res = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array=[f"{arguments.prompt_prefix}\n\n{b}" for b in (batch)],
|
||||
inputs_show_user_array=[f"Show Nothing" for _ in (batch)],
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history_array=[[] for _ in (batch)],
|
||||
sys_prompt_array=[arguments.system_prompt for _ in (batch)],
|
||||
max_workers=10 # OpenAI所允许的最大并行过载
|
||||
)
|
||||
|
||||
with open(txt+'.generated.json', 'a+', encoding='utf8') as f:
|
||||
for b, r in zip(batch, res[1::2]):
|
||||
f.write(json.dumps({"content":b, "summary":r}, ensure_ascii=False)+'\n')
|
||||
|
||||
promote_file_to_downloadzone(txt+'.generated.json', rename_file='generated.json', chatbot=chatbot)
|
||||
return
|
||||
@ -211,22 +211,30 @@ def test_Latex():
|
||||
# # for cookies, cb, hist, msg in silence_stdout(编译Latex)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
# cli_printer.print(cb) # print(cb)
|
||||
|
||||
def test_chatglm_finetune():
|
||||
from crazy_functions.chatglm微调工具 import 微调数据集生成
|
||||
txt = 'build/dev.json'
|
||||
plugin_kwargs = {"advanced_arg":"--llm_to_learn=gpt-3.5-turbo --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、人设进行描写。要求：100字以内，用第二人称。' --system_prompt=''" }
|
||||
|
||||
for cookies, cb, hist, msg in (微调数据集生成)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
cli_printer.print(cb)
|
||||
|
||||
|
||||
# test_解析一个Python项目()
|
||||
# test_Latex英文润色()
|
||||
# test_Markdown中译英()
|
||||
# test_批量翻译PDF文档()
|
||||
# test_谷歌检索小助手()
|
||||
# test_总结word文档()
|
||||
# test_下载arxiv论文并翻译摘要()
|
||||
# test_解析一个Cpp项目()
|
||||
# test_联网回答问题()
|
||||
# test_解析ipynb文件()
|
||||
# test_数学动画生成manim()
|
||||
# test_Langchain知识库()
|
||||
# test_Langchain知识库读取()
|
||||
if __name__ == "__main__":
|
||||
test_Latex()
|
||||
# test_解析一个Python项目()
|
||||
# test_Latex英文润色()
|
||||
# test_Markdown中译英()
|
||||
# test_批量翻译PDF文档()
|
||||
# test_谷歌检索小助手()
|
||||
# test_总结word文档()
|
||||
# test_下载arxiv论文并翻译摘要()
|
||||
# test_解析一个Cpp项目()
|
||||
# test_联网回答问题()
|
||||
# test_解析ipynb文件()
|
||||
# test_数学动画生成manim()
|
||||
# test_Langchain知识库()
|
||||
# test_Langchain知识库读取()
|
||||
# test_Latex()
|
||||
test_chatglm_finetune()
|
||||
input("程序完成，回车退出。")
|
||||
print("退出。")
|
||||
@ -130,6 +130,11 @@ def request_gpt_model_in_new_thread_with_ui_alive(
|
||||
yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
|
||||
return final_result
|
||||
|
||||
def can_multi_process(llm):
|
||||
if llm.startswith('gpt-'): return True
|
||||
if llm.startswith('api2d-'): return True
|
||||
if llm.startswith('azure-'): return True
|
||||
return False
|
||||
|
||||
def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array, inputs_show_user_array, llm_kwargs,
|
||||
@ -175,7 +180,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
except: 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-')):
|
||||
if not can_multi_process(llm_kwargs['llm_model']):
|
||||
max_workers = 1
|
||||
|
||||
executor = ThreadPoolExecutor(max_workers=max_workers)
|
||||
|
||||
@ -189,6 +189,18 @@ def rm_comments(main_file):
|
||||
main_file = re.sub(r'(?<!\\)%.*', '', main_file) # 使用正则表达式查找半行注释, 并替换为空字符串
|
||||
return main_file
|
||||
|
||||
def find_tex_file_ignore_case(fp):
|
||||
dir_name = os.path.dirname(fp)
|
||||
base_name = os.path.basename(fp)
|
||||
if not base_name.endswith('.tex'): base_name+='.tex'
|
||||
if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)
|
||||
# go case in-sensitive
|
||||
import glob
|
||||
for f in glob.glob(dir_name+'/*.tex'):
|
||||
base_name_s = os.path.basename(fp)
|
||||
if base_name_s.lower() == base_name.lower(): return f
|
||||
return None
|
||||
|
||||
def merge_tex_files_(project_foler, main_file, mode):
|
||||
"""
|
||||
Merge Tex project recrusively
|
||||
@ -197,15 +209,11 @@ def merge_tex_files_(project_foler, main_file, mode):
|
||||
for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
|
||||
f = s.group(1)
|
||||
fp = os.path.join(project_foler, f)
|
||||
if os.path.exists(fp):
|
||||
# e.g., \input{srcs/07_appendix.tex}
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as fx:
|
||||
c = fx.read()
|
||||
else:
|
||||
# e.g., \input{srcs/07_appendix}
|
||||
assert os.path.exists(fp+'.tex'), f'即找不到{fp}，也找不到{fp}.tex，Tex源文件缺失！'
|
||||
with open(fp+'.tex', 'r', encoding='utf-8', errors='replace') as fx:
|
||||
c = fx.read()
|
||||
fp = find_tex_file_ignore_case(fp)
|
||||
if fp:
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as fx: c = fx.read()
|
||||
else:
|
||||
raise RuntimeError(f'找不到{fp}，Tex源文件缺失！')
|
||||
c = merge_tex_files_(project_foler, c, mode)
|
||||
main_file = main_file[:s.span()[0]] + c + main_file[s.span()[1]:]
|
||||
return main_file
|
||||
@ -324,7 +332,7 @@ def split_subprocess(txt, project_folder, return_dict, opts):
|
||||
# 吸收在42行以内的begin-end组合
|
||||
text, mask = set_forbidden_text_begin_end(text, mask, r"\\begin\{([a-z\*]*)\}(.*?)\\end\{\1\}", re.DOTALL, limit_n_lines=42)
|
||||
# 吸收匿名公式
|
||||
text, mask = set_forbidden_text(text, mask, [ r"\$\$(.*?)\$\$", r"\\\[.*?\\\]" ], re.DOTALL)
|
||||
text, mask = set_forbidden_text(text, mask, [ r"\$\$([^$]+)\$\$", r"\\\[.*?\\\]" ], re.DOTALL)
|
||||
# 吸收其他杂项
|
||||
text, mask = set_forbidden_text(text, mask, [ r"\\section\{(.*?)\}", r"\\section\*\{(.*?)\}", r"\\subsection\{(.*?)\}", r"\\subsubsection\{(.*?)\}" ])
|
||||
text, mask = set_forbidden_text(text, mask, [ r"\\bibliography\{(.*?)\}", r"\\bibliographystyle\{(.*?)\}" ])
|
||||
|
||||
63
crazy_functions/交互功能函数模板.py
Normal file
63
crazy_functions/交互功能函数模板.py
Normal file
@ -0,0 +1,63 @@
|
||||
from toolbox import CatchException, update_ui
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
|
||||
|
||||
@CatchException
|
||||
def 交互功能模板函数(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, 如温度和top_p等, 一般原样传递下去就行
|
||||
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
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append(("这是什么功能？", "交互功能函数模板。在执行完成之后, 可以将自身的状态存储到cookie中, 等待用户的再次调用。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
state = chatbot._cookies.get('plugin_state_0001', None) # 初始化插件状态
|
||||
|
||||
if state is None:
|
||||
chatbot._cookies['lock_plugin'] = 'crazy_functions.交互功能函数模板->交互功能模板函数' # 赋予插件锁定 锁定插件回调路径，当下一次用户提交时，会直接转到该函数
|
||||
chatbot._cookies['plugin_state_0001'] = 'wait_user_keyword' # 赋予插件状态
|
||||
|
||||
chatbot.append(("第一次调用：", "请输入关键词, 我将为您查找相关壁纸, 建议使用英文单词, 插件锁定中，请直接提交即可。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
if state == 'wait_user_keyword':
|
||||
chatbot._cookies['lock_plugin'] = None # 解除插件锁定，避免遗忘导致死锁
|
||||
chatbot._cookies['plugin_state_0001'] = None # 解除插件状态，避免遗忘导致死锁
|
||||
|
||||
# 解除插件锁定
|
||||
chatbot.append((f"获取关键词：{txt}", ""))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
page_return = get_image_page_by_keyword(txt)
|
||||
inputs=inputs_show_user=f"Extract all image urls in this html page, pick the first 5 images and show them with markdown format: \n\n {page_return}"
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=inputs, inputs_show_user=inputs_show_user,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt="When you want to show an image, use markdown format. e.g. . If there are no image url provided, answer 'no image url provided'"
|
||||
)
|
||||
chatbot[-1] = [chatbot[-1][0], gpt_say]
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------------
|
||||
|
||||
def get_image_page_by_keyword(keyword):
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
response = requests.get(f'https://wallhaven.cc/search?q={keyword}', timeout=2)
|
||||
res = "image urls: \n"
|
||||
for image_element in BeautifulSoup(response.content, 'html.parser').findAll("img"):
|
||||
try:
|
||||
res += image_element["data-src"]
|
||||
res += "\n"
|
||||
except:
|
||||
pass
|
||||
return res
|
||||
@ -14,17 +14,19 @@ def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot
|
||||
doc = Document(fp)
|
||||
file_content = "\n".join([para.text for para in doc.paragraphs])
|
||||
else:
|
||||
import win32com.client
|
||||
word = win32com.client.Dispatch("Word.Application")
|
||||
word.visible = False
|
||||
# 打开文件
|
||||
print('fp', os.getcwd())
|
||||
doc = word.Documents.Open(os.getcwd() + '/' + fp)
|
||||
# file_content = doc.Content.Text
|
||||
doc = word.ActiveDocument
|
||||
file_content = doc.Range().Text
|
||||
doc.Close()
|
||||
word.Quit()
|
||||
try:
|
||||
import win32com.client
|
||||
word = win32com.client.Dispatch("Word.Application")
|
||||
word.visible = False
|
||||
# 打开文件
|
||||
doc = word.Documents.Open(os.getcwd() + '/' + fp)
|
||||
# file_content = doc.Content.Text
|
||||
doc = word.ActiveDocument
|
||||
file_content = doc.Range().Text
|
||||
doc.Close()
|
||||
word.Quit()
|
||||
except:
|
||||
raise RuntimeError('请先将.doc文档转换为.docx文档。')
|
||||
|
||||
print(file_content)
|
||||
# private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
|
||||
|
||||
@ -1,121 +1,107 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str
|
||||
from toolbox import CatchException, report_execption, write_results_to_file
|
||||
import re
|
||||
import unicodedata
|
||||
fast_debug = False
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import read_and_clean_pdf_text
|
||||
from .crazy_utils import input_clipping
|
||||
|
||||
def is_paragraph_break(match):
|
||||
"""
|
||||
根据给定的匹配结果来判断换行符是否表示段落分隔。
|
||||
如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。
|
||||
也可以根据之前的内容长度来判断段落是否已经足够长。
|
||||
"""
|
||||
prev_char, next_char = match.groups()
|
||||
|
||||
# 句子结束标志
|
||||
sentence_endings = ".!?"
|
||||
|
||||
# 设定一个最小段落长度阈值
|
||||
min_paragraph_length = 140
|
||||
|
||||
if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length:
|
||||
return "\n\n"
|
||||
else:
|
||||
return " "
|
||||
|
||||
def normalize_text(text):
|
||||
"""
|
||||
通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。
|
||||
例如，将连字 "fi" 转换为 "f" 和 "i"。
|
||||
"""
|
||||
# 对文本进行归一化处理，分解连字
|
||||
normalized_text = unicodedata.normalize("NFKD", text)
|
||||
|
||||
# 替换其他特殊字符
|
||||
cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text)
|
||||
|
||||
return cleaned_text
|
||||
|
||||
def clean_text(raw_text):
|
||||
"""
|
||||
对从 PDF 提取出的原始文本进行清洗和格式化处理。
|
||||
1. 对原始文本进行归一化处理。
|
||||
2. 替换跨行的连词
|
||||
3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换
|
||||
"""
|
||||
# 对文本进行归一化处理
|
||||
normalized_text = normalize_text(raw_text)
|
||||
|
||||
# 替换跨行的连词
|
||||
text = re.sub(r'(\w+-\n\w+)', lambda m: m.group(1).replace('-\n', ''), normalized_text)
|
||||
|
||||
# 根据前后相邻字符的特点，找到原文本中的换行符
|
||||
newlines = re.compile(r'(\S)\n(\S)')
|
||||
|
||||
# 根据 heuristic 规则，用空格或段落分隔符替换原换行符
|
||||
final_text = re.sub(newlines, lambda m: m.group(1) + is_paragraph_break(m) + m.group(2), text)
|
||||
|
||||
return final_text.strip()
|
||||
|
||||
def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import time, glob, os, fitz
|
||||
print('begin analysis on:', file_manifest)
|
||||
for index, fp in enumerate(file_manifest):
|
||||
with fitz.open(fp) as doc:
|
||||
file_content = ""
|
||||
for page in doc:
|
||||
file_content += page.get_text()
|
||||
file_content = clean_text(file_content)
|
||||
print(file_content)
|
||||
file_write_buffer = []
|
||||
for file_name in file_manifest:
|
||||
print('begin analysis on:', file_name)
|
||||
############################## <第 0 步，切割PDF> ##################################
|
||||
# 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
|
||||
# 的长度必须小于 2500 个 Token
|
||||
file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
page_one = str(page_one).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 2500
|
||||
|
||||
prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
|
||||
i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
|
||||
i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
|
||||
chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
from request_llm.bridge_all import model_info
|
||||
enc = model_info["gpt-3.5-turbo"]['tokenizer']
|
||||
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
|
||||
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
|
||||
page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
|
||||
# 为了更好的效果，我们剥离Introduction之后的部分（如果有）
|
||||
paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
|
||||
|
||||
############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
|
||||
final_results = []
|
||||
final_results.append(paper_meta)
|
||||
|
||||
if not fast_debug:
|
||||
msg = '正常'
|
||||
# ** gpt request **
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say_show_user,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=[],
|
||||
sys_prompt="总结文章。"
|
||||
) # 带超时倒计时
|
||||
|
||||
############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
|
||||
i_say_show_user = f'首先你在中文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。" # 用户提示
|
||||
chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[]) # 更新UI
|
||||
|
||||
chatbot[-1] = (i_say_show_user, gpt_say)
|
||||
history.append(i_say_show_user); history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
if not fast_debug: time.sleep(2)
|
||||
iteration_results = []
|
||||
last_iteration_result = paper_meta # 初始值是摘要
|
||||
MAX_WORD_TOTAL = 4096 * 0.7
|
||||
n_fragment = len(paper_fragments)
|
||||
if n_fragment >= 20: print('文章极长，不能达到预期效果')
|
||||
for i in range(n_fragment):
|
||||
NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
|
||||
i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i]}"
|
||||
i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i][:200]}"
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
|
||||
llm_kwargs, chatbot,
|
||||
history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
|
||||
sys_prompt="Extract the main idea of this section with Chinese." # 提示
|
||||
)
|
||||
iteration_results.append(gpt_say)
|
||||
last_iteration_result = gpt_say
|
||||
|
||||
all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
|
||||
i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
|
||||
chatbot.append((i_say, "[Local Message] waiting gpt response."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
if not fast_debug:
|
||||
msg = '正常'
|
||||
# ** gpt request **
|
||||
############################## <第 3 步，整理history，提取总结> ##################################
|
||||
final_results.extend(iteration_results)
|
||||
final_results.append(f'Please conclude this paper discussed above。')
|
||||
# This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py
|
||||
NUM_OF_WORD = 1000
|
||||
i_say = """
|
||||
1. Mark the title of the paper (with Chinese translation)
|
||||
2. list all the authors' names (use English)
|
||||
3. mark the first author's affiliation (output Chinese translation only)
|
||||
4. mark the keywords of this article (use English)
|
||||
5. link to the paper, Github code link (if available, fill in Github:None if not)
|
||||
6. summarize according to the following four points.Be sure to use Chinese answers (proper nouns need to be marked in English)
|
||||
- (1):What is the research background of this article?
|
||||
- (2):What are the past methods? What are the problems with them? Is the approach well motivated?
|
||||
- (3):What is the research methodology proposed in this paper?
|
||||
- (4):On what task and what performance is achieved by the methods in this paper? Can the performance support their goals?
|
||||
Follow the format of the output that follows:
|
||||
1. Title: xxx\n\n
|
||||
2. Authors: xxx\n\n
|
||||
3. Affiliation: xxx\n\n
|
||||
4. Keywords: xxx\n\n
|
||||
5. Urls: xxx or xxx , xxx \n\n
|
||||
6. Summary: \n\n
|
||||
- (1):xxx;\n
|
||||
- (2):xxx;\n
|
||||
- (3):xxx;\n
|
||||
- (4):xxx.\n\n
|
||||
Be sure to use Chinese answers (proper nouns need to be marked in English), statements as concise and academic as possible,
|
||||
do not have too much repetitive information, numerical values using the original numbers.
|
||||
"""
|
||||
# This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py
|
||||
file_write_buffer.extend(final_results)
|
||||
i_say, final_results = input_clipping(i_say, final_results, max_token_limit=2000)
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=history,
|
||||
sys_prompt="总结文章。"
|
||||
) # 带超时倒计时
|
||||
inputs=i_say, inputs_show_user='开始最终总结',
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=final_results,
|
||||
sys_prompt= f"Extract the main idea of this paper with less than {NUM_OF_WORD} Chinese characters"
|
||||
)
|
||||
final_results.append(gpt_say)
|
||||
file_write_buffer.extend([i_say, gpt_say])
|
||||
############################## <第 4 步，设置一个token上限> ##################################
|
||||
_, final_results = input_clipping("", final_results, max_token_limit=3200)
|
||||
yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
|
||||
|
||||
chatbot[-1] = (i_say, gpt_say)
|
||||
history.append(i_say); history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
res = write_results_to_file(history)
|
||||
chatbot.append(("完成了吗？", res))
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
res = write_results_to_file(file_write_buffer, file_name=gen_time_str())
|
||||
promote_file_to_downloadzone(res.split('\t')[-1], chatbot=chatbot)
|
||||
yield from update_ui(chatbot=chatbot, history=final_results) # 刷新界面
|
||||
|
||||
|
||||
@CatchException
|
||||
@ -151,10 +137,7 @@ def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
return
|
||||
|
||||
# 搜索需要处理的文件清单
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
|
||||
# [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
|
||||
# [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
|
||||
# [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
|
||||
|
||||
# 如果没找到任何文件
|
||||
if len(file_manifest) == 0:
|
||||
|
||||
@ -6,7 +6,7 @@ def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
|
||||
"""
|
||||
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，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，用于灵活调整复杂功能的各种参数
|
||||
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
|
||||
@ -35,7 +35,7 @@ def 同时问询_指定模型(txt, llm_kwargs, plugin_kwargs, chatbot, history,
|
||||
"""
|
||||
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，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，用于灵活调整复杂功能的各种参数
|
||||
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
|
||||
|
||||
@ -1,13 +1,12 @@
|
||||
from toolbox import CatchException, update_ui
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
import datetime, re
|
||||
|
||||
import datetime
|
||||
@CatchException
|
||||
def 高阶功能模板函数(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
|
||||
plugin_kwargs 插件模型的参数，用于灵活调整复杂功能的各种参数
|
||||
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
|
||||
@ -19,34 +18,12 @@ def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
for i in range(5):
|
||||
currentMonth = (datetime.date.today() + datetime.timedelta(days=i)).month
|
||||
currentDay = (datetime.date.today() + datetime.timedelta(days=i)).day
|
||||
i_say = f'历史中哪些事件发生在{currentMonth}月{currentDay}日？用中文列举两条，然后分别给出描述事件的两个英文单词。' + '当你给出关键词时，使用以下json格式：{"KeyWords":[EnglishKeyWord1,EnglishKeyWord2]}。'
|
||||
i_say = f'历史中哪些事件发生在{currentMonth}月{currentDay}日？列举两条并发送相关图片。发送图片时，请使用Markdown，将Unsplash API中的PUT_YOUR_QUERY_HERE替换成描述该事件的一个最重要的单词。'
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt='输出格式示例：1908年，美国消防救援事业发展的“美国消防协会”成立。关键词：{"KeyWords":["Fire","American"]}。'
|
||||
sys_prompt="当你想发送一张照片时，请使用Markdown, 并且不要有反斜线, 不要用代码块。使用 Unsplash API (https://source.unsplash.com/1280x720/? < PUT_YOUR_QUERY_HERE >)。"
|
||||
)
|
||||
gpt_say = get_images(gpt_say)
|
||||
chatbot[-1] = (i_say, gpt_say)
|
||||
history.append(i_say);history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
|
||||
def get_images(gpt_say):
|
||||
def get_image_by_keyword(keyword):
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
response = requests.get(f'https://wallhaven.cc/search?q={keyword}', timeout=2)
|
||||
for image_element in BeautifulSoup(response.content, 'html.parser').findAll("img"):
|
||||
if "data-src" in image_element: break
|
||||
return image_element["data-src"]
|
||||
|
||||
for keywords in re.findall('{"KeyWords":\[(.*?)\]}', gpt_say):
|
||||
keywords = [n.strip('"') for n in keywords.split(',')]
|
||||
try:
|
||||
description = keywords[0]
|
||||
url = get_image_by_keyword(keywords[0])
|
||||
img_tag = f"\n\n"
|
||||
gpt_say += img_tag
|
||||
except:
|
||||
continue
|
||||
return gpt_say
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
@ -6,7 +6,7 @@
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_nolocalllms:
|
||||
image: ghcr.io/binary-husky/gpt_academic_nolocal:master
|
||||
image: ghcr.io/binary-husky/gpt_academic_nolocal:master # (Auto Built by Dockerfile: docs/GithubAction+NoLocal)
|
||||
environment:
|
||||
# 请查阅 `config.py` 以查看所有的配置信息
|
||||
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
|
||||
@ -33,7 +33,7 @@ services:
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_with_chatglm:
|
||||
image: ghcr.io/binary-husky/gpt_academic_chatglm_moss:master
|
||||
image: ghcr.io/binary-husky/gpt_academic_chatglm_moss:master # (Auto Built by Dockerfile: docs/Dockerfile+ChatGLM)
|
||||
environment:
|
||||
# 请查阅 `config.py` 以查看所有的配置信息
|
||||
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
|
||||
@ -63,7 +63,7 @@ services:
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_with_rwkv:
|
||||
image: fuqingxu/gpt_academic:jittorllms # [option 2] 如果需要运行ChatGLM本地模型
|
||||
image: fuqingxu/gpt_academic:jittorllms
|
||||
environment:
|
||||
# 请查阅 `config.py` 以查看所有的配置信息
|
||||
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
|
||||
@ -111,7 +111,7 @@ services:
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_with_latex:
|
||||
image: ghcr.io/binary-husky/gpt_academic_with_latex:master
|
||||
image: ghcr.io/binary-husky/gpt_academic_with_latex:master # (Auto Built by Dockerfile: docs/GithubAction+NoLocal+Latex)
|
||||
environment:
|
||||
# 请查阅 `config.py` 以查看所有的配置信息
|
||||
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
|
||||
|
||||
@ -1667,5 +1667,294 @@
|
||||
"段音频的主要内容": "The main content of the segment audio is",
|
||||
"z$ 分别是空间直角坐标系中的三个坐标": "z$, respectively, are the three coordinates in the spatial rectangular coordinate system",
|
||||
"这个是怎么识别的呢我也不清楚": "I'm not sure how this is recognized",
|
||||
"从现在起": "From now on"
|
||||
"从现在起": "From now on",
|
||||
"连接bing搜索回答问题": "ConnectBingSearchAnswerQuestion",
|
||||
"联网的ChatGPT_bing版": "OnlineChatGPT_BingEdition",
|
||||
"Markdown翻译指定语言": "TranslateMarkdownToSpecifiedLanguage",
|
||||
"Langchain知识库": "LangchainKnowledgeBase",
|
||||
"Latex英文纠错加PDF对比": "CorrectEnglishInLatexWithPDFComparison",
|
||||
"Latex输出PDF结果": "OutputPDFFromLatex",
|
||||
"Latex翻译中文并重新编译PDF": "TranslateChineseToEnglishInLatexAndRecompilePDF",
|
||||
"sprint亮靛": "SprintIndigo",
|
||||
"寻找Latex主文件": "FindLatexMainFile",
|
||||
"专业词汇声明": "ProfessionalTerminologyDeclaration",
|
||||
"Latex精细分解与转化": "DecomposeAndConvertLatex",
|
||||
"编译Latex": "CompileLatex",
|
||||
"如果您是论文原作者": "If you are the original author of the paper",
|
||||
"正在编译对比PDF": "Compiling the comparison PDF",
|
||||
"将 \\include 命令转换为 \\input 命令": "Converting the \\include command to the \\input command",
|
||||
"取评分最高者返回": "Returning the highest-rated one",
|
||||
"不要修改!! 高危设置！通过修改此设置": "Do not modify!! High-risk setting! By modifying this setting",
|
||||
"Tex源文件缺失！": "Tex source file is missing!",
|
||||
"6.25 加入判定latex模板的代码": "Added code to determine the latex template on June 25",
|
||||
"正在精细切分latex文件": "Finely splitting the latex file",
|
||||
"获取response失败": "Failed to get response",
|
||||
"手动指定语言": "Manually specify the language",
|
||||
"输入arxivID": "Enter arxivID",
|
||||
"对输入的word文档进行摘要生成": "Generate a summary of the input word document",
|
||||
"将指定目录下的PDF文件从英文翻译成中文": "Translate PDF files from English to Chinese in the specified directory",
|
||||
"如果分析错误": "If the analysis is incorrect",
|
||||
"尝试第": "Try the",
|
||||
"用户填3": "User fills in 3",
|
||||
"请在此处追加更细致的矫错指令": "Please append more detailed correction instructions here",
|
||||
"为了防止大语言模型的意外谬误产生扩散影响": "To prevent the accidental spread of errors in large language models",
|
||||
"前面是中文冒号": "The colon before is in Chinese",
|
||||
"内含已经翻译的Tex文档": "Contains a Tex document that has been translated",
|
||||
"成功啦": "Success!",
|
||||
"刷新页面即可以退出UpdateKnowledgeArchive模式": "Refresh the page to exit UpdateKnowledgeArchive mode",
|
||||
"或者不在环境变量PATH中": "Or not in the environment variable PATH",
|
||||
"--读取文件": "--Read the file",
|
||||
"才能继续下面的步骤": "To continue with the next steps",
|
||||
"代理数据解析失败": "Proxy data parsing failed",
|
||||
"详见项目主README.md": "See the main README.md of the project for details",
|
||||
"临时存储用于调试": "Temporarily stored for debugging",
|
||||
"屏蔽空行和太短的句子": "Filter out empty lines and sentences that are too short",
|
||||
"gpt 多线程请求": "GPT multi-threaded request",
|
||||
"编译已经开始": "Compilation has started",
|
||||
"无法找到一个主Tex文件": "Cannot find a main Tex file",
|
||||
"修复括号": "Fix parentheses",
|
||||
"请您不要删除或修改这行警告": "Please do not delete or modify this warning",
|
||||
"请登录OpenAI查看详情 https": "Please log in to OpenAI to view details at https",
|
||||
"调用函数": "Call a function",
|
||||
"请查看终端的输出或耐心等待": "Please check the output in the terminal or wait patiently",
|
||||
"LatexEnglishCorrection+高亮修正位置": "Latex English correction + highlight correction position",
|
||||
"行": "line",
|
||||
"Newbing 请求失败": "Newbing request failed",
|
||||
"转化PDF编译是否成功": "Check if the conversion to PDF and compilation were successful",
|
||||
"建议更换代理协议": "Recommend changing the proxy protocol",
|
||||
"========================================= 插件主程序1 =====================================================": "========================================= Plugin Main Program 1 =====================================================",
|
||||
"终端": "terminal",
|
||||
"请先上传文件素材": "Please upload file materials first",
|
||||
"前面是中文逗号": "There is a Chinese comma in front",
|
||||
"请尝试把以下指令复制到高级参数区": "Please try copying the following instructions to the advanced parameters section",
|
||||
"翻译-": "Translation -",
|
||||
"请耐心等待": "Please be patient",
|
||||
"将前后断行符脱离": "Remove line breaks before and after",
|
||||
"json等": "JSON, etc.",
|
||||
"生成中文PDF": "Generate Chinese PDF",
|
||||
"用红色标注处保留区": "Use red color to highlight the reserved area",
|
||||
"对比PDF编译是否成功": "Compare if the PDF compilation was successful",
|
||||
"回答完问题后": "After answering the question",
|
||||
"其他操作系统表现未知": "Unknown performance on other operating systems",
|
||||
"-构建知识库": "Build knowledge base",
|
||||
"还原原文": "Restore original text",
|
||||
"或者重启之后再度尝试": "Or try again after restarting",
|
||||
"免费": "Free",
|
||||
"仅在Windows系统进行了测试": "Tested only on Windows system",
|
||||
"欢迎加REAME中的QQ联系开发者": "Feel free to contact the developer via QQ in REAME",
|
||||
"当前知识库内的有效文件": "Valid files in the current knowledge base",
|
||||
"您可以到Github Issue区": "You can go to the Github Issue area",
|
||||
"刷新Gradio前端界面": "Refresh the Gradio frontend interface",
|
||||
"吸收title与作者以上的部分": "Include the title and the above part of the author",
|
||||
"给出一些判定模板文档的词作为扣分项": "Provide some words in the template document as deduction items",
|
||||
"--读取参数": "-- Read parameters",
|
||||
"然后进行问答": "And then perform question-answering",
|
||||
"根据自然语言执行插件命令": "Execute plugin commands based on natural language",
|
||||
"*{\\scriptsize\\textbf{警告": "*{\\scriptsize\\textbf{Warning",
|
||||
"但请查收结果": "But please check the results",
|
||||
"翻译内容可靠性无保障": "No guarantee of translation accuracy",
|
||||
"寻找主文件": "Find the main file",
|
||||
"消耗时间的函数": "Time-consuming function",
|
||||
"当前语言模型温度设定": "Current language model temperature setting",
|
||||
"这需要一段时间计算": "This requires some time to calculate",
|
||||
"为啥chatgpt会把cite里面的逗号换成中文逗号呀": "Why does ChatGPT change commas inside 'cite' to Chinese commas?",
|
||||
"发现已经存在翻译好的PDF文档": "Found an already translated PDF document",
|
||||
"待提取的知识库名称id": "Knowledge base name ID to be extracted",
|
||||
"文本碎片重组为完整的tex片段": "Reassemble text fragments into complete tex fragments",
|
||||
"注意事项": "Notes",
|
||||
"参数说明": "Parameter description",
|
||||
"或代理节点": "Or proxy node",
|
||||
"构建知识库": "Building knowledge base",
|
||||
"报错信息如下. 如果是与网络相关的问题": "Error message as follows. If it is related to network issues",
|
||||
"功能描述": "Function description",
|
||||
"禁止移除或修改此警告": "Removal or modification of this warning is prohibited",
|
||||
"Arixv翻译": "Arixv translation",
|
||||
"读取优先级": "Read priority",
|
||||
"包含documentclass关键字": "Contains the documentclass keyword",
|
||||
"根据文本使用GPT模型生成相应的图像": "Generate corresponding images using GPT model based on the text",
|
||||
"图像生成所用到的提示文本": "Prompt text used for image generation",
|
||||
"Your account is not active. OpenAI以账户失效为由": "Your account is not active. OpenAI states that it is due to account expiration",
|
||||
"快捷的调试函数": "Convenient debugging function",
|
||||
"在多Tex文档中": "In multiple Tex documents",
|
||||
"因此选择GenerateImage函数": "Therefore, choose the GenerateImage function",
|
||||
"当前工作路径为": "The current working directory is",
|
||||
"实际得到格式": "Obtained format in reality",
|
||||
"这段代码定义了一个名为TempProxy的空上下文管理器": "This code defines an empty context manager named TempProxy",
|
||||
"吸收其他杂项": "Absorb other miscellaneous items",
|
||||
"请输入要翻译成哪种语言": "Please enter which language to translate into",
|
||||
"的单词": "of the word",
|
||||
"正在尝试自动安装": "Attempting automatic installation",
|
||||
"如果有必要": "If necessary",
|
||||
"开始下载": "Start downloading",
|
||||
"项目Github地址 \\url{https": "Project GitHub address \\url{https",
|
||||
"将根据报错信息修正tex源文件并重试": "The Tex source file will be corrected and retried based on the error message",
|
||||
"发送至azure openai api": "Send to Azure OpenAI API",
|
||||
"吸收匿名公式": "Absorb anonymous formulas",
|
||||
"用该压缩包+ConversationHistoryArchive进行反馈": "Provide feedback using the compressed package + ConversationHistoryArchive",
|
||||
"需要特殊依赖": "Requires special dependencies",
|
||||
"还原部分原文": "Restore part of the original text",
|
||||
"构建完成": "Build completed",
|
||||
"解析arxiv网址失败": "Failed to parse arXiv URL",
|
||||
"输入问题后点击该插件": "Click the plugin after entering the question",
|
||||
"请求子进程": "Requesting subprocess",
|
||||
"请务必用 pip install -r requirements.txt 指令安装依赖": "Please make sure to install the dependencies using the 'pip install -r requirements.txt' command",
|
||||
"如果程序停顿5分钟以上": "If the program pauses for more than 5 minutes",
|
||||
"转化PDF编译已经成功": "Conversion to PDF compilation was successful",
|
||||
"虽然PDF生成失败了": "Although PDF generation failed",
|
||||
"分析上述回答": "Analyze the above answer",
|
||||
"吸收在42行以内的begin-end组合": "Absorb the begin-end combination within 42 lines",
|
||||
"推荐http": "Recommend http",
|
||||
"Latex没有安装": "Latex is not installed",
|
||||
"用latex编译为PDF对修正处做高亮": "Compile to PDF using LaTeX and highlight the corrections",
|
||||
"reverse 操作必须放在最后": "'reverse' operation must be placed at the end",
|
||||
"AZURE OPENAI API拒绝了请求": "AZURE OPENAI API rejected the request",
|
||||
"该项目的Latex主文件是": "The main LaTeX file of this project is",
|
||||
"You are associated with a deactivated account. OpenAI以账户失效为由": "You are associated with a deactivated account. OpenAI considers it as an account expiration",
|
||||
"它*必须*被包含在AVAIL_LLM_MODELS列表中": "It *must* be included in the AVAIL_LLM_MODELS list",
|
||||
"未知指令": "Unknown command",
|
||||
"尝试执行Latex指令失败": "Failed to execute the LaTeX command",
|
||||
"摘要生成后的文档路径": "Path of the document after summary generation",
|
||||
"GPT结果已输出": "GPT result has been outputted",
|
||||
"使用Newbing": "Using Newbing",
|
||||
"其他模型转化效果未知": "Unknown conversion effect of other models",
|
||||
"P.S. 但愿没人把latex模板放在里面传进来": "P.S. Hopefully, no one passes a LaTeX template in it",
|
||||
"定位主Latex文件": "Locate the main LaTeX file",
|
||||
"后面是英文冒号": "English colon follows",
|
||||
"文档越长耗时越长": "The longer the document, the longer it takes.",
|
||||
"压缩包": "Compressed file",
|
||||
"但通常不会出现在正文": "But usually does not appear in the body.",
|
||||
"正在预热文本向量化模组": "Preheating text vectorization module",
|
||||
"5刀": "5 dollars",
|
||||
"提问吧! 但注意": "Ask questions! But be careful",
|
||||
"发送至AZURE OPENAI API": "Send to AZURE OPENAI API",
|
||||
"请仔细鉴别并以原文为准": "Please carefully verify and refer to the original text",
|
||||
"如果需要使用AZURE 详情请见额外文档 docs\\use_azure.md": "If you need to use AZURE, please refer to the additional document docs\\use_azure.md for details",
|
||||
"使用正则表达式查找半行注释": "Use regular expressions to find inline comments",
|
||||
"只有第二步成功": "Only the second step is successful",
|
||||
"P.S. 顺便把CTEX塞进去以支持中文": "P.S. By the way, include CTEX to support Chinese",
|
||||
"安装方法https": "Installation method: https",
|
||||
"则跳过GPT请求环节": "Then skip the GPT request process",
|
||||
"请切换至“UpdateKnowledgeArchive”插件进行知识库访问": "Please switch to the 'UpdateKnowledgeArchive' plugin for knowledge base access",
|
||||
"=================================== 工具函数 ===============================================": "=================================== Utility functions ===============================================",
|
||||
"填入azure openai api的密钥": "Fill in the Azure OpenAI API key",
|
||||
"上传Latex压缩包": "Upload LaTeX compressed file",
|
||||
"远程云服务器部署": "Deploy to remote cloud server",
|
||||
"用黑色标注转换区": "Use black color to annotate the conversion area",
|
||||
"音频文件的路径": "Path to the audio file",
|
||||
"必须包含documentclass": "Must include documentclass",
|
||||
"再列出用户可能提出的三个问题": "List three more questions that the user might ask",
|
||||
"根据需要切换prompt": "Switch the prompt as needed",
|
||||
"将文件复制一份到下载区": "Make a copy of the file in the download area",
|
||||
"次编译": "Second compilation",
|
||||
"Latex文件融合完成": "LaTeX file merging completed",
|
||||
"返回": "Return",
|
||||
"后面是英文逗号": "Comma after this",
|
||||
"对不同latex源文件扣分": "Deduct points for different LaTeX source files",
|
||||
"失败啦": "Failed",
|
||||
"编译BibTex": "Compile BibTeX",
|
||||
"Linux下必须使用Docker安装": "Must install using Docker on Linux",
|
||||
"报错信息": "Error message",
|
||||
"删除或修改歧义文件": "Delete or modify ambiguous files",
|
||||
"-预热文本向量化模组": "- Preheating text vectorization module",
|
||||
"将每次对话记录写入Markdown格式的文件中": "Write each conversation record into a file in Markdown format",
|
||||
"其他类型文献转化效果未知": "Unknown conversion effect for other types of literature",
|
||||
"获取线程锁": "Acquire thread lock",
|
||||
"使用英文": "Use English",
|
||||
"如果存在调试缓存文件": "If there is a debug cache file",
|
||||
"您需要首先调用构建知识库": "You need to call the knowledge base building first",
|
||||
"原始PDF编译是否成功": "Whether the original PDF compilation is successful",
|
||||
"生成 azure openai api请求": "Generate Azure OpenAI API requests",
|
||||
"正在编译PDF": "Compiling PDF",
|
||||
"仅调试": "Debug only",
|
||||
"========================================= 插件主程序2 =====================================================": "========================================= Plugin Main Program 2 =====================================================",
|
||||
"多线程翻译开始": "Multithreaded translation begins",
|
||||
"出问题了": "There is a problem",
|
||||
"版权归原文作者所有": "Copyright belongs to the original author",
|
||||
"当前大语言模型": "Current large language model",
|
||||
"目前对机器学习类文献转化效果最好": "Currently, the best conversion effect for machine learning literature",
|
||||
"这个paper有个input命令文件名大小写错误！": "This paper has an input command with a filename case error!",
|
||||
"期望格式例如": "Expected format, for example",
|
||||
"解决部分词汇翻译不准确的问题": "Resolve the issue of inaccurate translation for some terms",
|
||||
"待注入的知识库名称id": "Name/ID of the knowledge base to be injected",
|
||||
"精细切分latex文件": "Fine-grained segmentation of LaTeX files",
|
||||
"永远给定None": "Always given None",
|
||||
"work_folder = Latex预处理": "work_folder = LaTeX preprocessing",
|
||||
"请直接去该路径下取回翻译结果": "Please directly go to the path to retrieve the translation results",
|
||||
"寻找主tex文件": "Finding the main .tex file",
|
||||
"模型参数": "Model parameters",
|
||||
"返回找到的第一个": "Return the first one found",
|
||||
"编译转化后的PDF": "Compile the converted PDF",
|
||||
"\\SEAFILE_LOCALŅ03047\\我的资料库\\music\\Akie秋绘-未来轮廓.mp3": "\\SEAFILE_LOCALŅ03047\\My Library\\music\\Akie秋绘-未来轮廓.mp3",
|
||||
"拆分过长的latex片段": "Splitting overly long LaTeX fragments",
|
||||
"没有找到任何可读取文件": "No readable files found",
|
||||
"暗色模式 / 亮色模式": "Dark mode / Light mode",
|
||||
"检测到arxiv文档连接": "Detected arXiv document link",
|
||||
"此插件Windows支持最佳": "This plugin has best support for Windows",
|
||||
"from crazy_functions.虚空终端 import 终端": "from crazy_functions.null_terminal import Terminal",
|
||||
"本地论文翻译": "Local paper translation",
|
||||
"输出html调试文件": "Output HTML debugging file",
|
||||
"以下所有配置也都支持利用环境变量覆写": "All the following configurations can also be overridden using environment variables",
|
||||
"PDF文件所在的路径": "Path of the PDF file",
|
||||
"也是可读的": "It is also readable",
|
||||
"将消耗较长时间下载中文向量化模型": "Downloading Chinese vectorization model will take a long time",
|
||||
"环境变量配置格式见docker-compose.yml": "See docker-compose.yml for the format of environment variable configuration",
|
||||
"编译文献交叉引用": "Compile bibliographic cross-references",
|
||||
"默认为default": "Default is 'default'",
|
||||
"或者使用此插件继续上传更多文件": "Or use this plugin to continue uploading more files",
|
||||
"该PDF由GPT-Academic开源项目调用大语言模型+Latex翻译插件一键生成": "This PDF is generated by the GPT-Academic open-source project using a large language model + LaTeX translation plugin",
|
||||
"使用latexdiff生成论文转化前后对比": "Use latexdiff to generate before and after comparison of paper transformation",
|
||||
"正在编译PDF文档": "Compiling PDF document",
|
||||
"读取config.py文件中关于AZURE OPENAI API的信息": "Read the information about AZURE OPENAI API from the config.py file",
|
||||
"配置教程&视频教程": "Configuration tutorial & video tutorial",
|
||||
"临时地启动代理网络": "Temporarily start proxy network",
|
||||
"临时地激活代理网络": "Temporarily activate proxy network",
|
||||
"功能尚不稳定": "Functionality is unstable",
|
||||
"默认为Chinese": "Default is Chinese",
|
||||
"请查收结果": "Please check the results",
|
||||
"将 chatglm 直接对齐到 chatglm2": "Align chatglm directly to chatglm2",
|
||||
"中读取数据构建知识库": "Build a knowledge base by reading data in",
|
||||
"用于给一小段代码上代理": "Used to proxy a small piece of code",
|
||||
"分析结果": "Analysis results",
|
||||
"依赖不足": "Insufficient dependencies",
|
||||
"Markdown翻译": "Markdown translation",
|
||||
"除非您是论文的原作者": "Unless you are the original author of the paper",
|
||||
"test_LangchainKnowledgeBase读取": "test_LangchainKnowledgeBase read",
|
||||
"将多文件tex工程融合为一个巨型tex": "Merge multiple tex projects into one giant tex",
|
||||
"吸收iffalse注释": "Absorb iffalser comments",
|
||||
"您接下来不能再使用其他插件了": "You can no longer use other plugins next",
|
||||
"正在构建知识库": "Building knowledge base",
|
||||
"需Latex": "Requires Latex",
|
||||
"即找不到": "That is not found",
|
||||
"保证括号正确": "Ensure parentheses are correct",
|
||||
"= 2 通过一些Latex模板中常见": "= 2 through some common Latex templates",
|
||||
"请立即终止程序": "Please terminate the program immediately",
|
||||
"解压失败! 需要安装pip install rarfile来解压rar文件": "Decompression failed! Install 'pip install rarfile' to decompress rar files",
|
||||
"请在此处给出自定义翻译命令": "Please provide custom translation command here",
|
||||
"解压失败! 需要安装pip install py7zr来解压7z文件": "Decompression failed! Install 'pip install py7zr' to decompress 7z files",
|
||||
"执行错误": "Execution error",
|
||||
"目前仅支持GPT3.5/GPT4": "Currently only supports GPT3.5/GPT4",
|
||||
"P.S. 顺便把Latex的注释去除": "P.S. Also remove comments from Latex",
|
||||
"写出文件": "Write out the file",
|
||||
"当前报错的latex代码处于第": "The current error in the LaTeX code is on line",
|
||||
"主程序即将开始": "Main program is about to start",
|
||||
"详情信息见requirements.txt": "See details in requirements.txt",
|
||||
"释放线程锁": "Release thread lock",
|
||||
"由于最为关键的转化PDF编译失败": "Due to the critical failure of PDF conversion and compilation",
|
||||
"即将退出": "Exiting soon",
|
||||
"尝试下载": "Attempting to download",
|
||||
"删除整行的空注释": "Remove empty comments from the entire line",
|
||||
"也找不到": "Not found either",
|
||||
"从一批文件": "From a batch of files",
|
||||
"编译结束": "Compilation finished",
|
||||
"调用缓存": "Calling cache",
|
||||
"只有GenerateImage和生成图像相关": "Only GenerateImage and image generation related",
|
||||
"待处理的word文档路径": "Path of the word document to be processed",
|
||||
"是否在提交时自动清空输入框": "Whether to automatically clear the input box upon submission",
|
||||
"检查结果": "Check the result",
|
||||
"生成时间戳": "Generate a timestamp",
|
||||
"编译原始PDF": "Compile the original PDF",
|
||||
"填入ENGINE": "Fill in ENGINE",
|
||||
"填入api版本": "Fill in the API version",
|
||||
"中文Bing版": "Chinese Bing version",
|
||||
"当前支持的格式包括": "Currently supported formats include"
|
||||
}
|
||||
@ -90,62 +90,29 @@
|
||||
|
||||
到现在为止，申请操作就完成了，需要记下来的有下面几个东西：
|
||||
|
||||
● 密钥（1或2都可以）
|
||||
● 密钥（对应AZURE_API_KEY，1或2都可以）
|
||||
|
||||
● 终结点
|
||||
● 终结点 （对应AZURE_ENDPOINT）
|
||||
|
||||
● 部署名（对应AZURE_ENGINE，不是模型名）
|
||||
|
||||
● 部署名（不是模型名）
|
||||
|
||||
# 修改 config.py
|
||||
|
||||
```
|
||||
AZURE_ENDPOINT = "填入终结点"
|
||||
LLM_MODEL = "azure-gpt-3.5" # 指定启动时的默认模型，当然事后从下拉菜单选也ok
|
||||
|
||||
AZURE_ENDPOINT = "填入终结点" # 见上述图片
|
||||
AZURE_API_KEY = "填入azure openai api的密钥"
|
||||
AZURE_API_VERSION = "2023-05-15" # 默认使用 2023-05-15 版本，无需修改
|
||||
AZURE_ENGINE = "填入部署名"
|
||||
|
||||
```
|
||||
# API的使用
|
||||
|
||||
接下来就是具体怎么使用API了，还是可以参考官方文档：[快速入门 - 开始通过 Azure OpenAI 服务使用 ChatGPT 和 GPT-4 - Azure OpenAI Service | Microsoft Learn](https://learn.microsoft.com/zh-cn/azure/cognitive-services/openai/chatgpt-quickstart?pivots=programming-language-python)
|
||||
|
||||
和openai自己的api调用有点类似，都需要安装openai库，不同的是调用方式
|
||||
|
||||
```
|
||||
import openai
|
||||
openai.api_type = "azure" #固定格式，无需修改
|
||||
openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT") #这里填入“终结点”
|
||||
openai.api_version = "2023-05-15" #固定格式，无需修改
|
||||
openai.api_key = os.getenv("AZURE_OPENAI_KEY") #这里填入“密钥1”或“密钥2”
|
||||
|
||||
response = openai.ChatCompletion.create(
|
||||
engine="gpt-35-turbo", #这里填入的不是模型名，是部署名
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},
|
||||
{"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."},
|
||||
{"role": "user", "content": "Do other Azure Cognitive Services support this too?"}
|
||||
]
|
||||
)
|
||||
|
||||
print(response)
|
||||
print(response['choices'][0]['message']['content'])
|
||||
AZURE_ENGINE = "填入部署名" # 见上述图片
|
||||
|
||||
```
|
||||
|
||||
需要注意的是：
|
||||
|
||||
1. engine那里填入的是部署名，不是模型名
|
||||
|
||||
2. 通过openai库获得的这个 response 和通过 request 库访问 url 获得的 response 不同，不需要 decode，已经是解析好的 json 了，直接根据键值读取即可。
|
||||
|
||||
更细节的使用方法，详见官方API文档。
|
||||
|
||||
# 关于费用
|
||||
|
||||
Azure OpenAI API 还是需要一些费用的（免费订阅只有1个月有效期），费用如下：
|
||||
|
||||
|
||||
Azure OpenAI API 还是需要一些费用的（免费订阅只有1个月有效期）
|
||||
|
||||
具体可以可以看这个网址 ：[Azure OpenAI 服务 - 定价| Microsoft Azure](https://azure.microsoft.com/zh-cn/pricing/details/cognitive-services/openai-service/?cdn=disable)
|
||||
|
||||
|
||||
51
main.py
51
main.py
@ -4,13 +4,12 @@ def main():
|
||||
import gradio as gr
|
||||
if gr.__version__ not in ['3.28.3','3.32.2']: assert False, "需要特殊依赖，请务必用 pip install -r requirements.txt 指令安装依赖，详情信息见requirements.txt"
|
||||
from request_llm.bridge_all import predict
|
||||
from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
|
||||
from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
|
||||
# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
|
||||
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS = \
|
||||
get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS')
|
||||
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = \
|
||||
get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
|
||||
ENABLE_AUDIO, AUTO_CLEAR_TXT = \
|
||||
get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT')
|
||||
|
||||
# 如果WEB_PORT是-1, 则随机选取WEB端口
|
||||
PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
|
||||
if not AUTHENTICATION: AUTHENTICATION = None
|
||||
@ -47,23 +46,23 @@ def main():
|
||||
proxy_info = check_proxy(proxies)
|
||||
|
||||
gr_L1 = lambda: gr.Row().style()
|
||||
gr_L2 = lambda scale: gr.Column(scale=scale)
|
||||
gr_L2 = lambda scale, elem_id: gr.Column(scale=scale, elem_id=elem_id)
|
||||
if LAYOUT == "TOP-DOWN":
|
||||
gr_L1 = lambda: DummyWith()
|
||||
gr_L2 = lambda scale: gr.Row()
|
||||
gr_L2 = lambda scale, elem_id: gr.Row()
|
||||
CHATBOT_HEIGHT /= 2
|
||||
|
||||
cancel_handles = []
|
||||
with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
|
||||
gr.HTML(title_html)
|
||||
cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL})
|
||||
cookies = gr.State(load_chat_cookies())
|
||||
with gr_L1():
|
||||
with gr_L2(scale=2):
|
||||
chatbot = gr.Chatbot(label=f"当前模型：{LLM_MODEL}")
|
||||
chatbot.style(height=CHATBOT_HEIGHT)
|
||||
with gr_L2(scale=2, elem_id="gpt-chat"):
|
||||
chatbot = gr.Chatbot(label=f"当前模型：{LLM_MODEL}", elem_id="gpt-chatbot")
|
||||
if LAYOUT == "TOP-DOWN": chatbot.style(height=CHATBOT_HEIGHT)
|
||||
history = gr.State([])
|
||||
with gr_L2(scale=1):
|
||||
with gr.Accordion("输入区", open=True) as area_input_primary:
|
||||
with gr_L2(scale=1, elem_id="gpt-panel"):
|
||||
with gr.Accordion("输入区", open=True, elem_id="input-panel") as area_input_primary:
|
||||
with gr.Row():
|
||||
txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False)
|
||||
with gr.Row():
|
||||
@ -76,14 +75,14 @@ def main():
|
||||
with gr.Row():
|
||||
audio_mic = gr.Audio(source="microphone", type="numpy", streaming=True, show_label=False)
|
||||
with gr.Row():
|
||||
status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}")
|
||||
with gr.Accordion("基础功能区", open=True) as area_basic_fn:
|
||||
status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}", elem_id="state-panel")
|
||||
with gr.Accordion("基础功能区", open=True, elem_id="basic-panel") as area_basic_fn:
|
||||
with gr.Row():
|
||||
for k in functional:
|
||||
if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue
|
||||
variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
|
||||
functional[k]["Button"] = gr.Button(k, variant=variant)
|
||||
with gr.Accordion("函数插件区", open=True) as area_crazy_fn:
|
||||
with gr.Accordion("函数插件区", open=True, elem_id="plugin-panel") as area_crazy_fn:
|
||||
with gr.Row():
|
||||
gr.Markdown("插件可读取“输入区”文本/路径作为参数（上传文件自动修正路径）")
|
||||
with gr.Row():
|
||||
@ -105,7 +104,7 @@ def main():
|
||||
with gr.Row():
|
||||
with gr.Accordion("点击展开“文件上传区”。上传本地文件/压缩包供函数插件调用。", open=False) as area_file_up:
|
||||
file_upload = gr.Files(label="任何文件, 但推荐上传压缩文件(zip, tar)", file_count="multiple")
|
||||
with gr.Accordion("更换模型 & SysPrompt & 交互界面布局", open=(LAYOUT == "TOP-DOWN")):
|
||||
with gr.Accordion("更换模型 & SysPrompt & 交互界面布局", open=(LAYOUT == "TOP-DOWN"), elem_id="interact-panel"):
|
||||
system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt)
|
||||
top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
|
||||
temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
|
||||
@ -114,7 +113,7 @@ def main():
|
||||
md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
|
||||
|
||||
gr.Markdown(description)
|
||||
with gr.Accordion("备选输入区", open=True, visible=False) as area_input_secondary:
|
||||
with gr.Accordion("备选输入区", open=True, visible=False, elem_id="input-panel2") as area_input_secondary:
|
||||
with gr.Row():
|
||||
txt2 = gr.Textbox(show_label=False, placeholder="Input question here.", label="输入区2").style(container=False)
|
||||
with gr.Row():
|
||||
@ -181,9 +180,9 @@ def main():
|
||||
return {chatbot: gr.update(label="当前模型："+k)}
|
||||
md_dropdown.select(on_md_dropdown_changed, [md_dropdown], [chatbot] )
|
||||
# 随变按钮的回调函数注册
|
||||
def route(k, *args, **kwargs):
|
||||
def route(request: gr.Request, k, *args, **kwargs):
|
||||
if k in [r"打开插件列表", r"请先从插件列表中选择"]: return
|
||||
yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(*args, **kwargs)
|
||||
yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(request, *args, **kwargs)
|
||||
click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo)
|
||||
click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot])
|
||||
cancel_handles.append(click_handle)
|
||||
@ -191,12 +190,6 @@ def main():
|
||||
stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
|
||||
stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
|
||||
|
||||
def init_cookie(cookies, chatbot):
|
||||
# 为每一位访问的用户赋予一个独一无二的uuid编码
|
||||
cookies.update({'uuid': uuid.uuid4()})
|
||||
return cookies
|
||||
demo.load(init_cookie, inputs=[cookies, chatbot], outputs=[cookies])
|
||||
|
||||
if ENABLE_AUDIO:
|
||||
from crazy_functions.live_audio.audio_io import RealtimeAudioDistribution
|
||||
rad = RealtimeAudioDistribution()
|
||||
@ -204,6 +197,14 @@ def main():
|
||||
rad.feed(cookies['uuid'].hex, audio)
|
||||
audio_mic.stream(deal_audio, inputs=[audio_mic, cookies])
|
||||
|
||||
def init_cookie(cookies, chatbot):
|
||||
# 为每一位访问的用户赋予一个独一无二的uuid编码
|
||||
cookies.update({'uuid': uuid.uuid4()})
|
||||
return cookies
|
||||
demo.load(init_cookie, inputs=[cookies, chatbot], outputs=[cookies])
|
||||
demo.load(lambda: 0, inputs=None, outputs=None, _js='()=>{ChatBotHeight();}')
|
||||
|
||||
|
||||
# gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
|
||||
def auto_opentab_delay():
|
||||
import threading, webbrowser, time
|
||||
|
||||
@ -33,7 +33,7 @@ import pickle
|
||||
import time
|
||||
|
||||
CACHE_FOLDER = "gpt_log"
|
||||
blacklist = ['multi-language', 'gpt_log', '.git', 'private_upload', 'multi_language.py']
|
||||
blacklist = ['multi-language', 'gpt_log', '.git', 'private_upload', 'multi_language.py', 'build', '.github', '.vscode', '__pycache__', 'venv']
|
||||
|
||||
# LANG = "TraditionalChinese"
|
||||
# TransPrompt = f"Replace each json value `#` with translated results in Traditional Chinese, e.g., \"原始文本\":\"翻譯後文字\". Keep Json format. Do not answer #."
|
||||
@ -301,6 +301,7 @@ def step_1_core_key_translate():
|
||||
elif isinstance(node, ast.ImportFrom):
|
||||
for n in node.names:
|
||||
if contains_chinese(n.name): syntax.append(n.name)
|
||||
# if node.module is None: print(node.module)
|
||||
for k in node.module.split('.'):
|
||||
if contains_chinese(k): syntax.append(k)
|
||||
return syntax
|
||||
@ -310,6 +311,7 @@ def step_1_core_key_translate():
|
||||
for root, dirs, files in os.walk(directory_path):
|
||||
if any([b in root for b in blacklist]):
|
||||
continue
|
||||
print(files)
|
||||
for file in files:
|
||||
if file.endswith('.py'):
|
||||
file_path = os.path.join(root, file)
|
||||
@ -505,6 +507,6 @@ def step_2_core_key_translate():
|
||||
with open(file_path_new, 'w', encoding='utf-8') as f:
|
||||
f.write(content)
|
||||
os.remove(file_path)
|
||||
|
||||
step_1_core_key_translate()
|
||||
step_2_core_key_translate()
|
||||
print('Finished, checkout generated results at ./multi-language/')
|
||||
@ -16,15 +16,9 @@ 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
|
||||
|
||||
from .bridge_azure_test import predict_no_ui_long_connection as azure_noui
|
||||
from .bridge_azure_test import predict as azure_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
|
||||
|
||||
@ -51,10 +45,11 @@ class LazyloadTiktoken(object):
|
||||
return encoder.decode(*args, **kwargs)
|
||||
|
||||
# Endpoint 重定向
|
||||
API_URL_REDIRECT, = get_conf("API_URL_REDIRECT")
|
||||
API_URL_REDIRECT, AZURE_ENDPOINT, AZURE_ENGINE = get_conf("API_URL_REDIRECT", "AZURE_ENDPOINT", "AZURE_ENGINE")
|
||||
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"
|
||||
azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
|
||||
# 兼容旧版的配置
|
||||
try:
|
||||
API_URL, = get_conf("API_URL")
|
||||
@ -124,10 +119,10 @@ model_info = {
|
||||
},
|
||||
|
||||
# azure openai
|
||||
"azure-gpt35":{
|
||||
"fn_with_ui": azure_ui,
|
||||
"fn_without_ui": azure_noui,
|
||||
"endpoint": get_conf("AZURE_ENDPOINT"),
|
||||
"azure-gpt-3.5":{
|
||||
"fn_with_ui": chatgpt_ui,
|
||||
"fn_without_ui": chatgpt_noui,
|
||||
"endpoint": azure_endpoint,
|
||||
"max_token": 4096,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
@ -169,16 +164,6 @@ 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,
|
||||
},
|
||||
|
||||
}
|
||||
|
||||
@ -267,6 +252,23 @@ if "newbing-free" in AVAIL_LLM_MODELS:
|
||||
})
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
if "newbing" in AVAIL_LLM_MODELS: # same with newbing-free
|
||||
try:
|
||||
from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
|
||||
from .bridge_newbingfree import predict as newbingfree_ui
|
||||
# claude
|
||||
model_info.update({
|
||||
"newbing": {
|
||||
"fn_with_ui": newbingfree_ui,
|
||||
"fn_without_ui": newbingfree_noui,
|
||||
"endpoint": newbing_endpoint,
|
||||
"max_token": 4096,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
}
|
||||
})
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
|
||||
def LLM_CATCH_EXCEPTION(f):
|
||||
"""
|
||||
|
||||
@ -1,241 +0,0 @@
|
||||
"""
|
||||
该文件中主要包含三个函数
|
||||
|
||||
不具备多线程能力的函数：
|
||||
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
|
||||
|
||||
具备多线程调用能力的函数
|
||||
2. predict_no_ui：高级实验性功能模块调用，不会实时显示在界面上，参数简单，可以多线程并行，方便实现复杂的功能逻辑
|
||||
3. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
|
||||
"""
|
||||
|
||||
import logging
|
||||
import traceback
|
||||
import importlib
|
||||
import openai
|
||||
import time
|
||||
|
||||
|
||||
# 读取config.py文件中关于AZURE OPENAI API的信息
|
||||
from toolbox import get_conf, update_ui, clip_history, trimmed_format_exc
|
||||
TIMEOUT_SECONDS, MAX_RETRY, AZURE_ENGINE, AZURE_ENDPOINT, AZURE_API_VERSION, AZURE_API_KEY = \
|
||||
get_conf('TIMEOUT_SECONDS', 'MAX_RETRY',"AZURE_ENGINE","AZURE_ENDPOINT", "AZURE_API_VERSION", "AZURE_API_KEY")
|
||||
|
||||
|
||||
def get_full_error(chunk, stream_response):
|
||||
"""
|
||||
获取完整的从Openai返回的报错
|
||||
"""
|
||||
while True:
|
||||
try:
|
||||
chunk += next(stream_response)
|
||||
except:
|
||||
break
|
||||
return chunk
|
||||
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
发送至azure openai api，流式获取输出。
|
||||
用于基础的对话功能。
|
||||
inputs 是本次问询的输入
|
||||
top_p, temperature是chatGPT的内部调优参数
|
||||
history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
|
||||
chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
|
||||
additional_fn代表点击的哪个按钮，按钮见functional.py
|
||||
"""
|
||||
print(llm_kwargs["llm_model"])
|
||||
|
||||
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"]
|
||||
|
||||
raw_input = inputs
|
||||
logging.info(f'[raw_input] {raw_input}')
|
||||
chatbot.append((inputs, ""))
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
|
||||
|
||||
|
||||
payload = generate_azure_payload(inputs, llm_kwargs, history, system_prompt, stream)
|
||||
|
||||
history.append(inputs); history.append("")
|
||||
|
||||
retry = 0
|
||||
while True:
|
||||
try:
|
||||
|
||||
openai.api_type = "azure"
|
||||
openai.api_version = AZURE_API_VERSION
|
||||
openai.api_base = AZURE_ENDPOINT
|
||||
openai.api_key = AZURE_API_KEY
|
||||
response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break
|
||||
|
||||
except:
|
||||
retry += 1
|
||||
chatbot[-1] = ((chatbot[-1][0], "获取response失败，重试中。。。"))
|
||||
retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
|
||||
if retry > MAX_RETRY: raise TimeoutError
|
||||
|
||||
gpt_replying_buffer = ""
|
||||
is_head_of_the_stream = True
|
||||
if stream:
|
||||
|
||||
stream_response = response
|
||||
|
||||
while True:
|
||||
try:
|
||||
chunk = next(stream_response)
|
||||
|
||||
except StopIteration:
|
||||
from toolbox import regular_txt_to_markdown; tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 远程返回错误: \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk)}")
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="远程返回错误:" + chunk) # 刷新界面
|
||||
return
|
||||
|
||||
if is_head_of_the_stream and (r'"object":"error"' not in chunk):
|
||||
# 数据流的第一帧不携带content
|
||||
is_head_of_the_stream = False; continue
|
||||
|
||||
if chunk:
|
||||
#print(chunk)
|
||||
try:
|
||||
if "delta" in chunk["choices"][0]:
|
||||
if chunk["choices"][0]["finish_reason"] == "stop":
|
||||
logging.info(f'[response] {gpt_replying_buffer}')
|
||||
break
|
||||
status_text = f"finish_reason: {chunk['choices'][0]['finish_reason']}"
|
||||
gpt_replying_buffer = gpt_replying_buffer + chunk["choices"][0]["delta"]["content"]
|
||||
|
||||
history[-1] = gpt_replying_buffer
|
||||
chatbot[-1] = (history[-2], history[-1])
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
|
||||
|
||||
except Exception as e:
|
||||
traceback.print_exc()
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
|
||||
chunk = get_full_error(chunk, stream_response)
|
||||
|
||||
error_msg = chunk
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
|
||||
"""
|
||||
发送至AZURE OPENAI API，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
|
||||
inputs：
|
||||
是本次问询的输入
|
||||
sys_prompt:
|
||||
系统静默prompt
|
||||
llm_kwargs：
|
||||
chatGPT的内部调优参数
|
||||
history：
|
||||
是之前的对话列表
|
||||
observe_window = None：
|
||||
用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
|
||||
"""
|
||||
watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
|
||||
payload = generate_azure_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
|
||||
retry = 0
|
||||
while True:
|
||||
|
||||
try:
|
||||
openai.api_type = "azure"
|
||||
openai.api_version = AZURE_API_VERSION
|
||||
openai.api_base = AZURE_ENDPOINT
|
||||
openai.api_key = AZURE_API_KEY
|
||||
response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break
|
||||
|
||||
except:
|
||||
retry += 1
|
||||
traceback.print_exc()
|
||||
if retry > MAX_RETRY: raise TimeoutError
|
||||
if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
|
||||
|
||||
|
||||
stream_response = response
|
||||
result = ''
|
||||
while True:
|
||||
try: chunk = next(stream_response)
|
||||
except StopIteration:
|
||||
break
|
||||
except:
|
||||
chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
|
||||
|
||||
if len(chunk)==0: continue
|
||||
if not chunk.startswith('data:'):
|
||||
error_msg = get_full_error(chunk, stream_response)
|
||||
if "reduce the length" in error_msg:
|
||||
raise ConnectionAbortedError("AZURE OPENAI API拒绝了请求:" + error_msg)
|
||||
else:
|
||||
raise RuntimeError("AZURE OPENAI API拒绝了请求：" + error_msg)
|
||||
if ('data: [DONE]' in chunk): break
|
||||
|
||||
delta = chunk["delta"]
|
||||
if len(delta) == 0: break
|
||||
if "role" in delta: continue
|
||||
if "content" in delta:
|
||||
result += delta["content"]
|
||||
if not console_slience: print(delta["content"], end='')
|
||||
if observe_window is not None:
|
||||
# 观测窗，把已经获取的数据显示出去
|
||||
if len(observe_window) >= 1: observe_window[0] += delta["content"]
|
||||
# 看门狗，如果超过期限没有喂狗，则终止
|
||||
if len(observe_window) >= 2:
|
||||
if (time.time()-observe_window[1]) > watch_dog_patience:
|
||||
raise RuntimeError("用户取消了程序。")
|
||||
else: raise RuntimeError("意外Json结构："+delta)
|
||||
if chunk['finish_reason'] == 'length':
|
||||
raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
|
||||
return result
|
||||
|
||||
|
||||
def generate_azure_payload(inputs, llm_kwargs, history, system_prompt, stream):
|
||||
"""
|
||||
整合所有信息，选择LLM模型，生成 azure openai api请求，为发送请求做准备
|
||||
"""
|
||||
|
||||
conversation_cnt = len(history) // 2
|
||||
|
||||
messages = [{"role": "system", "content": system_prompt}]
|
||||
if conversation_cnt:
|
||||
for index in range(0, 2*conversation_cnt, 2):
|
||||
what_i_have_asked = {}
|
||||
what_i_have_asked["role"] = "user"
|
||||
what_i_have_asked["content"] = history[index]
|
||||
what_gpt_answer = {}
|
||||
what_gpt_answer["role"] = "assistant"
|
||||
what_gpt_answer["content"] = history[index+1]
|
||||
if what_i_have_asked["content"] != "":
|
||||
if what_gpt_answer["content"] == "": continue
|
||||
messages.append(what_i_have_asked)
|
||||
messages.append(what_gpt_answer)
|
||||
else:
|
||||
messages[-1]['content'] = what_gpt_answer['content']
|
||||
|
||||
what_i_ask_now = {}
|
||||
what_i_ask_now["role"] = "user"
|
||||
what_i_ask_now["content"] = inputs
|
||||
messages.append(what_i_ask_now)
|
||||
|
||||
payload = {
|
||||
"model": llm_kwargs['llm_model'],
|
||||
"messages": messages,
|
||||
"temperature": llm_kwargs['temperature'], # 1.0,
|
||||
"top_p": llm_kwargs['top_p'], # 1.0,
|
||||
"n": 1,
|
||||
"stream": stream,
|
||||
"presence_penalty": 0,
|
||||
"frequency_penalty": 0,
|
||||
"engine": AZURE_ENGINE
|
||||
}
|
||||
try:
|
||||
print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
|
||||
except:
|
||||
print('输入中可能存在乱码。')
|
||||
return payload
|
||||
|
||||
|
||||
@ -22,8 +22,8 @@ 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, trimmed_format_exc
|
||||
proxies, API_KEY, TIMEOUT_SECONDS, MAX_RETRY = \
|
||||
get_conf('proxies', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY')
|
||||
proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG = \
|
||||
get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG')
|
||||
|
||||
timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
|
||||
'网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
|
||||
@ -101,6 +101,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
if (time.time()-observe_window[1]) > watch_dog_patience:
|
||||
raise RuntimeError("用户取消了程序。")
|
||||
else: raise RuntimeError("意外Json结构："+delta)
|
||||
if json_data['finish_reason'] == 'content_filter':
|
||||
raise RuntimeError("由于提问含不合规内容被Azure过滤。")
|
||||
if json_data['finish_reason'] == 'length':
|
||||
raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
|
||||
return result
|
||||
@ -205,6 +207,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
chunk = get_full_error(chunk, stream_response)
|
||||
chunk_decoded = chunk.decode()
|
||||
error_msg = chunk_decoded
|
||||
openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
|
||||
if "reduce the length" in error_msg:
|
||||
if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
|
||||
history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
|
||||
@ -214,9 +217,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
elif "does not exist" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
|
||||
elif "Incorrect API key" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务.")
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
|
||||
elif "exceeded your current quota" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务.")
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
|
||||
elif "account is not active" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
|
||||
elif "associated with a deactivated account" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
|
||||
elif "bad forward key" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
|
||||
elif "Not enough point" in error_msg:
|
||||
@ -241,6 +248,8 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {api_key}"
|
||||
}
|
||||
if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG})
|
||||
if llm_kwargs['llm_model'].startswith('azure-'): headers.update({"api-key": api_key})
|
||||
|
||||
conversation_cnt = len(history) // 2
|
||||
|
||||
|
||||
@ -1,254 +0,0 @@
|
||||
"""
|
||||
========================================================================
|
||||
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```\n' + trimmed_format_exc() + '\n```\n'
|
||||
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。")
|
||||
|
||||
@ -89,9 +89,6 @@ class NewBingHandle(Process):
|
||||
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
|
||||
@ -101,7 +98,7 @@ class NewBingHandle(Process):
|
||||
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"
|
||||
wss_link=endpoint, # "wss://sydney.bing.com/sydney/ChatHub"
|
||||
):
|
||||
if not final:
|
||||
print(response)
|
||||
@ -121,14 +118,26 @@ class NewBingHandle(Process):
|
||||
self.local_history = []
|
||||
if (self.newbing_model is None) or (not self.success):
|
||||
# 代理设置
|
||||
proxies, = get_conf('proxies')
|
||||
proxies, NEWBING_COOKIES = get_conf('proxies', 'NEWBING_COOKIES')
|
||||
if proxies is None:
|
||||
self.proxies_https = None
|
||||
else:
|
||||
self.proxies_https = proxies['https']
|
||||
|
||||
if (NEWBING_COOKIES is not None) and len(NEWBING_COOKIES) > 100:
|
||||
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] NEWBING_COOKIES未填写或有格式错误。')
|
||||
self.child.send('[Fail]'); self.child.send('[Finish]')
|
||||
raise RuntimeError(f"NEWBING_COOKIES未填写或有格式错误。")
|
||||
else:
|
||||
cookies = None
|
||||
|
||||
try:
|
||||
self.newbing_model = NewbingChatbot(proxy=self.proxies_https)
|
||||
self.newbing_model = NewbingChatbot(proxy=self.proxies_https, cookies=cookies)
|
||||
except:
|
||||
self.success = False
|
||||
tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
|
||||
@ -143,7 +152,7 @@ class NewBingHandle(Process):
|
||||
asyncio.run(self.async_run())
|
||||
except Exception:
|
||||
tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
|
||||
self.child.send(f'[Local Message] Newbing失败 {tb_str}.')
|
||||
self.child.send(f'[Local Message] Newbing 请求失败，报错信息如下. 如果是与网络相关的问题，建议更换代理协议（推荐http）或代理节点 {tb_str}.')
|
||||
self.child.send('[Fail]')
|
||||
self.child.send('[Finish]')
|
||||
|
||||
@ -151,18 +160,14 @@ class NewBingHandle(Process):
|
||||
"""
|
||||
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
|
||||
self.threadLock.acquire() # 获取线程锁
|
||||
self.parent.send(kwargs) # 请求子进程
|
||||
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()
|
||||
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 # 失败
|
||||
else: yield res # newbing回复的片段
|
||||
self.threadLock.release() # 释放线程锁
|
||||
|
||||
|
||||
"""
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
from .bridge_newbing import preprocess_newbing_out, preprocess_newbing_out_simple
|
||||
from .bridge_newbingfree import preprocess_newbing_out, preprocess_newbing_out_simple
|
||||
from multiprocessing import Process, Pipe
|
||||
from toolbox import update_ui, get_conf, trimmed_format_exc
|
||||
import threading
|
||||
|
||||
@ -1,409 +0,0 @@
|
||||
"""
|
||||
========================================================================
|
||||
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))
|
||||
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
./docs/gradio-3.32.2-py3-none-any.whl
|
||||
pydantic==1.10.11
|
||||
tiktoken>=0.3.3
|
||||
requests[socks]
|
||||
transformers
|
||||
@ -15,4 +16,4 @@ pymupdf
|
||||
openai
|
||||
numpy
|
||||
arxiv
|
||||
rich
|
||||
rich
|
||||
|
||||
69
toolbox.py
69
toolbox.py
@ -4,6 +4,7 @@ import time
|
||||
import inspect
|
||||
import re
|
||||
import os
|
||||
import gradio
|
||||
from latex2mathml.converter import convert as tex2mathml
|
||||
from functools import wraps, lru_cache
|
||||
pj = os.path.join
|
||||
@ -40,7 +41,7 @@ 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。
|
||||
"""
|
||||
def decorated(cookies, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg, *args):
|
||||
def decorated(request: gradio.Request, cookies, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg, *args):
|
||||
txt_passon = txt
|
||||
if txt == "" and txt2 != "": txt_passon = txt2
|
||||
# 引入一个有cookie的chatbot
|
||||
@ -54,13 +55,21 @@ def ArgsGeneralWrapper(f):
|
||||
'top_p':top_p,
|
||||
'max_length': max_length,
|
||||
'temperature':temperature,
|
||||
'client_ip': request.client.host,
|
||||
}
|
||||
plugin_kwargs = {
|
||||
"advanced_arg": plugin_advanced_arg,
|
||||
}
|
||||
chatbot_with_cookie = ChatBotWithCookies(cookies)
|
||||
chatbot_with_cookie.write_list(chatbot)
|
||||
yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
|
||||
if cookies.get('lock_plugin', None) is None:
|
||||
# 正常状态
|
||||
yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
|
||||
else:
|
||||
# 处理个别特殊插件的锁定状态
|
||||
module, fn_name = cookies['lock_plugin'].split('->')
|
||||
f_hot_reload = getattr(importlib.import_module(module, fn_name), fn_name)
|
||||
yield from f_hot_reload(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
|
||||
return decorated
|
||||
|
||||
|
||||
@ -68,8 +77,21 @@ def update_ui(chatbot, history, msg='正常', **kwargs): # 刷新界面
|
||||
"""
|
||||
刷新用户界面
|
||||
"""
|
||||
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
|
||||
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。"
|
||||
cookies = chatbot.get_cookies()
|
||||
|
||||
# 解决插件锁定时的界面显示问题
|
||||
if cookies.get('lock_plugin', None):
|
||||
label = cookies.get('llm_model', "") + " | " + "正在锁定插件" + cookies.get('lock_plugin', None)
|
||||
chatbot_gr = gradio.update(value=chatbot, label=label)
|
||||
if cookies.get('label', "") != label: cookies['label'] = label # 记住当前的label
|
||||
elif cookies.get('label', None):
|
||||
chatbot_gr = gradio.update(value=chatbot, label=cookies.get('llm_model', ""))
|
||||
cookies['label'] = None # 清空label
|
||||
else:
|
||||
chatbot_gr = chatbot
|
||||
|
||||
yield cookies, chatbot_gr, history, msg
|
||||
|
||||
def update_ui_lastest_msg(lastmsg, chatbot, history, delay=1): # 刷新界面
|
||||
"""
|
||||
@ -192,7 +214,7 @@ def write_results_to_file(history, file_name=None):
|
||||
# remove everything that cannot be handled by utf8
|
||||
f.write(content.encode('utf-8', 'ignore').decode())
|
||||
f.write('\n\n')
|
||||
res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')
|
||||
res = '以上材料已经被写入:\t' + os.path.abspath(f'./gpt_log/{file_name}')
|
||||
print(res)
|
||||
return res
|
||||
|
||||
@ -445,8 +467,11 @@ def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
|
||||
import shutil
|
||||
if rename_file is None: rename_file = f'{gen_time_str()}-{os.path.basename(file)}'
|
||||
new_path = os.path.join(f'./gpt_log/', rename_file)
|
||||
# 如果已经存在，先删除
|
||||
if os.path.exists(new_path) and not os.path.samefile(new_path, file): os.remove(new_path)
|
||||
# 把文件复制过去
|
||||
if not os.path.exists(new_path): shutil.copyfile(file, new_path)
|
||||
# 将文件添加到chatbot cookie中，避免多用户干扰
|
||||
if chatbot:
|
||||
if 'file_to_promote' in chatbot._cookies: current = chatbot._cookies['file_to_promote']
|
||||
else: current = []
|
||||
@ -505,16 +530,24 @@ def on_report_generated(cookies, files, chatbot):
|
||||
chatbot.append(['报告如何远程获取？', f'报告已经添加到右侧“文件上传区”（可能处于折叠状态），请查收。{file_links}'])
|
||||
return cookies, report_files, chatbot
|
||||
|
||||
def load_chat_cookies():
|
||||
API_KEY, LLM_MODEL, AZURE_API_KEY = get_conf('API_KEY', 'LLM_MODEL', 'AZURE_API_KEY')
|
||||
if is_any_api_key(AZURE_API_KEY):
|
||||
if is_any_api_key(API_KEY): API_KEY = API_KEY + ',' + AZURE_API_KEY
|
||||
else: API_KEY = AZURE_API_KEY
|
||||
return {'api_key': API_KEY, 'llm_model': LLM_MODEL}
|
||||
|
||||
def is_openai_api_key(key):
|
||||
API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
|
||||
return bool(API_MATCH_ORIGINAL)
|
||||
|
||||
def is_azure_api_key(key):
|
||||
API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{32}$", key)
|
||||
return bool(API_MATCH_ORIGINAL) or bool(API_MATCH_AZURE)
|
||||
return bool(API_MATCH_AZURE)
|
||||
|
||||
def is_api2d_key(key):
|
||||
if key.startswith('fk') and len(key) == 41:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
API_MATCH_API2D = re.match(r"fk[a-zA-Z0-9]{6}-[a-zA-Z0-9]{32}$", key)
|
||||
return bool(API_MATCH_API2D)
|
||||
|
||||
def is_any_api_key(key):
|
||||
if ',' in key:
|
||||
@ -523,10 +556,10 @@ def is_any_api_key(key):
|
||||
if is_any_api_key(k): return True
|
||||
return False
|
||||
else:
|
||||
return is_openai_api_key(key) or is_api2d_key(key)
|
||||
return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key)
|
||||
|
||||
def what_keys(keys):
|
||||
avail_key_list = {'OpenAI Key':0, "API2D Key":0}
|
||||
avail_key_list = {'OpenAI Key':0, "Azure Key":0, "API2D Key":0}
|
||||
key_list = keys.split(',')
|
||||
|
||||
for k in key_list:
|
||||
@ -537,7 +570,11 @@ def what_keys(keys):
|
||||
if is_api2d_key(k):
|
||||
avail_key_list['API2D Key'] += 1
|
||||
|
||||
return f"检测到： OpenAI Key {avail_key_list['OpenAI Key']} 个，API2D Key {avail_key_list['API2D Key']} 个"
|
||||
for k in key_list:
|
||||
if is_azure_api_key(k):
|
||||
avail_key_list['Azure Key'] += 1
|
||||
|
||||
return f"检测到： OpenAI Key {avail_key_list['OpenAI Key']} 个, Azure Key {avail_key_list['Azure Key']} 个, API2D Key {avail_key_list['API2D Key']} 个"
|
||||
|
||||
def select_api_key(keys, llm_model):
|
||||
import random
|
||||
@ -552,8 +589,12 @@ def select_api_key(keys, llm_model):
|
||||
for k in key_list:
|
||||
if is_api2d_key(k): avail_key_list.append(k)
|
||||
|
||||
if llm_model.startswith('azure-'):
|
||||
for k in key_list:
|
||||
if is_azure_api_key(k): avail_key_list.append(k)
|
||||
|
||||
if len(avail_key_list) == 0:
|
||||
raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源。")
|
||||
raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源（右下角更换模型菜单中可切换openai,azure和api2d请求源）")
|
||||
|
||||
api_key = random.choice(avail_key_list) # 随机负载均衡
|
||||
return api_key
|
||||
|
||||
4
version
4
version
@ -1,5 +1,5 @@
|
||||
{
|
||||
"version": 3.42,
|
||||
"version": 3.44,
|
||||
"show_feature": true,
|
||||
"new_feature": "完善本地Latex矫错和翻译功能 <-> 增加gpt-3.5-16k的支持 <-> 新增最强Arxiv论文翻译插件 <-> 修复gradio复制按钮BUG <-> 修复PDF翻译的BUG, 新增HTML中英双栏对照 <-> 添加了OpenAI图片生成插件 <-> 添加了OpenAI音频转文本总结插件 <-> 通过Slack添加对Claude的支持"
|
||||
"new_feature": "[改善UI] 动态ChatBot窗口高度 <-> 修复Azure接口的BUG <-> 完善多语言模块 <-> 完善本地Latex矫错和翻译功能 <-> 增加gpt-3.5-16k的支持 <-> 新增最强Arxiv论文翻译插件 <-> 修复gradio复制按钮BUG <-> 修复PDF翻译的BUG, 新增HTML中英双栏对照 <-> 添加了OpenAI图片生成插件"
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user