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Merge branch 'frontier' of https://github.com/binary-husky/chatgpt_academic into frontier

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qingxu fu 2023-11-19 21:50:15 +08:00
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@ -146,9 +146,9 @@ debug*
private* private*
crazy_functions/test_project/pdf_and_word crazy_functions/test_project/pdf_and_word
crazy_functions/test_samples crazy_functions/test_samples
request_llm/jittorllms request_llms/jittorllms
multi-language multi-language
request_llm/moss request_llms/moss
media media
flagged flagged
request_llm/ChatGLM-6b-onnx-u8s8 request_llms/ChatGLM-6b-onnx-u8s8

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README.md
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@ -1,24 +1,25 @@
> **Note** > **Note**
> >
> 2023.10.8: Gradio, Pydantic依赖调整，已修改 `requirements.txt`。请及时**更新代码**，安装依赖时，请严格选择`requirements.txt`中**指定的版本**。 > 2023.11.12: 紧急修复了endpoint异常的问题。
> >
> `pip install -r requirements.txt` > 2023.11.7: 安装依赖时，请选择`requirements.txt`中**指定的版本**。 安装命令：`pip install -r requirements.txt`。本项目开源免费，近期发现有人蔑视开源协议并利用本项目违规圈钱，请提高警惕，谨防上当受骗。
# <div align=center><img src="docs/logo.png" width="40"> GPT 学术优化 (GPT Academic)</div> # <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！** **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. If you like this project, please give it a Star. We also have a README in [English|](docs/README.English.md)[日本語|](docs/README.Japanese.md)[한국어|](docs/README.Korean.md)[Русский|](docs/README.Russian.md)[Français](docs/README.French.md) translated by this project itself.
To translate this project to arbitrary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental). To translate this project to arbitrary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).
> **Note** > **Note**
> >
> 1.请注意只有 **高亮** 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** 标识的插件（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[自译解报告`self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告)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[`wiki`](https://github.com/binary-husky/gpt_academic/wiki)。[常规安装方法](#installation) | [一键安装脚本](https://github.com/binary-husky/gpt_academic/releases) | [配置说明](https://github.com/binary-husky/gpt_academic/wiki/%E9%A1%B9%E7%9B%AE%E9%85%8D%E7%BD%AE%E8%AF%B4%E6%98%8E)。 > 2.本项目中每个文件的功能都在[自译解报告`self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告)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[`wiki`](https://github.com/binary-husky/gpt_academic/wiki)。[常规安装方法](#installation) | [一键安装脚本](https://github.com/binary-husky/gpt_academic/releases) | [配置说明](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
> >
> 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,azure-key3,api2d-key4"`。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,azure-key3,api2d-key4"`。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。
@ -27,41 +28,38 @@ To translate this project to arbitrary language with GPT, read and run [`multi_l
功能（⭐= 近期新增功能） | 描述 功能（⭐= 近期新增功能） | 描述
--- | --- --- | ---
⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | 百度[千帆](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)与文心一言, [通义千问](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)，[LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) ⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | 百度[千帆](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)与文心一言, [通义千问](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)，[LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)，智谱API，DALLE3
一键润色 | 支持一键润色、一键查找论文语法错误 润色、翻译、代码解释 | 一键润色、翻译、查找论文语法错误、解释代码
一键中英互译 | 一键中英互译
一键代码解释 | 显示代码、解释代码、生成代码、给代码加注释
[自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键 [自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键
模块化设计 | 支持自定义强大的[函数插件](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions)，插件支持[热更新](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) 模块化设计 | 支持自定义强大的[插件](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions)，插件支持[热更新](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)
[自我程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] [一键读懂](https://github.com/binary-husky/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)本项目的源代码 [程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [插件] 一键可以剖析Python/C/C++/Java/Lua/...项目树 或 [自我剖析](https://www.bilibili.com/video/BV1cj411A7VW)
[程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] 一键可以剖析其他Python/C/C++/Java/Lua/...项目树 读论文、[翻译](https://www.bilibili.com/video/BV1KT411x7Wn)论文 | [插件] 一键解读latex/pdf论文全文并生成摘要
读论文、[翻译](https://www.bilibili.com/video/BV1KT411x7Wn)论文 | [函数插件] 一键解读latex/pdf论文全文并生成摘要 Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [插件] 一键翻译或润色latex论文
Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [函数插件] 一键翻译或润色latex论文 批量注释生成 | [插件] 一键批量生成函数注释
批量注释生成 | [函数插件] 一键批量生成函数注释 Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [插件] 看到上面5种语言的[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)了吗？
Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [函数插件] 看到上面5种语言的[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)了吗？ chat分析报告生成 | [插件] 运行后自动生成总结汇报
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) | [插件] PDF论文提取题目&摘要+翻译全文（多线程）
[PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [函数插件] PDF论文提取题目&摘要+翻译全文（多线程） [Arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
[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 Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF
Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF [谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [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/)
[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 | [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
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 ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [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
⭐Arxiv论文精细翻译 ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [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 ⭐[实时语音对话输入](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [插件] 异步[监听音频](https://www.bilibili.com/video/BV1AV4y187Uy/)，自动断句，自动寻找回答时机
⭐[实时语音对话输入](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [函数插件] 异步[监听音频](https://www.bilibili.com/video/BV1AV4y187Uy/)，自动断句，自动寻找回答时机
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 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 ⭐AutoGen多智能体插件 | [插件] 借助微软AutoGen，探索多Agent的智能涌现可能！
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 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ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)、[复旦MOSS](https://github.com/OpenLMLab/MOSS)同时伺候的感觉一定会很不错吧？ [多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持 | 同时被GPT3.5、GPT4、[清华ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)、[复旦MOSS](https://github.com/OpenLMLab/MOSS)同时伺候的感觉一定会很不错吧？
⭐ChatGLM2微调模型 | 支持加载ChatGLM2微调模型，提供ChatGLM2微调辅助插件 ⭐ChatGLM2微调模型 | 支持加载ChatGLM2微调模型，提供ChatGLM2微调辅助插件
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/)
⭐[void-terminal](https://github.com/binary-husky/void-terminal) pip包 | 脱离GUI，在Python中直接调用本项目的所有函数插件（开发中） ⭐[void-terminal](https://github.com/binary-husky/void-terminal) pip包 | 脱离GUI，在Python中直接调用本项目的所有函数插件（开发中）
⭐虚空终端插件 | [函数插件] 用自然语言，直接调度本项目其他插件 ⭐虚空终端插件 | [插件] 用自然语言，直接调度本项目其他插件
更多新功能展示 (图像生成等) …… | 见本文档结尾处 …… 更多新功能展示 (图像生成等) …… | 见本文档结尾处 ……
</div> </div>
- 新界面（修改`config.py`中的LAYOUT选项即可实现“左右布局”和“上下布局”的切换） - 新界面（修改`config.py`中的LAYOUT选项即可实现“左右布局”和“上下布局”的切换）
<div align="center"> <div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" > <img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
</div> </div>
@ -101,16 +99,16 @@ cd gpt_academic
2. 配置API_KEY 2. 配置API_KEY
在`config.py`中，配置API KEY等设置，[点击查看特殊网络环境设置方法](https://github.com/binary-husky/gpt_academic/issues/1) 。[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/%E9%A1%B9%E7%9B%AE%E9%85%8D%E7%BD%AE%E8%AF%B4%E6%98%8E)。 在`config.py`中，配置API KEY等设置，[点击查看特殊网络环境设置方法](https://github.com/binary-husky/gpt_academic/issues/1) 。[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
「 程序会优先检查是否存在名为`config_private.py`的私密配置文件，并用其中的配置覆盖`config.py`的同名配置。如您能理解该读取逻辑，我们强烈建议您在`config.py`旁边创建一个名为`config_private.py`的新配置文件，并把`config.py`中的配置转移（复制）到`config_private.py`中（仅复制您修改过的配置条目即可）。 」 「 程序会优先检查是否存在名为`config_private.py`的私密配置文件，并用其中的配置覆盖`config.py`的同名配置。如您能理解该读取逻辑，我们强烈建议您在`config.py`旁边创建一个名为`config_private.py`的新配置文件，并把`config.py`中的配置转移（复制）到`config_private.py`中（仅复制您修改过的配置条目即可）。 」
「 支持通过`环境变量`配置项目，环境变量的书写格式参考`docker-compose.yml`文件或者我们的[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/%E9%A1%B9%E7%9B%AE%E9%85%8D%E7%BD%AE%E8%AF%B4%E6%98%8E)。配置读取优先级: `环境变量` > `config_private.py` > `config.py`。 」 「 支持通过`环境变量`配置项目，环境变量的书写格式参考`docker-compose.yml`文件或者我们的[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。配置读取优先级: `环境变量` > `config_private.py` > `config.py`。 」
3. 安装依赖 3. 安装依赖
```sh ```sh
# （选择I: 如熟悉python）（python版本3.9以上，越新越好），备注：使用官方pip源或者阿里pip源,临时换源方法：python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ # （选择I: 如熟悉python, python>=3.9）备注：使用官方pip源或者阿里pip源, 临时换源方法：python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt python -m pip install -r requirements.txt
# （选择II: 使用Anaconda）步骤也是类似的 (https://www.bilibili.com/video/BV1rc411W7Dr)： # （选择II: 使用Anaconda）步骤也是类似的 (https://www.bilibili.com/video/BV1rc411W7Dr)：
@ -126,17 +124,17 @@ python -m pip install -r requirements.txt # 这个步骤和pip安装一样的步
【可选步骤】如果需要支持清华ChatGLM2/复旦MOSS作为后端，需要额外安装更多依赖（前提条件：熟悉Python + 用过Pytorch + 电脑配置够强）： 【可选步骤】如果需要支持清华ChatGLM2/复旦MOSS作为后端，需要额外安装更多依赖（前提条件：熟悉Python + 用过Pytorch + 电脑配置够强）：
```sh ```sh
# 【可选步骤I】支持清华ChatGLM2。清华ChatGLM备注：如果遇到"Call ChatGLM fail 不能正常加载ChatGLM的参数" 错误，参考如下： 1：以上默认安装的为torch+cpu版，使用cuda需要卸载torch重新安装torch+cuda； 2：如因本机配置不够无法加载模型，可以修改request_llm/bridge_chatglm.py中的模型精度, 将 AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) 都修改为 AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True) # 【可选步骤I】支持清华ChatGLM2。清华ChatGLM备注：如果遇到"Call ChatGLM fail 不能正常加载ChatGLM的参数" 错误，参考如下： 1：以上默认安装的为torch+cpu版，使用cuda需要卸载torch重新安装torch+cuda； 2：如因本机配置不够无法加载模型，可以修改request_llm/bridge_chatglm.py中的模型精度, 将 AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) 都修改为 AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llm/requirements_chatglm.txt python -m pip install -r request_llms/requirements_chatglm.txt
# 【可选步骤II】支持复旦MOSS # 【可选步骤II】支持复旦MOSS
python -m pip install -r request_llm/requirements_moss.txt python -m pip install -r request_llms/requirements_moss.txt
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llm/moss # 注意执行此行代码时，必须处于项目根路径 git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # 注意执行此行代码时，必须处于项目根路径
# 【可选步骤III】支持RWKV Runner # 【可选步骤III】支持RWKV Runner
参考wiki：https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner 参考wiki：https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
# 【可选步骤IV】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)： # 【可选步骤IV】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"] AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
``` ```
</p> </p>
@ -151,11 +149,11 @@ python main.py
### 安装方法II：使用Docker ### 安装方法II：使用Docker
0. 部署项目的全部能力（这个是包含cuda和latex的大型镜像。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个，建议使用方案1）（需要熟悉[Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian)运行时） 0. 部署项目的全部能力（这个是包含cuda和latex的大型镜像。但如果您网速慢、硬盘小，则不推荐使用这个）
[![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml) [![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
``` sh ``` sh
# 修改docker-compose.yml，保留方案0并删除其他方案。修改docker-compose.yml中方案0的配置，参考其中注释即可 # 修改docker-compose.yml，保留方案0并删除其他方案。然后运行：
docker-compose up docker-compose up
``` ```
@ -165,7 +163,7 @@ docker-compose up
[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml) [![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
``` sh ``` sh
# 修改docker-compose.yml，保留方案1并删除其他方案。修改docker-compose.yml中方案1的配置，参考其中注释即可 # 修改docker-compose.yml，保留方案1并删除其他方案。然后运行：
docker-compose up docker-compose up
``` ```
@ -175,48 +173,30 @@ P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以
[![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml) [![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
``` sh ``` sh
# 修改docker-compose.yml，保留方案2并删除其他方案。修改docker-compose.yml中方案2的配置，参考其中注释即可 # 修改docker-compose.yml，保留方案2并删除其他方案。然后运行：
docker-compose up
```
3. ChatGPT + LLAMA + 盘古 + RWKV（需要熟悉[Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian)运行时）
[![jittorllms](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-jittorllms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-jittorllms.yml)
``` sh
# 修改docker-compose.yml，保留方案3并删除其他方案。修改docker-compose.yml中方案3的配置，参考其中注释即可
docker-compose up docker-compose up
``` ```
### 安装方法III：其他部署姿势 ### 安装方法III：其他部署姿势
1. 一键运行脚本。 1. **Windows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。 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)。 脚本的贡献来源是[oobabooga](https://github.com/oobabooga/one-click-installers)。
2. 使用docker-compose运行。 2. 使用第三方API、Azure等、文心一言、星火等，见[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)
请阅读docker-compose.yml后，按照其中的提示操作即可
3. 如何使用反代URL 3. 云服务器远程部署避坑指南。
按照`config.py`中的说明配置API_URL_REDIRECT即可。 请访问[云服务器远程部署wiki](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
4. 微软云AzureAPI 4. 一些新型的部署平台或方法
按照`config.py`中的说明配置即可（AZURE_ENDPOINT等四个配置） - 使用Sealos[一键部署](https://github.com/binary-husky/gpt_academic/issues/993)。
- 使用WSL2（Windows Subsystem for Linux 子系统）。请访问[部署wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
5. 远程云服务器部署（需要云服务器知识与经验）。 - 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)
请访问[部署wiki-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
6. 使用Sealos[一键部署](https://github.com/binary-husky/gpt_academic/issues/993)。
7. 使用WSL2（Windows Subsystem for Linux 子系统）。
请访问[部署wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
8. 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 # Advanced Usage
### I：自定义新的便捷按钮（学术快捷键） ### 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。） 任意文本编辑器打开`core_functional.py`，添加条目如下，然后重启程序。（如按钮已存在，那么前缀、后缀都支持热修改，无需重启程序即可生效。）
例如 例如
``` ```
"超级英译中": { "超级英译中": {
@ -232,14 +212,13 @@ docker-compose up
</div> </div>
### II：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。 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。 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)。 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 # Updates
### I：新功能动态 ### I：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， 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。 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。
@ -280,28 +259,23 @@ 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
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" > <img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div> </div>
7. 新增MOSS大语言模型支持 7. OpenAI图像生成
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
</div>
8. OpenAI图像生成
<div align="center"> <div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" > <img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div> </div>
9. OpenAI音频解析与总结 8. OpenAI音频解析与总结
<div align="center"> <div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" > <img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div> </div>
10. Latex全文校对纠错 9. Latex全文校对纠错
<div align="center"> <div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===> <img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200"> <img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
</div> </div>
11. 语言、主题切换 10. 语言、主题切换
<div align="center"> <div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" > <img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
</div> </div>
@ -309,7 +283,10 @@ 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
### II：版本: ### II：版本:
- version 3.60（todo）: 优化虚空终端，引入code interpreter和更多插件 - version 3.70（todo）: 优化AutoGen插件主题并设计一系列衍生插件
- version 3.60: 引入AutoGen作为新一代插件的基石
- version 3.57: 支持GLM3，星火v3，文心一言v4，修复本地模型的并发BUG
- version 3.56: 支持动态追加基础功能按钮，新汇报PDF汇总页面
- version 3.55: 重构前端界面，引入悬浮窗口与菜单栏 - version 3.55: 重构前端界面，引入悬浮窗口与菜单栏
- version 3.54: 新增动态代码解释器（Code Interpreter）（待完善） - version 3.54: 新增动态代码解释器（Code Interpreter）（待完善）
- version 3.53: 支持动态选择不同界面主题，提高稳定性&解决多用户冲突问题 - version 3.53: 支持动态选择不同界面主题，提高稳定性&解决多用户冲突问题
@ -344,7 +321,13 @@ GPT Academic开发者QQ群：`610599535`
1. `Chuanhu-Small-and-Beautiful` [网址](https://github.com/GaiZhenbiao/ChuanhuChatGPT/) 1. `Chuanhu-Small-and-Beautiful` [网址](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
### IV：参考与学习 ### IV：本项目的开发分支
1. `master` 分支: 主分支，稳定版
2. `frontier` 分支: 开发分支，测试版
### V：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： 代码中参考了很多其他优秀项目中的设计，顺序不分先后：

8
check_proxy.py
View File

@ -46,7 +46,7 @@ def backup_and_download(current_version, remote_version):
return new_version_dir return new_version_dir
os.makedirs(new_version_dir) os.makedirs(new_version_dir)
shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history']) shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
proxies, = get_conf('proxies') proxies = get_conf('proxies')
r = requests.get( r = requests.get(
'https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True) 'https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
zip_file_path = backup_dir+'/master.zip' zip_file_path = backup_dir+'/master.zip'
@ -113,7 +113,7 @@ def auto_update(raise_error=False):
import requests import requests
import time import time
import json import json
proxies, = get_conf('proxies') proxies = get_conf('proxies')
response = requests.get( response = requests.get(
"https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5) "https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
remote_json_data = json.loads(response.text) remote_json_data = json.loads(response.text)
@ -156,7 +156,7 @@ def auto_update(raise_error=False):
def warm_up_modules(): def warm_up_modules():
print('正在执行一些模块的预热...') print('正在执行一些模块的预热...')
from toolbox import ProxyNetworkActivate from toolbox import ProxyNetworkActivate
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
with ProxyNetworkActivate("Warmup_Modules"): with ProxyNetworkActivate("Warmup_Modules"):
enc = model_info["gpt-3.5-turbo"]['tokenizer'] enc = model_info["gpt-3.5-turbo"]['tokenizer']
enc.encode("模块预热", disallowed_special=()) enc.encode("模块预热", disallowed_special=())
@ -167,5 +167,5 @@ if __name__ == '__main__':
import os import os
os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染 os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
from toolbox import get_conf from toolbox import get_conf
proxies, = get_conf('proxies') proxies = get_conf('proxies')
check_proxy(proxies) check_proxy(proxies)

46
config.py
View File

@ -49,6 +49,10 @@ THEME = "Default"
AVAIL_THEMES = ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast", "Gstaff/Xkcd", "NoCrypt/Miku"] AVAIL_THEMES = ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast", "Gstaff/Xkcd", "NoCrypt/Miku"]
# 默认的系统提示词（system prompt）
INIT_SYS_PROMPT = "Serve me as a writing and programming assistant."
# 对话窗的高度 （仅在LAYOUT="TOP-DOWN"时生效） # 对话窗的高度 （仅在LAYOUT="TOP-DOWN"时生效）
CHATBOT_HEIGHT = 1115 CHATBOT_HEIGHT = 1115
@ -83,17 +87,23 @@ DEFAULT_FN_GROUPS = ['对话', '编程', '学术', '智能体']
# 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 ) # 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 ↓↓↓ LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", AVAIL_LLM_MODELS = ["gpt-3.5-turbo-1106","gpt-4-1106-preview",
"api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k', "api2d-gpt-4", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
"gpt-4", "gpt-4-32k", "azure-gpt-4", "chatglm", "moss", "newbing", "stack-claude"] "api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
# P.S. 其他可用的模型还包括 ["qianfan", "llama2", "qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random" "gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4",
# "spark", "sparkv2", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"] "chatglm3", "moss", "newbing", "claude-2"]
# P.S. 其他可用的模型还包括 ["zhipuai", "qianfan", "llama2", "qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random"
# "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]
# 定义界面上“询问多个GPT模型”插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
# 百度千帆（LLM_MODEL="qianfan"） # 百度千帆（LLM_MODEL="qianfan"）
BAIDU_CLOUD_API_KEY = '' BAIDU_CLOUD_API_KEY = ''
BAIDU_CLOUD_SECRET_KEY = '' BAIDU_CLOUD_SECRET_KEY = ''
BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot' # 可选 "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat" BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot' # 可选 "ERNIE-Bot-4"(文心大模型4.0), "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat"
# 如果使用ChatGLM2微调模型，请把 LLM_MODEL="chatglmft"，并在此处指定模型路径 # 如果使用ChatGLM2微调模型，请把 LLM_MODEL="chatglmft"，并在此处指定模型路径
@ -135,17 +145,21 @@ SSL_CERTFILE = ""
API_ORG = "" API_ORG = ""
# 如果需要使用Slack Claude，使用教程详情见 request_llm/README.md # 如果需要使用Slack Claude，使用教程详情见 request_llms/README.md
SLACK_CLAUDE_BOT_ID = '' SLACK_CLAUDE_BOT_ID = ''
SLACK_CLAUDE_USER_TOKEN = '' SLACK_CLAUDE_USER_TOKEN = ''
# 如果需要使用AZURE 详情请见额外文档 docs\use_azure.md # 如果需要使用AZURE（方法一：单个azure模型部署）详情请见额外文档 docs\use_azure.md
AZURE_ENDPOINT = "https://你亲手写的api名称.openai.azure.com/" 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_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 AZURE_ENGINE = "填入你亲手写的部署名" # 读 docs\use_azure.md
# 如果需要使用AZURE（方法二：多个azure模型部署+动态切换）详情请见额外文档 docs\use_azure.md
AZURE_CFG_ARRAY = {}
# 使用Newbing (不推荐使用，未来将删除) # 使用Newbing (不推荐使用，未来将删除)
NEWBING_STYLE = "creative" # ["creative", "balanced", "precise"] NEWBING_STYLE = "creative" # ["creative", "balanced", "precise"]
NEWBING_COOKIES = """ NEWBING_COOKIES = """
@ -167,6 +181,11 @@ XFYUN_API_SECRET = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
# 接入智谱大模型
ZHIPUAI_API_KEY = ""
ZHIPUAI_MODEL = "chatglm_turbo"
# Claude API KEY # Claude API KEY
ANTHROPIC_API_KEY = "" ANTHROPIC_API_KEY = ""
@ -193,7 +212,7 @@ ALLOW_RESET_CONFIG = False
# 在使用AutoGen插件时，是否使用Docker容器运行代码 # 在使用AutoGen插件时，是否使用Docker容器运行代码
AUTOGEN_USE_DOCKER = True AUTOGEN_USE_DOCKER = False
# 临时的上传文件夹位置，请勿修改 # 临时的上传文件夹位置，请勿修改
@ -209,6 +228,10 @@ WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
"Warmup_Modules", "Nougat_Download", "AutoGen"] "Warmup_Modules", "Nougat_Download", "AutoGen"]
# *实验性功能*: 自动检测并屏蔽失效的KEY，请勿使用
BLOCK_INVALID_APIKEY = False
# 自定义按钮的最大数量限制 # 自定义按钮的最大数量限制
NUM_CUSTOM_BASIC_BTN = 4 NUM_CUSTOM_BASIC_BTN = 4
@ -226,13 +249,16 @@ LATEX_EXPERIMENTAL = False
│ ├── API_ORG（不常用） │ ├── API_ORG（不常用）
│ └── API_URL_REDIRECT（不常用） │ └── API_URL_REDIRECT（不常用）
│ │
├── "azure-gpt-3.5" 等azure模型 ├── "azure-gpt-3.5" 等azure模型（单个azure模型，不需要动态切换）
│ ├── API_KEY │ ├── API_KEY
│ ├── AZURE_ENDPOINT │ ├── AZURE_ENDPOINT
│ ├── AZURE_API_KEY │ ├── AZURE_API_KEY
│ ├── AZURE_ENGINE │ ├── AZURE_ENGINE
│ └── API_URL_REDIRECT │ └── API_URL_REDIRECT
│ │
├── "azure-gpt-3.5" 等azure模型（多个azure模型，需要动态切换，高优先级）
│ └── AZURE_CFG_ARRAY
│
├── "spark" 星火认知大模型 spark & sparkv2 ├── "spark" 星火认知大模型 spark & sparkv2
│ ├── XFYUN_APPID │ ├── XFYUN_APPID
│ ├── XFYUN_API_SECRET │ ├── XFYUN_API_SECRET

44
crazy_functional.py
View File

@ -1,4 +1,5 @@
from toolbox import HotReload # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效 from toolbox import HotReload # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效
from toolbox import trimmed_format_exc
def get_crazy_functions(): def get_crazy_functions():
@ -292,6 +293,7 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -316,6 +318,7 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -331,6 +334,7 @@ def get_crazy_functions():
}, },
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -346,22 +350,35 @@ def get_crazy_functions():
}, },
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
from crazy_functions.图片生成 import 图片生成 from crazy_functions.图片生成 import 图片生成_DALLE2, 图片生成_DALLE3
function_plugins.update({ function_plugins.update({
"图片生成（先切换模型到openai或api2d）": { "图片生成_DALLE2 （先切换模型到openai或api2d）": {
"Group": "对话", "Group": "对话",
"Color": "stop", "Color": "stop",
"AsButton": False, "AsButton": False,
"AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False） "AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
"ArgsReminder": "在这里输入分辨率, 如256x256（默认）", # 高级参数输入区的显示提示 "ArgsReminder": "在这里输入分辨率, 如1024x1024（默认），支持 256x256, 512x512, 1024x1024", # 高级参数输入区的显示提示
"Info": "图片生成 | 输入参数字符串，提供图像的内容", "Info": "使用DALLE2生成图片 | 输入参数字符串，提供图像的内容",
"Function": HotReload(图片生成) "Function": HotReload(图片生成_DALLE2)
},
})
function_plugins.update({
"图片生成_DALLE3 （先切换模型到openai或api2d）": {
"Group": "对话",
"Color": "stop",
"AsButton": False,
"AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
"ArgsReminder": "在这里输入分辨率, 如1024x1024（默认），支持 1024x1024, 1792x1024, 1024x1792。如需生成高清图像，请输入 1024x1024-HD, 1792x1024-HD, 1024x1792-HD。", # 高级参数输入区的显示提示
"Info": "使用DALLE3生成图片 | 输入参数字符串，提供图像的内容",
"Function": HotReload(图片生成_DALLE3)
}, },
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -378,6 +395,7 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -392,6 +410,7 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -407,6 +426,7 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -422,6 +442,7 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -437,6 +458,7 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -450,6 +472,7 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -494,11 +517,12 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
from toolbox import get_conf from toolbox import get_conf
ENABLE_AUDIO, = get_conf('ENABLE_AUDIO') ENABLE_AUDIO = get_conf('ENABLE_AUDIO')
if ENABLE_AUDIO: if ENABLE_AUDIO:
from crazy_functions.语音助手 import 语音助手 from crazy_functions.语音助手 import 语音助手
function_plugins.update({ function_plugins.update({
@ -511,6 +535,7 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -524,6 +549,7 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
@ -537,19 +563,21 @@ def get_crazy_functions():
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
try: try:
from crazy_functions.多智能体 import 多智能体终端 from crazy_functions.多智能体 import 多智能体终端
function_plugins.update({ function_plugins.update({
"多智能体终端（微软AutoGen）": { "AutoGen多智能体终端（仅供测试）": {
"Group": "智能体", "Group": "智能体",
"Color": "stop", "Color": "stop",
"AsButton": True, "AsButton": False,
"Function": HotReload(多智能体终端) "Function": HotReload(多智能体终端)
} }
}) })
except: except:
print(trimmed_format_exc())
print('Load function plugin failed') print('Load function plugin failed')
# try: # try:

22
crazy_functions/Latex全文润色.py
View File

@ -1,5 +1,5 @@
from toolbox import update_ui, trimmed_format_exc, promote_file_to_downloadzone, get_log_folder from toolbox import update_ui, trimmed_format_exc, promote_file_to_downloadzone, get_log_folder
from toolbox import CatchException, report_execption, write_history_to_file, zip_folder from toolbox import CatchException, report_exception, write_history_to_file, zip_folder
class PaperFileGroup(): class PaperFileGroup():
@ -11,7 +11,7 @@ class PaperFileGroup():
self.sp_file_tag = [] self.sp_file_tag = []
# count_token # count_token
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
enc = model_info["gpt-3.5-turbo"]['tokenizer'] enc = model_info["gpt-3.5-turbo"]['tokenizer']
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
self.get_token_num = get_token_num self.get_token_num = get_token_num
@ -146,7 +146,7 @@ def Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
try: try:
import tiktoken import tiktoken
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。") b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -157,12 +157,12 @@ def Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en') yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en')
@ -184,7 +184,7 @@ def Latex中文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
try: try:
import tiktoken import tiktoken
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。") b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -195,12 +195,12 @@ def Latex中文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh') yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh')
@ -220,7 +220,7 @@ def Latex英文纠错(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
try: try:
import tiktoken import tiktoken
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。") b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -231,12 +231,12 @@ def Latex英文纠错(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='proofread') yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='proofread')

16
crazy_functions/Latex全文翻译.py
View File

@ -1,5 +1,5 @@
from toolbox import update_ui, promote_file_to_downloadzone from toolbox import update_ui, promote_file_to_downloadzone
from toolbox import CatchException, report_execption, write_history_to_file from toolbox import CatchException, report_exception, write_history_to_file
fast_debug = False fast_debug = False
class PaperFileGroup(): class PaperFileGroup():
@ -11,7 +11,7 @@ class PaperFileGroup():
self.sp_file_tag = [] self.sp_file_tag = []
# count_token # count_token
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
enc = model_info["gpt-3.5-turbo"]['tokenizer'] enc = model_info["gpt-3.5-turbo"]['tokenizer']
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
self.get_token_num = get_token_num self.get_token_num = get_token_num
@ -117,7 +117,7 @@ def Latex英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
try: try:
import tiktoken import tiktoken
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。") b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -128,12 +128,12 @@ def Latex英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh') yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
@ -154,7 +154,7 @@ def Latex中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
try: try:
import tiktoken import tiktoken
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。") b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -165,12 +165,12 @@ def Latex中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en') yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')

14
crazy_functions/Latex输出PDF结果.py
View File

@ -1,5 +1,5 @@
from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone
from toolbox import CatchException, report_execption, update_ui_lastest_msg, zip_result, gen_time_str from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip_result, gen_time_str
from functools import partial from functools import partial
import glob, os, requests, time import glob, os, requests, time
pj = os.path.join pj = os.path.join
@ -129,7 +129,7 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):
yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history) # 刷新界面 yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history) # 刷新界面
else: else:
yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history) # 刷新界面 yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history) # 刷新界面
proxies, = get_conf('proxies') proxies = get_conf('proxies')
r = requests.get(url_tar, proxies=proxies) r = requests.get(url_tar, proxies=proxies)
with open(dst, 'wb+') as f: with open(dst, 'wb+') as f:
f.write(r.content) f.write(r.content)
@ -171,12 +171,12 @@ def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, histo
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
@ -249,7 +249,7 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
history = [] history = []
txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache) txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache)
if txt.endswith('.pdf'): if txt.endswith('.pdf'):
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"发现已经存在翻译好的PDF文档") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"发现已经存在翻译好的PDF文档")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
@ -258,13 +258,13 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无法处理: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无法处理: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return

6
crazy_functions/agent_fns/auto_agent.py
View File

@ -1,8 +1,8 @@
from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
from toolbox import report_execption, get_log_folder, update_ui_lastest_msg, Singleton from toolbox import report_exception, get_log_folder, update_ui_lastest_msg, Singleton
from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
from crazy_functions.agent_fns.autogen_general import AutoGenGeneral from crazy_functions.agent_fns.general import AutoGenGeneral
import time
class AutoGenMath(AutoGenGeneral): class AutoGenMath(AutoGenGeneral):

85
crazy_functions/agent_fns/autogen_general.py
View File

@ -1,85 +0,0 @@
from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
from toolbox import report_execption, get_log_folder, update_ui_lastest_msg, Singleton
from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
import time
class AutoGenGeneral(PluginMultiprocessManager):
def gpt_academic_print_override(self, user_proxy, message, sender):
# ⭐⭐ 子进程执行
self.child_conn.send(PipeCom("show", sender.name + '\n\n---\n\n' + message['content']))
def gpt_academic_get_human_input(self, user_proxy, message):
# ⭐⭐ 子进程执行
patience = 300
begin_waiting_time = time.time()
self.child_conn.send(PipeCom("interact", message))
while True:
time.sleep(0.5)
if self.child_conn.poll():
wait_success = True
break
if time.time() - begin_waiting_time > patience:
self.child_conn.send(PipeCom("done", ""))
wait_success = False
break
if wait_success:
return self.child_conn.recv().content
else:
raise TimeoutError("等待用户输入超时")
def define_agents(self):
raise NotImplementedError
def do_audogen(self, input):
# ⭐⭐ 子进程执行
input = input.content
with ProxyNetworkActivate("AutoGen"):
config_list = self.get_config_list()
code_execution_config={"work_dir": self.autogen_work_dir, "use_docker":self.use_docker}
agents = self.define_agents()
user_proxy = None
assistant = None
for agent_kwargs in agents:
agent_cls = agent_kwargs.pop('cls')
kwargs = {
'llm_config':{
"config_list": config_list,
},
'code_execution_config':code_execution_config
}
kwargs.update(agent_kwargs)
agent_handle = agent_cls(**kwargs)
agent_handle._print_received_message = lambda a,b: self.gpt_academic_print_override(agent_kwargs, a, b)
if agent_kwargs['name'] == 'user_proxy':
agent_handle.get_human_input = lambda a: self.gpt_academic_get_human_input(user_proxy, a)
user_proxy = agent_handle
if agent_kwargs['name'] == 'assistant': assistant = agent_handle
try:
if user_proxy is None or assistant is None: raise Exception("用户代理或助理代理未定义")
user_proxy.initiate_chat(assistant, message=input)
except Exception as e:
tb_str = '```\n' + trimmed_format_exc() + '```'
self.child_conn.send(PipeCom("done", "AutoGen 执行失败: \n\n" + tb_str))
def get_config_list(self):
model = self.llm_kwargs['llm_model']
api_base = None
if self.llm_kwargs['llm_model'].startswith('api2d-'):
model = self.llm_kwargs['llm_model'][len('api2d-'):]
api_base = "https://openai.api2d.net/v1"
config_list = [{
'model': model,
'api_key': self.llm_kwargs['api_key'],
},]
if api_base is not None:
config_list[0]['api_base'] = api_base
return config_list
def subprocess_worker(self, child_conn):
# ⭐⭐ 子进程执行
self.child_conn = child_conn
while True:
msg = self.child_conn.recv() # PipeCom
self.do_audogen(msg)

134
crazy_functions/agent_fns/general.py Normal file
View File

@ -0,0 +1,134 @@
from toolbox import trimmed_format_exc, get_conf, ProxyNetworkActivate
from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
from request_llms.bridge_all import predict_no_ui_long_connection
import time
def gpt_academic_generate_oai_reply(
self,
messages,
sender,
config,
):
llm_config = self.llm_config if config is None else config
if llm_config is False:
return False, None
if messages is None:
messages = self._oai_messages[sender]
inputs = messages[-1]['content']
history = []
for message in messages[:-1]:
history.append(message['content'])
context=messages[-1].pop("context", None)
assert context is None, "预留参数 context 未实现"
reply = predict_no_ui_long_connection(
inputs=inputs,
llm_kwargs=llm_config,
history=history,
sys_prompt=self._oai_system_message[0]['content'],
console_slience=True
)
assumed_done = reply.endswith('\nTERMINATE')
return True, reply
class AutoGenGeneral(PluginMultiprocessManager):
def gpt_academic_print_override(self, user_proxy, message, sender):
# ⭐⭐ run in subprocess
self.child_conn.send(PipeCom("show", sender.name + "\n\n---\n\n" + message["content"]))
def gpt_academic_get_human_input(self, user_proxy, message):
# ⭐⭐ run in subprocess
patience = 300
begin_waiting_time = time.time()
self.child_conn.send(PipeCom("interact", message))
while True:
time.sleep(0.5)
if self.child_conn.poll():
wait_success = True
break
if time.time() - begin_waiting_time > patience:
self.child_conn.send(PipeCom("done", ""))
wait_success = False
break
if wait_success:
return self.child_conn.recv().content
else:
raise TimeoutError("等待用户输入超时")
def define_agents(self):
raise NotImplementedError
def exe_autogen(self, input):
# ⭐⭐ run in subprocess
input = input.content
with ProxyNetworkActivate("AutoGen"):
code_execution_config = {"work_dir": self.autogen_work_dir, "use_docker": self.use_docker}
agents = self.define_agents()
user_proxy = None
assistant = None
for agent_kwargs in agents:
agent_cls = agent_kwargs.pop('cls')
kwargs = {
'llm_config':self.llm_kwargs,
'code_execution_config':code_execution_config
}
kwargs.update(agent_kwargs)
agent_handle = agent_cls(**kwargs)
agent_handle._print_received_message = lambda a,b: self.gpt_academic_print_override(agent_kwargs, a, b)
for d in agent_handle._reply_func_list:
if hasattr(d['reply_func'],'__name__') and d['reply_func'].__name__ == 'generate_oai_reply':
d['reply_func'] = gpt_academic_generate_oai_reply
if agent_kwargs['name'] == 'user_proxy':
agent_handle.get_human_input = lambda a: self.gpt_academic_get_human_input(user_proxy, a)
user_proxy = agent_handle
if agent_kwargs['name'] == 'assistant': assistant = agent_handle
try:
if user_proxy is None or assistant is None: raise Exception("用户代理或助理代理未定义")
user_proxy.initiate_chat(assistant, message=input)
except Exception as e:
tb_str = '```\n' + trimmed_format_exc() + '```'
self.child_conn.send(PipeCom("done", "AutoGen 执行失败: \n\n" + tb_str))
def subprocess_worker(self, child_conn):
# ⭐⭐ run in subprocess
self.child_conn = child_conn
while True:
msg = self.child_conn.recv() # PipeCom
self.exe_autogen(msg)
class AutoGenGroupChat(AutoGenGeneral):
def exe_autogen(self, input):
# ⭐⭐ run in subprocess
import autogen
input = input.content
with ProxyNetworkActivate("AutoGen"):
code_execution_config = {"work_dir": self.autogen_work_dir, "use_docker": self.use_docker}
agents = self.define_agents()
agents_instances = []
for agent_kwargs in agents:
agent_cls = agent_kwargs.pop("cls")
kwargs = {"code_execution_config": code_execution_config}
kwargs.update(agent_kwargs)
agent_handle = agent_cls(**kwargs)
agent_handle._print_received_message = lambda a, b: self.gpt_academic_print_override(agent_kwargs, a, b)
agents_instances.append(agent_handle)
if agent_kwargs["name"] == "user_proxy":
user_proxy = agent_handle
user_proxy.get_human_input = lambda a: self.gpt_academic_get_human_input(user_proxy, a)
try:
groupchat = autogen.GroupChat(agents=agents_instances, messages=[], max_round=50)
manager = autogen.GroupChatManager(groupchat=groupchat, **self.define_group_chat_manager_config())
manager._print_received_message = lambda a, b: self.gpt_academic_print_override(agent_kwargs, a, b)
manager.get_human_input = lambda a: self.gpt_academic_get_human_input(manager, a)
if user_proxy is None:
raise Exception("user_proxy is not defined")
user_proxy.initiate_chat(manager, message=input)
except Exception:
tb_str = "```\n" + trimmed_format_exc() + "```"
self.child_conn.send(PipeCom("done", "AutoGen exe failed: \n\n" + tb_str))
def define_group_chat_manager_config(self):
raise NotImplementedError

68
crazy_functions/agent_fns/pipe.py
View File

@ -2,28 +2,28 @@ from toolbox import get_log_folder, update_ui, gen_time_str, get_conf, promote_f
from crazy_functions.agent_fns.watchdog import WatchDog from crazy_functions.agent_fns.watchdog import WatchDog
import time, os import time, os
class PipeCom(): class PipeCom:
def __init__(self, cmd, content) -> None: def __init__(self, cmd, content) -> None:
self.cmd = cmd self.cmd = cmd
self.content = content self.content = content
class PluginMultiprocessManager(): class PluginMultiprocessManager:
def __init__(self, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): def __init__(self, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
# ⭐ 主进程 # ⭐ run in main process
self.autogen_work_dir = os.path.join(get_log_folder('autogen'), gen_time_str()) self.autogen_work_dir = os.path.join(get_log_folder("autogen"), gen_time_str())
self.previous_work_dir_files = {} self.previous_work_dir_files = {}
self.llm_kwargs = llm_kwargs self.llm_kwargs = llm_kwargs
self.plugin_kwargs = plugin_kwargs self.plugin_kwargs = plugin_kwargs
self.chatbot = chatbot self.chatbot = chatbot
self.history = history self.history = history
self.system_prompt = system_prompt self.system_prompt = system_prompt
self.web_port = web_port # self.web_port = web_port
self.alive = True self.alive = True
self.use_docker, = get_conf('AUTOGEN_USE_DOCKER') self.use_docker = get_conf("AUTOGEN_USE_DOCKER")
self.last_user_input = ""
# create a thread to monitor self.heartbeat, terminate the instance if no heartbeat for a long time # create a thread to monitor self.heartbeat, terminate the instance if no heartbeat for a long time
timeout_seconds = 5*60 timeout_seconds = 5 * 60
self.heartbeat_watchdog = WatchDog(timeout=timeout_seconds, bark_fn=self.terminate, interval=5) self.heartbeat_watchdog = WatchDog(timeout=timeout_seconds, bark_fn=self.terminate, interval=5)
self.heartbeat_watchdog.begin_watch() self.heartbeat_watchdog.begin_watch()
@ -35,8 +35,9 @@ class PluginMultiprocessManager():
return self.alive return self.alive
def launch_subprocess_with_pipe(self): def launch_subprocess_with_pipe(self):
# ⭐ 主进程 # ⭐ run in main process
from multiprocessing import Process, Pipe from multiprocessing import Process, Pipe
parent_conn, child_conn = Pipe() parent_conn, child_conn = Pipe()
self.p = Process(target=self.subprocess_worker, args=(child_conn,)) self.p = Process(target=self.subprocess_worker, args=(child_conn,))
self.p.daemon = True self.p.daemon = True
@ -46,15 +47,22 @@ class PluginMultiprocessManager():
def terminate(self): def terminate(self):
self.p.terminate() self.p.terminate()
self.alive = False self.alive = False
print('[debug] instance terminated') print("[debug] instance terminated")
def subprocess_worker(self, child_conn): def subprocess_worker(self, child_conn):
# ⭐⭐ 子进程 # ⭐⭐ run in subprocess
raise NotImplementedError raise NotImplementedError
def send_command(self, cmd): def send_command(self, cmd):
# ⭐ 主进程 # ⭐ run in main process
repeated = False
if cmd == self.last_user_input:
repeated = True
cmd = ""
else:
self.last_user_input = cmd
self.parent_conn.send(PipeCom("user_input", cmd)) self.parent_conn.send(PipeCom("user_input", cmd))
return repeated, cmd
def immediate_showoff_when_possible(self, fp): def immediate_showoff_when_possible(self, fp):
# ⭐ 主进程 # ⭐ 主进程
@ -63,7 +71,10 @@ class PluginMultiprocessManager():
# 如果是文本文件, 则直接显示文本内容 # 如果是文本文件, 则直接显示文本内容
if file_type.lower() in ['png', 'jpg']: if file_type.lower() in ['png', 'jpg']:
image_path = os.path.abspath(fp) image_path = os.path.abspath(fp)
self.chatbot.append(['检测到新生图像:', f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>']) self.chatbot.append([
'检测到新生图像:',
f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
])
yield from update_ui(chatbot=self.chatbot, history=self.history) yield from update_ui(chatbot=self.chatbot, history=self.history)
def overwatch_workdir_file_change(self): def overwatch_workdir_file_change(self):
@ -78,7 +89,7 @@ class PluginMultiprocessManager():
file_path = os.path.join(root, file) file_path = os.path.join(root, file)
if file_path not in self.previous_work_dir_files.keys(): if file_path not in self.previous_work_dir_files.keys():
last_modified_time = os.stat(file_path).st_mtime last_modified_time = os.stat(file_path).st_mtime
self.previous_work_dir_files.update({file_path:last_modified_time}) self.previous_work_dir_files.update({file_path: last_modified_time})
change_list.append(file_path) change_list.append(file_path)
else: else:
last_modified_time = os.stat(file_path).st_mtime last_modified_time = os.stat(file_path).st_mtime
@ -86,11 +97,11 @@ class PluginMultiprocessManager():
self.previous_work_dir_files[file_path] = last_modified_time self.previous_work_dir_files[file_path] = last_modified_time
change_list.append(file_path) change_list.append(file_path)
if len(change_list) > 0: if len(change_list) > 0:
file_links = '' file_links = ""
for f in change_list: for f in change_list:
res = promote_file_to_downloadzone(f) res = promote_file_to_downloadzone(f)
file_links += f'<br/><a href="file={res}" target="_blank">{res}</a>' file_links += f'<br/><a href="file={res}" target="_blank">{res}</a>'
yield from self.immediate_showoff_when_possible(file_path) yield from self.immediate_showoff_when_possible(f)
self.chatbot.append(['检测到新生文档.', f'文档清单如下: {file_links}']) self.chatbot.append(['检测到新生文档.', f'文档清单如下: {file_links}'])
yield from update_ui(chatbot=self.chatbot, history=self.history) yield from update_ui(chatbot=self.chatbot, history=self.history)
@ -102,7 +113,7 @@ class PluginMultiprocessManager():
if create_or_resume == 'create': if create_or_resume == 'create':
self.cnt = 1 self.cnt = 1
self.parent_conn = self.launch_subprocess_with_pipe() # ⭐⭐⭐ self.parent_conn = self.launch_subprocess_with_pipe() # ⭐⭐⭐
self.send_command(txt) repeated, cmd_to_autogen = self.send_command(txt)
if txt == 'exit': if txt == 'exit':
self.chatbot.append([f"结束", "结束信号已明确，终止AutoGen程序。"]) self.chatbot.append([f"结束", "结束信号已明确，终止AutoGen程序。"])
yield from update_ui(chatbot=self.chatbot, history=self.history) yield from update_ui(chatbot=self.chatbot, history=self.history)
@ -117,19 +128,27 @@ class PluginMultiprocessManager():
# the heartbeat watchdog might have it killed # the heartbeat watchdog might have it killed
self.terminate() self.terminate()
return "terminate" return "terminate"
if self.parent_conn.poll(): if self.parent_conn.poll():
self.feed_heartbeat_watchdog() self.feed_heartbeat_watchdog()
if "[GPT-Academic] 等待中" in self.chatbot[-1][-1]:
self.chatbot.pop(-1) # remove the last line
if "等待您的进一步指令" in self.chatbot[-1][-1]:
self.chatbot.pop(-1) # remove the last line
if '[GPT-Academic] 等待中' in self.chatbot[-1][-1]: if '[GPT-Academic] 等待中' in self.chatbot[-1][-1]:
self.chatbot.pop(-1) # remove the last line self.chatbot.pop(-1) # remove the last line
msg = self.parent_conn.recv() # PipeCom msg = self.parent_conn.recv() # PipeCom
if msg.cmd == "done": if msg.cmd == "done":
self.chatbot.append([f"结束", msg.content]); self.cnt += 1 self.chatbot.append([f"结束", msg.content])
self.cnt += 1
yield from update_ui(chatbot=self.chatbot, history=self.history) yield from update_ui(chatbot=self.chatbot, history=self.history)
self.terminate(); break self.terminate()
break
if msg.cmd == "show": if msg.cmd == "show":
yield from self.overwatch_workdir_file_change() yield from self.overwatch_workdir_file_change()
self.chatbot.append([f"运行阶段-{self.cnt}", msg.content]); self.cnt += 1 notice = ""
if repeated: notice = "（自动忽略重复的输入）"
self.chatbot.append([f"运行阶段-{self.cnt}（上次用户反馈输入为: 「{cmd_to_autogen}」{notice}", msg.content])
self.cnt += 1
yield from update_ui(chatbot=self.chatbot, history=self.history) yield from update_ui(chatbot=self.chatbot, history=self.history)
if msg.cmd == "interact": if msg.cmd == "interact":
yield from self.overwatch_workdir_file_change() yield from self.overwatch_workdir_file_change()
@ -159,13 +178,13 @@ class PluginMultiprocessManager():
return "terminate" return "terminate"
def subprocess_worker_wait_user_feedback(self, wait_msg="wait user feedback"): def subprocess_worker_wait_user_feedback(self, wait_msg="wait user feedback"):
# ⭐⭐ 子进程 # ⭐⭐ run in subprocess
patience = 5 * 60 patience = 5 * 60
begin_waiting_time = time.time() begin_waiting_time = time.time()
self.child_conn.send(PipeCom("interact", wait_msg)) self.child_conn.send(PipeCom("interact", wait_msg))
while True: while True:
time.sleep(0.5) time.sleep(0.5)
if self.child_conn.poll(): if self.child_conn.poll():
wait_success = True wait_success = True
break break
if time.time() - begin_waiting_time > patience: if time.time() - begin_waiting_time > patience:
@ -173,4 +192,3 @@ class PluginMultiprocessManager():
wait_success = False wait_success = False
break break
return wait_success return wait_success

10
crazy_functions/crazy_utils.py
View File

@ -5,7 +5,7 @@ import logging
def input_clipping(inputs, history, max_token_limit): def input_clipping(inputs, history, max_token_limit):
import numpy as np import numpy as np
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
enc = model_info["gpt-3.5-turbo"]['tokenizer'] enc = model_info["gpt-3.5-turbo"]['tokenizer']
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
@ -63,7 +63,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
""" """
import time import time
from concurrent.futures import ThreadPoolExecutor from concurrent.futures import ThreadPoolExecutor
from request_llm.bridge_all import predict_no_ui_long_connection from request_llms.bridge_all import predict_no_ui_long_connection
# 用户反馈 # 用户反馈
chatbot.append([inputs_show_user, ""]) chatbot.append([inputs_show_user, ""])
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
@ -177,11 +177,11 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
""" """
import time, random import time, random
from concurrent.futures import ThreadPoolExecutor from concurrent.futures import ThreadPoolExecutor
from request_llm.bridge_all import predict_no_ui_long_connection from request_llms.bridge_all import predict_no_ui_long_connection
assert len(inputs_array) == len(history_array) assert len(inputs_array) == len(history_array)
assert len(inputs_array) == len(sys_prompt_array) assert len(inputs_array) == len(sys_prompt_array)
if max_workers == -1: # 读取配置文件 if max_workers == -1: # 读取配置文件
try: max_workers, = get_conf('DEFAULT_WORKER_NUM') try: max_workers = get_conf('DEFAULT_WORKER_NUM')
except: max_workers = 8 except: max_workers = 8
if max_workers <= 0: max_workers = 3 if max_workers <= 0: max_workers = 3
# 屏蔽掉 chatglm的多线程，可能会导致严重卡顿 # 屏蔽掉 chatglm的多线程，可能会导致严重卡顿
@ -603,7 +603,7 @@ def get_files_from_everything(txt, type): # type='.md'
import requests import requests
from toolbox import get_conf from toolbox import get_conf
from toolbox import get_log_folder, gen_time_str from toolbox import get_log_folder, gen_time_str
proxies, = get_conf('proxies') proxies = get_conf('proxies')
try: try:
r = requests.get(txt, proxies=proxies) r = requests.get(txt, proxies=proxies)
except: except:

2
crazy_functions/latex_fns/latex_actions.py
View File

@ -174,7 +174,7 @@ class LatexPaperFileGroup():
self.sp_file_tag = [] self.sp_file_tag = []
# count_token # count_token
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
enc = model_info["gpt-3.5-turbo"]['tokenizer'] enc = model_info["gpt-3.5-turbo"]['tokenizer']
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
self.get_token_num = get_token_num self.get_token_num = get_token_num

45
crazy_functions/multi_stage/multi_stage_utils.py Normal file
View File

@ -0,0 +1,45 @@
from pydantic import BaseModel, Field
from typing import List
from toolbox import update_ui_lastest_msg, disable_auto_promotion
from request_llms.bridge_all import predict_no_ui_long_connection
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
import time
import pickle
def have_any_recent_upload_files(chatbot):
_5min = 5 * 60
if not chatbot: return False # chatbot is None
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
if not most_recent_uploaded: return False # most_recent_uploaded is None
if time.time() - most_recent_uploaded["time"] < _5min: return True # most_recent_uploaded is new
else: return False # most_recent_uploaded is too old
class GptAcademicState():
def __init__(self):
self.reset()
def reset(self):
pass
def lock_plugin(self, chatbot):
chatbot._cookies['plugin_state'] = pickle.dumps(self)
def unlock_plugin(self, chatbot):
self.reset()
chatbot._cookies['plugin_state'] = pickle.dumps(self)
def set_state(self, chatbot, key, value):
setattr(self, key, value)
chatbot._cookies['plugin_state'] = pickle.dumps(self)
def get_state(chatbot, cls=None):
state = chatbot._cookies.get('plugin_state', None)
if state is not None: state = pickle.loads(state)
elif cls is not None: state = cls()
else: state = GptAcademicState()
state.chatbot = chatbot
return state
class GatherMaterials():
def __init__(self, materials) -> None:
materials = ['image', 'prompt']

6
crazy_functions/pdf_fns/parse_pdf.py
View File

@ -14,7 +14,7 @@ import math
class GROBID_OFFLINE_EXCEPTION(Exception): pass class GROBID_OFFLINE_EXCEPTION(Exception): pass
def get_avail_grobid_url(): def get_avail_grobid_url():
GROBID_URLS, = get_conf('GROBID_URLS') GROBID_URLS = get_conf('GROBID_URLS')
if len(GROBID_URLS) == 0: return None if len(GROBID_URLS) == 0: return None
try: try:
_grobid_url = random.choice(GROBID_URLS) # 随机负载均衡 _grobid_url = random.choice(GROBID_URLS) # 随机负载均衡
@ -82,7 +82,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
# title # title
title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n' title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n'
# authors # authors
authors = article_dict.get('authors', '无法获取 authors'); prompt += f'authors:{authors}\n\n' authors = article_dict.get('authors', '无法获取 authors')[:100]; prompt += f'authors:{authors}\n\n'
# abstract # abstract
abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n' abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n'
# command # command
@ -103,7 +103,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
inputs_show_user_array = [] inputs_show_user_array = []
# get_token_num # get_token_num
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
enc = model_info[llm_kwargs['llm_model']]['tokenizer'] enc = model_info[llm_kwargs['llm_model']]['tokenizer']
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))

2
crazy_functions/vt_fns/vt_call_plugin.py
View File

@ -1,7 +1,7 @@
from pydantic import BaseModel, Field from pydantic import BaseModel, Field
from typing import List from typing import List
from toolbox import update_ui_lastest_msg, disable_auto_promotion from toolbox import update_ui_lastest_msg, disable_auto_promotion
from request_llm.bridge_all import predict_no_ui_long_connection from request_llms.bridge_all import predict_no_ui_long_connection
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
import copy, json, pickle, os, sys, time import copy, json, pickle, os, sys, time

6
crazy_functions/vt_fns/vt_modify_config.py
View File

@ -1,13 +1,13 @@
from pydantic import BaseModel, Field from pydantic import BaseModel, Field
from typing import List from typing import List
from toolbox import update_ui_lastest_msg, get_conf from toolbox import update_ui_lastest_msg, get_conf
from request_llm.bridge_all import predict_no_ui_long_connection from request_llms.bridge_all import predict_no_ui_long_connection
from crazy_functions.json_fns.pydantic_io import GptJsonIO from crazy_functions.json_fns.pydantic_io import GptJsonIO
import copy, json, pickle, os, sys import copy, json, pickle, os, sys
def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention): def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
ALLOW_RESET_CONFIG, = get_conf('ALLOW_RESET_CONFIG') ALLOW_RESET_CONFIG = get_conf('ALLOW_RESET_CONFIG')
if not ALLOW_RESET_CONFIG: if not ALLOW_RESET_CONFIG:
yield from update_ui_lastest_msg( yield from update_ui_lastest_msg(
lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。", lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。",
@ -66,7 +66,7 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
) )
def modify_configuration_reboot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention): def modify_configuration_reboot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
ALLOW_RESET_CONFIG, = get_conf('ALLOW_RESET_CONFIG') ALLOW_RESET_CONFIG = get_conf('ALLOW_RESET_CONFIG')
if not ALLOW_RESET_CONFIG: if not ALLOW_RESET_CONFIG:
yield from update_ui_lastest_msg( yield from update_ui_lastest_msg(
lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。", lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。",

10
crazy_functions/下载arxiv论文翻译摘要.py
View File

@ -1,6 +1,6 @@
from toolbox import update_ui, get_log_folder from toolbox import update_ui, get_log_folder
from toolbox import write_history_to_file, promote_file_to_downloadzone from toolbox import write_history_to_file, promote_file_to_downloadzone
from toolbox import CatchException, report_execption, get_conf from toolbox import CatchException, report_exception, get_conf
import re, requests, unicodedata, os import re, requests, unicodedata, os
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
def download_arxiv_(url_pdf): def download_arxiv_(url_pdf):
@ -43,7 +43,7 @@ def download_arxiv_(url_pdf):
file_path = download_dir+title_str file_path = download_dir+title_str
print('下载中') print('下载中')
proxies, = get_conf('proxies') proxies = get_conf('proxies')
r = requests.get(requests_pdf_url, proxies=proxies) r = requests.get(requests_pdf_url, proxies=proxies)
with open(file_path, 'wb+') as f: with open(file_path, 'wb+') as f:
f.write(r.content) f.write(r.content)
@ -77,7 +77,7 @@ def get_name(_url_):
# print('在缓存中') # print('在缓存中')
# return arxiv_recall[_url_] # return arxiv_recall[_url_]
proxies, = get_conf('proxies') proxies = get_conf('proxies')
res = requests.get(_url_, proxies=proxies) res = requests.get(_url_, proxies=proxies)
bs = BeautifulSoup(res.text, 'html.parser') bs = BeautifulSoup(res.text, 'html.parser')
@ -144,7 +144,7 @@ def 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, hi
try: try:
import bs4 import bs4
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a = f"解析项目: {txt}", a = f"解析项目: {txt}",
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4```。") b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -157,7 +157,7 @@ def 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, hi
try: try:
pdf_path, info = download_arxiv_(txt) pdf_path, info = download_arxiv_(txt)
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a = f"解析项目: {txt}", a = f"解析项目: {txt}",
b = f"下载pdf文件未成功") b = f"下载pdf文件未成功")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

169
crazy_functions/图片生成.py
View File

@ -1,13 +1,12 @@
from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from crazy_functions.multi_stage.multi_stage_utils import GptAcademicState
import datetime
def gen_image(llm_kwargs, prompt, resolution="256x256"): def gen_image(llm_kwargs, prompt, resolution="1024x1024", model="dall-e-2", quality=None):
import requests, json, time, os import requests, json, time, os
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
proxies, = get_conf('proxies') proxies = get_conf('proxies')
# Set up OpenAI API key and model # Set up OpenAI API key and model
api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model']) api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint'] chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
@ -23,6 +22,48 @@ def gen_image(llm_kwargs, prompt, resolution="256x256"):
'prompt': prompt, 'prompt': prompt,
'n': 1, 'n': 1,
'size': resolution, 'size': resolution,
'model': model,
'response_format': 'url'
}
if quality is not None: data.update({'quality': quality})
response = requests.post(url, headers=headers, json=data, proxies=proxies)
print(response.content)
try:
image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
except:
raise RuntimeError(response.content.decode())
# 文件保存到本地
r = requests.get(image_url, proxies=proxies)
file_path = f'{get_log_folder()}/image_gen/'
os.makedirs(file_path, exist_ok=True)
file_name = 'Image' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.png'
with open(file_path+file_name, 'wb+') as f: f.write(r.content)
return image_url, file_path+file_name
def edit_image(llm_kwargs, prompt, image_path, resolution="1024x1024", model="dall-e-2"):
import requests, json, time, os
from request_llms.bridge_all import model_info
proxies = get_conf('proxies')
api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
# 'https://api.openai.com/v1/chat/completions'
img_endpoint = chat_endpoint.replace('chat/completions','images/edits')
# # Generate the image
url = img_endpoint
headers = {
'Authorization': f"Bearer {api_key}",
'Content-Type': 'application/json'
}
data = {
'image': open(image_path, 'rb'),
'prompt': prompt,
'n': 1,
'size': resolution,
'model': model,
'response_format': 'url' 'response_format': 'url'
} }
response = requests.post(url, headers=headers, json=data, proxies=proxies) response = requests.post(url, headers=headers, json=data, proxies=proxies)
@ -42,23 +83,22 @@ def gen_image(llm_kwargs, prompt, resolution="256x256"):
return image_url, file_path+file_name return image_url, file_path+file_name
@CatchException @CatchException
def 图片生成(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): def 图片生成_DALLE2(prompt, 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 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 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,暂时没有用武之地
chatbot 聊天显示框的句柄，用于显示给用户 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 history 聊天历史,前情提要
system_prompt 给gpt的静默提醒 system_prompt 给gpt的静默提醒
web_port 当前软件运行的端口号 web_port 当前软件运行的端口号
""" """
history = [] # 清空历史，以免输入溢出 history = [] # 清空历史,以免输入溢出
chatbot.append(("这是什么功能？", "[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文效果不理想, 请尝试英文Prompt。正在处理中 .....")) chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 由于请求gpt需要一段时间,我们先及时地做一次界面更新
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg") if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
resolution = plugin_kwargs.get("advanced_arg", '256x256') resolution = plugin_kwargs.get("advanced_arg", '1024x1024')
image_url, image_path = gen_image(llm_kwargs, prompt, resolution) image_url, image_path = gen_image(llm_kwargs, prompt, resolution)
chatbot.append([prompt, chatbot.append([prompt,
f'图像中转网址: <br/>`{image_url}`<br/>'+ f'图像中转网址: <br/>`{image_url}`<br/>'+
@ -66,4 +106,99 @@ def 图片生成(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
f'本地文件地址: <br/>`{image_path}`<br/>'+ f'本地文件地址: <br/>`{image_path}`<br/>'+
f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>' f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
]) ])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
@CatchException
def 图片生成_DALLE3(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
history = [] # 清空历史,以免输入溢出
chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 由于请求gpt需要一段时间,我们先及时地做一次界面更新
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
resolution = plugin_kwargs.get("advanced_arg", '1024x1024').lower()
if resolution.endswith('-hd'):
resolution = resolution.replace('-hd', '')
quality = 'hd'
else:
quality = 'standard'
image_url, image_path = gen_image(llm_kwargs, prompt, resolution, model="dall-e-3", quality=quality)
chatbot.append([prompt,
f'图像中转网址: <br/>`{image_url}`<br/>'+
f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
f'本地文件地址: <br/>`{image_path}`<br/>'+
f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
class ImageEditState(GptAcademicState):
# 尚未完成
def get_image_file(self, x):
import os, glob
if len(x) == 0: return False, None
if not os.path.exists(x): return False, None
if x.endswith('.png'): return True, x
file_manifest = [f for f in glob.glob(f'{x}/**/*.png', recursive=True)]
confirm = (len(file_manifest) >= 1 and file_manifest[0].endswith('.png') and os.path.exists(file_manifest[0]))
file = None if not confirm else file_manifest[0]
return confirm, file
def get_resolution(self, x):
return (x in ['256x256', '512x512', '1024x1024']), x
def get_prompt(self, x):
confirm = (len(x)>=5) and (not self.get_resolution(x)[0]) and (not self.get_image_file(x)[0])
return confirm, x
def reset(self):
self.req = [
{'value':None, 'description': '请先上传图像（必须是.png格式）, 然后再次点击本插件', 'verify_fn': self.get_image_file},
{'value':None, 'description': '请输入分辨率,可选：256x256, 512x512 或 1024x1024', 'verify_fn': self.get_resolution},
{'value':None, 'description': '请输入修改需求,建议您使用英文提示词', 'verify_fn': self.get_prompt},
]
self.info = ""
def feed(self, prompt, chatbot):
for r in self.req:
if r['value'] is None:
confirm, res = r['verify_fn'](prompt)
if confirm:
r['value'] = res
self.set_state(chatbot, 'dummy_key', 'dummy_value')
break
return self
def next_req(self):
for r in self.req:
if r['value'] is None:
return r['description']
return "已经收集到所有信息"
def already_obtained_all_materials(self):
return all([x['value'] is not None for x in self.req])
@CatchException
def 图片修改_DALLE2(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
# 尚未完成
history = [] # 清空历史
state = ImageEditState.get_state(chatbot, ImageEditState)
state = state.feed(prompt, chatbot)
if not state.already_obtained_all_materials():
chatbot.append(["图片修改（先上传图片,再输入修改需求,最后输入分辨率）", state.next_req()])
yield from update_ui(chatbot=chatbot, history=history)
return
image_path = state.req[0]
resolution = state.req[1]
prompt = state.req[2]
chatbot.append(["图片修改, 执行中", f"图片:`{image_path}`<br/>分辨率:`{resolution}`<br/>修改需求:`{prompt}`"])
yield from update_ui(chatbot=chatbot, history=history)
image_url, image_path = edit_image(llm_kwargs, prompt, image_path, resolution)
chatbot.append([state.prompt,
f'图像中转网址: <br/>`{image_url}`<br/>'+
f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
f'本地文件地址: <br/>`{image_path}`<br/>'+
f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新

40
crazy_functions/多智能体.py
View File

@ -3,11 +3,6 @@
测试： 测试：
- show me the solution of $x^2=cos(x)$, solve this problem with figure, and plot and save image to t.jpg - show me the solution of $x^2=cos(x)$, solve this problem with figure, and plot and save image to t.jpg
Testing:
- Crop the image, keeping the bottom half.
- Swap the blue channel and red channel of the image.
- Convert the image to grayscale.
- Convert the CSV file to an Excel spreadsheet.
""" """
@ -19,6 +14,11 @@ from crazy_functions.agent_fns.persistent import GradioMultiuserManagerForPersis
from crazy_functions.agent_fns.auto_agent import AutoGenMath from crazy_functions.agent_fns.auto_agent import AutoGenMath
import time import time
def remove_model_prefix(llm):
if llm.startswith('api2d-'): llm = llm.replace('api2d-', '')
if llm.startswith('azure-'): llm = llm.replace('azure-', '')
return llm
@CatchException @CatchException
def 多智能体终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): def 多智能体终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
@ -32,16 +32,27 @@ def 多智能体终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
web_port 当前软件运行的端口号 web_port 当前软件运行的端口号
""" """
# 检查当前的模型是否符合要求 # 检查当前的模型是否符合要求
supported_llms = ['gpt-3.5-turbo-16k', 'gpt-4', 'gpt-4-32k', supported_llms = [
'api2d-gpt-3.5-turbo-16k', 'api2d-gpt-4'] "gpt-3.5-turbo-16k",
llm_kwargs['api_key'] = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model']) 'gpt-3.5-turbo-1106',
if llm_kwargs['llm_model'] not in supported_llms: "gpt-4",
chatbot.append([f"处理任务: {txt}", f"当前插件只支持{str(supported_llms)}, 当前模型{llm_kwargs['llm_model']}."]) "gpt-4-32k",
'gpt-4-1106-preview',
"azure-gpt-3.5-turbo-16k",
"azure-gpt-3.5-16k",
"azure-gpt-4",
"azure-gpt-4-32k",
]
from request_llms.bridge_all import model_info
if model_info[llm_kwargs['llm_model']]["max_token"] < 8000: # 至少是8k上下文的模型
chatbot.append([f"处理任务: {txt}", f"当前插件只支持{str(supported_llms)}, 当前模型{llm_kwargs['llm_model']}的最大上下文长度太短, 不能支撑AutoGen运行。"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
if model_info[llm_kwargs['llm_model']]["endpoint"] is not None: # 如果不是本地模型，加载API_KEY
llm_kwargs['api_key'] = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
# 检查当前的模型是否符合要求 # 检查当前的模型是否符合要求
API_URL_REDIRECT, = get_conf('API_URL_REDIRECT') API_URL_REDIRECT = get_conf('API_URL_REDIRECT')
if len(API_URL_REDIRECT) > 0: if len(API_URL_REDIRECT) > 0:
chatbot.append([f"处理任务: {txt}", f"暂不支持中转."]) chatbot.append([f"处理任务: {txt}", f"暂不支持中转."])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -49,7 +60,9 @@ def 多智能体终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
# 尝试导入依赖，如果缺少依赖，则给出安装建议 # 尝试导入依赖，如果缺少依赖，则给出安装建议
try: try:
import autogen, docker import autogen
if get_conf("AUTOGEN_USE_DOCKER"):
import docker
except: except:
chatbot.append([ f"处理任务: {txt}", chatbot.append([ f"处理任务: {txt}",
f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pyautogen docker```。"]) f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pyautogen docker```。"])
@ -60,7 +73,8 @@ def 多智能体终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
try: try:
import autogen import autogen
import glob, os, time, subprocess import glob, os, time, subprocess
subprocess.Popen(['docker', '--version']) if get_conf("AUTOGEN_USE_DOCKER"):
subprocess.Popen(["docker", "--version"])
except: except:
chatbot.append([f"处理任务: {txt}", f"缺少docker运行环境！"]) chatbot.append([f"处理任务: {txt}", f"缺少docker运行环境！"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

10
crazy_functions/总结word文档.py
View File

@ -1,5 +1,5 @@
from toolbox import update_ui from toolbox import update_ui
from toolbox import CatchException, report_execption from toolbox import CatchException, report_exception
from toolbox import write_history_to_file, promote_file_to_downloadzone from toolbox import write_history_to_file, promote_file_to_downloadzone
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
fast_debug = False fast_debug = False
@ -32,7 +32,7 @@ def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot
print(file_content) 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 # 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
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
max_token = model_info[llm_kwargs['llm_model']]['max_token'] max_token = model_info[llm_kwargs['llm_model']]['max_token']
TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4 TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
@ -97,7 +97,7 @@ def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pr
try: try:
from docx import Document from docx import Document
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。") b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -111,7 +111,7 @@ def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pr
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
@ -124,7 +124,7 @@ def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pr
# 如果没找到任何文件 # 如果没找到任何文件
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return

12
crazy_functions/总结音视频.py
View File

@ -1,4 +1,4 @@
from toolbox import CatchException, report_execption, select_api_key, update_ui, get_conf from toolbox import CatchException, report_exception, select_api_key, update_ui, get_conf
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
from toolbox import write_history_to_file, promote_file_to_downloadzone, get_log_folder from toolbox import write_history_to_file, promote_file_to_downloadzone, get_log_folder
@ -41,7 +41,7 @@ def split_audio_file(filename, split_duration=1000):
def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history): def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
import os, requests import os, requests
from moviepy.editor import AudioFileClip from moviepy.editor import AudioFileClip
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
# 设置OpenAI密钥和模型 # 设置OpenAI密钥和模型
api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model']) api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
@ -79,7 +79,7 @@ def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
chatbot.append([f"将 {i} 发送到openai音频解析终端 (whisper)，当前参数：{parse_prompt}", "正在处理 ..."]) chatbot.append([f"将 {i} 发送到openai音频解析终端 (whisper)，当前参数：{parse_prompt}", "正在处理 ..."])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
proxies, = get_conf('proxies') proxies = get_conf('proxies')
response = requests.post(url, headers=headers, files=files, data=data, proxies=proxies).text response = requests.post(url, headers=headers, files=files, data=data, proxies=proxies).text
chatbot.append(["音频解析结果", response]) chatbot.append(["音频解析结果", response])
@ -144,7 +144,7 @@ def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
try: try:
from moviepy.editor import AudioFileClip from moviepy.editor import AudioFileClip
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade moviepy```。") b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade moviepy```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -158,7 +158,7 @@ def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
@ -174,7 +174,7 @@ def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
# 如果没找到任何文件 # 如果没找到任何文件
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何音频或视频文件: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何音频或视频文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return

24
crazy_functions/批量Markdown翻译.py
View File

@ -1,6 +1,6 @@
import glob, time, os, re, logging import glob, time, os, re, logging
from toolbox import update_ui, trimmed_format_exc, gen_time_str, disable_auto_promotion from toolbox import update_ui, trimmed_format_exc, gen_time_str, disable_auto_promotion
from toolbox import CatchException, report_execption, get_log_folder from toolbox import CatchException, report_exception, get_log_folder
from toolbox import write_history_to_file, promote_file_to_downloadzone from toolbox import write_history_to_file, promote_file_to_downloadzone
fast_debug = False fast_debug = False
@ -13,7 +13,7 @@ class PaperFileGroup():
self.sp_file_tag = [] self.sp_file_tag = []
# count_token # count_token
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
enc = model_info["gpt-3.5-turbo"]['tokenizer'] enc = model_info["gpt-3.5-turbo"]['tokenizer']
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
self.get_token_num = get_token_num self.get_token_num = get_token_num
@ -118,7 +118,7 @@ def get_files_from_everything(txt, preference=''):
if txt.startswith('http'): if txt.startswith('http'):
import requests import requests
from toolbox import get_conf from toolbox import get_conf
proxies, = get_conf('proxies') proxies = get_conf('proxies')
# 网络的远程文件 # 网络的远程文件
if preference == 'Github': if preference == 'Github':
logging.info('正在从github下载资源 ...') logging.info('正在从github下载资源 ...')
@ -165,7 +165,7 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
try: try:
import tiktoken import tiktoken
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。") b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -177,12 +177,12 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
if not success: if not success:
# 什么都没有 # 什么都没有
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
@ -205,7 +205,7 @@ def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
try: try:
import tiktoken import tiktoken
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。") b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -215,11 +215,11 @@ def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
if not success: if not success:
# 什么都没有 # 什么都没有
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en') yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')
@ -238,7 +238,7 @@ def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history,
try: try:
import tiktoken import tiktoken
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。") b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -248,11 +248,11 @@ def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history,
if not success: if not success:
# 什么都没有 # 什么都没有
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return

10
crazy_functions/批量总结PDF文档.py
View File

@ -1,5 +1,5 @@
from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str
from toolbox import CatchException, report_execption from toolbox import CatchException, report_exception
from toolbox import write_history_to_file, promote_file_to_downloadzone from toolbox import write_history_to_file, promote_file_to_downloadzone
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive 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 read_and_clean_pdf_text
@ -21,7 +21,7 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
TOKEN_LIMIT_PER_FRAGMENT = 2500 TOKEN_LIMIT_PER_FRAGMENT = 2500
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
enc = model_info["gpt-3.5-turbo"]['tokenizer'] enc = model_info["gpt-3.5-turbo"]['tokenizer']
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
@ -119,7 +119,7 @@ def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
try: try:
import fitz import fitz
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a = f"解析项目: {txt}", a = f"解析项目: {txt}",
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。") b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -133,7 +133,7 @@ def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
@ -142,7 +142,7 @@ def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
# 如果没找到任何文件 # 如果没找到任何文件
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return

8
crazy_functions/批量总结PDF文档pdfminer.py
View File

@ -1,5 +1,5 @@
from toolbox import update_ui from toolbox import update_ui
from toolbox import CatchException, report_execption from toolbox import CatchException, report_exception
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
from toolbox import write_history_to_file, promote_file_to_downloadzone from toolbox import write_history_to_file, promote_file_to_downloadzone
@ -138,7 +138,7 @@ def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, histo
try: try:
import pdfminer, bs4 import pdfminer, bs4
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a = f"解析项目: {txt}", a = f"解析项目: {txt}",
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。") b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -147,7 +147,7 @@ def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, histo
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \ file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
@ -155,7 +155,7 @@ def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, histo
# [f for f in glob.glob(f'{project_folder}/**/*.cpp', 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)] # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或pdf文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或pdf文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

41
crazy_functions/批量翻译PDF文档_NOUGAT.py
View File

@ -1,4 +1,4 @@
from toolbox import CatchException, report_execption, get_log_folder, gen_time_str from toolbox import CatchException, report_exception, get_log_folder, gen_time_str
from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
from toolbox import write_history_to_file, promote_file_to_downloadzone from toolbox import write_history_to_file, promote_file_to_downloadzone
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
@ -57,29 +57,34 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
"批量翻译PDF文档。函数插件贡献者: Binary-Husky"]) "批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
# 尝试导入依赖，如果缺少依赖，则给出安装建议
try:
import nougat
import tiktoken
except:
report_execption(chatbot, history,
a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade nougat-ocr tiktoken```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# 清空历史，以免输入溢出 # 清空历史，以免输入溢出
history = [] history = []
from .crazy_utils import get_files_from_everything from .crazy_utils import get_files_from_everything
success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf') success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf')
if len(file_manifest) > 0:
# 尝试导入依赖，如果缺少依赖，则给出安装建议
try:
import nougat
import tiktoken
except:
report_exception(chatbot, history,
a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade nougat-ocr tiktoken```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
success_mmd, file_manifest_mmd, _ = get_files_from_everything(txt, type='.mmd')
success = success or success_mmd
file_manifest += file_manifest_mmd
chatbot.append(["文件列表：", ", ".join([e.split('/')[-1] for e in file_manifest])]);
yield from update_ui( chatbot=chatbot, history=history)
# 检测输入参数，如没有给定输入参数，直接退出 # 检测输入参数，如没有给定输入参数，直接退出
if not success: if not success:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
# 如果没找到任何文件 # 如果没找到任何文件
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {txt}") a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
@ -101,9 +106,13 @@ def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwa
from crazy_functions.pdf_fns.report_gen_html import construct_html from crazy_functions.pdf_fns.report_gen_html import construct_html
nougat_handle = nougat_interface() nougat_handle = nougat_interface()
for index, fp in enumerate(file_manifest): for index, fp in enumerate(file_manifest):
chatbot.append(["当前进度：", f"正在解析论文，请稍候。（第一次运行时，需要花费较长时间下载NOUGAT参数）"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 if fp.endswith('pdf'):
fpp = yield from nougat_handle.NOUGAT_parse_pdf(fp, chatbot, history) chatbot.append(["当前进度：", f"正在解析论文，请稍候。（第一次运行时，需要花费较长时间下载NOUGAT参数）"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
promote_file_to_downloadzone(fpp, rename_file=os.path.basename(fpp)+'.nougat.mmd', chatbot=chatbot) fpp = yield from nougat_handle.NOUGAT_parse_pdf(fp, chatbot, history)
promote_file_to_downloadzone(fpp, rename_file=os.path.basename(fpp)+'.nougat.mmd', chatbot=chatbot)
else:
chatbot.append(["当前论文无需解析：", fp]); yield from update_ui( chatbot=chatbot, history=history)
fpp = fp
with open(fpp, 'r', encoding='utf8') as f: with open(fpp, 'r', encoding='utf8') as f:
article_content = f.readlines() article_content = f.readlines()
article_dict = markdown_to_dict(article_content) article_dict = markdown_to_dict(article_content)

15
crazy_functions/批量翻译PDF文档_多线程.py
View File

@ -1,4 +1,4 @@
from toolbox import CatchException, report_execption, get_log_folder, gen_time_str from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages
from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
from toolbox import write_history_to_file, promote_file_to_downloadzone from toolbox import write_history_to_file, promote_file_to_downloadzone
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
@ -6,9 +6,8 @@ from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_an
from .crazy_utils import read_and_clean_pdf_text from .crazy_utils import read_and_clean_pdf_text
from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf
from colorful import * from colorful import *
import copy
import os import os
import math
@CatchException @CatchException
def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
@ -22,11 +21,9 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
# 尝试导入依赖，如果缺少依赖，则给出安装建议 # 尝试导入依赖，如果缺少依赖，则给出安装建议
try: try:
import fitz check_packages(["fitz", "tiktoken", "scipdf"])
import tiktoken
import scipdf
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken scipdf_parser```。") b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken scipdf_parser```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -43,7 +40,7 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
# 如果没找到任何文件 # 如果没找到任何文件
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {txt}") a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
@ -95,7 +92,7 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
# 递归地切割PDF文件 # 递归地切割PDF文件
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
enc = model_info["gpt-3.5-turbo"]['tokenizer'] enc = model_info["gpt-3.5-turbo"]['tokenizer']
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(

12
crazy_functions/理解PDF文档内容.py
View File

@ -1,5 +1,5 @@
from toolbox import update_ui from toolbox import update_ui
from toolbox import CatchException, report_execption from toolbox import CatchException, report_exception
from .crazy_utils import read_and_clean_pdf_text from .crazy_utils import read_and_clean_pdf_text
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
fast_debug = False fast_debug = False
@ -19,7 +19,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
TOKEN_LIMIT_PER_FRAGMENT = 2500 TOKEN_LIMIT_PER_FRAGMENT = 2500
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
enc = model_info["gpt-3.5-turbo"]['tokenizer'] enc = model_info["gpt-3.5-turbo"]['tokenizer']
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
@ -49,7 +49,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
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 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, llm_kwargs, chatbot,
history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果 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." # 提示 sys_prompt="Extract the main idea of this section, answer me with Chinese." # 提示
) )
iteration_results.append(gpt_say) iteration_results.append(gpt_say)
last_iteration_result = gpt_say last_iteration_result = gpt_say
@ -81,7 +81,7 @@ def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chat
try: try:
import fitz import fitz
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a = f"解析项目: {txt}", a = f"解析项目: {txt}",
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。") b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -96,7 +96,7 @@ def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chat
else: else:
if txt == "": if txt == "":
txt = '空空如也的输入栏' txt = '空空如也的输入栏'
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}") a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
@ -105,7 +105,7 @@ def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chat
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', 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 # 如果没找到任何文件
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}") a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return

6
crazy_functions/生成函数注释.py
View File

@ -1,5 +1,5 @@
from toolbox import update_ui from toolbox import update_ui
from toolbox import CatchException, report_execption from toolbox import CatchException, report_exception
from toolbox import write_history_to_file, promote_file_to_downloadzone from toolbox import write_history_to_file, promote_file_to_downloadzone
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
fast_debug = False fast_debug = False
@ -43,14 +43,14 @@ def 批量生成函数注释(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)] + \ file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)] + \
[f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

4
crazy_functions/联网的ChatGPT.py
View File

@ -2,7 +2,7 @@ from toolbox import CatchException, update_ui
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
import requests import requests
from bs4 import BeautifulSoup from bs4 import BeautifulSoup
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
def google(query, proxies): def google(query, proxies):
query = query # 在此处替换您要搜索的关键词 query = query # 在此处替换您要搜索的关键词
@ -72,7 +72,7 @@ def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
# ------------- < 第1步：爬取搜索引擎的结果 > ------------- # ------------- < 第1步：爬取搜索引擎的结果 > -------------
from toolbox import get_conf from toolbox import get_conf
proxies, = get_conf('proxies') proxies = get_conf('proxies')
urls = google(txt, proxies) urls = google(txt, proxies)
history = [] history = []
if len(urls) == 0: if len(urls) == 0:

4
crazy_functions/联网的ChatGPT_bing版.py
View File

@ -2,7 +2,7 @@ from toolbox import CatchException, update_ui
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
import requests import requests
from bs4 import BeautifulSoup from bs4 import BeautifulSoup
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
def bing_search(query, proxies=None): def bing_search(query, proxies=None):
@ -72,7 +72,7 @@ def 连接bing搜索回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, histor
# ------------- < 第1步：爬取搜索引擎的结果 > ------------- # ------------- < 第1步：爬取搜索引擎的结果 > -------------
from toolbox import get_conf from toolbox import get_conf
proxies, = get_conf('proxies') proxies = get_conf('proxies')
urls = bing_search(txt, proxies) urls = bing_search(txt, proxies)
history = [] history = []
if len(urls) == 0: if len(urls) == 0:

2
crazy_functions/虚空终端.py
View File

@ -48,7 +48,7 @@ from pydantic import BaseModel, Field
from typing import List from typing import List
from toolbox import CatchException, update_ui, is_the_upload_folder from toolbox import CatchException, update_ui, is_the_upload_folder
from toolbox import update_ui_lastest_msg, disable_auto_promotion from toolbox import update_ui_lastest_msg, disable_auto_promotion
from request_llm.bridge_all import predict_no_ui_long_connection from request_llms.bridge_all import predict_no_ui_long_connection
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
from crazy_functions.crazy_utils import input_clipping from crazy_functions.crazy_utils import input_clipping
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError

8
crazy_functions/解析JupyterNotebook.py
View File

@ -1,5 +1,5 @@
from toolbox import update_ui from toolbox import update_ui
from toolbox import CatchException, report_execption from toolbox import CatchException, report_exception
from toolbox import write_history_to_file, promote_file_to_downloadzone from toolbox import write_history_to_file, promote_file_to_downloadzone
fast_debug = True fast_debug = True
@ -13,7 +13,7 @@ class PaperFileGroup():
self.sp_file_tag = [] self.sp_file_tag = []
# count_token # count_token
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
enc = model_info["gpt-3.5-turbo"]['tokenizer'] enc = model_info["gpt-3.5-turbo"]['tokenizer']
def get_token_num(txt): return len( def get_token_num(txt): return len(
enc.encode(txt, disallowed_special=())) enc.encode(txt, disallowed_special=()))
@ -131,7 +131,7 @@ def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
else: else:
if txt == "": if txt == "":
txt = '空空如也的输入栏' txt = '空空如也的输入栏'
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}") a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
@ -141,7 +141,7 @@ def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
file_manifest = [f for f in glob.glob( file_manifest = [f for f in glob.glob(
f'{project_folder}/**/*.ipynb', recursive=True)] f'{project_folder}/**/*.ipynb', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}") a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return

48
crazy_functions/解析项目源代码.py
View File

@ -1,5 +1,5 @@
from toolbox import update_ui, promote_file_to_downloadzone, disable_auto_promotion from toolbox import update_ui, promote_file_to_downloadzone, disable_auto_promotion
from toolbox import CatchException, report_execption, write_history_to_file from toolbox import CatchException, report_exception, write_history_to_file
from .crazy_utils import input_clipping from .crazy_utils import input_clipping
def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
@ -113,7 +113,7 @@ def 解析项目本身(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
[f for f in glob.glob('./*/*.py')] [f for f in glob.glob('./*/*.py')]
project_folder = './' project_folder = './'
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@ -126,12 +126,12 @@ def 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)] file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@ -144,12 +144,12 @@ def 解析一个Matlab项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析Matlab项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析Matlab项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.m', recursive=True)] file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.m', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析Matlab项目: {txt}", b = f"找不到任何`.m`源文件: {txt}") report_exception(chatbot, history, a = f"解析Matlab项目: {txt}", b = f"找不到任何`.m`源文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@ -162,14 +162,14 @@ def 解析一个C项目的头文件(txt, llm_kwargs, plugin_kwargs, chatbot, his
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)] + \ file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)] + \
[f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] #+ \ [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] #+ \
# [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)] # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@ -182,7 +182,7 @@ def 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)] + \ file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)] + \
@ -190,7 +190,7 @@ def 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system
[f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] + \ [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] + \
[f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)] [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@ -204,7 +204,7 @@ def 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.java', recursive=True)] + \ file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.java', recursive=True)] + \
@ -212,7 +212,7 @@ def 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
[f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \ [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
[f for f in glob.glob(f'{project_folder}/**/*.sh', recursive=True)] [f for f in glob.glob(f'{project_folder}/**/*.sh', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@ -226,7 +226,7 @@ def 解析一个前端项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.ts', recursive=True)] + \ file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.ts', recursive=True)] + \
@ -241,7 +241,7 @@ def 解析一个前端项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
[f for f in glob.glob(f'{project_folder}/**/*.css', recursive=True)] + \ [f for f in glob.glob(f'{project_folder}/**/*.css', recursive=True)] + \
[f for f in glob.glob(f'{project_folder}/**/*.jsx', recursive=True)] [f for f in glob.glob(f'{project_folder}/**/*.jsx', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何前端相关文件: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何前端相关文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@ -255,7 +255,7 @@ def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.go', recursive=True)] + \ file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.go', recursive=True)] + \
@ -263,7 +263,7 @@ def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
[f for f in glob.glob(f'{project_folder}/**/go.sum', recursive=True)] + \ [f for f in glob.glob(f'{project_folder}/**/go.sum', recursive=True)] + \
[f for f in glob.glob(f'{project_folder}/**/go.work', recursive=True)] [f for f in glob.glob(f'{project_folder}/**/go.work', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@ -276,14 +276,14 @@ def 解析一个Rust项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.rs', recursive=True)] + \ file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.rs', recursive=True)] + \
[f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)] + \ [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)] + \
[f for f in glob.glob(f'{project_folder}/**/*.lock', recursive=True)] [f for f in glob.glob(f'{project_folder}/**/*.lock', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}") report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@ -296,7 +296,7 @@ def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.lua', recursive=True)] + \ file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.lua', recursive=True)] + \
@ -304,7 +304,7 @@ def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
[f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \ [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
[f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)] [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何lua文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何lua文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@ -318,13 +318,13 @@ def 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.cs', recursive=True)] + \ file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.cs', recursive=True)] + \
[f for f in glob.glob(f'{project_folder}/**/*.csproj', recursive=True)] [f for f in glob.glob(f'{project_folder}/**/*.csproj', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何CSharp文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何CSharp文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@ -352,7 +352,7 @@ def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
# 若上传压缩文件, 先寻找到解压的文件夹路径, 从而避免解析压缩文件 # 若上传压缩文件, 先寻找到解压的文件夹路径, 从而避免解析压缩文件
@ -365,7 +365,7 @@ def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
file_manifest = [f for pattern in pattern_include for f in glob.glob(f'{extract_folder_path}/**/{pattern}', recursive=True) if "" != extract_folder_path and \ file_manifest = [f for pattern in pattern_include for f in glob.glob(f'{extract_folder_path}/**/{pattern}', recursive=True) if "" != extract_folder_path and \
os.path.isfile(f) and (not re.search(pattern_except, f) or pattern.endswith('.' + re.search(pattern_except, f).group().split('.')[-1]))] os.path.isfile(f) and (not re.search(pattern_except, f) or pattern.endswith('.' + re.search(pattern_except, f).group().split('.')[-1]))]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

7
crazy_functions/询问多个大语言模型.py
View File

@ -1,4 +1,4 @@
from toolbox import CatchException, update_ui from toolbox import CatchException, update_ui, get_conf
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
import datetime import datetime
@CatchException @CatchException
@ -13,11 +13,12 @@ def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
web_port 当前软件运行的端口号 web_port 当前软件运行的端口号
""" """
history = [] # 清空历史，以免输入溢出 history = [] # 清空历史，以免输入溢出
chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……")) MULTI_QUERY_LLM_MODELS = get_conf('MULTI_QUERY_LLM_MODELS')
chatbot.append((txt, "正在同时咨询" + MULTI_QUERY_LLM_MODELS))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
# llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔 # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔 llm_kwargs['llm_model'] = MULTI_QUERY_LLM_MODELS # 支持任意数量的llm接口，用&符号分隔
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=txt, inputs_show_user=txt, inputs=txt, inputs_show_user=txt,
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,

2
crazy_functions/语音助手.py
View File

@ -2,7 +2,7 @@ from toolbox import update_ui
from toolbox import CatchException, get_conf, markdown_convertion from toolbox import CatchException, get_conf, markdown_convertion
from crazy_functions.crazy_utils import input_clipping from crazy_functions.crazy_utils import input_clipping
from crazy_functions.agent_fns.watchdog import WatchDog from crazy_functions.agent_fns.watchdog import WatchDog
from request_llm.bridge_all import predict_no_ui_long_connection from request_llms.bridge_all import predict_no_ui_long_connection
import threading, time import threading, time
import numpy as np import numpy as np
from .live_audio.aliyunASR import AliyunASR from .live_audio.aliyunASR import AliyunASR

6
crazy_functions/读文章写摘要.py
View File

@ -1,5 +1,5 @@
from toolbox import update_ui from toolbox import update_ui
from toolbox import CatchException, report_execption from toolbox import CatchException, report_exception
from toolbox import write_history_to_file, promote_file_to_downloadzone from toolbox import write_history_to_file, promote_file_to_downloadzone
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
@ -51,14 +51,14 @@ def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
project_folder = txt project_folder = txt
else: else:
if txt == "": txt = '空空如也的输入栏' if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \ file_manifest = [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}/**/*.cpp', recursive=True)] + \
# [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)] # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
if len(file_manifest) == 0: if len(file_manifest) == 0:
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return return
yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

8
crazy_functions/谷歌检索小助手.py
View File

@ -1,5 +1,5 @@
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
from toolbox import CatchException, report_execption, promote_file_to_downloadzone from toolbox import CatchException, report_exception, promote_file_to_downloadzone
from toolbox import update_ui, update_ui_lastest_msg, disable_auto_promotion, write_history_to_file from toolbox import update_ui, update_ui_lastest_msg, disable_auto_promotion, write_history_to_file
import logging import logging
import requests import requests
@ -17,7 +17,7 @@ def get_meta_information(url, chatbot, history):
from urllib.parse import urlparse from urllib.parse import urlparse
session = requests.session() session = requests.session()
proxies, = get_conf('proxies') proxies = get_conf('proxies')
headers = { headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36',
'Accept-Encoding': 'gzip, deflate, br', 'Accept-Encoding': 'gzip, deflate, br',
@ -29,7 +29,7 @@ def get_meta_information(url, chatbot, history):
try: try:
session.proxies.update(proxies) session.proxies.update(proxies)
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a=f"获取代理失败 无代理状态下很可能无法访问OpenAI家族的模型及谷歌学术 建议：检查USE_PROXY选项是否修改。", a=f"获取代理失败 无代理状态下很可能无法访问OpenAI家族的模型及谷歌学术 建议：检查USE_PROXY选项是否修改。",
b=f"尝试直接连接") b=f"尝试直接连接")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@ -146,7 +146,7 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
import math import math
from bs4 import BeautifulSoup from bs4 import BeautifulSoup
except: except:
report_execption(chatbot, history, report_exception(chatbot, history,
a = f"解析项目: {txt}", a = f"解析项目: {txt}",
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4 arxiv```。") b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4 arxiv```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

2
docker-compose.yml
View File

@ -137,7 +137,7 @@ services:
# P.S. 通过对 command 进行微调，可以便捷地安装额外的依赖 # P.S. 通过对 command 进行微调，可以便捷地安装额外的依赖
# command: > # command: >
# bash -c "pip install -r request_llm/requirements_qwen.txt && python3 -u main.py" # bash -c "pip install -r request_llms/requirements_qwen.txt && python3 -u main.py"
### =================================================== ### ===================================================
### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型 ### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型

10
docs/GithubAction+AllCapacity
View File

@ -19,13 +19,13 @@ RUN python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad sc
WORKDIR /gpt WORKDIR /gpt
RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
WORKDIR /gpt/gpt_academic WORKDIR /gpt/gpt_academic
RUN git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llm/moss RUN git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss
RUN python3 -m pip install -r requirements.txt RUN python3 -m pip install -r requirements.txt
RUN python3 -m pip install -r request_llm/requirements_moss.txt RUN python3 -m pip install -r request_llms/requirements_moss.txt
RUN python3 -m pip install -r request_llm/requirements_qwen.txt RUN python3 -m pip install -r request_llms/requirements_qwen.txt
RUN python3 -m pip install -r request_llm/requirements_chatglm.txt RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
RUN python3 -m pip install -r request_llm/requirements_newbing.txt RUN python3 -m pip install -r request_llms/requirements_newbing.txt
RUN python3 -m pip install nougat-ocr RUN python3 -m pip install nougat-ocr

10
docs/GithubAction+ChatGLM+Moss
View File

@ -14,12 +14,12 @@ RUN python3 -m pip install torch --extra-index-url https://download.pytorch.org/
WORKDIR /gpt WORKDIR /gpt
RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
WORKDIR /gpt/gpt_academic WORKDIR /gpt/gpt_academic
RUN git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss RUN git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss
RUN python3 -m pip install -r requirements.txt RUN python3 -m pip install -r requirements.txt
RUN python3 -m pip install -r request_llm/requirements_moss.txt RUN python3 -m pip install -r request_llms/requirements_moss.txt
RUN python3 -m pip install -r request_llm/requirements_qwen.txt RUN python3 -m pip install -r request_llms/requirements_qwen.txt
RUN python3 -m pip install -r request_llm/requirements_chatglm.txt RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
RUN python3 -m pip install -r request_llm/requirements_newbing.txt RUN python3 -m pip install -r request_llms/requirements_newbing.txt

8
docs/GithubAction+JittorLLMs
View File

@ -16,12 +16,12 @@ WORKDIR /gpt
RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
WORKDIR /gpt/gpt_academic WORKDIR /gpt/gpt_academic
RUN python3 -m pip install -r requirements.txt RUN python3 -m pip install -r requirements.txt
RUN python3 -m pip install -r request_llm/requirements_chatglm.txt RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
RUN python3 -m pip install -r request_llm/requirements_newbing.txt RUN python3 -m pip install -r request_llms/requirements_newbing.txt
RUN python3 -m pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I RUN python3 -m pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I
# 下载JittorLLMs # 下载JittorLLMs
RUN git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llm/jittorllms RUN git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llms/jittorllms
# 禁用缓存，确保更新代码 # 禁用缓存，确保更新代码
ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache

344
docs/README.Arabic.md Normal file
View File

@ -0,0 +1,344 @@
> **ملحوظة**
>
> تمت ترجمة هذا الملف README باستخدام GPT (بواسطة المكون الإضافي لهذا المشروع) وقد لا تكون الترجمة 100٪ موثوقة، يُرجى التمييز بعناية بنتائج الترجمة.
>
> 2023.11.7: عند تثبيت التبعيات، يُرجى اختيار الإصدار المُحدد في `requirements.txt`. الأمر للتثبيت: `pip install -r requirements.txt`.
# <div align=center><img src="logo.png" width="40"> GPT الأكاديمي</div>
**إذا كنت تحب هذا المشروع، فيُرجى إعطاؤه Star. لترجمة هذا المشروع إلى لغة عشوائية باستخدام GPT، قم بقراءة وتشغيل [`multi_language.py`](multi_language.py) (تجريبي).
> **ملحوظة**
>
> 1. يُرجى ملاحظة أنها الإضافات (الأزرار) المميزة فقط التي تدعم قراءة الملفات، وبعض الإضافات توجد في قائمة منسدلة في منطقة الإضافات. بالإضافة إلى ذلك، نرحب بأي Pull Request جديد بأعلى أولوية لأي إضافة جديدة.
>
> 2. تُوضّح كل من الملفات في هذا المشروع وظيفتها بالتفصيل في [تقرير الفهم الذاتي `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). يمكنك في أي وقت أن تنقر على إضافة وظيفة ذات صلة لاستدعاء GPT وإعادة إنشاء تقرير الفهم الذاتي للمشروع. للأسئلة الشائعة [`الويكي`](https://github.com/binary-husky/gpt_academic/wiki). [طرق التثبيت العادية](#installation) | [نصب بنقرة واحدة](https://github.com/binary-husky/gpt_academic/releases) | [تعليمات التكوين](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
>
> 3. يتم توافق هذا المشروع مع ودعم توصيات اللغة البيجائية الأكبر شمولًا وشجاعة لمثل ChatGLM. يمكنك توفير العديد من مفاتيح Api المشتركة في تكوين الملف، مثل `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. عند تبديل مؤقت لـ `API_KEY`، قم بإدخال `API_KEY` المؤقت في منطقة الإدخال ثم اضغط على زر "إدخال" لجعله ساري المفعول.
<div align="center">
الوظائف (⭐= وظائف مُضافة حديثًا) | الوصف
--- | ---
⭐[التوصل لنموذج جديد](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)! | بحث بيدو[تشيان فان](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu) ووينسين[جينرال](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)، مختبرات شنغهاي للذكاء الصناعي[شو شينغ](https://github.com/InternLM/InternLM)، إكسنفلام[زينغهو]https://xinghuo.xfyun.cn/)، [LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)، واجهة بيانية ذكية و3 خدمات إضافية [DALLE3]
الجودة الفائقة، الترجمة، شرح الكود | الإصلاح الفوري للاخطاء النحوية في الأبحاث وترجمة وتحسين التصريف اللغوي للأكواد
[اختصارات مخصصة](https://www.bilibili.com/video/BV14s4y1E7jN) | دعم الاختصارات المخصصة
تصميم قابل للتوسيع | دعم الإضافات القوية المخصصة (الوظائف)، الإضافات قابلة للتحديث بشكل فوري
[تحليل البرنامج](https://www.bilibili.com/video/BV1cj411A7VW) | [وظائف] التحليل الشجري بناءً على البرنامج من Python/C/C++/Java/Lua/..., أو [التحليل الذاتي](https://www.bilibili.com/video/BV1cj411A7VW)
قراءة وترجمة الأبحاث | [وظائف] فك تشفير كامل لأوراق البحث بتنسيق LaTeX/PDF وإنشاء مستخلص
ترجمة وتحسين أوراق اللاتكس | [وظائف] ترجمة أو تحسين الأوراق المكتوبة بلاتكس
إنشاء تعليقات الدوال دفعة واحدة | [وظائف] إنشاء تعليقات الدوال بدفعة واحدة
ترجمة Markdown بين اللغتين العربية والإنجليزية | [وظائف] هل رأيت الـ 5 لغات المستخدمة في منشور [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) ؟
إنشاء تقرير تحليل الدردشة | [وظائف] إنشاء تقرير ملخص بعد تشغيله
ترجمة كاملة لأوراق PDF | [وظائف] تحليل الأوراق بتنسيق PDF لتحديد العنوان وملخصها وترجمتها (متعدد الخيوط)
مساعدة Arxiv | [وظائف] قم بإدخال رابط مقال Arxiv لترجمة الملخص وتحميل ملف PDF
تصحيح لاتكس بضغطة زر واحدة | [وظائف] إكمال تصحيح لاتكس بناءً على التركيبة النحوية، إخراج همز المقابل للمقارنة PDF
مساعد بحث Google بنسخة محلية | [وظائف] قم بتقديم رابط لصفحة بحث Google Scholar العشوائي حتى يساعدك GPT في كتابة [الأبحاث المتعلقة](https://www.bilibili.com/video/BV1GP411U7Az/)
تجميع معلومات الويب + GPT | [وظائف] جمع المعلومات من الويب بشكل سهل للرد على الأسئلة لجعل المعلومات محدثة باستمرار
⭐ترجمة دقيقة لأوراق Arxiv ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [وظائف] ترجمة مقالات Arxiv عالية الجودة بنقرة واحدة، أفضل أداة حاليا للترجمة
⭐[إدخال الصوت الفوري](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [وظائف] (غير متزامن) استماع الصوت وقطعه تلقائيًا وتحديد وقت الإجابة تلقائيًا
عرض الصيغ/الصور/الجداول | يمكن عرض الصيغ بشكل [TEX](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png) وأيضًا بتنسيق رسومي، يدعم عرض الصيغ وإبراز الكود
⭐إضغط على وكيل "شارلوت الذكي" | [وظائف] استكمال الذكاء للكأس الأول للذكاء المكتسب من مايكروسوفت، اكتشاف وتطوير عالمي العميل
تبديل الواجهة المُظلمة | يمكنك التبديل إلى الواجهة المظلمة بإضافة ```/?__theme=dark``` إلى نهاية عنوان URL في المتصفح
دعم المزيد من نماذج LLM | دعم لجميع GPT3.5 وGPT4 و[ChatGLM2 في جامعة ثوه في لين](https://github.com/THUDM/ChatGLM2-6B) و[MOSS في جامعة فودان](https://github.com/OpenLMLab/MOSS)
⭐تحوي انطباعة "ChatGLM2" | يدعم استيراد "ChatGLM2" ويوفر إضافة المساعدة في تعديله
دعم المزيد من نماذج "LLM"، دعم [نشر الحديس](https://huggingface.co/spaces/qingxu98/gpt-academic) | انضم إلى واجهة "Newbing" (Bing الجديدة)،نقدم نماذج Jittorllms الجديدة تؤيدهم [LLaMA](https://github.com/facebookresearch/llama) و [盘古α](https://openi.org.cn/pangu/)
⭐حزمة "void-terminal" للشبكة (pip) | قم بطلب كافة وظائف إضافة هذا المشروع في python بدون واجهة رسومية (قيد التطوير)
⭐PCI-Express لإعلام (PCI) | [وظائف] باللغة الطبيعية، قم بتنفيذ المِهام الأخرى في المشروع
المزيد من العروض (إنشاء الصور وغيرها)……| شاهد أكثر في نهاية هذا المستند ...
</div>
- شكل جديد (عن طريق تعديل الخيار LAYOUT في `config.py` لقانون التوزيع "اليمين أو اليسار" أو "الأعلى أو الأسفل")
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
</div>
- جميع الأزرار يتم إنشاؤها ديناميكيًا من خلال قراءة functional.py ويمكن إضافة وظائف مخصصة بحرية وتحرير الحافظة
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- التجميل / التحوير
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- إذا تضمّن الإخراج معادلات، فسيتم عرضها بشكلٍ يمكّن من النسخ والقراءة على النحوين: TEX ورسومية.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- هل تشعر بالكسل من قراءة كود المشروع؟ قم بمدها مباشرةً إلى ChatGPT
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- دمج نماذج اللغات الكبيرة المختلفة (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
# Installation
### طريقة التثبيت الأولى: التشغيل المباشر (Windows، Linux أو MacOS)
1. قم بتنزيل المشروع
```sh
git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. قم بتكوين لغة البرمجة Python
في ملف `config.py`، قم بتكوين مفتاح الواجهة API والإعدادات الأخرى، [انقر هنا للاطلاع على طريقة تكوين الإعدادات في بيئة شبكة خاصة](https://github.com/binary-husky/gpt_academic/issues/1). [انقر هنا لزيارة صفحة الويكي](https://github.com/binary-husky/gpt_academic/wiki/توضيحات-تكوين-المشروع).
" ستقوم البرنامج بفحص وجود ملف تكوين خاص يسمى `config_private.py` بأولوية، وسيستخدم التكوينات الموجودة فيه لتجاوز التكوينات ذات الأسماء المطابقة في `config.py`. إذا كنت تفهم هذه الطريقة ونظام القراءة، فإننا نوصي بشدة بإنشاء ملف تكوين جديد يسمى `config_private.py` بجوار `config.py` ونقل (نسخ) التكوينات الموجودة في `config.py` إلى `config_private.py` (يجب نسخ العناصر التي قمت بتعديلها فقط). "
" يدعم المشروع التكوين من خلال `المتغيرات المحيطية`، ويمكن تحديد تنسيق كتابة المتغيرات المحيطية من خلال ملف `docker-compose.yml` أو صفحة الويكي الخاصة بنا. تعتمد أولوية القراءة على التكوينات على التالي: `المتغيرات المحيطية` > `config_private.py` > `config.py`. "
3. قم بتثبيت التبعيات
```sh
# (الخيار الأول: إذا كنت تعرف Python، python>=3.9) الملحوظة: استخدم مستودع pip الرسمي أو مستودع pip آلي بباي، يمكن تغيير المستودع المؤقت بواسطة الأمر: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (الخيار الثاني: باستخدام Anaconda) الخطوات مشابهة (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # إنشاء بيئة Anaconda
conda activate gptac_venv # تنشيط بيئة Anaconda
python -m pip install -r requirements.txt # هذه الخطوة مطابقة لخطوة تثبيت pip
```
<details><summary>إذا كنت بحاجة إلى دعم ChatGLM2 من الجامعة الصينية للاقتصاد وإدارة الأعمال وموس من جامعة فودان كخادم وجودة عالية لطرح الأسئلة، انقر هنا للعرض</summary>
<p>
【خطوات اختيارية】إذا كنت بحاجة إلى دعم جودة عالية لتشات جامعة تسينهوا (ChatGLM2) الصينية وجامعة فودان (MOSS)، يتعين عليك تثبيت تبعيات إضافية (شرط مسبق: التعامل مع Python واستخدام Pytorch وتوفر الحاسوب الشخصي بمواصفات قوية):
```sh
# 【خطوات اختيارية 1】دعم جودة عالية لتشات جامعة تسينهوا (ChatGLM2)
python -m pip install -r request_llms/requirements_chatglm.txt
# 【خطوات اختيارية 2】دعم جودة عالية لتشات جامعة فودان (MOSS)
python -m pip install -r request_llms/requirements_moss.txt
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # عند تنفيذ هذا الأمر، يجب أن تكون في مسار المشروع الرئيسي
# 【خطوات اختيارية 3】دعم RWKV Runner
راجع الويكي: https://github.com/binary-husky/gpt_academic/wiki/دليل-تكوين-RWKV
# 【خطوات اختيارية 4】تأكد من أن ملف التكوين config.py يحتوي على النماذج المرجوة، وهناك النماذج المدعومة حاليًا التالية (توجد خطط لتشغيل "jittorllms" في docker فقط):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. تشغيل البرنامج
```sh
python main.py
```
### طريقة التثبيت الثانية: استخدام Docker
0. نصب القدرات الكاملة للمشروع (هذا هو الصورة الكبيرة التي تحتوي على CUDA و LaTeX. ولكن إذا كانت سرعة الإنترنت بطيئة أو القرص الصلب صغير، فإننا لا نوصي باستخدام هذا الخيار)
[![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
``` sh
# قم بتعديل ملف docker-compose.yml للحفاظ على الخطة رقم 0 وحذف الخطط الأخرى. ثم أشغل:
docker-compose up
```
1. تشغيل نموذج ChatGPT فقط + 文心一言 (Wenxin YIYan) + Spark عبر الإنترنت (يُوصى بهذا الخيار للمعظم)
[![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
[![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
``` sh
# قم بتعديل ملف docker-compose.yml للحفاظ على الخطة رقم 1 وحذف الخطط الأخرى. ثم أشغل:
docker-compose up
```
P.S. للاستفادة من إمكانية اللافتكس الإضافية، يرجى الرجوع إلى الويكي. بالإضافة إلى ذلك، يمكنك استخدام الخطة 4 أو الخطة 0 مباشرة للحصول على إمكانية اللافتكس.
2. تشغيل نموذج ChatGPT + نموذج ChatGLM2 + نموذج MOSS + نموذج LLAMA2 + تون يي تشين ون (QiChaYiWen) (يتطلب معرفة بتشغيل نيفيديا دوكر (Nvidia Docker))
[![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
``` sh
# قم بتعديل ملف docker-compose.yml للحفاظ على الخطة رقم 2 وحذف الخطط الأخرى. ثم أشغل:
docker-compose up
```
### طريقة التثبيت الثالثة: طرائق نشر أخرى
1. **نصوص بنقرة واحدة لأنظمة Windows**.
يمكن لمستخدمي Windows الذين لا يعرفون بيئة Python تنزيل سكربت التشغيل بنقرة واحدة من [الإصدارات](https://github.com/binary-husky/gpt_academic/releases) المنشورة لتثبيت الإصدار الذي لا يحتوي على نماذج محلية.
المساهمة في السكربت تعود لـ[oobabooga](https://github.com/oobabooga/one-click-installers).
2. استخدام واجهة برمجة تطبيقات (API) مطراف ثالثة، Microsoft Azure، ونشوة النص، وغيرها، يرجى الرجوع إلى [صفحة الويكي](https://github.com/binary-husky/gpt_academic/wiki/إعدادات-التكوين-للمشروع) الخاصة بنا
3. دليل تجنب المشاكل عند نشر المشروع في خوادم السحابة.
يرجى زيارة صفحة [دليل نشر خوادم السحابة في المحيط](https://github.com/binary-husky/gpt_academic/wiki/دليل-نشر-خوادم-السحابة)
4. طرائق نشر المشروع بأحدث الأساليب
- استخدام Sealos للنشر السريع [بنقرة واحدة](https://github.com/binary-husky/gpt_academic/issues/993).
- استخدم WSL2 (Windows Subsystem for Linux). يُرجى زيارة صفحة الويكي [لدليل التثبيت-2](https://github.com/binary-husky/gpt_academic/wiki/دليل-تشغيل-WSL2-(Windows-Subsystem-for-Linux)
- كيفية تشغيل البرنامج تحت عنوان فرعي (على سبيل المثال: `http://localhost/subpath`). يُرجى زيارة [إرشادات FastAPI](docs/WithFastapi.md)
# الاستخدام المتقدم
### I: إنشاء أزرار مخصصة (اختصارات أكاديمية)
افتح أي محرر نصوص وافتح `core_functional.py` وأضف الإدخالات التالية ثم أعد تشغيل البرنامج. (إذا كانت الأزرار موجودة بالفعل، بإمكانك تعديل البادئة واللاحقة حراريًا دون الحاجة لإعادة تشغيل البرنامج)
على سبيل المثال:
```
"ترجمة سوبر الإنجليزية إلى العربية": {
# البادئة، ستتم إضافتها قبل إدخالاتك. مثلاً، لوصف ما تريده مثل ترجمة أو شرح كود أو تلوين وهلم جرا
"بادئة": "يرجى ترجمة النص التالي إلى العربية ثم استخدم جدول Markdown لشرح المصطلحات المختصة المذكورة في النص:\n\n",
# اللاحقة، سيتم إضافتها بعد إدخالاتك. يمكن استخدامها لوضع علامات اقتباس حول إدخالك.
"لاحقة": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
### II: إنشاء مكونات وظيفية مخصصة
قم بكتابة مكونات وظيفية قوية لتنفيذ أي مهمة ترغب في الحصول عليها وحتى تلك التي لم تخطر لك على بال.
إن إنشاء وتصحيح المكونات في هذا المشروع سهل للغاية، فما عليك سوى أن تمتلك بعض المعرفة الأساسية في لغة البرمجة بايثون وتستند على القالب الذي نقدمه.
للمزيد من التفاصيل، يُرجى الاطلاع على [دليل المكونات الوظيفية](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).
# التحديثات
### I: تحديثات
1. ميزة حفظ الدردشة: يمكن حفظ الدردشة الحالية كملف HTML قابل للقراءة والاسترداد ببساطة عند استدعاء الوظيفة في منطقة المكونات `حفظ الدردشة الحالية` ، ويمكن استرجاع المحادثة السابقة ببساطة عند استدعاء الوظيفة في منطقة المكونات (القائمة المنسدلة) `تحميل سجل الدردشة` .
نصيحة: يمكنك النقر المباشر على `تحميل سجل الدردشة` بدون تحديد ملف لعرض ذاكرة التخزين المؤقت لسجلات HTML.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. ميزة ترجمة المقالات العلمية بواسطة Latex/Arxiv
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
</div>
3. محطة فراغ (فهم نغمة المستخدم من داخل اللغة الطبيعية واستدعاء وظائف أخرى تلقائيًا)
- الخطوة 1: اكتب "بالرجاء استدعاء وظيفة ترجمة المقالة الأكاديمية من PDF وعنوان المقال هو https://openreview.net/pdf?id=rJl0r3R9KX".
- الخطوة 2: انقر فوق "محطة الفراغ".
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
</div>
4. تصميم الوظائف المتعددة القادرة على توفير وظائف قوية بواجهات بسيطة
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
5. ترجمة وإلغاء ترجمة المشاريع الأخرى مفتوحة المصدر
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
</div>
6. ميزة تزيين [live2d](https://github.com/fghrsh/live2d_demo) (مغلقة بشكل افتراضي، يتطلب تعديل `config.py`)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. إنتاج الصور من OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
8. تحليل وإجماع الصوت من OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
9. إصلاح أخطاء اللغة الطبيعة في Latex
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
</div>
10. تغيير اللغة والموضوع
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
</div>
### II: الإصدارات:
- الإصدار 3.70 (قريبًا): تحسينات لوظائف AutoGen وتصميم سلسلة من المكونات المشتقة
- الإصدار 3.60: إدخال AutoGen كأساس لوظائف الجيل الجديد
- الإصدار 3.57: دعم GLM3، نار النجوم v3، وشجرة الكلمات v4، وإصلاح خطأ الازدحام في النماذج المحلية
- الإصدار 3.56: الدعم لإضافة مزامنة الأزرار الأساسية حسب الطلب، وصفحة تجميع تقارير البيانات في ملف PDF
- الإصدار 3.55: إعادة هيكلة واجهة المستخدم الأمامية، وإضافة نافذة عائمة وشريط قائمة
- الإصدار 3.54: إضافة مترجم الكود المباشر (Code Interpreter) (قيد الانجاز)
- الإصدار 3.53: دعم اختيار موضوعات واجهة مختلفة، وزيادة الاستقرار وحل مشاكل التعارض بين المستخدمين المتعدد
- الإصدار 3.50: استخدام اللغة الطبيعية لاستدعاء جميع وظائف المشروع هذا (محطة فراغ)، ودعم تصنيف الوظائف وتحسين واجهة المستخدم وتصميم مواضيع جديدة
- الإصدار 3.49: دعم المنصات البحثية في بيدو كونفان وشجرة الكلمات
- الإصدار 3.48: دعم علي بابا, بوكما رش حتكيا, إكسونامبلومانت النار
- الإصدار 3.46: دعم محادثة نصية في الوقت الحقيقي غير مراقبة
- الإصدار 3.45: دعم تخصيص LatexChatglm النموذج التعديل
- الإصدار 3.44: دعم Azure رسميًا، وتحسين سهولة الاستخدام للواجهات الأمامية
- الإصدار 3.4: +ترجمة النصوص الكاملة للمقالات من خلال ملف PDF، +اختيار موضع المنطقة النصية، +خيار التخطيط الرأسي، +تحسينات في وظائف التداخل العديدة
- الإصدار 3.3: +وظائف متكاملة للمعلومات عبر الإنترنت
- الإصدار 3.2: دعم وظائف المكونات التي تحتوي معلمات أكثر (حفظ النص، فهم أي لغة برمجة، طلب أي تركيبة LLM في وقت واحد)
- الإصدار 3.1: دعم السؤال نحو نماذج GPT المتعددة! دعم واجهة api2d، دعم توازن الأحمال بين المفاتيح الخاصة المتعددة
- الإصدار 3.0: دعم لنماذج جات، واحدة منها لشتلس الصغيرة
- الإصدار 2.6: إعادة تصميم بنية الوظائف، وتحسين التفاعل وإضافة مزيد من الوظائف
- الإصدار 2.5: التحديث التلقائي، وحل مشكلة النص الطويل عند ملخص المشاريع الضخمة وتجاوز النصوص.
- الإصدار 2.4: (١) إضافة ميزة ترجمة المقالات الدورية. (٢) إضافة ميزة لتحويل مكان منطقة الإدخال. (٣) إضافة خيار التخطيط العمودي (vertical layout). (٤) تحسين وظائف المكونات متعددة الخيوط.
- الإصدار 2.3: تحسين التفاعل مع مواضيع متعددة
- الإصدار 2.2: دعم إعادة تحميل الوظائف المكونة حراريًا
- الإصدار 2.1: تصميم قابل للطي
- الإصدار 2.0: إدخال وحدات الوظائف المكونة
- الإصدار 1.0: الوظائف الأساسية
مجموعة المطورين GPT Academic QQ: `610599535`
- مشكلات معروفة
- بعض ملحقات متصفح الترجمة تتداخل مع تشغيل الواجهة الأمامية لهذا البرنامج
- يحتوي Gradio الرسمي حاليًا على عدد كبير من مشاكل التوافق. يُرجى استخدام `requirement.txt` لتثبيت Gradio.
### III: الأنساق
يمكن تغيير الأنساق بتعديل خيار `THEME` (config.py)
1. `Chuanhu-Small-and-Beautiful` [الرابط](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
### IV: فروع تطوير هذا المشروع
1. الفرع `master`: الفرع الرئيسي، إصدار مستقر
2. الفرع `frontier`: الفرع التطويري، إصدار تجريبي
### V: المراجع والفروض التعليمية
```
استخدمت العديد من التصاميم الموجودة في مشاريع ممتازة أخرى في الأكواد التالية، للمراجع عشوائية:
# ViewGradio:
https://github.com/THUD
# مُثبّت بضغطة واحدة Oobabooga:
https://github.com/oobabooga/one-click-installers
# المزيد:
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo

357
docs/README.English.md Normal file
View File

@ -0,0 +1,357 @@
> **Note**
>
> This README was translated by GPT (implemented by the plugin of this project) and may not be 100% reliable. Please carefully check the translation results.
>
> 2023.11.7: When installing dependencies, please select the **specified versions** in the `requirements.txt` file. Installation command: `pip install -r requirements.txt`.
# <div align=center><img src="logo.png" width="40"> GPT Academic Optimization</div>
**If you like this project, please give it a Star.**
To translate this project to arbitrary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).
> **Note**
>
> 1.Please note that only plugins (buttons) highlighted in **bold** support reading files, and some plugins are located in the **dropdown menu** in the plugin area. Additionally, we welcome and process any new plugins with the **highest priority** through PRs.
>
> 2.The functionalities of each file in this project are described in detail in the [self-analysis report `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). As the version iterates, you can also click on the relevant function plugin at any time to call GPT to regenerate the project's self-analysis report. Common questions are in the [`wiki`](https://github.com/binary-husky/gpt_academic/wiki). [Regular installation method](#installation) | [One-click installation script](https://github.com/binary-husky/gpt_academic/releases) | [Configuration instructions](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
>
> 3.This project is compatible with and encourages the use of domestic large-scale language models such as ChatGLM. Multiple api-keys can be used together. You can fill in the configuration file with `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"` to temporarily switch `API_KEY` during input, enter the temporary `API_KEY`, and then press enter to apply it.
<div align="center">
Feature (⭐ = Recently Added) | Description
--- | ---
⭐[Integrate New Models](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B) | Baidu [Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu) and Wenxin Yiyu, [Tongyi Qianwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), Shanghai AI-Lab [Shusheng](https://github.com/InternLM/InternLM), Xunfei [Xinghuo](https://xinghuo.xfyun.cn/), [LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), Zhifu API, DALLE3
Proofreading, Translation, Code Explanation | One-click proofreading, translation, searching for grammar errors in papers, explaining code
[Custom Shortcuts](https://www.bilibili.com/video/BV14s4y1E7jN) | Support for custom shortcuts
Modular Design | Support for powerful [plugins](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions), plugins support [hot updates](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)
[Program Profiling](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin] One-click to profile Python/C/C++/Java/Lua/... project trees or [self-profiling](https://www.bilibili.com/video/BV1cj411A7VW)
Read Papers, [Translate](https://www.bilibili.com/video/BV1KT411x7Wn) Papers | [Plugin] One-click to interpret full-text latex/pdf papers and generate abstracts
Full-text Latex [Translation](https://www.bilibili.com/video/BV1nk4y1Y7Js/), [Proofreading](https://www.bilibili.com/video/BV1FT411H7c5/) | [Plugin] One-click translation or proofreading of latex papers
Batch Comment Generation | [Plugin] One-click batch generation of function comments
Markdown [Translation](https://www.bilibili.com/video/BV1yo4y157jV/) | [Plugin] Did you see the [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) in the top five languages?
Chat Analysis Report Generation | [Plugin] Automatically generates summary reports after running
[PDF Paper Full-text Translation](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin] Extract title & abstract of PDF papers + translate full-text (multi-threaded)
[Arxiv Helper](https://www.bilibili.com/video/BV1LM4y1279X) | [Plugin] Enter the arxiv article URL to translate the abstract + download PDF with one click
One-click Proofreading of Latex Papers | [Plugin] Syntax and spelling correction of Latex papers similar to Grammarly + output side-by-side PDF
[Google Scholar Integration Helper](https://www.bilibili.com/video/BV19L411U7ia) | [Plugin] Given any Google Scholar search page URL, let GPT help you [write related works](https://www.bilibili.com/video/BV1GP411U7Az/)
Internet Information Aggregation + GPT | [Plugin] One-click to let GPT retrieve information from the Internet to answer questions and keep the information up to date
⭐Arxiv Paper Fine Translation ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [Plugin] One-click [high-quality translation of arxiv papers](https://www.bilibili.com/video/BV1dz4y1v77A/), the best paper translation tool at present
⭐[Real-time Speech Input](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [Plugin] Asynchronously [listen to audio](https://www.bilibili.com/video/BV1AV4y187Uy/), automatically segment sentences, and automatically find the best time to answer
Formula/Image/Table Display | Can simultaneously display formulas in [TeX form and rendered form](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), support formula and code highlighting
⭐AutoGen Multi-Agent Plugin | [Plugin] Explore the emergence of multi-agent intelligence with Microsoft AutoGen!
Start Dark [Theme](https://github.com/binary-husky/gpt_academic/issues/173) | Add ```/?__theme=dark``` to the end of the browser URL to switch to the dark theme
[More LLM Model Support](https://www.bilibili.com/video/BV1wT411p7yf) | It must be great to be served by GPT3.5, GPT4, [THU ChatGLM2](https://github.com/THUDM/ChatGLM2-6B), and [Fudan MOSS](https://github.com/OpenLMLab/MOSS) at the same time, right?
⭐ChatGLM2 Fine-tuning Model | Support for loading ChatGLM2 fine-tuning models and providing ChatGLM2 fine-tuning assistant plugins
More LLM Model Access, support for [huggingface deployment](https://huggingface.co/spaces/qingxu98/gpt-academic) | Join NewBing interface (New Bing), introduce Tsinghua [JittorLLMs](https://github.com/Jittor/JittorLLMs) to support [LLaMA](https://github.com/facebookresearch/llama) and [Pangu](https://openi.org.cn/pangu/)
⭐[void-terminal](https://github.com/binary-husky/void-terminal) pip package | Use this project's all function plugins directly in Python without GUI (under development)
⭐Void Terminal Plugin | [Plugin] Schedule other plugins of this project directly in natural language
More New Feature Demonstrations (Image Generation, etc.)...... | See the end of this document ........
</div>
- New interface (modify the LAYOUT option in `config.py` to switch between "left-right layout" and "top-bottom layout")
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
</div>
- All buttons are dynamically generated by reading `functional.py` and can be added with custom functions to free up the clipboard
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- Proofreading/Correction
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- If the output contains formulas, they will be displayed in both tex format and rendered format for easy copying and reading.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Too lazy to look at the project code? Show off the whole project directly in chatgpt's mouth
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Multiple large language models mixed calling (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
# Installation
### Installation Method I: Run directly (Windows, Linux or MacOS)
1. Download the project
```sh
git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configure API_KEY
In `config.py`, configure API KEY and other settings, [click here to see special network environment configuration methods](https://github.com/binary-husky/gpt_academic/issues/1). [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
「 The program will first check if a secret configuration file named `config_private.py` exists and use the configurations from that file to override the ones in `config.py` with the same names. If you understand this logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py` and move (copy) the configurations from `config.py` to `config_private.py` (only copy the configuration items you have modified). 」
「 Project configuration can be done via `environment variables`. The format of the environment variables can be found in the `docker-compose.yml` file or our [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明). Configuration priority: `environment variables` > `config_private.py` > `config.py`. 」
3. Install dependencies
```sh
# (Option I: If you are familiar with python, python>=3.9) Note: Use the official pip source or the Aliyun pip source. Temporary method for switching the source: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Option II: Using Anaconda) The steps are similar (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # Create the anaconda environment
conda activate gptac_venv # Activate the anaconda environment
python -m pip install -r requirements.txt # This step is the same as the pip installation process
```
<details><summary>If you need to support THU ChatGLM2, Fudan MOSS, or RWKV Runner as backends, click here to expand</summary>
<p>
【Optional Step】If you need to support THU ChatGLM2 or Fudan MOSS as backends, you need to install additional dependencies (Prerequisites: Familiar with Python + Familiar with Pytorch + Sufficient computer configuration):
```sh
# 【Optional Step I】Support THU ChatGLM2. Note: If you encounter the "Call ChatGLM fail unable to load ChatGLM parameters" error, refer to the following: 1. The default installation above is for torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2. If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py. Change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llms/requirements_chatglm.txt
# 【Optional Step II】Support Fudan MOSS
python -m pip install -r request_llms/requirements_moss.txt
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # When executing this line of code, make sure you are in the root directory of the project
# 【Optional Step III】Support RWKV Runner
Refer to wiki: https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
# 【Optional Step IV】Make sure that the AVAIL_LLM_MODELS in the config.py configuration file includes the expected models. The currently supported models are as follows (jittorllms series currently only supports the docker solution):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Run
```sh
python main.py
```
### Installation Method II: Use Docker
0. Deploy all capabilities of the project (this is a large image that includes cuda and latex. Not recommended if you have slow internet speed or small hard drive)
[![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
``` sh
# Modify docker-compose.yml, keep scheme 0 and delete other schemes. Then run:
docker-compose up
```
1. ChatGPT + Wenxin + Spark online models only (recommended for most people)
[![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
[![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
``` sh
# Modify docker-compose.yml, keep scheme 1 and delete other schemes. Then run:
docker-compose up
```
P.S. If you need the latex plugin functionality, please see the Wiki. Also, you can directly use scheme 4 or scheme 0 to get the Latex functionality.
2. ChatGPT + ChatGLM2 + MOSS + LLAMA2 + Intelligent Questions (requires familiarity with [Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian) runtime)
[![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
``` sh
# Modify docker-compose.yml, keep scheme 2 and delete other schemes. Then run:
docker-compose up
```
### Installation Method III: Other deployment methods
1. **Windows one-click running script**.
Windows users who are completely unfamiliar with the python environment can download the one-click running script from the [Release](https://github.com/binary-husky/gpt_academic/releases) to install the version without local models.
The script is contributed by [oobabooga](https://github.com/oobabooga/one-click-installers).
2. Use third-party APIs, Azure, Wenxin, Xinghuo, etc., see [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)
3. Pitfall guide for deploying on cloud servers.
Please visit [Cloud Server Remote Deployment Wiki](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
4. Some new deployment platforms or methods
- Use Sealos [to deploy with one click](https://github.com/binary-husky/gpt_academic/issues/993).
- Use WSL2 (Windows Subsystem for Linux). Please refer to [Deployment Wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
- How to run under a subpath (such as `http://localhost/subpath`). Please visit [FastAPI Run Instructions](docs/WithFastapi.md)
# Advanced Usage
### I: Customizing new convenient buttons (academic shortcuts)
Open `core_functional.py` with any text editor, add the following entry, and then restart the program. (If the button already exists, both the prefix and suffix can be modified on-the-fly without restarting the program.)
For example:
```
"Super Translation": {
# Prefix: will be added before your input. For example, used to describe your request, such as translation, code explanation, proofreading, etc.
"Prefix": "Please translate the following paragraph into Chinese and then explain each proprietary term in the text using a markdown table:\n\n",
# Suffix: will be added after your input. For example, used to wrap your input in quotation marks along with the prefix.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
### II: Custom function plugins
Write powerful function plugins to perform any task you desire and can't imagine.
The difficulty of writing and debugging plugins in this project is very low. As long as you have a certain knowledge of Python, you can implement your own plugin functionality by following the template we provide.
For more details, please refer to the [Function Plugin Guide](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).
# Updates
### I: Dynamics
1. Conversation-saving feature. Call `Save the current conversation` in the function plugin area to save the current conversation as a readable and restorable HTML file. Additionally, call `Load conversation history archive` in the function plugin area (drop-down menu) to restore previous sessions.
Tip: Clicking `Load conversation history archive` without specifying a file allows you to view the cached historical HTML archive.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. ⭐Latex/Arxiv paper translation feature⭐
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
</div>
3. Void Terminal (understanding user intent from natural language input and automatically calling other plugins)
- Step 1: Enter " Please call the plugin to translate the PDF paper, the address is https://openreview.net/pdf?id=rJl0r3R9KX"
- Step 2: Click "Void Terminal"
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
</div>
4. Modular function design, simple interface supporting powerful functionality
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
5. Translate and interpret other open-source projects
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
</div>
6. Added small features that decorate [live2d](https://github.com/fghrsh/live2d_demo) (disabled by default, needs modification in `config.py`)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. OpenAI image generation
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
8. OpenAI audio parsing and summarization
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
9. Latex full-text proofreading and correction
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
</div>
10. Language and theme switching
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
</div>
### II: Versions:
- version 3.70 (todo): Optimize the AutoGen plugin theme and design a series of derivative plugins
- version 3.60: Introduce AutoGen as the cornerstone of the new generation of plugins
- version 3.57: Support GLM3, Spark v3, Wenxin Quote v4, and fix concurrency bugs in local models
- version 3.56: Support dynamically adding basic functional buttons and a new summary PDF page
- version 3.55: Refactor the frontend interface and introduce floating windows and a menu bar
- version 3.54: Add a dynamic code interpreter (Code Interpreter) (to be improved)
- version 3.53: Support dynamically choosing different interface themes, improve stability, and resolve conflicts between multiple users
- version 3.50: Use natural language to call all function plugins of this project (Void Terminal), support plugin classification, improve UI, and design new themes
- version 3.49: Support Baidu Qianfan Platform and Wenxin Quote
- version 3.48: Support Ali Dharma Academy Tongyi Qianwen, Shanghai AI-Lab Shusheng, and Xunfei Spark
- version 3.46: Support fully hands-off real-time voice conversation
- version 3.45: Support customizing ChatGLM2 fine-tuned models
- version 3.44: Officially support Azure, optimize interface usability
- version 3.4: + Arxiv paper translation, latex paper correction functionality
- version 3.3: + Internet information integration functionality
- version 3.2: Function plugins support more parameter interfaces (conversation saving functionality, interpreting any code language + asking any combination of LLMs simultaneously)
- version 3.1: Support querying multiple GPT models simultaneously! Support API2D, support load balancing for multiple API keys
- version 3.0: Support chatglm and other small-scale LLMs
- version 2.6: Refactored plugin structure, improved interactivity, added more plugins
- version 2.5: Self-updating, fix the problem of text being too long and token overflowing when summarizing large code projects
- version 2.4: (1) Add PDF full-text translation functionality; (2) Add functionality to switch the position of the input area; (3) Add vertical layout option; (4) Optimize multi-threaded function plugins.
- version 2.3: Enhance multi-threaded interactivity
- version 2.2: Function plugin hot-reloading support
- version 2.1: Collapsible layout
- version 2.0: Introduce modular function plugins
- version 1.0: Basic functionality
GPT Academic Developer QQ Group: `610599535`
- Known Issues
- Some browser translation plugins interfere with the frontend operation of this software
- Official Gradio currently has many compatibility bugs, please make sure to install Gradio using `requirement.txt`
### III: Themes
You can change the theme by modifying the `THEME` option (config.py).
1. `Chuanhu-Small-and-Beautiful` [Website](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
### IV: Development Branches of This Project
1. `master` branch: Main branch, stable version
2. `frontier` branch: Development branch, test version
### V: References and Learning
```
The code references the designs of many other excellent projects, in no particular order:
# THU ChatGLM2-6B:
https://github.com/THUDM/ChatGLM2-6B
# THU JittorLLMs:
https://github.com/Jittor/JittorLLMs
# ChatPaper:
https://github.com/kaixindelele/ChatPaper
# Edge-GPT:
https://github.com/acheong08/EdgeGPT
# ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Oobabooga one-click installer:
https://github.com/oobabooga/one-click-installers
# More:
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo

357
docs/README.French.md Normal file
View File

@ -0,0 +1,357 @@
> **Remarque**
>
> Ce README a été traduit par GPT (implémenté par le plugin de ce projet) et n'est pas fiable à 100 %. Veuillez examiner attentivement les résultats de la traduction.
>
> 7 novembre 2023 : Lors de l'installation des dépendances, veuillez choisir les versions **spécifiées** dans le fichier `requirements.txt`. Commande d'installation : `pip install -r requirements.txt`.
# <div align=center><img src="logo.png" width="40"> Optimisation académique GPT (GPT Academic)</div>
**Si vous aimez ce projet, merci de lui donner une étoile ; si vous avez inventé des raccourcis ou des plugins utiles, n'hésitez pas à envoyer des demandes d'extraction !**
Si vous aimez ce projet, veuillez lui donner une étoile.
Pour traduire ce projet dans une langue arbitraire avec GPT, lisez et exécutez [`multi_language.py`](multi_language.py) (expérimental).
> **Remarque**
>
> 1. Veuillez noter que seuls les plugins (boutons) marqués en **surbrillance** prennent en charge la lecture de fichiers, et certains plugins se trouvent dans le **menu déroulant** de la zone des plugins. De plus, nous accueillons avec la plus haute priorité les nouvelles demandes d'extraction de plugins.
>
> 2. Les fonctionnalités de chaque fichier de ce projet sont spécifiées en détail dans [le rapport d'auto-analyse `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic个项目自译解报告). Vous pouvez également cliquer à tout moment sur les plugins de fonctions correspondants pour appeler GPT et générer un rapport d'auto-analyse du projet. Questions fréquemment posées [wiki](https://github.com/binary-husky/gpt_academic/wiki). [Méthode d'installation standard](#installation) | [Script d'installation en un clic](https://github.com/binary-husky/gpt_academic/releases) | [Instructions de configuration](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)..
>
> 3. Ce projet est compatible avec et recommande l'expérimentation de grands modèles de langage chinois tels que ChatGLM, etc. Prend en charge plusieurs clés API, vous pouvez les remplir dans le fichier de configuration comme `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. Pour changer temporairement la clé API, entrez la clé API temporaire dans la zone de saisie, puis appuyez sur Entrée pour soumettre et activer celle-ci.
<div align="center">
Fonctionnalités (⭐ = fonctionnalité récemment ajoutée) | Description
--- | ---
⭐[Modèles acquis](https://github.com/binary-husky/gpt_academic/wiki/如何切换模型)！ | Baidu [Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu) et Wenxin Yiyuan, [Tongyi Qianwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), Shanghai AI-Lab [Shusheng](https://github.com/InternLM/InternLM), Xunfei [Xinghuo](https://xinghuo.xfyun.cn/), [LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), Zhifu API, DALLE3
Amélioration, traduction, explication du code | Correction, traduction, recherche d'erreurs de syntaxe dans les articles, explication du code
[Raccourcis personnalisés](https://www.bilibili.com/video/BV14s4y1E7jN) | Prise en charge de raccourcis personnalisés
Conception modulaire | Prise en charge de plugins puissants personnalisables, prise en charge de la [mise à jour à chaud](https://github.com/binary-husky/gpt_academic/wiki/函数插件指南) des plugins
[Analyse de programme](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin] Analyse en profondeur d'un arbre de projets Python/C/C++/Java/Lua/... d'un simple clic ou [auto-analyse](https://www.bilibili.com/video/BV1cj411A7VW)
Lecture d'articles, traduction d'articles | [Plugin] Lecture automatique des articles LaTeX/PDF et génération du résumé
Traduction complète de [LaTeX](https://www.bilibili.com/video/BV1nk4y1Y7Js/) ou amélioration de leur qualité | [Plugin] Traduction ou amélioration rapide des articles LaTeX
Génération de commentaires en masse | [Plugin] Génération facile de commentaires de fonctions
Traduction [chinois-anglais](https://www.bilibili.com/video/BV1yo4y157jV/) du Markdown | [Plugin] Avez-vous vu le [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) dans les cinq langues ci-dessus ?
Génération de rapports d'analyse du chat | [Plugin] Génération automatique d'un rapport récapitulatif après l'exécution du chat
[Fonction de traduction complète des articles PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin] Extraction du titre et du résumé d'un article PDF, ainsi que traduction intégrale (multithreading)
Assistant Arxiv | [Plugin] Saisissez l'URL d'un article Arxiv pour traduire automatiquement le résumé et télécharger le PDF
Correction automatique d'articles LaTeX | [Plugin] Correction de la grammaire, de l'orthographe et comparaison avec le PDF correspondant, à la manière de Grammarly
Assistant Google Scholar | [Plugin] Donner l'URL d'une page de recherche Google Scholar pour obtenir de l'aide sur l'écriture des références
Agrégation d'informations sur Internet + GPT | [Plugin] Obtenez les informations de l'Internet pour répondre aux questions à l'aide de GPT, afin que les informations ne soient jamais obsolètes
⭐Traduction détaillée des articles Arxiv ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [Plugin] Traduction de haute qualité d'articles Arxiv en un clic, le meilleur outil de traduction d'articles à ce jour
⭐[Saisie orale en temps réel](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [Plugin] Écoute asynchrone de l'audio, découpage automatique et recherche automatique du meilleur moment pour répondre
Affichage des formules, images, tableaux | Affichage simultané de la forme [TeX et rendue](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png) des formules, prise en charge de la mise en évidence des formules et du code
⭐Plugin AutoGen multi-agents | [Plugin] Explorez les émergences intelligentes à plusieurs agents avec Microsoft AutoGen !
Activation du [thème sombre](https://github.com/binary-husky/gpt_academic/issues/173) | Ajouter ```/?__theme=dark``` à l'URL du navigateur pour basculer vers le thème sombre
Prise en charge de plusieurs modèles LLM | Expérimentez avec GPT 3.5, GPT4, [ChatGLM2 de Tsinghua](https://github.com/THUDM/ChatGLM2-6B), [MOSS de Fudan](https://github.com/OpenLMLab/MOSS) simultanément !
⭐Modèle ChatGLM2 fine-tuned | Chargez et utilisez un modèle fine-tuned de ChatGLM2, disponible avec un plugin d'assistance
Prise en charge de plus de modèles LLM, déploiement sur [Huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Ajout de l'interface de connaissance-API, support de [LLaMA](https://github.com/facebookresearch/llama) et [PanGuα](https://openi.org.cn/pangu/)
⭐Paquet pip [void-terminal](https://github.com/binary-husky/void-terminal) | Accédez à toutes les fonctions et plugins de ce projet directement depuis Python (en cours de développement)
⭐Plugin terminal du vide | [Plugin] Utilisez un langage naturel pour interagir avec les autres plugins du projet
Affichage de nouvelles fonctionnalités (génération d'images, etc.) …… | Voir à la fin de ce document ……
</div>
- Nouvelle interface (modifiez l'option LAYOUT dans `config.py` pour basculer entre la disposition "gauche-droite" et "haut-bas")
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
</div>
- Tous les boutons sont générés dynamiquement en lisant `functional.py`, vous pouvez donc ajouter de nouvelles fonctionnalités personnalisées et libérer le presse-papiers.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- Retouche/correction
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- If the output contains formulas, they will be displayed in both tex and rendered forms for easy copying and reading.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Don't feel like looking at the project code? Just give it to ChatGPT to show off.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Multiple large language models are mixed and used together (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4).
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
# Installation
### Method I: Run directly (Windows, Linux, or MacOS)
1. Download the project
```sh
git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configure API_KEY
In `config.py`, configure the API KEY and other settings. [Click here to see methods for special network environment configurations](https://github.com/binary-husky/gpt_academic/issues/1). [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
「 The program will first check if there is a confidential configuration file named `config_private.py`, and use the configurations in that file to override the corresponding configurations in `config.py`. If you understand this logic, we strongly recommend creating a new configuration file named `config_private.py` right next to `config.py`, and move (copy) the configurations from `config.py` to `config_private.py` (only copy the configurations that you have modified). 」
「 You can also configure the project using `environment variables`. The format of the environment variables can be found in the `docker-compose.yml` file or on our [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明). The priority of configuration reading is: `environment variables` > `config_private.py` > `config.py`. 」
3. Install dependencies
```sh
# (Option I: If you are familiar with Python, python>=3.9) Note: Use the official pip source or the Ali pip source. Temporary change of source method: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Option II: Use Anaconda) The steps are similar (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # Create an anaconda environment
conda activate gptac_venv # Activate the anaconda environment
python -m pip install -r requirements.txt # This step is the same as the pip installation step
```
<details><summary>If you need to support Tsinghua ChatGLM2/Fudan MOSS/RWKV as backends, click here to expand</summary>
<p>
[Optional Steps] If you need to support Tsinghua ChatGLM2/Fudan MOSS as backends, you need to install additional dependencies (Prerequisites: Familiar with Python + Have used PyTorch + Sufficient computer configuration):
```sh
# [Optional Step I] Support Tsinghua ChatGLM2. Comment on this note: If you encounter the error "Call ChatGLM generated an error and cannot load the parameters of ChatGLM", refer to the following: 1: The default installation is the torch+cpu version. To use cuda, you need to uninstall torch and reinstall torch+cuda; 2: If the model cannot be loaded due to insufficient computer configuration, you can modify the model precision in request_llm/bridge_chatglm.py. Change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).
python -m pip install -r request_llms/requirements_chatglm.txt
# [Optional Step II] Support Fudan MOSS
python -m pip install -r request_llms/requirements_moss.txt
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # Note: You need to be at the root directory of the project when executing this line of code
# [Optional Step III] Support RWKV Runner
Refer to the wiki: https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
# [Optional Step IV] Make sure that the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. The currently supported models are as follows (jittorllms series currently only support the docker solution):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Run
```sh
python main.py
```
### Method II: Use Docker
0. Deploy all capabilities of the project (this is a large image that includes cuda and latex. But if you have a slow internet speed or a small hard drive, it is not recommended to use this)
``` sh
# Modify the docker-compose.yml file, keep scheme 0 and delete the other schemes. Then run:
docker-compose up
```
1. ChatGPT + Wenxin Yiyu + Spark and other online models (recommended for most people)
``` sh
# Modify the docker-compose.yml file, keep scheme 1 and delete the other schemes. Then run:
docker-compose up
```
NOTE: If you need Latex plugin functionality, please refer to the Wiki. Additionally, you can also use scheme 4 or scheme 0 directly to obtain Latex functionality.
2. ChatGPT + ChatGLM2 + MOSS + LLAMA2 + Tongyi Qianwen (requires familiarity with [Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian) runtime)
``` sh
# Modify the docker-compose.yml file, keep scheme 2 and delete the other schemes. Then run:
docker-compose up
```
### Method III: Other deployment methods
1. **One-click run script for Windows**.
Windows users who are completely unfamiliar with the Python environment can download the one-click run script without local models from the [Release](https://github.com/binary-husky/gpt_academic/releases) section.
The script was contributed by [oobabooga](https://github.com/oobabooga/one-click-installers).
2. Use third-party APIs, Azure, Wenxin Yiyu, Xinghuo, etc., see the [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
3. Pitfall guide for deploying on cloud servers.
Please visit the [cloud server remote deployment wiki](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97).
4. Some new deployment platforms or methods
- Use Sealos [one-click deployment](https://github.com/binary-husky/gpt_academic/issues/993).
- Use WSL2 (Windows Subsystem for Linux). Please visit the [deployment wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
- How to run under a subpath (such as `http://localhost/subpath`). Please see [FastAPI running instructions](docs/WithFastapi.md)
# Utilisation avancée
### I: Personnalisation des nouveaux boutons d'accès rapide (raccourcis académiques)
Ouvrez `core_functional.py` avec n'importe quel éditeur de texte, ajoutez les entrées suivantes, puis redémarrez le programme. (Si le bouton existe déjà, le préfixe et le suffixe peuvent être modifiés à chaud sans redémarrer le programme).
Par exemple:
```
"Traduction avancée de l'anglais vers le français": {
# Préfixe, ajouté avant votre saisie. Par exemple, utilisez-le pour décrire votre demande, telle que la traduction, l'explication du code, l'amélioration, etc.
"Prefix": "Veuillez traduire le contenu suivant en français, puis expliquer chaque terme propre à la langue anglaise utilisé dans le texte à l'aide d'un tableau markdown : \n\n",
# Suffixe, ajouté après votre saisie. Par exemple, en utilisant le préfixe, vous pouvez entourer votre contenu par des guillemets.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
### II: Personnalisation des plugins de fonction
Écrivez de puissants plugins de fonction pour accomplir toutes les tâches que vous souhaitez ou ne pouvez pas imaginer.
Le développement et le débogage de ces plugins dans ce projet sont très faciles. Tant que vous avez des connaissances de base en python, vous pouvez implémenter vos propres fonctionnalités grâce à notre modèle fourni.
Veuillez consulter le [Guide des plugins de fonction](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) pour plus de détails.
# Mises à jour
### I: Dynamique
1. Fonction de sauvegarde de conversation. Appelez `Enregistrer la conversation en cours` dans la zone des plugins fonctionnels pour enregistrer la conversation en cours sous la forme d'un fichier HTML lisible et récupérable. En outre, appelez `Charger les archives de conversation` dans la zone des plugins fonctionnels (menu déroulant) pour restaurer les conversations précédentes.
Astuce: Si aucun fichier n'est spécifié, cliquez directement sur `Charger les archives de conversation` pour afficher le cache des archives HTML.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. ⭐ Fonction de traduction des articles Latex/Arxiv ⭐
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
</div>
3. Terminal du néant (comprendre l'intention de l'utilisateur à partir de la saisie en langage naturel et appeler automatiquement d'autres plugins)
- Étape 1: Saisissez "Veuillez appeler le plugin de traduction pour le document PDF, l'URL est https://openreview.net/pdf?id=rJl0r3R9KX".
- Étape 2 : Cliquez sur "Terminal du néant".
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
</div>
4. Conception de fonctionnalités modulaires, une interface simple peut prendre en charge des fonctionnalités puissantes
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
5. Traduction et interprétation d'autres projets open-source
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
</div>
6. Fonctionnalités supplémentaires intégrant [live2d](https://github.com/fghrsh/live2d_demo) (désactivé par défaut, nécessite des modifications dans `config.py`)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. Génération d'images par OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
8. Analyse et résumé audio par OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
9. Vérification et correction orthographique complète du document en Latex
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
</div>
10. Changement de langue et de thème
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
</div>
### II: Versions:
- version 3.70(tâche à accomplir) : Optimisation de la fonction AutoGen et création d'une série de plugins dérivés
- version 3.60 : Introduction d'AutoGen comme base des nouveaux plugins
- version 3.57 : Prise en charge de GLM3, Starlight v3, Zen v4 et correction de l'incompatibilité des modèles locaux
- version 3.56 : Possibilité d'ajouter dynamiquement des boutons de fonction de base et nouvelle page de synthèse des PDF
- version 3.55: Refonte de l'interface utilisateur avec fenêtres flottantes et barre de menu
- version 3.54 : Nouvel interpréteur de code dynamique (Code Interpreter) (à améliorer)
- version 3.53 : Possibilité de choisir dynamiquement différents thèmes d'interface, amélioration de la stabilité et résolution des problèmes de conflit entre utilisateurs multiples
- version 3.50 : Utiliser le langage naturel pour appeler toutes les fonctions du projet (Terminal du néant), prise en charge de la classification des plugins, amélioration de l'interface utilisateur, conception de nouveaux thèmes
- version 3.49 : Prise en charge de Baidu Qianfan et Xiaomi-Wenyiyan
- version 3.48 : Prise en charge d'Ali-DA, Shanghai AI-Lab-Shusheng et Xunfei Xinghuo
- version 3.46 : Prise en charge de la conversation audio temps réel sans intervention
- version 3.45 : Prise en charge de la personnalisation du modèle ChatGLM2
- version 3.44 : Prise en charge officielle d'Azure, amélioration de l'utilisabilité de l'interface
- version 3.4 : +traduction complète des articles Arxiv, +correction des articles Latex
- version 3.3 : +fonction d'intégration d'informations Internet
- version 3.2 : Les plugins de fonction prennent en charge plus de paramètres (fonction d'enregistrement de conversation, débogage de code de n'importe quel langage + demandes d'LLM arbitraires)
- version 3.1 : Prise en charge de l'interrogation simultanée de plusieurs modèles gpt ! Prise en charge de l'API2D, répartition de charge entre plusieurs clés API
- version 3.0 : Prise en charge de chatglm et d'autres petits llm
- version 2.6 : Refonte de la structure des plugins, amélioration de l'interactivité, ajout de nouveaux plugins
- version 2.5 : Auto-mise à jour, résolution des problèmes de dépassement de longueur de texte et de jeton pendant la consolidation de grands projets de codes sources
- version 2.4 : (1) Nouvelle fonctionnalité de traduction complète des documents PDF ; (2) Nouvelle fonctionnalité de changement de position de la zone de saisie ; (3) Nouvelle option de disposition verticale ; (4) Optimisation des plugins de fonction multithreads.
- version 2.3 : Amélioration de l'interactivité multi-threads
- version 2.2 : Prise en charge du rechargement à chaud des plugins de fonction
- version 2.1 : Mise en page pliable
- version 2.0 : Introduction de plugins de fonction modulaires
- version 1.0: Fonctionnalités de base
Groupe QQ des développeurs de GPT Academic: `610599535`
- Problèmes connus
- Certains plugins de traduction de navigateurs peuvent nuire au fonctionnement de l'interface utilisateur de ce logiciel.
- Gradio officiel a actuellement de nombreux bugs de compatibilité. Veuillez utiliser `requirement.txt` pour installer Gradio.
### III: Thèmes
Vous pouvez modifier le thème en modifiant l'option `THEME` (config.py).
1. `Chuanhu-Small-and-Beautiful` [Lien](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
### IV: Branches de développement de ce projet
1. Branche `master` : Branche principale, version stable
2. Branche `frontier` : Branche de développement, version de test
### V: Références et apprentissage
```
De nombreux designs de codes de projets exceptionnels ont été référencés dans le développement de ce projet, sans ordre spécifique :
# ChatGLM2-6B de l'Université Tsinghua:
https://github.com/THUDM/ChatGLM2-6B
# JittorLLMs de l'Université Tsinghua:
https://github.com/Jittor/JittorLLMs
# ChatPaper :
https://github.com/kaixindelele/ChatPaper
# Edge-GPT :
https://github.com/acheong08/EdgeGPT
# ChuanhuChatGPT :
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Oobabooga installeur en un clic :
https://github.com/oobabooga/one-click-installers
# Plus：
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo

364
docs/README.German.md Normal file
View File

@ -0,0 +1,364 @@
> **Hinweis**
>
> Dieses README wurde mithilfe der GPT-Übersetzung (durch das Plugin dieses Projekts) erstellt und ist nicht zu 100 % zuverlässig. Bitte überprüfen Sie die Übersetzungsergebnisse sorgfältig.
>
> 7. November 2023: Beim Installieren der Abhängigkeiten bitte nur die in der `requirements.txt` **angegebenen Versionen** auswählen. Installationsbefehl: `pip install -r requirements.txt`.
# <div align=center><img src="logo.png" width="40"> GPT Academic (GPT Akademisch)</div>
**Wenn Ihnen dieses Projekt gefällt, geben Sie ihm bitte einen Star. Wenn Sie praktische Tastenkombinationen oder Plugins entwickelt haben, sind Pull-Anfragen willkommen!**
Wenn Ihnen dieses Projekt gefällt, geben Sie ihm bitte einen Star.
Um dieses Projekt mit GPT in eine beliebige Sprache zu übersetzen, lesen Sie [`multi_language.py`](multi_language.py) (experimentell).
> **Hinweis**
>
> 1. Beachten Sie bitte, dass nur die mit **hervorgehobenen** Plugins (Schaltflächen) Dateien lesen können. Einige Plugins befinden sich im **Drop-down-Menü** des Plugin-Bereichs. Außerdem freuen wir uns über jede neue Plugin-PR mit **höchster Priorität**.
>
> 2. Die Funktionen jeder Datei in diesem Projekt sind im [Selbstanalysebericht `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT-Academic-Selbstanalysebericht) ausführlich erläutert. Sie können jederzeit auf die relevanten Funktions-Plugins klicken und GPT aufrufen, um den Selbstanalysebericht des Projekts neu zu generieren. Häufig gestellte Fragen finden Sie im [`Wiki`](https://github.com/binary-husky/gpt_academic/wiki). [Standardinstallationsmethode](#installation) | [Ein-Klick-Installationsskript](https://github.com/binary-husky/gpt_academic/releases) | [Konfigurationsanleitung](https://github.com/binary-husky/gpt_academic/wiki/Projekt-Konfigurationsanleitung).
>
> 3. Dieses Projekt ist kompatibel mit und unterstützt auch die Verwendung von inländischen Sprachmodellen wie ChatGLM. Die gleichzeitige Verwendung mehrerer API-Schlüssel ist möglich, indem Sie sie in der Konfigurationsdatei wie folgt angeben: `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. Wenn Sie den `API_KEY` vorübergehend ändern möchten, geben Sie vorübergehend den temporären `API_KEY` im Eingabebereich ein und drücken Sie die Eingabetaste, um die Änderung wirksam werden zu lassen.
<div align="center">
Funktionen (⭐= Kürzlich hinzugefügte Funktion) | Beschreibung
--- | ---
⭐[Neues Modell integrieren](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)! | Baidu [Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu) und Wenxin Yanyi, [Tongyi Qianwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), Shanghai AI-Lab [Shusheng](https://github.com/InternLM/InternLM), Xunfei [Xinghuo](https://xinghuo.xfyun.cn/), [LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), Cognitive Graph API, DALLE3
Verfeinern, Übersetzen, Codierung erläutern | Ein-Klick-Verfeinerung, Übersetzung, Suche nach grammatikalischen Fehlern in wissenschaftlichen Arbeiten, Erklärung von Code
[Eigene Tastenkombinationen](https://www.bilibili.com/video/BV14s4y1E7jN) definieren | Eigene Tastenkombinationen definieren
Modulare Gestaltung | Ermöglicht die Verwendung benutzerdefinierter leistungsstarker [Plugins](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions), Plugins unterstützen [Hot-Reload](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)
[Programmanalyse](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin] Ermöglicht die Erstellung einer Projekthierarchie für Python/C/C++/Java/Lua/... mit nur einem Klick oder [Selbstanalyse](https://www.bilibili.com/video/BV1cj411A7VW)
Lesen von Forschungsarbeiten, Übersetzen von Forschungsarbeiten | [Plugin] Ermöglicht eine Umwandlung des gesamten Latex-/PDF-Forschungspapiers mit nur einem Klick und generiert eine Zusammenfassung
Latex-Übersetzung des vollständigen Textes, Ausbesserung | [Plugin] Ermöglicht eine Übersetzung oder Verbesserung der Latex-Forschungsarbeit mit nur einem Klick
Erzeugen von Batch-Anmerkungen | [Plugin] Erzeugt Funktionserläuterungen in Stapeln
Markdown- [En-De-Übersetzung](https://www.bilibili.com/video/BV1yo4y157jV/) | [Plugin] Haben Sie die [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) in den oben genannten 5 Sprachen gesehen?
Erzeugen eines Chat-Analyseberichts | [Plugin] Generiert einen zusammenfassenden Bericht nach der Ausführung
PDF-Textübersetzungsmerkmal | [Plugin] Extrahiert Titel und Zusammenfassung des PDF-Dokuments und übersetzt den vollständigen Text (mehrfädig)
Arxiv-Assistent | [Plugin] Geben Sie die URL eines Arxiv-Artikels ein, um eine Zusammenfassung zu übersetzen und die PDF-Datei herunterzuladen
Automatische Überprüfung von Latex-Artikeln | [Plugin] Überprüft die Grammatik und Rechtschreibung von Latex-Artikeln nach dem Vorbild von Grammarly und generiert eine PDF-Vergleichsdatei
Google Scholar Integration Assistant | [Plugin] Geben Sie eine beliebige URL der Google Scholar-Suchseite ein und lassen Sie GPT Ihre [Verwandten Arbeiten](https://www.bilibili.com/video/BV1GP411U7Az/) schreiben
Internetinformationsaggregation + GPT | [Plugin] Ermöglicht es GPT, Fragen durch das Durchsuchen des Internets zu beantworten und Informationen immer auf dem neuesten Stand zu halten
⭐Feine Übersetzung von Arxiv-Artikeln ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [Plugin] Übersetzt Arxiv-Artikel [mit hoher Qualität](https://www.bilibili.com/video/BV1dz4y1v77A/) mit einem Klick - das beste Übersetzungstool für wissenschaftliche Artikel
⭐[Echtzeit-Spracheingabe](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [Plugin] [Asynchrones Lauschen auf Audio-Eingabe](https://www.bilibili.com/video/BV1AV4y187Uy/), automatisches Zerschneiden des Textes, automatische Suche nach dem richtigen Zeitpunkt zur Beantwortung
Darstellen von Formeln/Bildern/Tabellen | Zeigt Formeln sowohl in [TEX-](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)- als auch in gerenderten Formen an, unterstützt Formeln und Code-Hervorhebung
⭐AutoGen Multi-Agent Plugin | [Plugin] Erforscht die Möglichkeiten des emergenten Verhaltens von Multi-Agent-Systemen mit Microsoft AutoGen!
Start im Dark-Theme | Um das Dark-Theme zu aktivieren, fügen Sie ```/?__theme=dark``` am Ende der URL im Browser hinzu
[Mehrsprachige LLM-Modelle](https://www.bilibili.com/video/BV1wT411p7yf) unterstützt | Es ist sicherlich beeindruckend, von GPT3.5, GPT4, [ChatGLM2 der Tsinghua University](https://github.com/THUDM/ChatGLM2-6B), [MOSS der Fudan University](https://github.com/OpenLMLab/MOSS) bedient zu werden, oder?
⭐ChatGLM2 Feinabstimmungsmodell | Unterstützt das Laden von ChatGLM2-Feinabstimmungsmodellen und bietet Unterstützung für ChatGLM2-Feinabstimmungsassistenten
Integration weiterer LLM-Modelle, Unterstützung von [Huggingface-Deployment](https://huggingface.co/spaces/qingxu98/gpt-academic) | Hinzufügen der Newbing-Schnittstelle (neues Bing), Einführung der [Jittorllms der Tsinghua University](https://github.com/Jittor/JittorLLMs) zur Unterstützung von LLaMA und PanGu Alpha
⭐[void-terminal](https://github.com/binary-husky/void-terminal) Pip-Paket | Verwenden Sie das Projekt in Python direkt, indem Sie das gesamte Funktionsplugin verwenden (in Entwicklung)
⭐Void-Terminal-Plugin | [Plugin] Verwenden Sie natürliche Sprache, um andere Funktionen dieses Projekts direkt zu steuern
Weitere Funktionen anzeigen (z. B. Bildgenerierung) …… | Siehe das Ende dieses Dokuments ……
</div>
- Neues Interface (Ändern Sie die LAYOUT-Option in der `config.py`, um zwischen "Links-Rechts-Layout" und "Oben-Unten-Layout" zu wechseln)
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
</div>
- Alle Schaltflächen werden dynamisch aus der `functional.py` generiert und ermöglichen das beliebige Hinzufügen benutzerdefinierter Funktionen zur Befreiung der Zwischenablage.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- Überarbeiten/Korrigieren
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- If the output contains formulas, they will be displayed in both tex format and rendering format for easy copying and reading.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Don't want to look at the project code? Show off the whole project directly in chatgpt's mouth.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Multiple large language models mixed calling (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
# Installation
### Installation Method I: Run directly (Windows, Linux or MacOS)
1. Download the project
```sh
git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configure API_KEY
In `config.py`, configure API KEY and other settings, [click to view special network environment configuration methods](https://github.com/binary-husky/gpt_academic/issues/1). [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/Project-Configuration-Instructions).
「 The program will first check if there is a confidential configuration file named `config_private.py` and use its configuration to override the configuration with the same name in `config.py`. If you understand this reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py` and move (copy) the configuration in `config.py` to `config_private.py` (only copy the configuration items that you have modified). 」
「 You can configure the project through `environment variables`. The format of environment variables can refer to the `docker-compose.yml` file or our [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/Project-Configuration-Instructions). The priority of configuration reading is: `environment variables` > `config_private.py` > `config.py`. 」
3. Install dependencies
```sh
# (Option I: if you are familiar with python, python>=3.9) Note: Use the official pip source or Ali pip source, temporary method to change the source: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Option II: Using Anaconda) The steps are similar (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # Create an anaconda environment
conda activate gptac_venv # Activate the anaconda environment
python -m pip install -r requirements.txt # This step is the same as installing with pip
```
<details><summary>If you need support for Tsinghua ChatGLM2/Fudan MOSS/RWKV as backend, please click to expand.</summary>
<p>
[Optional] If you need to support Tsinghua ChatGLM2/Fudan MOSS as the backend, you need to install additional dependencies (Prerequisites: Familiar with Python + Have used PyTorch + Strong computer configuration):
```sh
# [Optional Step I] Support Tsinghua ChatGLM2. Tsinghua ChatGLM note: If you encounter the error "Call ChatGLM fail cannot load ChatGLM parameters normally", refer to the following: 1: The default installation above is torch+cpu version. To use cuda, you need to uninstall torch and reinstall torch+cuda; 2: If you cannot load the model due to insufficient computer configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py. Change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llms/requirements_chatglm.txt
# [Optional Step II] Support Fudan MOSS
python -m pip install -r request_llms/requirements_moss.txt
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # When executing this line of code, you must be in the root path of the project
# [Optional Step III] Support RWKV Runner
Refer to the wiki: https://github.com/binary-husky/gpt_academic/wiki/Support-RWKV-Runner
# [Optional Step IV] Make sure the AVAIL_LLM_MODELS in config.py includes the expected models. The currently supported models are as follows (the jittorllms series only supports the docker solution at present):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Run
```sh
python main.py
```
### Installation Method II: Use Docker
0. Deploy all capabilities of the project (this is a large image that includes cuda and latex. But if you have a slow internet speed or a small hard drive, it is not recommended to use this)
[![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
``` sh
# Modify docker-compose.yml, keep solution 0 and delete other solutions. Then run:
docker-compose up
```
1. ChatGPT + Wenxin's words + spark and other online models (recommended for most people)
[![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
[![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
``` sh
# Modify docker-compose.yml, keep solution 1 and delete other solutions. Then run:
docker-compose up
```
P.S. If you need the Latex plugin functionality, please refer to the Wiki. Also, you can directly use solution 4 or 0 to get the Latex functionality.
2. ChatGPT + ChatGLM2 + MOSS + LLAMA2 + Thousand Questions (Requires familiarity with [Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian) runtime)
[![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
``` sh
# Modify docker-compose.yml, keep solution 2 and delete other solutions. Then run:
docker-compose up
```
### Installation Method III: Other Deployment Methods
1. **Windows One-Click Script**.
Windows users who are completely unfamiliar with the python environment can download the one-click script for installation without local models in the published [Release](https://github.com/binary-husky/gpt_academic/releases).
The script is contributed by [oobabooga](https://github.com/oobabooga/one-click-installers).
2. Use third-party APIs, Azure, Wenxin's words, Spark, etc., see [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/Project-Configuration-Instructions)
3. Pit avoidance guide for cloud server remote deployment.
Please visit the [Cloud Server Remote Deployment Wiki](https://github.com/binary-husky/gpt_academic/wiki/Cloud-Server-Remote-Deployment-Guide)
4. Some new deployment platforms or methods
- Use Sealos [one-click deployment](https://github.com/binary-husky/gpt_academic/issues/993).
- Use WSL2 (Windows Subsystem for Linux). Please visit the [deployment wiki-2](https://github.com/binary-husky/gpt_academic/wiki/Deploy-on-Windows-Subsystem-for-Linux-WSL2)
- How to run under a subpath (such as `http://localhost/subpath`). Please visit [FastAPI Running Instructions](docs/WithFastapi.md)
# Fortgeschrittene Nutzung
### I: Benutzerdefinierte Tasten hinzufügen (akademische Hotkeys)
Öffnen Sie die Datei `core_functional.py` mit einem beliebigen Texteditor und fügen Sie folgenden Eintrag hinzu. Starten Sie dann das Programm neu. (Wenn die Schaltfläche bereits vorhanden ist, können sowohl das Präfix als auch das Suffix schnell geändert werden, ohne dass das Programm neu gestartet werden muss.)
Beispiel:
```
"Übersetzung von Englisch nach Chinesisch": {
# Präfix, wird vor Ihrer Eingabe hinzugefügt. Zum Beispiel, um Ihre Anforderungen zu beschreiben, z.B. Übersetzen, Code erklären, verbessern usw.
"Präfix": "Bitte übersetzen Sie den folgenden Abschnitt ins Chinesische und erklären Sie dann jedes Fachwort in einer Markdown-Tabelle:\n\n",
# Suffix, wird nach Ihrer Eingabe hinzugefügt. Zum Beispiel, um Ihre Eingabe in Anführungszeichen zu setzen.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
### II: Benutzerdefinierte Funktionsplugins
Schreiben Sie leistungsstarke Funktionsplugins, um beliebige Aufgaben zu erledigen, die Sie wünschen oder nicht erwartet haben.
Das Erstellen und Debuggen von Plugins in diesem Projekt ist einfach und erfordert nur Grundkenntnisse in Python. Sie können unser bereitgestelltes Template verwenden, um Ihre eigene Plugin-Funktion zu implementieren.
Weitere Informationen finden Sie in der [Plugin-Anleitung](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).
# Aktualisierungen
### I: Neuigkeiten
1. Dialogspeicherungsfunktion. Rufen Sie im Funktionspluginbereich "Aktuellen Dialog speichern" auf, um den aktuellen Dialog als lesbare und wiederherstellbare HTML-Datei zu speichern.
Darüber hinaus können Sie im Funktionspluginbereich (Dropdown-Menü) "Dialoghistorie laden" aufrufen, um frühere Sitzungen wiederherzustellen.
Tipp: Wenn kein Dateiname angegeben ist, können Sie direkt auf "Dialoghistorie laden" klicken, um den Verlauf des HTML-Archivs anzuzeigen.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. ⭐ Latex/Arxiv-Papierübersetzungsfunktion ⭐
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
</div>
3. Leere Terminaloberfläche (Verständnis der Benutzerabsicht und automatischer Aufruf anderer Plugins aus natürlicher Spracheingabe)
- Schritt 1: Geben Sie "Bitte Plugin aufrufen, um das PDF-Papier zu übersetzen, dessen Adresse https://openreview.net/pdf?id=rJl0r3R9KX ist" ein.
- Schritt 2: Klicken Sie auf "Leere Terminaloberfläche".
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
</div>
4. Modulare Funktionsgestaltung mit einfacher Schnittstelle für leistungsstarke Funktionen
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
5. Übersetzung und Lösung anderer Open-Source-Projekte
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
</div>
6. Funktionen zur Dekoration von [live2d](https://github.com/fghrsh/live2d_demo) (standardmäßig deaktiviert, config.py muss geändert werden)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-677397d7-abe6-4358-9bef-3c14a7041b59.png" width="500" >
</div>
7. OpenAI-Bildgenerierung
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
8. OpenAI-Audioanalyse und Zusammenfassung
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
9. Latex-Volltextkorrektur
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
</div>
10. Sprach- und Themenwechsel
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
</div>
### II: Versionen:
- Version 3.70 (ausstehend): Optimierung des AutoGen-Plugin-Themas und Entwicklung einer Reihe von abgeleiteten Plugins
- Version 3.60: Einführung von AutoGen als Grundlage für neue Plugin-Generation
- Version 3.57: Unterstützung von GLM3, SparkV3, WenxinYiyanV4, Behebung von Problemen bei gleichzeitiger Verwendung von lokalen Modellen
- Version 3.56: Dynamische Hinzufügung von Basisfunktionsbuttons, neue Übersichtsseite für PDFs
- Version 3.55: Überarbeitung der Benutzeroberfläche, Hinzufügung von Schwebefenstern und Menüleiste
- Version 3.54: Neuer dynamischer Code interpretier (Code Interpreter) (unfertig)
- Version 3.53: Unterstützung für dynamische Auswahl verschiedener Oberflächenthemen, Verbesserung der Stabilität und Behebung von Mehrbenutzerkonflikten
- Version 3.50: Verwenden Sie natürliche Sprache, um alle Funktionen dieses Projekts aufzurufen (leeres Terminal), Unterstützung für Plugin-Kategorien, verbesserte Benutzeroberfläche, neue Themen
- Version 3.49: Unterstützung für Baidu Qianfan Platform und WenxinYiyan
- Version 3.48: Unterstützung für Alibaba Damo Academy Tongyi Qianwen, Shanghai AI-Lab Shusheng, Xunfei Spark
- Version 3.46: Vollständig automatisierter Echtzeit-Sprachdialog
- Version 3.45: Anpassbare ChatGLM2-Feinjustierung
- Version 3.44: Offizielle Unterstützung für Azure, Verbesserung der Benutzerfreundlichkeit der Benutzeroberfläche
- Version 3.4: Hinzufügen von Arxiv-Papierübersetzung, LaTeX-Papierkorrektur
- Version 3.3: Hinzufügen von Internet-Informationen
- Version 3.2: Funktionsplugins unterstützen weitere Parameter (Dialog speichern, beliebigen Code analysieren und nach beliebigen LLM-Kombinationen fragen)
- Version 3.1: Unterstützung für die gleichzeitige Abfrage mehrerer GPT-Modelle! Unterstützung für API-Schlüssel-Lastenausgleich
- Version 3.0: Unterstützung von ChatGLM und anderen kleinen LLMs
- Version 2.6: Neugestaltung der Plugin-Struktur, Verbesserung der Interaktivität, Hinzufügen weiterer Plugins
- Version 2.5: Auto-Update zur Lösung von Problemen mit zu langem Text oder Tokenüberschuss beim Zusammenfassen von Code
- Version 2.4: (1) Hinzufügen der Funktion zur Übersetzung des vollständigen PDF-Texts; (2) Neues Feature zum Wechseln der Position des Eingabebereichs; (3) Hinzufügen der Option für eine vertikale Ausrichtung; (4) Verbesserung der Multithreading-Funktionen von Plugins.
- Version 2.3: Verbesserte Multithreading-Interaktivität
- Version 2.2: Funktionsplugins können heiß neu geladen werden
- Version 2.1: Faltbare Layouts
- Version 2.0: Einführung modularer Funktionsplugins
- Version 1.0: Grundfunktionen
Entwickler-QQ-Gruppe von GPT Academic: `610599535`
- Bekannte Probleme
- Einige Browserübersetzungsplugins beeinflussen die Frontend-Ausführung dieser Software
- Die offizielle Version von Gradio hat derzeit viele Kompatibilitätsprobleme. Installieren Sie Gradio daher unbedingt über `requirement.txt`.
### III: Themen
Sie können das Theme ändern, indem Sie die Option `THEME` (config.py) ändern.
1. `Chuanhu-Small-and-Beautiful` [Link](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
### IV: Entwicklungszweige dieses Projekts
1. `master` Branch: Hauptzweig, stabile Version
2. `frontier` Branch: Entwicklungsbranch, Testversion
### V: Referenzen und Lernen
```
Der Code basiert auf dem Design anderer herausragender Projekte. Die Reihenfolge ist beliebig:
# ChatGLM2-6B von Tsinghua:
https://github.com/THUDM/ChatGLM2-6B
# JittorLLMs von Tsinghua:
https://github.com/Jittor/JittorLLMs
# ChatPaper:
https://github.com/kaixindelele/ChatPaper
# Edge-GPT:
https://github.com/acheong08/EdgeGPT
# ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Oobabooga One-Click-Installations:
https://github.com/oobabooga/one-click-installers
# Weitere:
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo

361
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@ -0,0 +1,361 @@
> **Nota**
>
> Questo README è stato tradotto da GPT (implementato da un plugin di questo progetto) e non è al 100% affidabile, per favore valuta attentamente i risultati della traduzione.
>
> 2023.11.7: Quando installi le dipendenze, seleziona le versioni **specificate** nel file `requirements.txt`. Comando di installazione: `pip install -r requirements.txt`.
# <div align=center><img src="logo.png" width="40"> GPT Ottimizzazione Accademica (GPT Academic)</div>
**Se ti piace questo progetto, per favore dagli una stella; se hai idee o plugin utili, fai una pull request!**
Se ti piace questo progetto, dagli una stella.
Per tradurre questo progetto in qualsiasi lingua con GPT, leggi ed esegui [`multi_language.py`](multi_language.py) (sperimentale).
> **Nota**
>
> 1. Fai attenzione che solo i plugin (pulsanti) **evidenziati** supportano la lettura dei file, alcuni plugin si trovano nel **menu a tendina** nell'area dei plugin. Inoltre, accogliamo e gestiamo con **massima priorità** qualsiasi nuovo plugin attraverso pull request.
>
> 2. Le funzioni di ogni file in questo progetto sono descritte in dettaglio nel [rapporto di traduzione automatica del progetto `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). Con l'iterazione della versione, puoi anche fare clic sui plugin delle funzioni rilevanti in qualsiasi momento per richiamare GPT e rigenerare il rapporto di auto-analisi del progetto. Domande frequenti [`wiki`](https://github.com/binary-husky/gpt_academic/wiki) | [Metodo di installazione standard](#installazione) | [Script di installazione one-click](https://github.com/binary-husky/gpt_academic/releases) | [Configurazione](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
>
> 3. Questo progetto è compatibile e incoraggia l'uso di modelli di linguaggio di grandi dimensioni nazionali, come ChatGLM. Supporto per la coesistenza di più chiavi API, puoi compilare nel file di configurazione come `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. Quando è necessario sostituire temporaneamente `API_KEY`, inserisci temporaneamente `API_KEY` nell'area di input e premi Invio per confermare.
<div align="center">
Funzionalità (⭐ = Nuove funzionalità recenti) | Descrizione
--- | ---
⭐[Integrazione di nuovi modelli](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | Baidu [Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu) e [Wenxin](https://cloud.baidu.com/doc/GUIDE/5268.9) Intelligence, [Tongyi Qianwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), Shanghai AI-Lab [bookbrain](https://github.com/InternLM/InternLM), Xunfei [Xinghuo](https://xinghuo.xfyun.cn/), [LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), Zhipu API, DALLE3
Revisione, traduzione, spiegazione del codice | Revisione, traduzione, ricerca errori grammaticali nei documenti e spiegazione del codice con un clic
[Tasti di scelta rapida personalizzati](https://www.bilibili.com/video/BV14s4y1E7jN) | Supporta tasti di scelta rapida personalizzati
Design modulare | Supporto per plugin personalizzati potenti, i plugin supportano l'[aggiornamento in tempo reale](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)
[Analisi del codice](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin] Un clic per analizzare alberi di progetti Python/C/C++/Java/Lua/... o [autoanalisi](https://www.bilibili.com/video/BV1cj411A7VW)
Lettura di documenti, traduzione di documenti | [Plugin] Un clic per interpretare documenti completi in latex/pdf e generare un riassunto
Traduzione completa di testi in Latex, revisione completa di testi in Latex | [Plugin] Un clic per tradurre o correggere documenti in latex
Generazione automatica di commenti in batch | [Plugin] Un clic per generare commenti di funzione in batch
Traduzione [cinese-inglese](https://www.bilibili.com/video/BV1yo4y157jV/) in Markdown | [Plugin] Hai visto sopra i README in 5 lingue diverse ([Inglese](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md))?
Generazione di rapporti di analisi chat | [Plugin] Genera automaticamente un rapporto di sintesi dopo l'esecuzione
Funzionalità di traduzione di testo completo in PDF | [Plugin] Estrai il titolo e il riassunto dei documenti PDF e traduci tutto il testo (multithreading)
Aiutante per Arxiv | [Plugin] Inserisci l'URL dell'articolo Arxiv per tradurre riassunto e scaricare PDF in un clic
Controllo completo dei documenti in Latex | [Plugin] Rileva errori grammaticali e ortografici nei documenti in Latex simile a Grammarly + Scarica un PDF per il confronto
Assistente per Google Scholar | [Plugin] Dato qualsiasi URL della pagina di ricerca di Google Scholar, fai scrivere da GPT gli *articoli correlati* per te
Concentrazione delle informazioni di Internet + GPT | [Plugin] [Recupera informazioni da Internet](https://www.bilibili.com/video/BV1om4y127ck) utilizzando GPT per rispondere alle domande e rendi le informazioni sempre aggiornate
⭐Traduzione accurata di articoli Arxiv ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [Plugin] [Traduci articoli Arxiv ad alta qualità](https://www.bilibili.com/video/BV1dz4y1v77A/) con un clic, lo strumento di traduzione degli articoli migliore al mondo al momento
⭐[Inserimento della conversazione vocale in tempo reale](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [Plugin] [Ascolta l'audio](https://www.bilibili.com/video/BV1AV4y187Uy/) in modo asincrono, taglia automaticamente le frasi e trova automaticamente il momento giusto per rispondere
Visualizzazione di formule, immagini, tabelle | Mostra contemporaneamente formule in formato tex e renderizzato, supporta formule e evidenziazione del codice
⭐Plugin multi-agente AutoGen | [Plugin] Esplora le possibilità dell'emergenza intelligence multi-agente con l'aiuto di Microsoft AutoGen!
Attiva il tema scuro [qui](https://github.com/binary-husky/gpt_academic/issues/173) | Aggiungi ```/?__theme=dark``` alla fine dell'URL del browser per passare al tema scuro
Supporto di più modelli LLM | Essere servito contemporaneamente da GPT3.5, GPT4, [ChatGLM2 di Tsinghua](https://github.com/THUDM/ChatGLM2-6B), [MOSS di Fudan](https://github.com/OpenLMLab/MOSS)
⭐Modello di fine-tuning ChatGLM2 | Supporto per l'importazione del modello di fine-tuning di ChatGLM2, fornendo plug-in di assistenza per il fine tuning di ChatGLM2
Più supporto per modelli LLM, supporto del [deploy di Huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Aggiungi interfaccia Newbing (Bing Translator), introduce il supporto di [JittorLLMs](https://github.com/Jittor/JittorLLMs) di Tsinghua, supporto per [LLaMA](https://github.com/facebookresearch/llama) e [Panguα](https://openi.org.cn/pangu/)
⭐Pacchetto pip [void-terminal](https://github.com/binary-husky/void-terminal) | Fornisce funzionalità di tutti i plugin di questo progetto direttamente in Python senza GUI (in sviluppo)
⭐Plugin terminale virtuale | [Plugin] Richiama altri plugin di questo progetto utilizzando linguaggio naturale
Altre nuove funzionalità (come la generazione di immagini) ... | Vedi alla fine di questo documento ...
</div>
- Nuovo layout (modifica l'opzione LAYOUT in `config.py` per passare tra "layout sinistra / destra" e "layout sopra / sotto")
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
</div>
- Tutti i pulsanti vengono generati dinamicamente leggendo `functional.py`, puoi aggiungere liberamente funzionalità personalizzate, liberando la clipboard
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- Revisione / correzione
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- Se l'output contiene formule, saranno visualizzate sia in formato tex che in formato renderizzato per facilitarne la copia e la lettura.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Non hai voglia di guardare il codice del progetto? Mostralo direttamente al chatgpt in bocca.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Chiamate miste di modelli di grandi dimensioni (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
# Installazione
### Metodo di installazione I: Esegui direttamente (Windows, Linux o MacOS)
1. Scarica il progetto
```sh
git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configura l'API_KEY
Nel file `config.py`, configura l'API KEY e altre impostazioni, [clicca qui per vedere come configurare l'API in ambienti di rete speciali](https://github.com/binary-husky/gpt_academic/issues/1) . [Pagina Wiki](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
「 Il programma controllerà prima se esiste un file di configurazione privata chiamato `config_private.py` e utilizzerà le configurazioni in esso contenute per sovrascrivere le configurazioni con lo stesso nome in `config.py`. Se comprendi questa logica di lettura, ti consigliamo vivamente di creare un nuovo file di configurazione chiamato `config_private.py` accanto a `config.py` e spostare (copiare) le configurazioni da `config.py` a `config_private.py` (basta copiare le voci di configurazione che hai modificato). 」
「 Supporta la configurazione del progetto tramite `variabili d'ambiente`, il formato di scrittura delle variabili d'ambiente è descritto nel file `docker-compose.yml` o nella nostra [pagina Wiki](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明) priorità di lettura della configurazione: `variabili d'ambiente` > `config_private.py` > `config.py`. 」
3. Installa le dipendenze
```sh
# (Scelta I: Se familiarizzato con python, python>=3.9) Nota: Usa il repository delle fonti ufficiale di pip o Ali pip per temporaneamente cambiare la fonte: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Scelta II: Usa Anaconda) Anche in questo caso, i passaggi sono simili (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # Crea l'ambiente anaconda
conda activate gptac_venv # Attiva l'ambiente anaconda
python -m pip install -r requirements.txt # Questo passaggio è identico alla procedura di installazione con pip
```
<details><summary>Se desideri utilizzare il backend di ChatGLM2 di Tsinghua/Fudan MOSS/RWKV, fai clic per espandere</summary>
<p>
[Optional] Se desideri utilizzare ChatGLM2 di Tsinghua/Fudan MOSS come backend, è necessario installare ulteriori dipendenze (Requisiti: conoscenza di Python + esperienza con Pytorch + hardware potente):
```sh
# [Optional Step I] Supporto per ChatGLM2 di Tsinghua. Note di ChatGLM di Tsinghua: Se si verifica l'errore "Call ChatGLM fail non può caricare i parametri di ChatGLM", fare riferimento a quanto segue: 1: L'installazione predefinita è la versione torch+cpu, per usare cuda è necessario disinstallare torch ed installare nuovamente la versione con torch+cuda; 2: Se il modello non può essere caricato a causa di una configurazione insufficiente, è possibile modificare la precisione del modello in request_llm/bridge_chatglm.py, sostituendo AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) con AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llms/requirements_chatglm.txt
# [Optional Step II] Supporto per Fudan MOSS
python -m pip install -r request_llms/requirements_moss.txt
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # Attenzione: eseguire questo comando nella directory principale del progetto
# [Optional Step III] Supporto per RWKV Runner
Consulta il Wiki: https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
# [Optional Step IV] Assicurati che il file di configurazione config.py includa i modelli desiderati. Di seguito sono elencati i modelli attualmente supportati (gli llm di jittorllms supportano solo la soluzione Docker):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Esegui
```sh
python main.py
```
### Metodo di installazione II: Utilizzo di Docker
0. Installa tutte le funzionalità del progetto (Questo è un'immagine di grandi dimensioni che include cuda e latex. Potrebbe non essere adatta se hai una connessione lenta o uno spazio su disco limitato)
[![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
``` sh
# Modifica il file docker-compose.yml: mantieni solo la configurazione 0 e rimuovi le altre configurazioni. Avvia il seguente comando:
docker-compose up
```
1. ChatGPT + Wenxin Yiyu (Poem) + Spark, solo modelli online (Consigliato per la maggior parte delle persone)
[![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
[![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
``` sh
# Modifica il file docker-compose.yml: mantieni solo la configurazione 1 e rimuovi le altre configurazioni. Avvia il seguente comando:
docker-compose up
```
P.S. Se hai bisogno del plugin LaTeX, consulta la pagina Wiki. In alternativa, puoi utilizzare le configurazioni 4 o 0 direttamente per ottenere questa funzionalità.
2. ChatGPT + ChatGLM2 + MOSS + LLAMA2 + Tongyi Q&W (Richiede conoscenze su Nvidia Docker)
[![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
``` sh
# Modifica il file docker-compose.yml: mantieni solo la configurazione 2 e rimuovi le altre configurazioni. Avvia il seguente comando:
docker-compose up
```
### Metodo di installazione III: Altre opzioni di distribuzione
1. **Script di esecuzione con un clic per Windows**.
Se non conosci affatto l'ambiente python in Windows, puoi scaricare uno script di esecuzione con un clic dalla sezione [Release](https://github.com/binary-husky/gpt_academic/releases) per installare la versione che non richiede modelli locali.
Lo script è stato fornito da [oobabooga](https://github.com/oobabooga/one-click-installers).
2. Utilizzo di API di terze parti, Azure, Wenxin Yiyu (Poem), Xinghuo, ecc. vedi [pagina Wiki](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)
3. Guida all'installazione del server cloud remoto.
Visita la [pagina Wiki sull'installazione del server cloud remoto](https://github.com/binary-husky/gpt_academic/wiki/云服务器远程部署指南).
4. Altre nuove piattaforme o metodi di distribuzione:
- Uso di Sealos per il [deployment con un clic](https://github.com/binary-husky/gpt_academic/issues/993).
- Uso di WSL2 (Windows Subsystem for Linux). Vedi [Guida all'installazione](https://github.com/binary-husky/gpt_academic/wiki/使用WSL2（Windows-Subsystem-for-Linux-子系统）部署) per maggiori informazioni.
- Funzionamento su un sotto-percorso URL (`http://localhost/subpath`). Vedi [istruzioni FastAPI](docs/WithFastapi.md) per maggiori dettagli.
# Utilizzo avanzato
### I: Personalizzare nuovi pulsanti rapidi (tasti di scelta rapida accademici)
Apri `core_functional.py` con qualsiasi editor di testo e aggiungi le seguenti voci, quindi riavvia il programma. (Se il pulsante esiste già, sia il prefisso che il suffisso possono essere modificati a caldo senza la necessità di riavviare il programma.)
Ad esempio,
```
"Traduzione avanzata Cinese-Inglese": {
# Prefisso, sarà aggiunto prima del tuo input. Ad esempio, utilizzato per descrivere la tua richiesta, come traduzione, spiegazione del codice, rifinitura, ecc.
"Prefisso": "Si prega di tradurre il seguente testo in cinese e fornire spiegazione per i termini tecnici utilizzati, utilizzando una tabella in markdown uno per uno:\n\n",
# Suffisso, sarà aggiunto dopo il tuo input. Ad esempio, in combinazione con il prefisso, puoi circondare il tuo input con virgolette.
"Suffisso": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
### II: Plugin di funzioni personalizzate
Scrivi potentissimi plugin di funzioni per eseguire qualsiasi compito che desideri, sia che tu lo pensi o meno.
La scrittura di plugin per questo progetto è facile e richiede solo conoscenze di base di Python. Puoi seguire il [Guida ai Plugin di Funzione](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) per maggiori dettagli.
# Aggiornamenti
### I: Aggiornamenti
1. Funzionalità di salvataggio della conversazione. Chiamare `Salva la conversazione corrente` nell'area del plugin per salvare la conversazione corrente come un file html leggibile e ripristinabile.
Inoltre, nella stessa area del plugin (menu a tendina) chiamare `Carica la cronologia della conversazione` per ripristinare una conversazione precedente.
Suggerimento: fare clic su `Carica la cronologia della conversazione` senza specificare un file per visualizzare la tua cronologia di archiviazione HTML.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. ⭐ Funzionalità di traduzione articoli Latex/Arxiv ⭐
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
</div>
3. Terminale vuoto (Comprensione dell'intento dell'utente dai testi liberi + Chiamata automatica di altri plugin)
- Passaggio 1: Digitare "Chiamare il plugin per tradurre un documento PDF, l'indirizzo è https://openreview.net/pdf?id=rJl0r3R9KX"
- Passaggio 2: Fare clic su "Terminale vuoto"
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
</div>
4. Design modulare, interfacce semplici che supportano funzionalità potenti
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
5. Traduzione e interpretazione di altri progetti open source
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
</div>
6. Funzionalità leggera per [live2d](https://github.com/fghrsh/live2d_demo) (disabilitata per impostazione predefinita, richiede modifica di `config.py`)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. Generazione di immagini di OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
8. Elaborazione e riepilogo audio di OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
9. Correzione totale del testo di Latex
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
</div>
10. Cambio linguaggio e tema
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
</div>
### II: Versioni:
- versione 3.70 (todo): Ottimizzazione della visualizzazione del tema AutoGen e sviluppo di una serie di plugin correlati.
- versione 3.60: Introduzione di AutoGen come fondamento per i plugin della nuova generazione.
- versione 3.57: Supporto per GLM3, StarFirev3, Wenxin-yiyanv4 e correzione di bug sulla concorrenza dell'uso di modelli locali.
- versione 3.56: Possibilità di aggiungere dinamicamente pulsanti per funzionalità di base e nuova pagina di riepilogo del PDF.
- versione 3.55: Ristrutturazione dell'interfaccia utente, introduzione di finestre fluttuanti e barre dei menu.
- versione 3.54: Nuovo interprete di codice dinamico (Code Interpreter) (da perfezionare).
- versione 3.53: Possibilità di selezionare dinamicamente diversi temi dell'interfaccia utente, miglioramento della stabilità e risoluzione dei conflitti tra utenti multipli.
- versione 3.50: Utilizzo del linguaggio naturale per chiamare tutte le funzioni dei plugin di questo progetto (Terminale vuoto), supporto per la classificazione dei plugin, miglioramento dell'interfaccia utente e design di nuovi temi.
- versione 3.49: Supporto per la piattaforma Baidu Qianfan e Wenxin-yiyan.
- versione 3.48: Supporto per Alibaba DAXI 所见即所答, Shanghai AI-Lab Shusheng, Xunfei StarFire.
- versione 3.46: Supporto per la chat vocale in tempo reale completamente automatica.
- versione 3.45: Supporto personalizzato per il micro-aggiustamento del modello ChatGLM2.
- versione 3.44: Supporto ufficiale per Azure, miglioramento dell'usabilità dell'interfaccia.
- versione 3.4: + Funzionalità di traduzione di documenti arXiv e correzione di documenti LaTeX.
- versione 3.3: + Funzionalità di sintesi delle informazioni su Internet.
- versione 3.2: Il plugin di funzione supporta più interfacce dei parametri (funzionalità di salvataggio della conversazione, interpretazione di codici in qualsiasi linguaggio contemporaneamente, interrogare qualsiasi combinazione di LLM).
- versione 3.1: Supporto per l'interrogazione simultanea di più modelli GPT! Supporto per api2d, equilibrio del carico con più apikey.
- versione 3.0: Supporto per chatglm e altri piccoli llm.
- versione 2.6: Rielaborazione della struttura del plugin, miglioramento dell'interattività, aggiunta di ulteriori plugin.
- versione 2.5: Aggiornamento automatico, risoluzione del problema della lunghezza eccessiva del testo durante il riepilogo di grandi blocchi di codice che supera i token.
- versione 2.4: (1) Nuova funzionalità di traduzione di documenti PDF; (2) Nuova funzionalità di scambio delle posizioni tra l'area di input (input area); (3) Nuova opzione di layout verticale; (4) Ottimizzazione del plugin a threading multiplo.
- versione 2.3: Miglioramento dell'interattività con threading multiplo.
- versione 2.2: Supporto per il plugin con ricarica a caldo.
- versione 2.1: Layout pieghevole.
- versione 2.0: Introduzione di plugin modulari.
- versione 1.0: Funzioni di base
GPT Academic Developer QQ Group: `610599535`
- Problemi noti
- Alcuni plug-in di traduzione del browser possono interferire con il funzionamento del frontend di questo software
- L'app Gradio ufficiale ha molti bug di compatibilità, si consiglia di installare Gradio tramite `requirement.txt`
### III: Temi
Il tema può essere modificato modificando l'opzione `THEME` (config.py)
1. `Chuanhu-Small-and-Beautiful` [Website](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
### IV: Branch di Sviluppo di questo progetto
1. `master` branch: branch principale, versione stabile
2. `frontier` branch: branch di sviluppo, versione di test
### V: Riferimenti e Risorse di Apprendimento
```
Nel codice sono state utilizzate diverse idee dagli altri progetti, senza un ordine specifico:
# ChatGLM2-6B di Tsinghua:
https://github.com/THUDM/ChatGLM2-6B
# JittorLLMs di Tsinghua:
https://github.com/Jittor/JittorLLMs
# ChatPaper:
https://github.com/kaixindelele/ChatPaper
# Edge-GPT:
https://github.com/acheong08/EdgeGPT
# ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Installazione con un solo clic di Oobabooga:
https://github.com/oobabooga/one-click-installers
# Altre risorse:
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo

345
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> **注意**
>
> 此READMEはGPTによる翻訳で生成されました（このプロジェクトのプラグインによって実装されています）、翻訳結果は100%正確ではないため、注意してください。
>
> 2023年11月7日: 依存関係をインストールする際は、`requirements.txt`で**指定されたバージョン**を選択してください。 インストールコマンド: `pip install -r requirements.txt`。
# <div align=center><img src="logo.png" width="40"> GPT 学術最適化 (GPT Academic)</div>
**このプロジェクトが気に入った場合は、Starを付けてください。また、便利なショートカットキーまたはプラグインを作成した場合は、プルリクエストを歓迎します！**
GPTを使用してこのプロジェクトを任意の言語に翻訳するには、[`multi_language.py`](multi_language.py)を読み込んで実行します（実験的な機能）。
> **注意**
>
> 1. **強調された** プラグイン（ボタン）のみがファイルを読み込むことができることに注意してください。一部のプラグインは、プラグインエリアのドロップダウンメニューにあります。また、新しいプラグインのPRを歓迎し、最優先で対応します。
>
> 2. このプロジェクトの各ファイルの機能は、[自己分析レポート`self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E5%A0%82)で詳しく説明されています。バージョンが進化するにつれて、関連する関数プラグインをクリックして、プロジェクトの自己分析レポートをGPTで再生成することもできます。よくある質問については、[`wiki`](https://github.com/binary-husky/gpt_academic/wiki)をご覧ください。[標準的なインストール方法](#installation) | [ワンクリックインストールスクリプト](https://github.com/binary-husky/gpt_academic/releases) | [構成の説明](https://github.com/binary-husky/gpt_academic/wiki/Project-Configuration-Explain)。
>
> 3. このプロジェクトは、[ChatGLM](https://www.chatglm.dev/)などの中国製の大規模言語モデルも互換性があり、試してみることを推奨しています。複数のAPIキーを共存させることができ、設定ファイルに`API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`のように記入できます。`API_KEY`を一時的に変更する必要がある場合は、入力エリアに一時的な`API_KEY`を入力し、Enterキーを押して提出すると有効になります。
<div align="center">
機能（⭐= 最近追加された機能） | 説明
--- | ---
⭐[新しいモデルの追加](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | Baidu [Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)とWenxin Yiyu, [Tongyi Qianwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), Shanghai AI-Lab [Shusheng](https://github.com/InternLM/InternLM), Xunfei [Xinghuo](https://xinghuo.xfyun.cn/), [LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), Zhantu API, DALLE3
校正、翻訳、コード解説 | 一括校正、翻訳、論文の文法エラーの検索、コードの解説
[カスタムショートカットキー](https://www.bilibili.com/video/BV14s4y1E7jN) | カスタムショートカットキーのサポート
モジュール化された設計 | カスタムでパワフルな[プラグイン](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions)のサポート、プラグインの[ホットリロード](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)
[プログラム解析](https://www.bilibili.com/video/BV1cj411A7VW) | [プラグイン] Python/C/C++/Java/Lua/...のプロジェクトツリーを簡単に解析するか、[自己解析](https://www.bilibili.com/video/BV1cj411A7VW)
論文の読み込み、[翻訳](https://www.bilibili.com/video/BV1KT411x7Wn) | [プラグイン] LaTeX/PDFの論文全文を翻訳して要約を作成する
LaTeX全文の[翻訳](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[校正](https://www.bilibili.com/video/BV1FT411H7c5/) | [プラグイン] LaTeX論文を翻訳や校正する
一括コメント生成 | [プラグイン] 関数コメントを一括生成する
Markdownの[日英翻訳](https://www.bilibili.com/video/BV1yo4y157jV/) | [プラグイン] 5つの言語（[英語](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)など）のREADMEをご覧になりましたか？
チャット分析レポートの生成 | [プラグイン] 実行後にサマリーレポートを自動生成する
[PDF論文全文の翻訳機能](https://www.bilibili.com/video/BV1KT411x7Wn) | [プラグイン] PDF論文のタイトルと要約を抽出し、全文を翻訳する（マルチスレッド）
[Arxivアシスタント](https://www.bilibili.com/video/BV1LM4y1279X) | [プラグイン] arxiv論文のURLを入力すると、要約を翻訳してPDFをダウンロードできます
LaTeX論文の一括校正 | [プラグイン] Grammarlyのように、LaTeX論文の文法とスペルを修正して対照PDFを出力する
[Google Scholar統合アシスタント](https://www.bilibili.com/video/BV19L411U7ia) | [プラグイン] 任意のGoogle Scholar検索ページのURLを指定して、関連資料をGPTに書かせることができます
インターネット情報の集約+GPT | [プラグイン] インターネットから情報を取得して質問に答え、情報が常に最新になるようにします
⭐Arxiv論文の詳細な翻訳 ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [プラグイン] arxiv論文を超高品質で翻訳します。最高の論文翻訳ツールです
⭐[リアルタイム音声入力](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [プラグイン] 非同期[音声をリッスン(https://www.bilibili.com/video/BV1AV4y187Uy/)し、自動で文章を区切り、回答のタイミングを自動で探します
公式/画像/表の表示 | 公式の[tex形式とレンダリング形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)を同時に表示し、公式とコードのハイライトをサポートします
⭐AutoGenマルチエージェントプラグイン | [プラグイン] Microsoft AutoGenを利用して、マルチエージェントのインテリジェントなエマージェンスを探索します
ダーク[テーマ](https://github.com/binary-husky/gpt_academic/issues/173)を起動 | ブラウザのURLに```/?__theme=dark```を追加すると、ダークテーマに切り替えられます
[複数のLLMモデル](https://www.bilibili.com/video/BV1wT411p7yf)のサポート | GPT3.5、GPT4、[Tsinghua ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)、[Fudan MOSS](https://github.com/OpenLMLab/MOSS)などを同時に使えるのは最高の感じですよね？
⭐ChatGLM2ファインチューニングモデル | ChatGLM2ファインチューニングモデルをロードして使用することができ、ChatGLM2ファインチューニングの補助プラグインが用意されています
さらなるLLMモデルの導入、[HuggingFaceデプロイのサポート](https://huggingface.co/spaces/qingxu98/gpt-academic) | Newbingインターフェース（新しいBing）の追加、Tsinghua [Jittorllms](https://github.com/Jittor/JittorLLMs)の導入、[LLaMA](https://github.com/facebookresearch/llama)および[盤古α](https://openi.org.cn/pangu/)のサポート
⭐[void-terminal](https://github.com/binary-husky/void-terminal) pipパッケージ | GUIから独立して、Pythonから直接このプロジェクトのすべての関数プラグインを呼び出せます（開発中）
⭐Void Terminalプラグイン | [プラグイン] 自然言語で、このプロジェクトの他のプラグインを直接実行します
その他の新機能の紹介（画像生成など）...... | 末尾をご覧ください ......
</div>
- もし出力に数式が含まれている場合、TeX形式とレンダリング形式の両方で表示されます。これにより、コピーと読み取りが容易になります。
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- プロジェクトのコードを見るのがめんどくさい？プロジェクト全体を`chatgpt`に広報口頭発表してもらえるよ
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- 異なる言語モデルの組み合わせ呼び出し（ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4）
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
# インストール
### 方法I：直接実行（Windows、Linux、またはMacOS）
1. プロジェクトをダウンロード
```sh
git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. APIキーを設定する
`config.py`でAPIキーやその他の設定を設定します。特殊なネットワーク環境の設定方法については、[こちらをクリックして確認してください](https://github.com/binary-husky/gpt_academic/issues/1)。[Wikiページ](https://github.com/binary-husky/gpt_academic/wiki/Getting-Started)も参照してください。
「プログラムは、`config.py`と同じ場所にある`config_private.py`という名前のプライベート設定ファイルが存在するかどうかを優先的にチェックし、同じ名前の設定をコピーします。この読み込みロジックを理解できる場合、`config.py`の横に`config_private.py`という名前の新しい設定ファイルを作成し、`config.py`の設定を転送（コピー）することを強くお勧めします（変更した設定項目だけをコピーします）。」
「プロジェクトを環境変数で設定することもサポートしています。環境変数の書式は、`docker-compose.yml`ファイルや[Wikiページ](https://github.com/binary-husky/gpt_academic/wiki/Getting-Started)を参考にしてください。設定の優先度は、`環境変数` > `config_private.py` > `config.py`の順です。」
3. 依存関係をインストールする
```sh
# （オプションI：Pythonに詳しい場合、Python 3.9以上）注：公式のpipソースまたは阿里pipソースを使用し、一時的なソースの変更方法は、python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/です。
python -m pip install -r requirements.txt
# （オプションII：Anacondaを使用する場合）手順は同様です (https://www.bilibili.com/video/BV1rc411W7Dr)：
conda create -n gptac_venv python=3.11 # Anaconda環境を作成
conda activate gptac_venv # Anaconda環境をアクティベート
python -m pip install -r requirements.txt # この手順はpipのインストール手順と同じです
```
<details><summary>清華ChatGLM2/復旦MOSS/RWKVがバックエンドとしてサポートされている場合、ここをクリックして展開してください</summary>
<p>
【オプションステップ】 清華ChatGLM2/復旦MOSSをバックエンドとしてサポートする場合は、さらに追加の依存関係をインストールする必要があります（前提条件：Pythonに精通していて、PytorchとNVIDIA GPUを使用したことがあり、十分なコンピュータの構成を持っていること）：
```sh
# 【オプションステップI】 清華ChatGLM2のサポートを追加する。 清華ChatGLM2に関する注意点： "Call ChatGLM fail 不能正常加载ChatGLM的参数" のエラーが発生した場合、次の手順を参照してください。 1: デフォルトでインストールされているのはtorch+cpu版です。CUDAを使用するにはtorchをアンインストールしてtorch+cuda版を再インストールする必要があります。 2: モデルをロードできない場合は、request_llm/bridge_chatglm.pyのモデル精度を変更できます。AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)をAutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)に変更します。
python -m pip install -r request_llms/requirements_chatglm.txt
# 【オプションステップII】 復旦MOSSのサポートを追加する
python -m pip install -r request_llms/requirements_moss.txt
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # このコマンドを実行するときは、プロジェクトのルートパスである必要があります。
# 【オプションステップIII】 RWKV Runnerのサポートを追加する
Wikiを参照してください: https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
# 【オプションステップIV】 config.py設定ファイルに、以下のすべてのモデルが含まれていることを確認します。以下のモデルがすべてサポートされています（jittorllmsはDockerのみサポートされています）：
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. 実行する
```sh
python main.py
```
### 方法II：Dockerを使用する
0. プロジェクトのフルスケールデプロイ（これは、CUDAとLaTeXを含む大規模なイメージですが、ネットワーク速度が遅いまたはディスク容量が小さい場合はおすすめしません）
[![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
```sh
# docker-compose.ymlを編集し、スキーム0を残し、その他を削除してから実行する：
docker-compose up
```
1. ChatGPT + 文心一言 + sparkなどのオンラインモデルのみを含む（ほとんどの人におすすめ）
[![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
[![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
```sh
# docker-compose.ymlを編集し、スキーム1を残し、その他を削除してから実行する：
docker-compose up
```
P.S. LaTeXプラグインの機能を使用する場合は、Wikiを参照してください。また、LaTeX機能を使用するためには、スキーム4またはスキーム0を直接使用することもできます。
2. ChatGPT + ChatGLM2 + MOSS + LLAMA2 + 通慧千問（Nvidia Dockerに精通している場合）
[![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
```sh
# docker-compose.ymlを編集し、スキーム2を残し、その他を削除してから実行する：
docker-compose up
```
### 方法III：その他のデプロイメントオプション
1. **Windowsのワンクリック実行スクリプト**。
Python環境に詳しくないWindowsユーザーは、[リリース](https://github.com/binary-husky/gpt_academic/releases)からワンクリック実行スクリプトをダウンロードして、ローカルモデルのないバージョンをインストールできます。
スクリプトの貢献者は[oobabooga](https://github.com/oobabooga/one-click-installers)です。
2. 第三者のAPI、Azureなど、文心一言、星火などを使用するには、[Wikiページ](https://github.com/binary-husky/gpt_academic/wiki/Getting-Started)を参照してください。
3. クラウドサーバーでのリモートデプロイの回避策ガイドを参照してください。
[クラウドサーバーでのリモートデプロイの回避策ガイドwiki](https://github.com/binary-husky/gpt_academic/wiki/Getting-Started#%E4%BA%91%E3%82%B5%E3%83%BC%E3%83%90%E3%83%BC%E3%83%AA%E3%82%BC%E3%83%A0%E3%82%B5%E3%83%BC%E3%83%90%E3%81%AE%E3%83%AA%E3%83%A2%E3%83%BC%E3%83%88%E3%83%87%E3%83%97%E3%83%AD%E3%82%A4%E6%8C%87%E5%8D%97)
4. その他の新しいデプロイプラットフォームや方法
- Sealosを使用した[ワンクリックデプロイ](https://github.com/binary-husky/gpt_academic/issues/993)
- WSL2（Windows Subsystem for Linux）の使用方法については、[デプロイwiki-2](https://github.com/binary-husky/gpt_academic/wiki/Getting-Started)を参照してください。
- サブパス（例：`http://localhost/subpath`）でFastAPIを実行する方法については、[FastAPIの実行方法](docs/WithFastapi.md)を参照してください。
# 高度な使用法
### I：カスタムショートカットボタンの作成（学術的なショートカットキー）
テキストエディタで`core_functional.py`を開き、次の項目を追加し、プログラムを再起動します。（ボタンが存在する場合、プレフィックスとサフィックスはホット変更に対応しており、プログラムを再起動せずに有効にすることができます。）
例：
```
"超级英译中"： {
# プレフィックス、入力の前に追加されます。例えば、要求を記述するために使用されます。翻訳、コードの解説、校正など
"プレフィックス"： "下記の内容を中国語に翻訳し、専門用語を一つずつマークダウンテーブルで解説してください：\n\n"、
# サフィックス、入力の後に追加されます。プレフィックスと一緒に使用して、入力内容を引用符で囲むことができます。
"サフィックス"： ""、
}、
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
### II：関数プラグインのカスタマイズ
自分の望む任意のタスクを実行するために、強力な関数プラグインを作成できます。
このプロジェクトのプラグインの作成とデバッグの難易度は非常に低く、一定のPythonの基礎知識があれば、提供されたテンプレートを参考に自分自身のプラグイン機能を実装することができます。
詳細については、[関数プラグインガイド](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)を参照してください。
# 更新
### I：ダイナミック
1. 会話の保存機能。プラグインエリアで `Save Current Conversation` を呼び出すだけで、現在の会話を読み取り可能で復旧可能なhtmlファイルとして保存できます。
また、プラグインエリア（ドロップダウンメニュー）で `Load Conversation History Archive` を呼び出すことで、以前の会話を復元できます。
ヒント：ファイルを指定せずに `Load Conversation History Archive` をクリックすると、履歴のhtmlアーカイブのキャッシュを表示することができます。
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. ⭐Latex/Arxiv論文の翻訳機能⭐
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
</div>
3. ゼロのターミナル（自然言語入力からユーザの意図を理解+他のプラグインを自動的に呼び出す）
- ステップ1：「プラグインのPDF論文の翻訳を呼び出してください、アドレスはhttps://openreview.net/pdf?id=rJl0r3R9KX」と入力します。
- ステップ2：「Zero Terminal」をクリックします。
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
</div>
4. モジュール化された機能設計、シンプルなインターフェイスで強力な機能をサポートする
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
5. 他のオープンソースプロジェクトの翻訳
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
</div>
6. [live2d](https://github.com/fghrsh/live2d_demo)のデコレーション機能（デフォルトでは無効で、`config.py`を変更する必要があります）
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. OpenAI画像生成
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
8. OpenAIオーディオ解析と要約
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
9. Latex全体の校正と修正
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
</div>
10. 言語、テーマの切り替え
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
</div>
### II：バージョン:
- version 3.70（todo）: AutoGenプラグインのテーマを最適化し、一連の派生プラグインを設計する
- version 3.60: AutoGenを次世代プラグインの基盤として導入
- version 3.57: GLM3、星火v3、文心一言v4をサポート、ローカルモデルの並行バグを修正
- version 3.56: 基本機能ボタンを動的に追加、新しい報告書PDF集約ページ
- version 3.55: フロントエンドのデザインを再構築し、浮動ウィンドウとメニューバーを導入
- version 3.54: 新しい動的コードインタプリタ（Code Interpreter）の追加（未完成）
- version 3.53: 異なるテーマを動的に選択できるように、安定性の向上と複数ユーザの競合問題の解決
- version 3.50: 自然言語でこのプロジェクトのすべての関数プラグインを呼び出すことができるようになりました（ゼロのターミナル）プラグインの分類をサポートし、UIを改善し、新しいテーマを設計
- version 3.49: Baidu Qianfanプラットフォームと文心一言をサポート
- version 3.48: Alibaba DAMO Academy Tongyi Qianwen、Shanghai AI-Lab Shusheng、Xunfei Xinghuoをサポート
- version 3.46: 完全なオートモードのリアルタイム音声対話をサポート
- version 3.45: カスタムChatGLM2ファインチューニングモデルをサポート
- version 3.44: 公式にAzureをサポート、UIの使いやすさを最適化
- version 3.4: +arxiv論文の翻訳、latex論文の校閲機能
- version 3.3: +インターネット情報の総合機能
- version 3.2: 関数プラグインがさらに多くのパラメータインターフェースをサポート（会話の保存機能、任意の言語のコードの解釈、同時に任意のLLMの組み合わせを尋ねる）
- version 3.1: 複数のgptモデルに同時に質問できるようにサポートされました！ api2dをサポートし、複数のapikeyの負荷分散をサポートしました
- version 3.0: chatglmと他の小さなllmのサポート
- version 2.6: プラグインの構造を再構築し、対話性を高め、より多くのプラグインを追加しました
- version 2.5: 自己更新、ソースコード全体の要約時のテキストの長さ、トークンのオーバーフローの問題を解決しました
- version 2.4: (1)新しいPDF全文翻訳機能を追加しました。(2)入力エリアの位置を切り替えるための新しい機能を追加しました。(3)垂直レイアウトオプションを追加しました。(4)マルチスレッド関数プラグインを最適化しました。
- version 2.3: マルチスレッドの対話を強化しました
- version 2.2: 関数プラグインのホットリロードをサポート
- version 2.1: 折りたたみ式のレイアウト
- version 2.0: モジュール化された関数プラグインの導入
- version 1.0: 基本機能
GPT Academic開発者QQグループ：`610599535`
-既知の問題
- 一部のブラウザ翻訳プラグインがこのソフトウェアのフロントエンドの実行を妨げる
- 公式Gradioには互換性の問題があり、必ず`requirement.txt`を使用してGradioをインストールしてください
### III：テーマ
`THEME`オプション（`config.py`）を変更することで、テーマを変更できます
1. `Chuanhu-Small-and-Beautiful` [リンク](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
### IV：本プロジェクトの開発ブランチ
1. `master`ブランチ：メインブランチ、安定版
2. `frontier`ブランチ：開発ブランチ、テスト版
### V：参考と学習
```
コードの中には、他の優れたプロジェクトのデザインを参考にしたものが多く含まれています。順序は問いません：
# 清華ChatGLM2-6B:
https://github.com/THUDM/ChatGLM2-6B
# 清華JittorLLMs:
https://github.com/Jittor/JittorLLMs
# ChatPaper:
https://github.com/kaixindelele/ChatPaper
# Edge-GPT:
https://github.com/acheong08/EdgeGPT
# ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Oobaboogaワンクリックインストーラー：
https://github.com/oobabooga/one-click-installers
# その他：
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo

364
docs/README.Korean.md Normal file
View File

@ -0,0 +1,364 @@
> **참고**
>
> 이 README는 GPT 번역으로 생성되었습니다 (이 프로젝트의 플러그인에 의해 구현됨) . 100% 신뢰할 수 없으므로 번역 결과를 주의 깊게 검토하십시오.
>
> 2023.11.7: 종속성을 설치할 때, `requirements.txt`에 **지정된 버전**을 선택하십시오. 설치 명령어: `pip install -r requirements.txt`.
# <div align=center><img src="logo.png" width="40"> GPT 학술 최적화 (GPT Academic)</div>
**이 프로젝트가 마음에 드신다면, Star를 부탁드립니다. 편리한 단축키나 플러그인을 발견하셨다면 Pull Request를 환영합니다!**
GPT를 사용하여 이 프로젝트를 임의의 언어로 번역하려면 [`multi_language.py`](multi_language.py)를 읽고 실행하십시오 (실험적).
> **참고**
>
> 1. **강조 표시**된 플러그인 (버튼)만 파일을 읽을 수 있습니다. 일부 플러그인은 플러그인 영역의 **드롭다운 메뉴**에 있습니다. 또한 새로운 플러그인에 대한 모든 PR을 환영하며, 이를 **가장 우선적**으로 처리합니다.
>
> 2. 이 프로젝트의 각 파일의 기능은 [자체 분석 보고서 `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic%EC%A0%9C%ED%94%84%EB%AA%85%EC%84%B1%EB%B0%A9%EC%8B%9D%EC%9D%98_%EA%B2%B0%EA%B3%BC)에서 자세히 설명되어 있습니다. 버전이 반복됨에 따라, 관련 기능 플러그인을 언제든지 클릭하여 GPT를 호출하여 프로젝트의 자체 분석 보고서를 다시 생성할 수 있습니다. 자주 묻는 질문은 [`위키`](https://github.com/binary-husky/gpt_academic/wiki)를 참조하십시오. [일반적인 설치 방법](#installation) | [원클릭 설치 스크립트](https://github.com/binary-husky/gpt_academic/releases) | [설정 설명서](https://github.com/binary-husky/gpt_academic/wiki/%EC%84%A4%EC%A0%95%EC%82%AC%EB%AA%85_%EA%B0%84%EB%8B%A8_%EC%84%B8%ED%8A%B8%EB%B2%84_%EC%B6%94%EA%B0%80)
> 3. 이 프로젝트는 ChatGLM 등 대형 언어 모델 (ChatGLM 등) 실행을 지원하고 권장합니다. 여러 개의 API 키를 동시에 사용할 수 있으며, 구성 파일에 `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`와 같이 입력할 수 있습니다. `API_KEY`를 일시적으로 변경해야 하는 경우, 입력 영역에 임시 `API_KEY`를 입력한 다음 Enter 키를 누르면 적용됩니다.
<div align="center">
기능 (⭐= 최근 추가 기능) | 설명
--- | ---
⭐[새 모델 추가](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)! | Baidu [Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)와 Wenxin Yiyan, [Tongyi Qianwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), Shanghai AI-Lab [Shusheng](https://github.com/InternLM/InternLM), Xunfei [Star](https://xinghuo.xfyun.cn/), [LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), Zhipu API, DALLE3
문체 개선, 번역, 코드 설명 | 일괄적인 문체 개선, 번역, 논문 문법 오류 탐색, 코드 설명
[사용자 정의 단축키](https://www.bilibili.com/video/BV14s4y1E7jN) | 사용자 정의 단축키 지원
모듈화 설계 | 사용자 정의 가능한 강력한 [플러그인](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions) 지원, 플러그인 지원 [핫 업데이트](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)
[프로그램 분석](https://www.bilibili.com/video/BV1cj411A7VW) | [플러그인] 한 번에 Python/C/C++/Java/Lua/... 프로젝트 트리를 분석하거나 [자체 분석](https://www.bilibili.com/video/BV1cj411A7VW)
논문 읽기, 논문 [번역](https://www.bilibili.com/video/BV1KT411x7Wn) | [플러그인] LaTeX/PDF 논문 전문을 읽고 요약 생성
LaTeX 전체 [번역](https://www.bilibili.com/video/BV1nk4y1Y7Js/), [개선](https://www.bilibili.com/video/BV1FT411H7c5/) | [플러그인] LaTeX 논문 번역 또는 개선
일괄 주석 생성 | [플러그인] 함수 주석 일괄 생성
Markdown [한 / 영 번역](https://www.bilibili.com/video/BV1yo4y157jV/) | 위의 5개 언어로 작성된 [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)를 살펴보셨나요?
채팅 분석 보고서 생성 | [플러그인] 실행 후 요약 보고서 자동 생성
[PDF 논문 전체 번역](https://www.bilibili.com/video/BV1KT411x7Wn) 기능 | [플러그인] PDF 논문 제목 및 요약 추출 + 전체 번역 (멀티 스레드)
[Arxiv 도우미](https://www.bilibili.com/video/BV1LM4y1279X) | [플러그인] arxiv 논문 url 입력시 요약 번역 + PDF 다운로드
LaTeX 논문 일괄 교정 | [플러그인] Grammarly를 모사하여 LaTeX 논문에 대한 문법 및 맞춤법 오류 교정 + 대조 PDF 출력
[Google 학술 통합 도우미](https://www.bilibili.com/video/BV19L411U7ia) | 임의의 Google 학술 검색 페이지 URL을 지정하여 gpt가 [related works를 작성](https://www.bilibili.com/video/BV1GP411U7Az/)하게 해주세요.
인터넷 정보 집계 + GPT | [플러그인] [인터넷에서 정보를 가져와서](https://www.bilibili.com/video/BV1om4y127ck) 질문에 대답하도록 GPT를 자동화하세요. 정보가 절대로 오래되지 않도록 해줍니다.
⭐Arxiv 논문 세심한 번역 ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [플러그인] [arxiv 논문을 고품질 번역으로](https://www.bilibili.com/video/BV1dz4y1v77A/) 번역하는 최고의 도구
⭐[실시간 음성 대화 입력](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [플러그인] 비동기적으로 [오디오를 모니터링](https://www.bilibili.com/video/BV1AV4y187Uy/)하여 문장을 자동으로 분절하고 대답 시기를 자동으로 찾습니다.
수식/이미지/표 표시 | [tex 형식 및 렌더링 형식](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)의 수식을 동시에 표시하며, 수식 및 코드 하이라이트 지원
⭐AutoGen multi-agent 플러그인 | [플러그인] Microsoft AutoGen을 활용하여 여러 개의 에이전트가 지능적으로 발생하는 가능성을 탐색하세요!
다크 모드 주제 지원 | 브라우저의 URL 뒤에 ```/?__theme=dark```를 추가하여 다크 모드로 전환하세요.
[다양한 LLM 모델](https://www.bilibili.com/video/BV1wT411p7yf) 지원 | GPT3.5, GPT4, [Tsinghua ChatGLM2](https://github.com/THUDM/ChatGLM2-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS)을 함께 사용하는 느낌은 좋을 것입니다, 그렇지 않습니까?
⭐ChatGLM2 fine-tuned 모델 | ChatGLM2 fine-tuned 모델 로드를 지원하며, ChatGLM2 fine-tuned 보조 플러그인 제공
더 많은 LLM 모델 연결, [huggingface 배포](https://huggingface.co/spaces/qingxu98/gpt-academic) 지원 | Newbing 인터페이스(신 밍), Tsinghua [Jittorllms](https://github.com/Jittor/JittorLLMs) 도입, [LLaMA](https://github.com/facebookresearch/llama)와 [Pangu-alpha](https://openi.org.cn/pangu/)를 지원합니다.
⭐[void-terminal](https://github.com/binary-husky/void-terminal) 패키지 | GUI에서 독립, Python에서 이 프로젝트의 모든 함수 플러그인을 직접 호출 (개발 중)
⭐Void 터미널 플러그인 | [플러그인] 자연어로 이 프로젝트의 다른 플러그인을 직접 영속합니다.
기타 새로운 기능 소개 (이미지 생성 등) …… | 본 문서 맨 끝 참조 ……
</div>
- 새로운 인터페이스(`config.py`의 LAYOUT 옵션 수정으로 "왼쪽-오른쪽 레이아웃"과 "위-아래 레이아웃"을 전환할 수 있음)
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
</div>
- 모든 버튼은 functional.py를 동적으로 읽어 생성되므로 원하는대로 사용자 정의 기능을 추가할 수 있으며 클립 보드를 해제할 수 있습니다.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- 문체 개선/오류 수정
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- If the output contains equations, they will be displayed in both tex format and rendered format for easy copying and reading.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Don't feel like looking at the project code? Just give it to ChatGPT and let it dazzle you.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Mix and match multiple powerful language models (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
# Installation
### Installation Method I: Run Directly (Windows, Linux or MacOS)
1. Download the project
```sh
git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configure API_KEY
In `config.py`, configure the API KEY and other settings, [click here to view special network environment configuration methods](https://github.com/binary-husky/gpt_academic/issues/1). [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
" The program will first check if there is a confidential configuration file named `config_private.py` and use its configuration to override the configuration with the same name in `config.py`. If you can understand this reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py` and move (copy) the configuration from `config.py` to `config_private.py` (only copy the modified configuration items). "
" You can configure the project through `environment variables`. The format of the environment variables can be found in the `docker-compose.yml` file or our [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明). The priority of the configuration reading is: `environment variables` > `config_private.py` > `config.py`. "
3. Install dependencies
```sh
# (Option I: if familiar with python, python>=3.9) Note: Use the official pip source or Aliyun pip source. Temporary switching source method: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Option II: using Anaconda) The steps are similar (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # Create an Anaconda environment
conda activate gptac_venv # Activate the Anaconda environment
python -m pip install -r requirements.txt # This step is the same as the pip installation step
```
<details><summary>Click here to expand if you need support for Tsinghua ChatGLM2/Fudan MOSS/RWKV backend</summary>
<p>
[Optional Step] If you need support for Tsinghua ChatGLM2/Fudan MOSS as the backend, you need to install additional dependencies (Prerequisites: Familiar with Python + Have used Pytorch + Sufficient computer configuration):
```sh
# [Optional Step I] Support for Tsinghua ChatGLM2. Note for Tsinghua ChatGLM: If you encounter the error "Call ChatGLM fail cannot load ChatGLM parameters", refer to the following: 1: The default installation above is torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2: If you cannot load the model due to insufficient computer configuration, you can modify the model precision in request_llm/bridge_chatglm.py, change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llms/requirements_chatglm.txt
# [Optional Step II] Support for Fudan MOSS
python -m pip install -r request_llms/requirements_moss.txt
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # When executing this line of code, make sure you are in the project root path
# [Optional Step III] Support for RWKV Runner
Refer to the wiki: https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
# [Optional Step IV] Make sure that the AVAIL_LLM_MODELS in the config.py configuration file includes the expected models. The currently supported models are as follows (the jittorllms series only supports the docker solution):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Run
```sh
python main.py
```
### Installation Method II: Use Docker
0. Deploy all the capabilities of the project (this is a large image that includes cuda and latex. However, it is not recommended if your internet speed is slow or your hard disk is small)
[![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
``` sh
# Modify docker-compose.yml, keep scheme 0 and delete the others. Then run:
docker-compose up
```
1. ChatGPT+Random Quotes+Wikipedia Summary+Spark and other online models (recommended for most people)
[![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
[![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
``` sh
# Modify docker-compose.yml, keep scheme 1 and delete the others. Then run:
docker-compose up
```
P.S. If you need the Latex plugin feature, please refer to the Wiki. Additionally, you can also use scheme 4 or scheme 0 directly to get the Latex feature.
2. ChatGPT + ChatGLM2 + MOSS + LLAMA2 + Thousand Questions (Requires familiarity with [Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian) runtime)
[![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
``` sh
# Modify docker-compose.yml, keep scheme 2 and delete the others. Then run:
docker-compose up
```
### Installation Method III: Other Deployment Methods
1. **One-click run script for Windows**.
Windows users who are completely unfamiliar with the Python environment can download the one-click run script without local models from the [Release](https://github.com/binary-husky/gpt_academic/releases) section.
The script contribution comes from [oobabooga](https://github.com/oobabooga/one-click-installers).
2. Use third-party APIs, Azure, etc., Random Quotes, Spark, etc., see the [Wiki page](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
3. Pitfall guide for remote deployment on cloud servers.
Please visit the [cloud server remote deployment wiki](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
4. Some new deployment platforms or methods
- Use Sealos for [one-click deployment](https://github.com/binary-husky/gpt_academic/issues/993).
- Use WSL2 (Windows Subsystem for Linux). Please visit [deployment wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
- How to run in a subpath (such as `http://localhost/subpath`). Please refer to [FastAPI running instructions](docs/WithFastapi.md)
# 고급 사용법
### I: 사용자 정의 바로 가기 버튼 추가 (학술 단축키)
임의의 텍스트 편집기로 `core_functional.py` 파일을 열고 다음과 같은 항목을 추가한 다음 프로그램을 다시 시작하십시오. (이미 버튼이 있는 경우에는 접두사와 접미사를 실시간으로 수정할 수 있으므로 프로그램을 다시 시작할 필요가 없습니다.)
예시:
```
"초급영문 번역": {
# 접두사, 입력 내용 앞에 추가됩니다. 예를 들어 요구 사항을 설명하는 데 사용됩니다. 예를 들어 번역, 코드 설명, 교정 등
"Prefix": "다음 내용을 한국어로 번역하고 전문 용어에 대한 설명을 적용한 마크다운 표를 사용하세요:\n\n",
# 접미사, 입력 내용 뒤에 추가됩니다. 예를 들어 접두사와 함께 입력 내용을 따옴표로 감쌀 수 있습니다.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
### II: 사용자 정의 함수 플러그인
원하는 작업을 수행하기 위해 능력있는 함수 플러그인을 작성하세요.
이 프로젝트의 플러그인 작성 및 디버깅은 난이도가 낮으며, 일정한 Python 기본 지식만 있으면 우리가 제공하는 템플릿을 본따서 고유한 플러그인 기능을 구현할 수 있습니다.
자세한 내용은 [함수 플러그인 가이드](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)를 참조하세요.
# 업데이트
### I: 다이나믹
1. 대화 저장 기능. 플러그인 영역에서 '현재 대화 저장'을 호출하여 현재 대화를 볼 수 있고, html 파일을 복구할 수 있습니다.
또한 플러그인 영역에서 '대화 기록 불러오기'를 호출하여 이전 대화를 복원할 수 있습니다.
팁: 파일을 지정하지 않고 '대화 기록 불러오기'를 바로 클릭하면 이전 html 기록 캐시를 볼 수 있습니다.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. ⭐Latex/Arxiv 논문 번역 기능⭐
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
</div>
3. 빈 터미널 (자연어 입력에서 사용자 의도 이해 + 자동 플러그인 호출)
- 단계 1: "플러그인을 사용하여 PDF 논문을 번역하십시오. 주소는 https://openreview.net/pdf?id=rJl0r3R9KX입니다." 입력
- 단계 2: "빈 터미널" 클릭
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
</div>
4. 모듈화된 기능 디자인, 간단한 인터페이스로 강력한 기능 제공
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
5. 다른 오픈 소스 프로젝트 번역
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
</div>
6. [live2d](https://github.com/fghrsh/live2d_demo)의 작은 기능 추가 (기본 설정은 닫혀 있으며, `config.py`를 수정해야 합니다.)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. OpenAI 이미지 생성
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
8. OpenAI 오디오 분석 및 요약
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
9. Latex 전체 교정 오류
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
</div>
10. 언어, 테마 변경
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
</div>
### II: 버전:
- 버전 3.70 (예정): AutoGen 플러그인 테마 개선 및 다른 테마 플러그인 디자인
- 버전 3.60: AutoGen을 새로운 세대 플러그인의 기반으로 도입
- 버전 3.57: GLM3, Starfire v3, 文心一言 v4 지원, 로컬 모델의 동시성 버그 수정
- 버전 3.56: 동적으로 기본 기능 버튼 추가, 새로운 보고서 PDF 요약 페이지
- 버전 3.55: 프론트 엔드 인터페이스 리팩토링, 화면 따라다니는 윈도우 및 메뉴 바 도입
- 버전 3.54: 새로운 동적 코드 해석기 (Code Interpreter) 추가 (완벽하게 완성되지 않음)
- 버전 3.53: 다른 인터페이스 테마 동적 선택 기능 추가, 안정성 향상 및 다중 사용자 충돌 문제 해결
- 버전 3.50: 자연어로 이 프로젝트의 모든 함수 플러그인을 호출하는 기능 (빈 터미널) 추가, 플러그인 분류 지원, UI 개선, 새로운 테마 설계
- 버전 3.49: Baidu Qianfan 플랫폼 및 문심일언 지원
- 버전 3.48: Ali DameiYuan Sematic Query, Shanghai AI-Lab Shusheng, Xunfei Starfire 지원
- 버전 3.46: 완전 자동 운전 가능한 실시간 음성 대화 지원
- 버전 3.45: 사용자 정의 ChatGLM2 fine-tuning 모델 지원
- 버전 3.44: Azure 정식 지원, 인터페이스의 사용 편의성 개선
- 버전 3.4: +arxiv 논문 번역, latex 논문 교정 기능 추가
- 버전 3.3: +인터넷 정보 종합 기능
- 버전 3.2: 함수 플러그인이 더 많은 매개변수 인터페이스를 지원합니다 (대화 저장 기능, 임의의 언어 코드 해석 + 임의의 LLM 조합을 동시에 요청)
- 버전 3.1: 여러 GPT 모델에 동시에 질문할 수 있는 기능 추가! api2d 지원, 여러 개의 apikey 부하 균형 조정 지원
- 버전 3.0: chatglm 및 기타 소규모 llm 지원
- 버전 2.6: 플러그인 구조를 재구성하여 상호 작용성 향상, 더 많은 플러그인 추가
- 버전 2.5: 자동 업데이트, 소스 코드 요약 중 텍스트가 너무 길고 토큰이 오버플로되는 문제 해결
- 버전 2.4: (1)PDF 전체 번역 기능 추가; (2)입력 영역 위치 전환 기능 추가; (3)수직 레이아웃 옵션 추가; (4)멀티 스레드 함수 플러그인 최적화
- 버전 2.3: 멀티 스레드 상호 작용성 강화
- 버전 2.2: 함수 플러그인의 핫 리로드 지원
- 버전 2.1: 접을 수 있는 레이아웃
- 버전 2.0: 모듈화 함수 플러그인 도입
- 버전 1.0: 기본 기능
GPT Academic 개발자 QQ 그룹: `610599535`
- 알려진 문제
- 특정 웹 브라우저 번역 플러그인이 이 소프트웨어의 프론트엔드 실행에 방해가 되는 경우가 있습니다.
- 공식 Gradio에는 호환성 문제가 많기 때문에 `requirement.txt`를 사용하여 Gradio를 설치하십시오.
### III: 테마
`THEME` 옵션 (`config.py`)을 수정하여 테마를 변경할 수 있습니다.
1. `Chuanhu-Small-and-Beautiful` [URL](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
### IV: 이 프로젝트의 개발 브랜치
1. `master` 브랜치: 메인 브랜치, 안정 버전
2. `frontier` 브랜치: 개발 브랜치, 테스트 버전
### V: 참고 및 학습
```
코드에서는 다른 우수한 프로젝트의 디자인을 많이 참고했습니다. 순서는 문제 없이 나열됩니다:
# 清华ChatGLM2-6B:
https://github.com/THUDM/ChatGLM2-6B
# 清华JittorLLMs:
https://github.com/Jittor/JittorLLMs
# ChatPaper:
https://github.com/kaixindelele/ChatPaper
# Edge-GPT:
https://github.com/acheong08/EdgeGPT
# ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Oobabooga 원 클릭 설치 프로그램:
https://github.com/oobabooga/one-click-installers
# 더보기：
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo

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> **Nota**
>
> Este README foi traduzido pelo GPT (implementado por um plugin deste projeto) e não é 100% confiável. Por favor, verifique cuidadosamente o resultado da tradução.
>
> 7 de novembro de 2023: Ao instalar as dependências, favor selecionar as **versões especificadas** no `requirements.txt`. Comando de instalação: `pip install -r requirements.txt`.
# <div align=center><img src="logo.png" width="40"> GPT Acadêmico</div>
**Se você gosta deste projeto, por favor, dê uma estrela nele. Se você inventou atalhos de teclado ou plugins úteis, fique à vontade para criar pull requests!**
Para traduzir este projeto para qualquer idioma utilizando o GPT, leia e execute [`multi_language.py`](multi_language.py) (experimental).
> **Nota**
>
> 1. Observe que apenas os plugins (botões) marcados em **destaque** são capazes de ler arquivos, alguns plugins estão localizados no **menu suspenso** do plugin area. Também damos boas-vindas e prioridade máxima a qualquer novo plugin via PR.
>
> 2. As funcionalidades de cada arquivo deste projeto estão detalhadamente explicadas em [autoanálise `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). Com a iteração das versões, você também pode clicar nos plugins de funções relevantes a qualquer momento para chamar o GPT para regerar o relatório de autonálise do projeto. Perguntas frequentes [`wiki`](https://github.com/binary-husky/gpt_academic/wiki) | [Método de instalação convencional](#installation) | [Script de instalação em um clique](https://github.com/binary-husky/gpt_academic/releases) | [Explicação de configuração](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
>
> 3. Este projeto é compatível e encoraja o uso de modelos de linguagem chineses, como ChatGLM. Vários api-keys podem ser usados simultaneamente, podendo ser especificados no arquivo de configuração como `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. Quando precisar alterar temporariamente o `API_KEY`, insira o `API_KEY` temporário na área de entrada e pressione Enter para que ele seja efetivo.
<div align="center">
Funcionalidades (⭐= funcionalidade recentemente adicionada) | Descrição
--- | ---
⭐[Integração com novos modelos](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | [Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu) da Baidu, Wenxin e [Tongyi Qianwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), [Shusheng](https://github.com/InternLM/InternLM) da Shanghai AI-Lab, [Xinghuo](https://xinghuo.xfyun.cn/) da Iflytek, [LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), Zhipu API, DALLE3
Aprimoramento, tradução, explicação de códigos | Aprimoramento com um clique, tradução, busca de erros gramaticais em artigos e explicação de códigos
[Atalhos de teclado personalizados](https://www.bilibili.com/video/BV14s4y1E7jN) | Suporte para atalhos de teclado personalizados
Design modular | Suporte a plugins poderosos e personalizáveis, plugins com suporte a [atualização a quente](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)
[Análise de código](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin] Análise instantânea da estrutura de projetos em Python/C/C++/Java/Lua/... ou [autoanálise](https://www.bilibili.com/video/BV1cj411A7VW)
Leitura de artigos, [tradução](https://www.bilibili.com/video/BV1KT411x7Wn) de artigos | [Plugin] Interpretação instantânea de artigos completos em latex/pdf e geração de resumos
Tradução completa de artigos em latex [PDF](https://www.bilibili.com/video/BV1nk4y1Y7Js/), [aprimoramento](https://www.bilibili.com/video/BV1FT411H7c5/) | [Plugin] Tradução completa ou aprimoramento de artigos em latex com um clique
Geração em lote de comentários | [Plugin] Geração em lote de comentários de funções com um clique
Tradução (inglês-chinês) de Markdown | [Plugin] Você já viu o [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) nas 5 línguas acima?
Criação de relatório de análise de bate-papo | [Plugin] Geração automática de relatório de resumo após a execução
Tradução [completa de artigos em PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin] Extração de título e resumo de artigos em PDF + tradução completa (multithreading)
Auxiliar Arxiv | [Plugin] Insira o URL de um artigo Arxiv para traduzir o resumo + baixar o PDF com um clique
Correção automática de artigos em latex | [Plugin] Correções gramaticais e ortográficas de artigos em latex semelhante ao Grammarly + saída PDF comparativo
Auxiliar Google Scholar | [Plugin] Insira qualquer URL da busca do Google Acadêmico e deixe o GPT [escrever trabalhos relacionados](https://www.bilibili.com/video/BV1GP411U7Az/) para você
Agregação de informações da Internet + GPT | [Plugin] Capturar informações da Internet e obter respostas de perguntas com o GPT em um clique, para que as informações nunca fiquem desatualizadas
⭐Tradução refinada de artigos do Arxiv ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [Plugin] Tradução de alta qualidade de artigos do Arxiv com um clique, a melhor ferramenta de tradução de artigos atualmente
⭐Entrada de conversa de voz em tempo real | [Plugin] Monitoramento de áudio [assíncrono](https://www.bilibili.com/video/BV1AV4y187Uy/), segmentação automática de frases, detecção automática de momentos de resposta
Exibição de fórmulas, imagens e tabelas | Exibição de fórmulas em formato tex e renderizadas simultaneamente, suporte a fórmulas e destaque de código
⭐Plugin AutoGen para vários agentes | [Plugin] Explore a emergência de múltiplos agentes com o AutoGen da Microsoft!
Ativar o tema escuro | Adicione ```/?__theme=dark``` ao final da URL para alternar para o tema escuro
Suporte a múltiplos modelos LLM | Ser atendido simultaneamente pelo GPT3.5, GPT4, [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B) do Tsinghua University e [MOSS](https://github.com/OpenLMLab/MOSS) da Fudan University se sente incrível, não é mesmo?
⭐Modelo de ajuste fino ChatGLM2 | Suporte para carregar o modelo ChatGLM2 ajustado e fornecer plugins de assistência ao ajuste fino do ChatGLM2
Mais modelos LLM e suporte para [implantação pela HuggingFace](https://huggingface.co/spaces/qingxu98/gpt-academic) | Integração com a interface Newbing (Bing novo), introdução do [Jittorllms](https://github.com/Jittor/JittorLLMs) da Tsinghua University com suporte a [LLaMA](https://github.com/facebookresearch/llama) e [Panguα](https://openi.org.cn/pangu/)
⭐Pacote pip [void-terminal](https://github.com/binary-husky/void-terminal) | Chame todas as funções plugins deste projeto diretamente em Python, sem a GUI (em desenvolvimento)
⭐Plugin Terminal do Vácuo | [Plugin] Chame outros plugins deste projeto diretamente usando linguagem natural
Apresentação de mais novas funcionalidades (geração de imagens, etc.) ... | Veja no final deste documento ...
</div>
- Nova interface (altere a opção LAYOUT em `config.py` para alternar entre os "Layouts de lado a lado" e "Layout de cima para baixo")
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
</div>
- Todos os botões são gerados dinamicamente através da leitura do `functional.py`, você pode adicionar funcionalidades personalizadas à vontade, liberando sua área de transferência
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- Aprimoramento/Correção
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- Se a saída contiver fórmulas, elas serão exibidas tanto em formato tex quanto renderizado para facilitar a cópia e a leitura.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Não tem vontade de ver o código do projeto? O projeto inteiro está diretamente na boca do chatgpt.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Combinação de vários modelos de linguagem (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
# Instalação
### Método de instalação I: Executar diretamente (Windows, Linux ou MacOS)
1. Baixe o projeto
```sh
git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configure a API_KEY
No arquivo `config.py`, configure a API KEY e outras configurações. [Clique aqui para ver o método de configuração em redes especiais](https://github.com/binary-husky/gpt_academic/issues/1). [Página Wiki](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
「 O programa verificará primeiro se existe um arquivo de configuração privada chamado `config_private.py` e substituirá as configurações correspondentes no arquivo `config.py`. Se você entender essa lógica de leitura, é altamente recomendável criar um novo arquivo de configuração chamado `config_private.py` ao lado do `config.py` e copiar as configurações do `config.py` para o `config_private.py` (copiando apenas os itens de configuração que você modificou). 」
「 Suporte para configurar o projeto por meio de `variáveis de ambiente`, o formato de gravação das variáveis de ambiente pode ser encontrado no arquivo `docker-compose.yml` ou em nossa [página Wiki](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明). A prioridade de leitura das configurações é: `variáveis de ambiente` > `config_private.py` > `config.py`. 」
3. Instale as dependências
```sh
# (Opção I: Se você está familiarizado com o Python, Python>=3.9) Observação: Use o pip oficial ou o pip da Aliyun. Método temporário para alternar fontes: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Opção II: Use o Anaconda) Os passos também são semelhantes (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # Crie um ambiente do Anaconda
conda activate gptac_venv # Ative o ambiente do Anaconda
python -m pip install -r requirements.txt # Este passo é igual ao da instalação do pip
```
<details><summary>Se você quiser suporte para o ChatGLM2 do THU/ MOSS do Fudan/RWKV como backend, clique para expandir</summary>
<p>
[Opcional] Se você quiser suporte para o ChatGLM2 do THU/ MOSS do Fudan, precisará instalar dependências extras (pré-requisitos: familiarizado com o Python + já usou o PyTorch + o computador tem configuração suficiente):
```sh
# [Opcional Passo I] Suporte para ChatGLM2 do THU. Observações sobre o ChatGLM2 do THU: Se você encontrar o erro "Call ChatGLM fail 不能正常加载ChatGLM的参数" (Falha ao chamar o ChatGLM, não é possível carregar os parâmetros do ChatGLM), consulte o seguinte: 1: A versão instalada por padrão é a versão torch+cpu. Se você quiser usar a versão cuda, desinstale o torch e reinstale uma versão com torch+cuda; 2: Se a sua configuração não for suficiente para carregar o modelo, você pode modificar a precisão do modelo em request_llm/bridge_chatglm.py, alterando todas as ocorrências de AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) para AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llms/requirements_chatglm.txt
# [Opcional Passo II] Suporte para MOSS do Fudan
python -m pip install -r request_llms/requirements_moss.txt
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # Observe que você deve estar no diretório raiz do projeto ao executar este comando
# [Opcional Passo III] Suporte para RWKV Runner
Consulte a página Wiki: https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
# [Opcional Passo IV] Verifique se o arquivo de configuração config.py contém os modelos desejados, os modelos compatíveis são os seguintes (a série jittorllms suporta apenas a solução Docker):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Execute
```sh
python main.py
```
### Método de instalação II: Usando o Docker
0. Implante todas as capacidades do projeto (este é um contêiner grande que inclui CUDA e LaTeX. Não recomendado se você tiver uma conexão lenta com a internet ou pouco espaço em disco)
[![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
``` sh
# Modifique o arquivo docker-compose.yml para incluir apenas a seção 0 e excluir as outras seções. Em seguida, execute:
docker-compose up
```
1. ChatGPT + 文心一言 + spark + outros modelos online (recomendado para a maioria dos usuários)
[![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
[![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
``` sh
# Modifique o arquivo docker-compose.yml para incluir apenas a seção 1 e excluir as outras seções. Em seguida, execute:
docker-compose up
```
Obs.: Se você precisar do plugin Latex, consulte a Wiki. Além disso, você também pode usar a seção 4 ou 0 para obter a funcionalidade do LaTeX.
2. ChatGPT + ChatGLM2 + MOSS + LLAMA2 + 通义千问 (você precisa estar familiarizado com o [Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian) para executar este modo)
[![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
``` sh
# Modifique o arquivo docker-compose.yml para incluir apenas a seção 2 e excluir as outras seções. Em seguida, execute:
docker-compose up
```
### Método de instalação III: Outros métodos de implantação
1. **Script de execução com um clique para Windows**.
Usuários do Windows que não estão familiarizados com o ambiente Python podem baixar o script de execução com um clique da [Release](https://github.com/binary-husky/gpt_academic/releases) para instalar a versão sem modelos locais.
A contribuição do script vem de [oobabooga](https://github.com/oobabooga/one-click-installers).
2. Usar APIs de terceiros, Azure, etc., 文心一言, 星火, consulte a [página Wiki](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
3. Guia para evitar armadilhas na implantação em servidor em nuvem.
Consulte o [wiki de implantação em servidor em nuvem](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97).
4. Algumas novas plataformas ou métodos de implantação
- Use Sealos [implantação com um clique](https://github.com/binary-husky/gpt_academic/issues/993).
- Use o WSL2 (Subsistema do Windows para Linux). Consulte [wiki de implantação](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2).
- Como executar em um subdiretório da URL (como `http://localhost/subpath`). Consulte [instruções de execução com o FastAPI](docs/WithFastapi.md)
# Uso Avançado
### I: Personalização de Novos Botões de Atalho (Atalhos Acadêmicos)
Abra o arquivo `core_functional.py` em qualquer editor de texto, adicione o seguinte item e reinicie o programa. (Se o botão já existir, o prefixo e o sufixo podem ser modificados a qualquer momento sem reiniciar o programa).
Por exemplo:
```
"超级英译中": {
# Prefixo, adicionado antes do seu input. Por exemplo, usado para descrever sua solicitação, como traduzir, explicar o código, revisar, etc.
"Prefix": "Por favor, traduza o parágrafo abaixo para o chinês e explique cada termo técnico dentro de uma tabela markdown:\n\n",
# Sufixo, adicionado após o seu input. Por exemplo, em conjunto com o prefixo, pode-se colocar seu input entre aspas.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
### II: Personalização de Funções Plugins
Crie poderosos plugins de função para executar tarefas que você pode e não pode imaginar.
Criar plugins neste projeto é fácil, basta seguir o modelo fornecido, desde que você tenha conhecimento básico de Python.
Consulte o [Guia dos Plugins de Função](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) para mais detalhes.
# Atualizações
### I: Dinâmico
1. Função de salvar conversas. Chame a função "Salvar a conversa atual" na área de plugins para salvar a conversa atual em um arquivo HTML legível e recuperável. Além disso, chame a função "Carregar histórico de conversas" na área de plugins (menu suspenso) para restaurar conversas anteriores.
Dica: Se você clicar diretamente em "Carregar histórico de conversas" sem especificar o arquivo, poderá visualizar o cache do histórico do arquivo HTML.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. ⭐Tradução de artigos Latex/Arxiv⭐
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
</div>
3. Terminal vazio (entendendo a intenção do usuário a partir do texto em linguagem natural e chamando automaticamente outros plugins)
- Passo 1: Digite "Por favor, chame o plugin 'Traduzir artigo PDF' e forneça o link https://openreview.net/pdf?id=rJl0r3R9KX"
- Passo 2: Clique em "Terminal vazio"
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
</div>
4. Design de recursos modular, interface simples com suporte a recursos poderosos
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
5. Tradução e interpretação de outros projetos de código aberto
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
</div>
6. Recursos adicionais para [live2d](https://github.com/fghrsh/live2d_demo) (desativados por padrão, requer modificação no arquivo `config.py`)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. Geração de imagens pela OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
8. Análise e resumo de áudio pela OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
9. Correção de erros em texto e código LaTeX
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
</div>
10. Alternância de idioma e tema
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
</div>
### II: Versões:
- Versão 3.70 (a fazer): Melhorar o plugin AutoGen e projetar uma série de plugins relacionados.
- Versão 3.60: Introdução do AutoGen como base para a próxima geração de plugins.
- Versão 3.57: Suporte para GLM3, Starfire v3, Wenxin Yiyan v4, correção de bugs relacionados a modelos locais executados simultaneamente.
- Versão 3.56: Suporte para adicionar dinamicamente botões de função básicos e nova página de resumo em PDF.
- Versão 3.55: Reformulação da interface do usuário, introdução de janelas flutuantes e menus.
- Versão 3.54: Novo interpretador de código dinâmico (Code Interpreter) (em desenvolvimento)
- Versão 3.53: Suporte para alterar dinamicamente o tema da interface, melhorias de estabilidade e correção de conflitos entre vários usuários.
- Versão 3.50: Chamada de todas as funções de plugins deste projeto usando linguagem natural (Terminal vazio), suporte a categorização de plugins, melhorias na interface do usuário e design de novos temas.
- Versão 3.49: Suporte para Baidu Qianfan Platform e Wenxin Yiyan.
- Versão 3.48: Suporte para Alibaba DAMO Academy Tongyi Qianwen, Shanghai AI-Lab Shusheng e Xunfei Xinghuo.
- Versão 3.46: Suporte para diálogos em tempo real totalmente automáticos.
- Versão 3.45: Suporte para personalização do modelo ChatGLM2.
- Versão 3.44: Suporte oficial ao Azure, aprimoramentos na usabilidade da interface.
- Versão 3.4: Tradução completa de artigos Arxiv/Latex, correção de artigos Latex.
- Versão 3.3: Funcionalidade de consulta a informações na internet.
- Versão 3.2: Maior suporte para parâmetros de função de plugins (função de salvar conversas, interpretação de código em qualquer linguagem + perguntas sobre combinações LLM arbitrariamente).
- Versão 3.1: Suporte para fazer perguntas a modelos GPT múltiplos! Suporte para API2D, balanceamento de carga em vários APIKeys.
- Versão 3.0: Suporte para chatglm e outros pequenos modelos LLM.
- Versão 2.6: Refatoração da estrutura de plugins, melhoria na interação, adição de mais plugins.
- Versão 2.5: Auto-atualizável, resolve problemas de texto muito longo ou estouro de tokens ao resumir grandes projetos de código.
- Versão 2.4: (1) Novo recurso de tradução completa de PDF; (2) Nova função para alternar a posição da área de input; (3) Nova opção de layout vertical; (4) Melhoria dos plugins de função em várias threads.
- Versão 2.3: Melhorias na interação em várias threads.
- Versão 2.2: Suporte para recarregar plugins sem reiniciar o programa.
- Versão 2.1: Layout dobrável.
- Versão 2.0: Introdução de plugins de função modular.
- Versão 1.0: Funcionalidades básicas.
GPT Academic QQ Group: `610599535`
- Problemas conhecidos
- Alguns plugins de tradução de navegadores podem interferir na execução deste software.
- A biblioteca Gradio possui alguns bugs de compatibilidade conhecidos. Certifique-se de instalar o Gradio usando o arquivo `requirement.txt`.
### III: Temas
Você pode alterar o tema atualizando a opção `THEME` (config.py).
1. `Chuanhu-Small-and-Beautiful` [Link](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
### IV: Branches de Desenvolvimento deste Projeto
1. Branch `master`: Branch principal, versão estável.
2. Branch `frontier`: Branch de desenvolvimento, versão de teste.
### V: Referências para Aprendizado
```
O código referenciou muitos projetos excelentes, em ordem aleatória:
# Tsinghua ChatGLM2-6B:
https://github.com/THUDM/ChatGLM2-6B
# Tsinghua JittorLLMs:
https://github.com/Jittor/JittorLLMs
# ChatPaper:
https://github.com/kaixindelele/ChatPaper
# Edge-GPT:
https://github.com/acheong08/EdgeGPT
# ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Oobabooga instalador com um clique:
https://github.com/oobabooga/instaladores-de-um-clique
# Mais:
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo

361
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@ -0,0 +1,361 @@
> **Примечание**
>
> Этот README был переведен с помощью GPT (реализовано с помощью плагина этого проекта) и не может быть полностью надежным, пожалуйста, внимательно проверьте результаты перевода.
>
> 7 ноября 2023 года: При установке зависимостей, пожалуйста, выберите **указанные версии** из `requirements.txt`. Команда установки: `pip install -r requirements.txt`.
# <div align=center><img src="logo.png" width="40"> GPT Academic (GPT Академический)</div>
**Если вам нравится этот проект, пожалуйста, поставьте звезду; если у вас есть удобные горячие клавиши или плагины, приветствуются pull requests!**
Чтобы перевести этот проект на произвольный язык с помощью GPT, прочтите и выполните [`multi_language.py`](multi_language.py) (экспериментально).
> **Примечание**
>
> 1. Пожалуйста, обратите внимание, что только плагины (кнопки), выделенные **жирным шрифтом**, поддерживают чтение файлов, некоторые плагины находятся в выпадающем меню **плагинов**. Кроме того, мы с радостью приветствуем и обрабатываем PR для любых новых плагинов с **наивысшим приоритетом**.
>
> 2. Функции каждого файла в этом проекте подробно описаны в [отчете о самостоятельном анализе проекта `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). С каждым новым релизом вы также можете в любое время нажать на соответствующий функциональный плагин, вызвать GPT для повторной генерации сводного отчета о самоанализе проекта. Часто задаваемые вопросы [`wiki`](https://github.com/binary-husky/gpt_academic/wiki) | [обычные методы установки](#installation) | [скрипт одношаговой установки](https://github.com/binary-husky/gpt_academic/releases) | [инструкции по настройке](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
>
> 3. Этот проект совместим и настоятельно рекомендуется использование китайской NLP-модели ChatGLM и других моделей больших языков производства Китая. Поддерживает одновременное использование нескольких ключей API, которые можно указать в конфигурационном файле, например, `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. Если нужно временно заменить `API_KEY`, введите временный `API_KEY` в окне ввода и нажмите Enter для его подтверждения.
<div align="center">
Функции (⭐= Недавно добавленные функции) | Описание
--- | ---
⭐[Подключение новой модели](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | Baidu [QianFan](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu) и WenxinYiYan, [TongYiQianWen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), Shanghai AI-Lab [ShuSheng](https://github.com/InternLM/InternLM), Xunfei [XingHuo](https://xinghuo.xfyun.cn/), [LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), ZhiPu API, DALLE3
Улучшение, перевод, объяснение кода | Одним нажатием выполнить поиск синтаксических ошибок в научных статьях, переводить, объяснять код
[Настройка горячих клавиш](https://www.bilibili.com/video/BV14s4y1E7jN) | Поддержка настройки горячих клавиш
Модульный дизайн | Поддержка настраиваемых мощных [плагинов](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions), плагины поддерживают [горячую замену](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)
[Профилирование кода](https://www.bilibili.com/video/BV1cj411A7VW) | [Плагин] Одним нажатием можно профилировать дерево проекта Python/C/C++/Java/Lua/... или [проанализировать самого себя](https://www.bilibili.com/video/BV1cj411A7VW)
Просмотр статей, перевод статей | [Плагин] Одним нажатием прочитать полный текст статьи в формате LaTeX/PDF и сгенерировать аннотацию
Перевод LaTeX статей, [улучшение](https://www.bilibili.com/video/BV1FT411H7c5/)| [Плагин] Одним нажатием перевести или улучшить статьи в формате LaTeX
Генерация пакетного комментария | [Плагин] Одним нажатием сгенерировать многострочный комментарий к функции
Перевод Markdown на английский и китайский | [Плагин] Вы видели документацию на сверху на пяти языках? [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)`
Анализ и создание отчета в формате чата | [Плагин] Автоматически генерируйте сводный отчет после выполнения
Функция перевода полноценной PDF статьи | [Плагин] Изъять название и аннотацию статьи из PDF + переводить полный текст (многопоточно)
[Arxiv помощник](https://www.bilibili.com/video/BV1LM4y1279X) | [Плагин] Просто введите URL статьи на arXiv, чтобы одним нажатием выполнить перевод аннотации + загрузить PDF
Одним кликом проверить статью на LaTeX | [Плагин] Проверка грамматики и правописания статьи LaTeX, добавление PDF в качестве справки
[Помощник Google Scholar](https://www.bilibili.com/video/BV19L411U7ia) | [Плагин] Создайте "related works" с помощью Google Scholar URL по вашему выбору.
Агрегирование интернет-информации + GPT | [Плагин] [GPT получает информацию из интернета](https://www.bilibili.com/video/BV1om4y127ck) и отвечает на вопросы, чтобы информация никогда не устаревала
⭐Точный перевод статей Arxiv ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [Плагин] [Переводите статьи Arxiv наивысшего качества](https://www.bilibili.com/video/BV1dz4y1v77A/) всего одним нажатием. Сейчас это лучший инструмент для перевода научных статей
⭐[Реальное время ввода голосом](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [Плагин] Асинхронно [слушать аудио](https://www.bilibili.com/video/BV1AV4y187Uy/), автоматически разбивать на предложения, автоматически находить момент для ответа
Отображение формул/изображений/таблиц | Поддержка отображения формул в форме [tex и рендеринга](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), поддержка подсветки синтаксиса формул и кода
⭐Плагин AutoGen для множества интеллектуальных агентов | [Плагин] Используйте Microsoft AutoGen для исследования возможностей интеллектуального всплытия нескольких агентов!
Запуск [темной темы](https://github.com/binary-husky/gpt_academic/issues/173) | Добавьте `/?__theme=dark` в конец URL в браузере, чтобы переключиться на темную тему
[Поддержка нескольких моделей LLM](https://www.bilibili.com/video/BV1wT411p7yf) | Быть обслуживаемым GPT3.5, GPT4, [ChatGLM2 из Цинхуа](https://github.com/THUDM/ChatGLM2-6B), [MOSS из Фуданя](https://github.com/OpenLMLab/MOSS) одновременно должно быть очень приятно, не так ли?
⭐Модель ChatGLM2 Fine-tune | Поддержка загрузки модели ChatGLM2 Fine-tune, предоставляет вспомогательный плагин ChatGLM2 Fine-tune
Больше моделей LLM, поддержка [развертывания huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Включение интерфейса Newbing (новый Bing), введение поддержки китайских [Jittorllms](https://github.com/Jittor/JittorLLMs) для поддержки [LLaMA](https://github.com/facebookresearch/llama) и [Panguα](https://openi.org.cn/pangu/)
⭐Пакет pip [void-terminal](https://github.com/binary-husky/void-terminal) | Без GUI вызывайте все функциональные плагины этого проекта прямо из Python (разрабатывается)
⭐Плагин пустого терминала | [Плагин] Используя естественный язык, напрямую распоряжайтесь другими плагинами этого проекта
Больше новых функций (генерация изображений и т. д.) ... | Смотрите в конце этого документа ...
</div>
- Новый интерфейс (изменение опции LAYOUT в `config.py` позволяет переключиться между "расположением слева и справа" и "расположением сверху и снизу")
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
</div>
- Все кнопки генерируются динамически на основе `functional.py` и могут быть свободно дополнены, освобождая буфер обмена
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- Улучшение/исправление
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- Если вывод содержит формулы, они отображаются одновременно в виде tex и отрендеренного вида для удобства копирования и чтения
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Не хочешь смотреть код проекта? Весь проект сразу в уста ChatGPT
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Смешанное использование нескольких больших языковых моделей (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
# Установка
### Метод установки I: Прямой запуск (Windows, Linux или MacOS)
1. Скачайте проект
```sh
git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Настройте API_KEY
В файле `config.py` настройте API KEY и другие настройки, [нажмите здесь, чтобы узнать способы настройки в специальных сетевых средах](https://github.com/binary-husky/gpt_academic/issues/1). [Инструкции по настройке проекта](https://github.com/binary-husky/gpt_academic/wiki/Сonfig-Instructions).
「 Программа будет в первую очередь проверять наличие файла config_private.py с приватными настройками и заменять соответствующие настройки в файле config.py на те, которые указаны в файле config_private.py. Если вы понимаете эту логику, мы настоятельно рекомендуем вам создать новый файл настроек config_private.py рядом с файлом config.py и скопировать туда настройки из config.py (только те, которые вы изменяли). 」
「 Поддерживается настроить проект с помощью `переменных среды`. Пример настройки переменных среды можно найти в файле docker-compose.yml или на нашей [странице вики](https://github.com/binary-husky/gpt_academic/wiki/Сonfig-Instructions). Приоритет настроек: `переменные среды` > `config_private.py` > `config.py`. 」
3. Установите зависимости
```sh
# (Выбор I: Если знакомы с Python, python>=3.9). Примечание: используйте официальный pip-репозиторий или пакетный репозиторий Alibaba, временный способ изменить источник: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Выбор II: Используйте Anaconda). Шаги аналогичны (https://www.bilibili.com/video/BV1rc411W7Dr)：
conda create -n gptac_venv python=3.11 # Создание среды Anaconda
conda activate gptac_venv # Активация среды Anaconda
python -m pip install -r requirements.txt # Здесь все тоже самое, что и с установкой для pip
```
<details><summary>Если вам нужна поддержка ChatGLM2 от Цинхуа/MOSS от Фуданя/Раннера RWKV как бэкенда, нажмите, чтобы развернуть</summary>
<p>
【Опциональный шаг】Если вам нужна поддержка ChatGLM2 от Цинхуа/Сервиса MOSS от Фуданя, вам понадобится дополнительно установить дополнительные зависимости (предполагается, что вы знакомы с Python + PyTorch + у вас достаточно мощный компьютер):
```sh
# 【Опциональный шаг I】Поддержка ChatGLM2 от Цинхуа. Примечание к ChatGLM от Цинхуа: Если вы столкнулись с ошибкой "Call ChatGLM fail 不能正常加载ChatGLM的参数", обратите внимание на следующее: 1: По умолчанию установлена версия torch+cpu, для использования cuda необходимо удалить torch и установить версию torch+cuda; 2: Если вы не можете загрузить модель из-за недостаточной мощности компьютера, вы можете изменить точность модели в файле request_llm/bridge_chatglm.py, заменив AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) на AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llms/requirements_chatglm.txt
# 【Опциональный шаг II】Поддержка MOSS от Фуданя
python -m pip install -r request_llms/requirements_moss.txt
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # Обратите внимание, что когда вы запускаете эту команду, вы должны находиться в корневой папке проекта
# 【Опциональный шаг III】Поддержка RWKV Runner
Смотрите вики: https://github.com/binary-husky/gpt_academic/wiki/Поддержка-RWKV-Runner
# 【Опциональный шаг IV】Убедитесь, что config.py содержит все нужные вам модели. Пример:
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Запустите программу
```sh
python main.py
```
### Метод установки II: Используйте Docker
0. Установка всех возможностей проекта (это большой образ с поддержкой cuda и LaTeX; но если у вас медленный интернет или маленький жесткий диск, мы не рекомендуем использовать этот метод).
[![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
``` sh
# Измените файл docker-compose.yml, сохраните метод 0 и удалите другие методы. Затем запустите:
docker-compose up
```
1. Чат GPT + 文心一言 + Spark и другие онлайн-модели (рекомендуется для большинства пользователей)
[![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
[![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
``` sh
# Измените файл docker-compose.yml, сохраните метод 1 и удалите другие методы. Затем запустите:
docker-compose up
```
P.S. Если вам нужен функционал, связанный с LaTeX, обратитесь к разделу Wiki. Кроме того, вы также можете использовать схему 4 или схему 0 для доступа к функционалу LaTeX.
2. Чат GPT + ChatGLM2 + MOSS + LLAMA2 + TakyiQ & Другие попытки ввести в обиход
[![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
``` sh
# Измените файл docker-compose.yml, сохраните метод 2 и удалите другие методы. Затем запустите:
docker-compose up
```
### Метод установки III: Другие способы развертывания
1. **Скрипты запуска одним нажатием для Windows**.
Пользователи Windows, не знакомые с окружением Python, могут загрузить одну из версий в разделе [Релизы](https://github.com/binary-husky/gpt_academic/releases) для установки версии без локальных моделей.
Скрипты взяты из вкладки [oobabooga](https://github.com/oobabooga/one-click-installers).
2. Использование сторонних API, Azure и т. д., см. страницу [вики](https://github.com/binary-husky/gpt_academic/wiki/Сonfig-Instructions)
3. Руководство по развертыванию на удаленном сервере.
Пожалуйста, посетите [вики-страницу развертывания на облачном сервере](https://github.com/binary-husky/gpt_academic/wiki/Руководство-по-развертыванию-на-облаке).
4. Некоторые новые платформы или методы развертывания
- Использование Sealos [для однократного развертывания](https://github.com/binary-husky/gpt_academic/issues/993)
- Использование WSL2 (Windows Subsystem for Linux). См. [Руководство развертывания-2](https://github.com/binary-husky/gpt_academic/wiki/Using-WSL2-for-deployment)
- Как запустить на вложенном URL-адресе (например, `http://localhost/subpath`). См. [Инструкции по работе с FastAPI](docs/WithFastapi.md)
# Расширенное использование
### I: Пользовательские удобные кнопки (академические сочетания клавиш)
Откройте файл `core_functional.py` в любом текстовом редакторе и добавьте следующие записи, затем перезапустите программу. (Если кнопка уже существует, то префикс и суффикс поддерживают горячую замену без перезапуска программы.)
Например,
```
"Супер-англо-русский перевод": {
# Префикс, который будет добавлен перед вашим вводом. Например, используется для описания вашего запроса, например, перевода, объяснения кода, редактирования и т.д.
"Префикс": "Пожалуйста, переведите следующий абзац на русский язык, а затем покажите каждый термин на экране с помощью таблицы Markdown:\n\n",
# Суффикс, который будет добавлен после вашего ввода. Например, можно использовать с префиксом, чтобы заключить ваш ввод в кавычки.
"Суффикс": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
### II: Пользовательские функциональные плагины
Создавайте мощные функциональные плагины для выполнения любых задач, которые вам нужны и которых вы и не можете себе представить.
Создание плагина для этого проекта и его отладка являются простыми задачами, и если у вас есть базовые знания Python, вы можете реализовать свой собственный функциональный плагин, используя наши предоставленные шаблоны.
Дополнительную информацию см. в [Руководстве по функциональным плагинам](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).
# Обновления
### I: Динамические
1. Функция сохранения диалога. Вызовите "Сохранить текущий диалог" в области функциональных плагинов, чтобы сохранить текущий диалог в виде читаемого и восстанавливаемого html-файла.
Кроме того, можно использовать "Загрузить архивный файл диалога" в области функциональных плагинов (выпадающее меню), чтобы восстановить предыдущий разговор.
Подсказка: если не указывать файл и просто щелкнуть "Загрузить архивный файл диалога", можно просмотреть кэш сохраненных html-архивов.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. ⭐Перевод Latex/Arxiv статей⭐
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
</div>
3. Void Terminal (понимание пользовательских намерений из естественного языка и автоматическое вызов других плагинов)
- Шаг 1: Введите "Пожалуйста, вызовите плагин для перевода PDF-статьи, адрес которой https://openreview.net/pdf?id=rJl0r3R9KX".
- Шаг 2: Нажмите "Void Terminal".
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
</div>
4. Модульный дизайн функционала, позволяющий реализовать мощные функции с помощью простых интерфейсов
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
5. Перевод и анализ других открытых проектов
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
</div>
6. Функциональность для украшения[meme](https://github.com/fghrsh/live2d_demo) (по умолчанию отключена, требуется изменение файла `config.py`)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. Генерация изображений с помощью OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
8. Анализ и обобщение аудио с помощью OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
9. Проверка и исправление ошибок во всем тексте LaTeX
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
</div>
10. Изменение языка и темы
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
</div>
### II: Версии:
- Версия 3.70 (в планах): Оптимизация темы AutoGen и разработка ряда дополнительных плагинов
- Версия 3.60: Внедрение AutoGen в качестве фундамента нового поколения плагинов
- Версия 3.57: Поддержка GLM3, Starfire v3, Wenxin One Word v4, исправление ошибок при совместном использовании локальной модели
- Версия 3.56: Поддержка добавления дополнительных функциональных кнопок в реальном времени, новая страница отчетов в формате PDF
- Версия 3.55: Переработка пользовательского интерфейса, внедрение плавающего окна и панели меню
- Версия 3.54: Добавлен интерпретатор кода (Code Interpreter) (в разработке)
- Версия 3.53: Динамический выбор различных тем интерфейса, повышение стабильности и решение проблемы конфликтов между несколькими пользователями
- Версия 3.50: Использование естественного языка для вызова всех функциональных плагинов проекта (Void Terminal), поддержка категоризации плагинов, улучшение пользовательского интерфейса, разработка новых тем
- Версия 3.49: Поддержка платформы Baidu Qianfan и Wenxin One Word
- Версия 3.48: Поддержка Ali Dharma Institute, Shanghai AI-Lab Scholar, Xunfei Starfire
- Версия 3.46: Поддержка реального голосового диалога с полной автоматизацией
- Версия 3.45: Поддержка настраиваемой модели ChatGLM2
- Версия 3.44: Официальная поддержка Azure, улучшение удобства пользовательского интерфейса
- Версия 3.4: +Перевод полных текстов PDF, +корректировка латексных документов
- Версия 3.3: +Интернет-информационные функции
- Версия 3.2: Поддержка дополнительных параметров в функциональных плагинах (функция сохранения диалога, интерпретация кода на любом языке + одновременный вопрос о любом комбинированном LLM)
- Версия 3.1: Поддержка одновременного обращения к нескольким моделям gpt! Поддержка API2D, поддержка равномерной нагрузки нескольких api-ключей
- Версия 3.0: Поддержка chatglm и других небольших моделей llm
- Версия 2.6: Переработка структуры плагинов для повышения интерактивности, добавление дополнительных плагинов
- Версия 2.5: Автоматическое обновление, решение проблемы с длиной текста и переполнением токенов при обработке текста
- Версия 2.4: (1) Добавление функции полного перевода PDF; (2) Добавление функции изменения позиции объекта ввода; (3) Добавление функции вертикального размещения; (4) Оптимизация многопоточных функциональных плагинов.
- Версия 2.3: Улучшение интерактивности многопоточности
- Версия 2.2: Поддержка живой перезагрузки функциональных плагинов
- Версия 2.1: Складываемый макет
- Версия 2.0: Введение модульных функциональных плагинов
- Версия 1.0: Базовые функции
GPT Academic Группа QQ разработчиков: `610599535`
- Известные проблемы
- Некоторые расширения для браузера могут мешать работе пользовательского интерфейса этого программного обеспечения
- У официального Gradio есть много проблем совместимости, поэтому обязательно установите Gradio с помощью `requirement.txt`
### III: Темы
Вы можете изменить тему путем изменения опции `THEME` (config.py)
1. `Chuanhu-Small-and-Beautiful` [ссылка](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
### IV: Ветви разработки этого проекта
1. Ветка `master`: Основная ветка, стабильная версия
2. Ветвь `frontier`: Ветвь разработки, версия для тестирования
### V: Справочники и обучение
```
В коде использовались многие функции, представленные в других отличных проектах, поэтому их порядок не имеет значения:
# ChatGLM2-6B от Тиньхуа:
https://github.com/THUDM/ChatGLM2-6B
# Линейные модели с ограниченной памятью от Тиньхуа:
https://github.com/Jittor/JittorLLMs
# ChatPaper:
https://github.com/kaixindelele/ChatPaper
# Edge-GPT:
https://github.com/acheong08/EdgeGPT
# ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Установщик с одним щелчком Oobabooga:
https://github.com/oobabooga/one-click-installers
# Больше:
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo

307
docs/README.md.German.md
View File

@ -1,307 +0,0 @@
> **Hinweis**
>
> Bei der Installation von Abhängigkeiten sollten nur die in **requirements.txt** **angegebenen Versionen** streng ausgewählt werden.
>
> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`
# <img src="docs/logo.png" width="40" > GPT Akademisch optimiert (GPT Academic)
**Wenn Ihnen dieses Projekt gefällt, geben Sie ihm bitte einen Stern; wenn Sie bessere Tastenkombinationen oder Funktions-Plugins entwickelt haben, können Sie gerne einen Pull Request eröffnen.**
Wenn Sie dieses Projekt mögen, geben Sie ihm bitte einen Stern. Wenn Sie weitere nützliche wissenschaftliche Abkürzungen oder funktionale Plugins entwickelt haben, können Sie gerne ein Problem oder eine Pull-Anforderung öffnen. Wir haben auch ein README in [Englisch|](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), das von diesem Projekt selbst übersetzt wurde.
Um dieses Projekt in eine beliebige Sprache mit GPT zu übersetzen, lesen Sie `multi_language.py` (experimentell).
> **Hinweis**
>
> 1. Beachten Sie bitte, dass nur Funktionserweiterungen (Schaltflächen) mit **roter Farbe** Dateien lesen können und einige Erweiterungen im **Dropdown-Menü** des Erweiterungsbereichs zu finden sind. Außerdem begrüßen wir jede neue Funktionserweiterung mit **höchster Priorität** und bearbeiten sie.
>
> 2. Die Funktionalität jeder Datei in diesem Projekt wird in der Selbstanalyse [`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) detailliert beschrieben. Mit der Weiterentwicklung der Versionen können Sie jederzeit die zugehörigen Funktions-Erweiterungen aufrufen, um durch Aufruf von GPT einen Selbstanalysebericht des Projekts zu erstellen. Häufig gestellte Fragen finden Sie in der [`Wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Installationsanweisungen](#Installation).
>
> 3. Dieses Projekt ist kompatibel und fördert die Verwendung von inländischen Sprachmodellen wie ChatGLM und RWKV, Pangu, etc. Es unterstützt das Vorhandensein mehrerer api-keys, die in der Konfigurationsdatei wie folgt angegeben werden können: `API_KEY="openai-key1,openai-key2,api2d-key3"`. Wenn ein `API_KEY` temporär geändert werden muss, geben Sie den temporären `API_KEY` im Eingabebereich ein und drücken Sie dann die Eingabetaste, um ihn zu übernehmen.Funktion | Beschreibung
--- | ---
Ein-Klick-Polieren | Unterstützt ein-Klick-Polieren und ein-Klick-Suche nach grammatikalischen Fehlern in wissenschaftlichen Arbeiten
Ein-Klick Chinesisch-Englisch Übersetzung | Ein-Klick Chinesisch-Englisch Übersetzung
Ein-Klick-Code-Erklärung | Zeigt Code, erklärt Code, erzeugt Code und fügt Kommentare zum Code hinzu
[Benutzerdefinierte Tastenkombinationen](https://www.bilibili.com/video/BV14s4y1E7jN) | Unterstützt benutzerdefinierte Tastenkombinationen
Modulare Gestaltung | Unterstützt leistungsstarke individuelle [Funktions-Plugins](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions). Plugins unterstützen [Hot-Updates](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)
[Selbstprogramm-Analyse](https://www.bilibili.com/video/BV1cj411A7VW) | [Funktions-Plugin] [Ein-Klick Verstehen](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) der Quellcode dieses Projekts
[Programmanalyse](https://www.bilibili.com/video/BV1cj411A7VW) | [Funktions-Plugin] Ein-Klick-Analyse des Projektbaums anderer Python/C/C++/Java/Lua/...-Projekte
Lesen von Papieren, [Übersetzen](https://www.bilibili.com/video/BV1KT411x7Wn) von Papieren | [Funktions-Plugin] Ein-Klick Erklärung des gesamten LaTeX/PDF-Artikels und Erstellung einer Zusammenfassung
LaTeX-Volltext-Übersetzung und [Polieren](https://www.bilibili.com/video/BV1FT411H7c5/) | [Funktions-Plugin] Ein-Klick-Übersetzung oder-Polieren des LaTeX-Artikels
Bulk-Kommentargenerierung | [Funktions-Plugin] Ein-Klick Massenerstellung von Funktionskommentaren
Markdown [Chinesisch-Englisch Übersetzung](https://www.bilibili.com/video/BV1yo4y157jV/) | [Funktions-Plugin] Haben Sie die [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) in den oben genannten 5 Sprachen gesehen?
Analyse-Berichtserstellung von chat | [Funktions-Plugin] Automatische Zusammenfassung nach der Ausführung
[Funktion zur vollständigen Übersetzung von PDF-Artikeln](https://www.bilibili.com/video/BV1KT411x7Wn) | [Funktions-Plugin] Extrahiert Titel und Zusammenfassung der PDF-Artikel und übersetzt den gesamten Text (mehrere Threads)
[Arxiv-Assistent](https://www.bilibili.com/video/BV1LM4y1279X) | [Funktions-Plugin] Geben Sie die Arxiv-Artikel-URL ein und klicken Sie auf Eine-Klick-Übersetzung-Zusammenfassung + PDF-Download
[Google Scholar Integrations-Assistent](https://www.bilibili.com/video/BV19L411U7ia) | [Funktions-Plugin] Geben Sie eine beliebige Google Scholar Such-URL ein und lassen Sie gpt Ihnen bei der Erstellung von [relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/) helfen
Internet-Informationen Aggregation + GPT | [Funktions-Plugin] Lassen Sie GPT eine Frage beantworten, indem es [zuerst Informationen aus dem Internet](https://www.bilibili.com/video/BV1om4y127ck/) sammelt und so die Informationen nie veralten
Anzeige von Formeln / Bildern / Tabellen | Zeigt Formeln in beiden Formen, [TeX-Format und gerendeter Form](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), unterstützt Formeln und Code-Highlights
Unterstützung von PlugIns mit mehreren Threads | Unterstützt den Aufruf mehrerer Threads in Chatgpt, um Text oder Programme [Batch zu verarbeiten](https://www.bilibili.com/video/BV1FT411H7c5/)
Starten Sie das dunkle Gradio-[Thema](https://github.com/binary-husky/gpt_academic/issues/173) | Fügen Sie ```/?__theme=dark``` an das Ende der Browser-URL an, um das dunkle Thema zu aktivieren
[Unterstützung für mehrere LLM-Modelle](https://www.bilibili.com/video/BV1wT411p7yf), [API2D](https://api2d.com/) Interface-Unterstützung | Das Gefühl, gleichzeitig von GPT3.5, GPT4, [Tshinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS) bedient zu werden, muss toll sein, oder?
Zugriff auf weitere LLM-Modelle, Unterstützung von [huggingface deployment](https://huggingface.co/spaces/qingxu98/gpt-academic) | Hinzufügen der Newbing-Schnittstelle (neues Bing), Einführung der Unterstützung von [Jittorllms](https://github.com/Jittor/JittorLLMs) der Tsinghua-Universität, [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) und [Pangu alpha](https://openi.org.cn/pangu/)
Weitere neue Funktionen (wie Bildgenerierung) …… | Siehe Ende dieses Dokuments ……
- Neue Oberfläche (Ändern Sie die LAYOUT-Option in `config.py`, um zwischen "Seitenlayout" und "Oben-unten-Layout" zu wechseln)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
</div>- All buttons are dynamically generated by reading `functional.py`, and custom functions can be easily added, freeing up the clipboard.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- Proofreading/Correcting
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- If the output contains formulas, they will be displayed in both tex format and rendered format for easy copying and reading.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Don't feel like reading the project code? Show off the entire project to chatgpt.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Multiple large language models are mixed and called together (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4).
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
---
# Installation
## Installation-Method 1: Run directly (Windows, Linux or MacOS)
1. Download the project
```sh
git clone https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configure API_KEY
Configure API KEY and other settings in `config.py`. [Special Network Environment Settings](https://github.com/binary-husky/gpt_academic/issues/1).
(P.S. When the program is running, it will first check whether there is a "config_private.py" private configuration file, and use the configuration defined in it to override the configuration of "config.py". Therefore, if you understand our configuration reading logic, we strongly recommend that you create a new configuration file named "config_private.py" next to "config.py" and transfer (copy) the configurations in "config.py" to "config_private.py". "config_private.py" is not controlled by git, which can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`, and the writing format of environment variables refers to the `docker-compose` file. Reading priority: `environment variable` > `config_private.py` >`config.py`)
3. Install dependencies
```sh
# (Option I: If familar with Python) (Python version 3.9 or above, the newer the better), Note: Use the official pip source or Ali pip source, temporary switching method: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Option II: If not familiar with Python) Use anaconda with similar steps (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # Create an anaconda environment
conda activate gptac_venv # Activate the anaconda environment
python -m pip install -r requirements.txt # Same step as pip installation
```
<details><summary>Click to expand if supporting Tsinghua ChatGLM/Fudan MOSS as backend</summary>
<p>
[Optional Step] If supporting Tsinghua ChatGLM/Fudan MOSS as backend, additional dependencies need to be installed (Prerequisites: Familiar with Python + Used Pytorch + Sufficient computer configuration):
```sh
# [Optional Step I] Support Tsinghua ChatGLM. Remark: If encountering "Call ChatGLM fail Cannot load ChatGLM parameters", please refer to the following: 1: The above default installation is torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2: If the model cannot be loaded due to insufficient machine configuration, you can modify the model precision in `request_llm/bridge_chatglm.py`, and modify all AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llm/requirements_chatglm.txt
# [Optional Step II] Support Fudan MOSS
python -m pip install -r request_llm/requirements_moss.txt
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # When executing this line of code, you must be in the project root path
# [Optional Step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. Currently supported models are as follows (jittorllms series currently only supports docker solutions):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Run
```sh
python main.py
```5. Testing Function Plugin
```
- Test function plugin template function (requires gpt to answer what happened today in history), you can use this function as a template to implement more complex functions
Click "[Function Plugin Template Demo] Today in History"
```
## Installation-Method 2: Using Docker
1. Only ChatGPT (Recommended for most people)
``` sh
git clone https://github.com/binary-husky/gpt_academic.git # Download the project
cd gpt_academic # Enter the path
nano config.py # Edit config.py with any text editor, Configure "Proxy","API_KEY"and"WEB_PORT" (e.g 50923) etc.
docker build -t gpt-academic . # Install
# (Last step-option 1) Under Linux environment, use `--net=host` is more convenient and quick
docker run --rm -it --net=host gpt-academic
# (Last step-option 2) Under macOS/windows environment, can only use the -p option to expose the container's port(eg.50923) to the port on the host.
docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
```
2. ChatGPT + ChatGLM + MOSS (Requires familiarity with Docker)
``` sh
# Modify docker-compose.yml, delete solution 1 and solution 3, and retain solution 2. Modify the configuration of solution 2 in docker-compose.yml, referring to the comments in it.
docker-compose up
```
3. ChatGPT+LLAMA+Pangu+RWKV(Requires familiarity with Docker)
``` sh
# Modify docker-compose.yml, delete solution 1 and solution 2, and retain solution 3. Modify the configuration of solution 3 in docker-compose.yml, referring to the comments in it.
docker-compose up
```
## Installation-Method 3: Other Deployment Options
1. How to use reverse proxy URL/Microsoft Azure API
Configure API_URL_REDIRECT according to the instructions in `config.py`.
2. Remote cloud server deployment (requires cloud server knowledge and experience)
Please visit [Deployment wiki-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
3. Using WSL 2 (Windows subsystem for Linux)
Please visit [Deployment wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
4. How to run at a secondary URL (such as `http://localhost/subpath`)
Please visit [FastAPI operating instructions](docs/WithFastapi.md)
5. Use docker-compose to run
Please read docker-compose.yml and follow the prompts to operate.
---
# Advanced Usage
## Customize new convenience buttons / custom function plugins.
1. Customize new convenience buttons (Academic Shortcut Keys)
Open `core_functional.py` with any text editor, add an entry as follows, and then restart the program. (If the button has been added successfully and is visible, then the prefix and suffix can be hot-modified, and it will take effect without restarting the program.)
For example
```
"Super English to Chinese": {
# Prefix, will be added before your input. For example, used to describe your requirements, such as translation, explaining code, polishing, etc.
"Prefix": "Please translate the following content into Chinese, and then use a markdown table to explain the proper nouns that appear in the text one by one:\n\n",
# Suffix, will be added after your input. For example, combined with prefix, you can enclose your input content in quotes.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
2. Custom function plugins
Write powerful function plugins to perform any task you want and can't think of.
The difficulty of plugin writing and debugging is very low in this project. As long as you have a certain knowledge of Python, you can implement your own plugin functions by imitating the template we provided.
For more information, please refer to the [Function Plugin Guide](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
## New feature dynamics1. Funktion zur Speicherung von Dialogen. Rufen Sie im Bereich der Funktions-Plugins "Aktuellen Dialog speichern" auf, um den aktuellen Dialog als lesbares und wiederherstellbares HTML-Datei zu speichern. Darüber hinaus können Sie im Funktions-Plugin-Bereich (Dropdown-Menü) "Laden von Dialogverlauf" aufrufen, um den vorherigen Dialog wiederherzustellen. Tipp: Wenn Sie keine Datei angeben und stattdessen direkt auf "Laden des Dialogverlaufs" klicken, können Sie das HTML-Cache-Archiv anzeigen. Durch Klicken auf "Löschen aller lokalen Dialogverlaufsdatensätze" können alle HTML-Archiv-Caches gelöscht werden.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. Berichterstellung. Die meisten Plugins generieren nach Abschluss der Ausführung einen Arbeitsbericht.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
</div>
3. Modularisierte Funktionsgestaltung, einfache Schnittstellen mit leistungsstarken Funktionen.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
4. Dies ist ein Open-Source-Projekt, das sich "selbst übersetzen" kann.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
</div>
5. Die Übersetzung anderer Open-Source-Projekte ist kein Problem.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
</div>
6. Dekorieren Sie [`live2d`](https://github.com/fghrsh/live2d_demo) mit kleinen Funktionen (standardmäßig deaktiviert, Änderungen an `config.py` erforderlich).
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. Neue MOSS-Sprachmodellunterstützung.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
</div>
8. OpenAI-Bildgenerierung.
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
9. OpenAI-Audio-Analyse und Zusammenfassung.
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
10. Latex-Proofreading des gesamten Textes.
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
</div>
## Version:
- Version 3.5 (Todo): Rufen Sie alle Funktionserweiterungen dieses Projekts mit natürlicher Sprache auf (hohe Priorität).
- Version 3.4 (Todo): Verbesserte Unterstützung mehrerer Threads für Local Large Model (LLM).
- Version 3.3: + Internet-Informationssynthese-Funktion
- Version 3.2: Funktionserweiterungen unterstützen mehr Parameter-Schnittstellen (Speicherung von Dialogen, Interpretation beliebigen Sprachcodes + gleichzeitige Abfrage jeder LLM-Kombination)
- Version 3.1: Unterstützung mehrerer GPT-Modelle gleichzeitig! Unterstützung für API2D, Unterstützung für Lastenausgleich von mehreren API-Schlüsseln.
- Version 3.0: Unterstützung von Chatglm und anderen kleinen LLMs
- Version 2.6: Umstrukturierung der Plugin-Struktur zur Verbesserung der Interaktivität, Einführung weiterer Plugins
- Version 2.5: Automatische Aktualisierung, Problembehebung bei Quelltexten großer Projekte, wenn der Text zu lang ist oder Token überlaufen.
- Version 2.4: (1) Neue Funktion zur Übersetzung des gesamten PDF-Texts; (2) Neue Funktion zum Wechseln der Position des Eingabebereichs; (3) Neue Option für vertikales Layout; (4) Optimierung von Multithread-Funktions-Plugins.
- Version 2.3: Verbesserte Interaktivität mit mehreren Threads
- Version 2.2: Funktionserweiterungen unterstützen "Hot-Reload"
- Version 2.1: Faltbares Layout
- Version 2.0: Einführung von modularisierten Funktionserweiterungen
- Version 1.0: Grundlegende Funktionengpt_academic Entwickler QQ-Gruppe-2: 610599535
- Bekannte Probleme
- Einige Browser-Übersetzungs-Plugins können die Frontend-Ausführung dieser Software stören.
- Sowohl eine zu hohe als auch eine zu niedrige Version von Gradio führt zu verschiedenen Ausnahmen.
## Referenz und Lernen
```
Der Code bezieht sich auf viele Designs von anderen herausragenden Projekten, insbesondere:
# Projekt 1: ChatGLM-6B der Tsinghua Universität:
https://github.com/THUDM/ChatGLM-6B
# Projekt 2: JittorLLMs der Tsinghua Universität:
https://github.com/Jittor/JittorLLMs
# Projekt 3: Edge-GPT:
https://github.com/acheong08/EdgeGPT
# Projekt 4: ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Projekt 5: ChatPaper:
https://github.com/kaixindelele/ChatPaper
# Mehr:
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo
```

316
docs/README.md.Italian.md
View File

@ -1,316 +0,0 @@
> **Nota**
>
> Durante l'installazione delle dipendenze, selezionare rigorosamente le **versioni specificate** nel file requirements.txt.
>
> ` pip install -r requirements.txt`
# <img src="logo.png" width="40" > GPT Ottimizzazione Accademica (GPT Academic)
**Se ti piace questo progetto, ti preghiamo di dargli una stella. Se hai sviluppato scorciatoie accademiche o plugin funzionali più utili, non esitare ad aprire una issue o pull request. Abbiamo anche una README in [Inglese|](README_EN.md)[Giapponese|](README_JP.md)[Coreano|](https://github.com/mldljyh/ko_gpt_academic)[Russo|](README_RS.md)[Francese](README_FR.md) tradotta da questo stesso progetto.
Per tradurre questo progetto in qualsiasi lingua con GPT, leggere e eseguire [`multi_language.py`](multi_language.py) (sperimentale).
> **Nota**
>
> 1. Si prega di notare che solo i plugin (pulsanti) contrassegnati in **rosso** supportano la lettura di file, alcuni plugin sono posizionati nel **menu a discesa** nella zona dei plugin. Accettiamo e gestiamo PR per qualsiasi nuovo plugin con **massima priorità**!
>
> 2. Le funzionalità di ogni file di questo progetto sono descritte dettagliatamente nella propria analisi di autotraduzione [`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). Con l'iterazione delle versioni, è possibile fare clic sui plugin funzionali correlati in qualsiasi momento per richiamare GPT e generare nuovamente il rapporto di analisi automatica del progetto. Le domande frequenti sono riassunte nella [`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Metodo di installazione] (#installazione).
>
> 3. Questo progetto è compatibile e incoraggia l'utilizzo di grandi modelli di linguaggio di produzione nazionale come chatglm, RWKV, Pangu ecc. Supporta la coesistenza di più api-key e può essere compilato nel file di configurazione come `API_KEY="openai-key1,openai-key2,api2d-key3"`. Per sostituire temporaneamente `API_KEY`, inserire `API_KEY` temporaneo nell'area di input e premere Invio per renderlo effettivo.
<div align="center">
Funzione | Descrizione
--- | ---
Correzione immediata | Supporta correzione immediata e ricerca degli errori di grammatica del documento con un solo clic
Traduzione cinese-inglese immediata | Traduzione cinese-inglese immediata con un solo clic
Spiegazione del codice immediata | Visualizzazione del codice, spiegazione del codice, generazione del codice, annotazione del codice con un solo clic
[Scorciatoie personalizzate](https://www.bilibili.com/video/BV14s4y1E7jN) | Supporta scorciatoie personalizzate
Design modularizzato | Supporta potenti [plugin di funzioni](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions) personalizzati, i plugin supportano l'[aggiornamento in tempo reale](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)
[Auto-profiling del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] [Comprensione immediata](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) del codice sorgente di questo progetto
[Analisi del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] Un clic può analizzare l'albero di altri progetti Python/C/C++/Java/Lua/...
Lettura del documento, [traduzione](https://www.bilibili.com/video/BV1KT411x7Wn) del documento | [Plugin di funzioni] La lettura immediata dell'intero documento latex/pdf di un documento e la generazione di un riassunto
Traduzione completa di un documento Latex, [correzione immediata](https://www.bilibili.com/video/BV1FT411H7c5/) | [Plugin di funzioni] Una traduzione o correzione immediata di un documento Latex
Generazione di annotazioni in batch | [Plugin di funzioni] Generazione automatica delle annotazioni di funzione con un solo clic
[Traduzione cinese-inglese di Markdown](https://www.bilibili.com/video/BV1yo4y157jV/) | [Plugin di funzioni] Hai letto il [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) delle cinque lingue sopra?
Generazione di report di analisi di chat | [Plugin di funzioni] Generazione automatica di un rapporto di sintesi dopo l'esecuzione
[Funzione di traduzione di tutto il documento PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin di funzioni] Estrarre il titolo e il sommario dell'articolo PDF + tradurre l'intero testo (multithreading)
[Assistente di Arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plugin di funzioni] Inserire l'URL dell'articolo di Arxiv e tradurre il sommario con un clic + scaricare il PDF
[Assistente integrato di Google Scholar](https://www.bilibili.com/video/BV19L411U7ia) | [Plugin di funzioni] Con qualsiasi URL di pagina di ricerca di Google Scholar, lascia che GPT ti aiuti a scrivere il tuo [relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
Aggregazione delle informazioni su Internet + GPT | [Plugin di funzioni] Fai in modo che GPT rilevi le informazioni su Internet prima di rispondere alle domande, senza mai diventare obsolete
Visualizzazione di formule/img/tabelle | È possibile visualizzare un'equazione in forma [tex e render](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png) contemporaneamente, supporta equazioni e evidenziazione del codice
Supporto per plugin di funzioni multithreading | Supporto per chiamata multithreaded di chatgpt, elaborazione con un clic di grandi quantità di testo o di un programma
Avvia il tema di gradio [scuro](https://github.com/binary-husky/gpt_academic/issues/173) | Aggiungere ```/?__theme=dark``` dopo l'URL del browser per passare a un tema scuro
Supporto per maggiori modelli LLM, supporto API2D | Sentirsi serviti simultaneamente da GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS) deve essere una grande sensazione, giusto?
Ulteriori modelli LLM supportat,i supporto per l'implementazione di Huggingface | Aggiunta di un'interfaccia Newbing (Nuovo Bing), introdotta la compatibilità con Tsinghua [Jittorllms](https://github.com/Jittor/JittorLLMs), [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) e [PanGu-α](https://openi.org.cn/pangu/)
Ulteriori dimostrazioni di nuove funzionalità (generazione di immagini, ecc.)... | Vedere la fine di questo documento...
</div>
- Nuova interfaccia (modificare l'opzione LAYOUT in `config.py` per passare dal layout a sinistra e a destra al layout superiore e inferiore)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
</div>Sei un traduttore professionista di paper accademici.
- Tutti i pulsanti vengono generati dinamicamente leggendo il file functional.py, e aggiungerci nuove funzionalità è facile, liberando la clipboard.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- Revisione/Correzione
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- Se l'output contiene una formula, viene visualizzata sia come testo che come formula renderizzata, per facilitare la copia e la visualizzazione.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Non hai tempo di leggere il codice del progetto? Passa direttamente a chatgpt e chiedi informazioni.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Chiamata mista di vari modelli di lingua di grandi dimensioni (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
---
# Installazione
## Installazione - Metodo 1: Esecuzione diretta (Windows, Linux o MacOS)
1. Scarica il progetto
```sh
git clone https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configura API_KEY
In `config.py`, configura la tua API KEY e altre impostazioni, [configs for special network environments](https://github.com/binary-husky/gpt_academic/issues/1).
(N.B. Quando il programma viene eseguito, verifica prima se esiste un file di configurazione privato chiamato `config_private.py` e sovrascrive le stesse configurazioni in `config.py`. Pertanto, se capisci come funziona la nostra logica di lettura della configurazione, ti consigliamo vivamente di creare un nuovo file di configurazione chiamato `config_private.py` accanto a `config.py`, e spostare (copiare) le configurazioni di `config.py` in `config_private.py`. 'config_private.py' non è sotto la gestione di git e può proteggere ulteriormente le tue informazioni personali. NB Il progetto supporta anche la configurazione della maggior parte delle opzioni tramite "variabili d'ambiente". La sintassi della variabile d'ambiente è descritta nel file `docker-compose`. Priorità di lettura: "variabili d'ambiente" > "config_private.py" > "config.py")
3. Installa le dipendenze
```sh
# (Scelta I: se sei familiare con python) (python 3.9 o superiore, più nuovo è meglio), N.B.: utilizza il repository ufficiale pip o l'aliyun pip repository, metodo temporaneo per cambiare il repository: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Scelta II: se non conosci Python) utilizza anaconda, il processo è simile (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # crea l'ambiente anaconda
conda activate gptac_venv # attiva l'ambiente anaconda
python -m pip install -r requirements.txt # questo passaggio funziona allo stesso modo dell'installazione con pip
```
<details><summary>Se si desidera supportare ChatGLM di Tsinghua/MOSS di Fudan come backend, fare clic qui per espandere</summary>
<p>
【Passaggio facoltativo】 Se si desidera supportare ChatGLM di Tsinghua/MOSS di Fudan come backend, è necessario installare ulteriori dipendenze (prerequisiti: conoscenza di Python, esperienza con Pytorch e computer sufficientemente potente):
```sh
# 【Passaggio facoltativo I】 Supporto a ChatGLM di Tsinghua. Note su ChatGLM di Tsinghua: in caso di errore "Call ChatGLM fail 不能正常加载ChatGLM的参数" , fare quanto segue: 1. Per impostazione predefinita, viene installata la versione di torch + cpu; per usare CUDA, è necessario disinstallare torch e installare nuovamente torch + cuda; 2. Se non è possibile caricare il modello a causa di una configurazione insufficiente del computer, è possibile modificare la precisione del modello in request_llm/bridge_chatglm.py, cambiando AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) in AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llm/requirements_chatglm.txt
# 【Passaggio facoltativo II】 Supporto a MOSS di Fudan
python -m pip install -r request_llm/requirements_moss.txt
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Si prega di notare che quando si esegue questa riga di codice, si deve essere nella directory radice del progetto
# 【Passaggio facoltativo III】 Assicurati che il file di configurazione config.py includa tutti i modelli desiderati, al momento tutti i modelli supportati sono i seguenti (i modelli della serie jittorllms attualmente supportano solo la soluzione docker):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Esegui
```sh
python main.py
```5. Plugin di test delle funzioni
```
- Funzione plugin di test (richiede una risposta gpt su cosa è successo oggi in passato), puoi utilizzare questa funzione come template per implementare funzionalità più complesse
Clicca su "[Demo del plugin di funzione] Oggi nella storia"
```
## Installazione - Metodo 2: Utilizzo di Docker
1. Solo ChatGPT (consigliato per la maggior parte delle persone)
``` sh
git clone https://github.com/binary-husky/gpt_academic.git # scarica il progetto
cd gpt_academic # entra nel percorso
nano config.py # con un qualsiasi editor di testo, modifica config.py configurando "Proxy", "API_KEY" e "WEB_PORT" (ad esempio 50923)
docker build -t gpt-academic . # installa
#(ultimo passaggio - selezione 1) In un ambiente Linux, utilizzare '--net=host' è più conveniente e veloce
docker run --rm -it --net=host gpt-academic
#(ultimo passaggio - selezione 2) In un ambiente MacOS/Windows, l'opzione -p può essere utilizzata per esporre la porta del contenitore (ad es. 50923) alla porta della macchina
docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
```
2. ChatGPT + ChatGLM + MOSS (richiede familiarità con Docker)
``` sh
# Modifica docker-compose.yml, elimina i piani 1 e 3, mantieni il piano 2. Modifica la configurazione del piano 2 in docker-compose.yml, si prega di fare riferimento alle relative annotazioni
docker-compose up
```
3. ChatGPT + LLAMA + Pangu + RWKV (richiede familiarità con Docker)
``` sh
# Modifica docker-compose.yml, elimina i piani 1 e 2, mantieni il piano 3. Modifica la configurazione del piano 3 in docker-compose.yml, si prega di fare riferimento alle relative annotazioni
docker-compose up
```
## Installazione - Metodo 3: Altre modalità di distribuzione
1. Come utilizzare un URL di reindirizzamento / AzureAPI Cloud Microsoft
Configura API_URL_REDIRECT seguendo le istruzioni nel file `config.py`.
2. Distribuzione su un server cloud remoto (richiede conoscenze ed esperienza di server cloud)
Si prega di visitare [wiki di distribuzione-1] (https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
3. Utilizzo di WSL2 (Windows Subsystem for Linux)
Si prega di visitare [wiki di distribuzione-2] (https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
4. Come far funzionare ChatGPT all'interno di un sottodominio (ad es. `http://localhost/subpath`)
Si prega di visitare [Istruzioni per l'esecuzione con FastAPI] (docs/WithFastapi.md)
5. Utilizzo di docker-compose per l'esecuzione
Si prega di leggere il file docker-compose.yml e seguire le istruzioni fornite.
---
# Uso avanzato
## Personalizzazione dei pulsanti / Plugin di funzione personalizzati
1. Personalizzazione dei pulsanti (scorciatoie accademiche)
Apri `core_functional.py` con qualsiasi editor di testo e aggiungi la voce seguente, quindi riavvia il programma (se il pulsante è già stato aggiunto con successo e visibile, il prefisso e il suffisso supportano la modifica in tempo reale, senza bisogno di riavviare il programma).
ad esempio
```
"超级英译中": {
# Prefisso, verrà aggiunto prima del tuo input. Ad esempio, descrivi la tua richiesta, come tradurre, spiegare il codice, correggere errori, ecc.
"Prefix": "Per favore traduci questo testo in Cinese, e poi spiega tutti i termini tecnici nel testo con una tabella markdown:\n\n",
# Suffisso, verrà aggiunto dopo il tuo input. Ad esempio, con il prefisso puoi circondare il tuo input con le virgolette.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
2. Plugin di funzione personalizzati
Scrivi plugin di funzione personalizzati e esegui tutte le attività che desideri o non hai mai pensato di fare.
La difficoltà di scrittura e debug dei plugin del nostro progetto è molto bassa. Se si dispone di una certa conoscenza di base di Python, è possibile realizzare la propria funzione del plugin seguendo il nostro modello. Per maggiori dettagli, consultare la [guida al plugin per funzioni](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).
---
# Ultimo aggiornamento
## Nuove funzionalità dinamiche
1. Funzionalità di salvataggio della conversazione. Nell'area dei plugin della funzione, fare clic su "Salva la conversazione corrente" per salvare la conversazione corrente come file html leggibile e ripristinabile, inoltre, nell'area dei plugin della funzione (menu a discesa), fare clic su "Carica la cronologia della conversazione archiviata" per ripristinare la conversazione precedente. Suggerimento: fare clic su "Carica la cronologia della conversazione archiviata" senza specificare il file consente di visualizzare la cache degli archivi html di cronologia, fare clic su "Elimina tutti i record di cronologia delle conversazioni locali" per eliminare tutte le cache degli archivi html.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. Generazione di rapporti. La maggior parte dei plugin genera un rapporto di lavoro dopo l'esecuzione.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
</div>
3. Progettazione modulare delle funzioni, semplici interfacce ma in grado di supportare potenti funzionalità.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
4. Questo è un progetto open source che può "tradursi da solo".
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
</div>
5. Tradurre altri progetti open source è semplice.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
</div>
6. Piccola funzione decorativa per [live2d](https://github.com/fghrsh/live2d_demo) (disattivata per impostazione predefinita, è necessario modificare `config.py`).
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. Supporto del grande modello linguistico MOSS
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
</div>
8. Generazione di immagini OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
9. Analisi e sintesi audio OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
10. Verifica completa dei testi in LaTeX
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
</div>
## Versione:
- versione 3.5(Todo): utilizzo del linguaggio naturale per chiamare tutti i plugin di funzioni del progetto (alta priorità)
- versione 3.4(Todo): supporto multi-threading per il grande modello linguistico locale Chatglm
- versione 3.3: +funzionalità di sintesi delle informazioni su Internet
- versione 3.2: i plugin di funzioni supportano più interfacce dei parametri (funzionalità di salvataggio della conversazione, lettura del codice in qualsiasi lingua + richiesta simultanea di qualsiasi combinazione di LLM)
- versione 3.1: supporto per interrogare contemporaneamente più modelli gpt! Supporto api2d, bilanciamento del carico per più apikey
- versione 3.0: supporto per Chatglm e altri piccoli LLM
- versione 2.6: ristrutturazione della struttura del plugin, miglioramento dell'interattività, aggiunta di più plugin
- versione 2.5: auto-aggiornamento, risoluzione del problema di testo troppo lungo e overflow del token durante la sintesi di grandi progetti di ingegneria
- versione 2.4: (1) funzionalità di traduzione dell'intero documento in formato PDF aggiunta; (2) funzionalità di scambio dell'area di input aggiunta; (3) opzione di layout verticale aggiunta; (4) ottimizzazione della funzione di plugin multi-threading.
- versione 2.3: miglioramento dell'interattività multi-threading
- versione 2.2: i plugin di funzioni supportano l'hot-reload
- versione 2.1: layout ripiegabile
- versione 2.0: introduzione di plugin di funzioni modulari
- versione 1.0: funzione di basegpt_academic sviluppatori gruppo QQ-2: 610599535
- Problemi noti
- Alcuni plugin di traduzione del browser interferiscono con l'esecuzione del frontend di questo software
- La versione di gradio troppo alta o troppo bassa può causare diversi malfunzionamenti
## Riferimenti e apprendimento
```
Il codice fa riferimento a molte altre eccellenti progettazioni di progetti, principalmente:
# Progetto 1: ChatGLM-6B di Tsinghua:
https://github.com/THUDM/ChatGLM-6B
# Progetto 2: JittorLLMs di Tsinghua:
https://github.com/Jittor/JittorLLMs
# Progetto 3: Edge-GPT:
https://github.com/acheong08/EdgeGPT
# Progetto 4: ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Progetto 5: ChatPaper:
https://github.com/kaixindelele/ChatPaper
# Altro:
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo
```

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@ -1,270 +0,0 @@
> **노트**
>
> 의존성을 설치할 때는 반드시 requirements.txt에서 **지정된 버전**을 엄격하게 선택하십시오.
>
> `pip install -r requirements.txt`
# <img src="docs/logo.png" width="40" > GPT 학술 최적화 (GPT Academic)
**이 프로젝트가 마음에 드신다면 Star를 주세요. 추가로 유용한 학술 단축키나 기능 플러그인이 있다면 이슈나 pull request를 남기세요. 이 프로젝트에 대한 [영어 |](docs/README_EN.md)[일본어 |](docs/README_JP.md)[한국어 |](https://github.com/mldljyh/ko_gpt_academic)[러시아어 |](docs/README_RS.md)[프랑스어](docs/README_FR.md)로 된 README도 있습니다.
GPT를 이용하여 프로젝트를 임의의 언어로 번역하려면 [`multi_language.py`](multi_language.py)를 읽고 실행하십시오. (실험적)
> **노트**
>
> 1. 파일을 읽기 위해 **빨간색**으로 표시된 기능 플러그인 (버튼) 만 지원됩니다. 일부 플러그인은 플러그인 영역의 **드롭다운 메뉴**에 있습니다. 또한 새로운 플러그인은 **가장 높은 우선순위**로 환영하며 처리합니다!
>
> 2. 이 프로젝트의 각 파일의 기능을 [`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)에서 자세히 설명합니다. 버전이 업데이트 됨에 따라 관련된 기능 플러그인을 클릭하고 GPT를 호출하여 프로젝트의 자체 분석 보고서를 다시 생성할 수도 있습니다. 자주 묻는 질문은 [`위키`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)에서 볼 수 있습니다. [설치 방법](#installation).
>
> 3. 이 프로젝트는 국내 언어 모델 chatglm과 RWKV, 판고 등의 시도와 호환 가능합니다. 여러 개의 api-key를 지원하며 설정 파일에 "API_KEY="openai-key1,openai-key2,api2d-key3""와 같이 작성할 수 있습니다. `API_KEY`를 임시로 변경해야하는 경우 입력 영역에 임시 `API_KEY`를 입력 한 후 엔터 키를 누르면 즉시 적용됩니다.
<div align="center">
기능 | 설명
--- | ---
원 키워드 | 원 키워드 및 논문 문법 오류를 찾는 기능 지원
한-영 키워드 | 한-영 키워드 지원
코드 설명 | 코드 표시, 코드 설명, 코드 생성, 코드에 주석 추가
[사용자 정의 바로 가기 키](https://www.bilibili.com/video/BV14s4y1E7jN) | 사용자 정의 바로 가기 키 지원
모듈식 설계 | 강력한[함수 플러그인](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions) 지원, 플러그인이 [램 업데이트](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)를 지원합니다.
[자체 프로그램 분석](https://www.bilibili.com/video/BV1cj411A7VW) | [함수 플러그인] [원 키 우드] 프로젝트 소스 코드의 내용을 이해하는 기능을 제공
[프로그램 분석](https://www.bilibili.com/video/BV1cj411A7VW) | [함수 플러그인] 프로젝트 트리를 분석할 수 있습니다 (Python/C/C++/Java/Lua/...)
논문 읽기, 번역 | [함수 플러그인] LaTex/PDF 논문의 전문을 읽고 요약을 생성합니다.
LaTeX 텍스트[번역](https://www.bilibili.com/video/BV1nk4y1Y7Js/), [원 키워드](https://www.bilibili.com/video/BV1FT411H7c5/) | [함수 플러그인] LaTeX 논문의 번역 또는 개량을 위해 일련의 모드를 번역할 수 있습니다.
대량의 주석 생성 | [함수 플러그인] 함수 코멘트를 대량으로 생성할 수 있습니다.
Markdown 한-영 번역 | [함수 플러그인] 위의 5 종 언어의 [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)를 볼 수 있습니다.
chat 분석 보고서 생성 | [함수 플러그인] 수행 후 요약 보고서를 자동으로 생성합니다.
[PDF 논문 번역](https://www.bilibili.com/video/BV1KT411x7Wn) | [함수 플러그인] PDF 논문이 제목 및 요약을 추출한 후 번역됩니다. (멀티 스레드)
[Arxiv 도우미](https://www.bilibili.com/video/BV1LM4y1279X) | [함수 플러그인] Arxiv 논문 URL을 입력하면 요약을 번역하고 PDF를 다운로드 할 수 있습니다.
[Google Scholar 통합 도우미](https://www.bilibili.com/video/BV19L411U7ia) | [함수 플러그인] Google Scholar 검색 페이지 URL을 제공하면 gpt가 [Related Works 작성](https://www.bilibili.com/video/BV1GP411U7Az/)을 도와줍니다.
인터넷 정보 집계+GPT | [함수 플러그인] 먼저 GPT가 인터넷에서 정보를 수집하고 질문에 대답 할 수 있도록합니다. 정보가 절대적으로 구식이 아닙니다.
수식/이미지/표 표시 | 급여, 코드 강조 기능 지원
멀티 스레드 함수 플러그인 지원 | Chatgpt를 여러 요청에서 실행하여 [대량의 텍스트](https://www.bilibili.com/video/BV1FT411H7c5/) 또는 프로그램을 처리 할 수 있습니다.
다크 그라디오 테마 시작 | 어둡게 주제를 변경하려면 브라우저 URL 끝에 ```/?__theme=dark```을 추가하면됩니다.
[다중 LLM 모델](https://www.bilibili.com/video/BV1wT411p7yf) 지원, [API2D](https://api2d.com/) 인터페이스 지원됨 | GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS)가 모두 동시에 작동하는 것처럼 느낄 수 있습니다!
LLM 모델 추가 및[huggingface 배치](https://huggingface.co/spaces/qingxu98/gpt-academic) 지원 | 새 Bing 인터페이스 (새 Bing) 추가, Clearing House [Jittorllms](https://github.com/Jittor/JittorLLMs) 지원 [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) 및 [盘古α](https://openi.org.cn/pangu/)
기타 새로운 기능 (이미지 생성 등) ... | 이 문서의 끝부분을 참조하세요. ...- 모든 버튼은 functional.py를 동적으로 읽어와서 사용자 정의 기능을 자유롭게 추가할 수 있으며, 클립 보드를 해제합니다.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- 검수/오타 교정
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- 출력에 수식이 포함되어 있으면 텍스와 렌더링의 형태로 동시에 표시되어 복사 및 읽기가 용이합니다.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- 프로젝트 코드를 볼 시간이 없습니까? 전체 프로젝트를 chatgpt에 직접 표시하십시오
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- 다양한 대형 언어 모델 범용 요청 (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
---
# 설치
## Installation-Method 1: Run directly (Windows, Linux or MacOS)
1. 프로젝트 다운로드
```sh
git clone https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. API_KEY 구성
`config.py`에서 API KEY 등 설정을 구성합니다. [특별한 네트워크 환경 설정](https://github.com/binary-husky/gpt_academic/issues/1) .
(P.S. 프로그램이 실행될 때, 이름이 `config_private.py`인 기밀 설정 파일이 있는지 우선적으로 확인하고 해당 설정으로 `config.py`의 동일한 이름의 설정을 덮어씁니다. 따라서 구성 읽기 논리를 이해할 수 있다면, `config.py` 옆에 `config_private.py`라는 새 구성 파일을 만들고 `config.py`의 구성을 `config_private.py`로 이동(복사)하는 것이 좋습니다. `config_private.py`는 git으로 관리되지 않으며 개인 정보를 더 안전하게 보호할 수 있습니다. P.S. 프로젝트는 또한 대부분의 옵션을 `환경 변수`를 통해 설정할 수 있으며, `docker-compose` 파일을 참조하여 환경 변수 작성 형식을 확인할 수 있습니다. 우선순위: `환경 변수` > `config_private.py` > `config.py`)
3. 의존성 설치
```sh
# (I 선택: 기존 python 경험이 있다면) (python 버전 3.9 이상, 최신 버전이 좋습니다), 참고: 공식 pip 소스 또는 알리 pip 소스 사용, 일시적인 교체 방법: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (II 선택: Python에 익숙하지 않은 경우) anaconda 사용 방법은 비슷함(https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # anaconda 환경 만들기
conda activate gptac_venv # anaconda 환경 활성화
python -m pip install -r requirements.txt # 이 단계도 pip install의 단계와 동일합니다.
```
<details><summary>추가지원을 위해 Tsinghua ChatGLM / Fudan MOSS를 사용해야하는 경우 지원을 클릭하여 이 부분을 확장하세요.</summary>
<p>
[Tsinghua ChatGLM] / [Fudan MOSS]를 백엔드로 사용하려면 추가적인 종속성을 설치해야합니다 (전제 조건 : Python을 이해하고 Pytorch를 사용한 적이 있으며, 컴퓨터가 충분히 강력한 경우) :
```sh
# [선택 사항 I] Tsinghua ChatGLM을 지원합니다. Tsinghua ChatGLM에 대한 참고사항 : "Call ChatGLM fail cannot load ChatGLM parameters normally" 오류 발생시 다음 참조:
# 1 : 기본 설치된 것들은 torch + cpu 버전입니다. cuda를 사용하려면 torch를 제거한 다음 torch + cuda를 다시 설치해야합니다.
# 2 : 모델을 로드할 수 없는 기계 구성 때문에, AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)를
# AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)로 변경합니다.
python -m pip install -r request_llm/requirements_chatglm.txt
# [선택 사항 II] Fudan MOSS 지원
python -m pip install -r request_llm/requirements_moss.txt
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # 다음 코드 줄을 실행할 때 프로젝트 루트 경로에 있어야합니다.
# [선택 사항III] AVAIL_LLM_MODELS config.py 구성 파일에 기대하는 모델이 포함되어 있는지 확인하십시오.
# 현재 지원되는 전체 모델 :
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. 실행
```sh
python main.py
```5. 테스트 함수 플러그인
```
- 테스트 함수 플러그인 템플릿 함수 (GPT에게 오늘의 역사에서 무슨 일이 일어났는지 대답하도록 요청)를 구현하는 데 사용할 수 있습니다. 이 함수를 기반으로 더 복잡한 기능을 구현할 수 있습니다.
"[함수 플러그인 템플릿 데모] 오늘의 역사"를 클릭하세요.
```
## 설치 - 방법 2 : 도커 사용
1. ChatGPT 만 (대부분의 사람들이 선택하는 것을 권장합니다.)
``` sh
git clone https://github.com/binary-husky/gpt_academic.git # 다운로드
cd gpt_academic # 경로 이동
nano config.py # 아무 텍스트 에디터로 config.py를 열고 "Proxy","API_KEY","WEB_PORT" (예 : 50923) 등을 구성합니다.
docker build -t gpt-academic . # 설치
#(마지막 단계-1 선택) Linux 환경에서는 --net=host를 사용하면 더 편리합니다.
docker run --rm -it --net=host gpt-academic
#(마지막 단계-2 선택) macOS / windows 환경에서는 -p 옵션을 사용하여 컨테이너의 포트 (예 : 50923)를 호스트의 포트로 노출해야합니다.
docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
```
2. ChatGPT + ChatGLM + MOSS (Docker에 익숙해야합니다.)
``` sh
#docker-compose.yml을 수정하여 계획 1 및 계획 3을 삭제하고 계획 2를 유지합니다. docker-compose.yml에서 계획 2의 구성을 수정하면 됩니다. 주석을 참조하십시오.
docker-compose up
```
3. ChatGPT + LLAMA + Pangu + RWKV (Docker에 익숙해야합니다.)
``` sh
#docker-compose.yml을 수정하여 계획 1 및 계획 2을 삭제하고 계획 3을 유지합니다. docker-compose.yml에서 계획 3의 구성을 수정하면 됩니다. 주석을 참조하십시오.
docker-compose up
```
## 설치 - 방법 3 : 다른 배치 방법
1. 리버스 프록시 URL / Microsoft Azure API 사용 방법
API_URL_REDIRECT를 `config.py`에 따라 구성하면됩니다.
2. 원격 클라우드 서버 배치 (클라우드 서버 지식과 경험이 필요합니다.)
[배치위키-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)에 방문하십시오.
3. WSL2 사용 (Windows Subsystem for Linux 하위 시스템)
[배치 위키-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)에 방문하십시오.
4. 2 차 URL (예 : `http : //localhost/subpath`)에서 실행하는 방법
[FastAPI 실행 설명서] (docs / WithFastapi.md)를 참조하십시오.
5. docker-compose 실행
docker-compose.yml을 읽은 후 지시 사항에 따라 작업하십시오.
---
# 고급 사용법
## 사용자 정의 바로 가기 버튼 / 사용자 정의 함수 플러그인
1. 사용자 정의 바로 가기 버튼 (학술 바로 가기)
임의의 텍스트 편집기로 'core_functional.py'를 엽니다. 엔트리 추가, 그런 다음 프로그램을 다시 시작하면됩니다. (버튼이 이미 추가되어 보이고 접두사, 접미사가 모두 변수가 효과적으로 수정되면 프로그램을 다시 시작하지 않아도됩니다.)
예 :
```
"超级英译中": {
# 접두사. 당신이 요구하는 것을 설명하는 데 사용됩니다. 예를 들어 번역, 코드를 설명, 다듬기 등
"Prefix": "下面翻译成中文，然后用一个 markdown 表格逐一解释文中出现的专有名词：\n\n",
# 접미사는 입력 내용 앞뒤에 추가됩니다. 예를 들어 전위를 사용하여 입력 내용을 따옴표로 묶는데 사용할 수 있습니다.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
2. 사용자 지정 함수 플러그인
강력한 함수 플러그인을 작성하여 원하는 작업을 수행하십시오.
이 프로젝트의 플러그인 작성 및 디버깅 난이도는 매우 낮으며, 일부 파이썬 기본 지식만 있으면 제공된 템플릿을 모방하여 플러그인 기능을 구현할 수 있습니다. 자세한 내용은 [함수 플러그인 가이드]를 참조하십시오. (https://github.com/binary -husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E 4%BB%B6%E6%8C%87%E5%8D%97).
---
# 최신 업데이트
## 새로운 기능 동향1. 대화 저장 기능.
1. 함수 플러그인 영역에서 '현재 대화 저장'을 호출하면 현재 대화를 읽을 수 있고 복원 가능한 HTML 파일로 저장할 수 있습니다. 또한 함수 플러그인 영역(드롭다운 메뉴)에서 '대화 기록 불러오기'를 호출하면 이전 대화를 복원할 수 있습니다. 팁: 파일을 지정하지 않고 '대화 기록 불러오기'를 클릭하면 기록된 HTML 캐시를 볼 수 있으며 '모든 로컬 대화 기록 삭제'를 클릭하면 모든 HTML 캐시를 삭제할 수 있습니다.
2. 보고서 생성. 대부분의 플러그인은 실행이 끝난 후 작업 보고서를 생성합니다.
3. 모듈화 기능 설계, 간단한 인터페이스로도 강력한 기능을 지원할 수 있습니다.
4. 자체 번역이 가능한 오픈 소스 프로젝트입니다.
5. 다른 오픈 소스 프로젝트를 번역하는 것은 어렵지 않습니다.
6. [live2d](https://github.com/fghrsh/live2d_demo) 장식 기능(기본적으로 비활성화되어 있으며 `config.py`를 수정해야 합니다.)
7. MOSS 대 언어 모델 지원 추가
8. OpenAI 이미지 생성
9. OpenAI 음성 분석 및 요약
10. LaTeX 전체적인 교정 및 오류 수정
## 버전:
- version 3.5 (TODO): 자연어를 사용하여 이 프로젝트의 모든 함수 플러그인을 호출하는 기능(우선순위 높음)
- version 3.4(TODO): 로컬 대 모듈의 다중 스레드 지원 향상
- version 3.3: 인터넷 정보 종합 기능 추가
- version 3.2: 함수 플러그인이 더 많은 인수 인터페이스를 지원합니다.(대화 저장 기능, 임의의 언어 코드 해석 및 동시에 임의의 LLM 조합을 확인하는 기능)
- version 3.1: 여러 개의 GPT 모델에 대한 동시 쿼리 지원! api2d 지원, 여러 개의 apikey 로드 밸런싱 지원
- version 3.0: chatglm 및 기타 소형 llm의 지원
- version 2.6: 플러그인 구조를 재구성하여 상호 작용성을 향상시켰습니다. 더 많은 플러그인을 추가했습니다.
- version 2.5: 자체 업데이트, 전체 프로젝트를 요약할 때 텍스트가 너무 길어지고 토큰이 오버플로우되는 문제를 해결했습니다.
- version 2.4: (1) PDF 전체 번역 기능 추가; (2) 입력 영역 위치 전환 기능 추가; (3) 수직 레이아웃 옵션 추가; (4) 다중 스레드 함수 플러그인 최적화.
- version 2.3: 다중 스레드 상호 작용성 강화
- version 2.2: 함수 플러그인 히트 리로드 지원
- version 2.1: 접는 레이아웃 지원
- version 2.0: 모듈화 함수 플러그인 도입
- version 1.0: 기본 기능
gpt_academic 개발자 QQ 그룹-2 : 610599535
- 알려진 문제
- 일부 브라우저 번역 플러그인이이 소프트웨어의 프론트 엔드 작동 방식을 방해합니다.
- gradio 버전이 너무 높거나 낮으면 여러 가지 이상이 발생할 수 있습니다.
## 참고 및 학습 자료
```
많은 우수 프로젝트의 디자인을 참고했습니다. 주요 항목은 다음과 같습니다.
# 프로젝트 1 : Tsinghua ChatGLM-6B :
https://github.com/THUDM/ChatGLM-6B
# 프로젝트 2 : Tsinghua JittorLLMs:
https://github.com/Jittor/JittorLLMs
# 프로젝트 3 : Edge-GPT :
https://github.com/acheong08/EdgeGPT
# 프로젝트 4 : ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# 프로젝트 5 : ChatPaper :
https://github.com/kaixindelele/ChatPaper
# 더 많은 :
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo
```

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@ -1,324 +0,0 @@
> **Nota**
>
> Ao instalar as dependências, por favor, selecione rigorosamente as versões **especificadas** no arquivo requirements.txt.
>
> `pip install -r requirements.txt`
>
# <img src="logo.png" width="40" > Otimização acadêmica GPT (GPT Academic)
**Se você gostou deste projeto, por favor dê um Star. Se você criou atalhos acadêmicos mais úteis ou plugins funcionais, sinta-se livre para abrir uma issue ou pull request. Nós também temos um README em [Inglês|](README_EN.md)[日本語|](README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](README_RS.md)[Français](README_FR.md) traduzidos por este próprio projeto.
Para traduzir este projeto para qualquer idioma com o GPT, leia e execute [`multi_language.py`](multi_language.py) (experimental).
> **Nota**
>
> 1. Por favor, preste atenção que somente os plugins de funções (botões) com a cor **vermelha** podem ler arquivos. Alguns plugins estão localizados no **menu suspenso** na área de plugins. Além disso, nós damos as boas-vindas com a **maior prioridade** e gerenciamos quaisquer novos plugins PR!
>
> 2. As funções de cada arquivo neste projeto são detalhadas em [`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), auto-análises do projeto geradas pelo GPT também estão podem ser chamadas a qualquer momento ao clicar nos plugins relacionados. As perguntas frequentes estão resumidas no [`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Instruções de Instalação](#installation).
>
> 3. Este projeto é compatível com e incentiva o uso de modelos de linguagem nacionais, como chatglm e RWKV, Pangolin, etc. Suporta a coexistência de várias chaves de API e pode ser preenchido no arquivo de configuração como `API_KEY="openai-key1,openai-key2,api2d-key3"`. Quando precisar alterar temporariamente o `API_KEY`, basta digitar o `API_KEY` temporário na área de entrada e pressionar Enter para que ele entre em vigor.
<div align="center">
Funcionalidade | Descrição
--- | ---
Um clique de polimento | Suporte a um clique polimento, um clique encontrar erros de gramática no artigo
Tradução chinês-inglês de um clique | Tradução chinês-inglês de um clique
Explicação de código de um único clique | Exibir código, explicar código, gerar código, adicionar comentários ao código
[Teclas de atalho personalizadas](https://www.bilibili.com/video/BV14s4y1E7jN) | Suporte a atalhos personalizados
Projeto modular | Suporte para poderosos plugins[de função personalizada](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions), os plugins suportam[hot-reload](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)
[Análise automática do programa](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin de função][um clique para entender](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) o código-fonte do projeto
[Análise do programa](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin de função] Um clique pode analisar a árvore de projetos do Python/C/C++/Java/Lua/...
Leitura de artigos, [tradução](https://www.bilibili.com/video/BV1KT411x7Wn) de artigos | [Plugin de função] um clique para interpretar o resumo de artigos LaTeX/PDF e gerar resumo
Tradução completa LATEX, polimento|[Plugin de função] Uma clique para traduzir ou polir um artigo LATEX
Geração em lote de comentários | [Plugin de função] Um clique gera comentários de função em lote
[Tradução chinês-inglês](https://www.bilibili.com/video/BV1yo4y157jV/) markdown | [Plugin de função] Você viu o README em 5 linguagens acima?
Relatório de análise de chat | [Plugin de função] Gera automaticamente um resumo após a execução
[Funcionalidade de tradução de artigos completos em PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin de função] Extrai o título e o resumo do artigo PDF e traduz o artigo completo (multithread)
Assistente arXiv | [Plugin de função] Insira o url do artigo arXiv para traduzir o resumo + baixar PDF
Assistente de integração acadêmica do Google | [Plugin de função] Dê qualquer URL de página de pesquisa acadêmica do Google e deixe o GPT escrever[trabalhos relacionados](https://www.bilibili.com/video/BV1GP411U7Az/)
Agregação de informações da Internet + GPT | [Plugin de função] Um clique para obter informações do GPT através da Internet e depois responde a perguntas para informações nunca ficarem desatualizadas
Exibição de fórmulas/imagem/tabela | Pode exibir simultaneamente a forma de renderização e[TEX] das fórmulas, suporte a fórmulas e realce de código
Suporte de plugins de várias linhas | Suporte a várias chamadas em linha do chatgpt, um clique para processamento[de massa de texto](https://www.bilibili.com/video/BV1FT411H7c5/) ou programa
Tema gradio escuro | Adicione ``` /?__theme=dark``` ao final da url do navegador para ativar o tema escuro
[Suporte para vários modelos LLM](https://www.bilibili.com/video/BV1wT411p7yf), suporte para a nova interface API2D | A sensação de ser atendido simultaneamente por GPT3.5, GPT4, [Chatglm THU](https://github.com/THUDM/ChatGLM-6B), [Moss Fudan](https://github.com/OpenLMLab/MOSS) deve ser ótima, certo?
Mais modelos LLM incorporados, suporte para a implantação[huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Adicione interface Newbing (New Bing), suporte [JittorLLMs](https://github.com/Jittor/JittorLLMs) THU Introdução ao suporte do LLaMA, RWKV e Pan Gu Alpha
Mais recursos novos mostrados (geração de imagens, etc.) ... | Consulte o final deste documento ...
</div>
- Nova interface (Modifique a opção LAYOUT em `config.py` para alternar entre o layout esquerdo/direito e o layout superior/inferior)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
</div>- All buttons are dynamically generated by reading functional.py, and you can add custom functions at will, liberating the clipboard
<div align="center">
<img src = "https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700">
</div>
- Proofreading/errors correction
<div align="center">
<img src = "https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700">
</div>
- If the output contains formulas, it will be displayed in both tex and rendering format at the same time, which is convenient for copying and reading
<div align="center">
<img src = "https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700">
</div>
- Don't want to read the project code? Just show the whole project to chatgpt
<div align="center">
<img src = "https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700">
</div>
- Mix the use of multiple large language models (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
<div align="center">
<img src = "https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700">
</div>
---
# Instalação
## Installation-Method 1: Run directly (Windows, Linux or MacOS)
1. Download the project
```sh
git clone https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configure the API KEY
In `config.py`, configure API KEY and other settings, [Special Network Environment Settings] (https://github.com/binary-husky/gpt_academic/issues/1).
(P.S. When the program runs, it will first check whether there is a private configuration file named `config_private.py`, and use the configuration in it to cover the configuration with the same name in `config.py`. Therefore, if you can understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py`, and transfer (copy) the configuration in `config.py` to `config_private.py`. `config_private.py` is not controlled by git and can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`. The writing format of environment variables is referenced to the `docker-compose` file. Reading priority: `environment variable` > `config_private.py` > `config.py`)
3. Install dependencies
```sh
# (Option I: for those familiar with python)(python version is 3.9 or above, the newer the better), note: use the official pip source or the Alibaba pip source. Temporary solution for changing source: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Option II: for those who are unfamiliar with python) use anaconda, the steps are also similar (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # create anaconda environment
conda activate gptac_venv # activate anaconda environment
python -m pip install -r requirements.txt # This step is the same as the pip installation step
```
<details><summary>If you need to support Tsinghua ChatGLM / Fudan MOSS as the backend, click to expand here</summary>
<p>
[Optional Step] If you need to support Tsinghua ChatGLM / Fudan MOSS as the backend, you need to install more dependencies (prerequisite: familiar with Python + used Pytorch + computer configuration is strong):
```sh
# 【Optional Step I】support Tsinghua ChatGLM。Tsinghua ChatGLM Note: If you encounter a "Call ChatGLM fails cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installed is torch+cpu version, and using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient computer configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llm/requirements_chatglm.txt
# 【Optional Step II】support Fudan MOSS
python -m pip install -r request_llm/requirements_moss.txt
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Note: When executing this line of code, you must be in the project root path
# 【Optional Step III】Make sure that the AVAIL_LLM_MODELS in the config.py configuration file contains the expected model. Currently, all supported models are as follows (jittorllms series currently only supports docker solutions):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Run
```sh
python main.py
```5. Plugin de Função de Teste
```
- Função de modelo de plug-in de teste (exige que o GPT responda ao que aconteceu hoje na história), você pode usar esta função como modelo para implementar funções mais complexas
Clique em "[Função de plug-in de modelo de demonstração] O que aconteceu hoje na história?"
```
## Instalação - Método 2: Usando o Docker
1. Apenas ChatGPT (recomendado para a maioria das pessoas)
``` sh
git clone https://github.com/binary-husky/gpt_academic.git # Baixar o projeto
cd gpt_academic # Entrar no caminho
nano config.py # Editar config.py com qualquer editor de texto configurando "Proxy", "API_KEY" e "WEB_PORT" (por exemplo, 50923), etc.
docker build -t gpt-academic . # Instale
# (Ùltima etapa - escolha 1) Dentro do ambiente Linux, é mais fácil e rápido usar `--net=host`
docker run --rm -it --net=host gpt-academic
# (Última etapa - escolha 2) Em ambientes macOS/windows, você só pode usar a opção -p para expor a porta do contêiner (por exemplo, 50923) para a porta no host
docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
```
2. ChatGPT + ChatGLM + MOSS (conhecimento de Docker necessário)
``` sh
# Edite o arquivo docker-compose.yml, remova as soluções 1 e 3, mantenha a solução 2, e siga as instruções nos comentários do arquivo
docker-compose up
```
3. ChatGPT + LLAMA + Pangu + RWKV (conhecimento de Docker necessário)
``` sh
# Edite o arquivo docker-compose.yml, remova as soluções 1 e 2, mantenha a solução 3, e siga as instruções nos comentários do arquivo
docker-compose up
```
## Instalação - Método 3: Outros Métodos de Implantação
1. Como usar URLs de proxy inverso/microsoft Azure API
Basta configurar o API_URL_REDIRECT de acordo com as instruções em `config.py`.
2. Implantação em servidores em nuvem remotos (requer conhecimento e experiência de servidores em nuvem)
Acesse [Wiki de implementação remota do servidor em nuvem](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
3. Usando a WSL2 (sub-sistema do Windows para Linux)
Acesse [Wiki da implantação da WSL2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
4. Como executar em um subdiretório (ex. `http://localhost/subpath`)
Acesse [Instruções de execução FastAPI](docs/WithFastapi.md)
5. Execute usando o docker-compose
Leia o arquivo docker-compose.yml e siga as instruções.
# Uso Avançado
## Customize novos botões de acesso rápido / plug-ins de função personalizados
1. Personalizar novos botões de acesso rápido (atalhos acadêmicos)
Abra `core_functional.py` em qualquer editor de texto e adicione os seguintes itens e reinicie o programa (Se o botão já foi adicionado e pode ser visto, prefixos e sufixos são compatíveis com modificações em tempo real e não exigem reinício do programa para ter efeito.)
Por exemplo,
```
"Super Eng:": {
  # Prefixo, será adicionado antes da sua entrada. Por exemplo, para descrever sua solicitação, como tradução, explicação de código, polimento, etc.
  "Prefix": "Por favor, traduza o seguinte conteúdo para chinês e use uma tabela em Markdown para explicar termos próprios no texto: \n \n",
  # Sufixo, será adicionado após a sua entrada. Por exemplo, emparelhado com o prefixo, pode colocar sua entrada entre aspas.
  "Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
2. Personalizar plug-ins de função
Escreva plug-ins de função poderosos para executar tarefas que você deseja e não pensava possível.
A dificuldade geral de escrever e depurar plug-ins neste projeto é baixa e, se você tem algum conhecimento básico de python, pode implementar suas próprias funções sobre o modelo que fornecemos.
Para mais detalhes, consulte o [Guia do plug-in de função.](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).
---
# Última atualização
## Novas funções dinâmicas.
1. Função de salvamento de diálogo. Ao chamar o plug-in de função "Salvar diálogo atual", é possível salvar o diálogo atual em um arquivo html legível e reversível. Além disso, ao chamar o plug-in de função "Carregar arquivo de histórico de diálogo" no menu suspenso da área de plug-in, é possível restaurar uma conversa anterior. Dica: clicar em "Carregar arquivo de histórico de diálogo" sem especificar um arquivo permite visualizar o cache do arquivo html de histórico. Clicar em "Excluir todo o registro de histórico de diálogo local" permite excluir todo o cache de arquivo html.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. Geração de relatório. A maioria dos plug-ins gera um relatório de trabalho após a conclusão da execução.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
</div>
3. Design modular de funcionalidades, com interfaces simples, mas suporte a recursos poderosos
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
4. Este é um projeto de código aberto que é capaz de "auto-traduzir-se".
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
</div>
5. A tradução de outros projetos de código aberto é simples.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
</div>
6. Recursos decorativos para o [live2d](https://github.com/fghrsh/live2d_demo) (desativados por padrão, é necessário modificar o arquivo `config.py`)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. Suporte ao modelo de linguagem MOSS
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
</div>
8. Geração de imagens pelo OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
9. Análise e resumo de áudio pelo OpenAI
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
10. Revisão e correção de erros de texto em Latex.
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
</div>
## Versão:
- Versão 3.5(Todo): Usar linguagem natural para chamar todas as funções do projeto (prioridade alta)
- Versão 3.4(Todo): Melhorar o suporte à multithread para o chatglm local
- Versão 3.3: +Funções integradas de internet
- Versão 3.2: Suporte a mais interfaces de parâmetros de plug-in (função de salvar diálogo, interpretação de códigos de várias linguagens, perguntas de combinações LLM arbitrárias ao mesmo tempo)
- Versão 3.1: Suporte a perguntas a vários modelos de gpt simultaneamente! Suporte para api2d e balanceamento de carga para várias chaves api
- Versão 3.0: Suporte ao chatglm e outros LLMs de pequeno porte
- Versão 2.6: Refatoração da estrutura de plug-in, melhoria da interatividade e adição de mais plug-ins
- Versão 2.5: Autoatualização, resolvendo problemas de token de texto excessivamente longo e estouro ao compilar grandes projetos
- Versão 2.4: (1) Adição de funcionalidade de tradução de texto completo em PDF; (2) Adição de funcionalidade de mudança de posição da área de entrada; (3) Adição de opção de layout vertical; (4) Otimização de plug-ins de multithread.
- Versão 2.3: Melhoria da interatividade de multithread
- Versão 2.2: Suporte à recarga a quente de plug-ins
- Versão 2.1: Layout dobrável
- Versão 2.0: Introdução de plug-ins de função modular
- Versão 1.0: Funcionalidades básicasgpt_academic desenvolvedores QQ grupo-2: 610599535
- Problemas conhecidos
- Extensões de tradução de alguns navegadores podem interferir na execução do front-end deste software
- Uma versão muito alta ou muito baixa do Gradio pode causar vários erros
## Referências e Aprendizado
```
Foi feita referência a muitos projetos excelentes em código, principalmente:
# Projeto1: ChatGLM-6B da Tsinghua:
https://github.com/THUDM/ChatGLM-6B
# Projeto2: JittorLLMs da Tsinghua:
https://github.com/Jittor/JittorLLMs
# Projeto3: Edge-GPT:
https://github.com/acheong08/EdgeGPT
# Projeto4: ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Projeto5: ChatPaper:
https://github.com/kaixindelele/ChatPaper
# Mais:
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo
```

322
docs/README_EN.md
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@ -1,322 +0,0 @@
> **Note**
>
> This English README is automatically generated by the markdown translation plugin in this project, and may not be 100% correct.
>
> When installing dependencies, **please strictly select the versions** specified in requirements.txt.
>
> `pip install -r requirements.txt`
# GPT Academic Optimization (GPT Academic)
**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.
To translate this project to arbitary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).**
> Note:
>
> 1. Please note that only the function plugins (buttons) marked in **red** support reading files. Some plugins are in the **drop-down menu** in the plugin area. We welcome and process any new plugins with the **highest priority**!
> 2. The function of each file in this project is detailed in the self-translation analysis [`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). With version iteration, you can also click on related function plugins at any time to call GPT to regenerate the project's self-analysis report. Common questions are summarized in the [`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Installation method](#installation).
> 3. This project is compatible with and encourages trying domestic large language models such as chatglm, RWKV, Pangu, etc. Multiple API keys are supported and can be filled in the configuration file like `API_KEY="openai-key1,openai-key2,api2d-key3"`. When temporarily changing `API_KEY`, enter the temporary `API_KEY` in the input area and press enter to submit, which will take effect.
<div align="center">
Function | Description
--- | ---
One-click polishing | Supports one-click polishing and one-click searching for grammar errors in papers.
One-click Chinese-English translation | One-click Chinese-English translation.
One-click code interpretation | Displays, explains, generates, and adds comments to code.
[Custom shortcut keys](https://www.bilibili.com/video/BV14s4y1E7jN) | Supports custom shortcut keys.
Modular design | Supports custom powerful [function plug-ins](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions), plug-ins support [hot update](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).
[Self-program profiling](https://www.bilibili.com/video/BV1cj411A7VW) | [Function plug-in] [One-click understanding](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) of the source code of this project
[Program profiling](https://www.bilibili.com/video/BV1cj411A7VW) | [Function plug-in] One-click profiling of other project trees in Python/C/C++/Java/Lua/...
Reading papers, [translating](https://www.bilibili.com/video/BV1KT411x7Wn) papers | [Function Plug-in] One-click interpretation of latex/pdf full-text papers and generation of abstracts.
Latex full-text [translation](https://www.bilibili.com/video/BV1nk4y1Y7Js/), [polishing](https://www.bilibili.com/video/BV1FT411H7c5/) | [Function plug-in] One-click translation or polishing of latex papers.
Batch annotation generation | [Function plug-in] One-click batch generation of function annotations.
Markdown [Chinese-English translation](https://www.bilibili.com/video/BV1yo4y157jV/) | [Function plug-in] Have you seen the [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) in the five languages above?
Chat analysis report generation | [Function plug-in] Automatically generate summary reports after running.
[PDF full-text translation function](https://www.bilibili.com/video/BV1KT411x7Wn) | [Function plug-in] PDF paper extract title & summary + translate full text (multi-threaded)
[Arxiv Assistant](https://www.bilibili.com/video/BV1LM4y1279X) | [Function plug-in] Enter the arxiv article url and you can translate abstracts and download PDFs with one click.
[Google Scholar Integration Assistant](https://www.bilibili.com/video/BV19L411U7ia) | [Function plug-in] Given any Google Scholar search page URL, let GPT help you [write relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
Internet information aggregation+GPT | [Function plug-in] One-click [let GPT get information from the Internet first](https://www.bilibili.com/video/BV1om4y127ck), then answer questions, and let the information never be outdated.
Formula/image/table display | Can display formulas in both [tex form and render form](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), support formulas and code highlighting.
Multi-threaded function plug-in support | Supports multi-threaded calling of chatgpt, and can process [massive text](https://www.bilibili.com/video/BV1FT411H7c5/) or programs with one click.
Start Dark Gradio [theme](https://github.com/binary-husky/gpt_academic/issues/173) | Add ```/?__theme=dark``` after the browser URL to switch to the dark theme.
[Multiple LLM models](https://www.bilibili.com/video/BV1wT411p7yf) support, [API2D](https://api2d.com/) interface support | The feeling of being served by GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), and [Fudan MOSS](https://github.com/OpenLMLab/MOSS) at the same time must be great, right?
More LLM model access, support [huggingface deployment](https://huggingface.co/spaces/qingxu98/gpt-academic) | Add Newbing interface (New Bing), introduce Tsinghua [Jittorllms](https://github.com/Jittor/JittorLLMs) to support [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) and [Panguα](https://openi.org.cn/pangu/)
More new feature displays (image generation, etc.)…… | See the end of this document for more...
</div>
- New interface (modify the LAYOUT option in `config.py` to switch between "left and right layout" and "up and down layout")
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
</div>- All buttons are dynamically generated by reading `functional.py`, and you can add custom functions freely to unleash the power of clipboard.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- polishing/correction
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- If the output contains formulas, they will be displayed in both `tex` and render form, making it easy to copy and read.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Tired of reading the project code? ChatGPT can explain it all.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Multiple large language models are mixed, such as ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
---
# Installation
## Method 1: Directly running (Windows, Linux or MacOS)
1. Download the project
```sh
git clone https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configure the API_KEY
Configure the API KEY in `config.py`, [special network environment settings](https://github.com/binary-husky/gpt_academic/issues/1).
(P.S. When the program is running, it will first check if there is a private configuration file named `config_private.py` and use the configurations in it to override the same configurations in `config.py`. Therefore, if you can understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py` and transfer (copy) the configurations in `config.py` to `config_private.py`. `config_private.py` is not controlled by git and can make your private information more secure. P.S. The project also supports configuring most options through `environment variables`. Please refer to the format of `docker-compose` file when writing. Reading priority: `environment variables` > `config_private.py` > `config.py`)
3. Install the dependencies
```sh
# (Option I: If familiar with python) (python version 3.9 or above, the newer the better), note: use official pip source or Ali pip source, temporary switching method: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Option II: If not familiar with python) Use anaconda, the steps are similar (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # create anaconda environment
conda activate gptac_venv # activate anaconda environment
python -m pip install -r requirements.txt # this step is the same as pip installation
```
<details><summary>If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, click to expand</summary>
<p>
[Optional step] If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, you need to install more dependencies (prerequisites: familiar with Python + used Pytorch + computer configuration is strong enough):
```sh
# [Optional Step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: if you encounter the "Call ChatGLM fail cannot load ChatGLM parameters" error, refer to this: 1: The default installation above is torch + cpu version, to use cuda, you need to uninstall torch and reinstall torch + cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code = True)
python -m pip install -r request_llm/requirements_chatglm.txt
# [Optional Step II] Support Fudan MOSS
python -m pip install -r request_llm/requirements_moss.txt
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # When executing this line of code, you must be in the root directory of the project
# [Optional Step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file includes the expected models. Currently supported models are as follows (the jittorllms series only supports the docker solution for the time being):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Run it
```sh
python main.py
```5. Test Function Plugin
```
- Test function plugin template function (ask GPT what happened today in history), based on which you can implement more complex functions as a template
Click "[Function Plugin Template Demo] Today in History"
```
## Installation - Method 2: Using Docker
1. ChatGPT Only (Recommended for Most People)
``` sh
git clone https://github.com/binary-husky/gpt_academic.git # Download project
cd gpt_academic # Enter path
nano config.py # Edit config.py with any text editor, configure "Proxy", "API_KEY" and "WEB_PORT" (e.g. 50923), etc.
docker build -t gpt-academic . # Install
#(Last step - option 1) In a Linux environment, use `--net=host` for convenience and speed.
docker run --rm -it --net=host gpt-academic
#(Last step - option 2) On macOS/windows environment, only -p option can be used to expose the container's port (e.g. 50923) to the port of the main machine.
docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
```
2. ChatGPT + ChatGLM + MOSS (Requires Docker Knowledge)
``` sh
# Modify docker-compose.yml, delete Plan 1 and Plan 3, and keep Plan 2. Modify the configuration of Plan 2 in docker-compose.yml, refer to the comments in it for configuration.
docker-compose up
```
3. ChatGPT + LLAMA + Pangu + RWKV (Requires Docker Knowledge)
``` sh
# Modify docker-compose.yml, delete Plan 1 and Plan 2, and keep Plan 3. Modify the configuration of Plan 3 in docker-compose.yml, refer to the comments in it for configuration.
docker-compose up
```
## Installation - Method 3: Other Deployment Options
1. How to Use Reverse Proxy URL/Microsoft Cloud Azure API
Configure API_URL_REDIRECT according to the instructions in 'config.py'.
2. Deploy to a Remote Server (Requires Knowledge and Experience with Cloud Servers)
Please visit [Deployment Wiki-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
3. Using WSL2 (Windows Subsystem for Linux)
Please visit [Deployment Wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
4. How to Run Under a Subdomain (e.g. `http://localhost/subpath`)
Please visit [FastAPI Running Instructions](docs/WithFastapi.md)
5. Using docker-compose to Run
Read the docker-compose.yml and follow the prompts.
---
# Advanced Usage
## Custom New Shortcut Buttons / Custom Function Plugins
1. Custom New Shortcut Buttons (Academic Hotkey)
Open `core_functional.py` with any text editor, add an entry as follows and restart the program. (If the button has been successfully added and is visible, the prefix and suffix can be hot-modified without having to restart the program.)
For example,
```
"Super English-to-Chinese": {
# Prefix, which will be added before your input. For example, used to describe your requests, such as translation, code explanation, polishing, etc.
"Prefix": "Please translate the following content into Chinese and then use a markdown table to explain the proprietary terms that appear in the text：\n\n",
# Suffix, which is added after your input. For example, with the prefix, your input content can be surrounded by quotes.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
2. Custom Function Plugins
Write powerful function plugins to perform any task you can think of, even those you cannot think of.
The difficulty of plugin writing and debugging in this project is very low. As long as you have a certain knowledge of Python, you can implement your own plug-in functions based on the template we provide.
For details, please refer to the [Function Plugin Guide](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
## New Feature Dynamics
1. Conversation saving function. Call `Save current conversation` in the function plugin area to save the current conversation as a readable and recoverable HTML file. In addition, call `Load conversation history archive` in the function plugin area (dropdown menu) to restore previous sessions. Tip: Clicking `Load conversation history archive` without specifying a file will display the cached history of HTML archives, and clicking `Delete all local conversation history` will delete all HTML archive caches.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. Report generation. Most plugins will generate work reports after execution.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
</div>
3. Modular function design with simple interfaces that support powerful functions.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
4. This is an open-source project that can "self-translate".
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
</div>
5. Translating other open-source projects is a piece of cake.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
</div>
6. A small feature decorated with [live2d](https://github.com/fghrsh/live2d_demo) (disabled by default, need to modify `config.py`).
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. Added MOSS large language model support.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
</div>
8. OpenAI image generation.
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
9. OpenAI audio parsing and summarization.
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
10. Full-text proofreading and error correction of LaTeX.
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
</div>
## Versions:
- version 3.5(Todo): Use natural language to call all function plugins of this project (high priority).
- version 3.4(Todo): Improve multi-threading support for chatglm local large models.
- version 3.3: +Internet information integration function.
- version 3.2: Function plugin supports more parameter interfaces (save conversation function, interpretation of any language code + simultaneous inquiry of any LLM combination).
- version 3.1: Support simultaneous inquiry of multiple GPT models! Support api2d, and support load balancing of multiple apikeys.
- version 3.0: Support chatglm and other small LLM models.
- version 2.6: Refactored plugin structure, improved interactivity, and added more plugins.
- version 2.5: Self-updating, solving the problem of text overflow and token overflow when summarizing large engineering source codes.
- version 2.4: (1) Added PDF full-text translation function; (2) Added the function of switching the position of the input area; (3) Added vertical layout option; (4) Optimized multi-threading function plugins.
- version 2.3: Enhanced multi-threading interactivity.
- version 2.2: Function plugin supports hot reloading.
- version 2.1: Collapsible layout.
- version 2.0: Introduction of modular function plugins.
- version 1.0: Basic functions.
gpt_academic Developer QQ Group-2: 610599535
- Known Issues
- Some browser translation plugins interfere with the front-end operation of this software.
- Both high and low versions of gradio can lead to various exceptions.
## Reference and Learning
```
Many other excellent designs have been referenced in the code, mainly including:
# Project 1: THU ChatGLM-6B:
https://github.com/THUDM/ChatGLM-6B
# Project 2: THU JittorLLMs:
https://github.com/Jittor/JittorLLMs
# Project 3: Edge-GPT:
https://github.com/acheong08/EdgeGPT
# Project 4: ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Project 5: ChatPaper:
https://github.com/kaixindelele/ChatPaper
# More:
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo
```

323
docs/README_FR.md
View File

@ -1,323 +0,0 @@
> **Note**
>
> Ce fichier README est généré automatiquement par le plugin de traduction markdown de ce projet et n'est peut - être pas correct à 100%.
>
> During installation, please strictly select the versions **specified** in requirements.txt.
>
> `pip install -r requirements.txt`
>
# <img src="logo.png" width="40" > Optimisation académique GPT (GPT Academic)
**Si vous aimez ce projet, veuillez lui donner une étoile. Si vous avez trouvé des raccourcis académiques ou des plugins fonctionnels plus utiles, n'hésitez pas à ouvrir une demande ou une pull request.
Pour traduire ce projet dans une langue arbitraire avec GPT, lisez et exécutez [`multi_language.py`](multi_language.py) (expérimental).
> **Note**
>
> 1. Veuillez noter que seuls les plugins de fonctions (boutons) **en rouge** prennent en charge la lecture de fichiers. Certains plugins se trouvent dans le **menu déroulant** de la zone de plugins. De plus, nous accueillons et traitons les nouvelles pull requests pour les plugins avec **la plus haute priorité**!
>
> 2. Les fonctions de chaque fichier de ce projet sont expliquées en détail dans l'auto-analyse [`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). Avec l'itération des versions, vous pouvez également cliquer sur les plugins de fonctions pertinents et appeler GPT pour régénérer le rapport d'auto-analyse du projet à tout moment. Les FAQ sont résumées dans [le wiki](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Méthode d'installation](#installation).
>
> 3. Ce projet est compatible avec et encourage l'utilisation de grands modèles de langage nationaux tels que chatglm, RWKV, Pangu, etc. La coexistence de plusieurs clés API est prise en charge et peut être remplie dans le fichier de configuration, tel que `API_KEY="openai-key1,openai-key2,api2d-key3"`. Lorsque vous souhaitez remplacer temporairement `API_KEY`, saisissez temporairement `API_KEY` dans la zone de saisie, puis appuyez sur Entrée pour soumettre et activer.
<div align="center">
Functionnalité | Description
--- | ---
Révision en un clic | prend en charge la révision en un clic et la recherche d'erreurs de syntaxe dans les articles
Traduction chinois-anglais en un clic | Traduction chinois-anglais en un clic
Explication de code en un clic | Affichage, explication, génération et ajout de commentaires de code
[Raccourcis personnalisés](https://www.bilibili.com/video/BV14s4y1E7jN) | prend en charge les raccourcis personnalisés
Conception modulaire | prend en charge de puissants plugins de fonction personnalisée, les plugins prennent en charge la [mise à jour à chaud](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)
[Autoscanner](https://www.bilibili.com/video/BV1cj411A7VW) | [Plug-in de fonction] [Compréhension instantanée](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) du code source de ce projet
[Analyse de programme](https://www.bilibili.com/video/BV1cj411A7VW) | [Plug-in de fonction] Analyse en un clic de la structure d'autres projets Python / C / C ++ / Java / Lua / ...
Lecture d'articles, [traduction](https://www.bilibili.com/video/BV1KT411x7Wn) d'articles | [Plug-in de fonction] Compréhension instantanée de l'article latex / pdf complet et génération de résumés
[Traduction](https://www.bilibili.com/video/BV1nk4y1Y7Js/) et [révision](https://www.bilibili.com/video/BV1FT411H7c5/) complets en latex | [Plug-in de fonction] traduction ou révision en un clic d'articles en latex
Génération de commentaires en masse | [Plug-in de fonction] Génération en un clic de commentaires de fonction en masse
Traduction [chinois-anglais](https://www.bilibili.com/video/BV1yo4y157jV/) en Markdown | [Plug-in de fonction] avez-vous vu la [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) pour les 5 langues ci-dessus?
Génération de rapports d'analyse de chat | [Plug-in de fonction] Génère automatiquement un rapport de résumé après l'exécution
[Traduction intégrale en pdf](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plug-in de fonction] Extraction de titre et de résumé de l'article pdf + traduction intégrale (multi-thread)
[Aide à arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plug-in de fonction] Entrer l'url de l'article arxiv pour traduire et télécharger le résumé en un clic
[Aide à la recherche Google Scholar](https://www.bilibili.com/video/BV19L411U7ia) | [Plug-in de fonction] Donnez l'URL de la page de recherche Google Scholar, laissez GPT vous aider à [écrire des ouvrages connexes](https://www.bilibili.com/video/BV1GP411U7Az/)
Aggrégation d'informations en ligne et GPT | [Plug-in de fonction] Permet à GPT de [récupérer des informations en ligne](https://www.bilibili.com/video/BV1om4y127ck), puis de répondre aux questions, afin que les informations ne soient jamais obsolètes
Affichage d'équations / images / tableaux | Fournit un affichage simultané de [la forme tex et de la forme rendue](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), prend en charge les formules mathématiques et la coloration syntaxique du code
Prise en charge des plugins à plusieurs threads | prend en charge l'appel multithread de chatgpt, un clic pour traiter [un grand nombre d'articles](https://www.bilibili.com/video/BV1FT411H7c5/) ou de programmes
Thème gradio sombre en option de démarrage | Ajoutez```/?__theme=dark``` à la fin de l'URL du navigateur pour basculer vers le thème sombre
[Prise en charge de plusieurs modèles LLM](https://www.bilibili.com/video/BV1wT411p7yf), [API2D](https://api2d.com/) | Sera probablement très agréable d'être servi simultanément par GPT3.5, GPT4, [ChatGLM de Tsinghua](https://github.com/THUDM/ChatGLM-6B), [MOSS de Fudan](https://github.com/OpenLMLab/MOSS)
Plus de modèles LLM, déploiement de [huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Ajout prise en charge de l'interface Newbing (nouvelle bing), introduction du support de [Jittorllms de Tsinghua](https://github.com/Jittor/JittorLLMs), [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) et [Panguα](https://openi.org.cn/pangu/)
Plus de nouvelles fonctionnalités (génération d'images, etc.) ... | Voir la fin de ce document pour plus de détails ...
</div>
- Nouvelle interface (modifier l'option LAYOUT de `config.py` pour passer d'une disposition ``gauche-droite`` à une disposition ``haut-bas``)
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
</div>- Tous les boutons sont générés dynamiquement en lisant functional.py et peuvent être facilement personnalisés pour ajouter des fonctionnalités personnalisées, ce qui facilite l'utilisation du presse-papiers.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- Correction d'erreurs/lissage du texte.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- Si la sortie contient des équations, elles sont affichées à la fois sous forme de tex et sous forme rendue pour faciliter la lecture et la copie.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Pas envie de lire les codes de ce projet? Tout le projet est directement exposé par ChatGPT.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Appel à une variété de modèles de langage de grande envergure (ChatGLM + OpenAI-GPT3.5 + [API2D] (https://api2d.com/)-GPT4).
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
---
# Installation
## Installation-Method 1: running directly (Windows, Linux or MacOS)
1. Télécharger le projet
```sh
git clone https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configuration de la clé API
Dans `config.py`, configurez la clé API et d'autres paramètres. Consultez [Special network environment settings] (https://github.com/binary-husky/gpt_academic/issues/1).
(P.S. Lorsque le programme est exécuté, il vérifie en premier s'il existe un fichier de configuration privé nommé `config_private.py` et remplace les paramètres portant le même nom dans `config.py` par les paramètres correspondants dans `config_private.py`. Par conséquent, si vous comprenez la logique de lecture de nos configurations, nous vous recommandons vivement de créer un nouveau fichier de configuration nommé `config_private.py` à côté de `config.py` et de transférer (copier) les configurations de `config.py`. `config_private.py` n'est pas contrôlé par Git et peut garantir la sécurité de vos informations privées. P.S. Le projet prend également en charge la configuration de la plupart des options via "variables d'environnement", le format d'écriture des variables d'environnement est référencé dans le fichier `docker-compose`. Priorité de lecture: "variables d'environnement" > `config_private.py` > `config.py`)
3. Installer les dépendances
```sh
# (Option I: python users instalation) (Python version 3.9 or higher, the newer the better). Note: use official pip source or ali pip source. To temporarily change the source: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Option II: non-python users instalation) Use Anaconda, the steps are similar (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # Create anaconda env
conda activate gptac_venv # Activate anaconda env
python -m pip install -r requirements.txt # Same step as pip instalation
```
<details><summary>Cliquez ici pour afficher le texte si vous souhaitez prendre en charge THU ChatGLM/FDU MOSS en tant que backend.</summary>
<p>
【Optional】 Si vous souhaitez prendre en charge THU ChatGLM/FDU MOSS en tant que backend, des dépendances supplémentaires doivent être installées (prérequis: compétent en Python + utilisez Pytorch + configuration suffisante de l'ordinateur):
```sh
# 【Optional Step I】 Support THU ChatGLM. Remarque sur THU ChatGLM: Si vous rencontrez l'erreur "Appel à ChatGLM échoué, les paramètres ChatGLM ne peuvent pas être chargés normalement", reportez-vous à ce qui suit: 1: La version par défaut installée est torch+cpu, si vous souhaitez utiliser cuda, vous devez désinstaller torch et réinstaller torch+cuda; 2: Si le modèle ne peut pas être chargé en raison d'une configuration insuffisante de l'ordinateur local, vous pouvez modifier la précision du modèle dans request_llm/bridge_chatglm.py, modifier AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) par AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llm/requirements_chatglm.txt
# 【Optional Step II】 Support FDU MOSS
python -m pip install -r request_llm/requirements_moss.txt
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Note: When running this line of code, you must be in the project root path.
# 【Optional Step III】Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the desired model. Currently, all models supported are as follows (the jittorllms series currently only supports the docker scheme):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Exécution
```sh
python main.py
```5. Plugin de fonction de test
```
- Fonction de modèle de plugin de test (requiert que GPT réponde à ce qui s'est passé dans l'histoire aujourd'hui), vous pouvez utiliser cette fonction comme modèle pour mettre en œuvre des fonctionnalités plus complexes.
Cliquez sur "[Démo de modèle de plugin de fonction] Aujourd'hui dans l'histoire"
```
## Installation - Méthode 2: Utilisation de Docker
1. ChatGPT uniquement (recommandé pour la plupart des gens)
``` sh
git clone https://github.com/binary-husky/gpt_academic.git # Télécharger le projet
cd gpt_academic # Accéder au chemin
nano config.py # Editez config.py avec n'importe quel éditeur de texte en configurant "Proxy", "API_KEY" et "WEB_PORT" (p. ex. 50923)
docker build -t gpt-academic . # Installer
# (Dernière étape - choix1) Dans un environnement Linux, l'utilisation de `--net=host` est plus facile et rapide
docker run --rm -it --net=host gpt-academic
# (Dernière étape - choix 2) Dans un environnement macOS/Windows, seule l'option -p permet d'exposer le port du récipient (p.ex. 50923) au port de l'hôte.
docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
```
2. ChatGPT + ChatGLM + MOSS (il faut connaître Docker)
``` sh
# Modifiez docker-compose.yml, supprimez la solution 1 et la solution 3, conservez la solution 2. Modifiez la configuration de la solution 2 dans docker-compose.yml en suivant les commentaires.
docker-compose up
```
3. ChatGPT + LLAMA + PanGu + RWKV (il faut connaître Docker)
``` sh
# Modifiez docker-compose.yml, supprimez la solution 1 et la solution 2, conservez la solution 3. Modifiez la configuration de la solution 3 dans docker-compose.yml en suivant les commentaires.
docker-compose up
```
## Installation - Méthode 3: Autres méthodes de déploiement
1. Comment utiliser une URL de proxy inversé / Microsoft Azure Cloud API
Configurez simplement API_URL_REDIRECT selon les instructions de config.py.
2. Déploiement distant sur un serveur cloud (connaissance et expérience des serveurs cloud requises)
Veuillez consulter [Wiki de déploiement-1] (https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97).
3. Utilisation de WSL2 (sous-système Windows pour Linux)
Veuillez consulter [Wiki de déploiement-2] (https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2).
4. Comment exécuter sous un sous-répertoire (tel que `http://localhost/subpath`)
Veuillez consulter les [instructions d'exécution de FastAPI] (docs/WithFastapi.md).
5. Utilisation de docker-compose
Veuillez lire docker-compose.yml, puis suivre les instructions fournies.
# Utilisation avancée
## Personnalisation de nouveaux boutons pratiques / Plugins de fonctions personnalisées
1. Personnalisation de nouveaux boutons pratiques (raccourcis académiques)
Ouvrez core_functional.py avec n'importe quel éditeur de texte, ajoutez une entrée comme suit, puis redémarrez le programme. (Si le bouton a été ajouté avec succès et est visible, le préfixe et le suffixe prennent en charge les modifications à chaud et ne nécessitent pas le redémarrage du programme pour prendre effet.)
Par exemple
```
"Super coller sens": {
# Préfixe, sera ajouté avant votre entrée. Par exemple, pour décrire votre demande, telle que traduire, expliquer du code, faire la mise en forme, etc.
"Prefix": "Veuillez traduire le contenu suivant en chinois, puis expliquer chaque terme proprement nommé qui y apparaît avec un tableau markdown:\n\n",
# Suffixe, sera ajouté après votre entrée. Par exemple, en utilisant le préfixe, vous pouvez entourer votre contenu d'entrée de guillemets.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
2. Plugins de fonctions personnalisées
Écrivez des plugins de fonctions puissants pour effectuer toutes les tâches que vous souhaitez ou que vous ne pouvez pas imaginer.
Les plugins de ce projet ont une difficulté de programmation et de débogage très faible. Si vous avez des connaissances de base en Python, vous pouvez simuler la fonctionnalité de votre propre plugin en suivant le modèle que nous avons fourni.
Veuillez consulter le [Guide du plugin de fonction] (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) pour plus de détails.
---
# Latest Update
## Nouvelles fonctionnalités en cours de déploiement.
1. Fonction de sauvegarde de la conversation.
Appelez simplement "Enregistrer la conversation actuelle" dans la zone de plugin de fonction pour enregistrer la conversation actuelle en tant que fichier html lisible et récupérable. De plus, dans la zone de plugin de fonction (menu déroulant), appelez "Charger une archive de l'historique de la conversation" pour restaurer la conversation précédente. Astuce : cliquer directement sur "Charger une archive de l'historique de la conversation" sans spécifier de fichier permet de consulter le cache d'archive html précédent. Cliquez sur "Supprimer tous les enregistrements locaux de l'historique de la conversation" pour supprimer le cache d'archive html.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
</div>
2. Générer un rapport. La plupart des plugins génèrent un rapport de travail après l'exécution.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
</div>
3. Conception de fonctionnalités modulaires avec une interface simple mais capable d'une fonctionnalité puissante.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
</div>
4. C'est un projet open source qui peut "se traduire de lui-même".
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
</div>
5. Traduire d'autres projets open source n'est pas un problème.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
</div>
6. Fonction de décoration de live2d (désactivée par défaut, nécessite une modification de config.py).
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
</div>
7. Prise en charge du modèle de langue MOSS.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
</div>
8. Génération d'images OpenAI.
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
</div>
9. Analyse et synthèse vocales OpenAI.
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
</div>
10. Correction de la totalité des erreurs de Latex.
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
</div>
## Versions :
- version 3.5 (À faire) : appel de toutes les fonctions de plugin de ce projet en langage naturel (priorité élevée)
- version 3.4 (À faire) : amélioration du support multi-thread de chatglm en local
- version 3.3 : Fonctionnalité intégrée d'informations d'internet
- version 3.2 : La fonction du plugin de fonction prend désormais en charge des interfaces de paramètres plus nombreuses (fonction de sauvegarde, décodage de n'importe quel langage de code + interrogation simultanée de n'importe quelle combinaison de LLM)
- version 3.1 : Prise en charge de l'interrogation simultanée de plusieurs modèles GPT ! Support api2d, équilibrage de charge multi-clé api.
- version 3.0 : Prise en charge de chatglm et autres LLM de petite taille.
- version 2.6 : Refonte de la structure des plugins, amélioration de l'interactivité, ajout de plus de plugins.
- version 2.5 : Auto-mise à jour, résolution des problèmes de texte trop long et de dépassement de jetons lors de la compilation du projet global.
- version 2.4 : (1) Nouvelle fonction de traduction de texte intégral PDF ; (2) Nouvelle fonction de permutation de position de la zone d'entrée ; (3) Nouvelle option de mise en page verticale ; (4) Amélioration des fonctions multi-thread de plug-in.
- version 2.3 : Amélioration de l'interactivité multithread.
- version 2.2 : Les plugins de fonctions peuvent désormais être rechargés à chaud.
- version 2.1 : Disposition pliable
- version 2.0 : Introduction de plugins de fonctions modulaires
- version 1.0 : Fonctionnalités de base
gpt_academic développeur QQ groupe-2：610599535
- Problèmes connus
- Certains plugins de traduction de navigateur perturbent le fonctionnement de l'interface frontend de ce logiciel
- Des versions gradio trop hautes ou trop basses provoquent de nombreuses anomalies
## Référence et apprentissage
```
De nombreux autres excellents projets ont été référencés dans le code, notamment :
# Projet 1 : ChatGLM-6B de Tsinghua :
https://github.com/THUDM/ChatGLM-6B
# Projet 2 : JittorLLMs de Tsinghua :
https://github.com/Jittor/JittorLLMs
# Projet 3 : Edge-GPT :
https://github.com/acheong08/EdgeGPT
# Projet 4 : ChuanhuChatGPT :
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Projet 5 : ChatPaper :
https://github.com/kaixindelele/ChatPaper
# Plus :
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo
```

329
docs/README_JP.md
View File

@ -1,329 +0,0 @@
> **Note**
>
> このReadmeファイルは、このプロジェクトのmarkdown翻訳プラグインによって自動的に生成されたもので、100%正確ではない可能性があります。
>
> When installing dependencies, please strictly choose the versions specified in `requirements.txt`.
>
> `pip install -r requirements.txt`
>
# <img src="logo.png" width="40" > GPT 学术优化 (GPT Academic)
**もしこのプロジェクトが好きなら、星をつけてください。もしあなたがより良いアカデミックショートカットまたは機能プラグインを思いついた場合、Issueをオープンするか pull request を送信してください。私たちはこのプロジェクト自体によって翻訳された[英語 |](README_EN.md)[日本語 |](README_JP.md)[한국어 |](https://github.com/mldljyh/ko_gpt_academic)[Русский |](README_RS.md)[Français](README_FR.md)のREADMEも用意しています。
GPTを使った任意の言語にこのプロジェクトを翻訳するには、[`multi_language.py`](multi_language.py)を読んで実行してください。 (experimental)。
> **注意**
>
> 1. **赤色**で表示された関数プラグイン(ボタン)のみ、ファイルの読み取りをサポートしています。一部のプラグインは、プラグインエリアの**ドロップダウンメニュー**内にあります。また、私たちはどんな新しいプラグインのPRでも、**最優先**で歓迎し、処理します！
>
> 2. このプロジェクトの各ファイルの機能は、自己解析の詳細説明書である[`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)で説明されています。バージョンが進化するにつれて、関連する関数プラグインをいつでもクリックし、GPTを呼び出してプロジェクトの自己解析レポートを再生成することができます。よくある問題は[`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)にまとめられています。[インストール方法](#installation)。
> 3. このプロジェクトは、chatglmやRWKV、パンクなど、国内の大規模自然言語モデルを利用することをサポートし、試みることを奨励します。複数のAPIキーを共存することができ、設定ファイルに`API_KEY="openai-key1,openai-key2,api2d-key3"`のように記入することができます。`API_KEY`を一時的に変更する場合は、入力エリアに一時的な`API_KEY`を入力してEnterキーを押せば、それが有効になります。
<div align="center">
機能 | 説明
--- | ---
一键校正 | 一键で校正可能、論文の文法エラーを検索することができる
一键中英翻訳 | 一键で中英翻訳可能
一键コード解説 | コードを表示し、解説し、生成し、コードに注釈をつけることができる
[自分でカスタマイズ可能なショートカットキー](https://www.bilibili.com/video/BV14s4y1E7jN) | 自分でカスタマイズ可能なショートカットキーをサポートする
モジュール化された設計 | カスタマイズ可能な[強力な関数プラグイン](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions)をサポートし、プラグインは[ホットアップデート](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)に対応している
[自己プログラム解析](https://www.bilibili.com/video/BV1cj411A7VW) | [関数プラグイン] [一键読解](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)このプロジェクトのソースコード
プログラム解析 | [関数プラグイン] 一鍵で他のPython/C/C++/Java/Lua/...プロジェクトを分析できる
論文の読み、[翻訳](https://www.bilibili.com/video/BV1KT411x7Wn) | [関数プラグイン] LaTex/ PDF論文の全文を一鍵で読み解き、要約を生成することができる
LaTex全文[翻訳](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[校正](https://www.bilibili.com/video/BV1FT411H7c5/) | [関数プラグイン] LaTex論文の翻訳または校正を一鍵で行うことができる
一括で注釈を生成 | [関数プラグイン] 一鍵で関数に注釈をつけることができる
Markdown[中英翻訳](https://www.bilibili.com/video/BV1yo4y157jV/) | [関数プラグイン] 上記の5種類の言語の[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)を見たことがありますか？
チャット分析レポート生成 | [関数プラグイン] 実行後、自動的に概要報告書を生成する
[PDF論文全文翻訳機能](https://www.bilibili.com/video/BV1KT411x7Wn) | [関数プラグイン] PDF論文からタイトルと要約を抽出し、全文を翻訳する（マルチスレッド）
[Arxivアシスタント](https://www.bilibili.com/video/BV1LM4y1279X) | [関数プラグイン] arxiv記事のURLを入力するだけで、要約を一鍵翻訳し、PDFをダウンロードできる
[Google Scholar 総合アシスタント](https://www.bilibili.com/video/BV19L411U7ia) | [関数プラグイン] 任意のGoogle Scholar検索ページURLを指定すると、gptが[related works](https://www.bilibili.com/video/BV1GP411U7Az/)を作成する
インターネット情報収集＋GPT | [関数プラグイン] まずGPTに[インターネットから情報を収集](https://www.bilibili.com/video/BV1om4y127ck)してから質問に回答させ、情報が常に最新であるようにする
数式/画像/表表示 | 数式の[tex形式とレンダリング形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)を同時に表示し、数式、コードハイライトをサポートしている
マルチスレッド関数プラグインがサポートされている | chatgptをマルチスレッドで呼び出し、[大量のテキスト](https://www.bilibili.com/video/BV1FT411H7c5/)またはプログラムを一鍵で処理できる
ダークグラジオ[テーマの起動](https://github.com/binary-husky/gpt_academic/issues/173) | ブラウザのURLの後ろに```/?__theme=dark```を追加すると、ダークテーマを切り替えることができます。
[多数のLLMモデル](https://www.bilibili.com/video/BV1wT411p7yf)がサポートされ、[API2D](https://api2d.com/)がサポートされている | 同時にGPT3.5、GPT4、[清華ChatGLM](https://github.com/THUDM/ChatGLM-6B)、[復旦MOSS](https://github.com/OpenLMLab/MOSS)に対応
より多くのLLMモデルが接続され、[huggingfaceデプロイ](https://huggingface.co/spaces/qingxu98/gpt-academic)がサポートされている | Newbingインターフェイス（Newbing）、清華大学の[Jittorllm](https://github.com/Jittor/JittorLLMs)のサポート[LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV)と[盘古α](https://openi.org.cn/pangu/)
さらに多くの新機能（画像生成など）を紹介する... | この文書の最後に示す...
</div>
- 新しいインターフェース（`config.py`のLAYOUTオプションを変更することで、「左右配置」と「上下配置」を切り替えることができます）
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
</div>- All buttons are dynamically generated by reading functional.py, and custom functions can be freely added to free the clipboard.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- Polishing/Correction
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- If the output contains formulas, they are displayed in both TeX and rendering forms, making it easy to copy and read.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Don't feel like looking at the project code? Just ask chatgpt directly.
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Mixed calls of multiple large language models (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4）
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
---
# Installation
## Installation-Method 1: Directly run (Windows, Linux or MacOS)
1. Download the project.
```sh
git clone https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configure the API_KEY.
Configure the API KEY and other settings in `config.py` and [special network environment settings](https://github.com/binary-husky/gpt_academic/issues/1).
(P.S. When the program is running, it will first check if there is a private configuration file named `config_private.py`, and use the configuration in it to override the same name configuration in `config.py`. Therefore, if you can understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py`, and transfer (copy) the configuration in `config.py` to `config_private.py`. `config_private.py` is not controlled by git and can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`, and the writing format of environment variables refers to the `docker-compose` file. Reading priority: `environment variables` > `config_private.py` > `config.py`)
3. Install dependencies.
```sh
# （Choose I: If familiar with Python）(Python version 3.9 or above, the newer the better) Note: Use the official pip source or Ali pip source. Temporary switching source method: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# (Choose II: If not familiar with Python) Use anaconda, the steps are the same (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # Create anaconda environment.
conda activate gptac_venv # Activate the anaconda environment.
python -m pip install -r requirements.txt # This step is the same as the pip installation step.
```
<details><summary>If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, click to expand.</summary>
<p>
[Optional Steps] If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, you need to install more dependencies (precondition: familiar with Python + used Pytorch + computer configuration). Strong enough):
```sh
# Optional step I: support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: If you encounter the error "Call ChatGLM fail cannot load ChatGLM parameters normally", refer to the following: 1: The version installed above is torch+cpu version, using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).
python -m pip install -r request_llm/requirements_chatglm.txt
# Optional Step II: Support Fudan MOSS.
python -m pip install -r request_llm/requirements_moss.txt
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Note that when executing this line of code, it must be in the project root.
# 【Optional Step III】Ensure that the AVAIL_LLM_MODELS in the config.py configuration file contains the expected model. Currently, all supported models are as follows (jittorllms series currently only supports the docker solution):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Run.
```sh
python main.py
```5. Testing Function Plugin
```
- Test function plugin template function (requires gpt to answer what happened today in history), you can use this function as a template to implement more complex functions
Click "[Function Plugin Template Demo] Today in History"
```
## Installation-Methods 2: Using Docker
1. Only ChatGPT (recommended for most people)
``` sh
git clone https://github.com/binary-husky/gpt_academic.git # Download project
cd gpt_academic # Enter path
nano config.py # Edit config.py with any text editor ‑ configure "Proxy," "API_KEY," "WEB_PORT" (e.g., 50923) and more
docker build -t gpt-academic . # installation
#(Last step-Option 1) In a Linux environment, `--net=host` is more convenient and quick
docker run --rm -it --net=host gpt-academic
#(Last step-Option 2) In a macOS/windows environment, the -p option must be used to expose the container port (e.g., 50923) to the port on the host.
docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
```
2. ChatGPT + ChatGLM + MOSS (requires familiarity with Docker)
``` sh
# Modify docker-compose.yml, delete plans 1 and 3, and retain plan 2. Modify the configuration of plan 2 in docker-compose.yml, and reference the comments for instructions.
docker-compose up
```
3. ChatGPT + LLAMA + Pangu + RWKV (requires familiarity with Docker)
``` sh
# Modify docker-compose.yml, delete plans 1 and 2, and retain plan 3. Modify the configuration of plan 3 in docker-compose.yml, and reference the comments for instructions.
docker-compose up
```
## Installation-Method 3: Other Deployment Methods
1. How to use proxy URL/Microsoft Azure API
Configure API_URL_REDIRECT according to the instructions in `config.py`.
2. Remote Cloud Server Deployment (requires cloud server knowledge and experience)
Please visit [Deployment Wiki-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
3. Using WSL2 (Windows Subsystem for Linux Subsystem)
Please visit [Deployment Wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
4. How to run on a secondary URL (such as `http://localhost/subpath`)
Please visit [FastAPI Running Instructions](docs/WithFastapi.md)
5. Run with docker-compose
Please read docker-compose.yml and follow the instructions provided therein.
---
# Advanced Usage
## Customize new convenience buttons/custom function plugins
1. Custom new convenience buttons (academic shortcut keys)
Open `core_functional.py` with any text editor, add the item as follows, and restart the program. (If the button has been added successfully and is visible, the prefix and suffix support hot modification without restarting the program.)
example:
```
"Super English to Chinese Translation": {
# Prefix, which will be added before your input. For example, used to describe your request, such as translation, code interpretation, polish, etc.
"Prefix": "Please translate the following content into Chinese, and explain the proper nouns in the text in a markdown table one by one:\n\n",
# Suffix, which will be added after your input. For example, in combination with the prefix, you can surround your input content with quotation marks.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
2. Custom function plugins
Write powerful function plugins to perform any task you can and cannot think of.
The difficulty of writing and debugging plugins in this project is low, and as long as you have a certain amount of python basic knowledge, you can follow the template provided by us to achieve your own plugin functions.
For details, please refer to the [Function Plugin Guide](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
## New feature dynamics.
1. ダイアログの保存機能。関数プラグインエリアで '現在の会話を保存' を呼び出すと、現在のダイアログを読み取り可能で復元可能なHTMLファイルとして保存できます。さらに、関数プラグインエリア（ドロップダウンメニュー）で 'ダイアログの履歴保存ファイルを読み込む' を呼び出すことで、以前の会話を復元することができます。Tips:ファイルを指定せずに 'ダイアログの履歴保存ファイルを読み込む' をクリックすることで、過去のHTML保存ファイルのキャッシュを表示することができます。'すべてのローカルダイアログの履歴を削除' をクリックすることで、すべてのHTML保存ファイルのキャッシュを削除できます。
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500">
</div>
2. 報告書を生成します。ほとんどのプラグインは、実行が終了した後に作業報告書を生成します。
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300">
<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300">
<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300">
</div>
3. モジュール化された機能設計、簡単なインターフェースで強力な機能をサポートする。
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400">
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400">
</div>
4. 自己解決可能なオープンソースプロジェクトです。
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500">
</div>
5. 他のオープンソースプロジェクトの解読、容易である。
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500">
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500">
</div>
6. [Live2D](https://github.com/fghrsh/live2d_demo)のデコレート小機能です。（デフォルトでは閉じてますが、 `config.py`を変更する必要があります。）
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500">
</div>
7. 新たにMOSS大言語モデルのサポートを追加しました。
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500">
</div>
8. OpenAI画像生成
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500">
</div>
9. OpenAIオーディオの解析とサマリー
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500">
</div>
10. 全文校正されたLaTeX
<div align="center">
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500">
</div>
## バージョン：
- version 3.5（作業中）：すべての関数プラグインを自然言語で呼び出すことができるようにする（高い優先度）。
- version 3.4（作業中）：chatglmのローカルモデルのマルチスレッドをサポートすることで、機能を改善する。
- version 3.3：+Web情報の総合機能
- version 3.2：関数プラグインでさらに多くのパラメータインターフェイスをサポートする（ダイアログの保存機能、任意の言語コードの解読+同時に任意のLLM組み合わせに関する問い合わせ）
- version 3.1：複数のGPTモデルを同時に質問できるようになりました！ api2dをサポートし、複数のAPIキーを均等に負荷分散することができます。
- version 3.0：chatglmとその他の小型LLMのサポート。
- version 2.6：プラグイン構造を再構築し、対話内容を高め、より多くのプラグインを追加しました。
- version 2.5：自己アップデートし、長文書やトークンのオーバーフローの問題を解決しました。
- version 2.4：（1）全文翻訳のPDF機能を追加しました。（2）入力エリアの位置切り替え機能を追加しました。（3）垂直レイアウトオプションを追加しました。（4）マルチスレッド関数プラグインを最適化しました。
- version 2.3：マルチスレッド性能の向上。
- version 2.2：関数プラグインのホットリロードをサポートする。
- version 2.1：折りたたみ式レイアウト。
- version 2.0：モジュール化された関数プラグインを導入。
- version 1.0：基本機能
gpt_academic開発者QQグループ-2：610599535
- 既知の問題
- 一部のブラウザ翻訳プラグインが、このソフトウェアのフロントエンドの実行を妨害する
- gradioバージョンが高すぎるか低すぎると、多くの異常が引き起こされる
## 参考学習
```
コードの中には、他の優れたプロジェクトの設計から参考にしたものがたくさん含まれています：
# プロジェクト1：清華ChatGLM-6B:
https://github.com/THUDM/ChatGLM-6B
# プロジェクト2：清華JittorLLMs:
https://github.com/Jittor/JittorLLMs
# プロジェクト3：Edge-GPT:
https://github.com/acheong08/EdgeGPT
# プロジェクト4：ChuanhuChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# プロジェクト5：ChatPaper:
https://github.com/kaixindelele/ChatPaper
# その他：
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo
```

278
docs/README_RS.md
View File

@ -1,278 +0,0 @@
> **Note**
>
> Этот файл самовыражения автоматически генерируется модулем перевода markdown в этом проекте и может быть не на 100% правильным.
>
# <img src="logo.png" width="40" > GPT Академическая оптимизация (GPT Academic)
**Если вам нравится этот проект, пожалуйста, поставьте ему звезду. Если вы придумали более полезные языковые ярлыки или функциональные плагины, не стесняйтесь открывать issue или pull request.
Чтобы перевести этот проект на произвольный язык с помощью GPT, ознакомьтесь и запустите [`multi_language.py`](multi_language.py) (экспериментальный).
> **Примечание**
>
> 1. Обратите внимание, что только функциональные плагины (кнопки), помеченные **красным цветом**, поддерживают чтение файлов, некоторые плагины находятся в **выпадающем меню** в области плагинов. Кроме того, мы с наивысшим приоритетом рады и обрабатываем pull requests для любых новых плагинов!
>
> 2. В каждом файле проекта функциональность описана в документе самоанализа [`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). С каждой итерацией выполнения версии вы можете в любое время вызвать повторное создание отчета о самоанализе этого проекта, щелкнув соответствующий функциональный плагин и вызвав GPT. Вопросы сборки описаны в [`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Метод установки](#installation).
>
> 3. Этот проект совместим и поощряет использование китайских языковых моделей chatglm и RWKV, пангу и т. Д. Поддержка нескольких api-key, которые могут существовать одновременно, может быть указан в файле конфигурации, например `API_KEY="openai-key1,openai-key2,api2d-key3"`. Если требуется временно изменить `API_KEY`, введите временный `API_KEY` в области ввода и нажмите клавишу Enter, чтобы он вступил в силу.
> **Примечание**
>
> При установке зависимостей строго выбирайте версии, **указанные в файле requirements.txt**.
>
> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`## Задание
Вы профессиональный переводчик научных статей.
Переведите этот файл в формате Markdown на русский язык. Не изменяйте существующие команды Markdown, ответьте только переведенными результатами.
## Результат
Функция | Описание
--- | ---
Однокнопочный стиль | Поддержка однокнопочного стиля и поиска грамматических ошибок в научных статьях
Однокнопочный перевод на английский и китайский | Однокнопочный перевод на английский и китайский
Однокнопочное объяснение кода | Показ кода, объяснение его, генерация кода, комментирование кода
[Настройка быстрых клавиш](https://www.bilibili.com/video/BV14s4y1E7jN) | Поддержка настройки быстрых клавиш
Модульный дизайн | Поддержка пользовательских функциональных плагинов мощных [функциональных плагинов](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions), плагины поддерживают [горячую замену](https://github.com/binary-husky/gpt_academic/wiki/Function-Plug-in-Guide)
[Анализ своей программы](https://www.bilibili.com/video/BV1cj411A7VW) | [Функциональный плагин] [Однокнопочный просмотр](https://github.com/binary-husky/gpt_academic/wiki/chatgpt-academicProject-Self-analysis-Report) исходного кода этого проекта
[Анализ программы](https://www.bilibili.com/video/BV1cj411A7VW) | [Функциональный плагин] Однокнопочный анализ дерева других проектов Python/C/C++/Java/Lua/...
Чтение статей, [перевод](https://www.bilibili.com/video/BV1KT411x7Wn) статей | [Функциональный плагин] Однокнопочное чтение полного текста научных статей и генерация резюме
Полный перевод [LaTeX](https://www.bilibili.com/video/BV1nk4y1Y7Js/) и совершенствование | [Функциональный плагин] Однокнопочный перевод или совершенствование LaTeX статьи
Автоматическое комментирование | [Функциональный плагин] Однокнопочное автоматическое генерирование комментариев функций
[Перевод](https://www.bilibili.com/video/BV1yo4y157jV/) Markdown на английский и китайский | [Функциональный плагин] Вы видели обе версии файлов [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) для этих 5 языков?
Отчет о чат-анализе | [Функциональный плагин] После запуска будет автоматически сгенерировано сводное извещение
Функция перевода полного текста [PDF-статьи](https://www.bilibili.com/video/BV1KT411x7Wn) | [Функциональный плагин] Извлечение заголовка и резюме [PDF-статьи](https://www.bilibili.com/video/BV1KT411x7Wn) и перевод всего документа (многопоточность)
[Arxiv Helper](https://www.bilibili.com/video/BV1LM4y1279X) | [Функциональный плагин] Введите URL статьи на arxiv и одним щелчком мыши переведите резюме и загрузите PDF
[Google Scholar Integration Helper](https://www.bilibili.com/video/BV19L411U7ia) | [Функциональный плагин] При заданном любом URL страницы поиска в Google Scholar позвольте gpt вам помочь [написать обзор](https://www.bilibili.com/video/BV1GP411U7Az/)
Сбор Интернет-информации + GPT | [Функциональный плагин] Однокнопочный [запрос информации из Интернета GPT](https://www.bilibili.com/video/BV1om4y127ck), затем ответьте на вопрос, чтобы информация не устарела никогда
Отображение формул / изображений / таблиц | Может одновременно отображать формулы в [формате Tex и рендеринге](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), поддерживает формулы, подсвечивает код
Поддержка функций с многопоточностью | Поддержка многопоточного вызова chatgpt, однокнопочная обработка [больших объемов текста](https://www.bilibili.com/video/BV1FT411H7c5/) или программ
Темная тема gradio для запуска приложений | Добавьте ```/?__theme=dark``` после URL в браузере, чтобы переключиться на темную тему
[Поддержка нескольких моделей LLM](https://www.bilibili.com/video/BV1wT411p7yf), [API2D](https://api2d.com/) | Они одновременно обслуживаются GPT3.5, GPT4, [Clear ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS)
Подключение нескольких новых моделей LLM, поддержка деплоя[huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Подключение интерфейса Newbing (новый Bing), подключение поддержки [LLaMA](https://github.com/facebookresearch/llama), поддержка [RWKV](https://github.com/BlinkDL/ChatRWKV) и [Pangu α](https://openi.org.cn/pangu/)
Больше новых функций (генерация изображения и т. д.) | См. на конце этого файла…- All buttons are dynamically generated by reading functional.py, and custom functions can be freely added to liberate the clipboard
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
</div>
- Revision/Correction
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
</div>
- If the output contains formulas, they will be displayed in both tex and rendered form for easy copying and reading
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
</div>
- Don't feel like looking at project code? Show the entire project directly in chatgpt
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
</div>
- Mixing multiple large language models (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4）
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
</div>
---
# Installation
## Installation-Method 1: Run directly (Windows, Linux or MacOS)
1. Download the project
```sh
git clone https://github.com/binary-husky/gpt_academic.git
cd gpt_academic
```
2. Configure API_KEY
In `config.py`, configure API KEY and other settings, [special network environment settings] (https://github.com/binary-husky/gpt_academic/issues/1).
(P.S. When the program is running, it will first check whether there is a secret configuration file named `config_private.py` and use the configuration in it to replace the same name in` config.py`. Therefore, if you understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py`, and transfer (copy) the configuration in `config.py` to `config_private.py`. `config_private.py` is not controlled by git, which can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`, and the writing format of environment variables refers to the `docker-compose` file. Priority of read: `environment variable`>`config_private.py`>`config.py`)
3. Install dependencies
```sh
# （Option I: If familiar with Python）(Python version 3.9 or above, the newer the better), note: use the official pip source or the aliyun pip source, temporary switching source method: python -m pip install -r requirements.txt - i https://mirrors.aliyun.com/pypi/simple/
python -m pip install -r requirements.txt
# （Option II: If unfamiliar with Python）Use Anaconda, the steps are also similar (https://www.bilibili.com/video/BV1rc411W7Dr):
conda create -n gptac_venv python=3.11 # create an Anaconda environment
conda activate gptac_venv # activate Anaconda environment
python -m pip install -r requirements.txt # This step is the same as the pip installation
```
<details><summary> If you need to support Tsinghua ChatGLM/Fudan MOSS as backend, click here to expand </summary>
<p>
[Optional step] If you need to support Tsinghua ChatGLM/Fudan MOSS as backend, you need to install more dependencies (prerequisites: familiar with Python + have used Pytorch + computer configuration is strong):
```sh
# [Optional step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM note: If you encounter the "Call ChatGLM fail cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installation above is torch+cpu version, and cuda is used Need to uninstall torch and reinstall torch+cuda; 2: If you cannot load the model due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) Modify to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
python -m pip install -r request_llm/requirements_chatglm.txt
# [Optional step II] Support Fudan MOSS
python -m pip install -r request_llm/requirements_moss.txt
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Note that when executing this line of code, you must be in the project root path
# [Optional step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. Currently, all supported models are as follows (the jittorllms series currently only supports the docker solution):
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
```
</p>
</details>
4. Run
```sh
python main.py
```5. Testing Function Plugin
```
- Testing function plugin template function (requires GPT to answer what happened in history today), you can use this function as a template to implement more complex functions
Click "[Function plugin Template Demo] On this day in history"
```
## Installation - Method 2: Using Docker
1. ChatGPT only (recommended for most people)
``` sh
git clone https://github.com/binary-husky/gpt_academic.git # download the project
cd gpt_academic # enter the path
nano config.py # edit config.py with any text editor to configure "Proxy", "API_KEY", and "WEB_PORT" (eg 50923)
docker build -t gpt-academic . # install
# (Last step-Option 1) In a Linux environment, using `--net=host` is more convenient and faster
docker run --rm -it --net=host gpt-academic
# (Last step-Option 2) In macOS/windows environment, only -p option can be used to expose the port on the container (eg 50923) to the port on the host
docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
```
2. ChatGPT + ChatGLM + MOSS (requires familiarity with Docker)
``` sh
# Edit docker-compose.yml, delete solutions 1 and 3, and keep solution 2. Modify the configuration of solution 2 in docker-compose.yml, refer to the comments in it
docker-compose up
```
3. ChatGPT + LLAMA + PanGu + RWKV (requires familiarity with Docker)
``` sh
# Edit docker-compose.yml, delete solutions 1 and 2, and keep solution 3. Modify the configuration of solution 3 in docker-compose.yml, refer to the comments in it
docker-compose up
```
## Installation Method 3: Other Deployment Methods
1. How to use reverse proxy URL/Microsoft Azure API
Configure API_URL_REDIRECT according to the instructions in `config.py`.
2. Remote Cloud Server Deployment (Requires Knowledge and Experience of Cloud Servers)
Please visit [Deployment Wiki-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
3. Using WSL2 (Windows Subsystem for Linux subsystem)
Please visit [Deployment Wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
4. How to run at the secondary URL (such as `http://localhost/subpath`)
Please visit [FastAPI Operation Instructions](docs/WithFastapi.md)
5. Using docker-compose to run
Please read docker-compose.yml and follow the prompts to operate.
---
# Advanced Usage
## Customize new convenient buttons / custom function plugins
1. Customize new convenient buttons (academic shortcuts)
Open `core_functional.py` with any text editor, add an entry as follows, and then restart the program. (If the button has been added successfully and is visible, both prefixes and suffixes can be hot-modified without having to restart the program.)
For example:
```
"Super English to Chinese": {
# Prefix, will be added before your input. For example, describe your requirements, such as translation, code interpretation, polishing, etc.
"Prefix": "Please translate the following content into Chinese, and then explain each proper noun that appears in the text with a markdown table:\n\n",
# Suffix, will be added after your input. For example, with the prefix, you can enclose your input content in quotes.
"Suffix": "",
},
```
<div align="center">
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
</div>
2. Custom function plugin
Write powerful function plugins to perform any task you can and can't imagine.
The difficulty of debugging and writing plugins in this project is very low. As long as you have a certain knowledge of python, you can implement your own plugin function by imitating the template we provide.
Please refer to the [Function Plugin Guide](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) for details.
---
# Latest Update
## New feature dynamic
1. Сохранение диалогов. Вызовите "Сохранить текущий диалог" в разделе функций-плагина, чтобы сохранить текущий диалог как файл HTML, который можно прочитать и восстановить. Кроме того, вызовите «Загрузить архив истории диалога» в меню функций-плагина, чтобы восстановить предыдущую сессию. Совет: если нажать кнопку "Загрузить исторический архив диалога" без указания файла, можно просмотреть кэш исторических файлов HTML. Щелкните "Удалить все локальные записи истории диалогов", чтобы удалить все файловые кэши HTML.
2. Создание отчетов. Большинство плагинов создают рабочий отчет после завершения выполнения.
 
3. Модульный дизайн функций, простой интерфейс, но сильный функционал.
4. Это проект с открытым исходным кодом, который может «сам переводить себя».
5. Перевод других проектов с открытым исходным кодом - это не проблема.
6. Мелкие функции декорирования [live2d](https://github.com/fghrsh/live2d_demo) (по умолчанию отключены, нужно изменить `config.py`).
7. Поддержка большой языковой модели MOSS.
8. Генерация изображений с помощью OpenAI.
9. Анализ и подведение итогов аудиофайлов с помощью OpenAI.
10. Полный цикл проверки правописания с использованием LaTeX.
## Версии:
- Версия 3.5 (Todo): использование естественного языка для вызова функций-плагинов проекта (высокий приоритет)
- Версия 3.4 (Todo): улучшение многопоточной поддержки локальных больших моделей чата.
- Версия 3.3: добавлена функция объединения интернет-информации.
- Версия 3.2: функции-плагины поддерживают большое количество параметров (сохранение диалогов, анализирование любого языка программирования и одновременное запрос LLM-групп).
- Версия 3.1: поддержка одновременного запроса нескольких моделей GPT! Поддержка api2d, сбалансированное распределение нагрузки по нескольким ключам api.
- Версия 3.0: поддержка chatglm и других небольших LLM.
- Версия 2.6: перестройка структуры плагинов, улучшение интерактивности, добавлено больше плагинов.
- Версия 2.5: автоматическое обновление для решения проблемы длинного текста и переполнения токенов при обработке больших проектов.
- Версия 2.4: (1) добавлена функция полного перевода PDF; (2) добавлена функция переключения положения ввода; (3) добавлена опция вертикального макета; (4) оптимизация многопоточности плагинов.
- Версия 2.3: улучшение многопоточной интерактивности.
- Версия 2.2: функции-плагины поддерживают горячую перезагрузку.
- Версия 2.1: раскрывающийся макет.
- Версия 2.0: использование модульных функций-плагинов.
- Версия 1.0: базовые функции.
gpt_academic Разработчик QQ-группы-2: 610599535
- Известные проблемы
- Некоторые плагины перевода в браузерах мешают работе фронтенда этого программного обеспечения
- Высокая или низкая версия gradio может вызвать множество исключений
## Ссылки и учебные материалы
```
Мы использовали многие концепты кода из других отличных проектов, включая:
# Проект 1: Qinghua ChatGLM-6B:
https://github.com/THUDM/ChatGLM-6B
# Проект 2: Qinghua JittorLLMs:
https://github.com/Jittor/JittorLLMs
# Проект 3: Edge-GPT:
https://github.com/acheong08/EdgeGPT
# Проект 4: Chuanhu ChatGPT:
https://github.com/GaiZhenbiao/ChuanhuChatGPT
# Проект 5: ChatPaper:
https://github.com/kaixindelele/ChatPaper
# Больше:
https://github.com/gradio-app/gradio
https://github.com/fghrsh/live2d_demo
```

4
docs/WithFastapi.md
View File

@ -16,7 +16,7 @@ nano config.py
+ demo.queue(concurrency_count=CONCURRENT_COUNT) + demo.queue(concurrency_count=CONCURRENT_COUNT)
- # 如果需要在二级路径下运行 - # 如果需要在二级路径下运行
- # CUSTOM_PATH, = get_conf('CUSTOM_PATH') - # CUSTOM_PATH = get_conf('CUSTOM_PATH')
- # if CUSTOM_PATH != "/": - # if CUSTOM_PATH != "/":
- # from toolbox import run_gradio_in_subpath - # from toolbox import run_gradio_in_subpath
- # run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH) - # run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
@ -24,7 +24,7 @@ nano config.py
- # demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png") - # demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
+ 如果需要在二级路径下运行 + 如果需要在二级路径下运行
+ CUSTOM_PATH, = get_conf('CUSTOM_PATH') + CUSTOM_PATH = get_conf('CUSTOM_PATH')
+ if CUSTOM_PATH != "/": + if CUSTOM_PATH != "/":
+ from toolbox import run_gradio_in_subpath + from toolbox import run_gradio_in_subpath
+ run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH) + run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)

98
docs/self_analysis.md
View File

@ -38,20 +38,20 @@
| crazy_functions\读文章写摘要.py | 对论文进行解析和全文摘要生成 | | crazy_functions\读文章写摘要.py | 对论文进行解析和全文摘要生成 |
| crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 | | crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
| crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 | | crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 | | request_llms\bridge_all.py | 基于不同LLM模型进行对话。 |
| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 | | request_llms\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 | | request_llms\bridge_chatgpt.py | 基于GPT模型完成对话。 |
| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 | | request_llms\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 | | request_llms\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 | | request_llms\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 | | request_llms\bridge_moss.py | 加载Moss模型完成对话功能。 |
| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 | | request_llms\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 | | request_llms\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 | | request_llms\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 | | request_llms\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 | | request_llms\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 | | request_llms\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
| request_llm\test_llms.py | 对llm模型进行单元测试。 | | request_llms\test_llms.py | 对llm模型进行单元测试。 |
## 接下来请你逐文件分析下面的工程[0/48] 请对下面的程序文件做一个概述: check_proxy.py ## 接下来请你逐文件分析下面的工程[0/48] 请对下面的程序文件做一个概述: check_proxy.py
@ -129,7 +129,7 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
1. `input_clipping`: 该函数用于裁剪输入文本长度，使其不超过一定的限制。 1. `input_clipping`: 该函数用于裁剪输入文本长度，使其不超过一定的限制。
2. `request_gpt_model_in_new_thread_with_ui_alive`: 该函数用于请求 GPT 模型并保持用户界面的响应，支持多线程和实时更新用户界面。 2. `request_gpt_model_in_new_thread_with_ui_alive`: 该函数用于请求 GPT 模型并保持用户界面的响应，支持多线程和实时更新用户界面。
这两个函数都依赖于从 `toolbox` 和 `request_llm` 中导入的一些工具函数。函数的输入和输出有详细的描述文档。 这两个函数都依赖于从 `toolbox` 和 `request_llms` 中导入的一些工具函数。函数的输入和输出有详细的描述文档。
## [12/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文润色.py ## [12/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文润色.py
@ -137,7 +137,7 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
## [13/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文翻译.py ## [13/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文翻译.py
这个文件包含两个函数 `Latex英译中` 和 `Latex中译英`，它们都会对整个Latex项目进行翻译。这个文件还包含一个类 `PaperFileGroup`，它拥有一个方法 `run_file_split`，用于把长文本文件分成多个短文件。其中使用了工具库 `toolbox` 中的一些函数和从 `request_llm` 中导入了 `model_info`。接下来的函数把文件读取进来，把它们的注释删除，进行分割，并进行翻译。这个文件还包括了一些异常处理和界面更新的操作。 这个文件包含两个函数 `Latex英译中` 和 `Latex中译英`，它们都会对整个Latex项目进行翻译。这个文件还包含一个类 `PaperFileGroup`，它拥有一个方法 `run_file_split`，用于把长文本文件分成多个短文件。其中使用了工具库 `toolbox` 中的一些函数和从 `request_llms` 中导入了 `model_info`。接下来的函数把文件读取进来，把它们的注释删除，进行分割，并进行翻译。这个文件还包括了一些异常处理和界面更新的操作。
## [14/48] 请对下面的程序文件做一个概述: crazy_functions\__init__.py ## [14/48] 请对下面的程序文件做一个概述: crazy_functions\__init__.py
@ -217,7 +217,7 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
## [31/48] 请对下面的程序文件做一个概述: crazy_functions\读文章写摘要.py ## [31/48] 请对下面的程序文件做一个概述: crazy_functions\读文章写摘要.py
这个程序文件是一个Python模块，文件名为crazy_functions\读文章写摘要.py。该模块包含了两个函数，其中主要函数是"读文章写摘要"函数，其实现了解析给定文件夹中的tex文件，对其中每个文件的内容进行摘要生成，并根据各论文片段的摘要，最终生成全文摘要。第二个函数是"解析Paper"函数，用于解析单篇论文文件。其中用到了一些工具函数和库，如update_ui、CatchException、report_execption、write_results_to_file等。 这个程序文件是一个Python模块，文件名为crazy_functions\读文章写摘要.py。该模块包含了两个函数，其中主要函数是"读文章写摘要"函数，其实现了解析给定文件夹中的tex文件，对其中每个文件的内容进行摘要生成，并根据各论文片段的摘要，最终生成全文摘要。第二个函数是"解析Paper"函数，用于解析单篇论文文件。其中用到了一些工具函数和库，如update_ui、CatchException、report_exception、write_results_to_file等。
## [32/48] 请对下面的程序文件做一个概述: crazy_functions\谷歌检索小助手.py ## [32/48] 请对下面的程序文件做一个概述: crazy_functions\谷歌检索小助手.py
@ -227,19 +227,19 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
该程序文件定义了一个名为高阶功能模板函数的函数，该函数接受多个参数，包括输入的文本、gpt模型参数、插件模型参数、聊天显示框的句柄、聊天历史等，并利用送出请求，使用 Unsplash API 发送相关图片。其中，为了避免输入溢出，函数会在开始时清空历史。函数也有一些 UI 更新的语句。该程序文件还依赖于其他两个模块：CatchException 和 update_ui，以及一个名为 request_gpt_model_in_new_thread_with_ui_alive 的来自 crazy_utils 模块（应该是自定义的工具包）的函数。 该程序文件定义了一个名为高阶功能模板函数的函数，该函数接受多个参数，包括输入的文本、gpt模型参数、插件模型参数、聊天显示框的句柄、聊天历史等，并利用送出请求，使用 Unsplash API 发送相关图片。其中，为了避免输入溢出，函数会在开始时清空历史。函数也有一些 UI 更新的语句。该程序文件还依赖于其他两个模块：CatchException 和 update_ui，以及一个名为 request_gpt_model_in_new_thread_with_ui_alive 的来自 crazy_utils 模块（应该是自定义的工具包）的函数。
## [34/48] 请对下面的程序文件做一个概述: request_llm\bridge_all.py ## [34/48] 请对下面的程序文件做一个概述: request_llms\bridge_all.py
该文件包含两个函数：predict和predict_no_ui_long_connection，用于基于不同的LLM模型进行对话。该文件还包含一个lazyloadTiktoken类和一个LLM_CATCH_EXCEPTION修饰器函数。其中lazyloadTiktoken类用于懒加载模型的tokenizer，LLM_CATCH_EXCEPTION用于错误处理。整个文件还定义了一些全局变量和模型信息字典，用于引用和配置LLM模型。 该文件包含两个函数：predict和predict_no_ui_long_connection，用于基于不同的LLM模型进行对话。该文件还包含一个lazyloadTiktoken类和一个LLM_CATCH_EXCEPTION修饰器函数。其中lazyloadTiktoken类用于懒加载模型的tokenizer，LLM_CATCH_EXCEPTION用于错误处理。整个文件还定义了一些全局变量和模型信息字典，用于引用和配置LLM模型。
## [35/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatglm.py ## [35/48] 请对下面的程序文件做一个概述: request_llms\bridge_chatglm.py
这是一个Python程序文件，名为`bridge_chatglm.py`，其中定义了一个名为`GetGLMHandle`的类和三个方法：`predict_no_ui_long_connection`、 `predict`和 `stream_chat`。该文件依赖于多个Python库，如`transformers`和`sentencepiece`。该文件实现了一个聊天机器人，使用ChatGLM模型来生成回复，支持单线程和多线程方式。程序启动时需要加载ChatGLM的模型和tokenizer，需要一段时间。在配置文件`config.py`中设置参数会影响模型的内存和显存使用，因此程序可能会导致低配计算机卡死。 这是一个Python程序文件，名为`bridge_chatglm.py`，其中定义了一个名为`GetGLMHandle`的类和三个方法：`predict_no_ui_long_connection`、 `predict`和 `stream_chat`。该文件依赖于多个Python库，如`transformers`和`sentencepiece`。该文件实现了一个聊天机器人，使用ChatGLM模型来生成回复，支持单线程和多线程方式。程序启动时需要加载ChatGLM的模型和tokenizer，需要一段时间。在配置文件`config.py`中设置参数会影响模型的内存和显存使用，因此程序可能会导致低配计算机卡死。
## [36/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatgpt.py ## [36/48] 请对下面的程序文件做一个概述: request_llms\bridge_chatgpt.py
该文件为 Python 代码文件，文件名为 request_llm\bridge_chatgpt.py。该代码文件主要提供三个函数：predict、predict_no_ui和 predict_no_ui_long_connection，用于发送至 chatGPT 并等待回复，获取输出。该代码文件还包含一些辅助函数，用于处理连接异常、生成 HTTP 请求等。该文件的代码架构清晰，使用了多个自定义函数和模块。 该文件为 Python 代码文件，文件名为 request_llms\bridge_chatgpt.py。该代码文件主要提供三个函数：predict、predict_no_ui和 predict_no_ui_long_connection，用于发送至 chatGPT 并等待回复，获取输出。该代码文件还包含一些辅助函数，用于处理连接异常、生成 HTTP 请求等。该文件的代码架构清晰，使用了多个自定义函数和模块。
## [37/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_llama.py ## [37/48] 请对下面的程序文件做一个概述: request_llms\bridge_jittorllms_llama.py
该代码文件实现了一个聊天机器人，其中使用了 JittorLLMs 模型。主要包括以下几个部分： 该代码文件实现了一个聊天机器人，其中使用了 JittorLLMs 模型。主要包括以下几个部分：
1. GetGLMHandle 类：一个进程类，用于加载 JittorLLMs 模型并接收并处理请求。 1. GetGLMHandle 类：一个进程类，用于加载 JittorLLMs 模型并接收并处理请求。
@ -248,17 +248,17 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
这个文件中还有一些辅助函数和全局变量，例如 importlib、time、threading 等。 这个文件中还有一些辅助函数和全局变量，例如 importlib、time、threading 等。
## [38/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_pangualpha.py ## [38/48] 请对下面的程序文件做一个概述: request_llms\bridge_jittorllms_pangualpha.py
这个文件是为了实现使用jittorllms（一种机器学习模型）来进行聊天功能的代码。其中包括了模型加载、模型的参数加载、消息的收发等相关操作。其中使用了多进程和多线程来提高性能和效率。代码中还包括了处理依赖关系的函数和预处理函数等。 这个文件是为了实现使用jittorllms（一种机器学习模型）来进行聊天功能的代码。其中包括了模型加载、模型的参数加载、消息的收发等相关操作。其中使用了多进程和多线程来提高性能和效率。代码中还包括了处理依赖关系的函数和预处理函数等。
## [39/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_rwkv.py ## [39/48] 请对下面的程序文件做一个概述: request_llms\bridge_jittorllms_rwkv.py
这个文件是一个Python程序，文件名为request_llm\bridge_jittorllms_rwkv.py。它依赖transformers、time、threading、importlib、multiprocessing等库。在文件中，通过定义GetGLMHandle类加载jittorllms模型参数和定义stream_chat方法来实现与jittorllms模型的交互。同时，该文件还定义了predict_no_ui_long_connection和predict方法来处理历史信息、调用jittorllms模型、接收回复信息并输出结果。 这个文件是一个Python程序，文件名为request_llm\bridge_jittorllms_rwkv.py。它依赖transformers、time、threading、importlib、multiprocessing等库。在文件中，通过定义GetGLMHandle类加载jittorllms模型参数和定义stream_chat方法来实现与jittorllms模型的交互。同时，该文件还定义了predict_no_ui_long_connection和predict方法来处理历史信息、调用jittorllms模型、接收回复信息并输出结果。
## [40/48] 请对下面的程序文件做一个概述: request_llm\bridge_moss.py ## [40/48] 请对下面的程序文件做一个概述: request_llms\bridge_moss.py
该文件为一个Python源代码文件，文件名为 request_llm\bridge_moss.py。代码定义了一个 GetGLMHandle 类和两个函数 predict_no_ui_long_connection 和 predict。 该文件为一个Python源代码文件，文件名为 request_llms\bridge_moss.py。代码定义了一个 GetGLMHandle 类和两个函数 predict_no_ui_long_connection 和 predict。
GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个子进程并加载 MOSS 模型参数，通过 Pipe 进行主子进程的通信。该类还定义了 check_dependency、moss_init、run 和 stream_chat 等方法，其中 check_dependency 和 moss_init 是子进程的初始化方法，run 是子进程运行方法，stream_chat 实现了主进程和子进程的交互过程。 GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个子进程并加载 MOSS 模型参数，通过 Pipe 进行主子进程的通信。该类还定义了 check_dependency、moss_init、run 和 stream_chat 等方法，其中 check_dependency 和 moss_init 是子进程的初始化方法，run 是子进程运行方法，stream_chat 实现了主进程和子进程的交互过程。
@ -266,7 +266,7 @@ GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个
函数 predict 是单线程方法，通过调用 update_ui 将交互过程中 MOSS 的回复实时更新到UI（User Interface）中，并执行一个 named function（additional_fn）指定的函数对输入进行预处理。 函数 predict 是单线程方法，通过调用 update_ui 将交互过程中 MOSS 的回复实时更新到UI（User Interface）中，并执行一个 named function（additional_fn）指定的函数对输入进行预处理。
## [41/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbing.py ## [41/48] 请对下面的程序文件做一个概述: request_llms\bridge_newbing.py
这是一个名为`bridge_newbing.py`的程序文件，包含三个部分： 这是一个名为`bridge_newbing.py`的程序文件，包含三个部分：
@ -276,11 +276,11 @@ GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个
第三部分定义了一个名为`newbing_handle`的全局变量，并导出了`predict_no_ui_long_connection`和`predict`这两个方法，以供其他程序可以调用。 第三部分定义了一个名为`newbing_handle`的全局变量，并导出了`predict_no_ui_long_connection`和`predict`这两个方法，以供其他程序可以调用。
## [42/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbingfree.py ## [42/48] 请对下面的程序文件做一个概述: request_llms\bridge_newbingfree.py
这个Python文件包含了三部分内容。第一部分是来自edge_gpt_free.py文件的聊天机器人程序。第二部分是子进程Worker，用于调用主体。第三部分提供了两个函数：predict_no_ui_long_connection和predict用于调用NewBing聊天机器人和返回响应。其中predict函数还提供了一些参数用于控制聊天机器人的回复和更新UI界面。 这个Python文件包含了三部分内容。第一部分是来自edge_gpt_free.py文件的聊天机器人程序。第二部分是子进程Worker，用于调用主体。第三部分提供了两个函数：predict_no_ui_long_connection和predict用于调用NewBing聊天机器人和返回响应。其中predict函数还提供了一些参数用于控制聊天机器人的回复和更新UI界面。
## [43/48] 请对下面的程序文件做一个概述: request_llm\bridge_stackclaude.py ## [43/48] 请对下面的程序文件做一个概述: request_llms\bridge_stackclaude.py
这是一个Python源代码文件，文件名为request_llm\bridge_stackclaude.py。代码分为三个主要部分： 这是一个Python源代码文件，文件名为request_llm\bridge_stackclaude.py。代码分为三个主要部分：
@ -290,21 +290,21 @@ GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个
第三部分定义了predict_no_ui_long_connection和predict两个函数，主要用于通过调用ClaudeHandle对象的stream_chat方法来获取Claude的回复，并更新ui以显示相关信息。其中predict函数采用单线程方法，而predict_no_ui_long_connection函数使用多线程方法。 第三部分定义了predict_no_ui_long_connection和predict两个函数，主要用于通过调用ClaudeHandle对象的stream_chat方法来获取Claude的回复，并更新ui以显示相关信息。其中predict函数采用单线程方法，而predict_no_ui_long_connection函数使用多线程方法。
## [44/48] 请对下面的程序文件做一个概述: request_llm\bridge_tgui.py ## [44/48] 请对下面的程序文件做一个概述: request_llms\bridge_tgui.py
该文件是一个Python代码文件，名为request_llm\bridge_tgui.py。它包含了一些函数用于与chatbot UI交互，并通过WebSocket协议与远程LLM模型通信完成文本生成任务，其中最重要的函数是predict()和predict_no_ui_long_connection()。这个程序还有其他的辅助函数，如random_hash()。整个代码文件在协作的基础上完成了一次修改。 该文件是一个Python代码文件，名为request_llm\bridge_tgui.py。它包含了一些函数用于与chatbot UI交互，并通过WebSocket协议与远程LLM模型通信完成文本生成任务，其中最重要的函数是predict()和predict_no_ui_long_connection()。这个程序还有其他的辅助函数，如random_hash()。整个代码文件在协作的基础上完成了一次修改。
## [45/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt.py ## [45/48] 请对下面的程序文件做一个概述: request_llms\edge_gpt.py
该文件是一个用于调用Bing chatbot API的Python程序，它由多个类和辅助函数构成，可以根据给定的对话连接在对话中提出问题，使用websocket与远程服务通信。程序实现了一个聊天机器人，可以为用户提供人工智能聊天。 该文件是一个用于调用Bing chatbot API的Python程序，它由多个类和辅助函数构成，可以根据给定的对话连接在对话中提出问题，使用websocket与远程服务通信。程序实现了一个聊天机器人，可以为用户提供人工智能聊天。
## [46/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt_free.py ## [46/48] 请对下面的程序文件做一个概述: request_llms\edge_gpt_free.py
该代码文件为一个会话API，可通过Chathub发送消息以返回响应。其中使用了 aiohttp 和 httpx 库进行网络请求并发送。代码中包含了一些函数和常量，多数用于生成请求数据或是请求头信息等。同时该代码文件还包含了一个 Conversation 类，调用该类可实现对话交互。 该代码文件为一个会话API，可通过Chathub发送消息以返回响应。其中使用了 aiohttp 和 httpx 库进行网络请求并发送。代码中包含了一些函数和常量，多数用于生成请求数据或是请求头信息等。同时该代码文件还包含了一个 Conversation 类，调用该类可实现对话交互。
## [47/48] 请对下面的程序文件做一个概述: request_llm\test_llms.py ## [47/48] 请对下面的程序文件做一个概述: request_llms\test_llms.py
这个文件是用于对llm模型进行单元测试的Python程序。程序导入一个名为"request_llm.bridge_newbingfree"的模块，然后三次使用该模块中的predict_no_ui_long_connection()函数进行预测，并输出结果。此外，还有一些注释掉的代码段，这些代码段也是关于模型预测的。 这个文件是用于对llm模型进行单元测试的Python程序。程序导入一个名为"request_llms.bridge_newbingfree"的模块，然后三次使用该模块中的predict_no_ui_long_connection()函数进行预测，并输出结果。此外，还有一些注释掉的代码段，这些代码段也是关于模型预测的。
## 用一张Markdown表格简要描述以下文件的功能： ## 用一张Markdown表格简要描述以下文件的功能：
check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, crazy_functional.py, main.py, multi_language.py, theme.py, toolbox.py, crazy_functions\crazy_functions_test.py, crazy_functions\crazy_utils.py, crazy_functions\Latex全文润色.py, crazy_functions\Latex全文翻译.py, crazy_functions\__init__.py, crazy_functions\下载arxiv论文翻译摘要.py。根据以上分析，用一句话概括程序的整体功能。 check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, crazy_functional.py, main.py, multi_language.py, theme.py, toolbox.py, crazy_functions\crazy_functions_test.py, crazy_functions\crazy_utils.py, crazy_functions\Latex全文润色.py, crazy_functions\Latex全文翻译.py, crazy_functions\__init__.py, crazy_functions\下载arxiv论文翻译摘要.py。根据以上分析，用一句话概括程序的整体功能。
@ -355,24 +355,24 @@ crazy_functions\代码重写为全英文_多线程.py, crazy_functions\图片生
概括程序的整体功能：提供了一系列处理文本、文件和代码的功能，使用了各类语言模型、多线程、网络请求和数据解析技术来提高效率和精度。 概括程序的整体功能：提供了一系列处理文本、文件和代码的功能，使用了各类语言模型、多线程、网络请求和数据解析技术来提高效率和精度。
## 用一张Markdown表格简要描述以下文件的功能： ## 用一张Markdown表格简要描述以下文件的功能：
crazy_functions\谷歌检索小助手.py, crazy_functions\高级功能函数模板.py, request_llm\bridge_all.py, request_llm\bridge_chatglm.py, request_llm\bridge_chatgpt.py, request_llm\bridge_jittorllms_llama.py, request_llm\bridge_jittorllms_pangualpha.py, request_llm\bridge_jittorllms_rwkv.py, request_llm\bridge_moss.py, request_llm\bridge_newbing.py, request_llm\bridge_newbingfree.py, request_llm\bridge_stackclaude.py, request_llm\bridge_tgui.py, request_llm\edge_gpt.py, request_llm\edge_gpt_free.py, request_llm\test_llms.py。根据以上分析，用一句话概括程序的整体功能。 crazy_functions\谷歌检索小助手.py, crazy_functions\高级功能函数模板.py, request_llms\bridge_all.py, request_llms\bridge_chatglm.py, request_llms\bridge_chatgpt.py, request_llms\bridge_jittorllms_llama.py, request_llms\bridge_jittorllms_pangualpha.py, request_llms\bridge_jittorllms_rwkv.py, request_llms\bridge_moss.py, request_llms\bridge_newbing.py, request_llms\bridge_newbingfree.py, request_llms\bridge_stackclaude.py, request_llms\bridge_tgui.py, request_llms\edge_gpt.py, request_llms\edge_gpt_free.py, request_llms\test_llms.py。根据以上分析，用一句话概括程序的整体功能。
| 文件名 | 功能描述 | | 文件名 | 功能描述 |
| --- | --- | | --- | --- |
| crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 | | crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
| crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 | | crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 | | request_llms\bridge_all.py | 基于不同LLM模型进行对话。 |
| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 | | request_llms\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 | | request_llms\bridge_chatgpt.py | 基于GPT模型完成对话。 |
| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 | | request_llms\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 | | request_llms\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 | | request_llms\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 | | request_llms\bridge_moss.py | 加载Moss模型完成对话功能。 |
| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 | | request_llms\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 | | request_llms\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 | | request_llms\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 | | request_llms\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 | | request_llms\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 | | request_llms\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
| request_llm\test_llms.py | 对llm模型进行单元测试。 | | request_llms\test_llms.py | 对llm模型进行单元测试。 |
| 程序整体功能 | 实现不同种类的聊天机器人，可以根据输入进行文本生成。 | | 程序整体功能 | 实现不同种类的聊天机器人，可以根据输入进行文本生成。 |

129
docs/translate_english.json
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@ -265,7 +265,7 @@
"例如chatglm&gpt-3.5-turbo&api2d-gpt-4": "e.g. chatglm&gpt-3.5-turbo&api2d-gpt-4", "例如chatglm&gpt-3.5-turbo&api2d-gpt-4": "e.g. chatglm&gpt-3.5-turbo&api2d-gpt-4",
"先切换模型到openai或api2d": "Switch the model to openai or api2d first", "先切换模型到openai或api2d": "Switch the model to openai or api2d first",
"在这里输入分辨率": "Enter the resolution here", "在这里输入分辨率": "Enter the resolution here",
"如256x256": "e.g. 256x256", "如1024x1024": "e.g. 1024x1024",
"默认": "Default", "默认": "Default",
"建议您复制一个config_private.py放自己的秘密": "We suggest you to copy a config_private.py file to keep your secrets, such as API and proxy URLs, from being accidentally uploaded to Github and seen by others.", "建议您复制一个config_private.py放自己的秘密": "We suggest you to copy a config_private.py file to keep your secrets, such as API and proxy URLs, from being accidentally uploaded to Github and seen by others.",
"如API和代理网址": "Such as API and proxy URLs", "如API和代理网址": "Such as API and proxy URLs",
@ -430,7 +430,6 @@
"并显示到聊天当中": "And display it in the chat", "并显示到聊天当中": "And display it in the chat",
"插件调度异常": "Plugin scheduling exception", "插件调度异常": "Plugin scheduling exception",
"异常原因": "Exception reason", "异常原因": "Exception reason",
"实验性函数调用出错": "Experimental function call error",
"当前代理可用性": "Current proxy availability", "当前代理可用性": "Current proxy availability",
"异常": "Exception", "异常": "Exception",
"将文本按照段落分隔符分割开": "Split the text into paragraphs according to the paragraph separator", "将文本按照段落分隔符分割开": "Split the text into paragraphs according to the paragraph separator",
@ -502,7 +501,8 @@
"环境变量": "Environment variable", "环境变量": "Environment variable",
"不支持通过环境变量设置!": "Setting through environment variables is not supported!", "不支持通过环境变量设置!": "Setting through environment variables is not supported!",
"加载失败!": "Loading failed!", "加载失败!": "Loading failed!",
"成功读取环境变量": "Successfully read environment variables", "如": " e.g., ",
"成功读取环境变量": "Successfully read environment variable: ",
"本项目现已支持OpenAI和API2D的api-key": "This project now supports api-keys for OpenAI and API2D", "本项目现已支持OpenAI和API2D的api-key": "This project now supports api-keys for OpenAI and API2D",
"也支持同时填写多个api-key": "It also supports filling in multiple api-keys at the same time", "也支持同时填写多个api-key": "It also supports filling in multiple api-keys at the same time",
"您既可以在config.py中修改api-key": "You can modify the api-key in config.py", "您既可以在config.py中修改api-key": "You can modify the api-key in config.py",
@ -513,7 +513,7 @@
"请在config文件中修改API密钥之后再运行": "Please modify the API key in the config file before running", "请在config文件中修改API密钥之后再运行": "Please modify the API key in the config file before running",
"网络代理状态": "Network proxy status", "网络代理状态": "Network proxy status",
"未配置": "Not configured", "未配置": "Not configured",
"无代理状态下很可能无法访问OpenAI家族的模型": "It is very likely that you cannot access OpenAI's models without a proxy", "无代理状态下很可能无法访问OpenAI家族的模型": "",
"建议": "Suggestion", "建议": "Suggestion",
"检查USE_PROXY选项是否修改": "Check if the USE_PROXY option has been modified", "检查USE_PROXY选项是否修改": "Check if the USE_PROXY option has been modified",
"已配置": "Configured", "已配置": "Configured",
@ -1184,7 +1184,7 @@
"Call ChatGLM fail 不能正常加载ChatGLM的参数": "Call ChatGLM fail, unable to load parameters for ChatGLM", "Call ChatGLM fail 不能正常加载ChatGLM的参数": "Call ChatGLM fail, unable to load parameters for ChatGLM",
"不能正常加载ChatGLM的参数！": "Unable to load parameters for ChatGLM!", "不能正常加载ChatGLM的参数！": "Unable to load parameters for ChatGLM!",
"多线程方法": "Multithreading method", "多线程方法": "Multithreading method",
"函数的说明请见 request_llm/bridge_all.py": "For function details, please see request_llm/bridge_all.py", "函数的说明请见 request_llms/bridge_all.py": "For function details, please see request_llms/bridge_all.py",
"程序终止": "Program terminated", "程序终止": "Program terminated",
"单线程方法": "Single-threaded method", "单线程方法": "Single-threaded method",
"等待ChatGLM响应中": "Waiting for response from ChatGLM", "等待ChatGLM响应中": "Waiting for response from ChatGLM",
@ -1543,7 +1543,7 @@
"str类型": "str type", "str类型": "str type",
"所有音频都总结完成了吗": "Are all audio summaries completed?", "所有音频都总结完成了吗": "Are all audio summaries completed?",
"SummaryAudioVideo内容": "SummaryAudioVideo content", "SummaryAudioVideo内容": "SummaryAudioVideo content",
"使用教程详情见 request_llm/README.md": "See request_llm/README.md for detailed usage instructions", "使用教程详情见 request_llms/README.md": "See request_llms/README.md for detailed usage instructions",
"删除中间文件夹": "Delete intermediate folder", "删除中间文件夹": "Delete intermediate folder",
"Claude组件初始化成功": "Claude component initialized successfully", "Claude组件初始化成功": "Claude component initialized successfully",
"$c$ 是光速": "$c$ is the speed of light", "$c$ 是光速": "$c$ is the speed of light",
@ -2788,5 +2788,120 @@
"加载已保存": "Load saved", "加载已保存": "Load saved",
"打开浏览器页面": "Open browser page", "打开浏览器页面": "Open browser page",
"解锁插件": "Unlock plugin", "解锁插件": "Unlock plugin",
"如果话筒激活 / 如果处于回声收尾阶段": "If the microphone is active / If it is in the echo tail stage" "如果话筒激活 / 如果处于回声收尾阶段": "If the microphone is active / If it is in the echo tail stage",
"分辨率": "Resolution",
"分析行业动态": "Analyze industry trends",
"在项目实施过程中提供支持": "Provide support during project implementation",
"azure 对齐支持 -=-=-=-=-=-=-": "Azure alignment support -=-=-=-=-=-=-",
"默认的系统提示词": "Default system prompts",
"为您解释复杂的技术概念": "Explain complex technical concepts to you",
"提供项目管理和协作建议": "Provide project management and collaboration advice",
"请从AVAIL_LLM_MODELS中选择": "Please select from AVAIL_LLM_MODELS",
"提高编程能力": "Improve programming skills",
"请注意Newbing组件已不再维护": "Please note that the Newbing component is no longer maintained",
"用于定义和切换多个azure模型 --": "Used to define and switch between multiple Azure models --",
"支持 256x256": "Supports 256x256",
"定义界面上“询问多个GPT模型”插件应该使用哪些模型": "Define which models the 'Ask multiple GPT models' plugin should use on the interface",
"必须是.png格式": "Must be in .png format",
"tokenizer只用于粗估token数量": "The tokenizer is only used to estimate the number of tokens",
"协助您进行文案策划和内容创作": "Assist you in copywriting and content creation",
"帮助您巩固编程基础": "Help you consolidate your programming foundation",
"修改需求": "Modify requirements",
"确保项目顺利进行": "Ensure the smooth progress of the project",
"帮助您了解市场发展和竞争态势": "Help you understand market development and competitive situation",
"不需要动态切换": "No need for dynamic switching",
"解答您在学习过程中遇到的问题": "Answer the questions you encounter during the learning process",
"Endpoint不正确": "Endpoint is incorrect",
"提供编程思路和建议": "Provide programming ideas and suggestions",
"先上传图片": "Upload the image first",
"提供计算机科学、数据科学、人工智能等相关领域的学习资源和建议": "Provide learning resources and advice in computer science, data science, artificial intelligence, and other related fields",
"提供写作建议和技巧": "Provide writing advice and tips",
"间隔": "Interval",
"此后不需要在此处添加api2d的接口了": "No need to add the api2d interface here anymore",
"4. 学习辅导": "4. Learning guidance",
"智谱AI大模型": "Zhipu AI large model",
"3. 项目支持": "3. Project support",
"但这是意料之中的": "But this is expected",
"检查endpoint是否可用": "Check if the endpoint is available",
"接入智谱大模型": "Access the intelligent spectrum model",
"如果您有任何问题或需要解答的议题": "If you have any questions or topics that need answers",
"api2d 对齐支持 -=-=-=-=-=-=-": "api2d alignment support -=-=-=-=-=-=-",
"支持多线程": "Support multi-threading",
"再输入修改需求": "Enter modification requirements again",
"Endpoint不满足要求": "Endpoint does not meet the requirements",
"检查endpoint是否合法": "Check if the endpoint is valid",
"为您制定技术战略提供参考和建议": "Provide reference and advice for developing your technical strategy",
"支持 1024x1024": "Support 1024x1024",
"因为下面的代码会自动添加": "Because the following code will be automatically added",
"尝试加载模型": "Try to load the model",
"使用DALLE3生成图片 | 输入参数字符串": "Use DALLE3 to generate images | Input parameter string",
"当前论文无需解析": "The current paper does not need to be parsed",
"单个azure模型部署": "Deploy a single Azure model",
"512x512 或 1024x1024": "512x512 or 1024x1024",
"至少是8k上下文的模型": "A model with at least 8k context",
"自动忽略重复的输入": "Automatically ignore duplicate inputs",
"让您更好地掌握知识": "Help you better grasp knowledge",
"文件列表": "File list",
"并在不同模型之间用": "And use it between different models",
"插件调用出错": "Plugin call error",
"帮助您撰写文章、报告、散文、故事等": "Help you write articles, reports, essays, stories, etc.",
"*实验性功能*": "*Experimental feature*",
"2. 编程": "2. Programming",
"让您更容易理解": "Make it easier for you to understand",
"的最大上下文长度太短": "The maximum context length is too short",
"方法二": "Method 2",
"多个azure模型部署+动态切换": "Deploy multiple Azure models + dynamic switching",
"详情请见额外文档 docs\\use_azure.md": "For details, please refer to the additional document docs\\use_azure.md",
"包括但不限于 Python、Java、C++ 等": "Including but not limited to Python, Java, C++, etc.",
"为您提供业界最新的新闻和技术趋势": "Providing you with the latest industry news and technology trends",
"自动检测并屏蔽失效的KEY": "Automatically detect and block invalid keys",
"请勿使用": "Please do not use",
"最后输入分辨率": "Enter the resolution at last",
"图片": "Image",
"请检查AZURE_ENDPOINT的配置! 当前的Endpoint为": "Please check the configuration of AZURE_ENDPOINT! The current Endpoint is",
"图片修改": "Image modification",
"已经收集到所有信息": "All information has been collected",
"加载API_KEY": "Loading API_KEY",
"协助您编写代码": "Assist you in writing code",
"我可以为您提供以下服务": "I can provide you with the following services",
"排队中请稍后 ...": "Please wait in line ...",
"建议您使用英文提示词": "It is recommended to use English prompts",
"不能支撑AutoGen运行": "Cannot support AutoGen operation",
"帮助您解决编程问题": "Help you solve programming problems",
"上次用户反馈输入为": "Last user feedback input is",
"请随时告诉我您的需求": "Please feel free to tell me your needs",
"有 sys_prompt 接口": "There is a sys_prompt interface",
"可能会覆盖之前的配置": "May overwrite previous configuration",
"5. 行业动态和趋势分析": "5. Industry dynamics and trend analysis",
"正在等待线程锁": "Waiting for thread lock",
"请输入分辨率": "Please enter the resolution",
"接驳void-terminal": "Connecting to void-terminal",
"启动DALLE2图像修改向导程序": "Launching DALLE2 image modification wizard program",
"加载模型失败": "Failed to load the model",
"是否使用Docker容器运行代码": "Whether to run the code using Docker container",
"请输入修改需求": "Please enter modification requirements",
"作为您的写作和编程助手": "As your writing and programming assistant",
"然后再次点击本插件": "Then click this plugin again",
"需要动态切换": "Dynamic switching is required",
"文心大模型4.0": "Wenxin Large Model 4.0",
"找不到任何.pdf拓展名的文件": "Cannot find any file with .pdf extension",
"在使用AutoGen插件时": "When using the AutoGen plugin",
"协助您规划项目进度和任务分配": "Assist you in planning project schedules and task assignments",
"1. 写作": "1. Writing",
"你亲手写的api名称": "The API name you wrote yourself",
"使用DALLE2生成图片 | 输入参数字符串": "Generate images using DALLE2 | Input parameter string",
"方法一": "Method 1",
"我会尽力提供帮助": "I will do my best to provide assistance",
"多个azure模型": "Multiple Azure models",
"准备就绪": "Ready",
"请随时提问": "Please feel free to ask",
"如果需要使用AZURE": "If you need to use AZURE",
"如果不是本地模型": "If it is not a local model",
"AZURE_CFG_ARRAY中配置的模型必须以azure开头": "The models configured in AZURE_CFG_ARRAY must start with 'azure'",
"API key has been deactivated. OpenAI以账户失效为由": "API key has been deactivated. OpenAI considers it as an account failure",
"请先上传图像": "Please upload the image first",
"高优先级": "High priority",
"请配置ZHIPUAI_API_KEY": "Please configure ZHIPUAI_API_KEY",
"单个azure模型": "Single Azure model",
"预留参数 context 未实现": "Reserved parameter 'context' not implemented"
} }

7
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@ -352,7 +352,6 @@
"感谢热情的": "熱心な感謝", "感谢热情的": "熱心な感謝",
"是本次输出": "今回の出力です", "是本次输出": "今回の出力です",
"协议": "プロトコル", "协议": "プロトコル",
"实验性函数调用出错": "実験的な関数呼び出しエラー",
"例如需要翻译的一段话": "翻訳が必要な例文", "例如需要翻译的一段话": "翻訳が必要な例文",
"本地文件地址": "ローカルファイルアドレス", "本地文件地址": "ローカルファイルアドレス",
"更好的UI视觉效果": "より良いUI視覚効果", "更好的UI视觉效果": "より良いUI視覚効果",
@ -782,7 +781,7 @@
"主进程统一调用函数接口": "メインプロセスが関数インターフェースを統一的に呼び出します", "主进程统一调用函数接口": "メインプロセスが関数インターフェースを統一的に呼び出します",
"再例如一个包含了待处理文件的路径": "処理待ちのファイルを含むパスの例", "再例如一个包含了待处理文件的路径": "処理待ちのファイルを含むパスの例",
"负责把学术论文准确翻译成中文": "学術論文を正確に中国語に翻訳する責任があります", "负责把学术论文准确翻译成中文": "学術論文を正確に中国語に翻訳する責任があります",
"函数的说明请见 request_llm/bridge_all.py": "関数の説明については、request_llm/bridge_all.pyを参照してください", "函数的说明请见 request_llms/bridge_all.py": "関数の説明については、request_llms/bridge_all.pyを参照してください",
"然后回车提交": "そしてEnterを押して提出してください", "然后回车提交": "そしてEnterを押して提出してください",
"防止爆token": "トークンの爆発を防止する", "防止爆token": "トークンの爆発を防止する",
"Latex项目全文中译英": "LaTeXプロジェクト全文の中国語から英語への翻訳", "Latex项目全文中译英": "LaTeXプロジェクト全文の中国語から英語への翻訳",
@ -854,7 +853,7 @@
"查询版本和用户意见": "バージョンとユーザーの意見を検索する", "查询版本和用户意见": "バージョンとユーザーの意見を検索する",
"提取摘要": "要約を抽出する", "提取摘要": "要約を抽出する",
"在gpt输出代码的中途": "GPTがコードを出力する途中で", "在gpt输出代码的中途": "GPTがコードを出力する途中で",
"如256x256": "256x256のように", "如1024x1024": "1024x1024のように",
"概括其内容": "内容を要約する", "概括其内容": "内容を要約する",
"剩下的情况都开头除去": "残りの場合はすべて先頭を除去する", "剩下的情况都开头除去": "残りの場合はすべて先頭を除去する",
"至少一个线程任务意外失败": "少なくとも1つのスレッドタスクが予期しない失敗をした", "至少一个线程任务意外失败": "少なくとも1つのスレッドタスクが予期しない失敗をした",
@ -1616,7 +1615,7 @@
"正在重试": "再試行中", "正在重试": "再試行中",
"从而更全面地理解项目的整体功能": "プロジェクトの全体的な機能をより理解するために", "从而更全面地理解项目的整体功能": "プロジェクトの全体的な機能をより理解するために",
"正在等您说完问题": "質問が完了するのをお待ちしています", "正在等您说完问题": "質問が完了するのをお待ちしています",
"使用教程详情见 request_llm/README.md": "使用方法の詳細については、request_llm/README.mdを参照してください", "使用教程详情见 request_llms/README.md": "使用方法の詳細については、request_llms/README.mdを参照してください",
"6.25 加入判定latex模板的代码": "6.25 テンプレートの判定コードを追加", "6.25 加入判定latex模板的代码": "6.25 テンプレートの判定コードを追加",
"找不到任何音频或视频文件": "音声またはビデオファイルが見つかりません", "找不到任何音频或视频文件": "音声またはビデオファイルが見つかりません",
"请求GPT模型的": "GPTモデルのリクエスト", "请求GPT模型的": "GPTモデルのリクエスト",

5
docs/translate_std.json
View File

@ -94,5 +94,8 @@
"解析一个Matlab项目": "AnalyzeAMatlabProject", "解析一个Matlab项目": "AnalyzeAMatlabProject",
"函数动态生成": "DynamicFunctionGeneration", "函数动态生成": "DynamicFunctionGeneration",
"多智能体终端": "MultiAgentTerminal", "多智能体终端": "MultiAgentTerminal",
"多智能体": "MultiAgent" "多智能体": "MultiAgent",
"图片生成_DALLE2": "ImageGeneration_DALLE2",
"图片生成_DALLE3": "ImageGeneration_DALLE3",
"图片修改_DALLE2": "ImageModification_DALLE2"
} }

7
docs/translate_traditionalchinese.json
View File

@ -123,7 +123,7 @@
"的第": "的第", "的第": "的第",
"减少重复": "減少重複", "减少重复": "減少重複",
"如果超过期限没有喂狗": "如果超過期限沒有餵狗", "如果超过期限没有喂狗": "如果超過期限沒有餵狗",
"函数的说明请见 request_llm/bridge_all.py": "函數的說明請見 request_llm/bridge_all.py", "函数的说明请见 request_llms/bridge_all.py": "函數的說明請見 request_llms/bridge_all.py",
"第7步": "第7步", "第7步": "第7步",
"说": "說", "说": "說",
"中途接收可能的终止指令": "中途接收可能的終止指令", "中途接收可能的终止指令": "中途接收可能的終止指令",
@ -780,7 +780,6 @@
"检测到程序终止": "偵測到程式終止", "检测到程序终止": "偵測到程式終止",
"对整个Latex项目进行润色": "對整個Latex專案進行潤色", "对整个Latex项目进行润色": "對整個Latex專案進行潤色",
"方法则会被调用": "方法則會被調用", "方法则会被调用": "方法則會被調用",
"实验性函数调用出错": "實驗性函數調用出錯",
"把完整输入-输出结果显示在聊天框": "把完整輸入-輸出結果顯示在聊天框", "把完整输入-输出结果显示在聊天框": "把完整輸入-輸出結果顯示在聊天框",
"本地文件预览": "本地檔案預覽", "本地文件预览": "本地檔案預覽",
"接下来请你逐文件分析下面的论文文件": "接下來請你逐檔案分析下面的論文檔案", "接下来请你逐文件分析下面的论文文件": "接下來請你逐檔案分析下面的論文檔案",
@ -1147,7 +1146,7 @@
"Y+回车=确认": "Y+回車=確認", "Y+回车=确认": "Y+回車=確認",
"正在同时咨询ChatGPT和ChatGLM……": "正在同時諮詢ChatGPT和ChatGLM……", "正在同时咨询ChatGPT和ChatGLM……": "正在同時諮詢ChatGPT和ChatGLM……",
"根据 heuristic 规则": "根據heuristic規則", "根据 heuristic 规则": "根據heuristic規則",
"如256x256": "如256x256", "如1024x1024": "如1024x1024",
"函数插件区": "函數插件區", "函数插件区": "函數插件區",
"*** API_KEY 导入成功": "*** API_KEY 導入成功", "*** API_KEY 导入成功": "*** API_KEY 導入成功",
"请对下面的程序文件做一个概述文件名是": "請對下面的程序文件做一個概述文件名是", "请对下面的程序文件做一个概述文件名是": "請對下面的程序文件做一個概述文件名是",
@ -1887,7 +1886,7 @@
"请继续分析其他源代码": "請繼續分析其他源代碼", "请继续分析其他源代码": "請繼續分析其他源代碼",
"质能方程式": "質能方程式", "质能方程式": "質能方程式",
"功能尚不稳定": "功能尚不穩定", "功能尚不稳定": "功能尚不穩定",
"使用教程详情见 request_llm/README.md": "使用教程詳情見 request_llm/README.md", "使用教程详情见 request_llms/README.md": "使用教程詳情見 request_llms/README.md",
"从以上搜索结果中抽取信息": "從以上搜索結果中抽取信息", "从以上搜索结果中抽取信息": "從以上搜索結果中抽取信息",
"虽然PDF生成失败了": "雖然PDF生成失敗了", "虽然PDF生成失败了": "雖然PDF生成失敗了",
"找图片": "尋找圖片", "找图片": "尋找圖片",

39
docs/use_azure.md
View File

@ -1,3 +1,42 @@
# 微软Azure云接入指南
## 方法一（旧方法，只能接入一个Azure模型）
- 通过以下教程，获取AZURE_ENDPOINT，AZURE_API_KEY，AZURE_ENGINE，直接修改 config 配置即可。配置的修改方法见本项目wiki。
## 方法二（新方法，接入多个Azure模型，并支持动态切换）
- 在方法一的基础上，注册并获取多组 AZURE_ENDPOINT，AZURE_API_KEY，AZURE_ENGINE
- 修改config中的AZURE_CFG_ARRAY和AVAIL_LLM_MODELS配置项，按照格式填入多个Azure模型的配置，如下所示：
```
AZURE_CFG_ARRAY = {
"azure-gpt-3.5": # 第一个模型，azure模型必须以"azure-"开头，注意您还需要将"azure-gpt-3.5"加入AVAIL_LLM_MODELS（模型下拉菜单）
{
"AZURE_ENDPOINT": "https://你亲手写的api名称.openai.azure.com/",
"AZURE_API_KEY": "cccccccccccccccccccccccccccccccc",
"AZURE_ENGINE": "填入你亲手写的部署名1",
"AZURE_MODEL_MAX_TOKEN": 4096,
},
"azure-gpt-4": # 第二个模型，azure模型必须以"azure-"开头，注意您还需要将"azure-gpt-4"加入AVAIL_LLM_MODELS（模型下拉菜单）
{
"AZURE_ENDPOINT": "https://你亲手写的api名称.openai.azure.com/",
"AZURE_API_KEY": "dddddddddddddddddddddddddddddddd",
"AZURE_ENGINE": "填入你亲手写的部署名2",
"AZURE_MODEL_MAX_TOKEN": 8192,
},
"azure-gpt-3.5-16k": # 第三个模型，azure模型必须以"azure-"开头，注意您还需要将"azure-gpt-3.5-16k"加入AVAIL_LLM_MODELS（模型下拉菜单）
{
"AZURE_ENDPOINT": "https://你亲手写的api名称.openai.azure.com/",
"AZURE_API_KEY": "eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee",
"AZURE_ENGINE": "填入你亲手写的部署名3",
"AZURE_MODEL_MAX_TOKEN": 16384,
},
}
```
# 通过微软Azure云服务申请 Openai API # 通过微软Azure云服务申请 Openai API
由于Openai和微软的关系，现在是可以通过微软的Azure云计算服务直接访问openai的api，免去了注册和网络的问题。 由于Openai和微软的关系，现在是可以通过微软的Azure云计算服务直接访问openai的api，免去了注册和网络的问题。

18
main.py
View File

@ -1,26 +1,25 @@
import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染 import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
import pickle import pickle
import codecs
import base64 import base64
def main(): def main():
import gradio as gr import gradio as gr
if gr.__version__ not in ['3.32.6']: if gr.__version__ not in ['3.32.6']:
raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.") raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.")
from request_llm.bridge_all import predict from request_llms.bridge_all import predict
from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, 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 # 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 = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION') proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT') CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME') ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME')
DARK_MODE, NUM_CUSTOM_BASIC_BTN, SSL_KEYFILE, SSL_CERTFILE = get_conf('DARK_MODE', 'NUM_CUSTOM_BASIC_BTN', 'SSL_KEYFILE', 'SSL_CERTFILE') DARK_MODE, NUM_CUSTOM_BASIC_BTN, SSL_KEYFILE, SSL_CERTFILE = get_conf('DARK_MODE', 'NUM_CUSTOM_BASIC_BTN', 'SSL_KEYFILE', 'SSL_CERTFILE')
INIT_SYS_PROMPT = get_conf('INIT_SYS_PROMPT')
# 如果WEB_PORT是-1, 则随机选取WEB端口 # 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 PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
from check_proxy import get_current_version from check_proxy import get_current_version
from themes.theme import adjust_theme, advanced_css, theme_declaration, load_dynamic_theme from themes.theme import adjust_theme, advanced_css, theme_declaration, load_dynamic_theme
initial_prompt = "Serve me as a writing and programming assistant."
title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}" title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
description = "Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic), " description = "Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic), "
description += "感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors)." description += "感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors)."
@ -32,6 +31,7 @@ def main():
description += "</br></br>虚空终端使用说明: 点击虚空终端, 然后根据提示输入指令, 再次点击虚空终端" description += "</br></br>虚空终端使用说明: 点击虚空终端, 然后根据提示输入指令, 再次点击虚空终端"
description += "</br></br>如何保存对话: 点击保存当前的对话按钮" description += "</br></br>如何保存对话: 点击保存当前的对话按钮"
description += "</br></br>如何语音对话: 请阅读Wiki" description += "</br></br>如何语音对话: 请阅读Wiki"
description += "</br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"
# 问询记录, python 版本建议3.9+（越新越好） # 问询记录, python 版本建议3.9+（越新越好）
import logging, uuid import logging, uuid
@ -48,7 +48,7 @@ def main():
# 高级函数插件 # 高级函数插件
from crazy_functional import get_crazy_functions from crazy_functional import get_crazy_functions
DEFAULT_FN_GROUPS, = get_conf('DEFAULT_FN_GROUPS') DEFAULT_FN_GROUPS = get_conf('DEFAULT_FN_GROUPS')
plugins = get_crazy_functions() plugins = get_crazy_functions()
all_plugin_groups = list(set([g for _, plugin in plugins.items() for g in plugin['Group'].split('|')])) all_plugin_groups = list(set([g for _, plugin in plugins.items() for g in plugin['Group'].split('|')]))
match_group = lambda tags, groups: any([g in groups for g in tags.split('|')]) match_group = lambda tags, groups: any([g in groups for g in tags.split('|')])
@ -94,7 +94,7 @@ def main():
clearBtn = gr.Button("清除", elem_id="elem_clear", variant="secondary", visible=False); clearBtn.style(size="sm") clearBtn = gr.Button("清除", elem_id="elem_clear", variant="secondary", visible=False); clearBtn.style(size="sm")
if ENABLE_AUDIO: if ENABLE_AUDIO:
with gr.Row(): with gr.Row():
audio_mic = gr.Audio(source="microphone", type="numpy", streaming=True, show_label=False).style(container=False) audio_mic = gr.Audio(source="microphone", type="numpy", elem_id="elem_audio", streaming=True, show_label=False).style(container=False)
with gr.Row(): 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}", elem_id="state-panel") 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.Accordion("基础功能区", open=True, elem_id="basic-panel") as area_basic_fn:
@ -153,7 +153,7 @@ def main():
top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",) 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",) temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
max_length_sl = gr.Slider(minimum=256, maximum=1024*32, value=4096, step=128, interactive=True, label="Local LLM MaxLength",) max_length_sl = gr.Slider(minimum=256, maximum=1024*32, value=4096, step=128, interactive=True, label="Local LLM MaxLength",)
system_prompt = gr.Textbox(show_label=True, lines=2, placeholder=f"System Prompt", label="System prompt", value=initial_prompt) system_prompt = gr.Textbox(show_label=True, lines=2, placeholder=f"System Prompt", label="System prompt", value=INIT_SYS_PROMPT)
with gr.Tab("界面外观", elem_id="interact-panel"): with gr.Tab("界面外观", elem_id="interact-panel"):
theme_dropdown = gr.Dropdown(AVAIL_THEMES, value=THEME, label="更换UI主题").style(container=False) theme_dropdown = gr.Dropdown(AVAIL_THEMES, value=THEME, label="更换UI主题").style(container=False)
@ -433,16 +433,16 @@ def main():
server_port=PORT, server_port=PORT,
favicon_path=os.path.join(os.path.dirname(__file__), "docs/logo.png"), favicon_path=os.path.join(os.path.dirname(__file__), "docs/logo.png"),
auth=AUTHENTICATION if len(AUTHENTICATION) != 0 else None, auth=AUTHENTICATION if len(AUTHENTICATION) != 0 else None,
blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"]) blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile",f"{PATH_LOGGING}/admin"])
# 如果需要在二级路径下运行 # 如果需要在二级路径下运行
# CUSTOM_PATH, = get_conf('CUSTOM_PATH') # CUSTOM_PATH = get_conf('CUSTOM_PATH')
# if CUSTOM_PATH != "/": # if CUSTOM_PATH != "/":
# from toolbox import run_gradio_in_subpath # from toolbox import run_gradio_in_subpath
# run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH) # run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
# else: # else:
# demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png", # demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png",
# blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"]) # blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile",f"{PATH_LOGGING}/admin"])
if __name__ == "__main__": if __name__ == "__main__":
main() main()

16
multi_language.py
View File

@ -13,6 +13,7 @@
4. Run `python multi_language.py`. 4. Run `python multi_language.py`.
Note: You need to run it multiple times to increase translation coverage because GPT makes mistakes sometimes. Note: You need to run it multiple times to increase translation coverage because GPT makes mistakes sometimes.
(You can also run `CACHE_ONLY=True python multi_language.py` to use cached translation mapping)
5. Find the translated program in `multi-language\English\*` 5. Find the translated program in `multi-language\English\*`
@ -35,7 +36,9 @@ import pickle
import time import time
from toolbox import get_conf from toolbox import get_conf
CACHE_FOLDER, = get_conf('PATH_LOGGING') CACHE_ONLY = os.environ.get('CACHE_ONLY', False)
CACHE_FOLDER = get_conf('PATH_LOGGING')
blacklist = ['multi-language', CACHE_FOLDER, '.git', 'private_upload', 'multi_language.py', 'build', '.github', '.vscode', '__pycache__', 'venv'] blacklist = ['multi-language', CACHE_FOLDER, '.git', 'private_upload', 'multi_language.py', 'build', '.github', '.vscode', '__pycache__', 'venv']
@ -336,7 +339,10 @@ def step_1_core_key_translate():
if d not in cached_translation_keys: if d not in cached_translation_keys:
need_translate.append(d) need_translate.append(d)
need_translate_mapping = trans(need_translate, language=LANG_STD, special=True) if CACHE_ONLY:
need_translate_mapping = {}
else:
need_translate_mapping = trans(need_translate, language=LANG_STD, special=True)
map_to_json(need_translate_mapping, language=LANG_STD) map_to_json(need_translate_mapping, language=LANG_STD)
cached_translation = read_map_from_json(language=LANG_STD) cached_translation = read_map_from_json(language=LANG_STD)
cached_translation = dict(sorted(cached_translation.items(), key=lambda x: -len(x[0]))) cached_translation = dict(sorted(cached_translation.items(), key=lambda x: -len(x[0])))
@ -476,8 +482,10 @@ def step_2_core_key_translate():
if d not in cached_translation_keys: if d not in cached_translation_keys:
need_translate.append(d) need_translate.append(d)
if CACHE_ONLY:
up = trans_json(need_translate, language=LANG, special=False) up = {}
else:
up = trans_json(need_translate, language=LANG, special=False)
map_to_json(up, language=LANG) map_to_json(up, language=LANG)
cached_translation = read_map_from_json(language=LANG) cached_translation = read_map_from_json(language=LANG)
LANG_STD = 'std' LANG_STD = 'std'

167
request_llm/bridge_chatglm.py
View File

@ -1,167 +0,0 @@
from transformers import AutoModel, AutoTokenizer
import time
import threading
import importlib
from toolbox import update_ui, get_conf, ProxyNetworkActivate
from multiprocessing import Process, Pipe
load_message = "ChatGLM尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，ChatGLM消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
#################################################################################
class GetGLMHandle(Process):
def __init__(self):
super().__init__(daemon=True)
self.parent, self.child = Pipe()
self.chatglm_model = None
self.chatglm_tokenizer = None
self.info = ""
self.success = True
self.check_dependency()
self.start()
self.threadLock = threading.Lock()
def check_dependency(self):
try:
import sentencepiece
self.info = "依赖检测通过"
self.success = True
except:
self.info = "缺少ChatGLM的依赖，如果要使用ChatGLM，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_chatglm.txt`安装ChatGLM的依赖。"
self.success = False
def ready(self):
return self.chatglm_model is not None
def run(self):
# 子进程执行
# 第一次运行，加载参数
retry = 0
LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE')
if LOCAL_MODEL_QUANT == "INT4": # INT4
_model_name_ = "THUDM/chatglm2-6b-int4"
elif LOCAL_MODEL_QUANT == "INT8": # INT8
_model_name_ = "THUDM/chatglm2-6b-int8"
else:
_model_name_ = "THUDM/chatglm2-6b" # FP16
while True:
try:
with ProxyNetworkActivate('Download_LLM'):
if self.chatglm_model is None:
self.chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True)
if device=='cpu':
self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).float()
else:
self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).half().cuda()
self.chatglm_model = self.chatglm_model.eval()
break
else:
break
except:
retry += 1
if retry > 3:
self.child.send('[Local Message] Call ChatGLM fail 不能正常加载ChatGLM的参数。')
raise RuntimeError("不能正常加载ChatGLM的参数！")
while True:
# 进入任务等待状态
kwargs = self.child.recv()
# 收到消息，开始请求
try:
for response, history in self.chatglm_model.stream_chat(self.chatglm_tokenizer, **kwargs):
self.child.send(response)
# # 中途接收可能的终止指令（如果有的话）
# if self.child.poll():
# command = self.child.recv()
# if command == '[Terminate]': break
except:
from toolbox import trimmed_format_exc
self.child.send('[Local Message] Call ChatGLM fail.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
# 请求处理结束，开始下一个循环
self.child.send('[Finish]')
def stream_chat(self, **kwargs):
# 主进程执行
self.threadLock.acquire()
self.parent.send(kwargs)
while True:
res = self.parent.recv()
if res != '[Finish]':
yield res
else:
break
self.threadLock.release()
global glm_handle
glm_handle = None
#################################################################################
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
"""
多线程方法
函数的说明请见 request_llm/bridge_all.py
"""
global glm_handle
if glm_handle is None:
glm_handle = GetGLMHandle()
if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + glm_handle.info
if not glm_handle.success:
error = glm_handle.info
glm_handle = None
raise RuntimeError(error)
# chatglm 没有 sys_prompt 接口，因此把prompt加入 history
history_feedin = []
history_feedin.append(["What can I do?", sys_prompt])
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 = ""
for response in glm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
if len(observe_window) >= 1: observe_window[0] = response
if len(observe_window) >= 2:
if (time.time()-observe_window[1]) > watch_dog_patience:
raise RuntimeError("程序终止。")
return 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, ""))
global glm_handle
if glm_handle is None:
glm_handle = GetGLMHandle()
chatbot[-1] = (inputs, load_message + "\n\n" + glm_handle.info)
yield from update_ui(chatbot=chatbot, history=[])
if not glm_handle.success:
glm_handle = None
return
if additional_fn is not None:
from core_functional import handle_core_functionality
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
# 处理历史信息
history_feedin = []
history_feedin.append(["What can I do?", system_prompt] )
for i in range(len(history)//2):
history_feedin.append([history[2*i], history[2*i+1]] )
# 开始接收chatglm的回复
response = "[Local Message]: 等待ChatGLM响应中 ..."
for response in glm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history)
# 总结输出
if response == "[Local Message]: 等待ChatGLM响应中 ...":
response = "[Local Message]: ChatGLM响应异常 ..."
history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history)

180
request_llm/local_llm_class.py
View File

@ -1,180 +0,0 @@
from transformers import AutoModel, AutoTokenizer
import time
import threading
import importlib
from toolbox import update_ui, get_conf, Singleton
from multiprocessing import Process, Pipe
def SingletonLocalLLM(cls):
"""
一个单实例装饰器
"""
_instance = {}
def _singleton(*args, **kargs):
if cls not in _instance:
_instance[cls] = cls(*args, **kargs)
return _instance[cls]
elif _instance[cls].corrupted:
_instance[cls] = cls(*args, **kargs)
return _instance[cls]
else:
return _instance[cls]
return _singleton
class LocalLLMHandle(Process):
def __init__(self):
# ⭐主进程执行
super().__init__(daemon=True)
self.corrupted = False
self.load_model_info()
self.parent, self.child = Pipe()
self.running = True
self._model = None
self._tokenizer = None
self.info = ""
self.check_dependency()
self.start()
self.threadLock = threading.Lock()
def load_model_info(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
raise NotImplementedError("Method not implemented yet")
self.model_name = ""
self.cmd_to_install = ""
def load_model_and_tokenizer(self):
"""
This function should return the model and the tokenizer
"""
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
raise NotImplementedError("Method not implemented yet")
def llm_stream_generator(self, **kwargs):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
raise NotImplementedError("Method not implemented yet")
def try_to_import_special_deps(self, **kwargs):
"""
import something that will raise error if the user does not install requirement_*.txt
"""
# ⭐主进程执行
raise NotImplementedError("Method not implemented yet")
def check_dependency(self):
# ⭐主进程执行
try:
self.try_to_import_special_deps()
self.info = "依赖检测通过"
self.running = True
except:
self.info = f"缺少{self.model_name}的依赖，如果要使用{self.model_name}，除了基础的pip依赖以外，您还需要运行{self.cmd_to_install}安装{self.model_name}的依赖。"
self.running = False
def run(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
# 第一次运行，加载参数
try:
self._model, self._tokenizer = self.load_model_and_tokenizer()
except:
self.running = False
from toolbox import trimmed_format_exc
self.child.send(f'[Local Message] 不能正常加载{self.model_name}的参数.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
self.child.send('[FinishBad]')
raise RuntimeError(f"不能正常加载{self.model_name}的参数！")
while True:
# 进入任务等待状态
kwargs = self.child.recv()
# 收到消息，开始请求
try:
for response_full in self.llm_stream_generator(**kwargs):
self.child.send(response_full)
self.child.send('[Finish]')
# 请求处理结束，开始下一个循环
except:
from toolbox import trimmed_format_exc
self.child.send(f'[Local Message] 调用{self.model_name}失败.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
self.child.send('[Finish]')
def stream_chat(self, **kwargs):
# ⭐主进程执行
self.threadLock.acquire()
self.parent.send(kwargs)
while True:
res = self.parent.recv()
if res == '[Finish]':
break
if res == '[FinishBad]':
self.running = False
self.corrupted = True
break
else:
yield res
self.threadLock.release()
def get_local_llm_predict_fns(LLMSingletonClass, model_name):
load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
"""
⭐多线程方法
函数的说明请见 request_llm/bridge_all.py
"""
_llm_handle = LLMSingletonClass()
if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + _llm_handle.info
if not _llm_handle.running: raise RuntimeError(_llm_handle.info)
# chatglm 没有 sys_prompt 接口，因此把prompt加入 history
history_feedin = []
history_feedin.append([sys_prompt, "Certainly!"])
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 = ""
for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
if len(observe_window) >= 1:
observe_window[0] = response
if len(observe_window) >= 2:
if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
return 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, ""))
_llm_handle = LLMSingletonClass()
chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.info)
yield from update_ui(chatbot=chatbot, history=[])
if not _llm_handle.running: raise RuntimeError(_llm_handle.info)
if additional_fn is not None:
from core_functional import handle_core_functionality
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
# 处理历史信息
history_feedin = []
history_feedin.append([system_prompt, "Certainly!"])
for i in range(len(history)//2):
history_feedin.append([history[2*i], history[2*i+1]] )
# 开始接收回复
response = f"[Local Message]: 等待{model_name}响应中 ..."
for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history)
# 总结输出
if response == f"[Local Message]: 等待{model_name}响应中 ...":
response = f"[Local Message]: {model_name}响应异常 ..."
history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history)
return predict_no_ui_long_connection, predict

2
request_llm/README.md → request_llms/README.md
View File

@ -2,7 +2,7 @@
## ChatGLM ## ChatGLM
- 安装依赖 `pip install -r request_llm/requirements_chatglm.txt` - 安装依赖 `pip install -r request_llms/requirements_chatglm.txt`
- 修改配置，在config.py中将LLM_MODEL的值改为"chatglm" - 修改配置，在config.py中将LLM_MODEL的值改为"chatglm"
``` sh ``` sh

138
request_llm/bridge_all.py → request_llms/bridge_all.py
View File

@ -8,7 +8,7 @@
具备多线程调用能力的函数：在函数插件中被调用，灵活而简洁 具备多线程调用能力的函数：在函数插件中被调用，灵活而简洁
2. predict_no_ui_long_connection(...) 2. predict_no_ui_long_connection(...)
""" """
import tiktoken import tiktoken, copy
from functools import lru_cache from functools import lru_cache
from concurrent.futures import ThreadPoolExecutor from concurrent.futures import ThreadPoolExecutor
from toolbox import get_conf, trimmed_format_exc from toolbox import get_conf, trimmed_format_exc
@ -16,12 +16,15 @@ 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_no_ui_long_connection as chatgpt_noui
from .bridge_chatgpt import predict as chatgpt_ui from .bridge_chatgpt import predict as chatgpt_ui
from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui from .bridge_chatgpt_vision import predict_no_ui_long_connection as chatgpt_vision_noui
from .bridge_chatglm import predict as chatglm_ui from .bridge_chatgpt_vision import predict as chatgpt_vision_ui
from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
from .bridge_chatglm import predict as chatglm_ui from .bridge_chatglm import predict as chatglm_ui
from .bridge_chatglm3 import predict_no_ui_long_connection as chatglm3_noui
from .bridge_chatglm3 import predict as chatglm3_ui
from .bridge_qianfan import predict_no_ui_long_connection as qianfan_noui from .bridge_qianfan import predict_no_ui_long_connection as qianfan_noui
from .bridge_qianfan import predict as qianfan_ui from .bridge_qianfan import predict as qianfan_ui
@ -56,7 +59,7 @@ if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15' azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
# 兼容旧版的配置 # 兼容旧版的配置
try: try:
API_URL, = get_conf("API_URL") API_URL = get_conf("API_URL")
if API_URL != "https://api.openai.com/v1/chat/completions": if API_URL != "https://api.openai.com/v1/chat/completions":
openai_endpoint = API_URL openai_endpoint = API_URL
print("警告！API_URL配置选项将被弃用，请更换为API_URL_REDIRECT配置") print("警告！API_URL配置选项将被弃用，请更换为API_URL_REDIRECT配置")
@ -94,7 +97,7 @@ model_info = {
"fn_with_ui": chatgpt_ui, "fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui, "fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint, "endpoint": openai_endpoint,
"max_token": 1024*16, "max_token": 16385,
"tokenizer": tokenizer_gpt35, "tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35, "token_cnt": get_token_num_gpt35,
}, },
@ -112,7 +115,16 @@ model_info = {
"fn_with_ui": chatgpt_ui, "fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui, "fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint, "endpoint": openai_endpoint,
"max_token": 1024 * 16, "max_token": 16385,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gpt-3.5-turbo-1106": {#16k
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 16385,
"tokenizer": tokenizer_gpt35, "tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35, "token_cnt": get_token_num_gpt35,
}, },
@ -135,6 +147,15 @@ model_info = {
"token_cnt": get_token_num_gpt4, "token_cnt": get_token_num_gpt4,
}, },
"gpt-4-1106-preview": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 128000,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
},
"gpt-3.5-random": { "gpt-3.5-random": {
"fn_with_ui": chatgpt_ui, "fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui, "fn_without_ui": chatgpt_noui,
@ -144,6 +165,16 @@ model_info = {
"token_cnt": get_token_num_gpt4, "token_cnt": get_token_num_gpt4,
}, },
"gpt-4-vision-preview": {
"fn_with_ui": chatgpt_vision_ui,
"fn_without_ui": chatgpt_vision_noui,
"endpoint": openai_endpoint,
"max_token": 4096,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
},
# azure openai # azure openai
"azure-gpt-3.5":{ "azure-gpt-3.5":{
"fn_with_ui": chatgpt_ui, "fn_with_ui": chatgpt_ui,
@ -159,11 +190,11 @@ model_info = {
"fn_without_ui": chatgpt_noui, "fn_without_ui": chatgpt_noui,
"endpoint": azure_endpoint, "endpoint": azure_endpoint,
"max_token": 8192, "max_token": 8192,
"tokenizer": tokenizer_gpt35, "tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt35, "token_cnt": get_token_num_gpt4,
}, },
# api_2d # api_2d (此后不需要在此处添加api2d的接口了，因为下面的代码会自动添加)
"api2d-gpt-3.5-turbo": { "api2d-gpt-3.5-turbo": {
"fn_with_ui": chatgpt_ui, "fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui, "fn_without_ui": chatgpt_noui,
@ -182,15 +213,6 @@ model_info = {
"token_cnt": get_token_num_gpt4, "token_cnt": get_token_num_gpt4,
}, },
"api2d-gpt-3.5-turbo-16k": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": api2d_endpoint,
"max_token": 1024*16,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
# 将 chatglm 直接对齐到 chatglm2 # 将 chatglm 直接对齐到 chatglm2
"chatglm": { "chatglm": {
"fn_with_ui": chatglm_ui, "fn_with_ui": chatglm_ui,
@ -208,6 +230,14 @@ model_info = {
"tokenizer": tokenizer_gpt35, "tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35, "token_cnt": get_token_num_gpt35,
}, },
"chatglm3": {
"fn_with_ui": chatglm3_ui,
"fn_without_ui": chatglm3_noui,
"endpoint": None,
"max_token": 8192,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"qianfan": { "qianfan": {
"fn_with_ui": qianfan_ui, "fn_with_ui": qianfan_ui,
"fn_without_ui": qianfan_noui, "fn_without_ui": qianfan_noui,
@ -218,6 +248,20 @@ model_info = {
}, },
} }
# -=-=-=-=-=-=- api2d 对齐支持 -=-=-=-=-=-=-
for model in AVAIL_LLM_MODELS:
if model.startswith('api2d-') and (model.replace('api2d-','') in model_info.keys()):
mi = copy.deepcopy(model_info[model.replace('api2d-','')])
mi.update({"endpoint": api2d_endpoint})
model_info.update({model: mi})
# -=-=-=-=-=-=- azure 对齐支持 -=-=-=-=-=-=-
for model in AVAIL_LLM_MODELS:
if model.startswith('azure-') and (model.replace('azure-','') in model_info.keys()):
mi = copy.deepcopy(model_info[model.replace('azure-','')])
mi.update({"endpoint": azure_endpoint})
model_info.update({model: mi})
# -=-=-=-=-=-=- 以下部分是新加入的模型，可能附带额外依赖 -=-=-=-=-=-=- # -=-=-=-=-=-=- 以下部分是新加入的模型，可能附带额外依赖 -=-=-=-=-=-=-
if "claude-1-100k" in AVAIL_LLM_MODELS or "claude-2" in AVAIL_LLM_MODELS: if "claude-1-100k" in AVAIL_LLM_MODELS or "claude-2" in AVAIL_LLM_MODELS:
from .bridge_claude import predict_no_ui_long_connection as claude_noui from .bridge_claude import predict_no_ui_long_connection as claude_noui
@ -451,6 +495,22 @@ if "sparkv2" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
}) })
except: except:
print(trimmed_format_exc()) print(trimmed_format_exc())
if "sparkv3" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
try:
from .bridge_spark import predict_no_ui_long_connection as spark_noui
from .bridge_spark import predict as spark_ui
model_info.update({
"sparkv3": {
"fn_with_ui": spark_ui,
"fn_without_ui": spark_noui,
"endpoint": None,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "llama2" in AVAIL_LLM_MODELS: # llama2 if "llama2" in AVAIL_LLM_MODELS: # llama2
try: try:
from .bridge_llama2 import predict_no_ui_long_connection as llama2_noui from .bridge_llama2 import predict_no_ui_long_connection as llama2_noui
@ -467,6 +527,46 @@ if "llama2" in AVAIL_LLM_MODELS: # llama2
}) })
except: except:
print(trimmed_format_exc()) print(trimmed_format_exc())
if "zhipuai" in AVAIL_LLM_MODELS: # zhipuai
try:
from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
from .bridge_zhipu import predict as zhipu_ui
model_info.update({
"zhipuai": {
"fn_with_ui": zhipu_ui,
"fn_without_ui": zhipu_noui,
"endpoint": None,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
# <-- 用于定义和切换多个azure模型 -->
AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY")
if len(AZURE_CFG_ARRAY) > 0:
for azure_model_name, azure_cfg_dict in AZURE_CFG_ARRAY.items():
# 可能会覆盖之前的配置，但这是意料之中的
if not azure_model_name.startswith('azure'):
raise ValueError("AZURE_CFG_ARRAY中配置的模型必须以azure开头")
endpoint_ = azure_cfg_dict["AZURE_ENDPOINT"] + \
f'openai/deployments/{azure_cfg_dict["AZURE_ENGINE"]}/chat/completions?api-version=2023-05-15'
model_info.update({
azure_model_name: {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": endpoint_,
"azure_api_key": azure_cfg_dict["AZURE_API_KEY"],
"max_token": azure_cfg_dict["AZURE_MODEL_MAX_TOKEN"],
"tokenizer": tokenizer_gpt35, # tokenizer只用于粗估token数量
"token_cnt": get_token_num_gpt35,
}
})
if azure_model_name not in AVAIL_LLM_MODELS:
AVAIL_LLM_MODELS += [azure_model_name]
@ -484,7 +584,7 @@ def LLM_CATCH_EXCEPTION(f):
return decorated return decorated
def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience=False): def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window=[], console_slience=False):
""" """
发送至LLM，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。 发送至LLM，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： inputs：

78
request_llms/bridge_chatglm.py Normal file
View File

@ -0,0 +1,78 @@
model_name = "ChatGLM"
cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
from toolbox import get_conf, ProxyNetworkActivate
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
# ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 Local Model
# ------------------------------------------------------------------------------------------------------------------------
class GetGLM2Handle(LocalLLMHandle):
def load_model_info(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
self.model_name = model_name
self.cmd_to_install = cmd_to_install
def load_model_and_tokenizer(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
import os, glob
import os
import platform
from transformers import AutoModel, AutoTokenizer
LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE')
if LOCAL_MODEL_QUANT == "INT4": # INT4
_model_name_ = "THUDM/chatglm2-6b-int4"
elif LOCAL_MODEL_QUANT == "INT8": # INT8
_model_name_ = "THUDM/chatglm2-6b-int8"
else:
_model_name_ = "THUDM/chatglm2-6b" # FP16
with ProxyNetworkActivate('Download_LLM'):
chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True)
if device=='cpu':
chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).float()
else:
chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).half().cuda()
chatglm_model = chatglm_model.eval()
self._model = chatglm_model
self._tokenizer = chatglm_tokenizer
return self._model, self._tokenizer
def llm_stream_generator(self, **kwargs):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
def adaptor(kwargs):
query = kwargs['query']
max_length = kwargs['max_length']
top_p = kwargs['top_p']
temperature = kwargs['temperature']
history = kwargs['history']
return query, max_length, top_p, temperature, history
query, max_length, top_p, temperature, history = adaptor(kwargs)
for response, history in self._model.stream_chat(self._tokenizer,
query,
history,
max_length=max_length,
top_p=top_p,
temperature=temperature,
):
yield response
def try_to_import_special_deps(self, **kwargs):
# import something that will raise error if the user does not install requirement_*.txt
# 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
import importlib
# importlib.import_module('modelscope')
# ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 GPT-Academic Interface
# ------------------------------------------------------------------------------------------------------------------------
predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetGLM2Handle, model_name)

77
request_llms/bridge_chatglm3.py Normal file
View File

@ -0,0 +1,77 @@
model_name = "ChatGLM3"
cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
from toolbox import get_conf, ProxyNetworkActivate
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
# ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 Local Model
# ------------------------------------------------------------------------------------------------------------------------
class GetGLM3Handle(LocalLLMHandle):
def load_model_info(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
self.model_name = model_name
self.cmd_to_install = cmd_to_install
def load_model_and_tokenizer(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
from transformers import AutoModel, AutoTokenizer
import os, glob
import os
import platform
LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE')
if LOCAL_MODEL_QUANT == "INT4": # INT4
_model_name_ = "THUDM/chatglm3-6b-int4"
elif LOCAL_MODEL_QUANT == "INT8": # INT8
_model_name_ = "THUDM/chatglm3-6b-int8"
else:
_model_name_ = "THUDM/chatglm3-6b" # FP16
with ProxyNetworkActivate('Download_LLM'):
chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True)
if device=='cpu':
chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True, device='cpu').float()
else:
chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True, device='cuda')
chatglm_model = chatglm_model.eval()
self._model = chatglm_model
self._tokenizer = chatglm_tokenizer
return self._model, self._tokenizer
def llm_stream_generator(self, **kwargs):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
def adaptor(kwargs):
query = kwargs['query']
max_length = kwargs['max_length']
top_p = kwargs['top_p']
temperature = kwargs['temperature']
history = kwargs['history']
return query, max_length, top_p, temperature, history
query, max_length, top_p, temperature, history = adaptor(kwargs)
for response, history in self._model.stream_chat(self._tokenizer,
query,
history,
max_length=max_length,
top_p=top_p,
temperature=temperature,
):
yield response
def try_to_import_special_deps(self, **kwargs):
# import something that will raise error if the user does not install requirement_*.txt
# 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
import importlib
# importlib.import_module('modelscope')
# ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 GPT-Academic Interface
# ------------------------------------------------------------------------------------------------------------------------
predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetGLM3Handle, model_name, history_format='chatglm3')

18
request_llm/bridge_chatglmft.py → request_llms/bridge_chatglmft.py
View File

@ -44,7 +44,7 @@ class GetGLMFTHandle(Process):
self.info = "依赖检测通过" self.info = "依赖检测通过"
self.success = True self.success = True
except: except:
self.info = "缺少ChatGLMFT的依赖，如果要使用ChatGLMFT，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_chatglm.txt`安装ChatGLM的依赖。" self.info = "缺少ChatGLMFT的依赖，如果要使用ChatGLMFT，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_chatglm.txt`安装ChatGLM的依赖。"
self.success = False self.success = False
def ready(self): def ready(self):
@ -59,11 +59,11 @@ class GetGLMFTHandle(Process):
if self.chatglmft_model is None: if self.chatglmft_model is None:
from transformers import AutoConfig from transformers import AutoConfig
import torch import torch
# conf = 'request_llm/current_ptune_model.json' # conf = 'request_llms/current_ptune_model.json'
# if not os.path.exists(conf): raise RuntimeError('找不到微调模型信息') # if not os.path.exists(conf): raise RuntimeError('找不到微调模型信息')
# with open(conf, 'r', encoding='utf8') as f: # with open(conf, 'r', encoding='utf8') as f:
# model_args = json.loads(f.read()) # model_args = json.loads(f.read())
CHATGLM_PTUNING_CHECKPOINT, = get_conf('CHATGLM_PTUNING_CHECKPOINT') CHATGLM_PTUNING_CHECKPOINT = get_conf('CHATGLM_PTUNING_CHECKPOINT')
assert os.path.exists(CHATGLM_PTUNING_CHECKPOINT), "找不到微调模型检查点" assert os.path.exists(CHATGLM_PTUNING_CHECKPOINT), "找不到微调模型检查点"
conf = os.path.join(CHATGLM_PTUNING_CHECKPOINT, "config.json") conf = os.path.join(CHATGLM_PTUNING_CHECKPOINT, "config.json")
with open(conf, 'r', encoding='utf8') as f: with open(conf, 'r', encoding='utf8') as f:
@ -87,7 +87,7 @@ class GetGLMFTHandle(Process):
new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict) model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
if model_args['quantization_bit'] is not None: if model_args['quantization_bit'] is not None and model_args['quantization_bit'] != 0:
print(f"Quantized to {model_args['quantization_bit']} bit") print(f"Quantized to {model_args['quantization_bit']} bit")
model = model.quantize(model_args['quantization_bit']) model = model.quantize(model_args['quantization_bit'])
model = model.cuda() model = model.cuda()
@ -140,7 +140,7 @@ glmft_handle = None
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
""" """
多线程方法 多线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
global glmft_handle global glmft_handle
if glmft_handle is None: if glmft_handle is None:
@ -171,7 +171,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
""" """
单线程方法 单线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
chatbot.append((inputs, "")) chatbot.append((inputs, ""))
@ -195,13 +195,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
history_feedin.append([history[2*i], history[2*i+1]] ) history_feedin.append([history[2*i], history[2*i+1]] )
# 开始接收chatglmft的回复 # 开始接收chatglmft的回复
response = "[Local Message]: 等待ChatGLMFT响应中 ..." response = "[Local Message] 等待ChatGLMFT响应中 ..."
for response in glmft_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']): for response in glmft_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
chatbot[-1] = (inputs, response) chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)
# 总结输出 # 总结输出
if response == "[Local Message]: 等待ChatGLMFT响应中 ...": if response == "[Local Message] 等待ChatGLMFT响应中 ...":
response = "[Local Message]: ChatGLMFT响应异常 ..." response = "[Local Message] ChatGLMFT响应异常 ..."
history.extend([inputs, response]) history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)

13
request_llm/bridge_chatglmonnx.py → request_llms/bridge_chatglmonnx.py
View File

@ -1,5 +1,5 @@
model_name = "ChatGLM-ONNX" model_name = "ChatGLM-ONNX"
cmd_to_install = "`pip install -r request_llm/requirements_chatglm_onnx.txt`" cmd_to_install = "`pip install -r request_llms/requirements_chatglm_onnx.txt`"
from transformers import AutoModel, AutoTokenizer from transformers import AutoModel, AutoTokenizer
@ -8,7 +8,7 @@ import threading
import importlib import importlib
from toolbox import update_ui, get_conf from toolbox import update_ui, get_conf
from multiprocessing import Process, Pipe from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
from .chatglmoonx import ChatGLMModel, chat_template from .chatglmoonx import ChatGLMModel, chat_template
@ -17,7 +17,6 @@ from .chatglmoonx import ChatGLMModel, chat_template
# ------------------------------------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 Local Model # 🔌💻 Local Model
# ------------------------------------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------------
@SingletonLocalLLM
class GetONNXGLMHandle(LocalLLMHandle): class GetONNXGLMHandle(LocalLLMHandle):
def load_model_info(self): def load_model_info(self):
@ -28,13 +27,13 @@ class GetONNXGLMHandle(LocalLLMHandle):
def load_model_and_tokenizer(self): def load_model_and_tokenizer(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行 # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
import os, glob import os, glob
if not len(glob.glob("./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/*.bin")) >= 7: # 该模型有七个 bin 文件 if not len(glob.glob("./request_llms/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/*.bin")) >= 7: # 该模型有七个 bin 文件
from huggingface_hub import snapshot_download from huggingface_hub import snapshot_download
snapshot_download(repo_id="K024/ChatGLM-6b-onnx-u8s8", local_dir="./request_llm/ChatGLM-6b-onnx-u8s8") snapshot_download(repo_id="K024/ChatGLM-6b-onnx-u8s8", local_dir="./request_llms/ChatGLM-6b-onnx-u8s8")
def create_model(): def create_model():
return ChatGLMModel( return ChatGLMModel(
tokenizer_path = "./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/sentencepiece.model", tokenizer_path = "./request_llms/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/sentencepiece.model",
onnx_model_path = "./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/chatglm-6b-int8.onnx" onnx_model_path = "./request_llms/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/chatglm-6b-int8.onnx"
) )
self._model = create_model() self._model = create_model()
return self._model, None return self._model, None

55
request_llm/bridge_chatgpt.py → request_llms/bridge_chatgpt.py
View File

@ -7,8 +7,7 @@
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程 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 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 2. predict_no_ui_long_connection：支持多线程
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 json import json
@ -23,8 +22,8 @@ import random
# config_private.py放自己的秘密如API和代理网址 # 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 # 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, is_the_upload_folder from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder
proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG = \ proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG') get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \ 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。' '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。'
@ -45,16 +44,28 @@ def decode_chunk(chunk):
chunk_decoded = chunk.decode() chunk_decoded = chunk.decode()
chunkjson = None chunkjson = None
has_choices = False has_choices = False
choice_valid = False
has_content = False has_content = False
has_role = False has_role = False
try: try:
chunkjson = json.loads(chunk_decoded[6:]) chunkjson = json.loads(chunk_decoded[6:])
has_choices = 'choices' in chunkjson has_choices = 'choices' in chunkjson
if has_choices: has_content = "content" in chunkjson['choices'][0]["delta"] if has_choices: choice_valid = (len(chunkjson['choices']) > 0)
if has_choices: has_role = "role" in chunkjson['choices'][0]["delta"] if has_choices and choice_valid: has_content = "content" in chunkjson['choices'][0]["delta"]
if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"]
except: except:
pass pass
return chunk_decoded, chunkjson, has_choices, has_content, has_role return chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role
from functools import lru_cache
@lru_cache(maxsize=32)
def verify_endpoint(endpoint):
"""
检查endpoint是否可用
"""
if "你亲手写的api名称" in endpoint:
raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
return endpoint
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False): def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
""" """
@ -77,7 +88,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
try: try:
# make a POST request to the API endpoint, stream=False # make a POST request to the API endpoint, stream=False
from .bridge_all import model_info from .bridge_all import model_info
endpoint = model_info[llm_kwargs['llm_model']]['endpoint'] endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
response = requests.post(endpoint, headers=headers, proxies=proxies, response = requests.post(endpoint, headers=headers, proxies=proxies,
json=payload, stream=True, timeout=TIMEOUT_SECONDS); break json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
except requests.exceptions.ReadTimeout as e: except requests.exceptions.ReadTimeout as e:
@ -86,7 +97,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
if retry > MAX_RETRY: raise TimeoutError if retry > MAX_RETRY: raise TimeoutError
if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……') if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
stream_response = response.iter_lines() stream_response = response.iter_lines()
result = '' result = ''
json_data = None json_data = None
while True: while True:
@ -169,14 +180,22 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面 yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
return return
# 检查endpoint是否合法
try:
from .bridge_all import model_info
endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
except:
tb_str = '```\n' + trimmed_format_exc() + '```'
chatbot[-1] = (inputs, tb_str)
yield from update_ui(chatbot=chatbot, history=history, msg="Endpoint不满足要求") # 刷新界面
return
history.append(inputs); history.append("") history.append(inputs); history.append("")
retry = 0 retry = 0
while True: while True:
try: try:
# make a POST request to the API endpoint, stream=True # make a POST request to the API endpoint, stream=True
from .bridge_all import model_info
endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
response = requests.post(endpoint, headers=headers, proxies=proxies, response = requests.post(endpoint, headers=headers, proxies=proxies,
json=payload, stream=True, timeout=TIMEOUT_SECONDS);break json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
except: except:
@ -208,7 +227,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
return return
# 提前读取一些信息 （用于判断异常） # 提前读取一些信息 （用于判断异常）
chunk_decoded, chunkjson, has_choices, has_content, has_role = decode_chunk(chunk) chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"content" not in chunk_decoded): if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"content" not in chunk_decoded):
# 数据流的第一帧不携带content # 数据流的第一帧不携带content
@ -216,6 +235,9 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
if chunk: if chunk:
try: try:
if has_choices and not choice_valid:
# 一些垃圾第三方接口的出现这样的错误
continue
# 前者是API2D的结束条件，后者是OPENAI的结束条件 # 前者是API2D的结束条件，后者是OPENAI的结束条件
if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0): if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0):
# 判定为数据流的结束，gpt_replying_buffer也写完了 # 判定为数据流的结束，gpt_replying_buffer也写完了
@ -265,6 +287,8 @@ def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, 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) 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: 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) 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 "API key has been deactivated" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. OpenAI以账户失效为由, 拒绝服务." + openai_website)
elif "bad forward key" in error_msg: elif "bad forward key" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.") chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
elif "Not enough point" in error_msg: elif "Not enough point" in error_msg:
@ -289,7 +313,11 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
"Authorization": f"Bearer {api_key}" "Authorization": f"Bearer {api_key}"
} }
if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG}) if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG})
if llm_kwargs['llm_model'].startswith('azure-'): headers.update({"api-key": api_key}) if llm_kwargs['llm_model'].startswith('azure-'):
headers.update({"api-key": api_key})
if llm_kwargs['llm_model'] in AZURE_CFG_ARRAY.keys():
azure_api_key_unshared = AZURE_CFG_ARRAY[llm_kwargs['llm_model']]["AZURE_API_KEY"]
headers.update({"api-key": azure_api_key_unshared})
conversation_cnt = len(history) // 2 conversation_cnt = len(history) // 2
@ -322,6 +350,7 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
model = random.choice([ model = random.choice([
"gpt-3.5-turbo", "gpt-3.5-turbo",
"gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k",
"gpt-3.5-turbo-1106",
"gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613",
"gpt-3.5-turbo-16k-0613", "gpt-3.5-turbo-16k-0613",
"gpt-3.5-turbo-0301", "gpt-3.5-turbo-0301",

329
request_llms/bridge_chatgpt_vision.py Normal file
View File

@ -0,0 +1,329 @@
"""
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_long_connection：支持多线程
"""
import json
import time
import logging
import requests
import base64
import os
import glob
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder, update_ui_lastest_msg, get_max_token
proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
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。'
def have_any_recent_upload_image_files(chatbot):
_5min = 5 * 60
if chatbot is None: return False, None # chatbot is None
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
if not most_recent_uploaded: return False, None # most_recent_uploaded is None
if time.time() - most_recent_uploaded["time"] < _5min:
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
path = most_recent_uploaded['path']
file_manifest = [f for f in glob.glob(f'{path}/**/*.jpg', recursive=True)]
file_manifest += [f for f in glob.glob(f'{path}/**/*.jpeg', recursive=True)]
file_manifest += [f for f in glob.glob(f'{path}/**/*.png', recursive=True)]
if len(file_manifest) == 0: return False, None
return True, file_manifest # most_recent_uploaded is new
else:
return False, None # most_recent_uploaded is too old
def report_invalid_key(key):
if get_conf("BLOCK_INVALID_APIKEY"):
# 实验性功能，自动检测并屏蔽失效的KEY，请勿使用
from request_llms.key_manager import ApiKeyManager
api_key = ApiKeyManager().add_key_to_blacklist(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 decode_chunk(chunk):
# 提前读取一些信息 （用于判断异常）
chunk_decoded = chunk.decode()
chunkjson = None
has_choices = False
choice_valid = False
has_content = False
has_role = False
try:
chunkjson = json.loads(chunk_decoded[6:])
has_choices = 'choices' in chunkjson
if has_choices: choice_valid = (len(chunkjson['choices']) > 0)
if has_choices and choice_valid: has_content = "content" in chunkjson['choices'][0]["delta"]
if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"]
except:
pass
return chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role
from functools import lru_cache
@lru_cache(maxsize=32)
def verify_endpoint(endpoint):
"""
检查endpoint是否可用
"""
return endpoint
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
raise NotImplementedError
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
if is_any_api_key(inputs):
chatbot._cookies['api_key'] = inputs
chatbot.append(("输入已识别为openai的api_key", what_keys(inputs)))
yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
return
elif not is_any_api_key(chatbot._cookies['api_key']):
chatbot.append((inputs, "缺少api_key。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。"))
yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
return
if not have_recent_file:
chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面
return
if os.path.exists(inputs):
chatbot.append((inputs, "已经接收到您上传的文件，您不需要再重复强调该文件的路径了，请直接输入您的问题。"))
yield from update_ui(chatbot=chatbot, history=history, msg="等待指令") # 刷新界面
return
user_input = inputs
if additional_fn is not None:
from core_functional import handle_core_functionality
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
raw_input = inputs
logging.info(f'[raw_input] {raw_input}')
def make_media_input(inputs, image_paths):
for image_path in image_paths:
inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
return inputs
chatbot.append((make_media_input(inputs, image_paths), ""))
yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
# check mis-behavior
if is_the_upload_folder(user_input):
chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
time.sleep(2)
try:
headers, payload, api_key = generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths)
except RuntimeError as e:
chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
return
# 检查endpoint是否合法
try:
from .bridge_all import model_info
endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
except:
tb_str = '```\n' + trimmed_format_exc() + '```'
chatbot[-1] = (inputs, tb_str)
yield from update_ui(chatbot=chatbot, history=history, msg="Endpoint不满足要求") # 刷新界面
return
history.append(make_media_input(inputs, image_paths))
history.append("")
retry = 0
while True:
try:
# make a POST request to the API endpoint, stream=True
response = requests.post(endpoint, headers=headers, proxies=proxies,
json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
except:
retry += 1
chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
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.iter_lines()
while True:
try:
chunk = next(stream_response)
except StopIteration:
# 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
chunk_decoded = chunk.decode()
error_msg = chunk_decoded
# 首先排除一个one-api没有done数据包的第三方Bug情形
if len(gpt_replying_buffer.strip()) > 0 and len(error_msg) == 0:
yield from update_ui(chatbot=chatbot, history=history, msg="检测到有缺陷的非OpenAI官方接口，建议选择更稳定的接口。")
break
# 其他情况，直接返回报错
chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key)
yield from update_ui(chatbot=chatbot, history=history, msg="非OpenAI官方接口返回了错误:" + chunk.decode()) # 刷新界面
return
# 提前读取一些信息 （用于判断异常）
chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"content" not in chunk_decoded):
# 数据流的第一帧不携带content
is_head_of_the_stream = False; continue
if chunk:
try:
if has_choices and not choice_valid:
# 一些垃圾第三方接口的出现这样的错误
continue
# 前者是API2D的结束条件，后者是OPENAI的结束条件
if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0):
# 判定为数据流的结束，gpt_replying_buffer也写完了
lastmsg = chatbot[-1][-1] + f"\n\n\n\n「{llm_kwargs['llm_model']}调用结束，该模型不具备上下文对话能力，如需追问，请及时切换模型。」"
yield from update_ui_lastest_msg(lastmsg, chatbot, history, delay=1)
logging.info(f'[response] {gpt_replying_buffer}')
break
# 处理数据流的主体
status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
# 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
if has_content:
# 正常情况
gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
elif has_role:
# 一些第三方接口的出现这样的错误，兼容一下吧
continue
else:
# 一些垃圾第三方接口的出现这样的错误
gpt_replying_buffer = gpt_replying_buffer + chunkjson['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:
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)
chunk_decoded = chunk.decode()
error_msg = chunk_decoded
chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key)
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
print(error_msg)
return
def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key=""):
from .bridge_all import model_info
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'],
max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
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. " + openai_website); report_invalid_key(api_key)
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." + openai_website); report_invalid_key(api_key)
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); report_invalid_key(api_key)
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); report_invalid_key(api_key)
elif "API key has been deactivated" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. OpenAI以账户失效为由, 拒绝服务." + openai_website); report_invalid_key(api_key)
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:
chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
else:
from toolbox import regular_txt_to_markdown
tb_str = '```\n' + trimmed_format_exc() + '```'
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
return chatbot, history
# Function to encode the image
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths):
"""
整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
"""
if not is_any_api_key(llm_kwargs['api_key']):
raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。")
api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
headers = {
"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})
if llm_kwargs['llm_model'] in AZURE_CFG_ARRAY.keys():
azure_api_key_unshared = AZURE_CFG_ARRAY[llm_kwargs['llm_model']]["AZURE_API_KEY"]
headers.update({"api-key": azure_api_key_unshared})
base64_images = []
for image_path in image_paths:
base64_images.append(encode_image(image_path))
messages = []
what_i_ask_now = {}
what_i_ask_now["role"] = "user"
what_i_ask_now["content"] = []
what_i_ask_now["content"].append({
"type": "text",
"text": inputs
})
for image_path, base64_image in zip(image_paths, base64_images):
what_i_ask_now["content"].append({
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
})
messages.append(what_i_ask_now)
model = llm_kwargs['llm_model']
if llm_kwargs['llm_model'].startswith('api2d-'):
model = llm_kwargs['llm_model'][len('api2d-'):]
payload = {
"model": model,
"messages": messages,
"temperature": llm_kwargs['temperature'], # 1.0,
"top_p": llm_kwargs['top_p'], # 1.0,
"n": 1,
"stream": True,
"max_tokens": get_max_token(llm_kwargs),
"presence_penalty": 0,
"frequency_penalty": 0,
}
try:
print(f" {llm_kwargs['llm_model']} : {inputs[:100]} ..........")
except:
print('输入中可能存在乱码。')
return headers, payload, api_key

3
request_llm/bridge_chatgpt_website.py → request_llms/bridge_chatgpt_website.py
View File

@ -7,8 +7,7 @@
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程 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 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 2. predict_no_ui_long_connection：支持多线程
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 json import json

2
request_llm/bridge_claude.py → request_llms/bridge_claude.py
View File

@ -7,7 +7,7 @@
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程 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 具备多线程调用能力的函数
2. 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 2. predict_no_ui_long_connection：支持多线程
""" """
import os import os

27
request_llm/bridge_internlm.py → request_llms/bridge_internlm.py
View File

@ -1,13 +1,13 @@
model_name = "InternLM" model_name = "InternLM"
cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`" cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
from transformers import AutoModel, AutoTokenizer from transformers import AutoModel, AutoTokenizer
import time import time
import threading import threading
import importlib import importlib
from toolbox import update_ui, get_conf from toolbox import update_ui, get_conf, ProxyNetworkActivate
from multiprocessing import Process, Pipe from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
# ------------------------------------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------------
@ -34,7 +34,6 @@ def combine_history(prompt, hist):
# ------------------------------------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 Local Model # 🔌💻 Local Model
# ------------------------------------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------------
@SingletonLocalLLM
class GetInternlmHandle(LocalLLMHandle): class GetInternlmHandle(LocalLLMHandle):
def load_model_info(self): def load_model_info(self):
@ -52,15 +51,16 @@ class GetInternlmHandle(LocalLLMHandle):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行 # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
import torch import torch
from transformers import AutoModelForCausalLM, AutoTokenizer from transformers import AutoModelForCausalLM, AutoTokenizer
device, = get_conf('LOCAL_MODEL_DEVICE') device = get_conf('LOCAL_MODEL_DEVICE')
if self._model is None: with ProxyNetworkActivate('Download_LLM'):
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True) if self._model is None:
if device=='cpu': tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16) if device=='cpu':
else: model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16)
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda() else:
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda()
model = model.eval() model = model.eval()
return model, tokenizer return model, tokenizer
def llm_stream_generator(self, **kwargs): def llm_stream_generator(self, **kwargs):
@ -94,8 +94,9 @@ class GetInternlmHandle(LocalLLMHandle):
inputs = tokenizer([prompt], padding=True, return_tensors="pt") inputs = tokenizer([prompt], padding=True, return_tensors="pt")
input_length = len(inputs["input_ids"][0]) input_length = len(inputs["input_ids"][0])
device = get_conf('LOCAL_MODEL_DEVICE')
for k, v in inputs.items(): for k, v in inputs.items():
inputs[k] = v.cuda() inputs[k] = v.to(device)
input_ids = inputs["input_ids"] input_ids = inputs["input_ids"]
batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
if generation_config is None: if generation_config is None:

20
request_llm/bridge_jittorllms_llama.py → request_llms/bridge_jittorllms_llama.py
View File

@ -28,8 +28,8 @@ class GetGLMHandle(Process):
self.success = True self.success = True
except: except:
from toolbox import trimmed_format_exc from toolbox import trimmed_format_exc
self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\ self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\ r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llms/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc() r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
self.success = False self.success = False
@ -45,15 +45,15 @@ class GetGLMHandle(Process):
env = os.environ.get("PATH", "") env = os.environ.get("PATH", "")
os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin') os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..') root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
os.chdir(root_dir_assume + '/request_llm/jittorllms') os.chdir(root_dir_assume + '/request_llms/jittorllms')
sys.path.append(root_dir_assume + '/request_llm/jittorllms') sys.path.append(root_dir_assume + '/request_llms/jittorllms')
validate_path() # validate path so you can run from base directory validate_path() # validate path so you can run from base directory
def load_model(): def load_model():
import types import types
try: try:
if self.jittorllms_model is None: if self.jittorllms_model is None:
device, = get_conf('LOCAL_MODEL_DEVICE') device = get_conf('LOCAL_MODEL_DEVICE')
from .jittorllms.models import get_model from .jittorllms.models import get_model
# availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"] # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
args_dict = {'model': 'llama'} args_dict = {'model': 'llama'}
@ -109,7 +109,7 @@ llama_glm_handle = None
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
""" """
多线程方法 多线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
global llama_glm_handle global llama_glm_handle
if llama_glm_handle is None: if llama_glm_handle is None:
@ -140,7 +140,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
""" """
单线程方法 单线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
chatbot.append((inputs, "")) chatbot.append((inputs, ""))
@ -163,13 +163,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
history_feedin.append([history[2*i], history[2*i+1]] ) history_feedin.append([history[2*i], history[2*i+1]] )
# 开始接收jittorllms的回复 # 开始接收jittorllms的回复
response = "[Local Message]: 等待jittorllms响应中 ..." response = "[Local Message] 等待jittorllms响应中 ..."
for response in llama_glm_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']): for response in llama_glm_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, response) chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)
# 总结输出 # 总结输出
if response == "[Local Message]: 等待jittorllms响应中 ...": if response == "[Local Message] 等待jittorllms响应中 ...":
response = "[Local Message]: jittorllms响应异常 ..." response = "[Local Message] jittorllms响应异常 ..."
history.extend([inputs, response]) history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)

20
request_llm/bridge_jittorllms_pangualpha.py → request_llms/bridge_jittorllms_pangualpha.py
View File

@ -28,8 +28,8 @@ class GetGLMHandle(Process):
self.success = True self.success = True
except: except:
from toolbox import trimmed_format_exc from toolbox import trimmed_format_exc
self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\ self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\ r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llms/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc() r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
self.success = False self.success = False
@ -45,15 +45,15 @@ class GetGLMHandle(Process):
env = os.environ.get("PATH", "") env = os.environ.get("PATH", "")
os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin') os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..') root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
os.chdir(root_dir_assume + '/request_llm/jittorllms') os.chdir(root_dir_assume + '/request_llms/jittorllms')
sys.path.append(root_dir_assume + '/request_llm/jittorllms') sys.path.append(root_dir_assume + '/request_llms/jittorllms')
validate_path() # validate path so you can run from base directory validate_path() # validate path so you can run from base directory
def load_model(): def load_model():
import types import types
try: try:
if self.jittorllms_model is None: if self.jittorllms_model is None:
device, = get_conf('LOCAL_MODEL_DEVICE') device = get_conf('LOCAL_MODEL_DEVICE')
from .jittorllms.models import get_model from .jittorllms.models import get_model
# availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"] # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
args_dict = {'model': 'pangualpha'} args_dict = {'model': 'pangualpha'}
@ -109,7 +109,7 @@ pangu_glm_handle = None
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
""" """
多线程方法 多线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
global pangu_glm_handle global pangu_glm_handle
if pangu_glm_handle is None: if pangu_glm_handle is None:
@ -140,7 +140,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
""" """
单线程方法 单线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
chatbot.append((inputs, "")) chatbot.append((inputs, ""))
@ -163,13 +163,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
history_feedin.append([history[2*i], history[2*i+1]] ) history_feedin.append([history[2*i], history[2*i+1]] )
# 开始接收jittorllms的回复 # 开始接收jittorllms的回复
response = "[Local Message]: 等待jittorllms响应中 ..." response = "[Local Message] 等待jittorllms响应中 ..."
for response in pangu_glm_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']): for response in pangu_glm_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, response) chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)
# 总结输出 # 总结输出
if response == "[Local Message]: 等待jittorllms响应中 ...": if response == "[Local Message] 等待jittorllms响应中 ...":
response = "[Local Message]: jittorllms响应异常 ..." response = "[Local Message] jittorllms响应异常 ..."
history.extend([inputs, response]) history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)

20
request_llm/bridge_jittorllms_rwkv.py → request_llms/bridge_jittorllms_rwkv.py
View File

@ -28,8 +28,8 @@ class GetGLMHandle(Process):
self.success = True self.success = True
except: except:
from toolbox import trimmed_format_exc from toolbox import trimmed_format_exc
self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\ self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\ r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llms/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc() r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
self.success = False self.success = False
@ -45,15 +45,15 @@ class GetGLMHandle(Process):
env = os.environ.get("PATH", "") env = os.environ.get("PATH", "")
os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin') os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..') root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
os.chdir(root_dir_assume + '/request_llm/jittorllms') os.chdir(root_dir_assume + '/request_llms/jittorllms')
sys.path.append(root_dir_assume + '/request_llm/jittorllms') sys.path.append(root_dir_assume + '/request_llms/jittorllms')
validate_path() # validate path so you can run from base directory validate_path() # validate path so you can run from base directory
def load_model(): def load_model():
import types import types
try: try:
if self.jittorllms_model is None: if self.jittorllms_model is None:
device, = get_conf('LOCAL_MODEL_DEVICE') device = get_conf('LOCAL_MODEL_DEVICE')
from .jittorllms.models import get_model from .jittorllms.models import get_model
# availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"] # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
args_dict = {'model': 'chatrwkv'} args_dict = {'model': 'chatrwkv'}
@ -109,7 +109,7 @@ rwkv_glm_handle = None
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
""" """
多线程方法 多线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
global rwkv_glm_handle global rwkv_glm_handle
if rwkv_glm_handle is None: if rwkv_glm_handle is None:
@ -140,7 +140,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
""" """
单线程方法 单线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
chatbot.append((inputs, "")) chatbot.append((inputs, ""))
@ -163,13 +163,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
history_feedin.append([history[2*i], history[2*i+1]] ) history_feedin.append([history[2*i], history[2*i+1]] )
# 开始接收jittorllms的回复 # 开始接收jittorllms的回复
response = "[Local Message]: 等待jittorllms响应中 ..." response = "[Local Message] 等待jittorllms响应中 ..."
for response in rwkv_glm_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']): for response in rwkv_glm_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, response) chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)
# 总结输出 # 总结输出
if response == "[Local Message]: 等待jittorllms响应中 ...": if response == "[Local Message] 等待jittorllms响应中 ...":
response = "[Local Message]: jittorllms响应异常 ..." response = "[Local Message] jittorllms响应异常 ..."
history.extend([inputs, response]) history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)

5
request_llm/bridge_llama2.py → request_llms/bridge_llama2.py
View File

@ -1,18 +1,17 @@
model_name = "LLaMA" model_name = "LLaMA"
cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`" cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from toolbox import update_ui, get_conf, ProxyNetworkActivate from toolbox import update_ui, get_conf, ProxyNetworkActivate
from multiprocessing import Process, Pipe from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
from threading import Thread from threading import Thread
# ------------------------------------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 Local Model # 🔌💻 Local Model
# ------------------------------------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------------
@SingletonLocalLLM
class GetONNXGLMHandle(LocalLLMHandle): class GetONNXGLMHandle(LocalLLMHandle):
def load_model_info(self): def load_model_info(self):

20
request_llm/bridge_moss.py → request_llms/bridge_moss.py
View File

@ -1,8 +1,6 @@
from transformers import AutoModel, AutoTokenizer
import time import time
import threading import threading
import importlib
from toolbox import update_ui, get_conf from toolbox import update_ui, get_conf
from multiprocessing import Process, Pipe from multiprocessing import Process, Pipe
@ -24,12 +22,12 @@ class GetGLMHandle(Process):
def check_dependency(self): # 主进程执行 def check_dependency(self): # 主进程执行
try: try:
import datasets, os import datasets, os
assert os.path.exists('request_llm/moss/models') assert os.path.exists('request_llms/moss/models')
self.info = "依赖检测通过" self.info = "依赖检测通过"
self.success = True self.success = True
except: except:
self.info = """ self.info = """
缺少MOSS的依赖，如果要使用MOSS，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_moss.txt`和`git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss`安装MOSS的依赖。 缺少MOSS的依赖，如果要使用MOSS，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_moss.txt`和`git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss`安装MOSS的依赖。
""" """
self.success = False self.success = False
return self.success return self.success
@ -110,8 +108,8 @@ class GetGLMHandle(Process):
def validate_path(): def validate_path():
import os, sys import os, sys
root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..') root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
os.chdir(root_dir_assume + '/request_llm/moss') os.chdir(root_dir_assume + '/request_llms/moss')
sys.path.append(root_dir_assume + '/request_llm/moss') sys.path.append(root_dir_assume + '/request_llms/moss')
validate_path() # validate path so you can run from base directory validate_path() # validate path so you can run from base directory
try: try:
@ -176,7 +174,7 @@ moss_handle = None
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
""" """
多线程方法 多线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
global moss_handle global moss_handle
if moss_handle is None: if moss_handle is None:
@ -206,7 +204,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
""" """
单线程方法 单线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
chatbot.append((inputs, "")) chatbot.append((inputs, ""))
@ -219,7 +217,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
moss_handle = None moss_handle = None
return return
else: else:
response = "[Local Message]: 等待MOSS响应中 ..." response = "[Local Message] 等待MOSS响应中 ..."
chatbot[-1] = (inputs, response) chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)
@ -238,7 +236,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)
# 总结输出 # 总结输出
if response == "[Local Message]: 等待MOSS响应中 ...": if response == "[Local Message] 等待MOSS响应中 ...":
response = "[Local Message]: MOSS响应异常 ..." response = "[Local Message] MOSS响应异常 ..."
history.extend([inputs, response.strip('<|MOSS|>: ')]) history.extend([inputs, response.strip('<|MOSS|>: ')])
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)

24
request_llm/bridge_newbingfree.py → request_llms/bridge_newbingfree.py
View File

@ -54,7 +54,7 @@ class NewBingHandle(Process):
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.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 self.success = True
except: 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.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_llms/requirements_newbing.txt`安装Newbing的依赖。"
self.success = False self.success = False
def ready(self): def ready(self):
@ -62,8 +62,8 @@ class NewBingHandle(Process):
async def async_run(self): async def async_run(self):
# 读取配置 # 读取配置
NEWBING_STYLE, = get_conf('NEWBING_STYLE') NEWBING_STYLE = get_conf('NEWBING_STYLE')
from request_llm.bridge_all import model_info from request_llms.bridge_all import model_info
endpoint = model_info['newbing']['endpoint'] endpoint = model_info['newbing']['endpoint']
while True: while True:
# 等待 # 等待
@ -141,10 +141,10 @@ class NewBingHandle(Process):
except: except:
self.success = False self.success = False
tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n' 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(f'[Local Message] 不能加载Newbing组件，请注意Newbing组件已不再维护。{tb_str}')
self.child.send('[Fail]') self.child.send('[Fail]')
self.child.send('[Finish]') self.child.send('[Finish]')
raise RuntimeError(f"不能加载Newbing组件。") raise RuntimeError(f"不能加载Newbing组件，请注意Newbing组件已不再维护。")
self.success = True self.success = True
try: try:
@ -181,7 +181,7 @@ newbingfree_handle = None
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
""" """
多线程方法 多线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
global newbingfree_handle global newbingfree_handle
if (newbingfree_handle is None) or (not newbingfree_handle.success): if (newbingfree_handle is None) or (not newbingfree_handle.success):
@ -199,7 +199,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可 watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
response = "" response = ""
if len(observe_window) >= 1: observe_window[0] = "[Local Message]: 等待NewBing响应中 ..." if len(observe_window) >= 1: observe_window[0] = "[Local Message] 等待NewBing响应中 ..."
for response in newbingfree_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']): for response in newbingfree_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']):
if len(observe_window) >= 1: observe_window[0] = preprocess_newbing_out_simple(response) if len(observe_window) >= 1: observe_window[0] = preprocess_newbing_out_simple(response)
if len(observe_window) >= 2: if len(observe_window) >= 2:
@ -210,9 +210,9 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
""" """
单线程方法 单线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
chatbot.append((inputs, "[Local Message]: 等待NewBing响应中 ...")) chatbot.append((inputs, "[Local Message] 等待NewBing响应中 ..."))
global newbingfree_handle global newbingfree_handle
if (newbingfree_handle is None) or (not newbingfree_handle.success): if (newbingfree_handle is None) or (not newbingfree_handle.success):
@ -231,13 +231,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
for i in range(len(history)//2): for i in range(len(history)//2):
history_feedin.append([history[2*i], history[2*i+1]] ) history_feedin.append([history[2*i], history[2*i+1]] )
chatbot[-1] = (inputs, "[Local Message]: 等待NewBing响应中 ...") chatbot[-1] = (inputs, "[Local Message] 等待NewBing响应中 ...")
response = "[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。") 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 newbingfree_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']): for response in newbingfree_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)) 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。") 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 ..." 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]) history.extend([inputs, response])
logging.info(f'[raw_input] {inputs}') logging.info(f'[raw_input] {inputs}')
logging.info(f'[response] {response}') logging.info(f'[response] {response}')

17
request_llm/bridge_qianfan.py → request_llms/bridge_qianfan.py
View File

@ -75,11 +75,12 @@ def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt): def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
BAIDU_CLOUD_QIANFAN_MODEL, = get_conf('BAIDU_CLOUD_QIANFAN_MODEL') BAIDU_CLOUD_QIANFAN_MODEL = get_conf('BAIDU_CLOUD_QIANFAN_MODEL')
url_lib = { url_lib = {
"ERNIE-Bot": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions" , "ERNIE-Bot-4": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro",
"ERNIE-Bot-turbo": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant" , "ERNIE-Bot": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions",
"ERNIE-Bot-turbo": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant",
"BLOOMZ-7B": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/bloomz_7b1", "BLOOMZ-7B": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/bloomz_7b1",
"Llama-2-70B-Chat": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_70b", "Llama-2-70B-Chat": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_70b",
@ -119,7 +120,7 @@ def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
""" """
⭐多线程方法 ⭐多线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
watch_dog_patience = 5 watch_dog_patience = 5
response = "" response = ""
@ -134,7 +135,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
""" """
⭐单线程方法 ⭐单线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
chatbot.append((inputs, "")) chatbot.append((inputs, ""))
@ -158,8 +159,8 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
return return
# 总结输出 # 总结输出
response = f"[Local Message]: {model_name}响应异常 ..." response = f"[Local Message] {model_name}响应异常 ..."
if response == f"[Local Message]: 等待{model_name}响应中 ...": if response == f"[Local Message] 等待{model_name}响应中 ...":
response = f"[Local Message]: {model_name}响应异常 ..." response = f"[Local Message] {model_name}响应异常 ..."
history.extend([inputs, response]) history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)

21
request_llm/bridge_qwen.py → request_llms/bridge_qwen.py
View File

@ -1,21 +1,20 @@
model_name = "Qwen" model_name = "Qwen"
cmd_to_install = "`pip install -r request_llm/requirements_qwen.txt`" cmd_to_install = "`pip install -r request_llms/requirements_qwen.txt`"
from transformers import AutoModel, AutoTokenizer from transformers import AutoModel, AutoTokenizer
import time import time
import threading import threading
import importlib import importlib
from toolbox import update_ui, get_conf from toolbox import update_ui, get_conf, ProxyNetworkActivate
from multiprocessing import Process, Pipe from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
# ------------------------------------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 Local Model # 🔌💻 Local Model
# ------------------------------------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------------
@SingletonLocalLLM
class GetONNXGLMHandle(LocalLLMHandle): class GetONNXGLMHandle(LocalLLMHandle):
def load_model_info(self): def load_model_info(self):
@ -30,13 +29,13 @@ class GetONNXGLMHandle(LocalLLMHandle):
import platform import platform
from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model_id = 'qwen/Qwen-7B-Chat' with ProxyNetworkActivate('Download_LLM'):
revision = 'v1.0.1' model_id = 'qwen/Qwen-7B-Chat'
self._tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, trust_remote_code=True) self._tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen-7B-Chat', trust_remote_code=True, resume_download=True)
# use fp16 # use fp16
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", revision=revision, trust_remote_code=True, fp16=True).eval() model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, fp16=True).eval()
model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参 model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
self._model = model self._model = model
return self._model, self._tokenizer return self._model, self._tokenizer

12
request_llm/bridge_spark.py → request_llms/bridge_spark.py
View File

@ -8,7 +8,7 @@ from multiprocessing import Process, Pipe
model_name = '星火认知大模型' model_name = '星火认知大模型'
def validate_key(): def validate_key():
XFYUN_APPID, = get_conf('XFYUN_APPID', ) XFYUN_APPID = get_conf('XFYUN_APPID')
if XFYUN_APPID == '00000000' or XFYUN_APPID == '': if XFYUN_APPID == '00000000' or XFYUN_APPID == '':
return False return False
return True return True
@ -16,7 +16,7 @@ def validate_key():
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
""" """
⭐多线程方法 ⭐多线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
watch_dog_patience = 5 watch_dog_patience = 5
response = "" response = ""
@ -36,13 +36,13 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
""" """
⭐单线程方法 ⭐单线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
chatbot.append((inputs, "")) chatbot.append((inputs, ""))
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)
if validate_key() is False: if validate_key() is False:
yield from update_ui_lastest_msg(lastmsg="[Local Message]: 请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET", chatbot=chatbot, history=history, delay=0) yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET", chatbot=chatbot, history=history, delay=0)
return return
if additional_fn is not None: if additional_fn is not None:
@ -57,7 +57,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)
# 总结输出 # 总结输出
if response == f"[Local Message]: 等待{model_name}响应中 ...": if response == f"[Local Message] 等待{model_name}响应中 ...":
response = f"[Local Message]: {model_name}响应异常 ..." response = f"[Local Message] {model_name}响应异常 ..."
history.extend([inputs, response]) history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history) yield from update_ui(chatbot=chatbot, history=history)

26
request_llm/bridge_stackclaude.py → request_llms/bridge_stackclaude.py
View File

@ -36,7 +36,7 @@ try:
CHANNEL_ID = None CHANNEL_ID = None
async def open_channel(self): async def open_channel(self):
response = await self.conversations_open(users=get_conf('SLACK_CLAUDE_BOT_ID')[0]) response = await self.conversations_open(users=get_conf('SLACK_CLAUDE_BOT_ID'))
self.CHANNEL_ID = response["channel"]["id"] self.CHANNEL_ID = response["channel"]["id"]
async def chat(self, text): async def chat(self, text):
@ -51,7 +51,7 @@ try:
# TODO：暂时不支持历史消息，因为在同一个频道里存在多人使用时历史消息渗透问题 # TODO：暂时不支持历史消息，因为在同一个频道里存在多人使用时历史消息渗透问题
resp = await self.conversations_history(channel=self.CHANNEL_ID, oldest=self.LAST_TS, limit=1) resp = await self.conversations_history(channel=self.CHANNEL_ID, oldest=self.LAST_TS, limit=1)
msg = [msg for msg in resp["messages"] msg = [msg for msg in resp["messages"]
if msg.get("user") == get_conf('SLACK_CLAUDE_BOT_ID')[0]] if msg.get("user") == get_conf('SLACK_CLAUDE_BOT_ID')]
return msg return msg
except (SlackApiError, KeyError) as e: except (SlackApiError, KeyError) as e:
raise RuntimeError(f"获取Slack消息失败。") raise RuntimeError(f"获取Slack消息失败。")
@ -99,7 +99,7 @@ class ClaudeHandle(Process):
self.info = "依赖检测通过，等待Claude响应。注意目前不能多人同时调用Claude接口（有线程锁），否则将导致每个人的Claude问询历史互相渗透。调用Claude时，会自动使用已配置的代理。" self.info = "依赖检测通过，等待Claude响应。注意目前不能多人同时调用Claude接口（有线程锁），否则将导致每个人的Claude问询历史互相渗透。调用Claude时，会自动使用已配置的代理。"
self.success = True self.success = True
except: except:
self.info = "缺少的依赖，如果要使用Claude，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_slackclaude.txt`安装Claude的依赖，然后重启程序。" self.info = "缺少的依赖，如果要使用Claude，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_slackclaude.txt`安装Claude的依赖，然后重启程序。"
self.success = False self.success = False
def ready(self): def ready(self):
@ -146,14 +146,14 @@ class ClaudeHandle(Process):
self.local_history = [] self.local_history = []
if (self.claude_model is None) or (not self.success): if (self.claude_model is None) or (not self.success):
# 代理设置 # 代理设置
proxies, = get_conf('proxies') proxies = get_conf('proxies')
if proxies is None: if proxies is None:
self.proxies_https = None self.proxies_https = None
else: else:
self.proxies_https = proxies['https'] self.proxies_https = proxies['https']
try: try:
SLACK_CLAUDE_USER_TOKEN, = get_conf('SLACK_CLAUDE_USER_TOKEN') SLACK_CLAUDE_USER_TOKEN = get_conf('SLACK_CLAUDE_USER_TOKEN')
self.claude_model = SlackClient(token=SLACK_CLAUDE_USER_TOKEN, proxy=self.proxies_https) self.claude_model = SlackClient(token=SLACK_CLAUDE_USER_TOKEN, proxy=self.proxies_https)
print('Claude组件初始化成功。') print('Claude组件初始化成功。')
except: except:
@ -204,7 +204,7 @@ claude_handle = None
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False): def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
""" """
多线程方法 多线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
global claude_handle global claude_handle
if (claude_handle is None) or (not claude_handle.success): if (claude_handle is None) or (not claude_handle.success):
@ -222,7 +222,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可 watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
response = "" response = ""
observe_window[0] = "[Local Message]: 等待Claude响应中 ..." observe_window[0] = "[Local Message] 等待Claude响应中 ..."
for response in claude_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']): for response in claude_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) observe_window[0] = preprocess_newbing_out_simple(response)
if len(observe_window) >= 2: if len(observe_window) >= 2:
@ -234,9 +234,9 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None): def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
""" """
单线程方法 单线程方法
函数的说明请见 request_llm/bridge_all.py 函数的说明请见 request_llms/bridge_all.py
""" """
chatbot.append((inputs, "[Local Message]: 等待Claude响应中 ...")) chatbot.append((inputs, "[Local Message] 等待Claude响应中 ..."))
global claude_handle global claude_handle
if (claude_handle is None) or (not claude_handle.success): if (claude_handle is None) or (not claude_handle.success):
@ -255,14 +255,14 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
for i in range(len(history)//2): for i in range(len(history)//2):
history_feedin.append([history[2*i], history[2*i+1]]) history_feedin.append([history[2*i], history[2*i+1]])
chatbot[-1] = (inputs, "[Local Message]: 等待Claude响应中 ...") chatbot[-1] = (inputs, "[Local Message] 等待Claude响应中 ...")
response = "[Local Message]: 等待Claude响应中 ..." response = "[Local Message] 等待Claude响应中 ..."
yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。") yield from update_ui(chatbot=chatbot, history=history, msg="Claude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 claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt): for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt):
chatbot[-1] = (inputs, preprocess_newbing_out(response)) chatbot[-1] = (inputs, preprocess_newbing_out(response))
yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。") yield from update_ui(chatbot=chatbot, history=history, msg="Claude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Claude响应中 ...": if response == "[Local Message] 等待Claude响应中 ...":
response = "[Local Message]: Claude响应异常，请刷新界面重试 ..." response = "[Local Message] Claude响应异常，请刷新界面重试 ..."
history.extend([inputs, response]) history.extend([inputs, response])
logging.info(f'[raw_input] {inputs}') logging.info(f'[raw_input] {inputs}')
logging.info(f'[response] {response}') logging.info(f'[response] {response}')

0
request_llm/bridge_tgui.py → request_llms/bridge_tgui.py
View File

59
request_llms/bridge_zhipu.py Normal file
View File

@ -0,0 +1,59 @@
import time
from toolbox import update_ui, get_conf, update_ui_lastest_msg
model_name = '智谱AI大模型'
def validate_key():
ZHIPUAI_API_KEY = get_conf("ZHIPUAI_API_KEY")
if ZHIPUAI_API_KEY == '': return False
return True
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
"""
⭐多线程方法
函数的说明请见 request_llms/bridge_all.py
"""
watch_dog_patience = 5
response = ""
if validate_key() is False:
raise RuntimeError('请配置ZHIPUAI_API_KEY')
from .com_zhipuapi import ZhipuRequestInstance
sri = ZhipuRequestInstance()
for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
if len(observe_window) >= 1:
observe_window[0] = response
if len(observe_window) >= 2:
if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
return 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_llms/bridge_all.py
"""
chatbot.append((inputs, ""))
yield from update_ui(chatbot=chatbot, history=history)
if validate_key() is False:
yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置ZHIPUAI_API_KEY", chatbot=chatbot, history=history, delay=0)
return
if additional_fn is not None:
from core_functional import handle_core_functionality
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
# 开始接收回复
from .com_zhipuapi import ZhipuRequestInstance
sri = ZhipuRequestInstance()
for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history)
# 总结输出
if response == f"[Local Message] 等待{model_name}响应中 ...":
response = f"[Local Message] {model_name}响应异常 ..."
history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history)

0
request_llm/chatglmoonx.py → request_llms/chatglmoonx.py
View File

10
request_llm/com_sparkapi.py → request_llms/com_sparkapi.py
View File

@ -64,6 +64,7 @@ class SparkRequestInstance():
self.api_key = XFYUN_API_KEY self.api_key = XFYUN_API_KEY
self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat" self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat"
self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat" self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat"
self.gpt_url_v3 = "ws://spark-api.xf-yun.com/v3.1/chat"
self.time_to_yield_event = threading.Event() self.time_to_yield_event = threading.Event()
self.time_to_exit_event = threading.Event() self.time_to_exit_event = threading.Event()
@ -87,6 +88,8 @@ class SparkRequestInstance():
def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt): def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt):
if llm_kwargs['llm_model'] == 'sparkv2': if llm_kwargs['llm_model'] == 'sparkv2':
gpt_url = self.gpt_url_v2 gpt_url = self.gpt_url_v2
elif llm_kwargs['llm_model'] == 'sparkv3':
gpt_url = self.gpt_url_v3
else: else:
gpt_url = self.gpt_url gpt_url = self.gpt_url
@ -168,6 +171,11 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
""" """
通过appid和用户的提问来生成请参数 通过appid和用户的提问来生成请参数
""" """
domains = {
"spark": "general",
"sparkv2": "generalv2",
"sparkv3": "generalv3",
}
data = { data = {
"header": { "header": {
"app_id": appid, "app_id": appid,
@ -175,7 +183,7 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
}, },
"parameter": { "parameter": {
"chat": { "chat": {
"domain": "generalv2" if llm_kwargs['llm_model'] == 'sparkv2' else "general", "domain": domains[llm_kwargs['llm_model']],
"temperature": llm_kwargs["temperature"], "temperature": llm_kwargs["temperature"],
"random_threshold": 0.5, "random_threshold": 0.5,
"max_tokens": 4096, "max_tokens": 4096,

67
request_llms/com_zhipuapi.py Normal file
View File

@ -0,0 +1,67 @@
from toolbox import get_conf
import threading
import logging
timeout_bot_msg = '[Local Message] Request timeout. Network error.'
class ZhipuRequestInstance():
def __init__(self):
self.time_to_yield_event = threading.Event()
self.time_to_exit_event = threading.Event()
self.result_buf = ""
def generate(self, inputs, llm_kwargs, history, system_prompt):
# import _thread as thread
import zhipuai
ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
zhipuai.api_key = ZHIPUAI_API_KEY
self.result_buf = ""
response = zhipuai.model_api.sse_invoke(
model=ZHIPUAI_MODEL,
prompt=generate_message_payload(inputs, llm_kwargs, history, system_prompt),
top_p=llm_kwargs['top_p'],
temperature=llm_kwargs['temperature'],
)
for event in response.events():
if event.event == "add":
self.result_buf += event.data
yield self.result_buf
elif event.event == "error" or event.event == "interrupted":
raise RuntimeError("Unknown error:" + event.data)
elif event.event == "finish":
yield self.result_buf
break
else:
raise RuntimeError("Unknown error:" + str(event))
logging.info(f'[raw_input] {inputs}')
logging.info(f'[response] {self.result_buf}')
return self.result_buf
def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
conversation_cnt = len(history) // 2
messages = [{"role": "user", "content": system_prompt}, {"role": "assistant", "content": "Certainly!"}]
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
if what_gpt_answer["content"] == timeout_bot_msg:
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)
return messages

0
request_llm/edge_gpt_free.py → request_llms/edge_gpt_free.py
View File

29
request_llms/key_manager.py Normal file
View File

@ -0,0 +1,29 @@
import random
def Singleton(cls):
_instance = {}
def _singleton(*args, **kargs):
if cls not in _instance:
_instance[cls] = cls(*args, **kargs)
return _instance[cls]
return _singleton
@Singleton
class OpenAI_ApiKeyManager():
def __init__(self, mode='blacklist') -> None:
# self.key_avail_list = []
self.key_black_list = []
def add_key_to_blacklist(self, key):
self.key_black_list.append(key)
def select_avail_key(self, key_list):
# select key from key_list, but avoid keys also in self.key_black_list, raise error if no key can be found
available_keys = [key for key in key_list if key not in self.key_black_list]
if not available_keys:
raise KeyError("No available key found.")
selected_key = random.choice(available_keys)
return selected_key

319
request_llms/local_llm_class.py Normal file
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@ -0,0 +1,319 @@
import time
import threading
from toolbox import update_ui, Singleton
from multiprocessing import Process, Pipe
from contextlib import redirect_stdout
from request_llms.queued_pipe import create_queue_pipe
class ThreadLock(object):
def __init__(self):
self._lock = threading.Lock()
def acquire(self):
# print("acquiring", self)
#traceback.print_tb
self._lock.acquire()
# print("acquired", self)
def release(self):
# print("released", self)
#traceback.print_tb
self._lock.release()
def __enter__(self):
self.acquire()
def __exit__(self, type, value, traceback):
self.release()
@Singleton
class GetSingletonHandle():
def __init__(self):
self.llm_model_already_running = {}
def get_llm_model_instance(self, cls, *args, **kargs):
if cls not in self.llm_model_already_running:
self.llm_model_already_running[cls] = cls(*args, **kargs)
return self.llm_model_already_running[cls]
elif self.llm_model_already_running[cls].corrupted:
self.llm_model_already_running[cls] = cls(*args, **kargs)
return self.llm_model_already_running[cls]
else:
return self.llm_model_already_running[cls]
def reset_tqdm_output():
import sys, tqdm
def status_printer(self, file):
fp = file
if fp in (sys.stderr, sys.stdout):
getattr(sys.stderr, 'flush', lambda: None)()
getattr(sys.stdout, 'flush', lambda: None)()
def fp_write(s):
print(s)
last_len = [0]
def print_status(s):
from tqdm.utils import disp_len
len_s = disp_len(s)
fp_write('\r' + s + (' ' * max(last_len[0] - len_s, 0)))
last_len[0] = len_s
return print_status
tqdm.tqdm.status_printer = status_printer
class LocalLLMHandle(Process):
def __init__(self):
# ⭐run in main process
super().__init__(daemon=True)
self.is_main_process = True # init
self.corrupted = False
self.load_model_info()
self.parent, self.child = create_queue_pipe()
self.parent_state, self.child_state = create_queue_pipe()
# allow redirect_stdout
self.std_tag = "[Subprocess Message] "
self.running = True
self._model = None
self._tokenizer = None
self.state = ""
self.check_dependency()
self.is_main_process = False # state wrap for child process
self.start()
self.is_main_process = True # state wrap for child process
self.threadLock = ThreadLock()
def get_state(self):
# ⭐run in main process
while self.parent_state.poll():
self.state = self.parent_state.recv()
return self.state
def set_state(self, new_state):
# ⭐run in main process or 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process
if self.is_main_process:
self.state = new_state
else:
self.child_state.send(new_state)
def load_model_info(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process
raise NotImplementedError("Method not implemented yet")
self.model_name = ""
self.cmd_to_install = ""
def load_model_and_tokenizer(self):
"""
This function should return the model and the tokenizer
"""
# 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process
raise NotImplementedError("Method not implemented yet")
def llm_stream_generator(self, **kwargs):
# 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process
raise NotImplementedError("Method not implemented yet")
def try_to_import_special_deps(self, **kwargs):
"""
import something that will raise error if the user does not install requirement_*.txt
"""
# ⭐run in main process
raise NotImplementedError("Method not implemented yet")
def check_dependency(self):
# ⭐run in main process
try:
self.try_to_import_special_deps()
self.set_state("`依赖检测通过`")
self.running = True
except:
self.set_state(f"缺少{self.model_name}的依赖，如果要使用{self.model_name}，除了基础的pip依赖以外，您还需要运行{self.cmd_to_install}安装{self.model_name}的依赖。")
self.running = False
def run(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process
# 第一次运行，加载参数
self.child.flush = lambda *args: None
self.child.write = lambda x: self.child.send(self.std_tag + x)
reset_tqdm_output()
self.set_state("`尝试加载模型`")
try:
with redirect_stdout(self.child):
self._model, self._tokenizer = self.load_model_and_tokenizer()
except:
self.set_state("`加载模型失败`")
self.running = False
from toolbox import trimmed_format_exc
self.child.send(
f'[Local Message] 不能正常加载{self.model_name}的参数.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
self.child.send('[FinishBad]')
raise RuntimeError(f"不能正常加载{self.model_name}的参数！")
self.set_state("`准备就绪`")
while True:
# 进入任务等待状态
kwargs = self.child.recv()
# 收到消息，开始请求
try:
for response_full in self.llm_stream_generator(**kwargs):
self.child.send(response_full)
# print('debug' + response_full)
self.child.send('[Finish]')
# 请求处理结束，开始下一个循环
except:
from toolbox import trimmed_format_exc
self.child.send(
f'[Local Message] 调用{self.model_name}失败.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
self.child.send('[Finish]')
def clear_pending_messages(self):
# ⭐run in main process
while True:
if self.parent.poll():
self.parent.recv()
continue
for _ in range(5):
time.sleep(0.5)
if self.parent.poll():
r = self.parent.recv()
continue
break
return
def stream_chat(self, **kwargs):
# ⭐run in main process
if self.get_state() == "`准备就绪`":
yield "`正在等待线程锁，排队中请稍后 ...`"
with self.threadLock:
if self.parent.poll():
yield "`排队中请稍后 ...`"
self.clear_pending_messages()
self.parent.send(kwargs)
std_out = ""
std_out_clip_len = 4096
while True:
res = self.parent.recv()
# pipe_watch_dog.feed()
if res.startswith(self.std_tag):
new_output = res[len(self.std_tag):]
std_out = std_out[:std_out_clip_len]
# print(new_output, end='')
std_out = new_output + std_out
yield self.std_tag + '\n```\n' + std_out + '\n```\n'
elif res == '[Finish]':
break
elif res == '[FinishBad]':
self.running = False
self.corrupted = True
break
else:
std_out = ""
yield res
def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='classic'):
load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
"""
refer to request_llms/bridge_all.py
"""
_llm_handle = GetSingletonHandle().get_llm_model_instance(LLMSingletonClass)
if len(observe_window) >= 1:
observe_window[0] = load_message + "\n\n" + _llm_handle.get_state()
if not _llm_handle.running:
raise RuntimeError(_llm_handle.get_state())
if history_format == 'classic':
# 没有 sys_prompt 接口，因此把prompt加入 history
history_feedin = []
history_feedin.append([sys_prompt, "Certainly!"])
for i in range(len(history)//2):
history_feedin.append([history[2*i], history[2*i+1]])
elif history_format == 'chatglm3':
# 有 sys_prompt 接口
conversation_cnt = len(history) // 2
history_feedin = [{"role": "system", "content": sys_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
history_feedin.append(what_i_have_asked)
history_feedin.append(what_gpt_answer)
else:
history_feedin[-1]['content'] = what_gpt_answer['content']
watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
response = ""
for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
if len(observe_window) >= 1:
observe_window[0] = response
if len(observe_window) >= 2:
if (time.time()-observe_window[1]) > watch_dog_patience:
raise RuntimeError("程序终止。")
return response
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
"""
refer to request_llms/bridge_all.py
"""
chatbot.append((inputs, ""))
_llm_handle = GetSingletonHandle().get_llm_model_instance(LLMSingletonClass)
chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.get_state())
yield from update_ui(chatbot=chatbot, history=[])
if not _llm_handle.running:
raise RuntimeError(_llm_handle.get_state())
if additional_fn is not None:
from core_functional import handle_core_functionality
inputs, history = handle_core_functionality(
additional_fn, inputs, history, chatbot)
# 处理历史信息
if history_format == 'classic':
# 没有 sys_prompt 接口，因此把prompt加入 history
history_feedin = []
history_feedin.append([system_prompt, "Certainly!"])
for i in range(len(history)//2):
history_feedin.append([history[2*i], history[2*i+1]])
elif history_format == 'chatglm3':
# 有 sys_prompt 接口
conversation_cnt = len(history) // 2
history_feedin = [{"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
history_feedin.append(what_i_have_asked)
history_feedin.append(what_gpt_answer)
else:
history_feedin[-1]['content'] = what_gpt_answer['content']
# 开始接收回复
response = f"[Local Message] 等待{model_name}响应中 ..."
for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history)
# 总结输出
if response == f"[Local Message] 等待{model_name}响应中 ...":
response = f"[Local Message] {model_name}响应异常 ..."
history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history)
return predict_no_ui_long_connection, predict

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