镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 14:36:48 +00:00
Merge branch 'master' into huggingface
这个提交包含在：
binary-husky
1
.gitignore
vendored
1
.gitignore
vendored
@@ -145,3 +145,4 @@ cradle*
|
||||
debug*
|
||||
private*
|
||||
crazy_functions/test_project/pdf_and_word
|
||||
crazy_functions/test_samples
|
||||
|
||||
21
README.md
21
README.md
@@ -32,20 +32,20 @@ If you like this project, please give it a Star. If you've come up with more use
|
||||
一键中英互译 | 一键中英互译
|
||||
一键代码解释 | 可以正确显示代码、解释代码
|
||||
[自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键
|
||||
[配置代理服务器](https://www.bilibili.com/video/BV1rc411W7Dr) | 支持配置代理服务器
|
||||
模块化设计 | 支持自定义高阶的函数插件与[函数插件]，插件支持[热更新](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
|
||||
[配置代理服务器](https://www.bilibili.com/video/BV1rc411W7Dr) | 支持代理连接OpenAI/Google等，秒解锁ChatGPT互联网[实时信息聚合](https://www.bilibili.com/video/BV1om4y127ck/)能力
|
||||
模块化设计 | 支持自定义强大的[函数插件](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions)，插件支持[热更新](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
|
||||
[自我程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] [一键读懂](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)本项目的源代码
|
||||
[程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] 一键可以剖析其他Python/C/C++/Java/Lua/...项目树
|
||||
读论文 | [函数插件] 一键解读latex论文全文并生成摘要
|
||||
Latex全文翻译、润色 | [函数插件] 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 | [函数插件] 一键批量生成函数注释
|
||||
chat分析报告生成 | [函数插件] 运行后自动生成总结汇报
|
||||
Markdown中英互译 | [函数插件] 看到上面5种语言的[README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)了吗？
|
||||
Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [函数插件] 看到上面5种语言的[README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)了吗？
|
||||
[arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [函数插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
|
||||
[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）
|
||||
[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帮你选择有趣的文章
|
||||
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，支持公式、代码高亮
|
||||
多线程函数插件支持 | 支持多线调用chatgpt，一键处理海量文本或程序
|
||||
[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/)
|
||||
公式/图片/表格显示 | 可以同时显示公式的[tex形式和渲染形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)，支持公式、代码高亮
|
||||
多线程函数插件支持 | 支持多线调用chatgpt，一键处理[海量文本](https://www.bilibili.com/video/BV1FT411H7c5/)或程序
|
||||
启动暗色gradio[主题](https://github.com/binary-husky/chatgpt_academic/issues/173) | 在浏览器url后面添加```/?__dark-theme=true```可以切换dark主题
|
||||
[多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持，[API2D](https://api2d.com/)接口支持 | 同时被GPT3.5、GPT4和[清华ChatGLM](https://github.com/THUDM/ChatGLM-6B)伺候的感觉一定会很不错吧？
|
||||
huggingface免科学上网[在线体验](https://huggingface.co/spaces/qingxu98/gpt-academic) | 登陆huggingface后复制[此空间](https://huggingface.co/spaces/qingxu98/gpt-academic)
|
||||
@@ -183,6 +183,8 @@ docker run --rm -it --net=host --gpus=all gpt-academic bash
|
||||
2. 使用WSL2（Windows Subsystem for Linux 子系统）
|
||||
请访问[部署wiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
|
||||
|
||||
3. 如何在二级网址（如`http://localhost/subpath`）下运行
|
||||
请访问[FastAPI运行说明](docs/WithFastapi.md)
|
||||
|
||||
## 安装-代理配置
|
||||
1. 常规方法
|
||||
@@ -278,12 +280,15 @@ docker run --rm -it --net=host --gpus=all gpt-academic bash
|
||||
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/233575247-fb00819e-6d1b-4bb7-bd54-1d7528f03dd9.png" width="800" >
|
||||
<img src="https://user-images.githubusercontent.com/96192199/233779501-5ce826f0-6cca-4d59-9e5f-b4eacb8cc15f.png" width="800" >
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
## Todo 与 版本规划:
|
||||
- version 3.2+ (todo): 函数插件支持更多参数接口
|
||||
- version 3.3+ (todo): NewBing支持
|
||||
- version 3.2: 函数插件支持更多参数接口 (保存对话功能, 解读任意语言代码+同时询问任意的LLM组合)
|
||||
- version 3.1: 支持同时问询多个gpt模型！支持api2d，支持多个apikey负载均衡
|
||||
- version 3.0: 对chatglm和其他小型llm的支持
|
||||
- version 2.6: 重构了插件结构，提高了交互性，加入更多插件
|
||||
|
||||
@@ -57,6 +57,9 @@ CONCURRENT_COUNT = 100
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||||
# [("username", "password"), ("username2", "password2"), ...]
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||||
AUTHENTICATION = []
|
||||
|
||||
# 重新URL重新定向，实现更换API_URL的作用（常规情况下，不要修改！！）
|
||||
# 重新URL重新定向，实现更换API_URL的作用（常规情况下，不要修改!!）
|
||||
# 格式 {"https://api.openai.com/v1/chat/completions": "重定向的URL"}
|
||||
API_URL_REDIRECT = {}
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||||
|
||||
# 如果需要在二级路径下运行（常规情况下，不要修改!!）（需要配合修改main.py才能生效!）
|
||||
CUSTOM_PATH = "/"
|
||||
|
||||
@@ -19,12 +19,25 @@ def get_crazy_functions():
|
||||
from crazy_functions.解析项目源代码 import 解析一个Lua项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个CSharp项目
|
||||
from crazy_functions.总结word文档 import 总结word文档
|
||||
from crazy_functions.解析JupyterNotebook import 解析ipynb文件
|
||||
from crazy_functions.对话历史存档 import 对话历史存档
|
||||
function_plugins = {
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||||
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||||
"解析整个Python项目": {
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||||
"Color": "stop", # 按钮颜色
|
||||
"Function": HotReload(解析一个Python项目)
|
||||
},
|
||||
"保存当前的对话": {
|
||||
"AsButton":False,
|
||||
"Function": HotReload(对话历史存档)
|
||||
},
|
||||
"[测试功能] 解析Jupyter Notebook文件": {
|
||||
"Color": "stop",
|
||||
"AsButton":False,
|
||||
"Function": HotReload(解析ipynb文件),
|
||||
"AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
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||||
"ArgsReminder": "若输入0，则不解析notebook中的Markdown块", # 高级参数输入区的显示提示
|
||||
},
|
||||
"批量总结Word文档": {
|
||||
"Color": "stop",
|
||||
"Function": HotReload(总结word文档)
|
||||
@@ -191,5 +204,25 @@ def get_crazy_functions():
|
||||
}
|
||||
})
|
||||
|
||||
from crazy_functions.解析项目源代码 import 解析任意code项目
|
||||
function_plugins.update({
|
||||
"解析项目源代码（手动指定和筛选源代码文件类型）": {
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
|
||||
"ArgsReminder": "输入时用逗号隔开, *代表通配符, 加了^代表不匹配; 不输入代表全部匹配。例如: \"*.c, ^*.cpp, config.toml, ^*.toml\"", # 高级参数输入区的显示提示
|
||||
"Function": HotReload(解析任意code项目)
|
||||
},
|
||||
})
|
||||
from crazy_functions.询问多个大语言模型 import 同时问询_指定模型
|
||||
function_plugins.update({
|
||||
"询问多个GPT模型（手动指定询问哪些模型）": {
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
|
||||
"ArgsReminder": "支持任意数量的llm接口，用&符号分隔。例如chatglm&gpt-3.5-turbo&api2d-gpt-4", # 高级参数输入区的显示提示
|
||||
"Function": HotReload(同时问询_指定模型)
|
||||
},
|
||||
})
|
||||
###################### 第n组插件 ###########################
|
||||
return function_plugins
|
||||
|
||||
@@ -108,6 +108,13 @@ def test_联网回答问题():
|
||||
print("当前问答：", cb[-1][-1].replace("\n"," "))
|
||||
for i, it in enumerate(cb): print亮蓝(it[0]); print亮黄(it[1])
|
||||
|
||||
def test_解析ipynb文件():
|
||||
from crazy_functions.解析JupyterNotebook import 解析ipynb文件
|
||||
txt = "crazy_functions/test_samples"
|
||||
for cookies, cb, hist, msg in 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
print(cb)
|
||||
|
||||
|
||||
# test_解析一个Python项目()
|
||||
# test_Latex英文润色()
|
||||
# test_Markdown中译英()
|
||||
@@ -116,9 +123,8 @@ def test_联网回答问题():
|
||||
# test_总结word文档()
|
||||
# test_下载arxiv论文并翻译摘要()
|
||||
# test_解析一个Cpp项目()
|
||||
|
||||
test_联网回答问题()
|
||||
|
||||
# test_联网回答问题()
|
||||
test_解析ipynb文件()
|
||||
|
||||
input("程序完成，回车退出。")
|
||||
print("退出。")
|
||||
42
crazy_functions/对话历史存档.py
普通文件
42
crazy_functions/对话历史存档.py
普通文件
@@ -0,0 +1,42 @@
|
||||
from toolbox import CatchException, update_ui
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
|
||||
def write_chat_to_file(chatbot, file_name=None):
|
||||
"""
|
||||
将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
|
||||
"""
|
||||
import os
|
||||
import time
|
||||
if file_name is None:
|
||||
file_name = 'chatGPT对话历史' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.html'
|
||||
os.makedirs('./gpt_log/', exist_ok=True)
|
||||
with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
|
||||
for i, contents in enumerate(chatbot):
|
||||
for content in contents:
|
||||
try: # 这个bug没找到触发条件，暂时先这样顶一下
|
||||
if type(content) != str: content = str(content)
|
||||
except:
|
||||
continue
|
||||
f.write(content)
|
||||
f.write('\n\n')
|
||||
f.write('<hr color="red"> \n\n')
|
||||
|
||||
res = '对话历史写入：' + os.path.abspath(f'./gpt_log/{file_name}')
|
||||
print(res)
|
||||
return res
|
||||
|
||||
@CatchException
|
||||
def 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数，暂时没有用武之地
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
web_port 当前软件运行的端口号
|
||||
"""
|
||||
|
||||
chatbot.append(("保存当前对话", f"[Local Message] {write_chat_to_file(chatbot)}"))
|
||||
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
|
||||
|
||||
145
crazy_functions/解析JupyterNotebook.py
普通文件
145
crazy_functions/解析JupyterNotebook.py
普通文件
@@ -0,0 +1,145 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_execption, write_results_to_file
|
||||
fast_debug = True
|
||||
|
||||
|
||||
class PaperFileGroup():
|
||||
def __init__(self):
|
||||
self.file_paths = []
|
||||
self.file_contents = []
|
||||
self.sp_file_contents = []
|
||||
self.sp_file_index = []
|
||||
self.sp_file_tag = []
|
||||
|
||||
# count_token
|
||||
from request_llm.bridge_all import model_info
|
||||
enc = model_info["gpt-3.5-turbo"]['tokenizer']
|
||||
def get_token_num(txt): return len(
|
||||
enc.encode(txt, disallowed_special=()))
|
||||
self.get_token_num = get_token_num
|
||||
|
||||
def run_file_split(self, max_token_limit=1900):
|
||||
"""
|
||||
将长文本分离开来
|
||||
"""
|
||||
for index, file_content in enumerate(self.file_contents):
|
||||
if self.get_token_num(file_content) < max_token_limit:
|
||||
self.sp_file_contents.append(file_content)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index])
|
||||
else:
|
||||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
segments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
file_content, self.get_token_num, max_token_limit)
|
||||
for j, segment in enumerate(segments):
|
||||
self.sp_file_contents.append(segment)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(
|
||||
self.file_paths[index] + f".part-{j}.txt")
|
||||
|
||||
|
||||
|
||||
def parseNotebook(filename, enable_markdown=1):
|
||||
import json
|
||||
|
||||
CodeBlocks = []
|
||||
with open(filename, 'r', encoding='utf-8', errors='replace') as f:
|
||||
notebook = json.load(f)
|
||||
for cell in notebook['cells']:
|
||||
if cell['cell_type'] == 'code' and cell['source']:
|
||||
# remove blank lines
|
||||
cell['source'] = [line for line in cell['source'] if line.strip()
|
||||
!= '']
|
||||
CodeBlocks.append("".join(cell['source']))
|
||||
elif enable_markdown and cell['cell_type'] == 'markdown' and cell['source']:
|
||||
cell['source'] = [line for line in cell['source'] if line.strip()
|
||||
!= '']
|
||||
CodeBlocks.append("Markdown:"+"".join(cell['source']))
|
||||
|
||||
Code = ""
|
||||
for idx, code in enumerate(CodeBlocks):
|
||||
Code += f"This is {idx+1}th code block: \n"
|
||||
Code += code+"\n"
|
||||
|
||||
return Code
|
||||
|
||||
|
||||
def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
|
||||
enable_markdown = plugin_kwargs.get("advanced_arg", "1")
|
||||
try:
|
||||
enable_markdown = int(enable_markdown)
|
||||
except ValueError:
|
||||
enable_markdown = 1
|
||||
|
||||
pfg = PaperFileGroup()
|
||||
|
||||
for fp in file_manifest:
|
||||
file_content = parseNotebook(fp, enable_markdown=enable_markdown)
|
||||
pfg.file_paths.append(fp)
|
||||
pfg.file_contents.append(file_content)
|
||||
|
||||
# <-------- 拆分过长的IPynb文件 ---------->
|
||||
pfg.run_file_split(max_token_limit=1024)
|
||||
n_split = len(pfg.sp_file_contents)
|
||||
|
||||
inputs_array = [r"This is a Jupyter Notebook file, tell me about Each Block in Chinese. Focus Just On Code." +
|
||||
r"If a block starts with `Markdown` which means it's a markdown block in ipynbipynb. " +
|
||||
r"Start a new line for a block and block num use Chinese." +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"{f}的分析如下" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array = ["You are a professional programmer."] * n_split
|
||||
|
||||
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array=inputs_array,
|
||||
inputs_show_user_array=inputs_show_user_array,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history_array=[[""] for _ in range(n_split)],
|
||||
sys_prompt_array=sys_prompt_array,
|
||||
# max_workers=5, # OpenAI所允许的最大并行过载
|
||||
scroller_max_len=80
|
||||
)
|
||||
|
||||
# <-------- 整理结果，退出 ---------->
|
||||
block_result = " \n".join(gpt_response_collection)
|
||||
chatbot.append(("解析的结果如下", block_result))
|
||||
history.extend(["解析的结果如下", block_result])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# <-------- 写入文件，退出 ---------->
|
||||
res = write_results_to_file(history)
|
||||
chatbot.append(("完成了吗？", res))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
@CatchException
|
||||
def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对IPynb文件进行解析。Contributor: codycjy."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
history = [] # 清空历史
|
||||
import glob
|
||||
import os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "":
|
||||
txt = '空空如也的输入栏'
|
||||
report_execption(chatbot, history,
|
||||
a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
if txt.endswith('.ipynb'):
|
||||
file_manifest = [txt]
|
||||
else:
|
||||
file_manifest = [f for f in glob.glob(
|
||||
f'{project_folder}/**/*.ipynb', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_execption(chatbot, history,
|
||||
a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, )
|
||||
@@ -264,3 +264,44 @@ def 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
|
||||
@CatchException
|
||||
def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
txt_pattern = plugin_kwargs.get("advanced_arg")
|
||||
txt_pattern = txt_pattern.replace("，", ",")
|
||||
# 将要匹配的模式(例如: *.c, *.cpp, *.py, config.toml)
|
||||
pattern_include = [_.lstrip(" ,").rstrip(" ,") for _ in txt_pattern.split(",") if _ != "" and not _.strip().startswith("^")]
|
||||
if not pattern_include: pattern_include = ["*"] # 不输入即全部匹配
|
||||
# 将要忽略匹配的文件后缀(例如: ^*.c, ^*.cpp, ^*.py)
|
||||
pattern_except_suffix = [_.lstrip(" ^*.,").rstrip(" ,") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^*.")]
|
||||
pattern_except_suffix += ['zip', 'rar', '7z', 'tar', 'gz'] # 避免解析压缩文件
|
||||
# 将要忽略匹配的文件名(例如: ^README.md)
|
||||
pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", "\.") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^") and not _.strip().startswith("^*.")]
|
||||
# 生成正则表达式
|
||||
pattern_except = '/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
|
||||
pattern_except += '|/(' + "|".join(pattern_except_name) + ')$' if pattern_except_name != [] else ''
|
||||
|
||||
history.clear()
|
||||
import glob, os, re
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
# 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
|
||||
maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
|
||||
if len(maybe_dir)>0 and maybe_dir[0].endswith('.extract'):
|
||||
extract_folder_path = maybe_dir[0]
|
||||
else:
|
||||
extract_folder_path = project_folder
|
||||
# 按输入的匹配模式寻找上传的非压缩文件和已解压的文件
|
||||
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]))]
|
||||
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}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
@@ -28,3 +28,32 @@ def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
|
||||
history.append(txt)
|
||||
history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
|
||||
@CatchException
|
||||
def 同时问询_指定模型(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数，如温度和top_p等，一般原样传递下去就行
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
web_port 当前软件运行的端口号
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……"))
|
||||
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'] = plugin_kwargs.get("advanced_arg", 'chatglm&gpt-3.5-turbo') # 'chatglm&gpt-3.5-turbo' # 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(
|
||||
inputs=txt, inputs_show_user=txt,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
sys_prompt=system_prompt,
|
||||
retry_times_at_unknown_error=0
|
||||
)
|
||||
|
||||
history.append(txt)
|
||||
history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
@@ -70,6 +70,7 @@ def 谷歌检索小助手(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
|
||||
try:
|
||||
import arxiv
|
||||
import math
|
||||
from bs4 import BeautifulSoup
|
||||
except:
|
||||
report_execption(chatbot, history,
|
||||
@@ -80,25 +81,26 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
|
||||
meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
|
||||
batchsize = 5
|
||||
for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):
|
||||
if len(meta_paper_info_list[:batchsize]) > 0:
|
||||
i_say = "下面是一些学术文献的数据，提取出以下内容：" + \
|
||||
"1、英文题目；2、中文题目翻译；3、作者；4、arxiv公开（is_paper_in_arxiv）；4、引用数量（cite）；5、中文摘要翻译。" + \
|
||||
f"以下是信息源：{str(meta_paper_info_list[:batchsize])}"
|
||||
|
||||
if len(meta_paper_info_list[:10]) > 0:
|
||||
i_say = "下面是一些学术文献的数据，请从中提取出以下内容。" + \
|
||||
"1、英文题目；2、中文题目翻译；3、作者；4、arxiv公开（is_paper_in_arxiv）；4、引用数量（cite）；5、中文摘要翻译。" + \
|
||||
f"以下是信息源：{str(meta_paper_info_list[:10])}"
|
||||
inputs_show_user = f"请分析此页面中出现的所有文章：{txt}，这是第{batch+1}批"
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=inputs_show_user,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt="你是一个学术翻译，请从数据中提取信息。你必须使用Markdown表格。你必须逐个文献进行处理。"
|
||||
)
|
||||
|
||||
inputs_show_user = f"请分析此页面中出现的所有文章：{txt}"
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=inputs_show_user,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt="你是一个学术翻译，请从数据中提取信息。你必须使用Markdown格式。你必须逐个文献进行处理。"
|
||||
)
|
||||
history.extend([ f"第{batch+1}批", gpt_say ])
|
||||
meta_paper_info_list = meta_paper_info_list[batchsize:]
|
||||
|
||||
history.extend([ "第一批", gpt_say ])
|
||||
meta_paper_info_list = meta_paper_info_list[10:]
|
||||
|
||||
chatbot.append(["状态？", "已经全部完成"])
|
||||
chatbot.append(["状态？",
|
||||
"已经全部完成，您可以试试让AI写一个Related Works，例如您可以继续输入Write a \"Related Works\" section about \"你搜索的研究领域\" for me."])
|
||||
msg = '正常'
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
res = write_results_to_file(history)
|
||||
|
||||
43
docs/WithFastapi.md
普通文件
43
docs/WithFastapi.md
普通文件
@@ -0,0 +1,43 @@
|
||||
# Running with fastapi
|
||||
|
||||
We currently support fastapi in order to solve sub-path deploy issue.
|
||||
|
||||
1. change CUSTOM_PATH setting in `config.py`
|
||||
|
||||
``` sh
|
||||
nano config.py
|
||||
```
|
||||
|
||||
2. Edit main.py
|
||||
|
||||
```diff
|
||||
auto_opentab_delay()
|
||||
- demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
|
||||
+ demo.queue(concurrency_count=CONCURRENT_COUNT)
|
||||
|
||||
- # 如果需要在二级路径下运行
|
||||
- # CUSTOM_PATH, = get_conf('CUSTOM_PATH')
|
||||
- # if CUSTOM_PATH != "/":
|
||||
- # from toolbox import run_gradio_in_subpath
|
||||
- # run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
|
||||
- # else:
|
||||
- # 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
|
||||
+ CUSTOM_PATH, = get_conf('CUSTOM_PATH')
|
||||
+ if CUSTOM_PATH != "/":
|
||||
+ from toolbox import run_gradio_in_subpath
|
||||
+ run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
|
||||
+ else:
|
||||
+ demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
```
|
||||
|
||||
|
||||
3. Go!
|
||||
|
||||
``` sh
|
||||
python main.py
|
||||
```
|
||||
31
main.py
31
main.py
@@ -89,9 +89,12 @@ def main():
|
||||
with gr.Row():
|
||||
with gr.Accordion("更多函数插件", open=True):
|
||||
dropdown_fn_list = [k for k in crazy_fns.keys() if not crazy_fns[k].get("AsButton", True)]
|
||||
with gr.Column(scale=1):
|
||||
with gr.Row():
|
||||
dropdown = gr.Dropdown(dropdown_fn_list, value=r"打开插件列表", label="").style(container=False)
|
||||
with gr.Column(scale=1):
|
||||
with gr.Row():
|
||||
plugin_advanced_arg = gr.Textbox(show_label=True, label="高级参数输入区", visible=False,
|
||||
placeholder="这里是特殊函数插件的高级参数输入区").style(container=False)
|
||||
with gr.Row():
|
||||
switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary")
|
||||
with gr.Row():
|
||||
with gr.Accordion("点击展开“文件上传区”。上传本地文件可供红色函数插件调用。", open=False) as area_file_up:
|
||||
@@ -101,7 +104,7 @@ def main():
|
||||
top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
|
||||
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=4096, value=512, step=1, interactive=True, label="Local LLM MaxLength",)
|
||||
checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区", "输入清除键"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
|
||||
checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区", "输入清除键", "插件参数区"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
|
||||
md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
|
||||
|
||||
gr.Markdown(description)
|
||||
@@ -123,11 +126,12 @@ def main():
|
||||
ret.update({area_input_secondary: gr.update(visible=("底部输入区" in a))})
|
||||
ret.update({clearBtn: gr.update(visible=("输入清除键" in a))})
|
||||
ret.update({clearBtn2: gr.update(visible=("输入清除键" in a))})
|
||||
ret.update({plugin_advanced_arg: gr.update(visible=("插件参数区" in a))})
|
||||
if "底部输入区" in a: ret.update({txt: gr.update(value="")})
|
||||
return ret
|
||||
checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, clearBtn, clearBtn2] )
|
||||
checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, clearBtn, clearBtn2, plugin_advanced_arg] )
|
||||
# 整理反复出现的控件句柄组合
|
||||
input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt]
|
||||
input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg]
|
||||
output_combo = [cookies, chatbot, history, status]
|
||||
predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=input_combo, outputs=output_combo)
|
||||
# 提交按钮、重置按钮
|
||||
@@ -154,8 +158,13 @@ def main():
|
||||
# 函数插件-下拉菜单与随变按钮的互动
|
||||
def on_dropdown_changed(k):
|
||||
variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
|
||||
return {switchy_bt: gr.update(value=k, variant=variant)}
|
||||
dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt] )
|
||||
ret = {switchy_bt: gr.update(value=k, variant=variant)}
|
||||
if crazy_fns[k].get("AdvancedArgs", False): # 是否唤起高级插件参数区
|
||||
ret.update({plugin_advanced_arg: gr.update(visible=True, label=f"插件[{k}]的高级参数说明：" + crazy_fns[k].get("ArgsReminder", [f"没有提供高级参数功能说明"]))})
|
||||
else:
|
||||
ret.update({plugin_advanced_arg: gr.update(visible=False, label=f"插件[{k}]不需要高级参数。")})
|
||||
return ret
|
||||
dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt, plugin_advanced_arg] )
|
||||
def on_md_dropdown_changed(k):
|
||||
return {chatbot: gr.update(label="当前模型："+k)}
|
||||
md_dropdown.select(on_md_dropdown_changed, [md_dropdown], [chatbot] )
|
||||
@@ -189,5 +198,13 @@ def main():
|
||||
auto_opentab_delay()
|
||||
demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", share=False, favicon_path="docs/logo.png")
|
||||
|
||||
# 如果需要在二级路径下运行
|
||||
# CUSTOM_PATH, = get_conf('CUSTOM_PATH')
|
||||
# if CUSTOM_PATH != "/":
|
||||
# from toolbox import run_gradio_in_subpath
|
||||
# run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
|
||||
# else:
|
||||
# demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
# 如何使用其他大语言模型（v3.0分支测试中）
|
||||
# 如何使用其他大语言模型
|
||||
|
||||
## ChatGLM
|
||||
|
||||
@@ -15,7 +15,7 @@ LLM_MODEL = "chatglm"
|
||||
|
||||
|
||||
---
|
||||
## Text-Generation-UI (TGUI)
|
||||
## Text-Generation-UI (TGUI，调试中，暂不可用)
|
||||
|
||||
### 1. 部署TGUI
|
||||
``` sh
|
||||
|
||||
@@ -1,12 +1,12 @@
|
||||
|
||||
"""
|
||||
该文件中主要包含2个函数
|
||||
该文件中主要包含2个函数，是所有LLM的通用接口，它们会继续向下调用更底层的LLM模型，处理多模型并行等细节
|
||||
|
||||
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
|
||||
不具备多线程能力的函数：正常对话时使用，具备完备的交互功能，不可多线程
|
||||
1. predict(...)
|
||||
|
||||
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
|
||||
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(...)
|
||||
"""
|
||||
import tiktoken
|
||||
from functools import lru_cache
|
||||
@@ -210,7 +210,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
|
||||
return_string_collect.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {future.result()} </font>" )
|
||||
|
||||
window_mutex[-1] = False # stop mutex thread
|
||||
res = '<br/>\n\n---\n\n'.join(return_string_collect)
|
||||
res = '<br/><br/>\n\n---\n\n'.join(return_string_collect)
|
||||
return res
|
||||
|
||||
|
||||
|
||||
@@ -32,6 +32,7 @@ class GetGLMHandle(Process):
|
||||
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
|
||||
while True:
|
||||
@@ -53,17 +54,24 @@ class GetGLMHandle(Process):
|
||||
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！")
|
||||
|
||||
# 进入任务等待状态
|
||||
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:
|
||||
self.child.send('[Local Message] Call ChatGLM fail.')
|
||||
# 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.parent.send(kwargs)
|
||||
while True:
|
||||
res = self.parent.recv()
|
||||
@@ -92,8 +100,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
|
||||
# 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(["What can I do?", sys_prompt] )
|
||||
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
|
||||
@@ -130,11 +138,17 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||
|
||||
# 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(["What can I do?", system_prompt] )
|
||||
history_feedin.append([history[2*i], history[2*i+1]] )
|
||||
|
||||
# 开始接收chatglm的回复
|
||||
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
|
||||
history.extend([inputs, response])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
@@ -21,7 +21,7 @@ import importlib
|
||||
|
||||
# config_private.py放自己的秘密如API和代理网址
|
||||
# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
|
||||
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys
|
||||
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
|
||||
proxies, API_KEY, TIMEOUT_SECONDS, MAX_RETRY = \
|
||||
get_conf('proxies', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY')
|
||||
|
||||
@@ -145,7 +145,7 @@ 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
|
||||
return
|
||||
|
||||
history.append(inputs); history.append(" ")
|
||||
history.append(inputs); history.append("")
|
||||
|
||||
retry = 0
|
||||
while True:
|
||||
@@ -198,14 +198,17 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
chunk_decoded = chunk.decode()
|
||||
error_msg = chunk_decoded
|
||||
if "reduce the length" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长，或历史数据过长. 历史缓存数据现已释放，您可以请再次尝试.")
|
||||
history = [] # 清除历史
|
||||
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.)")
|
||||
# history = [] # 清除历史
|
||||
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.")
|
||||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
|
||||
elif "Incorrect API key" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由，拒绝服务.")
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务.")
|
||||
elif "exceeded your current quota" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由，拒绝服务.")
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务.")
|
||||
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:
|
||||
|
||||
84
toolbox.py
84
toolbox.py
@@ -24,7 +24,7 @@ def ArgsGeneralWrapper(f):
|
||||
"""
|
||||
装饰器函数，用于重组输入参数，改变输入参数的顺序与结构。
|
||||
"""
|
||||
def decorated(cookies, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, *args):
|
||||
def decorated(cookies, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg, *args):
|
||||
txt_passon = txt
|
||||
if txt == "" and txt2 != "": txt_passon = txt2
|
||||
# 引入一个有cookie的chatbot
|
||||
@@ -40,7 +40,7 @@ def ArgsGeneralWrapper(f):
|
||||
'temperature':temperature,
|
||||
}
|
||||
plugin_kwargs = {
|
||||
# 目前还没有
|
||||
"advanced_arg": plugin_advanced_arg,
|
||||
}
|
||||
chatbot_with_cookie = ChatBotWithCookies(cookies)
|
||||
chatbot_with_cookie.write_list(chatbot)
|
||||
@@ -520,3 +520,83 @@ class DummyWith():
|
||||
|
||||
def __exit__(self, exc_type, exc_value, traceback):
|
||||
return
|
||||
|
||||
def run_gradio_in_subpath(demo, auth, port, custom_path):
|
||||
def is_path_legal(path: str)->bool:
|
||||
'''
|
||||
check path for sub url
|
||||
path: path to check
|
||||
return value: do sub url wrap
|
||||
'''
|
||||
if path == "/": return True
|
||||
if len(path) == 0:
|
||||
print("ilegal custom path: {}\npath must not be empty\ndeploy on root url".format(path))
|
||||
return False
|
||||
if path[0] == '/':
|
||||
if path[1] != '/':
|
||||
print("deploy on sub-path {}".format(path))
|
||||
return True
|
||||
return False
|
||||
print("ilegal custom path: {}\npath should begin with \'/\'\ndeploy on root url".format(path))
|
||||
return False
|
||||
|
||||
if not is_path_legal(custom_path): raise RuntimeError('Ilegal custom path')
|
||||
import uvicorn
|
||||
import gradio as gr
|
||||
from fastapi import FastAPI
|
||||
app = FastAPI()
|
||||
if custom_path != "/":
|
||||
@app.get("/")
|
||||
def read_main():
|
||||
return {"message": f"Gradio is running at: {custom_path}"}
|
||||
app = gr.mount_gradio_app(app, demo, path=custom_path)
|
||||
uvicorn.run(app, host="0.0.0.0", port=port) # , auth=auth
|
||||
|
||||
|
||||
def clip_history(inputs, history, tokenizer, max_token_limit):
|
||||
"""
|
||||
reduce the length of history by clipping.
|
||||
this function search for the longest entries to clip, little by little,
|
||||
until the number of token of history is reduced under threshold.
|
||||
通过裁剪来缩短历史记录的长度。
|
||||
此函数逐渐地搜索最长的条目进行剪辑，
|
||||
直到历史记录的标记数量降低到阈值以下。
|
||||
"""
|
||||
import numpy as np
|
||||
from request_llm.bridge_all import model_info
|
||||
def get_token_num(txt):
|
||||
return len(tokenizer.encode(txt, disallowed_special=()))
|
||||
input_token_num = get_token_num(inputs)
|
||||
if input_token_num < max_token_limit * 3 / 4:
|
||||
# 当输入部分的token占比小于限制的3/4时，裁剪时
|
||||
# 1. 把input的余量留出来
|
||||
max_token_limit = max_token_limit - input_token_num
|
||||
# 2. 把输出用的余量留出来
|
||||
max_token_limit = max_token_limit - 128
|
||||
# 3. 如果余量太小了，直接清除历史
|
||||
if max_token_limit < 128:
|
||||
history = []
|
||||
return history
|
||||
else:
|
||||
# 当输入部分的token占比 > 限制的3/4时，直接清除历史
|
||||
history = []
|
||||
return history
|
||||
|
||||
everything = ['']
|
||||
everything.extend(history)
|
||||
n_token = get_token_num('\n'.join(everything))
|
||||
everything_token = [get_token_num(e) for e in everything]
|
||||
|
||||
# 截断时的颗粒度
|
||||
delta = max(everything_token) // 16
|
||||
|
||||
while n_token > max_token_limit:
|
||||
where = np.argmax(everything_token)
|
||||
encoded = tokenizer.encode(everything[where], disallowed_special=())
|
||||
clipped_encoded = encoded[:len(encoded)-delta]
|
||||
everything[where] = tokenizer.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char
|
||||
everything_token[where] = get_token_num(everything[where])
|
||||
n_token = get_token_num('\n'.join(everything))
|
||||
|
||||
history = everything[1:]
|
||||
return history
|
||||
|
||||
4
version
4
version
@@ -1,5 +1,5 @@
|
||||
{
|
||||
"version": 3.1,
|
||||
"version": 3.2,
|
||||
"show_feature": true,
|
||||
"new_feature": "添加支持清华ChatGLM和GPT-4 <-> 改进架构，支持与多个LLM模型同时对话 <-> 添加支持API2D（国内，可支持gpt4）<-> 支持多API-KEY负载均衡（并列填写，逗号分割） <-> 添加输入区文本清除按键"
|
||||
"new_feature": "保存对话功能 <-> 解读任意语言代码+同时询问任意的LLM组合 <-> 添加联网（Google）回答问题插件 <-> 修复ChatGLM上下文BUG <-> 添加支持清华ChatGLM和GPT-4 <-> 改进架构，支持与多个LLM模型同时对话 <-> 添加支持API2D（国内，可支持gpt4）"
|
||||
}
|
||||
|
||||
在新工单中引用
屏蔽一个用户