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紫东太初大模型

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binary-husky
2024-06-06 09:05:06 +00:00
parent 3d5790cc2c
commit 24a21ae320
6 changed files with 151 additions and 5 deletions
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6
config.py
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@@ -229,9 +229,15 @@ MOONSHOT_API_KEY = ""
# 零一万物(Yi Model) API KEY
YIMODEL_API_KEY = ""
# 深度求索(DeepSeek) API KEY，默认请求地址为"https://api.deepseek.com/v1/chat/completions"
DEEPSEEK_API_KEY = ""
# 紫东太初
TAICHU_API_KEY = ""
# Mathpix 拥有执行PDF的OCR功能，但是需要注册账号
MATHPIX_APPID = ""
MATHPIX_APPKEY = ""

6
crazy_functions/Latex_Function.py
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@@ -233,7 +233,7 @@ def pdf2tex_project(pdf_file_path, plugin_kwargs):
def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
# <-------------- information about this plugin ------------->
chatbot.append(["函数插件功能？",
"对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。"])
"对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
# <-------------- more requirements ------------->
@@ -310,7 +310,7 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
# <-------------- information about this plugin ------------->
chatbot.append([
"函数插件功能？",
"对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
"对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
# <-------------- more requirements ------------->
@@ -404,7 +404,7 @@ def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, h
# <-------------- information about this plugin ------------->
chatbot.append([
"函数插件功能？",
"将PDF转换为Latex项目，翻译为中文后重新编译为PDF。函数插件贡献者: Marroh。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
"将PDF转换为Latex项目，翻译为中文后重新编译为PDF。函数插件贡献者: Marroh。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
# <-------------- more requirements ------------->

12
request_llms/bridge_all.py
View File

@@ -34,6 +34,9 @@ from .bridge_google_gemini import predict_no_ui_long_connection as genai_noui
from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
from .bridge_zhipu import predict as zhipu_ui
from .bridge_taichu import predict_no_ui_long_connection as taichu_noui
from .bridge_taichu import predict as taichu_ui
from .bridge_cohere import predict as cohere_ui
from .bridge_cohere import predict_no_ui_long_connection as cohere_noui
@@ -116,6 +119,15 @@ model_info = {
"token_cnt": get_token_num_gpt35,
},
"taichu": {
"fn_with_ui": taichu_ui,
"fn_without_ui": taichu_noui,
"endpoint": openai_endpoint,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gpt-3.5-turbo-16k": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,

72
request_llms/bridge_taichu.py Normal file
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@@ -0,0 +1,72 @@
import time
import os
from toolbox import update_ui, get_conf, update_ui_lastest_msg, log_chat
from toolbox import check_packages, report_exception, have_any_recent_upload_image_files
from toolbox import ChatBotWithCookies
model_name = 'Taichu-2.0'
taichu_default_model = 'taichu_llm'
def validate_key():
TAICHU_API_KEY = get_conf("TAICHU_API_KEY")
if TAICHU_API_KEY == '': return False
return True
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
observe_window:list=[], console_slience:bool=False):
"""
⭐多线程方法
函数的说明请见 request_llms/bridge_all.py
"""
watch_dog_patience = 5
response = ""
if llm_kwargs["llm_model"] == "taichu":
llm_kwargs["llm_model"] = taichu_default_model
if validate_key() is False:
raise RuntimeError('请配置 TAICHU_API_KEY')
# 开始接收回复
from .com_taichu import TaichuChatInit
zhipu_bro_init = TaichuChatInit()
for chunk, response in zhipu_bro_init.generate_chat(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:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=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)
chatbot[-1] = [inputs, ""]
yield from update_ui(chatbot=chatbot, history=history)
if llm_kwargs["llm_model"] == "taichu":
llm_kwargs["llm_model"] = taichu_default_model
# 开始接收回复
from .com_taichu import TaichuChatInit
zhipu_bro_init = TaichuChatInit()
for chunk, response in zhipu_bro_init.generate_chat(inputs, llm_kwargs, history, system_prompt):
chatbot[-1] = [inputs, response]
yield from update_ui(chatbot=chatbot, history=history)
history.extend([inputs, response])
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
yield from update_ui(chatbot=chatbot, history=history)

55
request_llms/com_taichu.py Normal file
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@@ -0,0 +1,55 @@
# encoding: utf-8
# @Time : 2024/1/22
# @Author : Kilig947 & binary husky
# @Descr : 兼容最新的智谱Ai
from toolbox import get_conf
from toolbox import get_conf, encode_image, get_pictures_list
import logging, os, requests
import json
class TaichuChatInit:
def __init__(self): ...
def __conversation_user(self, user_input: str, llm_kwargs:dict):
return {"role": "user", "content": user_input}
def __conversation_history(self, history:list, llm_kwargs:dict):
messages = []
conversation_cnt = len(history) // 2
if conversation_cnt:
for index in range(0, 2 * conversation_cnt, 2):
what_i_have_asked = self.__conversation_user(history[index], llm_kwargs)
what_gpt_answer = {
"role": "assistant",
"content": history[index + 1]
}
messages.append(what_i_have_asked)
messages.append(what_gpt_answer)
return messages
def generate_chat(self, inputs:str, llm_kwargs:dict, history:list, system_prompt:str):
TAICHU_API_KEY = get_conf("TAICHU_API_KEY")
params = {
'api_key': TAICHU_API_KEY,
'model_code': 'taichu_llm',
'question': '\n\n'.join(history) + inputs,
'prefix': system_prompt,
'temperature': llm_kwargs.get('temperature', 0.95),
'stream_format': 'json'
}
api = 'https://ai-maas.wair.ac.cn/maas/v1/model_api/invoke'
response = requests.post(api, json=params, stream=True)
results = ""
if response.status_code == 200:
response.encoding = 'utf-8'
for line in response.iter_lines(decode_unicode=True):
delta = json.loads(line)['choices'][0]['text']
results += delta
yield delta, results
else:
raise ValueError
if __name__ == '__main__':
zhipu = TaichuChatInit()
zhipu.generate_chat('你好', {'llm_model': 'glm-4'}, [], '你是WPSAi')

5
tests/test_llms.py
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@@ -14,12 +14,13 @@ validate_path() # validate path so you can run from base directory
if "在线模型":
if __name__ == "__main__":
from request_llms.bridge_cohere import predict_no_ui_long_connection
from request_llms.bridge_taichu import predict_no_ui_long_connection
# from request_llms.bridge_cohere import predict_no_ui_long_connection
# from request_llms.bridge_spark import predict_no_ui_long_connection
# from request_llms.bridge_zhipu import predict_no_ui_long_connection
# from request_llms.bridge_chatglm3 import predict_no_ui_long_connection
llm_kwargs = {
"llm_model": "command-r-plus",
"llm_model": "taichu",
"max_length": 4096,
"top_p": 1,
"temperature": 1,