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"""
该文件中主要包含2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：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 tiktoken
from concurrent.futures import ThreadPoolExecutor
from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui
from .bridge_chatgpt import predict as chatgpt_ui
from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
from .bridge_chatglm import predict as chatglm_ui
from .bridge_tgui import predict_no_ui_long_connection as tgui_noui
from .bridge_tgui import predict as tgui_ui
colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
get_token_num_gpt35 = lambda txt: len(tiktoken.encoding_for_model("gpt-3.5-turbo").encode(txt, disallowed_special=()))
get_token_num_gpt4 = lambda txt: len(tiktoken.encoding_for_model("gpt-4").encode(txt, disallowed_special=()))
model_info = {
# openai
"gpt-3.5-turbo": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": "https://api.openai.com/v1/chat/completions",
"max_token": 4096,
"tokenizer": tiktoken.encoding_for_model("gpt-3.5-turbo"),
"token_cnt": get_token_num_gpt35,
},
"gpt-4": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": "https://api.openai.com/v1/chat/completions",
"max_token": 8192,
"tokenizer": tiktoken.encoding_for_model("gpt-4"),
"token_cnt": get_token_num_gpt4,
},
# api_2d
"api2d-gpt-3.5-turbo": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": "https://openai.api2d.net/v1/chat/completions",
"max_token": 4096,
"tokenizer": tiktoken.encoding_for_model("gpt-3.5-turbo"),
"token_cnt": get_token_num_gpt35,
},
"api2d-gpt-4": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": "https://openai.api2d.net/v1/chat/completions",
"max_token": 8192,
"tokenizer": tiktoken.encoding_for_model("gpt-4"),
"token_cnt": get_token_num_gpt4,
},
# chatglm
"chatglm": {
"fn_with_ui": chatglm_ui,
"fn_without_ui": chatglm_noui,
"endpoint": None,
"max_token": 1024,
"tokenizer": tiktoken.encoding_for_model("gpt-3.5-turbo"),
"token_cnt": get_token_num_gpt35,
},
}
def LLM_CATCH_EXCEPTION(f):
"""
装饰器函数，将错误显示出来
"""
def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):
try:
return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
except Exception as e:
from toolbox import get_conf
import traceback
proxies, = get_conf('proxies')
tb_str = '\n```\n' + traceback.format_exc() + '\n```\n'
observe_window[0] = tb_str
return tb_str
return decorated
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。
inputs：
是本次问询的输入
sys_prompt:
系统静默prompt
llm_kwargs：
LLM的内部调优参数
history：
是之前的对话列表
observe_window = None：
用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
"""
import threading, time, copy
model = llm_kwargs['llm_model']
n_model = 1
if '&' not in model:
assert not model.startswith("tgui"), "TGUI不支持函数插件的实现"
# 如果只询问1个大语言模型：
method = model_info[model]["fn_without_ui"]
return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
else:
# 如果同时询问多个大语言模型：
executor = ThreadPoolExecutor(max_workers=4)
models = model.split('&')
n_model = len(models)
window_len = len(observe_window)
assert window_len==3
window_mutex = [["", time.time(), ""] for _ in range(n_model)] + [True]
futures = []
for i in range(n_model):
model = models[i]
method = model_info[model]["fn_without_ui"]
llm_kwargs_feedin = copy.deepcopy(llm_kwargs)
llm_kwargs_feedin['llm_model'] = model
future = executor.submit(LLM_CATCH_EXCEPTION(method), inputs, llm_kwargs_feedin, history, sys_prompt, window_mutex[i], console_slience)
futures.append(future)
def mutex_manager(window_mutex, observe_window):
while True:
time.sleep(0.5)
if not window_mutex[-1]: break
# 看门狗（watchdog）
for i in range(n_model):
window_mutex[i][1] = observe_window[1]
# 观察窗（window）
chat_string = []
for i in range(n_model):
chat_string.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {window_mutex[i][0]} </font>" )
res = '<br/><br/>\n\n---\n\n'.join(chat_string)
# # # # # # # # # # #
observe_window[0] = res
t_model = threading.Thread(target=mutex_manager, args=(window_mutex, observe_window), daemon=True)
t_model.start()
return_string_collect = []
while True:
worker_done = [h.done() for h in futures]
if all(worker_done):
executor.shutdown()
break
time.sleep(1)
for i, future in enumerate(futures): # wait and get
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)
return res
def predict(inputs, llm_kwargs, *args, **kwargs):
"""
发送至LLM，流式获取输出。
用于基础的对话功能。
inputs 是本次问询的输入
top_p, temperature是LLM的内部调优参数
history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
additional_fn代表点击的哪个按钮，按钮见functional.py
"""
method = model_info[llm_kwargs['llm_model']]["fn_with_ui"]
yield from method(inputs, llm_kwargs, *args, **kwargs)
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