更多模型切换
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@ -46,14 +46,12 @@ WEB_PORT = -1
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MAX_RETRY = 2
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# OpenAI模型选择是（gpt4现在只对申请成功的人开放）
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LLM_MODEL = "gpt-3.5-turbo" # 可选 "chatglm", "tgui:anymodel@localhost:7865"
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LLM_MODEL = "gpt-3.5-turbo" # 可选 "chatglm"
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AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "chatglm", "gpt-4", "api2d-gpt-4", "api2d-gpt-3.5-turbo"]
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# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
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LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
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# OpenAI的API_URL
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API_URL = "https://api.openai.com/v1/chat/completions"
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# 设置gradio的并行线程数（不需要修改）
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CONCURRENT_COUNT = 100
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6
main.py
6
main.py
@ -5,8 +5,8 @@ def main():
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from request_llm.bridge_all import predict
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from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
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# 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
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proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
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get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
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proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS = \
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get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS')
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# 如果WEB_PORT是-1, 则随机选取WEB端口
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PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
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@ -101,7 +101,7 @@ def main():
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temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
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max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label="MaxLength",)
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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")
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md_dropdown = gr.Dropdown(["gpt-3.5-turbo", "chatglm"], value=LLM_MODEL, label="").style(container=False)
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md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="").style(container=False)
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gr.Markdown(description)
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with gr.Accordion("备选输入区", open=True, visible=False) as area_input_secondary:
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@ -21,38 +21,42 @@ from .bridge_chatglm import predict as chatglm_ui
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from .bridge_tgui import predict_no_ui_long_connection as tgui_noui
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from .bridge_tgui import predict as tgui_ui
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methods = {
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"openai-no-ui": chatgpt_noui,
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"openai-ui": chatgpt_ui,
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"chatglm-no-ui": chatglm_noui,
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"chatglm-ui": chatglm_ui,
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"tgui-no-ui": tgui_noui,
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"tgui-ui": tgui_ui,
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}
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colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
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model_info = {
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# openai
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"gpt-3.5-turbo": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": "https://api.openai.com/v1/chat/completions",
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"max_token": 4096,
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"tokenizer": tiktoken.encoding_for_model("gpt-3.5-turbo"),
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"token_cnt": lambda txt: len(tiktoken.encoding_for_model("gpt-3.5-turbo").encode(txt, disallowed_special=())),
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},
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"gpt-4": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": "https://api.openai.com/v1/chat/completions",
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"max_token": 4096,
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"tokenizer": tiktoken.encoding_for_model("gpt-4"),
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"token_cnt": lambda txt: len(tiktoken.encoding_for_model("gpt-4").encode(txt, disallowed_special=())),
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},
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# api_2d
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"gpt-3.5-turbo-api2d": {
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"api2d-gpt-3.5-turbo": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": "https://openai.api2d.net/v1/chat/completions",
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"max_token": 4096,
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"tokenizer": tiktoken.encoding_for_model("gpt-3.5-turbo"),
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"token_cnt": lambda txt: len(tiktoken.encoding_for_model("gpt-3.5-turbo").encode(txt, disallowed_special=())),
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},
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"gpt-4-api2d": {
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"api2d-gpt-4": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": "https://openai.api2d.net/v1/chat/completions",
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"max_token": 4096,
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"tokenizer": tiktoken.encoding_for_model("gpt-4"),
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"token_cnt": lambda txt: len(tiktoken.encoding_for_model("gpt-4").encode(txt, disallowed_special=())),
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@ -60,18 +64,20 @@ model_info = {
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# chatglm
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"chatglm": {
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"fn_with_ui": chatglm_ui,
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"fn_without_ui": chatglm_noui,
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"endpoint": None,
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"max_token": 1024,
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"tokenizer": tiktoken.encoding_for_model("gpt-3.5-turbo"),
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"token_cnt": lambda txt: len(tiktoken.encoding_for_model("gpt-3.5-turbo").encode(txt, disallowed_special=())),
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},
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}
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def LLM_CATCH_EXCEPTION(f):
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"""
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装饰器函数，将错误显示出来
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装饰器函数，将错误显示出来
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"""
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def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):
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try:
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@ -85,21 +91,20 @@ def LLM_CATCH_EXCEPTION(f):
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return tb_str
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return decorated
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colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
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def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience=False):
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"""
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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。
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inputs：
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是本次问询的输入
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sys_prompt:
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系统静默prompt
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llm_kwargs：
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LLM的内部调优参数
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history：
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是之前的对话列表
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observe_window = None：
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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
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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。
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inputs：
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是本次问询的输入
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sys_prompt:
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系统静默prompt
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llm_kwargs：
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LLM的内部调优参数
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history：
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是之前的对话列表
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observe_window = None：
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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
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"""
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import threading, time, copy
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@ -109,12 +114,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
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assert not model.startswith("tgui"), "TGUI不支持函数插件的实现"
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# 如果只询问1个大语言模型：
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if model.startswith('gpt'):
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method = methods['openai-no-ui']
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elif model == 'chatglm':
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method = methods['chatglm-no-ui']
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elif model.startswith('tgui'):
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method = methods['tgui-no-ui']
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method = model_info[model]["fn_without_ui"]
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return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
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else:
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# 如果同时询问多个大语言模型：
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@ -129,12 +129,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
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futures = []
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for i in range(n_model):
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model = models[i]
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if model.startswith('gpt'):
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method = methods['openai-no-ui']
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elif model == 'chatglm':
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method = methods['chatglm-no-ui']
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elif model.startswith('tgui'):
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method = methods['tgui-no-ui']
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method = model_info[model]["fn_without_ui"]
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llm_kwargs_feedin = copy.deepcopy(llm_kwargs)
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llm_kwargs_feedin['llm_model'] = model
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future = executor.submit(LLM_CATCH_EXCEPTION(method), inputs, llm_kwargs_feedin, history, sys_prompt, window_mutex[i], console_slience)
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@ -176,20 +171,15 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
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def predict(inputs, llm_kwargs, *args, **kwargs):
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"""
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发送至LLM，流式获取输出。
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用于基础的对话功能。
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inputs 是本次问询的输入
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top_p, temperature是LLM的内部调优参数
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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）
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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
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additional_fn代表点击的哪个按钮，按钮见functional.py
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发送至LLM，流式获取输出。
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用于基础的对话功能。
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inputs 是本次问询的输入
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top_p, temperature是LLM的内部调优参数
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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）
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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
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additional_fn代表点击的哪个按钮，按钮见functional.py
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"""
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if llm_kwargs['llm_model'].startswith('gpt'):
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method = methods['openai-ui']
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elif llm_kwargs['llm_model'] == 'chatglm':
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method = methods['chatglm-ui']
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elif llm_kwargs['llm_model'].startswith('tgui'):
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method = methods['tgui-ui']
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method = model_info[llm_kwargs['llm_model']]["fn_with_ui"]
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yield from method(inputs, llm_kwargs, *args, **kwargs)
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@ -21,9 +21,9 @@ import importlib
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# config_private.py放自己的秘密如API和代理网址
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# 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
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from toolbox import get_conf, update_ui
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proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY = \
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get_conf('proxies', 'API_URL', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY')
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from toolbox import get_conf, update_ui, is_any_api_key, select_api_key
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proxies, API_KEY, TIMEOUT_SECONDS, MAX_RETRY = \
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get_conf('proxies', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY')
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timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
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'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。'
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@ -60,7 +60,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
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while True:
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try:
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# make a POST request to the API endpoint, stream=False
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response = requests.post(API_URL, headers=headers, proxies=proxies,
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response = requests.post(llm_kwargs['endpoint'], headers=headers, proxies=proxies,
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json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
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except requests.exceptions.ReadTimeout as e:
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retry += 1
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@ -113,14 +113,14 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
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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
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additional_fn代表点击的哪个按钮，按钮见functional.py
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"""
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if inputs.startswith('sk-') and len(inputs) == 51:
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if is_any_api_key(inputs):
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chatbot._cookies['api_key'] = inputs
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chatbot.append(("输入已识别为openai的api_key", "api_key已导入"))
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yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
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return
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elif len(chatbot._cookies['api_key']) != 51:
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elif not is_any_api_key(chatbot._cookies['api_key']):
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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。"))
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yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
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yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
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return
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if additional_fn is not None:
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@ -143,7 +143,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
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while True:
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try:
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# make a POST request to the API endpoint, stream=True
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response = requests.post(API_URL, headers=headers, proxies=proxies,
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response = requests.post(llm_kwargs['endpoint'], headers=headers, proxies=proxies,
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json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
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except:
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retry += 1
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@ -202,12 +202,14 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
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"""
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整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
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"""
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if len(llm_kwargs['api_key']) != 51:
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if not is_any_api_key(llm_kwargs['api_key']):
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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。")
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api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {llm_kwargs['api_key']}"
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"Authorization": f"Bearer {api_key}"
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}
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conversation_cnt = len(history) // 2
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@ -235,7 +237,7 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
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messages.append(what_i_ask_now)
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payload = {
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"model": llm_kwargs['llm_model'],
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"model": llm_kwargs['llm_model'].strip('api2d-'),
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"messages": messages,
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"temperature": llm_kwargs['temperature'], # 1.0,
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"top_p": llm_kwargs['top_p'], # 1.0,
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179
toolbox.py
179
toolbox.py
@ -1,13 +1,10 @@
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import markdown
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import mdtex2html
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import threading
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import importlib
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import traceback
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import inspect
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import re
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from latex2mathml.converter import convert as tex2mathml
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from functools import wraps, lru_cache
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############################### 插件输入输出接驳区 #######################################
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class ChatBotWithCookies(list):
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def __init__(self, cookie):
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@ -25,9 +22,10 @@ class ChatBotWithCookies(list):
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def ArgsGeneralWrapper(f):
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"""
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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。
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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。
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"""
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def decorated(cookies, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, *args):
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from request_llm.bridge_all import model_info
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txt_passon = txt
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if txt == "" and txt2 != "": txt_passon = txt2
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# 引入一个有cookie的chatbot
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@ -38,6 +36,7 @@ def ArgsGeneralWrapper(f):
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llm_kwargs = {
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'api_key': cookies['api_key'],
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'llm_model': llm_model,
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'endpoint': model_info[llm_model]['endpoint'],
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'top_p':top_p,
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'max_length': max_length,
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'temperature':temperature,
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@ -56,69 +55,10 @@ def update_ui(chatbot, history, msg='正常', **kwargs): # 刷新界面
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"""
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assert isinstance(chatbot, ChatBotWithCookies), "在传递chatbot的过程中不要将其丢弃。必要时，可用clear将其清空，然后用for+append循环重新赋值。"
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yield chatbot.get_cookies(), chatbot, history, msg
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############################### ################## #######################################
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##########################################################################################
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def get_reduce_token_percent(text):
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"""
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* 此函数未来将被弃用
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"""
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try:
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||||
# text = "maximum context length is 4097 tokens. However, your messages resulted in 4870 tokens"
|
||||
pattern = r"(\d+)\s+tokens\b"
|
||||
match = re.findall(pattern, text)
|
||||
EXCEED_ALLO = 500 # 稍微留一点余地，否则在回复时会因余量太少出问题
|
||||
max_limit = float(match[0]) - EXCEED_ALLO
|
||||
current_tokens = float(match[1])
|
||||
ratio = max_limit/current_tokens
|
||||
assert ratio > 0 and ratio < 1
|
||||
return ratio, str(int(current_tokens-max_limit))
|
||||
except:
|
||||
return 0.5, '不详'
|
||||
|
||||
|
||||
|
||||
def write_results_to_file(history, 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 = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
|
||||
file_name = 'chatGPT分析报告' + \
|
||||
time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
|
||||
os.makedirs('./gpt_log/', exist_ok=True)
|
||||
with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
|
||||
f.write('# chatGPT 分析报告\n')
|
||||
for i, content in enumerate(history):
|
||||
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
|
||||
if i % 2 == 0:
|
||||
f.write('## ')
|
||||
f.write(content)
|
||||
f.write('\n\n')
|
||||
res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')
|
||||
print(res)
|
||||
return res
|
||||
|
||||
|
||||
def regular_txt_to_markdown(text):
|
||||
"""
|
||||
将普通文本转换为Markdown格式的文本。
|
||||
"""
|
||||
text = text.replace('\n', '\n\n')
|
||||
text = text.replace('\n\n\n', '\n\n')
|
||||
text = text.replace('\n\n\n', '\n\n')
|
||||
return text
|
||||
|
||||
|
||||
def CatchException(f):
|
||||
"""
|
||||
装饰器函数，捕捉函数f中的异常并封装到一个生成器中返回，并显示到聊天当中。
|
||||
装饰器函数，捕捉函数f中的异常并封装到一个生成器中返回，并显示到聊天当中。
|
||||
"""
|
||||
@wraps(f)
|
||||
def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
|
||||
@ -155,9 +95,70 @@ def HotReload(f):
|
||||
return decorated
|
||||
|
||||
|
||||
####################################### 其他小工具 #####################################
|
||||
|
||||
def get_reduce_token_percent(text):
|
||||
"""
|
||||
* 此函数未来将被弃用
|
||||
"""
|
||||
try:
|
||||
# text = "maximum context length is 4097 tokens. However, your messages resulted in 4870 tokens"
|
||||
pattern = r"(\d+)\s+tokens\b"
|
||||
match = re.findall(pattern, text)
|
||||
EXCEED_ALLO = 500 # 稍微留一点余地，否则在回复时会因余量太少出问题
|
||||
max_limit = float(match[0]) - EXCEED_ALLO
|
||||
current_tokens = float(match[1])
|
||||
ratio = max_limit/current_tokens
|
||||
assert ratio > 0 and ratio < 1
|
||||
return ratio, str(int(current_tokens-max_limit))
|
||||
except:
|
||||
return 0.5, '不详'
|
||||
|
||||
|
||||
|
||||
def write_results_to_file(history, 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 = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
|
||||
file_name = 'chatGPT分析报告' + \
|
||||
time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
|
||||
os.makedirs('./gpt_log/', exist_ok=True)
|
||||
with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
|
||||
f.write('# chatGPT 分析报告\n')
|
||||
for i, content in enumerate(history):
|
||||
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
|
||||
if i % 2 == 0:
|
||||
f.write('## ')
|
||||
f.write(content)
|
||||
f.write('\n\n')
|
||||
res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')
|
||||
print(res)
|
||||
return res
|
||||
|
||||
|
||||
def regular_txt_to_markdown(text):
|
||||
"""
|
||||
将普通文本转换为Markdown格式的文本。
|
||||
"""
|
||||
text = text.replace('\n', '\n\n')
|
||||
text = text.replace('\n\n\n', '\n\n')
|
||||
text = text.replace('\n\n\n', '\n\n')
|
||||
return text
|
||||
|
||||
|
||||
|
||||
|
||||
def report_execption(chatbot, history, a, b):
|
||||
"""
|
||||
向chatbot中添加错误信息
|
||||
向chatbot中添加错误信息
|
||||
"""
|
||||
chatbot.append((a, b))
|
||||
history.append(a)
|
||||
@ -166,7 +167,7 @@ def report_execption(chatbot, history, a, b):
|
||||
|
||||
def text_divide_paragraph(text):
|
||||
"""
|
||||
将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。
|
||||
将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。
|
||||
"""
|
||||
if '```' in text:
|
||||
# careful input
|
||||
@ -182,7 +183,7 @@ def text_divide_paragraph(text):
|
||||
|
||||
def markdown_convertion(txt):
|
||||
"""
|
||||
将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。
|
||||
将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。
|
||||
"""
|
||||
pre = '<div class="markdown-body">'
|
||||
suf = '</div>'
|
||||
@ -274,7 +275,7 @@ def close_up_code_segment_during_stream(gpt_reply):
|
||||
|
||||
def format_io(self, y):
|
||||
"""
|
||||
将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化，并将输出部分的Markdown和数学公式转换为HTML格式。
|
||||
将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化，并将输出部分的Markdown和数学公式转换为HTML格式。
|
||||
"""
|
||||
if y is None or y == []:
|
||||
return []
|
||||
@ -290,7 +291,7 @@ def format_io(self, y):
|
||||
|
||||
def find_free_port():
|
||||
"""
|
||||
返回当前系统中可用的未使用端口。
|
||||
返回当前系统中可用的未使用端口。
|
||||
"""
|
||||
import socket
|
||||
from contextlib import closing
|
||||
@ -410,9 +411,43 @@ def on_report_generated(files, chatbot):
|
||||
return report_files, chatbot
|
||||
|
||||
def is_openai_api_key(key):
|
||||
# 正确的 API_KEY 是 "sk-" + 48 位大小写字母数字的组合
|
||||
API_MATCH = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
|
||||
return API_MATCH
|
||||
return bool(API_MATCH)
|
||||
|
||||
def is_api2d_key(key):
|
||||
if key.startswith('fk') and len(key) == 41:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
def is_any_api_key(key):
|
||||
if ',' in key:
|
||||
keys = key.split(',')
|
||||
for k in keys:
|
||||
if is_any_api_key(k): return True
|
||||
return False
|
||||
else:
|
||||
return is_openai_api_key(key) or is_api2d_key(key)
|
||||
|
||||
|
||||
def select_api_key(keys, llm_model):
|
||||
import random
|
||||
avail_key_list = []
|
||||
key_list = keys.split(',')
|
||||
|
||||
if llm_model.startswith('gpt-'):
|
||||
for k in key_list:
|
||||
if is_openai_api_key(k): avail_key_list.append(k)
|
||||
|
||||
if llm_model.startswith('api2d-'):
|
||||
for k in key_list:
|
||||
if is_api2d_key(k): avail_key_list.append(k)
|
||||
|
||||
if len(avail_key_list) == 0:
|
||||
raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。")
|
||||
|
||||
api_key = random.choice(avail_key_list) # 随机负载均衡
|
||||
return api_key
|
||||
|
||||
@lru_cache(maxsize=128)
|
||||
def read_single_conf_with_lru_cache(arg):
|
||||
@ -423,7 +458,7 @@ def read_single_conf_with_lru_cache(arg):
|
||||
r = getattr(importlib.import_module('config'), arg)
|
||||
# 在读取API_KEY时，检查一下是不是忘了改config
|
||||
if arg == 'API_KEY':
|
||||
if is_openai_api_key(r):
|
||||
if is_any_api_key(r):
|
||||
print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
|
||||
else:
|
||||
print亮红( "[API_KEY] 正确的 API_KEY 是 'sk-' + '48 位大小写字母数字' 的组合，请在config文件中修改API密钥, 添加海外代理之后再运行。" + \
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user