Version 3.74: Merge latest updates on dev branch (frontier) (#1621)
* Update version to 3.74 * Add support for Yi Model API (#1635) * 更新以支持零一万物模型 * 删除newbing * 修改config --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> * Refactor function signatures in bridge files * fix qwen api change * rename and ref functions * rename and move some cookie functions * 增加haiku模型，新增endpoint配置说明 (#1626) * haiku added * 新增haiku，新增endpoint配置说明 * Haiku added * 将说明同步至最新Endpoint --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> * private_upload目录下进行文件鉴权 (#1596) * private_upload目录下进行文件鉴权 * minor fastapi adjustment * Add logging functionality to enable saving conversation records * waiting to fix username retrieve * support 2rd web path * allow accessing default user dir --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> * remove yaml deps * fix favicon * fix abs path auth problem * forget to write a return * add `dashscope` to deps * fix GHSA-v9q9-xj86-953p * 用户名重叠越权访问patch (#1681) * add cohere model api access * cohere + can_multi_thread * fix block user access(fail) * fix fastapi bug * change cohere api endpoint * explain version --------- Co-authored-by: Menghuan1918 <menghuan2003@outlook.com> Co-authored-by: Skyzayre <120616113+Skyzayre@users.noreply.github.com> Co-authored-by: XIao <46100050+Kilig947@users.noreply.github.com>
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binary-husky
@@ -34,6 +34,9 @@ from .bridge_google_gemini import predict_no_ui_long_connection as genai_noui
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from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
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from .bridge_zhipu import predict as zhipu_ui
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from .bridge_cohere import predict as cohere_ui
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from .bridge_cohere import predict_no_ui_long_connection as cohere_noui
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colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
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class LazyloadTiktoken(object):
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@@ -64,6 +67,7 @@ newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
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gemini_endpoint = "https://generativelanguage.googleapis.com/v1beta/models"
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claude_endpoint = "https://api.anthropic.com/v1/messages"
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yimodel_endpoint = "https://api.lingyiwanwu.com/v1/chat/completions"
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cohere_endpoint = 'https://api.cohere.ai/v1/chat'
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if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
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azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
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@@ -82,6 +86,7 @@ if newbing_endpoint in API_URL_REDIRECT: newbing_endpoint = API_URL_REDIRECT[new
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if gemini_endpoint in API_URL_REDIRECT: gemini_endpoint = API_URL_REDIRECT[gemini_endpoint]
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if claude_endpoint in API_URL_REDIRECT: claude_endpoint = API_URL_REDIRECT[claude_endpoint]
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if yimodel_endpoint in API_URL_REDIRECT: yimodel_endpoint = API_URL_REDIRECT[yimodel_endpoint]
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if cohere_endpoint in API_URL_REDIRECT: cohere_endpoint = API_URL_REDIRECT[cohere_endpoint]
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# 获取tokenizer
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tokenizer_gpt35 = LazyloadTiktoken("gpt-3.5-turbo")
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@@ -310,6 +315,18 @@ model_info = {
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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# cohere
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"cohere-command-r-plus": {
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"fn_with_ui": cohere_ui,
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"fn_without_ui": cohere_noui,
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"can_multi_thread": True,
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"endpoint": cohere_endpoint,
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"max_token": 1024 * 4,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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}
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# -=-=-=-=-=-=- 月之暗面 -=-=-=-=-=-=-
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from request_llms.bridge_moonshot import predict as moonshot_ui
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@@ -359,7 +376,7 @@ for model in AVAIL_LLM_MODELS:
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# -=-=-=-=-=-=- 以下部分是新加入的模型，可能附带额外依赖 -=-=-=-=-=-=-
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# claude家族
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claude_models = ["claude-instant-1.2","claude-2.0","claude-2.1","claude-3-sonnet-20240229","claude-3-opus-20240229"]
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claude_models = ["claude-instant-1.2","claude-2.0","claude-2.1","claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229"]
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if any(item in claude_models for item in AVAIL_LLM_MODELS):
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from .bridge_claude import predict_no_ui_long_connection as claude_noui
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from .bridge_claude import predict as claude_ui
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@@ -393,6 +410,16 @@ if any(item in claude_models for item in AVAIL_LLM_MODELS):
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"token_cnt": get_token_num_gpt35,
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},
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})
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model_info.update({
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"claude-3-haiku-20240307": {
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"fn_with_ui": claude_ui,
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"fn_without_ui": claude_noui,
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"endpoint": claude_endpoint,
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"max_token": 200000,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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})
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model_info.update({
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"claude-3-sonnet-20240229": {
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"fn_with_ui": claude_ui,
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@@ -789,7 +816,7 @@ 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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"""
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def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):
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def decorated(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list, console_slience:bool):
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try:
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return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
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except Exception as e:
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@@ -799,9 +826,9 @@ def LLM_CATCH_EXCEPTION(f):
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return decorated
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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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def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list=[], console_slience:bool=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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发送至LLM，等待回复，一次性完成，不显示中间过程。但内部（尽可能地）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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@@ -819,7 +846,6 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
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model = llm_kwargs['llm_model']
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n_model = 1
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if '&' not in model:
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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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method = model_info[model]["fn_without_ui"]
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@@ -880,15 +906,22 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
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return res
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def predict(inputs, llm_kwargs, *args, **kwargs):
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def predict(inputs:str, llm_kwargs:dict, *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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完整参数列表：
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predict(
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inputs:str, # 是本次问询的输入
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llm_kwargs:dict, # 是LLM的内部调优参数
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plugin_kwargs:dict, # 是插件的内部参数
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chatbot:ChatBotWithCookies, # 原样传递，负责向用户前端展示对话，兼顾前端状态的功能
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history:list=[], # 是之前的对话列表
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system_prompt:str='', # 系统静默prompt
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stream:bool=True, # 是否流式输出（已弃用）
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additional_fn:str=None # 基础功能区按钮的附加功能
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):
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"""
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inputs = apply_gpt_academic_string_mask(inputs, mode="show_llm")
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