优化azure的体验
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@ -8,7 +8,7 @@
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"""
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# [step 1]>> API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下，还需要填写组织（格式如org-123456789abcdefghijklmno的），请向下翻，找 API_ORG 设置项
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API_KEY = "sk-此处填API密钥" # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey1,fkxxxx-api2dkey2"
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API_KEY = "此处填API密钥" # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
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# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
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@ -110,9 +110,8 @@ SLACK_CLAUDE_USER_TOKEN = ''
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# 如果需要使用AZURE 详情请见额外文档 docs\use_azure.md
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AZURE_ENDPOINT = "https://你亲手写的api名称.openai.azure.com/"
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AZURE_API_KEY = "填入azure openai api的密钥"
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AZURE_API_VERSION = "2023-05-15" # 一般不修改
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AZURE_ENGINE = "填入你亲手写的部署名" # 读 docs\use_azure.md
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AZURE_API_KEY = "填入azure openai api的密钥" # 建议直接在API_KEY处填写，该选项即将被弃用
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AZURE_ENGINE = "填入你亲手写的部署名" # 读 docs\use_azure.md
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# 使用Newbing
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8
main.py
8
main.py
@ -4,10 +4,10 @@ def main():
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import gradio as gr
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if gr.__version__ not in ['3.28.3','3.32.2']: assert False, "需要特殊依赖，请务必用 pip install -r requirements.txt 指令安装依赖，详情信息见requirements.txt"
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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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from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, 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, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = \
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get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
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proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = \
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get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
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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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@ -54,7 +54,7 @@ def main():
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cancel_handles = []
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with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
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gr.HTML(title_html)
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cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL})
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cookies = gr.State(load_chat_cookies())
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with gr_L1():
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with gr_L2(scale=2):
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chatbot = gr.Chatbot(label=f"当前模型：{LLM_MODEL}")
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@ -16,9 +16,6 @@ from toolbox import get_conf, trimmed_format_exc
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from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui
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from .bridge_chatgpt import predict as chatgpt_ui
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from .bridge_azure_test import predict_no_ui_long_connection as azure_noui
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from .bridge_azure_test import predict as azure_ui
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from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
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from .bridge_chatglm import predict as chatglm_ui
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@ -48,10 +45,11 @@ class LazyloadTiktoken(object):
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return encoder.decode(*args, **kwargs)
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# Endpoint 重定向
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API_URL_REDIRECT, = get_conf("API_URL_REDIRECT")
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API_URL_REDIRECT, AZURE_ENDPOINT, AZURE_ENGINE = get_conf("API_URL_REDIRECT", "AZURE_ENDPOINT", "AZURE_ENGINE")
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openai_endpoint = "https://api.openai.com/v1/chat/completions"
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api2d_endpoint = "https://openai.api2d.net/v1/chat/completions"
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newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
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azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
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# 兼容旧版的配置
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try:
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API_URL, = get_conf("API_URL")
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@ -122,9 +120,9 @@ model_info = {
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# azure openai
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"azure-gpt-3.5":{
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"fn_with_ui": azure_ui,
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"fn_without_ui": azure_noui,
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"endpoint": get_conf("AZURE_ENDPOINT"),
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": azure_endpoint,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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@ -1,237 +0,0 @@
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"""
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该文件中主要包含三个函数
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不具备多线程能力的函数：
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1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
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具备多线程调用能力的函数
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2. predict_no_ui：高级实验性功能模块调用，不会实时显示在界面上，参数简单，可以多线程并行，方便实现复杂的功能逻辑
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3. 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
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"""
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import logging
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import traceback
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import importlib
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import openai
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import time
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import requests
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import json
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# 读取config.py文件中关于AZURE OPENAI API的信息
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from toolbox import get_conf, update_ui, clip_history, trimmed_format_exc
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TIMEOUT_SECONDS, MAX_RETRY, AZURE_ENGINE, AZURE_ENDPOINT, AZURE_API_VERSION, AZURE_API_KEY = \
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get_conf('TIMEOUT_SECONDS', 'MAX_RETRY',"AZURE_ENGINE","AZURE_ENDPOINT", "AZURE_API_VERSION", "AZURE_API_KEY")
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def get_full_error(chunk, stream_response):
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"""
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获取完整的从Openai返回的报错
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"""
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while True:
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try:
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chunk += next(stream_response)
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except:
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break
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return chunk
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def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
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"""
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发送至azure openai api，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chatGPT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 additional_fn is not None:
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import core_functional
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importlib.reload(core_functional) # 热更新prompt
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core_functional = core_functional.get_core_functions()
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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）
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inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
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raw_input = inputs
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logging.info(f'[raw_input] {raw_input}')
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chatbot.append((inputs, ""))
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yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
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payload = generate_azure_payload(inputs, llm_kwargs, history, system_prompt, stream)
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history.append(inputs); history.append("")
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retry = 0
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while True:
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try:
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openai.api_type = "azure"
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openai.api_version = AZURE_API_VERSION
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openai.api_base = AZURE_ENDPOINT
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openai.api_key = AZURE_API_KEY
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response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break
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except openai.error.AuthenticationError:
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tb_str = '```\n' + trimmed_format_exc() + '```'
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chatbot[-1] = [chatbot[-1][0], tb_str]
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yield from update_ui(chatbot=chatbot, history=history, msg="openai返回错误") # 刷新界面
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return
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except:
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retry += 1
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traceback.print_exc()
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if retry > MAX_RETRY: raise TimeoutError
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if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
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gpt_replying_buffer = ""
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is_head_of_the_stream = True
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if stream:
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stream_response = response
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while True:
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try:
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chunk = next(stream_response)
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except StopIteration:
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from toolbox import regular_txt_to_markdown; tb_str = '```\n' + trimmed_format_exc() + '```'
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] 远程返回错误: \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk)}")
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yield from update_ui(chatbot=chatbot, history=history, msg="远程返回错误:" + chunk) # 刷新界面
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return
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if is_head_of_the_stream and (r'"object":"error"' not in chunk):
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# 数据流的第一帧不携带content
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is_head_of_the_stream = False; continue
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if chunk:
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#print(chunk)
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try:
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if "delta" in chunk["choices"][0]:
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if chunk["choices"][0]["finish_reason"] == "stop":
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logging.info(f'[response] {gpt_replying_buffer}')
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break
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status_text = f"finish_reason: {chunk['choices'][0]['finish_reason']}"
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gpt_replying_buffer = gpt_replying_buffer + chunk["choices"][0]["delta"]["content"]
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history[-1] = gpt_replying_buffer
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chatbot[-1] = (history[-2], history[-1])
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yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
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except Exception as e:
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traceback.print_exc()
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yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
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chunk = get_full_error(chunk, stream_response)
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error_msg = chunk
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yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
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return
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def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
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"""
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发送至AZURE OPENAI API，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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chatGPT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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watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
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payload = generate_azure_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
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retry = 0
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while True:
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try:
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openai.api_type = "azure"
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openai.api_version = AZURE_API_VERSION
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openai.api_base = AZURE_ENDPOINT
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openai.api_key = AZURE_API_KEY
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response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break
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except:
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retry += 1
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traceback.print_exc()
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if retry > MAX_RETRY: raise TimeoutError
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if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
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stream_response = response
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result = ''
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while True:
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try: chunk = next(stream_response)
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except StopIteration:
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break
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except:
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chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
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if len(chunk)==0: continue
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json_data = json.loads(str(chunk))['choices'][0]
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delta = json_data["delta"]
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if len(delta) == 0:
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break
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if "role" in delta:
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continue
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if "content" in delta:
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result += delta["content"]
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if not console_slience: print(delta["content"], end='')
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if observe_window is not None:
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# 观测窗，把已经获取的数据显示出去
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if len(observe_window) >= 1: observe_window[0] += delta["content"]
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# 看门狗，如果超过期限没有喂狗，则终止
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if len(observe_window) >= 2000:
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if (time.time()-observe_window[1]) > watch_dog_patience:
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raise RuntimeError("用户取消了程序。")
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else:
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raise RuntimeError("意外Json结构："+delta)
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if json_data['finish_reason'] == 'content_filter':
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raise RuntimeError("由于提问含不合规内容被Azure过滤。")
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if json_data['finish_reason'] == 'length':
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raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
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return result
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def generate_azure_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 azure openai api请求，为发送请求做准备
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"""
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conversation_cnt = len(history) // 2
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messages = [{"role": "system", "content": system_prompt}]
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if conversation_cnt:
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for index in range(0, 2*conversation_cnt, 2):
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what_i_have_asked = {}
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what_i_have_asked["role"] = "user"
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what_i_have_asked["content"] = history[index]
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what_gpt_answer = {}
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what_gpt_answer["role"] = "assistant"
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what_gpt_answer["content"] = history[index+1]
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if what_i_have_asked["content"] != "":
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if what_gpt_answer["content"] == "": continue
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messages.append(what_i_have_asked)
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messages.append(what_gpt_answer)
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else:
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messages[-1]['content'] = what_gpt_answer['content']
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what_i_ask_now = {}
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what_i_ask_now["role"] = "user"
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what_i_ask_now["content"] = inputs
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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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"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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"n": 1,
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"stream": stream,
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"presence_penalty": 0,
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"frequency_penalty": 0,
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"engine": AZURE_ENGINE
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}
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try:
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print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
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except:
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print('输入中可能存在乱码。')
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return payload
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@ -22,8 +22,8 @@ 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, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc
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proxies, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, API_ORG = \
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get_conf('proxies', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG')
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proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG = \
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get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG')
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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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@ -101,6 +101,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
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if (time.time()-observe_window[1]) > watch_dog_patience:
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raise RuntimeError("用户取消了程序。")
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else: raise RuntimeError("意外Json结构："+delta)
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if json_data['finish_reason'] == 'content_filter':
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raise RuntimeError("由于提问含不合规内容被Azure过滤。")
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if json_data['finish_reason'] == 'length':
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raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
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return result
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@ -247,6 +249,7 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
|
||||
"Authorization": f"Bearer {api_key}"
|
||||
}
|
||||
if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG})
|
||||
if llm_kwargs['llm_model'].startswith('azure-'): headers.update({"api-key": api_key})
|
||||
|
||||
conversation_cnt = len(history) // 2
|
||||
|
||||
|
||||
34
toolbox.py
34
toolbox.py
@ -505,16 +505,24 @@ def on_report_generated(cookies, files, chatbot):
|
||||
chatbot.append(['报告如何远程获取？', f'报告已经添加到右侧“文件上传区”（可能处于折叠状态），请查收。{file_links}'])
|
||||
return cookies, report_files, chatbot
|
||||
|
||||
def load_chat_cookies():
|
||||
API_KEY, LLM_MODEL, AZURE_API_KEY = get_conf('API_KEY', 'LLM_MODEL', 'AZURE_API_KEY')
|
||||
if is_any_api_key(AZURE_API_KEY):
|
||||
if is_any_api_key(API_KEY): API_KEY = API_KEY + ',' + AZURE_API_KEY
|
||||
else: API_KEY = AZURE_API_KEY
|
||||
return {'api_key': API_KEY, 'llm_model': LLM_MODEL}
|
||||
|
||||
def is_openai_api_key(key):
|
||||
API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
|
||||
return bool(API_MATCH_ORIGINAL)
|
||||
|
||||
def is_azure_api_key(key):
|
||||
API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{32}$", key)
|
||||
return bool(API_MATCH_ORIGINAL) or bool(API_MATCH_AZURE)
|
||||
return bool(API_MATCH_AZURE)
|
||||
|
||||
def is_api2d_key(key):
|
||||
if key.startswith('fk') and len(key) == 41:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
API_MATCH_API2D = re.match(r"fk[a-zA-Z0-9]{6}-[a-zA-Z0-9]{32}$", key)
|
||||
return bool(API_MATCH_API2D)
|
||||
|
||||
def is_any_api_key(key):
|
||||
if ',' in key:
|
||||
@ -523,10 +531,10 @@ def is_any_api_key(key):
|
||||
if is_any_api_key(k): return True
|
||||
return False
|
||||
else:
|
||||
return is_openai_api_key(key) or is_api2d_key(key)
|
||||
return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key)
|
||||
|
||||
def what_keys(keys):
|
||||
avail_key_list = {'OpenAI Key':0, "API2D Key":0}
|
||||
avail_key_list = {'OpenAI Key':0, "Azure Key":0, "API2D Key":0}
|
||||
key_list = keys.split(',')
|
||||
|
||||
for k in key_list:
|
||||
@ -537,7 +545,11 @@ def what_keys(keys):
|
||||
if is_api2d_key(k):
|
||||
avail_key_list['API2D Key'] += 1
|
||||
|
||||
return f"检测到： OpenAI Key {avail_key_list['OpenAI Key']} 个，API2D Key {avail_key_list['API2D Key']} 个"
|
||||
for k in key_list:
|
||||
if is_azure_api_key(k):
|
||||
avail_key_list['Azure Key'] += 1
|
||||
|
||||
return f"检测到： OpenAI Key {avail_key_list['OpenAI Key']} 个, Azure Key {avail_key_list['Azure Key']} 个, API2D Key {avail_key_list['API2D Key']} 个"
|
||||
|
||||
def select_api_key(keys, llm_model):
|
||||
import random
|
||||
@ -552,8 +564,12 @@ def select_api_key(keys, llm_model):
|
||||
for k in key_list:
|
||||
if is_api2d_key(k): avail_key_list.append(k)
|
||||
|
||||
if llm_model.startswith('azure-'):
|
||||
for k in key_list:
|
||||
if is_azure_api_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。您可能选择了错误的模型或请求源。")
|
||||
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。您可能选择了错误的模型或请求源（右下角更换模型菜单中可切换openai,azure和api2d请求源）")
|
||||
|
||||
api_key = random.choice(avail_key_list) # 随机负载均衡
|
||||
return api_key
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user