Merge pull request #1014 from hongyi-zhao/master
Fix the reverse proxy based OpenAI access via https://github.com/acheong08/ChatGPT-to-API/.
This commit is contained in:
binary-husky
GitHub
commit
aaf4f37403
@ -351,6 +351,22 @@ if "qwen" in AVAIL_LLM_MODELS:
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})
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})
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except:
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except:
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print(trimmed_format_exc())
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print(trimmed_format_exc())
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if "chatgpt_website" in AVAIL_LLM_MODELS: # 接入一些逆向工程https://github.com/acheong08/ChatGPT-to-API/
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try:
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from .bridge_chatgpt_website import predict_no_ui_long_connection as chatgpt_website_noui
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from .bridge_chatgpt_website import predict as chatgpt_website_ui
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model_info.update({
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"chatgpt_website": {
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"fn_with_ui": chatgpt_website_ui,
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"fn_without_ui": chatgpt_website_noui,
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"endpoint": None,
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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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}
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})
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except:
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print(trimmed_format_exc())
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def LLM_CATCH_EXCEPTION(f):
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def LLM_CATCH_EXCEPTION(f):
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"""
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"""
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297
request_llm/bridge_chatgpt_website.py
Normal file
297
request_llm/bridge_chatgpt_website.py
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@ -0,0 +1,297 @@
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# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
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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 json
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import time
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import gradio as gr
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import logging
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import traceback
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import requests
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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, 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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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_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chatGPT，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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headers, payload = generate_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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# make a POST request to the API endpoint, stream=False
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from .bridge_all import model_info
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endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
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response = requests.post(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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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.iter_lines()
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result = ''
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while True:
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try: chunk = next(stream_response).decode()
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except StopIteration:
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break
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except requests.exceptions.ConnectionError:
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chunk = next(stream_response).decode() # 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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if not chunk.startswith('data:'):
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error_msg = get_full_error(chunk.encode('utf8'), stream_response).decode()
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if "reduce the length" in error_msg:
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raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
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else:
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raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
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if ('data: [DONE]' in chunk): break # api2d 正常完成
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json_data = json.loads(chunk.lstrip('data:'))['choices'][0]
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delta = json_data["delta"]
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if len(delta) == 0: break
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if "role" in delta: 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) >= 2:
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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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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chatGPT，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 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", what_keys(inputs)))
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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 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="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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from core_functional import handle_core_functionality
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inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
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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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try:
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headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
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except RuntimeError as e:
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chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
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yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
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return
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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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# make a POST request to the API endpoint, stream=True
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from .bridge_all import model_info
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endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
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response = requests.post(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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chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
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retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
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yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
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if retry > MAX_RETRY: raise TimeoutError
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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.iter_lines()
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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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# 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
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chunk_decoded = chunk.decode()
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error_msg = chunk_decoded
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chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
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yield from update_ui(chatbot=chatbot, history=history, msg="非Openai官方接口返回了错误:" + chunk.decode()) # 刷新界面
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return
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# print(chunk.decode()[6:])
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if is_head_of_the_stream and (r'"object":"error"' not in chunk.decode()):
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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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try:
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chunk_decoded = chunk.decode()
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# 前者是API2D的结束条件，后者是OPENAI的结束条件
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if 'data: [DONE]' in chunk_decoded:
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# 判定为数据流的结束，gpt_replying_buffer也写完了
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logging.info(f'[response] {gpt_replying_buffer}')
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break
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# 处理数据流的主体
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chunkjson = json.loads(chunk_decoded[6:])
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status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
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delta = chunkjson['choices'][0]["delta"]
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if "content" in delta:
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gpt_replying_buffer = gpt_replying_buffer + 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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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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chunk_decoded = chunk.decode()
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error_msg = chunk_decoded
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chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
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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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print(error_msg)
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return
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def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
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from .bridge_all import model_info
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openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
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if "reduce the length" in error_msg:
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if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
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history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
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max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
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# history = [] # 清除历史
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elif "does not exist" in error_msg:
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
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elif "Incorrect API key" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
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elif "exceeded your current quota" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
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elif "account is not active" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
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elif "associated with a deactivated account" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
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elif "bad forward key" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
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elif "Not enough point" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
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else:
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from toolbox import regular_txt_to_markdown
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tb_str = '```\n' + trimmed_format_exc() + '```'
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
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return chatbot, history
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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 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 {api_key}"
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}
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if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG})
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|
if llm_kwargs['llm_model'].startswith('azure-'): headers.update({"api-key": api_key})
|
||||||
|
|
||||||
|
conversation_cnt = len(history) // 2
|
||||||
|
|
||||||
|
messages = [{"role": "system", "content": system_prompt}]
|
||||||
|
if conversation_cnt:
|
||||||
|
for index in range(0, 2*conversation_cnt, 2):
|
||||||
|
what_i_have_asked = {}
|
||||||
|
what_i_have_asked["role"] = "user"
|
||||||
|
what_i_have_asked["content"] = history[index]
|
||||||
|
what_gpt_answer = {}
|
||||||
|
what_gpt_answer["role"] = "assistant"
|
||||||
|
what_gpt_answer["content"] = history[index+1]
|
||||||
|
if what_i_have_asked["content"] != "":
|
||||||
|
if what_gpt_answer["content"] == "": continue
|
||||||
|
if what_gpt_answer["content"] == timeout_bot_msg: continue
|
||||||
|
messages.append(what_i_have_asked)
|
||||||
|
messages.append(what_gpt_answer)
|
||||||
|
else:
|
||||||
|
messages[-1]['content'] = what_gpt_answer['content']
|
||||||
|
|
||||||
|
what_i_ask_now = {}
|
||||||
|
what_i_ask_now["role"] = "user"
|
||||||
|
what_i_ask_now["content"] = inputs
|
||||||
|
messages.append(what_i_ask_now)
|
||||||
|
|
||||||
|
payload = {
|
||||||
|
"model": llm_kwargs['llm_model'].strip('api2d-'),
|
||||||
|
"messages": messages,
|
||||||
|
"temperature": llm_kwargs['temperature'], # 1.0,
|
||||||
|
"top_p": llm_kwargs['top_p'], # 1.0,
|
||||||
|
"n": 1,
|
||||||
|
"stream": stream,
|
||||||
|
"presence_penalty": 0,
|
||||||
|
"frequency_penalty": 0,
|
||||||
|
}
|
||||||
|
try:
|
||||||
|
print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
|
||||||
|
except:
|
||||||
|
print('输入中可能存在乱码。')
|
||||||
|
return headers,payload
|
||||||
|
|
||||||
|
|
||||||
Loading…
x
Reference in New Issue
Block a user