gpt-academicrrrr / request_llm /bridge_chatgpt.py
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# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
"""
该文件中主要包含三个函数
不具备多线程能力的函数:
1. predict: 正常对话时使用,具备完备的交互功能,不可多线程
具备多线程调用能力的函数
2. predict_no_ui: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
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
"""
import json
import time
import gradio as gr
import logging
import traceback
import requests
import importlib
# config_private.py放自己的秘密如API和代理网址
# 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
from toolbox import get_conf, update_ui
proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL = \
get_conf('proxies', 'API_URL', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'LLM_MODEL')
timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
'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。'
def get_full_error(chunk, stream_response):
"""
获取完整的从Openai返回的报错
"""
while True:
try:
chunk += next(stream_response)
except:
break
return chunk
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
"""
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。
inputs:
是本次问询的输入
sys_prompt:
系统静默prompt
llm_kwargs:
chatGPT的内部调优参数
history:
是之前的对话列表
observe_window = None:
用于负责跨越线程传递已经输出的部分,大部分时候仅仅为了fancy的视觉效果,留空即可。observe_window[0]:观测窗。observe_window[1]:看门狗
"""
watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
retry = 0
while True:
try:
# make a POST request to the API endpoint, stream=False
response = requests.post(API_URL, headers=headers, proxies=proxies,
json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
except requests.exceptions.ReadTimeout as e:
retry += 1
traceback.print_exc()
if retry > MAX_RETRY: raise TimeoutError
if MAX_RETRY!=0: print(f'请求超时,正在重试 ({retry}/{MAX_RETRY}) ……')
stream_response = response.iter_lines()
result = ''
while True:
try: chunk = next(stream_response).decode()
except StopIteration:
break
except requests.exceptions.ConnectionError:
chunk = next(stream_response).decode() # 失败了,重试一次?再失败就没办法了。
if len(chunk)==0: continue
if not chunk.startswith('data:'):
error_msg = get_full_error(chunk.encode('utf8'), stream_response).decode()
if "reduce the length" in error_msg:
raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
else:
raise RuntimeError("OpenAI拒绝了请求:" + error_msg)
json_data = json.loads(chunk.lstrip('data:'))['choices'][0]
delta = json_data["delta"]
if len(delta) == 0: break
if "role" in delta: continue
if "content" in delta:
result += delta["content"]
if not console_slience: print(delta["content"], end='')
if observe_window is not None:
# 观测窗,把已经获取的数据显示出去
if len(observe_window) >= 1: observe_window[0] += delta["content"]
# 看门狗,如果超过期限没有喂狗,则终止
if len(observe_window) >= 2:
if (time.time()-observe_window[1]) > watch_dog_patience:
raise RuntimeError("程序终止。")
else: raise RuntimeError("意外Json结构:"+delta)
if json_data['finish_reason'] == 'length':
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。")
return result
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
"""
发送至chatGPT,流式获取输出。
用于基础的对话功能。
inputs 是本次问询的输入
top_p, temperature是chatGPT的内部调优参数
history 是之前的对话列表(注意无论是inputs还是history,内容太长了都会触发token数量溢出的错误)
chatbot 为WebUI中显示的对话列表,修改它,然后yeild出去,可以直接修改对话界面内容
additional_fn代表点击的哪个按钮,按钮见functional.py
"""
if inputs.startswith('sk-') and len(inputs) == 51:
chatbot._cookies['api_key'] = inputs
chatbot.append(("输入已识别为openai的api_key", "api_key已导入"))
yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
return
elif len(chatbot._cookies['api_key']) != 51:
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。"))
yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
return
if additional_fn is not None:
import core_functional
importlib.reload(core_functional) # 热更新prompt
core_functional = core_functional.get_core_functions()
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)
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
if stream:
raw_input = inputs
logging.info(f'[raw_input] {raw_input}')
chatbot.append((inputs, ""))
yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
history.append(inputs); history.append(" ")
retry = 0
while True:
try:
# make a POST request to the API endpoint, stream=True
response = requests.post(API_URL, headers=headers, proxies=proxies,
json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
except:
retry += 1
chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
retry_msg = f",正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
if retry > MAX_RETRY: raise TimeoutError
gpt_replying_buffer = ""
is_head_of_the_stream = True
if stream:
stream_response = response.iter_lines()
while True:
chunk = next(stream_response)
# print(chunk.decode()[6:])
if is_head_of_the_stream:
# 数据流的第一帧不携带content
is_head_of_the_stream = False; continue
if chunk:
try:
if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
# 判定为数据流的结束,gpt_replying_buffer也写完了
logging.info(f'[response] {gpt_replying_buffer}')
break
# 处理数据流的主体
chunkjson = json.loads(chunk.decode()[6:])
status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
# 如果这里抛出异常,一般是文本过长,详情见get_full_error的输出
gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
history[-1] = gpt_replying_buffer
chatbot[-1] = (history[-2], history[-1])
yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
except Exception as e:
traceback.print_exc()
yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
chunk = get_full_error(chunk, stream_response)
error_msg = chunk.decode()
if "reduce the length" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长,或历史数据过长. 历史缓存数据现已释放,您可以请再次尝试.")
history = [] # 清除历史
elif "Incorrect API key" in error_msg:
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.")
elif "exceeded your current quota" in error_msg:
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.")
else:
from toolbox import regular_txt_to_markdown
tb_str = '```\n' + traceback.format_exc() + '```'
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk.decode()[4:])}")
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
return
def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
"""
整合所有信息,选择LLM模型,生成http请求,为发送请求做准备
"""
if len(llm_kwargs['api_key']) != 51:
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。")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {llm_kwargs['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'],
"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