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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 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
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(inputs, top_p, temperature, history=[], sys_prompt=""):
"""
发送至chatGPT,等待回复,一次性完成,不显示中间过程。
predict函数的简化版。
用于payload比较大的情况,或者用于实现多线、带嵌套的复杂功能。
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,然后raise ConnectionAbortedError)
"""
headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt=sys_prompt, stream=False)
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=False, timeout=TIMEOUT_SECONDS*2); 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}) ……')
try:
result = json.loads(response.text)["choices"][0]["message"]["content"]
return result
except Exception as e:
if "choices" not in response.text: print(response.text)
raise ConnectionAbortedError("Json解析不合常规,可能是文本过长" + response.text)
def predict_no_ui_long_connection(inputs, top_p, temperature, history=[], sys_prompt=""):
"""
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的方法避免有人中途掐网线。
"""
headers, payload = generate_payload(inputs, top_p, temperature, 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
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"]; print(delta["content"], end='')
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。")
return result
def predict(inputs, top_p, temperature, 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 additional_fn is not None:
import functional
importlib.reload(functional) # 热更新prompt
functional = functional.get_functionals()
if "PreProcess" in functional[additional_fn]: inputs = functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数(如果有的话)
inputs = functional[additional_fn]["Prefix"] + inputs + functional[additional_fn]["Suffix"]
if stream:
raw_input = inputs
logging.info(f'[raw_input] {raw_input}')
chatbot.append((inputs, ""))
yield chatbot, history, "等待响应"
headers, payload = generate_payload(inputs, top_p, temperature, 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 chatbot, history, "请求超时"+retry_msg
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 chatbot, history, status_text
except Exception as e:
traceback.print_exc()
yield chatbot, history, "Json解析不合常规"
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] Input (or history) is too long, please reduce input or clear history by refreshing this page.")
history = []
elif "Incorrect API key" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key provided.")
else:
from toolbox import regular_txt_to_markdown
tb_str = regular_txt_to_markdown(traceback.format_exc())
chatbot[-1] = (chatbot[-1][0], f"[Local Message] Json Error \n\n {tb_str} \n\n {regular_txt_to_markdown(chunk.decode()[4:])}")
yield chatbot, history, "Json解析不合常规" + error_msg
return
def generate_payload(inputs, top_p, temperature, 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
"""
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {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_MODEL,
"messages": messages,
"temperature": temperature, # 1.0,
"top_p": top_p, # 1.0,
"n": 1,
"stream": stream,
"presence_penalty": 0,
"frequency_penalty": 0,
}
print(f" {LLM_MODEL} : {conversation_cnt} : {inputs}")
return headers,payload
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