254 lines
12 KiB
Python
254 lines
12 KiB
Python
# 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("用户取消了程序。")
|
||
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
|
||
|
||
|