|
|
|
|
|
""" |
|
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 |
|
|
|
|
|
|
|
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: |
|
|
|
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: |
|
|
|
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) |
|
functional = functional.get_functionals() |
|
if "PreProcess" in functional[additional_fn]: inputs = functional[additional_fn]["PreProcess"](inputs) |
|
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: |
|
|
|
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) |
|
|
|
if is_head_of_the_stream: |
|
|
|
is_head_of_the_stream = False; continue |
|
|
|
if chunk: |
|
try: |
|
if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0: |
|
|
|
logging.info(f'[response] {gpt_replying_buffer}') |
|
break |
|
|
|
chunkjson = json.loads(chunk.decode()[6:]) |
|
status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}" |
|
|
|
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, |
|
"top_p": top_p, |
|
"n": 1, |
|
"stream": stream, |
|
"presence_penalty": 0, |
|
"frequency_penalty": 0, |
|
} |
|
|
|
print(f" {LLM_MODEL} : {conversation_cnt} : {inputs}") |
|
return headers,payload |
|
|
|
|
|
|
