238 lines
10 KiB
Python
238 lines
10 KiB
Python
# 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 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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try: from config_private import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
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except: from config import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
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timeout_bot_msg = '[local] Request timeout, network error. please check proxy settings in config.py.'
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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(inputs, top_p, temperature, history=[], sys_prompt=""):
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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。
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predict函数的简化版。
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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。
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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
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（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）
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"""
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headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt=sys_prompt, stream=False)
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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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response = requests.post(API_URL, headers=headers, proxies=proxies,
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json=payload, stream=False, timeout=TIMEOUT_SECONDS*2); 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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try:
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result = json.loads(response.text)["choices"][0]["message"]["content"]
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return result
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except Exception as e:
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if "choices" not in response.text: print(response.text)
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raise ConnectionAbortedError("Json解析不合常规，可能是文本过长" + response.text)
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def predict_no_ui_long_connection(inputs, top_p, temperature, history=[], sys_prompt=""):
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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的方法避免有人中途掐网线。
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"""
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headers, payload = generate_payload(inputs, top_p, temperature, 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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response = requests.post(API_URL, 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: break
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if len(chunk)==0: continue
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if not chunk.startswith('data:'):
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chunk = get_full_error(chunk.encode('utf8'), stream_response)
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raise ConnectionAbortedError("OpenAI拒绝了请求:" + chunk.decode())
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delta = json.loads(chunk.lstrip('data:'))['choices'][0]["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: result += delta["content"]; print(delta["content"], end='')
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else: raise RuntimeError("意外Json结构："+delta)
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return result
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def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='',
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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 additional_fn is not None:
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import functional
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importlib.reload(functional)
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functional = functional.get_functionals()
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inputs = functional[additional_fn]["Prefix"] + inputs + functional[additional_fn]["Suffix"]
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if stream:
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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 chatbot, history, "等待响应"
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headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt, stream)
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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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response = requests.post(API_URL, 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 chatbot, history, "请求超时"+retry_msg
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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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chunk = next(stream_response)
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# print(chunk.decode()[6:])
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if is_head_of_the_stream:
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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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if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
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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.decode()[6:])
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status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
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# 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
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gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk.decode()[6:])['choices'][0]["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 chatbot, history, status_text
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except Exception as e:
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traceback.print_exc()
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yield chatbot, history, "Json解析不合常规"
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chunk = get_full_error(chunk, stream_response)
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error_msg = chunk.decode()
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if "reduce the length" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Input (or history) is too long, please reduce input or clear history by refleshing this page.")
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history = []
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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 provided.")
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else:
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from toolbox import regular_txt_to_markdown
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tb_str = regular_txt_to_markdown(traceback.format_exc())
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] Json Error \n\n {tb_str} \n\n {regular_txt_to_markdown(chunk.decode()[4:])}")
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yield chatbot, history, "Json解析不合常规" + error_msg
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return
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def generate_payload(inputs, top_p, temperature, 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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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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conversation_cnt = len(history) // 2
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messages = [{"role": "system", "content": system_prompt}]
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if conversation_cnt:
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for index in range(0, 2*conversation_cnt, 2):
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what_i_have_asked = {}
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what_i_have_asked["role"] = "user"
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what_i_have_asked["content"] = history[index]
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what_gpt_answer = {}
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what_gpt_answer["role"] = "assistant"
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what_gpt_answer["content"] = history[index+1]
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if what_i_have_asked["content"] != "":
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if what_gpt_answer["content"] == "": continue
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if what_gpt_answer["content"] == timeout_bot_msg: continue
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messages.append(what_i_have_asked)
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messages.append(what_gpt_answer)
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else:
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messages[-1]['content'] = what_gpt_answer['content']
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what_i_ask_now = {}
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what_i_ask_now["role"] = "user"
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what_i_ask_now["content"] = inputs
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messages.append(what_i_ask_now)
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payload = {
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"model": LLM_MODEL,
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"messages": messages,
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"temperature": temperature, # 1.0,
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"top_p": top_p, # 1.0,
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"n": 1,
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"stream": stream,
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"presence_penalty": 0,
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"frequency_penalty": 0,
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}
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print(f" {LLM_MODEL} : {conversation_cnt} : {inputs}")
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return headers,payload
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