238 lines
10 KiB
Python
238 lines
10 KiB
Python
"""
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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 logging
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import traceback
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import importlib
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import openai
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import time
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import requests
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import json
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# 读取config.py文件中关于AZURE OPENAI API的信息
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from toolbox import get_conf, update_ui, clip_history, trimmed_format_exc
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TIMEOUT_SECONDS, MAX_RETRY, AZURE_ENGINE, AZURE_ENDPOINT, AZURE_API_VERSION, AZURE_API_KEY = \
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get_conf('TIMEOUT_SECONDS', 'MAX_RETRY',"AZURE_ENGINE","AZURE_ENDPOINT", "AZURE_API_VERSION", "AZURE_API_KEY")
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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(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
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"""
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发送至azure openai api，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 core_functional
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importlib.reload(core_functional) # 热更新prompt
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core_functional = core_functional.get_core_functions()
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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）
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inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
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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 from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
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payload = generate_azure_payload(inputs, llm_kwargs, 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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openai.api_type = "azure"
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openai.api_version = AZURE_API_VERSION
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openai.api_base = AZURE_ENDPOINT
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openai.api_key = AZURE_API_KEY
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response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break
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except openai.error.AuthenticationError:
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tb_str = '```\n' + trimmed_format_exc() + '```'
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chatbot[-1] = [chatbot[-1][0], tb_str]
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yield from update_ui(chatbot=chatbot, history=history, msg="openai返回错误") # 刷新界面
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return
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except:
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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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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
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while True:
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try:
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chunk = next(stream_response)
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except StopIteration:
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from toolbox import regular_txt_to_markdown; tb_str = '```\n' + trimmed_format_exc() + '```'
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] 远程返回错误: \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk)}")
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yield from update_ui(chatbot=chatbot, history=history, msg="远程返回错误:" + chunk) # 刷新界面
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return
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if is_head_of_the_stream and (r'"object":"error"' not in chunk):
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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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#print(chunk)
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try:
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if "delta" in chunk["choices"][0]:
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if chunk["choices"][0]["finish_reason"] == "stop":
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logging.info(f'[response] {gpt_replying_buffer}')
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break
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status_text = f"finish_reason: {chunk['choices'][0]['finish_reason']}"
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gpt_replying_buffer = gpt_replying_buffer + chunk["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 from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
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except Exception as e:
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traceback.print_exc()
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yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
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chunk = get_full_error(chunk, stream_response)
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error_msg = chunk
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yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
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return
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def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
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"""
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发送至AZURE OPENAI API，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。
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inputs：
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是本次问询的输入
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sys_prompt:
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系统静默prompt
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llm_kwargs：
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chatGPT的内部调优参数
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history：
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是之前的对话列表
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observe_window = None：
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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
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"""
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watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
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payload = generate_azure_payload(inputs, llm_kwargs, 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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openai.api_type = "azure"
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openai.api_version = AZURE_API_VERSION
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openai.api_base = AZURE_ENDPOINT
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openai.api_key = AZURE_API_KEY
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response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break
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except:
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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
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result = ''
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while True:
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try: chunk = next(stream_response)
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except StopIteration:
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break
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except:
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chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
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if len(chunk)==0: continue
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json_data = json.loads(str(chunk))['choices'][0]
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delta = json_data["delta"]
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if len(delta) == 0:
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break
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if "role" in delta:
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continue
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if "content" in delta:
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result += delta["content"]
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if not console_slience: print(delta["content"], end='')
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if observe_window is not None:
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# 观测窗，把已经获取的数据显示出去
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if len(observe_window) >= 1: observe_window[0] += delta["content"]
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# 看门狗，如果超过期限没有喂狗，则终止
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if len(observe_window) >= 2000:
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if (time.time()-observe_window[1]) > watch_dog_patience:
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raise RuntimeError("用户取消了程序。")
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else:
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raise RuntimeError("意外Json结构："+delta)
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if json_data['finish_reason'] == 'content_filter':
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raise RuntimeError("由于提问含不合规内容被Azure过滤。")
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if json_data['finish_reason'] == 'length':
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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。")
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return result
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def generate_azure_payload(inputs, llm_kwargs, 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 azure openai api请求，为发送请求做准备
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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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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_kwargs['llm_model'],
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"messages": messages,
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"temperature": llm_kwargs['temperature'], # 1.0,
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"top_p": llm_kwargs['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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"engine": AZURE_ENGINE
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}
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try:
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print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
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except:
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print('输入中可能存在乱码。')
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return payload
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