420 lines
16 KiB
Python
420 lines
16 KiB
Python
import json
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import time
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import traceback
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import requests
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from loguru import logger
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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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from toolbox import (
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get_conf,
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update_ui,
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is_the_upload_folder,
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)
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proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf(
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"proxies", "TIMEOUT_SECONDS", "MAX_RETRY"
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)
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timeout_bot_msg = (
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"[Local Message] Request timeout. Network error. Please check proxy settings in config.py."
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+ "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。"
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)
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def get_full_error(chunk, stream_response):
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"""
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尝试获取完整的错误信息
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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 decode_chunk(chunk):
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"""
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用于解读"content"和"finish_reason"的内容（如果支持思维链也会返回"reasoning_content"内容）
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"""
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chunk = chunk.decode()
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respose = ""
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finish_reason = "False"
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reasoning_content = ""
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try:
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chunk = json.loads(chunk[6:])
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except:
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respose = ""
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finish_reason = chunk
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# 错误处理部分
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if "error" in chunk:
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respose = "API_ERROR"
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try:
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chunk = json.loads(chunk)
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finish_reason = chunk["error"]["code"]
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except:
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finish_reason = "API_ERROR"
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return respose, finish_reason
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try:
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if chunk["choices"][0]["delta"]["content"] is not None:
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respose = chunk["choices"][0]["delta"]["content"]
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except:
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pass
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try:
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finish_reason = chunk["choices"][0]["finish_reason"]
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except:
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pass
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try:
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if chunk["choices"][0]["delta"]["reasoning_content"] is not None:
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reasoning_content = chunk["choices"][0]["delta"]["reasoning_content"]
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except:
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pass
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return respose, finish_reason, reasoning_content
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def generate_message(input, model, key, history, max_output_token, system_prompt, temperature):
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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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api_key = f"Bearer {key}"
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headers = {"Content-Type": "application/json", "Authorization": api_key}
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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"] == "":
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continue
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if what_gpt_answer["content"] == timeout_bot_msg:
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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"] = input
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messages.append(what_i_ask_now)
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playload = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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"stream": True,
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"max_tokens": max_output_token,
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}
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return headers, playload
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def get_predict_function(
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api_key_conf_name,
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max_output_token,
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disable_proxy = False
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):
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"""
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为openai格式的API生成响应函数，其中传入参数：
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api_key_conf_name：
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`config.py`中此模型的APIKEY的名字，例如"YIMODEL_API_KEY"
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max_output_token：
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每次请求的最大token数量，例如对于01万物的yi-34b-chat-200k，其最大请求数为4096
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⚠️请不要与模型的最大token数量相混淆。
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disable_proxy：
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是否使用代理，True为不使用，False为使用。
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"""
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APIKEY = get_conf(api_key_conf_name)
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def predict_no_ui_long_connection(
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inputs,
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llm_kwargs,
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history=[],
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sys_prompt="",
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observe_window=None,
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console_slience=False,
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):
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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秒不准咬人(咬的也不是人
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if len(APIKEY) == 0:
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raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
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if inputs == "":
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inputs = "你好👋"
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headers, playload = generate_message(
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input=inputs,
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model=llm_kwargs["llm_model"],
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key=APIKEY,
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history=history,
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max_output_token=max_output_token,
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system_prompt=sys_prompt,
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temperature=llm_kwargs["temperature"],
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)
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from .bridge_all import model_info
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reasoning = model_info[llm_kwargs['llm_model']].get('enable_reasoning', False)
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retry = 0
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while True:
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try:
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endpoint = model_info[llm_kwargs["llm_model"]]["endpoint"]
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response = requests.post(
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endpoint,
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headers=headers,
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proxies=None if disable_proxy else proxies,
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json=playload,
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stream=True,
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timeout=TIMEOUT_SECONDS,
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)
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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:
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raise TimeoutError
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if MAX_RETRY != 0:
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logger.error(f"请求超时，正在重试 ({retry}/{MAX_RETRY}) ……")
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result = ""
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finish_reason = ""
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if reasoning:
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resoning_buffer = ""
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stream_response = response.iter_lines()
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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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if result == "":
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raise RuntimeError(f"获得空的回复，可能原因:{finish_reason}")
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break
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except requests.exceptions.ConnectionError:
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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 reasoning:
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response_text, finish_reason, reasoning_content = decode_chunk(chunk)
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else:
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response_text, finish_reason = decode_chunk(chunk)
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# 返回的数据流第一次为空，继续等待
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if response_text == "" and (reasoning == False or reasoning_content == "") and finish_reason != "False":
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continue
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if response_text == "API_ERROR" and (
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finish_reason != "False" or finish_reason != "stop"
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):
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chunk = get_full_error(chunk, stream_response)
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chunk_decoded = chunk.decode()
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logger.error(chunk_decoded)
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raise RuntimeError(
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f"API异常,请检测终端输出。可能的原因是:{finish_reason}"
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)
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if chunk:
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try:
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if finish_reason == "stop":
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if not console_slience:
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print(f"[response] {result}")
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break
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result += response_text
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if reasoning:
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resoning_buffer += reasoning_content
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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:
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observe_window[0] += response_text
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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) >= 2:
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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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except Exception as e:
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chunk = get_full_error(chunk, stream_response)
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chunk_decoded = chunk.decode()
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error_msg = chunk_decoded
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logger.error(error_msg)
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raise RuntimeError("Json解析不合常规")
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if reasoning:
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return '\n'.join(map(lambda x: '> ' + x, resoning_buffer.split('\n'))) + '\n\n' + result
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return result
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def predict(
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inputs,
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llm_kwargs,
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plugin_kwargs,
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chatbot,
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history=[],
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system_prompt="",
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stream=True,
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additional_fn=None,
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):
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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 len(APIKEY) == 0:
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raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
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if inputs == "":
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inputs = "你好👋"
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if additional_fn is not None:
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from core_functional import handle_core_functionality
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inputs, history = handle_core_functionality(
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additional_fn, inputs, history, chatbot
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)
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logger.info(f"[raw_input] {inputs}")
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chatbot.append((inputs, ""))
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yield from update_ui(
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chatbot=chatbot, history=history, msg="等待响应"
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) # 刷新界面
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# check mis-behavior
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if is_the_upload_folder(inputs):
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chatbot[-1] = (
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inputs,
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f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。",
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)
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yield from update_ui(
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chatbot=chatbot, history=history, msg="正常"
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) # 刷新界面
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time.sleep(2)
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headers, playload = generate_message(
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input=inputs,
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model=llm_kwargs["llm_model"],
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key=APIKEY,
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history=history,
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max_output_token=max_output_token,
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system_prompt=system_prompt,
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temperature=llm_kwargs["temperature"],
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)
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from .bridge_all import model_info
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reasoning = model_info[llm_kwargs['llm_model']].get('enable_reasoning', False)
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history.append(inputs)
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history.append("")
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retry = 0
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while True:
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try:
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endpoint = model_info[llm_kwargs["llm_model"]]["endpoint"]
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response = requests.post(
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endpoint,
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headers=headers,
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proxies=None if disable_proxy else proxies,
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json=playload,
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stream=True,
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timeout=TIMEOUT_SECONDS,
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)
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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 = (
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f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
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)
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yield from update_ui(
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chatbot=chatbot, history=history, msg="请求超时" + retry_msg
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) # 刷新界面
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if retry > MAX_RETRY:
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raise TimeoutError
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gpt_replying_buffer = ""
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if reasoning:
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gpt_reasoning_buffer = ""
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stream_response = response.iter_lines()
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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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break
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except requests.exceptions.ConnectionError:
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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 reasoning:
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response_text, finish_reason, reasoning_content = decode_chunk(chunk)
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else:
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response_text, finish_reason = decode_chunk(chunk)
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# 返回的数据流第一次为空，继续等待
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if response_text == "" and (reasoning == False or reasoning_content == "") and finish_reason != "False":
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status_text = f"finish_reason: {finish_reason}"
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yield from update_ui(
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chatbot=chatbot, history=history, msg=status_text
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)
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continue
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if chunk:
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try:
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if response_text == "API_ERROR" and (
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finish_reason != "False" or finish_reason != "stop"
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):
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chunk = get_full_error(chunk, stream_response)
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chunk_decoded = chunk.decode()
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chatbot[-1] = (
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chatbot[-1][0],
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"[Local Message] {finish_reason},获得以下报错信息：\n"
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+ chunk_decoded,
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)
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yield from update_ui(
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chatbot=chatbot,
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history=history,
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msg="API异常:" + chunk_decoded,
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) # 刷新界面
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logger.error(chunk_decoded)
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return
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if finish_reason == "stop":
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logger.info(f"[response] {gpt_replying_buffer}")
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break
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status_text = f"finish_reason: {finish_reason}"
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if reasoning:
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gpt_replying_buffer += response_text
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gpt_reasoning_buffer += reasoning_content
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history[-1] = '\n'.join(map(lambda x: '> ' + x, gpt_reasoning_buffer.split('\n'))) + '\n\n' + gpt_replying_buffer
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else:
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gpt_replying_buffer += response_text
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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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history[-1] = gpt_replying_buffer
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chatbot[-1] = (history[-2], history[-1])
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yield from update_ui(
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chatbot=chatbot, history=history, msg=status_text
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) # 刷新界面
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except Exception as e:
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yield from update_ui(
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chatbot=chatbot, history=history, msg="Json解析不合常规"
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) # 刷新界面
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chunk = get_full_error(chunk, stream_response)
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chunk_decoded = chunk.decode()
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chatbot[-1] = (
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chatbot[-1][0],
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"[Local Message] 解析错误,获得以下报错信息：\n" + chunk_decoded,
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)
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yield from update_ui(
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chatbot=chatbot, history=history, msg="Json异常" + chunk_decoded
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) # 刷新界面
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logger.error(chunk_decoded)
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return
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return predict_no_ui_long_connection, predict
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