313 lines
16 KiB
Python
313 lines
16 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_long_connection：支持多线程
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"""
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import json
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import time
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import logging
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import requests
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import base64
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import os
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import glob
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from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder, \
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update_ui_lastest_msg, get_max_token, encode_image, have_any_recent_upload_image_files
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proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
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get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
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timeout_bot_msg = '[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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def report_invalid_key(key):
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if get_conf("BLOCK_INVALID_APIKEY"):
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# 实验性功能，自动检测并屏蔽失效的KEY，请勿使用
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from request_llms.key_manager import ApiKeyManager
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api_key = ApiKeyManager().add_key_to_blacklist(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 decode_chunk(chunk):
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# 提前读取一些信息 （用于判断异常）
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chunk_decoded = chunk.decode()
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chunkjson = None
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has_choices = False
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choice_valid = False
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has_content = False
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has_role = False
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try:
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chunkjson = json.loads(chunk_decoded[6:])
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has_choices = 'choices' in chunkjson
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if has_choices: choice_valid = (len(chunkjson['choices']) > 0)
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if has_choices and choice_valid: has_content = "content" in chunkjson['choices'][0]["delta"]
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if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"]
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except:
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pass
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return chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role
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from functools import lru_cache
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@lru_cache(maxsize=32)
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def verify_endpoint(endpoint):
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"""
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检查endpoint是否可用
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"""
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return endpoint
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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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raise NotImplementedError
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def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
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have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
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if is_any_api_key(inputs):
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chatbot._cookies['api_key'] = inputs
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chatbot.append(("输入已识别为openai的api_key", what_keys(inputs)))
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yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
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return
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elif not is_any_api_key(chatbot._cookies['api_key']):
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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。"))
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yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
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return
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if not have_recent_file:
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chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
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yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面
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return
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if os.path.exists(inputs):
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chatbot.append((inputs, "已经接收到您上传的文件，您不需要再重复强调该文件的路径了，请直接输入您的问题。"))
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yield from update_ui(chatbot=chatbot, history=history, msg="等待指令") # 刷新界面
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return
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user_input = inputs
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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(additional_fn, inputs, history, chatbot)
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raw_input = inputs
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logging.info(f'[raw_input] {raw_input}')
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def make_media_input(inputs, image_paths):
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for image_path in image_paths:
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inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
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return inputs
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chatbot.append((make_media_input(inputs, image_paths), ""))
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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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# check mis-behavior
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if is_the_upload_folder(user_input):
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chatbot[-1] = (inputs, 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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yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
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time.sleep(2)
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try:
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headers, payload, api_key = generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths)
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except RuntimeError as e:
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chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
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yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
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return
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# 检查endpoint是否合法
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try:
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from .bridge_all import model_info
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endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
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except:
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tb_str = '```\n' + trimmed_format_exc() + '```'
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chatbot[-1] = (inputs, tb_str)
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yield from update_ui(chatbot=chatbot, history=history, msg="Endpoint不满足要求") # 刷新界面
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return
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history.append(make_media_input(inputs, image_paths))
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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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# make a POST request to the API endpoint, stream=True
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response = requests.post(endpoint, 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 from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
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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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try:
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chunk = next(stream_response)
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except StopIteration:
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# 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
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chunk_decoded = chunk.decode()
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error_msg = chunk_decoded
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# 首先排除一个one-api没有done数据包的第三方Bug情形
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if len(gpt_replying_buffer.strip()) > 0 and len(error_msg) == 0:
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yield from update_ui(chatbot=chatbot, history=history, msg="检测到有缺陷的非OpenAI官方接口，建议选择更稳定的接口。")
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break
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# 其他情况，直接返回报错
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chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key)
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yield from update_ui(chatbot=chatbot, history=history, msg="非OpenAI官方接口返回了错误:" + chunk.decode()) # 刷新界面
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return
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# 提前读取一些信息 （用于判断异常）
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chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
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if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"content" not in chunk_decoded):
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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 has_choices and not choice_valid:
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# 一些垃圾第三方接口的出现这样的错误
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continue
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# 前者是API2D的结束条件，后者是OPENAI的结束条件
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if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['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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lastmsg = chatbot[-1][-1] + f"\n\n\n\n「{llm_kwargs['llm_model']}调用结束，该模型不具备上下文对话能力，如需追问，请及时切换模型。」"
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yield from update_ui_lastest_msg(lastmsg, chatbot, history, delay=1)
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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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status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
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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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if has_content:
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# 正常情况
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gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
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elif has_role:
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# 一些第三方接口的出现这样的错误，兼容一下吧
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continue
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else:
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# 一些垃圾第三方接口的出现这样的错误
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gpt_replying_buffer = gpt_replying_buffer + chunkjson['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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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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chunk_decoded = chunk.decode()
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error_msg = chunk_decoded
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chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key)
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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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print(error_msg)
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return
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def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key=""):
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from .bridge_all import model_info
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openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
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if "reduce the length" in error_msg:
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if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
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history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
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max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
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elif "does not exist" in error_msg:
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
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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. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website); report_invalid_key(api_key)
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elif "exceeded your current quota" in error_msg:
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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." + openai_website); report_invalid_key(api_key)
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elif "account is not active" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website); report_invalid_key(api_key)
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elif "associated with a deactivated account" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website); report_invalid_key(api_key)
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elif "API key has been deactivated" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. OpenAI以账户失效为由, 拒绝服务." + openai_website); report_invalid_key(api_key)
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elif "bad forward key" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
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elif "Not enough point" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
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else:
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from toolbox import regular_txt_to_markdown
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tb_str = '```\n' + trimmed_format_exc() + '```'
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
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return chatbot, history
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def generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths):
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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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if not is_any_api_key(llm_kwargs['api_key']):
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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。")
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api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
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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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if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG})
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if llm_kwargs['llm_model'].startswith('azure-'):
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headers.update({"api-key": api_key})
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if llm_kwargs['llm_model'] in AZURE_CFG_ARRAY.keys():
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azure_api_key_unshared = AZURE_CFG_ARRAY[llm_kwargs['llm_model']]["AZURE_API_KEY"]
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headers.update({"api-key": azure_api_key_unshared})
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base64_images = []
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for image_path in image_paths:
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base64_images.append(encode_image(image_path))
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messages = []
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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"] = []
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what_i_ask_now["content"].append({
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"type": "text",
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"text": inputs
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})
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for image_path, base64_image in zip(image_paths, base64_images):
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what_i_ask_now["content"].append({
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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})
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messages.append(what_i_ask_now)
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model = llm_kwargs['llm_model']
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if llm_kwargs['llm_model'].startswith('api2d-'):
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model = llm_kwargs['llm_model'][len('api2d-'):]
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payload = {
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"model": 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": True,
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"max_tokens": get_max_token(llm_kwargs),
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"presence_penalty": 0,
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"frequency_penalty": 0,
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}
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try:
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print(f" {llm_kwargs['llm_model']} : {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 headers, payload, api_key
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