374 lines
19 KiB
Python
374 lines
19 KiB
Python
# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
|
||
|
||
"""
|
||
该文件中主要包含三个函数
|
||
|
||
不具备多线程能力的函数：
|
||
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
|
||
|
||
具备多线程调用能力的函数
|
||
2. predict_no_ui：高级实验性功能模块调用，不会实时显示在界面上，参数简单，可以多线程并行，方便实现复杂的功能逻辑
|
||
3. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
|
||
"""
|
||
|
||
import json
|
||
import time
|
||
import gradio as gr
|
||
import logging
|
||
import traceback
|
||
import requests
|
||
import importlib
|
||
import random
|
||
|
||
# config_private.py放自己的秘密如API和代理网址
|
||
# 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
|
||
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
|
||
proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
|
||
get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
|
||
|
||
timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
|
||
'网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
|
||
|
||
def get_full_error(chunk, stream_response):
|
||
"""
|
||
获取完整的从Openai返回的报错
|
||
"""
|
||
while True:
|
||
try:
|
||
chunk += next(stream_response)
|
||
except:
|
||
break
|
||
return chunk
|
||
|
||
def decode_chunk(chunk):
|
||
# 提前读取一些信息 （用于判断异常）
|
||
chunk_decoded = chunk.decode()
|
||
chunkjson = None
|
||
has_choices = False
|
||
has_content = False
|
||
has_role = False
|
||
try:
|
||
chunkjson = json.loads(chunk_decoded[6:])
|
||
has_choices = ('choices' in chunkjson) and (len(chunkjson['choices']) > 0)
|
||
if has_choices: has_content = "content" in chunkjson['choices'][0]["delta"]
|
||
if has_choices: has_role = "role" in chunkjson['choices'][0]["delta"]
|
||
except:
|
||
pass
|
||
return chunk_decoded, chunkjson, has_choices, has_content, has_role
|
||
|
||
from functools import lru_cache
|
||
@lru_cache(maxsize=32)
|
||
def verify_endpoint(endpoint):
|
||
"""
|
||
检查endpoint是否可用
|
||
"""
|
||
if "你亲手写的api名称" in endpoint:
|
||
raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
|
||
print(endpoint)
|
||
return endpoint
|
||
|
||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
|
||
"""
|
||
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。
|
||
inputs：
|
||
是本次问询的输入
|
||
sys_prompt:
|
||
系统静默prompt
|
||
llm_kwargs：
|
||
chatGPT的内部调优参数
|
||
history：
|
||
是之前的对话列表
|
||
observe_window = None：
|
||
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
|
||
"""
|
||
watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
|
||
headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
|
||
retry = 0
|
||
while True:
|
||
try:
|
||
# make a POST request to the API endpoint, stream=False
|
||
from .bridge_all import model_info
|
||
endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
|
||
response = requests.post(endpoint, headers=headers, proxies=proxies,
|
||
json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
|
||
except requests.exceptions.ReadTimeout as e:
|
||
retry += 1
|
||
traceback.print_exc()
|
||
if retry > MAX_RETRY: raise TimeoutError
|
||
if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
|
||
|
||
stream_response = response.iter_lines()
|
||
result = ''
|
||
json_data = None
|
||
while True:
|
||
try: chunk = next(stream_response).decode()
|
||
except StopIteration:
|
||
break
|
||
except requests.exceptions.ConnectionError:
|
||
chunk = next(stream_response).decode() # 失败了，重试一次？再失败就没办法了。
|
||
if len(chunk)==0: continue
|
||
if not chunk.startswith('data:'):
|
||
error_msg = get_full_error(chunk.encode('utf8'), stream_response).decode()
|
||
if "reduce the length" in error_msg:
|
||
raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
|
||
else:
|
||
raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
|
||
if ('data: [DONE]' in chunk): break # api2d 正常完成
|
||
json_data = json.loads(chunk.lstrip('data:'))['choices'][0]
|
||
delta = json_data["delta"]
|
||
if len(delta) == 0: break
|
||
if "role" in delta: continue
|
||
if "content" in delta:
|
||
result += delta["content"]
|
||
if not console_slience: print(delta["content"], end='')
|
||
if observe_window is not None:
|
||
# 观测窗，把已经获取的数据显示出去
|
||
if len(observe_window) >= 1:
|
||
observe_window[0] += delta["content"]
|
||
# 看门狗，如果超过期限没有喂狗，则终止
|
||
if len(observe_window) >= 2:
|
||
if (time.time()-observe_window[1]) > watch_dog_patience:
|
||
raise RuntimeError("用户取消了程序。")
|
||
else: raise RuntimeError("意外Json结构："+delta)
|
||
if json_data and json_data['finish_reason'] == 'content_filter':
|
||
raise RuntimeError("由于提问含不合规内容被Azure过滤。")
|
||
if json_data and json_data['finish_reason'] == 'length':
|
||
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。")
|
||
return result
|
||
|
||
|
||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||
"""
|
||
发送至chatGPT，流式获取输出。
|
||
用于基础的对话功能。
|
||
inputs 是本次问询的输入
|
||
top_p, temperature是chatGPT的内部调优参数
|
||
history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
|
||
chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
|
||
additional_fn代表点击的哪个按钮，按钮见functional.py
|
||
"""
|
||
if is_any_api_key(inputs):
|
||
chatbot._cookies['api_key'] = inputs
|
||
chatbot.append(("输入已识别为openai的api_key", what_keys(inputs)))
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
|
||
return
|
||
elif not is_any_api_key(chatbot._cookies['api_key']):
|
||
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。"))
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
|
||
return
|
||
|
||
user_input = inputs
|
||
if additional_fn is not None:
|
||
from core_functional import handle_core_functionality
|
||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||
|
||
raw_input = inputs
|
||
logging.info(f'[raw_input] {raw_input}')
|
||
chatbot.append((inputs, ""))
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
|
||
|
||
# check mis-behavior
|
||
if is_the_upload_folder(user_input):
|
||
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。")
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
|
||
time.sleep(2)
|
||
|
||
try:
|
||
headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
|
||
except RuntimeError as e:
|
||
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。")
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
|
||
return
|
||
|
||
# 检查endpoint是否合法
|
||
try:
|
||
from .bridge_all import model_info
|
||
endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
|
||
except:
|
||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||
chatbot[-1] = (inputs, tb_str)
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="Endpoint不满足要求") # 刷新界面
|
||
return
|
||
|
||
history.append(inputs); history.append("")
|
||
|
||
retry = 0
|
||
while True:
|
||
try:
|
||
# make a POST request to the API endpoint, stream=True
|
||
response = requests.post(endpoint, headers=headers, proxies=proxies,
|
||
json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
|
||
except:
|
||
retry += 1
|
||
chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
|
||
retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
|
||
if retry > MAX_RETRY: raise TimeoutError
|
||
|
||
gpt_replying_buffer = ""
|
||
|
||
is_head_of_the_stream = True
|
||
if stream:
|
||
stream_response = response.iter_lines()
|
||
while True:
|
||
try:
|
||
chunk = next(stream_response)
|
||
except StopIteration:
|
||
# 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
|
||
chunk_decoded = chunk.decode()
|
||
error_msg = chunk_decoded
|
||
# 首先排除一个one-api没有done数据包的第三方Bug情形
|
||
if len(gpt_replying_buffer.strip()) > 0 and len(error_msg) == 0:
|
||
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。")
|
||
break
|
||
# 其他情况，直接返回报错
|
||
chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
|
||
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
|
||
return
|
||
|
||
# 提前读取一些信息 （用于判断异常）
|
||
chunk_decoded, chunkjson, has_choices, has_content, has_role = decode_chunk(chunk)
|
||
|
||
if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"content" not in chunk_decoded):
|
||
# 数据流的第一帧不携带content
|
||
is_head_of_the_stream = False; continue
|
||
|
||
if chunk:
|
||
try:
|
||
if not has_choices:
|
||
# 一些垃圾第三方接口的出现这样的错误
|
||
continue
|
||
# 前者是API2D的结束条件，后者是OPENAI的结束条件
|
||
if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0):
|
||
# 判定为数据流的结束，gpt_replying_buffer也写完了
|
||
logging.info(f'[response] {gpt_replying_buffer}')
|
||
break
|
||
# 处理数据流的主体
|
||
status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
|
||
# 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
|
||
if has_content:
|
||
# 正常情况
|
||
gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
|
||
elif has_role:
|
||
# 一些第三方接口的出现这样的错误，兼容一下吧
|
||
continue
|
||
else:
|
||
# 一些垃圾第三方接口的出现这样的错误
|
||
gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
|
||
|
||
history[-1] = gpt_replying_buffer
|
||
chatbot[-1] = (history[-2], history[-1])
|
||
yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
|
||
except Exception as e:
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
|
||
chunk = get_full_error(chunk, stream_response)
|
||
chunk_decoded = chunk.decode()
|
||
error_msg = chunk_decoded
|
||
chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
|
||
print(error_msg)
|
||
return
|
||
|
||
def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
|
||
from .bridge_all import model_info
|
||
openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
|
||
if "reduce the length" in error_msg:
|
||
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
|
||
history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
|
||
max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
|
||
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.)")
|
||
elif "does not exist" in error_msg:
|
||
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.")
|
||
elif "Incorrect API key" in error_msg:
|
||
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)
|
||
elif "exceeded your current quota" in error_msg:
|
||
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)
|
||
elif "account is not active" in error_msg:
|
||
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)
|
||
elif "associated with a deactivated account" in error_msg:
|
||
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)
|
||
elif "bad forward key" in error_msg:
|
||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
|
||
elif "Not enough point" in error_msg:
|
||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
|
||
else:
|
||
from toolbox import regular_txt_to_markdown
|
||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
|
||
return chatbot, history
|
||
|
||
def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
|
||
"""
|
||
整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
|
||
"""
|
||
if not is_any_api_key(llm_kwargs['api_key']):
|
||
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。")
|
||
|
||
api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
|
||
|
||
headers = {
|
||
"Content-Type": "application/json",
|
||
"Authorization": f"Bearer {api_key}"
|
||
}
|
||
if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG})
|
||
if llm_kwargs['llm_model'].startswith('azure-'):
|
||
headers.update({"api-key": api_key})
|
||
if llm_kwargs['llm_model'] in AZURE_CFG_ARRAY.keys():
|
||
azure_api_key_unshared = AZURE_CFG_ARRAY[llm_kwargs['llm_model']]["AZURE_API_KEY"]
|
||
headers.update({"api-key": azure_api_key_unshared})
|
||
|
||
conversation_cnt = len(history) // 2
|
||
|
||
messages = [{"role": "system", "content": system_prompt}]
|
||
if conversation_cnt:
|
||
for index in range(0, 2*conversation_cnt, 2):
|
||
what_i_have_asked = {}
|
||
what_i_have_asked["role"] = "user"
|
||
what_i_have_asked["content"] = history[index]
|
||
what_gpt_answer = {}
|
||
what_gpt_answer["role"] = "assistant"
|
||
what_gpt_answer["content"] = history[index+1]
|
||
if what_i_have_asked["content"] != "":
|
||
if what_gpt_answer["content"] == "": continue
|
||
if what_gpt_answer["content"] == timeout_bot_msg: continue
|
||
messages.append(what_i_have_asked)
|
||
messages.append(what_gpt_answer)
|
||
else:
|
||
messages[-1]['content'] = what_gpt_answer['content']
|
||
|
||
what_i_ask_now = {}
|
||
what_i_ask_now["role"] = "user"
|
||
what_i_ask_now["content"] = inputs
|
||
messages.append(what_i_ask_now)
|
||
model = llm_kwargs['llm_model']
|
||
if llm_kwargs['llm_model'].startswith('api2d-'):
|
||
model = llm_kwargs['llm_model'][len('api2d-'):]
|
||
|
||
if model == "gpt-3.5-random": # 随机选择, 绕过openai访问频率限制
|
||
model = random.choice([
|
||
"gpt-3.5-turbo",
|
||
"gpt-3.5-turbo-16k",
|
||
"gpt-3.5-turbo-0613",
|
||
"gpt-3.5-turbo-16k-0613",
|
||
"gpt-3.5-turbo-0301",
|
||
])
|
||
logging.info("Random select model:" + model)
|
||
|
||
payload = {
|
||
"model": model,
|
||
"messages": messages,
|
||
"temperature": llm_kwargs['temperature'], # 1.0,
|
||
"top_p": llm_kwargs['top_p'], # 1.0,
|
||
"n": 1,
|
||
"stream": stream,
|
||
"presence_penalty": 0,
|
||
"frequency_penalty": 0,
|
||
}
|
||
try:
|
||
print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
|
||
except:
|
||
print('输入中可能存在乱码。')
|
||
return headers,payload
|
||
|
||
|