Merge pull request #662 from sperjar/master

自动编译Docker镜像并上传到ghcr
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binary-husky 2023-05-04 01:32:52 +08:00 committed by GitHub
commit 30d16989b7
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@ -3,6 +3,7 @@ import importlib
import traceback import traceback
import inspect import inspect
import re import re
import os
from latex2mathml.converter import convert as tex2mathml from latex2mathml.converter import convert as tex2mathml
from functools import wraps, lru_cache from functools import wraps, lru_cache
@ -517,13 +518,72 @@ def select_api_key(keys, llm_model):
api_key = random.choice(avail_key_list) # 随机负载均衡 api_key = random.choice(avail_key_list) # 随机负载均衡
return api_key return api_key
def read_env_variable(arg, default_value):
"""
环境变量可以是 `GPT_ACADEMIC_CONFIG`(优先)也可以直接是`CONFIG`
例如在windows cmd中既可以写
set USE_PROXY=True
set API_KEY=sk-j7caBpkRoxxxxxxxxxxxxxxxxxxxxxxxxxxxx
set proxies={"http":"http://127.0.0.1:10085", "https":"http://127.0.0.1:10085",}
set AVAIL_LLM_MODELS=["gpt-3.5-turbo", "chatglm"]
set AUTHENTICATION=[("username", "password"), ("username2", "password2")]
也可以写
set GPT_ACADEMIC_USE_PROXY=True
set GPT_ACADEMIC_API_KEY=sk-j7caBpkRoxxxxxxxxxxxxxxxxxxxxxxxxxxxx
set GPT_ACADEMIC_proxies={"http":"http://127.0.0.1:10085", "https":"http://127.0.0.1:10085",}
set GPT_ACADEMIC_AVAIL_LLM_MODELS=["gpt-3.5-turbo", "chatglm"]
set GPT_ACADEMIC_AUTHENTICATION=[("username", "password"), ("username2", "password2")]
"""
from colorful import print亮红, print亮绿
arg_with_prefix = "GPT_ACADEMIC_" + arg
if arg_with_prefix in os.environ:
env_arg = os.environ[arg_with_prefix]
elif arg in os.environ:
env_arg = os.environ[arg]
else:
raise KeyError
print(f"[ENV_VAR] 尝试加载{arg},默认值:{default_value} --> 修正值:{env_arg}")
try:
if isinstance(default_value, bool):
r = bool(env_arg)
elif isinstance(default_value, int):
r = int(env_arg)
elif isinstance(default_value, float):
r = float(env_arg)
elif isinstance(default_value, str):
r = env_arg
elif isinstance(default_value, dict):
r = eval(env_arg)
elif isinstance(default_value, list):
r = eval(env_arg)
elif default_value is None:
assert arg == "proxies"
r = eval(env_arg)
else:
print亮红(f"[ENV_VAR] 环境变量{arg}不支持通过环境变量设置! ")
raise KeyError
except:
print亮红(f"[ENV_VAR] 环境变量{arg}加载失败! ")
raise KeyError(f"[ENV_VAR] 环境变量{arg}加载失败! ")
print亮绿(f"[ENV_VAR] 成功读取环境变量{arg}")
return r
@lru_cache(maxsize=128) @lru_cache(maxsize=128)
def read_single_conf_with_lru_cache(arg): def read_single_conf_with_lru_cache(arg):
from colorful import print亮红, print亮绿, print亮蓝 from colorful import print亮红, print亮绿, print亮蓝
try: try:
r = getattr(importlib.import_module('config_private'), arg) # 优先级1. 获取环境变量作为配置
default_ref = getattr(importlib.import_module('config'), arg) # 读取默认值作为数据类型转换的参考
r = read_env_variable(arg, default_ref)
except: except:
r = getattr(importlib.import_module('config'), arg) try:
# 优先级2. 获取config_private中的配置
r = getattr(importlib.import_module('config_private'), arg)
except:
# 优先级3. 获取config中的配置
r = getattr(importlib.import_module('config'), arg)
# 在读取API_KEY时检查一下是不是忘了改config # 在读取API_KEY时检查一下是不是忘了改config
if arg == 'API_KEY': if arg == 'API_KEY':
print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和API2D的api-key。也支持同时填写多个api-key如API_KEY=\"openai-key1,openai-key2,api2d-key3\"") print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和API2D的api-key。也支持同时填写多个api-key如API_KEY=\"openai-key1,openai-key2,api2d-key3\"")