mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
synced 2026-03-02 08:09:43 +08:00
feat: add options to set voice bot
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@@ -76,6 +76,16 @@ class ChatGPTBot(Bot):
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reply = Reply(ReplyType.ERROR, 'Bot不支持处理{}类型的消息'.format(context.type))
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return reply
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def compose_args(self):
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return {
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"model": conf().get("model") or "gpt-3.5-turbo", # 对话模型的名称
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"temperature":conf().get('temperature', 0.9), # 值在[0,1]之间,越大表示回复越具有不确定性
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# "max_tokens":4096, # 回复最大的字符数
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"top_p":1,
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"frequency_penalty":conf().get('frequency_penalty', 0.0), # [-2,2]之间,该值越大则更倾向于产生不同的内容
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"presence_penalty":conf().get('presence_penalty', 0.0), # [-2,2]之间,该值越大则更倾向于产生不同的内容
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}
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def reply_text(self, session, session_id, retry_count=0) -> dict:
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'''
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call openai's ChatCompletion to get the answer
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@@ -88,13 +98,7 @@ class ChatGPTBot(Bot):
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if conf().get('rate_limit_chatgpt') and not self.tb4chatgpt.get_token():
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return {"completion_tokens": 0, "content": "提问太快啦,请休息一下再问我吧"}
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response = openai.ChatCompletion.create(
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model= conf().get("model") or "gpt-3.5-turbo", # 对话模型的名称
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messages=session,
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temperature=conf().get('temperature', 0.9), # 值在[0,1]之间,越大表示回复越具有不确定性
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#max_tokens=4096, # 回复最大的字符数
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top_p=1,
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frequency_penalty=conf().get('frequency_penalty', 0.0), # [-2,2]之间,该值越大则更倾向于产生不同的内容
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presence_penalty=conf().get('presence_penalty', 0.0), # [-2,2]之间,该值越大则更倾向于产生不同的内容
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messages=session, **self.compose_args()
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)
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# logger.info("[ChatGPT] reply={}, total_tokens={}".format(response.choices[0]['message']['content'], response["usage"]["total_tokens"]))
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return {"total_tokens": response["usage"]["total_tokens"],
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@@ -156,53 +160,11 @@ class AzureChatGPTBot(ChatGPTBot):
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openai.api_type = "azure"
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openai.api_version = "2023-03-15-preview"
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def reply_text(self, session, session_id, retry_count=0) ->dict:
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'''
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call openai's ChatCompletion to get the answer
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:param session: a conversation session
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:param session_id: session id
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:param retry_count: retry count
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:return: {}
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'''
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try:
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if conf().get('rate_limit_chatgpt') and not self.tb4chatgpt.get_token():
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return {"completion_tokens": 0, "content": "提问太快啦,请休息一下再问我吧"}
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response = openai.ChatCompletion.create(
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engine=conf().get("model") or "gpt-3.5-turbo", # the model deployment name on Azure
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messages=session,
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temperature=conf().get('temperature', 0.9), # 值在[0,1]之间,越大表示回复越具有不确定性
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#max_tokens=4096, # 回复最大的字符数
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top_p=1,
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frequency_penalty=conf().get('frequency_penalty', 0.0), # [-2,2]之间,该值越大则更倾向于产生不同的内容
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presence_penalty=conf().get('presence_penalty', 0.0), # [-2,2]之间,该值越大则更倾向于产生不同的内容
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)
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# logger.info("[ChatGPT] reply={}, total_tokens={}".format(response.choices[0]['message']['content'], response["usage"]["total_tokens"]))
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return {"total_tokens": response["usage"]["total_tokens"],
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"completion_tokens": response["usage"]["completion_tokens"],
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"content": response.choices[0]['message']['content']}
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except openai.error.RateLimitError as e:
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# rate limit exception
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logger.warn(e)
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if retry_count < 1:
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time.sleep(5)
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logger.warn("[OPEN_AI] RateLimit exceed, 第{}次重试".format(retry_count+1))
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return self.reply_text(session, session_id, retry_count+1)
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else:
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return {"completion_tokens": 0, "content": "提问太快啦,请休息一下再问我吧"}
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except openai.error.APIConnectionError as e:
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# api connection exception
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logger.warn(e)
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logger.warn("[OPEN_AI] APIConnection failed")
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return {"completion_tokens": 0, "content":"我连接不到你的网络"}
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except openai.error.Timeout as e:
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logger.warn(e)
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logger.warn("[OPEN_AI] Timeout")
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return {"completion_tokens": 0, "content":"我没有收到你的消息"}
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except Exception as e:
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# unknown exception
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logger.exception(e)
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Session.clear_session(session_id)
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return {"completion_tokens": 0, "content": "请再问我一次吧"}
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def compose_args(self):
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args = super().compose_args()
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args["engine"] = args["model"]
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del(args["model"])
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return args
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class SessionManager(object):
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@@ -13,8 +13,8 @@ class Bridge(object):
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def __init__(self):
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self.btype={
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"chat": const.CHATGPT,
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"voice_to_text": "openai",
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"text_to_voice": "baidu"
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"voice_to_text": conf().get("voice_to_text", "openai"),
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"text_to_voice": conf().get("text_to_voice", "baidu")
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}
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model_type = conf().get("model")
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if model_type in ["text-davinci-003"]:
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85
config.py
85
config.py
@@ -4,7 +4,84 @@ import json
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import os
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from common.log import logger
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config = {}
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# 将所有可用的配置项写在字典里
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available_setting ={
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#openai api配置
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"open_ai_api_key": "", # openai api key
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"open_ai_api_base": "https://api.openai.com/v1", # openai apibase,当use_azure_chatgpt为true时,需要设置对应的api base
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"proxy": "", # openai使用的代理
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"model": "gpt-3.5-turbo", # chatgpt模型, 当use_azure_chatgpt为true时,其名称为Azure上model deployment名称
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"use_azure_chatgpt": False, # 是否使用azure的chatgpt
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#Bot触发配置
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"single_chat_prefix": ["bot", "@bot"], # 私聊时文本需要包含该前缀才能触发机器人回复
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"single_chat_reply_prefix": "[bot] ", # 私聊时自动回复的前缀,用于区分真人
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"group_chat_prefix": ["@bot"], # 群聊时包含该前缀则会触发机器人回复
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"group_name_white_list": ["ChatGPT测试群", "ChatGPT测试群2"], # 开启自动回复的群名称列表
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"group_chat_in_one_session": ["ChatGPT测试群"], # 支持会话上下文共享的群名称
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"image_create_prefix": ["画", "看", "找"], # 开启图片回复的前缀
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#chatgpt会话参数
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"expires_in_seconds": 3600, # 无操作会话的过期时间
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"character_desc": "你是ChatGPT, 一个由OpenAI训练的大型语言模型, 你旨在回答并解决人们的任何问题,并且可以使用多种语言与人交流。", # 人格描述
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"conversation_max_tokens": 1000, # 支持上下文记忆的最多字符数
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#chatgpt限流配置
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"rate_limit_chatgpt": 20, # chatgpt的调用频率限制
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"rate_limit_dalle": 50, # openai dalle的调用频率限制
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#chatgpt api参数 参考https://platform.openai.com/docs/api-reference/chat/create
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"temperature": 0.9,
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"top_p": 1,
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"frequency_penalty": 0,
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"presence_penalty": 0,
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#语音设置
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"speech_recognition": False, # 是否开启语音识别
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"voice_reply_voice": False, # 是否使用语音回复语音,需要设置对应语音合成引擎的api key
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"voice_to_text": "openai", # 语音识别引擎,支持openai和google
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"text_to_voice": "baidu", # 语音合成引擎,支持baidu和google
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# baidu api的配置, 使用百度语音识别和语音合成时需要
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'baidu_app_id': "",
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'baidu_api_key': "",
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'baidu_secret_key': "",
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#服务时间限制,目前支持itchat
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"chat_time_module": False, # 是否开启服务时间限制
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"chat_start_time": "00:00", # 服务开始时间
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"chat_stop_time": "24:00", # 服务结束时间
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# itchat的配置
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"hot_reload": False, # 是否开启热重载
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# chatgpt指令自定义触发词
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"clear_memory_commands": ['#清除记忆'], # 重置会话指令
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}
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class Config(dict):
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def __getitem__(self, key):
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if key not in available_setting:
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raise Exception("key {} not in available_setting".format(key))
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return super().__getitem__(key)
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def __setitem__(self, key, value):
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if key not in available_setting:
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raise Exception("key {} not in available_setting".format(key))
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return super().__setitem__(key, value)
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def get(self, key, default=None):
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try :
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return self[key]
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except KeyError as e:
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return default
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except Exception as e:
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raise e
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config = Config()
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def load_config():
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global config
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@@ -15,12 +92,14 @@ def load_config():
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config_str = read_file(config_path)
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# 将json字符串反序列化为dict类型
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config = json.loads(config_str)
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config = Config(json.loads(config_str))
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# override config with environment variables.
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# Some online deployment platforms (e.g. Railway) deploy project from github directly. So you shouldn't put your secrets like api key in a config file, instead use environment variables to override the default config.
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for name, value in os.environ.items():
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config[name] = value
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if name in available_setting:
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logger.info("[INIT] override config by environ args: {}={}".format(name, value))
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config[name] = value
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logger.info("[INIT] load config: {}".format(config))
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