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编写配置说明
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### 开始使用
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### 开始使用
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> 环境配置完成后,启动步骤为先启动chatchat-server,然后启动chatchat-frontend。
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> 环境配置完成后,启动步骤为先启动chatchat-server,然后启动chatchat-frontend。
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> chatchat可通过pypi安装一键启动,您也可以选择使用源码启动。(Tips:源码配置可以帮助我们更快的寻找bug,或者改进基础设施。我们不建议新手使用这个方式)
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> chatchat可通过pypi安装一键启动,您也可以选择使用[源码启动](README_dev.md)。(Tips:源码配置可以帮助我们更快的寻找bug,或者改进基础设施。我们不建议新手使用这个方式)
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#### pypi安装一键启动
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#### pypi安装一键启动
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- 安装chatchat
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- 安装chatchat
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```shell
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```shell
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pip install langchain-chatchat -U
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pip install langchain-chatchat -U
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```
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```
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- 复制配置文件
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> 后面我们会提供一个一键初始化的脚本,现在您可以手动复制配置文件
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> 工作空间配置
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> 请注意:这个命令会清空数据库,如果您有重要数据,请备份
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>
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> 操作指令` chatchat-config`
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```text
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options:
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-h, --help show this help message and exit
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-v {true,false}, --verbose {true,false}
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是否开启详细日志
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-d DATA, --data DATA 数据存放路径
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-f FORMAT, --format FORMAT
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日志格式
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--clear 清除配置
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```
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> 查看配置
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```shell
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```shell
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cd chatchat-server/chatchat
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chatchat-config --show ±[●●][dev_config_init]
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mkdir -p ~/.config/chatchat/
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{
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cp -r configs ~/.config/chatchat/
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"log_verbose": false,
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cp -r data ~/.config/chatchat/
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"CHATCHAT_ROOT": "/media/gpt4-pdf-chatbot-langchain/langchain-ChatGLM/libs/chatchat-server/chatchat",
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cp -r img ~/.config/chatchat/
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"DATA_PATH": "/media/gpt4-pdf-chatbot-langchain/langchain-ChatGLM/libs/chatchat-server/chatchat/data",
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"IMG_DIR": "/media/gpt4-pdf-chatbot-langchain/langchain-ChatGLM/libs/chatchat-server/chatchat/img",
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"NLTK_DATA_PATH": "/media/gpt4-pdf-chatbot-langchain/langchain-ChatGLM/libs/chatchat-server/chatchat/data/nltk_data",
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"LOG_FORMAT": "%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s",
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"LOG_PATH": "/media/gpt4-pdf-chatbot-langchain/langchain-ChatGLM/libs/chatchat-server/chatchat/data/logs",
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"MEDIA_PATH": "/media/gpt4-pdf-chatbot-langchain/langchain-ChatGLM/libs/chatchat-server/chatchat/data/media",
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"BASE_TEMP_DIR": "/media/gpt4-pdf-chatbot-langchain/langchain-ChatGLM/libs/chatchat-server/chatchat/data/temp",
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"class_name": "ConfigBasic"
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}
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```
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```
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> 当配置文件复制完成后,配置拷贝后路径的`model_providers.yaml`文件,即可完成自定义平台加载
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- 自定义平台加载
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> 配置*CHATCHAT_ROOT*文件夹configs中的`model_providers.yaml`文件,即可完成自定义平台加载
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```shell
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```shell
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cd ~/.config/chatchat/configs
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vim model_providers.yaml
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vim model_providers.yaml
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```
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```
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>
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>
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@ -38,35 +62,3 @@ chatchat-kb -r
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```shell
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```shell
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chatchat -a
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chatchat -a
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```
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```
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#### 源码启动chatchat-server
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- 安装chatchat
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```shell
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git clone https://github.com/chatchat-space/Langchain-Chatchat.git
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```
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- 修改`model_providers.yaml`文件,即可完成自定义平台加载
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```shell
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cd Langchain-Chatchat/libs/chatchat-server/chatchat/configs
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vim model_providers.yaml
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```
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- 平台配置
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> 注意: 在您配置平台之前,请确认平台依赖完整,例如智谱平台,您需要安装智谱sdk `pip install zhipuai`
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>
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> 详细配置请参考[README.md](../model-providers/README.md)
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- 初始化开发环境
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> [Code](../../docs/contributing/code.md): 源码配置可以帮助我们更快的寻找bug,或者改进基础设施。
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- 初始化仓库
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> 请注意:这个命令会清空数据库,如果您有重要数据,请备份
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```shell
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cd Langchain-Chatchat/libs/chatchat-server/chatchat
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python init_database.py --recreate-vs
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```
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- 启动服务
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```shell
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cd Langchain-Chatchat/libs/chatchat-server/chatchat
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python startup.py -a
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```
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50
libs/chatchat-server/README_dev.md
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50
libs/chatchat-server/README_dev.md
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#### 源码启动chatchat-server
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- 安装chatchat
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```shell
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git clone https://github.com/chatchat-space/Langchain-Chatchat.git
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```
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- 关于chatchat-config
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> chatchat-config由ConfigWorkSpace接口提供知识库配置载入存储
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>
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> 具体实现可以参考basic_config.py
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>
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- ConfigWorkSpace接口说明
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```text
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ConfigWorkSpace是一个配置工作空间的抽象类,提供基础的配置信息存储和读取功能。
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提供ConfigFactory建造方法产生实例。
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该类的实例对象用于存储工作空间的配置信息,如工作空间的路径等
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工作空间的配置信息存储在用户的家目录下的.chatchat/workspace/workspace_config.json文件中。
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注意:不存在则读取默认
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```
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- 关于`model_providers.yaml`文件,是平台配置文件
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> 我们抽象了不同平台提供的 全局配置`provider_credential`,和模型配置`model_credential`
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>关于设计文稿,请查看 [README_CN.md](model_providers/core/model_runtime/README_CN.md)
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- 平台配置
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> 注意: 在您配置平台之前,请确认平台依赖完整,例如智谱平台,您需要安装智谱sdk `pip install zhipuai`
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>
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> 详细配置请参考[README.md](../model-providers/README.md)
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- 初始化开发环境
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> [Code](../../docs/contributing/code.md): 源码配置可以帮助我们更快的寻找bug,或者改进基础设施。
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- 初始化仓库
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> 请注意:这个命令会清空数据库,如果您有重要数据,请备份
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```shell
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cd Langchain-Chatchat/libs/chatchat-server/chatchat
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python init_database.py --recreate-vs
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```
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- 启动服务
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```shell
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cd Langchain-Chatchat/libs/chatchat-server/chatchat
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python startup.py -a
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```
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from chatchat.configs import config_workspace as workspace
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from chatchat.configs import config_basic_workspace as workspace
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def main():
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def main():
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action="store_true",
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action="store_true",
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help="清除配置"
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help="清除配置"
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)
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)
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parser.add_argument(
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"--show",
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action="store_true",
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help="显示配置"
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)
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args = parser.parse_args()
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args = parser.parse_args()
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if args.verbose:
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if args.verbose:
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workspace.set_log_format(args.format)
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workspace.set_log_format(args.format)
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if args.clear:
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if args.clear:
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workspace.clear()
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workspace.clear()
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print(workspace.get_config())
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if args.show:
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print(workspace.get_config())
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if __name__ == "__main__":
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if __name__ == "__main__":
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return cls.__name__
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return cls.__name__
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def __str__(self):
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def __str__(self):
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return f"ConfigBasic(log_verbose={self.log_verbose}, CHATCHAT_ROOT={self.CHATCHAT_ROOT}, DATA_PATH={self.DATA_PATH}, IMG_DIR={self.IMG_DIR}, NLTK_DATA_PATH={self.NLTK_DATA_PATH}, LOG_FORMAT={self.LOG_FORMAT}, LOG_PATH={self.LOG_PATH}, MEDIA_PATH={self.MEDIA_PATH}, BASE_TEMP_DIR={self.BASE_TEMP_DIR})"
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return self.to_json()
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@dataclass
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@dataclass
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def to_json(self, **kwargs: Any) -> str:
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def to_json(self, **kwargs: Any) -> str:
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data = self.to_dict(**kwargs)
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data = self.to_dict(**kwargs)
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return json.dumps(data)
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return json.dumps(data, indent=4, ensure_ascii=False)
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F = TypeVar("F", bound=Config)
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F = TypeVar("F", bound=Config)
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