281 lines
8.2 KiB
Python
281 lines
8.2 KiB
Python
from dataclasses import dataclass, is_dataclass, fields, MISSING
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from typing import Any, Tuple, Type
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import yaml
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from pathlib import Path
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from typing import Dict
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import os
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from loguru import logger
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from dotenv import load_dotenv
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load_dotenv()
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## NOTE: base classes taken from nerfstudio
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class PrintableConfig:
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"""
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Printable Config defining str function
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定义 __str__ 方法的可打印配置类
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"""
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def __str__(self):
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lines = [self.__class__.__name__ + ":"]
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for key, val in vars(self).items():
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if self.is_secrete(key):
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val = str(val)
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val = val[:3] + "*"*(len(val) - 6) + val[-3:]
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if isinstance(val, Tuple):
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flattened_val = "["
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for item in val:
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flattened_val += str(item) + "\n"
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flattened_val = flattened_val.rstrip("\n")
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val = flattened_val + "]"
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lines += f"{key}: {str(val)}".split("\n")
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return "\n" + "\n ".join(lines)
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def is_secrete(self, inp:str):
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sec_list = ["secret", "api_key"]
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for sec in sec_list:
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if sec in inp:
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return True
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return False
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# Base instantiate configs
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@dataclass
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class InstantiateConfig(PrintableConfig):
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"""
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Config class for instantiating an the class specified in the _target attribute.
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用于实例化 _target 属性指定的类的配置类
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"""
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_target: Type
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def setup(self, **kwargs) -> Any:
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"""
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Returns the instantiated object using the config.
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使用配置返回实例化的对象
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"""
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return self._target(self, **kwargs)
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def save_config(self, filename: str) -> None:
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"""
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Save the config to a YAML file.
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将配置保存到 YAML 文件
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"""
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def mask_value(key, value):
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"""
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Apply masking if key is secret-like
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如果键是敏感的，应用掩码
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检查键是否敏感（如包含 "secret" 或 "api_key"），如果是，则对值进行掩码处理
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"""
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if isinstance(value, str) and self.is_secrete(key):
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sval = str(value)
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return sval[:3] + "*" * (len(sval) - 6) + sval[-3:]
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return value
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def to_masked_serializable(obj):
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"""
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Recursively convert dataclasses and containers to serializable with masked secrets
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递归地将数据类和容器转换为可序列化的格式，同时对敏感信息进行掩码处理
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"""
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if is_dataclass(obj):
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out = {}
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for k, v in vars(obj).items():
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if is_dataclass(v) or isinstance(v, (dict, list, tuple)):
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out[k] = to_masked_serializable(v)
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else:
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out[k] = mask_value(k, v)
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return out
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if isinstance(obj, dict):
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out = {}
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for k, v in obj.items():
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if is_dataclass(v) or isinstance(v, (dict, list, tuple)):
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out[k] = to_masked_serializable(v)
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else:
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# k might be a non-string; convert to str for is_secrete check consistency
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key_str = str(k)
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out[k] = mask_value(key_str, v)
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return out
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if isinstance(obj, list):
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return [to_masked_serializable(v) for v in obj]
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if isinstance(obj, tuple):
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return tuple(to_masked_serializable(v) for v in obj)
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return obj
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masked = to_masked_serializable(self)
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with open(filename, 'w') as f:
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yaml.dump(masked, f)
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logger.info(f"[yellow]config saved to: {filename}[/yellow]")
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def get_name(self):
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return self.__class__.__name__
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@dataclass
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class KeyConfig(InstantiateConfig):
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api_key:str = None
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"""api key for llm"""
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def __post_init__(self):
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if self.api_key == "wrong-key" or self.api_key is None:
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self.api_key = os.environ.get("ALI_API_KEY")
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if self.api_key is None:
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logger.error(f"no ALI_API_KEY provided for embedding")
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else:
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logger.info("ALI_API_KEY loaded from environ")
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@dataclass
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class LLMKeyConfig(KeyConfig):
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llm_name: str = "qwen-plus"
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"""name of llm"""
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llm_provider:str = "openai"
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"""provider of the llm"""
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base_url:str = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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"""base url; could be used to overwrite the baseurl in llm provider"""
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@dataclass
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class ToolConfig(InstantiateConfig):
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use_tool:bool = True
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"""
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specify to use tool or not
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指定是否使用工具
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"""
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def load_tyro_conf(filename: str, inp_conf = None) -> InstantiateConfig:
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"""
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load and overwrite config from file
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从文件加载并覆盖配置
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"""
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config = yaml.load(Path(filename).read_text(), Loader=yaml.Loader)
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config = ovewrite_config(config, inp_conf) if inp_conf is not None else config
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return config
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def is_default(instance, field_):
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"""
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Check if the value of a field in a dataclass instance is the default value.
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检查数据类实例中字段的值是否为默认值
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"""
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value = getattr(instance, field_.name)
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if field_.default is not MISSING:
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# Compare with default value
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"""
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与默认值进行比较
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如果字段有默认值，则将当前值与默认值进行比较
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"""
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return value == field_.default
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elif field_.default_factory is not MISSING:
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# Compare with value generated by the default factory
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"""
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与默认工厂生成的值进行比较
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如果字段有默认工厂，则将当前值与默认工厂生成的值进行比较
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"""
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return value == field_.default_factory()
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else:
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# No default value specified
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return False
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def ovewrite_config(loaded_conf, inp_conf):
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"""
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for non-default values in inp_conf, overwrite the corresponding values in loaded_conf
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对于 inp_conf 中的非默认值，覆盖 loaded_conf 中对应的配置
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"""
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if not (is_dataclass(loaded_conf) and is_dataclass(inp_conf)):
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return loaded_conf
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for field_ in fields(loaded_conf):
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field_name = field_.name
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"""
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if field_name in inp_conf:
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如果字段名在 inp_conf 中，则进行覆盖
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"""
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current_value = getattr(inp_conf, field_name)
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new_value = getattr(inp_conf, field_name)
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"""
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inp_conf[field_name]
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从 inp_conf 中获取字段值
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如果字段名在 inp_conf 中，则获取其值
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"""
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if is_dataclass(current_value):
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"""
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Recurse for nested dataclasses
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递归处理嵌套的数据类
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如果当前值是数据类，则递归调用 ovewrite_config 进行合并
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"""
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merged_value = ovewrite_config(current_value, new_value)
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setattr(loaded_conf, field_name, merged_value)
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elif not is_default(inp_conf, field_):
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"""
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Overwrite only if the current value is not default
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仅在当前值不是默认值时进行覆盖
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如果 inp_conf 中的字段值不是默认值，则覆盖 loaded_conf 中的对应值
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"""
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setattr(loaded_conf, field_name, new_value)
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return loaded_conf
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def mcp_langchain_to_ws_config(conf:Dict[str, Dict[str, str]]):
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serv_conf = {}
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for k, v in conf.items():
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if v["transport"] == "stdio":
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serv_conf[k] = {
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"type" : v["transport"],
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"command": v["command"],
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"args": v["args"],
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}
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else:
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logger.warning(f"Unsupported transport {v['transport']} for MCP {k}. Skipping...")
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continue
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return {"mcpServers":serv_conf}
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