1405b50d5a
Refactored configuration classes to inherit from a new ValidatedConfigBase using Pydantic for robust validation and error reporting. Updated api_ada_configs.py, config.py, config_base.py, and official_configs.py to replace dataclasses with Pydantic models, add field validation, and improve error messages. This change enhances configuration reliability and developer feedback for misconfigurations. Also includes minor code cleanups and removal of unused variables in other modules.
269 lines
9.6 KiB
Python
269 lines
9.6 KiB
Python
from typing import List, Dict, Any
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from pydantic import Field, field_validator
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from src.config.config_base import ValidatedConfigBase
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class APIProvider(ValidatedConfigBase):
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"""API提供商配置类"""
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name: str = Field(..., min_length=1, description="API提供商名称")
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base_url: str = Field(..., description="API基础URL")
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api_key: str = Field(..., min_length=1, description="API密钥")
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client_type: str = Field(default="openai", description="客户端类型（如openai/google等，默认为openai）")
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max_retry: int = Field(default=2, ge=0, description="最大重试次数（单个模型API调用失败，最多重试的次数）")
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timeout: int = Field(default=10, ge=1, description="API调用的超时时长（超过这个时长，本次请求将被视为'请求超时'，单位：秒）")
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retry_interval: int = Field(default=10, ge=0, description="重试间隔（如果API调用失败，重试的间隔时间，单位：秒）")
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enable_content_obfuscation: bool = Field(default=False, description="是否启用内容混淆（用于特定场景下的内容处理）")
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obfuscation_intensity: int = Field(default=1, ge=1, le=3, description="混淆强度（1-3级，数值越高混淆程度越强）")
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@field_validator('base_url')
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@classmethod
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def validate_base_url(cls, v):
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"""验证base_url，确保URL格式正确"""
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if v and not (v.startswith('http://') or v.startswith('https://')):
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raise ValueError("base_url必须以http://或https://开头")
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return v
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@field_validator('api_key')
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@classmethod
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def validate_api_key(cls, v):
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"""验证API密钥不能为空"""
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if not v or not v.strip():
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raise ValueError("API密钥不能为空")
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return v
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@field_validator('client_type')
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@classmethod
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def validate_client_type(cls, v):
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"""验证客户端类型"""
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allowed_types = ["openai", "gemini"]
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if v not in allowed_types:
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raise ValueError(f"客户端类型必须是以下之一: {allowed_types}")
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return v
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def get_api_key(self) -> str:
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return self.api_key
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class ModelInfo(ValidatedConfigBase):
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"""单个模型信息配置类"""
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model_identifier: str = Field(..., min_length=1, description="模型标识符（用于URL调用）")
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name: str = Field(..., min_length=1, description="模型名称（用于模块调用）")
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api_provider: str = Field(..., min_length=1, description="API提供商（如OpenAI、Azure等）")
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price_in: float = Field(default=0.0, ge=0, description="每M token输入价格")
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price_out: float = Field(default=0.0, ge=0, description="每M token输出价格")
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force_stream_mode: bool = Field(default=False, description="是否强制使用流式输出模式")
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extra_params: Dict[str, Any] = Field(default_factory=dict, description="额外参数（用于API调用时的额外配置）")
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@field_validator('price_in', 'price_out')
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@classmethod
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def validate_prices(cls, v):
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"""验证价格必须为非负数"""
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if v < 0:
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raise ValueError("价格不能为负数")
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return v
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@field_validator('model_identifier')
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@classmethod
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def validate_model_identifier(cls, v):
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"""验证模型标识符不能为空且不能包含特殊字符"""
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if not v or not v.strip():
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raise ValueError("模型标识符不能为空")
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# 检查是否包含危险字符
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if any(char in v for char in [' ', '\n', '\t', '\r']):
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raise ValueError("模型标识符不能包含空格或换行符")
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return v
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@field_validator('name')
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@classmethod
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def validate_name(cls, v):
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"""验证模型名称不能为空"""
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if not v or not v.strip():
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raise ValueError("模型名称不能为空")
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return v
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class TaskConfig(ValidatedConfigBase):
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"""任务配置类"""
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model_list: List[str] = Field(default_factory=list, description="任务使用的模型列表")
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max_tokens: int = Field(default=1024, ge=1, le=100000, description="任务最大输出token数")
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temperature: float = Field(default=0.3, ge=0.0, le=2.0, description="模型温度")
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concurrency_count: int = Field(default=1, ge=1, le=10, description="并发请求数量，默认为1（不并发）")
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@field_validator('model_list')
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@classmethod
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def validate_model_list(cls, v):
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"""验证模型列表不能为空"""
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if not v:
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raise ValueError("模型列表不能为空")
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if len(v) != len(set(v)):
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raise ValueError("模型列表中不能有重复的模型")
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return v
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@field_validator('max_tokens')
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@classmethod
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def validate_max_tokens(cls, v):
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"""验证最大token数"""
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if v <= 0:
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raise ValueError("最大token数必须大于0")
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if v > 100000:
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raise ValueError("最大token数不能超过100000")
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return v
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class ModelTaskConfig(ValidatedConfigBase):
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"""模型配置类"""
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utils: TaskConfig = Field(..., description="组件模型配置")
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# 可选配置项（有默认值）
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utils_small: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["qwen3-8b"],
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max_tokens=800,
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temperature=0.7
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),
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description="组件小模型配置"
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)
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replyer_1: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["siliconflow-deepseek-v3"],
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max_tokens=800,
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temperature=0.2
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),
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description="normal_chat首要回复模型模型配置"
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)
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replyer_2: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["siliconflow-deepseek-v3"],
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max_tokens=800,
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temperature=0.7
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),
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description="normal_chat次要回复模型配置"
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)
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maizone: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["siliconflow-deepseek-v3"],
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max_tokens=800,
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temperature=0.3
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),
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description="maizone专用模型"
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)
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emotion: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["siliconflow-deepseek-v3"],
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max_tokens=800,
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temperature=0.7
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),
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description="情绪模型配置"
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)
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vlm: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["qwen2.5-vl-72b"],
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max_tokens=1500,
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temperature=0.3
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),
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description="视觉语言模型配置"
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)
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voice: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["siliconflow-deepseek-v3"],
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max_tokens=800,
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temperature=0.3
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),
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description="语音识别模型配置"
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)
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tool_use: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["siliconflow-deepseek-v3"],
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max_tokens=800,
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temperature=0.1
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),
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description="专注工具使用模型配置"
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)
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planner: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["siliconflow-deepseek-v3"],
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max_tokens=800,
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temperature=0.3
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),
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description="规划模型配置"
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)
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embedding: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["text-embedding-3-large"],
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max_tokens=1024,
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temperature=0.0
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),
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description="嵌入模型配置"
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)
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lpmm_entity_extract: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["siliconflow-deepseek-v3"],
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max_tokens=2000,
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temperature=0.1
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),
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description="LPMM实体提取模型配置"
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)
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lpmm_rdf_build: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["siliconflow-deepseek-v3"],
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max_tokens=2000,
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temperature=0.1
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),
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description="LPMM RDF构建模型配置"
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)
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lpmm_qa: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["siliconflow-deepseek-v3"],
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max_tokens=2000,
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temperature=0.3
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),
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description="LPMM问答模型配置"
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)
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schedule_generator: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["siliconflow-deepseek-v3"],
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max_tokens=1500,
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temperature=0.3
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),
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description="日程生成模型配置"
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)
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# 可选配置项（有默认值）
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video_analysis: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["qwen2.5-vl-72b"],
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max_tokens=1500,
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temperature=0.3
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),
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description="视频分析模型配置"
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)
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emoji_vlm: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["qwen2.5-vl-72b"],
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max_tokens=800
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),
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description="表情包识别模型配置"
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)
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anti_injection: TaskConfig = Field(
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default_factory=lambda: TaskConfig(
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model_list=["qwen2.5-vl-72b"],
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max_tokens=200,
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temperature=0.1
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),
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description="反注入检测专用模型配置"
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)
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def get_task(self, task_name: str) -> TaskConfig:
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"""获取指定任务的配置"""
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if hasattr(self, task_name):
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config = getattr(self, task_name)
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if config is None:
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raise ValueError(f"任务 '{task_name}' 未配置")
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return config
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raise ValueError(f"任务 '{task_name}' 未找到对应的配置")
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