from threading import Lock from typing import Any, Literal from pydantic import Field from src.config.config_base import ValidatedConfigBase class APIProvider(ValidatedConfigBase): """API提供商配置类""" name: str = Field(..., min_length=1, description="API提供商名称") base_url: str = Field(..., description="API基础URL") api_key: str | list[str] = Field(..., min_length=1, description="API密钥，支持单个密钥或密钥列表轮询") client_type: Literal["openai", "gemini", "aiohttp_gemini"] = Field( default="openai", description="客户端类型（如openai/google等，默认为openai）" ) 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）") 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）" ) 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）") @classmethod def validate_base_url(cls, v): """验证base_url，确保URL格式正确""" if v and not (v.startswith("http://") or v.startswith("https://")): raise ValueError("base_url必须以http://或https://开头") return v @classmethod def validate_api_key(cls, v): """验证API密钥不能为空""" if isinstance(v, str): if not v.strip(): raise ValueError("API密钥不能为空") elif isinstance(v, list): if not v: raise ValueError("API密钥列表不能为空") for key in v: if not isinstance(key, str) or not key.strip(): raise ValueError("API密钥列表中的密钥不能为空") else: raise ValueError("API密钥必须是字符串或字符串列表") return v def __init__(self, **data): super().__init__(**data) self._api_key_lock = Lock() self._api_key_index = 0 def get_api_key(self) -> str: with self._api_key_lock: if isinstance(self.api_key, str): return self.api_key if not self.api_key: raise ValueError("API密钥列表为空") key = self.api_key[self._api_key_index] self._api_key_index = (self._api_key_index + 1) % len(self.api_key) return key class ModelInfo(ValidatedConfigBase): """单个模型信息配置类""" model_identifier: str = Field(..., min_length=1, description="模型标识符（用于URL调用）") name: str = Field(..., min_length=1, description="模型名称（用于模块调用）") api_provider: str = Field(..., min_length=1, description="API提供商（如OpenAI、Azure等）") price_in: float = Field(default=0.0, ge=0, description="每M token输入价格") price_out: float = Field(default=0.0, ge=0, description="每M token输出价格") force_stream_mode: bool = Field(default=False, description="是否强制使用流式输出模式") 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）") anti_truncation: bool = Field(default=False, description="是否启用反截断功能，防止模型输出被截断") enable_prompt_perturbation: bool = Field(default=False, description="是否启用提示词扰动（合并了内容混淆和注意力优化）") perturbation_strength: Literal["light", "medium", "heavy"] = Field( default="light", description="扰动强度（light/medium/heavy）" ) enable_semantic_variants: bool = Field(default=False, description="是否启用语义变体作为扰动策略") @classmethod def validate_prices(cls, v): """验证价格必须为非负数""" if v < 0: raise ValueError("价格不能为负数") return v @classmethod def validate_model_identifier(cls, v): """验证模型标识符不能为空且不能包含特殊字符""" if not v or not v.strip(): raise ValueError("模型标识符不能为空") # 检查是否包含危险字符 if any(char in v for char in [" ", "\n", "\t", "\r"]): raise ValueError("模型标识符不能包含空格或换行符") return v @classmethod def validate_name(cls, v): """验证模型名称不能为空""" if not v or not v.strip(): raise ValueError("模型名称不能为空") return v class TaskConfig(ValidatedConfigBase): """任务配置类""" model_list: list[str] = Field(..., description="任务使用的模型列表") max_tokens: int = Field(default=800, description="任务最大输出token数") temperature: float = Field(default=0.7, description="模型温度") concurrency_count: int = Field(default=1, description="并发请求数量") embedding_dimension: int | None = Field( default=None, description="嵌入模型输出向量维度，仅在嵌入任务中使用", ge=1, ) @classmethod def validate_model_list(cls, v): """验证模型列表不能为空""" if not v: raise ValueError("模型列表不能为空") if len(v) != len(set(v)): raise ValueError("模型列表中不能有重复的模型") return v class ModelTaskConfig(ValidatedConfigBase): """模型配置类""" # 必需配置项 utils: TaskConfig = Field(..., description="组件模型配置") utils_small: TaskConfig = Field(..., description="组件小模型配置") replyer: TaskConfig = Field(..., description="normal_chat首要回复模型模型配置") maizone: TaskConfig = Field(..., description="maizone专用模型") emotion: TaskConfig = Field(..., description="情绪模型配置") vlm: TaskConfig = Field(..., description="视觉语言模型配置") voice: TaskConfig = Field(..., description="语音识别模型配置") tool_use: TaskConfig = Field(..., description="专注工具使用模型配置") planner: TaskConfig = Field(..., description="规划模型配置") embedding: TaskConfig = Field(..., description="嵌入模型配置") lpmm_entity_extract: TaskConfig = Field(..., description="LPMM实体提取模型配置") lpmm_rdf_build: TaskConfig = Field(..., description="LPMM RDF构建模型配置") lpmm_qa: TaskConfig = Field(..., description="LPMM问答模型配置") schedule_generator: TaskConfig = Field(..., description="日程生成模型配置") monthly_plan_generator: TaskConfig = Field(..., description="月层计划生成模型配置") emoji_vlm: TaskConfig = Field(..., description="表情包识别模型配置") anti_injection: TaskConfig = Field(..., description="反注入检测专用模型配置") relationship_tracker: TaskConfig = Field(..., description="关系追踪模型配置") # 处理配置文件中命名不一致的问题 utils_video: TaskConfig = Field(..., description="视频分析模型配置（兼容配置文件中的命名）") # 记忆系统专用模型配置 memory_short_term_builder: TaskConfig = Field(..., description="短期记忆构建模型配置（感知→短期格式化）") memory_short_term_decider: TaskConfig = Field(..., description="短期记忆决策模型配置（合并/更新/新建/丢弃）") memory_long_term_builder: TaskConfig = Field(..., description="长期记忆构建模型配置（短期→长期图结构）") memory_judge: TaskConfig = Field(..., description="记忆检索裁判模型配置（判断检索是否充足）") @property def video_analysis(self) -> TaskConfig: """视频分析模型配置（提供向后兼容的属性访问）""" return self.utils_video def get_task(self, task_name: str) -> TaskConfig: """获取指定任务的配置""" # 处理向后兼容性：如果请求video_analysis，返回utils_video if task_name == "video_analysis": task_name = "utils_video" if hasattr(self, task_name): config = getattr(self, task_name) if config is None: raise ValueError(f"任务 '{task_name}' 未配置") return config raise ValueError(f"任务 '{task_name}' 未找到对应的配置") class APIAdapterConfig(ValidatedConfigBase): """API Adapter配置类""" models: list[ModelInfo] = Field(..., min_length=1, description="模型列表") model_task_config: ModelTaskConfig = Field(..., description="模型任务配置") api_providers: list[APIProvider] = Field(..., min_length=1, description="API提供商列表") def __init__(self, **data): super().__init__(**data) self.api_providers_dict = {provider.name: provider for provider in self.api_providers} self.models_dict = {model.name: model for model in self.models} @classmethod def validate_models_list(cls, v): """验证模型列表""" if not v: raise ValueError("模型列表不能为空，请在配置中设置有效的模型列表。") # 检查模型名称是否重复 model_names = [model.name for model in v] if len(model_names) != len(set(model_names)): raise ValueError("模型名称存在重复，请检查配置文件。") # 检查模型标识符是否有效 for model in v: if not model.model_identifier: raise ValueError(f"模型 '{model.name}' 的 model_identifier 不能为空") return v @classmethod def validate_api_providers_list(cls, v): """验证API提供商列表""" if not v: raise ValueError("API提供商列表不能为空，请在配置中设置有效的API提供商列表。") # 检查API提供商名称是否重复 provider_names = [provider.name for provider in v] if len(provider_names) != len(set(provider_names)): raise ValueError("API提供商名称存在重复，请检查配置文件。") return v def get_model_info(self, model_name: str) -> ModelInfo: """根据模型名称获取模型信息""" if not model_name: raise ValueError("模型名称不能为空") if model_name not in self.models_dict: raise KeyError(f"模型 '{model_name}' 不存在") return self.models_dict[model_name] def get_provider(self, provider_name: str) -> APIProvider: """根据提供商名称获取API提供商信息""" if not provider_name: raise ValueError("API提供商名称不能为空") if provider_name not in self.api_providers_dict: raise KeyError(f"API提供商 '{provider_name}' 不存在") return self.api_providers_dict[provider_name]