61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
from pydantic import BaseModel, Field
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from typing import Optional, Literal, List
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from app.schemas.auth import UserOut
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# LLM Provider 类型
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LLMProviderType = Literal["openai", "claude", "ollama", "deepseek", "custom"]
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LLMType = Literal["chat", "vlm", "embedding", "rerank"]
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# 单个模型配置
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class LLMModelConfig(BaseModel):
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name: str = "" # 模型名称/别名，用于标识
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# provider 已废弃为必填字段：优先通过 base_url + model 推断。
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provider: Optional[LLMProviderType] = None
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model: str = ""
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base_url: str = ""
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api_key: str = ""
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enabled: bool = True # 是否启用
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# LLM 配置输入 - 每种类型支持多个模型
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class LLMConfigIn(BaseModel):
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chat: Optional[List[LLMModelConfig]] = []
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vlm: Optional[List[LLMModelConfig]] = []
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embedding: Optional[List[LLMModelConfig]] = []
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rerank: Optional[List[LLMModelConfig]] = []
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# 定时任务配置
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class SchedulerConfigIn(BaseModel):
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daily_plan_time: Optional[str] = "08:00"
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forum_scan_interval_minutes: Optional[int] = 30
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todo_ai_generate_time: Optional[str] = "08:00"
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enabled: Optional[bool] = True
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# 用户资料更新
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class ProfileUpdateIn(BaseModel):
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full_name: Optional[str] = Field(None, min_length=2, max_length=50)
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password: Optional[str] = Field(None, min_length=8)
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current_password: Optional[str] = None
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# 完整设置输出
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class SettingsOut(BaseModel):
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profile: UserOut
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llm_config: Optional[dict] = None
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scheduler_config: Optional[dict] = None
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model_config = {"from_attributes": True}
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# 测试 LLM 连接请求
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class LLMTestIn(BaseModel):
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type: LLMType
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# provider 已废弃为必填字段：优先通过 base_url + model 推断。
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provider: Optional[LLMProviderType] = None
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model: str
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base_url: str
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api_key: str
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