from fastapi import Query from pydantic import BaseModel, ConfigDict, Field from app.common.enums import QueueEnum from app.core.base_schema import BaseSchema, UserBySchema from app.core.validator import DateTimeStr class ChatQuerySchema(BaseModel): """聊天查询模型""" message: str = Field(..., min_length=1, max_length=4000, description="聊天消息") knowledge_ids: list[int] | None = Field(None, description="知识库ID列表，用于RAG检索") agent_config_id: int | None = Field(None, description="智能体配置ID，用于指定使用的智能体配置") class AgentConfigSchema(BaseModel): """智能体配置参数""" provider: str = Field("openai", description="LLM 供应商") model: str = Field(..., description="LLM 名称") api_key: str = Field(..., description="LLM API Key") base_url: str | None = Field(None, description="自定义 LLM API 地址") temperature: float = Field(0.7, description="温度参数，控制随机性") system_prompt: str = Field("你是一个有用的AI助手，可以帮助用户回答问题和提供帮助。请用中文回答用户的问题。", description="系统提示词") class AgentConfigCreateSchema(BaseModel): """创建智能体配置参数""" name: str = Field(..., max_length=100, description="智能体名称") provider: str = Field("openai", max_length=50, description="LLM 供应商") model: str = Field(..., max_length=100, description="LLM 名称") api_key: str = Field(..., max_length=500, description="LLM API Key") base_url: str | None = Field(None, max_length=500, description="自定义 LLM API 地址") temperature: float = Field(0.7, ge=0.0, le=2.0, description="温度参数，控制随机性") system_prompt: str = Field( "你是一个有用的AI助手，可以帮助用户回答问题和提供帮助。请用中文回答用户的问题。", description="系统提示词", ) is_default: bool = Field(False, description="是否默认配置") is_active: bool = Field(True, description="是否启用") class AgentConfigUpdateSchema(BaseModel): """更新智能体配置参数""" name: str | None = Field(None, max_length=100, description="智能体名称") provider: str | None = Field(None, max_length=50, description="LLM 供应商") model: str | None = Field(None, max_length=100, description="LLM 名称") api_key: str | None = Field(None, max_length=500, description="LLM API Key") base_url: str | None = Field(None, max_length=500, description="自定义 LLM API 地址") temperature: float | None = Field(None, ge=0.0, le=2.0, description="温度参数，控制随机性") system_prompt: str | None = Field(None, description="系统提示词") is_default: bool | None = Field(None, description="是否默认配置") is_active: bool | None = Field(None, description="是否启用") class AgentConfigOutSchema(AgentConfigCreateSchema, BaseSchema, UserBySchema): """智能体配置详情""" model_config = ConfigDict(from_attributes=True) class AgentConfigQueryParam: """智能体配置查询参数""" def __init__( self, name: str | None = Query(None, description="智能体名称"), provider: str | None = Query(None, description="LLM 供应商"), is_default: bool | None = Query(None, description="是否默认配置"), is_active: bool | None = Query(None, description="是否启用"), created_time: list[DateTimeStr] | None = Query( None, description="创建时间范围", examples=["2025-01-01 00:00:00", "2025-12-31 23:59:59"], ), updated_time: list[DateTimeStr] | None = Query( None, description="更新时间范围", examples=["2025-01-01 00:00:00", "2025-12-31 23:59:59"], ), created_id: int | None = Query(None, description="创建人"), updated_id: int | None = Query(None, description="更新人"), ) -> None: self.name = (QueueEnum.like.value, name) self.provider = (QueueEnum.eq.value, provider) self.is_default = (QueueEnum.eq.value, is_default) self.is_active = (QueueEnum.eq.value, is_active) self.created_id = (QueueEnum.eq.value, created_id) self.updated_id = (QueueEnum.eq.value, updated_id) if created_time and len(created_time) == 2: self.created_time = (QueueEnum.between.value, (created_time[0], created_time[1])) if updated_time and len(updated_time) == 2: self.updated_time = (QueueEnum.between.value, (updated_time[0], updated_time[1])) class KnowledgeCreateSchema(BaseModel): """创建知识库参数""" name: str = Field(..., max_length=100, description="知识库名称") description: str | None = Field(None, max_length=500, description="知识库描述") embedding_model: str = Field("openai", max_length=100, description="嵌入模型") chunk_size: int = Field(500, ge=100, le=2000, description="分块大小") chunk_overlap: int = Field(50, ge=0, le=500, description="分块重叠大小") is_active: bool = Field(True, description="是否启用") class KnowledgeUpdateSchema(KnowledgeCreateSchema): """更新知识库参数""" class KnowledgeOutSchema(KnowledgeCreateSchema, BaseSchema, UserBySchema): """知识库详情""" model_config = ConfigDict(from_attributes=True) class KnowledgeQueryParam: """知识库查询参数""" def __init__( self, name: str | None = Query(None, description="知识库名称"), is_active: bool | None = Query(None, description="是否启用"), created_time: list[DateTimeStr] | None = Query( None, description="创建时间范围", examples=["2025-01-01 00:00:00", "2025-12-31 23:59:59"], ), updated_time: list[DateTimeStr] | None = Query( None, description="更新时间范围", examples=["2025-01-01 00:00:00", "2025-12-31 23:59:59"], ), created_id: int | None = Query(None, description="创建人"), updated_id: int | None = Query(None, description="更新人"), ) -> None: self.name = (QueueEnum.like.value, name) self.is_active = (QueueEnum.eq.value, is_active) self.created_id = (QueueEnum.eq.value, created_id) self.updated_id = (QueueEnum.eq.value, updated_id) if created_time and len(created_time) == 2: self.created_time = (QueueEnum.between.value, (created_time[0], created_time[1])) if updated_time and len(updated_time) == 2: self.updated_time = (QueueEnum.between.value, (updated_time[0], updated_time[1])) class KnowledgeDocumentCreateSchema(BaseModel): """创建知识库文档参数""" knowledge_id: int = Field(..., description="知识库ID") title: str = Field(..., max_length=200, description="文档标题") content: str = Field(..., description="文档内容") file_type: str = Field("text", max_length=50, description="文件类型") file_path: str | None = Field(None, max_length=500, description="文件路径") meta_data: dict[str, str] | None = Field(None, description="元数据") class KnowledgeDocumentUpdateSchema(BaseModel): """更新知识库文档参数""" title: str | None = Field(None, max_length=200, description="文档标题") content: str | None = Field(None, description="文档内容") meta_data: dict[str, str] | None = Field(None, description="元数据") chunk_count: int | None = Field(None, description="分块数量") is_indexed: bool | None = Field(None, description="是否已索引") model_config = ConfigDict(extra="allow") class KnowledgeDocumentOutSchema(KnowledgeDocumentCreateSchema, BaseSchema, UserBySchema): """知识库文档详情""" chunk_count: int = Field(..., description="分块数量") is_indexed: bool = Field(..., description="是否已索引") model_config = ConfigDict(from_attributes=True) class KnowledgeDocumentQueryParam: """知识库文档查询参数""" def __init__( self, knowledge_id: int | None = Query(None, description="知识库ID"), title: str | None = Query(None, description="文档标题"), file_type: str | None = Query(None, description="文件类型"), is_indexed: bool | None = Query(None, description="是否已索引"), created_time: list[DateTimeStr] | None = Query( None, description="创建时间范围", examples=["2025-01-01 00:00:00", "2025-12-31 23:59:59"], ), created_id: int | None = Query(None, description="创建人"), ) -> None: self.knowledge_id = (QueueEnum.eq.value, knowledge_id) self.title = (QueueEnum.like.value, title) self.file_type = (QueueEnum.eq.value, file_type) self.is_indexed = (QueueEnum.eq.value, is_indexed) self.created_id = (QueueEnum.eq.value, created_id) if created_time and len(created_time) == 2: self.created_time = (QueueEnum.between.value, (created_time[0], created_time[1])) class RAGQuerySchema(BaseModel): """RAG检索查询参数""" query: str = Field(..., description="检索查询") knowledge_ids: list[int] = Field(..., description="知识库ID列表") top_k: int = Field(3, ge=1, le=10, description="返回最相关的文档数量")