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https://github.com/fastapiadmin/FastapiAdmin.git
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zhangtao
45dad1e256
feat(backend): 添加ChromaDB向量数据库支持 feat(backend): 实现智能体配置、知识库和文档管理 feat(backend): 重构WebSocket聊天服务为AgentService feat(frontend): 实现完整的聊天界面组件 feat(frontend): 添加智能体配置、知识库和文档管理页面 fix(user): 修复用户导入时性别转换问题 style(import): 优化导入组件加载状态处理
217 lines
9.3 KiB
Python
217 lines
9.3 KiB
Python
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from fastapi import Query
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from pydantic import BaseModel, ConfigDict, Field
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from app.common.enums import QueueEnum
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from app.core.base_schema import BaseSchema, UserBySchema
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from app.core.validator import DateTimeStr
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class ChatQuerySchema(BaseModel):
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"""聊天查询模型"""
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message: str = Field(..., min_length=1, max_length=4000, description="聊天消息")
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knowledge_ids: list[int] | None = Field(None, description="知识库ID列表，用于RAG检索")
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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")
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class AgentConfigSchema(BaseModel):
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"""智能体配置参数"""
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provider: str = Field("openai", description="LLM 供应商")
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model: str = Field(..., description="LLM 名称")
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api_key: str = Field(..., description="LLM API Key")
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base_url: str | None = Field(None, description="自定义 LLM API 地址")
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temperature: float = Field(0.7, description="温度参数，控制随机性")
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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")
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class AgentConfigCreateSchema(BaseModel):
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"""创建智能体配置参数"""
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name: str = Field(..., max_length=100, description="智能体名称")
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provider: str = Field("openai", max_length=50, description="LLM 供应商")
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model: str = Field(..., max_length=100, description="LLM 名称")
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api_key: str = Field(..., max_length=500, description="LLM API Key")
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base_url: str | None = Field(None, max_length=500, description="自定义 LLM API 地址")
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temperature: float = Field(0.7, ge=0.0, le=2.0, description="温度参数，控制随机性")
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system_prompt: str = Field(
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"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。",
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description="系统提示词",
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)
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is_default: bool = Field(False, description="是否默认配置")
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is_active: bool = Field(True, description="是否启用")
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class AgentConfigUpdateSchema(BaseModel):
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"""更新智能体配置参数"""
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name: str | None = Field(None, max_length=100, description="智能体名称")
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provider: str | None = Field(None, max_length=50, description="LLM 供应商")
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model: str | None = Field(None, max_length=100, description="LLM 名称")
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api_key: str | None = Field(None, max_length=500, description="LLM API Key")
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base_url: str | None = Field(None, max_length=500, description="自定义 LLM API 地址")
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temperature: float | None = Field(None, ge=0.0, le=2.0, description="温度参数，控制随机性")
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system_prompt: str | None = Field(None, description="系统提示词")
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is_default: bool | None = Field(None, description="是否默认配置")
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is_active: bool | None = Field(None, description="是否启用")
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class AgentConfigOutSchema(AgentConfigCreateSchema, BaseSchema, UserBySchema):
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"""智能体配置详情"""
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model_config = ConfigDict(from_attributes=True)
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class AgentConfigQueryParam:
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"""智能体配置查询参数"""
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def __init__(
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self,
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name: str | None = Query(None, description="智能体名称"),
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provider: str | None = Query(None, description="LLM 供应商"),
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is_default: bool | None = Query(None, description="是否默认配置"),
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is_active: bool | None = Query(None, description="是否启用"),
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created_time: list[DateTimeStr] | None = Query(
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None,
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description="创建时间范围",
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examples=["2025-01-01 00:00:00", "2025-12-31 23:59:59"],
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),
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updated_time: list[DateTimeStr] | None = Query(
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None,
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description="更新时间范围",
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examples=["2025-01-01 00:00:00", "2025-12-31 23:59:59"],
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),
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created_id: int | None = Query(None, description="创建人"),
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updated_id: int | None = Query(None, description="更新人"),
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) -> None:
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self.name = (QueueEnum.like.value, name)
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self.provider = (QueueEnum.eq.value, provider)
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self.is_default = (QueueEnum.eq.value, is_default)
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self.is_active = (QueueEnum.eq.value, is_active)
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self.created_id = (QueueEnum.eq.value, created_id)
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self.updated_id = (QueueEnum.eq.value, updated_id)
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if created_time and len(created_time) == 2:
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self.created_time = (QueueEnum.between.value, (created_time[0], created_time[1]))
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if updated_time and len(updated_time) == 2:
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self.updated_time = (QueueEnum.between.value, (updated_time[0], updated_time[1]))
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class KnowledgeCreateSchema(BaseModel):
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"""创建知识库参数"""
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name: str = Field(..., max_length=100, description="知识库名称")
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description: str | None = Field(None, max_length=500, description="知识库描述")
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embedding_model: str = Field("openai", max_length=100, description="嵌入模型")
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chunk_size: int = Field(500, ge=100, le=2000, description="分块大小")
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chunk_overlap: int = Field(50, ge=0, le=500, description="分块重叠大小")
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is_active: bool = Field(True, description="是否启用")
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class KnowledgeUpdateSchema(KnowledgeCreateSchema):
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"""更新知识库参数"""
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class KnowledgeOutSchema(KnowledgeCreateSchema, BaseSchema, UserBySchema):
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"""知识库详情"""
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model_config = ConfigDict(from_attributes=True)
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class KnowledgeQueryParam:
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"""知识库查询参数"""
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def __init__(
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self,
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name: str | None = Query(None, description="知识库名称"),
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is_active: bool | None = Query(None, description="是否启用"),
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created_time: list[DateTimeStr] | None = Query(
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None,
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description="创建时间范围",
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examples=["2025-01-01 00:00:00", "2025-12-31 23:59:59"],
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),
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updated_time: list[DateTimeStr] | None = Query(
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None,
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description="更新时间范围",
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examples=["2025-01-01 00:00:00", "2025-12-31 23:59:59"],
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),
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created_id: int | None = Query(None, description="创建人"),
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updated_id: int | None = Query(None, description="更新人"),
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) -> None:
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self.name = (QueueEnum.like.value, name)
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self.is_active = (QueueEnum.eq.value, is_active)
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self.created_id = (QueueEnum.eq.value, created_id)
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self.updated_id = (QueueEnum.eq.value, updated_id)
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if created_time and len(created_time) == 2:
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self.created_time = (QueueEnum.between.value, (created_time[0], created_time[1]))
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if updated_time and len(updated_time) == 2:
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self.updated_time = (QueueEnum.between.value, (updated_time[0], updated_time[1]))
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class KnowledgeDocumentCreateSchema(BaseModel):
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"""创建知识库文档参数"""
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knowledge_id: int = Field(..., description="知识库ID")
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title: str = Field(..., max_length=200, description="文档标题")
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content: str = Field(..., description="文档内容")
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file_type: str = Field("text", max_length=50, description="文件类型")
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file_path: str | None = Field(None, max_length=500, description="文件路径")
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meta_data: dict[str, str] | None = Field(None, description="元数据")
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class KnowledgeDocumentUpdateSchema(BaseModel):
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"""更新知识库文档参数"""
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title: str | None = Field(None, max_length=200, description="文档标题")
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content: str | None = Field(None, description="文档内容")
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meta_data: dict[str, str] | None = Field(None, description="元数据")
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chunk_count: int | None = Field(None, description="分块数量")
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is_indexed: bool | None = Field(None, description="是否已索引")
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model_config = ConfigDict(extra="allow")
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class KnowledgeDocumentOutSchema(KnowledgeDocumentCreateSchema, BaseSchema, UserBySchema):
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"""知识库文档详情"""
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chunk_count: int = Field(..., description="分块数量")
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is_indexed: bool = Field(..., description="是否已索引")
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model_config = ConfigDict(from_attributes=True)
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class KnowledgeDocumentQueryParam:
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"""知识库文档查询参数"""
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def __init__(
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self,
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knowledge_id: int | None = Query(None, description="知识库ID"),
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title: str | None = Query(None, description="文档标题"),
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file_type: str | None = Query(None, description="文件类型"),
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is_indexed: bool | None = Query(None, description="是否已索引"),
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created_time: list[DateTimeStr] | None = Query(
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None,
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description="创建时间范围",
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examples=["2025-01-01 00:00:00", "2025-12-31 23:59:59"],
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),
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created_id: int | None = Query(None, description="创建人"),
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) -> None:
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self.knowledge_id = (QueueEnum.eq.value, knowledge_id)
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self.title = (QueueEnum.like.value, title)
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self.file_type = (QueueEnum.eq.value, file_type)
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self.is_indexed = (QueueEnum.eq.value, is_indexed)
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self.created_id = (QueueEnum.eq.value, created_id)
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if created_time and len(created_time) == 2:
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self.created_time = (QueueEnum.between.value, (created_time[0], created_time[1]))
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class RAGQuerySchema(BaseModel):
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"""RAG检索查询参数"""
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query: str = Field(..., description="检索查询")
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knowledge_ids: list[int] = Field(..., description="知识库ID列表")
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top_k: int = Field(3, ge=1, le=10, description="返回最相关的文档数量")
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