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deepdata: core product code (backend, frontend, runtime, deepflow module)

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figmar
2026-08-30 19:23:38 +08:00
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runtime/deepdata-python/app/module/rag/schema/response.py
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"""
RAG 模块响应 DTO
定义所有 API 响应的数据结构
与 Java 响应 DTO 保持字段一致
"""
from pydantic import BaseModel, Field, ConfigDict
from typing import List, Optional, Any
from datetime import datetime
from app.db.models.knowledge_gen import RagType, FileStatus
class ModelConfig(BaseModel):
"""模型配置信息
对应 Java: com.deepdata.common.setting.domain.entity.ModelConfig
"""
model_config = ConfigDict(from_attributes=True, populate_by_name=True)
id: str = Field(..., description="模型ID")
created_at: Optional[datetime] = Field(None, alias="createdAt", description="创建时间")
updated_at: Optional[datetime] = Field(None, alias="updatedAt", description="更新时间")
created_by: Optional[str] = Field(None, alias="createdBy", description="创建人")
updated_by: Optional[str] = Field(None, alias="updatedBy", description="更新人")
model_name: str = Field(..., alias="modelName", description="模型名称")
provider: str = Field(..., description="模型提供商")
base_url: str = Field(..., alias="baseUrl", description="API 基础地址")
api_key: str = Field(default="", alias="apiKey", description="API 密钥")
type: str = Field(..., description="模型类型")
is_enabled: bool = Field(default=True, alias="isEnabled", description="是否启用")
is_default: bool = Field(default=False, alias="isDefault", description="是否默认")
is_deleted: bool = Field(default=False, alias="isDeleted", description="是否删除")
class KnowledgeBaseResp(BaseModel):
"""知识库响应
对应 Java: com.deepdata.rag.indexer.interfaces.dto.KnowledgeBaseResp
"""
model_config = ConfigDict(
from_attributes=True,
populate_by_name=True,
json_schema_extra={
"example": {
"id": "kb-uuid-123",
"name": "my_knowledge_base",
"description": "我的知识库",
"type": "DOCUMENT",
"embeddingModel": "text-embedding-ada-002",
"chatModel": "gpt-4",
"fileCount": 10,
"chunkCount": 150,
"embedding": {
"id": "model-1",
"modelName": "text-embedding-ada-002",
"provider": "openai"
}
}
}
)
id: str = Field(..., description="知识库ID")
name: str = Field(..., description="知识库名称")
description: Optional[str] = Field(None, description="知识库描述")
type: RagType = Field(..., description="RAG类型")
embedding_model: str = Field(alias="embeddingModel", description="嵌入模型ID")
chat_model: Optional[str] = Field(None, alias="chatModel", description="聊天模型ID")
file_count: Optional[int] = Field(None, alias="fileCount", description="文件数量")
chunk_count: Optional[int] = Field(None, alias="chunkCount", description="分块数量")
embedding: Optional[ModelConfig] = Field(None, description="嵌入模型配置")
chat: Optional[ModelConfig] = Field(None, description="聊天模型配置")
created_at: Optional[datetime] = Field(None, alias="createdAt", description="创建时间")
updated_at: Optional[datetime] = Field(None, alias="updatedAt", description="更新时间")
created_by: Optional[str] = Field(None, alias="createdBy", description="创建人")
updated_by: Optional[str] = Field(None, alias="updatedBy", description="更新人")
class RagFileResp(BaseModel):
"""RAG 文件响应
对应 Java: com.deepdata.rag.indexer.domain.model.RagFile
"""
model_config = ConfigDict(
from_attributes=True,
populate_by_name=True,
json_schema_extra={
"example": {
"id": "rag-file-uuid-123",
"knowledgeBaseId": "kb-uuid-123",
"fileName": "document.pdf",
"fileId": "file-uuid-456",
"chunkCount": 15,
"metadata": {"size": 1024, "format": "pdf"},
"status": "PROCESSED",
"progress": 100,
"createdAt": "2025-01-01T00:00:00"
}
}
)
id: str = Field(..., description="RAG文件ID")
knowledge_base_id: str = Field(alias="knowledgeBaseId", description="知识库ID")
file_name: str = Field(alias="fileName", description="文件名")
file_id: str = Field(alias="fileId", description="原始文件ID")
chunk_count: Optional[int] = Field(None, alias="chunkCount", description="分块数量")
metadata: Optional[dict] = Field(None, validation_alias="file_metadata", description="元数据")
status: FileStatus = Field(..., description="处理状态")
err_msg: Optional[str] = Field(None, alias="errMsg", description="错误信息")
progress: int = Field(default=0, ge=0, le=100, description="处理进度(0-100)")
created_at: Optional[datetime] = Field(None, alias="createdAt", description="创建时间")
updated_at: Optional[datetime] = Field(None, alias="updatedAt", description="更新时间")
created_by: Optional[str] = Field(None, alias="createdBy", description="创建人")
updated_by: Optional[str] = Field(None, alias="updatedBy", description="更新人")
class RagChunkResp(BaseModel):
"""RAG 分块响应
对应 Milvus 查询结果
"""
id: str = Field(..., description="分块ID")
text: str = Field(..., description="分块文本")
metadata: dict = Field(..., description="元数据")
score: float = Field(..., description="相似度分数")
distance: Optional[float] = Field(None, description="距离（可选）")
class Config:
populate_by_name = True # 允许使用 snake_case 或 camelCase
json_schema_extra = {
"example": {
"id": "chunk-uuid-123",
"text": "这是文档的一个分块内容...",
"metadata": {
"fileName": "document.pdf",
"chunkIndex": 0
},
"score": 0.95
}
}
class SearchResult(BaseModel):
"""检索结果
对应 Java: io.milvus.v2.service.vector.response.SearchResp.SearchResult
"""
id: str = Field(..., description="结果ID")
score: float = Field(..., description="相似度分数")
text: str = Field(..., description="文本内容")
metadata: dict = Field(default_factory=dict, description="元数据")
class Config:
json_schema_extra = {
"example": {
"id": "chunk-uuid-123",
"score": 0.95,
"text": "相关文档内容...",
"metadata": {"file_name": "doc.pdf"}
}
}
class UnifiedSearchResult(BaseModel):
"""统一检索结果
所有知识库类型（向量、知识图谱）的检索接口必须返回此格式
"""
model_config = ConfigDict(
populate_by_name=True,
json_schema_extra={
"example": {
"id": "result-uuid-123",
"text": "相关文档内容...",
"score": 0.95,
"metadata": {"fileName": "document.pdf", "chunkIndex": 0},
"resultType": "vector",
"knowledgeBaseId": "kb-uuid-123",
"knowledgeBaseName": "my_knowledge_base"
}
}
)
id: str = Field(..., description="结果ID")
text: str = Field(..., description="文本内容")
score: float = Field(..., description="相似度分数")
metadata: dict = Field(default_factory=dict, description="元数据")
result_type: str = Field(alias="resultType", description="结果类型（vector/graph）")
knowledge_base_id: str = Field(alias="knowledgeBaseId", description="知识库ID")
knowledge_base_name: str = Field(alias="knowledgeBaseName", description="知识库名称")
class PagedResponse(BaseModel):
"""分页响应
对应 Java: com.deepdata.common.interfaces.PagedResponse
"""
content: List[Any] = Field(..., description="数据列表")
total_elements: int = Field(alias="totalElements", description="总记录数")
page: int = Field(..., description="当前页码")
size: int = Field(..., description="每页数量")
total_pages: int = Field(alias="totalPages", description="总页数")
@classmethod
def create(cls, content: List[Any], total_elements: int, page: int, size: int):
"""创建分页响应
Args:
content: 数据列表
total_elements: 总记录数
page: 当前页码
size: 每页数量
Returns:
PagedResponse 实例
"""
total_pages = (total_elements + size - 1) // size if size > 0 else 0
return cls(
content=content,
total_elements=total_elements,
page=page,
size=size,
total_pages=total_pages
)
class Config:
populate_by_name = True # 允许使用 snake_case 或 camelCase
json_schema_extra = {
"example": {
"content": [],
"totalElements": 100,
"page": 1,
"size": 10,
"totalPages": 10
}
}