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* feat: Enhance annotation module with template management and validation - Added DatasetMappingCreateRequest and DatasetMappingUpdateRequest schemas to handle dataset mapping requests with camelCase and snake_case support. - Introduced Annotation Template schemas including CreateAnnotationTemplateRequest, UpdateAnnotationTemplateRequest, and AnnotationTemplateResponse for managing annotation templates. - Implemented AnnotationTemplateService for creating, updating, retrieving, and deleting annotation templates, including validation of configurations and XML generation. - Added utility class LabelStudioConfigValidator for validating Label Studio configurations and XML formats. - Updated database schema for annotation templates and labeling projects to include new fields and constraints. - Seeded initial annotation templates for various use cases including image classification, object detection, and text classification. * feat: Enhance TemplateForm with improved validation and dynamic field rendering; update LabelStudio config validation for camelCase support * feat: Update docker-compose.yml to mark datamate dataset volume and network as external * feat: Add tag configuration management and related components - Introduced new components for tag selection and browsing in the frontend. - Added API endpoint to fetch tag configuration from the backend. - Implemented tag configuration management in the backend, including loading from YAML. - Enhanced template service to support dynamic tag rendering based on configuration. - Updated validation utilities to incorporate tag configuration checks. - Refactored existing code to utilize the new tag configuration structure. * feat: Refactor LabelStudioTagConfig for improved configuration loading and validation * feat: Update Makefile to include backend-python-docker-build in the build process * feat: Migrate to poetry for better deps management * Add pyyaml dependency and update Dockerfile to use Poetry for dependency management - Added pyyaml (>=6.0.3,<7.0.0) to pyproject.toml dependencies. - Updated Dockerfile to install Poetry and manage dependencies using it. - Improved layer caching by copying only dependency files before the application code. - Removed unnecessary installation of build dependencies to keep the final image size small. * feat: Remove duplicated backend-python-docker-build target from Makefile * fix: airflow is not ready for adding yet * feat: update Python version to 3.12 and remove project installation step in Dockerfile
335 lines
12 KiB
Python
335 lines
12 KiB
Python
from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.future import select
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from sqlalchemy import update, func
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from sqlalchemy.orm import aliased
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from typing import Optional, List, Tuple
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from datetime import datetime
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import uuid
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from app.core.logging import get_logger
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from app.db.models import LabelingProject
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from app.db.models.dataset_management import Dataset
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from app.module.annotation.schema import (
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DatasetMappingCreateRequest,
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DatasetMappingUpdateRequest,
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DatasetMappingResponse
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)
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logger = get_logger(__name__)
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class DatasetMappingService:
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"""数据集映射服务"""
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def __init__(self, db: AsyncSession):
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self.db = db
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def _build_query_with_dataset_name(self):
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"""Build base query with dataset name joined"""
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return select(
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LabelingProject,
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Dataset.name.label('dataset_name')
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).outerjoin(
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Dataset,
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LabelingProject.dataset_id == Dataset.id
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)
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def _to_response_from_row(self, row) -> DatasetMappingResponse:
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"""Convert query row (mapping + dataset_name) to response"""
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mapping = row[0] # LabelingProject object
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dataset_name = row[1] # dataset_name from join
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response_data = {
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"id": mapping.id,
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"dataset_id": mapping.dataset_id,
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"dataset_name": dataset_name,
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"labeling_project_id": mapping.labeling_project_id,
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"name": mapping.name,
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"description": getattr(mapping, 'description', None),
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"created_at": mapping.created_at,
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"updated_at": mapping.updated_at,
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"deleted_at": mapping.deleted_at,
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}
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return DatasetMappingResponse(**response_data)
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async def _to_response(self, mapping: LabelingProject) -> DatasetMappingResponse:
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"""Convert ORM model to response with dataset name (for single entity operations)"""
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# Fetch dataset name
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dataset_name = None
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dataset_id = getattr(mapping, 'dataset_id', None)
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if dataset_id:
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dataset_result = await self.db.execute(
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select(Dataset.name).where(Dataset.id == dataset_id)
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)
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dataset_name = dataset_result.scalar_one_or_none()
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# Create response dict with all fields
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response_data = {
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"id": mapping.id,
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"dataset_id": dataset_id,
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"dataset_name": dataset_name,
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"labeling_project_id": mapping.labeling_project_id,
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"name": mapping.name,
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"description": getattr(mapping, 'description', None),
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"created_at": mapping.created_at,
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"updated_at": mapping.updated_at,
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"deleted_at": mapping.deleted_at,
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}
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return DatasetMappingResponse(**response_data)
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async def create_mapping(
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self,
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labeling_project: LabelingProject
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) -> DatasetMappingResponse:
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"""创建数据集映射"""
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logger.debug(f"Create dataset mapping: {labeling_project.dataset_id} -> {labeling_project.labeling_project_id}")
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# Use the passed object directly
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self.db.add(labeling_project)
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await self.db.commit()
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await self.db.refresh(labeling_project)
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logger.debug(f"Mapping created: {labeling_project.id}")
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return await self._to_response(labeling_project)
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async def get_mapping_by_source_uuid(
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self,
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dataset_id: str
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) -> Optional[DatasetMappingResponse]:
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"""根据源数据集ID获取映射(返回第一个未删除的)"""
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logger.debug(f"Get mapping by source dataset id: {dataset_id}")
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result = await self.db.execute(
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select(LabelingProject).where(
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LabelingProject.dataset_id == dataset_id,
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LabelingProject.deleted_at.is_(None)
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)
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)
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mapping = result.scalar_one_or_none()
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if mapping:
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logger.debug(f"Found mapping: {mapping.id}")
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return await self._to_response(mapping)
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logger.debug(f"No mapping found for source dataset id: {dataset_id}")
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return None
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async def get_mappings_by_dataset_id(
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self,
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dataset_id: str,
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include_deleted: bool = False
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) -> List[DatasetMappingResponse]:
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"""根据源数据集ID获取所有映射关系"""
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logger.debug(f"Get all mappings by source dataset id: {dataset_id}")
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query = self._build_query_with_dataset_name().where(
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LabelingProject.dataset_id == dataset_id
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)
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if not include_deleted:
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query = query.where(LabelingProject.deleted_at.is_(None))
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result = await self.db.execute(
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query.order_by(LabelingProject.created_at.desc())
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)
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rows = result.all()
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logger.debug(f"Found {len(rows)} mappings")
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return [self._to_response_from_row(row) for row in rows]
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async def get_mapping_by_labeling_project_id(
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self,
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labeling_project_id: str
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) -> Optional[DatasetMappingResponse]:
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"""根据Label Studio项目ID获取映射"""
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logger.debug(f"Get mapping by Label Studio project id: {labeling_project_id}")
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result = await self.db.execute(
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select(LabelingProject).where(
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LabelingProject.labeling_project_id == labeling_project_id,
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LabelingProject.deleted_at.is_(None)
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)
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)
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mapping = result.scalar_one_or_none()
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if mapping:
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logger.debug(f"Found mapping: {mapping.id}")
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return await self._to_response(mapping)
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logger.debug(f"No mapping found for Label Studio project id: {labeling_project_id}")
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return None
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async def get_mapping_by_uuid(self, mapping_id: str) -> Optional[DatasetMappingResponse]:
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"""根据映射UUID获取映射"""
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logger.debug(f"Get mapping: {mapping_id}")
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result = await self.db.execute(
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select(LabelingProject).where(
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LabelingProject.id == mapping_id,
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LabelingProject.deleted_at.is_(None)
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)
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)
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mapping = result.scalar_one_or_none()
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if mapping:
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logger.debug(f"Found mapping: {mapping.id}")
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return await self._to_response(mapping)
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logger.debug(f"No mapping found for mapping id: {mapping_id}")
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return None
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async def update_mapping(
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self,
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mapping_id: str,
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update_data: DatasetMappingUpdateRequest
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) -> Optional[DatasetMappingResponse]:
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"""更新映射信息"""
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logger.info(f"Update mapping: {mapping_id}")
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mapping = await self.get_mapping_by_uuid(mapping_id)
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if not mapping:
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return None
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update_values = update_data.model_dump(exclude_unset=True)
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update_values["last_updated_at"] = datetime.now()
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result = await self.db.execute(
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update(LabelingProject)
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.where(LabelingProject.id == mapping_id)
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.values(**update_values)
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)
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await self.db.commit()
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if result.rowcount and result.rowcount > 0: # type: ignore
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return await self.get_mapping_by_uuid(mapping_id)
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return None
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async def soft_delete_mapping(self, mapping_id: str) -> bool:
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"""软删除映射"""
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logger.info(f"Soft delete mapping: {mapping_id}")
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result = await self.db.execute(
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update(LabelingProject)
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.where(
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LabelingProject.id == mapping_id,
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LabelingProject.deleted_at.is_(None)
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)
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.values(deleted_at=datetime.now())
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)
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await self.db.commit()
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success = result.rowcount and result.rowcount > 0 # type: ignore
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if success:
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logger.info(f"Mapping soft-deleted: {mapping_id}")
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else:
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logger.warning(f"Mapping not exists or already deleted: {mapping_id}")
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return success
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async def get_all_mappings(
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self,
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skip: int = 0,
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limit: int = 100
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) -> List[DatasetMappingResponse]:
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"""获取所有有效映射"""
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logger.debug(f"List all mappings, skip: {skip}, limit: {limit}")
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query = self._build_query_with_dataset_name().where(
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LabelingProject.deleted_at.is_(None)
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)
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result = await self.db.execute(
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query
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.offset(skip)
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.limit(limit)
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.order_by(LabelingProject.created_at.desc())
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)
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rows = result.all()
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logger.debug(f"Found {len(rows)} mappings")
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return [self._to_response_from_row(row) for row in rows]
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async def count_mappings(self, include_deleted: bool = False) -> int:
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"""统计映射总数"""
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query = select(func.count()).select_from(LabelingProject)
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if not include_deleted:
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query = query.where(LabelingProject.deleted_at.is_(None))
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result = await self.db.execute(query)
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return result.scalar_one()
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async def get_all_mappings_with_count(
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self,
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skip: int = 0,
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limit: int = 100,
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include_deleted: bool = False
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) -> Tuple[List[DatasetMappingResponse], int]:
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"""获取所有映射及总数(用于分页)"""
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logger.debug(f"List all mappings with count, skip: {skip}, limit: {limit}")
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# 构建查询
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query = self._build_query_with_dataset_name()
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if not include_deleted:
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query = query.where(LabelingProject.deleted_at.is_(None))
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# 获取总数
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count_query = select(func.count()).select_from(LabelingProject)
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if not include_deleted:
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count_query = count_query.where(LabelingProject.deleted_at.is_(None))
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count_result = await self.db.execute(count_query)
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total = count_result.scalar_one()
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# 获取数据
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result = await self.db.execute(
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query
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.offset(skip)
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.limit(limit)
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.order_by(LabelingProject.created_at.desc())
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)
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rows = result.all()
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logger.debug(f"Found {len(rows)} mappings, total: {total}")
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return [self._to_response_from_row(row) for row in rows], total
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async def get_mappings_by_source_with_count(
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self,
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dataset_id: str,
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skip: int = 0,
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limit: int = 100,
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include_deleted: bool = False
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) -> Tuple[List[DatasetMappingResponse], int]:
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"""根据源数据集ID获取映射关系及总数(用于分页)"""
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logger.debug(f"Get mappings by source dataset id with count: {dataset_id}")
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# 构建查询
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query = self._build_query_with_dataset_name().where(
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LabelingProject.dataset_id == dataset_id
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)
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if not include_deleted:
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query = query.where(LabelingProject.deleted_at.is_(None))
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# 获取总数
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count_query = select(func.count()).select_from(LabelingProject).where(
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LabelingProject.dataset_id == dataset_id
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)
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if not include_deleted:
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count_query = count_query.where(LabelingProject.deleted_at.is_(None))
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count_result = await self.db.execute(count_query)
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total = count_result.scalar_one()
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# 获取数据
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result = await self.db.execute(
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query
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.offset(skip)
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.limit(limit)
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.order_by(LabelingProject.created_at.desc())
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)
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rows = result.all()
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logger.debug(f"Found {len(rows)} mappings, total: {total}")
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return [self._to_response_from_row(row) for row in rows], total |