Merge branch 'main' into develop_deer

runtime/datamate-python/.env.example

@@ -0,0 +1,125 @@
+# ====================================
+# Label Studio Adapter Configuration
+# ====================================
+
+# =========================
+# 应用程序配置
+# =========================
+APP_NAME="Label Studio Adapter"
+APP_VERSION="1.0.0"
+APP_DESCRIPTION="Adapter for integrating Data Management System with Label Studio"
+DEBUG=true
+
+# =========================
+# 服务器配置
+# =========================
+HOST=0.0.0.0
+PORT=18000
+
+# =========================
+# 日志配置
+# =========================
+LOG_LEVEL=INFO
+
+# =========================
+# Label Studio 服务配置
+# =========================
+# Label Studio 服务地址（根据部署方式调整）
+# Docker 环境：http://label-studio:8080
+# 本地开发：http://127.0.0.1:8000
+LABEL_STUDIO_BASE_URL=http://label-studio:8080
+
+# Label Studio 用户名和密码（用于自动创建用户）
+LABEL_STUDIO_USERNAME=admin@example.com
+LABEL_STUDIO_PASSWORD=password
+
+# Label Studio API 认证 Token（Legacy Token，推荐使用）
+# 从 Label Studio UI 的 Account & Settings > Access Token 获取
+LABEL_STUDIO_USER_TOKEN=your-label-studio-token-here
+
+# Label Studio 本地文件存储基础路径（容器内路径，用于 Docker 部署时的权限检查）
+LABEL_STUDIO_LOCAL_BASE=/label-studio/local_files
+
+# Label Studio 本地文件服务路径前缀（任务数据中的文件路径前缀）
+LABEL_STUDIO_FILE_PATH_PREFIX=/data/local-files/?d=
+
+# Label Studio 容器中的本地存储路径（用于配置 Local Storage）
+LABEL_STUDIO_LOCAL_STORAGE_DATASET_BASE_PATH=/label-studio/local_files/dataset
+LABEL_STUDIO_LOCAL_STORAGE_UPLOAD_BASE_PATH=/label-studio/local_files/upload
+
+# Label Studio 任务列表分页大小
+LS_TASK_PAGE_SIZE=1000
+
+# =========================
+# Data Management 服务配置
+# =========================
+# DM 存储文件夹前缀（通常与 Label Studio 的 local-files 文件夹映射一致）
+DM_FILE_PATH_PREFIX=/
+
+# =========================
+# Adapter 数据库配置 (MySQL)
+# =========================
+# 优先级1：如果配置了 MySQL，将优先使用 MySQL 数据库
+MYSQL_HOST=adapter-db
+MYSQL_PORT=3306
+MYSQL_USER=label_studio_user
+MYSQL_PASSWORD=user_password
+MYSQL_DATABASE=label_studio_adapter
+
+# =========================
+# Label Studio 数据库配置 (PostgreSQL)
+# =========================
+# 仅在使用 docker-compose.label-studio.yml 启动 Label Studio 时需要配置
+POSTGRES_HOST=label-studio-db
+POSTGRES_PORT=5432
+POSTGRES_USER=labelstudio
+POSTGRES_PASSWORD=labelstudio@4321
+POSTGRES_DATABASE=labelstudio
+
+# =========================
+# SQLite 数据库配置（兜底选项）
+# =========================
+# 优先级3：如果没有配置 MySQL/PostgreSQL，将使用 SQLite
+SQLITE_PATH=./data/labelstudio_adapter.db
+
+# =========================
+# 可选：直接指定数据库 URL
+# =========================
+# 如果设置了此项，将覆盖上面的 MySQL/PostgreSQL/SQLite 配置
+# DATABASE_URL=postgresql+asyncpg://user:password@host:port/database
+
+# =========================
+# 安全配置
+# =========================
+# 密钥（生产环境务必修改）
+SECRET_KEY=your-secret-key-change-this-in-production
+
+# Token 过期时间（分钟）
+ACCESS_TOKEN_EXPIRE_MINUTES=30
+
+# =========================
+# CORS 配置
+# =========================
+# 允许的来源（生产环境建议配置具体域名）
+ALLOWED_ORIGINS=["*"]
+
+# 允许的 HTTP 方法
+ALLOWED_METHODS=["*"]
+
+# 允许的请求头
+ALLOWED_HEADERS=["*"]
+
+# =========================
+# Docker Compose 配置
+# =========================
+# Docker Compose 项目名称前缀
+COMPOSE_PROJECT_NAME=ls-adapter
+
+# =========================
+# 同步配置（未来扩展）
+# =========================
+# 批量同步任务的批次大小
+SYNC_BATCH_SIZE=100
+
+# 同步失败时的最大重试次数
+MAX_RETRIES=3

runtime/datamate-python/.gitignore

@@ -0,0 +1,6 @@
+# Local Development Environment Files
+.env
+.dev.env
+
+# logs
+logs/

runtime/datamate-python/README.md

@@ -0,0 +1,86 @@
+# Label Studio Adapter (DataMate)
+
+这是 DataMate 的 Label Studio Adapter 服务，负责将 DataMate 的项目与 Label Studio 同步并提供对外的 HTTP API（基于 FastAPI）。
+
+## 简要说明
+
+- 框架：FastAPI
+- 异步数据库/ORM：SQLAlchemy (async)
+- 数据库迁移：Alembic
+- 运行器：uvicorn
+
+## 快速开始（开发）
+
+1. 克隆仓库并进入项目目录
+2. 创建并激活虚拟环境：
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+```
+
+3. 安装依赖：
+
+```bash
+pip install -r requirements.txt
+```
+
+4. 准备环境变量（示例）
+
+创建 `.env` 并设置必要的变量，例如：
+
+- DATABASE_URL（或根据项目配置使用具体变量）
+- LABEL_STUDIO_BASE_URL
+- LABEL_STUDIO_USER_TOKEN
+
+（具体变量请参考 `.env.example`）
+
+5. 数据库迁移（开发环境）：
+
+```bash
+alembic upgrade head
+```
+
+6. 启动开发服务器（示例与常用参数）：
+
+- 本地开发（默认 host/port，自动重载）：
+
+```bash
+uvicorn app.main:app --reload
+```
+
+- 指定主机与端口并打开调试日志：
+
+```bash
+uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload --log-level debug
+```
+
+- 在生产环境使用多个 worker（不使用 --reload）：
+
+```bash
+uvicorn app.main:app --host 0.0.0.0 --port 8000 --workers 4 --log-level info --proxy-headers
+```
+
+- 使用环境变量启动（示例）：
+
+```bash
+HOST=0.0.0.0 PORT=8000 uvicorn app.main:app --reload
+```
+
+注意：
+
+- `--reload` 仅用于开发，会监视文件变化并重启进程；不要在生产中使用。
+- `--workers` 提供并发处理能力，但会增加内存占用；生产时通常配合进程管理或容器编排（Kubernetes）使用。
+- 若需要完整的生产部署建议使用 ASGI 服务器（如 gunicorn + uvicorn workers / 或直接使用 uvicorn 在容器中配合进程管理）。
+
+访问 API 文档：
+
+- Swagger UI: http://127.0.0.1:8000/docs
+- ReDoc: http://127.0.0.1:8000/redoc （推荐使用）
+
+## 使用（简要）
+
+- 所有 API 路径以 `/api` 前缀注册（见 `app/main.py` 中 `app.include_router(api_router, prefix="/api")`）。
+- 根路径 `/` 返回服务信息和文档链接。
+
+更多细节请查看 `doc/usage.md`（接口使用）和 `doc/development.md`（开发说明）。

runtime/datamate-python/app/__init__.py

@@ -0,0 +1 @@
+# app/__init__.py

runtime/datamate-python/app/api/__init__.py

@@ -0,0 +1,19 @@
+"""
+API 路由模块
+
+集中管理所有API路由的组织结构
+"""
+from fastapi import APIRouter
+
+from .system import router as system_router
+from .project import project_router
+
+# 创建主API路由器
+api_router = APIRouter()
+
+# 注册到主路由器
+api_router.include_router(system_router, tags=["系统"])
+api_router.include_router(project_router, prefix="/project", tags=["项目"])
+
+# 导出路由器供 main.py 使用
+__all__ = ["api_router"]

runtime/datamate-python/app/api/project/__init__.py

@@ -0,0 +1,11 @@
+"""
+标注工程相关API路由模块
+"""
+from fastapi import APIRouter
+
+project_router = APIRouter()
+
+from . import create
+from . import sync
+from . import list
+from . import delete

runtime/datamate-python/app/api/project/create.py

@@ -0,0 +1,130 @@
+from fastapi import APIRouter, Depends, HTTPException
+from sqlalchemy.ext.asyncio import AsyncSession
+from typing import Optional
+
+from app.db.database import get_db
+from app.services.dataset_mapping_service import DatasetMappingService
+from app.infrastructure import DatamateClient, LabelStudioClient
+from app.schemas.dataset_mapping import (
+    DatasetMappingCreateRequest,
+    DatasetMappingCreateResponse,
+)
+from app.schemas import StandardResponse
+from app.core.logging import get_logger
+from app.core.config import settings
+from . import project_router
+
+logger = get_logger(__name__)
+
+@project_router.post("/create", response_model=StandardResponse[DatasetMappingCreateResponse], status_code=201)
+async def create_dataset_mapping(
+    request: DatasetMappingCreateRequest,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    创建数据集映射
+    
+    根据指定的DM程序中的数据集，创建Label Studio中的数据集，
+    在数据库中记录这一关联关系，返回Label Studio数据集的ID
+    
+    注意：一个数据集可以创建多个标注项目
+    """
+    try:
+        dm_client = DatamateClient(db)
+        ls_client = LabelStudioClient(base_url=settings.label_studio_base_url,
+                                      token=settings.label_studio_user_token)
+        service = DatasetMappingService(db)
+        
+        logger.info(f"Create dataset mapping request: {request.dataset_id}")
+        
+        # 从DM服务获取数据集信息
+        dataset_info = await dm_client.get_dataset(request.dataset_id)
+        if not dataset_info:
+            raise HTTPException(
+                status_code=404,
+                detail=f"Dataset not found in DM service: {request.dataset_id}"
+            )
+        
+        # 确定数据类型（基于数据集类型）
+        data_type = "image"  # 默认值
+        if dataset_info.type and dataset_info.type.code:
+            type_code = dataset_info.type.code.lower()
+            if "audio" in type_code:
+                data_type = "audio"
+            elif "video" in type_code:
+                data_type = "video"
+            elif "text" in type_code:
+                data_type = "text"
+        
+        project_name = f"{dataset_info.name}"
+        
+        # 在Label Studio中创建项目
+        project_data = await ls_client.create_project(
+            title=project_name,
+            description=dataset_info.description or f"Imported from DM dataset {dataset_info.id}",
+            data_type=data_type
+        )
+        
+        if not project_data:
+            raise HTTPException(
+                status_code=500,
+                detail="Fail to create Label Studio project."
+            )
+        
+        project_id = project_data["id"]
+        
+        # 配置本地存储：dataset/<id>
+        local_storage_path = f"{settings.label_studio_local_storage_dataset_base_path}/{request.dataset_id}"
+        storage_result = await ls_client.create_local_storage(
+            project_id=project_id,
+            path=local_storage_path,
+            title="Dataset_BLOB",
+            use_blob_urls=True,
+            description=f"Local storage for dataset {dataset_info.name}"
+        )
+
+        # 配置本地存储：upload
+        local_storage_path = f"{settings.label_studio_local_storage_upload_base_path}"
+        storage_result = await ls_client.create_local_storage(
+            project_id=project_id,
+            path=local_storage_path,
+            title="Upload_BLOB",
+            use_blob_urls=True,
+            description=f"Local storage for dataset {dataset_info.name}"
+        )
+        
+        if not storage_result:
+            # 本地存储配置失败，记录警告但不中断流程
+            logger.warning(f"Failed to configure local storage for project {project_id}")
+        else:
+            logger.info(f"Local storage configured for project {project_id}: {local_storage_path}")
+        
+        # 创建映射关系，包含项目名称
+        mapping = await service.create_mapping(
+            request, 
+            str(project_id),
+            project_name
+        )
+        
+        logger.debug(
+            f"Dataset mapping created: {mapping.mapping_id} -> S {mapping.dataset_id} <> L {mapping.labelling_project_id}"
+        )
+        
+        response_data = DatasetMappingCreateResponse(
+            mapping_id=mapping.mapping_id,
+            labelling_project_id=mapping.labelling_project_id,
+            labelling_project_name=mapping.labelling_project_name or project_name,
+            message="Dataset mapping created successfully"
+        )
+        
+        return StandardResponse(
+            code=201,
+            message="success",
+            data=response_data
+        )
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error while creating dataset mapping: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error")

runtime/datamate-python/app/api/project/delete.py

@@ -0,0 +1,106 @@
+from fastapi import Depends, HTTPException, Query
+from sqlalchemy.ext.asyncio import AsyncSession
+from typing import Optional
+
+from app.db.database import get_db
+from app.services.dataset_mapping_service import DatasetMappingService
+from app.infrastructure import DatamateClient, LabelStudioClient
+from app.schemas.dataset_mapping import DeleteDatasetResponse
+from app.schemas import StandardResponse
+from app.core.logging import get_logger
+from app.core.config import settings
+
+from . import project_router
+
+logger = get_logger(__name__)
+
+@project_router.delete("/mappings", response_model=StandardResponse[DeleteDatasetResponse])
+async def delete_mapping(
+    m: Optional[str] = Query(None, description="映射UUID"),
+    proj: Optional[str] = Query(None, description="Label Studio项目ID"),
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    删除映射关系和对应的 Label Studio 项目
+    
+    可以通过以下任一方式指定要删除的映射：
+    - m: 映射UUID
+    - proj: Label Studio项目ID
+    - 两者都提供（优先使用 m）
+    
+    此操作会：
+    1. 删除 Label Studio 中的项目
+    2. 软删除数据库中的映射记录
+    """
+    try:
+        # 至少需要提供一个参数
+        if not m and not proj:
+            raise HTTPException(
+                status_code=400,
+                detail="Either 'm' (mapping UUID) or 'proj' (project ID) must be provided"
+            )
+
+        ls_client = LabelStudioClient(base_url=settings.label_studio_base_url,
+                                      token=settings.label_studio_user_token)
+        service = DatasetMappingService(db)
+        
+        # 优先使用 mapping_id 查询
+        if m:
+            logger.debug(f"Deleting by mapping UUID: {m}")
+            mapping = await service.get_mapping_by_uuid(m)
+        # 如果没有提供 m，使用 proj 查询
+        elif proj:
+            logger.debug(f"Deleting by project ID: {proj}")
+            mapping = await service.get_mapping_by_labelling_project_id(proj)
+        else:
+            mapping = None
+        
+        if not mapping:
+            raise HTTPException(
+                status_code=404,
+                detail=f"Mapping either not found or not specified."
+            )
+        
+        mapping_id = mapping.mapping_id
+        labelling_project_id = mapping.labelling_project_id
+        labelling_project_name = mapping.labelling_project_name
+        
+        logger.debug(f"Found mapping: {mapping_id}, Label Studio project ID: {labelling_project_id}")
+        
+        # 1. 删除 Label Studio 项目
+        try:
+            delete_success = await ls_client.delete_project(int(labelling_project_id))
+            if delete_success:
+                logger.debug(f"Successfully deleted Label Studio project: {labelling_project_id}")
+            else:
+                logger.warning(f"Failed to delete Label Studio project or project not found: {labelling_project_id}")
+        except Exception as e:
+            logger.error(f"Error deleting Label Studio project: {e}")
+            # 继续执行，即使 Label Studio 项目删除失败也要删除映射记录
+        
+        # 2. 软删除映射记录
+        soft_delete_success = await service.soft_delete_mapping(mapping_id)
+        
+        if not soft_delete_success:
+            raise HTTPException(
+                status_code=500,
+                detail="Failed to delete mapping record"
+            )
+
+        logger.info(f"Successfully deleted mapping: {mapping_id}, Label Studio project: {labelling_project_id}")
+
+        return StandardResponse(
+            code=200,
+            message="success",
+            data=DeleteDatasetResponse(
+                mapping_id=mapping_id,
+                status="success",
+                message=f"Successfully deleted mapping and Label Studio project '{labelling_project_name}'"
+            )
+        )
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error deleting mapping: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error")

runtime/datamate-python/app/api/project/list.py

@@ -0,0 +1,152 @@
+from fastapi import APIRouter, Depends, HTTPException, Query
+from sqlalchemy.ext.asyncio import AsyncSession
+from typing import List
+import math
+
+from app.db.database import get_db
+from app.services.dataset_mapping_service import DatasetMappingService
+from app.schemas.dataset_mapping import DatasetMappingResponse
+from app.schemas.common import StandardResponse, PaginatedData
+from app.core.logging import get_logger
+from . import project_router
+
+logger = get_logger(__name__)
+
+@project_router.get("/mappings/list", response_model=StandardResponse[PaginatedData[DatasetMappingResponse]])
+async def list_mappings(
+    page: int = Query(1, ge=1, description="页码（从1开始）"),
+    page_size: int = Query(20, ge=1, le=100, description="每页记录数"),
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    查询所有映射关系（分页）
+    
+    返回所有有效的数据集映射关系（未被软删除的），支持分页查询
+    """
+    try:
+        service = DatasetMappingService(db)
+        
+        # 计算 skip
+        skip = (page - 1) * page_size
+        
+        logger.info(f"Listing mappings, page={page}, page_size={page_size}")
+        
+        # 获取数据和总数
+        mappings, total = await service.get_all_mappings_with_count(
+            skip=skip, 
+            limit=page_size
+        )
+        
+        # 计算总页数
+        total_pages = math.ceil(total / page_size) if total > 0 else 0
+        
+        # 构造分页响应
+        paginated_data = PaginatedData(
+            page=page,
+            size=page_size,
+            total_elements=total,
+            total_pages=total_pages,
+            content=mappings
+        )
+        
+        logger.info(f"Found {len(mappings)} mappings on page {page}, total: {total}")
+        
+        return StandardResponse(
+            code=200,
+            message="success",
+            data=paginated_data
+        )
+        
+    except Exception as e:
+        logger.error(f"Error listing mappings: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error")
+
+
+@project_router.get("/mappings/{mapping_id}", response_model=StandardResponse[DatasetMappingResponse])
+async def get_mapping(
+    mapping_id: str,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    根据 UUID 查询单个映射关系
+    """
+    try:
+        service = DatasetMappingService(db)
+        
+        logger.info(f"Get mapping: {mapping_id}")
+        
+        mapping = await service.get_mapping_by_uuid(mapping_id)
+        
+        if not mapping:
+            raise HTTPException(
+                status_code=404,
+                detail=f"Mapping not found: {mapping_id}"
+            )
+        
+        logger.info(f"Found mapping: {mapping.mapping_id}")
+        
+        return StandardResponse(
+            code=200,
+            message="success",
+            data=mapping
+        )
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error getting mapping: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error")
+
+
+@project_router.get("/mappings/by-source/{dataset_id}", response_model=StandardResponse[PaginatedData[DatasetMappingResponse]])
+async def get_mappings_by_source(
+    dataset_id: str,
+    page: int = Query(1, ge=1, description="页码（从1开始）"),
+    page_size: int = Query(20, ge=1, le=100, description="每页记录数"),
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    根据源数据集 ID 查询所有映射关系（分页）
+    
+    返回该数据集创建的所有标注项目（不包括已删除的），支持分页查询
+    """
+    try:
+        service = DatasetMappingService(db)
+        
+        # 计算 skip
+        skip = (page - 1) * page_size
+        
+        logger.info(f"Get mappings by source dataset id: {dataset_id}, page={page}, page_size={page_size}")
+        
+        # 获取数据和总数
+        mappings, total = await service.get_mappings_by_source_with_count(
+            dataset_id=dataset_id,
+            skip=skip,
+            limit=page_size
+        )
+        
+        # 计算总页数
+        total_pages = math.ceil(total / page_size) if total > 0 else 0
+        
+        # 构造分页响应
+        paginated_data = PaginatedData(
+            page=page,
+            size=page_size,
+            total_elements=total,
+            total_pages=total_pages,
+            content=mappings
+        )
+        
+        logger.info(f"Found {len(mappings)} mappings on page {page}, total: {total}")
+        
+        return StandardResponse(
+            code=200,
+            message="success",
+            data=paginated_data
+        )
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error getting mappings: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error")

runtime/datamate-python/app/api/project/sync.py

@@ -0,0 +1,71 @@
+from fastapi import APIRouter, Depends, HTTPException, Query
+from sqlalchemy.ext.asyncio import AsyncSession
+from typing import List, Optional
+
+from app.db.database import get_db
+from app.services.dataset_mapping_service import DatasetMappingService
+from app.services.sync_service import SyncService
+from app.infrastructure import DatamateClient, LabelStudioClient
+from app.exceptions import NoDatasetInfoFoundError, DatasetMappingNotFoundError
+from app.schemas.dataset_mapping import (
+    DatasetMappingResponse,
+    SyncDatasetRequest,
+    SyncDatasetResponse,
+)
+from app.schemas import StandardResponse
+from app.core.logging import get_logger
+from app.core.config import settings
+from . import project_router
+
+logger = get_logger(__name__)
+
+@project_router.post("/sync", response_model=StandardResponse[SyncDatasetResponse])
+async def sync_dataset_content(
+    request: SyncDatasetRequest,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    同步数据集内容
+    
+    根据指定的mapping ID，同步DM程序数据集中的内容到Label Studio数据集中，
+    在数据库中记录更新时间，返回更新状态
+    """
+    try:
+        ls_client = LabelStudioClient(base_url=settings.label_studio_base_url,
+                                      token=settings.label_studio_user_token)
+        dm_client = DatamateClient(db)
+        mapping_service = DatasetMappingService(db)
+        sync_service = SyncService(dm_client, ls_client, mapping_service)
+
+        logger.info(f"Sync dataset content request: mapping_id={request.mapping_id}")
+        
+        # 根据 mapping_id 获取映射关系
+        mapping = await mapping_service.get_mapping_by_uuid(request.mapping_id)
+        if not mapping:
+            raise HTTPException(
+                status_code=404,
+                detail=f"Mapping not found: {request.mapping_id}"
+            )
+        
+        # 执行同步（使用映射中的源数据集UUID）
+        result = await sync_service.sync_dataset_files(request.mapping_id, request.batch_size)
+        
+        logger.info(f"Sync completed: {result.synced_files}/{result.total_files} files")
+        
+        return StandardResponse(
+            code=200,
+            message="success",
+            data=result
+        )
+        
+    except HTTPException:
+        raise
+    except NoDatasetInfoFoundError as e:
+        logger.error(f"Failed to get dataset info: {e}")
+        raise HTTPException(status_code=404, detail=str(e))
+    except DatasetMappingNotFoundError as e:
+        logger.error(f"Mapping not found: {e}")
+        raise HTTPException(status_code=404, detail=str(e))
+    except Exception as e:
+        logger.error(f"Error syncing dataset content: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error")

runtime/datamate-python/app/api/system.py

@@ -0,0 +1,33 @@
+from fastapi import APIRouter
+from typing import Dict, Any
+from app.core.config import settings
+from app.schemas import StandardResponse
+
+router = APIRouter()
+
+@router.get("/health", response_model=StandardResponse[Dict[str, Any]])
+async def health_check():
+    """健康检查端点"""
+    return StandardResponse(
+        code=200,
+        message="success",
+        data={
+            "status": "healthy",
+            "service": "Label Studio Adapter",
+            "version": settings.app_version
+        }
+    )
+
+@router.get("/config", response_model=StandardResponse[Dict[str, Any]])
+async def get_config():
+    """获取配置信息"""
+    return StandardResponse(
+        code=200,
+        message="success",
+        data={
+            "app_name": settings.app_name,
+            "version": settings.app_version,
+            "label_studio_url": settings.label_studio_base_url,
+            "debug": settings.debug
+        }
+    )

runtime/datamate-python/app/core/__init__.py

@@ -0,0 +1 @@
+# app/core/__init__.py

runtime/datamate-python/app/core/config.py

@@ -0,0 +1,145 @@
+from pydantic_settings import BaseSettings
+from typing import Optional
+import os
+from pathlib import Path
+
+class Settings(BaseSettings):
+    """应用程序配置"""
+
+    class Config:
+        env_file = ".env"
+        case_sensitive = False
+        extra = 'ignore'  # 允许额外字段（如 Shell 脚本专用的环境变量）
+
+    # =========================
+    # Adapter 服务配置
+    # =========================
+    app_name: str = "Label Studio Adapter"
+    app_version: str = "1.0.0"
+    app_description: str = "Adapter for integrating Data Management System with Label Studio"
+    debug: bool = True
+
+    # 服务器配置
+    host: str = "0.0.0.0"
+    port: int = 8000
+
+    # CORS配置
+    allowed_origins: list = ["*"]
+    allowed_methods: list = ["*"]
+    allowed_headers: list = ["*"]
+
+    # MySQL数据库配置 (优先级1)
+    mysql_host: Optional[str] = None
+    mysql_port: int = 3306
+    mysql_user: Optional[str] = None
+    mysql_password: Optional[str] = None
+    mysql_database: Optional[str] = None
+
+    # PostgreSQL数据库配置 (优先级2)
+    postgres_host: Optional[str] = None
+    postgres_port: int = 5432
+    postgres_user: Optional[str] = None
+    postgres_password: Optional[str] = None
+    postgres_database: Optional[str] = None
+
+    # SQLite数据库配置 (优先级3 - 兜底)
+    sqlite_path: str = "data/labelstudio_adapter.db"
+
+    # 直接数据库URL配置（如果提供，将覆盖上述配置）
+    database_url: Optional[str] = None
+
+    # 日志配置
+    log_level: str = "INFO"
+
+    # 安全配置
+    secret_key: str = "your-secret-key-change-this-in-production"
+    access_token_expire_minutes: int = 30
+
+    # =========================
+    # Label Studio 服务配置
+    # =========================
+    label_studio_base_url: str = "http://label-studio:8080"
+    label_studio_username: Optional[str] = None  # Label Studio 用户名（用于登录）
+    label_studio_password: Optional[str] = None  # Label Studio 密码（用于登录）
+    label_studio_user_token: Optional[str] = None  # Legacy Token
+
+    label_studio_local_storage_dataset_base_path: str = "/label-studio/local_files/dataset"  # Label Studio容器中的本地存储基础路径
+    label_studio_local_storage_upload_base_path: str = "/label-studio/local_files/upload"  # Label Studio容器中的本地存储基础路径
+    label_studio_file_path_prefix: str = "/data/local-files/?d="  # Label Studio本地文件服务路径前缀
+
+    ls_task_page_size: int = 1000
+
+
+    # =========================
+    # Data Management 服务配置
+    # =========================
+    dm_file_path_prefix: str = "/"  # DM存储文件夹前缀
+
+
+    @property
+    def computed_database_url(self) -> str:
+        """
+        根据优先级自动选择数据库连接URL
+        优先级：MySQL > PostgreSQL > SQLite3
+        """
+        # 如果直接提供了database_url，优先使用
+        if self.database_url:
+            return self.database_url
+
+        # 优先级1: MySQL
+        if all([self.mysql_host, self.mysql_user, self.mysql_password, self.mysql_database]):
+            return f"mysql+aiomysql://{self.mysql_user}:{self.mysql_password}@{self.mysql_host}:{self.mysql_port}/{self.mysql_database}"
+
+        # 优先级2: PostgreSQL
+        if all([self.postgres_host, self.postgres_user, self.postgres_password, self.postgres_database]):
+            return f"postgresql+asyncpg://{self.postgres_user}:{self.postgres_password}@{self.postgres_host}:{self.postgres_port}/{self.postgres_database}"
+
+        # 优先级3: SQLite (兜底)
+        sqlite_full_path = Path(self.sqlite_path).absolute()
+        # 确保目录存在
+        sqlite_full_path.parent.mkdir(parents=True, exist_ok=True)
+        return f"sqlite+aiosqlite:///{sqlite_full_path}"
+
+    @property
+    def sync_database_url(self) -> str:
+        """
+        用于数据库迁移的同步连接URL
+        将异步驱动替换为同步驱动
+        """
+        async_url = self.computed_database_url
+
+        # 替换异步驱动为同步驱动
+        sync_replacements = {
+            "mysql+aiomysql://": "mysql+pymysql://",
+            "postgresql+asyncpg://": "postgresql+psycopg2://",
+            "sqlite+aiosqlite:///": "sqlite:///"
+        }
+
+        for async_driver, sync_driver in sync_replacements.items():
+            if async_url.startswith(async_driver):
+                return async_url.replace(async_driver, sync_driver)
+
+        return async_url
+
+    def get_database_info(self) -> dict:
+        """获取数据库配置信息"""
+        url = self.computed_database_url
+
+        if url.startswith("mysql"):
+            db_type = "MySQL"
+        elif url.startswith("postgresql"):
+            db_type = "PostgreSQL"
+        elif url.startswith("sqlite"):
+            db_type = "SQLite"
+        else:
+            db_type = "Unknown"
+
+        return {
+            "type": db_type,
+            "url": url,
+            "sync_url": self.sync_database_url
+        }
+
+
+# 全局设置实例
+settings = Settings()

runtime/datamate-python/app/core/logging.py

@@ -0,0 +1,53 @@
+import logging
+import sys
+from pathlib import Path
+from app.core.config import settings
+
+def setup_logging():
+    """配置应用程序日志"""
+    
+    # 创建logs目录
+    log_dir = Path("logs")
+    log_dir.mkdir(exist_ok=True)
+    
+    # 配置日志格式
+    log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    date_format = "%Y-%m-%d %H:%M:%S"
+    
+    # 创建处理器
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setLevel(getattr(logging, settings.log_level.upper()))
+    
+    file_handler = logging.FileHandler(
+        log_dir / "app.log",
+        encoding="utf-8"
+    )
+    file_handler.setLevel(getattr(logging, settings.log_level.upper()))
+    
+    error_handler = logging.FileHandler(
+        log_dir / "error.log", 
+        encoding="utf-8"
+    )
+    error_handler.setLevel(logging.ERROR)
+    
+    # 设置格式
+    formatter = logging.Formatter(log_format, date_format)
+    console_handler.setFormatter(formatter)
+    file_handler.setFormatter(formatter)
+    error_handler.setFormatter(formatter)
+    
+    # 配置根日志器
+    root_logger = logging.getLogger()
+    root_logger.setLevel(getattr(logging, settings.log_level.upper()))
+    root_logger.addHandler(console_handler)
+    root_logger.addHandler(file_handler)
+    root_logger.addHandler(error_handler)
+    
+    # 配置第三方库日志级别（减少详细日志）
+    logging.getLogger("uvicorn").setLevel(logging.WARNING)
+    logging.getLogger("sqlalchemy.engine").setLevel(logging.ERROR)  # 隐藏SQL查询日志
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+
+def get_logger(name: str) -> logging.Logger:
+    """获取指定名称的日志器"""
+    return logging.getLogger(name)

runtime/datamate-python/app/db/__init__.py

@@ -0,0 +1 @@
+# app/db/__init__.py

runtime/datamate-python/app/db/database.py

@@ -0,0 +1,39 @@
+from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine, async_sessionmaker
+from sqlalchemy.orm import declarative_base
+from app.core.config import settings
+from app.core.logging import get_logger
+from typing import AsyncGenerator
+
+logger = get_logger(__name__)
+
+# 获取数据库配置信息
+db_info = settings.get_database_info()
+logger.info(f"使用数据库: {db_info['type']}")
+logger.info(f"连接URL: {db_info['url']}")
+
+# 创建数据库引擎
+engine = create_async_engine(
+    settings.computed_database_url,
+    echo=False,  # 关闭SQL调试日志以减少输出
+    future=True,
+    # SQLite特殊配置
+    connect_args={"check_same_thread": False} if "sqlite" in settings.computed_database_url else {}
+)
+
+# 创建会话工厂
+AsyncSessionLocal = async_sessionmaker(
+    engine, 
+    class_=AsyncSession, 
+    expire_on_commit=False
+)
+
+# 创建基础模型类
+Base = declarative_base()
+async def get_db() -> AsyncGenerator[AsyncSession, None]:
+    """获取数据库会话"""
+    async with AsyncSessionLocal() as session:
+        try:
+            yield session
+        finally:
+            await session.close()
+            

runtime/datamate-python/app/exceptions.py

@@ -0,0 +1,31 @@
+"""
+自定义异常类定义
+"""
+
+class LabelStudioAdapterException(Exception):
+    """Label Studio Adapter 基础异常类"""
+    pass
+
+class DatasetMappingNotFoundError(LabelStudioAdapterException):
+    """数据集映射未找到异常"""
+    def __init__(self, mapping_id: str):
+        self.mapping_id = mapping_id
+        super().__init__(f"Dataset mapping not found: {mapping_id}")
+
+class NoDatasetInfoFoundError(LabelStudioAdapterException):
+    """无法获取数据集信息异常"""
+    def __init__(self, dataset_uuid: str):
+        self.dataset_uuid = dataset_uuid
+        super().__init__(f"Failed to get dataset info: {dataset_uuid}")
+
+class LabelStudioClientError(LabelStudioAdapterException):
+    """Label Studio 客户端错误"""
+    pass
+
+class DMServiceClientError(LabelStudioAdapterException):
+    """DM 服务客户端错误"""
+    pass
+
+class SyncServiceError(LabelStudioAdapterException):
+    """同步服务错误"""
+    pass

runtime/datamate-python/app/infrastructure/__init__.py

@@ -0,0 +1,6 @@
+# app/clients/__init__.py
+
+from .label_studio import Client as LabelStudioClient
+from .datamate import Client as DatamateClient
+
+__all__ = ["LabelStudioClient", "DatamateClient"]

runtime/datamate-python/app/infrastructure/datamate.py

@@ -0,0 +1,159 @@
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.future import select
+from sqlalchemy import func
+from typing import Optional
+from app.core.config import settings
+from app.core.logging import get_logger
+from app.schemas.dm_service import DatasetResponse, PagedDatasetFileResponse, DatasetFileResponse
+from app.models.dm.dataset import Dataset
+from app.models.dm.dataset_files import DatasetFiles
+
+logger = get_logger(__name__)
+
+class Client:
+    """数据管理服务客户端 - 直接访问数据库"""
+    
+    def __init__(self, db: AsyncSession):
+        """
+        初始化 DM 客户端
+        
+        Args:
+            db: 数据库会话
+        """
+        self.db = db
+        logger.info("Initialize DM service client (Database mode)")
+
+    async def get_dataset(self, dataset_id: str) -> Optional[DatasetResponse]:
+        """获取数据集详情"""
+        try:
+            logger.info(f"Getting dataset detail: {dataset_id} ...")
+            
+            result = await self.db.execute(
+                select(Dataset).where(Dataset.id == dataset_id)
+            )
+            dataset = result.scalar_one_or_none()
+            
+            if not dataset:
+                logger.error(f"Dataset not found: {dataset_id}")
+                return None
+            
+            # 将数据库模型转换为响应模型
+            # type: ignore 用于忽略 SQLAlchemy 的类型检查问题
+            return DatasetResponse(
+                id=dataset.id,  # type: ignore
+                name=dataset.name,  # type: ignore
+                description=dataset.description or "",  # type: ignore
+                datasetType=dataset.dataset_type,  # type: ignore
+                status=dataset.status,  # type: ignore
+                fileCount=dataset.file_count or 0,  # type: ignore
+                totalSize=dataset.size_bytes or 0,  # type: ignore
+                createdAt=dataset.created_at,  # type: ignore
+                updatedAt=dataset.updated_at,  # type: ignore
+                createdBy=dataset.created_by  # type: ignore
+            )
+        except Exception as e:
+            logger.error(f"Failed to get dataset {dataset_id}: {e}")
+            return None
+    
+    async def get_dataset_files(
+        self, 
+        dataset_id: str, 
+        page: int = 0, 
+        size: int = 100,
+        file_type: Optional[str] = None,
+        status: Optional[str] = None
+    ) -> Optional[PagedDatasetFileResponse]:
+        """获取数据集文件列表"""
+        try:
+            logger.info(f"Get dataset files: dataset={dataset_id}, page={page}, size={size}")
+            
+            # 构建查询
+            query = select(DatasetFiles).where(DatasetFiles.dataset_id == dataset_id)
+            
+            # 添加可选过滤条件
+            if file_type:
+                query = query.where(DatasetFiles.file_type == file_type)
+            if status:
+                query = query.where(DatasetFiles.status == status)
+            
+            # 获取总数
+            count_query = select(func.count()).select_from(DatasetFiles).where(
+                DatasetFiles.dataset_id == dataset_id
+            )
+            if file_type:
+                count_query = count_query.where(DatasetFiles.file_type == file_type)
+            if status:
+                count_query = count_query.where(DatasetFiles.status == status)
+                
+            count_result = await self.db.execute(count_query)
+            total = count_result.scalar_one()
+            
+            # 分页查询
+            query = query.offset(page * size).limit(size).order_by(DatasetFiles.created_at.desc())
+            result = await self.db.execute(query)
+            files = result.scalars().all()
+            
+            # 转换为响应模型
+            # type: ignore 用于忽略 SQLAlchemy 的类型检查问题
+            content = [
+                DatasetFileResponse(
+                    id=f.id,  # type: ignore
+                    fileName=f.file_name,  # type: ignore
+                    fileType=f.file_type or "",  # type: ignore
+                    filePath=f.file_path,  # type: ignore
+                    originalName=f.file_name,  # type: ignore
+                    size=f.file_size,  # type: ignore
+                    status=f.status,  # type: ignore
+                    uploadedAt=f.upload_time,  # type: ignore
+                    description=None,
+                    uploadedBy=None,
+                    lastAccessTime=f.last_access_time  # type: ignore
+                )
+                for f in files
+            ]
+            
+            total_pages = (total + size - 1) // size if size > 0 else 0
+            
+            return PagedDatasetFileResponse(
+                content=content,
+                totalElements=total,
+                totalPages=total_pages,
+                page=page,
+                size=size
+            )
+        except Exception as e:
+            logger.error(f"Failed to get dataset files for {dataset_id}: {e}")
+            return None
+    
+    async def download_file(self, dataset_id: str, file_id: str) -> Optional[bytes]:
+        """
+        下载文件内容
+        注意：此方法保留接口兼容性，但实际文件下载可能需要通过文件系统或对象存储
+        """
+        logger.warning(f"download_file is deprecated when using database mode. Use get_file_download_url instead.")
+        return None
+    
+    async def get_file_download_url(self, dataset_id: str, file_id: str) -> Optional[str]:
+        """获取文件下载URL（或文件路径）"""
+        try:
+            result = await self.db.execute(
+                select(DatasetFiles).where(
+                    DatasetFiles.id == file_id,
+                    DatasetFiles.dataset_id == dataset_id
+                )
+            )
+            file = result.scalar_one_or_none()
+            
+            if not file:
+                logger.error(f"File not found: {file_id} in dataset {dataset_id}")
+                return None
+            
+            # 返回文件路径（可以是本地路径或对象存储URL）
+            return file.file_path  # type: ignore
+        except Exception as e:
+            logger.error(f"Failed to get file path for {file_id}: {e}")
+            return None
+    
+    async def close(self):
+        """关闭客户端连接（数据库模式下无需操作）"""
+        logger.info("DM service client closed (Database mode)")

runtime/datamate-python/app/infrastructure/label_studio.py

@@ -0,0 +1,469 @@
+import httpx
+from typing import Optional, Dict, Any, List
+import json
+
+from app.core.config import settings
+from app.core.logging import get_logger
+from app.schemas.label_studio import (
+    LabelStudioProject, 
+    LabelStudioCreateProjectRequest,
+    LabelStudioCreateTaskRequest
+)
+
+logger = get_logger(__name__)
+
+class Client:
+    """Label Studio服务客户端
+    
+    使用 HTTP REST API 直接与 Label Studio 交互
+    认证方式：使用 Authorization: Token {token} 头部进行认证
+    """
+    
+    # 默认标注配置模板
+    DEFAULT_LABEL_CONFIGS = {
+        "image": """
+        <View>
+          <Image name="image" value="$image"/>
+          <RectangleLabels name="label" toName="image">
+            <Label value="Object" background="red"/>
+          </RectangleLabels>
+        </View>
+        """,
+        "text": """
+        <View>
+          <Text name="text" value="$text"/>
+          <Choices name="sentiment" toName="text">
+            <Choice value="positive"/>
+            <Choice value="negative"/>
+            <Choice value="neutral"/>
+          </Choices>
+        </View>
+        """,
+        "audio": """
+        <View>
+          <Audio name="audio" value="$audio"/>
+          <AudioRegionLabels name="label" toName="audio">
+            <Label value="Speech" background="red"/>
+            <Label value="Noise" background="blue"/>
+          </AudioRegionLabels>
+        </View>
+        """,
+        "video": """
+        <View>
+          <Video name="video" value="$video"/>
+          <VideoRegionLabels name="label" toName="video">
+            <Label value="Action" background="red"/>
+          </VideoRegionLabels>
+        </View>
+        """
+    }
+    
+    def __init__(
+        self, 
+        base_url: Optional[str] = None, 
+        token: Optional[str] = None,
+        timeout: float = 30.0
+    ):
+        """初始化 Label Studio 客户端
+        
+        Args:
+            base_url: Label Studio 服务地址
+            token: API Token（使用 Authorization: Token {token} 头部）
+            timeout: 请求超时时间（秒）
+        """
+        self.base_url = (base_url or settings.label_studio_base_url).rstrip("/")
+        self.token = token or settings.label_studio_user_token
+        self.timeout = timeout
+        
+        if not self.token:
+            raise ValueError("Label Studio API token is required")
+        
+        # 初始化 HTTP 客户端
+        self.client = httpx.AsyncClient(
+            base_url=self.base_url,
+            timeout=self.timeout,
+            headers={
+                "Authorization": f"Token {self.token}",
+                "Content-Type": "application/json"
+            }
+        )
+        
+        logger.info(f"Label Studio client initialized: {self.base_url}")
+    
+    def get_label_config_by_type(self, data_type: str) -> str:
+        """根据数据类型获取标注配置"""
+        return self.DEFAULT_LABEL_CONFIGS.get(data_type.lower(), self.DEFAULT_LABEL_CONFIGS["image"])
+    
+    async def create_project(
+        self, 
+        title: str, 
+        description: str = "", 
+        label_config: Optional[str] = None,
+        data_type: str = "image"
+    ) -> Optional[Dict[str, Any]]:
+        """创建Label Studio项目"""
+        try:
+            logger.info(f"Creating Label Studio project: {title}")
+            
+            if not label_config:
+                label_config = self.get_label_config_by_type(data_type)
+            
+            project_data = {
+                "title": title,
+                "description": description,
+                "label_config": label_config.strip()
+            }
+            
+            response = await self.client.post("/api/projects", json=project_data)
+            response.raise_for_status()
+            
+            project = response.json()
+            project_id = project.get("id")
+            
+            if not project_id:
+                raise Exception("Label Studio response does not contain project ID")
+            
+            logger.info(f"Project created successfully, ID: {project_id}")
+            return project
+        
+        except httpx.HTTPStatusError as e:
+            logger.error(f"Create project failed HTTP {e.response.status_code}: {e.response.text}")
+            return None
+        except Exception as e:
+            logger.error(f"Error while creating Label Studio project: {e}")
+            return None
+    
+    async def import_tasks(
+        self,
+        project_id: int,
+        tasks: List[Dict[str, Any]],
+        commit_to_project: bool = True,
+        return_task_ids: bool = True
+    ) -> Optional[Dict[str, Any]]:
+        """批量导入任务到Label Studio项目"""
+        try:
+            logger.info(f"Importing {len(tasks)} tasks into project {project_id}")
+            
+            response = await self.client.post(
+                f"/api/projects/{project_id}/import",
+                json=tasks,
+                params={
+                    "commit_to_project": str(commit_to_project).lower(),
+                    "return_task_ids": str(return_task_ids).lower()
+                }
+            )
+            response.raise_for_status()
+            
+            result = response.json()
+            task_count = result.get("task_count", len(tasks))
+            
+            logger.info(f"Tasks imported successfully: {task_count}")
+            return result
+            
+        except httpx.HTTPStatusError as e:
+            logger.error(f"Import tasks failed HTTP {e.response.status_code}: {e.response.text}")
+            return None
+        except Exception as e:
+            logger.error(f"Error while importing tasks: {e}")
+            return None
+    
+    async def create_tasks_batch(
+        self,
+        project_id: str,
+        tasks: List[Dict[str, Any]]
+    ) -> Optional[Dict[str, Any]]:
+        """批量创建任务的便利方法"""
+        try:
+            pid = int(project_id)
+            return await self.import_tasks(pid, tasks)
+        except ValueError as e:
+            logger.error(f"Invalid project ID format: {project_id}, error: {e}")
+            return None
+        except Exception as e:
+            logger.error(f"Error while creating tasks in batch: {e}")
+            return None
+    
+    async def create_task(
+        self,
+        project_id: str,
+        data: Dict[str, Any],
+        meta: Optional[Dict[str, Any]] = None
+    ) -> Optional[Dict[str, Any]]:
+        """创建单个任务"""
+        try:
+            task = {"data": data}
+            if meta:
+                task["meta"] = meta
+            
+            return await self.create_tasks_batch(project_id, [task])
+            
+        except Exception as e:
+            logger.error(f"Error while creating single task: {e}")
+            return None
+    
+    async def get_project_tasks(
+        self,
+        project_id: str,
+        page: Optional[int] = None,
+        page_size: int = 1000
+    ) -> Optional[Dict[str, Any]]:
+        """获取项目任务信息
+        
+        Args:
+            project_id: 项目ID
+            page: 页码（从1开始）。如果为None，则获取所有任务
+            page_size: 每页大小
+            
+        Returns:
+            如果指定了page参数，返回包含分页信息的字典：
+            {
+                "count": 总任务数,
+                "page": 当前页码,
+                "page_size": 每页大小,
+                "project_id": 项目ID,
+                "tasks": 当前页的任务列表
+            }
+            
+            如果page为None，返回包含所有任务的字典：
+            
+                "count": 总任务数,
+                "project_id": 项目ID,
+                "tasks": 所有任务列表
+            }
+        """
+        try:
+            pid = int(project_id)
+            
+            # 如果指定了page，直接获取单页任务
+            if page is not None:
+                logger.info(f"Fetching tasks for project {pid}, page {page} (page_size={page_size})")
+                
+                response = await self.client.get(
+                    f"/api/projects/{pid}/tasks",
+                    params={
+                        "page": page,
+                        "page_size": page_size
+                    }
+                )
+                response.raise_for_status()
+                
+                result = response.json()
+                
+                # 返回单页结果，包含分页信息
+                return {
+                    "count": result.get("total", len(result.get("tasks", []))),
+                    "page": page,
+                    "page_size": page_size,
+                    "project_id": pid,
+                    "tasks": result.get("tasks", [])
+                }
+            
+            # 如果未指定page，获取所有任务
+            logger.info(f"Start fetching all tasks for project {pid} (page_size={page_size})")
+            all_tasks = []
+            current_page = 1
+            
+            while True:
+                try:
+                    response = await self.client.get(
+                        f"/api/projects/{pid}/tasks",
+                        params={
+                            "page": current_page,
+                            "page_size": page_size
+                        }
+                    )
+                    response.raise_for_status()
+                    
+                    result = response.json()
+                    tasks = result.get("tasks", [])
+                    
+                    if not tasks:
+                        logger.debug(f"No more tasks on page {current_page}")
+                        break
+                    
+                    all_tasks.extend(tasks)
+                    logger.debug(f"Fetched page {current_page}, {len(tasks)} tasks")
+                    
+                    # 检查是否还有更多页
+                    total = result.get("total", 0)
+                    if len(all_tasks) >= total:
+                        break
+                    
+                    current_page += 1
+                    
+                except httpx.HTTPStatusError as e:
+                    if e.response.status_code == 404:
+                        # 超出页数范围，结束分页
+                        logger.debug(f"Reached last page (page {current_page})")
+                        break
+                    else:
+                        raise
+            
+            logger.info(f"Fetched all tasks for project {pid}, total {len(all_tasks)}")
+            
+            # 返回所有任务，不包含分页信息
+            return {
+                "count": len(all_tasks),
+                "project_id": pid,
+                "tasks": all_tasks
+            }
+            
+        except httpx.HTTPStatusError as e:
+            logger.error(f"获取项目任务失败 HTTP {e.response.status_code}: {e.response.text}")
+            return None
+        except Exception as e:
+            logger.error(f"获取项目任务时发生错误: {e}")
+            return None
+    
+    async def delete_task(
+        self,
+        task_id: int
+    ) -> bool:
+        """删除单个任务"""
+        try:
+            logger.info(f"Deleting task: {task_id}")
+            
+            response = await self.client.delete(f"/api/tasks/{task_id}")
+            response.raise_for_status()
+            
+            logger.info(f"Task deleted: {task_id}")
+            return True
+            
+        except httpx.HTTPStatusError as e:
+            logger.error(f"Delete task {task_id} failed HTTP {e.response.status_code}: {e.response.text}")
+            return False
+        except Exception as e:
+            logger.error(f"Error while deleting task {task_id}: {e}")
+            return False
+    
+    async def delete_tasks_batch(
+        self,
+        task_ids: List[int]
+    ) -> Dict[str, int]:
+        """批量删除任务"""
+        try:
+            logger.info(f"Deleting {len(task_ids)} tasks in batch")
+            
+            successful_deletions = 0
+            failed_deletions = 0
+            
+            for task_id in task_ids:
+                if await self.delete_task(task_id):
+                    successful_deletions += 1
+                else:
+                    failed_deletions += 1
+            
+            logger.info(f"Batch deletion finished: success {successful_deletions}, failed {failed_deletions}")
+            
+            return {
+                "successful": successful_deletions,
+                "failed": failed_deletions,
+                "total": len(task_ids)
+            }
+            
+        except Exception as e:
+            logger.error(f"Error while deleting tasks in batch: {e}")
+            return {
+                "successful": 0,
+                "failed": len(task_ids),
+                "total": len(task_ids)
+            }
+    
+    async def get_project(self, project_id: int) -> Optional[Dict[str, Any]]:
+        """获取项目信息"""
+        try:
+            logger.info(f"Fetching project info: {project_id}")
+            
+            response = await self.client.get(f"/api/projects/{project_id}")
+            response.raise_for_status()
+            
+            return response.json()
+            
+        except httpx.HTTPStatusError as e:
+            logger.error(f"Get project info failed HTTP {e.response.status_code}: {e.response.text}")
+            return None
+        except Exception as e:
+            logger.error(f"Error while getting project info: {e}")
+            return None
+    
+    async def delete_project(self, project_id: int) -> bool:
+        """删除项目"""
+        try:
+            logger.info(f"Deleting project: {project_id}")
+            
+            response = await self.client.delete(f"/api/projects/{project_id}")
+            response.raise_for_status()
+            
+            logger.info(f"Project deleted: {project_id}")
+            return True
+            
+        except httpx.HTTPStatusError as e:
+            logger.error(f"Delete project {project_id} failed HTTP {e.response.status_code}: {e.response.text}")
+            return False
+        except Exception as e:
+            logger.error(f"Error while deleting project {project_id}: {e}")
+            return False
+    
+    async def create_local_storage(
+        self,
+        project_id: int,
+        path: str,
+        title: str,
+        use_blob_urls: bool = True,
+        regex_filter: Optional[str] = None,
+        description: Optional[str] = None
+    ) -> Optional[Dict[str, Any]]:
+        """创建本地文件存储配置
+        
+        Args:
+            project_id: Label Studio 项目 ID
+            path: 本地文件路径（在 Label Studio 容器中的路径）
+            title: 存储配置标题
+            use_blob_urls: 是否使用 blob URLs（建议 True）
+            regex_filter: 文件过滤正则表达式（可选）
+            description: 存储描述（可选）
+            
+        Returns:
+            创建的存储配置信息，失败返回 None
+        """
+        try:
+            logger.info(f"Creating local storage for project {project_id}: {path}")
+            
+            storage_data = {
+                "project": project_id,
+                "path": path,
+                "title": title,
+                "use_blob_urls": use_blob_urls
+            }
+            
+            if regex_filter:
+                storage_data["regex_filter"] = regex_filter
+            if description:
+                storage_data["description"] = description
+            
+            response = await self.client.post(
+                "/api/storages/localfiles/",
+                json=storage_data
+            )
+            response.raise_for_status()
+            
+            storage = response.json()
+            storage_id = storage.get("id")
+            
+            logger.info(f"Local storage created successfully, ID: {storage_id}")
+            return storage
+            
+        except httpx.HTTPStatusError as e:
+            logger.error(f"Create local storage failed HTTP {e.response.status_code}: {e.response.text}")
+            return None
+        except Exception as e:
+            logger.error(f"Error while creating local storage: {e}")
+            return None
+
+    async def close(self):
+        """关闭客户端连接"""
+        try:
+            await self.client.aclose()
+            logger.info("Label Studio client closed")
+        except Exception as e:
+            logger.error(f"Error while closing Label Studio client: {e}")

runtime/datamate-python/app/main.py

@@ -0,0 +1,160 @@
+from fastapi import FastAPI, Request, HTTPException, status
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from fastapi.exceptions import RequestValidationError
+from starlette.exceptions import HTTPException as StarletteHTTPException
+from contextlib import asynccontextmanager
+from typing import Dict, Any
+
+from .core.config import settings
+from .core.logging import setup_logging, get_logger
+from .infrastructure import LabelStudioClient
+from .api import api_router
+from .schemas import StandardResponse
+
+# 设置日志
+setup_logging()
+logger = get_logger(__name__)
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """应用程序生命周期管理"""
+    
+    # 启动时初始化
+    logger.info("Starting Label Studio Adapter...")
+    
+    # 初始化 Label Studio 客户端，使用 HTTP REST API + Token 认证
+    ls_client = LabelStudioClient(
+        base_url=settings.label_studio_base_url,
+        token=settings.label_studio_user_token
+    )
+    
+    logger.info("Label Studio Adapter started")
+    
+    yield
+    
+    # 关闭时清理
+    logger.info("Shutting down Label Studio Adapter...")
+    
+    # 客户端清理会在客户端管理器中处理
+    logger.info("Label Studio Adapter stopped")
+
+# 创建FastAPI应用
+app = FastAPI(
+    title=settings.app_name,
+    description=settings.app_description,
+    version=settings.app_version,
+    debug=settings.debug,
+    lifespan=lifespan
+)
+
+# 配置CORS中间件
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=settings.allowed_origins,
+    allow_credentials=True,
+    allow_methods=settings.allowed_methods,
+    allow_headers=settings.allowed_headers,
+)
+
+# 自定义异常处理器：StarletteHTTPException (包括404等)
+@app.exception_handler(StarletteHTTPException)
+async def starlette_http_exception_handler(request: Request, exc: StarletteHTTPException):
+    """将Starlette的HTTPException转换为标准响应格式"""
+    return JSONResponse(
+        status_code=exc.status_code,
+        content={
+            "code": exc.status_code,
+            "message": "error",
+            "data": {
+                "detail": exc.detail
+            }
+        }
+    )
+
+# 自定义异常处理器：FastAPI HTTPException
+@app.exception_handler(HTTPException)
+async def fastapi_http_exception_handler(request: Request, exc: HTTPException):
+    """将FastAPI的HTTPException转换为标准响应格式"""
+    return JSONResponse(
+        status_code=exc.status_code,
+        content={
+            "code": exc.status_code,
+            "message": "error",
+            "data": {
+                "detail": exc.detail
+            }
+        }
+    )
+
+# 自定义异常处理器：RequestValidationError
+@app.exception_handler(RequestValidationError)
+async def validation_exception_handler(request: Request, exc: RequestValidationError):
+    """将请求验证错误转换为标准响应格式"""
+    return JSONResponse(
+        status_code=422,
+        content={
+            "code": 422,
+            "message": "error",
+            "data": {
+                "detail": "Validation error",
+                "errors": exc.errors()
+            }
+        }
+    )
+
+# 自定义异常处理器：未捕获的异常
+@app.exception_handler(Exception)
+async def general_exception_handler(request: Request, exc: Exception):
+    """将未捕获的异常转换为标准响应格式"""
+    logger.error(f"Unhandled exception: {exc}", exc_info=True)
+    return JSONResponse(
+        status_code=500,
+        content={
+            "code": 500,
+            "message": "error",
+            "data": {
+                "detail": "Internal server error"
+            }
+        }
+    )
+
+# 注册路由
+app.include_router(api_router, prefix="/api")
+
+# 测试端点：验证异常处理
+@app.get("/test-404", include_in_schema=False)
+async def test_404():
+    """测试404异常处理"""
+    raise HTTPException(status_code=404, detail="Test 404 error")
+
+@app.get("/test-500", include_in_schema=False)
+async def test_500():
+    """测试500异常处理"""
+    raise Exception("Test uncaught exception")
+
+# 根路径重定向到文档
+@app.get("/", response_model=StandardResponse[Dict[str, Any]], include_in_schema=False)
+async def root():
+    """根路径，返回服务信息"""
+    return StandardResponse(
+        code=200,
+        message="success",
+        data={
+            "message": f"{settings.app_name} is running",
+            "version": settings.app_version,
+            "docs_url": "/docs",
+            "label_studio_url": settings.label_studio_base_url
+        }
+    )
+
+if __name__ == "__main__":
+    import uvicorn
+    
+    uvicorn.run(
+        "app.main:app",
+        host=settings.host,
+        port=settings.port,
+        reload=settings.debug,
+        log_level=settings.log_level.lower()
+    )

runtime/datamate-python/app/models/README.md

@@ -0,0 +1,138 @@
+# DataMate 数据模型结构
+
+本文档列出了根据 `scripts/db` 中的 SQL 文件创建的所有 Python 数据模型。
+
+## 模型组织结构
+
+```
+app/models/
+├── __init__.py                 # 主模块导出文件
+├── dm/                         # 数据管理 (Data Management) 模块
+│   ├── __init__.py
+│   ├── annotation_template.py  # 标注模板
+│   ├── labeling_project.py     # 标注项目
+│   ├── dataset.py              # 数据集
+│   ├── dataset_files.py        # 数据集文件
+│   ├── dataset_statistics.py   # 数据集统计
+│   ├── dataset_tag.py          # 数据集标签关联
+│   ├── tag.py                  # 标签
+│   └── user.py                 # 用户
+├── cleaning/                   # 数据清洗 (Data Cleaning) 模块
+│   ├── __init__.py
+│   ├── clean_template.py       # 清洗模板
+│   ├── clean_task.py           # 清洗任务
+│   ├── operator_instance.py    # 算子实例
+│   └── clean_result.py         # 清洗结果
+├── collection/                 # 数据归集 (Data Collection) 模块
+│   ├── __init__.py
+│   ├── task_execution.py       # 任务执行明细
+│   ├── collection_task.py      # 数据归集任务
+│   ├── task_log.py             # 任务执行记录
+│   └── datax_template.py       # DataX模板配置
+├── common/                     # 通用 (Common) 模块
+│   ├── __init__.py
+│   └── chunk_upload_request.py # 文件切片上传请求
+└── operator/                   # 算子 (Operator) 模块
+    ├── __init__.py
+    ├── operator.py             # 算子
+    ├── operator_category.py    # 算子分类
+    └── operator_category_relation.py  # 算子分类关联
+```
+
+## 模块详情
+
+### 1. Data Management (DM) 模块
+对应 SQL: `data-management-init.sql` 和 `data-annotation-init.sql`
+
+#### 模型列表：
+- **AnnotationTemplate** (`t_dm_annotation_templates`) - 标注模板
+- **LabelingProject** (`t_dm_labeling_projects`) - 标注项目
+- **Dataset** (`t_dm_datasets`) - 数据集（支持医学影像、文本、问答等多种类型）
+- **DatasetFiles** (`t_dm_dataset_files`) - 数据集文件
+- **DatasetStatistics** (`t_dm_dataset_statistics`) - 数据集统计信息
+- **Tag** (`t_dm_tags`) - 标签
+- **DatasetTag** (`t_dm_dataset_tags`) - 数据集标签关联
+- **User** (`users`) - 用户
+
+### 2. Data Cleaning 模块
+对应 SQL: `data-cleaning-init.sql`
+
+#### 模型列表：
+- **CleanTemplate** (`t_clean_template`) - 清洗模板
+- **CleanTask** (`t_clean_task`) - 清洗任务
+- **OperatorInstance** (`t_operator_instance`) - 算子实例
+- **CleanResult** (`t_clean_result`) - 清洗结果
+
+### 3. Data Collection (DC) 模块
+对应 SQL: `data-collection-init.sql`
+
+#### 模型列表：
+- **TaskExecution** (`t_dc_task_executions`) - 任务执行明细
+- **CollectionTask** (`t_dc_collection_tasks`) - 数据归集任务
+- **TaskLog** (`t_dc_task_log`) - 任务执行记录
+- **DataxTemplate** (`t_dc_datax_templates`) - DataX模板配置
+
+### 4. Common 模块
+对应 SQL: `data-common-init.sql`
+
+#### 模型列表：
+- **ChunkUploadRequest** (`t_chunk_upload_request`) - 文件切片上传请求
+
+### 5. Operator 模块
+对应 SQL: `data-operator-init.sql`
+
+#### 模型列表：
+- **Operator** (`t_operator`) - 算子
+- **OperatorCategory** (`t_operator_category`) - 算子分类
+- **OperatorCategoryRelation** (`t_operator_category_relation`) - 算子分类关联
+
+## 使用方式
+
+```python
+# 导入所有模型
+from app.models import (
+    # DM 模块
+    AnnotationTemplate,
+    LabelingProject,
+    Dataset,
+    DatasetFiles,
+    DatasetStatistics,
+    DatasetTag,
+    Tag,
+    User,
+    # Cleaning 模块
+    CleanTemplate,
+    CleanTask,
+    OperatorInstance,
+    CleanResult,
+    # Collection 模块
+    TaskExecution,
+    CollectionTask,
+    TaskLog,
+    DataxTemplate,
+    # Common 模块
+    ChunkUploadRequest,
+    # Operator 模块
+    Operator,
+    OperatorCategory,
+    OperatorCategoryRelation
+)
+
+# 或者按模块导入
+from app.models.dm import Dataset, DatasetFiles
+from app.models.collection import CollectionTask
+from app.models.operator import Operator
+```
+
+## 注意事项
+
+1. **UUID 主键**: 大部分表使用 UUID (String(36)) 作为主键
+2. **时间戳**: 使用 `TIMESTAMP` 类型，并配置自动更新
+3. **软删除**: 部分模型（如 AnnotationTemplate, LabelingProject）支持软删除，包含 `deleted_at` 字段和 `is_deleted` 属性
+4. **JSON 字段**: 配置信息、元数据等使用 JSON 类型存储
+5. **字段一致性**: 所有模型字段都严格按照 SQL 定义创建，确保与数据库表结构完全一致
+
+## 更新记录
+
+- 2025-10-25: 根据 `scripts/db` 中的 SQL 文件创建所有数据模型
+- 已更新现有的 `annotation_template.py`、`labeling_project.py`、`dataset_files.py` 以匹配 SQL 定义

runtime/datamate-python/app/models/__init__.py

@@ -0,0 +1,69 @@
+# app/models/__init__.py
+
+# Data Management (DM) 模块
+from .dm import (
+    AnnotationTemplate,
+    LabelingProject,
+    Dataset,
+    DatasetFiles,
+    DatasetStatistics,
+    DatasetTag,
+    Tag,
+    User
+)
+
+# Data Cleaning 模块
+from .cleaning import (
+    CleanTemplate,
+    CleanTask,
+    OperatorInstance,
+    CleanResult
+)
+
+# Data Collection (DC) 模块
+from .collection import (
+    TaskExecution,
+    CollectionTask,
+    TaskLog,
+    DataxTemplate
+)
+
+# Common 模块
+from .common import (
+    ChunkUploadRequest
+)
+
+# Operator 模块
+from .operator import (
+    Operator,
+    OperatorCategory,
+    OperatorCategoryRelation
+)
+
+__all__ = [
+    # DM 模块
+    "AnnotationTemplate",
+    "LabelingProject",
+    "Dataset",
+    "DatasetFiles",
+    "DatasetStatistics",
+    "DatasetTag",
+    "Tag",
+    "User",
+    # Cleaning 模块
+    "CleanTemplate",
+    "CleanTask",
+    "OperatorInstance",
+    "CleanResult",
+    # Collection 模块
+    "TaskExecution",
+    "CollectionTask",
+    "TaskLog",
+    "DataxTemplate",
+    # Common 模块
+    "ChunkUploadRequest",
+    # Operator 模块
+    "Operator",
+    "OperatorCategory",
+    "OperatorCategoryRelation"
+]

runtime/datamate-python/app/models/cleaning/__init__.py

@@ -0,0 +1,13 @@
+# app/models/cleaning/__init__.py
+
+from .clean_template import CleanTemplate
+from .clean_task import CleanTask
+from .operator_instance import OperatorInstance
+from .clean_result import CleanResult
+
+__all__ = [
+    "CleanTemplate",
+    "CleanTask",
+    "OperatorInstance",
+    "CleanResult"
+]

runtime/datamate-python/app/models/cleaning/clean_result.py

@@ -0,0 +1,22 @@
+from sqlalchemy import Column, String, BigInteger, Text
+from app.db.database import Base
+
+class CleanResult(Base):
+    """清洗结果模型"""
+    
+    __tablename__ = "t_clean_result"
+    
+    instance_id = Column(String(64), primary_key=True, comment="实例ID")
+    src_file_id = Column(String(64), nullable=True, comment="源文件ID")
+    dest_file_id = Column(String(64), primary_key=True, comment="目标文件ID")
+    src_name = Column(String(256), nullable=True, comment="源文件名")
+    dest_name = Column(String(256), nullable=True, comment="目标文件名")
+    src_type = Column(String(256), nullable=True, comment="源文件类型")
+    dest_type = Column(String(256), nullable=True, comment="目标文件类型")
+    src_size = Column(BigInteger, nullable=True, comment="源文件大小")
+    dest_size = Column(BigInteger, nullable=True, comment="目标文件大小")
+    status = Column(String(256), nullable=True, comment="处理状态")
+    result = Column(Text, nullable=True, comment="处理结果")
+    
+    def __repr__(self):
+        return f"<CleanResult(instance_id={self.instance_id}, dest_file_id={self.dest_file_id}, status={self.status})>"

runtime/datamate-python/app/models/cleaning/clean_task.py

@@ -0,0 +1,27 @@
+from sqlalchemy import Column, String, BigInteger, Integer, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+
+class CleanTask(Base):
+    """清洗任务模型"""
+    
+    __tablename__ = "t_clean_task"
+    
+    id = Column(String(64), primary_key=True, comment="任务ID")
+    name = Column(String(64), nullable=True, comment="任务名称")
+    description = Column(String(256), nullable=True, comment="任务描述")
+    status = Column(String(256), nullable=True, comment="任务状态")
+    src_dataset_id = Column(String(64), nullable=True, comment="源数据集ID")
+    src_dataset_name = Column(String(64), nullable=True, comment="源数据集名称")
+    dest_dataset_id = Column(String(64), nullable=True, comment="目标数据集ID")
+    dest_dataset_name = Column(String(64), nullable=True, comment="目标数据集名称")
+    before_size = Column(BigInteger, nullable=True, comment="清洗前大小")
+    after_size = Column(BigInteger, nullable=True, comment="清洗后大小")
+    file_count = Column(Integer, nullable=True, comment="文件数量")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    started_at = Column(TIMESTAMP, nullable=True, comment="开始时间")
+    finished_at = Column(TIMESTAMP, nullable=True, comment="完成时间")
+    created_by = Column(String(256), nullable=True, comment="创建者")
+    
+    def __repr__(self):
+        return f"<CleanTask(id={self.id}, name={self.name}, status={self.status})>"

runtime/datamate-python/app/models/cleaning/clean_template.py

@@ -0,0 +1,18 @@
+from sqlalchemy import Column, String, Text, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+
+class CleanTemplate(Base):
+    """清洗模板模型"""
+    
+    __tablename__ = "t_clean_template"
+    
+    id = Column(String(64), primary_key=True, unique=True, comment="模板ID")
+    name = Column(String(64), nullable=True, comment="模板名称")
+    description = Column(String(256), nullable=True, comment="模板描述")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    updated_at = Column(TIMESTAMP, server_default=func.current_timestamp(), onupdate=func.current_timestamp(), comment="更新时间")
+    created_by = Column(String(256), nullable=True, comment="创建者")
+    
+    def __repr__(self):
+        return f"<CleanTemplate(id={self.id}, name={self.name})>"

runtime/datamate-python/app/models/cleaning/operator_instance.py

@@ -0,0 +1,15 @@
+from sqlalchemy import Column, String, Integer, Text
+from app.db.database import Base
+
+class OperatorInstance(Base):
+    """算子实例模型"""
+    
+    __tablename__ = "t_operator_instance"
+    
+    instance_id = Column(String(256), primary_key=True, comment="实例ID")
+    operator_id = Column(String(256), primary_key=True, comment="算子ID")
+    op_index = Column(Integer, primary_key=True, comment="算子索引")
+    settings_override = Column(Text, nullable=True, comment="配置覆盖")
+    
+    def __repr__(self):
+        return f"<OperatorInstance(instance_id={self.instance_id}, operator_id={self.operator_id}, index={self.op_index})>"

runtime/datamate-python/app/models/collection/__init__.py

@@ -0,0 +1,13 @@
+# app/models/collection/__init__.py
+
+from .task_execution import TaskExecution
+from .collection_task import CollectionTask
+from .task_log import TaskLog
+from .datax_template import DataxTemplate
+
+__all__ = [
+    "TaskExecution",
+    "CollectionTask",
+    "TaskLog",
+    "DataxTemplate"
+]

runtime/datamate-python/app/models/collection/collection_task.py

@@ -0,0 +1,28 @@
+from sqlalchemy import Column, String, Text, Integer, BigInteger, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+
+class CollectionTask(Base):
+    """数据归集任务模型"""
+    
+    __tablename__ = "t_dc_collection_tasks"
+    
+    id = Column(String(36), primary_key=True, comment="任务ID（UUID）")
+    name = Column(String(255), nullable=False, comment="任务名称")
+    description = Column(Text, nullable=True, comment="任务描述")
+    sync_mode = Column(String(20), default='ONCE', comment="同步模式：ONCE/SCHEDULED")
+    config = Column(Text, nullable=False, comment="归集配置（DataX配置），包含源端和目标端配置信息")
+    schedule_expression = Column(String(255), nullable=True, comment="Cron调度表达式")
+    status = Column(String(20), default='DRAFT', comment="任务状态：DRAFT/READY/RUNNING/SUCCESS/FAILED/STOPPED")
+    retry_count = Column(Integer, default=3, comment="重试次数")
+    timeout_seconds = Column(Integer, default=3600, comment="超时时间（秒）")
+    max_records = Column(BigInteger, nullable=True, comment="最大处理记录数")
+    sort_field = Column(String(100), nullable=True, comment="增量字段")
+    last_execution_id = Column(String(36), nullable=True, comment="最后执行ID（UUID）")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    updated_at = Column(TIMESTAMP, server_default=func.current_timestamp(), onupdate=func.current_timestamp(), comment="更新时间")
+    created_by = Column(String(255), nullable=True, comment="创建者")
+    updated_by = Column(String(255), nullable=True, comment="更新者")
+    
+    def __repr__(self):
+        return f"<CollectionTask(id={self.id}, name={self.name}, status={self.status})>"

runtime/datamate-python/app/models/collection/datax_template.py

@@ -0,0 +1,23 @@
+from sqlalchemy import Column, String, Text, Boolean, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+
+class DataxTemplate(Base):
+    """DataX模板配置模型"""
+    
+    __tablename__ = "t_dc_datax_templates"
+    
+    id = Column(String(36), primary_key=True, comment="模板ID（UUID）")
+    name = Column(String(255), nullable=False, unique=True, comment="模板名称")
+    source_type = Column(String(50), nullable=False, comment="源数据源类型")
+    target_type = Column(String(50), nullable=False, comment="目标数据源类型")
+    template_content = Column(Text, nullable=False, comment="模板内容")
+    description = Column(Text, nullable=True, comment="模板描述")
+    version = Column(String(20), default='1.0.0', comment="版本号")
+    is_system = Column(Boolean, default=False, comment="是否系统模板")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    updated_at = Column(TIMESTAMP, server_default=func.current_timestamp(), onupdate=func.current_timestamp(), comment="更新时间")
+    created_by = Column(String(255), nullable=True, comment="创建者")
+    
+    def __repr__(self):
+        return f"<DataxTemplate(id={self.id}, name={self.name}, source={self.source_type}, target={self.target_type})>"

runtime/datamate-python/app/models/collection/task_execution.py

@@ -0,0 +1,34 @@
+from sqlalchemy import Column, String, Text, Integer, BigInteger, DECIMAL, JSON, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+
+class TaskExecution(Base):
+    """任务执行明细模型"""
+    
+    __tablename__ = "t_dc_task_executions"
+    
+    id = Column(String(36), primary_key=True, comment="执行记录ID（UUID）")
+    task_id = Column(String(36), nullable=False, comment="任务ID")
+    task_name = Column(String(255), nullable=False, comment="任务名称")
+    status = Column(String(20), default='RUNNING', comment="执行状态：RUNNING/SUCCESS/FAILED/STOPPED")
+    progress = Column(DECIMAL(5, 2), default=0.00, comment="进度百分比")
+    records_total = Column(BigInteger, default=0, comment="总记录数")
+    records_processed = Column(BigInteger, default=0, comment="已处理记录数")
+    records_success = Column(BigInteger, default=0, comment="成功记录数")
+    records_failed = Column(BigInteger, default=0, comment="失败记录数")
+    throughput = Column(DECIMAL(10, 2), default=0.00, comment="吞吐量（条/秒）")
+    data_size_bytes = Column(BigInteger, default=0, comment="数据量（字节）")
+    started_at = Column(TIMESTAMP, nullable=True, comment="开始时间")
+    completed_at = Column(TIMESTAMP, nullable=True, comment="完成时间")
+    duration_seconds = Column(Integer, default=0, comment="执行时长（秒）")
+    config = Column(JSON, nullable=True, comment="执行配置")
+    error_message = Column(Text, nullable=True, comment="错误信息")
+    datax_job_id = Column(Text, nullable=True, comment="datax任务ID")
+    result = Column(Text, nullable=True, comment="执行结果")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    updated_at = Column(TIMESTAMP, server_default=func.current_timestamp(), onupdate=func.current_timestamp(), comment="更新时间")
+    created_by = Column(String(255), nullable=True, comment="创建者")
+    updated_by = Column(String(255), nullable=True, comment="更新者")
+    
+    def __repr__(self):
+        return f"<TaskExecution(id={self.id}, task_id={self.task_id}, status={self.status})>"

runtime/datamate-python/app/models/collection/task_log.py

@@ -0,0 +1,26 @@
+from sqlalchemy import Column, String, Text, Integer, BigInteger, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+
+class TaskLog(Base):
+    """任务执行记录模型"""
+    
+    __tablename__ = "t_dc_task_log"
+    
+    id = Column(String(36), primary_key=True, comment="执行记录ID（UUID）")
+    task_id = Column(String(36), nullable=False, comment="任务ID")
+    task_name = Column(String(255), nullable=False, comment="任务名称")
+    sync_mode = Column(String(20), default='FULL', comment="同步模式：FULL/INCREMENTAL")
+    status = Column(String(20), default='RUNNING', comment="执行状态：RUNNING/SUCCESS/FAILED/STOPPED")
+    start_time = Column(TIMESTAMP, nullable=True, comment="开始时间")
+    end_time = Column(TIMESTAMP, nullable=True, comment="结束时间")
+    duration = Column(BigInteger, nullable=True, comment="执行时长(毫秒)")
+    process_id = Column(String(50), nullable=True, comment="进程ID")
+    log_path = Column(String(500), nullable=True, comment="日志文件路径")
+    error_msg = Column(Text, nullable=True, comment="错误信息")
+    result = Column(Text, nullable=True, comment="执行结果")
+    retry_times = Column(Integer, default=0, comment="重试次数")
+    create_time = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    
+    def __repr__(self):
+        return f"<TaskLog(id={self.id}, task_id={self.task_id}, status={self.status})>"

runtime/datamate-python/app/models/common/__init__.py

@@ -0,0 +1,7 @@
+# app/models/common/__init__.py
+
+from .chunk_upload_request import ChunkUploadRequest
+
+__all__ = [
+    "ChunkUploadRequest"
+]

runtime/datamate-python/app/models/common/chunk_upload_request.py

@@ -0,0 +1,19 @@
+from sqlalchemy import Column, String, Integer, Text, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+
+class ChunkUploadRequest(Base):
+    """文件切片上传请求模型"""
+    
+    __tablename__ = "t_chunk_upload_request"
+    
+    id = Column(String(36), primary_key=True, comment="UUID")
+    total_file_num = Column(Integer, nullable=True, comment="总文件数")
+    uploaded_file_num = Column(Integer, nullable=True, comment="已上传文件数")
+    upload_path = Column(String(256), nullable=True, comment="文件路径")
+    timeout = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="上传请求超时时间")
+    service_id = Column(String(64), nullable=True, comment="上传请求所属服务：DATA-MANAGEMENT(数据管理)")
+    check_info = Column(Text, nullable=True, comment="业务信息")
+    
+    def __repr__(self):
+        return f"<ChunkUploadRequest(id={self.id}, service_id={self.service_id}, progress={self.uploaded_file_num}/{self.total_file_num})>"

runtime/datamate-python/app/models/dm/__init__.py

@@ -0,0 +1,21 @@
+# app/models/dm/__init__.py
+
+from .annotation_template import AnnotationTemplate
+from .labeling_project import LabelingProject
+from .dataset import Dataset
+from .dataset_files import DatasetFiles
+from .dataset_statistics import DatasetStatistics
+from .dataset_tag import DatasetTag
+from .tag import Tag
+from .user import User
+
+__all__ = [
+    "AnnotationTemplate",
+    "LabelingProject",
+    "Dataset",
+    "DatasetFiles",
+    "DatasetStatistics",
+    "DatasetTag",
+    "Tag",
+    "User"
+]

runtime/datamate-python/app/models/dm/annotation_template.py

@@ -0,0 +1,24 @@
+from sqlalchemy import Column, String, JSON, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+import uuid
+
+class AnnotationTemplate(Base):
+    """标注模板模型"""
+    
+    __tablename__ = "t_dm_annotation_templates"
+    
+    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="UUID主键ID")
+    name = Column(String(32), nullable=False, comment="模板名称")
+    description = Column(String(255), nullable=True, comment="模板描述")
+    configuration = Column(JSON, nullable=True, comment="配置信息（JSON格式）")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    deleted_at = Column(TIMESTAMP, nullable=True, comment="删除时间（软删除）")
+    
+    def __repr__(self):
+        return f"<AnnotationTemplate(id={self.id}, name={self.name})>"
+    
+    @property
+    def is_deleted(self) -> bool:
+        """检查是否已被软删除"""
+        return self.deleted_at is not None

runtime/datamate-python/app/models/dm/dataset.py

@@ -0,0 +1,35 @@
+from sqlalchemy import Column, String, Text, BigInteger, Integer, Boolean, JSON, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+import uuid
+
+class Dataset(Base):
+    """数据集模型（支持医学影像、文本、问答等多种类型）"""
+    
+    __tablename__ = "t_dm_datasets"
+    
+    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="UUID")
+    name = Column(String(255), nullable=False, comment="数据集名称")
+    description = Column(Text, nullable=True, comment="数据集描述")
+    dataset_type = Column(String(50), nullable=False, comment="数据集类型：IMAGE/TEXT/QA/MULTIMODAL/OTHER")
+    category = Column(String(100), nullable=True, comment="数据集分类：医学影像/问答/文献等")
+    path = Column(String(500), nullable=True, comment="数据存储路径")
+    format = Column(String(50), nullable=True, comment="数据格式：DCM/JPG/JSON/CSV等")
+    schema_info = Column(JSON, nullable=True, comment="数据结构信息")
+    size_bytes = Column(BigInteger, default=0, comment="数据大小(字节)")
+    file_count = Column(BigInteger, default=0, comment="文件数量")
+    record_count = Column(BigInteger, default=0, comment="记录数量")
+    retention_days = Column(Integer, default=0, comment="数据保留天数（0表示长期保留）")
+    tags = Column(JSON, nullable=True, comment="标签列表")
+    metadata = Column(JSON, nullable=True, comment="元数据信息")
+    status = Column(String(50), default='DRAFT', comment="状态：DRAFT/ACTIVE/ARCHIVED")
+    is_public = Column(Boolean, default=False, comment="是否公开")
+    is_featured = Column(Boolean, default=False, comment="是否推荐")
+    version = Column(BigInteger, nullable=False, default=0, comment="版本号")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    updated_at = Column(TIMESTAMP, server_default=func.current_timestamp(), onupdate=func.current_timestamp(), comment="更新时间")
+    created_by = Column(String(255), nullable=True, comment="创建者")
+    updated_by = Column(String(255), nullable=True, comment="更新者")
+    
+    def __repr__(self):
+        return f"<Dataset(id={self.id}, name={self.name}, type={self.dataset_type})>"

runtime/datamate-python/app/models/dm/dataset_files.py

@@ -0,0 +1,27 @@
+from sqlalchemy import Column, String, JSON, BigInteger, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+import uuid
+
+class DatasetFiles(Base):
+    """DM数据集文件模型"""
+    
+    __tablename__ = "t_dm_dataset_files"
+    
+    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="UUID")
+    dataset_id = Column(String(36), nullable=False, comment="所属数据集ID（UUID）")
+    file_name = Column(String(255), nullable=False, comment="文件名")
+    file_path = Column(String(1000), nullable=False, comment="文件路径")
+    file_type = Column(String(50), nullable=True, comment="文件格式：JPG/PNG/DCM/TXT等")
+    file_size = Column(BigInteger, default=0, comment="文件大小(字节)")
+    check_sum = Column(String(64), nullable=True, comment="文件校验和")
+    tags = Column(JSON, nullable=True, comment="文件标签信息")
+    metadata = Column(JSON, nullable=True, comment="文件元数据")
+    status = Column(String(50), default='ACTIVE', comment="文件状态：ACTIVE/DELETED/PROCESSING")
+    upload_time = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="上传时间")
+    last_access_time = Column(TIMESTAMP, nullable=True, comment="最后访问时间")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    updated_at = Column(TIMESTAMP, server_default=func.current_timestamp(), onupdate=func.current_timestamp(), comment="更新时间")
+    
+    def __repr__(self):
+        return f"<DatasetFiles(id={self.id}, dataset_id={self.dataset_id}, file_name={self.file_name})>"

runtime/datamate-python/app/models/dm/dataset_statistics.py

@@ -0,0 +1,25 @@
+from sqlalchemy import Column, String, Date, BigInteger, JSON, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+import uuid
+
+class DatasetStatistics(Base):
+    """数据集统计信息模型"""
+    
+    __tablename__ = "t_dm_dataset_statistics"
+    
+    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="UUID")
+    dataset_id = Column(String(36), nullable=False, comment="数据集ID（UUID）")
+    stat_date = Column(Date, nullable=False, comment="统计日期")
+    total_files = Column(BigInteger, default=0, comment="总文件数")
+    total_size = Column(BigInteger, default=0, comment="总大小(字节)")
+    processed_files = Column(BigInteger, default=0, comment="已处理文件数")
+    error_files = Column(BigInteger, default=0, comment="错误文件数")
+    download_count = Column(BigInteger, default=0, comment="下载次数")
+    view_count = Column(BigInteger, default=0, comment="查看次数")
+    quality_metrics = Column(JSON, nullable=True, comment="质量指标")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    updated_at = Column(TIMESTAMP, server_default=func.current_timestamp(), onupdate=func.current_timestamp(), comment="更新时间")
+    
+    def __repr__(self):
+        return f"<DatasetStatistics(id={self.id}, dataset_id={self.dataset_id}, date={self.stat_date})>"

runtime/datamate-python/app/models/dm/dataset_tag.py

@@ -0,0 +1,15 @@
+from sqlalchemy import Column, String, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+
+class DatasetTag(Base):
+    """数据集标签关联模型"""
+    
+    __tablename__ = "t_dm_dataset_tags"
+    
+    dataset_id = Column(String(36), primary_key=True, comment="数据集ID（UUID）")
+    tag_id = Column(String(36), primary_key=True, comment="标签ID（UUID）")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    
+    def __repr__(self):
+        return f"<DatasetTag(dataset_id={self.dataset_id}, tag_id={self.tag_id})>"

runtime/datamate-python/app/models/dm/labeling_project.py

@@ -0,0 +1,26 @@
+from sqlalchemy import Column, String, Integer, JSON, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+import uuid
+
+class LabelingProject(Base):
+    """DM标注项目模型（原 DatasetMapping）"""
+    
+    __tablename__ = "t_dm_labeling_projects"
+    
+    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="UUID主键ID")
+    dataset_id = Column(String(36), nullable=False, comment="数据集ID")
+    name = Column(String(32), nullable=False, comment="项目名称")
+    labeling_project_id = Column(Integer, nullable=False, comment="Label Studio项目ID")
+    configuration = Column(JSON, nullable=True, comment="标签配置")
+    progress = Column(JSON, nullable=True, comment="标注进度统计")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    deleted_at = Column(TIMESTAMP, nullable=True, comment="删除时间（软删除）")
+    
+    def __repr__(self):
+        return f"<LabelingProject(id={self.id}, dataset_id={self.dataset_id}, name={self.name})>"
+    
+    @property
+    def is_deleted(self) -> bool:
+        """检查是否已被软删除"""
+        return self.deleted_at is not None

runtime/datamate-python/app/models/dm/tag.py

@@ -0,0 +1,21 @@
+from sqlalchemy import Column, String, Text, BigInteger, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+import uuid
+
+class Tag(Base):
+    """标签模型"""
+    
+    __tablename__ = "t_dm_tags"
+    
+    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="UUID")
+    name = Column(String(100), nullable=False, unique=True, comment="标签名称")
+    description = Column(Text, nullable=True, comment="标签描述")
+    category = Column(String(50), nullable=True, comment="标签分类")
+    color = Column(String(7), nullable=True, comment="标签颜色(十六进制)")
+    usage_count = Column(BigInteger, default=0, comment="使用次数")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    updated_at = Column(TIMESTAMP, server_default=func.current_timestamp(), onupdate=func.current_timestamp(), comment="更新时间")
+    
+    def __repr__(self):
+        return f"<Tag(id={self.id}, name={self.name}, category={self.category})>"

runtime/datamate-python/app/models/dm/user.py

@@ -0,0 +1,24 @@
+from sqlalchemy import Column, String, BigInteger, Boolean, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+
+class User(Base):
+    """用户模型"""
+    
+    __tablename__ = "users"
+    
+    id = Column(BigInteger, primary_key=True, autoincrement=True, comment="用户ID")
+    username = Column(String(255), nullable=False, unique=True, comment="用户名")
+    email = Column(String(255), nullable=False, unique=True, comment="邮箱")
+    password_hash = Column(String(255), nullable=False, comment="密码哈希")
+    full_name = Column(String(255), nullable=True, comment="真实姓名")
+    avatar_url = Column(String(500), nullable=True, comment="头像URL")
+    role = Column(String(50), nullable=False, default='USER', comment="角色：ADMIN/USER")
+    organization = Column(String(255), nullable=True, comment="所属机构")
+    enabled = Column(Boolean, nullable=False, default=True, comment="是否启用")
+    last_login_at = Column(TIMESTAMP, nullable=True, comment="最后登录时间")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    updated_at = Column(TIMESTAMP, server_default=func.current_timestamp(), onupdate=func.current_timestamp(), comment="更新时间")
+    
+    def __repr__(self):
+        return f"<User(id={self.id}, username={self.username}, role={self.role})>"

runtime/datamate-python/app/models/operator/__init__.py

@@ -0,0 +1,11 @@
+# app/models/operator/__init__.py
+
+from .operator import Operator
+from .operator_category import OperatorCategory
+from .operator_category_relation import OperatorCategoryRelation
+
+__all__ = [
+    "Operator",
+    "OperatorCategory",
+    "OperatorCategoryRelation"
+]

runtime/datamate-python/app/models/operator/operator.py

@@ -0,0 +1,24 @@
+from sqlalchemy import Column, String, Text, Boolean, TIMESTAMP
+from sqlalchemy.sql import func
+from app.db.database import Base
+
+class Operator(Base):
+    """算子模型"""
+    
+    __tablename__ = "t_operator"
+    
+    id = Column(String(64), primary_key=True, comment="算子ID")
+    name = Column(String(64), nullable=True, comment="算子名称")
+    description = Column(String(256), nullable=True, comment="算子描述")
+    version = Column(String(256), nullable=True, comment="版本")
+    inputs = Column(String(256), nullable=True, comment="输入类型")
+    outputs = Column(String(256), nullable=True, comment="输出类型")
+    runtime = Column(Text, nullable=True, comment="运行时信息")
+    settings = Column(Text, nullable=True, comment="配置信息")
+    file_name = Column(Text, nullable=True, comment="文件名")
+    is_star = Column(Boolean, nullable=True, comment="是否收藏")
+    created_at = Column(TIMESTAMP, server_default=func.current_timestamp(), comment="创建时间")
+    updated_at = Column(TIMESTAMP, server_default=func.current_timestamp(), onupdate=func.current_timestamp(), comment="更新时间")
+    
+    def __repr__(self):
+        return f"<Operator(id={self.id}, name={self.name}, version={self.version})>"

runtime/datamate-python/app/models/operator/operator_category.py

@@ -0,0 +1,15 @@
+from sqlalchemy import Column, String, Integer
+from app.db.database import Base
+
+class OperatorCategory(Base):
+    """算子分类模型"""
+    
+    __tablename__ = "t_operator_category"
+    
+    id = Column(Integer, primary_key=True, autoincrement=True, comment="分类ID")
+    name = Column(String(64), nullable=True, comment="分类名称")
+    type = Column(String(64), nullable=True, comment="分类类型")
+    parent_id = Column(Integer, nullable=True, comment="父分类ID")
+    
+    def __repr__(self):
+        return f"<OperatorCategory(id={self.id}, name={self.name}, type={self.type})>"

runtime/datamate-python/app/models/operator/operator_category_relation.py

@@ -0,0 +1,13 @@
+from sqlalchemy import Column, String, Integer
+from app.db.database import Base
+
+class OperatorCategoryRelation(Base):
+    """算子分类关联模型"""
+    
+    __tablename__ = "t_operator_category_relation"
+    
+    category_id = Column(Integer, primary_key=True, comment="分类ID")
+    operator_id = Column(String(64), primary_key=True, comment="算子ID")
+    
+    def __repr__(self):
+        return f"<OperatorCategoryRelation(category_id={self.category_id}, operator_id={self.operator_id})>"

runtime/datamate-python/app/schemas/__init__.py

@@ -0,0 +1,29 @@
+# app/schemas/__init__.py
+
+from .common import *
+from .dataset_mapping import *
+from .dm_service import *
+from .label_studio import *
+
+__all__ = [
+    # Common schemas
+    "StandardResponse",
+    
+    # Dataset Mapping schemas
+    "DatasetMappingBase",
+    "DatasetMappingCreateRequest", 
+    "DatasetMappingUpdateRequest",
+    "DatasetMappingResponse",
+    "DatasetMappingCreateResponse",
+    "SyncDatasetResponse", 
+    "DeleteDatasetResponse",
+    
+    # DM Service schemas
+    "DatasetFileResponse",
+    "PagedDatasetFileResponse", 
+    "DatasetResponse",
+    
+    # Label Studio schemas
+    "LabelStudioProject",
+    "LabelStudioTask"
+]

runtime/datamate-python/app/schemas/common.py

@@ -0,0 +1,63 @@
+"""
+通用响应模型
+"""
+from typing import Generic, TypeVar, Optional, List
+from pydantic import BaseModel, Field
+
+# 定义泛型类型变量
+T = TypeVar('T')
+
+# 定义一个将 snake_case 转换为 camelCase 的函数
+def to_camel(string: str) -> str:
+    """将 snake_case 字符串转换为 camelCase"""
+    components = string.split('_')
+    # 首字母小写，其余单词首字母大写
+    return components[0] + ''.join(x.title() for x in components[1:])
+
+class BaseResponseModel(BaseModel):
+    """基础响应模型，启用别名生成器"""
+    
+    class Config:
+        populate_by_name = True
+        alias_generator = to_camel
+
+class StandardResponse(BaseResponseModel, Generic[T]):
+    """
+    标准API响应格式
+    
+    所有API端点应返回此格式，确保响应的一致性
+    """
+    code: int = Field(..., description="HTTP状态码")
+    message: str = Field(..., description="响应消息")
+    data: Optional[T] = Field(None, description="响应数据")
+    
+    class Config:
+        populate_by_name = True
+        alias_generator = to_camel
+        json_schema_extra = {
+            "example": {
+                "code": 200,
+                "message": "success",
+                "data": {}
+            }
+        }
+
+
+class PaginatedData(BaseResponseModel, Generic[T]):
+    """分页数据容器"""
+    page: int = Field(..., description="当前页码（从1开始）")
+    size: int = Field(..., description="页大小")
+    total_elements: int = Field(..., description="总条数")
+    total_pages: int = Field(..., description="总页数")
+    content: List[T] = Field(..., description="当前页数据")
+    
+    class Config:
+        json_schema_extra = {
+            "example": {
+                "page": 1,
+                "size": 20,
+                "totalElements": 100,
+                "totalPages": 5,
+                "content": []
+            }
+        }

runtime/datamate-python/app/schemas/dataset_mapping.py

@@ -0,0 +1,56 @@
+from pydantic import Field
+from typing import Optional
+from datetime import datetime
+
+from .common import BaseResponseModel
+
+class DatasetMappingBase(BaseResponseModel):
+    """数据集映射 基础模型"""
+    dataset_id: str = Field(..., description="源数据集ID")
+
+class DatasetMappingCreateRequest(DatasetMappingBase):
+    """数据集映射 创建 请求模型"""
+    pass
+
+class DatasetMappingCreateResponse(BaseResponseModel):
+    """数据集映射 创建 响应模型"""
+    mapping_id: str = Field(..., description="映射UUID")
+    labelling_project_id: str = Field(..., description="Label Studio项目ID")
+    labelling_project_name: str = Field(..., description="Label Studio项目名称")
+    message: str = Field(..., description="响应消息")
+
+class DatasetMappingUpdateRequest(BaseResponseModel):
+    """数据集映射 更新 请求模型"""
+    dataset_id: Optional[str] = Field(None, description="源数据集ID")
+
+class DatasetMappingResponse(DatasetMappingBase):
+    """数据集映射 查询 响应模型"""
+    mapping_id: str = Field(..., description="映射UUID")
+    labelling_project_id: str = Field(..., description="标注项目ID")
+    labelling_project_name: Optional[str] = Field(None, description="标注项目名称")
+    created_at: datetime = Field(..., description="创建时间")
+    last_updated_at: datetime = Field(..., description="最后更新时间")
+    deleted_at: Optional[datetime] = Field(None, description="删除时间")
+    
+    class Config:
+        from_attributes = True
+        populate_by_name = True
+
+class SyncDatasetRequest(BaseResponseModel):
+    """同步数据集请求模型"""
+    mapping_id: str = Field(..., description="映射ID（mapping UUID）")
+    batch_size: int = Field(50, ge=1, le=100, description="批处理大小")
+
+class SyncDatasetResponse(BaseResponseModel):
+    """同步数据集响应模型"""
+    mapping_id: str = Field(..., description="映射UUID")
+    status: str = Field(..., description="同步状态")
+    synced_files: int = Field(..., description="已同步文件数量")
+    total_files: int = Field(0, description="总文件数量")
+    message: str = Field(..., description="响应消息")
+
+class DeleteDatasetResponse(BaseResponseModel):
+    """删除数据集响应模型"""
+    mapping_id: str = Field(..., description="映射UUID")
+    status: str = Field(..., description="删除状态")
+    message: str = Field(..., description="响应消息")

runtime/datamate-python/app/schemas/dm_service.py

@@ -0,0 +1,58 @@
+from pydantic import BaseModel, Field
+from typing import List, Optional, Dict, Any
+from datetime import datetime
+
+class DatasetFileResponse(BaseModel):
+    """DM服务数据集文件响应模型"""
+    id: str = Field(..., description="文件ID")
+    fileName: str = Field(..., description="文件名")
+    fileType: str = Field(..., description="文件类型")
+    filePath: str = Field(..., description="文件路径")
+    originalName: Optional[str] = Field(None, description="原始文件名")
+    size: Optional[int] = Field(None, description="文件大小（字节）")
+    status: Optional[str] = Field(None, description="文件状态")
+    uploadedAt: Optional[datetime] = Field(None, description="上传时间")
+    description: Optional[str] = Field(None, description="文件描述")
+    uploadedBy: Optional[str] = Field(None, description="上传者")
+    lastAccessTime: Optional[datetime] = Field(None, description="最后访问时间")
+
+class PagedDatasetFileResponse(BaseModel):
+    """DM服务分页文件响应模型"""
+    content: List[DatasetFileResponse] = Field(..., description="文件列表")
+    totalElements: int = Field(..., description="总元素数")
+    totalPages: int = Field(..., description="总页数")
+    page: int = Field(..., description="当前页码")
+    size: int = Field(..., description="每页大小")
+    
+class DatasetTypeResponse(BaseModel):
+    """数据集类型响应模型"""
+    code: str = Field(..., description="类型编码")
+    name: str = Field(..., description="类型名称")
+    description: Optional[str] = Field(None, description="类型描述")
+    supportedFormats: List[str] = Field(default_factory=list, description="支持的文件格式")
+    icon: Optional[str] = Field(None, description="图标")
+
+class DatasetResponse(BaseModel):
+    """DM服务数据集响应模型"""
+    id: str = Field(..., description="数据集ID")
+    name: str = Field(..., description="数据集名称")
+    description: Optional[str] = Field(None, description="数据集描述")
+    datasetType: str = Field(..., description="数据集类型", alias="datasetType")
+    status: str = Field(..., description="数据集状态")
+    fileCount: int = Field(..., description="文件数量")
+    totalSize: int = Field(..., description="总大小（字节）")
+    createdAt: Optional[datetime] = Field(None, description="创建时间")
+    updatedAt: Optional[datetime] = Field(None, description="更新时间")
+    createdBy: Optional[str] = Field(None, description="创建者")
+    
+    # 为了向后兼容，添加一个属性方法返回类型对象
+    @property
+    def type(self) -> DatasetTypeResponse:
+        """兼容属性：返回类型对象"""
+        return DatasetTypeResponse(
+            code=self.datasetType,
+            name=self.datasetType,
+            description=None,
+            supportedFormats=[],
+            icon=None
+        )

runtime/datamate-python/app/schemas/label_studio.py

@@ -0,0 +1,38 @@
+from pydantic import Field
+from typing import Dict, Any, Optional, List
+from datetime import datetime
+from .common import BaseResponseModel
+
+class LabelStudioProject(BaseResponseModel):
+    """Label Studio项目模型"""
+    id: int = Field(..., description="项目ID")
+    title: str = Field(..., description="项目标题")
+    description: Optional[str] = Field(None, description="项目描述")
+    label_config: str = Field(..., description="标注配置")
+    created_at: Optional[datetime] = Field(None, description="创建时间")
+    updated_at: Optional[datetime] = Field(None, description="更新时间")
+
+class LabelStudioTaskData(BaseResponseModel):
+    """Label Studio任务数据模型"""
+    image: Optional[str] = Field(None, description="图像URL")
+    text: Optional[str] = Field(None, description="文本内容")
+    audio: Optional[str] = Field(None, description="音频URL")
+    video: Optional[str] = Field(None, description="视频URL")
+    filename: Optional[str] = Field(None, description="文件名")
+
+class LabelStudioTask(BaseResponseModel):
+    """Label Studio任务模型"""
+    data: LabelStudioTaskData = Field(..., description="任务数据")
+    project: Optional[int] = Field(None, description="项目ID")
+    meta: Optional[Dict[str, Any]] = Field(None, description="元数据")
+
+class LabelStudioCreateProjectRequest(BaseResponseModel):
+    """创建Label Studio项目请求模型"""
+    title: str = Field(..., description="项目标题")
+    description: str = Field("", description="项目描述")
+    label_config: str = Field(..., description="标注配置")
+    
+class LabelStudioCreateTaskRequest(BaseResponseModel):
+    """创建Label Studio任务请求模型"""
+    data: Dict[str, Any] = Field(..., description="任务数据")
+    project: Optional[int] = Field(None, description="项目ID")

runtime/datamate-python/app/services/__init__.py

@@ -0,0 +1,6 @@
+# app/services/__init__.py
+
+from .dataset_mapping_service import DatasetMappingService
+from .sync_service import SyncService
+
+__all__ = ["DatasetMappingService", "SyncService"]

runtime/datamate-python/app/services/dataset_mapping_service.py

@@ -0,0 +1,298 @@
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.future import select
+from sqlalchemy import update, func
+from typing import Optional, List, Tuple
+from datetime import datetime
+import uuid
+
+from app.models.dm.labeling_project import LabelingProject
+from app.schemas.dataset_mapping import (
+    DatasetMappingCreateRequest, 
+    DatasetMappingUpdateRequest, 
+    DatasetMappingResponse
+)
+from app.core.logging import get_logger
+
+logger = get_logger(__name__)
+
+class DatasetMappingService:
+    """数据集映射服务"""
+    
+    def __init__(self, db: AsyncSession):
+        self.db = db
+    
+    async def create_mapping(
+        self, 
+        mapping_data: DatasetMappingCreateRequest, 
+        labelling_project_id: str,
+        labelling_project_name: str
+    ) -> DatasetMappingResponse:
+        """创建数据集映射"""
+        logger.info(f"Create dataset mapping: {mapping_data.dataset_id} -> {labelling_project_id}")
+        
+        db_mapping = LabelingProject(
+            mapping_id=str(uuid.uuid4()),
+            dataset_id=mapping_data.dataset_id,
+            labelling_project_id=labelling_project_id,
+            labelling_project_name=labelling_project_name
+        )
+        
+        self.db.add(db_mapping)
+        await self.db.commit()
+        await self.db.refresh(db_mapping)
+        
+        logger.info(f"Mapping created: {db_mapping.id}")
+        return DatasetMappingResponse.model_validate(db_mapping)
+    
+    async def get_mapping_by_source_uuid(
+        self, 
+        dataset_id: str
+    ) -> Optional[DatasetMappingResponse]:
+        """根据源数据集ID获取映射（返回第一个未删除的）"""
+        logger.debug(f"Get mapping by source dataset id: {dataset_id}")
+        
+        result = await self.db.execute(
+            select(LabelingProject).where(
+                LabelingProject.dataset_id == dataset_id,
+                LabelingProject.deleted_at.is_(None)
+            )
+        )
+        mapping = result.scalar_one_or_none()
+        
+        if mapping:
+            logger.debug(f"Found mapping: {mapping.id}")
+            return DatasetMappingResponse.model_validate(mapping)
+        
+        logger.debug(f"No mapping found for source dataset id: {dataset_id}")
+        return None
+    
+    async def get_mappings_by_dataset_id(
+        self, 
+        dataset_id: str,
+        include_deleted: bool = False
+    ) -> List[DatasetMappingResponse]:
+        """根据源数据集ID获取所有映射关系"""
+        logger.debug(f"Get all mappings by source dataset id: {dataset_id}")
+        
+        query = select(LabelingProject).where(
+            LabelingProject.dataset_id == dataset_id
+        )
+        
+        if not include_deleted:
+            query = query.where(LabelingProject.deleted_at.is_(None))
+        
+        result = await self.db.execute(
+            query.order_by(LabelingProject.created_at.desc())
+        )
+        mappings = result.scalars().all()
+        
+        logger.debug(f"Found {len(mappings)} mappings")
+        return [DatasetMappingResponse.model_validate(mapping) for mapping in mappings]
+    
+    async def get_mapping_by_labelling_project_id(
+        self, 
+        labelling_project_id: str
+    ) -> Optional[DatasetMappingResponse]:
+        """根据Label Studio项目ID获取映射"""
+        logger.debug(f"Get mapping by Label Studio project id: {labelling_project_id}")
+        
+        result = await self.db.execute(
+            select(LabelingProject).where(
+                LabelingProject.labeling_project_id == labelling_project_id,
+                LabelingProject.deleted_at.is_(None)
+            )
+        )
+        mapping = result.scalar_one_or_none()
+        
+        if mapping:
+            logger.debug(f"Found mapping: {mapping.mapping_id}")
+            return DatasetMappingResponse.model_validate(mapping)
+        
+        logger.debug(f"No mapping found for Label Studio project id: {labelling_project_id}")
+        return None
+    
+    async def get_mapping_by_uuid(self, mapping_id: str) -> Optional[DatasetMappingResponse]:
+        """根据映射UUID获取映射"""
+        logger.debug(f"Get mapping: {mapping_id}")
+        
+        result = await self.db.execute(
+            select(LabelingProject).where(
+                LabelingProject.id == mapping_id,
+                LabelingProject.deleted_at.is_(None)
+            )
+        )
+        mapping = result.scalar_one_or_none()
+        
+        if mapping:
+            logger.debug(f"Found mapping: {mapping.id}")
+            return DatasetMappingResponse.model_validate(mapping)
+        
+        logger.debug(f"Mapping not found: {mapping_id}")
+        return None
+    
+    async def update_mapping(
+        self, 
+        mapping_id: str, 
+        update_data: DatasetMappingUpdateRequest
+    ) -> Optional[DatasetMappingResponse]:
+        """更新映射信息"""
+        logger.info(f"Update mapping: {mapping_id}")
+        
+        mapping = await self.get_mapping_by_uuid(mapping_id)
+        if not mapping:
+            return None
+        
+        update_values = update_data.model_dump(exclude_unset=True)
+        update_values["last_updated_at"] = datetime.now()
+        
+        result = await self.db.execute(
+            update(LabelingProject)
+            .where(LabelingProject.id == mapping_id)
+            .values(**update_values)
+        )
+        await self.db.commit()
+        
+        if result.rowcount > 0:
+            return await self.get_mapping_by_uuid(mapping_id)
+        return None
+    
+    async def update_last_updated_at(self, mapping_id: str) -> bool:
+        """更新最后更新时间"""
+        logger.debug(f"Update mapping last updated at: {mapping_id}")
+        
+        result = await self.db.execute(
+            update(LabelingProject)
+            .where(
+                LabelingProject.id == mapping_id,
+                LabelingProject.deleted_at.is_(None)
+            )
+            .values(last_updated_at=datetime.utcnow())
+        )
+        await self.db.commit()
+        return result.rowcount > 0
+    
+    async def soft_delete_mapping(self, mapping_id: str) -> bool:
+        """软删除映射"""
+        logger.info(f"Soft delete mapping: {mapping_id}")
+        
+        result = await self.db.execute(
+            update(LabelingProject)
+            .where(
+                LabelingProject.id == mapping_id,
+                LabelingProject.deleted_at.is_(None)
+            )
+            .values(deleted_at=datetime.now())
+        )
+        await self.db.commit()
+        
+        success = result.rowcount > 0
+        if success:
+            logger.info(f"Mapping soft-deleted: {mapping_id}")
+        else:
+            logger.warning(f"Mapping not exists or already deleted: {mapping_id}")
+        
+        return success
+    
+    async def get_all_mappings(
+        self, 
+        skip: int = 0, 
+        limit: int = 100
+    ) -> List[DatasetMappingResponse]:
+        """获取所有有效映射"""
+        logger.debug(f"List all mappings, skip: {skip}, limit: {limit}")
+        
+        result = await self.db.execute(
+            select(LabelingProject)
+            .where(LabelingProject.deleted_at.is_(None))
+            .offset(skip)
+            .limit(limit)
+            .order_by(LabelingProject.created_at.desc())
+        )
+        mappings = result.scalars().all()
+        
+        logger.debug(f"Found {len(mappings)} mappings")
+        return [DatasetMappingResponse.model_validate(mapping) for mapping in mappings]
+    
+    async def count_mappings(self, include_deleted: bool = False) -> int:
+        """统计映射总数"""
+        query = select(func.count()).select_from(LabelingProject)
+        
+        if not include_deleted:
+            query = query.where(LabelingProject.deleted_at.is_(None))
+        
+        result = await self.db.execute(query)
+        return result.scalar_one()
+    
+    async def get_all_mappings_with_count(
+        self, 
+        skip: int = 0, 
+        limit: int = 100,
+        include_deleted: bool = False
+    ) -> Tuple[List[DatasetMappingResponse], int]:
+        """获取所有映射及总数（用于分页）"""
+        logger.debug(f"List all mappings with count, skip: {skip}, limit: {limit}")
+        
+        # 构建查询
+        query = select(LabelingProject)
+        if not include_deleted:
+            query = query.where(LabelingProject.deleted_at.is_(None))
+        
+        # 获取总数
+        count_query = select(func.count()).select_from(LabelingProject)
+        if not include_deleted:
+            count_query = count_query.where(LabelingProject.deleted_at.is_(None))
+        
+        count_result = await self.db.execute(count_query)
+        total = count_result.scalar_one()
+        
+        # 获取数据
+        result = await self.db.execute(
+            query
+            .offset(skip)
+            .limit(limit)
+            .order_by(LabelingProject.created_at.desc())
+        )
+        mappings = result.scalars().all()
+        
+        logger.debug(f"Found {len(mappings)} mappings, total: {total}")
+        return [DatasetMappingResponse.model_validate(mapping) for mapping in mappings], total
+    
+    async def get_mappings_by_source_with_count(
+        self,
+        dataset_id: str,
+        skip: int = 0,
+        limit: int = 100,
+        include_deleted: bool = False
+    ) -> Tuple[List[DatasetMappingResponse], int]:
+        """根据源数据集ID获取映射关系及总数（用于分页）"""
+        logger.debug(f"Get mappings by source dataset id with count: {dataset_id}")
+        
+        # 构建查询
+        query = select(LabelingProject).where(
+            LabelingProject.dataset_id == dataset_id
+        )
+        
+        if not include_deleted:
+            query = query.where(LabelingProject.deleted_at.is_(None))
+        
+        # 获取总数
+        count_query = select(func.count()).select_from(LabelingProject).where(
+            LabelingProject.dataset_id == dataset_id
+        )
+        if not include_deleted:
+            count_query = count_query.where(LabelingProject.deleted_at.is_(None))
+        
+        count_result = await self.db.execute(count_query)
+        total = count_result.scalar_one()
+        
+        # 获取数据
+        result = await self.db.execute(
+            query
+            .offset(skip)
+            .limit(limit)
+            .order_by(LabelingProject.created_at.desc())
+        )
+        mappings = result.scalars().all()
+        
+        logger.debug(f"Found {len(mappings)} mappings, total: {total}")
+        return [DatasetMappingResponse.model_validate(mapping) for mapping in mappings], total

runtime/datamate-python/app/services/sync_service.py

@@ -0,0 +1,275 @@
+from typing import Optional, List, Dict, Any, Tuple
+from app.infrastructure import LabelStudioClient, DatamateClient
+from app.services.dataset_mapping_service import DatasetMappingService
+from app.schemas.dataset_mapping import SyncDatasetResponse
+from app.core.logging import get_logger
+from app.core.config import settings
+from app.exceptions import NoDatasetInfoFoundError, DatasetMappingNotFoundError
+
+logger = get_logger(__name__)
+
+class SyncService:
+    """数据同步服务"""
+    
+    def __init__(
+        self, 
+        dm_client: DatamateClient, 
+        ls_client: LabelStudioClient,
+        mapping_service: DatasetMappingService
+    ):
+        self.dm_client = dm_client
+        self.ls_client = ls_client
+        self.mapping_service = mapping_service
+    
+    def determine_data_type(self, file_type: str) -> str:
+        """根据文件类型确定数据类型"""
+        file_type_lower = file_type.lower()
+        
+        if any(ext in file_type_lower for ext in ['jpg', 'jpeg', 'png', 'gif', 'bmp', 'svg', 'webp']):
+            return 'image'
+        elif any(ext in file_type_lower for ext in ['mp3', 'wav', 'flac', 'aac', 'ogg']):
+            return 'audio'
+        elif any(ext in file_type_lower for ext in ['mp4', 'avi', 'mov', 'wmv', 'flv', 'webm']):
+            return 'video'
+        elif any(ext in file_type_lower for ext in ['txt', 'doc', 'docx', 'pdf']):
+            return 'text'
+        else:
+            return 'image'  # 默认为图像类型
+    
+    async def get_existing_dm_file_mapping(self, project_id: str) -> Dict[str, int]:
+        """
+        获取Label Studio项目中已存在的DM文件ID到任务ID的映射
+        
+        Args:
+            project_id: Label Studio项目ID
+            
+        Returns:
+            dm_file_id到task_id的映射字典
+        """
+        try:
+            logger.info(f"Fetching existing task mappings for project {project_id} (page_size={settings.ls_task_page_size})")
+            dm_file_to_task_mapping = {}
+
+            # 使用Label Studio客户端封装的方法获取所有任务
+            page_size = getattr(settings, 'ls_task_page_size', 1000)
+            
+            # 调用封装好的方法获取所有任务，page=None表示获取全部
+            result = await self.ls_client.get_project_tasks(
+                project_id=project_id,
+                page=None,  # 不指定page，获取所有任务
+                page_size=page_size
+            )
+            
+            if not result:
+                logger.warning(f"Failed to fetch tasks for project {project_id}")
+                return {}
+            
+            all_tasks = result.get("tasks", [])
+
+            # 遍历所有任务，构建映射
+            for task in all_tasks:
+                # 检查任务的meta字段中是否有dm_file_id
+                meta = task.get('meta')
+                if meta:
+                    dm_file_id = meta.get('dm_file_id')
+                    if dm_file_id:
+                        task_id = task.get('id')
+                        if task_id:
+                            dm_file_to_task_mapping[str(dm_file_id)] = task_id
+
+            logger.info(f"Found {len(dm_file_to_task_mapping)} existing task mappings")
+            return dm_file_to_task_mapping
+
+        except Exception as e:
+            logger.error(f"Error while fetching existing tasks: {e}")
+            return {}  # 发生错误时返回空字典，会同步所有文件
+    
+    async def sync_dataset_files(
+        self, 
+        mapping_id: str, 
+        batch_size: int = 50
+    ) -> SyncDatasetResponse:
+        """同步数据集文件到Label Studio"""
+        logger.info(f"Start syncing dataset by mapping: {mapping_id}")
+        
+        # 获取映射关系
+        mapping = await self.mapping_service.get_mapping_by_uuid(mapping_id)
+        if not mapping:
+            logger.error(f"Dataset mapping not found: {mapping_id}")
+            return SyncDatasetResponse(
+                mapping_id="",
+                status="error",
+                synced_files=0,
+                total_files=0,
+                message=f"Dataset mapping not found: {mapping_id}"
+            )
+        
+        try:
+            # 获取数据集信息
+            dataset_info = await self.dm_client.get_dataset(mapping.dataset_id)
+            if not dataset_info:
+                raise NoDatasetInfoFoundError(mapping.dataset_id)
+            
+            synced_files = 0
+            deleted_tasks = 0
+            total_files = dataset_info.fileCount
+            page = 0
+            
+            logger.info(f"Total files in dataset: {total_files}")
+            
+            # 获取Label Studio中已存在的DM文件ID到任务ID的映射
+            existing_dm_file_mapping = await self.get_existing_dm_file_mapping(mapping.labelling_project_id)
+            existing_dm_file_ids = set(existing_dm_file_mapping.keys())
+            logger.info(f"{len(existing_dm_file_ids)} tasks already exist in Label Studio")
+            
+            # 收集DM中当前存在的所有文件ID
+            current_dm_file_ids = set()
+            
+            # 分页获取并同步文件
+            while True:
+                files_response = await self.dm_client.get_dataset_files(
+                    mapping.dataset_id, 
+                    page=page, 
+                    size=batch_size,
+                    status="COMPLETED"  # 只同步已完成的文件
+                )
+                
+                if not files_response or not files_response.content:
+                    logger.info(f"No more files on page {page + 1}")
+                    break
+                
+                logger.info(f"Processing page {page + 1}, total {len(files_response.content)} files")
+                
+                # 筛选出新文件并批量创建任务
+                tasks = []
+                new_files_count = 0
+                existing_files_count = 0
+                
+                for file_info in files_response.content:
+                    # 记录当前DM中存在的文件ID
+                    current_dm_file_ids.add(str(file_info.id))
+                    
+                    # 检查文件是否已存在
+                    if str(file_info.id) in existing_dm_file_ids:
+                        existing_files_count += 1
+                        logger.debug(f"Skip existing file: {file_info.originalName} (ID: {file_info.id})")
+                        continue
+                    
+                    new_files_count += 1
+                    
+                    # 确定数据类型
+                    data_type = self.determine_data_type(file_info.fileType)
+                    
+                    # 替换文件路径前缀:只替换开头的前缀，不影响路径中间可能出现的相同字符串
+                    file_path = file_info.filePath.removeprefix(settings.dm_file_path_prefix)
+                    file_path = settings.label_studio_file_path_prefix + file_path
+                    
+                    # 构造任务数据
+                    task_data = {
+                        "data": {
+                            data_type: file_path
+                        },
+                        "meta": {
+                            "file_size": file_info.size,
+                            "file_type": file_info.fileType,
+                            "dm_dataset_id": mapping.dataset_id,
+                            "dm_file_id": file_info.id,
+                            "original_name": file_info.originalName,
+                        }
+                    }
+                    tasks.append(task_data)
+                
+                logger.info(f"Page {page + 1}: new files {new_files_count}, existing files {existing_files_count}")
+                
+                # 批量创建Label Studio任务
+                if tasks:
+                    batch_result = await self.ls_client.create_tasks_batch(
+                        mapping.labelling_project_id,
+                        tasks
+                    )
+                    
+                    if batch_result:
+                        synced_files += len(tasks)
+                        logger.info(f"Successfully synced {len(tasks)} files")
+                    else:
+                        logger.warning(f"Batch task creation failed, fallback to single creation")
+                        # 如果批量创建失败，尝试单个创建
+                        for task_data in tasks:
+                            task_result = await self.ls_client.create_task(
+                                mapping.labelling_project_id,
+                                task_data["data"],
+                                task_data.get("meta")
+                            )
+                            if task_result:
+                                synced_files += 1
+                
+                # 检查是否还有更多页面
+                if page >= files_response.totalPages - 1:
+                    break
+                page += 1
+            
+            # 清理在DM中不存在但在Label Studio中存在的任务
+            tasks_to_delete = []
+            for dm_file_id, task_id in existing_dm_file_mapping.items():
+                if dm_file_id not in current_dm_file_ids:
+                    tasks_to_delete.append(task_id)
+                    logger.debug(f"Mark task for deletion: {task_id} (DM file ID: {dm_file_id})")
+            
+            if tasks_to_delete:
+                logger.info(f"Deleting {len(tasks_to_delete)} tasks not present in DM")
+                delete_result = await self.ls_client.delete_tasks_batch(tasks_to_delete)
+                deleted_tasks = delete_result.get("successful", 0)
+                logger.info(f"Successfully deleted {deleted_tasks} tasks")
+            else:
+                logger.info("No tasks to delete")
+            
+            # 更新映射的最后更新时间
+            await self.mapping_service.update_last_updated_at(mapping.mapping_id)
+            
+            logger.info(f"Sync completed: total_files={total_files}, created={synced_files}, deleted={deleted_tasks}")
+            
+            return SyncDatasetResponse(
+                mapping_id=mapping.mapping_id,
+                status="success",
+                synced_files=synced_files,
+                total_files=total_files,
+                message=f"Sync completed: created {synced_files} files, deleted {deleted_tasks} tasks"
+            )
+            
+        except Exception as e:
+            logger.error(f"Error while syncing dataset: {e}")
+            return SyncDatasetResponse(
+                mapping_id=mapping.mapping_id,
+                status="error",
+                synced_files=0,
+                total_files=0,
+                message=f"Sync failed: {str(e)}"
+            )
+    
+    async def get_sync_status(
+        self, 
+        dataset_id: str
+    ) -> Optional[Dict[str, Any]]:
+        """获取同步状态"""
+        mapping = await self.mapping_service.get_mapping_by_source_uuid(dataset_id)
+        if not mapping:
+            return None
+        
+        # 获取DM数据集信息
+        dataset_info = await self.dm_client.get_dataset(dataset_id)
+        
+        # 获取Label Studio项目任务数量
+        tasks_info = await self.ls_client.get_project_tasks(mapping.labelling_project_id)
+        
+        return {
+            "mapping_id": mapping.mapping_id,
+            "dataset_id": dataset_id,
+            "labelling_project_id": mapping.labelling_project_id,
+            "last_updated_at": mapping.last_updated_at,
+            "dm_total_files": dataset_info.fileCount if dataset_info else 0,
+            "ls_total_tasks": tasks_info.get("count", 0) if tasks_info else 0,
+            "sync_ratio": (
+                tasks_info.get("count", 0) / dataset_info.fileCount 
+                if dataset_info and dataset_info.fileCount > 0 and tasks_info else 0
+            )
+        }

runtime/datamate-python/deploy/docker-entrypoint.sh

@@ -0,0 +1,64 @@
+#!/bin/bash
+set -e
+
+echo "=========================================="
+echo "Label Studio Adapter Starting..."
+echo "=========================================="
+
+# Label Studio 本地存储基础路径（从环境变量获取，默认值）
+LABEL_STUDIO_LOCAL_BASE="${LABEL_STUDIO_LOCAL_BASE:-/label-studio/local_files}"
+
+echo "=========================================="
+echo "Ensuring Label Studio local storage directories exist..."
+echo "Base path: ${LABEL_STUDIO_LOCAL_BASE}"
+echo "=========================================="
+
+# 创建必要的目录
+mkdir -p "${LABEL_STUDIO_LOCAL_BASE}/dataset"
+mkdir -p "${LABEL_STUDIO_LOCAL_BASE}/upload"
+
+echo "✓ Directory 'dataset' ready: ${LABEL_STUDIO_LOCAL_BASE}/dataset"
+echo "✓ Directory 'upload' ready: ${LABEL_STUDIO_LOCAL_BASE}/upload"
+
+echo "=========================================="
+echo "Directory initialization completed"
+echo "=========================================="
+
+# 等待数据库就绪（如果配置了数据库）
+if [ -n "$MYSQL_HOST" ] || [ -n "$POSTGRES_HOST" ]; then
+    echo "Waiting for database to be ready..."
+    sleep 5
+fi
+
+# 运行数据库迁移
+echo "=========================================="
+echo "Running database migrations..."
+echo "=========================================="
+alembic upgrade head
+
+if [ $? -eq 0 ]; then
+    echo "✓ Database migrations completed successfully"
+else
+    echo "⚠️  WARNING: Database migrations failed"
+    echo "    The application may not work correctly"
+fi
+
+echo "=========================================="
+
+# 启动应用
+echo "Starting Label Studio Adapter..."
+echo "Host: ${HOST:-0.0.0.0}"
+echo "Port: ${PORT:-18000}"
+echo "Debug: ${DEBUG:-false}"
+echo "Label Studio URL: ${LABEL_STUDIO_BASE_URL}"
+echo "=========================================="
+
+# 转换 LOG_LEVEL 为小写（uvicorn 要求小写）
+LOG_LEVEL_LOWER=$(echo "${LOG_LEVEL:-info}" | tr '[:upper:]' '[:lower:]')
+
+# 使用 uvicorn 启动应用
+exec uvicorn app.main:app \
+    --host "${HOST:-0.0.0.0}" \
+    --port "${PORT:-18000}" \
+    --log-level "${LOG_LEVEL_LOWER}" \
+    ${DEBUG:+--reload}

runtime/datamate-python/uvicorn_start.sh

@@ -0,0 +1,5 @@
+uvicorn app.main:app \
+    --host 0.0.0.0 \
+    --port 18000 \
+    --reload \
+    --log-level debug