Jerry Yan
71f8f7d1c3
## 功能概述 实现完整的任务拆分、分配和进度跟踪功能，支持将任务拆分为子任务并分配给不同用户。 ## Phase 1: 数据库层 - 新增 t_task_meta 表（任务元数据协调表） - 新增 t_task_assignment_log 表（分配日志表） - 新增 3 个权限条目（read/write/assign） - 新增 SQLAlchemy ORM 模型 ## Phase 2: 后端 API (Java) - 新增 task-coordination-service 模块（32 个文件） - 实现 11 个 API 端点： - 任务查询（列表、子任务、我的任务） - 任务拆分（支持 4 种策略） - 任务分配（单个、批量、重新分配、撤回） - 进度管理（查询、更新、聚合） - 分配日志 - 集成权限控制和路由规则 ## Phase 3: 前端 UI (React + TypeScript) - 新增 10 个文件（模型、API、组件、页面） - 实现 5 个核心组件： - SplitTaskDialog - 任务拆分对话框 - AssignTaskDialog - 任务分配对话框 - BatchAssignDialog - 批量分配对话框 - TaskProgressPanel - 进度面板 - AssignmentLogDrawer - 分配记录 - 实现 2 个页面： - TaskCoordination - 任务管理主页 - MyTasks - 我的任务页面 - 集成侧边栏菜单和路由 ## 问题修复 - 修复 getMyTasks 分页参数缺失 - 修复子任务 assignee 信息缺失（批量查询优化） - 修复 proportion 精度计算（余量分配） ## 技术亮点 - 零侵入设计：通过独立协调表实现，不修改现有模块 - 批量查询优化：避免 N+1 查询问题 - 4 种拆分策略：按比例/数量/文件/手动 - 进度自动聚合：子任务更新自动聚合到父任务 - 权限细粒度控制：read/write/assign 三级权限 ## 验证 - Maven 编译：✅ 零错误 - TypeScript 编译：✅ 零错误 - Vite 生产构建：✅ 成功
DataMate All-in-One Data Work Platform
DataMate is an enterprise-level data processing platform for model fine-tuning and RAG retrieval, supporting core functions such as data collection, data management, operator marketplace, data cleaning, data synthesis, data annotation, data evaluation, and knowledge generation.
If you like this project, please give it a Star⭐️!
🌟 Core Features
- Core Modules: Data Collection, Data Management, Operator Marketplace, Data Cleaning, Data Synthesis, Data Annotation, Data Evaluation, Knowledge Generation.
- Visual Orchestration: Drag-and-drop data processing workflow design.
- Operator Ecosystem: Rich built-in operators and support for custom operators.
🚀 Quick Start
Prerequisites
- Git (for pulling source code)
- Make (for building and installing)
- Docker (for building images and deploying services)
- Docker-Compose (for service deployment - Docker method)
- Kubernetes (for service deployment - k8s method)
- Helm (for service deployment - k8s method)
This project supports deployment via two methods: docker-compose and helm. After executing the command, please enter the corresponding number for the deployment method. The command echo is as follows:
Choose a deployment method:
1. Docker/Docker-Compose
2. Kubernetes/Helm
Enter choice:
Clone the Code
git clone git@github.com:ModelEngine-Group/DataMate.git
cd DataMate
Deploy the basic services
make install
If the machine you are using does not have make installed, please run the following command to deploy it:
# Windows
set REGISTRY=ghcr.io/modelengine-group/
docker compose -f ./deployment/docker/datamate/docker-compose.yml up -d
docker compose -f ./deployment/docker/milvus/docker-compose.yml up -d
# Linux/Mac
export REGISTRY=ghcr.io/modelengine-group/
docker compose -f ./deployment/docker/datamate/docker-compose.yml up -d
docker compose -f ./deployment/docker/milvus/docker-compose.yml up -d
Once the container is running, access http://localhost:30000 in a browser to view the front-end interface.
To list all available Make targets, flags and help text, run:
make help
Build and deploy Mineru Enhanced PDF Processing
make build-mineru
make install-mineru
Deploy the DeerFlow service
make install-deer-flow
Local Development and Deployment
After modifying the local code, please execute the following commands to build the image and deploy using the local image.
make build
make install dev=true
Uninstall
make uninstall
When running make uninstall, the installer will prompt once whether to delete volumes; that single choice is applied to all components. The uninstall order is: milvus -> label-studio -> datamate, which ensures the datamate network is removed cleanly after services that use it have stopped.
🤝 Contribution Guidelines
Thank you for your interest in this project! We warmly welcome contributions from the community. Whether it's submitting bug reports, suggesting new features, or directly participating in code development, all forms of help make the project better.
• 📮 GitHub Issues: Submit bugs or feature suggestions.
• 🔧 GitHub Pull Requests: Contribute code improvements.
📄 License
DataMate is open source under the MIT license. You are free to use, modify, and distribute the code of this project in compliance with the license terms.