Jerry Yan
8f21798d57
实现功能： - 替换 Label Studio 自带的侧边栏，使用自定义结果面板 - 支持通用区域关系标注（任意标注区域之间建立关系） - 实时同步 Label Studio 的标注变更 - 双向联动：点击面板区域可高亮 LS 内对应区域，反之亦然 - 快捷标注关系：关系拾取模式、CRUD 操作、自动切换 Tab - 保存联动：自动合并面板关系到标注结果，判断是否已标注 技术实现： - 新增 5 个组件： - annotation-result.types.ts: TypeScript 类型定义 - RegionList.tsx: 区域列表组件 - RelationEditor.tsx: 关系编辑弹窗 - RelationList.tsx: 关系列表组件 - AnnotationResultPanel.tsx: 主面板组件（300px，可折叠，Tabs 切换） - 修改 2 个文件： - lsf.html: 消息协议扩展、防抖广播、区域选择监听、事件绑定/解绑 - LabelStudioTextEditor.tsx: 移除 LS 侧边栏、集成自定义面板、消息处理、taskId 校验 关键设计： - 单向读取 + 保存时合并：避免复杂的双向同步 - _source: 'panel' 标记：区分面板创建的关系和 LS 原生关系 - 150ms 防抖广播：避免消息洪泛 - 幂等事件绑定：避免监听器累积 - taskId 校验：防止跨任务消息混乱 代码审查： - 经过 3 轮 codex 审查，所有问题已修复 - 构建成功，Lint 检查通过 - 事件绑定/解绑结构清晰，幂等性处理合理 - 跨任务消息校验与状态更新路径一致性明显提升
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.