Jerry Yan
4765349fd7
- 数据库层面： - 创建 RBAC 核心表（角色、菜单权限） - 扩展现有表支持数据共享 - 初始化基础数据 - 后端层面： - 实现 UserContext 用户上下文管理 - 实现数据集访问权限服务 - 实现菜单权限服务 - 添加数据集共享功能 - 修复前端命名不匹配问题（snake_case vs camelCase） - 修复请求头不匹配问题（X-User-Roles vs X-Role-Codes） - 修复 Mapper 方法未实现问题 - 修复共享设置持久化缺失问题 - 前端层面： - 创建菜单权限工具 - 更新 Redux Store 支持菜单过滤 - 创建数据集共享设置组件 - 添加用户信息到请求头 - 实现 Token 刷新逻辑 - 数据隔离： - 实现 MyBatis 查询权限检查 - 实现数据文件访问控制 参考： - Codex 生成的实施方案 - kimI-cli 实施结果 - Codex Review 审核报告 修复的问题： 1. 前端命名不匹配（is_shared -> isShared, shared_with -> sharedWith） 2. 请求头不匹配（X-User-Roles -> X-Role-Codes） 3. Mapper 方法未实现（添加 findFilesWithAccessCheck 等方法声明） 4. 共享设置持久化缺失（添加 isShared 和 sharedWith 字段到 UpdateDatasetRequest） 5. 用户上下文加载问题（实现 Token 刷新逻辑）
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.