Jerry Yan
ebb4548ca5
新增功能： - 补全 4 类实体同步：Workflow、Job、LabelTask、KnowledgeSet - 补全 7 类关系构建：USES_DATASET、PRODUCES、ASSIGNED_TO、TRIGGERS、DEPENDS_ON、IMPACTS、SOURCED_FROM - 新增 39 个测试用例，总计 111 个测试 问题修复（三轮迭代）： 第一轮（6 个问题）： - toStringList null/blank 过滤 - mergeUsesDatasetRelations 统一逻辑 - fetchAllPaged 去重抽取 - IMPACTS 占位标记 - 测试断言增强 - syncAll 固定索引改 Map 第二轮（2 个问题）： - 活跃 ID 空值/空白归一化（两层防御） - 关系构建 N+1 查询消除（预加载 Map） 第三轮（1 个问题）： - 空元素 NPE 防御（GraphSyncService 12 处 + GraphSyncStepService 6 处） 代码变更：+1936 行，-101 行 测试结果：111 tests, 0 failures 已知 P3 问题（非阻塞）： - 安全注释与实现不一致（待权限过滤任务一起处理） - 测试覆盖缺口（可后续补充）
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.