Jerry Yan
f5cb265667
Add support for detecting new file versions and switching to them: Backend Changes: - Add file_version column to AnnotationResult model - Create Alembic migration for database schema update - Implement check_file_version() method to compare annotation and file versions - Implement use_new_version() method to clear annotations and update version - Update upsert_annotation() to record file version when saving - Add new API endpoints: GET /version and POST /use-new-version - Add FileVersionCheckResponse and UseNewVersionResponse schemas Frontend Changes: - Add checkFileVersionUsingGet and useNewVersionUsingPost API calls - Add version warning banner showing current vs latest file version - Add 'Use New Version' button with confirmation dialog - Clear version info state when switching files to avoid stale warnings Bug Fixes: - Fix previousFileVersion returning updated value (save before update) - Handle null file_version for historical data compatibility - Fix segmented annotation clearing (preserve structure, clear results) - Fix files without annotations incorrectly showing new version warnings - Preserve total_segments when clearing segmented annotations Files Modified: - frontend/src/pages/DataAnnotation/Annotate/LabelStudioTextEditor.tsx - frontend/src/pages/DataAnnotation/annotation.api.ts - runtime/datamate-python/app/db/models/annotation_management.py - runtime/datamate-python/app/module/annotation/interface/editor.py - runtime/datamate-python/app/module/annotation/schema/editor.py - runtime/datamate-python/app/module/annotation/service/editor.py New Files: - runtime/datamate-python/alembic.ini - runtime/datamate-python/alembic/env.py - runtime/datamate-python/alembic/script.py.mako - runtime/datamate-python/alembic/versions/20250205_0001_add_file_version.py
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.