DataMate All-in-One Data Work Platform

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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.

简体中文 | English

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🌟 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)
K8s deployment additionally requires: Sealed Secrets Controller (for encrypted secret management)

Secret Management (K8s deployment only)

DataMate K8s deployment uses Bitnami Sealed Secrets to manage sensitive configuration such as database passwords and JWT secrets. All secrets are stored in encrypted form in Git (deployment/kubernetes/sealed-secrets/) and automatically decrypted by the Sealed Secrets Controller in the cluster at deploy time.

Online environment - install Sealed Secrets Controller:

bash
# Install via Helm (recommended)
helm repo add sealed-secrets https://bitnami-labs.github.io/sealed-secrets
helm install sealed-secrets sealed-secrets/sealed-secrets -n kube-system

# Verify installation
kubectl get pods -n kube-system | grep sealed-secrets

Air-gapped / offline environment:

Download the Sealed Secrets image on an internet-connected machine:

bash
# Download controller image (~60MB)
docker pull bitnami/sealed-secrets-controller:latest
docker save bitnami/sealed-secrets-controller:latest -o sealed-secrets-controller.tar

# Download kubeseal CLI (for updating secrets)
# macOS:
brew install kubeseal
# Linux:
wget https://github.com/bitnami-labs/sealed-secrets/releases/latest/download/kubeseal-linux-amd64

Transfer the image to your offline registry, then install via Helm with the custom image reference.

Updating secrets:

bash
# When passwords change, re-encrypt with kubeseal
echo -n "new-password" | kubeseal --raw --name datamate-conf --namespace datamate --scope namespace-wide

Note: Docker deployments do not require Sealed Secrets — secrets are managed via the .env file (excluded from Git via .gitignore).

Docker Quick deploy

shell
wget -qO docker-compose.yml https://raw.githubusercontent.com/ModelEngine-Group/DataMate/refs/heads/main/deployment/docker/datamate/docker-compose.yml \
 && REGISTRY=ghcr.io/modelengine-group/ docker compose up -d

Clone the Code

bash
git clone git@github.com:ModelEngine-Group/DataMate.git
cd DataMate

Deploy the basic services

bash
make install

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:

shell
Choose a deployment method:
1. Docker/Docker-Compose
2. Kubernetes/Helm
Enter choice:

If the machine you are using does not have make installed, please run the following command to deploy it:

bash
REGISTRY=ghcr.io/modelengine-group/ docker compose -f deployment/docker/datamate/docker-compose.yml --profile milvus 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:

bash
make help

If you are in an offline environment, you can run the following command to download all dependent images:

bash
make download

Deploy Label Studio as an annotation tool

bash
make install-label-studio

Build and deploy Mineru Enhanced PDF Processing

bash
make build-mineru
make install-mineru

Deploy the DeerFlow service

bash
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.

bash
make build
make install dev=true

Uninstall

bash
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.

📚 Documentation

Core Documentation

DEVELOPMENT.md - Local development environment setup and workflow
AGENTS.md - AI assistant guidelines and code style

Backend Documentation

backend/README.md - Backend architecture, services, and technology stack
backend/api-gateway/README.md - API Gateway configuration and routing
backend/services/main-application/README.md - Main application modules
backend/shared/README.md - Shared libraries (domain-common, security-common)

Runtime Documentation

runtime/README.md - Runtime architecture and components
runtime/datamate-python/README.md - FastAPI backend service
runtime/python-executor/README.md - Ray executor framework
runtime/ops/README.md - Operator ecosystem
runtime/datax/README.md - DataX data framework
runtime/deer-flow/README.md - DeerFlow LLM service

Frontend Documentation

frontend/README.md - React frontend application

🤝 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 a 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.