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RAG & Knowledge Systems

Datamate

Datamate is an open-source enterprise data-processing application for preparing data for model fine-tuning and RAG retrieval. Its listed capabilities include data collection and management, cleaning, synthesis, annotation, evaluation and knowledge generation, along with visual workflow orchestration and support for built-in and custom operators.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Datamate — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Datamate when a fine-tuning or RAG project needs a workflow-oriented workspace for multiple data-preparation stages, rather than a single-purpose annotation or evaluation tool.

Who it is for

  • Teams preparing data for model fine-tuning.
  • Teams building data workflows for RAG retrieval.
  • Developers deploying an enterprise data-processing application with Docker Compose or Kubernetes and Helm.

Documented uses

  • Collect and manage project data.
  • Design drag-and-drop workflows for processing data.
  • Clean, synthesize and annotate datasets.
  • Evaluate data and generate knowledge for fine-tuning or retrieval work.
Before you choose

Requirements & limits

The project is publicly available on GitHub under the MIT license. The README documents Docker Compose and Kubernetes/Helm deployment; Kubernetes requires the Sealed Secrets Controller for secret management.

  • The README lists a broad set of data-processing modules, but does not specify supported data formats, model integrations or evaluation methods.
  • The documented Docker path requires Docker and Docker Compose; the Kubernetes path additionally requires Kubernetes, Helm and the Sealed Secrets Controller.
  • The repository identifies TypeScript as the primary language and provides frontend and backend CI workflow badges.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Deploy Datamate with Docker Compose

Start Datamate's basic services using the repository's documented Docker Compose quick-start.

Before you start

  • Docker
  • Docker Compose

Steps

  1. Download the project's Docker Compose file with the documented wget command.

  2. Run the documented docker compose up -d command with the ghcr.io/modelengine-group registry setting.

  3. After the containers are running, open http://localhost:30000 in a browser.

Expected outcome

The README directs users to the Datamate front-end interface at localhost:30000 after the containers are running.

Guide limitations

  • The README's quick-start command describes Docker deployment; Kubernetes deployment has separate requirements.
  • The README says Docker deployments manage secrets through an environment file.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
ModelEngine-Group/DataMate · Official homepage title · retained in its source language
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