---
title: "easy-dataset"
type: "tool"
slug: "conardli-easy-dataset"
canonical_url: "https://www.graphcanon.com/tools/conardli-easy-dataset"
github_url: "https://github.com/ConardLi/easy-dataset"
homepage_url: "https://docs.easy-dataset.com"
stars: 14605
forks: 1490
primary_language: "JavaScript"
license: "Other"
categories: ["model-training", "evaluation-observability"]
tags: ["fine-tuning", "llm", "dataset", "javascript", "rag"]
updated_at: "2026-07-07T18:30:26.390525+00:00"
---

# easy-dataset

> A powerful tool for creating datasets for LLM fine-tuning, RAG, and evaluations.

ConardLi/easy-dataset is a JavaScript application designed to facilitate the creation of high-quality datasets specifically tailored for Large Language Models (LLMs). It supports model fine-tuning, retrieval-augmented generation (RAG), and evaluation tasks.

## Facts

- Repository: https://github.com/ConardLi/easy-dataset
- Homepage: https://docs.easy-dataset.com
- Stars: 14,605 · Forks: 1,490 · Open issues: 122 · Watchers: 66
- Primary language: JavaScript
- License: Other
- Last pushed: 2026-05-01T15:03:32+00:00

## Categories

- [Model Training](/categories/model-training.md)
- [Evaluation & Observability](/categories/evaluation-observability.md)

## Tags

fine-tuning, llm, dataset, javascript, rag

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## README (excerpt)

```text
<div align="center">



<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/ConardLi/easy-dataset">
<img alt="GitHub Downloads (all assets, all releases)" src="https://img.shields.io/github/downloads/ConardLi/easy-dataset/total">
<img alt="GitHub Release" src="https://img.shields.io/github/v/release/ConardLi/easy-dataset">
<img src="https://img.shields.io/badge/license-AGPL--3.0-green.svg" alt="AGPL 3.0 License"/>
<img alt="GitHub contributors" src="https://img.shields.io/github/contributors/ConardLi/easy-dataset">
<img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/ConardLi/easy-dataset">
<a href="https://arxiv.org/abs/2507.04009v1" target="_blank">
  <img src="https://img.shields.io/badge/arXiv-2507.04009-b31b1b.svg" alt="arXiv:2507.04009">
</a>

<a href="https://trendshift.io/repositories/13944" target="_blank"><img src="https://trendshift.io/api/badge/repositories/13944" alt="ConardLi%2Feasy-dataset | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>

**A powerful tool for creating fine-tuning datasets for Large Language Models**

[简体中文](./README.zh-CN.md) | [English](./README.md) | [Türkçe](./README.tr.md)

[Features](#features) • [Quick Start](#local-run) • [Documentation](https://docs.easy-dataset.com/ed/en) • [Contributing](#contributing) • [License](#license)

If you like this project, please give it a Star⭐️, or buy the author a coffee => [Donate](./public/imgs/aw.jpg) ❤️!

</div>

## Overview

Easy Dataset is an application specifically designed for building large language model (LLM) datasets. It features an intuitive interface, along with built-in powerful document parsing tools, intelligent segmentation algorithms, data cleaning and augmentation capabilities. The application can convert domain-specific documents in various formats into high-quality structured datasets, which are applicable to scenarios such as model fine-tuning, retrieval-augmented generation (RAG), and model performance evaluation.



## News

🎉🎉 Easy Dataset Version 1.7.0 launches brand-new evaluation capabilities! You can effortlessly convert domain-specific documents into evaluation datasets (test sets) and automatically run multi-dimensional evaluation tasks. Additionally, it comes with a human blind test system, enabling you to easily meet needs such as vertical domain model evaluation, post-fine-tuning model performance assessment, and RAG recall rate evaluation. Tutorial: [https://www.bilibili.com/video/BV1CRrVB7Eb4/](https://www.bilibili.com/video/BV1CRrVB7Eb4/)

## Features

### 📄 Document Processing & Data Generation

- **Intelligent Document Processing**: Supports PDF, Markdown, DOCX, TXT, EPUB and more formats with intelligent recognition
- **Intelligent Text Splitting**: Multiple splitting algorithms (Markdown structure, recursive separators, fixed length, code-aware chunking), with customizable visual segmentation
- **Intelligent Question Generation**: Auto-extract relevant questions from text segments, with question templates and batch generation
- **Domain Label Tree**: Intelligently builds global domain label trees based on document structure, with auto-tagging capabilities
- **Answer Generation**: Uses LLM API to generate comprehensive answers and Chain of Thought (COT), with AI optimization
- **Data Cleaning**: Intelligent text cleaning to remove noise and improve data quality

### 🔄 Multiple Dataset Types

- **Single-Turn QA Datasets**: Standard question-answer pairs for basic fine-tuning
- **Multi-Turn Dialogue Datasets**: Customizable roles and scenarios for conversational format
- **Image QA Datasets**: Generate visual QA data from images, with multiple import methods (directory, PDF, ZIP)
- **Data Distillation**: Generate label trees and questions directly from domain topics without uploading documents

### 📊 Model Evaluation System

- **Evaluation Datasets**: Generate true/false, single-choice, multiple-choice, short-answer, and open-ended questi
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/conardli-easy-dataset`](/api/graphcanon/tools/conardli-easy-dataset)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_