T8 is a text visualization solution for unstructured data within the AntV technology stack, where T stands for Text, and 8 represents a byte of 8 bits, symbolizing that it can deeply uncover insights hidden beneath the text.
T8-Preview-Lite.mp4
T8 is a declarative markdown syntax that can be used to describe the content of data interpretation reports. Technically, based on the assumption that the markdown syntax is generated by the server, the frontend simply consumes the syntax for rendering. As the demand for diversity and immediacy in data representation grows, along with the increasing application of AI and NLP technologies, maintaining text templates on the frontend will become unsustainable. In this context, using T8 for unified rendering will be the optimal choice.
- Quick Start - Get started with T8 in minutes with installation and basic usage examples
- Tutorial - Learn about themes, events, plugins, streaming, and LLM integration
- Syntax - Complete T8 syntax reference and structure guide
- API Reference - Detailed API documentation and usage guides
- ๐ซ Technology stack agnostic - Can be used into
React,Vue, and other frontend stack. - ๐ค LLM friendly - The T8's
syntaxis easy to be generated byAIwith prompt. - ๐ ๏ธ Extensible - Register custom
EntityPhraseto easily customize the T8's ui elements. - ๐ชฉ Lightweight - Few dependencies, small footprint, before gzip it was less than
20Kb.
Install T8 using npm or yarn:
npm install @antv/t8yarn add @antv/t8import { Text } from '@antv/t8';
const text = new Text({ container: 'container' });
text.theme('light').render(`
# Sales Report
Total sales reached [ยฅ1,234,567](metric_value, origin=1234567).
`);For complete usage examples and advanced features, see the Quick Start Guide.
T8 is designed to be LLM-friendly. The T8 syntax can be easily generated by AI models, making it ideal for automated data narrative generation. To help you integrate T8 with AI agents:
- Prompt Templates - Ready-to-use prompts for LLMs to generate T8 syntax
- LLM Integration Guide - Complete guide for using T8 with large language models
- Examples - Sample T8 syntax outputs
This project exists thanks to all the people who contribute. And thank you to all our backers! ๐
- Issues - report bugs or request features
- Discussions - discuss on GitHub
MIT@AntV.