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@nlux/hf-react

v2.17.1

Published

The Hugging Face adapters for nlux React, the React JS library for building conversational AI interfaces.

Downloads

27

Readme

NLUX React Hugging Face Adapter

Free And Open Source Docs https://docs.nlkit.com/nlux

This package enables the integration between NLUX and Hugging Face, the AI models platform.
More specifically ― the package include the adapter to connect to chat models hosted and exposed via Hugging Face Inference Endpoints.

For more information on how to use this package, please visit:
docs.nlkit.com/nlux/reference/adapters/hugging-face

Vanilla JS 🟨 vs React JS ⚛️

This package @nlux/hf-react is meant for use with the React JS version of NLUX. If you're looking for the vanilla JS version, please check the @nlux/hf package.

About NLUX

NLUX (for Natural Language User Experience) is an open-source JavaScript library that makes it simple to integrate powerful large language models (LLMs) like ChatGPT into your web app or website. With just a few lines of code, you can add conversational AI capabilities and interact with your favorite LLM.

Key Features 🌟

  • Build AI Chat Interfaces In Minutes ― High quality conversational AI interfaces with just a few lines of code.
  • React Components & Hooks<AiChat /> for UI and useChatAdapter hook for easy integration.
  • LLM Adapters ― For ChatGPT / LangChain 🦜 LangServe / HuggingFace 🤗 Inference.
  • A flexible interface to Create Your Own Adapter for any LLM or API.
  • Assistant and User Personas ― Customize the assistant and user personas with names, images, and more.
  • Streaming LLM Output ― Stream the chat response to the UI as it's being generated.
  • Customizable Theme - Easily customize the look and feel of the chat interface using CSS variables.
  • Event Listeners - Listen to messages, errors, and other events to customize the UI and behaviour.
  • Zero Dependencies ― Lightweight codebase, with zero-dep except for LLM front-end libraries.

Docs & Examples 📖

For developer documentation, examples, and API reference ― you can visit: docs.nlkit.com/nlux