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1.0.15 • Public • Published

nlux JS LangChain Adapter

Free And Open Source Docs nlux.ai

This package enables the integration between nlux and LangChain, the LLM framework.
More specifically ― the package includes the adapter to connect nlux JS to backends built using LangServe.


  • Support for both /invoke and /stream endpoints to allow for responses to be streamed back as they are generated.
  • Can utilize the /input_schema to construct a matching payload.
  • Ability to customize the payloads, both sent and received.

For more information on how to use this package, please visit:

Vanilla JS 🟨 vs React JS ⚛️

This package @nlux/langchain is meant for use with the vanilla JS version of nlux. If you're looking for the React JS version, please check the @nlux/langchain-react 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 favourite 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.
  • Bot and User Personas ― Customize the bot 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: nlux.ai

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npm i @nlux/langchain



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