@nlux/langchain-react

NLUX React LangChain Adapter

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

Features:

• 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: docs.nlkit.com/nlux/reference/adapters/langchain-langserve

Vanilla JS 🟨 vs React JS ⚛️

This package @nlux/langchain-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/langchain 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