The LangChain adapters for nlux, the javascript library for building conversational AI interfaces.
npm install @nlux/langchain!Free And Open Source
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.
#### 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
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.
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.
* Build AI Chat Interfaces In Minutes ― High quality conversational AI interfaces with just a few lines of code.
* React Components & Hooks ― 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.
For developer documentation, examples, and API reference ― you can visit: docs.nlkit.com/nlux