harehimself / langchain-nextjs-rag

A demonstration of RAG that utilizes Next.js, LangChain, and OpenAI. RAG (Retrieval Augmented Generation) is a technique that combines information retrieval and language generation to produce more informed and contextual responses.

This repository contains a simple experimental app built with Next.js, LangChain, and OpenAI to demonstrate RAG (retrieval augmented generation). RAG is a technique that combines information retrieval and language generation to produce more informed and contextual responses. The app leverages Next.js for the front-end, LangChain for managing the retrieval and generation process, and OpenAI's language model for the generation component. It serves as a proof-of-concept for integrating RAG into web applications.

Table of Contents

• Table of Contents
• Features
• Benefits
• How It Compares
• License

Features

• Next.js front-end for a seamless user experience
• LangChain integration for retrieval and generation management
• OpenAI language model for generation
• Customizable information retrieval sources
• Simple and intuitive user interface

Benefits

• Provides more informed and contextual responses
• Allows for dynamic and up-to-date information retrieval
• Scalable and modular architecture
• Easy to integrate into existing web applications
• Demonstrates the potential of RAG in a practical setting

How It Compares

• Offers a more comprehensive solution compared to traditional question-answering systems
• Leverages cutting-edge technologies like Next.js, LangChain, and OpenAI
• Provides a practical example of RAG implementation in a web application
• Serves as a starting point for further experimentation and development
• Showcases the potential of combining retrieval and generation for enhanced responses

License

Released under the permissive MIT License. Allows free use, modification, and distribution.