AI Chatbot — Next.js App

A minimal Next.js (TypeScript) starter app for an AI-powered chatbot UI. This repository was bootstrapped with Create Next App and contains a small, focused front-end built with Next.js and Tailwind/PostCSS tooling. The project is lightweight and ready to be extended with your preferred AI backend or API. (GitHub)

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Table of contents

• Project overview
• Key features & tech stack
• Repo structure
• Quick start (dev / build / production)
• Environment & configuration notes
• Deployment (Vercel recommended)
• How to extend for AI integrations
• Contributing & code style
• Suggested license

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Project overview

This repository contains a Next.js app (TypeScript) created with create-next-app and intended to be used as the front end for an AI chatbot or similar interactive UI. It includes default scripts for running, building, and linting the app, and uses the Geist font via next/font. (GitHub)

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Key features & tech stack

• Next.js (React + TypeScript) — app routes and client/server rendering patterns. (GitHub)
• TypeScript — typed codebase (tsconfig.json present). (GitHub)
• PostCSS / Tailwind-ready (postcss.config.mjs is present). (GitHub)
• ESLint config file present (eslint.config.mjs). (GitHub)
• Vercel-friendly configuration (next.config.ts) — straightforward to deploy to Vercel. (GitHub)

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Repository structure

A high-level view of the important files and folders:

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├─ app/                   # Next.js app directory (routes, pages, components)
├─ public/                # Static assets
├─ src/                   # Source files (if present)
├─ package.json           # Scripts & dependencies
├─ next.config.ts         # Next.js configuration
├─ tsconfig.json          # TypeScript configuration
├─ postcss.config.mjs     # PostCSS configuration
├─ eslint.config.mjs      # ESLint configuration
└─ README.md

(Exact folder names may vary — see the repository root for full layout.) (GitHub)

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Quick start

Prerequisites

• Node.js (recommended LTS — Node 18/20)
• npm, yarn, or pnpm

Install dependencies

Install using npm (or substitute yarn / pnpm):

npm install

Run development server

npm run dev

Open your browser at:

http://localhost:3000

The default Next.js dev server will hot-reload while you edit source files. (GitHub)

Build for production

npm run build
npm run start

The build command compiles the app for production; start runs the compiled app.

Other scripts

Check package.json for available scripts. Typical useful scripts include:

• npm run lint — run ESLint checks
• npm run format — run code formatter (if present)
• npm run test — run tests (if present)

(If a script is missing, add it to package.json as needed.)

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Environment & configuration

This front end is UI-only and does not ship with an AI backend out of the box. To connect it to an AI service you will typically:

1. Add environment variables (e.g., OPENAI_API_KEY or your own API base URL).
2. Create a serverless API route (Next.js API routes) or point client code to your backend.
3. Ensure you do not expose secret API keys to the browser — proxy requests via your own serverless functions or backend service.

Add environment variables in .env.local for local development. Example:

NEXT_PUBLIC_API_URL=http://localhost:3000/api

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Deployment

Vercel is the easiest way to deploy a Next.js app. This repository includes the configuration files expected by Next.js and is ready for Vercel deployment. To deploy:

1. Push the repo to GitHub (or your Git host).
2. Import the repo on Vercel and follow prompts.
3. Add any required environment variables in the Vercel dashboard (server-side secrets should be stored as environment secrets). (GitHub)

Other deployment targets (Netlify, Render, Fly, Docker) also work — build the app (npm run build) and serve the output with a Node server or static host as appropriate.

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How to extend for AI integrations

This repo is a front-end skeleton. To turn it into an AI chatbot:

1. Implement an API on the server (Next.js API route or external backend) that accepts prompts and returns responses from an LLM provider (OpenAI, Anthropic, local LLM, etc).
2. Add a client-side component that streams or polls responses and displays them in the chat UI. Use web sockets or server-sent events for streaming if desired.
3. Securely store API keys on the server; do not embed secret keys in client-side bundles.
4. Add conversation state (local storage, IndexedDB, or server-side persistence) depending on privacy and persistence needs.
5. Consider rate-limiting, caching, and cost controls when invoking paid LLM APIs.

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Code style & contribution

• The project contains ESLint config; please run linting before submitting PRs. (GitHub)
• Keep TypeScript types strict where practical.
• Open issues and PRs are welcome — include a clear description of changes and how to test them.

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License

Open Source License — MIT (Citation Required)

Anyone can copy, modify, and distribute this project as long as proper citation is provided.

Author: Mansoor Khan

GitHub: https://github.com/rebase-master

See the LICENSE file for full terms.

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