RAG Chatbot

An Open-Source RAG Chatbot Template Built With Ragie, Next.js, and the AI SDK by Vercel.

Features · Model Providers · Deploy Your Own · Running locally

Features

• Ragie.ai
  • Fully Managed RAG-as-a-service Platform for Developers
• Next.js App Router
  • Advanced routing for seamless navigation and performance
  • React Server Components (RSCs) and Server Actions for server-side rendering and increased performance
• AI SDK
  • Unified API for generating text, structured objects, and tool calls with LLMs
  • Hooks for building dynamic chat and generative user interfaces
  • Supports OpenAI (default), Anthropic, Cohere, and other model providers
• shadcn/ui
  • Styling with Tailwind CSS
  • Component primitives from Radix UI for accessibility and flexibility
• Data Persistence
  • Vercel Postgres powered by Neon for saving chat history and user data
  • Vercel Blob for efficient file storage
• NextAuth.js
  • Simple and secure authentication

Model Providers

This template ships with OpenAI GPT4-o as the default. However, with the AI SDK, you can switch LLM providers to OpenAI, Anthropic, Cohere, and many more with just a few lines of code.

Deploy Your Own

You can deploy your own version of the Next.js AI Chatbot to Vercel with one click:

Running locally

You will need to use the environment variables defined in .env.example to run Next.js AI Chatbot. It's recommended you use Vercel Environment Variables for this, but a .env file is all that is necessary.

Note: You should not commit your .env file or it will expose secrets that will allow others to control access to your various OpenAI and authentication provider accounts.

1. Install Vercel CLI: npm i -g vercel
2. Link local instance with Vercel and GitHub accounts (creates .vercel directory): vercel link
3. Download your environment variables: vercel env pull

pnpm install
pnpm dev

Your app template should now be running on localhost:3000.

By default, the Chatbot acts as an HR/People Ops assistant designed to help onboard new employees at PostHog. It was trained using their open-source Handbook, you can adjust the system prompt and customize the RAG Chatbot for any use-case.