ChatGPT reset expectations for how we interact with technology.
Now, users want smart, context-aware interfaces that respond naturally, remember past conversations, and look great doing it. If you're not building intelligent, user-centric experiences, your competition will --- and fast.
Enter this tutorial from Traversy Media.
Follow along to build an AI-powered chat experience with chat history, persistence, and a polished frontend, using Vue 3, Node.js, TypeScript, OpenAI, Stream, and Neon DB.
The final product is a sleek, modern chat app where users can register, send messages to an AI assistant, and return to view their conversation history anytime.
Project Overview
In this tutorial, you'll build a full-stack chat application where:
- Users register with a name and email.
- Messages are sent to OpenAI's GPT model.
- Responses are displayed instantly in a real-time UI.
- Conversations are stored and synced using Stream and Neon.
- Users can return later and view the full chat history.
Key technologies include:
- Frontend: Vue 3, Tailwind CSS, Pinia (for state management)
- Backend: Node.js, Express, TypeScript
- AI: OpenAI GPT (via API)
- Chat Infrastructure: Stream Chat
- Database: Neon (PostgreSQL), Drizzle ORM
This decoupled architecture allows you to iterate independently on the UI and backend.
Overview of the Build
Create a Node.js Backend with Express and TypeScript
Start by initializing the backend API using Express and TypeScript. This API handles user registration, message processing, and communication with third-party services like Stream and OpenAI. Environment variables are configured for secure access to credentials.
Register Users in Stream and Neon
When a user registers via the frontend, the backend creates their profile in Stream Chat (for messaging) and Neon (for persistence). This dual registration ensures chat metadata and history are available for retrieval and display.
Set Up Chat with OpenAI Integration
The application's core is the /chat route, which retrieves the last few messages from Neon, sends them along with the latest prompt to OpenAI, and returns a response. The AI-generated message is saved in the database and sent to Stream for UI rendering.
Scaffold the Vue 3 Frontend with Tailwind CSS
A new Vue 3 project is initialized using Vite and styled with Tailwind CSS. Pinia is used for state management, allowing the app to persist user sessions and chat history between visits.
Build the Chat UI and Message Handling Logic
The chat interface includes a header, a scrollable message container, and an input field. Messages are styled differently based on sender (user vs. AI) and formatted to display line breaks, markdown, and lists clearly. New messages are sent to the backend and appended to the chat view.
Display and Persist Chat History
Chat logs are fetched from Neon each time a user logs in, ensuring continuity across sessions. The frontend uses a dedicated store to manage message state and format conversations on load.
Deploy Backend to Render and Frontend to Vercel
The app is deployed using Render for the backend and Vercel for the frontend. Both services handle environment variables, build scripts, and deploy automation, making launching and scaling easy.
What's Next?
Congratulations! You've built a fully functional AI chat app with:
💬 Smart, context-aware AI messaging
📦 Persistent chat history
⚙️ A scalable backend
🧩 Real-time chat infrastructure
🎨 A sleek, responsive UI built
Want to explore more features? Try adding:
Check out Travery Media's full video tutorial to follow along step-by-step, dive deeper into the code, and extend this project for your use case.