Build & Deploy An AI-Powered Chat App | Vue, Node, TypeScript, Open AI, Stream & Neon Database

Traversy Media · Intermediate ·🔧 Backend Engineering ·1y ago

Key Takeaways

Build and deploy a full stack AI-powered chat app using Vue, Node, TypeScript, Open AI, Stream, and Neon Database, with features like user registration, chat history, and context-based conversation.

Original Description

In this project, we will build a full stack app that works similar to ChatGPT and answers prompts and keeps the context throughout the conversation. We will use Vue.js with Pinia for state management, Node and Express on the backend with TypeScript. Stream for the chat featrues and a Neon PostgreSQL database to store users and chat logs. Stream Chat: https://getstream.io/chat/sdk/react/?utm_source=youtube&utm_medium=referral&utm_content=&utm_campaign=traversymedia Neon Database: https://fyi.neon.tech/1traversy Code (Node/Express Backend): https://github.com/bradtraversy/chat-ai-api Code (Vue.js Frontend): https://github.com/bradtraversy/chat-ai-ui Timestamps: 0:00 - Intro 2:43 - Documentation URLs 4:13 - Backend Init & Install Dependencies 7:56 - Backend TypeScript Config 10:03 - Express Server Setup 13:35 - /register-user Endpoint Setup 17:37 - Initialize Stream With API Keys 21:17 - Register User With Stream Chat 27:41 - /chat Endpoint & Open AI Init 42:49 - Neon PostgreSQL Database Setup 46:00 - Database Config & Neon Adapter 48:46 - Drizzle Schema 54:26 - Drizzle Config 57:31 - Database Migration 59:11 - Save Users In Neon PostgreSQL 1:04:58 - Save Chats In Neon PostgreSQL 1:09:38 - Get User Chat History 1:15:22 - Frontend Init & Install Dependencies 1:19:21 - TailwindCSS Setup & Clean Up 1:22:02 - Router & Page Setup 1:27:54 - Home Form Display 1:31:52 - Reactive Variables & Input Bind 1:37:24 - Pinia Initialization 1:39:19 - Pinia User Store 1:42:57 - Hook Up Home Form 1:50:02 - Header & Leave Chat/Logout 1:57:20 - Pinia Chat Store 2:01:45 - Load Chat History Function/Request 2:08:42 - Load Chat Into UI 2:17:50 - Send Message Function/Request 2:22:21 - Send Message Form 2:29:32 - Get Context Of Chat 2:37:37 - Format Output Text 2:40:52 - Backend API Deploy To Render 2:43:55 - Frontend Deploy To Vercel
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In this project, we build a full stack AI-powered chat app using Vue, Node, TypeScript, Open AI, Stream, and Neon Database. We cover features like user registration, chat history, and context-based conversation, and deploy the app to Render and Vercel.

Key Takeaways
  1. Initialize backend with Node and Express
  2. Set up TypeScript configuration
  3. Create Express server and register user endpoint
  4. Initialize Stream Chat with API keys
  5. Register user with Stream Chat
  6. Set up Open AI and chat endpoint
  7. Configure Neon PostgreSQL database
  8. Save users and chats in database
  9. Get user chat history
  10. Initialize frontend with Vue.js and Pinia
💡 To build a full stack AI-powered chat app, it's essential to integrate multiple tools and technologies, including Open AI, Stream Chat, and Neon Database, and to manage state and chat history effectively.

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Chapters (35)

Intro
2:43 Documentation URLs
4:13 Backend Init & Install Dependencies
7:56 Backend TypeScript Config
10:03 Express Server Setup
13:35 /register-user Endpoint Setup
17:37 Initialize Stream With API Keys
21:17 Register User With Stream Chat
27:41 /chat Endpoint & Open AI Init
42:49 Neon PostgreSQL Database Setup
46:00 Database Config & Neon Adapter
48:46 Drizzle Schema
54:26 Drizzle Config
57:31 Database Migration
59:11 Save Users In Neon PostgreSQL
1:04:58 Save Chats In Neon PostgreSQL
1:09:38 Get User Chat History
1:15:22 Frontend Init & Install Dependencies
1:19:21 TailwindCSS Setup & Clean Up
1:22:02 Router & Page Setup
1:27:54 Home Form Display
1:31:52 Reactive Variables & Input Bind
1:37:24 Pinia Initialization
1:39:19 Pinia User Store
1:42:57 Hook Up Home Form
1:50:02 Header & Leave Chat/Logout
1:57:20 Pinia Chat Store
2:01:45 Load Chat History Function/Request
2:08:42 Load Chat Into UI
2:17:50 Send Message Function/Request
2:22:21 Send Message Form
2:29:32 Get Context Of Chat
2:37:37 Format Output Text
2:40:52 Backend API Deploy To Render
2:43:55 Frontend Deploy To Vercel
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