What is Generative UI and how to get started?

Generative UI (genUI) is an innovative approach to user interface design that leverages artificial intelligence to create dynamic, personalized experiences for users in real-time. This cutting-edge technology is poised to revolutionize the way we interact with digital products and services.

Key Features of Generative UI

1. Real-time customization: Interfaces are dynamically generated to suit individual users' preferences and requirements.
2. Context-awareness: The UI adapts based on factors like user behavior, device type, and environmental conditions.
3. Personalization at scale: Generative UI can accommodate a wide variety of user profiles and experiences.
4. Continuous improvement: Machine learning models learn from user interactions to refine and optimize the interface over time.

Getting started with Generative UI

To get started, we'll leverage Vercel's AI SDK which makes it easier to help developers build AI-powered applications.

We'll be using the following tech stack for this project:

• Next.js (w/ App Router)
• Groq w/ Llama 3.1

Let's start!

1. Create a project:

   npm create next-app@latest my-ai-app

2. Install dependencies:

   npm add ai @ai-sdk/openai zod

3. Grab the API key from Groq's Cloud console

4. Create a route handler, app/api/chat/route.ts, and add the following code:

   import { streamText } from "ai";
   import { createOpenAI as createGroq } from "@ai-sdk/openai";
    
   // Allow streaming responses up to 30 seconds
   export const maxDuration = 30;
    
   export async function POST(req: Request) {
     // Conversation history
     const { messages } = await req.json();
    
     // Configuring Groq
     const groq = createGroq({
       baseURL: "https://api.groq.com/openai/v1",
       apiKey: process.env.GROQ_API_KEY,
     });
    
     // Calling LLM
     const result  = await streamText({
       model: groq("llama-3.1-70b-versatile"),
       messages: messages,
     });
    
     // Return result as a streamed response object
     return result.toAIStreamResponse();
   }

Let's understand what this code is doing:

• Define an asynchronous POST request handler and extract messages from the body of the request. The messages variable contains a history of the conversation between you and the chatbot and provides the chatbot with the necessary context to make the next generation.
• Create a Groq client instance with your API key using createGroq and set the base URL for Groq.
• Calls the LLM llama-3.1-70b-versatile using the streamText function.
• The streamText function returns a StreamTextResult. This result object contains the toDataStreamResponse function which converts the result to a streamed response object.

This route handler creates a POST request endpoint at /api/chat.

5. Use the useChat hook to handle the response from LLM in our root component file page.tsx:

   "use client";
    
   import { useChat } from "ai/react";
    
   export default function Home() {
     const { messages, input, handleInputChange, handleSubmit } = useChat();
     return (
       <div className="flex flex-col w-full max-w-md py-24 mx-auto stretch">
         <h5 className="font-semibold text-center">
           Generative UI starter app with Vercel AI SDK, Llama 3.1, and Groq
         </h5>
         {messages.map((m) => (
           <div
             key={m.id}
             className="whitespace-pre-wrap p-2 m-2 bg-gray-100 text-black border-1 border-gray-50 rounded-lg"
           >
             {m.role === "user" ? "User: " : "AI: "}
             {m.content}
           </div>
         ))}
    
         <form onSubmit={handleSubmit}>
           <input
             className="fixed bottom-0 w-full max-w-md p-2 mb-8 border border-gray-300 rounded shadow-xl"
             value={input}
             placeholder="Say something..."
             onChange={handleInputChange}
           />
         </form>
       </div>
     );
   }

Let's understand what's happening here:

• The useChat hook is used to handle the response from the LLM.
• The messages variable contains the conversation history between the user and the LLM.
• The input variable contains the current user input.
• The handleInputChange function is used to update the user input.
• The handleSubmit function is used to send the user input to the LLM.

Voila, you're done! See for yourself by trying it out and playing around with it.

You can find the complete code here.

Feel free to reach out if you have any questions or need further clarification.

Happy coding!