This is a Next.js project bootstrapped with create-next-app.

Note: This Project uses the Vercel AI SDK!

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.

RSC & Client Server Design Pattern

This project demos three components of the NextJS design pattern nd can be run two ways.

• Client only
• Server only
• [Current Build ] Server and Client.

Routes

Routes are handled in JavaScript via the route.ts. There is one route, POST to the openAI model

Route.js

/api/chat/route.ts

Streaming Responses

uses streamText rather than block responses for a better user experience

 const result = await streamText({
    model: openai(`gpt-4-turbo`),
    messages,
  });

Optional - Streaming Data

possible to add previews or other inline items such as images to rest data route.ts

  // stream extra data here
  const data = new StreamData();
  data.append({ test: 'value' });

add this to the await stream to show data

const result = await streamText({
    model: openai(`gpt-4-turbo`),
    messages,
    onFinish() {
      data.close();
    },
  });

Use

Currently the project is set up as Client <---> Server

lets look at the differences.

Without the client using the useChat, direct from the api, you have to manage your own state using the useState hook to manage the user's input and messages, respectively. The biggest change in your implementation is how you manage the form submission behaviour:

• Define a new variable to house the existing messages and append the user's message.
• Update the messages state by passing newMessages to the setMessages function.
• Clear the input state with setInput("").
• Call your Server Action just like any other asynchronous function, passing the newMessages variable declared in the first step. This function will return a streamable value.
• Use an asynchronous for-loop in conjunction with the readStreamableValue function to iterate over the stream returned by the Server Action and read its value.
• Finally, update the messages state with the content streamed via the Server Action. To learn more about Next.js, take a look at the following resources:

To run the project client only:

• comment out the top of the client.ts file, and uncomment the bottom of the client code.
• This will remove the dependency of the server from the client but still use the route.ts

important changes in the design pattern when using state and the server architecture

you need to create a actions.ts that inits with use server and implements the calls to the UI, streamingText etc. In the code above, you first create a new variable to manage the state of the additional data (data). Then, you update the state of the additional data with setData(result.data). Just like that, you've sent additional data alongside the model's response.

Its import to know that the 'use server' is called both at the top of the actions.ts as well as inside the export const function

The ai/rsc library is designed to give you complete control over streamable values.

This unlocks LLM applications beyond the traditional chat format.

This project uses next/font to automatically optimize and load Inter, a custom Google Font.

Learn More

• Next.js Documentation - learn about Next.js features and API.
• Learn Next.js - an interactive Next.js tutorial.
• [https://sdk.vercel.ai/docs/getting-started/nextjs-app-router] - a tutorial that covers the app router

You can check out the Next.js GitHub repository - your feedback and contributions are welcome!

Deploy on Vercel

The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.

Check out our Next.js deployment documentation for more details.