Next.js Pages Router Quickstart
The Vercel AI SDK is a powerful Typescript library designed to help developers build AI-powered applications.
In this quickstart tutorial, you'll build a simple AI-chatbot with a streaming user interface. Along the way, you'll learn key concepts and techniques that are fundamental to using the SDK in your own projects.
If you are unfamiliar with the concepts of Prompt Engineering and HTTP Streaming, you can optionally read these documents first.
Prerequisites
To follow this quickstart, you'll need:
- Node.js 18+ and pnpm installed on your local development machine.
- An OpenAI API key.
If you haven't obtained your OpenAI API key, you can do so by signing up on the OpenAI website.
Setup Your Application
Start by creating a new Next.js application. This command will create a new directory named my-ai-app
and set up a basic Next.js application inside it.
Be sure to select no when prompted to use the App Router. If you are looking for the Next.js App Router quickstart guide, you can find it here.
pnpm create next-app@latest my-ai-app
Navigate to the newly created directory:
cd my-ai-app
Install dependencies
Install ai
and @ai-sdk/openai
, Vercel AI SDK's OpenAI provider.
Vercel AI SDK is designed to be a unified interface to interact with any large language model. This means that you can change model and providers with just one line of code! Learn more about available providers and building custom providers in the providers section.
pnpm install ai @ai-sdk/openai zod
Make sure you are using ai
version 3.1 or higher.
Configure OpenAI API Key
Create a .env.local
file in your project root and add your OpenAI API Key. This key is used to authenticate your application with the OpenAI service.
touch .env.local
Edit the .env.local
file:
OPENAI_API_KEY=xxxxxxxxx
Replace xxxxxxxxx
with your actual OpenAI API key.
Vercel AI SDK's OpenAI Provider will default to using the OPENAI_API_KEY
environment variable.
Create a Route Handler
As long as you are on Next.js 13+, you can use Route Handlers (using the App Router) alongside the Pages Router. This is recommended to enable you to use the Web APIs interface/signature and to better support streaming.
Create a Route Handler (app/api/chat/route.ts
) and add the following code:
import { openai } from '@ai-sdk/openai';import { StreamingTextResponse, streamText } from 'ai';
// Allow streaming responses up to 30 secondsexport const maxDuration = 30;
export async function POST(req: Request) { const { messages } = await req.json();
const result = await streamText({ model: openai('gpt-4-turbo'), messages, });
return result.toAIStreamResponse();}
Let's take a look at what is happening in this code:
- Define an asynchronous
POST
request and extractmessages
from the body of the request. Themessages
variable contains a history of the conversation with you and the chatbot and will provide the chatbot with the necessary context to make the next generation. - Call the
streamText
function which is imported from theai
package. To use this function, you pass it a configuration object that contains amodel
provider (imported from@ai-sdk/openai
) andmessages
(defined in step 2). You can use pass additional settings in this configuration object to further customise the models behaviour. - The
streamText
function returns aStreamTextResult
. This result object contains thetoAIStreamResponse
function which converts the result to a streamed response object. - Return the result to the client to stream the response.
This Route Handler creates a POST request endpint at /api/chat
.
Wire up the UI
Now that you have an API route that can query an LLM, it's time to setup your frontend. Vercel AI SDK's UI package abstract the complexity of a chat interface into one hook, useChat
.
Update your root page (pages/index.tsx
) with the following code to show a list of chat messages and provide a user message input:
import { useChat } from 'ai/react';
export default function Chat() { const { messages, input, handleInputChange, handleSubmit } = useChat(); return ( <div className="flex flex-col w-full max-w-md py-24 mx-auto stretch"> {messages.map(m => ( <div key={m.id} className="whitespace-pre-wrap"> {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> );}
This page utilizes the useChat
hook, which will, by default, use the POST
API route you created earlier (/api/chat
). The hook provides functions and state for handling user input and form submission. The useChat
hook provides multiple utility functions and state variables:
messages
- the current chat messages (an array of objects withid
,role
, andcontent
properties).input
- the current value of the user's input field.handleInputChange
andhandleSubmit
- functions to handle user interactions (typing into the input field and submitting the form, respectively).isLoading
- boolean that indicates whether the API request is in progress.
Running Your Application
With that, you have built everything you need for your chatbot! To start your application, use the command:
pnpm run dev
Head to your browser and open http://localhost:3000. You should see an input field. Test it out by entering a message and see the AI chatbot respond in real-time! Vercel AI SDK makes it fast and easy to build AI chat interfaces with Next.js.
Stream Data Alongside Response
Depending on your use case, you may want to stream additional data alongside the model's response. This can be done using StreamData
.
Update your Route Handler
Make the following changes to your Route Handler (app/api/chat/route.ts
)
import { openai } from '@ai-sdk/openai';import { StreamingTextResponse, streamText, StreamData } from 'ai';
// Allow streaming responses up to 30 secondsexport const maxDuration = 30;
export async function POST(req: Request) { const { messages } = await req.json();
const result = await streamText({ model: openai('gpt-4-turbo'), messages, });
const data = new StreamData();
data.append({ test: 'value' });
const stream = result.toAIStream({ onFinal(_) { data.close(); }, });
return new StreamingTextResponse(stream, {}, data);}
In this code, you:
- Create a new instance of
StreamData
. - Append the data you want to stream alongside the model's response.
- Create a new AI stream with the
toAIStream
method on theStreamTextResult
object. - Listen for the
onFinal
callback on the AI Stream created above. - Pass the data alongside the stream to the new
StreamingTextResponse
.
Update your frontend
To access this data on the frontend, the useChat
hook returns an optional value that stores this data. Update your root route with the following code to render the streamed data:
import { useChat } from 'ai/react';
export default function Chat() { const { messages, input, handleInputChange, handleSubmit, data } = useChat(); return ( <div className="flex flex-col w-full max-w-md py-24 mx-auto stretch"> {data && <pre>{JSON.stringify(data, null, 2)}</pre>} {messages.map(m => ( <div key={m.id} className="whitespace-pre-wrap"> {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> );}
Head back to your browser (http://localhost:3000) and enter a new message. You should see a JSON object appear with the value you sent from your API route!
Where to Next?
You've built an AI chatbot using the Vercel AI SDK! Experiment and extend the functionality of this application further by exploring tool calling or persisting chat history.
If you are looking to leverage the broader capabilities of LLMs, Vercel AI SDK Core provides a comprehensive set of lower-level tools and APIs that will help you unlock a wider range of AI functionalities beyond the chatbot paradigm.