Multi-Modal Chatbot

In this guide, you will build a multi-modal AI-chatbot with a streaming user interface.

Multi-modal refers to the ability of the chatbot to understand and generate responses in multiple formats, such as text, images, and videos. In this example, we will focus on sending images and generating text-based responses.

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.

Create Your Application

Start by creating a new Next.js application. This command will create a new directory named multi-modal-chatbot and set up a basic Next.js application inside it.

pnpm create next-app@latest multi-modal-chatbot

Navigate to the newly created directory:

cd multi-modal-chatbot

Install dependencies

Install ai and @ai-sdk/openai, the Vercel AI package and Vercel AI SDK's OpenAI provider respectively.

pnpm add ai @ai-sdk/openai

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.

Implementation Plan

To build a multi-modal chatbot, you will need to:

• Create a Route Handler to handle incoming chat messages and generate responses.
• Wire up the UI to display chat messages, provide a user input, and handle submitting new messages.
• Add the ability to upload images and attach them alongside the chat messages.

Create a Route Handler

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

import { openai } from '@ai-sdk/openai';
import { convertToCoreMessages, streamText } from 'ai';

// Allow streaming responses up to 30 seconds
export const maxDuration = 30;

export async function POST(req: Request) {
  const { messages } = await req.json();

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

  return result.toDataStreamResponse();
}

Let's take a look at what is happening in this code:

1. 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.
2. Call streamText, which is imported from the ai package. This function accepts a configuration object that contains a model provider (imported from @ai-sdk/openai) and messages (defined in step 1). You can pass additional settings to further customise the model's behaviour.
3. The streamText function returns a StreamTextResult. This result object contains the toDataStreamResponse function which converts the result to a streamed response object.
4. Finally, return the result to the client to stream the response.

This Route Handler creates a POST request endpoint at /api/chat.

Wire up the UI

Now that you have a Route Handler that can query a large language model (LLM), it's time to setup your frontend. AI SDK UI abstracts the complexity of a chat interface into one hook, useChat.

Update your root page (app/page.tsx) with the following code to show a list of chat messages and provide a user message input:

'use client';

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}
        className="fixed bottom-0 w-full max-w-md mb-8 border border-gray-300 rounded shadow-xl"
      >
        <input
          className="w-full p-2"
          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 with id, role, and content properties).
• input - the current value of the user's input field.
• handleInputChange and handleSubmit - 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.

Add Image Upload

To make your chatbot multi-modal, let's add the ability to upload and send images to the model. There are two ways to send attachments alongside a message with the useChat hook: by providing a FileList object or a list of URLs to the handleSubmit function. In this guide, you will be using the FileList approach as it does not require any additional setup.

Update your root page (app/page.tsx) with the following code:

'use client';

import { useChat } from 'ai/react';
import { useRef, useState } from 'react';
import Image from 'next/image';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat();

  const [files, setFiles] = useState<FileList | undefined>(undefined);
  const fileInputRef = useRef<HTMLInputElement>(null);

  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>
            {m?.experimental_attachments
              ?.filter(attachment =>
                attachment?.contentType?.startsWith('image/'),
              )
              .map((attachment, index) => (
                <Image
                  key={`${m.id}-${index}`}
                  src={attachment.url}
                  width={500}
                  height={500}
                  alt={attachment.name ?? `attachment-${index}`}
                />
              ))}
          </div>
        </div>
      ))}

      <form
        className="fixed bottom-0 w-full max-w-md p-2 mb-8 border border-gray-300 rounded shadow-xl space-y-2"
        onSubmit={event => {
          handleSubmit(event, {
            experimental_attachments: files,
          });

          setFiles(undefined);

          if (fileInputRef.current) {
            fileInputRef.current.value = '';
          }
        }}
      >
        <input
          type="file"
          className=""
          onChange={event => {
            if (event.target.files) {
              setFiles(event.target.files);
            }
          }}
          multiple
          ref={fileInputRef}
        />
        <input
          className="w-full p-2"
          value={input}
          placeholder="Say something..."
          onChange={handleInputChange}
        />
      </form>
    </div>
  );
}

In this code, you:

1. Create state to hold the files and create a ref to the file input field.
2. Display the "uploaded" files in the UI.
3. Update the onSubmit function, to call the handleSubmit function manually, passing the the files as an option using the experimental_attachments key.
4. Add a file input field to the form, including an onChange handler to handle updating the files state.

Running Your Application

With that, you have built everything you need for your multi-modal 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 and a button to upload an image.

Upload a file and ask the model to describe what it sees. Watch as the model's response is streamed back to you!

Where to Next?

You've built a multi-modal AI chatbot using the Vercel AI SDK! Experiment and extend the functionality of this application further by exploring tool calling or introducing more granular control over AI and UI states.

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.