Chatbot
An example of how to use the AI Elements to build a chatbot.
Can you explain how to use React hooks effectively?
React Hooks Best Practices
React hooks are a powerful feature that let you use state and other React features without writing classes. Here are some tips for using them effectively:
Rules of Hooks
- Only call hooks at the top level of your component or custom hooks
- Don't call hooks inside loops, conditions, or nested functions
Common Hooks
- useState: For local component state
- useEffect: For side effects like data fetching
- useContext: For consuming context
- useReducer: For complex state logic
- useCallback: For memoizing functions
- useMemo: For memoizing values
Example of useState and useEffect
Would you like me to explain any specific hook in more detail?
Yes, could you explain useCallback and useMemo in more detail? When should I use one over the other?
useCallback vs useMemo
Both hooks help with performance optimization, but they serve different purposes:
useCallback
useCallback memoizes functions to prevent unnecessary re-renders of child components that receive functions as props.
useMemo
useMemo memoizes values to avoid expensive recalculations on every render.
When to use which?
-
Use useCallback when:
- Passing callbacks to optimized child components that rely on reference equality
- Working with event handlers that you pass to child components
-
Use useMemo when:
- You have computationally expensive calculations
- You want to avoid recreating objects that are used as dependencies for other hooks
Performance Note
Don't overuse these hooks! They come with their own overhead. Only use them when you have identified a genuine performance issue.
Tutorial
Let's walk through how to build a chatbot using AI Elements and AI SDK. Our example will include reasoning, web search with citations, and a model picker.
Setup
First, set up a new Next.js repo and cd into it by running the following command (make sure you choose to use Tailwind the project setup):
npx create-next-app@latest ai-chatbot && cd ai-chatbotRun the following command to install AI Elements. This will also set up shadcn/ui if you haven't already configured it:
npx ai-elements@latestNow, install the AI SDK dependencies:
pnpm add ai @ai-sdk/react zod
In order to use the providers, let's configure an AI Gateway API key. Create a .env.local in your root directory and navigate here to create a token, then paste it in your .env.local.
We're now ready to start building our app!
Client
In your app/page.tsx, replace the code with the file below.
Here, we use the PromptInput component with its compound components to build a rich input experience with file attachments, model picker, and action menu. The input component uses the new PromptInputMessage type for handling both text and file attachments.
The whole chat lives in a Conversation. We switch on message.parts and render the respective part within Message, Reasoning, and Sources. We also use status from useChat to stream reasoning tokens, as well as render Loader.
'use client';
import { Conversation, ConversationContent, ConversationScrollButton,} from '@/components/ai-elements/conversation';import { Message, MessageContent } from '@/components/ai-elements/message';import { PromptInput, PromptInputActionAddAttachments, PromptInputActionMenu, PromptInputActionMenuContent, PromptInputActionMenuTrigger, PromptInputAttachment, PromptInputAttachments, PromptInputBody, PromptInputButton, type PromptInputMessage, PromptInputModelSelect, PromptInputModelSelectContent, PromptInputModelSelectItem, PromptInputModelSelectTrigger, PromptInputModelSelectValue, PromptInputSubmit, PromptInputTextarea, PromptInputFooter, PromptInputTools,} from '@/components/ai-elements/prompt-input';import { Action, Actions } from '@/components/ai-elements/actions';import { Fragment, useState } from 'react';import { useChat } from '@ai-sdk/react';import { Response } from '@/components/ai-elements/response';import { CopyIcon, GlobeIcon, RefreshCcwIcon } from 'lucide-react';import { Source, Sources, SourcesContent, SourcesTrigger,} from '@/components/ai-elements/sources';import { Reasoning, ReasoningContent, ReasoningTrigger,} from '@/components/ai-elements/reasoning';import { Loader } from '@/components/ai-elements/loader';
const models = [ { name: 'GPT 4o', value: 'openai/gpt-4o', }, { name: 'Deepseek R1', value: 'deepseek/deepseek-r1', },];
const ChatBotDemo = () => { const [input, setInput] = useState(''); const [model, setModel] = useState<string>(models[0].value); const [webSearch, setWebSearch] = useState(false); const { messages, sendMessage, status, regenerate } = useChat();
const handleSubmit = (message: PromptInputMessage) => { const hasText = Boolean(message.text); const hasAttachments = Boolean(message.files?.length);
if (!(hasText || hasAttachments)) { return; }
sendMessage( { text: message.text || 'Sent with attachments', files: message.files }, { body: { model: model, webSearch: webSearch, }, }, ); setInput(''); };
return ( <div className="max-w-4xl mx-auto p-6 relative size-full h-screen"> <div className="flex flex-col h-full"> <Conversation className="h-full"> <ConversationContent> {messages.map((message) => ( <div key={message.id}> {message.role === 'assistant' && message.parts.filter((part) => part.type === 'source-url').length > 0 && ( <Sources> <SourcesTrigger count={ message.parts.filter( (part) => part.type === 'source-url', ).length } /> {message.parts.filter((part) => part.type === 'source-url').map((part, i) => ( <SourcesContent key={`${message.id}-${i}`}> <Source key={`${message.id}-${i}`} href={part.url} title={part.url} /> </SourcesContent> ))} </Sources> )} {message.parts.map((part, i) => { switch (part.type) { case 'text': return ( <Fragment key={`${message.id}-${i}`}> <Message from={message.role}> <MessageContent> <Response> {part.text} </Response> </MessageContent> </Message> {message.role === 'assistant' && i === messages.length - 1 && ( <Actions className="mt-2"> <Action onClick={() => regenerate()} label="Retry" > <RefreshCcwIcon className="size-3" /> </Action> <Action onClick={() => navigator.clipboard.writeText(part.text) } label="Copy" > <CopyIcon className="size-3" /> </Action> </Actions> )} </Fragment> ); case 'reasoning': return ( <Reasoning key={`${message.id}-${i}`} className="w-full" isStreaming={status === 'streaming' && i === message.parts.length - 1 && message.id === messages.at(-1)?.id} > <ReasoningTrigger /> <ReasoningContent>{part.text}</ReasoningContent> </Reasoning> ); default: return null; } })} </div> ))} {status === 'submitted' && <Loader />} </ConversationContent> <ConversationScrollButton /> </Conversation>
<PromptInput onSubmit={handleSubmit} className="mt-4" globalDrop multiple> <PromptInputBody> <PromptInputAttachments> {(attachment) => <PromptInputAttachment data={attachment} />} </PromptInputAttachments> <PromptInputTextarea onChange={(e) => setInput(e.target.value)} value={input} /> </PromptInputBody> <PromptInputFooter> <PromptInputTools> <PromptInputActionMenu> <PromptInputActionMenuTrigger /> <PromptInputActionMenuContent> <PromptInputActionAddAttachments /> </PromptInputActionMenuContent> </PromptInputActionMenu> <PromptInputButton variant={webSearch ? 'default' : 'ghost'} onClick={() => setWebSearch(!webSearch)} > <GlobeIcon size={16} /> <span>Search</span> </PromptInputButton> <PromptInputModelSelect onValueChange={(value) => { setModel(value); }} value={model} > <PromptInputModelSelectTrigger> <PromptInputModelSelectValue /> </PromptInputModelSelectTrigger> <PromptInputModelSelectContent> {models.map((model) => ( <PromptInputModelSelectItem key={model.value} value={model.value}> {model.name} </PromptInputModelSelectItem> ))} </PromptInputModelSelectContent> </PromptInputModelSelect> </PromptInputTools> <PromptInputSubmit disabled={!input && !status} status={status} /> </PromptInputFooter> </PromptInput> </div> </div> );};
export default ChatBotDemo;Server
Create a new route handler app/api/chat/route.ts and paste in the following code. We're using perplexity/sonar for web search because by default the model returns search results. We also pass sendSources and sendReasoning to toUIMessageStreamResponse in order to receive as parts on the frontend. The handler now also accepts file attachments from the client.
import { streamText, UIMessage, convertToModelMessages } from 'ai';
// Allow streaming responses up to 30 secondsexport const maxDuration = 30;
export async function POST(req: Request) { const { messages, model, webSearch, }: { messages: UIMessage[]; model: string; webSearch: boolean; } = await req.json();
const result = streamText({ model: webSearch ? 'perplexity/sonar' : model, messages: convertToModelMessages(messages), system: 'You are a helpful assistant that can answer questions and help with tasks', });
// send sources and reasoning back to the client return result.toUIMessageStreamResponse({ sendSources: true, sendReasoning: true, });}You now have a working chatbot app with file attachment support! The chatbot can handle both text and file inputs through the action menu. Feel free to explore other components like Tool or Task to extend your app, or view the other examples.