Call Tools in Multiple Steps
Some language models are great at calling tools in multiple steps to achieve a more complex task. This is particularly useful when the tools are dependent on each other and need to be executed in sequence during the same generation step.
Client
Let's create a React component that imports the useChat
hook from the ai/react
module. The useChat
hook will call the /api/chat
endpoint when the user sends a message. The endpoint will generate the assistant's response based on the conversation history and stream it to the client. If the assistant responds with a tool call, the hook will automatically display them as well.
To call tools in multiple steps, you can use the maxSteps
option to specify the maximum number of steps that can be made before the model or the user responds with a text message. In this example, you will set it to 5
to allow for multiple tool calls.
'use client';
import { useChat } from 'ai/react';
export default function Page() { const { messages, input, setInput, append } = useChat({ api: '/api/chat', maxSteps: 5, });
return ( <div> <input value={input} onChange={event => { setInput(event.target.value); }} onKeyDown={async event => { if (event.key === 'Enter') { append({ content: input, role: 'user' }); } }} />
{messages.map((message, index) => ( <div key={index}>{message.content}</div> ))} </div> );}
Server
You will create a new route at /api/chat
that will use the streamText
function from the ai
module to generate the assistant's response based on the conversation history.
You will use the tools
parameter to specify two tools called getLocation
and getWeather
that will first get the user's location and then use it to get the weather.
You will add the two functions mentioned earlier and use zod to specify the schema for its parameters.
import { ToolInvocation, streamText } from 'ai';import { openai } from '@ai-sdk/openai';import { z } from 'zod';
interface Message { role: 'user' | 'assistant'; content: string; toolInvocations?: ToolInvocation[];}
function getLocation({ lat, lon }) { return { lat: 37.7749, lon: -122.4194 };}
function getWeather({ lat, lon, unit }) { return { value: 25, description: 'Sunny' };}
export async function POST(req: Request) { const { messages }: { messages: Message[] } = await req.json();
const result = streamText({ model: openai('gpt-4o'), system: 'You are a helpful assistant.', messages, tools: { getLocation: { description: 'Get the location of the user', parameters: z.object({}), execute: async () => { const { lat, lon } = getLocation(); return `Your location is at latitude ${lat} and longitude ${lon}`; }, }, getWeather: { description: 'Get the weather for a location', parameters: z.object({ lat: z.number().describe('The latitude of the location'), lon: z.number().describe('The longitude of the location'), unit: z .enum(['C', 'F']) .describe('The unit to display the temperature in'), }), execute: async ({ lat, lon, unit }) => { const { value, description } = getWeather({ lat, lon, unit }); return `It is currently ${value}°${unit} and ${description}!`; }, }, }, });
return result.toDataStreamResponse();}