tool use

Call Tools

Some models allow developers to provide a list of tools that can be called at any time during a generation. This is useful for extending the capabilites of a language model to either use logic or data to interact with systems external to the model.

http://localhost:3000

User: How is it going?

Assistant: All good, how may I help you?

What is the weather in Paris and New York?

Send Message

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.

We will use the maxSteps to specify the maximum number of steps (i.e., LLM calls) that can be made to prevent infinite loops. In this example, you will set it to 2 to allow for two backend calls to happen.

'use client';

import { useChat } from 'ai/react';

export default function Page() {
  const { messages, input, setInput, append } = useChat({
    api: '/api/chat',
    maxSteps: 2,
  });

  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 a tool called celsiusToFahrenheit that will convert a user given value in celsius to fahrenheit.

You will also use zod to specify the schema for the celsiusToFahrenheit function's 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[];
}

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: {
      getWeather: {
        description: 'Get the weather for a location',
        parameters: z.object({
          city: z.string().describe('The city to get the weather for'),
          unit: z
            .enum(['C', 'F'])
            .describe('The unit to display the temperature in'),
        }),
        execute: async ({ city, unit }) => {
          const weather = {
            value: 24,
            description: 'Sunny',
          };

          return `It is currently ${weather.value}°${unit} and ${weather.description} in ${city}!`;
        },
      },
    },
  });

  return result.toDataStreamResponse();
}

View Example on GitHub