Get started with OpenAI o1

With the release of OpenAI's o1 series models, there has never been a better time to start building AI applications, particularly those that require complex reasoning capabilities.

The AI SDK is a powerful TypeScript toolkit for building AI applications with large language models (LLMs) like OpenAI o1 alongside popular frameworks like React, Next.js, Vue, Svelte, Node.js, and more.

OpenAI o1

OpenAI released a series of AI models designed to spend more time thinking before responding. They can reason through complex tasks and solve harder problems than previous models in science, coding, and math. These models, named the o1 series, are trained with reinforcement learning and can "think before they answer". As a result, they are able to produce a long internal chain of thought before responding to a prompt.

The main reasoning model available in the API is:

1. o1: Designed to reason about hard problems using broad general knowledge about the world.

Benchmarks

OpenAI o1 models excel in scientific reasoning, with impressive performance across various domains:

• Ranking in the 89th percentile on competitive programming questions (Codeforces)
• Placing among the top 500 students in the US in a qualifier for the USA Math Olympiad (AIME)
• Exceeding human PhD-level accuracy on a benchmark of physics, biology, and chemistry problems (GPQA)

Source

Prompt Engineering for o1 Models

The o1 models perform best with straightforward prompts. Some prompt engineering techniques, like few-shot prompting or instructing the model to "think step by step," may not enhance performance and can sometimes hinder it. Here are some best practices:

1. Keep prompts simple and direct: The models excel at understanding and responding to brief, clear instructions without the need for extensive guidance.
2. Avoid chain-of-thought prompts: Since these models perform reasoning internally, prompting them to "think step by step" or "explain your reasoning" is unnecessary.
3. Use delimiters for clarity: Use delimiters like triple quotation marks, XML tags, or section titles to clearly indicate distinct parts of the input, helping the model interpret different sections appropriately.
4. Limit additional context in retrieval-augmented generation (RAG): When providing additional context or documents, include only the most relevant information to prevent the model from overcomplicating its response.

Getting Started with the AI SDK

The AI SDK is the TypeScript toolkit designed to help developers build AI-powered applications with React, Next.js, Vue, Svelte, Node.js, and more. Integrating LLMs into applications is complicated and heavily dependent on the specific model provider you use.

The AI SDK abstracts away the differences between model providers, eliminates boilerplate code for building chatbots, and allows you to go beyond text output to generate rich, interactive components.

At the center of the AI SDK is AI SDK Core, which provides a unified API to call any LLM. The code snippet below is all you need to call OpenAI o1 with the AI SDK:

import { generateText } from 'ai';
import { openai } from '@ai-sdk/openai';

const { text } = await generateText({
  model: openai('o1'),
  prompt: 'Explain the concept of quantum entanglement.',
});

AI SDK Core abstracts away the differences between model providers, allowing you to focus on building great applications. The unified interface also means that you can easily switch between models by changing just one line of code.

import { generateText } from 'ai';
import { openai } from '@ai-sdk/openai';

const { text } = await generateText({
  model: openai('o1'),
  prompt: 'Explain the concept of quantum entanglement.',
});

Refining Reasoning Effort

You can control the amount of reasoning effort expended by o1 through the reasoningEffort parameter. This parameter can be set to 'low', 'medium', or 'high' to adjust how much time and computation the model spends on internal reasoning before producing a response.

import { generateText } from 'ai';
import { openai } from '@ai-sdk/openai';

// Reduce reasoning effort for faster responses
const { text } = await generateText({
  model: openai('o1'),
  prompt: 'Explain quantum entanglement briefly.',
  providerOptions: {
    openai: { reasoningEffort: 'low' },
  },
});

Generating Structured Data

While text generation can be useful, you might want to generate structured JSON data. For example, you might want to extract information from text, classify data, or generate synthetic data. AI SDK Core provides two functions (generateObject and streamObject) to generate structured data, allowing you to constrain model outputs to a specific schema.

import { generateObject } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { object } = await generateObject({
  model: openai('o1'),
  schema: z.object({
    recipe: z.object({
      name: z.string(),
      ingredients: z.array(z.object({ name: z.string(), amount: z.string() })),
      steps: z.array(z.string()),
    }),
  }),
  prompt: 'Generate a lasagna recipe.',
});

This code snippet will generate a type-safe recipe that conforms to the specified zod schema.

Tools

While LLMs have incredible generation capabilities, they struggle with discrete tasks (e.g. mathematics) and interacting with the outside world (e.g. getting the weather). The solution: tools, which are like programs that you provide to the model, which it can choose to call as necessary.

Using Tools with the AI SDK

The AI SDK supports tool usage across several of its functions, like generateText and streamText. By passing one or more tools to the tools parameter, you can extend the capabilities of LLMs, allowing them to perform discrete tasks and interact with external systems.

Here's an example of how you can use a tool with the AI SDK and o1:

import { generateText, tool } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { text } = await generateText({
  model: openai('o1'),
  prompt: 'What is the weather like today?',
  tools: {
    getWeather: tool({
      description: 'Get the weather in a location',
      inputSchema: z.object({
        location: z.string().describe('The location to get the weather for'),
      }),
      execute: async ({ location }) => ({
        location,
        temperature: 72 + Math.floor(Math.random() * 21) - 10,
      }),
    }),
  },
});

In this example, the getWeather tool allows the model to fetch real-time weather data (simulated for simplicity), enhancing its ability to provide accurate and up-to-date information.

Building Interactive Interfaces

AI SDK Core can be paired with AI SDK UI, another powerful component of the AI SDK, to streamline the process of building chat, completion, and assistant interfaces with popular frameworks like Next.js, Nuxt, and SvelteKit.

AI SDK UI provides robust abstractions that simplify the complex tasks of managing chat streams and UI updates on the frontend, enabling you to develop dynamic AI-driven interfaces more efficiently.

With four main hooks — useChat, useCompletion, and useObject — you can incorporate real-time chat capabilities, text completions, streamed JSON, and interactive assistant features into your app.

Let's explore building a chatbot with Next.js, the AI SDK, and OpenAI o1:

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

// Allow responses up to 5 minutes
export const maxDuration = 300;

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

  const result = streamText({
    model: openai('o1'),
    messages: convertToModelMessages(messages),
  });

  return result.toUIMessageStreamResponse();
}

'use client';

import { useChat } from '@ai-sdk/react';

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

  return (
    <>
      {messages.map(message => (
        <div key={message.id}>
          {message.role === 'user' ? 'User: ' : 'AI: '}
          {message.content}
        </div>
      ))}
      <form onSubmit={handleSubmit}>
        <input name="prompt" value={input} onChange={handleInputChange} />
        <button type="submit">Submit</button>
      </form>
    </>
  );
}

The useChat hook on your root page (app/page.tsx) will make a request to your AI provider endpoint (app/api/chat/route.ts) whenever the user submits a message. The messages are then displayed in the chat UI.

Get Started

Ready to get started? Here's how you can dive in:

1. Explore the documentation at ai-sdk.dev/docs to understand the full capabilities of the AI SDK.
2. Check out our support for the o1 series of reasoning models in the OpenAI Provider.
3. Check out practical examples at ai-sdk.dev/examples to see the SDK in action and get inspired for your own projects.
4. Dive deeper with advanced guides on topics like Retrieval-Augmented Generation (RAG) and multi-modal chat at ai-sdk.dev/docs/guides.
5. Check out ready-to-deploy AI templates at vercel.com/templates?type=ai.