Generate Object with a Reasoning Model

Reasoning models, like DeepSeek's R1, are gaining popularity due to their ability to understand and generate better responses to complex queries than non-reasoning models. You may want to use these models to generate structured data. However, most (like R1 and OpenAI's o1) do not support tool-calling or structured outputs.

One solution is to pass the output from a reasoning model through a smaller model that can output structured data (like gpt-4o-mini). These lightweight models can efficiently extract the structured data while adding very little overhead in terms of speed and cost.

import { deepseek } from '@ai-sdk/deepseek';
import { openai } from '@ai-sdk/openai';
import { generateObject, generateText } from 'ai';
import 'dotenv/config';
import { z } from 'zod';

async function main() {
  const { text: rawOutput } = await generateText({
    model: deepseek('deepseek-reasoner'),
    prompt:
      'Predict the top 3 largest city by 2050. For each, return the name, the country, the reason why it will on the list, and the estimated population in millions.',
  });

  const { object } = await generateObject({
    model: openai('gpt-4o-mini'),
    prompt: 'Extract the desired information from this text: \n' + rawOutput,
    schema: z.object({
      name: z.string().describe('the name of the city'),
      country: z.string().describe('the name of the country'),
      reason: z
        .string()
        .describe(
          'the reason why the city will be one of the largest cities by 2050',
        ),
      estimatedPopulation: z.number(),
    }),
    output: 'array',
  });

  console.log(object);
}

main().catch(console.error);