Structured AI Extraction with Instructor

import instructor
from openai import OpenAI
from pydantic import BaseModel, Field
from typing import Optional

client = instructor.from_openai(OpenAI())

class Person(BaseModel):
    name:        str
    age:         Optional[int] = None
    occupation:  str
    skills:      list[str]     = Field(default_factory=list)

class ExtractedPeople(BaseModel):
    people: list[Person]

text = 'Alice (30) is a data engineer who knows Python, SQL and Spark. Bob is a senior ML engineer skilled in PyTorch and transformers.'

result = client.chat.completions.create(
    model='gpt-4o-mini',
    response_model=ExtractedPeople,
    messages=[{'role':'user','content':f'Extract people from: {text}'}],
)
for p in result.people:
    print(p.model_dump())