Simple Object Extraction Pattern

Learn how to extract structured objects from text using LLMs in this comprehensive tutorial. We'll cover the fundamental pattern of transforming unstructured text into validated Python objects using Instructor with GPT-4, Claude, and other language models.

from pydantic import BaseModel
import instructor
# Define your LLM extraction schema
class Person(BaseModel):
    name: str
    age: int
    occupation: str

# Extract structured data from LLM
client = instructor.from_provider("openai/gpt-5-nano")
person = client.create(
    model="gpt-3.5-turbo",  # Works with GPT-4, Claude, Gemini
    messages=[
        {"role": "user", "content": "John Smith is a 35-year-old software engineer."}
    ],
    response_model=Person  # Type-safe LLM extraction
)

print(f"Name: {person.name}")
print(f"Age: {person.age}")
print(f"Occupation: {person.occupation}")

┌───────────────┐            ┌───────────────┐
│ Define Model  │            │ Extracted     │
│ name: str     │  Extract   │ name: "John"  │
│ age: int      │ ─────────> │ age: 35       │
│ occupation: str│            │ occupation:   │
└───────────────┘            │ "software..." │
                             └───────────────┘

Guide your LLM with clear field descriptions for more accurate extraction:

from pydantic import BaseModel, Field

class Book(BaseModel):
    title: str = Field(description="The full title of the book")
    author: str = Field(description="The author's full name")
    publication_year: int = Field(description="The year the book was published")

Field descriptions serve as prompts for the LLM, improving extraction accuracy and reducing errors in your structured outputs.

Handle Missing Data in LLM Responses¶

Real-world LLM extractions often encounter missing information. Here's how to handle it gracefully:

from typing import Optional
from pydantic import BaseModel

class MovieReview(BaseModel):
    title: str
    director: Optional[str] = None  # Optional field
    rating: float

Using Optional fields ensures your LLM extraction remains robust when dealing with incomplete or partial information.

Validate LLM Outputs with Pydantic¶

Ensure LLM outputs meet your requirements with built-in validation:

from pydantic import BaseModel, Field

class Product(BaseModel):
    name: str
    price: float = Field(gt=0, description="The product price in USD")
    in_stock: bool

Pydantic validation ensures your LLM outputs are not just structured, but also correct and business-rule compliant.

Here's a complete example showing nested object extraction from LLMs:

from pydantic import BaseModel
from typing import Optional

class Address(BaseModel):
    street: str
    city: str
    state: str
    zip_code: str

class ContactInfo(BaseModel):
    name: str
    email: str
    phone: Optional[str] = None
    address: Optional[Address] = None

# Extract structured data
client = instructor.from_provider("openai/gpt-5-nano")
contact = client.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "user", "content": """
        Contact information:
        Name: Sarah Johnson
        Email: sarah.j@example.com
        Phone: (555) 123-4567
        Address: 123 Main St, Boston, MA 02108
        """}
    ],
    response_model=ContactInfo
)

print(f"Name: {contact.name}")
print(f"Email: {contact.email}")

Contact Information: Extract names, emails, phones from unstructured text
Product Details: Parse product descriptions into structured catalogs
Event Information: Extract dates, locations, attendees from event descriptions
Entity Recognition: Identify and structure people, places, organizations

Continue Your LLM Tutorial Journey¶

List Extraction Tutorial - Extract multiple objects from LLM responses
Nested Structures - Handle complex hierarchical data from LLMs
Advanced Validation - Implement business rules for LLM outputs

Master these patterns to build production-ready LLM applications with reliable structured outputs!