import instructor from pydantic import BaseModel # Define what you want class User(BaseModel): name: str age: int # Extract it from natural language client = instructor.from_provider("openai/gpt-4o-mini") user = client.chat.completions.create( response_model=User, messages=[{"role": "user", "content": "John is 25 years old"}], ) print(user) # User(name='John', age=25)

# OpenAI client = instructor.from_provider("openai/gpt-4o") # Anthropic client = instructor.from_provider("anthropic/claude-3-5-sonnet") # Google client = instructor.from_provider("google/gemini-pro") # Ollama (local) client = instructor.from_provider("ollama/llama3.2") # With API keys directly (no environment variables needed) client = instructor.from_provider("openai/gpt-4o", api_key="sk-...") client = instructor.from_provider("anthropic/claude-3-5-sonnet", api_key="sk-ant-...") client = instructor.from_provider("groq/llama-3.1-8b-instant", api_key="gsk_...") # All use the same API! user = client.chat.completions.create( response_model=User, messages=[{"role": "user", "content": "..."}], )

from pydantic import BaseModel, field_validator class User(BaseModel): name: str age: int @field_validator('age') def validate_age(cls, v): if v < 0: raise ValueError('Age must be positive') return v # Instructor automatically retries when validation fails user = client.chat.completions.create( response_model=User, messages=[{"role": "user", "content": "..."}], max_retries=3, )

from instructor import Partial for partial_user in client.chat.completions.create( response_model=Partial[User], messages=[{"role": "user", "content": "..."}], stream=True, ): print(partial_user) # User(name=None, age=None) # User(name="John", age=None) # User(name="John", age=25)

from typing import List class Address(BaseModel): street: str city: str country: str class User(BaseModel): name: str age: int addresses: List[Address] # Instructor handles nested objects automatically user = client.chat.completions.create( response_model=User, messages=[{"role": "user", "content": "..."}], )

from pydantic import BaseModel import instructor client = instructor.from_provider("openai/gpt-4o-mini") class Product(BaseModel): name: str price: float in_stock: bool product = client.chat.completions.create( response_model=Product, messages=[{"role": "user", "content": "iPhone 15 Pro, $999, available now"}], ) print(product) # Product(name='iPhone 15 Pro', price=999.0, in_stock=True)