Understanding Response Models
Instructor uses Pydantic models to define the structure of your LLM outputs. Here's how to create effective models.
from pydantic import BaseModel
class User(BaseModel):
name: str
age: int
from pydantic import BaseModel
from typing import List, Optional
class Address(BaseModel):
street: str
city: str
state: Optional[str] = None
country: str
class User(BaseModel):
name: str
age: int
addresses: List[Address]Add descriptions to help guide the LLM:
from pydantic import BaseModel, Field
class WeatherForecast(BaseModel):
"""Weather forecast for a specific location"""
temperature: float = Field(
description="Current temperature in Celsius"
)
condition: str = Field(
description="Weather condition (sunny, cloudy, rainy, etc.)"
)
humidity: int = Field(
description="Humidity percentage from 0-100"
)Add validation constraints to ensure quality data:
from pydantic import BaseModel, Field
class Product(BaseModel):
name: str = Field(min_length=3)
price: float = Field(gt=0) # greater than 0
quantity: int = Field(ge=0) # greater than or equal to 0
description: str = Field(max_length=500)
import instructor
from openai import OpenAI
client = instructor.from_openai(OpenAI())
forecast = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=WeatherForecast,
messages=[
{"role": "user", "content": "What's the weather in New York today?"}
]
)
print(forecast.model_dump_json(indent=2))Running the Example
First, install Instructor and any dependencies
$ pip install instructor pydantic
Run the Python script
$ python response-models.py