Validation

Validation Basics

Instructor leverages Pydantic's validation framework to ensure that outputs from LLMs match your expected schema. This is crucial for building reliable applications.

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

Initialize the client with instructor

client = instructor.from_openai(OpenAI())

Define a model with basic validation

class User(BaseModel):
    name: str = Field(..., description="User's full name")
    age: int = Field(...,
                    description="User's age in years",
                    ge=0, le=120)  # Must be between 0 and 120
    email: str = Field(..., description="User's email address")

Extract user information with validation

def extract_user(text: str) -> User:
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {
                "role": "user",
                "content": f"Extract user information from this text: {text}"
            }
        ],
        response_model=User
    )

Example usage

text = "John Doe is 25 years old and his email is john.doe@example.com."
user = extract_user(text)
print(user.model_dump_json(indent=2))

When an LLM output fails validation, Instructor can automatically retry the request with the validation error message:

import instructor
from openai import OpenAI
from pydantic import BaseModel, Field, field_validator

Initialize the client with instructor

client = instructor.from_openai(OpenAI())

Define a model with custom validation

class User(BaseModel):
    name: str
    age: int

    @field_validator("age")
    def validate_age(cls, v):
        if v < 0 or v > 120:
            raise ValueError("Age must be between 0 and 120")
        return v

Extract with automatic retries

user = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {
            "role": "user",
            "content": "Extract: John Doe, age: 150"
        }
    ],
    response_model=User,
    max_retries=2  # Try up to 2 more times if validation fails
)

print(user.model_dump_json(indent=2))

Running the Example

First, install Instructor and any dependencies

$ pip install instructor pydantic

Run the Python script

$ python validation-basics.py

Further Information

Documentation link 1