Models ↗

Original Documentation

Define LLM output schemas using pydantic.BaseModel. For more details, see the Pydantic documentation.

After defining a Pydantic model, use it as the response_model in your client create calls. The response_model parameter:

Defines the schema and prompts for the language model
Validates the response from the API
Returns a Pydantic model instance

Prompting#

Use docstrings and field annotations to define the prompt for generating responses.

from pydantic import BaseModel, Field
import instructor


class User(BaseModel):
    """
    This is the prompt that will be used to generate the response.
    Any instructions here will be passed to the language model.
    """

    name: str = Field(description="The name of the user.")
    age: int = Field(description="The age of the user.")


client = instructor.from_provider("openai/gpt-4o-mini")

user = client.create(
    response_model=User,
    messages=[{"role": "user", "content": "Extract: John is 30 years old"}],
)

Docstrings, types, and field annotations are used to generate the prompt. The create method uses this prompt to generate the response.

Optional Values#

Use Optional and default to make fields optional when sent to the language model.

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


class User(BaseModel):
    name: str = Field(description="The name of the user.")
    age: int = Field(description="The age of the user.")
    email: Optional[str] = Field(description="The email of the user.", default=None)


client = instructor.from_provider("openai/gpt-4o-mini")

user = client.create(
    response_model=User,
    messages=[{"role": "user", "content": "Extract: John is 30 years old"}],
)

Fields can also be omitted from the schema sent to the language model using Pydantic’s SkipJsonSchema annotation. See Fields for details.

Dynamic Model Creation#

Create models at runtime using Pydantic’s create_model function:

from pydantic import BaseModel, create_model


class FooModel(BaseModel):
    foo: str
    bar: int = 123


BarModel = create_model(
    'BarModel',
    apple=(str, 'russet'),
    banana=(str, 'yellow'),
    __base__=FooModel,
)
print(BarModel)
#> <class '__main__.BarModel'>
print(BarModel.model_fields.keys())
#> dict_keys(['foo', 'bar', 'apple', 'banana'])

When would I use this?

Consider a situation where the model is dynamically defined, based on some configuration or database. For example, we could have a database table that stores the properties of a model for some model name or id. We could then query the database for the properties of the model and use that to create the model.

SELECT property_name, property_type, description
FROM prompt
WHERE model_name = {model_name}

We can then use this information to create the model.

from pydantic import BaseModel, create_model, Field
from typing import List

types = {
    'string': str,
    'integer': int,
    'boolean': bool,
    'number': float,
    'List[str]': List[str],
}

# Mocked cursor.fetchall()
cursor = [
    ('name', 'string', 'The name of the user.'),
    ('age', 'integer', 'The age of the user.'),
    ('email', 'string', 'The email of the user.'),
]

BarModel = create_model(
    'User',
    **{
        property_name: (types[property_type], Field(description=description))
        for property_name, property_type, description in cursor
    },
    __base__=BaseModel,
)

print(BarModel.model_json_schema())
"""
{
    'properties': {
        'name': {
            'description': 'The name of the user.',
            'title': 'Name',
            'type': 'string',
        },
        'age': {
            'description': 'The age of the user.',
            'title': 'Age',
            'type': 'integer',
        },
        'email': {
            'description': 'The email of the user.',
            'title': 'Email',
            'type': 'string',
        },
    },
    'required': ['name', 'age', 'email'],
    'title': 'User',
    'type': 'object',
}
"""

This would be useful when different users have different descriptions for the same model. We can use the same model but have different prompts for each user.

Adding Behavior#

Add methods to Pydantic models like any Python class. This lets you add custom logic to your models.

from pydantic import BaseModel
from typing import Literal

import instructor

client = instructor.from_provider("openai/gpt-4.1-mini")


class SearchQuery(BaseModel):
    query: str
    query_type: Literal["web", "image", "video"]

    def execute(self):
        print(f"Searching for {self.query} of type {self.query_type}")
        #> Searching for cat of type image
        return "Results for cat"


query = client.create(
    model="gpt-4.1-mini",
    messages=[{"role": "user", "content": "Search for a picture of a cat"}],
    response_model=SearchQuery,
)

results = query.execute()
print(results)
#> Results for cat