Pydantic Factories

Common errors

• No factories for type <class 'your_module.YourCustomType'>

  cause The `ModelFactory` does not know how to generate data for a custom type used in your Pydantic model because no explicit builder or `FactoryField` was provided.
  fix Define a custom builder for `YourCustomType` directly on your `ModelFactory` class, or use `FactoryField(callable=your_generation_function)` on the Pydantic model field where `YourCustomType` is used.

• RecursionError: maximum recursion depth exceeded while calling a Python object

  cause This typically occurs with self-referencing Pydantic models where `pydantic-factories` attempts to generate an infinitely nested structure.
  fix Set `__recursion_limit__` on your `ModelFactory` to a positive integer (e.g., `__recursion_limit__ = 2`) to limit the depth of recursion, or strategically use `FactoryField` to break the recursive generation path.

• pydantic.ValidationError: X validation error for YourModel

  cause The mock data generated by `pydantic-factories` failed to pass the Pydantic validation rules defined in your model (e.g., `min_length`, `max_length`, `ge`, `le`, `regex`).
  fix Inspect the `ValidationError` details to identify which field failed. Then, adjust your `ModelFactory` (or use `FactoryField`) to ensure the generated values satisfy the Pydantic field's constraints. For example, if a `str` field has `min_length=10`, ensure your factory generates strings of at least 10 characters.

Warnings

• breaking The `pydantic-factories` library is no longer actively maintained. Version 1.17.3 is the final release, and users are strongly advised to migrate to its successor, `polyfactory`.
  Fix: Install `polyfactory` (`pip install polyfactory`) and refactor your code. While the APIs are similar, `polyfactory` offers enhanced features and better maintenance. Refer to the `polyfactory` documentation for migration guidance.
• gotcha Default generators for basic types (e.g., `str`, `int`, `float`) might produce values that violate specific Pydantic model constraints (e.g., `min_length`, `ge`, `le`, `regex`).
  Fix: Explicitly define generation parameters using `FactoryField` on the model field, or define custom builders within your factory to ensure generated data conforms to your Pydantic model's validation rules. Example: `name: str = FactoryField(min_length=5, max_length=20)`.
• gotcha Generating data for complex or custom types (e.g., a custom class not derived from Pydantic `BaseModel` or `dataclass`) without explicit instructions will result in errors.
  Fix: Provide a custom builder method on your `ModelFactory` for the specific type, or use `FactoryField(callable=...)` on the Pydantic field definition to tell the factory how to generate that type.

Install

• pip install pydantic-factories Install latest version

Imports

• ModelFactory

  from pydantic_factories import ModelFactory

• FactoryField

  from pydantic_factories.fields import FactoryField

Quickstart

Demonstrates defining a simple Pydantic model and creating a corresponding `ModelFactory` to generate single instances or batches of mock data. This is the primary usage pattern for `pydantic-factories`.

from pydantic import BaseModel
from pydantic_factories import ModelFactory
from datetime import date

class User(BaseModel):
    id: int
    name: str
    email: str
    birth_date: date

# Define a factory for your Pydantic model
class UserFactory(ModelFactory):
    __model__ = User

# Generate a single instance
user_instance = UserFactory.build()
print(f"Generated User: {user_instance}")

# Generate a batch of instances
users_batch = UserFactory.batch(size=3)
print(f"Generated Batch of Users: {len(users_batch)}")