name: pydantic
description: >
Pydantic models and validation. Use when: (1) Defining schemas,
(2) Validating input/output, (3) Generating JSON schema.

pydantic

Type-driven validation and serialization using Pydantic models.

Overview

Pydantic validates data using Python type hints and provides rich serialization via model_dump() and JSON schema output.

When to Use

• Validating request/response payloads
• Normalizing untrusted input
• Generating JSON schema for docs

Quick Start

uv pip install pydantic

from pydantic import BaseModel

class User(BaseModel):
    id: int
    email: str

user = User(id=1, email="[email protected]")

Core Patterns

1. Typed fields: strict schema definitions.
2. Field validators: custom validation logic.
3. Model validators: cross-field checks.
4. Serialization: model_dump() and model_dump_json().
5. Settings: environment-driven config via BaseSettings.

Example: field_validator

from pydantic import BaseModel, field_validator

class Model(BaseModel):
    name: str

    @field_validator("name")
    @classmethod
    def ensure_not_empty(cls, v: str):
        if not v:
            raise ValueError("name required")
        return v

Example: model_validate + model_dump

from pydantic import BaseModel

class Model(BaseModel):
    foo: int

model = Model.model_validate({"foo": 1})
print(model.model_dump())

Troubleshooting

• Coercion surprises: use strict types if needed
• Slow validators: keep them minimal
• Mutable defaults: use default_factory

References

• https://docs.pydantic.dev/