pydantic-validation

Pydantic Validation

Define strict, self-documenting data schemas with Pydantic v2. Pydantic validates data at the boundary between your application and the outside world, catching bad data before it causes bugs deep in business logic.

When to Use

• User defines FastAPI request/response models
• User validates configuration, API payloads, or form data
• User asks about data validation or serialization
• User builds complex nested data structures
• User needs discriminated unions or custom type validation

Core Patterns

BaseModel and Field Configuration

from pydantic import BaseModel, Field
from datetime import datetime

class CreateUserRequest(BaseModel):
    """Request body for creating a user."""

    name: str = Field(min_length=1, max_length=100)
    email: str = Field(pattern=r"^[^@]+@[^@]+\.[^@]+$")
    age: int = Field(ge=0, le=150)
    role: str = Field(default="user", description="User role")
    tags: list[str] = Field(default_factory=list, max_length=10)

    model_config = {
        "str_strip_whitespace": True,
        "json_schema_extra": {
            "examples": [
                {"name": "Alice", "email": "[email protected]", "age": 30}
            ]
        },
    }

Field Validators

Use @field_validator for single-field validation and transformation.

from pydantic import BaseModel, field_validator

class Product(BaseModel):
    name: str
    sku: str
    price_cents: int
    category: str

    @field_validator("sku")
    @classmethod
    def validate_sku(cls, v: str) -> str:
        if not v.startswith(("SKU-", "PRD-")):
            raise ValueError("SKU must start with 'SKU-' or 'PRD-'")
        return v.upper()

    @field_validator("price_cents")
    @classmethod
    def validate_price(cls, v: int) -> int:
        if v < 0:
            raise ValueError("Price cannot be negative")
        return v

    @field_validator("category", mode="before")
    @classmethod
    def normalize_category(cls, v: str) -> str:
        return v.lower().strip().replace(" ", "-")