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": "alice@example.com", "age": 30}
            ]
        },
    }

Field Validators

Use

@field_validator

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(" ", "-")

Model Validators

Use

@model_validator

from pydantic import BaseModel, model_validator

class DateRange(BaseModel):
    start_date: datetime
    end_date: datetime
    label: str = ""

    @model_validator(mode="after")
    def validate_date_range(self) -> "DateRange":
        if self.end_date <= self.start_date:
            raise ValueError("end_date must be after start_date")
        if not self.label:
            # Compute default label from dates
            return DateRange(
                start_date=self.start_date,
                end_date=self.end_date,
                label=f"{self.start_date.date()} to {self.end_date.date()}",
            )
        return self

    @model_validator(mode="before")
    @classmethod
    def preprocess(cls, data: dict) -> dict:
        """Transform raw input before field validation."""
        if isinstance(data, dict) and "dates" in data:
            start, end = data.pop("dates").split("/")
            return {**data, "start_date": start, "end_date": end}
        return data

Computed Fields

from pydantic import BaseModel, computed_field
from decimal import Decimal

class OrderItem(BaseModel):
    product_name: str
    quantity: int
    unit_price: Decimal

    @computed_field
    @property
    def total_price(self) -> Decimal:
        return self.quantity * self.unit_price

class Order(BaseModel):
    items: list[OrderItem]
    discount_percent: Decimal = Decimal("0")

    @computed_field
    @property
    def subtotal(self) -> Decimal:
        return sum(item.total_price for item in self.items)

    @computed_field
    @property
    def total(self) -> Decimal:
        discount = self.subtotal * self.discount_percent / 100
        return self.subtotal - discount

Discriminated Unions

Use discriminated unions for polymorphic data with a type field.

from pydantic import BaseModel, Field
from typing import Annotated, Literal, Union

class EmailNotification(BaseModel):
    type: Literal["email"] = "email"
    to_address: str
    subject: str
    body: str

class SmsNotification(BaseModel):
    type: Literal["sms"] = "sms"
    phone_number: str
    message: str = Field(max_length=160)

class PushNotification(BaseModel):
    type: Literal["push"] = "push"
    device_token: str
    title: str
    body: str

# Discriminated union -- Pydantic checks "type" field first for fast routing
Notification = Annotated[
    Union[EmailNotification, SmsNotification, PushNotification],
    Field(discriminator="type"),
]

class NotificationBatch(BaseModel):
    notifications: list[Notification]

# Parsing automatically routes to the correct model
batch = NotificationBatch.model_validate({
    "notifications": [
        {"type": "email", "to_address": "a@b.com", "subject": "Hi", "body": "Hello"},
        {"type": "sms", "phone_number": "+1234567890", "message": "Hey"},
    ]
})

Custom Types with Annotated

from typing import Annotated
from pydantic import AfterValidator, BeforeValidator, PlainSerializer

def validate_non_empty(v: str) -> str:
    if not v.strip():
        raise ValueError("String must not be empty or whitespace")
    return v.strip()

def validate_positive(v: int) -> int:
    if v <= 0:
        raise ValueError("Must be positive")
    return v

NonEmptyStr = Annotated[str, AfterValidator(validate_non_empty)]
PositiveInt = Annotated[int, AfterValidator(validate_positive)]

# Custom serialization
from datetime import datetime

UnixTimestamp = Annotated[
    datetime,
    BeforeValidator(lambda v: datetime.fromtimestamp(v) if isinstance(v, (int, float)) else v),
    PlainSerializer(lambda v: int(v.timestamp()), return_type=int),
]

class Event(BaseModel):
    name: NonEmptyStr
    priority: PositiveInt
    created_at: UnixTimestamp

Separating Input and Output Models

class UserBase(BaseModel):
    name: str
    email: str

class UserCreate(UserBase):
    password: str = Field(min_length=8)

class UserUpdate(BaseModel):
    name: str | None = None
    email: str | None = None

class UserResponse(UserBase):
    id: int
    created_at: datetime

    model_config = {"from_attributes": True}  # Enable ORM mode

Anti-Patterns

• Using dicts instead of models: Raw dicts bypass validation entirely. Define a Pydantic model for any external data boundary.
• Validating inside business logic: Validate at the boundary (API layer), not deep in service functions. By the time data reaches business logic, it should already be a validated model.
• Single model for create/read/update: Use separate models (

  UserCreate

  ,

  UserResponse

  ,

  UserUpdate

  ) to keep concerns clean.
• Overusing

  model_validator(mode="before")

  : Prefer field validators when possible.

  mode="before"

  receives raw unvalidated data and is harder to type correctly.
• Mutable model instances: Use

  model_copy(update={...})

  to create modified copies instead of mutating fields directly.

Quick Reference

Field(ge=0, max_length=100)

@field_validator("field_name")

@model_validator(mode="after")

@computed_field

@property

Field(discriminator="type")

Annotated[str, AfterValidator(fn)]

model_config = {"from_attributes": True}

obj.model_copy(update={"field": value})

Model.model_json_schema()