#Locale

Examples use Indian conventions: ₹ amounts, IST, dd/mm/yyyy, Aadhaar and DPDP Act where a standard mentions identity or privacy law. Keep them when you copy an example.

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#Purpose

Rules for using Pydantic v2 (2.x) as the boundary layer of a Python service: parse untrusted input once, hand typed objects inward, serialize deliberately on the way out.

v1 APIs (@validator, .dict(), class Config) still import with deprecation warnings. Do not write new code against them.

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#Model design

python
class OrderIn(BaseModel):
    model_config = ConfigDict(extra="forbid", str_strip_whitespace=True, frozen=True)

    customer_id: PositiveInt
    items: list[LineItem] = Field(min_length=1, max_length=200)
    note: str | None = Field(default=None, max_length=500)

• extra="forbid" on every input model. The default ignore turns a client typo into silently dropped data.
• Constrained types (PositiveInt, Field(max_length=...), Annotated[str, StringConstraints(...)]) over hand-written checks — they document themselves in the schema.
• frozen=True for value objects; a hashable, immutable model cannot be mutated halfway through a service call.
• Separate input, output and internal models. One Order model that is simultaneously the request body, the response, and the ORM mirror will leak a field in one direction or the other.

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#Validators — where rules live

python
class DateRange(BaseModel):
    start: date
    end: date

    @field_validator("end")
    @classmethod
    def end_after_start(cls, v: date, info: ValidationInfo) -> date:
        if "start" in info.data and v < info.data["start"]:
            raise ValueError("end must be on or after start")
        return v

    @model_validator(mode="after")
    def max_span(self) -> "DateRange":
        if (self.end - self.start).days > 366:
            raise ValueError("range exceeds one year")
        return self

• field_validator for one field; model_validator(mode="after") for cross-field rules. In an after validator self is the built instance.
• Validators raise ValueError (or AssertionError); Pydantic wraps it into a ValidationError with the field path. Raising HTTPException here couples the model to a framework.
• Rules that need I/O — "customer exists", "SKU is in stock" — do not belong in a validator. They belong in the service, where a database session is available and the failure is a domain error, not a 422.
• mode="before" validators receive raw input; use them to coerce legacy shapes ("1,2,3" → [1, 2, 3]), nothing else.

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#Settings

python
class Settings(BaseSettings):
    model_config = SettingsConfigDict(env_prefix="APP_", env_file=".env", extra="ignore")

    database_url: PostgresDsn
    redis_url: RedisDsn | None = None
    log_level: Literal["DEBUG", "INFO", "WARNING"] = "INFO"

pydantic-settings is a separate package. Instantiate once at startup and inject; a Settings() per request re-reads .env every time. Invalid or missing values fail at boot with a field-level message — that is the point.

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#Serialization

python
order.model_dump()                          # dict, Python types
order.model_dump(mode="json")               # dict, JSON-safe (datetime → str)
order.model_dump_json(exclude_none=True)    # str
order.model_dump(include={"id", "status"})  # projection

class OrderOut(BaseModel):
    model_config = ConfigDict(from_attributes=True)   # build from ORM objects
    id: int
    created_at: datetime = Field(serialization_alias="createdAt")

• mode="json" when the dict will be JSON-encoded by something else; plain model_dump() keeps datetime and Decimal as objects and json.dumps will fail on them.
• serialization_alias/alias_generator=to_camel for wire-format naming; keep Python attributes snake_case.
• exclude/include at dump time for projections, Field(exclude=True) for fields that must never serialize (password_hash).
• from_attributes=True is how you build an output model from an ORM instance without a hand-written mapping.

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#Strict versus lax

python
Model(age="3")                              # lax: "3" → 3
Model.model_validate({"age": "3"}, strict=True)   # ValidationError
class Model(BaseModel):
    model_config = ConfigDict(strict=True)   # whole model
    age: Annotated[int, Strict()]            # one field

Lax mode coerces "3" to 3, 1 to True, "2024-01-01" to date. That is right for form data and query strings, and wrong for JSON from your own clients, where "3" where 3 was expected is a bug worth surfacing. Use strict for internal and typed JSON contracts; lax at the edges that genuinely receive strings.

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#Performance

• Validation runs in Rust and is fast; constructing models in a hot loop is not free. Validate at the boundary once; pass the instance around, do not re-validate the same data three layers deep.
• TypeAdapter(list[Item]) for validating collections without a wrapper model; build it once at module level, not per call.
• model_construct() skips validation — only for data you already validated (e.g. rows from your own database), never for input.
• defer_build=True on models with huge unions if import time matters; measure before using it.
• Discriminated unions (Field(discriminator="type")) validate in one pass instead of trying each member; always tag polymorphic payloads.

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#Testing

python
def test_rejects_unknown_field():
    with pytest.raises(ValidationError) as e:
        OrderIn(customer_id=1, items=[item], bogus=1)
    assert e.value.errors()[0]["type"] == "extra_forbidden"

def test_schema_is_stable(snapshot):
    assert OrderIn.model_json_schema() == snapshot

Assert on errors()[...]["type"] and ["loc"], not on the message string, which changes between minor versions. Snapshot model_json_schema() for models that form a public contract. → Testing/pytest

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#Anti-patterns

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#Checklist

• Verify: Every input model sets extra="forbid"
• Verify: Constraints expressed with Field/constrained types, not manual checks
• Verify: Input, output and internal models are distinct
• Verify: Cross-field rules in model_validator(mode="after")
• Verify: No I/O and no framework exceptions inside validators
• Verify: Settings built once from BaseSettings and injected
• Verify: JSON output uses model_dump(mode="json") or model_dump_json()
• Verify: Wire naming handled by aliases; attributes stay snake_case
• Verify: Sensitive fields marked exclude=True
• Verify: Strict mode on internal/typed contracts; lax only at string edges
• Verify: Validation happens once at the boundary
• Verify: TypeAdapters are module-level
• Verify: Polymorphic payloads use a discriminator
• Verify: Tests assert error type/loc; public schemas are snapshotted