modmex 1.1.10

Lightweight Python models built on dataclasses with validation, serialization, and type-safe data mapping

modmex

Lightweight Python models built on dataclasses with validation, serialization, and type-safe data mapping.

modmex gives you a small but powerful toolkit for:

• Typed models with minimal boilerplate.
• Automatic coercion and validation at initialization time.
• Recursive serialization to Python primitives and JSON.
• Per-field and per-model validation hooks.
• Type-based custom serializers to adapt output for different consumers.

Why modmex

If you want stricter models than plain dataclasses, but without the weight of a large framework, modmex is designed for that middle ground.

It focuses on:

• Simplicity: small API surface.
• Predictability: explicit model lifecycle.
• Flexibility: configurable serialization without changing your model definitions.

Installation

With pip:

pip install modmex

With Poetry:

poetry add modmex

Quick Start

from decimal import Decimal

from modmex import BaseModel, Field


class User(BaseModel):
    id: int
    name: str
    balance: Decimal = Decimal("0")
    password: str = Field("", exclude=True)


user = User(id="1", name=123, balance="10.50")

# Type coercion happens during initialization.
assert user.id == 1
assert user.name == "123"
assert user.balance == Decimal("10.50")

# model_dump returns primitive/serializable values.
assert user.model_dump() == {
    "id": 1,
    "name": "123",
    "balance": 10.5,
}

# model_dump_json returns a JSON string.
assert user.model_dump_json() == '{"id":1,"name":"123","balance":10.5}'

Field Configuration

Use Field(...) to add serialization metadata to a model field.

Main options:

• exclude=True
  • Always excludes this field from model_dump and model_dump_json.
• exclude_from={"profile_name"}
  • Excludes this field only for selected serialization profiles.

Example:

from modmex import BaseModel, Field


class Session(BaseModel):
    id: str
    secret: str = Field("", exclude_from={"public"})


class User(BaseModel):
    id: int
    private_note: str = Field("x", exclude=True)
    sessions: list[Session] = Field(default_factory=list)


user = User(id=1, sessions=[Session(id="s1", secret="abc")])

assert user.model_dump(profile="public") == {
    "id": 1,
    "sessions": [{"id": "s1"}],
}

Tip:

• Use exclude=True for values that should never leave the model.
• Use exclude_from={...} when omission depends on the output profile.

Everyday Usage

1) Parse and normalize input data

from modmex import BaseModel


class Product(BaseModel):
    id: int
    name: str
    active: bool


product = Product(id="10", name=123, active="true")

assert product.id == 10
assert product.name == "123"
assert product.active is True

2) Work with nested models

from modmex import BaseModel


class Address(BaseModel):
    zipcode: int


class User(BaseModel):
    id: int
    address: Address


user = User(id="1", address={"zipcode": "90210"})
assert user.address.zipcode == 90210

3) Prepare different payloads from the same model

api_payload = account.model_dump(profile="public")
internal_payload = account.model_dump()

4) Build JSON directly

json_payload = account.model_dump_json(profile="public")

Dynamic Models

Use create_model when you need to define a model at runtime.

Field definitions can use one of these forms:

• (annotation, default)
• (annotation, Field(...))
• typing.Annotated[annotation, Field(...)]

from typing import Annotated

from modmex import BaseModel, Field, create_model, field_validator, model_validator


def normalize_name(self, value: str) -> str:
    return value.strip().title()


DynamicUser = create_model(
    "DynamicUser",
    id=(int, ...),
    name=(str, Field(default="anonymous")),
    slug=Annotated[str, Field(default="user", exclude=True)],
    __validators__={"normalize_name": field_validator("name")(normalize_name)},
)


class StaticUser(BaseModel):
    id: int
    name: str = "anonymous"
    slug: str = Field(default="user", exclude=True)

    @field_validator("name")
    def normalize_name(self, value: str) -> str:
        return value.strip().title()


dynamic_user = DynamicUser(id="1", name="  john ")
static_user = StaticUser(id="1", name="  john ")

assert dynamic_user.model_dump() == static_user.model_dump()
assert dynamic_user.name == static_user.name == "Jhon"

Validators

Field validators

Use @field_validator("field_name") to transform or validate a single field.

from modmex import BaseModel, field_validator


class Product(BaseModel):
    name: str

    @field_validator("name")
    def normalize_name(self, value: str) -> str:
        return value.strip().title()

Model validators

Use @model_validator(mode="before" | "after") to work with full model state.

• before: runs before type coercion.
• after: runs after field-level validation.

from modmex import BaseModel, model_validator


class Product(BaseModel):
    name: str
    slug: str = ""

    @model_validator(mode="before")
    def build_slug(self, values: dict) -> dict:
        values["slug"] = values["name"].lower().replace(" ", "-")
        return values

Serialization

model_dump(...)

Use model_dump when you need a dictionary payload.

Most common options:

• exclude={...} to omit fields for a specific call.
• profile="..." to apply exclude_from rules.
• include_excluded=True to force metadata-excluded fields into the payload.
• type_serializers={...} to control how specific Python types are represented.

model_dump_json(...)

Use model_dump_json when you need a JSON string output.

It supports the same practical options as model_dump (exclude, profile, include_excluded, type_serializers).

Omitting Fields During Serialization

Use this feature when the same model must produce different payloads depending on where the data is going.

• exclude_from defines where a field should be omitted.
• profile selects which omission rules to apply in a specific dump call.

What each option does

• exclude_from={"public"}
  • Omit this field when serializing with profile="public".
• profile="public"
  • Apply all field rules tagged for public during serialization.

Common pattern

You may want one shape for API responses and another for internal flows (logs, queues, exports, persistence payloads, etc.).

• API payload (profile="public"): hide internal fields.
• Internal payload (no profile, or another profile): keep those fields.

Example

from modmex import BaseModel, Field


class Account(BaseModel):
    id: int
    email: str = Field("", exclude_from={"public"})
    internal_note: str = Field("", exclude=True)


account = Account(id=1, email="[e-mail]", internal_note="secret")

# No profile: only always-excluded fields are removed.
assert account.model_dump() == {
    "id": 1,
    "email": "[e-mail]",
}

# public profile: profile-based exclusions are applied.
assert account.model_dump(profile="public") == {
    "id": 1,
}

# include_excluded=True: ignore Field exclusion metadata.
assert account.model_dump(profile="public", include_excluded=True) == {
    "id": 1,
    "email": "[e-mail]",
    "internal_note": "secret",
}

# Dynamic omission for one call (without metadata changes).
assert account.model_dump(exclude={"email"}) == {
    "id": 1,
}

Type-Based Custom Serializers

You can override serialization behavior by type with type_serializers.

Shape:

type_serializers = {
    SomeType: serializer_function,
}

Keep Decimal values as Decimal in model_dump

from decimal import Decimal

dumped = model.model_dump(
    type_serializers={
        Decimal: lambda value: value,
    }
)

Convert float to Decimal for a specific output contract

from decimal import Decimal

from modmex import BaseModel


class Price(BaseModel):
    amount: float


p = Price(amount=10.25)
dumped = p.model_dump(
    type_serializers={
        float: lambda value: Decimal(str(value)),
    }
)

assert dumped["amount"] == Decimal("10.25")

Emit Decimal as string in JSON

from decimal import Decimal

dumped_json = model.model_dump_json(
    type_serializers={
        Decimal: lambda value: str(value),
    }
)

Note: some client libraries expect Decimal instead of float values (for example, common boto3 workflows). Type serializers let you adapt output contracts cleanly, without hard-coding backend-specific behavior into your models.

Error Handling

Validation issues raise ValidationError.

Each error includes:

• loc: location path (supports nested structures).
• msg: human-readable message.
• type: error category.

Example locations:

• ["address", "zipcode"]
• ["tags", 1]

Practical Usage Pattern

Use this rule of thumb:

• Keep rich Python types in the in-memory model instance.
• Use model_dump / model_dump_json to produce transport-friendly payloads.
• Use type_serializers when a specific consumer requires a different type format.

Compatibility

• Python 3.10+