base-typed-id 0.2.0

Strict typed UUID identifier base classes with exact runtime subtype preservation and optional Pydantic v2 support.

base-typed-id

base_typed_id is a small Python library for building domain-specific UUID identifier types that remain real str objects at runtime.

It provides two base classes:

• BaseTypedId for plain UUID-backed typed identifiers
• BasePrefixedTypedId for canonical prefixed identifiers like user_<uuid>

Strict typed UUID identifier base classes with exact runtime subtype preservation and optional Pydantic v2 support.

---

Why

Sometimes an identifier is semantically important enough to deserve its own type, but operationally it should still behave like a normal string.

Examples:

• UserId
• OrderId
• ExternalEventId
• WorkspaceId
• IntegrationId

Sometimes you want plain UUID text:

• 123e4567-e89b-42d3-a456-426614174000

Sometimes you want a canonical prefixed form:

• user_123e4567-e89b-42d3-a456-426614174000
• workspace_123e4567-e89b-42d3-a456-426614174000

Using plain str everywhere loses domain meaning. Using plain uuid.UUID changes runtime behavior. Using wrappers adds interoperability friction. Using NewType helps only static typing.

base_typed_id gives you a middle ground: domain-specific UUID-backed identifiers with validation, while keeping real str behavior at runtime.

---

What it provides

BaseTypedId

A transparent domain-typed UUID identifier.

Canonical runtime form:

• <uuid>

Example:

from base_typed_id import BaseTypedId


class UserId(BaseTypedId):
    pass

Runtime value:

UserId("123e4567-e89b-42d3-a456-426614174000")

BasePrefixedTypedId

A transparent domain-typed UUID identifier with a class-level canonical prefix.

Canonical runtime form:

• <prefix>_<uuid>

Example:

from base_typed_id import BasePrefixedTypedId


class UserId(BasePrefixedTypedId):
    prefix = "user"

Runtime value:

UserId("user_123e4567-e89b-42d3-a456-426614174000")

---

What it guarantees

Shared guarantees

Both base classes:

• accept valid UUID strings and uuid.UUID values
• support auto-generation when called without an explicit value when uuid_version is 4 or None
• validate UUID structure at construction time
• preserve the exact subclass type at construction and validation boundaries
• behave like normal str
• normal string operations return plain str
• preserve subtype through pickle roundtrip
• support Pydantic v2, but do not require it
• serialize and export as plain string
• ship py.typed

Additional guarantees for BaseTypedId

• canonical runtime form is plain UUID text

• Pydantic / OpenAPI schema uses:

  • type: string
  • format: uuid

Additional guarantees for BasePrefixedTypedId

• canonical runtime form is <prefix>_<uuid>

• prefix is a class-level invariant

• prefix must be canonical lowercase snake case

• regex is derived from the declared prefix and cannot be overridden

• Pydantic / OpenAPI schema uses:

  • type: string
  • pattern: ^<prefix>_<uuid-regex>$

---

What it intentionally does not do

• no built-in domain rules beyond UUID parsing, prefix invariants, and optional version checks
• no normalization layer beyond canonical runtime construction
• no non-UUID identifier support
• no domain-specific methods
• no wrapper objects
• no built-in deterministic factory for prefixed identifiers in the current implementation

This package is intentionally minimal.

Domain rules should live in your subclasses or in your application layer.

---

Why not plain str / uuid.UUID / NewType / Annotated / custom wrapper?

Why not plain str?

Because plain str does not communicate domain intent.

def get_user(user_id: str, workspace_id: str) -> None:
    ...

This is easy to misuse:

• parameters can be swapped accidentally
• type annotations do not explain domain meaning
• static analysis cannot distinguish semantic identifier types

With typed subclasses:

def get_user(user_id: UserId, workspace_id: WorkspaceId) -> None:
    ...

the intent is explicit.

Why not uuid.UUID?

uuid.UUID validates structure, but it is not the same thing as a domain identifier type.

from uuid import UUID

def get_user(user_id: UUID, workspace_id: UUID) -> None:
    ...

This still loses semantic distinction:

• user_id and workspace_id are the same runtime type
• static analysis cannot distinguish one UUID-based domain identifier from another
• annotations do not preserve domain meaning
• exported JSON often requires explicit conversion to string
• many integrations expect plain str, not UUID
• exact domain subtype identity such as type(user_id) is UserId is not available

base_typed_id keeps UUID validation while preserving domain-specific type identity and plain string interoperability.

Why not typing.NewType?

NewType is a static typing tool, not a runtime type.

from typing import NewType

UserId = NewType("UserId", str)

user_id: UserId = UserId("123e4567-e89b-42d3-a456-426614174000")

assert type(user_id) is str
assert isinstance(user_id, str)

This means:

• runtime values are still plain str
• there is no real subclass at runtime
• runtime boundaries cannot preserve a concrete semantic subtype
• introspection and runtime behavior cannot distinguish UserId from plain str
• UUID validation is not part of construction

base_typed_id creates a real runtime subtype with UUID validation.

Why not Annotated[str, ...]?

Annotated can attach metadata for validators and frameworks, but it still does not create a distinct runtime type.

That means:

• runtime values are still plain str
• type(value) is not your domain identifier type
• exact subtype identity is not preserved inside Python objects

If you need runtime identity such as type(user_id) is UserId, Annotated[str, ...] is not enough.

Why not a custom wrapper class?

A wrapper can model a domain value, but it stops being a real string.

Typical trade-offs:

• isinstance(value, str) becomes False
• JSON serialization often needs custom handling
• many libraries expect plain str, not wrapper objects
• you often need explicit .value extraction
• interoperability becomes noisier

A wrapper is useful when you want rich behavior and strict encapsulation.

base_typed_id is for the opposite case: keep the identifier operationally identical to str, while still having a named domain type with UUID guarantees.

When base_typed_id is the right choice

Use it when you want:

• semantic identifier types in annotations
• UUID validation at construction and validation boundaries
• real str behavior at runtime
• plain string serialization
• clean interoperability with Python and library code
• Pydantic / OpenAPI compatibility

Use BaseTypedId when you want plain UUID runtime strings.

Use BasePrefixedTypedId when you want canonical prefixed runtime strings.

Do not use it when you need:

• heavy domain logic on the identifier itself
• mutable state
• multiple fields
• non-UUID runtime representation
• non-UUID identifiers

---

Installation

Base package

pip install base-typed-id

With Pydantic v2 support

pip install "base-typed-id[pydantic]"

For development

pip install "base-typed-id[dev]"

---

Quick start

Plain UUID typed identifier

from base_typed_id import BaseTypedId


class UserId(BaseTypedId):
    pass


user_id: UserId = UserId("123e4567-e89b-42d3-a456-426614174000")
generated_user_id: UserId = UserId()

assert user_id == "123e4567-e89b-42d3-a456-426614174000"
assert isinstance(user_id, str)
assert isinstance(user_id, UserId)
assert type(user_id) is UserId
assert type(generated_user_id) is UserId

Prefixed typed identifier

from base_typed_id import BasePrefixedTypedId


class UserId(BasePrefixedTypedId):
    prefix = "user"


user_id_from_plain_uuid: UserId = UserId("123e4567-e89b-42d3-a456-426614174000")
user_id_from_prefixed_string: UserId = UserId(
    "user_123e4567-e89b-42d3-a456-426614174000"
)
generated_user_id: UserId = UserId()

assert user_id_from_plain_uuid == "user_123e4567-e89b-42d3-a456-426614174000"
assert user_id_from_prefixed_string == "user_123e4567-e89b-42d3-a456-426614174000"
assert isinstance(user_id_from_plain_uuid, str)
assert isinstance(user_id_from_plain_uuid, UserId)
assert type(user_id_from_plain_uuid) is UserId
assert type(generated_user_id) is UserId

---

How to use it in your project

Create a module for your domain identifier types.

For example, create a file named domain_identifiers.py:

from base_typed_id import BasePrefixedTypedId, BaseTypedId


class UserId(BasePrefixedTypedId):
    """User identifier."""

    prefix = "user"


class WorkspaceId(BasePrefixedTypedId):
    """Workspace identifier."""

    prefix = "workspace"


class ExternalEventId(BaseTypedId):
    """External event identifier."""

    uuid_version = 5

Then use these types in your application code:

from .domain_identifiers import ExternalEventId, UserId, WorkspaceId


def get_user(
    user_id: UserId,
    workspace_id: WorkspaceId,
    external_event_id: ExternalEventId,
) -> None:
    print(user_id, workspace_id, external_event_id)

This gives you:

• domain-specific names in type annotations
• UUID validation at boundaries
• real str values at runtime
• plain string serialization behavior
• reconstruction through validation layers such as Pydantic

---

Runtime behavior

Both base classes are real str subclasses backed by UUID validation.

BaseTypedId

from base_typed_id import BaseTypedId


class UserId(BaseTypedId):
    pass


user_id: UserId = UserId("123e4567-e89b-42d3-a456-426614174000")

assert isinstance(user_id, str)
assert isinstance(user_id, UserId)
assert type(user_id) is UserId
assert user_id == "123e4567-e89b-42d3-a456-426614174000"

BasePrefixedTypedId

from base_typed_id import BasePrefixedTypedId


class UserId(BasePrefixedTypedId):
    prefix = "user"


user_id: UserId = UserId("123e4567-e89b-42d3-a456-426614174000")

assert isinstance(user_id, str)
assert isinstance(user_id, UserId)
assert type(user_id) is UserId
assert user_id == "user_123e4567-e89b-42d3-a456-426614174000"

Normal string operations return plain str

from base_typed_id import BasePrefixedTypedId


class UserId(BasePrefixedTypedId):
    prefix = "user"


user_id: UserId = UserId("user_123e4567-e89b-42d3-a456-426614174000")

uppercased_value: str = user_id.upper()
concatenated_value: str = user_id + "_debug"
replaced_value: str = user_id.replace("-", "_")

assert type(uppercased_value) is str
assert type(concatenated_value) is str
assert type(replaced_value) is str

This behavior is intentional.

The typed subtype is preserved at construction and validation boundaries, not across ordinary string operations.

---

Constructor rules

BaseTypedId

Valid inputs are:

• no argument
• UUID string
• uuid.UUID

Calling the constructor without an argument auto-generates a UUID when uuid_version is 4 or None.

from uuid import UUID

from base_typed_id import BaseTypedId


class UserId(BaseTypedId):
    pass


value_from_string: UserId = UserId("123e4567-e89b-42d3-a456-426614174000")
value_from_uuid: UserId = UserId(UUID("123e4567-e89b-42d3-a456-426614174000"))
generated_value: UserId = UserId()

BasePrefixedTypedId

Valid inputs are:

• no argument
• canonical prefixed UUID string
• raw UUID string
• uuid.UUID

Calling the constructor without an argument auto-generates a UUID when uuid_version is 4 or None.

from uuid import UUID

from base_typed_id import BasePrefixedTypedId


class UserId(BasePrefixedTypedId):
    prefix = "user"


value_from_prefixed_string: UserId = UserId(
    "user_123e4567-e89b-42d3-a456-426614174000"
)
value_from_plain_uuid_string: UserId = UserId(
    "123e4567-e89b-42d3-a456-426614174000"
)
value_from_uuid: UserId = UserId(UUID("123e4567-e89b-42d3-a456-426614174000"))
generated_value: UserId = UserId()

assert value_from_prefixed_string == "user_123e4567-e89b-42d3-a456-426614174000"
assert value_from_plain_uuid_string == "user_123e4567-e89b-42d3-a456-426614174000"
assert value_from_uuid == "user_123e4567-e89b-42d3-a456-426614174000"

Invalid input raises BaseTypedIdInvalidInputValueError.

from base_typed_id import (
    BasePrefixedTypedId,
    BaseTypedId,
    BaseTypedIdInvalidInputValueError,
)


class UserId(BaseTypedId):
    pass


class PrefixedUserId(BasePrefixedTypedId):
    prefix = "user"


try:
    UserId("not-a-uuid")
except BaseTypedIdInvalidInputValueError:
    pass

try:
    PrefixedUserId("user_not-a-uuid")
except BaseTypedIdInvalidInputValueError:
    pass

try:
    PrefixedUserId(123)
except BaseTypedIdInvalidInputValueError:
    pass

---

Prefix rules for BasePrefixedTypedId

Each concrete subclass must declare its own class-level prefix.

from base_typed_id import BasePrefixedTypedId


class UserId(BasePrefixedTypedId):
    prefix = "user"

Rules:

• prefix must be declared directly on the subclass
• prefix must be a str
• prefix must be canonical lowercase snake case
• regex is derived from prefix
• regex cannot be overridden

Examples of invalid subclasses:

from base_typed_id import BasePrefixedTypedId


class MissingPrefix(BasePrefixedTypedId):
    pass

from base_typed_id import BasePrefixedTypedId


class InvalidPrefixType(BasePrefixedTypedId):
    prefix = 123

from base_typed_id import BasePrefixedTypedId


class InvalidPrefixFormat(BasePrefixedTypedId):
    prefix = "User"

import re

from base_typed_id import BasePrefixedTypedId


class InvalidRegexOverride(BasePrefixedTypedId):
    prefix = "user"
    regex = re.compile("^custom$")

These raise BaseTypedIdInvariantViolationError.

---

UUID version control

By default, both base classes enforce UUID v4.

from base_typed_id import BasePrefixedTypedId, BaseTypedId


class UserId(BaseTypedId):
    pass


class WorkspaceId(BasePrefixedTypedId):
    prefix = "workspace"

Use another version explicitly:

from base_typed_id import BasePrefixedTypedId, BaseTypedId


class ExternalEventId(BaseTypedId):
    uuid_version = 5


class ImportedEntityId(BasePrefixedTypedId):
    prefix = "imported_entity"
    uuid_version = 5

Disable version restriction:

from base_typed_id import BasePrefixedTypedId, BaseTypedId


class FlexibleId(BaseTypedId):
    uuid_version = None


class FlexiblePrefixedId(BasePrefixedTypedId):
    prefix = "flexible"
    uuid_version = None

When uuid_version is not 4 or None, auto-generation from None is intentionally rejected.

---

Deterministic identifiers

For idempotent identifiers, the package provides deterministically_from_words.

This factory currently works with BaseTypedId subclasses.

from base_typed_id import BaseTypedId, deterministically_from_words


class ExternalEventId(BaseTypedId):
    uuid_version = 5


event_id: ExternalEventId = deterministically_from_words(
    ExternalEventId,
    words=[
        "workspace:house-of-ai",
        "provider:telegram",
        "event:message-created",
        "message:42",
    ],
)

Properties:

• same words -> same identifier
• order matters
• deterministic generation requires uuid_version = 5 or uuid_version = None

---

Pydantic v2 support

BaseTypedId

When used as a Pydantic field type:

• validation accepts UUID objects and UUID strings
• runtime model values preserve the exact subtype
• exported payloads are plain strings
• generated schema keeps type: string and format: uuid

from pydantic import BaseModel

from base_typed_id import BaseTypedId


class UserId(BaseTypedId):
    pass


class UserModel(BaseModel):
    user_id: UserId


user_model: UserModel = UserModel.model_validate(
    {
        "user_id": "123e4567-e89b-42d3-a456-426614174000",
    }
)

assert type(user_model.user_id) is UserId

dumped_python: dict[str, object] = user_model.model_dump()

assert dumped_python == {
    "user_id": "123e4567-e89b-42d3-a456-426614174000",
}
assert type(dumped_python["user_id"]) is str

BasePrefixedTypedId

When used as a Pydantic field type:

• Python-side validation accepts:

  • UUID objects
  • raw UUID strings
  • canonical prefixed strings

• runtime model values preserve the exact subtype

• exported payloads are plain strings

• generated schema keeps type: string and a prefix-derived pattern

from pydantic import BaseModel

from base_typed_id import BasePrefixedTypedId


class UserId(BasePrefixedTypedId):
    prefix = "user"


class UserModel(BaseModel):
    user_id: UserId


user_model: UserModel = UserModel.model_validate(
    {
        "user_id": "123e4567-e89b-42d3-a456-426614174000",
    }
)

assert type(user_model.user_id) is UserId
assert user_model.user_id == "user_123e4567-e89b-42d3-a456-426614174000"

dumped_python: dict[str, object] = user_model.model_dump()

assert dumped_python == {
    "user_id": "user_123e4567-e89b-42d3-a456-426614174000",
}
assert type(dumped_python["user_id"]) is str

Important boundary for BasePrefixedTypedId

Inside the validated model, the exact subtype is preserved.

After serialization or export, values intentionally become plain strings.

For JSON input validation through Pydantic, the current implementation accepts canonical prefixed strings and rejects raw UUID strings.

from pydantic import BaseModel, ValidationError

from base_typed_id import BasePrefixedTypedId


class UserId(BasePrefixedTypedId):
    prefix = "user"


class UserModel(BaseModel):
    user_id: UserId


UserModel.model_validate_json(
    '{"user_id":"user_123e4567-e89b-42d3-a456-426614174000"}'
)

try:
    UserModel.model_validate_json(
        '{"user_id":"123e4567-e89b-42d3-a456-426614174000"}'
    )
except ValidationError:
    pass

This is a feature of the current schema design, not an accidental test artifact.

---

Pickle support

Pickle roundtrip preserves the exact subtype for both base classes.

BaseTypedId

import pickle

from base_typed_id import BaseTypedId


class UserId(BaseTypedId):
    pass


source_user_id: UserId = UserId("123e4567-e89b-42d3-a456-426614174000")
serialized_user_id: bytes = pickle.dumps(source_user_id)
restored_user_id: object = pickle.loads(serialized_user_id)

assert restored_user_id == "123e4567-e89b-42d3-a456-426614174000"
assert type(restored_user_id) is UserId

BasePrefixedTypedId

import pickle

from base_typed_id import BasePrefixedTypedId


class UserId(BasePrefixedTypedId):
    prefix = "user"


source_user_id: UserId = UserId("user_123e4567-e89b-42d3-a456-426614174000")
serialized_user_id: bytes = pickle.dumps(source_user_id)
restored_user_id: object = pickle.loads(serialized_user_id)

assert restored_user_id == "user_123e4567-e89b-42d3-a456-426614174000"
assert type(restored_user_id) is UserId

---

JSON behavior

Since both base classes inherit from str, standard JSON serialization naturally produces plain JSON strings.

BaseTypedId

import json

from base_typed_id import BaseTypedId


class UserId(BaseTypedId):
    pass


value: UserId = UserId("123e4567-e89b-42d3-a456-426614174000")
serialized_value: str = json.dumps(value)
restored_value: object = json.loads(serialized_value)

assert serialized_value == '"123e4567-e89b-42d3-a456-426614174000"'
assert restored_value == "123e4567-e89b-42d3-a456-426614174000"
assert type(restored_value) is str

BasePrefixedTypedId

import json

from base_typed_id import BasePrefixedTypedId


class UserId(BasePrefixedTypedId):
    prefix = "user"


value: UserId = UserId("user_123e4567-e89b-42d3-a456-426614174000")
serialized_value: str = json.dumps(value)
restored_value: object = json.loads(serialized_value)

assert serialized_value == '"user_123e4567-e89b-42d3-a456-426614174000"'
assert restored_value == "user_123e4567-e89b-42d3-a456-426614174000"
assert type(restored_value) is str

This behavior is intentional.

JSON is a plain data boundary.

The exact runtime subtype exists only inside Python objects. After serialization, values become plain strings and do not carry subtype information.

---

Public API

from base_typed_id import BasePrefixedTypedId
from base_typed_id import BaseTypedId
from base_typed_id import BaseTypedIdError
from base_typed_id import BaseTypedIdInvalidInputValueError
from base_typed_id import BaseTypedIdInvariantViolationError
from base_typed_id import deterministically_from_words

Exceptions

BaseTypedIdError

Root exception for all package-specific errors.

BaseTypedIdInvalidInputValueError

Raised when an invalid UUID input value is provided.

BaseTypedIdInvariantViolationError

Raised when an internal invariant or contract is violated.

---

Examples

Current examples:

• examples/basic_usage.py
• examples/deterministic_factory_usage.py
• examples/pickle_roundtrip.py
• examples/pydantic_roundtrip_from_dump.py
• examples/pydantic_runtime_vs_dump.py
• examples/prefixed_basic_usage.py
• examples/prefixed_pydantic_runtime_vs_dump.py

---

Design notes

BaseTypedId is intended for projects that want domain-specific UUID identifier names without giving up normal str runtime behavior.

BasePrefixedTypedId is intended for projects that want the same runtime behavior, but with canonical prefixed string values such as:

• user_<uuid>
• workspace_<uuid>
• integration_<uuid>

This is especially useful when you have many semantic identifiers such as:

• UserId
• WorkspaceId
• OrderId
• ExternalEventId
• IntegrationId

The base classes stay intentionally small so that your domain layer remains explicit and predictable.

---

Development

Run tests

pytest

Run lint

ruff check .

Run type checking

mypy src tests
pyright

Build package

python -m build

Validate distribution metadata

twine check dist/*

---

Compatibility

• Python 3.10+
• CPython
• optional Pydantic v2 support

---

License

MIT