Exacting

(adj.) making great demands on one's skill, attention, or other resources.

exacting is a picky dataclass runtime utility collection, making sure all type annotations are followed.

Essentially... THE go-to option for dataclasses. heh.

🔑 Key features:

100% static typing. Because I hate nothing too.
Generally faster than pydantic!

static typing

🛍️ Get exacting:

pipuvgit

pip install -U exacting

uv pip install -U exacting

git clone https://github.com/AWeirdDev/exacting

🔥 Define some model:

Python 3.10+Python <= 3.9

from exacting import Exact

class Actor(Exact):
    name: str
    portrays: str

class Show(Exact):
    name: str
    description: str | None
    actors: list[Actor]

from typing import List, Optional
from exacting import Exact

class Actor(Exact):
    name: str
    portrays: str

class Show(Exact):
    name: str
    description: Optional[str]
    actors: List[Actor]

📦 Build 'em:

# (1) ✅ OK, exacting is happi
Show(
    name="Severance",
    description="great show",
    actors=[
        Actor(name="Adam Scott", portrays="Mark S."),
        Actor(name="Britt Lower", portrays="Helly R."),
    ]
)

# (2) ❌ Nuh-uh, exacting is angri
Show(
    name=123,
    description=False,
    actors=[
        "Walter White",
        "Jesse Pinkman"
    ]
)

ValidationError: During validation of…

ValidationError:
During validation of dataclass Show at field 'name', got:
  • Expected type <class 'str'>, got <class 'int'>

Normally, when you use the parameters passed in example (2) above, the Python dataclasses library might as well just go with it, because they only put the additional static typing to the model, but not at runtime. Exacting makes sure that at both times, types are all enforced. It even gives you a detailed error message on where this occurs! (In a cool way)

It's worth noting that error generations are lazy, which means once Exacting finds out about a problem about a dataclass, it raises a ValidationError. This saves a lot of computation time if you have a larger model.