A model-driven configuration object for TOML or dict-based configs.
Project description
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MDTC - Model-driven TOML Configuration.
A lightweight config singleton meant for storing your application's config state no matter where or how many times it is instantiated. You can pass this object around across your entire app and not worry about config mutations, unvalidated config values or lack of IDE completions. Originally meant for use with TOML key/value-based configs, but any k/v object should work as long as it complies with the model.
The source documentation can be found here
What is MDTC for?
- Avoids having to use or chain
.get()
or retrieve config values viacfg["foo"]["bar"]["baz"]
. - Code-completion-friendly via model-driven approach.
- Custom configuration validation (either via Pydantic's interfaces or custom-built validators you define).
- Immutable config state support. The config itself is immutable by default - you cannot replace
config.foo
with another value, for instance. - Supports nicer type hints instead of a huge TypeDict or another approach for a config dictionary loaded into Python.
What MDTC is not for
- It is not meant to replace other methods of loading TOML or dict configs, it simply provides an alternative for housing your TOML config values.
- It is not meant as "less code". The guarantees it provides require a different implementation approach, and won't always result in less upfront code.
- Codebases using other approaches or small configs won't benefit from this approach as much.
Dependencies
None, just the Python standard library.
Examples
Simple Configuration
import tomllib # python3.11-only, use tomli for <=3.10
from dataclasses import dataclass
from mdtc import Config
@dataclass
class FooCfg:
foo: str
bar: str
_name: str = "misc"
_key: str = "config.misc"
class MyConf(Config):
misc: FooCfg
cfg = """
[config.misc]
foo="bar"
bar="baz"
"""
toml = tomllib.loads(cfg)
config = MyConf(toml)
Pydantic Models in your Configuration
import tomllib # python3.11-only, use tomli for <=3.10
from pydantic import BaseModel
from mdtc import Config
class FooCfg(BaseModel):
_name: str = "misc"
_key: str = "config.misc"
foo: str
bar: str
class MyConf(Config):
misc: FooCfg
cfg = """
[config.misc]
foo="bar"
bar="baz"
"""
toml = tomllib.loads(cfg)
config = MyConf(toml)
Pydantic dataclass
Example
import tomllib # python3.11-only, use tomli for <=3.10
from pydantic import Field, validator
from pydantic.dataclasses import dataclass
from mdtc import Config
@dataclass
class FooCfg:
foo: str
bar: str = Field(title="A bar to get drinks in..")
_name: str = "misc"
_key: str = "config.misc"
@validator("foo")
def name_must_contain_space(cls, v):
if " " in v:
raise ValueError("must NOT contain a space!")
return v.title()
class MyConf(Config):
misc: FooCfg
cfg = """
[config.misc]
foo="bar"
bar="baz"
"""
toml = tomllib.loads(cfg)
config = MyConf(toml)
Contributing
Coming soon..
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