classconf 0.3.0

Dataclass companion for config metadata and parsing, with a ConfigParser that generates and loads configs from files.

classconf

Dataclass companion for config metadata and parsing, with a ConfigParser that generates and loads typed configs from files.

Core ideas

• Use @configclass to attach config metadata to dataclasses.
• Provide config classes to ConfigParser.
• Parse config files into dataclass instances.

Basic usage

from dataclasses import dataclass
from pathlib import Path

from classconf import ConfigParser, configclass
from classconf.format import JSONFormat


@configclass
class PathsConfig:
    output_dir: Path = Path("./out")


@configclass(top_level=True)
class AppConfig:
    name: str = "demo"
    paths: PathsConfig = field(default_factory=PathsConfig)


parser = ConfigParser(
    "config.json",
    AppConfig,
    format=JSONFormat(),
    create_noexist=True,
)

config = parser.get(AppConfig)

Generated config

{
  "name": "demo",
  "paths": {
    "output_dir": "out"
  }
}

Formats

• TOMLFormat (default if format is None)
• JSONFormat

from classconf.format import TOMLFormat

parser = ConfigParser("config.toml", AppConfig, PathsConfig, format=TOMLFormat())

TOMLFormat accepts none_value to control how None is written. Use none_value=None to omit None fields entirely.

Custom formats

To add a new format, implement ConfigFormat with read and write methods. read should return None when the file does not exist.

from pathlib import Path
from typing import Any

from classconf.format import ConfigFormat


class YAMLFormat(ConfigFormat):
    def read(self, path: Path) -> dict[str, Any] | None:
        ...

    def write(self, path: Path, data: dict[str, Any]) -> None:
        ...

Field mappings, serializers, deserializers

from dataclasses import dataclass
from classconf import ConfigParser, configclass
from classconf.format import JSONFormat


def deserialize_num(value: str, **_) -> int:
    return int(value.rstrip("x"))


def serialize_num(value: int) -> str:
    return f"{value}x"


@configclass(
    name="metrics",
    field_deserialzers={"count": deserialize_num},
    field_serializers={"count": serialize_num},
    field_name_mappings={"count": "count_value"},
)
class MetricsConfig:
    count: int = 3


parser = ConfigParser(
    "config.json",
    MetricsConfig,
    format=JSONFormat(),
    create_noexist=True,
)

metrics = parser.get(MetricsConfig)

Generated config file

{
  "metrics": {
    "count_value": "3x"
  }
}

Deserializers can also accept a parser to resolve other configs. This is useful when a field is typed as a protocol/ABC and a string selects which config section to load.

from dataclasses import dataclass, field
from typing import Protocol, runtime_checkable

from classconf import ConfigParser, configclass
from classconf.format import TOMLFormat


@runtime_checkable
class DatabaseConfig(Protocol):
    driver: ClassVar[str]


@configclass(name="sqlite")
class SQLiteConfig:
    driver: ClassVar[str] = "sqlite"
    path: str = "app.db"


@configclass(name="postgres")
class PostgresConfig:
    driver: ClassVar[str] = "postgres"
    host: str = "localhost"
    port: int = 5432


def resolve_database(name: str, parser: ConfigParser) -> DatabaseConfig:
    return parser.get(SQLiteConfig if name == "sqlite" else PostgresConfig)


def serialize_database(db: DatabaseConfig) -> str:
    return db.driver


@configclass(
    top_level=True,
    field_deserialzers={"database": resolve_database},
    field_serializers={"database": serialize_database},
)
class AppConfig:
    database: DatabaseConfig = field(default_factory=SQLiteConfig)


parser = ConfigParser(
    "config.json",
    AppConfig,
    SQLiteConfig,
    PostgresConfig,
    format=TOMLFormat(),
    create_noexist=True,
)

config = parser.get(AppConfig)
print(config.database.driver)

Generated config

database = "sqlite"

[postgres]
driver = "postgres"
host = "localhost"
port = 5432

[sqlite]
driver = "sqlite"
path = "app.db"

Generating configs from instances

ConfigParser.generate_config writes a config file from config class instances. This is useful for preset generation when a CLI or UI offers a few known configurations and only the selected one should be saved.

from dataclasses import dataclass
from classconf import ConfigParser, configclass
from classconf.format import JSONFormat


@configclass(name="logging")
class LoggingConfig:
    level: str = "INFO"
    file: str = "app.log"


preset = "debug"  # could come from CLI/UI

if preset == "debug":
    config = LoggingConfig(level="DEBUG", file="debug.log")
else:
    config = LoggingConfig(level="INFO", file="app.log")

ConfigParser.generate_config(
    "logging_preset.json",
    config,
    format=JSONFormat(),
    override_existing=True,
)

Adding configs later

parser.add(OtherConfig)
other = parser.get(OtherConfig)

Quirks and constraints

• Only one top-level config class is allowed per parser.
• create_noexist=False requires the file to exist.
• Missing config keys raise KeyError during parsing.
• get() raises if the class was not provided to the parser.
• With JSON/TOML, fields without defaults are written as null/None placeholders.