confident 2.0.0b1

Loading configurations from multiple sources into a data model.

Confident

Documentation

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Confident helps you create configuration objects from multiple sources such as files, environment variables and maps. Confident's BaseConfig class is a data model that enforces validation and type hints by using pydantic library.

With Confident you can manage multiple configurations depending on the environment your code is deployed in. While having lots of flexibility in how to describe your config objects, Confident will provide visibility of the process and help you expose misconfiguration as soon as possible.

Example

import os

from confident import BaseConfig


# Creating your own config class by inheriting from `BaseConfig`.
class MyAppConfig(BaseConfig):
    port: int = 5000
    host: str = 'localhost'
    labels: list


# Illustrates some environment variables.
os.environ['host'] = '127.0.0.1'
os.environ['labels'] = '["FOO", "BAR"]'  # JSON strings can be used for more types.

# Creating the config object. `BaseConfig` will load the values of the properties.
config = MyAppConfig()

print(config.host)
# > 127.0.0.1
print(config.model_dump_json())
# > {"port":5000,"host":"127.0.0.1","labels":["FOO","BAR"]}
print(config)
# > port=5000 host='127.0.0.1' labels=['FOO', 'BAR']
print(config.full_fields())
# > {
# 'port': ConfigField(name='port', value=5000, origin_value=5000, source_name='MyAppConfig', source_type='class_default', source_location=PosixPath('~/confident/readme_example.py')),
# 'host': ConfigField(name='host', value='127.0.0.1', origin_value='127.0.0.1', source_name='host', source_type='env_var', source_location='host'),
# 'labels': ConfigField(name='labels', value=['FOO', 'BAR'], origin_value='["FOO", "BAR"]', source_name='labels', source_type='env_var', source_location='labels')
# }

Installation

pip install confident

Capabilities

Customized Fields Loaders

Built-in loaders:

• Environment variables.
• Config files such as 'json' and 'yaml'.
• Config maps to load fields depending on the environment. (See documentation)

It is possible to configure the loading priority and add your own loader classes.

Full Support of Pydantic BaseSettings

Confident core functionality is based on pydantic library. That means BaseConfig object has all the benefits of pydantic's BaseModel and BaseSettings including type validation, object serialization and many more features.

Config Loading Visibility

BaseConfig object stores details about the fields loading process and offers ways to understand the source of each loaded field. Details about the origin value (before conversion), the location of the source and the type of loader, can all be accessed from the object.

Examples

More examples can be found in the project's repository.

Contributing

To contribute to Confident, please make sure any new features or changes to existing functionality include test coverage.