Map environment variables into typed settings
Environment-based application configuration
Pydantic · Class
SettingsConfigDict
SettingsConfigDict configures Pydantic settings models through named options such as environment-variable prefixes and dotenv-file loading. It is used as model_config data on a settings class.
How it works
SettingsConfigDict is the settings-specific configuration dictionary used with Pydantic Settings models. It supplies options such as environment-variable naming and dotenv-file loading while retaining the typed configuration model used by Pydantic.
Syntax
model_config = SettingsConfigDict(**options)Key points
- Use SettingsConfigDict to declare model configuration for a Pydantic settings class.
- The configuration is dictionary-shaped and provides named options rather than requiring a custom configuration class.
- Configuration can control how extra input is handled during model initialization.
- Strict validation can be enabled across a model through its configuration.
Common mistakes
- Use it as configuration data rather than as a runtime model or validation schema.
Example: Configure a settings model
from pydantic_settings import BaseSettings, SettingsConfigDict
class AppSettings(BaseSettings):
model_config = SettingsConfigDict(env_prefix="APP_", env_file=".env")
debug: bool = False
settings = AppSettings()The settings model reads configuration using the declared environment prefix and optional dotenv file.
When to use it
Load values from a dotenv file
Local development configuration
Declare validation and input-handling policy
Consistent model behavior
Interview questions
QWhat is SettingsConfigDict used for?
SettingsConfigDict is a typed configuration dictionary for settings models. It is assigned to model_config to control how configuration values are sourced and interpreted.
Follow-up: How would you make environment variables use a common prefix? Set env_prefix in SettingsConfigDict and name variables with that prefix.
QGive two examples of settings configuration options.
It lets a settings model declare options such as an environment-variable prefix and a dotenv file. The settings model then applies those options when it is instantiated.
Follow-up: What is the practical effect of env_file=".env"? Values from that dotenv file become one of the configured sources for settings loading.
QWhat kind of object underlies Pydantic configuration dictionaries?
The configuration is expressed as a dictionary-shaped object because the underlying configuration type is ConfigDict, documented as a TypedDict. This provides a structured set of named configuration fields rather than an untyped mapping.
Follow-up: Why prefer the documented configuration type over arbitrary keys? The typed fields communicate supported behavior and help catch misspelled or unsupported options.
Related
ConfigDictwith_configBaseSettings