`OSSS.ai.config.logging_config`¶

`OSSS.ai.config.logging_config` ¶

Configuration mapper for supporting both flat chart format and nested Pydantic format.

This module handles the mapping between: 1. Flat format (chart workflows): custom_constraints: ["term1", "term2"] 2. Nested format (Pydantic): behavioral_config.custom_constraints: ["term1", "term2"]

Modernized to use Pydantic's native validation and transformation capabilities while maintaining backward compatibility with existing chart workflows.

`ConfigMapper` ¶

Maps between flat configuration format (used in chart workflows) and nested Pydantic format (used in our configuration classes).

`map_flat_to_nested(flat_config, agent_type)` `classmethod` ¶

Convert flat configuration format to nested Pydantic format.

Parameters:

Name	Type	Description	Default
`flat_config`	`Dict[str, Any]`	Configuration in flat format (e.g., from chart workflows)	required
`agent_type`	`str`	Type of agent ('refiner', 'critic', 'historian', 'synthesis')	required

Returns:

Type	Description
`Dict[str, Any]`	Configuration in nested format suitable for Pydantic validation

`create_agent_config(flat_config, agent_type)` `classmethod` ¶

Create a Pydantic agent configuration from flat configuration format.

Parameters:

Name	Type	Description	Default
`flat_config`	`Dict[str, Any]`	Configuration in flat format	required
`agent_type`	`str`	Type of agent ('refiner', 'critic', 'historian', 'synthesis')	required

Returns:

Type	Description
`AgentConfigType`	Appropriate Pydantic configuration object

Raises:

Type	Description
`ValueError`	If agent_type is not supported

`model_validate_config(config_data, agent_type)` `classmethod` ¶

model_validate_config(
    config_data: Optional[Dict[str, Any]],
    agent_type: Literal["refiner"],
) -> Optional[RefinerConfig]

model_validate_config(
    config_data: Optional[Dict[str, Any]],
    agent_type: Literal["critic"],
) -> Optional[CriticConfig]

model_validate_config(
    config_data: Optional[Dict[str, Any]],
    agent_type: Literal["historian"],
) -> Optional[HistorianConfig]

model_validate_config(
    config_data: Optional[Dict[str, Any]],
    agent_type: Literal["synthesis"],
) -> Optional[SynthesisConfig]

model_validate_config(
    config_data: Optional[Dict[str, Any]], agent_type: str
) -> Optional[AgentConfigType]

Validate configuration data using Pydantic's model_validate().

This is the modernized approach that leverages Pydantic's native validation and error handling capabilities.

Parameters:

Name	Type	Description	Default
`config_data`	`Optional[Dict[str, Any]]`	Configuration data in either format	required
`agent_type`	`str`	Type of agent	required

Returns:

Type	Description
`Optional[AgentConfigType]`	Pydantic configuration object or None if validation fails

`validate_and_create_config(config_data, agent_type)` `classmethod` ¶

validate_and_create_config(
    config_data: Dict[str, Any],
    agent_type: Literal["refiner"],
) -> Optional[RefinerConfig]

validate_and_create_config(
    config_data: Dict[str, Any],
    agent_type: Literal["critic"],
) -> Optional[CriticConfig]

validate_and_create_config(
    config_data: Dict[str, Any],
    agent_type: Literal["historian"],
) -> Optional[HistorianConfig]

validate_and_create_config(
    config_data: Dict[str, Any],
    agent_type: Literal["synthesis"],
) -> Optional[SynthesisConfig]

validate_and_create_config(
    config_data: Dict[str, Any], agent_type: str
) -> Optional[AgentConfigType]

Validate configuration data and create appropriate config object.

Maintained for backward compatibility. New code should use model_validate_config().

Parameters:

Name	Type	Description	Default
`config_data`	`Dict[str, Any]`	Configuration data in either format	required
`agent_type`	`str`	Type of agent	required

Returns:

Type	Description
`Optional[AgentConfigType]`	Pydantic configuration object or None if creation fails

OSSS.ai.config.logging_config¶