Class swarmauri_standard.logger_formatters.BorderFormatter.BorderFormatter

swarmauri_standard.logger_formatters.BorderFormatter.BorderFormatter

Bases: FormatterBase

A formatter that surrounds log messages with borders for visual emphasis.

This formatter adds configurable border lines above and below messages, with options for border character, width, and padding.

border_char class-attribute instance-attribute

border_char = '-'

border_width class-attribute instance-attribute

border_width = 80

padding class-attribute instance-attribute

padding = 1

message_format class-attribute instance-attribute

message_format = None

type class-attribute instance-attribute

type = 'ObserveBase'

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

name class-attribute instance-attribute

name = None

version class-attribute instance-attribute

version = '0.1.0'

format class-attribute instance-attribute

format = '[%(name)s][%(levelname)s] %(message)s'

date_format class-attribute instance-attribute

date_format = None

model_post_init

model_post_init(*args, **kwargs)

Initialize the formatter after model creation.

This method is called after the model is created and sets up the formatting string with the border configuration.

Source code in swarmauri_standard/logger_formatters/BorderFormatter.py

def model_post_init(self, *args, **kwargs):
    """
    Initialize the formatter after model creation.

    This method is called after the model is created and sets up the
    formatting string with the border configuration.
    """
    super().model_post_init(*args, **kwargs)
    # Keep the original format to be used inside the border
    self.message_format = self.format

compile_formatter

compile_formatter()

Create and return a custom logging.Formatter with border decoration.

RETURNS	DESCRIPTION
Formatter	logging.Formatter: A formatter that adds borders to log messages

Source code in swarmauri_standard/logger_formatters/BorderFormatter.py

def compile_formatter(self) -> logging.Formatter:
    """
    Create and return a custom logging.Formatter with border decoration.

    Returns:
        logging.Formatter: A formatter that adds borders to log messages
    """
    original_formatter = logging.Formatter(self.message_format, self.date_format)

    # Create a custom formatter that wraps the original formatter's output with borders
    class BorderedFormatter(logging.Formatter):
        def __init__(self, parent_formatter, border_formatter):
            super().__init__()
            self.parent_formatter = parent_formatter
            self.border_formatter = border_formatter

        def format(self, record):
            # Get the formatted message from the original formatter
            formatted_message = self.parent_formatter.format(record)

            # Apply the border to the formatted message
            return self.border_formatter._apply_border(formatted_message)

    return BorderedFormatter(original_formatter, self)

set_border_style

set_border_style(char='-', width=80, padding=1)

Update the border style configuration.

PARAMETER	DESCRIPTION
char	The character to use for the border TYPE: str DEFAULT: '-'
width	The width of the border TYPE: int DEFAULT: 80
padding	Number of empty lines between border and message TYPE: int DEFAULT: 1

Source code in swarmauri_standard/logger_formatters/BorderFormatter.py

def set_border_style(
    self, char: str = "-", width: int = 80, padding: int = 1
) -> None:
    """
    Update the border style configuration.

    Args:
        char: The character to use for the border
        width: The width of the border
        padding: Number of empty lines between border and message
    """
    self.border_char = char
    self.border_width = width
    self.padding = padding

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER	DESCRIPTION
resource_type	The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)