Class swarmauri_standard.logger_formatters.IndentedFormatter.IndentedFormatter

swarmauri_standard.logger_formatters.IndentedFormatter.IndentedFormatter

Bases: FormatterBase

A formatter that adds indentation to each line of a log message.

This formatter preserves the original message formatting while adding indentation to improve readability of multi-line log messages.

ATTRIBUTE	DESCRIPTION
indent_width	Number of spaces to use for indentation. TYPE: int
indent_first_line	Whether to indent the first line of the message. TYPE: bool
format	The log message format string. TYPE: str
date_format	The date format string for timestamps. TYPE: Optional[str]

indent_width class-attribute instance-attribute

indent_width = 4

indent_first_line class-attribute instance-attribute

indent_first_line = False

format class-attribute instance-attribute

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

date_format class-attribute instance-attribute

date_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'

compile_formatter

compile_formatter()

Create and return a logging.Formatter with indentation functionality.

RETURNS	DESCRIPTION
Formatter	A custom logging.Formatter that adds indentation to log messages.

Source code in swarmauri_standard/logger_formatters/IndentedFormatter.py

def compile_formatter(self) -> logging.Formatter:
    """
    Create and return a logging.Formatter with indentation functionality.

    Returns:
        A custom logging.Formatter that adds indentation to log messages.
    """
    # Create a custom formatter that inherits from logging.Formatter
    parent_formatter = super().compile_formatter()

    # Create a wrapper class to modify the formatting behavior
    class IndentedLoggingFormatter(logging.Formatter):
        def __init__(self, parent_fmt, indent_width, indent_first_line):
            self.parent_formatter = parent_fmt
            self.indent_width = indent_width
            self.indent_first_line = indent_first_line
            # Initialize with the same format as the parent
            super().__init__(fmt=parent_fmt._fmt, datefmt=parent_fmt.datefmt)

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

            # Split the message into lines
            lines = formatted_msg.splitlines()

            # Create the indentation string
            indent = " " * self.indent_width

            # Apply indentation to lines
            if len(lines) > 0:
                # Process first line
                if self.indent_first_line:
                    lines[0] = indent + lines[0]

                # Process subsequent lines
                for i in range(1, len(lines)):
                    lines[i] = indent + lines[i]

            # Join the lines back together
            return "\n".join(lines)

    # Return the custom formatter instance
    return IndentedLoggingFormatter(
        parent_formatter, self.indent_width, self.indent_first_line
    )

model_post_init

model_post_init(*args, **kwargs)

Post-initialization processing.

Validates that indent_width is a positive integer.

PARAMETER	DESCRIPTION
*args	Variable length argument list. DEFAULT: ()
**kwargs	Arbitrary keyword arguments. DEFAULT: {}

RAISES	DESCRIPTION
ValueError	If indent_width is not a positive integer.

Source code in swarmauri_standard/logger_formatters/IndentedFormatter.py

def model_post_init(self, *args, **kwargs):
    """
    Post-initialization processing.

    Validates that indent_width is a positive integer.

    Args:
        *args: Variable length argument list.
        **kwargs: Arbitrary keyword arguments.

    Raises:
        ValueError: If indent_width is not a positive integer.
    """
    # Validate indent_width
    if self.indent_width < 0:
        raise ValueError("indent_width must be a positive integer")

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)