Class swarmauri_standard.logger_handlers.TimedRotatingFileLoggingHandler.TimedRotatingFileLoggingHandler

swarmauri_standard.logger_handlers.TimedRotatingFileLoggingHandler.TimedRotatingFileLoggingHandler

Bases: HandlerBase

A handler that extends FileHandler to rollover log files based on time intervals.

This handler uses TimedRotatingFileHandler from the standard logging module to rotate log files at specified time intervals, such as daily at midnight.

Attributes

type : Literal["TimedRotatingFileLoggingHandler"] The type identifier for this handler filename : str The path to the log file when : str The type of interval - 'S' (seconds), 'M' (minutes), 'H' (hours), 'D' (days), 'W0'-'W6' (weekday, 0=Monday), 'midnight' interval : int The interval count (e.g., 1 for once per day with when='D') backupCount : int The number of backup files to keep encoding : Optional[str] The encoding to use for the log file delay : bool If True, the file opening is deferred until the first log record is emitted utc : bool If True, times in UTC will be used; otherwise local time is used atTime : Optional[datetime] The time of day to rotate (only relevant for 'midnight' or 'W' whens) level : int The logging level for this handler formatter : Optional[Union[str, FullUnion[FormatterBase]]] The formatter to use for log messages

type class-attribute instance-attribute

type = 'TimedRotatingFileLoggingHandler'

filename instance-attribute

filename

when class-attribute instance-attribute

when = 'midnight'

interval class-attribute instance-attribute

interval = 1

backupCount class-attribute instance-attribute

backupCount = 7

encoding class-attribute instance-attribute

encoding = None

delay class-attribute instance-attribute

delay = False

utc class-attribute instance-attribute

utc = False

atTime class-attribute instance-attribute

atTime = None

level class-attribute instance-attribute

level = INFO

formatter class-attribute instance-attribute

formatter = None

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_handler

compile_handler()

Compiles a timed rotating file handler using the specified parameters.

This method creates a TimedRotatingFileHandler with the configured rotation settings, log level, and formatter.

Returns

logging.Handler The configured TimedRotatingFileHandler instance

Source code in swarmauri_standard/logger_handlers/TimedRotatingFileLoggingHandler.py

def compile_handler(self) -> logging.Handler:
    """
    Compiles a timed rotating file handler using the specified parameters.

    This method creates a TimedRotatingFileHandler with the configured
    rotation settings, log level, and formatter.

    Returns
    -------
    logging.Handler
        The configured TimedRotatingFileHandler instance
    """
    # Create the timed rotating file handler with the specified parameters
    handler = TimedRotatingFileHandler(
        filename=self.filename,
        when=self.when,
        interval=self.interval,
        backupCount=self.backupCount,
        encoding=self.encoding,
        delay=self.delay,
        utc=self.utc,
        atTime=self.atTime,
    )

    # Set the log level
    handler.setLevel(self.level)

    # Apply formatter if specified, otherwise use a default formatter
    if self.formatter:
        if isinstance(self.formatter, str):
            # If formatter is a string, create a Formatter with the string as format
            handler.setFormatter(logging.Formatter(self.formatter))
        else:
            # If formatter is a FormatterBase instance, compile it
            handler.setFormatter(self.formatter.compile_formatter())
    else:
        # Use default formatter if none specified
        default_formatter = logging.Formatter(
            "[%(asctime)s][%(name)s][%(levelname)s] %(message)s"
        )
        handler.setFormatter(default_formatter)

    return handler

get_handler_config

get_handler_config()

Returns the configuration of this handler as a dictionary.

This method is useful for serialization or debugging purposes.

Returns

dict[str, Any] A dictionary containing the handler's configuration

Source code in swarmauri_standard/logger_handlers/TimedRotatingFileLoggingHandler.py

def get_handler_config(self) -> dict[str, Any]:
    """
    Returns the configuration of this handler as a dictionary.

    This method is useful for serialization or debugging purposes.

    Returns
    -------
    dict[str, Any]
        A dictionary containing the handler's configuration
    """
    return {
        "type": self.type,
        "filename": self.filename,
        "when": self.when,
        "interval": self.interval,
        "backupCount": self.backupCount,
        "encoding": self.encoding,
        "delay": self.delay,
        "utc": self.utc,
        "atTime": self.atTime,
        "level": self.level,
        "formatter": self.formatter,
    }

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)