Class swarmauri_standard.logger_handlers.MemoryLoggingHandler.MemoryLoggingHandler

swarmauri_standard.logger_handlers.MemoryLoggingHandler.MemoryLoggingHandler

MemoryLoggingHandler(**data)

Bases: HandlerBase

A handler that stores logging records in memory until a capacity is reached, then flushes to a target handler.

This handler buffers log records in memory and flushes them to a target handler when the buffer is full or when a record with a level greater than or equal to flushLevel is seen.

Initialize the MemoryLoggingHandler with the provided configuration.

PARAMETER	DESCRIPTION
**data	Configuration options for the handler. TYPE: Any DEFAULT: {}

Source code in swarmauri_standard/logger_handlers/MemoryLoggingHandler.py

def __init__(self, **data: Any):
    """
    Initialize the MemoryLoggingHandler with the provided configuration.

    Args:
        **data: Configuration options for the handler.
    """
    super().__init__(**data)
    self._memory_handler = None
    self._target_handler = None

type class-attribute instance-attribute

type = 'MemoryLoggingHandler'

capacity class-attribute instance-attribute

capacity = 100

flushLevel class-attribute instance-attribute

flushLevel = ERROR

target class-attribute instance-attribute

target = 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'

level class-attribute instance-attribute

level = INFO

formatter class-attribute instance-attribute

formatter = None

compile_handler

compile_handler()

Compiles a memory logging handler using the specified capacity, flushLevel, and target handler.

RETURNS	DESCRIPTION
Handler	logging.Handler: The configured memory handler.

RAISES	DESCRIPTION
ValueError	If no target handler is specified.

Source code in swarmauri_standard/logger_handlers/MemoryLoggingHandler.py

def compile_handler(self) -> logging.Handler:
    """
    Compiles a memory logging handler using the specified capacity,
    flushLevel, and target handler.

    Returns:
        logging.Handler: The configured memory handler.

    Raises:
        ValueError: If no target handler is specified.
    """
    if not self.target:
        raise ValueError(
            "MemoryLoggingHandler requires a target handler for flushing records"
        )

    # Resolve the target handler
    if isinstance(self.target, str):
        # This is a placeholder for resolving handler by name
        # In a real implementation, you would look up the handler by name
        raise ValueError(
            f"Target handler resolution by name '{self.target}' not implemented"
        )
    else:
        self._target_handler = self.target.compile_handler()

    # Create the memory handler with the target
    self._memory_handler = MemoryHandler(
        capacity=self.capacity,
        flushLevel=self.flushLevel,
        target=self._target_handler,
    )
    self._memory_handler.setLevel(self.level)

    # Set formatter if provided
    if self.formatter:
        if isinstance(self.formatter, str):
            self._memory_handler.setFormatter(logging.Formatter(self.formatter))
        else:
            self._memory_handler.setFormatter(self.formatter.compile_formatter())
    else:
        default_formatter = logging.Formatter(
            "[%(name)s][%(levelname)s] %(message)s"
        )
        self._memory_handler.setFormatter(default_formatter)

    return self._memory_handler

flush

flush()

Manually flush all buffered log records to the target handler.

Source code in swarmauri_standard/logger_handlers/MemoryLoggingHandler.py

def flush(self) -> None:
    """
    Manually flush all buffered log records to the target handler.
    """
    if self._memory_handler:
        self._memory_handler.flush()

close

close()

Close the memory handler and its target handler.

Source code in swarmauri_standard/logger_handlers/MemoryLoggingHandler.py

def close(self) -> None:
    """
    Close the memory handler and its target handler.
    """
    if self._memory_handler:
        self._memory_handler.close()
    if self._target_handler:
        self._target_handler.close()

setTarget

setTarget(target_handler)

Set a new target handler for this memory handler.

PARAMETER	DESCRIPTION
target_handler	The new target handler to use for flushing. TYPE: Handler

Source code in swarmauri_standard/logger_handlers/MemoryLoggingHandler.py

def setTarget(self, target_handler: logging.Handler) -> None:
    """
    Set a new target handler for this memory handler.

    Args:
        target_handler: The new target handler to use for flushing.
    """
    if self._memory_handler:
        self._memory_handler.setTarget(target_handler)
    self._target_handler = target_handler

to_dict

to_dict()

Convert the handler configuration to a dictionary.

RETURNS	DESCRIPTION
Dict[str, Any]	Dict[str, Any]: Dictionary representation of the handler.

Source code in swarmauri_standard/logger_handlers/MemoryLoggingHandler.py

def to_dict(self) -> Dict[str, Any]:
    """
    Convert the handler configuration to a dictionary.

    Returns:
        Dict[str, Any]: Dictionary representation of the handler.
    """
    # Start with base attributes
    result = {
        "type": self.type,
        "level": self.level,
        "formatter": str(self.formatter) if self.formatter else None,
    }

    # Add MemoryLoggingHandler specific attributes
    result.update(
        {
            "capacity": self.capacity,
            "flushLevel": self.flushLevel,
            "target": self.target.to_dict()
            if hasattr(self.target, "to_dict")
            else self.target,
        }
    )
    return result

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)