Class swarmauri_standard.logger_handlers.FileLoggingHandler.FileLoggingHandler

swarmauri_standard.logger_handlers.FileLoggingHandler.FileLoggingHandler

Bases: HandlerBase

A handler for logging messages to a file on disk.

This handler writes logging records to a specified file path using Python's built-in FileHandler, managing file operations including opening, writing, and closing the file.

ATTRIBUTE	DESCRIPTION
type	The type identifier for this handler. TYPE: Literal['FileLoggingHandler']
level	The logging level for this handler. TYPE: int
formatter	Optional formatter for log messages. TYPE: Optional[Union[str, FullUnion[FormatterBase]]]
file_path	Path to the log file. TYPE: str
file_mode	Mode for opening the file ('a' for append, 'w' for write). TYPE: str
encoding	Character encoding for the file. TYPE: str
max_bytes	Maximum file size before rotation (0 means no rotation). TYPE: int
backup_count	Number of backup files to keep when rotating. TYPE: int

type class-attribute instance-attribute

type = 'FileLoggingHandler'

level class-attribute instance-attribute

level = INFO

formatter class-attribute instance-attribute

formatter = None

file_path class-attribute instance-attribute

file_path = 'logs/app.log'

file_mode class-attribute instance-attribute

file_mode = 'a'

encoding class-attribute instance-attribute

encoding = 'utf-8'

max_bytes class-attribute instance-attribute

max_bytes = 0

backup_count class-attribute instance-attribute

backup_count = 0

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 file logging handler using the specified parameters.

Creates a FileHandler or RotatingFileHandler based on configuration, sets the appropriate logging level and formatter.

RETURNS	DESCRIPTION
Handler	logging.Handler: Configured file handler for logging.

RAISES	DESCRIPTION
IOError	If the file path is invalid or inaccessible.

Source code in swarmauri_standard/logger_handlers/FileLoggingHandler.py

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

    Creates a FileHandler or RotatingFileHandler based on configuration,
    sets the appropriate logging level and formatter.

    Returns:
        logging.Handler: Configured file handler for logging.

    Raises:
        IOError: If the file path is invalid or inaccessible.
    """
    # Ensure the directory exists
    log_dir = os.path.dirname(self.file_path)
    if log_dir and not os.path.exists(log_dir):
        os.makedirs(log_dir)
    # Create the appropriate file handler based on rotation settings
    if self.max_bytes > 0:
        # Create a rotating file handler
        handler = RotatingFileHandler(
            filename=self.file_path,
            mode=self.file_mode,
            maxBytes=self.max_bytes,
            backupCount=self.backup_count,
            encoding=self.encoding,
        )
    else:
        # Create a standard file handler
        handler = logging.FileHandler(
            filename=self.file_path, mode=self.file_mode, encoding=self.encoding
        )

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

    # Configure the formatter
    if self.formatter:
        if isinstance(self.formatter, str):
            handler.setFormatter(logging.Formatter(self.formatter))
        else:
            handler.setFormatter(self.formatter.compile_formatter())
    else:
        # Apply default formatter if none is specified
        default_formatter = logging.Formatter(
            "[%(asctime)s][%(name)s][%(levelname)s] %(message)s",
            "%Y-%m-%d %H:%M:%S",
        )
        handler.setFormatter(default_formatter)

    return handler

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)