Class swarmauri_standard.logger_formatters.ContextualFormatter.ContextualFormatter

swarmauri_standard.logger_formatters.ContextualFormatter.ContextualFormatter

Bases: FormatterBase

A formatter that adds contextual information to log messages.

This formatter enhances log messages by including contextual information such as request IDs, user IDs, or any other attributes attached to the LogRecord object.

format class-attribute instance-attribute

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

date_format class-attribute instance-attribute

date_format = '%Y-%m-%d %H:%M:%S'

context_keys class-attribute instance-attribute

context_keys = ['request_id', 'user_id', 'correlation_id']

custom_context_keys class-attribute instance-attribute

custom_context_keys = []

context_as_prefix class-attribute instance-attribute

context_as_prefix = False

context_separator class-attribute instance-attribute

context_separator = ' '

context_prefix class-attribute instance-attribute

context_prefix = '['

context_suffix class-attribute instance-attribute

context_suffix = ']'

context_item_format class-attribute instance-attribute

context_item_format = '{key}={value}'

include_empty_context class-attribute instance-attribute

include_empty_context = False

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'

model_post_init

model_post_init(*args, **kwargs)

Initialize the formatter after model creation.

Combines the base format with context formatting logic.

Source code in swarmauri_standard/logger_formatters/ContextualFormatter.py

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

    Combines the base format with context formatting logic.
    """
    # We'll modify the format string in compile_formatter
    super().model_post_init(*args, **kwargs)

compile_formatter

compile_formatter()

Create and return a logging.Formatter with context handling.

RETURNS	DESCRIPTION
Formatter	logging.Formatter: A formatter that will include context information.

Source code in swarmauri_standard/logger_formatters/ContextualFormatter.py

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

    Returns:
        logging.Formatter: A formatter that will include context information.
    """
    # Create a custom formatter that processes context
    return ContextualFormatterImpl(
        fmt=self.format,
        datefmt=self.date_format,
        context_keys=self.context_keys + self.custom_context_keys,
        context_as_prefix=self.context_as_prefix,
        context_separator=self.context_separator,
        context_prefix=self.context_prefix,
        context_suffix=self.context_suffix,
        context_item_format=self.context_item_format,
        include_empty_context=self.include_empty_context,
    )

add_context_key

add_context_key(key)

Add a new context key to be included in log messages.

PARAMETER	DESCRIPTION
key	The name of the context key to add TYPE: str

Source code in swarmauri_standard/logger_formatters/ContextualFormatter.py

def add_context_key(self, key: str) -> None:
    """
    Add a new context key to be included in log messages.

    Args:
        key: The name of the context key to add
    """
    if key not in self.context_keys and key not in self.custom_context_keys:
        self.custom_context_keys.append(key)

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)