Class swarmauri_standard.logger_formatters.KeyValueFormatter.KeyValueFormatter

swarmauri_standard.logger_formatters.KeyValueFormatter.KeyValueFormatter

Bases: FormatterBase

A formatter that renders log record attributes as key=value pairs.

This formatter outputs log messages in a structured format where each attribute is represented as a key-value pair, making it easier to parse logs programmatically.

ATTRIBUTE	DESCRIPTION
key_value_separator	Character used to separate keys from values TYPE: str
pair_delimiter	Character used to separate key-value pairs TYPE: str
include_extra	Whether to include extra attributes from the LogRecord TYPE: bool
fields	List of fields to include in the output, in the desired order TYPE: List[str]
format	The format string (constructed dynamically) TYPE: str
date_format	Format for timestamps TYPE: str

key_value_separator class-attribute instance-attribute

key_value_separator = '='

pair_delimiter class-attribute instance-attribute

pair_delimiter = ' '

include_extra class-attribute instance-attribute

include_extra = False

fields class-attribute instance-attribute

fields = ['levelname', 'name', 'message']

format class-attribute instance-attribute

format = '%(message)s'

date_format class-attribute instance-attribute

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

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 the model is created.

This method constructs the format string based on the configured fields and other parameters.

Source code in swarmauri_standard/logger_formatters/KeyValueFormatter.py

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

    This method constructs the format string based on the configured fields
    and other parameters.
    """
    # The base format is constructed dynamically in compile_formatter
    # We just ensure format exists for compatibility with base class
    self.format = "%(message)s"

compile_formatter

compile_formatter()

Create and return a custom logging.Formatter that formats records as key-value pairs.

RETURNS	DESCRIPTION
Formatter	A logging Formatter instance that uses our custom format method

Source code in swarmauri_standard/logger_formatters/KeyValueFormatter.py

def compile_formatter(self) -> logging.Formatter:
    """
    Create and return a custom logging.Formatter that formats records as key-value pairs.

    Returns:
        A logging Formatter instance that uses our custom format method
    """
    # Create a custom formatter that uses our format_record method
    formatter = logging.Formatter(self.format, self.date_format)
    formatter.format = self.format_record
    return formatter

format_record

format_record(record)

Format a log record as key-value pairs.

PARAMETER	DESCRIPTION
record	The LogRecord to format TYPE: LogRecord

RETURNS	DESCRIPTION
str	A formatted string with key-value pairs

Source code in swarmauri_standard/logger_formatters/KeyValueFormatter.py

def format_record(self, record: LogRecord) -> str:
    """
    Format a log record as key-value pairs.

    Args:
        record: The LogRecord to format

    Returns:
        A formatted string with key-value pairs
    """
    # Dictionary to store the key-value pairs
    output_parts = []

    # Process the standard fields in the specified order
    for field in self.fields:
        if field == "message":
            # Special handling for message to ensure it's formatted
            value = record.getMessage()
        elif hasattr(record, field):
            value = getattr(record, field)
        else:
            continue

        # Add the key-value pair to our output
        output_parts.append(f"{field}{self.key_value_separator}{value}")

    # Include any extra attributes if specified
    if self.include_extra and hasattr(record, "extra"):
        extra = record.extra
        if isinstance(extra, dict):
            for key, value in extra.items():
                if key not in self.fields:
                    output_parts.append(f"{key}{self.key_value_separator}{value}")

    # Include any non-standard attributes directly attached to the LogRecord
    if self.include_extra:
        for key, value in record.__dict__.items():
            # Skip standard attributes and internal ones (starting with _)
            if (
                key not in self.fields
                and not key.startswith("_")
                and key
                not in (
                    "args",
                    "exc_info",
                    "exc_text",
                    "stack_info",
                    "lineno",
                    "funcName",
                    "created",
                    "msecs",
                    "relativeCreated",
                    "levelno",
                    "msg",
                )
            ):
                output_parts.append(f"{key}{self.key_value_separator}{value}")

    # Join all parts with the specified delimiter
    return self.pair_delimiter.join(output_parts)

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)