Class swarmauri_base.programs.ProgramBase.ProgramBase

swarmauri_base.programs.ProgramBase.ProgramBase

Bases: IProgram, ComponentBase

Abstract base class for program representation with serialization, diffing, and validation.

This class provides reusable logic for program representation, implementing the core functionality required by the IProgram interface including serialization, diffing, validation, and diff application workflows.

ATTRIBUTE	DESCRIPTION
_program_type	Class variable to store the program type identifier TYPE: str

resource class-attribute instance-attribute

resource = PROGRAM.value

type class-attribute instance-attribute

type = 'ComponentBase'

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="allow", 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

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

name class-attribute instance-attribute

name = None

version class-attribute instance-attribute

version = '0.1.0'

clone

clone()

Create a deep copy of this program.

RETURNS	DESCRIPTION
IProgram	A new program instance with the same state

Source code in swarmauri_base/programs/ProgramBase.py

def clone(self) -> "IProgram":
    """
    Create a deep copy of this program.

    Returns:
        A new program instance with the same state
    """
    return copy.deepcopy(self)

diff

diff(other)

Calculate the difference between this program and another.

PARAMETER	DESCRIPTION
other	Another program to compare against TYPE: IProgram

RETURNS	DESCRIPTION
DiffType	A structured representation of the differences between programs

RAISES	DESCRIPTION
TypeError	If the other program is not compatible for diffing

Source code in swarmauri_base/programs/ProgramBase.py

def diff(self, other: "IProgram") -> DiffType:
    """
    Calculate the difference between this program and another.

    Args:
        other: Another program to compare against

    Returns:
        A structured representation of the differences between programs

    Raises:
        TypeError: If the other program is not compatible for diffing
    """
    if not isinstance(other, IProgram):
        raise TypeError(f"Expected IProgram instance, got {type(other)}")

    # Log the diff operation for auditability
    logger.info(f"Computing diff between {self._program_type} programs")

    # This is a base implementation that should be overridden by subclasses
    # for more specific diff logic
    self_dict = self.model_dump()
    other_dict = other.model_dump()

    diff = {}

    # Identify removed keys
    for key in self_dict:
        if key not in other_dict:
            diff[key] = {"action": "remove"}

    # Identify added or modified keys
    for key, value in other_dict.items():
        if key not in self_dict:
            diff[key] = {"action": "add", "value": value}
        elif self_dict[key] != value:
            diff[key] = {"action": "modify", "value": value}

    logger.debug(f"Diff result: {diff}")
    return diff

apply_diff

apply_diff(diff)

Apply a diff to this program to create a new modified program.

PARAMETER	DESCRIPTION
diff	The diff structure to apply to this program TYPE: DiffType

RETURNS	DESCRIPTION
IProgram	A new program instance with the diff applied

RAISES	DESCRIPTION
ValueError	If the diff cannot be applied to this program
TypeError	If the diff is not in the expected format

Source code in swarmauri_base/programs/ProgramBase.py

def apply_diff(self, diff: DiffType) -> "IProgram":
    """
    Apply a diff to this program to create a new modified program.

    Args:
        diff: The diff structure to apply to this program

    Returns:
        A new program instance with the diff applied

    Raises:
        ValueError: If the diff cannot be applied to this program
        TypeError: If the diff is not in the expected format
    """
    if not isinstance(diff, dict):
        raise TypeError(f"Expected dict for diff, got {type(diff)}")

    logger.info(f"Applying diff to {self._program_type} program")

    # Create a new program by cloning the current one
    new_program = self.clone()
    new_dict = new_program.model_dump()

    try:
        # Apply each diff operation
        for key, operation in diff.items():
            if not isinstance(operation, dict) or "action" not in operation:
                raise ValueError(f"Invalid diff operation for key {key}")

            action = operation["action"]

            if action == "remove":
                if key in new_dict:
                    del new_dict[key]
            elif action == "add" or action == "modify":
                if "value" not in operation:
                    raise ValueError(
                        f"Missing 'value' for {action} operation on key {key}"
                    )
                new_dict[key] = operation["value"]
            else:
                raise ValueError(f"Unknown diff action '{action}' for key {key}")

        # Create a new program instance from the modified dictionary
        result = self.__class__.from_dict(new_dict)
        logger.debug("Diff applied successfully")
        return result

    except Exception as e:
        logger.error(f"Error applying diff: {e}")
        raise

validate

validate()

Validate that this program is well-formed and executable.

This base implementation always returns True. Subclasses should override this method to implement specific validation logic.

RETURNS	DESCRIPTION
bool	True if the program is valid, False otherwise

Source code in swarmauri_base/programs/ProgramBase.py

def validate(self) -> bool:
    """
    Validate that this program is well-formed and executable.

    This base implementation always returns True. Subclasses should override
    this method to implement specific validation logic.

    Returns:
        True if the program is valid, False otherwise
    """
    logger.info(f"Validating {self._program_type} program")
    # Base implementation doesn't implement specific validation
    # Subclasses should override this with actual validation logic
    return True

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_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML 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 TOML
    return toml.dumps(filtered_data)

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)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger