Class swarmauri_standard.toolkits.AccessibilityToolkit.AccessibilityToolkit

swarmauri_standard.toolkits.AccessibilityToolkit.AccessibilityToolkit

Bases: ToolkitBase

type class-attribute instance-attribute

type = 'AccessibilityToolkit'

tools class-attribute instance-attribute

tools = {
    "AutomatedReadabilityIndexTool": AutomatedReadabilityIndexTool(),
    "ColemanLiauIndexTool": ColemanLiauIndexTool(),
    "FleschKincaidTool": FleschKincaidTool(),
    "FleschReadingEaseTool": FleschReadingEaseTool(),
    "GunningFogTool": GunningFogTool(),
}

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="forbid", 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

resource class-attribute instance-attribute

resource = Field(default=TOOLKIT.value, frozen=True)

version class-attribute instance-attribute

version = '0.1.0'

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

add_tools

add_tools(tools)

Add multiple tools to the toolkit.

PARAMETER	DESCRIPTION
tools	A dictionary of tool objects keyed by their names. TYPE: Dict[str, Tool]

Source code in swarmauri_base/toolkits/ToolkitBase.py

def add_tools(self, tools: Dict[str, SubclassUnion[ToolBase]]) -> None:
    """
    Add multiple tools to the toolkit.

    Parameters:
        tools (Dict[str, Tool]): A dictionary of tool objects keyed by their names.
    """
    self.tools.update(tools)

add_tool

add_tool(tool)

Add a single tool to the toolkit.

PARAMETER	DESCRIPTION
tool	The tool instance to be added to the toolkit. TYPE: Tool

Source code in swarmauri_base/toolkits/ToolkitBase.py

def add_tool(self, tool: SubclassUnion[ToolBase]) -> None:
    """
    Add a single tool to the toolkit.

    Parameters:
        tool (Tool): The tool instance to be added to the toolkit.
    """
    self.tools[tool.name] = tool

remove_tool

remove_tool(tool_name)

Remove a tool from the toolkit by name.

PARAMETER	DESCRIPTION
tool_name	The name of the tool to be removed from the toolkit. TYPE: str

Source code in swarmauri_base/toolkits/ToolkitBase.py

def remove_tool(self, tool_name: str) -> None:
    """
    Remove a tool from the toolkit by name.

    Parameters:
        tool_name (str): The name of the tool to be removed from the toolkit.
    """
    if tool_name in self.tools:
        del self.tools[tool_name]
    else:
        raise ValueError(f"Tool '{tool_name}' not found in the toolkit.")

get_tool_by_name

get_tool_by_name(tool_name)

Get a tool from the toolkit by name.

PARAMETER	DESCRIPTION
tool_name	The name of the tool to retrieve. TYPE: str

RETURNS	DESCRIPTION
Tool	The tool instance with the specified name. TYPE: SubclassUnion[ToolBase]

Source code in swarmauri_base/toolkits/ToolkitBase.py

def get_tool_by_name(self, tool_name: str) -> SubclassUnion[ToolBase]:
    """
    Get a tool from the toolkit by name.

    Parameters:
        tool_name (str): The name of the tool to retrieve.

    Returns:
        Tool: The tool instance with the specified name.
    """
    if tool_name in self.tools:
        return self.tools[tool_name]
    else:
        raise ValueError(f"Tool '{tool_name}' not found in the toolkit.")

get_tools

get_tools(
    include=None,
    exclude=None,
    by_alias=False,
    exclude_unset=False,
    exclude_defaults=False,
    exclude_none=False,
)

List all tools in the toolkit with options to include or exclude specific fields.

PARAMETER	DESCRIPTION
include	Fields to include in the returned dictionary. TYPE: List[str] DEFAULT: None
exclude	Fields to exclude from the returned dictionary. TYPE: List[str] DEFAULT: None

RETURNS	DESCRIPTION
Dict[str, SubclassUnion[ToolBase]]	Dict[str, SubclassUnion[ToolBase]]: A dictionary of tools with specified fields included or excluded.

Source code in swarmauri_base/toolkits/ToolkitBase.py

def get_tools(
    self,
    include: Optional[List[str]] = None,
    exclude: Optional[List[str]] = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
) -> Dict[str, SubclassUnion[ToolBase]]:
    """
    List all tools in the toolkit with options to include or exclude specific fields.

    Parameters:
        include (List[str], optional): Fields to include in the returned dictionary.
        exclude (List[str], optional): Fields to exclude from the returned dictionary.

    Returns:
        Dict[str, SubclassUnion[ToolBase]]: A dictionary of tools with specified fields included or excluded.
    """
    return [
        tool.model_dump(
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
        )
        for name, tool in self.tools.items()
    ]