Class swarmauri_standard.tools.CodeExtractorTool.CodeExtractorTool

swarmauri_standard.tools.CodeExtractorTool.CodeExtractorTool

Bases: ToolBase

version class-attribute instance-attribute

version = '1.0.0'

parameters class-attribute instance-attribute

parameters = Field(
    default_factory=lambda: [
        Parameter(
            name="file_name",
            input_type="string",
            description="The name of the Python file to extract code from.",
            required=True,
        ),
        Parameter(
            name="extract_documentation",
            input_type="bool",
            description="Whether to start extracting code from the documentation string.",
            required=False,
            default=True,
        ),
        Parameter(
            name="to_be_ignored",
            input_type="list",
            description="A list of function or variable names to be ignored during code extraction.",
            required=False,
            default=[],
        ),
    ]
)

name class-attribute instance-attribute

name = 'CodeExtractorTool'

description class-attribute instance-attribute

description = 'Extracts code from a Python file.'

type class-attribute instance-attribute

type = 'CodeExtractorTool'

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

resource class-attribute instance-attribute

resource = Field(default=TOOL.value)

extract_code

extract_code(
    file_name, extract_documentation=True, to_be_ignored=[]
)

Extracts code from a Python file.

PARAMETER	DESCRIPTION
file_name	The name of the Python file to extract code from. TYPE: str
extract_documentation	Whether to start extracting code from the documentation string. TYPE: bool DEFAULT: True
to_be_ignored	A list of function or variable names to be ignored during code extraction. TYPE: List[str] DEFAULT: []

RETURNS	DESCRIPTION
str	Extracted code. TYPE: str

Source code in swarmauri_standard/tools/CodeExtractorTool.py

def extract_code(
    self,
    file_name: str,
    extract_documentation: bool = True,
    to_be_ignored: List[str] = [],
) -> str:
    """
    Extracts code from a Python file.

    Args:
        file_name (str): The name of the Python file to extract code from.
        extract_documentation (bool): Whether to start extracting code from the documentation string.
        to_be_ignored (List[str]): A list of function or variable names to be ignored during code extraction.

    Returns:
        str: Extracted code.
    """
    response_lines = []

    # Read the current file and collect relevant lines
    with open(file_name, "r", encoding="utf-8") as f:
        documentation_start = False
        first = not extract_documentation

        for line in f:
            stripped_line = line.strip()

            # Check if the line starts or ends the documentation string
            if first and '"""' in stripped_line:
                documentation_start = not documentation_start
                first = False
                continue

            if documentation_start and '"""' in stripped_line:
                documentation_start = not documentation_start
                continue

            if documentation_start and not extract_documentation:
                continue

            # Stop collecting lines when reaching the specified comment
            if "#" in stripped_line and "non-essentials" in stripped_line:
                break

            # Collect the line
            response_lines.append(line)

    response = "".join(response_lines)

    # Parse the response with AST
    tree = ast.parse(response)

    # Filter out nodes based on the `to_be_ignored` set
    class CodeCleaner(ast.NodeTransformer):
        def visit_FunctionDef(self, node):
            if any(pattern in node.name for pattern in to_be_ignored):
                return None
            return node

        def visit_Assign(self, node):
            if any(
                isinstance(target, ast.Name)
                and any(pattern in target.id for pattern in to_be_ignored)
                for target in node.targets
            ):
                return None
            return node

    # Transform the AST to remove ignored nodes
    cleaned_tree = CodeCleaner().visit(tree)

    # Convert the cleaned AST back to source code
    cleaned_code = ast.unparse(cleaned_tree)

    # Return the cleaned code
    return cleaned_code

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

call

call(*args, **kwargs)

File: ToolBase.py Class: ToolBase Method: call

Alias for call to conform to ITool interface.

Source code in swarmauri_base/tools/ToolBase.py

def call(self, *args, **kwargs) -> Any:
    """
    File: ToolBase.py
    Class: ToolBase
    Method: call

    Alias for __call__ to conform to ITool interface.
    """
    return self.__call__(*args, **kwargs)

batch

batch(inputs, *args, **kwargs)

File: ToolBase.py Class: ToolBase Method: batch

Default batch implementation: calls call on each item in inputs. Subclasses can override for optimized bulk behavior.

Source code in swarmauri_base/tools/ToolBase.py

def batch(self, inputs: List[Any], *args, **kwargs) -> List[Any]:
    """
    File: ToolBase.py
    Class: ToolBase
    Method: batch

    Default batch implementation: calls __call__ on each item in `inputs`.
    Subclasses can override for optimized bulk behavior.
    """
    results: List[Any] = []
    for inp in inputs:
        results.append(self.__call__(inp, **kwargs))
    return results