Class swarmauri_tool_jupyterexecutenotebook.JupyterExecuteNotebookTool.JupyterExecuteNotebookTool

swarmauri_tool_jupyterexecutenotebook.JupyterExecuteNotebookTool.JupyterExecuteNotebookTool

Bases: ToolBase

JupyterExecuteNotebookTool is a tool that executes a Jupyter notebook by running all cells sequentially. It captures and logs the outputs or errors produced during the execution and returns the executed notebook object.

ATTRIBUTE	DESCRIPTION
version	The version of the JupyterExecuteNotebookTool. TYPE: str
parameters	A list of parameters required to execute the notebook. TYPE: List[Parameter]
name	The name of the tool. TYPE: str
description	A brief description of the tool's functionality. TYPE: str
type	The type identifier for the tool. TYPE: Literal['JupyterExecuteNotebookTool']

version class-attribute instance-attribute

version = '1.0.0'

parameters class-attribute instance-attribute

parameters = Field(
    default_factory=lambda: [
        Parameter(
            name="notebook_path",
            input_type="string",
            description="The path to the Jupyter notebook to be executed.",
            required=True,
        ),
        Parameter(
            name="timeout",
            input_type="number",
            description="Maximum time (in seconds) for each cell to execute. Defaults to 30.",
            required=False,
        ),
    ]
)

name class-attribute instance-attribute

name = 'JupyterExecuteNotebookTool'

description class-attribute instance-attribute

description = (
    "Executes a Jupyter notebook and captures outputs."
)

type class-attribute instance-attribute

type = 'JupyterExecuteNotebookTool'

NotebookClient class-attribute

NotebookClient = NotebookClient

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)

execute_notebook

execute_notebook(notebook_path, timeout=30)

Executes the given Jupyter notebook by running all cells sequentially. Captures all outputs and errors, updating the NotebookNode object with the results.

PARAMETER	DESCRIPTION
notebook_path	The file path to the Jupyter notebook to execute. TYPE: str
timeout	The maximum time (in seconds) allowed for each cell to execute. Defaults to 30. TYPE: int DEFAULT: 30

RETURNS	DESCRIPTION
NotebookNode	The notebook object after execution, containing updated outputs. If cell execution fails, the error is recorded in the notebook outputs. TYPE: NotebookNode

Source code in swarmauri_tool_jupyterexecutenotebook/JupyterExecuteNotebookTool.py

def execute_notebook(self, notebook_path: str, timeout: int = 30) -> NotebookNode:
    """
    Executes the given Jupyter notebook by running all cells sequentially. Captures
    all outputs and errors, updating the NotebookNode object with the results.

    Args:
        notebook_path (str): The file path to the Jupyter notebook to execute.
        timeout (int, optional): The maximum time (in seconds) allowed for each
                                 cell to execute. Defaults to 30.

    Returns:
        NotebookNode: The notebook object after execution, containing updated
                      outputs. If cell execution fails, the error is recorded
                      in the notebook outputs.
    """
    logger.info("Starting notebook execution with JupyterExecuteNotebookTool.")
    logger.debug(f"Notebook path: {notebook_path}")
    logger.debug(f"Execution timeout: {timeout} seconds")

    try:
        with open(notebook_path, "r", encoding="utf-8") as f:
            notebook: NotebookNode = nbformat.read(f, nbformat.NO_CONVERT)

        # Create a client to execute the notebook
        client = NotebookClient(
            notebook,
            timeout=timeout,
            kernel_name="python3",
            allow_errors=True,  # Continue execution even if a cell fails
        )

        logger.info("Executing notebook cells...")
        client.execute()
        logger.info("Notebook execution completed successfully.")
        return notebook

    except (CellExecutionError, CellTimeoutError) as e:
        logger.error("A cell execution error occurred.")
        logger.exception(e)
        # The executed notebook still contains partial output and the error details.
        return notebook

    except Exception as e:
        logger.error("An unexpected error occurred during notebook execution.")
        logger.exception(e)
        # Return the partially executed or unmodified notebook in case of failure.
        return notebook

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