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Argilla

zenml.integrations.argilla

Initialization of the Argilla integration.

Attributes

ARGILLA = 'argilla' module-attribute

ARGILLA_ANNOTATOR_FLAVOR = 'argilla' module-attribute

Classes

ArgillaIntegration

Bases: Integration

Definition of Argilla integration for ZenML.

Functions
flavors() -> List[Type[Flavor]] classmethod

Declare the stack component flavors for the Argilla integration.

Returns:

Type Description
List[Type[Flavor]]

List of stack component flavors for this integration.
Source code in src/zenml/integrations/argilla/__init__.py 
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@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Declare the stack component flavors for the Argilla integration.

    Returns:
        List of stack component flavors for this integration.
    """
    from zenml.integrations.argilla.flavors import (
        ArgillaAnnotatorFlavor,
    )

    return [ArgillaAnnotatorFlavor]

Flavor

Class for ZenML Flavors.

Attributes
config_class: Type[StackComponentConfig] abstractmethod property

Returns StackComponentConfig config class.

Returns:

Type Description
Type[StackComponentConfig]

The config class.
config_schema: Dict[str, Any] property

The config schema for a flavor.

Returns:

Type Description
Dict[str, Any]

The config schema.
docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
implementation_class: Type[StackComponent] abstractmethod property

Implementation class for this flavor.

Returns:

Type Description
Type[StackComponent]

The implementation class for this flavor.
logo_url: Optional[str] property

A url to represent the flavor in the dashboard.

Returns:

Type Description
Optional[str]

The flavor logo.
name: str abstractmethod property

The flavor name.

Returns:

Type Description
str

The flavor name.
sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
service_connector_requirements: Optional[ServiceConnectorRequirements] property

Service connector resource requirements for service connectors.

Specifies resource requirements that are used to filter the available service connector types that are compatible with this flavor.

Returns:

Type Description
Optional[ServiceConnectorRequirements]

Requirements for compatible service connectors, if a service
Optional[ServiceConnectorRequirements]

connector is required for this flavor.
type: StackComponentType abstractmethod property

The stack component type.

Returns:

Type Description
StackComponentType

The stack component type.
Functions
from_model(flavor_model: FlavorResponse) -> Flavor classmethod

Loads a flavor from a model.

Parameters:

Name Type Description Default
flavor_model FlavorResponse

The model to load from.

 required

Raises:

Type Description
CustomFlavorImportError

If the custom flavor can't be imported.
ImportError

If the flavor can't be imported.

Returns:

Type Description
Flavor

The loaded flavor.
Source code in src/zenml/stack/flavor.py 
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@classmethod
def from_model(cls, flavor_model: FlavorResponse) -> "Flavor":
    """Loads a flavor from a model.

    Args:
        flavor_model: The model to load from.

    Raises:
        CustomFlavorImportError: If the custom flavor can't be imported.
        ImportError: If the flavor can't be imported.

    Returns:
        The loaded flavor.
    """
    try:
        flavor = source_utils.load(flavor_model.source)()
    except (ModuleNotFoundError, ImportError, NotImplementedError) as err:
        if flavor_model.is_custom:
            flavor_module, _ = flavor_model.source.rsplit(".", maxsplit=1)
            expected_file_path = os.path.join(
                source_utils.get_source_root(),
                flavor_module.replace(".", os.path.sep),
            )
            raise CustomFlavorImportError(
                f"Couldn't import custom flavor {flavor_model.name}: "
                f"{err}. Make sure the custom flavor class "
                f"`{flavor_model.source}` is importable. If it is part of "
                "a library, make sure it is installed. If "
                "it is a local code file, make sure it exists at "
                f"`{expected_file_path}.py`."
            )
        else:
            raise ImportError(
                f"Couldn't import flavor {flavor_model.name}: {err}"
            )
    return cast(Flavor, flavor)
generate_default_docs_url() -> str

Generate the doc urls for all inbuilt and integration flavors.

Note that this method is not going to be useful for custom flavors, which do not have any docs in the main zenml docs.

Returns:

Type Description
str

The complete url to the zenml documentation
Source code in src/zenml/stack/flavor.py 
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def generate_default_docs_url(self) -> str:
    """Generate the doc urls for all inbuilt and integration flavors.

    Note that this method is not going to be useful for custom flavors,
    which do not have any docs in the main zenml docs.

    Returns:
        The complete url to the zenml documentation
    """
    from zenml import __version__

    component_type = self.type.plural.replace("_", "-")
    name = self.name.replace("_", "-")

    try:
        is_latest = is_latest_zenml_version()
    except RuntimeError:
        # We assume in error cases that we are on the latest version
        is_latest = True

    if is_latest:
        base = "https://docs.zenml.io"
    else:
        base = f"https://zenml-io.gitbook.io/zenml-legacy-documentation/v/{__version__}"
    return f"{base}/stack-components/{component_type}/{name}"
generate_default_sdk_docs_url() -> str

Generate SDK docs url for a flavor.

Returns:

Type Description
str

The complete url to the zenml SDK docs
Source code in src/zenml/stack/flavor.py 
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def generate_default_sdk_docs_url(self) -> str:
    """Generate SDK docs url for a flavor.

    Returns:
        The complete url to the zenml SDK docs
    """
    from zenml import __version__

    base = f"https://sdkdocs.zenml.io/{__version__}"

    component_type = self.type.plural

    if "zenml.integrations" in self.__module__:
        # Get integration name out of module path which will look something
        #  like this "zenml.integrations.<integration>....
        integration = self.__module__.split(
            "zenml.integrations.", maxsplit=1
        )[1].split(".")[0]

        return (
            f"{base}/integration_code_docs"
            f"/integrations-{integration}/#{self.__module__}"
        )

    else:
        return (
            f"{base}/core_code_docs/core-{component_type}/"
            f"#{self.__module__}"
        )
to_model(integration: Optional[str] = None, is_custom: bool = True) -> FlavorRequest

Converts a flavor to a model.

Parameters:

Name Type Description Default
integration Optional[str]

The integration to use for the model.

 None
is_custom bool

Whether the flavor is a custom flavor.

 True

Returns:

Type Description
FlavorRequest

The model.
Source code in src/zenml/stack/flavor.py 
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def to_model(
    self,
    integration: Optional[str] = None,
    is_custom: bool = True,
) -> FlavorRequest:
    """Converts a flavor to a model.

    Args:
        integration: The integration to use for the model.
        is_custom: Whether the flavor is a custom flavor.

    Returns:
        The model.
    """
    connector_requirements = self.service_connector_requirements
    connector_type = (
        connector_requirements.connector_type
        if connector_requirements
        else None
    )
    resource_type = (
        connector_requirements.resource_type
        if connector_requirements
        else None
    )
    resource_id_attr = (
        connector_requirements.resource_id_attr
        if connector_requirements
        else None
    )

    model = FlavorRequest(
        name=self.name,
        type=self.type,
        source=source_utils.resolve(self.__class__).import_path,
        config_schema=self.config_schema,
        connector_type=connector_type,
        connector_resource_type=resource_type,
        connector_resource_id_attr=resource_id_attr,
        integration=integration,
        logo_url=self.logo_url,
        docs_url=self.docs_url,
        sdk_docs_url=self.sdk_docs_url,
        is_custom=is_custom,
    )
    return model

Integration

Base class for integration in ZenML.

Functions
activate() -> None classmethod

Abstract method to activate the integration.

Source code in src/zenml/integrations/integration.py 
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@classmethod
def activate(cls) -> None:
    """Abstract method to activate the integration."""
check_installation() -> bool classmethod

Method to check whether the required packages are installed.

Returns:

Type Description
bool

True if all required packages are installed, False otherwise.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def check_installation(cls) -> bool:
    """Method to check whether the required packages are installed.

    Returns:
        True if all required packages are installed, False otherwise.
    """
    for r in cls.get_requirements():
        try:
            # First check if the base package is installed
            dist = pkg_resources.get_distribution(r)

            # Next, check if the dependencies (including extras) are
            # installed
            deps: List[Requirement] = []

            _, extras = parse_requirement(r)
            if extras:
                extra_list = extras[1:-1].split(",")
                for extra in extra_list:
                    try:
                        requirements = dist.requires(extras=[extra])  # type: ignore[arg-type]
                    except pkg_resources.UnknownExtra as e:
                        logger.debug(f"Unknown extra: {str(e)}")
                        return False
                    deps.extend(requirements)
            else:
                deps = dist.requires()

            for ri in deps:
                try:
                    # Remove the "extra == ..." part from the requirement string
                    cleaned_req = re.sub(
                        r"; extra == \"\w+\"", "", str(ri)
                    )
                    pkg_resources.get_distribution(cleaned_req)
                except pkg_resources.DistributionNotFound as e:
                    logger.debug(
                        f"Unable to find required dependency "
                        f"'{e.req}' for requirement '{r}' "
                        f"necessary for integration '{cls.NAME}'."
                    )
                    return False
                except pkg_resources.VersionConflict as e:
                    logger.debug(
                        f"Package version '{e.dist}' does not match "
                        f"version '{e.req}' required by '{r}' "
                        f"necessary for integration '{cls.NAME}'."
                    )
                    return False

        except pkg_resources.DistributionNotFound as e:
            logger.debug(
                f"Unable to find required package '{e.req}' for "
                f"integration {cls.NAME}."
            )
            return False
        except pkg_resources.VersionConflict as e:
            logger.debug(
                f"Package version '{e.dist}' does not match version "
                f"'{e.req}' necessary for integration {cls.NAME}."
            )
            return False

    logger.debug(
        f"Integration {cls.NAME} is installed correctly with "
        f"requirements {cls.get_requirements()}."
    )
    return True
flavors() -> List[Type[Flavor]] classmethod

Abstract method to declare new stack component flavors.

Returns:

Type Description
List[Type[Flavor]]

A list of new stack component flavors.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Abstract method to declare new stack component flavors.

    Returns:
        A list of new stack component flavors.
    """
    return []
get_requirements(target_os: Optional[str] = None, python_version: Optional[str] = None) -> List[str] classmethod

Method to get the requirements for the integration.

Parameters:

Name Type Description Default
target_os Optional[str]

The target operating system to get the requirements for.

 None
python_version Optional[str]

The Python version to use for the requirements.

 None

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def get_requirements(
    cls,
    target_os: Optional[str] = None,
    python_version: Optional[str] = None,
) -> List[str]:
    """Method to get the requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.
        python_version: The Python version to use for the requirements.

    Returns:
        A list of requirements.
    """
    return cls.REQUIREMENTS
get_uninstall_requirements(target_os: Optional[str] = None) -> List[str] classmethod

Method to get the uninstall requirements for the integration.

Parameters:

Name Type Description Default
target_os Optional[str]

The target operating system to get the requirements for.

 None

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def get_uninstall_requirements(
    cls, target_os: Optional[str] = None
) -> List[str]:
    """Method to get the uninstall requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.

    Returns:
        A list of requirements.
    """
    ret = []
    for each in cls.get_requirements(target_os=target_os):
        is_ignored = False
        for ignored in cls.REQUIREMENTS_IGNORED_ON_UNINSTALL:
            if each.startswith(ignored):
                is_ignored = True
                break
        if not is_ignored:
            ret.append(each)
    return ret
plugin_flavors() -> List[Type[BasePluginFlavor]] classmethod

Abstract method to declare new plugin flavors.

Returns:

Type Description
List[Type[BasePluginFlavor]]

A list of new plugin flavors.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def plugin_flavors(cls) -> List[Type["BasePluginFlavor"]]:
    """Abstract method to declare new plugin flavors.

    Returns:
        A list of new plugin flavors.
    """
    return []

Modules

annotators

Initialization of the Argilla annotators submodule.

Classes
ArgillaAnnotator(name: str, id: UUID, config: StackComponentConfig, flavor: str, type: StackComponentType, user: Optional[UUID], created: datetime, updated: datetime, labels: Optional[Dict[str, Any]] = None, connector_requirements: Optional[ServiceConnectorRequirements] = None, connector: Optional[UUID] = None, connector_resource_id: Optional[str] = None, *args: Any, **kwargs: Any)

Bases: BaseAnnotator, AuthenticationMixin

Class to interact with the Argilla annotation interface.

Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    created: datetime,
    updated: datetime,
    labels: Optional[Dict[str, Any]] = None,
    connector_requirements: Optional[ServiceConnectorRequirements] = None,
    connector: Optional[UUID] = None,
    connector_resource_id: Optional[str] = None,
    *args: Any,
    **kwargs: Any,
):
    """Initializes a StackComponent.

    Args:
        name: The name of the component.
        id: The unique ID of the component.
        config: The config of the component.
        flavor: The flavor of the component.
        type: The type of the component.
        user: The ID of the user who created the component.
        created: The creation time of the component.
        updated: The last update time of the component.
        labels: The labels of the component.
        connector_requirements: The requirements for the connector.
        connector: The ID of a connector linked to the component.
        connector_resource_id: The custom resource ID to access through
            the connector.
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.

    Raises:
        ValueError: If a secret reference is passed as name.
    """
    if secret_utils.is_secret_reference(name):
        raise ValueError(
            "Passing the `name` attribute of a stack component as a "
            "secret reference is not allowed."
        )

    self.id = id
    self.name = name
    self._config = config
    self.flavor = flavor
    self.type = type
    self.user = user
    self.created = created
    self.updated = updated
    self.labels = labels
    self.connector_requirements = connector_requirements
    self.connector = connector
    self.connector_resource_id = connector_resource_id
    self._connector_instance: Optional[ServiceConnector] = None
Attributes
config: ArgillaAnnotatorConfig property

Returns the ArgillaAnnotatorConfig config.

Returns:

Type Description
ArgillaAnnotatorConfig

The configuration.
settings_class: Type[ArgillaAnnotatorSettings] property

Settings class for the Argilla annotator.

Returns:

Type Description
Type[ArgillaAnnotatorSettings]

The settings class.
Functions
add_dataset(**kwargs: Any) -> Any

Create a dataset for annotation.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -dataset_name: The name of the dataset. -settings: The settings for the dataset. -workspace: The name of the workspace. By default, the first available.

 {}

Returns:

Type Description
Any

An Argilla dataset object.

Raises:

Type Description
ValueError

if dataset_name or settings aren't provided.
RuntimeError

if the workspace creation fails.
RuntimeError

if the dataset creation fails.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def add_dataset(self, **kwargs: Any) -> Any:
    """Create a dataset for annotation.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -dataset_name: The name of the dataset.
            -settings: The settings for the dataset.
            -workspace: The name of the workspace. By default, the first available.

    Returns:
        An Argilla dataset object.

    Raises:
        ValueError: if `dataset_name` or `settings` aren't provided.
        RuntimeError: if the workspace creation fails.
        RuntimeError: if the dataset creation fails.
    """
    dataset_name = kwargs.get("dataset_name")
    settings = kwargs.get("settings")
    workspace = kwargs.get("workspace")

    if dataset_name is None or settings is None:
        raise ValueError(
            "`dataset_name` and `settings` keyword arguments are required."
        )

    if workspace is None and not self._get_client().workspaces:
        workspace_to_create = rg.Workspace(name="argilla")
        try:
            workspace = workspace_to_create.create()
        except Exception as e:
            raise RuntimeError(
                "Failed to create the `argilla` workspace."
            ) from e

    try:
        dataset = rg.Dataset(
            name=dataset_name, workspace=workspace, settings=settings
        )
        logger.info(f"Creating the dataset '{dataset_name}' in Argilla...")
        dataset.create()
        logger.info(f"Dataset '{dataset_name}' successfully created.")
        return self.get_dataset(
            dataset_name=dataset_name, workspace=workspace
        )
    except Exception as e:
        logger.error(
            f"Failed to create dataset '{dataset_name}' in Argilla: {str(e)}"
        )
        raise RuntimeError(
            f"Failed to create the dataset '{dataset_name}' in Argilla: {str(e)}"
        ) from e
add_records(dataset_name: str, records: Union[Any, List[Dict[str, Any]]], workspace: Optional[str] = None, mapping: Optional[Dict[str, str]] = None) -> Any

Add records to an Argilla dataset for annotation.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset.

 required
records Union[Any, List[Dict[str, Any]]]

The records to add to the dataset.

 required
workspace Optional[str]

The name of the workspace. By default, the first available.

 None
mapping Optional[Dict[str, str]]

The mapping of the records to the dataset fields. By default, None.

 None

Raises:

Type Description
RuntimeError

If the records cannot be loaded to Argilla.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def add_records(
    self,
    dataset_name: str,
    records: Union[Any, List[Dict[str, Any]]],
    workspace: Optional[str] = None,
    mapping: Optional[Dict[str, str]] = None,
) -> Any:
    """Add records to an Argilla dataset for annotation.

    Args:
        dataset_name: The name of the dataset.
        records: The records to add to the dataset.
        workspace: The name of the workspace. By default, the first available.
        mapping: The mapping of the records to the dataset fields. By default, None.

    Raises:
        RuntimeError: If the records cannot be loaded to Argilla.
    """
    dataset = self.get_dataset(
        dataset_name=dataset_name, workspace=workspace
    )

    try:
        logger.info(
            f"Loading the records to '{dataset_name}' in Argilla..."
        )
        dataset.records.log(records=records, mapping=mapping)
        logger.info(
            f"Records loaded successfully to Argilla for '{dataset_name}'."
        )
    except Exception as e:
        logger.error(
            f"Failed to load the records to Argilla for '{dataset_name}': {str(e)}"
        )
        raise RuntimeError(
            f"Failed to load the records to Argilla: {str(e)}"
        ) from e
delete_dataset(**kwargs: Any) -> None

Deletes a dataset from the annotation interface.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -dataset_name: The name of the dataset. -workspace: The name of the workspace. By default, the first available

 {}

Raises:

Type Description
ValueError

If the dataset name is not provided or if the datasets is not found.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def delete_dataset(self, **kwargs: Any) -> None:
    """Deletes a dataset from the annotation interface.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -dataset_name: The name of the dataset.
            -workspace: The name of the workspace. By default, the first available

    Raises:
        ValueError: If the dataset name is not provided or if the datasets
            is not found.
    """
    dataset_name = kwargs.get("dataset_name")
    workspace = kwargs.get("workspace")

    if not dataset_name:
        raise ValueError("`dataset_name` keyword argument is required.")

    try:
        dataset = self.get_dataset(
            dataset_name=dataset_name, workspace=workspace
        )
        dataset.delete()
        logger.info(f"Dataset '{dataset_name}' deleted successfully.")
    except ValueError:
        logger.warning(
            f"Dataset '{dataset_name}' not found. Skipping deletion."
        )
get_dataset(**kwargs: Any) -> Any

Gets the dataset with the given name.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -dataset_name: The name of the dataset. -workspace: The name of the workspace. By default, the first available.

 {}

Returns:

Type Description
Any

The Argilla Dataset for the given name and workspace, if specified.

Raises:

Type Description
ValueError

If the dataset name is not provided or if the dataset does not exist.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_dataset(self, **kwargs: Any) -> Any:
    """Gets the dataset with the given name.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -dataset_name: The name of the dataset.
            -workspace: The name of the workspace. By default, the first available.

    Returns:
        The Argilla Dataset for the given name and workspace, if specified.

    Raises:
        ValueError: If the dataset name is not provided or if the dataset
            does not exist.
    """
    dataset_name = kwargs.get("dataset_name")
    workspace = kwargs.get("workspace")

    if not dataset_name:
        raise ValueError("`dataset_name` keyword argument is required.")

    try:
        dataset = self._get_client().datasets(
            name=dataset_name, workspace=workspace
        )
        if dataset is None:
            logger.error(f"Dataset '{dataset_name}' not found.")
        else:
            return dataset
    except ValueError as e:
        logger.error(f"Dataset '{dataset_name}' not found.")
        raise ValueError(f"Dataset '{dataset_name}' not found.") from e
get_dataset_names(**kwargs: Any) -> List[str]

Gets the names of the datasets.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -workspace: The name of the workspace. By default, the first available. If set, only the dataset names in the workspace will be returned.

 {}

Returns:

Type Description
List[str]

A list of dataset names.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_dataset_names(self, **kwargs: Any) -> List[str]:
    """Gets the names of the datasets.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -workspace: The name of the workspace. By default, the first available.
                If set, only the dataset names in the workspace will be returned.

    Returns:
        A list of dataset names.
    """
    workspace = kwargs.get("workspace")

    if workspace is None:
        dataset_names = [dataset.name for dataset in self.get_datasets()]
    else:
        dataset_names = [
            dataset.name
            for dataset in self.get_datasets(workspace=workspace)
        ]

    return dataset_names
get_dataset_stats(dataset_name: str, **kwargs: Any) -> Tuple[int, int]

Gets the statistics of the given dataset.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset.

 required
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -workspace: The name of the workspace. By default, the first available.

 {}

Returns:

Type Description
Tuple[int, int]

A tuple containing (labeled_task_count, unlabeled_task_count) for the dataset.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_dataset_stats(
    self, dataset_name: str, **kwargs: Any
) -> Tuple[int, int]:
    """Gets the statistics of the given dataset.

    Args:
        dataset_name: The name of the dataset.
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -workspace: The name of the workspace. By default, the first available.

    Returns:
        A tuple containing (labeled_task_count, unlabeled_task_count) for
            the dataset.
    """
    workspace = kwargs.get("workspace")

    labeled_task_count = len(
        self._get_data_by_status(
            dataset_name=dataset_name,
            status="completed",
            workspace=workspace,
        )
    )
    unlabeled_task_count = len(
        self._get_data_by_status(
            dataset_name=dataset_name,
            status="pending",
            workspace=workspace,
        )
    )

    return (labeled_task_count, unlabeled_task_count)
get_datasets(**kwargs: Any) -> List[Any]

Gets the datasets currently available for annotation.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -workspace: The name of the workspace. By default, the first available. If set, only the datasets in the workspace will be returned.

 {}

Returns:

Type Description
List[Any]

A list of datasets.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_datasets(self, **kwargs: Any) -> List[Any]:
    """Gets the datasets currently available for annotation.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -workspace: The name of the workspace. By default, the first available.
                If set, only the datasets in the workspace will be returned.

    Returns:
        A list of datasets.
    """
    workspace = kwargs.get("workspace")

    if workspace is None:
        datasets = list(self._get_client().datasets)
    else:
        datasets = list(self._get_client().workspaces(workspace).datasets)

    return datasets
get_labeled_data(**kwargs: Any) -> Any

Gets the dataset containing the labeled data.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -dataset_name: The name of the dataset. -workspace: The name of the workspace. By default, the first available.

 {}

Returns:

Type Description
Any

The list of annotated records.

Raises:

Type Description
ValueError

If the dataset name is not provided.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_labeled_data(self, **kwargs: Any) -> Any:
    """Gets the dataset containing the labeled data.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -dataset_name: The name of the dataset.
            -workspace: The name of the workspace. By default, the first available.

    Returns:
        The list of annotated records.

    Raises:
        ValueError: If the dataset name is not provided.
    """
    dataset_name = kwargs.get("dataset_name")
    workspace = kwargs.get("workspace")

    if not dataset_name:
        raise ValueError("`dataset_name` keyword argument is required.")

    return self._get_data_by_status(
        dataset_name, workspace=workspace, status="completed"
    )
get_unlabeled_data(**kwargs: str) -> Any

Gets the dataset containing the unlabeled data.

Parameters:

Name Type Description Default
**kwargs str

Additional keyword arguments to pass to the Argilla client.

 {}

Returns:

Type Description
Any

The list of pending records for annotation.

Raises:

Type Description
ValueError

If the dataset name is not provided.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_unlabeled_data(self, **kwargs: str) -> Any:
    """Gets the dataset containing the unlabeled data.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.

    Returns:
        The list of pending records for annotation.

    Raises:
        ValueError: If the dataset name is not provided.
    """
    dataset_name = kwargs.get("dataset_name")
    workspace = kwargs.get("workspace")

    if not dataset_name:
        raise ValueError("`dataset_name` keyword argument is required.")

    return self._get_data_by_status(
        dataset_name, workspace=workspace, status="pending"
    )
get_url() -> str

Gets the top-level URL of the annotation interface.

Returns:

Type Description
str

The URL of the annotation interface.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_url(self) -> str:
    """Gets the top-level URL of the annotation interface.

    Returns:
        The URL of the annotation interface.
    """
    return (
        f"{self.config.instance_url}:{self.config.port}"
        if self.config.port
        else self.config.instance_url
    )
get_url_for_dataset(dataset_name: str, **kwargs: Any) -> str

Gets the URL of the annotation interface for the given dataset.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset.

 required
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -workspace: The name of the workspace. By default, the first available.

 {}

Returns:

Type Description
str

The URL of of the dataset annotation interface.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_url_for_dataset(self, dataset_name: str, **kwargs: Any) -> str:
    """Gets the URL of the annotation interface for the given dataset.

    Args:
        dataset_name: The name of the dataset.
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -workspace: The name of the workspace. By default, the first available.

    Returns:
        The URL of of the dataset annotation interface.
    """
    workspace = kwargs.get("workspace")

    dataset_id = self.get_dataset(
        dataset_name=dataset_name, workspace=workspace
    ).id
    return f"{self.get_url()}/dataset/{dataset_id}/annotation-mode"
launch(**kwargs: Any) -> None

Launches the annotation interface.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client.

 {}
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def launch(self, **kwargs: Any) -> None:
    """Launches the annotation interface.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
    """
    url = kwargs.get("api_url") or self.get_url()

    if self._get_client():
        webbrowser.open(url, new=1, autoraise=True)
    else:
        logger.warning(
            "Could not launch annotation interface"
            "because the connection could not be established."
        )
Modules
argilla_annotator

Implementation of the Argilla annotation integration.

Classes
ArgillaAnnotator(name: str, id: UUID, config: StackComponentConfig, flavor: str, type: StackComponentType, user: Optional[UUID], created: datetime, updated: datetime, labels: Optional[Dict[str, Any]] = None, connector_requirements: Optional[ServiceConnectorRequirements] = None, connector: Optional[UUID] = None, connector_resource_id: Optional[str] = None, *args: Any, **kwargs: Any)

Bases: BaseAnnotator, AuthenticationMixin

Class to interact with the Argilla annotation interface.

Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    created: datetime,
    updated: datetime,
    labels: Optional[Dict[str, Any]] = None,
    connector_requirements: Optional[ServiceConnectorRequirements] = None,
    connector: Optional[UUID] = None,
    connector_resource_id: Optional[str] = None,
    *args: Any,
    **kwargs: Any,
):
    """Initializes a StackComponent.

    Args:
        name: The name of the component.
        id: The unique ID of the component.
        config: The config of the component.
        flavor: The flavor of the component.
        type: The type of the component.
        user: The ID of the user who created the component.
        created: The creation time of the component.
        updated: The last update time of the component.
        labels: The labels of the component.
        connector_requirements: The requirements for the connector.
        connector: The ID of a connector linked to the component.
        connector_resource_id: The custom resource ID to access through
            the connector.
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.

    Raises:
        ValueError: If a secret reference is passed as name.
    """
    if secret_utils.is_secret_reference(name):
        raise ValueError(
            "Passing the `name` attribute of a stack component as a "
            "secret reference is not allowed."
        )

    self.id = id
    self.name = name
    self._config = config
    self.flavor = flavor
    self.type = type
    self.user = user
    self.created = created
    self.updated = updated
    self.labels = labels
    self.connector_requirements = connector_requirements
    self.connector = connector
    self.connector_resource_id = connector_resource_id
    self._connector_instance: Optional[ServiceConnector] = None
Attributes
config: ArgillaAnnotatorConfig property

Returns the ArgillaAnnotatorConfig config.

Returns:

Type Description
ArgillaAnnotatorConfig

The configuration.
settings_class: Type[ArgillaAnnotatorSettings] property

Settings class for the Argilla annotator.

Returns:

Type Description
Type[ArgillaAnnotatorSettings]

The settings class.
Functions
add_dataset(**kwargs: Any) -> Any

Create a dataset for annotation.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -dataset_name: The name of the dataset. -settings: The settings for the dataset. -workspace: The name of the workspace. By default, the first available.

 {}

Returns:

Type Description
Any

An Argilla dataset object.

Raises:

Type Description
ValueError

if dataset_name or settings aren't provided.
RuntimeError

if the workspace creation fails.
RuntimeError

if the dataset creation fails.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def add_dataset(self, **kwargs: Any) -> Any:
    """Create a dataset for annotation.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -dataset_name: The name of the dataset.
            -settings: The settings for the dataset.
            -workspace: The name of the workspace. By default, the first available.

    Returns:
        An Argilla dataset object.

    Raises:
        ValueError: if `dataset_name` or `settings` aren't provided.
        RuntimeError: if the workspace creation fails.
        RuntimeError: if the dataset creation fails.
    """
    dataset_name = kwargs.get("dataset_name")
    settings = kwargs.get("settings")
    workspace = kwargs.get("workspace")

    if dataset_name is None or settings is None:
        raise ValueError(
            "`dataset_name` and `settings` keyword arguments are required."
        )

    if workspace is None and not self._get_client().workspaces:
        workspace_to_create = rg.Workspace(name="argilla")
        try:
            workspace = workspace_to_create.create()
        except Exception as e:
            raise RuntimeError(
                "Failed to create the `argilla` workspace."
            ) from e

    try:
        dataset = rg.Dataset(
            name=dataset_name, workspace=workspace, settings=settings
        )
        logger.info(f"Creating the dataset '{dataset_name}' in Argilla...")
        dataset.create()
        logger.info(f"Dataset '{dataset_name}' successfully created.")
        return self.get_dataset(
            dataset_name=dataset_name, workspace=workspace
        )
    except Exception as e:
        logger.error(
            f"Failed to create dataset '{dataset_name}' in Argilla: {str(e)}"
        )
        raise RuntimeError(
            f"Failed to create the dataset '{dataset_name}' in Argilla: {str(e)}"
        ) from e
add_records(dataset_name: str, records: Union[Any, List[Dict[str, Any]]], workspace: Optional[str] = None, mapping: Optional[Dict[str, str]] = None) -> Any

Add records to an Argilla dataset for annotation.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset.

 required
records Union[Any, List[Dict[str, Any]]]

The records to add to the dataset.

 required
workspace Optional[str]

The name of the workspace. By default, the first available.

 None
mapping Optional[Dict[str, str]]

The mapping of the records to the dataset fields. By default, None.

 None

Raises:

Type Description
RuntimeError

If the records cannot be loaded to Argilla.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def add_records(
    self,
    dataset_name: str,
    records: Union[Any, List[Dict[str, Any]]],
    workspace: Optional[str] = None,
    mapping: Optional[Dict[str, str]] = None,
) -> Any:
    """Add records to an Argilla dataset for annotation.

    Args:
        dataset_name: The name of the dataset.
        records: The records to add to the dataset.
        workspace: The name of the workspace. By default, the first available.
        mapping: The mapping of the records to the dataset fields. By default, None.

    Raises:
        RuntimeError: If the records cannot be loaded to Argilla.
    """
    dataset = self.get_dataset(
        dataset_name=dataset_name, workspace=workspace
    )

    try:
        logger.info(
            f"Loading the records to '{dataset_name}' in Argilla..."
        )
        dataset.records.log(records=records, mapping=mapping)
        logger.info(
            f"Records loaded successfully to Argilla for '{dataset_name}'."
        )
    except Exception as e:
        logger.error(
            f"Failed to load the records to Argilla for '{dataset_name}': {str(e)}"
        )
        raise RuntimeError(
            f"Failed to load the records to Argilla: {str(e)}"
        ) from e
delete_dataset(**kwargs: Any) -> None

Deletes a dataset from the annotation interface.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -dataset_name: The name of the dataset. -workspace: The name of the workspace. By default, the first available

 {}

Raises:

Type Description
ValueError

If the dataset name is not provided or if the datasets is not found.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def delete_dataset(self, **kwargs: Any) -> None:
    """Deletes a dataset from the annotation interface.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -dataset_name: The name of the dataset.
            -workspace: The name of the workspace. By default, the first available

    Raises:
        ValueError: If the dataset name is not provided or if the datasets
            is not found.
    """
    dataset_name = kwargs.get("dataset_name")
    workspace = kwargs.get("workspace")

    if not dataset_name:
        raise ValueError("`dataset_name` keyword argument is required.")

    try:
        dataset = self.get_dataset(
            dataset_name=dataset_name, workspace=workspace
        )
        dataset.delete()
        logger.info(f"Dataset '{dataset_name}' deleted successfully.")
    except ValueError:
        logger.warning(
            f"Dataset '{dataset_name}' not found. Skipping deletion."
        )
get_dataset(**kwargs: Any) -> Any

Gets the dataset with the given name.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -dataset_name: The name of the dataset. -workspace: The name of the workspace. By default, the first available.

 {}

Returns:

Type Description
Any

The Argilla Dataset for the given name and workspace, if specified.

Raises:

Type Description
ValueError

If the dataset name is not provided or if the dataset does not exist.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_dataset(self, **kwargs: Any) -> Any:
    """Gets the dataset with the given name.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -dataset_name: The name of the dataset.
            -workspace: The name of the workspace. By default, the first available.

    Returns:
        The Argilla Dataset for the given name and workspace, if specified.

    Raises:
        ValueError: If the dataset name is not provided or if the dataset
            does not exist.
    """
    dataset_name = kwargs.get("dataset_name")
    workspace = kwargs.get("workspace")

    if not dataset_name:
        raise ValueError("`dataset_name` keyword argument is required.")

    try:
        dataset = self._get_client().datasets(
            name=dataset_name, workspace=workspace
        )
        if dataset is None:
            logger.error(f"Dataset '{dataset_name}' not found.")
        else:
            return dataset
    except ValueError as e:
        logger.error(f"Dataset '{dataset_name}' not found.")
        raise ValueError(f"Dataset '{dataset_name}' not found.") from e
get_dataset_names(**kwargs: Any) -> List[str]

Gets the names of the datasets.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -workspace: The name of the workspace. By default, the first available. If set, only the dataset names in the workspace will be returned.

 {}

Returns:

Type Description
List[str]

A list of dataset names.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_dataset_names(self, **kwargs: Any) -> List[str]:
    """Gets the names of the datasets.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -workspace: The name of the workspace. By default, the first available.
                If set, only the dataset names in the workspace will be returned.

    Returns:
        A list of dataset names.
    """
    workspace = kwargs.get("workspace")

    if workspace is None:
        dataset_names = [dataset.name for dataset in self.get_datasets()]
    else:
        dataset_names = [
            dataset.name
            for dataset in self.get_datasets(workspace=workspace)
        ]

    return dataset_names
get_dataset_stats(dataset_name: str, **kwargs: Any) -> Tuple[int, int]

Gets the statistics of the given dataset.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset.

 required
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -workspace: The name of the workspace. By default, the first available.

 {}

Returns:

Type Description
Tuple[int, int]

A tuple containing (labeled_task_count, unlabeled_task_count) for the dataset.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_dataset_stats(
    self, dataset_name: str, **kwargs: Any
) -> Tuple[int, int]:
    """Gets the statistics of the given dataset.

    Args:
        dataset_name: The name of the dataset.
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -workspace: The name of the workspace. By default, the first available.

    Returns:
        A tuple containing (labeled_task_count, unlabeled_task_count) for
            the dataset.
    """
    workspace = kwargs.get("workspace")

    labeled_task_count = len(
        self._get_data_by_status(
            dataset_name=dataset_name,
            status="completed",
            workspace=workspace,
        )
    )
    unlabeled_task_count = len(
        self._get_data_by_status(
            dataset_name=dataset_name,
            status="pending",
            workspace=workspace,
        )
    )

    return (labeled_task_count, unlabeled_task_count)
get_datasets(**kwargs: Any) -> List[Any]

Gets the datasets currently available for annotation.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -workspace: The name of the workspace. By default, the first available. If set, only the datasets in the workspace will be returned.

 {}

Returns:

Type Description
List[Any]

A list of datasets.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_datasets(self, **kwargs: Any) -> List[Any]:
    """Gets the datasets currently available for annotation.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -workspace: The name of the workspace. By default, the first available.
                If set, only the datasets in the workspace will be returned.

    Returns:
        A list of datasets.
    """
    workspace = kwargs.get("workspace")

    if workspace is None:
        datasets = list(self._get_client().datasets)
    else:
        datasets = list(self._get_client().workspaces(workspace).datasets)

    return datasets
get_labeled_data(**kwargs: Any) -> Any

Gets the dataset containing the labeled data.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -dataset_name: The name of the dataset. -workspace: The name of the workspace. By default, the first available.

 {}

Returns:

Type Description
Any

The list of annotated records.

Raises:

Type Description
ValueError

If the dataset name is not provided.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_labeled_data(self, **kwargs: Any) -> Any:
    """Gets the dataset containing the labeled data.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -dataset_name: The name of the dataset.
            -workspace: The name of the workspace. By default, the first available.

    Returns:
        The list of annotated records.

    Raises:
        ValueError: If the dataset name is not provided.
    """
    dataset_name = kwargs.get("dataset_name")
    workspace = kwargs.get("workspace")

    if not dataset_name:
        raise ValueError("`dataset_name` keyword argument is required.")

    return self._get_data_by_status(
        dataset_name, workspace=workspace, status="completed"
    )
get_unlabeled_data(**kwargs: str) -> Any

Gets the dataset containing the unlabeled data.

Parameters:

Name Type Description Default
**kwargs str

Additional keyword arguments to pass to the Argilla client.

 {}

Returns:

Type Description
Any

The list of pending records for annotation.

Raises:

Type Description
ValueError

If the dataset name is not provided.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_unlabeled_data(self, **kwargs: str) -> Any:
    """Gets the dataset containing the unlabeled data.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.

    Returns:
        The list of pending records for annotation.

    Raises:
        ValueError: If the dataset name is not provided.
    """
    dataset_name = kwargs.get("dataset_name")
    workspace = kwargs.get("workspace")

    if not dataset_name:
        raise ValueError("`dataset_name` keyword argument is required.")

    return self._get_data_by_status(
        dataset_name, workspace=workspace, status="pending"
    )
get_url() -> str

Gets the top-level URL of the annotation interface.

Returns:

Type Description
str

The URL of the annotation interface.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_url(self) -> str:
    """Gets the top-level URL of the annotation interface.

    Returns:
        The URL of the annotation interface.
    """
    return (
        f"{self.config.instance_url}:{self.config.port}"
        if self.config.port
        else self.config.instance_url
    )
get_url_for_dataset(dataset_name: str, **kwargs: Any) -> str

Gets the URL of the annotation interface for the given dataset.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset.

 required
**kwargs Any

Additional keyword arguments to pass to the Argilla client. -workspace: The name of the workspace. By default, the first available.

 {}

Returns:

Type Description
str

The URL of of the dataset annotation interface.
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def get_url_for_dataset(self, dataset_name: str, **kwargs: Any) -> str:
    """Gets the URL of the annotation interface for the given dataset.

    Args:
        dataset_name: The name of the dataset.
        **kwargs: Additional keyword arguments to pass to the Argilla client.
            -workspace: The name of the workspace. By default, the first available.

    Returns:
        The URL of of the dataset annotation interface.
    """
    workspace = kwargs.get("workspace")

    dataset_id = self.get_dataset(
        dataset_name=dataset_name, workspace=workspace
    ).id
    return f"{self.get_url()}/dataset/{dataset_id}/annotation-mode"
launch(**kwargs: Any) -> None

Launches the annotation interface.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the Argilla client.

 {}
Source code in src/zenml/integrations/argilla/annotators/argilla_annotator.py 
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def launch(self, **kwargs: Any) -> None:
    """Launches the annotation interface.

    Args:
        **kwargs: Additional keyword arguments to pass to the Argilla client.
    """
    url = kwargs.get("api_url") or self.get_url()

    if self._get_client():
        webbrowser.open(url, new=1, autoraise=True)
    else:
        logger.warning(
            "Could not launch annotation interface"
            "because the connection could not be established."
        )
Functions

flavors

Argilla integration flavors.

Classes
ArgillaAnnotatorConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseAnnotatorConfig, ArgillaAnnotatorSettings, AuthenticationConfigMixin

Config for the Argilla annotator.

This class combines settings and authentication configurations for Argilla into a single, usable configuration object without adding additional functionality.

Source code in src/zenml/config/secret_reference_mixin.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references are only passed for valid fields.

    This method ensures that secret references are not passed for fields
    that explicitly prevent them or require pydantic validation.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using plain-text secrets.
        **kwargs: Arguments to initialize this object.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            or an attribute which explicitly disallows secret references
            is passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}`. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure values with secrets "
                    "here: https://docs.zenml.io/getting-started/deploying-zenml/secret-management"
                )
            continue

        if secret_utils.is_clear_text_field(field):
            raise ValueError(
                f"Passing the `{key}` attribute as a secret reference is "
                "not allowed."
            )

        requires_validation = has_validators(
            pydantic_class=self.__class__, field_name=key
        )
        if requires_validation:
            raise ValueError(
                f"Passing the attribute `{key}` as a secret reference is "
                "not allowed as additional validation is required for "
                "this attribute."
            )

    super().__init__(**kwargs)
ArgillaAnnotatorFlavor

Bases: BaseAnnotatorFlavor

Argilla annotator flavor.

Attributes
config_class: Type[ArgillaAnnotatorConfig] property

Returns ArgillaAnnotatorConfig config class.

Returns:

Type Description
Type[ArgillaAnnotatorConfig]

The config class.
docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
implementation_class: Type[ArgillaAnnotator] property

Implementation class for this flavor.

Returns:

Type Description
Type[ArgillaAnnotator]

The implementation class.
logo_url: str property

A url to represent the flavor in the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
ArgillaAnnotatorSettings(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseSettings

Argilla annotator settings.

If you are using a private Hugging Face Spaces instance of Argilla you must pass in https_extra_kwargs.

Attributes:

Name Type Description
instance_url str

URL of the Argilla instance.
api_key Optional[str]

The api_key for Argilla
port Optional[int]

The port to use for the annotation interface.
headers Optional[str]

Extra headers to include in the request.
httpx_extra_kwargs Optional[str]

Extra kwargs to pass to the client.
Source code in src/zenml/config/secret_reference_mixin.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references are only passed for valid fields.

    This method ensures that secret references are not passed for fields
    that explicitly prevent them or require pydantic validation.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using plain-text secrets.
        **kwargs: Arguments to initialize this object.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            or an attribute which explicitly disallows secret references
            is passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}`. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure values with secrets "
                    "here: https://docs.zenml.io/getting-started/deploying-zenml/secret-management"
                )
            continue

        if secret_utils.is_clear_text_field(field):
            raise ValueError(
                f"Passing the `{key}` attribute as a secret reference is "
                "not allowed."
            )

        requires_validation = has_validators(
            pydantic_class=self.__class__, field_name=key
        )
        if requires_validation:
            raise ValueError(
                f"Passing the attribute `{key}` as a secret reference is "
                "not allowed as additional validation is required for "
                "this attribute."
            )

    super().__init__(**kwargs)
Functions
ensure_instance_url_ends_without_slash(instance_url: str) -> str classmethod

Pydantic validator to ensure instance URL ends without a slash.

Parameters:

Name Type Description Default
instance_url str

The instance URL to validate.

 required

Returns:

Type Description
str

The validated instance URL.
Source code in src/zenml/integrations/argilla/flavors/argilla_annotator_flavor.py 
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@field_validator("instance_url")
@classmethod
def ensure_instance_url_ends_without_slash(cls, instance_url: str) -> str:
    """Pydantic validator to ensure instance URL ends without a slash.

    Args:
        instance_url: The instance URL to validate.

    Returns:
        The validated instance URL.
    """
    return instance_url.rstrip("/")
Modules
argilla_annotator_flavor

Argilla annotator flavor.

Classes
ArgillaAnnotatorConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseAnnotatorConfig, ArgillaAnnotatorSettings, AuthenticationConfigMixin

Config for the Argilla annotator.

This class combines settings and authentication configurations for Argilla into a single, usable configuration object without adding additional functionality.

Source code in src/zenml/config/secret_reference_mixin.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references are only passed for valid fields.

    This method ensures that secret references are not passed for fields
    that explicitly prevent them or require pydantic validation.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using plain-text secrets.
        **kwargs: Arguments to initialize this object.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            or an attribute which explicitly disallows secret references
            is passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}`. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure values with secrets "
                    "here: https://docs.zenml.io/getting-started/deploying-zenml/secret-management"
                )
            continue

        if secret_utils.is_clear_text_field(field):
            raise ValueError(
                f"Passing the `{key}` attribute as a secret reference is "
                "not allowed."
            )

        requires_validation = has_validators(
            pydantic_class=self.__class__, field_name=key
        )
        if requires_validation:
            raise ValueError(
                f"Passing the attribute `{key}` as a secret reference is "
                "not allowed as additional validation is required for "
                "this attribute."
            )

    super().__init__(**kwargs)
ArgillaAnnotatorFlavor

Bases: BaseAnnotatorFlavor

Argilla annotator flavor.

Attributes
config_class: Type[ArgillaAnnotatorConfig] property

Returns ArgillaAnnotatorConfig config class.

Returns:

Type Description
Type[ArgillaAnnotatorConfig]

The config class.
docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
implementation_class: Type[ArgillaAnnotator] property

Implementation class for this flavor.

Returns:

Type Description
Type[ArgillaAnnotator]

The implementation class.
logo_url: str property

A url to represent the flavor in the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
ArgillaAnnotatorSettings(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseSettings

Argilla annotator settings.

If you are using a private Hugging Face Spaces instance of Argilla you must pass in https_extra_kwargs.

Attributes:

Name Type Description
instance_url str

URL of the Argilla instance.
api_key Optional[str]

The api_key for Argilla
port Optional[int]

The port to use for the annotation interface.
headers Optional[str]

Extra headers to include in the request.
httpx_extra_kwargs Optional[str]

Extra kwargs to pass to the client.
Source code in src/zenml/config/secret_reference_mixin.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references are only passed for valid fields.

    This method ensures that secret references are not passed for fields
    that explicitly prevent them or require pydantic validation.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using plain-text secrets.
        **kwargs: Arguments to initialize this object.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            or an attribute which explicitly disallows secret references
            is passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}`. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure values with secrets "
                    "here: https://docs.zenml.io/getting-started/deploying-zenml/secret-management"
                )
            continue

        if secret_utils.is_clear_text_field(field):
            raise ValueError(
                f"Passing the `{key}` attribute as a secret reference is "
                "not allowed."
            )

        requires_validation = has_validators(
            pydantic_class=self.__class__, field_name=key
        )
        if requires_validation:
            raise ValueError(
                f"Passing the attribute `{key}` as a secret reference is "
                "not allowed as additional validation is required for "
                "this attribute."
            )

    super().__init__(**kwargs)
Functions
ensure_instance_url_ends_without_slash(instance_url: str) -> str classmethod

Pydantic validator to ensure instance URL ends without a slash.

Parameters:

Name Type Description Default
instance_url str

The instance URL to validate.

 required

Returns:

Type Description
str

The validated instance URL.
Source code in src/zenml/integrations/argilla/flavors/argilla_annotator_flavor.py 
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@field_validator("instance_url")
@classmethod
def ensure_instance_url_ends_without_slash(cls, instance_url: str) -> str:
    """Pydantic validator to ensure instance URL ends without a slash.

    Args:
        instance_url: The instance URL to validate.

    Returns:
        The validated instance URL.
    """
    return instance_url.rstrip("/")
Functions Modules